From da17809686c8bff5c31a38db6cd4eaba68f65cb5 Mon Sep 17 00:00:00 2001 From: "C.A.P. Linssen" Date: Fri, 19 Apr 2024 12:06:10 +0200 Subject: [PATCH] fix tutorial notebooks after module (re)loading changes in NEST 3.7 --- .../nestml_active_dendrite_tutorial.ipynb | 226 +- .../nestml_izhikevich_tutorial.ipynb | 136 +- .../nestml_ou_noise_tutorial.ipynb | 1166 +--- ..._spike_frequency_adaptation_tutorial.ipynb | 1763 +++-- .../stdp_dopa_synapse/stdp_dopa_synapse.ipynb | 5519 ++++++++-------- doc/tutorials/stdp_windows/stdp_windows.ipynb | 5731 +++++------------ .../triplet_stdp_synapse.ipynb | 2285 +++---- .../nest_code_generator_utils.py | 4 +- 8 files changed, 6519 insertions(+), 10311 deletions(-) diff --git a/doc/tutorials/active_dendrite/nestml_active_dendrite_tutorial.ipynb b/doc/tutorials/active_dendrite/nestml_active_dendrite_tutorial.ipynb index 999d328b1..78345262e 100644 --- a/doc/tutorials/active_dendrite/nestml_active_dendrite_tutorial.ipynb +++ b/doc/tutorials/active_dendrite/nestml_active_dendrite_tutorial.ipynb @@ -18,9 +18,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -29,8 +37,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -113,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -182,16 +190,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "line 1:28 extraneous input '*' expecting {'integer', 'real', 'string', 'boolean', 'void', '(', ',', NAME, UNSIGNED_INTEGER}\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -200,8 +201,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -211,10 +212,6 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -229,27 +226,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_da766667f5464640bcfeb61aee78a729_module Configuration Summary\n", + "active_dend_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_da766667f5464640bcfeb61aee78a729_module' using\n", + "You can now build and install 'active_dend_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_da766667f5464640bcfeb61aee78a729_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libactive_dend_module.so will be installed to\n", + " /tmp/nestml_target_gey6txpr\n", "The module can be loaded into NEST using\n", - " (nestml_da766667f5464640bcfeb61aee78a729_module) Install (in SLI)\n", - " nest.Install(nestml_da766667f5464640bcfeb61aee78a729_module) (in PyNEST)\n", + " (active_dend_module) Install (in SLI)\n", + " nest.Install(active_dend_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -261,31 +258,28 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.1s)\n", + "-- Configuring done (0.6s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_da766667f5464640bcfeb61aee78a729_module_module.dir/nestml_da766667f5464640bcfeb61aee78a729_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_da766667f5464640bcfeb61aee78a729_module_module.dir/af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml.cpp: In member function ‘void af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml.cpp:180:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 180 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/active_dend_module_module.dir/active_dend_module.o\n", + "[ 66%] Building CXX object CMakeFiles/active_dend_module_module.dir/iaf_psc_exp_active_dendrite_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_active_dendrite_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_neuron_nestml.cpp:187:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 187 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml.cpp: In member function ‘virtual void af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml.cpp:277:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 277 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_exp_active_dendrite_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_neuron_nestml.cpp:290:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 290 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_neuronda766667f5464640bcfeb61aee78a729_nestml.cpp:275:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 275 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_neuron_nestml.cpp:285:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 285 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "[100%] Linking CXX shared module nestml_da766667f5464640bcfeb61aee78a729_module.so\n", - "[100%] Built target nestml_da766667f5464640bcfeb61aee78a729_module_module\n", - "[100%] Built target nestml_da766667f5464640bcfeb61aee78a729_module_module\n", + "[100%] Linking CXX shared module active_dend_module.so\n", + "[100%] Built target active_dend_module_module\n", + "[100%] Built target active_dend_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_da766667f5464640bcfeb61aee78a729_module.so\n", - "\n", - "Oct 19 03:39:35 Install [Info]: \n", - " loaded module nestml_da766667f5464640bcfeb61aee78a729_module\n" + "-- Installing: /tmp/nestml_target_gey6txpr/active_dend_module.so\n" ] } ], @@ -306,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -400,37 +394,19 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:39:35 NodeManager::prepare_nodes [Info]: \n", - " Preparing 5 nodes for simulation.\n", - "\n", - "Oct 19 03:39:35 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 5\n", - " Simulation time (ms): 100\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:39:35 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_276055/1944179876.py:84: UserWarning:Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n" + "/tmp/ipykernel_328427/1260340709.py:84: UserWarning:FigureCanvasAgg is non-interactive, and thus cannot be shown\n" ] }, { "data": { - "image/png": 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\n", 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+7s78POnWKkvkrhTk6cIbd4ex6cWBjOxaNGHg18OXGPrxDjbFqSi8iddUVxW3yUouVz1yc3N59NFHcXd3p0GDBsydO7fUclFRUezatYtXX32VNm3a8Msvv5RaLjAwkODgYPr378/MmTOJjIy02atdVIy0zFWQoijkm8q3NZfFYiHfaEZrLLTLmDm9TmP3WT3x8fGMHTuW999/n5EjR5Kdnc327dtRFIX+/fvTokULvv32W1566SWgaEzJ999/z/vvv289R15eHh9++CHffvstarWaRx55hBdffJHvv//errHWZ0azhVyjGYPJ8S/qkXFZzPz9OAcupgPgolNzV4cG3NslhN7N/awzUa8nz1jIoYsZ7D6fwvqIRM4k5bDpZBKbTiYR6OHMQ7c0YVyvJuXuhoSiMYnjF+3lQmoejXz0fDexF80ud7tWl4beeuaN6cLjfZvxzh8n2Hchjd8vaji9cC/vju5E58szWUXJRJ7a3jJ35USUq6lVKlyumAx0s2WLx2lWhZdeeomtW7fy+++/ExgYyL///W8OHTpEly5dbMotWbKEESNG4OXlxSOPPMKiRYt4+OGHr3vu4rXkjEaZ2FJZksxVUL7JTNjMG+8ZWxUi3xpq9w9rfHw8hYWFjBo1iqZNmwLQsWNH6+MTJ05kyZIl1mRu9erVFBQU8OCDD1rLmEwmFi5cSMuWRV1CU6ZMqfFNh+ua4guWI8/Qt1gUFmw9x0cbTmO2KOh1Gp7o14wn+jYv9xitYq5OWvq19qdfa39eGhrKmcRsfgu/xMr9sSRlG/hk4xkWbjnHgz0b8eyAlteMWbuaodDMxGUHOJ2YQ5CnMyue6k1j35pbM6xTI29WPtObH/Zd5O3VxzmRkM3Iz3fyaJ9mvDysbZVetB2FtZu1lrfNXe96cXvbAJY8fov1dve3/y6zsaBXc19WPtPHervfe5uvmQh04d0RNxlt6XJycli0aBHfffcdgwYNAmDZsmU0amTbam2xWFi6dCmffvopAA899BAvvPCCdfJCaeLj4/nwww9p2LAhbdu2rZL46wPpZq3nOnfuzKBBg+jYsSMPPPAAX331Fenp6dbHJ0yYwNmzZ9mzZw9Q1K364IMP4uZW0mLh6upqTeSgaMuV4u1XhH2pcMxsLjPPxGNL9vHBulOYLQrD2gez8YUBvDQ0tMKJXGlaB3nw0tBQdr16B5+O7UqPpj4YzRa+2xPNwA+2MOOXo2Wuu6coCjN/i+DgxXQ8XbR880SvGk3kiqlUKu7v1pDXupi5r3MDLAos3XWBEZ/s4FB0+o1PUOc5RstcXXDu3DmMRiO9evWy3ufr63tN8rVhwwZyc3MZPnw4AP7+/gwZMoTFixdfc84mTZrQsGFDGjVqRG5uLj///DNOTk7XlBPlI3/eVZBepyHyraHlKmuxWMjOysbD08Nu3az2ptFo2LBhA7t27WL9+vV8+umnvPbaa+zdu5fmzZsTGBjIPffcw5IlS2jevDl//fUXW7ZssTnH1bPsVCoVsuKNfRXXpyO2zMWm5/H4kv2cScpBr9Mw+772PNC9UZUsBOqkVXNP5xDu6RzCnvOpfLLxDLvOpbJiXwy/HY7jmQEteLp/C5uWreX7oll5IAa1Cj59uBttg2vXzEh3HXx4X0dGd2/MKz8fJSoll/sX7GLy7a2Yekdr69Iq9U3xBhqWWv5dc73rxdVrPh58Y3C5y+545fYyStacRYsWkZaWZu02haLr4NGjR5k9e7bNdXDr1q2o1WpatGiBl5dXaacTFSDJXAWpVKpyd3FYLBYKnTS4Omlr9TpzKpWKvn370rdvX2bOnEnTpk359ddfmT59OgBPPvkkY8eOpVGjRrRs2ZK+ffve4IzC3hy1m/Viai5jvthDQlYBwZ4uLJ7Qs0JLltyM3i386N3Cj/0X0piz5gSHojP4799nWL43mpeHhTK6W0POJuXw1uqixYBfGhrKgDYB1RJbZfRvE8Daaf2Z9b8Ifj18iU83nWXTyST+O6YLrYNqVwJaHYZ3bEBmvqnW73lbkS7xqip7s1q2bIlOp2Pv3r00aVK0S0d6ejqnT59mwIABAKSmpvL777/zww8/0L59e+uxZrOZfv36sX79eoYNG2a9v3nz5qjVajw86sd793//+1+FjxkyZIhNYnw95Xo3jBo1qsJBLFy4kMDAwAofJ6rX3r172bhxI3feeSeBgYHs3buX5ORk2rVrZy0zdOhQPD09eeedd2QsXA0pbntwpG1tYtPzePirvSRkFdAq0J1vJ95CA6/yfTHZU89mvvw86VbWHEvg3bUniEnL58VVR1i5P5ocgxlDoYUBbQJ4dkDtX+PKS69j3pguDAkL4rVfjxERl8U9n+1g9r3tebBHY4d6f9ysWfe2v3EhYRfu7u5MnDiRl156CT8/PwIDA3nttddsGim+/fZb/Pz8ePDBB695Hw4fPpxFixbZJHP1zT/+8Y8KlVepVJw5c6bca++Vq7not99+w8nJCS8vr3L9/Pnnn+TklL2CuKg9PD092bZtG8OHD6dNmza8/vrrzJ07l7vuustaRq1WM2HCBMxmM48++mgNRlt/NfByoWczH5qVc6HampaZb+Kxxfu4lJFPC383lj/Vq0YSuWIqlYoRnRrw9/QBvHpXKHqdhv0X0jkRn4W3q44P7u/kUInQ8I4NWDetP7e19qfAZOGVn4/xrx/Cy9xxQ4ib9cEHH3Dbbbdxzz33MHjwYPr160f37iX7QS9evJiRI0eW+jkaPXo0//vf/0hJSanOkGudhIQELBZLuX5cXSv2XV+u7bzUajUJCQnlbmnz8PDgyJEjdWI1Z9nOq8jEiRNJTk6uVFPx1WQ7r+pnMls4nZhNbHo+8Rn55BrNmMwWtGoVXnodvm7ONPN3pYW/+w2XBrlRXReaLTy+dD/bz6TQwMuFX/55a40mcqW5lJHPO39EsuNMCvPGdGFwWFBNh1SqG9W1xaLwxbbzfLi+aGJJMz9XPh3bjY6N6v4YJEOhGUUBJ40atdo+ibhs51V96tt2Xo8//jiffPJJubuVJ02axNtvv42/v3+5yperm3Xz5s34+vreuOBlf/31Fw0bNix3eVF7ZWZmcuzYMZYvX26XRE5Un+jUPFYfjWPrqWSOxGZgKGOv1iupVNDMz40eTX24pbkv/Vr7VzgRe3/dKbafSUGv0/DVoz1qXSIHReu5LXikO4qiOFSL3NXUahWTBrbkluY+PLcinAupeYxasJN/D2/HhFubOfRru5E7PtzKpYx8fpvcly6y/p6o5ZYsWVKh8gsWLKhQ+XIlc8UDHG8kLS0NX19f+vXrV6EgRNWJjo4mLCyszMcjIyOtA1pLc99997Fv3z6effZZhgwZUhUhCjtSFIVNJ5P4Yut59l1Is3nM00VLc383Gnjp8dQXbU5eaLaQmW8iOdvA+ZRcMvJMRKXkEpWSy6qDsQB0aezNsA7BDO/Q4Ib7kW4+lcSX284D8NGDnenQsHa3ENWVZKd7U1/+fK4fL/90lPWRicxeHcmBC+m8d38n3J3r5jy3ku28avdsVkdws9cJcfMURWHt2rUsWrSIn376qcLH2+VTvn79er7++mtWr15Nfn6+PU4p7CQkJITw8PDrPn49Vy9DImrG51vOsnjHBR7q2ZgXh5a+sOausym8/ecJTsRnAUVLN9za0p+7OgbTq7kfLfzdbtgdlZpj4OilTPZFpbH7XCpHYjMIjyn6efevk9za0o8HuoVgKaWRLym7gBd/PALAY32aclfHBjf3okWFeLs68cX47izbdYH/W3OCP4/FczIhiy/Gd6+TG9GXLBosbtbNXidE5UVFRbF48WKWLl1KcnIygweXvTzN9VQ6mbt48SKLFy9m2bJlpKenc9ddd/HNN99U9nSiimi1Wlq1alXTYYiblGsoJCXHQI7h2i18MvKMzPw9gv8diQPAzUnDI32a8kTf5hXazgrAz92Z29sGcnvbovGxSVkFrI9M5K/j8ew6l2r9cdNquOh6jsf7tcDHrWihzzd+O05qrpF2DTyZMbzd9Z5GVBGVSsWEvs3p2Mibyd8f4lxyLvd9tpP37u/E3Z3q1gVZJYsG241cJ6qXwWDgp59+YtGiRezYsQOz2cyHH37IxIkT8fSs3NJNFUrmjEYjv/zyC19//TU7d+5k8ODBxMbGcvjwYZstoIQQ9lXWOnP7L6TxrxWHicssQK2CR3o3ZfqQNni72mcl9UBPFx7p3ZRHejclNj2PHw/E8uP+aBKyDHyy+Rxf7bjAmJ6NaeSjZ11EIlq1io8e7Gyzh6Soft2b+vDHc/2Yuvwwu8+nMmX5YQ5dzGDG8FB0GseejFWs5LMg2ZxwDAcPHmTRokWsWLGCVq1aMX78eFasWEGjRo2sS4BVVrmTualTp7JixQpat27NI488wsqVK/Hz80On06HR1P0vbktp/UqiUqQuK866ztwV23n9eCCGf/9yjMLLsxg/fqhrlW7E3sjHlelD2jDptqa8+/069ud4ExmfzdJdF6xlJg1sSbsG1bMosLg+f3dnvp14Cx+uP83CredYvDOKY5cymP9wNwIr2GJbGxXviFCbWubku61q1JV67dWrF1OnTmXPnj1234e23MncggULeOWVV3j11VfrzYrNAE5OTqjVauLi4ggICMDJyancg6YtFgtGo5GCggKHX5rEHhRFwWg0kpycjFqtln34KsByxXZeiqLw37/P8PHGMwCM6NSA90ZX30B3rUZNN3+F18b3Zn90Fp9vOcvOs6m0CXJn8u3SVVObaDVqXr0rlK5NvHnxxyPsv5DOiE938NnYrvRq4VfT4d2U4m9hSy1I5m7mOlFflef6WNeuGYMGDWLRokUkJSUxfvx4hg4darf3Sbm//b/99lsWL15MgwYNGDFiBOPHj7dZWLYytm3bxgcffMDBgweJj4/n119/tVklWVEU3nzzTb766isyMjLo27cvCxYsoHXr1tYyaWlpTJ06ldWrV6NWqxk9ejQff/wx7u7uNxVbMbVaTfPmzYmPjycuLq5CxyqKQn5+Pnq9Xj7YV3B1daVJkyaS4FbEFResuetP89nmswBMvaMVzw9uY7d1tipCpVLRt5U/fVv5E5WSi7+7k3Sv1lJD2wfTZqoHz357kFOJ2Tz89V5m3BXKxH7NHfa76bbW/rQJ8sDHtebXlbyZ60R9VZHroz2vGQsWLGDBggVcuHABgPbt2zNz5kxrPlNQUMALL7zADz/8gMFgYOjQoXz++ecEBZWsRRkdHc2kSZPYvHkz7u7uPPbYY8yZMwet9vop1bp164iJiWHJkiVMmjSJ/Px8xowZA9z8zPpyJ3Njx45l7NixREVFsXTpUiZPnkxeXh4Wi4XIyMjrTmsuS25uLp07d+aJJ54odcuw999/n08++YRly5bRvHlz3njjDYYOHUpkZKR14cBx48YRHx/Phg0bMJlMPP744zz99NMsX768wvGUxcnJiSZNmlBYWIjZbC73cSaTiW3bttG/f39ZyPYyjUaDVqt12AtITSnO5cJjMjh4MR2AmXeH8US/5jUX1BWa+7vVdAjiBpr7u/Hr5Fv59y/H+C08jnf+PEF4TAbvje6EmwMuXzL7vg41HYKNyl4n6qvyXh/tfc1o1KgR7777Lq1bt0ZRFJYtW8Z9993H4cOHad++Pc8//zx//vknq1atwsvLiylTpjBq1Ch27twJFO01O2LECIKDg9m1axfx8fE8+uij6HQ6/vOf/9zw+Rs3bszMmTOZOXMmGzZsYMmSJWi1Wu677z7uv/9+7r//frp161bxF6ZUksViUdauXas88MADirOzs9KwYUNl6tSplT2dAii//vqrzfmDg4OVDz74wHpfRkaG4uzsrKxYsUJRFEWJjIxUAGX//v3WMn/99ZeiUqmUS5cuVToWezEajcpvv/2mGI3Gmg6lzqvrdf319vPKrXM2Kk1f+UNp+sofytfbz9dYLHW9rmuTqqhri8WiLNlxXmk540+l6St/KEM+2qKcS8q22/kdmby3q09tqmsfHx/l66+/VjIyMhSdTqesWrXK+tiJEycUQNm9e7eiKIqyZs0aRa1WKwkJCdYyCxYsUDw9PRWDwVCp509LS1M++eQTpUuXLopara7UOSr955hKpWLo0KEMHTqU1NRUvv322wqvcHw9UVFRJCQk2Ky54uXlRa9evdi9ezcPPfQQu3fvxtvbmx49eljLDB48GLVazd69exk5cmSp5zYYDBgMBuvt4sGV9u6PLywsWkYiPz8fk0n2TKxKdb2uW/s5kZGbj7NG4Yk+TRnbLYi8vLwaiaWu13VtUlV1/WDXINr4O/PyLxFcTMnmgc938PZ9YQxsU76tg+oqeW9XH3vWtcFgQKVS2XTDOjs74+zsfN3jzGYzq1atIjc3lz59+nDw4EFMJpNN3hEaGkqTJk3YvXs3vXv3Zvfu3XTs2NGm23Xo0KFMmjSJiIgIunbtWuH4fXx8mDp1KlOnTuXQoUMVPh5uctFg5fKgbD8/P6ZNm8a0adNu5nQ2EhISAGwqrPh28WOl7Rer1Wrx9fW1linNnDlzmD17tvW2Xq9nxYoV9gr9Gps3b66ycwtbdbmu/1P8N4v5PBs2nK/RWKBu13VtU1V1/ZJ1dIwZ08XDbLhYJU9TJd4/oiEuD/4ZZqGNl31nQch7u/rYo65XrFjBypUrbe578803mTVrVqnljx07Rp8+fSgoKMDd3Z1ff/2VsLAwwsPDcXJywtvb26b81XlHaXlJ8WPllZ6ezqJFizhx4gQAYWFhPPHEE5XrYqWSydyiRYuYN28eZ84UzaZr3bo106ZN48knn6xUENVtxowZTJ8+3Xq7KlvmNm/ezO23337DgZHi5tTVurYoCs98f5gDFzNo6e/Gt0/0QF/Dkwzqal3XRtVR14UWhXkbz7J8XwwAvZv78p9/tK8VEwuuZ/65fSh5uXTt1o1bW5R/7/Drkfd29bFnXffv35+FCxde0zJXlrZt2xIeHk5mZiY//fQTjz32GFu3br2pGCpi27Zt3HvvvXh6elp7Fj/55BPeeustVq9eTf/+/St8zgrX4MyZM/noo4+YOnUqffr0AWD37t08//zzREdH89Zbb1U4iNIEBwcDkJiYSIMGJdsCJSYm0qVLF2uZpKQkm+MKCwtJS0uzHl+a8jS/2kNx07Fer5cJEFWsrtb1F1vPsfN8JjqNmrQCC78cSeap/i1qNKa6Wte1UXXV9Zv3daZLU39e/fkYW8+m8+DXB1n4SHc6Nqq9e+sWX7idnZ1xdb3+nsHlJe/t6mPPuq7o/7+Tk5N1x4vu3buzf/9+Pv74Y8aMGYPRaCQjI8OmdS4xMdGaUwQHB7Nv3z6b8yUmJlofK4/Jkyfz4IMPsmDBAus6vWazmX/+859MnjyZY8eOVej1AFR4nu+CBQv46quvmDNnDvfeey/33nsvc+bM4csvv+Tzzz+vcABlad68OcHBwWzcuNF6X1ZWFnv37rUmkX369CEjI4ODBw9ay2zatAmLxUKvXr3sFosQNeFsUg5zN5wGoGczXxIyC8jMl3E8omrc16Uhv06+lWZ+rlzKyGf0wl2s3B9d02GVSVULFw0WjslisWAwGOjevTs6nc4m7zh16hTR0dE2ecexY8dsGpI2bNiAp6dnuVf1OHv2LC+88ILNhgsajYbp06dz9uzZSr2GCrfMmUwmmwkHxbp3724d0FheOTk5NoFHRUURHh6Or68vTZo0Ydq0abzzzju0bt3aujRJSEiIdS26du3aMWzYMJ566ikWLlyIyWRiypQpPPTQQ7IxsHBoZovCSz8dwVhoYUCbAJr4urLrXOo123kJYU+hwZ78PqUfL/wYzt8nknjl52OEx2Qw6972OGtr1xqCJYsGSzYnym/GjBncddddNGnShOzsbJYvX86WLVtYt24dXl5eTJw4kenTp+Pr64unp6e1F7J3794A3HnnnYSFhTF+/Hjef/99EhISeP3115k8eXK5e/y6devGiRMnrtkF4sSJE3Tu3LlSr6vCydz48eNZsGABH330kc39X375JePGjavQuQ4cOMDtt99uvV08ju2xxx5j6dKlvPzyy+Tm5vL000+TkZFBv379WLt2rXWNOYDvv/+eKVOmMGjQIOuiwZ988klFX5YQtcqyXRc4HJ2Bh7OWOaM6smDLOeDmF5YU4ka89Dq+HN+Dz7ecZe6G06zYF0NkXBafP9Kdht76mg7PqvijIKmcqIikpCQeffRR4uPj8fLyolOnTqxbt44hQ4YAMG/ePGsuceWiwcU0Gg1//PEHkyZNok+fPri5ufHYY49VaIjZc889x7/+9S/Onj1rTRL37NnD/Pnzeffddzl69Ki1bKdOncp1zkpPgFi/fr01iL179xIdHc2jjz5qM7Hg6oTvagMHDrTOiC2NSqXirbfeum4l+fr62nWBYCHsxVBoxlBoQaNSoVGrcNaqy5WMpeYYmPd3Uffqq8NDCfHWo1y+ZEkqJ6qDWq1iyh2t6djIm3/9cJgjsZnc8+kOPh3blb6tasfyJWrJ5kQlLFq06LqPu7i4MH/+fObPn19mmaZNm7JmzZpKxzB27FgAXn755VIfU6lUKIqCSqUq9wLUFU7mjh8/bp06e+5cUWuBv78//v7+HD9+3FpOWhBEfXEpI59dZ1M4fimT04k5xKTnkZ5rJNdo+yF01qrxd3cm0NOZFv7utAlyp02QB50aeeHnXtI8/9GG02QXFBLWwJOHejYBSsYFycdKVKcBbQJYPaUfk74/yPFLWYxftJcXh7Zl0oCWNf4d372pDz5uTvi4OfZ+naL+iYqKsvs5K5zMyfo7QsD55Bx+O3yJ1UfjiUrJLdcxhkILlzLyuZSRz+HoDJvHWgS40bOpL22DPVixr2jQ+Zv3hKG5vOdqceODStrmRDVr7OvKT8/eyhu/HWfVwVjeX3uKIzEZfPhAZzxcam7G56x729fYcwtxM5o2bWr3c8pCOkKUk6Io7D6XyudbzrHjbIr1fo1aRadGXvRo6kObIA9aBLjh5+aMj5sTzlo1FkXBZFbIzDORnGMgIbOAs0k5nEnK5mRCNmeTcjifnMv55JKkcHjHYHq18LPe9nd3pkWAG75uslyCqH4uOg3v39+Jrk18mPW/CNZFJHImcSdfjO9O6yCPmg5PiFrvf//7H3fddVe5l2FZs2YNt99+O3p9+capliuZGzVqFEuXLsXT07NcJx03bhzz5s27ZncGIRzV8UuZzF4dwf4LRZvca9Qq+rf25x9dG3JHaGC5Wii89Dqa+F27HlJGnpEDF9LZdyGN7WdSMBaa+ffwdjZlpg9pw/QhbezzYoSoBJVKxcO9mhAW4smk7w5yPiWX++bv5IP7OzOiU4Mbn0CIemzkyJEkJCQQEBBQrvIPPfQQ4eHhtGhRvnVFy5XM/f777yQnJ5frhIqisHr1at5++21J5oTDyzMWMmfNSb7fexGLUjTu7aGejXnythY09rXPQqXerk4MDgticFjQjQsLUcO6NPZm9dR+TF1+mN3nU5m8/BDhMc15ZVgoWk2Fly6ttPGL9nLwYjqfPNRVPjui1lMUhQkTJpR7+ZKCgoIKnb9cyZyiKLRpI60Con45fimT51Yc5vzlMXH3dA7h38NDaeBVe5ZnEKIm+Ls78+3EW/hg/Sm+2Hqer7ZHcTg6g4/Hdq225UvyjWbyjGYKL2/HKERt9thjj1Wo/Lhx48rdGwrlTOYqM+mhYcOGFT5GiNrixwMxvP7rcYxmC8GeLsx9sHONLskwd/0p1kckMrFfcx7s2bjG4hCimFajZsZd7ejSyJuXfzrKgYvpDP94Ox8+0Jkh1dBSZl2ZRJYmEQ5gyZIlVXr+ciVzAwYMqNIghKgtFEXhow2n+XRT0c4kd4YF8d7oTjW+/EF8ZgGnErNJyzPWaBxCXO2ujg1oH+LFlBWHOBqbyVPfHGDCrc2YMTy0SneNKJ7ZLbmcEJXYm1WIuspiUXjl56PWRG7qHa34Ynz3Gk/koGTLIlmYRNRGTfyKli95sl9zAJbuusDoBbvKvWxPpUjLnBBWkswJQVEi99pvx/jxQCwatYr3RnfkhTvb1vjCqFayaLCo5Zy0al6/O4zFE3rg46rj+KUs7v5kO7+HX6qS5yv+KCjSNieEJHNCKIrC7NURrNgXg1oFHz3YmTGXd16oLWTRYOEo7ggNYs2/buOW5r7kGs3864dwXv7pCLmGQrs+T/F2XtIyJ4Qkc0KwaEcUy3ZfRKWCD+7vzH1dat/kneI9jKVlTjiCBl56lj/Zi+cGtUalgh8PxDLik+0cjk6323OENvDglma++NaCYRBClJeiKJw5c4aIiAgKC+33B44kc6Je23Qykf9bcwKA14a3Y3T3RjUcUemk8UE4Gq1GzfQhbVj+ZG9CvFy4kJrH/Qt3M2/DaUzmm19O5M172vPjs31qdJa5EBURFRVFp06dCA0NpVOnTrRs2ZIDBw7Y5dwVTuYSExMZP348ISEhaLVaNBqNzY8QjuJsUg5Tlx9GUWDsLY2ZeHnwdm3krdfRwMsFd2fZgU84lj4t/fhrWn/u6xKC2aLw8cYz3L9wd9VOjhCiFnrppZcoLCzku+++46effqJRo0Y888wzdjl3ha8MEyZMIDo6mjfeeIMGDRrUngHiQlRAgcnM1BWHyTWa6dXcl9n3dqjV7+XZ93Vg9n0dajoMISrFS6/j44e6MqhdEK//eowjMRkM/3g7b9wdxthbGtfqz54Q9rJjxw5++ukn+vXrB0Dv3r1p1KgRubm5uLm53dS5K5zM7dixg+3bt9OlS5ebemIhatJ7a09yIj4LXzcnPh3bFSetjDgQoqrd2zmEHk19eHHVEXadS+Xfvx5j44lE/jOqI0GeLhU613MrDrPrXAqz7m3P3Z1CqihiIewnKSmJ1q1bW283aNAAvV5PUlISzZvfXM9Qha9gjRs3tg7GFsIRbT6VxJKdFwD48IFOBFbwIiKEqLwQbz3fTezF6yPa4aRVs/FkEoM/2sqPB2IqdG3JzDeRkmOkwCTbeQnHoFKpyMnJISsry/qjVqvJzs62ua8yKpzM/fe//+XVV1/lwoULlXpCIWpSjqGQf/9yDIAJtzbjjlDH2KD7vbUnuW/+Tv46Fl/ToQhx09RqFU/e1oI/pvajcyMvsgsKefmnozy2ZD+XMvLLdY6S7bykcUE4huJ97n18fKw/OTk5dO3aFR8fH7y9vfHx8anUuSvczTpmzBjy8vJo2bIlrq6u6HQ6m8fT0tIqFYgQ1WHu+lPEZxbQ2FfPK8NCazqccotKzuVITAYpubKdl6g72gR58POkW1m0I4q5G06z7XQyQ+dtY8bwUB6+pcl1x9KVLBoshGOozD735VXhZG7evHkyWFU4pPCYDJbuugDA//2jI3onx5l9XbzKvXzyRF2j1ah5ZkBLBocF8fJPRzl4MZ3Xfj3On0fjeXdUJ5r4uZZ6nPU6JNmccBDl2ee+sg1ilZrNKoSjMVsUXvv1GIoCI7s2pH+bgJoOqUIU2c5L1HEtA9z58Zk+LNt1gffXnWTXuVTu/O9WnhvUmif7tbhmkpJs5yXqkvXr1/P111+zevVq8vPLN9TgShUeMzdgwAC++eabSj2ZEDXl18OXiIjLwsNZy2sj2tV0OBUm23mJ+kCjVvFEv+asm9afPi38KDBZeH/tKUZ8sp2951NtyqpkOy/h4C5evMibb75Js2bNeOCBB1Cr1XzzzTeVOleFk7muXbvy4osvEhwczFNPPcWePXsq9cRCVJd8o5kP150CYPIdrfB3d67hiCpOtvMS9UlTPzeWP9WLeWM64+fmxJmkHMZ8uYeXVh0h7fK40aZ+rnRo6Im3q2znJRyH0Wjkhx9+YPDgwYSGhnLo0CFiY2PZsWMHP/zwAw888EClzlup2axxcXEsWbKEpKQk+vfvT1hYGB9++CGJiYmVCkKIqrRox3kSsgpo6K1nwq3NajqcSrF2s9ZsGEJUG5VKxciujdj0wkAe7tUEgFUHY7lj7hZW7o/mteHt+GPqbQzrEFzDkQpRPlOnTiUkJISPP/6YkSNHEhsby+rVq1GpVDe9g1alVkrVarWMGjWK33//ndjYWB5++GHeeOMNGjduzD/+8Q82bdp0U0EJYS9puUYWbDkHwMvD2uKic5xJD1dyc9bipdfhrJPFjUX94uWq4z8jO/LzpFsJDfYgI8/EKz8fY+TnOzl4Mb2mwxOi3BYsWMAzzzzD+vXrmTx5Mn5+fnY7901dGfbt28ebb77J3LlzCQwMZMaMGfj7+3P33Xfz4osv2iXAWbNmoVKpbH5CQ0uWlCgoKLBWiru7O6NHj5YWQmH19fbz5BrNdGzoxb2dHXeV+E/GduXIm3cysmujmg5FiBrRvakPf0ztx+sj2uHurOVIbCajF+xi2g+Hic+UMdyifLZt28Y999xDSEgIKpWK3377zebxCRMmXJNzDBs2zKZMWloa48aNw9PTE29vbyZOnEhOTs4Nn/vbb79l3759NGjQgDFjxvDHH39gNpvt8roqnMwlJSUxd+5cOnTowG233UZycjIrVqzgwoULzJ49m6+//pr169ezcOFCuwQI0L59e+Lj460/O3bssD72/PPPs3r1alatWsXWrVuJi4tj1KhRdntu4bgy8owsu7wUyXODWsuSOkI4OK1GzZO3tWDTiwNoFVi0l+Vv4XHc8eFWPtl4hgKTfS6Mou7Kzc2lc+fOzJ8/v8wyw4YNs8k5VqxYYfP4uHHjiIiIYMOGDfzxxx9s27aNp59++obPPXbsWDZs2MCxY8cIDQ1l8uTJBAcHY7FYiIyMvKnXVeGlSRo1akTLli154oknmDBhAgEB1y7x0KlTJ3r27HlTgV1Jq9USHHztuIjMzEwWLVrE8uXLueOOOwBYsmQJ7dq1Y8+ePfTu3dtuMQjHs3hHFLlGM+0aeDK4XWBNhyOEsJNADxfaBnlyNimXxj56YtLz+WjDaVbuj+HlYW25p1MIarX88Sauddddd3HXXXddt4yzs3OpOQfAiRMnWLt2Lfv376dHjx4AfPrppwwfPpwPP/yQkJAb9wA1b96c2bNnM2vWLNavX8+iRYt45JFHmDZtGqNGjeKTTz6p8OuqcMvcxo0bOXHiBC+99FKpiRyAp6enXVc6PnPmDCEhIbRo0YJx48YRHR0NwMGDBzGZTAwePNhaNjQ0lCZNmrB79267Pb+ovRRFISm7gMj4LE5mqPj9SDzL90azdGeUdf/Vfw1q5fCtcnP+OsHYL/ew/UxyTYciRO1w+SP9eN/mfDK2Kw28XLiUkc+/fgjn7k93sPV0smz1JSply5YtBAYG0rZtWyZNmkRqasmyOLt378bb29uayAEMHjwYtVrN3r17K/Q8KpWKoUOH8uOPPxIXF8eLL77I1q1bKxVzhVvmevToQV5eHq6uRatyX7x4kV9//ZWwsDDuvPPOSgVxPb169WLp0qW0bduW+Ph4Zs+ezW233cbx48dJSEjAyckJb29vm2OCgoJISEgo85wGgwGDwWC9bbEUbdTs5GTfKe6FhYUA5OfnYzKZ7Hru+siiKJxJyiE8JpOIuCyiUvKISs0l11jctaKG00dtjmkf5M5tzT3Jy8ur/oDt6FhMOrvPp3Nfp0DyGrrVaCzyvq4+Utdls1wea2QyGRncOoh+/+zFt3ujWbb7IueSMnl62V56NvXmuTta0SHEs1znlPquPvasa4PBgEqlQq0uaZ9ydnbG2bniy1ANGzaMUaNG0bx5c86dO8e///1v7rrrLnbv3o1GoyEhIYHAQNueHq1Wi6+v73Xzjhvx9fVl2rRpTJs2rVLHVziZu++++xg1ahTPPvssGRkZ9OrVC51OR0pKCh999BGTJk2qVCBlubI5tFOnTvTq1YumTZvy448/otfrK3XOOXPmMHv2bOttvV5/TZ+4PVXlfmz1kS9wmx5uaww0vlHpTP7++++qD6qKpaSqATURx4+jTzxW0+EA8r6uTlLX10pMKPpMnDx1ig1ZJwFoBrzZ5cpSqcRHpBIfUbFzS31XH3vU9YoVK1i5cqXNfW+++SazZs2q8Lkeeugh6787duxIp06daNmyJVu2bGHQoEGVjnH69OnlKqdSqZg7d26Fz1/hZO7QoUPMmzcPgJ9++omgoCAOHz7Mzz//zMyZM+2ezF3N29ubNm3acPbsWYYMGYLRaCQjI8OmdS4xMbHM/m6AGTNm2FRsVbbMbd68mdtvvx2ttsJVXW8pisLRS1n8cjiODZGJ5BdarI+5OWno1MiLzg29aBXoTjN/Vxr7uKJWzHW6rpfHH4bMDDp27MiQjkE1Gou8r6uP1HXZ1mdHQGoSbdq0ZUjva/+qi88sYMG2KP44Fo+igFoFd7UP5sl+zWhWxn6vUt/Vx5513b9/fxYuXHhNy5w9tGjRAn9/f86ePcugQYMIDg4mKSnJpkxhYSFpaWnXzTsOHz5sc/vQoUMUFhbStm1bAE6fPo1Go6F79+6VirPCNZiXl4eHhwdQtJfYqFGjUKvV9O7dm4sXL1YqiIrIycnh3LlzjB8/nu7du6PT6di4cSOjR48G4NSpU0RHR9OnT58yz1HZ5teKKm461uv16HS6Kn8+R6coChtPJPHJpjMcjc203t/C350h7YO4MyyILo190JQysLmu17VaXbQ+nouLs3WIQ02p63Vdm0hdl013OQHQ6XSlfiZaurry4RhfnhqQzQfrTvL3iSR+OZLIb0cTuadzCFPvaEWrQA+bY6S+q48967oqvxNjY2NJTU2lQYMGAPTp04eMjAwOHjxoTbw2bdqExWKhV69eZZ7nyhbIjz76CA8PD5YtW4aPjw8A6enpPP7449x2222VirPCyVyrVq347bffGDlyJOvWreP5558HipYs8fQs37iEinjxxRe55557aNq0KXFxcbz55ptoNBrGjh2Ll5cXEydOZPr06fj6+uLp6cnUqVPp06ePzGR1MLvOpvB/a04QEZcFgItOzd2dQhh7S2O6NfFx+AkMN6t4M/H6XQtClAjydKFFgBueLtdPBNoGe/D1Yz05FpvJxxvP8PeJRH4Pj+N/R+K4u1MIz93RitZBHtc9h6g7cnJyOHv2rPV2VFQU4eHh+Pr64uvry+zZsxk9ejTBwcGcO3eOl19+mVatWjF06FAA2rVrx7Bhw3jqqadYuHAhJpOJKVOm8NBDD5VrJivA3LlzWb9+vTWRA/Dx8eGdd97hzjvv5IUXXqjw66pwMjdz5kwefvhhnn/+eQYNGmRtAVu/fj1du3atcAA3Ehsby9ixY0lNTSUgIIB+/fqxZ88e60zaefPmoVarGT16NAaDgaFDh/L555/bPQ5RNeIz83nnzxP8eTQeKOpGffTWZjzZrzl+DriHalWxbucl2ZwQAMwY3o4Zw9uVu3zHRl58/VgPjl/K5JONZ1gfmcjqI3H8cTSOIe2CeGZACzqFSFJX1x04cIDbb7/dert4yNVjjz3GggULOHr0KMuWLSMjI4OQkBDuvPNO3n77bZvevO+//54pU6YwaNAga/5RkeVEsrKySE6+dmWC5ORksrOzK/W6KpzM3X///fTr14/4+Hg6d+5svX/QoEGMHDnSejs2NpaQkBCbPuzK+OGHH677uIuLC/Pnz7/uAoCi9lEUhVUHYpm9OoJcoxm1Csb3bsq0wW3wcZONs6/mpFXjpFWjkWxOiJvSoaEXXz7ag4i4TD7deJa1EQmsj0xkfWQi3Zp401WvYphFljSpqwYOHHjdJWvWrVt3w3P4+vqyfPnySscwcuRIHn/8cebOncstt9wCwN69e3nppZcqvelBpUYdBgcHXzPQrzigYmFhYYSHh9OiRYtKBSbqrow8I6/+fIy1EUXTuLs18ebtf3SgfYhXDUdWe307seyxGEKIimsf4sXC8d05m5TNV9ui+PXwJQ5FZ3AIDZs+2cnTA1oysmtDh93PWdReCxcu5MUXX+Thhx+2jh3UarVMnDiRDz74oFLnrLJdu2WxRlGa04nZ3Dd/J2sjEtBpVLwyLJRVz94qiZwQokI+2nCaofO28cO+6Js6T6tAD967vxM7XrmdZ/s3R69RiErNY8Yvx7j13U28t/YksemOvU6lqF1cXV35/PPPSU1N5fDhwxw+fJi0tDQ+//xz3NxK1hGNjY21rrZxIzL3WlSbTScTmbr8MLlGMw299Sx8pDsdG0kSJ4SouMTMAk4lZpOaa7TL+QI9XXhhSGuaF5wh0689y3ZHcykjnwVbzvHF1nMMahfEo32a0q+Vf72fkCXsw83NjU6dOpX5eEV6OCWZE9Xi54OxvPzzUcwWhd4tfPl8XHd8ZWxcuf1nzQnOJeUw+Y5WdGvic+MDhKjjivMpe/cCuWhg1K1NeaJfCzaeTOLb3RfZcTaFDZGJbIhMpEWAG4/0asrIrg1lfK+oUhV5b0syJ6rc4h1RvPVHJACjuzXi3dEd0WmqrIe/Ttp/IY3D0RmM6XnDLS+EqBdKkrmqOb9Wo2Zo+2CGtg/mbFIO3+25yE8HYzmfnMtbf0Ty7l8nubN9EA/2aEzfVv6lrn8pRHWpsmROmqEFwLJdF6yJ3BN9m/P6iHao5UuvwoovWGr5XAlxWdFnoTpGZ7cKdGfWve15cWhbfj0Uy4p9MUTGZ/HH0Xj+OBpPiJcL93dvxAM9GtPYt2YX9Rb1U5UlczIBQqw6EMOb/yvaFHHy7S158c62kuRXUvHnSapPiCJV3TJXGndnLeP7NGN8n2Ycv5TJqgMx/BYeR1xmAZ9sOssnm87Su4Uv93VpyF0dgvF2lW5YUT2qLJmLjIws92rIou7ZeCKRV34+CsDjfZtJIneTiq9XUoVCFCn+KCjV0jZ3rQ4NvejQ0IsZw9uxPjKRVQdi2HE2hT3n09hzPo2Zvx9nQJsA7ukcwpCwIFydZFSTqJiKXDPL/e4q70J2v/zyCwCNG8vYnvrqRHwWz604jEWBB7o3YubdYZLI3STrDhCyoZcQAHi76gjxcsHduWaTJBedhns7h3Bv5xBi0/NYfSSe38MvcTIhm79PJPH3iST0Og2Dw4K4u1MD+rcOQO8ka9eJG6uSCRBeXrKEhLix5GwDTy47QK7RzK0t/fjPqI6SyNmBtfVBqlIIAF4aGspLQ0NrOgwbjXxcmTSwJZMGtuRMYjb/O1K0B+zF1DxWH4lj9ZE4XHRq+rcOYGj7YAa1C5SuWFGmivRwljuZW7JkSaUDEvWD2aIwdcUhLmXk09zfjc/HdZNZq3aiSC4nhENpHeTBC3e2ZfqQNhyJzWT1kTjWHk/gUka+dfswjVpFr+a+3BkWxJD2wTT01td02KIKVWUPp3TiC7v5eOMZ9pxPw81Jw9eP9ZC/OO1o9ZR+KIBMBBbCsahUKro09qZLY29eH9GOiLisomQuIoGTCdnsOpfKrnOpzFodSZsgdwa0CWBAm0B6NvfBWSvdsXVJVfZwSjIn7GLHmRQ+3XQGgP+M6kjLAPcajqhukeVchLD19fbzrD4az/3dGzG+d9OaDqdcVCqVdeLE9CFtuJCSy4bIRNZFJHAwOp3TiTmcTszhq+1R6HUabm3px4C2AQxoE0BTP7cbP4Go1aqyh1OSOXHTMvKMPP9jOIoCY29pzH1dGtZ0SEKIOi4uo4AjMRn0belX06FUWjN/N57q34Kn+rcgPdfIjrMpbDmVzNbTyaTkGNh4MomNJ5MAaOitp3cLP/q09KN3C18a+ch6dqKEJHPips36XwTJ2QZaBrjx5j3tazqcOuk/a05wKSOf5+5oTdtgj5oOR4gaVzyvylJHljT1cXPins4h3NM5BItF4URCFltPJ7P1VDIHL6ZzKSOfnw/F8vOhWAAa++rp3bwouevVwk/G29VzksyJm7I+IoHfwuNQq+DDBzrjopMxHlVhy6kkTifmMO6WJoAkc0LU9DpzVUmtVtE+xIv2IV78c2Arcg2FHLiYzp7zqew+l8qxS5nEpOUTkxbLqoNFyV2wpwvdmnrTrYkPXZv40KGhp4y5q0ckmROVlpln4t+/Hgfgqf4t6CobwFeZ4tYHWeZFiCKqkmyuznNz1l6eGBEAQI6hkAMX0th9PpU951I5HpdFQlYBa44lsOZYAgBOGjXtG3peTu686dTQm8a+evkOqaMkmROVNnfDKVJyirpXnx/cpqbDqdNkOy8hbBUnJfUgl7uGu7OWgW0DGdg2EIA8YyFHYzM5FJ3OoYsZHI5OJzXXyOHoDA5HZ1iP83DR0j7Ekw4hXrRvWPS7RYA7Gplg5fAkmROVEhGXyXd7LgLw9n0dpHu1ilm386rRKISoPawNc7IPOK5OWnq38KN3i6LJIIqiEJ2WZ03uwmMyOJWQTXZBoXW7sWIuOjXtGngS1sCTNkEel3/c8XN3rqmXIypBkjlRYRaLwszfI7AocHenBtzayr+mQ6rzFOlmFcKG3kmDt6sOvfwheQ2VSkVTPzea+rkxsmsjAExmC2cSc4iIyyQiLovjlzKJjM8iz2i+pgUPwM/NidZB7lckeB60DnTHx03WD62NJJkTFfZb+CUOXkzH1UnD6yPCajqcekG6WYWwNW1wG6bJ8I5y02nUhIV4EhbiyQOX7zNbFC6k5nL8UiYnE7I5k5jN6cQcYtLzSM01knpVKx6Al15HM383mvu5Fv32d6OZnxvN/N3w0uuq/4UJQJI5UUEFJjNz158GYModrQj2cqnhiOoH6WYVQtibRq2iZYA7LQPcue+K+/OMhZxLyuV0Yjank7I5nVCU5F3KyCcz38SRmAyOxGRccz5fNyea+bnSxNeVxr6uNPLR09jHlUY+rjTwdpHtHauQJHOiQr7bc5FLGfkEe7rwRN/mNR1OvfHXv27DbFGkS0kIUeVcnbR0bORFx0a220/lG81cTMvlQkouUSl5Rb9Ti24nZRtIyzWSlmvk0FVdtlC0FeFnD3djeMcG1fQq6hdJ5sS1LGZQX5s0ZOab+GzzWQCmD2lT+yY9KOaajqByyqjvK7k61bKPah2ua+EYfjwQw6+HLnFn+yAelz8sq4XeSUNosCehwZ7XPJZrKORCai4XUvKISc8jNj2vaC289Dxi0/MxFloI8JBJFVWlll0hRI1LOwQH/wXdPwbfbjYPfbH1HBl5JloHujOqWy3bsiv9MP0KXof0RhB4S01HU37Xqe9aS+pa1AKxaXnsPp9K6yDZB7o2cHPWWhc6vprFopCSY8DLVcbUVRXpwBYlLIVw7C3IOFr021JofSg918iyXRcAeHFoW7S1aeyDpRBN5Dt4Wi6iiXzHJu5a7Tr1fbX/rDnBS6uOEJOWV40BlqIe1LVwEMXrzMnKJLWeWq0i0NNFdqSoQrXoinxz5s+fT7NmzXBxcaFXr17s27evpkNyPBdXQtp+cA4o+h39o/WhJTujyDWaCWvgyZ1hQTUYZCkurkSVdgCDyhNV2gGbuGu169T31f44Eseqg7Gk5xmrMcBS1IO6Fo6hLm/nJapWXcwX6kQyt3LlSqZPn86bb77JoUOH6Ny5M0OHDiUpKammQ3McBclwci6gAifvot8nPoSCZLIKTCy53Co39Y5WtWuts8txKyoVhSo3FFVJ3LXadeq7NCWzWWuw7utJXQvHUPw1JC1zoiLqar5QJ8bMffTRRzz11FM8/vjjACxcuJA///yTxYsX8+qrr9ZYXOeTczmcArkHY9FoSm9ebnnFVirxmQVkF5jKPF8Lfzdr92ZSVgEZ+WWXbebnhpO2qGxytuG6LTpNfV1pduk/+OXEEqO0ITHPB5QgtJlpZK7/nP/l3kN2QSGNffT0u2KB4KSsAi5l5Jd53laB7ni46KwxxKaX3UXYwt/dOp4iNcdA9HW6E5v5uVkXrkw/8gkXkp1RnLqSm5uPG3pU2amw82to/SxNfF2tK5ln5ps4n5xT5nkb+ugJ9ChaaiW7wMTZpLLLhnjrCfIsKptrKOR0YnaZZYO9XGjgpQeKlnY5EZ8FZ76EZGdw7gomNSgNIDuFgEMLaHTrTAAMhWYi47Iu/9sC1PA6cyc/gvw4cAkGQ05RK1d+HJz6L3T+vxoM7AaK49ZfnkXn4iBxi+sq/sPm6s9peExGmbtCeLhoaRXoYb19LDaTQovFeruwsJAL2UXn8HR1oW1wSdnjlzIxmS2UxkWnoV2DkkkBkXFZGApLnyTkpFXbjCs7lZBNnrH0bn+tWm0zo/RMYjY5htLLqlUqOjf2tt4+m5Rz3evJlXtpR6XkknGda0TnRt6oL1+nLqbmkpZbdtkODb2sS5DEpOWRkmMAbK8HNam25gs3y+GTOaPRyMGDB5kxY4b1PrVazeDBg9m9e3epxxgMBgwGg/W25fKH2cnJvitbv782ks1nNVjORJZZxkmtWC/QJgtYlLKv1pUtW2gB83XKttNH83mTX7mIO18n9+CHtCFXlYgHICY9n+MxKXS+/OXy475oPvz7XJnnXTK+C72aF31hrD58ibfWnC6z7OcPdWRgm6JEcd2xeP79+8kyy84dHcZd7YNQpYezPeIUz51+/dpCkcCGXbxzbyijuhRdxHefSeXZFUfLPO9rw1oz7pai1dIPXUjnsW/Cyyw7fVALnuzbFICIuCwe/PpgmWUn9W/G1IFFs+3OJecycsE+oMvlH1tPpG/hxWa7wbszlzIKGPm57XvYaDCQl1f94+ZU6eFoLv6KovFDUVwoxIRRcUGl8UV14WcKA+4G787VHteNXBk3isvlJk4d1PK4ixUWFl248/PzMZnKvjDXR2ZzUX3sjUqz+UyM+WK39Y+fq/Vq5s2SR7tab49ftLeUP4q1zDu+j44hHqx8sof13ieX7Schy0BpWgW48b9JJZOBpiw/yPmU0j+njbxdWP9cH+vt6SsPExFf+h+Dfm46tr/Qz3r71Z+PcDA6s9Syep2GgzP6W2/P+v0YO86llVpWBUTMvN16e86fEaw/UXZL9aEZ/a2rF8xdd4L/HU0ss+zOF/vi41p0Lf1s4ylWHowDYNmjXejZrCSBtOd722AwoFKpUKtLOhudnZ1xdradPVuZfMFROHwyl5KSgtlsJijIdhxXUFAQJ0+WnhDMmTOH2bNnW2/r9XpWrFhh99ju9QNjppqIjLLLvNrZjPPlRrvfL6gJL/2zB8ALHc24X/7DZk2Mmv3X6SV6rr0Zn8vv4w2XVOxKLLtHfWTbEI7qPwbAYFDhm1t2v8WhA/tIOlH075hEFb7OZZ/3yOEDZBWtZML55OuXPX40HNPFouc9k3r9sicjjqGNK0rKItSP4OtcdrxnT0awIfl40Xkzr3/eC2dOsiGz6MVdyAZf57IH68acP8OGvKLk9FLu9cvGXzzHhg1FFZGcf/2yidrb2LA/CdhAhsG2bLBe4cKR3cQcK/PwKvZe0a9CQH/5d7HLMddOl+Mu7XpRq+MusXnz5poOodbR50FDVw2N3BQ2bCj5P/TSaigs4yNmyk6zKeuu1qAua7WM/EybsnpFg28ZZbXGbJuyWpMaX+fS/4B2NufblFUVlF3WFaNNWUtu2WWd1IU2ZY1ZZZdVgU3Z3LSyywJs2rQJ3eWvzqzk65fdumUrbpevU+kJJWXDDx0g48y15e3x3l6xYgUrV660ue/NN99k1qxZNvdVJl9wFCrFwXcpjouLo2HDhuzatYs+fUr+2nn55ZfZunUre/fuveaY6mqZy87OJiwsjMjISDw8PG58QE3JOIJ2/+MoaEB3xfpBpkxUWCjsuRS8O9VYeGW6Im6L2p2IiOO0b98BtSXbYeJ2mPqWuq4RDvMdUkdIfVcfe9Z1eVvmKpMvOAqHb5nz9/dHo9GQmGjb7JuYmEhwcHCpx5T2n1wVCgsLSUtLQ6/X4+rqWuXPV2mufaDpKDi/GJycQKUpWhTWnAYtJ+IU0rumIyzdFXGbNRoozMNJlY/GgeJ2mPqWuq4RDvMdUkdIfVcfe9Z1eY+vTL7gKBx+NquTkxPdu3dn48aN1vssFgsbN260ybzFDYQ+D/qQkhl+BclFt9tOq9Gwbuhy3CpjCkDRbweK26HqW+paCOHA6nK+4PDJHMD06dP56quvWLZsGSdOnGDSpEnk5uZaZ6uIcnAJgNAXAAWMGUW/271YdH9tVhy3ouDlStE6BY4UtyPVt9S1EMLB1dV8weG7WQHGjBlDcnIyM2fOJCEhgS5durB27dprBjlWN2dnZ958881q6dK1i6ZjIHoVJG2GwNuhyYM1HVH5NB2DcuEHGudtQPHt6VBxO1x9S11XK4f7DnFwUt/Vp6bqurbmCzfL4SdACDtz1P0rJe7q44gxg+PGLYQQNyDJnLiWxQxqB9xDT+KuPo4YMzhu3EIIcR2SzAkhhBBCOLA6MQFCCCGEEKK+kmROCCGEEMKBSTInhBBCCOHAJJkTQgghhHBgkswJIYQQQjgwSeaEEEIIIRyYJHNCCCGEEA5MkjkhhBBCCAcmyZwQQgghhAOTZE4IIYQQwoFJMieEEEII4cAkmRNCCCGEcGCSzFUhRVEwmUwoilLTodR5UtfVR+q6+khdVy+p7+ojdW1fksxVocLCQtasWUNhYWFNh1LnSV1XH6nr6iN1Xb2kvquP1LV9STInhBBCCOHAJJkTQgghhHBg2poOQAghHEWBycyljHxSc4yk5xlJzzWSXVCI0Wyh0KxgMlswKwpOGjVOWjXOWjXOOg3uzhq8XZ3wdXXCx9UJHzcd7s5aVCpVTb8kIUQdIMmcEKJWyDMWklNQSJ7RTK6xkAKTGYsCahWoVCo0KhUatQp3Zy0eLlrcXbQ4azVVEoux0MLpxGyOXcrk2KVMziXlEJ2WR0JWAfYar+2iUxPiraeht54QLz0h3npCvF1o4utKiwB3/N2d7PNEQog6T5I5IUS1UBSFmLR8ziRlcyYph7NJOcSm55GUZSAp20COoeIDoZ20ajxddPi7OxHo6UKgh7P1J8RbTxM/Vxr7uOLmfP2vOkVROJWYzfbTKWw/m8K+qFQKTJZSy7o5aQjwcMbHraiVzdNFi5NWjVajRqdWoVarMJktGEwWjGYLBSYzOYZC0nNNZOQZScszUmCyUGCycD45l/PJuaU+j4eLluZ+rjgZ1ERtOU/rIE/aBrvTzM8NrUZGyAghSkgyV06KolBYWIjZbC73MSaTCa1WS0FBQYWOExVXW+pao9Gg1Ur3GYDFonAkNoNd51I5dDGdg9HpZOSZrnuMWgWuTlr0ThpcnTRoVCosioJZUbBYwGS2kGsoJNdY9H9sLLSQkmMgJcfAyYTsMs/r7+5EY9+ixK6pnystAtxoGeCOs1bDmmPxrD4Sx/kU26TK00VLx0ZedGzoTWiwB038XGnq64qvm9NN///mG80kZRdwKSOfuIwC4jLyicvI51JGPhdSc4lNzye7oJCjl7IANQc2nrUe66RV0ybIndBgT0KDPYp+N/DA3935pmISQjguSebKwWg0Eh8fT15eXoWOUxSF4OBgYmJi5OJexWpTXbu6utKgQQOcnKq/m0xRFNJyjcRnFlxOcoyk5BjIzDdRYDJTYLJgMJkxmi2oL3dbqlUq1CrQO2lwdy7qvvSw/tYR4OGMv4cz/u5ON+zWNJktbD2VzLqIBDafSiIlx2jzuJNWTQt/N1oHedA60J2mfq4EergQ5OlMoKcLbk6acv3/mS0KOYZCcgyFZOQZSc4uat1LzjaQlFVAUraBSxn5RKflkZFnulwPRg5HZ5R5Tmetmt4t/LittT+3tQ6gTZB7lb2X9E4amvq50dTPrdTHC0xmLqbmcSYhk7W7DqHza0xUSh6nE7PJM5o5fimL45eybI7xd3emXQMP2od40bFh0U9jX32Nfx6EEFVPkrkbsFgsREVFodFoCAkJwcmp/H+VWywWcnJycHd3R62WbpGqVBvqWlEUjEYjycnJREVF0bp16yqNJc9YyPFLWRyJySAiLpOolFyiUnLJKqi6dZu89EVdmmqDmq0Fx2ns60ZDbz0BHs7sPJvCb+GXbBI4D2ctfVv507O5L92b+hDWwBMn7c3XiUatwkuvw0uvo6G3/rplM/NNxKTlFf2k5xGVksf55BzOJeeSmW+kbyt/7u0cwp3tg3G/QXdsdXHRaWgb7EELPxfMFxWGD++ATqfDYlGITc/nREIWJ+OzOZmQxcmEbC6k5pKSY2D7GQPbz6RYz+PpoqXD5cSu+HcTX1fUaknwhKhLasc3Vy1mNBqxWCw0btwYV1fXCh1rsVgwGo24uLhIMlfFaktd6/V6dDodFy9etMZjL4ZCMwcupLPtdDLbz6RwMiELSxmD8QM8nPF3L2pN83d3xkuvQ++kQa/T4KJTo9OosShFXaFmRcFsUSgwmckuKGrtyr3c6pWZbyIl20ByjgGTWSEz30RmvglQc+ZwXKnP7e/uxN2dQhgSFkTPZr52Sd5uhpdeh9flZOZqiqI4VMuVWq2iiZ8rTfxcGdo+2Hp/nrGQ04k5RMZlcexSJhFxmZyMzyaroJBd51LZdS7VWtbDRUv7EE+bBK+Zn5skeEI4MEnmykmSMVFeVfFeORabybiv91zT6hbs6UKnRl50auRFq0B3mvm70dTXDb2TfWd5KkpRIpecbSAhI4912/cS2Kwt8VlG4jLyScgsoLm/G/d3b8SAtgHoHGSAviMlctfj6qSlS2NvujT2tt5XPCP3+KVMjsdlcuxSFifis8guKGTP+TT2nE+zlnV31hIW4kmHEC86Nir63SLAHY0keEI4BEnmhHAAe6NSySooxMNFy5CwIAa0CaBXcz+CvezX8nc9KpUKb1cnvF2daObrQtpJheEDWqDT6arl+UXFOWnVdLiqRdJktnAmMYfjcZkcv7zsSmRcFjmGQvZFpbEvqiTB0+s0lxM8T9pfbsFrFejuMIm6EPWJJHPCLiZMmEBGRga//fZbTYdy0y5cuEDz5s05fPgwXbp0qelwAKxrmw1uF8RHD3ap0ViE49Jp1ISFeBIW4smDPRoDUGi2cDY55/KkiqIu2oi4LPKMZg5eTOfgxXTr8U5aNe0aFCV4xV20rYPcq2y9PyFE+UgyV0cNHDiQLl268N///rdajnNUpSWhjRs3Jj4+Hn9//5oL7CqWy9lcHekVFLWIVqO+vMyJJ/d3bwQUzRaOSsklIi6TY7FF3bQRl7LINhRyJCaDIzEZ1uN1GhVtgjzo2NDL2oIXGuyBi04SPCGqiyRzQlxFo9EQHBx844LVqHiegwrJ5kTV06hVtAp0p1WgO/d1aQgUTZaJTsu7PP6uKLk7dimTzHwTEXFZRMRlwf6YkuMD3Alt4EHbYA/aXV4LL9jTpc6MUxSiNpHBD3XQhAkT2Lp1Kx9//DEqlQqVSsWFCxcA2Lp1K7fccgvOzs40aNCAV199lcLCwuseZzabmThxIs2bN0ev19O2bVs+/vjjCsV08eJF7rnnHnx8fHBzc6N9+/asWbPG+vj14oKiFsOpU6cybdo0fHx8CAoK4quvviI3N5fHH38cLy8vunXrxl9//WU95kZxz5o1i2XLlvH7779bX++WLVu4cOECKpWK8PBwa9mIiAjuvvtuPD098fDw4LbbbuPcuXMVqoObUdwyJ+PRRU1Rq1U083fj7k4hzLirHd892YvwmUPY/vLtLHykG5Nvb8mANgH4uTlhthTtqPF7eBzvrz3F40v302fOJjrPXs+DX+zmzd+Ps3xvNIei08mtxM4fQghb0jJXB3388cecPn2aDh068NZbbwEQEBDApUuXGD58OBMmTOCbb77h5MmTPPXUU7i4uDBr1qwyj7NYLDRq1IhVq1bh5+fHrl27ePrpp2nQoAEPPvhguWKaPHkyRqORbdu24ebmRmRkJO7u7gA3jKvYsmXLePnll9m3bx8rV65k0qRJ/Prrr4wcOZJXX32V999/n8cee4zo6GhcXV1vGPeLL77IiRMnyMrKYsmSJQD4+voSF2e75MalS5fo378/AwcOZNOmTXh6erJz506bZLOqFY+ZU0urhqhFVCpV0c4avq4M69AAKJr5nJBVwIn4LE7EZ3MqoWg9vHPJRWsgXj3RAqCJryuhwR60CfKgZaAbrQKKfrs6ySVKiPKQT0od5OXlhZOTE66urjbdhZ9//jmNGzfms88+Q6VSERoaSlxcHK+88gozZ84s8ziNRsPs2bOtt5s3b87u3bv58ccfy53MRUdHM3r0aDp27AhAixYtyh1X8VIfnTt35vXXXwdgxowZvPvuu/j7+/PUU09hsVh4+eWXWbx4MUePHqV3797odLrrxu3u7o5er8dgMFy3W3X+/Pl4eXnxww8/WGdvtmnTplyv214UGTMnHIRKpaKBl54GXnruCA2y3m8oNHMuKde60PHJhGxOxmeRlG0gOi2P6LQ81kcm2pyrobeeFgFu1i7fVgHutAx0x88OW6oJUZdIMlePnDhxgj59+th8Cfbt25ecnBxiY2Np0qRJmcfOnz+fxYsXEx0dTX5+PkajsUIzPZ977jkmTZrE+vXrGTx4MKNHj6ZTp04Viqu4PBQlmH5+ftbkECAwMBCApKQku8UNEB4ezm233Vajy3AUt8zJBUw4KmetxjqT9kppucaiBC8+mzNJOZxLzuFcUg6puUYuXd6v9spdLQC8XXW0CnCnf5sApt7RSj4Xot6TZE7c0A8//MCLL77I3Llz6dOnDx4eHnzwwQfs3bu33Od48sknGTp0KH/++Sfr169nzpw5zJ07l6lTp5b7HFcnUyqVyua+4i90i8Vit7ihaFeHmmaxJnM1G4cQ9ubr5sStLf25taXt7PH0XCPnknM4m1T0cy45h7PJOcSm55ORZ+LAxXQOXExnVLeGNPKp2O48QtQ1kszVUU5OTpjNZpv72rVrx88//2yzhdHOnTvx8PCgUaNGZR63c+dObr31Vv75z39a76vM4P/GjRvz7LPP8uyzzzJjxgy++uorpk6dWq64KqM8cZf2eq/WqVMnli1bhslkqrHWOZkAIeobHzcnerj50qOZr839+UYzUSm5DP9kOwCGQktNhCdErSKzWeuoZs2asXfvXi5cuEBKSgoWi4V//vOfxMTEMHXqVE6ePMnvv//Om2++yfTp063j0ko7rnXr1hw4cIB169Zx+vRp3njjDfbv31+heKZNm8a6deuIiori0KFDbN68mXbt2gGUK67KKE/czZo14+jRo5w6dYqUlBRMJtM155kyZQpZWVk89NBDHDhwgDNnzvDtt99y6tSpSsdWUcVLk8gECFHf6Z2Kumu9XYv+sCoeTypEfSbJXB314osvotFoCAsLIyAggOjoaBo2bMiaNWvYt28fnTt35tlnn2XixInWSQVlHffMM88watQoxowZQ69evUhNTbVp7SoPs9nM5MmTadeuHcOGDaNNmzZ8/vnnAOWKqzLKE/dTTz1F27Zt6dGjBwEBAezcufOa8/j5+bFp0yZycnIYMGAA3bt356uvvqrWVjrrBIhqe0Yharfiz4JFcjkhUCnyZ811FRQUEBUVRfPmzXFxqdg+mBaLhaysLDw9Patk83VRojbV9c28Z8ry4bpTfLb5LBNubcase9vb5ZyVZTKZWLNmDcOHD5e9WauY1HXZur+9gdRcI+um9adtsIddzin1XX2kru1LMgwhHIBs5yWEreLPgkXaI4SoXcncu+++i0qlYtq0adb7CgoKmDx5Mn5+fri7uzN69GgSE23XIoqOjmbEiBG4uroSGBjISy+9VK0LugpR1SyyaLAQNoonS0kuJ0Qtms26f/9+vvjiC5u1xACef/55/vzzT1atWoWXlxdTpkxh1KhR1rFNZrOZESNGEBwczK5du4iPj+fRRx9Fp9Pxn//8x+5xKopCvun6sx+LWSwW8o1mtMZCu3T96XUaWU+pnlKQ2axCXEktLXNCWNWKZC4nJ4dx48bx1Vdf8c4771jvz8zMZNGiRSxfvpw77rgDgCVLltCuXTv27NlD7969Wb9+PZGRkfz9998EBQXRpUsX3n77bV555RVmzZqFk5OTXWPNN5kJm7nOrucsr8i3hsr2NvWULBoshC0V0jInRLFa0c06efJkRowYweDBg23uP3jwICaTyeb+0NBQmjRpwu7duwHYvXs3HTt2JCioZNuYoUOHkpWVRURERPW8ACGqmGznJYSt4pY5BcnmhKjxZp4ffviBQ4cOlbpuWUJCAk5OTnh7e9vcHxQUREJCgrXMlYlc8ePFj5XGYDBgMBistxVFQaPR4OzsfE1Zk8mEoihYLBYsFgvOGhXHZw0p12tTFIWc7BzcPdzt0qLirFFZdzcQtoqTneL/q5pksVhQFAWTyYRGo7HLOQvNRa9JsVhKXQuvOhU/f03HUR9IXd+Y0VRot/qR+q4+9q5rrVZbr3suajSZi4mJ4V//+hcbNmyw2xIO5TFnzhybDdgBxowZw9ixY68pq9VqCQ4OJicnB6PRWOHn0jtpMBvyKx3rlbIL7HKaWik6OprOnTuzbds2m/1WKyo7O9uOUVWO0WgkPz+fbdu22W0izvkoNaDm/PnzrFlz1i7nvFkbNmyo6RDqDanraxUUaAAVO3fu5JJ9ViaxkvquPvaq6/q+xEmNJnMHDx4kKSmJbt26We8zm81s27aNzz77jHXr1mE0GsnIyLBpnUtMTCQ4OBiA4OBg9u3bZ3Pe4tmuxWWuNmPGDKZPn269fb2WuYKCAmJiYnB3d69wwqkoCtnZ2Xh4eDj8XwxmsxmVSnXNRA6j0WiXcYnu7u4AuLm54enpeYPS16pNdV1QUIBer6d///52+yPl4J8nISGa1i1bMnxIa7ucs7JMJhMbNmxgyJAh9frLszpIXZftg5PbSTPk06fPrXRt4m2Xc0p9Vx9717VWW+MdjTWqRl/9oEGDOHbsmM19jz/+OKGhobzyyis0btwYnU7Hxo0bGT16NACnTp0iOjqaPn36ANCnTx/+7//+j6SkJAIDA4GiTN/T05OwsLBSn9fZ2bnUxK00VyYxFZ2RWtzdV1oSVB0sFgsffvghX375JTExMQQFBfHMM8/Qt29fbr/9dtLT061Jcnh4OF27diUqKopmzZqxdOlSpk2bxjfffMOrr77K6dOnOXv2LAMHDmTixImcOXOG3377jVGjRrF06VJ27NjBjBkzOHDgAP7+/owcOZI5c+bg5uYGFG2b9fTTT3P27FlWrVqFj48Pr7/+Ok8//TQALVu2BKB79+4ADBgwgC1btlTotULN1fWV1Go1KpUKnU5ntwtCcYKq1WpqzUXGnq9PXJ/U9bU0lwfNaargMyH1XX2kru2jRq96Hh4edOjQwebHzc0NPz8/OnTogJeXFxMnTmT69Ols3ryZgwcP8vjjj9OnTx969+4NwJ133klYWBjjx4/nyJEjrFu3jtdff53JkyeXO2Grq2bMmMG7777LG2+8QWRkJMuXL79mfOH15OXl8d577/H1118TERFhTZY//PBDOnfuzOHDh3njjTc4d+4cw4YNY/To0Rw9epSVK1eyY8cOpkyZYnO+uXPn0qNHDw4fPsw///lPJk2aZN3ftLh19e+//yY+Pp5ffvnFTrVQNxQP8a7pVkchaoviNRdlOy8hasEEiBuZN28earWa0aNHYzAYGDp0qHVPTwCNRsMff/zBpEmT6NOnD25ubjz22GO89dZbNRh1zcvOzubjjz/ms88+47HHHgOKWr/69etX7hYvk8nE559/TufOnW3uv+OOO3jhhRest5988knGjRtnXey5devWfPLJJwwYMIAFCxZYuxqHDx9u3Rv1lVdeYd68eWzevJm2bdsSEBAAFO2DWlb3eH1mkb1ZhbBh3ZtVsjkhal8yd3Wi4eLiwvz585k/f36ZxzRt2pQ1a9ZUcWSO5cSJExgMBgYNGlTpczg5OV2ziDNAjx49bG4fOXKEo0eP8v3331vvK55VGhUVRbt27QBszqVSqQgODiYpKanS8dUniuwAIYQNlXVpEiFErUvmhH3o9foyHyseU6ZcsdpmadPD9Xp9qd16xePgiuXk5PDMM8/w3HPPXVO2SZMm1n9fPS5CpZKlVsrLYl00uGbjEKK2UFm7WSWdE0KSuTqqdevW6PV6Nm7cyJNPPmnzWHGXZnx8PD4+PkDRBIjK6tatG5GRkbRq1arS5yieEWs2l2+rtPqmOPGW7byEKGL9LEguJ0Tt2AFC2J+LiwuvvPIKL7/8Mt988w3nzp1jz549LFq0iFatWtG4cWNmzZrFmTNn+PPPP5k7d26ln+uVV15h165dTJkyhfDwcM6cOcPvv/9+zQSI6wkMDESv17N27VoSExPJzMysdDx1kWznJYQtmQAhRAlJ5uqwN954gxdeeIGZM2fSrl07xowZQ1JSEjqdjhUrVnDy5Ek6derEe++9Z7MnbkV16tSJrVu3cvr0aW677Ta6du3KzJkzCQkJKfc5tFotn3zyCV988QUhISHcd999lY6nLrLIdl5ClEq28xJCulnrNLVazWuvvcZrr712zWN9+/bl6NGjNvddOYZuwoQJTJgw4ZrjLly4UOpz9ezZk/Xr15cZS2nHXd21++STT17TJSyKFP/PyAQIIYpIy5wQJaRlTggHIEuTCGGr+O8amQAhhCRzQjgEWZpECFtqWZtECKsKdbNe3S1XHmFhYfV+zzQhbpYiY+aEsKGWljkhrCqUZXXp0gWVSmUztup61Go1p0+fpkWLFpUKTghRxCKzWYWwdfmzILmcEJWYALF3717rOmXXoygKHTp0qFRQtVF5E1ghquK9UjIBwu6nFsIhScucECUqlMwNGDCAVq1a4e3tXa7y/fv3v+5OBI6geNeCvLw8h38tonrk5eUB1+54cTNkAoQQtmTNYCFKVCiZ27x5c4VOXhf2S9VoNHh7e1v3EHV1dS13V5fFYsFoNFJQUGDdQktUjdpQ14qikJeXR1JSEt7e3mg0GjuevOiXWprmhABKJkBIr4kQVbDO3IkTJ1i0aBEffvihvU9dY4KDgwEqvCm8oijk5+eXucepsJ/aVNfe3t7W94y9SMucELZKliap2TiEqA3skszl5ubyww8/sGjRIvbs2UNYWFidSuZUKhUNGjQgMDCw1A3py2Iymdi2bRv9+/e3a5ebuFZtqWudTmffFrnLSnaAkHROCCj5LEjDnBA3mczt3LmTRYsW8eOPP5Kfn8/zzz/P4sWLCQ0NtVd8tYpGo6nQhVqj0VBYWIiLi4skc1Wsrte1rDMnhC2ZACFEiQoPLkpKSuL9998nNDSU+++/H29vb7Zs2YJareaJJ56os4mcEDWpZGmSmo1DiNpCdXnQgaRyQlSiZa5p06bcf//9fPzxxwwZMkQG9gtRLYouWTL/QYgixZcemQAhRCVa5po2bcqOHTvYtm0bp0+froqYhBBXsbbMyRQIIYCSz4J0swpRiWTu5MmTfPfdd8THx9OzZ0+6d+/OvHnzABmcLURVsch2XkLYsG7NKrmcEBVP5gD69u3L4sWLiY+P59lnn2XVqlWYzWb++c9/8tVXX5GcnGzvOIWo12QChBC2ij8LsjSJEJVM5oq5u7vz1FNPsWvXLiIiIujWrRuvv/46ISEh9opPCIG0zAlxtZKWOcnmhLDb7IV27doxd+5cYmNjWblypb1OK4S4grTMCVFELevMCWF1U+vMmc1mfv31V06cOAFAWFgY9913H6NGjbJLcEKIItIyJ4Stkr1ZJZsTotLJXEREBPfeey8JCQm0bdsWgPfee4+AgAD++OMP2rdvb7cghajvFOs6c5LNCQElnwUZMyfETXSzPvnkk7Rv357Y2FgOHTrEoUOHiImJoVOnTjz11FP2jFGIek/2ZhXClkp2gBDCqtItc+Hh4Rw4cAAfHx/rfT4+Pvzf//0fPXv2tEtwQogiFpnNKoQNtSxNIoRVpVvm2rRpQ2Ji4jX3JyUl0apVq3KdY86cOfTs2RMPDw8CAwP5xz/+walTp2zKFBQUMHnyZPz8/HB3d2f06NHXPG90dDQjRozA1dWVwMBAXnrpJQoLCyv70oSofazJXM2GIURtUTIBQrI5ISqdzM2ZM4fnnnuOn376idjYWGJjY/npp5+YNm0a7733HllZWdafsmzdupXJkyezZ88eNmzYgMlk4s477yQ3N9da5vnnn2f16tWsWrWKrVu3EhcXZzPBwmw2M2LECIxGI7t27WLZsmUsXbqUmTNnVvalCVHryAQIIWxZlyap2TCEqBUq3c169913A/Dggw9aB6IW/4V0zz33WG+rVCrMZnOp51i7dq3N7aVLlxIYGMjBgwfp378/mZmZLFq0iOXLl3PHHXcAsGTJEtq1a8eePXvo3bs369evJzIykr///pugoCC6dOnC22+/zSuvvMKsWbNwcnKq7EsUotYovmDJBAghilgnQMgMCCEqn8xt3rzZnnEAkJmZCYCvry8ABw8exGQyMXjwYGuZ0NBQmjRpwu7du+nduze7d++mY8eOBAUFWcsMHTqUSZMmERERQdeuXa95HoPBgMFgsN5WFAWNRoOzs7NdX4/JZLL5LapOXa9rs8UCgMVsrvHXWNfrujaRur6Oy0lcoR0/E1Lf1cfeda3Vauv1H7sVSuaOHj1Khw4dUKvVDBgw4IblIyIirMuW3IjFYmHatGn07duXDh06AJCQkICTkxPe3t42ZYOCgkhISLCWuTKRK368+LHSzJkzh9mzZ9vcN2bMGMaOHVuuWCtqw4YNVXJeca26Wtfp6RpAxaGDBzFG1Y6WiLpa17WR1PW14uPVgJqIyEjWpEfY9dxS39XHXnU9fPhwdDqdXc7liCqUzHXt2pWEhAQCAgLKVb5Pnz6Eh4fTokWLG5adPHkyx48fZ8eOHRUJqVJmzJjB9OnTrbersmVuw4YNDBkypF6/yapDXa/rRdF7ICeLnj17cHvb8n3+qkpdr+vaROq6bBtzj3EoNZ7Q0HYM79vMLueU+q4+9q5rrfam9kBweBV69Yqi8MYbb+Dq6lqu8kajsVzlpkyZwh9//MG2bdto1KiR9f7g4GCMRiMZGRk2rXOJiYkEBwdby+zbt8/mfMWzXYvLXM3Z2dnuidv16HQ6+WKoJnW1rpXLK8zptNpa8/rqal3XRlLX19JoiubvqdUau9eN1Hf1kbq2jwolc/37979m6ZDr6dOnD3q9vszHFUVh6tSp/Prrr2zZsoXmzZvbPN69e3d0Oh0bN25k9OjRAJw6dYro6Gj69OljfY7/+7//IykpicDAQKCo2dbT05OwsLCKvDwhaq3iLYvq8ZAQIWzIosFClKhQMrdlyxa7PvnkyZNZvnw5v//+Ox4eHtYxbl5eXuj1ery8vJg4cSLTp0/H19cXT09Ppk6dSp8+fejduzcAd955J2FhYYwfP57333+fhIQEXn/9dSZPnlytrW9CVKXL8x/q9QBfIa6kutxaLamcEDcxm9UeFixYAMDAgQNt7l+yZAkTJkwAYN68eajVakaPHo3BYGDo0KF8/vnn1rIajYY//viDSZMm0adPH9zc3Hjsscd46623qutlCFHlilsfZNFgIYqopWVOCKsaTebKs3K3i4sL8+fPZ/78+WWWadq0KWvWrLFnaELUSirZnVUI4IpFgyWXE6LyO0AIIaqPtMwJYUu28xKihCRzQjiA4uuVjJkTooh1BwjJ5YSQZE4IRyB7swphS7pZhSghyZwQDqD4eqWWbE4IQCZACHElSeaEcAAl3aw1G4cQtYUsTSJECUnmhHAAMgFCCFtqazerpHNCSDInhAOQCRBC2CqZACHJnBCSzAnhAKwTIGo4DiFqC5kAIUQJSeaEcADFFyyZACFEEbUsTSKElSRzQjgARZYmEcJG8UdBkSkQQkgyJ4QjsEjLnBA21OriHSBqOBAhaoEa3ZtVCFE2RVGIjM/i9/A40nKNNR2OELWKtWVOsjkhJJkTwh7MFoUcQyG5hkLyjGYsioLZomBRFCyWojLOOjVOGjXOOjXOWg3OWjUuOg2aq9YbMRSaWXMsnqU7L3AkNtN6f4iXC039XKvzZQlRa8l2XkKUkGROiBswFJq5mJrH2aQcolJyic/MJzHLQFJWAYlZBrIKTOQZzZU+v5uTBk+9Dk8XHZ56LVEpeaTkGABw0qgZ1C6Qf3RtyMC2AThrNfZ6WUI4NNkBQogSkswJcQVDoZnIuCzCYzI4HJ3BsUuZXEzNLfdf/zqNClcnLRq1CrVKhUZdMs7NWGjBUGjBUGjGZC45Ya7RTK7RTHxmgfW+IE9nHu3TjId6NsbP3dmur1GIukCWJhGihCRzol5TFIULqXlsPZXEltPJ7DmfSoHJck05D2ctLQPdaRHgRkNvPUGeLgR5uhDo4Yy3qw53Zy3uLtpyt5xZLApGs4VcQyHZBYVkFZjIyi/6rddp6NfaH51G5icJUZbiP5JkzJwQksyJeupiai7/C4/j9yNxnE3KsXnM182JLo296drYm86NvQkN9iDAw9muuy+o1Spc1BpcdBppeROiEkqWJhFCSDIn6g1joYW/jsfz7e6LHLiYbr1fp1HRs5kvA9oEMKBtAG2DPGTbLCFqOdnOS4gSksyJOi8zz8SSXVF8vzea5OyiiQVqFfRt5c+9nUMY2iEYTxddDUcphKiIkm7WGg5EiFpAkjlRZ2Xmm1i8I4rFO6LINhQCEOjhzLheTRl7S2MCPV1qOEIhRGUVN55vO5PMm78fp6mfG039XGnq50ZjX73M/Bb1iiRzotZRFIWMPBMJWQUkZhWQlGUgLc9I9hWTBLLyTeQYCq0zRI2FFjKzNfwnYismc9H6bnlGM8bCoskMbYM8+OftLbmrQwOctDKxQAhH19hXD0BMWj7Ldl+0eUylghAvPU18XWnm70oTXzea+bnSxM+VRt6ueOq1MpRC1CmSzIkak5pj4GRCNudTcolKziUqpWgdt7iMAozma2eU3pgKDAabe1oHujNtcBvu6hBs3f5HCOH4/tGlIU183TidmM2F1FyiU/O4kJpHdGouuUYzlzLyuZSRz+7zqdcc6+6spaG3nhBvFxr66Gno7UqItwvBHk5kGIoWAZeBF8KRSDInqkWesZCDF9M5dDGD43GZHL+UabOuWml83ZwI9HAmyNMFPzenywvraq0L7BYtBaJGp1GjxsKBfXsYeFs/9M5OaNSgVatp4usqSZwQdZBKpaJ7Ux+6N/WxuV9RFFJyjESn5XIhJY+LaXlcTM3lYmoe0Wl5pOUayTEUcioxm1OJ2aWcWcs7R/4m2MuFEC89wV4lyxAVL0kU5OlMoIcLeifpyhW1gyRzokoUmi3sv5DOjrPJ7DmfxpGYDAqvWnlXpYJmfm60DHCjub8bzfyLfjf2cSXQ07lCY15MJhOpJ6B9iCc6nfxNLUR9pVKpCPBwJsDDme5Nfa95PP+KVru4jHwupedbb19KzyM+Ix+Tuaj7NiYt/7rP5emiLVlz0rMo2Qtwd8bP3Qn/y7993ZzwdXVCK+tGiiokyZywm3yjmU0nk9gQmcDmU8lk5ptsHg/xcuGW5r50auRNh4ZehIV44u4sb0EhRPXRO2loFehOq0D3ax4zmUz88ecauve7g8QcE3EZ+SRlGUjMKiAx23B5DG8BCVkFFJgsZBUUklWQw5mr1qosjY+rDj93Z3zdnPB3d8LPreTfvm5Fi4976Yt+ihcil3F9orzkSipuisWisO9CGr8cimXNsQRyLs8ahaJu0oFtAujT0o/eLfxo5KOXLychRK2mVkEDLxea+HuUWUZRFLINhdb9mROv+J2cYyA1x0BarpHUHCNpeUYUBdLzTKTnmco859U0alVRYqfX4Xk5wfO+nOx5uTrZPOburMXDRWvdicbduWgIinzf1h91JpmbP38+H3zwAQkJCXTu3JlPP/2UW265pabDqrOyC0z8eCCWZbsuEJ2WZ72/kY+e4R0bMLhdEN2b+qCR8WpCiDpGpVLh6VI0drdVYNlJHxRNpkjPM5KWayQlx0BqjtGa7KXklvw7M99ERp6JjHwTxkILZotCWm7RcZWh06iuSO50eFyR6Lm7aPFw1uLmrMXVSYOrkxa9kxq9rui23kmDXlf029VJg6tOi4uTGieNJIi1VZ1I5lauXMn06dNZuHAhvXr14r///S9Dhw7l1KlTBAYG1nR4dUpMWh6Ld0ax6kCstRXO3VnLiI4NGN29ET2a+siEAyGEuEyjVuHv7oy/uzNtgq6f+BUrMJnJyDNdTvCMZOQX/Tszz0RGfknil5lvIqugkFxDITkFheQYCq3fyyazckVr4PXH/lXktbjqNLhcTvKuTPhctBqcdWrrb+crfrtc/j2kXRBN/FztEouwVSeSuY8++oinnnqKxx9/HICFCxfy559/snjxYl599dUajq5uiM/M59NNZ/lxf4x1IkOrQHee6NuckV0byqwuIYSwExedhmAvDcFeFV/Y3GJRyDVeTuwKCsm+KtG78t/ZBSbyjGbyjWbyTear/l1o/bfJXPSdb7YUdS9nXzGcpiKa+7tKMldFHD6ZMxqNHDx4kBkzZljvU6vVDB48mN27d5d6jMFgwHDFemSKoqDRaHB2tu+G5/M2nGbLCTU/Jx9ApXbcmUxmi8K+C+nWBXhvbeHLk/2a0a+V3+UmdwsmU2XWhbMfk8lk81tUHanr6iN1Xb3qSn27aMDFVYu/q30u8SazhYLLyV6ByXL5t5k8U1Hyl2c0X1683UxBoYUCkwVDoRmDqWhR94LL/w5w011Tx/aqa622fk8YUSmKY+9sFxcXR8OGDdm1axd9+vSx3v/yyy+zdetW9u7de80xs2bNYvbs2Tb3jRkzhrFjx9o1ti9OqInMcNwk7motPRSGNzHTyrOmIxFCCCFKDB8+vF4vS+XwLXOVMWPGDKZPn269XVUtc24tE9m0+xDt27dHo3Hsbshmfq50a+Jda//yMZlMbNiwgSFDhtTrD3R1kLquPlLX1Uvqu/rYu6612nqZzlg5/Kv39/dHo9GQmJhoc39iYiLBwcGlHuPs7Gz3xK00A9oGkXtOYXjPJvLFUE10Op3UdTWRuq4+UtfVS+q7+khd24fD9wE6OTnRvXt3Nm7caL3PYrGwceNGm25XIYQQQoi6yOFb5gCmT5/OY489Ro8ePbjlllv473//S25urnV2qxBCCCFEXeXwLXNQNHnhww8/ZObMmXTp0oXw8HDWrl1LUFBQjcZlMBhYsWKFzcxZUTWkrquP1HX1kbquXlLf1Ufq2r4cfjZrbZaZmYm3tzcZGRl4eXnVdDh1mtR19ZG6rj5S19VL6rv6SF3bV51omautimd+1tYZoHWJ1HX1kbquPlLX1Uvqu/pIXduXJHNCCCGEEA5MkjkhhBBCCAcmyVwVcnZ25s0336yWNe3qO6nr6iN1XX2krquX1Hf1kbq2L5kAIYQQQgjhwKRlTgghhBDCgUkyJ4QQQgjhwCSZE0IIIYRwYJLMCSGEEEI4MEnmhBBCCCEcmCRzQgghhBAOTJI5IYQQQggHJsmcEEIIIYQDk2ROCCGEEMKBSTInhBBCCOHAJJkTQgghhHBgkswJIYQQQjgwSeaEEEIIIRyYJHNCCCGEEA5MkrkqpCgKJpMJRVFqOpQ6T+q6+khdVx+p6+ol9V19pK7tS5K5KlRYWMiaNWsoLCys6VDqPKnr6iN1XX2krquX1Hf1kbq2L0nmhBBCCCEcmCRzQgghhBAOTJI5IUSdZDJbajoEIYSoFpLMCSHqlMSsAsZ9vYcus9dzLjmnpsNxeIqi8Hv4Je77bAdrjyfUdDhCiFJoazoAIYSwl22nk3l+ZTipuUYATsZn0zLAvYajclxxGfm8/ttxNp1MAuB/Ry4xrENwDUclhLiaJHMVYDabMZlM5S5vMpnQarUUFBRgNpurMLLaSafTodFoajoMUQ9YLAqfbT7LvL9Pc+VKBwqy7EFlWCwK3++L5r2/TpJjKLzi/hoMSghRJknmykFRFBISEsjIyKjwccHBwcTExKBSqaomuFrO29ub4ODgevv6RdXLzDfxwo/h/H2iqPVo7C1NOJmQxeHoDGQJq4q7mJrLSz8dZV9UGgDdmnjTpbEPi3dGYZEKFaJWkmSuHIoTucDAQFxdXcudmFgsFnJycnB3d0etrl/DExVFIS8vj6SkogtsgwYNajgiURedTMji2W8PciE1Dyetmnf+0YEHezRm7Jd7AKRdrgIURWHVgVhmr44g12jG1UnDy0PbMr5PM1bujykqU8MxCiFKJ8ncDZjNZmsi5+fnV6FjLRYLRqMRFxeXepfMAej1egCSkpIIDAyULldhV/87EscrPx0l32SmobeehY90p2MjLwCK/96S1eXLJy3XyKs/H2V9ZCIAvZr78uEDnWns6wpcWZ81FaEQ4nokmbuB4jFyrq6uNRyJYyquN5PJJMmcsAuzReH9dSf5Yut5AG5r7c/HD3XF183JWkZ69ctv86kkXv7pKMnZBnQaFS/c2ZanbmuBRl1SiSX/kmxOiNpIkrlykjFflSP1Juwp11DItJXhbLjcgvTPgS154c62NokHgOpy+iFjvMpmMlt476+TfL0jCoDWge7MG9OFDg29rikrLXNC1G6SzAkhHEJcRj4Tlx3gRHwWTlo1H9zfifu6NCy1rCQf1xefmc+U5Yc5eDEdgAm3NuPVu0Jx0ZXeel78R5kkx0LUTpLMCSFqvSMxGTz5zQGSsw34uzvxxfgedG/qU2b54uRDco9rbT+TzL9+CCct14iHs5YPHuh8w7Xjits9pTqFqJ3q36j8euCee+5h2LBhpT62fft2VCoVR48ereaohKicP4/G8+AXu0nONtA2yIPfJve9biIHknyUxmxRmLfhNI8u3kdarpGwBp788Vy/ci0CLMmxELWbtMzVQRMnTmT06NHExsbSqFEjm8eWLFlCjx496NSpUw1FJ0T5fb39PO/8eQKAO0ID+WRsV9ydb/y1JbNZbWUXmPjXD+HWnRzG3tKYN+9pX2a36tUkORaidpOWuTro7rvvJiAggKVLl9rcn5OTw6pVq5g4ceJ1j9+yZQsqlYp169bRtWtX9Ho9d9xxB0lJSfz111+0a9cOT09PHn74YfLy8qrwlYj6ymJR+L8/I62J3IRbm/HVoz3KlcjBFcmHZB9cSMll5Oe72HQyCWetmrkPdGbOqE7lTuRAkmMhajtpmasgRVHIN5Vvay6LxUK+0YzWWGiXdeb0Ok25ZodqtVoeffRRli5dymuvvWY9ZtWqVZjNZsaOHVuu55s1axafffYZrq6uPPjggzz44IM4OzuzfPlycnJyGDlyJJ9++imvvPLKTb0uIa5kLLTw0k9H+D08DoBX7wrlmf4tKjQz2totWM/bknacSWHy8kNk5psI9nThy0e706mRd4XPIxNKhKjdJJmroHyTmbCZ62rkuSPfGoqrU/n+y5544gk++OADtm7dysCBA4GiLtbRo0fj5XXt0gOleeedd+jbty9Q1HU7Y8YMzp07R4sWLQC4//772bx5syRzwm6yC0xM+u4QO86moFWreP/+Tozq1ujGB15FLckHy3Zd4K0/IjFbFLo09ubL8d0J9HSp1LnUkhwLUatJN2sdFRoayq233srixYsBOHv2LNu3b79hF+uVrhxXFxQUhKurqzWRK76veLsuIW5WUnYBY77Yw46zKbg6aVg0oWelErkixclH/WOxKLy1OpI3/xeB2aIwqltDfni6d6UTuSvV5+RYiNpMWuYqSK/TEPnW0HKVtVgsZGdl4+HpYbdu1oqYOHEiU6dOZf78+SxZsoSWLVsyYMCAch+v0+ms/1apVDa3i++zWCwVikmI0sSk5THu671Ep+Xh7+7E4gk9K9UdWKy+dgsWmMxM/zGcNccSAHh5WFsmDWh504t3y2xWIWq3Wt8yN2fOHHr27ImHhweBgYH84x//4NSpUzZlCgoKmDx5Mn5+fri7uzN69GgSExOrJB6VSoWrk7bcP3onTYXKX++nol/IDz74IGq1muXLl/PNN9/wxBNPyI4MotY5m5TDAwt3E52WRxNfV36edOtNJXJw5ezL+pN9ZOQZGb9oL2uOJaDTqPj4oS78c2Aru3zm62N9CuFI7NYyV5l1y8LCwtBqrx/C1q1bmTx5Mj179qSwsJB///vf3HnnnURGRuLm5gbA888/z59//smqVavw8vJiypQpjBo1ip07d1bqtdQV7u7ujBkzhhkzZpCVlcWECRNqOiQhbETGZTF+0V5Sc420DnTnuyd7EWSH7sDi/MVST3KPmLQ8Hluyj/PJuXi4aPlyfA/6tPSz2/nrW30K4Wjslsx16dIFlUpV7qnrarWa06dP24zBKs3atWttbi9dupTAwEAOHjxI//79yczMZNGiRSxfvpw77rgDKBro365dO/bs2UPv3r0r94LqiIkTJ7Jo0SKGDx9OSEhITYcjhNXh6HQeW7yPrIJCOjT05JsneuHr5mSXcxfvzVof+gXPJuXwyNd7ScgqIMTLhaVP3EKbIA+7Pofa2m9t19MKIezErmPm9u7dS0BAwA3LKYpChw4dKvUcmZmZAPj6+gJw8OBBTCYTgwcPtpYJDQ2lSZMm7N69u9RkzmAwYDAYbOLRaDQ4OztfU9ZkMqEoChaLpcLjw4oT2+Lja0KvXr0wm4uWUilvDP3797/mmEcffZRHH33U5hwzZ85k5syZ1z2vxWJBURRMJhMaTcXG/FWEyWSy+S2qjj3qem9UGs98d5hco5luTbz5enxXPJxUdvz/K/rsFZrNDv2euFFdR8Rl8fiyg6TnmWgV4MaSCd0J9nSx+2u2fh8oFoeuzxuR75HqY++61morPhSpLrFbMjdgwABatWqFt7d3ucr3798fvV5foeewWCxMmzaNvn37WpPBhIQEnJycrnneoKAgEhISSj3PnDlzmD17ts19Y8aMKXX9Na1WS3BwMDk5ORiNxgrFWyw7O7tSx9UFRqOR/Px8tm3bRmFhYZU/34YNG6r8OUSRytZ1RLqKJafUmBQVbbwsPBScwvZN9v1/S0hQA2qOH49gTepxu567JpRW1+ey4MuTGgrMKhq7KTzeNJNDOzZVyfMfSVUBGlLT0lmzZk2VPEdtIt8j1cdedT18+PBrJunVJ3ZL5jZv3lyh8pX5Qpg8eTLHjx9nx44dFT72SjNmzGD69OnW29drmSsoKCAmJgZ3d3dcXCo2lkdRFLKzs/Hw8KhVfzFMmjSJ77//vtTHxo0bx4IFC+z2XAUFBej1evr371/h+qsIk8nEhg0bGDJkSL3+QFeHm6nrv08ksWTfEUyKwqDQAD5+sBPOFZylXR7rso8QnppIWPv2DO/dxO7nry5l1fX2Myl8uSKcArOFns18+GJcVzxcqm5xAm1kIotPH8HHx4fhw2+psuepafI9Un3sXdc3Gn9f19n11b/44os8+eSThIaG2vO0AEyZMoU//viDbdu22ew3GhwcjNFoJCMjw6Z1LjExkeDg0jeQdnZ2LjVxK43ZbEalUqFWqyu8vEhx92Px8bXF22+/zUsvvVTqY56ennaNVa1WW5c1qY4vx+p6HlHxul4fkcBzK49gMivc3akB88Z0Qaepms9F8XtYpVLXiffDlXW9+WQSz34fjtFsYWDbABaM647eqeqGMIDthbIu1OeNyPdI9ZG6tg+7fpP+/vvvtG/f3rpYbW5u7k2fU1EUpkyZwq+//sqmTZto3ry5zePdu3dHp9OxceNG632nTp0iOjqaPn363PTz10WBgYG0atWq1J/AwMCaDk/UQesjEpi8/BAms8K9nUP4bxUmcnDldl51y+aTSTzz7UGMZgt3dQjmy/E9qjyRg5IJEDKbVYjaya7fpmfOnGHz5s20adOGf/3rXwQHB/PEE0+wa9euSp9z8uTJfPfddyxfvhwPDw8SEhJISEggPz8fAC8vLyZOnMj06dPZvHkzBw8e5PHHH6dPnz52nckqG0xXjtSbuDqR++jBzmirMJGDK7fzqjvvvy2nbBO5T8Z2xUlbPS3+JevMCSFqI7t/E/Tv35+lS5eSkJDAxx9/zJkzZ+jXrx/t2rXjww8/rPBivgsWLCAzM5OBAwfSoEED68/KlSutZebNm8fdd9/N6NGj6d+/P8HBwfzyyy92eT3Fzb95eXl2OV99U1xv0oxeP9VEIgclyUddsf1MCk9fTuSGtg/ik7Fdq7Rl82rWIb91KDkWoi6pshGDbm5uPPHEEzzxxBOcPXuWJUuWMGfOHF577TWbZUFupDx/Wbu4uDB//nzmz59/MyGXSqPR4O3tbd2D1NXV9f/bu/O4qKv1geOfmQGGfZdNQVFAXABxvYpmpeVSllmWZmpmdSuz1H63ssVss2wv89ZttW5WtmiZpWVmei1TAVFxARcUZZV932a+vz+QURQVbIbZnvfrNS9k5jvfc3ia8PGc85zT6mIGvV5PXV0dNTU1FrVmrj0oikJVVRX5+fl4e3ubdFsSYZnOTOTGtWMiB7Z1/NSBEhUffp5CXYOeq3sGsmRy33ZN5OCM49HatVUhRGuZvPyjsrKS//3vf2zatIni4mK6d+9u6iaNrqmQoq2HyiuKQnV1NS4uLhZVzdqevL29z1uIImzX2Ync6+2YyIHtHD+VnFnCh2lq6vR6ruoZyNu39m23qdUzNW3CbAvJsRC2yGTJ3JYtW/joo4/45ptvUBSFiRMnsnjxYhISEkzVpMmoVCqCg4MJCAho0waH9fX1bN68mcsuu8wupxkdHR1lRM4Ord+XZ9ZEDjBkc9a8YH9fdhl3/jeZOr2KyyL9WGqmRA7OPM7LigMqhA0zajKXk5PDJ598wrJly0hPT+cf//gHr732GpMmTcLd3d2YTZmFRqNpU3Ki0WhoaGjA2dnZLpM5YX82pZ9k1nIzJ3JY/0hSRkEl0z7aTnlNA+EeCm9P6mO2RA5sa9paCFtk1GQuNDQUPz8/pk6dysyZM+nRo4cxby+EsGBbDxdy96eJhmpLcyVycEY1qxVOs+aUVnPbB9soqKilR5AH00KL22X7kQuRalYhLJtRk7mvvvqK6667zu53YhbC3iQdK2bmJzuobdBzZXQAb06KN1siB2cs2Ley7KO4so6pH24nq6SacH83Pprel+2bN1z8jSamssGtXoSwJUbNuiZMmNDs+/z8fPLz8885iD02NtaYzQohzCg1q5TbP95OVZ2OoRH+/HuK+dZ2NVFZ4eYkNfU67vo0kUP5FQR7OfPZnYPwd7OMfxhbYzyFsCcm+U2RlJTE9OnT2b9/v+FfciqVCkVRUKlU6HQ6UzQrhGhnabnlTP1wG+U1DQzo4sN70/rhbIKzVtvKsGDfSiog9HqFh77aReKxYjycHfj0joF09HZpU8GVKUkBhBCWzSTJ3B133EFUVBQffvghgYGBdrsthxC27GhhJVM+SKS4qp64Tl58dPsAXJ0sZCTJyvZFe3HdAX7ck4OjRsV/pvYjMtDD3F1qxlqnrYWwFyb5zXvkyBG+/fZbIiIiTHF7IYSZldbBjGVJjYv0gz355I6BeDhbTsW2NVVffvLnUd7bfASAl2+KY0g3fzP36FyG6mAz90MI0TKTLGwZMWIEu3btMsWthRBmVl5Tz7v7NZwoqaGLnyv/nTkQb1cnc3erGWvZNPiXvbk8/cNeAP41qjvj4zuauUctkwIIISybSUbmPvjgA6ZPn05qaiq9e/c+Z4+16667zhTNCiFMrKZexz3LU8iuUtHB3Yn/zhyEv7vW3N06hzVMC+45UcoDX+5Er8DkgaHcd3k3c3fpvGRrEiEsm0mSua1bt/LHH3+wdu3ac16TAgghrJNOrzDnyxS2Hy1Gq1H4YFpfQn1dzd2tFln6tGB+eQ13fZpITb2e4VEdePb63ha9ttiapq2FsEcmmWadPXs2t912Gzk5Oej1+mYPSeSEsD6KorDg+1TW7c3FUaPiru56egZ7mrtb52XJ04I19Tr++d8kcstq6NbBjSW3mndPvtZQW3A8hRAmSuYKCwuZO3cugYGBpri9EKKd/fv3wyzflolKBa/eFEOkl2X/pW6YFrSwbiqKwuOrUtmZWYKnswMfTB+ApwUVjpyPtVUHC2FvTJLMTZgwgY0bN5ri1kKIdrZmdzYv/5wGwNPX9WJM7yAz9+jiDNOCFpZ+fLglg2+TT6BRq1g6pS/h/m7m7lIryTSrEJbMJGvmoqKimD9/Plu2bCEmJuacAogHHnjAFM0KIYxsZ2YxD33VWJk+c2g40wZ3sZiNbC/EEgsgNqbls+in/QA8cU0PhkV2MHOPWk9lxWfdCmEPTFbN6u7uzqZNm9i0aVOz11QqlSRzQliBE8VV3PVpIrUNekb2COCxsT3M3aVWs7QCiIyCSh74orFy9Zb+odw+pIu5u9QmljptLYRoZJJkLiMjwxS3FUK0k/KaemYuS6Sgoo6ewZ68OSkejdpyqy3PZkkjc1V1Ddzz3yTKaxro39mHZ8b3sujK1ZaopZpVCItm2SVUQoh216DTc//nO0nLKyfAQ8uHt/fHTWsZx3S11umRJPNmH00FD2l55fi7a/n3lL5oHcx/dm1bWXJ1sBDCiMncvHnzqKysbPX18+fPp6ioyFjNCyGMZNFPB9iUfhIXRw0fTh9AsJeLubvUZpZSfbl8WyardmY1FjzcGk+Ap7OZe3RpLG3aWgjRnNGSuTfffJOqqqpWX7906VJKSkqM1bwQwghWJp/goz8al0m8fkscMZ28zNyjS3N6WtB86UfK8RKe+WEfAI+M7s6grn5m68vfZUnT1kKIcxlt7kRRFKKiolq9FqQto3hCCNPbc6KU+Sv3APDAlRGM7h1s5h79DWZOPooq65i1PJk6nZ7RvYK4a1hX83TEyCypmlWnV9ieUUT3IA983SzrbGAh2pvRkrmPP/64ze+RTYWFsAwFFbX887+NlasjogOYMzLK3F36W8w5LajTK8xZkUJWSTVd/Fx5aWKs1RU8nK2p+3oLyeX0eoW5K1JYvSsbjVpFQoQ/18YGM6pnEF6ulr8JsxDGZrRkbvr06ca6lRCiHdXr9Mxankx2aQ1d/d14fVIf1FZUudqS08lH+2cf7/x+iM3pJ3F2VPPObf2s4oSHi7GkalZFUViwOpXVu7JRqRqT583pJ9mcfpLHNXsYFtmBa2ODuapnIB42EHshWsO6StSEEEa36Kf9bMsowl3rwHvTbCP5MNe+aEnHinj914MAPHN9b3pY8Pm1bXF6YNH82dyrv6Tz2V+NR8u9NSmeXiGe/Lg7hzW7c0jLK+e3A/n8diAfJwc1l0d14Nq4EEZEB1hdRbYQbSGfbiHs2KqdJ/j4j6MAvHZzHBEBHubtkJGozTCtWVpVzwNfpKDTK1zfJ4SJ/Tq1ex9MRWUhx3m9v/kIb288BMBz43szLi4EgNkjIpk9IpKDeeX8sDuHNbuzOXKykl/25fHLvjycHdWMiA7k2thgrogOwNnR+raHEeJCJJkTwk4dzCvnsZWpQGPBw9W9LP/M1dZq733RFEXh0ZW7ySqpprOfK8+N72316+TOZAlbvazYkcnzp45De3h0d6YM6nzONZGBHsy7yoO5IyM5kFvOmt3ZrNmdw7HCKn7ck8OPe3JwddIwskdjYndZVAdJ7IRNkGROCDtUVdfAvcuTqa7XMTTCnwetvODhbIZp1nZq7/PtmaxNzcVBreKtSfE2t1arKZ7mWIMIsGF/nqHS+p/Du3Lf5REXvF6lUtEj2JMewZ7839XdSc0qMyR2WSXVrN6Vzepd2bg6abiiewCjewdxRXQAWtlGX1gpi//obt68mXHjxhESEoJKpeK7775r9rqiKCxYsIDg4GBcXFwYOXIkBw8eNE9nhbACiqLwxKpUDuVXEOCh5Y1JfazqqK5WaccF+2m55WfsJxdNXKi36RttZyozFkDsPlHC/Z83nms7sV8nHh0d3ab3q1QqYjp5MX9sD7Y8cgWr7hvCzKHhdPR2oapOx497cpj9xU76Prueuz9LZlu+iuKqOhP9NEKYhklG5mpqaliyZAkbN24kPz8fvV7f7PXk5ORW36uyspK4uDjuuOMOJkyYcM7rL730Em+99RaffPIJ4eHhPPnkk4waNYp9+/bh7Gydu60LYUordhxn5alTCZZMjsffXWvuLhlde40k1dTrmP1FMrUNeoZHdWDm0HCTtmcu5jrO63hRFXcs20F1vY5hkf4smhDzt6avVSoV8WE+xIf58MQ1PdiTVcq61FzWpeZypKCSjWkFgIYVizfxj66+jO4dzKiegVZ7coewHyZJ5mbOnMkvv/zCTTfdxMCBA//W/3xjxoxhzJgxLb6mKApvvPEGTzzxBNdffz0An376KYGBgXz33XdMmjTpktsVwhbtyy7jqdV7AXjo6iirPpXgQtprjdeLaw+QnldBBw8tr94cZ/VbupxPe09bA5RU1XH7x9spqKijR7An/57SF0eN8SaTVCoVsZ28ie3kzb9GdedgfgVrdmXx7V+HyKqCPw4V8sehQhZ8n0rfMB/G9A5iVK8gQn1djdYHIYzFJMncmjVr+Omnn0hISDDF7Q0yMjLIzc1l5MiRhue8vLwYNGgQW7duPW8yV1tbS21treF7RVHQaDRotcYdoaivr2/2VZiOxPriymsauG950qlRJH9mDg67pHhZQ6yVU7MBOp3eZP3ccqiQZX8eBWDxDb3w0qqN3palxFqnawAap1nboy+1DXru+jSJwycrCfLU8t5tfXDWmLbtcF9n7hkaRrfqNLr3G8pvBwv5ZV8+KcdLSTpWTNKxYp77cT/Rge5cGR3AldEdiAnxtNkE3tSM/dl2cHCwqaKjtlIpJhg379mzJ19++SWxsbFGva9KpWLVqlWMHz8egD///JOEhASys7MJDj599NDNN9+MSqVixYoVLd5n4cKFPP30082eu+WWW5g8ebJR+yuEJfn0oJqkAjXeTgoPx+pws601+s38fELFT8c1DA7QM6mb/uJvaKOqBnhxl4bSOhVDA/VM7Gr8NizJyWp4LsUBrVrhpUE6k7alKPDZITWJBWqcNQoP9tIR4mbSJi+opBZ2F6nYXaTiUJkKhdMJg6ejQi8fhd4+ClFeCk5SGGs2Y8eOxdHRhn+pXYRJRuZeffVVHnnkEd599106dz63fNzc5s+fz7x58wzfm3Jkbv369Vx11VV2/SFrDxLrC/s+JZukralo1Cremz6Q+DDvS76XNcT66O9H+On4IUJDQxk7tpfR7z/3q92U1uXSxc+VpXf/A1cn02wMYCmxziyq4rmULWgcHBg7dpRJ2/rP5gwSCw6iUat4d2o/Erq131KA88X71lNfi6vq2JxewIYDJ9l8qICyWh1b81VszQdnRzUJ3fy4snsHrujegQ4etrcW1ZiM/dl2cLDvzTlM8tP379+fmpoaunbtiqur6zn/oYqKiozSTlBQ475YeXl5zUbm8vLy6NOnz3nfp9VqjZ64XYijo6PF/qVnayTW58osrGLhmgMAPDgikoHdOhjlvpYcaweHU0MkKpXR+/jDrmzW7MlFo1bx+i198HJzMer9W2LuWDudaltRMGk/NuzP49VTJ2gsHNeTy6PNs/fh+eId4OXITQPcuGlAZ+oa9GzLKOTXfXn8uj+frJJqNhw4yYYDJwHoE+rNyB4BXBEdQM9gT7ueArwQc3+2bYVJkrnJkyeTlZXFokWLCAwMNNmHODw8nKCgIDZs2GBI3srKyti2bRv33nuvSdoUwprU6/Q8uGInFbUNDOjiw6wrLrw/l60x9iKS3NIanviucaPlWVdEEB/mY9wGLJxiwhKI9LxyHvwyBUWBKYPCmDq4i8naMgYnBzXDIjswLLIDC69TOJBb3pjYHchn1/ESUk49XvklnQAPLcOjOjC8eweGRXTAy1WSF2FcJknm/vzzT7Zu3UpcXNzfvldFRQWHDh0yfJ+RkUFKSgq+vr6EhYUxZ84cnnvuOSIjIw1bk4SEhBjW1Qlhz5ZsOMjOzBI8nB14/RYb3E/uPAwHwxvxnoqi8PC3uymtrie2kxezr7SfxPj01iSmuX9xZR13fpJIRW0Dg8J9WXid8afGTenMTYpnj4gkv6yGDQfy2bA/jz8OFZJfXsvXSSf4OukEGrWK+FBvLu/egeFRAfSSIgphBCZJ5qKjo6murjbKvRITE7niiisM3zetdZs+fTrLli3j4YcfprKykrvvvpuSkhKGDh3KunXrZI85Yfe2ZxQZzrF8/oYYOvnYz5YKpkg+PvvrGJvTT6J1UPPazX2Muk2GpVOZIDlu0qDTc/8XyWQWVRHq68I7t/Wz+tgGeDozeWAYkweGUdugI/FoMb+n5fN72kkO5leQeKyYxGPFvPJLOv7uWi6L8ufy7gEMi/DHx83J3N0XVsgkydyLL77IQw89xPPPP09MTMw58+Genp6tvtfll19+wY0qVSoVzzzzDM8888wl91cIW1NaXc/cFSnoFbixbyeuO3Ugub04vS+acdKP40VVvLC2cd3ho2OiiQhwN8p9rYXahJsGv/xLGn8cKsTNScMH0wbga2PJjNZBQ0KEPwkR/jx+DZwormJzegG/p+Xzx6ECCipqWZmcxcrkLNQqiO3kzdBT1/ft7I3WQUpkxcWZJJkbPXo0ACNGjGj2vKIoqFQqdDrTlrYLYc8UReHxVXsMh74/fb11TVkZg8qIu9wqisIj3+6mqk7HoHBfplv4Wi5TUGGa47zWpebyn01HAHh5YhzdgzyM24AF6uTjyq2Dwrh1UBh1DXoSjxWxKe0km9JPciC33LDW7u2Nh3B2VDOgi68huesZLFOyomUmSeY2btxoitsKIVph9a7GA8U1ahVv3NIHd639lew3JR/GOM7ri+3H+fNwIc6OahbfGGuXf5ma4kSNIycr+L+vdwFw17BwxsYEX+QdtsfJQc2Qbv4M6ebP/LE9yCmtZsvBAv48XMiWQwWcLK/lfwcL+N/BAgB8XB0Z0q0xsRsa4U+Yn/0snRAXZpLf8sOHD2/Vdffddx/PPPMM/v7+puiGEHYnr6yGBd83Htc1+0r7q7ZsYqzkI7ukmkU/7Qfg/67uThd/M+5ea0aGgU4jDc1V1TVw72fJVNQ2MLCLLw+PjjbKfa1dsJcLE/uHMrF/KIqicDC/gi0HC/jjUAF/HSmkuKqeH/fk8OOeHAA6+bgwpJsfg8L9GBjuK0eN2TGz/pP9s88+4//+7/8kmRPCCBRFYf7KPZRW19O7o6fdbUNyJsOC/b+ReyiKwmOr9lBR20DfMG9mJIQbqXdWyIgjc43LAFJJyyvH313L27fGW33BgymoVCqiAj2ICvTgjqHh1Ov07D5RwpaDhfxxqIDkzGJOFFfzVeIJvko8AUBHbxcGhfsyqKsvg8L96OznKvvb2QmzJnOmWEwrhL36OvEEvx3Ix0ljf9WWZzPGkrlvk7P4Pe0kTg5qXropzm62dWmJMdfMLd+WyaqdWWjUKpbeGk+Ap+w80BqOGjX9OvvSr7MvD46MpLK2ge0ZRfyVUci2I0XsySolq6SalTuzWLkzC4BATy0Dw/0YFO7LP7r60q2DuyR3Nsr+FtMIYYNOFFfxzJp9ADx0dRRRgba/kPxCVH+z+jK/rIZnfmicrp47MsruqlfPdmYe21TIdikO5JYZPqePjO7OoK7td1SXrXHTOnBFdOMJEwCVtQ0kZxaz7UgR2zIK2XW8lLyyWn7Ylc0Pu7IB8HNzon8XH/p1bnz0CvHC2VGqZW2BJHNCWDm9XuHhb3ZTUdtAv84+3Dmsq7m7ZHan13i1/b2KovD4d6mU1TQQ09GLu4bZ8fTqKWcmb4pyRrVwG1TX6Zj9+U7qGvRcGR3AXfI5NSo3rYPhRAqAmnodOzNL2HZq5C45s5jCyjp+3pvHz3vzAHDSqOnV0ZN+YacTPBkptU6SzAlh5f771zH+PFyIi6OGVyfa93Rgk9Ob3LY9m/tpTy7r9+XhqFHx8sRYHOx4urrJmZ+oS51pfWbNPg7mVxDgoeXlm2Jlus/EnB01DO7mx+BujaOftQ069pwoJelYMUnHiknOLKagoo6dmSXszCzhgy0ZQGNRRVNi1zfMh+5BHna9ZMNaSDInhBXLKKjkhbWN1Zbzx0bbbbXl2S71BIjS6noWnppevffyCKKDWr/BuS1TnTXN2jy9u7if9uTwxfZMVCp4/ZY++LlrjdtBcVFaBw39u/jSv4sv0PjfMbOo6ozkroQDuWWcKK7mRHE136dkn3qfml4hnsR28iYu1IvYTt6E+7nZ5RY9lsyoyVxqaiq9e/du9fW33XZbm06DEEKcptcrPPLNbmrq9SRE+HHboM7m7pLFuNRq1pfWHeBkeS1d/d247/JuJuiZdVKdkbzp2xjTE8VVPPrtbgDuHd6NhAjZvcASqFQqOvu50dnPjQl9OwFQXlPPruOnRu8yi9mZWUx5TQPJmSUkZ5YY3uvh7EBMRy/iQr2J69SY4AV7OctoqxkZNZmLjY1lwIAB3HnnnUyaNAkPjwsvwn7nnXeM2bwQdmX59ky2Hy3C1UnDixPsczPb87mU47ySjhWxfFsm0HiWrSwMP011xixbW2LaoNMz58sUymoa6BPqzdyrokzQO2EsHs6ODI30Z2hkY8Kt1ytkFFay+0QJu46XsvtECXuzyyivaeDPw4X8ebjQ8F5/dy1xnbzo1dGLXiGe9O7oRYgkeO3GqMncpk2b+Pjjj3nooYeYO3cuN954I3feeSfDhg0zZjNC2L3skmoWnzor9F+justmoWdp6zRrXYOe+Sv3ADCxXyfDOiPRqNmauTaMzL3z+2ESjxXjoXVgyWTZT87aqNUqunVwp1sHd26Ibxy9q9fpSc8rZ/eJUnYdL2HXiVLS88opqKhlw4F8NhzIN7zf29WR3iGNyV3PEE8Gd/MjwEMKLEzBqMncsGHDGDZsGEuWLOGrr75i2bJlDB8+nIiICGbOnMn06dMJCgoyZpNC2B1FUXjiu1TDZrbT7PCs0Is5fZxX665//39HSM+rwNfNicfG9jBhz6zTpYyupGaV8uaGgwA8fX0v+QeHjXDUqOkV4kWvEC8mDwwDGiuV9+WUsvtEKXuzy9ibXcbBvHJKqurZcqiALYcajyNbMjmecXEh5uy+zTJJAYSbmxszZsxgxowZHDp0iI8//pilS5fy5JNPMnr0aFavXm2KZoWwC6t3ZRs2B158Y6xUr7bgdO5x8WzuWGElb51KOp64pgc+bk6m65iVauvIXG2Djoe+2kWDXmF0ryBuiO9osr4J83Nx0hg2NG5SU6/jYF4Fqdml7M1uTPJiOnqZsZe2zeTVrBERETz22GN07tyZ+fPn8+OPP5q6SSFsVmFFLQtXN1Zb3n9lBJF2vjnw+ahbOc3adLRUbUNjEYkkHS07c2BO34ps7rX16aeO63Li+Rt6y7opO+TsqCGmkxcxnSSBaw8mTeY2b97MRx99xLfffotarebmm29m5syZpmxSCJv2zJp9FFfVEx3kwT3DpdryfAzHT13kuu9SsthyqACtg5rnx8dI0nEe6jM3Db7ItTuOFvHe5iMALLohRrYhEaIdGD2Zy87OZtmyZSxbtoxDhw4xZMgQ3nrrLW6++Wbc3GQPLCEu1Yb9eXyfko1aBYtvjMXJQRaTn1crjvMqqarj2TWNe/Q9MCJS9uhrpQvFtLK2gYe+2oWiwE39OnF1L1kjLUR7MGoyN2bMGH799Vf8/f2ZNm0ad9xxB927dzdmE0LYFUVRyC+vZdfxEv71TeNeXXcO60pcqLd5O2bhmsaRLlQA8covaRRV1hEZ4C5HS11Es02DL3DdC2v3k1lURUdvFxaM62nyfgkhGhk1mXN0dOSbb77h2muvRaORPZqEaIvK2gYO5JZxILectKbHqYqwJp39XJk7UvbqupjTx3m1bM+JUsOecs+O7y2jnBdx5qbB5xuY++tIIZ/91RjTl26KxdPZsT26JoTAyMmcVKkK0TqFFbWGEv59OWXszS4lo6Cyxb8o1SoI93ejR7Anc0ZG4uIk/1C6GMOmwS0EVK9XePL7VBQFrosL4R9dZU+5i2m2lLCFz2hNvc6wT9/kgWFyyoMQ7UzOZhXChBRF4URxdWPSln16D6bcspoWrw/w0NIj2JPoIA+iAj3oHuRBRIC7nEbQRuoLDLR9k3SClOMluDlpePwa2VOuNc7M5VqqZn1zw0EyCioJ8NAyf2x0+3VMCAFIMieE0TTo9BwpqGzcUynr9KhbaXV9i9d38XOlV4gXPUM86RXiSa8QLzp4SOWfMRiqWc/KO0qq6nhxXePJGXNGRhHoKbvRt8aFqllTs0oN1avPju8t06tCmIEkc0Jcgpp6HQdyyw2bYe7NLuNAThm1DfpzrnXUqIgM8DAcadMrxIsewR54yF96JmM4zuus1OPVX9INRQ+3J3Rp/45ZqWYFEGdkyA06PY+u3I1OrzA2JohRUr0qhFlIMifERZRW1bM3p5R9p5K2vdmlHD5Zia6FUkk3Jw09gk+PtPUM8SQy0B2tg0yTmoP+jNw6NauUz7YdAxqPl5JzQltPdZ6RuQ+2ZJCaVYaXiyMLr+vV/h0TQgCSzAlhoCgKuWU1zZK2vdllnCiubvF6Pzcnw0hbr1NTpV383FDL8Vpmd7qatTH1UBSFp1bvRVFgXFwIQ7rJAv1L1TQwd7SgktfXpwPw+DU95AB1IcxIkjlhl5rODdyfW8b+nMbHgdzm24CcqZOPi2G0relroKdWTgywUGcf57Vmdw5Jx4pxcdTw+FgpergUKlVjPBVFQVEUFqzeazgGbWK/TubunhB2TZI5YbOq63ScKK4is+j043hRFRkFlRwtrGpxmlSjVhHRwb3Z+raeIZ54ucj6NmtyugBCT029jhfXNhY9/HN4V4K8ZATpUqhVKnSKDgVYl5rL5vSTOGnUPCfHoAlhdpLMCaul1zeejnC8uIqM/HJ+O65m4zd7OFFSQ2ZRFfnltRd8v4+r46ltQDzpEexBj2BP2QbERqhU4OlwHO/apSxdP5usEj1Bns7cfZmc9HCpvByOE+32DYezg3j6hzIA7hnelXA5Bk0Is5NkTli0ytoGjhdXkVl4emTNMMpWXE1ds+pRNZzIafZ+D60DYX6uhPk2PkJPfe0e5EGAh0yT2ipF30CU2zo0uuNs3/M+Km7n4dHdcXWSX3mXQqdvoJvrOjwccvj8t1fJK7uVUF937rsiwtxdE0JgQ8nc0qVLefnll8nNzSUuLo4lS5YwcOBAc3dLXERNvY7skmqySqrJKm78euaUaEFF3QXfr1Gr6OjtQicfZygvYEhcd7p0cDckb14ujpKw2aGc/N/wdsykWueGh+Yog0LSGN9nnLm7ZbWS037EyyGTWr07uqp0gp13snDcPTKKLYSFsIlkbsWKFcybN493332XQYMG8cYbbzBq1CjS0tIICAgwd/fsVk29joKKWk6W15JfXktOU9JWUk1WSQ1ZxdUUVFx4KhTA29Wx2ajamY9gL2ccNGrq6+v56aefGHtZOI6Osr7NnpVXFXLk2AoUVDQoLmhU9US5/05lzR14uMrRXW1VXlXIhqQPUcAQzz6+/2Ng5/vN3TUhxCk2kcy99tpr3HXXXcyYMQOAd999lx9//JGPPvqIRx991Gz92p9TTlqpCq/DhThoHM7YJqHx9abl92duwqmc9Ydz3nOe9565lF856ybnvuc89252j+bFAWe/R6+HyroGyqrrKatpoLymnrLqBooq6zhZUUt+WQ1lNQ3nxKQlrk4aOnq70NHHhY7eLs2mRENPja4J0Vobkz+mpraAWr07AM5OPtTXF7Jx5zKuS3jIzL2zPhuTP6a0Mp96pTGe9Yo7/o7lEk8hLIjVJ3N1dXUkJSUxf/58w3NqtZqRI0eydevWFt9TW1tLbe3pESFFUdBoNGi1xj1K6dX16Ww6qOHf+5KMel9r4qhREeChxd9dS6Cnlo7eLoR4O9PRq/FriLcz3heZCq2vb3m7kJauac214u+x5Fhn5qWSeGANDhoXQI0KCPJyoaFBT+KBH+jdZQShAdazua25Y90UTycHV5r+udfBwwVnR41VxvNizB1ve2LsWDs4ONj1khqVcvYQjJXJzs6mY8eO/PnnnwwePNjw/MMPP8ymTZvYtm3bOe9ZuHAhTz/9dLPnbrnlFiZPnmzUvn1zRM2h8sYPV9NH7OyPWkufvXOuOc+1bbnX+a9VWnz9Qu01fXXWgItD01cFFw24OoCnE3g6Kng6gYum5X4JYQqHi3/meOkfOGm8KapV4erQ+JlUFIVaXTGhXkPp5jPK3N20Gk3x1Gq8qWhQUa8HX63EU1iesWPH2vUSG6sfmbsU8+fPZ968eYbvTTUyd1V9PevXr+eqq66y6w9Ze6iXWLcbS4718fzOLFu7D5VKTTdvD8Pz1bXlOOHL+KvutqqRJHPH+sx4emutP54XY+542xNjx9rBwS7TGQOr/+n9/f3RaDTk5eU1ez4vL4+goJYPfdZqtUZP3C7E0dFRfjG0E4l1+7HEWHft2If+0deyde83ODu5oVZr0Ot11DVUMbj3RLp27GPuLl4Sc8XaVuN5MZb42bZVEmvjsPqTpp2cnOjXrx8bNmwwPKfX69mwYUOzaVchhH24ou8MvNwCqKguAqCiuggvtwCuiL/dvB2zUhJPISyf1SdzAPPmzeP999/nk08+Yf/+/dx7771UVlYaqluFEPbDw9WPEf1mAgrVtWWAwoj+d8q2JJdI4imE5bOJZO6WW27hlVdeYcGCBfTp04eUlBTWrVtHYGCgWftVW1vLF1980axyVpiGxLr9WEOs+3a/hrDAWCqqiwgLiqVv1Fhzd+mSWEqsbSWeF2Mp8bYHEmvjsvpqVktWWlqKt7c3JSUleHl5mbs7Nk1i3X6sJdbH81L5+vdnmXj5k4QG9jZ3dy6JJcXaFuJ5MZYUb1snsTYuqy+AsGRNe97Y89437UVi3X6sJdahgb2ZM/Fz1GrrPXLKkmJtC/G8GEuKt62TWBuXTUyzCiFES2w58TAHiacQlkmSOSGEEEIIKybJnAlptVqeeuqpdt3Tzl5JrNuPxLr9SKzbl8S7/UisjUsKIIQQQgghrJiMzAkhhBBCWDFJ5oQQQgghrJgkc0IIIYQQVkySOSGEEEIIKybJnIksXbqULl264OzszKBBg9i+fbu5u2T1XnjhBQYMGICHhwcBAQGMHz+etLS0ZtfU1NQwa9Ys/Pz8cHd358YbbyQvL89MPbYdL774IiqVijlz5hiek1gbV1ZWFrfddht+fn64uLgQExNDYmKi4XVFUViwYAHBwcG4uLgwcuRIDh48aMYeWyedTseTTz5JeHg4Li4udOvWjWeffZYzawEl1pdm8+bNjBs3jpCQEFQqFd99912z11sT16KiIqZMmYKnpyfe3t7MnDmTioqKdvwprJMkcyawYsUK5s2bx1NPPUVycjJxcXGMGjWK/Px8c3fNqm3atIlZs2bx119/sX79eurr67n66quprKw0XDN37lx++OEHvv76azZt2kR2djYTJkwwY6+t344dO/jPf/5DbGxss+cl1sZTXFxMQkICjo6OrF27ln379vHqq6/i4+NjuOall17irbfe4t1332Xbtm24ubkxatQoampqzNhz67N48WLeeecd3n77bfbv38/ixYt56aWXWLJkieEaifWlqaysJC4ujqVLl7b4emviOmXKFPbu3cv69etZs2YNmzdv5u67726vH8F6KcLoBg4cqMyaNcvwvU6nU0JCQpQXXnjBjL2yPfn5+QqgbNq0SVEURSkpKVEcHR2Vr7/+2nDN/v37FUDZunWrubpp1crLy5XIyEhl/fr1yvDhw5UHH3xQURSJtbE98sgjytChQ8/7ul6vV4KCgpSXX37Z8FxJSYmi1WqVL774oj26aDOuueYa5Y477mj23IQJE5QpU6YoiiKxNhZAWbVqleH71sR13759CqDs2LHDcM3atWsVlUqlZGVltVvfrZGMzBlZXV0dSUlJjBw50vCcWq1m5MiRbN261Yw9sz2lpaUA+Pr6ApCUlER9fX2z2EdHRxMWFiaxv0SzZs3immuuaRZTkFgb2+rVq+nfvz8TJ04kICCA+Ph43n//fcPrGRkZ5ObmNou3l5cXgwYNkni30ZAhQ9iwYQPp6ekA7Nq1iy1btjBmzBhAYm0qrYnr1q1b8fb2pn///oZrRo4ciVqtZtu2be3eZ2viYO4O2JqCggJ0Oh2BgYHNng8MDOTAgQNm6pXt0ev1zJkzh4SEBHr37g1Abm4uTk5OeHt7N7s2MDCQ3NxcM/TSun355ZckJyezY8eOc16TWBvXkSNHeOedd5g3bx6PPfYYO3bs4IEHHsDJyYnp06cbYtrS7xWJd9s8+uijlJWVER0djUajQafT8fzzzzNlyhQAibWJtCauubm5BAQENHvdwcEBX19fif1FSDInrNKsWbNITU1ly5Yt5u6KTTp+/DgPPvgg69evx9nZ2dzdsXl6vZ7+/fuzaNEiAOLj40lNTeXdd99l+vTpZu6dbfnqq69Yvnw5n3/+Ob169SIlJYU5c+YQEhIisRZWS6ZZjczf3x+NRnNOVV9eXh5BQUFm6pVtuf/++1mzZg0bN26kU6dOhueDgoKoq6ujpKSk2fUS+7ZLSkoiPz+fvn374uDggIODA5s2beKtt97CwcGBwMBAibURBQcH07Nnz2bP9ejRg8zMTABDTOX3yt/3r3/9i0cffZRJkyYRExPD1KlTmTt3Li+88AIgsTaV1sQ1KCjonELBhoYGioqKJPYXIcmckTk5OdGvXz82bNhgeE6v17NhwwYGDx5sxp5ZP0VRuP/++1m1ahW//fYb4eHhzV7v168fjo6OzWKflpZGZmamxL6NRowYwZ49e0hJSTE8+vfvz5QpUwx/llgbT0JCwjnb7KSnp9O5c2cAwsPDCQoKahbvsrIytm3bJvFuo6qqKtTq5n/1aTQa9Ho9ILE2ldbEdfDgwZSUlJCUlGS45rfffkOv1zNo0KB277NVMXcFhi368ssvFa1WqyxbtkzZt2+fcvfddyve3t5Kbm6uubtm1e69917Fy8tL+f3335WcnBzDo6qqynDNPffco4SFhSm//fabkpiYqAwePFgZPHiwGXttO86sZlUUibUxbd++XXFwcFCef/555eDBg8ry5csVV1dX5bPPPjNc8+KLLyre3t7K999/r+zevVu5/vrrlfDwcKW6utqMPbc+06dPVzp27KisWbNGycjIUFauXKn4+/srDz/8sOEaifWlKS8vV3bu3Kns3LlTAZTXXntN2blzp3Ls2DFFUVoX19GjRyvx8fHKtm3blC1btiiRkZHK5MmTzfUjWQ1J5kxkyZIlSlhYmOLk5KQMHDhQ+euvv8zdJasHtPj4+OOPDddUV1cr9913n+Lj46O4uroqN9xwg5KTk2O+TtuQs5M5ibVx/fDDD0rv3r0VrVarREdHK++9916z1/V6vfLkk08qgYGBilarVUaMGKGkpaWZqbfWq6ysTHnwwQeVsLAwxdnZWenatavy+OOPK7W1tYZrJNaXZuPGjS3+jp4+fbqiKK2La2FhoTJ58mTF3d1d8fT0VGbMmKGUl5eb4aexLipFOWPbayGEEEIIYVVkzZwQQgghhBWTZE4IIYQQwopJMieEEEIIYcUkmRNCCCGEsGKSzAkhhBBCWDFJ5oQQQgghrJgkc0IIIYQQVkySOSGEEEIIKybJnBDCbG6//XbGjx/f7u0uW7YMlUqFSqVizpw5Jmvn6NGjhnb69OljsnaEEPbNwdwdEELYJpVKdcHXn3rqKd58803MdQiNp6cnaWlpuLm5mayN0NBQcnJyeOWVV/j1119N1o4Qwr5JMieEMImcnBzDn1esWMGCBQtIS0szPOfu7o67u7s5ugY0JptBQUEmbUOj0RAUFGTWn1MIYftkmlUIYRJBQUGGh5eXlyF5anq4u7ufM816+eWXM3v2bObMmYOPjw+BgYG8//77VFZWMmPGDDw8PIiIiGDt2rXN2kpNTWXMmDG4u7sTGBjI1KlTKSgoaHOfu3TpwnPPPce0adNwd3enc+fOrF69mpMnT3L99dfj7u5ObGwsiYmJhvccO3aMcePG4ePjg5ubG7169eKnn3665LgJIURbSTInhLAon3zyCf7+/mzfvp3Zs2dz7733MnHiRIYMGUJycjJXX301U6dOpaqqCoCSkhKuvPJK4uPjSUxMZN26deTl5XHzzTdfUvuvv/46CQkJ7Ny5k2uuuYapU6cybdo0brvtNpKTk+nWrRvTpk0zTA/PmjWL2tpaNm/ezJ49e1i8eLGMxAkh2pUkc0IIixIXF8cTTzxBZGQk8+fPx9nZGX9/f+666y4iIyNZsGABhYWF7N69G4C3336b+Ph4Fi1aRHR0NPHx8Xz00Uds3LiR9PT0Nrc/duxY/vnPfxraKisrY8CAAUycOJGoqCgeeeQR9u/fT15eHgCZmZkkJCQQExND165dufbaa7nsssuMGhMhhLgQSeaEEBYlNjbW8GeNRoOfnx8xMTGG5wIDAwHIz88HYNeuXWzcuNGwBs/d3Z3o6GgADh8+/Lfab2rrQu0/8MADPPfccyQkJPDUU08ZkkwhhGgvkswJISyKo6Njs+9VKlWz55qqZPV6PQAVFRWMGzeOlJSUZo+DBw9e0ghZS21dqP0777yTI0eOMHXqVPbs2UP//v1ZsmRJm9sVQohLJcmcEMKq9e3bl71799KlSxciIiKaPUy57ciZQkNDueeee1i5ciUPPfQQ77//fru0K4QQIMmcEMLKzZo1i6KiIiZPnsyOHTs4fPgwP//8MzNmzECn05m8/Tlz5vDzzz+TkZFBcnIyGzdupEePHiZvVwghmkgyJ4SwaiEhIfzxxx/odDquvvpqYmJimDNnDt7e3qjVpv8Vp9PpmDVrFj169GD06NFERUXx73//2+TtCiFEE5Viru3XhRDCTJYtW8acOXMoKSlpl/YWLlzId999R0pKSru0J4SwLzIyJ4SwS6Wlpbi7u/PII4+YrI3MzEzc3d1ZtGiRydoQQggZmRNC2J3y8nLDPnHe3t74+/ubpJ2GhgaOHj0KgFarJTQ01CTtCCHsmyRzQgghhBBWTKZZhRBCCCGsmCRzQgghhBBWTJI5IYQQQggrJsmcEEIIIYQVk2ROCCGEEMKKSTInhBBCCGHFJJkTQgghhLBikswJIYQQQlix/we7Ak6VJ41zrwAAAABJRU5ErkJggg==\n", 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" ] @@ -456,37 +432,19 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:39:35 NodeManager::prepare_nodes [Info]: \n", - " Preparing 5 nodes for simulation.\n", - "\n", - "Oct 19 03:39:35 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 5\n", - " Simulation time (ms): 100\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:39:35 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_276055/1944179876.py:84: UserWarning:Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n" + "/tmp/ipykernel_328427/1260340709.py:84: UserWarning:FigureCanvasAgg is non-interactive, and thus cannot be shown\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -564,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -641,16 +599,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "line 1:28 extraneous input '*' expecting {'integer', 'real', 'string', 'boolean', 'void', '(', ',', NAME, UNSIGNED_INTEGER}\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -659,8 +610,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -670,10 +621,6 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -688,27 +635,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_33fb1d40309a4c51b738570b49ff66b9_module Configuration Summary\n", + "active_dend_reset_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_33fb1d40309a4c51b738570b49ff66b9_module' using\n", + "You can now build and install 'active_dend_reset_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_33fb1d40309a4c51b738570b49ff66b9_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libactive_dend_reset_module.so will be installed to\n", + " /tmp/nestml_target_f0e0du76\n", "The module can be loaded into NEST using\n", - " (nestml_33fb1d40309a4c51b738570b49ff66b9_module) Install (in SLI)\n", - " nest.Install(nestml_33fb1d40309a4c51b738570b49ff66b9_module) (in PyNEST)\n", + " (active_dend_reset_module) Install (in SLI)\n", + " nest.Install(active_dend_reset_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -720,31 +667,28 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_33fb1d40309a4c51b738570b49ff66b9_module_module.dir/nestml_33fb1d40309a4c51b738570b49ff66b9_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_33fb1d40309a4c51b738570b49ff66b9_module_module.dir/af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml.cpp: In member function ‘void af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml.cpp:182:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 182 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/active_dend_reset_module_module.dir/active_dend_reset_module.o\n", + "[ 66%] Building CXX object CMakeFiles/active_dend_reset_module_module.dir/iaf_psc_exp_active_dendrite_resetting_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_resetting_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_active_dendrite_resetting_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_resetting_neuron_nestml.cpp:189:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 189 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml.cpp: In member function ‘virtual void af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml.cpp:284:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 284 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_resetting_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_exp_active_dendrite_resetting_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_resetting_neuron_nestml.cpp:298:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 298 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/af_psc_exp_active_dendrite_resetting_neuron33fb1d40309a4c51b738570b49ff66b9_nestml.cpp:282:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 282 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/active_dendrite/target/iaf_psc_exp_active_dendrite_resetting_neuron_nestml.cpp:293:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 293 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "[100%] Linking CXX shared module nestml_33fb1d40309a4c51b738570b49ff66b9_module.so\n", - "[100%] Built target nestml_33fb1d40309a4c51b738570b49ff66b9_module_module\n", - "[100%] Built target nestml_33fb1d40309a4c51b738570b49ff66b9_module_module\n", + "[100%] Linking CXX shared module active_dend_reset_module.so\n", + "[100%] Built target active_dend_reset_module_module\n", + "[100%] Built target active_dend_reset_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_33fb1d40309a4c51b738570b49ff66b9_module.so\n", - "\n", - "Oct 19 03:39:47 Install [Info]: \n", - " loaded module nestml_33fb1d40309a4c51b738570b49ff66b9_module\n" + "-- Installing: /tmp/nestml_target_f0e0du76/active_dend_reset_module.so\n" ] } ], @@ -763,37 +707,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:39:47 NodeManager::prepare_nodes [Info]: \n", - " Preparing 5 nodes for simulation.\n", - "\n", - "Oct 19 03:39:47 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 5\n", - " Simulation time (ms): 100\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:39:47 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_276055/1944179876.py:84: UserWarning:Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n" + "/tmp/ipykernel_328427/1260340709.py:84: UserWarning:FigureCanvasAgg is non-interactive, and thus cannot be shown\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -846,7 +772,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/doc/tutorials/izhikevich/nestml_izhikevich_tutorial.ipynb b/doc/tutorials/izhikevich/nestml_izhikevich_tutorial.ipynb index 580ed9767..bef75f2a6 100644 --- a/doc/tutorials/izhikevich/nestml_izhikevich_tutorial.ipynb +++ b/doc/tutorials/izhikevich/nestml_izhikevich_tutorial.ipynb @@ -25,6 +25,14 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -33,8 +41,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -48,7 +56,7 @@ } ], "source": [ - "# %matplotlib inline\n", + "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import nest\n", "import numpy as np\n", @@ -61,6 +69,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Paths\n", + "\n", + "We assume here that we will generate code in a temporary directory `/tmp/nestml-component`. You can also create a unique temporary path using the [Python tempfile module](https://docs.python.org/3/library/tempfile.html).\n", + "\n", + "\n", "The Izhikevich model\n", "--------------------\n", "\n", @@ -112,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -123,8 +136,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -134,16 +147,12 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[11,zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml, WARNING, [12:8;12:17]]: Variable 'a' has the same name as a physical unit!\n", - "[12,zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml, WARNING, [15:8;15:17]]: Variable 'd' has the same name as a physical unit!\n", - "[16,zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml, WARNING, [12:8;12:17]]: Variable 'a' has the same name as a physical unit!\n", - "[17,zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml, WARNING, [15:8;15:17]]: Variable 'd' has the same name as a physical unit!\n", - "[22,zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml, WARNING, [12:8;12:17]]: Variable 'a' has the same name as a physical unit!\n", - "[23,zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml, WARNING, [15:8;15:17]]: Variable 'd' has the same name as a physical unit!\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", + "[12,izhikevich_tutorial_neuron_nestml, WARNING, [12:8;12:17]]: Variable 'a' has the same name as a physical unit!\n", + "[13,izhikevich_tutorial_neuron_nestml, WARNING, [15:8;15:17]]: Variable 'd' has the same name as a physical unit!\n", + "[17,izhikevich_tutorial_neuron_nestml, WARNING, [12:8;12:17]]: Variable 'a' has the same name as a physical unit!\n", + "[18,izhikevich_tutorial_neuron_nestml, WARNING, [15:8;15:17]]: Variable 'd' has the same name as a physical unit!\n", + "[23,izhikevich_tutorial_neuron_nestml, WARNING, [12:8;12:17]]: Variable 'a' has the same name as a physical unit!\n", + "[24,izhikevich_tutorial_neuron_nestml, WARNING, [15:8;15:17]]: Variable 'd' has the same name as a physical unit!\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -158,27 +167,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_fa48727ef3164c478caa483c02a70866_module Configuration Summary\n", + "nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_fa48727ef3164c478caa483c02a70866_module' using\n", + "You can now build and install 'nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_fa48727ef3164c478caa483c02a70866_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module.so will be installed to\n", + " /tmp/nestml_target_sii6g4x1\n", "The module can be loaded into NEST using\n", - " (nestml_fa48727ef3164c478caa483c02a70866_module) Install (in SLI)\n", - " nest.Install(nestml_fa48727ef3164c478caa483c02a70866_module) (in PyNEST)\n", + " (nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module) Install (in SLI)\n", + " nest.Install(nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -190,39 +199,38 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.1s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_fa48727ef3164c478caa483c02a70866_module_module.dir/nestml_fa48727ef3164c478caa483c02a70866_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_fa48727ef3164c478caa483c02a70866_module_module.dir/zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml.cpp: In member function ‘void zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml.cpp:177:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 177 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module_module.dir/nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module.o\n", + "[ 66%] Building CXX object CMakeFiles/nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module_module.dir/izhikevich_tutorial_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/izhikevich_tutorial_neuron_nestml.cpp: In member function ‘void izhikevich_tutorial_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/izhikevich_tutorial_neuron_nestml.cpp:184:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 184 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml.cpp: In member function ‘virtual void zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml.cpp:305:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 305 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/izhikevich_tutorial_neuron_nestml.cpp: In member function ‘virtual void izhikevich_tutorial_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/izhikevich_tutorial_neuron_nestml.cpp:318:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 318 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml.cpp:303:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 303 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/izhikevich_tutorial_neuron_nestml.cpp:313:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 313 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/zhikevich_tutorial_neuronfa48727ef3164c478caa483c02a70866_nestml.cpp:291:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 291 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/izhikevich/target/izhikevich_tutorial_neuron_nestml.cpp:303:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 303 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "[100%] Linking CXX shared module nestml_fa48727ef3164c478caa483c02a70866_module.so\n", - "[100%] Built target nestml_fa48727ef3164c478caa483c02a70866_module_module\n", - "[100%] Built target nestml_fa48727ef3164c478caa483c02a70866_module_module\n", + "[100%] Linking CXX shared module nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module.so\n", + "[100%] Built target nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module_module\n", + "[100%] Built target nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_fa48727ef3164c478caa483c02a70866_module.so\n" + "-- Installing: /tmp/nestml_target_sii6g4x1/nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module.so\n" ] } ], "source": [ "# generate and build code\n", "module_name, neuron_model_name = \\\n", - " NESTCodeGeneratorUtils.generate_code_for(\"izhikevich_solution.nestml\",\n", - " module_name=\"izhikevich_module\")" + " NESTCodeGeneratorUtils.generate_code_for(\"izhikevich_solution.nestml\")" ] }, { @@ -230,17 +238,6 @@ "metadata": {}, "source": [ "Check the generated log output for any potential error messages or warnings." - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:45:22 Install [Info]: \n", - " loaded module nestml_fa48727ef3164c478caa483c02a70866_module\n" - ] - } - ], ] }, { @@ -250,12 +247,12 @@ "### Instantiate model in NEST Simulator and run\n", "The generated extension module can be loaded using ``nest.Install()``.\n", "\n", - "Using the PyNEST API, the model can be instantiated and simulated in NEST. The following code will create one instance of the neuron model (`nest.Create(\"izhikevich_tutorial_neuron\")`), inject a constant current and run the simulation for 250 ms." + "Using the PyNEST API, the model can be instantiated and simulated in NEST. The following code will create one instance of the neuron model (`nest.Create(\"izhikevich_tutorial\")`), inject a constant current and run the simulation for 250 ms." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -263,19 +260,19 @@ "output_type": "stream", "text": [ "\n", - "Mar 26 14:32:10 Install [Info]: \n", - " loaded module izhikevich_module\n", + "Apr 19 10:44:47 Install [Info]: \n", + " loaded module nestml_004fd3f5ec9a44b9a28667ae47e2bc9e_module\n", "\n", - "Mar 26 14:32:10 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 10:44:47 NodeManager::prepare_nodes [Info]: \n", " Preparing 4 nodes for simulation.\n", "\n", - "Mar 26 14:32:10 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 10:44:47 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 4\n", " Simulation time (ms): 250\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Mar 26 14:32:10 SimulationManager::run [Info]: \n", + "Apr 19 10:44:47 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, @@ -283,12 +280,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_276545/2635512647.py:30: UserWarning:Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n" + "/tmp/ipykernel_329471/1245292433.py:33: UserWarning:FigureCanvasAgg is non-interactive, and thus cannot be shown\n" ] }, { "data": { - "image/png": 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\n", 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w2vCpqKjw+qwuoVm4cCEWLlzI/ZyVlYWioiK88847nOHz2WefwWQy4YMPPoBGo0FOTg6OHj2KV199lRg+AkA8Ps50kUmMg4T9HJBcOAdkznCGy4Ub4OMiGPDa8OEbPVVVVfjpp59QV1cHm0szpkceeUQ46XqhtbXV6Tyw/Px8XH755dBoNNxrCxYswAsvvIDm5mZER0e7/Ryj0Qij0dFOne1MbTabYTabA5aT/QwhPktOOrsdYUWj2RKUf49UuqZpGka7d8NosQalLsSGr+suk4X5f6ttQOqCj8Ho+Pu7jKYBPYd0dDnm1W5TcM4Zroipa9bjYzIPzDnDFTF07e1n+Zzc/OGHH+LXv/41NBoNYmNjQVEU9x5FUZIYPiUlJXjrrbc4bw8A1NTUIDMz0+m6xMRE7j1Phs+qVavw7LPP9nh9y5YtPp1P1hd5eXmCfZYcHKylACgBAGfOlmCD8ay8AvWC2Lpm5i/m0ampq8eGDRtE/b5gJi8vD5U1SgAUbDSwbv0GKKg+f+2S5ViF4znZu+8AWoqE2933tznkYgfAPidnz53Hhg0lssrjC2Lous3APCfNrW0Des5wRUhdGwwGr67z2fD5y1/+gqeeegorV64MOM/nySefxAsvvNDrNadPn8aIESO4nysrK7Fw4ULcdNNNgpTPr1y5EitWrOB+bmtrQ1paGubPnw+9Xh/w55vNZuTl5WHevHlQq9UBf55cNO4rA86fAQCkZ2Ri8aLhMkvUE6l03WIwAwd2AAD0UTFYvHiqaN8VrPB1/d+yw0A74ymdO38BQtRKmaWTjzNbi4Fy5rDW8RMnYe7IwNMD+usccqC0CThxCACQmjYYixePlFmivhFT108c3ArABm1oGBYvninoZ/dHxNC1t2eJ+mz4GAwG3HrrrYIkNz/++ONYtmxZr9dkZWVx/19VVYUrr7wSl112Gd5//32n65KSklBb63z4G/tzUlKSx8/XarXQarU9Xler1YIOfKE/T2r4XdZtNIL6bxFb1xZYuP+32uig1oXYqNVqGHmDg1YoB7Q++OeS2qAY0HOIhXasEdYgnzNcEVrXNhvNVflZBvic4YqQuvb2c3w2fO699158/fXXePLJJ30WypX4+HjEx8d7dW1lZSWuvPJKTJo0CatXr+5heOXm5uJPf/oTzGYz98fn5eVh+PDhHsNcBO9xKtMd4Ml5RBfOOFUyDfDKLlLV5aCLV9U10BPf+aX9A10XwYDPhs+qVauwdOlSbNq0CWPGjOlhYb366quCCcdSWVmJ2bNnY/DgwXj55ZdRX1/Pvcd6c26//XY8++yzuPfee/GHP/wBhYWFeOONN/Daa68JLs9AhDQwdEB04QypZHJAKpkc8FtgDPQO5/xxYRng4yIY8Mvw2bx5M4YPZ3I8XJObxSAvLw8lJSUoKSlBamqq03s0zTxQkZGR2LJlC5YvX45JkyYhLi4OTz311CVVym6z0VDIlDlKHlwHxPBxhujDAWna54CMCwdkcxBc+Gz4vPLKK/jggw/6zM0RkmXLlnn1fWPHjsWePXvEF0gG1hRU4Km1J/H+nZOROyRW8u8nE7oDEtpxhng5HJCmfQ66iC44+HMGCXXJj88ZylqtFjNmzBBDFkIv7DnbgPZuCw5caJLl+7vJg8thJDk+HFYb7TQeBrpR7LxBGNjPSbeFPCcsrt4vNlJBkAefDZ9HH30Ub731lhiyEHqh3chUEpms1j6uFAejhYS6WIgL3wE/jwMg+nBOfB/ouiCeURb+uKBpZsNAkA+fQ10HDhzA9u3bsW7dOuTk5PRIbv7uu+8EE47goNNu+PC9DVJiJDFqjm6npM2BPaF3uYzHgb7YO4V3LAP7OSEVbg74RiDAzKGqgdvuSnZ8NnyioqJwww03iCELoRc4w0emnRPZyTroMpHQDovRdUIf6Dt7EuriMJLcLw5Xw8dktSEUxPKRC58Nn9WrV4shB6EP2jnDR55QF//BHeheDtd8J5qmRatoDHa6XTw+xBAkiz2L83MysMdFz+dkYI8NuQm8/TJBEliPj0kujw+p6uLodjE+LQM4Xt/Vw4U/sCd0Ur3jgIS6HJDnJLjwyvCZOHEimpubvf7QmTNnorKy0m+hCD3p6JY31EV2sg7I7s2B63gc6Is9SXx3QHThoEeOzwDP/5Ibr0JdR48exbFjxxATE+PVhx49ehRGozEgwQgObDYanfakSdlyfEjuAofbSUwjkzAy0zNpc+CODZqmXULCA3txc256OtB14fKc2AbucxIMeJ3jM2fOHK97DwzUfAexMPAeGvlyfEhCL4u7RMWBiqsLfyAvcCarDfyo50AeFwAJ+/EhG4TgwivD58KFCz5/sOvREgT/YcNcgIw5PiS5mYNMYg5c2ysM5AWOJHo7YyShLo4eY4OEumTFK8Nn8ODBYstB6IUOo8PwCYYcH1Kh4XwPBrKXwzXReyAvcKS03xknL/GA1wXxEgcTpKqrH+Bk+MjQwNDW41iCgf3QuoZ3BvIk5trAcCAvcKRyx5ku0vSUg4yN4IIYPv2ATp7hI8ci6+plIqEuMomx9PByDOAFztUTOJANYsB9v6uBCqkEDS6I4dMPcPb4SJ/c7K7d+kDG1es2kCcxstg7IAaxM676GMj9rkhIOLgghk8/gJ/cLEeOj+tDO+B3b2QS4yCLvYOe4YyB+4wAxMvBp9vkMoeS5GZZ8dnwue+++7Bz504RRCF4otMks+HjJq9oIJ8u3EUmMY5uC1ncWIgR6MDqkhcIDOxKJrJZCi58Nnzq6+uxcOFCpKWl4YknnsCxY8fEkIvAgx/qkqOcnZ3QQ9WOQ/UG8m6WTGIOSBjUASlnd+Cu39hAbtrXoxJ0AOsiGPDZ8Fm7di2qq6vxl7/8BQcPHsTEiRORk5OD559/HqWlpSKISHDq42O1wSaxt4X1MkWEOLofDOxcDuZv16iYx2cgT2Ks4aNWMk1LiREIqBREF3yvqJLoo+fYGMDer2DArxyf6OhoPPDAA9i5cycuXryIZcuW4ZNPPkF2drbQ8vXAaDRi/PjxoCgKR48edXrv+PHjmDVrFkJCQpCWloYXX3xRdHkCxZtOzPyqLkB6o4N9aMN5ho9clV17iutxurpNlu9mYfURoWX0IVeo66tD5Vj53XFZw46sERgRogYg3+JWUNaMBz4+hNKGTlm+H+CNC/tzIpcuWrvMeOjTw9hyskaW7wccIVCNSgGtfYMg12K/auNpvLqlSJbvZulyGRtybRzXH6/G8v8d6bGmDDQCSm42m804dOgQ9u/fj9LSUiQmJgoll0d+//vfIyUlpcfrbW1tmD9/PgYPHozDhw/jpZdewjPPPIP3339fdJn8ZfPJGox+ejPWFFT0el27yyCVOs+HH+py7Galn8SqW7tw1wcHcP/HhyT/bj7BssC9sqUInx8oR2FlqyzfDzjCfuF2I1Cuxe2T/IvYcqoW645XyfL9AH9c2I1AmXoabT9Ti42FNfjPT7533BcKVhchKgXUSmaZkWOxb+gw4r1d5/Hm9pIeYVkpMQbJBuGfO0qw/ng18s81yvL9wYJfhs+OHTtw//33IzExEcuWLYNer8e6detQUdH7Ah4oGzduxJYtW/Dyyy/3eO+zzz6DyWTCBx98gJycHNx666145JFH8Oqrr4oqUyDsP98Es5XGgQvNvV7nap1LfV4Xu6sPUSu5SUyOB7es0QCaBmrbuiX/bharjeaMPn2ofJOY1Uajvp05CLhDxt0bOzb0ofIagXV2XbhuEqSk2yUkLFeH87o2+7jolnNc2A0fmecMVhdAz3lUSoJls1Tfzsydcs4ZwYDXh5SyDBo0CE1NTVi4cCHef/99XH311dBqtWLI5kRtbS3uv/9+fP/999DpdD3ez8/Px+WXXw6NxnFM9oIFC/DCCy+gubkZ0dHRbj/XaDQ6nSTf1saEUcxmM8xmc8Bys5/h7rOaOuyDsNvU63e1dzu/19llgjlE6eFq4ensNgEANEoKKiUFmIEuowlms1oyGQCgpsUAgPE2dXYZuRwblt50LRT8yTNcw9yDbpMwY8UXGjqM3IGYbQaj5N/Pfl+XveKQ1YXRbJVcFoA3oXdJfy9Y2OckXMvowmwVRhe+juva1i5GHqNFNl10dDG6CFEruI0CM2dIK09NiyP02Wrohl7b+15frDmEC3Vp2TlD+ntjsdrQ2Mncl7Yu6ecMV8TQtbef5bPh88wzz+Cmm25CVFSUr7/qNzRNY9myZXjwwQcxefJkt0nUNTU1yMzMdHqNDb3V1NR4NHxWrVqFZ599tsfrW7ZscWtg+UteXl6P185eVABQ4EJ5FTZs8Owtq6xVAnCceJ+3fQcSQwUTrU8O1VEAlGhtagBtoQBQ2LZzF1KEU49X7Kxm5ACAtes3IcyD3eVO10LRYQbYx6ajpQGAAgXHTiCs9rho3+mOyk6HHHsPHIbpgjzehfqmVgAUOlsaAShQXtX7WBaLykbmGTl74SI2bJAnxHPK/jwb7LroMpqxYcMGwT7f23F9rJiRo7m9U9Dv94WiVuZZNXcZwDgFKez+6WeUR0grx4E6x5yxeetOpIR593tCziE0DXSZmPHJPienzpzFhs4zgn2HN7SaAJpm5ozDxwoRWX9C0u/3hJC6NhgMXl3ns+Fz//33+yyMJ5588km88MILvV5z+vRpbNmyBe3t7Vi5cqVg382ycuVKrFixgvu5ra0NaWlpmD9/PvR6fcCfbzabkZeXh3nz5kGtdl6pV1fsB1paERYVi8WLp3j8jDdLfgY6HDuX6ZfNwshk6WaQpv1lwLkzSB+UjMbyFnS0GZF72UzkpASuH184taUYKGUWtRlXXImUKGfrrzddC0VlSxdwaA80KgXSByXheFMNho0chcW50h7ku6e4ATh+BAAwbNQYLJ6SKun3s7pWh+oAQxeGZqTiRHMVYuISsHjxRGllsdrw2L6tAIDo+CQsXjxe0u9nObT+DFBVhmy7LmhKgcWLFwT8ub6O6y9XHwIammBVqAT5fn8IKaoHThUgPiYSnSYrGuo7MXnqdEzLjJFUjvLdF4BzxQCASdMvw4S0qF6vF2MOMVlsoO3jkx0bGVlDsHjeUEE+31tOVrUBh/cBAAYPGYbFVw6R9PtdEUPXbMSmL3w2fITk8ccfx7Jly3q9JisrC9u3b0d+fn6PkNrkyZNxxx134KOPPkJSUhJqa2ud3md/TkpK8vj5Wq3WbahOrVYLuni6+7zWLiZMYDDZev2uTqNzTo8VlGgLuzvYFhQ6jYqL19OUQlIZAKDR4HBjmmyedSD0veNjoZmwaKhaCa2aeXxstLT3AwCauhxjwmilJf9+7rvtycyROibEbKUhgy66wTYS77bIpwu2ui8ylNGF2UpDpVKBoqjefs1rvB3XDfZwhsFkFfT7fYGdM0I1Ku7/bZB+zmgy8FuBeP+cCjmHGCyOeStKxuekuZs3Z8j4nLgipK69/RxZDZ/4+HjEx8f3ed2bb76Jv/3tb9zPVVVVWLBgAb788ktMmzYNAJCbm4s//elPMJvN3B+fl5eH4cOHewxzyU2LgZmg+kq6Y99XKShYbLQMVV3M92nVSmi4REXpQytsMi8AdJrkqdBwJG0quN41cpxB5KQLo3zVKq6VTHI02OTrwiBrcjOjCzbpHWDGBjtOpILVB00zz26oRrp8QBZ+QQQ7RuTod1XfwZ8z5Bkb7PmKCgrQadj8L7nnz4Gd3NwvzupKT0/H6NGjuX/Dhg0DAAwZMgSpqYyL//bbb4dGo8G9996LkydP4ssvv8Qbb7zhFMYKJmw2Gq1dzE6gt0FI0zQ67O9HhzG7BbnK2ZnFXsYKjSBY4NiKOn61iuyLvYyTGDs29PZqFdmNQJkMYqCnLgDpnxOTxYZmnmdUrgXO3ZwhR78rNukdkO854RuBGiVr+Mi9QZDvOQkGZPX4CElkZCS2bNmC5cuXY9KkSYiLi8NTTz2FBx54QG7R3NLebeGqcnrbsRtMVs6NHxumQX27UfKFljW0tColU9UF+R9cuRa4LpN9ElPJW6YbDDtZmnaUcOtl7E8SLEZgl9m5nB2w9zXSePoN4WnsNDr9bDBagXDpvp/FqfdXsMwZMi32XcGoiwHu8emXhk9GRobb08HHjh2LPXv2yCCR7zTbw1wAMwhpmnYbi2fDXArK4UKXvo+PO4+PtLs3q41GU6f8CxynC40SapWckxhvJyvThG6lwRnlbFdvWbxfHfIvboBjbIRp5Tvahb+4AcHg8ZF5gxAERjFfF7KmCnTwdTGwPT79ItR1KdLS5XBH07Tngcg2ZAvTqhCiZnulyJPj4/zgSitDI69vDSDfAsfmcYSoFEGU7yTPhG7iDQHi8XHkcoTK+Jy4Gj7BEd6RRxfdZivaeE0cZZsz7ONCy8sLlKOLtbP3a2B7fIjhIxN8jw/geSCyr4drVdwEIvlZXbzFXi5XbV0QTuhytuJ3XuzlmdBZ+1tBgUugldsINJiskh/iy9Ll5OWQ5znp4fGRObzj/JxIe18aOoJjzuCHutTcuWUyHN8RBHMGAFS1dMn2jLIQw0cmWg3OHSY9tRDv4Bk+WjVzu4wSnznDJhKHapSyhbrqO4JtQueF/WRINnfeycozobOGT4ha6TiIUmYjEHDcI6lxGMUKxwInu8cnCKofZVrsg0cXwXHkT1Bslqw2XPHSDox6elOPzayUEMNHJlw9Pp4GInveTphWxS0uUld1lTYy3TDTonWyPbjB4sKvamGOA4iP0Mq2q++5k5VnEms2Mn9/XLg2aBK9AXlCf1YbjRr7GXJ8fUhdyeSqC7mek+pWRhex4XI+J97NsWJTbT9CJDZMI1t4vNtsdTrHTq5xcbGxE2YrDSVFIT5cwqx/F4jhIxMtXnp82Ek8XKuCVmXP8ZHQ8Ok2W3GxkekanZ0Y7uhdI7cLX6YHt7i2HQAwLDHCsdhL7LYNlp1sDTOfYxhvXMhd2g/Ik+xd1mSAyWJDiFqB1Ghd0OT4yFX9eJZ9ThLCg0cXMnlGz9Z2AHCeM2RPepfJY15s10V2QrgsjTVZiOEjEy1e5vi0cx4fRzhBysXlQkMnbDQQGapGPH8nK3Woy/7gspOoXJVMxXXMgzs0IUK2UBeri1B7srtsu3oDM3Fl83Uh8bgwmCzcpsGhD+nHBrvQD4kPh1JByZ7jw+lChsW+02hBRTNjFQ91WuzlmTPkHBeAY7M0lLdBkHxcdDjrostshVWGPBvWCByaKPGhbS4Qw0cm+FVdgGePzzn7Qjs4NowX6pLuAWYXetZClyu5mZ3E0mKY87nk8Ph0mawoa2LCfsEwiQ2OZU6JlWv3VtvF/P3DEsOhsY9NqbvzNrQ7TgFP0DNHz8hhCJbUOXb1AGTPhePGhgyL/bl6Rhdx4RrEhGk4XUjuJe5gwm0OXUg/Lmiadt4ssc+JTEYgqwtAnly4s3Wsx1yG5lI8iOEjE80uoS5Pi9epaubQtVHJellyfErY3UoCM1A1Mk1idfa+NZlxzPHKcuzeztV3gKaBmDAN4sK13GIv+eJmn8QyYhldyLV7q7YfhOwU9rPSbntsiQW70MdHaBGmYfrnyLHYsx6fbPtzopI5vMOODTk8PtyuPoE1AuXdLDl0If24qG83orXLDAUFZMWHQa2Qd1ykxeigsEeY5BgbJS5jQy6I4SMTrfZQV4S92Zm7XarNRuN0NTOhjkrRQ2t3U0oZ6uJ7fADI7rYebJ/E5IjXF9c5L25yx+sHx8m3e2vsMKLTQoGimPCOincelZSGIKuL+HAtwrTyhXeKa509PhoZFvtOo4XbELBjQw4jkH1OhiY6PydybRAcupBjznB47PltDuSaMxJk3CCYrTacb2BDXcTjMyBhPT6DopnQjbtQV3mzAR1GCzQqBbLiwjhvi6QenzrnmKxKhuRmm41GbRu7e2MmMXnyONjFzXVCl3YSYytmUqPl272V1DMJ76lRoQjVOJrUAdLqg62YiY/QQifThG610Vx4Z2iCfGODHRc6jRLx4fKF/Ypd8jjk2iCw+uA8PjJ6ArlxIVObA6fnxL5BkHrzeLHRALOVhk6jREpkqKTf7QoxfGSCTW5OtRs+7gbhqSomzDUiKQIqpcLRx0eiHB+z1YYLDfaKLpdQl5QP7umaNnSZrQjTKLkdtRyTWHEQuPBtNhqHLzYDAEan6GXbvTk8gcyiopbJ8DnE6SLS4fGReLEvazLAaLFBq1IgLYYxzOXwjB6+2ASA1QXrSZZvsR/GLfb250TCDVtFswHVrd1QKihMTI8GII8ReLaHJ5AtiJDW+8U+JzkpkdycIbWXuJhnBCoU8lV0AcTwkQWL1cY1oBsUxXp8eg5Cfn4PAEeOj0RHVlxs7ITFRiNMo0RKZAgAedzW+84zE/qUzBhE6pijEWTZyXpw4UuZqHimph2tXWaEaZQYPShStt1bSR1jELM7WaWC4rxPUu3saZrG/vONAIDpQ2IdHh+JczmKefk9SrsS5Gjal3/OrousGOg08hiBBpNzRRcgz2Zpv33OGDMoEgkRjPer22yTPBeuxGPYTzpd1LV143x9JygKmJoRw3VZl3rOcGyW5M3vAYjhIwutvIqulKi+PT6jUljDx57jI9FD467nghxVXY4JPdbh4ZB4cXOq6EqQz4W/z77QT86IgVqp4BZ7qXf23CQW74jVS20Un6vvQEOHCSFqBcamRiJMpsXeUbXj0IXUOT40TXMbBDmfEzY0zlZ0AXJtlhxzBrs5AKQdGzRNe0z0lnTOuMCMi1HJekTq1NzYkHrO4DyBMuf3AMTwkQW2lD0iRMWduO7ugXT1+Ggk9victn//kAR3i5s0MlhtNA5cYCax3KxYbicrdSVTcV07aBqI1qkRF85O6NIbgfwJHQCnDykTN2022sNiL62XI9++0E8aHA2tSolQmRb7MzXsrt6xk5X6ObnYaEBNWzc0SgUmpEfL5vHhdJHQUxfSLvYO75dGqeA8cVIu9rVtzhVdgDwenx5zhkxe4qIaR/NXuSGGjwyw+T1ROjUXi3dNbm7qNHHJeSNcQ10S5fhsOVULAJiWGcO9JnXL9dPVbWjrtiBcq0JOip7TFyBtjHrr6ToAwMT0aM77pZY4Xm+z0dh/gd3VM/eEi9dLOKEXlDej2WCGVkljqD3HB5A+cZOb0DOZCV0Oj4/JYsOuIsfYYJE6x4fVxfi0KIRqlNBp5TECt51m5owJ6VHca1JvECqaDShv6oJSQWFyRgwoinJsECRc7LfadZGTEokQe0WuHC0wXA0fOTw+Fxs7UVzXAQUFjE2NlOx7PUEMHxlgj6uI1mkQzlnfzoPwYCmzwGXE6hBun8SkPLKipK4dZ2raoVJQWJCTxL0udaiLfWinZEQzCd4qheSVTDRNY93xKgDA0nHJ3OsaiRd61/weQJ7d24/HqgEAY6JprsUCIK0b3zW/BwC32Es5of9UUo+2bgviI7SYytsgSN20z7G4sQax9EZge7cZO4rqAQBLx6Zwr2skbtrHz+9h5045FntuzhjrmDPYcWG10ZKcUO6a3wPI4yVed5yZM2ZkxyHWXnEoJ8TwkQH+YYaOWLzzIPzhGPPQzB2ZyL2mkfDIivXHawAAs4bGIUrnOExOalftzyUNAIBc++JGUZTklUynq9txvr4TGpXC6X5I7cJndcHm9wDST+hWG40NJ5hJbEKc88QtZbJ3UW27U34PIM9iv85uBC4Zk8yFUwBAo5Jug2C10dh7zjWcIX2139bTtTBZbMiKD8PIZPlCXexzwuoCkH6DUNfWzXlnlzgZPrx+VxJ0Of/5HKMLNr8HgKPiT0JvIGv48I1AOelXhs/69esxbdo0hIaGIjo6Gtddd53T+2VlZViyZAl0Oh0SEhLwxBNPwGKR5xyj3ihvYqoe0qJD3Ya6OowWzmV87fhB3OtSdm5ef4IxvJbwdm6AtKGuimYDdp1ldpBXDk/gXpd6EmN3blcOj0dEiJp7XUoXvs1G4/ODZQCAq0bwdCHx7u1gaRPq2o3Qh6gwItK94SOFPj7fz+hiZnY85wmVOrzTbbZy4WDXCV2lkC7Utf1MHerajYgMVWPiYCbcxhqBJotNMq8T6wm8emyK0wGUKoV0z0lzpwkbChk55ox0PCdSbxDWn6gGTQMT06OQGu1oNOrc9kH8sfH5/nIAwBwZ54ySug6crm7rET2QE1XflwQH3377Le6//348//zzuOqqq2CxWFBYWMi9b7VasWTJEiQlJWHv3r2orq7GXXfdBbVajeeff15GyXtS3sxUB6XF6Nz228g7VYNusw1ZcWEYPUjPvS5VH5+zte04W9sBjVKBeaMSnd6TMtT1Sf5F2GhgRnasU+IoM4kZJZnEmDAXu1uRzwjcVVyP8/WdiNCqcOOkVO51qXdvP9o9kfNGJUClKHN6T6pQV2uXGV8frgAA3DMjg3tdao/PzqJ6dBgtSI4MccrvAaQ1Alf/fAEAcOvUNC6XhK32AwCD2Qq9Utw9bovBhD3FzCbl6nHORqCUuV+fHyxDt9mGUcl6TB7suCeOZG9pnxPXOcPJ8LHYABGjPoWVrThQ2gSVgsId0wdzr3MFIhLrwjV6ICf9wvCxWCx49NFH8dJLL+Hee+/lXh81ahT3/1u2bMGpU6ewdetWJCYmYvz48fjrX/+KP/zhD3jmmWeg0QSHwgGgvIlv+Disb5qmQVEU1h5lBsrV45x3TlLl+PxrRwkAYPbweESGqp3ek2pC7zRa8PkBZmH91YxMp/d0Wul2LBsLa1DWZECYRum0gwQc5zGx8Xoxm3Kt/rkUAHDzlDQubwGQdvdW196N745UAgCuHpuM1iJXw0caQ/DrQ+UwmKwYnhiBy4bwwhkShkBpmsZ7u88BAK4Zl9Lj3kvVtO9MTRv2nmuEUkHhrtwM7nWNSgG1koLZSsNgtEIfovb8IQLwwU8XYLbSGJms79GnRaqmfWarDZ/kXwTAGMT8uTOMC/2J/5wcLG3CkbIWKBWUU5gLcPS7stHiz6Ef2A3iJWOTkagP4V6X8jnpMFrw6T7mnvCjF3LTLwyfI0eOoLKyEgqFAhMmTEBNTQ3Gjx+Pl156CaNHjwYA5OfnY8yYMUhMdHgoFixYgIceeggnT57EhAkT3H620WiE0Wjkfm5rY0q4zWYzzGaz29/xBfYz+J/FGj7JERpoFcxkQNNAa2c3WrrM2FPMxGUX5yQ4/Z6CZgaq0WITRDZ3FNW0Y63dQv/NFZk9vkcB5mE1iSgDAHxxoAxt3RYMjtFhZla003eF2ne17Qaj0+vudB0IFqsNL28+AwC457LBUFO082fbHBOHwWjiQpFCU1zbgd1n60FRwO1TBjnJEGJfYDu6hRmvvfHm1rPoMlsxPi0SU9IisLXIWddsSKPLaBJNFpPFhg/tE/qd09OcQtlaJfMsdRotouti6+k6FJS1IFStwF3T03p8H5vybTQHLktv4/rfduNr3sgEJISpnK7RaZRo7bKgtbMbsTplj98VisYOI/7zE3NP3M0ZFM3OGVZR78uPx6tR3dqNmDA1Fo2Kd54z7M9me1fvYzPQOYSmafxjw2kAwC8mDkJMqLLHZ6mVChgtNhiMJpjN4tyX2rZuztNy57Q0lzmD+W9Ht3jPKcu/d51DY6cJg2N0mD8yTtT52pfP6heGz/nz5wEAzzzzDF599VVkZGTglVdewezZs3H27FnExMSgpqbGyegBwP1cU1Pj8bNXrVqFZ599tsfrW7ZsgU6nc/Mb/pGXlwcA6LYAzQZG7acP7sF5JUBBCRoUfti4BZsqFLDaFBiqt+HMwV04w/uMdjMAqGCy2LB+/QZQIjgY/n1GAZpWYHysDReP/oSLR53fL2ymACjR0NiMDRs2CC8AgA4z8MpRJQAKUyLbsWnTRqf3O1sUABTIP1QAuqznLpLVdaDk11I436BEmIpGWudZbNhw1ul9pp0Scy/Xb9yEEBHmMJoG/nmK0cXYaBsK9+1EIe/90mrmfpSUlmPDhovCC2CnoRv4n/2ezIpoxNatWwE467qjlXl//8HDMJ4XZ3e/pYJCRYsS4Woa2urj2LDhOPdebRcAqNDa0SXa2ASY3foLx5i/dUaCBYf2bOtxTWkFc1/OXbiIDRsuCPK9ruO6tB34rpCRYwRViQ0bKp3ep6zMe3k7diFNxJ5x315QwGBSIC2MhrX0MFyHYWk7wNyXTtHuS7cVWGUfn9Oiu7Etb7PT+411zJxRcOIk4poK3X4GH3/nkJPNFA6XKaGmaIyylWLDhtIe11C0/b5s24EEkY6s+vCsAmarApkRNCqP/4xKx2OCogZmbJZX14n6nHSYgXcLmL91dmw78jZvcnudUPM1ABgMBq+uk9XwefLJJ/HCCy/0es3p06dhs2e//+lPf8KNN94IAFi9ejVSU1Px9ddf49e//rXfMqxcuRIrVqzgfm5ra0NaWhrmz58PvV7fy296h9lsRl5eHubNmwe1Ws2ctn4wH9E6NW64Zj4A4M8F29FhtCB9zDQcPHoEAI1nbprKlR+ytHdb8OdD2wEAc+cvcColFoLtRfUozC+AUkHhhTtmcU23+ESUNODfZ45AF6HH4sW5gn4/yxPfnIDBUo0RSRF47u5pTnFxANjcfgynWmqRPSIHi6enc6+76joQGjtN+Os/9wIw4ZG5w3EDL5eExWqj8bv9zEN75Zy5iBYhfv1dQSVK9p1EiFqB15fN4s52Y+k8XIHvSk8hMjYBixdPFPz7AWYX+8CnBbDRDbh8aCweuW2SW11/WXsI59qbMGbceCwWoXrjYqMBvz+4F4ANz1w7Fte65JJUt3bj+aO7YYYCixcvEPz7WVbvvYiariJEhqrwwt2zuCakfMp3X8CG8mIkDUrF4sWjA/o+d7o2W2244Z19oNGB68cnY/mNY3r83pslP6OlvhPjp0x36sUlJCer2pB/YD8AGn+9aTJm8EKP/GteK9wHtSYEixdfIYocz28sQovpIlKjQ/HCPZdxxzKwHNlwBvvqypCWkY3F84Z6/JxA5hCDyYLX/7UPgAF3z8jE7QuGub3umWM70G0wY8bMy0U5pXzX2XoU5BdAQQGv/TIXOSnO61hoUT0+Ki5AaEQUFi+eLvj3szy5phBGaxVGJkXgj7+c3iMcLOR8zcJGbPpCVsPn8ccfx7Jly3q9JisrC9XVTHIpP6dHq9UiKysLZWVMnkFSUhIOHDjg9Lu1tbXce57QarXQantmmKnVasFuBv/zqtuZ5oXpMTru88O0SnQYLXh1awnMVhqXDYnFjKGJPT4jnHI8zDaFUlD56tq6sXLNSQDAr2ZkYHhKlNvrQu25UhYbLej3s+w4U4fvj1WDooB/3DgWupCe9ybcnq/QbXEvQ6D3jqZp/On7o2joMGFoQjjunpEFtRsjUw0mZm+10QAl7P0AgJrWbvxjE+Nl+u3cYchM6GmIR4Qy+uky20S5HwDw0d5S7DzbAI1KgT8tyXH6Hr6uNXYd2aAQXBaL1Ya//HAaRosNM7PjcOOkNKccDgCItDtozVYaNKXk2j8IyamqNry8pRgA8LsFIxCrd+8VDrHnUVhpCKYLvq7f21OMM7UdiNKp8eelOW6/g81rMdmEk4FPl8mKx785AbOVxvxRiZg9wv08qwth5gyzSHPG4YvN+Mie2/O360ZDHxbS4xp2zujyMGe44s8csuqH07jQaECSPgT/d9Uwj7/PjksbJfxz0mow45l1TJzgVzMyMX5wT0NUr2PmDIPJKtqcse54Fb49UgWKAp65JgdaredNoZBrrbefI6vhEx8fj/j4+D6vmzRpErRaLYqKijBz5kwAjLVYWlqKwYOZbPXc3Fz8/e9/R11dHRISmCTUvLw86PV6J4NJbtj8ntQYx4TJTFBGHK9oBQA8Osf9joTfA8JotgE9n2+/sFhtWPHVMTR1mjAyWY/fLRju8VpWBjFKZEsbOvHoFwUAgLtzMzA+LcrtdY7zqcRJVFz9cym2namDRqXAm7dN4Cpl3KFWMoaP0JVM3WYrfv3JITQbzBiVrMevZma6vc5xIrk4iYqFla34uz1n4Y+LRmB4kud282Imvr+w6QzyzzciVK3E364b3cPoAeC0yzeYLNCohPXAdRgtePSLApisNswdmYBfTkv3eK2YzS13FNXhlTzGIP7zklEeG8I5uhULPzZomsYzP5zEufpOJERo8Y8bx3q81tHhXHhd1LZ146FPD8NGA9eOT8Hs4QlurxN7zvjxWBW+OFgOigJevWUc1zPHHWI9J1YbjUe+KEBFcxdSo0Px23nuPU5iV7iVNRqw8rsTAIDls7MxLaun8SU3/aKPj16vx4MPPoinn34aW7ZsQVFRER566CEAwE033QQAmD9/PkaNGoU777wTx44dw+bNm/HnP/8Zy5cvd+vRkQuuoovX2yGMV3q6eEySx4FCUZTgx1bQNI0/rSnETyUNCFEr8Oat47nqMXeIVbnT2mXG/R8fQlu3BRPTo7By8QiP14Z56HYtBJtP1uBv608BAFYuGoGRyb2HO8XQh81G44/fncCxilZE6dR495eTeoT7WMSc0MubDLjnw4MwWWy4akQC7r4so9frNSJ1K/7mcAX+vYfJk3nl5nHIiOsZggUYY4OVQehJ3WSx4cFPDqO4rgPxEVq8cONYt8YXi1jPSUldOx75vAA0Ddw2NR2/4LU2cCVMxLHxr53n8OUh+0J/83juQFJ3cP2uBG7YZzBZ8OtPDqOu3YjhiRF4/vqe4T6WMK76Ufg548CFJjz+9TEAwINXDMFlQ+J6vZ57TgTs3EzTNP6x8TR2na1HiFqB9+6c5HS8Dx+dh6a5QtDYYcRdH+xHe7cF49Oi8Ohcz2FFOekXyc0A8NJLL0GlUuHOO+9EV1cXpk2bhu3btyM6munVoFQqsW7dOjz00EPIzc1FWFgY7r77bjz33HMyS+5MeTPTvDCd5/E5UdnK/f+fl/TundKqmIoAIbo30zSNlzYX4ctD5VBQwFu3TXTql+MOMbqwtnWbcdd/96O4rgOJei3e/eWkXo0vsRb7/HONeOTzAtho4LapaVjWx0IPCL/Y0zSNp34oxHcFlVBQwD9vm4j0WM9J9mI1Zqtr78bdqw+gvt2IEUkReO2W8b0u9ICjx5OQTfvWH6/GH75lMjOXXzkEi8f0njuk0yphMtgEHRsWqw2/+/oYfippgE6jxH/vntxn230xmvaVNnbijv8eQnu3BZMGR+PZa3J6vV6sIzw+P1CGlzYXAQCeWjoKM4f2vtCLYQR2m62476NDOFregshQNd6/y/NCDzh0IXTvmpNVrbjvI2ZzMHdkIh734GXhw/VCE9AD9sa2Ym5z8NIvxiEnxfN5WOzGUeizDtu6zfjVhwdR2mjAoKhQvH+n5w2b3PQbw0etVuPll1/Gyy+/7PGawYMHi5qlLgSOHj6OJNXbpqbh8wPleODyLKRE9Z7mr1EpAVgC7uVjs9H46/pTXH+Yv143ukezQncIHepq7jThVx8dxLGKVkTr1PjoV1ORoO89hifG7m3HmTo8+OlhGC1MGOOv17oPp7iiErBpn9XGhA8+3VcGigJevmlcn4uKGF2sK5oN+OV/9qO00YCUyBB8eM/UHv2c3CG0C//HY1X47ZdHYbXRuGlSKh6f5zkEyxKmUaHFYBbMG2i0WPHYF0exsbAGKgWFf90xEWNTo/r8PaFDXTUG4PkPDqHOboj++67JfeYwhYkQ0vjgpwt4bh3jEf31FVm4Z4b7ECwf/vlUVhvtdLSHP3QYLXjo08PYe64RYRolPlg2BYNj3XsBWTwdDRQIhy82457VBzgv9Vu3TeB6e/WGkJtHmqbx2tZivLmNyTv785KRuHpcSq+/w24czVYaJotNkFy4xg4j7l59AIWVbYjSqfHxvX3P43LSbwyfSwGaph1dm3mhricXjcS14wd5VXkhxLEV3WYrfv/Nce48sGevycEd0wb38VsMQu7eypsMuHv1AZyv70RkqBqf3jcNI5L6rqTjdrICTWJfHSrHn9YwSZpzRiTgn7dP9GoCA4TTR7fZike/KMDmk0xC/gs3jMUNEz2HMVj4Hh+2AWYgFFa24v6PD6G6tRup0aH47L5pSIr0bgITKpeDpmn8e895PL+BSdK8fsIg/OPGsV41iBSyoWOLwYTffHYEe881QqNU4J+3T/CYQ+KKWsCmffsvNOH1QiW6rEZkJ4Tj0/um9RpaYhEypGGz0Xh5SxH+tZPpG3TfzEw8udBzOJqP0/lUVhuUCv+rUevaunHPhwdxsqoNoWolVt8zFZN4HZo9oRM4F27LyRo89uVRGExWTB4cjf8um9KjkswTQs0ZZqsNf1pzAl8dYrqY/2HhCNw3K6vP39MJnAt3oaET9350EOfrOxEbpsHH907FkHgR+ycIADF8JKS+w4husw0KCk6enchQtdOBer3BHVvhp5uyqqULD316GMcqWqFSUHjhxrFORyD0Bdt+PtDdyu6z9Xj0iwI0G8yMV+FXUzGsjzAbS5Td+3CuvjOgxd5kseEfG89wHU6vGZeCV24e55N7ViOAl6Os0YCHPjuMk1Vt0KgUeO3m8T06vnpCH6oCRTH5AhcbDR7zX7zhh2NV+P03x9BttmFIfBg+vW8akiO9bzSiEeA4ky6TFX/6/gTXIfqeGRn485JRXnsJouxJpefqOvrMteiNopp2/PqTQyhtNECnUeL9Oyf36X3jI8SunqZpfLLvIv667hTMVgoT0iLx32VTvTJ6AIcuSuo6/JYBYPLvHv/qGLbazw/87dxheGROttfPnfP5VLZeiwV64/DFJvzmsyOobTMiLlyD/949BeM8FEC4ws4ZZU0GGC3WXkPpvWGz0fjXzhK8kncWNM0cw/DenZOcjgjpCyHmjLr2bjz8WQEOlDZBQTEee182r+FaFTqMFpTUdWByRt8bbk/sLKrDI58XoK3bgpTIEHxy37SgN3oAYvhISqU9vydRH+K3e5F9YP2ZULecrMET3xxHa5cZUTo13rljEnfqubeoFYGFukwWG97aXox/7igBTQNjBkXiP3dPdmqp3hczsuMQplGirMmAfeebfP4bAGYxeOzLAhRWMn0fHps7FI9cNdTnYycC9XKsO16FP60pRGuXGTFhGrz7y0mY6kPPFZ1GhVlD47H7bD2+OlSO33u5C+fT3m3GMz+cwrdHmJ3jFcPi8eZtE7wKb/FxLPb+7WRPV7fht18exZmadigo4E9LRuFeD9VsnliQk4SDpc346lAF7uQd4eAtNhuND/eW4h+bzsBksWFQVCj+c/fkPpPcXQn0ANsWgwl/WXuS6747PtaGj++ZjAgfekXNz0nEq3lnsf1MHerbjYiP8L3II/9cIx7/6iiqWruhUSnw4o1jcd0E344eCPRgTquNxvu7z+OVLUWw2GhkJ4Tjg7un9Jr75srY1Cgk6UNQ09aNLSdr+wwHuaOqpQsrvjqKfeeZU9fvzh2MPy8d5XMeC3eciZ9jY2dRHX7/zXHUtRsRrlXhjVvHY87IvtMU+MzPScR3Ryrx1aFyvwyfbrMVL2w6w6VKTEyPwrt3TkJCRPCGt/gQw0dCqlq6AQCD+sjj6Y1Y+27vp5IGzBradysAAGjoMOKv605xZ4CNTY3sM2nWE+xDbqPhc3z4ZFUrHv/qGM7UtANgcpuevjrH5x1gmFaFa8YPwucHyvDlwTKfDB+apvHp/jL8ff0pdJttiNKp8cKNY/0+NZidxLp9rLJr7DDiqbUnsf4E06NqfFoU/nXHxD5zvNxx65Q07D5bj68PV2DFvGFeh+kA4FBpE3771VGUN3VBQQG/mZ2N384b5lceBusN7PbRG2mx2vDurnN4Y1sxzFYasWEavHX7BL88NtdPGIQXNp3BicpWFFa2YvQgz0mertS2deN3Xx/jjoy5cng8Xr5pXJ+JzO5gd/W+6gIAtp6qxco1J1DfboRSQeGJ+UOR1HLK5+dkRJIe49OicLS8Bd8dqcCvrxji9e+aLDa8kleE93efB00DGbE6vHHrBK89LHyUCorrd+WrPs7Vd+B3Xx9DQVkLAObcqRdvHNtrIrMnGW6enIo3t5fgy4PlPhs+Pxyrwp/XnEBbtwU6jRLPXJODmyen+fQZLGo/x0Z7txl/X38aXxxkTlwfmhCO9+6chCw/PCy3TknHd0cq8eOxavxl6ShE+HCW2+nqNjz6RQHO1jKexDunD8afl47024smB8GZcn2JUtXCeHz8WdxY2NOoV/9cyn2eJ2iaxlcHyzHnlV1Ye7QKCgp44PIsfPPgZX4ZPQAQEaLiXO3f2T0EfdFttuKNrcW49p8/40xNO2LCNHj79olYdcNYv93et05hJp0NhTVoNXh3PktxbTvu/O8B/OX7QnSbbZg1NA6bH7vcb6MHADLsSZXfHPZOFzRNY/3xasx/bTfWn6iGUkHhkTlD8dWvc/0eF3NHJiI2TIP6diN2FNV79TutBjOe+eEkbn4vH+VNTN+PL3+di98tGO538mmmXRfrT1R7XVV1uroN1/9rL17echZmK415oxKx8bFZfoepYsO1mD+KuZ9fHSr36nesNhqf7ruIBa/vxp7iBmhVCvz12hx8sGyKX0YPwBxArKCYcOyRsmavfqep04THvzqG+z4+hPp2I4bEh+GbB3Nx74wMv4+nYZ+TLw+Wg6a987bkn2vENf/8Ce/tYoyeW6ekYf0js/wyelgy7PPNt14+J2arDf/Zcx6L39iDgrIWRGhVePHGsfjnbRN8NnpYbpqcBopiNo1skUlflDcZ8OAnh7lwzri0KKx/ZJbfRg/gmDO+PVzp9T3ZdbYeC1/fwxk998zIwA8Pz/TL6AGAKRnRyIoPQ5fZinXHq736HYPJgle2FOHaf/6Ms7UdiAvXYPWyKfjrdaP7ldEDEMNHUioFMHyuGpGAaZkxzI5sy1mP1+0734hfvJuP33/LhLZGJevx/fIZ+OPikQFl8auUCiy/MhsA8NrWs72Wh9psNNYercScV3bhta1nYbHRWJiThC2/vdzrHBZPjE2NxIikCJgsNny6v/czqthFfuEbe/BTCdOB+Kmlo/DRPVN9CrG54/+uGgqKAjacqEFBHwvckbJm3PxePpb/7wgaO00YnhiBtctnYMW8YQHdE41KweVprf75Amy99Aex2mh8tv8iZr+8Ax/uLYWNBm6YMAgbHp2FKQHE+gHgugmDkBYTivp2I/67p/fzqWpau/H7b45hyZt7cKKyFZGharx2yzi8L4C7/Bb7Yr+moBKNHcZer913vhFL3tyDP39fiBaDGaMH6bH+kZm4MzcjoETxtBgdbrQnp/9j45leF7husxXv7DyHK17agW+PVICigF9fnoX1j8zChPS+E3d7Y+m4FOg0Spxv6MTOPoziimYDfvPZYdz27304U9OOaHsPqX/44WFx5dG5TJn3e7vP93pPaJrGpsIaLHhtN/62nunUPWtoHDb/9nLcPKVnt25fSIvRYWY2Y1CzeX2eYBf5Oa/uwqaTNVBQwCNzhuKbB3ORGUAeHcBUw2lVChwobcK203W9Xnumpg13f3AAd39wAJUtXUiP0eGLB6bj6atzvE6mdgdFUZxR/NHe0l7bo9A0M49f9fIuvLW9hGvguemxy3HlCO+S/YMNEuqSENZDMyjaf8OHoiisXDwS1739M749UoGIEBVWLh4BrUqJtm4zdpypw6f7LuJgKbMIh6qVWDFvGO6ZkeFTCKQ3fjk9Hat/voCK5i68s7MEK+Y7lxnTNI3dxQ14eXMR16MoSR+CPy4ZiavHJgdceQQwerhnRgb+8O0JvJZ3FhPTozE53TkPo63bjE/3XcR/9lxAUydzVMj8UYn485JRfnu8XBmeFIEbJ6bim8MVeH7DaXx23/QeRkxxbTte31rMhbVC1Ao8cPkQLL9yiGA7pdunpuPDn0ux91wj3thW3KNrq9XGLChvbS/mQo3DEsPx1NIcn5J2e0OjUuB384fj0S+O4r3d53HjpNQeRn5Tpwmrf76Af+85j27mlFcsHpOEp6/OCdgIZZmZHYfshHCU1HXgoc+O4NN7p/W4J8fKW/DPHSXIO8Uk7EaGqrFi3jDcMS1dsOfkt/OG4YdjVThwoQkbC2t69CAyWWxYe7QSr+WdRVUrEwYfmazH367LwaTBwpytFa5V4ebJafhwbylWfHUUPzw8E2kxzmO/prUb/9lzHp/suwijhSm+uGPaYKyYNwzRXiZS98XSMcn49+7zOFHZipe3FOH568c4zQM0TWP/hSa8sqWIm7tiwzR4YsFw3BKgwcNn2WUZ2FPcgNU/l2JqRgwWudyTbrMVu6spPP/Gz6htYwy03KxYPH3NKK8qTr0hOTIUv5qZiXd2nsMLm87gsuzYHsnRZY0G/HNHMb45XAEbzfSFuis3A4/PHxawEcpy48RUvLWtBGdq2vHsjyfxd5cGkDRNY2dRPd7cXsyFGlOjQ/HnJaOwICdRsHsiBxTtra9tgNDW1obIyEi0trYKdkjphg0bsHjxYlz/7j4UVrbhg2WTcdUI35LRXHl961m8vpXp3RCiViA2TIuq1i6wd1OloHDb1HQ8fFW2YAsKn+8LKvHYl0cBMA/QTZNTQdPAwdImrD1aiXP1nQCYifeh2UPwqxmZAe1Q3EHTNB778ijWHq1ClE6N5bOzYCg7icycidhV3ITNJ2vQYS/lzU4Ix9NXj/I6L8oXqlq6cOXLO2G02JCTosfj84dBH6LGicpWbDlZi/zzjQAAigJ+MTEVK+YP86laylu+PlSOJ75hmv3dPysT83OS0NFtsd+TKs7jqA9RYcW8Yfjl9MF+L/L8cc0/H8dmo3HN2z+hsLIN0To1Vi4eiSHxYShrMmBXUT02FNZwu8tJg6Pxx8UjvSpH9pWSunZc//ZetBstuHJ4PO66LANalQKnq9vxw7EqHCtvAQAoKOD2aelYMW+419VSvvCPjWfw7q5zXP7UrKFxMJis+LmkAd8frUKD3fuRHBmC380fjusnDHJ7mKM7XXtLt9mKm9/Lx/GKVmTFh+E3s7ORGReG0oZObDpZg51FdVzS8fSsGDx9dY7Pydze8HNJA+74z34AwNyRCbhnRiaUCgqHLzZj3fFqnK5mCg20KgXum5WJB68Y4lPuibc89+MpfPDzBYSqlXh07lBMzYxBfbsRu8/WY/3xarR0MaHzQVGh+POSkVg4OknwRb61y4wrXtqBFoMZWXFh+P3CEYiP0OB0dTu2na7FzrP13Fy+eEwSfr9gREAVm57YfqYW9350iOsEfu34FBgtNhwta8Hao5U438DM46FqJZZfOQT3zcryOz3BlUDHtTu8Xb+J4eOCmIbP1FU70GwwY9NjswTZPWw7XYvff3McjXZvBgBkxoXh2vEpuH1quqgNpGiaxutbi/HW9mK4i6yEaZS4eUoall+ZjTg/8yS8odtsxS3v5eNYRavb94fYJ/prxqeI2kV0x5k6rPjqKJrd5BspKGDeqEQ8NneYKAsKn7+tO4X//OTejR8ZqsayyzKw7LKMgHfyvU1a/PJ8d4wZFInlVw7BghzhFxQ+O87U4d6PDrodn0oFhWvHp+A3s4cgO8G7Ngr+0G224o/fncB3BZVu30/Ua3HPjEwsuyzD44IixAJR09qNa/75E+ra3YeZpmRE4zdXZmP2sHhR78lHe0vx9/Wn3ValhqgVuH5CKh6Zky3KxoDFYrXhVx8dwu6z7kN/MVoaj8wbiVuneb4nQnCotAkP/68ANW3dbt+/fFg8Hp0zVJSNAZ93d53DPzaecfueTqPEL6cPxn0zMwVfT4jhE0SIZfjMnjsf4/66HQBw/Jn50Au0kzFZbKhu7UJDhxFp0TrJu2UeuNCEN7adRXVrN0wWG8alRmHm0DgsHZssym7NHR1GC74+VI51x6pQUtOMzMQojBkUhWvGp2BSerTPJer+UtvWjVUbTuNMTTvauy3Iig9D7pBYXDt+UECVfL5A0zQ2FtZg7dFKHClrQVy4FlnxYVg8OhlzRiZItlszWqx4e3sJthfVobXLjGidBrlZsVgwOgkT0qIkc5MfK2/B14fLseNMPbRqBdKidZg9PB5LxiZLWnr7w7EqfJJfisYOE0ABUwbH4MoR8ZgzMrFPg1yoBaKmtRtfHizHxsJqtHdbMCgqFJMyonHd+EG9Hj4rNKer2/DS5iKUNxnQZbYiJ0WPmdlxuHpcCqJ8KNcPhG6zFd8eqcC6Y9U4V9+B5MgQDE+KwKKcRLQU7cfSJcItxr3RYjDhhU1ncLS8FW1dZqRGh+KyIXG4elyy34nLvkLTNHaerceaI5XYd74R0ToNMuJ0mD8qCQtGJyFcoNCaK8TwCSLEMnyGT7kCC9/8GRFaFU48u0AASQmuiPEgEdxDdC0dRNfSQXQtHXIaPqSqSyKqWgOv6CIQCAQCgRAYxPCRiGp788KUqP7R2ZJAIBAIhEsRYvhIBFuuGkgpO4FAIBAIhMAgho9EsIYPCXURCAQCgSAfxPCRiGq2eSExfAgEAoFAkA1i+EgE8fgQCAQCgSA/xPCRABsNVBPDh0AgEAgE2ek3hs/Zs2dx7bXXIi4uDnq9HjNnzsSOHTucrikrK8OSJUug0+mQkJCAJ554AhaLd6dEi4mNBh69Kht3Th+MxAjxuhgTCAQCgUDonX5zSOnSpUsxdOhQbN++HaGhoXj99dexdOlSnDt3DklJSbBarViyZAmSkpKwd+9eVFdX46677oJarcbzzz8vq+wqBfDryzNJQywCgUAgEGSmX3h8GhoaUFxcjCeffBJjx47F0KFD8Y9//AMGgwGFhYUAgC1btuDUqVP49NNPMX78eCxatAh//etf8fbbb8NkMvXxDQQCgUAgEAYC/cLjExsbi+HDh+Pjjz/GxIkTodVq8d577yEhIQGTJk0CAOTn52PMmDFITHScer5gwQI89NBDOHnyJCZMmOD2s41GI4xGx8F9bW3MwYpmsxlmc88DJ32F/QwhPovQO0TX0kF0LR1E19JBdC0dYuja28/qF4YPRVHYunUrrrvuOkREREChUCAhIQGbNm1CdDRzcm1NTY2T0QOA+7mmpsbjZ69atQrPPvtsj9e3bNkCnU4n2N+Ql5cn2GcReofoWjqIrqWD6Fo6iK6lQ0hdGwwGr66T1fB58skn8cILL/R6zenTpzF8+HAsX74cCQkJ2LNnD0JDQ/Gf//wHV199NQ4ePIjk5GS/ZVi5ciVWrFjB/dzW1oa0tDTMnz9fsENK8/LyMG/ePJLjIzJE19JBdC0dRNfSQXQtHWLomo3Y9IWshs/jjz+OZcuW9XpNVlYWtm/fjnXr1qG5uZkzRv71r38hLy8PH330EZ588kkkJSXhwIEDTr9bW1sLAEhKSvL4+VqtFlqto9KKPay+q6tLkJthNpthMBjQ1dUVFBVmlzJE19JBdC0dRNfSQXQtHWLouquLaRTMruOekNXwiY+PR3x8fJ/Xse4rhcI5F1uhUMBmswEAcnNz8fe//x11dXVISEgAwLjQ9Ho9Ro0a5bVM7e3tAIC0tDSvf4dAIBAIBEJw0N7ejsjISI/vU3RfplEQ0NDQgBEjRuCKK67AU089hdDQUPz73//GG2+8gYMHD2LcuHGwWq0YP348UlJS8OKLL6KmpgZ33nkn7rvvPp/K2W02G6qqqhAREQGKogKWnQ2dlZeXCxI6I3iG6Fo6iK6lg+haOoiupUMMXdM0jfb2dqSkpPRwlPDpF8nNcXFx2LRpE/70pz/hqquugtlsRk5ODtauXYtx48YBAJRKJdatW4eHHnoIubm5CAsLw913343nnnvOp+9SKBRITU0V/G/Q6/XkQZIIomvpILqWDqJr6SC6lg6hdd2bp4elXxg+ADB58mRs3ry512sGDx6MDRs2SCQRgUAgEAiE/ka/aGBIIBAIBAKBIATE8BEZrVaLp59+2qlyjCAORNfSQXQtHUTX0kF0LR1y6rpfJDcTCAQCgUAgCAHx+BAIBAKBQBgwEMOHQCAQCATCgIEYPgQCgUAgEAYMxPAhEAgEAoEwYCCGD4FAIBAIhAEDMXxE5u2330ZGRgZCQkIwbdq0HgepEnzjmWeeAUVRTv9GjBjBvd/d3Y3ly5cjNjYW4eHhuPHGG7nDagm9s3v3blx99dVISUkBRVH4/vvvnd6naRpPPfUUkpOTERoairlz56K4uNjpmqamJtxxxx3Q6/WIiorCvffei46ODgn/iv5BX7petmxZj3G+cOFCp2uIrr1j1apVmDJlCiIiIpCQkIDrrrsORUVFTtd4M2+UlZVhyZIl0Ol0SEhIwBNPPEEOMnXBG13Pnj27x9h+8MEHna4RW9fE8BGRL7/8EitWrMDTTz+NI0eOYNy4cViwYAHq6urkFq1fk5OTg+rqau7fTz/9xL3329/+Fj/++CO+/vpr7Nq1C1VVVbjhhhtklLb/0NnZiXHjxuHtt992+/6LL76IN998E++++y7279+PsLAwLFiwAN3d3dw1d9xxB06ePIm8vDysW7cOu3fvxgMPPCDVn9Bv6EvXALBw4UKncf755587vU907R27du3C8uXLsW/fPuTl5cFsNmP+/Pno7Ozkrulr3rBarViyZAlMJhP27t2Ljz76CB9++CGeeuopOf6koMUbXQPA/fff7zS2X3zxRe49SXRNE0Rj6tSp9PLly7mfrVYrnZKSQq9atUpGqfo3Tz/9ND1u3Di377W0tNBqtZr++uuvuddOnz5NA6Dz8/MlkvDSAAC9Zs0a7mebzUYnJSXRL730EvdaS0sLrdVq6c8//5ymaZo+deoUDYA+ePAgd83GjRtpiqLoyspKyWTvb7jqmqZp+u6776avvfZaj79DdO0/dXV1NAB6165dNE17N29s2LCBVigUdE1NDXfNO++8Q+v1etpoNEr7B/QjXHVN0zR9xRVX0I8++qjH35FC18TjIxImkwmHDx/G3LlzudcUCgXmzp2L/Px8GSXr/xQXFyMlJQVZWVm44447UFZWBgA4fPgwzGazk85HjBiB9PR0ovMAuXDhAmpqapx0GxkZiWnTpnG6zc/PR1RUFCZPnsxdM3fuXCgUCuzfv19ymfs7O3fuREJCAoYPH46HHnoIjY2N3HtE1/7T2toKAIiJiQHg3byRn5+PMWPGIDExkbtmwYIFaGtrw8mTJyWUvn/hqmuWzz77DHFxcRg9ejRWrlwJg8HAvSeFrvvNIaX9jYaGBlitVqebBwCJiYk4c+aMTFL1f6ZNm4YPP/wQw4cPR3V1NZ599lnMmjULhYWFqKmpgUajQVRUlNPvJCYmoqamRh6BLxFY/bkbz+x7NTU1SEhIcHpfpVIhJiaG6N9HFi5ciBtuuAGZmZk4d+4c/vjHP2LRokXIz8+HUqkkuvYTm82Gxx57DDNmzMDo0aMBwKt5o6amxu3YZ98j9MSdrgHg9ttvx+DBg5GSkoLjx4/jD3/4A4qKivDdd98BkEbXxPAh9CsWLVrE/f/YsWMxbdo0DB48GF999RVCQ0NllIxAEI5bb72V+/8xY8Zg7NixGDJkCHbu3Ik5c+bIKFn/Zvny5SgsLHTKCySIgydd8/PQxowZg+TkZMyZMwfnzp3DkCFDJJGNhLpEIi4uDkqlskdlQG1tLZKSkmSS6tIjKioKw4YNQ0lJCZKSkmAymdDS0uJ0DdF54LD66208JyUl9Ujct1gsaGpqIvoPkKysLMTFxaGkpAQA0bU/PPzww1i3bh127NiB1NRU7nVv5o2kpCS3Y599j+CMJ127Y9q0aQDgNLbF1jUxfERCo9Fg0qRJ2LZtG/eazWbDtm3bkJubK6NklxYdHR04d+4ckpOTMWnSJKjVaiedFxUVoaysjOg8QDIzM5GUlOSk27a2Nuzfv5/TbW5uLlpaWnD48GHumu3bt8Nms3GTG8E/Kioq0NjYiOTkZABE175A0zQefvhhrFmzBtu3b0dmZqbT+97MG7m5uThx4oSTsZmXlwe9Xo9Ro0ZJ84f0A/rStTuOHj0KAE5jW3RdC5IiTXDLF198QWu1WvrDDz+kT506RT/wwAN0VFSUU7Y6wTcef/xxeufOnfSFCxfon3/+mZ47dy4dFxdH19XV0TRN0w8++CCdnp5Ob9++nT506BCdm5tL5+bmyix1/6C9vZ0uKCigCwoKaAD0q6++ShcUFNAXL16kaZqm//GPf9BRUVH02rVr6ePHj9PXXnstnZmZSXd1dXGfsXDhQnrChAn0/v376Z9++okeOnQofdttt8n1JwUtvem6vb2d/t3vfkfn5+fTFy5coLdu3UpPnDiRHjp0KN3d3c19BtG1dzz00EN0ZGQkvXPnTrq6upr7ZzAYuGv6mjcsFgs9evRoev78+fTRo0fpTZs20fHx8fTKlSvl+JOClr50XVJSQj/33HP0oUOH6AsXLtBr166ls7Ky6Msvv5z7DCl0TQwfkXnrrbfo9PR0WqPR0FOnTqX37dsnt0j9mltuuYVOTk6mNRoNPWjQIPqWW26hS0pKuPe7urro3/zmN3R0dDSt0+no66+/nq6urpZR4v7Djh07aAA9/t199900TTMl7X/5y1/oxMREWqvV0nPmzKGLioqcPqOxsZG+7bbb6PDwcFqv19P33HMP3d7eLsNfE9z0pmuDwUDPnz+fjo+Pp9VqNT148GD6/vvv77FhIrr2Dnd6BkCvXr2au8abeaO0tJRetGgRHRoaSsfFxdGPP/44bTabJf5rgpu+dF1WVkZffvnldExMDK3Vauns7Gz6iSeeoFtbW50+R2xdU3ZhCQQCgUAgEC55SI4PgUAgEAiEAQMxfAgEAoFAIAwYiOFDIBAIBAJhwEAMHwKBQCAQCAMGYvgQCAQCgUAYMBDDh0AgEAgEwoCBGD4EAoFAIBAGDMTwIRAIBAKBMGAghg+BQCAQCIQBAzF8CAQCgUAgDBhUcgsQbNhsNlRVVSEiIgIURcktDoFAIBAIBC+gaRrt7e1ISUmBQuHZr0MMHxeqqqqQlpYmtxgEAoFAIBD8oLy8HKmpqR7fJ4aPCxEREQAYxen1+oA/z2w2Y8uWLZg/fz7UanXAn0fwDNG1dBBdSwfRtXQQXUuHGLpua2tDWloat457ghg+LrDhLb1eL5jho9PpoNfryYMkMkTX0kF0LR1E19JBdC0dYuq6rzQVktxMIBAIBAJhwEAMHwKBQCAQCAMGYvgQCAQCgUAYMBDDh9Dv6DRa8Nd1p7D/fKPcogQF/959Hh/nl8otRlCw+2w9nvnhJLrNVrlFkZ3yJgNWfncC5+o75BZFdsxWG/6+/hS2nKyRW5Sg4IsDZXh7RwlompZbFFkghg+h37GnuAH//ekCVq45MWAfXJYOowV/33AaT609ifImg9ziyM4b24rx4d5SfHekUm5RZOfbIxX4/EAZXt5cJLcosnO8ogX/3nMBT3xzHGarTW5xZOe5dafw0uYiHC1vkVsUWSCGD6Hf0WW2AADO13eiuG5g72b5no3NZDcLg4nRx8bCapklkZ8uuy52FtVz/z9QYcdFa5cZ+wa4p5imaU4fmwoH5pxBDB+CT9A0jeWfHcFvvzwqm7fFbHF874YT8i5wXxwow+3/3oemTpMs38/fvcqti8LKVvzinb2yhiBZfeSfa0SrwSybHAaTBXd/cAD/2XNeNhlMdl10ma3YdbZONjkAYNWG03jk8wJYZPK28J+TjTIv9psKq3Hzu/moaJbHQ2uxOebPjYU1A9JrTgwfgk+0dpmx/kQ11hRU4mRVmywymG2OSUzuHcvnB8qw91wjvj1cIcv3843AI2UtqGntlkUOgLkXhy424587SmSTgV3gLDYaeadrZZPjeEUrdp2txytbzsrmbQmmxX71z6X44VgV9p1vkuX7zVbHc7LlZA2sNvkW+68PVeBAaRM+yb8oy/dbeLooazLgVLU887icEMOH4BMmp8lUHg+D2eKQ4UxNOy40dMoiBwAY7bLIpgub8w5aznCXKQi8LfxJfZOM4S6TRX5vC18X20/XwWiRxwCjaZobG7I9J7x5q6HDhEOl8hhgAHi6kMfbYnLxusm9eZQDYvj0M7afqcXK707AYLLI8v38yVSuB5e/e2PkkG+BYydUubwtromacuqCXezl9LbwJ/XdxQ3oMMrznASDt4Wvi3ajBT+XNMgiB/953XyyVhZvS8/nRMYNgv05kcvbEky6kAti+PQz3tpegs8PlOHrQzKFVngPzfn6TpTIkFzMejm0Kmb4yrljcZ7UpZeDNURZXRy40ITGDqPkcgDOY0MubwubQxKiVsBksWH7GXm8LXxdyOVtYcdGiJoZGxtPyPOcOHtbjDh8sVkGGZx1sflkDWwyhbucnxP55gyKAjRKBUrqOlBS1y65HHJCDJ9+BpsvIFciazDsFti8ltnD46GgmHwKuRIFzTKH/thdfYJeizGDImGjgS2n5PG2mIPA28IucHNGJAKQzwDjG8RyeVvY+8HqIu90rSyl3JYg8NCyf/e0zFiEaZSobu3GsYoWyeUAeiYXSw2rixCVEjOyYxk5ZDKK5YIYPv0M9qE5UNqE+nbpd/Ymi+skJt+Dm6QPwZSMGADyeX34C5wc3hY230mtUGDh6CQA8rmu+QucXN4W1hC8elwKAGDHGXlKuXtsEGRYWFgZLsuORUyYBi0GeUq5XXNKNssQImefk3CtCleOSAAgYwiSl6Moh7eFvR8qJYVFo5MBABsGWLiLGD79DHYyo2lgkxyhFXuYKSJEBaWCwunqNlxslDa5mA11qZUKLB7DPLjyGT6MLJGhalm8LawhrFYqsMhu+OwtaZAludjE0wUgj7eFDXVNTI/CoKhQ2ZKLzS66yDtdK3kpNxfeUSmxIIfx+si5UVEqKIRplKhq7caxilZJZXA8JxQ3Z2wsrJYpR9F5bEhtFLMbFI1SgXmjEmWbx+WEGD79DH5F00YZwl3sQxsTpkFult1NKvFkyoa61CoFFuQwi/2hi82obZMvuXiRTN4W1thQqyhkxYdjeGKEbMnFrrqQ2ttitdFgowgalUK2ewIAJvviMiUjhvO27L8gbSWRmRsbCiy07+zlKOV2hFYUPG+LtHMX95woFZg9PB4hagXKm7pkacnBGqRyjU8zTxfRMs7jckIMn36GiRdO2He+UfLQChvqUivlC62wXie1gkJSZAgmpkcBAPJOy7ezv8YeWpHa28IawioF8yiz90QObws7oU9Mj+Z5W+ol/H7HpkClVGDRGEYXciQXs96dUI0S80ex3hZp7wm3wCko5GbFQh+ikqWUmx0XapWCC61skjjcxXo5VEoFdBoVrhgWz8khNezYWDwmGUoFhVPVbShrlC5H0cwLdQGQPUQuB8Tw6Wewi74+RCVLaIW/W5ifkwiKAo6Vt6CqpUsWGQBwk+nmk9LqgqZpblIfmhjBeVu2SuhtYV34GrsuWDe+HMnFZp73SQ4DjG/4qJUUJqRFI1GvlSW5mG90sLrYfLJW0koizuBQKqBRKTB3lDzhLm6hVTDeFq1KgYuNBpyuli63hZVBY1/s2TlDnoIE5r7ER2gxPStGcjnMVuc5Q655XE6I4dPPYHf47AIndXUX521RUkiICMGUwdInF5t4oS7AsWM5UNqMDglTW/jVGRqZPGB8YwMAhiWGIysuTJbkYr5Byupim4TeFn6iuVqhgEJBcaFQqfMo+EbHZUPiEBGiQn27EYfLpCvl5oe6AP4GQdpSbr7REaZV4XLO2yLd3GVy2SxdNTIBaiWFc/WdKK6VNrnY6TnJkXHOsOtCrnlcTojh089gJ9RrxttDK+ca0SzhOVH8UBfAD61I98CwxpdKwSz2aTE6jB6kh40GTjRRksnh5GFQUVxoZXdxvWTeFpNLqIui5PG2AM6L/aT0aMRHMN6WvSXSVBLxk2gVCmc3vtSl3Ox9Uasoxtsy0u5tkdAA43udAGDW0DhZSrl7GmDSL/b8UBcA6EPUmJkdJ7kcAN8QZHIUKQo4Wt6C6lZpvC2uoS5AnnlcTojh04/gt34fmhCBkcl6WCVOZHXsFpwXloMXm1DXLk1yMTdxqBzDl93NHpPS8OGV9quVCgxPjECm3duyQyJvC7+qi4XVhdTJxfwJnfG2SJvb4jo2AWBqRgxiZSjltticd9WOcJd0uS0Wq7NnNEStxFUjpQ938Q1iAJgzMhFqJYXiug7JGqC6hroAYJFcXnPuvlBI0IdgUno0AOmMDtf7Acgzj8sJMXz6EVaX0Mpiduck4YPr6iZNiQrFuNRI0DSwRaIcG9brxHo5AMcu8mwrhbYuaeJd/HOyVArKxdsi1STWc7EfPUiP1Gg5kovZXbVzHkXeKWlKubkJnTcuVPZcNEDexf6KYfHQaZSobOnCcYlKubl+LQreYs95W6Qr5Ta7yBEZqsZlQxhvi1ReSYeXwzE25o1kSrnP1LSjVKLz/vibV9eCBKnGp7s5IyUqFOPTokDT0udKygExfPoRTjkMKorbsfxU0oBWiRZ7i9vdgrS9dPh5RixZ8eEYlhAOK01he5E0iz1/AqEodrG3l3IX1aHbLL63xd3ujaIoLndAjuRiVpZpmTGI1qnRLFEpt8UlpMIiRyk3F+qyj9EQtRJXDpe2cZ67Z1WOUm73HlqpF/ueupCjlNs1LxDgeVskakrr+pyyLJKxIlRqiOHTj+B3QFUpFMhOCMewxHCYrTS2SlTdZXKzW2AfmPzz0uQbuZtIAWBBDrOwbJJox2K29JxMxwyKxKCoUBhM0nhbPE5iY+RILnaWRWVvkAZIYxS7G5sAZCnldndf+LlXUnhb3D0ncpRyu+YFAsC8UYlQUMDJqjaUN4lfyu3OywFI3/7BNS8QAFKjdRhr95rnSTCPuzMCAYeHdt/5JknzRuWAGD79CNdyXUD6skx3E3pGXBhGJEVIlm/EhVQULoaPfZHdU9IoSXKxa6UIwHhbFuRIF+4yW9xP6HKUcrOyaNzkG22SwNviaVzIUcrtztty5YgEaFQKlDYacKZG/Eoid6EuQPo5w52HNjZci2mZrLdFfDnc3Q+AV8pd0YpKCUq5nbz2boxiaXThfs5Ij9VhFJs3KtN5f1JBDJ9+hOPhdYRWuL4tZxvQ3i1+uMvi0gOChZVj/XHxH1xPu7dhieGID6ElSy52N6EDwGK7tyXvVK3o4S6zm+RmALKUcrOy8KtFLsuO5Uq5xfa2WDx4AgHpS7ndjdFwrcPbIsVz4mmxl7qU25NXkn1OpNCFu00KIH0pt1OTTZ5Butg+PveeE78praf7ATjngF3KEMOnH+FuwA5LDMeQ+DCYrNL0beEfcMdn6Vjmwf25pEF0N6lreSwLRVEYF8NM9tJ4W9wvLBPTo5EcGYIOo0X0cFdvk5jUpdzuZNGqlJwBtk7kBc5TqAuQvpTb00LLPifrT4gf7vIUEtaHqDFrKGOASeEB8/ScLBydDIXd2yJ2uMvTZglwhIWlCHe5ywsEGK/56EGMt0Xs5GKTB4MYgFPeaJsEG2m5IIZPP8Kd65qiKJm8Lc5DJys+HKOS9bDYaNEPT7W4qd5hGRfLyLf9TJ3opdyeFjeFgsISie6Jp1AXwJRyS3kqt7tQF+BY7DcWVota3eUp1AVIX8rt6TmZMzIRWpUCFxo6RU0upmmaS6R1DXUBDqNYilJuTwYp07mYCXeJbRR78n4BDl1Icd6fJyMQAJaMYXqzrTteJaoMFg+bVwDITgjH0AQmb3SbDOf9SQUxfPoRXJhJ5T7MtPNsvehWem8ehqXjGDnEfnB729mnhYEr5RbbA9bbLnKp/eyuradrRTXA3PXxYVHxOiivOyaFUezoT8JnRnYconVqNHSYRK3u8lTVxbKEF1oRO9zlKSQcrlXhKvtBnWIu9s4VoD31MX9UIlT2Uu6SOnHDXZbe5oyx0iz2njYpAJAcGYqJ6Uwpt9iFEWZbb7pgk4sbRa3u4vfbcge7nkgxZ8gFMXz6EZ6MjhFJEchOCIfJYkOeyA8uP8/IlaX2HUv+OWkeXHcTOkUBS+0L3I/HxN45eTY6xqVGIjWaqe7aUSSeAdbbhA4AV9sXlo2F1VyJtRjQNM1N6q4eF/4RFmIucO6a1PGZPTwB4VoVKlu6UFAu7tERnkLCgPNiL1a4ix/adLfARek03NERP4q8wHmqIgIYb4tSQeFkVRsuiNhLp7dNCgBcbd+orBc5H643OdJidBiXFgUbLW6OTW+hLgC4ehx73l89WgyXZnUXMXz6EZ4WOYqiuAXuRxl3TumxTFmmjYao4a7eQl2AY2e/vahO1ITv3rxfFEVhyVjxPWC9ua0BYGpmDBIitGjrtmBPsXj5RlYbDXYNd7fQsm78jYU1ouUb9RbqAphwF3tSuviLveexcdWIBOg0SlQ0d+GYSM0MLTyPj7tQF+BY4H4U0QADevfQxoRpMMN+dMQ6ETcqvW1SAGDJmGRQFFBQ3opGEaNdvYW6AODqseJ7W/qaM7LtpwKYrTQ2i5y2IBfE8OlHsDkU7gYsO4n9VNyAJhGTi3ub0AGHu1bMScz1YE5XhieGOzxgIpZl9jahAw5vy/YzdegUqbze9aRlV5QKhwEmpgfMtbmmK9OzHEdH7D0nTr5Rb55AFnZnv+54tajl9b0ttKEaJebY843Eek7YsUlRzBhwx1x7vtH5+k6cqhYv36gvo4ObM0QN/fU+byXoQzDdXl5/tFG8Y296C3UBjjDTwYtNqGkVxwLrK9QF8IziSzTcRQyffgSbz+FuwGbFh2P0ICa5WEw3KbtjcVcyDABL7Iv9gdIm0RIF+9rZUxTFTabiLva9T2I5KXpkxOrQbbZhq0iJgr2FVFjYxT7vlHj5Rvzmmp7yjdjqGbEWe9dDOd0xIzsOUTo1GjqM2C9iwrejosq9LPzqLjHyjRy6UDhVD/GJCFFz+UZiLnB9PScLRiVBraRQVNsuWnm9u4M5XWGfkyON4i2LvRUjAMzREZMHR4OmmbEhigwuR8u4g9207T3XIEk3aakhhk8/wtt8DlEXe5eT0V0ZFOVIFBSrYsSbHQubR7GnWLzyek/J5iyMAWbPHRBpN9tb4ijLhLQoDIoKRaeI+Ub8ai1PY4PVxeaTNaLkG/WWS8KiUSm4XiVihoVNfRjnVwyLR4RWherWbhwpEz7fqLdcPD7sYv/jMfHzjTzJEqlT43J7ef2PIj0n3oyNhaOToFJQqOikcL5enHwjb+RYKnKIvC9DFGDyjcZLkG8kF8Tw6Uf0FuoCwIU09l8Q39vS+4PrCCeII0PvoS6AKcsUu7zeU2dcPmyl286z9aLkG/UV6gLsOWDjxDWKzbyF1pOHYUqGuPlG3oS6AH7CtzgGmJMsHu5LiFrJHechxnNi8lIXVw5P4A5PLShvEVwOJ1l6mzN4FaFiGGDebJZiwjS4bAjTzHC9SC0PvDE6FrP5RmUtqGgWvr+RNzIAkMRrLhfE8OlH9GV0pEbrMIl1k4pldFj6nlDZB/fwxWbBH1yapvsMdbGIv9j3PYEMT3RU3Ilxer03LnzAEbPffkachG9vdKFUOHpOiXFPvAl1AcC0rFjEhWvRYjCLdpyHpY9QF+DYqKw/IXy+keNE9N6fkVCNwwAT6znpK8cHYPKNNCLmG3kT3gF4LQ9O1IhqgPXmiUvQh2BqBmOAiWEUe+sNXDo2BRQFHCxtRpUEx3lICTF8+hHsEQm9JqWNdVRqiEFfJcMAkBQZgmmZzIP7g8CTqbvTjT3B7ljyzzeiTgQPmDdGIL/i7vujlcLL4OXubVSyHlnxYTBaxMk38sb7BQDXjGd0seVULQwmYRO+vfFGAowBJvZu1hvjfNbQeETp1KhvNyJf4IRvRx+hvhN1r+aFY8VI+PZmsY8IUeMq++n1PxwV0SjuY2zMG5kAJUXjXH0nikTIN3IYYL3LwT4na0XQhTceOICZx6fYDTApmuNKCTF8+hGmPhLjAGDxWKYNfEFZiyht4LnzmPrYSV43fhAAYG2BsA+uu9ONPZEWo8MEEfONeks253PdBGYS+7mkAXXtwhpg3i72fANMjIWlr3wnlglpURgcq4PBZBW84s6bECgL6wHbItJ5at4sLhqVguvwvaZAWKPY21AXAMwaFgd9iAp17UYcEKHBpLcL7XUT7HPG0SrRPGB9PasRIWqMimK+W0wDrC85loxJhlpJ4XR1G4oEPtDWWyMQcHjNhd7Ayk2/Mnx2796Nq6++GikpKaAoCt9//73T+zRN46mnnkJycjJCQ0Mxd+5cFBcXyyOsCHizW0iICOHawIsxWL3xcgDMmS8apQJFte04LaDr2uzUn8SLB5fztoi3c+rLyzE4NgwT0plEQaGrZ7zZTbOwk9geEVoeeDuZUhSFa+1G8fcCL/behncA5vT6QVGh6DBasO208AnffVV1sbCL/eaTNYJW3Jm9HJsAc54a22Dyh2NieCW9M86vHBEPfYgKNW3d2H9BHA9YX6EuAJgYZzd8jlUJXnHXVwsMliidBrPtHjChPcXehroAYLG9weSJylacq+8QVA456VeGT2dnJ8aNG4e3337b7fsvvvgi3nzzTbz77rvYv38/wsLCsGDBAnR3i3v+ilR4E+oCHN6WNQWVgsepvQl1AUBkqBpXjmAqNYR8cJ08Pl48uEvHMR6wo+UtgneG5ZqRebGr5jxgAk9i3i4qAJPwzbY8ELpixNtdPQBcZ3fj7y5uQIOAJ1F763UCmPPU2HCC0N4Wvix96WNSusMAEzIE6e33s7Djc93xasE9YH0dJcKiVSm5HDAhPcU0Tfs0PkdH0wjTMg0mDwtccefLfWHvyQ9HhTXAfNFFbLgWs4YyDSbXivCcyIVKbgF8YdGiRVi0aJHb92iaxuuvv44///nPuPbaawEAH3/8MRITE/H999/j1ltvdft7RqMRRqNj8m1rY7wTZrMZZnPgSaDsZwjxWd32nAgl1fvnzR0RC61KgZK6Dhwra0JOij7g72YxWZhJkQLd59+0dEwSNp+sxdqCSqy4aggUXuw++6LLyHgqVAoKFotzjog7XUeHKDEzOxa7ixvx7aEyPDonO2AZWLrt39PX/QCABSPj8Nw6CscrWlFU1YKs+DBBZODuB23zaoxdMzYZhZVt+O5IBW6bPMjv73XVteO+9K2LtCgtxgzS40RlG34oqMCd09P9loNPt5kZDwovxiYAXD0mEe/sPIedRXWobelETJhGEDkAXl8jm7VPWa4Zm4R3dl/AmiMVWDgqvsf7/swhXSbHc+LN701K0yNJr0VNmxF5J6uxMCfR6+/qC6PdkFJ4MUaXjknEFwfLsaGwGn9ZPAxatTLg73c6GLeP+2E2m6FRAvNGxOP7YzX45lA5xg+KCFgGlm6T93PG5dnRCNMyFXf7ztVjSka0IDKYfZ4zkrCzqB7fFVTi4dmZHqs2fZZDwLXR9TP7ol8ZPr1x4cIF1NTUYO7cudxrkZGRmDZtGvLz8z0aPqtWrcKzzz7b4/UtW7ZAp9MJJl9eXl7An3GikgKgRG11JTZsKO/12lGRChQ0KvDa93txQ4ZwJbuNzUoAFAoOH0L3ud53IWYbEKpUoqbNiLe+3IShkYHvWph28iooYMOGDRvcXuOq68E0o7fP888hu/ssBHpucfaiAoACFRdLsWHD+T6vH65X4FSLAq9+uweL04W5J63tzP04dGAfms70fX2oCaCgxNHyVnz47QYkhAb2/ayui1oZHXcZOj3eFz7ZagonoMTHu04jtqkwMCHsnDvP3I/S8+ewYYN3Ie7UMCUqOoGXvtyGWUnC7KppGjBbmPuye+cO6Puwp6IMAKDCzrN1+GrtBoSr3V/nyxxyoom5Hx1trV7dDwDICVegpk2B9zcXwHZRuDmjqoa5L6dOnsCGuuO9XmujgSiNEi3dFrzyxRaMjw38njARRGap27EtD1ovbKlB5koASvxQUI6pylJ44UT0iuNVzH2pq63Ghg19e1BG6xXYX6/AP3/cj1uGCHNPahuYsXni2FEoKgr6vN5qBbQKxgP29pcbkSXcPhqAMGsji8HgXV7rJWP41NQwfRcSE513KomJidx77li5ciVWrFjB/dzW1oa0tDTMnz8fen3gd9hsNiMvLw/z5s2DWu1hRvOS8zvOAWXnkDE4HYsXj+r12pAh9fj1pwU42R6Cdxdc3mcVgbe8VfIzYOjEjOnTMD0rps/r91tO4uvDlagLTceji3MC/v7z9Z1Awc8I0aixePECp/c86Xq2yYJvX9iFRqMVKWMuw4T0qIDlAICCDWeAqjIMyx6CxfOH9nm9ZVA1Hv/mBE4ZwvDWopmC7JxeOLUb6O7G5TNnYGxqpFe/k9d2GLuLG9EWPQzLrvLPA+aq67Cz9cCpAsRGRWLx4ul9/v6UdiPWvrQLpR0UcqbNxuDYwDcZP39/EqitxKgRw7D4iiyvfqc26iKe31iEEksMVi2eFrAMAONhoPdtBQAsnD8PUbq+n/u1dfk4Vd0Oc/IYLJ6a5vSeP3MIVVgDFB1HQlwMFi+e4tXvZNe2Y9s/83GmTYnc2VchWieMB+zzmoNAazMmTxiPxfZqut44rT6L9/eUokKZjD8uHh/w97d1mYEDOwAASxYt7DUUyur6wRvm4JuKfNS2GaHNnIQFAnnAyndfAC4WIyM9FYsXj+7z+qhzjdj/4WGcbNdg7vzZXoVx++K/ZfuA9jZMmzoZVw3v6WF0x15zIdYUVKFWl4GH+1h7vEXItZGFjdj0xSVj+PiLVquFVqvt8bparRbsZgj1eTSYhTJErezzs64amYSYMA0aOkzYf7GVS5QLFLaSKVTr3d9z/cRUfH24EhtP1uK568YgJFDXtYL5fbVS4fH7XXUdqVZjYU4SviuoxA8najB1iHcPe19YaeZ+aDUqr3SxaGwK/vLDKZQ3d6GwphMT0wN3XbNVdiFe3g8AuGFiGnYXN+KH4zVYMX9EQAYYq2ubPV1Qo/J8X/ikxKgxIzsOe4obsL6wDo/O7dtw7At7yhW0au/uBwBcNyEV/9hUhKPlrahsNSEjLvAQpBWOHBldiAZqdd/T7PUTUnGq+jTWHa/BshnujTZf5hDH/eh7rmDJSY3BqGQ9TlW3YfPpBtw5fbBXv9cXbD2Ct3PGDZPS8P6eUuw62wCDmensHBBGh6dEF6LxaryHaDW4bsIgvLfrPH44XoOl41MDk8GOzT6HeztGZw5LREKEFnXtRvx8vhnzc5ICloHt2Rmi8X483TgxDWsKqrCxsBbPXjsaWlXgIUgWIddabz+nXyU390ZSEjMgamudEwRra2u59/o7Jh8S49RKBdfTR8jkTV+SaQFgemYskvQhaO+2YKcAxyX4UorJ5/qJjuRNobr1epvozaLTqLjTwYWqaLJ4WR7LZ35OInQaJS42GnCkrEUQObztT8KHn/AtRBK+rwm9ANMsbuZQYZPw+eeWeVNFBDAVdxQFHLrYLEgbCl+q/fhcP0H4iru+ju9wZUSSHiOSImCy2gQ5LsHRU8lzV3F3sLrYUVSHFoMwVZDeNtlkUSooXDNO2J4+/oyN3CGxSNRr0dplxs4i4buuS80lY/hkZmYiKSkJ27Zt415ra2vD/v37kZubK6NkwuHo0uvdbbt+IrNL2XyyBh0CnQ7ubadgFoWCwrUCVs84+pP4NqFfNiQOCRFMt14hDDDAdyMQcJQvrzte7VShJqUMOo0KC+07xzUFFQHLAHhfcchnwegkhKgVON/QiWMVrQHL4O2RFa5cP8ExPoUwwMw8w1rt5WKfFBmCy4YwbSiEMDr8GRcA0zhPYe+6frFRmCpIb1tg8GGfk+8E0YV/m6URSXqMTNbDbKUF66DsS0UVC6uLvNO1aBOg67q3vYT4KBWONhRrjvT/6q5+Zfh0dHTg6NGjOHr0KAAmofno0aMoKysDRVF47LHH8Le//Q0//PADTpw4gbvuugspKSm47rrrZJVbKHz1MIxLjURWXBi6zTZsFujsGX8eGtbbsv1MXcD9Y7hdvZcLCouSZ4AJtbP31RAFgJnZcYgL16Kp04QdZwI3wLw5nd0dQnvAvGmu6Uq4VoUFdgPsuyOBG2C+7qZZFuQkcR4wIc6r4nsYfKlkvH4Cs1H5TgADzN/FPlEfghnZTPny9wKVlPvjYbh2POMBO3ChCWWNgXnAfN2s8WGNYqE8YL60wGDJSdFjWCJz7I0QHZT98c4CDg/Y9jN1aDUIf+yNlPQrw+fQoUOYMGECJkyYAABYsWIFJkyYgKeeegoA8Pvf/x7/93//hwceeABTpkxBR0cHNm3ahJCQEDnFFgxfd3EURXG7BeEWe993kiOS9Bg9iNk5/RCgHP5O6IBj57T1dB1au4TcOXk/oaqUCtxgNzq+ORz4Yu9PqAsQ3gPmr4fhF5OYxX7t0aqA+8f4K4NO4zDAhNjN+jtGF41mDLALDZ04fDGw/jH+hroAxwK3pqBCmBCklx3O+SRHhmKm3QD7NkCj2JuDfD1x7fhBUNhDkIEaYIDDM+rL2KAointOvj7UezWvN/g7NkYmO0KQ60XohC8l/crwmT17Nmia7vHvww8/BMAMkOeeew41NTXo7u7G1q1bMWzYMHmFFhB/XPnsJPZTSQOqWwM/aM5fD8Mv7GG3bwKexPwLdQHMeVXDEyNgstgEaeDn7wJ3o10X28/UoTGABn5WGw22r5mvMvA9YIEuLID/urhsSBySI0PQ2mUOuIOyv6EuwPGc/Hi8CkZLoAaYf89ImFbFNfAL1Cj21wgEGA9YqFqJ0kYDjgjQwI/1BvrqYWAX+2+PVATUwC+QzRLfAyboc+KjV/K68YOgVFA4UtYScAdlfwxRFvY5EUIXctKvDJ+Bji9t6FnSYnSYlhkDmga+E2A366+H4Zrxg6BWUiisbAvoCAtvT2Z3h/POSYhJzL/FZXhSBMamRsJiowM6SoOfI+SPG/8Xk5iy6W2nAzPA+LL4uotUKiieByyw3ay/iwoAzMiOQ5I+BC0GM7aeCtQA839hYcfnuuPVAR1h4U8YloVvgAnznPg3NhbkJCFCq0JFcxf2BXCERSChLsBxT745HJgBBgAmP0JdAJOEf8UwJgn/20CNYj8NUYAxfJQKCocvNvfrIyyI4dOP8KUlP5+bJzML3FeHygNyXQfiYYgJ02DuSKaiKZDdrD85RnyumzAIKgWFo+UtOBvg6cuBTKg38SbTQL8f8G83OzwpAuPsBligieeBeBhYA2zX2XrUtvl/vEwgMigVDqP4qwDDCYF4GKZmxCAthjnCYtNJ/8MJFj/CsHxunszo4sdjVTCYAiuM8NfDEKJWYqm9oimw58R/QxSwG2AhKlS2dCH/fGBniPkT6mJh54zvjlQGdIirt+eFuSNBH4LZdgNMCKNYLojh04/wpyIAABaNSUK4VoWLjYaATl92Phnd/93s9wWVflc0Bbp7i4/Q4soRTE+jQOPlgRhhV49LgUapwOnqNpys8q+iiX9gqz8TKQD8wm4Uf30osHyOQMJMmXFhmDw4GjY6sMo/oXb2u4vrAwoLBxKOVSgo/GIic08CWexNfiawskzNjMHgWB06TVZsOBFYYQRX1eXPYm83wDae8L8y1RLguAhRK7mScqGMYn8M0qtGJiBKp0ZNWzd+KmnwWwbWEPV3zrjJPmd8e6TC+TiQfgQxfPoR/u4kdRoVltp7+nwVgJXu1J/Ej3DCFcPiEReuRWOnye9eEIHs6llYD9iaAAwwwLe+Sq5E6TSYlxOYB4yddBQU47Hwh2vGpUCrUqCoth0nKv0vKQ8kzAQ4hxP8NcAsAe7sM+LCMFWAsLDZz8pDFjb0t/dcIyqa/UuotQTgdQKYsPBNAiXU+psXCAAT0qKQFR+GLrMVG/ysaPJ3w8iHnTM2FdYEVBjBhrr8MUi1KiWuDdADZrXRnLfIX31cNSIBMWEa1Lcbsbu4f/b0IYZPPyKQSg3WSt9wohrtfvaCsAToYeBXNPk7mQYSRmCZPZwxwBo6Aisp9/bUaU/wK5r8KSkXYkKPDFVj4WimoimQ3WygBumSsckIUTMH6x71s6Q8EK8TixBh4UDHaFqMDrlZsQEZYIF4FlhumJgKigL2X2hCaYP/PX0C8Yw65eX5mQMmxGZpbGokhiWGw2ix4cdjgeflBept2Xyyxq+ScufwuH9jQ6NScEnOXx3sn+EuYvj0IwJ5gCemO3ZO/vaCYB8apYLy28PATmL+VjRZAjD+WNRKBW60G2CBeMAC9XLMymZKyps6TdjuhwEmxIQOOBb7HwIoKQ/U6IgIUWPR6MAqmjjPgp/3AwAWj0lCmL2nj79h4UBCXSyBesACDXUBQEpUKGbZu1oH4mHwNy+Q5YYJqVBQwMHSZr8MMCHmDIqiuOckEA+YI8fHP1lyUuwl5RYbfvSjMtViCzw8DjjmjK2nawMujJADYvj0IwLZLTg9uP4uLH40qXNlWGJgCbWBhJf4sLkDO4rqUNfuX0ItZ3j4udgzHjD/E2qFmNABIDcrFoOiQtHWbcHmk/7lcwjhiWMX+x+OVflV0eTPkRWu6DQqXG0PJ/j7nAhhkC6yG2BlTQYcKPW9pDzQUBcLm+T87ZEKvxJqA80LBJiu1qwB5s9zIoRnFHAURhyraEVRjX+FEWwDQ3/DsXwPmD+6cOoqHoA++IURgVSmygUxfPoRgSZv3sArRSyp870U0eHhCHAyncIYYJ8fKPN5NyvUhJ6dEIEJ6VGw2mi/u7KaAkjaZGEXlp1Fdahq8S2hVqgJXaEIvMw/UO8XwBhgaTGhaO+2+NUgjQupBHiCNWsUrz9e7VdCrRDPiU6jwjX2Pktf+nFPAgmL85k7MhGRoWpUt/qXUOvUciGAsXHLFMemzde8PCEMYgCIC9fiqgALIxz5Tv7Lcv0EpjXI8YpWFPqYl2e2BZ4XyPILngdMiEaXUkIMn35EoLuFBH0IrhzOliL64WGwBebhYLlmXAp0GiXO1XfioI+7WaEmdMDhrv3yoH8PbqBuawDIig/H9KwY2Gjfd3BChboAh7fl53MNfh2SGaj3C2AMsFunpANgjGJfESLUBQAT06O5sPA6P/I5hAh1AcBtUxldbDpZi04f0zmEGhshaiWusxtgXx30w8MgQOUhwBhgceFMQq2vjS7FmDPWFFT6lZcnhCyx4Vqu0/gXB317Tvw9rsIdbGHEmZp2HBfgrD0pIYZPP8IcQA8IFjY57pvDFT53qBUi1AUw+RxseaivC5xQoS4AWDo2GaFq/wwwQLjFhV3gvjxY7lM4QahQF8Ak1M7MjgNNM3L4ihChLoDxtqjsXklfwwlC7ewpisItkx1eSV8RalyMGRSJnBTmqJcD9b7dY6HuB+Dw0G45VYP6dt/yOSwC5AUCjBeP7ffk+2IvnC5mD49HQgRTmbrllO9h4UArD1lut88Z3xf41meJDXUF+v0AUxixyF4Y4c9zIifE8OlHCLFbmDMiAYl65sHdfLLWz+8PfNiwi/36E9VoMXh/cKlQoS6AMcDYYxv+t/+iz78fSH8SPgtykhCtY8IJu856v5sVwm3O5/ZpdgPsULnP4QQhqogAICEiBHNGMuEEXydTIcfnLyalQqNU4Jg/4QSB5KAointO8usUPnklAw2L88lJicS4tCiYrbTPlVWBNMtz5dYpjkaXvpT5C+nlUCkVXNjtf/v9MYoDrzwEgOlZsciI1aHDaMG6Y96HhVkvtRDjAgBunzYYAFOZKsTJ8VJBDJ9+BHeybwAPMPPgMpOpr4u9kKGVsamRGJWsh8li86lkV0i3NQDcYX9wN5yo8fnkeKEm9RC1kju/63/7vV9YhLwfADBvVCLiwrWobzdi6ynfjOJA+pO4wi723x2p8LrKjKZpXmO2wMdGbLiWK/P/zMcFTsgxeu34FISqFajtonDoYosPMgjjWWC5Y5ojBOnLsQ2B9jTikxEXhhnZTJm/L2E3oeeMW6emQ0ExfZbO+3hsg1DhWIWCwq325+R/PmwQTAKsIXymZERjaEI4usxWrBXoBHspIIZPP0KoneStU9KgoIB955t8SnIWMrRCURRu402m3u5mhV7sx6RGYsygSJisNp/PigrksD9X2Els+5la1LR6V2UW6LEErqiVCtwyxW6A+ehtCaQVvyuzhsZzVWYbvExydsolCXA3zcJ6wNYerfSp95UQSe8sESFqrvmoL0nOQnq/AODqsSmICFGhvKkLe3xIchbKw8HChYUPlXvdNTjQY25cGRQVitnDg8MrqVYyx++cqmrz6fuF0gVFUdxz8tl+34tV5IIYPv0IoXYuKVGhXHWCLw+uUFVELMxuVoniug4cvuhdjo2QLnwWx2623OvdLL8DqhBejuyEcEzN9C3JOZCDKD1x65R0UBSwp7jBp54pQu6qlQqKC2t4Oz6dyqYF8C4AwLTMGAyJD4PBZMVaH0p2Az0SwJVb2GMbTtZ6HRYW+jkJ1fC9kt57ioX2tswflYTYMA1q24zY4WX3d0eoS/g54+vD3nslAf/PW3RHXLgW80f5luQsdKgLYPoshaiZJOcjZS2Cfa6YEMOnHyGkt4MN8Xzjw4MrZKwcAPQhalw9jtnNeuthEHonCzDnZoVrVbjQ0On1IYRCdEB15XYfk5wd40G4SSwtRsedAu2LUSxEGJbPTZPToFRQOFjajGIvDpN17ioujD6Y3SzznPiymzULVATAMnaQHoN0tE9hYSG9kSzszn7r6TqvvZLcnCGQMcokOTMGmK9GsZBzxuzhCUiJDEGLwYxNhd4nOQsV6mJhPWBrjlR61ftK6FAXAETq1Fg6lsmV/MyPXEk5IIZPP0LIB/jyYUw4obXL7EM4QdjQCsBLcj7uXZKzUFURfMK0Kq4Fu7cJi0J1QOWzcHQSIkPVqGzp8irJWYwJHXAYxV/7UPkntDcwKTKE80p6k2PDfj8lQH8SPjdOHAStijlMtsDLozSEvi8URSE3kfnM/3kZFhYy3MYyLDECUzNiYLXRXlf+CdVbiQ+bXLyzqM6rJGcxjEAlL8fGl8Ve6LFx2ZBYpMfo0G60eHWUhpAhaT6sB2ydl/O43BDDp58gdPKmUkHhtqnMBOJt8qYYC+34tCjkpOhhtNi8mkwDOfCwN9jd7OaTNV51chaqAyqfELWSOxjyo719T6ZiGT5XDo9Hkj4ETZ0mr3ezQvQ0coWdTL89XIHOPhoJ8psGUpRwMkTpNFhiz7Hx1igWsuUCy+Q4GjqNEiV1Hdh7rm+vpBghYcDxnHxxsMyrHBuhvV8A0/tqRnYsbDTw6T4vjGKLOLq4ZYrDK3nWC68kwEs6F8gQVCgcOTYf5Zf2aRQLHXpkGZ8WhZH2YpVvAzjgVyqI4dNPECN58+bJaVzPlNPVfSfHCZ1YDDC72btzMwAAn+y72GeIR6zFfmSyHhPTo2Cx0V5VjLByCNEBlc9duRmgKKZkt6+KETFCXQATyrzVV6M4wOaa7rh8aDwy48LQbrTguz4qRiwi6QJweMB+PFblpVdS+DEaqgKut7de+Ghvad8yCJxnxLJwtKP1gjc5NmaR5GDnjC8OlvUZqhdrzkjUh2CuvfXCZ/v63qjYBDgZ3R23TE6DVqXAyao2HCnrPVdSjFAXwMzjd0xzeMB8qfyTA2L49BPESN5M0Idgfk4iAODj/FKvZRB6cblmfAqidGpUNHf1eVinGKEull9OZxa4T/eV9dnHRujQDkt6rA5X2StGPs7vfTIVI7mZ5dYp6VApKBy40ISTVX33sRFDFoWCwp32e/Lx3t53s0L3NOIzMZ3ZzXrrlRTrObljmuNgyL5CPGJ4WgDGK8l2L/bGADNbxLkvc0YmYlBUKFoMZvzQR+K5UI0t3cHOGd8cruiz8o9tQAsI632KDtNw/cg+7MNTzCU3C7hZY7luwiCEa1U4X9/p1/EmUkIMn36CGMm0ALDsskwAwHdHKtHcRx8bMXayADOZsnH7viZTsUJdALBkbDLiwrWoaevuM8QjpgF212UZAJgQT29nRQldmsonKTKE62Pz4c+lfV4vZKM6Pr+YnAqdhqn86y3EI9auHmB2s/fY78nH+Rf7DPGIEeoCgKEJjhDPJ314GMSSAQDuzB0MBQX8VNLQZ4hHjLxAgPGy3pnLGB0f9mEUi2WIAsDM7DhkJ4Sj02Tt85w7vtde6Gf2bvv43HiiGrVtnkP1YuRcsYRrVVzi+eqfLwj++UJCDJ9+AvvQCJ28OSUjmsux+byPkkgxJ9NfTnNMpiV1nidTMRc4rUrJuWs/7MMAE7o/CZ9Z2XHIsod41hzxPJmKFepiuWcGYxSvPVaFxo7ejyoQa1etD1FzZdS9GcUOQ1QcXVwzPgUxYRpUtnRh6+nemzuKOUbZEM+XB8t7DfGIlcQKAKnROq6Muq/nRCzPKOAI8Zyqbuu1HYaYMlAUhWV2o+Oj/NJeQzxi5AWy5KREYkpGNCw2utdcNKGrL11ZdhkTqt9RVI8LPrTDkBpi+PQT+JOpkMmbFEVxC9wnfexmxZzQ02J0mDOSDbt53s2KmcsBAHdMT4dayeQ9Ha9o8Xid0GWpfBS83exH+Rc97mbFDHUBTIhnXGokTBZbn6XDYu4k776M0UVvIR4xQ10A45Vk2w180IcHTMhGn654G+IRK9TFsmxGBgCmu3ZveU9ihpmiwzS4bjxTjdmbAWYRuA2HKzdMHAR9iAoXGw3YUeQ5VC/kyejuYL0+/ztQ5vEAVbOIoS6A6a7Nhuq9CYXKBTF8+gmOqhXhB+zSscmIDdOgurW71/O7RJ9MeSEeT/FyMY0vgDkriu1JsbqXBU6MRG8+v5iUirA+qnjEDHUB9t2sfYH7ZN/FXvOexDQEsxMiMDM7rtcQj5jhDJZfTh/sVd6TmGPD2xCP2ONzWmYMRibr0W224Yte8p7Evi932Y3iTYU1HkM8YoXbWHQaFVfa7s2cIZYBtiAnCYl65siZjYXuW5Rwc7gIGxQWds74+lB50J7fRQyffoKYoZUQtSPE01tsVqwKDZbLhsRy8fJvDrsP8Yg9oQMOA2zd8SqPpe0WET0cAHNUwY32eLmn3azY3i8AWDImBfERWtS2GbGxl7wnsQ3Su+yLvacQj5ieBZakyBAsGsOUtveW9yRmaAVwDvEcchPioWma29mLJQM/76k3T7HY44If4vFUWSX2vAUAd053hOo9NdwU8mR0d6iVCq4C0eOcIUJPI1f4eU/f+HDMipQQwycIuNjYiY/zSz26JwHxF/xfTh8MtZLCoYvNOFHhfjcr9iRGURTnrl39c6nbyVSs/iR8xqVFYWI6cxr1Zx76hIiVzMuHXey3nq51Gy8XO7wDMIadN0axkK343TFnZCJSo5kQjzujWOyxycIaxb3lPYkZ6gKcQzz/2XO+x/tWGw3WESTm+OTnPeV5ONRWio0KO2d8ur/MbfdisSrL+KTF6DBvFBOqX+3R6BB/zrhtajo0SgUKylrc5j2J6Zllcc178qYLvdQQwycIeDXvLJ5aexLv7Dzn8RqxwxoJ+hAsse9mPS1wDjepeA/NLyamIlqnRlmTAZtO9vQwiK0HFjbv6bP9F912Lxa6Fb87shMicNWIBNA08G83C5wUXg6A6WPDTqZH3XQvttlo0frGsCgVFO6dydyT/+w532MylSLUBTjnPXlKIhW6SZ077pvF6GLLqdoe/Z7E6CrujhC1kmuC6inEI4VBujAnCanRoWjqNOEbN8UAYoe6WNg5w1Pek1g9dPjER2i5LvTv7+65nnDJzSKOTcAl76mPFiVyQAyfIKDZwMRBP84v9VipIYWnY5n9wf3xeBWqW7s8yiBUHyF3hGqUuMteufLervM9chik2EECTKO2JH0IGjpM+N5N8zwpYuUA8OvLswAwfULq2509DFJN6PERWiy1n6n27909DTCx+pO4cvPkNESGqlHaaMAWF6NYqnHBLwb4KP+i2+fVsasWT5ahiRGYwxnFzhsVE89TKrY+7pyeweQ9lTa5NYqlMEhVSgXu680oliDUBTjnPX3qJuwmlVfy/ssdRvG5HkaxNBtHnUbFHUf0vptNm9wQwycIYBfRxk4T1njoUCvFxD4+LQrTMmNgttL4z56eXh+pJpC7cgcjRK3AicrWHoeGSmEAAszfyHoY3t3VczJ1TCDiyjE1Mwbj06Jgsth6NJmUItTF8oDdANtQWN3TwyBifxI+YVoVF/57d7ezUSzVogIw/Z4GRYWiocPYR9hN3LHx6yuGAAC+PeJsFDuXTYsrQ1JkCK61h93e2VnS430xW2DwuXlKGqJ0alxsNGCzq1EsQagLYIziB69gnpMPfi7tEXaTalxkJ0Rg7shE0HTPUKgUoS6We2ZkQq1kigEOX2wS/ft8gRg+QQC/Wubfe8677QUh1cT+0GxmMv38QFmPhoZShLoAIDZci5smMS709108DFKFugDgtmnpiAxV40JDZ4/JVKoJnaIozuvzcf5FpzOrpAp1AcCIJD3nYfB0T6SQ5a7cDGhUChwrb8GBC47JVKpFhfkOBe63h5re232uRy4ad19E9gZOyYjmjGJ+6TAb6lIpKEFbX3iCXew3n6zt0YNLrKanrug0Ktxl76L83q5zTkaxFLk1LEvGJCMthgm7fXXIudpNKq8kAPzafk++PVLpVKAh1dgEGKP4hglMgUZvaRxyQAyfIMDMM3TO13e67QUh1cR+xbB45KToYTBZ8ZGLh0GKUBfLfbMyoaCAnUX1TueISbnYh2tVXOLkv3aWOHsYJNpFAsD8nCRkxOrQ2mV2mkylXOwB4DdXOjwMNbzSYXYXKVZ/Ej7xEVquOyzfABO7VNiVW6akIyZMg/KmLqw/4Vw6LJVxzvcwfLLPYRSLdSinJ4YmRmC+PbH33V3ujWKxN0sA0/Fcq1LgWEUr9jsZxdLNGSqlAg9czjwn7+8+77QpkNIrOXlwNCam9zSKxWyL4o5fX5EFigK2nq5DUY13B7lKATF8ggB2ER2RFAGg544akCYxDmAmU9br8+HeUicPg9idgvkMjg3DotE980rEPLLCHcsuy0CoWonCyjan82ekyq8BGGPifrvX5z97LnC7aCknUgCYNDgGUzOYUCj/TCCpjY77ZzGT6bYzdVzpsJSeQIDJRWPLud/Z6exhkDKcMG+UwyhmzxGTelwADk/x9wWVqGxx5Adyc4YEm6W4cIdR/N4uh4fBZJFWHzdNSkVcOFPtxm8yKeVGhaIozgD7JP8id/SN1GMjKz4ci+xH37gLhcoFMXyCANYVe/+sLKgUFPZfaOpRUi6lu3bR6GRkxOrQYjA7dewVs5eQO9i8kh+OVXGTqRR9KPjEhGm4k8r/tcMxmUqV78Ry48RUxNpLh1kPg5Q7WZaH7F6fzw9WoNPem8wisdGRGReGBfYjE96zG8VSe78AJuwWplHiTE07dvJOKpfyOeEbxf/96QLMVpvkzwgATEiPRm5WLCw22imvROyeRq6wRvGOonrOwyDl3Akw1W6/4vIDz3GpC1IbHfNGJSIrLgxt3RZ8YZ/HzRKGulgeuiIbAPDj8WqUN/V+uK5USPLXP/fcc73+G+iwgzE9VoerxzFdg13Ll6V8aJQKikuc/M+eC1w5t9QP7ri0KG4yfXfnOVhtNJdkLJV3AQDusxuk+ecbUVDG9MYwS7yLDFErud4Y/9xeAquNlmWxnz0sHiOTmVDoT7XM98ohB5vD8H1BJcoaDZJ7nQAgUqfGHfa8kn/xdrNiHmDrjhsnOjwMa45USh7qYmFDoV8cKEeTPT/QImGoC2COTGA9DG9tLwYgzwbhl9MHI0KrQnFdB3e2m9Ry8I3i93efR7fZ6igOkSjUBQBjUiMxa2gcrDbabTRDDiS5A2vWrHH699VXX+GFF17AK6+8gu+//14KEYIa/mBk+3OsP1GNskaHdSz24XKu3DBxEBIimJPK1xyptMspXaiL5ZE5QwEwHXv5uwUpZRgUFYrr7L0x3rZ7faSqLuNz94wM6EOYyXTDiWpZQhr8UOiuagU6jRbJwrB8JqRHY9bQOFhsNN7eUSKLLgDg3pmZ0CgVOFjazCVbSy1LiFqJX9vDGm/tKEaXvcReal3MzI7DmEGR6DJbuV5gZomNQAD4v6uYOWP9iWqcrW2XfJMCMIfr/tJegfi2PRQqtcccYIzilMgQ1LUb8fmBMlHP1OsNds746lC5x274UiLJX19QUOD0r7CwENXV1ZgzZw5++9vfSiFCUMOfKHNSInH5sHhYbTTe2eXYRUrRKZiPVqXkQk1vbS+B0WKV3G0NALlDYjE9KwYmqw1vbivmXpd6Un/wiiFQUEwX5WPlLZLnlADMZHrfLOaevLmtGEYZJnQAWDw6CYNjdOi0UPgov0w2o+OxucMAAN8cqeBK7KXIueKTqA/BLyYzeSUvbykCTdOyeFx+OX0w4sKZZGs210fKsQkwRvFyu9fng58uoKnTJGm+E8vIZD0WjU4CTTPPiVniUBfLr2ZkIkTNVCBuP1MneWIxwBg4y69iQk3v7DyH9m4m10fqZzU3KxYT0qNgtNjw9nb5c31ky/HR6/V49tln8Ze//EUuEYIGVxfoI/aB+s3hClSxuS0yLC6/nD4YiXotKlu68L/9ZbLIAACPzmEWuO94PY6kliE7IRzX20szX9pcJIv7HGAOAGS9PsfteWBShzRUSgUenWMPhf5cigb70Q1SLyyTBkdzm4Tv7UmkUoa6WP7vqmxoVAocuNCE3cUNsng5QjVKPGgPT7O9haQeFwBzUOaYQZHoNFnxrx0lkjX6dIX1FK8/UY1uszzzVnyEFssuYzz4L20ukm2jctOkNM7rs6eYKdCQ0hAFGKP4ifnDATCnx8ud6yNrcnNraytaWz2fcjxQcM2RmJwRg+lZTPUMW50gx2QaolZyE8g/t5egpcssuQwA4/WZkR3L/SxF2bQ7Hps7FGolhZ9KGrDrLJPMKvUkpg9Rc/lXLFLfDwBYMjoJKToa7d0WvGnfwUmtCwB4fN4wp5/lkCE5MpTrIfPS5jOyeEYB5miRJH0I97McuqAoCr9bwCxwH++7iPLmLllkGZmsx9KxyeA3fpdDHw9ekYWIEBXO1LTjO3vKgNRGoEalwKNzh/Z4TWouy47DjOxYmK00Xt9a3PcviIgkf/2bb77p9O+NN97Ak08+iVtuuQWLFi2SQoSgxl2o4BF7nPrzA+WobOmSvIyb5ebJaciI1aGx04Tz9Z2yyAAAf1o8ivt/uc68S4vRcW3Y2fb8Uns5ACavZFBUKPez1Ls3AFAoKCxJZ8bkMbsu5PC2jEuLwnXjU7if5bgfAPCbK7MRpmHaHjgS8KWVJVSjxBN2owMAWu0bFam5fGgcpmXGwGSxcT245Lgvf1g4wulnOeatKJ2Ga0DKzRkyPK+/mJSGkcl67mcxj1PpjScWMPdkTUEFSuo6+rhaPCT561977TWnf2+++SZ27tyJu+++G++9954UIgQ17pry5Q6JRW5WLExWG97Yela2PAq1UoGnr87p8ZrUjErRIzcrtu8LReaxucMQF67hfpZDFyFqJX6/0LHAyTGhA0BOFI0rhsZxP8swnwMAfs9b4Ora3J+YLjYxYRqsmD/c6TU5xgZ7QCUAVDT3PG9PCiiKwtNX5zgZ5HLoIi1Gxx1xAsjjGQWAe2dmITMujPtZDl0oFRT+smQk9zMNeXaP49OicNvUdDx9dQ7SYnSyyABIZPhcuHDB6d+5c+ewb98+PP/884iIiJBChKCFpvknWzsmCoqi8IR9cfvmcAXXk0KOh+bKEQm42Z7Aycggzwr3xm3jkRGr406Rl4OYMA3+dt0Y7uf2bnl21deMS8HckQmIj9BiaKI8zxBFAX+91uGJO1nV1svV4pESFYq/LB0FjUqBa3jeH6m557IM5KQ4dtVyLLQKBYXvfnMZIkJU+D97rqAcjErR42He9yskODrDHU8sGI6hCeGYkB4FrQzhHYDxxL34i7Hcz1VuDoCWgsuy43Dz5FREaFWYmB4tiwwAsOqGMbjb3mVbLkgDQ5kx8w54dA0VTEyPxtyRibDRwMZC5qwouYyOP9sXFqWCwqAoeSz1hIgQbH98Nt6+Y6Is38+ycHQSrhgWDwCYOFieCYSiKLx/52TsXzkH+hC1LDIAQHJkCJ67lvEI3jgxtY+rxePemZk49ewCzMiO6/tikVAoKLx52wQAQJI+RLaJfWJ6NAr+Mg+Pu3igpGb5ldlcSHZIfLgsMkSEqLHx0Vn47qHLJDm3zBNTMmK4zSM7d8jBCzeORcFT85DCC5UPRFRyCzDQ4Z/l4m6H+OSi4dh1tk62KiIWfYgaB/84F/UdRiRFhvT9CyKhkCue4sLqZVNQVNuO4TJ5W4Dg0cVduRmYkR3nlHckB3LkGLkyJD4c+/84BwqKkvX+BIMu1EoFtj1+BSqau5CdII/hAwSHLgDG6Lh3ZhaGxIf1fbFIUBQlW2g8mAiOESEQzzzzDCiKcvo3YsSIvn9RRixOHp+eAzI7IQIPzXa4jA0mqyRyuSNSp5Z1AgsmFAoKI5P1QWN8yM2Q+HCEqJVyixEUJOpDEB+hlVuMoCBErSRzhh2KojA8KSJoDLGBzCV3B3JyclBdXc39++mnn+QWqVdMPI+Pp+octikYADKhEggEAoEQAJdcqEulUiEpKcnr641GI4xGRyVIWxuToGk2m2E2B564yn6Gp8/qMjJn2qiVFCwWi9trFADyHpuBrw5V4sbxSYLIdSnSl64JwkF0LR1E19JBdC0dYuja28+iaJqWqSuK8DzzzDN46aWXEBkZiZCQEOTm5mLVqlVIT0/v9XeeffbZHq//73//g04nfhJvQzfw1wIVNAoaL02TL4xFIBAIBEJ/xmAw4Pbbb0drayv0er3H6y4pw2fjxo3o6OjA8OHDUV1djWeffRaVlZUoLCz0WDbvzuOTlpaGhoaGXhXnLWazGXl5eZg3bx7U6p7VN+fqO7HwzZ8RGarCoT9eFfD3DWT60jVBOIiupYPoWjqIrqVDDF23tbUhLi6uT8Pnkgp18btAjx07FtOmTcPgwYPx1Vdf4d5773X7O1qtFlptz7wZtVot6MD3+Hn2DppqpYI8aAIh9L0jeIboWjqIrqWD6Fo6hNS1t59zySU384mKisKwYcNQUiL/abCeMFvkLVMnEAgEAmEgcUmvth0dHTh37hySk+Xr9NsXZps8Z3ARCAQCgTAQuaQMn9/97nfYtWsXSktLsXfvXlx//fVQKpW47bbb5BbNI2aLPGdwEQgEAoEwELmkcnwqKipw2223obGxEfHx8Zg5cyb27duH+Hj5WoT3BdeRWabTcgkEAoFAGEhcUobPF198IbcIPsOGutQqEuoiEAgEAkFsiJtBZkioi0AgEAgE6SCrrcxYbCTURSAQCASCVJDVVmbY09lJqItAIBAIBPEhho/MsMnNKuLxIRAIBAJBdMhqKzOcx4fk+BAIBAKBIDpktZUZC2f4kFAXgUAgEAhiQwwfmTFZyZEVBAKBQCBIBVltZYaEuggEAoFAkA6y2soMCXURCAQCgSAdxPCRGRLqIhAIBAJBOshqKzOsx4eczk4gEAgEgvgQw0dm2BwfDfH4EAgEAoEgOmS1lRmugSHx+BAIBAKBIDrE8JEZUtVFIBAIBIJ0kNVWZojhQyAQCASCdJDVVmYsXFUXCXURCAQCgSA2xPCRGRPx+BAIBAKBIBlktZUZC5fcTG4FgUAgEAhiQ1ZbmXGUs5NQF4FAIBAIYkMMH5kx2+weHwW5FQQCgUAgiA1ZbWXGbLHn+KjIrSAQCAQCQWzIaiszFhsJdREIBALh/9u796Co6v4P4O8Fl1VcYdWVXUjkIiheAPHGEE/mk8QlczQbMzNUfoVJaJHmODQF2vRoWWNTZjmPM4Z/NGb+xks5XkIEHQ1Rbok3EkKp5KL4w0VQWOD7+yP3TCuIqOw5wL5fMzvDnnP2fL/73vWcj+d79hySCwsfhVluUsqhLiIiItvj3lZhHOoiIiKSD/e2CrMMdakdONRFRERkayx8FGa5SSmP+BAREdke97YKs1zHpw+P+BAREdkcCx+F8SalRERE8uHeVmGWW1Y4caiLiIjI5ri3VVgTh7qIiIhkw8JHYRzqIiIikg/3tgqzDHWx8CEiIrI97m0VJIRAc6ul8OFQFxERka2x8FGQ5Ro+ANCHR3yIiIhsjntbBVnO7wEAJxY+RERENse9rYKarY74cKiLiIjI1lj4KKjpH0d8+HN2IiIi22PhoyDLDUqdHB2gUrHwISIisjUWPgoyN/891MVhLiIiInmw8FFQEy9eSEREJCvucRVkGeriNXyIiIjkwcJHQZahLh7xISIikkev3ONu2rQJ3t7e6Nu3L0JDQ3Hq1Cmlu9Qu890jPjzHh4iISB69rvDZsWMHli9fjtTUVOTn5yM4OBhRUVGorq5WumttmJt5jg8REZGcet0ed8OGDYiPj0dcXBxGjx6NzZs3w9nZGVu3blW6a21Y7tPFqzYTERHJo4/SHehKTU1NyMvLQ3JysjTNwcEBERERyM7Obvc1jY2NaGxslJ6bTCYAgNlshtlsfuw+WdbR3rpuNzYBABwd2p9PD6ejrKlrMWv5MGv5MGv52CLrzq6rVxU+169fR0tLCwwGg9V0g8GAixcvtvuadevWYc2aNW2m//zzz3B2du6yvqWnp7eZVnRDBcAR9SYT9u/f32Vt2bv2sibbYNbyYdbyYdby6cqsGxoaOrVcryp8HkVycjKWL18uPTeZTPD09ERkZCRcXFwee/1msxnp6enwDn4SRRW3MHfiUGme6mwlUHwG+sED8dxzkx+7LXtnyfrZZ5+FWq1Wuju9GrOWD7OWD7OWjy2ytozYPEivKnz0ej0cHR1RVVVlNb2qqgpGo7Hd12g0Gmg0mjbT1Wp1l30Y1beBFf89DQFgko8eI40DAABC9fe5PRq1I/+RdaGu/OyoY8xaPsxaPsxaPl2ZdWfX06vOqnVycsKECROQkZEhTWttbUVGRgbCwsIU65dbP+DfI4egpVXg/T1FEOLvk5rNd+/O3sehV30MRERE3Vav2+MuX74cW7ZswbZt23DhwgUkJCSgvr4ecXFxivbrg+kBcHZyxOnL/4f/zfsTAGDmLSuIiIhk1auGugBg7ty5uHbtGlJSUlBZWYlx48bh4MGDbU54lpu7a1+8Pc0f6w5cxH/2X8C//PVovlv4OPXhBQyJiIjk0CsPNSxduhRXrlxBY2MjcnJyEBoaqnSXAAD/8y8fBA11RW2DGW9vL8Qd890rN3Ooi4iISBbc48pI7eiAL18OgVbTB6cu38C6Axek6URERGR73OPKzFvfH/95YSwA4O6Fm3l3diIiIpmw8FHAzHFPYGXUSOn5HXOLgr0hIiKyHyx8FJL4bz8se8YPjg4qhPvple4OERGRXeh1v+rqSVZEjkTC1OFwduLHQEREJAce8VEYix4iIiL5sPAhIiIiu8HCh4iIiOwGCx8iIiKyGyx8iIiIyG7wzNp7WO6cbjKZumR9ZrMZDQ0NMJlMUKvVXbJOah+zlg+zlg+zlg+zlo8tsrbsty378fth4XOPuro6AICnp6fCPSEiIqKHVVdXB1dX1/vOV4kHlUZ2prW1FVevXsWAAQOgUj3+rSRMJhM8PT3xxx9/wMXFpQt6SPfDrOXDrOXDrOXDrOVji6yFEKirq4OHhwccOrj5N4/43MPBwQFDhw7t8vW6uLjwH5JMmLV8mLV8mLV8mLV8ujrrjo70WPDkZiIiIrIbLHyIiIjIbrDwsTGNRoPU1FRoNBqlu9LrMWv5MGv5MGv5MGv5KJk1T24mIiIiu8EjPkRERGQ3WPgQERGR3WDhQ0RERHaDhQ8RERHZDRY+NrZp0yZ4e3ujb9++CA0NxalTp5TuUo+2evVqqFQqq0dAQIA0/86dO0hMTMTgwYOh1Wrx4osvoqqqSsEe9xzHjh3DjBkz4OHhAZVKhT179ljNF0IgJSUF7u7u6NevHyIiInDp0iWrZW7cuIH58+fDxcUFOp0Or732Gm7duiXju+gZHpT1okWL2nzPo6OjrZZh1p2zbt06TJo0CQMGDICbmxtmzZqF4uJiq2U6s90oLy/H9OnT4ezsDDc3N6xcuRLNzc1yvpVurzNZT506tc13e8mSJVbL2DprFj42tGPHDixfvhypqanIz89HcHAwoqKiUF1drXTXerQxY8agoqJCehw/flya98477+Cnn37Czp07cfToUVy9ehWzZ89WsLc9R319PYKDg7Fp06Z2569fvx5ffvklNm/ejJycHPTv3x9RUVG4c+eOtMz8+fNx7tw5pKenY9++fTh27BgWL14s11voMR6UNQBER0dbfc+3b99uNZ9Zd87Ro0eRmJiIkydPIj09HWazGZGRkaivr5eWedB2o6WlBdOnT0dTUxN++eUXbNu2DWlpaUhJSVHiLXVbnckaAOLj462+2+vXr5fmyZK1IJuZPHmySExMlJ63tLQIDw8PsW7dOgV71bOlpqaK4ODgdufV1tYKtVotdu7cKU27cOGCACCys7Nl6mHvAEDs3r1bet7a2iqMRqP49NNPpWm1tbVCo9GI7du3CyGEOH/+vAAgTp8+LS1z4MABoVKpxF9//SVb33uae7MWQoiFCxeKmTNn3vc1zPrRVVdXCwDi6NGjQojObTf2798vHBwcRGVlpbTMN998I1xcXERjY6O8b6AHuTdrIYR4+umnxdtvv33f18iRNY/42EhTUxPy8vIQEREhTXNwcEBERASys7MV7FnPd+nSJXh4eMDX1xfz589HeXk5ACAvLw9ms9kq84CAAAwbNoyZP6aysjJUVlZaZevq6orQ0FAp2+zsbOh0OkycOFFaJiIiAg4ODsjJyZG9zz1dVlYW3NzcMHLkSCQkJKCmpkaax6wf3c2bNwEAgwYNAtC57UZ2djYCAwNhMBikZaKiomAymXDu3DkZe9+z3Ju1xXfffQe9Xo+xY8ciOTkZDQ0N0jw5suZNSm3k+vXraGlpsfrwAMBgMODixYsK9arnCw0NRVpaGkaOHImKigqsWbMGTz31FM6ePYvKyko4OTlBp9NZvcZgMKCyslKZDvcSlvza+z5b5lVWVsLNzc1qfp8+fTBo0CDm/5Cio6Mxe/Zs+Pj4oLS0FO+99x5iYmKQnZ0NR0dHZv2IWltbkZSUhPDwcIwdOxYAOrXdqKysbPe7b5lHbbWXNQC88sor8PLygoeHB86cOYNVq1ahuLgYu3btAiBP1ix8qEeJiYmR/g4KCkJoaCi8vLzwww8/oF+/fgr2jKjrvPzyy9LfgYGBCAoKwvDhw5GVlYVp06Yp2LOeLTExEWfPnrU6L5Bs435Z//M8tMDAQLi7u2PatGkoLS3F8OHDZekbh7psRK/Xw9HRsc0vA6qqqmA0GhXqVe+j0+kwYsQIlJSUwGg0oqmpCbW1tVbLMPPHZ8mvo++z0Whsc+J+c3Mzbty4wfwfk6+vL/R6PUpKSgAw60exdOlS7Nu3D5mZmRg6dKg0vTPbDaPR2O533zKPrN0v6/aEhoYCgNV329ZZs/CxEScnJ0yYMAEZGRnStNbWVmRkZCAsLEzBnvUut27dQmlpKdzd3TFhwgSo1WqrzIuLi1FeXs7MH5OPjw+MRqNVtiaTCTk5OVK2YWFhqK2tRV5enrTMkSNH0NraKm3c6NH8+eefqKmpgbu7OwBm/TCEEFi6dCl2796NI0eOwMfHx2p+Z7YbYWFhKCoqsio209PT4eLigtGjR8vzRnqAB2XdnsLCQgCw+m7bPOsuOUWa2vX9998LjUYj0tLSxPnz58XixYuFTqezOludHs6KFStEVlaWKCsrEydOnBARERFCr9eL6upqIYQQS5YsEcOGDRNHjhwRubm5IiwsTISFhSnc656hrq5OFBQUiIKCAgFAbNiwQRQUFIgrV64IIYT4+OOPhU6nE3v37hVnzpwRM2fOFD4+PuL27dvSOqKjo0VISIjIyckRx48fF/7+/mLevHlKvaVuq6Os6+rqxLvvviuys7NFWVmZOHz4sBg/frzw9/cXd+7ckdbBrDsnISFBuLq6iqysLFFRUSE9GhoapGUetN1obm4WY8eOFZGRkaKwsFAcPHhQDBkyRCQnJyvxlrqtB2VdUlIiPvzwQ5GbmyvKysrE3r17ha+vr5gyZYq0DjmyZuFjYxs3bhTDhg0TTk5OYvLkyeLkyZNKd6lHmzt3rnB3dxdOTk7iiSeeEHPnzhUlJSXS/Nu3b4s333xTDBw4UDg7O4sXXnhBVFRUKNjjniMzM1MAaPNYuHChEOLvn7R/8MEHwmAwCI1GI6ZNmyaKi4ut1lFTUyPmzZsntFqtcHFxEXFxcaKurk6Bd9O9dZR1Q0ODiIyMFEOGDBFqtVp4eXmJ+Pj4Nv9hYtad017OAMS3334rLdOZ7cbly5dFTEyM6Nevn9Dr9WLFihXCbDbL/G66twdlXV5eLqZMmSIGDRokNBqN8PPzEytXrhQ3b960Wo+ts1bd7SwRERFRr8dzfIiIiMhusPAhIiIiu8HCh4iIiOwGCx8iIiKyGyx8iIiIyG6w8CEiIiK7wcKHiIiI7AYLHyIiIrIbLHyIqFtYtGgRZs2aJXu7aWlpUKlUUKlUSEpKslk7ly9fltoZN26czdohoo71UboDRNT7qVSqDuenpqbiiy++gFIXkndxcUFxcTH69+9vszY8PT1RUVGBzz77DIcPH7ZZO0TUMRY+RGRzFRUV0t87duxASkoKiouLpWlarRZarVaJrgH4uzAzGo02bcPR0RFGo1HR90lEHOoiIhkYjUbp4erqKhUalodWq20z1DV16lQsW7YMSUlJGDhwIAwGA7Zs2YL6+nrExcVhwIAB8PPzw4EDB6zaOnv2LGJiYqDVamEwGBAbG4vr168/dJ+9vb3x0UcfYcGCBdBqtfDy8sKPP/6Ia9euYebMmdBqtQgKCkJubq70mitXrmDGjBkYOHAg+vfvjzFjxmD//v2PnBsRdT0WPkTUbW3btg16vR6nTp3CsmXLkJCQgDlz5uDJJ59Efn4+IiMjERsbi4aGBgBAbW0tnnnmGYSEhCA3NxcHDx5EVVUVXnrppUdq//PPP0d4eDgKCgowffp0xMbGYsGCBXj11VeRn5+P4cOHY8GCBdIQXWJiIhobG3Hs2DEUFRXhk08+4REeom6GhQ8RdVvBwcF4//334e/vj+TkZPTt2xd6vR7x8fHw9/dHSkoKampqcObMGQDAV199hZCQEKxduxYBAQEICQnB1q1bkZmZid9+++2h23/uuefwxhtvSG2ZTCZMmjQJc+bMwYgRI7Bq1SpcuHABVVVVAIDy8nKEh4cjMDAQvr6+eP755zFlypQuzYSIHg8LHyLqtoKCgqS/HR0dMXjwYAQGBkrTDAYDAKC6uhoA8OuvvyIzM1M6Z0ir1SIgIAAAUFpa+ljtW9rqqP233noLH330EcLDw5GamioVZETUfbDwIaJuS61WWz1XqVRW0yy/FmttbQUA3Lp1CzNmzEBhYaHV49KlS4905KW9tjpq//XXX8fvv/+O2NhYFBUVYeLEidi4ceNDt0tEtsPCh4h6jfHjx+PcuXPw9vaGn5+f1cOWP1X/J09PTyxZsgS7du3CihUrsGXLFlnaJaLOYeFDRL1GYmIibty4gXnz5uH06dMoLS3FoUOHEBcXh5aWFpu3n5SUhEOHDqGsrAz5+fnIzMzEqFGjbN4uEXUeCx8i6jU8PDxw4sQJtLS0IDIyEoGBgUhKSoJOp4ODg+03dy0tLUhMTMSoUaMQHR2NESNG4Ouvv7Z5u0TUeSqh1KVSiYi6gbS0NCQlJaG2tlaW9lavXo09e/agsLBQlvaIyBqP+BCR3bt58ya0Wi1WrVplszbKy8uh1Wqxdu1am7VBRA/GIz5EZNfq6uqk6/DodDro9XqbtNPc3IzLly8DADQaDTw9PW3SDhF1jIUPERER2Q0OdREREZHdYOFDREREdoOFDxEREdkNFj5ERERkN1j4EBERkd1g4UNERER2g4UPERER2Q0WPkRERGQ3/h8lKi/Kt7TIZAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -310,7 +307,7 @@ "voltmeter.set({\"record_from\": [\"v\", \"u\"]})\n", "nest.Connect(voltmeter, neuron)\n", "\n", - "cgs = nest.Create(\"dc_generator\")\n", + "cgs = nest.Create('dc_generator')\n", "cgs.set({\"amplitude\": 25.})\n", "nest.Connect(cgs, neuron)\n", "\n", @@ -319,7 +316,7 @@ "\n", "nest.Simulate(250.)\n", "\n", - "spike_times = nest.GetStatus(sr, keys=\"events\")[0][\"times\"]\n", + "spike_times = nest.GetStatus(sr, keys='events')[0]['times']\n", "\n", "fig, ax = plt.subplots(nrows=2)\n", "ax[0].plot(voltmeter.get(\"events\")[\"times\"], voltmeter.get(\"events\")[\"v\"])\n", @@ -363,18 +360,11 @@ "\n", "You should have received a copy of the GNU General Public License along with NEST. If not, see .\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/doc/tutorials/ornstein_uhlenbeck_noise/nestml_ou_noise_tutorial.ipynb b/doc/tutorials/ornstein_uhlenbeck_noise/nestml_ou_noise_tutorial.ipynb index 130da3dbe..a090b2cb0 100644 --- a/doc/tutorials/ornstein_uhlenbeck_noise/nestml_ou_noise_tutorial.ipynb +++ b/doc/tutorials/ornstein_uhlenbeck_noise/nestml_ou_noise_tutorial.ipynb @@ -11,9 +11,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -22,8 +30,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -85,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -119,8 +127,10 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 3, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -130,8 +140,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -141,14 +151,10 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[11,rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml, WARNING, [2:0;16:0]]: Input block not defined!\n", - "[12,rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml, WARNING, [2:0;16:0]]: Output block not defined!\n", - "[16,rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml, WARNING, [2:0;16:0]]: Input block not defined!\n", - "[17,rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml, WARNING, [2:0;16:0]]: Output block not defined!\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", + "[12,ornstein_uhlenbeck_noise_neuron_nestml, WARNING, [2:0;16:0]]: Input block not defined!\n", + "[13,ornstein_uhlenbeck_noise_neuron_nestml, WARNING, [2:0;16:0]]: Output block not defined!\n", + "[17,ornstein_uhlenbeck_noise_neuron_nestml, WARNING, [2:0;16:0]]: Input block not defined!\n", + "[18,ornstein_uhlenbeck_noise_neuron_nestml, WARNING, [2:0;16:0]]: Output block not defined!\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -163,27 +169,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_fef4a05021ec458d98cf8834e662d18c_module Configuration Summary\n", + "nestml_164c82c56c3742f1a5e74f1f6d377d3d_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_fef4a05021ec458d98cf8834e662d18c_module' using\n", + "You can now build and install 'nestml_164c82c56c3742f1a5e74f1f6d377d3d_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_fef4a05021ec458d98cf8834e662d18c_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_164c82c56c3742f1a5e74f1f6d377d3d_module.so will be installed to\n", + " /tmp/nestml_target_p2osda7p\n", "The module can be loaded into NEST using\n", - " (nestml_fef4a05021ec458d98cf8834e662d18c_module) Install (in SLI)\n", - " nest.Install(nestml_fef4a05021ec458d98cf8834e662d18c_module) (in PyNEST)\n", + " (nestml_164c82c56c3742f1a5e74f1f6d377d3d_module) Install (in SLI)\n", + " nest.Install(nestml_164c82c56c3742f1a5e74f1f6d377d3d_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -195,39 +201,35 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_fef4a05021ec458d98cf8834e662d18c_module_module.dir/nestml_fef4a05021ec458d98cf8834e662d18c_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_fef4a05021ec458d98cf8834e662d18c_module_module.dir/rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml.cpp: In member function ‘void rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml.cpp:148:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 148 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/nestml_164c82c56c3742f1a5e74f1f6d377d3d_module_module.dir/nestml_164c82c56c3742f1a5e74f1f6d377d3d_module.o\n", + "[ 66%] Building CXX object CMakeFiles/nestml_164c82c56c3742f1a5e74f1f6d377d3d_module_module.dir/ornstein_uhlenbeck_noise_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/ornstein_uhlenbeck_noise_neuron_nestml.cpp: In member function ‘void ornstein_uhlenbeck_noise_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/ornstein_uhlenbeck_noise_neuron_nestml.cpp:156:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 156 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml.cpp: In member function ‘virtual void rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml.cpp:211:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 211 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/ornstein_uhlenbeck_noise_neuron_nestml.cpp: In member function ‘virtual void ornstein_uhlenbeck_noise_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/ornstein_uhlenbeck_noise_neuron_nestml.cpp:226:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 226 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml.cpp:209:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 209 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/ornstein_uhlenbeck_noise_neuron_nestml.cpp:221:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 221 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "[100%] Linking CXX shared module nestml_fef4a05021ec458d98cf8834e662d18c_module.so\n", - "[100%] Built target nestml_fef4a05021ec458d98cf8834e662d18c_module_module\n", - "[100%] Built target nestml_fef4a05021ec458d98cf8834e662d18c_module_module\n", + "[100%] Linking CXX shared module nestml_164c82c56c3742f1a5e74f1f6d377d3d_module.so\n", + "[100%] Built target nestml_164c82c56c3742f1a5e74f1f6d377d3d_module_module\n", + "[100%] Built target nestml_164c82c56c3742f1a5e74f1f6d377d3d_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_fef4a05021ec458d98cf8834e662d18c_module.so\n", - "\n", - "Oct 19 03:47:50 Install [Info]: \n", - " loaded module nestml_fef4a05021ec458d98cf8834e662d18c_module\n" + "-- Installing: /tmp/nestml_target_p2osda7p/nestml_164c82c56c3742f1a5e74f1f6d377d3d_module.so\n" ] } ], "source": [ "# generate and build code\n", "module_name, neuron_model_name_adapt_curr = \\\n", - " NESTCodeGeneratorUtils.generate_code_for(nestml_ou_model,\n", - " module_name=\"nestml_ou_module\")" + " NESTCodeGeneratorUtils.generate_code_for(nestml_ou_model)" ] }, { @@ -241,12 +243,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "def evaluate_ou_process(neuron_model_name: str, h: float=.1, t_sim:float=100.,\n", - " neuron_parms=None, title=None, plot=True):\n", + "def evaluate_ou_process(neuron_model_name: str, module_name: str, h: float=.1, t_sim:float=100., neuron_parms=None, title=None, plot=True):\n", " \"\"\"\n", " h : float\n", " timestep in ms\n", @@ -298,46 +299,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:47:50 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:50 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:50 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:50 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:50 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:50 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:50 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 1000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:50 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -375,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -383,892 +350,113 @@ "output_type": "stream", "text": [ "For h = 0.01, tau_noise = 10.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:50 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:50 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:50 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:50 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:51 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 0.01, tau_noise = 10.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:51 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:51 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:51 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:51 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:51 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 102.20988990712311\n", "Expected variance: 100.0\n", "For h = 0.01, tau_noise = 10.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:51 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:51 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:51 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:51 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:51 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 10220.988990712309\n", "Expected variance: 10000.0\n", "For h = 0.01, tau_noise = 10.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:52 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:52 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:52 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:52 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:52 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 1022098.8990712308\n", "Expected variance: 1000000.0\n", "For h = 0.01, tau_noise = 100.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:52 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:52 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:52 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:52 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:52 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 0.01, tau_noise = 100.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:52 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:52 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:52 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:52 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:52 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 114.93711386061999\n", "Expected variance: 100.0\n", "For h = 0.01, tau_noise = 100.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:53 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:53 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:53 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:53 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:53 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 11493.711386061985\n", "Expected variance: 10000.0\n", "For h = 0.01, tau_noise = 100.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:53 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:53 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:53 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:53 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:53 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 1149371.1386062026\n", "Expected variance: 1000000.0\n", "For h = 0.01, tau_noise = 1000.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:53 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:53 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:53 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:53 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:53 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 0.01, tau_noise = 1000.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:54 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:54 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:54 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:54 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:54 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 94.22840893430514\n", "Expected variance: 100.0\n", "For h = 0.01, tau_noise = 1000.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:54 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:54 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n", - "\n", - "Oct 19 03:47:54 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:54 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:54 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 9422.840893430452\n", "Expected variance: 10000.0\n", "For h = 0.01, tau_noise = 1000.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:54 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:54 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.01 ms.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:47:54 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:54 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:54 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 942284.0893430448\n", "Expected variance: 1000000.0\n", "For h = 0.1, tau_noise = 10.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 0.1, tau_noise = 10.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 99.45410074367867\n", "Expected variance: 100.0\n", "For h = 0.1, tau_noise = 10.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 9945.410074367865\n", "Expected variance: 10000.0\n", "For h = 0.1, tau_noise = 10.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 994541.0074367868\n", "Expected variance: 1000000.0\n", "For h = 0.1, tau_noise = 100.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 0.1, tau_noise = 100.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 96.9767512879476\n", "Expected variance: 100.0\n", "For h = 0.1, tau_noise = 100.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 9697.675128794752\n", "Expected variance: 10000.0\n", "For h = 0.1, tau_noise = 100.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 969767.5128794758\n", "Expected variance: 1000000.0\n", "For h = 0.1, tau_noise = 1000.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 0.1, tau_noise = 1000.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 137.92761151483887\n", "Expected variance: 100.0\n", "For h = 0.1, tau_noise = 1000.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 13792.761151483948\n", "Expected variance: 10000.0\n", "For h = 0.1, tau_noise = 1000.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 1379276.115148398\n", "Expected variance: 1000000.0\n", "For h = 1.0, tau_noise = 10.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 1.0, tau_noise = 10.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 102.54886144081759\n", "Expected variance: 100.0\n", "For h = 1.0, tau_noise = 10.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 10254.886144081758\n", "Expected variance: 10000.0\n", "For h = 1.0, tau_noise = 10.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 1025488.6144081757\n", "Expected variance: 1000000.0\n", "For h = 1.0, tau_noise = 100.0, sigma_noise = 0.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 1.0, tau_noise = 100.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 102.69332568184711\n", "Expected variance: 100.0\n", "For h = 1.0, tau_noise = 100.0, sigma_noise = 100.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 10269.332568184713\n", "Expected variance: 10000.0\n", "For h = 1.0, tau_noise = 100.0, sigma_noise = 1000.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", + "Actual variance: 1026933.2568184711\n", "Expected variance: 1000000.0\n", - "For h = 1.0, tau_noise = 1000.0, sigma_noise = 0.0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "For h = 1.0, tau_noise = 1000.0, sigma_noise = 0.0\n", "Actual variance: 0.0\n", "Expected variance: 0.0\n", "For h = 1.0, tau_noise = 1000.0, sigma_noise = 10.0\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", + "Actual variance: 65.7477856791866\n", "Expected variance: 100.0\n", "For h = 1.0, tau_noise = 1000.0, sigma_noise = 100.0\n", - "Actual variance: 6570.587968208678\n", + "Actual variance: 6574.778567918655\n", "Expected variance: 10000.0\n", "For h = 1.0, tau_noise = 1000.0, sigma_noise = 1000.0\n", - "Actual variance: 657058.7968208689\n", - "Expected variance: 1000000.0\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n", - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n" + "Actual variance: 657477.8567918665\n", + "Expected variance: 1000000.0\n" ] } ], @@ -1319,46 +507,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:47:55 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlomatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n", - "Oct 19 03:47:55 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 03:47:55 rnstein_uhlenbeck_noise_neuronfef4a05021ec458d98cf8834e662d18c_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 03:47:55 SimulationManager::set_status [Info]: \n", - " Temporal resolution changed from 0.1 to 1 ms.\n", - "\n", - "Oct 19 03:47:55 NodeManager::prepare_nodes [Info]: \n", - " Preparing 2 nodes for simulation.\n", - "\n", - "Oct 19 03:47:55 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 2\n", - " Simulation time (ms): 10000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:47:55 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1381,12 +535,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1421,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -1463,16 +617,18 @@ " \n", " integrate_odes()\n", " \n", - " onCondition(V_m > V_theta):\n", - " V_m = E_L\n", - " emit_spike()\n", + " if V_m > V_theta:\n", + " V_m = E_L\n", + " emit_spike()\n", "'''" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "execution_count": 10, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -1482,8 +638,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -1493,15 +649,15 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[13,af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml, WARNING, [33:18;33:103]]: Implicit casting from (compatible) type 'pA' to 'real'.\n" + "[14,iaf_psc_exp_neuron_nestml, WARNING, [33:18;33:103]]: Implicit casting from (compatible) type 'pA' to 'real'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "WARNING:root:Under certain conditions, the propagator matrix is singular (contains infinities).\n", - "WARNING:root:List of all conditions that result in a singular propagator:\n", + "WARNING:Under certain conditions, the propagator matrix is singular (contains infinities).\n", + "WARNING:List of all conditions that result in a singular propagator:\n", "WARNING:\ttau_m = tau_syn\n", "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n" ] @@ -1510,10 +666,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -1528,27 +680,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_6b65882369534c61b5c32ee135917a51_module Configuration Summary\n", + "nestml_2b1110b752954b51b154157ad07c9b4a_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_6b65882369534c61b5c32ee135917a51_module' using\n", + "You can now build and install 'nestml_2b1110b752954b51b154157ad07c9b4a_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_6b65882369534c61b5c32ee135917a51_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_2b1110b752954b51b154157ad07c9b4a_module.so will be installed to\n", + " /tmp/nestml_target_vgb_5w94\n", "The module can be loaded into NEST using\n", - " (nestml_6b65882369534c61b5c32ee135917a51_module) Install (in SLI)\n", - " nest.Install(nestml_6b65882369534c61b5c32ee135917a51_module) (in PyNEST)\n", + " (nestml_2b1110b752954b51b154157ad07c9b4a_module) Install (in SLI)\n", + " nest.Install(nestml_2b1110b752954b51b154157ad07c9b4a_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -1560,43 +712,35 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_6b65882369534c61b5c32ee135917a51_module_module.dir/nestml_6b65882369534c61b5c32ee135917a51_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_6b65882369534c61b5c32ee135917a51_module_module.dir/af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml.cpp: In member function ‘void af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml.cpp:170:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 170 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/nestml_2b1110b752954b51b154157ad07c9b4a_module_module.dir/nestml_2b1110b752954b51b154157ad07c9b4a_module.o\n", + "[ 66%] Building CXX object CMakeFiles/nestml_2b1110b752954b51b154157ad07c9b4a_module_module.dir/iaf_psc_exp_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/iaf_psc_exp_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/iaf_psc_exp_neuron_nestml.cpp:177:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 177 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml.cpp: In member function ‘virtual void af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml.cpp:258:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 258 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/iaf_psc_exp_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_exp_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/iaf_psc_exp_neuron_nestml.cpp:271:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 271 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/af_psc_exp_neuron6b65882369534c61b5c32ee135917a51_nestml.cpp:256:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 256 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/ornstein_uhlenbeck_noise/target/iaf_psc_exp_neuron_nestml.cpp:266:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 266 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "[100%] Linking CXX shared module nestml_6b65882369534c61b5c32ee135917a51_module.so\n", - "[100%] Built target nestml_6b65882369534c61b5c32ee135917a51_module_module\n", - "[100%] Built target nestml_6b65882369534c61b5c32ee135917a51_module_module\n", + "[100%] Linking CXX shared module nestml_2b1110b752954b51b154157ad07c9b4a_module.so\n", + "[100%] Built target nestml_2b1110b752954b51b154157ad07c9b4a_module_module\n", + "[100%] Built target nestml_2b1110b752954b51b154157ad07c9b4a_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_6b65882369534c61b5c32ee135917a51_module.so\n", - "\n", - "Oct 19 03:48:04 Install [Info]: \n", - " loaded module nestml_6b65882369534c61b5c32ee135917a51_module\n" - "[100%] Built target iaf_psc_exp_ou_module_module\n", - "\u001b[36mInstall the project...\u001b[0m\n", - "-- Install configuration: \"\"\n", - "-- Installing: /var/folders/2j/fb047q1177v9f56f_jktrb4c0000gn/T/nestml_target_csdcfkxn/iaf_psc_exp_ou_module.so\n" + "-- Installing: /tmp/nestml_target_vgb_5w94/nestml_2b1110b752954b51b154157ad07c9b4a_module.so\n" ] } ], "source": [ "# generate and build code\n", "module_name_ou, neuron_model_name = \\\n", - " NESTCodeGeneratorUtils.generate_code_for(nestml_iaf_psc_exp_model,\n", - " module_name=\"iaf_psc_exp_ou_module\")" + " NESTCodeGeneratorUtils.generate_code_for(nestml_iaf_psc_exp_model)" ] }, { @@ -1608,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1670,30 +814,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:48:04 NodeManager::prepare_nodes [Info]: \n", - " Preparing 3 nodes for simulation.\n", - "\n", - "Oct 19 03:48:04 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 3\n", - " Simulation time (ms): 300\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:48:04 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1715,30 +841,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 03:48:04 NodeManager::prepare_nodes [Info]: \n", - " Preparing 3 nodes for simulation.\n", - "\n", - "Oct 19 03:48:04 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 3\n", - " Simulation time (ms): 300\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:48:04 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1761,33 +869,21 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Oct 19 03:48:04 NodeManager::prepare_nodes [Info]: \n", - " Preparing 3 nodes for simulation.\n", - "\n", - "Oct 19 03:48:04 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 3\n", - " Simulation time (ms): 25000\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 19 03:48:04 SimulationManager::run [Info]: \n", - " Simulation finished.\n", "265 spikes recorded\n", - "Mean ISI: 94.38219696969698\n", - "ISI std. dev.: 88.8518443884758\n" + "Mean ISI: 94.38257575757575\n", + "ISI std. dev.: 88.84117979123644\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1881,7 +977,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/doc/tutorials/spike_frequency_adaptation/nestml_spike_frequency_adaptation_tutorial.ipynb b/doc/tutorials/spike_frequency_adaptation/nestml_spike_frequency_adaptation_tutorial.ipynb index a8f290ba4..d11707e4e 100644 --- a/doc/tutorials/spike_frequency_adaptation/nestml_spike_frequency_adaptation_tutorial.ipynb +++ b/doc/tutorials/spike_frequency_adaptation/nestml_spike_frequency_adaptation_tutorial.ipynb @@ -46,6 +46,14 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -54,8 +62,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -134,8 +142,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -153,8 +161,8 @@ "text": [ "WARNING:Under certain conditions, the propagator matrix is singular (contains infinities).\n", "WARNING:List of all conditions that result in a singular propagator:\n", - "WARNING:\ttau_m = tau_syn_exc\n", "WARNING:\ttau_m = tau_syn_inh\n", + "WARNING:\ttau_m = tau_syn_exc\n", "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n" ] }, @@ -162,10 +170,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -180,27 +184,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_7eebc3c02a3040df86acdab2a99d6e38_module Configuration Summary\n", + "nestml_4e02d9e66e71411b95e21d3a31d61a56_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_7eebc3c02a3040df86acdab2a99d6e38_module' using\n", + "You can now build and install 'nestml_4e02d9e66e71411b95e21d3a31d61a56_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_7eebc3c02a3040df86acdab2a99d6e38_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_4e02d9e66e71411b95e21d3a31d61a56_module.so will be installed to\n", + " /tmp/nestml_target_gw9m9vvq\n", "The module can be loaded into NEST using\n", - " (nestml_7eebc3c02a3040df86acdab2a99d6e38_module) Install (in SLI)\n", - " nest.Install(nestml_7eebc3c02a3040df86acdab2a99d6e38_module) (in PyNEST)\n", + " (nestml_4e02d9e66e71411b95e21d3a31d61a56_module) Install (in SLI)\n", + " nest.Install(nestml_4e02d9e66e71411b95e21d3a31d61a56_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -212,44 +216,35 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_7eebc3c02a3040df86acdab2a99d6e38_module_module.dir/nestml_7eebc3c02a3040df86acdab2a99d6e38_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_7eebc3c02a3040df86acdab2a99d6e38_module_module.dir/af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml.cpp: In member function ‘void af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml.cpp:190:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 190 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/nestml_4e02d9e66e71411b95e21d3a31d61a56_module_module.dir/nestml_4e02d9e66e71411b95e21d3a31d61a56_module.o\n", + "[ 66%] Building CXX object CMakeFiles/nestml_4e02d9e66e71411b95e21d3a31d61a56_module_module.dir/iaf_psc_alpha_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_neuron_nestml.cpp: In member function ‘void iaf_psc_alpha_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_neuron_nestml.cpp:198:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 198 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml.cpp: In member function ‘virtual void af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml.cpp:306:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 306 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_alpha_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_neuron_nestml.cpp:319:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 319 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml.cpp:304:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 304 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_neuron_nestml.cpp:314:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 314 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_neuron7eebc3c02a3040df86acdab2a99d6e38_nestml.cpp:292:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 292 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "[100%] Linking CXX shared module nestml_7eebc3c02a3040df86acdab2a99d6e38_module.so\n", - "[100%] Built target nestml_7eebc3c02a3040df86acdab2a99d6e38_module_module\n", - "[100%] Built target nestml_7eebc3c02a3040df86acdab2a99d6e38_module_module\n", + "[100%] Linking CXX shared module nestml_4e02d9e66e71411b95e21d3a31d61a56_module.so\n", + "[100%] Built target nestml_4e02d9e66e71411b95e21d3a31d61a56_module_module\n", + "[100%] Built target nestml_4e02d9e66e71411b95e21d3a31d61a56_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_7eebc3c02a3040df86acdab2a99d6e38_module.so\n", - "\n", - "Oct 19 03:51:59 Install [Info]: \n", - " loaded module nestml_7eebc3c02a3040df86acdab2a99d6e38_module\n" + "-- Installing: /tmp/nestml_target_gw9m9vvq/nestml_4e02d9e66e71411b95e21d3a31d61a56_module.so\n" ] } ], "source": [ "# generate and build code\n", - "module_name, neuron_model_name_no_sfa = \\\n", - " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha.nestml\")\n", - "\n", - "# load dynamic library (NEST extension module) into NEST kernel\n", - "nest.Install(module_name)" + "module_name_no_sfa, neuron_model_name_no_sfa = \\\n", + " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha.nestml\")" ] }, { @@ -265,7 +260,7 @@ "metadata": {}, "outputs": [], "source": [ - "def evaluate_neuron(neuron_name, neuron_parms=None, stimulus_type=\"constant\",\n", + "def evaluate_neuron(neuron_name, module_name, neuron_parms=None, stimulus_type=\"constant\",\n", " mu=500., sigma=0., t_sim=300., plot=True):\n", " \"\"\"\n", " Run a simulation in NEST for the specified neuron. Inject a stepwise\n", @@ -274,6 +269,7 @@ " dt = .1 # [ms]\n", "\n", " nest.ResetKernel()\n", + " nest.Install(module_name)\n", " neuron = nest.Create(neuron_name)\n", " if neuron_parms:\n", " for k, v in neuron_parms.items():\n", @@ -328,16 +324,19 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:51:59 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:10:06 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:10:06 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:51:59 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:10:06 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 300\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:51:59 SimulationManager::run [Info]: \n", + "Apr 19 11:10:06 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, @@ -354,7 +353,7 @@ }, { 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\n", 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\n", "text/plain": [ "
" ] @@ -364,7 +363,7 @@ } ], "source": [ - "evaluate_neuron(neuron_model_name_no_sfa)" + "evaluate_neuron(neuron_model_name_no_sfa, module_name_no_sfa)" ] }, { @@ -447,8 +446,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -477,10 +476,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -495,27 +490,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_cf4b4fa998ce469bb5922408c671e12d_module Configuration Summary\n", + "nestml_9c5da0c0943741c7b8b8c6f3f2696680_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_cf4b4fa998ce469bb5922408c671e12d_module' using\n", + "You can now build and install 'nestml_9c5da0c0943741c7b8b8c6f3f2696680_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_cf4b4fa998ce469bb5922408c671e12d_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_9c5da0c0943741c7b8b8c6f3f2696680_module.so will be installed to\n", + " /tmp/nestml_target_tl2yd4po\n", "The module can be loaded into NEST using\n", - " (nestml_cf4b4fa998ce469bb5922408c671e12d_module) Install (in SLI)\n", - " nest.Install(nestml_cf4b4fa998ce469bb5922408c671e12d_module) (in PyNEST)\n", + " (nestml_9c5da0c0943741c7b8b8c6f3f2696680_module) Install (in SLI)\n", + " nest.Install(nestml_9c5da0c0943741c7b8b8c6f3f2696680_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -527,44 +522,35 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_cf4b4fa998ce469bb5922408c671e12d_module_module.dir/nestml_cf4b4fa998ce469bb5922408c671e12d_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_cf4b4fa998ce469bb5922408c671e12d_module_module.dir/af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml.cpp: In member function ‘void af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml.cpp:196:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 196 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/nestml_9c5da0c0943741c7b8b8c6f3f2696680_module_module.dir/nestml_9c5da0c0943741c7b8b8c6f3f2696680_module.o\n", + "[ 66%] Building CXX object CMakeFiles/nestml_9c5da0c0943741c7b8b8c6f3f2696680_module_module.dir/iaf_psc_alpha_adapt_curr_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_curr_neuron_nestml.cpp: In member function ‘void iaf_psc_alpha_adapt_curr_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_curr_neuron_nestml.cpp:204:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 204 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml.cpp: In member function ‘virtual void af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml.cpp:319:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 319 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_curr_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_alpha_adapt_curr_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_curr_neuron_nestml.cpp:332:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 332 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml.cpp:317:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 317 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_curr_neuron_nestml.cpp:327:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 327 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_curr_neuroncf4b4fa998ce469bb5922408c671e12d_nestml.cpp:305:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 305 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "[100%] Linking CXX shared module nestml_cf4b4fa998ce469bb5922408c671e12d_module.so\n", - "[100%] Built target nestml_cf4b4fa998ce469bb5922408c671e12d_module_module\n", - "[100%] Built target nestml_cf4b4fa998ce469bb5922408c671e12d_module_module\n", + "[100%] Linking CXX shared module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module.so\n", + "[100%] Built target nestml_9c5da0c0943741c7b8b8c6f3f2696680_module_module\n", + "[100%] Built target nestml_9c5da0c0943741c7b8b8c6f3f2696680_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_cf4b4fa998ce469bb5922408c671e12d_module.so\n", - "\n", - "Oct 19 03:52:28 Install [Info]: \n", - " loaded module nestml_cf4b4fa998ce469bb5922408c671e12d_module\n" + "-- Installing: /tmp/nestml_target_tl2yd4po/nestml_9c5da0c0943741c7b8b8c6f3f2696680_module.so\n" ] } ], "source": [ "# generate and build code\n", - "module_name, neuron_model_name_adapt_curr = \\\n", - " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha_adapt_curr.nestml\")\n", - "\n", - "# load dynamic library (NEST extension module) into NEST kernel\n", - "nest.Install(module_name)" + "module_name_adapt_curr, neuron_model_name_adapt_curr = \\\n", + " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha_adapt_curr.nestml\")" ] }, { @@ -577,16 +563,19 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:52:28 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:11:02 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:11:02 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:28 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:11:02 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 300\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:28 SimulationManager::run [Info]: \n", + "Apr 19 11:11:02 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, @@ -602,7 +591,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -612,7 +601,7 @@ } ], "source": [ - "evaluate_neuron(neuron_model_name_adapt_curr)" + "evaluate_neuron(neuron_model_name_adapt_curr, module_name_adapt_curr)" ] }, { @@ -655,8 +644,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -684,10 +673,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -702,27 +687,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_3d5d4b21a5314a528dd55b99ac51154e_module Configuration Summary\n", + "nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_3d5d4b21a5314a528dd55b99ac51154e_module' using\n", + "You can now build and install 'nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_3d5d4b21a5314a528dd55b99ac51154e_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module.so will be installed to\n", + " /tmp/nestml_target_2p4fhhc6\n", "The module can be loaded into NEST using\n", - " (nestml_3d5d4b21a5314a528dd55b99ac51154e_module) Install (in SLI)\n", - " nest.Install(nestml_3d5d4b21a5314a528dd55b99ac51154e_module) (in PyNEST)\n", + " (nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module) Install (in SLI)\n", + " nest.Install(nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -734,44 +719,35 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_3d5d4b21a5314a528dd55b99ac51154e_module_module.dir/nestml_3d5d4b21a5314a528dd55b99ac51154e_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_3d5d4b21a5314a528dd55b99ac51154e_module_module.dir/af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml.cpp: In member function ‘void af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml.cpp:195:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 195 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module_module.dir/nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module.o\n", + "[ 66%] Building CXX object CMakeFiles/nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module_module.dir/iaf_psc_alpha_adapt_thresh_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_neuron_nestml.cpp: In member function ‘void iaf_psc_alpha_adapt_thresh_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_neuron_nestml.cpp:203:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 203 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml.cpp: In member function ‘virtual void af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml.cpp:316:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 316 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_alpha_adapt_thresh_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_neuron_nestml.cpp:329:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 329 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml.cpp:314:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 314 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_neuron_nestml.cpp:324:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 324 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_neuron3d5d4b21a5314a528dd55b99ac51154e_nestml.cpp:302:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 302 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "[100%] Linking CXX shared module nestml_3d5d4b21a5314a528dd55b99ac51154e_module.so\n", - "[100%] Built target nestml_3d5d4b21a5314a528dd55b99ac51154e_module_module\n", - "[100%] Built target nestml_3d5d4b21a5314a528dd55b99ac51154e_module_module\n", + "[100%] Linking CXX shared module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module.so\n", + "[100%] Built target nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module_module\n", + "[100%] Built target nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_3d5d4b21a5314a528dd55b99ac51154e_module.so\n", - "\n", - "Oct 19 03:52:48 Install [Info]: \n", - " loaded module nestml_3d5d4b21a5314a528dd55b99ac51154e_module\n" + "-- Installing: /tmp/nestml_target_2p4fhhc6/nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module.so\n" ] } ], "source": [ "# generate and build code\n", - "module_name, neuron_model_name_adapt_thresh = \\\n", - " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha_adapt_thresh.nestml\")\n", - "\n", - "# load dynamic library (NEST extension module) into NEST kernel\n", - "nest.Install(module_name)" + "module_name_adapt_thresh, neuron_model_name_adapt_thresh = \\\n", + " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha_adapt_thresh.nestml\")" ] }, { @@ -784,16 +760,19 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:11:53 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:11:53 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:11:53 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 300\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:11:53 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, @@ -809,7 +788,7 @@ }, { "data": { - "image/png": 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\n", 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p0SqPjF0qmzpw3avf46y7ItXcEWl4YdmEXkMPfYUziBvuSEkjlv/ze6EP0e0zB+H383MRRLkaAbFlXzF+995P4DhXDswzS8fh6nH91B6WLth9qgq3v7UfVrsTJhPwxytH4NaLB6k9LEIFSDkiCB1ic5dqfnNPoUfFtpTocNwwbYAuG/o1tFmx4rW9gmJ05Zh0/P3a8QgymQzXkdzucOLXWw5jm7vXyYiMWLxxywVUuaoXzleMlk3Jxl8XjkaIztaGEpyvGD101Uj8csZAlUelfcSKUVRYMP550wWGywWUC7FiFBJkwnPXjcdVY+VTOo2e08U6pBwRhM6wOZy4/c192HWquks0T02LBc9uP41DxQ145YZJulEKmjpsHorRVWMz8Ny14wXBdtWlQ1HS0I53fixGbEQIXrlhsm69aE4nhwf+06kYjeoXi42/nIqEAOLl9e7rqG62eChGKy7sj78uHE2FF/zgWJmnYvTXhaNww7QcWT7LSI9n64ESQTGKDg/BG7dcgEkDvAuP9Qcjze3XedUeitFLyyfi8lHpag+LUBH9SQYEYXDW7z6DXaeqAXQVavn//+JkFV7efQZ6wGp34s639guK0dXj+nkoRjxB7ts+3N2RXI+KEQCs/fgEPjjUqRhtutV/xcgIykGrxY5bXv9RUIxuuHCA5IqRAaYRgKv4ws0bfuxUjBaNlk0xMhK7T1XhN++KFaMpsipG56PnSNCjpY24U6QYrVNYMdLz3PKtENqsdrWH4jP6lA4IwqDYHE68uaewz/xvE4A395yDzeHs451s43Ry+M27h/HdGVdVsctGpuHvS8f1Ggql58von18X4J/fnAXg6mG08ZdTER9JFZZ6wuZw4u5NB3CktBEAsHRyFh5dOMoQSqHU1LdacdOGH1DtrvD10FUjccOFAxT7fL3u6yMljbh70wE4nBzCQ4KwYeUFmDSAqiZKAa/Mt1odMJmA/7dsAuaRx0gy/t/OfDyy7RiufeV7VDV3qD0cnyDliCB0xL7CetS0WPsMg+IAVLdYsK/Qu4purPLU56eEggMT+8fjhWUTelSM9C7vfvRTOdZ8dAIAkBoTjjdumRJQKJ3e4TgOf3z/CL487fKyzh6egsd+PoYUIz+w2B247c19Qrnu22cOohwjCSiua8PK139Am1t4f/66CbjAy0qTRO80ttlw079+ECopPnLVSFw5JkPlUemHDw+V4u87TgMAGtttCNbYuUrKEUHoiMZ2a99vCuD9LLHtcBnWu0MDByVH4bWbLpClRLAWOFHehNX/OQTAFXbz+sopyIw3qzsoxnnju0Js2edq8Do2Kw4vXj9Rt6GWcvPIh8eE0vlXj+uH38/PVeRzNSZv+US71YE73tovFNT5y4JRmD9aOa+GnpuUOpwcfvX2QRS4Q2nvmDkIN1+krDKv57V7tLQRv333JwBAbEQI/nXzBUgKpBWCCtBNQBA6Is7sm6fA1/ezwonyJvz23cMAgDhzKDasvMCwXpKGNivueGs/Omyu8rMvXj8BI/vFSvoZegtZ+uFsneBly4iLwGs3XYCocPnqE+lYDsLmvUV4+0dXH61JAxLw9JKxVK47QDiOw4PvH8Hxclce5S0XDcSNKuZu6Wz74+/bTwse4/mj0vE7hZR5I1DfasWdG/fDYnciyASsWz4JQ1K11z6ClCOC0BGTcxKQFBXa5/tMcJX1npyjvdj1xjabhzLw/5ZNwICkKB/+gn6ueqeTw71vH0JRXRsA4IF5wzFreKrKo2KbisYO3L1pP+xODmHBQVi/YhJSYrRl1WSFA0X1eGTbUQBASkw41i2fiPAQdby3+tnVLq/m+wdLAQAXDkrEg1eS8C4Vnx2rwIu78gEAQ1Oj8fTScaoq83pat7xHrqS+HQDw+ytyMWNossqj8g9SjghCR4QGB3mlKHAAbpw2QHNhRBzH4Q/v/yQoA7+5fDguGZbi1e/q0Zb9j68LBAvoFaPTcbcOm9lKidPJ4f53DgmhSn9dNArjs+MVHYNevHBNHTb86t8HYXNwCAkyYf3yiUiLpT5agXK0tBGPfdzp1Xzx+onUa0siyhra8Zv/uCIOYsJD8MoNkxAto8fYaLz85Rmh4fpVYzNwm4Yb6NKOIwgd8dXpahwoaujx57yCMCc3FXdqUJB+d38JPj5SAQCYOyIVd12ive8gFUdLG/H056cAADlJkXhqyTgqJtAH//i6AHsKXJUNl0zKwrUX9Fd5RNrlkQ+PCRbiP1w5ApNVKBSgt7yYdqsD977tUjiD3WWlk1XK1dDbUeJwG0b4MvNPLRmLQSlqhnvpa4IPFzfg79tdBRgGpUThyWvGavo+IuWIIHRCXasVq91WsciwYNxyUQ5CzgsXSI4Ox+rLhmmyAey52lb8edsxAK4QHq0fvoHQbnXgVyIh6rnrJshqAeV0EPxxvjL55wWjFPtsva3TDw+VCmFflwxLwS0X5ag7IJ3w2MfHccZd8e/+uUMxob/2wp5Z5dWvCrD3bB0AYNmUbMwfTZXppKLVYsd97xyC3ckhNNiE/3fdBFlzOJVA26MnCELgj+8fEXqM/HnBKCydnI3vC+pwvLwJYzPj3NbdBM0pRYArHOrXWw6j1eoAADy1eKzf1W/0ENb0xCcnhLLJ988dKltomF5keovdgfveOSSEgD2vg8tbLSoaO/CnD1x5RolRYXhqCRtGCk7jG3vXqSps/L4IAHBBTgLumjVE5RF1ovGpxfGyJjzjNowMSo7CQ1eNVHlEnWh9bgFX43G+ifbqecMxOjNO5REFDt0OBKEDPjtWgU+OusLN5o9Kx5JJWQA6hdvU2HBMG5yk1vACZuPec9jvLhV88/Qcv4oOMCC/ScL+c/V48/tzAIDJA9gSoljlpV1nkF/VAgC499KhGKdwnpEYrXvhHv7wKJrdoUlPXjMWqTGUZxQorRY7/vS+S+GMDg/Bs0vHI5gq/kmCw8nhD1t/gt3Je9nHIzKMRF+p+OFsHTbtdSn1UwcmajrPSIz2TMgEQXjQ1GHDwx+6LtY4cyj+umi0YMnlFQItW6fKG9vxt09dVr+sBDN+O3+4yiNSD6vdiT9s/QkcB4SFBOFvi8eSENUHpyubsX63qzrViIxYTebascKnR8vx+fFKAMCCcf1w2cg0lUekD575/DRKG1z5W7+7IhfZiZEqj0g/vP5dIQ6XNAJwNScemxWv7oDc6MFYZ7E78Ietrn5G4SFBePIa/dxHpBwRhMb526cnUdnkCqf745UjdFWWmOM4PPTBMbRYXJbqx34+xtBWv1e+PIPTlS4PyKrZQ5RLKNaocu10cvjD1iOwOTgEmYAnrxmjybBSFmhst+HhD105f/GRoXj4agZCk3Qghx0ubsDr350F4PIEL5/CRpEQHUwtSurbhHC6AUmRuPfSoSqPSF+8tOuMkCN339xhyEn2paUG29AtQRAa5mBRvRCnPm1QEpZMzlJ5RNLy+fFK7DjhslQvGt/P67LdvaFROR/nalvxgrs/x/C0GNyhQKU+rVs339lXLIRjrrxooGpWY41PIwDgmc9Poaq50wijVhU1PeFwupq9OjkgNNiEx38xhskGuloNBf3ztmNoc+eprv35GESEqtODq3e0ObcF1S0eHvlbLx6o8oikxbgmWIIAYHM4sa+wHo3tVsSZwzRVsIDjODz6v+MAgLDgIKz9xRgmEqOlwmJ3YK2730ecOTTgJFqtl/19/OOTsNqdAIC1vxiDsBBtrFO1aOqw4enPXFbjfnER+PVlw1QekXY5Xdks5BVMG5SExZPYM8JoUcR8d38xjpU1AQDuumQwhqbFqDwi/fB1XjV2nKgCAFwzMQsXDdFmM1JWWfvxCdgcHEwm4Ilf6M8jT8oRYUhsDifW7z6DN/cUCg0hASAlOhw3TBuAu2YNZn6zbztchoPunka3zBiIgd24tHmFQIuCwxvfFeJcravZ668vG+Z3dTo9sOdMLT495iq48YuJmZg0gEr89sVLu/JR2+ra23+4cgQz1em0lv/HcRz++r/jcDhdoYmPLBipKyOMWjR32PDUZ66+MBlxEVRYRULsDifW/M9lWIsKC8bvrmAvT1XLO0iseC6ZlKVqgRu5YOO2IAgFsTmcuP3Nfdh1qrrLAVXTYsGz20/jUHED072A2q0OPPnJSQBAcnQY7pmtryTzmhYLXtjpctkPSY3G9VPZiMNXA4fTJZwCgDk0GL+9PFfxMWhMnse52lZs+KYQADBpQAKuGks9Tfxl96lqoev9sin9kZseq/KIOtGygLlu9xnUtLjCFH9/RS7MYWyFfGlZ/337x2KcqmwGANw9ewhVVJSQ8xXPB+axp3hKAZuSH0HIyPrdZ7DrVDWArkIf//9fnKzCy7vPKDouX/jn1wUoa+wAADwwbzhiIkJVHpG0PLv9NJrdRRj+9LMRkiqpWuuH8u7+Yhwvd4fezBqM9Di66Pvi8Y9PwupwhSA+fJX6ng6tCpp2hxNrPnIp5jHhIRSaKBHFdW147WtXEYYJ/eOxYFw/lUfUO1o6Mps6bHh2u8sjlxlvxi9n6CsXRm3e2Xee4hmrz/uIlCPCUNgcTry5p7BPi6MJwJt7zsHmFrBYoqHNile/KgDgSoRcMjlb5RFJS2FNK975sRgAcMmwFL96GnWHFgXUDpsDz+3IA+DKm1G6h4QW87QOFzd0hiBOyNRlyIdSbD1QKlSjWnXpEKZDW7UkwD+/M09Q3h9iQHnXE//8+izqhHDaXEaLMHSipXXbYXPgefd9pHfFk5QjwlDsK6xHTYu1zzAhDkB1iwX7CuuVGJZPvPpVgeBV+e384b32Fejsc6SdE/j/7cyDw+ka7+/mKx9CxhJv/1CEcreH8L65w5gLvWER3mocGmzCr+ex5+nQyk602p14fqdLEMqIi8CN03LUHZBOKKhuwdYDJQBcDbsn9qf8Qamob7XiX9+4PHJjs+LwszEUTislG78/J1Ss/PVlw5hXPAOBlCPCUDS2W/t+UwDvl5vaFgte/64QADCxfzxmSVDamiXyq1rwwaFSAMCVY9Ixsh87+Q1K02514CV3aOeApEj8YmKmyiNin/3n6vDlaVfI7HUX9EdWAjXT9Jct+4qFxqT/N2cIk4KQFj0uz+/Mg5NzGa7upzBFSfnH1wVCT7z7LxvG9PpgeGjd0ma14+UvXffRoJQoLBzPdihooJByRBiKOHOYrO+Xm5e/PCP0bfj1ZcOZPvz94bkdpwXB4b658ggOWrHcb/z+HKrdVrp7Lx2KEBWLg2jF8/jM5y6vUVhIEO6ZzU71L62FJ3bYHHjxC1dBlKwEM5ZM0lforlqcrmzGtsNlAICfjcnA8HSWS3dra82KDYcTdGg4VJs3vjsnVPZV+z5SAn1/O4I4j8k5CUiO7lvhMcFV1ntyDjshD1XNHXhzzzkAwJSBibhoSJLKI5KWUxXN+OhIOQDg6rH9MMzAPT/EVrrBKVFYOF4dr5GWdO/vC2rx3ZlaAMDyqf2pcEUAvP1DESqaXOGcv5ozlHpqScTzO/LAcUCQjMYfOdCCaeSVrwoEw+FqDRkOtTC3LRY7XvnKdR8NS4vG1WP17TUCSDkiDEZocBBuuHBAn+/jANw4bQBTpbxf/7YQFncT0F97GTLAv0MLB/ArX54RBId75w6V/O9r46p0seXHYqFHz71zh/WaV0a4WO8OQYwIDcJds9gtbc+6E87mcOIf7kpq2grnZHtiz9a04uOjLuPPwvGZGJIarfKI9ENjuw2bvncbDnP0ZzhUm7d/KEJDmw2AS6kPMsB9xI7kRxAKMWVgYo8/47f8nNxU3MmQgNVisWOj+/Cf2D8eFw7S1+Ff1tAuhJvMH52OwSnGFRzsDif++U2ncEpJxX1zsqJJyDVaMimb+poEwMdHyoVco9tnDtJ9+IxS/PPrAkExZll51yKb9p5Dq9trdNfswZrwGmkl1NbmcApFLgYlR2H+qHSVR6QM1ASWMByvuTd6cJDreLI7Oy2OydHhuHHaANw5azBTXqN3fixGU4cr0fSOS/R3sW749qzwHO6Yqb/v5wufHK1ASb1LOL11xkAmvEZs2+QhlLYPMgG3Xqzf8rJyw3EcXvnSNZfJ0WG4ZmKWyiPqHfV3hnfUtFjw7n5Xhbo5uamaCBnWgH4BALDYHdjwbSEAYHhaDOUaScxHP5ULPRVvmznIEF4jgJQjwmDkVzVjx4kqAMDSyVmobLLgi5NVyE4w42+Lx2FyTgJTShHQ1XJz2Yg0lUckLU0dNvz7B1dfo6kDE2XvS8NyWBPHcYKgnxgVhsUqJ8Jr4Rosb2zHtkOdXscBSVEqj6grWhE0v82vFRoO3zQth8kKdVrkzT3nhJDo22cq26tMClguyPLhwTKhcM3tMwdpwmukFTiOwytf8caScPx8glZCbAOHLSmQIGTmH1+5lAyTCbj14kHgjSAxEaGYNjiJOcUIcFlu+DCXWy/20XLjvigYvtuweW+RUH71jkvkExy0cGnuKajFkdJGAMANFw6gvkZe8K9vOr2Otxvc6xgofNK1OTQYK7zIzWQJVs+4Nqsdb+4pBACMy4rD1F7CugnfcDo5Yc2mx0bg6nHaKxTAsuL5dV4NTriNJTdPH2AoYwl7kiBByERdqxXvu3vozB2R5s5rYV9g/te3LoUuOTpMQ8nR3mF3OPGGu/zq0NRozBqWqu6AVOZf3xQCAMJDgnDjNG0Jp2rQarHjbbfXccrARIyX2esoBRyjQYqnK5vxdV4NAODaC7KREMVWGwOt8v7BUiGZ/faZ2siH0Qpf5VXjTHUrAOCWGTmaqqqohWXAyx6RYdozlgSKdlYSQQTIln3FsLpDG345Qxt5CYeLG/BTicuTsHyq/iw3O09Wodwdz3zLjIGGiWfujpL6NnxxshIA8IuJmUiKDld5RJ2watz88FAZmt1eR63saVbhC74AwMqLctQbiA+wLmByHIe33O0XMuPNmD9aO8nsjE8tgM41aw4NxnVT+qs8Gn1RVNsmFLm5ZmIW4iONZSwh5YgwBE4nh017XQfp0NToLqENjMp+wuEfHGTCMh0e/vz3iwkPUazjNqthDP/+oQh8bRBWrHQsW7k5jhPWT0ZcBC7NNbbXMRBaLXZsPeDyqs8clsJk3pYWOVBUj5MVzQCAZVOymSiuohdK6tuw86Qrf3jRhEzERoSqPCJ9semHc4JRjJX7SElIOSIMwZd51Siuc+XtrLhwgCD0MSz7obHNJpS3vmxEml9NLVnuc3S2plUI47lmUhYiw4xbH8Zqd+KdH13hYRP6x2NUvziVR8Q+B4sbhOIBy6b0p5LTAfDhoTIh78+bPnAswuIZt/H7IgBAaLAJSy9Qt7iK3vj3D0Ui4V27hkMW122HzYH/7HNVV5ySk4jh6exXV5Qauk0IQ7DRHdoQGRaMn2skb+c/+4uFCkd6tNxsEoXxaPlyk4JPj1WgpsXV9FWrwqnS8Hs6JMiE6zQkeLLmuOQ4Dm+592K/uAjMIQ+cJNS2WPDRT66mr5ePSqfeWxIiNiZNJGOS5HxytBx17ibkyw16N5NyROie0oZ2fHHK5X5fOF4b7neO47B5r8vqODA5CtMH66vpa4fNgf+4+35cOCgRQ1KNZ5kSwyuKCZGhuJLBpq+MyfOob7Xif0dEgmcsCZ7+crC4QahItWxKf02FfrHcSPPd/SWwOvRr3FKTz0TGJK3OLbsrF9jk9ngmRYVpKk9OSkg5InTPe/tLBGvt8qndW0FYy0PZf64eBTWuKjzXT+mvu0IF249XorHdVcFp+VRlLze2njRQWNOKvWfrAACLJ2XpruiGHGw7XCYUV+lpT7MEy+G7/9nnssAHB5lwrYY8cCzDcRy2uOd1SDc5rlqA5XxDfm7jGTUmaZkz1S3Yd64eALBkcjbCQ4x5Hxk3yJ+QHJvDiX2F9WhstyLOHMZEQ1WO4/DeAZeHYmRGLEZnerrfWT3++W7qIUGmgMIA+fuNNeWP/35x5lBcNlKZpras3vVb3esTcF1GLMHolAnrJyvBjAsH6curqiQdNgf+d9jlgZs9PIU8cBJxqLhBKDG9ZFIW04qGN7B0fZQ3tuObfFeu6sJx/TRvTGJpbgGXMZlnyeQsFUeiLrpTjl5//XWsXLmy25+Vl5cjPb3TRZiTk4Nz5851ed8dd9yBl19+WbYx6g2bw4n1u8/gzT2FgqsbAFKiw3HDtAG4a9Zg1ZSkfefqca62DYAr6V8LdNgcQqz6rOEpSGaopLMUVDZ14Os8V4nQq8dlaP5yCwSnk8N77iphY7PiMCzN2OGF3nC6sllolPuLiVm686oqyWfHKoRS6NdM1Mb52BMsCZm8QS7IBPx8gjZyXLXC+wdLhWetlTtdKzicHN4/6LqPJvSPd/eCNCa6U454Hn30UQwc6Nn3Ij4+vsv7xo8fj9WrV3u8NmzYMDmHpitsDiduf3Mfdp2q7mJlrmmx4Nntp3GouAGv3DBJFQXpPZEHRqlS0YEiFlgW6/Dw/+BgqVCyevEktjwlSrP3bB1KG1xVFPX4rOVAbNm8RiPFVViFV8zjI0MxZwQVYpCCDpsD/3V742YOI2+clHAcJ+z/oanRGJOp3UIMLHoT95ypFfoOGv0+0q1ydMUVV2Dy5Ml9vi8zMxMrVqxQYET6ZP3uM9h1yuUFON9wx///Fyer8PLuM1h16VBFx9ZudeB/ggcmVTMeGD5kKD4yFLN1VjmK4zjh+w1OicK4LBUuN4YszPxchAabcPVYdpV3VsIy7Q4ntrotm1NyEjXTj4fFwgEVjR34xu3BXTCunyZzCxiUL7HzRJWQT6llbxyDU+sRrrhYB+GKrPHuflcuV1hIEK5i+D5SAl0XZGhubobD4ejzfVarFa2trQqMSF/YHE68uaewz0PUBODNPedgc1fuUYrPj1cIvTt6soKwdrZWNHbgW1E8daACC2NfD0dKG5FX1QLA5TVS8nJjTUBttdjxyVGX8j53RBoSohjsQM7WlOHr/BpUN1sAkGUzUN738ODSXEoFH1IXExGiWD6l3HCMWJQoXFE+mjts+PRYBQBg3sg0xJnZr+orJ7pVjmbPno3Y2FhERkZiwYIFyMvL6/Z9X3zxBSIjIxEdHY2cnBw8//zzff7tqqoqHDt2zONffn6+1F+BefYV1qOmxdrnsckBqG6xYF9hvRLDEth2yNVANT4yVDO9O/73U5kgsPxCw1bHnuCfiYkuN+w8WYU2q8t4o8dnLQf/da+fiNAgXDHGmCVmpYJvMD1E4+FJPCx4N+tbrfjqtMsbd9VY7RcLYAm7wynk4s4Yqp9wRRbWLQDsOFGJDpvLgK1lj6dU6C6sLjIyEjfffLOgHO3fvx/PPvsspk+fjgMHDiA7uzPHYezYsZgxYwaGDx+O2tpavP7667jvvvtQVlaGJ598ssfPWLduHf7yl78o8XWYprHd2vebAnh/IDS22/CVO2TkitHpCAvRhh2ADwMckBSJsWqEnMmI08nhY3dvmik5iUiP08fl5i8f/eQSTmMiQnDJsBSVR8M+HTYHPj9eCQCYk5uKGA30K+sOFoShM9UtQm+jBeP6UXiSRHx2rAJ2t3VrwThjhyVJzZ6CWtS3ucIVaW6lh69aGR8ZihlDk1UejfowrRw5nU5Yrd4J1OHh4TCZTFi6dCmWLl0qvL5o0SJcfvnlmDlzJh577DGPKnTbtm3z+BsrV67EFVdcgWeffRarVq1CVlb32vPdd9+NJUuWeLyWn5+PRYsWefnN9EGc2bcwIF/fHwifH6uAzeG6pH42pueDlA+1YkBeQXFdGw4VNwAAfjYmQ3cCy8HiepS5kz2vGqtebwoGHjWaO2xCrt7lo9hX3lmYs69OVwthsr3taaJveAs8APxMxb0YKKydkB+5jT8pMeGYosHeRmJYu3544T0sOEg34YqsIDYmzx+VrnoLFhZgega++uormM1mr/6dOnWqx78zY8YMTJ06FTt27Oj180wmE+6//37Y7Xbs3r27x/elpqZi1KhRHv+GDBni79fULJNzEpAcHeZVzlFKdDgm5yQoMSwAnZdUUlQYLhykjUuK96oAkCwZklewWFD+eK9YkAmYP1p5gYyly37niSqhiamaimJfMDRlwvoxhwZrJkyWh6W1B7jCdwFgREasocv1SkltiwXfnakFAFw5Oh3BOioxr/b9YXM4hXyYmcOSDZ8PIzViY7LRCzHwMO05ys3NxYYNG7x6b0ZG7wJGdnZ2rwqU+H0AUFdX59XnGpnQ4CDcOC0Hz24/3ev7OAA3ThugmDWioc2Kb/JcRQ3mj05HiEasILxCNyg5CiMy9NXvRhxSd+GgJKTEaKNyoFzwgn6cORQXDaEQhr7osDmw44QrpO7SEakwh1Euh7+crmzG6UpXURSWFXOt8emxCjjcIXU/IwFTUr7NrxEqAGrZ09kdDNgtBdkjUUPGZLlhWjlKT0/HzTffLMnfKigoQEpK33H9BQUFAODVewngrlmD8cmRcpyoaO7yMxNcG39ObirunDVYsTGJ4769PUjVrsZzrrYVP5W4Glv+bKz+Qur2natHZZOrypjeLjdfaWy3CUnbFMLgHbtExSu0LtCrbYX/nzikboy251KM2kImH/aVFhuOyQOUi5IwAvyaDQsJwtwRFFInJfWt2jQmy43uZqG6urrLax9//DH279+P+fPnC6/V1dV1KfNts9nwxBNPICwsDLNnz5Z9rHogNDgIGfHmbn+WHB2O1ZcNU7wB7EdHKoTPnzowqdf3sqKDfOweM6BPtzbvNQoOMmH+KHWrjKmdEL/zRCWs7rL2V43TiHCqsuTJWzajwoIxa7i2QupYg9+LozNjkZOsjT5RPcLI+V3TYsHes+6QujEZCNJRSJ3a2BxOfO4OqZs1LEWzhVjOhxXZY/vxSsGYrHXDk5Qw7Tnyh+nTp2PChAmYPHky4uLicODAAfzrX/9CdnY2HnzwQeF927Ztw5o1a7B48WIMHDgQdXV12Lx5M44ePYq1a9ciPZ3KxHpDm9Uu9OXhmTsiFb+cMQiTcxIUt4o3d9iw54xrPJePStNM3Pf2467Df1ByFIalSZcDwH97NT1jHMdhu7vK2NSBiUjSSDNeueDnIs4cigsH9a68E4DV7sRud/GK2bmpmiyPzMopVFDdgnx3n7ErVMj70ytfnKgSWjDoZV5Z6Qv3w9k6NHW4CrFcqSNPJyvwFUATo8L6NCYbCd0pR9deey0++ugjfP7552hra0NGRgZuu+02PPLII0hL63THjhkzBiNHjsTGjRtRXV2NsLAwjB8/Hlu2bOlSiY7oma/zamCxezZ3zUmKwrTB6myyr07XCImFWqloU91swUF3lbrLRqbpLqTuRHkzShvaAaj7TFiY1Q6bA1+6Q+rm5KYyH1LHwlrce7ZWqFKnlT3NKjtPVAn/TXMpHdtPdAqYk3QYUqems503JgUHmTCbvMaS0m514Jv8zvtIK8ZkJdCdcrRmzRqsWbOmz/dNmjSpSylvwnf4gysiNAhWu1OwnqkFn7QdFRbsk4Km5uG/62SV8Pl6FFj4ZwLA8PHi3xfUCrkzenzWcrDDfcaEBJkopC5AeCF+QFIkhqbqq0qdWmd4h82Br/NIwJQDjuOE+2NKTiLiIvURUueBirLHt/k1QuNXuo88YdtsSTCNw8nhi5MuS+SMISkwqxzuYnM4hfFcMjwF4SF9j4cBw7iHW3tCf/1ZHXkFOjc9BtmJkSqPRt30GX4uwoKDMFNDjV/VCst0CUeuPT11UKIuSviqtf7qWq3YV+iqwjp3hD481CyEfn2T1ylgGt34IzUnK5pRUu+KOpirM+GdhbUr3EchQbiYGr96QMoR4Tf7z9WjrtXVpHee6OBS6/LfV1gvlPvUyiWllFtbLatqeWM7jpS6qvAZ3TIltoJeODgJ0eG6c9xLjjgkUyt7ujtYUER2nezMi9HyXLIGv6f1JmAysGQFrzEAXEZrVlKcTg47T7rmd8aQZESG0X0khpQjwm/4IgImEzBnRKrqAgB/SQWZoJnYZLFbWw6BRe0LTpzjoLZApvZcHC1tEsqZXzZCG+tTbfmIQjKlg5/LOHOoog259YzT2enZvGhwEqJ0avBQy+DJr9nhaTHon6R+1IGeOFTSgJoWl3GbztaukHJE+M1OdwjbpP4JSFa5ApnYKj85JxEJUWG+/b4cg/ICvVodefjvlxoTjjGZcSqPRl22iwT9S+ky8gp+/bASkqlVLHaH0Ftr9vAU5guB+IMaoZ+HSxpQ0+IyeOgt7EttKps6cNjd+2/uSG0Yk/xBNdnjuPg+0u/8+ov+TkhCEYrr2lBQ3QrAVV5XjBohXGdrWnGutg2Ab+53NeN+OY7DrlP6tTp22Bz47oyr98elI9IM3/tjt/tZj+oXi3499AYjOqlutgiNkfVk2VTjfPzxbD1a3YVASIiXjl2nOvsq6mmNssCXornVozFJ7UgGfu2Oy4pDWmyEuoNhEFKOCL/4Kq/z4LrEnViu5l7nraKAqxiDFjhd2SKEWV0ic3K+GgLZ3rN1sLrLvMv9/XxBjbmoa7UKuVcszYW3qDFnfC4eoJ09zSr8eR1kAi4eop+5VFvA5O+d3PQYEjAl5kv3mo2PDMW4rHh1B6Mzqpo7cKK8CYA27yMlIOWI8Av+UkiKCsPIjFiVRwN8ledq/JoeG6GZErVihU6uymVqesb47xccZML0Ieo3l1MzJ+7rvGpBwdBSlTo1hc+vTrv2dEx4CMZnx6s3EAlQ22fK78Vx2fH6LIesAg1tVvxU0gBA/wImp7B1xOHk8I37Tr9oSDKVR5eYr91nK6Ct+0hJSDkifMbmcOK7fFe41MxhKaqHS1nsDuw5w48n2Tch2P1WpQ9/oNOam5VgxsDkKMU/X254gWxi/3jERhhbIOMF/aiwYEzUYbl2qXE6OaF3zPQhSbrMkVGKqqYOnKxoBgDMHEqCkFR8m18rVP8jAVNajpQ2CpVnL9H5mlVD9uDPVj0YnuSCbhzCZw4WNaDZ3bF+5rCuRQSUTozdX1iPdpsrnv5ijRykHTYHfjjr6jly8dAU1Sv9SU1ZQzvyqloAaOeZyAXHdQr60wYnISyEjt2+OF7eJFRS0tv6Ufp85L3qQPfntW5QWMbkjT8RoUGYNIAMHlIijqq4WM9rVgVchifXmTB9SBJCyPDULTQrhM94HFxiwUUl+Z6//E0mV71+LbD3bB0sQj6O/GNWWiD7Jo9dt73Sc3GqshlVza7cMtbmwluUNm5+LVo/eg9Zkhv+vI6JCNFd7oZaJiWO4wTP/4WDkhChcgN0vcGv2aGp0ciI02fxGrXW7vHyJtS6+1Nq9T5SAlKOCJ/hL4XRmbGql/AGOg/SsVnxPpfw5lHasf21KB9n2mAZlSOVTmBxMi0rJbzVuow8css05gVRK2eNn7OByVG6KOGtlmPY6eTwTb5L0ZwxJJmsxBJxproF5Y0dALS3p71FvGaVvB+bOmw4WNwAgIR3ORAX09Lr2pUCOikJn6gXVd3qaWMpaWWubrbguLvqykw/+gSpJjC7D6jx2fGIM+srH4eSaT3h842yE80YQI0M+6TVYse+c3zIqTY8waxyrKwJda36DE9Uky9PGyRUUQW+y6+Fw53MRftfevRmeJILUo4In9h7tlZQfs4PYVNDBP7uTOclpZXLv6qpA6crXfk4SllulFRYj5V1JtP6o7DqiQ6bAz8U6je3TA5+KKyDzcELR9rY06zCe40A/QuaSno3vnXPa7+4CAxO0UZ1VK3Az21YSBCmDlS/yqncKLlu260O7D9XD0D/50GgkHJE+ARfFS4sOAgTGUhC/b7ANR5zaLBmqq587y7EAAAXMVDiWmr4ZwIA0+UMGfQTJRXFQ8UNQq+nixicC29RMk+LXz9BJmDqoETFPlcplFx//Fz2T4zUpZVYDWOD3eHEj+4zfNpgH6ujEn3Cr9lJ/RNgDtNvLpcay+ZAUb1geGLxbmYJUo4In/i+wHUpjO8fz0QSKj+eyTkJgVUBU0FgiQgNwliZE6TVuLb5Z5IZb2ZLIFNhMsSKoh4FfTng18/ozDjdlIBXQ4C2OZzY5/ZaXkhrTzKOlzcJ1Vr1PK9qrNmaFotQ5fTCQfozHKqNx300UL9rVwpIOSK8prbFglOVrn4Z07o5uJQ+TCsaO3C2phWA/wepGhcAf0BNHpCou7LOYqsqXW6dz3poajQTxUt8Rent0dxhw1F3TiOtn8A4WtqIVqurxQHNpXSIBUzDzKtCxsO9BZ1RFXpWPNWCX7u56TF+F68yCvqSzAhZ+V50cE0brP6lsPes+JLSxkFa1dSBgmpeoVNuzEo5xoxiVfWGDpsDB4oaABhIiAqQfefqhWRso6+fQBGf11Np/UkGs55xHbCnwJVvFB4ShHEaCZMPFKXCbNutDhxyVwGk+6hvSDkivIa3OoSFBDGR3yPONxqTGa/uYLxEnG+kxwNKC1ZVpRRFcb4Rq3PhLUpd4OJ8o8k5+lSOlFp/4nyjzHh99ooRo8QaJc+4vPCK58T+CUyE7cuJ0lEr4nwjWrt9Q8oR4TV7RImSvR1cnEKSlBT5RvzxpLTAokS+EaB8WBTLVlWle/ZQvpHv6DHfSA2Mkm+k9PlGnnH5qG62IJ/yjWSD8o18g5Qjwiuqmjv6PLiUvKikyDdSA8o3Mg5azzdSGso3kg7KN5IHLXjGpUJ8nStRrVKLYfJagvKNfENf0hkhGz+cZStRUosHqVr5RgAUcY2RVbUTyjfyHco3kg7KN5IHlj3jWocX3o2Ub6QUlG/kOyG+vPnZZ58N6MOWLl2KrKysgP4GoQ5847Cw4L4PLiVC1Pa6lbWI0CDN5BvxzUABfQosP2gln0qBBfpTSaOQb0Qhdd7Brx895xspxQ9u41F2otkQ+UaA/N4Np5PDj+4znPa09PD7fwIjbUKUQgmv3MFicb4RrV1v8Ek5euCBB2AymfzKKTGZTBg/fjwpRxqFV45GZ8b2eHApGf59wD2eCdmB9TfiQwGVyJMSK5hjs+Jk/zxA2Twb/vulx0YgK8EYAllP8HMBABfoQNBXwuDBz1lueqy+841kPmucTk7wWuph7fWGkndOXlULmjtcnvEpOp9XpWlst+F0pSts3yhzq4a8BJDhyVt8Uo4A4O9//zsWLlzo0+/U1dVh0qRJvn4UwQhtVjuOlTUBYGNjNXXYhH5LkwYkqDwa7zkgUjDDQ/RlGeM4Dvvc32/SgAQmu8YrOSRe0M9KMCMtNkK5D5YYpZ6jzeHEYXfYh5b2tC+YTMpUVCuoaUFjuw2AfudSDcQGDyPMq3jry71uDxZ1zu1EA8yt0vBrNycpkvJfvcRn5Sg5ORkDBgzw6Xeio6N9/RiCIQ4XNwq5ABP7931wyX2QHipqED5DK5dUu9UhKJhqjFlu131JfTuqmy0A6HLjOA4HijoVRaJvjpc1weIOQ6Q5CwyjCfFKwc9rbEQIBqeQTCMlYs/GBC9kDMJ7xJ5ko9/NvuCTclRfX4+oqCifPyQpKQn19fWkJGmUA0XeXbZKWZn3exyk8ZL8TbkNuj+VNMDuVjD1KLB4u0ZYQG5FsbC2DXWtVgDszwUrkEAvHfxcxoSHYGhqjMqjUQ65jXL8GTdxQAKCgtjzjGuZ/e65HZYWjTizjkNqu0HudUueZP/wKVkjLi4Ozc3Nfn1QXFwcgoP1FUpkFPh+GQOSIpESo75Llr+kBqdEIT4ysJKUSl1x+8VhAwpaxpQKJeMFsvCQIIzMiFXmQxlFLOgr+azlRO4LnN8fydHhus9Xk9sQw6+/8f3jEaxzIV6p8622xSK0jtDLnmYFh5PDId6zYaS5VfhuBgw2vwHicyZ7eno6fv7zn+Pdd9+FxWKRY0wEQ4hdst5aHeS0zDucHA76OB4W4MMGshPNSNVwDkpP8Afw2Kw4Zvs3KaYIu+fCHBqM3HRtW+6VmrMDQr5aPJP5alKgxLeqb7XiTDUJ8VLD34GAtu4dLXCqolnoyUVhX9LD30fR4SEYlqbt+0hJfJZiFi9ejB07duDaa69FWloabrnlFuzcuVORal+E8pypZssle7qyGS3uXjosjMcbOI4TDqhJKgkscm7PVosdJ8pd+VR0uXUK+uOz4xESzKaiyBJlDe0ob+wAoJ09zSoHiyk8UQ748zvIBMP04BFXOpVTutuvoZBsLcKv3QkG8CRLic8396ZNm1BVVYWNGzfi4osvxqZNmzBv3jxkZmZi9erV2L9/vxzjJFRC7JKdPKD3SnVKbDu5chPkVB7O1rSivs2lYOpReThc3AAnXyDD4NbqxnYbTldpr5KimlC+kXTwc2kyucLqCGk4ICozHx3ucx0rohf4uY2PDMWgZN9z2rWOnIoneZL9xy+zptlsxrJly/Df//4XFRUVWLduHYYOHYrnnnsOU6ZMQW5uLtasWYOCggKpx0soDJ/f40ruVb+gBn+QxplDMSg58PEoEcKjZsyvEhFKHt9PA8KtnIrwoWLtVVL0DvkmTdz/a1Q/Zfp/qYmc64+fy+FpMfruFdUNck2r1e7E4ZIGAHrb02wgjqrQa0htdyjRg5A8yf4TcMxHQkIC7rjjDnz55ZcoKirCE088gcjISDz88MMYOnQopk+fLsU4CZU4XNwIABibHed1hR45L39eWZvQP14zFYP4ePXIMO3noHQH/0wGMN5DQYl794AMlRT1Dt/jZFQvDab1gNyCn93hFM5r45RDln9TnyjvLDM/cUC87J9nJGpaLCiqawOgDcOa1jhwrgEAeZL9QdKA+MzMTPzmN7/BG2+8gYULF4LjOOzdu1fKjyAUpM1qR547RGhsVnyf75db+Gxss6Gw1nWQjtdQ3PeR0gYAwOjMONVyUOTSVzmOw5FSl0CmpWciF/xcDEwOvJIiC8i9p612J06Uu84YWj+Bcaa6Fe02V2L7BJpLyfjJvacBYHy2cQR4JZrAHikRz228PB9iYPi1Ozgl2nCe5ECRLHi2qKgImzdvxr///W8cPXoUHMdh+vTpWL58uVQfQSjMsbImIZdkXJb64S5HyzoP0rESjYc//+WqsNdhc+CkW/gbm6n+HEpNeWMHalpcPX3G6PD7+QLHcfjJfdkbfS685XRlM6wOl1Veqj1tVH5yh34BwBiaS8k44p7XmIgQDEiMVHcwOuOwaM2OpjNTUjiOE9auHmUPuQlIOaqpqcGWLVuwefNm7NmzBxzHITc3F48++iiWL1+OnJwciYZJqMHh4gbhv73xHPHI5aUQH6RjMuNl+hRpOVnRLDR/HauCZUzuuGaxQKaVKk5yrU+Xouhqb6A3QV8uy7F4T/tyxhBd4RXziNAgJvJDlUauirn8vI7N8j60nPAO3nM0MDnKcM1fBWQ6W0vq24VCUHq7j5TAZ+WotbUV77//PjZv3oydO3fCZrMhIyMD9913H5YvX46JEyfKMU5CBfhLITk6HBlx3vTmkffi4A/SfnERTDSj9YYjYuFPh9Ybfo0EmYBR/dhu/iq/oij2bMbL+ll6gd/TMeEhGJhkjEpVcnmp+RCaUf3UC99VGrnDPtutDpyudHn+tWKQ0wocxwlr1oiedrnXrvg+GkP3kc/4rBylpqaio6MD0dHRuP7667F8+XLMmTMHQUHGOIyNBO8VGJcVx0QVmU4LXry6A/GBw+4xx0aEYECSeiEZcllV+RyboakxiAwzdolbPrdMC4qityh1gY/O1L9VXs5v58rdcvUaIyuxdBwvb2QqtFxJxOtVDoW+oqkD1c369LSzwE/u+yg4yKSb+0hJfJZm5s6di+XLl2PBggWIiPDGm0BoEXHxA1+VETnk8NoWC0ob2gFIHE/vvgHkTjgdmxXPhIIpJR45NnS5CXMxJDUaUdQLpU86bA6cquQLvtD6CYTTlc2w2il3S2r46n8AnXFSQ552efnJvXaHpcXougqoXPh8g3/44YdyjINgDN7qALjKeKuNuGKQVi5/cbU/tS5WOfWxoro2NLZrL6ZZDi+aZzGGeMn/vh45Xt4Eh9ssT4JnYHiE0Bh0/clh3+I944lRYciMN8vwCcaFj0zRk6fdH+TwyjmdHI66164ew/mVIGDzZmtrK9577z0UFBSgvr6+i+BhMpnw/PPPB/oxhMKIL9txXlp15BTExSU/x2rk8met2p/UaM3yJ+f6LK5rFxTFcQwYE6RGFsHTjzNGD8jhpeZDOqPDQzAo2Ri5W4D8XY54AX4sI6HlesLonnY5V1NhbSuaLXYAbBi3tUhAK3Lnzp1YsmQJGhoaenwPKUfahK9Ul5VgRmKU+v1a+EtqQFIk4iK1UdWGpYRIOYRb/pmEBJl02dzWFzwrKernMpKziAU/Z/GRochK0L9VXk7Zmg//Gp0Zq/vcLaVo7rChoKYVAFnfpUbcH8+onk45+UmDxmTWCKiKwj333IOoqCh89tlnaGhogNPp7PLP4XBINVZCQQSXrF8eD+lFcbn6x/DCnxzWXF55SIoKQz+vqv1pC/6ZDE+nmGb+og8JMmFEhnFDRHzhiGhPk1XefzpsnRXVtODB1QpHS5uEe0Ft45YqyNgEtriuHQ1UZlo2+Ls5LDgIww1uuPSXgDxHRUVFePLJJ3HZZZdJNR6CAepbrShr7ADgKgvrLXKJN1VNHajSYFUbwTKmw5AMp5PDsTJtVseSM0SMkl+9o81qR351CwDtrR/WEPdS05PXUm2OajDPVSscKRVHVdDcSg2/dnMzYhAWQpWk/SGgWRs7diwaGxv7fiOhKfiSsAAwkoFEyWOi8Yz2QVlTkzarHWfdIRlaGbMvFNe3ocUd0+yLAq1HOI7DcfcaHZ2p/n6RA6mLWJysaBas0XrcH70htXJ+vEx0PhpMOfIwOkk8sfyeTo4OR1qs/jz/anK8vLM/3kiDetr5pSu1V87p7LyPjH43B0JAytGTTz6JdevWYd++fVKNh2CA4yJlZJQfB5fUm118+UsdsiSXQ0cs/LGgYGrpmciFXL67ssYOoRiDVuZCbbS4fgJFrvwtXtCMDAvGgET1eqnpDX6NsnB+6w1+bgcmR5GnXWJK6tsFwyWtXf8JKKzukksuwXPPPYdp06ZhxIgRyM7ORnCw50I3mUxU/ltj8AdXUlQYUmLCvf49uRQN3pOVEReBBAaKQ3iD2PumR+GP/34mEwxfjOGESNDXmxVU7j0dFRaM/iTQB8SJcle+UW56DBVjkIgOmwNn3GGfIzKMeb6JlXmpvZ38mh1Jng3JERu3Rxp07UpBQMrRe++9hxUrVsDhcKCkpATNzc1d3qO3XAsjwG+ukf1imXh+/Hi0pGTwwp/a1ly5nt9x9+WWkxSluTKsknvRRJdRrobWqJrwc5abQdXVAsHp5ISzRkvnI+vkV7UIeVx6M3ioTV2rFRVNrpxmoyqeciK+j4an09r1l4Ckmt///vcYPnw43nvvPQwbNkyqMREqYrE7kF/lspj5eylIKXyKc3e0dJDy3je9WnM7BTLtPBO54OciM96MOLM2ysz7ipT6pMPJ4VSFS7mm9RMYRXVtaLO6KsIaPYRGymaantZ3Y8+r1Og9qsJbhEq5Ev9dfn5zkiIRrTHDJUsElHNUVlaGu+66ixQjHZFXKbKYMXDZnhLn7mRI74Ln1RYpE86dTg4nBeFP/TkEpD2AG9tsKG1oB6AxwUE2LxrlJvjCudrWToFehj3NLDIkYB83uKApl9mJN26FhQRhoIGa6irBiQBzmone4deuEc8DKQlIObrgggtQVFQk1VgIBgjEYiZHwjEfmwxox8qsd2vuiQpjC2RiWix2nKttA6DPuZBD+NTinmYVyv2TB35ec9NjEBJMpZCl1Oh5GcPXnGaibxrbNWq4ZJCAdv0LL7yAt99+G1u2bJFqPJKxY8cOzJkzB3FxcYiJicGkSZPwzjvvdHnftm3bMHHiRERERKB///545JFHYLfbVRgxG/BWh3BGLGYeuTtJ6o/HG1iy5soh3IorjelR+fOFUxUUfuMr/J4OMgG5FBMfEPxcDkyKQmQYhdBIAceJ8rgMvD7lSjcWVwFkIadZT5xkSPbQOgGdpsuXL4fdbseyZctw2223ISsrq9tqdYcPHw5okL6yYcMG/PKXv8Rll12GtWvXIjg4GKdOnUJxcbHH+z755BMsWrQIs2bNwgsvvIAjR45gzZo1qKqqwvr16xUdMyscl8BiJkfs9/D0GARrJHdH79Zc/vvFR4Yi3eD9P47ruFKdGDlCwXKSo2AOozK+gSCE0BjcSCElpQ3taOqgUshyYLU7RVUAaW4BaUP6PSJ/aO0GREDKUWJiIpKSkjB06FCpxhMwhYWFuOeee7Bq1So8//zzvb73gQcewNixY/H5558jJMQ1FbGxsVi7di3uvfde5ObmKjFkZvCwmPlxcEltBHI6OcESItdBKjRik/BvCj0cWLLmynAAj0jXluVPFi+aO0QsOjwEWQlmGT5Bfxi9uppUxqOGNivKGl1Vv/SsmPeGRw9YiY44z7BPY86rXORVNcPmcD0oo4fUynF18mdrnDkUGXHGNlwGSkCS2+7duyUahnS8/PLLcDgcePTRRwEALS0tiIqK6iLEHT9+HMePH8dLL70kKEYAcPfdd+Oxxx7Du+++iz/96U+Kjl1tShva0ey2mLFwKRTVtaFVSNxWfzzeIgh/OrTc2BxO5FW6qxnq8Pv5ynFR1T49ViWU+gavb7Wi3KACvdSrgyqqyYPYG5xrcAFeasSKp6GKsSiEUBwoQ1uGSxbRXabhjh07kJubi48//hhZWVmIiYlBUlISHnroITidTuF9Bw8eBABMnjzZ4/f79euHrKws4edG4nRl58EVSDiYdBY87cXPNrbZmLLmSn0+FlS3wupw7SOtPJPukCKUwVWS2theEF85QQK9ZJCHQx74NZqdaEZshD5L83uD+OqQKu6An9uw4CAMStFGDrFWsDucOF1JIYtS4ZPnqK6uDjExMQgN9f3AqKurQ1xcXJecJKnJy8tDcHAwVq5cid/+9rcYN24ctm7dijVr1sBut+Pxxx8HAJSXlwMAMjIyuvyNjIwMlJWV9fgZVVVVqK6u9ngtPz9fwm+hDqcqWoT/Hpbmu3IktZ3ilEhZG66R3B2PMfsxh6xzSiIFWg8U17Whw+ZSFLWyPtVGi3uaVU672wXEmUORFktVv6SCNxIOTyMBU2r4uR2cGo1QqgIoKYW1bbDaXfeR0e9mKfBJOUpJScFbb72F66+/3qcPqa2tRWpqKrZv3445c+Z4/XtOpxNWq9Wr94aHh8NkMqGlpQVOpxNPPPEEfve73wEArrnmGtTV1eH555/Hgw8+iJiYGLS3twu/dz4RERFoamrq8jrPunXr8Je//MXr76EV8twHV3J0OBKiwlQeDYTwrcx4s2zNzIRGbBKZxsTeN38UTLmQyvLHrxGTCRicEi3RX1UGqb1orD5rOZBq/fCWzZjwEIqJD5DTVa71Nywt2rAhNOL2EVKc4R02BwprXU3Hh6Vp63zTAvyZSXMr6rEo0d/LE91HQ2l+A8YniZPjONTW1vrc26iurs6vMJavvvoKs2fP9uq9J06cQG5uLsxmM1pbW7Fs2TKPny9btgyffvopDh48iJkzZ8JsdiVPWyyWLn+ro6ND+Hl33H333ViyZInHa/n5+Vi0aJFXY2UV/rIdnh7YxpJOkHKNR0sbnT+gzKHBukzQ559J/8RIw1cay6sSeVpT9akcSS1y8/tjiAEFepOE0hDHcch3K5pDda6YK0lBdSvcPdB1b/BQmsZ2GyqbXPIWza308IYngM4EKfDZHH/ffffhvvvu8/mD/LkIc3NzsWHDBq/ey4fH9evXD3l5eUhLS/P4eWpqKgCgvr7e4/3l5eXIzs72eG95eTmmTJnS42elpqYKf08vOJwc8t3C3lAGBD2r3YmzNbwFT/3xeAt/QA1JjWYiQV964ZadNaI2vKKYGhOOuEjj5iZ4C8dxnZZjWj8BUd7YgWaLq3jOsFTtGI9YJ6+KrO88YplNCq+ch2eD1qzk8MZtOSNtjIRPM+itotITo0aN8un96enpuPnmm336nUmTJiEvLw+lpaUYNGiQ8DqfQ5SSkgIAGD9+PABg3759HopQWVkZSkpKcPvtt/v0uVpHnD/hrzIipSX4XG0r7G4TnpwHaWcpb2n8Xbw3QY8Xq55CTjgu8DA7XlHUkvKuJtXNFqF/jB73h5J4eC1p/UkGv6eDNBg2zDq0ZuWl05NM61YKfFKObrrpJrnGIRnXXnst3n77bbz22mt47LHHALhylzZs2IDExERMmjQJgEtRy83Nxauvvoo77rhDKBSxfv16mEwmLF68WLXvoAanPRKl1d9cYhexVg7S+lYralrYDBuQwvKn9ZATk4R+NIeTE5oZGuEykqK6nxb3NKt45hfQXALShHPz9+CApChEhBo7bFhq+LkNDwlCdmKkyqNhAJN0+c42hxMFNWSskxLd+d4WLlyISy+9FI8//jhqamowbtw4fPDBB/jmm2/wyiuveBRgeOqpp7BgwQLMmzcP1113HY4ePYoXX3wRt956K0aMGKHit1AesVVnSIAhL1JsdrGyNkQjLvjTOg8boJCTTorq2mCxB+ZpNRpGKmDRHULxFwn+Fj+X8ZGhSI5Wv3iOWkidtsbfg1q5c7QE75UbnBKNYAZCzvXEudpWobkurV1p0F0tRZPJhA8++AC/+tWvsG3bNtx///2oqKjAxo0bu4TKXXXVVdi6dSvq6uqwatUqbN26FQ8++CBeeukllUavHqfcZWHTYyMQZ1Y/f4IXxLMSzIjSSPzsaQbDBqQMdeQFMgo5OV/Q1+9cSCl88ns6JiKESk8HCO+FG5YaY7jCFnLRYXPgnE7ChqVGGs8xVaqTC/LKS482pE4fiY6OxnPPPYfnnnuuz/cuWrRI81XmpEA4uBipj39aoXwOIedIwoRTc2gwMuP1WKnO9Uz0EHIS6OPO8/BssrFnWEe8p0mg9x+OExXPIUFTMs5Ut2g6bFhqpNyhjW02VDW7Qs4pDFR69B61oga68xwRvmN3OFFQ7baYSbCxAi1uYLU7UeiuVKely19cepyFSnVipCg40VnNUDvPRIyU8jgv6LPiaWUdj0p1GtrTLFLW2IEWvlIdCZqSkScuhUwGD0k5XWXskFq5EfeE1EqkDeuQckS4Ois73PkTAXiOpBI+z9Z0VqrTUslfPZe59gw50d/38xUt9uBSk8omC5r5SnU63B++EGiI0mlq9tgtUs1rkAkYlBIlxZAIN0YJQ/YFKc2nZHiSHlKOCI8QIRYE3zwNWplqWyyobbUCYOuAkuoAFoecGF0gszucKNBgDy5/kGr9aHFPS41UxqN8yi+QBb4YQ44OwoZZgzccRoQGITuBKtVJic2hzZ6QrBOw/621tRXvvfceCgoKUF9f38V6YzKZ8Pzzzwf6MYSM8CWJAYkqnQQYwSVOLhycKrcFT5oKUnrv4aC3kBPXOeWftFpU1waru1KdVkMMlcazezvNWSDwVuLEqDAkR1NhC6nII2+wB2JlPvD70TW3rDRH1xOFokgbyueSjoCUo507d2LJkiVoaGjo8T2kHLEPn2+UFhvORGflfFGlusgw9cfjDZ6l0Nm7XAMtOMFfbhRy4vmsjSJIBbp++D0dGxGC1BgS6ANBKDdt8IqRUtJhc6Corg0Am+e31uGNa7RmpYd12UOrBBRWd8899yAqKgqfffYZGhoa4HQ6u/xzOBxSjZWQCd5zNCg5sI0lVdgIr6xpqVx0gXsOw0OCdFmpjn8mWQmRmg05kcpeyc8FoK01qiZn3HM2KCWaKtUFAMdxwlkjv1edfaTybpyrbRPChmlPS0tzR2elOprbTjzWbgDWpwJR5M9ggxsupSQgs3xRURGefPJJXHbZZVKNh1AY12XLCy7qbyynkxPiZ5UYj5RFJABgYHIUU2EDUn8/FtaI2px1dyJPjApDfKS+G3BKpcjQ+ulUzgPxwtW1WtHkLmwRqDGL6ITf04BLgSeko7CmTfhvmlvp4fNfU2LCERNBlVOlIiDP0dixY9HY2CjVWAgVqG6xoNldFlaqgysQC15ZYzss7nwOJQ/SQMOGWFIwpcZDYdWJQBbI4xaedbL+nrUcNHfYUE2WY0ngBSFAn2eNWpyppnk9H5PI1x7I/VjgoXjS3EoN3UfyEJBy9OSTT2LdunXYt2+fVOMhFKZAwkvBJEHgkkfIkkY2u8XuQEm9yzrGqvIQyOVW2iBWWLXxTOSkgLwgPuFxxmhkT7OKOISGrPDSwa/R5OhwxJL1XVJ4xdNkckVWENIhDrOl80BaAgqru+SSS/Dcc89h2rRpGDFiBLKzsxEc7JmPYDKZ8OGHHwY0SEI+xIILC8mSWrz8xfHqehSY9WKtliJCrKHNijp3yXatrE8pCKSJcAGFLEkGf16HBpuQnaC/3Ea14Neols83VuHv9H5xZs3mq8rB+Z45f+6nWlGYLeUbSUtAytF7772HFStWwOFwoKSkBM3NzV3eQ8m3bMMXYwgLCUI/iQoJBJRc6BbEI8OCkRYrf1Ur0fHk999gW6GTwpsn+n6MesaUQhx+YwQrqBSnd4HIcjwgybg9Tvi7MJCQTn799U+MREgwtSmUIvRLnHdLAqb06DnkXG2kjPwhPAlIOfr973+P4cOH47333sOwYcOkGhOhILzgOzApCsEBFhKQQg8WFzbQimIt9qzoUWDmn0mUQgqrEvgrSJ0VPWsSpLyD3x+Z8WQ5DhS+cMBAgxsppKS+zYbGdhsAfZ7fUuCvQs9x4nxVmlupERcSoTNBWgIyPZWVleGuu+4ixUjDsJY/USAq+asVOuPVwxBnZjNeXYoCBANTtKOwygVvTAgOMqF/Iht7hnW0uKdZxO5wCr14SDGXDvKM94AER31FUwfaba52LrT/pYfCbOUjIOXoggsuQFFRkVRjIRTGYneg2H3ZSqkc+SuIt1sdKG1od41HQ1amAon6RLGKXr6fFIodfxllJ5gRFmKcsCZ/PW2uSof8+tHOnmaR4vp22ByuB8GKMUsPUGiSfNDcyguF2cpHQLP5wgsv4O2338aWLVukGg+hIEXiQgIMCL5nVUj85+XlwEqVsuV9ExOoPtBmtaOssQMAhZwAnYnbNBfeUd7UgQ4bVToEAu9zxHZuozp4nm/+TewZ954OCTIhO9G4OXFyQGu2Z6RoYNxZSITmVmoCyjlavnw57HY7li1bhttuuw1ZWVndVqs7fPhwQIMk5OGMuLNyauCbK1C7vEdVKwaUNW+oa7Wioc0Vr65H4U8NhVUJ/Km+5nByKKzlPa3aWJ+BEqhyTSFL0kEl0eWBn9f+SZEIJeu7pPCejYjQIGTERqg8Gn1hczhRVCt95A/hIiDlKDExEUlJSRg6dKhU4yEUhLXGd2fFlcAYGI83nNWIQudvBUHPAgTsfj8lKGtoh5X6PfmEXpVrNeA91HHmUCRGhak8Gv2gtwbXUuHh2Qjw/hiYHI2gAAs+EZ6U1LfD7g79GUxrV3ICUo52794t0TAINeAPLqkb3/kdNuIeT1psOKLDA1qainFGgwqdLxQYrHR1b5wxsBfE/1Aw1/oxhwYjnSzHASFUFtVQJU/WsTucOFfLbli01imgfEPZEHvl9Sh7qI2iPuSamhoMGjQIe/bsUfJjiR7gL4WBydLEWQd6YYsvf6Xg+2T4HfPrFv5Cgkzoz2C8esChju5nkhYbjiiNKKxyQcnFvsMrlDnJUWQ5DhCWcxvVwtO74fvvl4iLXJAALykdNgdK6t0FlmjNdkF8GvrjmaMwW3lRVDlyOBwoLCxEe3u7kh9L9ACfPzEgSf2NxXGccPlrqV5/oXvM2Yn6jFc/614jRvcaAUBhbWe/p9QYffR76gtTgOo1P2d0eUOQhvzJd2ux2FHdbAFAcyklZ2vJMy4XxXVtgsJKcys9/NqNjQihMFsZ0J80R3hFm7Xzsh0gscfDHy9MQ5sNzR12AEBOEnsemJ7ghb8BGhqzL/DexRwGFGgp8cfKzBsT+idRWJM3WO1OlLotx3rdH0pxTiTEs2DM0gvnRDlxOSTASwp/XgK0ZuVAuJspzFYWSDkyKOdEB1d/iQSXQLbnuTrxQaqcIBXImcJxnNCUUWoFkwUa221CJT6p1oiaBHp/FPGKsA6ftRyUNrQLrQJIOQqMolp1zke9w987EaFBhvEGe0ug4ranQk9rVmp4GY7FcH49QMqRQRErRyx4BcQHaf9E5cfjT8xvTYsVbVZX9+/+DMxhdwSiEHgIZCo8E5awO5xC/LwRL3p/QsHU3tN6wsN4RHMpGUUiAZOs79LCGw6jwoKRRGFfXQhkuVntTpQ1GPc+UgJSjgyKnFYdfxQNLVpGi+pEc6hD6825OrL88ZQ3dghlUw0VIhKIci0S6HMkKvqiZQJpAssbs+LMoYiLlK6yqNYR58T5E87NK52kvEvPOVFOMymevePr2vXwytPalQVSjgwKfynEmUMRH6m+VYcfT3K0dqqindOQQheIQAaw//3khubCd/g5CwsJQloMlfEOBN4QQ2tPOpxOUVg0zWuv+HN/0NzKh4dXnuZXFkg5Mijn5CgkIEEIl9IHqWDN9eN3xQJztg49R/wzSYwKQ4yEfbDUIpDKa2IvGsV4e4c4Jp7KeAcG5RdIT2Vzh9DUmQT4rgTi7XE4OZTU8wVsaG6lpkilHG0j4bNyVF9f7/eHhYWF4ZJLLkFCQoLff4OQhnMylvH2L7xBe8nu/AGVHhuBiNBglUfTPVIoBCSQdSqKocEm9Is3qzwa5fHPcqy9Pc0ilF8gDx5FiWiNSkpZQ2f/KAr7kh7yysuPz8pReno6fv7zn+Pdd9+FxWLx6XcTEhKwa9cuTJgwwdePJSTE47Jl4FJotzpQ2eRaS1qyMsnifWMItbx5LMJfRlkJkQgmL0ifiEOWtLSn5cRfS7xn1T8SNMUE0gS2iLGiRHqCPBt9Iz4PfF27gnGbvPKy4bNytHjxYuzYsQPXXnst0tLScMstt2Dnzp1+JeET6lBS3yZLiV1/t6hWD9JzGlIefK02ZrE7UN7UAYANBVpqfD2u+H5WRrMw+7unq5ot6LC5Q5YMNmdSU1ir78IvasHPa3CQCZkJxvMGy0lhLYUhy4neDbMs4LNytGnTJlRVVWHjxo24+OKLsWnTJsybNw+ZmZlYvXo19u/fL8c4CQk5x1hzNjVL/vLWG1+F5RaLHbWtVgBszKHUFNe1C3PCaplypfDoZ0WXkVdQ01LpKGLsvNYLfBGgfvERCA2m9OvzERtGfDWuGT0MWU48vPIUsigbfp0IZrMZy5Ytw3//+19UVFRg3bp1GDp0KJ577jlMmTIFubm5WLNmDQoKCqQeLyEBYsElRw5hz9fwBg16js5pxDLmb05tkQ7LePs7Fx79rBh+1nLia1yAuC8PhdUFBm/MCg+hRqVSIoQNk4ApORSGLB9VzRZYqJCI7ARsLklISMAdd9yBL7/8EkVFRXjiiScQGRmJhx9+GEOHDsX06dOlGCchIbzgYg4NRoqEl62/MfX8QRodHqKZZnFa7MvkC1S6uhOxoki5Cd7B748gE5BFIUsBUSQqjEL5BZ4EMhu8gYuUd+k5R552n/DFM0dlvJVBUl9yZmYmfvOb3+CNN97AwoULwXEc9u7dK+VHEBIgzpVhoTlbZyM+NsbjDVrrWO9vwmdkWDBSovVnrfbtMjKuoujvduT3R0acGeEhbFZyVBp+Ln3Nz9VSbqOa+LKnG9qsaOqwA6A8LqnhOA5FtVSpUi48ZQ+aX7mQrNtmUVERNm/ejH//+984evQoOI7D9OnTsXz5cqk+gpCIczInl/sen6x+cqG/AoteO9YXaVBhlQu997OSAxb2tB6g/AJ5MLLBwx98uR5rW61o5cOQydMuOZ5eeVq7chGQclRTU4MtW7Zg8+bN2LNnDziOQ25uLh599FEsX74cOTk5Eg2TkAqnk0NxvauMNwv5E3aHEyX8eDR0SWmlY73fln8dVmcLtJoiy/2sZMdXzyOF1UgC5RfIg0dOHCmd3eL/3UGeDTkRe+XDQqiQiFz4rBy1trbi/fffx+bNm7Fz507YbDZkZGTgvvvuw/LlyzFx4kQ5xklIRE2LRegKLrUV3J+ztLyxA3an9prF6bljvViBJoGMchN8pbHdhoY2GwASPAOF8gt6x++CMzSvsqHHYj5yEOjapbmVF5+Vo9TUVHR0dCA6OhrXX389li9fjjlz5iAoiDRYLcALvYB8idK+uOCL69XtUu7PAWV3OFHe6OoBpBXlyBfDf7VIgdbK9/MVX9ZoCUOeVqUx+WHyKFF5T7MKP5O+7MUS0XlNc9k7/uzppKgwRIdLll1AACip61yzFIbsHXQfsYfPp8LcuXOxfPlyLFiwABEREXKMiZARseDCQrxqiQLKmjf4IrBUNHXA4fZ2sTCHUsPaGlGTDpsDVc0WAFR1zVtY2dN6QDyXmdQvRjL4eaX16R3+KPTJ0WHGDUOWiTZrZ39FWrvy4rNy9OGHH8oxDkIh5BRc/PHCiMeTEa8NZdtDYGH8gPLP8q+d7+cL/qzPsgYSTn1Fr+tHDXhDRXJ0OAmaEsLPK63PnvHn7gCAkgZ+bo1tWJODUjpbFYNi4QwGL7gkRoUhioFwAn6zp8WGa6bkr94t42St7qS0QfysjXvZ+1KBkhc8I0KDNNO3jFXIwyE9TieHsgZXWLSR97Rc0JqVD0/Zg9aunJByZDB4wUXOg8u3+NnOTtpqIFjHfBhzqQaVB19KlbOmQMuBt7Ohd0W4L/zxtpUKwhGVgRfjz1zwyrkR1553dM6pt3u6usUCq8OVU0nzKi0uxZPWrDf4FdXRYOz7SElIOTIYpTJadQIJ4dLSRucVupQYfYa6KKFAawV+LoKDTMiI00bYp9pocU8ribd2CoeHoElWYqnwzKmkNSolVc0W2BzufFyNGA61BL92Q4NNSI2h+0hOSDkyEE4nJ1geWLhs7Q4nKppc4Q1a8cAAncKfJsYcgOVfE9/PBwJR3tNjIxASTMelNwj5HDpbP0pT2dTZ5oDyC6TDM2xY/XtQC3ir0FMxH3nh125GnBnBQeSVlxO67Q2EuESzrGF1XgY4aLXqG59wqkero6cCrb/v5yuComjwufBWOGrqsKGpww5AW3uaRYwe0ikXVDDEOwItsERrVnrIK68cpBwZCNbCCVg4SE0+phw5nBzKNZjM6+33q2kVK9Da+X5yQZeRb5QysKdZxVdZs7Sh87zOprnslkAE+ITIUOpxJDHiAjakePZOYPmcNLdyQ8qRgZC70omvm50F5chXxKEuWhmzL2jxmfiDNwUqrHYnKpu1pwhLiRH2tNJ461kXN9Ok8K++8bbojNpFgPQMP7eJUWGIDCPF01u8WbodNgdqWviee7R25YaUIwOhVIlmb0NwxFbmfhrJT9BaSIavximtfT9f8FXQL29sF9YyJRd7h9g7rbf1ozT8XkyKCoM5TH+FX9RCrzmVcuK1Qk+eDdmgFhvKQsqRgRBbdVgo0cxC1TfflQd9h7p4CLcGP4DJC9KJr6XPw0OCkBIdLt+ADICecxvVgnIq5YWUI/lgLS1C75ByZCBYO7hYGo/3IRkaDXXxutqQ6/vFR4YiJiJUxgGxD1Ve8h2hUl2CmXocBUjn+Uhrryd8XWE1ChUlMiJOJ0deOR8IJKojK5HOBLkh5chA8Jsrm5HLttMyysZ4vIE//JOj9RnqYpSET290RX6/BJmAdIP2OPK1/HkpQ60CWEMo/uLF4qNmmvLg2UST1qiU1Hg016W59QVvwhb5szUkyIS0GPLKyw0pRwZBbNWR+7L1RvD0rPqmncufV+gyNXL4+2q9F5KVteQVkwl+v6THRiAshI5Kb2DJG6xlPJpp0lxKhqf1nebVW7xR6IspDFlWhB5H8dRzTwl0O8M7duzAnDlzEBcXh5iYGEyaNAnvvPOOx3tycnJgMpm6/LvzzjtVGrV8VHtYdeQ5uHwRxD0aHKrogve3GpceE/Q5jutscEuXG82FCG/CTps7bGhoswGgsJpAocIW8kA5ld7j+91Ia1ZOqLm2sqiflS8DGzZswC9/+UtcdtllWLt2LYKDg3Hq1CkUFxd3ee/48eOxevVqj9eGDRum1FAVo4SxynCsJbt74+3ScqiLN9+vpsUKC8XjC1DJX98obWBrT2sZudsuGBV+XuPMlFMpNVRNTV4oB1FZdKccFRYW4p577sGqVavw/PPP9/n+zMxMrFixQoGRqUt5o3LKkTcueHGDQ61sdr2HupQaKB6/rzVqczhR0aS9sE+p8cV6XEoCfR94P5kezTRJ0OwRcbSCV/cOhX3KBr9mqZiPd4jP1r7WbofNgepmvscRrV0l0F1Y3csvvwyHw4FHH30UANDS0tJnSIjVakVra6sSw1MNPr8HAPrFqb+5SjVoZRIrdCx437zBl8gIz75TxixAwFPR2AF31KdmnrXakEDvHb4UA4mPDGWi7YJe4Nco7Wnp4e8PFuQLvVHeKJLfaO0qgu6Uox07diA3Nxcff/wxsrKyEBMTg6SkJDz00ENwOp1d3v/FF18gMjIS0dHRyMnJ8crbVFVVhWPHjnn8y8/Pl+PrSEaZ23MUGRaMWLM8l60vgji/2eMjQ1Wt+sZbHr2xOooPqAwdXgAe3kUdfj9fcuJ4rxEAZBi0Up2v8PsjJMiEFKqmFBAV7r2ox3NGTSrca7Qf7WnJEebW4IY1OdD73cwiujNJ5eXlITg4GCtXrsRvf/tbjBs3Dlu3bsWaNWtgt9vx+OOPC+8dO3YsZsyYgeHDh6O2thavv/467rvvPpSVleHJJ5/s8TPWrVuHv/zlL0p8HcngPUcZcREK9B/pW9PgBSktXf4e3jeNXQDeJNTzzyQiNAjxkcYOiyhrYCtHT2288XaUu+csLTYCwUHU4ygQykmIl5ymDhtaLHYAQAbt6T7xtYx/GSn0siGWPTI0JntoFaaVI6fTCavV6tV7w8PDYTKZ0NLSAqfTiSeeeAK/+93vAADXXHMN6urq8Pzzz+PBBx9ETEwMAGDbtm0ef2PlypW44oor8Oyzz2LVqlXIysrq9rPuvvtuLFmyxOO1/Px8LFq0yMdvqBz8wcWKoMcLn1q6/Pk5NIcGI86sP+WBt071izNAA88+pH1PL6F21qialDV2GmCIrviypfjzkQSh3vEpWqGB9rRctFjsaO7gFU+aW2/wRfkUe45o7SoD02F1X331Fcxms1f/Tp06BQAwm13C/7Jlyzz+1rJly9De3o6DBw/2+Hkmkwn3338/7HY7du/e3eP7UlNTMWrUKI9/Q4YMCfwLy0hZg/yCiy+XPy98qt1c05fLtaJRSe+bNPgmkLHxTFiA94JEh4dQcrGXVDCyp1mnLyduq8WOJl7QJCu81/TVSNNTwKR5lZLyBhLeA6EvzzwvL8WZQxEZxrRPQzcwPcu5ubnYsGGDV+/NyMgAAPTr1w95eXlIS0vz+HlqaioAoL6+vte/k52dDQCoq6vzdbjMYrE7UNPiqnTCwqXQZrWjsd3VD4UVT5Y3HaoFy7hOLWMVGgx19AV/cuLoovcOp5MT5Rzoc/0oBXkt5YHm1X/6CsvWez6u2tB9pDxMK0fp6em4+eabffqdSZMmIS8vD6WlpRg0aJDwellZGQAgJSWl198vKCjw6n1aorLRIvy3ErkyfVlGtXpJ8dYxLR7+fal+docTVc2UUMsjXEYk6APoe0/XtlqFJtNa2tMsQh4OeeDPb5OJvJve4FskCBUMkJMyqrKoOEyH1fnDtddeCwB47bXXhNecTic2bNiAxMRETJo0CYDLM+RwODx+12az4YknnkBYWBhmz56t3KBlpkyhy9bbw9Qz9lsbm91qd6La7X3TUp6Ut1Q2W4TS1Vp5JoHgbQiOHp+1HJBALx1aLvyiND6FDbsNHinR4QgN1p3ooyplojWbFkeVKr3BnzQEMjwpB9OeI39YuHAhLr30Ujz++OOoqanBuHHj8MEHH+Cbb77BK6+8gvBw18bdtm0b1qxZg8WLF2PgwIGoq6vD5s2bcfToUaxduxbp6ekqfxPp8GwAq/7mKmMpudDLA6qquUOwnqdrSPjz9vylmPFOXGGorkIwRrcwe5tbp1VvsJKI2pX2+j7xXKbF0lx6S1/ezQoSMGWDlzGSo8MQHqJeaw6t0lvYojgNgdaucuhOOTKZTPjggw/wpz/9Ce+88w5ef/11DB8+HBs3bsTy5cuF940ZMwYjR47Exo0bUV1djbCwMIwfPx5btmzpUolO65Qp7KnpM7lQNB5WhE+fQgEZUDClpkzn3w/w3lJXIW64pyFFWE08lGudrh+l4AXNpKgwRISSoCkVVGraf7y9H2lupYfyudRBd8oRAERHR+O5557Dc8891+N7Jk2a1KWUt17hL9vYiBAmuq1XNGnv8vfoe6PBA6pvqyqFRfHoXRH2B2+rKYUGm5AcRWE1gaD3wi9qwHFcZ68/mlfJobAv+aAeR+pAgbcGgN9ccifzeVu3v0yDl1SFzgVm/plEhQUjNkJ9BVpuelMWKX/Gd8Sl+YOoAWxAVJCHQ3Ka2u1ot7lyjLVo3FIDb3exS/GkggFyQcUu1IGUIwOgdHPGvst+ujZ7eqz6G91bhY4X/qLCghHDgPfNW7zPGXE/Ew31cJKLMmoWKeB1zhov0DOwp1nF14I1Rl973uC1QU4kYLISyq0XmjrsaLW6FE+aW+kRRzLQ/CoHKUcGQBBcGLHqdHqy2NnofYUNdXasN+tSeSg3QI8aXwV9VsJQtYAWvcFq0ZvtqLnDhmYLNYD1B2+9wSzdO1qht/uxnKUCSxrFm/lN1FAagh4g5UjntFsdaGhzN1yV+eDyRmfQ6uWv9ZjqvktXa/v7SQk1M+2GXiRPh5NDZRMlZEuB2EpMQrx0KF2UyEh4rlmaW6kpI0+yKpBypHOU6nHkLRUavfwFzwoDcyg1VrsTNe4eTiysESXoTVWky8g3alsssLubZGlpT7MIVaaSB/7eCTIBqTFUMMQbvA7JpjBkWamgSoCqQMqRzlGj0kmvgidjl79w/vcyaFffG7fyoDHhz5vrrbKps4cTCbfshaGqiTfyEWt7mlW8yY+hfmO+4W2EM28kTIuNQAg1gJUU/rw0magvly94q3zya5fuZmWhU0LnlDFW6USLl39lo0X4b62M2RfEZcq11ODWV7y5jNqtDtS7w1Az6KL3Ci3uaTXpLTeGVzRJ0PSdXvM2GjqrKRLSwnvaU6LDEUqKp1/0dCa0WOxo7nClIdDaVRZayTqnUsFKJ97YQco1ePnrobRzbwJZRZO46ak2nolciOeCPEfe4RkKZuz1Eyh8Ge/k6HCEhdD1LBX8vmbBQKhFer8/yNMuFxWMGbeNBJ2+Ooe/FOLMoYpVOvGmahArl78vCh2gT9e2R7KywS+4co9mv/p71v7iTTWl8JAgJEaFKTMgndKZ20hrTyo4juusNkrz6jVeV/dsoDUrF9RWQj3Ul04JWalscoWEpTPipalgbDw8vVVzE3sTtOLtEvDihuMrjUWHhyDaIKWre+rF5fGs6TLyCn5Pp8VSj6ze8GZq+ORrzZ0zDNPYboPF7gRAoUlSw3GccGbSmvUNb05K8X1Ea1dZSDnSOVXNrs2VGqtAhR4vbv8qDR6kvPIQFRaMmIhQlUcjPZXCM6EqTrwxAdDWGpULbwt6AOwZPLSIMJckCPlMTwYP2tPy0WKxo40awAZOD7bZKi0bZjUOKUc6p1IFZaS3EBwtCuJVIsu4Vukt1FGNNaIG3ljujehFCxT+AlfEAKMDevJSt1sdaHInX+t9LypJJQmYAdPTmvVUPGn/Sw0/v0qmRRAuSDnSMXaHE9XNvGCv/sFlsXdWAmPlkvJFYNar8FepA+VPKhT1tGqInpRrjuNo/UgEv/YA6sUjJZ7KEc2rlHh4NmJo/0uNFo3JeoGUIx1T22qFuzejIiEvfekZVQxbmXr1rDRr17PSV28VjuMMqRD09LgFQZ8ueq9ottjRbnOF1bC2p7UGhX/JQ1Vz57ym0r72Gq8Mh2KFntasT3g3v2R4UgtSjnSM2GLGwsFVpcGDVO+W8fo2G2wOl6pACgFZ6s6nrwILFBPvPX3JQhT+5Tu+eP5jI0JgDqPQJCmhsDp5EUKW6W5WHFKOdIxalkivEmM1stkb222wuisd6THUxUgCWV9yFMdxusgvUxLxnqYL3Dt68lJT+Fdg9OwN1q7nnxX6WrORYcGUoxkA3eV0OZ2c4PWk80B5SDnSMR5lIBm4GFi8/PsKO9N7qAuLz0QtGtpssDrcirAOn3Ug9JyQTetHKnhBKCwkCHFm/VXFVAs9e/7VporK+MtGbasVDndeBK1d5SHlSMfwLlmTCUiOlr85Y19nI6+shQabkBDJVrPInqyOmu5xhL6fiZE8R2K6s4RWkKDvMxWMhe5qmc4eR+EkaEqI3gvqyIU3a1CYWx1GVagNGZ7UhZQjHcNvruTocIQEq/+oeStTakwEgoK0cfnr/YASe8ZSDH7BGVVR7A1vi6xQ6fO+6UvYFMK/KDzRa/qaU8/QJJpXqdFysSK18SUHkQxPyqO+xEzIRmc4ARtCL4sWvL6MY+KEcy3nVPScB+b6fvGRBuij0GdxAe3lxKkNi3uadXryUpMQHyDdTKxHaJLBjT9S41msiOY2ELq7nvUe0s86pBzpGKU713trCWFR8OyriIReKx1R6epOPC11dNmL6Sshm9ZPYLgETVI0pYa8wdLQ3fYXFyuiuZUe8dpNiaYzQWlIOdIxnZetsgdXT4JUlQatTFqvdNRnWJQBexwB3RcY4ENEqBu595DlWBpaLHa0Wfl+Udo8a1hEi+0jtIJHpUqaW8nh125SVBjCQkhUVxqacZ1isTtQ32YDwIZVt9ViR7PFDkBbB6nem7BpXfmTEhL0u9JbJKK4gTCtn8CgfjH+0Xe0As2rXHh45Shk0Wf6zkF052jT2aoKpBzplCoVLoXeNru4SzlLglTfCef68Kx058xzODlUG6iPgrfPmqX1yTLiBsJ0gQeGRzNdBoxZeqFSJzmjLEIhi/JCDcnVhZQjnSIOJ0iLUzisrruQJcZ6Lp1Pd8qDuNIRi2MOlNoWC9y5yrr8fr5C/VB8g/U9zSrdJl+reF7rhe7vHdeeptCkAOlm0bJq8NQi3ckf/Nqls1Ud6LTQKRWNbFXe0mJJbD00YevNc2/omPHzbiOHk0N1i3G8aL7SfTUl7e1pVqHKVPJQpVLerRHg979eixWpic3hRG0rhdWpCSlHOoU1waVKg4I4a3MoNRQW0Ultq0XzirDSVJFALxn8XowKC6Z+UT7QZ5PrZgpNCoTejWsUhhwIvc1tTYtFMEjR2lUHUo50Cn8phAabkBAZpshn9nZP8QdpRGgQYiMYuvx7zZPST6WjPkN5DHAA93YZeSjvDHha2aHnSfMoNUsJ2X3izfojQVNaqFWBfFAYsnxUUs891SHlSKfwl21qTASCgvpKRZeW7gXxzoO0ryotamDEJmz89zOZgGSD91HQu5dQDnjl2hANhCWkt5xMrRd+UZPzz3C7w4kaCpWVhO5yYvRSrIgFzu+zSFEd6kPKkU5hrX+NFptF6qEJm6kXyz9/uSVFhSM02FhHwfmXvd4V4UDpLWFYS3uaVSqpJLrk1LRYBYVJ655/1hAXK6I1Kz1VZKxTHWNJRAaCL9GspFDfe9gIW8qaN+ip0lFv1mo6fClEzB+0uKdZhOM4ClGSAbK+B05PV3pdmxV2PkeTzkvJ4c+DIBOQpFHDrNbRtsRH9IigHKlwcPUWosZaPkdvAX688KdXYbnzmejz+/mCuBu50bxoveFNtUPW9jSr9DSXje02WO1OALQXfaU3z7hnjyOaVynxmFtSPP3Cmxzt5OhwBCucFkG4IClAh9gcTtS32QCwIdi3WuxotzkAsDEeb+Hj1bU0Zl/Q+/c7n94EqepmKwDjzEWgOJ2cUGqW5sxHzjMe8fsQoLkMhPNtcjUtVuG/aV6lheZWWrquXTpb1YaUIx1Sq9LB1ZPwWd2szctfTe+bVPRkrXYJt6QQ8FTTZdQr5ycMN7bbYHO4XqM5CwxxM02t5jayiPjeSYpWpmKrXjk/GqSa1qys0H2kPqQc6RDxwaVGFbLz81vEltFkxi6pnpQHjuME65geDv/zL7f6ts4Gt0asVHf+fNS494wR58IfWN7TWkNshU8mYUgy+DUaZw5FeAhVU5QSj/1Pa1ZyatyRDHQfqQcpRzqkuoWt5HIteo6a2u2wOlx5AFoZsy9UUyiPAMdxZKnrgZ4CEbW4p9XGK886CUM+0VtOnB48/2rTU9sNfm7NocGICiPF0y96mFunk6OwOgYg5UiHsHbZesTUMzCenhCHDlW3qOt9kxveMgXo8/t1R0+CVLPFLiTEkxfEO6o1sqe1AH8+hgSZEGcOVXk0+oGfV9rT0iPMbUwYk30LtUxju02oBGiUu5lFSDnSIapZdXs4I/nxmExAYpQ2Liq9WMZ7urdY8y6qiV6etZLQnPnP+cnX1aKQTqUbduuJLnkxgvWdqqkFyvmh8mq0CtEz4rVLUR1sQMqRDuFj2GPCQ1TpXN/1knKNJzEyDCGMlUnuMdRFZ56jLtVwDOg5EiO+7GtUztHTIvz+CA0mb0egCMpRjDYMR1qhU+mkeZWaasrRlA3PnHFau2rBlqRKSAJrsdasjacnxEpdjc4t47xwGxYShNiIEJVHoy5kqeuZnjyP4oRhCqvxjh7nsoWs8P7S08prtdjRZtVe+wjW6Gl+KScmcPqaW4D6c6kJKUc6pNMSqezG6muza8nKxAvMwUEmxOvQMl4jCoswinDb4/okz5HPVGtwT7MKWeGlp0Znnn+WEPdRpLmVHrWrDRMuSDnSIWpX3uoppl5LVqYaUUiGtvMAeg8bpDKsnopwQiSFMXhDjQb3NCuIC79QvzHp8AiVJW+wpIijKtTqo6hnxGuXQpbZgJQjHVLDULKkq18Qu7HfPRcs0Jc1t6cmfikMPhMl8AyhdOfERYUhWNOKsHz0lOzO4p7WEkbvNyYXrFVs1RPk2ZAXsSfZKFEdLELKkc5otzrQbLEDUN6q090+brbYYbFro1+QWP7Te0y13r+fL1RTzodPOJwc6sjbIQk1ZIWXhWqaV0no7k4nr5y8CM3naW5VhZQjncFET6EeChtoycqk5zwAsXCrx+/XE30lxFOIYVe6q+ZI3g7/6G75kRU+QPpoHwFop32EViCvnDT0GLWiY9lDS5BypDOqGKuyxno/lO7OJ6eTE+KqWRyzL3R3ANe2WuCWbTX//aSAenb4BlmOpYPmUh74eU2MCkMoY+0jtI5HmwsqPy85VL2SDejU0BmsKSPisBGtWEIadNmhWtzXR3vPRGr42fDIiaOLvkc8EobJ2xEQ4vBdssJLh0cjTepxJCndrdmosGBEhhm7DYRkuCfY4eRQS/cRE5BypDPUtER2F4JT3dwh/DcLylpv8FWk9G7Npb4+nTS222BzuJ47Cafeoff9oST8XIYFByHWTIKmVFBOpXzQ3MpHfZu1M6qD7iNVIeVIZ7AQa+1ZUtXlpdBSmWS9W3NrdP79eqI75Z0E/d7pLiyTNe+0Vuiu8pS4zQFVpvKd7vY0QKGyUtG9wZOUIynobW4BICUmQsnhEOdBypHOqGYs1prf7KyWSe5b+NOGQtcT3SaBe8SMG/uCqzKoohgI/P4ICwlCTDh5OwKBSqJLD8dxlNQuI3prc8ESniHLdCaoifrSMyEpalrMeiv7qSXB07Pinz6sN1w3FQTNocGICgtWaUTq0hlCSSV/vcFj/fDFSqgPh190lxtDay9w+Glt0VD7CK3Q3f1Bcysd/PRSJAM76E45mjVrFkwmU7f/QkO7dhvetm0bJk6ciIiICPTv3x+PPPII7Ha7CiOXBhbigT0ufw2VSeaHLVjGdZoHUC1K+DS6cEvFBXxHsMprYE+zTg1Z4SWH9rR8dNgcaOpwyUc0t9LjsXbpfFUV3Ul+f/zjH3Hrrbd6vNba2oo777wT8+bN83j9k08+waJFizBr1iy88MILOHLkCNasWYOqqiqsX79eyWFLBmtVemo0GPstDnXRo/KgRW+eJPTi2QwNNiHO3NV4YnT04g1mgfOnkprpBk7365O8wZJx3vzWttLcSkVvZ2s4hSyrju5m/7LLLuvy2saNGwEAy5cv93j9gQcewNixY/H5558jJMQ1FbGxsVi7di3uvfde5Obmyj9gCRHHWqtxcJ2/2V1lkt3NRhnN3ektSVoPlpvecqrI8tc5F0lR4QhiMCeORTrPGDb3tFYQ9xujvSgd5DmSD5pbeRHfzXo0zGoJ3YXVdcfmzZsRFRWFhQsXCq8dP34cx48fx+233y4oRgBw9913g+M4vPvuu2oMNSCsDifm5KZi8oAEDEmNVm0crVYHjpc1Yf+5elgd7thvDRykJ8qbcLysCWUN7QC0MWZvsTmcOF7m+n5VFDOOvKoWHC9rQlFtGwBjz4U3dNhde/poaSPq2jpzjgjfaWy34XhZE/YV1guv0foLnPKGdhwva8KxskbhNZpXaahpseB4WRMOFzcIr9HcSsfpymbXfVRH9xEr6M5zdD7V1dXYvn07rr32WkRFRQmvHzx4EAAwefJkj/f369cPWVlZws+7o6qqCtXV1R6v5efnSzhq/wgPCcb6FZPUHgZOlDfhyv/3tcdrWtjsC1781uP/9WQZa+qwd3kmevp+vrJyw48e/89KGCqrFNe1d10/GtjTLPLl6Wp8edrz/jDyXpSKdbvPYN3uM8L/B5nUa2ehN97dX4J395d4vEZnpnTc8NoPHv9P54H66N5z9M4778But3cJqSsvLwcAZGRkdPmdjIwMlJWV9fg3161bh9GjR3v8W7RokaTj1iIjM2K7fT3IBIzLild2MF7S05gBYNKABAVHIg8jM+J6/Jkevp8v6P1Zy0FvczaxP82ZL4zo1/1cRoUFY3hajMKj0Qfx5lBkxHVfUXR8djyT7SO0RE/7PzPejPRYfVRyVYsRdB8xjYnjxLXF2MLpdMJqtfb9RgDh4d3HaE6fPh35+fkoKyvzCJ/761//iocffhiVlZVITU31+J2ZM2eiqakJhw4d6vazevIcLVq0CEePHsWoUaO8GrPesDuc2Hu2Ds0dntX+hqfHYGByVA+/pT5HSxtRUt/u8VpKTDgm9o/XfNwvx3E4UNTgESsOAFkJZozO7Flx0iv5Vc3Ir2r1eC3OHIopAxNJkOoGp5PDD4V1aGizebw+OCUKQ0mg9wmL3YHvztTCYnN6vD4uOw4ZcWaVRqV96lut+KGwzqNKakiQCRcOTkI0JbUHRKvFjj1namF3dk6uyQRckJNIXjkJOF3ZjIJqz/soITIUF+QkUg6shBw7dgyjR4/2ST5n+uT46quvMHv2bK/ee+LEiS4FFAoKCrBnzx783//9n4diBABms+syslg8hUYA6OjoEH7eHampqV0UKgIICQ7CRUOS1R6Gz4zOjNOtomAymcgKJWJIagyGpJJQ7y1BQSZcOChJ7WHogvCQYMweTveG1CREheHyUelqD0OXRIWHYO7INLWHoVuGpcVgGBmZmIRp5Sg3NxcbNmzw6r3dhcdt3rwZQNcqdeL3l5eXIzs72+Nn5eXlmDJliq/DJQiCIAiCIAhCwzCtHKWnp+Pmm2/2+/c3b96MwYMH48ILL+zys/HjxwMA9u3b56EIlZWVoaSkBLfffrvfn0sQBEEQBEEQhPbQbUGGgwcP4sSJE7j++uu7/fmoUaOQm5uLV199FQ6HQ3h9/fr1MJlMWLx4sVJDJQiCIAiCIAiCAZj2HAXCpk2bAHQfUsfz1FNPYcGCBZg3bx6uu+46HD16FC+++CJuvfVWjBgxQqmhEgRBEARBEATBALr0HDmdTrz99tuYOHEihg8f3uP7rrrqKmzduhV1dXVYtWoVtm7digcffBAvvfSSgqMlCIIgCIIgCIIFdOk5CgoKQklJSd9vBLBo0SLqUUQQBEEQBEEQhD49RwRBEARBEARBEL5CyhFBEARBEARBEARIOSIIgiAIgiAIggCg05wjNbBYLACA/Px8lUdCEARBEARBEAQvl/NyujeQciQRxcXFAEDFHQiCIAiCIAiCIYqLizFx4kSv3mviOI6TeTyGoKGhAV9++SWys7MRHh6u2jjy8/OxaNEifPDBBxgyZIhq4yD8h56h9qFnqH3oGeoDeo7ah56h9lHzGVosFhQXF+OSSy5BfHy8V79DniOJiI+Px8KFC9UehsCQIUMwatQotYdBBAA9Q+1Dz1D70DPUB/QctQ89Q+2j1jP01mPEQwUZCIIgCIIgCIIgQMoRQRAEQRAEQRAEAFKOCIIgCIIgCIIgAJBypDtSUlLwyCOPICUlRe2hEH5Cz1D70DPUPvQM9QE9R+1Dz1D7aO0ZUrU6giAIgiAIgiAIkOeIIAiCIAiCIAgCAClHBEEQBEEQBEEQAEg5IgiCIAiCIAiCAEDKEUEQBEEQBEEQBABSjgiCIAiCIAiCIACQcqQbLBYLfve736Ffv34wm82YOnUqtm/frvawiG7YvXs3TCZTt/++//57j/d+9913mDFjBiIjI5Geno5f/epXaGlpUWnkxqSlpQWPPPII5s+fj8TERJhMJrz++uvdvvfEiROYP38+oqOjkZiYiBtuuAHV1dVd3ud0OvG3v/0NAwcOREREBMaOHYt///vfMn8TY+Ptc7z55pu73Zu5ubld3kvPUTl+/PFH/N///R9GjRqFqKgo9O/fH0uXLsXp06e7vJf2IZt4+wxpD7LLsWPHsGTJEgwaNAiRkZFITk7GzJkz8d///rfLe7W8D0NU/XRCMm6++Wa8++67uO+++zB06FC8/vrruPLKK7Fr1y7MmDFD7eER3fCrX/0KF1xwgcdrQ4YMEf770KFDuPTSSzFixAg8++yzKCkpwdNPP428vDx88sknSg/XsNTU1ODRRx9F//79MW7cOOzevbvb95WUlGDmzJmIi4vD2rVr0dLSgqeffhpHjhzBDz/8gLCwMOG9f/zjH/HEE0/gtttuwwUXXIAPP/wQ119/PUwmE6677jqFvpmx8PY5AkB4eDj++c9/erwWFxfX5X30HJXjySefxLfffoslS5Zg7NixqKiowIsvvoiJEyfi+++/x+jRowHQPmQZb58hQHuQVc6dO4fm5mbcdNNN6NevH9ra2vDee+9hwYIFeOWVV3D77bcD0ME+5AjNs3fvXg4A99RTTwmvtbe3c4MHD+amTZum4siI7ti1axcHgPvPf/7T6/uuuOIKLiMjg2tsbBRe+8c//sEB4D777DO5h0m46ejo4MrLyzmO47gff/yRA8Bt2LChy/vuuusuzmw2c+fOnRNe2759OweAe+WVV4TXSkpKuNDQUO6ee+4RXnM6ndzFF1/MZWVlcXa7Xb4vY2C8fY433XQTFxUV1effo+eoLN9++y1nsVg8Xjt9+jQXHh7OLV++XHiN9iG7ePsMaQ9qC7vdzo0bN44bPny48JrW9yGF1emAd999F8HBwYLGDgARERH45S9/iT179qC4uFjF0RG90dzcDLvd3uX1pqYmbN++HStWrEBsbKzw+o033ojo6Ghs2bJFyWEamvDwcKSnp/f5vvfeew9XXXUV+vfvL7w2d+5cDBs2zON5ffjhh7DZbLj77ruF10wmE+666y6UlJRgz5490n4BAoD3z5HH4XCgqampx5/Tc1SW6dOne1ibAWDo0KEYNWoUTpw4IbxG+5BdvH2GPLQHtUFwcDCys7PR0NAgvKb1fUjKkQ44ePAghg0b5iFEA8CUKVMAuMKzCPZYuXIlYmNjERERgdmzZ2Pfvn3Cz44cOQK73Y7Jkyd7/E5YWBjGjx+PgwcPKj1cohdKS0tRVVXV5XkBrn0ofl4HDx5EVFQURowY0eV9/M8JdWlra0NsbCzi4uKQmJiIe+65p0uuHz1H9eE4DpWVlUhOTgZA+1CLnP8MeWgPsk1raytqampw5swZ/P3vf8cnn3yCSy+9FIA+9iHlHOmA8vJyZGRkdHmdf62srEzpIRG9EBYWhmuuuQZXXnklkpOTcfz4cTz99NO4+OKL8d1332HChAkoLy8HgB6f69dff630sIle6Ot51dXVwWKxIDw8HOXl5UhLS4PJZOryPoD2q9pkZGTgt7/9LSZOnAin04lPP/0U69atw+HDh7F7926EhLiuTXqO6rNp0yaUlpbi0UcfBUD7UIuc/wwB2oNaYPXq1XjllVcAAEFBQfjFL36BF198EYA+9iEpRzqgvb0d4eHhXV6PiIgQfk6ww/Tp0zF9+nTh/xcsWIDFixdj7Nix+MMf/oBPP/1UeGY9PVd6pmzR1/Pi3xMeHk77lXEef/xxj/+/7rrrMGzYMPzxj3/Eu+++KyQI03NUl5MnT+Kee+7BtGnTcNNNNwGgfag1unuGAO1BLXDfffdh8eLFKCsrw5YtW+BwOGC1WgHoYx9SWJ0OMJvNsFgsXV7v6OgQfk6wzZAhQ7Bw4ULs2rULDodDeGY9PVd6pmzR1/MSv4f2q/a4//77ERQUhB07dgiv0XNUj4qKCvzsZz9DXFyckHML0D7UEj09w56gPcgWubm5mDt3Lm688Ub873//Q0tLC66++mpwHKeLfUjKkQ7IyMgQ3Jhi+Nf69eun9JAIP8jOzobVakVra6vgUu7pudIzZYu+nldiYqJgHcvIyEBFRQU4juvyPoD2K4uYzWYkJSWhrq5OeI2eozo0NjbiiiuuQENDAz799FOPeaZ9qA16e4Y9QXuQbRYvXowff/wRp0+f1sU+JOVIB4wfPx6nT5/uUtVl7969ws8J9ikoKEBERASio6MxevRohISEeBRpAACr1YpDhw7RM2WMzMxMpKSkdHleAPDDDz94PK/x48ejra2tS3Um2q/s0tzcjJqaGqSkpAiv0XNUno6ODlx99dU4ffo0/ve//2HkyJEeP6d9yD59PcOeoD3INnz4W2Njoz72oSoFxAlJ+f7777v0Oero6OCGDBnCTZ06VcWREd1RVVXV5bVDhw5xoaGh3IIFC4TX5s+fz2VkZHBNTU3Ca//85z85ANwnn3yiyFgJT3rrj3PnnXdyZrOZKyoqEl7bsWMHB4Bbv3698FpxcXGPfR0yMzOpN4cC9PQc29vbPfYbz29+8xsOALd161bhNXqOymK327kFCxZwISEh3EcffdTj+2gfsos3z5D2INtUVlZ2ec1qtXITJ07kzGYz19zczHGc9vchFWTQAVOnTsWSJUvwhz/8AVVVVRgyZAjeeOMNFBYW4rXXXlN7eMR5XHvttTCbzZg+fTpSU1Nx/PhxvPrqq4iMjMQTTzwhvO+xxx7D9OnTcckll+D2229HSUkJnnnmGcybNw/z589X8RsYjxdffBENDQ1C5Zz//ve/KCkpAQCsWrUKcXFxePDBB/Gf//wHs2fPxr333ouWlhY89dRTGDNmDFauXCn8raysLNx333146qmnYLPZcMEFF+CDDz7A119/jU2bNvUZe0/4T1/Psb6+HhMmTMCyZcuQm5sLAPjss8/w8ccfY/78+Vi4cKHwt+g5Ksvq1auxbds2XH311airq8PGjRs9fr5ixQoAoH3IMN48w4qKCtqDDHPHHXegqakJM2fORGZmJioqKrBp0yacPHkSzzzzDKKjowHoYB+qopIRktPe3s498MADXHp6OhceHs5dcMEF3Keffqr2sIhueP7557kpU6ZwiYmJXEhICJeRkcGtWLGCy8vL6/Ler7/+mps+fToXERHBpaSkcPfcc0+3VjVCXgYMGMAB6Pbf2bNnhfcdPXqUmzdvHhcZGcnFx8dzy5cv5yoqKrr8PYfDwa1du5YbMGAAFxYWxo0aNYrbuHGjgt/ImPT1HOvr67kVK1ZwQ4YM4SIjI7nw8HBu1KhR3Nq1azmr1drl79FzVI5LLrmkx2d3vihD+5BNvHmGtAfZ5t///jc3d+5cLi0tjQsJCeESEhK4uXPnch9++GGX92p5H5o47rwsKIIgCIIgCIIgCANCBRkIgiAIgiAIgiBAyhFBEARBEARBEAQAUo4IgiAIgiAIgiAAkHJEEARBEARBEAQBgJQjgiAIgiAIgiAIAKQcEQRBEARBEARBACDliCAIgiAIgiAIAgApRwRBEARBEARBEABIOSIIgiAIgiAIggBAyhFBEARBEARBEAQAUo4IgiAIRrn55puRk5Oj9jAE/vznP8NkMsFkMiE6Olrxzx8/frzw+VdddZXin08QBGEEQtQeAEEQBGEcTCaTV+/btWuXzCPxn7feeguhoaGKf+7atWtRV1eH+++/X/HPJgiCMAqkHBEEQRCK8dZbb3n8/5tvvont27d3eX3EiBH4xz/+AafTqeTwvGLFihWqfO6VV14JAPjTn/6kyucTBEEYAVKOCIIgCMU4X7H4/vvvsX37dtUUDoIgCIIQQzlHBEEQBJOcn3NUWFgIk8mEp59+Gi+99BIGDRqEyMhIzJs3D8XFxeA4Dn/961+RlZUFs9mMhQsXoq6ursvf/eSTT3DxxRcjKioKMTEx+NnPfoZjx44FNNacnBxcddVV2L17NyZPngyz2YwxY8Zg9+7dAICtW7dizJgxiIiIwKRJk3Dw4EGP36+oqMDKlSuRlZWF8PBwZGRkYOHChSgsLAxoXARBEIRvkOeIIAiC0BSbNm2C1WrFqlWrUFdXh7/97W9YunQp5syZg927d+N3v/sd8vPz8cILL+CBBx7Av/71L+F333rrLdx00024/PLL8eSTT6KtrQ3r16/HjBkzcPDgwYAKQOTn5+P666/HHXfcgRUrVuDpp5/G1VdfjZdffhkPPvgg7r77bgDA448/jqVLl+LUqVMICnLZKK+55hocO3YMq1atQk5ODqqqqrB9+3YUFRUxVZSCIAhC75ByRBAEQWiK0tJS5OXlIS4uDgDgcDjw+OOPo729Hfv27UNIiOtqq66uxqZNm7B+/XqEh4ejpaUFv/rVr3Drrbfi1VdfFf7eTTfdhOHDh2Pt2rUer/vKqVOn8N1332HatGkAgJEjR+Lyyy/HbbfdhpMnT6J///4AgISEBNxxxx346quvMGvWLDQ0NOC7777DU089hQceeED4e3/4wx/8HgtBEAThHxRWRxAEQWiKJUuWCIoRAEydOhWAK5+JV4z4161WK0pLSwEA27dvR0NDA5YtW4aamhrhX3BwMKZOnRpwhbyRI0cKipF4XHPmzBEUI/HrBQUFAACz2YywsDDs3r0b9fX1AY2BIAiCCAzyHBEEQRCaQqxoABAUpezs7G5f5xWOvLw8AC5lpTtiY2NVGVd4eDiefPJJrF69Gmlpabjwwgtx1VVX4cYbb0R6enpAYyIIgiB8g5QjgiAIQlMEBwf79DrHcQAglAV/6623ulU6xF4nJccFAPfddx+uvvpqfPDBB/jss8/w0EMP4fHHH8cXX3yBCRMmBDQugiAIwntIOSIIgiAMweDBgwEAqampmDt3rsqj6crgwYOxevVqrF69Gnl5eRg/fjyeeeYZbNy4Ue2hEQRBGAbKOSIIgiAMweWXX47Y2FisXbsWNputy8+rq6tVGBXQ1taGjo4Oj9cGDx6MmJgYWCwWVcZEEARhVMhzRBAEQRiC2NhYrF+/HjfccAMmTpyI6667DikpKSgqKsJHH32Eiy66CC+++KLi4zp9+jQuvfRSLF26FCNHjkRISAjef/99VFZW4rrrrlN8PARBEEaGlCOCIAjCMFx//fXo168fnnjiCTz11FOwWCzIzMzExRdfjJUrV6oypuzsbCxbtgw7d+7EW2+9hZCQEOTm5mLLli245pprVBkTQRCEUTFx4oxQgiAIgiC65c9//jP+8pe/oLq6GiaTCUlJSYp+fkNDA+x2OyZOnIixY8fif//7n6KfTxAEYQQo54ggCIIgfCAlJQUDBgxQ/HNnzZqFlJQUFBcXK/7ZBEEQRoE8RwRBEAThBQUFBULj1pCQEMyaNUvRz9+7dy+am5sBuBS0cePGKfr5BEEQRoCUI4IgCIIgCIIgCFBYHUEQBEEQBEEQBABSjgiCIAiCIAiCIACQckQQBEEQBEEQBAGAlCOCIAiCIAiCIAgApBwRBEEQBEEQBEEAIOWIIAiCIAiCIAgCAClHBEEQBEEQBEEQAEg5IgiCIAiCIAiCAEDKEUEQBEEQBEEQBABSjgiCIAiCIAiCIACQckQQBEEQBEEQBAEA+P/I7n0ymdwW2gAAAABJRU5ErkJggg==\n", 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" ] @@ -819,7 +798,7 @@ } ], "source": [ - "evaluate_neuron(neuron_model_name_adapt_thresh)" + "evaluate_neuron(neuron_model_name_adapt_thresh, module_name_adapt_thresh)" ] }, { @@ -851,7 +830,7 @@ "metadata": {}, "outputs": [], "source": [ - "def measure_fI_curve(I_stim_vec, neuron_model_name):\n", + "def measure_fI_curve(I_stim_vec, neuron_model_name, module_name):\n", " r\"\"\"For a range of stimulation currents ``I_stim_vec``, measure the steady\n", " state firing rate of the neuron ``neuron_model_name`` and return them as a\n", " vector ``rate_testant`` of the same size as ``I_stim_vec``.\"\"\"\n", @@ -860,6 +839,7 @@ " rate_testant = float(\"nan\") * np.ones_like(I_stim_vec)\n", " for i, I_stim in enumerate(I_stim_vec):\n", " nest.ResetKernel()\n", + " nest.Install(module_name)\n", " neuron = nest.Create(neuron_model_name)\n", "\n", " dc = nest.Create(\"dc_generator\", params={\"amplitude\": I_stim * 1E12}) # 1E12: convert A to pA\n", @@ -886,21 +866,24 @@ " marker = \"o\"\n", " else:\n", " marker = None\n", + "\n", " fig, ax = plt.subplots()\n", " ax = [ax]\n", " for label, rate_vec in label_to_rate_vec.items():\n", " ax[0].plot(I_stim_vec * 1E12, rate_vec, marker=marker, label=label)\n", + "\n", " for _ax in ax:\n", " _ax.legend(loc='upper right')\n", " _ax.grid()\n", " _ax.set_ylabel(\"Firing rate [Hz]\")\n", + "\n", " ax[0].set_xlabel(\"$I_{inj}$ [pA]\")\n", " plt.tight_layout()\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -908,736 +891,910 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:10 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:10 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:10 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:10 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:10 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:10 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:10 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:10 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:10 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:10 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:10 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:11 Install [Info]: \n", + " loaded module nestml_4e02d9e66e71411b95e21d3a31d61a56_module\n", + "\n", + "Apr 19 11:16:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:48 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:48 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", + " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:12 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_9c5da0c0943741c7b8b8c6f3f2696680_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:13 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:14 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:14 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:14 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:14 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:14 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:14 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:52:49 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:16:14 Install [Info]: \n", + " loaded module nestml_0caaf47dae6c4cb6bdf8977ac5d3c855_module\n", + "\n", + "Apr 19 11:16:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:52:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:16:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:52:49 SimulationManager::run [Info]: \n", + "Apr 19 11:16:14 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, { "data": { - "image/png": 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GcZOfwCuvr0ZrDyd4OMj0VmUr7Nq1Cy+99BKWLl2K8vJyDBgwAIcPH8bYsWN1xoWEhOD333/HkiVLMGvWLLi4uGDGjBmYMmUK5s+frx3n5uaGn376CUuWLMEzzzwDOzs7TJgwAXv27EHPnj313l8qleLgwYNYvHgx1q9fD7lcjueeew4bNmyo9/e2MYmEquWtqE6xsbEICQnB5cuXERxcv75VRERERESW6tatWwCAdu3aNXEkhhMEAaXlauQXK5FfUo6isnK9MSKRCHbWVnCUS+FoI4G1RL9gU23UgoCiUhVUghpWIs0zsjUlsc3B7NmzsW/fPigUikZ7z7ruHWNzLa7MEhERERFRs1afRFFzTTnyS8qRX6JEWblab4xELIKDjSZ5tbeRwEpc/5VgsUgEexumV42J320iIiIiImqW1IKAjIJSZCnKtBWEAU11YDd7a70tvOUqNRSl5cgvVqKgtFxn23AFmcQKjnIJHG2ksLW20hZNIsvDZJaIiIiIiJodtSAgMasIBSVKvXPlajXS8ktQXKaCt5MNCu6vvhaVqiA80LVWBBFsZfeff7WRQCY1bvtwS7V9+3Zs3769qcNoECazRERERETU7GQUlFabyFaVX6JEfjVjrEQiONhI4CiXwl4mgaQJetSS+TGZJSIiIiKiZkUtCMhSlBl1jbVErF19tZVJmnXxJTINJrNERERERNSsFJaW6zwjWxc/F1u42Er5/OtDhsksERERERE1ubJyFQpKyqEoLUdBiX7rnNpYicFE9iHEZJaIiIiIiBqdSq2GolQFRUk5FKVKlFbTOsdQViI+E/swYjJLRERERERmJwgCipX3V19LylFUpl95GNC03bGXWaGgpBwqQf98deNtZaxQ/DBiMktERERERGZRVq6GolSp3T5cXd9XkUgEO2sr2NtI4CCTwEaq6f2all+CtPySOt/Dzd6axZ4eUlyPJyIiIiIik1CpBeQXK3E3txjXUgsQl5qPOznFyCtW6iSyMokV3O1lCHC3Q5CPI9p52MPT1grylGiIrv4I3D4BD1tNdeLaONpI4eEgM/nnuHTpEubMmYO2bdvCxsYG9vb26NmzJ959911kZ2eb/P0a0/bt2yESiZCQkNDUoTQYV2aJiIiIiB5iSpUaZxNykFdcBie5NXoHuEBqYF9WQRBQolShoFSzdbiwTAWhmq3BVmIR7GUSONho+r5aS6rMr1ICxz4ETm8BCjO0h8X2nmjT+1lkdH8eWcWCTnVjiVgMN3treDjITL4q+/nnn+P5559Hp06d8OqrryIoKAhKpRJnz57F5s2bER0djf3795v0Pal+mMwSERERET2ElCo1Pj16EzuiE5BZpaerh70MM8L8sSg8sNqktkSpglgkQlJ2ERQl1bfQEUEEW2srONhIYG8jgfz+1mE9KiWwezpw4zcAD5xXZEB89G14pfwNjye/RlG5GCpBDSuR5hlZc2wtjo6OxqJFizBixAgcOHAAMlnlqu+IESOwZMkS/PLLLyZ5r+LiYtjY2FT7fSkqKoKtra1J3qcl4zZjIiIiIqKHjFKlxvwdZ/HB4evIqpLIAkCmohQfHL6OBV/9DaVKjRKlCiduZOCtn65g1EfHceRqGorKypFbVKaTyMokYrjZyxDgZocgXwcEetrD09EGttaSmtvm/PHh/UQWgF4xqPuvb/wK8Z8fw95GAie5NextJGZ7Rvbtt9+GSCTCli1bdBLZCtbW1nj88ce1r0UiEdasWaM3LiAgALNnz9a+rtja+9tvv2Hu3Lnw8PCAra0tSktLER4ejpCQEBw/fhz9+/eHra0t5s6dW2OMZ8+eRUREBAICAiCXyxEQEICnnnoKiYmJemNPnjyJAQMGwMbGBr6+vnj99dehVCr1xu3ZswcjR46Ej48P5HI5unTpgmXLlqGwsFBn3OzZs2Fvb4/Y2FgMHz4cdnZ28PDwwAsvvICioqIaYzYXrswSERERET1kPj16E1HXNFt6a0ghERmXjkc/OIbUvBKdtjljA5wAVG4dtpdpVl9lEiMrCquUmq3FEFUTRVUi4MwWYODLgFXtz9A2hEqlQmRkJHr16oXWrVub5T3mzp2LsWPH4quvvkJhYSGkUs3nuXfvHp555hksXboUb7/9NsTimtccExIS0KlTJ0RERMDV1RX37t3Dp59+ij59+uDKlStwd3cHAFy5cgXDhw9HQEAAtm/fDltbW2zatAm7du3Sm/PGjRsYM2YMXn75ZdjZ2SEuLg7vvPMOTp8+jcjISJ2xSqUSY8aMwYIFC7Bs2TL89ddfWL9+PRITE/Hjjz+a8LtVt2aXzBYUFGDdunW4cOECzp8/j8zMTKxevVrnNx4qlQoff/wxfvvtN1y+fBnZ2dnw9/fHhAkTsGzZMjg7O+vMWdNvgv71r39h2bJlZvw0RERERETNi1Klxo7ohDpTSABIzKpcbROLgNDWzujgaQ97mQQdfBz1f84+tAxIjTEskJJcnWdkayYAinRgSzhg42zY3ADg3RUY/W+Dh2dmZqKoqAht27Y1/D2MNHz4cHz22Wd6x7Ozs7F3714MGzaszjmmTp2KqVOnal+rVCqMGzcOXl5e2LVrFxYvXgwAePPNNyEIAiIjI+Hl5QUAGDt2LEJCQvTmXLFihfbvgiBgwIAB6NKlC4YMGYJLly6hW7du2vNlZWVYsmSJ9n1GjBgBqVSK5cuX488//8SAAQMM/G40XLNLZrOysrBlyxZ0794dEydOxNatW/XGFBcXY82aNXjqqafw7LPPwt3dHefOncP69evx448/4uzZs5DL5TrXTJ06FUuWLNE51qZNG7N+FiIiIiKi5uZsQo7OM7J1GdbZE0/09kNYoDuc5FLcunULQA0LRqkxQOIfpgpVV9pl88zbiKZMmVLtcRcXF4MSWQBQKBRYt24dvvvuOyQkJEClUmnPXb16Vfv3qKgoDB8+XJvIAoCVlRWefPJJrF27VmfOW7duYcWKFYiMjER6erpOEa+rV6/qJLMA8PTTT+u8nj59OpYvX46oqKiHO5n19/dHTk4ORCIRMjMzq01m5XI5bt++DTc3N+2x8PBwtGnTBtOmTcN3332HZ555RucaLy8v9OvXz+zxExERERE1V0qVGiduGLIaWumJ3n4YFeJj2GDvroZPXJJrXILqFWL8yqwR3N3dYWtri9u3bxt1nTF8fKr/PtZ0vDrTp0/HkSNHsHLlSvTp0weOjpoV8jFjxqC4uFg7LisrC97e3nrXP3hMoVBg0KBBsLGxwfr169GxY0fY2toiOTkZkydP1pkTACQSiU4eVnXOrKwsgz+HKTS7ZLbGh8OrsLKy0vsGAkDfvn0BAMnJySaPi4iIiIjIEgmCgPPJuThwPgUHL91DdqHhq7IA4CS3NnywEdt6oVICH3QBCjNR5zOz9h7A/KNmfWbWysoKw4cPx6FDh3Dnzh34+fnVeY1MJkNpaane8ZqSuppyHUNyIADIy8vDwYMHsXr1ap3HJUtLS/X637q5uSE1NVVvjgePRUZG4u7duzh69CiGDBmiPZ6bm1ttDOXl5cjKytLJxyrmrC5HM6cWVc244uHk4OBgvXO7du2CXC6HTCZDr1698L///a/O+dLT0xEbG6vzFR8fb/K4iYiIiIhM7XZmIT48fB3h7x3F5E1/YUd0olGJrAiaNj29A1zME6CVFOg7H3U/uSsAfeabNZGt8Prrr0MQBDz33HMoK9P/XimVSp0iRwEBAbh06ZLOmMjISCgUCrPEJxKJIAiCXqXlrVu36mw3BoChQ4fiyJEjSEtL0x5TqVTYs2eP3pwA9Oas7tneCjt37tR5XVFUKjw83LAPYiLNbmW2vlJSUrBs2TL07t0b48aN0zk3ffp0jB07Fq1bt0Z6ejq2bduGuXPn4tatW1i3bl2Nc27atElvPzkRERERUXOVqSjFwYt3sf/CXVxMztU5JxYBAzt4YGKoL25lFOK/UbUv0ggAZob5V9tr1mQGvgLcOQvc+BX6VY3vv+7wmKaScSMICwvDp59+iueffx69evXCokWLEBwcDKVSifPnz2PLli0ICQnB+PHjAQAzZszAypUrsWrVKgwZMgRXrlzBf//7Xzg5OZklPkdHRwwePBgbNmyAu7s7AgICcOzYMWzbtk2vCO6KFSvwww8/YNiwYVi1ahVsbW2xceNGvXY7/fv3h4uLCxYuXIjVq1dDKpVi586duHjxYrUxWFtb4/3334dCoUCfPn201YxHjx6NgQMHmuVz16RFJLPZ2dkYM2YMBEHAnj179EpZP/ibgylTpmD8+PH497//jcWLF8PDw6PaeZ9//nlMmzZN51h8fDwmTpxo0viJiIiIiOqruEyF366k4sD5FBy/kQmVWnels2srJ0zs0Qrju/vA08EGgObZ2Sv38hEZl15TColhnT2xMDzQvMFbSYGIncAfH2na7yjSK8/Ze2hWZM3ckudBzz33HPr27YsPP/wQ77zzDlJTUyGVStGxY0dMnz4dL7zwgnbsq6++ivz8fGzfvh3vvfce+vbti2+//RYTJkwwW3y7du3CSy+9hKVLl6K8vBwDBgzA4cOHMXbsWJ1xISEh+P3337FkyRLMmjULLi4umDFjBqZMmYL58+drx7m5ueGnn37CkiVL8Mwzz8DOzg4TJkzAnj170LNnT733l0qlOHjwIBYvXoz169dDLpfjueeew4YNG8z2mWsiEqqWqmpmMjMz4eHhodeap6qcnBw8+uijSExMRGRkpF6lrZrs2bMHERER+PnnnzF69GiDY4qNjUVISAguX75c7XZmIiIiIiJzU6kF/HUzE/vPp+DXy6koLNPdYtrKWY5JPVphYg9ftPd0qHYOpUqNzUdvYkd0IjIUlc99etjLMDPMHwvDA6tdla2oZtyuXTsTfiJonqFNOgkU5wByF6BNv0ZNYqlus2fPxr59++q9jbque8fYXMuiV2YrEtnbt2/jyJEjBieyALTlpmtrSExERERE1FwIgoDYu/k4cD4FP1y8i/QC3cJDTnIpxnbzwaQerdCrjQvE4tqLCkmtxHhxeAcsDA/E2YQc5BWXwUlujd4BLubdWlwTKynQdlDjvy9ZLItNZisS2Vu3buHw4cPo0aOHUdd/9dVXkEql6NWrl5kiJCIiIiJquOTsIvxw8S4OnE/BjXTdFTFriRjDO3tiYo9WCO/kAZnEyuj5pVZihAU2bhVaIlNolsnsoUOHUFhYiIKCAgDAlStXsG/fPgDAmDFjIBKJ8Nhjj+H8+fP46KOPUF5ejpMnT2qv9/DwQGCgZn//hg0bcOXKFQwfPhx+fn7aAlC//fYb1qxZA3d398b/gEREREREtcgrUuKnmHs4cD4FpxOy9c73a+eKST1aYVSID5zk3IpLjWP79u3Yvn17U4eh1SyT2UWLFiExMVH7eu/evdi7dy8AaJsYnzlzBgDw0ksv6V0/a9Ys7Te5c+fO+OGHH/DTTz8hJycHcrkcoaGh+OabbxAREWHmT0JEREREDzOlSm3wFt7SchWi4tKx/3wKouIyUKZS65zv6GWPST388HioL1o5yxsjfKJmrVkmswkJCXWOMbRu1fjx47Wls4mIiIiIGoNSpcanR29iR3QCMhWV/Uo97GWYEeaPRfeLK6nVAs4kZOPAhRT8dOke8kvKdebxcpRhYmgrTAhthS4+DtqeoETUTJNZIiIiIiJLpVSpMX/HWURdy8CDqWemohQfHL6OP+MzEdraGQcv3UNKbrHOGHuZBKNDvDGpRys80s4NVnUUciJ6WDGZJSIiIiIyoU+P3kTUtQwAuv1bq74+dTsbp25XPgsrEYsQ3skDE3u0wqNdvGAjNb6QE9HDhsksEREREZGJKFVq7IhOgAj6iWx1erR2wuSefhjbzReudtbmDo+oRWEyS0RERERkImcTcnSeka3L0lFd2BaHqJ6YzBIRERERNYAgCIhLLcBvsWnYdy7ZqGvzig1PfIlIF5NZIiIiIiIjlavUOJOQg8NX0vDblVTcySmu+6JqOMm5tbiCUq3EhfQLyCvNg5PMCaGeoZCK2UOXasZkloiIiIjIAEVl5Th+PRO/XUlFZFw6couUemM6edkjOacYRWWqWucSAXC3l6F3gIuZorUcSrUS22K2YXfcbmSVZGmPu8vd8WSnJzGv67xGSWoNbXsUFRUFABg6dCj27t2LqVOnmjMso23fvh1z5szB7du3ERAQ0NThmBWTWSIiIiKiGmQqSnHkahoOX0nDiRuZKC1X65wXi4A+Aa4YGeyNkUFeaO1qi/8cuYEPDl+vdV4BwMwwf0itxGaMvvlTqpV4KfIlnEg5AdEDjYyyirOw8cJGxGTG4KOhH5k9oY2OjtZ5vW7dOkRFRSEyMlLneFBQEM6dO2fWWMgwTGaJiIiIiKq4nVmIw1dS8VtsGv5OyoHwQFliG6kYQzp6YESQN4Z19tSrQrwoPBAXknMRGZeuV9W44vWwzp5YGB5o5k/S/G2L2YYTKScAAMID9Z8rXh+/cxxfxHyBBd0XmDWWfv366bz28PCAWCzWO24KRUVFsLW1Nfm8D5uH+1dBRERERPTQU6sFXEjOxbu/xGHEB8cw9L2jePvnOJxNrExk3eys8URvP2yd2RsXVo3EZzN6Y2ovv2rb6UitxPhsRi8sGdER7vYynXPu9jIsGdERn83oxVVZtRK743brrcg+SAQRdl/bDaVaf1t3U1MqlVi+fDl8fX3h6OiIRx99FNeuXdMZEx4ejpCQEBw/fhz9+/eHra0t5s6dW+OcZ8+eRUREBAICAiCXyxEQEICnnnoKiYmJemNPnjyJAQMGwMbGBr6+vnj99dehVOp/n/bs2YORI0fCx8cHcrkcXbp0wbJly1BYWKgzbvbs2bC3t0dsbCyGDx8OOzs7eHh44IUXXkBRUVE9v0vmw5VZIiIiInrolJarEH0zC4evaLYQpxeU6o3xd7PFyCAvjAz2Rs82LrASG/ZMJaBJaF8c3gELwwNxNiEHecVlcJJbo3eAS4tOYt85/Q7isuMMGptflq/zjGxNBAjILM5ExMEIOFo7GhxLZ9fOeK3vawaPr4833ngDAwYMwNatW5Gfn4/XXnsN48ePx9WrV2FlZaUdd+/ePTzzzDNYunQp3n77bYjFNd8DCQkJ6NSpEyIiIuDq6op79+7h008/RZ8+fXDlyhW4u7sDAK5cuYLhw4cjICAA27dvh62tLTZt2oRdu3bpzXnjxg2MGTMGL7/8Muzs7BAXF4d33nkHp0+f1ttGrVQqMWbMGCxYsADLli3DX3/9hfXr1yMxMRE//vijib5zpsFkloiIiIgsklKlNipRzC9RIiouHYevpOHotQwoSsv1xnT3c8LIYG+MCPJCB097g4sC1URqJX6o+sjGZcfhbNpZs8x9Paf255CbQlBQEL7++mvtaysrKzzxxBM4c+aMzvbk7Oxs7N27F8OGDatzzqlTp+oUlVKpVBg3bhy8vLywa9cuLF68GADw5ptvQhAEREZGwsvLCwAwduxYhISE6M25YsUK7d8FQcCAAQPQpUsXDBkyBJcuXUK3bt2058vKyrBkyRLt+4wYMQJSqRTLly/Hn3/+iQEDBhj67TE7JrNEREREZFGUKjU+PXoTO6ITkKmo7NPqYS/DjDB/LAoP1Ca19/KK8fuVNPx2JQ0nb2VBqdJ9LlNqJUJYoDtGBHlhRBcveDvZNOpnaWk6u3Y2eGx+Wb5RCWpHl45Gr8ya2+OPP67zuiIpTExM1ElmXVxcDEpkAUChUGDdunX47rvvkJCQAJWqsjL21atXtX+PiorC8OHDtYksoEmmn3zySaxdu1Znzlu3bmHFihWIjIxEeno6hCoPgl+9elUnmQWAp59+Wuf19OnTsXz5ckRFRTGZJSIiIiKqD6VKjfk7ziLqWobek5aZilJ8cPg6/ojPRP92boi8lo5Ld/L05nCQSRDe2RMjg7wwpJMHHG3Yy9RUjNnWq1QrMWLvCGSXZOsVf6pKBBHc5G7YPW53s+s76+amu+ouk2mekS4u1u077OPjY/Cc06dPx5EjR7By5Ur06dMHjo6OEIlEGDNmjM68WVlZ8Pb21rv+wWMKhQKDBg2CjY0N1q9fj44dO8LW1hbJycmYPHmyXqwSiUTvc1XMmZVV97bwxsRkloiIiIgsxqdHbyLqWgYA6KU/Fa9P387G6dvZOue8HW00q69BXujXzg3Wkpb73KqlkIqliOgcgY0XNtY6ToCAiE4RzS6RNYah29Xz8vJw8OBBrF69GsuWLdMeLy0tRXa27j3t5uaG1NRUvTkePBYZGYm7d+/i6NGjGDJkiPZ4bm5utTGUl5cjKytLJ6GtmPPBJLepMZklIiIiIougVKmxIzpBr91NTTp42uGxYB+MCPJC11ZOEBtRwIkax7yu8xCTGYPjd45DBJHOCm3F68F+gzG3a83Vf1sSkUgEQRC0K7wVtm7dqrPdGACGDh2KH374AWlpadqtxiqVCnv27NGbE4DenJ999lmNcezcuVP7zCwAbVGp8PBw4z6QmTGZJSIiIiKLcDYhW+cZ2bq8OaHrQ1V8yRJJxVJ8NPQjfBHzBXZf243M4kztOTe5GyI6RWBu17kWvSprDEdHRwwePBgbNmyAu7s7AgICcOzYMWzbtg3Ozs46Y1esWIEffvgBw4YNw6pVq2Bra4uNGzfqtdvp378/XFxcsHDhQqxevRpSqRQ7d+7ExYsXq43B2toa77//PhQKBfr06aOtZjx69GgMHDjQXB+9XoxKZs+dO1evNwkKCoKNDR+mJyIiIiLjJWcX4cD5FHx9Sr/PZm3yig1PfKnpSMVSLOi+AHO7zsWF9AvIK82Dk8wJoZ6hD00SW9WuXbvw0ksvYenSpSgvL8eAAQNw+PBhjB07VmdcSEgIfv/9dyxZsgSzZs2Ci4sLZsyYgSlTpmD+/PnacW5ubvjpp5+wZMkSPPPMM7Czs8OECROwZ88e9OzZU+/9pVIpDh48iMWLF2P9+vWQy+V47rnnsGHDBrN/dmOJhKqlrOogFovrVZ78zJkz1X6jLFFsbCxCQkJw+fJlBAcHN3U4RERERC1STmEZfoq5hwPnU3A2Madec3zzXD+uzJrBrVu3AADt2rVr4kjI1GbPno19+/ZBoVCYZf667h1jcy2jtxkvX74cgYGBBo1VqVR47rnnjH0LIiIiInoIlShViIxLx/7zKTh6LV2vjU4nL3sk5xSjuExV6zOzIgDu9jL0DnAxa7xE1LSMTmbHjRuHvn37GjRWpVLh2WefNTooIiIiIno4qNUCTt3OxoHzKfg55h4KSst1zvs42WBCaCtM7OGLzt6O+M+RG/jgcO29SQUAM8P8tb1miahlMiqZ3b9/Pzp16mTweCsrK+zfvx/t27c3OjAiIiIiarniUvOx/3wKfrhwF/fySnTOOcgkGNPVBxN7tMIjbV11qhAvCg/EheRcRMal61U1rng9rLMnFoYbtpOQiCpt374d27dvb+owDGZUMjthwgSj36A+1xARERFRy3Mvrxg/XLiL/edTEJdaoHNOaiVCeCdPTOrRCsM6e8JGalXtHFIrMT6b0Qubj97EjuhEZChKtefc7WWYGeaPheGBXJUlaoYEQahXDaaa1Ls1z/Hjx9GmTRsEBATonSsoKMD58+cxePDghsRGRERERBauoESJQ5dTceB8CqJvZeHB0qO9/V0wsUcrjO3qAxc7a4PmlFqJ8eLwDlgYHoizCTnIKy6Dk9wavQNcmMQ2ApFIBKVSafLEhFo2QRCgUqkglZquQnW9k9nw8HA4ODhg7969GDlypM65K1euYOjQoXqNfYmIiIio5SsrV+P49Qzsv5CC36+kobRcrXO+nYcdJvdohQmhrdDa1bbe7yO1ErNacROwt7dHZmYm7t27B09PT0gk9U4p6CFRXl6O9PR0qFQquLiYrjBbg+68gIAAjB8/Hp999hlmz55topCIiIiIqCkpVWqjVzwFQcC5pFwcOJ+Cg5fuIqdIqXPe3V6Gx7v7YlKPVghp5cgVPQvm4uKCoqIi5OXlIS8vDxKJpN4tPKllEwQBarUa5eWawm62trbNJ5n97LPPsHPnTsybNw9JSUlYtWqVqeIiIiIiokamVKnx6dGb2BGdgExFmfa4h70MM8L8saiaZ1FvZShw4MJdHDifgqTsIp1zcqkVRoV4Y2KPVhgQ6AYJtwC3CBKJBG3atEFBQQHy8/O1W46JHiQSiSCRSCCXy+Ho6AgHB4fm8cwsAIjFYnzyySdo1aoVli9fjuTkZHz22Wemio2IiIiIGolSpcb8HWcRdS0DD/6omakoxQeHr+NCci4+m9ELecVK/HhRk8BevJOnM9ZKLMKgDu6YGNoKI4K8YCfjFtSWSCQSwdHREY6Ojk0dCj3ETPLrsWXLluHLL7/Ejh07MH78eBQWFtZ7roKCAixduhQjR46Eh4cHRCIR1qxZU+3Yc+fO4dFHH4W9vT2cnZ0xefJk3Lp1q9qxn3zyCTp37gyZTIa2bdti7dq1UCqV1Y4lIiIieth8evQmoq5lANBtd1P1dWRcOkZ8eAyPvH0Ea3+8opPIdvdzwurxQTj5+nBsn9MXE3u0YiJLRGZlsv/CPPPMM/Dy8sLUqVNx7ty5es+TlZWFLVu2oHv37pg4cSK2bt1a7bi4uDiEh4cjNDQU3377LUpKSrBq1SoMGjQIFy5cgIeHh3bsW2+9hZUrV2LZsmUYOXIkzpw5gxUrViAlJQVbtmypd6xERERELYFSpcaO6AS9vq3VScis3Erc2lWOSaGtMKFHKwR62Js1RiKiB9U7mfX394dMJtM5NmLECBw7dgxjxoypd0D+/v7IycmBSCRCZmZmjcnsqlWrIJPJcPDgQe32hl69eqFDhw5477338M477wDQJMfr16/Hc889h7fffhuAphKzUqnEihUr8PLLLyMoKKje8RIRERFZurMJOTrPyNbl0S6eWBQeiJ5tXFj0h4iaTL23Gd++fRvdu3fXOx4aGopr167VuN23LiKRqM7/KJaXl+PgwYOYMmWKzj59f39/DB06FPv379ce++WXX1BSUoI5c+bozDFnzhwIgoADBw7UK04iIiKiliKv2PBEFgCm9vJDL39XJrJE1KTM8iCDg4MDHBwczDE1AODmzZsoLi5Gt27d9M5169YNhw8fRklJCWxsbHD58mUAQNeuXXXG+fj4wN3dXXu+Ounp6cjIyNA5Fh8fb4JPQERERNQ8pOaV4PCVNKOucZJbmykaIiLDGZXMvvnmmwaPFYlEWLlypdEBGSIrKwsA4OrqqnfO1dUVgiAgJycHPj4+yMrKgkwmg52dXbVjK+aqzqZNm7B27VrTBU5ERETUDKjVAv6Iz8TOU4n4/Wo6VGrD2qqIoOkX2zvAdH0iiYjqy6hktrqqwiKRqNq+UuZMZqu+hyHnDB33oOeffx7Tpk3TORYfH4+JEycaHiQRERFRM5GlKMW+v+9g1+kkJGbp9oT1dJAhvaC01usFADPD/PV6zRIRNQWjktkHt9yWl5fDx8cHR44cqXbLr7m4ubkBQLWrqtnZ2RCJRHB2dtaOLSkpQVFREWxtbfXG9urVq8b38fT0hKenp+kCJyIiImpkgiDgTEIOdp5KxKGYVJSp1NpzNlIxJnRvhaf7tUEXH0cs+OpvRMal61U1rng9rLMnFoYHNvInICKqnlHJbEUSWUGlUgEAnJyc9M6ZU2BgIORyOWJiYvTOxcTEoH379rCxsQFQ+axsTEwMHnnkEe241NRUZGZmIiQkpHGCJiIiImpE+SVK7D+Xgp2nEnE9TaFzroOnPZ5+pA0m9fSDk1yqPf7ZjF7YfPQmdkQnIkNRuUrrbi/DzDB/LAwP5KosETUbFtnJWiKRYPz48fj+++/x7rvvaotNJSUlISoqCq+88op27KhRo2BjY4Pt27frJLPbt2+HSCTilmEiIiJqUWLu5GHnqUT834W7KFaqtMelViKMDvHBM/380Seg+pY6UisxXhzeAQvDA3E2IQd5xWVwklujd4ALk1gianaaZTJ76NAhFBYWoqCgAABw5coV7Nu3DwAwZswY2NraYu3atejTpw/GjRuHZcuWoaSkBKtWrYK7uzuWLFmincvV1RUrVqzAypUr4erqipEjR+LMmTNYs2YNnn32WfaYJSIiIotXVFaOgxfvYeepRFy8k6dzro2rLaY/0gZTe/nB3V5m0HxSKzHCAhtv1x0RUX00y2R20aJFSExM1L7eu3cv9u7dC0DT3zYgIACdO3fG0aNH8dprr2Hq1KmQSCQYNmwY3nvvPXh4eOjMt3z5cjg4OGDjxo1477334O3tjWXLlmH58uWN+rmIiIiITOlGWgF2nkrCd+fuoKCkXHtcLAIe7eKFp/v5Y1B7d4jF7AdLRC2PSZJZUzfMTkhIMGhcr1698Pvvvxs0dvHixVi8eHEDoiIiIiJqeqXlKvxyORU7TyXh9O1snXNejjJE9GmDiL6t4eMkb6IIiYgah1HJbE0Vi5988kltwaUKIpEIFy9erH9kRERERKSVlFWEXaeTsPdsMrIKy3TODergjqcf8cejXTwh4bOtRPSQMCqZdXV11VuFHTJkiEkDIiIiImrJlCq1wcWVylVqRMal4+tTSTh+XbdFooutFE/0bo2n+rZBgLtdY4RORNSsGJXMHj161ExhEBEREbVsSpUanx69iR3RCchUVK6setjLMCPMH4uqtL1JzSvB7jNJ2H06Gan5JTrz9AlwwTP9/DEqxBsyiVWjfgYiouakWRaAIiIiImpJlCo15u84i6hrGXiw0kimohQfHL6O80k5mBHmjz1nkvH71XSo1IJ2jINMgsk9W2H6I/7o5O3QuMETETVTTGaJiIiIzOzTozcRdU2zTVh44FzF66hrGdoxFUJaOeKZR/wxvrsv7GT8sY2IqCqj/qvo6OiIqKgo9OrVy6DxarUazs7OOHHiBLp3716vAImIiIgsmVKlxo7oBIign8hWRyYRYUJoKzz9iD+6t3Y2c3RERJbLqGRWoVBArVYbPF4QBCgUCqhUKqMDIyIiImoJzibk6DwjW5eNT/fCo128zBgREVHLYPR+lX79+hk13tQ9aImIiIgsSV6x4YksoKlgTEREdTMqmV29enW93sTX17de1xERERFZKqVKjd9i0/DfqBtGXecktzZTRERELUujJLNERERED4u0/BLsOpWEb04nIb2g1ODrRADc7WXoHeBivuCIiFoQlsUjIiIiaiBBEBB9Kwtfn0zEr7FpOm11nORStPe0w9+JubXPAWBmmL+21ywREdWOySwRERFRPRWUKPH9uRR8dTIR8ekKnXNdWzlhRpg/Hu/uCyuxCAu++huRcel6VY0rXg/r7ImF4YGNGD0RkWVjMktERERkpLjUfHwVnYj951NQVFbZtcFaIsb4br6YGabfVuezGb2w+ehN7IhORIaicvuxu70MM8P8sTA8kKuyRERGYDJLREREZICycjV+iU3F19GJOJ2QrXOujastnunXBtN6tYaLXfUFnKRWYrw4vAMWhgfibEIO8orL4CS3Ru8AFyaxRET1wGSWiIiIqBZ3c4vxzekkfHM6GZlVVlRFImBoJ0/MCPPHkA4eEIsNa0cotRIjLNDNXOESET00mMwSERERPUAQBPwZn4WvTibg8JU0VKnnBBdbKZ7s0wZPP9IGrV1tmy5IIqKHXIOS2bi4OKxduxZHjx5FVlYWTp48iZ49e2Lt2rUYPHgwhg4daqo4iYiIiMwur1iJ7/6+g69PJuJWZqHOudDWzpgZ5o8xXX1gI7VqogiJiKhCvZPZCxcuYNCgQXBwcEB4eDi+/fZb7TmFQoHNmzczmSUiIiKLEHs3D1+fTMSB83dRrKws6GQjFWNC91Z4pp8/uvo5NWGERET0oHons8uWLUO3bt1w+PBhWFtbY8+ePdpzffv2xXfffWeSAImIiIgMpVSpDS6uVFquwqGYVOyITsC5pFydcwFutnimnz+m9WoNJ1tpI0RORETGqncy++eff+Lrr7+Gra0tVCqVzjkvLy+kpqY2ODgiIiIiQyhVanx69CZ2RCcgU1GmPe5hL8OMMH8sqtL25k5OEXaeSsK3Z5KRVVg5ViwChnfxwox+/hjY3t3ggk5ERNQ06p3MCoIAa+vqS8/n5ORAJpPVOygiIiIiQylVaszfcRZR1zLwYPqZqSjFB4ev43xSDp4J88c3p5IQGZeuU9DJzc4aEX1b46m+beDnwoJORESWot7JbLdu3bB//36MHj1a79wvv/yCXr16NSgwIiIiIkN8evQmoq5lAACEB85VvI66lqEdU6G3vwtmhPljVIg3ZBIWdCIisjT1TmZfeuklTJ8+HXZ2dpgxYwYAICkpCZGRkfjiiy+wb98+kwVJREREVB2lSo0d0QkQQT+RrY6NRIxJPf3wTL82CPZlQSciIktW72T2ySefxM2bN7FmzRr85z//AQBMmTIFEokEa9euxfjx400WJBEREVF1zibk6DwjW5eNT/fE8C5eZoyIiIgaS4P6zL7xxhuYOXMmfv31V6SlpcHd3R2PPfYY/P39TRUfERERUY3yig1PZAHNSi4REbUM9U5mjx8/jp49e8LPzw/z5s3TOadQKHDu3DkMHjy4wQESERERVedySh52nU4y6honefXFK4mIyPLUO5kdOnQooqOj0bdvX71z165dw9ChQ/Va9hARERE1RLlKjd+upGH7nwk4nZBt8HUiAO72MvQOcDFfcERE1Kga1JqnJkqlEmJx9Q3KiYiIiIyVW1SGb04n46voBNzNK9EeF4uAQA973EhX1Hq9AGBmmL+21ywREVk+o5LZ/Px85Obmal+npqYiKUl3e09xcTG+/PJLeHt7myRAIiIienhdSy3A9r9uY//5FJQoK593dZJL8VTfNpgR5g9PBxkWfPU3IuPS9aoaV7we1tkTC8MDGzl6IiIyJ6OS2Q8//BBvvvkmAEAkEmHSpEnVjhMEAW+88UbDo6vF7Nmz8eWXX9Z4Pjo6Gv369atxXKdOnRAXF2fOEImIiKgeVGoBkXHp+N+ft/HXzSydcx297DFnQFtMDG0FuXVlb9jPZvTC5qM3sSM6ERmKUu1xd3sZZob5Y2F4IFdliYhaGKOS2ZEjR8Le3h6CIGDp0qV48cUX0aZNG50xMpkMXbt2xZAhQ0wa6INWrlyJhQsX6h0fP348ZDIZ+vTpoz0ml8sRGRmpM04ul5s1PiIiIjJOfokS355Jxo7oRCRlF2mPi0TA8M5emDsgAGGBbhCJRHrXSq3EeHF4BywMD8TZhBzkFZfBSW6N3gEuTGKJiFooo5LZsLAwhIWFAQAKCwvx3HPPwdfX1yyB1SUwMBCBgbrbhY4dO4bMzEysWLECVlaVv60Vi8Xo169fY4dIREREBriZocCXfyVg3993UFRWWTzSQSbBE31aY1ZYANq42Ro0l9RKjLBAN3OFSkREzUi9C0CtXr3alHGYxLZt2yASiTB37tymDoWIiIhqoVYLOH4jA//7MwHHrmfonGvnbofZAwIwpacf7GT1/lGFiIhauAb9C6FSqXDo0CFcvXoVxcXFOudEIhFWrlzZoOCMkZeXh3379mH48OFo27atzrni4mJ4e3sjIyMDPj4+mDhxIt588024urrWOmd6ejoyMnT/gY2Pjzd57ERERA8LRWk5vj93B9v/SsCtjEKdc+GdPDC7fwAGd/CAWKy/lZiIiKiqeiezWVlZGDRoEOLi4iASibSteqo+x9KYyew333yD4uJizJs3T+d49+7d0b17d4SEhADQbEX+8MMPceTIEZw5cwb29vY1zrlp0yasXbvWrHETERE9DJKyivBldAK+PZOMgtJy7XFbaytM6+WHmf0DEOhR87/JRERED6p3Mrt8+XLY2NggMTER/v7+OHXqFFxdXbF582YcPHgQv//+uynjrNO2bdvg5uamV2H5lVde0Xk9YsQI9OjRA1OnTsXnn3+ud76q559/HtOmTdM5Fh8fj4kTJ5osbiIiopZKEAT8dTML//szAUfi0lC1RX0bV1vM6h+Aab394GgjbbogiYjIYtU7mT1y5AhWr16tLQAlFosRGBiIDRs2oKSkBP/85z/xzTffmCzQ2ly6dAlnz57FSy+9BJlMVuf4SZMmwc7ODidPnqx1nKenJzw9PU0VJhERkUVTqtQGVQouLlNh//kUbP/rNq6nKXTODWjvhjn922JoZ09YcSsxERE1QL2T2Tt37iAgIABWVlYQi8UoLKx87mX8+PGYPn26SQI0xLZt2wAAzz77rMHXCIIAsZil+omIiOqiVKnx6dGb2BGdgExFmfa4h70MM8L8seh+D9eU3GLsiE7A7tPJyCtWasfZSMWY1MMPs/sHoJO3Q1N8BCIiaoHqncy6u7sjLy8PAODr64vLly9j8ODBAIDs7GyUl5fXdrnJlJaW4uuvv0bfvn21z8XWZd++fSgqKmK7HiIiojooVWrM33EWUdcy8OA6aqaiFB8cvo6j19Lh4SDD4StpUFfZSuzrZIOZ/QMQ0ac1nG2tGzVuIiJq+eqdzPbq1QuxsbEYO3YsxowZgzfffBOOjo6wtrbGG2+80WiJ4oEDB5CdnV3tqmxiYiKmT5+OiIgItG/fHiKRCMeOHcNHH32E4OBgo1ZyiYiIHkafHr2JqGuayv7CA+cqXp9LytU53jfAFXMGBGBEkBck1WxDJiIiMoV6J7MvvPACbt68CQBYt24dTp48iZkzZwIAAgMD8fHHH5smwjps27YNdnZ2iIiI0Dvn6OgILy8vfPDBB0hLS4NKpYK/vz8WL16MN954A3Z2do0SIxERkSVSqtTYEZ0AEfQT2epM7tkKcwe0RUgrJ3OHRkREVP9k9tFHH8Wjjz4KAPDw8MD58+dx+fJliEQidO7cGRJJ4zQ5/+2332o85+Ligu+//75R4iAiImppzibk6DwjW5dpvVozkSUiokZTr70/xcXFGDBggE77HZFIhK5duyIkJKTRElkiIiIyn7xiwxPZ+ownIqLGpVQrcSb1DH5P/B1nUs9AqVbWfVEzVq+sUy6XIyYmhkkrERFRCxWfrsDOk0lGXeMkZ5EnIqLmSKlWYlvMNuyO242skiztcXe5O57s9CTmdZ0Hqdjyen7XOxsNCwvD6dOnER4ebsJwiIiIqCnFpebjk8h4/BxzD4IhD8oCEAFwt5ehd4CLWWMjIiLjKdVKvBT5Ek6knIDogbr0WcVZ2HhhI2IyY/DR0I8sLqGtdzL7/vvvY8KECfD29sbkyZNhb29vyriIiIioEV1OycN/jtzAb1fStMdEIqCDpz2upylqvVYAMDPMH1JWLiYiana2xWzDiZQTAADhgXJ+Fa+P3zmOL2K+wILuCxo9voao9786YWFhuHPnDubMmQMnJyc4ODjA0dFR++XkxAIQREREzd25pBzM+d9pjPvkD20iKxYBk3q0wuFXBuOnxYMwrLMnAOj1ma14PayzJxaGBzZe0EREZBClWondcbv1VmQfJIIIu6/ttrhnaOu9MjtlyhSIRLV/U4iIiKh5OnUrC59ExuOP+EztMYlYhMk9W+H58PYIcK9sX/fZjF7YfPQmdkQnIkNRqj3ubi/DzDB/LAwP5KosET30lGolLqRfQF5pHpxkTgj1DG30bbtqQY3M4kzcVdzFvcJ7OHXvlM4zsjURICCzOBMX0i+gj3efRojUNOqdzG7fvt2EYRAREZG5CYKAP+Oz8J/IGzh9O1t73NpKjGm9/bBwSCBau9rqXSe1EuPF4R2wMDwQZxNykFdcBie5NXoHuDCJJaKHXmMWV1KqlEgtSsU9xT3cLbyr92dqYWqDVlfzSvNMEmdjYTliIiKiFk4QBBy9loH/RN7A+aRc7XGZRIyn+rbBgiHt4OMkr3MeqZUYYYFuZoyUiMiymLq4UpGyCHcVd6tNVO8p7iGjOEPvuVdTcpJZ1qOiTGaJiIhaKLVawOGrafhvZDxiUip/2y6XWmFGmD+eHdQWng42TRghEZFlM6a40vxu85FbmluZoN7fCqz9s/BuvVZG3eXu8LXzhY+9j96fnnJPTPy/icguya41CRZBBDe5G0I9Q41+/6bEZJaIiKiFUakFHLp8D/+NjEdcaoH2uL1Mgln9/TFvYDu42rEnLBFRQ1QtrlTXaumnFz/F1pitKFGVGPUeEpEEXnZe8LHzga+9r96f3nbekFnJap0jonMENl7YWOsYAQIiOkU8PK15iIiIqHkpV6nxw8W72BgVj5sZhdrjjjYSzB3YFnP6t4WTrWX9oEJEBDSP4kqCICCvNA8pihTcUdxB9N1og4orAYBKUEGlUukdt7GyqXZF1dfOF772vvCQe8BKbNWguOd1nYeYzBgcv3NcL/GueD3YbzDmdp3boPdpCkxmiYiILFxZuRr7z9/BpqM3kZhVpD3uameNeQPbYmaYPxxsmMQSkeVpzOJKgOaZ1RRFivbrTsEdndeFysK6J6nB4FaD8YjPI9qVVR97H7jIXMzeIUYqluKjoR/hi5gvsPvabmQWV1axd5O7IaJTBOZ2nWtxq7IAk1kiIiKLVVquwrdn72Dz0ZtIyS3WHne3l2HB4HZ4ul8b2Frzn3oiskymLq5UMWeqIhV3FHeqTVazS7LrnqSeZofMbrK2N1KxFAu6L8DcrnObfIXblOr9L1xSUlKN58RiMZycnODg4FDf6YmIiKgGxWUqfHM6CZ8dv4m0/Mq+r96ONlg4pB0i+raBjbRh29KIiJqaMcWVFnRfAEDTZzWjKKNyZVVxBykFlclqWlEa1ILa4BhsrGzga++LVvat0Mq+Ffwc/LR/97LzwuT/m2xRxZWkYqlF9ZGtS72T2YCAgDqXxDt06IDXX38ds2bNqu/bEBERPRSUKnWdPVwLS8vx9clEfH7iFjIVZdrjrZzleH5oIKb28oNMwiSWiCyfMcWVtsZsxd9pf2srA5epy2odX5WVyAredt7ws/dDK4dW2kS1InF1s3GrNedpycWVLEG9k9ktW7bg7bffhq2tLZ544gl4eXnh3r172Lt3L4qLi7Fo0SIcPnwYc+fOhbW1NZ566ilTxk1ERNQiKFVqfHr0JnZEJ+gkqB72MswI88ei8EAUK1XY8VcCtv1xGzlFSu2YADdbPD+0PSb1aKWX+BIRWSqVWoXDiYcNLq5UoipB9L3oGs+7y931ktSKv3vbeUMirv/jGC25uJIlaNA24+DgYPzwww86v61YvXo1xo8fj+zsbPz222+YMmUKPvzwQyazRERED1Cq1Ji/4yyirmXgwd/7ZypK8cHh6/j+3B1kKUpRUFpZBbO9pz1eGNoe47r5QMIklogaqCkqBZeUl+BOwR0kFyRXfimStc+wlqvLjZqvlX0rdHHtoklS76+w+tn7wdfeFzYS8/XTbsnFlSxBvZPZ//3vf9i8ebPesrtIJMKCBQuwcOFC/Otf/8LTTz+NmTNnNjhQIiKilubTozcRdS0DAPQ20VW8TqhSnbiztwNeHNYBo0O8IRabt/olEbV85q4UnFeap5usVvlKL0o3xUfQWjdgHYsrPYTqncxmZmaiuLi42nMlJSXIyckBALi5uUEQat/nTkRE9LBRqtTYEZ0AEfQT2QdJxCL856keGBXMJJaITMMUlYLVghrpRelILkjWW2VNKkhCQVmBwfHYSmzR2qG15suxNXztfPHJ+U+QX5Zf63UsrvRwq3cyGxoairfffhvDhw+Hi4uL9nh2djbeeusthIaGAgCSk5Ph7e3d4ECJiIhakrMJOTrPyNamXC3AxdaaiSwRmYyhlYK3XtqK0W1HV7u6mqJIQamqVG/umrjauKKNQxtt0urn4Kf9u6uNq96Oz9zSXBZXolrVO5ndsGEDRo4cCX9/fwwbNgxeXl5IS0tDZGQkysvL8fvvvwMAzp8/j/Hjx5ssYCIiopYgr9jwapv1GU9EVBNjKgVvurgJmy5uMmhesUgMHzufyhXWKl9+Dn6wk9oZFSeLK1Fd6p3MDhw4ECdPnsT69etx/PhxZGVlwc3NDaNHj8by5cvRrVs3AMD7779vsmCJiIhaiqIyVd2DqnCSW5spEiJ6GJSUl2i2/+Yn4Y+7fxhcKfhBMiuZ3qpqxZevnS+kVqZbIWVxJapL/etQA+jWrRu+/fZbU8VCRETU4ilKy/FJ5A1sO3HLoPEiAO72MvQOcKlzLBE1P41ZKVipUiJZoUlYE/MTtX8mFiQirTCtzlXYmkR0isBjAY+htUNreNh6QCxqvCrqLK5EtWlQMktERESGEQQB/3fhLv516CrS8g1/xkwAMDPMn31kiSyMuSoFl6vLcVdxV5OsFugmrXcL70ItqE35MQAAIwNGord3b5PPawwWV6LqNCiZ/eOPP7Br1y4kJibqVTYWiUQ4cuRIg4IjIiJqCS6n5GHND7E4m5ijPRba2hkrx3bBxqM3ERmXrlfVuOL1sM6eWBge2MgRE1FDNLRSsFpQI7UwVbOqev8rqSAJSflJuFNwB+WCYT1YHaQOaOPYBm0c28Df0R9tHDR/trJvhSk/TEF2SXatq7XNqVIwUXUa1Gd23rx5cHV1RceOHSGTyXTOsx0PERE97HIKy/Deb9fwzekkqO//s+hub43XRnXGlJ5+EItF+GyGMzYfvYkd0YnIUFSu2LrbyzAzzB8LwwO5KktkYQytFPyfc//BYL/BuklrfhKSC5JRpjas6JtcItdJVKsmrtVVCK4Q0TmClYIfRiolkHQSKM4B5C5Am36ACZ9zbmwioZ5ZZ5cuXdC9e3d8+eWXeolsSxYbG4uQkBBcvnwZwcHBTR0OERE1Qyq1gF2nk/D+b9eQW6QEoOkVO6t/AF56tAMcbfR/cFCq1DibkIO84jI4ya3RO8CFSSyRBVKqlRixd0Sdq57GsBZbVyapjm3g71CZtHrIPWpMWOuK8+Wol+usFFxbn1myICol8MeHwOktQGFG5XF7T6DPc8DAV5pFUmtsrlXvldnExER88sknD1UiS0REVJczCdlY/X+xuHIvX3tsYHt3rB4fhA5eDjVeJ7USIyzQrTFCJCITU6qUSCpIQkJeAk6knKhXpWCJWAI/ez/4O/prvyoSVy87L5MXXWKl4IeISgnsng7c+A14YNs7FBlA1FvAnbNAxM5mkdAao97JbJcuXZCWlmbKWIxy9OhRDB06tNpz0dHR6Nevn/b1uXPnsHTpUpw8eRISiQTDhg3De++9h3bt2jVWuERE1MKl5pXg34eu4sCFu9pjrZzlWDmuCx4L9q7XygkR1a4xKwULgoCskiwk5CUgIT8Bt/NuIyE/AQl5CUhRpEAlGNduq8KCbgswof0E+Nj5QCJu3NqsrBT8kPjjw/uJLAC93QL3X9/4FfjjI2DIq40YWMPV+/8xb7/9Nv75z38iPDwcrVq1MmVMRsfxYFIbEhKi/XtcXBzCw8MRGhqKb7/9FiUlJVi1ahUGDRqECxcuwMPDo7FDJiKiFqS0XIUv/kjAJ5E3tL1jZRIxFg4JxMIhgZBbWzVxhEQtj7kqBQNAmaoMSflJuJ1/W5u4JuRpktcCZYGpPoLWIz6PoLVDa5PPawxWCm7BVErN1mK9MoMPEgFntgADX7ao1dl6J7MbN25EXl4eOnbsiNDQULi56W6NEolE+L//+78GB1iXDh066KzCPmjVqlWQyWQ4ePAgHB0dAQC9evVChw4d8N577+Gdd94xe4xERNQyRcWl482DV3A7s1B7bFSwN5aP7YLWrrZNGBlRy9XQSsGAZpU1szhTZ4X1dp4meTW0vY1ULIW/oz8CHAMQ4BSg/dPP3o+Vgqn5uHVU9xnZGgmAIl1THKrtIHNHZTL1TmYvXboEKysreHp64u7du7h7967O+eawnaq8vBwHDx7EzJkztYksAPj7+2Po0KHYv38/k1kiIjJaQmYh1h28giNx6dpj7T3tsXp8EAZ14I4fInMytFLwFzFfYHbIbCTmJ2pXVitWWRPyE6BQKgx6P3e5uzZRbevYVvunr70vrMTV77xgpeCHVFNUClarAUUqkJOg+5V9W/NnYXqtl+spzql7TDNS72Q2ISHBhGHU3z/+8Q9ERETA1tYWYWFhWLlyJQYOHAgAuHnzJoqLi9GtWze967p164bDhw+jpKQENjY21c6dnp6OjAzd32TEx8eb/kMQEZFFKCorx8aoeHx+/DbKVJqVGweZBC892gGz+gew+jCRmSnVSuyO261Xfbc6my5uwn8v/NegeSuqBbd1aosAxwDtnwFOAXCwrrlwW03mdZ2HmMyYOisFz+061+i5qRkyd6XgskIgJxHIua2ftOYkAqrS2q83htzFdHM1gsZ9ytyEnJyc8NJLLyE8PBxubm6Ij4/Hhg0bEB4ejp9++gmPPfYYsrI0z1C4urrqXe/q6gpBEJCTkwMfH59q32PTpk1Yu3atWT8HERE1f4Ig4OCle3j756u4l1eiPT61lx+WjuoET4fqfylKRKYjCAIiEyMNrhRc3VZhT7mnzpbgiqTVx86nxlXW+mCl4IeIKSoFq9VAwb1qEtWEeqyuigBHX8AlQPPl1Bo4uQkoza/7OnsPzWqyBbHYZLZHjx7o0aOH9vWgQYMwadIkdO3aFUuXLsVjjz2mPVfblufazj3//POYNm2azrH4+HhMnDix/oETEZFFuXovH2t+iMWp29naY938nLDm8WD0bGNZv8EmsgQVVYNv5t5EfG484nPjNX/PiTe6ANMI/xEY1mYY2jq2hb+jP+yt7c0UtT5WCn5IGFop+Ng7QPCk6rcC5yYZt7oqtQNc21YmrFW/nFoD0gd+wSq20iTVtRKAPvMtqvgTYGQya2VlhejoaPTt2xdisbjOJLG8vLzBARrD2dkZ48aNw+bNm1FcXKwtSlWxQltVdnY2RCIRnJ2da5zP09MTnp6e5gqXiIiasbwiJT44fA1fnUyE+v7PI2521lg6qhOm9WoNsbjpa0MQmUtjtbzJLcmtTFarJK45paZ5bu+pzk81eZVeVgpuwQyuFAzg+AbNl0FEgGOr6pNVlwDAzh0wpj7RwFc0q8M3fq0m1vuvOzymqWRsYYxKZletWgU/Pz/t35tDkacHCYLmfxyRSITAwEDI5XLExMTojYuJiUH79u1rfF6WiIgeTiq1gG/PJmPDr9eQXVgGALASizCjnz9eGdERTnLL+q01kTHM1fKmoKxAm7BWTVyrbr+tib3UHoHOgWjv3B5tHdtiS8wW5JfVvmWSlYJboKYorvSg0oL7z64maL4S/zKwUnA1rO2rSVTvr7Y6twYkMhMFDc33KWKnpo/smS2aqsUV7D00K7IW1pKnglHJ7OrVq7V/X7NmjaljabCcnBwcPHgQoaGh2iR1/Pjx+P777/Huu+/CwUHzAH9SUhKioqLwyiuvNGW4RETUzPydmIM1P8QiJiVPe6xfO1eseTwYnb0da7mSyPKZouVNkbIIt/JuaZLVnHjE52mS19TC1DrfXy6RI9ApEO1d2qO9c3ttAutl66WzgFKsKmal4IeJuYsrVaVWVf/sasV24KK6f/lSq37PAyFTNAmrrZtxq6sNZSUFhryqSVqb+pcCJlSvZ2aLi4vRvn17bN68GePHjzd1TAaZPn062rRpg969e8Pd3R03btzA+++/j7S0NGzfvl07bu3atejTpw/GjRuHZcuWoaSkBKtWrYK7uzuWLFnSJLETEVHjUqrUOJuQg7ziMjjJrdE7wEWn8nB6QQn+fSgO359L0R7zcbLB8rFdMLarT7PciURkasa0vJkVPAu3827rrbSmKFL05n2QzEqGdk7ttMlqReLqa+8LsajuiuCsFPwQMUVxpQfprK4+UB04NwlQlRken8QGKC+pe1yFTmMAv96GjzcHK6lF9ZGtS72SWblcjuLiYtjZ2Zk6HoN169YNe/bswebNm6FQKODq6oqBAwfiq6++Qp8+lc8ldO7cGUePHsVrr72GqVOnQiKRYNiwYXjvvffg4cFegERELZlSpcanR29iR3QCMhWVP6B42MswI8wf8wa2xa5TSfj4yA0oSjV1HqwlYiwY3A6LwgNha22xdRKJjGJsy5uNFzbWOU4iliDAMQAdnDtoEtf7K65+9n4NqhzMSsEPEUOLK/3xkWbVEdCsrubfrbkysFGrqyLAye/+FmD/ym3AFX/KHIAPg4DCzGrie2AeC6wUbAlEQsVDpkaaNm0aOnTogLffftvUMTVrsbGxCAkJweXLlxEcHNzU4RARUQ2UKjXm7ziLqGsZNZW7gK21FYrKVNrjI4K8sHJsENq42TZytERN6/S905j327x6XWslskIbxzY6q6wdnDugtWNrsyeUjVWoipqASgl80MWARBGAVA60CdOsrBq7umrtALgGVP/8qlNrQGJd+/XH3jWgUjCAoSsqE26qkbG5Vr1/5fzGG29gypQpsLGxweTJk+Hjo78Nq7r+rkRERI3h06M3EXVN83xVDb/P1yay7dztsGp8EMI7sYI9tXylqlLcyr2F6znXcS3nGq7nXMfljMtGzfFom0cxwn8EAp0D0dapLayt6viB30xYKbiFEgTg2s+GF1dSFgM3I6s/JxIDjn73V1YDNF+uVVZY5S4Ne3a1BVcKtgT1TmZ79eoFQFMIau3atdWOUalU1R4nIiIyJ6VKjR3RCYY0S4CdtRV+fHEg7GTcUkwtiyAIyCjOwLXsa9rE9UbODdzOuw2V0LCf0aZ3mc4ksiVpikrBKqVmFTXndmWBpezblc+xKouMm8/ZH/DpXn3f1bpWVxuiBVcKtgT1/pe7ubbmISIiOpuQo/OMbG0Ky1S4dCcPYYFuZo6KSMMcW2NLVaW4mXtTk7Rma5LWaznXkFuaW+t1UrFUuzU4MikSReW1JxBsedPCmLtScFlhZYL64J+5yUADf6miY8LGpits1EIrBVuCeiezzbE1DxEREQDkFRvxvFQ9xhPVhyl6uAqCgPSidO324OvZ13E95zoS8hPqXG31lHuig2sHdHLphI4uHdHJpRP8nfy177n54ma2vHmYmKJSsCAARVnVJ6zZt4DC9Oqvq46VrLLIkms7wLkNcOzfQEk+LKa4UgurFGwJuKeKiIhaHCe5cVvKjB1PZKz69HAtVZUiPjdem7BWbBXOK82r7i20KlZbO7p01CStrprk1cXGpdbr2PLmIWNopeATHwChT+knqjm3gewEoKzA8PeUOWmeV3Vtez9prfKngy8gfqA1U5nCgOJKgmYrL1dAH0pGJbM7duzA2LFj4ebmhh07dtQ5fubMmfUOjIiIqL56B7jA3d4aWYqyun6fD3d7GXoH1P5DPlFDGdrD9R+//wPOMmdcy7mGxPxEg1ZbO7p21K60dnTpqLPaagy2vHmIqJSarcWGVBY4+rbmy1AOPvqJasWfxhZbYnElqoNRrXnEYjFOnjyJvn37Qvzgb04enFgkapEFoNiah4jIMvznyA18cPh6neOWjOiIF4d3aISI6GGlVCsxYu8IZJdk19mbtSbWYmsEOgdqV1k7uXRCB5cOda621hdb3rRAZUX3iyzdAuKPAH9/Ub95xBLNFuDqElaXAMDaxK3NVMoaiit5srhSC2TW1jy3b9+Gr6+v9u9ERETN1ZwBAdgYFY/ScrXeuYrf7w/r7ImF4YGNHhs9HFRqFRLzE/HjzR91npGti7PMGV3du+psEfZ39IdE3HhPh7HljQk1ZqXgskJNsqrzdX9bcH5K/eftORsInlDZe9WqEZ9UZHElqoVRd+LGjRuxePFi+Pn5wd/fHwCgVqvrXKUlIiJqbF/+laBNZO1lEihKy7Xn3O1lmBnmj4XhgZBa8d8waji1oEZifiKuZF1BbFYsYjNjcTX7KorLi42ea3XYajzq/6gZoqRGZa5KwSX51Ser2bcARarp4q+q69SmL2zE4kpUDaOS2ffffx9Tp06Fn58fAE0fWWtra5w5cwY9e/Y0S4BERETGSssvwaajNwEAnb0dsP/5/riQnIe84jI4ya3RO8CFSSzVm1pQI7kgWZO4ZsYiNkuTuBYqC00yv5PMySTzUBNqaKXg4txqVljvf1VNjOti66apDPzgl1NrYPMAoDATFlMpmKgaRiWz1T1ea8Qjt0RERI3inV/iUFSmqduwalwQ5NYS9pF9SJj6WU9BEHBHcQexWbG4knkFV7I0XwXKmiu4SsQSdHTpiGC3YAS7BaOjS0e8EPkCckpyan1mlj1cWxBDKwX//Crg3x/IuqmbsBZnG/5edp4PJKttK/8ud675ur7zWSmYLB5b8xARUYtyITkX35/TPBs2MsgL/du7N3FE1BhM1cP1buFdxGbGarcLX8m6gvyy/BqvkYgk6ODSAUFuQQhyC0KwezA6OHeAtZVuu6enOj/FHq4PC2MqBf/9P81XXRx89BPVii+ZQ/3iZKVgagGYzBIRUYshCALe/DEWAGBtJcbysV2aOCJqDPXp4SoIAlILUyufcb2fuOaW5tb4PlYiK7R3bo9g92AEud5PXF06QGYlqzNG9nBtwYpzgeybmtXVrJtAUrRxW4ErOPrpJ6tugfcrBNuZOmrNamvEzhoqBXuwUjBZBKOT2WvXrkEi0VxW0XonLi6u2rF8jpaIiBrTDxfv4lxSLgBgzsAA+LuZ4QdAanYM7eG64o8V8HPw024Vzi6peSunWCRGoHMggt2CNSuu97cL20hs6hUje7iaSWNVCi5VVCasVRPX7JtAkeGVqqv12L+B3rMBqdwkoRqFlYLJwhmdzM6ePVvv2IwZM3ReC4LQYvvMEhFR81RcpsK/D2l+uepub40XhrZv4oioMSjVSuyO26232lmdn2//XO1xsUiMdk7tKrcKuwWjk2snyCWmTS6kYikWdF+AuV3nsodrQ5mjUrCyWPO8atWENfsWkBUPKNIMn8fWHSjKrHtcBe+Qpklkq2KlYLJQRiWz//ufAXv6iYiImsBnx2/iXl4JAOCfIzvBwYbJwcPgXNo5o3q4AtAmrhWrrp1dO8NWamumCPWxh2sDNaRScHkpkJPwQMJ6E8i6BeTfMTwGe2/NFuCKrcCugfe3BLfVvOcHXVgpmKgRGJXMzpo1y1xxEBER1dvd3GJsPqZpxRPk44hpvVs3cURkLsXlxbiceRkX0i/gfPp5nE07a9T1/xr0L4xrN85M0VGjMLRS8P+9APiG6iauecmAoDbsfWzd7iep7QG3dpUJqyFFl1gpmKhRsAAUERFZvHd+iUOJUvMD6urxQbASi+q4gixFRlEGzqefx/n087iQfgFx2XEoF8rrPZ+XrZcJo6NGZ0yl4Eu7NV+1sXGqkqQ+kLjW1tamLqwUTNQomMwSEZFF+zsxB/934S4AYExXbzzSjv1kLZVaUCM+N1676no+/TxSFCk1jm/t0BrdPbojKjkKhcrCWudmD1cLVZSteWa14ivppPGVgqV2mmS16nbgisTV1hUQmeGXX6wUTNQomMwSEZHFUqurtOKRiPH6aLbiMSelWmnSwkVFyiLEZMZoV10vZVxCgbKg2rESsQRBrkEI9QxFD88eCPUMhbtc00N488XN7OFqao1VJRioUngpHsi8cb9S8P3ktbjmitMGefwToMcM8ySsdWGlYCKzYzJLREQWa//5FFy8kwcAeG5QW7R2bbwiPg8TpVqJbTHbsDtut06xJXe5O57s9CTmdZ1nUJKYWpiKC+kXcCFDs/J6LfsaVEL1nQ+cZE4I9QhFqGcoQj1CEeIeUmNbHPZwNSFzVAkGALUKyE3STVSzblQ+x2oIkRiw8wQUqYa/r0vbpklkq2KlYCKzYTJLREQWqbC0HO/+qmnF4+kgw/PhbMVjDkq1Ei9FvoQTKScgeqBybFZxFjZe2IiYzBh8NPQjnYRWpVbhRu4Nnedd7xXeq/F9/B39EeqhWXXt4dkDAU4BEIvEBsXIHq4m0pAqwQAgCJoKvlk3qiSs95PX7FuAqsywOOw8AfcO97cGt6/8qkhMWSmYiO5jMktERBZp87GbSMsvBQAsHdUZdjL+k2YO22K24UTKCQDQ6+Na8fr4nePYfHEzenn10j7veinjEorKi6qdUyqWItgtWLtdONQzFK42rg2Kkz1cTcDQKsHH3gG6jL+/LTheN3EtzTPsvaztqySrHe7/ef95Vhun2q9lpWAiuo//8hMRkcW5k1OELcdvAQC6+Tlhco9WTRxRy6RUK7E7brfe1t3qbLm0pcZzLjIXdPfsrl11DXILgsxKZupwAbCHa70ZUyX4+AbNV13EEs1qqjZRbX9/xbU9YO9V/+2/rBRMRPcxmSUiIovzr0NxKC3XtOJZNS4IYrbiMYsL6Rd0npE1VFuntppV1/vbhv0d/SFq6ucWqXqlCs224KsHja8SXMHBV5OsViSqFV/ObcyzMspKwUR0H5NZIiKyKKdvZ+OnS5pnL8d390XvgIZtT6XqlZSX4NS9U0ZdMy9kHmYHz4azjbN5gmopGrNSMKB5llWRDmReAzKvayoGZ1zT/Jl/p35zhv0D6BYBuLYDZPamjdcQrBRMRGAyS0REFkStFvDmQU0rHhupGMtGd27iiFoOQRBwI/cGou9G46+7f+HvtL9Rqio1ao4BrQYwka2NuSoFV50/J+F+wnodyLhembwa+iyroTqOBny6mXbO+mClYKKHGpNZIiKyGPv+voPLKfkAgPmDA9HKWd7EEVm2rOIsnLx3En/d/QvRd6ORUVy/baYiiOAmd0OoZ6hpA2xJGlopuKqSfM3WYO0K6/2ENfsWoFbWHYtEDri3B9w7Au6dNNuDXdoBu6aySjARWRSLTWYjIyPx9ddf46+//kJycjKcnZ3Ru3dvrFq1Cr169dKOmz17Nr788ku96zt16oS4uLjGDJmIiBqgoESJd3+9BgDwdrTBwiHtmjgiy1OmKsOF9Av46+5f+OvuX7iafbXacb52vujfqj8G+A5AbFYstsZsrXVeAQIiOkWwanBtDK0U/MdHmu2zggAU3HtgW/D9vxfcNew97TzuJ6xVvjw6Ao5+gLiatkesEkxEFsZik9lPP/0UWVlZeOmllxAUFISMjAy8//776NevH3799VcMGzZMO1YulyMyMlLnermcv80nIrIkG6NuIlOh2fa6bHRn2Fpb7D9hjUYQBNzOv63dOnwm9QyKy4v1xtlKbNHXuy/6t+qP/r790cahjbZg05DWQ3A95zqO3zmuV9W44vVgv8GY23Vuo30ui2NMpeAT7wFxP2la3ZQV1D23SAy4BFSusLp3BDw6aQow2Rr5PDmrBBORhbHYnwQ2btwIT09PnWOjRo1C+/bt8fbbb+sks2KxGP36cTsMEZGlSsoqwhd/3AYA9GjjjAmhvk0cUfOVV5qHk/dOahPYe4X39MaIIEKQWxD6+2qS1+4e3SGtYaVNKpbio6Ef4YuYL7D72m5kFmdqz7nJ3RDRKQJzu87lqmxtkk4aXim4vAS4d17/uNSuMlmtWGF176gpwCQxUZsjVgkmIgtjscnsg4ksANjb2yMoKAjJyclNEBEREZnL2z9fRZmqshVPS2zzolQrcSH9AvJK8+Akc0KoZ6hBCaJSrURMRoz2udfLWZehFtR64zxtPTHAdwD6+/bHIz6PwMXGxeDYpGIpFnRfgLld59YrxodKUbZmS3BG3P0iTNeAexeMm8OjC+DfX7PCWpHAOraqf19WY7BKMBFZEItNZquTl5eHc+fO6azKAkBxcTG8vb2RkZEBHx8fTJw4EW+++SZcXWvffpOeno6MDN3fpMbHx5s8biIiqln0zSz8EpsKAJjUoxV6tDE8CbMESrUS22K2YXfcbp2eru5ydzzZ6UnM6zpPL2FMzk/WPvd6OvU0FEqF3rw2Vjbo7d1bu/razqldg38JIBVL0ce7T4PmMLvGaHtT8TxrxrX7z7Je01QOzogDijLrvr4uYzY0fYVeVgkmIgvQopLZf/zjHygsLMTy5cu1x7p3747u3bsjJCQEAHDs2DF8+OGHOHLkCM6cOQN7+5p7o23atAlr1641e9xERFQ9lVrAmwevAADkUiu8NqplteJRqpV4KfIlnEg5AdEDFW6zirOw8cJGxGTGYP2A9TiXfg7Rd6PxZ8qfuKOovjdoZ9fOCPMNQ3/f/ujh2QMyKxNtP7UE5mh7o1YBuYlVktb7CWvmDaA0v+7rrWSalVW39sCNw4CysI4LWCmYiMgYLSaZXblyJXbu3IlPPvlEp5rxK6+8ojNuxIgR6NGjB6ZOnYrPP/9c73xVzz//PKZNm6ZzLD4+HhMnTjRp7EREVL09Z5Jx9Z4maVgUHghvJ5smjsi0tsVsw4mUEwCgU1ip6uvjd45jyJ4heucBwM3GDf19+yPMNwxhvmFwl7ubP+jmqKFtb8pLgayblSusmRXJ6w3AkF671g73n2HtpNka7NFJszXYJQAQW2nGHHuXlYKJiEysRSSza9euxfr16/HWW2/hhRdeqHP8pEmTYGdnh5MnT9Y6ztPTs9pnc4mIyPzyS5R4/zdNK55WznLMH9yyWvEo1UrsjtutVyG4OhXnrcXW6OnVU7t1uKNLxxb5/LDRDG17c+wdoNOYymdZK7YIZ98GBFXd72PrXiVZ7aRJYD06Aw4+dT/PykrBREQmZ/HJ7Nq1a7FmzRqsWbMGb7zxhsHXCYIAcXU91oiIqFn45MgNZBWWAdC04rGRWjVxRKZ1If2CzjOydXml5yt4qstTkEvYWk6HMW1vjm/QfNXF0a8yUa1odePeCbBzq3+crBRMRGRyFp3Mrlu3DmvWrMGKFSuwevVqg6/bt28fioqK2K6HiKiZup1ZiO1/JQAA+gS4YFw3n6YNyISUKiX+vPsn/nf5f0Zd18axDRPZB5XkA5f2GN72piqRGHBpq0lYq24Rdu8IyGqup9EgrBRMRGRSFpvMvv/++1i1ahVGjRqFsWPH6m0Z7tevHxITEzF9+nRERESgffv2EIlEOHbsGD766CMEBwfj2WefbaLoiYioNm/9dAVKlQCRCFg1Ltjit9Kq1CqcTTuLQ7cP4XDiYeSXGVA86AFOMiczRGYhygo1W4LTrwIZVzV/pscB+dUXwqpV2D+A0GcAt0DT9Wc1FisFExGZhMUmsz/++CMA4JdffsEvv/yid14QBDg6OsLLywsffPAB0tLSoFKp4O/vj8WLF+ONN96AnZ1dY4dNRER1OHEjA79f1WzBnNrTD139LDOJEwQBlzIv4dDtQ/g14VdkFuu2bJGJZRAgoExdVus8IojgJndDqGeoGaM1kLnb3ihLNM+zapPWOCD9CpCbhDq3EBuq42jAK8g0cxERUZOy2GT26NGjdY5xcXHB999/b/5giIjIJMpVaqy734rHztoKrz7WqYkjMt71nOs4dPsQDt0+hBRFis45iViCgb4DMbrtaIS3DseOKzuw8cLGWucTICCiU4Rer9lGZeq2N+VlQFZ8lVXWq5qWN9m3AEFd+7ViCeDWAfDsovlyaw/8tESTYNea8LLtDRFRS2OxySwREbU835xOwvU0BQDg+aHt4eloGa14kvOT8fPtn/FLwi+Iz43XOSeCCH29+2J029F41P9Rne3C87rOQ0xmDI7fOa5X1bji9WC/wZjbdW6jfRY9DWl7oyrXJKhVV1kz4jSJrLq89vcViQHXQMCzM+AZpHm21bOL5pjEWndsVjzb3hARPYSYzBIRUbOQV6TEB4evAwBau8oxb2DbJo6odulF6fjl9i84dPsQLmdd1jvfzaMbRgeMxmMBj8HD1qPaOaRiKT4a+hG+iPkCu6/t1tmK7CZ3Q0SnCMztOrdpV2UNbXtzeBUQMLBylTX9qmbLsKr2bdSASNOP1bPL/YQ1SJPAunUApAb+MoNtb4iIHkpMZomIqFn46Mh15BQpAQBvjO7SLFvx5Jbk4rfE3/BLwi84m3pWrz9sB5cOGNN2DEYFjIKfg59Bc0rFUizovgBzu87FhfQLyCvNg5PMCaGeoU2bxALGtb05uUnzVRun1lWS1vvbhN07Ada2DYuTbW+IiB5KTGaJiKjJxacr8FV0IgDgkbauGBXi3cQRVSpUFiIyKRKHbh9C9N1olAu622P97P0wuu1ojGk7Bu1d2tf7faRiKfp492louKZTVgRc/KZ+bW8cfHRXWT2DNG1vZA6mj7MC294QET10mMwSEVGTW//TFZSr77fiGR9k8lY8SrXSqFXPUlUpTtw5gZ9v/4zjd46jVFWqc95T7onH2j6GMW3HINjNwlsHCQKQdwdIu6z5Sr0MpMUC2TfrLsb0oKErgL7PapLIpsK2N0REDw0ms0RE1KSirqXj6DXN6l9En9YI9jVdKx6lWoltMduwO243skqytMfd5e54stOTmNd1njapLVeX49S9U/j59s+ITIqEQqnQmctJ5oQR/iMwpu0Y9PTsCStx89sGXaeyIs2zrBWJa1qs5s+SPNPM36Zf0yayRET0UGEyS0RETUapUmP9/VY8DjIJlow0XSsepVqJlyJfwomUExA9UIU3qzgLGy9sxKWMS5gVPAuHEw/jt4TfkFOaozPOVmKLYW2GYXTb0QjzDTPvM6ym7OEqCEBecuUqa0XymnUTdT776ugHeIcAXsGarcKHXmPbGyIiapaYzBIRUZP5+mQibmYUAgBeHN4e7vYyk829LWYbTqScAAC9Qk0Vr0+knNCOqWAttsYgv0EY3XY0BvsNhlwiN1lM1WpoD9eywsrVVm3yGguU1rHaKpFrCjB5BQPeXTV/egXrr6zmJLDtDRERNUtMZomIqEnkFJbho99vAAD83Wwxq3+AyeZWqpXYHbdbr3drTcQQI8w3DKPbjsawNsPgYG3GQkVVGdPDVSwBcpN0V1pTL2v6uNb1GZ1a309WQyqTV9d2gCFbpdn2hoiImikms0RE1CQ+/P068oo1rXiWj+kCmcR0z6BeSL+g84xsXd4Pfx+P+j9qsvc3mKE9XP/TQ/Nca2l+7fNJ5IBX0P3EtWK1Nahhz7Gy7Q0RETVTTGaJiKjRXU8rwM5TSQCAAe3dMCLIy6Tz59W1xbY5MKaHa16y/jGnNvdXWUMqk1fXtoatthqLbW+IiKgZYjJLRESNShAErDt4BSq1ALEIWDnO9K14UhQpRo13kpmugnKtFBnAvYvAvfNA/BHjerh2eAxo/6gmefUMAuTOZguzRmx7Q0REzQiTWSIialRHrqbjxI1MAMD0R9qgs7ejyeZOLkjG+2ffx5GkIwaNF0EEN7kbQj1DTRaDVkGqJnG9e+F+AnsByDcuydbR4xkg6HFTRUdERGTxmMwSEVGjKStX462frwIAHG0k+H8jTNOKp0hZhK0xW/Fl7JcoU5cBACRiCcrV5bVeJ0BARKeIhrXcEQSg4N79pPVCZQKrSK39OjtPoDC99jFVsX8rERGRDiazRETUaL78KwG3MzWteF56tCNc7awbNJ8gCDh46yA++vsjpBdXJobj243HCz1ewFun3sLxO8f1qhpXvB7sNxhzu8415g2BvDuapLXqimtd24VdAgCfUMCnO+AbCnh3B2wcgQ+6AIWZYA9XIiIi4zGZJSKiRpGlKMV/jmha8bTzsMPMMP8GzRebGYt/nf4XLmZc1B4LcQvBskeWobtHdwDAR4M34Iuf52N31gVkWlU+l+umUiPCLRRzB2+oeVVWEIDcRN0V13sXgaI6qiS7BmoSVp/u9xPYbjWvqvadzx6uRERE9cRkloiIGsX7h6+joFSz7Xfl2CBIrcT1miezOBP/OfcfHIg/oF1tdbNxw8u9XsbjgY9DLLo/r0oJ6bezsODGb5gLES7YWCNPLIaTWo3QkjJIk5IBxWxN2xmRFZBz+4EV14tASW4tkYgA9w4PrLh2BWyMKCbFHq5ERET1xmSWiIjM7uq9fOw+rWnFM7ijB8I7eRg9h1KlxM6rO7H50mYUKjVblSViCWYEzcD8rvNhb22ve0GVHq5SCOhTUqo/6Y1fgU96aVrN1NbDVSQG3DtVJq0+oZqqwjIHoz+HDvZwJSIiqjcms0REZFaCIODNH69ALQBWYhFWju1idCue43eO490z7yIxP1F7bIjfELza51X4O1azXdmYHq65ibqvRVaAZ5fKbcK+oZo+rtZ2RsVsMPZwJSIiqhcms0REZFa/xqYh+pbmOdMZ/fzRwcvw1czbebfx7pl38UfKH9pjbZ3aYmmfpRjYamDNFyadNK6Ha/sRQKdRmuTVKxiQyg2/1lTYw5WIiMgoTGaJiMhsSstVePt+Kx5nWylefrSDQdcVlBXgs4ufYefVnSgXNM/ZOkgdsCh0ESI619JKJ+8OcPk74Mw24wLtOZM9XImIiCwMk1kiIjKbL/5IQFJ2EQDglUc7wtm29lY8akGNA/EH8PG5j5Fdkg1A00ZncofJeLHHi3CTu+lfpMgArhzQJLFJ0fULlD1ciYiILA6TWSIiMov0ghL8N1LTiqeDpz2efqRNreMvpF/Av07/C1eyrmiP9fTsidf6voYgtyDdwSV5wNWDwOV9wK1jgKDSPe8aCBTcA5TFYA9XIiKilonJLBERmcX7v15HYZkmyVw5LgiSGlrxpBam4sO/P8TPt3/WHvOy9cKS3kswKmBUZbGosiLg+i+aFdgbhwHVA9WJHf2AkMlAyBRN8abjG9jDlYiIqAVjMktERCZ3OSUP3/6dDAAY3tkTgzvqt+IpVZXiy9gvsTVmK4rLiwEAMisZZgfPxtyQubCV2gLlZcCtKCBmH3DtZ6BMoTuJrTsQPBEImQq0fgQQV0mY2cOViIioRWMyS0REJlXRikcQAIlYhOVju+idj0yKxIazG5CiSNEeH+E/Akt6L0ErW28g8U9NAnv1B02rmqpkjkCX8ZoV2LZDAKsa/iljD1ciIqIWjcksERGZ1M8xqTidoCneNKt/ANp52GvP3ci5gXdOv4NTqae0xzq4dMCyPq+hr0oC/PFf4PL3gCJVd1KJDdBxFNB1qqaNjtTGsGDYw5WIiKjFYjJLREQmU6KsbMXjameNxcM1rXjySvPw3/P/xbfXv4VaUAMAnGROeDFwKqbk5UHy7QIg57buZGIJEDhck8B2Gg3IDO9Pq4c9XImIiFocJrMtRFFJIQ6e2IbcojQ423ph3KB5sLWxa+qwdDBG07GEOBmjaVhajMk5NkjLDQYgw/8b0RF2MhF2x+3Gfy/8F3mleQAAK5EYTzh0xD/uJcMpbvUDs4mAgIGaLcRBEwBb10b/PERERGQZRIIg1NazoEVQKBRYsWIFvv32W2RnZ6Nz585YtmwZIiIijJ4rNjYWISEhuHz5MoKDg80QrXGKSgrx1jczcEJ9DTmSysInruVqDBR3wvKnvmryH3wZo+lYQpyM0TQsOUaXcjU6KPwx8/HX8HHMx7iRc0N77hG1FK/dTUIHpVJ3sla9NEWcgicBjj6N9RGIiIioGTE213ooktmRI0fizJkz+Pe//42OHTti165d2Lp1K3bu3Inp06cbNVdzSmaLSgox/8shuGhTCpEgQKhoXwFoX4eWyPDZrGNN9kMvY3y44mSMjBEAIAhA1dcAWinL8Wp2DoYVFUN7xjNIswIbMhlwbddosRMREVHzZGyuVX3Tvxbk559/xuHDh7Fp0yYsWLAAQ4cOxeeff44RI0bg1VdfhUqlauoQ6+2tb2bgoo2mz6LwwA+OFa8v2JTi7W9mNnpsFRij6VhCnIzRNCw9xqqJrEStxuLsXPxfyl0MLyqGyCUAGLQEWBQNPB8NDP4nE1kiIiKqlxb/zOz+/fthb2+PadOm6RyfM2cOpk+fjlOnTqF///5NFF39FZUU4oT6GkQikf4PklUJAn4X4uC6+3mIxY37P7daXY4jwjVAEOmt0uhgjHWyhDgZo2m0tBgdBAGzyuWwfuQZzTbiVj1rv4aIiIjIQC0+mb18+TK6dOkCiUT3o3br1k17vqZkNj09HRkZGTrH4uPjzROokQ6e2KbzjFqNRCIUWonwv9IT5g+qOlaM0WQsIU7GaBotKMYcKyscCF2AJ0a8ZP6YiIiI6KHS4pPZrKwstGunv4XN1dVVe74mmzZtwtq1a80WW0PkFqU1dQhERAbJLUpv6hCIiIioBWrxySwAiGrZ0lbbueeff15ve3J8fDwmTpxoqtDqzdnWC8g1fPx82xEIC3rcbPFUJ/rKD9hSdNjg8YyxZpYQJ2M0jZYYo7OtlxmjISIioodVi09m3dzcql19zc7OBlC5QlsdT09PeHp6mi22hhg3aB7+u/Mz5FrV/sysSBDgohIwb/y6Rq96GhTYB3t3/soYTcAS4mSMptESYxw3aF4jRkdEREQPixZfzbhr1664evUqysvLdY7HxMQAAEJCQpoirAaztbHDIHGn2os/QVNldJC4c5MkYIzRdCwhTsZoGoyRiIiIyDAtPpmdNGkSFAoFvvvuO53jX375JXx9ffHII480UWQNt/yprxBaIgOgWQGpquJ1aIkMbzy1o9Fjq8AYTccS4mSMpsEYiYiIiOrW4pPZ0aNHY8SIEVi0aBE+//xzREVFYf78+fjll1/w7rvvwsrKqqlDrDdbGzt8NusYJqg7wkWl+8Oki0rABHVHfDbrWJOuijBG07GEOBmjaTBGIiIiorqJBOGBX6m3QAqFAsuXL8e3336L7OxsdO7cGa+//joiIiKMnis2NhYhISG4fPkygoODzRBt/RSVFOLgiW3ILUqDs60Xxg2a1+x+iGSMpmMJcTJG02CMRERE9LAwNtd6KJJZU2quySwREREREZElMzbXavHbjImIiIiIiKjlYTJLREREREREFofJLBEREREREVkcSVMHYGlKS0sBAPHx8U0cCRERERERUctRkWNV5Fx1YTJrpOTkZADAxIkTmzYQIiIiIiKiFig5ORk9e/ascxyrGRspNzcXx44dQ+vWrSGTyRr1vePj4zFx4kQcOHAA7du3b9T3pocX7ztqCrzvqCnwvqPGxnuOmkJzvu9KS0uRnJyMIUOGwNnZuc7xXJk1krOzMyZMmNCkMbRv355tgajR8b6jpsD7jpoC7ztqbLznqCk01/vOkBXZCiwARURERERERBaHySwRERERERFZHCazREREREREZHGYzFoQDw8PrF69Gh4eHk0dCj1EeN9RU+B9R02B9x01Nt5z1BRa0n3HasZERERERERkcbgyS0RERERERBaHySwRERERERFZHCazREREREREZHGYzBIREREREZHFYTJLREREREREFofJrAVQKBR4+eWX4evrCxsbG4SGhmL37t1NHRZZmMjISMydOxedO3eGnZ0dWrVqhQkTJuDvv//WG3vu3Dk8+uijsLe3h7OzMyZPnoxbt25VO+8nn3yCzp07QyaToW3btli7di2USqW5Pw5ZsK1bt0IkEsHe3l7vHO89MqU//vgDY8aMgYuLC+RyOTp06IB169bpjOE9R6Zy/vx5TJw4Eb6+vrC1tUXnzp3x5ptvoqioSGcc7zmqr4KCAixduhQjR46Eh4cHRCIR1qxZU+1Yc9xn6enpmD17Ntzd3WFra4uwsDAcOXLElB/ReAI1eyNGjBCcnZ2FzZs3C5GRkcKzzz4rABB27tzZ1KGRBZk6daowdOhQYdOmTcLRo0eFvXv3Cv369RMkEolw5MgR7birV68KDg4OwqBBg4SffvpJ+O6774Tg4GDB19dXSE9P15lz/fr1gkgkEl5//XUhKipKePfddwVra2vhueeea+yPRxbizp07gpOTk+Dr6yvY2dnpnOO9R6a0c+dOQSwWCxEREcIPP/wgREZGCp9//rmwdu1a7Rjec2QqsbGxgo2NjdC9e3dhz549wpEjR4TVq1cLVlZWwuOPP64dx3uOGuL27duCk5OTMHjwYG0+sHr1ar1x5rjPSkpKhJCQEMHPz0/4+uuvhd9++02YMGGCIJFIhKNHj5rzY9eKyWwz99NPPwkAhF27dukcHzFihODr6yuUl5c3UWRkadLS0vSOFRQUCF5eXsLw4cO1x6ZNmya4u7sLeXl52mMJCQmCVCoVli5dqj2WmZkp2NjYCPPnz9eZ86233hJEIpEQGxtrhk9Blm7cuHHC+PHjhVmzZukls7z3yFTu3Lkj2NnZCYsWLap1HO85MpXly5cLAIT4+Hid4/PnzxcACNnZ2YIg8J6jhlGr1YJarRYEQRAyMjJqTGbNcZ9t3LhRACD89ddf2mNKpVIICgoS+vbta6qPaDRuM27m9u/fD3t7e0ybNk3n+Jw5c3D37l2cOnWqiSIjS+Pp6al3zN7eHkFBQUhOTgYAlJeX4+DBg5gyZQocHR214/z9/TF06FDs379fe+yXX35BSUkJ5syZozPnnDlzIAgCDhw4YJ4PQhbr66+/xrFjx7Bp0ya9c7z3yJS2bt2KwsJCvPbaazWO4T1HpiSVSgEATk5OOsednZ0hFothbW3Ne44aTCQSQSQS1TrGXPfZ/v370alTJ4SFhWmPSSQSPPPMMzh9+jRSUlIa+Onqh8lsM3f58mV06dIFEolE53i3bt2054nqKy8vD+fOnUNwcDAA4ObNmyguLtbeX1V169YN8fHxKCkpAVB573Xt2lVnnI+PD9zd3Xlvko709HS8/PLL+Pe//w0/Pz+987z3yJSOHz8OV1dXxMXFITQ0FBKJBJ6enli4cCHy8/MB8J4j05o1axacnZ2xaNEi3Lp1CwUFBTh48CA+++wz/OMf/4CdnR3vOWoU5rrPLl++XOOcABAbG2uyz2AMJrPNXFZWFlxdXfWOVxzLyspq7JCoBfnHP/6BwsJCLF++HEDl/VTTPScIAnJycrRjZTIZ7Ozsqh3Le5Oqev7559GpUycsWrSo2vO898iUUlJSUFRUhGnTpuHJJ5/E77//jldffRU7duzAmDFjIAgC7zkyqYCAAERHR+Py5csIDAyEo6Mjxo8fj1mzZuHjjz8GwP/OUeMw133WXHMSSd1DqKnVtp2grq0GRDVZuXIldu7ciU8++QS9evXSOWfoPcd7kwzx3Xff4ccff8T58+frvC9475EpqNVqlJSUYPXq1Vi2bBkAIDw8HNbW1nj55Zdx5MgR2NraAuA9R6aRkJCA8ePHw8vLC/v27YOHhwdOnTqF9evXQ6FQYNu2bdqxvOeoMZjjPmuO9yRXZps5Nze3an/TkZ2dDaD637oQ1WXt2rVYv3493nrrLbzwwgva425ubgCq/+1adnY2RCIRnJ2dtWNLSkr0Wg5UjOW9SYCmtdg//vEPvPjii/D19UVubi5yc3NRVlYGAMjNzUVhYSHvPTKpivvpscce0zk+evRoAJqWFbznyJSWLVuG/Px8/Prrr5gyZQoGDx6MV199FR999BG++OILHDt2jPccNQpz3WfNNSdhMtvMde3aFVevXkV5ebnO8ZiYGABASEhIU4RFFmzt2rVYs2YN1qxZgzfeeEPnXGBgIORyufb+qiomJgbt27eHjY0NgMrnKx4cm5qaiszMTN6bBADIzMxEWloa3n//fbi4uGi/vvnmGxQWFsLFxQVPP/007z0yqeqe6wIAQRAAAGKxmPccmdSFCxcQFBSkt12zT58+AKDdfsx7jszNXPdZ165da5wTaLqchMlsMzdp0iQoFAp89913Ose//PJL+Pr64pFHHmmiyMgSrVu3DmvWrMGKFSuwevVqvfMSiQTjx4/H999/j4KCAu3xpKQkREVFYfLkydpjo0aNgo2NDbZv364zx/bt2yESiTBx4kRzfQyyIN7e3oiKitL7euyxx2BjY4OoqCisX7+e9x6Z1JQpUwAAhw4d0jn+888/AwD69evHe45MytfXF7GxsVAoFDrHo6OjAQB+fn6856hRmOs+mzRpEuLi4nQ6qZSXl+Prr7/GI488Al9fX7N9plo1TUcgMsaIESMEFxcXYcuWLUJkZKTw3HPPCQCEr7/+uqlDIwvy3nvvCQCEUaNGCdHR0XpfFa5evSrY29sLgwcPFn7++Wfh+++/F0JCQmpttP3GG28IR48eFTZs2CDIZDI2dKc6VddnlvcemdL48eMFmUwmrFu3Tjh8+LDwr3/9S7CxsRHGjRunHcN7jkzl//7v/wSRSCT069dP2LNnj3DkyBHhrbfeEuzt7YWgoCChtLRUEATec9RwP//8s7B3717hiy++EAAI06ZNE/bu3Svs3btXKCwsFATBPPdZSUmJEBwcLLRu3VrYuXOncPjwYWHSpEmCRCIRjh492mif/0FMZi1AQUGBsHjxYsHb21uwtrYWunXrJnzzzTdNHRZZmCFDhggAavyq6uzZs8Lw4cMFW1tbwdHRUZg4caJeI/gKH3/8sdCxY0fB2tpaaNOmjbB69WqhrKysMT4SWbDqkllB4L1HplNUVCS89tprQuvWrQWJRCK0adNGeP3114WSkhKdcbznyFQiIyOFkSNHCt7e3oJcLhc6duwoLFmyRMjMzNQZx3uOGsLf37/Gn+Vu376tHWeO+yw1NVWYOXOm4OrqKtjY2Aj9+vUTDh8+bK6PahCRINx/gISIiIiIiIjIQvCZWSIiIiIiIrI4TGaJiIiIiIjI4jCZJSIiIiIiIovDZJaIiIiIiIgsDpNZIiIiIiIisjhMZomIiIiIiMjiMJklIiIiIiIii8NkloiIiIiIiCwOk1kiIiIiIiKyOExmiYiIiIiIyOIwmSUiImpkP//8M0QiEXbs2GHyuWfPng2RSASRSISQkJAGz/ef//ynzrkOHDigfU+RSISzZ882+H2JiIjqwmSWiIiokZ07dw4A0KtXL7PM7+3tjejoaOzatavBc33xxRcAgNjYWJw6daraMUOGDEF0dDRWrFjR4PcjIiIyFJNZIiKiRnbu3DnY2tqic+fOZplfJpOhX79+6NatW4PmOXv2LC5evIixY8cCALZt21btOBcXF/Tr1w+BgYENej8iIiJjMJklIiJqZH///Te6d+8OKyurRn3fNWvWQCQS4fz585g8eTIcHR3h5OSEZ555BhkZGXrjK5LXf//73+jfvz92796NoqKiRo2ZiIioJkxmiYiIGlFWVhaSkpK0W4xLS0vh7e2NvLy8Oq9Vq9Vo3bo1UlNTGxTDpEmT0L59e+zbtw9r1qzBgQMH8Nhjj0GpVGrHFBcX45tvvkGfPn0QEhKCuXPnoqCgAHv37m3QexMREZkKk1kiIqJG9ODzsjKZDKmpqXBycqrzWrFYjOTkZHh7ezcohsmTJ+Pdd9/FyJEj8corr2DLli04f/48vv32W+2Yffv2IS8vD/PmzQMAPPnkk7C3t69xqzEREVFjYzJLRETUiP7++28AQM+ePZsshqefflrn9RNPPAGJRIKoqCjtsW3btkEulyMiIgIAYG9vj2nTpuHEiRO4ceNGo8ZLRERUHSazREREjejcuXOwsbFBUFAQAODjjz/Gs88+qz3//vvv44knnsCsWbPg7OyMzp07Iy4uDgCwZcsWvUS0Ph5c2ZVIJHBzc0NWVhYAID4+HsePH8fYsWMhCAJyc3ORm5uLqVOnAqiscExERNSUmMwSERE1onPnzqF79+6QSCQAgEuXLqFr167a8zExMfjrr7+wcOFCZGVloXfv3vj8888BaNrjBAcHNziGB5+5LS8vR1ZWFtzc3ABoklVBELBv3z64uLhovyqqGn/55ZdQqVQNjoOIiKghmMwSERE1kry8PNy6dUtni/GlS5d0WujExMRg7dq1CAsLg5WVFTp16gSRSATAdMnszp07dV5/++23KC8vR3h4OFQqFb788ksEBgYiKipK72vJkiW4d+8eDh061OA4iIiIGkLS1AHQ/2/vjl0ay6I4AB9HE4iFNmlshBFB1Ej+BRsRRAstLAVbS1MrWtrbpk5lRBEE/wNBbIzBIog2giiiWNiYZIvFxw4j7uzoOvOY7+su753LaX/vXO4D4E9xfHwc7XY7ufyp1WpFvV5Pwmyz2Yx6vR5TU1NJTa1Wi8nJyYj4O8wWCoV391GtVqOrqysmJibi9PQ0VlZWolgsxvz8fOzv78fV1VVsbGzE+Pj4d7WFQiE2NzejXC7H9PT0u3sBgJ9lMgsAn+TlJuOXyWyj0Yje3t7keG+j0Yienp7o6+tLal4mt3d3d/Hw8BBfv359dx/VajXOzs5ibm4uVldXY2ZmJg4ODiKbzUa5XI5sNhuLi4uv1ubz+ZidnY29vb24vr5+dy8A8LNMZgHgk5RKpSiVSsn6tSPGxWIxWT89PcX5+XmMjo7G0dFRDA8Px5cvP/Yd+vn5OTo6OqKzs/O7Z/39/bG7u/tq3fb29r/uXalUolKpJOt2ux3NZjNardYP9QYAH8FkFgB+kZOTkzfDbK1Wi4GBgcjlcv/piPHl5WVkMplv9vo/7ezsRCaTSf5JCwCfoaPdbrd/dRMAwNuWlpZicHAwlpeX33zv4uIibm9vIyIil8t9c2HU2tparK+vx83NTeTz+Q/r7f7+PhqNRrIeGRmJ7u7uD9sfAF4jzALAb+7x8THGxsZia2sruTwKAP50jhkDwG/s8PAwhoaGYmFhQZAFgH8wmQUAACB1TGYBAABIHWEWAACA1BFmAQAASB1hFgAAgNQRZgEAAEgdYRYAAIDUEWYBAABIHWEWAACA1BFmAQAASB1hFgAAgNQRZgEAAEgdYRYAAIDUEWYBAABIHWEWAACA1PkLYF4ovGc7bxwAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ "
" ] @@ -1648,9 +1805,9 @@ ], "source": [ "I_stim_vec = np.linspace(10E-12, 1E-9, 20) # [A]\n", - "rate_vec = measure_fI_curve(I_stim_vec, neuron_model_name_no_sfa)\n", - "rate_vec_adapt = measure_fI_curve(I_stim_vec, neuron_model_name_adapt_curr)\n", - "rate_vec_thresh_adapt = measure_fI_curve(I_stim_vec, neuron_model_name_adapt_thresh)\n", + "rate_vec = measure_fI_curve(I_stim_vec, neuron_model_name_no_sfa, module_name_no_sfa)\n", + "rate_vec_adapt = measure_fI_curve(I_stim_vec, neuron_model_name_adapt_curr, module_name_adapt_curr)\n", + "rate_vec_thresh_adapt = measure_fI_curve(I_stim_vec, neuron_model_name_adapt_thresh, module_name_adapt_thresh)\n", "plot_fI_curve(I_stim_vec, {\"No adap\": rate_vec,\n", " \"Curr adap\" : rate_vec_adapt,\n", " \"Thr adap\" : rate_vec_thresh_adapt})" @@ -1712,7 +1869,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1723,8 +1880,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -1752,10 +1909,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", "CMake Warning (dev) at CMakeLists.txt:93 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -1770,27 +1923,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_7f7508b0764a4864aedebc17252a1670_module Configuration Summary\n", + "nestml_db743385b3864e6e9f1eb9b781769b0e_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_7f7508b0764a4864aedebc17252a1670_module' using\n", + "You can now build and install 'nestml_db743385b3864e6e9f1eb9b781769b0e_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_7f7508b0764a4864aedebc17252a1670_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_db743385b3864e6e9f1eb9b781769b0e_module.so will be installed to\n", + " /tmp/nestml_target_66ed1u9x\n", "The module can be loaded into NEST using\n", - " (nestml_7f7508b0764a4864aedebc17252a1670_module) Install (in SLI)\n", - " nest.Install(nestml_7f7508b0764a4864aedebc17252a1670_module) (in PyNEST)\n", + " (nestml_db743385b3864e6e9f1eb9b781769b0e_module) Install (in SLI)\n", + " nest.Install(nestml_db743385b3864e6e9f1eb9b781769b0e_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -1802,41 +1955,35 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target\n", - "[ 33%] Building CXX object CMakeFiles/nestml_7f7508b0764a4864aedebc17252a1670_module_module.dir/nestml_7f7508b0764a4864aedebc17252a1670_module.o\n", - "[ 66%] Building CXX object CMakeFiles/nestml_7f7508b0764a4864aedebc17252a1670_module_module.dir/af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml.cpp: In member function ‘void af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml.cpp:201:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 201 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 33%] Building CXX object CMakeFiles/nestml_db743385b3864e6e9f1eb9b781769b0e_module_module.dir/nestml_db743385b3864e6e9f1eb9b781769b0e_module.o\n", + "[ 66%] Building CXX object CMakeFiles/nestml_db743385b3864e6e9f1eb9b781769b0e_module_module.dir/iaf_psc_alpha_adapt_thresh_OU_neuron_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_OU_neuron_nestml.cpp: In member function ‘void iaf_psc_alpha_adapt_thresh_OU_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_OU_neuron_nestml.cpp:209:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 209 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml.cpp: In member function ‘virtual void af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml.cpp:329:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 329 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_OU_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_alpha_adapt_thresh_OU_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_OU_neuron_nestml.cpp:342:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 342 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/af_psc_alpha_adapt_thresh_OU_neuron7f7508b0764a4864aedebc17252a1670_nestml.cpp:327:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 327 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/spike_frequency_adaptation/target/iaf_psc_alpha_adapt_thresh_OU_neuron_nestml.cpp:337:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 337 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "[100%] Linking CXX shared module nestml_7f7508b0764a4864aedebc17252a1670_module.so\n", - "[100%] Built target nestml_7f7508b0764a4864aedebc17252a1670_module_module\n", - "[100%] Built target nestml_7f7508b0764a4864aedebc17252a1670_module_module\n", + "[100%] Linking CXX shared module nestml_db743385b3864e6e9f1eb9b781769b0e_module.so\n", + "[100%] Built target nestml_db743385b3864e6e9f1eb9b781769b0e_module_module\n", + "[100%] Built target nestml_db743385b3864e6e9f1eb9b781769b0e_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_7f7508b0764a4864aedebc17252a1670_module.so\n", - "\n", - "Oct 19 03:53:10 Install [Info]: \n", - " loaded module nestml_7f7508b0764a4864aedebc17252a1670_module\n" + "-- Installing: /tmp/nestml_target_66ed1u9x/nestml_db743385b3864e6e9f1eb9b781769b0e_module.so\n" ] } ], "source": [ "# generate and build code\n", - "module_name, neuron_model_name_adapt_thresh_ou = \\\n", - " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha_adapt_thresh_OU.nestml\")\n", - "\n", - "# load dynamic library (NEST extension module) into NEST kernel\n", - "nest.Install(module_name)" + "module_name_adapt_thresh_ou, neuron_model_name_adapt_thresh_ou = \\\n", + " NESTCodeGeneratorUtils.generate_code_for(\"models/iaf_psc_alpha_adapt_thresh_OU.nestml\")" ] }, { @@ -1848,7 +1995,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1856,28 +2003,34 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:53:10 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:12 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:10 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 300\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:10 SimulationManager::run [Info]: \n", + "Apr 19 11:17:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:10 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:12 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:10 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 300\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:10 SimulationManager::run [Info]: \n", + "Apr 19 11:17:12 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, @@ -1887,13 +2040,13 @@ "array([ 16.1, 36.1, 60.8, 90.5, 124.4, 161. , 198.9, 237.4, 276.1])" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1913,8 +2066,16 @@ } ], "source": [ - "evaluate_neuron(neuron_model_name_adapt_thresh_ou, stimulus_type=\"constant\", mu=500.)\n", - "evaluate_neuron(neuron_model_name_adapt_thresh_ou, stimulus_type=\"Ornstein-Uhlenbeck\", mu=500., sigma=0.)" + "evaluate_neuron(neuron_model_name_adapt_thresh_ou,\n", + " module_name_adapt_thresh_ou,\n", + " stimulus_type=\"constant\",\n", + " mu=500.)\n", + "\n", + "evaluate_neuron(neuron_model_name_adapt_thresh_ou,\n", + " module_name_adapt_thresh_ou,\n", + " stimulus_type=\"Ornstein-Uhlenbeck\",\n", + " mu=500.,\n", + " sigma=0.)" ] }, { @@ -1926,7 +2087,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1934,22 +2095,25 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:53:10 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:13 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:10 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 300\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:10 SimulationManager::run [Info]: \n", + "Apr 19 11:17:13 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1960,6 +2124,7 @@ ], "source": [ "spike_times = evaluate_neuron(neuron_model_name_adapt_thresh_ou,\n", + " module_name_adapt_thresh_ou,\n", " stimulus_type=\"Ornstein-Uhlenbeck\",\n", " mu=500.,\n", " sigma=200.)" @@ -1974,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1982,16 +2147,19 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:13 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:11 SimulationManager::run [Info]: \n", + "Apr 19 11:17:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "Mean: 35.57857142857143\n", "Std. dev.: 15.445684953198752\n" @@ -1999,7 +2167,7 @@ }, { "data": { - "image/png": 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81LVrV82aNUujR49u0XkzMzPVq1cvTZo0qcH+qqoqSVJISIhDX0hIiCzL0rlz5xQZGdng65cuXarc3NwW1QYAAADA8zgVbMrLy11UxjfWr1+v999/X4cPH3YITd91s/6b9WVmZmrkyJH12srKypSWltasWgEAAAB4BqeCjatdvnxZkydP1rPPPquoqCidP39eknTjxg1J0vnz5+Xj46PQ0FBJ3+zcfFt1dbVsNpuCg4MbnSciIkIREREurx8AAACAezgdbKqrq/Uf//Ef2rlzp6qqqhQWFqakpCRNnTpVHTp0aNa5Kisr9eWXX2rx4sVavHixQ3+HDh00bNgwrVu3Tv7+/iouLnYYU1xcrOjo6EY/XwMAAADgzuNUsDl9+rQeeughnTp1Svfdd5/uuecenTlzRvPnz9eqVav0xz/+sVlPRuvYsaOKiooc2vPy8rRnzx5t2bJFYWFh8vb2VmpqqjZs2KBf/epXuuuuuyRJp06dUlFRkaZNm+bMZQEAAAAwjFPBZvbs2bp27Zr279+v/v3717UfOHBAqampmj17tlauXNnk8/n5+SkhIcGhfeXKlWrTpk29vtzcXPXv318pKSnKysqS3W5Xdna2wsLCNH36dCeuCgAAAIBpnPoem61bt2rBggX1Qo0k9e/fX/PmzdOWLVucKu5mYmJitHv3bvn4+GjEiBF6+umnFR0drb179yo8PPy2zQsAAADA8zi1Y3PhwgV17dq1wb5u3brpwoULzpy+zsqVKxvc+YmLi9OOHTtcMgcAAAAAczm1Y9OtWzdt3ry5wb4tW7aoW7duzpweAAAAAJrEqR2bsWPHKisrS7W1tRozZowiIyP1+eef680339Srr76qvLw8V9UJAAAAAI1yKtg8//zzOnHihH7961/rtddeq2u3LEsTJkzQjBkznC4QAAAAAG7FqWBjs9n0+uuv67nnnlNRUZGqqqoUGhqqwYMHq2fPnq6qEQAAAABuqtmfsTl37pwyMjJUUFBQ19arVy9NnDhRc+bM0cSJE1VaWqqMjAxVVVW5tFgAAAAAaEizg83y5cv15z//WcnJyY2OSU5OVnFxcb3b0wAAAADgdml2sHn77bc1fvx4eXs3fhebt7e3xo8fr9///vdOFQcAAAAATdHsYFNaWqp//Md/vOW4H/7whyotLW1RUQAAAADQHM0ONjU1NfLx8bnlOB8fH3311VctKgoAAAAAmqPZwSYyMlLHjh275bijR4+qY8eOLSoKAAAAAJqj2cFm0KBBWrp06U13Y7766istW7ZMiYmJThUHAAAAAE3R7GAzbdo0HT9+XMOHD9eZM2cc+s+cOaO0tDR9/PHHmjZtmkuKBAAAAICbafYXdMbGxuq1115TZmamunXrpri4OHXr1k2S9Mknn+jgwYOqra3VsmXL1K9fP5cXDAAAAADf1exgI0njx49X37599dJLL6moqEgffPCBJKldu3ZKTk7WrFmzFB8f79JCAQAAAKAxLQo2kvTAAw/o/fffV21trSorKyVJYWFh8vJq9t1tAAAAAOCUFgebr3l5eSkiIsIVtQAAAABAi7C9AgAAAMB4BBsAAAAAxiPYAAAAADAewQYAAACA8Qg2AAAAAIxHsAEAAABgPIINAAAAAOMRbAAAAAAYj2ADAAAAwHgEGwAAAADGI9gAAAAAMB7BBgAAAIDxCDYAAAAAjEewAQAAAGA8gg0AAAAA4xFsAAAAABiPYAMAAADAeAQbAAAAAMYj2AAAAAAwHsEGAAAAgPG83V0AAOD26Jq12d0ltMj6kY5tDV1Led7QVqgGAGAKdmwAAAAAGI9gAwAAAMB4BBsAAAAAxiPYAAAAADAewQYAAACA8Qg2AAAAAIxHsAEAAABgPIINAAAAAOMRbAAAAAAYj2ADAAAAwHgEGwAAAADGI9gAAAAAMB7BBgAAAIDxCDYAAAAAjEewAQAAAGA8gg0AAAAA4xFsAAAAABiPYAMAAADAeB4VbHbt2qVx48YpJiZGAQEB+sEPfqBhw4bp4MGDDmMPHTqkpKQkBQYGKjg4WOnp6Tp58qQbqgYAAADgbh4VbJYtW6by8nJNmTJFhYWFWrJkiSoqKhQfH69du3bVjTt+/LgSEhJ048YNrV27VitWrFBpaakGDBigs2fPuvEKAAAAALiDt7sL+LbXXntNERER9dqSk5MVHR2tl156SYMHD5YkZWdny9fXVwUFBQoKCpIkxcXFqUePHsrPz9eiRYtavXYAAAAA7uNROzbfDTWSFBgYqN69e+uzzz6TJNXU1KigoEAZGRl1oUaSunTposTERG3cuLHV6gUAAADgGTxqx6YhFy5c0KFDh+p2a06cOKFr164pNjbWYWxsbKy2b98uu90uPz+/Rs9ZUVHhcMtaWVmZawsHAAAA0Go8PthMnjxZV65c0Zw5cyRJVVVVkqSQkBCHsSEhIbIsS+fOnVNkZGSj51y6dKlyc3NvT8EAAAAAWp1HB5sXX3xRb731ll599VXFxcXV67PZbI2+7mZ9kpSZmamRI0fWaysrK1NaWlqLawUAAADgPh4bbHJzc7VgwQItXLhQzzzzTF17aGiopG92br6turpaNptNwcHBNz13REREg5/nAQAAAGAmj3p4wNdyc3M1d+5czZ07V7Nnz67X1717d/n7+6u4uNjhdcXFxYqOjr7p52sAAAAA3Hk8LtjMnz9fc+fO1S9/+Uvl5OQ49Ht7eys1NVUbNmzQpUuX6tpPnTqloqIipaent2a5AAAAADyAR92KtnjxYmVnZys5OVlDhw7VBx98UK8/Pj5e0t93dPr376+UlBRlZWXJbrcrOztbYWFhmj59ujtKBwAAAOBGHhVs3n//fUnS1q1btXXrVod+y7IkSTExMdq9e7dmzpypESNGyNvbW4MHD1Z+fr7Cw8NbtWYAAAAA7udRwWb37t1NHhsXF6cdO3bcvmIAAAAAGMPjPmMDAAAAAM1FsAEAAABgPIINAAAAAOMRbAAAAAAYj2ADAAAAwHgEGwAAAADGI9gAAAAAMB7BBgAAAIDxCDYAAAAAjEewAQAAAGA8gg0AAAAA4xFsAAAAABiPYAMAAADAeAQbAAAAAMYj2AAAAAAwHsEGAAAAgPG83V0AAACtpWvWZneXcFuV5w11dwkA4Dbs2AAAAAAwHsEGAAAAgPEINgAAAACMR7ABAAAAYDyCDQAAAADjEWwAAAAAGI9gAwAAAMB4BBsAAAAAxiPYAAAAADAewQYAAACA8Qg2AAAAAIxHsAEAAABgPIINAAAAAOMRbAAAAAAYj2ADAAAAwHgEGwAAAADG83Z3AQAAwL26Zm12dwm3TXneUHeXAKCVsGMDAAAAwHgEGwAAAADGI9gAAAAAMB7BBgAAAIDxCDYAAAAAjEewAQAAAGA8gg0AAAAA4xFsAAAAABiPYAMAAADAeAQbAAAAAMYj2AAAAAAwHsEGAAAAgPEINgAAAACMR7ABAAAAYDyCDQAAAADjEWwAAAAAGI9gAwAAAMB4BBsAAAAAxiPYAAAAADCet7sLAAAA8DRdsza7u4TbpjxvaItex3sCT2fsjs3ly5c1depURUVFyc/PT/fff7/efvttd5cFAAAAwA2M3bFJT0/XgQMHlJeXp549e2r16tUaNWqUamtrNXr0aHeXBwAAAKAVGRlsCgsLtX379rowI0mJiYn69NNP9fzzz+vJJ59UmzZt3FwlAAAA7mTcnudZjLwVbePGjQoMDNTIkSPrtY8dO1ZnzpzR/v373VQZAAAAAHcwMtiUlJTovvvuk7d3/Q2n2NjYun4AAAAA3x9G3opWVVWle++916E9JCSkrv9mKioqdPbs2Xptx44dkySVlZW5qMqWuXH2U7fOf7sdPXq02a+5ne/JiRN/a9X5GuJp74knaMl78rcTNxza7qT36fu0Tpr65/L79J40VUveE+nOfl94TxzxnjjiPXHU0vfElb7+d/n169eb9gLLQD169LCSk5Md2s+cOWNJsl5++eWbvj4nJ8eSxMHBwcHBwcHBwcHh4cemTZualBGM3LEJDQ1tcFemurpa0jc7N43JzMx0+HzOxYsXVVpaqn79+snX19d1xcKjlZWVKS0tTZs2bVJ0dLS7y8EdgDUFV2NNwZVYT3C127mmrl+/rs8++0yDBg1q0ngjg02/fv20Zs0a1dTU1PucTXFxsSSpb9++N319RESEIiIiHNofeOAB1xYKY0RHR6tPnz7uLgN3ENYUXI01BVdiPcHVbtea+uEPf9jksUY+PGD48OG6fPmy1q9fX6/9d7/7naKiovSjH/3ITZUBAAAAcAcjd2yGDBmiRx55RJMmTdLFixcVHR2tNWvWaOvWrXrzzTf5DhsAAADge8bIYCNJGzZs0Jw5c5Sdna3q6mrFxMRozZo1+slPfuLu0gAAAAC0MmODTWBgoJYsWaIlS5a4uxQYLDw8XDk5OQoPD3d3KbhDsKbgaqwpuBLrCa7mSWvKZlmW5e4iAAAAAMAZRj48AAAAAAC+jWADAAAAwHgEGwAAAADGI9gAAAAAMB7BBnesXbt2ady4cYqJiVFAQIB+8IMfaNiwYTp48KDD2EOHDikpKUmBgYEKDg5Wenq6Tp486YaqYZrly5fLZrMpMDDQoY91hab6wx/+oMcff1wdOnSQv7+/evToofnz59cbw3pCUx0+fFhpaWmKiopSu3btFBMTo3nz5unq1av1xrGm8F2XLl3SCy+8oEcffVTh4eGy2WyaO3dug2Obs35effVVxcTEyNfXV926dVNubq6++uorl9dPsMEda9myZSovL9eUKVNUWFioJUuWqKKiQvHx8dq1a1fduOPHjyshIUE3btzQ2rVrtWLFCpWWlmrAgAE6e/asG68Anu706dOaMWOGoqKiHPpYV2iq1atXa9CgQWrfvr1WrVqlwsJCzZw5U99+aCnrCU117NgxPfjggyovL9crr7yigoIC/eQnP9G8efM0atSounGsKTSkqqpKb7zxhq5fv660tLRGxzVn/SxcuFBTpkxRenq6tm3bpszMTL300kuaPHmy6y/AAu5QX375pUPbpUuXrLvvvtt6+OGH69pGjhxphYWFWRcuXKhrKy8vt3x8fKwXXnihVWqFmVJSUqzU1FRrzJgxVkBAQL0+1hWa4n//93+tgIAAa9KkSTcdx3pCU82ZM8eSZJWVldVrnzBhgiXJqq6utiyLNYWG1dbWWrW1tZZlWdbZs2ctSVZOTo7DuKaun8rKSsvPz8+aMGFCvdcvXLjQstls1tGjR11aPzs2uGNFREQ4tAUGBqp379767LPPJEk1NTUqKChQRkaGgoKC6sZ16dJFiYmJ2rhxY6vVC7O8+eab2rNnj5YuXerQx7pCUy1fvlxXrlzRzJkzGx3DekJz+Pj4SJLat29frz04OFheXl5q27YtawqNstlsstlsNx3TnPWzdetW2e12jR07tt45xo4dK8uytGnTJpfWT7DB98qFCxd06NAh9enTR5J04sQJXbt2TbGxsQ5jY2NjVVZWJrvd3tplwsNVVFRo6tSpysvLU6dOnRz6WVdoqr179yokJETHjx/X/fffL29vb0VERGjixIm6ePGiJNYTmmfMmDEKDg7WpEmTdPLkSV26dEkFBQV6/fXXNXnyZAUEBLCm4JTmrJ+SkhJJUr9+/eqNi4yMVFhYWF2/qxBs8L0yefJkXblyRXPmzJH093tJJSkkJMRhbEhIiCzL0rlz51q1Rni+zMxM9erVS5MmTWqwn3WFpjp9+rSuXr2qkSNH6sknn9SOHTv0/PPPa9WqVXr88cdlWRbrCc3StWtX7du3TyUlJerevbuCgoKUmpqqMWPGaMmSJZL4fxSc05z1U1VVJV9fXwUEBDQ49utzuYq3S88GeLAXX3xRb731ll599VXFxcXV67vZtuuttmTx/bJ+/Xq9//77Onz48C3XBusKt1JbWyu73a6cnBxlZWVJkhISEtS2bVtNnTpVO3fuVLt27SSxntA05eXlSk1N1d13361169YpPDxc+/fv14IFC3T58mX99re/rRvLmoIzmrp+WnOdEWzwvZCbm6sFCxZo4cKFeuaZZ+raQ0NDJanBnxhUV1fLZrMpODi4tcqEh7t8+bImT56sZ599VlFRUTp//rwk6caNG5Kk8+fPy8fHh3WFJgsNDdVf//pXPfbYY/XahwwZoqlTp+rQoUMaNmyYJNYTmiYrK0sXL17UkSNH6n5KPnDgQIWFhWncuHF66qmn1LFjR0msKbRMc/6OCw0Nld1u19WrV+t+SPPtsd/9QbOzuBUNd7zc3FzNnTtXc+fO1ezZs+v1de/eXf7+/iouLnZ4XXFxsaKjo+Xn59dapcLDVVZW6ssvv9TixYvVoUOHumPNmjW6cuWKOnTooJ/+9KesKzRZQ/eoS6p71LOXlxfrCc1y5MgR9e7d2+HWn/79+0tS3S1qrCm0VHPWz9efrfnu2C+++EKVlZXq27evS2sj2OCONn/+fM2dO1e//OUvlZOT49Dv7e2t1NRUbdiwQZcuXaprP3XqlIqKipSent6a5cLDdezYUUVFRQ7HY489Jj8/PxUVFWnBggWsKzRZRkaGJGnLli312gsLCyVJ8fHxrCc0S1RUlI4eParLly/Xa9+3b58kqVOnTqwpOKU56yc5OVl+fn5auXJlvXOsXLlSNpvtpt+V0yIufXg04EHy8/MtSVZycrK1b98+h+Nrf/nLX6zAwEBr4MCBVmFhobVhwwarb9++VlRUlFVRUeHGK4ApGvoeG9YVmio1NdXy9fW15s+fb23fvt16+eWXLT8/PyslJaVuDOsJTfXee+9ZNpvNio+Pt9555x1r586d1sKFC63AwECrd+/e1vXr1y3LYk2hcYWFhda7775rrVixwpJkjRw50nr33Xetd99917py5YplWc1bPwsWLLBsNps1e/Zsa/fu3da//du/Wb6+vtb48eNdXjvBBnesQYMGWZIaPb7to48+sh5++GGrXbt2VlBQkJWWlubw5WZAYxoKNpbFukLTXL161Zo5c6bVuXNny9vb27rnnnusWbNmWXa7vd441hOaateuXdajjz5qdezY0fL397d69uxpTZ8+3aqsrKw3jjWFhnTp0qXRfzt98skndeOas36WLFli9ezZ02rbtq11zz33WDk5OdaNGzdcXrvNsv7/Rl4AAAAAMBSfsQEAAABgPIINAAAAAOMRbAAAAAAYj2ADAAAAwHgEGwAAAADGI9gAAAAAMB7BBgAAAIDxCDYAAAAAjEewAQAAAGA8gg0A4LZYuXKlbDabPvroo3rt27Zt06OPPqqoqCj5+voqKipKCQkJysvLqzeua9euSklJueU8Nput7sjPz3fpNdzKkSNH6s2/bt26Vp0fAPANgg0AoNX85je/UXJysoKCgvTrX/9a27Zt06JFi3Tfffc5FQp+/vOfa9++ffrpT3/qwmpvrWfPntq3b59ee+21Vp0XAODI290FAAC+P15++WUNHDjQIcT87Gc/U21tbYvP26lTJ8XHxztbXrO1a9dO8fHxstvtrT43AKA+dmwAAK2mqqpKkZGRDfZ5ebn2r6Svb4XbtWuXxo8fr9DQUAUFBempp57SlStX9MUXX+if//mfFRwcrMjISM2YMUNfffVVvXMsW7ZM//AP/6DAwEDdddddiomJ0ezZs11aJwDANdixAQC0mgceeEDr16/X3LlzNXz4cPXt21dt2rS5rXP+y7/8i9LT0/X222/r8OHDmj17tmpqavTxxx8rPT1dEyZM0I4dO7Ro0SJFRUXpueeekyS9/fbbyszM1LPPPqv8/Hx5eXmprKxMx44du631AgBahmADAGg1v/nNb5SWlqbc3Fzl5ubK399fDz74oIYPH64JEybIx8fH5XOmpKTUPVTgkUce0b59+7RmzRr9+7//u6ZNmyZJSkpK0rZt2/TWW2/VBZs//vGPCg4O1n/+53/Wnevhhx92eX0AANfgVjQAQKvp3r27/vznP2vPnj3Kzc1VUlKSDhw4oGeeeUYPPPDAbfmsynefrHbfffdJkoYOHerQ/umnn9b9+p/+6Z90/vx5jRo1Su+9954qKytdXhsAwHUINgCAVuXl5aWBAwcqOztbv//973XmzBk9+eSTOnjwoFasWOHy+UJCQur9um3bto22fztY/exnP9OKFSv06aefKiMjQxEREfrRj36k7du3u7xGAIDzCDYAALcKCAjQrFmzJEklJSVurqa+sWPH6k9/+pMuXLigzZs3y7IspaSk1NvZAQB4BoINAKDVfP755w22/+Uvf5EkRUVFtWY5TRYQEKAhQ4Zozpw5unHjho4ePerukgAA38HDAwAAraZPnz56+OGHNWTIEHXv3l12u1379+/X4sWLdffdd+vnP/+5u0usM378ePn7++uhhx5SZGSkvvjiC7388stq3769+vfv7+7yAADfQbABALSavLw8bdu2TQsXLtQXX3yhmpoade7cWaNHj9acOXMa/Y4bdxgwYIBWrlyptWvX6ty5cwoLC9OPf/xjrVq1SuHh4e4uDwDwHTbLsix3FwEAQEvZbDa9+OKLys7OVps2bWSz2Vp1/pqaGu3Zs0dJSUl69913NWLEiFadHwDwd3zGBgBgvPnz58vHx0eLFy9u1XmPHDkiHx8fJSUlteq8AABH3IoGADDagQMH6v67c+fOrTp3r1696s3fvXv3Vp0fAPANbkUDAAAAYDxuRQMAAABgPIINAAAAAOMRbAAAAAAYj2ADAAAAwHgEGwAAAADGI9gAAAAAMB7BBgAAAIDxCDYAAAAAjEewAQAAAGA8gg0AAAAA4/0fy1NzIUsrDv4AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -2010,6 +2178,7 @@ ], "source": [ "spike_times = evaluate_neuron(neuron_model_name_adapt_thresh_ou,\n", + " module_name_adapt_thresh_ou,\n", " stimulus_type=\"Ornstein-Uhlenbeck\",\n", " mu=500.,\n", " sigma=200.,\n", @@ -2059,7 +2228,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2067,7 +2236,7 @@ "output_type": "stream", "text": [ "For a Poisson process:\n", - "CV = 0.9727915126649517\n" + "CV = 0.9595231331304875\n" ] } ], @@ -2080,7 +2249,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2105,16 +2274,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "def cv_across_curr(neuron_model_name, neuron_params, N=40):\n", + "def cv_across_curr(neuron_model_name, module_name, neuron_params, N=40):\n", " t_sim = 25E3 # [ms]\n", " CV = np.nan * np.ones(N)\n", " rate = np.nan * np.ones(N)\n", " for i, mu in enumerate(np.logspace(np.log10(150.), np.log10(700.), N)):\n", " spike_times = evaluate_neuron(neuron_model_name,\n", + " module_name=module_name,\n", " stimulus_type=\"Ornstein-Uhlenbeck\",\n", " mu=mu,\n", " sigma=100.,\n", @@ -2135,7 +2305,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2143,985 +2313,1217 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:14 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:11 SimulationManager::run [Info]: \n", + "Apr 19 11:17:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:14 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:11 SimulationManager::run [Info]: \n", + "Apr 19 11:17:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:14 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:11 SimulationManager::run [Info]: \n", + "Apr 19 11:17:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:14 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:11 SimulationManager::run [Info]: \n", + "Apr 19 11:17:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:15 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:15 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:11 SimulationManager::run [Info]: \n", + "Apr 19 11:17:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:15 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:15 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:11 SimulationManager::run [Info]: \n", + "Apr 19 11:17:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:11 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:15 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:15 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:15 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:15 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:15 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:15 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:15 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:15 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:16 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:16 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:16 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:16 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:16 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:16 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:16 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:16 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:16 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:16 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:16 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:16 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:17 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:17 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:17 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:17 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:12 SimulationManager::run [Info]: \n", + "Apr 19 11:17:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:17 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:17 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:13 SimulationManager::run [Info]: \n", + "Apr 19 11:17:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:18 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:18 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:13 SimulationManager::run [Info]: \n", + "Apr 19 11:17:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:18 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:18 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:13 SimulationManager::run [Info]: \n", + "Apr 19 11:17:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:19 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:19 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:19 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:14 SimulationManager::run [Info]: \n", + "Apr 19 11:17:19 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:19 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:19 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:19 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:14 SimulationManager::run [Info]: \n", + "Apr 19 11:17:19 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:15 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:20 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:20 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:15 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:20 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:15 SimulationManager::run [Info]: \n", + "Apr 19 11:17:20 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:15 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:21 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:21 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:15 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:21 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:15 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Apr 19 11:17:21 SimulationManager::run [Info]: \n", + " Simulation finished.\n", + "\n", + "Apr 19 11:17:22 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", "\n", - "Oct 19 03:53:16 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:22 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:16 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:22 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:16 SimulationManager::run [Info]: \n", + "Apr 19 11:17:22 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:17 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:23 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:23 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:17 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:23 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:17 SimulationManager::run [Info]: \n", + "Apr 19 11:17:23 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:18 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:25 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:25 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:18 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:25 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:18 SimulationManager::run [Info]: \n", + "Apr 19 11:17:25 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:19 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:26 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:26 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:19 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:26 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:19 SimulationManager::run [Info]: \n", + "Apr 19 11:17:26 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:19 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:28 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:28 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:19 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:28 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:19 SimulationManager::run [Info]: \n", + "Apr 19 11:17:28 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:20 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:30 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:30 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:20 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:30 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:20 SimulationManager::run [Info]: \n", + "Apr 19 11:17:30 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:21 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:32 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:32 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:21 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:32 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:21 SimulationManager::run [Info]: \n", + "Apr 19 11:17:32 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:22 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:34 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:34 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:22 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:34 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:22 SimulationManager::run [Info]: \n", + "Apr 19 11:17:34 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:23 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:36 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:36 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:23 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:36 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:23 SimulationManager::run [Info]: \n", + "Apr 19 11:17:36 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:23 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:39 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:39 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:23 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:39 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:23 SimulationManager::run [Info]: \n", + "Apr 19 11:17:39 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:24 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:41 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:24 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:24 SimulationManager::run [Info]: \n", + "Apr 19 11:17:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:25 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:44 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:44 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:25 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:44 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:25 SimulationManager::run [Info]: \n", + "Apr 19 11:17:44 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:27 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:47 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:47 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:27 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:47 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:27 SimulationManager::run [Info]: \n", + "Apr 19 11:17:47 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:29 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:50 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:50 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:29 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:50 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:29 SimulationManager::run [Info]: \n", + "Apr 19 11:17:50 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:30 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:53 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:53 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:30 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:53 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:30 SimulationManager::run [Info]: \n", + "Apr 19 11:17:53 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:32 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:57 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:57 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:32 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:57 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:32 SimulationManager::run [Info]: \n", + "Apr 19 11:17:57 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:32 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:57 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:57 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:32 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:57 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:32 SimulationManager::run [Info]: \n", + "Apr 19 11:17:57 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:32 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:57 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:57 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:32 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:57 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:32 SimulationManager::run [Info]: \n", + "Apr 19 11:17:57 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:32 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:57 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:57 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:32 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:57 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:32 SimulationManager::run [Info]: \n", + "Apr 19 11:17:57 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:32 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:57 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:57 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:32 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:57 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:32 SimulationManager::run [Info]: \n", + "Apr 19 11:17:57 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:32 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:57 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:57 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:32 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:57 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:58 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:58 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:58 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:58 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:58 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:58 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:58 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:58 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:58 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:58 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:58 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:58 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:58 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:58 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:58 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:58 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:58 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:58 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:58 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:58 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:59 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:59 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:59 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:59 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Apr 19 11:17:59 SimulationManager::run [Info]: \n", + " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:59 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:59 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:59 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:59 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:17:59 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:17:59 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:17:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:17:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:00 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:00 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:00 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:18:00 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:33 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:00 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:00 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:00 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:33 SimulationManager::run [Info]: \n", + "Apr 19 11:18:00 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:34 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:00 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:00 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:00 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:34 SimulationManager::run [Info]: \n", + "Apr 19 11:18:00 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:34 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:00 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:00 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:00 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:34 SimulationManager::run [Info]: \n", + "Apr 19 11:18:00 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:34 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:01 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:01 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:01 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:34 SimulationManager::run [Info]: \n", + "Apr 19 11:18:01 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:34 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:01 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:01 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:01 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:34 SimulationManager::run [Info]: \n", + "Apr 19 11:18:01 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:34 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:01 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:01 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:01 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:34 SimulationManager::run [Info]: \n", + "Apr 19 11:18:01 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:34 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:02 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:02 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:02 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:34 SimulationManager::run [Info]: \n", + "Apr 19 11:18:02 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:35 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:02 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:02 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:35 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:02 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:35 SimulationManager::run [Info]: \n", + "Apr 19 11:18:02 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:35 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:02 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:02 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:35 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:02 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:35 SimulationManager::run [Info]: \n", + "Apr 19 11:18:03 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:35 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:03 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:03 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:35 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:03 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:35 SimulationManager::run [Info]: \n", + "Apr 19 11:18:03 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:35 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:03 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:03 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:35 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:03 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:35 SimulationManager::run [Info]: \n", + "Apr 19 11:18:04 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:35 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:04 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:04 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:35 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:04 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:35 SimulationManager::run [Info]: \n", + "Apr 19 11:18:04 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:36 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:05 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:05 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:36 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:05 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:36 SimulationManager::run [Info]: \n", + "Apr 19 11:18:05 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:36 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:05 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:05 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:36 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:05 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:36 SimulationManager::run [Info]: \n", + "Apr 19 11:18:05 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:36 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:06 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:06 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:36 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:06 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:36 SimulationManager::run [Info]: \n", + "Apr 19 11:18:06 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:36 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:07 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:07 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:36 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:07 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:36 SimulationManager::run [Info]: \n", + "Apr 19 11:18:07 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:37 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:07 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:07 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:37 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:07 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:37 SimulationManager::run [Info]: \n", + "Apr 19 11:18:07 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:37 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:08 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:08 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:37 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:08 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:37 SimulationManager::run [Info]: \n", + "Apr 19 11:18:08 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:37 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:09 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:09 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:37 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:09 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:37 SimulationManager::run [Info]: \n", + "Apr 19 11:18:09 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:38 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:10 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:10 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:38 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:10 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:38 SimulationManager::run [Info]: \n", + "Apr 19 11:18:10 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:38 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:11 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:11 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:38 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:39 SimulationManager::run [Info]: \n", - " Simulation finished.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Apr 19 11:18:11 SimulationManager::run [Info]: \n", + " Simulation finished.\n", + "\n", + "Apr 19 11:18:12 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", "\n", - "Oct 19 03:53:39 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:39 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:39 SimulationManager::run [Info]: \n", + "Apr 19 11:18:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:40 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:18:13 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:18:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:40 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:18:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 25000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:40 SimulationManager::run [Info]: \n", + "Apr 19 11:18:13 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, + { + "data": { + "image/png": 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\n", 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\n", @@ -3134,8 +3536,12 @@ } ], "source": [ - "CV_reg, rate_reg = cv_across_curr(neuron_model_name_adapt_thresh_ou, {\"Delta_Theta\" : 0.})\n", - "CV_adapt, rate_adapt = cv_across_curr(neuron_model_name_adapt_thresh_ou, {\"Delta_Theta\" : 5.})\n", + "CV_reg, rate_reg = cv_across_curr(neuron_model_name_adapt_thresh_ou,\n", + " module_name_adapt_thresh_ou,\n", + " {\"Delta_Theta\" : 0.})\n", + "CV_adapt, rate_adapt = cv_across_curr(neuron_model_name_adapt_thresh_ou,\n", + " module_name_adapt_thresh_ou,\n", + " {\"Delta_Theta\" : 5.})\n", "\n", "fig, ax = plt.subplots()\n", "ax.scatter(rate_reg, CV_reg, label=\"non-adapt\")\n", @@ -3165,7 +3571,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -3205,12 +3611,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3233,12 +3639,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3261,7 +3667,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -3269,34 +3675,40 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 03:53:41 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:19:52 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:19:52 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:41 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:19:52 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 2000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:41 SimulationManager::run [Info]: \n", + "Apr 19 11:19:52 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 03:53:41 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:19:52 Install [Info]: \n", + " loaded module nestml_db743385b3864e6e9f1eb9b781769b0e_module\n", + "\n", + "Apr 19 11:19:52 NodeManager::prepare_nodes [Info]: \n", " Preparing 3 nodes for simulation.\n", "\n", - "Oct 19 03:53:41 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:19:52 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 3\n", " Simulation time (ms): 2000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 03:53:41 SimulationManager::run [Info]: \n", + "Apr 19 11:19:52 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -3333,6 +3745,7 @@ "mu = 500.\n", "for i, dth in enumerate(np.linspace(0., 10., N)):\n", " spike_times = evaluate_neuron(neuron_model_name_adapt_thresh_ou,\n", + " module_name=module_name_adapt_thresh_ou,\n", " stimulus_type=\"Ornstein-Uhlenbeck\",\n", " mu=mu,\n", " sigma=500.,\n", diff --git a/doc/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.ipynb b/doc/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.ipynb index 71ed8f31d..3e9cd1d0b 100644 --- a/doc/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.ipynb +++ b/doc/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.ipynb @@ -60,6 +60,14 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -69,7 +77,7 @@ " Copyright (C) 2004 The NEST Initiative\n", "\n", " Version: 3.6.0-post0.dev0\n", - " Built: Oct 23 2023 17:52:55\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -227,28 +235,21 @@ "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:PyGSL is not available. The stiffness test will be skipped.\n", - "WARNING:root:Error when importing: No module named 'pygsl'\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "[1,GLOBAL, INFO]: List of files that will be processed:\n", - "[2,GLOBAL, INFO]: /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron.nestml\n", - "[3,GLOBAL, INFO]: /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse.nestml\n", - "[4,GLOBAL, INFO]: Target platform code will be generated in directory: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target'\n", + "[2,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/iaf_psc_delta_neuron.nestml\n", + "[3,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdp_synapse.nestml\n", + "[4,GLOBAL, INFO]: Target platform code will be generated in directory: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target'\n", + "[5,GLOBAL, INFO]: Target platform code will be installed in directory: '/tmp/nestml_target_2nbwam92'\n", "\n", " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", " Version: 3.6.0-post0.dev0\n", - " Built: Oct 23 2023 17:52:55\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -258,54 +259,50 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[5,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", - "[6,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/Users/pooja/.local/lib/python3.10/site-packages/NESTML-6.0.0.post0.dev0-py3.10.egg/pynestml/codegeneration/resources_nest/point_neuron'\n", - "[7,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/Users/pooja/.local/lib/python3.10/site-packages/NESTML-6.0.0.post0.dev0-py3.10.egg/pynestml/codegeneration/resources_nest/point_neuron'\n", - "[8,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/Users/pooja/.local/lib/python3.10/site-packages/NESTML-6.0.0.post0.dev0-py3.10.egg/pynestml/codegeneration/resources_nest/point_neuron'\n", - "[9,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /Users/pooja/conda/nestml_dev\n", - "[10,GLOBAL, INFO]: Start processing '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron.nestml'!\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[12,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [60:79;60:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", - "[13,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [60:15;60:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", - "[14,GLOBAL, INFO]: Start processing '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse.nestml'!\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[16,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[17,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[18,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "[21,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[22,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[23,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "[25,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [60:79;60:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", - "[26,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [60:15;60:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", - "[28,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[29,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[30,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "[31,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_tr']\n", - "[32,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_tr_post']\n", - "[33,GLOBAL, INFO]: Moving state var defining equation(s) post_tr\n", - "[34,GLOBAL, INFO]: Moving state variables for equation(s) post_tr\n", - "[35,GLOBAL, INFO]: Moving definition of post_tr from synapse to neuron\n", - "[36,GLOBAL, INFO]: \tMoving statement post_tr += 1.0\n", - "[37,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", - "[38,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", - "[39,GLOBAL, INFO]: Copying definition of tau_tr_post from synapse to neuron\n", - "[40,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", - "[41,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", - "[42,GLOBAL, INFO]: \t• Replacing variable post_tr\n", - "[43,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml) in expression: A_minus * post_tr\n", - "[46,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[47,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[48,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n" + "[6,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", + "[7,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[8,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[9,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[10,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /home/charl/julich/nest-simulator-install\n", + "[11,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/iaf_psc_delta_neuron.nestml'!\n", + "[13,iaf_psc_delta_neuron_nestml, INFO, [51:79;51:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[14,iaf_psc_delta_neuron_nestml, INFO, [51:15;51:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[15,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdp_synapse.nestml'!\n", + "[17,neuromodulated_stdp_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[18,neuromodulated_stdp_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[19,neuromodulated_stdp_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[22,neuromodulated_stdp_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[23,neuromodulated_stdp_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[24,neuromodulated_stdp_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[26,iaf_psc_delta_neuron_nestml, INFO, [51:79;51:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[27,iaf_psc_delta_neuron_nestml, INFO, [51:15;51:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[29,neuromodulated_stdp_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[30,neuromodulated_stdp_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[31,neuromodulated_stdp_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[32,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_tr']\n", + "[33,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_tr_post']\n", + "[34,GLOBAL, INFO]: Moving state var defining equation(s) post_tr\n", + "[35,GLOBAL, INFO]: Moving state variables for equation(s) post_tr\n", + "[36,GLOBAL, INFO]: Moving definition of post_tr from synapse to neuron\n", + "[37,GLOBAL, INFO]: \tMoving statement post_tr += 1.0\n", + "[38,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", + "[39,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", + "[40,GLOBAL, INFO]: Copying definition of tau_tr_post from synapse to neuron\n", + "[41,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", + "[42,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", + "[43,GLOBAL, INFO]: \t• Replacing variable post_tr\n", + "[44,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = post_tr__for_neuromodulated_stdp_synapse_nestml) in expression: A_minus * post_tr\n", + "[47,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[48,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[49,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Analysing input:\n", - "INFO:root:{\n", + "INFO:Analysing input:\n", + "INFO:{\n", " \"dynamics\": [\n", " {\n", " \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\",\n", @@ -329,40 +326,43 @@ " \"tau_syn\": \"2\"\n", " }\n", "}\n", - "INFO:root:Processing global options...\n", - "INFO:root:Processing input shapes...\n", - "INFO:root:\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "INFO:root:All known variables: [V_m], all parameters used in ODEs: {E_L, C_m, I_e, I_stim, tau_m}\n", - "INFO:root:No numerical value specified for parameter \"I_stim\"\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", + "INFO:All known variables: [V_m], all parameters used in ODEs: {tau_m, I_e, C_m, I_stim, E_L}\n", + "INFO:No numerical value specified for parameter \"I_stim\"\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "INFO:root:Finding analytically solvable equations...\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "INFO:root:Generating propagators for the following symbols: V_m\n" + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", + "INFO:Finding analytically solvable equations...\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[49,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml\n", - "[50,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml'\n", - "[51,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [52:0;99:0]]: Starts processing of the model 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml'\n" + "[50,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml, neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml\n", + "[51,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta_neuron_nestml'\n", + "[52,iaf_psc_delta_neuron_nestml, INFO, [43:0;94:0]]: Starts processing of the model 'iaf_psc_delta_neuron_nestml'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", - "WARNING:root:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "INFO:root:In ode-toolbox: returning outdict = \n", - "INFO:root:[\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n", + "INFO:Generating propagators for the following symbols: V_m\n", + "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", + "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", " {\n", " \"initial_values\": {\n", " \"V_m\": \"E_L\"\n", @@ -384,29 +384,9 @@ " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\"\n", " }\n", " }\n", - "]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Analysing input:\n", - "INFO:root:{\n", + "]\n", + "INFO:Analysing input:\n", + "INFO:{\n", " \"dynamics\": [\n", " {\n", " \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\",\n", @@ -415,9 +395,9 @@ " }\n", " },\n", " {\n", - " \"expression\": \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml' = (-post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\",\n", + " \"expression\": \"post_tr__for_neuromodulated_stdp_synapse_nestml' = (-post_tr__for_neuromodulated_stdp_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp_synapse_nestml\",\n", " \"initial_values\": {\n", - " \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\": \"0.0\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\": \"0.0\"\n", " }\n", " }\n", " ],\n", @@ -434,115 +414,85 @@ " \"refr_T\": \"2\",\n", " \"tau_m\": \"10\",\n", " \"tau_syn\": \"2\",\n", - " \"tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\": \"20\"\n", + " \"tau_tr_post__for_neuromodulated_stdp_synapse_nestml\": \"20\"\n", " }\n", "}\n", - "INFO:root:Processing global options...\n", - "INFO:root:Processing input shapes...\n", - "INFO:root:\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "INFO:root:\n", - "Processing differential-equation form shape post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\"\n", - "INFO:root:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\" of order 1\n", - "INFO:root:Shape post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml/tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\n", - "INFO:root:All known variables: [V_m, post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml], all parameters used in ODEs: {tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, E_L, C_m, I_e, I_stim, tau_m}\n", - "INFO:root:No numerical value specified for parameter \"I_stim\"\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", + "INFO:\n", + "Processing differential-equation form shape post_tr__for_neuromodulated_stdp_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdp_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp_synapse_nestml\"\n", + "INFO:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdp_synapse_nestml\" of order 1\n", + "INFO:Shape post_tr__for_neuromodulated_stdp_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp_synapse_nestml/tau_tr_post__for_neuromodulated_stdp_synapse_nestml\n", + "INFO:All known variables: [V_m, post_tr__for_neuromodulated_stdp_synapse_nestml], all parameters used in ODEs: {tau_m, I_e, C_m, I_stim, E_L, tau_tr_post__for_neuromodulated_stdp_synapse_nestml}\n", + "INFO:No numerical value specified for parameter \"I_stim\"\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:\n", - "Processing differential-equation form shape post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\"\n", - "INFO:root:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\" of order 1\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "INFO:root:Shape post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml/tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\n", - "INFO:root:Finding analytically solvable equations...\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "INFO:root:Shape post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml/tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\n", - "INFO:root:Generating propagators for the following symbols: V_m, post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\n" + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:\n", + "Processing differential-equation form shape post_tr__for_neuromodulated_stdp_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdp_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp_synapse_nestml\"\n", + "INFO:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdp_synapse_nestml\" of order 1\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", + "INFO:Shape post_tr__for_neuromodulated_stdp_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp_synapse_nestml/tau_tr_post__for_neuromodulated_stdp_synapse_nestml\n", + "INFO:Finding analytically solvable equations...\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[53,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml'\n", - "[54,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [52:0;99:0]]: Starts processing of the model 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml'\n" + "[54,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml'\n", + "[55,iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml, INFO, [43:0;94:0]]: Starts processing of the model 'iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", - "INFO:root:update_expr[post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml] = __P__post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml*post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\n", - "WARNING:root:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "WARNING:root:Not preserving expression for variable \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\" as it is solved by propagator solver\n", - "INFO:root:In ode-toolbox: returning outdict = \n", - "INFO:root:[\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", + "INFO:Shape post_tr__for_neuromodulated_stdp_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp_synapse_nestml/tau_tr_post__for_neuromodulated_stdp_synapse_nestml\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n", + "INFO:Generating propagators for the following symbols: V_m, post_tr__for_neuromodulated_stdp_synapse_nestml\n", + "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", + "INFO:update_expr[post_tr__for_neuromodulated_stdp_synapse_nestml] = __P__post_tr__for_neuromodulated_stdp_synapse_nestml__post_tr__for_neuromodulated_stdp_synapse_nestml*post_tr__for_neuromodulated_stdp_synapse_nestml\n", + "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"post_tr__for_neuromodulated_stdp_synapse_nestml\" as it is solved by propagator solver\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", " {\n", " \"initial_values\": {\n", " \"V_m\": \"E_L\",\n", - " \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\": \"0.0\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\": \"0.0\"\n", " },\n", " \"parameters\": {\n", " \"C_m\": \"250.000000000000\",\n", " \"E_L\": \"-70.0000000000000\",\n", " \"I_e\": \"0\",\n", " \"tau_m\": \"10.0000000000000\",\n", - " \"tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\": \"20.0000000000000\"\n", + " \"tau_tr_post__for_neuromodulated_stdp_synapse_nestml\": \"20.0000000000000\"\n", " },\n", " \"propagators\": {\n", " \"__P__V_m__V_m\": \"exp(-__h/tau_m)\",\n", - " \"__P__post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\": \"exp(-__h/tau_tr_post__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml)\"\n", + " \"__P__post_tr__for_neuromodulated_stdp_synapse_nestml__post_tr__for_neuromodulated_stdp_synapse_nestml\": \"exp(-__h/tau_tr_post__for_neuromodulated_stdp_synapse_nestml)\"\n", " },\n", " \"solver\": \"analytical\",\n", " \"state_variables\": [\n", " \"V_m\",\n", - " \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\"\n", " ],\n", " \"update_expressions\": {\n", " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\",\n", - " \"post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\": \"__P__post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml*post_tr__for_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\": \"__P__post_tr__for_neuromodulated_stdp_synapse_nestml__post_tr__for_neuromodulated_stdp_synapse_nestml*post_tr__for_neuromodulated_stdp_synapse_nestml\"\n", " }\n", " }\n", - "]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Analysing input:\n", - "INFO:root:{\n", + "]\n", + "INFO:Analysing input:\n", + "INFO:{\n", " \"dynamics\": [\n", " {\n", " \"expression\": \"pre_tr' = (-pre_tr) / tau_tr_pre\",\n", @@ -568,24 +518,41 @@ " \"tau_tr_pre\": \"20\"\n", " }\n", "}\n", - "INFO:root:Processing global options...\n", - "INFO:root:Processing input shapes...\n", - "INFO:root:\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", "Processing differential-equation form shape pre_tr with defining expression = \"(-pre_tr) / tau_tr_pre\"\n", - "INFO:root:\tReturning shape: Shape \"pre_tr\" of order 1\n", - "INFO:root:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", - "INFO:root:All known variables: [pre_tr], all parameters used in ODEs: {tau_tr_pre}\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"pre_tr\" of order 1\n", + "INFO:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", + "INFO:All known variables: [pre_tr], all parameters used in ODEs: {tau_tr_pre}\n", + "INFO:\n", "Processing differential-equation form shape pre_tr with defining expression = \"(-pre_tr) / tau_tr_pre\"\n", - "INFO:root:\tReturning shape: Shape \"pre_tr\" of order 1\n", - "INFO:root:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", - "INFO:root:Finding analytically solvable equations...\n", - "INFO:root:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", - "INFO:root:Generating propagators for the following symbols: pre_tr\n", - "INFO:root:update_expr[pre_tr] = __P__pre_tr__pre_tr*pre_tr\n", - "WARNING:root:Not preserving expression for variable \"pre_tr\" as it is solved by propagator solver\n", - "INFO:root:In ode-toolbox: returning outdict = \n", - "INFO:root:[\n", + "INFO:\tReturning shape: Shape \"pre_tr\" of order 1\n", + "INFO:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", + "INFO:Finding analytically solvable equations...\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n", + "INFO:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[57,GLOBAL, INFO]: Analysing/transforming synapse neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.\n", + "[58,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [2:0;66:0]]: Starts processing of the model 'neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n", + "INFO:Generating propagators for the following symbols: pre_tr\n", + "INFO:update_expr[pre_tr] = __P__pre_tr__pre_tr*pre_tr\n", + "WARNING:Not preserving expression for variable \"pre_tr\" as it is solved by propagator solver\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", " {\n", " \"initial_values\": {\n", " \"pre_tr\": \"0.0\"\n", @@ -611,370 +578,224 @@ "name": "stdout", "output_type": "stream", "text": [ - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[56,GLOBAL, INFO]: Analysing/transforming synapse neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.\n", - "[57,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [2:0;66:0]]: Starts processing of the model 'neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml'\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[59,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[60,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[61,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[63,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[64,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[65,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[66,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.cpp\n", - "[67,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h\n", - "[68,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [52:0;99:0]]: Successfully generated code for the model: 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml' in: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[69,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.cpp\n", - "[70,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.h\n", - "[71,iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml, INFO, [52:0;99:0]]: Successfully generated code for the model: 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml' in: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "Generating code for the synapse neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[72,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h\n", - "[73,neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml, INFO, [2:0;66:0]]: Successfully generated code for the model: 'neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml' in: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "[74,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/CMakeLists.txt\n", - "[75,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.h\n", - "[76,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp\n", - "[77,GLOBAL, INFO]: Successfully generated NEST module code in '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "\u001b[33mCMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\u001b[0m\n", - "\u001b[33mCMake Warning (dev) at CMakeLists.txt:95 (project):\n", + "[60,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[61,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[62,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[64,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[65,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[66,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[67,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml.cpp\n", + "[68,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml.h\n", + "[69,iaf_psc_delta_neuron_nestml, INFO, [43:0;94:0]]: Successfully generated code for the model: 'iaf_psc_delta_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "[70,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp\n", + "[71,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.h\n", + "[72,iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml, INFO, [43:0;94:0]]: Successfully generated code for the model: 'iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "Generating code for the synapse neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.\n", + "[73,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h\n", + "[74,neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [2:0;66:0]]: Successfully generated code for the model: 'neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "[75,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module.cpp\n", + "[76,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module.h\n", + "[77,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/CMakeLists.txt\n", + "[78,GLOBAL, INFO]: Successfully generated NEST module code in '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", " both commands.\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", - "\u001b[0m\n", - "-- The CXX compiler identification is AppleClang 14.0.3.14030022\n", + "\n", + "-- The CXX compiler identification is GNU 12.3.0\n", "-- Detecting CXX compiler ABI info\n", "-- Detecting CXX compiler ABI info - done\n", - "-- Check for working CXX compiler: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++ - skipped\n", + "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n", "-- Detecting CXX compile features\n", "-- Detecting CXX compile features - done\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0m-------------------------------------------------------\u001b[0m\n", - "\u001b[0mnestml_272f51c9bf654826ac5db45cd89ed321_module Configuration Summary\u001b[0m\n", - "\u001b[0m-------------------------------------------------------\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0mC++ compiler : /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++\u001b[0m\n", - "\u001b[0mBuild static libs : OFF\u001b[0m\n", - "\u001b[0mC++ compiler flags : \u001b[0m\n", - "\u001b[0mNEST compiler flags : -std=c++11 -Wall -Xclang -fopenmp -O2\u001b[0m\n", - "\u001b[0mNEST include dirs : -I/Users/pooja/conda/nestml_dev/include/nest -I/usr/local/include -I/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX13.3.sdk/usr/include -I/usr/local/Cellar/gsl/2.7/include -I/Users/pooja/conda/nestml_dev/include\u001b[0m\n", - "\u001b[0mNEST libraries flags : -L/Users/pooja/conda/nestml_dev/lib/nest -lnest -lsli -Xclang -fopenmp /usr/local/lib/libltdl.dylib /Users/pooja/conda/nestml_dev/lib/libreadline.dylib /Users/pooja/conda/nestml_dev/lib/libncurses.dylib /usr/local/Cellar/gsl/2.7/lib/libgsl.dylib /usr/local/Cellar/gsl/2.7/lib/libgslcblas.dylib\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0m-------------------------------------------------------\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0mYou can now build and install 'nestml_272f51c9bf654826ac5db45cd89ed321_module' using\u001b[0m\n", - "\u001b[0m make\u001b[0m\n", - "\u001b[0m make install\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0mThe library file libnestml_272f51c9bf654826ac5db45cd89ed321_module.so will be installed to\u001b[0m\n", - "\u001b[0m /Users/pooja/conda/nestml_dev/lib/nest\u001b[0m\n", - "\u001b[0mThe module can be loaded into NEST using\u001b[0m\n", - "\u001b[0m (nestml_272f51c9bf654826ac5db45cd89ed321_module) Install (in SLI)\u001b[0m\n", - "\u001b[0m nest.Install(nestml_272f51c9bf654826ac5db45cd89ed321_module) (in PyNEST)\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[33mCMake Warning (dev) in CMakeLists.txt:\n", + "\n", + "-------------------------------------------------------\n", + "nestml_9557cf48dd76469a8f5dc4ea86b34c42_module Configuration Summary\n", + "-------------------------------------------------------\n", + "\n", + "C++ compiler : /usr/bin/c++\n", + "Build static libs : OFF\n", + "C++ compiler flags : \n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", + "\n", + "-------------------------------------------------------\n", + "\n", + "You can now build and install 'nestml_9557cf48dd76469a8f5dc4ea86b34c42_module' using\n", + " make\n", + " make install\n", + "\n", + "The library file libnestml_9557cf48dd76469a8f5dc4ea86b34c42_module.so will be installed to\n", + " /tmp/nestml_target_2nbwam92\n", + "The module can be loaded into NEST using\n", + " (nestml_9557cf48dd76469a8f5dc4ea86b34c42_module) Install (in SLI)\n", + " nest.Install(nestml_9557cf48dd76469a8f5dc4ea86b34c42_module) (in PyNEST)\n", + "\n", + "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", "\n", - " cmake_minimum_required(VERSION 3.27)\n", + " cmake_minimum_required(VERSION 3.26)\n", "\n", " should be added at the top of the file. The version specified may be lower\n", " if you wish to support older CMake versions for this project. For more\n", " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", - "\u001b[0m\n", - "-- Configuring done (0.9s)\n", + "\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", - "-- Build files have been written to: /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target\n", - "[ 75%] \u001b[32mBuilding CXX object CMakeFiles/nestml_272f51c9bf654826ac5db45cd89ed321_module_module.dir/nestml_272f51c9bf654826ac5db45cd89ed321_module.o\u001b[0m\n", - "[ 75%] \u001b[32mBuilding CXX object CMakeFiles/nestml_272f51c9bf654826ac5db45cd89ed321_module_module.dir/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.o\u001b[0m\n", - "[ 75%] \u001b[32mBuilding CXX object CMakeFiles/nestml_272f51c9bf654826ac5db45cd89ed321_module_module.dir/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.o\u001b[0m\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.cpp:43:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:261:17: warning: 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.cpp:43:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.h:314:17: warning: 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.cpp:43:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.h:254:8: warning: 'register_stdp_connection' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]\n", - " void register_stdp_connection( double t_first_read, double delay );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:481:16: note: overridden virtual function is here\n", - " virtual void register_stdp_connection( double, double );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.cpp:166:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.cpp:251:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.cpp:176:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.cpp:272:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:47:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:261:17: warning: 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:49:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.h:314:17: warning: 'iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:49:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml.h:254:8: warning: 'register_stdp_connection' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]\n", - " void register_stdp_connection( double t_first_read, double delay );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:481:16: note: overridden virtual function is here\n", - " virtual void register_stdp_connection( double, double );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:516:18: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:692:12: warning: unused variable 'cd' [-Wunused-variable]\n", - " double cd;\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:842:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:855:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:1002:18: warning: unused variable '_tr_t' [-Wunused-variable]\n", - " const double _tr_t = start->t_;\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:984:10: warning: unused variable 'timestep' [-Wunused-variable]\n", - " double timestep = 0;\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:518:10: warning: unused variable 'get_thread' [-Wunused-variable]\n", - " auto get_thread = [tid]()\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:61:28: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " ConnectorModel* cf = new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( name );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml >( \"neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:588:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:613:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:648:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:928:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:583:9: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::update_internal_state_' requested here\n", - " update_internal_state_(t_lastspike_, (start->t_ + __dendritic_delay) - t_lastspike_, cp);\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:61:28: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " ConnectorModel* cf = new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( name );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml >( \"neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:499:7: warning: expression result unused [-Wunused-value]\n", - " dynamic_cast(t);\n", - " ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:286:14: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::check_connection' requested here\n", - " connection.check_connection( src, tgt, receptor_type, get_common_properties() );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:61:28: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " ConnectorModel* cf = new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( name );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml >( \"neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:518:10: warning: unused variable 'get_thread' [-Wunused-variable]\n", - " auto get_thread = [tid]()\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:67:14: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " cf = new GenericConnectorModel< ConnectionT< TargetIdentifierIndex > >( name + \"_hpc\" );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml >( \"neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:588:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:613:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:648:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:928:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:583:9: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::update_internal_state_' requested here\n", - " update_internal_state_(t_lastspike_, (start->t_ + __dendritic_delay) - t_lastspike_, cp);\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:67:14: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " cf = new GenericConnectorModel< ConnectionT< TargetIdentifierIndex > >( name + \"_hpc\" );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml >( \"neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml.h:499:7: warning: expression result unused [-Wunused-value]\n", - " dynamic_cast(t);\n", - " ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:286:14: note: in instantiation of member function 'nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml::check_connection' requested here\n", - " connection.check_connection( src, tgt, receptor_type, get_common_properties() );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:67:14: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " cf = new GenericConnectorModel< ConnectionT< TargetIdentifierIndex > >( name + \"_hpc\" );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_272f51c9bf654826ac5db45cd89ed321_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml >( \"neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml__with_iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml\" );\n", - " ^\n", - "3 warnings generated.\n", - "4 warnings generated.\n", - "21 warnings generated.\n", - "[100%] \u001b[32m\u001b[1mLinking CXX shared module nestml_272f51c9bf654826ac5db45cd89ed321_module.so\u001b[0m\n", - "[100%] Built target nestml_272f51c9bf654826ac5db45cd89ed321_module_module\n", - "[100%] Built target nestml_272f51c9bf654826ac5db45cd89ed321_module_module\n", - "\u001b[36mInstall the project...\u001b[0m\n", + "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target\n", + "[ 25%] Building CXX object CMakeFiles/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module_module.dir/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module_module.dir/iaf_psc_delta_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module_module.dir/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp:183:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 183 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp:287:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 287 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp:282:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 282 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 173 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml.cpp:266:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 266 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_delta_neuron_nestml.cpp:261:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 261 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + " | ^~~~~\n", + "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module.cpp:36:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:671:106: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:862:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 862 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:876:3: required from ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:671:106: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:849:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 849 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:671:106: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:862:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 862 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:876:3: required from ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:671:106: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:849:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 849 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:589:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 589 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:614:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 614 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:649:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 649 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:517:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 517 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:519:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 519 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::trigger_update_weight(size_t, const std::vector&, double, const CommonPropertiesType&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int; CommonPropertiesType = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:446:38: required from ‘void nest::Connector::trigger_update_weight(long int, size_t, const std::vector&, double, const std::vector&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:433:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:1009:18: warning: unused variable ‘_tr_t’ [-Wunused-variable]\n", + " 1009 | const double _tr_t = start->t_;\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:991:10: warning: unused variable ‘timestep’ [-Wunused-variable]\n", + " 991 | double timestep = 0;\n", + " | ^~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:589:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 589 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:614:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 614 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:649:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 649 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:517:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 517 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:519:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 519 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::trigger_update_weight(size_t, const std::vector&, double, const CommonPropertiesType&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int; CommonPropertiesType = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:446:38: required from ‘void nest::Connector::trigger_update_weight(long int, size_t, const std::vector&, double, const std::vector&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:433:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:1009:18: warning: unused variable ‘_tr_t’ [-Wunused-variable]\n", + " 1009 | const double _tr_t = start->t_;\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:991:10: warning: unused variable ‘timestep’ [-Wunused-variable]\n", + " 991 | double timestep = 0;\n", + " | ^~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::process_mod_spikes_spikes_(const std::vector&, double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:563:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:699:12: warning: unused variable ‘cd’ [-Wunused-variable]\n", + " 699 | double cd;\n", + " | ^~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:584:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:935:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 935 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::process_mod_spikes_spikes_(const std::vector&, double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:563:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:699:12: warning: unused variable ‘cd’ [-Wunused-variable]\n", + " 699 | double cd;\n", + " | ^~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:584:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:935:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 935 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "[100%] Linking CXX shared module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module.so\n", + "[100%] Built target nestml_9557cf48dd76469a8f5dc4ea86b34c42_module_module\n", + "[100%] Built target nestml_9557cf48dd76469a8f5dc4ea86b34c42_module_module\n", + "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /Users/pooja/conda/nestml_dev/lib/nest/nestml_272f51c9bf654826ac5db45cd89ed321_module.so\n", - "\n", - "Oct 23 18:08:40 Install [Info]: \n", - " loaded module nestml_272f51c9bf654826ac5db45cd89ed321_module\n" + "-- Installing: /tmp/nestml_target_2nbwam92/nestml_9557cf48dd76469a8f5dc4ea86b34c42_module.so\n" ] } ], @@ -984,10 +805,7 @@ " nestml_stdp_dopa_model,\n", " post_ports=[\"post_spikes\"],\n", " mod_ports=[\"mod_spikes\"],\n", - " logging_level=\"INFO\")\n", - "\n", - "# load dynamic library (NEST extension module) into NEST kernel\n", - "nest.Install(module_name)" + " logging_level=\"INFO\")\n" ] }, { @@ -1031,6 +849,7 @@ " nest.set_verbosity(\"M_ALL\")\n", "\n", " nest.ResetKernel()\n", + " nest.Install(module_name)\n", " nest.SetKernelStatus({'resolution': resolution})\n", "\n", " # create spike_generators with these times\n", @@ -1151,579 +970,644 @@ "output_type": "stream", "text": [ "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:12 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:12 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:12 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:12 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:12 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:12 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:12 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:12 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:12 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:12 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:12 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:13 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:13 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:13 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:13 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:14 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:14 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:14 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:14 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:14 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:14 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:14 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:14 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:08:43 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:14 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:14 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 i" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "af_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:14 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:43 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:14 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", "\n", - "Oct 23 18:08:43 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 11:33:14 NodeManager::prepare_nodes [Info]: \n", " Preparing 11 nodes for simulation.\n", "\n", - "Oct 23 18:08:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 11\n", " Simulation time (ms): 10000\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:08:43 SimulationManager::run [Info]: \n", + "Apr 19 11:33:14 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -1873,15 +1757,18 @@ "output_type": "stream", "text": [ "\n", - "Oct 23 18:08:56 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", + "Apr 19 11:33:15 Install [Info]: \n", + " loaded module nestml_9557cf48dd76469a8f5dc4ea86b34c42_module\n", + "\n", + "Apr 19 11:33:15 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:56 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", + "Apr 19 11:33:15 iaf_psc_delta_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:08:56 SimulationManager::set_status [Info]: \n", + "Apr 19 11:33:15 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n" ] } @@ -1906,6 +1793,7 @@ "nest.set_verbosity(\"M_ALL\")\n", "\n", "nest.ResetKernel()\n", + "nest.Install(module_name)\n", "nest.SetKernelStatus({'resolution': resolution})\n", "\n", "# Create the neurons\n", @@ -2040,4007 +1928,4007 @@ "output_type": "stream", "text": [ "0 out of 400\n", - "Oct 23 18:09:12 NodeManager::prepare_nodes [Info]: \n", - " Preparing 47 nodes for simulation.\n", "\n", + "Apr 19 11:33:15 NodeManager::prepare_nodes [Info]: \n", + " Preparing 47 nodes for simulation.\n", "1 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "2 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "2 out of 400\n", - "3 out of 400\n", - "4 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", - "5 out of 400\n", - "6 out of 400\n", - "7 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "3 out of 400\n", + "4 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "5 out of 400\n", + "6 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", + "7 out of 400\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 Simulat8 out of 400\n", - "9 out of 400\n", - "10 out of 400\n", - "ionManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "8 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "9 out of 400\n", + "10 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: 11 out of 400\n", - "12 out of 400\n", - "\n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "11 out of 400\n", + "12 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "13 out of 400\n", "14 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "15 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "16 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", - "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simulation finished.\n", - "16 out of 400\n", "17 out of 400\n", "18 out of 400\n", + "\n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", + " Simulation finished.\n", + "\n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", + " Simulation time (ms): 30\n", + " Number of OpenMP threads: 1\n", + " Not using MPI\n", + "\n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", + " Simulation finished.\n", "19 out of 400\n", "20 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "21 out of 400\n", + "22 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "23 out of 400\n", "\n", - "Oct 23 18:09:12 Simulat21 out of 400\n", - "22 out of 400\n", - "ionManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "23 out of 400\n", "24 out of 400\n", - "25 out of 400\n", - "26 out of 400\n", - "27 out of 400\n", - "28 out of 400\n", - "29 out of 400\n", - "30 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "25 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "26 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "27 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "28 out of 400\n", + "29 out of 400\n", + "30 out of 400\n", "31 out of 400\n", - "32 out of 400\n", - "33 out of 400\n", - "34 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "32 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 Simulat35 out of 400\n", - "36 out of 400\n", - "37 out of 400\n", - "38 out of 400\n", - "ionManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 Simulat33 out of 400\n", + "34 out of 400\n", + "35 out of 400\n", + "ionManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "36 out of 400\n", + "37 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updati39 out of 400\n", - "40 out of 400\n", - "41 out of 400\n", - "42 out of 400\n", - "ng_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "38 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "39 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "40 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "41 out of 400\n", + "42 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Number 43 out of 400\n", - "44 out of 400\n", - "45 out of 400\n", - "46 out of 400\n", - "of local nodes: 47\n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "43 out of 400\n", + "44 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "45 out of 400\n", + "46 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " S47 out of 400\n", - "48 out of 400\n", - "49 out of 400\n", - "50 out of 400\n", - "51 out of 400\n", - "52 out of 400\n", - "53 out of 400\n", - "54 out of 400\n", - "55 out of 400\n", - "imulation time (ms): 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "47 out of 400\n", + "48 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "49 out of 400\n", + "50 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - "56 out of 400\n", - "57 out of 400\n", - "58 out of 400\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "51 out of 400\n", + "52 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "53 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - " Number of OpenMP thr59 out of 400\n", - "60 out of 400\n", - "61 out of 400\n", - "62 out of 400\n", - "63 out of 400\n", - "64 out of 400\n", - "65 out of 400\n", - "eads: 1\n", + " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "54 out of 400\n", + "55 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "56 out of 400\n", + "57 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", - " Not using MP66 out of 400\n", - "67 out of 400\n", - "68 out of 400\n", - "69 out of 400\n", - "70 out of 400\n", - "I\n", + " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "58 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "59 out of 400\n", + "60 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "61 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 Simul71 out of 400\n", - "72 out of 400\n", - "73 out of 400\n", - "ationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "62 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "63 out of 400\n", + "64 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "65 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]74 out of 400\n", - "75 out of 400\n", - "76 out of 400\n", - "77 out of 400\n", - ": \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "66 out of 400\n", + "67 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "68 out of 400\n", + "69 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simulation finish78 out of 400\n", - "79 out of 400\n", - "80 out of 400\n", - "81 out of 400\n", - "82 out of 400\n", - "83 out of 400\n", - "84 out of 400\n", - "85 out of 400\n", - "ed.\n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", + " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "70 out of 400\n", + "71 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "72 out of 400\n", + "73 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "74 out of 400\n", "\n", - "Oct 23 18:09:12 Sim86 out of 400\n", - "87 out of 400\n", - "88 out of 400\n", - "89 out of 400\n", - "ulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "75 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "76 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "77 out of 400\n", + "78 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "79 out of 400\n", + "80 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_up90 out of 400\n", - "91 out of 400\n", - "92 out of 400\n", - "93 out of 400\n", - "94 out of 400\n", - "dating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "81 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simulation finished.\n", - "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Num95 out of 400\n", - "96 out of 400\n", - "97 out of 400\n", - "98 out of 400\n", - "ber of local nodes: 47\n", - " Simulation time (ms): 30\n", - " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "82 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "83 out of 400\n", + "84 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:15 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "85 out of 400\n", + "86 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " 99 out of 400\n", - "100 out of 400\n", - "101 out of 400\n", - "102 out of 400\n", - "103 out of 400\n", - " Simulation time (ms): 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "87 out of 400\n", + "88 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "89 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " Simulation time (ms):104 out of 400\n", - "105 out of 400\n", - " 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "90 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "91 out of 400\n", + "92 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "93 out of 400\n", + "94 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - " Number of OpenMP106 out of 400\n", - "107 out of 400\n", - "108 out of 400\n", - "109 out of 400\n", - " threads: 1\n", + " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "95 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "96 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", - " Not usin110 out of 400\n", - "111 out of 400\n", - "112 out of 400\n", - "113 out of 400\n", - "g MPI\n", + " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "97 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "98 out of 400\n", + "99 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "100 out of 400\n", + "101 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 S114 out of 400\n", - "115 out of 400\n", - "116 out of 400\n", - "117 out of 400\n", - "118 out of 400imulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "102 out of 400\n", + "103 out of 400\n", + "104 out of 400\n", + "105 out of 400\n", + "106 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "107 out of 400\n", + "108 out of 400\n", + "109 out of 400\n", + "110 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [I\n", - "nfo]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 Simulat111 out of 400\n", + "ionManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simulation fi119 out of 400\n", - "120 out of 400\n", - "121 out of 400\n", - "122 out of 400\n", - "123 out of 400\n", - "nished.\n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", + " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "112 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "113 out of 400\n", "\n", - "Oct 23 18:09:12124 out of 400\n", - "125 out of 400\n", - "126 out of 400\n", - "127 out of 400\n", - "128 out of 400\n", - "129 out of 400\n", - "130 out of 400\n", - "131 out of 400\n", - " SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "114 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "115 out of 400\n", + "116 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "117 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "118 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::star132 out of 400\n", - "133 out of 400\n", - "134 out of 400\n", - "135 out of 400\n", - "136 out of 400\n", - "137 out of 400\n", - "t_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "119 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "120 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "121 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " 138 out of 400\n", - "139 out of 400\n", - "140 out of 400\n", - "141 out of 400\n", - " Number of local nodes: 47\n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "122 out of 400\n", + "123 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "124 out of 400\n", + "125 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "126 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 142 out of 400\n", - "143 out of 400\n", - "144 out of 400\n", - "145 out of 400\n", - "146 out of 400\n", - "147 out of 400\n", - "148 out of 400\n", - "149 out of 400\n", - "150 out of 400\n", - "151 out of 400\n", - "152 out of 400\n", - "47\n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "127 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "128 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "129 out of 400\n", + "130 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " Simulation time (153 out of 400\n", - "154 out of 400\n", - "155 out of 400\n", - "156 out of 400\n", - "157 out of 400\n", - "158 out of 400\n", - "159 out of 400\n", - "160 out of 400\n", - "ms): 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "131 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "132 out of 400\n", + "133 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "134 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - " Number of Op161 out of 400\n", - "162 out of 400\n", - "163 out of 400\n", - "enMP threads: 1\n", + " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "135 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "136 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "137 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", - " Not 164 out of 400\n", - "165 out of 400\n", - "166 out of 400\n", - "167 out of 400\n", - "168 out of 400\n", - "169 out of 400\n", - "170 out of 400\n", - "171 out of 400\n", - "172 out of 400\n", - "173 out of 400\n", - "174 out of 400\n", - "using MPI\n", + " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "138 out of 400\n", + "139 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "140 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "141 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:175 out of 400\n", - "176 out of 400\n", - "12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "142 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "143 out of 400\n", + "144 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "145 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "146 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::ru177 out of 400\n", - "178 out of 400\n", - "179 out of 400\n", - "180 out of 400\n", - "181 out of 400\n", - "182 out of 400\n", - "183 out of 400\n", - "184 out of 400\n", - "n [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "147 out of 400\n", + "148 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "149 out of 400\n", + "150 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "151 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simulatio185 out of 400\n", - "186 out of 400\n", - "187 out of 400\n", - "188 out of 400\n", - "n finished.\n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", + " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "152 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "153 out of 400\n", + "154 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "155 out of 400\n", "\n", - "Oct 23 18:0189 out of 400\n", - "190 out of 400\n", - "191 out of 400\n", - "192 out of 400\n", - "193 out of 400\n", - "194 out of 400\n", - "195 out of 400\n", - "196 out of 400\n", - "197 out of 400\n", - "9:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "156 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "157 out of 400\n", + "158 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "159 out of 400\n", + "160 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::198 out of 400\n", - "199 out of 400\n", - "200 out of 400\n", - "201 out of 400\n", - "202 out of 400\n", - "start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "161 out of 400\n", + "162 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "163 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: 203 out of 400\n", - "204 out of 400\n", - "205 out of 400\n", - "206 out of 400\n", - "\n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "164 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "165 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "166 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Number of local nod207 out of 400\n", - "208 out of 400\n", - "209 out of 400\n", - "210 out of 400\n", - "es: 47\n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "167 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "168 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "169 out of 400\n", + "170 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "171 out of 400\n", + "172 out of 400\n", + "173 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " Simulation ti211 out of 400\n", - "212 out of 400\n", - "213 out of 400\n", - "214 out of 400\n", - "215 out of 400\n", - "me (ms): 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "174 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - " Number o216 out of 400\n", - "217 out of 400\n", - "218 out of 400\n", - "219 out of 400\n", - "f OpenMP threads: 1\n", + " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "175 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "176 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "177 out of 400\n", + "178 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "179 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", - " 220 out of 400\n", - "221 out of 400\n", - "222 out of 400\n", - "Not using MPI\n", + " Not using MPI\n", + "180 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "181 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "182 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18223 out of 400\n", - "224 out of 400\n", - "225 out of 400\n", - "226 out of 400\n", - ":09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "183 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "184 out of 400\n", + "185 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "186 out of 400\n", + "187 out of 400\n", + "188 out of 400\n", + "189 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager227 out of 400\n", - "228 out of 400\n", - "::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "190 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simul229 out of 400\n", - "230 out of 400\n", - "231 out of 400\n", - "232 out of 400\n", - "233 out of 400\n", - "234 out of 400\n", - "235 out of 400\n", - "ation finished.\n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", + " Simulation finished.\n", + "191 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "192 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "193 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "194 out of 400\n", + "195 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "196 out of 400\n", "\n", - "Oct 23 236 out of 400\n", - "237 out of 400\n", - "238 out of 400\n", - "239 out of 40018:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "197 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "198 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "199 out of 400\n", + "200 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManag\n", - "240 out of 400\n", - "241 out of 400\n", - "242 out of 400\n", - "er::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "201 out of 400\n", + "202 out of 400\n", + "203 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Inf243 out of 400\n", - "244 out of 400\n", - "245 out of 400\n", - "246 out of 400\n", - "o]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "204 out of 400\n", + "205 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "206 out of 400\n", + "207 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "208 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Number of local247 out of 400\n", - "248 out of 400\n", - "249 out of 400\n", - "250 out of 400\n", - "251 out of 400\n", - "252 out of 400\n", - "253 out of 400\n", - "254 out of 400\n", - " nodes: 47\n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "209 out of 400\n", + "210 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "211 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " Simulatio255 out of 400\n", - "256 out of 400\n", - "n time (ms): 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "212 out of 400\n", + "213 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "214 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "215 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "216 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "217 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "218 out of 400\n", + "219 out of 400\n", + "220 out of 400\n", + "221 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", - "257 out of 400\n", - "258 out of 400\n", - "259 out of 400\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "222 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "223 out of 400\n", + "224 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 2260 out of 400\n", - "261 out of 400\n", - "262 out of 400\n", - "263 out of 400\n", - "264 out of 400\n", - "265 out of 400\n", - "266 out of 400\n", - "267 out of 400\n", - "268 out of 400\n", - "269 out of 400\n", - "270 out of 400\n", - "271 out of 400\n", - "3 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "225 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "226 out of 400\n", + "227 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "228 out of 400\n", + "229 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationMan272 out of 400\n", - "273 out of 400\n", - "ager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "230 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "231 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " S274 out of 400\n", - "275 out of 400\n", - "276 out of 400\n", - "277 out of 400\n", - "imulation finished.\n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", + " Simulation finished.\n", + "232 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "233 out of 400\n", + "234 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "235 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "236 out of 400\n", "\n", - "Oct278 out of 400\n", - "279 out of 400\n", - "280 out of 400\n", - "281 out of 400\n", - "282 out of 400\n", - "283 out of 400\n", - "284 out of 400\n", - " 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "237 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "238 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "239 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "240 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationM285 out of 400\n", - "286 out of 400\n", - "287 out of 400\n", - "288 out of 400\n", - "anager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "241 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "242 out of 400\n", + "243 out of 400\n", + "244 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ 289 out of 400\n", - "290 out of 400\n", - "291 out of 400\n", - "[Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "245 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "246 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "247 out of 400\n", + "248 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Number of l292 out of 400\n", - "293 out of 400\n", - "294 out of 400\n", - "295 out of 400\n", - "ocal nodes: 47\n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "249 out of 400\n", + "250 out of 400\n", + "251 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "252 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "253 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " Simul296 out of 400\n", - "297 out of 400\n", - "298 out of 400\n", - "299 out of 400\n", - "300 out of 400\n", - "ation time (ms): 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "254 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "255 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "256 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - " 301 out of 400\n", - "302 out of 400\n", - "303 out of 400\n", - "304 out of 400\n", - "Number of OpenMP threads: 1\n", + " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "257 out of 400\n", + "258 out of 400\n", + "259 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "260 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - " Number of OpenMP threads305 out of 400\n", - "306 out of 400\n", - "307 out of 400\n", - "308 out of 400\n", - ": 1\n", + " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "261 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "262 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "263 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "264 out of 400\n", + "265 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "O309 out of 400\n", - "310 out of 400\n", - "311 out of 400\n", - "312 out of 400\n", - "ct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "266 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "267 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "268 out of 400\n", + "269 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 Simulatio313 out of 400\n", - "314 out of 400\n", - "315 out of 400\n", - "316 out of 400\n", - "nManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "270 out of 400\n", + "271 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "272 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " 317 out of 400\n", - "318 out of 400\n", - "319 out of 400\n", - "320 out of 400\n", - " Simulation finished.\n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", + " Simulation finished.\n", + "273 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "274 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "275 out of 400\n", + "276 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "277 out of 400\n", + "278 out of 400\n", + "279 out of 400\n", + "280 out of 400\n", + "281 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", - " Not using MPI\n", - "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simulation finished.\n", - "321 out of 400\n", - "322 out of 400\n", - "323 out of 400\n", - "324 out of 400\n", + " Not using MPI\n", + "\n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", + " Simulation finished.\n", + "282 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 Simulat325 out of 400\n", - "326 out of 400\n", - "327 out of 400\n", - "328 out of 400\n", - "329 out of 400\n", + "Apr 19 11:33:17 Simulat283 out of 400\n", "ionManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "284 out of 400\n", + "285 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "286 out of 400\n", + "287 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updati330 out of 400\n", - "331 out of 400\n", - "332 out of 400\n", - "333 out of 400\n", - "ng_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "288 out of 400\n", + "289 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Number 334 out of 400\n", - "335 out of 400\n", - "336 out of 400\n", - "337 out of 400\n", - "of local nodes: 47\n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "290 out of 400\n", + "291 out of 400\n", + "292 out of 400\n", + "293 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "294 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", - " S338 out of 400\n", - "339 out of 400\n", - "340 out of 400\n", - "341 out of 400\n", - "342 out of 400\n", - "343 out of 400\n", - "344 out of 400\n", - "345 out of 400\n", - "imulation time (ms): 30\n", + " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "295 out of 400\n", + "296 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "297 out of 400\n", + "298 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - "346 out of 400\n", - "347 out of 400\n", - "348 out of 400\n", - "349 out of 400\n", - "350 out of 400\n", - "351 out of 400\n", - "352 out of 400\n", - "353 out of 400\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "299 out of 400\n", + "300 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "301 out of 400\n", + "302 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", - " Number of OpenMP thr354 out of 400\n", - "355 out of 400\n", - "356 out of 400\n", - "357 out of 400\n", - "eads: 1\n", + " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "303 out of 400\n", + "304 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "305 out of 400\n", + "306 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", - " Not using MP358 out of 400\n", - "359 out of 400\n", - "360 out of 400\n", - "361 out of 400\n", - "I\n", + " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "307 out of 400\n", + "308 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "309 out of 400\n", + "310 out of 400\n", + "311 out of 400\n", + "312 out of 400\n", + "313 out of 400\n", + "314 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 Simul362 out of 400\n", - "363 out of 400\n", - "364 out of 400\n", - "365 out of 400\n", - "ationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "315 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]366 out of 400\n", - "367 out of 400\n", - "368 out of 400\n", - "369 out of 400\n", - "370 out of 400\n", - ": \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "316 out of 400\n", + "317 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "318 out of 400\n", + "319 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", - " Simulation finish371 out of 400\n", - "372 out of 400\n", - "373 out of 400\n", - "374 out of 400\n", - "375 out of 400\n", - "376 out of 400\n", - "377 out of 400\n", - "378 out of 400\n", - "379 out of 400\n", - "ed.\n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", + " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "320 out of 400\n", + "321 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "322 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 Sim380 out of 400\n", - "381 out of 400\n", - "382 out of 400\n", - "383 out of 400\n", - "384 out of 400\n", - "385 out of 400\n", - "386 out of 400\n", - "387 out of 400\n", - "388 out of 400ulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "323 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "324 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "325 out of 400\n", + "326 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_up\n", - "389 out of 400\n", - "390 out of 400\n", - "391 out of 400\n", - "392 out of 400\n", - "393 out of 400\n", - "394 out of 400\n", - "395 out of 400\n", - "dating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "327 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "328 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "329 out of 400\n", + "330 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "331 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", - " Num396 out of 400\n", - "397 out of 400\n", - "398 out of 400\n", - "399 out of 400\n", - "ber of local nodes: 47\n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "332 out of 400\n", + "333 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "334 out of 400\n", + "335 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "336 out of 400\n", + "337 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "338 out of 400\n", + "339 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "340 out of 400\n", + "341 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "342 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "343 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "344 out of 400\n", + "345 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "346 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "347 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "348 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "349 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "350 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "351 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "352 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "353 out of 400\n", + "354 out of 400\n", + "355 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "356 out of 400\n", + "357 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 Simulat358 out of 400\n", + "ionManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "359 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "360 out of 400\n", + "361 out of 400\n", + "362 out of 400\n", + "363 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "364 out of 400\n", + "365 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "366 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "367 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "368 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "369 out of 400\n", + "370 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "371 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "372 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "373 out of 400\n", + "374 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "375 out of 400\n", + "376 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "377 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "378 out of 400\n", + "379 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "380 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "381 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "382 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "383 out of 400\n", + "384 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "385 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "386 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "387 out of 400\n", + "388 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "389 out of 400\n", + "390 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "391 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", + "392 out of 400\n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "393 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "394 out of 400\n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "395 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "396 out of 400\n", + "397 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "398 out of 400\n", + "399 out of 400\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 23 18:09:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:33:18 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 47\n", " Simulation time (ms): 30\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 23 18:09:12 SimulationManager::run [Info]: \n", + "Apr 19 11:33:18 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] } @@ -6083,12 +5971,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/2j/fb047q1177v9f56f_jktrb4c0000gn/T/ipykernel_97085/2151756254.py:23: UserWarning:Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n" + "/tmp/ipykernel_335797/2151756254.py:23: UserWarning:FigureCanvasAgg is non-interactive, and thus cannot be shown\n" ] }, { "data": { - "image/png": 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", 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HySxRM3Xp0sXdJZDCMDMkB/NCcjAvJAfzQkrHySxRM/Xp08fdJZDCMDMkB/NCcjAvJAfzQkrHySxRM23ZssXdJZDCMDMkB/NCcjAvJAfzQkrHr+Zx4KkfO02ey2AwwN/f391lkIIwMyQH80JyMC8kB/NCrvLUORKvzBI108SJE91dAikMM0NyMC8kB/NCcjAvpHS8MuvAU3/qQERERERE5A6eOkfilVmiZgoODnZ3CaQwzAzJwbyQHMwLycG8kNLxyqwDT/2pAxERERERkTt46hyJV2aJmikiIsLdJZDCMDMkB/NCcjAvJAfzQkqn+Mnsvn378K9//QtDhw5FQEAA+vfvj5CQEJw+fdrdpdFV4oUXXnB3CaQwzAzJwbyQHMwLycG8kNIpfjL71VdfYc2aNXjooYfw3Xff4fXXX0dSUhJGjRqFo0ePurs8ugqkp6e7uwRSGGaG5GBeSA7mheRgXkjpvN1dQHP95z//QWxsLHx8fKRlzz33HIYPH47p06dj+fLlbqyOiIiIiIiIWoPiJ7N33313g2XXX389hg4dihMnTriholZmMQMlmUCXgYDay93VXF0a6fvhw4dfsX3ZPX4hA1CpgKBrXctCnQnI2gUMuAfw9rF/TNyeYLFuy9Vtym2H+P9O/YCybOtyACg6DZTlAJ37AddcX7+uYxvFNvS7E6jItW6nJLN+HcD17Yv12T4W2Bc4thZo3xXw8QEGPWDfV3UmIGMHAMG6D2+f+tps22oxA+eSgE59gc4DgOy91n5XewFFpzG8YyVQeNL6WNYuoGOv+m2U5wL+1wAFRwD/7kDXQdaaxbaJbWpsvExG4MhKQBCAfncA3W+01n10DdB7FHDNdda+EftO3EbnAdZtVuQBA++1ts12vMR+7jyg/vm2f9uOsThu4rq2OXZ83LYGcQyLTtvX4fg82z4vOAHkpQG9b7Vvm7OazLVAaTaggnUdMVMF6cBNTwJVBQ3b5bgt2xyo1daMAPXHltrLPvN1JuDMdsBYBAx7FvDxa3i82faTbYYv9vnwoUPsM5i1C+hzO6DbZ38sOI6fbb85ZqXOZM2omD3bxxp7TmNjUJIJBPSwP3b63WWtr7HzTVO5Epd16AUcX2fNbfcb63NvrgUqC6ztvHhMoSwHCOxdP0ZqL+vxnL234flCzIGXl3X81F7WHOUeqD/mgq617kvcrrdP03k211qPXdt1xWyL9djm29n5yHGMHfNkew4Q+148VvqOtrY1oDuGdygDTiVY1wu61v4c6XiucrZNZ+ddZ1mxmOvPx2XZzs9Hl3rdcTxfO7bLdoxLsqzHrtiusmygY29ru8XjwXY/zs41jq+ZjfWF+O+Ova31d+rb8Dzc2LHiuJ3GjpnGXhfFY9k2e47bdcyMyQgciwduesL5ecz2tVDMy8X+apX3MERXkOIns84IgoCCgoJLftJWYWEhioqK7JZptdrWLK15LGZgyTggO9X6JvWVzZzQXilN9H1cXFzLvhhcapwtZiBmLJCjsf6/72hgUkLTWagzAV8PAkyVgE8H4P1z9i/4tttzdZty2xG6EVj6uPX/Ph2stfQdDVgsQO5++31P3AT8+Jh9G19eD3xznfV5KrX1zUO7AKC2yrpOn9utbxhyNJfe/qQE678Xj7G+oDfGtq/qTMD0AUCdwX4dsV6xbX1uBwqP19dlW2u3m4Dc/YjbXo3hD7WH9Z2ZCx8ob9u2vqOtE1XbusU21ZmAaT3tn9trlHWyJ+5H7BvbvgMAb//6tvl0AN7VAsuetLbJdl3x+Y5/i2NsO262j72y2brMNmuONfS53do2cbzE/ld7Ncz8xE1AzDj7sXWlpsb8+s9Lt++VzdZc2+bA2986qRX7tMdQ67763QG89Cvw1SDAbLSuu+HfwAfZwPKn7OuxzStgfcymb+I0QRi+UWvdt3gci8R8OY6fs34T++7l9UDUYPu+t+1Xx/5q7Ji0zX1jnJ1vxPOCs1w5ZkLU5zYAKvvc2xxTTol94+x8YbuNa/4E5B2wX27bn7ZtaSrPjtt13JdjvoGG51rbc7VtnmyzIeZ6yaNOz1/15xeHOhzHzNk2nZ13nWWl921A8cmLWXQ4j4ltss1rUzlwPF5t29XYGIvPEcfWdrmz7Dv2g/gaINZg2xe2/7Ztm+15uLFzS1Pnasf+b+x10Vn2HPdt+77AZAS+7G3th1/fsO+fpl4LL64XVz4Bw6d/BSKlUvw9s86sWLECOp0Ozz33XJPrzZkzB8OGDbP7M378eABASkoKEhMTERUVBb1ej9DQUAD138cVHh4OrVaLmJgYxMfHQ6PRIDIyEgaDASEhIXbrRkREID09HbGxsYiNjUV6err06XHiOiEhITAYDIiMjIRGo0F8fDxiYmKg1WoRHh5uXXfc/wHZqQhdZ4T+9F5ETZ2CxMREJCQkIDo6GjqdDmFhYXbbDQsLg06nQ3R0NBISEjyvTRfXDQ0NhV6vR1RUlGe26WLfh6w2wHB2LyKnvCu16brrrmvZNv2yFInJuxC1qwb603sR+uKz9m3659+hPbIXMWkmxJ+ohSZ1DyKnvNt0m36YitgDJUgvMCNiczGQtat+nJ4OhuFcKiITa6DRmRF/ohYxG1KgPbCzeW2a9BJ0x/YgWmNCwo5dSFw1B1E/J0FvFBC6yvpDpOConUDufoQnVEOrt1jbtHUXNOsXIXJFMgy1AkJWG4AcDYIfvgcwVSJiezXS82sRm16L2IOl1jZtrwZ0+6zbAxCyohCGWgGRK5Kh2ZdqbVOaCVq9BeGLkoCSTOuY6vZZjyejgKhdNUjMrEOCtg7RGhN05RaErS2q76usXQhbdwG6cou1Tdo6JGbWIWplMvSHNyP0B+u+g79JBGqr6tt0sNo6TpnliIzbBUOtAK3e+uYrOM765ipiezXSC8zWNqXX1rcJQHCcAdDtQ8isnfVt0ux13qaH77GOk22b9miQoK21bxOA4J+sf4dtNFrbtLu0vk07LkC/e1l9my6uG55QDW1+uXWcDpdAozMjcusF6zjN3GHtq4tjELG9GunZZdY2/ZaC9F1bEPHOv6xjGWd9oxYSW2Rtk5i9bbsRs2m3tU0J1YCpEsGPPACUZCJ0dmJ9m1J2I+GnWYhet9vapo3WyWLw0kJrm9YWWdu0JhkJP81CYspu6/FkFBC6zljfr2KbxOydqIXmXBkiE2tgqKqwZs9UaV03OxUR7/wL6b8vQ2xaWf04bdHXr1NbVT9Oy5OgWfkN4o+W14/TZgOwb5HUR9I47bhgzd7O3Yhem2JtU3yxVOe00Xrr8ZS6DtEp+vpx2lUDvcFsbVOdob5N64uhTVyFmNlR1uNJZ7a2qVZAyKydwLF4aUztsrd5F9J/X4aIpUl2fRQyaycMJ7dbs2d7jkhchfCFiXbrNjiejpci+vP36s8RJZkI/nqHdZzii+uPp+Ol1jbt1Dsfp8Up0Kan1o+TzozIbXoYsvZZx8lmXalNR2qs45Rfaz2eaqvq27TaYB2nbXpo9mvsj6eEarv+bDBOO3Yh+ptI6I7vRdg8+/ZLx9OuEvtxMgoIjU4CcvfbZ+/IXus4ia9P74XVZ6+2qv54WpqE9AN76o+n35ch4qdk+3G62CY/b1X9OGlK6tuUo7EeT+L7iDOpiFqZXN+mz9+ztkk8nqKsWbEbJ7FNq3dBX1ZxcZwE521aON3+eHriEet2g4OBkkxE/JhoHacDJdZx2r8bEZNfsZ7Lbc8RWfvsX5/STNDml1vbJFjsx6msAlEfTUbixpXW40k879kcT8jRWI+n43ut5whtnfUcsTLZOk6zE23OU0L9OWLjrvrjaXkSDCe3W48n2+z9lFw/Tpt3If3g3vpzedROIGsXQmbuqD9HrF+E+N9T7NvkLHurUqznPW0dotckW8dJfM19+B5AsNRnz/Z42nGhvk2O2dNbEJOqx+0DAvh+j21yqU1xcXHwRG3ue2ZPnjyJO+64A0OHDkVycjK8vBq/stTYldnx48d73HcoAeCVWXdqou+Dg4OxYcOGK7Iv6XFemVX0ldngOAM2vOAPXpn1oCuzjW2jJa/MqtSXdWU2ON4HG9LyeWWWV2ZdujJbf34Br8xebVdmHfvHhSuzwbtHYMPGTSC6FE/9ntk2NZnNz8/HPffcg9raWuzduxe9e/eWvQ1PHSgJ75l1nyvZ97xntk3fMyvtj/fM8p5ZF+6ZdXoPIu+Z5T2zjdwzi/Jc+3tLec8s75lt4p7ZBuNC1AhPnSO1mclsWVkZHnzwQZw/fx7JyckYMmTIZW3HUweKPFdISAhWrVrl7jJIQZgZkoN5ITmYF5KDeSFXeeocqU1MZqurq/HII4/gwIED2LZtG+66667L3panDhR5LoPBAH9/f3eXQQrCzJAczAvJwbyQHMwLucpT50iK/wAos9mM5557Dnv27MHq1aubNZEluhwzZsxwdwmkMMwMycG8kBzMC8nBvJDSKf6red555x2sX78ewcHB0Ov1WL58ud3jEyZMcFNldLUYM2aMu0sghWFmSA7mheRgXkgO5oWUTvGT2UOHDgEANmzY4PQTZTmZpdam0+ncXQIpDDNDcjAvJAfzQnIwL6R0ip/M7ty5090l0FWupKTE3SWQwjAzJAfzQnIwLyQH80JKp/h7Zonc7f7773d3CaQwzAzJwbyQHMwLycG8kNJxMkvUTNHR0e4ugRSGmSE5mBeSg3khOZgXUro28dU8LclTP3aaiIiIiIjIHTx1jsQrs0TNFBwc7O4SSGGYGZKDeSE5mBeSg3khpeOVWQee+lMHIiIiIiIid/DUORKvzBI1U2hoqLtLIIVhZkgO5oXkYF5IDuaFlI5XZh146k8dyHPp9XoEBQW5uwxSEGaG5GBeSA7mheRgXshVnjpH4pVZomZavHixu0sghWFmSA7mheRgXkgO5oWUjpNZomYaPXq0u0sghWFmSA7mheRgXkgO5oWUjpNZomYyGo3uLoEUhpkhOZgXkoN5ITmYF1I6TmaJmikjI8PdJZDCMDMkB/NCcjAvJAfzQkqn+MnssWPH8Oyzz+Laa6+Fv78/rrnmGtx///3YsGGDu0ujq8T48ePdXQIpDDNDcjAvJAfzQnIwL6R0ip/MZmVloaKiAqGhofjuu+/w8ccfAwCeeOIJLFiwwM3V0dUgMjLS3SWQwjAzJAfzQnIwLyQH80JK1ya/msdsNuPWW29FdXU1Tp48Keu5nvqx00RERERERO7gqXMkxV+ZdcbLywv9+vVDaWlps7Zjtphxvvw8zBZzyxSmIC3R9qul/4KDg91dwlVPzJqpzqSIzDUnM3KPq6vlOLxcSugfV/NitphxruwczpWec6k9PM8rW2N9z9ckkoN5IaXzdncBLaWqqgpGoxFlZWVYv349Nm/ejOeee67J5xQWFqKoqMhumVarBWB9kZiYMBGHig5hRLcR+HHsj/BSe7Va/Z6kJdp+NfUf7892L9us+Xv7w1Bn8PjMXW5m5B5XV9NxeDmU0j+u5MVsMSN0cygOFx8GANzS7RYsHbu00fbwPK9sTfU9X5NIDuaFlK7NXJl955130K1bN1x33XV499138dRTT2H27NlNPmfOnDkYNmyY3R/xRviN2zdiV/IuFP1WhP3n9uO5CdaJsfgTrPDwcGi1WsTExCA+Ph4ajQaRkZEwGAwICQmxWzciIgLp6emIjY1FbGws0tPTERERYbdOSEgIDAYDIiMjodFoEB8fj5iYGGi1WoSHh9utGxoaCr1ej6ioKCQmJiIhIQHR0dHQ6XQICwuzWzcsLAw6nQ7R0dFISEhAYmIioqKioNfrERoa6rRNM+fORFJCEgxnDfh90e/QFmplt0lXqcP6T9fDUmPB74t+x6bETW5tU2uO04MPPtjm2qSkcdqeuh0JcxMAACeiTgAA1n+6HtpCrce2afDgwZc1TtpCLX5f9DsMZw1ISkjCzLkzm2zTx198jF3Ju1BxpALb47bjwOkDzJ5Nm3SVOmz+bjNqCmqwY80OLI5b7JFtCgsLu2SbdJU6rJ+yHgCQF5sHzVENZs6d2eg4jXlsDA4VHUL+L/nYe3AvopdEy27TrsO7sPm7zQCAXyN+ha5S55HnCE/MXnPbZHveE/tebNPtt9+uyDa1xXFSQpvGjBnT5trUFsfJE9oUFxcHT9Rm7pk9efIkcnJykJubi1WrVsHHxwdz585Fjx49Gn1OY1dmx48fj8NHDiMqO+qq/Ikzf2Ivj06nQ58+fdxdxlVLiVdmLzczvDLbspTSP67khVdmry5N9T1fk0gO5oVc5an3zLaZyayjRx55BKWlpUhNTYVKpXL5ebYD9aeb/gRdpQ59OvS56l6gzRZzs9veEttQgujoaLz55pvuLuOqJmatp39P5BvyPT5zzcmM3OPqajkOL5cS+sfVvJgtZpyvOA8IQP/A/pdsD8/zytZY3/M1ieRgXshVnjqZbTO/Zuzor3/9K/bt24fTp09f9ja81F4uvSFoi1qi7VdL/4m/MkruI2bNx9tHEZlrTmbkHldXy3F4uZTQP67mxUvthUGdBmFQ50EutYfneWVrrO/5mkRyMC+kdG12Mms0GgEAZWVlbq6E2jo/Pz93l0AKw8yQHMwLycG8kBzMCymd4iezhYWFDZbV1tbip59+gp+fH4YMGeKGquhqotFo3F0CKQwzQ3IwLyQH80JyMC+kdIr/ap5//OMfKC8vx/33348+ffogPz8fK1aswMmTJzFjxgx06NDB3SVSG/fqq6+6uwRSGGaG5GBeSA7mheRgXkjpFH9l9rnnnoNarcbcuXPxxhtvYObMmejbty9+/fVX/Oc//3F3eXQVED/OnMhVzAzJwbyQHMwLycG8kNK12U8zvlye+kldRERERERE7uCpcyTFX5klcjfxC6aJXMXMkBzMC8nBvJAczAspHa/MOvDUnzoQERERERG5g6fOkXhllqiZeL8JycXMkBzMC8nBvJAczAspHSezRM305ptvursEUhhmhuRgXkgO5oXkYF5I6TiZJWqmpKQkd5dACsPMkBzMC8nBvJAczAspHSezRM3UpUsXd5dACsPMkBzMC8nBvJAczAspHSezRM3Up08fd5dACsPMkBzMC8nBvJAczAspHSezRM20ZcsWd5dACsPMkBzMC8nBvJAczAspHb+ax4Gnfuw0eS6DwQB/f393l0EKwsyQHMwLycG8kBzMC7nKU+dIvDJL1EwTJ050dwmkMMwMycG8kBzMC8nBvJDS8cqsA0/9qQMREREREZE7eOociVdmiZopODjY3SWQwjAzJAfzQnIwLyQH80JK1yYns1988QVUKhWGDRvm7lLoKrBhwwZ3l0AKw8yQHMwLycG8kBzMCyldm5vM5uTkYNq0aQgICHB3KXSViIiIcHcJpDDMDMnBvJAczAvJwbyQ0nm7u4CW9u677+LOO++E2WxGcXGxu8uhq8ALL7zg7hJIYZgZkoN5ITmYF5KDeSGla1OT2aSkJPzyyy9IS0vD5MmTr+i+BbMZtTk5aNe3L1ReXld03+Re6enpGD58+GU993Jz05J5U0p2BbMZpqwsACr4DOjvkbXa9iWABv0qPn44LQ03BgZK69WcPYu6gkL4j74dKi8v1ObkwKt7dxgPHIQ6qAvMxRfg3aM7VGovqLy84DOgPywmEyoStqDD/z0My4ULaNe3L8xGI0pWxCLg/vvgd8MNUg2226orLIJKrULAnXdC5eXVoE9t2yCYzTDuPwC/226V1hXMFqi8vODdqyeq0w45faxd3z6ozclxul2v7t3tntdUf5mNRpSu3wCVCugUHAy1j4/T9QHYtcP2/2ItZmM1jEePwrvbNfDt3x++gwY12Kez8fPu1Qu1OTmwmGpRV1gIdZfOqDl5Cn43D0f7666z1hj3Mzq/8Dy8O3RosA2xDu9ePRuMp7qdT4O+cnxOddohHD59CjcEBDRYx2Kqhbm42C433r16wZSVBZMuF4AAn7594dO/v7QP2/05/rsuLw/evXqhLi/Prk9qzp6FSZcLwWKG2tsb/rffjrq8PLt2eXW7Biq1/TFpmwUxG17du6Fy6zZ0HDsGKi8vGDT74N2zB3wHDZLy5nvzcNQcSUf7kSNgLiy0Gw8xy949e8Cnf3+YsrJQm5ePdr172bXTWQ5s1xX3Z7t/x+O0sWw5O66dHUfVZ87AmH5Uyoq4P69u10DdzsfpeUzsb9vzgeOxVZeXZ9cPYpbFGo4cPoxhQ4bYtaHm7FnU5uVL5xFxfGxrsJhMMO4/gPYjR0jj6+y84Oz/zji2Re3jY7cfv9tuhdrHx+lx56x9Yr7FfDa2zHGfSnmNc5fmvIch8gRtZjJrNpsxefJk/P3vf3f5oCwsLERRUZHdMq1WK3vfgtmMrAkvwZiWBr+RIzFg+TKeMOmSLjc3LZk3pWRXMJuR+bcJqD50CADgN3IEBixf7lG12vZl+xEjoFIBxrRDUr8CkB7Pr6lBhq8v2o8YAUGwoObwEetG/P3Q/oYbre1UqYBGPmze5+bhMB09BlgswIcfAgDaDR+O2vR0AEDxrFnwHT4cam8vGNMa2ZafH3xvuAE1hw9b/ztyBPovXYrzoRNhTEuD7y23wKQ9A6HKAPj7w/f666V1AQBqtXX/zh7z9wMMRqfbtX1e+xtuQPWhQ077y2Iy4fTIUdImCz75FO1vvhnVhw/brd9+xAgAAqoPWffffsQtACD937YWW7633AK1WiXts//SH6UabbcPf3/AYHA6Dt5Dh6Lu2DEAQNGMGbgudS90YW9I27Ctq9HxtKmvQe0X+yqvvBxnAwOdr3NxG1Ju/PwAo0N7bZfZ9ofNv1UB/hCqDFAFBECoqpL6JPOll+3H1rEtTeS0wf5s5H34oV1dPjffjNoMrTVvoovttxsP2/21bw9UVzttZ4N+8msPGOvX9bn5Zpi0Z2z6fgQGrlgOAHbHsWO2VCpVg+Pa8dzUf+lSnJvwEkxHjtTvb/hwmDK0dn3heB4TzGace/Fv9f3t7wff62+w639xnGz7wfeWW6BS1be1qEtnZG3aJB0fducYB2INgtmM03fdDaGqym7bjsev47HS2OuGY1tUAf64Yc8eAJD2owoIwPUpycie9GrD486hfeKxKubTdl3bZbZtVQX44/qUFGn7nvwaR0SXr83cMztv3jxkZWUhMjLS5efMmTMHw4YNs/szfvx4AEBKSgoSExMRFRUFvV6P0NBQAPWf+hYeHg6tVouYmBj8smgRUnfvxtziYugPHMCzTz5pt25ERATS09MRGxuL2NhYpKenS/coiOuEhITAYDAgMjISGo0G8fHxiImJgVarRXh4uN26oaGh0Ov1iIqKQmJiIhISEhAdHQ2dToewsDC7dcPCwqDT6RAdHY2EhASX2xQfHw+NRoPIyEgYDAaEhISwTY20SavVXlabanNy8MqG9QCADzZvRub+/S616d+vv46TqalYU1aKDUlJ2L1x42W3qTYnB2GbNsJosWDm1q3YvXGjR45TbU4OJm20fkjFrKIiHN67F8uioz0qey+HhMCYloZ/5mSj+tAhfLJlC7JMJizfuQO/LFqE3Rs3YubWrTBaLIgvLAAATNq4ATWHj2BWURFO11RjY34+fklKxOmaaswqLAQA/DMn29pHuToYLRbMLS7G/lQNtpWVYU1ZKbJMJkwvLEBterq07od5uSg4dAizt2/HPoMByZUViC0pQUFtLT7Nz7du98xp1Bw+jE/z81FQW4vFf+zA+tnRSNq1C4v1F1Bw8CD+e/GHe/88fQo1hw9jemEBskwmrCkrxbayMhwxGjH3/HmUpqUhPFdXX6/BKLVpdWIilk77Eof37sGsoiLAYrm4jgFv/LYJRosFs7ZtQ+ruPdhWUYHlO3fgREoKJoc8Z9f+D3N1yD94EIv1F5C8ezd+T0lBbEkJsvbtw38TtkjrVh86jP8mbEFBbS1iS0qQXFSEfQYDFusvoNRsxod5uQCAVzdthDHtEKYXFuBkairmf/oZNiQl4YjRiFnbtkF/4KC1TQaDVIM0TuVl2FhehuMHD1jbdHHf+vkLpONp1rZt0OzZi20VFdZxqqnB9IvjbjtOpRWVWKy/gH0GA7bu2oWYHTvrx+liX93g6yONU8yOndi6a5d9mzIyUH3okHW7RqP9OFVU4Ihej7nFxTBaLAgXx9RhnDbk5WFjeRlO6S9gVlERjGlpePwvf0HN4cN22TtiNGJbeXl99gryG7bJbJbalFxU1DB7F9f99Nw5aZy279kNTVGx/ThdbL/t8bSmtMTaJqMRc3NyrG0Ss3fmtDROR1JT8UtSEjaWl1mPp/PZdvt+M2EzjJVVUps2Jidj4cyZOJGSgk+2JEjHZ/Whw1Kbov/4A0m7diO5qhKL//gDmfv34/WXX67vewAfbE5AxuYE/JiYiOSqSmmcCg8dwocZGXY1fLJlC06kpEjnvd0bN+Lb7dvr22Qw4tVNG+2yJ47T6WqjlL1XN21E9aH6cdKcPCkdTysSd+L0vv3Os2c2Y/b27dj+yy9YPzsaK3JyrOOUlyeta0w7hNdCnkOmRoPYkhL8npKM3xcuwuzt21BqNuPt3zahNienwbl84cyZ2JSSYh2n4mIYKirx13HjYNx/AG+cOgkAmJmZib3zF2B14k5sLC/DkdS9mPb7Vuu+s89L573StDTM3LoNR4xGbM3Px5qyUpzSaPDJFutxL24vfPNvKDiYJmUvqbAQM//9b2RqNPg0P9+a6TFjLnkuv9reGxUWFra5NrXFcfKENsXFxcETtYnvmb1w4QJuuOEGRERE4J133gEAPPjggyguLsbRo0cbfV5jV2bHjx8v6zuUlHJ1i1pHREQEpk2bJvt5vDLrurZ2Zfbb0lK83blzy1yZvcj2yiyANndlFiqVR1+ZBdBqV2ZnFRch/Jpuzte5uA2lXZl1rItXZlvuyuwPahXev+UWXpnllVmXXO57GLr6eOr3zLaJyewbb7yBbdu24dixY/C5eE+GK5NZZy53oHhPBl0O3jPrurZ0z2xT93jxnlneM+t4z6zjPYziOrxnlvfMOrtn1rFO8RzDe2Y9+zWOyNNxMttKzpw5gz/96U/49ttv7b74+fnnn0dJSQm2bNmCwMBABAUFubQ9Tx0o8lzBwcH8njaShZkhOZgXkoN5ITmYF3KVp86RFD+Z3blzJ/785z83uc6///1vfPvtty5tz1MHioiIiIiIyB08dY6k+A+AGjZsGOLj4xv8GTp0KPr374/4+Hi8+uqr7i6T2jDxJnwiVzEzJAfzQnIwLyQH80JK1+wrsydOnEBGRgYqKirQsWNHXHfddfjTn/7UUvVdtit9zyxdvQwGA/z9/d1dBikIM0NyMC8kB/NCcjAv5CpPnSNd9pXZ+fPno3///hg2bBiefPJJTJgwAU8++SSGDh2KQYMGYdGiRS1ZJ5HHmjFjhrtLIIVhZkgO5oXkYF5IDuaFlM77cp707rvvYubMmQgKCsKkSZMwbNgwdOjQAZWVlUhPT8e6devwj3/8A2fOnMFXX33V0jW7ZOfOnW7ZL119xlz83joiVzEzJAfzQnIwLyQH80JKJ3syq9FoMHPmTDz11FP46aefEBAQ0GCd7777DhMmTMA333yDZ599FrfddluLFEvkiXQ6nbtLIIVhZkgO5oXkYF5IDuaFlE72rxkvXrwYvXr1QmxsrNOJLAAEBAQgLi4OPXr0wOLFi5tdJJEnKykpcXcJpDDMDMnBvJAczAvJwbyQ0smezO7ZswfPPvssfH19m1yvffv2ePbZZ7Fr167LLo5ICe6//353l0AKw8yQHMwLycG8kBzMCymd7MlsdnY2brrpJpfWHTJkCLKzs2UXRaQk0dHR7i6BFIaZITmYF5KDeSE5mBdSOtmT2fLycnTs2NGldTt06ICKigrZRREpyaxZs9xdAikMM0NyMC8kB/NCcjAvpHSyJ7OCIEClUslan6gtCw4OdncJpDDMDMnBvJAczAvJwbyQ0qkEmbNNtVqNkSNHok+fPpdcV6fT4dChQzCbzZdd4JXmqV8ITERERERE5A6eOkeSfWW2f//+0Ov1SE9Pv+QfvV6P/v37t0bdRB4jNDTU3SWQwjAzJAfzQnIwLyQH80JKJ/vKbFvnqT91IM+l1+sRFBTk7jJIQZgZkoN5ITmYF5KDeSFXeeocSfaVWSKyx+9SJrmYGZKDeSE5mBeSg3khpeNklqiZRo8e7e4SSGGYGZKDeSE5mBeSg3khpfOW+4Sbb75Z1voqlQqHDx+WuxsixTAaje4ugRSGmSE5mBeSg3khOZgXUjrZk9mgoCBZX81zJezcuRN//vOfnT62Z88e3HnnnVe4IrqaZGRkuLsEUhhmhuRgXkgO5oXkYF5I6WRPZnfu3NkKZbSMt956C7fffrvdsuuuu85N1dDVYvz48e4ugRSGmSE5mBeSg3khOZgXUro2dc/sfffdhwkTJtj9ueaaa9xdFrVxkZGR7i6BFIaZITmYF5KDeSE5mBdSOtlXZj1dRUUF/Pz84O3d5prWJItFQHmxEYHX+EGt9qxfA2/r5s2b5+4SPI6zPIrL/Dv7oiCjDD0Gd0KlvhqCRYBarUKn7v4AYLdOt0GBKDpXjl7Xd4a3t1raTmmhQXpex2v8UF5shLnOAmOZCT2v7yxtVySuV1poQNWFavgH+cLbSw2/zr44e6AQ3QYGoksPf5QXG6ECpHUNJTXofWMXad+iujoLck+VICDIFx27+iH/TGmDfwd0aY9zaUUYNLIbDKU1du0UM2OxCCjOrcSFrAoMvr0HfHy8UFdnQd6Z0gZt1udXwVBSg57Xd0ZpoQHF58rR7dpO6NjVup/Bt3aHt7capYUG1JrM0GdXYvDtPaRlKsCujwOv8UNdnQUZBwox4JZrcCGrAr2u7wy1WiWtL/at+FyLRZBqU6tV0nYcx00cW8dt2I5VZZERBkMtBt/aA9XlJnQIag99fpXUF2q1qsG+OgS1b7AtZ/lxzJ0+vwpVF6rR4Zr26NIjQGpHUxm0bZe4PTF7KgB+nX2d9vuFrAp0u7YTOnXzQ0FGmd04imPbY3AnGEprEHiNHywWoUGW/Dr7QK2y7s9YZsLs2XMaHF+2x4Bt3c76SAUgIKg98s+UwjewHapLTfAP8gUEwFhmssu4mG2/zj7w9lKjU3f/Bm23HUdDSY3UrwDs1qnUV9tlwlBaA//Ovsg9oUdleQ16XNtZOu7E47f74E4ozChDQJAvuvQIcLrvSn21XTvF47hL7wCU5FTa9b9tX1+qj8R+tD1exPHx6eAN/flKBPXvCFNlLXrf2MUul87OF7bZE8854j70+VWoLDIC3ir0vTEI3t5qWCwCinIqkHNMj5se6IPqcpNdH3fq5m9Xq2PfBAS1R96pEnzwZiSKdBVQO9wSJuZJrNE2j+XFRrv6HY+bTt38Uamvlo4F2+c6Huu2uRTPMYNv7S4d07bPsW2HuL/2nX1g1NcA3ir0HNy5QR4cX2OcHfeO7W7svGDbZpPJjIwDheg3vCuy0y9g8K3d4ePj5XR/pYWGBvm33bfta4TjY9XVdUjfkY0u3f3h3d7Lbvxtz6PiucPx/G8orUGHoPbSeABokBvb1z9btucLcZvffx+N0kKDy+8fm/t+0zaz4vi39j5bc3uX2lZz9sX39q5pE98zK94z26FDB1RWVsLLywv33XcfoqKicNtttzX6vMLCQhQVFdkt02q1GD9+vMd9h1JTLBYB8TMOIj+jDD0Hd8JT74xi6K+g4OBgbNiwwd1leAxneQQgLYMKgJOzTo9BHaFSqZB/trzBOu18vTBpxn1Qq1VY+80BFJwtlx7z9lGjzmS5ZF1e7VQw1zZ+urPdju264r5t3+wv/k+SS/t0pue1gViS9BnWr1+PX77eh6LMSusDKuCVGfdh+Ye7UVtjtmvzmqj9KDxXccltdx/YEYWZFY0us+3jbgM7oCir0q6fvX3UCOoTIO3Ltk+6D+yAkjwjamvM8PZRo2vfDig4W44e1wZCBTgdN8dtNDVWam/AUlf//3a+Xg321dS2bNtmmzvHvus2sANKL7bDke02bNvVc3AnPBk+EutmHrTLXlP9LvaFOI4AEPNOsnW/Fx/rPqgj9LqqS2Zp/paPcPBMivTG1vEYsK3b1f62ZVujY7Z7XhuIJ/8zyq7tzrbbfVBHqAAUXOxrcfykTDRy3DdVY49rAzHeYd+2uWiybQ77tR3Pxvqox6COgE0bug/sAH2uwel+bHPp7HzR2HHbY1BHCIKAQvG4v/iciVH3In7GfhRnVTXaJNtanY2LaN7mKQgb90Wj22nn64WXv7oHP32wq8FxINby66yDdrWL/d5zcCcE/3sEfnwvpcFzHcek24AAFJ2vksZdyoQNsR3xMw5c8hzX49pAPP3urQDqX08cj1PH9z+NvR7Zjk07Xy9M+PJuLHkn2T6jKuC17x6At7fabn8QBCkjjrWJE33b48j2serqOix+O8lpn2/47pDda6TTsbB5rLbGjB7XBsJcV4fi84Ym+86uVocMzkuYgrCxX7j0/rG57zcdz189rw3EUxf7prX22Zrbu9S2mrMvT3xvz++ZbUU+Pj545pln8N133+HXX3/F559/jvT0dNx3331IS0tr9Hlz5szBsGHD7P6I9w6kpKQgMTERUVFR0Ov1CA0NBWCduABAeHg4tFotYmJiEB8fD41Gg8jISBgMBoSEhNitGxERgfT0dMTGxiI2Nhbp6emIiIiwWyckJAQGgwGRkZHQaDSIj49HTEwMtFotwsPD7dYNDQ2FXq9HVFQUEhMTsXbVr1j561KUVhVh1pJPUV5slNYNCwuDTqdDdHQ0EhISFNOmhIQEREdHQ6fTISwszG5dT2vTU0891eba1JxxenTsY8jPKENc0iycPKLFjK+/xdpVvyI5OQnbDq1ElbEcP+2YDsD6pgsA1uyeg6OHTiL+t5U4fC4FmQUnsfnAMphqq7F461TU1pgx9v8eRXmxEQvjZkJ34Sz2ndmOfWe2IytPi/Wpi+y2t3jrVJhqq7H5wDJkFp7E4XMpSEn/DUVlOqzZPcdu3Z92TEdVdTkSNHE4k3sYx89r8EdaPEqrihCXNEvatzhOaSnHsf3gWhw/r8GZ3MPWNlU7b1NRmQ57Tm62tqnQ2qbzpwrRzssH5cVGRM79NwBgfeoi6IrP4quIH7D76O/QXTiLNUnzkXemFI+OfQyF5yqctmnPyc12bZo69y27Nm07tBK7dqfg+HkNEo+uw6mjZzHrx88AwLpvAYhLmoXSqiIkHl2HI9q92LUrRWpTzJZpUpsKMyvx844fUFSmQ/KRTfh9+2/ILDyJH1dH4/ypQizeOhUQ6tu/PnURdBfOYs+xrdh3Zjt0F85ibfKCRsfpbK59m37e8QMAYPavH6LgrLV/y8pLse3QSpzJPYwj2r1IPLpOGqeCcxX4NHoyAGDWkk9xMj0DM77+FjsTt9uN04wlH6G2xux0nH7/YzP27tVg84FlyD5ViC/mvwsA+HT2v5B3prRB9nQXzmJ96iIUZlY0bNN+6zjtP5mIb6dHY8+2g1Kb5v1mXfebmCl2bRLHSWyT2Ff/GPM5Jv7tVeh0Osz4+lvs2LnNrk1RMVOQf7Yc8zZPQZ3JIrUp+cgmu+yJx5PtGKxPXYTM3DOYO2sR5s5a1OB4yj9bjvGPP43sU4VS9g6cSmqYvTlvoeBchZS9zamx1jZlXWxTpX2bAGv2iksKkHh0ndPjqeBsOcb+36MoOFsutSnp8EYcPpcCbc7xJtukK744Tqet47QwbqZdH4njtGHPUul4Wrd5FY4eOmlzPP0bdSaL3fEkjtP2g2txKj0DcUmzYK4V6s8nW79BWspxp9n7acd0FJyrwNSLx71tm+bPXIr9+w402aasPK2UvQP7DuHtf76Dgottss3egO5/avQcMW/zFNTWmPHX4OdQWlbSIHtF+ny89NwrKDxXYTdORfp8JB5dhz92bMXPi37F5tTYBuc92+ztObkZ23ZusTuXz9v4SYM2Hdh3CHNnLcKm39dJx1Nj54jUvRrE/rQSc76fh/SDx7Fm9xwUnC2XjvuvF32IrDM6u9enGV9/i5NHtIhLmoX8jDI8OvYxlBcb8d3SqdJ57/CZPYj5ZhW2pTmcy3+bgowDhfjXG5ORfvC49Ryx/Tekpu5rME6fRU9GebERERER2LFpt915b2HcTOl92bFEXYNz+f6Tifj6s++kNonn0doaM0LGv4BzJ3T145RVP05xSbNQcLYcn88Pl8ZJbFNTr08F5yowN/ZraZzuunEcMgtPYsmq2cg/f6HJ9xGL5/+IA5o0rE9dJPUn4Pr7iPJiI6IWR0jHU3JKMtau+rXJ90Yn0zOw8telOH5eg+TkJHz+2bRmvTcqLzZiQewM6C6cxYaEtVg8/8fLfm8kvt/5acd0ZBzLxuefTbN7b3QyPQOzlnwKwPo6Ul5sdPn9XnmxEdHLvkRRmQ7xm35G7E8r3f5+Ly4uDp6oTVyZdUar1eLmm2/G/fffj4SEBKfr8MostYSwsDD+qrENXpltWs9rA/F7xkLMnTuPV2Zt8Mps41bt+hbb09bxyix4ZdaVK7NxSbPwwv3hjW6HV2Z5ZdY2g3HJs/DCfeG8MtsK2+KV2SujzU5mAeCFF17A2rVrYTAY4OXl5dJzPHWgLoW/V+8+Op0Offr0cXcZHoX3zDZ9z2xeXi769OnDe2Z5z6xL98wKHQwYMKCf3fHFe2Z5z2xj98zmFuTiupsG8Z5Zm3bznlkrZ/fMmv2r0CXgGt4z20rbakv3zHrqHKlNT2bff/99REVFoaysDIGBgS49x1MHijxXdHQ03nzzTXeXQQrCzJAczAvJwbyQHMwLucpT50jNumdWEATMnz8fo0ePxjXXXAMvL68Gf9z5qcJnz55F+/bt0aFDB7fVQG3f4MGD3V0CKQwzQ3IwLyQH80JyMC+kdM2aab7//vuYOXMmRowYgQkTJqBLly4tVZcsRUVF6Natm92yw4cPY/369Rg3bhzU6jbxOVfkofz8/NxdAikMM0NyMC8kB/NCcjAvpHTNmswuXboUzzzzDFatWtVS9VyW5557Dn5+frj77rvRvXt3HD9+HAsWLIC/vz+mT5/u1tqo7dNoNHjggQfcXQYpCDNDcjAvJAfzQnIwL6R0zZrMGo1GPPzwwy1Vy2UbP348VqxYgZkzZ6K8vBzdunXD008/jU8++QTXXXedu8ujNu7VV191dwmkMMwMycG8kBzMC8nBvJDSNev3bx966CHs27evpWq5bG+99RZSU1Nx4cIF1NbWIjc3F8uWLeNElq4I8bu5iFzFzJAczAvJwbyQHMwLKV2zPs04NzcXY8aMwQsvvIB//OMf6Nq1a0vW5hae+kldRERERERE7uCpc6RmXZm98cYbcfbsWXz88cfo3r07AgICEBgYaPenU6dOLVUrkUcKDg52dwmkMMwMycG8kBzMC8nBvJDSNeue2WeeeQYqlfu/xJfInTZs2ODuEkhhmBmSg3khOZgXkoN5IaVr1mT2xx9/bKEyiJQrPDwcs2bNcncZpCDMDMnBvJAczAvJwbyQ0vELWIma6c0333R3CaQwzAzJwbyQHMwLycG8kNI1ezJbXl6Ozz77DKNHj0aPHj3Qo0cPjB49GlOnTkV5eXlL1Ejk0ZKSktxdAikMM0NyMC8kB/NCcjAvpHTNmszm5uZi5MiR+Oyzz1BZWYl77rkH99xzD6qqqvDpp59i1KhRyMvLa6laiTxSly5d3F0CKQwzQ3IwLyQH80JyMC+kdM26Z/aDDz5Afn4+Nm7ciEcffdTusc2bN+PZZ5/Ff//7XyxdurRZRRJ5sj59+ri7BFIYZobkYF5IDuaF5GBeSOmadWU2ISEBb7/9doOJLACMGzcOb731Fn777bfm7ILI423ZssXdJZDCMDMkB/NCcjAvJAfzQkrXrMlsVVUVevTo0ejjPXv2RFVVVXN2QeTx3nnnHXeXQArDzJAczAvJwbyQHMwLKV2zJrNDhgxBXFwcTCZTg8dqa2sRFxeHIUOGNGcXRB5v4sSJ7i6BFIaZITmYF5KDeSE5mBdSumZNZj/44AOkpqZi9OjRWLBgAXbu3ImdO3di/vz5GD16NDQaDf773/+2VK1NOnjwIJ544gkEBQXB398fw4YNw/fff39F9k1Xt1WrVrm7BFIYZobkYF5IDuaF5GBeSOmaNZl99tlnERMTg/z8fISFheGhhx7CQw89hDfeeAN5eXmIiYnBX//615aqtVG///477rrrLhQWFuLjjz/Gd999h8cffxw5OTmtvm+i4OBgd5dACsPMkBzMC8nBvJAczAspnUoQBOFynigIAioqKuDj4wNvb2/s378fWVlZAIABAwbgtttug7d3sz4s2SXl5eW44YYbcPfdd+OXX36BWt28r849duwYhg0bhqNHj2Lo0KEtVCUREREREZEyeeoc6bJnfiaTCUFBQfj+++/h7e2NO++8E8899xyee+453HnnnVdkIgsAsbGxKCgowBdffAG1Wo2qqipYLJYrsm9HFosZJfm5sFjMbtn/1cAT+zgiIsLdJbQKT+zrtsKdmeG4Kk9bPce0lLaQ6ZZsg6t5aQv9Rs3H8wsp3WXPOH19fdGzZ0/4+vq2ZD2ybdu2DYGBgdDpdBg/fjxOnz6NgIAAvPTSS5g1axbat2/f6HMLCwtRVFRkt0yr1V5WHRaLGSs//S9yT51A7xtvwnOfToda7XVZ2yLnPLWPX3jhBXeX0OI8ta/bCndlhuOqTG3xHNNS2kKmW7oNruSlLfQbtQyeX0jpmvU7uRMnTsRPP/3k9NOMr5QzZ86grq4OTz75JMaMGYM1a9Zg0qRJmDdvHl555ZUmnztnzhwMGzbM7s/48eMBACkpKUhMTERUVBT0ej1CQ0MB1N9bEB4eDq1Wi5iYGMTHx+OPLQlYunY9THVmTF+yAmWFBdK6ERERSE9PR2xsLGJjY5Geni79JExcJyQkBAaDAZGRkdBoNIiPj0dMTAy0Wi3Cw8Pt1g0NDYVer0dUVBQSExORkJCA6Oho6HQ6hIWF2a0bFhYGnU6H6OhoJCQkyGqTRqNBZGQkDAYDQkJC7NZ1R5seHTsWuadO4Jf96TiRloYZX33lEW364Ycf2tw4RX/3HY7s349f044j99QJPDp2rOLb5EnH02uvveaWNp04fAjfr7B+2Efkop9QVljAcVJAm9LT09tcm1pqnPIyMzF9yQq7TCutTdHffYct23fg/IVSLF27HnmZmc0ap6lTp16yTX97/nlojxzGjpMZSE5OwZqff75qjie2yb5NMTExba5NbXGcPKFNcXFx8ESXfc8sAKxcuRKRkZGoqanBxIkTMXDgQPj5+TVY7+mnn25WkU0ZPHgwzp49i7CwMMydO1daHhYWhvnz5+P06dO4/vrrnT63sSuz48ePl/374PwpZ+vz1D6OjY3Fiy++6O4yWpSn9nVb4a7McFyVqS2eY1pKW8h0S7fBlby0hX6jlsHzC7nKU++ZbdZk1pUPW1KpVDCbW+9+jGHDhuHYsWNITEzE/fffLy1PSkrCAw88gKVLl+Lll192eXvNGSiLxYyywgJ06t6DLwqtxBP7OD09HcOHD3d3GS3OE/u6rXBnZjiuytNWzzEtpS1kuiXb4Gpe2kK/UfPx/EKu8tTJbLM+pWnHjh0tVcdl6927N44dO4YePXrYLe/evTsAoKSk5IrVolZ7oUvP3ldsf1cjT+zjuLi4NvlC4Il93Va4MzMcV+Vpq+eYltIWMt2SbXA1L22h36j5eH4hpWvWZPaBBx5oqTou26233oqtW7dCp9PhxhtvlJbn5uYCALp16+au0ugqMW3aNHeXQArDzJAczAvJwbyQHMwLKV3zvpTVA4g3QC9evNhu+aJFi+Dt7Y0HH3zQDVXR1YRfOE5yMTMkB/NCcjAvJAfzQkrXrCuzf/nLXy65jkqlwvbt25uzmyaNHDkSkyZNQkxMDOrq6vDAAw9g586dWL16NT788EP07s1foaHWtWHDBneXQArDzJAczAvJwbyQHMwLKV2zrsxaLBYIgmD3p66uDhkZGdi5cydycnJgsVhaqtZGzZs3D59++ilSU1Px9ttvIy0tDbNmzeKvTtAVIf52AJGrmBmSg3khOZgXkoN5IaVr1qcZN2Xjxo14/fXXsWnTJowcObI1dtEqPPWTushzGQwG+Pv7u7sMUhBmhuRgXkgO5oXkYF7IVZ46R2q1e2Yff/xxTJgwAW+//XZr7YLII8yYMcPdJZDCMDMkB/NCcjAvJAfzQkrXqh8ANXjwYOzbt681d0HkdmPGjHF3CaQwzAzJwbyQHMwLycG8kNK12mS2rq4Oq1atwjXXXNNauyDyCDqdzt0lkMIwMyQH80JyMC8kB/NCStesTzOeNGmS0+WlpaXYu3cv8vPzMXPmzObsgsjjlZSUuLsEUhhmhuRgXkgO5oXkYF5I6Zo1mf3jjz+gUqnslqlUKnTp0gX33nsv/v73v+ORRx5pVoFEnu7+++93dwmkMMwMycG8kBzMC8nBvJDSNevXjDMzM3Hu3Dm7P2fPnsWBAwewatUqTmTpqhAdHe3uEkhhmBmSg3khOZgXkoN5IaVrta/mUSpP/dhpIiIiIiIid/DUOVKzPwCqvLwc06dPx5gxYzBy5EhoNBoAgF6vx8yZM6HVaptdJJEnCw4OdncJpDDMDMnBvJAczAvJwbyQ0jXrntmcnBw88MADyM7OxvXXX4+TJ0+isrISABAUFIT58+cjKysL3333XYsUS+SJNmzY4O4SSGGYGZKDeSE5mBeSg3khpWvWldn33nsPFRUVOHToEBITE+H4G8vjx4/Htm3bmlUgkacLDQ11dwmkMMwMycG8kBzMC8nBvJDSNWsy+/vvv+Ott97CkCFDGnyqMQBce+21yM7Obs4uiDzerFmz3F0CKQwzQ3IwLyQH80JyMC+kdM2azBqNRnTr1q3RxysqKpqzeSJFWLx4sbtLIIVhZkgO5oXkYF5IDuaFlK5Zk9khQ4YgKSmp0cfXrVuHkSNHNmcXLpk4cSJUKlWjf3Q6XavXQFev0aNHu7sEUhhmhuRgXkgO5oXkYF5I6Zr1AVBvv/02QkNDcfPNN+PZZ58FAFgsFmi1Wnz22WfYs2cP1qxZ0yKFNuUf//gHHn74YbtlgiAgLCwMAwcORJ8+fVq9Brp6GY1Gd5dACsPMkBzMC8nBvJAczAspXbMmsxMmTEBWVhY++ugjTJkyBQAwduxYCIIAtVqNadOmYfz48S1RZ5Puuusu3HXXXXbLUlJSYDAY8Le//a3V909Xt4yMDHeXQArDzJAczAvJwbyQHMwLKV2zJrMAMGXKFLz00ktYs2YNtFotLBYLBg8ejKeffhrXXnttS9R4WWJjY6FSqfDiiy+6rYamCBYBZn01vILaQ6Vu+OFZpBzjx4+XPZ6eMv4tUYentEVJrsQP+ajtYF5IDjl54fmbeH4hpWv2ZBYA+vfvj/Dw8JbYVIuora3FqlWrcPfdd2PgwIGNrldYWIiioiK7ZVqttpWrs754FM0/AlNWOXwGBKLbP27mi4iCTZ06FZEj/unyeHrK+LdEHZ7SFqWJjIzEvHnz3F0GKQTzQnK4mheevwng+YWUr1kfAOWptmzZggsXLlzyV4znzJmDYcOG2f0Rf0KVkpKCxMREREVFQa/XS9/DFRwcDAAIDw+HVqtFTEwM4uPjodFoEBkZCYPBgJCQELt1IyIikJ6ejtjYWMTGxuJQyn5M/ekbAMDfZvwTZn01QkJCYDAYEBkZCY1Gg/j4eMTExECr1Uo/KBC3FxoaCr1ej6ioKCQmJiIhIQHR0dHQ6XQICwuzWzcsLAw6nQ7R0dFISEhotTalp6cjIiLCbp2rpU23DRmJU4eO49PtP8CUVY7gcY812abzxzLwzoL/2Y2/W9o07jGYssrxxrpPUKYtxNQpn8gep99Wr8eC+B+RV1GEdxb8D2Z9tceOkydlT6/Xt7k2tcVx8pQ2zZs3r821qS2Ok6e0qU+fPi61qSgjF9+vmo895w9hy46t+CHqW49tU1scJ09p05gxY9pcm9riOHlCm+Li4uCJVIIgCHKecPPNN8vbgUqFw4cPy3pOc7344ov45ZdfkJeXh65duza6XmNXZsePH4+jR49i6NChrVIffxratgQ/HozFj33OK7PMssuCg4OxYcMGd5dBCsG8kByu5oXnbwJ4fiHXHTt2DMOGDWvVOdLlkD2ZffDBB6FSXfpkl5+fj1OnTkGlUsFsNl92gXJVVlaiR48e+Mtf/nJZB+eVGijep9K28J5Z97eFiIjk4fmbiFzlqZNZ2b9mvHPnTuzYsaPRP3FxcRgxYgSysrLg5eUlXTK/UtatW6eITzFWqVXwvsaPLx5tQFhYmOzx9JTxb4k6PKUtSiL+2hGRK5gXkkNOXnj+Jp5fSOlkX5ltTEFBAaZPn44FCxagtrYWEyZMwJQpUzB48OCW2LzLxo0bh5SUFBQUFMDf31/28z31pw7kuXQ6Hb/LmGRhZkgO5oXkYF5IDuaFXOWpc6RmfwBUfn4+wsPDce211yI6OhrPPfccTp48iZiYmCs+kS0qKsK2bdvw1FNPXdZEluhyrFu3zt0lkMIwMyQH80JyMC8kB/NCSnfZX82Tn5+P6dOnY+HChaitrcVLL72Ejz76CIMGDWrJ+mRZuXIl6urqPP5XjKltudI/tCHlY2ZIDuaF5GBeSA7mhZRO9mQ2Ly9PmsTW1dXh5ZdfxpQpU9w6iRWtWLEC3bt3x8MPP+zuUugq4ufn5+4SSGGYGZKDeSE5mBeSg3khpZM9mR08eDBqamowYsQIREREYNCgQSgpKUFJSUmjzxk1alSzinTVnj17rsh+iGxpNBo88MAD7i6DFISZITmYF5KDeSE5mBdSOtkfAKVW199me6mv6BEE4Yp/NU9zeerNzeS59Ho9goKC3F0GKQgzQ3IwLyQH80JyMC/kKk+dI8m+MrtkyZLWqINIscLDw7F06VJ3l0EKwsyQHMwLycG8kBzMCyldi301T1vhqT91ICIiIiIicgdPnSM1+6t5iK52wcHB7i6BFIaZITmYF5KDeSE5mBdSOl6ZdeCpP3UgIiIiIiJyB0+dI/HKLFEzhYeHu7sEUhhmhuRgXkgO5oXkYF5I6TiZJWqmN998090lkMIwMyQH80JyMC8kB/NCSsfJLFEzJSUlubsEUhhmhuRgXkgO5oXkYF5I6TiZJWqmLl26uLsEUhhmhuRgXkgO5oXkYF5I6TiZJWqmPn36uLsEUhhmhuRgXkgO5oXkYF5I6TiZJWqmLVu2uLsEUhhmhuRgXkgO5oXkYF5I6fjVPA489WOnyXMZDAb4+/u7uwxSEGaG5GBeSA7mheRgXshVnjpHahNXZs+cOYPnn38effv2hb+/P/70pz9h6tSpMBgM7i6NrgITJ050dwmkMMwMycG8kBzMC8nBvJDSKf7KbHZ2Nm6++WZ06tQJYWFhCAoKwp49e/Djjz/iiSeewK+//ipre576UwciIiIiIiJ38NQ5kuKvzC5btgylpaXYtGkT/vvf/+L111/HkiVL8PLLL2P9+vUoKSlxd4nUxgUHB7u7BFIYZobkYF5IDuaF5GBeSOm83V1Ac5WXlwMAevToYbe8V69eUKvV8PHxcUdZBMBisaCkpARdunSBWt1yPzdx3G5r7cdVGzZscLq8rq4OWVlZGDBgALy9L32oNdYOV9rb2n3QWFtc2a/JZMKxY8cwdOhQeHt748KFC1CpVOjcuTNKSkogCALUajWCgoLs2iOuFxQUBADSfsR/d+rUCRcuXEBJSQm8vLwwYMAAVFRUoGPHjsjKykLHjh3tarLdh+32AwMDkZGRAaPRiGHDhsHHx0d63LY2AHa1l5WVoVOnTigpKUFtbS0KCgowdOhQ6flFRUWoqKhA//79UVFRIdV+4cIFLFmyBHV1dbhw4QLKysoQGBgo1VVZWYn+/fujpKQEFRUV6Nu3L3Q6ndT3Yp+L+xZ/uUas02KxICsrC/369ZO2MXDgQOm5Yhs6dOiAEydO2NXsrI9LSkqkcXDsu86dO0tjoFKp0KVLF1xzzTWwWCzIyMgAAHTs2BEGg0Gqoa6uDufOnUNAQIDdcmc5F8fAZDLh7NmzuP7669GtWzeUlZVJ64nb69SpE4KCgqRxsV3HdluOeRD7a8CAAVCr1ZfMc2Pb8vf3x4EDB3D77bfD29sb586dkzKoVqsbZEbsv6KiIuTl5aFnz55Qq9XS+ItZPnfuHL7++mucOXMGgwYNgre3t3RM3XjjjcjLy0O/fv1QUVEhHRO2Y97UcSz+v0+fPsjOzkZAQAAqKyvRuXNnXHPNNVIfiH3csWNHWCwWFBQU4KabbkJZWZmU0YyMDFy4cAGjR4+GWq1Geno6/P390aVLF3h7ezfIZllZWYPj2zFv4vp9+vSRjgGLxSKdT3x8fOzOL47HnnjsOB4LAJCRkQG1Wi31qWOubXPkLE+NnYMbe05jWRK3Y5tDx/oASMeM7fjYnpOCgoJQV1eHo0ePYuHChbBYLHbbFNvoeF6yPY/YHhMZGRmoq6tDdXW19H6qsrISAwcOhFqtlrYj9qez+py9jjXWl7bLnZ3vxfN6p06dpG2Lz+3YsSOys7PtXp+cPWZbd9++fZGdne10ey3x2trY63ZjuWiJ/dj2m7P6HfMirvPrr7/iwoULbnsPRdRciv8144SEBIwbNw5PPPEEPvvsM3Tt2hW7d+/G66+/jkmTJmHWrFmNPrewsBBFRUV2y7RaLcaPH+9xl9CVxmKxYMmSJcjOzka/fv3wyiuvtMhJ0nG7oaGhWLp0aYvvR46IiAhMmzbNblldXR2+/vprmEwm+Pj44P33329yQttYf7nSXgCt0teXaosrY2wymfDll19Kb/zFN6UA0K5dO9TW1krr9u3bF5MmTQIAxMTEICcnR3qOSqVCTk4O+vbtCwDIycmR3oyLVCoVLnU669u3LyZOnIgff/xR2r4tlUqFDz74AMuXL7d7XPzqArF28U20YxvE5y9btkxaV6yrb9++EAQBOp0O27dvx5gxY+zqvxQfHx+8++67WLZsGbKzsxvsGwB69+6N4uJimEwmp8/96aefGrRbrHnFihXIzs6262PbfTjrO8cxEGsoLCxssNzHxwf/+c9/MGPGDLu6Hdsl5ryxMRL7vl+/fnjppZcQFRUlbU98zHadprZl21/t2rVDjx49kJOT02ieLRaLXTYb42xsnGXGy8sLZrO5wfMds7x9+3Y89NBD8PHxwdtvv42oqCi7x8X1bcfD8bzjeBz/5z//wcyZMxtkRSQejxaLBV999VWD9sjVWDZtj2/bvnG2vpeXFywWCwRBgEqlwjvvvIMZM2ZI/3c89pyx7Q/A+bHhmCPHPDV1Dnb2nMayJD6nT58+KCoqgslkalCft7c3VCpVg/53PCf16tULeXl5AKx5eemll6T+E9u4dOnSBuclx6w2dvyK2rVrh27duiE3N7fJ+sT8OL6ONdaX4nLb84/tvx23PXHiROm5YlvEzKvVaml/tu20rbup7TX3tbWp121nubjc90u2z7PtK2f1277+2I4PYL0ye/vtt7vtPRQpB3/NuJWMHTsWkZGR2Lp1K0aOHIn+/fvj+eefx+TJk5ucyALAnDlzMGzYMLs/48ePBwCkpKQgMTERUVFR0Ov1CA0NBVD/6xjh4eHQarWIiYlBfHw8NBoNIiMjYTAYEBISYrduREQE0tPTERsbi9jYWKSnpyMiIsJunZCQEBgMBkRGRkKj0SA+Ph4xMTHQarUIDw+3Wzc0NBR6vR5RUVFITExEQkICoqOjodPpEBYWZrduWFgYdDodoqOjkZCQcMXaVFJSgpkzZ6K2thbLly/H9u3bW6RNJSUlmDt3LsrLy7FmzRr89NNPSE5Oxq5du3D69Gm8+OKLV3yc2rdv36BNWVlZWLVqFYxGI3bs2IFVq1Y1OU6TJk3CsWPHoNFosGPHDmzcuBFRUVE4e/YsfvjhBwDA119/jaysLCxcuBB6vR7r16+X+nb58uWora3FzJkzUVJS0qLZy8rKwtKlSwEAq1atwuHDhxEVFYWNGzdix44d0Gg0OHbsmN0Lo5i9HTt2IDU1FVqtFpmZmVKfrFu3DrW1tYiLiwNg/aHUkSNHMHv2bCxfvhypqalITExEbW0tvv32W+Tk5CAuLg45OTlYunQpCgoKkJaWhvT0dBQUFGD79u0QBEHa3urVq1FbW4vExETodDqcOHECaWlpOHLkCF577TVpewCwbt06GI1G7Nq1C+fOncPs2bOxdu1alJeXY+PGjQCAb775BjqdDhs3bkR5eTlSUlKg1Wpx5swZ7Nq1S2qTIAh46KGHoNPpkJCQAL1ej4MHD+LEiRNITU1FbGwsamtrkZubi7q6OqmG7du3o6CgAOnp6XZtAiCts2LFCuzfvx/Lly+HTqfDkSNHkJaWBr1ej4SEBADAjBkzYDKZ7NqUmZmJ48eP43//+x+OHz8utUnc7oYNG/Dbb79hzZo10Gq1SElJwcqVK2E0GrF69Wpp3ZycHLz22mvSfk+cOIGsrCxpnMR1Z8yYgbq6ugZtys7OxmuvvWY37qtXr0ZVVRXefvtt7N27FydOnMD69euRmJiIRYsW2dUptmnHjh3IzMzEjh078L///Q8XLlyQ2iTmVBw/8RyRkpJiN07idnNzc7F+/Xro9XpoNBps3boVOp1O6mPHc8Q777yDAwcONDlOq1evhsFgaJC9/Px8JCQk2LV/zZo1duOk1Wqh0WhQVlZmN07Dhg3Dxo0bUVxcjPfeew9nzpxBZmam1Kb4+HgA1ttuxOMpPz8f33zzjXTee++991BVVYXVq1fDZDLhoYcegslkajR7OTk5GDduHLKysqTcOrbJNnvOjifbNp08eRJr1661W3fjxo04ceIE1q5d2+B4mjNnDkwmk905oqioSDqecnJy8Prrr8NkMmH16tV2x15Tx9OyZctQV1cnnSO2bt2KlStXYuvWrVKb1q9fb5cn8bz1888/Y9WqVQ3OeyUlJfj666/tsieeI5KTkzF16tQGr7klJSXSuXzjxo04fPiwVLvtsbds2TLU1tY2aNPBgwfx008/Sf2Zl5cntammpgb79u3D4cOHpey98sor0Ol0Un/Gx8fDaDRi586dDcbJ9hgRx6m8vBwajQYnTpzA7t27pXH65ZdfGpzL9Xo9NmzYgOXLl0Oj0WDKlCk4e/aslL24uDhkZ2dj8uTJ2L9/P9LT03HgwAEUFBRg6dKlduf7WbNmNcjehg0bkJiYiIULFwIAYmNjpXE6fPgwpk6diuTkZGi1WqSmpqK8vBzx8fHIzc112qadO3di1apV+Pnnn2E0GvHDDz+gpKQE48aNQ3Z2NhISEnD48GHMnj3bpfcRu3btwm+//Yb09HTs378fkydPRnZ2NuLi4qS8nj17FlOmTIFGo8Hy5cul89DChQvtXsObem80adIkabs5OTmYN2+edN775ZdfsHHjRqlNs2fPthv/RYsW4cCBA5g9ezbMZnOT572r8T0s2+S8TWJ+PI3ir8wCwPLly7F8+XI888wz6Nq1KzZt2oQlS5bg+++/x7/+9a9Gn8crs63naroyGxsbK02iRbwya8Urs86vzKanp2PkyJG8MuukXbwy2zDL6enpGD58OK/M8sqsXb8Bzq/MpqenY8yYMbwyyyuzLl2ZDQsLQ+/evXllli7JU6/MKn4y+/PPP2PSpEk4ffq0dDADwCuvvIJVq1bh/Pnz6Nq1q8vb89SBUqKr5Z5ZZ5NZgPfMinjPbMN7ZteuXYtXX32V98zynlmX7pldv349xo8fz3tmec+sS/fM7t27F2FhYbxnlvfMunTP7PLlyzFu3DjeM0uX5KlzJMVPZu+//36YzWbs2rXLbnl8fDyefvppbN26FQ8//LDL2/PUgSLPJV41IXIVM0NyMC8kB/NCcjAv5CpPnSMp/kcwBQUFTn9NS/yVFzm/xkd0OTz1HgLyXMwMycG8kBzMC8nBvJDSKf7KbHBwMH7//Xekp6fjhhtukJY/9dRTWL9+PbKzs9G7d2+Xt+epP3UgIiIiIiJyB0+dIyn+yux7770Hs9mM++67D5GRkZgzZw4effRRrFu3DpMmTZI1kSW6HPzCcZKLmSE5mBeSg3khOZgXUjrFX5kFAI1Gg08//RRpaWm4cOECBg0ahNDQ0Et+gqwznvpTByIiIiIiInfw1DmS4q/MAsDo0aPx22+/IS8vDyaTCadOnUJERITsiSzR5RC/64vIVcwMycG8kBzMC8nBvJDStYkrsy3JU3/qQJ7LYDDA39/f3WWQgjAzJAfzQnIwLyQH80Ku8tQ5Upu4MkvkTjNmzHB3CaQwzAzJwbyQHMwLycG8kNJxMkvUTGPGjHF3CaQwzAzJwbyQHMwLycG8kNJxMkvUTDqdzt0lkMIwMyQH80JyMC8kB/NCSsfJLFEzlZSUuLsEUhhmhuRgXkgO5oXkYF5I6TiZJWqm+++/390lkMIwMyQH80JyMC8kB/NCSsfJLFEzRUdHu7sEUhhmhuRgXkgO5oXkYF5I6fjVPA489WOniYiIiIiI3MFT50i8MkvUTMHBwe4ugRSGmSE5mBeSg3khOZgXUjpemXXgqT91ICIiIiIicgdPnSPxyixRM4WGhrq7BFIYZobkYF5IDuaF5GBeSOnaxGT2wIEDGDt2LAIDA9GxY0c88sgjOHTokLvLoqvErFmz3F0CKQwzQ3IwLyQH80JyMC+kdIqfzB48eBD33nsvzp49i08++QT/+9//cObMGTzwwAM4deqUu8ujq8DixYvdXQIpDDNDcjAvJAfzQnIwL6R03u4uoLk+/vhj+Pn5Yc+ePejatSsAYMKECbjhhhsQERGBNWvWuLlCautGjx7t7hJIYZgZkoN5ITmYF5KDeSGlU/xkNjk5GWPHjpUmsgDQq1cvPPDAA9i4cSMqKyvRoUMHN1ZIrU0QzDAas+Hn1w8qldcV377RaJT+bbGYUFK6D1063w612sfu/yqVFwyGTAAq+Pn1hdGYfcl/GwyZMBiyYTIVIbDTzfBSt4e//wCpDtvaAMBgyIQgWKBSedmtZ7uur28PlJTuR3vfnvDz64uS0v3w9ekGtdrH7jkWiwn6klS09+0Jf/8BqK7OdbofP7++qK7OhY/PNSgoTEBg4HB0CLjuYt/U95u4PV+fblCpvKQaBcGMktJ96NxpFGpqCtC+fW9UV+fa1RkQcC0EwWxXj8GQiZqaAnTufBuqq3MBqNC+fS9c0O9FrakIPXo8CpOp2Om4ibW08+6E8vKjUKnV6NXzSajVPnb9WVWVgZqaAnTpcgfUap9LZsS2Zn//AU7H1brcCEEwS3nw9e2OgsIEdOhwI+pqS+3aJI5JYzk0m43IL9iEwMDhCPAfJO2nffteKC07KGXPaMxG+/a97frNtr8b63fbrIn9IT7Xz6+fNH5i5sWa8vI3wNe3G7p0vh36kn2oqc6Db/seaO/bEyZTMXx9e0g58PXtjsKi39G92yOoqSmwa3djxyIAVFaeRlnZYfi274GuQfdApfJCVVUGjEYdVCo1goLuAgCUlO5Dh4AbcD47BgMHhKFdu052/e/vP6DR40dsd3V1Htq37y2Nq7PjTKzPsU/Fv8X+uqDfAwgWtG/fCwCkcXDMjPjckpJMWCx3NTjOAvwH2S3r0OFG1Jr0Ut+KNZlMRVKmbI9bsR0WiwkVFcfQMXAY1Kp2dsdm8YVdMNUUoGPgUGnbarWPVJ+YF8fj2rY/Adjt07bPHTPt2IeOebfNgG32xLE3GLJRW3sBPXsGw8vLz+k4i8eqvkQDCBb4+fVFQMC1dvWIx391dZ5dVh3P0Y7jbJsN22NQPDYaa5/4WtEp8BaUlqU5PSfbtkV8fRDPT7avL1VV5aisPC1lVjyOxX2I51pnrwXtvDuhquo0uncf6/RYFGuwWGqlbdj2jbMxs32e7XEj9rHYBgDQl6TCp10Qamv1duddx2Pf2b8d22gwZKKi4hi6dfs/lJUfgU+7IJhMxQ2OY0Ewo7o6DyqVGl26jEZ1dS4sllrpuLE91zl7TRRz2CnwFpSVH27y9d722GhsHyWl+9Cxw03IzfsFPXs8gfyC9ejb50V4efk1OF4c33PYsn3NtfaTDiZTETp1Him9RhsMmSgpOQtBuNel90+Ox9Plvue63PdtjeWgpd/7OdtPY+ckd3Psy9Z+T+yJFD+ZrampgZ+fX4Pl/v7+MJlMOHr0KO68806nzy0sLERRUZHdMq1W2yp1UusQBDMOHHweZWUH0anTKNw66ucWPXhd2X5GRgYA6wtHUvJtMJur4OUVgHvv2Y2UXXfDbK6CWu2PgIAbUFFxCACgVvvDYjE0+W+Vyg+CYISjToGjcOutPwOAVFtg4EhAAMor0hqsJ57cxHUBFQDxQ8xt/13/HEEwIzHpVqkWL68AmM1VTvcjPmarY8dboFZ5oazc2m8jR/yEpOTR0vak9TqMgMF45uLz1QAsNturr61Dh1tgMJxppG/s2yA6eepDa5scxs1iMdm1TXTq1EcIDLwF5eWHEBg4EoJFQEXlIamN99+33+mEtrG+bWxcOwWOglZ7L7peswjl5WkNtufYpk6BozBq1AocTPtbgxyazUbsTBxus08/WCxiv1j7U632R4cON6K8PM2h3+z721m/BwaOxG23rgQA7NsfIuVXfG7HjiNgMJyRnn//ffshCGbsTBzWSLuaduLEe9K/bfPr2M+BgSNhMVtQWXVYWl+t9oO//w2orKxfplL5Qa1W2+Xz/PkFuO/efTh85HWp/wM7jgTQ8PgZNWoF9h94wabd9mNpWydQfzw69qn4d8eOI1BZecrpce24bdvn/vFHLjp3mdHgOPPy8ofZbHC2KSfsjxPHdjjq2GEEKipPAqh2+rizvIic9afjPgM7joRKpZLOEbeOct6Htnm3zYBt9tRqf/j7X2839idPTcED96ch7dBEu3EW99mgvR1HQK1So6z8YIPjv7E2OI5vwz6tP8buv28/VCovp+0bOeInJKfc0WB8Aft87T/wnNQW22PZ8fUlYYsBvu3968cjcCRGjVxmsw9rXU29Fpw4+cEla3DkbMwA67Hr+LzAjiMhCPV9rFL5QaVS2fWfs34LDBwJFVTSOIn/bqofbdsiajz/jnmuP9dVVZ1uMPa2ObTddmOv986PjYbnU1FGxlfS34GBI1BefkjqX0Ew273nsH2Naux1TtSx4y1QQY3yijT88UcZ+vb9ze5864zjODqeo111ue/bHF8DbMe+Jd/7NbafxvLtTo59OWqk8/cKbZ3i75m98cYbsXfvXpjNZmmZyWRCamoqAECn0zX63Dlz5mDYsGF2f8aPHw8ASElJQWJiIqKioqDX66VPexO/jys8PBxarRYxMTGIj4+HRqNBZGQkDAYDQkJC7NaNiIhAeno6YmNjERsbi/T0dERERNitExISAoPBgMjISGg0GsTHxyMmJgZarRbh4eF264aGhkKv1yMqKgqJiYlISEhAdHQ0dDodwsLC7NYNCwuDTqdDdHQ0EhIS2lybfv11OZKSdmHlylJkZ+/DhAkhLdqm999/C2VlB/HRlHyUlR3EX//6RIM2VVdXQ6vV4p9vvgSzuQofTcmH2VyF5577P5SUlGPlylKkpV3A9u278eu6MhQX1WHmjCwAwEdT8mGxGPDtrCIUF9Uhfm0eNBoDDh824uef81BebsZX0wuldQHgy+m/4+jRZMybNwObNiXj5MlqzIn+A4VFBzB1aoG0bln5Qbz//ltIT0/Hjz/Oxtq1STh71oTFiy7YbE/A1KkFqK62YPmyEqRqdmPlykWYHf0JsrPLMGdOMQDgw/9aJ+xTIrYgR7cfK1eW4vBhIzQaA9auyUVxUR2+nVUkbbei4jA++2wLiovq8NPSHfh55TSkpV3AypWldm36978TYDZXYc6cYuh0Ndi8uRyJiYU4ebIay5fpUV1twdSpBaisPIyID88CABYv0iMjowzbt1di+/ZKnD1bg8WL9HZ9ZNemVGubxOz9882XYLEYpHW/ml6I8nIzVq4sQXLyHmg0Biz7aSfOndsvtenD/2agpHSf0+Np2rSPkJ2972KbBGm7s2efh05Xi82by5GUVHSxTSUoKNyPDRtWobw8TVp38SI9zp41OW3T5Mm/oaR0H955ZzOqqy2Inr0dyckbEB8fj+++exc6nUkap4gPz9m0qVbK3rZtu/DrujIUFVbYjFMuAGDGN5koLqrD2jW5F7NnkMZpSsQWGI3ZeOyxR1BRcejiONVi8+ZSpKRUYd++vVj6Yw6qqy345JOzKCndh7Fj72ukTaYmx+nkyWqkpFRh8+Zy6HS1+HL67zAas6Xjc8KEEGRn78PKlaVITt6NP3bskY6nb2cVwWIx4u1/bwEA6XhaF5+PPXuKcPiw0S572oxv8NbkzQCAOXOKceKkBitXJSIlpcpunJ5+eiwqKg7ZjZNWW2rXppkzt0l9VFZ2EFOnFqCqqgLLl5Xg2LELSEmpwsaNedDpavHVV9sgCEYn2bMeT3v3FkttmvFNppS9e+4JkMbp13Vl0jkiNjbX6TmifpzKbdpUfzxZs1J/PDkbp4rKQ/hoSmaj4yS2ac6cIrt9fzW9EDm6/Vi4aId0jnB23iuvSLM7R/z663L8/vtqLFjwB8rLzZj2hTXLk//1G4zGbISHh+Po0WT8/PPOBtn79NNzqKw87HA8VSN6ztuIj0+W2lRekYbJk39z2qbNm3fh55U7odPVYvr0rRfb33Cc0tIuSG0qyC/Dt7OKpPO+7bncOk6VF8dJh7PntmPChBDp9UQ87x0/norvvnvX5rxXYjdOkydb2//++2/h0KFUaZwyMsqk4yniw7OoqDgktSk/z2h3PJ04oUHYGyFSnYDlYpvqbM7lVXbHk+2YfvbZFmRk7MO3336Bbdt2NTiexHVt2zRv3gzpNfeTT95DYaH961N5RRq+/fYPKXvbthVBqy21O0eYzVV4+umxuHDhNKJnb8fJk9X47bcUu3EqK69/fX7+hUel11zH7Dm2aeaMrAbHk7VNdQ7HkwVz5hTj5Ml92LQpXzqexOy9994Wm36tP0esW5ciHU8LF+TYtfvd9xIcjqcKbN5cjpMn9+GHH7Ls6rTNnvj69NPSHcjI2IdJr4bYveeY9GqI9Pr0y5ofnL7mStv9ajtOnNRg8+ZytGunQqpmNz755L0m3xv9+ONsu+OprPwgHnvsEQDy3u/ZnsuTknbh11+Xy36/l5OzHxERCQ3OES3xHtb2XF5YeADR0dbsJSYWYvPmchw/norJk1+325673pcbjdmY/K/fLmZ6G3bt/hlr1yZh+/ZKpKXtxfvvv2W3vea+L4+Li4MnUvz3zM6bNw9vvPEGQkND8f7778NiseDzzz/H2rVrUVtbi2XLlmHChAlOn9vYldnx48d73HcokXOecGU2LCwM8+bN45VZG55wZVZqk4tXZgHVFbsyu3BhZ/z9tRJemW1Ca12ZBaC4K7MzvsnEO+8O5JVZGVdmAfVVe2X2229L8PbbXerH4zKuzLpSgyNPvTLrjFKuzNpqrSuz384qwiefjOGV2Uvsh1dmPfd7ZhU/mQWAKVOmICoqCrW1tQCA2267DWPGjMEXX3yB+Ph46WqrKzx1oKhx7r5n1hbvmeU9s67cMyu+Mec9s7xnFrj0PbO2f/OeWd4ze6l7ZsXHeM8s75l15Z5ZOfe/8p7Zq/ueWU+dI7WJySwAlJSU4NixY+jUqROGDx+OiIgIfPnllzh27BiGDBni8nY8daDIcwUHB2PDhg3uLoMUhJkhOZgXkoN5ITmYF3KVp86R2sxk1tHo0aORl5eHrKwsqNWu3xrsqQNFRERERETkDp46R1L8B0A5s3LlSuzbtw9vv/22rIks0eUQb+4nchUzQ3IwLyQH80JyMC+kdIq/MpuUlISpU6fikUceQdeuXbF3714sWbIE//d//4cNGzbA21vetw956k8dyHPpdDr06dPH3WWQgjAzJAfzQnIwLyQH80Ku8tQ5kuIvW/bp0wdeXl6IiorCm2++iZSUFHz++ef49ddfZU9kiS7HunXr3F0CKQwzQ3IwLyQH80JyMC+kdIqf7Q0ePBhbtmxxdxl0FRs8eLC7SyCFYWZIDuaF5GBeSA7mhZRO8VdmidzNz8/P3SWQwjAzJAfzQnIwLyQH80JKx8ksUTNpNBp3l0AKw8yQHMwLycG8kBzMCykdJ7NEzfTqq6+6uwRSGGaG5GBeSA7mheRgXkjpOJklaqbw8HB3l0AKw8yQHMwLycG8kBzMCymd4r+ap6V56sdOExERERERuYOnzpF4ZZaomYKDg91dAikMM0NyMC8kB/NCcjAvpHS8MuvAU3/qQERERERE5A6eOkfilVmiZuL9JiQXM0NyMC8kB/NCcjAvpHSczBI105tvvunuEkhhmBmSg3khOZgXkoN5IaXjZJaomZKSktxdAikMM0NyMC8kB/NCcjAvpHSczBI1U5cuXdxdAikMM0NyMC8kB/NCcjAvpHSczBI1U58+fdxdAikMM0NyMC8kB/NCcjAvpHQePZmtrKzEJ598grFjxyIoKAgqlQo//vij03VPnDiBsWPHokOHDggKCsJLL72EoqKiK1swXZW2bNni7hJIYZgZkoN5ITmYF5KDeSGl8+iv5snMzMSgQYPQv39/XHvttdi5cyeWLFmCiRMn2q2Xk5ODkSNHolOnTnjrrbdQWVmJb775Bv3794dGo4GPj4/L+/TUj50mz2UwGODv7+/uMkhBmBmSg3khOZgXkoN5IVd56hzJo6/M9urVC3l5ecjKykJUVFSj602bNg1VVVX4448/8NZbbyEiIgKrVq3C4cOHG72SS9RSHH+4QnQpzAzJwbyQHMwLycG8kNJ59GTW19cXPXv2vOR6a9asweOPP47+/ftLyx5++GHccMMNWLVqVWuWSA7MgoBzhhqYPfeCf4tjxtqu1srzlc6MJx6XTdXkifU6ulSNzWmfyWJBkr4CJosFAM8xtlzNhhIy1FpaKi9Xcx+2dbZjy/MLKZ1HT2ZdodPpUFhYiNtuu63BY6NHj0ZaWlqjzy0sLMSxY8fs/mi12tYst00zCwLGHzyDu1JPYPzBM1fNC2BwcLC7S6BW0Jp5vpKZ8cTjsqmaPLFeR5eqsTntM1ksGJJyFCGHMzAk5ShMFgvPMRe5mg0lZKg1tURervY+bMscx/Zxnl9I4RQ/mc3LywNg/ZVkR7169YJer0dNTY3T586ZMwfDhg2z+zN+/HgAQEpKChITExEVFQW9Xo/Q0FAA9S8S4eHh0Gq1iImJQXx8PDQaDSIjI2EwGBASEmK3bkREBNLT0xEbG4vY2Fikp6cjIiLCbp2QkBAYDAZERkZCo9EgPj4eMTEx0Gq1CA8Pt1s3NDQUer0eUVFRSExMREJCAqKjo6HT6RAWFma3blhYGHQ6HaKjo5GQkNCqbZr8/gfYV25AScS/sa/cgCf++qzi2+TKOD311FNtrk1tcZzktkmTcQ7bP7Nu9/d/vYbzRlOLtcnPz++KtWn179uQsm0rDOtWYm9GJl567XW3j9Mjjz2OfeUGlH72PjSFF/DeJ59Kbfpm3gLsOXESFdHfYF+5AY889rjHZe+HH39CUvwa1J49gz9mfoXzRpPdOJ26UIod0d+i9sRRJG/aiG/mLZDG6bzRhN//9RoAYFvEOziiy7dr0wffzEJZfj7KZ32OSrMFDz76GDZs2KD446klxinp6AkkrYxFdfIf2J2qwXuffOq0TZPf/wC7Dx2GcftmJMWvwe/7Dnpsm1pjnEaPHt3sNtnmdPtnEdBknLuqs9eW2nTeaMIfX05Fne48klbG4vG/vaT4NrXFcfLENsXFxcETefQHQNnav38/br/99gYfAJWcnIz7778fK1eulAZW9L///Q+RkZEoKSlB586dG2yzsLCwwScea7VajB8/3uNublYC8ad9+8oNuD3QH+tGXQ8vlcrdZbW6iIgITJs2zd1lUAtrzTxfycx44nHZVE2eWK+jS9XYnPaJV2YrzRZ08FLj+L3D8OlHH/EcA9ezoYQMtaaWOL9c7X3YljmO7dBflmD6l1+6uyxSAE/9AChvdxfQXOIVDmdXX6urq+3WcdS9e3d079699Yq7ynipVFg36nqcN5rQ38/nqnnhe+GFF9xdArWC1szzlcyMJx6XTdXkifU6ulSNzWmfj9o6gd1bWoU7OwfAR63mOeYiV7OhhAy1ppbIy9Xeh22Z49ge93nR3SURNYvif81Y/PVi8deNbeXl5SEoKAi+vr5XuqyrlpdKhUH+vlfVC196erq7S6BW0lp5vtKZ8cTjsqmaPLFeR5eqsTnt81GrcX9QR/iorS/RPMfUczUbSshQa2mpvFzNfdjW2Y4tzy+kdIqfzPbp0wfdunXD/v37Gzym0WgwYsSIK18UERERERERtSrFT2YB4JlnnsHGjRuRnZ0tLdu+fTtOnz6NZ5991o2V0dVg+PDh7i6BFIaZITmYF5KDeSE5mBdSOo+/Z3b27NkoLS1Fbm4uAGDDhg3IyckBAEyePBmdOnVCREQEVq9ejT//+c/497//jcrKSkRFRWH48OF45ZVX3Fk+XQXi4uL4YkCyMDMkB/NCcjAvJAfzQkrn8Z9mPHDgQGRlZTl97Ny5cxg4cCAA6yds/ec//0FKSgp8fHzw2GOPYcaMGejRo4es/XnqJ3URERERERG5g6fOkTz+14wzMzMhCILTP+JEFgCGDh2KLVu2oKqqCiUlJVi+fLnsiSzR5WiJL6inqwszQ3IwLyQH80JyMC+kdB5/ZfZK89SfOhAREREREbmDp86RPP7KLJGnCwkJcXcJpDDMDMnBvJAczAvJwbyQ0vHKrANP/akDeS6DwQB/f393l0EKwsyQHMwLycG8kBzMC7nKU+dIvDJL1EwzZsxwdwmkMMwMycG8kBzMC8nBvJDScTJL1ExjxoxxdwmkMMwMycG8kBzMC8nBvJDScTJL1Ew6nc7dJZDCMDMkB/NCcjAvJAfzQkrHySxRM5WUlLi7BFIYZobkYF5IDuaF5GBeSOk4mSVqpvvvv9/dJZDCMDMkB/NCcjAvJAfzQkrHySxRMxRUFuCv7/4VBZUF7i7F4xRUFmDyb5PZNw6YGZIrOjra3SWQUhQUIPqvfwUKeH6xU1AATJ7MfnHEvFAbwMksUTN8vONjHB55GP/b8T93l+JxPt7xMWbvm82+ccDMkFyzZs1ydwmkFB9/jFmHDwP/4/nFzscfA7Nns18cMS/UBnAyS3SZMkszseTQEiAWiDkUg6zSLHeX5DGkvgH7xhYzQ5cjODjY3SWQEmRmAkuWIBgAYmKALJ5fAEj9AoD9Yot5oTaCk1miyzQteRrqLHXAi0CdpQ7Tkqe5uySPIfUN2De2mBm6HBs2bHB3CaQE06YBdXXYAAB1ddb/k9QvANgvtpgXaiM4mSW6DLZXHhFv/YtX2qzs+uYi9g0zQ5cvNDTU3SWQp7O5+iilhVfb7K/KitgvzAu1KZzMEl0G2yuPuPh947zSZmXXNxexb5gZuny8Z5Yuyebqo5QWXm2zvyorYr8wL9SmcDJLJFODK49p9f+82q+0ObsqK7qa+4aZoeZYvHixu0sgT+Zw9dEuLVfz1TZnV2VF7Bfpv8wLKR0ns0QyNbjy2Kf+n1f7lTZnV2VFV3PfMDPUHKNHj3Z3CeTJHK4+2qXlar7a5uyqrIj9Iv2XeSGlUwmCILi7CE9y8OBB3HrrrVi3bh2uu+46d5dDHkZXrsOjsY/CbDHXL8wCMKD+v15qL2x+cTN6B/a+4vW5k9O+cXA19g0zQ82VkpKCe++9191lkCfS6YBHHwXM9eeXFAB2afHyAjZvBnpfRecXJ/3SAPsFAPNCrtNqtRg/fjwOHDiAUaNGubscCSezDpYuXYqJEye6uwwiIiIiIiKP8uOPP3rUBxN6u7sAT3PDDTcAAFatWoUhQ4a4uRrydOJPqXgln1zFzJAczAvJwbyQHMwLyXH8+HGEhIRIcyVPwcmsg8DAQADAkCFDMHToUDdXQ0px3XXXMS8kCzNDcjAvJAfzQnIwLySHOFfyFPwAKCIiIiIiIlIcTmaJiIiIiIhIcTiZJSIiIiIiIsXhZNZBt27d8Mknn6Bbt27uLoUUgHkhuZgZkoN5ITmYF5KDeSE5PDUv/GoeIiIiIiIiUhxemSUiIiIiIiLF4WSWiIiIiIiIFIeTWSIiIiIiIlIcTmaJiIiIiIhIcTiZJSIiIiIiIsXhZPaimpoafPDBB+jduzf8/Pxwxx13YOvWre4ui1rJvn378K9//QtDhw5FQEAA+vfvj5CQEJw+fbrBuidOnMDYsWPRoUMHBAUF4aWXXkJRUVGD9SwWC77++msMGjQI7du3x80334y4uDin+3d1m+SZvvjiC6hUKgwbNqzBY7t378a9994Lf39/9OzZE2+99RYqKysbrCfnnOPqNsmzHDx4EE888QSCgoLg7++PYcOG4fvvv7dbh3khADhz5gyef/559O3bF/7+/vjTn/6EqVOnwmAw2K3HvFxdKisr8cknn2Ds2LEICgqCSqXCjz/+6HRdd75XkbNNaj2u5MViseDHH3/EE088gX79+iEgIADDhg3D559/jurqaqfbXbx4MW666Sa0b98e119/PX744Qen6+l0OoSEhKBz584IDAzEk08+ibNnzzZrmy4RSBAEQXj++ecFb29v4d133xXmz58v3HXXXYK3t7eQnJzs7tKoFTzzzDNCz549hcmTJwsLFy4UIiMjhR49eggBAQFCenq6tF52drZwzTXXCIMHDxa+++474YsvvhC6dOki3HLLLUJNTY3dNv/73/8KAITXXntNWLBggfDYY48JAIS4uDi79eRskzxPdna24O/vLwQEBAhDhw61eywtLU1o3769MHLkSGHu3LnClClTBF9fX2Hs2LENtuPqOUfONslzbNmyRfDx8RHuuOMOYebMmcKCBQuEDz74QHjvvfekdZgXEgRBOH/+vNC5c2dhwIABwpdffinMnz9fmDhxogBAeOKJJ6T1mJerz7lz5wQAQv/+/YUHH3xQACAsWbKkwXrufq/i6japdbmSl4qKCgGAcOeddwqff/65sGDBAuGVV14R1Gq18OCDDwoWi8Vu/Xnz5gkAhGeeeUZYsGCB8NJLLwkAhOnTpzfY7vXXXy90795d+Oqrr4SZM2cK/fr1E/r27SsUFxdf1jZdxcmsIAipqakCACEqKkpaZjQahcGDBwt33XWXGyuj1rJr164GJ+PTp08Lvr6+wt/+9jdp2RtvvCH4+fkJWVlZ0rKtW7cKAIT58+dLy3JycoR27doJb775prTMYrEI9913n9C3b1+hrq5O9jbJMz333HPCX/7yF+GBBx5oMJkdN26c0KtXL6GsrExatnDhQgGAsGXLFmmZnHOOq9skz1FWVib06NFDeOqppwSz2dzoeswLCYIgfPHFFwIA4ejRo3bLX375ZQGAoNfrBUFgXq5G1dXVQl5eniAIgrBv375GJ7PufK8iZ5vUulzJS01NjbBr164Gz/3ss88EAMLWrVulZQaDQejatavw2GOP2a37t7/9TQgICJDOTYIgCF999ZUAQNBoNNKyEydOCF5eXsKHH354Wdt0FSezgiC89957gpeXl93JXBAEYdq0aQIA4fz5826qjK60UaNGCaNGjZL+3717d+HZZ59tsN4NN9wgPPTQQ9L/o6OjBQDCsWPH7NaLjY0VANj9NNzVbZLnSUxMFLy8vIQjR440mMyWlZUJ3t7edlfeBMH6wtGhQwfh1VdflZa5es6Rs03yHHPnzhUACMePHxcEQRAqKysbTGqZFxJ98MEHAgChqKiowXK1Wi1UVlYyL9TkZNad71XkbJOunKby4syRI0cEAML3338vLdu0aZMAQNi0aZPdurt37xYACMuWLZOW3X777cLtt9/eYLuPPPKIMHjw4Mvapqt4zyyAtLQ03HDDDQgMDLRbPnr0aADAoUOH3FAVXWmCIKCgoADXXHMNAOvv/hcWFuK2225rsO7o0aORlpYm/T8tLQ0BAQG46aabGqwnPi53m+RZzGYzJk+ejL///e8YPnx4g8fT09NRV1fXYGx9fHwwYsSIBnlx5ZwjZ5vkObZt24bAwEDodDrceOON6NChAwIDA/HGG29I9yQxLyR68MEHAQCvvvoqDh06hOzsbKxcuRJz587FW2+9hYCAAOaFGuXu9yqubpM8W35+PgBI74GB+rFzzMGtt94KtVotPW6xWHDkyJFG85KRkYGKigpZ25SDk1kAeXl56NWrV4Pl4rLc3NwrXRK5wYoVK6DT6fDcc88BsOYCQKPZ0Ov1qKmpkdbt0aMHVCpVg/WA+gzJ2SZ5lnnz5iErKwuRkZFOH7/U2NqeR1w958jZJnmOM2fOoK6uDk8++STGjBmDNWvWYNKkSZg3bx5eeeUVAMwL1Rs7diwiIyOxdetWjBw5Ev3798fzzz+PyZMnY9asWQCYF2qcu9+ruLpN8mxff/01AgMDMW7cOGlZXl4evLy80L17d7t1fXx80LVrV2lsxTy4et5xZZtyeMt+RhtkNBrh6+vbYHn79u2lx6ltO3nyJN58803cddddCA0NBVA/7pfKhq+vr8sZkrNN8hwXLlzA//73P3z88cfo1q2b03UuNba255GWygvPTZ6psrISBoMBYWFh0qcXP/300zCZTJg/fz6mTp3KvJCdgQMH4v7778czzzyDrl27YtOmTZg2bRp69uyJf/3rX8wLNcrd71X4Hlr5pk2bhm3btmHOnDno3LmztNxoNMLHx8fpc2zPEa7mRc425eBkFoCfn5/Tq2Hir4P5+fld6ZLoCsrPz8djjz2GTp064ZdffoGXlxeA+nF3JRuuZkjONslzfPTRRwgKCsLkyZMbXedSY2s7ri2VF2bFM4nj8sILL9gtf/HFFzF//nzs2bMH/v7+AJgXAn7++We8/vrrOH36NPr27QvA+sMPi8WCDz74AC+88ALPL9Qod79X4XtoZVu5ciU++ugjvPrqq3jjjTfsHvPz84PJZHL6PNtzhNy8uLJNOfhrxrBeAhd/pcKWuKx3795XuiS6QsrKyjBu3DiUlpYiISHBbqzFX41oLBtBQUHST6F69eqF/Px8CILQYD2gPkNytkme4cyZM1iwYAHeeust5ObmIjMzE5mZmaiurkZtbS0yMzOh1+svObaO2XLlnCNnm+Q5xHHp0aOH3XLx16pKSkqYF5LMmTMHI0eOlCayoieeeAIGgwFpaWnMCzXK3e9VXN0meZ6tW7fi5ZdfxmOPPYZ58+Y1eLxXr14wm80oLCy0W24ymXDhwgVpbMU8uHrecWWbcnAyC2DEiBE4ffo0ysvL7ZanpqZKj1PbU11djeDgYJw+fRobN27EkCFD7B7v06cPunXrhv379zd4rkajscvFiBEjYDAYcOLECbv1HDMkZ5vkGXQ6HSwWC9566y0MGjRI+pOamorTp09j0KBBmDp1KoYNGwZvb+8GY2symXDo0KEGeXHlnCNnm+Q5br31VgDW7NgS7wXq1q0b80KSgoICmM3mBstra2sBAHV1dcwLNcrd71Vc3SZ5ltTUVDz11FO47bbbsGrVKnh7N/xlXXHsHHOwf/9+WCwW6XG1Wo3hw4c7zUtqaiquvfZadOzYUdY2ZZH9+cdt0N69ext8J1t1dbVw3XXXCXfccYcbK6PWUldXJzzxxBOCt7d3g48HtxUWFib4+fnZfT3Ttm3bBADC3LlzpWXZ2dmNfs9anz597L5nzdVtkmcoKioS4uPjG/wZOnSo0L9/fyE+Pl44cuSIIAiCMHbsWKFXr15CeXm59PxFixYJAITNmzdLy+Scc1zdJnmOgwcPCgCEF1980W75Cy+8IHh7ews6nU4QBOaFrB5//HHBx8dHOHXqlN3y8ePHC2q1mnkhQRCa/qoVd75XkbNNunKaysvx48eFrl27CkOHDm3ye10NBoMQFBQkPP7443bLJ0yYIPj7+wsXLlyQlk2fPl0AIOzbt09advLkScHLy0v44IMPLmubruJk9qJnn31W+r61+fPnC3fffbfg7e0tJCYmurs0agX//ve/BQBCcHCwsGzZsgZ/ROfPnxe6du0qDB48WPj++++FadOmCV26dBGGDx8uVFdX223zvffeEwAIr7/+urBw4ULhscceEwAIK1assFtPzjbJczl+z6wgCMKBAwcEX19fYeTIkcLcuXOFKVOmCO3btxceeeSRBs939ZwjZ5vkOSZNmiQAEEJCQoTo6Gjh2WefFQDYfXk880KCUP/91d27dxemTp0qREdHC+PGjRMACH//+9+l9ZiXq9MPP/wgREZGCm+88YYAQHj66aeFyMhIITIyUigtLRUEwf3vVVzdJrW+S+WlvLxc6Nevn6BWq4Xp06c3eP+7e/duu+2J3yP817/+VVi4cKHw8ssvCwCEL774wm698vJyYfDgwUL37t2Fr7/+Wpg1a5bQr18/oXfv3kJhYeFlbdNVnMxeZDQahXfffVfo2bOn4OvrK9x+++1CQkKCu8uiVvLAAw8IABr9Y+vo0aPCI488Ivj7+wudO3cW/va3vwn5+fkNtmk2m4Vp06YJAwYMEHx8fIShQ4cKy5cvd7p/V7dJnsvZZFYQBCE5OVm4++67hfbt2wvdunUT3nzzTburHiI55xxXt0mew2QyCZ9++qkwYMAAoV27dsJ1110nzJo1q8F6zAsJgiCkpqYK48aNE3r27Cm0a9dOuOGGG4QvvvhCqK2ttVuPebn6DBgwoNH3KufOnZPWc+d7FTnbpNZ1qbycO3euyfe/oaGhDba5YMEC4cYbbxR8fHyEwYMHC7NmzRIsFkuD9bKzs4W//vWvQmBgoNChQwfh8ccfF86cOeO0Tle36QqVIDjcsU1ERERERETk4fgBUERERERERKQ4nMwSERERERGR4nAyS0RERERERIrDySwREREREREpDiezREREREREpDiczBIREREREZHicDJLREREREREisPJLBERERERESkOJ7NERERERESkOJzMEhERERERkeJwMktERNSEiRMnYuDAge4uQ/Lpp59CpVJBpVKhQ4cOV3z/I0aMkPb/+OOPX/H9ExERibzdXQAREdGVplKpXFpvx44drVzJ5Vu2bBnatWt3xfc7bdo06PV6hIeHX/F9ExER2eJkloiIrjrLli2z+/9PP/2ErVu3Nlh+0003YeHChbBYLFeyPJdMmDDBLft99NFHAQAfffSRW/ZPREQk4mSWiIiuOo4Twb1792Lr1q1umyASERGRfLxnloiIqAmO98xmZmZCpVLhm2++QXR0NK699lr4+/vjkUceQXZ2NgRBQGRkJPr27Qs/Pz88+eST0Ov1Dba7efNm3HfffQgICEDHjh3x2GOP4dixY82qdeDAgXj88cexc+dO3HbbbfDz88Pw4cOxc+dOAMDatWsxfPhwtG/fHrfeeivS0tLsnp+fn49XXnkFffv2ha+vL3r16oUnn3wSmZmZzaqLiIioNfDKLBER0WVYsWIFTCYTJk+eDL1ej6+//hohISH4y1/+gp07d+KDDz6AVqvFDz/8gHfffRcxMTHSc5ctW4bQ0FCMGTMGX331FQwGA+bOnYt7770XaWlpzfrAKa1WixdffBH/+Mc/MGHCBHzzzTcIDg7GvHnzEBERgX/+858AgC+//BIhISE4deoU1Grrz7afeeYZHDt2DJMnT8bAgQNRWFiIrVu34vz58x71IVhEREQAJ7NERESXRafT4cyZM+jUqRMAwGw248svv4TRaMT+/fvh7W19iS0qKsKKFSswd+5c+Pr6orKyEm+99Rb+/ve/Y8GCBdL2QkNDceONN2LatGl2y+U6deoUdu/ejbvuugsAMGTIEIwZMwavvfYaTp48if79+wMAunTpgn/84x9ISkrCgw8+iNLSUuzevRtRUVF49913pe19+OGHl10LERFRa+KvGRMREV2GZ599VprIAsAdd9wBwHo/rjiRFZebTCbodDoAwNatW1FaWooXXngBxcXF0h8vLy/ccccdzf4E5SFDhkgTWdu6/vKXv0gTWdvlZ8+eBQD4+fnBx8cHO3fuRElJSbNqICIiuhJ4ZZaIiOgy2E4MAUgT2379+jldLk4Qz5w5A8A6uXQmMDDQLXX5+vriq6++wjvvvIMePXrgzjvvxOOPP46XX34ZPXv2bFZNRERErYGTWSIiosvg5eUla7kgCAAgfc3PsmXLnE4Sba/qXsm6AODtt99GcHAw1q1bhy1btuDjjz/Gl19+iT/++AMjR45sVl1EREQtjZNZIiKiK2jw4MEAgO7du+Phhx92czUNDR48GO+88w7eeecdnDlzBiNGjMCMGTOwfPlyd5dGRERkh/fMEhERXUFjxoxBYGAgpk2bhtra2gaPFxUVuaEqwGAwoLq62m7Z4MGD0bFjR9TU1LilJiIioqbwyiwREdEVFBgYiLlz5+Kll17CqFGj8Pzzz6Nbt244f/48Nm3ahHvuuQezZ8++4nWdPn0aDz30EEJCQjBkyBB4e3sjPj4eBQUFeP755694PURERJfCySwREdEV9uKLL6J3796YPn06oqKiUFNTgz59+uC+++7DK6+84paa+vXrhxdeeAHbt2/HsmXL4O3tjT/96U9YtWoVnnnmGbfURERE1BSVYPvJD0REROTRPv30U3z22WcoKiqCSqVC165dr+j+S0tLUVdXh1GjRuHmm2/Gxo0br+j+iYiIRLxnloiISIG6deuGAQMGXPH9Pvjgg+jWrRuys7Ov+L6JiIhs8cosERGRgpw9exZnz54FYP0anwcffPCK7j81NRUVFRUArBPqW2655Yrun4iISMTJLBERERERESkOf82YiIiIiIiIFIeTWSIiIiIiIlIcTmaJiIiIiIhIcTiZJSIiIiIiIsXhZJaIiIiIiIgUh5NZIiIiIiIiUhxOZomIiIiIiEhxOJklIiIiIiIixeFkloiIiIiIiBSHk1kiIiIiIiJSHE5miYiIiIiISHH+H1RvD5I8AKSuAAAAAElFTkSuQmCC\n", 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", 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\n", 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", 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\n", 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J1NRUHDp0CDk5Ofjll1/QvHnzBj1HbXh7e2PTpk0IDw+X+/iKFSvw8OFDBAQE4OTJkxg4cCAcHR2RnJyM58+f4++//8b3338vnSxMR0cHCxYswOrVq9GlSxeMGzcOpaWluHDhAhwcHODg4FDnGLl8rc6dO4elS5diwIABaNmyJSwtLfH27VuEhYUhNjYWdnZ2Nd5/TgghRAFUtwoQIYQQlq15ndnq7N27l+3UqRNrYGDAWlpasl5eXuyjR4+k636GhIRUOiYiIoL9+OOPWQcHB1ZHR4e1sbFhBwwYwO7YsaPSvi9evGDnz5/Puri4sHp6eqyxsTHbunVrdtq0aeyxY8fq3FZfX1/p2p53795VWLsgZ51ZlmXZV69esZMmTWLNzc1ZoVDIdu/enT1y5Ei165xmZ2ez33//Pdu1a1fW0NCQFQqFbLNmzdgRI0awO3bsYHNzc1mWFa896+/vz3p6erIODg6srq4ua2dnx7q7u7NBQUEya51Wp6Y1VzMyMtivvvqKdXFxYXV1dVlTU1N28ODB7Pnz5+t8rupUXGe2onPnzklzV3GdWZZl2bKyMva3335jBw4cyJqbm7M6Ojqsg4MD269fP/b7779nX716VWn/H374gW3RogWro6PDOjk5sUuXLmXz8vKqXWe24pqw5SnytZIXQ1XCw8PZefPmsZ06dWItLS1ZbW1t1sTEhO3evTu7cuVKNi0trVbnIYQQ0jAMy7IsBzU0IYQQQgghhBBSb3TPLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtUPFLCGEEEIIIYQQtSPgOgBCMjMzERYWBicnJ+jp6XEdDiGEEEIIIYQDRUVFiI+Ph7u7O8zMzGrcn4pZwrmwsDB4eXlxHQYhhBBCCCGkEQgODsbYsWNr3I+KWcI5JycnAOIfWhcXF46jIco2f/58bN26leswNFJWURaOPDuCzjad0dW2K9fhUK55gvLMH5Rr/qBc80djy3V0dDS8vLyk9UFNGJZlWSXHREi1IiMj4ebmhoiICLRv357rcAhRWzse7sAv//wCcz1zhH0YBoZhuA6JEEIIIaTW6loX0ARQhBCVmjFjBtchaKykvCQAQEZRBgpKCziOhnLNF5Rn/qBc8wflmj/UPddUzBJCVGrjxo1ch6AxWJbFj7d/xOobqyEqEyGrKEv6WPmvuUK55gfKM39QrvmDcs0f6p5rKmYJISq1e/durkPQGFHpUdj/ZD8OPTuEO2/vILMoU/pYRlEGd4H9i3LND5Rn/qBc8wflmj/UPddUzBJCVKpnz55ch6AxyhesyfnJyCp+1xubWZjJQUSyKNf8QHnmD8o1f1Cu+UPdc02zGRNCVKqggPt7OTVF+ftiMwozZIYWl++l5Yoyc51fko9j0cfQ1aYr2lq2VdrzNEZR6VG49OoSJrWaBGsDa67DoWuaRyjX/KGIXLMsi5ycHGRnZ6OkpAQ052zjJBAIEBsbq9TnYBgGOjo6MDExgbGxsUInqKRilhCiUjExMVyHoDHKF7PphekyxWxjGGaszFz/79n/8NPdn+Bg6IDzE84r7XkaI//r/ohIi0B2UTaW9VrGdTh0TfMI5Zo/Gprr0tJSJCYmIj8/H4C4YNLS0qJZ9hshc3NzpZ6fZVmIRCIUFhYiJycHBgYGcHR0hECgmDKUillCiEp5eXlxHYLGKF/Mvsl7gyJRkfT7xtAzq8xcv8x+CQB4nfcahaWFEAqESnuuxuZN/hsAwOvc1xxHIkbXNH9QrvmjobnOyMhAfn4+TE1NYWNjo7DChShecXExdHV1lf48paWlSE5ORlZWFjIyMmBtrZiRRXTPLCFEpVavXs11CBqjoORdMRuXHSfzWGO4Z1aZuc4tyZV+nVHIfS+0KuWXiHs6yr8GXKJrmj8o1/zR0Fzn5uZCW1sb9vb2VMg2cklJSSp5HoFAAHt7e2hrayM3V3G/v6iYJYSoVEBAANchaIzyPbNxWXEyjzWGnlll5jq3+N0vwvTCdKU9T2NTxpZJ895Yilm6pvmDcs0fDc01y7IQCAQ0rFgNODs7q+y5GIaBtra2Qu+fpmKWEKJSo0eP5joEjVG+mM0vzZd5rDHcM1vfXBeJinAr6RYKSwur3Kd8IZdWmFav51FHhaWFYCH+IyCnOIfjaMTomuYPyjV/UK754/nz5yp9PkV/wEHFLA9dvnwZM2fORJs2bWBoaAhHR0eMHTsW9+7dq/HYwMBAMAwj99+bN29UED1RdydPnuQ6BI1RKKq62GsMw4zrm+sfbv2AWX/Nwvo766vcp3wxy6ee2fIfWuSV5HEYyTt0TfMH5Zo/KNf84erqynUIDdLoitnqiiWGYRAaGirdt1mzZvD29pZ+HxcXB4ZhEBgYWK/nrni+0NBQMAyDw4cP13isn59fpU8aPDw84OHhIbONYRj4+flJv3/8+DH8/PwQFxdXr5jrY/v27YiLi8OCBQtw5swZbN68GcnJyejduzcuX75cq3Ps3bsXN27ckPlnaWmp5MiJJpg7dy7XIWiM8j2zFTWGYcb1zXVUehQA4H7y/Sr34esw4/I5zy3ObRRLXdA1zR+Ua/6gXPPHy5cvuQ6hQRrtHdl79+5FmzZtKm1v165dlcfY29vjxo0baNmyZb2e89ixYzAxManXsbNmzcKwYcNq3O/GjRto0qSJ9PvHjx/D398fHh4eaNasWb2eu662bt0KGxsbmW3Dhg2Di4sL1qxZg4EDB9Z4Djc3N3Tv3l1ZIRINtmLFCq5D0BjlJ4CqKLMoEyzLcnq/Un1zLelxTMhJqLIN5YtZPk0AJZn8CQBK2VIUigqhL9DnMCK6pvmEcs0flGv+sLe3r/MxW7ZsQVBQEKKjo5GTkwNbW1v07dsXK1asQPv27ZUQZdUaXc+shJubG3r37l3pX3XFpp6eHnr37l3vqZ67dOlS70K4SZMm6N27d4379e7dW6aY5ULFQhYAjIyM0K5dO8THx3MQEeGT4OBgrkPQGNX1zBaJiqp9XBXqm2tJMVsoKpR7P2wZW8bbYcYVc1q+qOdKY7umL726BL/rfkgtSOU6FI3T2HJNlIdyzR+ZmZl1PiYtLQ3Dhw/Hrl278Ndff8Hf3x8PHjxAr1698PTpU8UHWY1GW8zWR1XDjI8fP46OHTtCT08PLVq0wObNm+UOC644zFiisLAQixcvhp2dHfT19eHu7o4HDx7I7CPvfPKUH2YcGBiIiRMnAgA8PT2lQ6kDAwOxevVqCAQCucXlzJkzYWlpicLCqu+Xq6usrCzcv3+/1p+mjBo1Ctra2rCwsMAHH3yAiIgIhcVCNFt9PzAildVUrGYVZakoEvnqm+vyhWpCTkKlxwtKC6STIAH8mgCqfM8s0DhmNG5s17TfdT8ceX4E+x/v5zoUjdPYck2Uh3KtXkpKSlBaWlqvY/X09Op8jL+/P1auXAkvLy+4u7vDx8cHwcHByMvLw4EDB+oVR3012mJWJBKhtLRU5p9IJKrzec6dO4cPPvgAlpaW+PPPP7Fu3TocPHgQ+/btq/U5li9fjtjYWOzatQu7du3C69ev4eHhgdjY2DrHU97IkSOxZs0aAOKhv5J7T0eOHIk5c+ZAIBBgx44dMsekp6fjjz/+gK+vL4RCYYOev7z58+cjLy8P33zzTbX72dnZ4ZtvvsGuXbsQEhKC1atX486dO+jduzcePnxY4/MkJycjMjJS5l90dLSimkHUgL4+t0MiNUlNxSzXMxrXJ9eiMpFMuxJyKxezFWfxTS+gnlkuNaZrulhULL1f/En6E26D0UCNKddEuSjX1ZN0YkVGRmLy5MkwNTWFra0tZs6ciaws2Q+SWZbFtm3b0LlzZ+jr68Pc3BwTJkyoVEdU1alWcQ4eyZw+v//+O5YsWQJHR0fo6elJ/57es2cPOnXqBKFQCAsLC4wbNw5Pnsi+H3p7e8PIyAjR0dGYOHEijIyM4OTkhCVLlqCoqKher4lkZKyq1xVutMVs7969oaOjI/OvPp8cfPvtt3B0dMT58+fh5eWFCRMm4OLFi8jJqf2SBtbW1jh27BhGjRqFqVOn4uLFiygoKMAPP/xQ53gqnlcyg1i7du2kQ6mtra1hY2ODjz76CDt37kRxcbH0mF27dqGoqAjz5s1r0HOXt2LFChw4cAAbN25Et27dqt132LBh+O677zBq1CgMGDAA8+fPx9WrV8EwDL799tsan2vbtm1wc3OT+efl5QUAuHbtGsLCwrB+/Xqkp6djxowZAN5ND79o0SJER0djz549OHbsGG7fvo3Vq1cjPz8fkyZNktl3+fLlCA8PR1BQEIKCghAeHo7ly5fL7DNp0iTk5+dj9erVuH37No4dO4Y9e/YgOjoaixYtktl3xowZSE9Px/r16xEWFoZz585h69atSExMlE6SINl37ty5SExMxNatW3Hu3DlqU4U23b59W+PaxFWeol9V/iBIwLz7JXLlzhVO27R169Y6t2nO53Nk2rP9wPZKefq/Hf8ns8+zhGeNOk+K/NkLfxYu0/ackhzO23T79u1Gcz1duXVF+tpEpUbx/j1C0W1as2aNxrVJE/OkiDatWbOmQW2SrJAhWfbl5cuXKC4uRnJyMrKyspCTk4M3b96gtLQUL168kNk3Pj4ehYWFSE1NRUZGBvLy8vD69WuIRCLExMTI7JuQkID8/HykpaUhLS0N+fn5SEhIkNknJiYGIpEIr1+/Rl5eHjIyMpCamorCwkLpCEjJvi9evEBpaSnevHmDnJwcZGVlITk5GcXFxdKJksovZfPBBx/A0dERv/32GxYuXIigoCAsWLBApk1z5szBwoUL4eHhgX379mH9+vUIDw9H79698fr1a2mbJD2rFdtUVlYmLTDLP/eyZcvw9OlTbNq0CQcPHoS2tjZWr14NX19ftG/fHv/3f/+HzZs34/79++jTpw+uX78ubVNhYSFKSkowfPhw9O3bF1u3bsXMmTOxceNGrFmzptZ5ysvLQ2JiIm7dugUfHx9YWVlh+vTp1eapqKgIWVlZVf7s1XWNY4ZtDFMhlhMYGAgfHx/89ttvaNu2rcxjDMPIFFvNmjWDh4eHdFhxXFwcmjdvjr1798Lb2xt5eXkwNjbGZ599hi1btsicy8fHB4GBgTIzQVY8X2hoKDw9PfHTTz9hyZIlMsd7eHggISFB+imIn58f/P39Zc4n+RSl/AzMDMNg5cqV0qHGhw8fxsSJExESElJp5uMHDx6ga9eu2L9/P6ZOnYqysjK0bNkSHTp0wIkTJ2r1etbE398ffn5++P7776U/VPUxfPhw3L9/H2/fvq12v+TkZKSkpMhsi46OhpeXFyIiIlR+07gqJeYmYt3tdRjYdCDGuozlOhzOpKenw8LCguswNMKwI8OQmJsos62ZSTPEZccBAH7s/yNGtBjBQWRi9cn1m7w3GHJ4iPR7LxcvrO63Wmaff5L/wcdnP5Z+r6Olg3vT7nE62ZWq/Bn1J7679Z30+589fsYQ5yHVHKF8jemajsuKw+jgd+tjXvvoGkz1TDmMSLM0plwT5WporiW9ji1atKj02I+3f5TOWs+1NhZt8HXPr+t8nOTv/nXr1mHp0qXS7fPnz8eePXuQn58PhmFw8+ZN9OnTBxs2bMDixYul+yUkJKBVq1b4/PPP8eOPPwKoXIdIVKwnJPXJgAEDEBYWJt0vMzMTDg4O8PT0xOnTp6Xb4+Pj4erqivHjx0uHAHt7e2Pfvn04dOgQxo0bJ+1NHTlyJGJiYhAVVbv8CIVCaaHdqlUrBAcHV6rfKqruZwMAIiMj4ebmVuu6oNHOZty2bdsGz5abkZEBlmVha2tb6TF526piZ2cnd1tthtU2RJcuXdC/f39s3boVU6dOxalTpxAXF1dp6HF9SQpZPz+/BhWygHgIhZZWzR39NjY2cieg4oNDTw/hcvxl3Hlzh9fF7KJFi+o0zJ9UTd4w4/LFLNfDjOuT64prp8q7Z7bifaIlZSXILcmFsa5x3YNUM+XXmQUaxzDjxnRNVxyC/jzjObrb0cz7itKYck2US5m5jkqPwt23d5VyblUbM2aMzPcdO3ZEYWEhkpOTYWtri1OnToFhGEybNk3mnlY7Ozt06tRJpsOrrsaPHy/z/Y0bN1BQUFBpqLKTkxMGDhyIS5cuyWxnGAajR49GfHw8mjdvLo2/tst0AsD169dRXFyMmJgYbNy4EZ6enrh06ZJKO6cabTGrCObm5mAYRm5v4Zs3b2p9Hnn7vnnzRiXrqn7xxReYOHEi7t+/j19++QWtWrXCkCEN/xR+9erV8PPzw3//+1+sXLmyQed68eIF/v77bwwePLjBcWkyycyaOSU5yC/Jh4GOAccRcYP+EFIcecWsk4kTGDBgwXK+1mx9cl2xUJV3z6y8Ai69MJ2XxWzF4o0LjemazimRfT2iM6OpmFWgxpRrolzKzHUbi8pLb3KlobFUrAUkt0QWFIh/P799+7bKjjWg6t7J2qi4pE5aWprc7QDg4OCACxcuyGwzMDCAUCiUFrKS+OsywWzXrl0BiG8PHTNmDFxcXLB8+XIcP3681udoKI0uZg0NDdG9e3cEBwfjp59+gq6uLgAgNzcXp06dqvV5Dh48iMWLF0uHsL18+RLXr1/H9OnTGxxjxR/6isaNG4emTZtiyZIlCAsLw8aNGxs8lG7Dhg349ttvMWzYMIwcORI3b96UeVyyxJCvry/27duHmJgYODs7AwAGDx6MAQMGoGPHjjAxMUF4eDjWrVsHhmGwevXqSs9F3skuypZ+nVaYxttidvTo0Th58iTXYag9lmVRWFr5F46F0ALGusbILs7mfP3V+uS6Ys/s27y3KBGVQEdbR7qtYsECiItZZxPn+gWqRiquLVzx9eJCY7qmKxb30Zk0waAiNaZcE+VSZq7rM6xXXVlZWYFhGFy9elXu3D/lt5UfslteamoqrKysKm2vWA9ICuukpKRK+75+/VruOQDxfa2SOXwawtjYGG3atMGzZ88afK66aLTFbEREhNwpplu2bFmndWRXrVqFkSNHYujQoViwYAFEIhHWr18PIyMjpKfXbgbM5ORkjBs3Dp988gmysrKwcuVKCIVCLFu2rNZxVMXNzQ0A8Ouvv8LY2Fj6CYnkB1JbWxvz58/H119/DUNDQ7mznNWV5M3p3LlzOHfuXKXHJff9ikQiiEQimfuAO3TogD///BM//fQTCgoKYGNjg4EDB2LFihVo1apVg2PTZNnF5YrZgjQ4GTtxGA136A8hxSgUFcosTyNhqmcKc6E5souzOV+apz65rlicsWDxOu+1TKGaV1y5gOPLWrOVemblFPaq1piuaXnDjIniNKZcE+WiXCvGqFGjsHbtWiQmJkon5KpKs2bN8OjRI5ltz549w9OnT6ssRMvr06cP9PX1sX//funSn4D4/tzLly9jwoQJco9TRCELiIvu8PBw9OvXTyHnq61GO5uxj48P+vTpU+lfXbuthw0bhiNHjiAtLQ0ffvghFi9ejHHjxmHs2LEwMzOr1TnWrFkDZ2dn+Pj4YObMmbC3t0dISIhC1uBq3rw5Nm3ahIcPH8LDwwM9evSo9Aby4YcfAgA+/vhjmJo2fCKL0NBQsCxb5T8JyQRZzZo1k27buHEjIiMjkZ2djZKSEiQmJuL333+nQrYWyhezfPnDWx7JbIikYapalsdMz0w64U1juGe2ruT1NCbmyE5yVVXPLB801ntmG4tKxWzmczSyeS7VWmPKNVEuyrVi9OvXD7Nnz4aPjw+++uornDp1CiEhIQgKCsK8efOwfft26b4ff/wxHj9+jHnz5uHSpUvYs2cPxowZU+tOPDMzM6xYsQInTpzA9OnTcfbsWezfvx+enp4QCoVV3lYomdG5trKystCzZ09s2rQJp0+fxuXLlxEQEID+/fujqKiowbcv1lWj65n19vaude9jXFyczPfNmjWT+0vLy8tLuvwLIF5YuHPnzpUmmKp4Pg8PD+n5pk2bhs2bN1cZi2QipfLk3dQtL74FCxZgwYIFVZ5bMnPxZ599VuU+pPEr30vGlz+85Zk/fz7XIWiE8sWspdASaYXie2VMdU1hIRTPQFmxCFS1+uRaXjFb8b5ZSQEn1BaiUCQeas2XtWbzSyoUsyXcF7ON6ZquWMzmFOcgOT8Ztoa1n/SRVK0x5ZooF+VacXbs2IHevXtjx44d2LZtG8rKyuDg4IB+/fqhZ8+e0v2mTJmC169fIyAgAHv37oWbmxu2b98Of3//Wj/XsmXLYGNjgy1btuDPP/+Evr4+PDw8sGbNmip7YOsy4hUQD4fu1KkTfv31V+kySnZ2dvDw8MCRI0fQrl27Op2voRpdMasMvr6+GDJkCOzt7fHmzRsEBATgyZMn1RanjcGDBw/w4sULrFq1CmPHjtXoZWv4oOIwY766cuUKXFxcuA5D7ZW/d9LRyPFdMatnis7WnREaH4qE3ATEZ8fDyYSbIe31yXX5nkYBI0ApW4qX2S9l9/m3gDMXmiOnOAe5Jbm8+YCoYo98Y+iZbUzXdPn3WYnnmc+pmFWQxpRrolyU6+rJ68QCqu6U8/HxgY+PT7XnZBgGS5culVnqBwA8PT1lvi/f2SaPr68vfH19q32uwMBA6RJAubm5EAqFAKpuV3l6enrYuXNntfuoUqMdZqxIOTk5+PLLL/H+++/D19cXIpEIZ86cafSz744bNw5TpkxB586d67yAMGlcCksLUSR6d1M/X/7wlsfc3JzrEDRC+aKmnaX4U1A9bT3YGdrhPcf3pI9de31N5bFJ1CfXeaXinlldLV20shDfvnAzSXaSOkkBZ6hjKO2F5ss1VWmYcSPomW1M17SkZ9ZE1wQMxJOj/J34N5chaZTGlGuiXJRr/tDW1uY6hAbhRc/soUOHuA6hXioOeybqq2JvgaQXjY8cHR25DkEjSIbXAsBg58Fws3KDg5EDTPVMYaJrAit9K6QWpOLvxL8xuc1kTmKsT64lkzsZ6RrBw8kDj9Me41nGM8TnxEsnTZMUcMa6xmBZFq9yXvHmmqo4zLgxLM3TmK5pyevRxLgJjHSMcPvNbZyPO48vu38JbS31/oONC1cTruLe23uY1WEWjHSNGlWuiXJRrvlDstqLuuJFzywhXCu/LA/An14kec6fP891CBqhfM+sgcAAY13GooddDwDioUr9HMSzCd5+cxvFomJOYqxPriU9s4Y6hhjoNFC6PeRViPRrScFipGMEeyPxenqP0x5XKvQ0UcVhxo1haR5lXNMsy+J17muUsWV1Oq78Bx3Dmw8HAKQUpODe23sKj1HTicpE+OrKV9gdsRuHnok7Bej9mz8o1/yRlcXtygcNRcUsaXTq+seLOsgqln2j4PM9s0uWLOE6BI1QfripvkC/0uOSocYFpQW4n3xfZXGVV59cS3pmDXUM0cq8FRyNxL0Dl+MvS/eRFCxGukYY1myY+LiSPJyP0/w/vhrjBFDKuKZ3PNqBoUeGYs2tNXU6TvJBh7GOMYY4D4FASzwA7cyLMwqPUdOlF6ZLf76epj8FQO/ffEK55g9bW/WeU0Dti9nr16/Dz88PmZmZXIfSIDExMdDT08ONGzfqdNyKFSvQtWtXlJVpRgH4y4Nf0OtAL5x7UXn9W3VGPbPvKGKtZCI7AZS+TuVito9DH2gx4rf4sPgwlcVVXn1yLfnj2VDHEAzDwNNJPPHFg+QHyCgULzUk6Y000jHCgCYDYK0vnonx8PPDCoi6cavYM1tQWoCSshKOohFTxjV947X4d+GZ2DMQlYlqfZzklg5jXWOY6plKRyhceHkBJSJuXyd1k1yQLP1aMgkbvX/zB+WaP1R9W6Oil0vTiGLW399f7YvZL7/8EkOGDEGfPn3qfNyLFy+wb98+JUWmWkeeH0GhqBDbH27XqLUBK94zm1mUyfkfoFxR13vYG5vyRY28nllTPVN0tekKADgec5yT4aj1ybUkTkMdQwDAwKbiocZlbBkuvLwAoNwwY10jCLQE8HLxAgA8SnmEZxnPGhp2oyUqE0nvlZasJQy8683mijKu6dSCVADiNYWfZjyt9XHSnlldYwCQDjXOLs7GxVcXFRylZkvOe1fMxmXHgWVZev/mkYbmmmEYlJaWatTfcpqqZcuWKnsulmUhEonAMIzCzqn2xWxdFRQU1LyTij158gTBwcH4/PPP63ysqakppk2bhrVr16r9G0axqFj6B0xsViwepz/mOCLFKb/GrERmYabqA2kERo8ezXUIGqGmYhYAprWdBkD8B/7R50dVEld59cl1xWK2i00X2BqIh0Dtf7IfhaWF0g+CjHXEBcs413HS4488O9KgmBuz8jmX9EYD3A81VsY1LfldAAB33typ1TElZSXS10hSzA5qOghmemYAxD8/pPZSClKkX+eV5CGtMI3ev3mkobk2MjKCSCRCUlISSktLFRQVUYbnz5+r5HlKS0uRlJQEkUgEIyMjhZ1XrWcz9vPzky4k3Lx5c+n2kJAQeHh4oFmzZnBzc8PMmTOxevVqPHnyBAsXLsTatWuxdetW/Pnnn4iKikJeXh5atGiBjz/+GIsWLYKOjo7M85w7dw7r16/H3bt3UVJSAmdnZ0yfPh3Lli2T7nP37l2sWrUK165dQ35+Ptq2bYtly5Zh0qRJNbZj+/btsLOzw5AhQyo9Vpvn/vjjj/HLL78gJCQEAwcOrHQOdVH+jxcAOBVzCu0tNWNtXXlrH6YXpsPaoG4LVWuCkydPch2CRpD80c6AgVBbKHcfDycPOJs442X2S/z++Hd81OYj6GjpyN1XGeqT64rFrEBLgGltp2HDvQ14kfVC5t5HI13xL0MnYyf0se+DG0k3cDL2JBZ1WwShQP5ros7K3ydtY2CD6MxoANwXs4q+pvNL8mXaevfNXcxoP6PG48qvuSspZoUCISa2moid4TvxKOURHqY8RCfrTgqNV1Ml5yfLfB+XFUfv3zzS0Fybm5sjPz8fWVlZyMrKgkAggJaWlkJ75IhiaGtrIzY2VmnnZ1kWZWVl0g81DAwMFLr0k1r3zM6aNUvam3n06FHcuHEDN27cQNeuXaX73L9/H0uXLsUXX3yBc+fOYfz48QDE96hOmTIFv//+O06dOgVfX1+sX78ec+bMkXmO3bt3Y8SIESgrK0NAQABOnjyJL774AgkJCdJ9QkJC0K9fP2RmZiIgIADHjx9H586d8eGHH0oXJK7O6dOnMWDAAGhpyaajNs8NAN26dYORkRFOnz5dp9evsSn/KTAgnrCjtEwzPs2TV8zydRKo5cuXcx2CRpAUs0KBsMo/DrS1tDG93XQAQFJeEs6+OKuy+ID65br8/bASE1pNkH7/y4NfpNvL7zO+lfi9Pac4RzocWdOUn/zJxsBG+jXXy/Mo+pqu+N547+29Wt03W/51kBSzAPBRm48gYMSf3R94fEBBUWq+ir+T47Lj6P2bRxqaa4FAgKZNm8LR0RHGxsYQCARUyDZS4eHhSj0/wzAQCAQwNjaGo6MjmjZtCoFAcf2pat0z26RJEzRt2hQA0KVLFzRr1qzSPsnJyXj8+DFatWols/3nn3+Wfl1WVob+/fvD0tISPj4+2LBhA8zNzZGbm4vFixejX79+uHz5svQiHDRokMy55s2bh/bt2+Py5cvS5AwdOhSpqalYvnw5pk+fXqlQLR9fbGwsZs+eLbO9ts8NiD9R6dSpE/7+W70Xhk/Jl/3FmV6YjmuJ1+Dh5MFNQAokGWasxWhJZ2vmy7qYFU2ezM2ap5pGUsxWNcRYYkzLMdj2zzakFaZh8/3NGNx0MAx0DFQRYp1zXVpWKr0nVNIzC4h7YCe2moi9kXtl/sAuX8wOdBoIC6EF0gvTceT5EYxuqXnDIcsPMy5fzHK9PI+ir+nUQtlROpL7ZttZtqv2uJyScsWszrti1sbABkObD8Xp2NP46+Vf8E33RWuL1gqNWRO9zX8r8/3L7Jf0/s0jisg1wzAwMTGBiYmJAiIiyiIZoaqu1LpntjY6duxYqZAFgAcPHmDMmDGwtLSEtrY2dHR0MH36dIhEIjx7Jp5A5Pr168jOzsa8efOq/DQpOjoaUVFRmDp1KgDxeHDJvxEjRiApKQlPn1Y9ecXr168BADY2NjLba/Pc5dnY2CAxMbHG/Rqz8n+kSv5A33hvo0bMQCnpmZUsMwLUbUbj1IJUHI8+LjOMTl0p+xNAvqhtMSsUCLGg6wIA4mGDu8J3KT02ibrmunxRVr6YBYCpbadWaqtkmDEA6GjrYEzLMQDEPXkvsl7UNdxGr/zQW8l9xAD3PbOKvqYr3nIC1O6+2ap6ZgHA180X2ow2RKwIq26u0sgl4BSt4gfMcdlx9P7NI5Rr/lD3XGt8MWtvb19p26tXr9C/f38kJiZi8+bNuHr1Ku7cuYOtW7cCeDdJVEqK+I28SZMmVZ7/7VvxJ5dffvkldHR0ZP7NmzcPAJCaWvkXs4TkuYRC2fu7avPc5QmFwkY5uVVdSH5xWulbYU5H8XDv2KxY7I3cy2VYCiEpZh2MHKT3LNalZ/a/f/8X//37v5h5fmalpTkIP9W2mAWAsS5j4WbpBgAIjAxETGaMUmOrr/LFbPleVwCwNbTFZ50/k9lWsWAZ7zpe+vWGuxvUflK8isoPM25ME0ApWvliVtLDejDqIIpERdUeV10x62ruKh1y/yjlEQ49pVl5a1KpmM2K4yYQQgiphsYXs/J6NYODg5GXl4ejR49i2rRpeO+999C9e3fo6urK7GdtLf5joeI9quVZWVkBAJYtW4Y7d+7I/de5c+caj09Pl+2lq81zl5eeni49l7qS/OJ0MHTA9PbT4WLmAgDY8XAHojOiuQytwSTrzJrpmcFCaAGg9vfMFpYW4lbSLQDAk/Qn+Pbvb9X6j/QOHTpwHYJGkBSzBoKahwxrMVpY1ks8aVxJWQnmX5qvkrWO65rr8sWsvKHQU9tOlfm+YsHSzLSZdCmWsIQwjVt3VmYCKMN3o3nic+K5CEdK0de0pJjVYrQwr7P4Q+HE3ETsidhT7XHli1kT3crDGud2mgsHQwcAwE93f8KjlEeKClnjFIuKkVEkXtdZm9EGACTkJKCdW/VDvYnmoN/V/KHuuVb7YlZPTw9A3ZbckRS4kmMB8UxbO3fulNmvb9++MDU1RUBAQJXFQ+vWreHq6oqHDx+ie/fucv8ZGxvLPRYAnJ2doa+vj5gY2Z6S2jx3ebGxsWjXTr1/yUiGGdsZ2kFHSwcr+6wEAwbFZcX47PJnyCjM4DjC+pP0zJromkiLWXlD6eSJTIuUmQjrXNw5/ProV8UHqSIHDx7kOgSNUH4CqNroaN0R8zvPByAuDL64/IXSh6fWNdfV9cwC4gmtjnsdR0vTlhjiPERamJT3Ta9vpENw199Zj3+S/6lb0I1Y+VEZZnpm6GLTBQAQ/DyY01sQFH1NSz7oM9czx0dtPkJrc/H9rbvDd1c7fLz8z3P5IegSBjoG8OvrB21GG0WiInx++XPEZ3P7QUBjVf62n/ZW4lUFStlS7D2i/iOlSO3Q72r+UPdcq30xK/k0YfPmzbhx4wbu3r2LnJzq/0AbMmQIdHV1MXnyZJw9exbHjh3D0KFDkZEhWywZGRlhw4YNuHLlCgYPHow//vgDISEh2LlzJz777N1wtx07duDSpUsYOnQoDh48iCtXriA4OBg//PADJk6cWG0surq66NOnD27evFmv5waAtLQ0PH/+HJ6enjW+Xo2ZpLhzMBL/gdrZpjM+6yJua2JuIhaGLJS7Xmtjx7KstGfWRNdE2r7rr69jd/juGj+suP/2vvRrO0M7AMAv//yCSy8vKSli5VqzZg3XIWiEugwzlpjTcQ5GtRgFAHiY8hDTz07Hm7w3SokPqHuuyw+XrXjPrEQL0xYI9grGzx4/yx15Y6pniu/f+x4MGBSUFuCTvz5BWHxY3QJvpMoPMzYQGMCnvQ8A8cRHh59x1wut6Gta8rvA2sAaAi0BlvcSz6paJCqC9zlvRKZFyj1O8qEhA6bKn58+Dn2k50svTMf0c9Oph1aO8kOMe9j2kH7df2p/LsIhHKDf1fyh7rlW+2LWw8MDy5Ytw8mTJ/Hee++hR48euHfvXrXHtGnTBkeOHEFGRgY++OADfP755+jcuTO2bNlSaV9fX1+cOXMGIpEIs2bNwqhRo7Bp0ybpLMoA4Onpidu3b8PMzAwLFy7E4MGD8emnn+LixYsYPHhwjW2YOnUqbt++jaSkpDo/NwAcP34cOjo6tVrTViI3NxcLFy6Eg4MDhEIhOnfujD/++KNWxyYnJ8Pb2xtWVlYwMDBAnz59cOlSwwurUlbc+2hv+O4+5086fIIRzUcAAO4n38eHpz5EZKr8P2Qaq/zSfGnbTPVMMavDLOkfWpvub8KCkAV4m/e2yuPvJ4uLWSdjJwQMDpAeu+zaMrXsdWroQuxErD7FLMMw8O/rD08n8Qdf0ZnRmHRyEs6+OKuUoet1zXV1E0DVRS/7XvDv6w8tRguFokJ8fvlzrL+zHoWlhfU+Z2NQfpixvkAf7k7uaGnaEgDw++Pfa7ynVFkUfU1LillLfUsAQFfbrpjbaS4AcQHqc84Hh54eqjSJk6Rn1kjXCFpM1X/eTGo9CbM7zpY+l885H+yL3IeSMvWfbFBRyq8x6+7kDjM9MwDAqiurNHJyNVIZ/a7mD3XPtdoXs4D4E4XExESIRCKwLAsPDw8AQFxcHE6dOiX3mFGjRuGff/5BQUEBEhISsG7dOgwbNkzmeInhw4cjNDQUubm5yMvLQ2RkJL766iuZfTp27Ig///wTb9++RXFxMZKSknDp0qVK69bKM2XKFFhYWOC3336r9Fhtnnvv3r2YOHEibG1tKx1flQ8++AD79u3DypUrcfbsWfTo0QOTJ09GUFBQtccVFRVh0KBBuHTpEjZv3ozjx4/D1tYWw4YNQ1iYYno/yhezkj++BzUVL0mUmJuIyacn45tr3+BV9iuFPJ+ySXplAXHPrJuVG34b/pu0lzUkPgRjj4/F5vubKw09FpWJ8DD5IQCgi00XtDRriXUD1sn0Ol1JuKK6xihAQxdiJ2IFJXUvZgFAV1sXGz02Su8/zSjKwFdXvsInf32CW0m3FFrU1jXXNQ0zrotxruOwyWMThNpCsGDx2+PfMDZ4LP737H8oFhU36NxckfTMajFa0NPWgxajBR83ce9sckEyFlxeINN7qyqKvqYl74NWwnfzQMzvPB/Lei6Tvvetvrka3ue8cTXhqvRnVjLUWt79shV93uVzrOi9AtqMNorLivHT3Z8w6eQknIk9Q0UtZIcZO5s4Y8174p4bVofFgpAFaj+PBakZ/a7mD3XPtUYUs+pOKBTC398fP//8M/Ly6rZe4JUrV3Dnzh2sXr261secOXMGFy5cwLZt2zBnzhx4enpi586dGDJkCJYuXQqRqOrF6Xfv3o2IiAgcOnQIU6dOxZAhQ3D48GG0atWqUpFdX5JhuBJCgRAbPTZicbfF0Ga0wYLFiZgTGHlsJHzP++Jg1EG8yn7VaCdFkgx9A8Q9swDQyrwV/hz1J0a2GAlA/Ef8rvBdGHJ4CBZcXoDTsaeRWpCK6Mxo6dqJ3Wy7AQAGNBmAVf1WSXud5l+aj2lnpuHPqD85nwimNuoygoBUrT49sxLaWtr4T8//YP2A9dJ7uG+9uYVZf83CmOAx2P7PdkSkRkBUVvV7QW3UNdc1TQBVV55NPfHn6D/RwUp8O8rrvNdYdWMVPA95YtWNVbiScIWT4q++yk/6JRliPaLFCOm9s3+//hvTzk7DsefHVHpLhiKv6TK2TDrTu5W+7KSGU9pOQcCQAOkHng+SH2DepXkYeWwkttzfgquJVwFUnhisyrhbT8Ku93ehmUkzAOKRCl9f/RpDDw/F2ttrcSvpFm9nj5esMSvQEsBMzwz9m/THJx0+AQC8yHqBiacm4rub3+F64nXevkaajn5X84e655phG2sFwDMikQjr1q3DqFGj6jSr2LFjx1BSUlKnH8RPPvkEf/zxBzIyMiAQCKTbDx48iClTpuDvv/9G37595R47ZMgQxMfHIyoqSmb7Dz/8gOXLlyMhIQGOjo5yj61KZGQk3Nzc4PK9C4SOQvw9+e8qP1l/kfUCm+9vxqVXlYc1G+sao7V5a7SxaIPmps1hrW8NGwMbWOlbwVjXGPoC/Vqt2atot5Nuw/cvXwDA7vd3o6d9T5nHr7++jm3/bMPDlIeVjjXRNZEWwye8TqC5aXPpY5deXsJXV75CcZlsL5OF0AKuZq5wMXdBC9MWsDWwhZW+FSz1LWEuNIeuli4nr4NEfn4+DAwaXqjwXZffu6C0rBS+br5Y2G1hvc+TUZiBXx/9iiPPj1T6o9RAYICWZi3RwrQFXMxc4GjsKP5ZElrCUt8S+gL9aodz1jXX2x9ux7Z/tgEAHnz8AAItQQ1H1E5pWSmOPj+KXeG7kJQnezuHFqOFpsZN4WruClczVzQ1aQprfWtY6lvCSt8KhjqGCoujofyu++HI8yOw0bfBpUnv3gPzS/KxOHQx/n79t8z+jkaOaGfZDm0t2sLeyB42+jawNrCGhdBCoe1S5DWdUZiBAX8OAAB83eNrTGs3rdI+ucW52PFoBw49PSQz9Fqih10P7Bla/czH5RWLivHb49/wW+Rv0hl8JQRaArQ0FV8Dzc2ao7lJc1jpW8FC3wKWQkuY6Jpw+n6qLMuuLsOp2FOwN7THXxP+AiAeKfTT7Z8Q9CxIZoi3gBGgjUUbtDBrgabGTdHUpCkcjBxgpmcGMz0zGOsaV/s+QRon+l3NH40t15K6ICIiAu3bt69x/8bxG5pAW1sby5Ytq/Nx48aNq/MxERERaNu2rUwhC4iHSkser6qYjYiIQP/+lSeAkBwbGRlZ52K2PEMdQ+m6gvI0N22OTZ6bEJMZgyPPj+Dci3PS4VA5xTm4+/Yu7r69K/dYBgwMdAxgKDCEgY4BhAIhtBltaGtpQ8AIoK2lLfv9v1+XPx54Nxu29Pt//w9G/j6ST7gBwESvcpHe16Ev+tj3wf3k+zgefRwXXl6QToQjKWQthBbS3gOJQc6DcGLcCRx+dhjHo49LX4f0wnTcenMLt97ckvs6aDFaMBAYwEBgAH0dfegL9KGjpQMtRgvajDYEWgLx1xVeB2k7IbvkVfntlb5nKm+PiIiQ+cBG5rxVPAeRxbKsdIbr+vTMlmcuNMfXPb/GJx0/wYnoEzgVewpPM54CEN+jGZ4ajvDUqhdU1xfoy/wTaguhpaUFASNA/Kt4tGjeAgLm3c+UNqMNLUYLDBgwDAPJfwCkzyvUFiq0gBRoCTCp9SSMcxmHi68u4lTsKVxPvI5SthRlbBnisuMQlx2HCy8vyD1eV0sXBjoG0Bfow0BgAD2BnvQ9Q4vRqvJreT/Pcn/e5Vwn8o55kPwAAKCvI5tzAx0D/N/A/8Ov4b/i8LPD0mG6ibmJSMxNrLZdhjqG0rbpauuKr/dq3hcrvf8xDB5HPkb79u2rfS/U1dLF+Fbj0cm6k9xYJMrfalGxZ1bCSNcIS7ovwScdP0Hw82Ccizsn8zNa/laV2tDV1sWsDrMwre00nH1xFqdjT+PO2zsoY8tQWlaKpxlPpT+b8pT/2dcX6EMoEErfRyXvq3L//+/PiVZVg+SqeAus+J4r81gV75tVHVPV9ttvbgMQT8Iloa2ljeILxQj6NAgb72/E3Td3IWJFKGVLEZEWgYi0CLnnkvzO0dPWg1AglPm/5PVgGEbm/1rQkr5+kn/Vtbv6h+r+etV4XD3PqU4ePnyITp2qv16JZnj48CH8xvuhu113rkOpFypmeSgtLQ0tWrSotN3CwkL6eHXHSvar67GAePKolBTZhdijo9/de+No5FirXwQtzVriqx5fYWn3pXia8RT33t7Ds4xneJL2BNGZ0XLveWLBIq8kTzyUkaNRUeZ65nK3MwyDbrbd0M22G1b0XoHw1HDceXMHT9KfICkvCdPaTpP7ujgaOWJB1wX4vMvneJbxDLeSbuFZxjM8z3iO2KxYuRPClLFlyC3JFRfMXLwOhsCrWPW431kd1HZIZU0shBbwdvOGt5s3knKTcDPpJiLTIhGTGYOYzJhKPVYSBaUFVQ8zFAIpSSnyH6uGZDi+oulo62B48+EY3nw48krycOfNHTxMeYjojGg8z3yOxNxEuccVlxWjuKgYmUWZSomrruSNXNHR1sH8zvMxu+Ns3Em6g/DUcDxOe4zH6Y+rnLFa0q6qcltrhkB8XM23ONx9exdnPjhT7T7li1nJBFBVMdE1wfT20zG9/XS8zXuLu2/v4lXOK0xwnVC7uCsQCoQY5zoO41zHIaMwA/fe3sP95PuIzYxFbFZspV59iWqvATVX8YOBoUOHor1Ve+x6fxeyi7NxK+kWHiQ/QGRqJF7lvJK75JzM7xyiPoyAlzEvuY6CqIKReL1ydS1madwHT1X7aWQNxWRDjt22bRvc3Nxk/nl5eQEALLQsMEAwAOvXr0d6ejpmzJgB4N0sa4sWLUJ0dDT27NmDY8eO4fbt2/juu+/QVNgUx1ceh39ffxT8WoBbU29hcMxgrGq9CuO1x2Oo1lB85PARXFNdMa3tNBi9MMKwZsNg9NYIfez6wLbAFm0M2sBZ2xn2rD1cjVxhnG+MVuatoJOlg5amLSHME8LZyBmmpaawFdjCWtsapmWmcDRwhLBACGcTZwhyBGhq3BTCQiHs9e1hUmYCC20LWAosYSQywoctPsRX876qsU1r16xFG+M2uPT9JWzy3IT8HfkY3XI0li9fjvDwcAQFBSEoKAjh4eFYvly8xMTYMWPRxqINTvufxjddv0GHfzrgF9dfsNhoMT4SfYT/tPkP2sS3wYKuC2D6xBTT2k6D5WtLDHQYiCZFTdBOvx1cdV3hUOoANzM3GGcbo4NVB+hm6KKVeSvo5+ujqWFTmJeZw0bbBrY6tjAtNUVTw6bQy9NDM5NmEGQL0MykGQwKDeAgdIAZawZLLUvY6trCqMQITYyaQCdPBxaMBQS5AjgZO8Gg2AA2ujYw+/c/a11rGBQbwNHIEYI8ARyNHKGTrwN7A3sYlhrCSscKZowZTFgT2OjZQFgklNlXt0AXdvp2MBIZwVJgCXMtcxiXGcNWaAu9Qj2ZffUK9WArtIVxmTHMtcylebLTt4Nuga7MvsIiIWz0bGDCmsCMMYOVjhUMSw1hb2APnXwdmX31i/VhrWsN03//s9a1hn6xvlLaZJplivebvS/9eZo7dy4SExOxdetWnDt3DmFhYXW6nlavXo38/HwsmLkA41zH4db3t7B32F689+g9BHQIwEzBTEzSnoS5znPRNqUt5nScA5NnJpjYaiJMEk3g7uAOuwI7tDNsh+bazWGabYo2xm1gkmeCdpbtoJuhCxczFwjzhGhq1BQmpSawEdhIrycHfQfo5+tjfuf5Cm+T5FYMyb7fr/welpmWsI6yRp+3fbC+1XoMfzYcwWODYRtmix/7/wjbSFvMbj8brbNbw9PcE511OsOFdUE3826wyLZAL/te0EvRQxebLjDMMkRr09YwLzGHg44DbLRtYF5mDicDJ+jn66O5aXPpNSIsEMLRwBGmZaaw0raCtcAaxqXGcDR0hF6+HpyMnSDIFaCJURPoF+nDVmgLE9YE5ow5HHQdYPLERG6bli9fjqjIKMSFxcE40hizrGehx/0euD75OhzOO2Dn+zvh8I8DFnZaiPaZ7THMchh66vRE27K26GXRC1ZZVujn2A/Ct0L0sOsBw0xDtDNvB8sSSzTRbQJbbVuYi8zR1LCpTJsstSwrtclKYAXjUmM4GDhAUCz+3Dw+Jx4ZhRnV5slvvZ/094WVvlW173uSdk+aNAnGjDHuH7iPbkXdcO38NezZswfR0dFYtGiRzL4zZsxAeno61q9fj7CwMJw7dw5bt25FYmIi5s6dK93XXGiOwz8cxlSHqejwrAMWGy3GhqYbMDptNDb03oBmkc3wVY+vYPrYFN7tvdEktQk8rD3gyrrCVeCKdobtYFNog67WXaGfro9O1p2gm66LdpbtYFJoAiehE6xgBQtYwF7PHobFhuL30Rzxz4huri6cjJxgVGIEW11bWGpZwow1g73QHvpF+mhq3FT6O0cvXw+Oho4wLjWGlcAKltqWMCkzgb2+PYSFQjQxaiL9earq95O9gX2l9z3jfGN4GHvI5Gnjxo3Snz0TXRNs+WwLvurxFRyvOmJL2y1YIlgCX4EvlrZcis5JnbG0+1KYPjHF1LZTYfbKDMOaDkOT/CboYNgBzbWbownbBO1M2sE01xQdrTtKXyP9HH20NGkJs1Iz2OnYVfqdW/61qup6amLYBHr5ejKvlX6RPuyF9jBjzWDBWMBGx0b8+8lQ/Pup/LVX8feTja7Nu99Pue/eyx0MHTTy95NOvo7GtUkT86SINpkxZvi/Df+n1N+5tX0vz8/PR0BAQLW1REV0zywP9enTByKRCLdv35bZLhmjvmPHDsyePVvusfb29ujfvz8OHToks/306dMYNWoUzp8/j/fff7/K566qZ9bLy6vWY+OJetuzZw9mzpzJdRhEBSjX/FBTnq8lXsOnFz8FAPw65Ff0cehT5b67wndh8/3NAIDrk68rbOQBUQy6pvmDcs0fjS3XdM8sqVGHDh1w8OBBlJaWytw3Gx4uvufIzc2t2mMl+5VXm2MBwMbGBjY2NvUJm2iIAQMGcB0CURHKNT/UlOc2Fm2kX0elR8ktZvNK8vDLg1/wR5R4vXN9gX6Dl2ciikfXNH9QrvlD3XNNw4x5aNy4ccjNzcWRI0dktu/btw8ODg7o1atXtcdGRUXh1q13kwuVlpZi//796NWrFxwcHKo8lhAA2Lp1K9chEBWhXPNDTXm20reCtb54IqGo9KhKj99KuoUPjn+A/U/2o5QtBQMGM91masxEOpqErmn+oFzzh7rnmoYZ89T777+Pu3fv4scff4SLiwsOHjyInTt3Yv/+/Zg6dSoAwNfXF/v27UNMTAycnZ0BAEVFRejWrRuys7Oxdu1a2NjYYNu2bTh58iQuXrwId3f3OsdS1+EERL2xLMu7P1L52GaAv+0mlX168VNcS7yGFqYtcNzruHT7qdhT+ObaN9KlXrrZdsN/ev5HpjeXNCIsC/DxmuZju/nYZtIo1LUuoJ5Znjp69Cg+/vhjfPvttxg2bBhu3bqFgwcPSgtZQLz2rUgkQvnPO/T09HDp0iV4enri888/x+jRo5GUlISzZ8/Wq5Al/HIx9iIEbQW4GHuR61BU5mLsReh/r8+rNgP8zDVfSSbvqE5bi7YAgLjsOESlRyEwIhAb7m6QFrJCbSGW91qOPUP3UCHbWF28iNECAXCRZ9f0xYuAvj6/2s3XXPNUbd7DGzPqmSWco55ZfmBZFj129sC9pHvobt8dtz+5rfG9dnxsM8DfdpOq/RX3F5aELZH7mL5AH9sGbVPbZSF4gWWBHj2Ae/eA7t2B27f50WvHx3bzsc2kUaGeWUJIo3T6+WncS7oHHAPuJt3FmefVrzepCaRtBn/aDPAz13wmWa6hOpKe2YocjRyxddBWKmQbu9OngXv3MAMA7t4FzvDkmv633QD4026+5prHavMe3phRMUsIUTqWZeEX6gcGDDAUYMDAL9QPmjwwRKbN4EebAX7mmu82btxY4z6Oxo4y349pOQb3p93H2Q/OooddD2WFRhSBZQE/P4BhsBEQ99L5+Ym3a7Jy7QbAj3bzNdc8V5v38MaMillCiNJJeupYsMADgAWr8T12Mm0GP9oM8DPXfLd79+4a99FitNDfsT8AoKlxU3zT6xvoaOvQ8HN1IOmdZFnsBsSFDR967Mq1GwA/2s3XXPNcbd7DGzMqZgkhSlWxhxL/dtBoco9dpTb/S5PbDPAz1wTo2bNnrfb7b+//Ykm3JQgcFggDHQMlR0UUokLvpDTTmt5jV7FXVkKT283XXJNav4c3VgKuAyCkqKgIABAdHc1xJEQZQuNCce/hvXcbUgAY/Ntjl3wX289sh3szzZoJu1Kb/6XJbQb4mWsCREVFwcrKqlb79mB6IPlFMpKRrOSoiEKEhr67ZxRAFAAr4F2P3fbtgCauZFCh3VKa3G6+5prU6T1cFST1gKQ+qAnNZkw4t2/fPnh7e3MdBiGEEEIIIaQRCA4OxtixY2vcj3pmCedatWoFADh06BDatWvHcTREmaKjo+Hl5YXg4GC4uLhwHQ5RIso1P1Ce+YNyzR+Ua/5ojLkuKipCfHw83Gs5EoCKWcI5ExMTAEC7du1onVmecHFxoVzzBOWaHyjP/EG55g/KNX80tlx37dq11vvSBFCEEEIIIYQQQtQOFbOEEEIIIYQQQtQOFbOEEEIIIYQQQtQOFbOEc9bW1li5ciWsra25DoUoGeWaPyjX/EB55g/KNX9QrvlDE3JNS/MQQgghhBBCCFE71DNLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtCLgOgJDMzEyEhYXByckJenp6XIdDCCGEEEII4UBRURHi4+Ph7u4OMzOzGvenYpZwLiwsDF5eXlyHQQghhBBCCGkEgoODMXbs2Br3o2KWcM7JyQmA+IfWxcWF42iIss2fPx9bt27lOgyiAorKNcuyyCnOQXJ+MrKLs1FcVoyi0iKZ/xeLxP/kHg+2ihNXtbmKB4hcFy9exODBg7kOgyiZo7Ejjq07hm1bt3EdClEB+l3NH40t19HR0fDy8pLWBzVhWJal39qEU5GRkXBzc0NERATat2/PdTiEkEYiMTcRh54ewoWXFxCfE891OITw3o4hO9DXoS/XYRBCNFhd6wLqmSWEqNSMGTOwb98+rsMgKtCQXN9MuonFIYuRU5JT62MEWgJoVTGvIcMw8rejiu1V7E8qKyoqovkONFxBaQEAwD/AH+dXnec4GqIK9LuaP9Q911TMEkJUauPGjVyHQFSkvrk+E3sG31z7BqVsKQCgs3VnuDu5w9nEGVb6VhBqCyEUCKX/19PWg562HrS1tBUZPqml9PR0WFhYcB0GUSL3P92RXpiOTh6duA6FqAj9ruYPdc81Lc1DCFGp3bt3cx0CUZH65PpRyiP89+//opQtha6WLn7s/yN+H/E7ZnWYhSHOQ9DFpgvaWrZFc9PmsDeyh7nQHAY6BlTIcoiuac3nbOIMALgXc4/jSIiq0HXNH+qea+qZJYSoVM+ePbkOgaiIJNdv8t7gZfZLvMl7g4LSAhSWFqJAVIASUQlYsGBZFpL/TsecRklZCQSMAFsHb0Vv+94ct4LUhK5pzdfUuCkeJD9AgbCA61CIitB1zR/qnmsqZgkhKlVQQH8M8cGbvDc4kXICm45vQnRmdJ2PX9pjKRWyaoKuac0n6ZnNKctBbnEujHSNOI6IKJsirmuWZZGXl4fs7GwUFRWB5pxtnAQCAWJjY5X6HAzDQE9PDyYmJjA0NFTovBRUzJJG40LcBUTpRCnkXMpYWkMp51SDN3ZFxxj2MgyZUZkKPSegPvlRRpyFpYXIKs5CdlE2souzUVJWgjK2DGVsGViWFX+NMmkPqLIVi4oRnhqOMras2v20GC3xhE2MeCImBgy0tbQxodUETG4zWelxEsWIiYnhOgSiZJJiFgBe5rxEe0taeUDTNfS6ZlkWycnJSE9PBwDo6OhAS4vubmyMzM3Nlf4cIpEIWVlZyMrKgoWFBWxsbBRW0FIxSxqNrQ+3Qpgq5DoMomxC4Oqtq1xHQVSktXlrvN/sfXSw6gBHI0cY6hhKJ20SaNGvIE3g5eXFdQhEycoXs6+yX1ExywMNva7z8vKQnp4OAwMD2NvbQ1dXVzGBEYUrLi5WSX6Ki4uRlJSE9PR0GBoawshIMSM86C8JQgjRAAItAcz0zGCiawJdbV0wYKDFaEGb0QbDiL9mwKhsyZkWpi3w7PAz7N+wXyXPR7izevVqBAQEcB0GUSInYyfp13HZcdwFQlSmodd1dnY2AFAhqwaSkpLg7Oxc844NpKurC3t7e8TExCA7O5uKWaJ5dg3ZhdbtWivsfFWtH9mgcyqhEFCXOBVNXdqtlDgVfE5dbV3oC/QbX977cB0AUQUqZDWfgY4BbAxskJyfjFfZr7gOh6hAQ6/roqIi6OjoUCGrBlRRyEro6upCR0cHRUVFCjsnFbOk0bAysIKdoR3XYRAlGz16NE6ePMl1GEQFKNf8QHnmB2cTZyTnJ+Nl9kuuQyEq0NDrmmVZukdWTTx//hyurq4qez6GYRQ6Jwn9lBFCVIr+6OUPyjU/UJ75oalxUwCgYpYn6LrmD1UWsoDiR9FRMSvH4cOHwTAM/vzzz0qPderUCQzD4Pz585Uea9myJbp27Vqn5/L29kazZs3qFaefnx8YhkFqamqN+65ZswbBwcH1eh5CFGnu3Llch0BUhHLND5Rnfmhm0gwAkF2cjczCTE5jIcpH1zV/vHyp3h9QUTErh4eHBxiGQUhIiMz29PR0hIeHw9DQsNJjCQkJiI2NhaenZ52ea8WKFTh27FiDY64JFbOksVixYgXXIRAVoVzzA+WZH8pPApWYl8hhJEQV6LrmD3t7+zofs2XLFvTu3RtWVlbQ09ND06ZN8dFHHyEyMlIJEVaPilk5rKys4ObmhtDQUJntYWFhEAgE8PX1rVTMSr6vazHbsmVLdOnSpUHxEqJO6EMV/qBc8wPlmR9M9UylX2cXZXMYCVEFuq75IzMzs87HpKWlYfjw4di1axf++usv+Pv748GDB+jVqxeePn2q+CCrQcVsFTw9PfH06VMkJSVJt4WGhqJHjx4YMWIE7t27h5ycHJnHtLW10b9/fwDiG9+3bduGzp07Q19fH+bm5pgwYQJiY2NlnkfeMOPMzEz4+vrCwsICRkZGGDlyJGJjY8EwDPz8/CrF+vbtW0yePBmmpqawtbXFzJkzkZWVJX2cYRjk5eVh3759YBjx0hweHh4Nf5EIqYeWLVtyHQJREco1P1Ce+cFEz0T6dVZxVjV7Ek1A17V6KSkpQWlpab2O1dPTq/Mx/v7+WLlyJby8vODu7g4fHx8EBwcjLy8PBw4cqFcc9UXFbBUkPazle2dDQkLg7u6Ofv36gWEYXL16Veaxrl27wtRU/MnlnDlzsHDhQgwePBjBwcHYtm0bIiMj0bdvX7x9+7bK5y0rK8Po0aMRFBSEr7/+GseOHUOvXr0wbNiwKo8ZP348WrVqhSNHjuA///kPgoKCsGjRIunjN27cgL6+PkaMGIEbN27gxo0b2LZtW31fGkIaRF9fn+sQiIpQrvmB8swPprrUM8sndF1XTzJvTWRkZLUdSkDtO7iaNWsGb2/vSs/l4eEh0wkVGhoKhmHw+++/Y8mSJXB0dISenh6io6MBAHv27EGnTp0gFAphYWGBcePG4cmTJzLn9Pb2hpGREaKjozFx4kQYGRnByckJS5YsqfeyOdbW1gAAgUC1i+VQMVsFd3d3aGlpSYvZtLQ0REREwN3dHUZGRujatat0aHF8fDxevHghLYBv3ryJnTt3Yu3atfjpp58wdOhQTJ48GRcuXEB2djZ+/vnnKp/33LlzuHbtGjZu3Iivv/4aQ4YMwbfffouZM2dWeYyvry/8/f0xePBgLFq0CL6+vjh48KB02uvevXtDS0sL1tbW6N27N3r37o127dop6JUipG5u377NdQhERSjX/EB55ofyPbPZxVTMajq6rmunpg4loP4dXDVZtmwZXr16hYCAAJw8eRI2Njb44Ycf4Ovri/bt2+Po0aPYvHkzHj16hD59+uD58+cyx5eUlGDMmDHo27cvjh8/jpkzZ2Ljxo348ccfax2DSCRCUVERoqKiMGvWLNjY2MDHx6febaoPWme2Cubm5ujUqZO0mA0LC4O2tjb69esHQFzsXr58GUDl+2VPnToFhmEwbdo0mS5/Ozs7mXPKExYWBgCYNGmSzPbJkydj2bJlco8ZM2aMzPcdO3ZEYWEhkpOTYWtrW8sWE6Iavr6+XIdAVIRyzQ+UZ34Qaguhq6WL4rJi6pnlAWVe1z/e/hFR6VFKO39dtLFog697fl3v4319fbF06VIAwODBgxEdHY09e/Zg9+7dYBhG2sG1YcMGLF68WHpc//790apVK/z88891Kh7La9myJf73v/9Jv8/MzMTq1asxYsQIBAUFSbd7eHjA1dUVfn5+MkOAi4uL4e/vj3HjxkEgEGDQoEG4e/cugoKC8O2339YqBkNDQ2lPbqtWrRAaGgonJ6cajlIsKmar4enpiZ9//hmvX79GSEgIunXrBiMjIwDiYnbDhg3IyspCSEgIBAIB3nvvPQDie1hZlq2ykGzRokWVz5mWlgaBQAALCwuZ7dUVpZaWljLfS8a+FxQU1NzIeggNDa1yoqsbN26gd+/eSnleohkWLVqEffv2cR0GUQHKNT9QnvmBYRiUFZQBenTPLB8o87qOSo/C3bd3lXJuVaupQ6khHVw1GT9+vMz3N27cQEFBQaWhyk5OThg4cCAuXboks51hGIwePRrx8fFo3ry5NH5JZ11tXL9+HcXFxYiJicHGjRvh6emJS5cuoX379vVrVD1QMVsNSTEbGhqK0NBQjBgxQvqYpHC9cuWKdGIoSaFrZWUlvadW3k3V1d1obWlpidLSUqSnp8sUtG/evFFUsxRmzZo1lYpaNzc3jqIh6oL+6OUPyjU/UJ75w9nGGTFZMdQzywPKvK7bWLRR2rnrqqGx1NSh1JAOrppUXFInLS1N7nYAcHBwwIULF2S2GRgYQCgUSgtZSfyFhYW1jqFr164AxLc0jhkzBi4uLli+fDmOHz9e63M0FBWz1RgwYAC0tbVx+PBhREZGYt26ddLHTE1N0blzZ+zbtw9xcXGYMmWK9LFRo0Zh7dq1SExMrDRcuCbu7u5Yt24d/vzzT3z66afS7X/88UeD2qKnp6fwnlpXV1fqhSV1Nnr0aJw8eZLrMIgKUK75gfLMHwkxCYAV9czygTKv64YM61U3dengEgqFcidfSk1NhZWVVaXtDMPIfC8prMuvxCLx+vVruecAgOfPn8PV1bX6htSCsbEx2rRpg2fPnjX4XHVBxWw1TExM0LVrVwQHB0NLS0t6v6yEu7s7Nm3aBEB2fdl+/fph9uzZ8PHxwd27dzFgwAAYGhoiKSkJ165dQ4cOHWQK1fKGDRuGfv36YcmSJcjOzka3bt1w48YN/PbbbwAALa36zdnVoUMHhIaG4uTJk7C3t4exsTFat25dr3MR0hD0Ry9/UK75gfLMH30690FoQij1zPIAXdeKUZcOrmbNmuHRo0cy2549e4anT59WWYiW16dPH+jr62P//v2YOHGidHtCQgIuX76MCRMmyD1OEYUsIC66w8PDK9VLykazGdfA09MTLMuiS5cuMDExkXnM3d0dLMtCV1cXffv2lXlsx44d+OWXX3DlyhV89NFHGDlyJL799lvk5eWhZ8+eVT6flpYWTp48iY8++ghr167F2LFjcfXqVezfvx8AYGZmVq92bN68Ga6urvjoo4/Qo0cPzJkzp17nKW/+/PkQCAQwMTHB0KFDce3atQafk2i+irP8Ec1FueYHyjN/PH30FADNZswHdF0rRvkOrq+++gqnTp1CSEgIgoKCMG/ePGzfvl2678cff4zHjx9j3rx5uHTpEvbs2YMxY8ZIl7ypiZmZGVasWIETJ05g+vTpOHv2LPbv3w9PT08IhUKsXLlS7nHx8fF1alNWVhZ69uyJTZs24fTp07h8+TICAgLQv39/FBUVVfk8ykI9szX48ccfq5xlbOzYsdLlb+Tx8fGpcXrqwMDAStvMzc2xZ88e7NmzR7pNMitZ9+7dpdv8/Pzg5+dX6Xhvb+9KN3936tRJYcWmqakpFixYAA8PD1haWiI6Ohrr16+Hh4cHTp8+jaFDh1Z5bHJyMlJSUmS2SdbFIvwwf/58rkMgKkK55gfKM390a98Np5JOIauIhhlrOrquFWfHjh3o3bs3duzYgW3btqGsrAwODg7o16+fTAfXlClT8Pr1awQEBGDv3r1wc3PD9u3b4e/vX+vnWrZsGWxsbLBlyxb8+eef0NfXh4eHB9asWVNlD2xti2UJoVCITp064ddff0V8fDwKCwthZ2cHDw8PHDlyRPXLf7Kk0QkKCmLXr1/Pnjt3jv3rr7/YVatWscbGxuyAAQO4Dq1KGRkZbJMmTdiOHTtWu9/KlStZAHL/BQQEsKGhoey6devYtLQ0dvr06SzLsuyoUaNYlmXZhQsXss+fP2d3797NHj16lL116xa7atUqNi8vj504caLMvsuWLWMfPXrEHjhwgD1w4AD76NEjdtmyZTL7TJw4kc3Ly2NXrVrF3rp1iz169Ci7e/du9vnz5+zChQtl9p0+fTqblpbGrlu3jg0NDWXPnj3L/vLLL2xCQgI7Z84cmX3nzJnDJiQksL/88gt79uxZalOFNu3evVvj2qSJeVJEm7y9vTWuTZqYp4a2affu3RrXJk3MkyLaNODrAaxboBvrFujGFouKNaJNmpgnRbTJy8urQW06dOgQGxMTwz579oxlWZaNi4tji4qK2Ldv37KZmZlsdnY2m5SUxJaUlLCxsbEsy7LSfV+9esUWFBSwKSkpbHp6Opubm8smJiaypaWlbHR0tMy+8fHxbF5eHpuamsqmpqayeXl5bHx8vMw+0dHRbGlpKZuYmMjm5uay6enpbEpKCltQUMC+evVKZt/Y2Fi2pKSETUpKYrOzs9nMzEz27du3bFFRERsXFyezr6a0KSUlRaVtevz4MXv//v0qf/Y+++wzFgAbERHB1gbDstV0LRJOnDp1Cn5+foiOjkZeXh7s7e3h5eWF7777rtJQ58bk008/RUBAAPLz86Gvry93n6p6Zr28vBAREaHSqbwJN44dO4Zx48ZxHQZRAco1P1Ce+ePrP7/GmcIzAIDQSaGw1Les4Qiirhp6XcfGxgJo2Gy9RDUyMjJgbm6usuer6WcjMjISbm5uta4LaJhxIzRq1CiMGjWK6zDqTPK5SMXZ1cqzsbGBjY2NqkIijZCjoyPXIRAVoVzzA+WZPxwtHIHX4q+zi7OpmNVgdF3zh66uLtchNAhNAEUUIiMjA6dOnULnzp0hFAq5Doc0YufPn+c6BKIilGt+oDzzR9Q/UdKv6b5ZzUbXNX9kZan3tUw9s6TOpkyZgqZNm6J79+6wsrLC8+fPsWHDBrx9+1buhFaElLdkyRKuQyAqQrnmB8ozf8z4aAauXr4KgGY01nR0XfOHra0t1yE0CK96Zq9fvw4/Pz9kZmZyHUqDxMTEQE9PDzdu3GjQeTIyMmBmZobg4OA6HdexY0ecP38es2bNwuDBg/HNN9+gXbt2uH79OgYPHtygmIjmqzjTNtFclGt+oDzzx5pv10i/pp5ZzUbXNX/ExcWp9PkUPV0TryaA+umnn7B06VK8ePECzZo14zqcehs3bhxKSkpw6tSpBp/L398f+/fvR2RkJGdj5ut6ozchhBBCVC+tIA0ehzwAAP/p+R9MbTuV24BIo/XixQuIRCK4uLhwHQppZKKjo6GtrY3mzZvLfbyudQGvembrqqCggOsQKnny5AmCg4Px+eefK+R8c+fORVxcHA4fPqyQ8xFSk9GjR3MdAlERyjU/UJ75Y8aHM6RfZxfRMGNN1tDrWk9PDyUlJSguLlZQRERZnj9/rrLnKi4uRklJCfT09BR2Tt4Us35+fli6dCkAoHnz5mAYBgzDIDQ0FADQrFkzjBo1CkePHkWXLl0gFAqlixRv3boVAwYMgI2NDQwNDdGhQwesW7cOJSUllZ7n3LlzGDRoEExNTWFgYIC2bdvihx9+kNnn7t27GDNmDCwsLCAUCtGlSxccOnSoVu3Yvn077OzsMGTIEOm2rVu3QktLC8nJydJtGzZsAMMwMotel5WVwdzcXOY+CFtbWwwZMgQBAQG1en5CGurkyZNch0BUhHLND5Rn/jh14hQMBAYA6J5ZTdfQ61qylGRSUhIVtI2cq6urSp6nuLgYSUlJAKDQpUZ5MwHUrFmzkJ6ejv/7v//D0aNHYW9vDwBo166ddJ/79+/jyZMn+O9//4vmzZvD0NAQgPge1SlTpqB58+bQ1dXFw4cP8f333yMqKgp79uyRHr9792588skncHd3R0BAAGxsbPDs2TNERERI9wkJCcGwYcPQq1cvBAQEwNTUFH/88Qc+/PBD5Ofn13iPwunTpzFgwABoab37HGLw4MFgWRaXLl3C5MmTAQAXL16Evr4+Lly4IN3v7t27yMzMrHRfq4eHB5YtW4bMzEyYmZnV7YUlpI6WL1+ONWvW1LwjUXuUa36gPPPH8uXLYdrVFPml+VTMariGXteGhoawsLBAeno6YmJioKOjI+1IIo1Lfn4+DAwMlHZ+lmXBsqy0E9DCwkJaYykCb4rZJk2aoGnTpgCALl26yL1nNjk5GY8fP0arVq1ktv/888/Sr8vKytC/f39YWlrCx8cHGzZsgLm5OXJzc7F48WL069cPly9fll6sgwYNkjnXvHnz0L59e1y+fBkCgfjlHzp0KFJTU7F8+XJMnz5dplCtGF9sbCxmz54ts71169Zo0qQJLl68iMmTJ6O4uBhXr17FF198gR9//BGvXr1C06ZNcfHiRejo6GDAgAEyx3ft2hVlZWW4efMmhg0bVotXk5D6k3zgQjQf5ZofKM/8MXnyZDx78QxJeUk0AZSGa+h1zTCMdERjdnY2ioqKFD7xD1EMeSNNFYlhGGhpacHAwAAmJiYwNDRU6IcavClma6Njx46VClkAePDgAVauXIm///4b6enpMo89e/YMvXr1wvXr15GdnY158+ZVmaDo6GhERUXhp59+AgCUlpZKHxsxYgROnTqFp0+fom3btnKPf/1avFK5jY1NpccGDRqEixcvAhDP2pyfn4/Fixdj9+7duHDhAnx9fXHx4kX06dOn0qchkvMlJibKfV5CFCk8PBwdOnTgOgyiApRrfqA880d4eDhMLU0B0DBjTaeI65phGBgZGcHIyEhBURFlCAoKwpQpU7gOo954c89sbUiGHpf36tUr9O/fH4mJidi8eTOuXr2KO3fuYOvWrQDeTRKVkpICQNwDXJW3b98CAL788kvo6OjI/Js3bx4AIDU1tcrjJc8lFAorPTZ48GC8evUKz58/x8WLF9GlSxfY2Nhg4MCBuHjxIgoKCqpcOkdyvsY44RUhhBBCGg8TXfG9btQzSwhpDKhnthx5ParBwcHIy8vD0aNH4ezsLN3+zz//yOxnbW0NAEhISKjy/FZWVgCAZcuW4YMPPpC7T+vWrWs8vmLvMPBuOPPFixdx4cIF6QRRgwYNwn//+19cuXIFRUVFcotZyfkk5ydEmagHhz8o1/xAeeaPDh064GnOUwDUM6vp6LrmD3XPNa96ZiXTQNelB1JS4JafQpplWezcuVNmv759+8LU1BQBAQFV3hPQunVruLq64uHDh+jevbvcf8bGxlXG4uzsDH19fcTExFR6zN7eHu3atcORI0dw7949aTE7ZMgQpKSk4Oeff4aJiQl69OhR6djY2FgAspNhEaIsBw8e5DoEoiKUa36gPPPHwYMHYaorHmacVZRF90BqMLqu+UPdc82rYlbyycPmzZtx48YN3L17Fzk5OdUeM2TIEOjq6mLy5Mk4e/Ysjh07hqFDhyIjI0NmPyMjI2zYsAFXrlzB4MGD8ccffyAkJAQ7d+7EZ599Jt1vx44duHTpEoYOHYqDBw/iypUrCA4Oxg8//ICJEydWG4uuri769OmDmzdvyn180KBBuHTpEnR1ddGvXz8A4mWImjdvjr/++gseHh7SSafKu3nzJiwtLdX+kxmiHmjWU/6gXPMD5Zk/1qxZA3OhOQCgpKwEmUWZ3AZElIaua/5Q91zzqpiVLEFz8uRJvPfee+jRowfu3btX7TFt2rTBkSNHkJGRgQ8++ACff/45OnfujC1btlTa19fXF2fOnIFIJMKsWbMwatQobNq0STqLMgB4enri9u3bMDMzw8KFCzF48GB8+umnuHjxotwhwBVNnToVt2/flq7TVJ7k+Pfee0/mvlrJdnnnZ1kWJ06cwJQpU2i6dKISDV2InagPyjU/UJ75Y/To0XAxc5F+H5UexWE0RJnouuYPdc81w9IYEbVSWFiIpk2bYsmSJfj6668bfL5Lly7h/fffR2RkJNq0aaOACOsuMjISbm5uiIiIQPv27TmJgRBCCCE1Sy1IhechTwDA4m6L4ePmw3FEhBBNUte6gFc9s5pAKBTC398fP//8M/Ly8hp8vu+++w4zZ87krJAl/DNp0iSuQyAqQrnmB8ozf0yaNAlW+law1hdPevkk/QnHERFloeuaP9Q91zSbsRqaPXs2MjMzERsb26D7XDMyMuDu7i5dFogQVQgMDOQ6BKIilGt+oDzzhyTXbSzaICUxBU/SqJjVVHRd84e655p6ZtWQtrY2li1b1uAJm8zNzeHn5wcbGxsFRUZIzTZs2MB1CERFKNf8QHnmD0mu21iIR3O9zH6J/JJ8LkMiSkLXNX+oe66pmCWEqNTQoUO5DoGoCOWaHyjP/CHJdTtL8VJ+LFg8y3jGZUhESei65g91zzUVs4QQlUpMTOQ6BKIilGt+oDzzhyTXkp5ZAHic9pircIgS0XXNH+qeaypmCSEqVXGNZqK5KNf8QHnmD0muHY0cYaxrDICW59FUdF3zh7rnmopZ0mC7du0CwzAwMjLiOhSiBgYMGMB1CERFKNf8QHnmD0muGYaR9s4+SnkEWuVR89B1zR/qnmsqZkmDJCYm4ssvv4SDgwPXoRA1sXXrVq5DICpCueYHyjN/lM91T7ueAICYrBjcenOLq5CIktB1zR/qnmuGpY/TSAOMHj0aDMPAwsIChw8fRm5ubp3PUdfFkYl6Y1kWDMNwHYZK8bHNAH/bTYjGYlng32s6ozADQ48MRUFpAXrZ9cKuobs4Dk6JyrWbN/jYZtIo1LUuoJ5ZUm/79+9HWFgYtm3bxnUoRE1cjL0IQVsBLsZe5DoUlbkYexH63+vzqs0AP3PNV6NHj+Y6BKIKFy9itEAAXBRf0+ZCc0xoNQEAcOvNLTxMechldMpz8SKgry9tNy9UyDXRbOr+Hk7FLKmX5ORkLFy4EGvXrkWTJk24DoeoAZZl8Z+L/0HZ5DIsu7iMF/dYSdpcJCriTZsBfuaaz06ePMl1CETZWBb4z39wsqwMWLZM/D2AGe1mQKAlAAAsu7oMsVmxXEapeP+2G0VFMu3WaFXkmmgudX8Pp2KW1Mu8efPQunVrfPrpp3U6Ljk5GZGRkTL/oqOjlRQlaUxOPz+Ne0n3gGPA3aS7OPP8DNchKZ20zeBPmwF+5prPZsyYwXUIRNlOnwbu3cMMALh7FzgjvqZtDW0xvd10AEB8TjymnZ6Gn+/+jAfJD5BZmKn+H2T9224AMu3WaFXkmmgudX8PF3AdAFE/R44cwcmTJ/HgwYM63w+3bds2+Pv7y33s2rVrSE1Nxe3bt+Hr64tFixZh3759GD16NE6ePIlFixZh/vz5uHLlCszNzeHo6Ijz589jyZIl8Pb2xqFDh6T7Ll++HJMnT0Z4eDgAoEOHDjh48CDWrFkj3WfSpEkIDAzEhg0bMHToUCQmJiIjIwMDBgzA1q1bsXHjRum+M2bMwMaNG7F792707NkTBQUFiImJgZeXF1avXo2AgADpvnPnzsWKFSsQHByMli1bQl9fn/dtOnfuHIItgoFDAEYBCAL87P1wbe81TJkyRS3bVFOeHj16hBUhKwBdAOEAM5jB1AlTkfEwAx9++KFatqk2efrpp59wsOAg8AKAPYA0YOa8mXhz4Q3GjBmjlm3SxDwpsk379u3TuDZpYp7q3aawMJivWQNHAI4A8gF4f/wxDqWlYfSYMThx4gT+Dv0bT62eIqckB3sj92Jv5F4AgIARQFQsgqWJJbLSsmBrY4vMjEwYGhqitLQUZaIy6OvrIzMzEzY2NkhISICTkxNev34NO1s7ZGZmQl9fH2wZi+LiYpiYmCAlJQUODg54+fIlnJ2d8fr1a1hbWyM7Oxu6urrQ0tJCQUEBzM3NkZSUhCZNmkj3TUpKgoWlBfLz8qGtrQ2BjgC5ObmwsrJCYmIinJycpPu+ffsWptnZKPzOBQAgBJAV8SVsS3fi5ctXcHZ2Rnx8PBwdHZGamgojYyOUlpRCJBLBwNAA6WnpsLe3l54vISEB9vb2yMjIgL6+PsrKyrhpk6kpCgsLxW0SCpGVlQVbW9t/92mK+CdRcPzOBSkAhgIoffwVRCnr1bhNmpgnxbbJdqwtBs4ZiMs7LjeK972AgIA61RY0ARSpk9zcXLi4uGDatGn473//K90+b948nDhxAgkJCdDR0YGhoaHc45OTk5GSkiKzLTo6Gl5eXjQBlAY79ewURh/8956MvwH0+3f75FMY2WokZ3Epk0yby2/X4DYD/Mw1361fvx5Lly7lOgyiLKdOAf/eU7cewNLy20e+u6ZDXoVgb+RePEh+oPIQCSENs6rvKoxzHcd1GADqPgEUFbOkTuLi4tC8efNq9xk7diyCg4NrfU6azVizsSyLHjt74H7SfbBggTgAzQAGDLrZd8PtT25r3Iy3ldr8L01uM8DPXBMgLCwM7u7uXIdBlIFlgR49gPv3AZZFGAB3QDzLbbduwO3blWa8fZP3BpGpkYjPiUdWcRYKSwtRKCpEYWkhytiyd++J0v+9e4+UfC3507T8YyrFssDly0BGRuXHzM2BgQM1b6bfCm1OAWAteUxT20wAACkpKfjsvc/Qy74X16EAqHtdQMOMSZ3Y2dkhJCSk0va1a9ciLCwMZ8+ehZWVVZ3OWVRUBAB076yGCo0Lxb2H995tSAFgIP4j5W7yXWw/sx3uzTTrD+FKbf6XJrcZ4GeuCRAVFVXn932iJkJD390zCiAKgBUgLnzu3gW2bwfkfJBhBzvYMXaAHsT/1E1oKPDdpioejAfs58htt1qr0OZrAN6TfqehbSYAgGtPr8Eo3QiR6ZFchwLgXT0gqQ9qQj2zRCG8vb3rvc7svn374O3trfigCCGEEEIIIWonODgYY8eOrXE/6pklnGvVqhUA4NChQ2jXrh3H0RBlktwfHRwcDBcXF67DIUpEueYHyjN/UK75g3LNH40x10VFRYiPj6/17StUzBKFCAwMRGBgYL2ONTExAQC0a9eO7pnlCRcXF8o1T1Cu+YHyzB+Ua/6gXPNHY8t1165da70vrTNLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLCCGEEEIIIUTtUDFLOGdtbY2VK1fC2tq65p2JWqNc8wflmh8oz/xBueYPyjV/aEKuaWkeQgghhBBCCCFqh3pmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHSpmCSGEEEIIIYSoHQHXARCSmZmJsLAwODk5QU9Pj+twCCGEEEIIIRwoKipCfHw83N3dYWZmVuP+VMwSzoWFhcHLy4vrMAghhBBCCCGNQHBwMMaOHVvjflTMEs45OTkBEP/Quri4cBwNUaak3CR8s/4b7Fy5E9pa2lyHQ5Rs/vz52Lp1K9dhECWjPPMH5Zo/KNf80dhyHR0dDS8vL2l9UBOGZVlWyTERUq3IyEi4ubkhIiIC7du35zocoiSiMhGGHhmKt/lv8UP/HzCqxSiuQyKEEEIIIY1IXesCmgCKEKISOcU5eJv/FgDw4O0DjqMhqjBjxgyuQyAqQHnmD8o1f1Cu+UPdc03FLCFEJbKLs6Vfv8x5yWEkRFU2btzIdQhEBSjP/EG55g/KNX+oe66pmCWEqIRMMZtNxSwf7N69m+sQiApQnvmDcs0flGv+UPdcUzFLCFGJ7KJ3xeybvDcoLC3kMBqiCi5dXVAsKuY6DKJkPXv25DoEoiKUa/6gXPOHuueaZjMmhKhE+Z5ZAHiV8wqtzFtxFA1RtrD4MHyb8C0OnT2EoJFBYBiG65CIkhQUFHAdAlERyjV/KCLXLMsiLy8P2dnZKCoqAs052zgJBALExsYq9TkYhoGenh5MTExgaGio0L8JqGeWp3Jzc7Fw4UI4ODhAKBSic+fO+OOPP2o8LiEhAQsXLpQuZMwwDAIDA5UfMFF7lYrZ7FccRUJU4fab2wCAiLQIpBSkcBwNUaaYmBiuQyAqQrnmj4bmmmVZJCcnIz4+HllZWRCJRAqKjCiaubm50p9DJBIhKysL8fHxSE5OVugHG9Qzy1MffPAB7ty5g7Vr16JVq1YICgrC5MmTUVZWhilTplR5XHR0NA4cOIDOnTtjxIgROHjwoAqjJuqsYjEblx3HTSBEJTIKM6RfP8t4BhsDGw6jIcrk5eXFdQhERSjX/NHQXOfl5SE9PR0GBgawt7eHrq6uYgIjCldcXKyS/BQXFyMpKQnp6ekwNDSEkZGRQs5LPbM8dObMGVy4cAHbtm3DnDlz4OnpiZ07d2LIkCFYunRptZ+eDRgwACkpKbhw4QIWL16swqiJuit/zyzAv57ZlPwU5BTncB2GyqQXpUu/fp7xnMNIiLKtXr2a6xCIilCu+aOhuc7OFv/Op0K28UtKSlLJ8+jq6sLe3h7Au58PRaBiloeOHTsGIyMjTJw4UWa7j48PXr9+jVu3blV5rJYW/ciQ+qnYM8unGY1js2Lx/uH3MfrYaOQW53IdjkpU7JklmisgIIDrEIiKUK75o6G5Lioqgo6ODhWyasDZ2Vllz6WrqwsdHR0UFRUp7JxUmfBQREQE2rZtC4FAdpR5x44dpY8Tomh8Lmbvvb2HUrYUaYVpuJ98n+twVCKzMFP6NRWzmm306NFch0BUhHLNHw3NNcuy1AGiJp4/V+3oKYZh6J5Z0jBpaWlo0aJFpe0WFhbSx5UlOTkZKSmyk8FER0cr7flI41GxmE0rTENucS6MdBVzz0RjlpL/7mf+UcojDGgygMNoVCOj6F3PbGxWLEpEJdDR1uEwIqIsJ0+e5DoEoiKUa/6gXPOHq6urSp9P0asb1Osjk8DAQDAMI/0nFAphZ2cHT09P/PDDD0hOTlZokFwLDQ0FwzAIDQ3lOhSFqe4HSZlLaGzbtg1ubm4y/ySTDFy7dg1hYWFYv3490tPTMWPGDADvPh1ctGgRoqOjsWfPHhw7dgy3b9/G6tWrkZ+fj0mTJsnsu3z5coSHhyMoKAhBQUEIDw/H8uXLZfaZNGkS8vPzsXr1aty+fRvHjh3Dnj17EB0djUWLFsnsO2PGDKSnp2P9+vUICwvDuXPnsHXrViQmJmLu3Lky+86dOxeJiYnYunUrzp07R236t02ZBZniH4JyH8Z99cNXat2m2ubpdNhpaZvDU8M1ok3V5enqzasoKH23rENpWSleZL9Q6zZpYp4U1aa5c+dqXJs0MU+KaFOPHj00rk2amCdFtKlHjx4NatO9e/cAvOv1e/nyJYqLi5GcnIysrCzk5OTgzZs3KC0txYsXL2T2jY+PR2FhIVJTU5GRkYG8vDy8fv0aIpFIOsuyZN+EhATk5+cjLS0NaWlpyM/PR0JCgsw+MTExEIlEeP36NfLy8pCRkYHU1FQUFhYiPj5eZt8XL16gtLQUb968QU5ODrKyspCcnIzi4mK8fPlSI9v08uVLlbapqKgIWVlZVf7s1XWIO8PWo583MDAQPj4+2Lt3L9q0aYOSkhIkJyfj2rVr2Lt3L7S1tfHnn39i8ODBdT11o5SdnY3Hjx+jXbt2MDEx4TqcBuvTpw9EIhFu374tsz0yMhJubm7YsWMHZs+eXeN57t69ix49emDv3r3w9vau1XNX1TPr5eWFiIgItG/fvtbtIOpl2JFhSMxNRAujFojNFa9n5tfHD+Nbjec4MuX7/NLnCE0IBQAY6xjj2uRr0GI0d/jV69zXGHpkqMy2tf3XYmSLkRxFRJQpMTERjo6OXIdBVIByzR8NzbVk3VJ5IwFJ41Kf2Yy3bNmCoKAgREdHIycnB7a2tujbty9WrFhR49/yNf1sSOqR2tYFDfprys3NDb1790b//v0xfvx4bNy4EY8ePYKhoSE++OADvH37tiGnbzRMTEzQu3dvjShkAaBDhw548uQJSktLZbaHh4cDEOdVWWxsbNC+fXuZfy4uLkp7PtJ4SIYZ66frw1TPFABwK6nqycY0Sfl1VnNKchCXFcddMCpQfvInCbpvVnMFBwdzHQJREco1f1Cu+SMzM7POx6SlpWH48OHYtWsX/vrrL/j7++PBgwfo1asXnj59qvggq6HwroGmTZtiw4YNyMnJwY4dO2QeO3HiBPr06QMDAwMYGxtjyJAhuHHjhsw+0dHR8PHxgaurKwwMDODo6IjRo0dLCy0JydDf/fv3Y/HixbCzs4O+vj7c3d3x4MEDmX29vb1hZGSEqKgoDB06FIaGhrC3t8fatWsBADdv3sR7770HQ0NDtGrVCvv27ZP7XOWHGUvOGR0djREjRsDIyAhOTk5YsmRJpRm6iouL8d1336FNmzbQ09ODtbU1fHx8KvVQqsq4ceOQm5uLI0eOyGzft28fHBwc0KtXL07iIpqrjC2TzuJrb26PnnY9AQC33txCGVvGZWgqUf6eWQB4lPqIo0hUo/z9sgzEty1QMau5WrZsyXUIREUo1/xBuVYvJSUllTqpaktPT6/Ox/j7+2PlypXw8vKCu7s7fHx8EBwcjLy8PBw4cKBecdSXUsa5jRgxAtra2rhy5Yp0W1BQEMaOHQsTExMcPHgQu3fvRkZGBjw8PHDt2jXpfq9fv4alpSXWrl0rHXsvEAiqrPSXL1+O2NhY7Nq1C7t27cLr16/h4eEh7cKWKCkpwQcffICRI0fi+PHjGD58OJYtW4bly5djxowZmDlzJo4dO4bWrVvD29tbOta/OiUlJRgzZgwGDRqE48ePY+bMmdi4cSN+/PFH6T5lZWUYO3Ys1q5diylTpuD06dNYu3YtLly4AA8PDxQUFFTzDMoxfPhwDBkyBJ9++il27tyJkJAQzJ49G+fOncO6deugra0NAPD19YVAIJDeIyBx+PBhHD58GJcvXwYgHm4s2UaIPDnFOWD/vVnWRNcEve17AwDSC9M1fg1SUZkIaYWyk6o9StHwYrZcz2x7S/EQoX+S/0GJqISrkIgS6evrcx0CURHKNX9Qrqvn5+cHhmEQGRmJyZMnw9TUFLa2tpg5cyaysrJk9mVZFtu2bUPnzp2hr68Pc3NzTJgwoVKt0qxZM7m37Xl4eMDDw0P6vaST7ffff8eSJUvg6OgIPT096YSqe/bsQadOnSAUCmFhYYFx48bhyZMnMucs3yk3ceLEajvlasva2hoAKq2WonRsPezdu5cFwN65c6fKfWxtbdm2bduyLMuyIpGIdXBwYDt06MCKRCLpPjk5OayNjQ3bt2/fKs9TWlrKFhcXs66uruyiRYuk20NCQlgAbNeuXdmysjLp9ri4OFZHR4edNWuWdNuMGTNYAOyRI0ek20pKSlhra2sWAHv//n3p9rS0NFZbW5tdvHhxpecKCQmpdM5Dhw7JxDtixAi2devW0u8PHjxY6blZlmXv3LnDAmC3bdtWZduVKScnh/3iiy9YOzs7VldXl+3YsSN78OBBmX0kbXzx4oXMdoin8JH7rz4iIiJYAGxERER9m0MauVfZr1i3QDfWLdCNnb1lNvsq6933gRGBXIenVCn5KdK2Sv5NODGB67CUKjAiUNrWP578If36SvwVrkMjSrBu3TquQyAqQrnmj4bmOiYmho2JiVFQNI3PypUrWQBs69at2W+//Za9cOEC+/PPP7N6enqsj4+PzL6ffPIJq6Ojwy5ZsoQ9d+4cGxQUxLZp04a1tbVl37x5I93P2dmZnTFjRqXncnd3Z93d3aXfS+oSR0dHdsKECeyJEyfYU6dOsWlpaeyaNWtYAOzkyZPZ06dPs7/99hvbokUL1tTUlH327Jn0HDNmzGB1dXXZtm3bsitXrmQvXrzIfvvttyzDMKy/v3+tX4fS0lK2sLCQffLkCTt27FjWxsaGffXqVbXH1PSzUde6QGmlM1tuXqmnT5/i9evXWLhwocyaU0ZGRhg/fjx27NiB/Px8GBgYoLS0FOvWrcP+/fsRHR2NkpJ3n+RX/FQBAKZMmSIz+66zszP69u2LkJAQmf0YhsGIESOk3wsEAri4uEAgEKBLly7S7RYWFrCxsanUGykPwzCV1uHq2LGjtMcSAE6dOgUzMzOMHj1apvu/c+fOsLOzQ2hoKD799NMan0vRjIyMsHnzZmzevLnKfQIDAxEYGFhpO6vAtaEIP5RflmfkoJFoYtwEjkaOSMxNxI2kG5jRfgaH0SlX+SHGzibOeJn9Es8ynuFt3lvYGtpyGJnySHpmtRltjGgxAj/e+RElZSW4+Ooi+jfpz3F0RNF8fX25DoGoCOWaP5SZ6x9v/4io9Cilnb8u2li0wdc9v6738b6+vli6dCkAYPDgwdKZpXfv3g2GYXDz5k3s3LkTGzZswOLFi6XH9e/fH61atcLPP/8sM6KzLlq2bIn//e9/0u8zMzOxevVqjBgxAkFBQdLtHh4ecHV1hZ+fn8wQ4OLiYvj7+2PcuHEQCAQYNGgQ7t69i6CgIHz77be1isHQ0FDak9uqVSuEhobCycmpXu2pL6UUs3l5eUhLS0OHDh0AvFu31N7evtK+Dg4OKCsrQ0ZGBgwMDLB48WJs3boVX3/9Ndzd3WFubg4tLS3MmjVL7pBcOzs7udsePnwos83AwABCoVBmm66urnRt1YrbCwsLa2ynvHPq6enJHPv27VtkZmZWOUtYampqjc9DiLrLLnpXzAbuCMSYzWPQ2743jjw/gvtv76NYVAxd7brNpKcuyk/+NLnNZKy9vRZlbBmOxxzH7I41zxqujiT3zDKFDIx1jdHXoS/CEsJw+dVlrOi9AgItWuJckyxatKjSXBNEM1Gu+UOZuY5Kj8Ldt3eVcm5VGzNmjMz3HTt2RGFhIZKTk2Fra4tTp06BYRhMmzZNplPLzs4OnTp1atCyn+PHy64GcePGDRQUFFQaquzk5ISBAwfi0qVLMtslnXLx8fFo3ry5NP7ynXI1uX79OoqLixETE4ONGzfC09MTly5dUunqJEr5i+L06dMQiUTS8d2WlpYAgKSkpEr7vn79GlpaWjA3NwcA7N+/H9OnT8eaNWtk9ktNTYWZmVml49+8eSN3m+Q5uWZlZQVLS0ucO3dO7uPGxsYqjogQ1SvfM7vWTzzxWm8HcTFbUFqAMy/OwMvFi6PolCu14N0HVu5N3HHs+TE8zXiKo8+PYlaHWRq5RE96YToAoLmd+JfjYOfBCEsIQ2ZRJu6/vY+e9j25DI8oGBU3/EG55g9l5rqNRRulnbuuGhpLxXpDMpmSpAPu7du3YFkWtrbyR2I1ZOmiip2ENXUeXrhwQWabpFNOUshK4q9Nh55E165dAQC9e/fGmDFj4OLiguXLl+P48eO1PkdDKbyYffXqFb788kuYmppizpw5AIDWrVvD0dERQUFB+PLLL6XDgvPy8nDkyBHpDMeA+FOCirNqnT59GomJiXKXcDl48CAWL14sPefLly9x/fp1TJ8+XdFNq5dRo0bhjz/+gEgkolmCCW+VL2YXzFmAs4fOYqDTQNjo2yC5IBm7wndhdIvR0NbS5jBK5UjOT5Z+bW1gjQ9cP8APt39AYm4ibiXdQh+HPhxGpxyZhZkAgPin4oXbPZ08IWAEKGVLcTL2JBWzGmb06NE4efIk12EQFaBc84cyc92QYb3qxsrKCgzD4OrVq3JnDS6/TSgUyp18KTU1FVZWVpW2l7/NEqi581DeOQDg+fPncHV1rb4htWBsbIw2bdrg2TPVrl7QoC6BiIgI3Lx5E9euXcPRo0exaNEidOzYEXl5eTh27Jh0VistLS2sW7cO//zzD0aNGoUTJ07gf//7Hzw9PZGZmSldIgcQF3+BgYHYtGkTLl++jPXr18PHxwdNmjSRG0NycjLGjRuH06dPIygoCIMHD4ZQKMSyZcsa0jSF+eijjzB8+HCMGDECq1atwrlz53Dp0iXs27cP3t7eOHbsGNchEqJ05YcZHzkgXhJKV1sXPm4+AICX2S9xPu48J7Epm6Rn1kTXBHraehjZYiT0tMW/vIKigqo7VG1Jhhm793QHAJjqmeK9Ju8BAI5HH0dEagRnsRHFo+KGPyjX/EG5VoxRo0aBZVkkJiaie/fulf5JbskExLMZP3oku9rBs2fPar1ua58+faCvr4/9+/fLbE9ISMDly5cxaNAguccpopAFxEV3eHi43M5HZWpQMevj44M+ffpg0KBB+PTTT/HgwQN8/fXXiIqKgqenp8y+U6ZMQXBwMNLS0vDhhx/Cx8cHJiYmCAkJwXvvvSfdb/PmzZg2bRp++OEHjB49GidOnMDRo0erXO9qzZo1cHZ2ho+PD2bOnAl7e3uEhIQ0mvWxtLW1ceLECSxfvhxHjx7FuHHj4OXlhbVr10IoFMr8EKtSbm4uFi5cCAcHBwiFQnTu3Bl//PFHrY5NTk6Gt7c3rKysYGBggD59+lQah09IeZKeWYGWAMu/Wi7dPr7VeFgIxfetb3+4XboWrSaRTABlrS/+cM9UzxRDmw0FAITGh+JEzAmuQlMayTDjyDuR0m2Luy2GQEsAFixW31wNUZmIq/CIgi1atIjrEIiKUK75g3KtGP369cPs2bPh4+ODr776CqdOnUJISAiCgoIwb948bN++Xbrvxx9/jMePH2PevHm4dOkS9uzZgzFjxkg7B2tiZmaGFStW4MSJE5g+fTrOnj2L/fv3w9PTE0KhECtXrpR7XHx8fJ3alJWVhZ49e2LTpk04ffo0Ll++jICAAPTv3x9FRUVVPo/S1Hru5UZGMi31//73P65DUUtDhgxhzczM2ICAAPby5cvsrFmzWADsgQMHqj2usLCQdXNzY5s0acLu37+f/euvv9ixY8eyAoGADQ0NrVcstDSP5vO77se6BbqxA/4YwD5//lzmsfLLuPie92WLS4s5ilI5Jp+aLG2bRHJeMjvgjwGsW6Ab22N/DzY6I5rDCBWrWFQszed3l76TeWzzvc3Sx9bcXMOKykRVnIWok4rXNNFclGv+aGiu+bI0T0pKisz2/2/vvuOiutI+gP/u9EYvKhJbFBQQiYmi7mKJYFuNZcXEmBU1amJM8mpcC2Q3aowxa6JZN2tbo29MFGKwZY2sCiImVmzxBdYSwBKx0KWMlJk57x/jXBkHEA0z15l5vn74AOeeezmH53KdZ86955iWL314WctNmzax8PBwplarmVKpZM8++yybOHEiO336NF/HYDCw5cuXsw4dOjCFQsFeeOEFlpqa2uDSPA3lQF9++SULDQ1lMpmMubm5sZEjR7KsrCyzOjExMUytVjPGGLt3755FvxpTVVXFpk6dyrp06cI0Gg2TSCTM39+fvfbaaxY/pz7NvTQPx5h9rrOSlpaGAQMGIDExEWPHjhW6OXYlKSkJf/jDHxAfH4/x48fz5YMGDUJWVhauX78Osbj+ZxfXrFmDmTNn4tixY+jd2/isn06nQ7du3aDRaHDy5MnHbk9WVhZCQkKQmZlp09nPiO3MSZuDA9cOoJ1rO4wuGY0pU6bw23QGHWanzUbar2kAgPBW4fig1wdo49pGmMY2s6jtUbhdeRvDOwzHsohlfPmxvGN4I8U4r4BGqsEHvT/AkHZDLJ6BsTcF2gK8mPgiAOBFw4tYNfnB8l9Vuir88d9/xPXy6wCAwe0GIy48jh+dJ/Zp06ZNZn/TxHFRrJ3Hb411bm4ugN82wRGxjYaeybWWR50bj5sXON40muSRdu3aBY1Gg+joaLPyyZMn4+bNm40mpLt27UJgYCCfyALGNXtfe+01pKenIy8vz2rtJvbLdJuxq9yVn7ncRCKSYHnf5Qj1CQUAnLx1EqO/H435P85HyrUU3K68bbdrGzPG+GdmfVTmtwn1ad0H/9P9fwAAFbUVmPfjPIz7YRy2XtiKS8WXUGuotTiePTA9LwsA3mrz/xwVEgW+HPQlOrgZ/wPbf3U/huwYgo9OfITDvx5GSVWJ3cbamT38N00cF8XaeVCsnUdDA1j2wm4X++vfvz+96HlCmZmZ6NKlCyQS8/CHhoby2/v06dPgvhERERblpn2zsrLQunXrJ2rXxoyNaFFZ/9TlxL5ll2YDME6C1Nrb8vxQSpRYG7kWK0+vxI5fdqDGUIOkK0lIupIEAFBJVPBQeMBD7gF3hTuUEiXEnBgiTgSJSAIxJ4ZYJObLODTP6OZvHSXVGXTQGYzrypmema1ratep6OjeEX89+leUVpfiYvFFfJJunBCPAwd3uTu8lF7wUnpBKVFCKpJCIpJAKpJCKpJCzIkbbePDv4e6dRvbVt/2prqjvcN/3danrcX2VppW+Hro15h7eC6O3zqOe7p72HZpG7Zd2gYAcJG5wEvhBVeZK1xkLlBJVXyMRZzI4rOtlzay9ch5c53L1nRbfBuX0207eyWxvQ7uHdDG7/HumGGM4VblLRTcK0BpVSlKq0tRVlMGnUEHPdPzn/UGPf/ZAMNj/wxr4jgOkW0i0b1F9ybvU1FTgfx7+Si6V4TCe4UorS5Fjb4GOoMOtYZa1BpqoTPoYGAP+vpwPxhYvV/XV7e5ScVSdPB+vBFVvUGPm5U3caviFspryiEtl8JH5cPPG2Hqg6ntD/fpt2ru4zkTHaeDvFYOlVQldFOeiN0ms+TJFRUV1Tu07+npyW9vbF9TvcfdFzBOHlVQUGBWlp1tTHT25O6BolrReOOJXXOXu2P//v3o2dNyaRZXmSsW9VmE0Z1G4+usr/FT3k+4pzOu06bVaaGt0CKvwn5H/n1VvvWW93+mP3aP3I3tl7dj+y/bcbvSuHY2A0NJdQlKqkv4NwPszf+d+D9ggGW5m9wN66PWI/12OjZmbMSp26egY8akv7ymHOU15TZuKfnNSh5dhdi/fnf6PXKZwZKqEiRfS0bytWRkFmaiotb+J/b7Pvt7pL2cBqlIWu92xhjOF5zH7uzdOHPnDK6WXbVtA63Aq9oLoyJGNVqn6F4RdmXvwrGbx3A+/zxqDDX8treeeQu9ZL3MlqcjTy+NXmO3ySzdZuykGh3NecTIw2/Zd82aNQgJCTH7GDVqFABADjmUnBJSgxQaiQbiWjFcpC7gaji4SF0g0UugEqsgZ3IooIBSdL+uVANRraj+uvf/qcQqSPQSszqiWhE0Ug2kBimUIiUUUEDO6q8rrhVDI7lfl1NCwSkgYzKoJWqIdebtFOvEUEvUkDEZFJyC+nS/T63VrZGxNQNz5szBiBEjAABxcXHIyMhAfHw84uPjkZGRgW2fb8OK/ivgk+iDjYM2okVWC7zc6WW0rWyLEHUI/ER+8GJeaK1sDWW1Eq01rSHWiuGt9IakxthHmUEGlUgFJaeEnMmhkWgg0UngKnOFqEYEV5krJDoJNBIN5Mx43qlEKsgMMv534yJzgahGBBeZCyQ6CdRiNd8nlUgFqUHK/87r1pXqpVCJVVDc/6cSq+Ch9UBE6wi+3+PGjYNWq8WSJUuQnp6OH/f9COkZKVZ3W43eF3pjye+WwO2/bhgbMBZuBW4I8giCR60HWkpbwlvsDY1BA0+ZJ6S1UngqPCGqEsFD7gFJrQRuUjcomAIqTgW1SA25QQ43qRvEtWK4yd0gqrbsf719+o3nXldJV3Rw74Ds7Gx+VkxT/2NiYlBSUoIft/yICfIJWOi9EMPuDcO0TtPQ8nZLDG43GIo7CoR4hUCpVcJP6QcXgwvcRe5wF7tDqVfCXeYOca244Zjifkz1MrhKH9QxfZbqpVCL1Xyc1GI1pHqpWR1RjQiuUlfI9MbzyfT3pBarIdFJzOIurhVDI9VAZpBBKVJCySkhYzLj34jO/HwS64x/ezImg5JTQilSQmaQQSPVmJ97dnKN0Eg1dN1z4D7JmIz/P/wudxdarRbjxo0z+5uOi4vDz//3M2Z/MxuR2yKx5MQSnLh1osmJLMc4yEQycDoOaqkaIp0IKonxmqTgFJBzckiZ8doq1omhlqrB1RrrinVivp0KTgEFp4DUIIVaYjyORqoBV3v/PNWLHytOMs7Y97KaMhw9dxRxcXFm/R43bhyybmeh34Z++NN//oQdv+xoUiLLgYMYYkiYBHKRHCK9CEqJEpyOg1KihEgvgkKsgJRJIefkxv4bpFBJVBDpRHz/n6RPjzr3OL3xdVyZpAzp6enYtWsXNm3aZHYt/8PIP+DzM5/jxYQXsersKpy6fcoskW1K/03/RJwIYDD/DPB3WXHgwHEN1zWV1z3e49Q1/ax660LUeDuZeTtN9a3Rzmar20Cf8u8Y33S4du0aampqkJ+fj7t376K8vBy3b9+GTqfDlStXABjXpQWMsyBXVVWhsLAQJSUlqKysxM2bN6HX65GTk2NW98aNG9BqtSgqKkJ1dTXu3r1b79+TVqvFunXrmnwuAYDgE0AdO3YMBw4cwKxZs+Du7i5kU36TnJwcBAUFIS0tzex50qa4fPkyQkJCcOLECXTv3vTbWJ5U7969odfrkZ6eblZueuB6/fr1mD59er37tmrVChEREfjuu+/Myvfu3Yvhw4dj//79GDRoUIM/u6GR2VGjRtEEUE5i3LhxFucPcUwUa+dAcXZs2lotwuONo7E+F3yQ+kmqRZ1qfTVmpszEydsP5tzwVfoiwj8CgZ6BaK1pbXxMRO4OV7krZGKZ8fEQ06MhT+nEd4euH8K7h94FAMQPi0dXH/PlFH/I/QEfHv+Qv4tIwknQs1VPdPPphjaubeCt9Ia3whvuCnfIxfIHj4iInt5nFNf8vAZrzxuXizn3p3OQiMxv4iyrKcO7qe/izJ0zfFk713bo1aoXAj0D4e/iD3e5O/SFesjFcnTo0IF/XOJpjbOzy8nJsemSpjk5OeA4rtkmgBL8NuNjx45h8eLFmDRpkl0ns3/+858RFRX12IksAAQEBGDChAmYPXs2Dh8+bIXWmevatSsSEhKg0+nMnpvNyMgAAISEhDS6r6leXU3ZFwB8fX3h61v/7ZbEOdCLXudBsXYOFGfHppQoIeEk0DEdRkSPsNiuN+gR+1Msn8j6a/yxoOcCRPhH2PyZ9uZWd+K+gnvmb8SfuHUCcT/FgYGBA4dJwZMwMXgivJW2mxXWGtzl7vzXpdWlZv2prK3ElH1TcKnkEgAg2CsYs5+fjZ4te1okqlfuXoFer7f7c8AZ2DKRBYy35YtEzXde2N0Zdu/ePaGbYOHChQvYvXs33nnnnSc+xttvv40ff/wRx44da8aW1W/06NGoqKjAjh07zMo3b94MPz+/Rp+HGT16NC5evGg247FOp8OWLVsQHh4OPz8/q7WbOAbT7STE8VGsnQPF2bFxHAdXuSsA4Lt/W75x8c+f/4nka8kAgOdbPI+dI3ei3zP9HCKJqTtxn2kiIwAorirmE1mFWIF1Uevw3gvv2X0iC8BsqbSSKvOH4df+vJZPZCPbRGLz0M0IbxVe74irXC5HbW0tamqafvsxEYbpVmBbqKmpQW1tLeRyebMdU9ArzaJFizB37lwAQPv27Y33xHMc0tLSAADt2rXD8OHDsXPnTjz33HNQKBRYvHgxAGD16tXo27cvfH19oVar0bVrVyxfvhy1tZbLWezbtw8DBw6Em5sbVCoVunTpgmXLlpnVOX36NF566SV4enpCoVDgueeea/K7zWvXrkXLli0RFRVlVt6/f3+EhITg1KlTiIiIgEqlQocOHfDJJ5/AYDCfre/5559Hly5dHvs+8ScxdOhQREVFYcaMGdiwYQMOHTqE6dOnY9++fVi+fDk/Rffrr78OiUSCa9eu8ftOmTIFwcHBiI6ORnx8PFJSUjBu3DhcunQJf/vb36zedmL/9uzZI3QTiI1QrJ0DxdnxucqMyWyfAeYrHdypvIPNWZsBAB3dO+IfL/4DSonS5u2zFi+lF3+LbN2R2cXHFvPfx4bHoo9f/StA2CMPxYMleeomszmlOdh6YSsAIMwnDJ/1+wxyccMJiaur8Zy5desWJbRPuU6dOtnk59TU1ODWrVsAHpwfzUHQ24ynTp2K4uJifPHFF9i5cydatWoFAAgKCuLrnD17FhcuXMBf/vIXtG/fHmq1GoDxfutXX30V7du3h0wmw/nz57F06VJcvHgRmzZt4vffuHEjpk2bhn79+mHdunXw9fXF5cuXkZmZydc5dOgQhgwZgvDwcKxbtw5ubm749ttv8fLLL0Or1WLSpEmN9mPv3r3o27dvvUPmt2/fxoQJEzBnzhwsXLgQu3btQmxsLPz8/DBx4kSzuv3790diYiIYY1Z/rmDnzp14//338cEHH6C4uBidO3dGQkICXnnlFb6OXq+HXq83mwJeLpfj4MGDmDdvHt555x1otVqEhYXhP//5D/r162fVNhPHEBcXh48//ljoZhAboFg7B4qz4zMls2f/exbo/6D8q6yv+DWxF/ZeyNdzFBKRBJ4KTxRVFfHJ65W7V5D6q/G54cHtBmN0x9FCNrHZmSWz99cNZ4xhWfoy6JgOIk6E93u9/8jnftVqNTw9PVFcXIycnBxIpVJ+0Io8XbRaLVQq681kzBgDY4wfcPT09OTzueYgaDLr7++PNm2Ma5Y999xzaNeunUWd/Px8/Pe//0VAQIBZ+cqVK/mvDQYDIiIi4OXlhcmTJ2PFihXw8PBARUUF3nvvPfzud79Damoq/wc0cOBAs2O99dZbCA4ORmpqKv8M6eDBg1FYWIi4uDhMnDixwXu78/PzkZub2+CESUVFRUhKSuKXIomMjERaWhri4+Mtktnu3btj7dq1uHTpEjp37tzQr61ZaDQarFq1CqtWrWqwzldffYWvvvrKorxFixbYvHmzFVtHHNn48eOFbgKxEYq1c6A4Oz4XuYvxs48LX1Z4rxCJlxMBAL1a9UKYb5gQTbM6H5WPMZm9f5vxjssPHtF6O+xth0vOPOSWI7OXSy7j5C3j42XRAdHo7Pno16gcx/F3T5aVlaG6utrq6+OSJ1PfXa3NieM4iEQiqFQquLq6Qq1WN+vfjeATQD1KaGioRSILAOfOncPChQtx9OhRFBcXm227fPkywsPDcezYMZSVleGtt95q8JeWnZ2Nixcv4rPPPgNgfP7TZNiwYfjhhx9w6dIldOnSpd79b968CQANTmrUsmVLizU1Q0ND8fPPP1vUNR0jLy/P6sksIULJyMhA165dH12R2D2KtXOgODs+04hrifbBbafxF+JRra8GALwR+oYg7bIFH6UPLuIiCu4VoEZfg+9zvgcA9GzZE+3c2gnbOCtwV7jzX5uS2QPXDvBlU0KmNPlYHMdBo9FAo9E0W/tI84uPj8err74qdDOe2FOfzJpuPa7r+vXriIiIQGBgIFatWoV27dpBoVAgPT0dM2fO5CeJMi0B4+/v3+Dx79y5A8A4G/Gf//zneusUFhY2uL/pZykUinq3e3l5WZTJ5fJ6J7IyHeNpnOSKEEIIIc7JlMxWoYovS7uRBgAI8QrBCy1fEKJZNmGa0bhAW4CD1w+itLoUADA2YKyArbIeqUgKF5kLymvKUVJdAsYYDlw1JrMhXiHw09BEn+Tp8tQns/WNqO7evRuVlZXYuXMn2rZty5c/PNrp42O8AN24caPB43t7G2eei42NxZgxY+qtExgY+Mj9Hx4dfhKmY5iOSYgjohEc50Gxdg4UZ8dnSmarUQ0DM6DoXhF+KTHOgBrhHyFk06zONKNxcVUxdvxivMXYXe6OgW0GNrabXfOQexiT2aoS5JTm4GrZVQBAZNtIYRtGrMLer+GCz5tumpr5cUYjTQlu3WmdGWPYsGGDWb0+ffrAzc0N69ata/A+/cDAQHTq1Annz5/HCy+8UO+Hi4tLvfsCQNu2baFUKpGTk9Pk9jckNzcXIpGo0eS5OVRUVGDWrFnw8/ODQqFAWFgYvv322ybte+PGDcyaNQv9+vWDu7s7OI6r97laQhqSkJAgdBOIjVCsnQPF2fG5yd0AAAwMFbUVOHHrBL+tt19voZplE6ZkloHh1O1TAICBbQZCJpYJ2SyrMk0CVVJVguTryXx5VNuohnYhdszer+GCj8ya3g1YtWoVYmJiIJVKERgY2GgCGRUVBZlMhvHjx2PevHmoqqrC2rVrUVJivh6WRqPBihUrMHXqVERGRmLatGlo0aIFsrOzcf78efzzn/8EAKxfvx5Dhw7F4MGDMWnSJLRu3RrFxcW4cOECzp49i8TExAbbIpPJ0Lt3b5w4caLBOk114sQJhIWFwcPD49GVf4MxY8bg1KlT+OSTTxAQEID4+HiMHz8eBoPhkffMZ2dnY+vWrQgLC8OwYcPs/g+A2B7Neuo8KNbOgeLs+OrOUlxWXYbjN48DANRSNUK8Q4Rqlk2YbjMGAAMzLqvYzaebUM2xCT6ZrS5ByrUUAEBnz85o49pGyGYRK7H3a7jgI7P9+/dHbGws9uzZg9///vfo0aMHzpw50+g+nTt3xo4dO1BSUoIxY8bgnXfeQVhYGP7xj39Y1H399deRlJQEvV6PqVOnYvjw4fj73//Oz6IMAAMGDEB6ejrc3d0xa9YsREZGYsaMGUhJSUFk5KNvqZgwYQLS09P5tZOeREVFBQ4ePIgJEyY88TGaIikpCcnJyVizZg3eeOMNDBgwABs2bEBUVBTmzp0LvV7f6P59+/ZFQUEBkpOT8d5771m1rcQxjRgxQugmEBuhWDsHirPjq5vM3q25i+O3jMlsz5Y9IRVJhWqWTZhGZuvq5uvgyez9GY1vVdzC5ZLLAIC+/n2FbBKxInu/hgs+MgsY3xGo712Bq1evNrjP8OHDMXz4cIvy+m4nHjp0KIYOHdpoG0JDQ7Ft27ZHN7Yer776KhYsWICvv/4a8+fP58vT0tLqrV/fbbnbtm0Dx3GYPHnyE7WhqXbt2gWNRoPo6Giz8smTJ+PVV1/FyZMn0adPw4t/N7REESFNtWfPHqGbQGyEYu0cKM6Oz1X+IJk9d+ccCu8ZJ8Z09FuMAfORWQBwkbmgnWs7YRpjI6aR2fLacr4syDNIqOYQK7P3azhlJs1AoVBg8eLFWLlyJSorKx97f51Oh7/97W+IjY21+i3GmZmZ6NKlC7+erkloaCi/nRBrGjdunNBNIDZCsXYOFGfHV3dkNuV6Cv9171aOn8x6Kb3A4cFkpKE+oRBxjv3y2VPhaVHWyaOTAC0htmDv1/CnYmTWEUyfPh2lpaXIzc197FnBfv31V7z22muYM2eOlVr3QFFRETp06GBR7unpyW+3pvz8fH7JJJPs7Gyr/kzydKEJw5wHxdo5UJwdX91kNrPQ+Ka3i9QFbV3bNrSLw5CKpPBQeKC4yrjiRDdvx77FGDDO1lyXUqKEv0vDy1wS+2bv13DHfmvJhsRiMWJjY59oeuv27dvjgw8+aHCt2oakpaWB47gmfdRdtqi+5Y6asq05rFmzBiEhIWYfo0aNAgAcOXIEhw8fxqeffori4mLExMQAeHAv/+zZs5GdnY1NmzZh165dSE9Px5IlS6DVavl3lUx14+LikJGRgfj4eMTHxyMjIwNxcXFmdcaNGwetVoslS5YgPT0du3btwqZNm5CdnY3Zs2eb1Y2JiUFxcTE+/fRTHD58GPv27cPq1auRl5eHN99806zum2++iby8PKxevRr79u2jPj3UpxUrVjhcnxwxTs3RpwkTJjhcnxwxTr+1TytWrHC4PjlinH5Lnw4fOAyTan01AKCNaxu89NJLdtunx4mTpOrB2E+oT6hD9KmxOKk4Fepyq3VDbk6uXffJEePUXH2q+7rsaejTunXr8Dg41tCaNeSpd+vWLezdu7dJdceMGQNPT0/07t0ber0e6enpZtuzsrIQEhKC9evXY/r06U065unTp9GjRw/87//+LyZNmtSkfRoamR01ahQyMzMRHBzcpOMQ+5Weno6ePXsK3QxiAxRr50BxdnyMMTz3zXPQsweTRA5tPxTL+y4XsFW2MyNlBo7kHQEAHB1/1Gyk2hFlFmZi/N7x/PdjOo3B4j6LBWwRsaan7RpuykmamhfQbcZ2rFWrVpg6depj7dO1a1ckJCRAp9OZPTebkZEBAAgJse4U+76+vvD19bXqzyBPt7y8PKGbQGyEYu0cKM6Oj+M4uMhcUFpdypc5wy3GJoEegTiSdwSh3qEOn8gClrcZB3gECNMQYhP2fg2n24ydzOjRo1FRUYEdO3aYlW/evBl+fn4IDw8XqGXEWTy8HjRxXBRr50Bxdg4PJ3FtXJxnzdHpodOx9PdLsaL/CqGbYhMPTwDVyZ0mf3Jk9n4Np5FZJzN06FBERUVhxowZKCsrQ8eOHZGQkIB9+/Zhy5YtEIvFfN3XX38dmzdvRk5ODtq2ffAO7Pbt2wEAubm5AIy3G2s0GgDA2LFjbdgbYo/69qW16pwFxdo5UJydw8PJrDONzKqkKrz07EtCN8NmlBIlZCIZagw1AGgmY0dn79dwGpl1Qjt37sSf/vQnfPDBBxgyZAhOnjyJhIQETJgwwayeXq+HXq+3WLs3Ojoa0dHR/Jq6q1ev5ssIeZTVq1cL3QRiIxRr50Bxdg5115oFnCuZdTYcx4GrMk4I6qP04dedJY7J3q/hNAEUEdzjPuhNCCGEENuae3gu9l3dBwBwk7vhyCtHBG4RsabXkl7D+YLziGgdgTWRa4RuDnEij5sX0MgsIcSmTFOwOxNnfc/QGWPtjCjOzqHubcZtXZxsVNYJr+F3Eu9gaLuheLf7u0I3hViZvV/DKZklhNhMSm4KksOTkZKbInRTbCYlNwXKpUqn6jPgnLF2Vnv27BG6CcQGXG8W81+3cXWeyZ+QkgIolcbPziIlBckJP2F57SB09uwsdGuIldn7NZySWUKITTDGsCBlAaq3VyM2JdYpRiv5Puudp8+Ac8bamcXExAjdBGJtjMF13yH+W6eZyZgxYMECoLoaiI11jhHa+32OcaY+Ozl7v4ZTMksIsYm9v+zFmVtngMHA6VunkfRLktBNsjq+z3CePgPOGWtn9vnnnwvdBGJte/fC9fJ1/tu2ucWNVHYge/cCZ4zXcJw+DSQ5wbXsfp8/B5ynz07O3q/hlMwSQqyOMYZFaYvAgQPOARw4LEpb5NAjdmZ9hnP0GXDOWDu7jRs3Ct0EYk2MAYsWwbtMxxe137DT8Ufs7vcbnPEaDo4zfu/I/a7T542Ac/SZ2P01nJJZQojVmUbqGBjQGmBgDj9iZ9ZnOEefAeeMtbPr2bOn0E0g1nR/pK5PRgXCj5RgclIBOu876/gjdqZRWVMix5jjj1TW6XNPwDn6TOz+Gi4RugGEVFdXAwCys7MFbgmxBsYY5m+fDxTcLygAoDJ+OT9+PtqObQvO9M63g7Docx2O2mfAOWNNgIsXL8Lb21voZhBrYAy4v6Y89Aw9vszD7wH8FzCWt237YOTSkdTt98Mctd8P9fkiAP6v2lH7TAA8fddwUz5gyg8ehdaZJYLbvHkzJk2aJHQzCCGEEEIIIU+B3bt3Y+TIkY+sRyOzRHABAQEAgO+++w5BQUECt4ZYU3Z2NkaNGoXdu3ejY8eOQjeHWBHF2jlQnJ0Hxdp5UKydx9MY6+rqavz666/o169fk+pTMksE5+pqXIg9KCgIwcHBAreG2ELHjh0p1k6CYu0cKM7Og2LtPCjWzuNpi3X37t2bXJcmgCKEEEIIIYQQYncomSWEEEIIIYQQYncomSWEEEIIIYQQYncomSWC8/HxwcKFC+Hj4yN0U4iVUaydB8XaOVCcnQfF2nlQrJ2HI8SaluYhhBBCCCGEEGJ3aGSWEEIIIYQQQojdoWSWEEIIIYQQQojdoWSWEEIIIYQQQojdoWSWEEIIIYQQQojdoWSWCKaiogKzZs2Cn58fFAoFwsLC8O233wrdLPKQ1NRUTJkyBZ07d4ZarUbr1q0xcuRInDlzxqLu2bNnERkZCY1GA3d3d4wZMwa5ubn1HveLL75A586dIZfL0b59eyxevBi1tbUW9fLz8zFp0iR4e3tDpVKhd+/eOHjwYLP3k1j68ssvwXEcNBqNxTaKtf07cuQIhg0bBg8PDyiVSnTq1AlLliwxq0Nxtn/nzp3DqFGj4OfnB5VKhc6dO+PDDz+EVqs1q0exti/l5eWYN28eBg0aBB8fH3Ach0WLFtVbV+jYpqSkoHfv3lCpVPD29sakSZOQn5//xH13Jk2Js16vx8qVKzFkyBD4+/tDpVKhS5cuWLBgAUpLS+s9rkPFmREikKioKObu7s7WrVvHUlNT2dSpUxkAtnXrVqGbRuoYO3YsGzBgAFuzZg1LS0tjiYmJrFevXkwikbCDBw/y9S5cuMBcXFxYREQE27t3L9uxYwcLDg5mfn5+LD8/3+yYH330EeM4jsXGxrJDhw6x5cuXM5lMxqZNm2ZWr6qqioWEhDB/f3+2ZcsWduDAATZy5EgmkUhYWlqaTfrvrG7cuMHc3NyYn58fU6vVZtso1vZv69atTCQSsVdeeYX9+9//ZqmpqWzDhg1s8eLFfB2Ks/3LyspiCoWCdevWjW3bto0dPHiQLVy4kInFYvbSSy/x9SjW9ufKlSvMzc2N9e3bl3/9tHDhQot6Qsc2LS2NSSQSNnLkSHbgwAG2ZcsW1rp1axYSEsKqqqqa/ffiaJoS5/Lycubi4sKmT5/OEhMT2aFDh9iKFSuYh4cHCwoKYlqt1qy+o8WZklkiiL179zIALD4+3qw8KiqK+fn5MZ1OJ1DLyMPu3LljUVZeXs5atGjBBg4cyJdFR0czb29vdvfuXb7s6tWrTCqVsnnz5vFlhYWFTKFQsOnTp5sdc+nSpYzjOJaVlcWXrV69mgFgx44d48tqa2tZUFAQ69mzZ7P0j9Rv+PDhbMSIESwmJsYimaVY27cbN24wtVrNZsyY0Wg9irP9e//99xkAlp2dbVY+ffp0BoAVFxczxijW9shgMDCDwcAYY6ygoKDBZFbo2Pbo0YMFBQWx2tpavuzo0aMMAFuzZs2Tdd6JNCXOOp2OFRYWWuybmJjIALBvvvmGL3PEOFMySwQxdepUptFozE56xhiLj49nANjRo0cFahlpqgEDBrCAgADGmPHiplQq2RtvvGFRb9CgQaxTp07891u2bGEA2PHjx83q3bx5kwFgS5cu5csiIyNZYGCgxTE//vhjBoDduHGjubpD6vjmm2+Yi4sL+/XXXy2SWYq1/Vu0aBEDwK5evdpgHYqzYzDFuqCgwKx83rx5TCQSsYqKCoq1A2goyRE6tjdu3GAA2LJlyyzqBgQEsKioqMfqp7Nr7E2L+ly7do0BYB9//DFf5ohxpmdmiSAyMzPRpUsXSCQSs/LQ0FB+O3l63b17F2fPnkVwcDAAICcnB/fu3ePjV1doaCiys7NRVVUF4EFsu3btalavVatW8Pb2Not9ZmZmg8cEgKysrObpEOHl5+dj1qxZ+OSTT+Dv72+xnWJt/3788Ud4enri4sWLCAsLg0Qiga+vL958802UlZUBoDg7ipiYGLi7u2PGjBnIzc1FeXk5fvjhB6xfvx4zZ86EWq2mWDswoWNr2qehuvRaz7pSU1MBgH+tBjhmnCmZJYIoKiqCp6enRbmprKioyNZNIo9h5syZqKysxPvvvw/gQbwaiiljDCUlJXxduVwOtVpdb926safzxPbeeustBAYGYsaMGfVup1jbv7y8PGi1WkRHR+Pll19GSkoK5s6di6+//hrDhg0DY4zi7CDatWuH48ePIzMzE88++yxcXV0xYsQIxMTEYNWqVQDob9qRCR3bR/18OgesJy8vDwsWLMALL7yA4cOH8+WOGGfJo6sQYh0cxz3RNiKsv/71r9i6dSu++OILPP/882bbmhrTx4k9nSe2s2PHDuzZswfnzp175O+WYm2/DAYDqqqqsHDhQixYsAAA0L9/f8hkMsyaNQsHDx6ESqUCQHG2d1evXsWIESPQokULbN++HT4+Pjh58iQ++ugjVFRUYOPGjXxdirXjEjq2DdWlc8A6iouL+Tcmt23bBpHIfOzS0eJMI7NEEF5eXvW+U1NcXAyg/nd3iPAWL16Mjz76CEuXLsXbb7/Nl3t5eQGo/5324uJicBwHd3d3vm5VVZXFshCmunVjT+eJ7VRUVGDmzJl455134Ofnh9LSUpSWlqKmpgYAUFpaisrKSoq1AzDFcPDgwWblQ4cOBWBcxoPi7BgWLFiAsrIy7N+/H3/84x/Rt29fzJ07F3//+9+xadMmHD58mGLtwISO7aN+Pp0Dza+kpARRUVHIy8tDcnIyOnToYLbdEeNMySwRRNeuXXHhwgXodDqz8oyMDABASEiIEM0ijVi8eDEWLVqERYsWIS4uzmzbs88+C6VSycevroyMDHTs2BEKhQLAg+c0Hq57+/ZtFBYWmsW+a9euDR4ToPOkORUWFuLOnTtYsWIFPDw8+I+EhARUVlbCw8MDEyZMoFg7gPqeawIAxhgAQCQSUZwdxM8//4ygoCCLWwp79OgBAPztxxRrxyR0bE2fG6pL50DzKikpQWRkJK5cuYLk5OR6r/UOGWerTi9FSAOSkpIYAPbtt9+alQ8ZMoSW5nkKffjhhwwA+8tf/tJgnXHjxjFfX19WVlbGl127do3JZDI2f/58vqyoqIgpFAr25ptvmu2/bNkyi2nh16xZwwCwEydO8GW1tbUsODiYhYeHN0fXyH337t1jhw4dsvgYPHgwUygU7NChQywjI4MxRrG2d/v377eYtZIxxlauXMkAsJ9++okxRnF2BAMGDGA+Pj6svLzcrPxf//oXA8B2797NGKNY27vGZrkVOrY9e/ZkISEhZq/rjh8/zgCwtWvXPnGfnVFjcS4uLmbdu3dn7u7u7NSpUw0ewxHjTMksEUxUVBTz8PBg//rXv1hqaiqbNm0aA8C2bNkidNNIHZ999hkDwIYMGcKOHz9u8WFy4cIFptFoWN++fVlSUhLbuXMnCwkJaXRh9ri4OJaWlsY+/fRTJpfL612wOzg4mD3zzDNs69atLDk5mY0ePbreBbuJddS3zizF2v6NGDGCyeVytmTJEpacnMyWLVvGFAoFGz58OF+H4mz/vv/+e8ZxHOvVqxfbtm0bO3jwIFu6dCnTaDQsKCiIVVdXM8Yo1vYqKSmJJSYmsk2bNjEALDo6miUmJrLExERWWVnJGBM+tocOHWISiYSNHj2aJScns61bt7JnnnmGhYSEsKqqKuv+ghzEo+Ks1WpZjx49GMdxbNWqVRav0x5eZ9rR4kzJLBFMeXk5e/fdd1nLli2ZTCZjoaGhLCEhQehmkYf069ePAWjwo67Tp0+zgQMHMpVKxVxdXdmoUaMsLqImq1atYgEBAUwmk7E2bdqwhQsXspqaGot6t2/fZhMnTmSenp5MoVCwXr16seTkZKv0lViqL5lljGJt77RaLZs/fz575plnmEQiYW3atGGxsbEWLzoozvYvNTWVDRo0iLVs2ZIplUoWEBDA5syZwwoLC83qUaztT9u2bRv8v/nKlSt8PaFje+DAAdarVy+mUCiYp6cnmzhxIrtz506z/A6cwaPifOXKlUZfp8XExFgc05HizDF2/yEZQgghhBBCCCHETtAEUIQQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7A4ls4QQQgghhBBC7M7/A8wm74Gcy8IrAAAAAElFTkSuQmCC", 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\n", 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", 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\n", 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7e3vY29s3u4zqfK/+/PNPbNu2DaNHj0b37t1hZGSE2NhYnD59GhUVFZg8eXKT5+wlhBDSchTMEkKIhnjppZegq6uLr776CgcPHoSenh5GjBiBn3/+GUeOHFEZ9E2ZMgW3bt3CZ599hosXL+Lff/+FmZkZPD09sWHDBoV1x44di7CwMHzxxRc4d+4cLl++DB0dHdjb22PMmDGYM2dOs8qrp6eHefPm4aeffgKguotxS89LFZFIhIsXL+LNN9/E+fPncfPmTfj4+CAgIADm5uYqg1lHR0fcvn0b33zzDY4cOcJlXLa1tYWXlxdee+01bhohFxcXbNmyBUFBQQgMDEROTg7Mzc3h4eGBbdu2KU3v0xLm5uYICQnBp59+iqNHj+LLL7+Enp4eBg0ahLfeegtPP/10q4/RFDweD9u3b1eaEkdOKBTi+PHjOHToEA4cOIBTp06hpKQEVlZW6N69O7Zu3YpFixYpbLNlyxbo6+tj3759+OGHH2Bra4tnn30WmzdvbjRDtirqfK9Gjx6Nx48f4+7duwgJCUFpaSlMTU3x1FNPYfHixVi8eHGbjWMmhBBSP4ZlWVbdhSCEEEIIIYQQQpqDxswSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4FMwSQgghhBBCCNE4AnUXgJCCggIEBwfDyckJurq66i4OIYQQQgghRA0qKyuRnJwMf39/mJqaNro+BbNE7YKDgzFz5kx1F4MQQgghhBDSCRw/fhwzZsxodD0KZonaOTk5AZB9aN3c3NRcGtLeXnnlFezevVvdxSAdQJPruqy4CpHX0mHvbgrbHibqLk6npsn1TJqH6lp7UF1rj85W1zExMZg5cyYXHzSGYVmWbecyEdKgiIgI+Pj44MGDB/D29lZ3cQghBP/+FIHom5kAgNXfjQbDMGouESGEENL1NTcuoARQhJAOtWTJEnUXgXQQTa5reSALAOXF1WosSeenyfVMmofqWntQXWsPTa9rCmYJIR1q586d6i4C6SCaXNdCEZ/7PS+9VI0l6fw0uZ5J81Bdaw+qa+2h6XVNwSwhpEP99NNP6i4C6SCaXNeGZiLu93wKZhukyfVMmofqWntQXWsPTa9rCmYJIR1q0KBB6i4C6SCaXNci/Sf5ESmYbZgm1zNpHqpr7UF1rT00va4pmzEhpEOVl5eruwikg2hyXVdXSbjf8zLK1FiSzk+T65k0D9W19miLumZZFsXFxSgqKkJ1dTUo52znJBAIEBcX167HYBgGQqEQxsbGMDIyatOkihTMEkI6VGxsrLqLQDqIJtd1deWTYJZaZhumyfVMmofqWnu0tq7FYjFSU1NRViZ7GCgQCMDj8SgzfCdkZmbWrvtnWRYSiQQVFRUoLi6Gvr4+HBwcIBC0TRhKwSwhpEPNnDlT3UUgHUST61pcK5gtK6pCRWk1RAZCNZao89LkeibNQ3WtPVpb1/n5+SgrK4OJiQmsra3bLHAhba+qqgo6OjrtfhyxWIysrCwUFhYiPz8fVlZWbbJfGjNLCOlQW7duVXcRSAfR5Lqu3TILAPnU1bhemlzPpHmorrVHa+u6pKQEfD4fdnZ2FMh2cunp6R1yHIFAADs7O/D5fJSUlLTZfimYJYR0qL1796q7CKSDaGpdsyyrHMxSV+N6aWo9k+ajutYera1rlmUhEAioW7EGcHZ27rBjMQwDPp/fpuOnKZglhHSoadOmqbsIpINoal1LqqWo+3c2L4OC2fpoaj2T5qO61h5U19ojOjq6Q4/X1g84KJglhHSokydPqrsIpINoal3XbZUFgLzUtusS1dVoaj2T5qO61h5U19rD3d1d3UVolS4XzB44cAAMw9T7ExQUxK3r4uKCpUuXcv9PSEgAwzA4cOBAi45dd39BQUFgGAZ//fVXo9tu3rxZ6UnFqFGjMGrUKIVlDMNg8+bN3P8fPnyIzZs3IyEhoUVlJqSjrVq1St1FIB1EU+taIZit+VpOjylUmK6HPKGp9Uyaj+pae1Bda4/ExER1F6FVuuyI7J9//hmenp5Ky728vOrdxs7ODiEhIXB1dW3RMY8dOwZjY+MWbfvCCy9g4sSJja4XEhICR0dH7v8PHz7Eli1bMGrUKLi4uLTo2IR0pPfff1/dRSAdRFPrunYw23OgDaJCMyGuliLlUR66+7ZN9sWuRFPrmTQf1bX2oLrWHnZ2ds3eZunSpTh48KDScg8PD0RGRrZFsZqsy7XMyvn4+GDIkCFKPw0Fm7q6uhgyZEiLU0X7+fm1OBB2dHTEkCFDGl1vyJAhCsEsIZrm+PHj6i4C6SCaWte1g9kefa3AF8r+VMbfz1FXkTo1Ta1n0nxU19qD6lp7FBQUtGg7PT09hISEKPz8/vvvbVu4JuiywWxL1NfN+MSJE+jTpw90dXXRo0cP7Nq1S2W34LrdjOUqKiqwfv162NraQk9PD/7+/rh7967COqr2p0rtbsYHDhzAvHnzAACjR4/mulIfOHAAW7duhUAgQHJystI+li9fDgsLC1RUVDR6PELaWksf+BDNo6l1XV3xJJjVM9aBk6dsQvmE8BxIpW2XgbGr0NR6Js1Hda09qK41S3V1NcRicYu21dXVbdF2PB5PqdHQ19e3RftqjS4bzEokEojFYoUfiaT5453Onj2L2bNnw8LCAr///js+//xzHD58WGXTen02btyIuLg4/Pjjj/jxxx+RlpaGUaNGIS4urtnlqW3KlCn45JNPAAC7d+/mnopMmTIFL730EgQCAb7//nuFbfLy8vDbb79hxYoVEIlErTo+IS2hp6en7iKQDqKpdV27ZVaoy4dLH0sAQHlxNTLji9RVrE5LU+uZNB/Vtfagum6YvBEqIiICCxYsgImJCWxsbLB8+XIUFhYqrMuyLPbs2YO+fftCT08PZmZmmDt3rlIcUF+jWN0cOvKcPP/3f/+HN954Aw4ODtDV1UVMTAwAYP/+/fD19YVIJIK5uTlmzZqFR48eKexz6dKlMDQ0RExMDObNmwdDQ0M4OTnhjTfeQGVlZdu8SR2kywazQ4YMgVAoVPhpyZOHDz74AA4ODjh37hxmzpyJuXPn4sKFCyguLm7yPqysrHDs2DFMnToVixYtwoULF1BeXo5PP/202eWpu195BjIvLy/uqYiVlRWsra3x7LPPYt++faiqquK2+fHHH1FZWYnVq1e36tiEtFRoaKi6i0A6iKbWde1ET1wwW9NxJv5+tppK1Xlpaj2T5qO61h5U100zZ84c9OzZE0eOHMG7776LgIAArFu3TmGdl156CWvXrsW4ceNw/Phx7NmzBxERERg2bBgyMzNbfOwNGzYgKSkJe/fuxcmTJ2FtbY1PP/0UK1asgLe3N44ePYpdu3YhLCwMQ4cOVZqCp7q6GtOnT8ewYcNw4sQJLF++HDt37sRnn33WpOOXl5fD1tYWfD4fjo6OePXVV5GXl9fi82mpLpsA6pdffkGvXr0UljV3XqPS0lLcunULr776KnR0dLjlhoaGmDZtWpOzHi9cuFDh2M7Ozhg2bBgCAwObVZ7mev3113Hw4EH8+eefWLRoEaRSKb777jtMmTKFkkURtVmxYoW6i0A6iKbWdd2WWQMTXdi4GCMzvggJYTkYNttNjaXrfDS1nknzUV1rj/as68t/RCEnuXNMd2bpZIgR83u2ePsVK1bgrbfeAgCMGzcOMTEx2L9/P3766ScwDIPr169j37592LFjB9avX89tN2LECPTs2RNffvllk4PHulxdXfHnn39y/y8oKMDWrVsxefJkBAQEcMtHjRoFd3d3bN68Gb/++iu3vKqqClu2bMGsWbMgEAgwduxY3Lp1CwEBAfjggw8aPLavry98fX3h4+MDAAgODsbOnTtx8eJF3Lx5E4aGhi06p5bossFsr169MGDAgFbtIz8/HyzLwsbGRuk1VcvqY2trq3LZ/fv3W1W+xvj5+WHEiBHYvXs3Fi1ahFOnTiEhIUGp6zEhHWndunXN6qZPNJem1nXtMbNCXT4AoLuvJTLji5CfUYaCzDKY2uirq3idjqbWM2k+qmvt0Z51nZNcgrTognbZd0ebPn26wv/79OmDiooKZGVlwcbGBqdOnQLDMHjuuecUxrTa2trC19dXYcrQ5pozZ47C/0NCQlBeXq7UVdnJyQljxozBxYsXFZYzDINp06YhOTkZ3bt358p/6dKlRo9dt/V5/Pjx8PPzw9y5c7Fv3z6l19tTlw1m24KZmRkYhlHZBSAjI6PJ+1G1bkZGBiwsLFpVvqZYs2YN5s2bhzt37uDbb79Fz549MX78+FbtMygoCKNHj1b5WkhISJOyMhPtRTdC2kNT67q68skNh0CnJpjtY4Xrx2Xjm+Lv58Dv6W5qKVtnpKn1TJqP6lp7tGddWzp1XKtdY1pblrr38vIhjeXl5QCAzMzMehvGAKBHjx4tPnbdKXVyc3NVLgcAe3t7nD9/XmGZvr4+RCIRF8jKy9/SBLGzZs2CgYEBrl+/3qLtW4qC2QYYGBhgwIABOH78OL744guuq3FJSQlOnTrV5P0cPnwY69ev57oaJyYm4tq1a3j++edbXca6F01ds2bNQrdu3fDGG29wXQCa2926Pp988olSUCvvbkBIfaZNm4aTJ0+quxikA2hqXcu7GQt0eODxZN+XZnb6MLHSQ2F2OeLDsimYrUVT65k0H9W19mjPum5Nt15NY2lpCYZhcPnyZZW5e2ovE4lEKpMv5eTkwNLSUml53ft5eWCdnp6utG5aWprKfQBAdHQ0l4OntViWBY/XsSmZumww++DBA5Upql1dXZs1j+yHH36IKVOmYMKECXj99dchkUiwfft2GBoaNnmQc1ZWFmbNmoUXX3wRhYWF2LRpE0QiETZs2NDkctRHHjz+8MMPMDIy4p6wyD/QfD4fr7zyCt555x0YGBiozJLWUu7u7tQKS5qNboS0h6bWtTyYlXcxBmQ3DS6+lrh/IRkZsYUoL66CnpFOfbvQKppaz6T5qK61B9V125g6dSq2bduG1NRUzJ8/v8F1XVxcEBYWprAsKioKjx8/rjcQrW3o0KHQ09PDoUOHuKk7ASAlJQWXLl3C3LlzVW7XVoHsX3/9hbKysg6PDbpsNuNly5Zh6NChSj8nTpxo1n4mTpyII0eOIDc3F8888wzWr1+PWbNmYcaMGTA1NW3SPj755BM4Oztj2bJlWL58Oezs7BAYGNgmc3h1794dX331Fe7fv49Ro0Zh4MCBSl9AzzzzDABg8eLFMDExafUxCWmNjhxHQdRLU+taVTALAD18ZTcTLCvrakxkNLWeSfNRXWsPquu2MXz4cKxcuRLLli3D22+/jVOnTiEwMBABAQFYvXo1vvvuO27dxYsX4+HDh1i9ejUuXryI/fv3Y/r06U1uhDM1NcX777+Pv//+G88//zz++ecfHDp0CKNHj4ZIJMKmTZtUbpecnNysc0pMTMTw4cPxzTff4J9//sHZs2exYcMGLFmyBN7e3njhhReatb9WY0mzVVVVsV5eXuz48ePVXZQm+frrr1kA7IMHD9pkf4GBgSwA1tramuXz+ayRkRH79NNPs5cvX27R/h48eNCm5SOdW3R0tLqLQDpIQ3WdGp3PRt3MYKVSaQeWqGlO77nPfvvSRfbwhzcUlkvEEvbnty9zr3XGstfn9rkE9o9PQtnc1JI23zdd09qD6lp7tLauY2Nj2djY2DYqTeezadMmFgCbnZ2tsPznn39mAbDx8fEKy/fv388OHjyYNTAwYPX09FhXV1f2+eefZ2/dusWtI5VK2c8//5zt0aMHKxKJ2AEDBrCXLl1i/f39WX9/f249+X34n3/+qbJsP/74I9unTx9WR0eHNTExYWfMmMFGREQorLNkyRLWwMCAZVmWLS8vVzqvhuTl5bGzZs1iXVxcWD09PVZHR4d1d3dn3377bbagoKDBbVm28c9Gc+MChmVZtmPDZ82zYsUKjB8/HnZ2dsjIyMDevXsRHByMf//9F+PGjVN38ep19+5dxMfH46WXXsLw4cNx/PjxNtvvwYMHMWrUKFhYWCAmJgbbt29HVFQUTp8+jQkTJtS7bVZWFrKzFedpjImJwcyZM/HgwQN4e3u3SRlJ57V//34sX75c3cUgHaC+ui4vrsLBDdcgEUsxZXUf2TyunciJr+4iJTIftj1MMOft/gqv3T6bwCWCmv56Xzj1MldHEZtFKpHi+9eDIRWz8Bxqi7FLvNp0/5p4TafFFODuv0nwG+8Ee3czdRdHY2hiXZOWaW1dx8XJvidbk+CIdIz6xuS2l8Y+GxEREfDx8WlyXNBluxm3peLiYrz55pt4+umnsWLFCkgkEpw5c6ZTB7KALPnTwoUL0bdvX+zdu7fN9uvn54evvvoKM2fOxIgRI7Bs2TJcu3YNdnZ2ePvttxvcds+ePfDx8VH4mTlzJgDgypUrCA4Oxvbt25GXl4clS5YAkCUhAGRdXuTzdx07dgyhoaHYunUrysrKuHEI8nU3btyI8PBwBAQEICAgAOHh4di4caPCOvPnz0dZWRm2bt2K0NBQHDt2DPv370dMTAzXvUa+7pIlS5CXl4ft27cjODgYZ8+exe7du5GamopVq1YprLtq1SqkpqZi9+7dOHv2LJ1TnXMyMzPrcufUFeupvnP69demn9OdO3dUntM7azZBIpYCAK79G672c6pbTw/CIgAADx8/UDqne0lB4AtkSTfuXUjutPVU+5yK8yogFcueW9+9EtXmnz0zMzONu57++jYYCWE5+Ofg7U5TT609p4747J0/f77LnVNXrKe2OKfz58+36pxu374NQJZcCJB1Ta2qqkJWVhYKCwtRXFyMjIwMiMVixMfHK6ybnJyMiooK5OTkID8/H6WlpUhLS4NEIkFsbKzCuikpKSgrK0Nubi5yc3NRVlaGlJQUhXViY2MhkUiQlpaG0tJS5OfnIycnBxUVFVwXW/m68fHxEIvFyMjIQHFxMQoLC5GVlYWqqiokJiZ2yXPi8/kdek6VlZUoLCys97PX3JiFWmZJm3n55Zexd+9elJWVQU9PT+U61DJLQkNDMWjQIHUXg7RA6uN8nP3hAdwHWGPkAo9G16+vru9dSMLVv2IAACbWenjuw6FtXtbWOPzhDeSllaJHXytMWtVb6fX/Dj9GeHAqAGDh5sEwszXo6CI2S2JELk5982Re88UfDYWxperv6JbQxGv653euoKywCsZWeli8tXN9/jozTaxr0jKtrWtqmdUcpaWlMDDouL9j1DJLOi35c5GGpv6xtraGt7e3wo+bm1tHFZF0AufOnVN3EUgLRd7IQEVpNcKDU1GUq3o6sNrqq+u8tFLu98KschTntWxOu/ZSXwIoOW9/B+73lMj8DilTaxRmlSn8PzWqbcusadd0daUEZYVVAICyoio1l0azqLOuK0qrkRFfCGqD6Riadl2TlissLFR3EVqFglnSJvLz83Hq1Cn07dsXIpFI3cUhndgbb7yh7iKQFiquFcBGhWZyv0uqpagoqVZav3ZdlxdXIfpmJqoqxMitFcwCbR9ctVZjway5nQF09WUz22UlFnVYuVqqIEvxwUPq44I23b+mXdNFOU/eD3GlBFUVytP4EdXUVdcsy+L4l3dw5LPbeHRNeQ5N0vY07bomLWdjY6PuIrSKVgez165dw+bNm1FQUKDuorRKbGwsdHV1ERIS0qzt3n//ffTr1w9SqbRZ2y1cuBDvvvsu/vrrLwQFBWHfvn0YOnQoMjMzsX379mbti2iftpzrmHSs4twnLahRNzLAsizKS6rw+yc38fPbVxAfpjhdTe26vnjwEf79KQJX/4pBfnqdYLaTtW42FswyDANrF2MAQGZCcbuVQyqR4v6lZCSEt24aIFUts23ZuqVp13RhtmJwT62zTaeuui7Oq0Buqux7Iyo0Qy1l0Daadl13FhKJFFKpZvUeSEhI6NDjtXXvCq0PZrds2aLxweybb76J8ePHY+jQ5o37efPNNxEfH4+DBw82a7s+ffrg3LlzeOGFFzBu3Di899578PLywrVr1zp9Uiyifn/88Ye6i0BaQCqRojivkvt/fkYZMuKK8M/ecOSnl0IqZRF0KBIVpU9aaGvXdWa8rAUz8no6FyyiZkRCyuO2Da5aQyqRQlIte8AnFKkOZgHA2tkIAJCfUdpuLXtx93Jw5Y9onN4dhty0khbvh2uZrXm/S/Ir8fBKWoNBHNuMm7HOdE03pdx1g9lyCmabTF11nVXroVF6dCEqy6k1vb21tq4ZhoFYLO403+0dQVwlQW5KCfLSSjUqoHV1de2wY7EsC4lE0uCQxObS6mC2ucrLGx8j1tEePXqE48eP47XXXmv2tiYmJnjuueewbdu2Zn3ZvPvuu7h79y4KCgogFouRlZWFo0ePYuDAgc0uA9E+8qx1RLOUFFQqBQpHv7iN9JgnY23KiqoQcjyW+7+8rivLxVyQK8+qCwAuvWVTAZTkV+Lghmu4+2+S2m98qque9FSpr2UWAKydZS2zYIHsxPZpnU1+mMv9fu/fpBbtQyKWorimW203LwtuedCvj3HogxCVAe21ozH4cf1/uHUmvknBYWe5pm+ejseeVwIRcTm1wfWKqGW2xdRV17W780ulLJIf5qmlHNqktXVtaGgIiUSC9PR0iMXa8fChqkL2oFYqkUJcJVFzaZpOnnG4vYnFYqSnp0MikcDQ0LDN9itosz1pmM2bN2PLli0AgO7du3PLAwMDMWrUKLi4uMDHxwfLly/H1q1b8ejRI6xduxbbtm3D7t278fvvvyMyMhKlpaXo0aMHFi9ejHXr1kEoFCoc5+zZs9i+fTtu3bqF6upqODs74/nnn8eGDRu4dW7duoUPP/wQV65cQVlZGXr16oUNGzZwKdUb8t1338HW1hbjx49Xeq0px168eDG+/fZbBAYGYsyYMc1+HwlprpMnT6q7CKQFancx1jUQoLJUDNTEObY9jMEX8pH6OB8PL6eh11A72PYw4eq69hjF2gZMckFKZB7EVVKUFlTi2tEYMDyg77hu7X4+9amueHID0lAwa9PdmPs9M7EIDh5tP1dp7cA6+lYWTKz1EXM7C31GO8LrKfsm7aMopxzy5wNu/a2gbyRE1M1MSCUsqiskSI3Kh/uAJ+OlWJZFeHAqxJUS3Pg7HhnxRZj4og8EOvW/F53hmmalLO5fTAZY4PqJOHgMsYVAqLrMhTkUzLaUuuo6K0FxbHpiRC7c+ltDIpEiISwHpjb6sLBvu5tj0vq6NjMzQ1lZGQoLC1FYWAiBQAAej9emLXLNxbKsrOcNw0AgbPv2PHGVBOKanj0FFbx6v4M6Gz6fz2UYbg8sy0IqlXIPNfT19WFm1nZ/M7W2ZfaFF17gWjOPHj2KkJAQhISEoF+/ftw6d+7cwVtvvYU1a9bg7NmzmDNnDgDZGNWFCxfi//7v/3Dq1CmsWLEC27dvx0svvaRwjJ9++gmTJ0+GVCrF3r17cfLkSaxZs4abKwqQBc/Dhw9HQUEB9u7dixMnTqBv37545plncODAgUbP4/Tp0xg5ciR4PMWqbMqxAaB///4wNDTE6dOnm/X+EdJS8nnFiGYpynkSzE5c2RsDp3ZHv4nOGPFMT0xb0xejn/MAXyD7Hgo5FguWZbm6rtsSBgB6RkLYdDfGcx8Oxchne8LARAcAcPVIDOLuZSut31GqK5+0IDQUzBqY6MLAVBeA8o12W6n9EEAiluLG33HITS1B4K+RSAjPQUVpdaOZoAtrJX8ysdbH2KVeWPHFCK7LcW6qYvfl0oJKiCufBPSJ4bm4dzG5wWN0hms6J6UElWWyuqsoqUbsnfo/QzRmtuXUUddSKYusJMXeD4kPcpEQnoM/Pr6Js98/wLEv7qCyTDkJHWm51ta1QCBAt27d4ODgACMjIwgEArUGsgBQVliFhPAcJIRlt8t1n5taipTIPKRE5iEnueVDQzpaeHh4u+6fYRgIBAIYGRnBwcEB3bp1g0DQdu2pWtsy6+joiG7dZE///fz84OLiorROVlYWHj58iJ49eyos//LLL7nfpVIpRowYAQsLCyxbtgw7duyAmZkZSkpKsH79egwfPhyXLl3iLuCxY8cq7Gv16tXw9vbGpUuXuIqdMGECcnJysHHjRjz//PNKgWrt8sXFxWHlypUKy5t6bED2NMbX1xdXr15t6O0ipM0sWLBA3UXodLISi5AeUwifkQ7gt8PT4rZQO5OxXQ8TONZpidQRCeAzygH3LyQjLboAiQ9yubqu2xIGAOb2sjntDEx10XuUI2xdTXDsizuorpTgyp/R6NHXqh3Ppn7iWq2hDbVGAoCNizHi7mUrjOdrS/W1aIMFzv3wAFIJCynLYvLLfdC9j6XKVQtqJX8ytdYHAOjoCWBqrY+CzDIuqY5cfqZisigAeHQtHX7juyEsMAW6egJ4DrUFj//kc9qaazo3tQSBhyJRXSmB+wAbeD1lD31jnWbvp25G7AfBKfAYbKu0nkQiRUmu4gOAssJKpfWaQ1ItBY/PgOGp90a9I6jj+7sgo4zrMWHtbISsxGKUF1Xh9O4wbp3KMjGib2XBZ6RDfbshzdQWdc0wDIyNjWFsbNz4yh0g5Hgsoi/KvlcleYaY9JJnm+4/7MRdpD6W7d/UhsGwcZoxx668l6mm6px3TZ1Enz59lAJZALh79y6mT58OCwsL8Pl8CIVCPP/885BIJIiKigIgSy5VVFSE1atX1/skKiYmBpGRkVi0aBEAWV9y+c/kyZORnp6Ox48f11u+tLQ0ALK5W2tryrFrs7a2Rmpqw2OMtNnl36NwcONVZCe1X9ZSbdLeTwA1TVW5GH/vuocrf0bj+t/t182nteTdjA1MdOoNuAdMdIFOTdKk68djERYmq2t5q27t7cztFLsEWjkZof8kZ+5YpQWtCzBaSqFltoEEUABg7SJLAlWcV4HSVgZEyuWQoLxY1tLk6GkGXQMBuvtaYtQiDwCAuLomYyYL3P4nod79yFtmhSI+9IyeDIOxcJA9TKjbMluQ8SSY9R3rBEDWsn56TxiuHYlB4KFIHNtxB+FBKbh7PglFueUtvqYfXk3Dn5/eQmZ8EfLSSnHj7zj8+enNFrWwpUYVKPw/I64Iv2y8ht8/DkVSxJOxxyV5FUqJWVS10OSklDRp/uPs5GIcfO8aft18XSum+FHH93ft8bIDp3bneoAAsu8U+TRZkSE0ZU9b6pJ/q2vlZEh+lIfqNh7XWvvvVkFmGao0JFGZptc1BbMNsLOzU1qWlJSEESNGIDU1Fbt27cLly5dx8+ZN7N69G8CTJFHZ2bIuTo6OjvXuPzNTNk/jm2++CaFQqPCzevVqAEBOTv1TMsiPVXde16YcuzaRSNQpk1t1BpJqKcICU1CSV4kTX91Vd3FIF/ToWjrXPfLhlTQu069UIkXc3Ww8CE7Bo2vpTQqWyourcPtsAuLuZSvdsLc2sVJRTTBrbKlX7zoiQyH8JsgC0tzUUlTnybrhylsYze0M4Pd0N5jZ6qsc82nbw4T7PbOduu42pqqJY2YBwM7VlPu9tQlppFJWIRiq3Srba5gdVnwxApNf7gPvEQ54ap47zO0NuIzKmfFFyE5W/bBN3jJraq2v8HDTwkH2MKE4t0IhM2xBTcusQMhDvwnO4NW0NtY+v4y4Ivz3WxSuHYnB8R13Ia1ufotkemwhAg9FQiKWgmEAUxtZq3FJfiXCg5v3cFUqZZEWXQAAcPAwhfw0i/MqkJNcgpPf3Me1ozEAFLsYy8fM1Q1m02IK8PvHoQjYckPhIWZVhRhp0QXcNVpRUo1/9oajvKgKhVnlrZ5CqTkKs8tx93ySQvZwTdGUpGLiagnyM0qR/CgPsXeyAMjqq5uXOaav7Yun5rljyuo+eP7jYeg9SnavkxlfhNTH+QgPSsHp3fdx5PNbrcoC3lyslEV4UAqSI1V/F+SmliDmdlazMoWTtlVa8ORar66QIL6Nh7TUfQibk0KNIB1Ba7sZN4WqVs3jx4+jtLQUR48ehbOzM7f83r17CutZWcm6yNUdo1qbpaWsW9iGDRswe/Zslet4eHg0un1enuIXZ1OOXVteXh63L6KopODJk/nKMjGqysXQ0aPLpjV69+6t7iJ0GlIpi7CgJ9dpVbkYj29kwN7NFBcPPkRWrSy5Zrb6ePb9QVz3zqqaDMFGFiJIqqV4fCMDIcdjZYmZABhb6cF3jBOsnY1w/UQscpJL0HOQLfqMceS6mzZHUU03YyMLUYPr9RntiLvnElFVIYGgQNZrRD5m1thShGGz3TBstpvKba26GcnGcrKy1pj26mocFZqBO/8mYdhsV4UMv8CTOWaBxoNZ2x7G0NUXoLJMjITwXHgOVX4AqkpVhRgMj4FQh4+i3HI8vp6BiP9SUVpUhaeXe8N9oI1CMGtsqafw98h3rBN8xzqhOK8C//feNbAsEPFfKkYtknWZk0pZ8HgMqirESI+VZZu2cFRsCZcHswCQl1oCOzdTAE+CWRNrfegb68CljyU3hpnhMXAfaI2o0Ewu+VdxXgXM053AStl6u9mWFVUhPbYAtj1MYGCiC4lYiqBfIwEW4At4mLbGF/bupvj9o5vITS1B2KVk9B3r1Gg3b7mc5GKuBcRjsC26+1oh+mYmDEx1kfwwD9WVEtz9NwmuftYK47dtuhsjNapAKZh9fD0DYAFxpQRnvgvDvA0DoW+sg3P7IpAUkQs9IyHcBtggLapAITFa4oNc9Byo3LW5PqyURXxYDqydjWBo1vB1VVfgoUikPs5H8sNcTH/dD4Asa3hmXCH4Ah5sehjj0dV0RFxJg1t/awyY5NKs/dfHzdkLpQWV3Hjx5nrwXyqu/BmNPqMdMXSWq8r7rMc3MhB8+LFCMjZA9v3A4/Ng72YK+5rPKwB4DrXFrTMJAIDjOxUfOt86k4AJL/i0qKzN9Tg0A//9FgWGx2DhpsHcAxpA9n19bMcdVJaJ4fd0t3q/AzsDqZRFamQ+PN291V2UNlf3ofCja+noOajp12xDqirECn8/ACArsRj27m2fHLCtafp9mVbflevqyr6Mm9MqKf/ilW8LyFo89u3bp7DesGHDYGJigr179+LZZ59V+YXt4eEBd3d33L9/H5988kmzy+/s7Aw9PT3ExsYqLG/KsWuLi4uDj0/HfNlrmpI8xS+++PvZ8BjStBtWotrhw4c1/ouzrSQ+yFVKjnTjRByqKsSQShSf3udnlCHmdhYsHY1w+1wCYu9kQ1IthZ6REOIqqdIf0aLsclz+PUphWXhQCiL+S4X/Ig94DW9aNlxANs6wNF92LTQWzOqIBPB6yh73LiSjMLUaWYlF3A1/Q6268m3NbA2Qn17a5kmVqirEEOrwUZxfgUv/FwlJtRS3ziQoBLMSsRQZcU+mGmosmOXxZS1F0beykPwwFxKJFHx+wx2e0qILcOKru2BZwNhCpJSM6OqRGPToa6WQcKu+983IXATn3pZICMvB4xsZqCgVoyCrDLmpJejmZQ6PIbbcnLmudR4M1A5mc9NKuWBWPmZWfiPuOdSWC2b7jnXCsDluGDG/JyRiKf47HIW4e9nIS6hCWGAK1y1ZXC3BpV8iUZhVBr6Qh8y4IkilLKydjTD33QG4ez4JeWmysboDJrvAoafsZq/fxG44/9NDlBdX49G1dK7FrT7VlRLc+TcRSQ+edCN26GkGY0vZgxxA1l34949CZe99TAF3M8sTMLDqZsQFsyzLgmEYsFIWCWFPWlhL8ivx708PMOKZnlx35fLiaoQHPnkIxfBk2yVF5HEPEprixt9xuH02EUbmIizcPBjVlRKkRhXApbdFg4G8VMois+ZzmvwoH6lR+QgPSkXc3SyuFyWPx3C9M3JTSiAyELZ6PGl6TAFC/y8HYQY3sOCDwTAwaTygrSwX48bxWJjaGsB7hD1CT8ZBUi3F3X+TINTlY+CU7grrPwhOQfDhKKX98AU8+PirLr+JlT7s3U251nkAYBhZj9K0qAKubttbaqRs3DYrZXH7XCI8B9vi/qVk9BnjhKKccq4Hzt3zSXD2seA+922pKKccd/5NAl/AYNgsN25oh7hagqSIPNi7m0JkIGxwH1f/jEZYYAoqBfnwG9CnSe9dXnopIv5LhddT9grfLZ1N3ZbTlMh85KQUw9LRqM33DUBjhqdp+n2ZVgez8orbtWsXlixZAqFQCA8PDxgZ1f+hHj9+PHR0dLBgwQK8/fbbqKiowHfffYf8fMXkE4aGhtixYwdeeOEFjBs3Di+++CJsbGwQExOD+/fv49tvvwUAfP/995g0aRImTJiApUuXwsHBAXl5eXj06BHu3LmDP//8s96y6OjoYOjQobh+/XqLjg0Aubm5iI6ObtE8tdqgJF9xzFTUzSwKZlupJQ9uuprSgkqEHI9FzG1Z9zkdER+9Rzni9tlErtsgw8imqPHxd8Bfn91CeXE1rp+IQ2VptUJXWPm4SkA2nnX4PHeUFVUh7FIyFxAxPAbWzkbIjJcFFYH/F4ni3AoMnv4k4UN4UApunUnAoGnd4T1C8aaxJK+Su0luLCAFgN6jHXH/YjJYFrj6Vwx3U92UbW1cjGTBbGJxi29CWZbF3X+TUJBVBvcBNkiMyEXYpRSYWutB30SXC/Cyk0vASlk8+C8VUaGZyM8o5W44eTym0Zs+AHDubYnoW1moqpAgI7YQdm6mDQYzN0/Hcw8qageyRuYi2djbgko8vJrGJc0S6CqOda3Le4Q9EsJyIK6Sct0xASApIg/ZNdk0dUR8OPUyV9jO2EIEoS4f1ZUS5KbI1hNXSbhxoma2smDWpbcl+k10RnW5GIOmyQIP+fsy5nlP5KQUoyinAjdPx8NjiC1EBkJE3chE9M1MpbJmJRYjI64Id84lApAlAfN7+sk0TG79rHHjRByKcipw599E9Bpu1+DUFnf+TcSt0wlPzslKT+kzZuFgAH0THZQVViEroYj7Tje3M+BaF6USFpVlYogMhMhMLOJaag3NdVGSV4nUxwW48PNDbp9mtvrIzyyDqbU+unmZw9hSD1f+jEZFiezhjW13EzSmrKhKNpUQZK3bt88m4vGNDBTnVqDfRGcMnela77ZFOeXc9B8AcPKb+9xnWq7uMIPLv0XB1EZfKXFbU7FSFlf+jAYDHipLxYi+mdmkKbSCAx5zn4X8jFKF76vQk/GIu5cNl96W6D/JGekxhVwgKxTxMWy2G0xt9GFopgtDM90GPwtPzXNH4KFImFjrobe/I7KTinHlz2iU1XQBr91K2l4y4p88gIu6noGYm5kQV0uREV8E49oPAVngwoGHePb9wdCt6elVWliJh1fS0N3XssWBlbzVW/5ZMDDRRb8JzpCIpfh71z2kxxTCycsc09f0rXcfRbnleFDTzV9XbIaMuCLYuTb+eb52NAaJ4bnITi7G7Df7t6j8HUF+bXfztkDyozywUhahJ+Mx+eU+rd53Sa1gVkfER1WFpNnBbEc9eKlL0+/LtDqYHTVqFDZs2ICDBw9i3759kEql3Dyz9fH09MSRI0fwv//9D7Nnz4aFhQUWLlyI9evXY9KkSQrrrlixAvb29vjss8/wwgsvgGVZuLi4YMmSJdw6o0ePRmhoKD7++GOsXbsW+fn5sLCwgJeXV5PmmV20aBFWrlyJ9PR0hTG+TTk2AJw4cQJCobBJx9JGxfl1nuI9ykN5SRX0DJufbZPITJs2rVPMS6lOIcdjZV0Za/Sf5IJew+3wKCQdZYVVcOplhiEzXWHtLMsA2Xd8N4QcjVXo0ujW3xq2PUyQmVAEHo9Bj75W6OZjzt3w9R7liPh72ciIK4TnUDtYOBgiLboA/3wfjoqSatw6k4DuvpawdjZGcV4Frv4VA4lYiuCAxzAw1YVL7ydDD2pnMm6sZRYAjC304NrPGjG3sxRaS0yaEMxaOxsjMiQDlWViFGaXc12iS/Ir8fBKKnh8Hvye7sYlgSkrqsLDK2lwH2gDEyvZ/tOiChByTNZj5dHVJ0lh8jPKkF8rwZG4UoKkh3n47zfFliBDc10Mn+MOHVHjfyK7eZtzXaMvHHiIsoIq+Pg7YMQzPVFdKUFRbjk3/2VeWilSalpvbHsYQ9dACHM7A7gPtIG5vQECNl2XBXLnErmbbxNLUYM3N87eFvAd54TUx/kQV8la6vMzylBRUo3ymhu37r5WSkm7GB4DCwcDZMQV4cF/qSjMKZcFsDUxkPz4DI+pN7DS1RfiqXnuOPNdOCrLxLhzLhHDZrsh9q6sJVeoy4e5vQGMLfW4gObiwYdc99HB03soJPPh8WXjdIN+fYySvEqEB6ZywW7oqXgkReTCyskIrv2t4eBuishrsrrVNRDAztUUfuOdlMrIMAxsXIwRfz8HadEFqCiRBVMOHmYKWZPLCqsgMhAi4f6TVtmpr/rWTPki5qbZcPAwxcx1/SCVSLku/yX5lbjyZzQAWW+LhoJZcZUEleVi3D2fpBCQyrvJAkBCWE6Dway8VVtOHrzY9jBG/0kuqCoXIzW6AKbW+rDuZiQLdsVS/PvjA8zfOBCJD3JRkFmGQdN6NNr7QC7mdpbCsIeY21lcMCuVSFGSXwkjC9lnNTEiF7mpJeDxGIWHGvIgSUdPAB6fQUVJNXKSS5CTXIKK0mruPRbo8jFznR/3/dcUVt2MMH/jQO7/OnpPzistuqDBYLa6SoK81FJYuxi1OJCoKK3muugDsocJ8gcK5UVV3LVoYq2HwqxylORV4uapeDw1zx2ALNFk7J1s3P4nEaMXe6rMxN2QopxyBB9+zF2/AHD7bCK8htsj5Hgs0mNqWvIf5iEvrZTLJg/IAqikiDwwDBB7J0vhQUh4UEqTgll5IrnM+CKIqyRNHiLQkaqrJNzDSjs3ExiY6ODRtXTE389BVmJRsz5vqtRumXXubYnom5k1f3NKYWZr0MCWMgnhObhw4CG8n3LA0Fn1X//tQdPvy7Q6mAVkTyNUPZFISEiod5upU6di6tSpSstVJViZNGmSUpBbV58+ffD77783XlgVFi5ciHfffRe//PIL3nnnnWYf++eff8a8efNgY2PTouN3dSV1glmplEXYpRSFFi3SPJr8hdlWUh/LAhpzewM8Nd8djh5mYBgG8zcORHWFROnGy2ekA+6cS+TGw45a5KHUeloXj8fAtZ81XPs9yXZu726K2W/2Q8CWGwALRN3IhLWzsazrn1h2Q8yywPmfIjDnnQEwt5P9Aa59k2bchGAWkHUfjb2brZDsxNiq8W2tXZ7cUGQlFMHUWh/XT8Ti7r9JXItmRnwhJr7oA4EOHxd+jkDyo3xEXEnFgg8GQ0ckQOSNDKX9mtvLui+zLLjgEwDunk/k1uk5yAaOnmboOchWIchqiJ6hDmy7GyMjrogblhAWmAIdPQEeXklDWVEVBkx2weDpPRBea3z02CVeSvXcf5ILAv8vEiX5ldx3T2Ot2QyPwVNz3RWWhQelKATorv2t624GADB3MERGnKw1KflhnkKSJ3nLbGNc+ljCztUE6bGFCAtMgccQW6TUJMBxH2SD0TXjeAuzy5GVUMRlV9Y30YFLbwul/fUaZof7F5ORn1GGW/8koNcwOxTnV+DmqXgAspvlB/+lwnuEPfceDZneAz7+9XdJtukuC2Zrj4116GkGoc6TOi4rroI5DBBf08XY2tkIFvaG6DfRGSFHnwzlkXfPrz01kaGZLiydDJGTXIKkB7kYPE3574NUIsW9C8kIPRWv0IqqZyRUaK0EZMFqQw9Nawez8u60xpYiTF7dh9um9jhA/4UeuPTLI5QXV+Pwlhtczw6GxzQ6djP+fjbCg1O5bs1ymfFFKMopR2ZCEUJPxqMgswxuA6zh1s8aZ3940OA+PQbbou84Jzz4LxVxd7NRmP2kNRAA+oxyaHVgYW5vyI1nT43OV5lwDpD9TT/1zX2kRRegt78DRi6oP09JQ2onrKtdp/rGOgqfuwkv+ODyH1FIjylEeGAKvIbbQ99Eh/vcScRSXPj5ISrLqtFntPLDmfrE3snmvtP6ju+Ge+eTUFUuxm9bb6C0UHFMeMTlVIx4RjZTB8uyuHYkBvcuqJ5LOvZ2FkrnujXYpVxcLeGuRamERVZiUaccJ1p7Ci4DE130HGiDx9czIJWyuH48FtPW9G1Vq2jtYNZvfDfuQc7DK2kYXuc7WpWIy2moLJU96OozxrFJ3fjbiqbfl1E2Yw0nEomwZcsWfPnllygtLW18g1r+++8/3Lx5E1u3bm2n0mk+eZc0CwcDWXIayMa7NGXKBqKatvcCKMot5/7wew61g5OnOfcH1MBEV2ULgo5IAP8FHjC3N8DoxZ6NBrINMbM1gIO7KQAg+nYmclKKEVnTSiwPYKoqJPj3xwjupjvmjqylTc9ICCOLxltXAdmYzOSyMIVlhuaNB7OWDobgCWTvR1ZCMUoLK3H7n0SFMcSJ4bk4vScMGfGFSH4kezBQkleJkKOxqK6UILam+7aTlzmemueOsUt74dn/DcKMdX5wH2iDyS/3AY8vO0bq4wIAgK6+AOOWeqHXMPsmB7JytR8YCGoCpFtnErib2FtnEnDlj2guyHb2sVBZzx5DbJWWN6Vrdl1eI+xhXNNKrSPio1udLsZyNi71BwxNTRLGMAzO3D0IQNZC+Peue1xd1R6n6z5AMaD2HGqnEBDK8fg8LsCqKhfjxt9xCLtUc6PNgPtsRFyWTU3HEzBwG9Dww1jrOufJMIC9mwn0jZ/cLJYVVSI9tpALFLv7ynom9BnlCEMz2Xq6+gL08FOdlMzZWxaYZyUWK/19kEikOPHVPYQci1XqDjzhBR9YOhly5yKXFJGHI5/fRsDm6ygvVgxG8moy9Bqa6WLUc55w7WeFaa/1rTf47TXMDl7DZT23ag9RCA9ORXmJ8rREcmnR+TizNxzJD/O47dIqnnS3Prr9Nv79MYJ72BVzK0tlIDt6sadCV3mvp+xhbKmHYbPdMOWVJ9ciIMtY7Du28e7LjeHxGG4cuHzcrCoPr6RxvUfCg1MVpnGSk0rZRrPBZ9bqYjzttb7o4WeFsUt6YfxyL265ub0BLJ0MMfLZnmAY2X4v/xElaw0Vy/Yv70Fx9UgMspOKERaYjBt/x0EqUfzcALJW/tSofEgkUsTUDDEwsdbDsNmusK/5jpcHsgYmOrDpLrsOIq9nICE8B7fOJODMd+EqA9kRz7hz5/7wSlqD516UU6HQIixPOtfZ1M5kbGCqA2NLPfSquS6SH+Ur9OJpqtqfC/n+dUR8WHUzgkNPUwCy97vuda9KTk1GelbKKvTc6giafl9GwWwXsHLlSqxduxZxcc2bozI3Nxe//PKLRk+U3N7kLS2G5iKuO5CkWoqQmmkeSPMdOHBA3UXoMCzL4sbfcQgKeMxlW5V39wKgkJGzMe4DbLDgg8HNStxU774Gym7+ywqrcPLr+wAra6WZtKo3+k2UT61TgpDjsSjJr0RqVD5XhqYmtwGAt3cs437XNRA0mhwJkN3MWdYkEMlKLFKYB3XUIg/uBiElMh+nvrmvsO2D/1IRHPCYS4blM9IBvmOd4DnEDgyPgUNPMzy9whvd+1gqdLMDZN1O68vG25jeox0xbpkX5m8ciEmrFJNoCGq6cd6/lAxxTbl6j1bdisjn8zBmsafCMmPL5mW5le9n/DIv2PYwwYhnetY7L3DPQTYYPKMHRi/2xIDJLgqvNSdr+9c/blP4TMm3d6g1PrN2wA/IAqz6OPe2gIOHKQBZnT6+IWvhcO1rhaeXK2ZY7eFr1ejYZmtnY1lrfA1LJyPo6guhb/Ik+MtJLsG5H2RzLfJ4DNz6y85HoMPH2KVesHYxxsgFPesdt1m79TsqVPFG9EFQKhcwmdnqY/D07nDtZ43hc93g4GGGSS/1Rt/x3TDnrf7c9XX1SAwy4gqRn1GGx3V6GuTWBNzm9gbwGm6PiSt7NzomdMQzPblgRv6vuFKC6yficO9CEtcyKFdZVo3zPz+UZZwW8uA2wBpPzXPHxm+XcceSB0oiQ6FiSz4DjHm+F0Yv9sSU1X3gNdweI57pCYbHwLWfFSxrZdY2szVA33FPWiC9RzoodP9uDXlAV5JfiUv/F8kln5IrK6rihiPIXfq/SIV5jtNjC7FvbTCOf3m3wXmEM+Nl3+umNvqw6maESS/1hudQOzh6mssCJgYYOKU7GIaBpaMRvGuScaVE5uPaEdn9hL6JDmas9ZMFumIWf267hcu/R+PWmQTuGqgt8FAkjn95F0e33+ES5rn1swbDMBjxjDt0DQTQMxKi34RumLdxINdlv6pcjNO7w3Dj7zgu2Zmxpewep5uXOYbOdkXvUY4wd5DVqarx77XVTWLXaYPZOi2zADBkpis3dv7Kn9EKWeQbE3I8FvvfusK9P/KWWfn+5L0BKkqqEXe/4SmAykuqFHoCPryS1urp9JpD0+/LKJjtAvh8PjZs2NDsTGSzZs3S+Kcx7U3eMmtkJoK9uyncam5Yom9lIfI6TdDeEjt27FB3ETpMZnwRbp1JQMR/qTizNwziagnSYwoAyFogLLupJ+ujaz9rrjVE3nroO8YRZrYGGDStOzd/6f2LyQg89Ih76t7cKQy+3bML09f2hVU3I4yY13g3Kzmrmi6G2SklyEl5Esx297XC1Fd9uV4S8vFPDh6mXIuo/MZfZCCEs49yN1buGN0Uk6y0JrMon8+Dx2BbWHUzQjcvC4x8tifM7Q0w5nlPTH/Nl2vp1TMSYtgcN3TzUt1SCgB2bqbwGPLkfW7JNEqAbM7eOW/3b3C6IIGQjwGTXOA13B4DJrtwQYq8VbKpduzYAf8FPWFUq+XdpY+FQgu3kbmIC1Cdepk1eF4Mw2D0c724gFreVb3PGCe49rPmvocBNGk6JF09AcxqBXvyByK6+gLuOrj7bxIXnD01310hOHT0MMO8dwc0OO2OpaMh94Dk8fUM7ka0rKgKoSdlD5pNrPUwf+NADJjcHRNX+nBjTo0t9TB8jhusnY1hVXPtldfqmlq7+7dEIuVaQs3tm/79IdDhY/ab/bD4o6GY83Z/LqB9eDkNV/+KwZk9YVxiLgC4diSGe5g7fI4bJrzgA9+xTvhy55fcgwtA9l3y3NahmP1mf1g7G4HhMRg+x62mNdgeLn1knyX3ATZ48auRKqfJ6T/JBU5e5rDpboz+E52VXm8peT0DQOS1dNw8nYBTu+9zDxav/BnN/S4/p9KCSvzz/QMu6A05FgNxlRRp0QU4v/+hUmItQPbQUt4yK39faxv9nCde/naUwud28PQeXP4Beau3+0Ab2LmaoH/NNEq1h2gk1pnDuLSwEtG3ZK2xtTO/yx8aWToaYcX2EVj2+VMYOkvWTdilj6XSlEq6BgI497bArDf6w3esE6at6Yt+TzuDYRikFD8CIMs1UHuoSV2FWYqvZcQWtttcuo+upeHvr+8hI775AXNZre7W8mBWZCDEmOdlDxCra6biasrczdWVEtw7n4SKkmpcOPgQ6TEFXAIo+Xvcw88Kugay77Db/yQ2+DAkJ0lxPuTC7HKFfBPtTdPvy7R+zCwh9amqEHM3y4bmsi+nobNdkfwoD5VlYgT+EgkDY104NXBjSpRNmDBB3UXoMGk1gSsg6856fv9D5KfLWlVsehg3qaWyPYgMhOjmZY6EcFmXOkdPMy7hBJ/Pw/gV3vjj45uorpRN5wAAJlZ6sHZpXpbNCRMmwMnTHE4bm3eNWNV0uxRXPpnUXs9IyLXYTFzpgz8+vcmNIR4+1x3FuRUIPBTJJfhxH2DdYHdhKycjPMKTB1ItzfKqSu9RjgrTyjz7/iCUFlTC1s2kSXU+aqEH+HwGLAs41tNFuK3xBTzMeqMfom9m1tuVtj4TJkyArr4Q41d449iOO2ClLDxUPPgYv8wb0bcym5TcxsRKD+OW9sKZ72StpZZOhrBzkyWi8V/gAVbKQt9Yp8EHA7XZdDfmkn/JH1wwDANzewMu8RAAeA6zq3cKmIYwDAOPwbYIORaL/IwyXDzwCCmReaiuknLBylPz3BtNjGPvZqrQZRUAUqMLuKQ6hZnlXDduC/vGk8rUxuPzuG7rA6d2V+rZEHIsFizLou/4blyg5OhppvB+TJgwAX6+3SCuksDCwRA9B9lwwyTmvjsAlaViiAxVt5QL6zl3HZGgwQy7LWXpZASb7sbIjC/ipipKiczHsS/voO+4J2Mae/hZYfxyL0iqpYi7l43Ux/k4/3MEfMc4KfSkSQjLQfDhx/Bf4KHQQyU3tYS7V7BVEcwyDAOGr9jrQ2QgxKRVvXH089tcIjD5NTNgigsyE4qQ+jgf+sY6KMmvRPKjPIWpvx7fyFAKGI2t9Lgu6wCUeprw+TxMfrk3okIzYe1shG7eFg32ahg41hMRx2QtlQnhOehro7r7t3wcvFxlmRh5GaVc4ru2IpFI8d/v0RBXyjLHT3vNl+tK3hTyllOegOGCTADo5mWBPmMcEXYpBbmppTizJwzT1vRtMDlaekwBdx1KxSz++T6c+788mBUI+fAZ6YDb/yQiN7UE5/ZFYPLq3ir/BmQnP0muJv+s3vg7TlaODkimpen3ZdQyS1qkpKQEa9euhb29PUQiEfr27YvffvtN3cVqU7W7fMgntDe20MOkVb3BE8i+bE5/F4bH1ELbLKmpqY2v1EVk1OluFXc3m7uhtnM1VUOJnvAd6wQwsq6KE170URi/aGqtj0kv91YIBmvftDZVS+u6dqupPEFR7VYoY0s9TFzZG8ZWevAd6wQrJyP06GuF57YOxYApLnDrb43+dbrNKh3D+ckx9Ix1YGbXflN3mNrow8HDrMkPLwQ6fIxe3Atjnu/VrG7draVvrAPfsU4KLaxNIa9nO1cTzHt3AKav6Ytu3sqt4gamuug7rhv0jJrWjbS7rxVGPtsTVt2MMPJZD+7zJzIUYuJLvTFygUeTu4bLH1YIdPmwq+l+CsjGrA6c2h39JjrjqXnuGLXQo8VJYNwH2nDdmR/fyEBpYRXX8ufsY6GQIbw+9rXKJieplnKtNLlpTwLvul3lm8PZ2wLjl3th6CxXTH3Vl+tyHXoyHimR+VxXfc+hdgrvR2pqKoS6smlzPAbbKrzGMEy9gaw68HgM5rzdHyu+GIEXvhrJtRLnJJdwUy0JdfkYMd8dDMNg/HIv7oFJ7J1snNh1T7YfPsN1o354OQ2nv72PCz8/xN+77iI5Mg9Bvz7mjmnfjB4eVk5GGLvUCwIhD869LbhAlM/nYdprvlj5lT+G1GS1lk/9BchaguWZvI0tRVzw1GuYXaOfXWtnYzw1zx09B9k22j0/rzSDG+aQUKcbem2F2bK/abWHJtT929cWclNKuKEa1ZUS/P3N/Ua7QNcm72ZsYKKr9D4Nn+vOtWqnxxbi6Be3G8yNIs9KL1deXM090Kjd+j1wandZxnsASRG5OLMnnHvgWpt8vKyBiQ7XMyc9phBn9oQpzSHfHjT9voxaZkmLzJ49Gzdv3sS2bdvQs2dPBAQEYMGCBZBKpVi4cKG6i9cmas8xK08AAsie6o9b6oXzP8kS5Fw48Ahp0QUYOsutU/0h76zqzsncVbEsi4yaDKDOPhYoKajk5vMEwN00qYujpzlWbB8BoYivsgXTydMcE1/ywdnvH4DHZ5rUnbOulta1ub0B93Rarm4rlKOHGRZvHaqwTFdPoDKTrCqWDoZgeAxYKctlkyYtU7ue63bfbq26rdwt5T7IFhIxCzM7A25uT0D2oGHQ1O6t3j9Q05W6pymXVMzSyRBWTkbgC3gY2MRj2LqagMdnIJWw8Bxii8ehmWClLJIe5qGbt8WTTMYMYGbX8mAWUBw2MOb5Xjj1zX1IJazCONK63e817fu7doA96SUfBAU8Vkj0M2had+5htUCHjymr++DEV/eQnVTMdTXuOVA2tvzUt/eRm1qKpFrdvuUJ6ACgzxhHLgN8U7n1t4ZLHwulsdgMw4AvZGQ9D2qyrydF5MKhpxky44u4h6LeIx3Qa5gdMuOLmtxLoakKCvLh3tsbYYEpSIspREVptcoAWD5m1qmXOVIiZT3XwoNkmc0bmhu4ueQPNuXElRL8+1ME4sNy4L+gJ3T1G77/qh3M1sXjMRi/zKumN1IucpJL8OenNzF8rrvKB7nJNRnbbbobw6a7McIuPclUX3v/fD4PE170wYmdd5GVWIykiFz8/kko/Bd4KDzcks8JbtnNCCOf7YnivAqkROYjJTIfv38cirFLvJo0RVJLadp1XRe1zJJmO3PmDM6fP489e/bgpZdewujRo7Fv3z6MHz8eb731FiSS9n+K1BFUtczKuQ+wwbTX+3Jf7A+vpuPQphDc+idBKfMkUTRy5Eh1F6FDFGaVc9MzOPtYYNprvtxTbh6PaXAuyo4iMhQ22BXXpbclFn88FIs+HNKirLotrWuBkK90o27h0LZd1gQ6fPQd5wRDc130qSchE2kaTbimeTwGXk/Zt+sNIQAMnekG2x4mGDi1O+a+MwBjnu8F/4UeTU5qJDIQYtwyL/iOccKIZ3ty3VaTInKRk1KMsEDZTbOptX6bdj909DCDjki2P/lDN1MbfYUHuYBm1HV9eHweRj9Xk+yMkY0rr3vt6+oLMevNftwYWh6PQd/x3WBoJsLsN/tzrWw6egKFxGrm9gYtnhu0oYBPz0iHm6Yo8YEsgAoPln0GGJ6sa7ueoQ5celuqzA7eGiNHjoRLzfh5Vsoi8YFypmeJWMrNf25qo4c+Y2TJvHJTS3Hj7/g2LY/84bDIUIjJL/fmHlJE38zEb1tDkRCe02DSpLJamZ1V4Qt5mLK6N/eZKC+uxoWfH+Lo9tuIu5f9ZN7gkioul4OjhxmG18mDUPea0REJMGOtH1z7yYZvlORV4vTuMJz8+h6SI/NQVSFGQc24YysnIwh0+Ji8ug83hK0wqxxHt9/GP3vDkRFX2C6JoTT5ugaoZZa0wLFjx2BoaIh58+YpLF+2bBkWLlyIGzduYNiwYWoqXdspqdXFxNBU+Umek6c55m0cgKBDkUh+lI/KUjFunIjDzdPxcOplDpfelrDtYQwzOwO1jY3sjHbv3o2dO3equxjtLj22gPvd1tUEBia6mLHWD9dPxMGpl1mzssWqU2vmumtNXVt1M1TIZGzu0LpWKFWGzXZrdJ5N0jhtuaabwqa7Mea83b9V+3AfYAP3mumGnLzMkR4ry2r8+0c3uXXaqjVZji/goZuPBWJqxsoCigmU5DS9rhmGweDpPdB7lCN0DQQqA0ChDh/jl3vBfaANRPoC7kGajp4AU1/1RUl+JQxMdVGQWYbggMcoK6rC0yu827QVsjZnb3NkJcgyu8fdy0b0TVkd9ehr1a5zke7evRtffLGDm6/37vkk9Bxoo9C1vyinHPLYysRKHz0H2yAxPAdZicW4dyEJuaklcOkt62LfkgeitckzRtv2MEF3Xys862KMS79EIikiFyX5sgDR1EYf7gOs4dTLHJbdjBQe+MjHzOqruJ+T4/F5sszfPYxx5c8YlBdVISOuCP/sDYe+iQ56+FpBKOJzSREde5mDx+fh6Rd9cOHnh5CKpdwDj9p09ASY8KIPHgSn4vrxWFRVSJD0MA9JD/NkD7pq9ifvai7U4WPqq764f0E2NZNELBvPHXcvG2a2+nDtbw0nTzNYORu3yUMtjb+u2Y7M/Uy6hKFDh0IikSA0NFRheUREBHx8fPD9999j5cqVTd6ffLsHDx7A29u78Q06yKVfHuHRtXToGetg+edP1bsey7KIu5eNm6fikZuqPNcvX8iDlZMhzGwNYGCmCyMz2RgXXX0BhCI+dEQC6Ij4EIoEHTo+Tl1YltWKLp2X/u8RHl1Nh1DEx4odI7TygUZr6josMBmXf4/m/v/iVyOhI9KMBwCEtJWCrDL8te0WNx4PkCUi7Pd022X9lYu+mYl/f4rg/v/0C95cUM1hWdlEvdpGjeedm1qC3z4KlQU8NV2OAeCZ/w2EpWPbdutXUHPOt87Ec62s45b2gseQJ0NOEsJzcHq3bD7xWW/2g72bKfIzSvHHJzchrlKcW9XQTDaPuom1Pkyt9WBYcy9kYKoDA1PdBv9GlhZW4sA7VwEAQ2b2QP+JLjVFZBFxOQ1X/4pWOh4YwMRSD+b2BjC10cfdf5OUtm9IZbkYd84m4sF/qdzY99r4Qh5e+HJEsx9ilBVVIfRUPCJD0pXmn1380VCloL8gqwy3zyYi6nqGcjZtBjC2EMHc3hBmtvowMNWFvrEODEx0oGsghFCHD6GID6EOH3whT2PuvZobF9CdAWm23NxclXPTmpubc6/XJysrC9nZivNtxcTI5lg7/9MDRNo2fY6v9iYfk2Jk1vCTT4Zh4OpnjR59rZAWXYCoGxmID8vhuphKqqXIiCtSGu+hCl/AA8NnwOczYHgMeDwGPL7sh+Ex4PF54PEALsuI/B8V3091v7SU1qlZoHrbRovaIgUVhXj06CF69fKCqUj93Wzbk3wuSKGtGAaf6uPUwlMY12OcmkvVcS7EXcCEyRNw7sy5Fp23pdOTmzRjSxEFsp3YtGnTcPLkSXUXo0sytdbH4o+HIeZWJhLCc+HQ01SWvK0ddPOx4MbrAiqmq7pwAdMmTMDJc+eAcdrzXYYLF4CpU4FTp9Ry3hYOhhgwyQW3ziRwgWx3X8v2DWRr1bXv2NEID05FWaFsXt7M+CJIpSykUhb56U+m5ZFPt2Vma4C57wxAeHAqEsNzuCFbJfmVKMmvVEqeBMgScU16uTecPFWP+82sdf9Ue4gOwzDwGekAt37WiLyejsiQjCc9eljZeN668+DWnZ6oPrp6Agyd5Yr+k5wRfTMTsXezkRqZzwWUzt7KY52bQt9YB6MWemDIjB6IDElH7J1sZMQXolsvc266ptpMrfUx9vleGDytB6JCMxB1M/NJ/g0WKMqpQFFOBRLCGj4uw8iG1/AE8nvJJ/eYqWkpmLPaX2H6KE2ifU0FpE009HSnodf27NkDHx8fhZ+ZM2cCAHJSSrmgrzP8yJ+Em1jrY8mSJcjLy8P27dsRHByMs2fPYvfu3UhNTcWqVasAANOnT4dDTzP8fnUXnl7THXCPhVU/MczdGUh0SyAybPxmXCKWQlwpQWWZGBUl1Sgrkk2kXZRTgcKscuSnlyI3tRS5qSWynxTZT06y8k92UrHCT1ZinZ+EImQlFCEzXvmnvd7TijQG3U28UZHGqL1+2/unumY6jsDs06g8V4kNFzZg2rRpAID58+ejrKwMW7duRWhoKI4dO4b9+/cjJiYG69atAwBu3aZ89uTrrlq1Cqmpqdi9ezfOnj2L4OBgbN++HXl5eViyZInCuuvWrUNMTAz279+PY8eOITQ0FFu3bkVZWRk3/7R83Y0bNyI8PBwBAQEICAhAeHg4Nm7cqLBO7XO6ceMGXtr+EqQeUqwLWIe1a9c2+5xWr1/OPazRNxeo/Zy6Yj211TmdPHmyy51TZ6qn++F3EJUTikzRLRi6VGH9+vXtck4rV62Ao6csgLVwMMR7m955ck5HjyL0tdcwSCpF2dtva089hYUhYOVKBFRWIvz117Fxwwa1nFO5URIkoifTtwyY7NK+n72338YgqRShr72GU6dPoNosDQBQWliF8OBURFxOw6Or6dw4VpYnQeida9w5VaAQv/23E89/MgxXsw5h8IweKEAiTO1EAE85r0p1pQSXDj7CzOmzlc7pr8PHceO8bM5bhge88f4rSucUHR+Jh1lXIHWNwZClFsgzCMOAyS7IKI+GibUeWFbWAsoyYuSUJzarni4FXUBQ2HH0n2OFcOkRTF/TFw/yz2PUIo9WffbCIu7iZMhBTHq1F/7L/QHT1vTF9OnT662nT7ZvQb8Jzvj1xlYs+/wpRBT/C9/xDqgS5UBkwgBMwx1tWVb2PleWilFeVIXSgkoU51WgMLschkILfPH5DpWfPXV87+3du7fBc6mLuhmTZmtNN+P6WmZnzpyJb975E93sWpZAob3o6AkxaFr3ZmcorI9ELEVpQSVKCypRVSFBVYUY1TX/VlVIIK6SQCplwUpYSCUs9/RTKpHKlklZ7qm5nNIVXGsBW+cXVmkBVKzbPl8J2WU5uJt+F8gCYA342fnBSr/xqSo0WR4vG6tz5qCcJ3uKemrBKUzpOUXNpWp/p6JOYdrhacAxALNaft4nvrqLlMh8+C/0gM/I5s/9STrGkiVLcPDgQXUXg7SBgswy3L+UDM+hdrBxqTVn6qlTwLRpWALgoPz/U7r+d5n8vBX+r6bzLiuqwrWjMbDqZgTfMe3TOg9AZV1LJ07Cvz9FICUyn5s3l8cADJ+BQMiH7xhH+Pg3LZEey7KoKK1GSb7sXij1cT7uXUgGAHiPsMdT892RlViM6JuZSInMR0Hmk9ZfSydDPPPeoGafklQiRVlRNUSGgnYb26xurFT2vpYVVaGssAoVZdUQV0lQXSlFdaUY4iopqislsntLiZS7n5RKWFwPuYEVb89S7o2hJs3tZkzBLGm2lStX4vDhw8jPz4dA8KS18bfffsOCBQtw9erVZiWA6qxjZknbYVkWA/cNxJ30O2DLWDD6DPrb9Ufoi6EaM4ajuRTOGSwYdP1zBtq2riXVUhTmlMPMVr9Lv2eaLi8vjxtmQroglgUGDgTu3EEey8KcYYD+/YHQ0K49frbWeXNjZrv6eauhrlkpixNf3UVqVEGD6wlFfIx+zlN5HDdptc72Hd7cuIC6GZNmmzVrFkpKSnDkyBGF5QcPHoS9vT0GDx6sppKRzup09GncTr8NFixwF2DB4lb6LZyJPqPuorUbhXOGdpwz0LZ1zRfyYG5nQIFsJ/fTTz+puwikPZ0+Ddy+DbAsfgJkAc+tW8CZrv1dVvu8AWjHeauhrhkeg1HPeUKgy1da7uBhisHTe2DO2/2xYscICmTbiaZ/h1NGDdJskyZNwvjx4/Hyyy+jqKgIbm5uOHz4MM6ePYtDhw6Bz++aXThIy7Asi81Bm8GAkQU4Nb1FGTDYHLQZk90nd7lgRemca3Tlcwa0s64JMGhQ87v9EQ3BssDmzbJWOZYFV9MMI1s+eXLXbKWsc96crnzeaqxrU2t9PPu/QUiLLkBxbjn0jHTg2s+6yfMzk9bR9O9wCmZJixw9ehTvvfcePvjgA+Tl5cHT0xOHDx/Gs88+2+x9VVbKMt3JsxqTriUoIQi3799+siAbgH5Ni13WLXx35jv4u/irrXztQemca3Tlcwa0s64JEBkZCUvLrj3+XWsFBcla6mpEArAEnrTYffcd4N8Fr+k6583pyufdGeraBDAwAYByxCcXtu+xCKezfYfL4wF5fNAYGjNL1O7gwYNYunSpuotBCCGEEEII6QSOHz+OGTNmNLoetcwStevZsycA4I8//oCXl5eaS0Pakzxz9fHjx+Hm5qbu4pB2RHWtHaietQfVtfagutYenbGuKysrkZycDP8m9gSgYJaonbGxLP2/l5cXZTPWEm5ublTXWoLqWjtQPWsPqmvtQXWtPTpbXffr16/J61I2Y0IIIYQQQgghGoeCWUIIIYQQQgghGoeCWUIIIYQQQgghGoeCWaJ2VlZW2LRpE6ysrNRdFNLOqK61B9W1dqB61h5U19qD6lp7dIW6pql5CCGEEEIIIYRoHGqZJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicSiYJYQQQgghhBCicQTqLgAhBQUFCA4OhpOTE3R1ddVdHEIIIYQQQogaVFZWIjk5Gf7+/jA1NW10fQpmidoFBwdj5syZ6i4GIYQQQgghpBM4fvw4ZsyY0eh6FMwStXNycgIg+9C6ubmpuTSkvb3yyivYvXu3uotBOkBz61oilqIgqwylhZWoLKmGRMKClQKslIVUyoKVsmBZFlIJC5Ztx4I3UScoQqdw8fx5jB0/Xt3FIO3M2FwXO/d/SN/fWoL+VmuPzlbXMTExmDlzJhcfNIZh2c5wS0C0WUREBHx8fPDgwQN4e3uruziEkA4krpYg9k42Hl1LR0ZcISTVUnUXiRBSj6df8Ib7ABt1F4MQ0oU1Ny6glllSrx9//BEvvvgiDAwMUFJSovDanTt38Pbbb+P69esQCAQYM2YMvvjiC/To0UNNpSWaYsmSJTh48KC6i0E6QGN1nZ1cjNO7w1BaUNnkfTIMwPAYMDym1eVr/R7aaiearbKykvIddHHiainAAn9+fx4bBzyn7uKQDkB/q7WHptc1BbNEpdTUVLz55puwt7dHYWGhwmuRkZEYNWoU+vbtiz/++AMVFRX44IMPMGLECNy7dw9WVlZqKjXRBDt37lR3EUgHaaiuM+ILceqb+6gsEwMA9IyEcPWzhp2bCUxt9GFgogu+kAcejwHDZ8DjM+AxbRPEkraVl5cHc3NzdReDtKMrf0Tj/qVkmPDtUZRTDmNLPXUXibQz+lutPTS9rmlqHqLSqlWrMHLkSIxXMQ7qgw8+gK6uLk6dOoXJkydj9uzZOH36NLKzs/HFF1+oobREk/z000/qLgLpIPXVdWlhpUIgO3SWK5Z8Mhz+Cz3Qc5AtrJ2NYWCqC5GBEDp6Agh1+ODzeRTIdlJ0TXd9vYbbcb9HhqSrsSSko9B1rT00va4pmCVKDh06hODgYOzZs0fpNbFYjFOnTmHOnDkwNjbmljs7O2P06NE4duxYRxaVaKBBgwapuwikg9RX16F/x3GBrP9CD/Sb4Ay+kP4caSq6prs+CwdDWDsbAQAehaRDKqV0K10dXdfaQ9PrmroZEwVZWVlYu3Yttm3bBkdHR6XXY2NjUV5ejj59+ii91qdPH5w/fx4VFRUQiUQdUVyigcrLy9VdBNJBVNV1TkoxHl6Ttey49LaAz0iHji4WaWN0TWsHz6F2yEosRkleJbITi2HT3bjxjYjGaovrmmVZlJaWoqioCJWVlaCcs52TQCBAXFxcux6DYRjo6urC2NgYBgYGYJi262lFwSxRsHr1anh4eODll19W+Xpubi4AqBwfZW5uDpZlkZ+fDzs7O6XXAVmwnJ2drbAsJiamlaUmmiQ2NlbdRSAdRFVdXzsSA7CyJE7D5tBUXF0BXdPaoZu3Bfd7ZkIRBbNdXGuva5ZlkZWVhby8PACAUCgEj0c9cDojMzOzdj+GRCJBYWEhCgsLYW5uDmtr6zYLaOlTRThHjhzByZMnsW/fvkY/YA293tBre/bsgY+Pj8LPzJkzAQBXrlxBcHAwtm/fjry8PCxZsgQAMG3aNADAunXrEBMTg/379+PYsWMIDQ3F1q1bUVZWhvnz5yusu3HjRoSHhyMgIAABAQEIDw/Hxo0bFdaZP38+ysrKsHXrVoSGhuLYsWPYv38/YmJisG7dOoV1lyxZgry8PGzfvh3BwcE4e/Ysdu/ejdTUVKxatUph3VWrViE1NRW7d+/G2bNn6ZzqnNPMmTO73Dl1xXpqi3OqqKhQOKdnZy1B8qN8AIDbQAv8+H97NO6cumI9tfacZs6c2eXOqSvWU2vPKTIuDBKmCgDw5y8nu8Q5dcV6aqtzSkhIaNU5bd68GXl5eaiuroarqyuEQiEcHR1haGgICwsLWFlZQV9fH926dQPDMOjRowckEgl69OgBoVAIe3t7GBsbw8zMDDY2NhCJRHB2dgbLsgrr6ujowNbWFiYmJjAxMYGtrS10dHQU1mFZFs7OzhCJRLCxsYGZmRmMjY1hb28PoVCosC7DMOjWrRv09fVhZWUFCwsLGBoawtHREXw+X2FdPp/fJc7Jzc2t3c9JJBLB3t4ePB4PCQkJCAsLq/ezt3fvXjQHzTNLAAAlJSVwc3PDc889h//973/c8tWrV+Pvv/9GSkoKhEIhUlJS4Onpid27d2P16tUK+3jrrbewY8cOlJWV1dvNuL6W2ZkzZ9I8s1pi1apVzf6iIpqpbl1fPRKDe+eTAACLPhwCU2t9dRWNtCG6prXHtlW/wgh2MLXRx6ItQ9RdHNKOWntdp6WlobCwEK6urtDR0WnDkpG2lpiYCGdn5w45VlVVFWJjY2FiYgJ7e3uV69A8s6RFcnJykJmZiR07dmDHjh1Kr5uZmWHGjBn466+/oKenh/DwcKV1wsPD4ebm1uB4WWtra1hbW7dp2YlmoZte7VG7riUSKR7fyAAA2fQ7FMh2GXRNa48xU4fh5ql4FGSWoaK0GiIDobqLRNpJa6/ryspKCIVCCmQ1QEcFsgCgo6MDoVCIysqmzy/fGOpmTAAAtra2CAwMVPqZMGECRCIRAgMD8dFHH0EgEGDatGk4evQoiouLue2TkpIQGBiI2bNnq/EsiCaQdychXV/tuk56kIvyIlkXxV7DVD+NJZqJrmnt8d2BL7nfsxOLG1iTaLrWXtcsy9IYWQ0RHR3docdjGKZNk4FRyywBAIhEIowaNUpp+YEDB8Dn8xVe27JlCwYOHIipU6fi3XffRUVFBT744ANYWlrijTfe6LhCE4108uRJdReBdJDadf2oJoOxUJcP135W6ioSaQd0TWuPPQd2YP+bVwDIkkA5eSkngyRdA13X2sPd3b1Dj9eWmYwBLWuZ/euvv8AwDH7//Xel13x9fcEwDM6dO6f0mqurK/r169esYy1duhQuLi4tKufmzZvBMAxycnIaXfeTTz7B8ePHW3SclvL09ERQUBCEQiHmzp2LpUuXws3NDf/99x+srOgmlTRMnuiCdH3yuq6ukiDpoSyjpaufFXRE9By1K6FrWnuse3MNjK30AMiCWdJ10XWtPRITE9VdhFbRqmB21KhRYBgGgYGBCsvz8vIQHh4OAwMDpddSUlIQFxeH0aNHN+tY77//Po4dO9bqMjemvYPZAwcOoKSkRGl5//79ceHCBZSWlqKwsBDHjh2Dq6tru5WDdB3vv/++uotAOoi8rlMj8yGplgIAuvelB15dDV3T2uP999+HjYtsSp7MhCKaN7QLo+tae9Q3nWZDli5dCoZhlH48PT3boYQN06pg1tLSEj4+PggKClJYHhwcDIFAgBUrVigFs/L/NzeYdXV1hZ+fX6vKS0hX1NE9CYj6yOs64YFsfmqegIGjZ/vPZ0c6Fl3T2uP48eOw7WECACgvqkJGbKGaS0TaC13X2qOgoKBF2+np6SEkJEThR1Xv1/amVcEsIAtKHz9+jPT0dG5ZUFAQBg4ciMmTJ+P27dsKiY2CgoLA5/MxYsQIALIB7Xv27EHfvn2hp6cHMzMzzJ07F3FxcQrHUdXNuKCgACtWrIC5uTkMDQ0xZcoUxMXFgWEYbN68WamsmZmZWLBgAUxMTGBjY4Ply5ejsPDJHw6GYVBaWoqDBw9yT0RUjXslpDOhFnzt4erqCpZlkRguGzLh0NOMuhh3QXRNaw9XV1e49bcGjycb8yYfC0+6HrquNUt1dTXEYnGLttXV1W3RdjweD0OGDFH48fX1bdG+WkMrg1kACq2zgYGB8Pf3x/Dhw8EwDC5fvqzwWr9+/WBiInsS+dJLL2Ht2rUYN24cjh8/jj179iAiIgLDhg1DZmZmvceVSqWYNm0aAgIC8M477+DYsWMYPHgwJk6cWO82c+bMQc+ePXHkyBG8++67CAgI4CasBoCQkBDo6elh8uTJ3BORPXv2tPStIaRD6OnpqbsIpIPo6ekhN7UEJfmyFPwuvS3UXCLSHuia1h56enrQN9aBSx9LAED07SxUVbTsBpp0bnRdN0ye3yYiIqLBhieg6Q1hLi4uWLp0qdKxRo0apdBYFRQUBIZh8H//939444034ODgAF1dXcTExAAA9u/fD19fX4hEIpibm2PWrFl49OiRwj6XLl0KQ0NDxMTEYN68eTA0NISTkxPeeOONNp02pyNoXTDr7+8PHo/HBbO5ubl48OAB/P39YWhoiH79+nFdi5OTkxEfH88FwNevX8e+ffuwbds2fPHFF5gwYQIWLFiA8+fPo6ioCF9++WV9h8XZs2dx5coV7Ny5E++88w7Gjx+PDz74AMuXL693mxUrVmDLli0YN24c1q1bhxUrVuDw4cPcGJUhQ4aAx+PBysqKeyLi5eXVRu8UIe0jNDRU3UUgHSQ0NBQJYbnc/519LNVYGtJe6JrWHvK67jVMNsZOXClBzO0sdRaJtBO6rpumsYYnoOUNYY3ZsGEDkpKSsHfvXpw8eRLW1tb49NNPsWLFCnh7e+Po0aPYtWsXwsLCMHToUKUpeKqrqzF9+nQMGzYMJ06cwPLly7Fz50589tlnTTp+eXk5bG1twefz4ejoiFdffRV5eXktPp+W0rr+XmZmZvD19eWC2eDgYPD5fAwfPhyALNi9dOkSAOXxsqdOnQLDMHjuuecUmvJtbW0V9qlKcHAwAGD+/PkKyxcsWIANGzao3Gb69OkK/+/Tpw8qKiqQlZUFGxubJp4xIZ3LihUr1F0E0kFWrFiB/w7KsiSa2erDxIqe9HdFdE1rD3ldd/M2h76JDsoKqxAWmAKPIbbg87WufaRLa8/r+vIfUchJVk4uqg6WToYYMb9ni7dfsWIF3nrrLQDAuHHjEBMTg/379+Onn34CwzBcQ9iOHTuwfv16brsRI0agZ8+e+PLLL5scPNbl6uqKP//8k/t/QUEBtm7dismTJyMgIIBbPmrUKLi7u2Pz5s349ddfueVVVVXYsmULZs2aBYFAgLFjx+LWrVsICAjABx980OCxfX194evrCx8fHwCyOGfnzp24ePEibt68CUNDwxadU0toXTALyILTL7/8EmlpaQgMDET//v25N93f3x87duxAYWEhAgMDIRAI8NRTTwGQjWFlWbbeQLJHjx71HjM3NxcCgQDm5opzsjUUlFpYKHbJk/dpLy8vb/wkCemk1q1bh4MHD6q7GKQDrFu3HsNNlwEA7FxN1Fwa0l7omtYe8rrm8XnwHuGAm6fikZtSglunEzB4ev33QETztOd1nZNcgrTognbZd0drrOGpNQ1hjZkzZ47C/0NCQlBeXq7UVdnJyQljxozBxYsXFZYzDINp06YhOTkZ3bt358ovb9RrSN3W5/Hjx8PPzw9z587Fvn37lF5vT1odzAYFBSEoKAiTJ0/mXpMHrv/99x+XGEoe6FpaWnJjalUNlm5oALWFhQXEYjHy8vIUAtqMjIy2Oi1CNALd9GqPLz/5Fr9tlXVVs+lBwWxXRde09qhd1/0mdEPc3Szkppbi9j8JcPAwg6MHZSvvKtrzurZ06rhWu8a0tiyNNTy1piGsMXWn1MnNzVW5HADs7e1x/vx5hWX6+voQiURcICsvf0VFRYvKM2vWLBgYGOD69est2r6ltDKYHTlyJPh8Pv766y9ERETg888/514zMTFB3759cfDgQSQkJGDhwoXca1OnTsW2bduQmpqq1F24Mf7+/vj888/x+++/4+WXX+aW//bbb606F11dXWqpJRpl2rRpOHnypLqLQTrA/9Z/DB+z8QAAm+7Gai4NaS90TWuP2nUtEPIxfrk3/vz0FiRiKU5+cw8j5rnDe4QDmJpsx0Rzted13ZpuvZqmOQ1hIpFIZfKlnJwcWFoq55xgGMXrTB5Y156xRS4tLU3lPgAgOjoa7u7uDZ9IE7EsCx6vY4ccaGUwa2xsjH79+uH48ePg8XjceFk5f39/fPXVVwAU55cdPnw4Vq5ciWXLluHWrVsYOXIkDAwMkJ6ejitXrqB3794KgWptEydOxPDhw/HGG2+gqKgI/fv3R0hICH755RcAaHHF9+7dG0FBQTh58iTs7OxgZGQEDw+PFu2LkI5AN73aY97U5/Hoajp0RHyY2xqouzikndA1rT3q1rWFgyFGPeeBwF8iIRWzCD4chdtnE+HW3xrdfCxg52oCgZCvptKS1qDrum00pyHMxcUFYWFhCsuioqLw+PHjegPR2oYOHQo9PT0cOnQI8+bN45anpKTg0qVLmDt3rsrt2iqQ/euvv1BWVoYhQ4a0yf6aSiuDWUAWpN68eRN+fn4wNlZsMfD398fOnTuho6ODYcOGKbz2/fffY8iQIfj++++xZ88eSKVS2NvbY/jw4Rg0aFC9x+PxeDh58iTeeOMNbNu2DVVVVRg+fDgOHTqEIUOGwNTUtEXnsWvXLrzyyit49tlnUVZWBn9//1b1vyekva1btw47d+5UdzFIB7h37TF0YQxrF2NqqenC6JrWHqrq2nOIHUws9XBu3wOUFlahJL8S9y4k496FZDAMYGKtD0MzXejqCaCrL4COngA8AQ88PgMej6n5lwemnmf6dVufGlTPqqp30bT9Mgzg1Mscpjb6TS9HF0DXddtoTkPY4sWL8dxzz2H16tWYM2cOEhMT8fnnn8PKyqpJxzI1NcX777+PjRs34vnnn8eCBQuQm5uLLVu2QCQSYdOmTSq3S05OhpOTU5PPKTExEQsXLsSzzz4LNzc3MAyD4OBgfPXVV/D29sYLL7zQ5H21Ba0NZj/77LN6s4fNmDGDm/5GlWXLlmHZsmUN7v/AgQNKy8zMzLB//37s37+fWybPNjZgwABu2ebNm7F582al7ZcuXao0qNvX1xdXrlxpsCyEdCavvPKKuotAOkBVuRi6rOxBoS2Nl+3S6JrWHvXVtZ2bKRZsGoyo0ExE38pERmwhWBZgWaAgswwFmWUdXNK2pW+sg+c/GQa+QHsyNtN13Xaa2hC2cOFCpKWlYe/evfj555/h4+OD7777Dlu2bGnysTZs2ABra2t8/fXX+P3336Gnp4dRo0bhk08+qbcFtqnBspyxsTFsbGzw5ZdfIjMzExKJBM7OzlizZg02btwIA4OO7YnFsA1FbaRNHT58GKmpqejduzd4PB6uX7+O7du3w8/Pj5u6RxtFRETAx8cHDx48gLe3t7qLQ9rZ/v37G5xfmXQNyZF5+PurewCAKav7wKUPzTHbVdE1rT2aWtcVpdVIfZyP7KRi5KWXory4CpVlYlSWi1FVLoZUwkIq0azbzznv9Idtd+15MNfa6zouLg5A6xIckY5R35jc9tLYZ6O5cYHWtsyqg5GREX777Td89NFHKC0thZ2dHZYuXYqPPvpI3UUjpMOYmVG2S22QGV/E/U7Jn7o2uqa1R1PrWmQghGs/a7j2s653HZZlwbKAVCKFVCL7XcVK9WzbpGLUrKxqUdN2UFpQid8/ugkAyIgt1Kpglq5r7cHna/a4dgpmO9DUqVMxdepUdReDELVycHBQdxFIB8hNKQEAGJrrQs9IR82lIe2Jrmnt0ZZ1zTAMGAbg8fiAsM1226ZEBkIYmOigtLAK6bGF6DtO3SXqOHRdaw8dHc3+G609nf8JIZ3CuXPn1F0E0gFyU2XBrKWjkZpLQtobXdPaQ9vqmmEY2LmZAgDSYwoazKfS1WhbXWuzwsJCdRehVSiYJYR0qDfeeEPdRSDtTFwl4RK+WDjQlDxdHV3T2kMb69rWVda1uLy4GoXZ5WouTcfRxrrWVjY2NuouQqtofDB77do1bN68GQUFBeouSqvExsZCV1cXISEhrdpPfn4+TE1Ncfz48WZtd+nSJSxfvhyenp4wMDCAg4MDZsyYgdu3byute+fOHYwbNw6GhoYwNTXF7NmzucHchDSmbkZu0vXkpZdyY9osHAzVWxjS7uia1h7aWNd2rk/GyWbEanYLVnNoY11rq4SEhA49Xlv3cOgSweyWLVs0Pph98803MX78eAwdOrRV+zEzM8O6devw1ltvoaqqqsnbfffdd0hISMDrr7+OM2fOYNeuXcjKysKQIUNw6dIlbr3IyEiMGjUKVVVV+OOPP7B//35ERUVhxIgRyM7OblXZiXb4448/1F0E0s5yasbLAoClIwWzXR1d09pDG+va0tEQAl1Zgpz0mAL1FqYDtbauGYaBVCpto9KQ9uTq6tqhx2NZtnnzRzdC44PZ5iov73xdRB49eoTjx4/jtddea5P9rVq1CgkJCfjrr7+avM3u3btx6dIlvPzyy/D398fcuXNx/vx5WFhY4JNPPuHW++CDD6Crq4tTp05h8uTJmD17Nk6fPo3s7Gx88cUXbVJ+0rVNmzZN3UUg7Uye/EnCimFipafm0pD2Rte09tDGuubxebCtycie9CgPEol2BGitrWtdXV1UV1c3q2GFqEd0dHSHHauqqgrV1dXQ1dVts31qdDC7efNmvPXWWwCA7t2712TGYxAUFAQAcHFxwdSpU3H06FH4+flBJBJxEw/v3r0bI0eOhLW1NQwMDNC7d298/vnnqK6uVjrO2bNnMXbsWJiYmEBfXx+9evXCp59+qrDOrVu3MH36dJibm0MkEsHPz6/JT7W+++472NraYvz48dyy3bt3g8fjISsri1u2Y8cOMAyjMJG1VCqFmZmZwtgGGxsbjB8/Hnv37m3S8QHA2lo5fb6hoSG8vLyQnJwMABCLxTh16hTmzJkDY+MnU204Oztj9OjROHbsWJOPR7TXyZMn1V0E0s7kyZ/sXMzA42v0nxnSBHRNaw9trWtXPysAQEleJSKvpau5NB2jtXUtv09MT0+ngLaTc3d375DjVFVVIT1ddv3UjiNaS6On5nnhhReQl5eHb775BkePHoWdnR0AwMvLi1vnzp07ePToEf73v/+he/fuMDCQJSOJjY3FwoUL0b17d+jo6OD+/fv4+OOPERkZif3793Pb//TTT3jxxRfh7++PvXv3wtraGlFRUXjw4AG3TmBgICZOnIjBgwdj7969MDExwW+//YZnnnkGZWVljY47OH36NEaOHAke78lN37hx48CyLC5evIgFCxYAAC5cuAA9PT2cP3+eW+/WrVsoKCjAuHGK+eJHjRqFDRs2oKCgAKamps17Y2sUFhbizp07GDNmDPeelZeXo0+fPkrr9unTB+fPn0dFRQVEIlGLjke0w8aNGxVa+0nXwrIscmqC2fj0SAAD1Vsg0u7omtYe2lrXvYbZ4/bZRJTkV+LWmQR4DrEDX9i1H9S1tq4NDAxgbm6OvLw8xMbGQigUco1OpHMpKyuDvr5+u+1fNqc0yzUYmpubc/FYW9DoYNbR0RHdunUDAPj5+cHFxUVpnaysLDx8+BA9e/ZUWP7ll19yv0ulUowYMQIWFhZYtmwZduzYATMzM5SUlGD9+vUYPnw4Ll26xF2AY8eOVdjX6tWr4e3tjUuXLkEgkL2lEyZMQE5ODjZu3Ijnn39eIVCtW764uDisXLlSYbmHhwccHR1x4cIFLFiwAFVVVbh8+TLWrFmDzz77DElJSejWrRsuXLgAoVCIkSNHKmzfr18/SKVSXL9+HRMnTmzCu6nslVdeQWlpKd577z0AQG5uLgDZh7Auc3NzsCyL/Px87qFCfedbd2xtTExMi8pHNJP84QxpHMuyEFdLUVUuRlW5GNWVErBS+R+GmiQK7JP/o9Zydc0gUVUuRmWpGADQZ1DPRtYmXQFd09pDW+uaL+Rh4JTuCDwUiZL8Sjz4LxW+Y53UXax21dq6ZhiG6/1YVFSEyspKrZraSJOo6pXalhiGAY/Hg76+PoyNjWFgYEBjZpujT58+SoEsANy9exfTp0+HhYUF+Hw+hEIhnn/+eUgkEkRFRQGQJZcqKirC6tWr633TY2JiEBkZiUWLFgGQdcWV/0yePBnp6el4/PhxveVLS0sDoLqb79ixY3HhwgWuLGVlZVi/fj0sLS251tkLFy5g6NChSk845PtLTU1t8P2pz/vvv49ff/0VO3fuRP/+/RVea+gD2NiHc8+ePfDx8VH4mTlzJgDgypUrCA4Oxvbt25GXl4clS5YAeDJuY926dYiJicH+/ftx7NgxhIaGYuvWrSgrK8P8+fMV1t24cSPCw8MREBCAgIAAhIeHY+PGjQrrzJ8/H2VlZdi6dStCQ0Nx7Ngx7N+/HzExMVi3bp3CukuWLEFeXh62b9+O4OBgnD17Frt370ZqaipWrVqlsO6qVauQmpqK3bt34+zZs3ROdc4pPDy8y51TW9XT3ZCH2PLqtzj7Qzi2rfgD368Jxg9rgnHgnasI2HwDf356C399dgtHPr+No9tv49gXd3Bsxx0c//IuTuy8ixNf3cPfu+7h5Nf3ceob9fz8+2MEd72f++9kl6wnOifFcwoPD+9y59QV66ktzunDDz/scufU1HqqNsyBmCebciz4j0fITirW+HNqqJ4+/PDDVp8TwzBYsGAB7O3t8dlnn0FXVxf//PMPoqKikJycjCNHjsDU1BRbtmxBjx498Prrr6NHjx745ptvIJVKERQUhPv37yMnJwe//vorbG1t8e677yqs++OPP6K0tBTXr1/H9evXUVpaih9//FFhnXfffRe2trb49ddfkZOTg/v37yMoKAhSqRTffPONwrpbtmyBqakpjhw5guTkZERFReGff/6Brq4uPv/8c4V1P//88y5xTo8ePWr3c9q3bx9yc3Px999/4/Dhww1+9pozTBIAGFbDH5N88cUXeOuttxAfH6/UMuvi4gJvb2+cPn1aYXlSUhK8vLzg4eGBdevWwcXFBSKRCKGhoXjllVcQGBiIUaNG4ddff8Vzzz2H//77DyNGjFB5/KtXr+Kpp55qsIwNbR8SEoJhw4YhICBA6SnYoUOHsHjxYkRFReHgwYP4559/cPv2bTzzzDPg8XjYv38/zMzM8N577+H9999X2DYqKgoeHh745ptv8OqrrzZYvrq2bNmCzZs34+OPP+Y+aADw+PFjeHp6Yvfu3Vi9erXCNm+99RZ27NiBsrKyBrsZ19cyO3PmTDx48ADe3t7NKivRPAEBAVi4cKG6i9GpZMYX4cbJOCQ/zFN3UdqMvokOdHySsWixdrbkaBO6prWHttd16uN8nNh1D6yUhZGFCDPX+cHYsmsmudP2utYmna2uIyIi4OPj0+S4QKO7GTeFqpbC48ePo7S0FEePHoWzszO3/N69ewrrWVnJBvynpKTUu39LS0sAwIYNGzB79myV63h4eDS6fV6e8k2svDvzhQsXcP78eS5B1NixY/G///0P//33HyorK5XGy9ben3z/TSUPZDdv3qwQyAKy1N16enoIDw9X2i48PBxubm6Njpe1trZW2QpNtEfv3r3VXYRO5dG1NAT9+hhSyZPnioZmurBwNISJlR70jXWgqyeAUCSAjogPhsfIflDz/caT/csAYHgAasYkqXtYkrm9AR5HmzS+ItF4dE1rD22vawcPMwyd6YprR2NQnFuBQx9ch2s/K7j0toRTL3PoG+uou4htRtvrWptoel1rfDArT+3cnCl35AFu7bTQLMti3759CusNGzYMJiYm2Lt3L5599lmVgbGHhwfc3d1x//79Fg2Ud3Z2hp6eHmJjY5Ves7Ozg5eXF44cOYLbt29z+x8/fjxeeuklfPnllzA2NsbAgcoJVuLi4gAoJsNqzNatW7F582b873//w6ZNm5ReFwgEmDZtGo4ePYrPP/8cRkZGAGQt3YGBgVz3E0IacvjwYY3/4mwrt88m4Ppx2bXK4zPwHuEA37GOMLFqv0QMHYnqWjtQPWsPqmug73gnFGSX4eHlNLBSFjG3shBzSzbzhIWDIRx7mcHJ0xymNvrQMxRCKOJrZNIjqmvtoel1rfHdjIOCgjB69Gi89NJLWLJkCYRCITw8PGBkZAQXFxf4+Pjg1KlTCttERkbC19cXw4YNw9tvv42Kigp89913SEhIQHR0NNfNGJBlM37hhRcwZswYvPjii7CxsUFMTAzu37+Pb7/9FoAsm/GkSZPg7++PpUuXwsHBAXl5eXj06BHu3LmDP//8s8FzGDt2LMrLy3Ht2jWl19asWYNvvvkGenp6yMvL41o+e/Togfj4eEyfPh0nTpxQuV1AQACys7Ob9CW6Y8cOvPnmm5g4caLKQHbIkCHcezdw4ED069cP7777LioqKvDBBx8gLy8P9+7d41qzm6O53QkI6QpSIvNwYtc9gAVEBkJMWtUb9u6m6i4WIYSQJshOKsbdfxMRH54LcaWk3vV4fAa6+gLwBTzwBDzw+Qx4fB54fFkvm8a0VRzc+H6erMAXMPAd64Tuvs2/pyOktbSum7F8CpqDBw9i3759kEqlCsGoKp6enjhy5Aj+97//Yfbs2bCwsMDChQuxfv16TJo0SWHdFStWcAPXX3jhBbAsCxcXF24APgCMHj0aoaGh+Pjjj7F27Vrk5+fDwsICXl5e3OD7hixatAgrV65Eenq6UibgcePG4ZtvvsFTTz2l0IV33Lhx2Ldvn8ouxizL4u+//8bChQub/DRQPp/Y2bNncfbsWZX7BGTvXVBQEN555x3MnTsXAoEAY8aMwRdffNGiQJZon2nTpmntXIVy5SVVOP/zQ4AFBEIeZr7hBwt7Q3UXq81RXWsHqmftQXX9hFU3Izz9gg8kYiky44uQHJmHlEd5yEwoBit90k4klbAoL27fbLHtIe5RGtZ+PwW8JgTcRLNp+nWt8S2zXUFFRQW6deuGN954A++8806r93fx4kU8/fTTiIiIgKenZxuUsH1RyyzRJizL4syeMCSEy6a6Gv2cJ7yesldzqQghhLSFynIxMuIKUVZYiYoSMSpKq1BRJoZUwkIqlkIqYSGp+bfRW/Am3KE36Sa+kePUfrmitBo5ybK5wme/2Q92bqZNOQIhbUbrWma7ApFIxCVeevXVV1s9kfBHH32E5cuXa0QgS7TP/Pnz8ccff6i7GGoTHpTCBbKu/azRa3j98zJrOm2va21B9aw9qK4bp6sngLO3hbqL0WJlRVX4+Z0rAAvEh+VQMKsFNP267vLzzGqKlStXYu3atVzippbKz8+Hv78/Pv744zYqGSFt68CBA+ougtrkppbg6pEYAICRuQijn/PQyMQgTaXNda1NqJ61B9V116dvrAPb7sYAgPj7OWouDekImn5dUzDbSfD5fGzYsKHV2cTMzMywefNmmv6GdFo7duxQdxHUgmVZXP4jGlIxC4bHYPwKb+jqC9VdrHalrXWtbaietQfVtXaQJ34qyCxDfkapmktD2pumX9cUzBJCOtSECRPUXQS1SHqYh9TH+QCA3v4OsHPt+nOwamtdaxuqZ+1Bda0dXPpYcr/Hh1HrbFen6dc1BbOEkA6Vmpqq7iJ0OKmURchR2VzSQhEfAya7qLdAHUQb61obUT1rD6pr7WBmqw++vmy6oQQKZrs8Tb+uKZglhHSo/Px8dRehwyVF5CI3VZYdst/TztAz0lFziTqGNta1NqJ61h5U19qBYRgwxrK/WRmxhSgvrlJziUh70vTrmoJZQkiHGjlypLqL0OFSImV/KHg8Bn3GOKq5NB1HG+taG1E9aw+qa+3Rb5Q7ANm0PQnh1DrblWn6dU3BLCGkQ+3evVvdRehwadEFAABrFyPoiLRnRjRtrGttRPWsPaiutUfA8f0QGciSFFJW465N069rhm10xmZC2ldzJ0cmmo1l2S49HU1dVeVi/Lj+P7As4Pd0Nwyb7abuInUYbatrQro8lgW08ZrWxvNmWVw8+AiR1zMg0OFhxRcjINDhq7tURAs0Ny7QniYCQojaXYi7gAmTJ+DcmXMY12OcuovTIf6/vXuPa7re/wD++m5jGwO5CYh4vyEColZeKC94lFLzgpaW2hFM85KnfqaZSuckZqXpsY6n4y3TtBQ1L1hesvCClZfwVoGJBngDFIRxHxts+/z+GBvMgaKyfdm+7+fjwUP57vPd3h/f29x7n8/38/n+VCIYM0yC8e/kwW8wNiTEXAvViBEjsH//fr7DINZ25AhGPPcc9v/wAzBYQK/pI0eA4cOBAweE0++qXP9n0w9IhQjaCj1ObL8Cv/buYAzQ6xiYnkGvZ2CMQSwWQSIVQeEug8JNCpGYM1x3y1Vdfysy/AlOeN8J2IPXXpuGrds3Q+psn2UhjcwS3tHIrDAwxtBzQ0+cv30eTzV/CkmvJTn8qB1jDNNj/4luOYPAoMfUlf0hd3H8xZ+EmGtCHBpjQM+ewPnzwFNPAUlJwqhKhNjvGn2u6PU0NvX6ELpKPd9RESv726RAdHnan+8wADx8XUDXzBJCbOLgXwdx/vZ5IB44d/scDv11iO+QrO7gXwfhovQFAGRJMnA0K4HniGxDiLkWsqioKL5DINZ28CBw/jyiAODcOeCQQF7TVf0GIJx+18i1NOkUBoao0bSlK0QiBy/iid2ikVnCO+M3MMtnxaGlb3u+wyFWkq5MQ7lWDegZIOLgLJGjg5djXz+arkyDm9oXYoiRqIjH9cAzDj9KaRyVvXD7ApiKgVNweLL5kw7fbyFTKpXw8vLiOwxiLcaRugsXoGQMXhwHPPmk449S1ui36ZpZR+/3fXKt1eqhLq0EJ+IgEnFmf+q0elRqdCgr0qC8pBKsavox0xv+TwBD1e9UcjRGpWVl6NDVHx6+Cr5DAUDXzBI7VpRbDoW2jO8wiJV4ojk8jb/oAWgBZbZj59sTzU1/vyq7iD+qRimfD3iex6isyzQqCwAXAfYMM43OOnK/hWzjxo2YN28e32EQa6kxOrkRwDzGqkcpn3fg13TNUVnAUOg5er/vk2vJ88/D1bP2BaCcZGLIXZzQxEtuw2BJQ1mxYgWeHGS/7+FUzJJGo2UXT7Tx9+E7DNLQGHDs+jEUqgsNv5QBcAEADh5yD/yt7d8AR/uS+54+35bcQLLsFDhwiE2MxbBOwxxylJIxhtjEWHDgwMCAFobjjt5voevVqxffIRBrYQyIjTWMRDIGU6Y5znB82DDHHKW8p98mjtxvoeaa2P17OBWzhHcajQYA0PwJEVp3pMu4HU3i9UT8O3l29YHbANpU/b0SaNdqNQa0HcBDZNZj0edKAOWGv57LPYe1h9Y6XJ8BQ7/P/15jJOMuAAXAwBy630KXmpoKb29vvsMg1pCYaDY6mQrAG6gepVy7FhjggK/pe/pt4sj9FmquSaN7D09LSwNQXR88CF0zS3i3ZcsWREdH8x0GIYQQQgghpBHYt28fRo0a9cB2NDJLeBcQEAAA+OabbxAUFMRzNMSa0tLSEBkZiX379qFjR8de/EnoKNfCQHkWDsq1cFCuhaMx5lqj0eDWrVsYUM+ZAFTMEt65ubkBAIKCgmifWYHo2LEj5VogKNfCQHkWDsq1cFCuhaOx5fqJJ56od1u6QJEQQgghhBBCiN2hYpYQQgghhBBCiN2hYpYQQgghhBBCiN2hYpbwzsfHB4sWLYKPD+0x6+go18JBuRYGyrNwUK6Fg3ItHI6Qa9qahxBCCCGEEEKI3aGRWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdoeKWUIIIYQQQgghdkfCdwCEFBYW4sSJE2jVqhVkMhnf4RBCCCGEEEJ4oNFocOvWLQwYMAAeHh4PbE/FLOHdiRMnEBkZyXcYhBBCCCGEkEZg3759GDVq1APbUTFLeNeqVSsAhidtx44deY6GWFOJshwfL/kP3l+5ACIRx3c4xMpmzZqF1atX8x0GsTLKs3BQroWDci0cjS3XaWlpiIyMNNUHD8IxxpiVYyLkvi5duoSQkBCkpKQgODiY73CIlej1DF+/ewqlBRoMnhyEzr39+A6JEEIIIYQ0Ig9bF9ACUIQQm6hQaVFaoAEA3E4v4jkaYgtRUVF8h0BsgPIsHJRr4aBcC4e955qKWUKITahVlaa/F+WqeIyE2Mqnn37KdwjEBijPwkG5Fg7KtXDYe66pmCWE2IRGpTX9vTCHilkh2LhxI98hEBugPAsH5Vo4KNfCYe+5pmKWEGITmhojs6UFGmgrdDxGQ6ytskKH9m5P4U4GTSl3dL169eI7BGIjlGvhoFwLh73nmlYzJoTYRM2RWQAouluOpi1ceYqGWNvlk9m4c5Hh4NU/MHn5MxCJ6btTR1VeXs53CMRGKNfC0RC5ZoyhrKwMxcXF0Gg0oDVnGyeJRIKMjAyrPgbHcZDJZHBzc4OLiws4ruF2tKBPFwJVWlqK2bNnw9/fH3K5HN27d8eOHTseeF5mZiZmz55t2siY4zhs3rzZ+gETu3dvMVtI1806tMIcwwchdVkliu7SB2BHlp6ezncIxEYo18LxuLlmjCE3Nxe3bt1CUVERdDqajdVYeXp6Wv0xdDodioqKcOvWLeTm5jboFxs0MitQY8aMwdmzZ7Fs2TIEBAQgLi4O48ePh16vx4QJE+o8Ly0tDdu2bUP37t0xbNgwbN++3YZRE3tWc5oxQNfNOrry0grT3/MyS+Hp58JjNMSaIiMj+Q6B2AjlWjgeN9dlZWVQKpVQKBRo3rw5pFJpwwRGGlxFRYVN8lNRUYHbt29DqVTCxcUFrq4NMzuPRmYF6NChQ0hISMCaNWswffp0DBw4EBs2bEBERATmzZt332/P+vfvj7t37yIhIQFz5syxYdTE3mnK7plmnCus0brb6UVQ3i7jOwybUZdWf3mhzBZOv4VoyZIlfIdAbIRyLRyPm+vi4mIAoELWDty+fdsmjyOVStG8eXMA1c+PhkDFrADFx8fD1dUVY8eONTs+efJkZGdn49dff63zXJGInjLk0ViMzApomnHO9WLsXXEeu5adQ3lJxYNPcADlJdX5zsss5TESYm3r1q3jOwRiI5Rr4XjcXGs0Gjg5OVEhawfatGljs8eSSqVwcnKCRqNpsPukykSAUlJS0KVLF0gk5rPMQ0NDTbcT0tAsr5kVzshs7nXDN5BajQ7ZaYX8BmMjNacZK7OpmHVkI0aM4DsEYiOUa+F43FwzxmgAxE789ddfNn08juPomlnyePLz89G+fXuL415eXqbbrSU3Nxd37941O5aWlma1xyONh6bcvJgtL65ARbkWUmfHfxtSFVcXdjnXitGhhy+P0VgfY8xsmnFxnhoVai2kcsfPtRDt37+f7xCIjVCuhYNyLRydOnWy6eM15ErGwCOOzG7evBkcx5l+5HI5/Pz8MHDgQCxduhS5ubkNGiTfEhMTwXEcEhMT+Q6lwdzvidTQT7Ka1qxZg5CQELMf4yIDv/zyC06cOIEVK1ZAqVQiKioKQPW3g2+99RbS0tKwadMmxMfHIykpCUuWLIFKpcK4cePM2sbExCA5ORlxcXGIi4tDcnIyYmJizNqMGzcOKpUKS5YsQVJSEuLj47Fp0yakpaXhrbfeMmsbFRUFpVKJFStW4MSJEzh8+DBWr16NrKwszJgxw6ztjBkzkJWVhdWrV+Pw4cPUp6o+GUfqGPSm58OHi1bYdZ/qm6cTR0+a+pxzrdgh+nS/PJ0+mQS9zvxbV2V2mV33yRHz1FB9mjFjhsP1yRHz1BB96tmzp8P1yRHz1BB96tmz52P16fz58wCqR/1u3LiBiooK5ObmoqioCCUlJbhz5w60Wi2uXbtm1vbWrVtQq9XIy8tDQUEBysrKkJ2dDZ1OZ1pl2dg2MzMTKpUK+fn5yM/Ph0qlQmZmplmb9PR06HQ6ZGdno6ysDAUFBcjLy4NarcatW7fM2l67dg1arRZ37txBSUkJioqKkJubi4qKCty4ccMh+3Tjxg2b9kmj0aCoqKjO597DTnHn2COM827evBmTJ0/Gl19+icDAQFRWViI3Nxe//PILvvzyS4jFYuzcuRODBw9+2LtulIqLi/Hnn38iKCgIbm5ufIfz2MLCwqDT6ZCUlGR2/NKlSwgJCcH69esxbdq0B97PuXPn0LNnT3z55ZeIjo6u12PXNTIbGRmJlJQUBAcH17sfxL58/c9TKM5Tw62ZDMU5hmslBr4SiKC+/jxHZn0H/vc7bqQYZjxIZGK89ml/iETW+9KIb0V3Vdj6rzNmxwZM6IyQ/i14iohYU1ZWFlq0oNwKAeVaOB4318Z9S2ubCUgal0dZzTg6OhpbtmyxON65c2ekpqbe99wHPTeM9Uh964LHmsweEhKCPn36oF+/fnjhhRfw6aef4o8//oCLiwvGjBmDnJycx7n7RsPNzQ19+vRxiEIWALp27YrLly9DqzWf9pmcnAzAkFdr8fX1RXBwsNlPx44drfZ4pPEwXjNbXHkbMoVhumlmqpLPkGymrKh6oQOtRufwq/vWXPzJSJlF1806qn379vEdArERyrVwUK6Fo7Cw8JHOc3Z2xunTp81+du7c2bDB1UODX5ndunVrrFy5EiUlJVi/fr3Zbd999x3CwsKgUCjQpEkTRERE4PTp02Zt0tLSMHnyZHTq1AkKhQItWrTAiBEjTIWWkXHq79atWzFnzhz4+fnB2dkZAwYMwMWLF83aRkdHw9XVFampqXjuuefg4uKC5s2bY9myZQCAM2fOoG/fvnBxcUFAQIDFNw21TTM23mdaWhqGDRsGV1dXtGrVCnPnzrVYoauiogIffPABAgMDIZPJ4OPjg8mTJ1uMUNrK6NGjUVpaij179pgd37JlC/z9/dG7d29e4iKOi+mZ6ZpZTx93tOhs2KA780pBgy4C0FipisxXMM65VsRTJLZR83pZscTw30weFbMOq0OHDnyHQGyEci0clGv7UllZaTFIVV8ymeyRzhOJROjTp4/ZT7du3R7pvh6HVZYZGzZsGMRiMX766SfTsbi4OIwaNQpubm7Yvn07Nm7ciIKCAoSHh+OXX34xtcvOzkbTpk2xbNky09x7iUSC3r1748qVKxaPFRMTg4yMDHzxxRf44osvkJ2djfDwcNMQtlFlZSXGjBmD559/Ht9++y2GDh2KhQsXIiYmBlFRUXj11VcRHx+Pzp07Izo62jTX/34qKysxcuRIDBo0CN9++y1effVVfPrpp/j4449NbfR6PUaNGoVly5ZhwoQJOHjwIJYtW4aEhASEh4ejvNz2K7oOHToUERERmDlzJjZs2IDjx49j2rRpOHz4MJYvXw6xWAwAmDJlCiQSiekaAaPdu3dj9+7dOHbsGADDdGPjMUJqoynXAlU1q0whQcuqYra8pNLhRyn1Oj1U92zHk3O94fZXa4xqrmRs/OIi51qxxfZMxDE4OzvzHQKxEcq1cFCu7y82NhYcx+HSpUsYP3483N3d0axZM7z66qsoKjL/wpoxhjVr1qB79+5wdnaGp6cnXnzxRYtapW3btrVethceHo7w8HDT78ZBtq+//hpz585FixYtIJPJTAuqbtq0Cd26dYNcLoeXlxdGjx6Ny5cvm91nzUG5sWPH3ndQrrGzytKSLi4u8Pb2RnZ2NgBDQTdv3jx07doV33//vWmp7mHDhqFDhw6YP38+Tp40LJDSv39/9O/f33RfOp0Ozz//PIKDg7F+/Xp88sknZo/l4+OD+Ph406JFffv2RadOnbB06VJs2LDB1M44OjpmzBgAhifGgQMHsHTpUly4cAE9evQAADz11FPw9fVFXFwcnnzyyfv2s6KiAosXLzbt1zpo0CCcO3cOcXFxeO+99wAA33zzDQ4fPow9e/aYHhsAunXrhp49e2Lz5s2YOXPmQ/4LP769e/fi3XffxXvvvQelUonAwEBs374dL7/8sqmNTqeDTqezGDm7d3/a1atXY/Xq1QAgiFE28vBqbstzI/MaBkVWj/5nphagaQtXPsKyifKSSlMhb5RzzdGL2eqiNbivP25eyodex3A9OR+de/vxGBmxhqSkJAwYMIDvMIgNUK6Fg3JdPy+88AJeeuklTJkyBcnJyVi4cCEAQ0FpNH36dGzevBlvvvkmPv74YyiVSrz//vt4+umn8fvvv6NZs2aP9NgLFy5EWFgY1q1bB5FIBF9fXyxduhQxMTEYP348li5divz8fMTGxiIsLAxnz541W7nYOCg3btw4zJ8/Hz/99BOWLFkCd3d3Ux1zP+Xl5fDz88Pdu3fRvHlzREZG4v333zftjmIrVtsnoWZRc+XKFWRnZ2P27Nlme065urrihRdewPr166FSqaBQKKDVarF8+XJs3boVaWlpqKys/lB077cKADBhwgSz1XfbtGmDp59+GsePHzdrx3Echg0bZvpdIpGgY8eOkEgkpkIWMGxP4+vrazEaWRuO4yz24QoNDTWNWALAgQMH4OHhgREjRpgN/3fv3h1+fn5ITEzkpZh1dXXFqlWrsGrVqjrbbN68GZs3b7Y4TgUreVg1R+QGPzcQ7r7OcPWUobRAg1upSnQb1IrH6Kyr5vWyns1dUHC7DMrbZSjOK4ebt2N+862uumZWJObQJrQpZAoJNCotMi7epWLWAU2ZMoXvEIiNUK6Fw5q5/vmbq8i71TguPfFu5Yp+4wIe+fwpU6Zg3rx5AIDBgwebVpbeuHEjOI7DmTNnsGHDBqxcuRJz5swxndevXz8EBATgk08+MZvR+TA6dOiAXbt2mX4vLCzEkiVLMGzYMMTFxZmOh4eHo1OnToiNjcW2bdtMx42DcqNHj4ZEIql1UK4u3bp1Q7du3Uzr7Jw4cQKffvopjh49irNnz8LV1XaDFFYpZsvKypCfn4+uXbsCqN63tHnz5hZt/f39odfrUVBQAIVCgTlz5mD16tWYP38+BgwYAE9PT4hEIkydOrXWKbl+fpYfjPz8/PD777+bHVMoFJDL5WbHpFJprd8eSKVSqNXqB/aztvuUyWRm5+bk5KCwsLDOVcLy8vIe+DiE2LuaI7Ofb1yL//VdjpadPZF65g6yrxZCp9NDLHbMzdVrXi/b7W8tkbjtCsCAy6duo/dIx1zl0TjNWK0thVgsQrtQb6SeuYObl/JRqdHBSSbmOULSkN56661aV7UkjodyLRzWzHXerVJk/1Volfu2tZEjR5r9HhoaCrVajdzcXDRr1gwHDhwAx3F45ZVXzAa1/Pz80K1bt8fa9vOFF14w+/306dMoLy+3mKrcqlUr/O1vf8PRo0fNjhsH5W7duoV27dqZ4q85KFcX4zZNRhEREejRowdefPFFbNiwweJ2a7JKMXvw4EHodDrT/O6mTZsCAG7fvm3RNjs7GyKRCJ6ehuuqtm7dikmTJuGjjz4ya5eXlwcPDw+L8+/cuVPrMeNj8s3b2xtNmzbF4cOHa729SZMmNo6IENurWcwu/sDwbV/LLl5IPXMHlRod0i/kIqCnY47Y1RyZbRXkhWbt3JBzrRiXT2aj5/NtIXLAIt64AFTzVoapU+17+CD1zB1oK/W4eSkfHZ7w5TM80sCouBEOyrVwWDPX3q0az6VFjxvLvfWGcTEl4wBcTk4OGGN1TiV+nK2L7h0kfNDgYUJCgtkx46CcsZA1xl+fAb3ajB49Gi4uLjhz5syDGzegBi9mb968ibfffhvu7u6YPn06AMOeQy1atEBcXBzefvtt07TgsrIy7Nmzx7TCMWD4luDeVbUOHjyIrKysWrdw2b59O+bMmWO6zxs3buDUqVOYNGlSQ3ftkQwfPhw7duyATqejVYKJYNWcZjz99dew+9vtaN/DB867nVBeUolzB6+j05PNwDng3quq4uqRWRc3GYL6+iPnWjHKiipwIyUf7br58BiddRivmb2SdglAL7QK8oJEJoZWo8OfJ7OpmHUwI0aMwP79+/kOg9gA5Vo4rJnrx5nWa2+8vb3BcRx+/vnnWlcNrnlMLpfXuvhSXl4evL29LY7XvMwSePDgYW33AQB//fWX2bW0j4MxZnZJqS08VjGbkpICrVYLrVaL3Nxc/Pzzz/jyyy8hFosRHx8PHx/DhzSRSITly5dj4sSJGD58OKZPnw6NRoMVK1agsLDQtEUOYCj+Nm/ejMDAQISGhuL8+fNYsWIFWrZsWWsMubm5GD16NF577TUUFRVh0aJFkMvlpguw+fbyyy9j27ZtGDZsGP7v//4PvXr1gpOTEzIzM3H8+HGMGjUKo0eP5jtMQqyq5sjs9m+2AgCcpGL0iGiDU3vTUHBHhfSLd9HxSccrcsqqphnLXCQQO4nQ6alm+GXXX6hU6/D70VtoG+pt8R+SvTMWs2F9ewIAJE5iBPRqhj9/zsbNS0pc/yMPbUNr/0+V2B8qboSDci0clOuGMXz4cCxbtgxZWVkYN27cfdu2bdsWf/zxh9mxq1ev4sqVK3UWojWFhYXB2dkZW7duNVusNTMzE8eOHcOLL75Y63kNVcju3r0bKpUKffr0aZD7q6/HKp0nT56MsLAwDBo0CDNnzsTFixcxf/58pKamYuDAgWZtJ0yYgH379iE/Px8vvfQSJk+eDDc3Nxw/fhx9+/Y1tVu1ahVeeeUVLF26FCNGjMB3332HvXv31rnf1UcffYQ2bdpg8uTJePXVV9G8eXMcP3680eyPJRaL8d133yEmJgZ79+7F6NGjERkZiWXLlkEul5uuK7a10tJSzJ49G/7+/pDL5ejevTt27NhRr3Nzc3MRHR0Nb29vKBQKhIWFWczDJ6Qm48isSMzhnflvm44H9/eH3NUJAJC0PwMV6kfbI60xU1VNM3ZxN3z76iQTI7CPYQpQ1tVC/HEsk7fYrEVdtRXR2YvVU416j2gPqbPh+9Ofv7kKbaWOl9hIw7PltVGEX5Rr4aBcN4xnnnkG06ZNw+TJk/HOO+/gwIEDOH78OOLi4vD6669j7dq1prZ///vf8eeff+L111/H0aNHsWnTJowcOdI0OPggHh4e+Ne//oXvvvsOkyZNwvfff4+tW7di4MCBkMvlWLRoUa3n3bp166H6dOPGDTzzzDP47LPP8P333+Pw4cNYuHAhoqKiEBwcjKlTpz7U/T2uRxqZjY6OrnUfpAcZNWoURo0add82Hh4e+OKLLyyO13WBtFwuf+RVeeu6z+vXr5v9Hh4ebrGCb133GRsbi9jYWLNjEokEc+fOxdy5c+uM0dbGjBmDs2fPYtmyZQgICEBcXBzGjx8PvV6PCRMm1HmeRqPBoEGDUFhYiFWrVsHX1xerV6/GkCFDcOTIEVrGndRKXTUyK1NIMGv6LNNxqVyCHhGtcTo+HQV3VDi8PhnPz+oGscRxriM1jswq3KoXges9sh2u/5GHEqUap+LT4NfeHc3aufEVYoPSafWoUBsK1Sd7V68Ur3CTovfIdvh5518ozlPj6JbLGBwd5FC5FqpZs2Y9uBFxCJRr4aBcN5z169ejT58+WL9+PdasWQO9Xg9/f38888wz6NWrl6ndhAkTkJ2djXXr1uHLL79ESEgI1q5di8WLF9f7sRYuXAhfX1/897//xc6dO+Hs7Izw8HB89NFHdY7A1rdYNnJzc0OzZs3wySefICcnBzqdDm3atMGbb76JmJgYuLi4PNT9PS6O2ek+K4mJiRg4cCB27dpV57A5qd2hQ4fw/PPPmwpYo2effRaXLl3CzZs3IRbXvtromjVrMGvWLJw6dQphYWEAAK1Wi27dusHV1RW//vrrQ8dz6dIlhISEICUlBcHBwY/WKdKoHf48BekXcuHRTAFNmz/x6quvmm7T6/Q4tC4ZN5INCxe0DPRE+MRAuPs4xrY1WxaeRGmBBp17+2Hw5CDT8TsZRdj77wtgegaJkwj9x3dGYJif3U85LivUYPMCw77hTm3yMW1h9VQnvU6PPcvPI/dGCQCgdZAXwl8JRBMvea33RezDpk2bzF7TxHFRroXjcXOdkZEB4PEWOCK2Udc1udbyoOfGw9YF9JW4AMXHx8PV1dVsPj1gmDaenZ1934I0Pj4enTt3NhWygGHk+ZVXXkFSUhKysrKsFjexX8ZpxjKFxLRyuZFILMJzr4WYRiYzUwuw/f1fcXTLn7j2Rx7KijR2u7cx0zPTAlAKd/Ptufzau6PfuE7gOEBbqcexry5j5wdJ+O3ITeTeKIZOq+cj5Mdm3JYHAFw9zItUkViEEW92N+X65p9KbH3vNI5+dRnpF3PtOtdCdu9rmjguyrVwUK6Fo64BLHthla15bKG2qb+kflJSUtClSxdIJObpDw0NNd3+9NNP13luv379LI4bz7106RJatGjxSHGdP3wdyktOj3QuadyU2WUAAJnCCc1qeX44ScUY8WZ3nNz1Fy6fug1dpR6pp+8g9bRh6y2pXAy5qxPkrlLIXZzgJBWBE3PgOA4iMQeRiANn/JPjgAYa3Hzcu9HpGPQ6w/uU8ZrZmrqGt4SHnwIJGy+hvKQS+VllOLk7zXS73MUJCncpFG5SSKRiiMUcRBKR4U8xB04sMo/xnoAt4q856vvAtvXoYC3KCqtXYvT1t9wiTe7ihJH/1x3HtlxG+sW70GsZUk/dRuopw+qLUrkYzm5SyBROkCkkkMrFEIlF4ESAiOPAiap/RBwAG6+AbfNxczsYqNfd8cbP31zlOwxiZV7NXeDv7/9Q5zA9Q4lSDVVxBdSllVCXVUKj0kKn04PpDe+Pej0D0zEwZvj9oT/aPWR79pAncByH9j184N/Ro97nVKi1KCvUQFVcYeq7Tqs39Fenh05r+FNf8zvLezrO6vzF8veH7dODiCUieLlabu9yP8Zcl+SroSnXoriiHAp3WfX2dKwq7Br9pI/xjQOrlKBSo4WTzD7LQvuMmjyW/Pz8Wof2vby8TLff71xju4c9FzAsHnX37l2zY2lphg/vV3/NQclfjjG1lNRO7irBDz98Z3aNiJHMWYK/TeqCwKeb47eEm7h5SWkanaxQ61Ch1qE479H2PmsMXDwsi1kAaBXohfGLeuPPX7Jx6edslORX91FdZvjwZ/wywN78ev4UBo+y/GJMKpdgyPSuuHOtCOe/v4Fbf96b63IA5TaOljyOnD8dbyEzYqnYO+WB2wyqiiuQdj4X136/i5zrxahU2/9ib6mnb2Pyir4Q17EvOGMMOdeLcfnUbdz+qxAFd1Q2jrDhaZ2KMeDZ+7dRl1Ui9fRt3EjJx530Imgrq6vzToOc0TLQy+wLTtJ4aSv1cKr9Y0qjR9OMBep+1+U96Jq9xzl3zZo1CAkJMfuJjIw0nCtmEDkBTKSFk1wELauA1FmCSr0aUmcJdKiERMYBYj04CYNYCug5LaRyMbR6jVlbPVcJsZQDJ9aDE+shlnLQc5VmbbR6DaRyMfScFmIpwEkYINZDIuOgwz1tWQWc5CIwkRYiJ0AkYYBIB4lMBB3ujbMCEpkIEOkgklCfjH1q0lSGbxI+x9y5czFixAgAQExMDJKTkxEXF4e4uDgkJyfjf5uWY9jMUBy++V+MfLM70lWn0KVvM2ic78KthRhObjpwzhVw8ZKggitDEy85yrUlULhJUcnUkDqLwUSG/oucGCDWGeKEBjKFBBV6NWQKCbTQwEkuAsQ6iJwMcTKRFlJnMbTM0Layqq2h/xwg1pnnyVmMSr15Wz1n6D8n0YOTGP7tNZICtA72MvV73LhxUKlUWLJkCZKSknA44SB+zz6GsGgf3JAdw+DoLvir+DSC+7eAUncTTVspoJeqIHMDnFwY4FQBuasYWqjh3MQJGp0KclcnaKGGVCEGxFqIpDX6pBBDy9SQuUhQoS+v7lNV/+/tU0M895z9KuDp54K0tDTTqpjG/kdFRUGpVOLr3Z/DNbgArYeogbY3EfKsD/KRho5P+SJPfR2+bZpAjWK4eDkBThWQODNInAEmroTMRWLoU42cmvdJX9Wnyqo8mbetLU96rtKsjaFv4qr+G19PuqrXU4VZ3rVMAye5uPr15GR4PTnJRRZtdTC89iAyPPeqX0/mzz27eY+Qi+l9z5H7JKoxfKhxgkqlMm0xUvO9/Pff/sCmj/fhywU/4eedV5GZWlDvQpZBD7FEBK2+Ek5yMXTMECfjdBBJqvov0kEiFUEHQxut3vCaq71POjjJqvokN7xPG56nD5cnUdWQj0alxcWkPxATE2PW73HjxuH29Xys+r9vsefj8/jz5+z6F7IiBnB6iJ046KGFRCqCVl8BicwQp9iJA0SG92eRBGBcVU5ZBZzu7ZO04Z57embImUjrjKSkJMTHx2PTpk1m7+UjR0Ti1/0Z+HzOUZzcnYbM1AKzQvZhcBwHBmb4kzHT72azUjjDs6S+bS3a3NuWMz52LW2rfodF26rbRFVtRTXbokZbdp+2MGtr9tj3tK21T6J72t6vT7XGWUefOA45OTkADCsVV1RUIDc3F0VFRSgpKcGdO3eg1Wpx7do1AIZ9aQHDKshqtRp5eXkoKChAWVkZsrOzodPpkJ6ebtY2MzMTKpUK+fn50Gg0KCoqqvX1pFKpsG7duod7DvG9ANSpU6fw448/Yvbs2fDw8OAzlMeSnp6OoKAgJCYmml1PWh9Xr15FSEgIzpw5gyeeeMJKEVYLCwuDTqdDUlKS2XHjBdfr16/HtGnTaj23efPm6NevH7755huz4wcPHsTw4cPxww8/4Nln6/4qr66R2cjISFoASiDGjRtn8fwhjolyLQyUZ8dWqdHh8/87AQDIUJ3Byq9iam2z/7+/4XZ6kemYq6cMrYObwrulK9y8nSF3cYLcVQKZwgliJ5HhsgExB4578BfhfMn47S6+X5cMAHhxwVNo1tZ81fn0C7k4+tVlU9EuEnFo0dkDzdq7w8NXAYWb4TIRuasTJFIxRGIOYnHVpRKNtM9J+zNw9uB1AMCM1eEWo9EVai2+X5eMzNQC07EmTeVoFeQFn1ZN4OYth7OrFMqyO5BIRWjfrr1Z8UQan/T0dJtuaZqenm6Yvt9AC0DxPs341KlTWLx4MaKjo+26mH377bcRERHx0IUsAAQEBGDixIl46623cOLECStEZ65r167Yvn07tFqt2XWzycmGN+yQkJD7nmtsV1N9zgUAX19f+Pr6PkrYxEHQh17hoFwLA+XZsUmkIohEHPR6hhdHj7O4Xa/T44cvUkyFrKefAn3HdkKrLl7gbHxNe0OrudaBcc9wo+y/CvHDhhTTdZ/d/tYKPZ5tXeclJfZC7lq9WKGmTGu2rVylRodvP71oWpG+aUtXhEV2QOtgL4tCtfRaHnQ6nd0/B4TAloUsYJiWLxI13ORgu5tmXF7e+K6junz5Mvbt24c33njjke/jH//4B3766SecOnWqASOr3ejRo1FaWoo9e/aYHd+yZQv8/f3vez3M6NGjkZqaarbisVarxdatW9G7d++HXhyCCI9xOglxfJRrYaA8OzaO4yBzMXzx/e3eAxa3n4pPN9ta7aV3e6F1cFOHKGJqrkJv3DMcANSllUjYdAmMGRZLev71UPQd18nuC1kAcG5SvRBneUmF2W3nDl03FbJtQprihXeeRJuQprWOuMpkMlRWVqKiosLiNtK4GKcC20JFRQUqKyshkzXca4XXYjY2Nhbz5s0DALRr1w4cZ5h2kZiYCABo27Ythg8fjr1796JHjx6Qy+WmjYNXr16N/v37w9fXFy4uLujatSuWL1+OyspKi8c5fPgwBg0aBHd3dygUCnTp0gVLly41a3Pu3DmMHDkSXl5ekMvl6NGjR72/bV67di38/PwQERFhdjw8PBwhISE4e/Ys+vXrB4VCgfbt22PZsmXQ682vLXjyySfRpUuXh54n/iiGDh2KiIgIzJw5Exs2bMDx48cxbdo0HD58GMuXLzct0T1lyhRIJBLcuHHDdO6rr76K4OBgjB07FnFxcThy5AjGjRuHK1eu4OOPP7Z67MT+7d+/n+8QiI1QroWB8uz4ZApDgRPeb7DZ8RKlGsnHDYt/ebdyxdAZXSF2srtxkjrVHJUsqzEymxiXitICw+99x3ZE21Db7dFpbc6uNYrZ0urP1AV3yvDbkZsAAN+2bhg6syucpHVv6eLmZpiSffv2bSpoG7lOnTrZ5HEqKipw+7Zh5wLj86Mh8DrNeOrUqVAqlfjss8+wd+9eNG9uWAY8KCjI1ObChQu4fPky/vnPf6Jdu3ZwcXEBYJhvPWHCBLRr1w5SqRS///47PvzwQ6SmpmLTpk2m8zdu3IjXXnsNAwYMwLp16+Dr64urV68iJSXF1Ob48eMYMmQIevfujXXr1sHd3R07duzASy+9BJVKhejo6Pv24+DBg+jfv3+tQ+Z37tzBxIkTMXfuXCxatAjx8fFYuHAh/P39MWnSJLO24eHh2LVrl+mibmvau3cv3n33Xbz33ntQKpUIDAzE9u3b8fLLL5va6HQ66HQ6sy2QZDIZjh49infeeQdvvPEGVCoVunfvju+//x4DBgywaszEMcTExOCjjz7iOwxiA5RrYaA8Oz6ZwvBx8dIflzEE1ZcTXfzhhmn7sfAJgZDKeb96rUGJJSLIXZ2gLq2EqmpktuiuCukXDGt/tO/ug+D+j7YdYWPl3KS6gDeOzDLG8NOOq4Zcc8CA8QF1ruxs5OLiAi8vLyiVSqSnp8PJyck0aEUaF5VKBYVCYbX7Z8yw9ZZxwNHLy8tUzzUEXt91WrZsidatWwMAevTogbZt21q0yc3NxZ9//omAgACz45988onp73q9Hv369UPTpk0xefJkrFy5Ep6enigtLcWcOXPwzDPP4NixY6YX0KBBg8zu6/XXX0dwcDCOHTtmuob0ueeeQ15eHmJiYjBp0qQ653bn5uYiIyOjzgWT8vPzcejQIdNWJIMHD0ZiYiLi4uIsitknnngCa9euxZUrVxAYGFjXP1uDcHV1xapVq7Bq1ao622zevBmbN2+2ON6sWTNs2bLFitERRzZ+/Hi+QyA2QrkWBsqz4zMWs96ezUzHSgs0uHQyGwDQOsgLzdo13EhLY+LiLqsqZg0jsZdP3jbd1ntke4crzuQ1RmbVVSOz+VmlpgWfQvq1gG+bB+ea4zjT7Mni4mJoNBrwvOYsqUNts1obEsdxEIlEUCgUcHNzg4uLS4O+bhr9V2ihoaEWhSwAXLx4EYsWLcLJkyehVCrNbrt69Sp69+6NU6dOobi4GK+//nqd/2hpaWlITU3Fv//9bwCG6z+Nhg0bhgMHDuDKlSvo0qVLrednZxveyOta1MjPz89iT83Q0FD89ttvFm2N95GVlWX1YpYQviQnJ6Nr1658h0FsgHItDJRnx2ecZqwqqd4HOznxFvRaQ3Hy1PPteInLFlzcpcjPMlwzq9fpkXraUMw2a+cGL/+GG11qLOS1TDM2jkQDQI9nW9f7vjiOg6urK1xdXRsuQNLg4uLiMGHCBL7DeGSNvpg1Tj2u6ebNm+jXrx86d+6MVatWoW3btpDL5UhKSsKsWbNMi0QZt4Bp2bJlnfdv3Ffp7bffxttvv11rm7y8vDrPNz6WXC6v9famTZtaHJPJZLUuZGW8j8a4yBUhhBBChMk4Msu01bPUrlct+uTb1g3NO7jzEpctGBeBUhVpcPNPpWkhqKC+jrngpVgsgkwhgUalhbpqmnH6hVwAgE/rJnDzduYzPEIsNPpitrYR1X379qGsrAx79+5FmzZtTMfvHe308fEBYNioty7e3oaL9hcuXIgxY8bU2qZz584PPP/e0eFHYbwP430S4ohoBEc4KNfCQHl2fDWLWaZnUJVUQJldBgBoE+zFZ2hWp6jankdVUonLpwyjshKZGB2fdNxtBuUuTtCotCgvrYQyuwwFd1QAgA5P+PAcGbEGe38P533JOePSzA8zGmkscGsu68wYw4YNG8zaPf3003B3d8e6devqnKffuXNndOrUCb///jueeuqpWn+aNGlSZyxt2rSBs7Mz0tPT6x1/XTIyMiASie5bPDeE0tJSzJ49G/7+/pDL5ejevTt27NhRr3MzMzMxe/ZsDBgwAB4eHuA4rtbragmpy/bt2/kOgdgI5VoYKM+OzzjNGAAq1FpkXSkw/d4y0LGLWZeqkVmmZ6YtiNqFejvcYlc1GbfnKS+tRPrFXNPxDj0ct4AXMnt/D+f9lWj8NmDVqlWIioqCk5MTOnfufN8CMiIiAlKpFOPHj8c777wDtVqNtWvXoqCgwKydq6srVq5cialTp2Lw4MF47bXX0KxZM6SlpeH333/H//73PwDA+vXrMXToUDz33HOIjo5GixYtoFQqcfnyZVy4cAG7du2qMxapVIqwsDCcOXPmsf8tzpw5g+7du8PT0/Ox7+t+xowZg7Nnz2LZsmUICAhAXFwcxo8fD71e/8A582lpadi2bRu6d++OYcOG2f0LgNgerXoqHJRrYaA8Oz7jyCwAaFRa3KpaDEgiEzvswk9GCrfqgROd1rCtoiNPqwYAuauhgFeXViDjN8Mle01buMKjmfVWvCX8sff3cN5HZsPDw7Fw4ULs378fffv2Rc+ePXH+/Pn7nhMYGIg9e/agoKAAY8aMwRtvvIHu3bvjv//9r0XbKVOm4NChQ9DpdJg6dSqGDx+O//znP6ZVlAFg4MCBSEpKgoeHB2bPno3Bgwdj5syZOHLkCAYPHmxxn/eaOHEikpKSTHsnPYrS0lIcPXoUEydOfOT7qI9Dhw4hISEBa9aswfTp0zFw4EBs2LABERERmDdvHnQ63X3P79+/P+7evYuEhATMmTPHqrESxzRixAi+QyA2QrkWBsqz45PXGJnVqLTIvGy4LMq/owfEEt4/SlqVcWS2Jkcv4I17zZYoNci7VQoAaBtquQYMcQz2/h7O+8gsYPhGoLZvBa5fv17nOcOHD8fw4cMtjtc2nXjo0KEYOnTofWMIDQ3Fzp07HxxsLSZMmIAFCxbgq6++wvz5803HExMTa21f27TcnTt3guM4TJ48+ZFiqK/4+Hi4urpi7NixZscnT56MCRMm4Ndff8XTTz9d5/l1bVFESH3t37+f7xCIjVCuhYHy7PhqjszmXC9GaYFhm5pWXaw7k6wxMF4zayR2EqFpS8dendc4zbiivHqHD5/Wdc+YJPbN3t/DqTJpAHK5HIsXL8Ynn3yCsrKyhz5fq9Xi448/xsKFC60+xTglJQVdunQx7adrFBoaarqdEGsaN24c3yEQG6FcCwPl2fHJXKo/M2TUuIayZaDjF7P3jsz6tm4CsdixPz4bpxnX1NTfsQt4IbP39/BGMTLrCKZNm4bCwkJkZGQ89Kpgt27dwiuvvIK5c+daKbpq+fn5aN++vcVxLy8v0+3WlJuba9oyySgtLc2qj0kaF1owTDgo18JAeXZ8NReAup1RDACQSEWCKHAkUjGkzhLTKKWvg08xBqqnGRtJpCK4+dCWPI7K3t/DHfurJRsSi8VYuHDhIy1v3a5dO7z33nt17lVbl8TERHAcV6+fmtsW1bbdUX1uawhr1qxBSEiI2U9kZCQA4JdffsGJEyewYsUKKJVKREVFAaiey//WW28hLS0NmzZtQnx8PJKSkrBkyRKoVCrTt0rGtjExMUhOTkZcXBzi4uKQnJyMmJgYszbjxo2DSqXCkiVLkJSUhPj4eGzatAlpaWl46623zNpGRUVBqVRixYoVOHHiBA4fPozVq1cjKysLM2bMMGs7Y8YMZGVlYfXq1Th8+DD16Z4+rVy50uH65Ih5aog+TZw40eH65Ih5etw+rVy50uH65Ih5epw+/Xj0exhpNYa1Ndx9FRg5aqTd9ulh8qSF2tR/v3buDtGn++WJc9KjJp1TOTIy0u26T46Yp4bqU83PZY2hT+vWrcPD4Fhde9aQRu/27ds4ePBgvdqOGTMGXl5eCAsLg06nQ1JSktntly5dQkhICNavX49p06bV6z7PnTuHnj174ssvv0R0dHS9zqlrZDYyMhIpKSkIDg6u1/0Q+5WUlIRevXrxHQaxAcq1MFCeHR9jDGtnJYLpqz8ydnjCB0Om2ff+lPW179OLpu2IJn30NJp4Pdzgg73JuV6M3cvOmX7v8kxz/O3vXXiMiFhTY3sPN9Yk9a0LaJqxHWvevDmmTp36UOd07doV27dvh1arNbtuNjk5GQAQEhLSoDHey9fXF76+tE+ZkGVlZfEdArERyrUwUJ4dH8dxkDlLoC6rNB3z8BXONi1NmhqKVxcPGVw9ZQ9obf/unWYshOnkQmbv7+E0zVhgRo8ejdLSUuzZs8fs+JYtW+Dv74/evXvzFBkRinv3gyaOi3ItDJRnYai5ojFgmGYsFD0iWqPDEz4In9DZ6pdjNQbye4tZB1+9Wejs/T2cRmYFZujQoYiIiMDMmTNRXFyMjh07Yvv27Th8+DC2bt0KsVhsajtlyhRs2bIF6enpaNOmjen47t27AQAZGRkADNONXV0Nb3QvvviiDXtD7FH//v35DoHYCOVaGCjPwnBvMevRTDjFrFdzF8FMqQYAJ5kYIgkHvdYwrbxpCxeeIyLWZO/v4TQyK0B79+7F3//+d7z33nsYMmQIfv31V2zfvh0TJ040a6fT6aDT6Sz27h07dizGjh1r2lN39erVpmOEPMjq1av5DoHYCOVaGCjPwiBzMR+t8/Cl1W0dFcdx0OhUAACFuxTOtWzVQxyHvb+H0wJQhHcPe6E3IYQQQmzrhy9SkHbOsMesTCHBlJX9BDHlVqj2LD+HOxnFaBPSFMP/0Y3vcIiAPGxdQCOzhBCbMi7BLiRC/c5QiLkWIsqzMNTca9bdx1lYhawA38O//fULdHrKF71Htec7FGJl9v4eTsUsIcRmjmQcQULvBBzJOMJ3KDZzJOMInD90FlSfAWHmWqj279/PdwjEBmQ5maa/C2nxJxw5Ajg7G/4UiiNH8NXBzXi27R34tGrCdzTEyuz9PZyKWUKITTDGsODIAmh2a7DwyEJBjFaa+qwTTp8BYeZayKKiovgOgVgbY5AdOWT6VTDXyzIGLFgAaDTAwoXCGKGt6nOUkPoscPb+Hk7FLCHEJg7+dRDnb58HngPO3T6HQ38devBJds7UZwinz4Awcy1kn376Kd8hEGs7eBDyjFTTrx7ZV3kMxoYOHgTOG97Dce4ccEgA72VVff4UEE6fBc7e38OpmCWEWB1jDLGJseDAARcBDhxiE2MdesTOrM8QRp8BYeZa6DZu3Mh3CMSaGANiY6EoV5oOeW5b6/gjdlX9hvHaYI4z/O7I/a7R542AMPpM7P49nIpZQojVGUfqGBjQAmBgDj9iZ9ZnCKPPgDBzLXS9evXiOwRiTVUjda1unYPHlcPo8dtOeP/8neOP2BlHZY2FHGOOP1JZo8+9AGH0mdj9e7jkwU0IsS6NRgMASEtL4zkSYg2MMczfPR+4W3XgLoCqtUPmx81HmxfbONyqmBZ9rsFR+wwIM9cESE1Nhbe3N99hEGtgDKjaUx56LUoTV8AdwJ+A4XibNtUjl46kZr/v5aj9vqfPqQBMr2pH7TMB0Pjew431gLE+eBDaZ5bwbsuWLYiOjuY7DEIIIYQQQkgjsG/fPowaNeqB7WhklvAuICAAAPDNN98gKCiI52iINaWlpSEyMhL79u1Dx44d+Q6HWBHlWhgoz8JBuRYOyrVwNMZcazQa3Lp1CwMGDKhXeypmCe/c3NwAAEFBQQgODuY5GmILHTt2pFwLBOVaGCjPwkG5Fg7KtXA0tlw/8cQT9W5LC0ARQgghhBBCCLE7VMwSQgghhBBCCLE7VMwSQgghhBBCCLE7VMwS3vn4+GDRokXw8fHhOxRiZZRr4aBcCwPlWTgo18JBuRYOR8g1bc1DCCGEEEIIIcTu0MgsIYQQQgghhBC7Q8UsIYQQQgghhBC7Q8UsIYQQQgghhBC7Q8UsIYQQQgghhBC7Q8Us4U1paSlmz54Nf39/yOVydO/eHTt27OA7LHKPY8eO4dVXX0VgYCBcXFzQokULjBo1CufPn7doe+HCBQwePBiurq7w8PDAmDFjkJGRUev9fvbZZwgMDIRMJkO7du2wePFiVFZWWrTLzc1FdHQ0vL29oVAoEBYWhqNHjzZ4P4mlL774AhzHwdXV1eI2yrX9++WXXzBs2DB4enrC2dkZnTp1wpIlS8zaUJ7t38WLFxEZGQl/f38oFAoEBgbi/fffh0qlMmtHubYvJSUleOedd/Dss8/Cx8cHHMchNja21rZ85/bIkSMICwuDQqGAt7c3oqOjkZub+8h9F5L65Fmn0+GTTz7BkCFD0LJlSygUCnTp0gULFixAYWFhrffrUHlmhPAkIiKCeXh4sHXr1rFjx46xqVOnMgBs27ZtfIdGanjxxRfZwIED2Zo1a1hiYiLbtWsX69OnD5NIJOzo0aOmdpcvX2ZNmjRh/fr1YwcPHmR79uxhwcHBzN/fn+Xm5prd5wcffMA4jmMLFy5kx48fZ8uXL2dSqZS99tprZu3UajULCQlhLVu2ZFu3bmU//vgjGzVqFJNIJCwxMdEm/ReqzMxM5u7uzvz9/ZmLi4vZbZRr+7dt2zYmEonYyy+/zL777jt27NgxtmHDBrZ48WJTG8qz/bt06RKTy+WsW7dubOfOnezo0aNs0aJFTCwWs5EjR5raUa7tz7Vr15i7uzvr37+/6fPTokWLLNrxndvExEQmkUjYqFGj2I8//si2bt3KWrRowUJCQpharW7wfxdHU588l5SUsCZNmrBp06axXbt2sePHj7OVK1cyT09PFhQUxFQqlVl7R8szFbOEFwcPHmQAWFxcnNnxiIgI5u/vz7RaLU+RkXvl5ORYHCspKWHNmjVjgwYNMh0bO3Ys8/b2ZkVFRaZj169fZ05OTuydd94xHcvLy2NyuZxNmzbN7D4//PBDxnEcu3TpkunY6tWrGQB26tQp07HKykoWFBTEevXq1SD9I7UbPnw4GzFiBIuKirIoZinX9i0zM5O5uLiwmTNn3rcd5dn+vfvuuwwAS0tLMzs+bdo0BoAplUrGGOXaHun1eqbX6xljjN29e7fOYpbv3Pbs2ZMFBQWxyspK07GTJ08yAGzNmjWP1nkBqU+etVoty8vLszh3165dDAD7+uuvTcccMc9UzBJeTJ06lbm6upo96RljLC4ujgFgJ0+e5CkyUl8DBw5kAQEBjDHDm5uzszObPn26Rbtnn32WderUyfT71q1bGQB2+vRps3bZ2dkMAPvwww9NxwYPHsw6d+5scZ8fffQRA8AyMzMbqjukhq+//po1adKE3bp1y6KYpVzbv9jYWAaAXb9+vc42lGfHYMz13bt3zY6/8847TCQSsdLSUsq1A6iryOE7t5mZmQwAW7p0qUXbgIAAFhER8VD9FLr7fWlRmxs3bjAA7KOPPjIdc8Q80zWzhBcpKSno0qULJBKJ2fHQ0FDT7aTxKioqwoULFxAcHAwASE9PR3l5uSl/NYWGhiItLQ1qtRpAdW67du1q1q558+bw9vY2y31KSkqd9wkAly5dapgOEZPc3FzMnj0by5YtQ8uWLS1up1zbv59++gleXl5ITU1F9+7dIZFI4OvrixkzZqC4uBgA5dlRREVFwcPDAzNnzkRGRgZKSkpw4MABrF+/HrNmzYKLiwvl2oHxnVvjOXW1pc961nXs2DEAMH1WAxwzz1TMEl7k5+fDy8vL4rjxWH5+vq1DIg9h1qxZKCsrw7vvvgugOl915ZQxhoKCAlNbmUwGFxeXWtvWzD09T2zv9ddfR+fOnTFz5sxab6dc27+srCyoVCqMHTsWL730Eo4cOYJ58+bhq6++wrBhw8AYozw7iLZt2+L06dNISUlBhw4d4ObmhhEjRiAqKgqrVq0CQK9pR8Z3bh/0+PQcsJ6srCwsWLAATz31FIYPH2467oh5ljy4CSHWwXHcI91G+PWvf/0L27Ztw2effYYnn3zS7Lb65vRhck/PE9vZs2cP9u/fj4sXLz7w35Zybb/0ej3UajUWLVqEBQsWAADCw8MhlUoxe/ZsHD16FAqFAgDl2d5dv34dI0aMQLNmzbB79274+Pjg119/xQcffIDS0lJs3LjR1JZy7bj4zm1dbek5YB1KpdL0xeTOnTshEpmPXTpanmlklvCiadOmtX5To1QqAdT+7Q7h3+LFi/HBBx/gww8/xD/+8Q/T8aZNmwKo/Zt2pVIJjuPg4eFhaqtWqy22hTC2rZl7ep7YTmlpKWbNmoU33ngD/v7+KCwsRGFhISoqKgAAhYWFKCsro1w7AGMOn3vuObPjQ4cOBWDYxoPy7BgWLFiA4uJi/PDDD3jhhRfQv39/zJs3D//5z3+wadMmnDhxgnLtwPjO7YMen54DDa+goAARERHIyspCQkIC2rdvb3a7I+aZilnCi65du+Ly5cvQarVmx5OTkwEAISEhfIRF7mPx4sWIjY1FbGwsYmJizG7r0KEDnJ2dTfmrKTk5GR07doRcLgdQfZ3GvW3v3LmDvLw8s9x37dq1zvsE6HnSkPLy8pCTk4OVK1fC09PT9LN9+3aUlZXB09MTEydOpFw7gNquawIAxhgAQCQSUZ4dxG+//YagoCCLKYU9e/YEANP0Y8q1Y+I7t8Y/62pLz4GGVVBQgMGDB+PatWtISEio9b3eIfNs1eWlCKnDoUOHGAC2Y8cOs+NDhgyhrXkaoffff58BYP/85z/rbDNu3Djm6+vLiouLTcdu3LjBpFIpmz9/vulYfn4+k8vlbMaMGWbnL1261GJZ+DVr1jAA7MyZM6ZjlZWVLDg4mPXu3bshukaqlJeXs+PHj1v8PPfcc0wul7Pjx4+z5ORkxhjl2t798MMPFqtWMsbYJ598wgCwn3/+mTFGeXYEAwcOZD4+PqykpMTs+Oeff84AsH379jHGKNf27n6r3PKd2169erGQkBCzz3WnT59mANjatWsfuc9CdL88K5VK9sQTTzAPDw929uzZOu/DEfNMxSzhTUREBPP09GSff/45O3bsGHvttdcYALZ161a+QyM1/Pvf/2YA2JAhQ9jp06ctfowuX77MXF1dWf/+/dmhQ4fY3r17WUhIyH03Zo+JiWGJiYlsxYoVTCaT1bphd3BwMGvVqhXbtm0bS0hIYKNHj651w25iHbXtM0u5tn8jRoxgMpmMLVmyhCUkJLClS5cyuVzOhg8fbmpDebZ/3377LeM4jvXp04ft3LmTHT16lH344YfM1dWVBQUFMY1GwxijXNurQ4cOsV27drFNmzYxAGzs2LFs165dbNeuXaysrIwxxn9ujx8/ziQSCRs9ejRLSEhg27ZtY61atWIhISFMrVZb9x/IQTwozyqVivXs2ZNxHMdWrVpl8Tnt3n2mHS3PVMwS3pSUlLA333yT+fn5MalUykJDQ9n27dv5DovcY8CAAQxAnT81nTt3jg0aNIgpFArm5ubGIiMjLd5EjVatWsUCAgKYVCplrVu3ZosWLWIVFRUW7e7cucMmTZrEvLy8mFwuZ3369GEJCQlW6SuxVFsxyxjl2t6pVCo2f/581qpVKyaRSFjr1q3ZwoULLT50UJ7t37Fjx9izzz7L/Pz8mLOzMwsICGBz585leXl5Zu0o1/anTZs2df7ffO3aNVM7vnP7448/sj59+jC5XM68vLzYpEmTWE5OToP8GwjBg/J87dq1+35Oi4qKsrhPR8ozx1jVRTKEEEIIIYQQQoidoAWgCCGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYHSpmCSGEEEIIIYTYnf8H71z61ReXBv4AAAAASUVORK5CYII=", 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\n", 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", + "image/png": 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\n", 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", 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\n", 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", 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\n", 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", + "image/png": 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\n", 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", + "image/png": 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\n", "text/plain": [ "
" ] @@ -6282,7 +6170,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "scrolled": true, "tags": [] @@ -6293,15 +6181,16 @@ "output_type": "stream", "text": [ "[1,GLOBAL, INFO]: List of files that will be processed:\n", - "[2,GLOBAL, INFO]: /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron.nestml\n", - "[3,GLOBAL, INFO]: /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse.nestml\n", - "[4,GLOBAL, INFO]: Target platform code will be generated in directory: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target'\n", + "[2,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/iaf_psc_exp_neuron.nestml\n", + "[3,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdp_synapse.nestml\n", + "[4,GLOBAL, INFO]: Target platform code will be generated in directory: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target'\n", + "[5,GLOBAL, INFO]: Target platform code will be installed in directory: '/tmp/nestml_target_nxqmze5r'\n", "\n", " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", " Version: 3.6.0-post0.dev0\n", - " Built: Oct 23 2023 17:52:55\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -6311,54 +6200,50 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[5,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", - "[6,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/Users/pooja/.local/lib/python3.10/site-packages/NESTML-6.0.0.post0.dev0-py3.10.egg/pynestml/codegeneration/resources_nest/point_neuron'\n", - "[7,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/Users/pooja/.local/lib/python3.10/site-packages/NESTML-6.0.0.post0.dev0-py3.10.egg/pynestml/codegeneration/resources_nest/point_neuron'\n", - "[8,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/Users/pooja/.local/lib/python3.10/site-packages/NESTML-6.0.0.post0.dev0-py3.10.egg/pynestml/codegeneration/resources_nest/point_neuron'\n", - "[9,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /Users/pooja/conda/nestml_dev\n", - "[10,GLOBAL, INFO]: Start processing '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron.nestml'!\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[12,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [71:39;71:63]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", - "[13,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [71:15;71:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", - "[14,GLOBAL, INFO]: Start processing '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse.nestml'!\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[16,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[17,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[18,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "[21,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[22,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[23,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "[25,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [71:39;71:63]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", - "[26,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [71:15;71:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", - "[28,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[29,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[30,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "[31,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_tr']\n", - "[32,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_tr_post']\n", - "[33,GLOBAL, INFO]: Moving state var defining equation(s) post_tr\n", - "[34,GLOBAL, INFO]: Moving state variables for equation(s) post_tr\n", - "[35,GLOBAL, INFO]: Moving definition of post_tr from synapse to neuron\n", - "[36,GLOBAL, INFO]: \tMoving statement post_tr += 1.0\n", - "[37,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", - "[38,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", - "[39,GLOBAL, INFO]: Copying definition of tau_tr_post from synapse to neuron\n", - "[40,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", - "[41,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", - "[42,GLOBAL, INFO]: \t• Replacing variable post_tr\n", - "[43,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml) in expression: A_minus * post_tr\n", - "[46,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[47,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[48,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n" + "[6,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", + "[7,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[8,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[9,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[10,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /home/charl/julich/nest-simulator-install\n", + "[11,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/iaf_psc_exp_neuron.nestml'!\n", + "[13,iaf_psc_exp_neuron_nestml, INFO, [67:39;67:63]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[14,iaf_psc_exp_neuron_nestml, INFO, [67:15;67:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[15,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/neuromodulated_stdp_synapse.nestml'!\n", + "[17,neuromodulated_stdp_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[18,neuromodulated_stdp_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[19,neuromodulated_stdp_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[22,neuromodulated_stdp_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[23,neuromodulated_stdp_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[24,neuromodulated_stdp_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[26,iaf_psc_exp_neuron_nestml, INFO, [67:39;67:63]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[27,iaf_psc_exp_neuron_nestml, INFO, [67:15;67:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[29,neuromodulated_stdp_synapse_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[30,neuromodulated_stdp_synapse_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[31,neuromodulated_stdp_synapse_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[32,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_tr']\n", + "[33,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_tr_post']\n", + "[34,GLOBAL, INFO]: Moving state var defining equation(s) post_tr\n", + "[35,GLOBAL, INFO]: Moving state variables for equation(s) post_tr\n", + "[36,GLOBAL, INFO]: Moving definition of post_tr from synapse to neuron\n", + "[37,GLOBAL, INFO]: \tMoving statement post_tr += 1.0\n", + "[38,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", + "[39,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", + "[40,GLOBAL, INFO]: Copying definition of tau_tr_post from synapse to neuron\n", + "[41,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", + "[42,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", + "[43,GLOBAL, INFO]: \t• Replacing variable post_tr\n", + "[44,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = post_tr__for_neuromodulated_stdp_synapse_nestml) in expression: A_minus * post_tr\n", + "[47,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[48,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[49,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Analysing input:\n", - "INFO:root:{\n", + "INFO:Analysing input:\n", + "INFO:{\n", " \"dynamics\": [\n", " {\n", " \"expression\": \"I_syn_exc' = (-I_syn_exc) / tau_syn_exc\",\n", @@ -6394,66 +6279,69 @@ " \"tau_syn_inh\": \"2\"\n", " }\n", "}\n", - "INFO:root:Processing global options...\n", - "INFO:root:Processing input shapes...\n", - "INFO:root:\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", "Processing differential-equation form shape I_syn_exc with defining expression = \"(-I_syn_exc) / tau_syn_exc\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", - "INFO:root:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", + "INFO:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", + "INFO:\n", "Processing differential-equation form shape I_syn_inh with defining expression = \"(-I_syn_inh) / tau_syn_inh\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", - "INFO:root:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", + "INFO:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + (I_syn_exc - I_syn_inh + I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", - "INFO:root:All known variables: [I_syn_exc, I_syn_inh, V_m], all parameters used in ODEs: {C_m, tau_syn_exc, I_e, I_stim, tau_syn_inh, tau_m, E_L}\n", - "INFO:root:No numerical value specified for parameter \"I_stim\"\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", + "INFO:All known variables: [I_syn_exc, I_syn_inh, V_m], all parameters used in ODEs: {tau_syn_exc, E_L, tau_m, I_e, C_m, I_stim, tau_syn_inh}\n", + "INFO:No numerical value specified for parameter \"I_stim\"\n", + "INFO:\n", "Processing differential-equation form shape I_syn_exc with defining expression = \"(-I_syn_exc) / tau_syn_exc\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", + "INFO:\n", "Processing differential-equation form shape I_syn_inh with defining expression = \"(-I_syn_inh) / tau_syn_inh\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + (I_syn_exc - I_syn_inh + I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", - "INFO:root:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", - "INFO:root:Finding analytically solvable equations...\n", - "INFO:root:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", - "INFO:root:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", - "INFO:root:Generating propagators for the following symbols: I_syn_exc, I_syn_inh, V_m\n" + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", + "INFO:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", + "INFO:Finding analytically solvable equations...\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[49,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml\n", - "[50,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml'\n", - "[51,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [59:0;118:0]]: Starts processing of the model 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml'\n" + "[50,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml, neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml\n", + "[51,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_neuron_nestml'\n", + "[52,iaf_psc_exp_neuron_nestml, INFO, [55:0;115:0]]: Starts processing of the model 'iaf_psc_exp_neuron_nestml'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "WARNING:root:Under certain conditions, the propagator matrix is singular (contains infinities).\n", - "WARNING:root:List of all conditions that result in a singular propagator:\n", - "WARNING:root:\ttau_m = tau_syn_exc\n", - "WARNING:root:\ttau_m = tau_syn_inh\n", - "INFO:root:update_expr[I_syn_exc] = I_syn_exc*__P__I_syn_exc__I_syn_exc\n", - "INFO:root:update_expr[I_syn_inh] = I_syn_inh*__P__I_syn_inh__I_syn_inh\n", - "INFO:root:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + I_syn_exc*__P__V_m__I_syn_exc + I_syn_inh*__P__V_m__I_syn_inh + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", - "WARNING:root:Not preserving expression for variable \"I_syn_exc\" as it is solved by propagator solver\n", - "WARNING:root:Not preserving expression for variable \"I_syn_inh\" as it is solved by propagator solver\n", - "WARNING:root:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "INFO:root:In ode-toolbox: returning outdict = \n", - "INFO:root:[\n", + "INFO:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", + "INFO:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n", + "INFO:Generating propagators for the following symbols: I_syn_exc, I_syn_inh, V_m\n", + "WARNING:Under certain conditions, the propagator matrix is singular (contains infinities).\n", + "WARNING:List of all conditions that result in a singular propagator:\n", + "WARNING:\ttau_m = tau_syn_exc\n", + "WARNING:\ttau_m = tau_syn_inh\n", + "INFO:update_expr[I_syn_exc] = I_syn_exc*__P__I_syn_exc__I_syn_exc\n", + "INFO:update_expr[I_syn_inh] = I_syn_inh*__P__I_syn_inh__I_syn_inh\n", + "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + I_syn_exc*__P__V_m__I_syn_exc + I_syn_inh*__P__V_m__I_syn_inh + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", + "WARNING:Not preserving expression for variable \"I_syn_exc\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"I_syn_inh\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", " {\n", " \"initial_values\": {\n", " \"I_syn_exc\": \"0\",\n", @@ -6472,7 +6360,7 @@ " \"__P__I_syn_exc__I_syn_exc\": \"exp(-__h/tau_syn_exc)\",\n", " \"__P__I_syn_inh__I_syn_inh\": \"exp(-__h/tau_syn_inh)\",\n", " \"__P__V_m__I_syn_exc\": \"tau_m*tau_syn_exc*(-exp(__h/tau_m) + exp(__h/tau_syn_exc))*exp(-__h*(tau_m + tau_syn_exc)/(tau_m*tau_syn_exc))/(C_m*(tau_m - tau_syn_exc))\",\n", - " \"__P__V_m__I_syn_inh\": \"tau_m*tau_syn_inh*(exp(__h/tau_m) - exp(__h/tau_syn_inh))*exp(-__h*(tau_m + tau_syn_inh)/(tau_m*tau_syn_inh))/(C_m*(tau_m - tau_syn_inh))\",\n", + " \"__P__V_m__I_syn_inh\": \"tau_m*tau_syn_inh*(exp(__h/tau_m) - exp(__h/tau_syn_inh))*exp(-__h/tau_syn_inh - __h/tau_m)/(C_m*(tau_m - tau_syn_inh))\",\n", " \"__P__V_m__V_m\": \"exp(-__h/tau_m)\"\n", " },\n", " \"solver\": \"analytical\",\n", @@ -6487,49 +6375,9 @@ " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + I_syn_exc*__P__V_m__I_syn_exc + I_syn_inh*__P__V_m__I_syn_inh + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\"\n", " }\n", " }\n", - "]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Analysing input:\n", - "INFO:root:{\n", + "]\n", + "INFO:Analysing input:\n", + "INFO:{\n", " \"dynamics\": [\n", " {\n", " \"expression\": \"I_syn_exc' = (-I_syn_exc) / tau_syn_exc\",\n", @@ -6550,9 +6398,9 @@ " }\n", " },\n", " {\n", - " \"expression\": \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml' = (-post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml) / tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\",\n", + " \"expression\": \"post_tr__for_neuromodulated_stdp_synapse_nestml' = (-post_tr__for_neuromodulated_stdp_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp_synapse_nestml\",\n", " \"initial_values\": {\n", - " \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\": \"0.0\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\": \"0.0\"\n", " }\n", " }\n", " ],\n", @@ -6569,85 +6417,88 @@ " \"tau_m\": \"10\",\n", " \"tau_syn_exc\": \"2\",\n", " \"tau_syn_inh\": \"2\",\n", - " \"tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\": \"20\"\n", + " \"tau_tr_post__for_neuromodulated_stdp_synapse_nestml\": \"20\"\n", " }\n", "}\n", - "INFO:root:Processing global options...\n", - "INFO:root:Processing input shapes...\n", - "INFO:root:\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", "Processing differential-equation form shape I_syn_exc with defining expression = \"(-I_syn_exc) / tau_syn_exc\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", - "INFO:root:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", + "INFO:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", + "INFO:\n", "Processing differential-equation form shape I_syn_inh with defining expression = \"(-I_syn_inh) / tau_syn_inh\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", - "INFO:root:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", + "INFO:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + (I_syn_exc - I_syn_inh + I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", - "INFO:root:\n", - "Processing differential-equation form shape post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml) / tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\"\n", - "INFO:root:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\" of order 1\n", - "INFO:root:Shape post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml/tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\n", - "INFO:root:All known variables: [I_syn_exc, I_syn_inh, V_m, post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml], all parameters used in ODEs: {C_m, tau_syn_exc, I_e, I_stim, tau_syn_inh, tau_m, E_L, tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml}\n", - "INFO:root:No numerical value specified for parameter \"I_stim\"\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", + "INFO:\n", + "Processing differential-equation form shape post_tr__for_neuromodulated_stdp_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdp_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp_synapse_nestml\"\n", + "INFO:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdp_synapse_nestml\" of order 1\n", + "INFO:Shape post_tr__for_neuromodulated_stdp_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp_synapse_nestml/tau_tr_post__for_neuromodulated_stdp_synapse_nestml\n", + "INFO:All known variables: [I_syn_exc, I_syn_inh, V_m, post_tr__for_neuromodulated_stdp_synapse_nestml], all parameters used in ODEs: {tau_syn_exc, tau_tr_post__for_neuromodulated_stdp_synapse_nestml, E_L, tau_m, I_e, C_m, I_stim, tau_syn_inh}\n", + "INFO:No numerical value specified for parameter \"I_stim\"\n", + "INFO:\n", "Processing differential-equation form shape I_syn_exc with defining expression = \"(-I_syn_exc) / tau_syn_exc\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_exc\" of order 1\n", + "INFO:\n", "Processing differential-equation form shape I_syn_inh with defining expression = \"(-I_syn_inh) / tau_syn_inh\"\n", - "INFO:root:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"I_syn_inh\" of order 1\n", + "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + (I_syn_exc - I_syn_inh + I_e + I_stim) / C_m\"\n", - "INFO:root:\tReturning shape: Shape \"V_m\" of order 1\n", - "INFO:root:\n", - "Processing differential-equation form shape post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml) / tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\"\n", - "INFO:root:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\" of order 1\n", - "INFO:root:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", - "INFO:root:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", - "INFO:root:Shape post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml/tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\n", - "INFO:root:Finding analytically solvable equations...\n", - "INFO:root:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", - "INFO:root:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", - "INFO:root:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", - "INFO:root:Shape post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml/tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\n", - "INFO:root:Generating propagators for the following symbols: I_syn_exc, I_syn_inh, V_m, post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\n" + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:\n", + "Processing differential-equation form shape post_tr__for_neuromodulated_stdp_synapse_nestml with defining expression = \"(-post_tr__for_neuromodulated_stdp_synapse_nestml) / tau_tr_post__for_neuromodulated_stdp_synapse_nestml\"\n", + "INFO:\tReturning shape: Shape \"post_tr__for_neuromodulated_stdp_synapse_nestml\" of order 1\n", + "INFO:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", + "INFO:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", + "INFO:Shape post_tr__for_neuromodulated_stdp_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp_synapse_nestml/tau_tr_post__for_neuromodulated_stdp_synapse_nestml\n", + "INFO:Finding analytically solvable equations...\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[53,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml'\n", - "[54,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [59:0;118:0]]: Starts processing of the model 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml'\n" + "[54,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml'\n", + "[55,iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml, INFO, [55:0;115:0]]: Starts processing of the model 'iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "WARNING:root:Under certain conditions, the propagator matrix is singular (contains infinities).\n", - "WARNING:root:List of all conditions that result in a singular propagator:\n", - "WARNING:root:\ttau_m = tau_syn_exc\n", - "WARNING:root:\ttau_m = tau_syn_inh\n", - "INFO:root:update_expr[I_syn_exc] = I_syn_exc*__P__I_syn_exc__I_syn_exc\n", - "INFO:root:update_expr[I_syn_inh] = I_syn_inh*__P__I_syn_inh__I_syn_inh\n", - "INFO:root:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + I_syn_exc*__P__V_m__I_syn_exc + I_syn_inh*__P__V_m__I_syn_inh + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", - "INFO:root:update_expr[post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml] = __P__post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml*post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\n", - "WARNING:root:Not preserving expression for variable \"I_syn_exc\" as it is solved by propagator solver\n", - "WARNING:root:Not preserving expression for variable \"I_syn_inh\" as it is solved by propagator solver\n", - "WARNING:root:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "WARNING:root:Not preserving expression for variable \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\" as it is solved by propagator solver\n", - "INFO:root:In ode-toolbox: returning outdict = \n", - "INFO:root:[\n", + "INFO:Shape I_syn_exc: reconstituting expression -I_syn_exc/tau_syn_exc\n", + "INFO:Shape I_syn_inh: reconstituting expression -I_syn_inh/tau_syn_inh\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m + I_syn_exc/C_m - I_syn_inh/C_m\n", + "INFO:Shape post_tr__for_neuromodulated_stdp_synapse_nestml: reconstituting expression -post_tr__for_neuromodulated_stdp_synapse_nestml/tau_tr_post__for_neuromodulated_stdp_synapse_nestml\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n", + "INFO:Generating propagators for the following symbols: I_syn_exc, I_syn_inh, V_m, post_tr__for_neuromodulated_stdp_synapse_nestml\n", + "WARNING:Under certain conditions, the propagator matrix is singular (contains infinities).\n", + "WARNING:List of all conditions that result in a singular propagator:\n", + "WARNING:\ttau_m = tau_syn_exc\n", + "WARNING:\ttau_m = tau_syn_inh\n", + "INFO:update_expr[I_syn_exc] = I_syn_exc*__P__I_syn_exc__I_syn_exc\n", + "INFO:update_expr[I_syn_inh] = I_syn_inh*__P__I_syn_inh__I_syn_inh\n", + "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + I_syn_exc*__P__V_m__I_syn_exc + I_syn_inh*__P__V_m__I_syn_inh + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", + "INFO:update_expr[post_tr__for_neuromodulated_stdp_synapse_nestml] = __P__post_tr__for_neuromodulated_stdp_synapse_nestml__post_tr__for_neuromodulated_stdp_synapse_nestml*post_tr__for_neuromodulated_stdp_synapse_nestml\n", + "WARNING:Not preserving expression for variable \"I_syn_exc\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"I_syn_inh\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"post_tr__for_neuromodulated_stdp_synapse_nestml\" as it is solved by propagator solver\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", " {\n", " \"initial_values\": {\n", " \"I_syn_exc\": \"0\",\n", " \"I_syn_inh\": \"0\",\n", " \"V_m\": \"E_L\",\n", - " \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\": \"0.0\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\": \"0.0\"\n", " },\n", " \"parameters\": {\n", " \"C_m\": \"250.000000000000\",\n", @@ -6656,79 +6507,33 @@ " \"tau_m\": \"10.0000000000000\",\n", " \"tau_syn_exc\": \"2.00000000000000\",\n", " \"tau_syn_inh\": \"2.00000000000000\",\n", - " \"tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\": \"20.0000000000000\"\n", + " \"tau_tr_post__for_neuromodulated_stdp_synapse_nestml\": \"20.0000000000000\"\n", " },\n", " \"propagators\": {\n", " \"__P__I_syn_exc__I_syn_exc\": \"exp(-__h/tau_syn_exc)\",\n", " \"__P__I_syn_inh__I_syn_inh\": \"exp(-__h/tau_syn_inh)\",\n", " \"__P__V_m__I_syn_exc\": \"tau_m*tau_syn_exc*(-exp(__h/tau_m) + exp(__h/tau_syn_exc))*exp(-__h*(tau_m + tau_syn_exc)/(tau_m*tau_syn_exc))/(C_m*(tau_m - tau_syn_exc))\",\n", - " \"__P__V_m__I_syn_inh\": \"tau_m*tau_syn_inh*(exp(__h/tau_m) - exp(__h/tau_syn_inh))*exp(-__h*(tau_m + tau_syn_inh)/(tau_m*tau_syn_inh))/(C_m*(tau_m - tau_syn_inh))\",\n", + " \"__P__V_m__I_syn_inh\": \"tau_m*tau_syn_inh*(exp(__h/tau_m) - exp(__h/tau_syn_inh))*exp(-__h/tau_syn_inh - __h/tau_m)/(C_m*(tau_m - tau_syn_inh))\",\n", " \"__P__V_m__V_m\": \"exp(-__h/tau_m)\",\n", - " \"__P__post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\": \"exp(-__h/tau_tr_post__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml)\"\n", + " \"__P__post_tr__for_neuromodulated_stdp_synapse_nestml__post_tr__for_neuromodulated_stdp_synapse_nestml\": \"exp(-__h/tau_tr_post__for_neuromodulated_stdp_synapse_nestml)\"\n", " },\n", " \"solver\": \"analytical\",\n", " \"state_variables\": [\n", " \"I_syn_exc\",\n", " \"I_syn_inh\",\n", " \"V_m\",\n", - " \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\"\n", " ],\n", " \"update_expressions\": {\n", " \"I_syn_exc\": \"I_syn_exc*__P__I_syn_exc__I_syn_exc\",\n", " \"I_syn_inh\": \"I_syn_inh*__P__I_syn_inh__I_syn_inh\",\n", " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + I_syn_exc*__P__V_m__I_syn_exc + I_syn_inh*__P__V_m__I_syn_inh + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\",\n", - " \"post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\": \"__P__post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml*post_tr__for_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml\"\n", + " \"post_tr__for_neuromodulated_stdp_synapse_nestml\": \"__P__post_tr__for_neuromodulated_stdp_synapse_nestml__post_tr__for_neuromodulated_stdp_synapse_nestml*post_tr__for_neuromodulated_stdp_synapse_nestml\"\n", " }\n", " }\n", - "]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:Analysing input:\n", - "INFO:root:{\n", + "]\n", + "INFO:Analysing input:\n", + "INFO:{\n", " \"dynamics\": [\n", " {\n", " \"expression\": \"pre_tr' = (-pre_tr) / tau_tr_pre\",\n", @@ -6754,24 +6559,41 @@ " \"tau_tr_pre\": \"20\"\n", " }\n", "}\n", - "INFO:root:Processing global options...\n", - "INFO:root:Processing input shapes...\n", - "INFO:root:\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", "Processing differential-equation form shape pre_tr with defining expression = \"(-pre_tr) / tau_tr_pre\"\n", - "INFO:root:\tReturning shape: Shape \"pre_tr\" of order 1\n", - "INFO:root:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", - "INFO:root:All known variables: [pre_tr], all parameters used in ODEs: {tau_tr_pre}\n", - "INFO:root:\n", + "INFO:\tReturning shape: Shape \"pre_tr\" of order 1\n", + "INFO:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", + "INFO:All known variables: [pre_tr], all parameters used in ODEs: {tau_tr_pre}\n", + "INFO:\n", "Processing differential-equation form shape pre_tr with defining expression = \"(-pre_tr) / tau_tr_pre\"\n", - "INFO:root:\tReturning shape: Shape \"pre_tr\" of order 1\n", - "INFO:root:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", - "INFO:root:Finding analytically solvable equations...\n", - "INFO:root:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", - "INFO:root:Generating propagators for the following symbols: pre_tr\n", - "INFO:root:update_expr[pre_tr] = __P__pre_tr__pre_tr*pre_tr\n", - "WARNING:root:Not preserving expression for variable \"pre_tr\" as it is solved by propagator solver\n", - "INFO:root:In ode-toolbox: returning outdict = \n", - "INFO:root:[\n", + "INFO:\tReturning shape: Shape \"pre_tr\" of order 1\n", + "INFO:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", + "INFO:Finding analytically solvable equations...\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n", + "INFO:Shape pre_tr: reconstituting expression -pre_tr/tau_tr_pre\n", + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[57,GLOBAL, INFO]: Analysing/transforming synapse neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.\n", + "[58,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [2:0;66:0]]: Starts processing of the model 'neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n", + "INFO:Generating propagators for the following symbols: pre_tr\n", + "INFO:update_expr[pre_tr] = __P__pre_tr__pre_tr*pre_tr\n", + "WARNING:Not preserving expression for variable \"pre_tr\" as it is solved by propagator solver\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", " {\n", " \"initial_values\": {\n", " \"pre_tr\": \"0.0\"\n", @@ -6797,412 +6619,235 @@ "name": "stdout", "output_type": "stream", "text": [ - "[56,GLOBAL, INFO]: Analysing/transforming synapse neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.\n", - "[57,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [2:0;66:0]]: Starts processing of the model 'neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml'\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[59,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[60,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[61,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[63,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", - "[64,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", - "[65,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[66,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.cpp\n", - "[67,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h\n", - "[68,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [59:0;118:0]]: Successfully generated code for the model: 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml' in: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[69,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.cpp\n", - "[70,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.h\n", - "[71,iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml, INFO, [59:0;118:0]]: Successfully generated code for the model: 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml' in: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "Generating code for the synapse neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "ANTLR runtime and generated code versions disagree: 4.10!=4.13.1\n", - "[72,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h\n", - "[73,neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml, INFO, [2:0;66:0]]: Successfully generated code for the model: 'neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml' in: '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "[74,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/CMakeLists.txt\n", - "[75,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.h\n", - "[76,GLOBAL, INFO]: Rendering template /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp\n", - "[77,GLOBAL, INFO]: Successfully generated NEST module code in '/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target' !\n", - "\u001b[33mCMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\u001b[0m\n", - "\u001b[33mCMake Warning (dev) at CMakeLists.txt:95 (project):\n", + "[60,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[61,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[62,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[64,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, WARNING, [12:8;12:28]]: Variable 'd' has the same name as a physical unit!\n", + "[65,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [40:13;40:20]]: Implicit casting from (compatible) type '1 / ms' to 'real'.\n", + "[66,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [63:13;63:123]]: Implicit casting from (compatible) type 'ms' to 'real'.\n", + "[67,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml.cpp\n", + "[68,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml.h\n", + "[69,iaf_psc_exp_neuron_nestml, INFO, [55:0;115:0]]: Successfully generated code for the model: 'iaf_psc_exp_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "[70,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp\n", + "[71,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.h\n", + "[72,iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml, INFO, [55:0;115:0]]: Successfully generated code for the model: 'iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "Generating code for the synapse neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.\n", + "[73,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h\n", + "[74,neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml, INFO, [2:0;66:0]]: Successfully generated code for the model: 'neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "[75,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module.cpp\n", + "[76,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module.h\n", + "[77,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/CMakeLists.txt\n", + "[78,GLOBAL, INFO]: Successfully generated NEST module code in '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target' !\n", + "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", " both commands.\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", - "\u001b[0m\n", - "-- The CXX compiler identification is AppleClang 14.0.3.14030022\n", + "\n", + "-- The CXX compiler identification is GNU 12.3.0\n", "-- Detecting CXX compiler ABI info\n", "-- Detecting CXX compiler ABI info - done\n", - "-- Check for working CXX compiler: /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++ - skipped\n", + "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n", "-- Detecting CXX compile features\n", "-- Detecting CXX compile features - done\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0m-------------------------------------------------------\u001b[0m\n", - "\u001b[0mnestml_e73da659413e460d859f52a1d18a5dc3_module Configuration Summary\u001b[0m\n", - "\u001b[0m-------------------------------------------------------\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0mC++ compiler : /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin/c++\u001b[0m\n", - "\u001b[0mBuild static libs : OFF\u001b[0m\n", - "\u001b[0mC++ compiler flags : \u001b[0m\n", - "\u001b[0mNEST compiler flags : -std=c++11 -Wall -Xclang -fopenmp -O2\u001b[0m\n", - "\u001b[0mNEST include dirs : -I/Users/pooja/conda/nestml_dev/include/nest -I/usr/local/include -I/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX13.3.sdk/usr/include -I/usr/local/Cellar/gsl/2.7/include -I/Users/pooja/conda/nestml_dev/include\u001b[0m\n", - "\u001b[0mNEST libraries flags : -L/Users/pooja/conda/nestml_dev/lib/nest -lnest -lsli -Xclang -fopenmp /usr/local/lib/libltdl.dylib /Users/pooja/conda/nestml_dev/lib/libreadline.dylib /Users/pooja/conda/nestml_dev/lib/libncurses.dylib /usr/local/Cellar/gsl/2.7/lib/libgsl.dylib /usr/local/Cellar/gsl/2.7/lib/libgslcblas.dylib\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0m-------------------------------------------------------\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0mYou can now build and install 'nestml_e73da659413e460d859f52a1d18a5dc3_module' using\u001b[0m\n", - "\u001b[0m make\u001b[0m\n", - "\u001b[0m make install\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[0mThe library file libnestml_e73da659413e460d859f52a1d18a5dc3_module.so will be installed to\u001b[0m\n", - "\u001b[0m /Users/pooja/conda/nestml_dev/lib/nest\u001b[0m\n", - "\u001b[0mThe module can be loaded into NEST using\u001b[0m\n", - "\u001b[0m (nestml_e73da659413e460d859f52a1d18a5dc3_module) Install (in SLI)\u001b[0m\n", - "\u001b[0m nest.Install(nestml_e73da659413e460d859f52a1d18a5dc3_module) (in PyNEST)\u001b[0m\n", - "\u001b[0m\u001b[0m\n", - "\u001b[33mCMake Warning (dev) in CMakeLists.txt:\n", + "\n", + "-------------------------------------------------------\n", + "nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module Configuration Summary\n", + "-------------------------------------------------------\n", + "\n", + "C++ compiler : /usr/bin/c++\n", + "Build static libs : OFF\n", + "C++ compiler flags : \n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", + "\n", + "-------------------------------------------------------\n", + "\n", + "You can now build and install 'nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module' using\n", + " make\n", + " make install\n", + "\n", + "The library file libnestml_6a0e5b32a83c4b3a83ce6c87a2445436_module.so will be installed to\n", + " /tmp/nestml_target_nxqmze5r\n", + "The module can be loaded into NEST using\n", + " (nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module) Install (in SLI)\n", + " nest.Install(nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module) (in PyNEST)\n", + "\n", + "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", "\n", - " cmake_minimum_required(VERSION 3.27)\n", + " cmake_minimum_required(VERSION 3.26)\n", "\n", " should be added at the top of the file. The version specified may be lower\n", " if you wish to support older CMake versions for this project. For more\n", " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", - "\u001b[0m\n", - "-- Configuring done (0.9s)\n", + "\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", - "-- Build files have been written to: /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target\n", - "[ 75%] \u001b[32mBuilding CXX object CMakeFiles/nestml_e73da659413e460d859f52a1d18a5dc3_module_module.dir/nestml_e73da659413e460d859f52a1d18a5dc3_module.o\u001b[0m\n", - "[ 75%] \u001b[32mBuilding CXX object CMakeFiles/nestml_e73da659413e460d859f52a1d18a5dc3_module_module.dir/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.o\u001b[0m\n", - "[ 75%] \u001b[32mBuilding CXX object CMakeFiles/nestml_e73da659413e460d859f52a1d18a5dc3_module_module.dir/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.o\u001b[0m\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.cpp:43:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:280:17: warning: 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.cpp:43:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.h:333:17: warning: 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.cpp:43:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.h:263:8: warning: 'register_stdp_connection' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]\n", - " void register_stdp_connection( double t_first_read, double delay );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:481:16: note: overridden virtual function is here\n", - " virtual void register_stdp_connection( double, double );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.cpp:179:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.cpp:274:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.cpp:189:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:47:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:280:17: warning: 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.cpp:295:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:49:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.h:333:17: warning: 'iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]\n", - " inline double get_C_m() const\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:747:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0)\n", - " virtual double get_C_m( int comp );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:49:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml.h:263:8: warning: 'register_stdp_connection' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]\n", - " void register_stdp_connection( double t_first_read, double delay );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/node.h:481:16: note: overridden virtual function is here\n", - " virtual void register_stdp_connection( double, double );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:516:18: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:692:12: warning: unused variable 'cd' [-Wunused-variable]\n", - " double cd;\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:842:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:855:16: warning: unused variable '__resolution' [-Wunused-variable]\n", - " const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:1002:18: warning: unused variable '_tr_t' [-Wunused-variable]\n", - " const double _tr_t = start->t_;\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:984:10: warning: unused variable 'timestep' [-Wunused-variable]\n", - " double timestep = 0;\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:518:10: warning: unused variable 'get_thread' [-Wunused-variable]\n", - " auto get_thread = [tid]()\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:61:28: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " ConnectorModel* cf = new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( name );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml >( \"neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:588:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:613:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:648:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:928:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:583:9: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::update_internal_state_' requested here\n", - " update_internal_state_(t_lastspike_, (start->t_ + __dendritic_delay) - t_lastspike_, cp);\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:61:28: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " ConnectorModel* cf = new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( name );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml >( \"neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:499:7: warning: expression result unused [-Wunused-value]\n", - " dynamic_cast(t);\n", - " ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:286:14: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::check_connection' requested here\n", - " connection.check_connection( src, tgt, receptor_type, get_common_properties() );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:61:28: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " ConnectorModel* cf = new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( name );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml >( \"neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:518:10: warning: unused variable 'get_thread' [-Wunused-variable]\n", - " auto get_thread = [tid]()\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:67:14: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " cf = new GenericConnectorModel< ConnectionT< TargetIdentifierIndex > >( name + \"_hpc\" );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml >( \"neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:588:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:613:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:648:14: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model \n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:928:10: warning: unused variable 'get_t' [-Wunused-variable]\n", - " auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:583:9: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::update_internal_state_' requested here\n", - " update_internal_state_(t_lastspike_, (start->t_ + __dendritic_delay) - t_lastspike_, cp);\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:381:18: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::send' requested here\n", - " C_[ lcid ].send( e, tid, cp );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_base.h:223:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here\n", - " explicit Connector( const synindex syn_id )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:281:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here\n", - " thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:67:14: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " cf = new GenericConnectorModel< ConnectionT< TargetIdentifierIndex > >( name + \"_hpc\" );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml >( \"neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml\" );\n", - " ^\n", - "In file included from /Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:52:\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml.h:499:7: warning: expression result unused [-Wunused-value]\n", - " dynamic_cast(t);\n", - " ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:286:14: note: in instantiation of member function 'nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml::check_connection' requested here\n", - " connection.check_connection( src, tgt, receptor_type, get_common_properties() );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model_impl.h:262:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here\n", - " add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/connector_model.h:156:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here\n", - " GenericConnectorModel( const std::string name )\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/model_manager_impl.h:67:14: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here\n", - " cf = new GenericConnectorModel< ConnectionT< TargetIdentifierIndex > >( name + \"_hpc\" );\n", - " ^\n", - "/Users/pooja/conda/nestml_dev/include/nest/nest_impl.h:35:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here\n", - " kernel().model_manager.register_connection_model< ConnectorModelT >( name );\n", - " ^\n", - "/Users/pooja/nestml/integrate_specific_odes/doc/tutorials/stdp_dopa_synapse/target/nestml_e73da659413e460d859f52a1d18a5dc3_module.cpp:111:11: note: in instantiation of function template specialization 'nest::register_connection_model' requested here\n", - " nest::register_connection_model< nest::neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml >( \"neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml__with_iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml\" );\n", - " ^\n", - "3 warnings generated.\n", - "4 warnings generated.\n", - "21 warnings generated.\n", - "[100%] \u001b[32m\u001b[1mLinking CXX shared module nestml_e73da659413e460d859f52a1d18a5dc3_module.so\u001b[0m\n", - "[100%] Built target nestml_e73da659413e460d859f52a1d18a5dc3_module_module\n", - "[100%] Built target nestml_e73da659413e460d859f52a1d18a5dc3_module_module\n", - "\u001b[36mInstall the project...\u001b[0m\n", + "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target\n", + "[ 25%] Building CXX object CMakeFiles/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module_module.dir/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module_module.dir/iaf_psc_exp_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module_module.dir/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp: In member function ‘void iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp:196:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 196 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp:310:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 310 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml.cpp:305:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 305 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml.cpp:186:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 186 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_exp_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml.cpp:289:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 289 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/iaf_psc_exp_neuron_nestml.cpp:284:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 284 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + " | ^~~~~\n", + "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module.cpp:36:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:671:104: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:862:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 862 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:876:3: required from ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:671:104: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:849:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 849 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:671:104: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:862:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 862 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:876:3: required from ‘nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:671:104: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:849:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 849 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:589:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 589 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:614:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 614 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:649:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 649 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:517:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 517 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:519:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 519 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::trigger_update_weight(size_t, const std::vector&, double, const CommonPropertiesType&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int; CommonPropertiesType = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:446:38: required from ‘void nest::Connector::trigger_update_weight(long int, size_t, const std::vector&, double, const std::vector&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:433:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:1009:18: warning: unused variable ‘_tr_t’ [-Wunused-variable]\n", + " 1009 | const double _tr_t = start->t_;\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:991:10: warning: unused variable ‘timestep’ [-Wunused-variable]\n", + " 991 | double timestep = 0;\n", + " | ^~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:589:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 589 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:614:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 614 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:649:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 649 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:517:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 517 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:519:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 519 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::trigger_update_weight(size_t, const std::vector&, double, const CommonPropertiesType&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int; CommonPropertiesType = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:446:38: required from ‘void nest::Connector::trigger_update_weight(long int, size_t, const std::vector&, double, const std::vector&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:433:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:1009:18: warning: unused variable ‘_tr_t’ [-Wunused-variable]\n", + " 1009 | const double _tr_t = start->t_;\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:991:10: warning: unused variable ‘timestep’ [-Wunused-variable]\n", + " 991 | double timestep = 0;\n", + " | ^~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::process_mod_spikes_spikes_(const std::vector&, double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:563:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:699:12: warning: unused variable ‘cd’ [-Wunused-variable]\n", + " 699 | double cd;\n", + " | ^~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::update_internal_state_(double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:584:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:935:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 935 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::process_mod_spikes_spikes_(const std::vector&, double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:563:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:699:12: warning: unused variable ‘cd’ [-Wunused-variable]\n", + " 699 | double cd;\n", + " | ^~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h: In instantiation of ‘void nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::update_internal_state_(double, double, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:584:9: required from ‘bool nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml::send(nest::Event&, size_t, const nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_dopa_synapse/target/neuromodulated_stdp_synapse_nestml__with_iaf_psc_exp_neuron_nestml.h:935:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 935 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "[100%] Linking CXX shared module nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module.so\n", + "[100%] Built target nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module_module\n", + "[100%] Built target nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module_module\n", + "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /Users/pooja/conda/nestml_dev/lib/nest/nestml_e73da659413e460d859f52a1d18a5dc3_module.so\n", - "\n", - "Oct 23 18:10:33 nestml_e73da659413e460d859f52a1d18a5dc3_module [Debug]: \n", - " Initializing.\n", - "\n", - "Oct 23 18:10:33 Install [Info]: \n", - " loaded module nestml_e73da659413e460d859f52a1d18a5dc3_module\n" + "-- Installing: /tmp/nestml_target_nxqmze5r/nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module.so\n" ] } ], "source": [ "# generate and build code\n", "\n", - "\n", "module_name, neuron_model_name, synapse_model_name = NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_exp_neuron.nestml\",\n", " nestml_stdp_dopa_model,\n", " post_ports=[\"post_spikes\"],\n", " mod_ports=[\"mod_spikes\"],\n", - " logging_level=\"INFO\")\n", - "\n", - "# load dynamic library (NEST extension module) into NEST kernel\n", - "nest.ResetKernel()\n", - "nest.Install(module_name)" + " logging_level=\"INFO\")" ] }, { @@ -7215,7 +6860,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -7304,7 +6949,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -7312,42 +6957,29 @@ "output_type": "stream", "text": [ "\n", - "Oct 23 18:10:44 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 23 18:10:44 iaf_psc_delta272f51c9bf654826ac5db45cd89ed321_neuron_nestml__with_neuromodulated_stdp272f51c9bf654826ac5db45cd89ed321_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 23 18:10:44 iaf_psc_exp397a116eaa754e0e911785dd403176af_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 23 18:10:44 iaf_psc_exp397a116eaa754e0e911785dd403176af_neuron_nestml__with_neuromodulated_stdp397a116eaa754e0e911785dd403176af_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "Apr 19 11:34:22 Install [Info]: \n", + " loaded module nestml_6a0e5b32a83c4b3a83ce6c87a2445436_module\n", "\n", - "Oct 23 18:10:44 iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml [Warning]: \n", + "Apr 19 11:34:22 iaf_psc_exp_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:10:44 iaf_psc_expe73da659413e460d859f52a1d18a5dc3_neuron_nestml__with_neuromodulated_stdpe73da659413e460d859f52a1d18a5dc3_synapse_nestml [Warning]: \n", + "Apr 19 11:34:22 iaf_psc_exp_neuron_nestml__with_neuromodulated_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 23 18:10:44 SimulationManager::set_status [Info]: \n", + "Apr 19 11:34:22 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 0.1 ms.\n", - "--> Stimuli will be presented at times: [306.0, 316.0, 581.0, 656.0, 721.0, 778.0, 817.0, 879.0, 1341.0, 1600.0, 2073.0, 2129.0, 2239.0, 2450.0, 2554.0, 2710.0, 3275.0, 3416.0, 3675.0, 3800.0, 3866.0, 3941.0, 4169.0, 4892.0, 5061.0, 5508.0, 5766.0, 5802.0, 5942.0, 6165.0, 6175.0, 6186.0, 6278.0, 6318.0, 6407.0, 6696.0, 6828.0, 7216.0, 7317.0, 7536.0, 7654.0, 7664.0, 7854.0, 7953.0, 8167.0, 8245.0, 8450.0, 8898.0, 8945.0, 9069.0, 9088.0, 9347.0, 10180.0]\n", - "--> t_dopa_spikes = [609.0, 746.0, 800.0, 830.0, 891.0, 1613.0, 2139.0, 2265.0, 2728.0, 5816.0, 5962.0, 6203.0, 6300.0, 6436.0, 6849.0, 7667.0, 7869.0, 7981.0, 8196.0, 9090.0, 9370.0, 10197.0]\n" + "--> Stimuli will be presented at times: [137.0, 168.0, 197.0, 242.0, 351.0, 400.0, 410.0, 712.0, 1254.0, 1284.0, 1651.0, 1858.0, 1921.0, 2093.0, 2210.0, 2242.0, 2252.0, 2675.0, 2734.0, 2768.0, 3022.0, 3040.0, 3229.0, 3371.0, 3434.0, 3712.0, 3887.0, 4080.0, 4209.0, 4808.0, 5490.0, 5581.0, 5621.0, 5701.0, 6040.0, 6262.0, 6491.0, 6754.0, 6837.0, 6980.0, 7042.0, 7222.0, 7273.0, 7472.0, 8103.0, 8126.0, 8451.0, 8489.0, 8676.0, 8745.0, 8876.0, 9068.0, 9152.0, 9224.0, 9433.0, 9447.0, 9580.0, 9633.0, 9643.0, 9920.0, 10113.0]\n", + "--> t_dopa_spikes = [163.0, 209.0, 425.0, 731.0, 1883.0, 2106.0, 2230.0, 2701.0, 2785.0, 3050.0, 3052.0, 3395.0, 3904.0, 5514.0, 5633.0, 5721.0, 6055.0, 6290.0, 6510.0, 6781.0, 6999.0, 7069.0, 7500.0, 8126.0, 8463.0, 8768.0, 9164.0, 9237.0, 9449.0, 9459.0, 9593.0]\n" ] } ], "source": [ "nest.ResetKernel()\n", + "nest.Install(module_name) # load dynamic library (NEST extension module) into NEST kernel\n", "nest.set_verbosity(\"M_ALL\")\n", "nest.local_num_threads = 4\n", - "\n", "nest.resolution = dt\n", "nest.print_time = True\n", "nest.overwrite_files = True\n", @@ -7476,7 +7108,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -7493,7 +7125,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -7539,7 +7171,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": { "scrolled": true, "tags": [] @@ -7549,1208 +7181,1208 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Oct 23 18:11:50 NodeManager::prepare_nodes [Info]: \n", "0.0%\n", + "\n", + "Apr 19 11:34:22 NodeManager::prepare_nodes [Info]: \n", " Preparing 1087 nodes for simulation.\n", "\n", - "Oct 23 18:11:50 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:22 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 100.0 ms, Real-time factor: 1.5894\n", + "[ 100% ] Model time: 100.0 ms, Real-time factor: 3.9082\n", "\n", - "Oct 23 18:11:51 SimulationManager::run [Info]: \n", + "Apr 19 11:34:23 SimulationManager::run [Info]: \n", " Simulation finished.\n", "1.0%\n", "\n", - "Oct 23 18:11:52 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:25 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 200.0 ms, Real-time factor: 2.924570el time: 154.0 ms, Real-time factor: 4.3283\n", + "[ 100% ] Model time: 200.0 ms, Real-time factor: 8.043620\n", "\n", - "Oct 23 18:11:52 SimulationManager::run [Info]: \n", + "Apr 19 11:34:26 SimulationManager::run [Info]: \n", " Simulation finished.\n", "2.0%\n", "\n", - "Oct 23 18:11:53 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:29 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 300.0 ms, Real-time factor: 4.507370\n", + "[ 100% ] Model time: 300.0 ms, Real-time factor: 12.32186\n", "\n", - "Oct 23 18:11:53 SimulationManager::run [Info]: \n", + "Apr 19 11:34:29 SimulationManager::run [Info]: \n", " Simulation finished.\n", "3.0%\n", "\n", - "Oct 23 18:11:54 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:31 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 400.0 ms, Real-time factor: 6.318550l time: 389.0 ms, Real-time factor: 6.8330\n", + "[ 100% ] Model time: 400.0 ms, Real-time factor: 17.752280\n", "\n", - "Oct 23 18:11:55 SimulationManager::run [Info]: \n", + "Apr 19 11:34:32 SimulationManager::run [Info]: \n", " Simulation finished.\n", "4.0%\n", "\n", - "Oct 23 18:11:56 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:35 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 500.0 ms, Real-time factor: 7.741430\n", + "[ 100% ] Model time: 500.0 ms, Real-time factor: 22.306230\n", "\n", - "Oct 23 18:11:56 SimulationManager::run [Info]: \n", + "Apr 19 11:34:35 SimulationManager::run [Info]: \n", " Simulation finished.\n", "5.0%\n", "\n", - "Oct 23 18:11:57 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 600.0 ms, Real-time factor: 9.175423al-time factor: 10.2354\n", + "[ 100% ] Model time: 600.0 ms, Real-time factor: 26.830080\n", "\n", - "Oct 23 18:11:57 SimulationManager::run [Info]: \n", + "Apr 19 11:34:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "6.0%\n", "\n", - "Oct 23 18:11:58 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 700.0 ms, Real-time factor: 10.78751\n", + "[ 100% ] Model time: 700.0 ms, Real-time factor: 31.369480\n", "\n", - "Oct 23 18:11:59 SimulationManager::run [Info]: \n", + "Apr 19 11:34:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "7.0%\n", "\n", - "Oct 23 18:12:00 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:43 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 800.0 ms, Real-time factor: 12.853210, Real-time factor: 14.2922\n", + "[ 100% ] Model time: 800.0 ms, Real-time factor: 35.899100\n", "\n", - "Oct 23 18:12:00 SimulationManager::run [Info]: \n", + "Apr 19 11:34:44 SimulationManager::run [Info]: \n", " Simulation finished.\n", "8.0%\n", "\n", - "Oct 23 18:12:01 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:46 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 900.0 ms, Real-time factor: 15.162880\n", + "[ 100% ] Model time: 900.0 ms, Real-time factor: 40.469433\n", "\n", - "Oct 23 18:12:01 SimulationManager::run [Info]: \n", + "Apr 19 11:34:47 SimulationManager::run [Info]: \n", " Simulation finished.\n", "9.0%\n", "\n", - "Oct 23 18:12:02 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:49 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1000.0 ms, Real-time factor: 16.61390\n", + "[ 100% ] Model time: 1000.0 ms, Real-time factor: 44.99197\n", "\n", - "Oct 23 18:12:03 SimulationManager::run [Info]: \n", + "Apr 19 11:34:50 SimulationManager::run [Info]: \n", " Simulation finished.\n", "10.0%\n", "\n", - "Oct 23 18:12:04 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:52 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1100.0 ms, Real-time factor: 18.111950\n", + "[ 100% ] Model time: 1100.0 ms, Real-time factor: 49.572998\n", "\n", - "Oct 23 18:12:04 SimulationManager::run [Info]: \n", + "Apr 19 11:34:53 SimulationManager::run [Info]: \n", " Simulation finished.\n", "11.0%\n", "\n", - "Oct 23 18:12:05 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:55 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1200.0 ms, Real-time factor: 19.601210\n", + "[ 100% ] Model time: 1200.0 ms, Real-time factor: 54.278378\n", "\n", - "Oct 23 18:12:05 SimulationManager::run [Info]: \n", + "Apr 19 11:34:56 SimulationManager::run [Info]: \n", " Simulation finished.\n", "12.0%\n", "\n", - "Oct 23 18:12:06 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:34:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1300.0 ms, Real-time factor: 21.216480l-time factor: 30.0847\n", + "[ 100% ] Model time: 1300.0 ms, Real-time factor: 58.832242\n", "\n", - "Oct 23 18:12:06 SimulationManager::run [Info]: \n", + "Apr 19 11:34:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "13.0%\n", "\n", - "Oct 23 18:12:07 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:01 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1400.0 ms, Real-time factor: 22.674240\n", + "[ 100% ] Model time: 1400.0 ms, Real-time factor: 63.269854\n", "\n", - "Oct 23 18:12:07 SimulationManager::run [Info]: \n", + "Apr 19 11:35:02 SimulationManager::run [Info]: \n", " Simulation finished.\n", "14.0%\n", "\n", - "Oct 23 18:12:08 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:04 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1500.0 ms, Real-time factor: 24.144500 35% ] Model time: 1435.0 ms, Real-time factor: 66.2431\n", + "[ 100% ] Model time: 1500.0 ms, Real-time factor: 67.680667\n", "\n", - "Oct 23 18:12:08 SimulationManager::run [Info]: \n", + "Apr 19 11:35:05 SimulationManager::run [Info]: \n", " Simulation finished.\n", "15.0%\n", "\n", - "Oct 23 18:12:09 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:07 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1600.0 ms, Real-time factor: 25.620830 35% ] Model time: 1535.0 ms, Real-time factor: 70.4493\n", + "[ 100% ] Model time: 1600.0 ms, Real-time factor: 72.092067\n", "\n", - "Oct 23 18:12:10 SimulationManager::run [Info]: \n", + "Apr 19 11:35:08 SimulationManager::run [Info]: \n", " Simulation finished.\n", "16.0%\n", "\n", - "Oct 23 18:12:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:10 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1700.0 ms, Real-time factor: 27.138090\n", + "[ 100% ] Model time: 1700.0 ms, Real-time factor: 76.708270\n", "\n", - "Oct 23 18:12:11 SimulationManager::run [Info]: \n", + "Apr 19 11:35:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "17.0%\n", "\n", - "Oct 23 18:12:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1800.0 ms, Real-time factor: 28.643200eal-time factor: 42.6238\n", + "[ 100% ] Model time: 1800.0 ms, Real-time factor: 81.506157\n", "\n", - "Oct 23 18:12:12 SimulationManager::run [Info]: \n", + "Apr 19 11:35:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "18.0%\n", "\n", - "Oct 23 18:12:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 1900.0 ms, Real-time factor: 30.130005ime factor: 34.7828\n", + "[ 100% ] Model time: 1900.0 ms, Real-time factor: 87.605451\n", "\n", - "Oct 23 18:12:13 SimulationManager::run [Info]: \n", + "Apr 19 11:35:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "19.0%\n", "\n", - "Oct 23 18:12:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:19 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2000.0 ms, Real-time factor: 31.599960 Model time: 1952.0 ms, Real-time factor: 59.4103\n", + "[ 100% ] Model time: 2000.0 ms, Real-time factor: 93.715430\n", "\n", - "Oct 23 18:12:14 SimulationManager::run [Info]: \n", + "Apr 19 11:35:20 SimulationManager::run [Info]: \n", " Simulation finished.\n", "20.0%\n", "\n", - "Oct 23 18:12:15 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:23 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2100.0 ms, Real-time factor: 33.082523al-time factor: 38.2321\n", + "[ 100% ] Model time: 2100.0 ms, Real-time factor: 99.787984\n", "\n", - "Oct 23 18:12:15 SimulationManager::run [Info]: \n", + "Apr 19 11:35:23 SimulationManager::run [Info]: \n", " Simulation finished.\n", "21.0%\n", "\n", - "Oct 23 18:12:16 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:26 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2200.0 ms, Real-time factor: 34.618013\n", + "[ 100% ] Model time: 2200.0 ms, Real-time factor: 105.96668\n", "\n", - "Oct 23 18:12:17 SimulationManager::run [Info]: \n", + "Apr 19 11:35:26 SimulationManager::run [Info]: \n", " Simulation finished.\n", "22.0%\n", "\n", - "Oct 23 18:12:18 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:29 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2300.0 ms, Real-time factor: 36.207920\n", + "[ 100% ] Model time: 2300.0 ms, Real-time factor: 110.354440\n", "\n", - "Oct 23 18:12:18 SimulationManager::run [Info]: \n", + "Apr 19 11:35:29 SimulationManager::run [Info]: \n", " Simulation finished.\n", "23.0%\n", "\n", - "Oct 23 18:12:19 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:32 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2400.0 ms, Real-time factor: 37.699463ime: 2369.0 ms, Real-time factor: 53.9353\n", + "[ 100% ] Model time: 2400.0 ms, Real-time factor: 114.504260\n", "\n", - "Oct 23 18:12:19 SimulationManager::run [Info]: \n", + "Apr 19 11:35:32 SimulationManager::run [Info]: \n", " Simulation finished.\n", "24.0%\n", "\n", - "Oct 23 18:12:20 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:35 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2500.0 ms, Real-time factor: 39.230143ms, Real-time factor: 45.3883\n", + "[ 100% ] Model time: 2500.0 ms, Real-time factor: 118.613960\n", "\n", - "Oct 23 18:12:20 SimulationManager::run [Info]: \n", + "Apr 19 11:35:35 SimulationManager::run [Info]: \n", " Simulation finished.\n", "25.0%\n", "\n", - "Oct 23 18:12:21 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2600.0 ms, Real-time factor: 40.745190 ms, Real-time factor: 47.1460\n", + "[ 100% ] Model time: 2600.0 ms, Real-time factor: 122.836110\n", "\n", - "Oct 23 18:12:21 SimulationManager::run [Info]: \n", + "Apr 19 11:35:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "26.0%\n", "\n", - "Oct 23 18:12:22 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2700.0 ms, Real-time factor: 42.199393odel time: 2669.0 ms, Real-time factor: 60.5093\n", + "[ 100% ] Model time: 2700.0 ms, Real-time factor: 127.048340\n", "\n", - "Oct 23 18:12:22 SimulationManager::run [Info]: \n", + "Apr 19 11:35:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "27.0%\n", "\n", - "Oct 23 18:12:23 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:43 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2800.0 ms, Real-time factor: 43.657078\n", + "[ 100% ] Model time: 2800.0 ms, Real-time factor: 131.240150\n", "\n", - "Oct 23 18:12:24 SimulationManager::run [Info]: \n", + "Apr 19 11:35:44 SimulationManager::run [Info]: \n", " Simulation finished.\n", "28.0%\n", "\n", - "Oct 23 18:12:25 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:46 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 2900.0 ms, Real-time factor: 45.151995] Model time: 2869.0 ms, Real-time factor: 64.7927\n", + "[ 100% ] Model time: 2900.0 ms, Real-time factor: 135.360930\n", "\n", - "Oct 23 18:12:25 SimulationManager::run [Info]: \n", + "Apr 19 11:35:47 SimulationManager::run [Info]: \n", " Simulation finished.\n", "29.0%\n", "\n", - "Oct 23 18:12:26 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:49 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3000.0 ms, Real-time factor: 46.666743 2986.0 ms, Real-time factor: 54.0219\n", + "[ 100% ] Model time: 3000.0 ms, Real-time factor: 139.438050\n", "\n", - "Oct 23 18:12:26 SimulationManager::run [Info]: \n", + "Apr 19 11:35:50 SimulationManager::run [Info]: \n", " Simulation finished.\n", "30.0%\n", "\n", - "Oct 23 18:12:27 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:52 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3100.0 ms, Real-time factor: 48.177717\n", + "[ 100% ] Model time: 3100.0 ms, Real-time factor: 143.641890\n", "\n", - "Oct 23 18:12:27 SimulationManager::run [Info]: \n", + "Apr 19 11:35:53 SimulationManager::run [Info]: \n", " Simulation finished.\n", "31.0%\n", "\n", - "Oct 23 18:12:28 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:55 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3200.0 ms, Real-time factor: 49.605260\n", + "[ 100% ] Model time: 3200.0 ms, Real-time factor: 147.738860\n", "\n", - "Oct 23 18:12:28 SimulationManager::run [Info]: \n", + "Apr 19 11:35:56 SimulationManager::run [Info]: \n", " Simulation finished.\n", "32.0%\n", "\n", - "Oct 23 18:12:29 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:35:58 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3300.0 ms, Real-time factor: 51.078475\n", + "[ 100% ] Model time: 3300.0 ms, Real-time factor: 151.965970\n", "\n", - "Oct 23 18:12:29 SimulationManager::run [Info]: \n", + "Apr 19 11:35:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "33.0%\n", "\n", - "Oct 23 18:12:30 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:01 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3400.0 ms, Real-time factor: 52.595992\n", + "[ 100% ] Model time: 3400.0 ms, Real-time factor: 157.258930\n", "\n", - "Oct 23 18:12:31 SimulationManager::run [Info]: \n", + "Apr 19 11:36:02 SimulationManager::run [Info]: \n", " Simulation finished.\n", "34.0%\n", "\n", - "Oct 23 18:12:31 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:04 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3500.0 ms, Real-time factor: 54.088506ctor: 104.6056\n", + "[ 100% ] Model time: 3500.0 ms, Real-time factor: 162.003430\n", "\n", - "Oct 23 18:12:32 SimulationManager::run [Info]: \n", + "Apr 19 11:36:05 SimulationManager::run [Info]: \n", " Simulation finished.\n", "35.0%\n", "\n", - "Oct 23 18:12:33 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:07 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3600.0 ms, Real-time factor: 55.611776792\n", + "[ 100% ] Model time: 3600.0 ms, Real-time factor: 166.656710\n", "\n", - "Oct 23 18:12:33 SimulationManager::run [Info]: \n", + "Apr 19 11:36:08 SimulationManager::run [Info]: \n", " Simulation finished.\n", "36.0%\n", "\n", - "Oct 23 18:12:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:10 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3700.0 ms, Real-time factor: 57.295654ctor: 110.7112\n", + "[ 100% ] Model time: 3700.0 ms, Real-time factor: 171.139480\n", "\n", - "Oct 23 18:12:34 SimulationManager::run [Info]: \n", + "Apr 19 11:36:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "37.0%\n", "\n", - "Oct 23 18:12:35 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3800.0 ms, Real-time factor: 59.275798ctor: 114.6020\n", + "[ 100% ] Model time: 3800.0 ms, Real-time factor: 175.338290\n", "\n", - "Oct 23 18:12:35 SimulationManager::run [Info]: \n", + "Apr 19 11:36:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "38.0%\n", "\n", - "Oct 23 18:12:37 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 3900.0 ms, Real-time factor: 61.047414r: 176.3137\n", + "[ 100% ] Model time: 3900.0 ms, Real-time factor: 179.436560\n", "\n", - "Oct 23 18:12:37 SimulationManager::run [Info]: \n", + "Apr 19 11:36:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "39.0%\n", "\n", - "Oct 23 18:12:38 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:19 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4000.0 ms, Real-time factor: 62.593382 86% ] Model time: 3986.0 ms, Real-time factor: 72.5292\n", + "[ 100% ] Model time: 4000.0 ms, Real-time factor: 183.708100\n", "\n", - "Oct 23 18:12:38 SimulationManager::run [Info]: \n", + "Apr 19 11:36:20 SimulationManager::run [Info]: \n", " Simulation finished.\n", "40.0%\n", "\n", - "Oct 23 18:12:39 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:22 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4100.0 ms, Real-time factor: 64.355690actor: 124.5728\n", + "[ 100% ] Model time: 4100.0 ms, Real-time factor: 187.850330\n", "\n", - "Oct 23 18:12:39 SimulationManager::run [Info]: \n", + "Apr 19 11:36:23 SimulationManager::run [Info]: \n", " Simulation finished.\n", "41.0%\n", "\n", - "Oct 23 18:12:40 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:25 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4200.0 ms, Real-time factor: 66.161172actor: 128.0125\n", + "[ 100% ] Model time: 4200.0 ms, Real-time factor: 192.070740\n", "\n", - "Oct 23 18:12:41 SimulationManager::run [Info]: \n", + "Apr 19 11:36:26 SimulationManager::run [Info]: \n", " Simulation finished.\n", "42.0%\n", "\n", - "Oct 23 18:12:42 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:28 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4300.0 ms, Real-time factor: 67.984037actor: 131.5794\n", + "[ 100% ] Model time: 4300.0 ms, Real-time factor: 196.311790\n", "\n", - "Oct 23 18:12:42 SimulationManager::run [Info]: \n", + "Apr 19 11:36:29 SimulationManager::run [Info]: \n", " Simulation finished.\n", "43.0%\n", "\n", - "Oct 23 18:12:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:31 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4400.0 ms, Real-time factor: 69.556182.5550\n", + "[ 100% ] Model time: 4400.0 ms, Real-time factor: 200.388120\n", "\n", - "Oct 23 18:12:43 SimulationManager::run [Info]: \n", + "Apr 19 11:36:31 SimulationManager::run [Info]: \n", " Simulation finished.\n", "44.0%\n", "\n", - "Oct 23 18:12:44 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:34 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4500.0 ms, Real-time factor: 71.123008\n", + "[ 100% ] Model time: 4500.0 ms, Real-time factor: 204.522525\n", "\n", - "Oct 23 18:12:45 SimulationManager::run [Info]: \n", + "Apr 19 11:36:35 SimulationManager::run [Info]: \n", " Simulation finished.\n", "45.0%\n", "\n", - "Oct 23 18:12:46 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4600.0 ms, Real-time factor: 72.619003factor: 140.9496\n", + "[ 100% ] Model time: 4600.0 ms, Real-time factor: 208.628985\n", "\n", - "Oct 23 18:12:46 SimulationManager::run [Info]: \n", + "Apr 19 11:36:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "46.0%\n", "\n", - "Oct 23 18:12:47 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4700.0 ms, Real-time factor: 74.132144r: 86.9696\n", + "[ 100% ] Model time: 4700.0 ms, Real-time factor: 212.699730\n", "\n", - "Oct 23 18:12:47 SimulationManager::run [Info]: \n", + "Apr 19 11:36:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "47.0%\n", "\n", - "Oct 23 18:12:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:43 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4800.0 ms, Real-time factor: 75.584097tor: 219.5399\n", + "[ 100% ] Model time: 4800.0 ms, Real-time factor: 217.743655\n", "\n", - "Oct 23 18:12:48 SimulationManager::run [Info]: \n", + "Apr 19 11:36:44 SimulationManager::run [Info]: \n", " Simulation finished.\n", "48.0%\n", "\n", - "Oct 23 18:12:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:46 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 4900.0 ms, Real-time factor: 77.021637ctor: 90.3425\n", + "[ 100% ] Model time: 4900.0 ms, Real-time factor: 221.758590\n", "\n", - "Oct 23 18:12:49 SimulationManager::run [Info]: \n", + "Apr 19 11:36:47 SimulationManager::run [Info]: \n", " Simulation finished.\n", "49.0%\n", "\n", - "Oct 23 18:12:50 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:49 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5000.0 ms, Real-time factor: 78.500863factor: 92.0991\n", + "[ 100% ] Model time: 5000.0 ms, Real-time factor: 225.805775\n", "\n", - "Oct 23 18:12:51 SimulationManager::run [Info]: \n", + "Apr 19 11:36:49 SimulationManager::run [Info]: \n", " Simulation finished.\n", "50.0%\n", "\n", - "Oct 23 18:12:52 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:52 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5100.0 ms, Real-time factor: 80.338753r: 97.6487\n", + "[ 100% ] Model time: 5100.0 ms, Real-time factor: 229.738330\n", "\n", - "Oct 23 18:12:52 SimulationManager::run [Info]: \n", + "Apr 19 11:36:52 SimulationManager::run [Info]: \n", " Simulation finished.\n", "51.0%\n", "\n", - "Oct 23 18:12:53 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:55 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5200.0 ms, Real-time factor: 81.7854895166.0 ms, Real-time factor: 123.1774\n", + "[ 100% ] Model time: 5200.0 ms, Real-time factor: 233.717475\n", "\n", - "Oct 23 18:12:53 SimulationManager::run [Info]: \n", + "Apr 19 11:36:55 SimulationManager::run [Info]: \n", " Simulation finished.\n", "52.0%\n", "\n", - "Oct 23 18:12:54 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:36:58 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5300.0 ms, Real-time factor: 83.235081ime factor: 121.7321\n", + "[ 100% ] Model time: 5300.0 ms, Real-time factor: 237.654490\n", "\n", - "Oct 23 18:12:54 SimulationManager::run [Info]: \n", + "Apr 19 11:36:58 SimulationManager::run [Info]: \n", " Simulation finished.\n", "53.0%\n", "\n", - "Oct 23 18:12:55 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:01 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5400.0 ms, Real-time factor: 84.731604\n", + "[ 100% ] Model time: 5400.0 ms, Real-time factor: 241.620775e: 5385.0 ms, Real-time factor: 283.5706\n", "\n", - "Oct 23 18:12:55 SimulationManager::run [Info]: \n", + "Apr 19 11:37:01 SimulationManager::run [Info]: \n", " Simulation finished.\n", "54.0%\n", "\n", - "Oct 23 18:12:56 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:04 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5500.0 ms, Real-time factor: 86.409668\n", + "[ 100% ] Model time: 5500.0 ms, Real-time factor: 245.678255\n", "\n", - "Oct 23 18:12:56 SimulationManager::run [Info]: \n", + "Apr 19 11:37:04 SimulationManager::run [Info]: \n", " Simulation finished.\n", "55.0%\n", "\n", - "Oct 23 18:12:58 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:07 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5600.0 ms, Real-time factor: 88.435181\n", + "[ 100% ] Model time: 5600.0 ms, Real-time factor: 249.818500\n", "\n", - "Oct 23 18:12:58 SimulationManager::run [Info]: \n", + "Apr 19 11:37:07 SimulationManager::run [Info]: \n", " Simulation finished.\n", "56.0%\n", "\n", - "Oct 23 18:12:59 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:10 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5700.0 ms, Real-time factor: 90.407850actor: 133.8435\n", + "[ 100% ] Model time: 5700.0 ms, Real-time factor: 254.011505\n", "\n", - "Oct 23 18:12:59 SimulationManager::run [Info]: \n", + "Apr 19 11:37:10 SimulationManager::run [Info]: \n", " Simulation finished.\n", "57.0%\n", "\n", - "Oct 23 18:13:00 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:13 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5800.0 ms, Real-time factor: 92.186038e factor: 179.0993\n", + "[ 100% ] Model time: 5800.0 ms, Real-time factor: 258.028620\n", "\n", - "Oct 23 18:13:01 SimulationManager::run [Info]: \n", + "Apr 19 11:37:13 SimulationManager::run [Info]: \n", " Simulation finished.\n", "58.0%\n", "\n", - "Oct 23 18:13:02 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:16 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 5900.0 ms, Real-time factor: 94.246186.5291\n", + "[ 100% ] Model time: 5900.0 ms, Real-time factor: 261.964470\n", "\n", - "Oct 23 18:13:02 SimulationManager::run [Info]: \n", + "Apr 19 11:37:16 SimulationManager::run [Info]: \n", " Simulation finished.\n", "59.0%\n", "\n", - "Oct 23 18:13:03 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:19 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6000.0 ms, Real-time factor: 95.898651e factor: 186.4894\n", + "[ 100% ] Model time: 6000.0 ms, Real-time factor: 265.921750\n", "\n", - "Oct 23 18:13:03 SimulationManager::run [Info]: \n", + "Apr 19 11:37:19 SimulationManager::run [Info]: \n", " Simulation finished.\n", "60.0%\n", "\n", - "Oct 23 18:13:04 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:22 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6100.0 ms, Real-time factor: 97.537260time factor: 142.7093\n", + "[ 100% ] Model time: 6100.0 ms, Real-time factor: 269.958005\n", "\n", - "Oct 23 18:13:05 SimulationManager::run [Info]: \n", + "Apr 19 11:37:22 SimulationManager::run [Info]: \n", " Simulation finished.\n", "61.0%\n", "\n", - "Oct 23 18:13:06 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:25 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6200.0 ms, Real-time factor: 99.303123actor: 288.8664\n", + "[ 100% ] Model time: 6200.0 ms, Real-time factor: 274.147785\n", "\n", - "Oct 23 18:13:06 SimulationManager::run [Info]: \n", + "Apr 19 11:37:25 SimulationManager::run [Info]: \n", " Simulation finished.\n", "62.0%\n", "\n", - "Oct 23 18:13:07 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:28 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6300.0 ms, Real-time factor: 101.15041actor: 293.9338\n", + "[ 100% ] Model time: 6300.0 ms, Real-time factor: 278.180890me: 6268.0 ms, Real-time factor: 407.2064\n", "\n", - "Oct 23 18:13:07 SimulationManager::run [Info]: \n", + "Apr 19 11:37:28 SimulationManager::run [Info]: \n", " Simulation finished.\n", "63.0%\n", "\n", - "Oct 23 18:13:08 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:31 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6400.0 ms, Real-time factor: 102.931180Real-time factor: 120.7797\n", + "[ 100% ] Model time: 6400.0 ms, Real-time factor: 282.224865\n", "\n", - "Oct 23 18:13:09 SimulationManager::run [Info]: \n", + "Apr 19 11:37:31 SimulationManager::run [Info]: \n", " Simulation finished.\n", "64.0%\n", "\n", - "Oct 23 18:13:10 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:34 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6500.0 ms, Real-time factor: 104.923030l-time factor: 153.3514\n", + "[ 100% ] Model time: 6500.0 ms, Real-time factor: 287.381755l time: 6485.0 ms, Real-time factor: 337.1852\n", "\n", - "Oct 23 18:13:10 SimulationManager::run [Info]: \n", + "Apr 19 11:37:34 SimulationManager::run [Info]: \n", " Simulation finished.\n", "65.0%\n", "\n", - "Oct 23 18:13:11 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6600.0 ms, Real-time factor: 107.196000\n", + "[ 100% ] Model time: 6600.0 ms, Real-time factor: 292.719700\n", "\n", - "Oct 23 18:13:11 SimulationManager::run [Info]: \n", + "Apr 19 11:37:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", "66.0%\n", "\n", - "Oct 23 18:13:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6700.0 ms, Real-time factor: 108.661230Real-time factor: 127.5671\n", + "[ 100% ] Model time: 6700.0 ms, Real-time factor: 297.928300time: 6668.0 ms, Real-time factor: 435.7229\n", "\n", - "Oct 23 18:13:13 SimulationManager::run [Info]: \n", + "Apr 19 11:37:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", "67.0%\n", "\n", - "Oct 23 18:13:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:43 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6800.0 ms, Real-time factor: 111.398350factor: 110.2985\n", + "[ 100% ] Model time: 6800.0 ms, Real-time factor: 303.177260\n", "\n", - "Oct 23 18:13:14 SimulationManager::run [Info]: \n", + "Apr 19 11:37:44 SimulationManager::run [Info]: \n", " Simulation finished.\n", "68.0%\n", "\n", - "Oct 23 18:13:15 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:46 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 6900.0 ms, Real-time factor: 111.759100e factor: 325.9254\n", + "[ 100% ] Model time: 6900.0 ms, Real-time factor: 308.374353odel time: 6885.0 ms, Real-time factor: 361.9108\n", "\n", - "Oct 23 18:13:15 SimulationManager::run [Info]: \n", + "Apr 19 11:37:47 SimulationManager::run [Info]: \n", " Simulation finished.\n", "69.0%\n", "\n", - "Oct 23 18:13:16 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:49 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7000.0 ms, Real-time factor: 113.412050\n", + "[ 100% ] Model time: 7000.0 ms, Real-time factor: 313.505170\n", "\n", - "Oct 23 18:13:16 SimulationManager::run [Info]: \n", + "Apr 19 11:37:50 SimulationManager::run [Info]: \n", " Simulation finished.\n", "70.0%\n", "\n", - "Oct 23 18:13:17 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:53 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7100.0 ms, Real-time factor: 114.847030\n", + "[ 100% ] Model time: 7100.0 ms, Real-time factor: 319.070813\n", "\n", - "Oct 23 18:13:17 SimulationManager::run [Info]: \n", + "Apr 19 11:37:53 SimulationManager::run [Info]: \n", " Simulation finished.\n", "71.0%\n", "\n", - "Oct 23 18:13:18 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:56 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7200.0 ms, Real-time factor: 116.306090e factor: 339.3000\n", + "[ 100% ] Model time: 7200.0 ms, Real-time factor: 323.023983\n", "\n", - "Oct 23 18:13:18 SimulationManager::run [Info]: \n", + "Apr 19 11:37:56 SimulationManager::run [Info]: \n", " Simulation finished.\n", "72.0%\n", "\n", - "Oct 23 18:13:19 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:37:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7300.0 ms, Real-time factor: 118.112340al-time factor: 172.9209\n", + "[ 100% ] Model time: 7300.0 ms, Real-time factor: 327.331420\n", "\n", - "Oct 23 18:13:20 SimulationManager::run [Info]: \n", + "Apr 19 11:37:59 SimulationManager::run [Info]: \n", " Simulation finished.\n", "73.0%\n", "\n", - "Oct 23 18:13:21 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:02 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7400.0 ms, Real-time factor: 119.750570time factor: 233.3627\n", + "[ 100% ] Model time: 7400.0 ms, Real-time factor: 332.593820 Model time: 7385.0 ms, Real-time factor: 390.3997\n", "\n", - "Oct 23 18:13:21 SimulationManager::run [Info]: \n", + "Apr 19 11:38:02 SimulationManager::run [Info]: \n", " Simulation finished.\n", "74.0%\n", "\n", - "Oct 23 18:13:22 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:05 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7500.0 ms, Real-time factor: 121.208970al-time factor: 177.5584\n", + "[ 100% ] Model time: 7500.0 ms, Real-time factor: 336.802090\n", "\n", - "Oct 23 18:13:22 SimulationManager::run [Info]: \n", + "Apr 19 11:38:05 SimulationManager::run [Info]: \n", " Simulation finished.\n", "75.0%\n", "\n", - "Oct 23 18:13:23 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:08 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7600.0 ms, Real-time factor: 122.673650\n", + "[ 100% ] Model time: 7600.0 ms, Real-time factor: 340.891773\n", "\n", - "Oct 23 18:13:23 SimulationManager::run [Info]: \n", + "Apr 19 11:38:08 SimulationManager::run [Info]: \n", " Simulation finished.\n", "76.0%\n", "\n", - "Oct 23 18:13:24 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7700.0 ms, Real-time factor: 124.123060, Real-time factor: 145.7690\n", + "[ 100% ] Model time: 7700.0 ms, Real-time factor: 345.941543\n", "\n", - "Oct 23 18:13:24 SimulationManager::run [Info]: \n", + "Apr 19 11:38:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "77.0%\n", "\n", - "Oct 23 18:13:25 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7800.0 ms, Real-time factor: 126.531770 402.8230\n", + "[ 100% ] Model time: 7800.0 ms, Real-time factor: 351.548327Model time: 7768.0 ms, Real-time factor: 514.7829\n", "\n", - "Oct 23 18:13:26 SimulationManager::run [Info]: \n", + "Apr 19 11:38:15 SimulationManager::run [Info]: \n", " Simulation finished.\n", "78.0%\n", "\n", - "Oct 23 18:13:27 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 7900.0 ms, Real-time factor: 128.088320, Real-time factor: 150.4262\n", + "[ 100% ] Model time: 7900.0 ms, Real-time factor: 356.748190\n", "\n", - "Oct 23 18:13:27 SimulationManager::run [Info]: \n", + "Apr 19 11:38:18 SimulationManager::run [Info]: \n", " Simulation finished.\n", "79.0%\n", "\n", - "Oct 23 18:13:28 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:20 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8000.0 ms, Real-time factor: 129.740680eal-time factor: 190.0747\n", + "[ 100% ] Model time: 8000.0 ms, Real-time factor: 361.131403\n", "\n", - "Oct 23 18:13:28 SimulationManager::run [Info]: \n", + "Apr 19 11:38:21 SimulationManager::run [Info]: \n", " Simulation finished.\n", "80.0%\n", "\n", - "Oct 23 18:13:29 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:23 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8100.0 ms, Real-time factor: 131.451670l-time factor: 256.2948\n", + "[ 100% ] Model time: 8100.0 ms, Real-time factor: 365.055957\n", "\n", - "Oct 23 18:13:29 SimulationManager::run [Info]: \n", + "Apr 19 11:38:24 SimulationManager::run [Info]: \n", " Simulation finished.\n", "81.0%\n", "\n", - "Oct 23 18:13:30 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:26 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8200.0 ms, Real-time factor: 133.018790Real-time factor: 194.8809\n", + "[ 100% ] Model time: 8200.0 ms, Real-time factor: 369.042590\n", "\n", - "Oct 23 18:13:30 SimulationManager::run [Info]: \n", + "Apr 19 11:38:27 SimulationManager::run [Info]: \n", " Simulation finished.\n", "82.0%\n", "\n", - "Oct 23 18:13:31 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:29 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8300.0 ms, Real-time factor: 134.679390\n", + "[ 100% ] Model time: 8300.0 ms, Real-time factor: 373.174913\n", "\n", - "Oct 23 18:13:31 SimulationManager::run [Info]: \n", + "Apr 19 11:38:30 SimulationManager::run [Info]: \n", " Simulation finished.\n", "83.0%\n", "\n", - "Oct 23 18:13:32 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:32 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8400.0 ms, Real-time factor: 136.142560ime factor: 397.5512\n", + "[ 100% ] Model time: 8400.0 ms, Real-time factor: 377.813927\n", "\n", - "Oct 23 18:13:33 SimulationManager::run [Info]: \n", + "Apr 19 11:38:33 SimulationManager::run [Info]: \n", " Simulation finished.\n", "84.0%\n", "\n", - "Oct 23 18:13:34 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:35 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8500.0 ms, Real-time factor: 137.691110l-time factor: 268.5108\n", + "[ 100% ] Model time: 8500.0 ms, Real-time factor: 381.832757\n", "\n", - "Oct 23 18:13:34 SimulationManager::run [Info]: \n", + "Apr 19 11:38:36 SimulationManager::run [Info]: \n", " Simulation finished.\n", "85.0%\n", "\n", - "Oct 23 18:13:35 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8600.0 ms, Real-time factor: 139.169980ime factor: 406.5911\n", + "[ 100% ] Model time: 8600.0 ms, Real-time factor: 386.786580 85% ] Model time: 8585.0 ms, Real-time factor: 454.1770\n", "\n", - "Oct 23 18:13:35 SimulationManager::run [Info]: \n", + "Apr 19 11:38:39 SimulationManager::run [Info]: \n", " Simulation finished.\n", "86.0%\n", "\n", - "Oct 23 18:13:36 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8700.0 ms, Real-time factor: 140.709380Real-time factor: 206.2303\n", + "[ 100% ] Model time: 8700.0 ms, Real-time factor: 391.085007\n", "\n", - "Oct 23 18:13:36 SimulationManager::run [Info]: \n", + "Apr 19 11:38:42 SimulationManager::run [Info]: \n", " Simulation finished.\n", "87.0%\n", "\n", - "Oct 23 18:13:37 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:44 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8800.0 ms, Real-time factor: 142.450240 Real-time factor: 208.6890\n", + "[ 100% ] Model time: 8800.0 ms, Real-time factor: 395.228320\n", "\n", - "Oct 23 18:13:37 SimulationManager::run [Info]: \n", + "Apr 19 11:38:45 SimulationManager::run [Info]: \n", " Simulation finished.\n", "88.0%\n", "\n", - "Oct 23 18:13:39 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:47 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 8900.0 ms, Real-time factor: 144.354810ms, Real-time factor: 169.5216\n", + "[ 100% ] Model time: 8900.0 ms, Real-time factor: 399.164097\n", "\n", - "Oct 23 18:13:39 SimulationManager::run [Info]: \n", + "Apr 19 11:38:48 SimulationManager::run [Info]: \n", " Simulation finished.\n", "89.0%\n", "\n", - "Oct 23 18:13:40 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:50 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9000.0 ms, Real-time factor: 146.360850time factor: 426.4250\n", + "[ 100% ] Model time: 9000.0 ms, Real-time factor: 403.140237\n", "\n", - "Oct 23 18:13:40 SimulationManager::run [Info]: \n", + "Apr 19 11:38:51 SimulationManager::run [Info]: \n", " Simulation finished.\n", "90.0%\n", "\n", - "Oct 23 18:13:41 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:53 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9100.0 ms, Real-time factor: 148.432540 factor: 227.4094\n", + "[ 100% ] Model time: 9100.0 ms, Real-time factor: 407.147390\n", "\n", - "Oct 23 18:13:41 SimulationManager::run [Info]: \n", + "Apr 19 11:38:54 SimulationManager::run [Info]: \n", " Simulation finished.\n", "91.0%\n", "\n", - "Oct 23 18:13:43 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:56 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9200.0 ms, Real-time factor: 150.281770time factor: 438.4394\n", + "[ 100% ] Model time: 9200.0 ms, Real-time factor: 413.148308\n", "\n", - "Oct 23 18:13:43 SimulationManager::run [Info]: \n", + "Apr 19 11:38:57 SimulationManager::run [Info]: \n", " Simulation finished.\n", "92.0%\n", "\n", - "Oct 23 18:13:44 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:38:59 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9300.0 ms, Real-time factor: 152.141140actor: 315.0733\n", + "[ 100% ] Model time: 9300.0 ms, Real-time factor: 417.084235\n", "\n", - "Oct 23 18:13:44 SimulationManager::run [Info]: \n", + "Apr 19 11:39:00 SimulationManager::run [Info]: \n", " Simulation finished.\n", "93.0%\n", "\n", - "Oct 23 18:13:45 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:39:02 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9400.0 ms, Real-time factor: 154.109710\n", + "[ 100% ] Model time: 9400.0 ms, Real-time factor: 421.012175\n", "\n", - "Oct 23 18:13:45 SimulationManager::run [Info]: \n", + "Apr 19 11:39:03 SimulationManager::run [Info]: \n", " Simulation finished.\n", "94.0%\n", "\n", - "Oct 23 18:13:47 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:39:05 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9500.0 ms, Real-time factor: 155.855950-time factor: 455.1112\n", + "[ 100% ] Model time: 9500.0 ms, Real-time factor: 425.045858\n", "\n", - "Oct 23 18:13:47 SimulationManager::run [Info]: \n", + "Apr 19 11:39:06 SimulationManager::run [Info]: \n", " Simulation finished.\n", "95.0%\n", "\n", - "Oct 23 18:13:48 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:39:08 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9600.0 ms, Real-time factor: 157.818760-time factor: 460.8055\n", + "[ 100% ] Model time: 9600.0 ms, Real-time factor: 429.079772\n", "\n", - "Oct 23 18:13:48 SimulationManager::run [Info]: \n", + "Apr 19 11:39:08 SimulationManager::run [Info]: \n", " Simulation finished.\n", "96.0%\n", "\n", - "Oct 23 18:13:49 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:39:11 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9700.0 ms, Real-time factor: 159.341810eal-time factor: 311.0069\n", + "[ 100% ] Model time: 9700.0 ms, Real-time factor: 433.271405\n", "\n", - "Oct 23 18:13:49 SimulationManager::run [Info]: \n", + "Apr 19 11:39:11 SimulationManager::run [Info]: \n", " Simulation finished.\n", "97.0%\n", "\n", - "Oct 23 18:13:51 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:39:14 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9800.0 ms, Real-time factor: 161.361290actor: 333.8900\n", + "[ 100% ] Model time: 9800.0 ms, Real-time factor: 437.196325\n", "\n", - "Oct 23 18:13:51 SimulationManager::run [Info]: \n", + "Apr 19 11:39:14 SimulationManager::run [Info]: \n", " Simulation finished.\n", "98.0%\n", "\n", - "Oct 23 18:13:52 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:39:17 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 9900.0 ms, Real-time factor: 163.321690Real-time factor: 318.4846\n", + "[ 100% ] Model time: 9900.0 ms, Real-time factor: 441.222980\n", "\n", - "Oct 23 18:13:52 SimulationManager::run [Info]: \n", + "Apr 19 11:39:17 SimulationManager::run [Info]: \n", " Simulation finished.\n", "99.0%\n", "\n", - "Oct 23 18:13:53 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 11:39:20 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 1087\n", " Simulation time (ms): 100\n", " Number of OpenMP threads: 4\n", " Not using MPI\n", "\n", - "[ 100% ] Model time: 10000.0 ms, Real-time factor: 165.17810s, Real-time factor: 242.1239\n", + "[ 100% ] Model time: 10000.0 ms, Real-time factor: 445.25468\n", "\n", - "Oct 23 18:13:53 SimulationManager::run [Info]: \n", + "Apr 19 11:39:20 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] } @@ -8778,7 +8410,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -8790,15 +8422,16 @@ "Number of synapses: 100000\n", " Exitatory : 81000\n", " Inhibitory : 20000\n", - "Excitatory rate : 12.48 Hz\n", - "Inhibitory rate : 2.86 Hz\n", - "Actual times of stimulus presentation: [ 307. 317. 657. 1342. 2074. 2451. 2555. 3276. 3417. 3676. 3801. 3867.\n", - " 3942. 4170. 4893. 5062. 5509. 5767. 6166. 6187. 6319. 6697. 7217. 7318.\n", - " 7537. 7665. 8246. 8451. 8899. 8946. 9089. 582. 722. 779. 818. 880.\n", - " 1601. 2130. 2240. 2711. 5803. 5943. 6176. 6279. 6408. 6829. 7655. 7855.\n", - " 7954. 8168. 9070. 9348.]\n", - "Actual t_dopa_spikes = [ 610. 747. 801. 831. 892. 1614. 2140. 2266. 2729. 5817. 5963. 6204.\n", - " 6301. 6437. 6850. 7668. 7870. 7982. 8197. 9091. 9371.]\n" + "Excitatory rate : 14.43 Hz\n", + "Inhibitory rate : 3.42 Hz\n", + "Actual times of stimulus presentation: [ 169. 243. 352. 401. 1255. 1285. 1652. 1922. 2243. 2253. 2735. 3230.\n", + " 3435. 3713. 4081. 4210. 4809. 5582. 6838. 7223. 7274. 8127. 8490. 8677.\n", + " 8877. 9069. 9634. 9644. 9921. 138. 198. 411. 713. 1859. 2094. 2211.\n", + " 2676. 2769. 3023. 3041. 3372. 3888. 5491. 5622. 5702. 6041. 6263. 6492.\n", + " 6755. 6981. 7043. 7473. 8104. 8452. 8746. 9153. 9225. 9434. 9448. 9581.]\n", + "Actual t_dopa_spikes = [ 164. 210. 426. 732. 1884. 2107. 2231. 2702. 2786. 3051. 3053. 3396.\n", + " 3905. 5515. 5634. 5722. 6056. 6291. 6511. 6782. 7000. 7070. 7501. 8127.\n", + " 8464. 8769. 9165. 9238. 9450. 9460. 9594.]\n" ] } ], @@ -8835,12 +8468,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -8943,12 +8576,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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i616fZf9OXq4XvZ+W1ln7ryEHo7wH48JYKRtiAyDs/NQvIRXv7T5Wlr2UZ54pEszeR0OsmIi2Eg/I9n0xM0g0zbjUxYPvbMVvnl2JrL3/Fa4DUXrmNaH7jcY+OVf66ojR9an8pshrfQBXNinLvpZ+7grViXru4ms3bEDO7ECztwor39zRvzfbA1m1+moAFNu23aU95vG1j+MDD3wA/1ryrwNTERyANu3NnEAMxkFdwnWKrnvvMSGIHJKUMmvfgdh3yrJPpWyIDYCwqqrXHysYF6OhVoQHMi2r59EwnqisnO17ueKKK0o+tj++D43xHhh7Yogx00xLTxbXTF+G29/chH/M1KAuAy0HMKqT29KFjifWo/O5jciuaB2w+5TUp4J+UB7yZelHoZSiITUy/NsABag/KB0rDdc9AFDzEuQXb/8CDnVw0+Kb9ndT1NJXL/BejnNa5AKcjlLCvfqy7mnlANJXCGHV7fdGH9+fsm7+23jgZ1dh6/Kl+7spAy5lQ2wAhJ1w+mMe4K6hUSIHykCilGL6nxbgzv/3piLb0YEzyenEyw1cBsl9JT/72c9i93Pdox8QMS9mAd8jRIypydfaG1F5Xl/X0udrHeyS3xbFr+QYdKqYFFOCRCnWp4C9pDaV5eCS/nReUAqPuR7hklG9R/vcgRK7zWh0cQ41tQxcUh6AR8Ti1phASpmj+taA/r1c3yV6Zg4RK4tSnr7+t2hauxpvPnRPH8/c7x+6z1I2xAZABtLvUcp835/oWKbbxq5NXfAcihdvK57mOeN6cA6pgTt43xXM1UnTL3+JtSe9H92zZu3vpuyVPPnkk7H7bQAzjj4ZMw8/Hp63hz2uxDV7j2LESETJcJn2vfemy30g/eBQKWX8F+tTANQe9fJHi5Xc1i50vrgZbs9/bm2yUoT3LVIQ4in2vIfkQEFeuLiIvbrQHpwRfw4HiJXwukqao96jQnWx0v2ku1FK4ThO8QP7QbyMg9aHVqNnzsBQipvWrSnhqANk/O2hlA2xARCWvuX1OyRWyuH91ylZND3b3gOvSF2X6zfvhHPEINgnDQPdz72r48GHQPN5bL/im8UPPoBl4sSJsfvfGjIWW4aOwppRE7B43KHKY+a092D6rnZtf4zrMXQvPaVsjJhH2WDvvl/roJd+yJBaSppmbZ9i4zgKiBilFMH/yhIvzTcvQffMbeh6WV/c/D0p/fjpKUImIoDAECv0tT7e6C+bd+Kq1duQ21MH1IDI/pvYuDisfq75V0z69O1K0FGKrXsl3eYAmrPYllA6cEYSpRT33nsvbrjhBrS0DDzrpPP5TcgsbkbH0xvgZfeN8XewibW/G3AwCjsN9/s0sI/n+IwbpeJ3czbaHnxIPohRGO/fwcS1mGU7vz+ksrIydn9rqiL83VEl17/bnbNxwWI/HkvbfUo13vfEEGP8PezCuC+7cjm2kRdtnzJIhIQV/nWRxeYP/AyEEgyid+6jFr63pXfeTjR8png9t/9IoZSLnSbwQkOsL9TEVT0Z/GGTn95+VCqBHx46ssgZ+0r241zDBajvmxixTGYbtm67C0bdh2KPYzMlljIdF1v3+ir7vY4YZZ/fRX57N3rebur3OoZtbW3YtMkvD/DUU0/ha1/7Wr9eX5Tsuvbwt5d2YFSUzYq+SllTHmDp96yJJXjJ+5WayBSHpsRE2913ywexE4xm+/4SCsA23tvd/J133ondz9XkUQS6s3XtHtyhTgBR6qcySN/fJZ++/gDgxh+EUFxfDU1dn+Ljyf1rtiSeQb52O3J129DiHSA1ksqyb6WfY8RkRMyft/qCYOzKR8Vv53b2T81GlVCPIpd5j3j69yJlcwllxJSybv0fsH37PWja8Iv463usIVK8bcXWvfee8IbY7psWI71gF6iiQHqcbNy4EQ8//DC2bt6s3M+GJ2QyGeUx/SVOZw5uRxSHv69RWKW8B9f397aG+h8oC731uPfee9HVxSfOGCgInp0wKTFBi/CO+4WK2U9CASyYfBjaTrsM259fuL+bs8dSzKPF2ekKik5PPvpmTZ183ZvSFHiKhoZGVFe37XWMmEP1dWj2mRxAfbS/hEMaS3iv2j5FZGqiS6LFPE/LCVYOVrl92e34zFOfweq21QN+L3aWYqmJfYFlTGY2Gkj9b/r1C3DXj97Erk2lFnjef4ogZWqw9Vkp3kMGenPzCwAAO9cYexyrG5RSTnKgkZx9L+z32HMq7b333otVq1bhzjvvwKo3ZsYe25krPeHTnkj7w0L81oFgiL0HpWyIDYD0+zRc6Nt5OHjXW4+NGzdi5sz4Adhf4nrMxG6YoApFvym9E6taV7FNPSDEI8Ch48/BpEEnAK/3vmezwP3gBz+I3c8bYjLi1NYbJQ5o6uA9ZLSEFOW1Q7Zg6jGv4YQTnwNot/5AAGhcKBk6XBZRZqJ+T/itOhuBVc8CzsGVfEHXp4gpe9QJZerf/IenXaaUHrRxEH9d+Fes71iPy166TN7Zz1OnDhHrCzWxKxshYp0ZO+bIvZOdG7vgOh5e+/eqmKOYdqcHruxEnGQ3dPAb9kIpdhwP+X7u531FxIqte30VZz8bCf3uLDdMPH/TDbGHtGYHti/mNgqGXtkQ2yMpG2IDIf3ucZevt3LlSu7vfcZ/ZhExCti1DcjWjcZXn/7qvrl/n4RgbPUR4V/UOQBocXsg99zTh/StiomQ9z4L+7ziQfLDxjLZMt0N8fe/7Qxg7YvcJhYR83AAJOvoy43vPAt4+IvAO7cOXHtY4QJMB25R0/YphdFM2Bg/cnAaIaVK232rsOOXbyO7pm1/N2XApD3XXvygvRAKfk4yOESsdFnTFDmFmrqyMUf2j3S3xtwjy6BlPTsHvC0qsXfwsUZ7k74+22Nj9oOlZKvrw9U99nfxtvVp3dNID2OsP7ukqeTzKKX9H1e8H5gY+zou7r3q7N7fUjbEBkDsxgii95y999TRELSIOrmYpnygqImeR9Fdtw7tQxbCNXLSZJIdOxFG9XCcsOsE7Nq4HvnswC+IfRHOg1/KJOHkgbvPAW77KJBPo7n5Fbw990zs3PnUwDWyiJx77rmx+zmlRoFYcluEVxAGUJeaq6OUef1Z3pPJxpW5B1R2sxKkc5v/70s/2Uc33LOFk0+CUvwa2j6liBHj058O3Pebsax0RWl/SWZFK0CBlrtW7O+m7FsZ0Bgxb49ixPa1xNa+SlRFv4cfNfCNUYgUvrsX6AQFsHberj1vi6LD8IhY8WsUW/dKkZ5c5Dh6dllpBnJnrhPnPnkuLn3hUjhefzqeij80qzu+J2V/IWLv8XCDsiE2AGJv2Rr+djv7gaOr6GN7XC+qj9Lb24NsVROcRA96Bq3VHjcsOwxPXf9bbiFNevufzsXFuJUySax4Atj8BtC4AFhwF5Yuuxzp9CasWPm/A9jKeHnmmWeKHMHSyRSoX8wk1VevqVGK5zrHx1JoEbH3Bjlx3woBdpIO7DT2nFJSynvV9SmiiBHjELEBNMS+ef9CrNtVhPpaFti70+h6bSvcrv0/v+6p8AWdEVES+4SM7eP07Jq5koIgs/q5aIOVGrA25Hf0oO2h1ci9+YpPm2ZF8JL1GZ3ox9fJJmgKhDVkS1l3iq97xYXswUPdvux2bOnagkW7F+Gtxrf2ug19kbbHH9/ra+y32Gvsw2QdXU1A1w5g9XOALSYkee/pFWVDbACE9UyJMVULtrTj2ieWYVNLX1KWKuhm/WSIOY6D119/HWvWqGkIrhspzo7Zi/XDG6KdQn/vbm3mNh7fubhf2rinQgngsYpjKZMEa0S0FqHhqe5JPXR3r4Lr5oofXKKIXPkXdvBpsTlETJFMhXLfiX8HITWx5PmzhElO8CKyizIbc/genC8HXFp62rD9lF+h88wfYkd+gfY46lFkVrXCaeUXofpcPeq21qG7W2/QuF05/OwH16p3GqxRHxhirFKlnnfWtK3BT978CZY0L9HetxSZvbZ5r84HAM91lbGsB4vs/sdidL20BS13Ly9+cB+FwkI+dTR6XVfc0X/3oDzuxaWv71Mtqv5rUymiQsQoTLSP/C2O887AttSIwtY9n9gya9qQiaG9Nt+6DOnFzWh+NgXceBQ/cYu3HWCaWF8Vfo9xzO6rGDHLFIxTSvHa6l3Y0KzPstmR6wh/t2X7j4JcyjPntm4tekxfZOuwo3DHmoEptKyUfWGIbXsH+POR/n8PXQS8+quBv+cAS9kQGwhhLDEqpBP/7D/n4IF5W/E/t74NoDSDKrvSnwzYLt5f/OU5c+bgtddew4MPPqhMdcp5yJHDxhG+IWYYasiebVWVt2epUx3Xw8Kt7cj1Q0wXW8Fe5yHc0tqLC25+C/94bjU2bauFS01QakgGRSnS2Pgg3pl/DpYu/UbRY7etasPOjZ3oac/h1XtWYuNitRL67W9/m/t7RSdfL4f1LhsqZYHZ1CsEYHslLNa0r4qFgMpxiJjn4nvH/ws/O+U6JI344uClCKUUK3d0oTfnYPeWLrQ2ahbYfhgv9zS24KvLNqEpN3BIxJrdK1Bb64/3poZrtMelF+xC6z0rsfPGBaBuFM/wsR0fQ11jHZ56Sk2ldbvzaLruXVyeOhstdy1H77sC/UhZEDaazza3dnHJXwL50owv4ekNT+Pi5y8u5TG1sqcB9TPvX43p1y/A9g1bcNu3v4J7fvQduP1AC98fkkunY9seZMYTY4L6ItSjSDfLbI3ehv9B54irceWqrUBPYT7K7/04le4vTClhsg6FzLvvYey+ZQm6XuOVVG49DK5HKTD/Dp/Z0N+i6Jp26nC4yXHoTNTi94cWn/O5y1EKakdzZb6xB613rUDrXSvw+k23q88R5u9XX3kZ//jHP7B79275WEUpk/gG9fHw7t3AnJuU+0RU/o1Xv4yNzZ9ARcNm/9wSmiaue3siorH43LImfPXud/H5W97W6hd8lt99S02sOvnk2P1OH75pU91gvDH5RPxkx248sn0z3tj+BrxSXjwj9u7daL75ZuTWrSvp+H2CiD1zJbh3Offm8Of21HB8vOq/8JO12we+Hf0oZUNsAIQHINQdf1dXDk8//TT+9Kc/Yfv2+E7T8XTfkZlSZfnyyKvaqaRRsk/jT1yTJ7+ND5z6MBoGy54Wykx8e0ILAIC/vboOF9w8B1c9unSPzufaw7ZBM0n8v8eWYuHWDnQ/24jnXx6ODZnr0Zh7ELmuQX2+35q1/wcAaGuPpzS0Nvbg6b8uxuPXLcCjv5+P1W/vxIxblimPff3118Pf2V7Z0HAZI8NQ9DevJ0JHRqT5QHwaImL8u8nldmPd+t+jo3MBKKM1xSlM0Q35xYuNEaunq3DssBU4pG47TmiQjYU317XgR48uwdbW0pS/Z5c24VN/ewPf+NscPPr7d/HQr99Bb2c8Grkn1A0K4Mdrt+P5lk78fuPexzLN2TEHb2x/Q96RaAJF8SW7c8Ym/4dD4Skyxq1fv577297lK+3pRbsBx/+G2TXtaH9sLbw8QxdlEbGAmsjEiO3sSuM3z/GJggAg4/RPvRp3Dxby1sYerHxjB9btWoTb/30X2kgCrdu3YsOC4nWIMjKDar9K247tuOWyi3HPVd+BJ6JS/SSUUjxy5ZO4+ydzue1JN4lM7VkAgGeaO4HrJwF/PQ74/RigXxVSvn8bDCJGFdTExHwH+c1d6HppC2dcKEulrJgOPPe/wKNfBjr6F10oJk2poX06vvW+VWj85dww81x2bYS+ZJe3YffmjbHnUwBvvDUHzc3NuPnmm9GTEwzzgU6c8MRlJcXOel4OefIGzEQWY079J4DSHMnsurenIs70f3zBL83Q1pvHLs06wTsO9etd3s33e0KPYtebv7l4Ip1gfdtdNzjc9qulz+Bbr34LD61+qE/tafzf/0XL3/6Ozf/9P6WdsJeGWH57N1rvWylnY+TuoZ+L/t/kH2KZORR3NLagOf/eccSVDbEBEGqwiJiuY1IsXLgQmUwGjz76aKlX3uu2iWIwbfU8D7lcDo899hhmz55duCPD6y4M8JGj1sMwPEydNku+IIug7UHxXwD422u+EvnMkr5D6rbdgZaWmfA8f5ItBRF7Z7O/ANYWDI4qYzKAauxe9ZE+379UWfFm9GzpIrEeDQ0RHVSlnHnMO1cl63BbotpPo3r42CMdIrZ8xZXYuvV2LFjwBd59XZJCxl+TXdgOd58Of1dZHdKZF98xD48u2I5vP1Ba3bfvPrjIv9a2KEnMhoXx9La4xS67di3aH3kEnpB0hkUFH9m591nlLn/5cnzr1W9JNZuytA7/D3/Bj/EX5JHUX8BkxpZHQUExIj1Ce/iuGxf6adcN2QilWaZPcbRqBTWRUExf2LeA8k1LmrF9jfzOurtlg25PDDEn7/f5TLXv0MqNGOf/3RUfn3vLpCRO+1gNpo9NqA9ItwEbZ6vjLgdIZt93Jxw7j/amRuxYNzD1vLI9Nlryg0AN/rnPbDxTPrh9k+9MdNVKzdLuNP64sQktyb3jGRuhg4dKym9DKurXbPFbDhELfqx5Idq4eV/E90R9wyWmT9wVY7Uohet4UrxydkUr4HhofcBPi0+YMW0Qs0D114s4UmatfJPfr5rbW9YBT3wT2KRwArHtixmGFD7akm5SOw5F6e6J+nGiusDuKWGcs+vengv7TAQWo+/YGiOLpdKrEDFqe9gwfT5++s8r8b2Z39ujtujEdePXV7sIItaydTPmPenrkyYzb/U4vj5028LfYs6cM7By1Y+LtgUAMu/69HgvXSIqvpeG2O6bFiOzvBXNt8Y44RXxhwCFbREsrz803JLuKyK8H8Xa3w04GOWNw6aEL5aWAAWrkShZBsK/xRpisx9ag4ajciFKdswxx/ATZhEkwTBN9eLYj5JOb8Ky5d/BkCGnY9LEH0n7Fy/5Orq6FmHC+MsLyAIfI+a5LrK9Paiqi9CuhGEgrxi0xK1AL6pRjRIpQF2lG46mqX6XlFIJsRkzZkzUJkM2bjfXRZ7YfEpevLj09UJ/1GVN7OiYF7XJi+5JCopHV2sGbzy0FhOOifcCN+dtUEaRZ1FSGuMHWtbYtyQ3LpevRDHmuG6s78eb/us8AIDd1ITUFV9Gp2VhvOP0nZ4JoHX7NmxYMA+om6Q95vlNz2PK4Cnh3zOrhmIH8Q2JV91P42zNeXwcqv/vh3Z9KLY99u40j3iF5zMZFxUxYqx1RkjfjJLta9rx/D99he1Lv/kA6oZWhvuaW16Rjt8TaqLO31OM9n37RD+pwu+OroBSnbrnv4Bdy7Dz1F/i+arz8IWTxvW5bX0V144MHnsAMtDOfWoDFszYotxX6VYqt/ui7v+feNdP4PT2MRW4aUHpqKgt9iPGENvYxGek5AwYxrhwXHYuKYjJGJd04A1owrStm1TjD/g2TutuxAeDJngU069fADTcjSGHv4Vpx9yEwYM/yM1HXk/hmzNjz4AR2393kg48l7kQYD7Z5tbt+BCiuUZp7Nx7PtC1HVjyAPCLvicSo5Ri4SGTMX/C0djQMxKvLODpmAQUFISbY9vb54qXKUlfZ9e9/pIEs+7qjBrWcegqnDBds7Yh9U4W38OFuLDyx+i1e1GdqC7h7iWEARQxxJQoMCP3XXMlbGICk47h+mYwfv+nIY9MdisyTVsxedLVSCT2ztj1rBy2nvh7EGph/PyrB4ya2NmxBCtX/xAjR34GhyoNMWDDIVWAEd3/vZRJv4yIDbDogsYPhDwF1KUwGLRj16YObFgTLdKdnZ08elDMEBMGyJ4or8Wg+RUrf4ientXYsuUWeJ7spe3q8hGSLVv/hUc/9mlcetpIrKjzu7nnenjoFz/GLZd/CdtWRt48NqCXbfG/JiZxGbkXz+I86T4ZJ4N/r/w3ljUvAzzP955LtaaYb+/kANcB/v0Z4NYzYEA94aoCwl98kanLVQh0X4j3YT38pB2Ui+OzsGzWdrz52Dq4jtz38tUVyvvFvXZPkb78lbtWYvOyVsx+QF9r5q2WNkx7czkeaj98wOPqKShaawy8Mq0Si3sUCiG7wFnx/qe2hil487VufPa+C/HpcaOxPJnEbvR9wfr31d/DGw/cHXuMuND3MmMogwrxcACFMSIaTCW8YGIaSkQMGmdLiIhR1hAr3cvY1b0caxdEns2tK/nAd4PI3yE2RbhGDI1Tw7H3kk63y58jRs75OX717Er8+UW/r1NQ7Jryb6xceS26WvVB/9T1kNvSBS/vIp8p3hbXsTnHmLh2ZNe0ofGns/bgQSLRGWF9EdUXmju0bz5dT0j6EibrIMDqrTwarqOXP7ZgG3NQ4f2ya5AGxWPFtjvR01ta/ItaovHbgsHII4mXOyOvfMv2Huzc2IX6iTPgul1YtPiSoMHSldjllYCAFtgPTksLWm75F3IbohCFZ1ML0FQpxAqLk7hi/kfX3sfOzJ9wNABgec3h0j6VQrlxoSLpkG7BoRRY/ACwYSa/7vWTsIiYo9HULSPqy67CmM+ujuax4fZg5N3SYoZLoTFSR36Dm9I5/HD1Vszt6FG/tqe+Dax/FdncTgya2AIj4beZCw0pGJdDLMaR4ex5jGkgHeNfQ27QZmTr16Nr1NzY6iZ7Y6QtWfYNpNObsHHjn8NnESVTyW+330Mp7cuG2ECLoGiNJc2oRumew92FQpUUwN+PqMKsw4+DtwcGjt2SQev9q5BZEdHUWu9dAbsxih+iADJd0eLlui6P6BGChppDtPcYVzWFb1lhZdm2/V4sX/592HY8pWvO9PW4+8dvYbRiMgqkh6E5BPRDndx2wRexpTaJr57s13hxszk0rV0N6nmYcdOfQQsLlaVSTgHcNsn3mD9ILpH23bz4Zlw3/zpc9PxFcO+5CPjTJJ/GxIhhMO/ud2OAf5wEbHgN2LEQxk419U5FFfzhd6/wDT34k/mm8YfjBnINfk7+gE7wcWyuVY3XH1qLJa9sw/LZPoWM7YH5mko4VjQBR5OjftKirCJu+xSFpvXFvanfX74OlBDsTI4HJVXS/kQhrsi2u6JFqoTJM1A4KKX4BSpxE6pgAbjvjFq8fWQl/q9CXmBiF0Fh3+Jjv4umUafi5FWfBQXBn4bUYzY5pWi7Aslt3IQtl1zKoRs6ERd6lgVqwEVHRwc2btwYesdnbZ2JDz/8YXQ5zLgtdYEziHLG585nx4IdKBgsIirfyxXosnnPQ0fnAsyffx7o8O+BmP44Zfv2ut4sclRW3lm60I5sHtkS6CWGAiUGgD/OWIlnl6pR6lKUom29g9CaiyCHu+f4inCmfh06xr+Kpp0P45m7/oDNy1qU53e9tAXN/1yCeT+fg9uvegO7t3Rh5t234p4ffQcdO/k4w5atm/HPb1yMTYsjpVWkIbfctQLU2bOgtvy2bWHAvec0wU6/AerpjUhZ+k+pEbMmAryB74hjAh56Cl2FRcR60tE4J0GWWkaJLiXZ0vz552PevE+ira3vNMZvbL8I122Mypp4fYk9FV/A+leBxiimkRAj/P6NP7wKzX/5CzZfeFFsnTVxGhgIdKJYfG0wfbDJOravkCmM2iRRK54Anvwm8O/zcdVlX+T3zb4OuOfcPjFP0o6BX+C3+BOuAQFFwiSw4OCfiRsx5pVvKWnHBjFQ41bh0OwYpSFGGMePSc0+1Bor/j0cKuuGX1y6Efc3teH8RevV33/RfcB9F2DRoksx/vQmjPuAb2wb3BxnYEzPGG4tpzGIMbVdrjyGY5p44YUXsGDBArS2tuLJJ5/Epk2b4CUiyqKTatf2ud4Fu7Djl2+j5y09rT3OCWfbUUgFNdROH0IBk9F2dNTTA1HK1MQBlu7ODgDAE8tX45c72nHpYe/iyOaX8L9VJwMdE8PjXr39H/jIJV/HO715PNjUhu9OGIGRhomP3TAbM1CFOUNNPHJIJYBDMKy7A0c3beZvRIERbSmk8gYo9Sl4z//9ehimiU9++wdovuVdeD1AZlkLxv7hwwCAbd6/MPGDryG36kPo6BgFEIq0Fw0sz/MkL1vFiKMAPBv+bRsmEoXJ7P2Dz+YmYAqC7iXzsLb1l/4GksQ7845E7vTT8JHZr8OgFM3dOVz+73dxzJhBGP7SblQOW4P/nTQLf9t+BrZ2+1SgxS89j6Z3XkRP5/vQ8AkSOkQodbDh3XlofmUxzPe/iRGTzlB+A7ewOrCL+EnJT2DHb+Zh+LePRcKUFTnPzAKo5bZlszn09vZgyJAhuHvF3QCAK3Z+Hk3tp6HBclC942VgUkTV49ADzwbaouBrI9MCYBQAIJdqRS7VguqeCbCbetE9twkvH1WDWYaNn40bhMR1X0cH/RqWHpLBrnQVFk6NjIKNiPqQ3+4KwAOGWwRVr21Bb30SKxkPUkWyFyOPfxbtyz4HN1sPz6XI2i6+MXM12k+qwseWZFCV5785O3kjn8XMrTMVbzmSJ4edgZvmLsB2ytTTIQlpHUo4aTQ1PY6Vq36MMaMvxIhBV+Ar2+7FrtQI2LXnYt7TGzH1YyPw9NNPY0Q6jUlz52H+t6/E1V0OLq5ZhQvpFnwMp4CAoNuieLbKf858Kmrv/E0L8dLdy3F0dwWOKGxr7+nC/JtuwLGf+BRGt84Eff0GzBt2E95obsCnQeCYabw51cEbU04E8W5BftMP8U4Fj04tWfAl9LRsQP3G32B37Sh85L+PgJnw79v4/e/7Su+x/LdhpdI+ClXO8eht4rn37CtaYRyNf/3rX8hkMjh+9KmYsjuJt9O34bsL29BzQiuqC/0HrldSIdyOp9Yjt7kL109JoTlF8KtlWVR4CDU46lHYTNZJuvI5vL22Csn2ZuAwfxvbp3MZB3/9853otXfjK1sGYXtdO5467WwcOnsJvmA/i3MsAEYPKoduQHrXUaAb3wDOuBBP7mrHFSu3YDKtwy+ENrqFMfpmezc+t3gDptZU4o/tKWBTFw4ZnMLqU4fjtzubcdnYofjgYw/C6+6BdfE3MfSY6aisasTWLdMQ4Nqe6+E7DyzCOdNGAwBWrVqF1atXI3tIFktf3QmcqJ4vAGDr8iV4dOs0ABTfOfxtAC6qDrkJ2Pwz5GsiVCE1qBEv3rocl//9dOka3bP944bVbMaIia9i1sNN2LbAj5H8669+g9dO6cInDj8SPznlWtz4xJOY9/5P4IPzX0Ndr+/kSC9fjnVXXY3BX/4y1tQkMRwj8Y61HqO9BkzyRkr3y/b24JV77sCCzCAk31eNLmMhvnv8dzHcrsScC7+AlspKHHP0tWjvWY31uUWg7i4Al+nfAalGfgRw6K5e9AeH47XVu/D8sp04ryoPllNHQJn09QECFt3v5VEp/Pq4Gpy/3cbfuKLA7FpTEM4Qc/FqaxfSrodzh9f72wQlPpP1E3qsWn0NPnhq6ckhfjP3N/hy92lYW8sotgojRYfWSnLfBYBzFoDvAvANsa7XXgM+eBrS83ya+IrRo7Em9To+bB8pnd5VUYV3JxyJyY0mPthSUEZZY6d1A/D69fFtKDaFNC7A8GFLAXwq3NRsHwYgKvkyITcC65NRBseOjg70JMYA4LM6qhR2Simctx6ARX0f7j+u/QquumM2KKX4yxNv4agXHsGYqk5MTf0I2z70SXR3L8fhk38Ky6rFs0t34PW1zbjqrCMwvLYCO3M22mwH07PTsM7yqd8nDZ6DRJbgc+brONucD2wCsPJJYOpnkc9m8c69P8XI6laMG9+Mv7Wdi1Etp2FRwxrgGKGhjLPKhMEZYpRSdL28Bet2bcbOhh6cccYZqK4OaIulUBNl5/LGTLRNZVukCcG7FSnYaT+2fsikdmAHT5utsetwSvMpqJiwE0h0F9oaGS03z30JO1q24v9OPh2JwYdh118XwWnNYOOJP4DRvBzdg5qwcq5PMU0kEnDdNHp6/4kxo6PxRInHNdDtyqPj+Y2oPGIw2h/1Kcwdz2xEzQd9yqnj9GLR4kthmdU47rg7cdVjyyBWa62f2Ima0dEauROjcNwhP8f7Bi3CHSv/L9xu54FuZxhMhmmUZ56/eWs35jzBJ7A6kKRsiA2wLF4wDzj7THx7VxpeohJ/nPR1DEvOhkWXcIbY4pdnoGbocHx2kE83e7erF9dW1qO7UBl+W1U04bdV10n3qUobOHuuvzg3rV6Fra2dWPO2H5A7ftgo1PeMl85pO/R5mACOOnom5rx1EQCKbnSgupAkwPO8WK/aDHwa93/oHJy0eRVO2OoPNNYQM4iHF667GYO/5v+9a9cL2Lq1Ghg5Eo1jx2Dctu345TMrsHBrBxZu7cCPUIkJZ/iLxf+OWIUrZ/0e6c4OvHqHn57UTNXjdfwXOlGLL+JueNTB0luexpgzNqMLc9G1fo7+QwBhxq1BiaEYVjEWNOug8/lNUq0RALArWgEM47b98S83gWa7cfHFF8MyLDieg/PafWWu3fk+qq2XueNJTHFSpz1SeLsa/JgIz8yh/c560JyL7w/3PWM9HY34vXcVAGDqpgasb+uE9+GoL5gQvFqUYujUJ3DIoE6kVn4Z7Y+uxVNnRQblkIZtGNowD6lkDo1vfRueS/HnV9diZgOAhgoABOfOFxAljmru4nszv4cr8Ffts11x1C8ggr7KxC2UYOWq/wcAaNzxAJY+7KDGTaMmvQmpRAbvPr8Za9vmYsPmtVgDYOjbb+PbF30LAPDPziPwIXotxjUMRlX7EZgMNVJw/+2vYnLriWjEWBxmVSPh9OK5d2bD9TysfGMmfnjkG9ieOxYLNqVQhTR2Dzsem8ZuwhtTfGcFNaqwaviXkOjgEcyWzjlAAshWPYjmuRfhn93NOOu/pmLymMElpfrdOtFPTW+t5mk77Dq7kkzFRzL+4rGscR5OzJ2OL+Gb6N58Gaxjd2HHtKcwqPHDoO7xRe8HAPnNXVjUYOLhCf74PqbTxZc226Ehll7IK0vNC17GnC09OPHYQ5WG2BuPrkHa3gECwGs4Gd94/nk8cL6fvv7BxHk4h97rn1ModeGtfh5ofT+uWOkrAuvIFHiUwGA6WJAB9HurfAV5eU8GM5/dgf+pspDZBHx2qN+xrli5FTP/fhOahx6LrfQfGHXyDAwF0Nk5HJ0dvoFqCDyZhx9+GACwc+1OHLPpC8CJ+nf1zlOPFX4R7MjUwahuRqLCfz/Ei5ZNw7Dh2PGe1y0f+AXqATijl2DbAl8JqWjbija6GQ+tWYzzJ5+HO6adDgDorarF557331vzv27B6I5erP/rjXj7iHEYdvinsNHcjbXYgcOyw2EI8Oase2/HmtmvoAbAvxu2wjUpmjPNuD59DhaMGYJjGj6CwypSQMUnsD29DlknPqvgug/7xpJlesBeJArNeR429GbxlbvfBQGQrdwOTOORfCOG8vrL431a8PRxSfyVQUjZaSlEo5gEJKucJL641Hd+PZ2chPfX1wBPXA6M/7V0D8cpDR3MOlls6dqC15e9hkvxEaSRA+Ar2ipDTBWTqRPC9FcDBjqfn4Hc/0TlIJYedywAB68kZYRpxtRT0F5dh++PBN59saBos2jyfRcA7ZtLbotq5crf9TEceuoQbtuDO76Go3Fj+PdftlyFvwx7HJsG+Sjxo48+imwyWkftXBVyqWZYnpxYaPd1f0LbXSsw7JgaDD26B1f94PsAgFlrmrH2uUfg9YzE8s6ROGzTa1g73DcKkonBmDjx/+E7D/ghCY+8ux0rfnMWPjp/NdpsFyONqdG9jQQqTQMjCUORbvb1llcfux3zdtTguONno8FuQ9cJqzDqpdMwbf544At8O9lvalKDS+iRW9uOrte24tmKWf49bRvnnPoJZNe2gxxVAkOiCIqj+i6/HjoYz9ZU4y/gnXqsIVZnNwDYLCBifruXdKfxq8xwoHo4Js28F188/Wo4Lf48O3bUEXizdhLsZJRky7ZtHHrYEowatZ6fYYnLJQlve3QNcus6kNGU5dm27c4wlKSl5TVMX5jH/4LRbQ2KQ87kHSd34HLssurw3LDTsCM5DKPz/rXnvtuA1vqJsBhDzGZ010d+Nx9NbXyisgNJytTEARanUA/G09BnWFk3LzIkNmfySBgKjx8gBGH60tAeKaIrX34J3S1R52+aKxsoLUwmPdN0C/fgPZGe58EThz6zrtxHvgpKCN459Cjl86QqLaw5cnT0DIwHpqe6BgCwoVnNU65N+tudfASPN9Z14VHrv/ES+RRm4kxQL4/RlRORaVirvIYkgeefeSanNcPxxoPHY5WtsP1Zf4F7/PHHYSniW0SJi6dx2+UsRHaqI6wRFMjLeTEImAJWhM5kwAfYuyMcDD3qeXSNeQsd41+V7hEop7VjFgPw6QBvb4wWpsWHpaRz+NvvaQC8bChZAqOjpyOirlLqj5sdTRH6YCfl7HbZuk0A9P764YwDwk4UKKrCYtfhjgp/tzccjt5K/mo5s16ZVhsAcjXbQFNtaNn1Gm659dY+F1pfPoW3BjzNg4iI144P3YXuUXOx/X1/wvynHkf76tJKXHQkohssqfe/SYAUOy18n+yxg36mTtaxfVXUb3rqePSYlWAcUGr6WdsYcYV+EcRt5JlF1I2ZOpdNvQzJuojuUlcbzWuqeRIARmZkNEkUk4kj9CgBqAESIDAspdIo/XtbFRFywNKGdvREysam8VHcTRBjm7P8d7TFiJ7NUQRjbFw4P/yddPxz5zXNA0n442ZExYRwf7UlO/N00jhKiFWkUNac1MkXH3sBH313LeqHORhRtRs7u3fKA5ZDxGKETSzDNSkwxKL+NM+tCX8/uqswt2xSo16uE09zDyTn5pB1s/h868cx11qLF5JLoqYpnE2GzhBTPSSJJkRCCCgBcutL8+K3K5yzXNbEEowwtkkdSYKZUysxvyMyUD3Fs9jpF7i/qeHhh02Xhn83NvJUNEo8dDWsUhocbXfdBQBoXuY/y/XX/RGAX+dzbDq6Tpb58rt2z5DY7LcvaURboT7bTmM4t88yCBzKzDkFNGveen9sBTUcAzFUWAVniJnobs3gqb8swuJXtsJpy3LvccmSJdh140J0PrcJXbO2ydcSpFiyDrbuXCDP1qgThbBzTOioYFHkwlrelI0MxNfICO75UoXzqKC/Dhok1J9EARFjjP/cug7dYwAA8gzdMJuVKYvEkAdJBxOr3W1F4Q5LVw4CpYSnJpZjxMpSTMSChwCkIp4WQ5ljFTRVnAaT2A6e68IwGUVCMYE+8cQTikaJlDQqEc9jQB5JKqpFxTkaJLTQJl18ViCGGU2aXTXRwroUx4FSBx48EK+0uAnP8RvPFTX0AFPRBkJVQyM6z9Rk7mHFiFHS4jzAxcSrjbzJ9+Jr3L5sfX34O9MgJ9IQn9RzPSQV1ExWuE9eUBY8EYkrIlQRYEuEqq58rI//fghhx4DcTlowDHRP4BlMn9Ok12PpDJSYUgkAjyS01D9KPGyoWoctg0fAqbKQLjXNLyPdrZGC7WpiMESHiFMdxeite/strL71kZLuZTGLU3gvj2JTOoeZpsN9VRFxAXjngiZmWnGOf1UPpk/RZcQVFJ0AEbOY96AzTgOhzPhn2yciYn0Rdt7xKAGlRjhn88lL9swxwb5ZXYxJMDwChcpgnWSKZ2ONR8K8NGIlCucw9eJKWPrD6FHC/gUsWrwIf/zjH0tKpuDl83hzmO+M23XCBPzmg7/F1z7+IOfY8KmJaqRLahNrXLD5akJELHoHScpkoCwSLxUXL8OK7dmwiAWbOFhhbefaoELEYu4obWERMR8nJqDOXiSc6WvaOGZu+MO0Crx5dCXOXRQZgqXUBQ3mYzaWjH0rYb24EmI/r/rfKwH4ehA791MmCQ+lMsq0MKbWlmUavPOnMB/p1BBiJvDY799CR3cuajeX3ZJg8cPN2L66HW89tj62DzR1FZ+jPVWCFUaMXF/mNNYQK2xRIGKsw982LA7xi37yz0UV+hElbky5JlkIifTDUrKLA37cdCCesABRz+QQsfwAZXAcCCkbYgeQuDaffcfSIWKKc7lST67LKRLi5EA9Knmqorvwx4oxYoYWe1BdjoJvOTuI/K6nogVy92cGk8koLB5MeJ4Dj7qA0mhSXUxGxKjradqgMM6CRQR8iludxCFiBtnTBZbCZQynDjJY2Mv0GYWSKCIQnkeLfgP+BgVDrM8KqPy+qGiIMX024DiwhphykQsXfvVdWYVVZQwCgEF4Y80Q+jwlJqjm+pS4WDlqFGYc8wE8/P4zkd2DIrxs4WFH8yA6RA7glepiS4/JHBDkxMk7Lj4wbxWusHrx/GiGdld495wixRi2RhEDPjrQ/wYeNaUECmJ/DGLE2C7pFnHW8IYYo+D3wSM68+5bkWOSP7AZYP3kSL4Zdt+EBG4fMRle4Z2z72NPRW+IBYZfoU2cISY/G+t8Y7PhkgKixjqgjBJqPAbP6BFeBX9+hh8j/Pbbbxe9BoTxYBCKZMIGTfBMCJ7GHfPdNOOLKgwxi3lep2hfiI7NruMVeauiI2yT4zkwDRN5YkstVSXr0CWFUW/mDWWPANTZ8/5V1NgRGrGtLXIizR2uqa1XTJTrGjMmQ3S8+Jrz+GM+lThhEi5BGbfGUVfqLU7Mc1sGgc0ZYgVHnurbgSJbuxkTxt6KWQ89hd53d6Hx53OQWxv1D5OaaN/MxHBJOk8kHRPkch2iuEUSf1DXRdZKIJ1IFdoYcyw7XwTIFhdX6b+nBPPsLjG5CZ+ExwrXVulcxOtTHTHCrMcqg1rVQzjES1zPqcHtLxtiZQlFlcKdgihHkCNUAmeRGg4RU8ziHgPjel68IaYfLFRCKcRMNiokTytESMnL0k8Kz5YoQtlk6woZzCTlwASlNig8EFqiaz5EOphn8qgGlVMoOoHSRfkUt6yw3ybeENvzBdaJQ6M89rsXN8SoS/X0mYJwym3Bg+j2VQFVUTmFV8wqkoFixC6QKnovLbxj3RPwiJi6nxDwxxiCd47C4MYXf7KLeYdGdLId2/vOQ6dM5i6npPElGLDE954/P/UU/PuUT6KrQs5QGYjFPFpQe62XUfauPzKioQVebc4JxCJOJca/8IgY3wMdoT8Gma4SmrlPJZTuPSK2cMbTmHXv7dF1BEQMAJzERPxlSgUeGHkIXscZ/nH9YIipMrMB0XsPYuhIUUOMQerY3YVnYWsqkhIMsQCtTBuV6CpQgPssWgNIWFdKRMS8fF69XWmIMRmAixlizFzZcudybtek//oRhk59EoBviFnECg0xZRuKiD47nGAoE8KX3ShRwqsXQyeE9P47OrNY2GDi/gk6I6z489Gi44EK/+rl5JPeBwAwDYNjM3gCvU40dlVlWwBfD7IMApdVews6hVINIR6aJz+KrjFvovawa9H+2FrA4e9lUpOv6Vj0qeJFV+4okG6P4oGTP4H7TvkEepIVeg8keL2PhoYYWx7D//6s08shJvcQalxTbUhTw+ERsWJsJ8LSv0vr5yYzRhxuPfdAqckbYmVqYlniRa0QStREDhEr4hVmaRquxxti4rGaCZoSKhlaImRczBBjFQRfkdPdy7+OihbIisMoqQbz24MJSh2/AHKJhhgNJ1HmvXoUpiJGTOWwYxExXYwYqwCROGpijDFVhEQDJ8bA4yZaxaLoCFQwz9UZopFw3mpvzxAxlREk9mk+w1hgYJWGiOkmMsohYuqjDM4QMyRHByWGUvEFAGp4nAJGe0uLNeGuwWaaKkWZE2ixhBjoGDQYW4eMRDpVgdmHH689lUPEglsxiqHNeUML92G+P0upLTURQWCsUPiIGLfwi9RET6Ym6hCxsFUcVXDPEDGAj9Hl50///kc4UcHsFSgkAdgLh0ogqqKxAIuIBdRElqYrzwEsimew1quhQMRKWPpDhZVQZKp2xx/cR+HnV4q4xEasOMwayQ6VcAwyzhyTUe6iqb8ElUfRlKFHPe9fx3NgEAN5QzbEOMU3LLEhX0uXVptwiJhPVNwTROwXU31nSlGamIC+2B7FZe+vwo1T+JjA8FlKuLfK+cfOH6V+ZwBoavRjqiyDcGuFyyFijtwu7S0ILJPwzp/AEFMhYsRFemihuLim3SYMyRDbG/W/WIzxLDeLvJWAZ5hYNP5wxBnHbH8METHh3QHCXCus08GjiXdRGmIiIlZUr4jGqpIarHjnbBiBw5xvwgY8Q0jW8d5JX182xPaHEFMdI2brY8SKCUdN9FwQZlF2xUHjepraOzw1kVIqeZtqqLrIrLJNMTNSqdREm/FAsYiYCxOeZ/vUsxKpicHCxN3R82uLlCKs11YXI0Y8tdIqShwiVnwij5l82UWmcP/RdhRYP0JIf+Z5VFuHib1OL6qRRyKMY/D6TK1UvC+JmsiuaIV3VzIiJiK5QTtLMMQEaqK06MCAp1MgiCt4wr2iBaNFYceYW4LXWTTEDJjcs3WnKsUzQmHqeYaIGFvTx2Vur0JN2JioEvIPFU4KqIkG4Dlg8oVIMWKOwhDzNPfJF5JQDEiMmGL+HJeLMsYFuN6AImIx1MQXEouxzWhBe3tEk2KNRxbADaiJrFOtFEQsMJL7UsRbkhIQMSLcI461ltcU6VZSExk0KUTENEyGUsXxHFBQ5EJqIqvsMoZyjBLouFQzybNrhwkQUjR5g0qeG5NAzkBxaqIQr5nRxOn0JelBqTFipYhRGBemQbg5VkbESmwbfHSNw+QLqKAK3VcalVIbZUNsb4QWobazdDtCqb8+am6qRsT4dwfwc61DRCef5tpKaqLLZdku5qjjqYlyP1fpyIYGETOJA0qFGLEyIrb/5c0338SnPvUpNDQ0oLKyEpMnT8avfy2nrV24cCHOPPNM1NTUoL6+HhdccAE2btyouOIeiqIvUg0i5ggxYixatLw+/lNx1ETHhWlF99jQzFOVqEeRsGT6gSoORUxfX0mTse0g2j+E6xqlIWJsdiWDixEzCoiYWzoiFl6LmYxc6rehBLdagHBRxFETmd+xCox+kig2fQzJdWr3cYtUYSEZn42ytYlvuxRErNVowLdxG36Ev8EO4kY0CqhW7Solps5iEWDZwJJiHRGhfuKCERQZZpE7Mbg3vK8QRyYhyMSITdYhesJZWlsp0tdMi2KWjDiKYDqRwtxDj8KSwvzBxr8F6BursLlcIHq8IUZKdWCEiJhviDGfWZE10W9LaYaYPxdRDSImxvoVE9YgNpj50x9TlEuQ6PWjIWZ76rTWIYmr8CpY2niXkcGLySW46aabwgQxbL/jEDEFNbE0RCwwxPZcodF/ApGaSLX7WLFZQ4xDxArPw1ETGe94fxliBRZGiIipUDkwRc5VIQiuDjdhjBfix4jpYuKKCQVKoCbyyq8CWwLAvLtShnswH2sO5gzuIuNz9Eg/46GPiOmoiXY4N9fRXozLN8UmfkgYBA5HTdTHiKGEsW3C4KZj/8v2fby0HvI8tp1wPZxEfBkFNp7UAAUlRJtATR0jxr5Hvw+zj+7A5AZtuEu4hxYR6wM1sRgiZijWa5Z6yKJ3ZtIEpQaHmBVL0HMgyUFpiD3wwAM47bTTMGjQINx77714/vnn8eMf/1ga+KtXr8bpp5+OfD6PRx55BHfeeSfWrl2LD3/4w2huVtc+6BfR0NpERIzt/TNHxAfPsk/muTwiJq2GLtVkIfekGLFinGW+DeJo1R8bImLFqImMkkiYCdaFBY/aPt1Gp6mJolqYPMrFqcW1JkS4KNUm6yjZEItRbuLjmClniH2EviZemLmO/5GzeSbLlAI5KlZ09Im6c2GTFFrIcCyBvziqqFEAkNdeSvW++IN5J32AXjILhwDBuLC01MTIEOsrNdGU2kURn6yDU8A8p8+GGEdN1NDUWCEiIiaMIfY9vXzUSVg8/nB87WQ5xbEKEePuU3irNqOQ7EmMWIiIwQJcm0/EsRfUxHzgTOIMMTbzXF+RHMYQY7POFj6+ySGXAW1z7+kvumQdYsyRKlGS67pYvXq1v99QI2KBpsUmXuoLIhaX/bW46BwY/N+lxojZXBZBdmwHiFi0TlpsTHFfDDFL368DRCxP8lJbVYiYSim3PU/zkLwThBLCZQjsqxSnJvL6RrfbpTzM6YNCWwxFooSguWYQXIOPlVMZZUsWLQTgO2tVNDsA8DwHlAJVyOOCypX4WN12DNqxRHlvAwSmJn29qWRbFH/3FjW5xUdMblaKeMRGy+GPID10OTLvU7c9lExEEQ4QMUOzOHVVRnN+WHmDNWKD8cHS1YW1xdJAYrpkHRwiplkGg29dFBFTlYNg1moWEbMMCnjGezZG7KAr6NzY2IjLLrsMl19+OW6++eZw+xlnnCEd+3//939IpVJ49tlnUVfn16448cQTMXnyZFx//fX44x//uNftUSbrKDHvs64fqWIfuEBuJlnHyBObUTFkDryNH4Xh+pQl6lI4OcXFSfB//j6XuqA96jpfKvEEhjQF1RocUfr60pN1cNthgnp9S9YReP4JIbArWrHtxOtQ0X0ozJ7vKlOqiBJmfEJpiFicAlNKKmClUL5PxcYOKjx6VDBZaAmImMtNmIW+oVmkViU1iKmqz4t9gztEpriIhpgDM6LCCJfO2i4qKnjFQBmnRnlqoipFPoip93IKhljGyaMm0beMY6wSYtvqZAScGPz1DcF2ZNvTVD9Ue5kga6LOcx4shM2pPILyqyaTpKAY9WQTDsOh2BiiRm4ha6LFxI6KMWK2Gxhi0TZdHbFcoa9x1ERmzIlJV4oJO62yNFkPBCCUQ9icEBHbi/TiBdFTE/m/dShD4HRg28whYmGMD0MrLQkRKzxjGBsrxxAXlRKUISLEiMWd4TAoDrGitSmcGxhFkk3WERliJSDzMSEBjucgaSYZqrIasS+KiCmXD8YQIwYowV6lry9KTRSSdRhuFoA8f9t9WKqoEhGLLvAMPoPHTzwDRw3L4lsuDcBa5Tr/yY9/DIAfvsDFiHEDw3/GyWbkOK9tXw/gJOl6KSMByzSQ46iJ/nyrpCYaLlowFK0YisOxWvm8JjW4uXePdH9mDnFHxgMAtlQ8nsDwAFfRrRcpahKq0tdz1ydWUSeBf66GyllCjBjNOyCpBI+IKZxRKpRSl6zDNDyJmlhGxPaj3H777ejt7cWPf/zj2OMcx8Gzzz6Lz372s6ERBgATJkzAGWecoa6ztSei7IvqGDFRqPBvaUf7E7BpmkgNymHk+1pQf2gjWiY+xe1X1ZGhAiLmei48YTKPa4sreKAJ9AhTsHCaRdAYV7OYeDBDRIzQEv0JzMDcNeXfsKt3oXvkXNSnFIHoYk018F7bvc2auCf0BUA24ERFliP4EFfeJlzPDaiZ4n3YGh/c/WXKHyt/Gtyg3E4VKLD4flhqYpi+nk3WIbTThRnGiImP0FMwaIohYhT8O6VC+l4ASHpV2qyJ1HA5BSxt7wTpY4xYX1dvs+EQ/m+BuhNXy4ZdP4siYoX3lWHrFzMGeDGddgMmF67DIGIeRwwKU6Q39vhFllWImA6NDBAxVfp6CsDsoyHGCpe+vtAAdmyH1ETW0N9DD2yxZB1hmzSzaYC+6NLXh/GSfYwRE9HKUlci/hStN5H/k5sLYqiJjGFiqZgQbGySEhEr7iTJx92/gCKJzBGAdxTF0Y3tEmpo+dTEPYsRC6UoNZF3+ri9cpFeALBp5IAsJupkHdHPR8gXAQArx1dwNbNchdL84P33AvDRKq6OWNHU9+r9S0dOhEE8IVlHECMm96Wc4eL75F/4Ffkt5uMU5TUtaoEwGZD2ZArgTikyLPMsM7cIIsZK8P6ox/ZRGdV1FdR8QH6jyjpiBl9HjGic7F4uX9i/d9RELlmH4YJ6QtbEsiG2/+T111/H4MGDsXr1ahx33HGwLAvDhw/HFVdcga6uCHrfsGEDMpkMpk2bJl1j2rRpWL9+PbLZrPY+u3fvxooVK7j/1q9frz2elb4iYvlS6C9Mn/M8F8Q0Yaai89INjEfHpRrthmICHcRcx+NSawPQIlweFCmVY2alQMlIFPGqd2WixYLzOMIE9ZyC8ahWsKVtzMB0KqM045Wm/jtH97OiGDFaYtbEAYgRI8z/++3S9yUa3j86XjLcNOn7WW9Vu8HUKguTYKiVx7T2eyoKOotxItxnlGPERLQqjprYlclJ7VSNO5cagvFqSO+fEhOu5ltSwmdNzNEtfTbEXEa5LGFNLQER00/rrGc5qFkmxoFGUjA2uCQCpVMTg9o/RsHjG9QR46mJfltzru+JdxTp67XUxEQMNZGQvifr4GLEWGpicH35elyM2B6u+44mdbOY6UxniEWIGBsjxhxAg3+Y5yth6Zfmij2hYZacvr40RCzPZhFU0aOY+3GU32BzCdTEtpze+HE8pySDO/gmqkPzrpqayI5No1DQGXtTR6xIcWAREbN61Zkx+4QsFBw1AZOBxDiFWAeQChG78AufA+A7ODhqooTO8OfpWpuxqrAlPzN0/gAI4+RUU8yOymjjI7hIeU2TmlxsveftgYu1DzGYLApEiU/Z1FVWYcW0/OekQnwdwL8vP329fEHX4JFSdUFnB2CdDBonO80HsWnsHCvHyRY3xFhEzJENsb1wxO1rOegMscbGRqTTaXz+85/Hf//3f+OVV17Bj370I9x777341Kc+FU6ira2+Ij548GDpGoMHDwallMtIJcrNN9+MqVOncv+df/75ymMvvfRSfoNGidfJbo3Se+655wIAvvCFL3CLQ09XN2bNmg3KpEBjU5l3tnUoNT6LUFQhUvLu+fc96O5U88ZF8YjBFc8FUPBOagy3grK4dcvm2Ove8MALyu0uTPzxj7/1OdlKQ0wxCYQTPwHxom/QtGNrdHT4Q0aeAiWUUooN6zYo21Vq+nonhvsfN68aAswoI2LMTgUiNgPncsdv2rgZy5ctFa5BuUmyyRoV/d7hpxTW1RHT1cFSIWJSfI0ifX13V3e0RaImWgwVhpc77rkH6XSaR8QUEI6PdpD4Y4ieSEqJyyFt9vo30JOOp/OK1+ru6gzniKL1jgA5a6Kpj6GIk2B6eOapZzRHBHFQ0ZagT5977rlFk3UEiMrIZICImWht3oVsOgpI90ID3b/W0mV+uuhN69ZF1ymWNZHKiBgIgQkHHxrzNjo63pXnYIUEjrcrrrgCvemouK1LCQgokrU7pHMCQ+zOO+/Ek08+WfQeqtGtL+gs3Iv5EPMOORKvTDkRjmFg5syZmD17NtrYDIpcPZNg3uorIiaOhdIUxmuvvRZAtD6pJC5GLE5s1w2vKxras2fPxssvvxzdg/Gyb9i8GevXr0d3Wu90C66rTp3iy09//lO/vcE9NEPAZdopyqzZb+D2229X7GHQvEIR+S17mDiMEoC6NPYb/P2vNwCInnvFyhXK4zZs3oIf/OAHWnoLO2Wxesa5554LwzC0NPzWlrZwXH7hC/8t7X/gvnvxzjvv4IEHHuSMJ1dY7x9+5GH+xJj5b2PXHL49nu23U5FV0FaUKBDFgolcPupTvd29KJ3D5IW/WFm2bBkeeOABPPDAA1i2bJlwjjDPEwKiKLTYKdSStEjgDIve3b3/vgsAcNWPfhRuc4iF+++/X7petoKnuCtRScOFnbfxhS98AQCwo0meLwFgzdLluPbaa7kYsddee1k6TlV0Xpe+PpftAajJfVvbo7jiiivQ2NiobMeBJAe0ITZr1iwQQkr6b/HixQB8FCebzeLaa6/FNddcg9NPPx0/+tGP8Pvf/x5vvfUWXn31Ve4ecR6buH3f+ta3sHz5cu4/1UJMQXDXXXdyiSZoiaF5gXGlS539zDO+AvXII49wfNqKVApnnHEGKDNAWcpAbU2tklrhWxHR9gu/eCFqqmtKayshEjXRch09N7Gw/fBJEwsXkA9JmTl88VOncfcIxIWJq666EhQuZ1RJN2DbyKavZ5S3wyaMjjnLFwcW57WdetRU5XFsHElc+npVcHDYzhj9VkpKIRli0aIQLIoeo8m6gkE0bux4nHji8cL9Xc4QY2XUyCBZh9oQy+lQEuX1+KfhHGCF8VJXxyC0UrIOPTXxhPefgqqqqqJZE13Kxx/QQnFk/j4kJn09r0QTmkFdRfxCLF4/YRi45557AACVJRS2FJVoQkRDLA4RiySoI/apsz+tOdh/XyYIsqgo0HP99/nMM88UTfKSL8Sb1CUCapyFIfWDUFcTzSmisn/EkUcCAI6eckR0DPNxTcZznkvI8SyEQYEPm7wFXzn6QSxY+N+4445/xLYVAFKpFADglltuQX1DRLENqYlK2pW/7atf/arWEceJEsTROav492sWTt40ZBQWTTgC60eMw6Jxh+NDH/oQTjvtNIwYOTK6jUI58xjDROVtrqvjqWmik4f1MplmHiJCGOz93e9+B6CwPumezcpwf5eKiPV094br3sQRtdy+0047DR8/82PRdRhEbPS48Zg0aRJqB6mp02F7AYyMCRv4yc9+UkAWg2P0KKV/PflpTv7Aqfj6176mvQfgI2IeIRg3ZnTscTqhAOB64TOp5LvfvAJA9NxHHnW08rhR48fhxhtv1N9LoWcQEDzzzDMwYxIX1dUOCue9+x94EK0NDdhw6MSw33/+s+fj/e9/Py688EK+oLPwzj//+S9o78HKse3zUVGZ5A0x1/bbWZhjPIa6lyXFmTIWNZGqiuahilRF6YiYRj845phjcNFFF+Giiy7CMcccI54UNd0wtdTEB0/+hPI0Fsn64kX/AwC47rrrwm0OMfHFL35R1VjuL6qgBVPiwjJMPPLIIwCAMePHKq4DTJpwKH73u99xBZ1PP+3D0nFqQyyaw2wG3a6tToB6Fre25inFLbfcgjFjxijbcSDJAZ2s44gjjsBtt91W0rHjx48HAAwZMgTr1q3DWWedxe0/++yzceWVV4bp6ocM8WvCBMgYK21tbSCEoL6+Xnu/4cOHY/jw4SW1zXUcmJ4HJwgCJpbaEBJEtzQVO9NzHQCU91SxCqOjTvFA4aFiyAa8/4gXsW3rVLj0YxI1UY8M+Moq4Y7Vp5IIqYkxgdFDK1tBMZHZwhpiQdZEdfp6cbL2N7KIWHROwpSrN4kEA98QY5J1lERNjKEfxsDmfUHEJK+1F6ENwTdnvVd1tJM/3KNyZiTiaQ2xKEZMbTBkdVmWFFNNpkdAo7k0wEGwO7MAC33FRgK0QHsT71pR11BoJ2OIKWhJG3sGo5ahoHjEki4WV2RZLJrtEQPwSki4wV6fKVJ7eG8jFtZPjj1eih0ywVViLjVGLEzWoY1n8T/IsrrJuB9X4HS8hi+R+8K9xRCxwBALlDMPJuDmhBgxMX29HCPGBqInvAghy4eGGBOXEIxRQjD5iC3h9mxW7Z3lhLKGRtRXXBog+8w7DkjCLOpdCphpUHiCkeQUixEr/GNQX4V8cerJ4TE7Bw0uSk0M5jKPS9Yhz5nHHvcSgMujdklOHv+iNTWtOPa4F9Db0wDggnC/S/g6dYDayKQANhK+j/NZE/UvcuXy1fjQ+acVrq09rHAhRmkLDjaLx4gtqQd+ib/io3gJZ+M5ruV5z/ZLVIRb1FKMmlis7YQEWROLozIAYHge56iiQJ+zJuree7GsidRDOHdTIXlNnCHGUhNzuRxeOcs3HvKpWhyNRixZOB/vO9dvF5eZVszsLLZHc7+heWA7FTgbYdbEABEzECSKEud2VbIagxqgDDdQFeumE0q8gr7QB2oiozesHH0oPllI1lFMAj1Blb6eSxZl6JJ18KKmJooxYpr1ISf3aTU1Ub4Hl4yD0VEsItMs30vJOg5oQ2zUqFH4+te/3qdzpk2bhrlz50rbg84WBGVOnDgRlZWVEvQL+NDwpEmTUFFRevHiOHFtm0NKlDQthTiM4VBUWLTdcf3FgjmNnVSo608AGVQghRyCqBhKKAZPewI5pDBp8jvI57+hCDpWd24VIrZg8nH4ZOM87fFAfEFnxzO1gc0OLFDP8ZN1eKrJXoGIMZx51nizigQYB/cLlS5Klck6KCUCNTGmaPMezhEi0UP2WrM0O9lIEbM/UY/CsoRMitAbYgFSxVL+PBDswkiMRBNyOiOgcL1MPoHKpEa54Kx4OfuWkppYMAjFKTtdiLti652pxp0nLupKaiKUyW38BvIf0gVBcdYXf8/q+gblriqqoTgKTTRM3vhSFb4OhFuoAi+wSzXcCH/jfWN9BWkmPo5LyD3RXjFtvvDtA0MsyN7pUQtwu5UxYsF4CMZ7Qkpf7x+QpAgx37wiO2WYqZQYnMHjlYA0ctdhFMiAFqVCmXiUrPigJiYFo0vghDX1cN96C7jwDOlYkbhkQI5f9IgRJetgakOaXNZEOVmHSskRRUrWUejrR0x5E4bhobaOd2I6CkNMJevBG2EUpORaZV0ctVBxDhcjxhhiwVpagiH23fdVI0/qcB++irMpY4gRF7ZrKxVyUeKyJuZt3boQXdMIknUIhphjGOhJVaE+w9ebMqjH0fd8RKzIOxWSdajqiAJR1kTt1Wg0PsVkHaokGIGw8X4duyMK+vJjpuACPI/xYyM0UK4j1vcFlAJwqSMhYgBjiLHJLMT1m3gckwbwETHKWEKe0xdDrLT4P5PacAvJs3oFfYwQo6RkHUHmY/b5qMJhmCelZfz1NMk6wD6/pf72niIsg6oMMYWziKUm5plYaRM2RGdZXwqR7285oKmJeyKf/exnAQAzZszgtj///PMAgFNO8bPfWJaFc889F9OnT0d3dzQJbN26FTNnzsQFF1yA/hLPcWBwA6g0QyzKVqT2+egoLa4q0xKbtMClaKmuwbdxB36B34VX94iL3+EX+BbuxGYcgq3vdkqB/KxXldtOiB8jxhy+dtwkrB0yQXl84FNMFlAO1VSSMBw/sFkhDix4NO8vigpqohTQC99ADe7FGWKGjIiJE/0qHI1bpnwRC8YfDkopTGXKOFPImhgzEezhJFEMEas0I5pElDWRmZzAT7Se68ESUUlC9YZYQT10Gc/nHbgcV5Gb8BzOwyd708qzIiOIaTyh8FzmPpwWFxi9DK1SeOcurNDYFFPdZm1FQWeF8TwomeO+tEcsebTFxIiJ4hkEpEj0tLiXTdbB9nYlqguACs4LItTZKVUCEM3TeQ4V9LUoYQ2VMp6KBuBw+FQ3HhGzuUXHFWLE1HXEouOTjHIZh4iBEI4y5YlKp/RkekTAo6RgcCkMsT4m6+D6BgWmbRgE0plTHqvOmigbv4HSbzIJRljlLJjDi1ETRXGEuSV4P6apNmptlTahmOdsRYp0DhGL6coOi7BIWRqo8KeippCpKa/BSF6D4hDDRT5McFGiIaYQW5sJkTC/CunrbTssyA0ATx73ETz0/jOxcego7kxDUNA9QktIX8+PCd3RdmwtUQMcuUMwLkzT1CajyDKOUTZLf/D9Mz1+fDqlPN3aFdRWSim37urmQps4cEWHTJC+Pqy1x1ybYX0QUGVdMYtaHPysT3ykkOBdFZm6LQ7JFthRhllSsg6voHOyz+d5ASIWHecjYqoLlkJNdIQ6Ypr1Kxtka4yO9RRF7VVGPPsuWETMDBExhpr4HkLEDjpD7BOf+ATOPfdc/OpXv8JvfvMbvPLKK/jDH/6Aa6+9Fueccw4+9KEPhcf+8pe/RDqdxjnnnIMZM2bgiSeewKc//WkMHToUP/zhD/unQYTAsW2upg2FJXnUVF0m9ESJzLEgHTQzQfJgglsYTMzkxFIGXA+zjpqCHKnABnI4GuFzebsqPawkxyBHKvB3/BBtG7MSNVFEvQKpa9gpZ00EsLlew3EvNDiOmmgZDmyHRTRYaqJfR8zfoagPJcb5kMgQA8AZbwm2YK2mLbeQ76EjOQjzDz2qcI7sOaJI8HXE4gLQYxa3ojFizHsQETEuA6ICkROP9xR1xCg8EKg9Y4S64TGBzCIfBwA8SC5BdRGaGy+USz3Oe2SLI2I2EtLCH0imoAix9c5UaJfrEe6Fqwo6+0hPaYaOS4wSDDGhbzJaCIdiQ+P5FZpiGBRDKtR8fPnekYSKs1ZhUxhihT7teFRaaF3TREU2Su4T1q4JsyZagGfDZLOMBZSZsCn+LzZrIrvmM4lgQ0SMfWchNdEg8JhkRSrqS6ww87VHia8AqBS8GNRbJWyq65DepulaVDAIDBDpWM9gEDHmeBYRC2jQbDIlXbIOgzFeJNpzOJ9pkBMVFUlJTVQhi6Ul63DZbJZiO8Q1j1m7nD4YYtwl2TaaDmzb5g12jdIfa4g5+aLKboCIweYLxLfU1gMAXjr6ZO5MQ7ieA8pTE1Vp+12Rmqh+lvjCuAafiS9InlR4L36yDrXkHNaAYQ1x/35Ozo8j9A0xdizLulOdzcccqoXIiFhATTRZamJhl0CzFKmKAGDBEBAxL5Zay19P52jnhS1izB46orMVhFjKZB2ihLVAqTwnsve3tUwY0cmhmD8Ml0NhddRELx/obez1VHXE4pN15Jg+TeBKBv97qaDzQWeIAcDDDz+MK6+8ErfeeivOPvts/POf/8QPfvADPPbYY9xxU6ZMwaxZs5BIJPC5z30OX/7ylzFp0iS8/vrrGDZsWL+0xUdiHG6iJEr4V+60AbQqDtTKSl/ZYSd7ts+5juOfwSp1AiLmMkptBpUAPM6r1IOawj1E6pW6cx8+5W2lkeZqqAmBURVQAlRD1jfEYqiJ1AEBUSJi4qJiG6zCSwCWzqiKd4qN76LKGDHREIutI6b5tlQxLWeZVyjObY5gMHGJJwoLSRyV0fOohNxRQrmMRqxEi5jO66jxginriIH/DiY7MwfUniLURI0inHUUiFihDWyiFIfyhpenMNZ09cdU4hGjhMQDggHD0I8Ef6e6DwkGkGECCSOlaZH+1k4YoK57Gvm5g/bYricttK5pwvRYRd5/3zwilhcQMeGbBoaYRE30JcG0NR9S8aJtLDUxFhFTdVPWq878dguGmOpTcNTEPUDECluVx1LBSWUqvodLDLy48UW0Zdt4RIxVzsJ2M+9Js/RzWcmkGLH4B7SL64NalJcdM3FEAg4Vl+wwT/sNQ+94CenrWWEZBMSw/TpmNGqjrqlRjBiVjrJdGQEVFVsDZoiIlVIOQ6yZ5xHKUxNVBqiEEqufJhe8O9WnMwyfux1cQ4WIaa6bZw0xzrj1/xk+dHBhHzhqoshfoRQYzNUS1XdET4wRC6iJASLGUveE2nYqRMykJk/TL0YHZSV8V/Hn8AkqovYlXQfE6Bs1kRXqyTFiqoRW/rEd/N+6GDF2LdFQE2kYI8YiYjJNUoXa8zFijO6jqHWXj0VyDyw5oGPE9lQqKyvxhz/8AX/4wx+KHnviiSfilVdeGbjGEALXsWFynUJhiBEieR1CjquIiBW6m7ZoJKWyB4Px7lDH49rjwoJh8DXDAm+oI9QxcTUIhAdDStYB6DO4hd5y9RMAAJKGDcdlPWXshG+EcLYqWYdkiBEgFVATCbgi0EScnIu0zIOuoHMCBuN5j0tfr82UBjnFSY9FUJH3jzfAP5sUI6ZCLJn3xlETiQfPpX4OE/aWxINuaiC2T+PVxUjYukUhMMSYx05VdIMyRjTnLIDCEDMNGK4Lr+AhdpAAJWpPaM4NDDFmkSx8M8Mw4Rb6v0t52iEtpI2Wmi8aTzA5mkS4nRSPdxH3OkyyDi7reMEQo0L/FmM5iOGVVn9McW8A2lgSVYKVAOW1HSoVdHZNEwaLQgSGGFtHzHVgcc8YBJHz1MSEjpooImKCbsRmTeSyuQmImCoLJjckWeWEkoICwBrYBQcS5wgoroQZpuK+IspFCAxKQ8cDX0dM6IeGgUR7Aje8ewM+ao6L7sO8J7fEYqmAr/QFb0oXI9aLGtyOyzgFESiNmijPV4VLs84LAmytH463J07F8VvXglAvdIZ4cckfbA9sOdr+QMQW4iScgjkA/Hp4eaePMWJ+S7h9tptXdBX+vYQFnR0HUMRCiiJSE3uHrAbNnhBtMC05L7+AiOlWqziFlhCemhhmTaSBo9XQlkLLMWu7eAsKYNOGtZgGyHXExAFDZURQJ54wjoOEJXyyjoJwY5uoETFqIseENniuVzK6ERp6faAmslmJTcMGIaVRE8NsySwbQYGI+QcrPpiXAVDJbFDNY6UhYjSs08c6TVSImHw+S81kY8R8RJVXZMoxYmUJhRIC13E4aiIhisVA0emiIFlhMXN873cc/QGUosKsZq7PHOtSbuJ2YRYUGMYLHHKKBUQsxhBTImI6Qyx43pjBYhkO5zWT7kltQKGcAMURMcLVHipOTWTFJQYSiqBvSq0+FHTWUFoUBM8so3sYhHCnijFfKn48u4VVhIjhKAtQ+tREtcJkZNuU28Pr9wERGzl+CWdksDEdESIWbfIMAwmX99jrELHAEDO5ApiFdOyMV9H2DLBviBoWVN9G/JQZblFimk0I+JWxeN/Ms8g2R000IoSHkd6UkL7eUpGC1aI6ztNQE1UJSgJEJ+/K93RNE7UZ2RAL5h4XPjWRXZ9F+ltQ0NnUImLRsWH6embQGQw1ka2j6Hqi91/1zOq36FKjoAwp+kVALyoxHoGYKjq5aFwVjNOgnxZ2E6pO1lHtVGNd+zotNdH15LimOEMsENlo8u/+SOJ/MId8BG+QM7i9TgnURBHBD67K0rgJBZ6fdiraq+vw2pHv45gaLCJW18nPRY5t8/djY8QK34cafTPEuhGlyA+piVzWxHhqIhWdXAAcFSIGft4xEGVNJEG9vJh2GgIi5lrpPaAmqiVAxJTPStTURLNg0BgxhjNLTRR1GRvAtKOODNvFxYgJaIxHbY5uGOeUonCFZB18QWfOEBN1HU2MGI+I6e8ttaXUZB0sSs2sXXV1zTCIBauEy5SKiPkHl0DjVg11o7QYMS8nG4BUFSNWhInCGmKgPrNHFyPWMIrRhQ9AKRtiAywUfnpqg4vnSsgxYkpDLOD2y6oyoI8RC444fshHow1sQKnrwWQXN1gwDJcbGL6C5EkBvzpqogdTLugMOcFC2IbAA6VpPwAkTJuPEZMCRvVpwiVll1AhRowxxBTemLhFzyVE6RGl4FO/xsWIaRdwUOllsF4wccBKhphKWTSjSc7hqDYOPFuRSplQ6BAxm6YKh2gQMe2UUkBHhK1cjBgHbxQUGWYy9QwDlssrirrFLFjk2cmcRcTCu1C+bphHZGUXCNCISBbjROV9fUSstMQDUVs16cs11MTOSsF7bsQVimCvpzHEtEaEXomyXZ4GBvjfh1WaHQ01kb2qmKwjSF/PzUVMl2IBpShrIuuFVVMTxYXeKxbHxyJiIEjAiU2I4rk0zqfEtE9xL/FaZmCICRRUyElZAtTMFp6PpSaqkkPoqYn6GLEAiZ1rnKo8V0VNFJU8LSIWE2tncIZY1G7L4Z/ZH/Os5yYaO+Fa6tVy5xT7ZPVoZ9rowHYccHXENF2Cz5ooOlJlpVM0xEihniF1nJKoiSIi5FGDh5lKoCbqCsHHxdoQYZwFxkrCY2jgmnfEImI9rXwNuxb3PCxa4GdcpsI7FOmtrpsHYfqtllZPFdREERHjqIkMi0iDiCWoydPfFU4tnZssYinF90KWjkcZB7BBPBBicCwBnQSUQ9YI9Wjw/QX2lNAvSPEm+ucRF2DXEo0hltugeEcFdM5m53KV8cgiXqxzV0FNfC+lry8bYgMslBApfT1UMWIqQ0zTkUIYnJ1oFYfWJYeo2+TwiJgD06c3sfUwiOUvjmKSIXFD0BYYSiMtrrgsEAuIxWZNBBSTKntdoWtnDcEQY6mJxO4TNVEb94YEt+jExojpDDEFApJlvOhi1kTREFO1mxjRwm8LhpirQBw94oEwSthQujvapwrSZSTVeqxye4CISQ4DlprIUZ0KihPrvDRMmIwhZjNZE0XJeTIiFhhiFuOldYTnoSrPMQDD5VuegzomyyWm8N1V31m4liYhjZjWOzBOnAS/QBHLK2km1/Vofb0h/UVtFSJm8bTOiJrIpq/nETGRyBOkr2evzSJi7PXzcQWdCfEz9ATnCdREVapuzmgQk3UQR6tQAYBbLENd0D4WEdNY6Y4RGLAC8qm6b+GYvKA8mZwhli+c3zdETKImFp7fVjE6wNSlk0+JjtEh7VzCJMEAZY17FvWzeGQpl3e4BYWt1Rhk1Y5DxILvPy4dlY1g3wcxbNiu0ydqIlW4P3TURHaTn6wDXIxYnFppCtw+SgxQNpW4Km1/CZlEAR5Z2A4+KRAhBndiMNatwvrhJ+tQXznHzDtiUrAe+xIcP+0Y5XlSHTEvx729wSl1grCUzRtX/sXsQjv9a3paaqIawbKoBddgDTH5e6sc1ABADTlWSiVsH8xzugcFiMmxBHTiqrIBe5rsnYLjxgDiFbXwQJdz3BNNIjan1d/ek4+ylQfURPbO6jkq+vY5ZixHY71MTSyLQigx/ILOHHUgIY89lSEWXEPzlYpRE7XiUo7K4MGEQTxYSb5uESEeiFAXQ4eIrcPhhQlH8P5JRktB0RKeV4mIGTZPTRSp4SE1UaFUCX/njQjdIyAgLuNZUmVUi4nzcQ1D5lUDkHj+sZUWI3ILd20iXzlnsulr+TPkZB0xt4JsiDmOJymYrgGOMphgggtyReqMVLQOUu/QBgFrELHCxCwiYgmHpSYmAMYQI8yxgROD9fwHyTrYtLgizUUnYmjPUDQrj2sxa0GYhVela0t9k0sMwSPAqkK30tgxKEpx/gkJIqPrak/We+JtBQLkGib4+MUCpaqg/MQhYsGlghgxLvmQ5hPlFIZYSE0khGWmSdREVYwYp1By38SnJrIok+hI8RxakteYTdYR2mQiylUwNjwpm6ksnmEgY2aQ9/hMfCzg56kQMY2DLI6auGfJOkRELKF0QhkxmWs5pyFLdRO+Yc7m1x+qiI1TUvSE41PM/fKMw4WYDuyMg8b1jVyCCpXErc2um4f4XiiVKdEhNTFExPT3FKmJHjV4B4sSESutoHPOC8YU8CAuEW4sOLIKxopVmNfjCjrnmHfkSbHoHtauXhG2jH1yERHzqM3FDlUl1GsQoVBQE4MMfsG8wxpiQtZEpSFmciV9qILqr0fE+pjJFYAtlS4xSjLEANkpriroDEDqF35MPX+IyrimxOEDDYuQNJ5a/1TUlsL8zBliRdzjHCLmyQYwl77+ADfKyobYAItrWnDsvEBNVGTcUyyMQf0OXR+KpSYqsjWF+1yPm7hdmD41UbgIETi/AOBpjItbyXfgEQpDmKzELDxBzEugTEpZAxnFPGE4XNFHSfnxbO1gFxGxHKEcbYDNtFjF0HpKYJLBJUSZbIMiIXg14xAx9dBziTxt55h3LtKT5BixeKNUQsQU1ETH5JXsbtSFv6cPOic4W9l+qjO4FHVMHDvJIWJgF7TAYGcpYqYBS4wRY/ob26fzYawRY+gVKIls6m5HiBErHCC13xDqpnga2t6IURs4NKWUGLEcl/2UUSQJHyMWOEHERZCYpSXr0PVGfaYvPXpgux6HOAB+jJjDvJagr9FCMLufrENExARqYhiPwlyXefXs9ih9PUNnCamJJJaaSBVjk5uLhGQdCbha6hbgI2Jet54qHQibrCMw7MS375gBIibvF41wFhFj288jYnsWIyanry9iiGmC81lRI2KkCCIWfas1Qxu0187ZLo+IqZJMxDxCgJomOUMsGgPEsJFb24t3XnwHyfQg5eWCMzlqovBaXE9GxERqIlBwnDg2SCLoD/q2i4YYhQGuLEUJiJjOH8MqtFwqdUDi2gbzsVlw5Bkx5WlsFhETmBk2XBw2wU8+Q6lIqeOv6bpZbr8qthUIqJeCIRbESQXt4OZ5gZpoyv3JoiY3D3oKhomu5E9oiBVN7sTEPXFjjIAQA4kSbQxXoImGMWJiZ1RRE0vwMlEBEdNJYrhfa3RDx7ro3BARY/paTDFwQI4RAxHeFS3elgNFyobYAItrmFKyDhA5Rkyl/EUMYglU9vc7Gmi5iFCXgngRrcOBpaTREcOVDDFXU9DZ3+dJk4pI4xPvIyaGpEyB34TBx4ipEDECKCcycUtHEkxGH8JREy2aV1wiDhEzld5DSgVELC5Zh+byLqi04OZYaqJwqsqAjxNesXB9Q0xsg0k5JaaHRHEVm5Pj/XO1d9DsUSTryPYOBrg6Yiwipo4R4xExnprIUpiCmBAuRixsQ3ScI8SI8XsjqcoL3jadkUIUHkbVQey1YuAstg+FFBfhkxPT01JdWfGIptsJ93fCS+nRAz9GTLiMaXDzWPiOSBBx6lMTWZKMmL7eVbwLl/ECqwwxViJqosHa9VJ6ZE81Nvm0idGxlMCEqx9rxIPreOh+fbt6P3uowhCTELEgWYe0JijejWHApKZPTWR2G1yyDlWMWF+TdchsB1FKyZpoa6mJ0fUlRIy5xoIxw9mLc8elbZfbJjoKVOdw8aEFxZQrGs7Ol6YDr61Qd8oJtovfzn8JLCImOk5cNy8bcFTlnPURMVjxLAQAMEWHKfUHezh/Kg2xUrMmMga+cJRBTP4VFObjABETkVfCOcui67ZnWrnjHLhobS4UhAd/C1EDiWKdCvfQOEwylgFKhOT3AiLGGnlUcDqraPAWtbiEZn7yK+FbFN5ZIj2c326WhohxcVFMfLMHAhgmEm7xuR8IDFF2XrOxffUKvHrPnfyBCmqipB+pdC7igtrx4TIAJBqqv82/J7tH5SziDC2GnaMCHtj+dYADYmVDbKDFNU04thiDVFqMWEBZEmk0wQJtM/WHJA9bLDXRg0F4hdZPgc1PPMR0QSlFV6oKK0YdgpyV0PKdATUXWszWE3j4A5hcMjG5Qst8jJgSEdNSE/mu/a/Da8JRLnp4DFLck82Kaxia9yvEiJVETeTFViFirPJG1M8bXTZ+UrZJKjybGA4c25MD6g3K3YIoFRoNIqZFymQjyDBtITW7K/3mEDHD4GLERESMVUackJrIImKyslMqNfH9a/nnkmPzCvcowagTt3FxkCLtkBmTgWdVKsZbYvp6V0NNFAPMA4WaaIxNk7gFRIzfHiA5gQRKbKZ+PQyCsKCzGhErXKMYIsbSbAMFlUXENNREz+PjiVyzdCdWFCOmltoxC+A5FPbudNFr8dREHSKmpyZK44sQmNREj93DjRUWEXMKSg6nL2sQMYMzxKJjDMX6IIqj6oN9SF8fIJtiXSQx/ik6if8zLSJiqnkrZl2kBYWerVVnc44rRVInoQ3BusZlTRTEcW3po9u0TjouLOhcAjVRnKPDywdjW0XJFFBinYs1x1xbQsQMPo7VK6A8FtQUUvZ8m5l3OrMtoAB6khUAAIe4qK70aaFSsg4xa6KbKzl9PSEuX15EQMS461qRXkBBpJgxoEBNZBExBbvALdxPrHcaIGLFWs4bYuyzE4AYJRtirmFyPYh6eTz6q5+gtWmHcKBATQRKs2QMV0I2VaI6JjCmuYIBSl2GfRdisg4+aVU5RqwsobimBc8Wi92UZoi5VFZKgGiy5xExyRLTQt7Uoaiq62LOJDCInDidEA/UpXjm2A/ijcOPw6tTTtTC7IA6fkxMXx95rKNm8s2Ojk+Ydmz6ekr1E5mosKwZlJSy0QZiIq9OqaCBth1dJkipFsweJOtQcFmyBruQ6bNb+dctPvkERgQx/WQdsiIIbnGRFl9Am2hARwuBIlkHsfJ8tmkUR8R4amJCQMSiNu/q8RVvPlkHH48E+NRE2XiSn80SFjtbY6SoFOVikouhMrKUrfBLFeIIA0W5VGpir6UJmxe86fngEyrmKQogYeSRdxSeX5OneYbviFCYKFDd3Lwyfb00DzC/XQ3lLZcMvgEzPlhqIpe+Psud66qKuLP3F2LETOIiS7PKYwcdOgeu64FoCpiyokTERAeTyfdTjsateBVm6MxQXTsyxEqhJrKXZ+NP/X7oX38EbVKeq6QmKtPXa+aOoAaVuF2jUImbM1LWxL4xRgJELM04e0RqIiu7ahvw0lHv57a5IiKmWIfFeEUA6HEulqy6MEbMlOctWURaY6HPBIZBKck6Mur+zVMT+TWtbkw7T3croDxmgeLHpvoHeESNTUbmuR5em3Ii7vvAJ7Fq5AQ48EKDQMwJK8eI5bl3rJ0Lg+lT9SapjMa6lS38fZRZEy1urVTF20Y6k+BQ1yTr8KTYPWYeYwwxr2CIiYmkdCLqYh61lfGjIiJGCk00TVtqqyg8IqY51pHfdTD22Ccvlr6eTdYB6vmleNn1h0PEDmyjrGyIDbC4hgnXdfjuSyyJmqiMEQsMsVQXtz1IgMEjYqIyKStK4T7XQ6qa9d4StcfTcEBdgu5KvwbD1iEj4cYYF+r09YIhJmYiihnYCcPhvGbS/Tz1ROZvkSenyBDjUSULCkSMUKlgaSDt1bVqamIfYsQYMia3VWXo5kVELI5TLnj6feVNbUQEMWKiuAblJtH34Z3w90mZBfp7A1rDgxYUbovzsjtwragfEs5SLiCnQg2huPT1Bkt1qfH7rJqaGEmpiJiEYmkMsYASpDvP38a/oxwzDYt0NM4QIwGaDPwSv8V3cStaMaSAZhdfaO4dr0CgUUg0wYgdtkFliBlImnYBERP6rmGCXV7Zd2Qajo9+ug5XdDegJrLvhFLKG0K6GDErAQjOktDbTQwueYvn8oW/VUoV/6AsJdSPEWPvzbaXGA48h5ZmiKmSdQjiFjy9CgJPCW4WX7iCzsE8yXQtUxvLGQkXT0oi2vl4ull5roqaKMZpqRCxsGC1ERhiojMj+lmdt8NrirXgesWsiSolM0YCx55bHdHk2OyohjC3Pnn8hyNUtiCBky7KmihLp71F2uNgKlTGFGWdraXp2wCAJbX1/v0ZOr4kIjXRUTNDwhpsRP5+Vsrhpnxq+NewPNk5APBOPZYJ4Hke1o3wY8JmH3E8HLhw89GYZY0nMWui5+V4pKcvLypGxLlYV9CZcxYqELFQLxKZTRpDzM3w+p7+eQhgGFL8sm6OEBF2T1G7C5D7ASFAVW0nTj7lURx77IsxdxAMMd0xwTtimRGFtrCzrIo+rUtfr2pRXNmFA03KhtgAi2tacPN58BNhienrA2qiwQ+MhUOnAeARMfF0MQsR3ygedfHgZ2eTqIlEESMW1gSS2+unPRduJaBHYVYz4UhGDQt/WYaNPDuwRYPCzcmLdngVfvtZ27ujCUugaLHUxMg8olz9DlbenHyscrucNVH/DVTvDwBUSbKzrCGGeJ8USQiLKfEkhSXPGGKO7UlB2n6MGGuoRu8hpTJaGUlkNfsL6ApHjzJcrs+x6etpSE2MjveEwG8bCY4uwtJTdowcKTUhpCay6bCpKlmHqv2iMaumJrqusL0IvQIAXj90KrNLUFwY5S9YpNYNGYF1ZAq6SD3+ja+CWB6SI9UoBStdiiY7BPCExTMfDhPZ2PRgIGHYXDHWsH1ChjTWELMSebhI+ogYdz15sc0JKK0OEcsnEjA0qAclAmVKpCbG0oZFRIwgAVsots0YYqYN1/FALA3Sw1CS2GQdIX1QOC14j+JMUEyt0FETgzpipVAT2aPYJD2G4YVmoC7RUF5liAmN1o0b/x7+WJazpUV/D0tnYef9bykqyr3ZHNi3pCoSK79EZh0sIFVVVZ3htnwMNVHlPBUNMdVNl6Xv1XxMYU00En6MWMiMiWFCCLteGVyY/0JjR3FDh0fAqAZKyjLGtFjIPt9dyV3bMwuGGGWRbqZfstREFrEQDXaRmsiuA0L/owVDzDRtmKYcfxdKLJMkOIZ9Fv57ewoUPUkTcAVqonj/wLkqbvcM2UECAI5giInPG7XZT9xlCBk8tYnADJN/vmBOFN5LXkFNnHjSfJimi7pBzUiletWoInhDTPcdAso4ZzxTeY4ylcwjxtHHJevgneuAUP7pALfJyobYAItr+sk6WKEKzzxVZIgJ+rSOihYXI+Y6tp6aKCVoIAWlWFwIXIgZgD14eACX4HLcLV1XRU20hbic0MOvBoS4kZgwbNhsRjnh0GwwYSiTdfDPknK9cMIiAiJmKuuIAYkYo0OZNZHyMWJmbByKWgnzqJysI8sYIAYptiALsQLEkYq3ioiYuHQ4Jrj4Ct4LFRhUGqWzaIxYJKbpgI/vKU5N5NopGL7KOAF2AVcony4lJdHfxTGY1yiUfuIM5e2121aPOjS6j4DQmYyhGSTKcZj+0ItqEMMDSRQP/FZRx/IGQAWnTaRQy8/ogSBp5pHOO9J7E6mJnCEW1HwSFnkpMx+ArM23x9EgYrlEEoS66nlOzC7o8YiYK8X7UO2fLjWQhK0dd4aZ95FlXYY4xhDjEDFXRgP9thWUeelCVBmnBPhUYddjnRIMnclVUBNjinUH8iY5Pbo+iaiJOsUwqyjgKs0tMSURIkRMEOb75k0TTs7vSyLbojuX4xExQbn3YjIJA6yxzsx3QrKOYhLE94Vrs+J2SSKvHwSrpOOIlQK17T7goJF8oNMvrxHWElNNcjY/JnT3YamJU4R2GpbLUxNDREx2egECNTHmW9lwke7qLLSLR8Q8YYF03TQSVh4nvf8JnHzK44DFP5cocdREVjwrMlQpiDLBT4omuWQ4ftZA/lrRilAiIpbu5I+LczZbnmSI6canuIZ5Bbq2+OS2IpmGaRVhEARtcpyob2uhOdVGeewbiphuVvJK6jyz/rCGfuyV9r+UDbEBFtcwYedzvHKtKoipsP5jfDsAhBgx0fOY4z3A/iEFalPe5TqsVzDExPFuKAwxFx6eI+chTWqk66uoiaLXUDROQuJEiFZHDyIVdBYmkpwbJOuQRZy8sgaRIPywTazBFZwWQ030r68y/nhqohmDiOljxGRzNsco0CYhsRSVCgGVoIasQIaIWMGTL3mtTV5RYM/PaYq5hqJLOaswxAzDA2FRR46aKNcRc01TSN3Pt0Xt0GX6eaGWDvu4qvT1ylEnjM8cKlRH+Whr/Poh3S9uH4uIOeFiJZxvesXKGgEA6vOqjJwEni14Egv9jSi+NQVB0rCRybsyNTEGETMLge+ek+dmiQARY/tYRjDE8gzSxBd01iNiYlptERHzzHjFgisG7BmoICKrgbmVacPOuyAKQwQADBYRsxikOYjtED3iBbpbQGhnXBVIptRKZpg5MfibiRtxFMiQqVFytMam4Yb0V52il1HYdpKSVwIiJvbvBPPMtmmEa5vYl3uzfAyL6FjMelRtkBTELdBXWbZCHCKmkgARy+eDb0ElR0GSUKakQOEoymcNBACYSXh5VfFnlfDPemimx7+uE4OI5fm6obo5JKR4GRaS4MeRYXncshwgYgkmGy7nnGTme5sxvlzhuzjwUJPy0TdpnpGoiVmMGLsGiUQOpunAGrkCOql0KuUeLoaOBNetbA9/++iT/P0rvCSPiClixBxNcDrVZE10sqVREykISMINY8SCW+vGp2vwWLPrFsICxHEiUhNVF9P0FWraQJGEHTR0iMeLpQIsmN9ZI8VsJ/Bjpxn9gKWtH+A0xbIhNsDimhbsrBAES5JSsoPAYGEDk3MBNVEzE3OImLgvm4XkgSkMfJpzwA8DAsN0ZOOCePCEQFDdpAKo45tEEXn2MrLEGGKmDdvRQ929rluYQ4ojYlmTrQUlKLskStbBqHzScfz1Vfes4M4Rn5UXDcpJVYZYNEwtkNi3XGHx2pBn5qQ7BcqQYeX8rInifkuct1jFpKBI6SKiDQNJqnICBBnuhMMZhZgLhg8TsUQNcUyLu20QvyFSL3XiGnJyB6dQv6fKACrCa8vPlk0JdemY2BFWPBiwk/zYEiUe0RT6JmPM22GiG2GBMnVl1oFD6Ibwd621WzouY8o0ZpZiJlLYHFhImDYytiuNAdfkCzrbHCLmz4FOjk8xLqavB4Cs0Cc5Q4zdXkDEpLpqxAU1TE6/cIUYMSmmTvwkbCkEz0QF8nojxcrBzrkwajRGBpMZ1GCqryYKaQbF69qW/95E37of9atug+mZfja+grDJZfKujOybmsLsun7kO9CC9UjdBiUiJszveaSkewTPdOL7nsXw4RvlC7OImGHAycvIFQC8KWR7Fe+TVpRcYMVxAsNEZ4gVR51FRKxQ3oiTJAGytkALRBXEIy0jASebDfuiXM6APV/cUOhbAa1GiYjxWT51ekZQ0BlWBRICXd9M8Ih0gIglCxRtv8f6+7OoQDsZEt2edbIJU7hDXFAuyRkzZwusgc7dW4RVUf+RU25KRsTsdPh+2H1yP5V1n5SX4pBoZfKrYnXERNQ40y3cN8YQs5xQrwnAT9WcCgDEEubewpwojRNNrCBzJcVZvnhmLr7PASEiZgrrGPGDZMK/1c6iaH/arBC2Cw5FyseYH8hSNsQGWBzLQj7dC07FNxTe9IKyzWIqITdbMQ4p4uuI5bNpaREMYs28rM11WQ+kQBOTtBExFj70yqvEgaflDgdiFryKYSYw8XCmCQnDgcNx7fn2pb3CthJcNhmTwIDBDPToxgZRL7C6JxnTvjtEati6ah7l+fNx1MRgcpWpO/I5LDXRR8TiFHmBjmPlZA54QbkwrBxcW8YxbZMI1ETm3AJKomsBJQZMEUYFEGVN5J+XNVYNRtEJAudZcRJ80dMlOAGAP/lDbJWCHuGaKWmbQ00A9Tiz1sJH6yyfNqR4LrEIuxYRA2BXqL3BYdOEvw/dulZdfBbReAGisUdNORZRZxdXIFL4Osa+ozDEiJROmFWoxcUyhwqkzDzSeVd6ED8GIfqbR8T8b2QL54UxYswD+GhbdIytQRjzivT1gD/uRKq3iIgVS8XOik0NJKkeEXMy9XDyLsxqHdrDzDUWY4i56uU3n4jem2sI85umP1nUQp7Jxpdg+Jx2QbFilRzL0CHbAtpQ+JulEevqqSkRMWGCz8aMGwA4Yspb8jVYRN4yQ0RMnAZfr0wiTgnvdWUUl/3LzndL98szDhdiFVNQAaegPIaIGBXvAiQNIOuK8VlVksJtkkL8kS59PyvCuwhK3tBcMBerELHi5RYAIOMGJSHkqxCBmhggYkmvYIgxJzyH/+LOZXMEGQL67sAFRQpw8pIx61C+o6197QmQGL0kEArAoIZsUNnp6LnYJUSMRVM4oStokosHVCW/soM1XZOsQ5y73XyP0O44QyxCxIKra2PKLP5FhoiYcP1eBSLGJWSRzmD2GTm8sHiHZm9wkH92QjC0UiZ/X1VCIfZd9JqVsfsBJuHdAW6PlQ2xAZYtYydhvWfwho+K8qOgdGVjeo9jmBwiJqEe2SxcCPSHYODnXKHDEmVq0goDQqV5IK9JYAEAmWKZyBBDTZS2+DFibPp6ccJKF7LvqNAp0WubKXjVTZLw97C1TTR1xHQToEcMFGJLBY91BfchjFgqi0apg6xUs4aYVaSAs/gmPFNOSSwaYuL7y1sGRwnkiyjGUxOpYSrfWxAjRgHcg6/hVnwLHgyYDCJmsFQNKn8Tx7I4r3AXGQQPBmjI5Y/anGS56gUJDDF2q+sRWJgGQggShGBsQn6/FTQjxdToFEoPBvKVbDanYgQMwHTdMAGByONn41JsvhBedA8rr/WWs4ZvDhVSezImJGdLmlkfReQkhxQSMdREHSJmJAqGmJfkjHyV0iCibXm2oDNr6CXVfdE0HYkiK2ZN1CFinc1prHxrB1zOOCVIIac1dj27AnbOLWmxZxExK0DExEQwnCHGZNRk2slKUNTZ1iBiOVeeAywtIiY6bfwx4zMmCtR2LTWxOCJWzBAr1qacacDO+N/SKvq+BcddTAZeAMhnO6X7sc4Ew5KRflGkGDGFJAlFVkyUgSrpOMuw4BoEXkaNXPDnCwyQQVsA+GEI/gEqRIynJmYVzi8A6OVqefLf3rD4lTZg3aSCpEXMfefiVO5cjxljQ/N8HTUHLiitAPI9HKoGyIgYMT2YzHqhf0/EL8AsJupZ9xLTTj0ipqu0xsYmqwyxiEUkOsY1iFhWRMQ0MV8wQEyXiX2PP15M0+rHiMl09V6Fg98ooQwL4Bviv316RdAQpQTUxKRQ2y5p8mWERCegeMk0Y4jpvnmIupYNsbLccOgJvCdC5Y1UFDgODDGVMueYvCEmLniZ7i64wsRKCx4HmuepiR5IwfPO32dIQkbEbEsf5J02xSeQRaYm6o/VZWcLpNfTQ+QyIua/X9XgZqmJTMukawRimxacwoTLLUKo5FoTi4hptDolIsZSEwkpKR4ovI+Zk96QzRhinkel4Nm8RYT+FN0wS1LSNlZ2NQxVGgWBIbbIPAEvkU9hNvkYZuFjnLHKGWJQGVKm9CwZVCoRMcc0C/GFzCJpJqWx5FDCnadKfOfX2SstRoyCwE5ZSg9rJIKym0jK9OXgSEZpCBZ0saCza2ZKMPfUbU6bMobdyyFisgGaLFATRfFj+Jhrk+owOUNi7Dz/GWgFZ4iFdcTYdto81VJLTbTUNakMwwEV0DkZEeOfOjj03j/PxMx/r8a2tXycRspzuH7NZU208rBzLqT0owrhDDENIsYaYg4TH6r7xo5hwqQmFwvGom126OGOrmAQQ+rTL+DTaCJjuG2B4WSaUfKnviTrECf4HGQvdjHhShsQgq5MLtwTdz9xrPcWM8RyXdJ5vCGmHqOsiDFivvEsIGIEyDpirJ9MTTRJAo5hwEurkYvYdtT4WVRpPsY5KiBiutCC6L3Jc4VpFacm6pKGsWEHRDCuHOLCQ5VEnwQAR1gAjQTlan7qDBEKP4mIlP3w6e9qjmedIESLoltM21WOTVtjwFFTbdjLhphaRGpiIHpETL6aofBmZBR1xAhIVOA9ZprzzBxSxfppYZFICrXtKoQwimLUxIxZwdAwiRQjBrx3UtiXDbF9ID0JPp7ENVKQ6ogZQQHEaLAHhV5Viq1tWnz6emH/hnfnwaH8gAqpiXk5a6JpOnzFeQAGoVyNIwBoHDRcaksgvRoKEctZDqmJQotVQ7fCyiHv6KmJOaQKWchKR8QaUiPhLyjROYQpDxB6lRQ0jEBs04KT94Qz4HvwGNkTRMxWJetgFByriGdKQsSsrMThyQXJOgIPr1hHyjK0VLlMgVary46YS8oxIP4J/qS7i4wIN+3AGM5YlQ1XHqX1hGQdAJBGVWiIcfQJAvRkMvwLIQY8wQvnF3RmkAQfLuWOoQAgLAo6z34n6tFdE3m3leigSJ9IJJHPqpMwsDFiIS1YMAj8b1x8Ks+gUup2GYXlmbbY/paAwVBNs6hEBckinXcl/r1rWtL3eQoXAADMcb4hZtMUKBtToagjlhEMMdckcBTdzU4kCgiiSE20JURMzJpIpfT1/jWMdt9Q2L2NVwCTQsILtr2GlfXnA61PSEdNVI+hvMUjYhGNWeOUM0yYnhmmqQf89PWB3pgPqYm8WAK6/W/yVena2YLhxMeIaQwxxWaxj2QVqGwxEY/vSvvPYyhfOGMUiOntXS/W86emJvbREJOyJsr3SxGKtJCxkJBKqAyxzcPq4fUWkCvhtYkFelnJtR3m3z6OmihlTVRLjxtdQzRyTE2yjmSIiGmbGDozAcATjKsNdVWgtBLIp6UU+GKm1cFVQ7ki8dps0SCwPAuugrkT35MKbdTExyeY9riOJ32naN4WEDFNf8pLyTr06euJZYeJeYLvoKUyKoyuRBVP9wd8Cq8oW41x+CbuxI34fwiMHuU9TF30NCOFz54Q1uKUgDirETHRwZ4Kt/O4oC9hDbwDHBIrG2L7SLjq6Ioq9zTIysZM3NmwkKLciWRqojz4RETMSxSyQqVz4MmAQbIOXgxC0ZnkB8OMKadCJxlT3dnZoWkGqEc4aYgUoejvKotXpCXKHioAS0cBUSNip438PCbWHssfaaq48vrA+LxlwVVVh4doiFGhSDF7dR0iFp+swySKulfsdYVdngIRyxRoMIFiIcYIydRE5lwS79F2TVMZzE8RURPZrQajEFtCDIbd+0JYXyQ8Q1CuMqgK6ZccTZQY2LzptxDflY+KMXQ91wRlsmZaRL4HhSHFxbCGGOuNvZlcie9/7DfIJvQUTun7JlN6RIyhbtqFviR+Y6owtqN7MQYOqqR7p025PWlGz0kYKaSYOLMcKlBn9CKTd6QTHUteOJeBH2sOreAMMZWSkbFlml9G44y1TSINhyhGjDHiPf79esWSdQiSpHwyI9YYCZJ1lMJNNBPRMVYhjiuemljcaMklkhI1EYgMPVWyDsBPBlFMQkTMiJ6/b9RE9fXiRDdnBtKTtQvHKZxFVD1vAUDadSE7WaK/bbtH2sYZYonSEbEwWYeiJUPyQ5AWaIFEgRRaxMK2IXWhISY+T/Xwnait261sB/H86/WFmkg1xe17gvWBymuimRTS15t5UFCkvHg0A+ARMRFteuioyQVErBee7aHeYdR7YewOqR6GarMOD+MiPIQvavtne2qQT01U9S8qr+fypKB2TiaZ+7kKBo+t6c+uFegd/PPk853CXfVzO7GcsAwGKXa8IT9RqiYph3yIpRUI8PfEd5AmNXiXnIycQn8N22rmUAkCJ87pQSzYO3dKiFiqBGqiKHycmGxuhU6EA9sOKxti+0PiqYlRV8wpp3FfbNPEokWLQlRMWUJJSJrghQPf4ukeCJJ1CO2EJ+W09ZRF9nxJa5QG1hArTk2MNlRYfOZHUdfMogIw1XWExC1ZhlJ5SO1U7hxiZmFIk6x+5OatRFgOyeDoC7KSYWji5qL3H91nyJCtsBo2SxNjRkDE4qiJ4kKpihGLDLEggQK/INumwaXhZa/pkIRvVGmolX6iAcW+MCaFpVrx79hK5GGmXAw6tAvE9ODZ6+E5jfzzqRCxwJMm7GtveUT6jK6Z4rY51IRHWURM83Ir+BgGluaXFOrN7aoajuUTD1dfx28of61kZYiISYqOIn29iH65Zga6qZy9XlphiGVMIscIMIiYb4hFz5dFBeqNLqTzqqyJsne1C4O4v22aAlsTQ+WQSOfkMROMAbH9jqlGxLyKaq475HLbuWOKJ+sQ6GQe76iyhdghO+9yzgvdtbisiTpETBMjBqiNoNcPP06iJgKRoRcaYkLfThQrRYGogG8p1MRMWDqK9Z7xx5RiiIkOGbGP9ORcuD2ycZnK5bkbKqmJcenr17XD7eazY7Lj3LSyRRU627TggSBboCZ6rgtPSAY1MjsSPUJCBkIMuLSBaTuwaeQopCuqtMk6xh2xBMcd9yJSqR5pDJMC7XTFzG2Fubzw7dj2C9REjR0WKrOqrJ1m0pHWX8/MClkTg2fiz+3YHd1fNB46K1IhIta7vAODXH2/oWYeq+qPxtPks3iGXIAFVeq5d03DRFiuBScOEWMLHgs6ksohDvjUxLrxc5GsbQKhchdZbm4rXEODiAnXzec7APi1YD1Pdh6wbYJlh+M8arcmfb1lSPcafpycWKNVeABKgW5Sy9xXRVIt3NvMoQYE3VnZUReKYaHzySclQyslUDVVBZ3FS0aGGFFSE7sUFPoDUcqG2D4Sk1lcPMOUPMFhsg5mscxRv1up+rNTyJw265l3/dNKaEPggSGEJ3VQEAwbtkWmLcEtJewhOl4VYAM+LiBQLHXURHZrpZXlBvygSTxkn0MFiCUnGfGvL6QbV8QXRTenBaNP2qE83DVMfGr1SfjBji+F23pN4EvvOx5X1tyANPe8pSF29fU7cNTRszHiuMdhp9q5fWKyjthvXQIi1otqAJEhtqzrIW5/3jLgxWTqooaiFktwbkJXX8vwkSWuqaICTTHhY4049BONGPX+5sLNBJqGYISkUQWqQMT8vxUosSkTJ9i6NJbmOsbgw7htWc4Qk7n+bXX1hfaq0EHBEEtVhAkIxCaz6f11yTo8K4uUVS3dR5Q0qiQDRJXpLi0ZYtHYyKICg8weJWrlWpakEHainvvblhCxEBqPzsnY0jcI2ynSfkxD0Y/8OcazeI/r4CHbwt/dlUI/KDKBWh6fVVZM4uDk5CySgbAKmDJZh4KqOqxiHC6YcCWGHH0xoySqr7+rbjASXgK2p0bEbDenPF2fOTGSdMFpYzLlTXRUqcBYTs+fH24Tlc+4bKNhu6TshMJasSaFpt/MQzXhv2Euxdd0ahrG7+9x1EpZb+EZaT6L7lnbuO/Ri6heppHIcvXZVJK1EnjsxNNxy/FnYEsmh5feeAbtw+dzxxAzh95emYXR7V4U/r5/QgI3fPwMTP/kxWhraIBjykmQAiO5rq4ZEvJf+DvdkkVrYw8c4mL22BG4O38MlncU6OFOBmDGYooZH9wzeRROwZiTDLGUzaG8AOAlepF0A6s8/D9JKICmDT76I1LhXcMIY8Qyazq5fiThVGYOW1NRWvztiWHK+wFAhZuUi7mPPl5joJeiVQGjxy/E6FPuwLjTboT/VALvg1D0QtYvIsc4Lzm7E04+j/uv+QH+8dULtQiXBwOwHJhGDdda3fi0U/L2mnG7UDued8T+ZvDx3N8EOh1GcQ8zg1q4yjk8vJ6ZwMI1TRDLzlw05fESEDHBuWIWxq5SgwR2r/Xr8x3ooWJlQ2wfSVU1DzeLKeyDlNRsf9lUQLtUKnFAgVjyhqLuSiCG6IFhBz7v7QGA+kE7+TYRB57OTaaQtAZJDhR/ALBEaqI4QrhshhRJJqVp/WG8FzGLCiCpy3jIi2cQ5Az9EeMtP1A0fCukiPOTVuITnR8I/5wz1MLihlqstyZjCaKJzEqUlpFx6LAt/m0NF5n6ddy+bgbNs0h8gQDpua0sxMkpHRpihcVBCGK3LUObrAPwDTGd+DFi6oXDhAUwfHoxxsMgQN04f1EYPq1NeQ1PQF1zqFBSE4N2Txj9LlL1W8MtjlkheyYpa+j657Giep4sY2yLiBgA9FaWnpTAsRLozqifwWT6jx0EmYvIs+HCUtACRcmgCq7gdey15P7EUhOTRgVSjKGZRSVqEj3+QiuMXduypHdVBXaRp8h71cqsiex5/iLOX7vHikHEhNcROD+cQYO57UcfPSv8TQx1TJ5Okp7LKThsYWLDyiOfycVQcZi2MYZYMvBkC+3PJ1KYNvwDSI99B1UTJoOw5Qo0umHSS3J1xAAWEStsr+nkitKKMWIq6YHvCWfT1+s87sE32nrJpdHGErImin3GSgiJVYTjMwWn5RBjEETpYmyt1iG8IdayYKHUnvvwFVyOe/AyzoJrpdE7n0cIsqQyTDhjWFkQJnX6MLpLuv+6UWPRVjMIuUQSv1zXiMadW6VjzGQGHV3xNMe/TPHfU9PI8XjxrE9gzqmnSt8+mMethOxsC/BbiwCZrjw2Du6Ec5iLCR9vwku7JzEPGOkltEY/JnpDVEz4VnXd0rFuohcp1+9bVFVIrSD1FsGSV/z3o7JvV9SMx0Ube7FmtECFE5k7Vq+8USMpJwVbSJPu2Hm09MjONLGf6+pVjprol1xIVLUDybRyfe4mcl1XT5HtFwBstxstWzejeetm5DNp7Xq6hRwGYjrIjzmSu4RufFr1PUo6b9UIRWZl9pY0J7dB87rfrVyBoalFaGppj1WglqzdhTbhnY+s3s0bYopkHRLKrUhhz0rTvKbCiQe2JVY2xPaRSBOY0IEiQ0zo4UdXK6l3tmmCEBd1I5cil9sN22pA+oizMKRiHHMUfy3PiiZakZoIAEOGCYsGoaAoruAFoouL6UYEa+sW2aA1ovenIiFPkIH0ogYklYNqxCspTxrEbismoOvEw9B6lMg31k/unYIXcHNNNJS2Y3z4e+rU15Tni9d23WixcRO8h6ozEZksJlEQvYUrs+JZaZkDXvAAE3M7KM3DcgXPspVEMqZIsqcILA+vnarQ7quhprCv+OQoUS0FRMwDkd4Xu2/yhDkYc+rN4bbdw0+QjxMNMSlGjEg1ZHKkIlTQVIZYd7WvJKkRMVnaM+nC8fx2FknPIog7UVxTU2xWpCZ6QkB0V0K+1hPjknh7iI8gJ4wUKhE5cHpRjZpEBm29EQ0sb1p45MQz8OAHPw5bMAjHIvKyG1YOGVoL6kXGAJuIIpCOTF56R51JtSEmFtoGVIhKJEFMTUtNPbdd/k4CNdF1uG/DUhMBIJvpLmmtN1NRUdWKfJAxkr/31rETkTnybeycege2n3AjzESNeBlJKtwUbI/39CcdA6BAzs6g4fAO4Oz7sf2EG6L9ZnF0qqeACFlWNM/qCsZ2JBX9Ungp/vXileZi1MQgq6eKKtbl6VWal3e24s0KPivky+RsUGLgbnIZ3EQPaF5WZHs5x1W0T6Xw2gzKuCGtVrTNZA96u4unwg8kl0iiacxoaXuAiCUU/d0uvJsEIchlHOyszaALdXgbp4LWMO8z7Tu8et95B7PtM7Vt6AlqiQnfwqhQIz2VdpBEQU8VHZE0kE0XnM3Cp6zNZnDbhBPxWvIQPHhsfD91kz0lrCS+pNwk8gVErMc5G23299HdnkZjR7xjJk4XoF5knJOkbJgCvhNtrXEYHsCX0I4GAIBnRvfcjnHIFkI4XKRBuLIV+j49r+FEwLTC4vBxbe1FDaqrOqXtbAKhQFpTzDVo6X3VSGRAiI3VyxfHHjc034vunLxm6YzdaD8vHVYtt1fc31Vyz9i/UjbE9pFkhDohKWFxFYu0hueNrce8MXL2PcewMH7CUhz2vkexZOllWHPE/+IfZ3wYzSdfipQh1yQBADcRKVQiNREATEtMjOACQorjuiyPSnFtTaRAFNpIN6L4mgQT8JxNpUA2zcXRpo/ErR+ZwDetW/EALgmPqWSOl/i/qANSas+iajLqVCidAPASzsayuir84xjeEItTGNotIf6O+c1iDKlUGmpMMyISAIBjR8YQ+50AIG8aHIUsdlEQJh4n2SUdE3pSK7Jwc8uQ8HilMpNMcRkfJaqeUaVtQTqpz5lURXl6aLHi3yqhhmgkGXA1i1/wjpM1rSAFQ2X72DOQzgpGMBN0ZxEiWUMUBEjJ/b6r0K9V1MTOhlppm9guVpq75UQBAGAlou+QDWJNFMadqzE+xGQdIiLWnpRjxADgu++rwoLBJhJGiouf7MIgVCRy6EjbYcfYPGQk2moGYUfDMKQr+bmnh3HCkIp2ZLw6UJftW/IS1JG2JaOmXTN2xw+pQYWgkCeSerRhzOjVAIAl4ybxO0SEXri/6Xjcu8wLhlg+3xrjdWVQfouGKaMr8vrl985D/Oy0ubotMOorwqvoapkd3320FCM2ru0QfGX+72F0TcPgw30FLD1kVRiztH24rNyL0lv4fuw79ag6TrgrEWW3DN6jaIixa4FOilETA+eB0qFh6xX/JYcfhQtHnaO9r5tQr20BPdFIZGINC1HWZPLIm5Y0XI2KLqQ74pVbi7FM0sng+/MXCnQKS1HfzCuUQUkSINtjg3gE/8D3cRP5Id448ePRgcseBQDs+vVvsDJ5tLY9bbZPTdXR5FhxEz2otosjnwYBsr1+X3SFj5lybDw3cmL498jKEdqYRk/z3UQh1EPCS8I28/BoBTqcbyPtfhx2+nOIcu7FfV/1+DbsiPFjVMjrLeA70W5LXoznyPmFzIM+Iram8x08NaIOPyZ/wZ/wEwCAS8Qac/p3/nLdRwEAVz/MxuKp55Ve1CjnRrOib4aYfvRF+l1Pt25NLlzSSCifKsGu5cqYPP6sdXQU0o7llwMi8rvqjpkrDiQpG2IDKIduXRP+ZhUSALBEL6dlITtyvHKhvW/E8dI2xzQxYsQGAEBn9wp0VdYDAG44sgI7T/1g4SgRHWGRA9mDIsLWniFn8OtJ6qHgbCKptF16GJ59ooBwURCsnzwJpGM7TkpsQyXJYc6RFUiTGjxHzgvvyxpirs13104MAkllle9MNXm1sh5bZpDPJNHC5DCPFzd425N6z41oYFQpvFBSPSsnWmRsS0Z4OjSKqOLmnLhJ2ScUeHitShdOZhYqc4IhlkjCrZGDeAPxzFqtRpjRpa8HUOXxcQ7FnoiY8jsWCx5TEDiFyVvKGMncwWQWSNH76gqIGBEUTUoMICnz+YNEFCqDsqtej2Ko+ua8HcuUxxpMDGQ2KDyuQtkS6ngDVjxioifJO1vak3pz+PGxCSSNCs7z34U6JJM59OQcuIUX2Vmpf1YWDe8dNQfrrAYOEQu+0bDaChw7rt6/Xkb2lK4eZIKvCueLXWnhsAp+XgjmGNWTBYpIJilQw8N3qi6ySh0CllabFbKHOm5beIqf6ZJ11PDXsir95w8QMdVAeIZ8JrrXZRsK19Cj9HXeMBzuHMVtO7rpZKTcKtRlPoTaMVH/cCrasb2S4LqzPqq8FivdhbnbV7DiqYkA0JLwn9ttbwcgf68u1MWeD8gIj3iNQElUIWItzDDoa5r8YB4R2xesX8SgGHfY4j5df+YRJ0BCxEwHTntr7HmD86whpnZuhYZYIi/NCUFNsBQhyPTYaPaA5eQ4AMDcw0+HHTifZv8B2L0KuXXrcFT3Wm17mvNB+QL5mT0QtGJI+JRuIo1qu8I3wmlMYgcQdLVkkLVd7OjiEam0wK6ZMupknDL8XP8PcY1L9KIkdgUxMO/wM5EzXVBUYvkgA2trDeS901AVMH9Y+0PUkTTJOirz9eHv1Af+wjGPwucheWw1fLbSBlJIJkIoOsg2PDPKX0dWk6ORQxKukYXTGekMcf0sVwhxWfnBzzKsIvX42oTDkE/KThRDocu0JBlEToGI6VoUzL2ZTFbrmPIA2ClVNDZQnWIN2eLf9PX0FDy+9VgABJVD12E++QC3v9sCqEsPeEusbIgNoAxpaw5/d4NXVpKW7Bm0G4ZLGdF0YpsmUil/wNsCffD2qadhUq3CeEupB3fwm00MAABtRhadqWZuW1zWxI1DR6PXkJUy1gtaUdGLSZPfRqIyg6aRo8Ltg2keW4ZH9LxgkfnGh+7DiBOaMXRqmzSuu1EHJGXutfh8gTRVULQnCHaRDqQVKAYAZE98wT9fkYGHlTbBi8RmMhQXnhPf9yxWDRM9hIT/h5nkbQXCw9F+Cj/rqRxHJbY5PXQFzOoWbluQQMEwKYyEh1FN7dz+bCIFw3QZQ0agB5oNMPNdoJ7viaykkeGY0WVNBDC+XaQFxs+OiUoZCfaELHIUBG6I+on3jf4eedI9SA5bq7ytaIip0ofRlGwcRxkBFehrbW3YPula0hZgfVcLeh0XjhDXCUJw+BFzUFXVEVITlVlCNTVpxPvvErphnCHWmiKosPjx3IVBSKTyMImDfCElfFVej0Cxhthxx7+A7PgNaO1S11mrr/THf0daDvR+cEISvz66QqIQqtqfiEkznkxkUVEhj6/EMBtOVS1cqD3atpuAuFw6zLxL0V7IcOaLAb2X3qr0jzOoenyJQqttJCrF2o+8tCYJPkI/ipGVUVIZk3EoOAyk7lS04t3BpVHOAwdiIpELHR0e9GvAmxVb0Tp4MJzdPgVUrCNGicn1CQBoBl+bUqav8+8n8tbLb+TiYZ/GvaPO1bYvTjoqc9j2vj9K21kn6rhDF2LIkK2Fu/v9YSjdjWFd7dJ5ALBp2GgsGzGJ6ysAkDDjqXD1jCH23LQPYtXICdgyZAR3TJBkJGGpYsQKhpjhx4htE3TtV5KM/rFxNgBgRJ5fJ1jZnfcRcBUidhe+ge+RWzEdXwAA7Dr6LnSPexk0k+fiQcXvSAlAPYrVO7slo1oMc9hVQTC6KkCxxTmSgnLZmPXjacuwQ9BadyhWVk3El0+pxpdPrsKWKoJRVE7wUSoiZuYZ1N/Ko+uIh6VjMkRTvDnVjjXVkaG9C6OAShuNV18d0w61kHB8qo9vI0NxNfmzFKfp2fJ615IiyJjABmMnNo4SMs7GtCEwxFR1MSmA9TUGPvkxgp//4FtwBskILKuj9pgGFuB9yDPxuFKiq6pBOGX4VTiq4li0DJGT8bSmDHidpVNX95eUHgBUlj5LLVMbyCV8wCkx6zB78nHYOWgwPrl8LgZli3u0WXGYQEZxIQOAE4d+ArvIm+HfHgierT0K3rgenNjUpKQmOm6CdfqiLVmBxPC1AN5XUpu6qtSe8S4Mwos4Gy/gHHwNt2DqqGWw4GD1ug9j9JiVGDp0KwYtSGMppkT3xhBUIY0VmIrpJ30Bn8ZTktegC4PQW5NGPVT3lSejX07zkaAzVq/Bp8xd6MRRuA9f5o7518iP4tylFB2WhSxRUzwBoK1aqL1V2QZgVOHO0b4dGI378BVMnTQXNzT7E7QHoPG0zXhqWCeO2+zgw+OfRFVVpBw6ipintoIh1mi0IVDJatGNDvAJCdgF7SFcjBYMk2ixbQXvJYGvFFZm+ft5hoHb6LcwFx/E5bhJakt9ehxGt8zHlPoqNGEi2LT9mYQ+WUdD82fRas4I/y5GTSRDUgjCrxaPnYQtQ0aipU54XhB0JpvxasMG9KSO4/blkMR8vB+HYhP+OuIb6BpRj8+8uQr1RgOSrd044gMUFUPTcBYzCExFG8YeugBgEq4AQIfgkAAiaqJKmusHgSQ8jPu0yvMtv59l0y7ExDeWAfUf5LZTACNGbIRZmcFfc9egOZPAYd3RIhcUNw1qBIqynhzB/b11xGYAES2vPUmQQR5gEuoE0pIyUGXWgi143YU6pKoyuPmMH8NxEkh0fgq9qRiUnFQhTxNIwkYX6vDMkR/GciNSKANlNpHP4asP/gbjpo3GKxM/gfm75DH9zNgExvfy4+6tYRZurT8Ph2ESrsavQcAYYopuWFXdiZPe/ySOo0diMYnmtT99+jf40rrpqN2+AZ/qrcPi9A60geDU4T46/7z9NExDROTrMAS+M6RuwjtYUnUPaqYeh+HLv4IkWoHAUCcULgwsw7E4DBtw/KhTMTu7Dfn6wUjt3q6lG6ZRiSr433XC6MNx89GT0VZ5iPLY1oIuN6HmSLRgLQ75+HbkOmZj1/Ip6Bq8CW9Xfghv4HRcjLsxsrIFD00obelvL8wviUQOOdKFtz6QxC5jsPb49FEzsWbISDRdfxVGHz4Ok8+9UjpGHDe3ku/gOLoAgwpGcEpyegiGWOCt17y3/3f4Vahd/n5QqFFmnbRiKGoHr5LmrzYM4f4eOmwLWlvHhwrvsViEzrbxaK5rUF531sSTMJSeh8/g8XCbylmw1NyCZ8Yk0TJ4AnoMG+xiPPsI2bEatEs0XAHAK5SISBIg3ZkDTZzGDfH51jCsTByJXe5knPcahXl5AxYNniJdJ5CtnRtBQTGd/A+3nQJ4jZwFAJhO/hufpY8AAHYe8ShunjEBm2oG44JsxIIRz62stXDXW5swrYFnYIgOt7WHvo3hmQ0gWwhgKpzVJouyx8cZZWsm4+OHfgMAkDcJnhmTwBGb/XX57qpLkUIGX8MtCsPRw3ajFRY1MJI2YNmIXfjLUcCpVh0+xF5/9DvAen7eXWfxRq4NCwk4aBlLuBIVTRiNsXVbsctpwPZPXoWdlQRuCfW0hg+PkrbFGW4dZDBW0qNxAhaE2zxFXOVtk5L4w9EVOKqxGR9ZLzilCbToYNCve3OtuL17GV4/aRK+uDmPuw5LYXk978CZlzofn8NMbpub7MSyQQZumZTCvKFTAFyDj9MZmIzVeBQXSU4cOnQi3mpejVOdo3ANuVFqz7YKFy/+9vtIu1/QvpMDQcqI2ADKxLxeke+xDsGq0YegvboOi8b7UHVfrHY2DfJMyEG2PUJmoA2YhNsrP487jxqFF4amoaImZtL8AtmZrAURPfR7ILsxAvfjUuwmI/EwLgYADBu1GQBw2GELMGhQM3DcdukcAHgCn8dqcjTuxjek99OBeuRqe5ULctxk9MbkY0EJxdP4DDaTidy+3WQk3hiVx5PDR2jO9mXB8DFoYVCq3oaI1sFmA3wZn8QScgLuT34LTmFh7ahP4JVxx2Fp5aG498jJSFXxiofKENtR6Q/VRdYmBA/MZ6QLxL93M4bhGfIZvE0+hI2Ej4exSTKKb6pWp5mdTT6GHKnAQ7hYepf1mUMwteGDmFR7DI6v4heJ3lSllGI+FINPjxzn53scX8DfPvYjbB8yCh6AuROnoql+qAIRM+BVtOO5Q2SP5rM4HzeTH+BH5G/YTiagiwzCwsMHIzd8OJJVCVROWw0yeiuSUyIjq3Ps66iuk40nouDWtxYUIdX7664YhEFH5lE3VPaUq/pmT6V6rggMlQ11h2Dj8DF4ZEIF1tdECl8Q7wdTgR4qricqlW1JA5uqtyjv3ZoiqLJqufa2YSgoAMuyUVGRxshR67BwwhHSuSmmgHJLwVH0Fj6MJQaPigbXru1owfBt62CfnsBmBtURpUtIoDZ/iIW0mcBychx2FhwhFQr0UhTWCAP8+nhzJxyDvJlDRdLEtMGnY3jFeIytPgLjqo/Ace5JsARnGusEqR27EB7JoGv023CMNLqcCFmjAJ7E5/An8lPcgKtRPciEPWI83Oo65IaPhW4k7MLI8HfzpGOxZMQwbKtTx3V1VOfRPPEJpGodDD68AzWjMhhy5DJg2ErYlV14CBdjBZmGh/FFZCvasL42XrkLipQHTj5CKBrH1eK18ZGBPIjKfTtbZ+KQyYux1bMwd8E2dG5dKR3ThqHSthmNF8LL+dcWDTFxfO2obwUlbmw9xUZ7qn6nRtrC8cxfuBn83BK0L6T0w0NdJr7PPY7/BuDHFu7GCFRU8IipAxfvWOvx7ORD8caIFHbUFE+kEoyrRELOahdQE01C4DZ3YVnyOG5/k3UoOjMn4/+z997hdpTl+v/nnbL6Wru37PTeEwgkoffeO2IBFRQ72EVR1GM7eBTPOaLHgnrsFQQRVHrvJCENEtKTnb77XnVmfn/MrDXvlLX23iEe+Prjua5c2WvqOzNvee6n3E+i0My26FYGF+yiR1QH2Cu6HuWV2PjAdr8RuPyt1jCHOxqXsHzMdDa2dgTOAzuKpNBf5MFn11KXrE1Is7V1C90T/k7b1CKlRNAAJqRwQKMKSKhcq+V4z+/y2tpV38wzkaU8Kk7gKY4KvNOcyHNvZBl3R16gWwzw8+l5lkem8gPx/mF1t7VN9Z7f+5wxMJTxzildjEGJWKw75HR+PTHCg23DF123hMnESS9Wfg8X+uv/ZmGhifscEqTVnZODzxbyfnPEWMd0IlE7TNQqDnL32A5eaNT42KGJAAgD2CHGBraVoj38dFKEp5vd+ekf4gx+y9vYI9oCxvFtcYtN6h7ubApnWd4UL/JMahxD+eA89EaSf1kg9thjj3HmmWfS0NBAPB5n2rRpfPnLX/Ycc9VVVyGECPybObO6ZWg0kog2EyuFD9E9EbcTru2YOOprq1KIzmMcH9i/qmk3QlKI1+MWObxrwdGsxl2oyhNOItnjuUaPnhl1nH2Y7Kat4hGUQYEQZsWdrjd6w4W6sBWONcJu5z7RQgnvpGQJlf5EItQCVqvdJVVDACtEkEEPYH19lr+0VK9FArA/WscFx7gmxkLMnQhkcoO/i7Mqf2+I24xd2ZjCfeKMynZZ4QKwPHHStmxN2N9yp9JTsaALLL5c+qT3XOe5B0O9hK7sdZSLaH2BWpBot2inUPAqBT3pJBNT9ndJqd5QREOtHrakxl+o2SZZ/iQuoy+S4Yk5izFqhMNaQCSaZUNrZ2DfPQQT8zc22v1KzbjP1DPp75W/+9qfDl1U1RCrcxedThuC76870ojaEsyxHK2Ur/0KLtjZmqqr/F1eVMPuE/beAoqTgI2tQRpusKnto2rC83w5Ea94SQDSqfBcl5aSawHeUpgIwAqflxFcpSG921bqX0wGj5GlJ1J9ySp/Dz2S97D8jVTWRO0cqx5lkNbYOGKqO747Y52BYbKTDgr5oDewkOxiZ1YG7hZ3cx5geyh7kvsoafZzGMnqc+xOXNC1MT3Ms4ztYf+UP5Nd/EdSY9zoCq1ho/1MjpK9TCwiG++pfS2gId9bOa9MTLK1zdt3xrA9cN4uBwxHW+327t+8NnDMDoJjdYcxHqtnAmCHr8vifz/b9QyDjauQv2/a6vUcY0e/jm7tqg7EvM9dBlFlI4mCRXqYiBbLIb36Jp/henErq1onk5PYVkuYmEJQ1IZXvP3tikcHyVi+nEXLNsyUBPx8vM7dGa+nfVCfi+K0f38iSLHvl91WPf1qcE35Lz7q+V1+h3uld/a4fjQ38RX2CK9x0wIiJYu6Un5YEpCy0atheg9miNFKDs0ercZSBmKDUv77cywJpG8MOiUvLAHr1J08kLBD6wyh00O4N7Qse6U5G2Cv5bwfX/h92ZhUaAga1hJWeLizUWc4pGDlttZW68tGblFOlpZ0Sd0KKcUSjXt0GjOklum3+SQ3ia9xp3ohkegQUTPKuro6hpPydzcMe3wMxnfycAj43CfC9bEeJwprVTKcHMQ2DAuKkdo5ma+3/EsCsV/96lccd9xx1NXV8b//+7/89a9/5VOf+lSwZhUQj8d58sknPf9++9tgjO+BSMv+XXRmw93k+9Taiv4ca0XN/ZoEsuaxLLD/5fo+4lJ+h7+QZkmy7lZyxHxMgHtijQcltnavzwJaTn6OxyUCBV9XDFustxK0yO2NNJJT7QlKyzYR7R8HlvBMoqGTSxVmSQAr0kNjcfgihnIBa+/kZ9Hfby8cbVZXZetTDbYyrRqWZ/smbA/AEAmbWj7EC7k5GRyqAiugDJVDE/05CeBVVrYwEYBYw/DUtLtz3m+xp7F+mOCPcIlmdg5/kP/eDS0BL5gstRYdfzgwwIBDkmPJBX2FRZkNLToYbr2NRIOhf2F9tNIuIdhlTgjdFxYKUvU6Tj+Wx4cizWN5UR7X3qVxRmlMgOofoEsE27wn0hTYVpYhNaiYys+dSPbS2he0Ro7TN1b+3mpMAqCdIAFMeSEuRONYWHTQFThmpLKRSZW/08WNboZjTifzygUjvs4+YS/qcsHjukgTRcs7J2xlAkNDQWUjn9lCb8GrGLjfCdZlfHlTVfp3lwTEkmo4WC7L/oTdjmLdVk/OR9JnXAPoTg5fQ60p75630zEUNRu7Pce0sZOTjL/R3N9T8aCV+0a81Z5z9+wIFgneTtASrhGlabAMxPwKp7M+OeF2ORFnY9sWZCDWZnnnlu6OPsxqMZ9VxDVq+IAf4zy/y7nZ5b4rsKgbxiMGttdglZgPwO0t59AjbOW5aOYxMD0haiOR3bTaIFkx0X1rRskhi1mbUXh4QpDsw9Rdg6hh2muFvCb5ZTvjGAgx7GwQ0wLHAZ4i8Oui01kngsZtS0BGFTQZVoCEyS9lgKI3DCDC1mbN3RYkMa8t2+MCsFClHM+tjPf1A+FZabZouzx6xUa8XnzNAYYNpr3epPLeMbevYL83PeEF8OUxr6SDofDjCAfM2YyPXGoYKLqLDvbsGc+0V4LGg7B77EtmUCTyIcNfxxJYKRYA8EdxOYl4H2KEcLhsEB4crLf/T4dHZ1STbCRGXtMxrXBDSEnV6I0nMSNBYPtGkn85ILZ9+3be85738N73vpdf//rXnHPOOZxwwglcffXVfP7znw8crygKS5cu9fxbsGDBQWnL2N4+ZveOTGUd0r1sczNZzdusn1Q9vlkqCJ0maA1Y3aiiSIqYUSPBulpV8m2JjtA6SKMVy0eB/6qTo5Kpcxf2os/bVfYACMudAGSvXlnWMZNiwwbnWIXxT30e8din2LfPXezHERzcWyNjA1bUspRie2kLKdZZS2SLnoJFIR+nu7uDDgkoPdQ8zz5WEXRISul6prOXZq7jVq7n1oriYz+T3StW1KsEU1FBp8Si0rOB7f73CbYFO2plnXvaC0GiLTusCXGXLy+roGtslIDhSJe9SPvIxoL/et2J6lTw5WPTpeAYGGeFT+pFRcWIe9/PqxEbOKiFDGEvRJfq5bRb9rfbwkTyVCcn2VE3OXRfyXBBcnNxGEt6OX9TAtaa5X2PgyQCi+/S0vQRK3Yb4q7Cf27hHmb3uj1tRUhIyTrJOxeP93sUmLJk6KPBsq2QrzhKWFi9tTKQLkWioIQfM1KR54e6jKvImEIQ6fUqSnqV2jiDkRi7FdtA1BJz55C03hhQ7NczraJAyJKv38i+vK3UFjEoCYNOywUky9Np5MBRI+YaheZte7Xyd1n5BBhKhofepKx+py3T3bEw1u1TqbqdAcV0Rbp6iYmytGZdj+YOBzhFfCQnCibvUn7AO9bewaS99vO+zEwsINliA5Pde4KW6DDrdkdWI9Fnh4kLX+hT2Qghz+MrG0xPbt1k61XPOfvahkbtGikbxIJAbGyFGKMskYhbaFdgEi8Ob9Aqz7llWe0o4fvzOzHF6IGYIXQ2YL8z05eKYGJQNPOVmmt+Gairr/ytODUSw9aMsuwTLbyQqW54Kkv5GWtdy20jNGiCFsMib9Ye9+uYgQUoiawnB9uVYKrFSKU7qpBVVc/73yU6gteR+uVupZdxJTedwq+bZOrs+afRsoGY/9uuN21dIJr0Gh26GGPnb9cFjZYT2RjYBrC70WsM8hu1Ez4QuJEpDGbr2O948eTnHB9yj55ECg2p5IhQKa+8pqmws8ub+qA11Kaul6X83gYHHB0jtZOm/OjMvDszjQFAe57l5mPuyjRiREfuaX495F8OiP3oRz9icHCQT33qU69rO2ZYq1Fbd7Oo24vE60Pi6gHWtY0lmXT3CSzO4C9kfGChbIUxkq6iETbpvZCeyJA0ORs1eFnKA3G5L3RoR6ydfVHX5V4NuBxv/aPqtcPkSSe1tUy/DwTCDneIcWxiIs24CsHLwqVnjjoL3zMsxYiUk4EtFEvHKmTYvdf1Rswj6F18IbGATBWGtFJ6M/mkOxFeuzkYghMQqShymYSit7cVeYF4vO5ocumxmC3TPJPfMyxlDXMYFGkGRJof8IHKvjE99nce0AWPNKkkLa8SpVkq78r/ofK7YNrvIswjpmAyHbukwnMsIUeUREsOLen20WQYMHDAeERSXu9rl68fvvAdbjxFzHIXgVRrt0cllAFJISsVtPa1/eW2oCe0LJZTbyAjegL79CpK/ea2VooJb397ufNeTEwspRS6kMuL2zyWA3au3VPFo6su/MtaZlTZ526bmw1aPr33dUI3pHcifJDczmfwio5KVPLCjLc2Ba6dMe3xvCHhArHp+gvc8tK2StjK39u1wDM8xZGe9yH0IBATWMxxyBLWRafSRyYwxkEKi+7vxtKDDLDVZH538J6rmceegh120zh2V0VxMoVA75lQCX+xCM43ZVnXOpZtyj5yFJiQ8rJ6yR4ygJeZxeaBoHFoqHUFvYYNTPpFFrDI4M6dT6qLaWh0DTGlpJvz0jTYy/hue98KFlaKvOb18L5cjobYI9oqSrBdNNqWdNNmtLh3TD+kHht6LVlihnu/fMnuR4rPk1YOx2pp2cSYHnue7hP1rGYuDW27ISbY2e+2JW5U98TtHHyV7u3hjNflvjaNVyqGpGeSYzClmngzrDWec0qNuxktEisXSC6fV15rLaHwFN7QvpaWTRUjQjn+Yqq5rrL/LOvPges/YnnLBTwwYRALi66hDQfkEQN40mlXKeL1yG1Njmd3dgshhHgArGhrpuDULlUUW/ENGxMyI+79DacO256nOAoDper4ksUSMEZXmFgykYO4M0ZQke8XdazE9ibWt7ljqae7PXCsLG8d+jltVlfFKFRNVrY2B0K513jSN0AuNhwxo6gSeHyaIys54ACtLTag6TDrEZbA8EUnPBeZTpYY6N4OPyjSvMwsRGtwXZDnEIAWwx5zq9IzPPPxCz5ytZKvRm2PaGBDdBLbrI7Ks5VlNisD9zWFQDYDazHXeFsqRVi3zksZv6G1AzBJhzAn+uUJjgFgcNCeSxS1RKY0fDRS0upHdYz061vHosW9OdxncBfxkrsfTaP4GqIt/tnyL8ea+Mgjj9DY2MjatWs577zzWLlyJY2NjVx44YX8+7//O5mMN9Ezm83S3t7Onj176Ojo4Pzzz+dLX/oSjY3VE1dHIholxNF/p7l7GzHrY5Ukw5Y9eeLN2+lSvNalZybNZX+HvNDbw2Op9Th/F2cCcKn1C+7mPIpEyCd3YSJYzbxKHaxmazdn8Bd+Lt7FfqWZaxZOYjGXcg63Q7YNqkTjmSisYTa/5y3eHUJwZ8fpnmcKf1aDydb6ACkE2N6DncKbYP4Ex3AVPyST2Us/KZaxiLsIhg59g8/TJ8LjjKft2sbKsVPYI9r4s3UBF/F7tooUfxq/l654lFcajqwcO6l3K/G6QbLCzfl4MVE9F6VT2cAzLAJsBXbG3lUwwf5eR+x4kSfHuOfeFX2aekVlvypR2GIypMZ4sHgqy8SiyvZepYkj5/8HU9Q1LJfy07pFE9/nw+5xwgW/S8Tj3G2eSV6JcuOhKtNznazXpzn3sZcHmX2wV8uzhxb+hpubJssxPMhLLGRAZPicdTNTxSsMHu/29cX5Z3lQPy703DFsp2Dp7BDjeHTcPs7U/8yT8XbyIvjtAFoLe3hn8Sd8L/N+wAYVciz94EADhTqd5RzCq8ymVFRAN5nCOs911oyZGHp9gL7+FnINMbar4wL7/EV3y/LwtEPYbypsJMUZ/IUoeVZMncDvJ2ynKbIgQGgB3jy+eSzjCetoBkWa+80zKm//0MJy3vI0/O2ILu7TTudldQY3WV8NXEteNMfl1tKQTtEtwsMDH+BUFEweFie5G31kBr/gKs7gL55tX5nTxTbJk7lo6AV2JDo9IclHi4f4q5O7VBYBdC/6MnNzH+OlxCzuHBsBnyV/m5jAjdbXOZfbWcKTKIlwkHAUj/AYx1NUdD5vfT2QI2K/C0EXHayadBq7r53KGryGhmixwJSeHaxumejZHlGXMyHbxOa4a3ApCZ2b1K8yh+XEW2wApHAMlibonlUinv8QjYndHMODVQllXhg/nbld63mw6fekkyVWp8azO1JHVkTZmPB+I1Oo/HTyJbxgTiGhDFJHDydzL3V6H5lJPSzrHSDZvgxVNTyGkS1iEr+aeyZtub1oZgnDlMLGLZjfu4YtDWPoF3V8pvBfTNdW8pgSPiaX8ATPWEdiCJVvcgPzWE6cITRKNmmQAoVF3nHwklNPqpZowv2m/YNNmHUKyxXvnFnvMEaOG/8Sp3cXedqaSVFEuYVPMF9bRvStRdoHeirHLzae4hHluNB3L4B1/Y1ke89mrc8LWx4vOkU62cYGptFLPfkxKwB7Hp2lrmKMta1CAKAnej0K5lhrM9tEeKhwWcpGgf1ODuQcXmKLNYH9opn/5WqeHzyB1uQGjuMBJk56sWI0ULDQtBynizv5bz4G2Aptn5nhUeWEyvUfV7wA+IGxc+htWM1hj/XRpa5hsHXkwDFj9dAn6rlPnMFuq50NmneNfaHpcLrmfIot6ROB0wLnD6kx/nDo8YzNdtGSyfA4c0PX2imsZ4/Vyi7Rwavq7MB+xTIwpYiX7WIcN1rfYLOoTrhTFgvYG80zu0nlVSkH9+jCc/w1fkLg+O/wceazjNT0gQowGNAbSLM/YEQuy3hjM9/ig1jA86s+zC2zjwntf3fOmORhoga4W3jnRlOYxGL9mK1ZtmTGsC7qhlvuFGO40foGS81nOEv5I02tW4jFN3KPmWGv0sSTmXmea/VpUT5n3RxqRP8PPo0RDarmApPTrbu4V5xD0upnZv8z7Kk/kx3KWG60vsF4NrGNcYFw0XG9fUztWsff5i6tbPtly6U8GV9KO5t5GhdITeZVzzgq31kGYpNmPE9H2XNmBf2Tv4teztwjX6Jfr86mW5YXxWHcZ53GeLOPPbSwgoVsTA7vsS8Q4VCe4VmOYF3bONZJ4cOXWr8kTT9LdnXzUGczWxvbuGPh0cRTy+D3w176dZF/OSC2fft2hoaGuOSSS/jMZz7DLbfcwrPPPssXvvAFVq5cyaOPPopwLPwLFixgwYIFzJ1rWz4efvhhvv3tb3P//ffz7LPPkkpVJzzYvXs3e/Z4rRbr16+v/B0lh6IXaGhdxlv4BT/hPQCkChZnbFjFf071AjFDEbyccEN+yqBnybaXWDVuHjtpZwEvcq95Mag2GcOX+DdP7HWEPCfzNx4zjmOjOoVtYjzbGE+LtZs/J46mmtwlLuQuLvRsO9H6W4WWtixKaEgAgMV1/Dv/bn2ObcLrvbjM/AXfVa73KICG0MiZMdaLGdzCxz0ACeAI61GeFMdUBWEAs7s28XLHeIqqzkoWUE8PP41fjTkr2KXzq1p5z9Jb+Y76iarX04xSxXK0jhmsFvbEqVFk3VCW8198hK0NrczYvoOB5hQvRezJThn3NOsmJHhaXF+51mrm8fv6K8g3Bpmvdmjt7KC2FU+W8XWvcDl7+RnXkFOirEjMd9tMCUwvOH48dQR3cK5ncSxLkkGO4Aketk5ilZhPl+i0CQ6k+XJx8hEGLZ1nxJGB84Vp0arsZgfjGIoU+Pj4k9zilMBp1t0s7z+OnRl73EQosGXTAhxDJk9wrKdPbS9M5FPWf7Jbaa1qJBhO1pSmc38IxT5QVfEqiggvqIfzAoeTsfoYw3b+qFyOjQGCygbAT8V7Kn/HyDGNl1nGYfSYjQizACro+QaOGkrTs+55ls3czV7RGgjDGiTBzfpnK7+t2G6u4kd8m3AP/h3iksA2PbMTcMOnCyLGCmuh55jbx3o9NY1GN8d0PcuDY9zvegr3cp91OgXhXfhKsW7ezve4yfoaQ9LYnJ1fx+5iC3tT9WwSU/i+9SGa2VMZ863WTnYLu283sZd5LGd+z2ZW1E8IBWFgF5n9hPWfWE0KIfiX6dn1fKLpJvqsDLfykcq4rEvv4Wx+x0183XN8j1bP4/hAiwr2Om33h/WWV1GRpaBH6Fz0PCKW5Qt81mMUkeUI6zGeFEczGE1UPPwAg1aKd3AbeqJEafqtmGNXY+GypJVls5jM5hB2yKb6bZzf9nM2Wi2sEvPZHWlit/95JGlmD6fs/gv3tp3HgMjwpGNhDjz/KGV8xxrAVob7zDpu4itslcZTh7WdI7JPYUYVFMVkUuNaLua3/Jp3MCRSPMXRoIPMYTBRf5UkfQHwD2AlD+d7J0bY1DA/sM9dO6zKGrSLdlYIV/lWMLmRG3kfdji/gcofcKnWD1mxhW0LagOxbC7Nb3PvpK+hHrDn13fwY75jfYKS0FiemgHMYKU1n6uUH0o5qBaHL74dlRL7rWb6ix3M15cxTbzMVmMCm9RwUFIQUZ5MHYJ+WA6RKPBw4vTAMWOsrcxhJSux52vdKnAmd7GUx7nJ+ip5EfO8B49M30mBlchAbLy1kXq6WSEOpS+RYnViGn5DiywCi6v4Ed+0PhNKo15eZ440H2FtdgH7k3UjAmEAP54S5cdTWpiYH+TU3csr2+fEHmePFedZYQOHadZa1omZZEWSp32eSUKi1vdq7kSSTPU4zwEn7B9i6+Y/84eJtuGw2drN5ZvX8t0JRwdAmF9UDCITnmT3xLP4tXh3IN0CbAPLFnUSzcWdPKIdz8pMeIrLlPyrvBqdEjBQH249xbNiKUMiXO/s72vmsvj/0qlvo9PYzp93DTE2vZBt6hg2icmV0Fq/LNi1h8u7mjm08HM2LzT5lXIlOTXO6roZrJbCzMHu81fxQ77KFyvb2sZs5mnhRgfsoo2fcA0mCp+wvh7wJJaEzjI9nAgNIGP1cvHWu7htvM2g/Yy1lK76Pfyd60P1ljAZz2Yu55esseYyILyd4FT+CsBFjV9mmfUFekQjO+uaKTVOCrvUG0Le0EDsoYce4oQTgpaRMHnxxRdZuHAhpmmSy+X4whe+wKedonjHH388kUiE6667jvvvv5+TT7bp3q+//nrPNU455RQOOeQQLr74Yn74wx8G9sty66238sUvfjF0X8boZebmnfSkO4nG+jhOe4iM0sez2RP5wMaJTB9aSlPxCe6alKAxuoMlPMHf+t7K1ngLA7pOE/tYwlMAxHKCi598muZxrzCuMBF1bARUeFx4F+e4Nchp3INqlTjrjjtZfvLhPFpnKwi/5u2BNk4wN7BZCR+4Y60tvJMfkrQGuUu4AK2aRyzBEE3s43M7f8julM7n024u3u7nF3CJ+Sib2mfz5EQXgDy94nz+MP9kshJpRsQqsKj4PO8b+B1Ld43n7y1NbE9q9MSDgObw/ERWd+dZ2ayzxZrMT8V0dxBbFqppoloWU/b2kVj1CpY+jo9N/hbZpM6U2MvcxXm8ynRUSix6YQslVePPC20l5nfirZX7dOzdh2lqtPftp90hJZi7z+QlJ32jfvIW/o3/8rTtBXG4GxVjWcSLec7fuJZ9HXt4JTmFXqWOIhE0w+DErie4a+zJFIVrtZ6ybwv9mRgtYhcziy8TJ0u91stD6onsEJ3kiBGhwEn8DSwTK+Lmj+zSJZBnWYzft5N5OzaSn9fLKdyLgskn+r/Po3u/zENjB+nWExSJYKIwgY3MZDWzrZXMH9jH8v0zmLhnGyvHTGZvqo4ZO3aybUwdZGCLmOh55mZrN8cVn+R92xR+PCtLDw0cPrCS5yzX4FD27Jbl5VbvNcLk2NxT5DTBVmUcrezlIuOPvKAs4A7V7perW10vbNTKMYcVvCAWe65xSH4Z8e0x0vkhxmZe4fmWuSyL2MaXvxgXsFvzgoSU1U/J1MmpwX43trSDw5cdyUPTWqAOeqLpClNka287EfKcseU8oqVHeHBKE+ujE9mqusaJX3Elex1wlrL6WJR+klZ28Xnzszy9/wSO0x/jz8lzeFpbFLh3We4XQQv3ZryLjGIZTr6VxVhrK/VbS8zYu5uGgftY3TKe1sGdtHbuZixb2CApYl3bpzMxuZ2mWA+fV2/kbvUcNoipWChcvKmf5OZePnfyIoqKoCCi3FK4ibLj8RTjIRaqT7CRySzmSQRw8/ouvhMf4sX2RnKxNIqicXr/o/yl+VB6FSdHwbFQC6fNEQqczx+YYbzC2NQmVGHQQDcft77KX81z2SQmc7JxL2OsLr4kPs0TytEcZj7NS/1LeFYsYSiexIxYmMJw/MUKRilCQVOxhMLL1txA1NoC64WKl1qL5flPPu4BYbpVQMVAweRYHuSt/IwZ1moe3X0OvelG9sV1LKHQQwMvsIiHFp/MJcqvSAMvchj7hQ3EZu7aSYOynW0NLQwoSQoiUvHEtFtdnNz6RxRMPs7XuLtwMU/kTqAvEsPQBIpicJzxIH/VvQWLT9t9FxMbNvG4fhw7zLEUiFJSFExTtUv+OOy0rcZejl+9jRfG1rEr00inuo080QqBhCyqNNff3XiGZ9+51h+5jF/x6o5F7N3/VhrHLUNL7eI8/UHaxSCPFS7i2XSwtiVYvIWfM9bayg/EBz17npoZrGPnlyQDFe//LuHmz0221pGiHwHErCw5EedvnFnpV5ncAMl89SLfZemKt3NnzI0kWMCLHM4zfJYvcNfQW1iesI00XaKT/zY+XgG4U1iPptnv66zcfdQ9eS0DSzMo8R6+aH6Wl5nBV9WbPPda3L2WZxpsI+qyptkeo2OHtZ2r+CHrmM4xPEwzexlYO49ss0ZLs0tTflPuS/yu8Bm2KBaFuJ17PKi4Suk+mhmUkMrbrR+zhCdIMcA9xrm8oCxiH82U0DBRMFECIGBgoBFtcx2XqveweuJ4dkVb2KmElFDYkeK8zY/xytQxdLWm6aahqpffL5uiE2kZ69bBUrD4IN/ivsJpDOlJzuYOlluH8vfCWXTRSVHX7PxAS6Fk6uR9TJNDDtupsCxP+oEV6+dk7S6mWSt4hONZwhMc0rGSDPfzd+tMtjKeHHGWGE9Qv6HEk01L2OoU0R7DNjYok/iVuLJyPWGZZOjlxG1P8nJ6CqvrbVDzU97DkHCtm6pVQsVAo8jJ/I0jtj3K3cbVrG/tIJfUKGmC6azlw/wHf9ryLp5LHcJQWmef7n1/A33NTHz+K1jj7ybdewx/Sv0Zs+uLHFP3FrbHp7BB80aFHLL5ZRZsW8/kfJrJ5iHsWdvKlG27mDl7LXfVT2RDuogRKVFEw0LhEJ6jKZ+ledMEPj/jBr4k7GiOO2Pedfs/hWvQvi3/AfqnupEXk/dtIxHtYW+8hSElRop+3lL6OWuUOTRa+1lkPkN+bws7NhxOa+M+dqeaeNWazqq2eZUUCGEZNLKf9/LfdJg7GBpo5NcD7yGvZ3jXRoMHG4boHFyN2p7gxsbPc0/kTNYzg0GSHMljxB2imMbYNr7Mp7jTupCXmckeenmjcie+oYHYjBkz+OEPfziiY8ePt5WdpqYm1q1bx2mneZWVM844g+uuu44XXnihAsTC5IILLiCZTPLUU0/VvN/73/9+LrnEa61ev349559/PncduYQ5c94F2IDELBjM/fwTHjh0SfNJXHPiFB58cAZEYGbmc6H3qd+zliN/8SLp838AgL/0wrgdG/hsx5cqhB2mAfE9+1h896M8eoUNxPpEfeC683qjbK43IcRNH6GAgsVJ/N3jKQvziCmWwTE8BMCuZ69kS0LDU9bMUmgYzHP9K1/l4cJ4PjH94wC81DCDIQeEfXxNjou2FtGFQFx5GXd8ewYp4EIKQIHfXpTgFU2Kzy4ZHJFt5U8OE2ROsS36wrT40cOf41TxJHueSdO7McGehkk8O16hf7AeVjkTWyzHNYu/X7ne/qcO4f4jg3kTl1q/ZA9/ITZ0FjlPsWpXi3uAUz0gSpaj1y1nVtcmVMsCy6Jup2DawKPEd2wAw+CYsVNZ/PWbmbljP1/d4MYvn7TyRTvcpXcf67s6EZZFhB5O5U8cddnbmVoSrH/xswyeYmJFgzlDimXwyeLX0f58NcKMIkQ9M+b9R2X/nCe/zlwEp2nfpzjOSym/Zs0xvH/v7ewqLievKiRFkePWLavs35mZjr+O8bd7vk1r3WPE+2dSHHsvl7MNLdfIhJe+SC4Dfwp9O64cu+MpPn7MZeze8wxX93lB0Qeem8c0w6Ll2kPQ2hII8Q52b9jIHZu98fLnWn/kQn7Hw5zIC7hArMnawxW7f0DziwuZl3oLMA0Ti8XO9OAHYfc+OEBzAfJiiB+M7+NnM12F8rK7bmPc9o08gUUp0gjzJ3jo+i/aZse2x4iQ6DqCsR0dfGuLxX/peW6bYvfRRx3vxmzrJa7nG5WCvWMHd7JvVS9z797OZPE9Pvmhz9CbbuDQFXfxH+Ln7Dn63zmN6lbGshGi1drJv/FJ0JpZ8cgSSiX7voMOZfH451/i2mU/pP74AdZ1xvAX1d6/v5OXXj2VWM8ersrczpXieT7LB3h//gzKH77rlTy3zLTH3n6H8vn6CW1c1/oJHntmsYfJM1GKcPnKLcx59JfM2LGPiXt7KGrwwBe/Q6+Dc6JWjs/yBSaxAasARl4lki5WFF3LhHHPF5mW3cuZi3I8NngMyx+aRjn+4HDgrPdfywdPdb1OG//tR2w48muV3zMe+CnXHxLn0VaFkpSLs2jzWs4bexs9SgPLnff7AodVPHsf6aznuinjiasKRbPIIw+5EQin8DeuvOlB+9vf/AP2p9KYCP5D3AAqrLHm8N2hj/Db0juhDqIFk9OejvKZuo/CFb/jS7f9DTOWYMHCe8hk9rrTSgk6/svk/et/z4fr7+emTx7BS312iYVnAca7QEwAmUQDx+iPcAyPsGXbXDZvsj0kWl83sa6tHHnRR1h64dEc89tjubu+DwaAAYuPjctyMzeEdSeiBMkn5lrL+RDfIsUAu3ZNZsf2WcwujWXOCjdsdirwVuCp5iE+uMjr5raBrMWx1oMBIFaWzn0lLn20n23HruD3ja6xsdHay2ncEyiBcFT+Ud4T+W8EsOvFJsx5KuguuM9kB/n19A7MmZfym03h+dkeEQLVKnEDNzETO+9sFqs5fOc9/GddO/c32Upnmcr9Xdb3WcKTACTWXEJ00xC3TrmFc7ZCajpoWsEDasty4l/vY/UVYxgQmQoIS5r9nPnYXs5Y+O9E07uZ6+RY5g2FqRd/gZd7N9J7d5q6+Y8A0NKvc89lrgf0mZde4FypbvBnhTvnAywqvERDpAeAc5U/cS5/Qss2MKf+6zy57aMkWga51fqwx8hbKkYZ2DuZqwqH0rglxcvqDv5zSg/PTPJGDoisRrRU5JidTzKv9X4A7rbO9QCXWuJhhcVEw+B03fZskBec8a3nOXfrCxQ1wVWfivHjU3/MwtaF3Ln5aa7dGJ6Pdgm/oklSvQcO+y1pYC4rmFvOG4/AHFYypnsfjS2f4pEHHgKgoBQ4q+tJbl94DLvqmgDBvU64v2oaXHrnj+ncuRVLVRmcvpB4dDOrl9pAbMgJyZvda/CtF7LkZv4Xg+3PVdrxSIvGW2cnOXvKsXR1L2fd8ssq+2b1radtY56xRhNfOO5Iz/pSLMRIF5uZ/6r9Tgv1f6ZoDrF2/4+59qnvcOuZ/exL28BbNQyWbPLmTfYW+2keSHPmq52cSZH+1ufYsdAbTdKy6TIaN59B6+CDUN0WWJFn6w+Fevf31K1b+cRP/5stx7+dTfnF2KEu72URsLTrH7TN+kbl2D+U7PDnssFTtUp8ii8zi9VE905k/EvXoxZTCASnpHSuab2Rm9t3YQmLnv5FtD/yaWLRPq4572NV29fIfq7iRwBsosDVwz/S6yJvaCDW0dHB1VeP7tXNnz8/FESVqeuVESTEWpY17HGtra20toZZ/YIiQpgHMyeNR4lqw1LOTdg0gBxb4r/SkmWPkO5wk1uNUtmqUPu6jUYhFIRBdZKDMCA233yJMYqdXG6ZOqlO/zUthBFlXGQ5iuWiyN4GO0ykJWdy6ZYiCqC3JzA1r2taKIJ0fQwG3ITzeXu6iZnRwDPO2baDs5THKvf1/u9KcnB3YJvwZYnHrCHO4s/8wAwhvZAOXc5CAOK5IbIS+1kin2XODpdCu0J4sW8nwjBIZ/Ms+sznELoe+KblgtCRvV2edkWTSQ4750IG7vLmA/nPX8CLjNuSZKdpL1D74jt9x9tnDBQsT0bOwEADe/dMxLIE/1m6kFOjW8DwJs7qajB+u9G0mZ9K0R6KDslJZNMhaMU0io8QJTk0wGDCa3W9aPdzLB5/LXfv8T7JvB6DKYMmiaPGoLe7FnPhG5u6VeBc/oRezouR5ET+gbqnyNIrr2bwj3ZuVbAKiisRh6QiaumkLffbt+7tYvx212ob91EoT+0dYq7EkPqcpfCJU6aj/tab71YOZzqXP1VAGMCePRPpiAyS7h+gmIxx2V/sEKu9mRxiRj3KtNNhXbDf+uU07ibJILfuKDGvFPxWWm8/Y6f2sTU9BugJALHymzmi/1na6+339XzumNBjyhJVBB8Y34pSCNa5UQt1WOxBLxlM2tODAjw2Q3iuspgnmeLAqr2v1qMnijYQc6TYFWVGmcXvuE/D3733SdRFmDB3eOu7/1kn9wxw+Ka1TBq7kWWSNrHCGdMxTD48ZRxxtUzKIGyqt3L3cz63kslU5iKZ2GRIJNm4so0dh9se0DlbB8lkLZh3BEw/DbjPOcn7RmOrBNF1zj2j0WGooC0inS7w7d7veqAj+7pQ1fG0T5mNonhZdIcjsoiZQ4GQxnP5Eynsd791yzzPNRT2YUqxpZGG4PXd3M/q9z5ibZZU3vKUaAA4ib+TYCjwDc/K/RnNoaa2TBHYf3xxgEVTp/D85iCNfjWZkV3HzLhXiTVFkHanzuzlBGF/w1wuSWrrUWD90W6D9E3DWP4m7twWaGvzvh3E9jQG+kNPZDJnth7Cur7NZOPuuNB9lPBaRIdQbl1bjK1zEeP2Y0nEHg27ltB81snwm3JbvW1STZOpRgdNlhT+FcKo0tlTTtOQn9t7XLyQJRsJzxv6mrhJOs/7vtSXY0S22s/VH4cTxp3AoW220aTaXK5bBU7j7tB9YdK1eyEnnHYkjzzwPAARM+J5BgO1Mi8ctqObsTsdmnfnXfifFeBtmwo0Fyx2+NqY0lOcP/V8u26tf5A5l9kX3xu4Zt2Ql/yi0k1CXkEYk+2AMUCzLltRvScKQ6d+64nO38MTZoTJ4hUvELGChoe63lfJ9G32bBM+9t95LGOOQxYSX3csWtHtc4lFbWzZ7eox5TI9ljVygpuYORHYNtxhr4v8y7EmXnTRRQDcc889nu1//attXVm6dGngHFn+8Ic/MDQ0NOxxoxLfQFEbomhNzoQ0DGASPkwkgwBhmozb4aUbzQ85A9usfeG2UvVQjbI11A+8wgjU5+Tdjm2iEvUtwAIwtG6E8E5WG5yCuod2G5VOqLcn8WPWlnEpVB8F77Tuvsq1ZVm60/XuDDnesrCihWrJ+1KFZQUmvZmsRsMga4H/I8mDZpOw6YPH71rvOWZMz97g/GiaqFlbkWnVY2jNzYHn0Ax7EhOlIoOalz1qzPRZaLqO/yX52z6LVQztcWO/96aCk0+xNETJ9H7fnh7bC/CqNYa91BH1gWKAkggqFZrDXlSUmCbzu2ylTPEpFeO6Nnl+R0oGExwCHX95ksP22e8iOs2bp6P4DAiTWV8JR/B/q1msQuxrIOYvAhpGzwZEpMerN1xP51ip3alcAR8RFTN7vd9qZ0ajsz4e6M8AqlVkBt5Ctz097cS04MGWAKXzMJQahbJlKVvRN+SrGVmiJFoLbMs74TuBCchClEosbrAZTdcqUwLX8Je5OySdIKWpKCHFaLV8PQCNA9nKuFk5QdCddBfZWayq/D2wPYF/ZCf3O/NO8wzIdATGVef0BoS/84R8Xn+7Z3Tb38yyvAp8Oe9xYTpOUpWNYMJ7XefvxOLDKz+eE951Y11pFqaTQzFxl6PcTD7Bc76/qZGXJUbKaCTQ32URWChRh0nXUCv1CzFKKLkhFG0czeNsw0et6/hF6fPN/VaJaQ7jaiGXJJvNOPd32qF4gYte7/0eCWuQCWyyzy9UT+KfsLtUeS5Zymxu8pqUyQ7QjKScGSJgUDu2pd5u/yjKsEzOBym8S2oQiM0svVIx/PT0tNNbgqS6H8XyflP/eYnsEFO2bw484+BOHSvkjKZGOy9KEQrFrJtzWlfvZfWMRquHd3ZY2ylunxYAefUZx+3hbPe3qWGojw7TO//6n6cpb9I46I4l9zhvH5rQ410jq4lf71C2uvNwNgKHtx/u7qvyWSeznliIVzdMTFOhqXkJakidtLKBZY2Yy4Cw+/y83XI0RtlIE5TD9znzlu+dt6TGVYzzYWMyauk8U/98YPvEXf56YY5zwSqTdrmimcF12rBMby1dvwGodzKK6RjvCrXLqoSJZpQ47KVlCMUKfPuG7lcYTtktr10Aao+XQyE2pd7zO6074ZCjqBWYTs4Z/qDXSd7QHrEDkVNPPZVzzjmHL33pS5imydKlS3nuuef44he/yNlnn83RR9vheps3b+aKK67g8ssvZ+rUqQghePjhh7nllluYM2fOqD1xNcXXV/Q2acIcBohpu7wny8pEXX9PRXEvSy7rTAwhA1GWllL1SSqOPQj9k7ISAsRmrJ9NZNIY4t3T0TONFESw3k1C2wYWKFawTTP7JFrU1jgF37tqHp8OWAvG9duWIb9itWjoxcq77iFFkqzrIZEmvICF2Qp6xMqFDbOmCHjeQuot05rf56gptjQNBqnxlUK2cp+mVjeXS5GUBM2w34eSG2JfukhmyFVuWydOLj9ATZnEq+S6XQrbvkgwJCc3sD0wiZUpZFdZEwGIaCp5XzcphfQBFTO4dX8zJAl4qOr6vW2JGgZ1zfak6//O0/vt/qK3+kKcfKvveLlOnGl5LPkT2IgVWxJYsQVWKLW8LnXRxftKqKaFapgsXvZoZXs6V6Cge8NRxw1mKTOOlDBpGOdYHpWgP6OdnYF6WUND9USTIXW2BKhtc0ZExK1axUrR5FLIGTMG6+gpQTRToqF9Klm2B5SeaKlIZGiASEsK8j38Z+Qa8K3JAYU0ZSvWwldAWykmUcwIWPY7K8uWVkEu4ubfTcZV0LJ7Y2Sme8dO/ZADYNpmO/fxKaqdIQpoyAvzb2oZ6Jf2BQf1/DpvDG4gssE5JTptWtUIhN8vccOO2rstuxWts2q2TN8heZoiUURNIObK0FA9lmV3fjWfRQB6vJ10CGlQVI1if9gqDe+2PEQbnWwlgv0dsr0uGLB7t4GSSGNIjkot6TPAsLnS1sHBegg2ibpBg0ShrFx6zx/rzMfyd2oe6EWNueufaXiBsmKaHN5ut1UZBRDrKARprktacLYYK9UpHBqqY8iyaNS2IPADMe8Ym7RjG4ppBebGgX3OS/E9+4TmI53rCHJbl1Ka9hClbD1TZp3iOS4VS0KVkixj2Uq+txP/LNs4o0w0E/5+mgZ6aTInhu4ry5R+E+HMqLL4x1Tb0AbW4mUPDBN/LrrYFwEneiAXgVmN7vipZlyoVnMrTIaG6hjfMSnU8xw2L4zrGaDc1YXi5Pb59Id0doiGYnmbt43plEukJALgT9BkptgY3+7RSZK5IRKFUijxztiiBcLyHB+ma+H7Rv7njfVNrPxtDfUgLKNCSqJaZoCC3y9NA71kBgZQkkHDdnJwB5bq6x++NsrFpJVi3POs+hjvHB/XnKiaUXjE0k3+efeNI/9yQAzgt7/9LV/84hf5wQ9+wBe/+EXGjBnD9ddfzxe+8IXKMZlMhra2Nr71rW+xa9cuDMNgwoQJfPjDH+aGG24gmRw+eXjE4lsENFmxHCUQk3819PYEzs8O2R3T8nnE0vkB+qNuSFhDqcjErevYNG4aU0trWa+5YQ6JChAb3iM2eX+MSV12UmemLcaQfy6zBE3KFjDCJ7UxWXeb3pIIKDt1zXGEzy04ZqjgtM97vXnG2kqP7iNBkiyqEbyn6ptQRIhK3u7UnMiZ/mWVQNgMQKPqdeU3DAVroSgFF9U0T3KJUuTWaE5IgVLM050uMGGX21caOqoV1PS2p8Xazb4BV1kacvICZDGH9gYW/OyQrXhuNO1EeF1VAjZFIyTsQMXw9oxcHAxbKfcrVKmBXs9vzSiRabWTjP260tisidAFar03xM4fmtiJG3ZUKqgeFsgYeSKNQVIaYQWNaYphoFpu3uSYnMVX/t7Ljv71RCVgncoVyOteTbJjyPUwD2AwrsmZP4LdhzZfPZNcLolh6ERC6nEB0DhxRIpkOzvRqoQmLci2s7vrCaJWCaFAT14QJTiG6rKDqPkcygefgeIga27bQgRvSEwAMCecHE2f107LO0oKFqm8Oz62NUG8kCPrgDG5uHlhQKegeued1oJz/0an4K+vAY0dwbk6zFjiB0uJobJRQASUYoDxcS/YFgiPciCcTx6dPMX1vPuk5HgJhWlSP+h8w+ayIlYOr/GtD12SV2HY0ERXcll3flcccorGjjEVb6GstHYkO4CggaYspneY0oZb4D5XLr6K/U40sY0hQ/EQcasJ1RMl1ymFBA0N1YUyZNYPuN89ENbm9EHTVCvfIJ0bQklIhWYNr2czlR+is9V+137jTS1pKwVrOBmqEvC+jNXcUOV8LkXWgIy6C0HSCwh9zzJhp22YlNfXmJXFyttqshxuZVowucUGYopQKA62sP6ubwKC407yeqq0EI9OWTrMnZSy0zzt0nL1JKbZc2N5SfO/96bBIVJhqFmSSYNmZZqTV9KAMm7uYCSSxBt6LLplj5hgbNpNcaj2VVul/jqcDA3VMXNGS2g6ip8REKCuZ7cExBqddniftbWnhwqlo298t9UvqPwtQoLSElaUouI1pWVyQQ9VGVQ1OTVM5flN9xjoqyiZvnbpWTfH1hrc61m6Wo0hujRvSoFfWnr3IwBFDwFiQ12QDhZyl2WMNEd4AGNEQUl6jXyaooc+gyymqVRq5AHEM2OrHvt6y78kEIvH43z961/n61//etVjGhoa+NOfhqMR+OeI1jB8nQSwF3olJyhJY1VWMNKDA/inosGhctFSrzKTKXiBWAKTUx/5M6+On8GZC/7KxzLfq+wre8TUYgy5FFNYjlijY8HsLg7R0tbCLl97FENnjLIbjHAA0znkXlNtiCFK3nukm2IouEpcpFggZjphFL7LdQo3hybrFD5WsNCTZwLuPtVPVRviEWtzQl6yJoF5LMwG0+GbKJK+avbgBWKZTpfhSNaxKxNoqUhB976LVGOTc3x1cA5Qj8E+Kb+poBTYsWM67e3rWbPqeJuwdmBvIDovn7dBX5dTR0fXgk9aFGFAzHshczBR0Vf9bUv3+4CYWaK5zQFivqM7h0zUhngg7MwPxBqs/ZUbmUWlAsTKxdDrO2eErNjBvmi/e58COGiRypWQYXasWKLkA2L10rcdpERnffXwqzafklBw3rvuABl5nJRUC5HuqBqCI0tjDU6oVzfdjcBillPU2NSCoYmaUSJSKqKUSoh0m7N/S+Baatw7fsZEnXwKRcGy3P6s5V1FMV6w32B/DIq6YMn6FTw0ezGHl55CU22NvTCgYVmpSvx/WRrMnP39mpwwSV//T9aHzafBF+bvzbH8IDnKoYnBuW18zAfEfPdVnFQbfUwHYmswP06WdC5LxSBc77BohulHJVCkSynJ5DAg3L1ILufO78IJv043u99Akd5AW7INWF39qv0+ryMuOClkvQqZJnZTVOq9QEzDA8TkvlmeZ/xSPyh/A/e5YtZQ5WsOWS7oThRyCGFQ/rKW6Q1NTBVyJBL2vWq9Q8WyMMuMbaYZKJwLYFmpwHrTrLjMGCVDp2hliSpDKCR9HjHvic093QjLuz1t9NFPUK8cshQiWsK5TiUQFIBknbff15ojGvODznnuQWquHiXmHcv+trbkzBBDgPeYjqxJsay2e0ITvcfpIdEyYZKWvHq63ogw3WsWogpNMRfFV/OIjQaIFfIJ6uvrQ681GPHO8/GShTrojgXhsFT6+0bd4CBlIOZ/e511bqFoxaeLWEDlcaW+nM4NBa5U3psRGWeXe3xEAmJyv7F8PVMWXQp7tQpDzpxorxdNRm5YINbYZ48bLW4EXkg03wMpbwkP2SMmLJN6egLPBo5e6Bu/qgPEwqJaypLNZkgm3WtGoyPjdHg95F8uR+wNKX7Le0pasmp4xBRnPuqT1q28ZKCJh1QuH8zaBwifJyhZ9FpUhLBDGw9d9TQpxatEqL0lLFPQseqd3vb4lJVT+u6vPNrTvVvINMQCXg3F1BnngKAwINeek4BYSg8MuHRjzPP6koUcajmeXXrERK5AXPJKDZVpKCwLNeJNalY13+Tnc+sDNNBNwVAxQlwaEdM7bFSjREemzrMtUQjm4IlyxXjLIj3WpTSXE45VJ6TUtIzAolwGYghvuGRwwfO2xRLw6volPPH45fTst61CYnCvZ4G1LCgWbfCwy2qgLq6HKi9WSMiDaXm9MKXBmC9+3ZXMYI/nt2oUidbbSr//fpkSKKkgI6W/j9RbrmVflNx9jeyllFWpnzQlMAbDQsni+VzguHyIpyNaNChJ+RgqFsmSu/D1Y7hALOQdNvgAU6FoL/aak5NXN5Qnnc2jGiZPzt2PSLePyCNWX8PDoSVOQkucxkT2MUCCMeMm2s2T+k6ikLctmtI3EyI4Ram+YqOt8m/Z4p4vh/YJIiW7j3Sn7OeYuWMDVz7xV67O/6ByfHFAR9ETHg9RaUglWs5LzIxxruYVv0Lqb0flWaRtUcNCmC54Dnu7Y2PBvieL6oAVrbk51NvvaaMc4xvx5ytKoYj9IOTfyeSwOWJlyeclQhtnrknJBgHpIW2PWHUxAkDM7bNF+T4IVLGfmFQ8HECo3jbXSUpWsUqOWJ1klJOVUFlBK1ru2hnF8gwvs+S7p1GqeDlq5YipnnUkh6qEeZXrAn2kThpvlqmgCVsRFd7mB7ylTU4ki/ztkiUb1euq4ukPeYlOyd8P/AYqVa3eT5L5cr6Se4yWkwk4QgwXpkFTaXgVsWyMDZzvXzeV4YGYapVI4pKJRKOtXoSZ8EbNVAOfjewN3xEihUKcVCqFGlLDasgHxJoKFjkpBlcoTli271kz2UHpl9fD2ZqWomH8Y9sSlBRnLZGeM50fwlDzvkPte6bVRoSwvB4xaT3SZLOoPLf7PWI5yU1dzHn6e505fL5dU689HrSY4TFsKWYRrZQN5mVLP5PFQa9+KB3rj4gB0BwAa9YITSwUvN9O1+uHe4TXTd4EYv8HEkDzSWmBr7F+q732eb1S5E13xL1WPBscHENFe7/l6/QpPxCThpke84bVDa6Msu+3Z5Lq9rEySbHbLdYuTuv/BwJY2f04Q2aJeCrIAIglaDbt6/uVcmFZZKRbKyk9oBHF07pnsk3msxXLrny1+kEvKK2QdYS8YEX1eq+ykaArPU0fOaM8Abj72s16FF/+XWZwkLqUVxGJF4LfRjhWqkjJINLmWmfk7lGO7TZUM9DytATEvOILnRTetrgMQ2rlu6vZXs+8WCzGKiExO61GGpMRzJA8w7B1z58bUBxSKxOp6jsj5cud04wCJMt5HMFrqyk9uM3nEasTPdLN3X0NdFMc0ki2tIV4EYP9IpHP4n+Xecs+WpZoqYSZcPOH0sL0vIN+DFrSTriICL6zlC/0plgoAzGN8t2OfmUbJ63eRH+yBMnWEU3UNYFYdB5adA5pfR99agNPP/Oscy8ZiNnGA1VzF7AwAKhGvApLayScPlp1iDoQEHWAWI+TO2QB8WLBo6yVcipKJO75AmZWevKUnVfpUUCFPUcEJKQvyZua8xYlZz6r5hFr0kOKw0uHKU70sdrUXPGoVJOyR9DbHmdcStvKc37lHslkzdBEeU+xKCntDhBL1IWDyZZ4S+B8WYoD3ncqGw+KsucNUMVOtu2UFF9VoPlAgdw3q5F1JPLhipgM4kwpNjSmqJ6+YJa8b6pRerP+cHRZZCCWymZRQoCYIlKeMSgsizrJc2aaKqroq+yTRQx5fzf2dgeAWLRorxcRTUFWy0pSXS8hBPdN+xmDeg+7DlsWaGOtHpjIBo/RCzIQK++XwGE+R8oKKsF+I1Zjwar0UW+YrW9d0rxAbLb1En5J0+eZEyKRFjz53Ulv36lmpEhXyZULk0IxRjweD6wRyUQC0x99UbDIG66uIXCUM987SWUlfUt6JwOmIKFLIcQh4C/vAK6CRH4UKxSwFP8cYt80oTY4c5grcmhiXDJeeNdqn0FZ6g9WIScVUoeIPnwdvpZu+/tqcdPTjyL5Xqdt3jnWE0ZcHPTsk9upJoPzeyU00dfre3rccjS93d56d7VybV9veeO27F9YFElxqDV5KmUglpAWG2myiOWCgyPrKNP+RMhUDRYcxZdEGS3msUpqQFmN4t4vQ2+l8XnDdpvXtSaCQQyWIOMo9AGrUdHtgEpCQ6hKYDKMJnSPx8hW3pxJX3rEhiHvQM5WansFFW7Nx+6W14KLS5whCpYD5qR9zWYa4fMAZQb7SUW9bvuwgVUGYtGSgdYiJ7xL5zmLeF4jEKIVqTD/+SxZeCdoTW2QflmeMNXKdF0Y9CwQsnK0z8pQF9fJZoMeV7+SAcF2FnPuROp/r3rJ21bVLELEYXULCycLAWKKD7E1SECsIDEdZuillNWIpdOBgVby0x4CiWIOub+YlkUhDIgVDbSEax1JK34gVqIu7rQ7REH3KwmGUyJBkzy1AtAcS7qqRUfoEeuxr1fDuBPX+xnSG7n44kud+7h9owzE9GR9ZdtIgFhLRGq31PG1gu2ZVSz3WbrT5cY5/0t90MirKPGEd8TmZCDmGC+kJsXTEZQQT4Df2ltuR1ma8hZFKXYuDJjXhbCGeq7XLxCxGEoyQXEYVst4wWEDDKGV9njEfPqjkkyMWIEoSeUKyh6xREbKrym547ku6vWa+8XIeZ9HNh4UJI+YYZVIqv9g1twF7r01JTDeZK9WNSAW93hW3L/lUMGSlE0R0xSENJebJYWiNK6bpNBq/5whiyZb5guFUCAmtIxnKCeLJU8+pmUp6Eq5PIZ31bGy3r7V2NcTAGKRgh1KGtEUL5jRXIOPQLC++QV+ftgX6J8SpOOv5fVLOpEylpDIsQz32u60Lhlminlilnf+DTMKNOWtUBInv0fMjHjn/sMJlhnyh4VGI63eOTThA2JVNCi/sauWGFYKRVECz5ZMBENo6wsWeVNaF5Vy2Gh1j5g8Fw1Z3qifsLE9qA4GtkVLBYTpZ020JaI1gql41mYZiMUq+SU+Y5NvylNK0vMWvbrl1IZwY5ssjb09gOMRk8ZhpOBMarI3LqZ5dINUoPSJdGw8uFarlRwx7/vr3d3JqlXHs2zZaZh5ySBtjYx1+PWSN4HY6yCqFG4VYnCqiOJgJ9kjJksiGwLEygHGPm9Gwk9XL08G/hACx5vjT3iPSkxvCYlKrWjmGDuriTHT6gPToilM4uVaG768mrqie4Oywu1fS6JxzbMtWiqiVjxi0mKd8w7kgpOxEAYcVN07qRQ0+2qyKFgUzXJ7JesMil01W5LM4ADtyeHjj2WPmFpf795LDrVw0GVW9d6jljIWkYDYZGs90ZirZNUvNTxAqXwnqzDkUQBLJbdP9pOgPqGTCwH6YeLXefNSuGmANMH3PVSjVPnonly5MggJC030WSllT+2gFJaZpo/ikEosVTu2vSyJghd4unqhD/gaBtGo26443jyKAQwJiAXv4wdilhPqqunhi11KT9X0iviva4SAEIFAMQpokRL5aCP/8e1vB5oXLdrjO1FfO6lajXkXtWiVmotqsQywXRkoR2yGPE8pr6LG454u4jECx20Dg6zIxBJV0pxDFl5PqE3RYoJp5yzoeiEwB8aFRSwM4El/K/2gNjQghKAwDGhLDQ2BZTJv5f9IbSz/J43/Qe97GU2OmOwRK881cWn8DEjKTibiq8ruEz/rWkIKFzOK7jW3Dz6NKnp44NEn3HvrSsBTkcIlLyqF1LcDLxArSNb4eo9HzL1uXNMQUjK+kVcpSsaMtPRNaik6uvTpY0YJJaxEh57xfHt/HUHTVIhWSK6805zpC92rG+gHywsqhXOMPy9X1Wp/J1lq9ZOEw6Jlae6croog0JCvECkViThttELeSVkaClblRC9Zh/ccS1rTIlY+lCDHD8R0vR5FCu1UfCRqYTVagQArbS1RdKcIso+YIxoLkpQ0FEwKskfMeYcBIiCPbiats8J7TUV45y/TVBnQgiAyVgo+T1n/0fQg842cIxZx8sUtK1/TI6aY0lrru9+YxPBApm7AXn/UqAWaey29GASWSkLz9LVEybv2CslTqCbCPGLOe/OtdWpJsH/fOPr7WtGF2waV8LzUN4q8CcReBxGyIlPD0KA4Y3mgCmlRPBcSmmiW61p4O2jE8A4sQ4ohDsRyF+wB65/iIhKHngvEBEUzz7zjx6IoIjAxWsJl/1E0rzUrIwOxKoqrPw5eLxXRy0QUUrPTBe9gL1QWuTAg5lXui1qwgCiA4cTnW9KkELE0LJ9HrG6gHzXEw+KXsnKkmaaHYU4ehOVFJ6+YnjlGlxeFkMSda6xbOcR6jo9wM9FoPed+eCFLzp1M41FmSNAVWL7J0Si5HbGATn08vGMOlwsDUMi5HiL/BOMHxpoEauVj485mJUTRrmX5VQx3X4p+zCEdRVGrLtiyJIvexaBYBYipFkSk2nYNepK04U70fRhkanrEvAut6YAGvQoQUxV1RGQdZWa5UpVPpBk5VN3CiGT4+Mc/YTdP6h3lxTvV4CZV50tG4IsrkZFZF5WSkz8hW4TLQ88ZU15FWkGJ+Rkyk3DijXD1/eGAPRreFssKAnjZKJAsWRxdnMX0kp0rFaifVOURIzJx0pBATdmKYXGY8d+yfwvHPP4pmve7NdPCsLXis30MB8TkdsvGFOGUwdDj7oPISlg6IoWlhV3XN06TkuHNNCT23YgNsE48+XT3XC0IxOLS+aVSeD+PF9y+0CetFXJooiGBl6iqePqvkVewpPtmZCBW4x3KHrF4qYQIAR16pNEzP5VzHstiWSopYc+pfn4n33JBIp8DC3ISG6HlTDaaKhCSVq+qIzMiQXVQAqAWnL6huXqAprhzlqLZjfR46UpFImWPmCZ7j70ir+Nesg5X4rksW4uCGZZNEHMWd4aGA/s9WaqWRs27uoqa9L6PamvBCKZL9xrOOw5E40SjZOQQQ6Aub3iLtYhg+gK4Rq1AaxSvMudnTTQMne4QluNoMRja7Laz1X8XWhxjiWopTDaccD2r4JkDwqIGKuKb9OuVUvhxktjkcaDopoeWXzOcSU0y7ig+L1fUp59GhBRmHaID6JX0Ej8Qkzy+kjFOFQeRBf2fIG8CsddBRqIUAoicfVyuSs54LB+0NOSrKGFRX6V0UwoR80+IkWIey7ICVp6oBMTiTg0aARStfMVjEwBikrdC8XnE5AlcRMLPBy9hQsQoopfzBGTFyg8syl07BGDpulfZK2iE4TUMh37PlECTjgpGMDRxJAVDK0DMFzvmSXlxJquc4lUNI/HwpPuyHM/9fJyv0cxeItF6xs1u5LAzJ6LoAjkkQUHYhBvFrNcjZniVo0w1IBbWv3yzSCmvSGQd7j00oxRQ8HRTXuDdY1Xnm4sQpd9Pky7LEbueJ2LmSVr9HMeDkKsOiPxiW+UkxdbldA5IRJUVvigTpMTgoma5dNki+M5k7wBIHjGtykBnZHNGrEzxXWW/WsqiRkysSJJbbvlP+97SxyuHsySbpNIHhZC6cbr7/qM1EGIZiMlfKxd1jlfK4929vllSUKO6Z8iqIgbHfhzGunXxPEAsVgWImUHrldzSZMkiToRjS05tMt9bqx/GwwU2aBJOuHAxpJi1LHUD/eglr1JnlZUVWXn1RQMriURAWZNFfibZI1aOiIhEwwFiGYhVNaz4xqnsEbMsV4HvjPdCJM19DzzgtklX8DtJ41JYu1UlTCgmLV790loh52IZ0ruI+W5Synuvm5Fy/MK8m2XRpPcfM0oeIGQ3WBCJeqM9Ir76naapkHRyxIZ87TJ8lpFkNotAeICY4eR2a4qCkEgZNE3K25GV6JD5oNYcYRaD40EGeWqkPA5lIFZyPWJaOKMleD2K8h5P2HMuy8a8wsf4Oh+zvsoF/C4ULMV8pTI0NYkilb7QUl4DwsHI+9H0cLAbi0Y5bc0y7zZfaH25dqL/WTxATAY8fo+Yv68YOj16T6At0RoeMT1mAy1Dmoubijrn5Q/n/MLhbvkBq+AZ10YNL6d/WqjTahzrSH2f4xHTLE9fVMtATAJnStzrEYuZ7nu1DNDV2kBMVcusiV7RJF1RlUI5NeVNIPamHKCU69TkIuETbCyktoRihi9yuk+hMg05b8gr0UIer4pmSyQAxGwpGPlKWJV/YbeEBPj0Gh4xx7IdtpYMSMBHXhzkqSFd8oNSEdoeADXmbUdBCxbXtNtuT2CmZO3WLY2i79hENhsgkAiKVVGOVF/YqAeAOBNlQRGeDxOR2jycUh6L1lf+VoT/yQQlw8mFknYYPiCWjlWz8Icobj5lvJRXK2Qxsk4TL+QC30OTzMXyZSq5g2EejxrPP6v/FW7acxPf4Voy9EFuZKUiAFJFPxCr3DBwrC4pdglFQbdcBaEo46mQhC2/slFmftIi1YHYSCbqskesWo6YZuRQIiZEUnzgAx8CvKxsEcdLnmhqqGwLB2Lu+0jWUHCVkj1+JKcX2fIjOouyInUQyxQo8Ygnz0wNq2EkfX+9qncuJKRVei8J32MFCo9X8U56rpcHJR6e7+SXZEi+peXMGYoqUU3nDyw00TIVTFPOMSwraeHvp5ZHbOa6FQGDiQzEIoX6yt9TxRaIpjn++OPde2vBXF9/kd4wiUvrwZTSxsrfk1lX+duUOkfMB5aNgvd3vQTEdEXh6HXLGbd/F3O3vVq1DQnTCHjElFKceDzp84j5gJilkBC2gaVfER4cWzK81yvnycqRFoZDMqSZFormrrORUYQmCiGYtCe8VpdZCo4jTXf7gBIpe8RciRhFImXQrIVPKorli5ypQl8fL+SwEHRv6uRQnkfFDPWIxfC6hFUtiSJF/ug+IOYngzoQiej2Ow7ziHX27icmEW9FfOV1cMLf/OMlUpQZWeUJrXZoYsnQGNCCel20WAxY9MqvOhK1DWdFaS5OGhYtVoYGywWZ/tDEIB2YfHHvz4Yq318Wr0dMYud00mKELnlg4xqGdEzccvVE0xREJM+hEmIU1v0liByRPdWqxG49Gs/y6yFvArE3sCgVIBa+XzeDVhKlCp2n5otTX3KhnayvhITURIp5LIKWfBmaeXLErHxFifQnzypSMWZF98bpJiSNsez5CAMZg9LkFykV0Z3FISeFh6VL3pAGq3ydEI+YFpGU8xIUtfDjLCf+25CBGBpZ3yuOFQskpElw8u7tgWspuIuc5luY/WxcAHlV8UyaXo/YMEAs7iapCkQgR8wwnEVCJkrwhQulouGKaCmkwKW/OUbeTVeXa4yN27sz6BGTa4l4AKmzLRSI1ciXKxZgTX/FAm/21g7BkiVhZL2hXlLLPKKCLvW9hCKwJMBgxtz2WT4go1rFQNHlMlulplcHjSMpSBsf1iOWR9VNRDTNn++6C4CcVP267BFL1NVVtg0Vggq0TI6RGAEQk7951jeXCYkYwTJBjenoUkFnTQ0CnZGEJobRcUtRqyR9XooAa2oIY2KgHXkxYiAWVmqkDEZVKewnEJqYGBlZh2lECDMYRKoYVKrliB2z7DHOfPAPgVBYuc/qxTrOM+/nIv7KBLaDFmXFihWV/bZHrHabw+bIqATETsov5yzrDq6yfsBENlW2yzliEVXFkv2tprfRGZlERgjm7tjIWS89ScpX41EOvo2bwRpIihElFov7QIpvDJsqUSepe0BRPIH9Rb/uTlAKdqIyqoEHiEWl7+Rnp/WLEIKpu7cFt1smZjHYTyNSLnHZI2b6POS6Y/QUsjHYE8pZvU3ymIo55A+yDhFmJPUDMU1NoeakfKd0nWf/SIud15J4NHyNiMZimMKiJEVgRAJG1DLTrS80MYQlFajQ3ZfFT5lvlHRySjDlJFrym3/d/hB1DK8yYZCW9+qG63KGE5roiin1c8NfpsAPxNTafQ8g6hDOKLqFKQHMskfM7HPHfHxusyecOyEZZC1DoEn5XSLEmKQpkbBmEpU81ZohkfWowxvWXk/5lyzo/K8ildDEKn1IWMHBLnc+z/aCdwJpaB/D1f/1I7RIlKdeOMyzL1LMgxrMEZNDKRK4RTaLZh61SmiOJk2siq8mR8wTau0MtuE8YkapEpqYlcZnphROVxs22Wuyd6kARdVLX69aDsOZEh6aOORP6i0U0FWFE9c8x7aGVo7YsDJwT/mr6L7QOlmxLCutOQUkx4MXiA0j0Ui9dG1vbJyKgumEAchKsN8jlqjiaSiEfWef0mWWlEo82thBg5ldm9mfTLMkxNKuyXHjskesDMTCQhOlA5e89CK49TExCirdG+qIpgoUBnRiXvx8MwAAjExJREFUgyO3KKd8oWOhHjHLwtLAEDIQ8z1/RLI6Fgwkp5MnvLdyyXJoYiRW1W8wEnVjuNBEzcih6BZqLMUhhx7KAA94xnTEsdTH61zFxLSCi50iEQokQoB55TjD7rMy8UMAiPk8Ynoigi4t+poaDCmRjTVaFSAWpqBlpdVuOCAWG0nOZx5EvEoCr0+SIUCs7BFTZY9YWI5YbvgcMcuoYiEepUesY892VNP0k5EFZJ5Yg1YmCdKijB07FjY57LiaMizL5/ztr7KhtdOzTc7hi2hDXMHPA+fJq1JU0xjUTkQp/IP+5UHCggbJw1wriqBoWZRHWJhHTBgR4nEvI7AeEpqoO3GlA4riqUXmB2JhUsg7Cr3h9YjF9IZqp4RK2HoXIR/uEYvXV/5WdCck3jMflCpED0rUvbIn98vnfvcwPrqvlUQ+S6/mA2IGAQ00zCOm5WUg5p3PhwP8I5Fk3Clu7+sjsWgUAxNDmuNipuWZoyvn+F67LoUSliRgpfjnM989TVMjJ0IM7GEROxWyDvubycCmp38Pq7pXM1Dqpj3ZybriXPzZi3JoounTG/1G+KQyfCfWnTw2RbMoSfOR5hh+zf4uGi6ehjlUIj6/mdIm970mhGyQ83INhEXFaM43qQXEVJkMp4oH7Y0io27dCy+8cEA3mj17NrEQFpo3pboI2SMWFhFmBdW2jR1DzNwSVD7VYvACda3toffViwUsxQow3smTdLzCECUomQUUtRxq5J1YdMudVITm9YjFpElcqXjE3P3lvwcMr0esvDjkNPfgjOFjogv5qyxa3G2HKNiWcnniKde8UZQQK6KlkfV5ASLFAkIoTN+9jekhFknAQwai+5Q8+Y2VJ9yCWvIMTj0mvTvfOy6aAl36WLoccoJCQRRpMTPsFf2cVJiHZdreQ6FLi4UPiMWrALEwUgI/i6FZEpWi2QqC41950f67XCTSsirPIHvEPIWtnccJDU1UFG7+zld5Yv6hvO2eO8h9w91VKihgCXY+Zycwj29ylLGRkHWYVXLEJNFMC1NTKEodJumP84/4PGLDATHnubVItCoQq1XUtywVIFbFeKkaeRTVQo1l2LdjL9G0NzTR9YjVDuPwALEaHjG17BGT8lOzUe93kJVeyxRoiQia1JfLYUPVZDQeMdmDPhwQiw9DRw9OaGJspB6xEAbScnimKnvEfBEFiQRKvlaOmN1u0wg3hFV7P4nKXOyz5DveIkWtPV5UuWSGFmVwcJCk4121PWLDjbdhLOxVgKUMxCK6hqFN4HMb41z1TJC1tkEifqkGxDQBBWmXDcT8HrEI8XjSC8T8oYmGiu4s2Fkh0KVPlg+ZR/ySyzuh/SXLQ2CTiIQDsTBDgxAiNIc3SiE0RyySDoJX2UMeMVzWRDWmSnOT5EEMq33tiGKKigUyVhyytU1pHlPM4DNEQzxiMmtiJF3vvcdB8YiFj+FoNIohLDe6BjvgOWyO9s8fsvGppLpGGNUHxJSACi7IKyNjfCw4aR+aMx8XpVBdNZ9lZc9jAJjKdorqXMDy1Jc1PUDMN0/4+pESUp7AL2UdR9FNinnJIyYxdicPc3VOWZdIS3O+ZfjWiBBdpJqhR648IYetihEY1l5PGXXrDjvssBGTTcjy7LPPcuihh476vH8ZUQSYFvF5zcMfWz5FBmKO/qaalp2UaRVDPWK7mvI8vHAPH/uTd7oI0IhLUuzVoF4+1sK75DntkCbpOFkQFqZlYGGhSMVoZdEtdxCKiHfCi0pGlrDQxLK1e0gGYlK4hKxY1Vu9lZublqi0JIy+XvcBMVMRREpFosUCeT3CW/hfe2eIO1tHRfVVmY8VCsNa5jw1PnysjfKiWvGIqSUP4WrEY8TwAzEVXQ5vkuOrhYKpWJxTWESBEjEiDJg20BQSg9ZIPWKlEAVV+JQ2q6RUCi6GzRRC4uSMSDO+rLtVQhPD2iEEh619icPW2gVB5ayIYsl7fK28K7+kzEGGyxFTTRNDVTxwKu4DI1ZUYkPzeaJDgZhVBmJBZenL6UOAQBpeqJSvXc12qZh24T49kaZ/YDNRfBbwMmtivPY7UyUts1aOmHAKoluSdTjoEfMBsWQETTIa6VK+Y1kMKVS5KhAL6XlD0qH+HDF/vor/m4aJkht5aGJyqHodx+E8YqKnelvK39wyq5DrVOk4epVQnUi5qPcwoZkeFUCLkc8XoAzEQnLERivKjsUYLWsD22WlOK5HHEt3+L3qqwAxj0dHUShI3z5mFgNATBhREvEEJelMfz1ELI2I4xHLCYGs9+VHwDRbcuYty+c+S0TlWkjDXib0oCi5UI9YpC6oi/g9YmXmOT2uU8IMvOlEIDTRPcKSKKEjJbufyiBTCTGWxH35s6rmNQpFU97QxJHUVxxOIlXWiEg0GCoeBcJGcjCNQ/I8SSUDdN/z+CnzAXJKASs01993E+fRy8XTZc9deRwDRDx18WSPmATK/JFUgecZgVvXEUWFogTsKqyJPpF1iYw0XvxALNwYG9pMSlLdOw8QqxG58UaQA4KJn/3sZ5kyZcqIjjUMg2uuueZAbvMvJa0fWEhu7X6SSztGfI7LmigqQOxHzwzx7XGwWflm0B2+RYCAjWOGaO33ArFa09W++1vgIu82i6BHLC8lwJdDCEwntrfsEQvQ5ksTq6InkA0rsZAcMc/zRILuZ80ooTlxb5LRhTqjp9Kbc0Qkg3hwYVKjscr7FAWBqQg0y+S8ZY8Sn7GPJeknnZ3BiVi3VI7Y+RJ3TV7sPmPR9QhWk1pATE6aLR9X0osoUuK+B1D4PWKG4hnJiuJtt4WFglIp7GiVQxN1V5mo5Ig5L64qEAvLEfMpe2ZJoIqgoldeNIXlOizk8EuPZ7BM9hEyCddS8kqWd0obFRAzsshBpFWBmKZ6mDwTQiGt/op+4wqiyjKIuLGSdo6Ye02/xdc+qDoQO7duuq8F1aV8zL294Uq2YhYRAvR4mnnzF9Bl/oa8COaIJasw7VWuo40QiJWf26gOxBTZEloGYv2SBTzmVbwAinkpX6laaGKIkpcdjUdsBMhX5EFJjBCIheWIOaKqEqFRSI5YLWWz3J9MvwdJJNGTp4ecUVvKNSQ1Pfy7WiFeDLQoHR0d0OtESYTUEas0K2RODRNl+1LqU73sm/Jnz3a5EHtdPBrqtS5LNDq8RyyhKmSlXdFSMUjWYUZRIho5mSTG8K+vSsUjZgmBJoXX5kYExOxrWz4yiGQk3HAb9jzxeJxMSM3EKPlQj5hW3xjYJgMx3ShVanZqcZ3yoinfOWDQkN5dwZK9aw7Al72/IWM0mCPm9SA1Nozx3u9gkHVUWSM0LTgPxqrcLhgSWg2IeT38QgTnr4IoYhkjT0XQQuaqSF66p1KqNEduZUmRCTJqe8TCcuhrSVEy7KpG0PgIXo9YnZSDaJWG94gZnkIWrhQk8C/bh4fTz15vOSAgdvbZZ7N48eLhD8QGYldfffWB3OZfSiKdKSKdo2NuEU7/lXPE5vWaXLF7H9+ctxUr+U7E4D1suGc2Mxo20/jnArx/9G3buac+7O6BKa4oATHdKZhoOOGR5TpagYLM8sQaSSAbvDw5YuVwLun81gnBPAbdMCrhCHlppGWEa6fKoVc8LmEeMbnYtShSoZFuHOpnqrGqEh7YnJgYvD8ajYV93mcsDB9KICxZefRO/AUP5ardtqJaQpMsQx6CEd8zFU2v0iMDsbyRD0ZpOaFiQlIADUeRE05ySExXSSQSDPks+WFADEkZtyxHoXaAmAdkOp3DlLyHEdlS7Sls7fwfBghreB/9sfR6ZGSKH0DKHPKQbpS/mJ9Wv6Sp5KQ+lFAVMtrviSjriChrUSN/rOwzCyYyEKtVaFSPBecHJWL7RUdSHmHSY1+nGN/Li63fDd2vmPZYjSbq+OWvf8Pis333LxcB1msvWoY07kbiOZKBWDXiIbAtoWpCQxmSPODxYGhWSfIyalXbWjs00R9SFcgRG0HuiZ0jZitMJ655jgdmHVb12FoeMUX2EoSwJnqb5t0fq+IRi9W/t1bTq4rmeC+rFRc3jQgKPm+QFmPVspUsUCc7v5Wq5DKKEgQEoWJEaX71AvZPvAdLtdu0edN8mOQeUp9IsrevOhtjNQVblriqIE+fkVIhNDRR6CoFagAxS0FXJKOj1C1HBMQMe5Yx/VT3kkdsOLIORVE45bTT+f3L3vD4CHlMIziO1FTwW8hrvCYBMRGXwnKkZvjHkazQl0rNlTqpqkMsJhPTWCKofvoNVarqTWeY0DbDu/8g5IhFQzxfED4GEiPEfTJToinliEU0v04TvKAlLIyB+SO7EV4G37JES+57S+sG5WVHJgcpqVJJiWFCE0fjEQMomcMDMdkjVidbZIs+IKYFn8+qEipZrALE3ug5YqPuxbfffjszZswY/kBHVFXl9ttvZ+rUqaO91f/vpRwq7FdeLGFhWRFUfQaR9OXke68l/dskaq9jVRsm0/q941pq7gd7wPqnCDmfJEreDjFzYqGVSk6QV3Qp8TTv89R4PGKOBTbdGGPywhaS9VGOvnRaoF3yAliULdyWuwhmqa18WxJwEgUXiAEI4V4/rqe5VPEuYBoKlq+4YayQH97iIinufnAgK/WqaaB37yGSsLxATFosrGLRM1EWfNYsGYjljJwnFhzAchRjoUlAzCwDMfueTcko73jHO1i4cCHTdDepPiw0UZGUYXtCF6hKuc6HHHoYkhMgKWye0MTyH1rIyldj8T2k0cvGVnlvI8kRs7xeizAaeNW0WbSy0jeLC4EQReLqs6ii3+vZKnnDeWrliOmJEEONwzQ6klCvyFA7P9VWUs1/pjieUC2e4gMf/HDwVkYJLGvY/B6ZNXQkgMWSgFi+BnmVZQqMmIUiUSVHEkGPQKkoFe2MVLm/z/pgDT5GzpLKb0gft+XlywJArFbuW1lEARQnZHj67m1M37ml6rGJGh4x/zVlURIJT4FugW+sOwqSh6xjFMZr/5dWy7UOo+EgxjIiaP5GqhGOPPJI95q6NzRRM4Jh06H1CENv6F5n27bZnl1NqQSibmzVU+UC6dXGT1xRMKX+rpcKKIpXyRNGBKErFGWDTKlIz3a7flM2mwJLVEITAWS9z8+yGybVPDvJSNBrVUuMkPk5Sp4w9hUlxPNtSP1LN0pozoquJMIBdMw3SWazGR4b0FifU2BgQmW76swBiuq+27wIXjNA1uEDYv5Q4IORI6ZXMTqEesSqzHf+VpRD84GKIQEgpvs9YkGSjGLvQpSeU2nps1MIpu7aWrXtAJEQoBIXs1C0iaiRubTG3PvLkSolaRwXc75vEfCIDZ8jJktJKqVRDYjJoZQpqWC4VRz+m5pVsqmLkm/JE5oY4nl8I8mogdiRRx5JnURvPBI577zzyGRGzl72pthS9oj5lRcLC0wdXQgUbQxCiXs7e0i+gKZp/P2w6Xxhyhg+PjGcpEOWsNCyglxziAIINzSxTM7gV3gj0sSa9SkRMTlHzAFiQgjOuHYeV37tSOpavJMwgCYVAC5Kl0tKrracFakcFyn0UZ/0Kg5GwbVOiyKeei5CqnsW0eKopncCUlAwhdciHCkWQkNx4rIV3APEvEpOTlrMIoN9RPdso6VZRZXCgDQpnNEqeu8f9Ii5x+ZKueAqUQ5N1NzJrBzaJFA4fU4709tStLe3c/7551OnuADBCA1NlDxiDigsW9481PkhylBMUoLkrMQKa2KYAlUDlGT0AlOHXI+lNgqCoKS/vleZkQ4ZKFv0KCkPEIv5cuSiNWpQhQGxshU5kgiZI53ae0IIPmR8k+Ot+6pe+7Jpn+AvjY9U3V8BYtEUn/r0ZwL7NcMgtn1D1fPLIpMbxEaSvFZyn7k2EIOCbqBKQCyaDAFiskesah0xX7jswCvkpMVbBmLRgXEhZB0jAGKWQEj9y6zRL5PZ6h4xzzV93UOJxz314xVUTttpf6N51rLKdtkjJqoUTB723paJ4txMr+IlsIGYr5FajHvuuce9jq565oSIlCOohCjfAGc9660DWX6T7aveDUC+vxnTt641pFKIkNDVyn2rsiZKZBOq4rEA6aUimzYe4rmOYkQQmuIx1GtGiZ1rZrNr12ReeeVI9IiO6hjxlmRzCKkPZ2XUGUKwVUt0ieBKJlqoBtyyRtBzUc0LL4d8lUNOvUDMqNxHyGGeclmVwO0Ef+iO8N97YpRMybvmGEFkkFsQQbAf8c2P/jB7/3rwWnMRwXYWhEkYEEuGgJ5U31Qa9vd6tvUW9mI6Rup+3d0X0/391dt+DZXcjsuJKSlOW/U0x7yyjKPXl0tDBK0XF3RbREPaFDUjRNIXoidPRVdkIOY+k9HfSaFfwywK9m2Y7L1AIGVsdKGJXiA2fMSQvPZaJcETu+/EsAxe7n029HijEO6hk4GYx/B7EPrJP1NGDcQ6Ozu5+OKLueeeezwTw5vyTxBnzg4CMXvhXatLluFSjt8fXfaIBSc4TdOYn07wvvGtpLXhF2otxJXbys7K3+UCn4YvR8yvl8kWrpyvu3k9Yn5K9/CBI2+VLZlJKTQxT4SeaMo53uKsk/2sfu6gr+URi+gxhBlcOC3h3RYtlQJ5Usc8/AgTN21yN0hW4VoesVjffoRhUAqEJkpArDQ6IBYwboUBMdNlOfz+2xdVff+lEIugIi1YllOPpDzhe0ITQ86NSQqvbHHWa0RCDLf4yg5ALTpyIJZoHu/5XemekvKkWCYblA4PeE74rG3VGCchPEeszFilJ0KUStX9lkvEk1zD96peu0/zFzX3Sjk0UYun+PebvxnYX7duGdM2bKx5DYCcFEobGwFgKQ3urvxt1QJupqDoUxqjqSAbnkzWoYYoIeCG2Fbum8t7Si/IYdFYUPJF6cdHGPKkSEAszFtcFs0YmUW54/P/5vktVBU5bUgRGofu2cbFzz/Iddxc2e5hTawSEfH+hXbc+vWLrg/dr5omwlEetWisEgK9dPsyz310fyKbFuWMM85w26wJj2Eq4vGIhXvaDt3gVcDLvSS963AmPvFlul68NHBOOh6vzAUvTfYqwoppjtgjpu5214MFuRx79kxkaMAdi8KIIhRBQXom1ShRGGzglZePoq+3jfoWN5fpy3v2IUfdyUAsjGCrlsjtlpXhqkDMDE6cejUgJo3FDX9bSKFfwyq670w3pfto4cA8Uo2iFYgV3e+eyu4HQJGiSYohQCzmA2JCKCjpGrUgRwlswyQMcFXbngzJnYwPjeHYh5/ybCuYOe7b8b88svMPmFIuVkz3Pou/X7aYGec4lVQhx5yuTcT8xDCOfH33Xs7q1kI9Yponl9P9RrJeV7BgzW+nsPIX0zByXqN33Ywhmi173j4lshJrlB4xwxreIyZLQjrG2K+xdXANf9r8bZbtfyD0+GKVmhAyEEvOcb3J/3IesUsuuYR77rmHs88+m3HjxvG5z32OV1+tXqn+TTlwKU+CBd98YAFYER6IF1keKXFq6ma2nd3PH49yPmcVj1g1aYwGlcPu/K7AtrfxU5qsPRxpPUIj9sRqVYBYOGtiTF60De+E4skRq6JQAXxkgh0CUnbVh4lsUXnGnMFdk4+iq66N+KGHUnfyCZ5j9ekT3fuWBDOkUKeoBG7rkplQIJbb56Phx/J6xCyLMV1d3vw0SRHTfJTX8uKpOe8oZ+TISrSSDWPc8EC/R6xk+kGs25ajO48OxneHADHDuYaiBPuODLTqhwYC+2WQqKi2YhpK1qEIFp19oWdbTFJe81IYUq0FfjgRMcli2zDy0J7EGV/2/JYIiCt/KabFrkSjh6RC9UHdWI0cqzDrdNkjpvrIOoRigeMREkK8ZqNexSMWS/DlL38psH/atp1M21V9jJVFNhzUAiyb+leysvtxSoNdI2qfaQryRp7sXikEOtUWOM7rERvZEhadOoWC1E+99Y8Uj7cfRujpA4QD9K+++upQb3HlDhXyGfc+6mB/4LiG8y8KbCvJZT7QMEolmgd6PUYuOTRRVFnW37fgfTz5lid519x3he5XTbMCvrRYlEuef5Cj1y1nUdeqyjFDu2aFeMSiPPLgw+79dbXiDQCISvN+aI5YiEG34olBEB0Yh2XY327yHjf0WNf1ynHPz+yhqMFZKx6ns3s3Z614wuPpqArEVAVtdQ9K1xD6iv10FAqccP+DZFbIY96+d1EOtzRLHiY2+V4dhuEBYoPSXCHM4PwpyzFvmV51n/xOw9j27GOC23Qpp08ZtOdlrcfrfR/Y3sDqX03juNW2t7VuaIA6uShwlfsFPWKunLvjVRp7uxmzZxeztz5hX2YYj5geEjEw7vvfIzpzJu1f+mLwJkpIOPcoxLJG6RGLhrv0w7w+3YVddGVfRZeAeFyq8RkmR5TstJ9olTWkb98mANLqrzhrcAhhRYiMJE/XEbkP9Sv9WIaCWVBRfHNGamKOz/BF3mbdxjXJZ+xwhVFISXIGKObwHrF0vpnsvijFQZXBpzJOW6uDv1IpfJ9sUIu2S+zY/IsBsV/+8pd0dXXx3e9+l87OTr761a8yffp0TjjhBH7xi1+QC6uX8qaMWiLr3Unf8IU+mZhYpk5WAXVxE9NST5BrNyoeIsvHHNds7qmZo5ca78bea471pbe4l+wz/1PZPlMtkOwr8B2u5QN8x9lqgRO6pzheNn/MdkSKQz5N9Sp5YTliYfKRCW2ctPpZTlv1tGf7EWtt8BXLm/zGPIE7jSNYZU7gv0oXMBBJ8I3LvsjEX/0SoevMXLMGgPkvv0KkwWWu1DcLpkgulLYml8FgascsRMusQHtye71Aqn7uXHTJMl6/x7Yk6Z0ueIpJClhqtjfXoVPKx2jZZwPgKfVTeHl8P7sacrTNms2UQ11yHD8Qm9U4L9DGskyun8zXjv26d2PRXux6nzvR/plLUSzYz5RQgqFgespdtE94+YWK1bwssnU2Vd/GmOmzGXIscvWmayme0t7K0Vdc5Tk3LnlnZeKOBT0js8DVX3opDQ12fsqELbZXNNIoeWw6yt/avbaZ99acq7Q95WXoKis1QmIWrBss8tfJR/L16XZuyrxUHNWXYC+Hiqh1Uc+I0EJi28tAzK8s7lrUCFNOqvzu66te+uLUvY9X3Vdpi1PTT48kue7DH7XPs/4KwJStm5jZtR89JLTJr9u1RlxlZG4qnN3LLAme3ns3q3oe8yTlF3qqE1pYpiAdSfPyo+1k90bpeqYFPVkfOK5BWmDTTVU8npJXqLHhaFquu44p3Xsr2/wkA2Pw5haOiIQEEM7YHTt2LMn6YFv9kjr22MrfsR0b0PftpJSvDRgmNblW9LiuUSqFeOkNidE2GwSvlftHvIrrVlwvcF6PIByQHYnGaBrsY+6OjSjbB+l+9ViG9k5h7+pzKiF4FdFiLF7kzk82fb37/pZI5EaZTPD7h5VW8b+G8s9j1i3nkC0vc/6LDwcUZdWC8bu3c86KJ+js3esl/5EMBvKcFFMEh7fXEVnRjdqVJdbeRuuePR4CJuG822bJmBYf6mPMNDeH2d+W0jZ3jAwxQJ1qv9dU9y94dE51kD/v2M6q+5K62+4ZDeF5+me31gUYP2XjT91/Rkk+pNLwX/5QWbt9E3v2cdmz93HhCw+hy8WXJR3EkN5lrciF5kyK397wIf73po8SSdl6oQzEwvp6NMRQlVi0iMl33E7DpUGvaGSUOXR+2bxp4aiAWCTmfldTc3PtZa/PIekEm+PjAHh+aoydJfd9xavUhQOI759J2rLntrPmjgkeIKBr/SOsbfg7Ge03AOQtnaimcNiZE6tetxxKqsYWs3lgNXkji5LU6GpyIxV0yXAqYhpKehzt7OQM7mZ8/awKF8Bw0jSrnxeYRVPEJYwpv5vEEUs9x06Vaq4qSpyX/zCJVb+cRrJQI34dW984dN5RDOp9njw3rWChS+trJCrNm3FvtMsbTQ6IciaTyXDttdfy9NNPs2rVKq6//nrWrl3LO97xDtrb23nf+97Hs8+Gx3a+KV6ZMHAxard3Imj4H42GH7qTwDeO+YZn/57kNuaOaeSSRWP53Nmz4az/QLS4E3N9LE7b//wPb9v6CGOMHZyz5XmOP/74qm0w1BjzVz+LapQ4677fVbaXdjzPZ8Y2MT8V5yPJCC+vPQZLCoMTwiXrcFkTvZPrurEXQMNEOPUrNM061bPPU0esBhBLqArHTbLQzV7WNaxhW2QXtzfez7Ers5zy4iBve7ifl+d/kg8XP8RZha+xDzus5NNnuiDqjKveyWXrX+W066+jufl4YlsS6JsFiScUDjvrI3R0dDBlyhSWHnYd06Z9joULfoquZ6BtLnuLT5CwohxZtN+x4QuH6Hz3O0kkk8yYPIlENMIprW3oY8eSPOqoyjGTD1tCGoXJJYXWt7zFc/5bxzRxYmOaEyMwP9fHMVdcxQ1LbmBs4wTSVx5HV7LFQwbiB2J6SOFpWQ5p8+Y8xGbYoHxw4yK2Pf4+tjz4CfRsK2oxQWs0CNhloNUy0Mt7//GHqvfS9QQXffarrC5kyJkWfQWd9vwAmd7dnHzJ5YH+ITP0HRI1OXHvy0weMLhiU+0QnrHf/x4Nb30rLR/5MHPnfJt5825l0ozPQN04jEnHVI5TpfCk/Ko/YQ7tJ/fsj/hofYTGknfR9yvS5e4ptLE0LDmFdYkpfGbxRxhI1nNCU4bnjpjNXxZNI++jnJZDExvfOhMRd8dyGBCLFDVOnG4vGmP+45uYCO4bt4grx9zgISZ55eUj6ev1Eu28/Y+3cuWKP/LV9bdUf1nld+FYxhVN45f3PknL6jm8Y80dzH51B+/4rT3ufzjHS6X43SsOpTHhtVxf3NbAyU0ZTm3KcHqzN5yyse9QzEGV3uUu61fT9R9nc/MEHjj/WvJdFzLw6sfYkkuj+kJu6luncdaks3h1ToY1t0+ivm8qIkQZOuzMiXTOqGfOsZ00diQD+wGazm6gacupJBjPtOmfQ2tq4nunHc3YmM5VagzNp/yn8XqnRkJCAqC3SwVKaxAH1F10IfqE8bR81A0LVEpFYru3sfFvnehqC5MnXRd67rhG9xkTerQCxLZvn1nZnqrLsCChsyC6nVnjbK/KSVcFjUiydGy/mt3Cly/seKf0WIy2fD96926aZh/BX7a8lS0PfBojn/bU0wJAi7LpVTekVeiCKRPHccbeHUzs2ccNRx5NS/MpJJPTmDr1WhaMOYbIPjfMPYy4o9qbjBcLLNm4hva+blRV9dYHMyG+7VWU3BAnGI96ztN1nUWLFtFQX894zTVoRBSFmy9ZwOETG/jcWbNoeve7iM6YgdrZKl3X7oOXpE0aB3qZtGcHY8wSc49zFUu/Mn9ndCysFHQ9odEfMfnKuCHqdn2NSPYF/uschWlPPsHnkysYa27ng8p2OuvjfPjEqQghmD/v+2h6M9Om3ei55tGdR7OkfQkzGmZw8fSLQ99PRzTC40tm8YHxbvsLu9xSQ5Ouv5nW5bOZdKM3xDnT7BgKLWgYGiBqlNAkY0Z0Wj0iZhMxGXLYqWOtOqI4nbq6Op5oe6KyL3HReaTnzyN9+OGcdc23mGwKihKN/lvNPeiWdw6Wc8R2757LcDIlEeXc1npaqtS9y/WEA9uXXjqJzZvms337zNGFJmaSDMTtQthTzr6cdIPGvIZHmXr94XxyYjszkjH+fcZYTvrAJ9l01DV88Jqv8Lv9EfaWBI/2ax4WzLJMyHyIWO9k2ta8HRTBqbPbuOaYMMO5hYnBYKS7UiYgj05UU1ly7mSueKiPzKDBKS+G56NqsSMw4yfy0Jwk7Z9azJGTjq7sS6hxMqdPRK2L0HTFTBrO/zPNsfmk03MZO/Zt+KtTNhd6AtffeMQYjNka31TeyUkXtZBWdrOg9Wma3nI5+rhxtPnykj+gZDlk81qu27eZcSdcTlxYKIbF0g9+hBOutEtezTzquMrxn178adoSbXznhO9w2vwpLJ+f5G9TC3SO+yixSDvz1vZxIk8AFg0NDUwYfwbx+Hh0vYHOTq/O9UaT18zpOGvWLL75zW/yjW98g7vvvpvbbruN2267jR/84AfMmTOHFStWDH+R/x/L1HO/wVS+wf0PuJNlfLlXCThz8plsw11YVrY9Slu6g5tPWmBvOPxqaGyDx24AYGJThsbjjuWblK2vZ9Zsg1Eqcdojf+akx+/2MFwBfGhCGx+ZFmH58uUsz6VZsfxUFh5yLwD9/c0VNp1y/o9/AR1smAFXLpe2uAtweRJX0jp6a5CYQ5b3nPkeOBPm/Wwe19TbIQrXbjiWpa/YE/fSpVP59Yt7Ksf/8X1Hcuj4+srvujNOp+4Mt65O+x8mUFhvh9TGkyne+16X7nn8uHe6NxaCh6d3cc8ad9LyA7HMxIkIIXjLO670bN/ygBvfnGps4j2/vTP02RKqwq8WON//qB9Xtt91wV0APFx42HO8H4hh1bYg+c3L0UkTaPnOd1Duj9C/3ba81WH/P/vECYHT/aIWJSuU33KtRBBCMGDC3xxq6Ss//28kMjqKomL4YmfiUt6Opgh+tOIX7C9+0t5QQxdOH388acm40NpyGrScBkd8GONbX3OvKYWjFdbdS2Gd3Xc/echsPgk83TPAhcvWc1gmSaOuevwirkdM0HLSJdy7+0X73o7Ha2zMvvbsMd4wn5jk5YuOz5A+qhM22f1edYDYhAnXUsjvIZWayUknuqFidWedxQKvDllpQzZbx/LlpzN36gZWdk7myOceoH3PDt7VeycNU7JAbatfNLeLqwqf5KfA4iVLmH+c/Z5PBhas6ufetsNZ3jyFb0nnnDW/g1NaMuz+zgvu86kKv5jvS+525JDzfw/A4DHd/H7nZ2no6GThJRchLrUVxxmrd/GzJzfx4VNnsHBcPfc/8JvKuae+5xoSeoJbr70D4+oiqhber2NJnfOvP7TmsyaOns/Co73K5tx0gueOmINlWRjzJ7Lz62Vjoa1kJK1+BoUNiKMjCE1MnXQSsbmushgAKJKM+cpXqu4b7Eoyc8KPaZ1ov9PWT32K3d/4Bo3vsvuFHPasKZJHTBpKE+d1MOOiz9qbTYuh/gLJutrssa2lc8BvGHAMa5F4nKs+exPdO7bRMXUGxz65kwd/bhdYjrVPxsNgr0ZprGsAZ/oVqs2a+JNLpHVnzPcrf57x1iU89unfV36HecSCQ79azrAExCxQc4MkN65myfRlgWPPOeccAP5n625Yb5eB1wRMak7y+2td1sfJf76D3KpP0bfrD851nfImUYVLH7fn9JlHLnEJqggq7RsaIlynx3FsgkyIR4nkVzsNFWgNDbx/8Tsq1WY+5+qbtLScwnEtpwTaH1Ej/Oi0H4W+B1nGxCIsqUtSLmLRvqGZMn95/Rmn0HDmqYFzwoZawRpCnZwic2gHaipC+0cXsfMlHeMVNxWlHJo4xxjHaddfwW0/u62yT09n6PjNryu//zz7HG6++cPU1e1icKCRY+vb+fschRPcyNdKGGXq0c+hHjdp2GdVhOAHcybSXcgz6/E1nn0P9mns645zcb3vHCNDT/cYerrtNW80HjEtFuFj/30rO3ftZeqUCfAWwNGzPgp8dJJt2Jh3+CTOO9xuf/ejMf6ty+7RH1ODY7Iz/Q5iTy8CQMRUfvCOalEDglhuL0PSPF9Ar+SITdlV4iN/6Q05zwmLFipqZDpnHD0HJaKSljxGAJnjx5E5fpzzq4EFR97u3lmJcLj1FM+KpSze8RALk4s4enyRb/SnWTWY44KGDO9v+ygYFk0iSvNxp/COeRshfT7o4VELb7nkEmR4dPVP/0ipUCCRqaPNsphy2BIyza5B4a2z3spbZ7218vuX10hjpP1iuG86jXRxPT8i/r6NRCIRjlh6H6ZZQlWjVCaoN6AcNHJ9VVU599xzWbJkCf/+7//OLbfcwqpVq4Y/8U0BYNy4d7Jx889puc1XB6kzaNEpaPlQMo3KOSF5PrXEcBR7PwgDKhbpsuWxv7+FTZsWENFzdO2YwUwnh6q8379c6pHq7YyYEJ3cRePl59fMEZPllAmn8I/N/+CrR3+VrhdVijmDecd1Bu68aEL1EADAG0yvVm9jWPiEH4jF1XAlwZRyaZTXUO8km/Wy+VnFoudxrWGAmPC1T9E1Mqcdj/XQ45Rrw4yZVk/T2BRza4TGlEWTbh71PZcQaqATaLqKUiXHIBGR2+5jxBpF7LssJQkoVlPmy7KkPsWao+eRVJXAt5ZtgPIuP0tVR53XI6nX6MuaU/9E1zJMnfKJmm2rJketX8ERD/+Zuj475KtGalJFpq37PbncAA+ZC4Fgn0q1tfBiNMVlh40LOXv0kqxv4Kr/uDWw/eTZbZw8Ozx0LpFxrcXDfbfXIkIItHpJOXBcMnKJUH+/LsvuoqBVt+gqCk767n979hVfQxKf/LxN77yK+gvOR3U8tDLY0BStMq94WEYldjmhiGFBGNiAzS/lK8biSeKpNPHpQa9aoHtrUcySQUWdGGF+XeWeIUDMqNdhSIofLeWR0zxOPNEOqy6F5PACaGJkoc2RKoWnTYlUo+wR888PhhSq6Ffm8z6Cgojq9Sr/s+Xkpgzv7mwmb1pM2La/sr1arpzheKflvYZVoO6dM0g4JTTUTBQ1qXtCE2vliIU98+BgI089eSmmqXL+KXFmtR7G4q0v80xf1nN/YSZHleaiS8/VYO3jr706j/RrjEl4qd8bNp1OLHkm4EZsjQaIKVGNVCrJ1FS4Jz5MbjziRm58/EY6U52k9No5bbXWvPjQXlKDW1k7IHvhw8k6PNf0/VbK+oA8xw1DvNc55nLevfZELij9hpvXfJCjjm7h1FkzWVws8VzfEGOKgntYg4dTs3F4IC1LJBYnEnNZgutah2f3DpM6BsAxwAqhVv2+byQ5KEDMMAzuvPNOfvKTn3DvvfdSKpWYP38+7373uw/G5f9/IdOnfY53//EwfvCCG7pS/5bLabj88sCxFpanMJ9faoG0MDGknINOtZc9uRTzt9rxw2FMd1u3uGFHpj9fyDfqozUUKt0ELWOhZka+SH3zuG+yP7ef5ngz+z45wJaV+5l1ZAer99dOgg6IXNS5xuQXxk4VAGJVlLZaC/VoJECGUywiM1QLhgFiPmVDccCxzD53yKnjmTivSg6S7xWkJk+s/H3tuBbokazSQg30Aa1G2GmsBokMVQDucFKSPIYVj1iNS2WqsIgqugKGQcfUOk8/CCugWZZdVj26TxE1JNdFOTxQVGGPqyaeECygrr/bVVpUC+ZfBv1PVo45tPVQkAgQx21/iG0pN6zR36d+efUS7luziwsOCQLx/yvmXxFC8PJ/IVYFiEn5ilXAxPf3RFmQMHh+UOVtvn0dVokdIUVqRyKKrw+qUpispyaXPLdb4UBsxFKDECcSqx6hoPhdWFqM3FCWNLaF3W/48YsQwkOBroQogWZUqQCx/sJ2jMEN0GH33xmTOjjWybXLSgy4ZiaC0ucUDh5hoTKtyne2pDleWOHfVM7V8yvtedMHxEY53l+rKELwFSeP9bvWumGPN0LyDsFEV4Nj0psjVv09VzMIlxl6hWa/k1umdfL2ex7irBbXOy6EGBUJXEJVmWWt5BVm8CG+xff6ynUs5RurtL5yOcVFrchAbLjQxEx2kL54krasiVo/+jnqnMnn0BhrZFr9tOHp9muMndTgbiyh0p8zKoWyC9bwQMyf5auqbqkg94ja40XTUjyw77v8dcV2SpZeIQip1zVObsrw6h5X/zLfZFMftbwmILZq1Spuu+02fvGLX7B3714ymQxXX3017373u1m0aNHBauP/b+Rz5yyEn7q/2z75yUABQ7ApwQ+qR0zK1Tg0voPSC5ERl0k0faDEP9FEItXbEjEtRI2aS2GiCIXmuA0YmsakaBrj0NTvr3VWUDzJpzVA0kiAWFihYvB6xF4LEDv//PM9v61i0TNyhdBrFnH1N09xvklJooBVa4AlvzRm6vj+7Am82D/E+8a1sq5HvpcWeGfytf1vKi4zBQqB/CAH6hEz5NBJ/cAVoJPfPYcNm/uZfdQYHpGsymFA7D2F63m7+g++WbqUD/r2FyVlpZwjpowAdBwxuan6Tqn/ipmnwqlfgT+eWNn2szN+xk1P3+Rth6KiOoqnv09NbE5y9THh4Yb/VyJGaUA6eGK/y9IIgNh+Q+HB/vB+eYU5yKvZIj1Jb8jPzK5NwMKaLVBrGCRkRjN5bhcSyZB6AEAszCNWlmi8usU/CMQitDS1wA7HgzFKA0ooZpLaNlDqAomVTpPKf8hAjLNnk/ztM8ys2z1i44Fe5ThLCtkUZvjcXcvQVvCx6IXVmXwjiVl5Fkk5FyZaiGFBzhGL1vKIDQc+nfE+Lh7llDXPsaTFNSQpqhKYo2qJEAo3cBNZ4iQZAoKGhHKIqeJb64bziJ3x0pOs7JzMDZvbUCeNHoipisqxY4+tul/ugv4yOLIohomlKHQ2xMHBPQX04VkTfQOs7BHz6GojAE8lU6HkRN/4wZ+sA70uOOwNXidsOBn17NDX18f3v/99Fi9ezPz587nllluYPXs2P/vZz+jq6uLWW299E4QdoJzqC9kRVYpqmsKsCcRG7RGTPQiYoSCsmiXHz6bjZ02M1QBaugmiSpLtaGXUhR2lyULUAmLOdfNGtuox1eRgecS+/GUvrXrqhBM9HrF4pnaYhN+rqToeMVNi+6tWjylMItEo57c18MWpnaR8lnxFaAG0VevbxD3Frf1A7MAmV7k/qzUMAcNJXUeSxWdPItXgZT4Ms0D+3TyctxdvYLk1Fc3X7oIVBGKihrHkQydOZWpris+e5YaF+d/hzC47LLGlbxCx6K0gebuqSUm4QMzfp94IohygN+m1iuUAGrmwe/QAFOdWTC5/7n5PiPfY/btZvHFNjbNsqRWKWc0jJhQ59PlAPGLVd0Xj1Ws3BYFYnG1b3BCwsCiKWqKEtUMCYoqwvEWEJSCWK7nha/Hx7bx32tMc31a7Fp5sGNGrfOdyrUX7R3hoYi2P2Dtmv6NmG/4v5Yz3ziOW0jnigilVjzGdZ5Gf0MIKD82XYqFrsSZWo9h3D7D7fNg9hKKMao4SQqBgOSDMlcaYVEuqXKLFRw1fzSNWbldDdoBj1q9gRr9ZiSb5Z0mtFI30QD+mohCXdKYCGvpwHmjf73AgNnzbihLLtT88X/Fgujc9YqOVUfeq9vZ28vk8HR0dfPrTn+Zd73oXU6ZUH+BvysglUDW+yoJmYdX0eo3eI+YuKKqoMbOGiD800e8ditaIjY+YjNojVk1GrbLLhS9rKA5l786ju/7A0paz2TiwcsS3OFhA7Pvf/77nd3LpEhL9hzOETec/nHfF368Uh3LbkKjKRwPEaj2LCAFitSQZq+4RCyajjEymH3EMO1+1w3EONM4cQEhMJPIrrBWaCKAr1T1iqpMjVuubfezUGXzs1HB66rJM3t1DU3+WdK6AqBFGJktJ0Sphk/4+9UYQEVJX6P/mxkHFoZpHbCRiSsr9os1rOW7h/BpH26LU8oj5yDrc7a8NiNXyiMVi1XNZFD9yiqaZNGEcxe2OmX6UBpQwsg7ZrC4EWDIYlUCr7BGLabERGcaL0rWrfeewHDFv8yzGjHEpxidOnAjPu/vfM/89/M8Kt/zLqA2FB1EmH9LC5ENqG2rMkILjVuiHwZcj9hqUbiUc4IKt+7yWOerdc9/NHevv4L0L3s3QppvsazrWS3+kxWjW5lrszgdFaoydqevWU9LinvXVcMixZEk3xujfL+fX+T1i5dBERTpiBB4xSV/wGyPlKJg3YdjoZdS96vTTT+fOO+9ky5YtfOUrX3kThL0OYoqDmyNmypa9dGuNI4PiLxbs94jVyhETRvHgAbHROsSkOjFKLJzVx76w/d++/A7u3vYDVvc8Uf1YnxwsIFZm+5JFHeN6T4dTYP1G34qXyMNXUv0FRnZ6rYwB4hE5f0moowLFCQ+I8BsiDkx5OfSMc1hywWWc9eFPkMjUOe0a/bW8C7Z7fmQYRdPvESuO0iM2orYB9dk8qmWhxEaWOF5SFAYLdp8M61OvtyivV2hiCLHDgQCxch8zpfGRjkUrxBK1pBqZDXiVHE9o4mv2iNUITUz4gJg8V/iB6/ilbFjv5vMM78n2jfMQ1U22BwrFC251KdxYHtdpvboXTxbPeKwW6SF5xCoKvK8uWWdnJ6eddhonn3wycyX2TAgSVYSFuL+RxAgh6jKr5NmZIyTrGFaU2h6x0c5RDdk4WBYLIxdy3aLrePDSB5nVKJHNjDI0MUxGSip2oFJtzTvzL3eTyGaxFMUzXkxpPjjhbTNpHJPkhHfMDLtERVyyjtGGJkqeZB+Y9XzCN5HYqGXUK9+f/vSnf0Y73hRHUscfz8BDD9U8xiQYmihbNF5LaKI68zS454/Be5rhM26g0J+frCMk2dc92QjnzT0AGe1CZ+XdZGpRA4hVu+7EiMamQjhjV1kOVo7YXXfdFdjmURSG9ST4PWLBd67U8PIoBQOkW9T0iClaTdDj3xWTQwf9HrEDDE1UNZ2jL3/7AZ0ri6xQyu0eLjnaHyoSniN24GQdgX2x2nXkylKS5oWwPvV6i3idQhOVYtD7U401cbSyYO5c4iF5voE21LhfdY+YCyAPCIgZFtXc1xFfWLxpyKBPGqPNMyDVyqQJkyjtHio3bPh7S5dQTFAUgSkDQ+lvoYAlXTMiAbG3z347v3/59+iqzlmTzxr+vvgUymr5vVIecGKmbZyUQ67K3+uII44Y0T3f6GKzXvokBIgJIYgX3LWzoTA6rftwlvEsCzmM5aAuqFwzcB9FGfUcdcgJD1HsepbIpFNDr1vxbPqMjqNhNNaqFZB/LSK3s8qaF3EMx5aioEneWkMCYrOPHsPso4OFoP2GjkpoouzFGlFoouQR8+kLymuIIDg48nrf/7XJa15t7rjjDi655JJKzpj8b8GCBQejjf+/kqb3vofYgvl0/Fv1+GgrhKyjaLiDc7ShiSWJrEOdenzoMdWAmDmMRyyu1VA4TeOgecRGK/WXXVb5W0lUD+2qBsRuGNfGxHiEb82sTvd9sDxi1157bWCbJ3RmmNBEf6K4EpI3VcsjZgZYlw5eaGJcl5VUL5vaP836OFLvWJX7DxeaqPlDE0M8Yspr9IjJIiIjA3UloZBwik2H9amR3ezAThvRpV8n1sRE9wxyeENBIwcplGyknrVaeVVVc8SEDAwOQmii3E+jXoXTMOR7SXN+p13PbVeXVKD5AMg6/EYPGQMowvR6YXT3WZvjzdx3yX387aK/kYrUpgYviydncwSsiYn5HcBrK0fyRveImWEesSqPeNimtdQP9jN23y4O6R5ZmYCynMFDvIvfcBoP1wxNVFRl1HOUSDQTmXJG1XoeFbKOUawrhxXtiK9Di5O4b8fPUaL/5ByxKmuL4vQ9U1HRpb5pjsigV4U1UfGOueGkJM0BQbKOand7U0Yir0nTufnmm7nwwgt55JFH0HWdpqYmz7/GxmAV8X+2XHXVVTY9bpV/Tz31lOf4F154gZNPPplUKkV9fT0XXnghGzZs+D9vd1kShxzCpN/+lvqLL656jCmCQEyupzJaICYvwGoVljkjJIbcPnUY+voarHWWWXrdQhOb3v0uGq98B53f+Q5KFVKUWnJGeyNPLZ3NFR3Vme0OFhC78cYbA9ssU2LlGs6T4I8k1INtqeUR88eP1wZi6qjCAKO63yMm/TxAj9iwUosyX76/tNjILRkWiPnJOkI8YqMFHTU9YiMEYobEmhjWp0Yk/0TL5+uVRyMslS7xn55tBxKaGGasqkYGEWjDCPJUwR+aKHvEDmAe9QExzXSvp/n6lEzso3jiBu25oKlBIkUYJdupYnkt6oqmIDzeJ284nO5bUxJ6gpg2ck+F7KGuBrjliAMtaRuLXhMQ+3+E1c3jJamSIxYvFbj8ufs5/aVn3KVlhONFwWI8XegYldDE0HYoyoHPUdWuWYWso5YsNCbyttyxHGpMZl9+x0FtT5hUW/NUR5ewhIKGu/YbI6hPV52sQ1rfRqCfeMZNjRyxN+nrRy+vCd7feuutvOtd7+J//ud/3jBF02688cZQS8o555xDNBrl8MMPr2xbu3Ytxx9/PAsXLuR3v/sduVyOz3/+8xxzzDEsW7aMlpbhmcheD7FCQhOL8sLxGnIttCrAqBoQ83vEAqFntUIPX0ePmFpXR9tnPnPA52sjsKrJ7+y1FHS+4447+MAHPuDZJofODFtHzPIBnBDmp1pkHf5pdViP2ChE1WUQ7Os8/yQgNlJWt2rx+sOxVAU9YlKIasUjdvCIKUY6hkqKWvEAhPWp6jeQ/jzAkgJvdFFVBaQprlpJinnN83hp70tcMfOKwL6wOXLkHrFa4bzhHjEZEB2IN9HPbqZIQEP3hybKYdZKkOgoOzhEvBy/POqCzpYXiKnC4xHzk3W8Vl2jOAKPmCfiwAEMr8Ww9kb3iIWJNYpHPCCjmVqDoEZVRjdHVRMPM6CT6zfKSIvYMOvraxZ5HFZ5j2XDhKWq3tDEEY173zgvR79ItxqJflKqFZo4unSzN8UnrwmI7du3jyuuuOINA8IApkyZEiAQefjhh9m7dy+f+9znPG39/Oc/TzQa5S9/+QuZTAaARYsWMW3aNL75zW/yjW984/+07SMVEytQ30P2iL0WIKZWKW470hyxQGiiz3o5WdPYUCYHsQzEwcoRk8OsD6LV3s8KWbnfCO5xsHLEwghxPDliw1jD/aEYIuQbKzUW0tF5xGr3vQCDY2ABeO11xKrc2P37AL6FnFfiB1p+CZB1SF3oQEMTD4ZHzKavt9t+oCRLokYI6//LomqC0xoy/G1vX83jvnfy91i+ZzlLO5YG9oUBsegI+5q/6Los8hxUnazjAOZRw+8Rc68n1+oCMEpeD5XUCPt4RasA2eELOnvzVoTlnX8UVXiYFBXh9Yipr9F4V5I0xWo5YnJoYpnhdLQesSl1U3i191U6U53/T6awVPOIhcqBrLmSnlLWvyq7FPWgE8FVqyP2eovlyYesAsTKxyoKugTEzCrj/oz6r7Fs8DyWpn+J4UPUYayJtciCylIrNPF19/i+3vd/jfKaeuRRRx3FmjXD10h5veXHP/4xQgje9a53VbaVSiX+8pe/cNFFF3kmgQkTJnDCCSdw++23vx5NHZmIINiSPWKjDk2UL13FSlU1NNGfI+YbEP4csQ/V1RMrWVy7Lo9lGojXUOdJFtniWM3KeSBSsmoTctSSgxWaGJbsL1tskQrR9vYeHTg2EHYYEppR0yMmRg7ERlsLyjOBCzgYdcSGvecBfAs5UVkdpl0B+npJkT7Q0MRaMlIgtr5+bGVsjIRAIlT+WeGir7OoiuC6Ce3MTcX5/JRgwntZ6qJ1HDv22AArHoQbqyIj9b7WUCSqGdnkgs4HwsLpzxFTZM+tL3xXJutQO2ZDrB7UKBz7SftcGWkcAH298AAxBcUXmngwPWKFkZB1mMEc3NECsU8u/iTHjj2Wbxz7jf8nPWJV6liHygHN1VKfveaaa3zXUw58jpJFLoPgPNA/nYJ+tCIbRIYxPrbWJ9EkncSokiM2OfYMFzZ9ljGR1YF9Ze+z/MVGFppYg77ekyP2pktstPKaPGK33HILF1xwAePGjeP0008nMkKF4P9Sent7+cMf/sBJJ53EpEmTKttfffVVstks8+cHa7zMnz+ff/zjH+RyOWJVGPV2797Nnj17PNvWr19/cBtfRcwQsg5PjlgtpsIQmXX08ax57CHnV7WFqQpZhz9HLOAR87blwlSao3+1EQEY/6QcseFyeEYjhhnGJjX6BeK1KA/PPPMMxx13XI0jNJ595jzS6X0Ui/OGvV5YsrIyCrKOmkrIKAvheq8l8MZL/HOUF+UAFnhDVt6GaZffIybru9rrFJr4XOsM7pp8FK1O24bvU+GiRN440Q8HUzRFcEgmwX2H167fVkvCjFUHYy7y5/8eeeyRPPLII8Tj7ncfrpZgqPiAmGoaLG3awvhkT/BQ2SMWjcF1K8AoQrIZgEKpWAlNHEm0gCUbzkxvjpgeVRGGDDKtg+oRk1esaqGjXm9BEIiNZD4/csyRHDnmSAA29W5yr/3/CCirRtZRFk14YnlHfwNJT0mnvaUHFFXhmWeePqA5ShbLctsoTLt/vuE8YhIQGy7iIBbVyVkDld/VPGKyVLtiGAtoLfF4xAKhiXKO2LCXelN88pp65NSpUzn55JO54IILSCQSZDIZz7+6urqD1c4Dll//+tdks1ne/e53e7bv27cPIJRQpLGxEcuy6O7urnrdW2+9lblz53r+nX/++QA89thjPPzww9x8883s37+fK6+8EnBr91x//fWsX7+e2267jdtvv51nnnmGL3/5ywwNDXHppZd6jr3hhht46aWXPPcu54iVj7n00ktZ0rLEPWA93Hbbbaxfv57rr7/ec70rr7yS/fv3c/PNN/Pwww9z7733ssHUmHfGeWxNtwRG7fbt2/nud7/Lyy+/HHgHU4w2LCwWnnZW5ZmWv7Sisl8xTdK65X0mIbnZsbj9kUd46aWXuOGGGzztvPTSSxkaGuLLX/4yzzzzDLfffnvNZ+rr663c1zRKfPe732X79u2VfMHysddee23lme69995hv9P+nv0AbOwYrFw/mphc9Tv96le/4le/+hUvvfRShfTFNE0aGhpG/Uzl7zRz5kzuvfdezzONG2u3VVEi3PbjO8nlMuzZM4nBwXzoM8myZdtWbrvtNs+2XC5b9ZkyqpeNTJFqvFx66aUMDrrkNute2RC4tv+ZZPnhD3/ofichsKQpKVfIjfg7DTeeLn//+1GSdr2t/Pnneb6Tv++VRf5ODz78SGW7pio1n+nn//szT9/r+vkP7fdmlhjDdkxT47HHVo/qmZ577jmqyee++EVeeukl2rBry53cfDI33HADM2bYoKK5cyI3HnkNBVWnr6ebZ555hra2thHPEQ8++yglx/ty+9YHPceOdjz5v9OePbZxbPu2tOfYsPH0WucI/7x3h2LTrW/BQFXV1/xMmzdvDnybqNRX/M8kS61neuzJxyrHaYrGLbfcwtvf/nYGB3sq2194YUVgjhjuO+UGsyze6yb+a4bBUa2bGZfsDXynh5a5VOIT5jZxziVvg2Rz5Zk2NNpjwNThi9/8Ss1n+stf76FpcADVYepb8nKOLTvXo0XssT/zmGaQQM+GDes9QOzfvvrVqs/kl7Dv1PmqHcUjLIvZuhI672nq2fbzmBHWr9/PDTfc4AFiP/nJTwLfab9usys+kp/tud6VV15JX68b8qqgHLT1abR6xEjmvbJki7nAePrmN78Zemy+mOe73/0uU9NTAbhk+iWhzyRL78Bg4JnKsmXrFtra2l7zM1133dcr4fINW04C4O57/uK5l/ydZLn33nsDz/nP+E6RcekKwcXvXrqn8kyhoqn0799V+TlYMEPnPa94kVG57/33f/1XZZuiqsM+k5wj9p1v/4fnmZa9+EJlX9Eph/R/OZd/7ye/wog7xGkn3xT6nR57zJ1H32giLH/G7ijkYx/7GN/+9rdZuHAhs2bNCvWI/eQnPzngxj300EOccMIJIzr2xRdfZOHChYHthx9+OBs3bmT79u1EpQTkJ554gqOOOorf/OY3XCZRmQN87Wtf44YbbqCrq4v29vbQ+1XziJ1//vmsXLmSOXPmjKjdI5Vtn3608vcVUz/NtUd9gCtmeRPGV+1dBcCc5gO/d3b5cjZddrn9Q1WZtWolAPfdd1+lIy/QJtM6lGKM2QiHRRl37qIKy9Y/7n2Mm3p72NzUzjkvLON7n7zac/38pl72fN8Ga1prhPaPLuFgyMs7+zntFltZbkpGeP7GUw7KdW964ib+uO6PTN+S4siV9kCfOX8WZ3325mHPtSyLTZs2kU6naW5uPuA2XHnllfzsZz/zXduku+dpYtF2VHUMX/va1wA48sgjOfXUUwPXkPtP+ycPR2uM8d1rH6hs+8D3qxedLe4a5Cvfc5/39NNPZ+lSN0dm/fpvsHnLDwCYOPGDTJl8fc1rtz+4zG3XMXPRymFQO5Yx9L0b2V+0w53i85tpumIWB0sK27ZR2LyZ5NKlVUMx5Pc09uvHVP7+xVOb+dwd9lh4y+JxfO1Cryd94qfvrvz94o2n0JB050LLsniiZ4AWXaW58CKx2FgSiQmjanupVOLf/u3fKr8v+81vK3/PWmsrlt25bpbvWc4RY44gqkbJ5/Ns3LiRXVaGK3/2IgBTWpLc/7HjQ/tUzfvvy1LanyM6pf6AC22HiWEMsX//E9TXL0bXM8OfcBDl3O88SrJriDUY3HTZAs4/pPM1Xe93v/sdq1ev5vvHnV/Z9o8WjXm+Yr9l+Y/Lzq78/bH/r707D2+qSv8A/r1Juu8rUKCgRVrbUnABERBFQFyolEUUZ6AVUGBggKoj2BkGZHEZXOBhYNzYFEFRQFkUUFZREGeAny2KMwVRQYFCWQql0OX8/ihNkyZtk+Yk9/bm+/HpY0lvct+bvDm5b86553yw3u42ALA4bzFe/c+rAIDM5Ew83fFpAMDeb/uhqKgyJ2/vvAWBga2divfMez9g+7ECjO1U+bzHXijEd/v7V/5x2nmrbYUQOHW0CAajgph424WTszKz8PpfX4MpOgCmyPpnMDz+zQG8uSIXl0JaonVBBa5vH42ug9rgwukStEiMwHd//RJR1yYZuuq/Bx9dOoXia1+kDB06tPbrh6ZZfPlb4xgsj+Xb85cQYjLixmD7veNCVODs2d0ICGiJgIB4AMDatWuxb1/lCWffvn1x6623Wt/p0mng2L+BhB6Ayfoau18u/IIH1lSucxZgCsDeP+yt/clRQVUuGhO749y1LyguRhfi5XHWs4kWFBRYFVQjSyqLG2OEH5pN6oRzJedwoOAAOjfrbH8mS8vXZ9SXQLPqdnTL1urX9LrT7+G5Df9yqo2qzaVLh3H8n1sQcDYJChSEj78JL79ZXVBOmzat1t8tPw8++OmlOt+nrig9cQkVJWXwbRVqHqZsGUtVex94220oSfkFicX/AQDMCJ+JKRP/bPuAFs/z/y53webzfzH/u+rz+H97v8baVyq/1Ei4tTMy/vK3OmPsOOsLFBRVriH3xZN3ok1s9Re054tL0X76ZgCVk1n9b9b9Dh23VOePA6e+B67vYXcimIMHDyI1NdUt5+eucmlo4pIlSzBp0iTzSaBsiYmJeOuttxzaNj4+3ua27777Dv/+978xYcIEqyIMAKKiKk+qq3rGLBUWFkJRFISHh9e6v9jYWMTGxjoUmxQmBbg2PETYmb4ecK0AM7PoYrY8WQ269iEIAFFKCFpVVBYWEQkJVlMdl4ty9Prh3yhXDPC/Un0fewwB8hZHtByaWHNomCsshwVV8XFwaIOiKFbDYRvK3oeRohgQGVG9mOjgwYNx4sQJdOtme42YzX2dfX5qnHjXHMagWAyzU5T6h+zccvQQ/tM6CcnHj8BgsChoavSIyR6a6NuiBXxbtGjQfS2/DXR2sg5FUdA1ourktWuD9u/IxdAR/hG4q+Vd5n/7+fkhKSkJhYdPV8d2LXZnT3BMUQEwRUm4ZqMGozEQMTG9pD+uI64Kge+uDRWVMcGPveHbJgkTWdU27Lx1qzHIzaucVc7Pr4nTjxvSoyUCF1d/mRhccrnWbRVFQZPrai+Ulyxd4tS+g8LCEVvSHCipfM4MBgVhMYEIi6lc09HXoJgn//AxKVY9Yq7MQAtUHkun8LrXHFMUAyIjrd+r9V4jFhQNJN5b7/5rTrSlLRZDzOyEWVs7VDWxUrh/uFUbVKe6LqFI8JdShAFAUFACAs9WTz3vzNDEqz/vgm+rbjhT4t6p632a1n2uVEUxWi/o7MjQxMsV4XZvF5b57MAQasvh+X41rxGz+KdqsyaGNa/8aYRcahHKy8vRu7ecngd7mjVrhpEjR9a/YS0WLlwIAHYfIyEhAQEBATbD/gAgNzcXbdq0qfX6MLUJCJcm5KiT5TvK4oLtW2+9FYcOHYLJZELCr00hqiYdsPP+VQCYRIXVdQBmlgOI3TRUu74TZWeUW4wxN5P4+I5IT0/HunXr6twmOTkZycnJjj3gtULBP9gHJRdL69kYNjMS1TXToSPTg9368yHccOoYQi9fhKL0t76vxQxPMnteXGU1a2J9k3VobIp3y/H7VQWHIzmld9YzYbqnEHO1aABqX5okJuYepLV7AwGBrWA0Ol8k+8YFo21ROVqeOYHzgcHoln9tWPkNfZx+LGfzqeZ7u+aXQyF+JqC48jPGx2SSOllHQ8la0NnowAx1arGaaMHOe6LWL4RcnDWxJpO/j9vaqLrWETOZTCgrK0PqtV7sK9+9j73+p1BQ8qv0OBrEYITRckFnB9YRq6jlNL/C4ppWxYGcLC2rfbKO2pb7IMe49Clxzz332CyQrBVXrlzBsmXL0KlTJ/ObypLJVHmN1erVq1FUVGS+/ZdffsG2bdswYMAAT4brFHuTdUhTS4+Yj48PHnvsMQwdOhSKqL3XwnIBZ0OZ7cmB1YWpEk9YLaNwR4+Y1bo2Hv4glf1hVPW8PzihA5q1CcPdw+oe/lfz2bQ5CbGcmcqR/QMIv3wRBtT4YBcVEJYfGhotxOpd0NkNcbsyPbDlh2TVe8PbizAAqLB4TQ1uKsRkFA1WhZhFb4qiGBAT0wvBQTc0+LENAB7I24Mhe79AYOkVYPC7wIA3nH4cZ/Op5lp+NZ9/67/WmKyjERZilg2j2z67JbAsxIQT74kGfWlWVyHma3JbG1VXj9jo0aNx33334YEHKoeRovwqjhf/D1crStwSi9OMBphE9XWdFUa/Oja+tk0tC8JVOLnO6VWLUSE2PWIWv4cHam/SPq1z6Ux4ypQpWLZsGebOnYv8/HwUFhba/Kjl448/RmFhYZ09as899xyKi4vRt29ffPbZZ1izZg0eeOABREdH46mnnvJgtA6wKASEYrugszSWs4mbatmH5Yl3jQY4PDwKAZeaw7ckEr7FtsNlrKZMdtOJtsx1xKp6xCwXRTbUsRClO9i/ANcF107GY1qGYMDTt+DGLs3q3l4BmlRUjzmv2WhfvVo99K3qg9wvsPI5Su5W+1TgNirKYTkVv7umr28Io0UxExZQd2+0zPyrjW+I48sq2FtjT3pONUKNpUfsann1iZe9afNdYbx2CqAACA0KAZIfBAIinH4cZ/PJZj3BGu91q+FNinC8R+zeF4HwVsCwtU7F4whZCzobHRi+rRarHjE77W+tXwg1aEHn2ttRo9HotjaqrkIsOjoat912m92p82W8l12lGKwXdC4zBtZ7n3CT/WGV5WXVnyGOzERaZjU00TqHA32NSG0eCkUB5g25qd7HImsuZVb79u1x6NAhPPnkk0hMTERMTIzNj1oWLlyIoKAgPPLII7Vuk5SUhO3bt8PHxweDBg1CVlYW2rRpg507d6oae30qINzWmFt9U1nL4s6oo5hSFAXBRQkIO5dq9eFpVl57EeeKciH3pKpKVY+Y1QKjHi7Exo4dK/XxnH7ejQYEi+phujU/jH/7faX59zNnKidMGZzTET2zbkS3h5z4tr6iDMKqEFP/g6/K4I4tERXki8ggX/zxtron2vDE4pYt7ihEaHwxWt5pe41rTdZT71c+p7JzqjGyfF5kXiPW8afKtXtiLxTWefIW0cyx6xmSIpPMvzcPlnsNhAEGtClvCj9hwqB7Mxr8OE7nk1J3j5hv82CrTR2+RqzzmMqp9a93bdpze1zpEbMscLTcI1ZuMbm/vXas1mvEJPeIGYxGt7VR9pZvqcv1p87Cr7QM/R7Ocks8TjEYsDKucuKNIxVNcca3/i86m/vaXn4DAH6B1UVcYGj9M5zf2Kx6kh57Czp/NLoL9ub0wu0JUfU+FllzqUX4+9//rv6K2rXYvHmzQ9vdcsst+OKLL9wcjXyeGZpofx91rwRvcbGvYuf6qoq6v3FrKMs1LmReI1a1TlqpyeLboCDHLqyVZefOnWjTpo28B2zA8255IlHXe15cG78eGh2A0Ggnr1upKAU0OjQxLMAHX02unG3Kv45rDNyl5nPuF1qG5l3OOXRfewWH9JxqhCqE3Daj6kT9pl/+i2bnzyD64nkYbqr9OX7wqRx8u3YV2vWs+5qsPq37YOexnQjxDUGvVvInNrmrNAUVEGgWa3+GYEc4nU81nu6aX7pEDGqLwg9+hH9CGEoOfNPohybWtii31pRUFFcXSM4cYoMKsdp7YRSDATt3bnNLG+XsKWvS74VI/L0QTeNbS4/FWYrBgJ8D09DtyhycEaG4y4EvhRXY9tQDwPU3d0Jk85You3oVHfsNqvdxXhqYhpFL/417kpvY/eLK38eoymejHrjUIlhOr0luZlG/lCvlninEahuaaPm+tpkhzmIzxbYBqLuIazhnJlNw6nGvFRY/NbuEdodD0aK0HLfcfZe0x3dERITzQ4Xq4uyXJ4ZAk9V0HHWdhDgya2KtKsrcOmuiq9T8kLF8zUJCQoCiOjauwd57Q3ZONUZlbuoRMwCIO1/ZU1lX0RDdshXuG/tkvY/ra/TF7DvrXy7DFQYoLr3fnM0nm6GJNfZtCvdD7KjKGVVL/k+x+mBRa4iYK4VYbdf5aU19X7jV9tlhCGjAMdUoIq4W+cA3pBSXT/tBSTS4rY1qSOeBgjrOhzzJaISiAMdE5Yzdjlzbqtg5DwMAk68vMl/+JxTF4NBzkhIXhq8n363ZzpfGTDtjf6ge1Q1kuVuvEbM/WYd1KI4VU8JOj5hi0aVtipE3HXapxYWkof7yZpSs+vAtMwms6f4bRiZ8C79gz6531Ly5ulOyGnyN8GldPSyh7kLMhSbF4AMDqi+KNkXUfyGybIE3VX7AhfS0XQ5DbTfddBP8/Pzw0EMPOXU/y6n3qwoOtXNKC8olf3njrmvEPMWVEyyn86meyTrqolaPmOUSLoGB9V+bY6lFcAvzZ/YzHZ+RGpfb2HlJbAroYB9AAUL7tHb+8Wucwxze0BK/743B4c9awmAweKSNsnctWK1UyjtLNSe5ceQyDKXGgs6WDAajU+97FmHu0Xg+JbydxXupAhWe+Vatlm+A6ppwQ7Eammh7YuKfGAm/68Ngig1ASDd5DW1qXBiigytP3J+5N1Ha41pOXz/iwvnKOtXDQ0s2bdrk0f3ZFVD9IVT3yaULTUr87Qi6oRQ+Pr/CJ9YPgbc4vzaSqyIG3oDYP9+EUA0WYv369cOkSZPsrplYF+tJKSpfH03klMpkXyNmOZlDlcZUiLnSI+Z0PtU4oat7Yp6avWfqPKe9e/dGREQEbrjhBsTFOTEJEYBAn0B80u8TLOqzCLfH3V7/HTys/6Sp8A8JRYXFU+vIiJWmT96Cpn/pCN9mDRiuX2No4pXzfji5PxplxT5QDEa3tlE9e/ZEREQEhgwZ4vB9FJOblgyqRa9eveDn54c+4RY9gwaD1bvBkXarrkKMtMHpM8q0tDQsX77c7pTw9lRUVKBDhw744IMPcOONdU+TTbWL+mMyzrz7PY76/ebe6estvtV1ZNZEm0LMYFmI2ekRMyqIeSINQgip364E+BqxObs7zl8uxXXR8q7hshzb3+Xytd4ayTOX1UcLM3gKi9fcbUMTDQYomR+iiagcIKPGt2+KyWA1UYDW2Dz3Ea3rvY/l9ZM+1054tZBTaitvJLMmeowLT4Gz+VTzJL/OHrF61zH0jJCQEIwfP77B7VJ8aDziQ7X3BQ8AXH9zR4x8fTGmz5oOn2tvC0eGJhoCfWAIbGCBUtdkHQaDW9uoO+64A3fccYdT91Fqm7zMTbp164YuXbqg8O2FqFp2XTEarN4OjvWI2R+aSNrhdIuWl5eHy5cvO7y9EMLp+5CtgJQoPJX0Gia2/geguO+CX1FaXXjUOjTR4n1tszCnxae5UGr/JsYdJ9mRQb5SizDAukfMVFWM1DHtrjtkZWV5dH/21HV9hI9P9Td2/v7OfVNsl6JwCER9+i0Aru8BPPphvZu2jKweftOhZTgAbeSU2tw1a6IltYbRNYgLz4Gz+aTU7OVyYmioms+pntslRVFgsDgltNcjJvX46/pCz2iQ2kYFd6n8XPJrE97gx1DjGjGDwWD9BYnBWGNx8PpP4dkjpn0NyqyMjAz4+Tl+/YaeGy9P+tX3BK6UVq4p47YesXKL9YkcaXic7BFrbMorLNaOqfrFw0MTV65cWf9GHlTz/dyyRSaO/DQHAODrG61CRF7opj9U/jggrUU4xvVog8LiqxjR7ToA2sspNZTbGbLpiri4OJw5Y72cQGPqEXPlc9rpfKrjc8OGRnrEvIHlepmu9JC6ymAwSm2jwu5rDb+EMPi2rn+a9lp5eNmaKlbvy5qT2jj05QkLMa1zOrMyMzMbtKPoaJ6guapqBj/AjT1iFtc5OPINUM2x/ZZthtBBA2DZI2Y094h5dmhieno61q1b59F91tSrVy8cPnwYwcHBNtcoKRbXKyq87FSTnu5jfd2kFnJKbZZrD8qYoPPee+9FcXExDh8+XP24jalocOE5cDqf6lnQuS6N6jltRJRr/5n/7e4esbpiMRiktlGKjxEBKa6dgyo+Ks2aaDmBmsFo9daptSf/sY3AN/8Cbh4GpfQyMNfNMZJLnM6sxYsXuyMOcoBl74y7Juswhoebfw+9//7671CzIbAsxOoYmthYWBVi5l88OzRRCyfMzZo1w/jx4xEQEAAfH+vjF9ZjVet9rPDwcJw7d05yhOQMLeSU2iqsrnt0/QQzKCgIQ4cOtVrWxVuGJjqdT87MmljjTyzE3MeqEHNi+nrpcRgMmmujar1Uw/17rv7V0WvEWt1e+WO21S2RkRxs0RqRqsWFAff1iPklJCBmwniE9euH8AH9692+zmvEdHCRqNXQxKrzNg8PTczJyfHo/moTGRlpf7pfi7ysee2HPUOHDsXtt9+OMWPGyAyPnKCVnFKT5ZxDRjedYDamosGVp8D5fHKiR6zmDIu81ME9an6nquI6jgaDQXNtlGrriNXoEbN8oYwS10wl9WhghTpylCeGJgJAtDMnyDUbAstOMB30iPmb/Kt/F+bppDwagzNT7KqhouKq+XeDof5hm1FRUejTp487Q6J6aD2nPM3AQsylHjFn86lm4eXM0ETyDLWHJmqujdJAIVazJ1nGbK+kvkb0KUGW3FmIOcOmsRaWPWKNvxCb0nkKTIoJN165iuZlZfXfwQ1yc3NV2a/jLId4eX4RZnKe9nPKs9xVLzWqQsyFk2yn88nmGrG6lsRQ0O3LL6FUVOD6s2cbEh45wGYmSzvDzD1ZiGmtjVJtaKLl/Ck1hiY6MmsiaR9fxUbK6Mp6TTLZFGIWv+qgRywlOgXbH96O5c3uq3yzRLdVOyTNadkyC0ZjEEymMDRvrrFvMYkc4K4escZ0jZgDl3e6bWdtO9a1eLuC5sd/Q//Va9Dtt9/dGxeZqT00UWtUG5pYbnGJh8Fotaansz1iiZ2byoqKJNJGtwo5TbM9YtBXjxgAhPmFAffMBGKSgOvv9Pj+27Vr5/JjBLSLxuXc0/BPiZIQkTVf32h07fIlFMUIk0m7iyFTNRk5pSfuKsQa1fVMLsTqdD7V+NzwDaj/88ynrEyTJ+h6Ze+59mSPmObaKJW+VBEW16krRkOD1j986NlbcfzHc0jpLmGdT5KOrVoj5WPw7Mx9taoxtl9YNBJ6KcQAAH7BQOfRQOyNHt/1ihUrXH6MiIfaIuqxFEQ+nFj/xg3g4xPGIqwRkZFTeuKu83tvKcSczSenCt86rpEheWoOTVR11kTFoLk2SlEr92r0iJU1oBCLbRWKm+6Jh6+/Nr7AJ2tSX5VffvkFS5cuRXl5ORITE5GWloakpKRGNTyjsdBuj1g1oTT+WRO14Pnnn3f5MQy+RgQkRkqIhvRARk7pibt6xBoVF84znc6nhj7fdVxLRnKpOTRRURS2UddY9ojBaEBFRcOHJpI2SW3V7r//fuzbtw9Xr17FypUr8eCDDyI4OBgdOnSQuRuCdgqxmkNMBGsv6dLT09UOgeoxNT0ZzcMD8O6ITmqH4hDmlDV3TV/fmLjS2+F0PjnTu2A5fbdHL2TzLjVffzWHJgJso8wsCi9FMVj+0+EeMdI2qWfzv//+O7777jurN/DFixeRl5cnczcE+zMaqaJGw2w0VcdV7Hve09HoktYWtiRbj3W9Do91vU7tMBzGnLLGHjHXOJ1PTg1NtPidQxM9Rs2hiQDbKLMaPWLlwvmhiaRtUlu1P/7xj9iyZYvVbcHBwejcubPM3ZCG1By+EJcYhpPBR1HkW4hv4terFJW+DB48WO0QSGeYU9a08r1WY+V0PjXwBFLh0ESPUbtHjG1UJWFxjZhitJ41kYWYPkjtEZs8eTLuvvtuDBo0CPfddx/S0tIQHMwL+GVpF90OuadzbS6q1RKj0YA1qa8BUHSxoLMWLFmyRO0QSGeYU9Y4NNE1zuaTUyf0ltuyYnYbm8k63LHI9pAPgF2vAj1y6t2UbdQ1FdaTdVj9k+2WLkht1fr374+goCD8+OOPGDVqFKKiotCmTRsMHDhQ5m681vPdnkf3Ft0xq9sstUOplQKlciiJIjRdMDYmr7zyitohkM4wp6zxhMY1zuZTg59tDk30GHuTdbjcI5Z4LzBiM3D9XfVuyjaqktX09QYFFZY9Ymy3dEFqj9gPP/yAgoIC+Pr6AgBKS0vx/fffa26F9MaqdVhrzO85X+0wHKaZ69gauT59+qgdAukMc8qazPP7Rx99FGvXrkXXrl3lPaib+KdEoeTgGZcfx535ZHXyz6FYbmMzWYedz29PDk1kG3VNjenrLSfrMPD9oAtSC7G+ffsiNzcXt9xyCwDAx8cH7du3R/v27WXuhjTMcu2wQFOgipHox/Hjx9UOgXSGOWVNZo9Y27Zt8fTTT0t7PHeKGHADiluFwj/JtaUtnM4ny6e7vql2OWuiKtRePJttVKWaCzpbXyOmRkQkm9SXsbS0FBkZGViyZAlOnjwp86GpkQgwBSA+JB4AMKPbDJWj0YezZ8+qHQLpDHPKmrcOTTQG+SCkewv4xLr2pZkr+eR35ZzjG3NNUo9Re7IOLbRRTXIqr2ULf2iQekFYdoEpBquhid7abumN0z1ix44dQ4sWLez+rVu3bggNDcWCBQswbtw4hISEmHvEXnrpJZeDJe0zKAa8d/97+O3Sb0iOSlY7HF3o3r272iGQzjCnrHGEj2uczSejyYCQC0dRFBKPG39cBqCuE12LHjG+UB6jdiGmhTYqcthQhPS8G6a4OPWCqLmgc1n1P1mI6YPTPWJJSUmYNWsWrl69avO38ePH4+2338bevXtRVFSEnTt34vHHH0dAQICUYEkbfFuF1vn3cP9wFmESzZ/feK4LpMaBOWXNkyeYeuRsPimKglv2v4Kuu/+GyLM/OnFHjsXyFLWLXq20UT7Nm6vaPtScvr6C09frjtOtWvfu3TFlyhQkJyfjk08+qXU7RVFwww03YODAgZg2bZorMZLGRP3hRoTc1RKxYzuoHYpXeO2119QOgXSGOUUyNSSfDKICflfP17+h1WQdLMQ8pb5rxG6++Wap+4uJb231b7ZR11jOV68YICwn62AdpgtOt2qffvopPvvsM/j5+WHAgAHo06cPDh065I7YSKOMob4Iu7c1fFuGqB2KV0hPT1c7BNIZ5hTJ5NZ8Ujg0UQ1Gg/3r8YYPH45evXpJn9UwY9JUdOo3CH98cS4AtlFVak7WwWvE9KdBXy/16dMH3333HebOnYt9+/ahffv2eOqpp3DhwgXZ8TktKysLiqLU+rNnz556t01KSlLxCIisrVu3Tu0QSGeYUySTx/KpluKA5KttOF58fDy6desGPz8/qfsLjY7BHY9mocl1CQDYRpnZTF/PoYl60+B+fqPRiHHjxuF///sfRo8ejX/+859o27YtFi9eLDM+p02ZMgW7d++2+YmOjkbz5s3RsWNHq+0DAgJstv3ggw9Uip7IVmZmptohkM4wp0gmt+aT5bkmTzzd6qsmX6ECFTgeeBwGuZNqO41tVCVRXnP6+uq/cR0xfXB5HbHw8HDMnTsXf/rTn/Dkk09ixIgReP311zFv3jx06tRJRoxOSUhIQEJCgtVtO3bswOnTp/G3v/0NxhrT3xoMBnTu3NmTIRI5hWPlSTbmFMnkznxSuI6Yx5wIPIH18etRaihFJ8Xz52+W2EZdU245a6KJQxN1SFqrlpiYiA0bNuCzzz7DpUuX0KVLFzz22GOyHt4lCxcuhKIoGD58uNqhEDlt4cKFaodAOsOcAv756E0wKMCQTi3VDqXR81g+cbIOtys1lgKK+jOJso2qJCosZ000WC0rZmQhpgvSWrWSkhLs27cPJ0+eRO/eveHn54d33nlH1sM32Pnz5/HRRx+hZ8+euO6662z+fvnyZTRt2hRGoxEtWrTAuHHjUFhYqEKkRPap0bNM+sacAvqmxeHA1HvwwoA0tUNp9BqST6bYWABA1KhRdW9oNWsiTzw9xaBy7yPbqGvKLRcOM0JY9ojxewldcHpoYllZGX788Ufk5eWZfw4ePIiffvoJFRUVEELAZDKhbdu2SEtT/wNuxYoVuHz5MkaMGGHzt6rFplNTUwFUDmF87bXXsGXLFnz77bcIDg6u9XFPnTqFgoICq9vy8/PlBk+Eyi8LiGRiTlUK9fdROwRdaEg+Xbd6FS7n5iK4a1eH76Nwsg6PUfsaMbZRlYzR0ebfDYGBVj1iHJqoD06/0wIDA5GWloYhQ4Zg1qxZ+Oabb3D99ddj4sSJWLJkCfbv34+LFy8iLy8Py5cvdym47du31zkDouXPgQMH7D7GwoULERUVhf79+9v8LTs7G9nZ2ejduzd69+6NmTNn4p133sGhQ4fw1ltv1RnbggULkJqaavWTkZEBANi1axd27NiB2bNno7Cw0HzRadV0rNnZ2cjPz8eiRYuwZs0a7N27FzNmzEBxcTEGDx5stW1OTg5yc3OxfPlyLF++HLm5ucjJybHaZvDgwSguLsaMGTOwd+9erFmzBosWLUJ+fj6ys7Otts3MzERhYSFmz56NHTt2YOPGjZg/fz6OHz+O0aNHW207evRoHD9+HPPnz8fGjRt5TCod08aNG3V3THp8nRrTMa1evVp3x6TH16mxHNP777/v9DEdPXcOf1+7Foqvb53H9NXXX8PMYODr5MZjspSXl6fqMa1evZqvU24u3j53HqZmzXDE3x9BXW7H//5X/YX/fw8dapTHpMbrtGvXLmiVIiz7OR0wfPhwtG/fHmlpaUhLS0NUVJS7YsPvv/+ODRs2OLTtgAEDEBkZaXXbd999h/bt22PChAmYM2eOQ49TUVGB0NBQPPDAA3XOnlhbj1hGRgby8vKQkpLi0P6I6nP8+HE0b95c7TBIR5hTJJM78+nMosU49Y9/AADChzyCZlOnumU/BLRb2s78+zMdn8HQ5KGqxcI2qpqoqIBybRxi+rxdyD1euRD6qjFdcEurCDVDazQOHjyI1NRUTZ6fOz00cdGiRe6Iw65mzZph5MiRDb5/1cWezj6GEKLeVeVjY2MRe22MO5E7zZgxA6+//rraYZCOMKdIJk/lE4cmeo7a14ixjaqmWJyPch0x/dHtpX5XrlzBsmXL0KlTJ/M1YI746KOPUFxczCntSTP4YUSyMadIJrfmk9VkHbo9ZdEcBeqe5LONss/6GjH14iB5dNuqffzxxygsLKy1N+znn39G165dMW/ePHz22WfYuHEjnn32WWRmZiIlJcWlnjgimarGOxPJwpwimTyVTwrPPD1G7R4xtlH2Ca4jpjsuL+isVQsXLkRQUBAeeeQRu38PDQ1FkyZN8Oqrr+LkyZMoLy9Hq1atMH78eOTk5CAoKMjDERPZt27dOrVDIJ1hTpFMbs0ny8vYuaCzx6hdiLGNso9DE/VHt63a5s2bcfHiRYSEhNj9e0REBFavXo2ffvoJxcXFuHLlCv773//ipZdeQlhYmIejJapd1UxARLIwp0gmt+ZTRXn17xya6DFqL+jMNso+Tl+vP2zViDRuypQpaodAOsOcIpncmU/C4sxTMfKUxVPUXkeMbZR91j1iKgZC0vBlJNK4jz/+WO0QSGeYUySTW/PJskeMQxM9Ru2hiWyjasEeMd1hq0akcQkJCWqHQDrDnCKZ3JlPoqKi+h/sAvAYtYcmso2yr5yTdegOWzUijQsICFA7BNIZ5hTJ5NZ8sjjxVLs48CZq94ixjbKvgoWY7rAQI9K4vXv3qh0C6QxzimRyaz6VW/SIcWiix6i9jhjbKPusJhFlHaYLbNWING7EiBFqh0A6w5wimdyZT0JwaKIa1O4RYxtlHwsx/WGrRqRx2dnZaodAOsOcIpncmk+WsyZy+nqPUbsQYxtlH4cm6g9bNSKNW7p0qdohkM4wp0gmt+YTZ01UhdrX47GNsq+8ggs66w1bNSKNS09PVzsE0hnmFMnkznyynDWR64h5jtrriLGNsq/U4ppJH74fdIGvIpHGrVu3Tu0QSGeYUySTW/OpwvKiGJ6yeIraQxPZRtl3fUyw+Xc/E98PesBXkUjjOFaeZGNOkUzuvUbMYrIOXiPmMWoPTWQbZd+rg9sjqWkIRna7DkF+JrXDIQn4KhJp3NixY9UOgXSGOUUyuTOfLGdNVHhNjMeoPTSRbZR9raKCsHFid7XDIIn49RKRxu3cuVPtEEhnmFMkk1vzieuIqULtoYlso8hbsFUj0riIiAi1QyCdYU6RTG7NJ64jpgq1hyayjSJvwVaNSOOaN2+udgikM8wpksmd+STKLYcm8pTFU9TuEWMbRd6CrRqRxm3atEntEEhnmFMkk1vzqYJDE9Wg9jVibKPIW7BVI9K4p556Su0QSGeYUySTe/PJYvp6TtbhMWoPTWQbRd6ChRiRxmVlZakdAukMc4pkcmc+cWiiOtQemsg2irwFWzUijVu5cqXaIZDOMKdIJrfmk9U6Ykb37YesqF2IsY0ib8FCjEjj0tPT1Q6BdIY5RTK5NZ8sZ03k0ESPUaDuc802irwFCzEijVu3bp3aIZDOMKdIJnfmE4cmqkPtHjG2UeQt2KoRaVxOTo7aIZDOMKdIJrfmE2dNVIXahRjbKPIWbNWING7IkCFqh0A6w5wimdyZT4JDE1Wh9qyJbKPIW7AQI9K43NxctUMgnWFOkUxuzaeycvOvitHkvv2QFbXXEWMbRd6ChRgRERFpkrAcmmjkKYunqD00kchb8J1GpHHt2rVTOwTSGeYUyeTWfCorM//KHjHPUXtoItso8hYsxIg0bsWKFWqHQDrDnCKZ3JlPotxiaKKJ64h5ito9YmyjyFuwECPSuOeff17tEEhnmFMkk1vzqaK6EIORhZinqL2OGNso8ha6LMT279+PjIwMxMXFITAwEElJSZg+fTqKi4tttt23bx969eqF4OBghIeHY8CAAThy5IgKURPZx4UtSTbmFMnkznyKemKU+feAtDS37YesGRV1i162UeQtdFeIff/99+jSpQuOHj2KOXPmYP369XjkkUcwffp0m+lQDx06hLvuugtXr17FypUrsWjRIvz3v//FHXfcgYKCApWOgMgaF7Yk2ZhTJJM78ynwtk5o/f4KJGzeBGNIiNv2Q9bUHprINoq8he4KseXLl6OkpASrVq3C4MGDcffdd2PatGkYMWIE1q5di7Nnz5q3/fvf/w4/Pz+sX78e999/PwYMGIANGzagoKAAL7/8sopHQVRt8ODBaodAOsOcIpncmU+KoiCgQwf4xse7bR9kS+3JOthGkbfQXSHm4+MDAAgLC7O6PTw8HAaDAb6+vgCAsrIyrF+/HgMHDkRoaKh5u1atWqFHjx5Ys2aN54ImqsOSJUvUDoF0hjlFMjGf9EftHjHmFHkL3RVimZmZCA8Px5gxY3DkyBEUFRVh/fr1eOONNzB27FgEBQUBAA4fPozLly8jzc6Y87S0NOTn56OkpKTW/Zw6dQoHDx60+snPz3fbcZH3euWVV9QOgXSGOUUyMZ/0R+0FnZlT5C10V4i1bt0au3fvRl5eHhISEhAaGor09HRkZmZi7ty55u3OnDkDAIiMjLR5jMjISAghrIYx1rRgwQKkpqZa/WRkZAAAdu3ahR07dmD27NkoLCxEZmYmgOqLT7Ozs5Gfn49FixZhzZo12Lt3L2bMmIHi4mJzd3zVtjk5OcjNzcXy5cuxfPly5ObmIicnx2qbwYMHo7i4GDNmzMDevXuxZs0aLFq0CPn5+cjOzrbaNjMzE4WFhZg9ezZ27NiBjRs3Yv78+Th+/DhGjx5tte3o0aNx/PhxzJ8/Hxs3buQxqXRMMTExujsmPb5OjemY/Pz8dHdMenydGssxAdDdMenxdarvmCx9/vnnqh6Tn58fXycek7Rj2rVrF7RKEUIItYOozfbt29GjRw+Htt2/fz86dOiAo0ePonfv3mjSpAmys7MRExODb775BjNnzsSgQYOwcOFCAMDXX3+Nrl274v3338fDDz9s9VgvvPACcnJy8Pvvv6Np06Z293fq1CmbCT3y8/ORkZGBvLw8pKSkNOCIiWytWbMG/fv3VzsM0hHmFMnEfNKHdkurF1HeNHAT4oLjVIuFOUUyHTx4EKmpqZo8P9f0MvWJiYl46623HNo2/tqFvJMnT8aFCxdw4MAB8zDE7t27Izo6GsOHD8ewYcNw5513IioqCkB1z5ilwsJCKIqC8PDwWvcXGxuL2NhYJ4+IyHl19cwSNQRzimRiPumP2teIMafIW2i6EGvWrBlGjhzp1H0OHDiA5ORkcxFWpWPHjgCAvLw83HnnnUhISEBAQIB5WIWl3NxctGnTBv7+/g0PnkiS7t27qx0C6QxzimRiPumP2gs6M6fIW+juGrG4uDgcPHgQFy9etLp99+7dAIAWLVoAAEwmE9LT07F69WoUFRWZt/vll1+wbds2DBgwwHNBE9Vh/vz5aodAOsOcIpmYT/qjdo8Yc4q8haavEWuItWvXIiMjA7fddhuys7MRHR2NPXv24IUXXkB8fDz2799vnsL+0KFD6NixI26++WZMnjwZJSUl+Pvf/47CwkIcOHAAMTExTu1by2NQiYiIiGpjeY3YtsHbEB0QrWI0RPJo+fxcdz1iDz74ILZs2YLQ0FBMmDABffv2xdKlSzFq1Cjs3LnTXIQBQFJSErZv3w4fHx8MGjQIWVlZaNOmDXbu3Ol0EUbkLlUzABHJwpwimZhP+qN2jxhziryF7nrE1KTlipuIiIioNpY9Yl8+/CXC/cPVC4ZIIi2fn+uuR4xIb6rW8CCShTlFMjGf9EdR1J2sgzlF3oKFGJHGvfbaa2qHQDrDnCKZmE/6o/bQROYUeQsWYkQaV7UIOZEszCmSifmkP2oXYswp8hYsxIg0rlOnTmqHQDrDnCKZmE/6o/Y6Yswp8hYsxIg07vLly2qHQDrDnCKZmE/6o3aPGHOKvAULMSKNO3z4sNohkM4wp0gm5pP+qF2IMafIW7AQI9K4jIwMtUMgnWFOkUzMJ/1Re9ZE5hR5CxZiRBo3Y8YMtUMgnWFOkUzMJ/0xqHx6yJwib8FCjEjjXn/9dbVDIJ1hTpFMzCf9UXtoInOKvAULMSKNS09PVzsE0hnmFMnEfNIftYcmMqfIW7AQI9K4devWqR0C6QxzimRiPpFszCnyFizEiDRu9OjRaodAOsOcIpmYTyQbc4q8BQsxIo2bMmWK2iGQzjCnSCbmE8nGnCJvwUKMSOM+/vhjtUMgnWFOkUzMJ5KNOUXegoUYkcYlJCSoHQLpDHOKZGI+kWzMKfIWLMSINC4gIEDtEEhnmFMkE/OJZGNOkbdgIUakcXv37lU7BNIZ5hTJxHwi2ZhT5C1YiBFp3IgRI9QOgXSGOUUyMZ9INuYUeQsWYkQal52drXYIpDPMKZKJ+USyMafIW7AQI9K4pUuXqh0C6QxzimRiPpFszCnyFizEiDQuPT1d7RBIZ5hTJBPziWRjTpG3YCFGpHHr1q1TOwTSGeYUycR8ItmYU+QtWIgRaRzHypNszCmSiflEsjGnyFuwECPSuLFjx6odAukMc4pkYj6RbMwp8hYsxIg0bufOnWqHQDrDnCKZmE8kG3OKvAULMSKNi4iIUDsE0hnmFMnEfCLZmFPkLViIEWlc8+bN1Q6BdIY5RTIxn0g25hR5C10WYvv370dGRgbi4uIQGBiIpKQkTJ8+HcXFxVbbZWVlQVEUm5+kpCSVIieytWnTJrVDIJ1hTpFMzCeSjTlF3sKkdgCyff/99+jSpQsSExMxZ84cREdHY+fOnZg+fTr+85//4JNPPrHaPiAgAFu3brW5jUgrnnrqKbVDIJ1hTpFMzCeSjTlF3kJ3hdjy5ctRUlKCVatWISEhAQBw99134/fff8ebb76Js2fPWo09NhgM6Ny5s1rhEtUrKysLK1euVDsM0hHmFMnEfCLZmFPkLXQ3NNHHxwcAEBYWZnV7eHg4DAYDfH191QiLqMH4YUSyMadIJuYTycacIm+hu0IsMzMT4eHhGDNmDI4cOYKioiKsX78eb7zxBsaOHYugoCCr7S9fvoymTZvCaDSiRYsWGDduHAoLC+vdz6lTp3Dw4EGrn/z8fHcdFnmx9PR0tUMgnWFOkUzMJ5KNOUXeQneFWOvWrbF7927k5eUhISEBoaGhSE9PR2ZmJubOnWu1bfv27fHyyy/j3XffxcaNG5GVlYXFixeja9euuHjxYp37WbBgAVJTU61+MjIyAAC7du3Cjh07MHv2bBQWFiIzMxNAdcOSnZ2N/Px8LFq0CGvWrMHevXsxY8YMFBcXY/DgwVbb5uTkIDc3F8uXL8fy5cuRm5uLnJwcq20GDx6M4uJizJgxA3v37sWaNWuwaNEi5Ofnm1enr9o2MzMThYWFmD17Nnbs2IGNGzdi/vz5OH78OEaPHm217ejRo3H8+HHMnz8fGzdu5DGpdExPP/207o5Jj69TYzqm4cOH6+6Y9Pg6NZZjGjJkiO6OSY+vU33HZEntYxo+fDhfJx6TtGPatWsXtEoRQgi1g6jN9u3b0aNHD4e23b9/Pzp06ICjR4+id+/eaNKkCbKzsxETE4NvvvkGM2fOxKBBg7Bw4cI6H2fVqlUYNGgQXn31VXMC2HPq1CkUFBRY3Zafn4+MjAzk5eUhJSXFobiJ6pOTk4Pnn39e7TBIR5hTJBPzSR/aLW1n/j03M1fFSJhTJNfBgweRmpqqyfNzTU/WkZiYiLfeesuhbePj4wEAkydPxoULF3DgwAHzMMTu3bsjOjoaw4cPx7Bhw3DnnXfW+jj9+/dHUFAQ9uzZU+f+YmNjERsb6+CREDXckCFD1A6BdIY5RTIxn0g25hR5C00XYs2aNcPIkSOdus+BAweQnJxscy1Yx44dAQB5eXl1FmIAIISAwaC7UZvUSOXm5qJdu3b1b0jkIOYUycR8ItmYU+QtdFdtxMXF4eDBgzbXeO3evRsA0KJFizrv/9FHH6G4uJhT2hMRERERkdtoukesISZOnIiMjAz07t0b2dnZiI6Oxp49e/DCCy8gOTkZ9913HwDg559/xqOPPopHHnkEbdq0gaIo2LFjB+bMmYOUlBSne+KI3IXfCpJszCmSiflEsjGnyFvorkfswQcfxJYtWxAaGooJEyagb9++WLp0KUaNGoWdO3ea1xELDQ1FkyZN8Oqrr2LgwIHo168fVq1ahfHjx+Orr76yGdpIpJYVK1aoHQLpDHOKZGI+kWzMKfIWmp41sbHR8qwsRERERLXR0qyJRDJp+fxcdz1iRHrDhS1JNuYUycR8ItmYU+QtWIgRady6devUDoF0hjlFMjGfSDbmFHkLFmJEGle1Uj2RLMwpkon5RLIxp8hbsBAj0rglS5aoHQLpDHOKZGI+kWzMKfIWLMSINO6VV15ROwTSGeYUycR8ItmYU+QtWIgRaVyfPn3UDoF0hjlFMjGf9KF3q94AgKHJQ1WOhDlF3kN3CzoT6c3x48fVDoF0hjlFMjGf9OGlO15CVkoWUqLUn96bOUXegoUYkcadPXtW7RBIZ5hTJBPzSR98jD5Ii0lTOwwAzCnyHhyaSKRx3bt3VzsE0hnmFMnEfCLZmFPkLViIEWnc/Pnz1Q6BdIY5RTIxn0g25hR5C0UIIdQOQi8OHjyI1NRU5OXlISVF/THWRERERETeTMvn5+wRI9K49PR0tUMgnWFOkUzMJ5KNOUXegoUYkcatW7dO7RBIZ5hTJBPziWRjTpG3YCFGpHGZmZlqh0A6w5wimZhPJBtzirwFCzEijXvttdfUDoF0hjlFMjGfSDbmFHkLFmJEGrdw4UK1QyCdYU6RTMwnko05Rd6ChRiRxnXq1EntEEhnmFMkE/OJZGNOkbcwqR2Anly5cgUAkJ+fr3IkpCeHDh1CdHS02mGQjjCnSCbmE8nGnCKZqs7Lq87TtYSFmES5ubkAgIyMDHUDISIiIiIis9zcXNx8881qh2GFhZhEbdu2BQCsXLkSycnJKkdDepCfn4+MjAx8/PHHaNOmjdrhkA4wp0gm5hPJxpwi2b7//nsMHjzYfJ6uJSzEJAoNDQUAJCcna27lbmrc2rRpw5wiqZhTJBPziWRjTpFsVefpWsLJOoiIiIiIiDyMhRgREREREZGHsRAjIiIiIiLyMBZiEsXExGDq1KmIiYlROxTSCeYUycacIpmYTyQbc4pk03JOKUIIoXYQRERERERE3oQ9YkRERERERB7GQoyIiIiIiMjDWIgRERERERF5GAsxIiIiIiIiD2MhJsHFixcxceJExMXFwd/fHx06dMD777+vdlikkq1bt2L48OFISkpCUFAQmjdvjn79+uE///mPzbb79u1Dr169EBwcjPDwcAwYMABHjhyx+7jz5s1DUlIS/Pz8cN111+G5555DaWmpzXanTp1CVlYWoqOjERgYiNtvvx1btmyRfpyknrfffhuKoiA4ONjmb8wpctSuXbtw//33IyIiAgEBAbjhhhswY8YMq22YT+So/fv3IyMjA3FxcQgMDERSUhKmT5+O4uJiq+2YU1RTUVERnnnmGdxzzz2IiYmBoiiYNm2a3W3Vzp8vvvgCt99+OwIDAxEdHY2srCycOnWqwccOQS7r3bu3CA8PF6+//rrYunWrGDlypAAg3nvvPbVDIxUMGjRI9OjRQyxYsEBs375dfPjhh6Jz587CZDKJLVu2mLf74YcfREhIiLjjjjvEhg0bxKpVq0RKSoqIi4sTp06dsnrMmTNnCkVRxLPPPiu2bdsm/vGPfwhfX1/x+OOPW21XUlIiUlNTRYsWLcSyZcvE5s2bRb9+/YTJZBLbt2/3yPGTex07dkyEhYWJuLg4ERQUZPU35hQ56r333hMGg0E88sgjYu3atWLr1q3irbfeEs8995x5G+YTOergwYPC399ftG/fXnzwwQdiy5YtYurUqcJoNIoHH3zQvB1ziuz56aefRFhYmOjevbv5HHrq1Kk226mdP9u3bxcmk0n069dPbN68WSxbtkw0b95cpKamipKSkgYdOwsxF23YsEEAEMuXL7e6vXfv3iIuLk6UlZWpFBmp5eTJkza3FRUViSZNmoiePXuab3vooYdEdHS0OH/+vPm2o0ePCh8fH/HMM8+Ybzt9+rTw9/cXTzzxhNVjzpo1SyiKIg4ePGi+bf78+QKA+Prrr823lZaWiuTkZNGpUycpx0fq6tu3r0hPTxeZmZk2hRhzihxx7NgxERQUJMaMGVPndswnctRf//pXAUDk5+db3f7EE08IAKKwsFAIwZwi+yoqKkRFRYUQQoiCgoJaCzG186djx44iOTlZlJaWmm/76quvBACxYMGCBh07CzEXjRw5UgQHB1u9KEIIsXz5cgFAfPXVVypFRlrTo0cP0bZtWyFE5Zs8ICBAjBo1yma7e+65R9xwww3mfy9btkwAELt377ba7rfffhMAxKxZs8y39erVSyQmJto85vPPPy8AiGPHjsk6HFLBu+++K0JCQsSvv/5qU4gxp8hR06ZNEwDE0aNHa92G+UTOqMqpgoICq9ufeeYZYTAYxMWLF5lT5JDaCjG18+fYsWMCgHjhhRdstm3btq3o3bu3U8dZhdeIuSgvLw833ngjTCaT1e1paWnmvxOdP38e+/btQ0pKCgDg8OHDuHz5sjlPLKWlpSE/Px8lJSUAqnOoXbt2Vts1a9YM0dHRVjmWl5dX62MCwMGDB+UcEHncqVOnMHHiRLz44oto0aKFzd+ZU+SonTt3IjIyEocOHUKHDh1gMpkQGxuL0aNH48KFCwCYT+SczMxMhIeHY8yYMThy5AiKioqwfv16vPHGGxg7diyCgoKYU+QStfOn6j61bdvQ830WYi46c+YMIiMjbW6vuu3MmTOeDok0aOzYsbh06RL++te/AqjOi9pyRwiBs2fPmrf18/NDUFCQ3W0tc4z5qF9/+tOfkJiYiDFjxtj9O3OKHHX8+HEUFxfjoYcewsMPP4wvvvgCf/nLX/DOO+/g/vvvhxCC+UROad26NXbv3o28vDwkJCQgNDQU6enpyMzMxNy5cwGwjSLXqJ0/9e2/oXlmqn8Tqo+iKA36G3mHKVOm4L333sO8efNwyy23WP3N0dxxJseYj/qzatUqrFu3Dvv376/3NWROUX0qKipQUlKCqVOnYvLkyQCAu+66C76+vpg4cSK2bNmCwMBAAMwncszRo0eRnp6OJk2a4KOPPkJMTAy++eYbzJw5ExcvXsTChQvN2zKnyBVq509t2zY0z9gj5qKoqCi7VXBhYSEA+5UzeY/nnnsOM2fOxKxZszBu3Djz7VFRUQDsf1NXWFgIRVEQHh5u3rakpMRmCuCqbS1zjPmoPxcvXsTYsWPx5z//GXFxcTh37hzOnTuHq1evAgDOnTuHS5cuMafIYVW50qdPH6vb77vvPgCV00Mzn8gZkydPxoULF7Bp0yYMHDgQ3bt3x1/+8hfMmTMHixYtwo4dO5hT5BK186e+/Tc0z1iIuahdu3b44YcfUFZWZnV7bm4uACA1NVWNsEgDnnvuOUybNg3Tpk1DTk6O1d8SEhIQEBBgzhNLubm5aNOmDfz9/QFUj3Guue2JEydw+vRpqxxr165drY8JMB8bo9OnT+PkyZN45ZVXEBERYf5ZsWIFLl26hIiICPzhD39gTpHD7F3jAABCCACAwWBgPpFTDhw4gOTkZJuhYB07dgQA85BF5hQ1lNr5U/X/2rZtcJ41aIoPMvv0008FAPH+++9b3X7vvfdy+novNn36dAFA/O1vf6t1m8GDB4vY2Fhx4cIF820///yz8PX1FZMmTTLfdubMGeHv7y9Gjx5tdf8XXnjBZhrWBQsWCABiz5495ttKS0tFSkqKuO2222QcGnnY5cuXxbZt22x++vTpI/z9/cW2bdtEbm6uEII5RY7ZtGmTzcxhQgjx6quvCgDiyy+/FEIwn8hxPXr0EDExMaKoqMjq9jfffFMAEB9//LEQgjlF9atr+nq186dTp04iNTXV6tx+9+7dAoD417/+1aDjZSEmQe/evUVERIR48803xdatW8Xjjz8uAIhly5apHRqp4OWXXxYAxL333it2795t81Plhx9+EMHBwaJ79+7i008/FatXrxapqal1LkyYk5Mjtm/fLmbPni38/PzsLkyYkpIiWrZsKd577z3x+eefi/79+3NhSx2yt44Yc4oclZ6eLvz8/MSMGTPE559/Ll544QXh7+8v+vbta96G+USO+uSTT4SiKKJz587mBZ1nzZolgoODRXJysrhy5YoQgjlFtfv000/Fhx9+KBYtWiQAiIceekh8+OGH4sMPPxSXLl0SQqifP9u2bRMmk0n0799ffP755+K9994TLVu25ILOaisqKhLjx48XTZs2Fb6+viItLU2sWLFC7bBIJXfeeacAUOuPpX//+9+iZ8+eIjAwUISGhoqMjAybBTGrzJ07V7Rt21b4+vqK+Ph4MXXqVHH16lWb7U6cOCGGDRsmIiMjhb+/v+jcubP4/PPP3XKspB57hZgQzClyTHFxsZg0aZJo2bKlMJlMIj4+Xjz77LM2JxPMJ3LU1q1bxT333COaNm0qAgICRNu2bcVTTz0lTp8+bbUdc4rsadWqVa3nTT/99JN5O7XzZ/PmzaJz587C399fREZGimHDhomTJ082+LgVIa4NCiciIiIiIiKP4GQdREREREREHsZCjIiIiIiIyMNYiBEREREREXkYCzEiIiIiIiIPYyFGRERERETkYSzEiIiIiIiIPIyFGBERERERkYexECMiIiIiIvIwFmJEREREREQexkKMiIg8SlEUh362b9+OrKwstG7dWu2QzZYsWWIV4+nTpz26/4kTJ5r3HRwc7NF9ExGRXCa1AyAiIu+ye/duq3/PmDED27Ztw9atW61uT05ORsuWLTFhwgRPhueQ1atXo1mzZggPD/fofrOzs/HII49gxowZ2LFjh0f3TUREcrEQIyIij+rcubPVv2NiYmAwGGxuB4DQ0FBPheWUm266SZWeulatWqFVq1aIiYnx+L6JiEguDk0kIiLNsjc0UVEUjBs3DosXL0ZiYiICAgJw6623Ys+ePRBCYPbs2bjuuusQHByMu+++G/n5+TaP+8UXX6Bnz54IDQ1FYGAgunbtii1btrgU61133YXU1FTs3r0bXbp0QUBAAFq3bo3FixcDADZs2ICbb74ZgYGBaNeuHTZu3Gh1/4KCAjzxxBNo2bIl/Pz8EBMTg65du+KLL75wKS4iItIm9ogREVGjs379euzfvx8vvvgiFEXBpEmT8MADDyAzMxNHjhzBP//5T5w/fx5PPvkkBg4ciAMHDkBRFADAsmXLMGzYMPTr1w9Lly6Fj48P3njjDfTp0webNm1Cz549GxzXiRMn8Nhjj+GZZ55BixYtMG/ePAwfPhy//vorPvroI+Tk5CAsLAzTp09HRkYGjhw5gri4OADA0KFDsW/fPsyaNQtt27bFuXPnsG/fPpw5c0bKc0ZERNrCQoyIiBqdK1euYPPmzQgKCgJQ2UuWkZGBbdu2Yd++feaiq6CgABMnTkReXh7atWuH4uJiTJgwAX379sWaNWvMj3f//ffj5ptvRk5ODr755psGx3XmzBls2rQJt9xyCwDg1ltvRWxsLF588UXk5+ebi664uDh06NABq1atwp///GcAwFdffYWRI0fi8ccfNz9ev379GhwLERFpG4cmEhFRo9OjRw9zEQYAN954IwDgvvvuMxdhlrf//PPPAICvv/4ahYWFyMzMRFlZmfmnoqIC9957L7799ltcunSpwXE1a9bMXIQBQGRkJGJjY9GhQwdzEWYvLgDo1KkTlixZgpkzZ2LPnj0oLS1tcBxERKR9LMSIiKjRiYyMtPq3r69vnbeXlJQAAE6ePAkAGDRoEHx8fKx+XnrpJQghUFhYKC2uqhjqiwsAPvjgA2RmZuLtt9/G7bffjsjISAwbNgwnTpxocDxERKRdHJpIREReIzo6GgAwb948u7M0AkCTJk08GZJZdHQ05syZgzlz5uCXX37B2rVrMXnyZJw6dcpmYg8iImr8WIgREZHX6Nq1K8LDw/H9999j3LhxaodTq/j4eIwbNw5btmzBV199pXY4RETkBizEiIjIawQHB2PevHnIzMxEYWEhBg0ahNjYWBQUFOD//u//UFBQgH/9618ej+v8+fPo0aMHHn30USQlJSEkJATffvstNm7ciAEDBng8HiIicj8WYkRE5FX++Mc/Ij4+Hv/4xz8watQoFBUVmSfUyMrKUiUmf39/3HbbbXj33Xdx9OhRlJaWIj4+HpMmTcIzzzyjSkxEROReihBCqB0EERFRY7BkyRI89thjyM/PR6tWrWAyefb7zIqKClRUVGDEiBFYtWoVLl686NH9ExGRPJw1kYiIyElt2rSBj48PTp8+7dH9Pvnkk/Dx8cE777zj0f0SEZF87BEjIiJy0JkzZ/DTTz+Z/92hQweP9or9+uuv5in4jUYjbrrpJo/tm4iI5GIhRkRERERE5GEcmkhERERERORhLMSIiIiIiIg8jIUYERERERGRh7EQIyIiIiIi8jAWYkRERERERB7GQoyIiIiIiMjDWIgRERERERF5GAsxIiIiIiIiD2MhRkRERERE5GEsxIiIiIiIiDzs/wE+gxX9KXuLEQAAAABJRU5ErkJggg==", + "image/png": 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\n", 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" ] @@ -8982,12 +8615,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -9022,12 +8655,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -9051,12 +8684,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -9094,12 +8727,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", 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" ] @@ -9184,7 +8817,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -9198,7 +8831,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/doc/tutorials/stdp_windows/stdp_windows.ipynb b/doc/tutorials/stdp_windows/stdp_windows.ipynb index b4195a115..6bb2f7782 100644 --- a/doc/tutorials/stdp_windows/stdp_windows.ipynb +++ b/doc/tutorials/stdp_windows/stdp_windows.ipynb @@ -14,6 +14,14 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -22,8 +30,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -281,8 +289,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -292,10 +300,10 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[16,stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[22,stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[30,stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[50,stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n" + "[17,stdp_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[23,stdp_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[31,stdp_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[51,stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n" ] }, { @@ -310,12 +318,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[64,stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[69,stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", + "[65,stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[70,stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -330,27 +334,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_484e294c98d344e8b3ee6c4b42d9261c_module Configuration Summary\n", + "nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_484e294c98d344e8b3ee6c4b42d9261c_module' using\n", + "You can now build and install 'nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_484e294c98d344e8b3ee6c4b42d9261c_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module.so will be installed to\n", + " /tmp/nestml_target_i1uuk7tq\n", "The module can be loaded into NEST using\n", - " (nestml_484e294c98d344e8b3ee6c4b42d9261c_module) Install (in SLI)\n", - " nest.Install(nestml_484e294c98d344e8b3ee6c4b42d9261c_module) (in PyNEST)\n", + " (nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module) Install (in SLI)\n", + " nest.Install(nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -362,142 +366,133 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.3s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target\n", - "[ 25%] Building CXX object CMakeFiles/nestml_484e294c98d344e8b3ee6c4b42d9261c_module_module.dir/nestml_484e294c98d344e8b3ee6c4b42d9261c_module.o\n", - "[ 75%] Building CXX object CMakeFiles/nestml_484e294c98d344e8b3ee6c4b42d9261c_module_module.dir/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml.o\n", - "[ 75%] Building CXX object CMakeFiles/nestml_484e294c98d344e8b3ee6c4b42d9261c_module_module.dir/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.cpp: In member function ‘void iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.cpp:166:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 166 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 25%] Building CXX object CMakeFiles/nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module_module.dir/nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module_module.dir/iaf_psc_delta_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module_module.dir/iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 173 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.cpp:253:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 253 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:266:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 266 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.cpp:251:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 251 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:261:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 261 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml.cpp: In member function ‘void iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml.cpp:176:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 176 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml.cpp:183:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 183 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml.cpp:274:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 274 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml.cpp:287:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 287 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml.cpp:272:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 272 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml.cpp:282:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 282 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_484e294c98d344e8b3ee6c4b42d9261c_module.cpp:52:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_484e294c98d344e8b3ee6c4b42d9261c_module.cpp:111:163: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:676:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 676 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module.cpp:36:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:568:91: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:683:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 683 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_484e294c98d344e8b3ee6c4b42d9261c_module.cpp:111:163: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:676:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:688:3: required from ‘nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_484e294c98d344e8b3ee6c4b42d9261c_module.cpp:111:163: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:664:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 664 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:695:3: required from ‘nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:568:91: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:671:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 671 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:688:3: required from ‘nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_484e294c98d344e8b3ee6c4b42d9261c_module.cpp:111:163: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:664:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:483:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 483 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:568:91: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:683:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 683 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:695:3: required from ‘nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:568:91: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:671:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 671 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:484:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 484 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:508:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 508 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:509:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 509 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:544:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 544 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:545:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 545 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:416:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 416 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:417:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 417 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:418:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 418 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:419:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 419 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:483:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 483 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:484:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 484 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:508:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 508 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:509:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 509 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:544:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 544 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:545:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 545 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:416:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 416 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:417:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 417 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:418:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 418 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:419:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 419 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::update_internal_state_(double, double, const nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:478:9: required from ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:738:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 738 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:479:9: required from ‘bool nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:745:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 745 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:739:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 739 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:746:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 746 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h: In instantiation of ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::update_internal_state_(double, double, const nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:478:9: required from ‘void nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:738:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 738 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:479:9: required from ‘bool nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:745:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 745 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml__with_iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml.h:739:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 739 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[100%] Linking CXX shared module nestml_484e294c98d344e8b3ee6c4b42d9261c_module.so\n", - "[100%] Built target nestml_484e294c98d344e8b3ee6c4b42d9261c_module_module\n", - "[100%] Built target nestml_484e294c98d344e8b3ee6c4b42d9261c_module_module\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:746:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 746 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "[100%] Linking CXX shared module nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module.so\n", + "[100%] Built target nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module_module\n", + "[100%] Built target nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_484e294c98d344e8b3ee6c4b42d9261c_module.so\n", - "\n", - "Oct 19 05:06:46 Install [Info]: \n", - " loaded module nestml_484e294c98d344e8b3ee6c4b42d9261c_module\n" + "-- Installing: /tmp/nestml_target_i1uuk7tq/nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module.so\n" ] } ], @@ -505,9 +500,7 @@ "module_name, neuron_model_name, synapse_model_name = NESTCodeGeneratorUtils.generate_code_for(\n", " \"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n", " nestml_stdp_model,\n", - " post_ports=[\"post_spikes\"])\n", - "\n", - "nest.Install(module_name)" + " post_ports=[\"post_spikes\"])" ] }, { @@ -526,19 +519,20 @@ "outputs": [], "source": [ "def run_network(pre_spike_time, post_spike_time,\n", - " neuron_model_name,\n", - " synapse_model_name,\n", - " resolution=1., # [ms]\n", - " delay=1., # [ms]\n", - " lmbda=1E-6,\n", - " sim_time=None, # if None, computed from pre and post spike times\n", - " synapse_parameters=None, # optional dictionary passed to the synapse\n", - " fname_snip=\"\"):\n", + " module_name,\n", + " neuron_model_name,\n", + " synapse_model_name,\n", + " resolution=1., # [ms]\n", + " delay=1., # [ms]\n", + " lmbda=1E-6,\n", + " sim_time=None, # if None, computed from pre and post spike times\n", + " synapse_parameters=None, # optional dictionary passed to the synapse\n", + " fname_snip=\"\"):\n", "\n", + " nest.ResetKernel()\n", + " nest.Install(module_name)\n", " nest.set_verbosity(\"M_WARNING\")\n", " #nest.set_verbosity(\"M_ALL\")\n", - "\n", - " nest.ResetKernel()\n", " nest.SetKernelStatus({'resolution': resolution})\n", "\n", " wr = nest.Create('weight_recorder')\n", @@ -608,33 +602,6 @@ "execution_count": 5, "metadata": {}, "outputs": [], - "source": [ - "def stdp_window(neuron_model_name, synapse_model_name, synapse_parameters=None):\n", - " sim_time = 1000. # [ms]\n", - " pre_spike_time = 100. #sim_time / 2 # [ms]\n", - " delay = 10. # dendritic delay [ms]\n", - "\n", - " dt_vec = []\n", - " dw_vec = []\n", - " for post_spike_time in np.arange(25, 175).astype(float):\n", - " dt, dw = run_network(pre_spike_time, post_spike_time,\n", - " neuron_model_name,\n", - " synapse_model_name,\n", - " resolution=1., # [ms]\n", - " delay=delay, # [ms]\n", - " synapse_parameters=synapse_parameters,\n", - " sim_time=sim_time)\n", - " dt_vec.append(dt)\n", - " dw_vec.append(dw)\n", - " \n", - " return dt_vec, dw_vec, delay" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], "source": [ "def plot_stdp_window(dt_vec, dw_vec, delay):\n", " fig, ax = plt.subplots(dpi=120)\n", @@ -655,6 +622,34 @@ " ax.set_ylim(ylim)" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def stdp_window(module_name, neuron_model_name, synapse_model_name, synapse_parameters=None):\n", + " sim_time = 1000. # [ms]\n", + " pre_spike_time = 100. #sim_time / 2 # [ms]\n", + " delay = 10. # dendritic delay [ms]\n", + "\n", + " dt_vec = []\n", + " dw_vec = []\n", + " for post_spike_time in np.arange(25, 175).astype(float):\n", + " dt, dw = run_network(pre_spike_time, post_spike_time,\n", + " module_name,\n", + " neuron_model_name,\n", + " synapse_model_name,\n", + " resolution=1., # [ms]\n", + " delay=delay, # [ms]\n", + " synapse_parameters=synapse_parameters,\n", + " sim_time=sim_time)\n", + " dt_vec.append(dt)\n", + " dw_vec.append(dw)\n", + " \n", + " return dt_vec, dw_vec, delay" + ] + }, { "cell_type": "code", "execution_count": 7, @@ -665,1228 +660,1213 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 Install [Info]: \n", + " loaded module nestml_c74fe99fdb8a4a94b0aaeb52ce20cf1a_module\n", + "\n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:46 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:50 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:51 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:47 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:52 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1896,7 +1876,7 @@ } ], "source": [ - "dt_vec, dw_vec, delay = stdp_window(neuron_model_name, synapse_model_name,\n", + "dt_vec, dw_vec, delay = stdp_window(module_name, neuron_model_name, synapse_model_name,\n", " synapse_parameters={\"alpha\": .5})\n", "\n", "plot_stdp_window(dt_vec, dw_vec, delay)" @@ -1924,1234 +1904,1210 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:53 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:54 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:48 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:55 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:06:49 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:51:56 iaf_psc_delta_neuron_nestml__with_stdp_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n" ] }, { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -3161,7 +3117,7 @@ } ], "source": [ - "dt_vec, dw_vec, delay = stdp_window(neuron_model_name, synapse_model_name,\n", + "dt_vec, dw_vec, delay = stdp_window(module_name, neuron_model_name, synapse_model_name,\n", " synapse_parameters={\"alpha\": -1.})\n", "plot_stdp_window(dt_vec, dw_vec, delay)" ] @@ -3265,8 +3221,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -3276,10 +3232,10 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[16,stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", - "[24,stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", - "[34,stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", - "[63,stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n" + "[17,stdp_windowed_synapse_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", + "[25,stdp_windowed_synapse_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", + "[35,stdp_windowed_synapse_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", + "[64,stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n" ] }, { @@ -3288,7 +3244,7 @@ "text": [ "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "WARNING:Not preserving expression for variable \"post_nn_trace__for_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"post_nn_trace__for_stdp_windowed_synapse_nestml\" as it is solved by propagator solver\n", "WARNING:Not preserving expression for variable \"pre_nn_trace\" as it is solved by propagator solver\n" ] }, @@ -3296,12 +3252,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[78,stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", - "[84,stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", + "[79,stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", + "[85,stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [10:8;10:28]]: Variable 'd' has the same name as a physical unit!\n", "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -3316,27 +3268,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module Configuration Summary\n", + "nestml_81cef8d40f4e47aea7c9c5093486008d_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module' using\n", + "You can now build and install 'nestml_81cef8d40f4e47aea7c9c5093486008d_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_81cef8d40f4e47aea7c9c5093486008d_module.so will be installed to\n", + " /tmp/nestml_target_kubgfbw2\n", "The module can be loaded into NEST using\n", - " (nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module) Install (in SLI)\n", - " nest.Install(nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module) (in PyNEST)\n", + " (nestml_81cef8d40f4e47aea7c9c5093486008d_module) Install (in SLI)\n", + " nest.Install(nestml_81cef8d40f4e47aea7c9c5093486008d_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -3348,139 +3300,133 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.2s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target\n", - "[ 25%] Building CXX object CMakeFiles/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module_module.dir/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.o\n", - "[ 50%] Building CXX object CMakeFiles/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module_module.dir/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.o\n", - "[ 75%] Building CXX object CMakeFiles/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module_module.dir/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.cpp: In member function ‘void iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.cpp:166:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 166 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 25%] Building CXX object CMakeFiles/nestml_81cef8d40f4e47aea7c9c5093486008d_module_module.dir/nestml_81cef8d40f4e47aea7c9c5093486008d_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_81cef8d40f4e47aea7c9c5093486008d_module_module.dir/iaf_psc_delta_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_81cef8d40f4e47aea7c9c5093486008d_module_module.dir/iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 173 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.cpp:253:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 253 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:266:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 266 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.cpp:251:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 251 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:261:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 261 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml.cpp: In member function ‘void iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml.cpp:181:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 181 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml.cpp:188:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 188 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml.cpp:284:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 284 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml.cpp:297:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 297 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml.cpp:282:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 282 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml.cpp:292:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 292 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.cpp:52:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.cpp:111:172: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:732:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 732 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_81cef8d40f4e47aea7c9c5093486008d_module.cpp:36:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:611:100: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:739:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 739 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.cpp:111:172: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:732:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:746:3: required from ‘nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.cpp:111:172: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:719:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 719 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:753:3: required from ‘nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:611:100: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:726:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 726 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:746:3: required from ‘nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.cpp:111:172: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:719:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:516:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 516 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:611:100: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:739:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 739 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:753:3: required from ‘nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:611:100: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:726:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 726 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:517:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 517 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:544:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 544 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:545:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 545 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:584:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 584 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:585:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 585 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:449:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 449 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:450:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 450 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:451:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 451 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:452:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 452 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:516:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 516 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:517:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 517 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:544:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 544 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:545:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 545 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:584:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 584 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:585:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 585 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:449:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 449 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:450:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 450 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:451:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 451 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:452:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 452 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::update_internal_state_(double, double, const nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:511:9: required from ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:800:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 800 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:512:9: required from ‘bool nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:807:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 807 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:801:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 801 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:808:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 808 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::update_internal_state_(double, double, const nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:511:9: required from ‘void nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:800:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 800 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:512:9: required from ‘bool nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:807:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 807 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml__with_iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml.h:801:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 801 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[100%] Linking CXX shared module nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.so\n", - "[100%] Built target nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module_module\n", - "[100%] Built target nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module_module\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_windowed_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:808:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 808 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "[100%] Linking CXX shared module nestml_81cef8d40f4e47aea7c9c5093486008d_module.so\n", + "[100%] Built target nestml_81cef8d40f4e47aea7c9c5093486008d_module_module\n", + "[100%] Built target nestml_81cef8d40f4e47aea7c9c5093486008d_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_79a98d48ee834ca4914e80a2fa3e1cfb_module.so\n" + "-- Installing: /tmp/nestml_target_kubgfbw2/nestml_81cef8d40f4e47aea7c9c5093486008d_module.so\n" ] } ], @@ -3488,8 +3434,7 @@ "module_name, neuron_model_name, synapse_model_name = NESTCodeGeneratorUtils.generate_code_for(\n", " \"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n", " nestml_windowed_stdp_model,\n", - " post_ports=[\"post_spikes\"])\n", - "nest.Install(module_name)" + " post_ports=[\"post_spikes\"])" ] }, { @@ -3502,1276 +3447,1221 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:39 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:40 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:41 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:06 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", + "Apr 19 11:52:42 iaf_psc_delta_neuron_nestml__with_stdp_windowed_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", + " model have been reset!\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " model have been reset!\n", "\n", "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", @@ -5049,28 +4939,6 @@ "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ "\n", "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", @@ -5246,2874 +5114,337 @@ "\n", "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n" + " model have been reset!\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:07 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dt_vec, dw_vec, delay = stdp_window(neuron_model_name, synapse_model_name)\n", - "plot_stdp_window(dt_vec, dw_vec, delay)" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Symmetric inhibitory STDP\n", - "--------------------\n", - "\n", - "\n", - "
\n", - "\n", - "
\n", - "\n", - "The symmetric STDP window in the figure can be observed experimentally and was used to achieve a self-organised balance between excitation and inhibition in recurrent networks [4]_." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "nestml_stdp_vogels_model = \"\"\"\n", - "model stdp_vogels_synapse:\n", - "\n", - " state:\n", - " w real = 1.\n", - "\n", - " parameters:\n", - " d ms = 1 ms @nest::delay # !!! cannot have a variable called \"delay\"\n", - " lambda real = .01\n", - " offset real = 1.\n", - " tau_tr_pre ms = 20 ms\n", - " tau_tr_post ms = 20 ms\n", - " alpha real = 1\n", - " mu_plus real = 1\n", - " mu_minus real = 1\n", - " Wmax real = 100.\n", - " Wmin real = 0.\n", - "\n", - " equations:\n", - " kernel pre_trace_kernel = exp(-t / tau_tr_pre)\n", - " inline pre_trace real = convolve(pre_trace_kernel, pre_spikes)\n", - "\n", - " # all-to-all trace of postsynaptic neuron\n", - " kernel post_trace_kernel = exp(-t / tau_tr_post)\n", - " inline post_trace real = convolve(post_trace_kernel, post_spikes)\n", - "\n", - " input:\n", - " pre_spikes <- spike\n", - " post_spikes <- spike\n", - "\n", - " output:\n", - " spike\n", - "\n", - " onReceive(post_spikes, priority=2):\n", - " w += lambda * (pre_trace + post_trace)\n", - "\n", - " onReceive(pre_spikes, priority=1):\n", - " w += lambda * (pre_trace + post_trace - offset)\n", - "\n", - " # deliver spike to postsynaptic partner\n", - " emit_spike(w, d)\n", - " \n", - " update:\n", - " integrate_odes()\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save to a temporary file and make the model available to instantiate in NEST:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " -- N E S T --\n", - " Copyright (C) 2004 The NEST Initiative\n", - "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", - "\n", - " This program is provided AS IS and comes with\n", - " NO WARRANTY. See the file LICENSE for details.\n", - "\n", - " Problems or suggestions?\n", - " Visit https://www.nest-simulator.org\n", - "\n", - " Type 'nest.help()' to find out more about NEST.\n", - "\n", - "[16,stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[22,stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[30,stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[51,stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[65,stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "[70,stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", - "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", - " cmake_minimum_required() should be called prior to this top-level project()\n", - " call. Please see the cmake-commands(7) manual for usage documentation of\n", - " both commands.\n", - "This warning is for project developers. Use -Wno-dev to suppress it.\n", - "\n", - "-- The CXX compiler identification is GNU 12.3.0\n", - "-- Detecting CXX compiler ABI info\n", - "-- Detecting CXX compiler ABI info - done\n", - "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n", - "-- Detecting CXX compile features\n", - "-- Detecting CXX compile features - done\n", - "\n", - "-------------------------------------------------------\n", - "nestml_913e0b5b31a0403a9a7bfc51a747debd_module Configuration Summary\n", - "-------------------------------------------------------\n", - "\n", - "C++ compiler : /usr/bin/c++\n", - "Build static libs : OFF\n", - "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", - "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", - "\n", - "-------------------------------------------------------\n", - "\n", - "You can now build and install 'nestml_913e0b5b31a0403a9a7bfc51a747debd_module' using\n", - " make\n", - " make install\n", - "\n", - "The library file libnestml_913e0b5b31a0403a9a7bfc51a747debd_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", - "The module can be loaded into NEST using\n", - " (nestml_913e0b5b31a0403a9a7bfc51a747debd_module) Install (in SLI)\n", - " nest.Install(nestml_913e0b5b31a0403a9a7bfc51a747debd_module) (in PyNEST)\n", - "\n", - "CMake Warning (dev) in CMakeLists.txt:\n", - " No cmake_minimum_required command is present. A line of code such as\n", - "\n", - " cmake_minimum_required(VERSION 3.26)\n", - "\n", - " should be added at the top of the file. The version specified may be lower\n", - " if you wish to support older CMake versions for this project. For more\n", - " information run \"cmake --help-policy CMP0000\".\n", - "This warning is for project developers. Use -Wno-dev to suppress it.\n", - "\n", - "-- Configuring done (0.2s)\n", - "-- Generating done (0.0s)\n", - "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target\n", - "[ 25%] Building CXX object CMakeFiles/nestml_913e0b5b31a0403a9a7bfc51a747debd_module_module.dir/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.o\n", - "[ 50%] Building CXX object CMakeFiles/nestml_913e0b5b31a0403a9a7bfc51a747debd_module_module.dir/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.o\n", - "[ 75%] Building CXX object CMakeFiles/nestml_913e0b5b31a0403a9a7bfc51a747debd_module_module.dir/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.cpp: In member function ‘void iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.cpp:166:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 166 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.cpp:253:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 253 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", - " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.cpp:251:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 251 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml.cpp: In member function ‘void iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml.cpp:176:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 176 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml.cpp:274:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 274 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", - " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml.cpp:272:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 272 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", - " | ^~~~~\n", - "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.cpp:52:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.cpp:111:170: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:688:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 688 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.cpp:111:170: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:688:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:701:3: required from ‘nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.cpp:111:170: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:676:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 676 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:701:3: required from ‘nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.cpp:111:170: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:676:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:494:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 494 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:518:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 518 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:553:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 553 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:427:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 427 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:429:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 429 | auto get_thread = [tid]()\n", - " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:494:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 494 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:518:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 518 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:553:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 553 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:427:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 427 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:429:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 429 | auto get_thread = [tid]()\n", - " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::update_internal_state_(double, double, const nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:489:9: required from ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:752:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 752 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:753:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 753 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::update_internal_state_(double, double, const nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:489:9: required from ‘void nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:752:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 752 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml__with_iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml.h:753:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 753 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[100%] Linking CXX shared module nestml_913e0b5b31a0403a9a7bfc51a747debd_module.so\n", - "[100%] Built target nestml_913e0b5b31a0403a9a7bfc51a747debd_module_module\n", - "[100%] Built target nestml_913e0b5b31a0403a9a7bfc51a747debd_module_module\n", - "Install the project...\n", - "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.so\n" - ] - } - ], - "source": [ - "module_name, neuron_model_name, synapse_model_name = NESTCodeGeneratorUtils.generate_code_for(\n", - " \"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n", - " nestml_stdp_vogels_model,\n", - " post_ports=[\"post_spikes\"])\n", - "nest.Install(module_name)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:22 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dt_vec, dw_vec, delay = stdp_window(module_name, neuron_model_name, synapse_model_name)\n", + "plot_stdp_window(dt_vec, dw_vec, delay)" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Symmetric inhibitory STDP\n", + "--------------------\n", + "\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "The symmetric STDP window in the figure can be observed experimentally and was used to achieve a self-organised balance between excitation and inhibition in recurrent networks [4]_." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "nestml_stdp_vogels_model = \"\"\"\n", + "model stdp_vogels_synapse:\n", + "\n", + " state:\n", + " w real = 1.\n", + "\n", + " parameters:\n", + " d ms = 1 ms @nest::delay # !!! cannot have a variable called \"delay\"\n", + " lambda real = .01\n", + " offset real = 1.\n", + " tau_tr_pre ms = 20 ms\n", + " tau_tr_post ms = 20 ms\n", + " alpha real = 1\n", + " mu_plus real = 1\n", + " mu_minus real = 1\n", + " Wmax real = 100.\n", + " Wmin real = 0.\n", + "\n", + " equations:\n", + " kernel pre_trace_kernel = exp(-t / tau_tr_pre)\n", + " inline pre_trace real = convolve(pre_trace_kernel, pre_spikes)\n", + "\n", + " # all-to-all trace of postsynaptic neuron\n", + " kernel post_trace_kernel = exp(-t / tau_tr_post)\n", + " inline post_trace real = convolve(post_trace_kernel, post_spikes)\n", + "\n", + " input:\n", + " pre_spikes <- spike\n", + " post_spikes <- spike\n", + "\n", + " output:\n", + " spike\n", + "\n", + " onReceive(post_spikes, priority=2):\n", + " w += lambda * (pre_trace + post_trace)\n", + "\n", + " onReceive(pre_spikes, priority=1):\n", + " w += lambda * (pre_trace + post_trace - offset)\n", + "\n", + " # deliver spike to postsynaptic partner\n", + " emit_spike(w, d)\n", + " \n", + " update:\n", + " integrate_odes()\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save to a temporary file and make the model available to instantiate in NEST:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " -- N E S T --\n", + " Copyright (C) 2004 The NEST Initiative\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " This program is provided AS IS and comes with\n", + " NO WARRANTY. See the file LICENSE for details.\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " Problems or suggestions?\n", + " Visit https://www.nest-simulator.org\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " Type 'nest.help()' to find out more about NEST.\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "[17,stdp_vogels_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[23,stdp_vogels_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[31,stdp_vogels_synapse_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[52,stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[66,stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "[71,stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [8:8;8:28]]: Variable 'd' has the same name as a physical unit!\n", + "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", + " cmake_minimum_required() should be called prior to this top-level project()\n", + " call. Please see the cmake-commands(7) manual for usage documentation of\n", + " both commands.\n", + "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "-- The CXX compiler identification is GNU 12.3.0\n", + "-- Detecting CXX compiler ABI info\n", + "-- Detecting CXX compiler ABI info - done\n", + "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n", + "-- Detecting CXX compile features\n", + "-- Detecting CXX compile features - done\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "-------------------------------------------------------\n", + "nestml_8e9daa037f484288a6e9ba0cf082d818_module Configuration Summary\n", + "-------------------------------------------------------\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "C++ compiler : /usr/bin/c++\n", + "Build static libs : OFF\n", + "C++ compiler flags : \n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "-------------------------------------------------------\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "You can now build and install 'nestml_8e9daa037f484288a6e9ba0cf082d818_module' using\n", + " make\n", + " make install\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta484e294c98d344e8b3ee6c4b42d9261c_neuron_nestml__with_stdp484e294c98d344e8b3ee6c4b42d9261c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "The library file libnestml_8e9daa037f484288a6e9ba0cf082d818_module.so will be installed to\n", + " /tmp/nestml_target_creme1gl\n", + "The module can be loaded into NEST using\n", + " (nestml_8e9daa037f484288a6e9ba0cf082d818_module) Install (in SLI)\n", + " nest.Install(nestml_8e9daa037f484288a6e9ba0cf082d818_module) (in PyNEST)\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "CMake Warning (dev) in CMakeLists.txt:\n", + " No cmake_minimum_required command is present. A line of code such as\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta79a98d48ee834ca4914e80a2fa3e1cfb_neuron_nestml__with_stdp_windowed79a98d48ee834ca4914e80a2fa3e1cfb_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " cmake_minimum_required(VERSION 3.26)\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " should be added at the top of the file. The version specified may be lower\n", + " if you wish to support older CMake versions for this project. For more\n", + " information run \"cmake --help-policy CMP0000\".\n", + "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "Oct 19 05:07:23 iaf_psc_delta913e0b5b31a0403a9a7bfc51a747debd_neuron_nestml__with_stdp_vogels913e0b5b31a0403a9a7bfc51a747debd_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n" + "-- Configuring done (0.5s)\n", + "-- Generating done (0.0s)\n", + "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target\n", + "[ 25%] Building CXX object CMakeFiles/nestml_8e9daa037f484288a6e9ba0cf082d818_module_module.dir/nestml_8e9daa037f484288a6e9ba0cf082d818_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_8e9daa037f484288a6e9ba0cf082d818_module_module.dir/iaf_psc_delta_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_8e9daa037f484288a6e9ba0cf082d818_module_module.dir/iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml.cpp:183:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 183 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml.cpp:287:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 287 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml__with_stdp_vogels_synapse_nestml.cpp:282:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 282 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 173 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:266:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 266 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/iaf_psc_delta_neuron_nestml.cpp:261:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 261 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + " | ^~~~~\n", + "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/nestml_8e9daa037f484288a6e9ba0cf082d818_module.cpp:36:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:576:98: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:695:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 695 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:708:3: required from ‘nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:576:98: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:683:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 683 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:576:98: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:695:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 695 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:708:3: required from ‘nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:576:98: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:683:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 683 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:495:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 495 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:519:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 519 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:554:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 554 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:428:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 428 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:430:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 430 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:495:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 495 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:519:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 519 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:554:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 554 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:428:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 428 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:430:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 430 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/stdp_windows/target/stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:490:9: required from ‘bool nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_vogels_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-i" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[100%] Linking CXX shared module nestml_913e0b5b31a0403a9a7bfc51a747debd_module.so\n", + "[100%] Built target nestml_913e0b5b31a0403a9a7bfc51a747debd_module_module\n", + "[100%] Built target nestml_913e0b5b31a0403a9a7bfc51a747debd_module_module\n", + "Install the project...\n", + "-- Install configuration: \"\"\n", + "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_913e0b5b31a0403a9a7bfc51a747debd_module.so\n" ] + } + ], + "source": [ + "module_name, neuron_model_name, synapse_model_name = NESTCodeGeneratorUtils.generate_code_for(\n", + " \"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n", + " nestml_stdp_vogels_model,\n", + " post_ports=[\"post_spikes\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "name": "stdout", @@ -9922,7 +7253,7 @@ } ], "source": [ - "dt_vec, dw_vec, delay = stdp_window(neuron_model_name,\n", + "dt_vec, dw_vec, delay = stdp_window(module_name, neuron_model_name,\n", " synapse_model_name,\n", " synapse_parameters={\"offset\": .6})\n", "plot_stdp_window(dt_vec, dw_vec, delay)" diff --git a/doc/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.ipynb b/doc/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.ipynb index 76398e3b3..792e15cdb 100644 --- a/doc/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.ipynb +++ b/doc/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.ipynb @@ -22,6 +22,14 @@ "id": "orange-zambia", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/charl/.local/lib/python3.11/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -30,8 +38,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -248,15 +256,16 @@ "text": [ "Automatic pdb calling has been turned ON\n", "[1,GLOBAL, INFO]: List of files that will be processed:\n", - "[2,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron.nestml\n", - "[3,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse.nestml\n", + "[2,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/iaf_psc_delta_neuron.nestml\n", + "[3,GLOBAL, INFO]: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/stdp_triplet_synapse.nestml\n", "[4,GLOBAL, INFO]: Target platform code will be generated in directory: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target'\n", + "[5,GLOBAL, INFO]: Target platform code will be installed in directory: '/tmp/nestml_target_yocmq5xi'\n", "\n", " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -266,87 +275,68 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[5,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: v3.6.0\n", - "[6,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[6,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", "[7,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", "[8,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", - "[9,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /home/charl/julich/nest-simulator-install\n", - "[10,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron.nestml'!\n", - "[11,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, DEBUG, [52:0;99:0]]: Start building symbol table!\n", - "[12,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [60:79;60:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", - "[13,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [60:15;60:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", - "[14,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse.nestml'!\n", - "[15,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[16,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[17,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[18,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[19,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, WARNING, [47:16;47:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "[20,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[21,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[22,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, WARNING, [56:16;56:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "[23,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, DEBUG, [52:0;99:0]]: Start building symbol table!\n", - "[24,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[25,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[26,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[27,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[28,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[29,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[30,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, DEBUG, [52:0;99:0]]: Start building symbol table!\n", - "[31,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [60:79;60:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", - "[32,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [60:15;60:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", - "[33,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[34,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[35,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[36,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[37,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, WARNING, [47:16;47:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "[38,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[39,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[40,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, WARNING, [56:16;56:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "[41,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['tr_o1', 'tr_o2']\n", - "[42,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_y', 'tau_minus']\n", - "[43,GLOBAL, INFO]: Moving state var defining equation(s) tr_o1\n", - "[44,GLOBAL, INFO]: Moving state var defining equation(s) tr_o2\n", - "[45,GLOBAL, INFO]: Moving state variables for equation(s) tr_o1\n", - "[46,GLOBAL, INFO]: Moving definition of tr_o1 from synapse to neuron\n", - "[47,GLOBAL, INFO]: Moving state variables for equation(s) tr_o2\n", - "[48,GLOBAL, INFO]: Moving definition of tr_o2 from synapse to neuron\n", - "Eval stmt = # increment post trace values\n", - "tr_o1 += 1\n", - "\n", - "[49,GLOBAL, INFO]: \tMoving statement # increment post trace values\n", + "[9,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/pynestml/codegeneration/resources_nest/point_neuron'\n", + "[10,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /home/charl/julich/nest-simulator-install\n", + "[11,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/iaf_psc_delta_neuron.nestml'!\n", + "[12,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n", + "[13,iaf_psc_delta_neuron_nestml, INFO, [51:79;51:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[14,iaf_psc_delta_neuron_nestml, INFO, [51:15;51:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[15,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/stdp_triplet_synapse.nestml'!\n", + "[16,stdp_triplet_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", + "[17,stdp_triplet_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[18,stdp_triplet_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[19,stdp_triplet_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[20,stdp_triplet_synapse_nestml, WARNING, [47:16;47:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[21,stdp_triplet_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[22,stdp_triplet_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[23,stdp_triplet_synapse_nestml, WARNING, [56:16;56:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[24,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n", + "[25,stdp_triplet_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", + "[26,stdp_triplet_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[27,stdp_triplet_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[28,stdp_triplet_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[29,stdp_triplet_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[30,stdp_triplet_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[31,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n", + "[32,iaf_psc_delta_neuron_nestml, INFO, [51:79;51:79]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[33,iaf_psc_delta_neuron_nestml, INFO, [51:15;51:74]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[34,stdp_triplet_synapse_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", + "[35,stdp_triplet_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[36,stdp_triplet_synapse_nestml, INFO, [43:17;43:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[37,stdp_triplet_synapse_nestml, INFO, [44:17;44:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[38,stdp_triplet_synapse_nestml, WARNING, [47:16;47:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[39,stdp_triplet_synapse_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[40,stdp_triplet_synapse_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[41,stdp_triplet_synapse_nestml, WARNING, [56:16;56:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[42,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['tr_o1', 'tr_o2']\n", + "[43,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_y', 'tau_minus']\n", + "[44,GLOBAL, INFO]: Moving state var defining equation(s) tr_o1\n", + "[45,GLOBAL, INFO]: Moving state var defining equation(s) tr_o2\n", + "[46,GLOBAL, INFO]: Moving state variables for equation(s) tr_o1\n", + "[47,GLOBAL, INFO]: Moving definition of tr_o1 from synapse to neuron\n", + "[48,GLOBAL, INFO]: Moving state variables for equation(s) tr_o2\n", + "[49,GLOBAL, INFO]: Moving definition of tr_o2 from synapse to neuron\n", + "[50,GLOBAL, INFO]: \tMoving statement # increment post trace values\n", "tr_o1 += 1\n", - "Eval stmt = tr_o2 += 1\n", - "\n", - "[50,GLOBAL, INFO]: \tMoving statement tr_o2 += 1\n", - "Eval stmt = # potentiate synapse\n", - "w_ nS = w + tr_r1 * (A2_plus + A3_plus * tr_o2)\n", - "\n", - "Eval stmt = w = min(Wmax,w_)\n", - "\n", - "[51,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", - "[52,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", - "[53,GLOBAL, INFO]: Copying definition of tau_y from synapse to neuron\n", - "[54,GLOBAL, INFO]: Copying definition of tau_minus from synapse to neuron\n", - "[55,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", - "[56,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", - "[57,GLOBAL, INFO]: \t• Replacing variable tr_o1\n", - "[58,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) in expression: tr_o1 * (A2_minus + A3_minus * tr_r2)\n", - "[59,GLOBAL, INFO]: \t• Replacing variable tr_o2\n", - "[60,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) in expression: A3_plus * tr_o2\n", - "[61,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, DEBUG, [52:0;99:0]]: Start building symbol table!\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[62,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[63,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[64,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[65,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[66,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml\n", - "[67,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml'\n", - "[68,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [52:0;99:0]]: Starts processing of the model 'iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml'\n" + "[51,GLOBAL, INFO]: \tMoving statement tr_o2 += 1\n", + "[52,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", + "[53,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", + "[54,GLOBAL, INFO]: Copying definition of tau_y from synapse to neuron\n", + "[55,GLOBAL, INFO]: Copying definition of tau_minus from synapse to neuron\n", + "[56,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", + "[57,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", + "[58,GLOBAL, INFO]: \t• Replacing variable tr_o1\n", + "[59,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = tr_o1__for_stdp_triplet_synapse_nestml) in expression: tr_o1 * (A2_minus + A3_minus * tr_r2)\n", + "[60,GLOBAL, INFO]: \t• Replacing variable tr_o2\n", + "[61,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = tr_o2__for_stdp_triplet_synapse_nestml) in expression: A3_plus * tr_o2\n", + "[62,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n", + "[63,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", + "[64,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[65,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[66,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n" ] }, { @@ -389,7 +379,7 @@ "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m, nonlin_term = I_stim/C_m\n", "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "INFO:All known variables: [V_m], all parameters used in ODEs: {I_stim, E_L, C_m, I_e, tau_m}\n", + "INFO:All known variables: [V_m], all parameters used in ODEs: {I_stim, C_m, tau_m, I_e, E_L}\n", "INFO:No numerical value specified for parameter \"I_stim\"\n", "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n", @@ -404,12 +394,27 @@ "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n", "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n", "DEBUG:\tnonlinear term: 0.0\n", - "DEBUG:Initializing system of shapes with x = Matrix([[V_m]]), A = Matrix([[-1/tau_m]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m]]), c = Matrix([[0]])\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m]]), A = Matrix([[-1/tau_m]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m]]), c = Matrix([[0.0]])\n", "INFO:Finding analytically solvable equations...\n", "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n", "DEBUG:os.makedirs('/tmp')\n", "DEBUG:write lines to '/tmp/ode_dependency_graph.dot'\n", - "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n", + "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[67,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml\n", + "[68,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta_neuron_nestml'\n", + "[69,iaf_psc_delta_neuron_nestml, INFO, [43:0;94:0]]: Starts processing of the model 'iaf_psc_delta_neuron_nestml'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols [V_m])\n", "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n", @@ -462,7 +467,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[69,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, DEBUG, [52:0;99:0]]: Start building symbol table!\n" + "[70,iaf_psc_delta_neuron_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n" ] }, { @@ -479,15 +484,15 @@ " }\n", " },\n", " {\n", - " \"expression\": \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml' = (-tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) / tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\",\n", + " \"expression\": \"tr_o1__for_stdp_triplet_synapse_nestml' = (-tr_o1__for_stdp_triplet_synapse_nestml) / tau_minus__for_stdp_triplet_synapse_nestml\",\n", " \"initial_values\": {\n", - " \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"0.0\"\n", + " \"tr_o1__for_stdp_triplet_synapse_nestml\": \"0.0\"\n", " }\n", " },\n", " {\n", - " \"expression\": \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml' = (-tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) / tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\",\n", + " \"expression\": \"tr_o2__for_stdp_triplet_synapse_nestml' = (-tr_o2__for_stdp_triplet_synapse_nestml) / tau_y__for_stdp_triplet_synapse_nestml\",\n", " \"initial_values\": {\n", - " \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"0.0\"\n", + " \"tr_o2__for_stdp_triplet_synapse_nestml\": \"0.0\"\n", " }\n", " }\n", " ],\n", @@ -503,9 +508,9 @@ " \"V_th\": \"(-55)\",\n", " \"refr_T\": \"2\",\n", " \"tau_m\": \"10\",\n", - " \"tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"33.7\",\n", + " \"tau_minus__for_stdp_triplet_synapse_nestml\": \"33.7\",\n", " \"tau_syn\": \"2\",\n", - " \"tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"125\"\n", + " \"tau_y__for_stdp_triplet_synapse_nestml\": \"125\"\n", " }\n", "}\n", "INFO:Processing global options...\n", @@ -520,160 +525,154 @@ "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", "INFO:\n", - "Processing differential-equation form shape tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml with defining expression = \"(-tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) / tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\"\n", - "DEBUG:Splitting expression -tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols [tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml])\n", - "DEBUG:\tlinear factors: Matrix([[-1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]])\n", + "Processing differential-equation form shape tr_o1__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o1__for_stdp_triplet_synapse_nestml) / tau_minus__for_stdp_triplet_synapse_nestml\"\n", + "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols [tr_o1__for_stdp_triplet_synapse_nestml])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_minus__for_stdp_triplet_synapse_nestml]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", - "DEBUG:Created Shape with symbol tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, derivative_factors = [-1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", - "INFO:\tReturning shape: Shape \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\" of order 1\n", - "INFO:Shape tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml: reconstituting expression -tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", + "DEBUG:Created Shape with symbol tr_o1__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_minus__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\tReturning shape: Shape \"tr_o1__for_stdp_triplet_synapse_nestml\" of order 1\n", + "INFO:Shape tr_o1__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml\n", "INFO:\n", - "Processing differential-equation form shape tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml with defining expression = \"(-tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) / tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\"\n", - "DEBUG:Splitting expression -tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols [tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml])\n", - "DEBUG:\tlinear factors: Matrix([[-1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]])\n", + "Processing differential-equation form shape tr_o2__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o2__for_stdp_triplet_synapse_nestml) / tau_y__for_stdp_triplet_synapse_nestml\"\n", + "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols [tr_o2__for_stdp_triplet_synapse_nestml])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_y__for_stdp_triplet_synapse_nestml]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", - "DEBUG:Created Shape with symbol tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, derivative_factors = [-1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", - "INFO:\tReturning shape: Shape \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\" of order 1\n", - "INFO:Shape tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml: reconstituting expression -tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", - "INFO:All known variables: [V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], all parameters used in ODEs: {I_stim, E_L, C_m, tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tau_m, tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, I_e}\n", + "DEBUG:Created Shape with symbol tr_o2__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_y__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\tReturning shape: Shape \"tr_o2__for_stdp_triplet_synapse_nestml\" of order 1\n", + "INFO:Shape tr_o2__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml\n", + "INFO:All known variables: [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml], all parameters used in ODEs: {tau_y__for_stdp_triplet_synapse_nestml, I_stim, tau_m, E_L, C_m, tau_minus__for_stdp_triplet_synapse_nestml, I_e}\n", "INFO:No numerical value specified for parameter \"I_stim\"\n", "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + 0 * (1.0 / 1.0) + (I_e + I_stim) / C_m\"\n", - "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + I_stim)/C_m (symbols [V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, V_m])\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + I_stim)/C_m (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml, V_m])\n", "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0], [0], [0]])\n", "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n", "DEBUG:\tnonlinear term: 0.0\n", "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m + I_stim/C_m, nonlin_term = 0\n", "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", "INFO:\n", - "Processing differential-equation form shape tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml with defining expression = \"(-tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) / tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\"\n", - "DEBUG:Splitting expression -tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols [V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml])\n", - "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [0], [0], [0]])\n", + "Processing differential-equation form shape tr_o1__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o1__for_stdp_triplet_synapse_nestml) / tau_minus__for_stdp_triplet_synapse_nestml\"\n", + "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml, V_m, tr_o1__for_stdp_triplet_synapse_nestml])\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_triplet_synapse_nestml], [0], [0], [0]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", - "DEBUG:Created Shape with symbol tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, derivative_factors = [-1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], inhom_term = 0.0, nonlin_term = 0\n", - "INFO:\tReturning shape: Shape \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\" of order 1\n", + "DEBUG:Created Shape with symbol tr_o1__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_minus__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0\n", + "INFO:\tReturning shape: Shape \"tr_o1__for_stdp_triplet_synapse_nestml\" of order 1\n", "INFO:\n", - "Processing differential-equation form shape tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml with defining expression = \"(-tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml) / tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\"\n", - "DEBUG:Splitting expression -tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols [V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml])\n", - "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [0], [0], [0]])\n" + "Processing differential-equation form shape tr_o2__for_stdp_triplet_synapse_nestml with defining expression = \"(-tr_o2__for_stdp_triplet_synapse_nestml) / tau_y__for_stdp_triplet_synapse_nestml\"\n", + "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml, V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_triplet_synapse_nestml], [0], [0], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "DEBUG:Created Shape with symbol tr_o2__for_stdp_triplet_synapse_nestml, derivative_factors = [-1/tau_y__for_stdp_triplet_synapse_nestml], inhom_term = 0.0, nonlin_term = 0\n", + "INFO:\tReturning shape: Shape \"tr_o2__for_stdp_triplet_synapse_nestml\" of order 1\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]))\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0], [0]])\n", + "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n", + "DEBUG:\tnonlinear term: 0.0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[71,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml'\n", + "[72,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, INFO, [43:0;94:0]]: Starts processing of the model 'iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "INFO:Shape tr_o1__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml\n", + "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_triplet_synapse_nestml], [0]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", - "DEBUG:Created Shape with symbol tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, derivative_factors = [-1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], inhom_term = 0.0, nonlin_term = 0\n", - "INFO:\tReturning shape: Shape \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\" of order 1\n", - "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols Matrix([[V_m], [tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]]))\n", - "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0], [0]])\n", - "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n", - "DEBUG:\tnonlinear term: 0.0\n", - "INFO:Shape tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml: reconstituting expression -tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", - "DEBUG:Splitting expression -tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols Matrix([[V_m], [tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]]))\n", - "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [0]])\n", - "DEBUG:\tinhomogeneous term: 0.0\n", - "DEBUG:\tnonlinear term: 0.0\n", - "INFO:Shape tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml: reconstituting expression -tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", - "DEBUG:Splitting expression -tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols Matrix([[V_m], [tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]]))\n", - "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]])\n", + "INFO:Shape tr_o2__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml\n", + "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_triplet_synapse_nestml]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", - "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]]), A = Matrix([[-1/tau_m, 0, 0], [0, -1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, 0], [0, 0, -1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0], [0]]), c = Matrix([[0], [0], [0]])\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]), A = Matrix([[-1/tau_m, 0, 0], [0, -1/tau_minus__for_stdp_triplet_synapse_nestml, 0], [0, 0, -1/tau_y__for_stdp_triplet_synapse_nestml]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0]]), c = Matrix([[0.0], [0.0], [0.0]])\n", "INFO:Finding analytically solvable equations...\n", "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n", "DEBUG:os.makedirs('/tmp')\n", "DEBUG:write lines to '/tmp/ode_dependency_graph.dot'\n", "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n", "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m\n", - "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols [V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml])\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + I_stim/C_m (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n", "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [0], [0]])\n", "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n", "DEBUG:\tnonlinear term: 0.0\n", - "INFO:Shape tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml: reconstituting expression -tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", - "DEBUG:Splitting expression -tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols [V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml])\n", - "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [0]])\n", + "INFO:Shape tr_o1__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml\n", + "DEBUG:Splitting expression -tr_o1__for_stdp_triplet_synapse_nestml/tau_minus__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_minus__for_stdp_triplet_synapse_nestml], [0]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", - "INFO:Shape tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml: reconstituting expression -tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", - "DEBUG:Splitting expression -tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml (symbols [V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml])\n", - "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]])\n", + "INFO:Shape tr_o2__for_stdp_triplet_synapse_nestml: reconstituting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml\n", + "DEBUG:Splitting expression -tr_o2__for_stdp_triplet_synapse_nestml/tau_y__for_stdp_triplet_synapse_nestml (symbols [V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_y__for_stdp_triplet_synapse_nestml]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot\n", - "DEBUG:os.makedirs('/tmp')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[70,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml'\n", - "[71,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [52:0;99:0]]: Starts processing of the model 'iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "DEBUG:os.makedirs('/tmp')\n", "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable_before_propagated.dot'\n", "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable_before_propagated.dot']\n", "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph_analytically_solvable.dot\n", "DEBUG:os.makedirs('/tmp')\n", "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable.dot'\n", "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable.dot']\n", - "INFO:Generating propagators for the following symbols: V_m, tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", - "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]]), A = Matrix([[-1/tau_m, 0, 0], [0, -1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, 0], [0, 0, -1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0], [0]]), c = Matrix([[0], [0], [0]])\n", + "INFO:Generating propagators for the following symbols: V_m, tr_o1__for_stdp_triplet_synapse_nestml, tr_o2__for_stdp_triplet_synapse_nestml\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]]), A = Matrix([[-1/tau_m, 0, 0], [0, -1/tau_minus__for_stdp_triplet_synapse_nestml, 0], [0, 0, -1/tau_y__for_stdp_triplet_synapse_nestml]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0]]), c = Matrix([[0], [0], [0]])\n", "DEBUG:System of equations:\n", - "DEBUG:x = Matrix([[V_m], [tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml], [tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]])\n", + "DEBUG:x = Matrix([[V_m], [tr_o1__for_stdp_triplet_synapse_nestml], [tr_o2__for_stdp_triplet_synapse_nestml]])\n", "DEBUG:A = Matrix([\n", - "[-1/tau_m, 0, 0],\n", - "[ 0, -1/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, 0],\n", - "[ 0, 0, -1/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml]])\n", - "DEBUG:b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0], [0]])\n", + "[-1/tau_m, 0, 0],\n", + "[ 0, -1/tau_minus__for_stdp_triplet_synapse_nestml, 0],\n", + "[ 0, 0, -1/tau_y__for_stdp_triplet_synapse_nestml]])\n", + "DEBUG:b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0]])\n", "DEBUG:c = Matrix([[0], [0], [0]])\n", "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\n", - "INFO:update_expr[tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml] = __P__tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml*tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", - "INFO:update_expr[tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml] = __P__tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml*tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\n", + "INFO:update_expr[tr_o1__for_stdp_triplet_synapse_nestml] = __P__tr_o1__for_stdp_triplet_synapse_nestml__tr_o1__for_stdp_triplet_synapse_nestml*tr_o1__for_stdp_triplet_synapse_nestml\n", + "INFO:update_expr[tr_o2__for_stdp_triplet_synapse_nestml] = __P__tr_o2__for_stdp_triplet_synapse_nestml__tr_o2__for_stdp_triplet_synapse_nestml*tr_o2__for_stdp_triplet_synapse_nestml\n", "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "WARNING:Not preserving expression for variable \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\" as it is solved by propagator solver\n", - "WARNING:Not preserving expression for variable \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"tr_o1__for_stdp_triplet_synapse_nestml\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"tr_o2__for_stdp_triplet_synapse_nestml\" as it is solved by propagator solver\n", "INFO:In ode-toolbox: returning outdict = \n", "INFO:[\n", " {\n", " \"initial_values\": {\n", " \"V_m\": \"E_L\",\n", - " \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"0.0\",\n", - " \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"0.0\"\n", + " \"tr_o1__for_stdp_triplet_synapse_nestml\": \"0.0\",\n", + " \"tr_o2__for_stdp_triplet_synapse_nestml\": \"0.0\"\n", " },\n", " \"parameters\": {\n", " \"C_m\": \"250.000000000000\",\n", " \"E_L\": \"-70.0000000000000\",\n", " \"I_e\": \"0\",\n", " \"tau_m\": \"10.0000000000000\",\n", - " \"tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"33.7000000000000\",\n", - " \"tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"125.000000000000\"\n", + " \"tau_minus__for_stdp_triplet_synapse_nestml\": \"33.7000000000000\",\n", + " \"tau_y__for_stdp_triplet_synapse_nestml\": \"125.000000000000\"\n", " },\n", " \"propagators\": {\n", " \"__P__V_m__V_m\": \"exp(-__h/tau_m)\",\n", - " \"__P__tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"exp(-__h/tau_minus__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml)\",\n", - " \"__P__tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"exp(-__h/tau_y__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml)\"\n", + " \"__P__tr_o1__for_stdp_triplet_synapse_nestml__tr_o1__for_stdp_triplet_synapse_nestml\": \"exp(-__h/tau_minus__for_stdp_triplet_synapse_nestml)\",\n", + " \"__P__tr_o2__for_stdp_triplet_synapse_nestml__tr_o2__for_stdp_triplet_synapse_nestml\": \"exp(-__h/tau_y__for_stdp_triplet_synapse_nestml)\"\n", " },\n", " \"solver\": \"analytical\",\n", " \"state_variables\": [\n", " \"V_m\",\n", - " \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\",\n", - " \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\"\n", + " \"tr_o1__for_stdp_triplet_synapse_nestml\",\n", + " \"tr_o2__for_stdp_triplet_synapse_nestml\"\n", " ],\n", " \"update_expressions\": {\n", " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + V_m*__P__V_m__V_m - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m - I_stim*__P__V_m__V_m*tau_m/C_m + I_stim*tau_m/C_m\",\n", - " \"tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"__P__tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml*tr_o1__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\",\n", - " \"tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\": \"__P__tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml*tr_o2__for_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml\"\n", + " \"tr_o1__for_stdp_triplet_synapse_nestml\": \"__P__tr_o1__for_stdp_triplet_synapse_nestml__tr_o1__for_stdp_triplet_synapse_nestml*tr_o1__for_stdp_triplet_synapse_nestml\",\n", + " \"tr_o2__for_stdp_triplet_synapse_nestml\": \"__P__tr_o2__for_stdp_triplet_synapse_nestml__tr_o2__for_stdp_triplet_synapse_nestml*tr_o2__for_stdp_triplet_synapse_nestml\"\n", " }\n", " }\n", "]\n" @@ -683,9 +682,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[72,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, DEBUG, [52:0;99:0]]: Start building symbol table!\n", - "[73,GLOBAL, INFO]: Analysing/transforming synapse stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.\n", - "[74,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [2:0;63:0]]: Starts processing of the model 'stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml'\n" + "[73,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, DEBUG, [43:0;94:0]]: Start building symbol table!\n" ] }, { @@ -772,12 +769,26 @@ "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_x]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", - "DEBUG:Initializing system of shapes with x = Matrix([[tr_r1], [tr_r2]]), A = Matrix([[-1/tau_plus, 0], [0, -1/tau_x]]), b = Matrix([[0], [0]]), c = Matrix([[0], [0]])\n", + "DEBUG:Initializing system of shapes with x = Matrix([[tr_r1], [tr_r2]]), A = Matrix([[-1/tau_plus, 0], [0, -1/tau_x]]), b = Matrix([[0.0], [0.0]]), c = Matrix([[0.0], [0.0]])\n", "INFO:Finding analytically solvable equations...\n", "INFO:Saving dependency graph plot to /tmp/ode_dependency_graph.dot\n", "DEBUG:os.makedirs('/tmp')\n", "DEBUG:write lines to '/tmp/ode_dependency_graph.dot'\n", - "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n", + "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph.dot']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[74,GLOBAL, INFO]: Analysing/transforming synapse stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.\n", + "[75,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [2:0;63:0]]: Starts processing of the model 'stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "INFO:Shape tr_r1: reconstituting expression -tr_r1/tau_plus\n", "DEBUG:Splitting expression -tr_r1/tau_plus (symbols [tr_r1, tr_r2])\n", "DEBUG:\tlinear factors: Matrix([[-1/tau_plus], [0]])\n", @@ -797,13 +808,13 @@ "DEBUG:write lines to '/tmp/ode_dependency_graph_analytically_solvable.dot'\n", "DEBUG:run [PosixPath('dot'), '-Kdot', '-Tpdf', '-O', 'ode_dependency_graph_analytically_solvable.dot']\n", "INFO:Generating propagators for the following symbols: tr_r1, tr_r2\n", - "DEBUG:Initializing system of shapes with x = Matrix([[tr_r1], [tr_r2]]), A = Matrix([[-1/tau_plus, 0], [0, -1/tau_x]]), b = Matrix([[0], [0]]), c = Matrix([[0], [0]])\n", + "DEBUG:Initializing system of shapes with x = Matrix([[tr_r1], [tr_r2]]), A = Matrix([[-1/tau_plus, 0], [0, -1/tau_x]]), b = Matrix([[0.0], [0.0]]), c = Matrix([[0], [0]])\n", "DEBUG:System of equations:\n", "DEBUG:x = Matrix([[tr_r1], [tr_r2]])\n", "DEBUG:A = Matrix([\n", "[-1/tau_plus, 0],\n", "[ 0, -1/tau_x]])\n", - "DEBUG:b = Matrix([[0], [0]])\n", + "DEBUG:b = Matrix([[0.0], [0.0]])\n", "DEBUG:c = Matrix([[0], [0]])\n", "INFO:update_expr[tr_r1] = __P__tr_r1__tr_r1*tr_r1\n", "INFO:update_expr[tr_r2] = __P__tr_r2__tr_r2*tr_r2\n", @@ -841,30 +852,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[75,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[76,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[77,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[78,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[79,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[80,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[81,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[82,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", - "[83,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.cpp\n", - "[84,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h\n", - "[85,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [52:0;99:0]]: Successfully generated code for the model: 'iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", - "[86,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.cpp\n", - "[87,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.h\n", - "[88,iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml, INFO, [52:0;99:0]]: Successfully generated code for the model: 'iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", - "[89,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h\n", - "[90,stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml, INFO, [2:0;63:0]]: Successfully generated code for the model: 'stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", - "[91,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.cpp\n", - "[92,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.h\n", - "[93,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/CMakeLists.txt\n", - "[94,GLOBAL, INFO]: Successfully generated NEST module code in '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", + "[76,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", + "[77,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[78,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[79,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[80,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, DEBUG, [2:0;63:0]]: Start building symbol table!\n", + "[81,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[82,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [52:17;52:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[83,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [53:17;53:17]]: Implicit casting from (compatible) type 'integer' to 'real'.\n", + "[84,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp\n", + "[85,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.h\n", + "[86,iaf_psc_delta_neuron_nestml, INFO, [43:0;94:0]]: Successfully generated code for the model: 'iaf_psc_delta_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", + "[87,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp\n", + "[88,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.h\n", + "[89,iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml, INFO, [43:0;94:0]]: Successfully generated code for the model: 'iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", + "[90,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h\n", + "[91,stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml, INFO, [2:0;63:0]]: Successfully generated code for the model: 'stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml' in: '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", + "[92,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module.cpp\n", + "[93,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module.h\n", + "[94,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/CMakeLists.txt\n", + "[95,GLOBAL, INFO]: Successfully generated NEST module code in '/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target' !\n", "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -879,27 +886,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module Configuration Summary\n", + "nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module' using\n", + "You can now build and install 'nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_3c70a8ed53be4c46854ae5b0aa248ab9_module.so will be installed to\n", + " /tmp/nestml_target_yocmq5xi\n", "The module can be loaded into NEST using\n", - " (nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module) Install (in SLI)\n", - " nest.Install(nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module) (in PyNEST)\n", + " (nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module) Install (in SLI)\n", + " nest.Install(nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -911,71 +918,133 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.3s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target\n", - "[ 25%] Building CXX object CMakeFiles/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module_module.dir/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.o\n", - "[ 50%] Building CXX object CMakeFiles/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module_module.dir/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.o\n", - "[ 75%] Building CXX object CMakeFiles/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module_module.dir/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.cpp: In member function ‘void iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.cpp:166:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 166 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 25%] Building CXX object CMakeFiles/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module_module.dir/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module_module.dir/iaf_psc_delta_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module_module.dir/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 173 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.cpp:253:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 253 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:266:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 266 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.cpp:251:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 251 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:261:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 261 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.cpp: In member function ‘void iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.cpp:181:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 181 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp:188:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 188 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.cpp:284:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 284 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp:297:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 297 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml.cpp:282:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 282 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", - " | ^~~~~\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.cpp:52:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h: In instantiation of ‘nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.cpp:111:171: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h:724:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 724 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml.cpp:292:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 292 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + " | ^~~~~\n", + "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module.cpp:36:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:99: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:731:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 731 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:745:3: required from ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:99: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:718:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 718 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h: In instantiation of ‘nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.cpp:111:171: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h:724:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h: In instantiation of ‘void nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h:738:3: required from ‘nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.cpp:111:171: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h:711:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 711 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:99: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:731:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 731 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h: In instantiation of ‘void nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h:738:3: required from ‘nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_b1d27d2bd4604585bbb6bb0e9e18dc93_module.cpp:111:171: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml__with_iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml.h:711:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n" + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:745:3: required from ‘nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:99: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:718:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 718 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:518:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 518 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:543:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 543 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:580:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 580 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:451:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 451 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:453:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 453 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:518:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 518 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:543:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 543 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:580:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 580 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:451:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 451 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:453:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 453 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:513:9: required from ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:799:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 799 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:800:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 800 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:513:9: required from ‘bool nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:799:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 799 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:800:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 800 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "[100%] Linking CXX shared module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module.so\n", + "[100%] Built target nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module_module\n", + "[100%] Built target nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module_module\n", + "Install the project...\n", + "-- Install configuration: \"\"\n", + "-- Installing: /tmp/nestml_target_yocmq5xi/nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module.so\n" ] }, { @@ -1062,8 +1131,7 @@ "module_name, neuron_model_name, synapse_model_name = NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n", " nestml_triplet_stdp_model,\n", " post_ports=[\"post_spikes\"],\n", - " logging_level=\"DEBUG\")\n", - "nest.Install(module_name)" + " logging_level=\"DEBUG\")" ] }, { @@ -1172,8 +1240,8 @@ " -- N E S T --\n", " Copyright (C) 2004 The NEST Initiative\n", "\n", - " Version: 3.6.0\n", - " Built: Sep 25 2023 02:58:38\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Mar 26 2024 08:52:51\n", "\n", " This program is provided AS IS and comes with\n", " NO WARRANTY. See the file LICENSE for details.\n", @@ -1183,25 +1251,14 @@ "\n", " Type 'nest.help()' to find out more about NEST.\n", "\n", - "[16,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[19,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml, WARNING, [48:16;48:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "[22,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml, WARNING, [58:16;58:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "[25,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[34,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[37,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml, WARNING, [48:16;48:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "[40,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml, WARNING, [58:16;58:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", - "Eval stmt = # increment post trace values\n", - "tr_o1 = 1\n", - "\n", - "Eval stmt = tr_o2 = 1\n", - "\n", - "Eval stmt = # potentiate synapse\n", - "#w_ nS = Wmax * ( w / Wmax + tr_r1 * ( A2_plus + A3_plus * tr_o2 ) )\n", - "w_ nS = w + tr_r1 * (A2_plus + A3_plus * tr_o2)\n", - "\n", - "Eval stmt = w = min(Wmax,w_)\n", - "\n", - "[63,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n" + "[17,stdp_triplet_nn_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[20,stdp_triplet_nn_synapse_nestml, WARNING, [48:16;48:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[23,stdp_triplet_nn_synapse_nestml, WARNING, [58:16;58:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[26,stdp_triplet_nn_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[35,stdp_triplet_nn_synapse_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[38,stdp_triplet_nn_synapse_nestml, WARNING, [48:16;48:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[41,stdp_triplet_nn_synapse_nestml, WARNING, [58:16;58:16]]: Implicit casting from (compatible) type 'nS' to 'real'.\n", + "[64,stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n" ] }, { @@ -1210,8 +1267,8 @@ "text": [ "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", - "WARNING:Not preserving expression for variable \"tr_o1__for_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml\" as it is solved by propagator solver\n", - "WARNING:Not preserving expression for variable \"tr_o2__for_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"tr_o1__for_stdp_triplet_nn_synapse_nestml\" as it is solved by propagator solver\n", + "WARNING:Not preserving expression for variable \"tr_o2__for_stdp_triplet_nn_synapse_nestml\" as it is solved by propagator solver\n", "WARNING:Not preserving expression for variable \"tr_r1\" as it is solved by propagator solver\n", "WARNING:Not preserving expression for variable \"tr_r2\" as it is solved by propagator solver\n" ] @@ -1220,12 +1277,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[76,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "[80,stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", - "CMake Warning:\n", - " Ignoring empty string (\"\") provided on the command line.\n", - "\n", - "\n", + "[77,stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", + "[81,stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml, WARNING, [13:8;13:28]]: Variable 'd' has the same name as a physical unit!\n", "CMake Warning (dev) at CMakeLists.txt:95 (project):\n", " cmake_minimum_required() should be called prior to this top-level project()\n", " call. Please see the cmake-commands(7) manual for usage documentation of\n", @@ -1240,27 +1293,27 @@ "-- Detecting CXX compile features - done\n", "\n", "-------------------------------------------------------\n", - "nestml_82892aea3e8a4a86adc97b5e80f33e7c_module Configuration Summary\n", + "nestml_4940100b28f446be95429cd7b36f439e_module Configuration Summary\n", "-------------------------------------------------------\n", "\n", "C++ compiler : /usr/bin/c++\n", "Build static libs : OFF\n", "C++ compiler flags : \n", - "NEST compiler flags : -std=c++11 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", + "NEST compiler flags : -std=c++17 -Wall -fopenmp -O2 -fdiagnostics-color=auto\n", "NEST include dirs : -I/home/charl/julich/nest-simulator-install/include/nest -I/usr/include -I/usr/include -I/usr/include\n", - "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli -fopenmp /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so\n", + "NEST libraries flags : -L/home/charl/julich/nest-simulator-install/lib/nest -lnest -lsli /usr/lib/x86_64-linux-gnu/libltdl.so /usr/lib/x86_64-linux-gnu/libgsl.so /usr/lib/x86_64-linux-gnu/libgslcblas.so /usr/lib/gcc/x86_64-linux-gnu/12/libgomp.so /usr/lib/x86_64-linux-gnu/libpthread.a\n", "\n", "-------------------------------------------------------\n", "\n", - "You can now build and install 'nestml_82892aea3e8a4a86adc97b5e80f33e7c_module' using\n", + "You can now build and install 'nestml_4940100b28f446be95429cd7b36f439e_module' using\n", " make\n", " make install\n", "\n", - "The library file libnestml_82892aea3e8a4a86adc97b5e80f33e7c_module.so will be installed to\n", - " /home/charl/julich/nest-simulator-install/lib/nest\n", + "The library file libnestml_4940100b28f446be95429cd7b36f439e_module.so will be installed to\n", + " /tmp/nestml_target_tz6lmulj\n", "The module can be loaded into NEST using\n", - " (nestml_82892aea3e8a4a86adc97b5e80f33e7c_module) Install (in SLI)\n", - " nest.Install(nestml_82892aea3e8a4a86adc97b5e80f33e7c_module) (in PyNEST)\n", + " (nestml_4940100b28f446be95429cd7b36f439e_module) Install (in SLI)\n", + " nest.Install(nestml_4940100b28f446be95429cd7b36f439e_module) (in PyNEST)\n", "\n", "CMake Warning (dev) in CMakeLists.txt:\n", " No cmake_minimum_required command is present. A line of code such as\n", @@ -1272,151 +1325,142 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.4s)\n", + "-- Configuring done (0.5s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target\n", - "[ 25%] Building CXX object CMakeFiles/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module_module.dir/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.o\n", - "[ 50%] Building CXX object CMakeFiles/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module_module.dir/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.o\n", - "[ 75%] Building CXX object CMakeFiles/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module_module.dir/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml.o\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml.cpp: In member function ‘void iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml.cpp:181:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 181 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "[ 25%] Building CXX object CMakeFiles/nestml_4940100b28f446be95429cd7b36f439e_module_module.dir/nestml_4940100b28f446be95429cd7b36f439e_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_4940100b28f446be95429cd7b36f439e_module_module.dir/iaf_psc_delta_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_4940100b28f446be95429cd7b36f439e_module_module.dir/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:173:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 173 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml.cpp:284:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 284 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:266:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 266 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml.cpp:282:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 282 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml.cpp:261:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 261 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.cpp: In member function ‘void iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.cpp:166:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 166 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp: In member function ‘void iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp:188:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 188 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.cpp:253:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 253 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp:297:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 297 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.cpp:251:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 251 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml.cpp:292:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 292 | auto get_t = [origin, lag](){ return nest::Time( nest::Time::step( origin.get_steps() + lag + 1) ).get_ms(); };\n", " | ^~~~~\n", - "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.cpp:52:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.cpp:111:174: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:724:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 724 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "In file included from /home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_4940100b28f446be95429cd7b36f439e_module.cpp:36:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:102: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:731:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 731 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.cpp:111:174: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:724:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:738:3: required from ‘nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:61:24: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.cpp:111:174: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:711:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 711 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:745:3: required from ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierPtrRport]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:62:5: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:102: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:718:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 718 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:738:3: required from ‘nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:158:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:10: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:35:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.cpp:111:174: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:711:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:517:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 517 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:102: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:731:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 731 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:745:3: required from ‘nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml() [with targetidentifierT = nest::TargetIdentifierIndex]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_model.h:164:25: required from ‘nest::GenericConnectorModel::GenericConnectorModel(std::string) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:103:34: required from ‘void nest::ModelManager::register_specific_connection_model_(const std::string&) [with CompleteConnecionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/model_manager_impl.h:67:80: required from ‘void nest::ModelManager::register_connection_model(const std::string&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/nest_impl.h:37:70: required from ‘void nest::register_connection_model(const std::string&) [with ConnectorModelT = stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:603:102: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:718:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 718 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:518:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 518 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:542:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 542 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:543:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 543 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:579:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 579 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:580:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 580 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:450:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 450 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:451:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 451 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:452:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 452 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:453:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 453 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:517:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 517 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:518:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 518 | auto get_t = [_tr_t](){ return _tr_t; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:542:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 542 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:543:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 543 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:579:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 579 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:580:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 580 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:450:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 450 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:451:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 451 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:452:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 452 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:453:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 453 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::update_internal_state_(double, double, const nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:512:9: required from ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:792:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 792 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:513:9: required from ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:799:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 799 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:793:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 793 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:800:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 800 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", " | ^~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::update_internal_state_(double, double, const nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:512:9: required from ‘void nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:381:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml; size_t = long unsigned int]’\n", - "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:373:3: required from here\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:792:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 792 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h: In instantiation of ‘void nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::update_internal_state_(double, double, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:513:9: required from ‘bool nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml::send(nest::Event&, size_t, const nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml; size_t = long unsigned int]’\n", + "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:799:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 799 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", " | ^~~~~~~~~~~~\n", - "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml__with_iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml.h:793:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", - " 793 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", - " | ^~~~~\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[100%] Linking CXX shared module nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.so\n", - "[100%] Built target nestml_82892aea3e8a4a86adc97b5e80f33e7c_module_module\n", - "[100%] Built target nestml_82892aea3e8a4a86adc97b5e80f33e7c_module_module\n", + "/home/charl/julich/nestml-fork-integrate_specific_odes/nestml/doc/tutorials/triplet_stdp_synapse/target/stdp_triplet_nn_synapse_nestml__with_iaf_psc_delta_neuron_nestml.h:800:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 800 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model\n", + " | ^~~~~\n", + "[100%] Linking CXX shared module nestml_4940100b28f446be95429cd7b36f439e_module.so\n", + "[100%] Built target nestml_4940100b28f446be95429cd7b36f439e_module_module\n", + "[100%] Built target nestml_4940100b28f446be95429cd7b36f439e_module_module\n", "Install the project...\n", "-- Install configuration: \"\"\n", - "-- Installing: /home/charl/julich/nest-simulator-install/lib/nest/nestml_82892aea3e8a4a86adc97b5e80f33e7c_module.so\n", - "\n", - "Oct 19 05:02:12 Install [Info]: \n", - " loaded module nestml_82892aea3e8a4a86adc97b5e80f33e7c_module\n" + "-- Installing: /tmp/nestml_target_tz6lmulj/nestml_4940100b28f446be95429cd7b36f439e_module.so\n" ] } ], "source": [ "# Generate code for nearest spike interaction model\n", - "module_name, neuron_model_name_nn, synapse_model_name_nn = NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n", - " nestml_triplet_stdp_nn_model,\n", - " post_ports=[\"post_spikes\"])\n", - "nest.Install(module_name)" + "module_name_nn, neuron_model_name_nn, synapse_model_name_nn = \\\n", + " NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_delta_neuron.nestml\",\n", + " nestml_triplet_stdp_nn_model,\n", + " post_ports=[\"post_spikes\"])" ] }, { @@ -1480,7 +1524,7 @@ "metadata": {}, "outputs": [], "source": [ - "def run_triplet_stdp_network(neuron_model_name, synapse_model_name, neuron_opts,\n", + "def run_triplet_stdp_network(module_name, neuron_model_name, synapse_model_name, neuron_opts,\n", " nest_syn_opts, pre_spike_times, post_spike_times, \n", " resolution=1., delay=1., sim_time=None):\n", " \"\"\"\n", @@ -1488,6 +1532,7 @@ " \"\"\"\n", " nest.set_verbosity(\"M_ALL\")\n", " nest.ResetKernel()\n", + " nest.Install(module_name)\n", " nest.SetKernelStatus({'resolution': resolution})\n", " \n", " # Set defaults for neuron\n", @@ -1569,9 +1614,9 @@ "outputs": [], "source": [ "# Simulate the network\n", - "def run_frequency_simulation(neuron_model_name, synapse_model_name,\n", - " neuron_opts, nest_syn_opts,\n", - " freqs, delta_t, n_spikes):\n", + "def run_frequency_simulation(module_name, neuron_model_name, synapse_model_name,\n", + " neuron_opts, nest_syn_opts,\n", + " freqs, delta_t, n_spikes):\n", " \"\"\"\n", " Runs the spike pair simulation for given frequencies and given time difference between spikes.\n", " \"\"\"\n", @@ -1587,7 +1632,7 @@ "\n", " sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 10. + 3 * syn_opts[\"delay\"]\n", "\n", - " dw = run_triplet_stdp_network(neuron_model_name, synapse_model_name, \n", + " dw = run_triplet_stdp_network(module_name, neuron_model_name, synapse_model_name, \n", " neuron_opts, nest_syn_opts,\n", " pre_spike_times=pre_spike_times,\n", " post_spike_times=post_spike_times,\n", @@ -1619,14 +1664,6 @@ "n_spikes = 60" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "f80eae4e-0749-449c-a2b8-dcb2248d1fb0", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 10, @@ -1638,502 +1675,436 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "Initial weight: 1.0, Updated weight: 1.000062712440608\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 59034\n", - " Number of OpenMP threads: 1\n", + " Number of OpenMP thInitial weight: 1.0, Updated weight: 1.045481723674705\n", + "reads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "Initial weight: 1.0, Updated weight: 1.000062712440608\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 11834\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "Initial weight: 1.0, Updated weight: 1.045481723674705\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", + " Simulation resolution has changed. InternaInitial weight: 1.0, Updated weight: 1.1180707933363045\n", + "l state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 5934\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "Initial weight: 1.0, Updated weight: 1.1180707933363045\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Initial weight: 1.0, Updated weight: 1.205329009261286\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have beInitial weight: 1.0, Updated weight: 1.4186655196495506\n", - "Initial weight: 1.0, Updated weight: 1.5813821544865971\n", - "en reset!\n", - "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.205329009261286\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 2984\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", + " SimulatInitial weight: 1.0, Updated weight: 1.4186655196495506\n", + "ion resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1509\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", + " SimInitial weight: 1.0, Updated weight: 1.5813821544865971\n", + "ulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1214\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", - "Initial weight: 1.0, Updated weight: 0.6678711978627694\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 59024\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "Initial weight: 1.0, Updated weight: 0.6678711978627694\n", + "\n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updaInitial weight: 1.0, Updated weight: 0.6653426131462727\n", + "ting_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 11824\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "Initial weight: 1.0, Updated weight: 0.6653426131462727\n", - "Initial weight: 1.0, Updated weight: 0.6450780469148971\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 0.6450780469148971\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 5924\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "Initial weight: 1.0, Updated weight: 0.6180411107607721\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:37 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:37 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:37 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5Initial weight: 1.0, Updated weight: 1.068737821702289\n", - "Initial weight: 1.0, Updated weight: 1.5937453662768748\n", - "e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:37 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:37 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 0.6180411107607721\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:37 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:37 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 2974\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:37 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", + " SimulatInitial weight: 1.0, Updated weight: 1.068737821702289\n", + "ion resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1499\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", + " SimInitial weight: 1.0, Updated weight: 1.5937453662768748\n", + "ulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1204\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:12 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] } @@ -2162,7 +2133,7 @@ "nest_syn_opts.pop('tau_minus')\n", "nest_syn_opts.pop('tau_y')\n", "\n", - "dw_dict = run_frequency_simulation(neuron_model_name, synapse_model_name, \n", + "dw_dict = run_frequency_simulation(module_name, neuron_model_name, synapse_model_name, \n", " neuron_opts, nest_syn_opts,\n", " freqs, delta_t, n_spikes)" ] @@ -2186,502 +2157,436 @@ "output_type": "stream", "text": [ "\n", - "Oct 19 05:02:12 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "Initial weight: 1.0, Updated weight: 1.0000000027196625\n", + "Initial weight: 1.0, Updated weight: 1.0086627050654013\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:12 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:12 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:12 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:12 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 59034\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "Initial weight: 1.0, Updated weight: 1.0000000027196625\n", - "Initial weight: 1.0, Updated weight: 1.0086627050654013\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5Initial weight: 1.0, Updated weight: 1.0903003652138468\n", - "Initial weight: 1.0, Updated weight: 1.2776911537160713\n", - "Initial weight: 1.0, Updated weight: 1.4771400111243256\n", - "e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 11834\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:13 NodeManager::preInitial weight: 1.0, Updated weight: 1.530550096954562\n", - "pare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 5934\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "Initial weight: 1.0, Updated weight: 1.0903003652138468\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. InternalInitial weight: 1.0, Updated weight: 0.554406040254968\n", - " state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.2776911537160713\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 2984\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: Initial weight: 1.0, Updated weight: 0.5544062835543123\n", - "Initial weight: 1.0, Updated weight: 0.5555461935366892\n", - "Initial weight: 1.0, Updated weight: 0.632456315445355\n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.4771400111243256\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1509\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", + " SimuInitial weight: 1.0, Updated weight: 1.530550096954562\n", + "lation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", - " Preparing 6 nodes for sInitial weight: 1.0, Updated weight: 1.2001792723059206\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial weight: 1.0, Updated weight: 1.5398255566140917\n", - "imulation.\n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", + " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1214\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 0.554406040254968\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 59024\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", + " SimInitial weight: 1.0, Updated weight: 0.5544062835543123\n", + "ulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 11824\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 0.5555461935366892\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 5924\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 0.632456315445355\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 2974\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.2001792723059206\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1499\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:13 correlation_detector [Info]: \n", + "Apr 19 12:03:38 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:38 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:13 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:38 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:38 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:13 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:13 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:38 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.5398255566140917\n", "\n", - "Oct 19 05:02:13 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:38 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:13 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:38 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 1204\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:13 SimulationManager::run [Info]: \n", + "Apr 19 12:03:38 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] } @@ -2710,9 +2615,9 @@ "nest_syn_opts_nn.pop('tau_minus')\n", "nest_syn_opts_nn.pop('tau_y')\n", "\n", - "dw_dict_nn = run_frequency_simulation(neuron_model_name_nn, synapse_model_name_nn,\n", - " neuron_opts_nn, nest_syn_opts_nn,\n", - " freqs, delta_t, n_spikes)" + "dw_dict_nn = run_frequency_simulation(module_name_nn, neuron_model_name_nn, synapse_model_name_nn,\n", + " neuron_opts_nn, nest_syn_opts_nn,\n", + " freqs, delta_t, n_spikes)" ] }, { @@ -2732,7 +2637,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -2741,7 +2646,7 @@ }, { "data": { - "image/png": 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pUihcuDCaNm2Kv/76K9vtIyKitCWdF7R/P2BtDYwfn7l5QSnFxgKTJ8vX3boBFlERwNmzkqo+/hho1QooVEhXoTEyEjh2DDhxQv9vLAu4tF7PlAIiInL/dQsWzPpM/S1btqBmzZqoWbMm3n77bXz66af4+uuvocnGlP8vvvgCN27cQGhoKFavXg0AKF68OCIiItC1a1e0aNEC586dQ2BgIN577z2MGjUqw/D14sULvP7665g5cybs7Oywdu1a9OjRA97e3qhQoUKW20dERMnFxgLLl0u9oOBgOda7t9QLqlYtGxcMDIT7zKe4das2HG1DMf63jsCsC+l0DwGoUgXo3Blo2FDW3hsRw5CeRUQAhQvn/uu+eCFhOyvc3d3x9ttvAwC6du2KFy9e4NChQ+jUqVOWX79w4cIoUKAAoqOj4ezsnHh87dq1iIyMxLp161DovwYuWbIEPXr0wNy5c1GqVKk0r9egQQM0aNAg8f7MmTOxY8cO7N69G6NGjcpy+4iISGfv3uT1gurXl3pBHTpk4skJCcCdO4CXV7LbC/9QTMUdAMA30V/B/tZ5Od/JCWjUSELPnTuyWdmECTIT20QwDOVT3t7eOHv2LLZv3w4AsLKywoABA7Bq1aqXhiE/Pz/Url078f6XX36JL7/8Ms1zb9y4gQYNGiQGIQBo3bo1EhIS4O3tnTgMpvX222/jp59+Qnh4OKZNm4Y9e/bg0aNHiIuLQ2RkJOc1ERHlwPXrwOefy3AYIPOCvv0WePfddIbDIiKAq1eTB5/Ll2UCdAoL8TUewxlVCj3Gh5PKAy77JAAl+QMZgMzGbtdOr+8rpxiG9KxgQemlMcbrZoW7uzvi4uJQtmzZxGNKKVhbWyNY21+ajjJlysDLyyvxfvHixdM9VymV7rCb9njSa9nb2wMAxo8fjwMHDuC7775DtWrVUKBAAbz55puIiYl52VsjIqIUgoJkk9Rly2SZvLW1rBb76qsky+QDA1P19sDbO+1hLjs76U5q2BBo2BCBFVwwb6AL8AL49pdSsBk4QXfuixeyD4edndzPVPdT7mIY0jONJuvDVbktLi4O69atw/fffw9XV9dkj/Xr1w8bNmxA3bp1032+lZUVqqUxoGxjY4P4+Phkx2rXro21a9ciPDw8sXfo77//hoWFBWrUqAEAaV7rxIkTGDZsGPr06QNA5hD5+vpm6X0SEeV3ac4L6qUwf9S/qPbsLDDXSxd8/P3TvkjJkrphLu2tevVkG42F3pGSQWFhQP/+SZ4bHw+4uQFPnkj1xZS9RCaCYSgf2rNnD4KDgzFixAg4pKic9eabb8Ld3R0LFy7M8nUrVaqEAwcOwNvbGyVKlICDgwMGDx6MKVOmYOjQoZg6dSqePHmCTz/9FEOGDEl3vhAgAWn79u3o0aMHNBoNvv76aySkNwmPiIhS2bcPGPdZAm56y8Lx+iUeYKHzXHT4azWwK/UwFzQaCTlJQ492mOslC2uqVZPRr+fPAYuk69QnTgT27JFeofv3GYbIdLi7u6NTp06pghAgPUOzZs3ChQsXsnzd999/H0ePHoWLiwtevHiBI0eOoF27djhw4ADGjBmDpk2bomDBgujXrx8WLFiQ4bUWLlyId999F61atYKjoyMmTpyI0NDQLLeJiCjfePIE8PLC9QP38fnGJtjv3wCABUoiEDPxP4wIcodl0H9/VKYY5kLDhlJpMQcrgDQaoFixJAfc3YHvv5ev16wBmjbN9rUNTaOUCWwKYsJCQ0Ph4OCAkJCQxPksWlFRUfDx8UHlypVhpx0LJcokfv8QUbYkJAB376aa3/PsUSSmYiqW4WPEwwrWiMEY/ID/lfgJDo2rJh/qSjHMlV3nzwObNgFffgmUKJHkgWPHgE6dpAz1lCkyTpfLMvr9nRJ7hoiIiExVZGTq1VyXLiVbzRULK/yEkZiCaQiGLGjpXcsb88f5o9rrg4HSXxhky3ilZIX8kSMyR3rFiv8euHNHKkzHxQEDBkgYMnEMQ0RERKbgv2GuZLebN9NfzVWvHvaVeBvjLg7BzccyPlWvHrBoEdChQ00ANQ3a3P37JQjZ2sqqNACSkIYPB549k2Gx1asNEsT0jWGIiIgoN6UzzIVHj9I+39Ex1WquG/E18PlEK+zbpztl5kzgvfeytn1GdsXHy9xoABg1CkjcGECjkflBH30ErF0re4+ZAYYhIiIiQ4mMBK5dSz3MlV5BurRWc5Uundi78uyZTL9JWi9ozBjgf/9LUi8oF6xfD1y5AhQtKvOFkqlaVbf3mJlgGNIDzkGn7OD3DVEe8/Rp2sNcKeqvAZCxpXr1dIGnUSO5X6RImpeOjQV++kmm32jrBfXqBXz3XTb3EcuBqCjg66/l68mTgeLFAfzyC1C2rOzOaoYYhnLA2toaABAREYECZtIVSKZDW03bMjf6tIlIfxISgHv3Ugefhw/TPt/RMXVvT82amV7NtX+/7CN244bcr1dP9hHr2DGH7yObliyRkkHlyslu9/DwAEaOlPlCZ86Y9BL69DAM5YClpSWKFi2KwMBAAEDBggWzteM75T8JCQl48uQJChYsCCs9LG8lIgOJikp7NVd6w1zVqqUOPmXKZGsS8c2bEoKMNS8oPYMHA7dvy0bzBf69Cbz1lvR+vfOOlKE2Q/wpnEPaHdq1gYgosywsLFChQgUGaCJT4u0tFZMvXszaMFfDhlLEMJ1hrqx49kz2EVu6VDcvaPRomRdUtGiOL59jpUv/t4w+KAho0QMICQFatwZWrjSLlWNpYRjKIY1Gg9KlS8PJyQmxsbHGbg6ZERsbG1gkq1tPREbx4AGwZQuwcSOQVvX9EiVS782VhWGuzEpvXtD8+TKv2thiYyWYAQBiYoA335SaQpUqAdu3S0A0UwxDemJpacm5H0RE5uLZM2DrVglAx4/LfBdAxp86d5aeDm0AyuYwV1aknBdUt67UCzLWvKC0fPCBzBH/br5CzQWjgKNHpSfsjz8AJydjNy9HGIaIiCh/CA8Hdu+WAHTggHR1aLVpAwwaJL0djo651qT05gWNGKH3jqccuXJFygYpBfxv8n9FIC0sgM2bJbmZORP6qImIiPQsJkZq3mzcCOzaBURE6B5r2BBwcwMGDkxSNTB3aOcFLVsmu1aY2ryglCZNkiD05ptA81aWQMsVUlixUSNjN00vGIaIiChvSUgATpyQALR1qyQPrapVJQC5uQG1a+d602JjZfLxlCm6ZvXsKfWCTGFeUFqOHgX27gWsrBRmTY8HYCXDhnkkCAEMQ0RElBcoJSvANm2SW9KaP6VKSe/PoEFSA8dIK54OHAA++yz5vKCFC2Vzd1Ol3YwVAD4osB7VP9sgk81zs9x1LmAYIiIi83X7toSfjRtlWbyWgwPQr58EoHbtjFqY5+ZN4PPPpXcFkHlBM2ZIvSBTmheUlq1bgXPngMIWEfgm7Avgrr2M6+UxJv6/gYiIKIVHj3RL4c+f1x23swN69JAA1K2b0Zd6m9u8oLSsWKEAaPBFwlyUcoiWlWMlShi7WXrHMERERKYvOBjYtk0C0NGjqZfCDxokRXns7Y3aTECCz4oVwDffmM+8oPTsafsdfj7kh2EWvwK//w688oqxm2QQDENERGSaIiKkJ2LjRll7nnQpfOvWEoDeegsoWdJ4bUzhwAFZKn/9utw3h3lB6dqxA3bfTMCnALB4qYTOPIphiIiITEdsrGz8uXEjsHOn1AbSql9fAtDAgUDFikZrYlrMeV5QWi78HYn6H34iIWHUKODjj43dJIMyw/9FRESUpyQkAH//LQHo999lzyutypUlALm5AXXqGK+N6Xj2DJg+XfYRi4uT4DN6NPD11+YzLyilgADgtS4FUMHpHg51mIzSC+cbu0kGxzBERES5TynZ/X3jRqlifP++7jEnJ+n9cXMDmjc3yc0/05sXNH8+UKOGcduWU9OnS4ecvZMdnDctBEzv49c7hiEiIso9d+7olsLfvKk7bm8P9O0rvUDt25v02FJa84IWLMgDU2qUwq0hM7By89cANJg71yRzqEGY7ncbERHlDf7+shR+0ybg7FndcVtboHt3CUCvvy5L402Yt7fMC/rzT7lv7vOCUpk+HV9uqIN4aPBG5xi0bWtj7Bblmrzwv4+IiExNcDCwfbsEoCNHZF4QIJt7duokAah3b7OoZBwcLENHS5bknXlBqWzZgjNT92EbpsBCk4A5C/JPEAIYhoiISF8iIoA9eyQA7d0rm6RqtWypWwpfqpTx2pgFac0L6tFD6gWZ+7ygZM6ehRo6DBOxHwAwdJhFXtiIPksYhoiIKPtiY4G//pIAtGMH8OKF7rG6dXVL4StXNl4bs+HgQdlHTDsvqE4dqRdk9vOCUrp/H+jVC6HRNkDxErCLUJg2LZ9MFEqCYYiIiLImIQE4dUoC0G+/AU+f6h6rWFG3FL5ePeO1MRuUkrc1e7ZuXlCJEjIv6P3388i8oKTCw2UJXEAAHOrVw9GTFXHLX4Py5Y3dsNyX1/7XEhGRISgFXL6s2xXez0/3WMmSwIABEoJatDC7JUh+fsCvvwJr1shiNyCPzgtKS5UqstfbH39AY18ENY2/m4lRMAwREVH67t3TLYXXjhkBQJEishTezQ3o2NHsuk0iImR+95o1wOHDuq3OCheWaU2TJuWxeUFpKVQIket+x9wvQzC6SDEUN3Z7jMi8vnuJiMjwAgJk+GvjRuCff3THbWxkKbybG/DGG0CBAsZrYzYoJYWu16yRtxcWpnusQwdg2DDJd4UKGauFueTyZRnC1Gjw4xILTPuxGHYdBy5cMLtOPb2xMHYDsmrZsmWoXLky7Ozs0KRJE5w4cSJTz/v7779hZWWFhg0bGraBRETmKCQEWL1aZgiXLQuMGSNByMJCjq1aBTx+LDvHv/mmWQUhPz9g5kzp6WnTBnB3lyBUpYosmffxAQ4dAoYMyQdB6ORJoGlTYPBgBD2KxuzZcnjcuPwbhAAz6xnasmULxo4di2XLlqF169ZYsWIFunXrhuvXr6NChQrpPi8kJATvvPMOOnbsiMePH+dii4mITFhkpMwU3rhRlsJHR+sea95c5gD17w84OxuvjdkUHi6L29IaBuvfX3qBXn01nwUAX1+gTx8peRAdjVnf2SAkBGjQABg82NiNMy6NUtpvEdPXvHlzNG7cGMuXL088VqtWLfTu3RuztfE2DQMHDkT16tVhaWmJnTt3wsvLK9OvGRoaCgcHB4SEhMDePp/OLCOivCMuTrpBNm6UtJB0rKh2bd1S+KpVjdfGbOIwWAZCQ4FWrYBr14DGjfHv+hOo0bAgYmKAffuArl2N3UD9y8rvb7PpGYqJiYGnpycmTZqU7LirqytOnTqV7vNWr16Nu3fvYv369Zg5c+ZLXyc6OhrRSf46Cg0NzX6jiYhMgVLA6dMSgH77DXjyRPdYhQoyB2jQoMR5JObm33+BdeuAtWuBu3d1x6tUkQD0zjuy4j/fio+X/8fXrgGlSwO7d+PryRKEOnQAunQxdgONz2zC0NOnTxEfH49SKSqXlipVCgEBAWk+5/bt25g0aRJOnDgBq0yudJg9ezamTZuW4/YSERndlSu6pfC+vrrjjo4yVjRokFSGtjC76aMID0++GkyrcGFZ5T90aD4cBkvP+PEyDGpnB+zejUtPy2L9enlo3jx+RoAZhSEtTYr/a0qpVMcAID4+HoMGDcK0adNQIwvrIydPnoxx48Yl3g8NDUX5/FiBiojMk48PsHmz9AJdvao7XriwzBdxc5O9waytjdfGbFJK5v9qh8G0xa41Gt0wWJ8++XQYLD23b8umaoB0n7m4wMlfNpeNjASaNDFu80yF2YQhR0dHWFpapuoFCgwMTNVbBABhYWE4f/48Ll68iFGjRgEAEhISoJSClZUVDh48iA4dOqR6nq2tLWxtbQ3zJoiIDOHxY0kHmzbJcJiWjY3sBj9okCyFL1jQeG3MgfSGwapWlQA0ZEg+HwbLSPXqgIcH4OkpBZQgI2UrV+r2ziUzCkM2NjZo0qQJPDw80KdPn8TjHh4e6NWrV6rz7e3tceXKlWTHli1bhsOHD2Pr1q2obGb75BARJRMaKhOgN26UvcG0v9m03SRubjJbuFgx47Yzm9IbBitSRLcarHVrDvFkStu2ckvBDEdHDcZswhAAjBs3DkOGDIGLiwtatmyJlStXws/PDyNHjgQgQ1wPHz7EunXrYGFhgboptt11cnKCnZ1dquNERGYhKkrmfmzcKLvDJ10K36yZBKD+/YEyZYzXxhxQCjhxQgLQ779zGCzbgoNlrfz8+bLD7H+0o6dz5sjCQdIxqzA0YMAABAUFYfr06fD390fdunWxd+9eVPyvf9Tf3x9+SffLISIyd3FxwJEj8lts+3bpEdJ65RXdpqjVqhmvjTnk66sbBrt3T3ecw2DZEBsrgfivv2RH+kuXAAsLxMQAX30ln2+zZgxDKZlVnSFjYJ0hIsp1Skn1540bgS1bgMBA3WPly0sdoEGDpFqemY4ThYdLMes1ayTraRUpIqvBhg2Tsjhm+vaM55NPgGXLpPvs77/lewTAjz9KUXFnZ9mMNj/0ruXJOkNERHnetWsSgDZtklVhWiVKyOTXQYNkooyZTvbIaBisY0fdMJiZzvM2viVLJAhpNMCGDYlBKDQUmDFDTpk6NX8EoaxiGCIiMiZfX5nMsWmTbKCpVagQ0Lu3BKDOnc1yKbxWesNg1arphsEy2FGJMuPAAen6AWRSUJKFRfPmAU+fAjVrAiNGGKl9Jo5hiIgotwUGStfIpk0ylKFlbQ106yYBqHt3s/4T/sULGQZbu5bDYAZ386bME0pIkGqT48cnPvToEbBggXw9ezaQyfrD+Q4/FiKi3BAaCuzcKQHIw0O2SAAkDbRrJwGob1+geHFjtjJHEhKSD4OFh8txDoMZmJOTVE+MjQVWrEiWMBctkuKKrVpJRyOljWGIiMhQoqJkF8xNm4A//pD7Wi4uul3hy5Y1Xhv1wMdHNwyWdKpT9eq6YTAW8jeg4sVlmOzFCyBF0eDp0yUrsRcuYwxDRET6FB8v40KbNsk4UUiI7rGaNXW7wmdhmyBTpB0GW7MGOHpUd7xIEXl7w4bJtmf8BWwgSskH37693Le2TrPApp0d8MUXuds0c8QwRESUU0oBZ89KANqyBUi6bVDZslIHyM0NaNTIrNNBRsNgnTpJAOrdm8NguWLBAkk5Y8cCCxemejggQPbj5RyhzOHHRESUXdevSwDauDH5MqnixYE335ReoDZtzHYpvBaHwUzMH3/oJklXqpTqYaXk2y84GFi/XjI4ZYxhiIgoK/z8dPsaXLqkO16woCxnHjQIcHWVTVLN2IsXwNatEoCSDoPZ2+tWg3EYzAguX5bvMaWADz8ERo9Odcru3bJIsUABII19zCkNDENERC/z9KmMC23cCJw8qTtuZQV07Sq/nHr2NOul8IAMgx0/LsNgW7cmHwbr3Fk3DFaggBEbmZ89fgz06CFJtUMHYPHiVGk0Lg6YNEm+/uwzs92mLtcxDBERpSUsDNi1SwKQh4f8lgHkl89rr0kA6tdPqkObuXv3dMNgvr664zVqSAB6+20OgxldVJTUJfDzk/HJ339PsxDn6tVSdqhECWDCBCO000wxDBERaUVHA/v3SwD64w8p0KLVuLEEoAEDgHLljNdGPdEOg61ZAxw7pjtub69bDdaiBYfBTMahQ8CZM0DRosCePWnWo4qIAKZMka//9z/AwSF3m2jOGIaIKH+Lj5c0sHGjrBV//lz3WPXqul3ha9Y0WhP1hcNgZuyNN4AdO4DChdMty7BoEeDvL3OqP/ooV1tn9hiGiCj/UQo4f163K7y/v+6xMmWka8TNTar65oGukZcNgw0Zkic6u/ImpXTfg0n2G0vrtFOn5Otvv01Ve5FegmGIiPKPmzd1u8LfuaM7XrSoblf4Nm0AS0ujNVFfwsJ0w2DHj+uOcxjMjHh6AqNGyerFihUzPFWjkZHdQ4dkbjVlDcMQEeVt9+/rdoW/eFF3vEAB+UvbzQ3o0iVP/CmdkCAjftphsIgIOa7RyGr/oUM5DGY2Hj2SFYqPHskEoF9/felTtMUvKesYhogo7wkKkjSwcWPybhErKwk+bm4ShAoXNl4b9ejuXd0w2L//6o7XrKlbDcZhMDMSEaELQrVrA0uWZHj6pk1S4SGN3TgokxiGiChvePFCqs1t3CibVmqXwgMy9DVokJTldXQ0Xhv1KL1hMAcH3TBY8+YcBjM7CQnShefpKevj//gjw2VhFy7It3aJEsDt2wxE2cUwRETmKyZGgs/GjRKEtONCANCwoW4pfIUKRmuiPr1sGGzYMOnw4jCYGZs6Vf7nWlvL6rEqVTI8feJE+S97hnKGYYiIzEt8vOwWunGj/NIIDtY9VrWqbil8rVrGa6OecRgsn/j9d2DGDPl65Urp0czAwYPAX3/Jzi8zZ+ZC+/IwhiEiMn1KyXjAxo0yGfrRI91jzs4yLjRoEODikmfGhcLC5HfjmjWS/bQ4DJaHtWolxT07dpT/wRlISND1Cn38cZr7tVIWMAwRkeny9pbZoZs2Abdu6Y47OMj8Hzc3oF27PLEUHpBfcEePSgDatk03DGZhoSuKyGGwPKxsWUm+mVjZuGkT4OUlpRK++srwTcvrGIaIyLQ8eCCFEDdtkkmkWnZ2ssLGzQ3o1i1PLIXXuntXhsDWrpWtp7Rq1gSGD5dhsLJljdc+MqAXL2QG/Ouvy/2CBV/6lOhoWW0PyKaseWRNgFExDBGR8T17JvN/Nm2SGcJKyXFLS5kZPGiQdIkUKWLcdupRRsNgbm7SC9SsGYfB8rSEBGDwYJn8v2gRMGZMpp4WGSkjaQcPZvop9BIMQ0RkHOHh8ktg0ybZHDU2VvfYq69KInjrLaBkSeO1Uc8yGgZLuhrMzs6IjaTcM3my/BuwtZUJYJlUtCjwyy/yTygTHUmUCQxDRJR7YmLkz9lNm4CdO5MvhW/QQALQwIEv3XrA3Ny5I0Ng69YlHwZ75RXdajAOg+Uza9YA8+bJ16tWyd4oWVSokH6blJ8xDBGRYSUkyDjQpk0yLvTsme6xKlUkALm5AXXqGK+NBhAaqhsGO3lSd5zDYIQTJ4APPpCvv/5ahoEz4cEDYNw4YNq0PFU5wiQwDBGR/iklS120S+EfPNA9VqqUFEIcNCjPpYGEBODIEd0wWGSkHLewkF1Ahg2TOeAcBsvH7t0D+vSRYeE335Qii5k0ZYoE7CdP5PuM9IdhiIj05/Zt6QHauFGWxWvZ2wP9+kkAatdO9gjLQ7TDYGvXyr6wWq+8olsNVqaM8dpHJmT3btk7r0kT+YaxsMjU065dk5ANALNmGa55+VXe+olERLnv0SNZCr9xI3D+vO64rS3Qo4cEoG7d8lx3SHrDYEWL6obBmjbNUx1fpA9jx8pa+PbtszT7efJk6Xns0wdo2dJwzcuvGIaIKOuCg2UcaONGWR6VdCl8p04SgHr3lh6hPCQhATh8WALQ9u0cBqMsiIvT9Yi+/XaWnnrihOzXamkJzJ5tgLYRwxARZUFEBPDNN8DixbIyTKtVKwlAb70FODkZr30Gcvu2bjVY0mGwWrV0q8E4DEbpWrECWL9eEnQWS0UoBUyYIF+/954U4iT9Yxgiosw5fhwYMUImyABAvXoSgAYOzJMbI4WE6IbB/v5bd7xoUXnbw4blqa3QyFAOHwY++UQ2GN60CRg9OktP370bOHNGRtSmTDFQG4lhiIhe4sULmbCwZIncL1tW/tJ94w3jtssA4uN1q8FSDoN17SoBqEcPDoNRJt26JQsH4uOl0vSnn2b5Eq6uunJEpUvruX2UiGGIiNL311/A++8Dvr5y/733gO++k2I5eUhGw2DDh8vvMQ6DUZY8ewZ07w48fy4znn/5JVvdiAUKAOPH6795lBzDEBGlFhIiP4F//lnuV6woX3fubNx26VFICPDbb9ILdOqU7jiHwSjHYmNl/tzt20CFCsCOHVnuToyOlvnWlpYGaiMlwzBERMnt2yfVcbWFEj/5RJaw5IFNUuPjk68Gi4qS4xwGI72aNEm+0QoXlmVgpUpl+RLz5knFih9/BDp0MEAbKRmGISISwcFSA2XdOrlftSrg7g60bWvUZunDrVu6YbCkxbBr19atBuN8DNKbDz4A/vwTmD8fqF8/y08PDJQw9OKFfE2GxzBERMCuXcDIkUBAgIwLjR0LzJxp1ltipzcMVqyYbhisSRMOg5EB1KwJXLkCWFtn6+kzZkgQatIE6N9fz22jNDEMEeVnT5/KCpfNm+V+zZqyg3arVsZtVzZlNAzWrZtuGMzW1pitpDzp2jXA31+KjgLZDkJ37gA//SRfz5uX6d06KIcYhojyI6WkiM6oUbLro4WFTJieOtUsJ8x4e+uGwR4+1B2vXVu3GozDYGQwT55IyvbzA7Zulerr2fTVV1KsumtXzhXKTQxDRPlNQIBMit6+Xe7XrSu9QU2bGrddWRQSIhNM16wBTp/WHecwGOWq6Gigb1/AxweoUgV49dVsX+rcORna1WiAuXP12EZ6KYYhovxCKWDDBmDMGKmBYmUFfPml3Mxk3Cg+Hjh0SALQjh26YTBLy+Srwczk7ZC5U0rm2p08Kfvw/fGHbMKaTZs2yX+HDMnWvGvKAYYhovzg4UP5ob1nj9xv1Eh6gxo2NGqzMiu9YbA6dSQAcRiMjOK77ySZW1hIl07t2jm63Pffy+LNRo300zzKPIYhorxMKQk948YBoaGAjY1stDphQrYneOaW5891q8E4DEYmZ/duYOJE+XrRIqBLlxxfUqMBevXK8WUoGxiGiPKqf/+VeicHD8r9Zs0kGNWpY9x2ZSA+XnYA0Q6DRUfLcUtL3Wqw7t05DEYm4OBB+WPjo49kIUIOeHpKWa+iRfXTNMo6hiGivCYhQTZSnTBBipXY2UnhkrFjZZ6QCbp5U4bBfv019TCYdjWYs7Px2keUyuLFQOvWwJtv5qh7MioK6NMHCA8H9u83u3UMeYZp/mQkouy5e1c2Uz16VO63bi29QTVqGLVZaXn+XLca7MwZ3fHixXXDYI0bcxiMTEjSDcM0GsDNLceXXLxYNgcuV04WdpJxMAwR5QXx8cCSJbIyLCJCKkfPni3d9yZUtY3DYGS2lJJuyrAwYONGvezVFxwMzJolX8+YITvUk3EwDBGZO29v4N13dXtOtGsH/PKLTEIwEdphsHXrgEePdMfr1tWtBuMwGJm0b7+Vte9WVsDly9LrmkOzZ0sPad26spyejIdhiMhcxcUBCxbI6rDoaNkhe/58mTRtAr1BwcG6YbB//tEdL15cws+wYbKEmMNgZPJ+/x34+mv5etkyvQQhPz/ZkR4A5syR3lEyHoYhInN09ar0Bp07J/ddXYGffwYqVDBqs+LjAQ8PCUA7dyYfBnv9dQlAb7zBYTAyI+fPA0OHyteffQa8/75eLqv9G6ZtW/m3QcbFMERkTmJjpU7/9OnytYMDsHChpAwjdrHcuKFbDZZyGEy7GqxUKaM1jyh7Hj4EevYEIiMlscyfr5fLJiRIyS9LS9mMlb2jxqdRSiljN8KUhYaGwsHBASEhIbC3tzd2cyg/8/KSZOHlJfe7d5ftrcuWNUpzOAxGeZpSwGuvyVYbderInDw9/w64fx8oX16vl6QksvL72/gTC7Jo2bJlqFy5Muzs7NCkSROcOHEi3XO3b9+Ozp07o2TJkrC3t0fLli1x4MCBXGwtkR5ER0ufetOmEoSKFwfWr5cKuLkchF68kP1dBw6U7S8++kiCkKWl7Am2bZv0DP34I5fFk5nTaKTXtUED2XPMAH8MMwiZDrMKQ1u2bMHYsWPx1Vdf4eLFi2jTpg26desGPz+/NM8/fvw4OnfujL1798LT0xPt27dHjx49cPHixVxuOVE2nTsne07MmCETpvv1A65fl66XXEoajx5JDcfXXwdKlJAmbNkiGa1ePZnD/fChZLO+fTkfiPIQFxfgwgWgcmW9XE4p2cHj5k29XI70yKyGyZo3b47GjRtj+fLlicdq1aqF3r17Y/bs2Zm6Rp06dTBgwAB88803aT4eHR2NaO2sT0g3W/ny5TlMRrkrMhKYOlU2gkxIAEqWlFUsb75p8JdWSuZn79olAUc7R1uralXZP+ntt2WfV/b+UJ6yYYMUKTVAKejffwf695dtNx48AAoV0vtLUBJZGSYzmwnUMTEx8PT0xKRJk5Idd3V1xSltfZWXSEhIQFhYGIoXL57uObNnz8a0adNy1FaiHDl1SlaKeXvLfTc3GXdydDTYS8bGAidOSPjZvRvw8dE9ptEAzZvLPNJevYBatRiAKI9avRoYMUK6N8+ckSEyPYmNlZqogOyMwyBkWswmDD19+hTx8fEolWJJSqlSpRAQEJCpa3z//fcIDw9H//790z1n8uTJGDduXOJ9bc8QkcGFhwNffSXBRymZlLN8ucG2sQ4Nlb2Qdu0C9u6V4m9adnZA584SgLp3Z0FEyge0QUhbabp+fb1efuVK4M4dWVX5+ed6vTTpgdmEIS1Nij9JlVKpjqVl06ZNmDp1Knbt2gUnJ6d0z7O1tYUtJz1Qbjt6VH4Q37sn94cNk8k4xYrp9WXu39f1/hw5In+tapUsKcGnVy+gUyf+5Ur5yKpVsqefUsDHH8vWNnrs/gwLA7QDDlOmSH1UMi1mE4YcHR1haWmZqhcoMDAwVW9RSlu2bMGIESPw+++/o1OnToZsJlHWhIXJjErtPLhy5aR4Yteuerm8UrIAbfdu6QFKuXagZk0JPz17Ai1asAou5UNJg9Ann8jOqXoeB/7uO+DJE5mK9N57er006YnZhCEbGxs0adIEHh4e6NOnT+JxDw8P9MpgGGHTpk149913sWnTJrzxxhu50VSizDl4UKrZaldDfvihVGDL4UT9mBjg2DHdBOj793WPaTSyk0DPnnKrWTNHL0Vk3g4e1AWhUaNkiFrPQSggAPj+e/l61izA2lqvlyc9MZswBADjxo3DkCFD4OLigpYtW2LlypXw8/PDyJEjAch8n4cPH2LdunUAJAi98847+OGHH9CiRYvEXqUCBQrAwcHBaO+D8rnnz4EvvgDc3eV+pUqysWrHjtm+ZHAwsG+fBKD9+2U+kFbBgrJbR69eshVGyZI5aj1R3tGunYwNV6xokCAESJH4b76RYem+ffV+edITs1paD0jRxXnz5sHf3x9169bFwoUL8dprrwEAhg0bBl9fXxw9ehQA0K5dOxw7dizVNYYOHYo1a9Zk6vVYgZr06s8/pQfo4UO5/+mn8udiNiYR+Pjo5v8cPy5liLRKldL1/nTsCBQooKf2E+U1sbGyE72Bl0gqxVWYuS0rv7/NLgzlNoYh0otnz4AxY6RyNABUry49Q23aZPoSCQmAp6du/s+VK8kfr1NHt/y9aVOT2LieyPT88ov841m0iAEoj8uTdYaIzNb27bJC5fFjSSiffSYbrRYs+NKnRkVJ9/quXbIjQNJNUC0tJUtpe4CqVjXgeyDKC37+GfjgA/m6bVuDjludOSMvNWcOd6U3BwxDRIYSGCjDYL/9Jvdr1ZKVKy1aZPi0oCAZTdu9W+b/hIfrHitcWBaa9eoFdOsm22MQUSasXClD1ID00iZZiKNvSgETJkgH1NatDEPmgGGISN+Uks27Pv0UePpUunAmTgS+/lqqGabhzh3d8NfJkzIkplW2rK73p3177v1FlGVJg9DYsVLDy4DjV3v2SEV3OztdfSEybQxDRPrk7y9bue/aJffr15feoCZNkp2WkCC7vWsD0I0byS/ToIFu/g93fyfKgRUrgP9WHOdGEIqPB7S7Ro0ezZ3pzQXDEJE+KAX8+qv8sA0OltUp//sfMHkyYGMDQPZe/esvCT979sgUIi0rK5nC0KsX0KOHrLYnohy6d0/qBwEyV+/77w3+l8XatcD161I8PsVWmmTCGIaIcurBA+mC37tX7jdpIr1B9esjMFCCz+7dUt8tMlL3NHt7mUvQs6fM/yla1CitJ8q7qlSRP1I8PaWgqYGDUESE1BQCZJtBPe+mQwbEMESUXUrJMt0vvpAqhzY2wLRpuNn9C+zea4VdHwGnT8tpWhUq6Ia/XnstsdOIiPQpMlJXXGvgQLnlgt9+kxJiFSrIzh5kPhiGiLLD11fK+B86hHhY4HTtD7Cr+SzsXl0CtyYnP7VxY93+Xw0acP4PkUEtWyY1hI4ckdUHuWjoUOkNsrBId60EmSiGIaKsSEgAli9H+IRpOBjRGrst12KP3Zt4er0gcF1OsbYGOnTQzf8pV864TSbKN5Yu1c0R2rgRGD8+V19eo5F/92R+GIaIMsn/lA/+GLYNu2+/gr/gh2jYAfEAwuWvwTfekN6fLl1yvNcqEWVV0iA0frwMX+eSwEAZ8ua8P/PFMESUDqWAa9eA3TsTsOvnxzjrVxmA7gds5coKvXpp0KuX7ATP3aiJjCRpEJowQco+5+J49Oefy/qJX34xaC1HMiCGIaIk4uKk6OGuXbIC7N49ALAAUBoA0Mz+Bnq9Xwo9hxVHnToazv8hMrYlS6TAKSDFTWfPztUg5OUFbNggfzxVqJBrL0t6xjBE+V5YmGx7sXu3bIMRHKx7zBZR6IS/0NP2IHrMaIbSXwzmDGgiUxEZKWEIMEoQAqSWkFKyYC1FbVUyIwxDlC89eCAbn+7aJYtOYmJ0jzkWi0N3i73oGbQaneGBwt1ekyq2LCVLZFoKFAAOHwY2bQLGjcv1IHToEHDggAyRf/ttrr406RnDEOULSgGXL+uGvzw9kz9evTrQq3s8eoWsQ8t1H8EyLlpmQy5aCrzzDnuDiEzJrVtAjRrydZkyMmknlyUkyPQkQHbgqVIl15tAesQwRHlWbCxw7JiEn927gX//1T2m0QAtW+rq/7wScQEYPlwSEyAPLF8OlC5tnMYTUdp++EF6gdauBd5+22jN2LIFuHABKFJEdt4h88YwRHnK8+cy/2fXLmDfPiAkRPdYgQKAq6uEn+7dAScnANHRwPTpwNy5ssNiiRIyB2HAAPYGEZmaH36Q/f8A4OZNozbl+nX5ETFhAlCypFGbQnrAMERmLyhI6qvt3g0cPSorwrScnKTwYa9eQMeOQMGCSZ74zz/SG6TdMv6ttyQIOTnlZvOJKDMWLZLNVgHZ+GvGDKM2Z8YM+ZupcmWjNoP0hGGIzNbz58CCBfIzMixMd7x2bd3+X82aSWn8ZCIjga+/BhYulIF/Jycp4d+vXy62nogybeFCGRoDZExq+nST6LmtW9fYLSB9YRgisxMaKr3l33+vGwarX1/mOffsKZOh03XyJPDuu8Dt23L/7bclTZUoYehmE1F2mFgQ2rIFaNgQqFnTaE0gA2AYIrPx4oWMYs2fDzx7Jsfq1gWmTQN6906jByip8HBg8mS5gFKyAmXFCpk8RESmy89P/vv11/KP3YhB6NEjGVmPiZEVqQ0aGK0ppGcMQ2TyIiJkYdfcucCTJ3LslVeAqVNlmk+GIQiQOiTvvQf4+Mj9ESOA777jRkJE5mDBAln50LWr0YfGpk6VUfZWraQ3mvKOl/0aITKaqCjgxx+BqlVlz8UnT+TrdeuAq1dl8mKGQSg0FBg5UmZO+/hIrfwDB2QDIQYhItO1fbus9AQkAHXrZvQgdPMm4O4uX8+bZ/TmkJ4xDJHJiYmRnqBq1YAxY4CAAKBSJflBdPMmMGQIYGn5kovs3y9jaCtWyP2PPpIE5epq6OYTUU58950sZujXL/nSUCObPFnWW2g3Zqa8JcthyNvb2xDtIEJsrHTaVK8OfPwx8PAhUK4c8NNPgLe3zHu2etnAbnCwDOp36wbcvy9lYY8ckdViRYrkyvsgomyaPx8YP16+dnHJxD/43PH338DOndITPWuWsVtDhpDlMFS/fn28/vrrOHjwoCHaQ/lQXJwUk33lFeD992W+ZOnSwOLFwJ07wIcfAjY2mbjQ7t1AnTrAmjXShz1mjFSUbtfOwO+AiHJs3jzd/hZTp8rNBCila9a770rpDsp7shyGfHx80KxZMwwfPhy1atXCTz/9hIiICEO0jfK4+HgpllinDjBsGHDvnpT8WbAAuHsXGDUKsLXNxIWePAEGD5b+a39/2bPoxAlZMl+okIHfBRHl2Ny5sus8ICFoyhSjNiep+Hgp2VGmjCxmo7xJo5RS2XliXFwctm/fjmXLluHy5ct477338Mknn6BixYr6bqNRhYaGwsHBASEhIbC3tzd2c/KEhASZHzllipS0B6TMz4QJwCefZCG/xMQAS5fKT6iQEOnD/uIL+WFaoIChmk9E+pS0jtC0acA33xi3PemIiclkDzWZjKz8/s5yGIqMjERwcHDi7dmzZzh58iSWL1+O6OhoxMTE5KjxpoZhSH+UkpGsKVOAS5fkWNGisuH0mDFZmNKjFPDnn/LEW7fkWMOGMlm6WTMDtJyIDObvv2XZ/PjxJhuEyDxl5fd3lmenFSpUCPb29ihZsiSKFCkCe3t72Nvbo1evXgwLlCalZNPUb76RQmWABJ/PPpNblla5X78uT9LOWXNyAr79ViZNv3SJGRGZnNat5d91+fLGbkky4eGyoG3MGJMocUQGluUw9NZbb+HgwYPo2rUrxowZg2rVqhmiXZQHKAX89ZeEoDNn5FihQsDo0TKaVbx4Fi4WFCTDX8uXyyC+jY3sXv3VVwBDOJF5WbQIaNsWaNRI7ptYEAKkiQcOyEpWb28OkeV1WZ5AvWXLFly5cgWFChVCixYt0LNnTxw5csQQbSMzduyY/KxzdZUgVKCABCAfH1mamukgFBsry8qqV5etNOLjZe+N69dl0iWDEJF5mTVLenc7dQIePzZ2a9L05In8eAGk45lBKO/LVtHFcuXKYc6cOfDz80O3bt3w0UcfoUGDBli9erW+20dm5tQpKfjcrp0s6LK1lW7me/ekhEjJklm42P79svnP6NFSP6hePeDQIWDHDilFTUTm5dtvpTcXkEnTpUoZtz3pmDkTCAsDGjcGBg40dmsoN2R5AvUPP/yAsLAwvHjxIvG/ISEhOHz4MMLDwxEfH2+othoFJ1BnztmzMhx24IDct7aWmkGTJ0vhxCzx9pYflHv3yn1HR2DGDNlfzESKsBFRFn37rew6r/36yy+N25503LsnNc9iYwEPD+nAIvNk0AnUmzdvRtGiRVGsWDEULVoU5cqVQ926ddG9e3cUK1Ys240m83TxooSgPXvkvpWVzGX+6isgy1UWgoOB6dNlOCwuTi42erTsVs29xIjM18yZ8u8YkGGyyZON2550XLkC9OkjQcjVlUEoP8lyGDp9+nS6j3l5eeWkLWRGrlyRJfI7dsh9CwvgnXfk512VKlm8WFwcsHKlpKqgIDnWvTvw/fdSQJGIzNevv+qC0OzZwKRJxm1PBm7dkoKvlSrJ32SUf+R4o9aQkBAsW7YMTZo0gYuLiz7aRCbsxg3ZLb5+fQlCGo0Uf75xA1i9OhtB6K+/ZEXJJ59IEKpdW8ba/viDQYgoL+jbV1ZTmHgQAmQp/erVwPnzsmaD8o9sh6HDhw/j7bffRunSpTF16lRUqlQJ2SxmTWbg9m3ZLb5uXeC33+TYW2/JRvDr12cjt9y+LdtndO4sFyleXP4Uu3SJO8sT5SWFCsnkGxMMQk+eyB93Dx/qjg0bJhXxKX/JUhh68OABZs6ciapVq6Jnz55QSmHr1q3w9/fHNG7akif5+MjmhLVqSehJSJCV7ZcuSSjK8qaFISFSabZOHSlHbWkp84Ju35beIU6QJjJ/06cn32jV2tpoTUnPuXNAkybyc2zYMGO3howt0795Xn/9dRw5cgQdOnTA9OnT0bt3bxRKsomUhuU58xQ/P1nwsWqVTOkBgDfekK2DmjTJxgXj4wF3d1lN8uSJHOvaVXZlrVVLb+0mIiObNk0XhFxdgVatjNqctKxaBXz8MRAdLb3aP/xg7BaRsWU6DO3fvx+DBg3C2LFjOTcoD3v4UIb2f/5ZNiYE5OfZ9OlA8+bZvOjRo1ItWrshWc2aEoJef10PLSYikzF1qm5r93nzTC4IRUdL3bMVK+R+z57AunWAg4Nx20XGl+lhsr///hsFChRAhw4dULNmTUyfPh137twxZNsolyglXcbvvy+1DJculSDUvr0UTjxwIJtB6N49mZHYvr0EoaJFpcb9lSsMQkR5TdIgNH++DIebkMePpRjsihWy8GPGDFkEwiBEQDaKLkZERGDz5s1YtWoVTp8+jaZNm2Lw4MGoU6cOOnfuzKKLZiQkBNi4UVa1J62K0Lq1/KBo3z6bFw4LkzG2hQslVVlYACNHyg9KR0d9NJ2ITIVSEoSmT5f78+fL3jsmJjwcaNkSuH9ffu5162bsFpGhZeX3d5bDUFLe3t5wd3fHr7/+isePH0Oj0TAMmTilpFr0ypXA5s1ARIQct7WV1WEffAC8+mo2d2hOSADWrJHKsto9hzp1klBUt66+3gIRmZJz54BmzeTr774DPv/cuO1JQvvbTfvz7N49OcbdfPKHXAtDWvHx8fjjjz+watUq7N69O6eXMyl5JQyFhAAbNkgX8eXLuuO1agEffijL5rO0i3xKJ07IvKALF+R+tWpSNLFHj2wmKyIyG8uWAZGRJhWEIiPlZ1utWiZb8JoMLNfDUF5mzmFIKeCff3S9QJGRctzWFujfX3qBWrfOYVbx9QUmTAB+/13u29tLJelPP+VWz0R5lVLyA6VgQWO3JE2+vlLr8eJFWdV/9y5QvryxW0W5zaB7k5Hpe/5cagKtXClzlbVq15a/lN5+O4e9QADw4gUwZ450i0dHS6J6/32ZbOTklMOLE5HJUkr+4PnjD+DQIZOrUOjhITvNP3sGlCwJbNnCIEQvxzCURygFnDkjw2C//abrBbKzkwqrH3wgkwdzPGKVkCB7DU2eDPj7y7F27WSVWIMGObw4EZk0pWSfsW+/lft798oYuwlQSlbzf/ml/Jhq2hTYto1BiDKHYcjMBQfreoGuXtUdr1tXeoEGDwaKFdPTi506JfOCzp2T+1WqSM9Q796cF0SU1yklRVNnzZL7CxeaVBAaPBjYtEnujxghu/vY2Rm3XWQ+GIbMkFKSS1aulF6gqCg5XqCArheoRQs95pP794GJE3U/aQoXlh+KY8bwpw1RfqAU8NVXUpEVkJ7gMWOM2qSkNBqgTRtg61YJQe+/z7/PKGsYhsxIcLCMUK1cCVy7pjter56uF6hoUT2+YESE9DvPmyfjbhoNMHy4dJE7O+vxhYjIZJlwEAoPl31gASll1rmzLGQlyiqGIROnFPD33xKAfv9d1wtUsKBMEvzgAynxode/gpSSqmSTJgEPHsixV1+VDXwaN9bjCxGRyXv2TP4KA+RnwOjRxm0PZKvDqVNllezZszIVQKNhEKLsYxgyUc+eyZ45K1cCN27ojtevr+sFMkgZ+bNn5a++M2fkfsWKUlH2zTfZ70yUH5UoARw5Ahw7JpNxjCw4WH7+7dsn97dulWExopwwuzC0bNkyzJ8/H/7+/qhTpw4WLVqENm3apHv+sWPHMG7cOFy7dg1lypTBhAkTMHLkyFxsceYpBZw8KSvCtm6VFeuA9AK5uUkvUNOmWcgk8fGyzv7pUyAoKHP/1e4oX6iQrBgbN04mIxFR/qEUcPOmVCwEpMvFBLpdLl8G+vSRStJ2dvLHoonM4SYzZ1ZhaMuWLRg7diyWLVuG1q1bY8WKFejWrRuuX7+OChUqpDrfx8cHr7/+Ot5//32sX78ef//9Nz7++GOULFkS/fr1M8I7SFtQkK4X6OZN3fGGDaUXaNAgwL5gnHQX3cxkqAkKkvOzU1PznXdkfkCZMnp7j0RkJpSSIfJFi2Rtevfuxm4RABkSGzFCpjJWqgRs3w40amTsVlFeYVYVqJs3b47GjRtj+fLlicdq1aqF3r17Y7Z2cl8SEydOxO7du3EjyTjTyJEjcenSJZw+fTpTr2moCtQq+DmO73yGlRsKYesxR8TEWQIACllHw63iaXzgtBMucWegCfov2Dx/nv0Xs7eXrm5Hx5f/t0wZbqZKlF8pJStH58+X+0uXAh9/bNw2AfjlF91QmKurTGk0sVqPZILyZAXqmJgYeHp6YtKkScmOu7q64tSpU2k+5/Tp03B1dU12rEuXLnB3d0dsbCysra1TPSc6OhrR2vEpyIdpCJ+43sLy880S7zfCBXyIFXCL3QT7O2HAnXSeWKxYxoEm5bHixbktBhG9nFKytc5338l9EwlCgAyNffutTBeYMQOwtDR2iyivMZsw9PTpU8THx6NUqVLJjpcqVQoBAQFpPicgICDN8+Pi4vD06VOULl061XNmz56NadOm6a/h6XjDJRC/er7AIPs/8UHFA2hSKei/ADMy/bBTrBhgZTb/y4jIXCgFjB8vmysDJhGEfH1lOAyQH4GXLwNFihizRZSXmd1vVk2K2cNKqVTHXnZ+Wse1Jk+ejHHjxiXeDw0NRXkD1HPvuqQ7Hs0DihQZAGCA3q9PRJQpKYPQsmXARx8ZtUmrVkkWW7pUt4CNQYgMycLYDcgsR0dHWFpapuoFCgwMTNX7o+Xs7Jzm+VZWViiRzoCzra0t7O3tk90MwdKS/7iJyAQopVtFauQgFB0txRNHjJCvDx7M3hoQoqwymzBkY2ODJk2awMPDI9lxDw8PtGrVKs3ntGzZMtX5Bw8ehIuLS5rzhYiI8h0LC+mK+esvowahhw9lz+cVK6R8yIwZsgMQy5tRbjCbMAQA48aNwy+//IJVq1bhxo0b+Oyzz+Dn55dYN2jy5Ml45513Es8fOXIk/v33X4wbNw43btzAqlWr4O7uji+++MJYb4GIyPi0Vebj4+W+pSXQsaPRmnPiBNCkidR6LVoU+PNP2f7Qwqx+Q5E5M6s5QwMGDEBQUBCmT58Of39/1K1bF3v37kXFihUBAP7+/vDz80s8v3Llyti7dy8+++wzLF26FGXKlMGPP/5oUjWGiIhylVJSTHXRIinjvG6dUbtfHjwAOnUCYmJkn8UdO4CqVY3WHMqnzKrOkDEYqs4QEVGuSxqEABmT+uADozYJkCGx69elnpB241WinMqTdYaIiCgHlAI++0w2WwWMGoR8fKQzSrt0/quv5D7nB5GxcESWiCivSxmEVq40WhA6eBBwcZFCihERcszCgkGIjIthiIgor5swQReEfv7ZKNu8KwXMmQN06ybbJlpZAQYq8E+UZQxDRER5XceOss37L78A772X6y8fFga89RYweTKQkAC8+66sIHN2zvWmEKWJc4aIiPK6rl2Bu3dlI+Zc5u0tQ2I3bgDW1sDixTJCx2ExMiXsGSIiymuUAqZMAW7f1h0zQhACgFGjJAiVKQMcPw58+CGDEJkehiEiorxEKeDTT4Hp04EOHYDwcKM2Z9Uq6Rny9ARatDBqU4jSxTBERJRXKCVdMUuXSvfL9Om5XrgnOBjYsEF3v3x5YPt2zg8i08Y5Q0REeYFSwCefAMuXSxBatQoYNixXm3DlivQC3b0rGax371x9eaJsY88QEZG5S0gwehDavFmGwe7elWKK/+2SRGQWGIaIiMzdd9/pgtDq1bkahOLigC++ANzcpIiiqytw/jzQqFGuNYEoxxiGiIjM3XvvSVnn1auBoUNz7WWfPJHw8/33cn/SJGDvXqBEiVxrApFecM4QEZE5Ukq3Rr14ceD0aSnrnIsOHQKOHAEKFwbWrAH69cvVlyfSG4YhIiJzo50jVLu2LKMHcj0IAcDAgYCvL9CzpzSFyFxxmIyIyJwkJAAffQT89BMwdqyUeM4l0dGypUZgoO7YpEkMQmT+2DNERGQuEhKAkSNls1ULC2DtWqBmzVx56YcPgTffBM6cAc6eBf76i5WkKe9gzxARkTlIKwi9/XauvPSJE0CTJhKEihaV1WMMQpSXMAwREZm6hATZ1EsbhNaty5UgpBTw44+yq8fjx0C9erJsvls3g780Ua5iGCIiMnX79wO//CJB6NdfgcGDDf6SERGySn/MGKkl5OYmC9aqVjX4SxPlOs4ZIiIyda+/DsycCVSuDAwalCsvGRsL/PMPYGkJzJ8vc7U5NEZ5lUYppYzdCFMWGhoKBwcHhISEwN7e3tjNIaL8IiEBiIoCChY0WhOuX5eVY+3aGa0JRNmWld/fHCYjIjI1CQlSVbpLF+DFi1x5SaWAuXOBJUt0x2rXZhCi/IHDZEREpkQbhFavljlCp07JnhcGFBYGDB8ObNsmw2KdO+fain0ik8AwRERkKuLjJQitWSNBaONGgwchb2+gTx/gxg3A2lp6hmrUMOhLEpkchiEiIlMQHw+MGCH1gywtgQ0bgAEDDPqSu3cDQ4YAoaFAmTLSM9SihUFfksgkcc4QEZGxpQxCGzcaPAhNnw706iVBqE0bwNOTQYjyL4YhIiJju38f2LNHF4T69zf4SxYuLP8dPVp2n3d2NvhLEpksDpMRERlbpUrA4cPA7dtAv34Ge5mEBJmKBACffSZbbLRta7CXIzIb7BkiIjKG+Hgp5KNVv75Bg9DmzUDz5rJyDJACigxCRIJhiIgot8XHy1r2Zs1kF1QDiouTjVXd3GRfsR9/NOjLEZklDpMREeWm+Hhg2DBg/XrAygp48sRgL/XkiczDPnJE7k+aJDciSo5hiIgot6QMQlu2AH37GuSlzp2TUbf792Wy9Jo1Bh2FIzJrDENERLkhPl62gd+wweBB6M8/JfhER0sBxR07ZGsNIkobwxARkaHFxwPvvCPL5q2sgN9+k7LPBtKkCVCiBODiAqxbBzg4GOyliPIEhiEiIkOLj5cNVw0YhEJDAe3G3M7OwOnTQLlyuqX0RJQ+/jMhIjI0Gxvg999lJrMBgtCJE7Kx6saNumMVKjAIEWUW/6kQERlCXJyMUSkl921sgFdf1etLKAUsXgx06AAEBMiy+YQEvb4EUb7AMEREpG9xcbID6tChwOefG+QlIiPl8qNHy8sNHCjbarA3iCjrOGeIiEif4uKAt9+W1WLW1gYp8+zrKwvRLl6U7czmzwfGjpWq0kSUdQxDRET6kjIIbd0K9Oyp15cICpJVYkFBQMmS8lLt2+v1JYjyHXaoEhHpQ1wcMHiwQYMQIEvm339fApGnJ4MQkT5olNLO7qO0hIaGwsHBASEhIbDXrlslIkpp0CBg0yYJQtu2AT166O3SYWFAeLgsmQdkpX5sLGBnp7eXIMpzsvL7mz1DRET60LMnUKCA3oOQt7fsNt+7t1SUBmSeEIMQkf4wDBER6cPAgcC9e3oNQrt3y8b2N27IHmO+vnq7NBElwTBERJQdsbHAuHHAw4e6Y9pxrByKjwe++Qbo1UsqS7dpI/ODatbUy+WJKAWGISKirIqNBdzcgIULga5dZfK0ngQHS+fSjBly/9NPpX6QnnIWEaWBS+uJiLJCG4S2bZOq0nPnyp5jevLuu8C+fTInaOVKqd1IRIbFMERElFmxsTI3aPt2CUI7dgCvv67Xl/juO+DffwF3d6BRI71emojSwWEyIqLMSBmEdu7USxCKi5NhMK2qVWV+EIMQUe5hGCIiyoyJEyUI2dpKEOrWLceXfPIEcHUFOncGDhzQHee2GkS5i2GIiCgzxo8H6tfXWxA6dw5o0gQ4cgQoVAiIisp5E4koezhniIgoPUrpumlKlwYuXJCKhzm0ahXw8cdSRLFGDZl6VLt2ji9LRNnEniEiorTExAD9+gHr1+uO5TAIRUcDI0cCI0bI1z17AmfPMggRGRvDEBFRSjExwFtvSZfNhx8CgYF6uewffwArVkhn04wZcnkHB71cmohywGzCUHBwMIYMGQIHBwc4ODhgyJAheP78ebrnx8bGYuLEiahXrx4KFSqEMmXK4J133sGjR49yr9FEZH5iYoA335S9MGxtJbE4Oenl0v36SdHqP/8E/vc/wMJsfgIT5W1m809x0KBB8PLywv79+7F//354eXlhSAbVyCIiInDhwgV8/fXXuHDhArZv345bt26hZ8+eudhqIjIr0dEShP74Q6oe7t4ty72ySSmZH/TsmdzXaIDvv9fL/Gsi0iONUkoZuxEvc+PGDdSuXRtnzpxB8+bNAQBnzpxBy5YtcfPmTdTM5IY9586dQ7NmzfDvv/+iQoUKmXpOaGgoHBwcEBISAnt7+2y/ByIycdHRMjSmDUK7duUoCEVEyPygX3+VHTv27NHL3GsiyqSs/P42i9Vkp0+fhoODQ2IQAoAWLVrAwcEBp06dynQYCgkJgUajQdGiRdM9Jzo6GtHR0Yn3Q0NDs91uIjIjGzYk7xHq3Dnbl/L1Bfr0Aby8JAC5unJIjMiUmUUYCggIgFMaY/ZOTk4ICAjI1DWioqIwadIkDBo0KMOEOHv2bEybNi3bbSUiMzV8OODtDXTqlKMg5OEhhaqfPQNKlgS2bAHat9djO4lI74z6t8rUqVOh0WgyvJ0/fx4AoEmjJKtSKs3jKcXGxmLgwIFISEjAsmXLMjx38uTJCAkJSbzdv38/e2+OiExfdLTcAJnQM3dutoOQUsCcOTIk9uwZ4OIi22owCBGZPqP2DI0aNQoDBw7M8JxKlSrh8uXLePz4carHnjx5glKlSmX4/NjYWPTv3x8+Pj44fPjwS8cNbW1tYWtr+/LGE5F5i44G+vaVr7XbbORAaKgsm09IkJ3nly6VETciMn1GDUOOjo5wdHR86XktW7ZESEgIzp49i2bNmgEA/vnnH4SEhKBVq1bpPk8bhG7fvo0jR46gRIkSems7EZmxqChZ5753L1CgAHD1quyNkQMODpKpzp4FPviA+4sRmROzWE0GAN26dcOjR4+wYsUKAMAHH3yAihUr4o8//kg855VXXsHs2bPRp08fxMXFoV+/frhw4QL27NmTrAepePHisLGxydTrcjUZUR6TMgjt2QN06JCtS+3eDQQHA0OH6rmNRJRjWfn9bTbrGzZs2IB69erB1dUVrq6uqF+/Pn799ddk53h7eyMkJAQA8ODBA+zevRsPHjxAw4YNUbp06cTbqVOnjPEWiMjYoqJkaCyHQSghAfjmG6BXL+kFunTJAG0lolxjFqvJAOnNWZ90j6A0JO3kqlSpEsyk04uIcoM2CO3bJ0Hozz+zNbs5OBh4+23JU4DUEuLeYkTmzWzCEBFRjty6BZw4IUFo716gXbssX+LKFakfdPeuTI7++WcJRkRk3hiGiCh/qF8f2L8fiI3NVhDaskVWiUVEAJUqyWTpRo303koiMgKGISLKuyIjpRx0rVpyv3XrbF/q9m0JQp07A5s2AVycSpR3MAwRUd4UGQn07g2cPw/89VeOu3G+/BKoUAEYPJh7jBHlNWazmoyIKNMiI2Wp18GDMnE6LCzLlzh/HujZU3qDANlb7J13GISI8iKGISLKW7RByMMDKFRIVo+99lqWLrFqFfDqq7Jv69SphmkmEZkOhiEiyjtSBqG9e7MUhGJigI8+AkaMkN06evYEvvrKgO0lIpPAOUNElDdERkp6+esvXY9QmzaZfvqjR1KY+swZ2Upj+nSZJ2TBPxmJ8jyGISLKGxISgLi4bAWhc+eAHj2Ax4+BokWBDRuA1183XFOJyLQwDBFR3lCokGyvcfNmljdddXaWLFWvHrBjB1C1qoHaSEQmiR3ARGS+IiKAtWt19wsVynQQio/XfV2+vEwzOn2aQYgoP2IYIiLzFBEhY1vDhgGzZ2fpqT4+QLNmwM6dumMNGkiWIqL8h2GIiMxPRATQvTtw+DBQuDDQtm2mn3rwIODiAly4AHzxhezOQUT5G8MQEZmX8HAJQkeOAEWKAAcOAK1avfRpSgFz5gDdugHPngFNm8olrK1zoc1EZNI4gZqIzIc2CB09KkFo//5MBaGwMGD4cGDbNrk/YgSwZInsPE9ExDBEROYhPl7mCGmD0IEDQMuWL33aixdAixbA9evSC7RkCfDBB4ZvLhGZDw6TEZF5sLQE3nwTsLfPdBACZEpRp05AmTLA8eMMQkSUmkYppYzdCFMWGhoKBwcHhISEwN7e3tjNIaInT4CSJTM8JSFBhsYcHOR+bCwQHAw4OeVC+4jIJGTl9zd7hojIdL14IZuFBQXpjr0kCAUHy2har166lWLW1gxCRJQ+zhkiItP04oXsiXHiBHDjhiz90mgyfMqVK0CfPsDduzI5+uJFqSdERJQR9gwRkelJGoTs7YG5c18ahDZvlonSd+8ClSoBp04xCBFR5jAMEZFpSRmEPDyA5s3TPT0uToonurlJLcbOnYHz54FGjXKxzURk1hiGiMh0vHghVRFPnJDZzx4eL+3e+fhj4Pvv5etJk2TD+hIlcqGtRJRnMAwRkel4/33g5MlMByEA+OwzoHRpYOtW2aLM0jIX2klEeQonUBOR6Zg5E7h2DXB3l/0y0nHjBlCrlnxdqxZw7x6rSRNR9rFniIiMK2mps6pVAS+vdINQdDQwciRQrx5w7JjuOIMQEeUEwxARGU9YGNCxI7Bnj+6YRdo/lh4+BNq1A1askKKKXl650kIiygc4TEZExhEaKpOlT52SjcPu3gUKFUrz1BMngLfeAh4/BooWBTZulKcSEekDe4aIKPeFhgJdu0oQKlYM+PPPNIOQUsDixUCHDhKE6tWTZfMMQkSkTwxDRJS7tEHo9GkJQn/9BTRpkuapBw4Ao0dLLaGBA+UpVavmcnuJKM/jMBkR5Z60glDjxume3qUL8M47QMOGwNixLy1CTUSULQxDRJR7li59aRA6elQO29tL+FmzhiGIiAyLw2RElHsmTJBxr0OHUgUhpWQLso4dgaFDZcUYwCBERIbHniEiMqywMKBAAcDKSspD//BDmqcMHw5s2yb3ixeXeUI2NrncViLKlxiGjOj332VPJWtr+aFvY5P86/HjdZtNnjsn56d1no2NbE5ZpYqc++gRcPFi2te1tgbKlpXdDgAgNhaIitI9lk6JF6LsCQkBXF2BatWAdevS3Cvj1i2gTx9ZXW9tLavHPviAPUJElHsYhozo0SPgn3/Sf3zoUN3Xly4B8+enf+62bbowdPy47OCdnnXrgCFD5Ov9+4GePXWP2dkBlSvL767q1YH+/TPcMJwofc+fywzos2eBO3cAX99US8F275bvxdBQoEwZ+T5u0cIorSUTFR8fj9jYWGM3g0yQtbU1LPW0GSHDkBH17CkBJiZGemhiYnS32FjglVd059arB3z+eepztV+XK6c718EBcHFJ+7yYGKBgQd25MTHJ2xQVJfs+3bihe11tGDp2TLZC0AalatV0X1eowA0yKYmkQah4cZkjlCIIRUYCn3wiQahNG+C33wBnZ+M0l0yPUgoBAQF4/vy5sZtCJqxo0aJwdnaGJoddyRqlkm4MRCmFhobCwcEBISEhsLe3N3Zz9C4hQfZ70gam0FApBHznDnD7NjBiBFCnjpy7YoWEobRYWwPr10tPEgA8eCDDHgxK+dDz5zI0du4cUKKEBKEGDdI89cwZYMsWYN48+R4i0vL398fz58/h5OSEggUL5viXHeUtSilEREQgMDAQRYsWRenSpVOdk5Xf3+wZyucsLGRua4ECct/RUXqrOndOfW6/fvLHvTYoaf97966EqaR/1e/bJ/M+APklV6VK8p6knj2B8uUN//4ol70kCF2+LDvM9+4t91u04LAYpRYfH58YhEqUKGHs5pCJKvDfL67AwEA4OTnlaMiMYYgyzdER6NRJbknFx8smmk5OumPW1kDt2hKUoqMBb2+5adWurQtDO3YAv/ySPCxVqwZUqiQLkMiMXL4sN0dHCUL16yc+tHmz9DQqJaWG0uksIkqcI1Qw6Zg+URq03yOxsbEMQ2RclpYyFJbUsGFy0wYlbU+Stjcp6Xyoc+eAvXtTX9fKSgLR779LBWJArhURIcc5rGKCXnsN2LULKF06MQjFxQETJwILFsgprq7J57gRpYdDY/Qy+voeYRgig9IGpQoVpJheWtzcJNwkHX67c0cmc9+5I8WKtZYvB779Vq5bqZKuF6lqVVmN1K0bUKRIbrwzShQcDAQFyf8IQCZO/ycwEBgwQKpKA8DkycCMGZxDRkSmhWGIjK5ePbkllZAgpQfu3EneixAdLfObIiNlCO7u3eTP+/dfXRiaOlUmdZcuLfOZtDft/bZt09wonbIiOFgmmPn7S+KpXj3xoXPngL59ZTJ94cLA2rVyn4jI1DAMkUmysJAQlHI4Zf58WXn06FHySdw+PvL7uFQp3bk+PmkHJi0/P10Y+vZbGY5LKzQ5OwPNmukmmdN/goNlAtmFCzJHKCoq2cO7dkkQqlFD5oXVrm2kdhIZwalTp9C6dWt06dIF+/fvT/X42LFj4evri507d+rtNY8fP4758+fD09MT/v7+2LFjB3prVysksWzZMsyfPx/+/v6oU6cOFi1ahDZt2uitHeaIYYjMjkYjVbTLlgXatUv/vG+/Bd5/X0JSQIDupr2fNDh5e0thy0uX0r7W/fu6YDZ3rhQLTBmYtF/Xq5cPtpF49kx6hC5cAEqWBA4fBurWTXbKtGlSxPPTT3UVz4nyi1WrVsHNzQ3btm2Dn58fKqSYWHnu3Dm88cYben3N8PBwNGjQAMOHD0e/fv3SPGfLli0YO3Ysli1bhtatW2PFihXo1q0brl+/nqqN+QnrDL1EXq8zREI7Tymt0BQQAFy9qpuwPWSIDL+l58EDCWqATBrevz95WEoaoKpVM7MVc0pJV5ybW6og9OgRMHMmsHAhYGtr7IaSOYuKioKPjw8qV64MOzs7+b6LiDBOYwoWzPLeMOHh4XB2dsahQ4cwZcoUtGzZEt988w0AWfVUqFChZFW1mzVrhn8y2o4gGzQaTZo9Q82bN0fjxo2xfPnyxGO1atVC7969MXv2bACAr68vKleujG3btmHRokU4d+4cateujW3btsHX1xcTJkzA1atX0bx5c2zbtg3FixcHACQkJGDOnDlYvXo17t+/DwcHB7i6uuLXX3/V63tLKtX3ShKsM0SURdpl/ZkxcaLUyUkamJIGp6QlBi5cADw80r/Ww4cy8RsAliyRXJEyMGnvly5tpInHCQm6Teu8vYFateTrkiWBI0eAOnVw4gTw1lvA48fSK7ZokRHaSXlXRIRMPDOGFy+yPLlwy5YtcHZ2RrNmzTB48GB8/fXX+Prrr6HRaGBpaYmTJ0+iefPm8PLyQqlSpVL9Ep81axZmzZqV4Wvs27cvy0NbMTEx8PT0xKRJk5Idd3V1xalTpxLve3l5AZDhtFmzZqFw4cLo3bs3hgwZgsKFC2Pp0qVQSuH111+Hu7s7xo8fDwCYPXs2Nm3ahJUrV6JKlSp48OABbt68maU2GgvDEFEW1a2bakQoXWPHylLypGFJ+/Xjx5IntE6flrk16Xn0SAIRINXAT55Mu7epdGkZlsr2itPoaODvvyXFeXgANWsCGzbIYzVryjK+atWAH36AqlUbSxYD48bJEvp69WRYjCg/c3d3x+DBgwEAvXv3xocffohDhw6hU6dOsLCwwKNHj1CiRAk0SKfY1siRI9FfW84/HWW13c9Z8PTpU8THx6NU0jkCAEqVKoWAgIDE+5cuXUKxYsWwefNmODo6AgDat2+Pw4cP4/r16yj0Xzhs2rRpsucdOHAAb7zxBtq3bw8AqFixIlq3bp3ldhoDwxCRAbm4yC0zRo4EWrdO3dMUEAA8eZI8OB07BmzalP61kganVatkZVdaE8Odnf+b33T1KnDwoISf48eTD0n4+ckwhUYjt9u3ASsrREYCHw4FtD3gAwdK8Uyu0CO9K1hQemiM9dpZ4O3tjVOnTmH16tUAgMKFC6NXr15YtWoVOv1XsfbixYvpBiEAKF68eOLQkyGkrM2jlEp2zMvLCz179kwMQgDg5+cHNze3xCCkPZZ03lPPnj0xceJEXLx4EX379kX//v0N+j70iWGIyES0aSO3tCQdqQKA4cOlEGXK3iZ/fyAsLPlQnYeHVH9OT0AAUGrYMMDTE+sxGJcwFc5FwuFcvxScX62G0t0awjlYg2LF/uttsrLCv/8CffoAFy/K0N38+dILxhp5ZBAajdmkbHd3dzRt2hQ1atRIPDZ48GD069cPwcHBKFasGLy8vDIMQ4YaJnN0dISlpWWy3hxAtrNI2lt06dIlTJgwIdk5Xl5eGDVqVOL9qKgo3Lp1Cw21FXEBfPHFF+jZsyd27tyJxYsX48svv4SnpycqV66cpXYaA8MQkRlIGoQAWciV1v5xgKxwTzq3yM0NqFE5FgFeAfD3DkGAv0JApAMCrMsjPl4DR0cAvXoBjo7Y8/h/2OL1ChAG4O//bnPlOjY2MsfJ0VGGxHx8pLdqyxbgv15xonwtLi4O69atSzUnp0uXLihSpAg2bNiAUaNG4cqVK+jTp0+61zHUMJmNjQ2aNGkCDw+PZK/v4eGBXr16AZBJx76+vmjUqFHi4//++y+ePXuW7Ni1a9cQHx+fKtTVqFEDEyZMwJgxY+Dg4IDr168zDBFR7kuci+ntDWzfjp4eHuj599+ym66WRoOEA0fwvEFbCU5ffw0A6LMFKHs29aq64GDZWkXb4121qtQRqlyZG+4Sae3ZswePHz9G3bp1cfXq1WSPtWnTBu7u7hg1ahQSEhJw+fJlPHr0CIUKFYJDitoT2R0me/HiBe7cuZN438fHB15eXihevHjisvlx48ZhyJAhcHFxQcuWLbFy5Ur4+flh5MiRAKRXyMLCAvWT7Cvo5eWFokWLolKlSonHLl26hCpVqqDIf1Vu582bh1KlSqFp06awtLTEL7/8gmLFiqFVq1ZZfh/GwDBElFf8+6/MnC5aVO4fPAh8+aXu8QoVdF1KHTvCwtERKX/cDhggt5Sio2XeUtIeqtde0/cbIDJv7u7uAIDO6XXbArhw4QJmzpyJiRMnYuHChRg3bhy+//57vbz++fPnEycvAxJ8AGDo0KFYs2YNAGDAgAEICgrC9OnT4e/vj7p162Lv3r2oWLEiAAk5r7zySuKO8EDac5wuXbqUbIgsKioKs2bNgp+fHwoXLozWrVvj8OHDKJZ0PyUTxjpDL8E6Q2SyQkNlabuHhwSf27eBlSul0iQA3LoFTJigC0DVq3NSD5mFjGrHECWlrzpDFhk+akKCg4MxZMgQODg4wMHBAUOGDMHz588z/fwPP/wQGo0Gi1gAhczZ06ey6Vrr1jJm1bs3sHSpBCFLS8DXV3dujRrAzp3AJ5/I1wxCRERpMpthskGDBuHBgweJe7x88MEHGDJkCP7444+XPnfnzp34559/UEZb3Y7IHGirPT9/LpujARJ4ZsyQ5WWA9PZoe37at+e+F0RE2WAWYejGjRvYv38/zpw5g+bNmwMAfv75Z7Rs2RLe3t6oWbNmus99+PAhRo0alVgM6mWio6MRHR2deD80NDTnb4Aos54+BQ4d0hU89PMDWrYEtNVhixWTEthVqkgA+m+cn4iIss8swtDp06fh4OCQGIQAoEWLFnBwcMCpU6fSDUMJCQkYMmQIxo8fjzp16mTqtWbPno1p06bppd1EmTZ7NrBtm+zfkXQan42NbEMQH69bL/+S+iNERJQ1ZjFnKCAgAE5Jq8j9x8nJKVXxqKTmzp0LKysrjB49OtOvNXnyZISEhCTe7t+/n602E6VJKeDKFeCnn5KHnvPnAU9POVa3LvDZZ8C+fbI7/MGDRtqUjIgofzBqz9DUqVNf2gtz7tw5AKnLhwOpS4gn5enpiR9++AEXLlxI95y02NrawpZbbpM++fvrhr3++kuK9wBAx44y5weQSc69ewOdOun20SAiolxh1DA0atQoDBw4MMNzKlWqhMuXL+Px48epHnvy5EmqDee0Tpw4gcDAwMRCUwAQHx+Pzz//HIsWLYJv0lU3RIawYwfwzTey71dSBQoAbdsm3/+rQ4fcbRsRESUyahhydHRMthFcelq2bImQkBCcPXsWzf5bVfPPP/8gJCQk3eqWQ4YMSdwUT6tLly4YMmQIhg8fnvPGE2nFx8smXR4eQLdusmkYIENbV6/KkvYmTXSrvlq1Atj7SERkMsxiAnWtWrXQtWtXvP/++1ixYgUAWVrfvXv3ZJOnX3nlFcyePRt9+vRBiRIlUKJEiWTXsba2hrOzc4arz4gyxddXN/R16JDM7QGA8HBdGGrfXjbu6tgRSPG9SEREpsMswhAAbNiwAaNHj4arqysAoGfPnliyZEmyc7y9vRESEmKM5lF+8eCBDGndvp38uL29hJ/GjXXHihQBXrLZIhERGZ9ZrCYDZOO69evXIzQ0FKGhoVi/fj2Kavdg+o9SCsOGDUv3Gr6+vhg7dqxB20l5RGwscPIkMGUKMGeO7njp0kBQkAyBtWolj//9txzbuRPo29doTSYi03Hq1CloNBp07do1zcfHjh2L3r17526jMun48ePo0aMHypQpA41Gg507d6Y6Z9myZYlbYDRp0gQnTpzI/Ybqkdn0DBEZlFKyl5d26OvIESAsTB4rV04KHWo0EoL275ftLVjtmYjSsWrVKri5uWHbtm3w8/NLtpgHkJXSmSkEbAzh4eFo0KABhg8fjn79+qV6fMuWLRg7diyWLVuG1q1bY8WKFejWrRuuX7+e6n2aDUUZCgkJUQBUSEiIsZtChuTqqpREIt2tRAml+vdX6pdflIqNNXYLifKNyMhIdf36dRUZGZn8gRcv0r9l5dyIiMydm00vXrxQhQsXVv/884/q2rWrmjZtWuJjMTExytraWgFIvDVr1izbr2VoANSOHTuSHWvWrJkaOXJksmOvvPKKmjRpUuJ9Hx8fBUBt27ZNtWnTRtnZ2anGjRsrHx8fdeTIEdW0aVNVoEAB1a5dOxUUFJT4vPj4ePXtt9+qatWqKVtbW+Xk5KTefvvtdNuX7veKytrvb7MZJiPKsagomew8aZIMcUVF6R6rXVuqPXfoINWgz58HAgNlAvSIEYAVO1GJjK5w4fRvKXswnJzSP7dbt+TnVqqU9nnZtGXLFjg7O6NZs2YYPHgwVq9eDfVfkVVLS0ucPHkSAODl5QV/f38cOHAg2fNnzZqFwoULZ3gz1rBUTEwMPD09E+fvarm6uuKUdtsgyHsDZDht1qxZOH36NIKCgjBkyBDMnTsXS5cuxdGjR3HlyhW4u7snPm/27NnYuHEjVq5cCW9vb2zfvh3t2rUz+PviT3jKu7TVnrVDX8ePA5GRusf//ltWegHAV18B334LFCxonLYSUZ7h7u6OwYMHAwB69+6NDz/8EIcOHUKnTp1gYWGBR48eoUSJEmjQoEGazx85ciT6v2TxRdmyZfXe7sx4+vQp4uPjU9X4K1WqVLIdIS5duoRixYph8+bNiSV02rdvj8OHD+P69esoVKgQAKBp06bJnqfdR7R9+/YAgIoVK6J169aGflsMQ5THKCVzewDgxx+BlBPmS5fW1ftp1Eh3PBP1rojIyF68SP+xlFvWBAamf65FikERPRbh9fb2xqlTp7B69WoAQOHChdGrVy+sWrUqsfbdxYsX0w1CgCwYKl68eI7akdkdHlxcXLJ1/ZQ7O6gUO0J4eXmhZ8+eyWoJ+vn5wc3NLTEIaY8lnTvVs2dPTJw4ERcvXkTfvn3Rv3//HH8WmcEwZEyhoUBwcPqPOzlJtWJAfggEBaV/rqMjoP0GCw+X3c/TU6KErgs4IgJ48kT3mFJAQoLu5uQEaFfthYUBd+8mfzzprXJlQPvXyvPnwNmzaZ8XHy/DUrVqybnPngF//pn+dRs1Alq0kHODgwF399TnPHkiQ2ATJwJDhsi5bdtKT0/bthJ+XF3ldbOwPQsRmZAkv0SNdu5LuLu7o2nTpqhRo0biscGDB6Nfv34IDg5GsWLF4OXllWEYmjVrFma9ZEPmffv2oU2bNuk+ntkdHrLK0dERlpaWqfYFDQwMTNZbdOnSJUyYMCHZOV5eXhg1alTi/aioKNy6dQsNtbXZAHzxxRfo2bMndu7cicWLF+PLL7+Ep6cnKleunOW2ZslLZxXlcwadQP3jj6kn7Sa97dunO/eXXzI+d+tW3bmbNmV87rp1unN378743J9+0p3r4ZHxuQsW6M49dSrjc2fM0J3r5ZXxuUkm5anbtzM+N+lEu/h4paKicv7/iYhyVUaTYk1ZbGysKlWqlFq4cGGq446Ojmrx4sVKKaUqV66sVq9ene51goKC1O3btzO8RaScBG4gSGcC9UcffZTsWK1atRInUIeEhCiNRqPOnDmT+Livr68CoHx8fBKPnT9/Xmk0GhUaGprma0dFRSlbW1u1Z8+edNunrwnU7BkyJktLwM4u/ceTduXq89yk3ckWFqnPtbSU4xYWgLW17ridnQwzaR9LeUu61LxwYanEnN655crpznVwALp0Sfs8jQaoU0d3rr299PykPK9gQaB16+R7fFlYcNsLIso1e/bswePHj1G3bl1cTbEnYZs2beDu7o5Ro0YhISEBly9fxqNHj1CoUCE4pCjToY9hspx48eIF7ty5k3jfx8cHXl5eKF68OCpUqIBx48ZhyJAhcHFxQcuWLbFy5Ur4+flh5MiRAKRXyMLCAvXr10+8hpeXF4oWLZqsN+rSpUuoUqUKihQpAgCYN28eSpUqhaZNm8LS0hK//PILihUrlu62W3r10riUz3FpPRFR7jLXnqHu3bsnWzKf1s3T01P9+uuvqkyZMgqAGjdunLGbncqRI0fSbPvQoUMTz1m6dKmqWLGisrGxUY0bN1bHjh1LfGzx4sWqTp06ya45ZcoU1bZt22THRo8erfr165d4f9q0aapGjRrKzs5OOTo6ql69eqnr169n2FZ99QxplPpvvR+lKTQ0FA4ODggJCYG9vb2xm0NElOdFRUXBx8cnscIxUXoy+l7Jyu9v1hkiIiKifI1hiIiIiPI1hiEiIiLK1xiGiIiIKF9jGCIiIpPE9T30Mvr6HmEYIiIik2L9X32ziIgII7eETJ32e8Q6aU28bGDRRSIiMimWlpYoWrQoAv/bX6xgwYKp9sKi/E0phYiICAQGBqJo0aKwTLk3XRYxDBERkclxdnYGgMRARJSWokWLJn6v5ATDEBERmRyNRoPSpUvDyckJsbGxxm4OmSBra+sc9whpMQwREZHJsrS01NsvPKL0cAI1ERER5WsMQ0RERJSvMQwRERFRvsY5Qy+hLegUGhpq5JYQERFRZml/b2emMCPD0EuEhYUBAMqXL2/klhAREVFWhYWFwcHBIcNzNIr1zjOUkJCAR48eoUiRIpku+hUaGory5cvj/v37sLe3N3ALiZ937uLnnbv4eecuft65y5Cft1IKYWFhKFOmDCwsMp4VxJ6hl7CwsEC5cuWy9Vx7e3v+Y8pF/LxzFz/v3MXPO3fx885dhvq8X9YjpMUJ1ERERJSvMQwRERFRvsYwZAC2traYMmUKbG1tjd2UfIGfd+7i5527+HnnLn7euctUPm9OoCYiIqJ8jT1DRERElK8xDBEREVG+xjBERERE+RrDEBEREeVrDEMGsGzZMlSuXBl2dnZo0qQJTpw4Yewm5QnHjx9Hjx49UKZMGWg0GuzcuTPZ40opTJ06FWXKlEGBAgXQrl07XLt2zTiNzQNmz56Npk2bokiRInByckLv3r3h7e2d7Bx+5vqzfPly1K9fP7H4XMuWLbFv377Ex/lZG87s2bOh0WgwduzYxGP8vPVr6tSp0Gg0yW7Ozs6Jjxv782YY0rMtW7Zg7Nix+Oqrr3Dx4kW0adMG3bp1g5+fn7GbZvbCw8PRoEEDLFmyJM3H582bhwULFmDJkiU4d+4cnJ2d0blz58T95Shrjh07hk8++QRnzpyBh4cH4uLi4OrqivDw8MRz+JnrT7ly5TBnzhycP38e58+fR4cOHdCrV6/EXwj8rA3j3LlzWLlyJerXr5/sOD9v/atTpw78/f0Tb1euXEl8zOiftyK9atasmRo5cmSyY6+88oqaNGmSkVqUNwFQO3bsSLyfkJCgnJ2d1Zw5cxKPRUVFKQcHB/XTTz8ZoYV5T2BgoAKgjh07ppTiZ54bihUrpn755Rd+1gYSFhamqlevrjw8PFTbtm3VmDFjlFL83jaEKVOmqAYNGqT5mCl83uwZ0qOYmBh4enrC1dU12XFXV1ecOnXKSK3KH3x8fBAQEJDss7e1tUXbtm352etJSEgIAKB48eIA+JkbUnx8PDZv3ozw8HC0bNmSn7WBfPLJJ3jjjTfQqVOnZMf5eRvG7du3UaZMGVSuXBkDBw7EvXv3AJjG582NWvXo6dOniI+PR6lSpZIdL1WqFAICAozUqvxB+/mm9dn/+++/xmhSnqKUwrhx4/Dqq6+ibt26APiZG8KVK1fQsmVLREVFoXDhwtixYwdq166d+AuBn7X+bN68GRcuXMC5c+dSPcbvbf1r3rw51q1bhxo1auDx48eYOXMmWrVqhWvXrpnE580wZAAajSbZfaVUqmNkGPzsDWPUqFG4fPkyTp48meoxfub6U7NmTXh5eeH58+fYtm0bhg4dimPHjiU+zs9aP+7fv48xY8bg4MGDsLOzS/c8ft76061bt8Sv69Wrh5YtW6Jq1apYu3YtWrRoAcC4nzeHyfTI0dERlpaWqXqBAgMDUyVe0i/tqgR+9vr36aefYvfu3Thy5AjKlSuXeJyfuf7Z2NigWrVqcHFxwezZs9GgQQP88MMP/Kz1zNPTE4GBgWjSpAmsrKxgZWWFY8eO4ccff4SVlVXiZ8rP23AKFSqEevXq4fbt2ybx/c0wpEc2NjZo0qQJPDw8kh338PBAq1atjNSq/KFy5cpwdnZO9tnHxMTg2LFj/OyzSSmFUaNGYfv27Th8+DAqV66c7HF+5oanlEJ0dDQ/az3r2LEjrly5Ai8vr8Sbi4sLBg8eDC8vL1SpUoWft4FFR0fjxo0bKF26tGl8f+fKNO18ZPPmzcra2lq5u7ur69evq7Fjx6pChQopX19fYzfN7IWFhamLFy+qixcvKgBqwYIF6uLFi+rff/9VSik1Z84c5eDgoLZv366uXLmi3NzcVOnSpVVoaKiRW26ePvroI+Xg4KCOHj2q/P39E28RERGJ5/Az15/Jkyer48ePKx8fH3X58mX15ZdfKgsLC3Xw4EGlFD9rQ0u6mkwpft769vnnn6ujR4+qe/fuqTNnzqju3burIkWKJP5uNPbnzTBkAEuXLlUVK1ZUNjY2qnHjxolLkSlnjhw5ogCkug0dOlQpJcszp0yZopydnZWtra167bXX1JUrV4zbaDOW1mcNQK1evTrxHH7m+vPuu+8m/twoWbKk6tixY2IQUoqftaGlDEP8vPVrwIABqnTp0sra2lqVKVNG9e3bV127di3xcWN/3hqllMqdPigiIiIi08M5Q0RERJSvMQwRERFRvsYwRERERPkawxARERHlawxDRERElK8xDBEREVG+xjBERERE+RrDEBEREeVrDENERESUrzEMERERUb7GMEREeVpQUBCcnJzg6+ur1+u++eabWLBggV6vSUTGwTBERHna7Nmz0aNHD1SqVCnZ8ddeew0jRoxIdf6yZctQsGBBxMfHZ3jdb775Bt9++y1CQ0P12VwiMgKGISLKsyIjI+Hu7o733nsv2XGlFLy8vNCkSZNUz/H09ESDBg1gaWmZ4bXr16+PSpUqYcOGDXptMxHlPoYhIjJ7J0+eRLNmzWBnZwdHR0csXLgQALBv3z5YWVmhZcuWyc6/ffs2wsLC0g1D2uOzZs2CRqNJddMOj/Xs2RObNm0y8LsjIkNjGCIis7Z371706dMHH3/8MS5fvowPP/wQ48aNw927d3H8+HG4uLikeo6npycsLS1Rv379ZMejo6Nx7dq1xDD06aefwt/fP/H20UcfoWLFiujfvz8AoFmzZjh79iyio6MN/0aJyGAYhojIbEVFReHDDz/EDz/8gGHDhqFGjRqYPn06ihQpgmPHjsHX1xdlypRJ9bwLFy4gPj4eBQsWTNbjY2dnh7i4uMQwVKRIETg7O8PZ2RkrVqzA3r17cezYMZQrVw4AULZsWURHRyMgICBX3zcR6ZeVsRtARJRdhw8fRmRkJAYMGJB4zNLSElZWVrC1tUVkZCTs7OxSPc/T0xN9+vTBN998k+z477//jgULFqB27drJjk+bNg2rV6/GsWPHULFixcTjBQoUAABERETo820RUS5jzxARma0jR46kmuzs4+OD4OBgNG7cGI6OjggODk71vIsXL6Jdu3Zo2LBhstuzZ89Qv359WFnp/k5MLwgBwLNnzwAAJUuWNNA7JKLcwDBERGbr4sWLiImJSXZs8eLFaNy4MWrVqoVGjRrh+vXryR6/d+8enj9/jsaNG6e63oULF5JNqs4oCAHA1atXUa5cOTg6OurpHRGRMTAMEZHZ8vLywrVr17B27VrcunULM2fOxPLly/Hzzz8DALp06YJr164l6x3y9PSEhYUFGjZsmOxacXFxuHz5cmIYmjlzJpYsWYItW7bA1tYWAQEBCAgISDZZ+sSJE3B1dTX8GyUig2IYIiKz5Ofnh6CgIKxfvx7fffcd6tWrh127dmH//v2JvT716tWDi4sLfvvtt8TnXbhwAdWrV0fhwoWTXe/atWuIiopC48aNoZTC/Pnz8fTpU7Ro0QKlS5dOvHl5eQGQyds7duzA+++/n2vvmYgMQ6OUUsZuBBFRVu3evRvDhw9HUFBQhuft3bsXX3zxBa5evQoLC/39/bd06VLs2rULBw8e1Ns1icg4uJqMiMzSxYsXUa9evZee9/rrr+P27dt4+PAhypcvr7fXt7a2xuLFi/V2PSIyHvYMEZFZ6t27NypUqIAff/zR2E0hIjPHMERERET5GidQExERUb7GMERERET5GsMQERER5WsMQ0RERJSvMQwRERFRvsYwRERERPkawxARERHlawxDRERElK8xDBEREVG+xjBERERE+dr/AW2f8+mhtHPsAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -2817,7 +2722,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -2883,7 +2788,7 @@ "outputs": [], "source": [ "# Simulation\n", - "def run_triplet_protocol_simulation(neuron_model_name, synapse_model_name,\n", + "def run_triplet_protocol_simulation(module_name, neuron_model_name, synapse_model_name,\n", " neuron_opts, nest_syn_opts,\n", " spike_delays, n_triplets = 1, triplet_delay = 1000,\n", " pre_post_pre=True):\n", @@ -2898,11 +2803,11 @@ " sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 10. + 3 * syn_opts[\"delay\"]\n", "\n", " print('Simulating for (delta_t1, delta_t2) = ({}, {})'.format(_delays[0], _delays[1]))\n", - " dw = run_triplet_stdp_network(neuron_model_name, synapse_model_name,\n", - " neuron_opts, nest_syn_opts,\n", - " pre_spike_times=pre_spike_times,\n", - " post_spike_times=post_spike_times,\n", - " sim_time=sim_time)\n", + " dw = run_triplet_stdp_network(module_name, neuron_model_name, synapse_model_name,\n", + " neuron_opts, nest_syn_opts,\n", + " pre_spike_times=pre_spike_times,\n", + " post_spike_times=post_spike_times,\n", + " sim_time=sim_time)\n", " dw_vec.append(dw)\n", " \n", " return dw_vec" @@ -3014,345 +2919,299 @@ "text": [ "Simulating for (delta_t1, delta_t2) = (5, -5)\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "Initial weight: 1.0, Updated weight: 1.0003735276417982\n", + "Simulating for (delta_t1, delta_t2) = (10, -10)\n", + "\n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5Initial weight: 1.0, Updated weight: 1.0003735276417982\n", - "Simulating for (delta_t1, delta_t2) = (10, -10)\n", - "Initial weight: 1.0, Updated weight: 0.9998230228609227\n", - "Simulating for (delta_t1, delta_t2) = (15, -5)\n", - "e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 24\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbbInitial weight: 1.0, Updated weight: 0.9984719712644969\n", - "Simulating for (delta_t1, delta_t2) = (5, -15)\n", - "6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 0.9998230228609227\n", + "Simulating for (delta_t1, delta_t2) = (15, -5)\n", "\n", - "Oct 19 05:02:14 NodeManager::preparInitial weight: 1.0, Updated weight: 1.001383086591746\n", - "Simulating for (delta_t1, delta_t2) = (5, -5)\n", - "e_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 34\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal statInitial weight: 1.0, Updated weight: 1.0005542494412774\n", - "Simulating for (delta_t1, delta_t2) = (10, -10)\n", - "e and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", + "Initial weight: 1.0, Updated weight: 0.9984719712644969\n", + "Simulating for (delta_t1, delta_t2) = (5, -15)\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 34\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", - " DeInitial weight: 1.0, Updated weight: 1.0000931206450185\n", - "Simulating for (delta_t1, delta_t2) = (15, -5)\n", - "fault for delta_tau changed from 0.5 to 5 ms\n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", + " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", + " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + " model have been resetInitial weight: 1.0, Updated weight: 1.001383086591746\n", + "Simulating for (delta_t1, delta_t2) = (5, -5)\n", + "!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aeaInitial weight: 1.0, Updated weight: 0.9991105337807658\n", - "Simulating for (delta_t1, delta_t2) = (5, -15)\n", - "Initial weight: 1.0, Updated weight: 1.0012383200640604\n", - "3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 34\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", - " Default for delta_tau changed from 0.5 to 5 ms\n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", - " Default for delta_tau changed from 0.1 to 1 ms\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", + " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", + " Default fInitial weight: 1.0, Updated weight: 1.0005542494412774\n", + "or delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 24\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "Simulating for (delta_t1, delta_t2) = (10, -10)\n", + "\n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.0000931206450185\n", + "Simulating for (delta_t1, delta_t2) = (15, -5)\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 34\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", + "Initial weight: 1.0, Updated weight: 0.9991105337807658\n", + "Simulating for (delta_t1, delta_t2) = (5, -15)\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "\n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 34\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:40 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:40 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:40 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:40 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:40 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.0012383200640604\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:40 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:40 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 34\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:40 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] } @@ -3361,18 +3220,18 @@ "pre_post_pre_delays = [(5, -5), (10, -10), (15, -5), (5, -15)]\n", "\n", "# All-to-All interation\n", - "dw_vec = run_triplet_protocol_simulation(neuron_model_name, synapse_model_name,\n", + "dw_vec = run_triplet_protocol_simulation(module_name, neuron_model_name, synapse_model_name,\n", " neuron_opts, nest_syn_opts,\n", " pre_post_pre_delays,\n", " n_triplets=1,\n", " triplet_delay=1000)\n", "\n", "# Nearest spike interaction\n", - "dw_vec_nn = run_triplet_protocol_simulation(neuron_model_name_nn, synapse_model_name_nn,\n", - " neuron_opts_nn, nest_syn_opts_nn,\n", - " pre_post_pre_delays,\n", - " n_triplets=1,\n", - " triplet_delay=1000)" + "dw_vec_nn = run_triplet_protocol_simulation(module_name_nn, neuron_model_name_nn, synapse_model_name_nn,\n", + " neuron_opts_nn, nest_syn_opts_nn,\n", + " pre_post_pre_delays,\n", + " n_triplets=1,\n", + " triplet_delay=1000)" ] }, { @@ -3383,7 +3242,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3427,339 +3286,299 @@ "text": [ "Simulating for (delta_t1, delta_t2) = (-5, 5)\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5Initial weight: 1.0, Updated weight: 1.0452168105331474\n", - "Simulating for (delta_t1, delta_t2) = (-10, 10)\n", - "e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.0452168105331474\n", + "Simulating for (delta_t1, delta_t2) = (-10, 10)\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 9114\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bInitial weight: 1.0, Updated weight: 1.0275785817728278\n", - "bb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepSimulating for (delta_t1, delta_t2) = (-5, 15)\n", - "are_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 9214\n", - " Number of OpenMP threads: 1\n", + " Number of OpenMP thrInitial weight: 1.0, Updated weight: 1.0275785817728278\n", + "Simulating for (delta_t1, delta_t2) = (-5, 15)\n", + "eads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", + "\n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:41 SimulationManager::start_updaInitial weight: 1.0, Updated weight: 1.008936270857372\n", + "ting_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 9214\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", - "Initial weight: 1.0, Updated weight: 1.008936270857372\n", - "Simulating for (delta_t1, delta_t2) = (-15, 5)\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "Simulating for (delta_t1, delta_t2) = (-15, 5)\n", + "\n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_3c70a8ed53be4c46854ae5b0aa248ab9_module\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 9214\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "Initial weight: 1.0, Updated weight: 1.050539844879153\n", "Simulating for (delta_t1, delta_t2) = (-5, 5)\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5Initial weight: 1.0, Updated weight: 1.048644757755009\n", - "e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 9114\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", + "Initial weight: 1.0, Updated weight: 1.048644757755009\n", "Simulating for (delta_t1, delta_t2) = (-10, 10)\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", + "Initial weight: 1.0, Updated weight: 1.026345906763637\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 9214\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", - "Initial weight: 1.0, Updated weight: 1.026345906763637\n", "Simulating for (delta_t1, delta_t2) = (-5, 15)\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", - " Number of local nodes: 6Initial weight: 1.0, Updated weight: 1.0099778920748412\n", - "Simulating for (delta_t1, delta_t2) = (-15, 5)\n", - "\n", + "Apr 19 12:03:41 SimulationManager::start_updating_ [Info]: \n", + " Number of local nodes: 6\n", " Simulation time (ms): 9214\n", - " Number of OpenMP threads: 1\n", + " Number of OpenMP Initial weight: 1.0, Updated weight: 1.0099778920748412\n", + "Simulating for (delta_t1, delta_t2) = (-15, 5)\n", + "threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n", "\n", - "Oct 19 05:02:14 correlation_detector [Info]: \n", + "Apr 19 12:03:41 Install [Info]: \n", + " loaded module nestml_4940100b28f446be95429cd7b36f439e_module\n", + "\n", + "Apr 19 12:03:41 correlation_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlomatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlomatrix_detector [Info]: \n", " Default for delta_tau changed from 0.5 to 5 ms\n", "\n", - "Oct 19 05:02:14 correlospinmatrix_detector [Info]: \n", + "Apr 19 12:03:41 correlospinmatrix_detector [Info]: \n", " Default for delta_tau changed from 0.1 to 1 ms\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_deltab1d27d2bd4604585bbb6bb0e9e18dc93_neuron_nestml__with_stdp_tripletb1d27d2bd4604585bbb6bb0e9e18dc93_synapse_nestml [Warning]: \n", + "Apr 19 12:03:41 iaf_psc_delta_neuron_nestml__with_stdp_triplet_nn_synapse_nestml [Warning]: \n", " Simulation resolution has changed. Internal state and parameters of the \n", " model have been reset!\n", "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml [Warning]: \n", - " Initial weight: 1.0, Updated weight: 1.0466078732990223\n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 iaf_psc_delta82892aea3e8a4a86adc97b5e80f33e7c_neuron_nestml__with_stdp_triplet_nn82892aea3e8a4a86adc97b5e80f33e7c_synapse_nestml [Warning]: \n", - " Simulation resolution has changed. Internal state and parameters of the \n", - " model have been reset!\n", - "\n", - "Oct 19 05:02:14 SimulationManager::set_status [Info]: \n", + "Apr 19 12:03:41 SimulationManager::set_status [Info]: \n", " Temporal resolution changed from 0.1 to 1 ms.\n", + "Initial weight: 1.0, Updated weight: 1.0466078732990223\n", "\n", - "Oct 19 05:02:14 NodeManager::prepare_nodes [Info]: \n", + "Apr 19 12:03:41 NodeManager::prepare_nodes [Info]: \n", " Preparing 6 nodes for simulation.\n", "\n", - "Oct 19 05:02:14 SimulationManager::start_updating_ [Info]: \n", + "Apr 19 12:03:41 SimulationManager::start_updating_ [Info]: \n", " Number of local nodes: 6\n", " Simulation time (ms): 9214\n", " Number of OpenMP threads: 1\n", " Not using MPI\n", "\n", - "Oct 19 05:02:14 SimulationManager::run [Info]: \n", + "Apr 19 12:03:41 SimulationManager::run [Info]: \n", " Simulation finished.\n" ] } @@ -3768,20 +3587,20 @@ "post_pre_post_delays = [(-5, 5), (-10, 10), (-5, 15), (-15, 5)]\n", "\n", "# All-to-All interaction\n", - "dw_vec = run_triplet_protocol_simulation(neuron_model_name, synapse_model_name,\n", - " neuron_opts, nest_syn_opts,\n", - " post_pre_post_delays,\n", - " n_triplets=10,\n", - " triplet_delay=1000,\n", - " pre_post_pre=False)\n", + "dw_vec = run_triplet_protocol_simulation(module_name, neuron_model_name, synapse_model_name,\n", + " neuron_opts, nest_syn_opts,\n", + " post_pre_post_delays,\n", + " n_triplets=10,\n", + " triplet_delay=1000,\n", + " pre_post_pre=False)\n", "\n", "# Nearest spike interaction\n", - "dw_vec_nn = run_triplet_protocol_simulation(neuron_model_name_nn, synapse_model_name_nn,\n", - " neuron_opts_nn, nest_syn_opts_nn,\n", - " post_pre_post_delays,\n", - " n_triplets=10,\n", - " triplet_delay=1000,\n", - " pre_post_pre=False)" + "dw_vec_nn = run_triplet_protocol_simulation(module_name_nn, neuron_model_name_nn, synapse_model_name_nn,\n", + " neuron_opts_nn, nest_syn_opts_nn,\n", + " post_pre_post_delays,\n", + " n_triplets=10,\n", + " triplet_delay=1000,\n", + " pre_post_pre=False)" ] }, { @@ -3792,7 +3611,7 @@ "outputs": [ { "data": { - "image/png": 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w2PsAANP5HICioqK0bNkyHTlyRE888YQ2bdqkkSNH6mc/+5mOHDnSHWMEAADoUj7/GOrp06d1/Phx9+PYsWN69913tWbNGjU3N6ulpaW7xnrF8GOo3T0AfgzV9jnALgBsZfcxQOqb7wW+vH/7+brygQMHKjg4WEOHDtXgwYMVHBys4OBg3XPPPd0SFgAAALqazwHoBz/4gQoLC3XHHXfoJz/5icaOHdsd4wIAAOg2Pl8DlJ+frwMHDmjgwIGaPn26Zs+erV27dnXH2AAAALpFh26EOHLkSD399NOqqqrSrFmz9G//9m+aMGGCXnnlla4eHwAAQJfz+SLo3/3udzpx4oS++uor9/82NjbqnXfe0cmTJ9Xa2tpdY71iuAi6uwfAFbi2zwF2AWAru48BUt98L+jWi6A3bdqkIUOGKDQ0VEOGDNHIkSMVFxenu+66S6GhoR0eNIAryO6jLwkMgM18DkAul+uSz5WVlXVmLAAAAFdEp38MtbGxUatXr5bT6VR8fHxXjAkAAKBbdTgAvfPOO3rwwQc1fPhwLVu2TKNGjZKPlxMBAADYwqcA9Nlnn+lXv/qVxowZo9mzZ8uyLG3evFk1NTV64oknumuMAAAAXard1wClpqZq165dmjlzppYvX660tDQNHDjQ/bzD7osqAQAA2qndAejtt9/W/fffr8zMTK71AQAAvVq7PwJ77733NGDAAM2cOVPjxo3T8uXL9be//a07xwYAANAt2h2AEhIS9PLLL6u2tlaLFi1SYWGhxo0bp+nTp+vFF1/UF1980Z3jBAAA6DI+3wn6Hx06dEh5eXl69dVX9cUXX8jhcHAn6Haw+3Kpvnj3z96GOcAcgNnsPgZIffM44Mv7d6fuAzRu3DitWLFCn332mbZu3ao777yzM6sDAAC4Ijp1Bqiv4gxQdw+AKcccYA7AbHYfA6S+eRy4YmeAAAAAeiMCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxrE9AK1evVqxsbEKCgqS0+nU3r17L1u/e/duOZ1OBQUFafTo0crNzfV4/uWXX1ZSUpJCQ0MVGhqq22+/XX/+85+7cxMAAEAvY2sAys/PV2ZmppYuXarS0lIlJSVp1qxZqqqqarO+srJSqampSkpKUmlpqZYsWaIFCxZoy5Yt7pri4mLdd9992rVrl1wul6655holJyfr6NGjV2qzAABAD+ewLMuyq/Np06Zp8uTJWrNmjbtt/PjxSktLU05Ojlf9okWLtH37dlVUVLjbMjIyVF5eLpfL1WYfra2tCg0N1cqVK/XQQw+1a1xNTU0KCQlRY2OjgoODfdyqb+ZwdPkqfWLJ7gHYNuV6DOYAcwBms/sYIPXN44Av79+2nQFqaWnR/v37lZyc7NGenJyskpKSNpdxuVxe9SkpKdq3b5/OnDnT5jKnTp3SmTNndPXVV19yLM3NzWpqavJ4AACAvsu2AFRfX6/W1lZFRER4tEdERKi2trbNZWpra9usP3v2rOrr69tc5rHHHtOIESN0++23X3IsOTk5CgkJcT+io6N93BoAANCb2H4RtOOi84CWZXm1fVN9W+2StGLFCm3cuFFbt25VUFDQJde5ePFiNTY2uh/V1dW+bAIAAOhl/OzqODw8XP379/c621NXV+d1lueCyMjINuv9/PwUFhbm0f7ss8/qqaee0s6dO3XjjTdediyBgYEKDAzswFYAAIDeyLYzQAEBAXI6nSoqKvJoLyoqUmJiYpvLJCQkeNUXFhYqPj5e/v7+7rZf//rX+uUvf6m3335b8fHxXT94AADQq9n6EVh2drbWrl2rdevWqaKiQllZWaqqqlJGRoak8x9N/eM3tzIyMnTkyBFlZ2eroqJC69atU15enhYuXOiuWbFihX7+859r3bp1GjVqlGpra1VbW6uvvvrqim8fAADomWz7CEyS0tPT1dDQoOXLl6umpkZxcXEqKChQTEyMJKmmpsbjnkCxsbEqKChQVlaWVq1apaioKL3wwguaM2eOu2b16tVqaWnRvffe69HX448/rmXLll2R7QIAAD2brfcB6qm4D1B3D4ApxxxgDsBsdh8DpL55HOgV9wECAACwCwEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABjH9gC0evVqxcbGKigoSE6nU3v37r1s/e7du+V0OhUUFKTRo0crNzfX4/mPPvpIc+bM0ahRo+RwOPT888934+gBAEBvZGsAys/PV2ZmppYuXarS0lIlJSVp1qxZqqqqarO+srJSqampSkpKUmlpqZYsWaIFCxZoy5Yt7ppTp05p9OjRevrppxUZGXmlNgUAAPQiDsuyLLs6nzZtmiZPnqw1a9a428aPH6+0tDTl5OR41S9atEjbt29XRUWFuy0jI0Pl5eVyuVxe9aNGjVJmZqYyMzN9GldTU5NCQkLU2Nio4OBgn5ZtD4ejy1fpE0t2D8C2KddjMAeYAzCb3ccAqW8eB3x5/7btDFBLS4v279+v5ORkj/bk5GSVlJS0uYzL5fKqT0lJ0b59+3TmzJkOj6W5uVlNTU0eDwAA0HfZFoDq6+vV2tqqiIgIj/aIiAjV1ta2uUxtbW2b9WfPnlV9fX2Hx5KTk6OQkBD3Izo6usPrAoBeweGw9wHYzPaLoB0XvRAsy/Jq+6b6ttp9sXjxYjU2Nrof1dXVHV4XAADo+fzs6jg8PFz9+/f3OttTV1fndZbngsjIyDbr/fz8FBYW1uGxBAYGKjAwsMPLAwCA3sW2M0ABAQFyOp0qKiryaC8qKlJiYmKbyyQkJHjVFxYWKj4+Xv7+/t02VgAA0LfY+hFYdna21q5dq3Xr1qmiokJZWVmqqqpSRkaGpPMfTT300EPu+oyMDB05ckTZ2dmqqKjQunXrlJeXp4ULF7prWlpaVFZWprKyMrW0tOjo0aMqKyvT3/72tyu+fQAAoIeybLZq1SorJibGCggIsCZPnmzt3r3b/dzDDz9s3XLLLR71xcXF1qRJk6yAgABr1KhR1po1azyer6ystCR5PS5ez+U0NjZakqzGxsbObNolnf/un30P+wcA23eB7QOA7ZgDtrL7z99XjwO+vH/beh+gnor7AHX3AJhyzAHmgO1sn4RmzwG7//xS3zwO9Ir7AAEAANjFtm+BAYCpesb/+wfMxhkgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDgEIAAAYBwCEAAAMA4BCAAAGIcABAAAjEMAAgAAxiEAAQAA4xCAAACAcQhAAADAOAQgAABgHAIQAAAwDgEIAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAc2wPQ6tWrFRsbq6CgIDmdTu3du/ey9bt375bT6VRQUJBGjx6t3Nxcr5otW7bo+uuvV2BgoK6//npt27atu4YPAAB6IVsDUH5+vjIzM7V06VKVlpYqKSlJs2bNUlVVVZv1lZWVSk1NVVJSkkpLS7VkyRItWLBAW7Zscde4XC6lp6dr7ty5Ki8v19y5c/VP//RP+r//+78rtVkAAKCHc1iWZdnV+bRp0zR58mStWbPG3TZ+/HilpaUpJyfHq37RokXavn27Kioq3G0ZGRkqLy+Xy+WSJKWnp6upqUlvvfWWu+aOO+5QaGioNm7c2K5xNTU1KSQkRI2NjQoODu7o5l2Sw9Hlq/SJJbsHYNuU6zGYA2bPAbv3v8QcsBtzQN0yB3x5//br8t7bqaWlRfv379djjz3m0Z6cnKySkpI2l3G5XEpOTvZoS0lJUV5ens6cOSN/f3+5XC5lZWV51Tz//POXHEtzc7Oam5vd/25sbJR0/g/ZF9m+VX3079qb2L4HmAO2s30PMAdsZ/se6IY5cOF9uz3ndmwLQPX19WptbVVERIRHe0REhGpra9tcpra2ts36s2fPqr6+XsOHD79kzaXWKUk5OTl64oknvNqjo6Pbuzm9SojtA7B9BMazfQ8wB2xn+x5gDtjO9j3QjXPgxIkTCvmG9dsWgC5wXHQe0LIsr7Zvqr+43dd1Ll68WNnZ2e5/nzt3TseOHVNYWNhllzNRU1OToqOjVV1d3S0fD6LnYw6AOYCeOgcsy9KJEycUFRX1jbW2BaDw8HD179/f68xMXV2d1xmcCyIjI9us9/PzU1hY2GVrLrVOSQoMDFRgYKBH25AhQ9q7KUYKDg7uUZMeVx5zAMwB9MQ58E1nfi6w7VtgAQEBcjqdKioq8mgvKipSYmJim8skJCR41RcWFio+Pl7+/v6XrbnUOgEAgHls/QgsOztbc+fOVXx8vBISEvTSSy+pqqpKGRkZks5/NHX06FFt2LBB0vlvfK1cuVLZ2dmaP3++XC6X8vLyPL7d9ZOf/EQ333yznnnmGd1zzz364x//qJ07d+rdd9+1ZRsBAEDPY2sASk9PV0NDg5YvX66amhrFxcWpoKBAMTExkqSamhqPewLFxsaqoKBAWVlZWrVqlaKiovTCCy9ozpw57prExERt2rRJP//5z/WLX/xCY8aMUX5+vqZNm3bFt68vCgwM1OOPP+71kSHMwRwAcwB9YQ7Yeh8gAAAAO9j+UxgAAABXGgEIAAAYhwAEAACMQwAyRENDg4YNG6bDhw/bPRRJ0pQpU7R161a7h9Fn9LT92xXq6uo0dOhQHT161O6h9Eq9bU40Nzfrmmuu0f79++0eSp/BHLg8ApAhcnJydPfdd2vUqFGXrDl8+LAcDofX4+233/apr2XLlnmtIzIy0qPmF7/4hR577DGdO3euI5uDi7Rn/3799dd65JFHdMMNN8jPz09paWlt1u3evVtOp1NBQUEaPXq0cnNzfR7P1q1blZKSovDwcDkcDpWVlXnVNDc36z/+4z8UHh6ugQMHavbs2frss8/czw8bNkxz587V448/7nP/uLKv+T179ujuu+9WVFSUHA6H3njjDa+aRx55xKuf6dOnu58PDAzUwoULtWjRIp/6xqV11XGhuLi4zXnyl7/8xafx9LQ5QAAywOnTp5WXl6cf/vCH7arfuXOnampq3I+ZM2f63Oe3v/1tj3UcOHDA4/k777xTjY2N2rFjh8/rhqf27t/W1lYNGDBACxYs0O23395mTWVlpVJTU5WUlKTS0lItWbJECxYs0JYtW3wa08mTJ3XTTTfp6aefvmRNZmamtm3bpk2bNundd9/VV199pbvuukutra3umnnz5un111/X8ePHferfdFf6NX/y5ElNmDBBK1euvGzdHXfc4dFPQUGBx/MPPPCA9u7dq4qKCp/6h7euPC5ccOjQIY/9961vfcvncfWkOWD7b4Gh+7311lvy8/NTQkJCu+rDwsK8ztj4ys/P77Lr6N+/v1JTU7Vx40bNmjWrU32Zrr37d+DAgVqzZo0k6b333tOXX37pVZObm6trrrlGzz//vCRp/Pjx2rdvn5599lmP+219k7lz50rSJU+9NzY2Ki8vT6+++qr7oPvaa68pOjpaO3fuVEpKiiTphhtuUGRkpLZt26Z/+Zd/aXf/prvSr/lZs2a163UcGBh42X7CwsKUmJiojRs3avny5R0eD7r2uHDBsGHDOv0zUT1pDnAGyAB79uxRfHx8u+tnz56tYcOG6aabbtLmzZs71Ocnn3yiqKgoxcbG6p//+Z/16aefetVMnTpVe/fu7dD68Xe+7t/LcblcSk5O9mhLSUnRvn37dObMmS7pQ5L279+vM2fOePQVFRWluLg4lZSUeNQyT3xnx2u+PYqLizVs2DBde+21mj9/vurq6rxq2N9doyuPCxdMmjRJw4cP12233aZdu3Z1aB09aQ4QgAxw+PDhdv0y7qBBg/Tcc89p8+bNKigo0G233ab09HS99tprPvU3bdo0bdiwQTt27NDLL7+s2tpaJSYmqqGhwaNuxIgRqqqq4jqgTmrv/m2P2tparx8OjoiI0NmzZ1VfX98lfVzoJyAgQKGhoV59XfxjxiNGjOg1F3H2FFf6Nd8es2bN0uuvv6533nlHv/nNb/T+++9r5syZam5u9qhjf3eNrjwuDB8+XC+99JK2bNmirVu3aty4cbrtttu0Z88en9bT0+YAH4EZ4PTp0woKCvJo+/a3v60jR45IkpKSkvTWW28pPDxcWVlZ7pr4+HgdP35cK1as0IMPPtju/v7xVPgNN9yghIQEjRkzRv/1X/+l7Oxs93MDBgzQuXPn1NzcrAEDBnR084zX3v3bXg6Hw+PfF24Wf3F7d7Asy6ufAQMG6NSpU93ed19ypV/z7ZGenu7+77i4OMXHxysmJkZvvvmmvv/977ufY393ja48LowbN07jxo1z/zshIUHV1dV69tlndfPNN7d7TD1tDhCADBAeHu51EWlBQYH7I43LhY/p06dr7dq1nep/4MCBuuGGG/TJJ594tB87dkxXXXUV4aeTOrN/LxYZGel1Bqaurk5+fn4KCwvr/GD/oZ+WlhYdP37c4yxQXV2dEhMTPWqPHTumoUOHdlnfJrD7Nd8ew4cPV0xMTJvHBfZ353XlcaEt06dP7/SZQrvnAB+BGWDSpEn6+OOPPdpiYmI0duxYjR07ViNGjLjksqWlpRo+fHin+m9ublZFRYXXeg4ePKjJkyd3at3o3P69WEJCgoqKijzaCgsLFR8fL39//y4ZryQ5nU75+/t79FVTU6ODBw96BaCDBw9q0qRJXda3Cex+zbdHQ0ODqqur2zwusL87ryuPC23pinli+xyw0Od9+OGHlp+fn3Xs2LHL1q1fv956/fXXrY8//tj6y1/+Yv3617+2/P39reeee86n/n76059axcXF1qeffmr97//+r3XXXXdZgwcPtg4fPuxRd8stt1jLly/3eXvgqb3717Is66OPPrJKS0utu+++25oxY4ZVWlpqlZaWup//9NNPrauuusrKysqyPv74YysvL8/y9/e3Nm/e7NOYGhoarNLSUuvNN9+0JFmbNm2ySktLrZqaGndNRkaGNXLkSGvnzp3WBx98YM2cOdOaMGGCdfbsWXfNyZMnrQEDBlh79uzxqX/TXenX/IkTJ9xzSZL13HPPWaWlpdaRI0fcz//0pz+1SkpKrMrKSmvXrl1WQkKCNWLECKupqcljXTExMdaGDRt822B46crjwm9/+1tr27Zt1l//+lfr4MGD1mOPPWZJsrZs2dLu8fTEOUAAMsT06dOt3Nzcy9asX7/eGj9+vHXVVVdZgwcPtpxOp/Xqq6961FRWVlqSrF27dl1yPenp6dbw4cMtf39/Kyoqyvr+979vffTRRx41n332meXv729VV1d3eJvwd+3Zv5Z1/sAiyevxj4qLi61JkyZZAQEB1qhRo6w1a9Z4PL9r1y5LklVZWXnJfl555ZU2+3n88cfdNadPn7Z+/OMfW1dffbU1YMAA66677rKqqqo81vP73//eGjdu3Df/AeDlSr7mL8yJix8PP/ywZVmWderUKSs5OdkaOnSo5e/vb11zzTXWww8/7LW/S0pKrCFDhlinTp3q0DbDU1cdF5555hlrzJgxVlBQkBUaGmp95zvfsd58802PdXzTcaEnzgECkCHefPNNa/z48VZra2un1rNr1y5ryJAh7fp/FZezcOFCa/78+Z1aB/6uq/Zve7zyyivW2LFjrZaWlm7va8qUKdbrr7/e7f30RT3tNd8e9957r/Xkk092ez+m6I3HhSs5B7gI2hCpqan65JNPdPToUUVHR3d4PW+//baWLFni9fVlXw0bNkwLFy7s1Drwd121f9vj7bff1lNPPdWl1wS1pa6uTvfee6/uu+++bu2nr+ppr/lv0tzcrAkTJnh8Kw2d09uOC1d6Djgs6/9/xxUAAMAQfAsMAAAYhwAEAACMQwACAADGIQABAADjEIAAAIBxCEAAAMA4BCAAAGAcAhAAADAOAQhApzQ0NGjYsGE6fPiw3UPpUvfee6+ee+65y9bMmDFDDodDDodDZWVl3TaWRx55xN3PG2+80W39ACYhAAHolJycHN19990aNWqUR3tJSYkcDofuuOOONpfLzMxUWlpal46lK/v8z//8Tz355JNqamq6bN38+fNVU1OjuLg4X4fbbr/73e9UU1PTbesHTEQAAtBhp0+fVl5enn74wx96Pbdu3Trdd9992rVrl6qqqryef//99zV16tQuHU9X9nnjjTdq1KhRev311y9bd9VVVykyMlJ+ft3304ohISGKjIzstvUDJiIAAeiwt956S35+fkpISPBoP3nypPLz85WZmamZM2dq/fr17ufOnDmjgIAAlZSUaOnSpXI4HJo2bVqnx9Idfc6ePVsbN270aRyHDx+Ww+HQ1q1bdfPNN2vAgAFyOp06fPiwiouLNXXqVF111VW69dZbdezYMUnSuXPn9NRTT+lb3/qWgoKCFBERoblz5/rULwDfEIAAdNiePXsUHx/v1Z6fn6/IyEhNnTpVDzzwgF555RVd+N3l/v37691335UklZWVqaamRjt27Oj0WLqjz6lTp+rPf/6zmpub2z2OC9cCrV69Wk899ZRcLpcaGho0d+5cPfPMM1q1apWKi4t14MAB5eXlSTr/MeLvf/97vfTSSzp06JC2bt2qGTNm+PYHAOATAhCADjt8+LCioqK82vPy8vTAAw9IktLS0lRXV6c//elPkqR+/frp888/V1hYmCZMmKDIyEgNGTJEkvS9731PoaGhuvfee30eS0f6rK6u1owZM3T99dfrxhtv1H//9397rHPEiBFqbm5WbW1tu8dRXl6u0NBQbdq0Sd/5znc0ceJE3XrrraqqqtLmzZs1ZcoUTZ06VVOmTHGvd8eOHbrzzjt16623KiYmRjfddJMeffRRn/8GANqPAASgw06fPq2goCCPtkOHDqmkpET333+/JGnQoEG65557tG7dOndNaWmpJkyY4LW+BQsWaMOGDT6Po6N9+vn56fnnn9fHH3+snTt3KisrSydPnnQ/P2DAAEnSqVOn2j2WsrIyzZ49W+Hh4e62qqoq3XfffRo4cKBHW2xsrKTzH7U9++yzSk5OVm5urvujMQDdhwAEoMPCw8N1/Phxj7a8vDxNmTJF1157rbvtgQce0NatW921ZWVlbQagW2+9VYMHD/Z5HB3tc/jw4Zo4caIkadiwYbr66qs9wseF/x46dGi7x1JeXq7p06d7tJWVlXlcc/T111/rr3/9q7vvhQsXqqKiQrfffrtefPFFjR07VpWVle3uE4DvCEAAOmzSpEn6+OOP3f8+e/asNmzY4D4Tc0FKSooGDx7s/kbVgQMHdOONN3bJGLqqz3379uncuXOKjo52tx08eFAjR470OJtzOU1NTTp8+LAmTZrkbjty5IiOHTvm0fbRRx+ptbXVI5Bde+21+tnPfqYPPvhAp06d8vi7Auh63fe9TQB9XkpKihYvXqzjx48rNDRU//M//6MvvvhCcXFxOnjwoEdtUlKS8vLy9OMf/1jnzp3Thx9+qM8//1wDBw5USEhIh8fQFX02NDTooYce0tq1az2W37t3r5KTk9s9lvLycvXr188jaJWVlWnIkCEe90kqLy/X6NGjNXjwYK1YsUIRERGaMmWK+vfvr7Vr1yo0NFSJiYk+/iUA+IIABKDDbrjhBsXHx+sPf/iDfvSjH7m/1fTd7373kst88MEH+tWvfqVFixbpt7/9rbKzs/Wb3/zmsv2sX79e8+bNc3+r6x91ts/m5mZ973vf0+LFiz1Cx9dff61t27b59A218vJyXXfdde5rh6S2rz0qLy93f/z19ddf66mnnlJVVZUGDRqkm266Se+8845CQ0Pb3S8A3zmsto4oANBOBQUFWrhwoQ4ePKh+/Tr/qXpxcbFWrlypzZs3u9uWLVum4uJiFRcXd3r9/8iyLN1///0aN26cli1b5vHcqlWr9Mc//lGFhYWXXH7GjBmaOHGinn/++S4d16U4HA5t27aty++gDZiIa4AAdEpqaqp+9KMf6ejRo51eV0pKin7wgx+ooKBAI0eO1Pvvvy/p/NfEV6xY0en1X+y9995Tfn6+3njjDU2cOFETJ07UgQMHJEn+/v568cUXv3Edq1ev1qBBg9zLdYeMjAwNGjSo29YPmIgzQADQQUePHtXp06clSddcc40CAgK6pZ+6ujr3b5INHz7c4+v0ADqGAAQAAIzDR2AAAMA4BCAAAGAcAhAAADAOAQgAABiHAAQAAIxDAAIAAMYhAAEAAOMQgAAAgHEIQAAAwDj/D6p+AmBdNnHMAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] diff --git a/pynestml/codegeneration/nest_code_generator_utils.py b/pynestml/codegeneration/nest_code_generator_utils.py index 5d8513b45..54692eab8 100644 --- a/pynestml/codegeneration/nest_code_generator_utils.py +++ b/pynestml/codegeneration/nest_code_generator_utils.py @@ -86,7 +86,7 @@ def generate_code_for(cls, nestml_neuron_model = nestml_model_file.read() # update neuron model name inside the file - neuron_model_name = re.findall(r"model [^:\s]*:", nestml_neuron_model)[0][7:-1] + neuron_model_name = re.findall(r"model [^:\s]*:", nestml_neuron_model)[0][6:-1] neuron_fn = neuron_model_name + ".nestml" with open(neuron_fn, "w") as f: print(nestml_neuron_model, file=f) @@ -108,7 +108,7 @@ def generate_code_for(cls, nestml_synapse_model = nestml_model_file.read() # update synapse model name inside the file - synapse_model_name = re.findall(r"model [^:\s]*:", nestml_synapse_model)[0][8:-1] + synapse_model_name = re.findall(r"model [^:\s]*:", nestml_synapse_model)[0][6:-1] synapse_fn = synapse_model_name + ".nestml" with open(synapse_fn, "w") as f: print(nestml_synapse_model, file=f)