From f862badce6555d27788333a24e656b790f4592d5 Mon Sep 17 00:00:00 2001 From: "C.A.P. Linssen" Date: Thu, 21 Dec 2023 08:41:06 -0800 Subject: [PATCH] change third factor STDP plasticity unit test into a notebook tutorial --- ...rd_factor_active_dendrite-checkpoint.ipynb | 3319 +++++++++++++++++ .../stdp_third_factor_active_dendrite.ipynb | 3319 +++++++++++++++++ models/neurons/iaf_psc_exp_dend_neuron.nestml | 2 +- .../point_neuron/common/NeuronClass.jinja2 | 45 + .../point_neuron/common/NeuronHeader.jinja2 | 8 +- .../third_factor_stdp_synapse_test.py | 243 -- 6 files changed, 6691 insertions(+), 245 deletions(-) create mode 100644 doc/tutorials/stdp_third_factor_active_dendrite/.ipynb_checkpoints/stdp_third_factor_active_dendrite-checkpoint.ipynb create mode 100644 doc/tutorials/stdp_third_factor_active_dendrite/stdp_third_factor_active_dendrite.ipynb delete mode 100644 tests/nest_tests/third_factor_stdp_synapse_test.py diff --git a/doc/tutorials/stdp_third_factor_active_dendrite/.ipynb_checkpoints/stdp_third_factor_active_dendrite-checkpoint.ipynb b/doc/tutorials/stdp_third_factor_active_dendrite/.ipynb_checkpoints/stdp_third_factor_active_dendrite-checkpoint.ipynb new file mode 100644 index 000000000..cf0d9e6f9 --- /dev/null +++ b/doc/tutorials/stdp_third_factor_active_dendrite/.ipynb_checkpoints/stdp_third_factor_active_dendrite-checkpoint.ipynb @@ -0,0 +1,3319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NESTML active dendrite third-factor STDP synapse\n", + "==========================================\n", + "\n", + "Welcome ...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Introduction\n", + "------------\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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" + ] + }, + { + "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" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from typing import List, Optional\n", + "\n", + "import matplotlib as mpl\n", + "\n", + "mpl.rcParams['axes.formatter.useoffset'] = False\n", + "mpl.rcParams['axes.grid'] = True\n", + "mpl.rcParams['grid.color'] = 'k'\n", + "mpl.rcParams['grid.linestyle'] = ':'\n", + "mpl.rcParams['grid.linewidth'] = 0.5\n", + "mpl.rcParams['figure.dpi'] = 120\n", + "mpl.rcParams['figure.figsize'] = [8., 3.]\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import nest\n", + "import numpy as np\n", + "import os\n", + "import random\n", + "import re\n", + "\n", + "from pynestml.codegeneration.nest_code_generator_utils import NESTCodeGeneratorUtils\n", + "from pynestml.codegeneration.nest_tools import NESTTools" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "post_trace_var = \"I_dend\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating code with NESTML\n", + "\n", + "We will take a simple current-based integrate-and-fire model with alpha-shaped postsynaptic response kernels (``iaf_psc_alpha``) as the basis for our modifications. First, let's take a look at this base neuron without any modifications.\n", + "\n", + "We will use a helper function to generate the C++ code for the models, build it as a NEST extension module, and load the module into the kernel. Because NEST does not support un- or reloading of modules at the time of writing, we implement a workaround that appends a unique number to the name of each generated model, for example, \"iaf_psc_alpha_3cc945f\". The resulting neuron model name is returned by the function, so we do not have to think about these internals." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1,GLOBAL, INFO]: List of files that will be processed:\n", + "[2,GLOBAL, INFO]: /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron.nestml\n", + "[3,GLOBAL, INFO]: /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse.nestml\n", + "[4,GLOBAL, INFO]: Target platform code will be generated in directory: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target'\n", + "[5,GLOBAL, INFO]: Target platform code will be installed in directory: '/tmp/nestml_target_buhdamnv'\n", + "\n", + " -- N E S T --\n", + " Copyright (C) 2004 The NEST Initiative\n", + "\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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", + "[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-clopath_synapse/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-clopath_synapse/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-clopath_synapse/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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron.nestml'!\n", + "[12,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[13,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:39;50:47]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[14,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:15;50:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[15,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse.nestml'!\n", + "[16,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[17,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[18,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [73:11;73:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([73:11;73:28])\n", + "[19,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [80:11;80:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([80:11;80:28])\n", + "[20,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[21,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[22,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[23,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[24,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:39;50:47]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[25,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:15;50:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[26,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[27,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[28,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [73:11;73:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([73:11;73:28])\n", + "[29,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [80:11;80:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([80:11;80:28])\n", + "[30,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[31,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[32,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[33,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_trace', 'post_trace_kernel']\n", + "[34,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_trace', 'post_trace_kernel']\n", + "[35,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_tr_post']\n", + "[36,GLOBAL, INFO]: Synaptic state variables moved to neuron that will need continuous-time buffering: ['I_post_dend']\n", + "[37,GLOBAL, INFO]: Moving state var defining equation(s) post_trace\n", + "[38,GLOBAL, INFO]: Moving state var defining equation(s) post_trace_kernel\n", + "[39,GLOBAL, INFO]: Moving state variables for equation(s) post_trace\n", + "[40,GLOBAL, INFO]: Moving state variables for equation(s) post_trace_kernel\n", + "[41,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", + "[42,GLOBAL, INFO]: \t• Replacing variable I_post_dend\n", + "[43,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend <= 1pA\n", + "[44,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[45,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[46,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend <= 1pA\n", + "[47,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[48,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[49,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", + "[50,GLOBAL, INFO]: Copying definition of tau_tr_post from synapse to neuron\n", + "[51,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", + "[52,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", + "[53,GLOBAL, INFO]: \t• Replacing variable post_trace\n", + "[54,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = post_trace__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml) in expression: alpha * lambda * (w / Wmax) ** mu_minus * post_trace\n", + "[55,GLOBAL, INFO]: \t• Replacing variable post_trace_kernel\n", + "[56,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[57,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[58,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[59,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml\n", + "[60,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml'\n", + "[61,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [38:0;93:0]]: Starts processing of the model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Analysing input:\n", + "INFO:{\n", + " \"dynamics\": [\n", + " {\n", + " \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\",\n", + " \"initial_values\": {\n", + " \"V_m\": \"E_L\"\n", + " }\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_exc__X__exc_spikes = exp(-t / tau_syn_exc)\",\n", + " \"initial_values\": {}\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_inh__X__inh_spikes = exp(-t / tau_syn_inh)\",\n", + " \"initial_values\": {}\n", + " }\n", + " ],\n", + " \"options\": {\n", + " \"output_timestep_symbol\": \"__h\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250\",\n", + " \"E_L\": \"(-70)\",\n", + " \"I_e\": \"0\",\n", + " \"V_reset\": \"(-70)\",\n", + " \"V_th\": \"(-55)\",\n", + " \"refr_T\": \"2\",\n", + " \"tau_m\": \"10\",\n", + " \"tau_syn_exc\": \"2\",\n", + " \"tau_syn_inh\": \"2\"\n", + " }\n", + "}\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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n", + "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m\n", + "DEBUG:\tnonlinear term: 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m, nonlin_term = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + 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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "INFO:All known variables: [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes], all parameters used in ODEs: {tau_syn_inh, tau_syn_exc, tau_m, C_m, I_stim, E_L, 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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes, V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [-1.0/C_m], [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 = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [-1.0/C_m]])\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 I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_syn_inh]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, -1.0/C_m], [0, -1/tau_syn_exc, 0], [0, 0, -1/tau_syn_inh]]), 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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [-1.0/C_m]])\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 I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_syn_inh]])\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", + "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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "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, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, -1.0/C_m], [0, -1/tau_syn_exc, 0], [0, 0, -1/tau_syn_inh]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0]]), c = Matrix([[0], [0], [0]])\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", + "DEBUG:System of equations:\n", + "DEBUG:x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]])\n", + "DEBUG:A = Matrix([\n", + "[-1/tau_m, 1.0/C_m, -1.0/C_m],\n", + "[ 0, -1/tau_syn_exc, 0],\n", + "[ 0, 0, -1/tau_syn_inh]])\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 + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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[I_kernel_exc__X__exc_spikes] = I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\n", + "INFO:update_expr[I_kernel_inh__X__inh_spikes] = I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\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_kernel_exc__X__exc_spikes\": \"1\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"1\",\n", + " \"V_m\": \"E_L\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250.000000000000\",\n", + " \"E_L\": \"-70.0000000000000\",\n", + " \"I_e\": \"0\",\n", + " \"tau_m\": \"10.0000000000000\",\n", + " \"tau_syn_exc\": \"2.00000000000000\",\n", + " \"tau_syn_inh\": \"2.00000000000000\"\n", + " },\n", + " \"propagators\": {\n", + " \"__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\": \"1.0*exp(-__h/tau_syn_exc)\",\n", + " \"__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\": \"1.0*exp(-__h/tau_syn_inh)\",\n", + " \"__P__V_m__I_kernel_exc__X__exc_spikes\": \"1.0*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_kernel_inh__X__inh_spikes\": \"1.0*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\": \"1.0*exp(-__h/tau_m)\"\n", + " },\n", + " \"solver\": \"analytical\",\n", + " \"state_variables\": [\n", + " \"V_m\",\n", + " \"I_kernel_exc__X__exc_spikes\",\n", + " \"I_kernel_inh__X__inh_spikes\"\n", + " ],\n", + " \"update_expressions\": {\n", + " \"I_kernel_exc__X__exc_spikes\": \"I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\",\n", + " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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": [ + "[62,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Analysing input:\n", + "INFO:{\n", + " \"dynamics\": [\n", + " {\n", + " \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\",\n", + " \"initial_values\": {\n", + " \"V_m\": \"E_L\"\n", + " }\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_exc__X__exc_spikes = exp(-t / tau_syn_exc)\",\n", + " \"initial_values\": {}\n", + " },\n", + " {\n", + " \"expression\": \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml = exp(-t / tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\",\n", + " \"initial_values\": {}\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_inh__X__inh_spikes = exp(-t / tau_syn_inh)\",\n", + " \"initial_values\": {}\n", + " }\n", + " ],\n", + " \"options\": {\n", + " \"output_timestep_symbol\": \"__h\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250\",\n", + " \"E_L\": \"(-70)\",\n", + " \"I_e\": \"0\",\n", + " \"V_reset\": \"(-70)\",\n", + " \"V_th\": \"(-55)\",\n", + " \"refr_T\": \"2\",\n", + " \"tau_m\": \"10\",\n", + " \"tau_syn_exc\": \"2\",\n", + " \"tau_syn_inh\": \"2\",\n", + " \"tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"20\"\n", + " }\n", + "}\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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n", + "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m\n", + "DEBUG:\tnonlinear term: 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m, nonlin_term = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + 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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "INFO:\n", + "Processing function-of-time shape \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\" with defining expression = \"exp(-t/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, derivative_factors = [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml: reconstituting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "INFO:All known variables: [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes], all parameters used in ODEs: {tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, tau_syn_inh, tau_syn_exc, tau_m, C_m, I_stim, E_L, 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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes, V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [0], [-1.0/C_m], [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 = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\n", + "Processing function-of-time shape \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\" with defining expression = \"exp(-t/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, derivative_factors = [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [0], [-1.0/C_m]])\n", + "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml: reconstituting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "DEBUG:Splitting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, 0, -1.0/C_m], [0, -1/tau_syn_exc, 0, 0], [0, 0, -1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, 0], [0, 0, 0, -1/tau_syn_inh]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0], [0.0]]), c = Matrix([[0.0], [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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[63,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml'\n", + "[64,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, INFO, [38:0;93:0]]: Starts processing of the model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [0], [-1.0/C_m]])\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 I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml: reconstituting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "DEBUG:Splitting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh]])\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", + "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, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, 0, -1.0/C_m], [0, -1/tau_syn_exc, 0, 0], [0, 0, -1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, 0], [0, 0, 0, -1/tau_syn_inh]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0], [0.0]]), c = Matrix([[0], [0], [0], [0]])\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", + "DEBUG:System of equations:\n", + "DEBUG:x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]])\n", + "DEBUG:A = Matrix([\n", + "[-1/tau_m, 1.0/C_m, 0, -1.0/C_m],\n", + "[ 0, -1/tau_syn_exc, 0, 0],\n", + "[ 0, 0, -1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, 0],\n", + "[ 0, 0, 0, -1/tau_syn_inh]])\n", + "DEBUG:b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0], [0.0]])\n", + "DEBUG:c = Matrix([[0], [0], [0], [0]])\n", + "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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[I_kernel_exc__X__exc_spikes] = I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\n", + "INFO:update_expr[post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml] = __P__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml*post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "INFO:update_expr[I_kernel_inh__X__inh_spikes] = I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\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_kernel_exc__X__exc_spikes\": \"1\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"1\",\n", + " \"V_m\": \"E_L\",\n", + " \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"1\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250.000000000000\",\n", + " \"E_L\": \"-70.0000000000000\",\n", + " \"I_e\": \"0\",\n", + " \"tau_m\": \"10.0000000000000\",\n", + " \"tau_syn_exc\": \"2.00000000000000\",\n", + " \"tau_syn_inh\": \"2.00000000000000\",\n", + " \"tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"20.0000000000000\"\n", + " },\n", + " \"propagators\": {\n", + " \"__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\": \"1.0*exp(-__h/tau_syn_exc)\",\n", + " \"__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\": \"1.0*exp(-__h/tau_syn_inh)\",\n", + " \"__P__V_m__I_kernel_exc__X__exc_spikes\": \"1.0*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_kernel_inh__X__inh_spikes\": \"1.0*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\": \"1.0*exp(-__h/tau_m)\",\n", + " \"__P__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"1.0*exp(-__h/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\"\n", + " },\n", + " \"solver\": \"analytical\",\n", + " \"state_variables\": [\n", + " \"V_m\",\n", + " \"I_kernel_exc__X__exc_spikes\",\n", + " \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\",\n", + " \"I_kernel_inh__X__inh_spikes\"\n", + " ],\n", + " \"update_expressions\": {\n", + " \"I_kernel_exc__X__exc_spikes\": \"I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\",\n", + " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"__P__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml*post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\"\n", + " }\n", + " }\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[65,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Analysing input:\n", + "INFO:{\n", + " \"dynamics\": [\n", + " {\n", + " \"expression\": \"pre_trace_kernel__X__pre_spikes = exp(-t / tau_tr_pre)\",\n", + " \"initial_values\": {}\n", + " }\n", + " ],\n", + " \"options\": {\n", + " \"output_timestep_symbol\": \"__h\"\n", + " },\n", + " \"parameters\": {\n", + " \"Wmax\": \"100.0\",\n", + " \"Wmin\": \"0.0\",\n", + " \"alpha\": \"1.0\",\n", + " \"d\": \"1\",\n", + " \"lambda\": \"0.01\",\n", + " \"mu_minus\": \"1.0\",\n", + " \"mu_plus\": \"1.0\",\n", + " \"tau_tr_post\": \"20\",\n", + " \"tau_tr_pre\": \"20\"\n", + " }\n", + "}\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", + "Processing function-of-time shape \"pre_trace_kernel__X__pre_spikes\" with defining expression = \"exp(-t/tau_tr_pre)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol pre_trace_kernel__X__pre_spikes, derivative_factors = [-1/tau_tr_pre], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape pre_trace_kernel__X__pre_spikes: reconstituting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre\n", + "INFO:All known variables: [pre_trace_kernel__X__pre_spikes], all parameters used in ODEs: {tau_tr_pre}\n", + "INFO:\n", + "Processing function-of-time shape \"pre_trace_kernel__X__pre_spikes\" with defining expression = \"exp(-t/tau_tr_pre)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol pre_trace_kernel__X__pre_spikes, derivative_factors = [-1/tau_tr_pre], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape pre_trace_kernel__X__pre_spikes: reconstituting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre\n", + "DEBUG:Splitting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre (symbols Matrix([[pre_trace_kernel__X__pre_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_tr_pre]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "DEBUG:Initializing system of shapes with x = Matrix([[pre_trace_kernel__X__pre_spikes]]), A = Matrix([[-1/tau_tr_pre]]), b = Matrix([[0.0]]), 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", + "INFO:Shape pre_trace_kernel__X__pre_spikes: reconstituting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre\n", + "DEBUG:Splitting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre (symbols [pre_trace_kernel__X__pre_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_tr_pre]])\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", + "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: pre_trace_kernel__X__pre_spikes\n", + "DEBUG:Initializing system of shapes with x = Matrix([[pre_trace_kernel__X__pre_spikes]]), A = Matrix([[-1/tau_tr_pre]]), b = Matrix([[0.0]]), c = Matrix([[0]])\n", + "DEBUG:System of equations:\n", + "DEBUG:x = Matrix([[pre_trace_kernel__X__pre_spikes]])\n", + "DEBUG:A = Matrix([[-1/tau_tr_pre]])\n", + "DEBUG:b = Matrix([[0.0]])\n", + "DEBUG:c = Matrix([[0]])\n", + "INFO:update_expr[pre_trace_kernel__X__pre_spikes] = __P__pre_trace_kernel__X__pre_spikes__pre_trace_kernel__X__pre_spikes*pre_trace_kernel__X__pre_spikes\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", + " {\n", + " \"initial_values\": {\n", + " \"pre_trace_kernel__X__pre_spikes\": \"1\"\n", + " },\n", + " \"parameters\": {\n", + " \"tau_tr_pre\": \"20.0000000000000\"\n", + " },\n", + " \"propagators\": {\n", + " \"__P__pre_trace_kernel__X__pre_spikes__pre_trace_kernel__X__pre_spikes\": \"exp(-__h/tau_tr_pre)\"\n", + " },\n", + " \"solver\": \"analytical\",\n", + " \"state_variables\": [\n", + " \"pre_trace_kernel__X__pre_spikes\"\n", + " ],\n", + " \"update_expressions\": {\n", + " \"pre_trace_kernel__X__pre_spikes\": \"__P__pre_trace_kernel__X__pre_spikes__pre_trace_kernel__X__pre_spikes*pre_trace_kernel__X__pre_spikes\"\n", + " }\n", + " }\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[66,GLOBAL, INFO]: Analysing/transforming synapse third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.\n", + "[67,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [39:0;85:0]]: Starts processing of the model 'third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml'\n", + "[68,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[69,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[70,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[71,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[72,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp\n", + "[73,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h\n", + "[74,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [38:0;93:0]]: Successfully generated code for the model: 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml' in: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target' !\n", + "[75,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp\n", + "[76,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h\n", + "[77,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, INFO, [38:0;93:0]]: Successfully generated code for the model: 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml' in: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target' !\n", + "[78,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h\n", + "[79,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [39:0;85:0]]: Successfully generated code for the model: 'third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml' in: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target' !\n", + "[80,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp\n", + "[81,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.h\n", + "[82,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/CMakeLists.txt\n", + "[83,GLOBAL, INFO]: Successfully generated NEST module code in '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/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", + "\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_7c1adc9dbf3144fabdd9ebff0cce4e18_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 -g\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_7c1adc9dbf3144fabdd9ebff0cce4e18_module' using\n", + " make\n", + " make install\n", + "\n", + "The library file libnestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.so will be installed to\n", + " /tmp/nestml_target_buhdamnv\n", + "The module can be loaded into NEST using\n", + " (nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module) Install (in SLI)\n", + " nest.Install(nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target\n", + "[ 25%] Building CXX object CMakeFiles/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module.dir/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module.dir/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module.dir/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp:291:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 291 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "In file included from /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:43:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h: In constructor ‘continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml(double, double)’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h:105:10: warning: ‘continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::access_counter_’ will be initialized after [-Wreorder]\n", + " 105 | size_t access_counter_;\n", + " | ^~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h:102:10: warning: ‘double continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::I_post_dend’ [-Wreorder]\n", + " 102 | double I_post_dend;\n", + " | ^~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:46:1: warning: when initialized here [-Wreorder]\n", + " 46 | continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml( double t,\n", + " | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp: In member function ‘void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:202:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 202 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:321:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 321 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In file included from /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:52:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:727:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 727 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:727:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:739:3: required from ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:715:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 715 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:739:3: required from ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:715:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:522:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 522 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:551:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 551 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:591:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 591 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:455:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 455 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:457:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 457 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:522:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 522 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:551:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 551 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:591:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 591 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:455:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 455 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:457:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 457 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::update_internal_state_(double, double, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:517:9: required from ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:789:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 789 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:790:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 790 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::update_internal_state_(double, double, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:517:9: required from ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:789:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 789 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:790:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 790 | 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_7c1adc9dbf3144fabdd9ebff0cce4e18_module.so\n", + "[100%] Built target nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module\n", + "[100%] Built target nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module\n", + "Install the project...\n", + "-- Install configuration: \"\"\n", + "-- Installing: /tmp/nestml_target_buhdamnv/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.so\n", + "iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::init_state_internal_()\n", + "iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::init_state_internal_()\n", + "\n", + "Dec 21 08:04:39 Install [Info]: \n", + " loaded module nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module\n" + ] + } + ], + "source": [ + "# codegen_opts = {\"neuron_synapse_pairs\": [{\"neuron\": \"iaf_psc_exp_dend\",\n", + "# \"synapse\": \"third_factor_stdp_synapse\",\n", + "# \"post_ports\": [\"post_spikes\",\n", + "# [\"I_post_dend\", \"I_dend\"]]}]}\n", + "\n", + "# if not NESTTools.detect_nest_version().startswith(\"v2\"):\n", + "# codegen_opts[\"neuron_parent_class\"] = \"StructuralPlasticityNode\"\n", + "# codegen_opts[\"neuron_parent_class_include\"] = \"structural_plasticity_node.h\"\n", + "\n", + "# generate the \"jit\" model (co-generated neuron and synapse), that does not rely on ArchivingNode\n", + "# files = [os.path.join(\"models\", \"neurons\", \"iaf_psc_exp_dend_neuron.nestml\"),\n", + "# os.path.join(\"models\", \"synapses\", \"third_factor_stdp_synapse.nestml\")]\n", + "# input_path = [os.path.realpath(os.path.join(os.path.dirname(__file__), os.path.join(\n", + "# os.pardir, os.pardir, s))) for s in files]\n", + "# generate_nest_target(input_path=input_path,\n", + "# target_path=\"/tmp/nestml-jit\",\n", + "# logging_level=\"INFO\",\n", + "# module_name=\"nestml_jit_module\",\n", + "# codegen_opts=codegen_opts)\n", + "#nest.Install(\"nestml_jit_module\")\n", + "\n", + "# generate and build code\n", + "module_name, neuron_model_name, synapse_model_name = \\\n", + " NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_exp_dend_neuron.nestml\",\n", + " \"../../../models/synapses/third_factor_stdp_synapse.nestml\",\n", + " logging_level=\"DEBUG\",\n", + " post_ports=[\"post_spikes\", [\"I_post_dend\", \"I_dend\"]])\n", + "\n", + "# load dynamic library (NEST extension module) into NEST kernel\n", + "nest.Install(module_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the NESTML model is ready to be used in a simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def run_synapse_test(neuron_model_name,\n", + " synapse_model_name,\n", + " resolution=1., # [ms]\n", + " delay=1., # [ms]\n", + " sim_time=None, # if None, computed from pre and post spike times\n", + " pre_spike_times=None,\n", + " post_spike_times=None,\n", + " fname_snip=\"\"):\n", + "\n", + " if pre_spike_times is None:\n", + " pre_spike_times = []\n", + "\n", + " if post_spike_times is None:\n", + " post_spike_times = []\n", + "\n", + " if sim_time is None:\n", + " sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 5 * delay\n", + "\n", + " nest_version = NESTTools.detect_nest_version()\n", + "\n", + " nest.set_verbosity(\"M_ALL\")\n", + " nest.ResetKernel()\n", + "\n", + " print(\"Pre spike times: \" + str(pre_spike_times))\n", + " print(\"Post spike times: \" + str(post_spike_times))\n", + "\n", + " nest.set_verbosity(\"M_WARNING\")\n", + "\n", + " nest.ResetKernel()\n", + " nest.SetKernelStatus({\"resolution\": resolution})\n", + "\n", + " wr = nest.Create(\"weight_recorder\")\n", + " nest.CopyModel(synapse_model_name, \"stdp_nestml_rec\",\n", + " {\"weight_recorder\": wr[0], \"w\": 1., \"d\": 1., \"receptor_type\": 0, \"lambda\": .001})\n", + "\n", + " # create spike_generators with these times\n", + " pre_sg = nest.Create(\"spike_generator\",\n", + " params={\"spike_times\": pre_spike_times})\n", + " post_sg = nest.Create(\"spike_generator\",\n", + " params={\"spike_times\": post_spike_times,\n", + " \"allow_offgrid_times\": True})\n", + "\n", + " # create parrot neurons and connect spike_generators\n", + " pre_neuron = nest.Create(\"parrot_neuron\")\n", + " post_neuron = nest.Create(neuron_model_name)\n", + "\n", + " if nest_version.startswith(\"v2\"):\n", + " spikedet_pre = nest.Create(\"spike_detector\")\n", + " spikedet_post = nest.Create(\"spike_detector\")\n", + " else:\n", + " spikedet_pre = nest.Create(\"spike_recorder\")\n", + " spikedet_post = nest.Create(\"spike_recorder\")\n", + " mm = nest.Create(\"multimeter\", params={\"record_from\": [\"V_m\", post_trace_var]})\n", + "\n", + " nest.Connect(pre_sg, pre_neuron, \"one_to_one\", syn_spec={\"delay\": 1.})\n", + " nest.Connect(post_sg, post_neuron, \"one_to_one\", syn_spec={\"delay\": 1., \"weight\": 6000.})\n", + " if nest_version.startswith(\"v2\"):\n", + " nest.Connect(pre_neuron, post_neuron, \"all_to_all\", syn_spec={\"model\": \"stdp_nestml_rec\"})\n", + " else:\n", + " nest.Connect(pre_neuron, post_neuron, \"all_to_all\", syn_spec={\"synapse_model\": \"stdp_nestml_rec\"})\n", + " nest.Connect(mm, post_neuron)\n", + " nest.Connect(pre_neuron, spikedet_pre)\n", + " nest.Connect(post_neuron, spikedet_post)\n", + "\n", + " # get STDP synapse and weight before protocol\n", + " syn = nest.GetConnections(source=pre_neuron, synapse_model=\"stdp_nestml_rec\")\n", + "\n", + " t = 0.\n", + " t_hist = []\n", + " w_hist = []\n", + " state = 0\n", + " while t <= sim_time:\n", + " print(\"t = \" + str(t) + \" ms\")\n", + " if t > sim_time / 6. and state == 0:\n", + " nest.SetStatus(post_neuron, {\"I_dend\": 1.})\n", + " state = 1\n", + " if t > 2 * sim_time / 6 and state == 1:\n", + " nest.SetStatus(post_neuron, {\"I_dend\": 1.})\n", + " if t > 3 * sim_time / 6. and state == 1:\n", + " state = 2\n", + " if t > 5 * sim_time / 6. and state == 2:\n", + " nest.SetStatus(post_neuron, {\"I_dend\": 0.})\n", + " state = 3\n", + " nest.Simulate(resolution)\n", + " t += resolution\n", + " t_hist.append(t)\n", + " w_hist.append(nest.GetStatus(syn)[0][\"w\"])\n", + "\n", + " third_factor_trace = nest.GetStatus(mm, \"events\")[0][post_trace_var]\n", + " timevec = nest.GetStatus(mm, \"events\")[0][\"times\"]\n", + "\n", + " \n", + " \n", + " \n", + " fig, ax = plt.subplots(nrows=2)\n", + " ax1, ax2 = ax\n", + "\n", + " V_m = nest.GetStatus(mm, \"events\")[0][\"V_m\"]\n", + " ax2.plot(timevec, third_factor_trace, label=\"I_dend_post\")\n", + " ax1.plot(timevec, V_m, alpha=.7, linestyle=\":\")\n", + " ax1.set_ylabel(\"V_m\")\n", + "\n", + " for _ax in ax:\n", + " _ax.grid(which=\"major\", axis=\"both\")\n", + " _ax.grid(which=\"minor\", axis=\"x\", linestyle=\":\", alpha=.4)\n", + " _ax.set_xlim(0., sim_time)\n", + " _ax.legend()\n", + " fig.savefig(\"/tmp/stdp_third_factor_synapse_test\" + fname_snip + \"_V_m.png\", dpi=300)\n", + " \n", + " \n", + " \n", + " fig, ax = plt.subplots(nrows=5)\n", + " ax1, ax2, ax3, ax4, ax5 = ax\n", + "\n", + " pre_spike_times_ = nest.GetStatus(spikedet_pre, \"events\")[0][\"times\"]\n", + " print(\"Actual pre spike times: \" + str(pre_spike_times_))\n", + "\n", + " n_spikes = len(pre_spike_times_)\n", + " for i in range(n_spikes):\n", + " ax1.plot(2 * [pre_spike_times_[i] + delay], [0, 1], linewidth=2, color=\"blue\", alpha=.4)\n", + "\n", + " post_spike_times_ = nest.GetStatus(spikedet_post, \"events\")[0][\"times\"]\n", + " print(\"Actual post spike times: \" + str(post_spike_times_))\n", + " ax1.set_ylabel(\"Pre spikes\")\n", + "\n", + " n_spikes = len(post_spike_times_)\n", + " for i in range(n_spikes):\n", + " if i == 0:\n", + " _lbl = \"nestml\"\n", + " else:\n", + " _lbl = None\n", + " ax[-4].plot(2 * [post_spike_times_[i]], [0, 1], linewidth=2, color=\"black\", alpha=.4, label=_lbl)\n", + " ax[-4].set_ylabel(\"Post spikes\")\n", + "\n", + " ax[-3].plot(timevec, third_factor_trace)\n", + " ax[-3].set_ylabel(\"3rd factor\")\n", + "\n", + " ax[-2].plot(t_hist[:-1], np.diff(w_hist), marker=\"o\", label=u\"Δw\")\n", + " ax[-2].set_ylabel(u\"Δw\")\n", + "\n", + " ax[-1].plot(t_hist, w_hist, marker=\"o\")\n", + " ax[-1].set_ylabel(\"w\")\n", + " ax[-1].set_xlabel(\"Time [ms]\")\n", + " for _ax in ax:\n", + " if not _ax == ax[-1]:\n", + " _ax.set_xticklabels([])\n", + " _ax.grid(True)\n", + " _ax.set_xlim(0., sim_time)\n", + "\n", + " fig.savefig(\"/tmp/stdp_third_factor_synapse_test\" + fname_snip + \".png\", dpi=300)\n", + "\n", + " return timevec, t_hist, third_factor_trace, w_hist\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", + "DEBUG:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/charl/.local/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n", + "WARNING:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " -- N E S T --\n", + " Copyright (C) 2004 The NEST Initiative\n", + "\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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", + "[85,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", + "Pre spike times: [1.000e+00 2.000e+00 3.000e+00 4.000e+00 5.000e+00 7.000e+00 9.000e+00\n", + " 1.200e+01 1.700e+01 2.100e+01 2.300e+01 2.700e+01 2.800e+01 3.000e+01\n", + " 3.100e+01 3.300e+01 3.500e+01 3.600e+01 3.700e+01 3.800e+01 3.900e+01\n", + " 4.100e+01 4.400e+01 4.600e+01 4.900e+01 5.100e+01 5.300e+01 5.500e+01\n", + " 5.600e+01 5.700e+01 5.800e+01 5.900e+01 6.000e+01 6.100e+01 6.400e+01\n", + " 6.500e+01 6.700e+01 7.000e+01 7.300e+01 7.500e+01 7.700e+01 8.000e+01\n", + " 8.100e+01 8.200e+01 8.300e+01 8.600e+01 9.100e+01 9.300e+01 9.400e+01\n", + " 9.500e+01 9.600e+01 9.800e+01 9.900e+01 1.000e+02 1.010e+02 1.040e+02\n", + " 1.050e+02 1.080e+02 1.090e+02 1.100e+02 1.110e+02 1.130e+02 1.150e+02\n", + " 1.180e+02 1.190e+02 1.210e+02 1.240e+02 1.260e+02 1.270e+02 1.280e+02\n", + " 1.310e+02 1.320e+02 1.340e+02 1.350e+02 1.390e+02 1.430e+02 1.440e+02\n", + " 1.460e+02 1.470e+02 1.480e+02 1.490e+02 1.510e+02 1.520e+02 1.530e+02\n", + " 1.570e+02 1.580e+02 1.590e+02 1.600e+02 1.620e+02 1.630e+02 1.660e+02\n", + " 1.670e+02 1.680e+02 1.690e+02 1.700e+02 1.730e+02 1.750e+02 1.760e+02\n", + " 1.790e+02 1.800e+02 1.810e+02 1.820e+02 1.850e+02 1.880e+02 1.890e+02\n", + " 1.930e+02 1.940e+02 1.980e+02 2.000e+02 2.010e+02 2.030e+02 2.060e+02\n", + " 2.070e+02 2.080e+02 2.090e+02 2.100e+02 2.110e+02 2.130e+02 2.140e+02\n", + " 2.160e+02 2.180e+02 2.210e+02 2.250e+02 2.320e+02 2.330e+02 2.340e+02\n", + " 2.380e+02 2.400e+02 2.420e+02 2.430e+02 2.470e+02 2.480e+02 2.490e+02\n", + " 2.500e+02 2.530e+02 2.540e+02 2.560e+02 2.570e+02 2.580e+02 2.620e+02\n", + " 2.630e+02 2.650e+02 2.680e+02 2.700e+02 2.730e+02 2.750e+02 2.770e+02\n", + " 2.780e+02 2.800e+02 2.820e+02 2.830e+02 2.840e+02 2.870e+02 2.920e+02\n", + " 2.930e+02 2.990e+02 3.000e+02 3.010e+02 3.030e+02 3.040e+02 3.080e+02\n", + " 3.100e+02 3.110e+02 3.130e+02 3.170e+02 3.220e+02 3.230e+02 3.280e+02\n", + " 3.290e+02 3.350e+02 3.370e+02 3.380e+02 3.390e+02 3.410e+02 3.430e+02\n", + " 3.450e+02 3.490e+02 3.560e+02 3.590e+02 3.700e+02 3.710e+02 3.720e+02\n", + " 3.750e+02 3.770e+02 3.780e+02 3.810e+02 3.820e+02 3.840e+02 3.890e+02\n", + " 3.910e+02 3.960e+02 3.980e+02 3.990e+02 4.050e+02 4.070e+02 4.080e+02\n", + " 4.090e+02 4.110e+02 4.120e+02 4.130e+02 4.150e+02 4.180e+02 4.190e+02\n", + " 4.210e+02 4.220e+02 4.250e+02 4.280e+02 4.290e+02 4.300e+02 4.310e+02\n", + " 4.330e+02 4.340e+02 4.370e+02 4.390e+02 4.420e+02 4.440e+02 4.470e+02\n", + " 4.500e+02 4.540e+02 4.550e+02 4.590e+02 4.610e+02 4.620e+02 4.630e+02\n", + " 4.640e+02 4.650e+02 4.670e+02 4.680e+02 4.690e+02 4.710e+02 4.720e+02\n", + " 4.750e+02 4.810e+02 4.910e+02 4.920e+02 4.970e+02 5.020e+02 5.050e+02\n", + " 5.070e+02 5.110e+02 5.140e+02 5.160e+02 5.180e+02 5.190e+02 5.200e+02\n", + " 5.250e+02 5.270e+02 5.280e+02 5.320e+02 5.330e+02 5.350e+02 5.380e+02\n", + " 5.410e+02 5.430e+02 5.440e+02 5.460e+02 5.470e+02 5.500e+02 5.530e+02\n", + " 5.550e+02 5.580e+02 5.590e+02 5.630e+02 5.660e+02 5.700e+02 5.750e+02\n", + " 5.780e+02 5.850e+02 5.900e+02 5.950e+02 6.010e+02 6.030e+02 6.050e+02\n", + " 6.060e+02 6.070e+02 6.110e+02 6.120e+02 6.150e+02 6.230e+02 6.290e+02\n", + " 6.300e+02 6.310e+02 6.320e+02 6.330e+02 6.400e+02 6.430e+02 6.470e+02\n", + " 6.480e+02 6.500e+02 6.510e+02 6.520e+02 6.590e+02 6.600e+02 6.620e+02\n", + " 6.640e+02 6.680e+02 6.730e+02 6.750e+02 6.760e+02 6.790e+02 6.800e+02\n", + " 6.820e+02 6.870e+02 6.880e+02 6.970e+02 7.020e+02 7.090e+02 7.150e+02\n", + " 7.180e+02 7.190e+02 7.200e+02 7.220e+02 7.240e+02 7.250e+02 7.330e+02\n", + " 7.390e+02 7.410e+02 7.420e+02 7.470e+02 7.510e+02 7.530e+02 7.550e+02\n", + " 7.600e+02 7.650e+02 7.690e+02 7.720e+02 7.770e+02 7.780e+02 7.890e+02\n", + " 7.900e+02 7.910e+02 7.980e+02 8.030e+02 8.090e+02 8.130e+02 8.160e+02\n", + " 8.190e+02 8.310e+02 8.340e+02 8.390e+02 8.420e+02 8.510e+02 8.530e+02\n", + " 8.680e+02 8.840e+02 8.900e+02 8.920e+02 8.930e+02 9.000e+02 9.030e+02\n", + " 9.090e+02 9.170e+02 9.300e+02 9.380e+02 9.390e+02 9.540e+02 9.570e+02\n", + " 9.640e+02 9.650e+02 9.660e+02 9.810e+02 9.850e+02 9.940e+02 9.950e+02\n", + " 1.001e+03 1.018e+03 1.023e+03 1.053e+03 1.068e+03 1.100e+03 1.109e+03\n", + " 1.146e+03 1.172e+03 1.226e+03 1.286e+03 1.287e+03 1.327e+03 1.353e+03\n", + " 1.396e+03 1.426e+03 1.488e+03 1.521e+03 1.539e+03 1.550e+03 1.571e+03]\n", + "Post spike times: [ 4. 7. 8. 9. 10. 11. 12. 14. 15. 16. 19. 20.\n", + " 21. 25. 26. 27. 29. 32. 33. 35. 36. 38. 40. 41.\n", + " 43. 45. 46. 47. 48. 50. 51. 52. 53. 54. 55. 57.\n", + " 58. 59. 61. 68. 69. 70. 73. 76. 77. 79. 81. 83.\n", + " 85. 86. 88. 92. 94. 96. 97. 98. 99. 102. 103. 104.\n", + " 105. 108. 110. 111. 114. 116. 117. 119. 121. 123. 125. 128.\n", + " 131. 135. 136. 140. 141. 142. 143. 146. 147. 148. 149. 151.\n", + " 153. 155. 156. 158. 159. 161. 164. 165. 168. 169. 171. 173.\n", + " 174. 178. 182. 184. 186. 187. 189. 191. 192. 193. 195. 196.\n", + " 198. 199. 200. 201. 202. 204. 210. 211. 214. 215. 216. 217.\n", + " 218. 223. 226. 229. 230. 231. 232. 237. 238. 239. 240. 241.\n", + " 242. 243. 244. 245. 247. 250. 251. 252. 253. 255. 256. 257.\n", + " 258. 260. 264. 266. 268. 270. 271. 272. 273. 274. 275. 278.\n", + " 279. 280. 281. 286. 287. 289. 290. 291. 295. 296. 299. 300.\n", + " 301. 305. 309. 312. 313. 315. 319. 322. 323. 324. 326. 327.\n", + " 328. 331. 333. 335. 338. 339. 343. 344. 345. 346. 347. 349.\n", + " 352. 355. 358. 359. 361. 362. 364. 367. 369. 381. 382. 384.\n", + " 385. 388. 393. 394. 396. 397. 402. 404. 412. 414. 415. 416.\n", + " 418. 419. 424. 425. 427. 430. 431. 433. 434. 437. 439. 441.\n", + " 443. 452. 453. 456. 457. 458. 459. 466. 467. 472. 474. 476.\n", + " 478. 481. 483. 484. 485. 486. 489. 490. 492. 494. 501. 507.\n", + " 509. 510. 512. 513. 516. 517. 522. 530. 537. 543. 545. 547.\n", + " 548. 550. 558. 559. 560. 562. 563. 567. 568. 575. 577. 579.\n", + " 584. 585. 587. 592. 596. 597. 600. 603. 604. 613. 617. 618.\n", + " 620. 623. 625. 628. 633. 637. 638. 639. 647. 648. 649. 651.\n", + " 654. 656. 657. 660. 666. 668. 670. 674. 676. 681. 683. 689.\n", + " 690. 698. 701. 702. 703. 706. 707. 710. 717. 723. 734. 744.\n", + " 746. 754. 762. 764. 765. 772. 775. 778. 779. 787. 788. 792.\n", + " 800. 802. 807. 829. 832. 836. 840. 841. 849. 863. 882. 894.\n", + " 896. 915. 922. 924. 926. 931. 933. 934. 961. 967. 971. 983.\n", + " 1014. 1015. 1016. 1030. 1047. 1061. 1068. 1083. 1126. 1144. 1145. 1150.\n", + " 1163. 1221. 1239. 1307. 1308. 1341. 1416. 1468. 1987.]\n", + "t = 0.0 ms\n", + "t = 0.5 ms\n", + "t = 1.0 ms\n", + "t = 1.5 ms\n", + "t = 2.0 ms\n", + "t = 2.5 ms\n", + "t = 3.0 ms\n", + "t = 3.5 ms\n", + "t = 4.0 ms\n", + "t = 4.5 ms\n", + "t = 5.0 ms\n", + "t = 5.5 ms\n", + "t = 6.0 ms\n", + "t = 6.5 ms\n", + "t = 7.0 ms\n", + "t = 7.5 ms\n", + "t = 8.0 ms\n", + "t = 8.5 ms\n", + "t = 9.0 ms\n", + "t = 9.5 ms\n", + "t = 10.0 ms\n", + "t = 10.5 ms\n", + "t = 11.0 ms\n", + "t = 11.5 ms\n", + "t = 12.0 ms\n", + "t = 12.5 ms\n", + "t = 13.0 ms\n", + "t = 13.5 ms\n", + "t = 14.0 ms\n", + "t = 14.5 ms\n", + "t = 15.0 ms\n", + "t = 15.5 ms\n", + "t = 16.0 ms\n", + "t = 16.5 ms\n", + "t = 17.0 ms\n", + "t = 17.5 ms\n", + "t = 18.0 ms\n", + "t = 18.5 ms\n", + "t = 19.0 ms\n", + "t = 19.5 ms\n", + "t = 20.0 ms\n", + "t = 20.5 ms\n", + "t = 21.0 ms\n", + "t = 21.5 ms\n", + "t = 22.0 ms\n", + "t = 22.5 ms\n", + "t = 23.0 ms\n", + "t = 23.5 ms\n", + "t = 24.0 ms\n", + "t = 24.5 ms\n", + "t = 25.0 ms\n", + "t = 25.5 ms\n", + "t = 26.0 ms\n", + "t = 26.5 ms\n", + "t = 27.0 ms\n", + "t = 27.5 ms\n", + "t = 28.0 ms\n", + "t = 28.5 ms\n", + "t = 29.0 ms\n", + "t = 29.5 ms\n", + "t = 30.0 ms\n", + "t = 30.5 ms\n", + "t = 31.0 ms\n", + "t = 31.5 ms\n", + "t = 32.0 ms\n", + "t = 32.5 ms\n", + "t = 33.0 ms\n", + "t = 33.5 ms\n", + "t = 34.0 ms\n", + "t = 34.5 ms\n", + "t = 35.0 ms\n", + "t = 35.5 ms\n", + "t = 36.0 ms\n", + "t = 36.5 ms\n", + "t = 37.0 ms\n", + "t = 37.5 ms\n", + "t = 38.0 ms\n", + "t = 38.5 ms\n", + "t = 39.0 ms\n", + "t = 39.5 ms\n", + "t = 40.0 ms\n", + "t = 40.5 ms\n", + "t = 41.0 ms\n", + "t = 41.5 ms\n", + "t = 42.0 ms\n", + "t = 42.5 ms\n", + "t = 43.0 ms\n", + "t = 43.5 ms\n", + "t = 44.0 ms\n", + "t = 44.5 ms\n", + "t = 45.0 ms\n", + "t = 45.5 ms\n", + "t = 46.0 ms\n", + "t = 46.5 ms\n", + "t = 47.0 ms\n", + "t = 47.5 ms\n", + "t = 48.0 ms\n", + "t = 48.5 ms\n", + "t = 49.0 ms\n", + "t = 49.5 ms\n", + "t = 50.0 ms\n", + "t = 50.5 ms\n", + "t = 51.0 ms\n", + "t = 51.5 ms\n", + "t = 52.0 ms\n", + "t = 52.5 ms\n", + "t = 53.0 ms\n", + "t = 53.5 ms\n", + "t = 54.0 ms\n", + "t = 54.5 ms\n", + "t = 55.0 ms\n", + "t = 55.5 ms\n", + "t = 56.0 ms\n", + "t = 56.5 ms\n", + "t = 57.0 ms\n", + "t = 57.5 ms\n", + "t = 58.0 ms\n", + "t = 58.5 ms\n", + "t = 59.0 ms\n", + "t = 59.5 ms\n", + "t = 60.0 ms\n", + "t = 60.5 ms\n", + "t = 61.0 ms\n", + "t = 61.5 ms\n", + "t = 62.0 ms\n", + "t = 62.5 ms\n", + "t = 63.0 ms\n", + "t = 63.5 ms\n", + "t = 64.0 ms\n", + "t = 64.5 ms\n", + "t = 65.0 ms\n", + "t = 65.5 ms\n", + "t = 66.0 ms\n", + "t = 66.5 ms\n", + "t = 67.0 ms\n", + "t = 67.5 ms\n", + "t = 68.0 ms\n", + "t = 68.5 ms\n", + "t = 69.0 ms\n", + "t = 69.5 ms\n", + "t = 70.0 ms\n", + "t = 70.5 ms\n", + "t = 71.0 ms\n", + "t = 71.5 ms\n", + "t = 72.0 ms\n", + "t = 72.5 ms\n", + "t = 73.0 ms\n", + "t = 73.5 ms\n", + "t = 74.0 ms\n", + "t = 74.5 ms\n", + "t = 75.0 ms\n", + "t = 75.5 ms\n", + "t = 76.0 ms\n", + "t = 76.5 ms\n", + "t = 77.0 ms\n", + "t = 77.5 ms\n", + "t = 78.0 ms\n", + "t = 78.5 ms\n", + "t = 79.0 ms\n", + "t = 79.5 ms\n", + "t = 80.0 ms\n", + "t = 80.5 ms\n", + "t = 81.0 ms\n", + "t = 81.5 ms\n", + "t = 82.0 ms\n", + "t = 82.5 ms\n", + "t = 83.0 ms\n", + "t = 83.5 ms\n", + "t = 84.0 ms\n", + "t = 84.5 ms\n", + "t = 85.0 ms\n", + "t = 85.5 ms\n", + "t = 86.0 ms\n", + "t = 86.5 ms\n", + "t = 87.0 ms\n", + "t = 87.5 ms\n", + "t = 88.0 ms\n", + "t = 88.5 ms\n", + "t = 89.0 ms\n", + "t = 89.5 ms\n", + "t = 90.0 ms\n", + "t = 90.5 ms\n", + "t = 91.0 ms\n", + "t = 91.5 ms\n", + "t = 92.0 ms\n", + "t = 92.5 ms\n", + "t = 93.0 ms\n", + "t = 93.5 ms\n", + "t = 94.0 ms\n", + "t = 94.5 ms\n", + "t = 95.0 ms\n", + "t = 95.5 ms\n", + "t = 96.0 ms\n", + "t = 96.5 ms\n", + "t = 97.0 ms\n", + "t = 97.5 ms\n", + "t = 98.0 ms\n", + "t = 98.5 ms\n", + "t = 99.0 ms\n", + "t = 99.5 ms\n", + "t = 100.0 ms\n", + "t = 100.5 ms\n", + "t = 101.0 ms\n", + "t = 101.5 ms\n", + "t = 102.0 ms\n", + "t = 102.5 ms\n", + "t = 103.0 ms\n", + "t = 103.5 ms\n", + "t = 104.0 ms\n", + "t = 104.5 ms\n", + "t = 105.0 ms\n", + "t = 105.5 ms\n", + "t = 106.0 ms\n", + "t = 106.5 ms\n", + "t = 107.0 ms\n", + "t = 107.5 ms\n", + "t = 108.0 ms\n", + "t = 108.5 ms\n", + "t = 109.0 ms\n", + "t = 109.5 ms\n", + "t = 110.0 ms\n", + "t = 110.5 ms\n", + "t = 111.0 ms\n", + "t = 111.5 ms\n", + "t = 112.0 ms\n", + "t = 112.5 ms\n", + "t = 113.0 ms\n", + "t = 113.5 ms\n", + "t = 114.0 ms\n", + "t = 114.5 ms\n", + "t = 115.0 ms\n", + "t = 115.5 ms\n", + "t = 116.0 ms\n", + "t = 116.5 ms\n", + "t = 117.0 ms\n", + "t = 117.5 ms\n", + "t = 118.0 ms\n", + "t = 118.5 ms\n", + "t = 119.0 ms\n", + "t = 119.5 ms\n", + "t = 120.0 ms\n", + "t = 120.5 ms\n", + "t = 121.0 ms\n", + "t = 121.5 ms\n", + "t = 122.0 ms\n", + "t = 122.5 ms\n", + "t = 123.0 ms\n", + "t = 123.5 ms\n", + "t = 124.0 ms\n", + "t = 124.5 ms\n", + "t = 125.0 ms\n", + "t = 125.5 ms\n", + "t = 126.0 ms\n", + "t = 126.5 ms\n", + "t = 127.0 ms\n", + "t = 127.5 ms\n", + "t = 128.0 ms\n", + "t = 128.5 ms\n", + "t = 129.0 ms\n", + "t = 129.5 ms\n", + "t = 130.0 ms\n", + "t = 130.5 ms\n", + "t = 131.0 ms\n", + "t = 131.5 ms\n", + "t = 132.0 ms\n", + "t = 132.5 ms\n", + "t = 133.0 ms\n", + "t = 133.5 ms\n", + "t = 134.0 ms\n", + "t = 134.5 ms\n", + "t = 135.0 ms\n", + "t = 135.5 ms\n", + "t = 136.0 ms\n", + "t = 136.5 ms\n", + "t = 137.0 ms\n", + "t = 137.5 ms\n", + "t = 138.0 ms\n", + "t = 138.5 ms\n", + "t = 139.0 ms\n", + "t = 139.5 ms\n", + "t = 140.0 ms\n", + "t = 140.5 ms\n", + "t = 141.0 ms\n", + "t = 141.5 ms\n", + "t = 142.0 ms\n", + "t = 142.5 ms\n", + "t = 143.0 ms\n", + "t = 143.5 ms\n", + "t = 144.0 ms\n", + "t = 144.5 ms\n", + "t = 145.0 ms\n", + "t = 145.5 ms\n", + "t = 146.0 ms\n", + "t = 146.5 ms\n", + "t = 147.0 ms\n", + "t = 147.5 ms\n", + "t = 148.0 ms\n", + "t = 148.5 ms\n", + "t = 149.0 ms\n", + "t = 149.5 ms\n", + "t = 150.0 ms\n", + "t = 150.5 ms\n", + "t = 151.0 ms\n", + "t = 151.5 ms\n", + "t = 152.0 ms\n", + "t = 152.5 ms\n", + "t = 153.0 ms\n", + "t = 153.5 ms\n", + "t = 154.0 ms\n", + "t = 154.5 ms\n", + "t = 155.0 ms\n", + "t = 155.5 ms\n", + "t = 156.0 ms\n", + "t = 156.5 ms\n", + "t = 157.0 ms\n", + "t = 157.5 ms\n", + "t = 158.0 ms\n", + "t = 158.5 ms\n", + "t = 159.0 ms\n", + "t = 159.5 ms\n", + "t = 160.0 ms\n", + "t = 160.5 ms\n", + "t = 161.0 ms\n", + "t = 161.5 ms\n", + "t = 162.0 ms\n", + "t = 162.5 ms\n", + "t = 163.0 ms\n", + "t = 163.5 ms\n", + "t = 164.0 ms\n", + "t = 164.5 ms\n", + "t = 165.0 ms\n", + "t = 165.5 ms\n", + "t = 166.0 ms\n", + "t = 166.5 ms\n", + "t = 167.0 ms\n", + "t = 167.5 ms\n", + "t = 168.0 ms\n", + "t = 168.5 ms\n", + "t = 169.0 ms\n", + "t = 169.5 ms\n", + "t = 170.0 ms\n", + "t = 170.5 ms\n", + "t = 171.0 ms\n", + "t = 171.5 ms\n", + "t = 172.0 ms\n", + "t = 172.5 ms\n", + "t = 173.0 ms\n", + "t = 173.5 ms\n", + "t = 174.0 ms\n", + "t = 174.5 ms\n", + "t = 175.0 ms\n", + "t = 175.5 ms\n", + "t = 176.0 ms\n", + "t = 176.5 ms\n", + "t = 177.0 ms\n", + "t = 177.5 ms\n", + "t = 178.0 ms\n", + "t = 178.5 ms\n", + "t = 179.0 ms\n", + "t = 179.5 ms\n", + "t = 180.0 ms\n", + "t = 180.5 ms\n", + "t = 181.0 ms\n", + "t = 181.5 ms\n", + "t = 182.0 ms\n", + "t = 182.5 ms\n", + "t = 183.0 ms\n", + "t = 183.5 ms\n", + "t = 184.0 ms\n", + "t = 184.5 ms\n", + "t = 185.0 ms\n", + "t = 185.5 ms\n", + "t = 186.0 ms\n", + "t = 186.5 ms\n", + "t = 187.0 ms\n", + "t = 187.5 ms\n", + "t = 188.0 ms\n", + "t = 188.5 ms\n", + "t = 189.0 ms\n", + "t = 189.5 ms\n", + "t = 190.0 ms\n", + "t = 190.5 ms\n", + "t = 191.0 ms\n", + "t = 191.5 ms\n", + "t = 192.0 ms\n", + "t = 192.5 ms\n", + "t = 193.0 ms\n", + "t = 193.5 ms\n", + "t = 194.0 ms\n", + "t = 194.5 ms\n", + "t = 195.0 ms\n", + "t = 195.5 ms\n", + "t = 196.0 ms\n", + "t = 196.5 ms\n", + "t = 197.0 ms\n", + "t = 197.5 ms\n", + "t = 198.0 ms\n", + "t = 198.5 ms\n", + "t = 199.0 ms\n", + "t = 199.5 ms\n", + "t = 200.0 ms\n", + "t = 200.5 ms\n", + "t = 201.0 ms\n", + "t = 201.5 ms\n", + "t = 202.0 ms\n", + "t = 202.5 ms\n", + "t = 203.0 ms\n", + "t = 203.5 ms\n", + "t = 204.0 ms\n", + "t = 204.5 ms\n", + "t = 205.0 ms\n", + "t = 205.5 ms\n", + "t = 206.0 ms\n", + "t = 206.5 ms\n", + "t = 207.0 ms\n", + "t = 207.5 ms\n", + "t = 208.0 ms\n", + "t = 208.5 ms\n", + "t = 209.0 ms\n", + "t = 209.5 ms\n", + "t = 210.0 ms\n", + "t = 210.5 ms\n", + "t = 211.0 ms\n", + "t = 211.5 ms\n", + "t = 212.0 ms\n", + "t = 212.5 ms\n", + "t = 213.0 ms\n", + "t = 213.5 ms\n", + "t = 214.0 ms\n", + "t = 214.5 ms\n", + "t = 215.0 ms\n", + "t = 215.5 ms\n", + "t = 216.0 ms\n", + "t = 216.5 ms\n", + "t = 217.0 ms\n", + "t = 217.5 ms\n", + "t = 218.0 ms\n", + "t = 218.5 ms\n", + "t = 219.0 ms\n", + "t = 219.5 ms\n", + "t = 220.0 ms\n", + "t = 220.5 ms\n", + "t = 221.0 ms\n", + "t = 221.5 ms\n", + "t = 222.0 ms\n", + "t = 222.5 ms\n", + "t = 223.0 ms\n", + "t = 223.5 ms\n", + "t = 224.0 ms\n", + "t = 224.5 ms\n", + "t = 225.0 ms\n", + "t = 225.5 ms\n", + "t = 226.0 ms\n", + "t = 226.5 ms\n", + "t = 227.0 ms\n", + "t = 227.5 ms\n", + "t = 228.0 ms\n", + "t = 228.5 ms\n", + "t = 229.0 ms\n", + "t = 229.5 ms\n", + "t = 230.0 ms\n", + "t = 230.5 ms\n", + "t = 231.0 ms\n", + "t = 231.5 ms\n", + "t = 232.0 ms\n", + "t = 232.5 ms\n", + "t = 233.0 ms\n", + "t = 233.5 ms\n", + "t = 234.0 ms\n", + "t = 234.5 ms\n", + "t = 235.0 ms\n", + "t = 235.5 ms\n", + "t = 236.0 ms\n", + "t = 236.5 ms\n", + "t = 237.0 ms\n", + "t = 237.5 ms\n", + "t = 238.0 ms\n", + "t = 238.5 ms\n", + "t = 239.0 ms\n", + "t = 239.5 ms\n", + "t = 240.0 ms\n", + "t = 240.5 ms\n", + "t = 241.0 ms\n", + "t = 241.5 ms\n", + "t = 242.0 ms\n", + "t = 242.5 ms\n", + "t = 243.0 ms\n", + "t = 243.5 ms\n", + "t = 244.0 ms\n", + "t = 244.5 ms\n", + "t = 245.0 ms\n", + "t = 245.5 ms\n", + "t = 246.0 ms\n", + "t = 246.5 ms\n", + "t = 247.0 ms\n", + "t = 247.5 ms\n", + "t = 248.0 ms\n", + "t = 248.5 ms\n", + "t = 249.0 ms\n", + "t = 249.5 ms\n", + "t = 250.0 ms\n", + "t = 250.5 ms\n", + "t = 251.0 ms\n", + "t = 251.5 ms\n", + "t = 252.0 ms\n", + "t = 252.5 ms\n", + "t = 253.0 ms\n", + "t = 253.5 ms\n", + "t = 254.0 ms\n", + "t = 254.5 ms\n", + "t = 255.0 ms\n", + "t = 255.5 ms\n", + "t = 256.0 ms\n", + "t = 256.5 ms\n", + "t = 257.0 ms\n", + "t = 257.5 ms\n", + "t = 258.0 ms\n", + "t = 258.5 ms\n", + "t = 259.0 ms\n", + "t = 259.5 ms\n", + "t = 260.0 ms\n", + "t = 260.5 ms\n", + "t = 261.0 ms\n", + "t = 261.5 ms\n", + "t = 262.0 ms\n", + "t = 262.5 ms\n", + "t = 263.0 ms\n", + "t = 263.5 ms\n", + "t = 264.0 ms\n", + "t = 264.5 ms\n", + "t = 265.0 ms\n", + "t = 265.5 ms\n", + "t = 266.0 ms\n", + "t = 266.5 ms\n", + "t = 267.0 ms\n", + "t = 267.5 ms\n", + "t = 268.0 ms\n", + "t = 268.5 ms\n", + "t = 269.0 ms\n", + "t = 269.5 ms\n", + "t = 270.0 ms\n", + "t = 270.5 ms\n", + "t = 271.0 ms\n", + "t = 271.5 ms\n", + "t = 272.0 ms\n", + "t = 272.5 ms\n", + "t = 273.0 ms\n", + "t = 273.5 ms\n", + "t = 274.0 ms\n", + "t = 274.5 ms\n", + "t = 275.0 ms\n", + "t = 275.5 ms\n", + "t = 276.0 ms\n", + "t = 276.5 ms\n", + "t = 277.0 ms\n", + "t = 277.5 ms\n", + "t = 278.0 ms\n", + "t = 278.5 ms\n", + "t = 279.0 ms\n", + "t = 279.5 ms\n", + "t = 280.0 ms\n", + "t = 280.5 ms\n", + "t = 281.0 ms\n", + "t = 281.5 ms\n", + "t = 282.0 ms\n", + "t = 282.5 ms\n", + "t = 283.0 ms\n", + "t = 283.5 ms\n", + "t = 284.0 ms\n", + "t = 284.5 ms\n", + "t = 285.0 ms\n", + "t = 285.5 ms\n", + "t = 286.0 ms\n", + "t = 286.5 ms\n", + "t = 287.0 ms\n", + "t = 287.5 ms\n", + "t = 288.0 ms\n", + "t = 288.5 ms\n", + "t = 289.0 ms\n", + "t = 289.5 ms\n", + "t = 290.0 ms\n", + "t = 290.5 ms\n", + "t = 291.0 ms\n", + "t = 291.5 ms\n", + "t = 292.0 ms\n", + "t = 292.5 ms\n", + "t = 293.0 ms\n", + "t = 293.5 ms\n", + "t = 294.0 ms\n", + "t = 294.5 ms\n", + "t = 295.0 ms\n", + "t = 295.5 ms\n", + "t = 296.0 ms\n", + "t = 296.5 ms\n", + "t = 297.0 ms\n", + "t = 297.5 ms\n", + "t = 298.0 ms\n", + "t = 298.5 ms\n", + "t = 299.0 ms\n", + "t = 299.5 ms\n", + "t = 300.0 ms\n", + "t = 300.5 ms\n", + "t = 301.0 ms\n", + "t = 301.5 ms\n", + "t = 302.0 ms\n", + "t = 302.5 ms\n", + "t = 303.0 ms\n", + "t = 303.5 ms\n", + "t = 304.0 ms\n", + "t = 304.5 ms\n", + "t = 305.0 ms\n", + "t = 305.5 ms\n", + "t = 306.0 ms\n", + "t = 306.5 ms\n", + "t = 307.0 ms\n", + "t = 307.5 ms\n", + "t = 308.0 ms\n", + "t = 308.5 ms\n", + "t = 309.0 ms\n", + "t = 309.5 ms\n", + "t = 310.0 ms\n", + "t = 310.5 ms\n", + "t = 311.0 ms\n", + "t = 311.5 ms\n", + "t = 312.0 ms\n", + "t = 312.5 ms\n", + "t = 313.0 ms\n", + "t = 313.5 ms\n", + "t = 314.0 ms\n", + "t = 314.5 ms\n", + "t = 315.0 ms\n", + "t = 315.5 ms\n", + "t = 316.0 ms\n", + "t = 316.5 ms\n", + "t = 317.0 ms\n", + "t = 317.5 ms\n", + "t = 318.0 ms\n", + "t = 318.5 ms\n", + "t = 319.0 ms\n", + "t = 319.5 ms\n", + "t = 320.0 ms\n", + "t = 320.5 ms\n", + "t = 321.0 ms\n", + "t = 321.5 ms\n", + "t = 322.0 ms\n", + "t = 322.5 ms\n", + "t = 323.0 ms\n", + "t = 323.5 ms\n", + "t = 324.0 ms\n", + "t = 324.5 ms\n", + "t = 325.0 ms\n", + "t = 325.5 ms\n", + "t = 326.0 ms\n", + "t = 326.5 ms\n", + "t = 327.0 ms\n", + "t = 327.5 ms\n", + "t = 328.0 ms\n", + "t = 328.5 ms\n", + "t = 329.0 ms\n", + "t = 329.5 ms\n", + "t = 330.0 ms\n", + "t = 330.5 ms\n", + "t = 331.0 ms\n", + "t = 331.5 ms\n", + "t = 332.0 ms\n", + "t = 332.5 ms\n", + "t = 333.0 ms\n", + "t = 333.5 ms\n", + "t = 334.0 ms\n", + "t = 334.5 ms\n", + "t = 335.0 ms\n", + "t = 335.5 ms\n", + "t = 336.0 ms\n", + "t = 336.5 ms\n", + "t = 337.0 ms\n", + "t = 337.5 ms\n", + "t = 338.0 ms\n", + "t = 338.5 ms\n", + "t = 339.0 ms\n", + "t = 339.5 ms\n", + "t = 340.0 ms\n", + "t = 340.5 ms\n", + "t = 341.0 ms\n", + "t = 341.5 ms\n", + "t = 342.0 ms\n", + "t = 342.5 ms\n", + "t = 343.0 ms\n", + "t = 343.5 ms\n", + "t = 344.0 ms\n", + "t = 344.5 ms\n", + "t = 345.0 ms\n", + "t = 345.5 ms\n", + "t = 346.0 ms\n", + "t = 346.5 ms\n", + "t = 347.0 ms\n", + "t = 347.5 ms\n", + "t = 348.0 ms\n", + "t = 348.5 ms\n", + "t = 349.0 ms\n", + "t = 349.5 ms\n", + "t = 350.0 ms\n", + "t = 350.5 ms\n", + "t = 351.0 ms\n", + "t = 351.5 ms\n", + "t = 352.0 ms\n", + "t = 352.5 ms\n", + "t = 353.0 ms\n", + "t = 353.5 ms\n", + "t = 354.0 ms\n", + "t = 354.5 ms\n", + "t = 355.0 ms\n", + "t = 355.5 ms\n", + "t = 356.0 ms\n", + "t = 356.5 ms\n", + "t = 357.0 ms\n", + "t = 357.5 ms\n", + "t = 358.0 ms\n", + "t = 358.5 ms\n", + "t = 359.0 ms\n", + "t = 359.5 ms\n", + "t = 360.0 ms\n", + "t = 360.5 ms\n", + "t = 361.0 ms\n", + "t = 361.5 ms\n", + "t = 362.0 ms\n", + "t = 362.5 ms\n", + "t = 363.0 ms\n", + "t = 363.5 ms\n", + "t = 364.0 ms\n", + "t = 364.5 ms\n", + "t = 365.0 ms\n", + "t = 365.5 ms\n", + "t = 366.0 ms\n", + "t = 366.5 ms\n", + "t = 367.0 ms\n", + "t = 367.5 ms\n", + "t = 368.0 ms\n", + "t = 368.5 ms\n", + "t = 369.0 ms\n", + "t = 369.5 ms\n", + "t = 370.0 ms\n", + "t = 370.5 ms\n", + "t = 371.0 ms\n", + "t = 371.5 ms\n", + "t = 372.0 ms\n", + "t = 372.5 ms\n", + "t = 373.0 ms\n", + "t = 373.5 ms\n", + "t = 374.0 ms\n", + "t = 374.5 ms\n", + "t = 375.0 ms\n", + "t = 375.5 ms\n", + "t = 376.0 ms\n", + "t = 376.5 ms\n", + "t = 377.0 ms\n", + "t = 377.5 ms\n", + "t = 378.0 ms\n", + "t = 378.5 ms\n", + "t = 379.0 ms\n", + "t = 379.5 ms\n", + "t = 380.0 ms\n", + "t = 380.5 ms\n", + "t = 381.0 ms\n", + "t = 381.5 ms\n", + "t = 382.0 ms\n", + "t = 382.5 ms\n", + "t = 383.0 ms\n", + "t = 383.5 ms\n", + "t = 384.0 ms\n", + "t = 384.5 ms\n", + "t = 385.0 ms\n", + "t = 385.5 ms\n", + "t = 386.0 ms\n", + "t = 386.5 ms\n", + "t = 387.0 ms\n", + "t = 387.5 ms\n", + "t = 388.0 ms\n", + "t = 388.5 ms\n", + "t = 389.0 ms\n", + "t = 389.5 ms\n", + "t = 390.0 ms\n", + "t = 390.5 ms\n", + "t = 391.0 ms\n", + "t = 391.5 ms\n", + "t = 392.0 ms\n", + "t = 392.5 ms\n", + "t = 393.0 ms\n", + "t = 393.5 ms\n", + "t = 394.0 ms\n", + "t = 394.5 ms\n", + "t = 395.0 ms\n", + "t = 395.5 ms\n", + "t = 396.0 ms\n", + "t = 396.5 ms\n", + "t = 397.0 ms\n", + "t = 397.5 ms\n", + "t = 398.0 ms\n", + "t = 398.5 ms\n", + "t = 399.0 ms\n", + "t = 399.5 ms\n", + "t = 400.0 ms\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual pre spike times: [ 2. 3. 4. 5. 6. 8. 10. 13. 18. 22. 24. 28. 29. 31.\n", + " 32. 34. 36. 37. 38. 39. 40. 42. 45. 47. 50. 52. 54. 56.\n", + " 57. 58. 59. 60. 61. 62. 65. 66. 68. 71. 74. 76. 78. 81.\n", + " 82. 83. 84. 87. 92. 94. 95. 96. 97. 99. 100. 101. 102. 105.\n", + " 106. 109. 110. 111. 112. 114. 116. 119. 120. 122. 125. 127. 128. 129.\n", + " 132. 133. 135. 136. 140. 144. 145. 147. 148. 149. 150. 152. 153. 154.\n", + " 158. 159. 160. 161. 163. 164. 167. 168. 169. 170. 171. 174. 176. 177.\n", + " 180. 181. 182. 183. 186. 189. 190. 194. 195. 199. 201. 202. 204. 207.\n", + " 208. 209. 210. 211. 212. 214. 215. 217. 219. 222. 226. 233. 234. 235.\n", + " 239. 241. 243. 244. 248. 249. 250. 251. 254. 255. 257. 258. 259. 263.\n", + " 264. 266. 269. 271. 274. 276. 278. 279. 281. 283. 284. 285. 288. 293.\n", + " 294. 300. 301. 302. 304. 305. 309. 311. 312. 314. 318. 323. 324. 329.\n", + " 330. 336. 338. 339. 340. 342. 344. 346. 350. 357. 360. 371. 372. 373.\n", + " 376. 378. 379. 382. 383. 385. 390. 392. 397. 399. 400.]\n", + "Actual post spike times: [ 6. 9. 11.5 14. 16.5 19.5 22. 25.5 28.5 31.5 34.5 37.5\n", + " 40.5 43. 46. 48.5 51.5 54. 56.5 59. 62. 65.5 70. 73.\n", + " 76. 78.5 81.5 84.5 87.5 90.5 93.5 96.5 99. 102. 104.5 107.\n", + " 109.5 112.5 115.5 118.5 121.5 124.5 127.5 130.5 133.5 137. 141. 143.5\n", + " 146.5 149. 152. 154.5 157.5 160.5 163.5 166.5 169.5 172.5 175.5 179.5\n", + " 183.5 186.5 189. 192. 194.5 197.5 200.5 203. 205.5 209.5 212.5 215.5\n", + " 218. 220.5 224.5 227.5 230.5 233. 236. 239. 241.5 244. 246.5 249.\n", + " 251.5 254. 256.5 259. 261.5 265.5 268.5 271.5 274. 276.5 279.5 282.\n", + " 285. 288. 290.5 293. 296.5 299.5 302. 305.5 309.5 313.5 316.5 320.5\n", + " 323.5 326. 328.5 331.5 334.5 337.5 340.5 344.5 347. 350. 353.5 356.5\n", + " 359.5 362.5 365.5 368.5 371.5 382.5 385.5 388.5 392. 395. 397.5 400.5]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fname_snip = \"\"\n", + "\n", + "post_spike_times = np.sort(np.unique(1 + np.round(500 * np.sort(np.abs(np.random.randn(500)))))) # [ms]\n", + "pre_spike_times = np.sort(np.unique(1 + np.round(500 * np.sort(np.abs(np.random.randn(500)))))) # [ms]\n", + "\n", + "# run the simulation\n", + "timevec, t_hist, third_factor_trace, w_hist = run_synapse_test(neuron_model_name=neuron_model_name,\n", + " synapse_model_name=synapse_model_name,\n", + " resolution=.5, # [ms]\n", + " delay=1.5, # [ms]\n", + " pre_spike_times=pre_spike_times,\n", + " post_spike_times=post_spike_times,\n", + " sim_time=400.,\n", + " fname_snip=fname_snip)\n", + "\n", + "# verify\n", + "assert np.any(np.abs(np.array(w_hist) - 1) > 0.), \"No change in the weight!\"\n", + "\n", + "# verify that weight does not change appreciably when third factor trace is close to zero\n", + "idx = np.where(np.abs(third_factor_trace) < 1E-12)[0] # find where third_factor_trace is (almost) zero\n", + "times_dw_should_be_zero = timevec[idx]\n", + "assert len(times_dw_should_be_zero) > 0 # make sure we have > 0 datapoints to check\n", + "for time_dw_should_be_zero in times_dw_should_be_zero[1:]:\n", + " _idx = np.argmin((time_dw_should_be_zero - np.array(t_hist))**2)\n", + " np.testing.assert_allclose(t_hist[_idx], time_dw_should_be_zero)\n", + " np.testing.assert_allclose(0., np.abs(w_hist[_idx - 1] - w_hist[_idx])) # make sure that weight does not change appreciably\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Detailed look at the numerics\n", + "------------------------\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " -- N E S T --\n", + " Copyright (C) 2004 The NEST Initiative\n", + "\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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", + "[87,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", + "Pre spike times: [40.0, 100.0]\n", + "Post spike times: [25.0, 75.0]\n", + "t = 0.0 ms\n", + "t = 0.1 ms\n", + "t = 0.2 ms\n", + "t = 0.30000000000000004 ms\n", + "t = 0.4 ms\n", + "t = 0.5 ms\n", + "t = 0.6 ms\n", + "t = 0.7 ms\n", + "t = 0.7999999999999999 ms\n", + "t = 0.8999999999999999 ms\n", + "t = 0.9999999999999999 ms\n", + "t = 1.0999999999999999 ms\n", + "t = 1.2 ms\n", + "t = 1.3 ms\n", + "t = 1.4000000000000001 ms\n", + "t = 1.5000000000000002 ms\n", + "t = 1.6000000000000003 ms\n", + "t = 1.7000000000000004 ms\n", + "t = 1.8000000000000005 ms\n", + "t = 1.9000000000000006 ms\n", + "t = 2.0000000000000004 ms\n", + "t = 2.1000000000000005 ms\n", + "t = 2.2000000000000006 ms\n", + "t = 2.3000000000000007 ms\n", + "t = 2.400000000000001 ms\n", + "t = 2.500000000000001 ms\n", + "t = 2.600000000000001 ms\n", + "t = 2.700000000000001 ms\n", + "t = 2.800000000000001 ms\n", + "t = 2.9000000000000012 ms\n", + "t = 3.0000000000000013 ms\n", + "t = 3.1000000000000014 ms\n", + "t = 3.2000000000000015 ms\n", + "t = 3.3000000000000016 ms\n", + "t = 3.4000000000000017 ms\n", + "t = 3.5000000000000018 ms\n", + "t = 3.600000000000002 ms\n", + "t = 3.700000000000002 ms\n", + "t = 3.800000000000002 ms\n", + "t = 3.900000000000002 ms\n", + "t = 4.000000000000002 ms\n", + "t = 4.100000000000001 ms\n", + "t = 4.200000000000001 ms\n", + "t = 4.300000000000001 ms\n", + "t = 4.4 ms\n", + "t = 4.5 ms\n", + "t = 4.6 ms\n", + "t = 4.699999999999999 ms\n", + "t = 4.799999999999999 ms\n", + "t = 4.899999999999999 ms\n", + "t = 4.999999999999998 ms\n", + "t = 5.099999999999998 ms\n", + "t = 5.1999999999999975 ms\n", + "t = 5.299999999999997 ms\n", + "t = 5.399999999999997 ms\n", + "t = 5.4999999999999964 ms\n", + "t = 5.599999999999996 ms\n", + "t = 5.699999999999996 ms\n", + "t = 5.799999999999995 ms\n", + "t = 5.899999999999995 ms\n", + "t = 5.999999999999995 ms\n", + "t = 6.099999999999994 ms\n", + "t = 6.199999999999994 ms\n", + "t = 6.299999999999994 ms\n", + "t = 6.399999999999993 ms\n", + "t = 6.499999999999993 ms\n", + "t = 6.5999999999999925 ms\n", + "t = 6.699999999999992 ms\n", + "t = 6.799999999999992 ms\n", + "t = 6.8999999999999915 ms\n", + "t = 6.999999999999991 ms\n", + "t = 7.099999999999991 ms\n", + "t = 7.19999999999999 ms\n", + "t = 7.29999999999999 ms\n", + "t = 7.39999999999999 ms\n", + "t = 7.499999999999989 ms\n", + "t = 7.599999999999989 ms\n", + "t = 7.699999999999989 ms\n", + "t = 7.799999999999988 ms\n", + "t = 7.899999999999988 ms\n", + "t = 7.999999999999988 ms\n", + "t = 8.099999999999987 ms\n", + "t = 8.199999999999987 ms\n", + "t = 8.299999999999986 ms\n", + "t = 8.399999999999986 ms\n", + "t = 8.499999999999986 ms\n", + "t = 8.599999999999985 ms\n", + "t = 8.699999999999985 ms\n", + "t = 8.799999999999985 ms\n", + "t = 8.899999999999984 ms\n", + "t = 8.999999999999984 ms\n", + "t = 9.099999999999984 ms\n", + "t = 9.199999999999983 ms\n", + "t = 9.299999999999983 ms\n", + "t = 9.399999999999983 ms\n", + "t = 9.499999999999982 ms\n", + "t = 9.599999999999982 ms\n", + "t = 9.699999999999982 ms\n", + "t = 9.799999999999981 ms\n", + "t = 9.89999999999998 ms\n", + "t = 9.99999999999998 ms\n", + "t = 10.09999999999998 ms\n", + "t = 10.19999999999998 ms\n", + "t = 10.29999999999998 ms\n", + "t = 10.399999999999979 ms\n", + "t = 10.499999999999979 ms\n", + "t = 10.599999999999978 ms\n", + "t = 10.699999999999978 ms\n", + "t = 10.799999999999978 ms\n", + "t = 10.899999999999977 ms\n", + "t = 10.999999999999977 ms\n", + "t = 11.099999999999977 ms\n", + "t = 11.199999999999976 ms\n", + "t = 11.299999999999976 ms\n", + "t = 11.399999999999975 ms\n", + "t = 11.499999999999975 ms\n", + "t = 11.599999999999975 ms\n", + "t = 11.699999999999974 ms\n", + "t = 11.799999999999974 ms\n", + "t = 11.899999999999974 ms\n", + "t = 11.999999999999973 ms\n", + "t = 12.099999999999973 ms\n", + "t = 12.199999999999973 ms\n", + "t = 12.299999999999972 ms\n", + "t = 12.399999999999972 ms\n", + "t = 12.499999999999972 ms\n", + "t = 12.599999999999971 ms\n", + "t = 12.69999999999997 ms\n", + "t = 12.79999999999997 ms\n", + "t = 12.89999999999997 ms\n", + "t = 12.99999999999997 ms\n", + "t = 13.09999999999997 ms\n", + "t = 13.199999999999969 ms\n", + "t = 13.299999999999969 ms\n", + "t = 13.399999999999968 ms\n", + "t = 13.499999999999968 ms\n", + "t = 13.599999999999968 ms\n", + "t = 13.699999999999967 ms\n", + "t = 13.799999999999967 ms\n", + "t = 13.899999999999967 ms\n", + "t = 13.999999999999966 ms\n", + "t = 14.099999999999966 ms\n", + "t = 14.199999999999966 ms\n", + "t = 14.299999999999965 ms\n", + "t = 14.399999999999965 ms\n", + "t = 14.499999999999964 ms\n", + "t = 14.599999999999964 ms\n", + "t = 14.699999999999964 ms\n", + "t = 14.799999999999963 ms\n", + "t = 14.899999999999963 ms\n", + "t = 14.999999999999963 ms\n", + "t = 15.099999999999962 ms\n", + "t = 15.199999999999962 ms\n", + "t = 15.299999999999962 ms\n", + "t = 15.399999999999961 ms\n", + "t = 15.499999999999961 ms\n", + "t = 15.59999999999996 ms\n", + "t = 15.69999999999996 ms\n", + "t = 15.79999999999996 ms\n", + "t = 15.89999999999996 ms\n", + "t = 15.99999999999996 ms\n", + "t = 16.09999999999996 ms\n", + "t = 16.19999999999996 ms\n", + "t = 16.29999999999996 ms\n", + "t = 16.399999999999963 ms\n", + "t = 16.499999999999964 ms\n", + "t = 16.599999999999966 ms\n", + "t = 16.699999999999967 ms\n", + "t = 16.79999999999997 ms\n", + "t = 16.89999999999997 ms\n", + "t = 16.99999999999997 ms\n", + "t = 17.099999999999973 ms\n", + "t = 17.199999999999974 ms\n", + "t = 17.299999999999976 ms\n", + "t = 17.399999999999977 ms\n", + "t = 17.49999999999998 ms\n", + "t = 17.59999999999998 ms\n", + "t = 17.69999999999998 ms\n", + "t = 17.799999999999983 ms\n", + "t = 17.899999999999984 ms\n", + "t = 17.999999999999986 ms\n", + "t = 18.099999999999987 ms\n", + "t = 18.19999999999999 ms\n", + "t = 18.29999999999999 ms\n", + "t = 18.39999999999999 ms\n", + "t = 18.499999999999993 ms\n", + "t = 18.599999999999994 ms\n", + "t = 18.699999999999996 ms\n", + "t = 18.799999999999997 ms\n", + "t = 18.9 ms\n", + "t = 19.0 ms\n", + "t = 19.1 ms\n", + "t = 19.200000000000003 ms\n", + "t = 19.300000000000004 ms\n", + "t = 19.400000000000006 ms\n", + "t = 19.500000000000007 ms\n", + "t = 19.60000000000001 ms\n", + "t = 19.70000000000001 ms\n", + "t = 19.80000000000001 ms\n", + "t = 19.900000000000013 ms\n", + "t = 20.000000000000014 ms\n", + "t = 20.100000000000016 ms\n", + "t = 20.200000000000017 ms\n", + "t = 20.30000000000002 ms\n", + "t = 20.40000000000002 ms\n", + "t = 20.50000000000002 ms\n", + "t = 20.600000000000023 ms\n", + "t = 20.700000000000024 ms\n", + "t = 20.800000000000026 ms\n", + "t = 20.900000000000027 ms\n", + "t = 21.00000000000003 ms\n", + "t = 21.10000000000003 ms\n", + "t = 21.20000000000003 ms\n", + "t = 21.300000000000033 ms\n", + "t = 21.400000000000034 ms\n", + "t = 21.500000000000036 ms\n", + "t = 21.600000000000037 ms\n", + "t = 21.70000000000004 ms\n", + "t = 21.80000000000004 ms\n", + "t = 21.90000000000004 ms\n", + "t = 22.000000000000043 ms\n", + "t = 22.100000000000044 ms\n", + "t = 22.200000000000045 ms\n", + "t = 22.300000000000047 ms\n", + "t = 22.40000000000005 ms\n", + "t = 22.50000000000005 ms\n", + "t = 22.60000000000005 ms\n", + "t = 22.700000000000053 ms\n", + "t = 22.800000000000054 ms\n", + "t = 22.900000000000055 ms\n", + "t = 23.000000000000057 ms\n", + "t = 23.10000000000006 ms\n", + "t = 23.20000000000006 ms\n", + "t = 23.30000000000006 ms\n", + "t = 23.400000000000063 ms\n", + "t = 23.500000000000064 ms\n", + "t = 23.600000000000065 ms\n", + "t = 23.700000000000067 ms\n", + "t = 23.800000000000068 ms\n", + "t = 23.90000000000007 ms\n", + "t = 24.00000000000007 ms\n", + "t = 24.100000000000072 ms\n", + "t = 24.200000000000074 ms\n", + "t = 24.300000000000075 ms\n", + "t = 24.400000000000077 ms\n", + "t = 24.500000000000078 ms\n", + "t = 24.60000000000008 ms\n", + "t = 24.70000000000008 ms\n", + "t = 24.800000000000082 ms\n", + "t = 24.900000000000084 ms\n", + "t = 25.000000000000085 ms\n", + "t = 25.100000000000087 ms\n", + "t = 25.200000000000088 ms\n", + "t = 25.30000000000009 ms\n", + "t = 25.40000000000009 ms\n", + "t = 25.500000000000092 ms\n", + "t = 25.600000000000094 ms\n", + "t = 25.700000000000095 ms\n", + "t = 25.800000000000097 ms\n", + "t = 25.900000000000098 ms\n", + "t = 26.0000000000001 ms\n", + "t = 26.1000000000001 ms\n", + "t = 26.200000000000102 ms\n", + "t = 26.300000000000104 ms\n", + "t = 26.400000000000105 ms\n", + "t = 26.500000000000107 ms\n", + "t = 26.600000000000108 ms\n", + "t = 26.70000000000011 ms\n", + "t = 26.80000000000011 ms\n", + "t = 26.900000000000112 ms\n", + "t = 27.000000000000114 ms\n", + "t = 27.100000000000115 ms\n", + "t = 27.200000000000117 ms\n", + "t = 27.300000000000118 ms\n", + "t = 27.40000000000012 ms\n", + "t = 27.50000000000012 ms\n", + "t = 27.600000000000122 ms\n", + "t = 27.700000000000124 ms\n", + "t = 27.800000000000125 ms\n", + "t = 27.900000000000126 ms\n", + "t = 28.000000000000128 ms\n", + "t = 28.10000000000013 ms\n", + "t = 28.20000000000013 ms\n", + "t = 28.300000000000132 ms\n", + "t = 28.400000000000134 ms\n", + "t = 28.500000000000135 ms\n", + "t = 28.600000000000136 ms\n", + "t = 28.700000000000138 ms\n", + "t = 28.80000000000014 ms\n", + "t = 28.90000000000014 ms\n", + "t = 29.000000000000142 ms\n", + "t = 29.100000000000144 ms\n", + "t = 29.200000000000145 ms\n", + "t = 29.300000000000146 ms\n", + "t = 29.400000000000148 ms\n", + "t = 29.50000000000015 ms\n", + "t = 29.60000000000015 ms\n", + "t = 29.700000000000152 ms\n", + "t = 29.800000000000153 ms\n", + "t = 29.900000000000155 ms\n", + "t = 30.000000000000156 ms\n", + "t = 30.100000000000158 ms\n", + "t = 30.20000000000016 ms\n", + "t = 30.30000000000016 ms\n", + "t = 30.400000000000162 ms\n", + "t = 30.500000000000163 ms\n", + "t = 30.600000000000165 ms\n", + "t = 30.700000000000166 ms\n", + "t = 30.800000000000168 ms\n", + "t = 30.90000000000017 ms\n", + "t = 31.00000000000017 ms\n", + "t = 31.100000000000172 ms\n", + "t = 31.200000000000173 ms\n", + "t = 31.300000000000175 ms\n", + "t = 31.400000000000176 ms\n", + "t = 31.500000000000178 ms\n", + "t = 31.60000000000018 ms\n", + "t = 31.70000000000018 ms\n", + "t = 31.800000000000182 ms\n", + "t = 31.900000000000183 ms\n", + "t = 32.000000000000185 ms\n", + "t = 32.100000000000186 ms\n", + "t = 32.20000000000019 ms\n", + "t = 32.30000000000019 ms\n", + "t = 32.40000000000019 ms\n", + "t = 32.50000000000019 ms\n", + "t = 32.60000000000019 ms\n", + "t = 32.700000000000195 ms\n", + "t = 32.800000000000196 ms\n", + "t = 32.9000000000002 ms\n", + "t = 33.0000000000002 ms\n", + "t = 33.1000000000002 ms\n", + "t = 33.2000000000002 ms\n", + "t = 33.3000000000002 ms\n", + "t = 33.400000000000205 ms\n", + "t = 33.500000000000206 ms\n", + "t = 33.60000000000021 ms\n", + "t = 33.70000000000021 ms\n", + "t = 33.80000000000021 ms\n", + "t = 33.90000000000021 ms\n", + "t = 34.00000000000021 ms\n", + "t = 34.100000000000215 ms\n", + "t = 34.200000000000216 ms\n", + "t = 34.30000000000022 ms\n", + "t = 34.40000000000022 ms\n", + "t = 34.50000000000022 ms\n", + "t = 34.60000000000022 ms\n", + "t = 34.70000000000022 ms\n", + "t = 34.800000000000225 ms\n", + "t = 34.900000000000226 ms\n", + "t = 35.00000000000023 ms\n", + "t = 35.10000000000023 ms\n", + "t = 35.20000000000023 ms\n", + "t = 35.30000000000023 ms\n", + "t = 35.40000000000023 ms\n", + "t = 35.500000000000234 ms\n", + "t = 35.600000000000236 ms\n", + "t = 35.70000000000024 ms\n", + "t = 35.80000000000024 ms\n", + "t = 35.90000000000024 ms\n", + "t = 36.00000000000024 ms\n", + "t = 36.10000000000024 ms\n", + "t = 36.200000000000244 ms\n", + "t = 36.300000000000246 ms\n", + "t = 36.40000000000025 ms\n", + "t = 36.50000000000025 ms\n", + "t = 36.60000000000025 ms\n", + "t = 36.70000000000025 ms\n", + "t = 36.80000000000025 ms\n", + "t = 36.900000000000254 ms\n", + "t = 37.000000000000256 ms\n", + "t = 37.10000000000026 ms\n", + "t = 37.20000000000026 ms\n", + "t = 37.30000000000026 ms\n", + "t = 37.40000000000026 ms\n", + "t = 37.50000000000026 ms\n", + "t = 37.600000000000264 ms\n", + "t = 37.700000000000266 ms\n", + "t = 37.80000000000027 ms\n", + "t = 37.90000000000027 ms\n", + "t = 38.00000000000027 ms\n", + "t = 38.10000000000027 ms\n", + "t = 38.20000000000027 ms\n", + "t = 38.300000000000274 ms\n", + "t = 38.400000000000276 ms\n", + "t = 38.50000000000028 ms\n", + "t = 38.60000000000028 ms\n", + "t = 38.70000000000028 ms\n", + "t = 38.80000000000028 ms\n", + "t = 38.90000000000028 ms\n", + "t = 39.000000000000284 ms\n", + "t = 39.100000000000286 ms\n", + "t = 39.20000000000029 ms\n", + "t = 39.30000000000029 ms\n", + "t = 39.40000000000029 ms\n", + "t = 39.50000000000029 ms\n", + "t = 39.60000000000029 ms\n", + "t = 39.700000000000294 ms\n", + "t = 39.800000000000296 ms\n", + "t = 39.9000000000003 ms\n", + "t = 40.0000000000003 ms\n", + "t = 40.1000000000003 ms\n", + "t = 40.2000000000003 ms\n", + "t = 40.3000000000003 ms\n", + "t = 40.400000000000304 ms\n", + "t = 40.500000000000306 ms\n", + "t = 40.60000000000031 ms\n", + "t = 40.70000000000031 ms\n", + "t = 40.80000000000031 ms\n", + "t = 40.90000000000031 ms\n", + "t = 41.00000000000031 ms\n", + "t = 41.100000000000314 ms\n", + "t = 41.200000000000315 ms\n", + "t = 41.30000000000032 ms\n", + "t = 41.40000000000032 ms\n", + "t = 41.50000000000032 ms\n", + "t = 41.60000000000032 ms\n", + "t = 41.70000000000032 ms\n", + "t = 41.800000000000324 ms\n", + "t = 41.900000000000325 ms\n", + "t = 42.00000000000033 ms\n", + "t = 42.10000000000033 ms\n", + "t = 42.20000000000033 ms\n", + "t = 42.30000000000033 ms\n", + "t = 42.40000000000033 ms\n", + "t = 42.500000000000334 ms\n", + "t = 42.600000000000335 ms\n", + "t = 42.70000000000034 ms\n", + "t = 42.80000000000034 ms\n", + "t = 42.90000000000034 ms\n", + "t = 43.00000000000034 ms\n", + "t = 43.10000000000034 ms\n", + "t = 43.200000000000344 ms\n", + "t = 43.300000000000345 ms\n", + "t = 43.40000000000035 ms\n", + "t = 43.50000000000035 ms\n", + "t = 43.60000000000035 ms\n", + "t = 43.70000000000035 ms\n", + "t = 43.80000000000035 ms\n", + "t = 43.900000000000354 ms\n", + "t = 44.000000000000355 ms\n", + "t = 44.10000000000036 ms\n", + "t = 44.20000000000036 ms\n", + "t = 44.30000000000036 ms\n", + "t = 44.40000000000036 ms\n", + "t = 44.50000000000036 ms\n", + "t = 44.600000000000364 ms\n", + "t = 44.700000000000365 ms\n", + "t = 44.80000000000037 ms\n", + "t = 44.90000000000037 ms\n", + "t = 45.00000000000037 ms\n", + "t = 45.10000000000037 ms\n", + "t = 45.20000000000037 ms\n", + "t = 45.300000000000374 ms\n", + "t = 45.400000000000375 ms\n", + "t = 45.50000000000038 ms\n", + "t = 45.60000000000038 ms\n", + "t = 45.70000000000038 ms\n", + "t = 45.80000000000038 ms\n", + "t = 45.90000000000038 ms\n", + "t = 46.000000000000384 ms\n", + "t = 46.100000000000385 ms\n", + "t = 46.20000000000039 ms\n", + "t = 46.30000000000039 ms\n", + "t = 46.40000000000039 ms\n", + "t = 46.50000000000039 ms\n", + "t = 46.60000000000039 ms\n", + "t = 46.700000000000394 ms\n", + "t = 46.800000000000395 ms\n", + "t = 46.9000000000004 ms\n", + "t = 47.0000000000004 ms\n", + "t = 47.1000000000004 ms\n", + "t = 47.2000000000004 ms\n", + "t = 47.3000000000004 ms\n", + "t = 47.400000000000404 ms\n", + "t = 47.500000000000405 ms\n", + "t = 47.600000000000406 ms\n", + "t = 47.70000000000041 ms\n", + "t = 47.80000000000041 ms\n", + "t = 47.90000000000041 ms\n", + "t = 48.00000000000041 ms\n", + "t = 48.10000000000041 ms\n", + "t = 48.200000000000415 ms\n", + "t = 48.300000000000416 ms\n", + "t = 48.40000000000042 ms\n", + "t = 48.50000000000042 ms\n", + "t = 48.60000000000042 ms\n", + "t = 48.70000000000042 ms\n", + "t = 48.80000000000042 ms\n", + "t = 48.900000000000425 ms\n", + "t = 49.000000000000426 ms\n", + "t = 49.10000000000043 ms\n", + "t = 49.20000000000043 ms\n", + "t = 49.30000000000043 ms\n", + "t = 49.40000000000043 ms\n", + "t = 49.50000000000043 ms\n", + "t = 49.600000000000435 ms\n", + "t = 49.700000000000436 ms\n", + "t = 49.80000000000044 ms\n", + "t = 49.90000000000044 ms\n", + "t = 50.00000000000044 ms\n", + "t = 50.10000000000044 ms\n", + "t = 50.20000000000044 ms\n", + "t = 50.300000000000445 ms\n", + "t = 50.400000000000446 ms\n", + "t = 50.50000000000045 ms\n", + "t = 50.60000000000045 ms\n", + "t = 50.70000000000045 ms\n", + "t = 50.80000000000045 ms\n", + "t = 50.90000000000045 ms\n", + "t = 51.000000000000455 ms\n", + "t = 51.100000000000456 ms\n", + "t = 51.20000000000046 ms\n", + "t = 51.30000000000046 ms\n", + "t = 51.40000000000046 ms\n", + "t = 51.50000000000046 ms\n", + "t = 51.60000000000046 ms\n", + "t = 51.700000000000465 ms\n", + "t = 51.800000000000466 ms\n", + "t = 51.90000000000047 ms\n", + "t = 52.00000000000047 ms\n", + "t = 52.10000000000047 ms\n", + "t = 52.20000000000047 ms\n", + "t = 52.30000000000047 ms\n", + "t = 52.400000000000475 ms\n", + "t = 52.500000000000476 ms\n", + "t = 52.60000000000048 ms\n", + "t = 52.70000000000048 ms\n", + "t = 52.80000000000048 ms\n", + "t = 52.90000000000048 ms\n", + "t = 53.00000000000048 ms\n", + "t = 53.100000000000485 ms\n", + "t = 53.200000000000486 ms\n", + "t = 53.30000000000049 ms\n", + "t = 53.40000000000049 ms\n", + "t = 53.50000000000049 ms\n", + "t = 53.60000000000049 ms\n", + "t = 53.70000000000049 ms\n", + "t = 53.800000000000495 ms\n", + "t = 53.900000000000496 ms\n", + "t = 54.0000000000005 ms\n", + "t = 54.1000000000005 ms\n", + "t = 54.2000000000005 ms\n", + "t = 54.3000000000005 ms\n", + "t = 54.4000000000005 ms\n", + "t = 54.500000000000504 ms\n", + "t = 54.600000000000506 ms\n", + "t = 54.70000000000051 ms\n", + "t = 54.80000000000051 ms\n", + "t = 54.90000000000051 ms\n", + "t = 55.00000000000051 ms\n", + "t = 55.10000000000051 ms\n", + "t = 55.200000000000514 ms\n", + "t = 55.300000000000516 ms\n", + "t = 55.40000000000052 ms\n", + "t = 55.50000000000052 ms\n", + "t = 55.60000000000052 ms\n", + "t = 55.70000000000052 ms\n", + "t = 55.80000000000052 ms\n", + "t = 55.900000000000524 ms\n", + "t = 56.000000000000526 ms\n", + "t = 56.10000000000053 ms\n", + "t = 56.20000000000053 ms\n", + "t = 56.30000000000053 ms\n", + "t = 56.40000000000053 ms\n", + "t = 56.50000000000053 ms\n", + "t = 56.600000000000534 ms\n", + "t = 56.700000000000536 ms\n", + "t = 56.80000000000054 ms\n", + "t = 56.90000000000054 ms\n", + "t = 57.00000000000054 ms\n", + "t = 57.10000000000054 ms\n", + "t = 57.20000000000054 ms\n", + "t = 57.300000000000544 ms\n", + "t = 57.400000000000546 ms\n", + "t = 57.50000000000055 ms\n", + "t = 57.60000000000055 ms\n", + "t = 57.70000000000055 ms\n", + "t = 57.80000000000055 ms\n", + "t = 57.90000000000055 ms\n", + "t = 58.000000000000554 ms\n", + "t = 58.100000000000556 ms\n", + "t = 58.20000000000056 ms\n", + "t = 58.30000000000056 ms\n", + "t = 58.40000000000056 ms\n", + "t = 58.50000000000056 ms\n", + "t = 58.60000000000056 ms\n", + "t = 58.700000000000564 ms\n", + "t = 58.800000000000566 ms\n", + "t = 58.90000000000057 ms\n", + "t = 59.00000000000057 ms\n", + "t = 59.10000000000057 ms\n", + "t = 59.20000000000057 ms\n", + "t = 59.30000000000057 ms\n", + "t = 59.400000000000574 ms\n", + "t = 59.500000000000576 ms\n", + "t = 59.60000000000058 ms\n", + "t = 59.70000000000058 ms\n", + "t = 59.80000000000058 ms\n", + "t = 59.90000000000058 ms\n", + "t = 60.00000000000058 ms\n", + "t = 60.100000000000584 ms\n", + "t = 60.200000000000585 ms\n", + "t = 60.30000000000059 ms\n", + "t = 60.40000000000059 ms\n", + "t = 60.50000000000059 ms\n", + "t = 60.60000000000059 ms\n", + "t = 60.70000000000059 ms\n", + "t = 60.800000000000594 ms\n", + "t = 60.900000000000595 ms\n", + "t = 61.0000000000006 ms\n", + "t = 61.1000000000006 ms\n", + "t = 61.2000000000006 ms\n", + "t = 61.3000000000006 ms\n", + "t = 61.4000000000006 ms\n", + "t = 61.500000000000604 ms\n", + "t = 61.600000000000605 ms\n", + "t = 61.70000000000061 ms\n", + "t = 61.80000000000061 ms\n", + "t = 61.90000000000061 ms\n", + "t = 62.00000000000061 ms\n", + "t = 62.10000000000061 ms\n", + "t = 62.200000000000614 ms\n", + "t = 62.300000000000615 ms\n", + "t = 62.40000000000062 ms\n", + "t = 62.50000000000062 ms\n", + "t = 62.60000000000062 ms\n", + "t = 62.70000000000062 ms\n", + "t = 62.80000000000062 ms\n", + "t = 62.900000000000624 ms\n", + "t = 63.000000000000625 ms\n", + "t = 63.10000000000063 ms\n", + "t = 63.20000000000063 ms\n", + "t = 63.30000000000063 ms\n", + "t = 63.40000000000063 ms\n", + "t = 63.50000000000063 ms\n", + "t = 63.600000000000634 ms\n", + "t = 63.700000000000635 ms\n", + "t = 63.80000000000064 ms\n", + "t = 63.90000000000064 ms\n", + "t = 64.00000000000064 ms\n", + "t = 64.10000000000063 ms\n", + "t = 64.20000000000063 ms\n", + "t = 64.30000000000062 ms\n", + "t = 64.40000000000062 ms\n", + "t = 64.50000000000061 ms\n", + "t = 64.6000000000006 ms\n", + "t = 64.7000000000006 ms\n", + "t = 64.8000000000006 ms\n", + "t = 64.90000000000059 ms\n", + "t = 65.00000000000058 ms\n", + "t = 65.10000000000058 ms\n", + "t = 65.20000000000057 ms\n", + "t = 65.30000000000057 ms\n", + "t = 65.40000000000056 ms\n", + "t = 65.50000000000055 ms\n", + "t = 65.60000000000055 ms\n", + "t = 65.70000000000054 ms\n", + "t = 65.80000000000054 ms\n", + "t = 65.90000000000053 ms\n", + "t = 66.00000000000053 ms\n", + "t = 66.10000000000052 ms\n", + "t = 66.20000000000051 ms\n", + "t = 66.30000000000051 ms\n", + "t = 66.4000000000005 ms\n", + "t = 66.5000000000005 ms\n", + "t = 66.60000000000049 ms\n", + "t = 66.70000000000049 ms\n", + "t = 66.80000000000048 ms\n", + "t = 66.90000000000047 ms\n", + "t = 67.00000000000047 ms\n", + "t = 67.10000000000046 ms\n", + "t = 67.20000000000046 ms\n", + "t = 67.30000000000045 ms\n", + "t = 67.40000000000045 ms\n", + "t = 67.50000000000044 ms\n", + "t = 67.60000000000043 ms\n", + "t = 67.70000000000043 ms\n", + "t = 67.80000000000042 ms\n", + "t = 67.90000000000042 ms\n", + "t = 68.00000000000041 ms\n", + "t = 68.1000000000004 ms\n", + "t = 68.2000000000004 ms\n", + "t = 68.3000000000004 ms\n", + "t = 68.40000000000039 ms\n", + "t = 68.50000000000038 ms\n", + "t = 68.60000000000038 ms\n", + "t = 68.70000000000037 ms\n", + "t = 68.80000000000037 ms\n", + "t = 68.90000000000036 ms\n", + "t = 69.00000000000036 ms\n", + "t = 69.10000000000035 ms\n", + "t = 69.20000000000034 ms\n", + "t = 69.30000000000034 ms\n", + "t = 69.40000000000033 ms\n", + "t = 69.50000000000033 ms\n", + "t = 69.60000000000032 ms\n", + "t = 69.70000000000032 ms\n", + "t = 69.80000000000031 ms\n", + "t = 69.9000000000003 ms\n", + "t = 70.0000000000003 ms\n", + "t = 70.10000000000029 ms\n", + "t = 70.20000000000029 ms\n", + "t = 70.30000000000028 ms\n", + "t = 70.40000000000028 ms\n", + "t = 70.50000000000027 ms\n", + "t = 70.60000000000026 ms\n", + "t = 70.70000000000026 ms\n", + "t = 70.80000000000025 ms\n", + "t = 70.90000000000025 ms\n", + "t = 71.00000000000024 ms\n", + "t = 71.10000000000024 ms\n", + "t = 71.20000000000023 ms\n", + "t = 71.30000000000022 ms\n", + "t = 71.40000000000022 ms\n", + "t = 71.50000000000021 ms\n", + "t = 71.60000000000021 ms\n", + "t = 71.7000000000002 ms\n", + "t = 71.8000000000002 ms\n", + "t = 71.90000000000019 ms\n", + "t = 72.00000000000018 ms\n", + "t = 72.10000000000018 ms\n", + "t = 72.20000000000017 ms\n", + "t = 72.30000000000017 ms\n", + "t = 72.40000000000016 ms\n", + "t = 72.50000000000016 ms\n", + "t = 72.60000000000015 ms\n", + "t = 72.70000000000014 ms\n", + "t = 72.80000000000014 ms\n", + "t = 72.90000000000013 ms\n", + "t = 73.00000000000013 ms\n", + "t = 73.10000000000012 ms\n", + "t = 73.20000000000012 ms\n", + "t = 73.30000000000011 ms\n", + "t = 73.4000000000001 ms\n", + "t = 73.5000000000001 ms\n", + "t = 73.6000000000001 ms\n", + "t = 73.70000000000009 ms\n", + "t = 73.80000000000008 ms\n", + "t = 73.90000000000008 ms\n", + "t = 74.00000000000007 ms\n", + "t = 74.10000000000007 ms\n", + "t = 74.20000000000006 ms\n", + "t = 74.30000000000005 ms\n", + "t = 74.40000000000005 ms\n", + "t = 74.50000000000004 ms\n", + "t = 74.60000000000004 ms\n", + "t = 74.70000000000003 ms\n", + "t = 74.80000000000003 ms\n", + "t = 74.90000000000002 ms\n", + "t = 75.00000000000001 ms\n", + "t = 75.10000000000001 ms\n", + "t = 75.2 ms\n", + "t = 75.3 ms\n", + "t = 75.39999999999999 ms\n", + "t = 75.49999999999999 ms\n", + "t = 75.59999999999998 ms\n", + "t = 75.69999999999997 ms\n", + "t = 75.79999999999997 ms\n", + "t = 75.89999999999996 ms\n", + "t = 75.99999999999996 ms\n", + "t = 76.09999999999995 ms\n", + "t = 76.19999999999995 ms\n", + "t = 76.29999999999994 ms\n", + "t = 76.39999999999993 ms\n", + "t = 76.49999999999993 ms\n", + "t = 76.59999999999992 ms\n", + "t = 76.69999999999992 ms\n", + "t = 76.79999999999991 ms\n", + "t = 76.8999999999999 ms\n", + "t = 76.9999999999999 ms\n", + "t = 77.0999999999999 ms\n", + "t = 77.19999999999989 ms\n", + "t = 77.29999999999988 ms\n", + "t = 77.39999999999988 ms\n", + "t = 77.49999999999987 ms\n", + "t = 77.59999999999987 ms\n", + "t = 77.69999999999986 ms\n", + "t = 77.79999999999986 ms\n", + "t = 77.89999999999985 ms\n", + "t = 77.99999999999984 ms\n", + "t = 78.09999999999984 ms\n", + "t = 78.19999999999983 ms\n", + "t = 78.29999999999983 ms\n", + "t = 78.39999999999982 ms\n", + "t = 78.49999999999982 ms\n", + "t = 78.59999999999981 ms\n", + "t = 78.6999999999998 ms\n", + "t = 78.7999999999998 ms\n", + "t = 78.89999999999979 ms\n", + "t = 78.99999999999979 ms\n", + "t = 79.09999999999978 ms\n", + "t = 79.19999999999978 ms\n", + "t = 79.29999999999977 ms\n", + "t = 79.39999999999976 ms\n", + "t = 79.49999999999976 ms\n", + "t = 79.59999999999975 ms\n", + "t = 79.69999999999975 ms\n", + "t = 79.79999999999974 ms\n", + "t = 79.89999999999974 ms\n", + "t = 79.99999999999973 ms\n", + "t = 80.09999999999972 ms\n", + "t = 80.19999999999972 ms\n", + "t = 80.29999999999971 ms\n", + "t = 80.39999999999971 ms\n", + "t = 80.4999999999997 ms\n", + "t = 80.5999999999997 ms\n", + "t = 80.69999999999969 ms\n", + "t = 80.79999999999968 ms\n", + "t = 80.89999999999968 ms\n", + "t = 80.99999999999967 ms\n", + "t = 81.09999999999967 ms\n", + "t = 81.19999999999966 ms\n", + "t = 81.29999999999966 ms\n", + "t = 81.39999999999965 ms\n", + "t = 81.49999999999964 ms\n", + "t = 81.59999999999964 ms\n", + "t = 81.69999999999963 ms\n", + "t = 81.79999999999963 ms\n", + "t = 81.89999999999962 ms\n", + "t = 81.99999999999962 ms\n", + "t = 82.09999999999961 ms\n", + "t = 82.1999999999996 ms\n", + "t = 82.2999999999996 ms\n", + "t = 82.3999999999996 ms\n", + "t = 82.49999999999959 ms\n", + "t = 82.59999999999958 ms\n", + "t = 82.69999999999958 ms\n", + "t = 82.79999999999957 ms\n", + "t = 82.89999999999957 ms\n", + "t = 82.99999999999956 ms\n", + "t = 83.09999999999955 ms\n", + "t = 83.19999999999955 ms\n", + "t = 83.29999999999954 ms\n", + "t = 83.39999999999954 ms\n", + "t = 83.49999999999953 ms\n", + "t = 83.59999999999953 ms\n", + "t = 83.69999999999952 ms\n", + "t = 83.79999999999951 ms\n", + "t = 83.89999999999951 ms\n", + "t = 83.9999999999995 ms\n", + "t = 84.0999999999995 ms\n", + "t = 84.19999999999949 ms\n", + "t = 84.29999999999949 ms\n", + "t = 84.39999999999948 ms\n", + "t = 84.49999999999947 ms\n", + "t = 84.59999999999947 ms\n", + "t = 84.69999999999946 ms\n", + "t = 84.79999999999946 ms\n", + "t = 84.89999999999945 ms\n", + "t = 84.99999999999945 ms\n", + "t = 85.09999999999944 ms\n", + "t = 85.19999999999943 ms\n", + "t = 85.29999999999943 ms\n", + "t = 85.39999999999942 ms\n", + "t = 85.49999999999942 ms\n", + "t = 85.59999999999941 ms\n", + "t = 85.6999999999994 ms\n", + "t = 85.7999999999994 ms\n", + "t = 85.8999999999994 ms\n", + "t = 85.99999999999939 ms\n", + "t = 86.09999999999938 ms\n", + "t = 86.19999999999938 ms\n", + "t = 86.29999999999937 ms\n", + "t = 86.39999999999937 ms\n", + "t = 86.49999999999936 ms\n", + "t = 86.59999999999935 ms\n", + "t = 86.69999999999935 ms\n", + "t = 86.79999999999934 ms\n", + "t = 86.89999999999934 ms\n", + "t = 86.99999999999933 ms\n", + "t = 87.09999999999933 ms\n", + "t = 87.19999999999932 ms\n", + "t = 87.29999999999932 ms\n", + "t = 87.39999999999931 ms\n", + "t = 87.4999999999993 ms\n", + "t = 87.5999999999993 ms\n", + "t = 87.69999999999929 ms\n", + "t = 87.79999999999929 ms\n", + "t = 87.89999999999928 ms\n", + "t = 87.99999999999928 ms\n", + "t = 88.09999999999927 ms\n", + "t = 88.19999999999926 ms\n", + "t = 88.29999999999926 ms\n", + "t = 88.39999999999925 ms\n", + "t = 88.49999999999925 ms\n", + "t = 88.59999999999924 ms\n", + "t = 88.69999999999924 ms\n", + "t = 88.79999999999923 ms\n", + "t = 88.89999999999922 ms\n", + "t = 88.99999999999922 ms\n", + "t = 89.09999999999921 ms\n", + "t = 89.1999999999992 ms\n", + "t = 89.2999999999992 ms\n", + "t = 89.3999999999992 ms\n", + "t = 89.49999999999919 ms\n", + "t = 89.59999999999918 ms\n", + "t = 89.69999999999918 ms\n", + "t = 89.79999999999917 ms\n", + "t = 89.89999999999917 ms\n", + "t = 89.99999999999916 ms\n", + "t = 90.09999999999916 ms\n", + "t = 90.19999999999915 ms\n", + "t = 90.29999999999914 ms\n", + "t = 90.39999999999914 ms\n", + "t = 90.49999999999913 ms\n", + "t = 90.59999999999913 ms\n", + "t = 90.69999999999912 ms\n", + "t = 90.79999999999912 ms\n", + "t = 90.89999999999911 ms\n", + "t = 90.9999999999991 ms\n", + "t = 91.0999999999991 ms\n", + "t = 91.1999999999991 ms\n", + "t = 91.29999999999909 ms\n", + "t = 91.39999999999908 ms\n", + "t = 91.49999999999908 ms\n", + "t = 91.59999999999907 ms\n", + "t = 91.69999999999906 ms\n", + "t = 91.79999999999906 ms\n", + "t = 91.89999999999905 ms\n", + "t = 91.99999999999905 ms\n", + "t = 92.09999999999904 ms\n", + "t = 92.19999999999904 ms\n", + "t = 92.29999999999903 ms\n", + "t = 92.39999999999903 ms\n", + "t = 92.49999999999902 ms\n", + "t = 92.59999999999901 ms\n", + "t = 92.69999999999901 ms\n", + "t = 92.799999999999 ms\n", + "t = 92.899999999999 ms\n", + "t = 92.99999999999899 ms\n", + "t = 93.09999999999899 ms\n", + "t = 93.19999999999898 ms\n", + "t = 93.29999999999897 ms\n", + "t = 93.39999999999897 ms\n", + "t = 93.49999999999896 ms\n", + "t = 93.59999999999896 ms\n", + "t = 93.69999999999895 ms\n", + "t = 93.79999999999895 ms\n", + "t = 93.89999999999894 ms\n", + "t = 93.99999999999893 ms\n", + "t = 94.09999999999893 ms\n", + "t = 94.19999999999892 ms\n", + "t = 94.29999999999892 ms\n", + "t = 94.39999999999891 ms\n", + "t = 94.4999999999989 ms\n", + "t = 94.5999999999989 ms\n", + "t = 94.6999999999989 ms\n", + "t = 94.79999999999889 ms\n", + "t = 94.89999999999888 ms\n", + "t = 94.99999999999888 ms\n", + "t = 95.09999999999887 ms\n", + "t = 95.19999999999887 ms\n", + "t = 95.29999999999886 ms\n", + "t = 95.39999999999885 ms\n", + "t = 95.49999999999885 ms\n", + "t = 95.59999999999884 ms\n", + "t = 95.69999999999884 ms\n", + "t = 95.79999999999883 ms\n", + "t = 95.89999999999883 ms\n", + "t = 95.99999999999882 ms\n", + "t = 96.09999999999881 ms\n", + "t = 96.19999999999881 ms\n", + "t = 96.2999999999988 ms\n", + "t = 96.3999999999988 ms\n", + "t = 96.49999999999879 ms\n", + "t = 96.59999999999879 ms\n", + "t = 96.69999999999878 ms\n", + "t = 96.79999999999878 ms\n", + "t = 96.89999999999877 ms\n", + "t = 96.99999999999876 ms\n", + "t = 97.09999999999876 ms\n", + "t = 97.19999999999875 ms\n", + "t = 97.29999999999875 ms\n", + "t = 97.39999999999874 ms\n", + "t = 97.49999999999874 ms\n", + "t = 97.59999999999873 ms\n", + "t = 97.69999999999872 ms\n", + "t = 97.79999999999872 ms\n", + "t = 97.89999999999871 ms\n", + "t = 97.9999999999987 ms\n", + "t = 98.0999999999987 ms\n", + "t = 98.1999999999987 ms\n", + "t = 98.29999999999869 ms\n", + "t = 98.39999999999868 ms\n", + "t = 98.49999999999868 ms\n", + "t = 98.59999999999867 ms\n", + "t = 98.69999999999867 ms\n", + "t = 98.79999999999866 ms\n", + "t = 98.89999999999866 ms\n", + "t = 98.99999999999865 ms\n", + "t = 99.09999999999864 ms\n", + "t = 99.19999999999864 ms\n", + "t = 99.29999999999863 ms\n", + "t = 99.39999999999863 ms\n", + "t = 99.49999999999862 ms\n", + "t = 99.59999999999862 ms\n", + "t = 99.69999999999861 ms\n", + "t = 99.7999999999986 ms\n", + "t = 99.8999999999986 ms\n", + "t = 99.9999999999986 ms\n", + "t = 100.09999999999859 ms\n", + "t = 100.19999999999858 ms\n", + "t = 100.29999999999858 ms\n", + "t = 100.39999999999857 ms\n", + "t = 100.49999999999856 ms\n", + "t = 100.59999999999856 ms\n", + "t = 100.69999999999855 ms\n", + "t = 100.79999999999855 ms\n", + "t = 100.89999999999854 ms\n", + "t = 100.99999999999854 ms\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual pre spike times: [ 41. 101.]\n", + "Actual post spike times: [26.8 76.8]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fname_snip = \"detailed\"\n", + "\n", + "pre_spike_times = [40., 100.] # [ms]\n", + "post_spike_times = [25., 75.] # [ms]\n", + "\n", + "# run the simulation\n", + "timevec, t_hist, third_factor_trace, w_hist = run_synapse_test(neuron_model_name=neuron_model_name,\n", + " synapse_model_name=synapse_model_name,\n", + " resolution=.1, # [ms]\n", + " delay=1., # [ms]\n", + " pre_spike_times=pre_spike_times,\n", + " post_spike_times=post_spike_times,\n", + " sim_time=101.,\n", + " fname_snip=fname_snip)\n", + "\n", + "# verify that weight stays zero: buffering ensures that the value of I_dend at the right time is used\n", + "np.testing.assert_allclose(w_hist, 0.)\n", + "\n", + "# idx = np.where(np.abs(third_factor_trace) < 1E-12)[0] # find where third_factor_trace is (almost) zero\n", + "# times_dw_should_be_zero = timevec[idx]\n", + "# assert len(times_dw_should_be_zero) > 0 # make sure we have > 0 datapoints to check\n", + "# for time_dw_should_be_zero in times_dw_should_be_zero[1:]:\n", + "# _idx = np.argmin((time_dw_should_be_zero - np.array(t_hist))**2)\n", + "# np.testing.assert_allclose(t_hist[_idx], time_dw_should_be_zero)\n", + "# np.testing.assert_allclose(0., np.abs(w_hist[_idx - 1] - w_hist[_idx])) # make sure that weight does not change appreciably\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "References\n", + "----------\n", + "\n", + "...\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/tutorials/stdp_third_factor_active_dendrite/stdp_third_factor_active_dendrite.ipynb b/doc/tutorials/stdp_third_factor_active_dendrite/stdp_third_factor_active_dendrite.ipynb new file mode 100644 index 000000000..cf0d9e6f9 --- /dev/null +++ b/doc/tutorials/stdp_third_factor_active_dendrite/stdp_third_factor_active_dendrite.ipynb @@ -0,0 +1,3319 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NESTML active dendrite third-factor STDP synapse\n", + "==========================================\n", + "\n", + "Welcome ...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Introduction\n", + "------------\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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" + ] + }, + { + "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" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "from typing import List, Optional\n", + "\n", + "import matplotlib as mpl\n", + "\n", + "mpl.rcParams['axes.formatter.useoffset'] = False\n", + "mpl.rcParams['axes.grid'] = True\n", + "mpl.rcParams['grid.color'] = 'k'\n", + "mpl.rcParams['grid.linestyle'] = ':'\n", + "mpl.rcParams['grid.linewidth'] = 0.5\n", + "mpl.rcParams['figure.dpi'] = 120\n", + "mpl.rcParams['figure.figsize'] = [8., 3.]\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import nest\n", + "import numpy as np\n", + "import os\n", + "import random\n", + "import re\n", + "\n", + "from pynestml.codegeneration.nest_code_generator_utils import NESTCodeGeneratorUtils\n", + "from pynestml.codegeneration.nest_tools import NESTTools" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "post_trace_var = \"I_dend\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating code with NESTML\n", + "\n", + "We will take a simple current-based integrate-and-fire model with alpha-shaped postsynaptic response kernels (``iaf_psc_alpha``) as the basis for our modifications. First, let's take a look at this base neuron without any modifications.\n", + "\n", + "We will use a helper function to generate the C++ code for the models, build it as a NEST extension module, and load the module into the kernel. Because NEST does not support un- or reloading of modules at the time of writing, we implement a workaround that appends a unique number to the name of each generated model, for example, \"iaf_psc_alpha_3cc945f\". The resulting neuron model name is returned by the function, so we do not have to think about these internals." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1,GLOBAL, INFO]: List of files that will be processed:\n", + "[2,GLOBAL, INFO]: /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron.nestml\n", + "[3,GLOBAL, INFO]: /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse.nestml\n", + "[4,GLOBAL, INFO]: Target platform code will be generated in directory: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target'\n", + "[5,GLOBAL, INFO]: Target platform code will be installed in directory: '/tmp/nestml_target_buhdamnv'\n", + "\n", + " -- N E S T --\n", + " Copyright (C) 2004 The NEST Initiative\n", + "\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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", + "[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-clopath_synapse/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-clopath_synapse/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-clopath_synapse/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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron.nestml'!\n", + "[12,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[13,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:39;50:47]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[14,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:15;50:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[15,GLOBAL, INFO]: Start processing '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse.nestml'!\n", + "[16,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[17,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[18,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [73:11;73:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([73:11;73:28])\n", + "[19,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [80:11;80:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([80:11;80:28])\n", + "[20,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[21,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[22,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[23,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[24,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:39;50:47]]: Implicit magnitude conversion from pA to pA buffer with factor 1.0 \n", + "[25,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [50:15;50:30]]: Implicit magnitude conversion from mV / ms to pA / pF with factor 1.0 \n", + "[26,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[27,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[28,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [73:11;73:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([73:11;73:28])\n", + "[29,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [80:11;80:28]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([80:11;80:28])\n", + "[30,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[31,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[32,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[33,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_trace', 'post_trace_kernel']\n", + "[34,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_trace', 'post_trace_kernel']\n", + "[35,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_tr_post']\n", + "[36,GLOBAL, INFO]: Synaptic state variables moved to neuron that will need continuous-time buffering: ['I_post_dend']\n", + "[37,GLOBAL, INFO]: Moving state var defining equation(s) post_trace\n", + "[38,GLOBAL, INFO]: Moving state var defining equation(s) post_trace_kernel\n", + "[39,GLOBAL, INFO]: Moving state variables for equation(s) post_trace\n", + "[40,GLOBAL, INFO]: Moving state variables for equation(s) post_trace_kernel\n", + "[41,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", + "[42,GLOBAL, INFO]: \t• Replacing variable I_post_dend\n", + "[43,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend <= 1pA\n", + "[44,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[45,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[46,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend <= 1pA\n", + "[47,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[48,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend / pA\n", + "[49,GLOBAL, INFO]: Copying parameters from synapse to neuron...\n", + "[50,GLOBAL, INFO]: Copying definition of tau_tr_post from synapse to neuron\n", + "[51,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", + "[52,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", + "[53,GLOBAL, INFO]: \t• Replacing variable post_trace\n", + "[54,GLOBAL, INFO]: \t -> ASTSimpleExpression replacement made (var = post_trace__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml) in expression: alpha * lambda * (w / Wmax) ** mu_minus * post_trace\n", + "[55,GLOBAL, INFO]: \t• Replacing variable post_trace_kernel\n", + "[56,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n", + "[57,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[58,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[59,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml\n", + "[60,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml'\n", + "[61,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [38:0;93:0]]: Starts processing of the model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Analysing input:\n", + "INFO:{\n", + " \"dynamics\": [\n", + " {\n", + " \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\",\n", + " \"initial_values\": {\n", + " \"V_m\": \"E_L\"\n", + " }\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_exc__X__exc_spikes = exp(-t / tau_syn_exc)\",\n", + " \"initial_values\": {}\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_inh__X__inh_spikes = exp(-t / tau_syn_inh)\",\n", + " \"initial_values\": {}\n", + " }\n", + " ],\n", + " \"options\": {\n", + " \"output_timestep_symbol\": \"__h\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250\",\n", + " \"E_L\": \"(-70)\",\n", + " \"I_e\": \"0\",\n", + " \"V_reset\": \"(-70)\",\n", + " \"V_th\": \"(-55)\",\n", + " \"refr_T\": \"2\",\n", + " \"tau_m\": \"10\",\n", + " \"tau_syn_exc\": \"2\",\n", + " \"tau_syn_inh\": \"2\"\n", + " }\n", + "}\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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n", + "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m\n", + "DEBUG:\tnonlinear term: 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m, nonlin_term = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + 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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "INFO:All known variables: [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes], all parameters used in ODEs: {tau_syn_inh, tau_syn_exc, tau_m, C_m, I_stim, E_L, 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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes, V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [-1.0/C_m], [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 = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [-1.0/C_m]])\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 I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_syn_inh]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, -1.0/C_m], [0, -1/tau_syn_exc, 0], [0, 0, -1/tau_syn_inh]]), 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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [-1.0/C_m]])\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 I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols [V_m, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_syn_inh]])\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", + "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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "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, I_kernel_exc__X__exc_spikes, I_kernel_inh__X__inh_spikes\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, -1.0/C_m], [0, -1/tau_syn_exc, 0], [0, 0, -1/tau_syn_inh]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0]]), c = Matrix([[0], [0], [0]])\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", + "DEBUG:System of equations:\n", + "DEBUG:x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [I_kernel_inh__X__inh_spikes]])\n", + "DEBUG:A = Matrix([\n", + "[-1/tau_m, 1.0/C_m, -1.0/C_m],\n", + "[ 0, -1/tau_syn_exc, 0],\n", + "[ 0, 0, -1/tau_syn_inh]])\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 + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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[I_kernel_exc__X__exc_spikes] = I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\n", + "INFO:update_expr[I_kernel_inh__X__inh_spikes] = I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\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_kernel_exc__X__exc_spikes\": \"1\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"1\",\n", + " \"V_m\": \"E_L\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250.000000000000\",\n", + " \"E_L\": \"-70.0000000000000\",\n", + " \"I_e\": \"0\",\n", + " \"tau_m\": \"10.0000000000000\",\n", + " \"tau_syn_exc\": \"2.00000000000000\",\n", + " \"tau_syn_inh\": \"2.00000000000000\"\n", + " },\n", + " \"propagators\": {\n", + " \"__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\": \"1.0*exp(-__h/tau_syn_exc)\",\n", + " \"__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\": \"1.0*exp(-__h/tau_syn_inh)\",\n", + " \"__P__V_m__I_kernel_exc__X__exc_spikes\": \"1.0*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_kernel_inh__X__inh_spikes\": \"1.0*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\": \"1.0*exp(-__h/tau_m)\"\n", + " },\n", + " \"solver\": \"analytical\",\n", + " \"state_variables\": [\n", + " \"V_m\",\n", + " \"I_kernel_exc__X__exc_spikes\",\n", + " \"I_kernel_inh__X__inh_spikes\"\n", + " ],\n", + " \"update_expressions\": {\n", + " \"I_kernel_exc__X__exc_spikes\": \"I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\",\n", + " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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": [ + "[62,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Analysing input:\n", + "INFO:{\n", + " \"dynamics\": [\n", + " {\n", + " \"expression\": \"V_m' = (-(V_m - E_L)) / tau_m + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\",\n", + " \"initial_values\": {\n", + " \"V_m\": \"E_L\"\n", + " }\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_exc__X__exc_spikes = exp(-t / tau_syn_exc)\",\n", + " \"initial_values\": {}\n", + " },\n", + " {\n", + " \"expression\": \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml = exp(-t / tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\",\n", + " \"initial_values\": {}\n", + " },\n", + " {\n", + " \"expression\": \"I_kernel_inh__X__inh_spikes = exp(-t / tau_syn_inh)\",\n", + " \"initial_values\": {}\n", + " }\n", + " ],\n", + " \"options\": {\n", + " \"output_timestep_symbol\": \"__h\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250\",\n", + " \"E_L\": \"(-70)\",\n", + " \"I_e\": \"0\",\n", + " \"V_reset\": \"(-70)\",\n", + " \"V_th\": \"(-55)\",\n", + " \"refr_T\": \"2\",\n", + " \"tau_m\": \"10\",\n", + " \"tau_syn_exc\": \"2\",\n", + " \"tau_syn_inh\": \"2\",\n", + " \"tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"20\"\n", + " }\n", + "}\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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m]])\n", + "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m\n", + "DEBUG:\tnonlinear term: 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Created Shape with symbol V_m, derivative_factors = [-1/tau_m], inhom_term = E_L/tau_m + I_e/C_m, nonlin_term = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + 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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "INFO:\n", + "Processing function-of-time shape \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\" with defining expression = \"exp(-t/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, derivative_factors = [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml: reconstituting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "INFO:All known variables: [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes], all parameters used in ODEs: {tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, tau_syn_inh, tau_syn_exc, tau_m, C_m, I_stim, E_L, 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 + ((I_kernel_exc__X__exc_spikes * 1.0 - I_kernel_inh__X__inh_spikes * 1.0) + I_e + I_stim) / C_m\"\n", + "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_e + 1.0*I_kernel_exc__X__exc_spikes - 1.0*I_kernel_inh__X__inh_spikes + I_stim)/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes, V_m])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [0], [-1.0/C_m], [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 = 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m\n", + "INFO:\tReturning shape: Shape \"V_m\" of order 1\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_exc__X__exc_spikes\" with defining expression = \"exp(-t/tau_syn_exc)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_exc__X__exc_spikes, derivative_factors = [-1/tau_syn_exc], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\n", + "Processing function-of-time shape \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\" with defining expression = \"exp(-t/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, derivative_factors = [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:\n", + "Processing function-of-time shape \"I_kernel_inh__X__inh_spikes\" with defining expression = \"exp(-t/tau_syn_inh)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol I_kernel_inh__X__inh_spikes, derivative_factors = [-1/tau_syn_inh], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape V_m: reconstituting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [0], [-1.0/C_m]])\n", + "DEBUG:\tinhomogeneous term: E_L/tau_m + I_e/C_m + I_stim/C_m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml: reconstituting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "DEBUG:Splitting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, 0, -1.0/C_m], [0, -1/tau_syn_exc, 0, 0], [0, 0, -1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, 0], [0, 0, 0, -1/tau_syn_inh]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0], [0.0]]), c = Matrix([[0.0], [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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[63,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml'\n", + "[64,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, INFO, [38:0;93:0]]: Starts processing of the model 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_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 + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m\n", + "DEBUG:Splitting expression E_L/tau_m - V_m/tau_m + I_e/C_m + 1.0*I_kernel_exc__X__exc_spikes/C_m - 1.0*I_kernel_inh__X__inh_spikes/C_m + I_stim/C_m (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_m], [1.0/C_m], [0], [-1.0/C_m]])\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 I_kernel_exc__X__exc_spikes: reconstituting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc\n", + "DEBUG:Splitting expression -I_kernel_exc__X__exc_spikes/tau_syn_exc (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_syn_exc], [0], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml: reconstituting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "DEBUG:Splitting expression -post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [0]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "INFO:Shape I_kernel_inh__X__inh_spikes: reconstituting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh\n", + "DEBUG:Splitting expression -I_kernel_inh__X__inh_spikes/tau_syn_inh (symbols [V_m, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh]])\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", + "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, I_kernel_exc__X__exc_spikes, post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, I_kernel_inh__X__inh_spikes\n", + "DEBUG:Initializing system of shapes with x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]]), A = Matrix([[-1/tau_m, 1.0/C_m, 0, -1.0/C_m], [0, -1/tau_syn_exc, 0, 0], [0, 0, -1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, 0], [0, 0, 0, -1/tau_syn_inh]]), b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0], [0.0]]), c = Matrix([[0], [0], [0], [0]])\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", + "DEBUG:System of equations:\n", + "DEBUG:x = Matrix([[V_m], [I_kernel_exc__X__exc_spikes], [post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml], [I_kernel_inh__X__inh_spikes]])\n", + "DEBUG:A = Matrix([\n", + "[-1/tau_m, 1.0/C_m, 0, -1.0/C_m],\n", + "[ 0, -1/tau_syn_exc, 0, 0],\n", + "[ 0, 0, -1/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, 0],\n", + "[ 0, 0, 0, -1/tau_syn_inh]])\n", + "DEBUG:b = Matrix([[E_L/tau_m + I_e/C_m + I_stim/C_m], [0.0], [0.0], [0.0]])\n", + "DEBUG:c = Matrix([[0], [0], [0], [0]])\n", + "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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[I_kernel_exc__X__exc_spikes] = I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\n", + "INFO:update_expr[post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml] = __P__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml*post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\n", + "INFO:update_expr[I_kernel_inh__X__inh_spikes] = I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\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_kernel_exc__X__exc_spikes\": \"1\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"1\",\n", + " \"V_m\": \"E_L\",\n", + " \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"1\"\n", + " },\n", + " \"parameters\": {\n", + " \"C_m\": \"250.000000000000\",\n", + " \"E_L\": \"-70.0000000000000\",\n", + " \"I_e\": \"0\",\n", + " \"tau_m\": \"10.0000000000000\",\n", + " \"tau_syn_exc\": \"2.00000000000000\",\n", + " \"tau_syn_inh\": \"2.00000000000000\",\n", + " \"tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"20.0000000000000\"\n", + " },\n", + " \"propagators\": {\n", + " \"__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\": \"1.0*exp(-__h/tau_syn_exc)\",\n", + " \"__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\": \"1.0*exp(-__h/tau_syn_inh)\",\n", + " \"__P__V_m__I_kernel_exc__X__exc_spikes\": \"1.0*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_kernel_inh__X__inh_spikes\": \"1.0*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\": \"1.0*exp(-__h/tau_m)\",\n", + " \"__P__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"1.0*exp(-__h/tau_tr_post__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml)\"\n", + " },\n", + " \"solver\": \"analytical\",\n", + " \"state_variables\": [\n", + " \"V_m\",\n", + " \"I_kernel_exc__X__exc_spikes\",\n", + " \"post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\",\n", + " \"I_kernel_inh__X__inh_spikes\"\n", + " ],\n", + " \"update_expressions\": {\n", + " \"I_kernel_exc__X__exc_spikes\": \"I_kernel_exc__X__exc_spikes*__P__I_kernel_exc__X__exc_spikes__I_kernel_exc__X__exc_spikes\",\n", + " \"I_kernel_inh__X__inh_spikes\": \"I_kernel_inh__X__inh_spikes*__P__I_kernel_inh__X__inh_spikes__I_kernel_inh__X__inh_spikes\",\n", + " \"V_m\": \"-E_L*__P__V_m__V_m + E_L + I_kernel_exc__X__exc_spikes*__P__V_m__I_kernel_exc__X__exc_spikes + I_kernel_inh__X__inh_spikes*__P__V_m__I_kernel_inh__X__inh_spikes + 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_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\": \"__P__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml*post_trace_kernel__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__X__post_spikes__for_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml\"\n", + " }\n", + " }\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[65,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, DEBUG, [38:0;93:0]]: Start building symbol table!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:Analysing input:\n", + "INFO:{\n", + " \"dynamics\": [\n", + " {\n", + " \"expression\": \"pre_trace_kernel__X__pre_spikes = exp(-t / tau_tr_pre)\",\n", + " \"initial_values\": {}\n", + " }\n", + " ],\n", + " \"options\": {\n", + " \"output_timestep_symbol\": \"__h\"\n", + " },\n", + " \"parameters\": {\n", + " \"Wmax\": \"100.0\",\n", + " \"Wmin\": \"0.0\",\n", + " \"alpha\": \"1.0\",\n", + " \"d\": \"1\",\n", + " \"lambda\": \"0.01\",\n", + " \"mu_minus\": \"1.0\",\n", + " \"mu_plus\": \"1.0\",\n", + " \"tau_tr_post\": \"20\",\n", + " \"tau_tr_pre\": \"20\"\n", + " }\n", + "}\n", + "INFO:Processing global options...\n", + "INFO:Processing input shapes...\n", + "INFO:\n", + "Processing function-of-time shape \"pre_trace_kernel__X__pre_spikes\" with defining expression = \"exp(-t/tau_tr_pre)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol pre_trace_kernel__X__pre_spikes, derivative_factors = [-1/tau_tr_pre], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape pre_trace_kernel__X__pre_spikes: reconstituting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre\n", + "INFO:All known variables: [pre_trace_kernel__X__pre_spikes], all parameters used in ODEs: {tau_tr_pre}\n", + "INFO:\n", + "Processing function-of-time shape \"pre_trace_kernel__X__pre_spikes\" with defining expression = \"exp(-t/tau_tr_pre)\"\n", + "DEBUG:Found t: 0\n", + "DEBUG:\tFinding ode for order 1...\n", + "DEBUG:Shape satisfies ODE of order = 1\n", + "DEBUG:Created Shape with symbol pre_trace_kernel__X__pre_spikes, derivative_factors = [-1/tau_tr_pre], inhom_term = 0.0, nonlin_term = 0.0\n", + "INFO:Shape pre_trace_kernel__X__pre_spikes: reconstituting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre\n", + "DEBUG:Splitting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre (symbols Matrix([[pre_trace_kernel__X__pre_spikes]]))\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_tr_pre]])\n", + "DEBUG:\tinhomogeneous term: 0.0\n", + "DEBUG:\tnonlinear term: 0.0\n", + "DEBUG:Initializing system of shapes with x = Matrix([[pre_trace_kernel__X__pre_spikes]]), A = Matrix([[-1/tau_tr_pre]]), b = Matrix([[0.0]]), 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", + "INFO:Shape pre_trace_kernel__X__pre_spikes: reconstituting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre\n", + "DEBUG:Splitting expression -pre_trace_kernel__X__pre_spikes/tau_tr_pre (symbols [pre_trace_kernel__X__pre_spikes])\n", + "DEBUG:\tlinear factors: Matrix([[-1/tau_tr_pre]])\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", + "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: pre_trace_kernel__X__pre_spikes\n", + "DEBUG:Initializing system of shapes with x = Matrix([[pre_trace_kernel__X__pre_spikes]]), A = Matrix([[-1/tau_tr_pre]]), b = Matrix([[0.0]]), c = Matrix([[0]])\n", + "DEBUG:System of equations:\n", + "DEBUG:x = Matrix([[pre_trace_kernel__X__pre_spikes]])\n", + "DEBUG:A = Matrix([[-1/tau_tr_pre]])\n", + "DEBUG:b = Matrix([[0.0]])\n", + "DEBUG:c = Matrix([[0]])\n", + "INFO:update_expr[pre_trace_kernel__X__pre_spikes] = __P__pre_trace_kernel__X__pre_spikes__pre_trace_kernel__X__pre_spikes*pre_trace_kernel__X__pre_spikes\n", + "INFO:In ode-toolbox: returning outdict = \n", + "INFO:[\n", + " {\n", + " \"initial_values\": {\n", + " \"pre_trace_kernel__X__pre_spikes\": \"1\"\n", + " },\n", + " \"parameters\": {\n", + " \"tau_tr_pre\": \"20.0000000000000\"\n", + " },\n", + " \"propagators\": {\n", + " \"__P__pre_trace_kernel__X__pre_spikes__pre_trace_kernel__X__pre_spikes\": \"exp(-__h/tau_tr_pre)\"\n", + " },\n", + " \"solver\": \"analytical\",\n", + " \"state_variables\": [\n", + " \"pre_trace_kernel__X__pre_spikes\"\n", + " ],\n", + " \"update_expressions\": {\n", + " \"pre_trace_kernel__X__pre_spikes\": \"__P__pre_trace_kernel__X__pre_spikes__pre_trace_kernel__X__pre_spikes*pre_trace_kernel__X__pre_spikes\"\n", + " }\n", + " }\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[66,GLOBAL, INFO]: Analysing/transforming synapse third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.\n", + "[67,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [39:0;85:0]]: Starts processing of the model 'third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml'\n", + "[68,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[69,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[70,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, DEBUG, [39:0;85:0]]: Start building symbol table!\n", + "[71,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, WARNING, [44:8;44:28]]: Variable 'd' has the same name as a physical unit!\n", + "[72,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp\n", + "[73,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h\n", + "[74,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [38:0;93:0]]: Successfully generated code for the model: 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml' in: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target' !\n", + "[75,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp\n", + "[76,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h\n", + "[77,iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml, INFO, [38:0;93:0]]: Successfully generated code for the model: 'iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml' in: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target' !\n", + "[78,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h\n", + "[79,third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml, INFO, [39:0;85:0]]: Successfully generated code for the model: 'third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml' in: '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target' !\n", + "[80,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp\n", + "[81,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.h\n", + "[82,GLOBAL, INFO]: Rendering template /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/CMakeLists.txt\n", + "[83,GLOBAL, INFO]: Successfully generated NEST module code in '/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/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", + "\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_7c1adc9dbf3144fabdd9ebff0cce4e18_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 -g\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_7c1adc9dbf3144fabdd9ebff0cce4e18_module' using\n", + " make\n", + " make install\n", + "\n", + "The library file libnestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.so will be installed to\n", + " /tmp/nestml_target_buhdamnv\n", + "The module can be loaded into NEST using\n", + " (nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module) Install (in SLI)\n", + " nest.Install(nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target\n", + "[ 25%] Building CXX object CMakeFiles/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module.dir/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.o\n", + "[ 50%] Building CXX object CMakeFiles/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module.dir/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.o\n", + "[ 75%] Building CXX object CMakeFiles/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module.dir/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.o\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp: In member function ‘void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp: In member function ‘virtual void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.cpp:291:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 291 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n", + "In file included from /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:43:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h: In constructor ‘continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml(double, double)’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h:105:10: warning: ‘continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::access_counter_’ will be initialized after [-Wreorder]\n", + " 105 | size_t access_counter_;\n", + " | ^~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.h:102:10: warning: ‘double continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::I_post_dend’ [-Wreorder]\n", + " 102 | double I_post_dend;\n", + " | ^~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:46:1: warning: when initialized here [-Wreorder]\n", + " 46 | continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::continuous_variable_histentry_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml( double t,\n", + " | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp: In member function ‘void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::init_state_internal_()’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:202:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 202 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp: In member function ‘virtual void iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::update(const nest::Time&, long int, long int)’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml.cpp:321:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 321 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + " | ~~^~~~~~~~~~~~~~~~~~~~~\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In file included from /home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:52:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:727:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 727 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:727:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:739:3: required from ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:715:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 715 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::recompute_internal_variables() [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:739:3: required from ‘nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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 = third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml; std::string = std::__cxx11::basic_string]’\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.cpp:111:179: required from here\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:715:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:522:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 522 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:551:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 551 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:591:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 591 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:455:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 455 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:457:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 457 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:522:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 522 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:551:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 551 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:591:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 591 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:455:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 455 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:457:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 457 | auto get_thread = [tid]()\n", + " | ^~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::update_internal_state_(double, double, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:517:9: required from ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:789:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 789 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:790:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 790 | 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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h: In instantiation of ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::update_internal_state_(double, double, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestmlCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex]’:\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:517:9: required from ‘void nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::send(nest::Event&, size_t, const nest::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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::third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_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-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:789:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 789 | const double __resolution = timestep; // do not remove, this is necessary for the resolution() function\n", + " | ^~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-clopath_synapse/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml__with_iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml.h:790:10: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", + " 790 | 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_7c1adc9dbf3144fabdd9ebff0cce4e18_module.so\n", + "[100%] Built target nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module\n", + "[100%] Built target nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module_module\n", + "Install the project...\n", + "-- Install configuration: \"\"\n", + "-- Installing: /tmp/nestml_target_buhdamnv/nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module.so\n", + "iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml::init_state_internal_()\n", + "iaf_psc_exp_dend7c1adc9dbf3144fabdd9ebff0cce4e18_neuron_nestml__with_third_factor_stdp7c1adc9dbf3144fabdd9ebff0cce4e18_synapse_nestml::init_state_internal_()\n", + "\n", + "Dec 21 08:04:39 Install [Info]: \n", + " loaded module nestml_7c1adc9dbf3144fabdd9ebff0cce4e18_module\n" + ] + } + ], + "source": [ + "# codegen_opts = {\"neuron_synapse_pairs\": [{\"neuron\": \"iaf_psc_exp_dend\",\n", + "# \"synapse\": \"third_factor_stdp_synapse\",\n", + "# \"post_ports\": [\"post_spikes\",\n", + "# [\"I_post_dend\", \"I_dend\"]]}]}\n", + "\n", + "# if not NESTTools.detect_nest_version().startswith(\"v2\"):\n", + "# codegen_opts[\"neuron_parent_class\"] = \"StructuralPlasticityNode\"\n", + "# codegen_opts[\"neuron_parent_class_include\"] = \"structural_plasticity_node.h\"\n", + "\n", + "# generate the \"jit\" model (co-generated neuron and synapse), that does not rely on ArchivingNode\n", + "# files = [os.path.join(\"models\", \"neurons\", \"iaf_psc_exp_dend_neuron.nestml\"),\n", + "# os.path.join(\"models\", \"synapses\", \"third_factor_stdp_synapse.nestml\")]\n", + "# input_path = [os.path.realpath(os.path.join(os.path.dirname(__file__), os.path.join(\n", + "# os.pardir, os.pardir, s))) for s in files]\n", + "# generate_nest_target(input_path=input_path,\n", + "# target_path=\"/tmp/nestml-jit\",\n", + "# logging_level=\"INFO\",\n", + "# module_name=\"nestml_jit_module\",\n", + "# codegen_opts=codegen_opts)\n", + "#nest.Install(\"nestml_jit_module\")\n", + "\n", + "# generate and build code\n", + "module_name, neuron_model_name, synapse_model_name = \\\n", + " NESTCodeGeneratorUtils.generate_code_for(\"../../../models/neurons/iaf_psc_exp_dend_neuron.nestml\",\n", + " \"../../../models/synapses/third_factor_stdp_synapse.nestml\",\n", + " logging_level=\"DEBUG\",\n", + " post_ports=[\"post_spikes\", [\"I_post_dend\", \"I_dend\"]])\n", + "\n", + "# load dynamic library (NEST extension module) into NEST kernel\n", + "nest.Install(module_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the NESTML model is ready to be used in a simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def run_synapse_test(neuron_model_name,\n", + " synapse_model_name,\n", + " resolution=1., # [ms]\n", + " delay=1., # [ms]\n", + " sim_time=None, # if None, computed from pre and post spike times\n", + " pre_spike_times=None,\n", + " post_spike_times=None,\n", + " fname_snip=\"\"):\n", + "\n", + " if pre_spike_times is None:\n", + " pre_spike_times = []\n", + "\n", + " if post_spike_times is None:\n", + " post_spike_times = []\n", + "\n", + " if sim_time is None:\n", + " sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 5 * delay\n", + "\n", + " nest_version = NESTTools.detect_nest_version()\n", + "\n", + " nest.set_verbosity(\"M_ALL\")\n", + " nest.ResetKernel()\n", + "\n", + " print(\"Pre spike times: \" + str(pre_spike_times))\n", + " print(\"Post spike times: \" + str(post_spike_times))\n", + "\n", + " nest.set_verbosity(\"M_WARNING\")\n", + "\n", + " nest.ResetKernel()\n", + " nest.SetKernelStatus({\"resolution\": resolution})\n", + "\n", + " wr = nest.Create(\"weight_recorder\")\n", + " nest.CopyModel(synapse_model_name, \"stdp_nestml_rec\",\n", + " {\"weight_recorder\": wr[0], \"w\": 1., \"d\": 1., \"receptor_type\": 0, \"lambda\": .001})\n", + "\n", + " # create spike_generators with these times\n", + " pre_sg = nest.Create(\"spike_generator\",\n", + " params={\"spike_times\": pre_spike_times})\n", + " post_sg = nest.Create(\"spike_generator\",\n", + " params={\"spike_times\": post_spike_times,\n", + " \"allow_offgrid_times\": True})\n", + "\n", + " # create parrot neurons and connect spike_generators\n", + " pre_neuron = nest.Create(\"parrot_neuron\")\n", + " post_neuron = nest.Create(neuron_model_name)\n", + "\n", + " if nest_version.startswith(\"v2\"):\n", + " spikedet_pre = nest.Create(\"spike_detector\")\n", + " spikedet_post = nest.Create(\"spike_detector\")\n", + " else:\n", + " spikedet_pre = nest.Create(\"spike_recorder\")\n", + " spikedet_post = nest.Create(\"spike_recorder\")\n", + " mm = nest.Create(\"multimeter\", params={\"record_from\": [\"V_m\", post_trace_var]})\n", + "\n", + " nest.Connect(pre_sg, pre_neuron, \"one_to_one\", syn_spec={\"delay\": 1.})\n", + " nest.Connect(post_sg, post_neuron, \"one_to_one\", syn_spec={\"delay\": 1., \"weight\": 6000.})\n", + " if nest_version.startswith(\"v2\"):\n", + " nest.Connect(pre_neuron, post_neuron, \"all_to_all\", syn_spec={\"model\": \"stdp_nestml_rec\"})\n", + " else:\n", + " nest.Connect(pre_neuron, post_neuron, \"all_to_all\", syn_spec={\"synapse_model\": \"stdp_nestml_rec\"})\n", + " nest.Connect(mm, post_neuron)\n", + " nest.Connect(pre_neuron, spikedet_pre)\n", + " nest.Connect(post_neuron, spikedet_post)\n", + "\n", + " # get STDP synapse and weight before protocol\n", + " syn = nest.GetConnections(source=pre_neuron, synapse_model=\"stdp_nestml_rec\")\n", + "\n", + " t = 0.\n", + " t_hist = []\n", + " w_hist = []\n", + " state = 0\n", + " while t <= sim_time:\n", + " print(\"t = \" + str(t) + \" ms\")\n", + " if t > sim_time / 6. and state == 0:\n", + " nest.SetStatus(post_neuron, {\"I_dend\": 1.})\n", + " state = 1\n", + " if t > 2 * sim_time / 6 and state == 1:\n", + " nest.SetStatus(post_neuron, {\"I_dend\": 1.})\n", + " if t > 3 * sim_time / 6. and state == 1:\n", + " state = 2\n", + " if t > 5 * sim_time / 6. and state == 2:\n", + " nest.SetStatus(post_neuron, {\"I_dend\": 0.})\n", + " state = 3\n", + " nest.Simulate(resolution)\n", + " t += resolution\n", + " t_hist.append(t)\n", + " w_hist.append(nest.GetStatus(syn)[0][\"w\"])\n", + "\n", + " third_factor_trace = nest.GetStatus(mm, \"events\")[0][post_trace_var]\n", + " timevec = nest.GetStatus(mm, \"events\")[0][\"times\"]\n", + "\n", + " \n", + " \n", + " \n", + " fig, ax = plt.subplots(nrows=2)\n", + " ax1, ax2 = ax\n", + "\n", + " V_m = nest.GetStatus(mm, \"events\")[0][\"V_m\"]\n", + " ax2.plot(timevec, third_factor_trace, label=\"I_dend_post\")\n", + " ax1.plot(timevec, V_m, alpha=.7, linestyle=\":\")\n", + " ax1.set_ylabel(\"V_m\")\n", + "\n", + " for _ax in ax:\n", + " _ax.grid(which=\"major\", axis=\"both\")\n", + " _ax.grid(which=\"minor\", axis=\"x\", linestyle=\":\", alpha=.4)\n", + " _ax.set_xlim(0., sim_time)\n", + " _ax.legend()\n", + " fig.savefig(\"/tmp/stdp_third_factor_synapse_test\" + fname_snip + \"_V_m.png\", dpi=300)\n", + " \n", + " \n", + " \n", + " fig, ax = plt.subplots(nrows=5)\n", + " ax1, ax2, ax3, ax4, ax5 = ax\n", + "\n", + " pre_spike_times_ = nest.GetStatus(spikedet_pre, \"events\")[0][\"times\"]\n", + " print(\"Actual pre spike times: \" + str(pre_spike_times_))\n", + "\n", + " n_spikes = len(pre_spike_times_)\n", + " for i in range(n_spikes):\n", + " ax1.plot(2 * [pre_spike_times_[i] + delay], [0, 1], linewidth=2, color=\"blue\", alpha=.4)\n", + "\n", + " post_spike_times_ = nest.GetStatus(spikedet_post, \"events\")[0][\"times\"]\n", + " print(\"Actual post spike times: \" + str(post_spike_times_))\n", + " ax1.set_ylabel(\"Pre spikes\")\n", + "\n", + " n_spikes = len(post_spike_times_)\n", + " for i in range(n_spikes):\n", + " if i == 0:\n", + " _lbl = \"nestml\"\n", + " else:\n", + " _lbl = None\n", + " ax[-4].plot(2 * [post_spike_times_[i]], [0, 1], linewidth=2, color=\"black\", alpha=.4, label=_lbl)\n", + " ax[-4].set_ylabel(\"Post spikes\")\n", + "\n", + " ax[-3].plot(timevec, third_factor_trace)\n", + " ax[-3].set_ylabel(\"3rd factor\")\n", + "\n", + " ax[-2].plot(t_hist[:-1], np.diff(w_hist), marker=\"o\", label=u\"Δw\")\n", + " ax[-2].set_ylabel(u\"Δw\")\n", + "\n", + " ax[-1].plot(t_hist, w_hist, marker=\"o\")\n", + " ax[-1].set_ylabel(\"w\")\n", + " ax[-1].set_xlabel(\"Time [ms]\")\n", + " for _ax in ax:\n", + " if not _ax == ax[-1]:\n", + " _ax.set_xticklabels([])\n", + " _ax.grid(True)\n", + " _ax.set_xlim(0., sim_time)\n", + "\n", + " fig.savefig(\"/tmp/stdp_third_factor_synapse_test\" + fname_snip + \".png\", dpi=300)\n", + "\n", + " return timevec, t_hist, third_factor_trace, w_hist\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", + "DEBUG:findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/home/charl/.local/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n", + "WARNING:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " -- N E S T --\n", + " Copyright (C) 2004 The NEST Initiative\n", + "\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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", + "[85,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", + "Pre spike times: [1.000e+00 2.000e+00 3.000e+00 4.000e+00 5.000e+00 7.000e+00 9.000e+00\n", + " 1.200e+01 1.700e+01 2.100e+01 2.300e+01 2.700e+01 2.800e+01 3.000e+01\n", + " 3.100e+01 3.300e+01 3.500e+01 3.600e+01 3.700e+01 3.800e+01 3.900e+01\n", + " 4.100e+01 4.400e+01 4.600e+01 4.900e+01 5.100e+01 5.300e+01 5.500e+01\n", + " 5.600e+01 5.700e+01 5.800e+01 5.900e+01 6.000e+01 6.100e+01 6.400e+01\n", + " 6.500e+01 6.700e+01 7.000e+01 7.300e+01 7.500e+01 7.700e+01 8.000e+01\n", + " 8.100e+01 8.200e+01 8.300e+01 8.600e+01 9.100e+01 9.300e+01 9.400e+01\n", + " 9.500e+01 9.600e+01 9.800e+01 9.900e+01 1.000e+02 1.010e+02 1.040e+02\n", + " 1.050e+02 1.080e+02 1.090e+02 1.100e+02 1.110e+02 1.130e+02 1.150e+02\n", + " 1.180e+02 1.190e+02 1.210e+02 1.240e+02 1.260e+02 1.270e+02 1.280e+02\n", + " 1.310e+02 1.320e+02 1.340e+02 1.350e+02 1.390e+02 1.430e+02 1.440e+02\n", + " 1.460e+02 1.470e+02 1.480e+02 1.490e+02 1.510e+02 1.520e+02 1.530e+02\n", + " 1.570e+02 1.580e+02 1.590e+02 1.600e+02 1.620e+02 1.630e+02 1.660e+02\n", + " 1.670e+02 1.680e+02 1.690e+02 1.700e+02 1.730e+02 1.750e+02 1.760e+02\n", + " 1.790e+02 1.800e+02 1.810e+02 1.820e+02 1.850e+02 1.880e+02 1.890e+02\n", + " 1.930e+02 1.940e+02 1.980e+02 2.000e+02 2.010e+02 2.030e+02 2.060e+02\n", + " 2.070e+02 2.080e+02 2.090e+02 2.100e+02 2.110e+02 2.130e+02 2.140e+02\n", + " 2.160e+02 2.180e+02 2.210e+02 2.250e+02 2.320e+02 2.330e+02 2.340e+02\n", + " 2.380e+02 2.400e+02 2.420e+02 2.430e+02 2.470e+02 2.480e+02 2.490e+02\n", + " 2.500e+02 2.530e+02 2.540e+02 2.560e+02 2.570e+02 2.580e+02 2.620e+02\n", + " 2.630e+02 2.650e+02 2.680e+02 2.700e+02 2.730e+02 2.750e+02 2.770e+02\n", + " 2.780e+02 2.800e+02 2.820e+02 2.830e+02 2.840e+02 2.870e+02 2.920e+02\n", + " 2.930e+02 2.990e+02 3.000e+02 3.010e+02 3.030e+02 3.040e+02 3.080e+02\n", + " 3.100e+02 3.110e+02 3.130e+02 3.170e+02 3.220e+02 3.230e+02 3.280e+02\n", + " 3.290e+02 3.350e+02 3.370e+02 3.380e+02 3.390e+02 3.410e+02 3.430e+02\n", + " 3.450e+02 3.490e+02 3.560e+02 3.590e+02 3.700e+02 3.710e+02 3.720e+02\n", + " 3.750e+02 3.770e+02 3.780e+02 3.810e+02 3.820e+02 3.840e+02 3.890e+02\n", + " 3.910e+02 3.960e+02 3.980e+02 3.990e+02 4.050e+02 4.070e+02 4.080e+02\n", + " 4.090e+02 4.110e+02 4.120e+02 4.130e+02 4.150e+02 4.180e+02 4.190e+02\n", + " 4.210e+02 4.220e+02 4.250e+02 4.280e+02 4.290e+02 4.300e+02 4.310e+02\n", + " 4.330e+02 4.340e+02 4.370e+02 4.390e+02 4.420e+02 4.440e+02 4.470e+02\n", + " 4.500e+02 4.540e+02 4.550e+02 4.590e+02 4.610e+02 4.620e+02 4.630e+02\n", + " 4.640e+02 4.650e+02 4.670e+02 4.680e+02 4.690e+02 4.710e+02 4.720e+02\n", + " 4.750e+02 4.810e+02 4.910e+02 4.920e+02 4.970e+02 5.020e+02 5.050e+02\n", + " 5.070e+02 5.110e+02 5.140e+02 5.160e+02 5.180e+02 5.190e+02 5.200e+02\n", + " 5.250e+02 5.270e+02 5.280e+02 5.320e+02 5.330e+02 5.350e+02 5.380e+02\n", + " 5.410e+02 5.430e+02 5.440e+02 5.460e+02 5.470e+02 5.500e+02 5.530e+02\n", + " 5.550e+02 5.580e+02 5.590e+02 5.630e+02 5.660e+02 5.700e+02 5.750e+02\n", + " 5.780e+02 5.850e+02 5.900e+02 5.950e+02 6.010e+02 6.030e+02 6.050e+02\n", + " 6.060e+02 6.070e+02 6.110e+02 6.120e+02 6.150e+02 6.230e+02 6.290e+02\n", + " 6.300e+02 6.310e+02 6.320e+02 6.330e+02 6.400e+02 6.430e+02 6.470e+02\n", + " 6.480e+02 6.500e+02 6.510e+02 6.520e+02 6.590e+02 6.600e+02 6.620e+02\n", + " 6.640e+02 6.680e+02 6.730e+02 6.750e+02 6.760e+02 6.790e+02 6.800e+02\n", + " 6.820e+02 6.870e+02 6.880e+02 6.970e+02 7.020e+02 7.090e+02 7.150e+02\n", + " 7.180e+02 7.190e+02 7.200e+02 7.220e+02 7.240e+02 7.250e+02 7.330e+02\n", + " 7.390e+02 7.410e+02 7.420e+02 7.470e+02 7.510e+02 7.530e+02 7.550e+02\n", + " 7.600e+02 7.650e+02 7.690e+02 7.720e+02 7.770e+02 7.780e+02 7.890e+02\n", + " 7.900e+02 7.910e+02 7.980e+02 8.030e+02 8.090e+02 8.130e+02 8.160e+02\n", + " 8.190e+02 8.310e+02 8.340e+02 8.390e+02 8.420e+02 8.510e+02 8.530e+02\n", + " 8.680e+02 8.840e+02 8.900e+02 8.920e+02 8.930e+02 9.000e+02 9.030e+02\n", + " 9.090e+02 9.170e+02 9.300e+02 9.380e+02 9.390e+02 9.540e+02 9.570e+02\n", + " 9.640e+02 9.650e+02 9.660e+02 9.810e+02 9.850e+02 9.940e+02 9.950e+02\n", + " 1.001e+03 1.018e+03 1.023e+03 1.053e+03 1.068e+03 1.100e+03 1.109e+03\n", + " 1.146e+03 1.172e+03 1.226e+03 1.286e+03 1.287e+03 1.327e+03 1.353e+03\n", + " 1.396e+03 1.426e+03 1.488e+03 1.521e+03 1.539e+03 1.550e+03 1.571e+03]\n", + "Post spike times: [ 4. 7. 8. 9. 10. 11. 12. 14. 15. 16. 19. 20.\n", + " 21. 25. 26. 27. 29. 32. 33. 35. 36. 38. 40. 41.\n", + " 43. 45. 46. 47. 48. 50. 51. 52. 53. 54. 55. 57.\n", + " 58. 59. 61. 68. 69. 70. 73. 76. 77. 79. 81. 83.\n", + " 85. 86. 88. 92. 94. 96. 97. 98. 99. 102. 103. 104.\n", + " 105. 108. 110. 111. 114. 116. 117. 119. 121. 123. 125. 128.\n", + " 131. 135. 136. 140. 141. 142. 143. 146. 147. 148. 149. 151.\n", + " 153. 155. 156. 158. 159. 161. 164. 165. 168. 169. 171. 173.\n", + " 174. 178. 182. 184. 186. 187. 189. 191. 192. 193. 195. 196.\n", + " 198. 199. 200. 201. 202. 204. 210. 211. 214. 215. 216. 217.\n", + " 218. 223. 226. 229. 230. 231. 232. 237. 238. 239. 240. 241.\n", + " 242. 243. 244. 245. 247. 250. 251. 252. 253. 255. 256. 257.\n", + " 258. 260. 264. 266. 268. 270. 271. 272. 273. 274. 275. 278.\n", + " 279. 280. 281. 286. 287. 289. 290. 291. 295. 296. 299. 300.\n", + " 301. 305. 309. 312. 313. 315. 319. 322. 323. 324. 326. 327.\n", + " 328. 331. 333. 335. 338. 339. 343. 344. 345. 346. 347. 349.\n", + " 352. 355. 358. 359. 361. 362. 364. 367. 369. 381. 382. 384.\n", + " 385. 388. 393. 394. 396. 397. 402. 404. 412. 414. 415. 416.\n", + " 418. 419. 424. 425. 427. 430. 431. 433. 434. 437. 439. 441.\n", + " 443. 452. 453. 456. 457. 458. 459. 466. 467. 472. 474. 476.\n", + " 478. 481. 483. 484. 485. 486. 489. 490. 492. 494. 501. 507.\n", + " 509. 510. 512. 513. 516. 517. 522. 530. 537. 543. 545. 547.\n", + " 548. 550. 558. 559. 560. 562. 563. 567. 568. 575. 577. 579.\n", + " 584. 585. 587. 592. 596. 597. 600. 603. 604. 613. 617. 618.\n", + " 620. 623. 625. 628. 633. 637. 638. 639. 647. 648. 649. 651.\n", + " 654. 656. 657. 660. 666. 668. 670. 674. 676. 681. 683. 689.\n", + " 690. 698. 701. 702. 703. 706. 707. 710. 717. 723. 734. 744.\n", + " 746. 754. 762. 764. 765. 772. 775. 778. 779. 787. 788. 792.\n", + " 800. 802. 807. 829. 832. 836. 840. 841. 849. 863. 882. 894.\n", + " 896. 915. 922. 924. 926. 931. 933. 934. 961. 967. 971. 983.\n", + " 1014. 1015. 1016. 1030. 1047. 1061. 1068. 1083. 1126. 1144. 1145. 1150.\n", + " 1163. 1221. 1239. 1307. 1308. 1341. 1416. 1468. 1987.]\n", + "t = 0.0 ms\n", + "t = 0.5 ms\n", + "t = 1.0 ms\n", + "t = 1.5 ms\n", + "t = 2.0 ms\n", + "t = 2.5 ms\n", + "t = 3.0 ms\n", + "t = 3.5 ms\n", + "t = 4.0 ms\n", + "t = 4.5 ms\n", + "t = 5.0 ms\n", + "t = 5.5 ms\n", + "t = 6.0 ms\n", + "t = 6.5 ms\n", + "t = 7.0 ms\n", + "t = 7.5 ms\n", + "t = 8.0 ms\n", + "t = 8.5 ms\n", + "t = 9.0 ms\n", + "t = 9.5 ms\n", + "t = 10.0 ms\n", + "t = 10.5 ms\n", + "t = 11.0 ms\n", + "t = 11.5 ms\n", + "t = 12.0 ms\n", + "t = 12.5 ms\n", + "t = 13.0 ms\n", + "t = 13.5 ms\n", + "t = 14.0 ms\n", + "t = 14.5 ms\n", + "t = 15.0 ms\n", + "t = 15.5 ms\n", + "t = 16.0 ms\n", + "t = 16.5 ms\n", + "t = 17.0 ms\n", + "t = 17.5 ms\n", + "t = 18.0 ms\n", + "t = 18.5 ms\n", + "t = 19.0 ms\n", + "t = 19.5 ms\n", + "t = 20.0 ms\n", + "t = 20.5 ms\n", + "t = 21.0 ms\n", + "t = 21.5 ms\n", + "t = 22.0 ms\n", + "t = 22.5 ms\n", + "t = 23.0 ms\n", + "t = 23.5 ms\n", + "t = 24.0 ms\n", + "t = 24.5 ms\n", + "t = 25.0 ms\n", + "t = 25.5 ms\n", + "t = 26.0 ms\n", + "t = 26.5 ms\n", + "t = 27.0 ms\n", + "t = 27.5 ms\n", + "t = 28.0 ms\n", + "t = 28.5 ms\n", + "t = 29.0 ms\n", + "t = 29.5 ms\n", + "t = 30.0 ms\n", + "t = 30.5 ms\n", + "t = 31.0 ms\n", + "t = 31.5 ms\n", + "t = 32.0 ms\n", + "t = 32.5 ms\n", + "t = 33.0 ms\n", + "t = 33.5 ms\n", + "t = 34.0 ms\n", + "t = 34.5 ms\n", + "t = 35.0 ms\n", + "t = 35.5 ms\n", + "t = 36.0 ms\n", + "t = 36.5 ms\n", + "t = 37.0 ms\n", + "t = 37.5 ms\n", + "t = 38.0 ms\n", + "t = 38.5 ms\n", + "t = 39.0 ms\n", + "t = 39.5 ms\n", + "t = 40.0 ms\n", + "t = 40.5 ms\n", + "t = 41.0 ms\n", + "t = 41.5 ms\n", + "t = 42.0 ms\n", + "t = 42.5 ms\n", + "t = 43.0 ms\n", + "t = 43.5 ms\n", + "t = 44.0 ms\n", + "t = 44.5 ms\n", + "t = 45.0 ms\n", + "t = 45.5 ms\n", + "t = 46.0 ms\n", + "t = 46.5 ms\n", + "t = 47.0 ms\n", + "t = 47.5 ms\n", + "t = 48.0 ms\n", + "t = 48.5 ms\n", + "t = 49.0 ms\n", + "t = 49.5 ms\n", + "t = 50.0 ms\n", + "t = 50.5 ms\n", + "t = 51.0 ms\n", + "t = 51.5 ms\n", + "t = 52.0 ms\n", + "t = 52.5 ms\n", + "t = 53.0 ms\n", + "t = 53.5 ms\n", + "t = 54.0 ms\n", + "t = 54.5 ms\n", + "t = 55.0 ms\n", + "t = 55.5 ms\n", + "t = 56.0 ms\n", + "t = 56.5 ms\n", + "t = 57.0 ms\n", + "t = 57.5 ms\n", + "t = 58.0 ms\n", + "t = 58.5 ms\n", + "t = 59.0 ms\n", + "t = 59.5 ms\n", + "t = 60.0 ms\n", + "t = 60.5 ms\n", + "t = 61.0 ms\n", + "t = 61.5 ms\n", + "t = 62.0 ms\n", + "t = 62.5 ms\n", + "t = 63.0 ms\n", + "t = 63.5 ms\n", + "t = 64.0 ms\n", + "t = 64.5 ms\n", + "t = 65.0 ms\n", + "t = 65.5 ms\n", + "t = 66.0 ms\n", + "t = 66.5 ms\n", + "t = 67.0 ms\n", + "t = 67.5 ms\n", + "t = 68.0 ms\n", + "t = 68.5 ms\n", + "t = 69.0 ms\n", + "t = 69.5 ms\n", + "t = 70.0 ms\n", + "t = 70.5 ms\n", + "t = 71.0 ms\n", + "t = 71.5 ms\n", + "t = 72.0 ms\n", + "t = 72.5 ms\n", + "t = 73.0 ms\n", + "t = 73.5 ms\n", + "t = 74.0 ms\n", + "t = 74.5 ms\n", + "t = 75.0 ms\n", + "t = 75.5 ms\n", + "t = 76.0 ms\n", + "t = 76.5 ms\n", + "t = 77.0 ms\n", + "t = 77.5 ms\n", + "t = 78.0 ms\n", + "t = 78.5 ms\n", + "t = 79.0 ms\n", + "t = 79.5 ms\n", + "t = 80.0 ms\n", + "t = 80.5 ms\n", + "t = 81.0 ms\n", + "t = 81.5 ms\n", + "t = 82.0 ms\n", + "t = 82.5 ms\n", + "t = 83.0 ms\n", + "t = 83.5 ms\n", + "t = 84.0 ms\n", + "t = 84.5 ms\n", + "t = 85.0 ms\n", + "t = 85.5 ms\n", + "t = 86.0 ms\n", + "t = 86.5 ms\n", + "t = 87.0 ms\n", + "t = 87.5 ms\n", + "t = 88.0 ms\n", + "t = 88.5 ms\n", + "t = 89.0 ms\n", + "t = 89.5 ms\n", + "t = 90.0 ms\n", + "t = 90.5 ms\n", + "t = 91.0 ms\n", + "t = 91.5 ms\n", + "t = 92.0 ms\n", + "t = 92.5 ms\n", + "t = 93.0 ms\n", + "t = 93.5 ms\n", + "t = 94.0 ms\n", + "t = 94.5 ms\n", + "t = 95.0 ms\n", + "t = 95.5 ms\n", + "t = 96.0 ms\n", + "t = 96.5 ms\n", + "t = 97.0 ms\n", + "t = 97.5 ms\n", + "t = 98.0 ms\n", + "t = 98.5 ms\n", + "t = 99.0 ms\n", + "t = 99.5 ms\n", + "t = 100.0 ms\n", + "t = 100.5 ms\n", + "t = 101.0 ms\n", + "t = 101.5 ms\n", + "t = 102.0 ms\n", + "t = 102.5 ms\n", + "t = 103.0 ms\n", + "t = 103.5 ms\n", + "t = 104.0 ms\n", + "t = 104.5 ms\n", + "t = 105.0 ms\n", + "t = 105.5 ms\n", + "t = 106.0 ms\n", + "t = 106.5 ms\n", + "t = 107.0 ms\n", + "t = 107.5 ms\n", + "t = 108.0 ms\n", + "t = 108.5 ms\n", + "t = 109.0 ms\n", + "t = 109.5 ms\n", + "t = 110.0 ms\n", + "t = 110.5 ms\n", + "t = 111.0 ms\n", + "t = 111.5 ms\n", + "t = 112.0 ms\n", + "t = 112.5 ms\n", + "t = 113.0 ms\n", + "t = 113.5 ms\n", + "t = 114.0 ms\n", + "t = 114.5 ms\n", + "t = 115.0 ms\n", + "t = 115.5 ms\n", + "t = 116.0 ms\n", + "t = 116.5 ms\n", + "t = 117.0 ms\n", + "t = 117.5 ms\n", + "t = 118.0 ms\n", + "t = 118.5 ms\n", + "t = 119.0 ms\n", + "t = 119.5 ms\n", + "t = 120.0 ms\n", + "t = 120.5 ms\n", + "t = 121.0 ms\n", + "t = 121.5 ms\n", + "t = 122.0 ms\n", + "t = 122.5 ms\n", + "t = 123.0 ms\n", + "t = 123.5 ms\n", + "t = 124.0 ms\n", + "t = 124.5 ms\n", + "t = 125.0 ms\n", + "t = 125.5 ms\n", + "t = 126.0 ms\n", + "t = 126.5 ms\n", + "t = 127.0 ms\n", + "t = 127.5 ms\n", + "t = 128.0 ms\n", + "t = 128.5 ms\n", + "t = 129.0 ms\n", + "t = 129.5 ms\n", + "t = 130.0 ms\n", + "t = 130.5 ms\n", + "t = 131.0 ms\n", + "t = 131.5 ms\n", + "t = 132.0 ms\n", + "t = 132.5 ms\n", + "t = 133.0 ms\n", + "t = 133.5 ms\n", + "t = 134.0 ms\n", + "t = 134.5 ms\n", + "t = 135.0 ms\n", + "t = 135.5 ms\n", + "t = 136.0 ms\n", + "t = 136.5 ms\n", + "t = 137.0 ms\n", + "t = 137.5 ms\n", + "t = 138.0 ms\n", + "t = 138.5 ms\n", + "t = 139.0 ms\n", + "t = 139.5 ms\n", + "t = 140.0 ms\n", + "t = 140.5 ms\n", + "t = 141.0 ms\n", + "t = 141.5 ms\n", + "t = 142.0 ms\n", + "t = 142.5 ms\n", + "t = 143.0 ms\n", + "t = 143.5 ms\n", + "t = 144.0 ms\n", + "t = 144.5 ms\n", + "t = 145.0 ms\n", + "t = 145.5 ms\n", + "t = 146.0 ms\n", + "t = 146.5 ms\n", + "t = 147.0 ms\n", + "t = 147.5 ms\n", + "t = 148.0 ms\n", + "t = 148.5 ms\n", + "t = 149.0 ms\n", + "t = 149.5 ms\n", + "t = 150.0 ms\n", + "t = 150.5 ms\n", + "t = 151.0 ms\n", + "t = 151.5 ms\n", + "t = 152.0 ms\n", + "t = 152.5 ms\n", + "t = 153.0 ms\n", + "t = 153.5 ms\n", + "t = 154.0 ms\n", + "t = 154.5 ms\n", + "t = 155.0 ms\n", + "t = 155.5 ms\n", + "t = 156.0 ms\n", + "t = 156.5 ms\n", + "t = 157.0 ms\n", + "t = 157.5 ms\n", + "t = 158.0 ms\n", + "t = 158.5 ms\n", + "t = 159.0 ms\n", + "t = 159.5 ms\n", + "t = 160.0 ms\n", + "t = 160.5 ms\n", + "t = 161.0 ms\n", + "t = 161.5 ms\n", + "t = 162.0 ms\n", + "t = 162.5 ms\n", + "t = 163.0 ms\n", + "t = 163.5 ms\n", + "t = 164.0 ms\n", + "t = 164.5 ms\n", + "t = 165.0 ms\n", + "t = 165.5 ms\n", + "t = 166.0 ms\n", + "t = 166.5 ms\n", + "t = 167.0 ms\n", + "t = 167.5 ms\n", + "t = 168.0 ms\n", + "t = 168.5 ms\n", + "t = 169.0 ms\n", + "t = 169.5 ms\n", + "t = 170.0 ms\n", + "t = 170.5 ms\n", + "t = 171.0 ms\n", + "t = 171.5 ms\n", + "t = 172.0 ms\n", + "t = 172.5 ms\n", + "t = 173.0 ms\n", + "t = 173.5 ms\n", + "t = 174.0 ms\n", + "t = 174.5 ms\n", + "t = 175.0 ms\n", + "t = 175.5 ms\n", + "t = 176.0 ms\n", + "t = 176.5 ms\n", + "t = 177.0 ms\n", + "t = 177.5 ms\n", + "t = 178.0 ms\n", + "t = 178.5 ms\n", + "t = 179.0 ms\n", + "t = 179.5 ms\n", + "t = 180.0 ms\n", + "t = 180.5 ms\n", + "t = 181.0 ms\n", + "t = 181.5 ms\n", + "t = 182.0 ms\n", + "t = 182.5 ms\n", + "t = 183.0 ms\n", + "t = 183.5 ms\n", + "t = 184.0 ms\n", + "t = 184.5 ms\n", + "t = 185.0 ms\n", + "t = 185.5 ms\n", + "t = 186.0 ms\n", + "t = 186.5 ms\n", + "t = 187.0 ms\n", + "t = 187.5 ms\n", + "t = 188.0 ms\n", + "t = 188.5 ms\n", + "t = 189.0 ms\n", + "t = 189.5 ms\n", + "t = 190.0 ms\n", + "t = 190.5 ms\n", + "t = 191.0 ms\n", + "t = 191.5 ms\n", + "t = 192.0 ms\n", + "t = 192.5 ms\n", + "t = 193.0 ms\n", + "t = 193.5 ms\n", + "t = 194.0 ms\n", + "t = 194.5 ms\n", + "t = 195.0 ms\n", + "t = 195.5 ms\n", + "t = 196.0 ms\n", + "t = 196.5 ms\n", + "t = 197.0 ms\n", + "t = 197.5 ms\n", + "t = 198.0 ms\n", + "t = 198.5 ms\n", + "t = 199.0 ms\n", + "t = 199.5 ms\n", + "t = 200.0 ms\n", + "t = 200.5 ms\n", + "t = 201.0 ms\n", + "t = 201.5 ms\n", + "t = 202.0 ms\n", + "t = 202.5 ms\n", + "t = 203.0 ms\n", + "t = 203.5 ms\n", + "t = 204.0 ms\n", + "t = 204.5 ms\n", + "t = 205.0 ms\n", + "t = 205.5 ms\n", + "t = 206.0 ms\n", + "t = 206.5 ms\n", + "t = 207.0 ms\n", + "t = 207.5 ms\n", + "t = 208.0 ms\n", + "t = 208.5 ms\n", + "t = 209.0 ms\n", + "t = 209.5 ms\n", + "t = 210.0 ms\n", + "t = 210.5 ms\n", + "t = 211.0 ms\n", + "t = 211.5 ms\n", + "t = 212.0 ms\n", + "t = 212.5 ms\n", + "t = 213.0 ms\n", + "t = 213.5 ms\n", + "t = 214.0 ms\n", + "t = 214.5 ms\n", + "t = 215.0 ms\n", + "t = 215.5 ms\n", + "t = 216.0 ms\n", + "t = 216.5 ms\n", + "t = 217.0 ms\n", + "t = 217.5 ms\n", + "t = 218.0 ms\n", + "t = 218.5 ms\n", + "t = 219.0 ms\n", + "t = 219.5 ms\n", + "t = 220.0 ms\n", + "t = 220.5 ms\n", + "t = 221.0 ms\n", + "t = 221.5 ms\n", + "t = 222.0 ms\n", + "t = 222.5 ms\n", + "t = 223.0 ms\n", + "t = 223.5 ms\n", + "t = 224.0 ms\n", + "t = 224.5 ms\n", + "t = 225.0 ms\n", + "t = 225.5 ms\n", + "t = 226.0 ms\n", + "t = 226.5 ms\n", + "t = 227.0 ms\n", + "t = 227.5 ms\n", + "t = 228.0 ms\n", + "t = 228.5 ms\n", + "t = 229.0 ms\n", + "t = 229.5 ms\n", + "t = 230.0 ms\n", + "t = 230.5 ms\n", + "t = 231.0 ms\n", + "t = 231.5 ms\n", + "t = 232.0 ms\n", + "t = 232.5 ms\n", + "t = 233.0 ms\n", + "t = 233.5 ms\n", + "t = 234.0 ms\n", + "t = 234.5 ms\n", + "t = 235.0 ms\n", + "t = 235.5 ms\n", + "t = 236.0 ms\n", + "t = 236.5 ms\n", + "t = 237.0 ms\n", + "t = 237.5 ms\n", + "t = 238.0 ms\n", + "t = 238.5 ms\n", + "t = 239.0 ms\n", + "t = 239.5 ms\n", + "t = 240.0 ms\n", + "t = 240.5 ms\n", + "t = 241.0 ms\n", + "t = 241.5 ms\n", + "t = 242.0 ms\n", + "t = 242.5 ms\n", + "t = 243.0 ms\n", + "t = 243.5 ms\n", + "t = 244.0 ms\n", + "t = 244.5 ms\n", + "t = 245.0 ms\n", + "t = 245.5 ms\n", + "t = 246.0 ms\n", + "t = 246.5 ms\n", + "t = 247.0 ms\n", + "t = 247.5 ms\n", + "t = 248.0 ms\n", + "t = 248.5 ms\n", + "t = 249.0 ms\n", + "t = 249.5 ms\n", + "t = 250.0 ms\n", + "t = 250.5 ms\n", + "t = 251.0 ms\n", + "t = 251.5 ms\n", + "t = 252.0 ms\n", + "t = 252.5 ms\n", + "t = 253.0 ms\n", + "t = 253.5 ms\n", + "t = 254.0 ms\n", + "t = 254.5 ms\n", + "t = 255.0 ms\n", + "t = 255.5 ms\n", + "t = 256.0 ms\n", + "t = 256.5 ms\n", + "t = 257.0 ms\n", + "t = 257.5 ms\n", + "t = 258.0 ms\n", + "t = 258.5 ms\n", + "t = 259.0 ms\n", + "t = 259.5 ms\n", + "t = 260.0 ms\n", + "t = 260.5 ms\n", + "t = 261.0 ms\n", + "t = 261.5 ms\n", + "t = 262.0 ms\n", + "t = 262.5 ms\n", + "t = 263.0 ms\n", + "t = 263.5 ms\n", + "t = 264.0 ms\n", + "t = 264.5 ms\n", + "t = 265.0 ms\n", + "t = 265.5 ms\n", + "t = 266.0 ms\n", + "t = 266.5 ms\n", + "t = 267.0 ms\n", + "t = 267.5 ms\n", + "t = 268.0 ms\n", + "t = 268.5 ms\n", + "t = 269.0 ms\n", + "t = 269.5 ms\n", + "t = 270.0 ms\n", + "t = 270.5 ms\n", + "t = 271.0 ms\n", + "t = 271.5 ms\n", + "t = 272.0 ms\n", + "t = 272.5 ms\n", + "t = 273.0 ms\n", + "t = 273.5 ms\n", + "t = 274.0 ms\n", + "t = 274.5 ms\n", + "t = 275.0 ms\n", + "t = 275.5 ms\n", + "t = 276.0 ms\n", + "t = 276.5 ms\n", + "t = 277.0 ms\n", + "t = 277.5 ms\n", + "t = 278.0 ms\n", + "t = 278.5 ms\n", + "t = 279.0 ms\n", + "t = 279.5 ms\n", + "t = 280.0 ms\n", + "t = 280.5 ms\n", + "t = 281.0 ms\n", + "t = 281.5 ms\n", + "t = 282.0 ms\n", + "t = 282.5 ms\n", + "t = 283.0 ms\n", + "t = 283.5 ms\n", + "t = 284.0 ms\n", + "t = 284.5 ms\n", + "t = 285.0 ms\n", + "t = 285.5 ms\n", + "t = 286.0 ms\n", + "t = 286.5 ms\n", + "t = 287.0 ms\n", + "t = 287.5 ms\n", + "t = 288.0 ms\n", + "t = 288.5 ms\n", + "t = 289.0 ms\n", + "t = 289.5 ms\n", + "t = 290.0 ms\n", + "t = 290.5 ms\n", + "t = 291.0 ms\n", + "t = 291.5 ms\n", + "t = 292.0 ms\n", + "t = 292.5 ms\n", + "t = 293.0 ms\n", + "t = 293.5 ms\n", + "t = 294.0 ms\n", + "t = 294.5 ms\n", + "t = 295.0 ms\n", + "t = 295.5 ms\n", + "t = 296.0 ms\n", + "t = 296.5 ms\n", + "t = 297.0 ms\n", + "t = 297.5 ms\n", + "t = 298.0 ms\n", + "t = 298.5 ms\n", + "t = 299.0 ms\n", + "t = 299.5 ms\n", + "t = 300.0 ms\n", + "t = 300.5 ms\n", + "t = 301.0 ms\n", + "t = 301.5 ms\n", + "t = 302.0 ms\n", + "t = 302.5 ms\n", + "t = 303.0 ms\n", + "t = 303.5 ms\n", + "t = 304.0 ms\n", + "t = 304.5 ms\n", + "t = 305.0 ms\n", + "t = 305.5 ms\n", + "t = 306.0 ms\n", + "t = 306.5 ms\n", + "t = 307.0 ms\n", + "t = 307.5 ms\n", + "t = 308.0 ms\n", + "t = 308.5 ms\n", + "t = 309.0 ms\n", + "t = 309.5 ms\n", + "t = 310.0 ms\n", + "t = 310.5 ms\n", + "t = 311.0 ms\n", + "t = 311.5 ms\n", + "t = 312.0 ms\n", + "t = 312.5 ms\n", + "t = 313.0 ms\n", + "t = 313.5 ms\n", + "t = 314.0 ms\n", + "t = 314.5 ms\n", + "t = 315.0 ms\n", + "t = 315.5 ms\n", + "t = 316.0 ms\n", + "t = 316.5 ms\n", + "t = 317.0 ms\n", + "t = 317.5 ms\n", + "t = 318.0 ms\n", + "t = 318.5 ms\n", + "t = 319.0 ms\n", + "t = 319.5 ms\n", + "t = 320.0 ms\n", + "t = 320.5 ms\n", + "t = 321.0 ms\n", + "t = 321.5 ms\n", + "t = 322.0 ms\n", + "t = 322.5 ms\n", + "t = 323.0 ms\n", + "t = 323.5 ms\n", + "t = 324.0 ms\n", + "t = 324.5 ms\n", + "t = 325.0 ms\n", + "t = 325.5 ms\n", + "t = 326.0 ms\n", + "t = 326.5 ms\n", + "t = 327.0 ms\n", + "t = 327.5 ms\n", + "t = 328.0 ms\n", + "t = 328.5 ms\n", + "t = 329.0 ms\n", + "t = 329.5 ms\n", + "t = 330.0 ms\n", + "t = 330.5 ms\n", + "t = 331.0 ms\n", + "t = 331.5 ms\n", + "t = 332.0 ms\n", + "t = 332.5 ms\n", + "t = 333.0 ms\n", + "t = 333.5 ms\n", + "t = 334.0 ms\n", + "t = 334.5 ms\n", + "t = 335.0 ms\n", + "t = 335.5 ms\n", + "t = 336.0 ms\n", + "t = 336.5 ms\n", + "t = 337.0 ms\n", + "t = 337.5 ms\n", + "t = 338.0 ms\n", + "t = 338.5 ms\n", + "t = 339.0 ms\n", + "t = 339.5 ms\n", + "t = 340.0 ms\n", + "t = 340.5 ms\n", + "t = 341.0 ms\n", + "t = 341.5 ms\n", + "t = 342.0 ms\n", + "t = 342.5 ms\n", + "t = 343.0 ms\n", + "t = 343.5 ms\n", + "t = 344.0 ms\n", + "t = 344.5 ms\n", + "t = 345.0 ms\n", + "t = 345.5 ms\n", + "t = 346.0 ms\n", + "t = 346.5 ms\n", + "t = 347.0 ms\n", + "t = 347.5 ms\n", + "t = 348.0 ms\n", + "t = 348.5 ms\n", + "t = 349.0 ms\n", + "t = 349.5 ms\n", + "t = 350.0 ms\n", + "t = 350.5 ms\n", + "t = 351.0 ms\n", + "t = 351.5 ms\n", + "t = 352.0 ms\n", + "t = 352.5 ms\n", + "t = 353.0 ms\n", + "t = 353.5 ms\n", + "t = 354.0 ms\n", + "t = 354.5 ms\n", + "t = 355.0 ms\n", + "t = 355.5 ms\n", + "t = 356.0 ms\n", + "t = 356.5 ms\n", + "t = 357.0 ms\n", + "t = 357.5 ms\n", + "t = 358.0 ms\n", + "t = 358.5 ms\n", + "t = 359.0 ms\n", + "t = 359.5 ms\n", + "t = 360.0 ms\n", + "t = 360.5 ms\n", + "t = 361.0 ms\n", + "t = 361.5 ms\n", + "t = 362.0 ms\n", + "t = 362.5 ms\n", + "t = 363.0 ms\n", + "t = 363.5 ms\n", + "t = 364.0 ms\n", + "t = 364.5 ms\n", + "t = 365.0 ms\n", + "t = 365.5 ms\n", + "t = 366.0 ms\n", + "t = 366.5 ms\n", + "t = 367.0 ms\n", + "t = 367.5 ms\n", + "t = 368.0 ms\n", + "t = 368.5 ms\n", + "t = 369.0 ms\n", + "t = 369.5 ms\n", + "t = 370.0 ms\n", + "t = 370.5 ms\n", + "t = 371.0 ms\n", + "t = 371.5 ms\n", + "t = 372.0 ms\n", + "t = 372.5 ms\n", + "t = 373.0 ms\n", + "t = 373.5 ms\n", + "t = 374.0 ms\n", + "t = 374.5 ms\n", + "t = 375.0 ms\n", + "t = 375.5 ms\n", + "t = 376.0 ms\n", + "t = 376.5 ms\n", + "t = 377.0 ms\n", + "t = 377.5 ms\n", + "t = 378.0 ms\n", + "t = 378.5 ms\n", + "t = 379.0 ms\n", + "t = 379.5 ms\n", + "t = 380.0 ms\n", + "t = 380.5 ms\n", + "t = 381.0 ms\n", + "t = 381.5 ms\n", + "t = 382.0 ms\n", + "t = 382.5 ms\n", + "t = 383.0 ms\n", + "t = 383.5 ms\n", + "t = 384.0 ms\n", + "t = 384.5 ms\n", + "t = 385.0 ms\n", + "t = 385.5 ms\n", + "t = 386.0 ms\n", + "t = 386.5 ms\n", + "t = 387.0 ms\n", + "t = 387.5 ms\n", + "t = 388.0 ms\n", + "t = 388.5 ms\n", + "t = 389.0 ms\n", + "t = 389.5 ms\n", + "t = 390.0 ms\n", + "t = 390.5 ms\n", + "t = 391.0 ms\n", + "t = 391.5 ms\n", + "t = 392.0 ms\n", + "t = 392.5 ms\n", + "t = 393.0 ms\n", + "t = 393.5 ms\n", + "t = 394.0 ms\n", + "t = 394.5 ms\n", + "t = 395.0 ms\n", + "t = 395.5 ms\n", + "t = 396.0 ms\n", + "t = 396.5 ms\n", + "t = 397.0 ms\n", + "t = 397.5 ms\n", + "t = 398.0 ms\n", + "t = 398.5 ms\n", + "t = 399.0 ms\n", + "t = 399.5 ms\n", + "t = 400.0 ms\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual pre spike times: [ 2. 3. 4. 5. 6. 8. 10. 13. 18. 22. 24. 28. 29. 31.\n", + " 32. 34. 36. 37. 38. 39. 40. 42. 45. 47. 50. 52. 54. 56.\n", + " 57. 58. 59. 60. 61. 62. 65. 66. 68. 71. 74. 76. 78. 81.\n", + " 82. 83. 84. 87. 92. 94. 95. 96. 97. 99. 100. 101. 102. 105.\n", + " 106. 109. 110. 111. 112. 114. 116. 119. 120. 122. 125. 127. 128. 129.\n", + " 132. 133. 135. 136. 140. 144. 145. 147. 148. 149. 150. 152. 153. 154.\n", + " 158. 159. 160. 161. 163. 164. 167. 168. 169. 170. 171. 174. 176. 177.\n", + " 180. 181. 182. 183. 186. 189. 190. 194. 195. 199. 201. 202. 204. 207.\n", + " 208. 209. 210. 211. 212. 214. 215. 217. 219. 222. 226. 233. 234. 235.\n", + " 239. 241. 243. 244. 248. 249. 250. 251. 254. 255. 257. 258. 259. 263.\n", + " 264. 266. 269. 271. 274. 276. 278. 279. 281. 283. 284. 285. 288. 293.\n", + " 294. 300. 301. 302. 304. 305. 309. 311. 312. 314. 318. 323. 324. 329.\n", + " 330. 336. 338. 339. 340. 342. 344. 346. 350. 357. 360. 371. 372. 373.\n", + " 376. 378. 379. 382. 383. 385. 390. 392. 397. 399. 400.]\n", + "Actual post spike times: [ 6. 9. 11.5 14. 16.5 19.5 22. 25.5 28.5 31.5 34.5 37.5\n", + " 40.5 43. 46. 48.5 51.5 54. 56.5 59. 62. 65.5 70. 73.\n", + " 76. 78.5 81.5 84.5 87.5 90.5 93.5 96.5 99. 102. 104.5 107.\n", + " 109.5 112.5 115.5 118.5 121.5 124.5 127.5 130.5 133.5 137. 141. 143.5\n", + " 146.5 149. 152. 154.5 157.5 160.5 163.5 166.5 169.5 172.5 175.5 179.5\n", + " 183.5 186.5 189. 192. 194.5 197.5 200.5 203. 205.5 209.5 212.5 215.5\n", + " 218. 220.5 224.5 227.5 230.5 233. 236. 239. 241.5 244. 246.5 249.\n", + " 251.5 254. 256.5 259. 261.5 265.5 268.5 271.5 274. 276.5 279.5 282.\n", + " 285. 288. 290.5 293. 296.5 299.5 302. 305.5 309.5 313.5 316.5 320.5\n", + " 323.5 326. 328.5 331.5 334.5 337.5 340.5 344.5 347. 350. 353.5 356.5\n", + " 359.5 362.5 365.5 368.5 371.5 382.5 385.5 388.5 392. 395. 397.5 400.5]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fname_snip = \"\"\n", + "\n", + "post_spike_times = np.sort(np.unique(1 + np.round(500 * np.sort(np.abs(np.random.randn(500)))))) # [ms]\n", + "pre_spike_times = np.sort(np.unique(1 + np.round(500 * np.sort(np.abs(np.random.randn(500)))))) # [ms]\n", + "\n", + "# run the simulation\n", + "timevec, t_hist, third_factor_trace, w_hist = run_synapse_test(neuron_model_name=neuron_model_name,\n", + " synapse_model_name=synapse_model_name,\n", + " resolution=.5, # [ms]\n", + " delay=1.5, # [ms]\n", + " pre_spike_times=pre_spike_times,\n", + " post_spike_times=post_spike_times,\n", + " sim_time=400.,\n", + " fname_snip=fname_snip)\n", + "\n", + "# verify\n", + "assert np.any(np.abs(np.array(w_hist) - 1) > 0.), \"No change in the weight!\"\n", + "\n", + "# verify that weight does not change appreciably when third factor trace is close to zero\n", + "idx = np.where(np.abs(third_factor_trace) < 1E-12)[0] # find where third_factor_trace is (almost) zero\n", + "times_dw_should_be_zero = timevec[idx]\n", + "assert len(times_dw_should_be_zero) > 0 # make sure we have > 0 datapoints to check\n", + "for time_dw_should_be_zero in times_dw_should_be_zero[1:]:\n", + " _idx = np.argmin((time_dw_should_be_zero - np.array(t_hist))**2)\n", + " np.testing.assert_allclose(t_hist[_idx], time_dw_should_be_zero)\n", + " np.testing.assert_allclose(0., np.abs(w_hist[_idx - 1] - w_hist[_idx])) # make sure that weight does not change appreciably\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Detailed look at the numerics\n", + "------------------------\n", + "..." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " -- N E S T --\n", + " Copyright (C) 2004 The NEST Initiative\n", + "\n", + " Version: 3.6.0-post0.dev0\n", + " Built: Nov 8 2023 01:11:46\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", + "[87,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: master\n", + "Pre spike times: [40.0, 100.0]\n", + "Post spike times: [25.0, 75.0]\n", + "t = 0.0 ms\n", + "t = 0.1 ms\n", + "t = 0.2 ms\n", + "t = 0.30000000000000004 ms\n", + "t = 0.4 ms\n", + "t = 0.5 ms\n", + "t = 0.6 ms\n", + "t = 0.7 ms\n", + "t = 0.7999999999999999 ms\n", + "t = 0.8999999999999999 ms\n", + "t = 0.9999999999999999 ms\n", + "t = 1.0999999999999999 ms\n", + "t = 1.2 ms\n", + "t = 1.3 ms\n", + "t = 1.4000000000000001 ms\n", + "t = 1.5000000000000002 ms\n", + "t = 1.6000000000000003 ms\n", + "t = 1.7000000000000004 ms\n", + "t = 1.8000000000000005 ms\n", + "t = 1.9000000000000006 ms\n", + "t = 2.0000000000000004 ms\n", + "t = 2.1000000000000005 ms\n", + "t = 2.2000000000000006 ms\n", + "t = 2.3000000000000007 ms\n", + "t = 2.400000000000001 ms\n", + "t = 2.500000000000001 ms\n", + "t = 2.600000000000001 ms\n", + "t = 2.700000000000001 ms\n", + "t = 2.800000000000001 ms\n", + "t = 2.9000000000000012 ms\n", + "t = 3.0000000000000013 ms\n", + "t = 3.1000000000000014 ms\n", + "t = 3.2000000000000015 ms\n", + "t = 3.3000000000000016 ms\n", + "t = 3.4000000000000017 ms\n", + "t = 3.5000000000000018 ms\n", + "t = 3.600000000000002 ms\n", + "t = 3.700000000000002 ms\n", + "t = 3.800000000000002 ms\n", + "t = 3.900000000000002 ms\n", + "t = 4.000000000000002 ms\n", + "t = 4.100000000000001 ms\n", + "t = 4.200000000000001 ms\n", + "t = 4.300000000000001 ms\n", + "t = 4.4 ms\n", + "t = 4.5 ms\n", + "t = 4.6 ms\n", + "t = 4.699999999999999 ms\n", + "t = 4.799999999999999 ms\n", + "t = 4.899999999999999 ms\n", + "t = 4.999999999999998 ms\n", + "t = 5.099999999999998 ms\n", + "t = 5.1999999999999975 ms\n", + "t = 5.299999999999997 ms\n", + "t = 5.399999999999997 ms\n", + "t = 5.4999999999999964 ms\n", + "t = 5.599999999999996 ms\n", + "t = 5.699999999999996 ms\n", + "t = 5.799999999999995 ms\n", + "t = 5.899999999999995 ms\n", + "t = 5.999999999999995 ms\n", + "t = 6.099999999999994 ms\n", + "t = 6.199999999999994 ms\n", + "t = 6.299999999999994 ms\n", + "t = 6.399999999999993 ms\n", + "t = 6.499999999999993 ms\n", + "t = 6.5999999999999925 ms\n", + "t = 6.699999999999992 ms\n", + "t = 6.799999999999992 ms\n", + "t = 6.8999999999999915 ms\n", + "t = 6.999999999999991 ms\n", + "t = 7.099999999999991 ms\n", + "t = 7.19999999999999 ms\n", + "t = 7.29999999999999 ms\n", + "t = 7.39999999999999 ms\n", + "t = 7.499999999999989 ms\n", + "t = 7.599999999999989 ms\n", + "t = 7.699999999999989 ms\n", + "t = 7.799999999999988 ms\n", + "t = 7.899999999999988 ms\n", + "t = 7.999999999999988 ms\n", + "t = 8.099999999999987 ms\n", + "t = 8.199999999999987 ms\n", + "t = 8.299999999999986 ms\n", + "t = 8.399999999999986 ms\n", + "t = 8.499999999999986 ms\n", + "t = 8.599999999999985 ms\n", + "t = 8.699999999999985 ms\n", + "t = 8.799999999999985 ms\n", + "t = 8.899999999999984 ms\n", + "t = 8.999999999999984 ms\n", + "t = 9.099999999999984 ms\n", + "t = 9.199999999999983 ms\n", + "t = 9.299999999999983 ms\n", + "t = 9.399999999999983 ms\n", + "t = 9.499999999999982 ms\n", + "t = 9.599999999999982 ms\n", + "t = 9.699999999999982 ms\n", + "t = 9.799999999999981 ms\n", + "t = 9.89999999999998 ms\n", + "t = 9.99999999999998 ms\n", + "t = 10.09999999999998 ms\n", + "t = 10.19999999999998 ms\n", + "t = 10.29999999999998 ms\n", + "t = 10.399999999999979 ms\n", + "t = 10.499999999999979 ms\n", + "t = 10.599999999999978 ms\n", + "t = 10.699999999999978 ms\n", + "t = 10.799999999999978 ms\n", + "t = 10.899999999999977 ms\n", + "t = 10.999999999999977 ms\n", + "t = 11.099999999999977 ms\n", + "t = 11.199999999999976 ms\n", + "t = 11.299999999999976 ms\n", + "t = 11.399999999999975 ms\n", + "t = 11.499999999999975 ms\n", + "t = 11.599999999999975 ms\n", + "t = 11.699999999999974 ms\n", + "t = 11.799999999999974 ms\n", + "t = 11.899999999999974 ms\n", + "t = 11.999999999999973 ms\n", + "t = 12.099999999999973 ms\n", + "t = 12.199999999999973 ms\n", + "t = 12.299999999999972 ms\n", + "t = 12.399999999999972 ms\n", + "t = 12.499999999999972 ms\n", + "t = 12.599999999999971 ms\n", + "t = 12.69999999999997 ms\n", + "t = 12.79999999999997 ms\n", + "t = 12.89999999999997 ms\n", + "t = 12.99999999999997 ms\n", + "t = 13.09999999999997 ms\n", + "t = 13.199999999999969 ms\n", + "t = 13.299999999999969 ms\n", + "t = 13.399999999999968 ms\n", + "t = 13.499999999999968 ms\n", + "t = 13.599999999999968 ms\n", + "t = 13.699999999999967 ms\n", + "t = 13.799999999999967 ms\n", + "t = 13.899999999999967 ms\n", + "t = 13.999999999999966 ms\n", + "t = 14.099999999999966 ms\n", + "t = 14.199999999999966 ms\n", + "t = 14.299999999999965 ms\n", + "t = 14.399999999999965 ms\n", + "t = 14.499999999999964 ms\n", + "t = 14.599999999999964 ms\n", + "t = 14.699999999999964 ms\n", + "t = 14.799999999999963 ms\n", + "t = 14.899999999999963 ms\n", + "t = 14.999999999999963 ms\n", + "t = 15.099999999999962 ms\n", + "t = 15.199999999999962 ms\n", + "t = 15.299999999999962 ms\n", + "t = 15.399999999999961 ms\n", + "t = 15.499999999999961 ms\n", + "t = 15.59999999999996 ms\n", + "t = 15.69999999999996 ms\n", + "t = 15.79999999999996 ms\n", + "t = 15.89999999999996 ms\n", + "t = 15.99999999999996 ms\n", + "t = 16.09999999999996 ms\n", + "t = 16.19999999999996 ms\n", + "t = 16.29999999999996 ms\n", + "t = 16.399999999999963 ms\n", + "t = 16.499999999999964 ms\n", + "t = 16.599999999999966 ms\n", + "t = 16.699999999999967 ms\n", + "t = 16.79999999999997 ms\n", + "t = 16.89999999999997 ms\n", + "t = 16.99999999999997 ms\n", + "t = 17.099999999999973 ms\n", + "t = 17.199999999999974 ms\n", + "t = 17.299999999999976 ms\n", + "t = 17.399999999999977 ms\n", + "t = 17.49999999999998 ms\n", + "t = 17.59999999999998 ms\n", + "t = 17.69999999999998 ms\n", + "t = 17.799999999999983 ms\n", + "t = 17.899999999999984 ms\n", + "t = 17.999999999999986 ms\n", + "t = 18.099999999999987 ms\n", + "t = 18.19999999999999 ms\n", + "t = 18.29999999999999 ms\n", + "t = 18.39999999999999 ms\n", + "t = 18.499999999999993 ms\n", + "t = 18.599999999999994 ms\n", + "t = 18.699999999999996 ms\n", + "t = 18.799999999999997 ms\n", + "t = 18.9 ms\n", + "t = 19.0 ms\n", + "t = 19.1 ms\n", + "t = 19.200000000000003 ms\n", + "t = 19.300000000000004 ms\n", + "t = 19.400000000000006 ms\n", + "t = 19.500000000000007 ms\n", + "t = 19.60000000000001 ms\n", + "t = 19.70000000000001 ms\n", + "t = 19.80000000000001 ms\n", + "t = 19.900000000000013 ms\n", + "t = 20.000000000000014 ms\n", + "t = 20.100000000000016 ms\n", + "t = 20.200000000000017 ms\n", + "t = 20.30000000000002 ms\n", + "t = 20.40000000000002 ms\n", + "t = 20.50000000000002 ms\n", + "t = 20.600000000000023 ms\n", + "t = 20.700000000000024 ms\n", + "t = 20.800000000000026 ms\n", + "t = 20.900000000000027 ms\n", + "t = 21.00000000000003 ms\n", + "t = 21.10000000000003 ms\n", + "t = 21.20000000000003 ms\n", + "t = 21.300000000000033 ms\n", + "t = 21.400000000000034 ms\n", + "t = 21.500000000000036 ms\n", + "t = 21.600000000000037 ms\n", + "t = 21.70000000000004 ms\n", + "t = 21.80000000000004 ms\n", + "t = 21.90000000000004 ms\n", + "t = 22.000000000000043 ms\n", + "t = 22.100000000000044 ms\n", + "t = 22.200000000000045 ms\n", + "t = 22.300000000000047 ms\n", + "t = 22.40000000000005 ms\n", + "t = 22.50000000000005 ms\n", + "t = 22.60000000000005 ms\n", + "t = 22.700000000000053 ms\n", + "t = 22.800000000000054 ms\n", + "t = 22.900000000000055 ms\n", + "t = 23.000000000000057 ms\n", + "t = 23.10000000000006 ms\n", + "t = 23.20000000000006 ms\n", + "t = 23.30000000000006 ms\n", + "t = 23.400000000000063 ms\n", + "t = 23.500000000000064 ms\n", + "t = 23.600000000000065 ms\n", + "t = 23.700000000000067 ms\n", + "t = 23.800000000000068 ms\n", + "t = 23.90000000000007 ms\n", + "t = 24.00000000000007 ms\n", + "t = 24.100000000000072 ms\n", + "t = 24.200000000000074 ms\n", + "t = 24.300000000000075 ms\n", + "t = 24.400000000000077 ms\n", + "t = 24.500000000000078 ms\n", + "t = 24.60000000000008 ms\n", + "t = 24.70000000000008 ms\n", + "t = 24.800000000000082 ms\n", + "t = 24.900000000000084 ms\n", + "t = 25.000000000000085 ms\n", + "t = 25.100000000000087 ms\n", + "t = 25.200000000000088 ms\n", + "t = 25.30000000000009 ms\n", + "t = 25.40000000000009 ms\n", + "t = 25.500000000000092 ms\n", + "t = 25.600000000000094 ms\n", + "t = 25.700000000000095 ms\n", + "t = 25.800000000000097 ms\n", + "t = 25.900000000000098 ms\n", + "t = 26.0000000000001 ms\n", + "t = 26.1000000000001 ms\n", + "t = 26.200000000000102 ms\n", + "t = 26.300000000000104 ms\n", + "t = 26.400000000000105 ms\n", + "t = 26.500000000000107 ms\n", + "t = 26.600000000000108 ms\n", + "t = 26.70000000000011 ms\n", + "t = 26.80000000000011 ms\n", + "t = 26.900000000000112 ms\n", + "t = 27.000000000000114 ms\n", + "t = 27.100000000000115 ms\n", + "t = 27.200000000000117 ms\n", + "t = 27.300000000000118 ms\n", + "t = 27.40000000000012 ms\n", + "t = 27.50000000000012 ms\n", + "t = 27.600000000000122 ms\n", + "t = 27.700000000000124 ms\n", + "t = 27.800000000000125 ms\n", + "t = 27.900000000000126 ms\n", + "t = 28.000000000000128 ms\n", + "t = 28.10000000000013 ms\n", + "t = 28.20000000000013 ms\n", + "t = 28.300000000000132 ms\n", + "t = 28.400000000000134 ms\n", + "t = 28.500000000000135 ms\n", + "t = 28.600000000000136 ms\n", + "t = 28.700000000000138 ms\n", + "t = 28.80000000000014 ms\n", + "t = 28.90000000000014 ms\n", + "t = 29.000000000000142 ms\n", + "t = 29.100000000000144 ms\n", + "t = 29.200000000000145 ms\n", + "t = 29.300000000000146 ms\n", + "t = 29.400000000000148 ms\n", + "t = 29.50000000000015 ms\n", + "t = 29.60000000000015 ms\n", + "t = 29.700000000000152 ms\n", + "t = 29.800000000000153 ms\n", + "t = 29.900000000000155 ms\n", + "t = 30.000000000000156 ms\n", + "t = 30.100000000000158 ms\n", + "t = 30.20000000000016 ms\n", + "t = 30.30000000000016 ms\n", + "t = 30.400000000000162 ms\n", + "t = 30.500000000000163 ms\n", + "t = 30.600000000000165 ms\n", + "t = 30.700000000000166 ms\n", + "t = 30.800000000000168 ms\n", + "t = 30.90000000000017 ms\n", + "t = 31.00000000000017 ms\n", + "t = 31.100000000000172 ms\n", + "t = 31.200000000000173 ms\n", + "t = 31.300000000000175 ms\n", + "t = 31.400000000000176 ms\n", + "t = 31.500000000000178 ms\n", + "t = 31.60000000000018 ms\n", + "t = 31.70000000000018 ms\n", + "t = 31.800000000000182 ms\n", + "t = 31.900000000000183 ms\n", + "t = 32.000000000000185 ms\n", + "t = 32.100000000000186 ms\n", + "t = 32.20000000000019 ms\n", + "t = 32.30000000000019 ms\n", + "t = 32.40000000000019 ms\n", + "t = 32.50000000000019 ms\n", + "t = 32.60000000000019 ms\n", + "t = 32.700000000000195 ms\n", + "t = 32.800000000000196 ms\n", + "t = 32.9000000000002 ms\n", + "t = 33.0000000000002 ms\n", + "t = 33.1000000000002 ms\n", + "t = 33.2000000000002 ms\n", + "t = 33.3000000000002 ms\n", + "t = 33.400000000000205 ms\n", + "t = 33.500000000000206 ms\n", + "t = 33.60000000000021 ms\n", + "t = 33.70000000000021 ms\n", + "t = 33.80000000000021 ms\n", + "t = 33.90000000000021 ms\n", + "t = 34.00000000000021 ms\n", + "t = 34.100000000000215 ms\n", + "t = 34.200000000000216 ms\n", + "t = 34.30000000000022 ms\n", + "t = 34.40000000000022 ms\n", + "t = 34.50000000000022 ms\n", + "t = 34.60000000000022 ms\n", + "t = 34.70000000000022 ms\n", + "t = 34.800000000000225 ms\n", + "t = 34.900000000000226 ms\n", + "t = 35.00000000000023 ms\n", + "t = 35.10000000000023 ms\n", + "t = 35.20000000000023 ms\n", + "t = 35.30000000000023 ms\n", + "t = 35.40000000000023 ms\n", + "t = 35.500000000000234 ms\n", + "t = 35.600000000000236 ms\n", + "t = 35.70000000000024 ms\n", + "t = 35.80000000000024 ms\n", + "t = 35.90000000000024 ms\n", + "t = 36.00000000000024 ms\n", + "t = 36.10000000000024 ms\n", + "t = 36.200000000000244 ms\n", + "t = 36.300000000000246 ms\n", + "t = 36.40000000000025 ms\n", + "t = 36.50000000000025 ms\n", + "t = 36.60000000000025 ms\n", + "t = 36.70000000000025 ms\n", + "t = 36.80000000000025 ms\n", + "t = 36.900000000000254 ms\n", + "t = 37.000000000000256 ms\n", + "t = 37.10000000000026 ms\n", + "t = 37.20000000000026 ms\n", + "t = 37.30000000000026 ms\n", + "t = 37.40000000000026 ms\n", + "t = 37.50000000000026 ms\n", + "t = 37.600000000000264 ms\n", + "t = 37.700000000000266 ms\n", + "t = 37.80000000000027 ms\n", + "t = 37.90000000000027 ms\n", + "t = 38.00000000000027 ms\n", + "t = 38.10000000000027 ms\n", + "t = 38.20000000000027 ms\n", + "t = 38.300000000000274 ms\n", + "t = 38.400000000000276 ms\n", + "t = 38.50000000000028 ms\n", + "t = 38.60000000000028 ms\n", + "t = 38.70000000000028 ms\n", + "t = 38.80000000000028 ms\n", + "t = 38.90000000000028 ms\n", + "t = 39.000000000000284 ms\n", + "t = 39.100000000000286 ms\n", + "t = 39.20000000000029 ms\n", + "t = 39.30000000000029 ms\n", + "t = 39.40000000000029 ms\n", + "t = 39.50000000000029 ms\n", + "t = 39.60000000000029 ms\n", + "t = 39.700000000000294 ms\n", + "t = 39.800000000000296 ms\n", + "t = 39.9000000000003 ms\n", + "t = 40.0000000000003 ms\n", + "t = 40.1000000000003 ms\n", + "t = 40.2000000000003 ms\n", + "t = 40.3000000000003 ms\n", + "t = 40.400000000000304 ms\n", + "t = 40.500000000000306 ms\n", + "t = 40.60000000000031 ms\n", + "t = 40.70000000000031 ms\n", + "t = 40.80000000000031 ms\n", + "t = 40.90000000000031 ms\n", + "t = 41.00000000000031 ms\n", + "t = 41.100000000000314 ms\n", + "t = 41.200000000000315 ms\n", + "t = 41.30000000000032 ms\n", + "t = 41.40000000000032 ms\n", + "t = 41.50000000000032 ms\n", + "t = 41.60000000000032 ms\n", + "t = 41.70000000000032 ms\n", + "t = 41.800000000000324 ms\n", + "t = 41.900000000000325 ms\n", + "t = 42.00000000000033 ms\n", + "t = 42.10000000000033 ms\n", + "t = 42.20000000000033 ms\n", + "t = 42.30000000000033 ms\n", + "t = 42.40000000000033 ms\n", + "t = 42.500000000000334 ms\n", + "t = 42.600000000000335 ms\n", + "t = 42.70000000000034 ms\n", + "t = 42.80000000000034 ms\n", + "t = 42.90000000000034 ms\n", + "t = 43.00000000000034 ms\n", + "t = 43.10000000000034 ms\n", + "t = 43.200000000000344 ms\n", + "t = 43.300000000000345 ms\n", + "t = 43.40000000000035 ms\n", + "t = 43.50000000000035 ms\n", + "t = 43.60000000000035 ms\n", + "t = 43.70000000000035 ms\n", + "t = 43.80000000000035 ms\n", + "t = 43.900000000000354 ms\n", + "t = 44.000000000000355 ms\n", + "t = 44.10000000000036 ms\n", + "t = 44.20000000000036 ms\n", + "t = 44.30000000000036 ms\n", + "t = 44.40000000000036 ms\n", + "t = 44.50000000000036 ms\n", + "t = 44.600000000000364 ms\n", + "t = 44.700000000000365 ms\n", + "t = 44.80000000000037 ms\n", + "t = 44.90000000000037 ms\n", + "t = 45.00000000000037 ms\n", + "t = 45.10000000000037 ms\n", + "t = 45.20000000000037 ms\n", + "t = 45.300000000000374 ms\n", + "t = 45.400000000000375 ms\n", + "t = 45.50000000000038 ms\n", + "t = 45.60000000000038 ms\n", + "t = 45.70000000000038 ms\n", + "t = 45.80000000000038 ms\n", + "t = 45.90000000000038 ms\n", + "t = 46.000000000000384 ms\n", + "t = 46.100000000000385 ms\n", + "t = 46.20000000000039 ms\n", + "t = 46.30000000000039 ms\n", + "t = 46.40000000000039 ms\n", + "t = 46.50000000000039 ms\n", + "t = 46.60000000000039 ms\n", + "t = 46.700000000000394 ms\n", + "t = 46.800000000000395 ms\n", + "t = 46.9000000000004 ms\n", + "t = 47.0000000000004 ms\n", + "t = 47.1000000000004 ms\n", + "t = 47.2000000000004 ms\n", + "t = 47.3000000000004 ms\n", + "t = 47.400000000000404 ms\n", + "t = 47.500000000000405 ms\n", + "t = 47.600000000000406 ms\n", + "t = 47.70000000000041 ms\n", + "t = 47.80000000000041 ms\n", + "t = 47.90000000000041 ms\n", + "t = 48.00000000000041 ms\n", + "t = 48.10000000000041 ms\n", + "t = 48.200000000000415 ms\n", + "t = 48.300000000000416 ms\n", + "t = 48.40000000000042 ms\n", + "t = 48.50000000000042 ms\n", + "t = 48.60000000000042 ms\n", + "t = 48.70000000000042 ms\n", + "t = 48.80000000000042 ms\n", + "t = 48.900000000000425 ms\n", + "t = 49.000000000000426 ms\n", + "t = 49.10000000000043 ms\n", + "t = 49.20000000000043 ms\n", + "t = 49.30000000000043 ms\n", + "t = 49.40000000000043 ms\n", + "t = 49.50000000000043 ms\n", + "t = 49.600000000000435 ms\n", + "t = 49.700000000000436 ms\n", + "t = 49.80000000000044 ms\n", + "t = 49.90000000000044 ms\n", + "t = 50.00000000000044 ms\n", + "t = 50.10000000000044 ms\n", + "t = 50.20000000000044 ms\n", + "t = 50.300000000000445 ms\n", + "t = 50.400000000000446 ms\n", + "t = 50.50000000000045 ms\n", + "t = 50.60000000000045 ms\n", + "t = 50.70000000000045 ms\n", + "t = 50.80000000000045 ms\n", + "t = 50.90000000000045 ms\n", + "t = 51.000000000000455 ms\n", + "t = 51.100000000000456 ms\n", + "t = 51.20000000000046 ms\n", + "t = 51.30000000000046 ms\n", + "t = 51.40000000000046 ms\n", + "t = 51.50000000000046 ms\n", + "t = 51.60000000000046 ms\n", + "t = 51.700000000000465 ms\n", + "t = 51.800000000000466 ms\n", + "t = 51.90000000000047 ms\n", + "t = 52.00000000000047 ms\n", + "t = 52.10000000000047 ms\n", + "t = 52.20000000000047 ms\n", + "t = 52.30000000000047 ms\n", + "t = 52.400000000000475 ms\n", + "t = 52.500000000000476 ms\n", + "t = 52.60000000000048 ms\n", + "t = 52.70000000000048 ms\n", + "t = 52.80000000000048 ms\n", + "t = 52.90000000000048 ms\n", + "t = 53.00000000000048 ms\n", + "t = 53.100000000000485 ms\n", + "t = 53.200000000000486 ms\n", + "t = 53.30000000000049 ms\n", + "t = 53.40000000000049 ms\n", + "t = 53.50000000000049 ms\n", + "t = 53.60000000000049 ms\n", + "t = 53.70000000000049 ms\n", + "t = 53.800000000000495 ms\n", + "t = 53.900000000000496 ms\n", + "t = 54.0000000000005 ms\n", + "t = 54.1000000000005 ms\n", + "t = 54.2000000000005 ms\n", + "t = 54.3000000000005 ms\n", + "t = 54.4000000000005 ms\n", + "t = 54.500000000000504 ms\n", + "t = 54.600000000000506 ms\n", + "t = 54.70000000000051 ms\n", + "t = 54.80000000000051 ms\n", + "t = 54.90000000000051 ms\n", + "t = 55.00000000000051 ms\n", + "t = 55.10000000000051 ms\n", + "t = 55.200000000000514 ms\n", + "t = 55.300000000000516 ms\n", + "t = 55.40000000000052 ms\n", + "t = 55.50000000000052 ms\n", + "t = 55.60000000000052 ms\n", + "t = 55.70000000000052 ms\n", + "t = 55.80000000000052 ms\n", + "t = 55.900000000000524 ms\n", + "t = 56.000000000000526 ms\n", + "t = 56.10000000000053 ms\n", + "t = 56.20000000000053 ms\n", + "t = 56.30000000000053 ms\n", + "t = 56.40000000000053 ms\n", + "t = 56.50000000000053 ms\n", + "t = 56.600000000000534 ms\n", + "t = 56.700000000000536 ms\n", + "t = 56.80000000000054 ms\n", + "t = 56.90000000000054 ms\n", + "t = 57.00000000000054 ms\n", + "t = 57.10000000000054 ms\n", + "t = 57.20000000000054 ms\n", + "t = 57.300000000000544 ms\n", + "t = 57.400000000000546 ms\n", + "t = 57.50000000000055 ms\n", + "t = 57.60000000000055 ms\n", + "t = 57.70000000000055 ms\n", + "t = 57.80000000000055 ms\n", + "t = 57.90000000000055 ms\n", + "t = 58.000000000000554 ms\n", + "t = 58.100000000000556 ms\n", + "t = 58.20000000000056 ms\n", + "t = 58.30000000000056 ms\n", + "t = 58.40000000000056 ms\n", + "t = 58.50000000000056 ms\n", + "t = 58.60000000000056 ms\n", + "t = 58.700000000000564 ms\n", + "t = 58.800000000000566 ms\n", + "t = 58.90000000000057 ms\n", + "t = 59.00000000000057 ms\n", + "t = 59.10000000000057 ms\n", + "t = 59.20000000000057 ms\n", + "t = 59.30000000000057 ms\n", + "t = 59.400000000000574 ms\n", + "t = 59.500000000000576 ms\n", + "t = 59.60000000000058 ms\n", + "t = 59.70000000000058 ms\n", + "t = 59.80000000000058 ms\n", + "t = 59.90000000000058 ms\n", + "t = 60.00000000000058 ms\n", + "t = 60.100000000000584 ms\n", + "t = 60.200000000000585 ms\n", + "t = 60.30000000000059 ms\n", + "t = 60.40000000000059 ms\n", + "t = 60.50000000000059 ms\n", + "t = 60.60000000000059 ms\n", + "t = 60.70000000000059 ms\n", + "t = 60.800000000000594 ms\n", + "t = 60.900000000000595 ms\n", + "t = 61.0000000000006 ms\n", + "t = 61.1000000000006 ms\n", + "t = 61.2000000000006 ms\n", + "t = 61.3000000000006 ms\n", + "t = 61.4000000000006 ms\n", + "t = 61.500000000000604 ms\n", + "t = 61.600000000000605 ms\n", + "t = 61.70000000000061 ms\n", + "t = 61.80000000000061 ms\n", + "t = 61.90000000000061 ms\n", + "t = 62.00000000000061 ms\n", + "t = 62.10000000000061 ms\n", + "t = 62.200000000000614 ms\n", + "t = 62.300000000000615 ms\n", + "t = 62.40000000000062 ms\n", + "t = 62.50000000000062 ms\n", + "t = 62.60000000000062 ms\n", + "t = 62.70000000000062 ms\n", + "t = 62.80000000000062 ms\n", + "t = 62.900000000000624 ms\n", + "t = 63.000000000000625 ms\n", + "t = 63.10000000000063 ms\n", + "t = 63.20000000000063 ms\n", + "t = 63.30000000000063 ms\n", + "t = 63.40000000000063 ms\n", + "t = 63.50000000000063 ms\n", + "t = 63.600000000000634 ms\n", + "t = 63.700000000000635 ms\n", + "t = 63.80000000000064 ms\n", + "t = 63.90000000000064 ms\n", + "t = 64.00000000000064 ms\n", + "t = 64.10000000000063 ms\n", + "t = 64.20000000000063 ms\n", + "t = 64.30000000000062 ms\n", + "t = 64.40000000000062 ms\n", + "t = 64.50000000000061 ms\n", + "t = 64.6000000000006 ms\n", + "t = 64.7000000000006 ms\n", + "t = 64.8000000000006 ms\n", + "t = 64.90000000000059 ms\n", + "t = 65.00000000000058 ms\n", + "t = 65.10000000000058 ms\n", + "t = 65.20000000000057 ms\n", + "t = 65.30000000000057 ms\n", + "t = 65.40000000000056 ms\n", + "t = 65.50000000000055 ms\n", + "t = 65.60000000000055 ms\n", + "t = 65.70000000000054 ms\n", + "t = 65.80000000000054 ms\n", + "t = 65.90000000000053 ms\n", + "t = 66.00000000000053 ms\n", + "t = 66.10000000000052 ms\n", + "t = 66.20000000000051 ms\n", + "t = 66.30000000000051 ms\n", + "t = 66.4000000000005 ms\n", + "t = 66.5000000000005 ms\n", + "t = 66.60000000000049 ms\n", + "t = 66.70000000000049 ms\n", + "t = 66.80000000000048 ms\n", + "t = 66.90000000000047 ms\n", + "t = 67.00000000000047 ms\n", + "t = 67.10000000000046 ms\n", + "t = 67.20000000000046 ms\n", + "t = 67.30000000000045 ms\n", + "t = 67.40000000000045 ms\n", + "t = 67.50000000000044 ms\n", + "t = 67.60000000000043 ms\n", + "t = 67.70000000000043 ms\n", + "t = 67.80000000000042 ms\n", + "t = 67.90000000000042 ms\n", + "t = 68.00000000000041 ms\n", + "t = 68.1000000000004 ms\n", + "t = 68.2000000000004 ms\n", + "t = 68.3000000000004 ms\n", + "t = 68.40000000000039 ms\n", + "t = 68.50000000000038 ms\n", + "t = 68.60000000000038 ms\n", + "t = 68.70000000000037 ms\n", + "t = 68.80000000000037 ms\n", + "t = 68.90000000000036 ms\n", + "t = 69.00000000000036 ms\n", + "t = 69.10000000000035 ms\n", + "t = 69.20000000000034 ms\n", + "t = 69.30000000000034 ms\n", + "t = 69.40000000000033 ms\n", + "t = 69.50000000000033 ms\n", + "t = 69.60000000000032 ms\n", + "t = 69.70000000000032 ms\n", + "t = 69.80000000000031 ms\n", + "t = 69.9000000000003 ms\n", + "t = 70.0000000000003 ms\n", + "t = 70.10000000000029 ms\n", + "t = 70.20000000000029 ms\n", + "t = 70.30000000000028 ms\n", + "t = 70.40000000000028 ms\n", + "t = 70.50000000000027 ms\n", + "t = 70.60000000000026 ms\n", + "t = 70.70000000000026 ms\n", + "t = 70.80000000000025 ms\n", + "t = 70.90000000000025 ms\n", + "t = 71.00000000000024 ms\n", + "t = 71.10000000000024 ms\n", + "t = 71.20000000000023 ms\n", + "t = 71.30000000000022 ms\n", + "t = 71.40000000000022 ms\n", + "t = 71.50000000000021 ms\n", + "t = 71.60000000000021 ms\n", + "t = 71.7000000000002 ms\n", + "t = 71.8000000000002 ms\n", + "t = 71.90000000000019 ms\n", + "t = 72.00000000000018 ms\n", + "t = 72.10000000000018 ms\n", + "t = 72.20000000000017 ms\n", + "t = 72.30000000000017 ms\n", + "t = 72.40000000000016 ms\n", + "t = 72.50000000000016 ms\n", + "t = 72.60000000000015 ms\n", + "t = 72.70000000000014 ms\n", + "t = 72.80000000000014 ms\n", + "t = 72.90000000000013 ms\n", + "t = 73.00000000000013 ms\n", + "t = 73.10000000000012 ms\n", + "t = 73.20000000000012 ms\n", + "t = 73.30000000000011 ms\n", + "t = 73.4000000000001 ms\n", + "t = 73.5000000000001 ms\n", + "t = 73.6000000000001 ms\n", + "t = 73.70000000000009 ms\n", + "t = 73.80000000000008 ms\n", + "t = 73.90000000000008 ms\n", + "t = 74.00000000000007 ms\n", + "t = 74.10000000000007 ms\n", + "t = 74.20000000000006 ms\n", + "t = 74.30000000000005 ms\n", + "t = 74.40000000000005 ms\n", + "t = 74.50000000000004 ms\n", + "t = 74.60000000000004 ms\n", + "t = 74.70000000000003 ms\n", + "t = 74.80000000000003 ms\n", + "t = 74.90000000000002 ms\n", + "t = 75.00000000000001 ms\n", + "t = 75.10000000000001 ms\n", + "t = 75.2 ms\n", + "t = 75.3 ms\n", + "t = 75.39999999999999 ms\n", + "t = 75.49999999999999 ms\n", + "t = 75.59999999999998 ms\n", + "t = 75.69999999999997 ms\n", + "t = 75.79999999999997 ms\n", + "t = 75.89999999999996 ms\n", + "t = 75.99999999999996 ms\n", + "t = 76.09999999999995 ms\n", + "t = 76.19999999999995 ms\n", + "t = 76.29999999999994 ms\n", + "t = 76.39999999999993 ms\n", + "t = 76.49999999999993 ms\n", + "t = 76.59999999999992 ms\n", + "t = 76.69999999999992 ms\n", + "t = 76.79999999999991 ms\n", + "t = 76.8999999999999 ms\n", + "t = 76.9999999999999 ms\n", + "t = 77.0999999999999 ms\n", + "t = 77.19999999999989 ms\n", + "t = 77.29999999999988 ms\n", + "t = 77.39999999999988 ms\n", + "t = 77.49999999999987 ms\n", + "t = 77.59999999999987 ms\n", + "t = 77.69999999999986 ms\n", + "t = 77.79999999999986 ms\n", + "t = 77.89999999999985 ms\n", + "t = 77.99999999999984 ms\n", + "t = 78.09999999999984 ms\n", + "t = 78.19999999999983 ms\n", + "t = 78.29999999999983 ms\n", + "t = 78.39999999999982 ms\n", + "t = 78.49999999999982 ms\n", + "t = 78.59999999999981 ms\n", + "t = 78.6999999999998 ms\n", + "t = 78.7999999999998 ms\n", + "t = 78.89999999999979 ms\n", + "t = 78.99999999999979 ms\n", + "t = 79.09999999999978 ms\n", + "t = 79.19999999999978 ms\n", + "t = 79.29999999999977 ms\n", + "t = 79.39999999999976 ms\n", + "t = 79.49999999999976 ms\n", + "t = 79.59999999999975 ms\n", + "t = 79.69999999999975 ms\n", + "t = 79.79999999999974 ms\n", + "t = 79.89999999999974 ms\n", + "t = 79.99999999999973 ms\n", + "t = 80.09999999999972 ms\n", + "t = 80.19999999999972 ms\n", + "t = 80.29999999999971 ms\n", + "t = 80.39999999999971 ms\n", + "t = 80.4999999999997 ms\n", + "t = 80.5999999999997 ms\n", + "t = 80.69999999999969 ms\n", + "t = 80.79999999999968 ms\n", + "t = 80.89999999999968 ms\n", + "t = 80.99999999999967 ms\n", + "t = 81.09999999999967 ms\n", + "t = 81.19999999999966 ms\n", + "t = 81.29999999999966 ms\n", + "t = 81.39999999999965 ms\n", + "t = 81.49999999999964 ms\n", + "t = 81.59999999999964 ms\n", + "t = 81.69999999999963 ms\n", + "t = 81.79999999999963 ms\n", + "t = 81.89999999999962 ms\n", + "t = 81.99999999999962 ms\n", + "t = 82.09999999999961 ms\n", + "t = 82.1999999999996 ms\n", + "t = 82.2999999999996 ms\n", + "t = 82.3999999999996 ms\n", + "t = 82.49999999999959 ms\n", + "t = 82.59999999999958 ms\n", + "t = 82.69999999999958 ms\n", + "t = 82.79999999999957 ms\n", + "t = 82.89999999999957 ms\n", + "t = 82.99999999999956 ms\n", + "t = 83.09999999999955 ms\n", + "t = 83.19999999999955 ms\n", + "t = 83.29999999999954 ms\n", + "t = 83.39999999999954 ms\n", + "t = 83.49999999999953 ms\n", + "t = 83.59999999999953 ms\n", + "t = 83.69999999999952 ms\n", + "t = 83.79999999999951 ms\n", + "t = 83.89999999999951 ms\n", + "t = 83.9999999999995 ms\n", + "t = 84.0999999999995 ms\n", + "t = 84.19999999999949 ms\n", + "t = 84.29999999999949 ms\n", + "t = 84.39999999999948 ms\n", + "t = 84.49999999999947 ms\n", + "t = 84.59999999999947 ms\n", + "t = 84.69999999999946 ms\n", + "t = 84.79999999999946 ms\n", + "t = 84.89999999999945 ms\n", + "t = 84.99999999999945 ms\n", + "t = 85.09999999999944 ms\n", + "t = 85.19999999999943 ms\n", + "t = 85.29999999999943 ms\n", + "t = 85.39999999999942 ms\n", + "t = 85.49999999999942 ms\n", + "t = 85.59999999999941 ms\n", + "t = 85.6999999999994 ms\n", + "t = 85.7999999999994 ms\n", + "t = 85.8999999999994 ms\n", + "t = 85.99999999999939 ms\n", + "t = 86.09999999999938 ms\n", + "t = 86.19999999999938 ms\n", + "t = 86.29999999999937 ms\n", + "t = 86.39999999999937 ms\n", + "t = 86.49999999999936 ms\n", + "t = 86.59999999999935 ms\n", + "t = 86.69999999999935 ms\n", + "t = 86.79999999999934 ms\n", + "t = 86.89999999999934 ms\n", + "t = 86.99999999999933 ms\n", + "t = 87.09999999999933 ms\n", + "t = 87.19999999999932 ms\n", + "t = 87.29999999999932 ms\n", + "t = 87.39999999999931 ms\n", + "t = 87.4999999999993 ms\n", + "t = 87.5999999999993 ms\n", + "t = 87.69999999999929 ms\n", + "t = 87.79999999999929 ms\n", + "t = 87.89999999999928 ms\n", + "t = 87.99999999999928 ms\n", + "t = 88.09999999999927 ms\n", + "t = 88.19999999999926 ms\n", + "t = 88.29999999999926 ms\n", + "t = 88.39999999999925 ms\n", + "t = 88.49999999999925 ms\n", + "t = 88.59999999999924 ms\n", + "t = 88.69999999999924 ms\n", + "t = 88.79999999999923 ms\n", + "t = 88.89999999999922 ms\n", + "t = 88.99999999999922 ms\n", + "t = 89.09999999999921 ms\n", + "t = 89.1999999999992 ms\n", + "t = 89.2999999999992 ms\n", + "t = 89.3999999999992 ms\n", + "t = 89.49999999999919 ms\n", + "t = 89.59999999999918 ms\n", + "t = 89.69999999999918 ms\n", + "t = 89.79999999999917 ms\n", + "t = 89.89999999999917 ms\n", + "t = 89.99999999999916 ms\n", + "t = 90.09999999999916 ms\n", + "t = 90.19999999999915 ms\n", + "t = 90.29999999999914 ms\n", + "t = 90.39999999999914 ms\n", + "t = 90.49999999999913 ms\n", + "t = 90.59999999999913 ms\n", + "t = 90.69999999999912 ms\n", + "t = 90.79999999999912 ms\n", + "t = 90.89999999999911 ms\n", + "t = 90.9999999999991 ms\n", + "t = 91.0999999999991 ms\n", + "t = 91.1999999999991 ms\n", + "t = 91.29999999999909 ms\n", + "t = 91.39999999999908 ms\n", + "t = 91.49999999999908 ms\n", + "t = 91.59999999999907 ms\n", + "t = 91.69999999999906 ms\n", + "t = 91.79999999999906 ms\n", + "t = 91.89999999999905 ms\n", + "t = 91.99999999999905 ms\n", + "t = 92.09999999999904 ms\n", + "t = 92.19999999999904 ms\n", + "t = 92.29999999999903 ms\n", + "t = 92.39999999999903 ms\n", + "t = 92.49999999999902 ms\n", + "t = 92.59999999999901 ms\n", + "t = 92.69999999999901 ms\n", + "t = 92.799999999999 ms\n", + "t = 92.899999999999 ms\n", + "t = 92.99999999999899 ms\n", + "t = 93.09999999999899 ms\n", + "t = 93.19999999999898 ms\n", + "t = 93.29999999999897 ms\n", + "t = 93.39999999999897 ms\n", + "t = 93.49999999999896 ms\n", + "t = 93.59999999999896 ms\n", + "t = 93.69999999999895 ms\n", + "t = 93.79999999999895 ms\n", + "t = 93.89999999999894 ms\n", + "t = 93.99999999999893 ms\n", + "t = 94.09999999999893 ms\n", + "t = 94.19999999999892 ms\n", + "t = 94.29999999999892 ms\n", + "t = 94.39999999999891 ms\n", + "t = 94.4999999999989 ms\n", + "t = 94.5999999999989 ms\n", + "t = 94.6999999999989 ms\n", + "t = 94.79999999999889 ms\n", + "t = 94.89999999999888 ms\n", + "t = 94.99999999999888 ms\n", + "t = 95.09999999999887 ms\n", + "t = 95.19999999999887 ms\n", + "t = 95.29999999999886 ms\n", + "t = 95.39999999999885 ms\n", + "t = 95.49999999999885 ms\n", + "t = 95.59999999999884 ms\n", + "t = 95.69999999999884 ms\n", + "t = 95.79999999999883 ms\n", + "t = 95.89999999999883 ms\n", + "t = 95.99999999999882 ms\n", + "t = 96.09999999999881 ms\n", + "t = 96.19999999999881 ms\n", + "t = 96.2999999999988 ms\n", + "t = 96.3999999999988 ms\n", + "t = 96.49999999999879 ms\n", + "t = 96.59999999999879 ms\n", + "t = 96.69999999999878 ms\n", + "t = 96.79999999999878 ms\n", + "t = 96.89999999999877 ms\n", + "t = 96.99999999999876 ms\n", + "t = 97.09999999999876 ms\n", + "t = 97.19999999999875 ms\n", + "t = 97.29999999999875 ms\n", + "t = 97.39999999999874 ms\n", + "t = 97.49999999999874 ms\n", + "t = 97.59999999999873 ms\n", + "t = 97.69999999999872 ms\n", + "t = 97.79999999999872 ms\n", + "t = 97.89999999999871 ms\n", + "t = 97.9999999999987 ms\n", + "t = 98.0999999999987 ms\n", + "t = 98.1999999999987 ms\n", + "t = 98.29999999999869 ms\n", + "t = 98.39999999999868 ms\n", + "t = 98.49999999999868 ms\n", + "t = 98.59999999999867 ms\n", + "t = 98.69999999999867 ms\n", + "t = 98.79999999999866 ms\n", + "t = 98.89999999999866 ms\n", + "t = 98.99999999999865 ms\n", + "t = 99.09999999999864 ms\n", + "t = 99.19999999999864 ms\n", + "t = 99.29999999999863 ms\n", + "t = 99.39999999999863 ms\n", + "t = 99.49999999999862 ms\n", + "t = 99.59999999999862 ms\n", + "t = 99.69999999999861 ms\n", + "t = 99.7999999999986 ms\n", + "t = 99.8999999999986 ms\n", + "t = 99.9999999999986 ms\n", + "t = 100.09999999999859 ms\n", + "t = 100.19999999999858 ms\n", + "t = 100.29999999999858 ms\n", + "t = 100.39999999999857 ms\n", + "t = 100.49999999999856 ms\n", + "t = 100.59999999999856 ms\n", + "t = 100.69999999999855 ms\n", + "t = 100.79999999999855 ms\n", + "t = 100.89999999999854 ms\n", + "t = 100.99999999999854 ms\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual pre spike times: [ 41. 101.]\n", + "Actual post spike times: [26.8 76.8]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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8efMAALq6ugAKztDZ2dlBLpdXqO0rV67A0dGx2Bm+Hj16AACuXr1a8cKJKon3FpBIzBOJwiyRKMwSicR76KqxgIAAeHp64vDhw5g3bx5kMplynYWFBT788MMKzzsRHx8PGxubYssLl8XFxZW6b0JCAq5fv67yiIqKqlAdRCXZunWr1CWQFmGeSBRmiURhlkgkTeaJHTo1paWloXnz5oiNjcWPP/4IhUKBxMREAEB+fj5SU1ORm5tbobYzMzNhaGhYbLmRkZFyfWnWr18PZ2dnlYe7uzsAICQkBMHBwfD29kZycrLyFHDhzZqenp6IioqCr68v/P39ERYWBi8vL2RkZMDDw0Nl2wULFiA8PBx79uzBnj17EB4ejgULFqhs4+HhgYyMDHh5eSEsLAz+/v7w9fVFVFQUPD09VbadOHEikpOT4e3tjeDgYAQGBsLHxwexsbGYPn26yrbTp09HbGwsfHx8EBgYyGPS8DG1adNG645JG9+nmnJMHTp0UOuY1qxZg8jISERERODMmTOIi4urdsekje9TTTgmf39/rTsmbXyfasIx9ejRQ+uOSRvfp5pyTEXzVPSYqmKgQ5niv8M20gs5OTlBT08PN2/eRF5eHhQKBVasWIG5c+ciLS0NDRs2hI2NDR4+fKh2287OzrC2tsbp06dVlt+4cQNOTk7YuHEjpk2bVuK+CQkJyo5l0f08PDwQEBAABwcHteshKiokJAR9+vSRugzSEurmKS0NOHny3+eDBgH16lVBYVTj8LOJRGGWSKTS8hQVFQV3d3dcvnwZXbp0EfJaHOVSTS1atMCxY8cwYsQITJgwAW+88QaAgvnjli9fjry8POUZNXXZ2NggNja22PL4+HgAgK2tban7WllZwcrKSmXZpUuXAEB5po6ISFssXix1BURERBUXExPDDp1Ubt26hVatWuGnn35CUFAQAGDp0qWYP38+8vLy0KtXL9y9e7dCbXfq1AlnzpzB06dPVQZGCQ0NVa5XR+HomwcOHEC7du0qVBMR8O9fk3i2l0RgnkgUZolEYZZIpBflKTs7GzExMejXr5+w12OHTk0xMTFYu3Yt2rZti507d2Lz5s1wcHBAjx494OHhgZs3b2LOnDkVanvUqFFYsWIFNm/erJyHLjs7G9u2bYOLiwvs7OzUaq+wU9iuXTs4OTlVqCaiohwcHJglEoZ5IlGYJRKFWSKRSsuTqDNzhdihU0NGRgZ0dHRw/PhxvPvuu2jbti1++OEHfPPNNxgwYAAAYNeuXbC3t69Q+y4uLhg9ejTmz5+PhIQEODg4YPv27YiOjubIS0REREREVAxHuVSDiYkJ8vLycPr0ady7d0+5vHDqgpMnT8LPzw+jR4+u8Gvs2LEDc+bMwc6dO/HBBx8gNzcXx44dg6ura6XrJyIiIiIi7cIOnZr69esHPT09dOrUCRMmTIBMJsO3336LPn364LXXXkOHDh2UQ6BWhJGREby9vREfH4+srCyEhYVh8ODBAo+AiIiIiIi0BTt0atq0aRPq16+PLl26IDo6GkZGRggODkZqaioWLVqEc+fOwcTEpMLtp6enY9GiRRgyZAgsLCwgk8ng5+dXobYsLS2xaNEiWFpaVrgeIoBZIrGYJxKFWSJRmCUSSdN54jx0ajI1NUV+fj5ycnIAAHp6esUmA5fL5UhPT69Q+9HR0WjevDns7e3RokULBAUFYdu2bZg0aVJlSyciIiIiIi3DQVHUZGBggCFDhhTrxBWKj4/Hb7/9VuH2bWxsEB8fj0aNGuHSpUvo3r17hdsiIiIiIiLtxg6dmiwsLBAZGYnTp0+jXr16KuuOHTuG0aNHlzgrfHkZGhqiUaNGlS0TAJCamorg4GDY2dmV2gElIiIiIiLNKDoPnbm5uZA22aErp6ysLBw5cgRvvPEGtmzZgldeeQVBQUEwNTUFAOzbtw8TJkzAoEGDcOjQIYmrLRAcHAx3d3epyyAiIiIioiICAgIwYsQIIW2xQ1cOCQkJ6N27N+7fvw+FQgGFQoGUlBT07t0bf/zxB3bt2oWZM2di1KhR2LVrF/T0NP+/NSEhAYmJiSrLCu/zK2mWeiJ1LV68GIsWLZK6jApLS0vDyZMnlc8HDRpU7Cw7aU5NzxNVH8xS7aCJz3BmiUQqLU9RUVFwd3eHnZ2dsNfiKJfl4OXlhejoaHh6euLYsWP4/vvvUa9ePdy4cQOdOnXCjBkzMHnyZOzdu1eSzhwArF+/Hs7OzioPDw8PAMDjx4+RlJSE48ePw8bGBsuXL4eTkxM+/fRTODk5YcuWLTA0NERoaCgiIyPx/PlzHD58GM2bN8fixYtVtt29ezfkcjmuXbuGa9euQS6XY/fu3SrbLF68GM2bN8fhw4fx/PlzREZGIjQ0FIaGhtiyZYvKtsuXL4eNjQ2OHz+OpKQkxMTEICgoCObm5li7dq3KtmvXroW5uTmCgoIQExPDY9LwMX3xxRc1+ph27twJW1tbHD58GLa2tlizZo1Wvk815ZiWLVumdcekje9TTTimlJQUrTsmbXyfKntMly9fhp6eHkJCQmBra6tsV+QxrV69mu8Tj0nYMRXNU9Fjevz4MQAIvR2Ko1yWITk5GT169ICLiwvWrl2rXO7v7493330XJiYm8PDwgLe3t3KCcaDgXrvKKhwUpTyjXJZ0hq7wLwARERFwcnKqdD1Uu/n4+GDWrFlSl1FhKSkpOHjwoPL56NGjUb9+fQkrqt1qep6o+mCWagdNfIYzSyRSaXm6fv06nJ2dhX4/5yWXZWjYsCEUCgXu3buHffv2qaxTKBTIyMjA9u3bsX37dpV1+fn5miwTVlZWsLKy0uhrUu3SsmVLqUsgLcI8kSjMEonCLJFImswTO3Rl+OKLL7B48WL873//Q4cOHZTLMzIysHz5ckyYMAHNmzeXsEIizTA2Npa6BNIizBOJwiyRKMwSiaTJPPEeujJ8+eWXkMlk6Nq1K9zc3JSPIUOGQCaToVevXirLCx9E2iYsLEzqEkiLME8kCrNEojBLJJIm88QzdOW0cOFCLFy4sNjymTNnqjxXKBSQyWSVuuRy3bp1SE1NRVxcHADg6NGjePToEQDg/fff58h8JIkpU6ZIXQJpEeaJRGGWSBRmiUTSZJ7YoSvDkiVL4O7uDjc3N+jo6ODIkSNV/porVqzAgwcPlM8PHz6Mw4cPAwDeeustduhIEp6ensXuFSWqKOaJRGGWSBRmiUTSZJ44ymUZdHR0IJPJkJmZCQMDA+jolH2VamXP0IlSFaPoENVUHOWSiKjm4mc4aYuq+H7Oe+jKIJfLkZ+fDwMDA+Xzsh7VoTNHJBrvDSWRmCcShVkiUZglEkmTeWKHjojK5ejRo1KXQFqEeSJRmCUShVkikTSZJ3boKig5ORkHDhzA8uXLsXz5chw4cAB///231GURVRlPT0+pSyAtwjyRKMwSicIskUiazBMHRamAL7/8Et9++y1ycnJQ9BZEAwMDzJs3D0uWLJGwOqKqMWvWLKlLIC3CPJEozBKJwiyRSJrME8/QqcnLywtLlizBwIEDcfz4cdy9exd3797F8ePHMXDgQCxduhReXl5Sl0kk3NmzZ6UugbQI80SiMEskCrNEImkyTzxDp6aNGzfCzc2t2PQFzZs3x5AhQ+Dm5oYNGzaUOGcdUU3G0cRIJOaJRGGWSBRmiUTSZJ54hk5NaWlpGDJkSKnrhw4dimfPnmmwIiLNaNy4sdQlkBZhnkgUZolEYZZIJE3miR06Nb300ksIDQ0tdX1oaCheeuklDVZEpBknTpyQugTSIswTicIskSjMEomkyTyxQ6emjRs34sKFC/D09ERUVJRy7rmoqCjMmTMHf/zxBzZu3Ch1mUTCzZ07V+oSSIswTyQKs0SiMEskkibzxA6dmjp06IBHjx5hzZo1aN26NQwNDWFoaIjWrVtj7dq1ePjwITp06AAzMzPlo169elKXTVRpkyZNkroE0iLME4nCLJEozBKJpMk8cVAUNb3xxhuQyWRSl0GkcQcOHJC6BNIizBOJwiyRKMwSiaTJPLFDpyY/Pz+pSyCShJubG44ePSp1GaQlmCcShVkiUZglEkmTeeIll0RULvwlRyIxTyQKs0SiMEskkibzxA6dmk6fPg1vb2+VZb6+vrC3t4e1tTU8PT2Rn58vUXVEVWfBggVSl0BahHkiUZglEoVZIpE0mSd26NT05Zdf4tq1a8rn4eHhmDZtGiwtLdG/f3+sWbMGK1askLBCoqoxbtw4qUsgLcI8kSjMEonCLJFImswTO3RqunnzJrp166Z8vnPnTpiZmeHcuXPYv38/3n33XezYsUPCComqRnh4uNQlkBZhnkgUZolEYZZIJE3miR06NT1//hxmZmbK54GBgRgyZAhMTEwAAN27d8eDBw+kKo+IiIiIiGoRdujUZGdnh4sXLwIAoqKiEBERgUGDBinXJycnw9DQUKryiKpM+/btpS6BtAjzRKIwSyQKs0QiaTJP7NCp6c0338TmzZvx+uuvY/Dgwahfvz5GjBihXH/58mU4OjpKWCFR1di7d6/UJZAWYZ5IFGaJRGGWSCRN5onz0Knps88+Q05ODo4fPw57e3v4+fnB3NwcQMHZuaCgIMyePVvaIomqwLJly6QugbQI80SiMEskCrNEImkyTzxDpyY9PT0sXboUV65cwZkzZ9C3b1/lOgsLCzx+/Bjz58+XsEKiquHm5iZ1CaRFmCcShVkiUZglEkmTeWKHjojKhROukkjME4nCLJEozBKJxInFiaja8fDwkLoE0iLME4nCLJEozBKJpMk8sUNHROXi5+cndQmkRZgnEoVZIlGYJRJJk3lih46IymXlypVSl0BahHkiUZglEoVZIpE0mSd26IioXAYPHix1CaRFmCcShVkiUZglEkmTeWKHTk1LlixBREREqeuvX7+OJUuWaLAiIs2IjY2VugTSIswTicIskSjMEomkyTyxQ6emL7/8En/99Vep6yMiIrB48WINVkSkGSkpKVKXQFqEeSJRmCUShVkikTSZJ3boBEtOToaBgYHUZRAJ5+rqKnUJpEWYJxKFWSJRmCUSSZN50tPYK9VgZ8+eRVBQkPL54cOHERUVVWy71NRU7N+/H+3bt9dgdUSa4ePjg9WrV0tdBmkJ5olEYZZIFGaJRNJkntihK4czZ84oL6OUyWQ4fPgwDh8+XOK27dq1w9q1ayv8WtnZ2fjiiy+wc+dOpKSkoEOHDvjqq6/w6quvVrhNIhH4S45EYp5IFGaJRGGWSCRN5qlWXXKZmZmJDz/8UO2Z2+fNm4fExEQkJCRAoVBg48aNSExMVHkkJSUhIyMDERERcHFxqXCNkyZNwqpVq/Dmm2/i+++/h66uLoYOHYqQkJAKt0kkgpubm9QlkBZhnkgUZolEYZZIJE3mqVadoTM2NsamTZvQrl07tfczNjYGANy/fx+WlpYwMTERXl9YWBj27dsHb29vfPTRRwCACRMmwNnZGfPmzcP58+eFvyZRean7hxCiF2GeSBRmiURhlkgkTeapVp2hA4CuXbu+cNqBsjRt2rRYZy4jIwO+vr7YsGEDHjx4UOG2Dx06BF1dXUydOlW5zMjICFOmTMGFCxcQExNT4baJKmvixIlSl0BahHkiUZglEoVZIpE0madadYYOAL777jsMHToUzs7OmDRpEvT01PtfMGXKFISGhio7hTk5OejZs6fyeb169fDbb7+hc+fOatd25coVODo6wszMTGV5jx49AABXr16FnZ1difsmJCQgMTFRZVlJA7cQVRTvLSCRmCcShVkiUZglEon30FWhSZMmQUdHB9OmTYOZmRlatWqFDh06qDw6duxY6v5nzpzByJEjlc/37NmDiIgI7N69GxEREWjUqFGF56GLj4+HjY1NseWFy+Li4krdd/369XB2dlZ5uLu7AwBCQkIQHBwMb29vJCcnK/9iUHhtr6enJ6KiouDr6wt/f3+EhYXBy8sLGRkZ8PDwUNl2wYIFCA8Px549e7Bnzx6Eh4djwYIFKtt4eHggIyMDXl5eCAsLg7+/P3x9fREVFQVPT0+VbSdOnIjk5GR4e3sjODgYgYGB8PHxQWxsLKZPn66y7fTp0xEbGwsfHx8EBgbymDR8TGvXrq3Rx1S47bp16wAAM2bM0Mr3qaYc04YNG7TumLTxfaoJx9SnTx+tOyZtfJ8qe0y7du1CQkICDhw4AAAYO3as8GPaunUr3ycek7BjKpqnosdUFeNiyBQKhUJ4q9VY//79IZPJytzuzJkzJS43MTHBunXr8H//938AAHd3d8TFxSEsLAwAsGrVKnh7eyM+Pl7t2lq2bInWrVvj+PHjKsvv3buHli1bYvXq1ZgzZ06J+5Z2hs7d3R0RERFwcnJSux6iooKDg9GvXz+py6iwlJQUHDx4UPl89OjRqF+/voQV1W41PU9UfTBLtYMmPsOZJRKptDxdv34dzs7OQr+f17pLLovOJ1cRderUQWpqKgAgLy8PQUFBeP/995Xr69ati7S0tAq1bWxsjOzs7GLLs7KylOtLY2VlBSsrK5VlhW3x0ksS4datW2jYsKHUZVRYfn4+nJ2dlc9jYmJeeNabqlZNzxNVH8xS7aCJz3BmiUQqLU+F38tL+s5fUbWuQ1dZXbp0wQ8//ICXX34ZP/30E549e6YyLOndu3dhbW1dobZtbGwQGxtbbHnh2T5bW1u12gsPDwcA5aWXREREREQkvZiYGHTp0kVIW7WyQ5efn49du3bh559/Vo5K2bRpUwwfPhxvvvkmdHV1S9136dKlGDx4MLp16waFQoFRo0YpBy0BAH9/f7z00ksVqqtTp044c+YMnj59qjIwSmhoqHK9OhwdHQEABw4cUHuqBqKiCi/fDQgIgIODg9TlUA3HPJEozBKJwiyRSC/KU3Z2NmJiYoRe3lvrOnRpaWkYPHgwLl68iLp166JFixYAgFOnTuHHH3/Ehg0bcOLEiWIjTRbq1q0bbt26hfPnz8Pc3FzlzUhNTcXMmTMr/AaNGjUKK1aswObNm5Xz0GVnZ2Pbtm1wcXEpdYTL0hQeQ7t27XgPHQnh4ODALJEwzBOJwiyRKMwSiVRankSdmStU6zp0n332GS5fvoy1a9fi3Xffhb6+PgAgNzcXW7ZswQcffIDPPvsMa9euLbUNS0tLjBgxothyc3NzzJ49u8K1ubi4YPTo0Zg/fz4SEhLg4OCA7du3Izo6Glu3bq1wu0REREREpJ1qXYfO398fM2fOxMyZM1WW6+vrY8aMGbh58yYOHTr0wg4dUDByTUmXbLq6ulaqvh07dmDhwoXYuXMnUlJS0KFDBxw7dqzS7RIRERERkfapdR26v//+G61bty51fZs2bZCcnFzq+pycHIwbNw4BAQFQKBQwNzcHUHC55cqVK/G///0Pe/fuVZ75U5eRkRG8vb3h7e1dof2JiIiIiKj2qHUTizs4OOCnn34qdf1PP/2Eli1blrp+8eLF8Pf3x9y5cxEfH4/k5GQkJyfj8ePH+Oijj3D48GEsWbKkKkpXm6WlJRYtWgRLS0upS6EajlkikZgnEoVZIlGYJRJJ03mqdROLr1+/Hu+99x6GDBmCOXPmKEeCvH37NtasWYPAwECsW7cOM2bMKHH/5s2bo3///ti2bVuJ6ydNmoSgoCBER0dX1SEQEREREREBqIWXXM6cORMJCQn45ptvcOLECZV1+vr6+OKLL0rtzAEFc8K5uLiUut7FxQX79u0TVm9lpKamIjg4GHZ2djA0NJS6HCIiIiKiWq3otAWFt25VVq07Q1coKSkJv/76q8qgJgMHDixxRveiHBwc0K1bt1I7bWPHjsWlS5eUs8BL6ciRI5xUnIiIiIiomgkICChx1PyKqHVn6M6ePYu2bdvC0tISY8eOLbY+KSkJN27cKHVUyYkTJ2LRokUwNzeHp6cnHBwcIJPJcOfOHXz33Xc4ePAgFi9eXNWHUS6F89ZxkszaY8u5e7j1+Ck+HdIWDeuKPSu7ePFiLFq0SGibVHmBEfHY9cdD5OTJpS5FLenP02Fax1TqMqo1KzNDzOjfEk629aQupVrjZxOJwiyRSKXlqXDScXXnl36RWneGTldXFzt37sT48eNLXL9//36MHz8e+fn5Ja7Pz8/HlClTsGPHDshkMujoFIwrI5fLoVAoMHHiRGzdulW5XErXr1+Hs7MzIiIiOElmLRCfloleX/8GABjb3Q7fvNFBaPuxsbFo3Lix0Dap8np/fRpxaVlSl0FVpK6RHg5M64W2NmZSl1Jt8bOJRGGWSKTS8lQV389r3Rm6svqv2dnZ0NXVLXW9rq4u/Pz88OGHH+Lnn3/Gw4cPARRcsjl06FB06CD2SzRRed1+/Ez574Crsfj0tTYwNzEQ1n5AQABmzZolrD2qvKdZucrOXBd7c7SpQV/6r0dch5Mz/9BUmsycfPhficWzrDxM9A3DjzN6w87CROqyqiV+NpEozBKJpMk81YoO3cOHD1VGnbx16xbOnj1bbLvU1FRs2rQJTZs2LbYuKysLR44cwf3799GwYUMMGzYM8+fPr8qyidQS+eTfDl1WrhwHLsVgqmvpU3Co60XTeZA07hR5z6f1a4nBTo0krEY9gcaxGDKkvdRlVGvtbMyw9PhNJDzLxttbQ3FoRm80NOUAV//FzyYShVkikTSZp1rRodu2bRsWL14MmUwGmUyGpUuXYunSpcW2UygU0NXVxaZNm1SWJyQkoHfv3rh//77yDJ+JiQkCAgIwcOBAjRwDUVkin6SrPN/5xwNM6dMCujoyIe0bGxsLaYfEKfqeO1rXlbAS9TFPZXvXtQWSnmdjU/A9RP+dgUnbwrD33Z6oa6QvdWnVCrNEojBLJJIm81QrOnQeHh5wdnaGQqGAh4cHPvjgA/Tt21dlG5lMhjp16qBTp06wtrZWWefl5YXo6Gh4enpiwIABiIqKgpeXF6ZNm4a7d+9q8lCISlV4tkZHBsgVQExyJoJuJ+CVttZl7Fk+YWFh6Nevn5C2SIzCs7KGejqwr2GX4zFP5fPpkDZITs/BwcuPEBH7FFN3XMa2yd1hpF/6rQG1DbNEojBLJJIm81QrOnRt27ZF27ZtARScrXN1dUXz5s3Lvf/JkycxYcIErFixQrnM2toa48ePx+3bt9G6dWvhNROpQy5XKM/WuHdujJPXnyA9Ow/bLzwQ1qGbMmWKkHZInMIOnYOVqbAzsZrCPJWPTCbD1yPbIyUjF7/efIIL9/6G5/6rWDe+S417z6sKs0SiMEskkibzJP1QjBr25ptvokGDBqWuf/r0KfLy8lSWPXz4EH369FFZ1qdPHygUCjx58qRK6iRSR2xqJjJzC0Zm7WxfH6O6NgEAnI1MxL3E9BftWm6enp5C2iFxCjvxNe1yS4B5Uoeerg7Wje+M7s3qAwB+iXiMhUciyhzkq7ZglkgUZolE0mSeal2H7oMPPkDv3r1LXf/SSy9h7ty5Ksuys7NhZGSksqzw+X87f0RSKDogiqOVKd7q+e/APjsuPBDyGtu3bxfSDomR8jwHic+yAdTMDh3zpB4jfV1smdgdbRoVvNd7Qh9i7W9REldVPTBLJAqzRCJpMk+1rkMXGBiIUaNGlbp+1KhROH78eLHl0dHR+PPPP5WPv/76CwBw584dleWFDyJNul20Q2ddFw5WpujbqiEA4MfLj5CeXfk/PLi5uVW6DRJHpRNvXfMm6Gae1FfPWB87/q8HmtQvuNF+7W93EJOcIXFV0mOWSBRmiUTSZJ5qxT10RcXFxb1w0khbW1vExsYWW75w4UIsXLiw2PKZM2eqPFcoFJDJZKVOTE5UFe78c+mdZV1D1K9TMPfchF7NcO5OEp5l58H/Size7ll8Og51HD16tNJ1kjiRCTV3hEuAeaooKzMj+IzvghE+vyM3X4E1p+/Ae3RHqcuSFLNEojBLJJIm81TrOnQNGjTA7du3S11/8+ZNmJmpTs67bdu2qi6LqFIKJxVvXeSL/YA2VmhS3xiPUjKx43w03nKxh0xW8UEUPD09sXr16krXSmJE/vOemxjoorF5zRtqm3mquI525hjsZI0T15/gxz8fYXr/lmhpWfPO0orCLJEozBKJpMk81boO3ZAhQ7Bp0ya8+eab6Ny5s8q6P//8E5s3b8bo0aNVlk+cOFGTJRKpJV+uwN1/Bj5pVeTSO10dGd7u2RRf/3ILdxLSceHe3+jdsmGFX2fWrFmVrpXEKbzkspWVKXRq4GiHzFPlfPhqa5y88QRyBbD6VCTWje8idUmSYZZIFGaJRNJknmrdPXReXl4wMzNDjx498MYbb+CLL77AF198gZEjR8LFxQX16tWDl5eX1GUSldvD5Axk58kBqJ6hAwCPbnYw1Cv4Md9+PrpSr3P27NlK7U/iKBQKZYeuJl5uCTBPldW6UV283tEWAHDsr3jciHsqcUXSYZZIFGaJRNJknmpdh87W1haXLl3C+PHjcfr0aXz11Vf46quv8Ntvv+HNN9/ExYsX0aRJE6nLJCq3wsstAaDVf77c169jgBGdCr70nbrxBLGpmRV+nfr161d4XxIrKT0HKRm5AGpuh455qjzPgY7KuehWnSr9VgJtxyyRKMwSiaTJPNW6Dh0A2NjYYPv27UhJScHjx4/x+PFjpKSkwM/PD7a2tlKXR6SWO0+KduiK30czoVczAIBcAez+o+JTGLxoMCHSrLLe85qAeaq8Zg3rYPQ/c07+ejMBVx6mSFyRNJglEoVZIpE0mada2aErJJPJYGVlBSsrq0oNFlFUdnY2PvnkE9ja2sLY2BguLi44depUmft9+eWXkMlkxR7/nf+O6L8KpyywrWcEMyP9YuudG9dDt6YFfyXadzEGWbkVG4H1xIkTFS+ShCo6TUXrRjXzDB3zJMb7r7SCgW7Br/KVJyMlrkYazBKJwiyRSJrMU60bFKXQ77//jj///BNpaWmQy+Uq62QyWYlTFJTHpEmTcOjQIcyZMwetWrWCn58fhg4dijNnzqBPnz5l7r9hwwaYmhYZ2EJXt0J1UO1ROGXBfy+3LGpC72a49CAFyc9z8PNf8Xijq/qXFc+dO7fCNZJYkf+853UN9dDIrGb+0Yd5EqOxuTHGu9jD73w0QqKScOHu3+jVsoHUZWkUs0SiMEskkibzVOs6dMnJyRg2bBjCwsKUc8YpFAoAUP67oh26sLAw7Nu3D97e3vjoo48AABMmTICzszPmzZuH8+fPl9nGqFGj0LBhxUcipNolN1+Oe0kFX+5fdKZmiFMjWNY1ROKzbKw7E4XX2jeCiYF6P/6TJk3CgQMHKlUviaEcEKVRXWFXF2ga8yTOzJdbYt/Fh8jKlWPFyds4NL1Xjc1FRTBLJAqzRCJpMk+17pLLjz/+GH/99Rf27NmDe/fuQaFQ4MSJE4iMjMT06dPRqVMnxMXFVajtQ4cOQVdXF1OnTlUuMzIywpQpU3DhwgXExMSU2YZCocDTp0+VnUyiF4lOeo7c/IKstLIq/V4qAz0dTHNtAQC4n/QcS3++qfZr8Zdc9aA6wmXNvH8OYJ5EsqprhEm9mwMALj9IQdDtRIkr0ixmiURhlkgkTeap1nXojh8/jmnTpmHMmDGoW7fgjIaOjg4cHBzg4+ODZs2aYc6cORVq+8qVK3B0dCw2MXmPHj0AAFevXi2zjRYtWqBevXqoW7cu3nrrLTx58qRCtVDtUHjpHVD2aIeTX2qOHs0tAAC7Qx/i1xvqZcvNzU39Akm4J0+z8SwrDwDQyqpm3j8HME+iTe/XAnUNC866rzh5G3J57fmjILNEojBLJJIm81TrOnSpqalwcnICAOW9aunp/34pHjRoUIVvYoyPj4eNjU2x5YXLXnTmr379+njvvfewadMmHDp0CO+88w7279+Pvn374unTsucXSkhIwPXr11UeUVFRFToOqjluqzHaoa6ODKs8OqKuUcGXvk9+/AuJz7LL/VpHjx6tWJEklDYMiAIwT6KZmxhgSt+Cs3TX454i8PpjiSvSHGaJRGGWSCRN5qnWdehsbW3x+HHBLzpDQ0NYWVnh2rVryvWxsbEVvvcgMzMThoaGxZYXjlSZmVn6HGCzZ8/G2rVrMX78eLzxxhv47rvvsH37dty5cwfr168v87XXr18PZ2dnlYe7uzsAICQkBMHBwfD29kZycjImTpwI4N+/HHh6eiIqKgq+vr7w9/dHWFgYvLy8kJGRAQ8PD5VtFyxYgPDwcOzZswd79uxBeHg4FixYoLKNh4cHMjIy4OXlhbCwMPj7+8PX1xdRUVHw9PRU2XbixIlITk6Gt7c3goODERgYCB8fH8TGxmL69Okq206fPh2xsbHw8fFBYGAgjyksDD8FhQEADHOfwcRAr8xjalLfBOZRgQCAv5/nYO7+P7FkSfmOydPTk+9TNTimw7/+ey/uLh/vGntMH3/8sVa/T1IcU9TPm2FmWDCQ1uIfL+PCH6E1/pjK8z61atVK645JG9+nmnBMCxYs0Lpj0sb3qaYcU9E8FT2mkJAQiCZT1LKbtSZPnoz79+8jKCgIQEFHauvWrZg/fz7kcjmWL1+OwYMH49ChQ2q37ezsDGtra5w+fVpl+Y0bN+Dk5ISNGzdi2rRparVpY2MDJycn/Prrry/cLiEhAYmJqvdNREVFwd3dHREREcqzkqRdXlkZhLuJzzGwrRW2TOxe7v0+2HsFP10rOGPsNcIJb/8zV92LhIeHo3379hUtlQT5+OA1HLz8CPVN9PHnwldr7OAXzFPV2BR8F1//cgsAMKFXUyx+3anGZqS8mCUShVkikUrL0/Xr1+Hs7Cz0+3mtO0P34Ycf4vXXX0d2dsGlZl9++SV69uyJhQsXYtGiRejatSvWrl1bobZtbGwQHx9fbHnhsopMWm5nZ4fk5OQyt7OysoKTk5PKw8HBQe3Xo5ojOy8f0X9nAHjxlAUl8XJ3RmNzYwDAVz/fRFTCszL2KPhgIulFJvw7TUVN/qLOPFWNib2boc0/l+LuuPAAW0PuS1xR1WOWSBRmiUTSZJ60vkP3119/IS0tTfm8ffv2+PDDD5WXRtavXx+//vorkpOTkZaWhqCgoBLvgyuPTp06ITIystg9b6Ghocr16lAoFIiOjoalpWWF6iHtdi/xOfL/GfigtZodunrG+ljp0REyGZCdJ8fsfVeRkycve0eSlFyuQJQWjHBJVcdIXxe+k7rD2qzgd9zS4zcRGFF77qcjIqqNtL5D17lzZ/z888/K5wMGDCh2SSQAmJubK0e9rKhRo0YhPz8fmzdvVi7Lzs7Gtm3b4OLiAjs7OwDAw4cPcevWLZV9/3u5JFAwyXhiYiKGDBlSqbpIO0WqMSBKSXq2aIBpri0BFAyisOpU5Au352Uo0otNzcTznHwA6nfiqxvmqerYmhvDd1J31DHQhUIBzNl/BVcepkhdVpVhlkgUZolE0mSetL5DZ2xsjIyMDOXzoKCgKpsKwMXFBaNHj8b8+fMxb948bN68GQMGDEB0dDSWL1+u3G7ChAlo27atyr5NmzbF5MmTsWrVKqxfvx7jx4/He++9h06dOql93x3VDoUdOh0Z0NKyYmdrPnzVEc6NC6bZ2HT2Li7c/bvUbffu3Vuh1yBx7iQU7cTX7A4d81S1nGzrYd34LtCRAVm5cryz/RJikjPK3rEGYpZIFGaJRNJknvQ09koS6dixI1atWgVdXV3Uq1cPAHDx4kXlyJOlGTlyZIVeb8eOHVi4cCF27tyJlJQUdOjQAceOHYOrq+sL93vzzTdx/vx5/Pjjj8jKykLTpk0xb948fPbZZzAxMalQLaTdCuega9agDoz0dSvUhoGeDr4b0xnD155DVq4cnvuvYve7LiV2EJctW1apeqnybj8u/7yD1R3zVPVebmOFJSOc8XlABP5+noNJ28JweMZLqGeiL3VpQjFLJAqzRCJpMk9aP8rlpUuXMGrUKDx8+BAAIJPJUNYhy2Qy5Ofna6K8KlUVo+hQ9dHP+wwe/J2BwU7W2PR2t0q1tfNCNBYeuQ4AMDPSw8a3uqK3Q0OVbdzc3DhHj8Q+3H8Vh6/EoqGpIS59PlDqciqFedKcr4/fxKaz9wAAPVtYYPv/9YChXsX+CFQdMUskCrNEIpWWp6r4fq71Z+i6deuGqKgo3L17F0+ePEH//v3x2WefYeDAmv1liGq3zJx8PPzn8ikR91K91bMpktJz8P3pO3ialYcJvmFY+j9njOlur9yGv+SkF5mgPQOiME+a88mQNohJycDx8Mf4414y5v8Y/s+gSDV3lNSimCUShVkikTixuGB6enpo3bo1XF1dMXHiRAwfPhz9+vV74YOoOrubmI7CE80i7qWSyWTwfNUR343pBANdHeTJFfjkx3B8ffwm5P+MpFk4QSdJI1+uwJ1/LrOt6ZdbAsyTJunoyLDKoxO62JsDAA5ficWc/VeRlpkrbWGCMEskCrNEImkyT7WiQ1dU4YiTRDXZ7cf/Do4h8su9e+fG2POuCyzqGAAANp29hxm7LyMjJw9+fn7CXofUF5Ocgex/ppbQhg4d86RZRvq6+GFCNzRtUHBP9pGrcRjy3Vn8HpUkcWWVxyyRKMwSiaTJPNW6Dh2RNii89E5PR4bmDesIbbtbMwsEzHwJDlYFl/WduP4EHpsuYIn3GqGvQ+q5XWSaitaNav4llytXrpS6hFqngakhDk7vhf6tC+Y2jU/LwptbQrH46HVk5dbc+8aZJRKFWSKRNJknduiIaqDIf87QNW9YBwZ64n+M7RuY4McZvdG3VcHAKBGxT3FS0RGBEfHKSzBJs+4U6dA5WNX8M3SDBw+WuoRayaquEbZN6o6l/3OG8T+j4277PRrD1pzDX49SpS2ugpglEoVZIpE0mSd26IhqoMIpCxwbVd0X+3rG+vCd1B3jXQoGRknOkmP6rj8xfG0ITt14UuZosSRW4XveyMwI9Yxr/rDzsbGxUpdQa8lkMrzp0hS/zO6rvK/ubuJzjFx/Ht//egd5+XJpC1QTs0SiMEskkibzxA4dUQ2Tnp2H2NRMAIBjFZ+p0dfVwVJ3Z3i5O8NYp+BL3o34p3h3xyWM8PkdZ24lsGOnIYUTyVdlJ16TUlJSpC6h1mvWsA4OTOuFjwe3hp6ODHlyBVb/GolBq8/CN+R+jRk0hVkiUZglEkmTeWKHjqiGuaPhe6lkMhne7tkU+95yxEeDHGFmVDDbyV+P0jDZ7yL+t/48zkYmsmNXhXLz5biX+BwA4GhV8++fAwBXV1epSyAAero6mPWyAwJmvaScDuNe0nMsOXYDPZedxqc//oWI2DSJq3wxZolEYZZIJE3mSevnoTt79myF9uMPNVVXhUPXA2KmLCgvvx82YvXq1Xi7VzP4htyHb8h9PMvOw9WYVEzwDUMLyzp4ta01BrazRhf7+tDV0Y45rqqDB38/R84/l8Fpyxk6Hx8frF69Wuoy6B/Ojevhp/f6YNcfD7Dzjwd48HcGMnPzse9iDPZdjEFne3O83bMphra3gZF+9ZqUnFkiUZglEkmTeZIptPzP6jo6OiqTpyoUinJNppqfX7ERv7Kzs/HFF19g586dSElJQYcOHfDVV1/h1VdfLXPf2NhYeHp64uTJk5DL5Xj55ZexevVqtGjRokK1VMVM9CQ9r2M3sDXkPgx0dXBjyWDo6Upzoj01Iwdbzt3Htt/v43mO6s9LfRN9vNzGCq+2tUZfR0uYGmr9346q1PHweMzc/ScAIGDWS+hkZy5tQaTV5HIFzkUlYeeFB/jt1hMUHQeprqEeuje3gEtzC/Rs0QBOtmaSfQYREdVEVfH9XOu/ZZ05c0bleXZ2NubNm4eMjAxMnToVrVu3BgDcunULP/zwA+rUqYPly5dX+PUmTZqEQ4cOYc6cOWjVqhX8/PwwdOhQnDlzBn369Cl1v/T0dLz88stIS0vDggULoK+vj9WrV6Nfv364evUqGjRoUOGaSLsU3kvV0spUo1+k3NzccPToUeVzcxMDfDS4Nf6vT3PsvxiDX28+wZ8PU6BQACkZuTj8ZywO/xkLA10ddG1aH+1szdCmUV20tTFDK2tTGOpVr7/yV2eRRS6zbaUll1z+N09UfejoyNDP0RL9HC0Rm5qJvaEPse/iQySl5+BZdh5+u5WA324lAABMDfXQrVl9uDRvgB7NLdC6UV2N/wGHWSJRmCUSSZN50vozdP/14YcfIiQkBGfPnoWRkZHKuoyMDPTr1w+urq4VmjsiLCwMLi4u8Pb2xkcffQQAyMrKgrOzM6ysrHD+/PlS912+fDk++eQThIWFoXv37gAKOpnOzs6YN28eli1bpnY9PEOnnXouO43HT7MwopMtvh/bWepyVCSlZ+O3Wwn49cYTnLuThMxS5rbS1ZGhpWUdtLUxQ+tGddHY3Bg29YxhU88IVmaG7Oz9x8zdl3E8/DGa1DdGyCcDpC6HaqGcPDlOXH+MM7cTEHovWTkwU0ms6hqiecM6aGFZB80b1kHzhqZo3rAOGpsbw9iAP9tEVLvxDJ0Au3fvxueff16sMwcAJiYmePvtt7F06dIKdegOHToEXV1dTJ06VbnMyMgIU6ZMwYIFCxATEwM7O7tS9+3evbuyMwcAbdq0wSuvvIIDBw5UqENH2ictMxePn2YBABw1eP8cAEycOBHbt29/4TYNTQ3h0c0OHt3skJWbj/N3k/DrzQRci0nFnSfpyvvA8uUKRD5JVw7FX7wdA1ibGcGmnhEs6hjAzEgf9Yz1Uc9EX/lvM2N91DXSg7G+LowNdGGsrwsjfV2tvHdPOU2Fht/zqlSePFH1YaCnA7eOtnDraAsAiEnOQOj9ZPxx72+E3v8bMcn/dvASnmUj4Vk2Qu8nF2unjoEuGpgaoqGpARqaGqKBqSEsTQ1gUccApkb6MDXUhamhPkyN9JT/rmNY8PNd2hUJzBKJwiyRSJrMU63r0D1//hzx8fGlro+Pj0dGRkaF2r5y5QocHR1hZmamsrxHjx4AgKtXr5bYoZPL5fjrr7/wf//3f8XW9ejRAydPnsSzZ89Qt664L3Pbfr+P7LyaNdcQAY/TspT/1vSXe3Vv7DXS18WANtYY0MYaQMFIjfeTnuNm/FPciH+KW/HPcDP+KRKeZRfbNyk9B0npObge91TtOg30dP7p3OnAQE8H+ro6MNAt+K++rqzguZ4OdHVk0NORQVf50IGuDAX/1QF0ZDLIZDLlvwue/7McUP4bMkCGwnUF/8Y/62X//KOwiyn7z/pCRdcrlxV5cj/pnxEutahDx4EHajY7CxPYWZhgVNcmAIDY1ExcfZiK+0npuJf0HPeTnuNe4vNiUx88z8nH8+QMPExW//esro4Mhno6/zx0YaivAyM9Xcg6TYa7z+/Q05FB75+f8YJ/F/zMy2Qy6MoKfs51/vmZLvx3waPg563wZ7ro88Kf7cKf+aLPX/Sz/d8/K/331v0X3cv/otv8ZcVaLv++VLaOYz/CxuC7UpdBWqK0PMXdfyj8tWpdh27gwIH4/vvv0a1bN4wcOVJl3Y8//ojvv/++wjO7x8fHw8bGptjywmVxcXEl7pecnIzs7Owy9y28368kCQkJSExMVFkWFRVV6vbfn76D1IyaMccQlaxwiHFN2bp1Kz7++OMK76+vqwNH67pwtK6LEZ0aK5c/zcrF47QsxKdl4XFa5j//zVL+NzUzB2mZucjKLd8fIHLy5MjJkyOt9CvCaixNv+dVqbJ5ouqlsbkxGpsbF1ue8jxH2cF78jQLSenZBX+weZaNv58X/DslIwflufkjX65ARk4+MnLyAfD3FxHVTDmJD4S3WeuGpvLx8UGTJk0wevRoNGnSBP3790f//v1hZ2cHDw8PNGnSBGvXrq1Q25mZmTA0NCy2vPDyzszMkr9hFi6vyL6F1q9fD2dnZ5WHu7s7ACAkJATBwcHw9vZGcnIyJk6cWO5jourJyVyOx1ER8PLyQkZGBjw8PAAU3IALAAsWLEB4eDj27NmDPXv2IDw8HAsWLFDZxsPDAxkZGfDy8kJYWBj8/f3h6+uLqKgoeHp6qmw7ceJEtGnTBt7e3ggODkZgYCB8fHwQGxuL6dOnq2w7ffp0xMbGwsfHB4GBgSVmr3BbT09PJDx6gJCfDyL5egiaKx7j2YX9+OI1B2QGrsAJT1dYXfget7xewxjZeewY2Riejs8wvWU6FvW3xEuy2/Ae1QG2ceewYGgbNE6+gnd626OdXgIGNDVCt4b5cKqbhT5NTWGdn4i+rRqizvM4dLY3R92cZLS2MkED3SzY1pHB2hiop5uLRnX1YSjPhLWZIfRyM9CgjgH05Tmoa6gLQ5kchrqAoS6gBzmM9HQgU+TDQFcHMnk+dHVkkCnkKLjqU+ztyfVkWTDPeFTm+5ScnFwl71NUVBR8fX3h7++PsLCwSmevQ4cO5c5eTTkmdX6eassxZaQm4oev52NU1yYIXOWJRW5OyA3ehBXD7DDCIALLuudj66A6mFz/No682xldE09i/9SeaPrgOL4f2wmdcm9iag9L9G+QjoG2+RjuYIy2eokY2ckGDZ7dw2Anaxj+fQd9WzWENVLhZGkIO5M8NDbOQzNzfZghAy0s68AgOw12FsYwyE2HdV1DGMtyYWYoQx09BYx15DA10IG+IhemhnrQyc+FkX7Bz7a+rgw6UEBXVvCFSQZFGefIiIg0r9YNigIUDFSyadMm/PLLL3jwoKCX3LRpUwwdOhTvvvsujI2L/5WxPJydnWFtbY3Tp0+rLL9x4wacnJywceNGTJs2rdh+SUlJsLS0xJIlS7Bw4UKVdevXr8esWbNw69Yttc/Q3bhxAx4eHggICICDg4PKuqxSBqug6k8GwFCCeaBCQkJeOFIrvZhCoVCehVD8Z/l/l5W0QO+fS8e0BfNEokidpf/+bP/3a9WLfrYV/1lbmW9kte7LXBW4cP48evXuLXUZpCVKy9Pdu1EYM+oNXL58GV26dBHyWrXqksusrCxs3rwZnTp1wuzZszF79myh7dvY2CA2NrbY8sJ79mxtbUvcz8LCAoaGhiXe21fWvoWsrKxgZWWlsuzSpUsAoDxTR0RERERE0ouJiWGHriKMjIzwySefYM2aNXB1dRXefqdOnXDmzBk8ffpUZWCU0NBQ5fqS6OjooH379soOWFGhoaFo0aJFhQZEcXR0BAAcOHAA7dq1U3t/okJRUVFwd3cv8WwvkbqYJxKFWSJRmCUS6UV5ys7ORkxMDPr16yfs9WpVhw4ouCwyOjq6StoeNWoUVqxYgc2bNyvnocvOzsa2bdvg4uKiHOHy4cOHyMjIQJs2bVT2/fTTT3Hp0iV069YNAHD79m389ttvyrbUVdipbNeuHeehIyEcHByYJRKGeSJRmCUShVkikUrLk6gzc4VqXYdu6dKlGD9+PF5++WUMHDhQaNsuLi4YPXo05s+fj4SEBDg4OGD79u2Ijo7G1q1bldtNmDABwcHBKtfZz5w5Ez/88AOGDRuGjz76CPr6+li1ahWsra0xd+5coXUSEREREZF2qHUdunXr1sHCwgKDBw9G8+bN0bx582KDoMhkMhw5cqRC7e/YsQMLFy7Ezp07kZKSgg4dOuDYsWNlXuJZt25dBAUFwdPTE1999RXkcjn69++P1atXw9LSskK1EBERERGRdqt1Hbq//voLMpkM9vb2yM/PL3GutsqMJGdkZARvb294e3uXuk1QUFCJy5s0aYKDBw9W+LWJiIiIiKh20cp56LKzs/HJJ5/A1tYWxsbGcHFxwalTpwAA0dHRuH//fqmPkJAQdOvWDebm5jAzM8OIESNw7969El9n69ataNu2LYyMjNCqVatS56+LjY2Fh4eH0DbLw9LSEosWLeIZPqo0ZolEYp5IFGaJRGGWSCRN50kr56EbN24cDh06hDlz5qBVq1bw8/PDxYsXcebMmRfOVZOeno4uXbogLS0Nc+fOhb6+PlavXg2FQoGrV6+iQYMGym03bdqE6dOn44033sDgwYNx7tw57Ny5E9988w0++eSTKm2TiIiIiIgI0MIOXVhYGFxcXODt7a0cHTIrKwvOzs6wsrLC+fPni+1z584dpKWlITAwEAsXLkRYWBi6d+8OALh16xacnZ0xb948LFu2DACQmZkJOzs79OzZE8eOHVO289ZbbyEgIAAxMTGoX78+AGD58uX45JNPhLZJREREREQEaGGHbt68eVi1ahWSk5NV5oIbOXIk/P39YWVlhcGDB2Pt2rXIzs7G66+/josXLyq3a9KkCR48eKDS5uDBg3H37l3l/XbHjx/HsGHD8PPPP2Po0KHK7S5cuIDevXtj586deOuttwAAPXr0AFDQ0RTVZnmlpqYiODgYdnZ2MDQ0VGtfIiIiIiISq+g8dObm5kLa1LpBUa5cuQJHR0eVztyxY8fg7+8PAGjatCn27t2L7Oxs5Ofno169eti4cSOeP3+ODz/8EA8fPsTPP/+MYcOGKffv0aMHTp48iWfPnqFu3bq4cuUKACjniyvUtWtX6Ojo4MqVK3jrrbcgl8vx119/4f/+7/+K1VnRNtURHBwMd3d3tfYhIiIiIqKqFRAQgBEjRghpS+s6dPHx8bCxsVFZ5u3tjW7duuHSpUuYMmUKMjIy8PHHH2Po0KE4ceIEACApKQmenp5o2LAh1qxZo9KhK2wvLi4OrVu3Rnx8PHR1dWFlZaXyOgYGBmjQoAHi4uIAAMnJycjOzi5WT2XaLE1CQgISExNVluXk5AAAGv7vM+jXt33h/kREREREVLVyU+KQ5L8UdnZ2wtrUug5dZmZmscsLb9y4gZkzZ+LSpUvIzMzEiBEjMHfuXHh4eKjsBxScEbt06ZLK/kZGRirbZGZmwsDAoMTXNzIyUtkOQImXO1a0zdKsX78eixcvLnGdfn1bGFg2feH+RERERESkGSJvh9K6Dp2xsTGys7NVlmVkZEBPT0+5vl69egAAW1tblf2Ags7Ts2fPVPbPyspS2cbY2Fh59uu/srKyVLYDUKyeyrRZmpkzZ2L06NEqy6KionjJJRERERGRFtO6Dp2NjQ1iY2NVljVq1Ah3794FAOXcdNOmTUOTJk2U21hYWMDQ0BBxcXEqUwkABZdxFu5b+Br5+flISEhQuUQyJycHf//9t3K7wjYL9xfRZmmsrKyKXa5JRERERETaTesmFu/UqRMiIyPx9OlT5bKuXbviwoULyvUmJibYsGEDHB0dldvo6Oigffv2uH37Ntq3b6/SZmhoKFq0aIG6desq2wBQ7NLMS5cuQS6XK9cXtvnf7SrTJhERERERUSGt69CNGjUK+fn52Lx5s3LZggULkJ6eDhcXF+UNiA8fPsStW7dU9h0yZAiePn2K/v37K5fdvn0bv/32m8rljAMGDICFhQU2bNigsv+GDRtgYmKiMqDKqFGjcPHiRZWOWmXbJCIiIiIiArTwkksXFxeMHj0a8+fPR0JCAhwcHLB9+3YkJCRg7969yu0mTJiA4OBgFJ2Gb968edi7dy++++476OnpQV9fH6tWrYK1tTXmzp2r3M7Y2BheXl6YNWsWRo8ejcGDB+PcuXPYtWsXli5dCgsLC+W2M2fOxA8//IBhw4bho48+EtImERERERERoIUdOgDYsWMHFi5ciJ07dyIlJQUdOnTAsWPH4Orq+sL96tati6CgIHh6euKrr76CXC5H//79sXr1alhaWqpsO3PmTOjr62PlypX46aefYGdnh9WrV2P27NlV3qa6+jk2gMzCAgoFkC+XQ1dXB/jPv3Pz85GVK4eRvi709Yqv537as191ro371e79Lt5PRp684HPLWE+Gzk3rv/Bziz8D3K+271eda+N+3I8/AyXvl23yFMcq9c2+OJmi6Ckq0irXr1+Hs7MzIiIi4OTkJHU5VMO5ubnh6NGjUpdBWqKkPL30zW+ITS2YoqWVlSlOfdhPitKohuFnE4nCLJFIpeWpKr6f6whphYi0Hn/JkUgl5UlXR1biv4lehJ9NJAqzRCJpMk/s0BFRuXh6ekpdAmmRkvKkV6QTp6/LX09UPvxsIlGYJRJJk3nib0wiKpdZs2ZJXQJpkZLypKfLM3SkPn42kSjMEomkyTxVqkN3+/ZtUXUQUTV39uxZqUsgLVJSnnR1/v2VpMcOHZUTP5tIFGaJRNJknio1ymXbtm1hZWWFPn36oG/fvujbty86d+4MmYy/iIm0Tf369aUugbRISXkq2okreraO6EX42USiMEskkibzVKkO3d69exESEoJz584hICAACoUCpqam6N27t7KD5+LiAgMDA1H1EpFEGjduLHUJpEVKylPRTpyeDu8IoPLhZxOJwiyRSJrMU6V+Y44ZMwZr167F1atX8ffff+Onn37CzJkz8ezZM3h5eaF///4wNzcXVCoRSenEiRNSl0BapKQ86XGUS6oAfjaRKMwSiaTJPAmbWLxevXpwcnJCcnIykpKSEBcXh+joaOjwr6xEWmHu3LlSl0BapKQ86aqMcskOHZUPP5tIFGaJRNJknirV24qIiMCGDRswfvx42NnZoUWLFpg9ezaePHmCGTNm4MKFC0hNTRVUKhFJadKkSVKXQFqkpDwVnaqAZ+iovPjZRKIwSySSJvNUqTN0HTp0gK6uLoYPH4758+ejb9++cHZ25qAoRFrowIEDUpdAWqSkPBXtxPEeOiovfjaRKMwSiaTJPFXqN6aTkxPkcjkCAwOxf/9+7N+/H6dOncKzZ89E1UdE1YSbm5vUJZAWKSlPHOWSKoKfTSQKs0QiaTJPlerQhYeHIykpCQcPHkTPnj3x22+/wc3NDRYWFujatSvmzJmDQ4cOiaqViCR09OhRqUsgLVJSnoqeleMll1Re/GwiUZglEkmTear0NS3169fH8OHD8e233+L8+fNIS0vDDz/8gIyMDKxduxZjxowRUScRSWzBggVSl0BapKQ86RY5K6fPSy6pnPjZRKIwSySSJvMkZJTL27dv4+zZszh37hzOnTuHhw8fQqFQoFGjRujbt6+IlyAiiY0bN07qEkiLlJQnlWkLeMkllRM/m0gUZolE0mSeKvUn0FGjRqFRo0Zo164dpk2bhtDQUAwYMABbt27FnTt3EBcXh/3794uqlYgkFB4eLnUJpEVKylPRSy71eMkllRM/m0gUZolE0mSeKnWG7v79+xg7diz69u2Lvn37wsrKqtg2KSkpqF+/fmVehoiIagE9jnJJRESktkp16C5fvlzi8uzsbPz000/YvXs3Tpw4gczMzMq8DBFVA+3bt5e6BNIiJeWp6GWWHOWSyoufTSQKs0QiaTJPwv4EqlAo8Ouvv2Ly5MmwtrbG2LFj8dNPPyEnJ0fUSxCRhPbu3St1CaRFSsqTftF76HjJJZUTP5tIFGaJRNJknio9KMrly5exe/du7Nu3D0+ePIGRkRGGDh0KDw8PRERE4KuvvhJRJxFJbNmyZVKXQFqkpDzpFrnMUp8dOionfjaRKMwSiaTJPFXoDN29e/fg5eWFNm3aoEePHli/fj169OiBXbt2ISEhAQcPHsTo0aNhYmIiul4ikggnXCWRSspT0T6cLu+ho3LiZxOJwiyRSJrMk9pn6Hr16oWwsDDo6+tj4MCBWLBgAdzd3WFmZlYV9RFRNcEJV0mksvLEe+iovPjZRKIwSyRStZ5YPDQ0FAYGBli4cCG2bduGCRMmsDNHVAt4eHhIXQJpkbLyxHvoqLz42USiMEskkibzpHaHbt26dejatSsWLlyIxo0bY+DAgdiyZQuSk5Oroj4iqib8/PykLoG0SFl5Yn+OyoufTSQKs0QiaTJPanfoZs6ciZCQENy7dw9ffPEF4uLiMHXqVNjY2GDo0KHYvn070tLSqqJWIpLQypUrpS6BtAjzRKIwSyQKs0QiaTJPFb7rvFmzZvj8889x48YNXLx4EbNmzcK1a9eU0xa4ubkhJCREZK1EJKHBgwdLXQJpEeaJRGGWSBRmiUTSZJ4qPW0BAHTt2hVdu3bFihUrcPr0aezevRv+/v549uwZZDJeN0OkDWJjY6UugbQI80SiMEskCrNEImkyT0LHhdbR0cGrr74KPz8/PHnyBHv37sXw4cNFvgQRSSQlJUXqEkiLlJUnGfjHQCoffjaRKMwSiaTJPFXZRD9GRkYYM2YMjhw5UlUvQUQa5OrqKnUJpEVKypNC5d+KYuuJSsLPJhKFWSKRNJknrZu5NTU1FVOnToWlpSXq1KmDl19+GX/++We597958yaGDBkCU1NTWFhY4O2330ZiYmKx7eRyOZYvX47mzZvDyMgIHTp0wN69eyvcZnR0NGQyWYmPffv2qfc/gagK+Pj4SF0CaRHmiURhlkgUZolE0mSehNxDV13I5XIMGzYM165dw8cff4yGDRti/fr16N+/Py5fvoxWrVq9cP9Hjx7B1dUV9erVw7Jly5Ceno4VK1YgPDwcYWFhMDAwUG772Wef4ZtvvsG7776L7t2748iRIxg/fjxkMhnGjh1boTYBYNy4cRg6dKjKsl69egn4v0NUOatXr5a6BNIiJeVJIf/3rNzDvzOQmy+Hvq7W/d2RBONnE4nCLJFIGs2TQovs379fAUBx8OBB5bKEhASFubm5Yty4cWXuP2PGDIWxsbHiwYMHymWnTp1SAFBs2rRJuezRo0cKfX19xaxZs5TL5HK5om/fvoomTZoo8vLy1G7z/v37CgAKb29v9Q+8FBEREQoAioiICGFtUu01fPhwqUsgLVI0Tzl5+Yrvf41UtPn8F0XTT44pH928Tim+/zVSkZOXL2GlVN3xs4lEYZZIpNLyVBXfz7XqT5+HDh2CtbU1Ro4cqVxmaWkJDw8PHDlyBNnZ2S/c/8cff8Tw4cNhb2+vXDZw4EA4OjriwIEDymVHjhxBbm4uZs6cqVwmk8kwY8YMPHr0CBcuXFC7zaKeP3+OnJyc8h84kQYcPXpU6hJIixTmKTdfjqk7LmHVqUhk5uarbJOUno1VpyIxbedl5ObLpSiTagB+NpEozBKJpMk8aVWH7sqVK+jSpQt0dFQPq0ePHsjIyEBkZGSp+8bGxiIhIQHdunUrtq5Hjx64cuWKyuvUqVMHbdu2LbZd4Xp12yy0ePFimJqawsjICN27d8fJkydfcMREmjNx4kSpSyAtUpinDUF3ceZ28fuUgX8HSfntVgI2Bt3VUGVU0/CziURhlkgkTeZJqzp08fHxsLGxKba8cFlcXNwL9y267X/3T05OVp7hi4+Ph7W1dbE59v77Ouq0qaOjg0GDBsHb2xs//fQTVq9ejYSEBLz22mv4+eefX3zgABISEnD9+nWVR1RUVJn7EZUX7y0gkVavXo3cfDl2XIguc4ICGYAdFx7wLB2ViJ9NJAqzRCJpMk/VtkMnl8uRlZVVrodCUfB33MzMTBgaGhZry8jISLm+NIXryrN/eV9HnTbt7e1x4sQJTJ8+HW5ubpg9ezauXLkCS0tLzJ07t9S6C61fvx7Ozs4qD3d3dwBASEgIgoOD4e3tjeTkZOVfDNzc3AAAnp6eiIqKgq+vL/z9/REWFgYvLy9kZGTAw8NDZdsFCxYgPDwce/bswZ49exAeHo4FCxaobOPh4YGMjAx4eXkhLCwM/v7+8PX1RVRUFDw9PVW2nThxIpKTk+Ht7Y3g4GAEBgbCx8cHsbGxmD59usq206dPR2xsLHx8fBAYGMhj0vAxrV27VuuOSRvfp5pyTBs2bMBsr++RlJ5T5gQFCgCJ6dnYeuRMtT4mbXyfasIx9enTR+uOSRvfp5pwTFu3btW6Y9LG96mmHFPRPBU9ppCQEIgmUxT2hqqZoKAgvPzyy+Xa9ubNm2jTpg1MTU0xZswYbN26VWX98ePHMWzYMAQGBmLw4MEltnHp0iV0794dO3bswNtvv62ybt68efD29kZWVhYMDQ0xfPhw3Lx5E3fvql4ClJGRgTp16uDTTz/F119/rVabpZk/fz6++eYbxMTEoEmTJqVul5CQUGwqhKioKLi7uyMiIgJOTk6l7ktUHsHBwejXr5/UZZCWCA4ORmYDR0zfVf5pZTa+1QVDnItf8UC1Gz+bSBRmiUQqLU/Xr1+Hs7Oz0O/n1XbagjZt2mDbtm3l2rbwkkYbGxvlZY5FFS6ztbUts43S9rewsFB2vGxsbHDmzBkoFAqVyy7/+zrqtFkaOzs7AEBycvILO3RWVlawsrJSWVZ4OScvvSQRbt26hYYNG0pdBmmJW7duwdReDzmJD8q9z98x5rguS67Cqqgm4mcTicIskUil5anwe3lZgzWqo9p26Bo1aoRJkyaptU+nTp1w7tw5yOVylYFRQkNDYWJiAkdHx1L3bdy4MSwtLXHp0qVi68LCwtCpUyeV19myZQtu3ryJdu3aqbxO4Xp12yzNvXv3ABSM1qmu8PBwAFBeeklEVJO95St1BURERGLExMSgS5cuQtqqth26ihg1ahQOHTqEw4cPY9SoUQCApKQkHDx4EG5ubipnwwovl2zZsqVy2RtvvIHt27cjJiZGeWbs9OnTiIyMVF43CwAjRoyAp6cn1q9fj3Xr1gEAFAoFNm7ciMaNG6N3795qt5mYmFis0xYbGwtfX1906NChxIFVylLYgT1w4IBKx5NIXYWX7wYEBMDBwUHqcqiGY55IFGaJRGGWSKQX5Sk7OxsxMTFCL+/Vug5dz549MXnyZNy4cQMNGzbE+vXrkZ+fj8WLF6ts+8orrwAAoqOjlcsWLFiAgwcP4uWXX8bs2bORnp4Ob29vtG/fHpMnT1Zu16RJE8yZMwfe3t7Izc1F9+7dERAQgHPnzmH37t3Q1dVVu8158+bh7t27eOWVV2Bra4vo6Ghs2rQJz58/x/fff1+h/x9mZmYAgHbt2vEeOhLCwcGBWSJhmCcShVkiUZglEqm0PIk6M1dIqzp0urq6OH78OD7++GOsWbMGmZmZ6N69O/z8/NC6desy97ezs0NwcDA+/PBDfPrppzAwMMCwYcOwcuXKYve6ffPNN6hfvz42bdoEPz8/tGrVCrt27cL48eMr1OagQYOwceNG+Pj4ICUlBebm5nB1dcXnn38u/E0nIiIiIiLtoFUdOgCoX78+tmzZgi1btrxwu6Jn5opycnLCiRMnynwdHR0dzJ8/H/Pnzy9z2/K0OW7cOIwbN67MtoiIiIiIiApV23noiIiIiIiI6MW0rkOXnp6ORYsWYciQIbCwsIBMJoOfn1+5909NTcXUqVNhaWmJOnXq4OWXX8aff5Y8T9JPP/2ELl26wMjICPb29li0aBHy8vI00mZ5WFpaYtGiRRUaIZOoKGaJRGKeSBRmiURhlkgkTeep2k4sXlHR0dFo3rw57O3t0aJFCwQFBWHbtm3lmgJBLpejb9++uHbtGj7++GPloCoxMTG4fPkyWrVqpdz2l19+wbBhw9C/f3+MGzcO4eHh8PHxwdSpU7Fhw4YqbZOIiIiIiAjQwg5ddnY2UlJS0KhRI1y6dAndu3cvd4fuwIEDGDNmDA4ePKic9iAxMRGOjo547bXXsGfPHuW2Tk5O0NfXx6VLl6CnV3Ar4ueff45ly5bhxo0baNOmTZW1SUREREREBGjhoCiGhoZo1KhRhfY9dOgQrK2tMXLkSOUyS0tLeHh4YNeuXcjOzoahoSFu3LiBGzduwMfHR9nxAoCZM2di6dKlOHToED7//PMqa7O8UlNTERwcDDs7u2KjdBIRERERkWYVnYfO3NxcSJta16GrjCtXrqBLly7Q0VG9tbBHjx7YvHkzIiMj0b59e1y5cgUA0K1bN5XtbG1t0aRJE+X6qmqzvIKDg+Hu7q72fkREREREVHUCAgIwYsQIIW2xQ1dEfHw8XF1diy23sbEBAMTFxaF9+/aIj49XWf7fbePi4qq0zZIkJCQgMTFRZVlOTg4AoOH/PoN+fdsX7k9EVN15j+qITvbmUpdB1czixYuxaNEiqcsgLcAskUil5SkqKgru7u6ws7MT9lpaN8plZWRmZpZ4aaKRkZFyfdH/lrZt4fqqarMk69evh7Ozs8rDw8MDAKBf3xYGlk354IMPPmr04/HTLMTExCAoKAjm5uZYu3YtnJyc8Omnn8LJyQlr166Fubk5goKCEBMTg6SkJBw/fhw2NjZYvny5yrZbtmyBoaEhQkNDERkZiefPn+Pw4cNo3rw5Fi9erLLt7t27IZfLce3aNVy7dg1yuRy7d+9W2Wbx4sVo3rw5Dh8+jOfPnyMyMhKhoaEwNDTEli1bVLZdvnw5bGxscPz4cSQlJfGYKnlMKSkpWndM2vg+1YRjWr16tdYdkza+TzXlmIrmqegxPX78uNTv/BWldYOiFKXuoCimpqYYM2YMtm7dqrL8+PHjGDZsGAIDAzF48GCsWLECH3/8MR4+fFisd92jRw/o6uriwoULVdZmSUo6Q1f4FwCb//OBgWXTMo+fiKg62/tuT/Rq2UDqMqia8fHxwaxZs6Qug7QAs0QilZan69evw9nZGREREXBychLyWrzksggbGxvlpY9FFS6ztbVVble4/L+dr/j4ePTo0aNK2yyJlZUVrKysXrgNEVFN1dDUAN2a1Ze6DKqGWrZsKXUJpCWYJRJJk3niJZdFdOrUCX/++SfkcrnK8tDQUJiYmMDR0VG5HVBwBrCouLg4PHr0SLm+qtokIqptJvZqBn1d/sqi4oyNjaUugbQEs0QiaTJPtfa3Y3x8PG7duoXc3FzlslGjRuHJkyc4fPiwcllSUhIOHjwINzc35bWuTk5OaNOmDTZv3oz8/Hzlths2bIBMJlPON1dVbRIR1Sb9HC0xvT//ck4lCwsLk7oE0hLMEomkyTxp5SWX69atQ2pqqnJkyKNHj+LRo0cAgPfffx/16tXD/PnzsX37dty/fx/NmjUDUND56tmzJyZPnowbN26gYcOGWL9+PfLz87F48WKV1/D29sbrr7+OQYMGYezYsYiIiMC6devwzjvvoG3btsrtqqJNIqLaQF9Xhhn9WuL9V1rx7ByVasqUKVKXQFqCWSKRNJknrRwUpVmzZnjw4EGJ6wo7cJMmTSrWoQOAlJQUfPzxxwgICEBmZia6d++OFStWFJsfDiiYP2Lx4sW4efMmLC0tMWnSJHzxxRfQ19dX2a4q2iyPwpsux36zDzILeygUQL5cDl1dHeA//87Nz0dWrhxG+rrQ1yu+nvtpz34VfY3wmzfRrLlDtTwm7lfz9ou4eRPO7dqqLM/KzsOVR08BADP7t4Tnq47syFGZJk6ciO3bt0tdBmkBZolEKi1PVTEoilZ26KhAVQSGiKiq3EtMx4CVwQCA78d2wohOjSWuiIiISKyq+H7OP30SUbm4ublJXQJpkZLyVPRsXG4+/9ZI5cPPJhKFWSKRNJknduiIqFyOHj0qdQmkRUrKk66OTPnv/P+MDExUGn42kSjMEomkyTyxQ0dE5eLp6Sl1CaRFSsqTXpEOXZ6cZ+iofPjZRKIwSySSJvPEDh0RlcusWbOkLoG0SEl50ityyWUeL7mkcuJnE4nCLJFImswTO3REVC5nz56VugTSIiXlSZdn6KgC+NlEojBLJJIm88QOHRGVS/369aUugbRISXnS4z10VAH8bCJRmCUSSZN5YoeOiMqlcWMOIU/ilJQnPd1/O3Qc5ZLKi59NJAqzRCJpMk/s0BFRuZw4cULqEkiLlJQnPZ1/fyXl85JLKid+NpEozBKJpMk8sUNHROUyd+5cqUsgLVJSnopcccl76Kjc+NlEojBLJJIm8yRJh65Xr1749NNPcezYMaSmpkpRAhGpadKkSVKXQFqkpDzJZDLo/3PZZV4+76Gj8uFnE4nCLJFImsyTJB06c3NzbNy4Ea+//joaNmyIDh06YNasWdi/fz/i4uKkKImIynDgwAGpSyAtUlqeCke65CWXVF78bCJRmCUSSZN5kqRD98svvyAlJQWXL1/GqlWr0KZNGxw+fBjjxo2DnZ0dWrZsicmTJ0tRGhGVws3NTeoSSIuUlqfC++g4KAqVFz+bSBRmiUTSZJ5kCoWiWvzWzMnJwe7du/Htt98iMjISMpkM+fn5UpdVo12/fh3Ozs6IiIiAk5OT1OUQEZWp05KTSM3IxcReTbF4hLPU5RAREQlVFd/PJRsUJT09HSdPnsTChQvRv39/mJub45133oGuri6mTZuGnTt3SlUaEZVgwYIFUpdAWqS0PBXORcdBUai8+NlEojBLJJIm86SnsVcqolu3brh27RpkMhk6duwIV1dXzJkzB3379kWDBg2kKImIyjBu3DipSyAtUlqeCu+hy+Mll1RO/GwiUZglEkmTeZLkDN2ff/4JhUKBESNGYObMmZgxYwbc3d3ZmSOqxsLDw6UugbRIaXkqvIeOZ+iovPjZRKIwSySSJvMkyRm6S5cu4dy5czh37hzmz5+PxMREWFlZoW/fvspHx44dIZPJym6MiIi0hp5u4SiXnLaAiIioPCQ5Q9elSxfMnj0bhw4dwuPHj3Hz5k189dVXMDExwcqVK9G1a1dYWFhIURoRlaJ9+/ZSl0BapLQ8FV5ymcszdFRO/GwiUZglEkmTeZJsUJRCmZmZePToEWJiYvDw4UMkJiZCoVAgPT1d6tKIqIi9e/dKXQJpkdLypP/PJZf5vIeOyomfTSQKs0QiaTJPkkxbcOzYMZw9exbnzp3Dn3/+idzcXBgZGaFHjx7KSy579+4NU1NTTZemVThtARHVNEO/P4cb8U8xsK01tkzsJnU5REREQmnNtAWvv/46tmzZgoYNG8LLywu///470tLSEBQUBC8vLwwaNIidOaJqhhOukkilTiyuWzhtAe+ho/LhZxOJwiyRSJrMkySDoly7dg3Ozs4c9ISoBjl69KjUJZAWKS1PhfPQ5fMeOionfjaRKMwSiaTJPElyhq59+/bszBHVMB4eHlKXQFqktDwppy3gPXRUTvxsIlGYJRJJk3mSfFAUIqoZ/Pz8pC6BtEhpeVJOLM5LLqmc+NlEojBLJJIm88QOHRGVy8qVK6UugbRIaXn69x46nqGj8uFnE4nCLJFImswTO3REVC6DBw+WugTSIqXlqfAeOl5ySeXFzyYShVkikTSZJ3boiKhcYmNjpS6BtEhpedItvIeOZ+ionPjZRKIwSySSJvPEDh0RlUtKSorUJZAWKS1P+rqFo1zyHjoqH342kSjMEomkyTxpZYcuOzsbn3zyCWxtbWFsbAwXFxecOnWqXPvu27cPXbp0gZGRESwtLTFlyhQkJSUV2+7JkyeYPHkyrKysYGxsjC5duuDgwYOValMmk5X4+Oabb9T7H0BUBVxdXaUugbRIaXnS5SWXpCZ+NpEozBKJpMk8STIPXVWbNGkSDh06hDlz5qBVq1bw8/PD0KFDcebMGfTp06fU/TZs2ICZM2filVdewapVq/Do0SN8//33uHTpEkJDQ2FkZAQAePr0Kfr06YMnT55g9uzZaNSoEQ4cOAAPDw/s3r0b48ePV7vNQq+++iomTJigsqxz584C/+8QVYyPjw9Wr14tdRmkJUrPU0FHLi41A+N/uACFouBsna6uDvCCf+fm5yMrVw4jfV3o64nblvtV//0iIyPRsuWTal9nVexXnWurifvdvhMFW7u4al8n96sZtUXeiYKjY2Kx/TITHgj/nSpTKBRa9WfQsLAwuLi4wNvbGx999BEAICsrC87OzrCyssL58+dL3C8nJwfW1tbo0KEDgoKClPPkHTt2DG5ublizZg3ef/99AIC3tzfmzZuH06dPY8CAAQAAuVyOnj17IiYmBg8ePICBgYFabQIFZ+hmzZqFdevWCfl/cf36dTg7OyMiIgJOTk5C2iQiqgoZOXn4P7+L+ONestSlEBERVZmcxAeI950l9Pu51l1yeejQIejq6mLq1KnKZUZGRpgyZQouXLiAmJiYEveLiIhAamoqxowZozLp+fDhw2Fqaop9+/Ypl507dw6WlpbKzhwA6OjowMPDA48fP0ZwcLDabRaVmZmJrKysiv0PIKoibm5uUpdAWqRonjJy8vDSN7+xM0dERFQBWtehu3LlChwdHWFmZqayvEePHgCAq1evlrhfdnY2AMDY2LjYOmNjY1y5cgXyf27Sz87OLnE7ExMTAMDly5fVbrOQn58f6tSpA2NjY7Rr1w579uwp9ViJNOno0aNSl0BapGie3tl+CSkZuRJWQ0REVHNpXYcuPj4eNjY2xZYXLouLiytxv1atWkEmk+H3339XWX779m0kJiYiMzNTOVpN69at8ejRIzx4oHoN7Llz5wD8O0ypOm0CQO/evbF06VIEBARgw4YN0NXVxZtvvokNGzaUedwJCQm4fv26yiMqKqrM/YjKa+LEiVKXQFqkME8ZOXk4f/dviashIiKqubSuQ5eZmQlDQ8NiywsHH8nMzCxxv4YNG8LDwwPbt2/HypUrce/ePZw7dw5jxoyBvr6+yr7vvPMOdHV14eHhgfPnz+Pu3bv4+uuv4e/vr7KdOm0CwO+//47Zs2fj9ddfx/Tp03H58mU4OztjwYIFpdZdaP369XB2dlZ5uLu7AwBCQkIQHBwMb29vJCcnK79IFV7y5OnpiaioKPj6+sLf3x9hYWHw8vJCRkYGPDw8VLZdsGABwsPDsWfPHuzZswfh4eFYsGCByjYeHh7IyMiAl5cXwsLC4O/vD19fX0RFRcHT01Nl24kTJyI5ORne3t4IDg5GYGAgfHx8EBsbi+nTp6tsO336dMTGxsLHxweBgYE8Jg0f0xdffKF1x6SN71NNOaalS5fCy8sL3x4MAREREVWc1g2K4uzsDGtra5w+fVpl+Y0bN+Dk5ISNGzdi2rRpJe6blpaGCRMm4KefflIue+utt5CRkYHDhw8jJSUF5ubmAAru1Zs+fTr+/rvgL8uNGjXCokWLMGPGDMyePRvfffed2m2WZNOmTZg+fTrOnTv3whE6ExISkJiYqLIsKioK7u7uHBSFhPD29sbHH38sdRmkJQrz9PXxG9h09r7U5RAREWlEVQyKonXTFtjY2JQ4M3t8fDwAwNbWttR969WrhyNHjuDhw4eIjo5G06ZN0bRpU/Tu3RuWlpYqHa9Ro0bh9ddfx7Vr15Cfn48uXbogKCgIAODo6FihNktiZ2cHAEhOfvFgAVZWVrCysnrhNkSVUXgfKpEIhXmyqmtUxpZERET0IlrXoevUqRPOnDmDp0+fqgyMEhoaqlxfFnt7e9jb2wMAUlNTcfnyZbzxxhvFtjMwMED37t2Vz3/99VcAwMCBAyvc5n/du3cPAGBpaVnmtv9VOCgL76UjEW7duoWGDRtKXQZpicI8daybh5xE8XPyEBERVUe5KQXjeRR+TxdCoWX++OMPBQCFt7e3cllWVpbCwcFB4eLiolz24MEDxc2bN8tsb/r06QodHR1FWFjYC7eLjIxU1K1bVzF8+PAKtZmQkFBsu6dPnypatmypaNiwoSI7O7vMdv/Lz89PgYJZevnggw8++OCDDz744IOPavIICAhQ+7t9abTuDJ2LiwtGjx6N+fPnIyEhAQ4ODti+fTuio6OxdetW5XYTJkxAcHAwFEVuIfzmm28QEREBFxcX6OnpISAgACdPnsRXX32lciYOANq1a4fRo0fD3t4e9+/fx4YNG2BhYYGNGzeqbFfeNn18fBAQEAA3NzfY29sjPj4evr6+ePjwIXbu3AkDAwO1/18UXvp54MABtGvXTu39iQoV3o8ZEBAABwcHqcuhGo55IlGYJRKFWSKRXpSn7OxsxMTEoF+/fsJeT+s6dACwY8cOLFy4EDt37kRKSgo6dOiAY8eOwdXV9YX7tW/fHv7+/vjpp5+Qn5+PDh064MCBAxg9enSxbTt27Iht27bhyZMnytEsFy9eXOw+tvK2+dJLL+H8+fPYsmUL/v77b9SpUwc9evSAr6+vygTm6ii85LRdu3YcFIWEcHBwYJZIGOaJRGGWSBRmiUQqLU9dunQR+jpa2aEzMjKCt7c3vL29S92mcACTooYNG4Zhw4aV6zX27t1bru3K2+arr76KV199tVxtEhERERERAVo4Dx0REREREVFtwQ4dERERERFRDcUOnRaztLTEokWLKjTlAVFRzBKJxDyRKMwSicIskUiazpNMUXSYRyIiIiIiIqoxeIaOiIiIiIiohmKHjoiIiIiIqIZih46IiIiIiKiGYoeOiIiIiIiohmKHjoiIiIiIqIZih04LZWdn45NPPoGtrS2MjY3h4uKCU6dOSV0WVWMXL17Ee++9BycnJ9SpUwf29vbw8PBAZGRksW1v3ryJIUOGwNTUFBYWFnj77beRmJgoQdVUUyxduhQymQzOzs7F1p0/fx59+vSBiYkJGjVqhA8++ADp6ekSVEnV2Z9//onXX38dFhYWMDExgbOzM9asWaOyDbNEZblz5w7Gjh2LJk2awMTEBG3atMGSJUuQkZGhsh2zREWlp6dj0aJFGDJkCCwsLCCTyeDn51fituX9jiSXy7F8+XI0b94cRkZG6NChA/bu3VvhGvUqvCdVW5MmTcKhQ4cwZ84ctGrVCn5+fhg6dCjOnDmDPn36SF0eVUPffvstfv/9d4wePRodOnTA48ePsW7dOnTp0gV//PGH8ov4o0eP4Orqinr16mHZsmVIT0/HihUrEB4ejrCwMBgYGEh8JFTdPHr0CMuWLUOdOnWKrbt69SpeeeUVtG3bFqtWrcKjR4+wYsUK3LlzB7/88osE1VJ1dPLkSbi5uaFz585YuHAhTE1NcffuXTx69Ei5DbNEZYmJiUGPHj1Qr149vPfee7CwsMCFCxewaNEiXL58GUeOHAHALFFxSUlJWLJkCezt7dGxY0cEBQWVuJ0635E+++wzfPPNN3j33XfRvXt3HDlyBOPHj4dMJsPYsWPVL1JBWiU0NFQBQOHt7a1clpmZqWjZsqWiV69eElZG1dnvv/+uyM7OVlkWGRmpMDQ0VLz55pvKZTNmzFAYGxsrHjx4oFx26tQpBQDFpk2bNFYv1RxjxoxRDBgwQNGvXz+Fk5OTyrrXXntNYWNjo0hLS1Mu++GHHxQAFCdOnNB0qVQNpaWlKaytrRX/+9//FPn5+aVuxyxRWZYuXaoAoIiIiFBZPmHCBAUARXJyskKhYJaouKysLEV8fLxCoVAoLl68qACg2LZtW7Htyvsd6dGjRwp9fX3FrFmzlMvkcrmib9++iiZNmijy8vLUrpGXXGqZQ4cOQVdXF1OnTlUuMzIywpQpU3DhwgXExMRIWB1VV7179y52dq1Vq1ZwcnLCzZs3lct+/PFHDB8+HPb29splAwcOhKOjIw4cOKCxeqlmOHv2LA4dOoTvvvuu2LqnT5/i1KlTeOutt2BmZqZcPmHCBJiamjJPBADYs2cPnjx5gqVLl0JHRwfPnz+HXC5X2YZZovJ4+vQpAMDa2lpluY2NDXR0dGBgYMAsUYkMDQ3RqFGjMrcr73ekI0eOIDc3FzNnzlQuk8lkmDFjBh49eoQLFy6oXSM7dFrmypUrcHR0VPkgAoAePXoAKLiUgKg8FAoFnjx5goYNGwIAYmNjkZCQgG7duhXbtkePHrhy5YqmS6RqLD8/H++//z7eeecdtG/fvtj68PBw5OXlFcuTgYEBOnXqxDwRAODXX3+FmZkZYmNj0bp1a5iamsLMzAwzZsxAVlYWAGaJyqd///4AgClTpuDq1auIiYnB/v37sWHDBnzwwQeoU6cOs0QVps53pCtXrqBOnTpo27Ztse0K16uLHTotEx8fDxsbm2LLC5fFxcVpuiSqoXbv3o3Y2FiMGTMGQEG2AJSar+TkZGRnZ2u0Rqq+Nm7ciAcPHsDLy6vE9WXliZ9VBBQMYpGXl4cRI0Zg8ODB+PHHH/F///d/2LhxIyZPngyAWaLyGTJkCLy8vHDq1Cl07twZ9vb2GDt2LN5//32sXr0aALNEFafOd6T4+HhYW1tDJpMV2w6o2Hd1DoqiZTIzM2FoaFhsuZGRkXI9UVlu3bqFWbNmoVevXpg4cSKAf7NTVr5KWk+1y99//40vvvgCCxcuhKWlZYnblJUnflYRUDC6XEZGBqZPn64c1XLkyJHIycnBpk2bsGTJEmaJyq1Zs2ZwdXXFG2+8gQYNGuDnn3/GsmXL0KhRI7z33nvMElWYOt+RquK7Ojt0WsbY2LjEsySFl6YYGxtruiSqYR4/foxhw4ahXr16ynsygX+zw3xRWT7//HNYWFjg/fffL3WbsvLELBHwb07GjRunsnz8+PHYtGkTLly4ABMTEwDMEr3Yvn37MHXqVERGRqJJkyYACv44IJfL8cknn2DcuHH8XKIKU+c7UlV8V+cll1rGxsZGedq3qMJltra2mi6JapC0tDS89tprSE1NRWBgoEpeCi8FKC1fFhYWPDtHuHPnDjZv3owPPvgAcXFxiI6ORnR0NLKyspCbm4vo6GgkJyeXmSd+VhHw7++s/w5kYWVlBQBISUlhlqhc1q9fj86dOys7c4Vef/11ZGRk4MqVK8wSVZg635FsbGzw+PFjKBSKYtsBFfuuzg6dlunUqRMiIyOVozkVCg0NVa4nKklWVhbc3NwQGRmJY8eOoV27dirrGzduDEtLS1y6dKnYvmFhYcwWASi4MVwul+ODDz5A8+bNlY/Q0FBERkaiefPmWLJkCZydnaGnp1csTzk5Obh69SrzRACArl27AijIVVGF95hYWloyS1QuT548QX5+frHlubm5AIC8vDxmiSpMne9InTp1QkZGhsoo4kDlvquzQ6dlRo0ahfz8fGzevFm5LDs7G9u2bYOLiwvs7OwkrI6qq/z8fIwZMwYXLlzAwYMH0atXrxK3e+ONN3Ds2DGV6S9Onz6NyMhIjB49WlPlUjXm7OwMf3//Yg8nJyfY29vD398fU6ZMQb169TBw4EDs2rULz549U+6/c+dOpKenM08EAPDw8AAAbN26VWX5li1boKenh/79+zNLVC6Ojo64cuUKIiMjVZbv3bsXOjo66NChA7NElVLe70gjRoyAvr4+1q9fr1ymUCiwceNGNG7cGL1791b7tWWK/57voxrPw8MD/v7+8PT0hIODA7Zv346wsDCcPn0arq6uUpdH1dCcOXPw/fffw83NTfkFqqi33noLABATE4POnTvD3Nwcs2fPRnp6Ory9vdGkSRNcvHiRl1xSqfr374+kpCREREQol/3555/o3bs32rVrh6lTp+LRo0dYuXIlXF1dceLECQmrpepkypQp8PX1hYeHB/r164egoCAcPHgQ8+fPx7JlywAwS1S2s2fPYsCAAWjQoAHee+89NGjQAMeOHcMvv/yCd955Bz/88AMAZolKtm7dOqSmpiIuLg4bNmzAyJEj0blzZwDA+++/j3r16qn1HWnevHnw9vbG1KlT0b17dwQEBODnn3/G7t27MX78ePULVHsqcqr2MjMzFR999JGiUaNGCkNDQ0X37t0VgYGBUpdF1Vi/fv0UAEp9FBUREaEYNGiQwsTERGFubq548803FY8fP5aocqop+vXrp3Byciq2/Ny5c4revXsrjIyMFJaWlopZs2Ypnj59KkGFVF3l5OQovvzyS0XTpk0V+vr6CgcHB8Xq1auLbccsUVlCQ0MVr732mqJRo0YKfX19haOjo2Lp0qWK3Nxcle2YJfqvpk2blvod6f79+8rtyvsdKT8/X7Fs2TJF06ZNFQYGBgonJyfFrl27Klwfz9ARERERERHVULyHjoiIiIiIqIZih46IiIiIiKiGYoeOiIiIiIiohmKHjoiIiIiIqIZih46IiIiIiKiGYoeOiIiIiIiohmKHjoiIiIiIqIZih46IiIiIiKiGYoeOiIiIiIiohmKHjoiIiIiIqIZih46IiKicJk2ahGbNmkldhtKXX34JmUwGmUwGU1NTjb9+p06dlK8/fPhwjb8+EREBelIXQEREJCWZTFau7c6cOVPFlVTczp07oa+vr/HXXbZsGZKTk+Hp6anx1yYiogLs0BERUa22c+dOlec7duzAqVOnii1v27YtfvjhB8jlck2WVy5vvfWWJK87dOhQAMDnn38uyesTERE7dEREVMv9tzP0xx9/4NSpU5J1koiIiNTBe+iIiIjK6b/30EVHR0Mmk2HFihXw8fFBixYtYGJigkGDBiEmJgYKhQJeXl5o0qQJjI2NMWLECCQnJxdr95dffkHfvn1Rp04d1K1bF8OGDcP169crVWuzZs0wfPhwBAUFoVu3bjA2Nkb79u0RFBQEADh8+DDat28PIyMjdO3aFVeuXFHZ//Hjx5g8eTKaNGkCQ0ND2NjYYMSIEYiOjq5UXUREJBbP0BEREVXS7t27kZOTg/fffx/JyclYvnw5PDw8MGDAAAQFBeGTTz5BVFQU1q5di48++gi+vr7KfXfu3ImJEydi8ODB+Pbbb5GRkYENGzagT58+uHLlSqUGYYmKisL48eMxbdo0vPXWW1ixYgXc3NywceNGLFiwADNnzgQAfP311/Dw8MDt27eho1Pwt9433ngD169fx/vvv49mzZohISEBp06dwsOHD6vVwDBERLUdO3RERESVFBsbizt37qBevXoAgPz8fHz99dfIzMzEpUuXoKdX8Os2MTERu3fvxoYNG2BoaIj09HR88MEHeOedd7B582ZlexMnTkTr1q2xbNkyleXqun37Ns6fP49evXoBANq1a4fBgwfj3Xffxa1bt2Bvbw8AqF+/PqZNm4azZ8+if//+SE1Nxfnz5+Ht7Y2PPvpI2d78+fMrXAsREVUNXnJJRERUSaNHj1Z25gDAxcUFQMH9eYWducLlOTk5iI2NBQCcOnUKqampGDduHJKSkpQPXV1duLi4VHpkzXbt2ik7c0XrGjBggLIzV3T5vXv3AADGxsYwMDBAUFAQUlJSKlUDERFVLZ6hIyIiqqSinSMAys6dnZ1dicsLO0l37twBUNDBKomZmZkkdRkaGuLbb7/F3LlzYW1tjZ49e2L48OGYMGECGjVqVKmaiIhILHboiIiIKklXV1et5QqFAgCUUyDs3LmzxI5S0bN7mqwLAObMmQM3NzcEBATgxIkTWLhwIb7++mv89ttv6Ny5c6XqIiIicdihIyIikkjLli0BAFZWVhg4cKDE1RTXsmVLzJ07F3PnzsWdO3fQqVMnrFy5Ert27ZK6NCIi+gfvoSMiIpLI4MGDYWZmhmXLliE3N7fY+sTERAmqAjIyMpCVlaWyrGXLlqhbty6ys7MlqYmIiErGM3REREQSMTMzw4YNG/D222+jS5cuGDt2LCwtLfHw4UP8/PPPeOmll7Bu3TqN1xUZGYlXXnkFHh4eaNeuHfT09ODv748nT55g7NixGq+HiIhKxw4dERGRhMaPHw9bW1t888038Pb2RnZ2Nho3boy+ffti8uTJktRkZ2eHcePG4fTp09i5cyf09PTQpk0bHDhwAG+88YYkNRERUclkiqJ3QBMREVGN8eWXX2Lx4sVITEyETCZDgwYNNPr6qampyMvLQ5cuXdChQwccO3ZMo69PRES8h46IiKjGs7S0RNOmTTX+uv3794elpSViYmI0/tpERFSAZ+iIiIhqqHv37iknA9fT00P//v01+vqhoaF49uwZgIJOZceOHTX6+kRExA4dERERERFRjcVLLomIiIiIiGooduiIiIiIiIhqKHboiIiIiIiIaih26IiIiIiIiGooduiIiIiIiIhqKHboiIiIiIiIaih26IiIiIiIiGooduiIiIiIiIhqKHboiIiIiIiIaih26IiIiIiIiGooduiIiIiIiIhqqP8H42eDiDRGRGEAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fname_snip = \"detailed\"\n", + "\n", + "pre_spike_times = [40., 100.] # [ms]\n", + "post_spike_times = [25., 75.] # [ms]\n", + "\n", + "# run the simulation\n", + "timevec, t_hist, third_factor_trace, w_hist = run_synapse_test(neuron_model_name=neuron_model_name,\n", + " synapse_model_name=synapse_model_name,\n", + " resolution=.1, # [ms]\n", + " delay=1., # [ms]\n", + " pre_spike_times=pre_spike_times,\n", + " post_spike_times=post_spike_times,\n", + " sim_time=101.,\n", + " fname_snip=fname_snip)\n", + "\n", + "# verify that weight stays zero: buffering ensures that the value of I_dend at the right time is used\n", + "np.testing.assert_allclose(w_hist, 0.)\n", + "\n", + "# idx = np.where(np.abs(third_factor_trace) < 1E-12)[0] # find where third_factor_trace is (almost) zero\n", + "# times_dw_should_be_zero = timevec[idx]\n", + "# assert len(times_dw_should_be_zero) > 0 # make sure we have > 0 datapoints to check\n", + "# for time_dw_should_be_zero in times_dw_should_be_zero[1:]:\n", + "# _idx = np.argmin((time_dw_should_be_zero - np.array(t_hist))**2)\n", + "# np.testing.assert_allclose(t_hist[_idx], time_dw_should_be_zero)\n", + "# np.testing.assert_allclose(0., np.abs(w_hist[_idx - 1] - w_hist[_idx])) # make sure that weight does not change appreciably\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "References\n", + "----------\n", + "\n", + "...\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/models/neurons/iaf_psc_exp_dend_neuron.nestml b/models/neurons/iaf_psc_exp_dend_neuron.nestml index f6bea1a0d..4f6195381 100644 --- a/models/neurons/iaf_psc_exp_dend_neuron.nestml +++ b/models/neurons/iaf_psc_exp_dend_neuron.nestml @@ -35,7 +35,7 @@ See also iaf_cond_exp """ -model iaf_psc_exp_dend: +model iaf_psc_exp_dend_neuron: state: V_m mV = E_L # Membrane potential diff --git a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 index e8220f542..b745e98be 100644 --- a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 +++ b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 @@ -980,6 +980,37 @@ const {{ type_symbol_printer.print(var_symbol.type_symbol) }} {{variable_name}}_ } {%- if state_vars_that_need_continuous_buffering | length > 0 %} +void {{neuronName}}::get_continuous_variable_history( double t1, + double t2, + std::deque< continuous_variable_histentry_{{ neuronName }} >::iterator* start, + std::deque< continuous_variable_histentry_{{ neuronName }} >::iterator* finish ) +{ + *finish = continuous_variable_history_.end(); + if ( continuous_variable_history_.empty() ) + { + *start = *finish; + return; + } + else + { + std::deque< continuous_variable_histentry_{{ neuronName }} >::iterator runner = continuous_variable_history_.begin(); + + // To have a well defined discretization of the integral, we make sure + // that we exclude the entry at t1 but include the one at t2 by subtracting + // a small number so that runner->t_ is never equal to t1 or t2. + while ( ( runner != continuous_variable_history_.end() ) and runner->t_ - 1.0e-6 < t1 ) + { + ++runner; + } + *start = runner; + while ( ( runner != continuous_variable_history_.end() ) and runner->t_ - 1.0e-6 < t2 ) + { + ( runner->access_counter_ )++; + ++runner; + } + *finish = runner; + } +} void {{neuronName}}::write_continuous_variable_history(nest::Time const &t, {%- for state_var in state_vars_that_need_continuous_buffering %} @@ -988,6 +1019,20 @@ void {{neuronName}}::write_continuous_variable_history(nest::Time const &t, { const double t_ms = t.get_ms(); + // prune all entries from history which are no longer needed + // except the penultimate one. we might still need it. + while ( continuous_variable_history_.size() > 1 ) + { + if ( continuous_variable_history_.front().access_counter_ >= n_incoming_ ) + { + continuous_variable_history_.pop_front(); + } + else + { + break; + } + } + continuous_variable_history_.push_back( continuous_variable_histentry_{{ neuronName }}( t_ms, {%- for state_var in state_vars_that_need_continuous_buffering %} {{ state_var }}{% if not loop.last %}, {% endif %} diff --git a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 index c6909725e..59691fb7f 100644 --- a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 +++ b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 @@ -357,7 +357,13 @@ public: const double {{ state_var }}{% if not loop.last %}, {% endif %} {%- endfor %}); - std::vector< continuous_variable_histentry_{{ neuronName }} > continuous_variable_history_; + void get_continuous_variable_history( double t1, + double t2, + std::deque< continuous_variable_histentry_{{ neuronName }} >::iterator* start, + std::deque< continuous_variable_histentry_{{ neuronName }} >::iterator* finish ); + + + std::deque< continuous_variable_histentry_{{ neuronName }} > continuous_variable_history_; {%- endif %} {%- endif %} diff --git a/tests/nest_tests/third_factor_stdp_synapse_test.py b/tests/nest_tests/third_factor_stdp_synapse_test.py deleted file mode 100644 index 9f9aa1227..000000000 --- a/tests/nest_tests/third_factor_stdp_synapse_test.py +++ /dev/null @@ -1,243 +0,0 @@ -# -*- coding: utf-8 -*- -# -# third_factor_stdp_synapse_test.py -# -# This file is part of NEST. -# -# Copyright (C) 2004 The NEST Initiative -# -# NEST is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 2 of the License, or -# (at your option) any later version. -# -# NEST is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with NEST. If not, see . -import numpy as np -import os -import unittest - -import nest - -from pynestml.codegeneration.nest_tools import NESTTools -from pynestml.frontend.pynestml_frontend import generate_nest_target - -try: - import matplotlib - matplotlib.use("Agg") - import matplotlib.ticker - import matplotlib.pyplot as plt - TEST_PLOTS = True -except Exception: - TEST_PLOTS = False - - -class NestThirdFactorSTDPSynapseTest(unittest.TestCase): - - neuron_model_name = "iaf_psc_exp_dend__with_third_factor_stdp_synapse" - synapse_model_name = "third_factor_stdp_synapse__with_iaf_psc_exp_dend" - - post_trace_var = "I_dend" - - def setUp(self): - r"""Generate the neuron model code""" - - codegen_opts = {"neuron_synapse_pairs": [{"neuron": "iaf_psc_exp_dend", - "synapse": "third_factor_stdp_synapse", - "post_ports": ["post_spikes", - ["I_post_dend", "I_dend"]]}]} - - if not NESTTools.detect_nest_version().startswith("v2"): - codegen_opts["neuron_parent_class"] = "StructuralPlasticityNode" - codegen_opts["neuron_parent_class_include"] = "structural_plasticity_node.h" - - # generate the "jit" model (co-generated neuron and synapse), that does not rely on ArchivingNode - files = [os.path.join("models", "neurons", "iaf_psc_exp_dend_neuron.nestml"), - os.path.join("models", "synapses", "third_factor_stdp_synapse.nestml")] - input_path = [os.path.realpath(os.path.join(os.path.dirname(__file__), os.path.join( - os.pardir, os.pardir, s))) for s in files] - generate_nest_target(input_path=input_path, - target_path="/tmp/nestml-jit", - logging_level="INFO", - module_name="nestml_jit_module", - codegen_opts=codegen_opts) - - def test_nest_stdp_synapse(self): - - fname_snip = "" - - post_spike_times = np.sort(np.unique(1 + np.round(500 * np.sort(np.abs(np.random.randn(500)))))) # [ms] - pre_spike_times = np.sort(np.unique(1 + np.round(500 * np.sort(np.abs(np.random.randn(500)))))) # [ms] - - self.run_synapse_test(neuron_model_name=self.neuron_model_name, - synapse_model_name=self.synapse_model_name, - resolution=.5, # [ms] - delay=1.5, # [ms] - pre_spike_times=pre_spike_times, - post_spike_times=post_spike_times, - sim_time=400., - fname_snip=fname_snip) - - def run_synapse_test(self, neuron_model_name, - synapse_model_name, - resolution=1., # [ms] - delay=1., # [ms] - sim_time=None, # if None, computed from pre and post spike times - pre_spike_times=None, - post_spike_times=None, - fname_snip=""): - - if pre_spike_times is None: - pre_spike_times = [] - - if post_spike_times is None: - post_spike_times = [] - - if sim_time is None: - sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 5 * delay - - nest_version = NESTTools.detect_nest_version() - - nest.set_verbosity("M_ALL") - nest.ResetKernel() - nest.Install("nestml_jit_module") - - print("Pre spike times: " + str(pre_spike_times)) - print("Post spike times: " + str(post_spike_times)) - - nest.set_verbosity("M_WARNING") - - nest.ResetKernel() - nest.SetKernelStatus({"resolution": resolution}) - - wr = nest.Create("weight_recorder") - nest.CopyModel(synapse_model_name, "stdp_nestml_rec", - {"weight_recorder": wr[0], "w": 1., "d": 1., "receptor_type": 0, "lambda": .001}) - - # create spike_generators with these times - pre_sg = nest.Create("spike_generator", - params={"spike_times": pre_spike_times}) - post_sg = nest.Create("spike_generator", - params={"spike_times": post_spike_times, - "allow_offgrid_times": True}) - - # create parrot neurons and connect spike_generators - pre_neuron = nest.Create("parrot_neuron") - post_neuron = nest.Create(neuron_model_name) - - if nest_version.startswith("v2"): - spikedet_pre = nest.Create("spike_detector") - spikedet_post = nest.Create("spike_detector") - else: - spikedet_pre = nest.Create("spike_recorder") - spikedet_post = nest.Create("spike_recorder") - mm = nest.Create("multimeter", params={"record_from": ["V_m", self.post_trace_var]}) - - nest.Connect(pre_sg, pre_neuron, "one_to_one", syn_spec={"delay": 1.}) - nest.Connect(post_sg, post_neuron, "one_to_one", syn_spec={"delay": 1., "weight": 9999.}) - if nest_version.startswith("v2"): - nest.Connect(pre_neuron, post_neuron, "all_to_all", syn_spec={"model": "stdp_nestml_rec"}) - else: - nest.Connect(pre_neuron, post_neuron, "all_to_all", syn_spec={"synapse_model": "stdp_nestml_rec"}) - nest.Connect(mm, post_neuron) - nest.Connect(pre_neuron, spikedet_pre) - nest.Connect(post_neuron, spikedet_post) - - # get STDP synapse and weight before protocol - syn = nest.GetConnections(source=pre_neuron, synapse_model="stdp_nestml_rec") - - t = 0. - t_hist = [] - w_hist = [] - state = 0 - while t <= sim_time: - if t > sim_time / 6. and state == 0: - nest.SetStatus(post_neuron, {"I_dend": 1.}) - state = 1 - if t > 2 * sim_time / 6 and state == 1: - nest.SetStatus(post_neuron, {"I_dend": 1.}) - if t > 3 * sim_time / 6. and state == 1: - state = 2 - if t > 5 * sim_time / 6. and state == 2: - nest.SetStatus(post_neuron, {"I_dend": 0.}) - state = 3 - nest.Simulate(resolution) - t += resolution - t_hist.append(t) - w_hist.append(nest.GetStatus(syn)[0]["w"]) - - third_factor_trace = nest.GetStatus(mm, "events")[0][self.post_trace_var] - timevec = nest.GetStatus(mm, "events")[0]["times"] - - if TEST_PLOTS: - fig, ax = plt.subplots(nrows=2) - ax1, ax2 = ax - - V_m = nest.GetStatus(mm, "events")[0]["V_m"] - ax2.plot(timevec, third_factor_trace, label="I_dend_post") - ax1.plot(timevec, V_m, alpha=.7, linestyle=":") - ax1.set_ylabel("V_m") - - for _ax in ax: - _ax.grid(which="major", axis="both") - _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4) - _ax.set_xlim(0., sim_time) - _ax.legend() - fig.savefig("/tmp/stdp_triplet_synapse_test" + fname_snip + "_V_m.png", dpi=300) - - if TEST_PLOTS: - fig, ax = plt.subplots(nrows=5) - ax1, ax2, ax3, ax4, ax5 = ax - - pre_spike_times_ = nest.GetStatus(spikedet_pre, "events")[0]["times"] - print("Actual pre spike times: " + str(pre_spike_times_)) - - n_spikes = len(pre_spike_times_) - for i in range(n_spikes): - ax1.plot(2 * [pre_spike_times_[i] + delay], [0, 1], linewidth=2, color="blue", alpha=.4) - - post_spike_times_ = nest.GetStatus(spikedet_post, "events")[0]["times"] - print("Actual post spike times: " + str(post_spike_times_)) - ax1.set_ylabel("Pre spikes") - - n_spikes = len(post_spike_times_) - for i in range(n_spikes): - if i == 0: - _lbl = "nestml" - else: - _lbl = None - ax[-4].plot(2 * [post_spike_times_[i]], [0, 1], linewidth=2, color="black", alpha=.4, label=_lbl) - ax[-4].set_ylabel("Post spikes") - - ax[-3].plot(timevec, third_factor_trace) - ax[-3].set_ylabel("3rd factor") - - ax[-2].plot(t_hist[:-1], np.diff(w_hist), marker="o", label=u"Δw") - ax[-2].set_ylabel(u"Δw") - - ax[-1].plot(t_hist, w_hist, marker="o") - ax[-1].set_ylabel("w") - ax[-1].set_xlabel("Time [ms]") - for _ax in ax: - if not _ax == ax[-1]: - _ax.set_xticklabels([]) - _ax.grid(True) - _ax.set_xlim(0., sim_time) - - fig.savefig("/tmp/stdp_third_factor_synapse_test" + fname_snip + ".png", dpi=300) - - # verify - idx = np.where(np.abs(third_factor_trace) < 1E-12)[0] # find where third_factor_trace is (almost) zero - times_dw_should_be_zero = timevec[idx] - assert len(times_dw_should_be_zero) > 0 # make sure we have > 0 datapoints to check - for time_dw_should_be_zero in times_dw_should_be_zero[1:]: - _idx = np.argmin((time_dw_should_be_zero - np.array(t_hist))**2) - np.testing.assert_allclose(t_hist[_idx], time_dw_should_be_zero) - np.testing.assert_allclose(0., np.abs(w_hist[_idx - 1] - w_hist[_idx])) # make sure that weight does not change appreciably - - assert np.any(np.abs(np.array(w_hist) - 1) > 0.), "No change in the weight!"