From 146c737281c06d35d2fef688b3af28cb44bdc83b Mon Sep 17 00:00:00 2001 From: "C.A.P. Linssen" Date: Mon, 6 May 2024 12:20:36 +0200 Subject: [PATCH] fix third-factor plasticity buffering and add third factor plasticity tutorial --- .../stdp_third_factor_active_dendrite.ipynb | 933 +++++++++++++++--- .../codegeneration/nest_code_generator.py | 8 +- .../point_neuron/common/NeuronClass.jinja2 | 130 ++- .../point_neuron/common/NeuronHeader.jinja2 | 49 +- .../common/SynapseHeader.h.jinja2 | 30 +- .../synapse_post_neuron_transformer.py | 10 +- 6 files changed, 966 insertions(+), 194 deletions(-) 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 index 7ed5d47e7..93b455dcb 100644 --- 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 @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -527,13 +527,13 @@ "[27,third_factor_stdp_synapse, WARNING, [18:8;18:17]]: Variable 'd' has the same name as a physical unit!\n", "[28,third_factor_stdp_synapse, WARNING, [48:11;48:26]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([48:11;48:26])\n", "[29,third_factor_stdp_synapse, WARNING, [55:11;55:26]]: SPL_COMPARISON_OPERATOR_VISITOR : Operands of a logical rhs not compatible.([55:11;55:26])\n", - "[30,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_trace_kernel', 'post_trace']\n", + "[30,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: ['post_trace', 'post_trace_kernel']\n", "[31,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: ['tau_tr_post']\n", "[32,GLOBAL, INFO]: Synaptic state variables moved to neuron that will need buffering: ['I_post_dend']\n", - "[33,GLOBAL, INFO]: Moving state var defining equation(s) post_trace_kernel\n", - "[34,GLOBAL, INFO]: Moving state var defining equation(s) post_trace\n", - "[35,GLOBAL, INFO]: Moving state variables for equation(s) post_trace_kernel\n", - "[36,GLOBAL, INFO]: Moving state variables for equation(s) post_trace\n", + "[33,GLOBAL, INFO]: Moving state var defining equation(s) post_trace\n", + "[34,GLOBAL, INFO]: Moving state var defining equation(s) post_trace_kernel\n", + "[35,GLOBAL, INFO]: Moving state variables for equation(s) post_trace\n", + "[36,GLOBAL, INFO]: Moving state variables for equation(s) post_trace_kernel\n", "[37,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with suffixed external variable references\n", "[38,GLOBAL, INFO]: \t• Replacing variable I_post_dend\n", "[39,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = I_post_dend) in expression: I_post_dend <= I_post_dend_peak\n", @@ -546,15 +546,10 @@ "[46,GLOBAL, INFO]: Copying definition of tau_tr_post from synapse to neuron\n", "[47,GLOBAL, INFO]: Adding suffix to variables in spike updates\n", "[48,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references\n", - "[49,GLOBAL, INFO]: \t• Replacing variable post_trace_kernel\n", - "[50,GLOBAL, INFO]: \t• Replacing variable post_trace\n", - "[51,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = post_trace__for_third_factor_stdp_synapse) in expression: alpha * lambda * (w / Wmax) ** mu_minus * post_trace\n", - "[52,iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[53,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, DEBUG, [13:0;63:0]]: Start building symbol table!\n", - "[54,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, WARNING, [18:8;18:17]]: Variable 'd' has the same name as a physical unit!\n", - "[55,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron\n", - "[56,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_active_dendrite_neuron'\n", - "[57,iaf_psc_exp_active_dendrite_neuron, INFO, [2:0;63:0]]: Starts processing of the model 'iaf_psc_exp_active_dendrite_neuron'\n" + "[49,GLOBAL, INFO]: \t• Replacing variable post_trace\n", + "[50,GLOBAL, INFO]: ASTSimpleExpression replacement made (var = post_trace__for_third_factor_stdp_synapse) in expression: alpha * lambda * (w / Wmax) ** mu_minus * post_trace\n", + "[51,GLOBAL, INFO]: \t• Replacing variable post_trace_kernel\n", + "[52,iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, DEBUG, [2:0;63:0]]: Start building symbol table!\n" ] }, { @@ -623,12 +618,29 @@ "Processing function-of-time shape \"syn_kernel__X__synaptic_spikes\" with defining expression = \"e*t*exp(-t/tau_syn)/tau_syn\"\n", "DEBUG:Found t: 1\n", "DEBUG:\tFinding ode for order 1...\n", - "DEBUG:\tFinding ode for order 2...\n", + "DEBUG:\tFinding ode for order 2...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[53,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, DEBUG, [13:0;63:0]]: Start building symbol table!\n", + "[54,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, WARNING, [18:8;18:17]]: Variable 'd' has the same name as a physical unit!\n", + "[55,GLOBAL, INFO]: Successfully constructed neuron-synapse pair iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron\n", + "[56,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_active_dendrite_neuron'\n", + "[57,iaf_psc_exp_active_dendrite_neuron, INFO, [2:0;63:0]]: Starts processing of the model 'iaf_psc_exp_active_dendrite_neuron'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "DEBUG:\tchecking whether shape definition is satisfied...\n", "DEBUG:Shape satisfies ODE of order = 2\n", "DEBUG:Created Shape with symbol syn_kernel__X__synaptic_spikes, derivative_factors = Matrix([[-1/tau_syn**2], [-2/tau_syn]]), inhom_term = 0.0, nonlin_term = 0.0\n", "INFO:Shape syn_kernel__X__synaptic_spikes: reconstituting expression -syn_kernel__X__synaptic_spikes/tau_syn**2 - 2*syn_kernel__X__synaptic_spikes__d/tau_syn\n", - "INFO:All known variables: [V_m, I_dAP, syn_kernel__X__synaptic_spikes, syn_kernel__X__synaptic_spikes'], all parameters used in ODEs: {tau_m, C_m, E_L, tau_syn, I_e, tau_dAP}\n", + "INFO:All known variables: [V_m, I_dAP, syn_kernel__X__synaptic_spikes, syn_kernel__X__synaptic_spikes'], all parameters used in ODEs: {C_m, tau_dAP, tau_syn, I_e, tau_m, E_L}\n", "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + ((syn_kernel__X__synaptic_spikes * 1.0) + I_dAP + I_e) / C_m + 0 * 1.0 / 1000.0\"\n", "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_dAP + I_e + 1.0*syn_kernel__X__synaptic_spikes)/C_m (symbols [V_m, I_dAP, syn_kernel__X__synaptic_spikes, syn_kernel__X__synaptic_spikes__d, V_m])\n", @@ -689,13 +701,7 @@ "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_syn**2], [-2/tau_syn]])\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" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "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", @@ -721,7 +727,13 @@ "INFO:update_expr[V_m] = -E_L*__P__V_m__V_m + E_L + I_dAP*__P__V_m__I_dAP + V_m*__P__V_m__V_m + __P__V_m__syn_kernel__X__synaptic_spikes*syn_kernel__X__synaptic_spikes + __P__V_m__syn_kernel__X__synaptic_spikes__d*syn_kernel__X__synaptic_spikes__d - I_e*__P__V_m__V_m*tau_m/C_m + I_e*tau_m/C_m\n", "INFO:update_expr[I_dAP] = I_dAP*__P__I_dAP__I_dAP\n", "INFO:update_expr[syn_kernel__X__synaptic_spikes] = __P__syn_kernel__X__synaptic_spikes__syn_kernel__X__synaptic_spikes*syn_kernel__X__synaptic_spikes + __P__syn_kernel__X__synaptic_spikes__syn_kernel__X__synaptic_spikes__d*syn_kernel__X__synaptic_spikes__d\n", - "INFO:update_expr[syn_kernel__X__synaptic_spikes__d] = __P__syn_kernel__X__synaptic_spikes__d__syn_kernel__X__synaptic_spikes*syn_kernel__X__synaptic_spikes + __P__syn_kernel__X__synaptic_spikes__d__syn_kernel__X__synaptic_spikes__d*syn_kernel__X__synaptic_spikes__d\n", + "INFO:update_expr[syn_kernel__X__synaptic_spikes__d] = __P__syn_kernel__X__synaptic_spikes__d__syn_kernel__X__synaptic_spikes*syn_kernel__X__synaptic_spikes + __P__syn_kernel__X__synaptic_spikes__d__syn_kernel__X__synaptic_spikes__d*syn_kernel__X__synaptic_spikes__d\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "WARNING:Not preserving expression for variable \"V_m\" as it is solved by propagator solver\n", "WARNING:Not preserving expression for variable \"I_dAP\" as it is solved by propagator solver\n", "INFO:In ode-toolbox: returning outdict = \n", @@ -773,10 +785,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[58,iaf_psc_exp_active_dendrite_neuron, DEBUG, [2:0;63:0]]: Start building symbol table!\n", - "[59,iaf_psc_exp_active_dendrite_neuron, INFO, [63:16;63:18]]: Implicit casting from (compatible) type 'pA' to 'real'.\n", - "[60,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse'\n", - "[61,iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, INFO, [2:0;63:0]]: Starts processing of the model 'iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse'\n" + "[58,iaf_psc_exp_active_dendrite_neuron, DEBUG, [2:0;63:0]]: Start building symbol table!\n" ] }, { @@ -850,7 +859,22 @@ "Processing function-of-time shape \"syn_kernel__X__synaptic_spikes\" with defining expression = \"e*t*exp(-t/tau_syn)/tau_syn\"\n", "DEBUG:Found t: 1\n", "DEBUG:\tFinding ode for order 1...\n", - "DEBUG:\tFinding ode for order 2...\n", + "DEBUG:\tFinding ode for order 2...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[59,iaf_psc_exp_active_dendrite_neuron, INFO, [63:16;63:18]]: Implicit casting from (compatible) type 'pA' to 'real'.\n", + "[60,GLOBAL, INFO]: Analysing/transforming model 'iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse'\n", + "[61,iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, INFO, [2:0;63:0]]: Starts processing of the model 'iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse'\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "DEBUG:\tchecking whether shape definition is satisfied...\n", "DEBUG:Shape satisfies ODE of order = 2\n", "DEBUG:Created Shape with symbol syn_kernel__X__synaptic_spikes, derivative_factors = Matrix([[-1/tau_syn**2], [-2/tau_syn]]), inhom_term = 0.0, nonlin_term = 0.0\n", @@ -862,7 +886,7 @@ "DEBUG:Shape satisfies ODE of order = 1\n", "DEBUG:Created Shape with symbol post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse, derivative_factors = [-1/tau_tr_post__for_third_factor_stdp_synapse], inhom_term = 0.0, nonlin_term = 0.0\n", "INFO:Shape post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse: reconstituting expression -post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse/tau_tr_post__for_third_factor_stdp_synapse\n", - "INFO:All known variables: [V_m, I_dAP, syn_kernel__X__synaptic_spikes, syn_kernel__X__synaptic_spikes', post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse], all parameters used in ODEs: {tau_m, C_m, E_L, tau_syn, I_e, tau_tr_post__for_third_factor_stdp_synapse, tau_dAP}\n", + "INFO:All known variables: [V_m, I_dAP, syn_kernel__X__synaptic_spikes, syn_kernel__X__synaptic_spikes', post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse], all parameters used in ODEs: {C_m, tau_dAP, tau_syn, tau_tr_post__for_third_factor_stdp_synapse, I_e, tau_m, E_L}\n", "INFO:\n", "Processing differential-equation form shape V_m with defining expression = \"(-(V_m - E_L)) / tau_m + ((syn_kernel__X__synaptic_spikes * 1.0) + I_dAP + I_e) / C_m + 0 * 1.0 / 1000.0\"\n", "DEBUG:Splitting expression (E_L - V_m)/tau_m + (I_dAP + I_e + 1.0*syn_kernel__X__synaptic_spikes)/C_m (symbols [V_m, I_dAP, syn_kernel__X__synaptic_spikes, syn_kernel__X__synaptic_spikes__d, post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse, V_m])\n", @@ -904,13 +928,7 @@ "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", "INFO:Shape syn_kernel__X__synaptic_spikes: reconstituting expression -syn_kernel__X__synaptic_spikes/tau_syn**2 - 2*syn_kernel__X__synaptic_spikes__d/tau_syn\n", - "DEBUG:Splitting expression -syn_kernel__X__synaptic_spikes/tau_syn**2 - 2*syn_kernel__X__synaptic_spikes__d/tau_syn (symbols Matrix([[V_m], [I_dAP], [syn_kernel__X__synaptic_spikes], [syn_kernel__X__synaptic_spikes__d], [post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse]]))\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "DEBUG:Splitting expression -syn_kernel__X__synaptic_spikes/tau_syn**2 - 2*syn_kernel__X__synaptic_spikes__d/tau_syn (symbols Matrix([[V_m], [I_dAP], [syn_kernel__X__synaptic_spikes], [syn_kernel__X__synaptic_spikes__d], [post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse]]))\n", "DEBUG:\tlinear factors: Matrix([[0], [0], [-1/tau_syn**2], [-2/tau_syn], [0]])\n", "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", @@ -932,7 +950,13 @@ "DEBUG:\tnonlinear term: 0.0\n", "INFO:Shape I_dAP: reconstituting expression -I_dAP/tau_dAP\n", "DEBUG:Splitting expression -I_dAP/tau_dAP (symbols [V_m, I_dAP, syn_kernel__X__synaptic_spikes, syn_kernel__X__synaptic_spikes__d, post_trace_kernel__for_third_factor_stdp_synapse__X__post_spikes__for_third_factor_stdp_synapse])\n", - "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_dAP], [0], [0], [0]])\n", + "DEBUG:\tlinear factors: Matrix([[0], [-1/tau_dAP], [0], [0], [0]])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "DEBUG:\tinhomogeneous term: 0.0\n", "DEBUG:\tnonlinear term: 0.0\n", "INFO:Shape syn_kernel__X__synaptic_spikes: reconstituting expression -syn_kernel__X__synaptic_spikes/tau_syn**2 - 2*syn_kernel__X__synaptic_spikes__d/tau_syn\n", @@ -1096,22 +1120,7 @@ "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[63,iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, INFO, [63:16;63:18]]: Implicit casting from (compatible) type 'pA' to 'real'.\n", - "[64,GLOBAL, INFO]: Analysing/transforming synapse third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.\n", - "[65,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, INFO, [13:0;63:0]]: Starts processing of the model 'third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron'\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "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", @@ -1151,6 +1160,9 @@ "name": "stdout", "output_type": "stream", "text": [ + "[63,iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse, INFO, [63:16;63:18]]: Implicit casting from (compatible) type 'pA' to 'real'.\n", + "[64,GLOBAL, INFO]: Analysing/transforming synapse third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.\n", + "[65,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, INFO, [13:0;63:0]]: Starts processing of the model 'third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron'\n", "[66,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, DEBUG, [13:0;63:0]]: Start building symbol table!\n", "[67,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, WARNING, [18:8;18:17]]: Variable 'd' has the same name as a physical unit!\n", "[68,third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron, DEBUG, [13:0;63:0]]: Start building symbol table!\n", @@ -1217,12 +1229,23 @@ " information run \"cmake --help-policy CMP0000\".\n", "This warning is for project developers. Use -Wno-dev to suppress it.\n", "\n", - "-- Configuring done (0.3s)\n", + "-- Configuring done (0.1s)\n", "-- Generating done (0.0s)\n", "-- Build files have been written to: /home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target\n", "[ 25%] Building CXX object CMakeFiles/nestmlmodule_module.dir/nestmlmodule.o\n", "[ 50%] Building CXX object CMakeFiles/nestmlmodule_module.dir/iaf_psc_exp_active_dendrite_neuron.o\n", "[ 75%] Building CXX object CMakeFiles/nestmlmodule_module.dir/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.o\n", + "In file included from /home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp:44:\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.h: In constructor ‘continuous_variable_histentry_iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse::continuous_variable_histentry_iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse(double, double)’:\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.h:108:10: warning: ‘continuous_variable_histentry_iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse::access_counter_’ will be initialized after [-Wreorder]\n", + " 108 | size_t access_counter_;\n", + " | ^~~~~~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.h:105:10: warning: ‘double continuous_variable_histentry_iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse::I_post_dend’ [-Wreorder]\n", + " 105 | double I_post_dend;\n", + " | ^~~~~~~~~~~\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp:47:1: warning: when initialized here [-Wreorder]\n", + " 47 | continuous_variable_histentry_iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse::continuous_variable_histentry_iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse( double t,\n", + " | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron.cpp: In member function ‘void iaf_psc_exp_active_dendrite_neuron::init_state_internal_()’:\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron.cpp:211:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", " 211 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", @@ -1232,12 +1255,12 @@ " 329 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp: In member function ‘void iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse::init_state_internal_()’:\n", - "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp:221:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 221 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp:230:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 230 | 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-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp: In member function ‘virtual void iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse::update(const nest::Time&, long int, long int)’:\n", - "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp:350:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", - " 350 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse.cpp:359:24: warning: comparison of integer expressions of different signedness: ‘long int’ and ‘const size_t’ {aka ‘const long unsigned int’} [-Wsign-compare]\n", + " 359 | for (long i = 0; i < NUM_SPIKE_RECEPTORS; ++i)\n", " | ~~^~~~~~~~~~~~~~~~~~~~~\n" ] }, @@ -1281,13 +1304,7 @@ "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:602:104: required from here\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:720:16: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", " 720 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", - " | ^~~~~~~~~~~~\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " | ^~~~~~~~~~~~\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h: In instantiation of ‘bool nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron::send(nest::Event&, size_t, const nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuronCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’:\n", "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron; size_t = long unsigned int]’\n", "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:383:3: required from here\n", @@ -1300,11 +1317,11 @@ "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:571:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", " 571 | 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-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:428:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 428 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:426:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 426 | 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-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:430:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 430 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:428:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 428 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h: In instantiation of ‘bool nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron::send(nest::Event&, size_t, const nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuronCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierIndex; size_t = long unsigned int]’:\n", "/home/charl/julich/nest-simulator-install/include/nest/connector_base.h:391:22: required from ‘void nest::Connector::send_to_all(size_t, const std::vector&, nest::Event&) [with ConnectionT = nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron; size_t = long unsigned int]’\n", @@ -1318,11 +1335,11 @@ "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:571:14: warning: variable ‘get_t’ set but not used [-Wunused-but-set-variable]\n", " 571 | 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-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:428:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", - " 428 | const double __resolution = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the resolution() function\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:426:18: warning: unused variable ‘__resolution’ [-Wunused-variable]\n", + " 426 | 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-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:430:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", - " 430 | auto get_thread = [tid]()\n", + "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:428:10: warning: variable ‘get_thread’ set but not used [-Wunused-but-set-variable]\n", + " 428 | auto get_thread = [tid]()\n", " | ^~~~~~~~~~\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h: In instantiation of ‘void nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron::update_internal_state_(double, double, const nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuronCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport]’:\n", "/home/charl/julich/nestml-fork-third_factor_stdp/nestml/doc/tutorials/stdp_third_factor_active_dendrite/target/third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron.h:493:9: required from ‘bool nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron::send(nest::Event&, size_t, const nest::third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuronCommonSynapseProperties&) [with targetidentifierT = nest::TargetIdentifierPtrRport; size_t = long unsigned int]’\n", @@ -1377,6 +1394,7 @@ " \"weight_variable\": {\"third_factor_stdp_synapse\": \"w\"}}\n", "input_path = [\"iaf_psc_exp_active_dendrite_neuron.nestml\",\n", " \"third_factor_stdp_synapse.nestml\"]\n", + "\n", "generate_nest_target(input_path=input_path,\n", " logging_level=\"DEBUG\",\n", " codegen_opts=codegen_opts)\n", @@ -1404,7 +1422,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -1592,7 +1610,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1602,7 +1620,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1640,8 +1658,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_225356/2573028503.py:137: UserWarning:Attempting to set identical low and high ylims makes transformation singular; automatically expanding.\n", - "/tmp/ipykernel_225356/2573028503.py:152: UserWarning:Data has no positive values, and therefore cannot be log-scaled.\n" + "/tmp/ipykernel_281287/2573028503.py:137: UserWarning:Attempting to set identical low and high ylims makes transformation singular; automatically expanding.\n", + "/tmp/ipykernel_281287/2573028503.py:152: UserWarning:Data has no positive values, and therefore cannot be log-scaled.\n" ] }, { @@ -1674,7 +1692,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1733,7 +1751,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1805,7 +1823,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1846,6 +1864,7 @@ "post_spike_times = np.array([60.]) # [ms]\n", "\n", "stepwise_times = [0., 64., 68.]\n", + "stepwise_values = [0., 100., 0.]\n", "\n", "# run the simulation\n", "timevec, t_hist, third_factor_trace, w_hist = run_synapse_test(neuron_model_name=neuron_model_name,\n", @@ -1870,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 16, "metadata": { "scrolled": true }, @@ -1879,6 +1898,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "Case 3b\n", "Pre spike times: [ 15. 65. 165.]\n", "Post spike times: [60.]\n", "~~~~~~~ at t = 16.0 , 3rd factor = 0.0\n", @@ -1899,7 +1919,7 @@ }, { "data": { - "image/png": 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069YNPj4+ePDBB/Hggw/Cx8cH3bp1w5YtW5QeHtUxycnJSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtz0mehpKQk9O3bF7m5uRg/fjy+/vprfP3113jrrbeQm5uLJ598EklJSRbff3FxMd5++234+vrCxcUFISEh+PHHHyu93dSpU6HRaMpcnJ2dLR4LyREZGan0EGwCO8vBzuKxsRzsLB4by8HOcqi1M4/eaaF3330XgYGB+Omnn9CwYUOz62JiYvDwww/j3XffxT//+U+L7n/06NFYu3YtXn/9dbRv3x6LFy9G//79sX37djz88MOV3v6LL75AgwYNTF/b29tbNA6SZ/bs2UoPwSawsxzsLB4by8HO4rGxHOwsh1o7c0+fhY4fP47nn3++zIQPABo1aoQxY8bgxIkTFt13eno6Vq5ciQ8++ABxcXEYO3Ystm3bhtatW2P8+PFVuo/Bgwdj1KhRpsvw4cMtGgvJs3DhQqWHYBPYWQ52Fo+N5WBn8dhYDnaWQ62dOemz0D333HPXg7acO3cOHTp0sOi+165dC3t7e4wdO9a0zNnZGWPGjMEvv/yC7OzsSu9Dr9fj8uXL4MFZ644ePXooPQSbwM5ysLN4bCwHO4vHxnKwsxxq7cy3d1ro448/xrBhw9CjRw8MGDDA7LqkpCR8+eWXWLVqlUX3vXfvXnTo0AGNGjUyW258Ee3btw8tW7a86320a9cOV69eRf369REeHo5Zs2ahadOmlT52Xl4ezp8/b7YsKyurmltAlrh+/brSQ7AJ7CwHO4vHxnKws3hsLAc7y6HWztzTZ6E5c+bAy8sLAwcORMuWLdG7d2/07t0bLVu2xODBg+Ht7Y3PP/8czzzzjOly5+SwIrm5ufDx8Smz3LjszJkzFd7Ww8MDr7zyCr788kusXbsWL7zwAlatWoVHHnkEly9frvSx582bh8DAQLNLeHg4ACAtLQ2pqamIi4uDVqs1fVDVeGja6OhoZGVlISEhAUlJSUhPT0dsbCwKCwsRERFhtm5MTAwyMjKQmJiIxMREZGRkICYmxmydiIgIFBYWIjY2Funp6UhKSkJCQgKysrIQHR1ttm5kZCS0Wi3i4uKQmpqKlJQUxMfHIycnB1FRUWbrRkVFIScnB/Hx8UhJSanxNs2dOxeAYbJ/8OABi7fp448/Vs02WePzZFx39uzZVrdNanye/vjjD+HbtGDBl7hxoxg//LABe/bssbnnacqUKVa3TWp8nnbu3Gl126S25+mtt96yum1S4/OUnJxsddukxufp2LFjQrfpzJkz2L59O44ePYrq4MnZLdSmTRtoNJpq3Uaj0eD48eOVrufn54eOHTti48aNZsuPHz8OPz8/zJ49G6+//nqVHzcxMREjR47EBx98gHfeeeeu61a0py88PJwnZy9HbZ4gOicnB82bN6+dgVGF2FkO0Z15cna+lmVhZ/HYWA52lkPWzz+enF2SkydP4sSJE9W6VGXCBwAuLi4oLi4us7yoqMh0fXWMGDECzZo1q9K5A729vREQEGB28ff3r9bjkWViY2OVHoJNYGc52Fk8NpaDncVjYznYWQ61duakT4V8fHyQm5tbZrlxma+vb7Xvs2XLltBqtTUeG4kzf/58pYdgE9hZDnYWj43lYGfx2FgOdpZDrZ056bPQ6dOnkZaWZrZs//79eO655zB06FCsX7/e4vsODg7GkSNHynwGb9euXabrq0Ov1+PkyZPw8vKyeEwknvH92iQWO8vBzuKxsRzsLB4by8HOcqi1Myd9Fnr11VcxdepU09fnzp1D7969sW7dOuzcuRODBg3CunXrLLrvwYMHQ6fTYcGCBaZlxcXFWLRoEUJCQkxH7jx9+jQOHz5sdts7P48HGE7Ufv78efTr18+i8ZAcycnJSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtz0meh9PR0PP7446avly5diuvXr2P//v3IycnBY489hpkzZ1p03yEhIRgyZAgmTJiA8ePHY8GCBXj00Udx8uRJ0xEeAeC5557Dvffea3bb1q1b4/nnn8cnn3yCefPmYcSIEXjllVcQHByMcePGWbaxJIXxCE0kFjvLwc7isbEc7CweG8vBznKotTPP02chrVYLb29v09cbNmxAz5494efnBwAYOHCg6fCtlli6dCkmTZqEZcuWIT8/H507d8aGDRsQGhp619uNHDkSP//8M7799lsUFRWhdevWGD9+PCZOnAhXV1eLx0PiTZo0Sekh2AR2loOdxWNjOdhZPDaWg53lUGtn7umzkJeXF06dOgUAKCgowK+//oq+ffuari8pKUFJSYnF9+/s7Iy4uDjk5uaiqKgI6enpZvcPADt27MCdZ9z46quvcODAAVy+fBk3btzA0aNH8eGHH6Jhw4YWj4XkqMnnQKnq2FkOdhaPjeVgZ/HYWA52lkOtnbmnz0J9+vTB559/jkaNGmHHjh0oLS01ncQcAA4ePGj67B1RVRj3EpNY7CwHO4vHxnKws3hsLAc7y6HWzpz0WejDDz/EkSNH8Oabb8LR0REzZ85E27ZtARgOurJ69WqMGDFC4VFSXVLd8y+SZdhZDnYWj43lYGfx2FgOdpZDrZ359k4LNW3aFP/73/+Qn5+Py5cv47XXXjNdV1paiq1bt5od3ZOoMunp6UoPwSawsxzsLB4by8HO4rGxHOwsh1o7c09fDbm5uZVZ5uLigvvuu0+B0VBdNmbMGKWHYBPYWQ52Fo+N5WBn8dhYDnaWQ62duaePSCWio6OVHoJNYGc52Fk8NpaDncVjYznYWQ61dtbo7zz8I9EdDhw4gMDAQGRmZiIgIEDp4ahKfj6wZs2tr4cMATw8lBsPka3g/3tERGSLjD//zpw5gGnTqv77Off0EalEWFiY0kOwCewsBzuLx8ZysLN4bCwHO8uh1s6c9BGpRHJystJDsAnsLAc7i8fGcrCzeGwsBzvLodbOnPQRqYRa3wNubdhZDnYWj43lYGfx2FgOdpZDrZ056asBnU6HlStXYty4cfjnP/+JjIwMAMClS5ewbt06nDt3TuERUl3y8ssvKz0Em8DOcrCzeGwsBzuLx8ZysLMcau3MSZ+FCgoK8NBDD2HEiBFYsWIFvv/+e5w/fx4A0KBBA7z66qv47LPPFB4l1SU7d+5Uegg2gZ3lYGfx2FgOdhaPjeVgZznU2pmTPgu98847OHDgADZt2oTjx4/j9oOg2tvbY/Dgwdi4caOCI6S6xoOHHpSCneVgZ/HYWA52Fo+N5WBnOdTamZM+C61fvx7/93//h8cffxwajabM9R06dMDJkyflD4zqrObNmys9BJvAznKws3hsLAc7i8fGcrCzHGrtzEmfhS5duoS2bdtWeP3NmzdRUlIicURU123atEnpIdgEdpaDncVjYznYWTw2loOd5VBrZ076LOTn54fff/+9wus3b96MTp06SRwR1XVvvPGG0kOwCewsBzuLx8ZysLN4bCwHO8uh1s6c9FnohRdeQEJCAlatWmX6PJ9Go0FxcTEmTpyIlJQUjBs3TuFRUl0yevRopYdgE9hZDnYWj43lYGfx2FgOdpZDrZ01+tuPQEJVptfrMXbsWCxcuBDu7u4oKChA06ZNcfHiRZSUlGDcuHH44osvlB5mrThw4AACAwORmZmJgIAApYejKvn5wJo1t74eMgRQ6ed3iawK/98jIiJbZPz5d+bMAUybVvXfz7mnz0IajQZfffUVdu7cieeeew5PPvkkgoODMXbsWOzYscNqJnwkT1hYmNJDsAnsLAc7i8fGcrCzeGwsBzvLodbODkoPoC4qLCzEqFGjMGjQIIwcORIPP/yw0kMiK5CcnKz0EGwCO8vBzuKxsRzsLB4by8HOcqi1M/f0WcDV1RVbtmxBYWGh0kMhKxITE6P0EGwCO8vBzuKxsRzsLB4by8HOcqi1Myd9Fnr44Yfxyy+/KD0MsiLDhw9Xegg2gZ3lYGfx2FgOdhaPjeVgZznU2pmTPgvNnTsXP/30E95991389ddfSg+HrEBGRobSQ7AJ7CwHO4vHxnKws3hsLAc7y6HWzpz0Wei+++7DX3/9hQ8++ACtW7eGk5MTGjVqZHZxc3NTephERERERGTjeCAXCw0aNAgajUbpYZAVCQoKUnoINoGd5WBn8dhYDnYWj43lYGc51NqZkz4LLV68WOkhkJVZsWKFar9RWBN2loOdxWNjOdhZPDaWg53lUGtnnpy9moqKivDdd9/hxIkTaNKkCZ566in4+PgoPSyheHL2ivEE0UTK4P97RERki3hydgny8vIQGBiIESNGICYmBmPHjkX79u2xZcsWpYdGVkCtJ/O0NuwsBzuLx8ZysLN4bCwHO8uh1s6c9FVDbGwsTp48iejoaGzYsAGffvopXFxcMG7cOKWHRlZArSfztDbsLAc7i8fGcrCzeGwsBzvLodbOnPRVw+bNm/Hcc89h5syZ6N+/P1599VXMnTsXJ0+exJ9//qn08KiOi4iIUHoINoGd5WBn8dhYDnYWj43lYGc51NqZk75qOH36NB5++GGzZQ8//DD0ej3OnTun0KjIWvDgQHKwsxzsLB4by8HO4rGxHOwsh1o7c9JXDcXFxXB2djZbZvy6pKREiSGRFZk1a5bSQ7AJ7CwHO4vHxnKws3hsLAc7y6HWzjxlQzWdPHkSv//+u+nrS5cuAQCOHj0Kd3f3Mut37dpV1tCojuvbt6/SQ7AJ7CwHO4vHxnKws3hsLAc7y6HWzpz0VdOkSZMwadKkMstfeukls6/1ej00Gg10Op2soVEdl5OTo/QQbAI7y8HO4rGxHOwsHhvLwc5yqLUzJ33VsGjRIqWHQFYsPz9f6SHYBHaWg53FY2M52Fk8NpaDneVQa2dO+qohMjJS2mMVFxdj8uTJWLZsGfLz89G5c2e89957ePzxxyu9bU5ODqKjo7F582aUlpaid+/emD17Ntq1aydh5GSp0NBQpYdgE9hZDnYWj43lYGfx2FgOdpZDrZ15IBeVGj16ND755BOMHDkSn332Gezt7dG/f3+kpaXd9XZXr15F7969kZqaipiYGEybNg179+5Fz549cfHiRUmjJ0vEx8crPQSbwM5ysLN4bCwHO4vHxnKwsxxq7azR6/V6pQdB5tLT0xESEoK4uDi8+eabAICioiIEBgbC29sbP//8c4W3/fjjj/H2228jPT0d3bt3BwAcPnwYgYGBGD9+PGbMmFHt8Rw4cACBgYHIzMxEQECAZRtlpfLzgTVrbn09ZAjg4aHceIhsBf/fIyIiW2T8+XfmzAFMm1b138+5p0+F1q5dC3t7e4wdO9a0zNnZGWPGjMEvv/yC7Ozsu962e/fupgkfANxzzz147LHHsHr1aqHjppoJCwtTegg2gZ3lYGfx2FgOdhaPjeVgZznU2pmf6VOhvXv3okOHDmjUqJHZ8h49egAA9u3bh5YtW5a5XWlpKf744w/861//KnNdjx49sHnzZly5cgUNGzas8LHz8vJw/vx5s2VZWVmWbAZVU3JystJDsAnsLAc7i8fGcrCzeGwsBzvLodbO3NOnQrm5ufDx8Smz3LjszJkz5d5Oq9WiuLjYotsazZs3D4GBgWaX8PBwAEBaWhpSU1MRFxcHrVZrOrCN8S8a0dHRyMrKQkJCApKSkpCeno7Y2FgUFhYiIiLCbN2YmBhkZGQgMTERiYmJyMjIQExMjNk6ERERKCwsRGxsLNLT05GUlISEhARkZWUhOjrabN3IyEhotVrExcUhNTUVKSkpiI+PR05ODqKioszWjYqKQk5ODuLj45GSklLjbZo7dy4AICkpCQcPHrB4mzp16qSabbLG58m4blBQkNVtkxqfp2HDhgnfpgULvsSNG8X44YcN2LNnj809T35+fla3TWp8np555hmr2ya1PU/GP2Rb0zap8Xnq06eP1W2TGp+nyMhIodt05swZbN++HUePHkV18DN9KuTn54eOHTti48aNZsuPHz8OPz8/zJ49G6+//nqZ22VnZ6NVq1b46KOPMH78eLPrEhISMGbMGOzduxfBwcEVPnZFe/rCw8P5mb5y1ObnirRaLTw9PWtnYFQhdpZDdGd+po+vZVnYWTw2loOd5ZD184+f6bMCLi4uKC4uLrO8qKjIdH1FtwNg0W2NvL29ERAQYHbx9/ev1vhtSaNGhl82jZc73pFbLQsXLqy9gVGF2FkO0Z1r8/+9uoqvZTnYWTw2loOd5ZD18++JJ6p3O36mT4V8fHyQk5NTZnlubi4AwNfXt9zbeXp6wsnJybRedW5LlrG3r729C8bPbJJY7CyH6M61+f9eXcXXshzsLB4by8HOcsj6+efmVr3bcdKnQsHBwdi+fTsuX75sdjCXXbt2ma4vj52dHYKCgrB79+4y1+3atQvt2rW760FcKmLcc8gDuoh1+PBhNGnSROlhWD12loOdxWNjOdhZPDaWg53lkNXZ+Ht5ee/wK5eeVOfXX3/VA9DHxcWZlhUVFen9/f31ISEhpmWnTp3SHzp0yOy2H374oR6A/rfffjMtO3z4sN7e3l7/9ttvWzSexYsX6wHwwgsvvPDCCy+88MILLyq6rF+/vkq/z/NALioVERGBpKQkREdHw9/fH0uWLEF6ejq2bt2K0NBQAECvXr2QmpqK25/CK1euoEuXLrhy5QrefPNN1KtXD5988gl0Oh327dsHLy+vao/ll19+wT/+8Q+sXr3adIRJql3Gg+WsX7+en6EUiJ3lYGfx2FgOdhaPjeVgZzlkdi4uLkZ2djZ69uwJd3f3Stfn2ztVaunSpZg0aRKWLVuG/Px8dO7cGRs2bDBN+CrSsGFD7NixA9HR0XjvvfdQWlqKXr16Yfbs2RZN+ACY3mLaqVMnHr1TMH9/fzaWgJ3lYGfx2FgOdhaPjeVgZzlkde7atWuV1+WkT6WcnZ0RFxeHuLi4CtfZsWNHuctbtGiBNbcfy5yIiIiIiGwWT9lARERERERkxTjpIyIiIiIismKc9FGlvLy8MGXKFIs/E0iVY2M52FkOdhaPjeVgZ/HYWA52lkPNnXn0TiIiIiIiIivGPX1ERERERERWjJM+IiIiIiIiK8ZJHxERERERkRXjpI+IiIiIiMiKcdJHRERERERkxTjpIyIiIiIismKc9BEREREREVkxTvqIiIiIiIisGCd9REREREREVoyTPiIiIiIiIivGSR8REREREZEV46SPiIiIiIjIijkoPQBSv4KCAqSmpqJly5ZwcnJSejhERERERDatuLgY2dnZ6NmzJ9zd3Stdn5M+qlRqairCw8OVHgYREREREd1m/fr1GDBgQKXrcdJHlWrZsiUAw4vK399f4dFYr3PnzqFp06ZKD8PqsbMc7CweG8vBzuKxsRzsLIeszllZWQgPDzf9nl4ZTvqoUsa3dPr7+yMgIEDh0VivOXPmYP78+UoPw+qxsxzsLB4by8HO4rGxHOwsh+zOVf3olUav1+sFj4XquAMHDiAwMBCZmZmc9BERERERKay6v59zTx9V2S+//ILc3NwKr/fw8MD9999v+jo/Px979uyp9H779Olj9vWWLVsqvc39998PDw8P09d79uxBfn7+XW/Trl07tGvXzvT18ePHcfz48bveRuY2ffHFF/j2229NX1vDNqnxeVq7di0GDx4MwHq26XZq2abbOxvV9W0qj5Lb9Pzzz2PkyJF3vU1d2yY1Pk9hYWFITk4GYD3bdDs1bJPx+4U1bZORmrapvO/LdX2byqP0NpXX+Xa1tU3Z2dmVju92nPRRleXn56N+/fpVXv/GjRs4f/58tR+nKre5ceNGmbFVdrs731997dq1ao9P5DbNmzfP7Gtr2CY1Pk89e/as8Lq6uk13o9Q23a3z7Y9bl7apKmRu08iRI61um9T4PBknfID1bNPt1LBNFX2/qMvbdLfHVWqbyutc17epPEpvU2U//2prm65fv16tsXHSR1Xm4eEBLy+vu15/O0dHx7uuX5Gq3MbR0fGuj12eOyes9evXr/SxZG7T9OnTER8fX+Fjl0ft26TG52nTpk3o27dvudfV1W26G6W2qbzOdX2byqPkNv3www946qmn7nqburZNanyeoqKiTJ/PsZZtup0atsn4/cKatqmi+1Vym8r7vlzXt6k8Sm/T3X7PMN733R67POVtk4uLS7XGxs/0UaX4mT45cnJy0Lx5c6WHYfXYWQ52Fo+N5WBn8dhYDnaWQ1bn6v5+bid8RERUJevXr1d6CDaBneVgZ/HYWA52Fo+N5WBnOdTamZM+IpXw8/NTegg2gZ3lYGfx2FgOdhaPjeVgZznU2pmTPiKVqO57s8ky7CwHO4vHxnKws3hsLAc7y6HWzpz0EalEenq60kOwCewsBzuLx8ZysLN4bCwHO8uh1s48kAtVigdykUOr1cLT01PpYVg9dpaDncVjYznYWTw2loOd5ZDVmQdyIaqjoqOjlR6CTWBnOdhZPDaWg53FY2M52FkOtXbmnj6qFPf0ERERERGpB/f0EdVRYWFhSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtzTx9Vinv6iIiIiIjUg3v6FHbq1ClMnz4do0ePxoABA/DMM8+YXQYMGKD0EEml1PoecGvDznKws3hsLAc7i8fGcrCzHGrt7KD0AKzJihUrEBkZiZKSEri7u8PNza3MOhqNRoGRUV3w8ssvKz0Em8DOcrCzeGwsBzuLx8ZysLMcau3MPX21aMKECbjnnntw+PBhaLVanDhxoszl+PHjFt33b7/9hldeeQUBAQGoX78+WrVqhYiICBw5cqTMuocOHUK/fv3QoEEDeHp64tlnn8X58+drunkk2M6dO5Uegk1gZznYWTw2loOdxWNjOdhZDrV25p6+WnThwgWMHz8eHTp0qPX7/uijj/C///0PQ4YMQefOnXH27FnMnTsXXbt2xa+//orAwEAAwF9//YXQ0FC4ublhxowZuHr1KmbOnImMjAykp6fD0dGx1sdGtcPDw0PpIdgEdpaDncVjYznYWTw2loOd5VBrZ076alFISAhOnz4t5L7/85//IDEx0WzSNnToUAQFBeHDDz/E8uXLAQAzZszAtWvXsGfPHrRq1QoA0KNHDzz++ONYvHgxxo4dK2R8VHPNmzdXegg2gZ3lYGfx2FgOdhaPjeVgZznU2plv76xFn376KZYvX461a9fW+n3/4x//KLOXrn379ggICMChQ4dMy7799ls8/fTTpgkfAPTp0wcdOnTA6tWra31cVHs2bdqk9BBsAjvLwc7isbEc7CweG8vBznKotTNP2VDLlixZgjFjxqB+/fpo0aIF7O3tza7XaDTYv39/rTyWXq9Hy5YtERAQgE2bNiEnJwctWrTARx99hPHjx5ut++yzz2Ljxo24ePHiXe8zLy+vzOf/srKyEB4ezlM2CFZYWAhXV1elh2H12FkOdhaPjeVgZ/HYWA52lkNWZ56yQUHz5s3Dv/71Lzg5OcHPzw/e3t5o3Lix2cXT07PWHu+bb75BTk4Ohg4dCgDIzc0FAPj4+JRZ18fHB1qtFsXFxZVuQ2BgoNklPDwcAJCWlobU1FTExcVBq9UiMjISwK2TUEZHRyMrKwsJCQlISkpCeno6YmNjUVhYiIiICLN1Y2JikJGRgcTERCQmJiIjIwMxMTFm60RERKCwsBCxsbFIT09HUlISEhISkJWVZTocrnHdyMhIaLVaxMXFITU1FSkpKYiPj0dOTg6ioqLM1o2KikJOTg7i4+ORkpKimm0KDg62um1S4/N0//33W902qfF5GjFihNVtk9qep06dOlndNqnxefrnP/9pdduktufJ39/f6rZJjc9Tv379rG6b1Pg8jR49Wso2paWloTq4p68WtWjRAm3btsWGDRvKPV1DbTp8+DBCQkIQEBCAn376Cfb29vjpp58QGhqKVatWmV6gRpMnT0ZsbCzy8/Ph7u5e4f1yTx8RERERkbpxT5+CLl26hJEjRwqf8J09exZPPfUU3NzcsHbtWtNbSF1cXACg3L15RUVFZutUxNvbGwEBAWYX41/gSCzjX3FILHaWg53FY2M52Fk8NpaDneVQa2cevbMW9ezZExkZGUIf49KlS3jyySdRUFCAn376Cb6+vqbrjG/rNL7N83a5ubnw9PSEk5OT0PGR5ZKTk5Uegk1gZznYWTw2loOdxWNjOdhZDrV25p6+WvTFF18gNTUVH3/8caUHTLFEUVERwsLCcOTIEWzYsAGdOnUyu7558+bw8vLC7t27y9w2PT3d9JkxUifj+8FJLHaWg53FY2M52Fk8NpaDneVQa2dO+mpRp06dcOLECUyYMAHe3t6oX78+GjVqZHax9K2fOp0OQ4cOxS+//II1a9bgwQcfLHe9QYMGYcOGDcjOzjYt27p1K44cOYIhQ4ZY9Ngkx/Dhw5Uegk1gZznYWTw2loOdxWNjOdhZDrV25ts7a9GgQYOg0WiE3Pcbb7yB77//HmFhYdBqtaaTsRuNGjUKgOGvC2vWrEHv3r3x2muv4erVq4iLi0NQUBCef/55IWOj2pGRkYGgoCClh2H12FkOdhaPjeVgZ/HYWA52lkOtnTnpq0WLFy8Wdt/79u0DYHifcHnvFTZO+lq2bInU1FT85z//wTvvvANHR0c89dRTmDVrFj/PR0RERERkgzjpU9C5c+fg6+uLH3/8EY8++uhd192xY0eV79d4snaqW9T4VyFrxM5ysLN4bCwHO4vHxnKwsxxq7czP9CmMp0kkoxUrVig9BJvAznKws3hsLAc7i8fGcrCzHGrtzJOzK+jcuXPw8fHBli1bKt3Tp6TqnvyRiIiIiIjE4cnZieootZ7M09qwsxzsLB4by8HO4rGxHOwsh1o7c0+fgrinj4iIiIiIqot7+ojqqIiICKWHYBPYWQ52Fo+N5WBn8dhYDnaWQ62duadPQdzTR7crLCyEq6ur0sOweuwsBzuLx8ZysLN4bCwHO8shqzP39BHVUbNmzVJ6CDaBneVgZ/HYWA52Fo+N5WBnOdTamZO+GsrMzLT4ti4uLoiMjISvr28tjojqqr59+yo9BJvAznKws3hsLAc7i8fGcrCzHGrtzElfDXXu3BldunTBzJkz8ddff1Xrto0aNcKiRYtwzz33CBod1SU5OTlKD8EmsLMc7CweG8vBzuKxsRzsLIdaO3PSV0MTJkzA5cuXMX78eLRp0waPPvooFi5ciEuXLik9NKpj8vPzlR6CTWBnOdhZPDaWg53FY2M52FkOtXbmpK+G3n//fRw7dgz/+9//8OKLL+LgwYP497//jWbNmmHQoEFYt24dbty4ofQwqQ4IDQ1Vegg2gZ3lYGfx2FgOdhaPjeVgZznU2pmTvlry4IMPYs6cOThz5gz++9//IiIiAlu2bMGQIUPQtGlTvPDCC9i2bZvSwyQVi4+PV3oINoGd5WBn8dhYDnYWj43lYGc51NqZp2wQqLi4GN9//z1WrFiB//73v7hx4wZ8fX2RnZ2t9NCqhadsICIiIiJSD56yQUWcnJwwcOBAPP/88+jVqxf0ej3OnDmj9LBIpcLCwpQegk1gZznYWTw2loOdxWNjOdhZDrV25p4+QXbu3InExER8++230Gq1cHFxQXh4OEaOHIknn3xS6eFVC/f0ERERERGpB/f0KWjfvn0YP348Wrdujd69e2PhwoXo3r07li5dinPnzmH58uV1bsJH8kRGRio9BJvAznKws3hsLAc7i8fGcrCzHGrtzD19NXT8+HEkJiZixYoVOHz4MPR6Pbp3745Ro0Zh2LBh8PLyUnqINcY9fXJotVp4enoqPQyrx85ysLN4bCwHO4vHxnKwsxyyOnNPn2T+/v6YPHkybt68icmTJ+PIkSPYtWsX/u///s8qJnwkz8KFC5Uegk1gZznYWTw2loOdxWNjOdhZDrV2dlB6AHXd//3f/2HkyJHo0aOH0kOhOo6vITnYWQ52Fo+N5WBn8dhYDnaWQ62dOemroc8++0zpIZCVuH79utJDsAnsLAc7i8fGcrCzeGwsBzvLodbOfHsnkUocO3ZM6SHYBHaWg53FY2M52Fk8NpaDneVQa2dO+ohUIjw8XOkh2AR2loOdxWNjOdhZPDaWg53lUGtnTvqIVCI2NlbpIdgEdpaDncVjYznYWTw2loOd5VBrZ56ygSrFUzYQEREREakHT9mgoOnTpyMzM7PC6w8cOIDp06dbfP9Xr17FlClT0K9fP3h6ekKj0WDx4sXlrnvo0CH069cPDRo0gKenJ5599lmcP3/e4scm8cLCwpQegk1gZznYWTw2loOdxWNjOdhZDrV25p6+WmRnZ4fly5djxIgR5V6/atUqjBgxAjqdzqL7P3nyJNq2bYtWrVqhXbt22LFjBxYtWoTRo0ebrffXX3+hS5cucHNzw6uvvoqrV69i5syZaNWqFdLT0+Ho6Fitx+WePiIiIiIi9eCePhXTarXVnnDdzsfHB7m5uTh16hTi4uIqXG/GjBm4du0atm3bhldffRUxMTFYvXo19u/fX+GeQVJeVFSU0kOwCewsBzuLx8ZysLN4bCwHO8uh1s48T18N7dy5Ezt27DB9vW7dOmRlZZVZr6CgAKtWrUJQUJDFj+Xk5IRmzZpVut63336Lp59+Gq1atTIt69OnDzp06IDVq1dj7NixFo+BxJk0aZLSQ7AJ7CwHO4vHxnKws3hsLAc7y6HWztzTV0Pbt2/H1KlTMXXqVGg0Gqxbt8709e2XTz/9FB4eHpgzZ47Q8eTk5CAvLw/dunUrc12PHj2wd+9eoY9Pllu/fr3SQ7AJ7CwHO4vHxnKws3hsLAc7y6HWztzTV0Pjx4/HK6+8Ar1eD29vb8yfPx+DBg0yW0ej0cDV1RXOzs7Cx5ObmwvA8FbQO/n4+ECr1aK4uBhOTk7l3j4vL6/MAV/K23NJtc/Pz0/pIdgEdpaDncVjYznYWTw2loOd5VBrZ+7pqyEXFxc0btwYTZo0wYkTJzBq1Cg0btzY7OLp6SllwgcA169fB4ByJ3XGMRjXKc+8efMQGBhodjGeZDItLQ2pqamIi4uDVqtFZGQkgFtHKYqOjkZWVhYSEhKQlJSE9PR0xMbGorCwEBEREWbrxsTEICMjA4mJiUhMTERGRgZiYmLM1omIiEBhYSFiY2ORnp6OpKQkJCQkICsrC9HR0WbrRkZGQqvVIi4uDqmpqUhJSUF8fDxycnJM7602rhsVFYWcnBzEx8cjJSVFNdu0cOFCq9smNT5Py5cvt7ptUuPzdPPmTavbJrU9T59++qnVbZMan6fc3Fyr2ya1PU/GI5tb0zap8Xn6448/rG6b1Pg8ubi4SNmmtLQ0VIuehLp27Zp+4cKF+nnz5ulPnjxZa/f722+/6QHoFy1aVO7ypUuXlrnNW2+9pQegLyoqqvB+z507p8/MzDS7rF+/Xg9An5mZWWvjp7I+/vhjpYdgE9hZDnYWj43lYGfx2FgOdpZDVufMzMxq/X7Ot3fWojFjxmDXrl2mc/XduHEDDzzwgOlrNzc3bNu2DV26dBE2BuPbOo1v87xdbm4uPD09K3xrJwB4e3vD29tb2PioYmPGjFF6CDaBneVgZ/HYWA52Fo+N5WBnOdTamW/vrEXbt2/HwIEDTV8nJiYiMzMT33zzDTIzM9GsWTNMmzZN6BiaN28OLy8v7N69u8x16enpCA4OFvr4ZDnj7n4Si53lYGfx2FgOdhaPjeVgZznU2pmTvlp09uxZtGnTxvT1+vXr0a1bNwwfPhydOnXCv//9b+zatUv4OAYNGoQNGzYgOzvbtGzr1q04cuQIhgwZIvzxyTJLlixRegg2gZ3lYGfx2FgOdhaPjeVgZznU2pmTvlpUv359FBQUAABKSkqwY8cO9O3b13R9w4YNcenSpRo9xty5c/Hee+8hISEBAJCcnIz33nsP7733num+Y2Ji4Orqit69e2POnDn44IMPMGTIEAQFBeH555+v0eOTOMYP6ZJY7CwHO4vHxnKws3hsLAc7y6HWzhq9Xq9XehDWom/fvjh9+jQSExPx/fffY/r06fjll1/Qo0cPAMA777yDVatW4cSJExY/Rps2bXDq1Klyrztx4oRpT+OBAwfwn//8B2lpaXB0dMRTTz2FWbNmoWnTptV+zAMHDiAwMBCZmZkICAiweOxERERERFRz1f39nHv6atH7779vOjH6tGnTMGjQINOEDwCSkpLw0EMP1egxTp48Cb1eX+7l9reWBgQEYNOmTbh27Rry8/OxfPlyiyZ8JI9a3wNubdhZDnYWj43lYGfx2FgOdpZDrZ159M5a1K1bNxw+fBg///wz3N3d0bNnT9N1BQUFeOmll8yWEd3u5ZdfVnoINoGd5WBn8dhYDnYWj43lYGc51NqZe/pqmZeXFwYMGFBmcufu7o7XXnuNR8+kCu3cuVPpIdgEdpaDncVjYznYWTw2loOd5VBrZ+7pEyA1NRU//PCD6bN3rVu3xtNPP43Q0FCFR0Zq5uHhofQQbAI7y8HO4rGxHOwsHhvLwc5yqLUzJ3216MaNGxg+fDjWr18PvV4Pd3d3AIa3ds6aNQv//Oc/sWLFCtSrV0/ZgZIqNW/eXOkh2AR2loOdxWNjOdhZPDaWg53lUGtnvr2zFk2bNg1JSUl44403kJubC61WC61Wi7Nnz+LNN9/EunXrMH36dKWHSSq1adMmpYdgE9hZDnYWj43lYGfx2FgOdpZDrZ15yoZa1LZtW/Tq1QuLFi0q9/rRo0djx44dOHnypNyB1RBP2SBHYWEhXF1dlR6G1WNnOdhZPDaWg53FY2M52FkOWZ15ygYF5ebmIiQkpMLrQ0JCcPbsWYkjorpk9OjRSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtzT18t8vf3R7du3bBy5cpyrx82bBh2796NrKwsySOrGe7pIyIiIiJSD+7pU1BkZCRWr16NqKgo/Pnnn9DpdCgtLcWff/6JF198EWvWrFHt7J+UFxYWpvQQbAI7y8HO4rGxHOwsHhvLwc5yqLUz9/TVIp1OhzFjxmDp0qXQaDSwszPMqUtLS6HX6xEZGYmFCxealtcV3NNHRERERKQe3NOnIHt7eyxevBj79u3De++9hxdeeAEvvPAC3n//fezbtw+LFi2qcxM+kicmJkbpIdgEdpaDncVjYznYWTw2loOd5VBrZ56nT4DOnTujc+fOSg+D6pjhw4crPQSbwM5ysLN4bCwHO4vHxnKwsxxq7czdTgJkZmbi448/xksvvYSXXnoJcXFxyMjIUHpYpHJ8jcjBznKws3hsLAc7i8fGcrCzHGrtzD19tai4uBjjxo3DsmXLoNfrzT7T984772DkyJH4+uuv4ejoqPBIiYiIiIjIVnBPXy16++23sXTpUrz44os4dOgQioqKUFxcjEOHDiEqKgrLly/H+PHjlR4mqVRQUJDSQ7AJ7CwHO4vHxnKws3hsLAc7y6HWzpz01aLly5fj2Wefxdy5c9GxY0c4ODjA3t4eHTt2RHx8PEaOHInly5crPUxSqRUrVig9BJvAznKws3hsLAc7i8fGcrCzHGrtzFM21CI3Nzd8+OGHePHFF8u9/osvvsCECRNQUFAgd2A1xFM2EBERERGpB0/ZoKC+ffti06ZNFV6fkpKCJ554QuKIqC5R68k8rQ07y8HO4rGxHOwsHhvLwc5yqLUz9/TVoj///BMRERHw8/PDyy+/DH9/fwDA0aNHER8fjxMnTmDVqlXw8vIyu52np6cSw60y7ukjIiIiIlIP7ulT0L333ouMjAysX78eTzzxBNq1a4d27dqhb9+++O677/DHH3+gU6dO8PLyMrsQAUBERITSQ7AJ7CwHO4vHxnKws3hsLAc7y6HWztzTV4umTp0KjUZT7dtNmTJFwGhqD/f0yVFYWAhXV1elh2H12FkOdhaPjeVgZ/HYWA52lkNW5+r+fs7z9NWiqVOnKj0EqsNmzZqFSZMmKT0Mq8fOcrCzeGwsBzuLx8ZysLMcau3Mt3cSqUTfvn2VHoJNYGc52Fk8NpaDncVjYznYWQ61duakj0glcnJylB6CTWBnOdhZPDaWg53FY2M52FkOtXbmpI9IJfLz85Uegk1gZznYWTw2loOdxWNjOdhZDrV25qTPShUXF+Ptt9+Gr68vXFxcEBISgh9//FHpYdFdhIaGKj0Em8DOclS388WrxTh89jKOn7+KnILrOH+lGJeu30TRTR1KS3m8sfLwtSwHO4vHxnKwsxxq7cwDuVip0aNHY+3atXj99dfRvn17LF68GP3798f27dvx8MMPKz08Kkd8fDxmz56t9DCsHjvLUZXOuZeuIyXzLP6bcRa/ndLibseSdrDTwNHBDk4OdnA0Xuzt4Ohgb1hubwenesZlt19v9/ft7G/d3t4O9ew1cLC3g4Od4b/17DVwsLODg73m1rK//2taZnfn7cpZZqeBvZ3GoiM5Vxdfy3Kws3hsLAc7y6HWzjxlgxVKT09HSEgI4uLi8OabbwIAioqKEBgYCG9vb/z888/Vuj+esoGIaku2thD/zczFfzPPYu/pAqWHI8ztk0h7Ow3sNX//104Du9v+bbzOzk4DezuY/u1wx3pmt/n733Z2GthrAHs7O8Ntb1vPTnNrAmp3+2NoDJNVw3owW99OA9jd9m+N5rblGg00f//X/Ppbj1vR9Yb7LHt/dhoN7OxQ5ccrO7Zb199a99ZtZUy8iYiUwlM2ENauXQt7e3uMHTvWtMzZ2RljxoxBTEwMsrOz0bJlS4vv/5djF6G77e1WepT9u0F5f0q4c1F5f28o9y8Q+ju/rOLj1eIYyq5m6TaXt45h4UcffYy33x5fjfuq2t9r7lzN0n7ld9FXuo4lz19VngNL7+eLeV/gxRejKlyn/BZV2M67qOhXz4p+Ka3od9UKf4W9yy+3FT92RevXfEwaDTDns8/w9ltvmPa0HTxzGf/NzEVmzuUy6zd3d8GTgc0Q3ModJTo9bpSUorhEh+KSUtzQleJGyW0XXSmKb95afmudv9e/bT3jv4tvWybLTZ0eN3U64Ka0h6Q73DkpNE4MNTD8v6cBgNsnibg1mQRuLdNqL8KrSRPT9QBgZ2f4f8W4jt3f/7j9vo33i9vu17DarUmpYX3zx9bg1n3dPvaKbnfrcYyT39vXLf92drdtvwa3xvb3vfw9Ztz23zuWofz1jd8Pbv/edvf1DP9NSkrCoIEDb905bo37tkWm7blzLMYFd95vhfdxxze0iu63oia3b6MGZcei0ZR9XJS7ftn7Na1edlH1vm+Xs+jzzz7Da6+9Vs79ll25Wo9f7sOXc5/V+DtMVcdUG49f9W2t2s/H999/H+9OnFjOehXcvoqPf+eyk8cvlHt/FeGePiv0+OOPIycnBwcPHjRbvnXrVvTp0wfff/89wsLCyr1tXl4ezp8/b7YsKysL4eHhpr8kBE3dhCtFJcLGT0TWr01jVzwZ5IMnA5shqLmblL0yer3eNBm8qdOjRFeKm6V66HR63CwtRYlOj5u6UpSU6qErNa5juE6n06PEuOzv/+pK/76P8pbddl2p3rDcdNHrUVqqh04PlJYabqsrhWm9Ur3hcW+td+u/JbfdX6keFdyv+ePdvj4REVmHG+dPITfh5Srv6eOBXKxQbm4ufHx8yiw3Ljtz5kyFt503bx4CAwPNLuHh4QCAtLQ0pKamori4WMi4ici6NSy9ilFdGmNc64t41a8AvT0v47tFc3D9+nVEREQAgOkPUjExMcjIyEBiYiISExORkZGBmJgYs3UiIiJQWFiI2NhYpKenIykpCQkJCcjKykJ0dLTZupGRkcjPz8fnsz/B77t+RvpP27Bm2ULgmhYzJv4Hfl4N8Oa/RyCwuRvmv/8Omjlcx57/rsTVY7thd/YQ9v6wFD18nbD2kwn4Z5cWWDbtJQzv0Qq/r/4Moc10cMhKhW9BBh5omI9ru1bj/0JbYf+SqZj6TAD2fj0B7/8zCHZ7VuK5e+zQreQgepQewthAB7gdWIsvn+2Gc2unIWF0d1xPmYkvhweh07kf8WYXe4xsdg5hzn9iVr9maH98HVJeD4XTtpnY+kYvnP5iDNa/cB/C7ffgo4ccEHt/Cf7leQQ/jAnAQ2eTcHB6P3TYPw9H3++PJ/KTkfbKfXijxUnMCbXDgifq49+eh5H6anf84+x67Hm3D+45vATpMY+hX/FPSBzWDq+2PY9377uBz5/wxGCXA/jhpRAEZa/H5uhQtM9ajR9efRj9NPvwSb+meNH/KqL8ruLjJ5qij+YPrBr7ANqe+B6JL4SgY+5mfDXyPvR1ysI7DzbCmA43MdS3AFP7+KLHzT8QP6IrWmb/iM+GBeOe/F8x/Sl/PFY/B2OCnPBsRw2e8LiAN3u1QKfCPzDtmQD45KZh0tOd0PH6Abz8kC9C3bQIb6vB4A6OCHHJQ9RDLdD6ygH836P+8Dr/O17s5Yf2JScxJMgDD3hcQ89mOvT3d0GAQx5G9WiBppcO4bkHW8NTm4lRD7SCP3LR178BunveQDfPG/DIP4R2OIfB97eAe/6fGNi1ORpfOYang5rCz16Lh1o4oUtjHTo1KEJo24bw1Z3Dk4HN0OjycTzRqSkaF+Wgp78H2tS7gvu87BHoCbR1LsQDrRqi8c08PNK+CRpc/QsP+TeG583z6NK8AVo6FaG9G9DBww4+DoW4r3lDNLpxAV1aucOl8Bzua+EG99JL6NDECT5ON9HctRSt3ezhaVeIDt714XIjH+29G8CpSAs/r/pohEL4NnRAY0cdPB11aNbAAfVRhObuLqh34wp83JzhWHINTRs6wVVzAx7OdnCrp0cDex08XezhpC9GkwZOcLhZiCYNHFFPVwQPVwc4a0rQsB5Q3wFwttOhkbM96pXegJtLPdjpitHQ2QEO+puo72gHR00pnO0BZ3ugHnRwqWcHu9KbcKlnD33JDTjXs4OdXgdHew3sNXrYa/RwsAPsUAoHOw00+lLY22kAfenfezP1hr1m1X7vBZFt4p4+K+Tn54eOHTti48aNZsuPHz8OPz8/zJ49G6+//nq5t63Knr49p/JResfLxtJd41V/S4OmCuuUd1+Wvb2gKvdVlV3vVb4dgEuXLsHNza2ScSrbz+J2Fjx+VZ67qrz74s77KSjIh4eHx13vpypvK6nqjqmKvsNW9I23om/JFa9/l8eu6FbVHlP17r9UD1y4mA/XBg1Nb7/0cHVE2yb1Kx4sVZtWq4Wnp6fSw7B67CxebTY2fg/V6299TzMtw63vZ8bvX7d/f7v9ulv/vnUf+jvWg77s/ejveLw77xdl7recbajixzBuvx/zdcu//aVLBXBzcy//jip5rIq+31fl4ygVjanidau6ZnUev9yb1+gjQxXd/tKly2jUqFH5N6jKY1VxTFl/HsLgx//Bz/TZMhcXl3L3xhUVFZmur4i3tze8vb3vev/3t/a46/Vkmbil8/HWW28pPQyrt3j+MnaWIPGruews2MKFC9lYAnYWrzYbmz5rZ/aHOfFvH68LVi6M52tZgrhvFkjp7HC5ahNLI7690wr5+PggNze3zHLjMl9fX9lDoiro0aOH0kOwCewsBzuLx8ZysLN4bCwHO8uh1s7c02eFgoODsX37dly+bL57edeuXabrq8O41zArK6vWxkhlHT58GE2aNFF6GFaPneVgZ/HYWA52Fo+N5WBnOWR1Nv5eXtVjbXDSZ4UGDx6MmTNnYsGCBabz9BUXF2PRokUICQmp9ukaMjIyAMB0QBciIiIiIlJednY2unbtWul6nPRZoZCQEAwZMgQTJkxAXl4e/P39sWTJEpw8eRILFy6s9v116NABALB69Wp06tSptodLuHWwnPXr18Pf31/p4VgtdpaDncVjYznYWTw2loOd5ZDZubi4GNnZ2ejZs2eV1uekz0otXboUkyZNwrJly5Cfn4/OnTtjw4YNCA0NrfZ9Gd8i2qlTpyodHYgs5+/vz8YSsLMc7CweG8vBzuKxsRzsLIeszlXZw2fESZ+VcnZ2RlxcHOLi4pQeChERERERKYhH7yQiIiIiIrJinPQRERERERFZMU76qFJeXl6YMmUKvLy8lB6K1WJjOdhZDnYWj43lYGfx2FgOdpZDzZ01er1er/QgiIiIiIiISAzu6SMiIiIiIrJinPQRERERERFZMU76iIiIiIiIrBgnfURERERERFaMkz4iIiIiIiIrxkkfERERERGRFeOkj4iIiIiIyIpx0kdERERERGTFOOkjIiIiIiKyYpz0ERERERERWTFO+oiIiIiIiKwYJ31ERERERERWzEHpAVD5rl69iri4OOzatQvp6enIz8/HokWLMHr06CrdvqCgAOPHj0dSUhIKCwvRo0cPzJo1C127dq32WAoKCpCamoqWLVvCycmp2rcnIiIiIqLaU1xcjOzsbPTs2RPu7u6Vrs9Jn0pduHAB06dPR6tWrXDfffdhx44dVb5taWkpnnrqKezfvx9vvfUWmjRpgnnz5qFXr17Ys2cP2rdvX62xpKamIjw8vHobQEREREREQq1fvx4DBgyodD1O+lTKx8cHubm5aNasGXbv3o3u3btX+bZr167Fzz//jDVr1mDw4MEAgIiICHTo0AFTpkxBYmJitcbSsmVLAIYXlb+/f7VuS1V37tw5NG3aVOlhWD12loOdxWNjOdhZPDaWg53lkNU5KysL4eHhpt/TK8NJn0o5OTmhWbNmFt127dq1aNq0KQYOHGha5uXlhYiICCxfvhzFxcXVepumcV1/f38EBARYNCaq3Jw5czB//nylh2H12FkOdhaPjeVgZ/HYWA52lkN256r+Ts8DuVihvXv3omvXrrCzM396e/TogcLCQhw5ckShkdHd8BuxHOwsx90663Q65Ofnmy46nU7iyKwHX8tysLN4bCwHO8uh1s6c9Fmh3Nxc+Pj4lFluXHbmzJkKb5uXl4cDBw6YXbKysoSNlW4JCwtTegg2gZ3luFvny5cvY82aNabL5cuXJY7MevC1LAc7i8fGcrCzHGrtzEmfFbp+/Xq5u3qdnZ1N11dk3rx5CAwMNLsYD+KSlpaG1NRUxMXFQavVIjIyEsCtF3d0dDSysrKQkJCApKQkpKenIzY2FoWFhYiIiDBbNyYmBhkZGUhMTERiYiIyMjIQExNjtk5ERAQKCwsRGxuL9PR0JCUlISEhAVlZWYiOjjZbNzIyElqtFnFxcUhNTUVKSgri4+ORk5ODqKgos3WjoqKQk5OD+Ph4pKSkqGabjJ+XtKZtUuPzFBQUZHXbpMbnacmSJRVu0/Lly7F3716cOHECP/zwQ53ZJrU9T56enla3TWp8nmbMmGF126S258nImrZJjc/Tyy+/bHXbpMbnKTk5Wco2paWloTo0er1eX61bkHTGA7lU9ZQNDRo0wNChQ7Fw4UKz5Rs3bsRTTz2FlJQU9O3bt9zb5uXl4fz582bLjB8UzczM5Gf6BIqKilLtWwKsCTvLcbfO+fn5WLNmjenrIUOGwMPDQ9bQrAZfy3Kws3hsLAc7yyGr84EDBxAYGFjl3895IBcrZDzy552My3x9fSu8rbe3N7y9vYWNjSo2adIkpYdgE9hZDnYWj43lYGfx2FgOdpZDrZ359k4rFBwcjN9//x2lpaVmy3ft2gVXV1d06NBBoZHR3axfv17pIdgEdpaDncVjYznYWTw2loOd5VBrZ076atnx48dx6NAhaY+Xm5uLw4cP4+bNm6ZlgwcPxrlz57Bu3TrTsgsXLmDNmjUICwur1ukaSB4/Pz+lh2AT2FkOdhaPjeVgZ/HYWA52lkOtnfn2Tgt9/vnn+Pnnn7Fy5UrTsueffx5Lly4FAHTp0gUbN26s0Vsl586di4KCAtPRNpOTk/HXX38BAP7v//4Pbm5umDBhApYsWYITJ06gTZs2AAyTvgceeADPP/88Dh48iCZNmmDevHnQ6XSYNm2axeMhsVxcXJQegk1gZznYWTw2loOdxWNjOdhZDrV25p4+C3399ddo2rSp6etNmzZhyZIlGDt2LObMmYPjx4/XeII1c+ZMTJo0CV988QUAYN26dZg0aRImTZqE/Pz8Cm9nb2+PjRs3YujQofj888/x1ltvoUmTJti2bRs6duxYozGROOnp6UoPwSawsxzsLB4by8HO4rGxHOwsh1o78+idFnJzc8NHH31kOpTqmDFjsGPHDhw7dgwAMHnyZCxbtgwnTpxQcpi1orpHByLLaLVaeHp6Kj0Mq8fOctytM4/eWTv4WpaDncVjYznYWQ5Znav7+zn39Fnozrny5s2b8eSTT5q+btOmDc6ePSt7WFSHGc/bQmKxsxzsLB4by8HO4rGxHOwsh1o7c9JnoQ4dOiApKQmA4a2dZ86cMZv0/fXXX3B3d1dodFQXLVmyROkh2AR2loOdxWNjOdhZPDaWg53lUGtnTvos9Oabb+LHH3+Eh4cHwsLCcO+995qd8Hzbtm0IDg5WboBU54SFhSk9BJvAznKws3hsLAc7i8fGcrCzHGrtzKN3WmjYsGFo3LgxNm7cCHd3d7z00ktwcDDkNL6X99lnn1V4lFSXJCcnKz0Em8DOcrCzeGwsBzuLx8ZysLMcau3MPX018Pjjj2P27NmYMmUKvLy8TMs9PT2xbt06/POf/1RwdFTXqPU94NaGneVgZ/HYWA52Fo+N5WBnOdTamXv6aignJwc7d+5EXl4eBg0ahBYtWkCn0+HSpUtwc3ODvb290kOkOuLll19Wegg2gZ3lYGfx2FgOdhaPjeVgZznU2pl7+iyk1+vxn//8B23btsXIkSPxn//8B0eOHAEAXL16FW3atMGcOXMUHiXVJTt37lR6CDaBneVgZ/HYWA52Fo+N5WBnOdTamZM+C8XFxeGzzz4zHdDl9lM4uLm5YeDAgfj2228VHCHVNTxPmRzsLAc7i8fGcrCzeGwsBzvLodbOnPRZ6KuvvsJzzz2HGTNmlHuUzs6dO5v2/BFVRfPmzZUegk1gZznYWTw2loOdxWNjOdhZDrV25qTPQtnZ2fjHP/5R4fX169fH5cuXJY6I6rpNmzYpPQSbwM5ysLN4bCwHO4vHxnKwsxxq7cxJn4W8vb2RnZ1d4fV79uxBq1atJI6I6ro33nhD6SHYBHaWg53FY2M52Fk8NpaDneVQa2dO+iw0cOBAzJ8/H8ePHzct02g0AIDNmzdj8eLFGDJkiFLDozpo9OjRSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtr9LcfgYSq7NKlSwgNDcWJEyfwyCOPICUlBY8//jiuXr2KX375BV26dMHOnTvh6uqq9FBr7MCBAwgMDERmZiYCAgKUHg4R1XH5+flYs2aN6eshQ4ao9oPvREREalTd38+5p89Cbm5u+PXXXzF+/Hjk5OTA2dkZqampKCgowJQpU/DTTz9ZxYSP5AkLC1N6CDaBneVgZ/HYWA52Fo+N5WBnOdTamXv6LHT+/Hl4eXnddZ3ffvsN3bt3lzQicbinj4hqE/f0ERER1Qz39Eny2GOPIT8/v8Lrt2/fjj59+kgcEdV1MTExSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtz0mehwsJCPP7447h06VKZ6zZs2ID+/fvj/vvvV2BkVFcNHz5c6SHYBHaWg53FY2M52Fk8NpaDneVQa2dO+iy0detWnD9/Hv369cPVq1dNy1euXImBAwfisccew8aNGxUcIdU1GRkZSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtz0meh1q1bY9u2bcjOzkb//v1RWFiIBQsWYNSoURg4cCDWr18PZ2dnpYdJREREREQ2zkHpAdRlfn5+2LJlC3r16oXg4GAcO3YM//rXv7BgwQLTOfuIqiooKEjpIdgEdpaDncVjYznYWTw2loOd5VBrZ+7pqyKtVlvuxdvbG6tWrcLZs2cRGRmJDz/8EPn5+abriapqxYoVSg/BJrCzHOwsHhvLwc7isbEc7CyHWjvzlA1VZGdnd9e9d3q9vtzrdTqdyGFJwVM2EFFt4ikbiIiIaqa6v5/z7Z1VNHnyZL5lk4QKCwtDcnKy0sOweuwsBzuLx8ZysLN4bCwHO8uh1s7c00eV4p4+IqpN3NNHRERUMzw5O1EdFRERofQQbAI7y8HO4rGxHOwsHhvLwc5yqLUz9/RV0fTp06HRaDBx4kTY2dlh+vTpld5Go9Fg0qRJEkYnFvf0yVFYWAhXV1elh2H12FmOu3Xmnr7awdeyHOwsHhvLwc5yyOrMz/QJMnXqVGg0Grz99ttwdHTE1KlTK72NtUz6SI5Zs2bx9SIBO8vBzuKxsRzsLB4by8HOcqi1Myd9VVRaWnrXr4lqqm/fvkoPwSawsxzsLB4by8HO4rGxHOwsh1o78zN9RCqRk5Oj9BBsAjvLwc7isbEc7CweG8vBznKotTP39NWQVqvFli1bcPLkSQBAmzZt8Nhjj6Fx48bKDozqnPz8fKWHYBPYWQ52Fo+N5WBn8dhYDnaWQ62dOemrgalTp+Kjjz7CjRs3cPvxcBwdHTF+/PgqHeyFyCg0NFTpIdgEdpaDncVjYznYWTw2loOd5VBrZ76900KxsbGYPn06+vTpg40bN+LYsWM4duwYNm7ciD59+uD9999HbGys0sOkOiQ+Pl7pIdgEdpaDncVjYznYWTw2loOd5VBrZ56ywULNmzdHt27d8N1335V7fVhYGPbs2YMzZ85IHlnt4ykbiKg28ZQNRERENcOTs0ty6dIl9OvXr8Lr+/fvjytXrkgcEdV1YWFhSg/BJrCzHOwsHhvLwc7isbEc7CyHWjtz0mehhx56CLt27arw+l27duGhhx6y+P6Li4vx9ttvw9fXFy4uLggJCcGPP/5Y6e2M5xO88+Ls7GzxWEiO5ORkpYdgE9hZDnYWj43lYGfx2FgOdpZDrZ056bPQ/Pnz8csvvyA6OhpZWVkoLS1FaWkpsrKy8Prrr+PXX3/F/PnzLb7/0aNH45NPPsHIkSPx2Wefwd7eHv3790daWlqVbv/FF19g2bJlpsuiRYssHgvJERkZqfQQbAI7y8HO4rGxHOwsHhvLwc5yqLUzP9NnoYYNG6K0tBRFRUUAADs7w/zZeNJ2JycnODiYHxxVo9Hg0qVLld53eno6QkJCEBcXhzfffBMAUFRUhMDAQHh7e+Pnn3+u8LZTp07FtGnTcP78eTRp0sSibbsTP9Mnh1arhaenp9LDsHrsLMfdOvMzfbWDr2U52Fk8NpaDneWQ1bm6v5/zlA0WGjRoEDQajZD7Xrt2Lezt7TF27FjTMmdnZ4wZMwYxMTHIzs5Gy5Yt73ofer0ely9fRsOGDYWNk2rXwoUL8dZbbyk9DKvHznKws3hsLAc7i8fGcrCzHGrtzEmfhRYvXizsvvfu3YsOHTqgUaNGZst79OgBANi3b1+lk7527drh6tWrqF+/PsLDwzFr1iw0bdpU2Jip5ozPL4nFznKws3hsLAc7i8fGcrCzHGrtzEmfCuXm5sLHx6fMcuOyu50GwsPDA6+88goefPBBODk54aeffkJ8fDzS09Oxe/fuMhPJO+Xl5eH8+fNmy7KysizYCqqu69evKz0Em8DOcrCzeGwsBzuLx8ZysLMcau3MA7lYaOvWrYiLizNblpCQgFatWqFp06aIjo6GTqez6L6vX78OJyenMsuNR+C824vptddew5w5czBixAgMGjQIn376KZYsWYKjR49i3rx5lT72vHnzEBgYaHYJDw8HAKSlpSE1NRVxcXHQarWmD6oaD01rPKhNQkICkpKSkJ6ejtjYWBQWFiIiIsJs3ZiYGGRkZCAxMRGJiYnIyMhATEyM2ToREREoLCxEbGws0tPTkZSUhISEBGRlZSE6Otps3cjISGi1WsTFxSE1NRUpKSmIj49HTk4OoqKizNaNiopCTk4O4uPjkZKSoppt+vjjj61um9T4PM2ePdvqtkmNz9Mff/xR4TYtX74ce/fuxYkTJ/DDDz/UmW1S2/M0ZcoUq9smNT5PO3futLptUtvzZHwrnDVtkxqfp+TkZKvbJjU+T8eOHZOyTVU9uKMRD+RioUceeQStW7fG8uXLAQAZGRno2rUrOnfuDH9/f6xduxYzZszA22+/Xe37DgwMRNOmTbF161az5QcPHkRAQADmz5+PcePGVes+fXx8EBAQgC1bttx1vYr29IWHh/NALoLl5OSgefPmSg/D6rGzHHfrzAO51A6+luVgZ/HYWA52lkNWZ56cXZJDhw6hW7dupq+XLVuGRo0a4aeffsKqVavw73//G0uXLrXovn18fJCbm1tmuXGZr69vte+zZcuW0Gq1la7n7e2NgIAAs4u/v3+1H4+qLzY2Vukh2AR2loOdxWNjOdhZPDaWg53lUGtnTvosdO3aNbPPx6WkpKBfv35wdXUFAHTv3h2nTp2y6L6Dg4Nx5MgRXL582Wy58WTwwcHB1bo/vV6PkydPwsvLy6LxkBw1Oa8jVR07y8HO4rGxHOwsHhvLwc5yqLUzJ30WatmyJX777TcAhrc/ZmZm4oknnjBdr9Vqy/1cXlUMHjwYOp0OCxYsMC0rLi7GokWLEBISYjpy5+nTp3H48GGz29751kzAcKL28+fPo1+/fhaNh+Qwvl+bxGJnOdhZPDaWg53FY2M52FkOtXbm0TstNHLkSEyfPh05OTk4cOAAPDw8MGDAANP1e/bsQYcOHSy675CQEAwZMgQTJkxAXl4e/P39sWTJEpw8eRILFy40rffcc88hNTUVt38ss3Xr1hg6dCiCgoLg7OyMtLQ0rFy5EsHBwdX+HCDJlZycrPQQbAI7y8HO4rGxHOwsHhvLwc5yqLUz9/RZaOLEiXjnnXeQnZ2NVq1aYf369XB3dwdg2Mu3Y8cOPPPMMxbf/9KlS/H6669j2bJlePXVV3Hz5k1s2LABoaGhd73dyJEjkZ6ejqlTp+L111/Hb7/9hvHjx2Pnzp2mt56SOhmP0ERisbMc7CweG8vBzuKxsRzsLIdaO/PonVSp6h4diCzDo2rJwc5y8Oid4vG1LAc7i8fGcrCzHDx6JxHd1fr165Uegk1gZznYWTw2loOdxWNjOdhZDrV25qSPSCX8/PyUHoJNYGc52Fk8NpaDncVjYznYWQ61duakj0glXFxclB6CTWBnOdhZPDaWg53FY2M52FkOtXbmpI9IJdLT05Uegk1gZznYWTw2loOdxWNjOdhZDrV25qSPSCXGjBmj9BBsAjvLwc7isbEc7CweG8vBznKotTMnfRaaPn06MjMzK7z+wIEDmD59usQRUV0XHR2t9BBsAjvLwc7isbEc7CweG8vBznKotTNP2WAhOzs7LF++HCNGjCj3+lWrVmHEiBHQ6XSSR1b7eMoGIqpNPGUDERFRzfCUDSqh1Wrh6Oio9DCoDgkLC1N6CDaBneVgZ/HYWA52Fo+N5WBnOdTa2UHpAdQlO3fuxI4dO0xfr1u3DllZWWXWKygowKpVqxAUFCRxdFTXJScnKz0Em8DOcrCzeGwsBzuLx8ZysLMcau3MPX3VsH37dkydOhVTp06FRqPBunXrTF/ffvn000/h4eGBOXPmKD1kqkPU+h5wa8POcrCzeGwsBzuLx8ZysLMcau3MPX3VMH78eLzyyivQ6/Xw9vbG/PnzMWjQILN1NBoNXF1d4ezsrNAoqa56+eWXlR6CTWBnOdhZPDaWg53FY2M52FkOtXbmnr5qcHFxQePGjdGkSROcOHECo0aNQuPGjc0unp6enPCRRXbu3Kn0EGwCO8vBzuKxsRzsLB4by8HOcqi1M/f0Wah169ZllhUWFmLlypUoLi5G//79y12HqCI8eqEc7CwHO4vHxnKws3hsLAc7y6HWzpz0WWjMmDHYtWuX6Vx9N27cwAMPPGD62s3NDdu2bUOXLl2UHCbVIc2bN1d6CDaBneVgZ/HYWA52Fo+N5WBnOdTamW/vtND27dsxcOBA09eJiYnIzMzEN998g8zMTDRr1gzTpk1TcIRU12zatEnpIdgEdpaDncVjYznYWTw2loOd5VBrZ56c3UKurq6YO3cu/vWvfwEAwsPDcebMGaSnpwMAPvnkE8TFxSE3N1fJYdYKnpxdjsLCQri6uio9DKvHznLcrTNPzl47+FqWg53FY2M52FkOWZ15cnZJ6tevj4KCAgBASUkJduzYgb59+5qub9iwIS5duqTQ6KguGj16tNJDsAnsLAc7i8fGcrCzeGwsBzvLodbO/Eyfhbp27YqvvvoKvXv3xvfff48rV64gLCzMdP2xY8fQtGlTBUdIdc3q1auVHoJNYGc52Fk8NpaDncVjYznYWQ61duaePgu9//77yMvLQ7du3TBt2jQMGjQIPXr0MF2flJSEhx56SMERUl1z+x8NSBx2loOdxWNjOdhZPDaWg53lUGtnfqavBs6fP4+ff/4Z7u7u6Nmzp2l5QUEBlixZgp49eyI4OFi5AdYSfqaPiGoTP9NHRERUM/xMn0ReXl4YMGCA2YQPANzd3fHaa69ZxYSP5ImJiVF6CDaBneVgZ/HYWA52Fo+N5WBnOdTamZ/pq6HU1FT88MMPOHXqFADDSduffvpphIaGKjwyqmuGDx+u9BBsAjvLwc7isbEc7CweG8vBznKotTP39Fnoxo0bGDRoEB599FHMnDkTP/74I3788UfMnDkTvXv3xuDBg3Hz5k2lh0l1SEZGhtJDsAnsLAc7i8fGcrCzeGwsBzvLodbOnPRZaNq0aUhKSsIbb7yB3NxcaLVaaLVanD17Fm+++SbWrVuH6dOnKz1MIiIiIiKycZz0WSgxMRGRkZH4+OOPzU7N4O3tjY8++gjPPfccli1bpuAIqa4JCgpSegg2gZ3lYGfx2FgOdhaPjeVgZznU2pmTPgvl5uYiJCSkwutDQkJw9uxZiSOium7FihVKD8EmsLMc7CweG8vBzuKxsRzsLIdaO/OUDRby9/dHt27dsHLlynKvHzZsGHbv3o2srCzJI6t9PGUDEdUmnrKBiIioZnjKBkkiIyOxevVqREVF4c8//4ROp0NpaSn+/PNPvPjii1izZg1Gjx6t9DCpDlHryTytDTvLwc7isbEc7CweG8vBznKotTP39FlIp9NhzJgxWLp0KTQaDezsDPPn0tJS6PV6REZGYuHChabldRn39BFRbeKePiIioprhnj5J7O3tsXjxYuzbtw/vvfceXnjhBbzwwgt4//33sW/fPixatMgqJnwkT0REhNJDsAnsLAc7i8fGcrCzeGwsBzvLodbO3NNXTUVFRfjuu+9w4sQJNGnSBE899RR8fHyUHpZQ3NMnR2FhIVxdXZUehtVjZznu1pl7+moHX8tysLN4bCwHO8shqzP39AmUl5eHwMBAjBgxAjExMRg7dizat2+PLVu2KD00sgKzZs1Segg2gZ3lYGfx2FgOdhaPjeVgZznU2pmTvmqIjY3FyZMnER0djQ0bNuDTTz+Fi4sLxo0bp/TQyAr07dtX6SHYBHaWg53FY2M52Fk8NpaDneVQa2cHpQdQl2zevBnPPfccZs6caVrWtGlTjBgxAn/++Sc6duyo4OiorsvJyVF6CDaBneVgZ/HYWA52Fo+N5WBnOdTamXv6quH06dN4+OGHzZY9/PDD0Ov1OHfuXK0+VnFxMd5++234+vrCxcUFISEh+PHHH6t025ycHERERMDd3R2NGjXCgAEDcPz48VodH9W+/Px8pYdgE9hZDnYWj43lYGfx2FgOdpZDrZ056auG4uJiODs7my0zfl1SUlKrjzV69Gh88sknGDlyJD777DPY29ujf//+SEtLu+vtrl69it69eyM1NRUxMTGYNm0a9u7di549e+LixYu1OkaqXaGhoUoPwSawsxzsLB4by8HO4rGxHOwsh1o7c9JXTSdPnsTvv/9uuvzxxx8AgKNHj5otN14skZ6ejpUrV+KDDz5AXFwcxo4di23btqF169YYP378XW87b948HD16FBs2bMD48eMRHR2NzZs3Izc3V7UfLCWD+Ph4pYdgE9hZDnYWj43lYGfx2FgOdpZDrZ15yoZqsLOzg0ajKbNcr9eXWW5cptPpqv0448ePxyeffAKtVotGjRqZln/wwQeIiYnB6dOn0bJly3Jv26NHDwCGiePt+vbti2PHjiErK6va4+EpG4ioNvGUDURERDVT3d/PeSCXali0aJGUx9m7dy86dOhgNuEDbk3o9u3bV+6kr7S0FH/88Qf+9a9/lbmuR48e2Lx5M65cuYKGDRuKGTjVSFhYGJKTk5UehtVjZznYWTw2loOdxWNjOdhZDrV25qSvGiIjI6U8Tm5ubrknfDcuO3PmTLm302q1KC4urvS2dzvKaF5eHs6fP2+2zJK9g1R9avwGYY3YWQ52Fo+N5WBn8dhYDnaWQ62d+Zk+Fbp+/TqcnJzKLDceNOb69esV3g6ARbc1mjdvHgIDA80u4eHhAIC0tDSkpqYiLi4OWq3WNAkOCwsDAERHRyMrKwsJCQlISkpCeno6YmNjUVhYiIiICLN1Y2JikJGRgcTERCQmJiIjIwMxMTFm60RERKCwsBCxsbFIT09HUlISEhISkJWVhejoaLN1IyMjodVqERcXh9TUVKSkpCA+Ph45OTmIiooyWzcqKgo5OTmIj49HSkqKarapU6dOVrdNanyegoKCrG6b1Pg8DRs2rMJtWr58Ofbu3YsTJ07ghx9+qDPbpLbnyc/Pz+q2SY3P0zPPPGN126S258n47iVr2iY1Pk99+vSxum1S4/MUGRkpZZsqO7jjnfiZPhUKDAxE06ZNsXXrVrPlBw8eREBAAObPn1/uCeEvXLgALy8vTJ8+HZMmTTK7bt68eXj55Zdx+PBhi/b0hYeH8zN9gmm1Wnh6eio9DKvHznLcrTM/01c7+FqWg53FY2M52FkOWZ2r+5k+7ulTIR8fH+Tm5pZZblzm6+tb7u08PT3h5ORk0W2NvL29ERAQYHbx9/ev7iaQBRYuXKj0EGwCO8txt86NGjXCkCFDTJc7P79MVcPXshzsLB4by8HOcqi1Myd9KhQcHIwjR47g8uXLZst37dplur48dnZ2CAoKwu7du8tct2vXLrRr144HcVEx44F6SCx2luNune3t7eHh4WG62NvbSxyZ9eBrWQ52Fo+N5WBnOdTamQdyUaHBgwdj5syZWLBgAd58800AhhPDL1q0CCEhIab3vp8+fRqFhYW45557zG77zjvvYPfu3ejWrRsA4M8//8S2bdtM91VdxcXFAHhAF9EOHz6MJk2aKD0Mq8fOcrCzeGwsBzuLx8ZysLMcsjobfy83/p5eGX6mT6UiIiKQlJSE6Oho+Pv7Y8mSJUhPT8fWrVsRGhoKAOjVqxdSU1Nx+1N45coVdOnSBVeuXMGbb76JevXq4ZNPPoFOp8O+ffvg5eVV7bEsWbIEo0ePrq1NIyIiIiKiWrB+/XoMGDCg0vW4p0+lli5dikmTJmHZsmXIz89H586dsWHDBtOEryINGzbEjh07EB0djffeew+lpaXo1asXZs+ebdGEDwA6dOgAAFi9erXpCJNUu4wHy1m/fj0/QykQO8vBzuKxsRzsLB4by8HOcsjsXFxcjOzsbPTs2bNK63PSp1LOzs6Ii4tDXFxchevs2LGj3OUtWrQwOzJeTRkPstCpUycevVMwf39/NpaAneVgZ/HYWA52Fo+N5WBnOWR17tq1a5XX5YFciIiIiIiIrBgnfURERERERFaMkz4iIiIiIiIrxkkfVcrLywtTpkyx+EAwVDk2loOd5WBn8dhYDnYWj43lYGc51NyZp2wgIiIiIiKyYtzTR0REREREZMU46SMiIiIiIrJinPQRERERERFZMU76iIiIiIiIrBgnfURERERERFaMkz4iIiIiIiIrxkkfERERERGRFeOkj4iIiIiIyIpx0kdERERERGTFOOkjIiIiIiKyYpz0ERERERERWTFO+oiIiIiIiKyYg9IDIPUrKChAamoqWrZsCScnJ6WHQ0RERERk04qLi5GdnY2ePXvC3d290vU56aNKpaamIjw8XOlhEBERERHRbdavX48BAwZUuh4nfVSpli1bAjC8qPz9/RUejfWaNm0apkyZovQwrB47y8HO4rGxHOwsHhvLwc5yyOqclZWF8PBw0+/pldHo9Xq94DFRHXfgwAEEBgYiMzMTAQEBSg+HiIiIiMimVff3cx7IhUglwsLClB6CTWBnOe7aWVcCnNkLHE81/FdXIm9gVoSvZTnYWTw2loOd5VBrZ+7pU6mrV68iLi4Ou3btQnp6OvLz87Fo0SKMHj26SrcvKCjA+PHjkZSUhMLCQvTo0QOzZs1C165dqz0W7ukjolqhKwH2fQNkfgsUnAZKSwA7B8C9NRA4EAgeCdjzUwdERESV4Z4+K3HhwgVMnz4dhw4dwn333Vet25aWluKpp55CYmIiXnnlFXz88cfIy8tDr169cPToUUEjppqKiopSegg2gZ3lKNNZVwJsngjsjANy9/894atn+G/uPsPyze9yr1818LUsBzuLx8ZysLMcau3MPX0qVVxcjPz8fDRr1gy7d+9G9+7dq7ynb/Xq1Rg6dCjWrFmDwYMHAwDOnz+PDh064Mknn0RiYmK1xsI9fXLk5OSgefPmSg/D6rGzHGU671limNjpbgANmgEaza3r9Hrg6lnA3hHoOR7o+pz8AddBfC3Lwc7isbEc7CyHrM7V/f2c76NRKScnJzRr1syi265duxZNmzbFwIEDTcu8vLwQERGB5cuXo7i4mOfbU6H169fj5ZdfVnoYVo+d5TDrrCtBacZaaIqvQO/e2nzCBxi+btAMmoJT0GR+C9w3gm/zrAK+luVgZ/HYuGJ6vR5XrlzB5cuXcfPmTdRkX01WVhaKi4trcXRUnpp01mg0qFevHho1aoSGDRtCc+fPyxrgT1UrtHfvXnTt2hV2dubv3u3RowcWLFiAI0eOICgoqNzb5uXl4fz582bLsrKyhI2VbvHz81N6CDaBneUwdtaV6hGfmITBp4/CXl8PRefz0bD0Mlz0Rbhk54Yrmoam23hqnOCRfwp25zIA3y5KDb3O4GtZDnYWj43LV1JSgpycHBQWFgIAHBwcYGdnZ/FEoHXr1rU5PKqApZ31ej10Oh2Kiopw5coVuLq6onnz5nBwqJ3pGj/TZ4Vyc3Ph4+NTZrlx2ZkzZyq87bx58xAYGGh2MZ6YPS0tDampqYiLi4NWq0VkZCSAW0cpio6ORlZWFhISEpCUlIT09HTExsaisLAQERERZuvGxMQgIyMDiYmJSExMREZGBmJiYszWiYiIQGFhIWJjY5Geno6kpCQkJCQgKysL0dHRZutGRkZCq9UiLi4OqampSElJQXx8PHJyckzvrTauGxUVhZycHMTHxyMlJUU127Rw4UKr2yY1Pk/Lly+3um1S4/N08+ZNREZG4sSFa/h93+9w1hfBVXcFTXXn4FJaCKAUDUqv4ObNmwAMb2kvKrVD3rlzQNFlVW6T2p6nTz/91Oq2SY3PU25urtVtk9qep+nTp1vdNtXG85SZmYnc3Fy4ubnB3t4e7du3B2CYVDg7O6Np06bw8PBAo0aN4Ovri3r16qFdu3bQ6XRo164dNBoNWrVqBVdXV3h5ecHNzQ0NGjRAixYtYG9vb7auvb09WrRogQYNGqBx48bw8vKCq6srWrVqBY1GY7ZuvXr14Ovri0aNGsHDwwNNmzaFs7MzWrduDb1eb7auo6MjmjVrBjc3N7i5uaFZs2ZwdHQ0W0ev11u8TY0bN1bdNvn6+lq0TfXr10ezZs3QtGlTODg44MqVK5gzZ06Fr720tDRUBz/TVwdU9zN99vb2GDduHObNm2e2fNu2bXjssceQlJRkmsjdqaI9feHh4fxMn2BxcXF46623lB6G1WNnOeLi4vDWG2/g5G8/4MKPs9G5ZD/sNDAcrfM2V+q3QYleg4LCm2hUegmN6rvAZcRS7umrAr6W5WBn8di4fCdOnMDNmzfRvn37Wnmb39mzZy3+6BBVXW101uv1OHr0KOrVq4e2bduWuw4/00dwcXEp973ERUVFpusr4u3tDW9vb2Fjo4qNGTNG6SHYBHaWoKQYUT19gVWj4HXhNG6WFqIE9eCCG9C4egAOTsDVcwAAD0c9bto7oeDaDbjiOgpd28OlaflvPydzfC3Lwc7isXH59Ho9HBwcau1zXU2aNKmV+6G7q43OGo0G9vb2NfoM55349k4r5OPjg9zc3DLLjct8fX1lD4mqwPhWExKLnQUqvgL8vgxIHIoLSTHA5RyU6vW4Zt8Avzg+CF39ZoC+FKhX/9ZtdEWwgx6eei0KNa7Ibv4kD+JSRXwty8HO4rGxHNnZ2UoPwSbUVufaPIgLwD19Frl+/TomTpyI3r17m95jqybBwcH46aefUFpaanYwl127dsHV1RUdOnRQcHRUkSVLlig9BJvAzgJcPQ9krgUOfg/cNBxwoG2bNkBDHxxv/QSmH2kDHezxYJsNcDj2X+ByDlBaCkAPFObD7vollGgc8avjg7jhE4ZgJbelDuFrWQ52Fo+N5ajobYJUu9TamXv6LODi4oIvv/wS586dU3ooyM3NxeHDh00HQwCAwYMH49y5c1i3bp1p2YULF7BmzRqEhYXxdA0qpcY/IFgjdq5F+aeAHR8BK4YB+1eaJnxo7IcpO0uAYd/gZLO+uKlxRKnGHro+0w3n4fMNBhwcAegBvQ7wCcZq1+FIcB2DIl3t/mXTmvG1LAc7i8fGchw9elTpIdgEtXbmnj4L3X///cjMzBT6GHPnzkVBQYHpaJvJycn466+/AAD/93//Bzc3N0yYMAFLlizBiRMn0KZNGwCGSd8DDzyA559/HgcPHkSTJk0wb9486HQ6TJs2TeiYyXLJyclKD8EmsHMtOHcQ2J8InEwznFjdyOc+IHgk0LIHpg02TN6Kb5aarnZ2cjKceP2+EcCPk4AjmwAHZ2hGrEJawl6U3tDh+k2d7K2ps/haloOdxWNjOYxH/ySx1NqZe/os9Omnn2LlypX4+uuvUVJSIuQxZs6ciUmTJuGLL74AAKxbtw6TJk3CpEmTkJ+fX+Ht7O3tsXHjRgwdOhSff/453nrrLTRp0gTbtm1Dx44dhYyVao6faZCDnS2k1wOndwHfvwqsfxE48ZNhmUYDtH0ECP8CeOZzoFUIoNGYOhsncfZ2GtSz//tHjr0D0LYn4NQQsK8HXDkDF0d7w/o3Sst9eCqLr2U52Fk8NpZDbZ/pW7x4MTQaDZydnZGTk1Pm+l69eiEwMND0dZs2baDRaMq99OvXz+y2aWlpePLJJ9G8eXM4OzujVatWCAsLQ2JiIgBg9OjRFd7X7RfjUfN79eoFjUZT4YTuxx9/NN1m/vz5ZbZx9+7dNc1VY9zTZ6HRo0fDzs4O48aNw6uvvormzZuXOSqmRqPB/v37LX6MkydPVrrO4sWLsXjx4jLLPTw88PXXX+Prr7+2+PFJrpdfflnpIdgEdq6mUh1wbDuwfwVwMevWcjsHoP0TwH3DAI+yJ6I1djZO+lzq2Zuv0Nj/1r8vZMHZoTEAoKiEe/qqiq9lOdhZPDaWw8vLS+khlKu4uBgffvih6Zx0dxMcHIw33nijzPLbD1K4Zs0aDB06FMHBwXjttdfg4eGBEydOYOfOnfjqq68wYsQIjBs3Dn369DHd5sSJE5g8eTLGjh2LRx55xLTcz8/P9G9nZ2dkZWUhPT0dPXr0MHv8b775Bs7OzigqKkKjRo2qtf2ycNJnIU9PTzRu3Jh7zqjW7Ny5E/7+/pWvSDXCzlV0swj4cyPwx2rgym1HA67nCtwbBgQNARpU/AuEsXPR35M+53p3vLHEo7Vh4lhaAlzMgouj4VQx129w0ldVfC3Lwc7isbEcV69ehbOzs9LDKCM4OBhfffUVJkyYUOkR5ps3b45Ro0bddZ2pU6eiU6dO+PXXX+Ho6Gh2XV5eHgDgwQcfxIMPPmhavnv3bkyePBkPPvhghffv5+eHkpISrFixwmzSV1RUhKSkJDz11FP49ttvTadIUxtO+iy0Y8cOpYdAVsbDw0PpIdgEdq5E0WXgQBKQ+S1QdOnWchcPIHAQ0GkA4Fz5XzGNnU17+hzv2NNnXw/waGPYe3gxC871HgYAFHNPX5XxtSwHO4vHxnLY29tXvpICYmJiMGLECHz44Yf4/PPPa3x/x44dw/Dhw8tM+ADU+FzUw4cPx5dffolZs2aZjpCfnJyMwsJCRERE4NtvvzU7cr6aqHNURDaoefPmSg/BJrBzBa7mAT/PBRIjgN0JtyZ8jXyBh6OBEauArs9WacIH3OpsPJCLs0M5v2wY3+J5MQvODoYfR9zTV3V8LcvBzuKxsRzlTYLUoG3btnjuuefw1VdfmQ5eWJGbN2/iwoULZS7Xr183rdO6dWts3brVdPDD2jRixAjk5uaa7fxJTEzEY489ZppQOjioc58aJ301oNPpsGTJEkRERCAkJAQhISGIiIjA0qVLodPxFxeqnk2bNik9BJvAznfIPwns+NBw2oWMNcDNv39wNvYHHpsMDF0OBIQDDtU71Yuxs3ES53znnj7jYwBA0SU01lw2rM+jd1YZX8tysLN4bCzHpUuXKl9JIRMnTkRJSQk++uiju663efNmeHl5lbl89tlnpnXefvttZGdnw8/PD48++igmT56MtLQ0lJbW/EBh7du3R7du3UwHhCkoKMDGjRsxYsQI0zqFhYU1fhwR1DkVrQMuXbqEvn374rfffkPDhg3Rrl07AIaj93z77bf44osvsGnTJtV+mJPUp7wPJlPtY+e/nc0E9iUCp/5nvrx5V8MpFVp0MxyZ00LGzhUeyAUAmtz6DE/TmzkAfFB0k0fvrCq+luVgZ/HYuPq+2nkcxy9crdZtSkv1sLO7UOtjadekAf4d2q5m99GuHZ599lksWLAA77zzDnx8fMpdLyQkBO+9916Z5bcfVfNf//oXmjdvjk8++QTbt2/H9u3bERsbi3bt2mHZsmX4xz/+UaOxjhgxArGxsZg3bx7Wrl0Le3t7/POf/8SePXsAAO7u7jW6f1E46bPQxIkTsWfPHsyZMwf//ve/Ua9ePQCG3c5ff/01Xn31VUycOLFKRyIiAgxHhF29erXSw7B6Nt1ZrwdO/2o4x17uH7eWazRAm0cM59jzvqdWHsrYucIDuQCA562jonnfyAbgwz191WDTr2WJ2Fk8Nq6+4xeuIjPncrVuU1xcDCen6r1rQ6Z3330Xy5Ytw4cffmi25+52TZo0MTvqZkX69u2Lvn37orCwEHv27MGqVaswf/58PP300zh8+HCNPts3bNgwvPnmm/jvf/+Lb775Bk8//TQaNmxout54sBi14aTPQklJSXjppZfw0ksvmS2vV68eXnzxRRw6dAhr167lpI+qjD/w5LDJzroS4Ng2w2RPe+LWcvt6QIe+QOdhgHvLWn1IY+e77ulzbgQ0aApcPYfGxacB9EDxTR30ej00NdjLaCts8rWsAHYWj42rr12TBkoPwaS2xtKuXTuMGjXKtLevNri6uuKRRx7BI488giZNmmDatGn473//i8jISIvv08fHB7169cKsWbPwv//9D99++63Z9c2aNavpsIXgpM9CFy9evOvpGu655x5otVqJI6K6LiwsDMnJyUoPw+rZVOeb14HDPxhOu3D13K3ljvWBe58xnHahfmMhD23sbDqQS3mTPsDwub6r5+B+/TQAoFQP3NCVwqm8A7+QGZt6LSuIncVj4+qz5O2UR48erfDk4mrx7rvvYvny5ZV+ts8S3bp1AwDk5uZWsmblRowYgRdeeAHu7u7o37+/2XW1cf8icNJnIX9/f3z//fdl9vQZff/992YndCSqDH/gyWETna8XGE67cGCd4RQMRi4eQOcIw4TPSexfiZOTk6HX600nW69w0tfEHzj1PzQoOgcnuyIUa5xRdJOTvqqwideyCrCzeGwsh9onfIDhXHijRo3Cl19+idatW1t0JMytW7fiscceK7N848aNAFAr59gePHgwsrOz0bFjxzJHRa3o84hK46TPQi+99BJeeeUV9O/fH6+//jo6dOgAAPjzzz/x+eef48cff8TcuXMVHiXVJTExMZgxY4bSw7B6Vt35ylnDXr3DPwAlt50c1q0F0Hko0KEf4CDnkN0xMTGYPC0Wer3h63Lf3gmYjuBppwF8dLk46dAWRTd1cHOpJ2WcdZlVv5ZVhJ3FY2M5/vrrL7Ro0ULpYVRq4sSJWLZsGf78808EBASYXZeTk4Ply5eXuU2DBg0QHh4OABgwYADatm2LsLAw+Pn54dq1a9iyZQuSk5PRvXt3hIWF1XiMbm5umDp1arnXXbx4scyyhIQEpKSklFn+2muvmX0eUCRO+iz00ksvIS8vDx9++GGZQw3Xq1cPkydPxosvvqjQ6KguGj58uNJDsAlW2Vl7HNi3AsjaAuhvO/qlV0cgeATQJhSQfLLY4cOHmw7iAtzt7Z2GvzzbaQBfXY5p0keVs8rXsgqxs3hsLIenp6fSQ6gSf39/jBo1CkuWLClz3b59+/Dss8+WWd66dWvTpO/rr7/Gd999h9WrV+PMmTPQ6/Vo164dJk6ciLffflv4efQaNCj7Tpovvvii3HVHjx7NSV9dMHXqVLzyyivYsmULTp06BcDwouvTpw+aNGmi8OiorsnIyEBQUJDSw7B6VtU59w/DaRdO/2K+vEU3w2kXmnet0WkXaiIjIwNerW69lcjFsYJJZ8NmgGN9aG5cRnNdDgCeq6+qrOq1rGLsLB4by3H9+nW4uroqPQyT0aNHY/To0eVet3jxYixevNhs2cmTJ6t0v8OGDcOwYcOqNZZu3bpBb3xrSjluPxl7RXr16gW9Xm+2p+9u2ygbJ30W2rlzJ+699154eXmV+8K6cOECDh48iNDQUAVGR0RWq7TUMMnblwicy7y1XGMHtOsJ3DfcsIdPBW6fvDlX9Bk9jQZo7Ae7a3vRvNQw6eO5+oiIiGoXJ30W6t27N5YtW4YRI0aUe/3WrVsxYsQI6HT8izVVDf/KKUed7ay7CWRtBfavAPJP3lpu7wh07Gf4zJ6bej6rERQUZP72Tse7HJilsT/ssvfBV5cDO70O12/w+2ZV1NnXch3DzuKxsRwuLi5KD8EmqLWz3A95WJG77QIGDCfAtLfn0eeo6lasWKH0EGxCnet8oxD4Yw2wYjiw44NbEz7HBkCXUcCIVcAjb6hqwgcYOt++p6/CA7kAQOP20GgAB30JvErP8zN9VVTnXst1FDuLx8Zy8FRicqi1M/f0VcPp06fN3k98+PBh7Ny5s8x6BQUFpkPNElUVj1wmR53pfD0fyFxnOPVC8ZVby10b/33ahTDD+fZUasaMGfg564LpayeHu/yNsbEf7P7+7KGv7gwnfVVUZ17LdRw7i8fGctSFI3daA7V25p6+ali0aBF69eqF3r17Q6PR4P3330fv3r3LXMLDw5Geno4JEyYoPWSqQ2rjEMJUOdV3vpwLpH0KJA4Ffl96a8Ln3hLoOR4YvhK4b5iqJ3yAobPxHH0A4HK3t3d6tIWdneH65rq/eCCXKlL9a9lKsLN4bCzH0aNHlR6CTVBrZ+7pq4aIiAgEBgZCr9cjIiICr776Kh555BGzdTQaDerXr4/g4GA0bdpUoZFSXcST08qh2s4Xsgyf1zu2zfy0C973Gk670Pph6addqInk5GT88Eeu6eu7vr3TwREaj9ZAXgZ8S3NwhQdyqRLVvpatDDuLx8Zy1IWTs1sDtXbmpK8a7r33Xtx7770ADHv9QkND0bZtW4VHRdYiIiICq1evVnoYVk9VnfV6IHe/4Uic2bvMr2sZAgQPB3yCFTvtQk1ERERg6NufmL6u8Dx9f9M0aQ+7I5loocvB79zTVyWqei1bMXYWj43lOHbsGPz8/JQehtVTa2dO+iw0cuRIFBYWVnj95cuX4erqKvwEkGQ97jwfDYmhis6lpcCpNMMJ1fMO3lqusQP8ehvOsdfEX7nx1YLFixdj3R/nARjmrI72leylbOwPjQZoUHoF+sKLAPgHtcqo4rVsA9hZPDaWo02bNkoPwSaotXPdea+Qyrz66qv4xz/+UeH1Dz30EN544w2JI6K6btasWUoPwSYo2rnkBnB4I7DmOWDzpFsTPntHICAcGPYN8NjkOj/hAwydjQdkcXawh51dJXsrG/ubDubieuWE6OFZBX7PkIOdxWNjOc6dO6f0EGyCWjtzN5SFUlJS8Nxzz1V4/eDBg7F8+XJ89tlnEkdFdVnfvn2VHoJNUKTzjWvAoWQgYw1w7dYRLeHU0DDZCxgIuHrKH5dAffv2RfpVw6TPqV4V/r7YuB2M88IGV06KG5gV4fcMOdhZPDaWw83NTekh2AS1duakz0JnzpxB8+bNK7ze19cXOTk5EkdEdR1fL3JI7VyoBTK/BQ6sB25cvbW8vpfhtAv3PA04usobj0Q5OTm47toIQCUHcTFy8cC1eh5wKLkIt8JTgkdnHfg9Qw52Fo+N5bhx4wbq11f3kZ+tgVo7c9JnocaNG+PPP/+s8PpDhw6hUaNGEkdEdV1+fr7SQ7AJUjpfygH+WAn8mQLobtxa7tEauG844N8HsK8nfhwKys/PR1E9w1E4KzuIi1GBSys0uX4R7tdPixya1eD3DDnYWTw2lkOn40GyZFBrZ36mz0L9+vXDl19+ib1795a57vfff8eCBQvw5JNPKjAyqqtCQ0OVHoJNENr5wlFgyzRg1Sjg4Pe3JnxNA4G+7wODFwMdn7T6CR9g6Gw8316V9vQBuOzaGgDgduMccLNI2NisBb9nyMHO4rGxHA0aNFB6CLVu2bJluOeee1CvXj24u7srPRwA6u3MSZ+FYmNj0ahRI/To0QODBg3C5MmTMXnyZAwcOBAhISFwc3NDbGys0sOkOiQ+Pl7pIdiEWu+s1wN/7QF+eAP49gXz8+y1egB45nNgwFygTd06z15NxcfHmw7kctcTs9/mSoM2hn/oS4F8HsylMvyeIQc7i8fGcpw/f/7WF7oS4Mxe4Hiq4b+6EuUGZqHDhw9j9OjR8PPzw1dffYUFCxYoPSQAd3RWEb6900K+vr7YvXs33nnnHXz33XdISkoCADRq1AgjR47EjBkz4Ovrq/AoqS6ZPXu20kOwCbXWubQUOLnTcNqF84dvLdfYGd6+ed8woLH6ztMjy+zZs/HyN78DqOKBXABcb2g4TUOpXm/Ya+p9r7DxWQN+z5CDncVjYzlatmxpmNzt+8bwefOC00BpCWDnALi3BgIHAsEjAfu6MT3YsWMHSktL8dlnn8HfXz1HvW7ZsqXSQyiX7fzZWQAfHx8sWbIE+fn5OHv2LM6ePYv8/HwsXryYEz6qtrCwMKWHYBNq3LnkhuFInKufBX6ccmvC5+Bk+IE5LBF4dKJNT/gAQ+fqvr2zpIEPbmicDJO+i1kih2cV+D1DDnYWj43lOPrnIWDzRGBnHJC7/+8JXz3Df3P3GZZvflfRvX7Xrl2r8rp5eXkAoJq3dRodPXpU6SGUi5O+WqDRaODt7Q1vb29oNJWci4qoAsnJyUoPwSZY3Ln4KrAvEVgxFNg5E7j0l2G5U0Pg/tHAiNXAQ68BjXxqbax1WXJyMq7f+Ps8fVWc9Lk41sMZe1/o9UApJ32V4vcMOdhZPDaWo/3VdODwD4bPm7u3BlwbA86NDP91b21YfngDsD9RynimTp0KjUaDgwcPYsSIEfDw8MDDDz8MAFi+fDnuv/9+uLi4wNPTE8OGDUN2drbptm3atMGUKVMAAF5eXtBoNJg6daqUcVemffv2Sg+hXHVj/62K/e9//8Pvv/+OS5cuobS01Ow6jUaDSZMmKTQyqmsiIyOxZMkSpYdh9ard+dpFIHMtcPA7w/n2jBp4A52HAvc8BdRzqf2B1nGRkZEoChkLoOp7+lwc7XHG3hdtSk5Af+GY4S20NvQ5yOri9ww52Fk8NpZAV4Lrvy2HS/EVwwTvzp0UGg3QoBlQcMrw1s/7Rkh7m+eQIUPQvn17zJgxA3q9Hu+//z4mTZqEiIgIvPDCCzh//jzmzJmD0NBQ7N27F+7u7vj000+xdOlSJCUl4YsvvkCDBg3QuXNnKeOtzIkTJ9C2bVulh1EGJ30W0mq1eOqpp5Ceng69Xg+NRgO9Xg8Apn9z0kfVwc80yFHlzgXZhtMuHNkE6G7eWu7RxvCZB79H68znHpTw8cxZeGGl4a2vVZ30OTnYI8fOcP7T0pvXYX85B3BX52cj1IDfM+RgZ/HY2AI/zzF89rmqCi/CWXsQgB64/FfF65XeNBycbE2kYQ9gVTRpD/zj/6o+ljvcd999SEw07F08deoU/Pz88N577yEmJsa0zsCBA9GlSxfMmzcPMTExCA8Px759+5CUlITBgwejSZMmFj9+beNn+qzMW2+9hT/++AOJiYk4fvw49Ho9Nm3ahCNHjiAqKgrBwcE4c+aM0sOkOmThwoVKD8EmVNo57zDw42TDZ/YObbg14WsWBPT7ABi8COjwBCd8lfg64dZf7at6IBcXR3vk2LcAAMMf0fgWz7vi9ww52Fk8NrbAhaOGz+VV9ZJ3CCgpNvxMu1lY8UV3E9AVGdav6n1XZ/JZjqioKNO/161bh9LSUkRERODChQumS7NmzdC+fXts3769puWEu3DhgtJDKBd/a7HQxo0bMW7cOAwdOhQXL14EANjZ2cHf3x/x8fEYOHAgXn/9daxYsULhkVJd0aNHD6WHYBPK7azXAzl7DJ/Zy9ljfl3rh4Dg4YZJH1VZUJeu+PVPw7+ruqfP2cEOufY+0MMOpXoYJn1+vcUNso7j9ww52Fk8NrZAk2p+bqzwIvRXz0EDPeDgXPF6JUUA7AxHT67Onr4auP2tkEePHoVer6/wc3H16qn/PLf169dXegjl4qTPQgUFBQgICABw6ySMV69eNV3/xBNPmO2WJqrM9evXlR6CTTDrXFoKHN8O7F9h/pdKO3vA/3HDaRc81fe+/Lrg8rUiAIYfzlU9kItzPXvc1DjivL0XWuovAxePCRxh3cfvGXKws3hsbIHqvp1SVwLd4mdgd/4A0KhF2c/0AYY/gBacAnyDgSFLpL2jxcXl1ufiS0tLodFo8N///hf29mV/dqj1xOe3u/MYH2rBSZ+FfH19cfbsWQCAk5MTvL29sX//fgwYMAAAkJOTU6MjeRYXF2Py5MlYtmwZ8vPz0blzZ7z33nt4/PHH73q7qVOnYtq0aWWWOzk5oaioyOLxkHjHjvEXXBmOHTtmeIvLkRRg/yrgcs6tK+u5APc8DQQNARo2VW6QVuDYqWzAvhOA6hy907Bejn1zBOsv8+2dleD3DDnYWTw2lsDeAdf9+qPe5dPA1bOGg7bc/nuqXm9Y7tQQCByk2EcY/Pz8oNfr0bZtW3To0EGRMdRUcXGx0kMoFyd9FgoNDcWPP/6IiRMnAgCGDh2Kjz/+GPb29igtLcWnn36Kvn37Wnz/o0ePxtq1a/H666+jffv2WLx4Mfr374/t27ebDmd7N8YjGRmV99cSUpfw8HClh2D9iq9gxL06IHEocD3/1nJnN8MPuYB/Gg5fTTX2UGhv7P7fOQDVeXvn35M+u+Yo1R8Erp03PE8uHsLGWZfxe4Yc7CweG8vh/MC/gOt/GU7LUHDKMMGzq2c4eEvxFcPX9zxtOHKnQgYOHIgJEyZg2rRpWL58udkOFL1eD61Wi8aNq/i2U4Wo7byBRpz0Weg///kPfvzxRxQXF8PJyQlTp07FgQMHTEfrDA0NxZw5cyy67/T0dKxcuRJxcXF48803AQDPPfccAgMDMX78ePz888+V3ofajmRElYuNjcX8+fOVHoZ1unr+79MufI/Lxw7Do1Urw/KGzQynXejYH6h3l884ULUtWLgIuKc/AMC5igdycXY0rJdj39xwgnYAuHgcaHG/kDHWdfyeIQc7i8fGcuSeO4/WT7wHNO1kOC1D/inDhM/OwfCWzsBBUk/VUB7jkTsnTJiAkydPIjw8HA0bNsSJEyeQlJSEsWPHmn43Vqvc3Fy0bt1a6WGUwUlfFf3xxx9o3bo13NzcAABBQUEICrp1YAcPDw9s2bIFBQUFsLe3R8OGDS1+rLVr18Le3h5jx441LXN2dsaYMWMQExOD7OzsSg8Hq9frcfnyZTRs2JAnjK8j+ANPgPxTwP6VwNHNQGkJAKB1q1aAZzsgeATQrjePwinIy69FY+Ymw5Fcqvz2znrGt3e2gHHOh4tZnPRVgN8z5GBn8dhYDtNEpOtzhsnduQyg6LLhHS5Ng1Tz8/Cdd95Bhw4dMHv2bNNHllq2bIknnngCzzzzjMKjq5waJ3wAT9lQZV26dMEPP/xg+vrRRx/F1q1by6zn7u5eowkfAOzduxcdOnRAo0bmbzMzHt1q3759ld5Hu3bt4ObmhoYNG2LUqFE4d+5clR47Ly8PBw4cMLtkZfFzNTKEhYUpPQTrce4gsPldw3mG/txomvDB5z68tV0PDE4A2j+umh9w1mjS1Ommfxs/q1cZ4+Twql1DFDn8/f2Pn+urEL9nyMHO4rGxHEeP3nbAMnsHwLcL0K6n4b8K/DycOnUq9Hp9ue9MGzhwIH766SdcvXoVV69exaFDhzB37lyzz/nd7fZKMuusIpz0VZGLiwsKCwtNX+/YsaPKE6nqys3NhY+PT5nlxmV3O/+fh4cHXnnlFXz55ZdYu3YtXnjhBaxatQqPPPIILl++XOljz5s3D4GBgWYX43vt09LSkJqairi4OGi1WkRGRgK49c06OjoaWVlZSEhIQFJSEtLT0xEbG4vCwkJERESYrRsTE4OMjAwkJiYiMTERGRkZpqOdGteJiIhAYWEhYmNjkZ6ejqSkJCQkJCArKwvR0dFm60ZGRkKr1SIuLg6pqalISUlBfHw8cnJyTOd/Ma4bFRWFnJwcxMfHIyUlRTXb5O/vb3XbJPV5OnoUG76YhBOfP4Nr3zyHMz+vhk6nw7Hjx4E2D+OVLfbAM5+jXruHkZGZWTe2qQ4/T+NeegUnTpwAADw3cniVtqmevR2yjh4BAPx+rhSF16/j1O9bVbNNanuePD09rW6b1Pg8zZgxw+q2SW3Pk5E1bVNtPE+XLl0y/e5pnEgcO3YMOp0OZ86cwbVr15Cfn48LFy6gqKgI2dnZZuueOHECJSUlOHv2LK5cuQJvb2/k5eXhxo0bOHXqlNm6p06dwo0bN5CXl4dLly7hypUrOHv2LEpKSkzfy43rZmdno6ioCBcuXEB+fj6uXbuGM2fOGH7m/n1QHuO6f/31FwoLC3Hx4kVcvHgRhYWF+Ouvv2ptmy5duqS6bWrfvn2tbFNpaSn27NlT4WsvLS0N1aHR601voqG7eOihh5Cfn4+33noLbm5uGDx4MF577TU88sgjd73dwIEDq/1Yfn5+6NixIzZu3Gi2/Pjx4/Dz88Ps2bPx+uuvV/n+EhMTMXLkSHzwwQd455137rpuXl4ezp8/b7YsKysL4eHhyMzMNJ2mgmpfVFQU3+JiiVIdcOzv0y7cvlfIzgFo/4ThtAset95qwc5yPPXqDNi1DwUAJL30DzjYV+1vjMMX/IqrxSV42zMVD1/dDGjsgH9tAhwcRQ63TuJrWQ52Fo+Ny3f8+HEAhndv1YZTp06p9q2H1qS2Olf2/B84cACBgYFV/v2c722qos8++wyDBw/GmDFjAAAajQafffYZPvvsswpvo9FooNPpqv1YLi4u5R7u1XjKhdvPZ1IVI0aMwBtvvIEtW7ZUOunz9vaGt7d3te6faofxIEBURTeLDG/d/GM1cCX31vJ6rsC9YYbTLjTwKnMzdpbj8X79sfXYVdSz11R5wgcYDvpytRg47/j3wXb0pUD+CcCro6CR1l18LcvBzuKxsRzlvYuMap9aO/PtnVXUrVs3ZGVl4dChQ9ixYwf0ej0mTpyI7du3V3jZtm2bRY/l4+OD3NzcMsuNy3x9fat9ny1btoRWq7VoPCTH+vXrlR5C3VB0Gfh9KZAYAfzvs1sTPhd3oPsLwIhVwIMvlTvhA9hZlt/3ZwCo+kFcjIyf/zvr0PzWQp6kvVx8LcvBzuKxsRwFBQVKD8EmqLUz9/RVg4ODAzp27IiOHTsiMjISTz/9NEJCQmr9cYKDg7F9+3ZcvnzZ7GAuu3btMl1fHXq9HidPnkSXLl1qc5hUy/z8/JQegrpdzTPs1Tu8Abh5/dbyRr5/n3bhScDBqdK7YWc5Gro3Rn5J1c/RZ2Q8V1+enZfh+SwpBi6q80PxSuNrWQ52Fo+N5XByqvxnJNVcbXXW6/W1egR+7umz0KJFi4RM+ADDOfZ0Oh0WLFhgWlZcXGx6TOPpGk6fPo3Dhw+b3fbOz+MBhhO1nz9/Hv369RMyXqod1X3brs3IPwns+BBYMQzIWHNrwtfYH3hsMjB0ORAQXqUJH8DOsujtDH9TrO6ePue/9/RdL9EbTq0B8AieFeBrWQ52Fo+Ny6fRaFBaWlpr92dnx1/7ZaitzrU96eOePhUKCQnBkCFDMGHCBOTl5cHf3x9LlizByZMnsXDhQtN6zz33HFJTU3H7sXhat26NoUOHIigoCM7OzkhLS8PKlSsRHByMcePGKbE5VEXp6eno2bOn0sNQj7OZwL5E4NT/zJf7dgGCRwItugEWfDNkZzmyc88BjdtW/+2df69//aYO8PUH8g4Z3t6p11v0fFszvpblYGfx2Lh8Tk5OuHTpEm7cuAFHx5ofzOratWs1Pq0YVa42Ot+4cQM3b96Eq6trLY2Kkz7VWrp0KSZNmoRly5YhPz8fnTt3xoYNGxAaGvr/7d15XFRV/wfwzwCyKiKKCu6BS4CKppI9uWQ+WSlmqaRt2GOp5WNlpT2SpYnWL0l9ylDTcnlyKTOxtHKp3CoTd8ElxRURAQXcgAFmzu+PaQZGUNZ77p2Zz/v1mld15s6d7/1wG+Zw7j3njq97+umn8ccff+Dbb79Ffn4+WrRogYkTJ+Ltt9+u0ROHap55kiCHJgRw/k/g0Eog7XBxu04HtOxh6uw1bFett2DOctzVui1OZxXAw7Vyf/F0r2XaPr/QANT/+5KvgpvA9UuAtzZvjlcLz2U5mLPymHHZvL29cfXqVctSXtXt+GltPTt7Vd2cCwoKLPN43Lpmd3Ww06dR7u7uiI2NRWxs7G232bZtW6m2RYsWKVgVKWn8+PFYtmyZ2mWow1AEnPrV1NnLOlPc7lwLaN3PtOyCT7MaeSuHzlmiXQl70Siog+UevYoqHukzAvVbFz9x5SQ7fbfguSwHc1YeMy6bl5eXZT3OU6dOoVatWtDpdFW+5O/mzZvw8vKq4SrpVlXNWQgBIQQKCwsBmNZircmfFzt9RBrhkL/wCvOA4z+YJmi5kV7cXssTCH4MaD8E8KrZv0w6ZM4q6HhPN1y6mm+ZjbOizJeD5hcYTPf06XSmEeAryUCrO1/p4Gh4LsvBnJXHjMum0+nQsGFDeHl54dq1a9Dr9ajO8trs8MlR1Zx1Oh2cnJzg6ekJb29veHl58Z4+InsUERGB9evXq12GHHk5wJF44Mha0xIMZh71TOvrBQ8E3JS578ChclbRroS9aNU2tPITuZg7fUUGiFoe0Hk3Aa5eAC5zMpdb8VyWgzkrjxnfnk6nQ+3atVG7du1q74s5y6HVnNnpq6AdO3ZU6XXl3YNHZKbFD4gad/3S38su/AAU5Re3121qWnahzcOAS/VvVr8Th8hZA+4O7YD8QmOVJ3IRAtAXGeFeP8jU6eMMnqXwXJaDOSuPGcvBnOXQas7s9FVQ7969rYZYKzqNqsFgULIssiPjx4/HnDlz1C5DGVmngYOrgOSfAVFi+mm/tkDH4UCrXoCkqaTtOmeNMBoFTp4+h2bNmlkmZqkotxLb5xcaTJ2+09tMl//mXwPca+6mdlvHc1kO5qw8ZiwHc5ZDqzmz01dBW7dutfpvvV6PiRMnIjc3F6NGjULbtm0BAMePH8eiRYvg5eWFmTNnqlEq2aixY8eqXULNSztsWnbh/C7r9ib3mGbibNJZ+jT8dpmzxuiLjGjY0A8AqjyRCwDkFxpN6zGaZZ0yLdlBAHguy8KclceM5WDOcmg1Z3b6KujW9WNef/11uLq64s8//4S7u7ulPSIiAmPHjkWvXr2wceNG/POf/5RdKtmoHTt2ICgoqPwNtc5oNHXyDq4E0pOK23VOwF29TCN7fm1VK89uctaw/EIDrl+/ATc390pP5FKy05dXaLDu9F1OZqevBJ7LcjBn5TFjOZizHFrNWc71VHZoxYoVePbZZ606fGaenp549tlnsXz5chUqI1tVr149tUuoHkMh8NdGYM3zwKbo4g6fsytwdwTw5JdA36mqdvgAO8jZBuQVGuD89wifRyXv6XOzGukzmGZvda9rauB9fVZ4LsvBnJXHjOVgznJoNWeO9FXRzZs3LQsnliUtLQ25ubkSKyJb16RJE7VLqJqC3L+XXfgauJlZ3O5aGwgZBIQOBjx9VSvvVjabsw3JLzTAtZZpQh63St7TV2qkT6czjfal7mOn7xY8l+VgzspjxnIwZzm0mjNH+qqob9+++Pjjj7F27dpSz3377bf4+OOP0bdvXxUqI1u1adMmtUuonLxsYM8XwMpIYNenxR0+z/pA+Bjg6dVAtxc11eEDbDBnG5RXaMC1a1cBVH6kr+TEL/kFf0+EZb7EM/usaUSZAPBcloU5K48Zy8Gc5dBqzhzpq6K4uDj06dMHQ4cOhb+/v+Xa3VOnTuHixYsIDAzE3LlzVa6SbMkbb7yhdgkVcy3NNKr3149Akb64vW5T0/16rR9SfNmF6rCZnG1YfqEBjRo1BoDKL9lQ4h7A/KJbOn3GIiD7HNBAe/dKqIHnshzMWXnMWA7mLIdWc+ZIXxU1adIEhw4dwuzZsxEaGor09HSkp6cjJCQEc+bMwaFDh9C0aVO1yyQbMmLECLVLuLPLycAvMcBXT5kWVjd3+BreDfxzGhD5JXD3AE13+AAbyNkO6AuNOHP2DIDKj/SVmr0TsO7k8RJPC57LcjBn5TFjOZizHFrNmSN9VZCfn4+FCxciLCwMr776Kl599VW1SyI7sHr1arVLKE0IIO2QaSbOlN3WzzXrZhrZC+gkfdmF6tBkznYmr9CAwLsCAaDSs3eWHBnMM1/eWbe5aUIgQwE7fSXwXJaDOSuPGcvBnOXQas4c6asCd3d3vPXWW/jrr7/ULoXsSEREhNolFDMagTM7gHUvA+tfLe7w6ZyAoAeBwV8Aj8aqss5edWkqZzuVV2hAcvJJAJVfp8/V2clySlku73R2Aeq1NP07O30WPJflYM7KY8ZyMGc5tJozR/qqKDQ0FGfPnlW7DLIj69evV7sEoKgASP4ZOLQSyEkpbnd2Bdo9CnR4EvAOUK++GqCJnO1cfqERQUGtAQDurpX726KTkw7uLs7IKzQUj/QBpvv6Lp8wdfqEsLk/NiiB57IczFl5zFgO5iyHVnPmSF8VzZgxA5999hl+/vlntUshOxEdHa3emxfcBA59BXw1HNj+YXGHz60O0OkZ4KmvgfvH23yHD1A5ZweRV2hAamoqnHSmkbvKMi/zoC8yFjea7+vTXwduZNREmTaP57IczFl5zFgO5iyHVnPmSF8Vffrpp/D19UW/fv3QqlUrtGrVCh4eHlbb6HQ6fPfddypVSLZm+PDh8t80NwtI+hY4sg4ouFHc7uUHdIgE2g0AXD3l16UgVXJ2MPpCA3x9feFWyxm6KozIedRyRg4KS4/0mV1JBuo0qoFKbRvPZTmYs/KYsRzMWQ6t5sxOXxUdPnwYOp0OzZs3h8FgQHJy6ftMqvJlhxxXYmIi2rdvL+fNrqYCh78C/tpomhzDzKf538su/BNwriWnFsmk5uyg8goMyMvLhUeDulV6vXkyl7zCEp0+38Dif7+SDLT8R3VKtAs8l+VgzspjxnIwZzm0mjM7fVXE+/nIJl0+aZqJ8/Q2QJS4dK5RCBD2FND8PsCJV31T9Zg7ayUXWq8M87IN+SU7fW61gTr+wPU0TuZCRERUSez0EWmEYn8VEgJI3W+anOXCXuvnmt9rGtnz7+gwE2No8a9v9ia/0AgPD89Kr9FnZl7mwWqkDzDd18dOnwXPZTmYs/KYsRzMWQ6t5sw/6degkydPYu/evcjNzVW7FLJBq1atqtkdGo2mEb34McAPrxd3+HROQOuHgCGLgUc+BALCHKbDByiQM5WSV2hAVlZWpdfoM7NM5FJotH7CfF/ftYuA/gYcHc9lOZiz8pixHMxZDq3mzE5fJX3++ecIDg5GQEAAnnvuOVy9ehUZGRm499570a5dO4SHh6Nhw4b4+OOP1S6VbMz7779fMzsqKgCOrQdWPwtsmQJkHje1u7gBoU8Aw1YCfd4G6gfeeT92qsZyptvSFxrQpEkTuFVyjT4zj7Lu6QOA+q2L/z3rVFXLsxs8l+VgzspjxnIwZzm0mjM7fZWwYcMGjBo1Cm5ubujSpQtWrVqFUaNG4eWXX0bdunWxYMECzJkzB23btsXrr7+OH374Qe2SyYZUezFP/Q3T/XqrngR2fARcvWBqd6sD3BMFPLUa+MergLd/9Yu1YVpdNNWemBdnd6/i5Z2WiVwKbu30lZzMhZ0+nstyMGflMWM5mLMcWs1ZJ4QQahdhK3r16gWdToetW7dCp9Nhzpw5mDBhAh599FF8//33lu2KiorQoUMHNGvWDJs2bVKx4ppx5MgRhIaGIikpCSEhIWqXQ7e6eQVIWgMc/c603p5Z7YamxdTbPmp3yy6Qto1YkoArNwrQ9+5GeLVv6/JfcIulv5/Bt/tT4eKsQ/zLJWbpFAJYFmFaq69df6DXxBqsmoiIyHZU9vs5R/oq4ejRoxg8eLBlKYbHHnsMRqMRkZGRVtu5uLjg6aefxr59+9Qok2zUredRuXJSgB2xppG9gyuLO3z1WgIPRAPDVgHth7DDd4tK50yVll9owKnTp6o+e+ff9wIWGQQKDSXu69Ppikf7OJkLz2VJmLPymLEczFkOrebM2TsrITc3F56exV+g69Y1rUEVEBBQatvGjRvj+vXr0moj27d06dKKbZhx3DQT55kdppEPs8ahQNjTQLN7uezCHVQ4Z6oSIQTyCo1o1bJVlSdyKXlZaH6hAbWcS5zP9VsDFw8CWWcAQxHg7Li/xnguy8GclceM5WDOcmg1Z34zrITGjRvj4sWLlv/28PDA6NGj0bRp01Lbpqamon79+jLLIxs3a9as2z8phGn2zQ2vA/GjgdPbizt8Lf4BPPYp8Fgc0ILr7JXnjjlTtRUaBIxGgfT0S3Cv4kQu1p2+W2fw/Hukz1AAXD1f1TLtAs9lOZiz8pixHMxZDq3m7Lh/Iq2Ce+65B7t27bL8t6enJ+bPn1/mtjt27NDsOh2kTf369SvdaDQCp7cCh1aZFlY3c3IGgvqa1tjzbSWvSDtQZs5UY/KLTJOveHvXhXsVR/o8bhnps1JyBs8rpwDfu6r0HvaA57IczFl5zFgO5iyHVnNmp68Spk6dinPnzpW7XWZmJry9vTFs2DAJVZG9SE1NLf6PIj1wYiNw6GvgWon2Wh5AuwFA+6FAnUbyi7QDVjlTjTN30goKC6q8OPutl3daqdcCcHIBjEWm+/pa/7PKtdo6nstyMGflMWM5mLMcWs2Znb5KCA4ORnBwcLnb+fn5Ye3atRIqInuSnZ1tmpXwyDog6VsgL7v4SXdvIHQwEPI44F5XtRrtQXZ2dvkbUZXlF5guxzQUGao+kcudLu90rmWarOhKsvXotwPiuSwHc1YeM5aDOcuh1ZzZ6SPSghuZiGh4AVgRCRTmFrfXaVy87EItd/XqsyM9e/ZUuwS7Zr68s06d2tUY6SvuLJZaoB0A6geZOn1Xkk33tv49o7Kj4bksB3NWHjOWgznLodWcOeMDkZqyzwHbPgRWDUP+n0uLO3y+dwF9JgNPrgBCn2CHrwbFxcWpXYJdMy+onpGRWe3F2YEyLu8ETJ0+AMi/CuReqdJ72AOey3IwZ+UxYzmYsxxazZkjfURqSD9qWnbh7G+WWTibNWsG+Hf8e9mFbg47eqG0OXPmqF2CXTN30po1a1blTl/JpR7KHukLLP73K8mAV4MqvY+t47ksB3NWHjOWgznLodWcOdJHJIsQwPndwPevAOteAs7sLF52oeX9+PfPzsDAT4Dm4ezwKSgiIkLtEuyauZOWnHyyxtbpK8U80gc49H19PJflYM7KY8ZyMGc5tJozR/qo4jKOAe3aOvRiyKUYioD0RCD/mmmylUbtS+djNACn/l524UpycbuTC9D6IaDjk0C9lvhUmzP82p3169erXYJdM3fSgoJaw92lan9XLPm6Mjt97t6Alx+Qc870/1bj0LL/37NzPJflYM7KY8ZyMGc5FM/Z/N0z5VClXsaRPo3S6/V46623EBAQAA8PD4SHh2PLli0Vem1qaioiIyPh4+MDb29vPPbYYzh9+nT1i9o0GVg+GNi3zHTCOTJDkSmH5U8A3zwPfDfW9M+S+RTmA0lrga+eBn6NKe7w1fI0Tc4y/Cug91ummQgBREVFqXc8DoQ5K8s82+aZM2eqPNLn4uyEWs6m0W7zPYIW5v/3Mo4C2WeBv34s/f+eg+C5LAdzVh4zloM5y6FYzrd+99w6vVIv1wlhvr6MtGT48OFYs2YNXnvtNbRu3RpLly7Fnj17sHXrVtx///23fd2NGzfQuXNnXL16FW+88QZq1aqFOXPmQAiBgwcPon79+pWu5ciRIwgNDUXSxECE1CsE3OqY1op7aLrD/WUdgOl/us1vA8d/MC2x4FYHcKoFGAtN/+1aG2gQZFpYXX+t+HUePkDoECD4MdNIxS2ysrLg6+sr7zgcFHNW1qqE81i5+zyKiorww6u94ORUtUuVn1r0J67nF6F/B3+M6fX3PXwl/9/LvWxaqw8606hfwU2H+2ziuSwHc1YeM5aDOcuhSM5lfPc8kl6E0A//QlJSEkJCQsrdBUf6NCghIQFfffUVPvjgA8TGxmLUqFH49ddf0aJFC0ycOPGOr503bx5OnjyJDRs2YOLEiRg/fjw2b96MtLQ0zJo1q3qFufsAPi0AQwFwfINpIhJHdHCF6X86Q4EpD8/6pk6ce13T4uk30oFzvwPZZ0zb1/EH7n8NeGo10PnZMjt8APDFF1/IOwYHxpyVZb4cM/tKZpU7fEDxWn1WI30l/9+rE2C6RNrJGXCr7ZCfTTyX5WDOymPGcjBnORTJuazvnm61K7UL+/9TqA1as2YNnJ2dMWrUKEubu7s7Ro4ciejoaKSkpJhmerzNa7t27YquXbta2tq1a4cHH3wQq1evxvvvv1+NygSgA4xejaC7eh7i8DcwBA82ffFyFMYiOCd+A53+OkTd5qa2Ij10edl/j+oJ05dQQyFQpIehVzREYB9TGwAYjLfddecuXVF4h+epZjBnZeX+3UmrX7dOtfZjnswlr9Bg+nkZi+CcuMb0/55PC8BYBEuXsjAfwtkdcLDPpns6d0JhgV7tMuwec1YeM5aDOctR4zmX9d1TCBgrebGmff9GtFEHDhxAmzZt4O1tPSLUrVs3AMDBgwfL7PQZjUYcPnwY//rXv0o9161bN2zevBnXr19HnTq3/zKWkZGBzMxMq7ajR48CAJKTTwHZLtAbjHASBuD8Tlze2wEFOrdKH6OtchV6NDBeBgAYcb3MbQp0rjDCHXpdERYv2YMU54wK7fv69euosyWlxmqlsjFnOXT66zhy5EiVX3/j0hlcv5KLzWlnsPm3vWhmOId/3UyCkxC4mWr6f7CRoRA6CAAX/n4ATnCczyaDwYgt63jBjtKYs/KYsRzMWY6azvl23z1PZZn+yKrXV6yDyU6fBqWlpcHf379Uu7nt4sWLZb4uKysLer2+3Ne2bdv2tu89b948vPfee2U+N2jl1TJay2qjYu+oXQCRakJn1+z+lldqa342ERGR/UtJSUHnzp3L3Y6dPg3Ky8uDm1vpv1C7u7tbnr/d6wBU6bVmL7/8MoYOHWrVdvDgQTzzzDNYvXo1goODyz8AqrTk5GQMGjQI69atQ1BQUPkvoCphznIwZ+UxYzmYs/KYsRzMWQ6ZOev1eqSkpKBXr14V2p6dPg3y8PAoc6g2Pz/f8vztXgeUPcxb3mvNGjZsiIYNG5b5XHBwcIVmB6KqCwoKYsYSMGc5mLPymLEczFl5zFgO5iyHrJwrMsJnxgt7Ncjf3x9paWml2s1tAQEBZb7O19cXbm5uVXotERERERHZJ3b6NCgsLAwnTpzAtWvXrNp3795teb4sTk5OaN++Pfbu3Vvqud27d+Ouu+664yQuRERERERkf9jp06AhQ4bAYDBg4cKFlja9Xo8lS5YgPDzcMnPn+fPncfz48VKv3bNnj1XH76+//sKvv/5a6l49IiIiIiKyf7ynT4PCw8MxdOhQTJo0CRkZGQgKCsKyZctw9uxZqwUfn3vuOWzfvh2ixDodL7/8MhYtWoT+/fvjzTffRK1atTB79mw0atQIb7zxRpXq8fPzw5QpU+Dn51ftY6OyMWM5mLMczFl5zFgO5qw8ZiwHc5ZDyznrhKjkyn4kRX5+Pt555x0sX74c2dnZ6NChA2JiYtCvXz/LNr179y7V6QOACxcuYPz48di8eTOMRiN69+6NOXPmcLYmIiIiIiIHxE4fERERERGRHeM9fURERERERHaMnT4iIiIiIiI7xk4fERERERGRHWOnj4iIiIiIyI6x00dERERERGTH2OkjIiIiIiKyY+z0ERERERER2TF2+oiIiIiIiOwYO31ERERERER2jJ0+O9e7d2+4u7ujdu3aqF27Nh555BG1SyIiIiIiIolc1C6AlPf555/jmWeeUbsMIiIiIiJSATt9VK6cnBxs374dzZo1g5ubm9rlEBERERE5NL1ej5SUFPTq1Qs+Pj7lv0CQ4q5fvy7effdd0a9fP1GvXj0BQCxZsqTMbfPz88XEiROFv7+/cHd3F926dRObN2+u8nv36tVLNGjQQDRo0ED07dtXHDp0qNL7WLdunQDABx988MEHH3zwwQcffGjosW7dugp9n+dInwSXL1/GtGnT0Lx5c3Ts2BHbtm277bYjRozAmjVr8Nprr6F169ZYunQpHn30UWzduhX3339/pd975syZCA4OhrOzM+bOnYtHHnkEx48fR506dSq8j2bNmgEA1q1bh6CgoErXQBWTnp6ORo0aqV2G3WPOcjBn5TFjOZiz8pixHMxZDlk5JycnY9CgQZbv6eWq9LAPVVp+fr5IS0sTQgixZ88eAZQ90rd7924BQMTGxlra8vLyRGBgoOjevbvVtv/4xz9u2+N/++23b1tL27ZtKz1ymJSUJACIpKSkSr2OKmf06NFql+AQmLMczFl5zFgO5qw8ZiwHc5ZDVs6V/X7OkT4J3Nzc0Lhx43K3W7NmDZydnTFq1ChLm7u7O0aOHIno6GikpKRYevO//fZblWpxcnKCEKJKryVlLViwQO0SHAJzloM5K48Zy8GclceM5WDOcmg1Zy7ZoCEHDhxAmzZt4O3tbdXerVs3AMDBgwcrtb+cnBxs2bIFer0eBQUFmDNnDrKyshAeHn7b12RkZODIkSNWj+Tk5EofC1VeRESE2iU4BOYsB3NWHjOWgzkrjxnLwZzl0GrO7PRpSFpaGvz9/Uu1m9suXrxYqf0VFhZi0qRJaNCgARo3boz169fjxx9/RN26dW/7mnnz5iE0NNTqMWjQIACm0cXt27cjNjYWWVlZiIqKAlB8co8fPx7JyclYvHgx4uPjkZCQgJiYGOTm5iIyMtJq2+joaCQmJmLlypVYuXIlEhMTER0dbbVNZGQkcnNzERMTg4SEBMTHx2Px4sVITk7G+PHjrbaNiopCVlYWYmNjsX37dmzcuBFxcXFITU3FmDFjrLYdM2YMUlNTERcXh40bN2rmmMz3S9rTMWnx59S+fXu7OyYt/pyWLVtmd8ektZ+Tr6+v3R2TFn9O77//vt0dk9Z+Tmb2dExa/DmNHTvW7o5Jiz+n9evXSzmmSl/1p+zVpnSrO93Td9ddd4lHHnmkVPupU6cEADFnzhzF60tPTxdJSUlWD/PsnbynT1m81l4O5iwHc1YeM5aDOSuPGcvBnOXgPX1ULg8PD+j1+lLt+fn5lueV1rBhQzRs2FDx96HS3nnnHbVLcAjMWQ7mrDxmLAdzVh4zloM5y6HVnHl5p4b4+/sjLS2tVLu5LSAgQHZJJNG6devULsEhMGc5mLPymLEczFl5zFgO5iyHVnNmp09DwsLCcOLECVy7ds2qfffu3ZbnyX4FBgaqXYJDYM5yMGflMWM5mLPymLEczFkOrebMTp+GDBkyBAaDAQsXLrS06fV6LFmyBOHh4RVffLGGTJ06FTqdDqGhoVLf11HJuHyXmLMszFl5zFgO5qw8ZiwHc5ZDqzmz0yfJp59+iunTp2Px4sUAgPXr12P69OmYPn06rl69CgAIDw/H0KFDMWnSJEycOBELFy5Enz59cPbsWcycOVN6zVOnToUQAklJSdLf2xElJCSoXYJDYM5yMGflMWM5mLPymLEczFkOreasE4IrdcvQsmVLnDt3rsznzpw5g5YtWwIwTdryzjvvYPny5cjOzkaHDh0QExODfv36SazW2pEjRxAaGoqkpCSEhISoVoe9y8rKgq+vr9pl2D3mLAdzVh4zloM5K48Zy8Gc5ZCVc2W/n3OkT5KzZ89CCFHmw9zhAwB3d3fExsYiLS0N+fn5SEhIULXDR/KY120hZTFnOZiz8pixHMxZecxYDuYsh1Zz5kgf3dbUqVPx3nvvWf6bI31EREREROrjSB/VGN7TJ1dERITaJTgE5iwHc1YeM5aDOSuPGcvBnOXQas4c6aNy8Z4+IiIiIiLt4EgfkY3S6jXg9oY5y8GclceM5WDOymPGcjBnObSaMzt9RBoxduxYtUtwCMxZDuasPGYsB3NWHjOWgznLodWc2ekj0ogdO3aoXYJDYM5yMGflMWM5mLPymLEczFkOrebMTh/d1tSpU6HT6RAaGqp2KQ6hXr16apfgEJizHMxZecxYDuasPGYsB3OWQ6s5s9NHt8XZO+Vq0qSJ2iU4BOYsB3NWHjOWgzkrjxnLwZzl0GrO7PQRacSmTZvULsEhMGc5mLPymLEczFl5zFgO5iyHVnPmkg1ULi7ZIEdubi48PT3VLsPuMWc5mLPymLEczFl5zFgO5iyHrJy5ZAORjRoxYoTaJTgE5iwHc1YeM5aDOSuPGcvBnOXQas4c6aNycaSPiIiIiEg7ONJHNYazd8oVERGhdgkOgTnLwZyVx4zlYM7KY8ZyMGc5tJozR/qoXBzpIyIiIiLSDo70Edmo6OhotUtwCMxZDuasPGYsB3NWHjOWgznLodWc2ekj0ojhw4erXYJDYM5yMGflMWM5mLPymLEczFkOrebMTh+RRiQmJqpdgkNgznIwZ+UxYzmYs/KYsRzMWQ6t5sxOHxERERERkR1jp49II9q3b692CQ6BOcvBnJXHjOVgzspjxnIwZzm0mjM7fUQasWrVKrVLcAjMWQ7mrDxmLAdzVh4zloM5y6HVnLlkA93W1KlT8d5771n+m0s2EBERERGpj0s2UI2ZOnUqhBBISkpSuxSHoNXFPO0Nc5aDOSuPGcvBnJXHjOVgznJoNWeO9FG5uDg7EREREZF2cKSPyEZFRkaqXYJDYM5yMGflMWM5mLPymLEczFkOrebMkT4qF0f65MjNzYWnp6faZdg95iwHc1YeM5aDOSuPGcvBnOWQlTNH+ohs1KxZs9QuwSEwZzmYs/KYsRzMWXnMWA7mLIdWc2anj0gj+vXrp3YJDoE5y8GclceM5WDOymPGcjBnObSas4vaBajp/PnzNbKf5s2b18h+yLGlpqaqXYJDYM5yMGflMWM5mLPymLEczFkOrebs0J2+li1bQqfTVfn1QgjodDoYDIYarIocVXZ2ttolOATmLAdzVh4zloM5K48Zy8Gc5dBqzg7d6du6davaJWjarYuzk7J69uypdgkOgTnLwZyVx4zlYM7KY8ZyMGc5tJozZ++kcnH2TjnGjx+POXPmqF2G3WPOcjBn5TFjOZiz8pixHMxZDlk5V/b7OTt9VC52+oiIiIiItINLNlRD9+7d8Z///AcbNmxATk6O2uWQg4mIiFC7BIfAnOVgzspjxnIwZ+UxYzmYsxxazZkjfSU88sgj2LVrF65duwYnJycEBwejR48e6NmzJ3r06IGAgAC1S1QFR/qIiIiIiLSDI33V8NNPPyE7Oxv79u3D7Nmz0a5dO6xduxbDhw9Hs2bNEBgYiOeff17tMslORUVFqV2CQ2DOcjBn5TFjOZiz8pixHMxZDq3mzJG+chQUFGDFihX48MMPceLECYdcooEjfXJkZWXB19dX7TLsHnOWgzkrjxnLwZyVx4zlYM5yyMqZI33VdOPGDWzevBnvvPMOevfuDR8fH7zwwgtwdnbG6NGj8eWXX6pdItmpL774Qu0SHAJzloM5K48Zy8GclceM5WDOcmg1Z4dep+9WXbp0waFDh6DT6dCxY0f07NkTr732Gnr06IH69eurXR7ZuW7duqldgkNgznIwZ+UxYzmYs/KYsRzMWQ6t5sxOXwn79++Hk5MTBg0ahEcffRQ9evRAUFCQ2mWRg8jLy1O7BIfAnOVgzspjxnIwZ+UxYzmYsxxazZmdvhL27t2LnTt3YufOnZg0aRIyMzPRsGFD9OjRw/Lo2LEjdDqd2qWSHTp16pTaJTgE5iwHc1YeM5aDOSuPGcvBnOXQas6cyOUOTpw4YekEbtu2DSkpKfD29kZ2drbapUnFiVzkSE1NRZMmTdQuw+4xZzmYs/KYsRzMWXnMWA7mLIesnDmRSw3Jy8vDhQsXkJKSgvPnzyMzMxNCCNy4cUPt0qSZOnUqdDodQkND1S7FIcTExKhdgkNgznIwZ+UxYzmYs/KYsRzMWQ6t5syRvhI2bNiAHTt2YOfOndi/fz8KCwvh7u6Obt26WS7vvO+++1C7dm21S5WKI31ERERERNrBkb5qGDhwID7//HM0aNAAMTEx+P3333H16lVs27YNMTExeOihhxyuw0fyREREqF2CQ2DOcjBn5TFjOZiz8pixHMxZDq3mzJG+EhITExEaGsqJWm7BkT4iIiIiIu3gSF81tG/f/rYdvsLCQqxbtw5DhgyRXBU5ijFjxqhdgkNgznIwZ+UxYzmYs/KYsRzMWQ6t5syRvnJs374dK1aswLfffoucnBwAgMFgULcoyTjSJwdn1ZKDOcvBnJXHjOVgzspjxnIwZzk4e6cNOXz4MN566y00b94cffr0wbJly3DvvffikUceUbs0smPr1q1TuwSHwJzlYM7KY8ZyMGflMWM5mLMcWs2Znb6/nT9/Hv/3f/+H9u3bo1OnTpg9ezbuvvtuLFq0CJcuXcIPP/yAHj16qF0m2bHAwEC1S3AIzFkO5qw8ZiwHc1YeM5aDOcuh1Zxd1C5AbZ999hlWrFiBP/74AwDQs2dPzJs3D4MHD0aDBg1Uro4ciYeHh9olOATmLAdzVh4zloM5K48Zy8Gc5dBqzg4/0vfSSy9hz549eO+995Camopff/0Vo0ePZoePpEtISFC7BIfAnOVgzspjxnIwZ+UxYzmYsxxazdnhO31dunSBXq9HTEwMRo0ahRUrVuD69etql0UOaOTIkWqX4BCYsxzMWXnMWA7mrDxmLAdzlkOrOTt8py8hIQEnTpzAW2+9hWPHjuHZZ59Fo0aNMHjwYKxevRq5ublql0gOYvz48WqX4BCYsxzMWXnMWA7mrDxmLAdzlkOrOXPJhlvs3r0by5cvx+rVq5GZmQlPT08MGDAAQ4cOxeHDhzF9+nQu2UBERERERKrhkg3VFB4ejrlz5+LixYvYsGEDHnvsMfzwww+IjIzE9OnT1S6P7FhERITaJTgE5iwHc1YeM5aDOSuPGcvBnOXQas4c6auA3NxcrF27FitWrMAvv/yCgoICtUuSiiN9RERERETawZE+BXh6euKZZ57BTz/9hNTUVLXLITul1WvA7Q1zloM5K48Zy8GclceM5WDOcmg1Z4dep2/atGk1sp933323RvZDjm3s2LFql+AQmLMczFl5zFgO5qw8ZiwHc5ZDqzk7dKdvyZIl1d6HTqez207f1KlT8d5776ldhsPYsWMHgoKC1C7D7jFnOZiz8pixHMxZecxYDuYsh1ZzduhO35kzZ9QuQdOmTp2KqVOnWq4ZJmXVq1dP7RIcAnOWgzkrjxnLwZyVx4zlYM5yaDVn3tNHpBFNmjRRuwSHwJzlYM7KY8ZyMGflMWM5mLMcWs2ZnT4ijdi0aZPaJTgE5iwHc1YeM5aDOSuPGcvBnOXQas5csoHKxSUb5MjNzYWnp6faZdg95iwHc1YeM5aDOSuPGcvBnOWQlTOXbCCyUSNGjFC7BIfAnOVgzspjxnIwZ+UxYzmYsxxazZkjfVQujvQREREREWkHR/qIbFRERITaJTgE5iwHc1YeM5aDOSuPGcvBnOXQas4c6aNycaSPiIiIiEg7ONJHZKOio6PVLsEhMGc5mLPymLEczFl5zFgO5iyHVnNmp49II4YPH652CQ6BOcvBnJXHjOVgzspjxnIwZzm0mjM7fUQakZiYqHYJDoE5y8GclceM5WDOymPGcjBnObSaMzt9REREREREdoydPiKNaN++vdolOATmLAdzVh4zloM5K48Zy8Gc5dBqzuz0EWnEqlWr1C7BITBnOZiz8pixHMxZecxYDuYsh1Zz5pINVC4u2UBEREREpB1csoHIRml1MU97w5zlYM7KY8ZyMGflMWM5mLMcWs2ZI31ULo70ERERERFpB0f6iGxUZGSk2iU4BOYsB3NWHjOWgzkrjxnLwZzl0GrOHOmjcnGkT47c3Fx4enqqXYbdY85yMGflMWM5mLPymLEczFkOWTlzpI/IRs2aNUvtEhwCc5aDOSuPGcvBnJXHjOVgznJoNWd2+uzczJkz0axZM9SpUwedOnXC9evX1S6JbqNfv35ql+AQmLMczFl5zFgO5qw8ZiwHc5ZDqzm7qF0AKScuLg4bN27E77//jmbNmiExMRGurq5ql0W3kZqaqnYJDoE5y8GclceM5WDOymPGcjBnObSaMzt9dspgMGDGjBnYuXMnmjdvDgDo0KGDylXRnWRnZ6tdgkNgznIonXOhwYi9Z7NxNa8AdT1c0aVlPdRydqyLV3guy8GclceM5WDOcmg1Z8f6DamCGzduYMqUKXj44Yfh6+sLnU6HpUuXlrmtXq/HW2+9hYCAAHh4eCA8PBxbtmyp0vteuHABubm5WLNmDRo1aoS2bdti0aJF1TgSUlrPnj3VLsEhMGc5lMq50GDEJ7+cRPcPfsHwRX9izPL9GL7oT9z3wa/45JeTKDQYFXlfLeK5LAdzVh4zloM5y6HVnNnpU9jly5cxbdo0HDt2DB07drzjtiNGjMDs2bPx9NNP4+OPP4azszMeffRR/Pbbb5V+39TUVFy9ehUnTpzA2bNn8c033yA6Oho7d+6s6qGQwuLi4tQuwSEwZzmUyLnQYMSo/+3F7C0ncOVGgdVzl2/oMXvLCYz+cp/DdPx4LsvBnJXHjOVgznJoNWcu2aAwvV6P7OxsNG7cGHv37kXXrl2xZMkSjBgxwmq7hIQEhIeHIzY2Fm+++SYAID8/H6GhoWjYsCH++OMPy7b3338/fv/99zLf7+2338b06dNx4MABdO7cGWfPnkWLFi0AAOPGjUPt2rXxwQcfVOoYuGQDEWnBJ7+cxOwtJ8rd7o1/tsG4B1tLqIiIiEgdXLJBY9zc3NC4ceNyt1uzZg2cnZ0xatQoS5u7uztGjhyJXbt2ISUlxdL+22+/QQhR5mP69OkAgDZt2sDV1RU6nc7yupL/TtoTERGhdgkOgTnLUdM5FxqM+N+usyjvU0wH4H+7zjnEaB/PZTmYs/KYsRzMWQ6t5sxOn0YcOHAAbdq0gbe3t1V7t27dAAAHDx6s1P68vLwwZMgQzJgxA3q9HseOHcPXX3+NRx999I6vy8jIwJEjR6weycnJlXpvqpr169erXYJDYM5y1HTOe89m4/KNApR3aYoAkHlDj71ntXkjfU3iuSwHc1YeM5aDOcuh1ZzZ6dOItLQ0+Pv7l2o3t128eLHS+4yLi8Ply5fRoEEDPProo4iJiUGPHj3u+Jp58+YhNDTU6jFo0CAAphHG7du3IzY2FllZWYiKigJQ/BeN8ePHIzk5GYsXL0Z8fDwSEhIQExOD3NxcREZGWm0bHR2NxMRErFy5EitXrkRiYiKio6OttomMjERubi5iYmKQkJCA+Ph4LF68GMnJyRg/frzVtlFRUcjKykJsbCy2b9+OjRs3Ii4uDqmpqRgzZozVtmPGjEFqaqplSQutHFNwcLDdHZMWf07t27e3u2PS4s9p2LBhNXpMh49X7o9Pc+Z9Zvc/p8DAQLs7Ji3+nAYOHGh3x6S1n1OzZs3s7pi0+HPq27ev3R2TFn9OUVFRUo6psnN+8J4+ie50T19gYCDatm2LH3/80ar99OnTCAwMxJw5c/Daa68pXmNGRgYyMzOt2pKTkzFo0CDe06ewrKws+Pr6ql2G3WPOctR0zrtOXcHwRX9WePtVL96L7oH1a+z9tYjnshzMWXnMWA7mLIesnHlPn43y8PCAXq8v1Z6fn295XoaGDRsiJCTE6hEUFCTlvR3dF198oXYJDoE5y1HTOXdpWQ8NartW6J4+v9pu6NKyXo2+vxbxXJaDOSuPGcvBnOXQas7s9GmEv78/0tLSSrWb2wICAmSXRJKZ798kZTFnOWo651rOTniue8sK3dP3XPcWDrFQO89lOZiz8pixHMxZDq3m7KJ2AWQSFhaGrVu34tq1a1aTuezevdvyvFrMI5Cc0EVZx48fR4MGDdQuw+4xZzmUyLmnnxFb61zH7tNZ0AFWHUDzf4ff5Ysefvk4cuRIjb63FvFcloM5K48Zy8Gc5ZCVs/l7eVlXCpZJkDR79uwRAMSSJUtKPffnn38KACI2NtbSlp+fL4KCgkR4eLjEKotNmTJFwPQ9ig8++OCDDz744IMPPvjQ2GPdunUV+l7PiVwk+PTTT5GTk4OLFy9i/vz5eOKJJ9CpUycApgXT69atC8A0e1F8fDzGjx+PoKAgLFu2DAkJCfjll1/Qs2dP1erftWsX7rvvPqxevdoywyTVLPNkOevWreM9lApiznIwZ+UxYzmYs/KYsRzMWQ6ZOev1eqSkpKBXr17w8fEpd3te3inBRx99hHPnzln+e+3atVi7di0A4JlnnrF0+v73v//hnXfewZdffons7Gx06NABGzZsULXDB8ByuWlwcDBn71RYUFAQM5aAOcvBnJXHjOVgzspjxnIwZzlk5dy5c+cKb8tOnwRnz56t0Hbu7u6IjY1FbGyssgUREREREZHDsP/pzYiIiIiIiBwYO31ERERERER2jJ0+Kpefnx+mTJkCPz8/tUuxW8xYDuYsB3NWHjOWgzkrjxnLwZzl0HLOnL2TiIiIiIjIjnGkj4iIiIiIyI6x00dERERERGTH2OkjIiIiIiKyY+z0ERERERER2TF2+oiIiIiIiOwYO31ERERERER2jJ0+IiIiIiIiO8ZOHxERERERkR1jp4+IiIiIiMiOsdNHRERERERkx9jpIyIiIiIismPs9BEREREREdkxF7ULIO3LycnB9u3b0axZM7i5ualdDhERERGRQ9Pr9UhJSUGvXr3g4+NT/guEhl2/fl28++67ol+/fqJevXoCgFiyZEmFX5+dnS1efPFF0aBBA+Hp6Sl69+4t9u3bV+a23333nejUqZNwc3MTzZo1E++++64oLCzU1D6/+uor8fTTT4ugoCABQPTq1eu2x56fny8mTpwo/P39hbu7u+jWrZvYvHnzbbe/k3Xr1gkAfPDBBx988MEHH3zwwYeGHuvWravQ93lNj/RdvnwZ06ZNQ/PmzdGxY0ds27atwq81Go3o378/Dh06hAkTJqBBgwaYN28eevfujX379qF169aWbX/66ScMGjQIvXv3xty5c5GYmIjp06cjIyMD8+fP18w+58+fj3379qFr1664cuXKHY9/xIgRWLNmDV577TW0bt0aS5cuxaOPPoqtW7fi/vvvr3COANCsWTMAwLp16xAUFFSp11LFpaeno1GjRmqXYfeYsxylcjYUAX98ApzZDhTcBFy9AJ0LIIqK/7tVL+C+VwDn2/xqqol92BGey3IwZ+UxYzmYsxyK5FzG77/kywYMWnzW8j29XFUa+pEkPz9fpKWlCSGE2LNnjwAqPtL39ddfCwDim2++sbRlZGQIHx8fMXz4cKttg4ODRceOHa1G4d5++22h0+nEsWPHNLPP8+fPC4PBIIQQIiQk5LYjfbt37xYARGxsrKUtLy9PBAYGiu7du5f5mjtJSkoSAERSUlKlX0sVN3r0aLVLcAjMWY5SOe9dKsTsECFiWwsxv4cQC3oWP+b3MLXPDhFi37Lb77Qm9mFHeC7LwZyVx4zlYM5yKJJzGb//kt69p1Lfz3VCCFGzXVFl7N27F127dsWSJUswYsSIcrePjIzEjh07cPHiRTg5Fc9XM3r0aCxfvhxZWVlwc3PD0aNHERISgri4OLz88suW7S5evIgmTZogJiYGkydPVn2ftwoNDUWDBg3KHP2cOHEiZs+ejaysLHh7e1vaP/jgA0RHR+P8+fMV/6sAgCNHjiA0NBRJSUkICQmp8OuIiACY/kK5/Akg7RDg0wKAESjIBYSxeBshgJuZgG8roNd/ACdn630YDcC2D4Dss4CXH6DTAQAKDEYUGYRlH64FV5Dn1RynQv4N6G7ZBxERka0RBgQemQuPmykocK1v+f139FI+us5OrvD3c7u9/uXAgQPo3LmzVUcKALp164aFCxfixIkTaN++PQ4cOAAA6NKli9V2AQEBaNq0qeV5tfdZ2WNv06aNVYfPvE8AOHjw4G07fRkZGcjMzLRqS05OrtT7U9VERERg/fr1apdh95izHFY5pycCOeeBWp5AbiaQdxWAsfSLjEXA5RPAr9NM25ZUmGvq8EEAN9NNmwvAWGS0moZawAC3q6fg++f/Qa9zV+DItEOv13NyLQmYs/KYsRzMWY6aztlN5MPNeAkCgFNRhqXdKa+oUvux2yUb0tLS4O/vX6rd3Hbx4kXLdiXbb93WvJ3a+6yM6uxz3rx5CA0NtXoMGjQIAPDbb79h+/btiI2NRVZWFqKiogCYvtwBwPjx45GcnIzFixcjPj4eCQkJiImJQW5uLiIjI622jY6ORmJiIlauXImVK1ciMTER0dHRVttERkYiNzcXMTExSEhIQHx8PBYvXozk5GSMHz/eatuoqChkZWUhNjYW27dvx8aNGxEXF4fU1FSMGTPGatsxY8YgNTUVcXFx2Lhxo2aOyXy/pD0dkxZ/Tu3bt7e7Y9Liz2nZsmWWY/roP6OB3CsouHYJxptZKCwqQFGRAQaDEQWFhRACyNfrAehQWFgIGA24kJqK3Lw8XMnKwpWsLOTn5aKoqAiADvn5egBAQUEBAMB0wYqAgIBRADoIGAtNz+n15m31EBAoLCyE0WiEwWBAUVERhBCW/RRvWwAhBIqKimAwGGA0GlFYWAgBgYICfYW2LbmN+Z+FhYUwCiMMhiIYDEUwinK2LadOnQ52d0xa/DnVcq1ld8ektZ+TmT0dkxZ/Ti4uLnZ3TFr8Obm5udXoMTnBCAgBAR3MF2gKYfqdVxl2e3mns7MzRo8ejXnz5lm1//rrr3jwwQcRHx+PQYMGISYmBu+++y7S09PRsGFDq2179uyJa9eu4eDBg6rv81Z3urwzMDAQbdu2xY8//mjVfvr0aQQGBmLOnDl47bXXysztdiN9gwYN4uWdChszZgwWLFigdhl2jznLMWbMGCyY8jJwaBWQ/HPxKJ2TC1DLC/CsBzjf8pfQ3CzT8xH/BRqFWj+XngSsf800GujpCwC4oS9CxnXTL03/uu5wcdZBl5cNnZMLrv0zFgY/+/68iomJwTvvvKN2GXaPOSuPGcvBnOWo6ZydM4/Ae8sECGMRhEc9S/uRtFx0mnGYl3d6eHhYetAl5efnW54v+c/bbWt+Xu19VkZ19tmwYcNSHVWSgx/EcjBnhRmNwPldmNVTD3w/ztTm4gE4uwLGQqBuc6BWGZ9BQpgu4QwIM83Aeevsm571Tff7pR0CdKZ7+oxOgAGmy1ucXWqhlpMOKMoDAsJQL/ifdj+D58ToqWjQuInaZdg95qw8ZiwHc5ajxnP2CwD232X6/efc0HJPXy3nwkrtxm4v7/T397dcZlmSuS0gIMCyXcn2W7c1b6f2PitDiX2S8tatW6d2CQ6BOSvEUASc2ASseR7YFI2bp3eb2p1rAcEDgZ5vAl4NgfwcUwevJCGAG5cAtzpA6OCyO2vOLqbn3OqYthUCVle2VGQfdobnshzMWXnMWA7mLEeN51zW778qsNtOX1hYGPbv3w+j0XqygN27d8PT0xNt2rSxbAeYLh8t6eLFi7hw4YLlebX3WRlhYWE4ceIErl27VmqfJesjbQkMDFS7BIfAnGtYQS5w+Bvgq+HA1vf/vowTcPHwBsKeBp5aberwdR8HtBtgGvHLOQfkXgHyr5n+mXPO1N5uANDxqdu/V9jTVvuopc+Cp7gJb+NVOF9Lqdg+7AjPZTmYs/KYsRzMWQ5Fcr7l9x9yrwD6G5XahV10+tLS0nD8+HHLTZQAMGTIEKSnp2Pt2rWWtsuXL+Obb75BRESEZVadkJAQtGvXDgsXLoTBYLBsO3/+fOh0OgwZMkQT+6yMIUOGwGAwYOHChZY2vV6PJUuWIDw8vFLLNZA8VbmUlyqPOdeQvBxg72JgZSSw61Pgxt8zinn6AuFjcCRsKhA+ynL/HZxdgIemA70mmi7hdHIxXe7p5GL6714TTc/faYTuln0IJxe4wACDzgVFjdpXbB92hOeyHMxZecxYDuYshyI5l/U7VFRu9k7N/2b89NNPkZOTY5lxcv369bhw4QIAYNy4cahbty4mTZqEZcuW4cyZM2jZsiUAU8fn3nvvxfPPP4+jR4+iQYMGmDdvHgwGA9577z2r94iNjcXAgQPx0EMPYdiwYUhKSsKnn36KF154AXfffbdlO7X3uWPHDuzYsQMAkJmZiZs3b2L69OkATBPE9OzZEwAQHh6OoUOHYtKkScjIyEBQUBCWLVuGs2fP4osvvqjuj4QUkpCQgF69eqldht1jztV0LQ04/DXw149AUYl7h+s2BToOB1o/BLi44s/YWPR48GHr1zq7AJ2fM43EpSeaRvrcvYFG7SveUSuxj8Rd2/F9wnHc1Hli0hND0cinds0dpw3guSwHc1YeM5aDOcuhWM63/g49fAiY+XzFX1/FdeGladGihWku7jIeZ86cEUIIERUVZfXfZllZWWLkyJGifv36wtPTU/Tq1Uvs2bOnzPeJj48XYWFhws3NTTRt2lRMnjxZFBQUlNpOzX1OmTLltllMmTLFatu8vDzx5ptvisaNGws3NzfRtWtXsXHjxjLrLE9SUpIAIJKSkqr0eqqYK1euqF2CQ2DOVXQ5WYhfYoT4rLcQC3oWP74dJcSpbUIYDFaby8h585FLYsAnO8WAT3aKS1fzFH8/reG5LAdzVh4zloM5yyEr58p+P7eZJRtIPUeOHEFoaCiXbFBYVFQUli1bpnYZdo85V4IQptnCDq4EUnZbP9e0KxA2HAjobJlJrCQZOW85mo5PfjkJAPgiqgsaetv3Yuy34rksB3NWHjOWgznLISvnyn4/Z6ePysVOH5GDMRqBc7+b1thLP1LcrnMC7uoNhD0FNGitWnlmm49cwtxfkwEAX4zogoZ1HKvTR0REjquy38/tYiIXInsQERGhdgkOgTnfgaEQOP4j8E0UsHlycYfP2RUIfgx4cjnQd0qFOnwycjaW+JOlDqVHG+0dz2U5mLPymLEczFkOrebMkT4qF0f6iOxcQS5wbD2Q+A1wM7O43a2OqbMXOrh4Fk4N2ZiUhritpwAAS57viga1Kz/bMRERkS3iSB+RjRo/frzaJTgE5lxCbhaQsMi07MKf84o7fF5+wL0vm9bY6/ZilTp8snN2vHE+nsuyMGflMWM5mLMcWs1Z80s2EDmKsWPHql2CQ2DOAK5dBA59Bfz1E2AoKG73aWaaCrr1PwHnWtV6Cxk5l7xORVfGZDL2jueyHMxZecxYDuYsh1Zz5kgfkUaY12AkZTl0zpeTgZ/fA756Gjj6XXGHr1GIadHXof8D2j1a7Q4fICfnkvcmOF6Xz8HPZYmYs/KYsRzMWQ6t5syRPiKNqFevntolOASHy1kI4OIB07ILF/ZYP9cs3DQTp3/HMpddqA4ZORtLDPU54ECf453LKmHOymPGcjBnObSaMzt9RBrRpEkTtUtwCA6Ts9EInN1p6uxlHi9u1zkBQQ8CHYcD9QMVe3vZOTvi7J0Ocy6rjDkrjxnLwZzl0GrOvLyTSCM2bdqkdgkOwe5zLioAjm0AVj8LbHm3uMPn4gaEPA4MWwn0maxohw+Qk7Nw8Os77f5c1gjmrDxmLAdzlkOrOXPJBioXl2yQIzc3F56enmqXYffsNueCm8DR703LLuReKW53qwOEPmHq8HnIu+RERs7fH7qIRTtOAwBWvBgOb/fq34toS+z2XNYY5qw8ZiwHc5ZDVs5csoHIRo0YMULtEhyC3eWcmwXsXgisiAR2Lyju8NVuCHT/t2nZhS7/ktrhA+TkXPJvlg440Gd/57JGMWflMWM5mLMcWs2ZI31ULo70EWnQ1QumZRdObLJedqFeS9PkLIEPAs72fdv2dwdT8fnOMwCAlS+Go46DjfQREZHj4kgfkY2KiIhQuwSHYPM5Z/4FbJkCfP0scGx9cYevcSjQ731gyBKgTT/VO3wyci75J0snB5y+0+bPZRvBnJXHjOVgznJoNWeO9FG5ONJHpDIhgNT9wMEVQOo+6+da3GeaidO/gzq1qSj+wAUs/u0sAODr0ffC09W+RzaJiIjMONJHZKOio6PVLsEh2FTORiNw6ldg7Sjgh9eLO3xOzqbRvKFLgIc/0GSHT0bOJf9k6YhLNtjUuWzDmLPymLEczFkOrebMP4sSacTw4cPVLsEh2ETORQXAiY3A4a9N9+6ZubgDdw8A2kcCdRqpV18FyMjZ0a9TsYlz2Q4wZ+UxYzmYsxxazZkjfUQakZiYqHYJDkHTOeuvAweWAysjgZ2zijt87t7APSOAp1cD943TfIcPkJ+zA97Sp+1z2Y4wZ+UxYzmYsxxazZkjfUREart5GUhcAxz9DijMLW6v0xjoEAm0fRSo5aFefRplLLlkgwN2+oiIiCqKnT4ijWjfvr3aJTgETeWck2JaduHkJsBQWNzue5dpcpbAPqrPwllVMnIueXWnI97Tp6lz2Y4xZ+UxYzmYsxxazZmXdxJpxKpVq9QuwSFoIueMY8Dmd4DVzwLHNxR3+Pw7Ao98CAxZDLR5yGY7fICknB38nj5NnMsOgDkrjxnLwZzl0GrOXLKBysUlG4hqgBDAhT3AwZXAxQPWz7W83zSy1zhUndps1Oo9Kfjyz3MAgLUv34dazvw7JhEROQYu2UBko7S6mKe9kZ6z0QAk/wJ8+wLw44TiDp+Ti+levchlQL8Zdtfhk7I4e4mhPse7uJOfGbIwZ+UxYzmYsxxazZkjfVQujvQRVUGRHvjrR+DQ18D1tOL2Wh7A3RGmZRdq+6lXnx1YlXAeK3efBwB8N/YfcHJyxK4fERE5Io70EdmoyMhItUtwCIrnnH8N2P+ladmF3/5b3OHz8AG6jgSeWg10H2v3HT4Z57Oj/8mSnxlyMGflMWM5mLMcWs2ZI31ULo70yZGbmwtPT0+1y7B7iuV8IxNIXA0c23DLsgv+QMcnTZdyurjV/PtqlIzzeeXu81iVYBrp+/7f/4DOwdZt4GeGHMxZecxYDuYsh6ycOdJHZKNmzZqldgkOocZzzj4LbPsQWDUMOLy6uMNXPwh48B1g2Aog5HGH6vABcs5nq3v6HKzDB/AzQxbmrDxmLAdzlkOrOdvufOBEdqZfv35ql+AQaiznS0nAoVXA2d+s2wPCgLCngaZdHXrFcBnns6Nfp8LPDDmYs/KYsRzMWQ6t5sxOH5FGpKamql2CQ6hWzkIAKbtNyy6kHSpu1+mAlj2AsKeAhndXv0g7ION8Nt+d4Kjzt/AzQw7mrDxmLAdzlkOrObPTR6QR2dnZapfgEKqUs9EAnPrV1NnLOl3c7uQCtOkHdBwG+DSvuSLtAM9n5TFjOZiz8pixHMxZDq3mzE4fkUb07NlT7RIcQqVyLswH/vrBdK/e9UvF7bU8geDHgPZDAK8GNV+kHZBxPluu7nTQy2j5mSEHc1YeM5aDOcuh1Zw5kQuRRsTFxaldgkOoUM75V4F9S4GVQ4HfPynu8HnUA7q9CDy9Grh3DDt8dyDjfDbf0+eYXT5+ZsjCnJXHjOVgznJoNWcu2UDl4pIN5DCupxcvu1CUX9zu3cR0CWebhwEXV/XqIytLfz+Db/enwsVZh/iX/6F2OURERNJwyQYiGxUREaF2CQ6hzJyzzgBb3we+Gg4krinu8DVoA/SdAjy5HAgeyA5fJcg4n81/sXTUkT5+ZsjBnJXHjOVgznJoNWeO9FG5ONJHdivtsGnZhXN/WLc3uce07EKTzg57v5gtWPzbGcQfSIWrixO+fek+tcshIiKSRvpIX/fu3fGf//wHGzZsQE5OTnV3Z+XGjRuYMmUKHn74Yfj6+kKn02Hp0qUVfn1OTg5GjRoFPz8/eHl54YEHHsD+/fvL3Pb7779H586d4e7ujubNm2PKlCkoKiqyyX0uXboUOp2uzMelS5fK3C+pLyoqSu0SHMKIqOeAs78D340Fvh9X3OHT6YC7egNPLAQGzAaa3sMOXzXIOJ8dfaSPnxlyMGflMWM5mLMcWs252rN3+vj4YMGCBZg5cyacnJwQHByMHj16oGfPnujRowcCAgKqvO/Lly9j2rRpaN68OTp27Iht27ZV+LVGoxH9+/fHoUOHMGHCBDRo0ADz5s1D7969sW/fPrRu3dqy7U8//YRBgwahd+/emDt3LhITEzF9+nRkZGRg/vz5NrdPs2nTpqFVq1ZWbT4+PhXOkOSaM2eO2iXYN0MRcOoXfPbPQmBTdHG7cy3TvXodngR8mqlXn52RcT6bL1Rx1L45PzPkYM7KY8ZyMGc5NJuzqAFGo1Hs379ffPzxx2Lo0KGicePGQqfTCScnJ3HXXXeJESNGVGm/+fn5Ii0tTQghxJ49ewQAsWTJkgq99uuvvxYAxDfffGNpy8jIED4+PmL48OFW2wYHB4uOHTuKwsJCS9vbb78tdDqdOHbsmM3tc8mSJQKA2LNnT/lBVUBSUpIAIJKSkmpkf1S2mTNnql2CfdLfFOLQaiGWDxFiQU+R9l5bIRb0FGLxI0L8+ZkQN6+oXaFdknE+L9pxSgz4ZKcYOv8Pxd9Li/iZIQdzVh4zloM5yyEr58p+P6+RiVx0Oh06deqEV155BatXr8a5c+fwxRdfoHXr1jhz5gz+97//VWm/bm5uaNy4cZVeu2bNGjRq1AhPPPGEpc3Pzw+RkZH47rvvoNfrAQBHjx7F0aNHMWrUKLi4FA98vvzyyxBCYM2aNTa3z5KuX78Og8FQufBIFd26dVO7BPuSlwPsXQysjAR2fQrcyAAAuNZtDISPAZ5aDYSPAjx91a3TTsk4n4WDX9/Jzww5mLPymLEczFkOreZcI52+GzduYPPmzXjnnXfQu3dv+Pj44IUXXoCzszNGjx6NL7/8sibeplIOHDiAzp07w8nJ+hC7deuG3NxcnDhxwrIdAHTp0sVqu4CAADRt2tTyvC3t0+yBBx6At7c3PD09MXDgQJw8ebLUNrfKyMjAkSNHrB7Jycnlvo6qLy8vT+0S7MO1NOC3/5o6e/uWAfrrpva6TYGeE7Cn9ZtA2HDArbaqZdo7Geez+PuuPgft8/EzQxLmrDxmLAdzlkOrOVe709elSxfUq1cPAwYMwMaNG3HPPfdg5cqVls7D/Pnz8dRTT9VErZWSlpYGf3//Uu3mtosXL1q2K9l+67bm7Wxpn56enhgxYgTi4uIQHx+PiRMn4pdffsF9992HlJSUUq8vad68eQgNDbV6DBo0CADw22+/Yfv27YiNjUVWVpblRlXz1LTjx49HcnIyFi9ejPj4eCQkJCAmJga5ubmIjIy02jY6OhqJiYlYuXIlVq5cicTERERHR1ttExkZidzcXMTExCAhIQHx8fFYvHgxkpOTMX78eKtto6KikJWVhdjYWGzfvh0bN25EXFwcUlNTMWbMGKttx4wZg9TUVMTFxWHjxo2aOaaZM2fa3THJ/Dmd2/8rDs4egqz5j+BmwnJcTDkLg9GI7ccygH9Ow8AVV4G7B2D2x3Nt5phs+ed0+PBhxY/pyy+Xw2g04vy5cw75c5oyZYrdHZMWf047duywu2PS2s9pwoQJdndMWvw5rV+/3u6OSYs/p1OnTkk5pt9++w2VUt3rSXU6nXB2dhZDhgwRixcvFidPnqzuLstU2Xv6nJycxEsvvVSq/ZdffhEARHx8vBBCiGnTpgkAIj09vdS2PXr0EB07drS5fZZl586dQqfTidGjR99xu/T0dJGUlGT1WLduHe/pk+DChQtql2B7jEYhUg8I8cME0316JR8b3hDiwl7TNiUwZzlk5Dx/W7IY8MlOMeyzXYq/lxbxXJaDOSuPGcvBnOWQlbP0e/r27t2LWbNmQQiBSZMmoW3btvD390dkZCTmzp2LgwcPWmZYk8nDw8NyP1xJ+fn5ludL/vN225qft6V9luX+++9HeHg4fv755ztu17BhQ4SEhFg9goKC7vgaqhkxMTFql2A7jEbgzE7TsgvrXwVSdpvadU5AYB9g8OdA/49M6+3dMrUjc5ZDZs6OOnsnz2U5mLPymLEczFkOreZc7U5f586d8eqrr2LNmjW4dOkSjh07hunTp8PT0xOzZs3CPffcA19f+RMl+Pv7Wy6JLMncZl5Kwny55O22LbnkhK3s83aaNWuGrKyscrcjdSxYsEDtErTPUAgc/xH4JgrYPBlIP2Jqd3YFgh8DnlwO9J0CNGh9210wZzlk5Gz+e6Kjdvp4LsvBnJXHjOVgznJoNecamcjFLC8vDxcuXEBKSgrOnz+PzMxMCCFw48aNmnybCgkLC8P+/fthNBqt2nfv3g1PT0+0adPGsh1gGrEs6eLFi7hw4YLleVva5+2cPn0afn5+5W5H6jBfr01lKMgFDn0NrBoObP8QyDlvanerA3R6Bnjqa6DH60DdJuXuijnLISNno3mdPgedyoXnshzMWXnMWA7mLIdWc652p2/Dhg2YOHEiunfvDh8fH/Tt2xczZ86E0WjE66+/jo0bNyI7O7smar2ttLQ0HD9+HIWFhZa2IUOGID09HWvXrrW0Xb58Gd988w0iIiLg5uYGAAgJCUG7du2wcOFCq6UN5s+fD51OhyFDhtjcPjMzM0tl9OOPP2Lfvn14+OGHKxIpqWD9+vVql6A9uVlAwiLTTJx/zgNu/n1ue/kB975sWnah24uVWnaBOcshM2dHHenjuSwHc1YeM5aDOcuh2ZyrexOhTqcT9erVEwMGDBAffvih+OOPP0RBQUF1d2sxd+5cERMTI1566SUBQDzxxBMiJiZGxMTEiJycHCGEEFFRUQKAOHPmjOV1RUVF4t577xW1a9cW7733noiLixMhISGiTp064vjx41bvsX79eqHT6USfPn3EwoULxSuvvCKcnJzEiy++aLWdrewzKChIDB06VHz44YdiwYIFYtSoUcLFxUU0a9ZMXLp0qdI/Ay7OLkd5k+w4lKupQuyYJcSivtaTs3z1tBDHfhCiqOqfMcxZDhk5f/rrSTHgk53imc//VPy9tIjnshzMWXnMWA7mLIesnCv7/bzanb7Dhw8L4y2z49WkFi1aCABlPsydvLI6fUIIkZWVJUaOHCnq168vPD09Ra9evcSePXvKfJ/4+HgRFhYm3NzcRNOmTcXkyZPL7Lzawj7ffvttERYWJurWrStq1aolmjdvLl566aUqdfiEYKdPFs6qJYTIPCnElqlCfNbburMX/5IQp3cIYTBU+y2Ysxwycp77ywkx4JOd4tkvdiv+XlrEc1kO5qw8ZiwHc5ZDq7N36oRQYWpNsilHjhxBaGgokpKSEBISonY5disuLg5jx45Vuwz5hAAuHgAOrgQu7LF+rlk4EPYU4N+xxq7hc9icJZOR89xfTmLz0XT4erli2b+6KfpeWsRzWQ7mrDxmLAdzlkNWzpX9fu6ieEVEVCGBgYFqlyCX0Qic3Wnq7GUeL27XOQFBDwIdhwP1az4Th8tZJTJyNjr47J08l+VgzspjxnIwZzm0mjM7fUQaUd5ai3ajqAA4uRk4tAq4eqG43cUNaPso0OFJwNtfsbd3mJxVxpyVx4zlYM7KY8ZyMGc5tJpzjS7ZQERVl5CQoHYJyiq4CRxcBawaBuyILe7wudUB7okyLbtw/2uKdvgAB8hZI2TkLGBessEx8VyWgzkrjxnLwZzl0GrOHOkj0oiRI0eqXYIycrOAxDXA0e+AghJrdtZuCLSPBNr1B1w9pZVjtzlrjIyczXekOzno9Z08l+VgzspjxnIwZzm0mjNH+og0Yvz48WqXULOuXgB2fASsfBI4uKK4w1evJfBANDBsFdBhqNQOH2CHOWuUjJzNs5A5aJ+P57IkzFl5zFgO5iyHVnPm7J1ULs7eSZWS+ZdpcpYzOwBhLG5vHAp0fApo3h1w4t+bqPpmb/4LW//KRCNvN3we1VXtcoiIiKSp7PdzfvMi0oiIiAi1S6g6IYAL+4ANrwNrRwGntxV3+FrcBwycCzwWB7T8h+odPpvO2YbIyLn4L5aOOdTHc1kO5qw8ZiwHc5ZDqzlzpI/KxZE+ui2jETizzTRBy+UTxe1OzkBQX6DjMMD3LtXKI/v20aa/sP1EJhrXdcei57qoXQ4REZE0HOkjslFavQa8TEUFwNHvgdXPAj+/V9zhc3EH2g8x3a/3QLQmO3w2lbMNk3NPn+lvlk6OOdDHc1kS5qw8ZiwHc5ZDqzlz9k4ijRg7dqzaJZRPf900C2fiGiAvu7jd3RsIeQIIfQJwr6tefRVgEznbARk5O/p1KjyX5WDOymPGcjBnObSaM0f6iDRix44dapdwezcvA38uAFZEAgmLijt8dRoD/3gFeGo10OV5zXf4AI3nbEdk5GyZvdNB7+njuSwHc1YeM5aDOcuh1Zw50kekEfXq1VO7hNJyUoBDXwEnNwGGwuJ237uAjsOBwD6As219jGgyZzskI2fzSJ+jLtnAc1kO5qw8ZiwHc5ZDqznb1rc1IjvWpEkTtUsolnHMtOzC2Z3W19D5dwTCngKahdvsN21N5WzHZORsvqfPRk/FauO5LAdzVh4zloM5y6HVnHl5J5FGbNq0Sd0ChABSEoD1rwHxY/5eZ+/vDl/L+01LLgz8BGh+r01/y1Y9ZwchJWfzSJ+DXt7Jc1kO5qw8ZiwHc5ZDqzlzyQYqF5dskCM3Nxeenp7y39hoMK2rd3AlcCW5uN3JBWj9T6DDk4BvK/l1KUS1nB2MjJw/+PEY/jh1Bc3reyLuqc6KvpcW8VyWgzkrjxnLwZzlkJUzl2wgslEjRoyQ+4ZFeuBIPPDV08Av04o7fLU8gA6RwPCvgN7/sasOH6BCzg5KRs7Gv/9m6ZjjfDyXZWHOymPGcjBnObSaM0f6qFwc6bMz+ddMyy4krQHycorbPXyA0MFA8CDTEgxEGjd9w1HsPpOFlg28MHd4J7XLISIikoYjfUQ2KiIiQtk3uJEJ7IoDVj4J7Pm8uMNXxx+4/zXTsgudn7P7Dp/iORMAOTkXL9ngmHguy8GclceM5WDOcmg1Z470Ubk40mfjss8Ch74GTm4GjEXF7fWDgLDhwF0PAE7OqpVHVFUxG44i4UwW7vLzwsfDONJHRESOgyN9RDYqOjq6Znd4KQnY9DawOgr468fiDl9AGPBoLDD4cyCor8N1+Go8ZyqTjJwt6/Qp/k7axHNZDuasPGYsB3OWQ6s5c50+Io0YPnx49XciBJCy2zQTZ9qh4nadDmjZw7TGXsO7q/8+NqxGcqZyyci5eJ0+x+z28VyWgzkrjxnLwZzl0GrOHOkj0ojExMSqv9hoAE5uAdb8C/jpreIOn5ML0K4/EPk/4KEYh+/wAdXMmSpMRs6OPtLHc1kO5qw8ZiwHc5ZDqzlzpI/IlhXmA3/9ABxeDVy/VNxeyxMIfgxoPwTwaqBefURERESkOnb6iDSiffv2Fd84/6ppjb2kb01LMJh51DN19IIfA9zq1HyRdqBSOVOVycjZPA+Zo17eyXNZDuasPGYsB3OWQ6s58/JOIo1YtWpV+RtdTwf+mAusiAT2Linu8Hk3AXq8YVp2odMz7PDdQYVypmqTkbNlyQbH7PPxXJaEOSuPGcvBnOXQas5csoHKxSUbNCDrDHBoFZD8s+n+PbMGbUzLLrTqDTjxbzjkWN5Zl4SDKTlo17gOYod2VLscIiIiabhkA5GNKnMxz7TDwMZJwDcjgBObijt8Te4B+s8GnlgIBPZhh68StLpoqr2Rszi7Y//NkueyHMxZecxYDuYsh1Zz5kgflYsjfZIZjcD5XcChlaa19sx0OqBVL6DjcKBhO/XqI9KIt+MTcfjCVdztXwczh3Ckj4iIHAdH+ohs1LDIIabRvDXPA5uiizt8zrWAuyOAyC+Bf77HDl81RUZGql2CQ5CRs/kvlk4OelMfz2U5mLPymLEczFkOrebMkT4qF0f6FFaQCxz/AYaDq+Ccd6W43dULCB5kmo3T01e18uxNbm4uPD091S7D7snIedLaRCSlXkVoE2988EQHRd9Li3guy8GclceM5WDOcsjKmSN9RLYiLwfYuxhYGQns+hTpp/8e2fP0BcLHmGbiDB/FDl8NmzVrltolOAQ5OTv23yx5LsvBnJXHjOVgznJoNWeu00ck27U04PDXwF8/AkV6S7N7oyCg58tA64cAF1cVC7Rv/fr1U7sEhyAj5+LrVBzz8k6ey3IwZ+UxYzmYsxxazZmdPiJZrpz6e9mFXwBhLG73aweEPYXtBy/j8bsHqFefg0hNTVW7BIcgI2dzp89Bb+njuSwJc1YeM5aDOcuh1ZzZ6aOKyzgGtGsLOPO0sTAUAemJpkXS3b2BRu2t8xECSDsEHFwJpOy2fm3TrqY19gI6Azodsrctllu7g8rOzla7BIcgI2fzkg1ODtrp47ksB3NWHjOWgznLodWcNX1P340bNzBlyhQ8/PDD8PX1hU6nw9KlSyv8+pycHIwaNQp+fn7w8vLCAw88gP3795e57ffff4/OnTvD3d0dzZs3x5QpU1BUVOQQ+6ywTZOB5YOBfctMnR1HZigy5bD8CeCb54Hvxpr+ac6nqAA4s9PUvv7V4g6fzsm0rt7gz4H+H5nW2/t7mKJnz54qHpDjYM5yyMjZ0ach47ksB3NWHjOWgznLodWcNd3pu3z5MqZNm4Zjx46hY8fKrcFkNBrRv39/rFy5Ev/+978xc+ZMZGRkoHfv3jh58qTVtj/99BMGDRoEHx8fzJ07F4MGDcL06dMxbtw4u99npYgiIO0gsCMW2DzZcTt+hiJg89umHNIOAcYiwKmW6Z9pB4FfpgFx4cCmt4H0I6bXOLsCwY8BTy4H+k4BGrQutdu4uDi5x+GgmLMcMnPWOeg9fTyX5WDOymPGcjBnOTSbs9Cw/Px8kZaWJoQQYs+ePQKAWLJkSYVe+/XXXwsA4ptvvrG0ZWRkCB8fHzF8+HCrbYODg0XHjh1FYWGhpe3tt98WOp1OHDt2zK73WRFJSUkCgEh69x4h5vcQIra1ELNDhNi3rNL7sgt7l5qOP7a1KY8FPYWYf78Qc9oLMSNAiPd8hZhW37TNkv5C7F4oxM0raldNZHde//qgGPDJTvHOukS1SyEiIpLK8v08KalC22v65iw3Nzc0bty4Sq9ds2YNGjVqhCeeeMLS5ufnh8jISCxfvhx6vR5ubm44evQojh49iri4OLi4FMfx8ssvY8aMGVizZg0mT55sl/usrLwbV5F7XQ8IV7jmX0He9jicuqwDdM6V3pfNEgYEHomDx80rKHCtD1zPhs5YiFqF16AThr83cobOaMBN4Y49Yf+FwbUOcLYQQPoddz19egwmT35H8UNwdMxZDhk5X80rAOCoc3cCERERWL9+vdpl2D3mrDxmLAdzlkOrOWu601cdBw4cQOfOneHkZH0Fa7du3bBw4UKcOHEC7du3x4EDBwAAXbp0sdouICAATZs2tTxvj/ssS0ZGBjIzM63akpOTAQBOeVfgdNN0yggY4Hb1FHz//D/ode5l7sseuYl8uBkvQQBwKsqwtJsvdC3SueCazhsuKELRjRv4futOnHIpfSlnWby7D8Mnv1Tj8luqEOYsh8ycdQ46facWv1TYI+asPGYsB3OWQ6s5a/qevupIS0uDv79/qXZz28WLFy3blWy/dVvzdva4z7LMmzcPoaGhVo9BgwYBMM+UJyDE33PmCSOcYIReb1prrqCgAEIIFBUVwWAwwGg0orCwEAAs25j/WVhYCKMwwmAogsFQBKMoZ1ujEQaDAUVFRRBCoKCgwGqbggI9BEQFty27TgGBggL9HbfVGQ2AEBDQQfy97IIQRhToXHHJ6IOLaIRrRjfohQ5Oogh5mecBAMnJpi+/p06fgtFoRFraRdy8eRPZOdm4fPky9Pp8HDmSZLXtmTNnUFRUhEuXLuH69eu4evUqMjIyUFBYgHPnzllte+7cORQUFiAjIwNXr17F9evXcenSJRQVFeHMmTNW26akpECvz8fly5eRnZONmzdvIi3tIoxGI06dPmW1bWpqKvLy8pCVdQVZWVeQl5dnmYq4IseUkpKiuWM6cuSI3R2TFn9Op0+flnJMEEak7PsVCQkJiI+Px+LFi5GcnIzx48cDMP3FFQCioqKQlZWF2NhYbN++HRs3bkRcXBxSU1MxZswYq23HjBmD1NRUxMXFYePGjdi+fTtiY2ORlZWFqKgoq23Hjx+P5ORkLF68GPHx8UhISEBMTAxyc3MRGRlptW10dDQSExOxcuVKrFy5EomJiYiOjrbaJjIyErm5uYiJiSn3mAIDA+3umLT4cxo4cKDdHZPWfk7NmjWzu2PS4s+pb9++dndMWvw5RUVFSTmm3377DZWhE8I25j/bu3cvunbtiiVLlmDEiBHlbu/s7IzRo0dj3rx5Vu2//vorHnzwQcTHx2PQoEGIiYnBu+++i/T0dDRs2NBq2549e+LatWs4ePCgXe6zLLcb6Rs0aBAOvN0BIf6eAABdXjZ0Ti649s9YGPxCytyXPXLOPALvLRMgjEUQHvX+btUBTs4oeZGZLvcK4OSCaxELUdSoYpMQ5WTnwKeeT43XTNaYsxyycnav5Qxv91qKv48WZWVlwdfXV+0y7B5zVh4zloM5yyEr5yNHjiA0NBRJSUkICSn/u7jdXt7p4eFhGa0pKT8/3/J8yX/eblvz8/a4z7I0bNiwVKfSrJZzLdRycTXNk16UBwSEoV7wPx1r3T6/AGD/XaZZO50blr0qtBBA4Q0gIAy+re6pcD7LFnyJCRMm1HDBdCvmLAdzVt4XX3zBjCVgzspjxnIwZzm0mrPdXt7p7+9vuSSyJHNbQECAZbuS7bdua97OHvdZJUIANy4BbnWA0MGO1eEDTMcbOth0/DculV4orBr5dOvWrYaLpbIwZzmYs/KYsRzMWXnMWA7mLIdWc7bbb+xhYWHYuXMnjEaj1YQmu3fvhqenJ9q0aWPZDjBdPlryh3Tx4kVcuHABo0aNstt9VpR51DD5QgZwrRBw9QJahQMuHYEjRyq1L7tQKwzwCAfObAdST5ry0LmY1jEsuFnlfI4fP44GDRooVzcBYM6yMGflMWM5mLPymLEczFkOWTmbJ1os6+q+Mim0dESNu9M6fRcvXhTHjh0TBQUFlravvvqq1Fp1mZmZwsfHRzz55JNWr2/Xrp3o2LGjKCoqsrRNnjxZ6HQ6cfToUbveZ0UsXbrUNIMLH3zwwQcffPDBBx988KGZx7p16yr0fV7zE7l8+umnyMnJwcWLFzF//nw88cQT6NSpEwBg3LhxqFu3LkaMGIFly5bhzJkzaNmyJQDAYDDg/vvvR1JSEiZMmIAGDRpg3rx5OH/+PPbs2YO2bdta3mPDhg0YOHAgHnjgAQwbNgxJSUn49NNPMXLkSCxcuNCynT3usyJ27dqF++67D6tXr0ZwcHClXksVY54sZ926dQgKClK7HLvFnOVgzspjxnIwZ+UxYzmYsxwyc9br9UhJSUGvXr3g4+NT/gsqPewjWYsWLW7bsz1z5owQQoioqCir/zbLysoSI0eOFPXr1xeenp6iV69eYs+ePWW+T3x8vAgLCxNubm6iadOmYvLkyVYjh/a8z/IkJSUJACIpKalKr6fyMWM5mLMczFl5zFgO5qw8ZiwHc5ZDyzlrfqSP1FfZKWGp8pixHMxZDuasPGYsB3NWHjOWgznLoeWc7Xb2TiIiIiIiImKnj4iIiIiIyK6x00fl8vPzw5QpU+Dn56d2KXaLGcvBnOVgzspjxnIwZ+UxYzmYsxxazpn39BEREREREdkxjvQRERERERHZMXb6iIiIiIiI7Bg7fURERERERHaMnT4iIiIiIiI7xk4fERERERGRHWOnj25Lr9fjrbfeQkBAADw8PBAeHo4tW7aoXZZN2rNnD/79738jJCQEXl5eaN68OSIjI3HixIlS2x47dgwPP/wwateuDV9fXzz77LPIzMxUoWrbN2PGDOh0OoSGhpZ67o8//sD9998PT09PNG7cGK+88gpu3LihQpW2af/+/Rg4cCB8fX3h6emJ0NBQfPLJJ1bbMOPqOXnyJIYNG4amTZvC09MT7dq1w7Rp05Cbm2u1HXOumBs3bmDKlCl4+OGH4evrC51Oh6VLl5a5bUU/h41GI2bOnIlWrVrB3d0dHTp0wKpVqxQ+Eu2qSMZGoxFLly7FwIED0axZM3h5eSE0NBTTp09Hfn5+mfv94osvcPfdd8Pd3R2tW7fG3LlzJRyNdlXmXDYrLCxEcHAwdDodPvroo1LP81wurTI5G41GzJ8/H2FhYfDw8ED9+vXRp08fHDp0qNR2quUsiG5j2LBhwsXFRbz55pvis88+E927dxcuLi5i586dapdmcwYPHiwaN24sxo0bJxYtWiRiYmJEo0aNhJeXl0hMTLRsl5KSIho0aCACAwPFxx9/LGbMmCHq1asnOnbsKPR6vYpHYHtSUlKEp6en8PLyEiEhIVbPHThwQLi7u4tOnTqJ+fPni7ffflu4ubmJhx9+WKVqbcumTZuEq6urCA8PF7NnzxYLFy4Ub731lpgwYYJlG2ZcPefPnxc+Pj6iRYsW4oMPPhCfffaZGDFihAAgBg4caNmOOVfcmTNnBADRvHlz0bt3bwFALFmypNR2lfkc/s9//iMAiBdffFEsXLhQ9O/fXwAQq1atknRU2lKRjK9fvy4AiHvvvVdMnz5dLFy4UDz//PPCyclJ9O7dWxiNRqvtFyxYIACIwYMHi4ULF4pnn31WABD/93//J/HItKWi53JJs2bNEl5eXgKAiI2NLfU8z+XSKpNzVFSUcHFxEf/617/EokWLxH//+18RFRUlNm/ebLWdmjmz00dl2r17d6kPhry8PBEYGCi6d++uYmW26ffffy/1ZeHEiRPCzc1NPP3005a2l156SXh4eIhz585Z2rZs2SIAiM8++0xavfbgySefFH369BG9evUq1el75JFHhL+/v7h69aqlbdGiRQKA2LRpk+xSbcrVq1dFo0aNxOOPPy4MBsNtt2PG1TNjxgwBQCQlJVm1P/fccwKAyMrKEkIw58rIz88XaWlpQggh9uzZc9svcBX9HL5w4YKoVauWGDt2rKXNaDSKHj16iKZNm4qioiLlDkajKpKxXq8Xv//+e6nXvvfeewKA2LJli6UtNzdX1K9fX/Tv399q26efflp4eXlZ/j9wNBU9l83S09NF3bp1xbRp08rs9PFcLltFc/76668FALF27do77k/tnHl5J5VpzZo1cHZ2xqhRoyxt7u7uGDlyJHbt2oWUlBQVq7M99913H1xdXa3aWrdujZCQEBw7dszS9u2332LAgAFo3ry5pa1v375o06YNVq9eLa1eW7djxw6sWbMG//3vf0s9d+3aNWzZsgXPPPMMvL29Le3PPfccateuzZzLsXLlSqSnp2PGjBlwcnLCzZs3YTQarbZhxtV37do1AECjRo2s2v39/eHk5ARXV1fmXElubm5o3LhxudtV9HP4u+++Q2FhIV5++WVLm06nw0svvYQLFy5g165dNXsANqAiGbu6uuK+++4r1f74448DgNXvxK1bt+LKlStWGQPA2LFjcfPmTfzwww81ULXtqei5bPaf//wHbdu2xTPPPFPm8zyXy1bRnGfPno1u3brh8ccfh9FoxM2bN8vcTu2c2emjMh04cABt2rSx+iIBAN26dQMAHDx4UIWq7IsQAunp6WjQoAEAIDU1FRkZGejSpUupbbt164YDBw7ILtEmGQwGjBs3Di+88ALat29f6vnExEQUFRWVytnV1RVhYWHMuRw///wzvL29kZqairZt26J27drw9vbGSy+9ZLkfhxlXX+/evQEAI0eOxMGDB5GSkoKvv/4a8+fPxyuvvAIvLy/mrIDKfA4fOHAAXl5euPvuu0ttZ36eKu7SpUsAYPmdCBRneOvP45577oGTkxMzroCEhAQsW7YM//3vf6HT6crchudy1V27dg0JCQno2rUroqOjUbduXdSuXRt33XVXqT+8qZ0zO31Ur6OzAQAADolJREFUprS0NPj7+5dqN7ddvHhRdkl2Z8WKFUhNTcWTTz4JwJQ5gNvmnpWVBb1eL7VGW7RgwQKcO3cOMTExZT5fXs48t+/s5MmTKCoqwmOPPYZ+/frh22+/xb/+9S8sWLAAzz//PABmXBMefvhhxMTEYMuWLejUqROaN2+OYcOGYdy4cZgzZw4A5qyEynwOp6WloVGjRqW+SPP3ZNXMnDkT3t7eeOSRRyxtaWlpcHZ2RsOGDa22dXV1Rf369ZlxOYQQGDduHJ588kl07979ttvxXK66U6dOQQiBr776CosXL8bMmTOxYsUK+Pn5YdiwYdi4caNlW7VzdlF072Sz8vLy4ObmVqrd3d3d8jxV3fHjxzF27Fh0794dUVFRAIozLS/3sp4nkytXruDdd9/FO++8Az8/vzK3KS9nntt3duPGDeTm5mLMmDGW2TqfeOIJFBQU4LPPPsO0adOYcQ1p2bIlevbsicGDB6N+/fr44Ycf8P7776Nx48b497//zZwVUJnPYf6erDnvv/8+fv75Z8ybNw8+Pj6W9ry8vFK3RpjxHC/f0qVLkZiYiDVr1txxO57LVWeeKfnKlSv4888/ER4eDgAYOHAgWrVqhenTp+Phhx8GoH7O7PRRmTw8PMocVTJfvuXh4SG7JLtx6dIl9O/fH3Xr1rXcOwkUZ8rcq27y5Mnw9fXFuHHjbrtNeTkz4zsz5zN8+HCr9qeeegqfffYZdu3aBU9PTwDMuDq++uorjBo1CidOnEDTpk0BmDrXRqMRb731FoYPH85zWQGV+Rzm78ma8fXXX2Py5MkYOXIkXnrpJavnPDw8UFBQUObreI7f2bVr1zBp0iRMmDABzZo1u+O2PJerzpxNq1atLB0+AKhduzYiIiKwfPlyFBUVwcXFRfWceXknlcnf399ymUtJ5raAgADZJdmFq1ev4pFHHkFOTg42btxolaN5eP92ufv6+nKU7w5OnjyJhQsX4pVXXsHFixdx9uxZnD17Fvn5+SgsLMTZs2eRlZVVbs48t+/MnM+tE4yYL7/Kzs5mxjVg3rx56NSpk6XDZzZw4EDk5ubiwIEDzFkBlfkc9vf3x6VLlyCEKLUdwN+TFbFlyxY899xz6N+/PxYsWFDqeX9/fxgMBmRkZFi1FxQU4MqVK8z4Dj766CMUFBTgySeftPw+vHDhAgDT5/TZs2ctHWqey1V3u9+JgOn3YmFhoWViF7VzZqePyhQWFoYTJ05YZpAz2717t+V5qpz8/HxERETgxIkT2LBhA4KDg62eb9KkCfz8/LB3795Sr01ISGDm5UhNTYXRaMQrr7yCVq1aWR67d+/GiRMn0KpVK0ybNg2hoaFwcXEplXNBQQEOHjzInMtxzz33ADDlXZL5XgQ/Pz9mXAPS09NhMBhKtRcWFgIAioqKmLMCKvM5HBYWhtzcXKvZJgH+nqyo3bt34/HHH0eXLl2wevVquLiUvvjMnOGtP4+9e/fCaDQy4zs4f/48srOzERISYvl92KNHDwCmy2lbtWqFo0ePAuC5XB0BAQFo3Lhxqd+JgOn3oru7O+rUqQNAAzkruiAE2aw///yz1Fou+fn5IigoSISHh6tYmW0qKioSAwcOFC4uLuKHH3647XZjxowRHh4e4vz585a2n3/+WQAQ8+fPl1GqzcrMzBTx8fGlHiEhIaJ58+YiPj5eHD58WAghxMMPPyz8/f3FtWvXLK///PPPBQDx008/qXUINmH//v0CgHjqqaes2ocPHy5cXFxEamqqEIIZV9eAAQOEq6ur+Ouvv6zaBw0aJJycnJhzNd1pza2Kfg6npKTcds2tJk2aOOzaZmZ3yvjo0aOifv36IiQk5I5r7eXm5gpfX18xYMAAq/ZnnnlGeHp6iitXrtR02Tbndjnv27ev1O/Dzz77TAAQI0aMEPHx8SInJ0cIwXO5Iu50Pr/66qsCgNVC7JmZmcLb21s8+uijlja1c+Y9fVSm8PBwDB06FJMmTUJGRgaCgoKwbNkynD17Fl988YXa5dmcN954A99//z0iIiKQlZWF5cuXWz1vXjsnOjoa33zzDR544AG8+uqruHHjBmJjY9G+fXvLzIhUtgYNGmDQoEGl2s1r9ZV8bsaMGbjvvvvQq1cvjBo1ChcuXMCsWbPw0EMPWW64prJ16tQJ//rXv7B48WIUFRWhV69e2LZtG7755htMmjTJcnkKM66eCRMm4KeffkKPHj3w73//G/Xr18eGDRvw008/4YUXXmDOVfTpp58iJyfHMjK9fv16yyVv48aNQ926dSv8Ody0aVO89tpriI2NRWFhIbp27Yp169Zh586dWLFiheV+bUdTXsZOTk7o168fsrOzMWHChFJr7QUGBlpmmvTw8EBMTAzGjh2LoUOHol+/fti5cyeWL1+OGTNmwNfXV+7BaUh5OXfu3BmdO3e2es3Zs2cBACEhIVa/E3ku315FPjMmTZqE1atXY/DgwXj99ddRt25dLFiwAIWFhXj//fct+1I9Z0W7lGTT8vLyxJtvvikaN24s3NzcRNeuXcXGjRvVLssm9erVSwC47aOkpKQk8dBDDwlPT0/h4+Mjnn76aXHp0iWVKrd9vXr1EiEhIaXad+7cKe677z7h7u4u/Pz8xNixY61GS+j2CgoKxNSpU0WLFi1ErVq1RFBQkJgzZ06p7Zhx9ezevVs88sgjonHjxqJWrVqiTZs2YsaMGaKwsNBqO+ZccS1atLjt5/CZM2cs21X0c9hgMIj3339ftGjRQri6uoqQkBCxfPlyiUekPeVlfObMmTv+PoyKiiq1z4ULF4q2bdsKV1dXERgYKObMmSOMRqP8g9OQip7LJZmzL3kVlxnP5bJVNOdTp06Jxx9/XHh7ewsPDw/Rp08fkZCQUGp/auasE+KWuwmJiIiIiIjIbnAiFyIiIiIiIjvGTh8REREREZEdY6ePiIiIiIjIjrHTR0REREREZMfY6SMiIiIiIrJj7PQRERERERHZMXb6iIiIiIiI7Bg7fURERERERHaMnT4iIiIiIiI7xk4fERERERGRHWOnj4iISLIRI0agZcuWapdhMXXqVOh0Ouh0OtSuXVv6+4eFhVnef8CAAdLfn4jI3rmoXQAREZE90Ol0Fdpu69atCldSdV9++SVq1aol/X3ff/99ZGVlYfz48dLfm4jIEbDTR0REVAO+/PJLq//+3//+hy1btpRqv/vuu7Fo0SIYjUaZ5VXIM888o8r7PvroowCAyZMnq/L+RET2jp0+IiKiGnBrh+nPP//Eli1bVOtIERERmfGePiIiIsluvafv7Nmz0Ol0+OijjxAXF4e77roLnp6eeOihh5CSkgIhBGJiYtC0aVN4eHjgscceQ1ZWVqn9/vTTT+jRowe8vLxQp04d9O/fH0eOHKlWrS1btsSAAQOwbds2dOnSBR4eHmjfvj22bdsGAFi7di3at28Pd3d33HPPPThw4IDV6y9duoTnn38eTZs2hZubG/z9/fHYY4/h7Nmz1aqLiIgqjiN9REREGrFixQoUFBRg3LhxyMrKwsyZMxEZGYk+ffpg27ZteOutt5CcnIy5c+fizTffxOLFiy2v/fLLLxEVFYV+/frhww8/RG5uLubPn4/7778fBw4cqNbEMcnJyXjqqacwevRoPPPMM/joo48QERGBBQsWIDo6Gi+//DIA4IMPPkBkZCT++usvODmZ/q48ePBgHDlyBOPGjUPLli2RkZGBLVu24Pz585qazIaIyJ6x00dERKQRqampOHnyJOrWrQsAMBgM+OCDD5CXl4e9e/fCxcX0azszMxMrVqzA/Pnz4ebmhhs3buCVV17BCy+8gIULF1r2FxUVhbZt2+L999+3aq+sv/76C3/88Qe6d+8OAAgODka/fv3w4osv4vjx42jevDkAoF69ehg9ejR27NiB3r17IycnB3/88QdiY2Px5ptvWvY3adKkKtdCRESVx8s7iYiINGLo0KGWDh8AhIeHAzDdL2ju8JnbCwoKkJqaCgDYsmULcnJyMHz4cFy+fNnycHZ2Rnh4eLVnDA0ODrZ0+ErW1adPH0uHr2T76dOnAQAeHh5wdXXFtm3bkJ2dXa0aiIio6jjSR0REpBElO1AALB3AZs2aldlu7kidPHkSgKkTVhZvb29V6nJzc8OHH36IN954A40aNcK9996LAQMG4LnnnkPjxo2rVRMREVUcO31EREQa4ezsXKl2IQQAWJZ/+PLLL8vsTJUcJZRZFwC89tpriIiIwLp167Bp0ya88847+OCDD/Drr7+iU6dO1aqLiIgqhp0+IiIiGxcYGAgAaNiwIfr27atyNaUFBgbijTfewBtvvIGTJ08iLCwMs2bNwvLly9UujYjIIfCePiIiIhvXr18/eHt74/3330dhYWGp5zMzM1WoCsjNzUV+fr5VW2BgIOrUqQO9Xq9KTUREjogjfURERDbO29sb8+fPx7PPPovOnTtj2LBh8PPzw/nz5/HDDz/gH//4Bz799FPpdZ04cQIPPvggIiMjERwcDBcXF8THxyM9PR3Dhg2TXg8RkaNip4+IiMgOPPXUUwgICMD//d//ITY2Fnq9Hk2aNEGPHj3w/PPPq1JTs2bNMHz4cPzyyy/48ssv4eLignbt2mH16tUYPHiwKjURETkinSh5tzURERE5nKlTp+K9995DZmYmdDod6tevL/X9c3JyUFRUhM6dO6NDhw7YsGGD1PcnIrJ3vKePiIiIAAB+fn5o0aKF9Pft3bs3/Pz8kJKSIv29iYgcAUf6iIiIHNzp06ctC6q7uLigd+/eUt9/9+7duH79OgBTx7Njx45S35+IyN6x00dERERERGTHeHknERERERGRHWOnj4iIiIiIyI6x00dERERERGTH2OkjIiIiIiKyY+z0ERERERER2TF2+oiIiIiIiOwYO31ERERERER2jJ0+IiIiIiIiO8ZOHxERERERkR1jp4+IiIiIiMiOsdNHRERERERkx/4fmEx7MPd4Rk8AAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -1909,9 +1929,11 @@ } ], "source": [ + "print(\"Case 3b\")\n", + "\n", "sim_time = 101.\n", "\n", - "stepwise_times = [0., 60., 63.]\n", + "stepwise_times = [0., 59., 63.]\n", "stepwise_values = [0., 100., 0.]\n", "\n", "# run the simulation\n", @@ -1928,20 +1950,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Case 3b: Third factor active during post but not pre spike\n", + "### Case 3c: Third factor active during post but not pre spike (long delays)\n", "\n", "Dendritic delay is now 10 ms. With the same third-factor timing, the gating is enabled during the somatic post spike, but not the post spike from the perspective of the synapse, due to the dendritic delay. Because the gate is closed at the time of arrival of the post spike, no change in the weight occurs." ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Case 3c\n", "Pre spike times: [ 15. 65. 165.]\n", "Post spike times: [60.]\n", "~~~~~~~ at t = 16.0 , 3rd factor = 0.0\n", @@ -1961,36 +1984,15 @@ ] }, { - "ename": "AssertionError", - "evalue": "\nNot equal to tolerance rtol=1e-07, atol=0\n\nMismatched elements: 1 / 1 (100%)\nMax absolute difference: 6.10617431e-05\nMax relative difference: 6.10580148e-05\n x: array(1.)\n y: array(1.000061)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn [76], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# run the simulation\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m timevec, t_hist, third_factor_trace, w_hist \u001b[38;5;241m=\u001b[39m \u001b[43mrun_synapse_test\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneuron_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mneuron_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43msynapse_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynapse_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# [ms]\u001b[39;49;00m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mdelay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# [ms]\u001b[39;49;00m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_spike_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_spike_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_spike_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_spike_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mstepwise_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstepwise_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mstepwise_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstepwise_values\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn [55], line 168\u001b[0m, in \u001b[0;36mrun_synapse_test\u001b[0;34m(neuron_model_name, synapse_model_name, resolution, delay, sim_time, pre_spike_times, post_spike_times, fname_snip, stepwise_times, stepwise_values, reset_I_dAP_after_AP, verbose)\u001b[0m\n\u001b[1;32m 166\u001b[0m np\u001b[38;5;241m.\u001b[39mtesting\u001b[38;5;241m.\u001b[39massert_allclose(t_log_ref[ref_idx], t_pre)\n\u001b[1;32m 167\u001b[0m np\u001b[38;5;241m.\u001b[39mtesting\u001b[38;5;241m.\u001b[39massert_allclose(t_hist[numeric_result_idx], t_pre)\n\u001b[0;32m--> 168\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtesting\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_allclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mw_log_ref\u001b[49m\u001b[43m[\u001b[49m\u001b[43mref_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_hist\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnumeric_result_idx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;66;03m# -----------\u001b[39;00m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m timevec, t_hist, third_factor_trace, w_hist\n", - " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", - "File \u001b[0;32m~/.local/lib/python3.11/site-packages/numpy/testing/_private/utils.py:844\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m 840\u001b[0m err_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(remarks)\n\u001b[1;32m 841\u001b[0m msg \u001b[38;5;241m=\u001b[39m build_err_msg([ox, oy], err_msg,\n\u001b[1;32m 842\u001b[0m verbose\u001b[38;5;241m=\u001b[39mverbose, header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[1;32m 843\u001b[0m names\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m), precision\u001b[38;5;241m=\u001b[39mprecision)\n\u001b[0;32m--> 844\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m 845\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m 846\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtraceback\u001b[39;00m\n", - "\u001b[0;31mAssertionError\u001b[0m: \nNot equal to tolerance rtol=1e-07, atol=0\n\nMismatched elements: 1 / 1 (100%)\nMax absolute difference: 6.10617431e-05\nMax relative difference: 6.10580148e-05\n x: array(1.)\n y: array(1.000061)" - ] - }, - { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "> \u001b[0;32m/home/charl/.local/lib/python3.11/site-packages/numpy/testing/_private/utils.py\u001b[0m(844)\u001b[0;36massert_array_compare\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m 842 \u001b[0;31m 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and therefore cannot be log-scaled.\n" ] }, { "data": { - "image/png": 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\n", 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" ] @@ -2000,6 +2002,8 @@ } ], "source": [ + "print(\"Case 3c\")\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", @@ -2015,75 +2019,698 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When the third factor timing is shifted to the time of the post spike, the gate is enabled at the time of the postsynaptic spike (from the timing perspective of the synapse, that is, at the time of the postsynaptic spike plus the dendritic propagation delay)." + "### Case 3d: Third factor active during post but not pre spike (long delays)\n", + "\n", + "We move the timing of the 3rd factor to coincide with the (delayed) post spike." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 18, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Case 3d\n", + "Pre spike times: [ 15. 65. 165.]\n", + "Post spike times: [60.]\n", + "~~~~~~~ at t = 16.0 , 3rd factor = 0.0\n", + "At t = 16.0, pre spike\n", + "\t---> post_tr = 0.0, new weight = 1.0\n", + "~~~~~~~ at t = 66.0 , 3rd factor = 0.0\n", + "At t = 66.0, pre spike\n", + "\t---> post_tr = 0.0, new weight = 1.0\n", + "~~~~~~~ at t = 76.0 , 3rd factor = 1.0\n", + "At t = 76.0, post spike\n", + "\t---> pre_tr = 0.37035819334810866, new weight = 1.000037035819335\n", + "~~~~~~~ at t = 166.0 , 3rd factor = 0.0\n", + "At t = 166.0, pre spike\n", + "\t---> post_tr = 0.00012340980408667956, new weight = 1.000037035819335\n", + "Actual pre spike times: [ 16. 66. 166.]\n", + "Actual post spike times: [61.]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## Pre and post at the same time\n", + "print(\"Case 3d\")\n", + "\n", + "stepwise_times = [0., 72., 80.]\n", + "stepwise_values = [0., 100., 0.]\n", "\n", - "## Check for different delays" + "# 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=15., # [ms]\n", + " pre_spike_times=pre_spike_times,\n", + " post_spike_times=post_spike_times,\n", + " stepwise_times=stepwise_times,\n", + " stepwise_values=stepwise_values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Case 4: Third factor active during both post- and pre-spike\n", - "\n", - "If the dAP lasts longer, the presynaptic spike at t = 66 ms causes a depression of the synapse that is orders of magnitude larger than the small depression, so the net result is a depression at t = 66 ms." + "When the third factor timing is shifted to the time of the post spike, the gate is enabled at the time of the postsynaptic spike (from the timing perspective of the synapse, that is, at the time of the postsynaptic spike plus the dendritic propagation delay)." ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# run the simulation\n", - "sim_time = 101.\n", - "stepwise_times = [0.,\n", - " 58.,\n", - " 73.] # try 66, 67 for edge case\n", + "### Case 3e: pre and post spike at the same time\n", "\n", - "stepwise_values = [0., 100., 0.]\n", - "\n", - "\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=10., # [ms]\n", - " pre_spike_times=pre_spike_times,\n", - " post_spike_times=post_spike_times,\n", - " stepwise_times=stepwise_times, stepwise_values=stepwise_values)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert len(np.unique(np.diff(w_hist))) == 2\n", - "np.testing.assert_allclose(np.amax(np.abs(np.diff(w_hist))), 6.647848e-05, rtol=1E-6)" + "..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Case 5: Non-forced dynamics\n", + "## Use example: Non-forced dynamics\n", "\n", "Rather than forcing the value of I_dAP, we can also allow it to following the (ODE) dynamics defined in the model (by setting ``reset_I_dAP_after_AP`` to false), showing the gating for values between zero and the maximum value." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre spike times: [ 5. 12. 16. 20. 22. 25. 27. 48. 57. 60. 61. 65. 69. 73.\n", + " 74. 81. 82. 85. 90. 91. 94. 113. 117. 120. 121. 126. 131. 135.\n", + " 137. 141. 143. 147. 153. 156. 162. 167. 169. 172. 174. 176. 177. 180.\n", + " 183. 192. 209. 211. 213. 214. 215. 217. 218. 223. 225. 232. 235. 238.\n", + " 242. 244. 246. 248. 249. 252. 266. 267. 278. 279. 285. 295. 297. 301.\n", + " 309. 312. 314. 319. 320. 333. 337. 349. 351. 352. 356. 366. 369. 373.\n", + " 386. 387. 392. 397. 398. 400. 401.]\n", + "Post spike times: [ 10. 50. 61. 63. 65. 68. 69. 70. 72. 73. 74. 78. 80. 84.\n", + " 87. 90. 91. 92. 94. 95. 96. 98. 102. 103. 107. 108. 115. 116.\n", + " 117. 118. 122. 123. 124. 127. 131. 133. 134. 138. 142. 148. 150. 152.\n", + " 153. 155. 156. 157. 158. 159. 166. 167. 169. 171. 174. 176. 179. 183.\n", + " 188. 191. 202. 204. 207. 208. 210. 211. 212. 213. 217. 219. 222. 224.\n", + " 230. 236. 237. 239. 240. 244. 245. 246. 248. 251. 257. 258. 261. 300.\n", + " 350.]\n", + "~~~~~~~ at t = 6.0 , 3rd factor = 0.0\n", + "At t = 6.0, pre spike\n", + "\t---> post_tr = 0.0, new weight = 1.0\n", + "~~~~~~~ at t = 12.5 , 3rd factor = 0.01\n", + "At t = 12.5, post spike\n", + "\t---> pre_tr = 0.522045776761016, new weight = 1.0000005220457768\n", + "~~~~~~~ at t = 13.0 , 3rd factor = 0.01\n", + "At t = 13.0, pre spike\n", + "\t---> post_tr = 0.951229424500714, new weight = 0.9999995708163524\n", + "~~~~~~~ at t = 17.0 , 3rd factor = 0.01\n", + "At t = 17.0, pre spike\n", + "\t---> post_tr = 0.6376281516217733, new weight = 0.9999989331882008\n", + "~~~~~~~ at t = 21.0 , 3rd factor = 0.01\n", + "At t = 21.0, pre spike\n", + "\t---> post_tr = 0.4274149319487267, new weight = 0.9999985057732688\n", + "~~~~~~~ at t = 23.0 , 3rd factor = 0.01\n", + "At t = 23.0, pre spike\n", + "\t---> post_tr = 0.3499377491111553, new weight = 0.9999981558355197\n", + "~~~~~~~ at t = 26.0 , 3rd factor = 0.01\n", + "At t = 26.0, pre spike\n", + "\t---> post_tr = 0.2592402606458915, new weight = 0.999997896595259\n", + "~~~~~~~ at t = 28.0 , 3rd factor = 0.01\n", + "At t = 28.0, pre spike\n", + "\t---> post_tr = 0.21224797382674304, new weight = 0.9999976843472851\n", + "~~~~~~~ at t = 49.0 , 3rd factor = 0.01\n", + "At t = 49.0, pre spike\n", + "\t---> post_tr = 0.025991128778755347, new weight = 0.9999976583561563\n", + "~~~~~~~ at t = 52.5 , 3rd factor = 0.01\n", + "At t = 52.5, post spike\n", + "\t---> pre_tr = 1.014365665454156, new weight = 0.9999986727218217\n", + "~~~~~~~ at t = 58.0 , 3rd factor = 0.01\n", + "At t = 58.0, pre spike\n", + "\t---> post_tr = 0.5875170147643394, new weight = 0.9999980852048069\n", + "~~~~~~~ at t = 61.0 , 3rd factor = 0.01\n", + "At t = 61.0, pre spike\n", + "\t---> post_tr = 0.4352433094979525, new weight = 0.9999976499614974\n", + "~~~~~~~ at t = 62.0 , 3rd factor = 0.01\n", + "At t = 62.0, pre spike\n", + "\t---> post_tr = 0.39382443238355336, new weight = 0.999997256137065\n", + "~~~~~~~ at t = 63.5 , 3rd factor = 0.01\n", + "At t = 63.5, post spike\n", + "\t---> pre_tr = 2.554111568202798, new weight = 0.9999998102486333\n", + "~~~~~~~ at t = 65.5 , 3rd factor = 0.01\n", + "At t = 65.5, post spike\n", + "\t---> pre_tr = 2.091129687679862, new weight = 1.000001901378321\n", + "~~~~~~~ at t = 66.0 , 3rd factor = 0.01\n", + "At t = 66.0, pre spike\n", + "\t---> post_tr = 1.994018619217422, new weight = 0.9999999073597018\n", + "~~~~~~~ at t = 67.5 , 3rd factor = 0.01\n", + "At t = 67.5, post spike\n", + "\t---> pre_tr = 2.572780160402916, new weight = 1.0000024801398621\n", + "~~~~~~~ at t = 70.0 , 3rd factor = 0.01\n", + "At t = 70.0, pre spike\n", + "\t---> post_tr = 2.1154314357011494, new weight = 1.0000003647084263\n", + "~~~~~~~ at t = 70.5 , 3rd factor = 0.01\n", + "At t = 70.5, post spike\n", + "\t---> pre_tr = 2.857191845135627, new weight = 1.0000032219002715\n", + "~~~~~~~ at t = 71.5 , 3rd factor = 0.01\n", + "At t = 71.5, post spike\n", + "\t---> pre_tr = 2.58529409198592, new weight = 1.0000058071943634\n", + "~~~~~~~ at t = 72.5 , 3rd factor = 0.01\n", + "At t = 72.5, post spike\n", + "\t---> pre_tr = 2.3392708310561603, new weight = 1.0000081464651944\n", + "~~~~~~~ at t = 74.0 , 3rd factor = 0.01\n", + "At t = 74.0, pre spike\n", + "\t---> post_tr = 3.7622129465796084, new weight = 1.000004384252248\n", + "~~~~~~~ at t = 74.5 , 3rd factor = 0.01\n", + "At t = 74.5, post spike\n", + "\t---> pre_tr = 2.8664623936646807, new weight = 1.0000072507146416\n", + "~~~~~~~ at t = 75.0 , 3rd factor = 0.01\n", + "At t = 75.0, pre spike\n", + "\t---> post_tr = 4.355420473185267, new weight = 1.0000028952941684\n", + "~~~~~~~ at t = 75.5 , 3rd factor = 0.01\n", + "At t = 75.5, post spike\n", + "\t---> pre_tr = 3.54491185568144, new weight = 1.000006440206024\n", + "~~~~~~~ at t = 76.5 , 3rd factor = 0.01\n", + "At t = 76.5, post spike\n", + "\t---> pre_tr = 3.2075688906598563, new weight = 1.0000096477749147\n", + "~~~~~~~ at t = 80.5 , 3rd factor = 0.01\n", + "At t = 80.5, post spike\n", + "\t---> pre_tr = 2.1500977264495993, new weight = 1.0000117978726413\n", + "~~~~~~~ at t = 82.0 , 3rd factor = 0.01\n", + "At t = 82.0, pre spike\n", + "\t---> post_tr = 4.122541362382591, new weight = 1.0000076753312788\n", + "~~~~~~~ at t = 82.5 , 3rd factor = 0.01\n", + "At t = 82.5, post spike\n", + "\t---> pre_tr = 2.7115805552680508, new weight = 1.000010386911834\n", + "~~~~~~~ at t = 83.0 , 3rd factor = 0.01\n", + "At t = 83.0, pre spike\n", + "\t---> post_tr = 4.681459106585423, new weight = 1.0000057054527274\n", + "~~~~~~~ at t = 86.0 , 3rd factor = 0.01\n", + "At t = 86.0, pre spike\n", + "\t---> post_tr = 3.4681102055348383, new weight = 1.0000022373425217\n", + "~~~~~~~ at t = 86.5 , 3rd factor = 0.01\n", + "At t = 86.5, post spike\n", + "\t---> pre_tr = 3.4735443168560516, new weight = 1.0000057108868385\n", + "~~~~~~~ at t = 89.5 , 3rd factor = 0.01\n", + "At t = 89.5, post spike\n", + "\t---> pre_tr = 2.5732649202723934, new weight = 1.0000082841517588\n", + "~~~~~~~ at t = 91.0 , 3rd factor = 0.01\n", + "At t = 91.0, pre spike\n", + "\t---> post_tr = 3.6018512989659937, new weight = 1.0000046823004598\n", + "~~~~~~~ at t = 92.0 , 3rd factor = 0.01\n", + "At t = 92.0, pre spike\n", + "\t---> post_tr = 3.259089829505857, new weight = 1.0000014232106302\n", + "~~~~~~~ at t = 92.5 , 3rd factor = 0.01\n", + "At t = 92.5, post spike\n", + "\t---> pre_tr = 3.718258940504649, new weight = 1.0000051414695708\n", + "~~~~~~~ at t = 93.5 , 3rd factor = 0.01\n", + "At t = 93.5, post spike\n", + "\t---> pre_tr = 3.364419819315349, new weight = 1.00000850588939\n", + "~~~~~~~ at t = 94.5 , 3rd factor = 0.01\n", + "At t = 94.5, post spike\n", + "\t---> pre_tr = 3.0442529424983102, new weight = 1.0000115501423323\n", + "~~~~~~~ at t = 95.0 , 3rd factor = 0.01\n", + "At t = 95.0, pre spike\n", + "\t---> post_tr = 5.005131312533589, new weight = 1.0000065450110198\n", + "~~~~~~~ at t = 96.5 , 3rd factor = 0.01\n", + "At t = 96.5, post spike\n", + "\t---> pre_tr = 3.3531314805965615, new weight = 1.0000098981425003\n", + "~~~~~~~ at t = 97.5 , 3rd factor = 0.01\n", + "At t = 97.5, post spike\n", + "\t---> pre_tr = 3.034038831238087, new weight = 1.0000129321813316\n", + "~~~~~~~ at t = 98.5 , 3rd factor = 0.01\n", + "At t = 98.5, post spike\n", + "\t---> pre_tr = 2.745311862278311, new weight = 1.0000156774931939\n", + "~~~~~~~ at t = 100.5 , 3rd factor = 0.01\n", + "At t = 100.5, post spike\n", + "\t---> pre_tr = 2.2476712484370385, new weight = 1.0000179251644423\n", + "~~~~~~~ at t = 104.5 , 3rd factor = 0.01\n", + "At t = 104.5, post spike\n", + "\t---> pre_tr = 1.5066590947252985, new weight = 1.0000194318235371\n", + "~~~~~~~ at t = 105.5 , 3rd factor = 0.01\n", + "At t = 105.5, post spike\n", + "\t---> pre_tr = 1.3632815251316353, new weight = 1.0000207951050621\n", + "~~~~~~~ at t = 109.5 , 3rd factor = 0.01\n", + "At t = 109.5, post spike\n", + "\t---> pre_tr = 0.9138349346857744, new weight = 1.0000217089399968\n", + "~~~~~~~ at t = 110.5 , 3rd factor = 0.01\n", + "At t = 110.5, post spike\n", + "\t---> pre_tr = 0.8268720428121358, new weight = 1.0000225358120396\n", + "~~~~~~~ at t = 114.0 , 3rd factor = 0.01\n", + "At t = 114.0, pre spike\n", + "\t---> post_tr = 3.742394862731172, new weight = 1.000018793417177\n", + "~~~~~~~ at t = 117.5 , 3rd factor = 0.01\n", + "At t = 117.5, post spike\n", + "\t---> pre_tr = 1.1153005942952012, new weight = 1.0000199087177712\n", + "~~~~~~~ at t = 118.0 , 3rd factor = 0.01\n", + "At t = 118.0, pre spike\n", + "\t---> post_tr = 3.459831721170213, new weight = 1.0000164488860501\n", + "~~~~~~~ at t = 118.5 , 3rd factor = 0.01\n", + "At t = 118.5, post spike\n", + "\t---> pre_tr = 1.9603951345767552, new weight = 1.0000184092811846\n", + "~~~~~~~ at t = 119.5 , 3rd factor = 0.01\n", + "At t = 119.5, post spike\n", + "\t---> pre_tr = 1.7738388719006888, new weight = 1.0000201831200564\n", + "~~~~~~~ at t = 120.5 , 3rd factor = 0.01\n", + "At t = 120.5, post spike\n", + "\t---> pre_tr = 1.6050357848624384, new weight = 1.0000217881558413\n", + "~~~~~~~ at t = 121.0 , 3rd factor = 0.01\n", + "At t = 121.0, pre spike\n", + "\t---> post_tr = 5.153844563532659, new weight = 1.0000166343112777\n", + "~~~~~~~ at t = 122.0 , 3rd factor = 0.01\n", + "At t = 122.0, pre spike\n", + "\t---> post_tr = 4.663391407825558, new weight = 1.00001197091987\n", + "~~~~~~~ at t = 124.5 , 3rd factor = 0.01\n", + "At t = 124.5, post spike\n", + "\t---> pre_tr = 2.5593765339879564, new weight = 1.000014530296404\n", + "~~~~~~~ at t = 125.5 , 3rd factor = 0.01\n", + "At t = 125.5, post spike\n", + "\t---> pre_tr = 2.3158196547954857, new weight = 1.000016846116059\n", + "~~~~~~~ at t = 126.5 , 3rd factor = 0.01\n", + "At t = 126.5, post spike\n", + "\t---> pre_tr = 2.0954402770820746, new weight = 1.000018941556336\n", + "~~~~~~~ at t = 127.0 , 3rd factor = 0.01\n", + "At t = 127.0, pre spike\n", + "\t---> post_tr = 5.419228051083838, new weight = 1.000013522328285\n", + "~~~~~~~ at t = 129.5 , 3rd factor = 0.01\n", + "At t = 129.5, post spike\n", + "\t---> pre_tr = 2.331141120684153, new weight = 1.0000158534694057\n", + "~~~~~~~ at t = 132.0 , 3rd factor = 0.01\n", + "At t = 132.0, pre spike\n", + "\t---> post_tr = 4.065728748028494, new weight = 1.0000117877406576\n", + "~~~~~~~ at t = 133.5 , 3rd factor = 0.01\n", + "At t = 133.5, post spike\n", + "\t---> pre_tr = 2.423318599757631, new weight = 1.0000142110592574\n", + "~~~~~~~ at t = 135.5 , 3rd factor = 0.01\n", + "At t = 135.5, post spike\n", + "\t---> pre_tr = 1.984045462127446, new weight = 1.0000161951047195\n", + "~~~~~~~ at t = 136.0 , 3rd factor = 0.01\n", + "At t = 136.0, pre spike\n", + "\t---> post_tr = 4.455369689119001, new weight = 1.0000117397350303\n", + "~~~~~~~ at t = 136.5 , 3rd factor = 0.01\n", + "At t = 136.5, post spike\n", + "\t---> pre_tr = 2.746467997718075, new weight = 1.000014486203028\n", + "~~~~~~~ at t = 138.0 , 3rd factor = 0.01\n", + "At t = 138.0, pre spike\n", + "\t---> post_tr = 4.508456157238271, new weight = 1.0000099777468707\n", + "~~~~~~~ at t = 140.5 , 3rd factor = 0.01\n", + "At t = 140.5, post spike\n", + "\t---> pre_tr = 2.619813337737195, new weight = 1.0000125975602083\n", + "~~~~~~~ at t = 142.0 , 3rd factor = 0.01\n", + "At t = 142.0, pre spike\n", + "\t---> post_tr = 3.882816515294677, new weight = 1.0000087147436931\n", + "~~~~~~~ at t = 144.0 , 3rd factor = 0.01\n", + "At t = 144.0, pre spike\n", + "\t---> post_tr = 3.178981289630836, new weight = 1.0000055357624036\n", + "~~~~~~~ at t = 144.5 , 3rd factor = 0.01\n", + "At t = 144.5, post spike\n", + "\t---> pre_tr = 3.486143604728897, new weight = 1.0000090219060083\n", + "~~~~~~~ at t = 148.0 , 3rd factor = 0.01\n", + "At t = 148.0, pre spike\n", + "\t---> post_tr = 2.8356229741304912, new weight = 1.000006186283034\n", + "~~~~~~~ at t = 150.5 , 3rd factor = 0.01\n", + "At t = 150.5, post spike\n", + "\t---> pre_tr = 2.6920369584413977, new weight = 1.0000088783199925\n", + "~~~~~~~ at t = 152.5 , 3rd factor = 0.01\n", + "At t = 152.5, post spike\n", + "\t---> pre_tr = 2.2040534462984853, new weight = 1.000011082373439\n", + "~~~~~~~ at t = 154.0 , 3rd factor = 0.01\n", + "At t = 154.0, pre spike\n", + "\t---> post_tr = 3.1216189499221354, new weight = 1.000007960754489\n", + "~~~~~~~ at t = 154.5 , 3rd factor = 0.01\n", + "At t = 154.5, post spike\n", + "\t---> pre_tr = 2.755755762412794, new weight = 1.0000107165102514\n", + "~~~~~~~ at t = 155.5 , 3rd factor = 0.01\n", + "At t = 155.5, post spike\n", + "\t---> pre_tr = 2.4935109287993096, new weight = 1.00001321002118\n", + "~~~~~~~ at t = 157.0 , 3rd factor = 0.01\n", + "At t = 157.0, pre spike\n", + "\t---> post_tr = 3.952060955624111, new weight = 1.0000092579602244\n", + "~~~~~~~ at t = 157.5 , 3rd factor = 0.01\n", + "At t = 157.5, post spike\n", + "\t---> pre_tr = 2.992743505044751, new weight = 1.0000122507037295\n", + "~~~~~~~ at t = 158.5 , 3rd factor = 0.01\n", + "At t = 158.5, post spike\n", + "\t---> pre_tr = 2.70794630594858, new weight = 1.0000149586500353\n", + "~~~~~~~ at t = 159.5 , 3rd factor = 0.01\n", + "At t = 159.5, post spike\n", + "\t---> pre_tr = 2.450251143654528, new weight = 1.0000174089011789\n", + "~~~~~~~ at t = 160.5 , 3rd factor = 0.01\n", + "At t = 160.5, post spike\n", + "\t---> pre_tr = 2.2170789183640203, new weight = 1.0000196259800973\n", + "~~~~~~~ at t = 161.5 , 3rd factor = 0.01\n", + "At t = 161.5, post spike\n", + "\t---> pre_tr = 2.0060959640744582, new weight = 1.0000216320760613\n", + "~~~~~~~ at t = 163.0 , 3rd factor = 0.01\n", + "At t = 163.0, pre spike\n", + "\t---> post_tr = 5.727711850216825, new weight = 1.0000159043642112\n", + "~~~~~~~ at t = 168.0 , 3rd factor = 0.01\n", + "At t = 168.0, pre spike\n", + "\t---> post_tr = 3.474032847155879, new weight = 1.000012430331364\n", + "~~~~~~~ at t = 168.5 , 3rd factor = 0.01\n", + "At t = 168.5, post spike\n", + "\t---> pre_tr = 2.524377008635836, new weight = 1.0000149547083725\n", + "~~~~~~~ at t = 169.5 , 3rd factor = 0.01\n", + "At t = 169.5, post spike\n", + "\t---> pre_tr = 2.284150774643389, new weight = 1.0000172388591473\n", + "~~~~~~~ at t = 170.0 , 3rd factor = 0.01\n", + "At t = 170.0, pre spike\n", + "\t---> post_tr = 4.6562349300953505, new weight = 1.0000125826242172\n", + "~~~~~~~ at t = 171.5 , 3rd factor = 0.01\n", + "At t = 171.5, post spike\n", + "\t---> pre_tr = 2.7308124602924955, new weight = 1.0000153134366774\n", + "~~~~~~~ at t = 173.0 , 3rd factor = 0.01\n", + "At t = 173.0, pre spike\n", + "\t---> post_tr = 4.310131652414358, new weight = 1.000011003305025\n", + "~~~~~~~ at t = 173.5 , 3rd factor = 0.01\n", + "At t = 173.5, post spike\n", + "\t---> pre_tr = 3.1870295666307253, new weight = 1.0000141903345916\n", + "~~~~~~~ at t = 175.0 , 3rd factor = 0.01\n", + "At t = 175.0, pre spike\n", + "\t---> post_tr = 4.389545310071512, new weight = 1.0000098007892815\n", + "~~~~~~~ at t = 176.5 , 3rd factor = 0.01\n", + "At t = 176.5, post spike\n", + "\t---> pre_tr = 3.221717549236458, new weight = 1.0000130225068307\n", + "~~~~~~~ at t = 177.0 , 3rd factor = 0.01\n", + "At t = 177.0, pre spike\n", + "\t---> post_tr = 4.545085161885486, new weight = 1.0000084774216689\n", + "~~~~~~~ at t = 178.0 , 3rd factor = 0.01\n", + "At t = 178.0, pre spike\n", + "\t---> post_tr = 4.112563122634014, new weight = 1.0000043648585464\n", + "~~~~~~~ at t = 178.5 , 3rd factor = 0.01\n", + "At t = 178.5, post spike\n", + "\t---> pre_tr = 4.449656636216688, new weight = 1.0000088145151826\n", + "~~~~~~~ at t = 181.0 , 3rd factor = 0.01\n", + "At t = 181.0, pre spike\n", + "\t---> post_tr = 3.8254624780223847, new weight = 1.0000049890527045\n", + "~~~~~~~ at t = 181.5 , 3rd factor = 0.01\n", + "At t = 181.5, post spike\n", + "\t---> pre_tr = 4.247616136387358, new weight = 1.000009236668841\n", + "~~~~~~~ at t = 184.0 , 3rd factor = 0.01\n", + "At t = 184.0, pre spike\n", + "\t---> post_tr = 3.612773089324623, new weight = 1.0000056238957515\n", + "~~~~~~~ at t = 185.5 , 3rd factor = 0.01\n", + "At t = 185.5, post spike\n", + "\t---> pre_tr = 3.7079702205099556, new weight = 1.000009331865972\n", + "~~~~~~~ at t = 190.5 , 3rd factor = 0.01\n", + "At t = 190.5, post spike\n", + "\t---> pre_tr = 2.2489976240407024, new weight = 1.000011580863596\n", + "~~~~~~~ at t = 193.0 , 3rd factor = 0.01\n", + "At t = 193.0, pre spike\n", + "\t---> post_tr = 2.7200112614591245, new weight = 1.0000088608523345\n", + "~~~~~~~ at t = 193.5 , 3rd factor = 0.01\n", + "At t = 193.5, post spike\n", + "\t---> pre_tr = 2.617327842659958, new weight = 1.0000114781801772\n", + "~~~~~~~ at t = 204.5 , 3rd factor = 0.01\n", + "At t = 204.5, post spike\n", + "\t---> pre_tr = 0.871232755379377, new weight = 1.0000123494129327\n", + "~~~~~~~ at t = 206.5 , 3rd factor = 0.01\n", + "At t = 206.5, post spike\n", + "\t---> pre_tr = 0.7133050499179624, new weight = 1.0000130627179826\n", + "~~~~~~~ at t = 209.5 , 3rd factor = 0.01\n", + "At t = 209.5, post spike\n", + "\t---> pre_tr = 0.528429377883509, new weight = 1.0000135911473604\n", + "~~~~~~~ at t = 210.0 , 3rd factor = 0.01\n", + "At t = 210.0, pre spike\n", + "\t---> post_tr = 2.9218184759271457, new weight = 1.0000106693288844\n", + "~~~~~~~ at t = 210.5 , 3rd factor = 0.01\n", + "At t = 210.5, post spike\n", + "\t---> pre_tr = 1.4293720983991764, new weight = 1.0000120987009828\n", + "~~~~~~~ at t = 212.0 , 3rd factor = 0.01\n", + "At t = 212.0, pre spike\n", + "\t---> post_tr = 3.2528906175780508, new weight = 1.0000088458103653\n", + "~~~~~~~ at t = 212.5 , 3rd factor = 0.01\n", + "At t = 212.5, post spike\n", + "\t---> pre_tr = 2.1215003190517265, new weight = 1.0000109673106843\n", + "~~~~~~~ at t = 213.5 , 3rd factor = 0.01\n", + "At t = 213.5, post spike\n", + "\t---> pre_tr = 1.9196128710532288, new weight = 1.0000128869235554\n", + "~~~~~~~ at t = 214.0 , 3rd factor = 0.01\n", + "At t = 214.0, pre spike\n", + "\t---> post_tr = 4.475178985935751, new weight = 1.0000084117445696\n", + "~~~~~~~ at t = 214.5 , 3rd factor = 0.01\n", + "At t = 214.5, post spike\n", + "\t---> pre_tr = 2.688166978373113, new weight = 1.000011099911548\n", + "~~~~~~~ at t = 215.0 , 3rd factor = 0.01\n", + "At t = 215.0, pre spike\n", + "\t---> post_tr = 5.000538823383603, new weight = 1.0000060993727244\n", + "~~~~~~~ at t = 215.5 , 3rd factor = 0.01\n", + "At t = 215.5, post spike\n", + "\t---> pre_tr = 3.3835834924613684, new weight = 1.000009482956217\n", + "~~~~~~~ at t = 216.0 , 3rd factor = 0.01\n", + "At t = 216.0, pre spike\n", + "\t---> post_tr = 5.475904062239708, new weight = 1.0000040070521548\n", + "~~~~~~~ at t = 218.0 , 3rd factor = 0.01\n", + "At t = 218.0, pre spike\n", + "\t---> post_tr = 4.483291056660296, new weight = 0.999999523761098\n", + "~~~~~~~ at t = 219.0 , 3rd factor = 0.01\n", + "At t = 219.0, pre spike\n", + "\t---> post_tr = 4.056649504012212, new weight = 0.999995467111594\n", + "~~~~~~~ at t = 219.5 , 3rd factor = 0.01\n", + "At t = 219.5, post spike\n", + "\t---> pre_tr = 4.784709333076619, new weight = 1.0000002518209272\n", + "~~~~~~~ at t = 221.5 , 3rd factor = 0.01\n", + "At t = 221.5, post spike\n", + "\t---> pre_tr = 3.9173886755290686, new weight = 1.0000041692096027\n", + "~~~~~~~ at t = 224.0 , 3rd factor = 0.01\n", + "At t = 224.0, pre spike\n", + "\t---> post_tr = 3.8769112345846324, new weight = 1.0000002922983682\n", + "~~~~~~~ at t = 224.5 , 3rd factor = 0.01\n", + "At t = 224.5, post spike\n", + "\t---> pre_tr = 3.85330233282487, new weight = 1.000004145600701\n", + "~~~~~~~ at t = 226.0 , 3rd factor = 0.01\n", + "At t = 226.0, pre spike\n", + "\t---> post_tr = 4.034854431133023, new weight = 1.00000011074627\n", + "~~~~~~~ at t = 226.5 , 3rd factor = 0.01\n", + "At t = 226.5, post spike\n", + "\t---> pre_tr = 4.106046545291564, new weight = 1.0000042167928151\n", + "~~~~~~~ at t = 232.5 , 3rd factor = 0.01\n", + "At t = 232.5, post spike\n", + "\t---> pre_tr = 2.2534461223996884, new weight = 1.0000064702389375\n", + "~~~~~~~ at t = 233.0 , 3rd factor = 0.01\n", + "At t = 233.0, pre spike\n", + "\t---> post_tr = 3.4769246147000366, new weight = 1.0000029933143229\n", + "~~~~~~~ at t = 236.0 , 3rd factor = 0.01\n", + "At t = 236.0, pre spike\n", + "\t---> post_tr = 2.5757691065065482, new weight = 1.0000004175452164\n", + "~~~~~~~ at t = 238.5 , 3rd factor = 0.01\n", + "At t = 238.5, post spike\n", + "\t---> pre_tr = 2.5924680467358043, new weight = 1.000003010013263\n", + "~~~~~~~ at t = 239.0 , 3rd factor = 0.01\n", + "At t = 239.0, pre spike\n", + "\t---> post_tr = 2.859406110869833, new weight = 1.0000001506071523\n", + "~~~~~~~ at t = 239.5 , 3rd factor = 0.01\n", + "At t = 239.5, post spike\n", + "\t---> pre_tr = 3.296991518249867, new weight = 1.0000034475986705\n", + "~~~~~~~ at t = 241.5 , 3rd factor = 0.01\n", + "At t = 241.5, post spike\n", + "\t---> pre_tr = 2.699348348628432, new weight = 1.0000061469470192\n", + "~~~~~~~ at t = 242.5 , 3rd factor = 0.01\n", + "At t = 242.5, post spike\n", + "\t---> pre_tr = 2.4424713901525816, new weight = 1.0000085894184094\n", + "~~~~~~~ at t = 243.0 , 3rd factor = 0.01\n", + "At t = 243.0, pre spike\n", + "\t---> post_tr = 4.433342726517339, new weight = 1.000004156075683\n", + "~~~~~~~ at t = 245.0 , 3rd factor = 0.01\n", + "At t = 245.0, pre spike\n", + "\t---> post_tr = 3.6297140291343344, new weight = 1.0000005263616538\n", + "~~~~~~~ at t = 246.5 , 3rd factor = 0.01\n", + "At t = 246.5, post spike\n", + "\t---> pre_tr = 3.202633600831582, new weight = 1.0000037289952546\n", + "~~~~~~~ at t = 247.0 , 3rd factor = 0.01\n", + "At t = 247.0, pre spike\n", + "\t---> post_tr = 3.9229879250315842, new weight = 0.9999998060073295\n", + "~~~~~~~ at t = 247.5 , 3rd factor = 0.01\n", + "At t = 247.5, post spike\n", + "\t---> pre_tr = 3.84909214279237, new weight = 1.0000036550994722\n", + "~~~~~~~ at t = 248.5 , 3rd factor = 0.01\n", + "At t = 248.5, post spike\n", + "\t---> pre_tr = 3.482802596266747, new weight = 1.0000071379020685\n", + "~~~~~~~ at t = 249.0 , 3rd factor = 0.01\n", + "At t = 249.0, pre spike\n", + "\t---> post_tr = 5.02380825910271, new weight = 1.0000021140938093\n", + "~~~~~~~ at t = 250.0 , 3rd factor = 0.01\n", + "At t = 250.0, pre spike\n", + "\t---> post_tr = 4.545729693874224, new weight = 0.9999975683641155\n", + "~~~~~~~ at t = 250.5 , 3rd factor = 0.01\n", + "At t = 250.5, post spike\n", + "\t---> pre_tr = 4.663414993389196, new weight = 1.0000022317791089\n", + "~~~~~~~ at t = 253.0 , 3rd factor = 0.01\n", + "At t = 253.0, pre spike\n", + "\t---> post_tr = 4.146360166587359, new weight = 0.9999980854189423\n", + "~~~~~~~ at t = 253.5 , 3rd factor = 0.01\n", + "At t = 253.5, post spike\n", + "\t---> pre_tr = 4.405972222203744, new weight = 1.0000024913911645\n", + "~~~~~~~ at t = 259.5 , 3rd factor = 0.01\n", + "At t = 259.5, post spike\n", + "\t---> pre_tr = 2.41804882385247, new weight = 1.0000049094399883\n", + "~~~~~~~ at t = 260.5 , 3rd factor = 0.01\n", + "At t = 260.5, post spike\n", + "\t---> pre_tr = 2.1879410544595577, new weight = 1.0000070973810429\n", + "~~~~~~~ at t = 263.5 , 3rd factor = 0.01\n", + "At t = 263.5, post spike\n", + "\t---> pre_tr = 1.620866598921211, new weight = 1.0000087182476418\n", + "~~~~~~~ at t = 267.0 , 3rd factor = 0.01\n", + "At t = 267.0, pre spike\n", + "\t---> post_tr = 2.9808205083554915, new weight = 1.0000057374271334\n", + "~~~~~~~ at t = 268.0 , 3rd factor = 0.01\n", + "At t = 268.0, pre spike\n", + "\t---> post_tr = 2.6971579324090196, new weight = 1.000003040269201\n", + "~~~~~~~ at t = 279.0 , 3rd factor = 0.01\n", + "At t = 279.0, pre spike\n", + "\t---> post_tr = 0.8978058838658618, new weight = 1.0000021424633172\n", + "~~~~~~~ at t = 280.0 , 3rd factor = 0.01\n", + "At t = 280.0, pre spike\n", + "\t---> post_tr = 0.8123683578546792, new weight = 1.0000013300949593\n", + "~~~~~~~ at t = 286.0 , 3rd factor = 0.01\n", + "At t = 286.0, pre spike\n", + "\t---> post_tr = 0.445837207585244, new weight = 1.0000008842577517\n", + "~~~~~~~ at t = 296.0 , 3rd factor = 0.01\n", + "At t = 296.0, pre spike\n", + "\t---> post_tr = 0.16401434277989588, new weight = 1.000000720243409\n", + "~~~~~~~ at t = 298.0 , 3rd factor = 0.01\n", + "At t = 298.0, pre spike\n", + "\t---> post_tr = 0.1342835863797744, new weight = 1.0000005859598224\n", + "~~~~~~~ at t = 302.0 , 3rd factor = 0.01\n", + "At t = 302.0, pre spike\n", + "\t---> post_tr = 0.09001297980392112, new weight = 1.0000004959468427\n", + "~~~~~~~ at t = 302.5 , 3rd factor = 0.01\n", + "At t = 302.5, post spike\n", + "\t---> pre_tr = 2.597001362474804, new weight = 1.000003092948205\n", + "~~~~~~~ at t = 310.0 , 3rd factor = 0.01\n", + "At t = 310.0, pre spike\n", + "\t---> post_tr = 0.512811991713415, new weight = 1.0000025801362133\n", + "~~~~~~~ at t = 313.0 , 3rd factor = 0.01\n", + "At t = 313.0, pre spike\n", + "\t---> post_tr = 0.37990046724537996, new weight = 1.000002200235746\n", + "~~~~~~~ at t = 315.0 , 3rd factor = 0.01\n", + "At t = 315.0, pre spike\n", + "\t---> post_tr = 0.31103619564248713, new weight = 1.0000018891995504\n", + "~~~~~~~ at t = 320.0 , 3rd factor = 0.01\n", + "At t = 320.0, pre spike\n", + "\t---> post_tr = 0.18865298893754545, new weight = 1.0000017005465613\n", + "~~~~~~~ at t = 321.0 , 3rd factor = 0.01\n", + "At t = 321.0, pre spike\n", + "\t---> post_tr = 0.17070028341501503, new weight = 1.0000015298462779\n", + "~~~~~~~ at t = 334.0 , 3rd factor = 0.01\n", + "At t = 334.0, pre spike\n", + "\t---> post_tr = 0.04652125431050819, new weight = 1.0000014833250235\n", + "~~~~~~~ at t = 338.0 , 3rd factor = 0.01\n", + "At t = 338.0, pre spike\n", + "\t---> post_tr = 0.031184129331055528, new weight = 1.0000014521408942\n", + "~~~~~~~ at t = 350.0 , 3rd factor = 0.01\n", + "At t = 350.0, pre spike\n", + "\t---> post_tr = 0.009392479258035455, new weight = 1.000001442748415\n", + "~~~~~~~ at t = 352.0 , 3rd factor = 0.01\n", + "At t = 352.0, pre spike\n", + "\t---> post_tr = 0.00768991161620069, new weight = 1.0000014350585034\n", + "~~~~~~~ at t = 352.5 , 3rd factor = 0.01\n", + "At t = 352.5, post spike\n", + "\t---> pre_tr = 2.2779991357556093, new weight = 1.0000037130576391\n", + "~~~~~~~ at t = 353.0 , 3rd factor = 0.01\n", + "At t = 353.0, pre spike\n", + "\t---> post_tr = 0.9581875442724417, new weight = 1.000002754870095\n", + "~~~~~~~ at t = 357.0 , 3rd factor = 0.01\n", + "At t = 357.0, pre spike\n", + "\t---> post_tr = 0.6422923187874793, new weight = 1.000002112577776\n", + "~~~~~~~ at t = 367.0 , 3rd factor = 0.01\n", + "At t = 367.0, pre spike\n", + "\t---> post_tr = 0.23628613930424777, new weight = 1.0000018762916367\n", + "~~~~~~~ at t = 370.0 , 3rd factor = 0.01\n", + "At t = 370.0, pre spike\n", + "\t---> post_tr = 0.17504507729112537, new weight = 1.0000017012465594\n", + "~~~~~~~ at t = 374.0 , 3rd factor = 0.01\n", + "At t = 374.0, pre spike\n", + "\t---> post_tr = 0.1173362242680992, new weight = 1.0000015839103351\n", + "~~~~~~~ at t = 387.0 , 3rd factor = 0.01\n", + "At t = 387.0, pre spike\n", + "\t---> post_tr = 0.03197785158762609, new weight = 1.0000015519324834\n", + "~~~~~~~ at t = 388.0 , 3rd factor = 0.01\n", + "At t = 388.0, pre spike\n", + "\t---> post_tr = 0.02893475666488471, new weight = 1.0000015229977268\n", + "~~~~~~~ at t = 393.0 , 3rd factor = 0.01\n", + "At t = 393.0, pre spike\n", + "\t---> post_tr = 0.01754981704857704, new weight = 1.0000015054479097\n", + "~~~~~~~ at t = 398.0 , 3rd factor = 0.01\n", + "At t = 398.0, pre spike\n", + "\t---> post_tr = 0.010644502112309454, new weight = 1.0000014948034075\n", + "~~~~~~~ at t = 399.0 , 3rd factor = 0.01\n", + "At t = 399.0, pre spike\n", + "\t---> post_tr = 0.009631543807580398, new weight = 1.0000014851718637\n", + "~~~~~~~ at t = 401.0 , 3rd factor = 0.01\n", + "At t = 401.0, pre spike\n", + "\t---> post_tr = 0.007885641114883877, new weight = 1.0000014772862227\n", + "~~~~~~~ at t = 402.0 , 3rd factor = 0.01\n", + "At t = 402.0, pre spike\n", + "\t---> post_tr = 0.00713522314594973, new weight = 1.0000014701509994\n", + "Actual pre spike times: [ 6. 13. 17. 21. 23. 26. 28. 49. 58. 61. 62. 66. 70. 74.\n", + " 75. 82. 83. 86. 91. 92. 95. 114. 118. 121. 122. 127. 132. 136.\n", + " 138. 142. 144. 148. 154. 157. 163. 168. 170. 173. 175. 177. 178. 181.\n", + " 184. 193. 210. 212. 214. 215. 216. 218. 219. 224. 226. 233. 236. 239.\n", + " 243. 245. 247. 249. 250. 253. 267. 268. 279. 280. 286. 296. 298. 302.\n", + " 310. 313. 315. 320. 321. 334. 338. 350. 352. 353. 357. 367. 370. 374.\n", + " 387. 388. 393. 398. 399.]\n", + "Actual post spike times: [ 11. 51. 62. 64. 66. 69. 70. 71. 73. 74. 75. 79. 81. 85.\n", + " 88. 91. 92. 93. 95. 96. 97. 99. 103. 104. 108. 109. 116. 117.\n", + " 118. 119. 123. 124. 125. 128. 132. 134. 135. 139. 143. 149. 151. 153.\n", + " 154. 156. 157. 158. 159. 160. 167. 168. 170. 172. 175. 177. 180. 184.\n", + " 189. 192. 203. 205. 208. 209. 211. 212. 213. 214. 218. 220. 223. 225.\n", + " 231. 237. 238. 240. 241. 245. 246. 247. 249. 252. 258. 259. 262. 301.\n", + " 351.]\n" + ] + }, + { + "ename": "AssertionError", + "evalue": "\nNot equal to tolerance rtol=1e-07, atol=0\n\nMismatched elements: 1 / 1 (100%)\nMax absolute difference: 1.06506985e-07\nMax relative difference: 1.06507224e-07\n x: array(0.999998)\n y: array(0.999998)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [20], line 12\u001b[0m\n\u001b[1;32m 9\u001b[0m stepwise_values \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0.\u001b[39m, \u001b[38;5;241m1.\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# run the simulation\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m timevec, t_hist, third_factor_trace, w_hist \u001b[38;5;241m=\u001b[39m \u001b[43mrun_synapse_test\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneuron_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mneuron_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43msynapse_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msynapse_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# [ms]\u001b[39;49;00m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mdelay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# [ms]\u001b[39;49;00m\n\u001b[1;32m 16\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_spike_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_spike_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[43m \u001b[49m\u001b[43mpost_spike_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpost_spike_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43msim_time\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m400.\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mstepwise_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstepwise_times\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstepwise_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstepwise_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset_I_dAP_after_AP\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn [9], line 168\u001b[0m, in \u001b[0;36mrun_synapse_test\u001b[0;34m(neuron_model_name, synapse_model_name, resolution, delay, sim_time, pre_spike_times, post_spike_times, fname_snip, stepwise_times, stepwise_values, reset_I_dAP_after_AP, verbose)\u001b[0m\n\u001b[1;32m 166\u001b[0m np\u001b[38;5;241m.\u001b[39mtesting\u001b[38;5;241m.\u001b[39massert_allclose(t_log_ref[ref_idx], t_pre)\n\u001b[1;32m 167\u001b[0m np\u001b[38;5;241m.\u001b[39mtesting\u001b[38;5;241m.\u001b[39massert_allclose(t_hist[numeric_result_idx], t_pre)\n\u001b[0;32m--> 168\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtesting\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massert_allclose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mw_log_ref\u001b[49m\u001b[43m[\u001b[49m\u001b[43mref_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_hist\u001b[49m\u001b[43m[\u001b[49m\u001b[43mnumeric_result_idx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;66;03m# -----------\u001b[39;00m\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m timevec, t_hist, third_factor_trace, w_hist\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/.local/lib/python3.11/site-packages/numpy/testing/_private/utils.py:844\u001b[0m, in \u001b[0;36massert_array_compare\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)\u001b[0m\n\u001b[1;32m 840\u001b[0m err_msg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(remarks)\n\u001b[1;32m 841\u001b[0m msg \u001b[38;5;241m=\u001b[39m build_err_msg([ox, oy], err_msg,\n\u001b[1;32m 842\u001b[0m verbose\u001b[38;5;241m=\u001b[39mverbose, header\u001b[38;5;241m=\u001b[39mheader,\n\u001b[1;32m 843\u001b[0m names\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m), precision\u001b[38;5;241m=\u001b[39mprecision)\n\u001b[0;32m--> 844\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(msg)\n\u001b[1;32m 845\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m 846\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtraceback\u001b[39;00m\n", + "\u001b[0;31mAssertionError\u001b[0m: \nNot equal to tolerance rtol=1e-07, atol=0\n\nMismatched elements: 1 / 1 (100%)\nMax absolute difference: 1.06506985e-07\nMax relative difference: 1.06507224e-07\n x: array(0.999998)\n y: array(0.999998)" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> \u001b[0;32m/home/charl/.local/lib/python3.11/site-packages/numpy/testing/_private/utils.py\u001b[0m(844)\u001b[0;36massert_array_compare\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m 842 \u001b[0;31m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 843 \u001b[0;31m names=('x', 'y'), precision=precision)\n", + "\u001b[0m\u001b[0;32m--> 844 \u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 845 \u001b[0;31m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\u001b[0;32m 846 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0m\n", + "ipdb> c\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fname_snip = \"\"\n", "\n", diff --git a/pynestml/codegeneration/nest_code_generator.py b/pynestml/codegeneration/nest_code_generator.py index 1335258a7..7107934f8 100644 --- a/pynestml/codegeneration/nest_code_generator.py +++ b/pynestml/codegeneration/nest_code_generator.py @@ -604,14 +604,14 @@ def _get_neuron_model_namespace(self, neuron: ASTModel) -> Dict: namespace = self._get_model_namespace(neuron) if "paired_synapse" in dir(neuron): - if "state_vars_that_need_post_spike_buffering" in dir(neuron): - namespace["state_vars_that_need_post_spike_buffering"] = neuron.state_vars_that_need_post_spike_buffering + if "state_vars_that_need_continuous_buffering" in dir(neuron): + namespace["state_vars_that_need_continuous_buffering"] = neuron.state_vars_that_need_continuous_buffering codegen_and_builder_opts = FrontendConfiguration.get_codegen_opts() xfrm = SynapsePostNeuronTransformer(codegen_and_builder_opts) - namespace["state_vars_that_need_post_spike_buffering"] = [xfrm.get_neuron_var_name_from_syn_port_name(port_name, removesuffix(neuron.unpaired_name, FrontendConfiguration.suffix), removesuffix(neuron.paired_synapse.get_name().split("__with_")[0], FrontendConfiguration.suffix)) for port_name in neuron.state_vars_that_need_post_spike_buffering] + namespace["state_vars_that_need_continuous_buffering_transformed"] = [xfrm.get_neuron_var_name_from_syn_port_name(port_name, removesuffix(neuron.unpaired_name, FrontendConfiguration.suffix), removesuffix(neuron.paired_synapse.get_name().split("__with_")[0], FrontendConfiguration.suffix)) for port_name in neuron.state_vars_that_need_continuous_buffering] else: - namespace["state_vars_that_need_post_spike_buffering"] = [] + namespace["state_vars_that_need_continuous_buffering"] = [] namespace["extra_on_emit_spike_stmts_from_synapse"] = neuron.extra_on_emit_spike_stmts_from_synapse namespace["paired_synapse"] = neuron.paired_synapse.get_name() namespace["post_spike_updates"] = neuron.post_spike_updates diff --git a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 index d93608626..ebf487354 100644 --- a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 +++ b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronClass.jinja2 @@ -68,8 +68,22 @@ along with NEST. If not, see . #include "lockptrdatum.h" #include "{{neuronName}}.h" +{%- if state_vars_that_need_continuous_buffering | length > 0 %} +continuous_variable_histentry_{{ neuronName }}::continuous_variable_histentry_{{ neuronName }}( double t, +{%- for state_var in state_vars_that_need_continuous_buffering %} + double {{ state_var }}{% if not loop.last %}, {% endif %} +{%- endfor %} ) +: t_( t ) +, access_counter_ ( 0 ) +{%- for state_var in state_vars_that_need_continuous_buffering %} +, {{ state_var }}( {{ state_var }} ) +{%- endfor %} +{ +} +{%- endif %} + {%- if not (nest_version.startswith("v2") or nest_version.startswith("v3.0") or nest_version.startswith("v3.1") or nest_version.startswith("v3.2") or nest_version.startswith("v3.3") or nest_version.startswith("v3.4") or nest_version.startswith("v3.5") or nest_version.startswith("v3.6")) %} void @@ -965,6 +979,15 @@ const double {{ variable_name }}__tmp = {{ printer.print(var_ast) }}; // voltage logging B_.logger_.record_data(origin.get_steps() + lag); {%- endif %} + +{%- if state_vars_that_need_continuous_buffering | length > 0 %} + write_continuous_variable_history( nest::Time::step( origin.get_steps() + lag + 1 ), +{%- for state_var_name in state_vars_that_need_continuous_buffering_transformed %} +{%- set state_var = utils.get_variable_by_name(astnode, state_var_name) %} + {{ printer.print(state_var) }}{% if not loop.last %}, {% endif %} +{%- endfor %} + ); +{%- endif %} } {%- if use_gap_junctions %} @@ -992,6 +1015,108 @@ const double {{ variable_name }}__tmp = {{ printer.print(var_ast) }}; {%- endif %} } +{%- if state_vars_that_need_continuous_buffering | length > 0 %} + +continuous_variable_histentry_{{ neuronName }} {{ neuronName }}::get_continuous_variable_history( double t ) +{ + std::deque< continuous_variable_histentry_{{ neuronName }} >::iterator runner; + if ( continuous_variable_history_.empty() or t < 0.0 ) + { + return continuous_variable_histentry_{{ neuronName }}(0., +{%- for state_var in state_vars_that_need_continuous_buffering %} + 0.{% if not loop.last %},{% endif %} +{%- endfor %}); // XXX: TODO: return initial value + } + else + { + runner = continuous_variable_history_.begin(); + while ( runner != continuous_variable_history_.end() ) + { + if ( fabs( t - runner->t_ ) < nest::kernel().connection_manager.get_stdp_eps() ) + { + return *runner; + } + ( runner->access_counter_ )++; + ++runner; + } + } + + // if we get here, there is no entry at time t + std::cout << "\n\n\nXXX FIX ME: no entry at time t!\n\n\n"; + return continuous_variable_histentry_{{ neuronName }}(0., +{%- for state_var in state_vars_that_need_continuous_buffering %} + 0.{% if not loop.last %}, {% endif %} +{%- endfor %}); // XXX: TODO: return initial value +} + +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 ) +{ +#ifdef DEBUG + std::cout << "{{neuronName}}::get_continuous_variable_history()" << std::endl; +#endif + *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 %} + const double {{ state_var }}{% if not loop.last %}, {% endif %} +{%- endfor %}) +{ +#ifdef DEBUG + std::cout << "{{neuronName}}::write_continuous_variable_history()" << std::endl; +#endif + 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 %} +{%- endfor %}) ); +#ifdef DEBUG + std::cout << "\thistory size = " << continuous_variable_history_.size() << std::endl; +#endif +} +{%- endif %} + // Do not move this function as inline to h-file. It depends on // universal_data_logger_impl.h being included here. void {{neuronName}}::handle(nest::DataLoggingRequest& e) @@ -1241,9 +1366,6 @@ void {%- endfor %} {%- for var in analytic_state_variables_moved|sort %} , get_{{ var }}() -{%- endfor %} -{%- for var in state_vars_that_need_post_spike_buffering|sort %} - , get_{{ var }}() {%- endfor %} , 0 // access counter ) ); @@ -1271,7 +1393,7 @@ void generate getter functions for the transferred variables #} -{%- for var in transferred_variables + state_vars_that_need_post_spike_buffering %} +{%- for var in transferred_variables %} {%- with variable_symbol = transferred_variables_syms[var] %} {%- if not variable_symbol %} diff --git a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 index 28f2cf6d7..cfaf2dc60 100644 --- a/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 +++ b/pynestml/codegeneration/resources_nest/point_neuron/common/NeuronHeader.jinja2 @@ -172,9 +172,6 @@ public: {%- endfor %} {%- for var in analytic_state_variables_moved|sort %} double {{ var }}, -{%- endfor %} -{%- for var in state_vars_that_need_post_spike_buffering|sort %} - double {{ var }}, {%- endfor %} size_t access_counter ) : t_( t ) @@ -183,9 +180,6 @@ public: {%- endfor %} {%- for var in analytic_state_variables_moved|sort %} , {{ var }}_( {{ var }} ) -{%- endfor %} -{%- for var in state_vars_that_need_post_spike_buffering|sort %} - , {{ var }}_( {{ var }} ) {%- endfor %} , access_counter_( access_counter ) { @@ -197,13 +191,27 @@ public: {%- endfor %} {%- for var in analytic_state_variables_moved|sort %} double {{ var }}_; -{%- endfor %} -{%- for var in state_vars_that_need_post_spike_buffering|sort %} - double {{ var }}_; {%- endfor %} size_t access_counter_; //!< access counter to enable removal of the entry, once all neurons read it }; +{%- if state_vars_that_need_continuous_buffering | length > 0 %} + +class continuous_variable_histentry_{{ neuronName }} +{ +public: + continuous_variable_histentry_{{ neuronName }}( double t, +{%- for state_var in state_vars_that_need_continuous_buffering %} + double {{ state_var }}{% if not loop.last %}, {% endif %} +{%- endfor %} ); + +{%- for state_var in state_vars_that_need_continuous_buffering %} + double {{ state_var }}; +{%- endfor %} + double t_; //!< point in time for history entry + size_t access_counter_; +}; +{%- endif %} {%- endif %} /* BeginDocumentation @@ -346,6 +354,25 @@ public: * with t > t_first_read. */ void register_stdp_connection( double t_first_read, double delay ); +{%- if state_vars_that_need_continuous_buffering | length > 0 %} + + /** + * write_continuous_variable_history + */ + void write_continuous_variable_history(nest::Time const &t, +{%- for state_var in state_vars_that_need_continuous_buffering %} + const double {{ state_var }}{% if not loop.last %}, {% endif %} +{%- endfor %}); + + 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 ); + + continuous_variable_histentry_{{ neuronName }} get_continuous_variable_history( double t ); + + std::deque< continuous_variable_histentry_{{ neuronName }} > continuous_variable_history_; +{%- endif %} {%- endif %} {%- if neuron.get_state_symbols()|length > 0 %} // ------------------------------------------------------------------------- @@ -394,7 +421,7 @@ public: /* getters/setters for variables transferred from synapse */ -{%- for var in transferred_variables + state_vars_that_need_post_spike_buffering %} +{%- for var in transferred_variables + state_vars_that_need_continuous_buffering %} double get_{{var}}( double t, const bool before_increment = true ); {%- endfor %} {%- endif %} @@ -457,7 +484,7 @@ private: std::deque< histentry__{{neuronName}} > history_; // cache for initial values -{%- for var in transferred_variables + state_vars_that_need_post_spike_buffering %} +{%- for var in transferred_variables + state_vars_that_need_continuous_buffering %} double {{var}}__iv; {%- endfor %} diff --git a/pynestml/codegeneration/resources_nest/point_neuron/common/SynapseHeader.h.jinja2 b/pynestml/codegeneration/resources_nest/point_neuron/common/SynapseHeader.h.jinja2 index 9b9715cdd..f68c2bb97 100644 --- a/pynestml/codegeneration/resources_nest/point_neuron/common/SynapseHeader.h.jinja2 +++ b/pynestml/codegeneration/resources_nest/point_neuron/common/SynapseHeader.h.jinja2 @@ -1,3 +1,4 @@ +#define DEBUG {#- SynapseHeader.h.jinja2 @@ -78,8 +79,6 @@ along with NEST. If not, see . {{ synapse.print_comment() }} **/ -//#define DEBUG - namespace nest { {%- if not (nest_version.startswith("v2") or nest_version.startswith("v3.0") or nest_version.startswith("v3.1") or nest_version.startswith("v3.2") @@ -727,18 +726,17 @@ public: &finish ); while ( start != finish ) { -{%- if paired_neuron_name is not none and paired_neuron_name|length > 0 %} -{%- if paired_neuron.state_vars_that_need_post_spike_buffering | length > 0 %} +{%- if paired_neuron_name is not none and paired_neuron_name|length > 0 and paired_neuron.state_vars_that_need_continuous_buffering | length > 0 %} /** - * grab state variables from the postsynaptic neuron + * grab state variables from the postsynaptic neuron at the time of the post spike **/ -{%- for var_name in paired_neuron.state_vars_that_need_post_spike_buffering %} + auto histentry = ((post_neuron_t*)(__target))->get_continuous_variable_history(start->t_ + __dendritic_delay); + +{%- for var_name in paired_neuron.state_vars_that_need_continuous_buffering %} {%- set var = utils.get_parameter_variable_by_name(astnode, var_name) %} -{%- set var_name_post = utils.get_var_name_tuples_of_neuron_synapse_pair(continuous_post_ports, var_name) %} - const double __{{ var_name }} = start->{{ var_name_post }}_; + const double __{{ var_name }} = histentry.{{ var_name }}; {%- endfor %} -{%- endif %} {%- endif %} {% if vt_ports is defined and vt_ports|length > 0 %} @@ -812,17 +810,15 @@ public: { 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 -{%- if paired_neuron_name is not none and paired_neuron_name|length > 0 %} -{%- if paired_neuron.state_vars_that_need_post_spike_buffering | length > 0 %} +{%- if paired_neuron_name is not none and paired_neuron_name|length > 0 and paired_neuron.state_vars_that_need_continuous_buffering | length > 0 %} /** * grab state variables from the postsynaptic neuron **/ -{%- for var_name in paired_neuron.state_vars_that_need_post_spike_buffering %} +{%- for var_name in paired_neuron.state_vars_that_need_continuous_buffering %} {%- set var_name_post = utils.get_var_name_tuples_of_neuron_synapse_pair(continuous_post_ports, var_name) %} - const double __{{ var_name }} = ((post_neuron_t*)(__target))->get_{{ var_name_post }}(_tr_t); + const double __{{ var_name }} = ((post_neuron_t*)(__target))->get_{{ var_name_post }}(); {%- endfor %} -{%- endif %} {%- endif %} {%- filter indent(8, True) %} @@ -859,14 +855,14 @@ public: 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 {%- if paired_neuron_name is not none and paired_neuron_name|length > 0 %} -{%- if paired_neuron.state_vars_that_need_post_spike_buffering | length > 0 %} +{%- if paired_neuron.state_vars_that_need_continuous_buffering | length > 0 %} /** * grab state variables from the postsynaptic neuron **/ -{%- for var_name in paired_neuron.state_vars_that_need_post_spike_buffering %} +{%- for var_name in paired_neuron.state_vars_that_need_continuous_buffering %} {%- set var_name_post = utils.get_var_name_tuples_of_neuron_synapse_pair(continuous_post_ports, var_name) %} - const double __{{ var_name }} = ((post_neuron_t*)(__target))->get_{{ var_name_post }}(_tr_t); + const double __{{ var_name }} = ((post_neuron_t*)(__target))->get_{{ var_name_post }}(); {%- endfor %} {%- endif %} {%- endif %} diff --git a/pynestml/transformers/synapse_post_neuron_transformer.py b/pynestml/transformers/synapse_post_neuron_transformer.py index 037c2287a..c840aa4e9 100644 --- a/pynestml/transformers/synapse_post_neuron_transformer.py +++ b/pynestml/transformers/synapse_post_neuron_transformer.py @@ -377,20 +377,20 @@ def transform_neuron_synapse_pair_(self, neuron: ASTModel, synapse: ASTModel): # collect all ``continuous`` type input ports, the value of which is used in event handlers -- these have to be buffered in the hist_entry for each post spike in the postsynaptic history # - state_vars_that_need_post_spike_buffering = [] + state_vars_that_need_continuous_buffering = [] for input_block in new_synapse.get_input_blocks(): for port in input_block.get_input_ports(): if self.is_continuous_port(port.name, new_synapse): - state_vars_that_need_post_spike_buffering.append(port.name) + state_vars_that_need_continuous_buffering.append(port.name) # check that they are not used in the update block update_block_var_names = [] for update_block in synapse.get_update_blocks(): update_block_var_names.extend([var.get_complete_name() for var in ASTUtils.collect_variable_names_in_expression(update_block)]) - assert all([var not in update_block_var_names for var in state_vars_that_need_post_spike_buffering]) + assert all([var not in update_block_var_names for var in state_vars_that_need_continuous_buffering]) - Logger.log_message(None, -1, "Synaptic state variables moved to neuron that will need buffering: " + str(state_vars_that_need_post_spike_buffering), None, LoggingLevel.INFO) + Logger.log_message(None, -1, "Synaptic state variables moved to neuron that will need buffering: " + str(state_vars_that_need_continuous_buffering), None, LoggingLevel.INFO) # # move state variable declarations from synapse to neuron @@ -580,7 +580,7 @@ def mark_post_port(_expr=None): new_neuron.unpaired_name = neuron.get_name() new_neuron.set_name(new_neuron_name) new_neuron.paired_synapse = new_synapse - new_neuron.state_vars_that_need_post_spike_buffering = state_vars_that_need_post_spike_buffering + new_neuron.state_vars_that_need_continuous_buffering = state_vars_that_need_continuous_buffering # # rename synapse