From febaad535a3285ea5b103a402800a8519cb71cf8 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 28 Nov 2018 20:27:49 +0100
Subject: [PATCH 01/42] fixed bug and added testbench for postsynaptic trace
 function

---
 nestkernel/archiving_node.cpp |  15 +-
 nestkernel/archiving_node.h   |   6 +
 nestkernel/nest_names.cpp     |   2 +
 nestkernel/nest_names.h       |   2 +
 pynest/examples/stdp_test.py  | 272 ++++++++++++++++++++++++++++++++++
 5 files changed, 294 insertions(+), 3 deletions(-)
 create mode 100644 pynest/examples/stdp_test.py

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 59e91604bb..c1bab4c946 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -98,20 +98,28 @@ Archiving_Node::register_stdp_connection( double t_first_read )
 double
 nest::Archiving_Node::get_K_value( double t )
 {
+  const bool include_overlapping_spikes = true;
+   std::cout << "* In Archiving_Node::get_K_value(t = " << t << ")" << std::endl; 
   if ( history_.empty() )
   {
+    _trace = Kminus_;
+    std::cout << "\thistory is empty: K_value = " << Kminus_ << std::endl; 
     return Kminus_;
   }
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
-    if ( t - history_[ i ].t_ > kernel().connection_manager.get_stdp_eps() )
+    if ( t >= history_[ i ].t_ ) //  kernel().connection_manager.get_stdp_eps() 
     {
-      return ( history_[ i ].Kminus_
+      _trace = ( history_[ i ].Kminus_
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
+      std::cout << "\tK_value = " << _trace << std::endl;
+      return _trace;
     }
-    i--;
+    --i;
   }
+  _trace = 0.;
+  std::cout << "\tfall-through: K_value = " << _trace << std::endl;
   return 0;
 }
 
@@ -226,6 +234,7 @@ nest::Archiving_Node::get_status( DictionaryDatum& d ) const
   def< double >( d, names::tau_Ca, tau_Ca_ );
   def< double >( d, names::beta_Ca, beta_Ca_ );
   def< double >( d, names::tau_minus_triplet, tau_minus_triplet_ );
+  def< double >( d, names::post_trace, _trace );
 #ifdef DEBUG_ARCHIVER
   def< int >( d, names::archiver_length, history_.size() );
 #endif
diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index 134de8b638..4f6279a06b 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -44,6 +44,10 @@
 // Includes from sli:
 #include "dictdatum.h"
 
+#include <iostream>
+#include <sstream>
+#include <fstream>
+
 #define DEBUG_ARCHIVER 1
 
 namespace nest
@@ -212,6 +216,8 @@ class Archiving_Node : public Node
   double tau_minus_;
   double tau_minus_inv_;
 
+  double _trace;
+
   // time constant for triplet low pass filtering of "post" spike train
   double tau_minus_triplet_;
   double tau_minus_triplet_inv_;
diff --git a/nestkernel/nest_names.cpp b/nestkernel/nest_names.cpp
index 2606014cb8..ffc5ce9edf 100644
--- a/nestkernel/nest_names.cpp
+++ b/nestkernel/nest_names.cpp
@@ -308,7 +308,9 @@ const Name port_name( "port_name" );
 const Name port_width( "port_width" );
 const Name ports( "ports" );
 const Name post_synaptic_element( "post_synaptic_element" );
+const Name post_trace( "post_trace" );
 const Name pre_synaptic_element( "pre_synaptic_element" );
+const Name pre_trace( "pre_trace" );
 const Name precise_times( "precise_times" );
 const Name precision( "precision" );
 const Name print_time( "print_time" );
diff --git a/nestkernel/nest_names.h b/nestkernel/nest_names.h
index 89d5f25a8f..5a148ae3fc 100644
--- a/nestkernel/nest_names.h
+++ b/nestkernel/nest_names.h
@@ -328,7 +328,9 @@ extern const Name port_name;
 extern const Name port_width;
 extern const Name ports;
 extern const Name post_synaptic_element;
+extern const Name post_trace;
 extern const Name pre_synaptic_element;
+extern const Name pre_trace;
 extern const Name precise_times;
 extern const Name precision;
 extern const Name print_time;
diff --git a/pynest/examples/stdp_test.py b/pynest/examples/stdp_test.py
new file mode 100644
index 0000000000..f545526396
--- /dev/null
+++ b/pynest/examples/stdp_test.py
@@ -0,0 +1,272 @@
+# -*- coding: utf-8 -*-
+#
+# test_stdp_multiplicity.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+# This script tests the parrot_neuron in NEST.
+
+import nest
+import unittest
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+
+@nest.check_stack
+class StdpSynapse(unittest.TestCase):
+	"""
+	...
+	"""
+
+	def run_protocol(self, pre_post_shift):
+		"""
+		"""
+
+		resolution = .1	 # [ms]
+
+		delay = 1.  # [ms]
+
+		pre_spike_times = [25., 100., 110., 120., 200.]	  # [ms]
+		post_spike_times = [50., 100., 110., 120., 150., 250.]	  # [ms]
+
+		# pre_spike_times = [220., 300.]	  # [ms]
+		# post_spike_times = [150., 250., 350.]	  # [ms]
+
+		# pre_spike_times = [301., 302.]	  # [ms]
+		# post_spike_times = [100., 300.]	  # [ms]
+
+		# pre_spike_times = [   4.,   6.  , 6.]	# [ms]
+		# post_spike_times = [  2.,   6. ]	  # [ms]
+
+		# pre_spike_times = 1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))	  # [ms]
+		# post_spike_times =  np.sort(np.unique(1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))))	 # [ms]
+
+		print("Pre spike times: " + str(pre_spike_times))
+		print("Post spike times: " + str(post_spike_times))
+
+		nest.set_verbosity("M_WARNING")
+
+		post_weights = {'parrot': [], 'parrot_ps': []}
+
+		nest.ResetKernel()
+		nest.SetKernelStatus({'resolution': resolution})
+
+		wr = nest.Create('weight_recorder')
+		nest.CopyModel("stdp_synapse", "stdp_synapse_rec",
+					   {"weight_recorder": wr[0], "weight": 1.})
+
+		# create spike_generators with these times
+		pre_sg = nest.Create("spike_generator",
+							 params={"spike_times": pre_spike_times,
+									 'allow_offgrid_spikes': True})
+		post_sg = nest.Create("spike_generator",
+							  params={"spike_times": post_spike_times,
+									  'allow_offgrid_spikes': True})
+		pre_sg_ps = nest.Create("spike_generator",
+								params={"spike_times": pre_spike_times,
+										'precise_times': True})
+		post_sg_ps = nest.Create("spike_generator",
+								 params={"spike_times": post_spike_times,
+										 'precise_times': True})
+
+		# create parrot neurons and connect spike_generators
+		pre_parrot = nest.Create("parrot_neuron")
+		post_parrot = nest.Create("parrot_neuron")
+		pre_parrot_ps = nest.Create("parrot_neuron_ps")
+		post_parrot_ps = nest.Create("parrot_neuron_ps")
+
+		nest.Connect(pre_sg, pre_parrot,
+					 syn_spec={"delay": delay})
+		nest.Connect(post_sg, post_parrot,
+					 syn_spec={"delay": delay})
+		nest.Connect(pre_sg_ps, pre_parrot_ps,
+					 syn_spec={"delay": delay})
+		nest.Connect(post_sg_ps, post_parrot_ps,
+					 syn_spec={"delay": delay})
+
+		# create spike detector --- debugging only
+		spikes = nest.Create("spike_detector",
+							 params={'precise_times': True})
+		nest.Connect(
+			pre_parrot + post_parrot +
+			pre_parrot_ps + post_parrot_ps,
+			spikes
+		)
+
+		# connect both parrot neurons with a stdp synapse onto port 1
+		# thereby spikes transmitted through the stdp connection are
+		# not repeated postsynaptically.
+		nest.Connect(
+			pre_parrot, post_parrot,
+			syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
+		nest.Connect(
+			pre_parrot_ps, post_parrot_ps,
+			syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
+
+		# get STDP synapse and weight before protocol
+		syn = nest.GetConnections(source=pre_parrot,
+								  synapse_model="stdp_synapse_rec")
+		w_pre = nest.GetStatus(syn)[0]['weight']
+		syn_ps = nest.GetConnections(source=pre_parrot_ps,
+									 synapse_model="stdp_synapse_rec")
+		w_pre_ps = nest.GetStatus(syn)[0]['weight']
+
+		print()
+		print("[py] w_pre = " + str(w_pre))
+		print("[py] w_pre_ps = " + str(w_pre_ps))
+		print()
+
+		sim_time = np.amax(np.concatenate((pre_spike_times, post_spike_times))) + 5 * delay
+		n_steps = int(np.ceil(sim_time / resolution)) + 1
+		trace_nest = []
+		trace_nest_t = []
+		t = nest.GetStatus([0], "time")[0]
+		trace_nest_t.append(t)
+		post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+		trace_nest.append(post_trace_value)
+		for step in range(n_steps):
+			nest.Simulate(resolution)
+			t = nest.GetStatus([0], "time")[0]
+			if np.any(np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.):
+				trace_nest_t.append(t)
+				post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+				trace_nest.append(post_trace_value)
+
+		# get weight post protocol
+		w_post = nest.GetStatus(syn)[0]['weight']
+		w_post_ps = nest.GetStatus(syn_ps)[0]['weight']
+
+
+		tau_minus = nest.GetStatus(post_parrot)[0]['tau_minus']
+		# post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+		# print("kljpjijiiiiiiiiiiiiiiiiiiiiiiiiiiiiii " + str(post_trace_value))
+
+		fig, ax = plt.subplots(nrows=3)
+		ax1, ax3, ax2 = ax
+
+		n_spikes = len(pre_spike_times)
+		for i in range(n_spikes):
+			ax1.plot(2 * [pre_spike_times[i] + delay], [0, 1], linewidth=2, color="blue", alpha=.4)
+
+		n_spikes = len(post_spike_times)
+		for i in range(n_spikes):
+				ax3.plot(2 * [post_spike_times[i] + delay], [0, 1], linewidth=2, color="red", alpha=.4)
+
+		ref_post_trace = np.zeros(1000)
+		n_spikes = len(post_spike_times)
+		for sp_idx in range(n_spikes):
+			t_sp = post_spike_times[sp_idx] + delay
+			for i in range(len(ref_post_trace)):
+				t = (i / float(len(ref_post_trace - 1))) * sim_time
+				if t >= t_sp:
+					ref_post_trace[i] += np.exp(-(t - t_sp) / tau_minus)
+
+		ax2.plot(np.linspace(0., sim_time, len(ref_post_trace)), ref_post_trace, label="Expected", color="cyan", alpha=.6)
+
+
+		# fn_nest_trace_values = "/tmp/trace_vals_0x7ff985894370.txt"
+		# print("Please enter fn_nest_trace_values now:")
+		# import pdb;pdb.set_trace()
+		# s = open(fn_nest_trace_values, "r")
+		# l = s.readlines()
+		# nest_spike_times = []
+		# nest_trace_values = []
+		# for line in l:
+		# 	line_split = line.split()
+		# 	nest_spike_times.append(float(line_split[0]))
+		# 	nest_trace_values.append(float(line_split[1]))
+		# ax2.scatter(nest_spike_times, nest_trace_values, label="NEST", color="orange")
+
+
+		ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
+
+
+		ax2.set_xlabel("Time [ms]")
+		ax1.set_ylabel("Pre spikes")
+		ax3.set_ylabel("Post spikes")
+		ax2.set_ylabel("Trace")
+		ax2.legend()
+		for _ax in ax:
+			_ax.grid()
+			_ax.set_xlim(0., sim_time)
+		fig.savefig("/tmp/traces.png")
+		import pdb;pdb.set_trace()
+
+		print("[py] w_post = " + str(w_post))
+		print("[py] w_post_ps = " + str(w_post_ps))
+		print()
+
+		wr_weights = nest.GetStatus(wr, "events")[0]["weights"]
+		print("[py] wr_weights = " + str(wr_weights))
+
+
+		assert w_post != w_pre, "Plain parrot weight did not change."
+		assert w_post_ps != w_pre_ps, "Precise parrot \
+			weight did not change."
+
+		post_weights['parrot'].append(w_post)
+		post_weights['parrot_ps'].append(w_post_ps)
+
+		return post_weights
+
+	def test_ParrotNeuronSTDPProtocolPotentiation(self):
+		"""Check weight convergence on potentiation."""
+
+		post_weights = self.run_protocol(pre_post_shift=10.0)
+		w_plain = np.array(post_weights['parrot'])
+		w_precise = np.array(post_weights['parrot_ps'])
+
+		assert all(w_plain == w_plain[0]), 'Plain weights differ'
+		dw = w_precise - w_plain
+		dwrel = dw[1:] / dw[:-1]
+		assert all(np.round(dwrel, decimals=3) ==
+				   0.5), 'Precise weights do not converge.'
+
+	# def test_ParrotNeuronSTDPProtocolDepression(self):
+	#	 """Check weight convergence on depression."""
+
+	#	 post_weights = self.run_protocol(pre_post_shift=-10.0)
+	#	 w_plain = np.array(post_weights['parrot'])
+	#	 w_precise = np.array(post_weights['parrot_ps'])
+
+	#	 assert all(w_plain == w_plain[0]), 'Plain weights differ'
+	#	 dw = w_precise - w_plain
+	#	 dwrel = dw[1:] / dw[:-1]
+	#	 assert all(np.round(dwrel, decimals=3) ==
+	#				0.5), 'Precise weights do not converge.'
+
+
+def suite():
+
+	# makeSuite is sort of obsolete http://bugs.python.org/issue2721
+	# using loadTestsFromTestCase instead.
+	suite = unittest.TestLoader().loadTestsFromTestCase(StdpSynapse)
+	return unittest.TestSuite([suite])
+
+
+def run():
+	runner = unittest.TextTestRunner(verbosity=99)
+	runner.run(suite())
+
+
+if __name__ == "__main__":
+	#unittest.findTestCases(__main__).debug()
+	run()

From d089391350e791f4912c1c789bac11077644dbe0 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 28 Nov 2018 20:52:02 +0100
Subject: [PATCH 02/42] added assertion failure upon fall-through case

---
 nestkernel/archiving_node.cpp | 6 ++----
 nestkernel/archiving_node.h   | 2 ++
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index c1bab4c946..a57bd98dbf 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -98,7 +98,6 @@ Archiving_Node::register_stdp_connection( double t_first_read )
 double
 nest::Archiving_Node::get_K_value( double t )
 {
-  const bool include_overlapping_spikes = true;
    std::cout << "* In Archiving_Node::get_K_value(t = " << t << ")" << std::endl; 
   if ( history_.empty() )
   {
@@ -118,9 +117,8 @@ nest::Archiving_Node::get_K_value( double t )
     }
     --i;
   }
-  _trace = 0.;
-  std::cout << "\tfall-through: K_value = " << _trace << std::endl;
-  return 0;
+  assert(false); // fall-through case: means that the trace value is requested at a time before the earliest postsynaptic spike in the history buffer. Something is wrong!
+  return 0; // just here to silence compiler warnings
 }
 
 void
diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index 4f6279a06b..a5cc198f9e 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -48,6 +48,8 @@
 #include <sstream>
 #include <fstream>
 
+#include <cassert>
+
 #define DEBUG_ARCHIVER 1
 
 namespace nest

From a725a1bd743b898a8677170a534b3e9445fb8efd Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 29 Nov 2018 15:07:45 +0100
Subject: [PATCH 03/42] updated the example

---
 models/stdp_connection.h      |  4 +++-
 nestkernel/archiving_node.cpp |  5 ++++-
 pynest/examples/stdp_test.py  | 19 +++++++++++++------
 3 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/models/stdp_connection.h b/models/stdp_connection.h
index f990bf54d6..1ed8c5fd8a 100644
--- a/models/stdp_connection.h
+++ b/models/stdp_connection.h
@@ -256,8 +256,10 @@ STDPConnection< targetidentifierT >::send( Event& e,
   }
 
   // depression due to new pre-synaptic spike
+  const double _K_value = target->get_K_value( t_spike - dendritic_delay );
+  std::cout << "In Synapse: got K_value = " << _K_value << std::endl;
   weight_ =
-    depress_( weight_, target->get_K_value( t_spike - dendritic_delay ) );
+    depress_( weight_, _K_value );
 
   e.set_receiver( *target );
   e.set_weight( weight_ );
diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index a57bd98dbf..72f921c802 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -108,7 +108,8 @@ nest::Archiving_Node::get_K_value( double t )
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
-    if ( t >= history_[ i ].t_ ) //  kernel().connection_manager.get_stdp_eps() 
+    if ( t >= history_[ i ].t_ )
+    // if (t - history_[i].t_ > kernel().connection_manager.get_stdp_eps())
     {
       _trace = ( history_[ i ].Kminus_
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
@@ -118,6 +119,7 @@ nest::Archiving_Node::get_K_value( double t )
     --i;
   }
   assert(false); // fall-through case: means that the trace value is requested at a time before the earliest postsynaptic spike in the history buffer. Something is wrong!
+  // _trace = 0.;
   return 0; // just here to silence compiler warnings
 }
 
@@ -233,6 +235,7 @@ nest::Archiving_Node::get_status( DictionaryDatum& d ) const
   def< double >( d, names::beta_Ca, beta_Ca_ );
   def< double >( d, names::tau_minus_triplet, tau_minus_triplet_ );
   def< double >( d, names::post_trace, _trace );
+  std::cout << "In Archiving_Node::get_status(): trace = " << _trace << std::endl;
 #ifdef DEBUG_ARCHIVER
   def< int >( d, names::archiver_length, history_.size() );
 #endif
diff --git a/pynest/examples/stdp_test.py b/pynest/examples/stdp_test.py
index f545526396..6b4f39a8e3 100644
--- a/pynest/examples/stdp_test.py
+++ b/pynest/examples/stdp_test.py
@@ -40,12 +40,15 @@ def run_protocol(self, pre_post_shift):
 		"""
 		"""
 
-		resolution = .1	 # [ms]
+		resolution = .5	 # [ms]
 
 		delay = 1.  # [ms]
 
-		pre_spike_times = [25., 100., 110., 120., 200.]	  # [ms]
-		post_spike_times = [50., 100., 110., 120., 150., 250.]	  # [ms]
+		pre_spike_times = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]	  # [ms]
+		post_spike_times = [3., 7., 8., 10., 12., 13., 14., 16., 17., 18., 19., 20., 21., 22.]	  # [ms]
+
+		# pre_spike_times = [10., 11., 12., 13., 14., 15., 25., 35., 45., 50., 51., 52., 70.]	  # [ms]
+		# post_spike_times = [10., 11., 12., 13., 30., 40., 50., 51., 52., 53., 54.]	  # [ms]
 
 		# pre_spike_times = [220., 300.]	  # [ms]
 		# post_spike_times = [150., 250., 350.]	  # [ms]
@@ -145,10 +148,12 @@ def run_protocol(self, pre_post_shift):
 		for step in range(n_steps):
 			nest.Simulate(resolution)
 			t = nest.GetStatus([0], "time")[0]
+			# if True:
 			if np.any(np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.):
 				trace_nest_t.append(t)
 				post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
 				trace_nest.append(post_trace_value)
+				print("In Python: Getting trace = " + str(post_trace_value) + " at time t = " + str(t))
 
 		# get weight post protocol
 		w_post = nest.GetStatus(syn)[0]['weight']
@@ -177,7 +182,7 @@ def run_protocol(self, pre_post_shift):
 			for i in range(len(ref_post_trace)):
 				t = (i / float(len(ref_post_trace - 1))) * sim_time
 				if t >= t_sp:
-					ref_post_trace[i] += np.exp(-(t - t_sp) / tau_minus)
+					ref_post_trace[i] += np.exp(-(t - t_sp -delay) / tau_minus)
 
 		ax2.plot(np.linspace(0., sim_time, len(ref_post_trace)), ref_post_trace, label="Expected", color="cyan", alpha=.6)
 
@@ -205,10 +210,12 @@ def run_protocol(self, pre_post_shift):
 		ax2.set_ylabel("Trace")
 		ax2.legend()
 		for _ax in ax:
-			_ax.grid()
+			_ax.grid(which="major", axis="both")
+			_ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
+			_ax.minorticks_on()
+			# _ax.grid(major=True, minor=True)
 			_ax.set_xlim(0., sim_time)
 		fig.savefig("/tmp/traces.png")
-		import pdb;pdb.set_trace()
 
 		print("[py] w_post = " + str(w_post))
 		print("[py] w_post_ps = " + str(w_post_ps))

From 85fa1eb23d3a66d4ce66ad63d67072f6843aa9f6 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 7 Jan 2019 18:44:20 +0100
Subject: [PATCH 04/42] refactor postsynaptic trace demo script to unit test

---
 pynest/examples/stdp_test.py         | 279 ---------------------------
 pynest/nest/tests/test_post_trace.py | 250 ++++++++++++++++++++++++
 2 files changed, 250 insertions(+), 279 deletions(-)
 delete mode 100644 pynest/examples/stdp_test.py
 create mode 100644 pynest/nest/tests/test_post_trace.py

diff --git a/pynest/examples/stdp_test.py b/pynest/examples/stdp_test.py
deleted file mode 100644
index 6b4f39a8e3..0000000000
--- a/pynest/examples/stdp_test.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# test_stdp_multiplicity.py
-#
-# This file is part of NEST.
-#
-# Copyright (C) 2004 The NEST Initiative
-#
-# NEST is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 2 of the License, or
-# (at your option) any later version.
-#
-# NEST is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
-
-# This script tests the parrot_neuron in NEST.
-
-import nest
-import unittest
-import math
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-
-
-@nest.check_stack
-class StdpSynapse(unittest.TestCase):
-	"""
-	...
-	"""
-
-	def run_protocol(self, pre_post_shift):
-		"""
-		"""
-
-		resolution = .5	 # [ms]
-
-		delay = 1.  # [ms]
-
-		pre_spike_times = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]	  # [ms]
-		post_spike_times = [3., 7., 8., 10., 12., 13., 14., 16., 17., 18., 19., 20., 21., 22.]	  # [ms]
-
-		# pre_spike_times = [10., 11., 12., 13., 14., 15., 25., 35., 45., 50., 51., 52., 70.]	  # [ms]
-		# post_spike_times = [10., 11., 12., 13., 30., 40., 50., 51., 52., 53., 54.]	  # [ms]
-
-		# pre_spike_times = [220., 300.]	  # [ms]
-		# post_spike_times = [150., 250., 350.]	  # [ms]
-
-		# pre_spike_times = [301., 302.]	  # [ms]
-		# post_spike_times = [100., 300.]	  # [ms]
-
-		# pre_spike_times = [   4.,   6.  , 6.]	# [ms]
-		# post_spike_times = [  2.,   6. ]	  # [ms]
-
-		# pre_spike_times = 1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))	  # [ms]
-		# post_spike_times =  np.sort(np.unique(1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))))	 # [ms]
-
-		print("Pre spike times: " + str(pre_spike_times))
-		print("Post spike times: " + str(post_spike_times))
-
-		nest.set_verbosity("M_WARNING")
-
-		post_weights = {'parrot': [], 'parrot_ps': []}
-
-		nest.ResetKernel()
-		nest.SetKernelStatus({'resolution': resolution})
-
-		wr = nest.Create('weight_recorder')
-		nest.CopyModel("stdp_synapse", "stdp_synapse_rec",
-					   {"weight_recorder": wr[0], "weight": 1.})
-
-		# create spike_generators with these times
-		pre_sg = nest.Create("spike_generator",
-							 params={"spike_times": pre_spike_times,
-									 'allow_offgrid_spikes': True})
-		post_sg = nest.Create("spike_generator",
-							  params={"spike_times": post_spike_times,
-									  'allow_offgrid_spikes': True})
-		pre_sg_ps = nest.Create("spike_generator",
-								params={"spike_times": pre_spike_times,
-										'precise_times': True})
-		post_sg_ps = nest.Create("spike_generator",
-								 params={"spike_times": post_spike_times,
-										 'precise_times': True})
-
-		# create parrot neurons and connect spike_generators
-		pre_parrot = nest.Create("parrot_neuron")
-		post_parrot = nest.Create("parrot_neuron")
-		pre_parrot_ps = nest.Create("parrot_neuron_ps")
-		post_parrot_ps = nest.Create("parrot_neuron_ps")
-
-		nest.Connect(pre_sg, pre_parrot,
-					 syn_spec={"delay": delay})
-		nest.Connect(post_sg, post_parrot,
-					 syn_spec={"delay": delay})
-		nest.Connect(pre_sg_ps, pre_parrot_ps,
-					 syn_spec={"delay": delay})
-		nest.Connect(post_sg_ps, post_parrot_ps,
-					 syn_spec={"delay": delay})
-
-		# create spike detector --- debugging only
-		spikes = nest.Create("spike_detector",
-							 params={'precise_times': True})
-		nest.Connect(
-			pre_parrot + post_parrot +
-			pre_parrot_ps + post_parrot_ps,
-			spikes
-		)
-
-		# connect both parrot neurons with a stdp synapse onto port 1
-		# thereby spikes transmitted through the stdp connection are
-		# not repeated postsynaptically.
-		nest.Connect(
-			pre_parrot, post_parrot,
-			syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
-		nest.Connect(
-			pre_parrot_ps, post_parrot_ps,
-			syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
-
-		# get STDP synapse and weight before protocol
-		syn = nest.GetConnections(source=pre_parrot,
-								  synapse_model="stdp_synapse_rec")
-		w_pre = nest.GetStatus(syn)[0]['weight']
-		syn_ps = nest.GetConnections(source=pre_parrot_ps,
-									 synapse_model="stdp_synapse_rec")
-		w_pre_ps = nest.GetStatus(syn)[0]['weight']
-
-		print()
-		print("[py] w_pre = " + str(w_pre))
-		print("[py] w_pre_ps = " + str(w_pre_ps))
-		print()
-
-		sim_time = np.amax(np.concatenate((pre_spike_times, post_spike_times))) + 5 * delay
-		n_steps = int(np.ceil(sim_time / resolution)) + 1
-		trace_nest = []
-		trace_nest_t = []
-		t = nest.GetStatus([0], "time")[0]
-		trace_nest_t.append(t)
-		post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
-		trace_nest.append(post_trace_value)
-		for step in range(n_steps):
-			nest.Simulate(resolution)
-			t = nest.GetStatus([0], "time")[0]
-			# if True:
-			if np.any(np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.):
-				trace_nest_t.append(t)
-				post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
-				trace_nest.append(post_trace_value)
-				print("In Python: Getting trace = " + str(post_trace_value) + " at time t = " + str(t))
-
-		# get weight post protocol
-		w_post = nest.GetStatus(syn)[0]['weight']
-		w_post_ps = nest.GetStatus(syn_ps)[0]['weight']
-
-
-		tau_minus = nest.GetStatus(post_parrot)[0]['tau_minus']
-		# post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
-		# print("kljpjijiiiiiiiiiiiiiiiiiiiiiiiiiiiiii " + str(post_trace_value))
-
-		fig, ax = plt.subplots(nrows=3)
-		ax1, ax3, ax2 = ax
-
-		n_spikes = len(pre_spike_times)
-		for i in range(n_spikes):
-			ax1.plot(2 * [pre_spike_times[i] + delay], [0, 1], linewidth=2, color="blue", alpha=.4)
-
-		n_spikes = len(post_spike_times)
-		for i in range(n_spikes):
-				ax3.plot(2 * [post_spike_times[i] + delay], [0, 1], linewidth=2, color="red", alpha=.4)
-
-		ref_post_trace = np.zeros(1000)
-		n_spikes = len(post_spike_times)
-		for sp_idx in range(n_spikes):
-			t_sp = post_spike_times[sp_idx] + delay
-			for i in range(len(ref_post_trace)):
-				t = (i / float(len(ref_post_trace - 1))) * sim_time
-				if t >= t_sp:
-					ref_post_trace[i] += np.exp(-(t - t_sp -delay) / tau_minus)
-
-		ax2.plot(np.linspace(0., sim_time, len(ref_post_trace)), ref_post_trace, label="Expected", color="cyan", alpha=.6)
-
-
-		# fn_nest_trace_values = "/tmp/trace_vals_0x7ff985894370.txt"
-		# print("Please enter fn_nest_trace_values now:")
-		# import pdb;pdb.set_trace()
-		# s = open(fn_nest_trace_values, "r")
-		# l = s.readlines()
-		# nest_spike_times = []
-		# nest_trace_values = []
-		# for line in l:
-		# 	line_split = line.split()
-		# 	nest_spike_times.append(float(line_split[0]))
-		# 	nest_trace_values.append(float(line_split[1]))
-		# ax2.scatter(nest_spike_times, nest_trace_values, label="NEST", color="orange")
-
-
-		ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
-
-
-		ax2.set_xlabel("Time [ms]")
-		ax1.set_ylabel("Pre spikes")
-		ax3.set_ylabel("Post spikes")
-		ax2.set_ylabel("Trace")
-		ax2.legend()
-		for _ax in ax:
-			_ax.grid(which="major", axis="both")
-			_ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
-			_ax.minorticks_on()
-			# _ax.grid(major=True, minor=True)
-			_ax.set_xlim(0., sim_time)
-		fig.savefig("/tmp/traces.png")
-
-		print("[py] w_post = " + str(w_post))
-		print("[py] w_post_ps = " + str(w_post_ps))
-		print()
-
-		wr_weights = nest.GetStatus(wr, "events")[0]["weights"]
-		print("[py] wr_weights = " + str(wr_weights))
-
-
-		assert w_post != w_pre, "Plain parrot weight did not change."
-		assert w_post_ps != w_pre_ps, "Precise parrot \
-			weight did not change."
-
-		post_weights['parrot'].append(w_post)
-		post_weights['parrot_ps'].append(w_post_ps)
-
-		return post_weights
-
-	def test_ParrotNeuronSTDPProtocolPotentiation(self):
-		"""Check weight convergence on potentiation."""
-
-		post_weights = self.run_protocol(pre_post_shift=10.0)
-		w_plain = np.array(post_weights['parrot'])
-		w_precise = np.array(post_weights['parrot_ps'])
-
-		assert all(w_plain == w_plain[0]), 'Plain weights differ'
-		dw = w_precise - w_plain
-		dwrel = dw[1:] / dw[:-1]
-		assert all(np.round(dwrel, decimals=3) ==
-				   0.5), 'Precise weights do not converge.'
-
-	# def test_ParrotNeuronSTDPProtocolDepression(self):
-	#	 """Check weight convergence on depression."""
-
-	#	 post_weights = self.run_protocol(pre_post_shift=-10.0)
-	#	 w_plain = np.array(post_weights['parrot'])
-	#	 w_precise = np.array(post_weights['parrot_ps'])
-
-	#	 assert all(w_plain == w_plain[0]), 'Plain weights differ'
-	#	 dw = w_precise - w_plain
-	#	 dwrel = dw[1:] / dw[:-1]
-	#	 assert all(np.round(dwrel, decimals=3) ==
-	#				0.5), 'Precise weights do not converge.'
-
-
-def suite():
-
-	# makeSuite is sort of obsolete http://bugs.python.org/issue2721
-	# using loadTestsFromTestCase instead.
-	suite = unittest.TestLoader().loadTestsFromTestCase(StdpSynapse)
-	return unittest.TestSuite([suite])
-
-
-def run():
-	runner = unittest.TextTestRunner(verbosity=99)
-	runner.run(suite())
-
-
-if __name__ == "__main__":
-	#unittest.findTestCases(__main__).debug()
-	run()
diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
new file mode 100644
index 0000000000..f35fd42da1
--- /dev/null
+++ b/pynest/nest/tests/test_post_trace.py
@@ -0,0 +1,250 @@
+# -*- coding: utf-8 -*-
+#
+# test_stdp_multiplicity.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+# This script tests the parrot_neuron in NEST.
+
+import nest
+import unittest
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+
+
+@nest.check_stack
+class PostTraceTestCase(unittest.TestCase):
+
+    def test_post_trace(self):
+        """
+        """
+
+        resolution = .5     # [ms]
+
+        delay = 1.  # [ms]
+
+        pre_spike_times1 = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]      # [ms]
+        post_spike_times1 = [3., 7., 8., 10., 12., 13., 14., 16., 17., 18., 19., 20., 21., 22.]      # [ms]
+        
+        pre_spike_times = [pre_spike_times1]
+        post_spike_times = [post_spike_times1]
+
+        # pre_spike_times = [10., 11., 12., 13., 14., 15., 25., 35., 45., 50., 51., 52., 70.]      # [ms]
+        # post_spike_times = [10., 11., 12., 13., 30., 40., 50., 51., 52., 53., 54.]      # [ms]
+
+        # pre_spike_times = [220., 300.]      # [ms]
+        # post_spike_times = [150., 250., 350.]      # [ms]
+
+        # pre_spike_times = [301., 302.]      # [ms]
+        # post_spike_times = [100., 300.]      # [ms]
+
+        # pre_spike_times = [   4.,   6.  , 6.]    # [ms]
+        # post_spike_times = [  2.,   6. ]      # [ms]
+
+        # pre_spike_times = 1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))      # [ms]
+        # post_spike_times =  np.sort(np.unique(1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))))     # [ms]
+
+        for spike_times_idx in range(len(pre_spike_times)):
+
+            print("Pre spike times: " + str(pre_spike_times[spike_times_idx]))
+            print("Post spike times: " + str(post_spike_times[spike_times_idx]))
+
+            nest.set_verbosity("M_WARNING")
+
+            post_weights = {'parrot': [], 'parrot_ps': []}
+
+            nest.ResetKernel()
+            nest.SetKernelStatus({'resolution': resolution})
+
+            wr = nest.Create('weight_recorder')
+            nest.CopyModel("stdp_synapse", "stdp_synapse_rec",
+                        {"weight_recorder": wr[0], "weight": 1.})
+
+            # create spike_generators with these times
+            pre_sg = nest.Create("spike_generator",
+                                params={"spike_times": pre_spike_times[spike_times_idx],
+                                        'allow_offgrid_spikes': True})
+            post_sg = nest.Create("spike_generator",
+                                params={"spike_times": post_spike_times[spike_times_idx],
+                                        'allow_offgrid_spikes': True})
+            pre_sg_ps = nest.Create("spike_generator",
+                                    params={"spike_times": pre_spike_times[spike_times_idx],
+                                            'precise_times': True})
+            post_sg_ps = nest.Create("spike_generator",
+                                    params={"spike_times": post_spike_times[spike_times_idx],
+                                            'precise_times': True})
+
+            # create parrot neurons and connect spike_generators
+            pre_parrot = nest.Create("parrot_neuron")
+            post_parrot = nest.Create("parrot_neuron")
+            pre_parrot_ps = nest.Create("parrot_neuron_ps")
+            post_parrot_ps = nest.Create("parrot_neuron_ps")
+
+            nest.Connect(pre_sg, pre_parrot,
+                        syn_spec={"delay": delay})
+            nest.Connect(post_sg, post_parrot,
+                        syn_spec={"delay": delay})
+            nest.Connect(pre_sg_ps, pre_parrot_ps,
+                        syn_spec={"delay": delay})
+            nest.Connect(post_sg_ps, post_parrot_ps,
+                        syn_spec={"delay": delay})
+
+            # create spike detector --- debugging only
+            spikes = nest.Create("spike_detector",
+                                params={'precise_times': True})
+            nest.Connect(
+                pre_parrot + post_parrot +
+                pre_parrot_ps + post_parrot_ps,
+                spikes
+            )
+
+            # connect both parrot neurons with a stdp synapse onto port 1
+            # thereby spikes transmitted through the stdp connection are
+            # not repeated postsynaptically.
+            nest.Connect(
+                pre_parrot, post_parrot,
+                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
+            nest.Connect(
+                pre_parrot_ps, post_parrot_ps,
+                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
+
+            # get STDP synapse and weight before protocol
+            syn = nest.GetConnections(source=pre_parrot,
+                                    synapse_model="stdp_synapse_rec")
+            w_pre = nest.GetStatus(syn)[0]['weight']
+            syn_ps = nest.GetConnections(source=pre_parrot_ps,
+                                        synapse_model="stdp_synapse_rec")
+            w_pre_ps = nest.GetStatus(syn)[0]['weight']
+
+            print()
+            print("[py] w_pre = " + str(w_pre))
+            print("[py] w_pre_ps = " + str(w_pre_ps))
+            print()
+
+            sim_time = np.amax(np.concatenate((pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx]))) + 5 * delay
+            n_steps = int(np.ceil(sim_time / resolution)) + 1
+            trace_nest = []
+            trace_nest_t = []
+            t = nest.GetStatus([0], "time")[0]
+            trace_nest_t.append(t)
+            post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+            trace_nest.append(post_trace_value)
+            for step in range(n_steps):
+                nest.Simulate(resolution)
+                t = nest.GetStatus([0], "time")[0]
+                # if True:
+                if np.any(np.abs(t - np.array(pre_spike_times[spike_times_idx]) - delay) < resolution/2.):
+                    trace_nest_t.append(t)
+                    post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+                    trace_nest.append(post_trace_value)
+                    print("In Python: Getting trace = " + str(post_trace_value) + " at time t = " + str(t))
+
+            # get weight post protocol
+            w_post = nest.GetStatus(syn)[0]['weight']
+            w_post_ps = nest.GetStatus(syn_ps)[0]['weight']
+
+
+            tau_minus = nest.GetStatus(post_parrot)[0]['tau_minus']
+            # post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+            # print("kljpjijiiiiiiiiiiiiiiiiiiiiiiiiiiiiii " + str(post_trace_value))
+
+            fig, ax = plt.subplots(nrows=3)
+            ax1, ax3, ax2 = ax
+
+            n_spikes = len(pre_spike_times[spike_times_idx])
+            for i in range(n_spikes):
+                ax1.plot(2 * [pre_spike_times[spike_times_idx][i] + delay], [0, 1], linewidth=2, color="blue", alpha=.4)
+
+            n_spikes = len(post_spike_times[spike_times_idx])
+            for i in range(n_spikes):
+                    ax3.plot(2 * [post_spike_times[spike_times_idx][i] + delay], [0, 1], linewidth=2, color="red", alpha=.4)
+
+            ref_post_trace = np.zeros(1000)
+            n_spikes = len(post_spike_times[spike_times_idx])
+            for sp_idx in range(n_spikes):
+                t_sp = post_spike_times[spike_times_idx][sp_idx] + delay
+                for i in range(len(ref_post_trace)):
+                    t = (i / float(len(ref_post_trace - 1))) * sim_time
+                    if t >= t_sp:
+                        ref_post_trace[i] += np.exp(-(t - t_sp -delay) / tau_minus)
+
+            ax2.plot(np.linspace(0., sim_time, len(ref_post_trace)), ref_post_trace, label="Expected", color="cyan", alpha=.6)
+
+
+            # fn_nest_trace_values = "/tmp/trace_vals_0x7ff985894370.txt"
+            # print("Please enter fn_nest_trace_values now:")
+            # import pdb;pdb.set_trace()
+            # s = open(fn_nest_trace_values, "r")
+            # l = s.readlines()
+            # nest_spike_times = []
+            # nest_trace_values = []
+            # for line in l:
+            #     line_split = line.split()
+            #     nest_spike_times.append(float(line_split[0]))
+            #     nest_trace_values.append(float(line_split[1]))
+            # ax2.scatter(nest_spike_times, nest_trace_values, label="NEST", color="orange")
+
+
+            ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
+
+
+            ax2.set_xlabel("Time [ms]")
+            ax1.set_ylabel("Pre spikes")
+            ax3.set_ylabel("Post spikes")
+            ax2.set_ylabel("Trace")
+            ax2.legend()
+            for _ax in ax:
+                _ax.grid(which="major", axis="both")
+                _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
+                _ax.minorticks_on()
+                # _ax.grid(major=True, minor=True)
+                _ax.set_xlim(0., sim_time)
+            fig.savefig("/tmp/traces.png")
+
+            print("[py] w_post = " + str(w_post))
+            print("[py] w_post_ps = " + str(w_post_ps))
+            print()
+
+            wr_weights = nest.GetStatus(wr, "events")[0]["weights"]
+            print("[py] wr_weights = " + str(wr_weights))
+
+
+            assert w_post != w_pre, "Plain parrot weight did not change."
+            assert w_post_ps != w_pre_ps, "Precise parrot \
+                weight did not change."
+
+            post_weights['parrot'].append(w_post)
+            post_weights['parrot_ps'].append(w_post_ps)
+
+
+def suite():
+    suite1 = unittest.TestLoader().loadTestsFromTestCase(PostTraceTestCase)
+    return unittest.TestSuite([suite1])
+
+
+def run():
+    runner = unittest.TextTestRunner(verbosity=99)
+    runner.run(suite())
+
+
+if __name__ == "__main__":
+    #unittest.findTestCases(__main__).debug()
+    run()

From 2ca0a857a282c8044174977fb94a4d5df4d9e47e Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 9 Jan 2019 17:28:13 +0100
Subject: [PATCH 05/42] replace history-based calculation of postsynaptic trace
 with private member variable

---
 models/stdp_connection.h             |  8 +--
 nestkernel/archiving_node.cpp        | 63 +++++++++++++++---
 pynest/nest/tests/test_post_trace.py | 95 +++++++++++++---------------
 3 files changed, 101 insertions(+), 65 deletions(-)

diff --git a/models/stdp_connection.h b/models/stdp_connection.h
index 1ed8c5fd8a..8c2376ea0e 100644
--- a/models/stdp_connection.h
+++ b/models/stdp_connection.h
@@ -220,7 +220,7 @@ STDPConnection< targetidentifierT >::send( Event& e,
   const CommonSynapseProperties& )
 {
   // synapse STDP depressing/facilitation dynamics
-  double t_spike = e.get_stamp().get_ms();
+  const double t_spike = e.get_stamp().get_ms();
 
   // use accessor functions (inherited from Connection< >) to obtain delay and
   // target
@@ -255,11 +255,9 @@ STDPConnection< targetidentifierT >::send( Event& e,
     weight_ = facilitate_( weight_, Kplus_ * std::exp( minus_dt / tau_plus_ ) );
   }
 
-  // depression due to new pre-synaptic spike
   const double _K_value = target->get_K_value( t_spike - dendritic_delay );
-  std::cout << "In Synapse: got K_value = " << _K_value << std::endl;
-  weight_ =
-    depress_( weight_, _K_value );
+  std::cout << "In Synapse: t_spike = " << t_spike << ", dendritic_delay = " << dendritic_delay << ", got K_value = " << _K_value << std::endl;
+  weight_ = depress_( weight_, _K_value );
 
   e.set_receiver( *target );
   e.set_weight( weight_ );
diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 72f921c802..c96c0334b3 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -96,20 +96,36 @@ Archiving_Node::register_stdp_connection( double t_first_read )
 }
 
 double
-nest::Archiving_Node::get_K_value( double t )
+nest::Archiving_Node::get_K_value( double t ) //const
 {
-   std::cout << "* In Archiving_Node::get_K_value(t = " << t << ")" << std::endl; 
+
+#ifdef DO_HISTORY_SEARCH
   if ( history_.empty() )
   {
     _trace = Kminus_;
     std::cout << "\thistory is empty: K_value = " << Kminus_ << std::endl; 
     return Kminus_;
   }
+
+  {
+    std::cout << "\thistory list dump: " << Kminus_ << std::endl; 
+    int i = history_.size() - 1;
+    while ( i >= 0 )
+    {
+        std::cout << "\t\thistory_[ " << i << " ].t_ = " << history_[i].t_ << std::endl;
+        --i;
+    }
+  }
+
+  std::cout << "\tACTUAL t_last_spike_ = " << last_spike_ << std::endl;
+  
+  // N.B. iterating over the private member `history_` directly instead of calling get_history() avoids incrementing any access counters
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
-    if ( t >= history_[ i ].t_ )
-    // if (t - history_[i].t_ > kernel().connection_manager.get_stdp_eps())
+      std::cout << "\t\tcomparing " << t << " >= " << history_[i].t_ << ": " << ( t >= history_[ i ].t_ ) << std::endl;
+    //if ( t >= history_[ i ].t_ )
+    if (t - history_[i].t_ > -kernel().connection_manager.get_stdp_eps())
     {
       _trace = ( history_[ i ].Kminus_
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
@@ -121,6 +137,16 @@ nest::Archiving_Node::get_K_value( double t )
   assert(false); // fall-through case: means that the trace value is requested at a time before the earliest postsynaptic spike in the history buffer. Something is wrong!
   // _trace = 0.;
   return 0; // just here to silence compiler warnings
+#endif
+
+  if (last_spike_ < 0) {
+      // neuron has not spiked yet!
+      return 0.;
+  }
+  
+  // decay from t = last_spike_ to t = t
+     std::cout << "* In Archiving_Node::get_K_value(t = " << t << "): decay from t = last_spike_ = " << last_spike_ << " to t = t = " << t << " --> new value = " << Kminus_ * std::exp( ( last_spike_ - t ) * tau_minus_inv_ ) <<  "\n";
+  return Kminus_ * std::exp( ( last_spike_ - t ) * tau_minus_inv_ );
 }
 
 void
@@ -150,7 +176,7 @@ nest::Archiving_Node::get_K_values( double t,
     i--;
   }
 
-  // we only get here if t< time of all spikes in history)
+  // we only get here if t < time of all spikes in history
 
   // return 0.0 for both K values
   triplet_K_value = 0.0;
@@ -192,6 +218,8 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
   update_synaptic_elements( t_sp_ms );
   Ca_minus_ += beta_Ca_;
 
+  std::cout << "In Archiving_Node(" << std::hex<<this << ")::set_spiketime(" << t_sp_ms << ")\n";
+
   if ( n_incoming_ )
   {
     // prune all spikes from history which are no longer needed
@@ -200,6 +228,7 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
     {
       if ( history_.front().access_counter_ >= n_incoming_ )
       {
+        std::cout << "\tpop_front()\n";
         history_.pop_front();
       }
       else
@@ -207,9 +236,21 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
         break;
       }
     }
-    // update spiking history
-    Kminus_ =
-      Kminus_ * std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_inv_ ) + 1.0;
+ 
+    // decay from t = last_spike_ to t = t_sp_ms
+    if (last_spike_ >= 0.) {
+        Kminus_ *= std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_inv_ );
+    }
+        
+    // bump the trace due to incoming spike
+    Kminus_ += 1.;
+        
+        // update spiking history
+    std::cout << "\tupdating trace from t = " << last_spike_ << " to t = " << t_sp_ms << "; new value = " << Kminus_<< "\n";
+    //Kminus_ =
+    //  Kminus_ * std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_inv_ ) + 1.0;
+      
+   
     triplet_Kminus_ = triplet_Kminus_
         * std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_triplet_inv_ )
       + 1.0;
@@ -234,8 +275,10 @@ nest::Archiving_Node::get_status( DictionaryDatum& d ) const
   def< double >( d, names::tau_Ca, tau_Ca_ );
   def< double >( d, names::beta_Ca, beta_Ca_ );
   def< double >( d, names::tau_minus_triplet, tau_minus_triplet_ );
-  def< double >( d, names::post_trace, _trace );
-  std::cout << "In Archiving_Node::get_status(): trace = " << _trace << std::endl;
+  //const double t_ms = kernel().simulation_manager.get_time().get_ms();
+  //const double K_value = get_K_value(t_ms);
+  def< double >( d, names::post_trace, Kminus_ );
+  std::cout << "In Archiving_Node::get_status(): trace = " << Kminus_ << std::endl;
 #ifdef DEBUG_ARCHIVER
   def< int >( d, names::archiver_length, history_.size() );
 #endif
diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index f35fd42da1..82d3cab33f 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -26,8 +26,7 @@
 import math
 import numpy as np
 import matplotlib.pyplot as plt
-
-
+import matplotlib.ticker as plticker
 
 
 @nest.check_stack
@@ -37,8 +36,9 @@ def test_post_trace(self):
         """
         """
 
-        resolution = .5     # [ms]
+        show_all_nest_trace_samples = True
 
+        resolution = .1     # [ms]
         delay = 1.  # [ms]
 
         pre_spike_times1 = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]      # [ms]
@@ -121,26 +121,20 @@ def test_post_trace(self):
             # not repeated postsynaptically.
             nest.Connect(
                 pre_parrot, post_parrot,
-                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
+                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
             nest.Connect(
                 pre_parrot_ps, post_parrot_ps,
-                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1})
+                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
 
-            # get STDP synapse and weight before protocol
+            # get STDP synapse
             syn = nest.GetConnections(source=pre_parrot,
                                     synapse_model="stdp_synapse_rec")
-            w_pre = nest.GetStatus(syn)[0]['weight']
             syn_ps = nest.GetConnections(source=pre_parrot_ps,
                                         synapse_model="stdp_synapse_rec")
-            w_pre_ps = nest.GetStatus(syn)[0]['weight']
 
-            print()
-            print("[py] w_pre = " + str(w_pre))
-            print("[py] w_pre_ps = " + str(w_pre_ps))
-            print()
 
             sim_time = np.amax(np.concatenate((pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx]))) + 5 * delay
-            n_steps = int(np.ceil(sim_time / resolution)) + 1
+            n_steps = int(np.ceil(sim_time / delay)) + 1
             trace_nest = []
             trace_nest_t = []
             t = nest.GetStatus([0], "time")[0]
@@ -148,44 +142,57 @@ def test_post_trace(self):
             post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
             trace_nest.append(post_trace_value)
             for step in range(n_steps):
-                nest.Simulate(resolution)
+                nest.Simulate(delay)
                 t = nest.GetStatus([0], "time")[0]
-                # if True:
-                if np.any(np.abs(t - np.array(pre_spike_times[spike_times_idx]) - delay) < resolution/2.):
+                if show_all_nest_trace_samples or np.any(np.abs(t - np.array(pre_spike_times[spike_times_idx]) - delay) < resolution/2.):
                     trace_nest_t.append(t)
                     post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
                     trace_nest.append(post_trace_value)
-                    print("In Python: Getting trace = " + str(post_trace_value) + " at time t = " + str(t))
+                    print("In Python: trace = " + str(post_trace_value) + " at time t = " + str(t))
 
-            # get weight post protocol
-            w_post = nest.GetStatus(syn)[0]['weight']
-            w_post_ps = nest.GetStatus(syn_ps)[0]['weight']
 
+            #
+            #   compute Python known-good reference of postsynaptic trace
+            #
 
             tau_minus = nest.GetStatus(post_parrot)[0]['tau_minus']
-            # post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
-            # print("kljpjijiiiiiiiiiiiiiiiiiiiiiiiiiiiiii " + str(post_trace_value))
+            n_timepoints = 10000
+            ref_post_trace = np.zeros(n_timepoints)
+            n_spikes = len(post_spike_times[spike_times_idx])
+            for sp_idx in range(n_spikes):
+                t_sp = post_spike_times[spike_times_idx][sp_idx] + delay
+                for i in range(n_timepoints):
+                    t = (i / float(n_timepoints - 1)) * sim_time
+                    if t >= t_sp:
+                        ref_post_trace[i] += np.exp(-(t - t_sp) / tau_minus)
+            
+            n_spikes = len(pre_spike_times[spike_times_idx])
+            for sp_idx in range(n_spikes):
+                t_sp = pre_spike_times[spike_times_idx][sp_idx] + delay
+                i = int(np.round(t_sp / sim_time * float(len(ref_post_trace - 1))))
+                print("* At t_sp = " + str(t_sp) + ", post_trace should be " + str(ref_post_trace[i]))
+                #import pdb;pdb.set_trace()
+
+
+            #
+            #   plotting
+            #
 
             fig, ax = plt.subplots(nrows=3)
             ax1, ax3, ax2 = ax
+            ax1.set_ylim([0., 1.])
+            ax3.set_ylim([0., 1.])
+            ax2.set_ylim([0., np.amax(ref_post_trace)])
 
             n_spikes = len(pre_spike_times[spike_times_idx])
             for i in range(n_spikes):
-                ax1.plot(2 * [pre_spike_times[spike_times_idx][i] + delay], [0, 1], linewidth=2, color="blue", alpha=.4)
+                for _ax in [ax1, ax2]:
+                    _ax.plot(2 * [pre_spike_times[spike_times_idx][i] + delay], _ax.get_ylim(), linewidth=2, color="blue", alpha=.4)
 
             n_spikes = len(post_spike_times[spike_times_idx])
             for i in range(n_spikes):
                     ax3.plot(2 * [post_spike_times[spike_times_idx][i] + delay], [0, 1], linewidth=2, color="red", alpha=.4)
 
-            ref_post_trace = np.zeros(1000)
-            n_spikes = len(post_spike_times[spike_times_idx])
-            for sp_idx in range(n_spikes):
-                t_sp = post_spike_times[spike_times_idx][sp_idx] + delay
-                for i in range(len(ref_post_trace)):
-                    t = (i / float(len(ref_post_trace - 1))) * sim_time
-                    if t >= t_sp:
-                        ref_post_trace[i] += np.exp(-(t - t_sp -delay) / tau_minus)
-
             ax2.plot(np.linspace(0., sim_time, len(ref_post_trace)), ref_post_trace, label="Expected", color="cyan", alpha=.6)
 
 
@@ -205,34 +212,22 @@ def test_post_trace(self):
 
             ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
 
-
             ax2.set_xlabel("Time [ms]")
             ax1.set_ylabel("Pre spikes")
             ax3.set_ylabel("Post spikes")
             ax2.set_ylabel("Trace")
             ax2.legend()
+                        
+            
             for _ax in ax:
+                _ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10*delay))
+                _ax.xaxis.set_minor_locator(plticker.MultipleLocator(base=delay))
                 _ax.grid(which="major", axis="both")
                 _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
-                _ax.minorticks_on()
-                # _ax.grid(major=True, minor=True)
+                #_ax.minorticks_on()
                 _ax.set_xlim(0., sim_time)
-            fig.savefig("/tmp/traces.png")
-
-            print("[py] w_post = " + str(w_post))
-            print("[py] w_post_ps = " + str(w_post_ps))
-            print()
-
-            wr_weights = nest.GetStatus(wr, "events")[0]["weights"]
-            print("[py] wr_weights = " + str(wr_weights))
-
-
-            assert w_post != w_pre, "Plain parrot weight did not change."
-            assert w_post_ps != w_pre_ps, "Precise parrot \
-                weight did not change."
 
-            post_weights['parrot'].append(w_post)
-            post_weights['parrot_ps'].append(w_post_ps)
+            fig.savefig("/tmp/traces.png", dpi=300.)
 
 
 def suite():

From fc879a2ab62752d0c4c6a8b5871c7fafc5b01974 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 10 Jan 2019 17:20:48 +0100
Subject: [PATCH 06/42] update unit test to account for multiple spikes within
 one (dmin) timestep

---
 pynest/nest/tests/test_post_trace.py | 47 ++++++++++++++++++++++------
 1 file changed, 37 insertions(+), 10 deletions(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 82d3cab33f..6a661e198e 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -13,7 +13,7 @@
 #
 # NEST is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See thed
 # GNU General Public License for more details.
 #
 # You should have received a copy of the GNU General Public License
@@ -34,12 +34,26 @@ class PostTraceTestCase(unittest.TestCase):
 
     def test_post_trace(self):
         """
+        construct a network of the form:
+        
+                               static_synapse                   stdp_synapse                    static_synapse
+            [ pre_spike_gen ] ----(delay)----o [ pre_parrot ] ----(delay)----o [ post_parrot ] o----(delay)---- [ post_spike_gen ]
+
+
+        The spike times of the spike generators are defined in `pre_spike_times` and `post_spike_times`. From the perspective of the stdp_synapse, spikes arrive with the following delays (with respect to the values in these lists):
+        
+        - for the presynaptic neuron: one synaptic delay in the leftmost static synapse
+        - for the postsynaptic neuron: one synaptic delay in the rightmost static synapse
+        - from the postsynaptic neuron: one dendritic delay between the post_parrot node and the synapse itself---see the C++ variable `dendritic_delay`).
+                
         """
 
         show_all_nest_trace_samples = True
 
         resolution = .1     # [ms]
-        delay = 1.  # [ms]
+        delay = 5.  # [ms]
+        
+        dendritic_delay = delay
 
         pre_spike_times1 = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]      # [ms]
         post_spike_times1 = [3., 7., 8., 10., 12., 13., 14., 16., 17., 18., 19., 20., 21., 22.]      # [ms]
@@ -132,7 +146,6 @@ def test_post_trace(self):
             syn_ps = nest.GetConnections(source=pre_parrot_ps,
                                         synapse_model="stdp_synapse_rec")
 
-
             sim_time = np.amax(np.concatenate((pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx]))) + 5 * delay
             n_steps = int(np.ceil(sim_time / delay)) + 1
             trace_nest = []
@@ -160,7 +173,7 @@ def test_post_trace(self):
             ref_post_trace = np.zeros(n_timepoints)
             n_spikes = len(post_spike_times[spike_times_idx])
             for sp_idx in range(n_spikes):
-                t_sp = post_spike_times[spike_times_idx][sp_idx] + delay
+                t_sp = post_spike_times[spike_times_idx][sp_idx] + delay + dendritic_delay
                 for i in range(n_timepoints):
                     t = (i / float(n_timepoints - 1)) * sim_time
                     if t >= t_sp:
@@ -169,9 +182,9 @@ def test_post_trace(self):
             n_spikes = len(pre_spike_times[spike_times_idx])
             for sp_idx in range(n_spikes):
                 t_sp = pre_spike_times[spike_times_idx][sp_idx] + delay
-                i = int(np.round(t_sp / sim_time * float(len(ref_post_trace - 1))))
+                i = int(np.round(t_sp / sim_time * float(len(ref_post_trace) - 1)))
                 print("* At t_sp = " + str(t_sp) + ", post_trace should be " + str(ref_post_trace[i]))
-                #import pdb;pdb.set_trace()
+                #import pdb;pdb.set_trace()`
 
 
             #
@@ -186,12 +199,11 @@ def test_post_trace(self):
 
             n_spikes = len(pre_spike_times[spike_times_idx])
             for i in range(n_spikes):
-                for _ax in [ax1, ax2]:
-                    _ax.plot(2 * [pre_spike_times[spike_times_idx][i] + delay], _ax.get_ylim(), linewidth=2, color="blue", alpha=.4)
+                ax1.plot(2 * [pre_spike_times[spike_times_idx][i] + delay], ax1.get_ylim(), linewidth=2, color="blue", alpha=.4)
 
             n_spikes = len(post_spike_times[spike_times_idx])
             for i in range(n_spikes):
-                    ax3.plot(2 * [post_spike_times[spike_times_idx][i] + delay], [0, 1], linewidth=2, color="red", alpha=.4)
+                ax3.plot(2 * [post_spike_times[spike_times_idx][i] + delay + dendritic_delay], [0, 1], linewidth=2, color="red", alpha=.4)
 
             ax2.plot(np.linspace(0., sim_time, len(ref_post_trace)), ref_post_trace, label="Expected", color="cyan", alpha=.6)
 
@@ -211,11 +223,24 @@ def test_post_trace(self):
 
 
             ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
+            n_points = len(trace_nest_t)
+            for i in range(n_points):
+                t = trace_nest_t[i]
+                print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
+                for i_search, t_search in enumerate(reversed(np.array(pre_spike_times[spike_times_idx]) + delay)):
+                    print("\t* Testing " + str(t_search) + "...")
+                    if t_search <= t:
+                        _trace_at_t_search = ref_post_trace[int(np.round(t_search / sim_time * float(len(ref_post_trace) - 1)))]
+                        if np.any((t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay))**2 < resolution/2.):
+                            _trace_at_t_search += 1.
+                        ax2.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="#FFFFFF7F")
+                        ax2.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
+                        break
 
             ax2.set_xlabel("Time [ms]")
             ax1.set_ylabel("Pre spikes")
             ax3.set_ylabel("Post spikes")
-            ax2.set_ylabel("Trace")
+            ax2.set_ylabel("Synaptic trace")
             ax2.legend()
                         
             
@@ -227,6 +252,8 @@ def test_post_trace(self):
                 #_ax.minorticks_on()
                 _ax.set_xlim(0., sim_time)
 
+            fig.suptitle("Postsynaptic trace testbench. Spike times are\nshown from the perspective of the STDP synapse.")
+
             fig.savefig("/tmp/traces.png", dpi=300.)
 
 

From 15825ec86f4d08b860ff16187c7ffb3be1e4fa70 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 10 Jan 2019 18:32:08 +0100
Subject: [PATCH 07/42] update postsynaptic trace unit test to account for
 multiple spikes within one (dmin) timestep

---
 pynest/nest/tests/test_post_trace.py | 58 ++++++++++++++++++++++------
 1 file changed, 46 insertions(+), 12 deletions(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 6a661e198e..605d75e895 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -25,6 +25,8 @@
 import unittest
 import math
 import numpy as np
+import scipy as sp
+import scipy.stats
 import matplotlib.pyplot as plt
 import matplotlib.ticker as plticker
 
@@ -58,9 +60,6 @@ def test_post_trace(self):
         pre_spike_times1 = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]      # [ms]
         post_spike_times1 = [3., 7., 8., 10., 12., 13., 14., 16., 17., 18., 19., 20., 21., 22.]      # [ms]
         
-        pre_spike_times = [pre_spike_times1]
-        post_spike_times = [post_spike_times1]
-
         # pre_spike_times = [10., 11., 12., 13., 14., 15., 25., 35., 45., 50., 51., 52., 70.]      # [ms]
         # post_spike_times = [10., 11., 12., 13., 30., 40., 50., 51., 52., 53., 54.]      # [ms]
 
@@ -73,14 +72,33 @@ def test_post_trace(self):
         # pre_spike_times = [   4.,   6.  , 6.]    # [ms]
         # post_spike_times = [  2.,   6. ]      # [ms]
 
-        # pre_spike_times = 1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))      # [ms]
-        # post_spike_times =  np.sort(np.unique(1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))))     # [ms]
+        #pre_spike_times1 = np.sort(np.unique(1 + np.round(100 * np.abs(np.random.randn(100)))))      # [ms]
+        #post_spike_times1 = np.sort(np.unique(1 + np.round(100 * np.sort(np.abs(np.random.randn(100))))))     # [ms]
 
-        for spike_times_idx in range(len(pre_spike_times)):
+        t_sp_min = 1.
+        t_sp_max = 50
+        n_spikes = int(t_sp_max)
+        pre_spike_times1 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
+        post_spike_times1 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
+        
+        #pre_spike_times1 = [2.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 11.0, 12.0, 13.0, 16.0, 17.0, 18.0, 20.0, 21.0, 22.0, 24.0, 26.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 46.0, 48.0, 50.0]
+        #post_spike_times1 = [2.0, 4.0, 5.0, 6.0, 8.0, 13.0, 14.0, 17.0, 18.0, 19.0, 21.0, 22.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 46.0, 48.0, 49.0, 50.0]
+
+        #pre_spike_times1 = np.array([2.0, 7.0, 13.0, 18, 23, 28, 33, 37])
+        #post_spike_times1 = np.array([2.0, 7.0, 13.0, 18, 23, 28, 33, 37])
 
-            print("Pre spike times: " + str(pre_spike_times[spike_times_idx]))
-            print("Post spike times: " + str(post_spike_times[spike_times_idx]))
 
+        pre_spike_times = [pre_spike_times1]
+        post_spike_times = [post_spike_times1]
+
+        pre_spike_times = [np.array(a) for a in pre_spike_times]
+        post_spike_times = [np.array(a) for a in post_spike_times]
+
+        for spike_times_idx in range(len(pre_spike_times)):
+
+            print("Pre spike times: [" + ", ".join([str(t) for t in pre_spike_times[spike_times_idx]]) + "]")
+            print("Post spike times: [" + ", ".join([str(t) for t in post_spike_times[spike_times_idx]]) + "]")
+        
             nest.set_verbosity("M_WARNING")
 
             post_weights = {'parrot': [], 'parrot_ps': []}
@@ -176,7 +194,7 @@ def test_post_trace(self):
                 t_sp = post_spike_times[spike_times_idx][sp_idx] + delay + dendritic_delay
                 for i in range(n_timepoints):
                     t = (i / float(n_timepoints - 1)) * sim_time
-                    if t >= t_sp:
+                    if t > t_sp + 1E-3:
                         ref_post_trace[i] += np.exp(-(t - t_sp) / tau_minus)
             
             n_spikes = len(pre_spike_times[spike_times_idx])
@@ -231,8 +249,23 @@ def test_post_trace(self):
                     print("\t* Testing " + str(t_search) + "...")
                     if t_search <= t:
                         _trace_at_t_search = ref_post_trace[int(np.round(t_search / sim_time * float(len(ref_post_trace) - 1)))]
-                        if np.any((t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay))**2 < resolution/2.):
-                            _trace_at_t_search += 1.
+                        #if (t_search - trace_nest_t[i])**2 > resolution/2. \
+                        #idx = np.argmin((t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay))**2)
+                        #t_found = (t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay)
+                        traces_match = (_trace_at_t_search - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
+                        if not traces_match:
+                            post_spike_occurred_at_t_search = np.any((t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay))**2 < resolution/2.)
+                            if post_spike_occurred_at_t_search:
+                                traces_match = (_trace_at_t_search + 1 - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
+                                if traces_match:
+                                    _trace_at_t_search += 1.
+                                    
+                                
+                            """_pre_spike_times = np.array(pre_spike_times[spike_times_idx])
+                            pre_spike_occurred_between_t_search_and_t = np.any(_pre_spike_times[np.logical_and(_pre_spike_times > t_search, _pre_spike_times < t)])
+                            pre_spike_occurred_between_t_search_and_t = False
+                            if not pre_spike_occurred_between_t_search_and_t:
+                                _trace_at_t_search += 1."""
                         ax2.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="#FFFFFF7F")
                         ax2.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
                         break
@@ -269,4 +302,5 @@ def run():
 
 if __name__ == "__main__":
     #unittest.findTestCases(__main__).debug()
-    run()
+    #run()
+    PostTraceTestCase().test_post_trace()

From 38010cb590eca4e865a2e1ffba2b98efded20910 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 10 Jan 2019 18:35:22 +0100
Subject: [PATCH 08/42] added debugging code to archiving node

---
 nestkernel/archiving_node.cpp | 103 ++++++++++++++--------------------
 nestkernel/archiving_node.h   |  16 ++----
 2 files changed, 46 insertions(+), 73 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index c96c0334b3..cc0ecfe3b7 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -53,6 +53,7 @@ nest::Archiving_Node::Archiving_Node()
   , Ca_minus_( 0.0 )
   , tau_Ca_( 10000.0 )
   , beta_Ca_( 0.001 )
+  , trace_(0.)
   , synaptic_elements_map_()
 {
 }
@@ -71,6 +72,7 @@ nest::Archiving_Node::Archiving_Node( const Archiving_Node& n )
   , Ca_minus_( n.Ca_minus_ )
   , tau_Ca_( n.tau_Ca_ )
   , beta_Ca_( n.beta_Ca_ )
+  , trace_(n.trace_)
   , synaptic_elements_map_( n.synaptic_elements_map_ )
 {
 }
@@ -96,57 +98,49 @@ Archiving_Node::register_stdp_connection( double t_first_read )
 }
 
 double
-nest::Archiving_Node::get_K_value( double t ) //const
+nest::Archiving_Node::get_K_value( double t )
 {
-
-#ifdef DO_HISTORY_SEARCH
+  std::cout << "* In Archiving_Node::get_K_value(t = " << t << ")\n";
   if ( history_.empty() )
   {
-    _trace = Kminus_;
-    std::cout << "\thistory is empty: K_value = " << Kminus_ << std::endl; 
-    return Kminus_;
+    trace_ = 0.;
+    std::cout << "\t--> trace = " << trace_ << std::endl;
+    return trace_;
   }
-
+  
+  
   {
-    std::cout << "\thistory list dump: " << Kminus_ << std::endl; 
-    int i = history_.size() - 1;
-    while ( i >= 0 )
-    {
-        std::cout << "\t\thistory_[ " << i << " ].t_ = " << history_[i].t_ << std::endl;
-        --i;
-    }
+  std::cout << "\tCurrent history list:\n";
+  int i = 0;
+  while ( i < history_.size() )
+  {
+    std::cout << "\t\thistory["<<i<<"] t = " << history_[ i ].t_ << std::endl;
+    ++i;
   }
 
-  std::cout << "\tACTUAL t_last_spike_ = " << last_spike_ << std::endl;
   
-  // N.B. iterating over the private member `history_` directly instead of calling get_history() avoids incrementing any access counters
+  }
+  
+  
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
-      std::cout << "\t\tcomparing " << t << " >= " << history_[i].t_ << ": " << ( t >= history_[ i ].t_ ) << std::endl;
-    //if ( t >= history_[ i ].t_ )
-    if (t - history_[i].t_ > -kernel().connection_manager.get_stdp_eps())
+    if ( t >= history_[ i ].t_)
     {
-      _trace = ( history_[ i ].Kminus_
+      trace_ = ( history_[ i ].Kminus_
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
-      std::cout << "\tK_value = " << _trace << std::endl;
-      return _trace;
+    std::cout << "\t   updating trace from t = " << history_[i].t_ << " to t = " << t << std::endl;
+    std::cout << "\t   --> trace = " << trace_ << std::endl;
+      return trace_;
     }
-    --i;
-  }
-  assert(false); // fall-through case: means that the trace value is requested at a time before the earliest postsynaptic spike in the history buffer. Something is wrong!
-  // _trace = 0.;
-  return 0; // just here to silence compiler warnings
-#endif
-
-  if (last_spike_ < 0) {
-      // neuron has not spiked yet!
-      return 0.;
+    i--;
   }
   
-  // decay from t = last_spike_ to t = t
-     std::cout << "* In Archiving_Node::get_K_value(t = " << t << "): decay from t = last_spike_ = " << last_spike_ << " to t = t = " << t << " --> new value = " << Kminus_ * std::exp( ( last_spike_ - t ) * tau_minus_inv_ ) <<  "\n";
-  return Kminus_ * std::exp( ( last_spike_ - t ) * tau_minus_inv_ );
+  
+  
+  trace_ = 0.;
+  std::cout << "\t--> fall-through: trace = " << trace_ << std::endl;
+  return trace_;
 }
 
 void
@@ -176,7 +170,7 @@ nest::Archiving_Node::get_K_values( double t,
     i--;
   }
 
-  // we only get here if t < time of all spikes in history
+  // we only get here if t< time of all spikes in history)
 
   // return 0.0 for both K values
   triplet_K_value = 0.0;
@@ -196,16 +190,16 @@ nest::Archiving_Node::get_history( double t1,
     return;
   }
   std::deque< histentry >::reverse_iterator runner = history_.rbegin();
-  const double t2_lim = t2 + kernel().connection_manager.get_stdp_eps();
-  const double t1_lim = t1 + kernel().connection_manager.get_stdp_eps();
+  const double t2_lim = t2;// + kernel().connection_manager.get_stdp_eps();
+  const double t1_lim = t1;// + kernel().connection_manager.get_stdp_eps();
   while ( runner != history_.rend() and runner->t_ >= t2_lim )
   {
     ++runner;
   }
   *finish = runner.base();
-  while ( runner != history_.rend() and runner->t_ >= t1_lim )
+  while ( runner != history_.rend() and runner->t_ > t1_lim )
   {
-    runner->access_counter_++;
+    //runner->access_counter_++;
     ++runner;
   }
   *start = runner.base();
@@ -214,12 +208,12 @@ nest::Archiving_Node::get_history( double t1,
 void
 nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
 {
+    std::cout << "* In Archiving_Node::set_spiketime(t_sp = " << t_sp << ", offset = " << offset << ")" << std::endl;
+
   const double t_sp_ms = t_sp.get_ms() - offset;
   update_synaptic_elements( t_sp_ms );
   Ca_minus_ += beta_Ca_;
 
-  std::cout << "In Archiving_Node(" << std::hex<<this << ")::set_spiketime(" << t_sp_ms << ")\n";
-
   if ( n_incoming_ )
   {
     // prune all spikes from history which are no longer needed
@@ -228,7 +222,6 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
     {
       if ( history_.front().access_counter_ >= n_incoming_ )
       {
-        std::cout << "\tpop_front()\n";
         history_.pop_front();
       }
       else
@@ -236,21 +229,9 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
         break;
       }
     }
- 
-    // decay from t = last_spike_ to t = t_sp_ms
-    if (last_spike_ >= 0.) {
-        Kminus_ *= std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_inv_ );
-    }
-        
-    // bump the trace due to incoming spike
-    Kminus_ += 1.;
-        
-        // update spiking history
-    std::cout << "\tupdating trace from t = " << last_spike_ << " to t = " << t_sp_ms << "; new value = " << Kminus_<< "\n";
-    //Kminus_ =
-    //  Kminus_ * std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_inv_ ) + 1.0;
-      
-   
+    // update spiking history
+    Kminus_ =
+      Kminus_ * std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_inv_ ) + 1.0;
     triplet_Kminus_ = triplet_Kminus_
         * std::exp( ( last_spike_ - t_sp_ms ) * tau_minus_triplet_inv_ )
       + 1.0;
@@ -275,10 +256,8 @@ nest::Archiving_Node::get_status( DictionaryDatum& d ) const
   def< double >( d, names::tau_Ca, tau_Ca_ );
   def< double >( d, names::beta_Ca, beta_Ca_ );
   def< double >( d, names::tau_minus_triplet, tau_minus_triplet_ );
-  //const double t_ms = kernel().simulation_manager.get_time().get_ms();
-  //const double K_value = get_K_value(t_ms);
-  def< double >( d, names::post_trace, Kminus_ );
-  std::cout << "In Archiving_Node::get_status(): trace = " << Kminus_ << std::endl;
+  def< double >( d, names::post_trace, trace_ );
+     std::cout << "In Archiving_Node::get_status(): trace = " << trace_ << std::endl;
 #ifdef DEBUG_ARCHIVER
   def< int >( d, names::archiver_length, history_.size() );
 #endif
diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index a5cc198f9e..e6d14a52b4 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -44,12 +44,6 @@
 // Includes from sli:
 #include "dictdatum.h"
 
-#include <iostream>
-#include <sstream>
-#include <fstream>
-
-#include <cassert>
-
 #define DEBUG_ARCHIVER 1
 
 namespace nest
@@ -94,10 +88,8 @@ class Archiving_Node : public Node
 
   /**
    * \fn int get_synaptic_elements_vacant(Name n)
-   * Get the number of synaptic elements of type n which are available
+   * get the number of synaptic elements of type n which are available
    * for new synapse creation
-   * Returns a negative number to indicate that synaptic elements
-   * must be deleted during the next update
    */
   int get_synaptic_elements_vacant( Name n ) const;
 
@@ -218,8 +210,6 @@ class Archiving_Node : public Node
   double tau_minus_;
   double tau_minus_inv_;
 
-  double _trace;
-
   // time constant for triplet low pass filtering of "post" spike train
   double tau_minus_triplet_;
   double tau_minus_triplet_inv_;
@@ -229,6 +219,10 @@ class Archiving_Node : public Node
   // spiking history needed by stdp synapses
   std::deque< histentry > history_;
 
+  
+  double trace_;    // XXX: DEBUGGING ONLY: REMOVE
+  
+  
   /*
    * Structural plasticity
    */

From abea27a567b62fe98722ecd2cd8480c3b3239356 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 10 Jan 2019 19:27:06 +0100
Subject: [PATCH 09/42] update postsynaptic trace unit test to account for
 multiple spikes within one (dmin) timestep

---
 pynest/nest/tests/test_post_trace.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 605d75e895..9498ee8e81 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -88,7 +88,7 @@ def test_post_trace(self):
         #post_spike_times1 = np.array([2.0, 7.0, 13.0, 18, 23, 28, 33, 37])
 
 
-        pre_spike_times = [pre_spike_times1]
+        pre_spike_times = [np.concatenate((pre_spike_times1, np.array([70.])))]
         post_spike_times = [post_spike_times1]
 
         pre_spike_times = [np.array(a) for a in pre_spike_times]
@@ -165,7 +165,8 @@ def test_post_trace(self):
                                         synapse_model="stdp_synapse_rec")
 
             sim_time = np.amax(np.concatenate((pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx]))) + 5 * delay
-            n_steps = int(np.ceil(sim_time / delay)) + 1
+            print("* Simulating for " + str(sim_time) + " ms...")
+            n_steps = int(np.ceil(sim_time / delay))
             trace_nest = []
             trace_nest_t = []
             t = nest.GetStatus([0], "time")[0]
@@ -241,12 +242,14 @@ def test_post_trace(self):
 
 
             ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
+            print("trace_nest_t = " + str(trace_nest_t) + ", trace_nest = " + str(trace_nest))
+            
             n_points = len(trace_nest_t)
             for i in range(n_points):
                 t = trace_nest_t[i]
-                print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
+                #print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
                 for i_search, t_search in enumerate(reversed(np.array(pre_spike_times[spike_times_idx]) + delay)):
-                    print("\t* Testing " + str(t_search) + "...")
+                    #print("\t* Testing " + str(t_search) + "...")
                     if t_search <= t:
                         _trace_at_t_search = ref_post_trace[int(np.round(t_search / sim_time * float(len(ref_post_trace) - 1)))]
                         #if (t_search - trace_nest_t[i])**2 > resolution/2. \
@@ -266,7 +269,7 @@ def test_post_trace(self):
                             pre_spike_occurred_between_t_search_and_t = False
                             if not pre_spike_occurred_between_t_search_and_t:
                                 _trace_at_t_search += 1."""
-                        ax2.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="#FFFFFF7F")
+                        ax2.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="none")#"#FFFFFF7F")
                         ax2.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
                         break
 

From aea2949446c313779f8f7fce30e09e51273edafe Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 21 Feb 2019 13:40:29 +0100
Subject: [PATCH 10/42] add new, stricter condition for removing spikes from
 the postsynaptic buffer

---
 nestkernel/archiving_node.cpp | 22 ++++++++++++----------
 nestkernel/archiving_node.h   |  3 +++
 2 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index cc0ecfe3b7..49e4bf772c 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -48,6 +48,7 @@ nest::Archiving_Node::Archiving_Node()
   , tau_minus_inv_( 1. / tau_minus_ )
   , tau_minus_triplet_( 110.0 )
   , tau_minus_triplet_inv_( 1. / tau_minus_triplet_ )
+  , max_delay_( -1.0 )
   , last_spike_( -1.0 )
   , Ca_t_( 0.0 )
   , Ca_minus_( 0.0 )
@@ -67,6 +68,7 @@ nest::Archiving_Node::Archiving_Node( const Archiving_Node& n )
   , tau_minus_inv_( n.tau_minus_inv_ )
   , tau_minus_triplet_( n.tau_minus_triplet_ )
   , tau_minus_triplet_inv_( n.tau_minus_triplet_inv_ )
+  , max_delay_( n.max_delay_ )
   , last_spike_( n.last_spike_ )
   , Ca_t_( n.Ca_t_ )
   , Ca_minus_( n.Ca_minus_ )
@@ -78,7 +80,7 @@ nest::Archiving_Node::Archiving_Node( const Archiving_Node& n )
 }
 
 void
-Archiving_Node::register_stdp_connection( double t_first_read )
+Archiving_Node::register_stdp_connection( double t_first_read, double delay )
 {
   // Mark all entries in the deque, which we will not read in future as read by
   // this input input, so that we savely increment the incoming number of
@@ -95,6 +97,8 @@ Archiving_Node::register_stdp_connection( double t_first_read )
   }
 
   n_incoming_++;
+
+  max_delay_ = std::max(delay, max_delay_);
 }
 
 double
@@ -107,8 +111,7 @@ nest::Archiving_Node::get_K_value( double t )
     std::cout << "\t--> trace = " << trace_ << std::endl;
     return trace_;
   }
-  
-  
+
   {
   std::cout << "\tCurrent history list:\n";
   int i = 0;
@@ -120,8 +123,7 @@ nest::Archiving_Node::get_K_value( double t )
 
   
   }
-  
-  
+
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
@@ -135,9 +137,7 @@ nest::Archiving_Node::get_K_value( double t )
     }
     i--;
   }
-  
-  
-  
+
   trace_ = 0.;
   std::cout << "\t--> fall-through: trace = " << trace_ << std::endl;
   return trace_;
@@ -199,7 +199,7 @@ nest::Archiving_Node::get_history( double t1,
   *finish = runner.base();
   while ( runner != history_.rend() and runner->t_ > t1_lim )
   {
-    //runner->access_counter_++;
+    runner->access_counter_++;
     ++runner;
   }
   *start = runner.base();
@@ -220,7 +220,9 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
     // except the penultimate one. we might still need it.
     while ( history_.size() > 1 )
     {
-      if ( history_.front().access_counter_ >= n_incoming_ )
+      const double next_t_sp = history_[1].t_;
+      if ( history_.front().access_counter_ >= n_incoming_
+           && abs(next_t_sp - t_sp_ms) > 2. * max_delay_ )
       {
         history_.pop_front();
       }
diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index e6d14a52b4..ddfb47ac5d 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -32,6 +32,7 @@
 #define ARCHIVING_NODE_H
 
 // C++ includes:
+#include <algorithm>
 #include <deque>
 
 // Includes from nestkernel:
@@ -214,6 +215,8 @@ class Archiving_Node : public Node
   double tau_minus_triplet_;
   double tau_minus_triplet_inv_;
 
+  double max_delay_;
+
   double last_spike_;
 
   // spiking history needed by stdp synapses

From cf2c991aa9e218087a9768340e6374fee39f9bf5 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 21 Feb 2019 13:56:54 +0100
Subject: [PATCH 11/42] added new parameter to register_stdp_connection() to
 pass synaptic delay

---
 models/stdp_connection.h              | 2 +-
 models/stdp_connection_facetshw_hom.h | 2 +-
 models/stdp_connection_hom.h          | 2 +-
 models/stdp_dopa_connection.h         | 2 +-
 models/stdp_pl_connection_hom.h       | 2 +-
 models/stdp_triplet_connection.h      | 2 +-
 models/vogels_sprekeler_connection.h  | 2 +-
 nestkernel/archiving_node.h           | 2 +-
 nestkernel/node.cpp                   | 2 +-
 9 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/models/stdp_connection.h b/models/stdp_connection.h
index 8c2376ea0e..a6f111a581 100644
--- a/models/stdp_connection.h
+++ b/models/stdp_connection.h
@@ -167,7 +167,7 @@ class STDPConnection : public Connection< targetidentifierT >
 
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
   void
diff --git a/models/stdp_connection_facetshw_hom.h b/models/stdp_connection_facetshw_hom.h
index c8eee868e8..829cf7534b 100644
--- a/models/stdp_connection_facetshw_hom.h
+++ b/models/stdp_connection_facetshw_hom.h
@@ -292,7 +292,7 @@ class STDPFACETSHWConnectionHom : public Connection< targetidentifierT >
 
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
   void
diff --git a/models/stdp_connection_hom.h b/models/stdp_connection_hom.h
index 3341e4fad7..41f5844353 100644
--- a/models/stdp_connection_hom.h
+++ b/models/stdp_connection_hom.h
@@ -219,7 +219,7 @@ class STDPConnectionHom : public Connection< targetidentifierT >
     ConnTestDummyNode dummy_target;
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
 private:
diff --git a/models/stdp_dopa_connection.h b/models/stdp_dopa_connection.h
index 87374d9d48..3d12766c3a 100644
--- a/models/stdp_dopa_connection.h
+++ b/models/stdp_dopa_connection.h
@@ -262,7 +262,7 @@ class STDPDopaConnection : public Connection< targetidentifierT >
     ConnTestDummyNode dummy_target;
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
   void
diff --git a/models/stdp_pl_connection_hom.h b/models/stdp_pl_connection_hom.h
index c141d23b7d..654f38999e 100644
--- a/models/stdp_pl_connection_hom.h
+++ b/models/stdp_pl_connection_hom.h
@@ -188,7 +188,7 @@ class STDPPLConnectionHom : public Connection< targetidentifierT >
 
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
   void
diff --git a/models/stdp_triplet_connection.h b/models/stdp_triplet_connection.h
index ac1560edcd..ce02d227dd 100644
--- a/models/stdp_triplet_connection.h
+++ b/models/stdp_triplet_connection.h
@@ -183,7 +183,7 @@ class STDPTripletConnection : public Connection< targetidentifierT >
 
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
   void
diff --git a/models/vogels_sprekeler_connection.h b/models/vogels_sprekeler_connection.h
index 6054e415ff..835c0de6c2 100644
--- a/models/vogels_sprekeler_connection.h
+++ b/models/vogels_sprekeler_connection.h
@@ -141,7 +141,7 @@ class VogelsSprekelerConnection : public Connection< targetidentifierT >
 
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
   void
diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index ddfb47ac5d..df9094741a 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -89,7 +89,7 @@ class Archiving_Node : public Node
 
   /**
    * \fn int get_synaptic_elements_vacant(Name n)
-   * get the number of synaptic elements of type n which are available
+   * Get the number of synaptic elements of type n which are available
    * for new synapse creation
    */
   int get_synaptic_elements_vacant( Name n ) const;
diff --git a/nestkernel/node.cpp b/nestkernel/node.cpp
index d8ff986303..82cafbc60b 100644
--- a/nestkernel/node.cpp
+++ b/nestkernel/node.cpp
@@ -229,7 +229,7 @@ Node::send_test_event( Node&, rport, synindex, bool )
  * throws IllegalConnection
  */
 void
-Node::register_stdp_connection( double )
+Node::register_stdp_connection( double, double )
 {
   throw IllegalConnection();
 }

From ee90db9b7fae3ded228683b091cc4667a951e5fc Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Thu, 21 Feb 2019 18:43:27 +0100
Subject: [PATCH 12/42] refactor testbench

---
 nestkernel/archiving_node.cpp        |  12 +-
 nestkernel/archiving_node.h          |   2 +-
 nestkernel/node.h                    |   2 +-
 pynest/nest/tests/test_post_trace.py | 410 ++++++++++++++-------------
 4 files changed, 223 insertions(+), 203 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 49e4bf772c..369053e0c6 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -128,6 +128,7 @@ nest::Archiving_Node::get_K_value( double t )
   while ( i >= 0 )
   {
     if ( t >= history_[ i ].t_)
+    //if ( t - history_[ i ].t_ > kernel().connection_manager.get_stdp_eps())
     {
       trace_ = ( history_[ i ].Kminus_
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
@@ -190,14 +191,17 @@ nest::Archiving_Node::get_history( double t1,
     return;
   }
   std::deque< histentry >::reverse_iterator runner = history_.rbegin();
-  const double t2_lim = t2;// + kernel().connection_manager.get_stdp_eps();
-  const double t1_lim = t1;// + kernel().connection_manager.get_stdp_eps();
+  //const double t2_lim = t2;// + kernel().connection_manager.get_stdp_eps();
+  //const double t1_lim = t1;// + kernel().connection_manager.get_stdp_eps();
+  const double t2_lim = t2 + kernel().connection_manager.get_stdp_eps();
+  const double t1_lim = t1 + kernel().connection_manager.get_stdp_eps();
   while ( runner != history_.rend() and runner->t_ >= t2_lim )
   {
     ++runner;
   }
   *finish = runner.base();
-  while ( runner != history_.rend() and runner->t_ > t1_lim )
+//  while ( runner != history_.rend() and runner->t_ > t1_lim )
+  while ( runner != history_.rend() and runner->t_ >= t1_lim )
   {
     runner->access_counter_++;
     ++runner;
@@ -217,7 +221,7 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
   if ( n_incoming_ )
   {
     // prune all spikes from history which are no longer needed
-    // except the penultimate one. we might still need it.
+    // the 
     while ( history_.size() > 1 )
     {
       const double next_t_sp = history_[1].t_;
diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index df9094741a..de1184adf2 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -166,7 +166,7 @@ class Archiving_Node : public Node
    * t_first_read: The newly registered synapse will read the history entries
    * with t > t_first_read.
    */
-  void register_stdp_connection( double t_first_read );
+  void register_stdp_connection( double t_first_read, double delay );
 
   void get_status( DictionaryDatum& d ) const;
   void set_status( const DictionaryDatum& d );
diff --git a/nestkernel/node.h b/nestkernel/node.h
index 85d3967bb8..ccc076b1db 100644
--- a/nestkernel/node.h
+++ b/nestkernel/node.h
@@ -478,7 +478,7 @@ class Node
    * @throws IllegalConnection
    *
    */
-  virtual void register_stdp_connection( double );
+  virtual void register_stdp_connection( double, double );
 
   /**
    * Handle incoming spike events.
diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 9498ee8e81..d26359287f 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -34,6 +34,209 @@
 @nest.check_stack
 class PostTraceTestCase(unittest.TestCase):
 
+    def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolution, delay, sim_time, tau_minus, show_all_nest_trace_samples=False):
+
+        print("Pre spike times: [" + ", ".join([str(t) for t in pre_spike_times]) + "]")
+        print("Post spike times: [" + ", ".join([str(t) for t in post_spike_times]) + "]")
+    
+        nest.set_verbosity("M_WARNING")
+
+        post_weights = {'parrot': [], 'parrot_ps': []}
+
+        nest.ResetKernel()
+        nest.SetKernelStatus({'resolution': resolution})
+
+        wr = nest.Create('weight_recorder')
+        nest.CopyModel("stdp_synapse", "stdp_synapse_rec",
+                    {"weight_recorder": wr[0], "weight": 1.})
+
+        # create spike_generators with these times
+        pre_sg = nest.Create("spike_generator",
+                            params={"spike_times": pre_spike_times,
+                                    'allow_offgrid_spikes': True})
+        post_sg = nest.Create("spike_generator",
+                            params={"spike_times": post_spike_times,
+                                    'allow_offgrid_spikes': True})
+        pre_sg_ps = nest.Create("spike_generator",
+                                params={"spike_times": pre_spike_times})
+                                        #'precise_times': True})
+        post_sg_ps = nest.Create("spike_generator",
+                                params={"spike_times": post_spike_times})
+#                                            'precise_times': True})
+
+        # create parrot neurons and connect spike_generators
+        pre_parrot = nest.Create("parrot_neuron")
+        post_parrot = nest.Create("parrot_neuron", params={"tau_minus" : tau_minus})
+        pre_parrot_ps = nest.Create("parrot_neuron_ps")
+        post_parrot_ps = nest.Create("parrot_neuron_ps", params={"tau_minus" : tau_minus})
+
+        nest.Connect(pre_sg, pre_parrot,
+                    syn_spec={"delay": delay})
+        nest.Connect(post_sg, post_parrot,
+                    syn_spec={"delay": delay})
+        nest.Connect(pre_sg_ps, pre_parrot_ps,
+                    syn_spec={"delay": delay})
+        nest.Connect(post_sg_ps, post_parrot_ps,
+                    syn_spec={"delay": delay})
+
+        # create spike detector --- debugging only
+        spikes = nest.Create("spike_detector")#,
+           #                 params={'precise_times': True})
+        nest.Connect(
+            pre_parrot + post_parrot +
+            pre_parrot_ps + post_parrot_ps,
+            spikes
+        )
+
+        # connect both parrot neurons with a stdp synapse onto port 1
+        # thereby spikes transmitted through the stdp connection are
+        # not repeated postsynaptically.
+        nest.Connect(
+            pre_parrot, post_parrot,
+            syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
+        nest.Connect(
+            pre_parrot_ps, post_parrot_ps,
+            syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
+
+        # get STDP synapse
+        syn = nest.GetConnections(source=pre_parrot,
+                                synapse_model="stdp_synapse_rec")
+        syn_ps = nest.GetConnections(source=pre_parrot_ps,
+                                    synapse_model="stdp_synapse_rec")
+
+        print("* Simulating for " + str(sim_time) + " ms...")
+        n_steps = int(np.ceil(sim_time / delay))
+        trace_nest = []
+        trace_nest_t = []
+        t = nest.GetStatus([0], "time")[0]
+        trace_nest_t.append(t)
+        post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+        trace_nest.append(post_trace_value)
+        for step in range(n_steps):
+            nest.Simulate(delay)
+            t = nest.GetStatus([0], "time")[0]
+            if show_all_nest_trace_samples or np.any(np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.):
+                trace_nest_t.append(t)
+                post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+                trace_nest.append(post_trace_value)
+                print("For NEST: trace = " + str(post_trace_value) + " at time t = " + str(t))
+
+        return trace_nest_t, trace_nest
+
+
+
+    def run_post_trace_test_python_reference_(self, pre_spike_times, post_spike_times, resolution, delay, dendritic_delay, sim_time, tau_minus):
+        """
+        compute Python known-good reference of postsynaptic trace
+        """
+
+        n_timepoints = 10000
+        trace_python_ref = np.zeros(n_timepoints)
+        n_spikes = len(post_spike_times)
+        for sp_idx in range(n_spikes):
+            t_sp = post_spike_times[sp_idx] + delay + dendritic_delay
+            for i in range(n_timepoints):
+                t = (i / float(n_timepoints - 1)) * sim_time
+                if t > t_sp + 1E-3:
+                    trace_python_ref[i] += np.exp(-(t - t_sp) / tau_minus)
+
+        n_spikes = len(pre_spike_times)
+        for sp_idx in range(n_spikes):
+            t_sp = pre_spike_times[sp_idx] + delay
+            i = int(np.round(t_sp / sim_time * float(len(trace_python_ref) - 1)))
+            print("* At t_sp = " + str(t_sp) + ", post_trace should be " + str(trace_python_ref[i]))
+
+        return trace_python_ref
+
+    def plot_run(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times, post_spike_times, resolution, delay, dendritic_delay, sim_time, fname):
+
+        #
+        #   plotting
+        #
+
+        fig, ax = plt.subplots(nrows=3)
+        ax1, ax3, ax2 = ax
+        ax1.set_ylim([0., 1.])
+        ax3.set_ylim([0., 1.])
+        ax2.set_ylim([0., np.amax(trace_python_ref)])
+
+        n_spikes = len(pre_spike_times)
+        for i in range(n_spikes):
+            ax1.plot(2 * [pre_spike_times[i] + delay], ax1.get_ylim(), linewidth=2, color="blue", alpha=.4)
+
+        n_spikes = len(post_spike_times)
+        for i in range(n_spikes):
+            ax3.plot(2 * [post_spike_times[i] + delay + dendritic_delay], [0, 1], linewidth=2, color="red", alpha=.4)
+
+        ax2.plot(np.linspace(0., sim_time, len(trace_python_ref)), trace_python_ref, label="Expected", color="cyan", alpha=.6)
+
+
+        # fn_nest_trace_values = "/tmp/trace_vals_0x7ff985894370.txt"
+        # print("Please enter fn_nest_trace_values now:")
+        # import pdb;pdb.set_trace()
+        # s = open(fn_nest_trace_values, "r")
+        # l = s.readlines()
+        # nest_spike_times = []
+        # nest_trace_values = []
+        # for line in l:
+        #     line_split = line.split()
+        #     nest_spike_times.append(float(line_split[0]))
+        #     nest_trace_values.append(float(line_split[1]))
+        # ax2.scatter(nest_spike_times, nest_trace_values, label="NEST", color="orange")
+
+
+        ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
+        print("trace_nest_t = " + str(trace_nest_t) + ", trace_nest = " + str(trace_nest))
+
+        n_points = len(trace_nest_t)
+        for i in range(n_points):
+            t = trace_nest_t[i]
+            #print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
+            for i_search, t_search in enumerate(reversed(np.array(pre_spike_times) + delay)):
+                #print("\t* Testing " + str(t_search) + "...")
+                if t_search <= t:
+                    _trace_at_t_search = trace_python_ref[int(np.round(t_search / sim_time * float(len(trace_python_ref) - 1)))]
+                    #if (t_search - trace_nest_t[i])**2 > resolution/2. \
+                    #idx = np.argmin((t_search - (np.array(post_spike_times) + delay + dendritic_delay))**2)
+                    #t_found = (t_search - (np.array(post_spike_times) + delay + dendritic_delay)
+                    traces_match = (_trace_at_t_search - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
+                    if not traces_match:
+                        post_spike_occurred_at_t_search = np.any((t_search - (np.array(post_spike_times) + delay + dendritic_delay))**2 < resolution/2.)
+                        if post_spike_occurred_at_t_search:
+                            traces_match = (_trace_at_t_search + 1 - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
+                            if traces_match:
+                                _trace_at_t_search += 1.
+                                
+                            
+                        """_pre_spike_times = np.array(pre_spike_times)
+                        pre_spike_occurred_between_t_search_and_t = np.any(_pre_spike_times[np.logical_and(_pre_spike_times > t_search, _pre_spike_times < t)])
+                        pre_spike_occurred_between_t_search_and_t = False
+                        if not pre_spike_occurred_between_t_search_and_t:
+                            _trace_at_t_search += 1."""
+                    ax2.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="none")#"#FFFFFF7F")
+                    ax2.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
+                    break
+
+        ax2.set_xlabel("Time [ms]")
+        ax1.set_ylabel("Pre spikes")
+        ax3.set_ylabel("Post spikes")
+        ax2.set_ylabel("Synaptic trace")
+        ax2.legend()
+                    
+        
+        for _ax in ax:
+            _ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10*delay))
+            _ax.xaxis.set_minor_locator(plticker.MultipleLocator(base=delay))
+            _ax.grid(which="major", axis="both")
+            _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
+            #_ax.minorticks_on()
+            _ax.set_xlim(0., sim_time)
+
+        fig.suptitle("Postsynaptic trace testbench. Spike times are\nshown from the perspective of the STDP synapse.")
+
+        fig.savefig(fname, dpi=300.)
+
+
     def test_post_trace(self):
         """
         construct a network of the form:
@@ -77,16 +280,19 @@ def test_post_trace(self):
 
         t_sp_min = 1.
         t_sp_max = 50
-        n_spikes = int(t_sp_max)
+        n_spikes = 10 #int(t_sp_max)
         pre_spike_times1 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
+        n_spikes = 50
         post_spike_times1 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
-        
+        tau_minus = 2.	# [ms]
+
         #pre_spike_times1 = [2.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 11.0, 12.0, 13.0, 16.0, 17.0, 18.0, 20.0, 21.0, 22.0, 24.0, 26.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 46.0, 48.0, 50.0]
         #post_spike_times1 = [2.0, 4.0, 5.0, 6.0, 8.0, 13.0, 14.0, 17.0, 18.0, 19.0, 21.0, 22.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 46.0, 48.0, 49.0, 50.0]
 
         #pre_spike_times1 = np.array([2.0, 7.0, 13.0, 18, 23, 28, 33, 37])
         #post_spike_times1 = np.array([2.0, 7.0, 13.0, 18, 23, 28, 33, 37])
 
+        sim_time = t_sp_max + 5 * delay
 
         pre_spike_times = [np.concatenate((pre_spike_times1, np.array([70.])))]
         post_spike_times = [post_spike_times1]
@@ -94,203 +300,13 @@ def test_post_trace(self):
         pre_spike_times = [np.array(a) for a in pre_spike_times]
         post_spike_times = [np.array(a) for a in post_spike_times]
 
+
         for spike_times_idx in range(len(pre_spike_times)):
+            fname = "/tmp/traces_" + str(spike_times_idx) + ".png"
+            trace_nest_t, trace_nest = self.run_post_trace_test_nest_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, sim_time, tau_minus, show_all_nest_trace_samples)
+            trace_python_ref = self.run_post_trace_test_python_reference_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, tau_minus)
+            self.plot_run(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, fname)
 
-            print("Pre spike times: [" + ", ".join([str(t) for t in pre_spike_times[spike_times_idx]]) + "]")
-            print("Post spike times: [" + ", ".join([str(t) for t in post_spike_times[spike_times_idx]]) + "]")
-        
-            nest.set_verbosity("M_WARNING")
-
-            post_weights = {'parrot': [], 'parrot_ps': []}
-
-            nest.ResetKernel()
-            nest.SetKernelStatus({'resolution': resolution})
-
-            wr = nest.Create('weight_recorder')
-            nest.CopyModel("stdp_synapse", "stdp_synapse_rec",
-                        {"weight_recorder": wr[0], "weight": 1.})
-
-            # create spike_generators with these times
-            pre_sg = nest.Create("spike_generator",
-                                params={"spike_times": pre_spike_times[spike_times_idx],
-                                        'allow_offgrid_spikes': True})
-            post_sg = nest.Create("spike_generator",
-                                params={"spike_times": post_spike_times[spike_times_idx],
-                                        'allow_offgrid_spikes': True})
-            pre_sg_ps = nest.Create("spike_generator",
-                                    params={"spike_times": pre_spike_times[spike_times_idx],
-                                            'precise_times': True})
-            post_sg_ps = nest.Create("spike_generator",
-                                    params={"spike_times": post_spike_times[spike_times_idx],
-                                            'precise_times': True})
-
-            # create parrot neurons and connect spike_generators
-            pre_parrot = nest.Create("parrot_neuron")
-            post_parrot = nest.Create("parrot_neuron")
-            pre_parrot_ps = nest.Create("parrot_neuron_ps")
-            post_parrot_ps = nest.Create("parrot_neuron_ps")
-
-            nest.Connect(pre_sg, pre_parrot,
-                        syn_spec={"delay": delay})
-            nest.Connect(post_sg, post_parrot,
-                        syn_spec={"delay": delay})
-            nest.Connect(pre_sg_ps, pre_parrot_ps,
-                        syn_spec={"delay": delay})
-            nest.Connect(post_sg_ps, post_parrot_ps,
-                        syn_spec={"delay": delay})
-
-            # create spike detector --- debugging only
-            spikes = nest.Create("spike_detector",
-                                params={'precise_times': True})
-            nest.Connect(
-                pre_parrot + post_parrot +
-                pre_parrot_ps + post_parrot_ps,
-                spikes
-            )
-
-            # connect both parrot neurons with a stdp synapse onto port 1
-            # thereby spikes transmitted through the stdp connection are
-            # not repeated postsynaptically.
-            nest.Connect(
-                pre_parrot, post_parrot,
-                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
-            nest.Connect(
-                pre_parrot_ps, post_parrot_ps,
-                syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
-
-            # get STDP synapse
-            syn = nest.GetConnections(source=pre_parrot,
-                                    synapse_model="stdp_synapse_rec")
-            syn_ps = nest.GetConnections(source=pre_parrot_ps,
-                                        synapse_model="stdp_synapse_rec")
-
-            sim_time = np.amax(np.concatenate((pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx]))) + 5 * delay
-            print("* Simulating for " + str(sim_time) + " ms...")
-            n_steps = int(np.ceil(sim_time / delay))
-            trace_nest = []
-            trace_nest_t = []
-            t = nest.GetStatus([0], "time")[0]
-            trace_nest_t.append(t)
-            post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
-            trace_nest.append(post_trace_value)
-            for step in range(n_steps):
-                nest.Simulate(delay)
-                t = nest.GetStatus([0], "time")[0]
-                if show_all_nest_trace_samples or np.any(np.abs(t - np.array(pre_spike_times[spike_times_idx]) - delay) < resolution/2.):
-                    trace_nest_t.append(t)
-                    post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
-                    trace_nest.append(post_trace_value)
-                    print("In Python: trace = " + str(post_trace_value) + " at time t = " + str(t))
-
-
-            #
-            #   compute Python known-good reference of postsynaptic trace
-            #
-
-            tau_minus = nest.GetStatus(post_parrot)[0]['tau_minus']
-            n_timepoints = 10000
-            ref_post_trace = np.zeros(n_timepoints)
-            n_spikes = len(post_spike_times[spike_times_idx])
-            for sp_idx in range(n_spikes):
-                t_sp = post_spike_times[spike_times_idx][sp_idx] + delay + dendritic_delay
-                for i in range(n_timepoints):
-                    t = (i / float(n_timepoints - 1)) * sim_time
-                    if t > t_sp + 1E-3:
-                        ref_post_trace[i] += np.exp(-(t - t_sp) / tau_minus)
-            
-            n_spikes = len(pre_spike_times[spike_times_idx])
-            for sp_idx in range(n_spikes):
-                t_sp = pre_spike_times[spike_times_idx][sp_idx] + delay
-                i = int(np.round(t_sp / sim_time * float(len(ref_post_trace) - 1)))
-                print("* At t_sp = " + str(t_sp) + ", post_trace should be " + str(ref_post_trace[i]))
-                #import pdb;pdb.set_trace()`
-
-
-            #
-            #   plotting
-            #
-
-            fig, ax = plt.subplots(nrows=3)
-            ax1, ax3, ax2 = ax
-            ax1.set_ylim([0., 1.])
-            ax3.set_ylim([0., 1.])
-            ax2.set_ylim([0., np.amax(ref_post_trace)])
-
-            n_spikes = len(pre_spike_times[spike_times_idx])
-            for i in range(n_spikes):
-                ax1.plot(2 * [pre_spike_times[spike_times_idx][i] + delay], ax1.get_ylim(), linewidth=2, color="blue", alpha=.4)
-
-            n_spikes = len(post_spike_times[spike_times_idx])
-            for i in range(n_spikes):
-                ax3.plot(2 * [post_spike_times[spike_times_idx][i] + delay + dendritic_delay], [0, 1], linewidth=2, color="red", alpha=.4)
-
-            ax2.plot(np.linspace(0., sim_time, len(ref_post_trace)), ref_post_trace, label="Expected", color="cyan", alpha=.6)
-
-
-            # fn_nest_trace_values = "/tmp/trace_vals_0x7ff985894370.txt"
-            # print("Please enter fn_nest_trace_values now:")
-            # import pdb;pdb.set_trace()
-            # s = open(fn_nest_trace_values, "r")
-            # l = s.readlines()
-            # nest_spike_times = []
-            # nest_trace_values = []
-            # for line in l:
-            #     line_split = line.split()
-            #     nest_spike_times.append(float(line_split[0]))
-            #     nest_trace_values.append(float(line_split[1]))
-            # ax2.scatter(nest_spike_times, nest_trace_values, label="NEST", color="orange")
-
-
-            ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
-            print("trace_nest_t = " + str(trace_nest_t) + ", trace_nest = " + str(trace_nest))
-            
-            n_points = len(trace_nest_t)
-            for i in range(n_points):
-                t = trace_nest_t[i]
-                #print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
-                for i_search, t_search in enumerate(reversed(np.array(pre_spike_times[spike_times_idx]) + delay)):
-                    #print("\t* Testing " + str(t_search) + "...")
-                    if t_search <= t:
-                        _trace_at_t_search = ref_post_trace[int(np.round(t_search / sim_time * float(len(ref_post_trace) - 1)))]
-                        #if (t_search - trace_nest_t[i])**2 > resolution/2. \
-                        #idx = np.argmin((t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay))**2)
-                        #t_found = (t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay)
-                        traces_match = (_trace_at_t_search - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
-                        if not traces_match:
-                            post_spike_occurred_at_t_search = np.any((t_search - (np.array(post_spike_times[spike_times_idx]) + delay + dendritic_delay))**2 < resolution/2.)
-                            if post_spike_occurred_at_t_search:
-                                traces_match = (_trace_at_t_search + 1 - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
-                                if traces_match:
-                                    _trace_at_t_search += 1.
-                                    
-                                
-                            """_pre_spike_times = np.array(pre_spike_times[spike_times_idx])
-                            pre_spike_occurred_between_t_search_and_t = np.any(_pre_spike_times[np.logical_and(_pre_spike_times > t_search, _pre_spike_times < t)])
-                            pre_spike_occurred_between_t_search_and_t = False
-                            if not pre_spike_occurred_between_t_search_and_t:
-                                _trace_at_t_search += 1."""
-                        ax2.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="none")#"#FFFFFF7F")
-                        ax2.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
-                        break
-
-            ax2.set_xlabel("Time [ms]")
-            ax1.set_ylabel("Pre spikes")
-            ax3.set_ylabel("Post spikes")
-            ax2.set_ylabel("Synaptic trace")
-            ax2.legend()
-                        
-            
-            for _ax in ax:
-                _ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10*delay))
-                _ax.xaxis.set_minor_locator(plticker.MultipleLocator(base=delay))
-                _ax.grid(which="major", axis="both")
-                _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
-                #_ax.minorticks_on()
-                _ax.set_xlim(0., sim_time)
-
-            fig.suptitle("Postsynaptic trace testbench. Spike times are\nshown from the perspective of the STDP synapse.")
-
-            fig.savefig("/tmp/traces.png", dpi=300.)
 
 
 def suite():

From e7e55acd9b989662b3c91a579c1f7f6dbf983e05 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 27 Feb 2019 20:50:54 +0100
Subject: [PATCH 13/42] postsynaptic trace fixes for #1034; updated testbench

---
 models/stdp_connection.h             |   1 +
 nestkernel/archiving_node.cpp        |  15 +-
 nestkernel/archiving_node.h          |   2 +-
 pynest/nest/tests/test_post_trace.py | 250 +++++++++++++++++----------
 4 files changed, 174 insertions(+), 94 deletions(-)

diff --git a/models/stdp_connection.h b/models/stdp_connection.h
index a6f111a581..4035a4da1d 100644
--- a/models/stdp_connection.h
+++ b/models/stdp_connection.h
@@ -247,6 +247,7 @@ STDPConnection< targetidentifierT >::send( Event& e,
   double minus_dt;
   while ( start != finish )
   {
+    std::cout << "\tlooping over the history: it->t_ = " << start->t_ << std::endl;
     minus_dt = t_lastspike_ - ( start->t_ + dendritic_delay );
     ++start;
     // get_history() should make sure that
diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 369053e0c6..23a1150e76 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -120,15 +120,14 @@ nest::Archiving_Node::get_K_value( double t )
     std::cout << "\t\thistory["<<i<<"] t = " << history_[ i ].t_ << std::endl;
     ++i;
   }
-
-  
   }
 
+  // search for the latest post spike in the history buffer that came strictly before `t`
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
-    if ( t >= history_[ i ].t_)
-    //if ( t - history_[ i ].t_ > kernel().connection_manager.get_stdp_eps())
+    // if ( t >= history_[ i ].t_)		// XXX: CAP: TRACES FIX
+    if ( t - history_[ i ].t_ > kernel().connection_manager.get_stdp_eps() )
     {
       trace_ = ( history_[ i ].Kminus_
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
@@ -184,6 +183,7 @@ nest::Archiving_Node::get_history( double t1,
   std::deque< histentry >::iterator* start,
   std::deque< histentry >::iterator* finish )
 {
+  std::cout << "* In Archiving_Node::get_history( t1 = " << t1 << " (excl.); t2 = " << t2 << " (incl.)\n";
   *finish = history_.end();
   if ( history_.empty() )
   {
@@ -221,13 +221,16 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
   if ( n_incoming_ )
   {
     // prune all spikes from history which are no longer needed
-    // the 
+    // only remove a spike if:
+    // - its access counter indicates it has been read out by all connected STDP synapses, and
+    // - there is another, later spike, that is strictly more than max_delay_ away from the current spike at t_sp_ms
     while ( history_.size() > 1 )
     {
       const double next_t_sp = history_[1].t_;
       if ( history_.front().access_counter_ >= n_incoming_
-           && abs(next_t_sp - t_sp_ms) > 2. * max_delay_ )
+           && t_sp_ms - next_t_sp > max_delay_ + kernel().connection_manager.get_stdp_eps() )	// XXX: CAP: TRACES FIX
       {
+        std::cout << "\tRemoving spike at t = " << history_[0].t_ << " from history\n";
         history_.pop_front();
       }
       else
diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index de1184adf2..7f0cdce270 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -129,7 +129,7 @@ class Archiving_Node : public Node
 
   /**
    * \fn double get_K_value(long t)
-   * return the Kminus value at t (in ms).
+   * return the Kminus (synaptic trace) value at t (in ms). When the trace is requested at the exact same time that the neuron emits a spike, the trace value as it was just before the spike is returned.
    */
   double get_K_value( double t );
 
diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index d26359287f..f82b16d5d1 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -31,14 +31,17 @@
 import matplotlib.ticker as plticker
 
 
-@nest.check_stack
+#@nest.check_stack
 class PostTraceTestCase(unittest.TestCase):
 
+    trace_match_atol_ = 1E-2
+    trace_match_rtol_ = 1E-2
+
     def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolution, delay, sim_time, tau_minus, show_all_nest_trace_samples=False):
 
         print("Pre spike times: [" + ", ".join([str(t) for t in pre_spike_times]) + "]")
         print("Post spike times: [" + ", ".join([str(t) for t in post_spike_times]) + "]")
-    
+
         nest.set_verbosity("M_WARNING")
 
         post_weights = {'parrot': [], 'parrot_ps': []}
@@ -51,12 +54,12 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
                     {"weight_recorder": wr[0], "weight": 1.})
 
         # create spike_generators with these times
-        pre_sg = nest.Create("spike_generator",
+        """pre_sg = nest.Create("spike_generator",
                             params={"spike_times": pre_spike_times,
                                     'allow_offgrid_spikes': True})
         post_sg = nest.Create("spike_generator",
                             params={"spike_times": post_spike_times,
-                                    'allow_offgrid_spikes': True})
+                                    'allow_offgrid_spikes': True})"""
         pre_sg_ps = nest.Create("spike_generator",
                                 params={"spike_times": pre_spike_times})
                                         #'precise_times': True})
@@ -65,15 +68,15 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
 #                                            'precise_times': True})
 
         # create parrot neurons and connect spike_generators
-        pre_parrot = nest.Create("parrot_neuron")
-        post_parrot = nest.Create("parrot_neuron", params={"tau_minus" : tau_minus})
+        #pre_parrot = nest.Create("parrot_neuron")
+        #post_parrot = nest.Create("parrot_neuron", params={"tau_minus" : tau_minus})
         pre_parrot_ps = nest.Create("parrot_neuron_ps")
         post_parrot_ps = nest.Create("parrot_neuron_ps", params={"tau_minus" : tau_minus})
 
-        nest.Connect(pre_sg, pre_parrot,
-                    syn_spec={"delay": delay})
-        nest.Connect(post_sg, post_parrot,
-                    syn_spec={"delay": delay})
+        #nest.Connect(pre_sg, pre_parrot,
+        #            syn_spec={"delay": delay})
+        #nest.Connect(post_sg, post_parrot,
+        #            syn_spec={"delay": delay})
         nest.Connect(pre_sg_ps, pre_parrot_ps,
                     syn_spec={"delay": delay})
         nest.Connect(post_sg_ps, post_parrot_ps,
@@ -83,7 +86,7 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
         spikes = nest.Create("spike_detector")#,
            #                 params={'precise_times': True})
         nest.Connect(
-            pre_parrot + post_parrot +
+            #pre_parrot + post_parrot +
             pre_parrot_ps + post_parrot_ps,
             spikes
         )
@@ -91,35 +94,36 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
         # connect both parrot neurons with a stdp synapse onto port 1
         # thereby spikes transmitted through the stdp connection are
         # not repeated postsynaptically.
-        nest.Connect(
-            pre_parrot, post_parrot,
-            syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
+        #nest.Connect(
+        #    pre_parrot, post_parrot,
+        #    syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
         nest.Connect(
             pre_parrot_ps, post_parrot_ps,
             syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
 
         # get STDP synapse
-        syn = nest.GetConnections(source=pre_parrot,
-                                synapse_model="stdp_synapse_rec")
+        #syn = nest.GetConnections(source=pre_parrot,
+        #                        synapse_model="stdp_synapse_rec")
         syn_ps = nest.GetConnections(source=pre_parrot_ps,
                                     synapse_model="stdp_synapse_rec")
 
-        print("* Simulating for " + str(sim_time) + " ms...")
+        print("[py] Total simulation time: " + str(sim_time) + " ms")
         n_steps = int(np.ceil(sim_time / delay))
         trace_nest = []
         trace_nest_t = []
         t = nest.GetStatus([0], "time")[0]
         trace_nest_t.append(t)
-        post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+        post_trace_value = nest.GetStatus(post_parrot_ps)[0]['post_trace']
         trace_nest.append(post_trace_value)
         for step in range(n_steps):
+            print("\n[py] simulating for " + str(delay) + " ms")
             nest.Simulate(delay)
             t = nest.GetStatus([0], "time")[0]
             if show_all_nest_trace_samples or np.any(np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.):
                 trace_nest_t.append(t)
-                post_trace_value = nest.GetStatus(post_parrot)[0]['post_trace']
+                post_trace_value = nest.GetStatus(post_parrot_ps)[0]['post_trace']
                 trace_nest.append(post_trace_value)
-                print("For NEST: trace = " + str(post_trace_value) + " at time t = " + str(t))
+                print("[py] Received NEST trace: " + str(post_trace_value) + " at time t = " + str(t))
 
         return trace_nest_t, trace_nest
 
@@ -130,7 +134,7 @@ def run_post_trace_test_python_reference_(self, pre_spike_times, post_spike_time
         compute Python known-good reference of postsynaptic trace
         """
 
-        n_timepoints = 10000
+        n_timepoints = 100 * int(np.ceil(max(np.amax(pre_spike_times), np.amax(post_spike_times))))
         trace_python_ref = np.zeros(n_timepoints)
         n_spikes = len(post_spike_times)
         for sp_idx in range(n_spikes):
@@ -148,81 +152,84 @@ def run_post_trace_test_python_reference_(self, pre_spike_times, post_spike_time
 
         return trace_python_ref
 
+
     def plot_run(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times, post_spike_times, resolution, delay, dendritic_delay, sim_time, fname):
 
+        fig, ax = plt.subplots(nrows=3)
+        ax1, ax2, ax3 = ax
+
+
         #
-        #   plotting
+        #   pre spikes
         #
 
-        fig, ax = plt.subplots(nrows=3)
-        ax1, ax3, ax2 = ax
         ax1.set_ylim([0., 1.])
-        ax3.set_ylim([0., 1.])
-        ax2.set_ylim([0., np.amax(trace_python_ref)])
-
+        ax1.set_ylabel("Pre spikes")
         n_spikes = len(pre_spike_times)
         for i in range(n_spikes):
             ax1.plot(2 * [pre_spike_times[i] + delay], ax1.get_ylim(), linewidth=2, color="blue", alpha=.4)
 
+
+        #
+        #   post spikes
+        #
+
+        ax2.set_ylim([0., 1.])
+        ax2.set_ylabel("Post spikes")
         n_spikes = len(post_spike_times)
         for i in range(n_spikes):
-            ax3.plot(2 * [post_spike_times[i] + delay + dendritic_delay], [0, 1], linewidth=2, color="red", alpha=.4)
+            ax2.plot(2 * [post_spike_times[i] + delay + dendritic_delay], [0, 1], linewidth=2, color="red", alpha=.4)
 
-        ax2.plot(np.linspace(0., sim_time, len(trace_python_ref)), trace_python_ref, label="Expected", color="cyan", alpha=.6)
 
+        #
+        #   traces
+        #
 
-        # fn_nest_trace_values = "/tmp/trace_vals_0x7ff985894370.txt"
-        # print("Please enter fn_nest_trace_values now:")
-        # import pdb;pdb.set_trace()
-        # s = open(fn_nest_trace_values, "r")
-        # l = s.readlines()
-        # nest_spike_times = []
-        # nest_trace_values = []
-        # for line in l:
-        #     line_split = line.split()
-        #     nest_spike_times.append(float(line_split[0]))
-        #     nest_trace_values.append(float(line_split[1]))
-        # ax2.scatter(nest_spike_times, nest_trace_values, label="NEST", color="orange")
-
+        ax3.legend()
+        ax3.set_ylabel("Synaptic trace")
+        ax3.set_ylim([0., np.amax(trace_python_ref)])
+        ax3.plot(np.linspace(0., sim_time, len(trace_python_ref)), trace_python_ref, label="Expected", color="cyan", alpha=.6)
+        ax3.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
+        # print("trace_nest_t = " + str(trace_nest_t) + ", trace_nest = " + str(trace_nest))
 
-        ax2.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
-        print("trace_nest_t = " + str(trace_nest_t) + ", trace_nest = " + str(trace_nest))
+        #
+        #   Trace values are returned from NEST at regular intervals, but only updated at presynaptic spike times.
+        #
+        #   Step backwards in time from the sampled value, to find the last time at which the trace value was updated, namely the time of occurrence of the last presynaptic spike.
+        #
 
-        n_points = len(trace_nest_t)
-        for i in range(n_points):
+        n_timepoints = len(trace_nest_t)
+        for i in range(n_timepoints):
             t = trace_nest_t[i]
-            #print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
+            print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
             for i_search, t_search in enumerate(reversed(np.array(pre_spike_times) + delay)):
-                #print("\t* Testing " + str(t_search) + "...")
                 if t_search <= t:
+                    print("\t* Testing " + str(t_search) + "...")
                     _trace_at_t_search = trace_python_ref[int(np.round(t_search / sim_time * float(len(trace_python_ref) - 1)))]
                     #if (t_search - trace_nest_t[i])**2 > resolution/2. \
                     #idx = np.argmin((t_search - (np.array(post_spike_times) + delay + dendritic_delay))**2)
                     #t_found = (t_search - (np.array(post_spike_times) + delay + dendritic_delay)
-                    traces_match = (_trace_at_t_search - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
+                    traces_match = np.allclose(_trace_at_t_search, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                    print("\t   traces_match = " + str(traces_match))
                     if not traces_match:
                         post_spike_occurred_at_t_search = np.any((t_search - (np.array(post_spike_times) + delay + dendritic_delay))**2 < resolution/2.)
+                        print("\t   post_spike_occurred_at_t_search = " + str(post_spike_occurred_at_t_search))
                         if post_spike_occurred_at_t_search:
-                            traces_match = (_trace_at_t_search + 1 - trace_nest[i])**2 < 1E-3  # XXX: try np.allclose
+                            traces_match = np.allclose(_trace_at_t_search + 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                            print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search+1) + ")")
                             if traces_match:
                                 _trace_at_t_search += 1.
-                                
-                            
-                        """_pre_spike_times = np.array(pre_spike_times)
-                        pre_spike_occurred_between_t_search_and_t = np.any(_pre_spike_times[np.logical_and(_pre_spike_times > t_search, _pre_spike_times < t)])
-                        pre_spike_occurred_between_t_search_and_t = False
-                        if not pre_spike_occurred_between_t_search_and_t:
-                            _trace_at_t_search += 1."""
-                    ax2.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="none")#"#FFFFFF7F")
-                    ax2.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
+
+                        if not traces_match and post_spike_occurred_at_t_search:
+                            traces_match = np.allclose(_trace_at_t_search - 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                            print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search-1) + ")")
+                            if traces_match:
+                                _trace_at_t_search -= 1.
+
+                    ax3.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="none")#"#FFFFFF7F")
+                    ax3.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
                     break
 
-        ax2.set_xlabel("Time [ms]")
-        ax1.set_ylabel("Pre spikes")
-        ax3.set_ylabel("Post spikes")
-        ax2.set_ylabel("Synaptic trace")
-        ax2.legend()
-                    
         
         for _ax in ax:
             _ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10*delay))
@@ -232,36 +239,83 @@ def plot_run(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times,
             #_ax.minorticks_on()
             _ax.set_xlim(0., sim_time)
 
+        ax3.set_xlabel("Time [ms]")
         fig.suptitle("Postsynaptic trace testbench. Spike times are\nshown from the perspective of the STDP synapse.")
 
+        print("* Saving to " + fname)
         fig.savefig(fname, dpi=300.)
 
 
+    def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times, post_spike_times, resolution, delay, dendritic_delay, sim_time):
+        """
+        Trace values are returned from NEST at regular intervals, but only updated at presynaptic spike times.
+        
+        To match the NEST samples with the continuous reference trace, step backwards in time from the sampled value, to find the last time at which the trace value was updated, namely the time of occurrence of the last presynaptic spike.
+        """
+
+        trace_nest_adjusted = trace_nest.copy()
+
+        n_timepoints = len(trace_nest_t)
+        for i in range(n_timepoints)[1:]:
+            t = trace_nest_t[i]
+            print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
+
+            traces_match = False
+            for i_search, t_search in enumerate(reversed(np.array(pre_spike_times) + delay)):
+                if t_search <= t:
+                    print("\t* Testing " + str(t_search) + "...")
+                    _trace_at_t_search = trace_python_ref[int(np.round(t_search / sim_time * float(len(trace_python_ref) - 1)))]
+                    traces_match = np.allclose(_trace_at_t_search, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                    print("\t   traces_match = " + str(traces_match))
+                    post_spike_occurred_at_t_search = np.any((t_search - (np.array(post_spike_times) + delay + dendritic_delay))**2 < resolution/2.)
+                    print("\t   post_spike_occurred_at_t_search = " + str(post_spike_occurred_at_t_search))
+
+                    if (not traces_match) and post_spike_occurred_at_t_search:
+                        traces_match = np.allclose(_trace_at_t_search + 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                        print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search+1) + ")")
+                        if traces_match:
+                            _trace_at_t_search += 1.
+
+                    if (not traces_match) and post_spike_occurred_at_t_search:
+                        traces_match = np.allclose(_trace_at_t_search - 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                        print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search-1) + ")")
+                        if traces_match:
+                            _trace_at_t_search -= 1.
+
+                    break
+
+            if (not traces_match) and i_search == len(pre_spike_times) - 1:
+                print("\tthe time before the first pre spike")
+                # the time before the first pre spike
+                traces_match = trace_nest[i] == 0.
+
+            if not traces_match:
+                return False
+
+        return True
+
     def test_post_trace(self):
         """
         construct a network of the form:
-        
+
                                static_synapse                   stdp_synapse                    static_synapse
             [ pre_spike_gen ] ----(delay)----o [ pre_parrot ] ----(delay)----o [ post_parrot ] o----(delay)---- [ post_spike_gen ]
 
 
-        The spike times of the spike generators are defined in `pre_spike_times` and `post_spike_times`. From the perspective of the stdp_synapse, spikes arrive with the following delays (with respect to the values in these lists):
-        
+        The spike times of the spike generators are defined in `pre_spike_times` and `post_spike_times`. From the perspective of the STDP synapse, spikes arrive with the following delays (with respect to the values in these lists):
+
         - for the presynaptic neuron: one synaptic delay in the leftmost static synapse
         - for the postsynaptic neuron: one synaptic delay in the rightmost static synapse
-        - from the postsynaptic neuron: one dendritic delay between the post_parrot node and the synapse itself---see the C++ variable `dendritic_delay`).
-                
+        - for the synapse itself: one dendritic delay between the post_parrot node and the synapse itself (see the C++ variable `dendritic_delay`).
         """
 
         show_all_nest_trace_samples = True
 
         resolution = .1     # [ms]
-        delay = 5.  # [ms]
-        
-        dendritic_delay = delay
+        delays = np.array([1., 5.])  # [ms]
 
-        pre_spike_times1 = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]      # [ms]
-        post_spike_times1 = [3., 7., 8., 10., 12., 13., 14., 16., 17., 18., 19., 20., 21., 22.]      # [ms]
+        # pre_spike_times1 = [2., 5., 7., 8., 10., 11., 15., 17., 20., 21., 22., 23., 26., 28.]      # [ms]
+        # post_spike_times1 = [3., 7., 8., 10., 12., 13., 14., 16., 17., 18., 19., 20., 21., 22.]      # [ms]
         
         # pre_spike_times = [10., 11., 12., 13., 14., 15., 25., 35., 45., 50., 51., 52., 70.]      # [ms]
         # post_spike_times = [10., 11., 12., 13., 30., 40., 50., 51., 52., 53., 54.]      # [ms]
@@ -281,9 +335,9 @@ def test_post_trace(self):
         t_sp_min = 1.
         t_sp_max = 50
         n_spikes = 10 #int(t_sp_max)
-        pre_spike_times1 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
+        pre_spike_times2 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
         n_spikes = 50
-        post_spike_times1 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
+        post_spike_times2 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
         tau_minus = 2.	# [ms]
 
         #pre_spike_times1 = [2.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 11.0, 12.0, 13.0, 16.0, 17.0, 18.0, 20.0, 21.0, 22.0, 24.0, 26.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 46.0, 48.0, 50.0]
@@ -292,20 +346,42 @@ def test_post_trace(self):
         #pre_spike_times1 = np.array([2.0, 7.0, 13.0, 18, 23, 28, 33, 37])
         #post_spike_times1 = np.array([2.0, 7.0, 13.0, 18, 23, 28, 33, 37])
 
-        sim_time = t_sp_max + 5 * delay
+        # pre_spike_times = [np.concatenate((pre_spike_times1, np.array([70.])))]
+        # post_spike_times = [post_spike_times1]
+
+        # pre_spike_times = [np.array(a) for a in pre_spike_times]
+        # post_spike_times = [np.array(a) for a in post_spike_times]
+
+        # pre_spike_times.append(post_spike_times[0].copy())
+        # post_spike_times.append(pre_spike_times[0].copy())
+
+        # minimal reproducing example of the original bug
+        pre_spike_times1 = np.array([2., 3., 10.])
+        post_spike_times1 = np.array([1., 2., 3.])
+
+        # pre_spike_times2 = np.array([3.0, 4.0, 9.0, 10.0, 11.0, 13.0, 14.0, 17.0, 18.0, 19.0, 20.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 34.0, 35.0, 37.0, 39.0, 40.0, 43.0, 44.0, 46.0, 47.0, 48.0, 49.0, 50.0])
+        # post_spike_times2 = np.array([5.0, 10.0, 15.0, 22.0, 23.0, 28.0, 39.0, 41.0, 47.0])#[1., 2., 3.])
 
-        pre_spike_times = [np.concatenate((pre_spike_times1, np.array([70.])))]
-        post_spike_times = [post_spike_times1]
 
-        pre_spike_times = [np.array(a) for a in pre_spike_times]
-        post_spike_times = [np.array(a) for a in post_spike_times]
+        # minimal reproducing example of the original bug
+        # pre_spike_times2 = np.array([16.0, 17.0, 18.0])
+        # post_spike_times2 = np.array([12.0, 14.0, 17.0, 18.0])
 
+        # for each parameter set, run the test
+        pre_spike_times = [pre_spike_times1, pre_spike_times2, post_spike_times2]
+        post_spike_times = [post_spike_times1, post_spike_times2, pre_spike_times2]
 
-        for spike_times_idx in range(len(pre_spike_times)):
-            fname = "/tmp/traces_" + str(spike_times_idx) + ".png"
-            trace_nest_t, trace_nest = self.run_post_trace_test_nest_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, sim_time, tau_minus, show_all_nest_trace_samples)
-            trace_python_ref = self.run_post_trace_test_python_reference_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, tau_minus)
-            self.plot_run(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, fname)
+        for delay in delays:
+            dendritic_delay = delay
+            for spike_times_idx in range(len(pre_spike_times)):
+                fname = "/tmp/traces_[delay=" + str(delay) + "]_[experiment=" + str(spike_times_idx) + "].png"
+                sim_time = np.amax(np.concatenate((pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx]))) + 5 * delay
+                trace_nest_t, trace_nest = self.run_post_trace_test_nest_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, sim_time, tau_minus, show_all_nest_trace_samples)
+                trace_python_ref = self.run_post_trace_test_python_reference_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, tau_minus)
+                self.plot_run(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, fname)
+                if not self.nest_trace_matches_ref_trace(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time):
+                    import pdb;pdb.set_trace()
+                self.assertTrue(self.nest_trace_matches_ref_trace(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time))
 
 
 

From 41060e3e9bcc67892a759ad05c1205dded196de7 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 27 Feb 2019 21:02:50 +0100
Subject: [PATCH 14/42] postsynaptic trace fixes for #1034: cleanup

---
 models/stdp_connection.h             |  2 --
 nestkernel/archiving_node.cpp        | 29 ++--------------------------
 pynest/nest/tests/test_post_trace.py |  6 ++----
 3 files changed, 4 insertions(+), 33 deletions(-)

diff --git a/models/stdp_connection.h b/models/stdp_connection.h
index 4035a4da1d..4b3dd4fd53 100644
--- a/models/stdp_connection.h
+++ b/models/stdp_connection.h
@@ -247,7 +247,6 @@ STDPConnection< targetidentifierT >::send( Event& e,
   double minus_dt;
   while ( start != finish )
   {
-    std::cout << "\tlooping over the history: it->t_ = " << start->t_ << std::endl;
     minus_dt = t_lastspike_ - ( start->t_ + dendritic_delay );
     ++start;
     // get_history() should make sure that
@@ -257,7 +256,6 @@ STDPConnection< targetidentifierT >::send( Event& e,
   }
 
   const double _K_value = target->get_K_value( t_spike - dendritic_delay );
-  std::cout << "In Synapse: t_spike = " << t_spike << ", dendritic_delay = " << dendritic_delay << ", got K_value = " << _K_value << std::endl;
   weight_ = depress_( weight_, _K_value );
 
   e.set_receiver( *target );
diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 23a1150e76..7a7f7e6d7d 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -104,43 +104,26 @@ Archiving_Node::register_stdp_connection( double t_first_read, double delay )
 double
 nest::Archiving_Node::get_K_value( double t )
 {
-  std::cout << "* In Archiving_Node::get_K_value(t = " << t << ")\n";
   if ( history_.empty() )
   {
     trace_ = 0.;
-    std::cout << "\t--> trace = " << trace_ << std::endl;
     return trace_;
   }
 
-  {
-  std::cout << "\tCurrent history list:\n";
-  int i = 0;
-  while ( i < history_.size() )
-  {
-    std::cout << "\t\thistory["<<i<<"] t = " << history_[ i ].t_ << std::endl;
-    ++i;
-  }
-  }
-
   // search for the latest post spike in the history buffer that came strictly before `t`
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
-    // if ( t >= history_[ i ].t_)		// XXX: CAP: TRACES FIX
     if ( t - history_[ i ].t_ > kernel().connection_manager.get_stdp_eps() )
     {
       trace_ = ( history_[ i ].Kminus_
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
-    std::cout << "\t   updating trace from t = " << history_[i].t_ << " to t = " << t << std::endl;
-    std::cout << "\t   --> trace = " << trace_ << std::endl;
       return trace_;
     }
     i--;
   }
 
-  trace_ = 0.;
-  std::cout << "\t--> fall-through: trace = " << trace_ << std::endl;
-  return trace_;
+  assert(0);  // this case should never happen, i.e. a suitable spike should always be present in the history buffer
 }
 
 void
@@ -183,7 +166,6 @@ nest::Archiving_Node::get_history( double t1,
   std::deque< histentry >::iterator* start,
   std::deque< histentry >::iterator* finish )
 {
-  std::cout << "* In Archiving_Node::get_history( t1 = " << t1 << " (excl.); t2 = " << t2 << " (incl.)\n";
   *finish = history_.end();
   if ( history_.empty() )
   {
@@ -191,8 +173,6 @@ nest::Archiving_Node::get_history( double t1,
     return;
   }
   std::deque< histentry >::reverse_iterator runner = history_.rbegin();
-  //const double t2_lim = t2;// + kernel().connection_manager.get_stdp_eps();
-  //const double t1_lim = t1;// + kernel().connection_manager.get_stdp_eps();
   const double t2_lim = t2 + kernel().connection_manager.get_stdp_eps();
   const double t1_lim = t1 + kernel().connection_manager.get_stdp_eps();
   while ( runner != history_.rend() and runner->t_ >= t2_lim )
@@ -200,7 +180,6 @@ nest::Archiving_Node::get_history( double t1,
     ++runner;
   }
   *finish = runner.base();
-//  while ( runner != history_.rend() and runner->t_ > t1_lim )
   while ( runner != history_.rend() and runner->t_ >= t1_lim )
   {
     runner->access_counter_++;
@@ -212,8 +191,6 @@ nest::Archiving_Node::get_history( double t1,
 void
 nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
 {
-    std::cout << "* In Archiving_Node::set_spiketime(t_sp = " << t_sp << ", offset = " << offset << ")" << std::endl;
-
   const double t_sp_ms = t_sp.get_ms() - offset;
   update_synaptic_elements( t_sp_ms );
   Ca_minus_ += beta_Ca_;
@@ -223,14 +200,13 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
     // prune all spikes from history which are no longer needed
     // only remove a spike if:
     // - its access counter indicates it has been read out by all connected STDP synapses, and
-    // - there is another, later spike, that is strictly more than max_delay_ away from the current spike at t_sp_ms
+    // - there is another, later spike, that is strictly more than (max_delay_ + eps) away from the new spike (at t_sp_ms)
     while ( history_.size() > 1 )
     {
       const double next_t_sp = history_[1].t_;
       if ( history_.front().access_counter_ >= n_incoming_
            && t_sp_ms - next_t_sp > max_delay_ + kernel().connection_manager.get_stdp_eps() )	// XXX: CAP: TRACES FIX
       {
-        std::cout << "\tRemoving spike at t = " << history_[0].t_ << " from history\n";
         history_.pop_front();
       }
       else
@@ -266,7 +242,6 @@ nest::Archiving_Node::get_status( DictionaryDatum& d ) const
   def< double >( d, names::beta_Ca, beta_Ca_ );
   def< double >( d, names::tau_minus_triplet, tau_minus_triplet_ );
   def< double >( d, names::post_trace, trace_ );
-     std::cout << "In Archiving_Node::get_status(): trace = " << trace_ << std::endl;
 #ifdef DEBUG_ARCHIVER
   def< int >( d, names::archiver_length, history_.size() );
 #endif
diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index f82b16d5d1..d4743451f4 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# test_stdp_multiplicity.py
+# test_post_trace.py
 #
 # This file is part of NEST.
 #
@@ -19,8 +19,6 @@
 # You should have received a copy of the GNU General Public License
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
-# This script tests the parrot_neuron in NEST.
-
 import nest
 import unittest
 import math
@@ -31,7 +29,7 @@
 import matplotlib.ticker as plticker
 
 
-#@nest.check_stack
+@nest.check_stack
 class PostTraceTestCase(unittest.TestCase):
 
     trace_match_atol_ = 1E-2

From fd2a9d7d6813457b36394e3cd5975ba5bad454e9 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 27 Feb 2019 21:04:37 +0100
Subject: [PATCH 15/42] postsynaptic trace fixes for #1034: cleanup

---
 nestkernel/archiving_node.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 7a7f7e6d7d..bf2d8fcfc2 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -205,7 +205,7 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
     {
       const double next_t_sp = history_[1].t_;
       if ( history_.front().access_counter_ >= n_incoming_
-           && t_sp_ms - next_t_sp > max_delay_ + kernel().connection_manager.get_stdp_eps() )	// XXX: CAP: TRACES FIX
+           && t_sp_ms - next_t_sp > max_delay_ + kernel().connection_manager.get_stdp_eps() )
       {
         history_.pop_front();
       }

From 7d4fb0e6b32ba9abe070b588b17d053b597caa3c Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 27 Feb 2019 21:11:32 +0100
Subject: [PATCH 16/42] postsynaptic trace fixes for #1034: cleanup

---
 nestkernel/archiving_node.h          | 5 +----
 pynest/nest/tests/test_post_trace.py | 6 +++---
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index 7f0cdce270..6d35e9a1be 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -216,16 +216,13 @@ class Archiving_Node : public Node
   double tau_minus_triplet_inv_;
 
   double max_delay_;
+  double trace_;
 
   double last_spike_;
 
   // spiking history needed by stdp synapses
   std::deque< histentry > history_;
 
-  
-  double trace_;    // XXX: DEBUGGING ONLY: REMOVE
-  
-  
   /*
    * Structural plasticity
    */
diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index d4743451f4..927fcf087c 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -63,7 +63,7 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
                                         #'precise_times': True})
         post_sg_ps = nest.Create("spike_generator",
                                 params={"spike_times": post_spike_times})
-#                                            'precise_times': True})
+                                        #'precise_times': True})
 
         # create parrot neurons and connect spike_generators
         #pre_parrot = nest.Create("parrot_neuron")
@@ -81,8 +81,8 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
                     syn_spec={"delay": delay})
 
         # create spike detector --- debugging only
-        spikes = nest.Create("spike_detector")#,
-           #                 params={'precise_times': True})
+        spikes = nest.Create("spike_detector",
+                           params={'precise_times': True})
         nest.Connect(
             #pre_parrot + post_parrot +
             pre_parrot_ps + post_parrot_ps,

From ba09243e9e61fda49a6edaa814a832bee19bbd39 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 27 Feb 2019 21:18:58 +0100
Subject: [PATCH 17/42] postsynaptic trace fixes for #1034: cleanup

---
 nestkernel/archiving_node.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 23a1150e76..53edbdba8e 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -135,9 +135,10 @@ nest::Archiving_Node::get_K_value( double t )
     std::cout << "\t   --> trace = " << trace_ << std::endl;
       return trace_;
     }
-    i--;
+    --i;
   }
 
+  // this case occurs when the trace was requested at a time precisely at or before the first spike in the history
   trace_ = 0.;
   std::cout << "\t--> fall-through: trace = " << trace_ << std::endl;
   return trace_;

From 73fc9c34beef4fc73e399622b69471a01cba9a73 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 27 Feb 2019 22:22:49 +0100
Subject: [PATCH 18/42] pep8

---
 pynest/nest/tests/test_post_trace.py | 308 +++++++++++++++++++--------
 1 file changed, 215 insertions(+), 93 deletions(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 3115242f7b..c19156e663 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -35,10 +35,14 @@ class PostTraceTestCase(unittest.TestCase):
     trace_match_atol_ = 1E-2
     trace_match_rtol_ = 1E-2
 
-    def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolution, delay, sim_time, tau_minus, show_all_nest_trace_samples=False):
+    def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
+                                  resolution, delay, sim_time, tau_minus,
+                                  show_all_nest_trace_samples=False):
 
-        print("Pre spike times: [" + ", ".join([str(t) for t in pre_spike_times]) + "]")
-        print("Post spike times: [" + ", ".join([str(t) for t in post_spike_times]) + "]")
+        print("Pre spike times: ["
+              + ", ".join([str(t) for t in pre_spike_times]) + "]")
+        print("Post spike times: ["
+              + ", ".join([str(t) for t in post_spike_times]) + "]")
 
         nest.set_verbosity("M_WARNING")
 
@@ -49,28 +53,29 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
 
         wr = nest.Create('weight_recorder')
         nest.CopyModel("stdp_synapse", "stdp_synapse_rec",
-                    {"weight_recorder": wr[0], "weight": 1.})
+                       {"weight_recorder": wr[0], "weight": 1.})
 
         # create spike_generators with these times
         pre_sg_ps = nest.Create("spike_generator",
                                 params={"spike_times": pre_spike_times,
                                         'precise_times': True})
         post_sg_ps = nest.Create("spike_generator",
-                                params={"spike_times": post_spike_times,
-                                        'precise_times': True})
+                                 params={"spike_times": post_spike_times,
+                                         'precise_times': True})
 
         # create parrot neurons and connect spike_generators
         pre_parrot_ps = nest.Create("parrot_neuron_ps")
-        post_parrot_ps = nest.Create("parrot_neuron_ps", params={"tau_minus" : tau_minus})
+        post_parrot_ps = nest.Create("parrot_neuron_ps",
+                                     params={"tau_minus": tau_minus})
 
         nest.Connect(pre_sg_ps, pre_parrot_ps,
-                    syn_spec={"delay": delay})
+                     syn_spec={"delay": delay})
         nest.Connect(post_sg_ps, post_parrot_ps,
-                    syn_spec={"delay": delay})
+                     syn_spec={"delay": delay})
 
         # create spike detector --- debugging only
         spikes = nest.Create("spike_detector",
-                           params={'precise_times': True})
+                             params={'precise_times': True})
         nest.Connect(
             pre_parrot_ps + post_parrot_ps,
             spikes
@@ -81,11 +86,13 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
         # not repeated postsynaptically.
         nest.Connect(
             pre_parrot_ps, post_parrot_ps,
-            syn_spec={'model': 'stdp_synapse_rec', 'receptor_type': 1, 'delay' : delay})
+            syn_spec={'model': 'stdp_synapse_rec',
+                      'receptor_type': 1,
+                      'delay': delay})
 
         # get STDP synapse
         syn_ps = nest.GetConnections(source=pre_parrot_ps,
-                                    synapse_model="stdp_synapse_rec")
+                                     synapse_model="stdp_synapse_rec")
 
         print("[py] Total simulation time: " + str(sim_time) + " ms")
         n_steps = int(np.ceil(sim_time / delay))
@@ -93,27 +100,33 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times, resolutio
         trace_nest_t = []
         t = nest.GetStatus([0], "time")[0]
         trace_nest_t.append(t)
-        post_trace_value = nest.GetStatus(post_parrot_ps)[0]['post_trace']
-        trace_nest.append(post_trace_value)
+        post_tr = nest.GetStatus(post_parrot_ps)[0]['post_trace']
+        trace_nest.append(post_tr)
         for step in range(n_steps):
             print("\n[py] simulating for " + str(delay) + " ms")
             nest.Simulate(delay)
             t = nest.GetStatus([0], "time")[0]
-            if show_all_nest_trace_samples or np.any(np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.):
+            nearby_pre_spike = np.any(
+                np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.)
+            if show_all_nest_trace_samples or nearby_pre_spike:
                 trace_nest_t.append(t)
-                post_trace_value = nest.GetStatus(post_parrot_ps)[0]['post_trace']
-                trace_nest.append(post_trace_value)
-                print("[py] Received NEST trace: " + str(post_trace_value) + " at time t = " + str(t))
+                post_tr = nest.GetStatus(post_parrot_ps)[0]['post_trace']
+                trace_nest.append(post_tr)
+                print("[py] Received NEST trace: " +
+                      str(post_tr) + " at time t = " + str(t))
 
         return trace_nest_t, trace_nest
 
-
-    def run_post_trace_test_python_reference_(self, pre_spike_times, post_spike_times, resolution, delay, dendritic_delay, sim_time, tau_minus):
+    def run_post_trace_test_python_reference_(self, pre_spike_times,
+                                              post_spike_times, resolution,
+                                              delay, dendritic_delay, sim_time,
+                                              tau_minus):
         """
         compute Python known-good reference of postsynaptic trace
         """
 
-        n_timepoints = 100 * int(np.ceil(max(np.amax(pre_spike_times), np.amax(post_spike_times))))
+        max_t_sp = max(np.amax(pre_spike_times), np.amax(post_spike_times))
+        n_timepoints = 100 * int(np.ceil(max_t_sp))
         trace_python_ref = np.zeros(n_timepoints)
         n_spikes = len(post_spike_times)
         for sp_idx in range(n_spikes):
@@ -126,18 +139,20 @@ def run_post_trace_test_python_reference_(self, pre_spike_times, post_spike_time
         n_spikes = len(pre_spike_times)
         for sp_idx in range(n_spikes):
             t_sp = pre_spike_times[sp_idx] + delay
-            i = int(np.round(t_sp / sim_time * float(len(trace_python_ref) - 1)))
-            print("* At t_sp = " + str(t_sp) + ", post_trace should be " + str(trace_python_ref[i]))
+            i = int(np.round(t_sp / sim_time
+                             * float(len(trace_python_ref) - 1)))
+            print("* At t_sp = " + str(t_sp)
+                  + ", post_trace should be " + str(trace_python_ref[i]))
 
         return trace_python_ref
 
-
-    def plot_run(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times, post_spike_times, resolution, delay, dendritic_delay, sim_time, fname):
+    def plot_run(self, trace_nest_t, trace_nest, trace_python_ref,
+                 pre_spike_times, post_spike_times, resolution, delay,
+                 dendritic_delay, sim_time, fname):
 
         fig, ax = plt.subplots(nrows=3)
         ax1, ax2, ax3 = ax
 
-
         #
         #   pre spikes
         #
@@ -146,8 +161,9 @@ def plot_run(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times,
         ax1.set_ylabel("Pre spikes")
         n_spikes = len(pre_spike_times)
         for i in range(n_spikes):
-            ax1.plot(2 * [pre_spike_times[i] + delay], ax1.get_ylim(), linewidth=2, color="blue", alpha=.4)
-
+            ax1.plot(2 * [pre_spike_times[i] + delay],
+                     ax1.get_ylim(),
+                     linewidth=2, color="blue", alpha=.4)
 
         #
         #   post spikes
@@ -157,8 +173,9 @@ def plot_run(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times,
         ax2.set_ylabel("Post spikes")
         n_spikes = len(post_spike_times)
         for i in range(n_spikes):
-            ax2.plot(2 * [post_spike_times[i] + delay + dendritic_delay], [0, 1], linewidth=2, color="red", alpha=.4)
-
+            ax2.plot(2 * [post_spike_times[i] + delay + dendritic_delay],
+                     [0, 1],
+                     linewidth=2, color="red", alpha=.4)
 
         #
         #   traces
@@ -167,97 +184,169 @@ def plot_run(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times,
         ax3.legend()
         ax3.set_ylabel("Synaptic trace")
         ax3.set_ylim([0., np.amax(trace_python_ref)])
-        ax3.plot(np.linspace(0., sim_time, len(trace_python_ref)), trace_python_ref, label="Expected", color="cyan", alpha=.6)
-        ax3.scatter(trace_nest_t, trace_nest, marker=".", alpha=.5, color="orange", label="NEST")
-
+        ax3.plot(np.linspace(0., sim_time, len(trace_python_ref)),
+                 trace_python_ref,
+                 label="Expected", color="cyan", alpha=.6)
+        ax3.scatter(trace_nest_t, trace_nest,
+                    marker=".", alpha=.5, color="orange", label="NEST")
 
         #
-        #   Trace values are returned from NEST at regular intervals, but only updated at presynaptic spike times.
+        #   Trace values are returned from NEST at regular intervals, but only
+        #   updated at presynaptic spike times.
         #
-        #   Step backwards in time from the sampled value, to find the last time at which the trace value was updated, namely the time of occurrence of the last presynaptic spike.
+        #   Step backwards in time from the sampled value, to find the last
+        #   time at which the trace value was updated, namely the time of
+        #   occurrence of the last presynaptic spike.
         #
 
+        pre_spike_times = np.array(pre_spike_times)
         n_timepoints = len(trace_nest_t)
         for i in range(n_timepoints):
             t = trace_nest_t[i]
-            print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
-            for i_search, t_search in enumerate(reversed(np.array(pre_spike_times) + delay)):
+            print("* Finding ref for NEST timepoint t = "
+                  + str(t) + ", trace = " + str(trace_nest[i]))
+            for i_search, t_search in enumerate(
+                                        reversed(pre_spike_times + delay)):
                 if t_search <= t:
                     print("\t* Testing " + str(t_search) + "...")
-                    _trace_at_t_search = trace_python_ref[int(np.round(t_search / sim_time * float(len(trace_python_ref) - 1)))]
-                    traces_match = np.allclose(_trace_at_t_search, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                    _idx = int(np.round(t_search / sim_time
+                               * float(len(trace_python_ref) - 1)))
+                    _trace_at_t_search = trace_python_ref[_idx]
+                    traces_match = np.allclose(_trace_at_t_search,
+                                               trace_nest[i],
+                                               atol=self.trace_match_atol_,
+                                               rtol=self.trace_match_rtol_)
                     print("\t   traces_match = " + str(traces_match))
                     if not traces_match:
-                        post_spike_occurred_at_t_search = np.any((t_search - (np.array(post_spike_times) + delay + dendritic_delay))**2 < resolution/2.)
-                        print("\t   post_spike_occurred_at_t_search = " + str(post_spike_occurred_at_t_search))
+                        post_spike_occurred_at_t_search = np.any(
+                            (t_search - (np.array(post_spike_times)
+                             + delay + dendritic_delay))**2 < resolution/2.)
+                        print("\t   post_spike_occurred_at_t_search = "
+                              + str(post_spike_occurred_at_t_search))
                         if post_spike_occurred_at_t_search:
-                            traces_match = np.allclose(_trace_at_t_search + 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
-                            print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search+1) + ")")
+                            traces_match = np.allclose(
+                                _trace_at_t_search + 1,
+                                trace_nest[i],
+                                atol=self.trace_match_atol_,
+                                rtol=self.trace_match_rtol_)
+                            print("\t   traces_match = " + str(traces_match)
+                                  + " (nest trace = " + str(trace_nest[i])
+                                  + ", ref trace = "
+                                  + str(_trace_at_t_search+1) + ")")
                             if traces_match:
                                 _trace_at_t_search += 1.
 
-                        if not traces_match and post_spike_occurred_at_t_search:
-                            traces_match = np.allclose(_trace_at_t_search - 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
-                            print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search-1) + ")")
+                        if not traces_match \
+                                and post_spike_occurred_at_t_search:
+                            traces_match = np.allclose(
+                                _trace_at_t_search - 1,
+                                trace_nest[i],
+                                atol=self.trace_match_atol_,
+                                rtol=self.trace_match_rtol_)
+                            print("\t   traces_match = "
+                                  + str(traces_match)
+                                  + " (nest trace = "
+                                  + str(trace_nest[i])
+                                  + ", ref trace = "
+                                  + str(_trace_at_t_search-1) + ")")
                             if traces_match:
                                 _trace_at_t_search -= 1.
 
-                    ax3.scatter(t_search, _trace_at_t_search, 100, marker=".", color="#A7FF00FF", facecolor="none")#"#FFFFFF7F")
-                    ax3.plot([trace_nest_t[i], t_search], [trace_nest[i], _trace_at_t_search], linewidth=.5, color="#0000007F")
+                    ax3.scatter(t_search, _trace_at_t_search, 100, marker=".",
+                                color="#A7FF00FF", facecolor="none")
+                    ax3.plot([trace_nest_t[i], t_search],
+                             [trace_nest[i], _trace_at_t_search],
+                             linewidth=.5, color="#0000007F")
                     break
 
-        
         for _ax in ax:
-            _ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10*delay))
-            _ax.xaxis.set_minor_locator(plticker.MultipleLocator(base=delay))
+            _ax.xaxis.set_major_locator(
+                plticker.MultipleLocator(base=10*delay))
+            _ax.xaxis.set_minor_locator(
+                plticker.MultipleLocator(base=delay))
             _ax.grid(which="major", axis="both")
             _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
-            #_ax.minorticks_on()
             _ax.set_xlim(0., sim_time)
 
         ax3.set_xlabel("Time [ms]")
-        fig.suptitle("Postsynaptic trace testbench. Spike times are\nshown from the perspective of the STDP synapse.")
+        fig.suptitle("""Postsynaptic trace testbench. Spike times are\n"""
+                     """shown from the perspective of the STDP synapse.""")
 
         print("* Saving to " + fname)
         fig.savefig(fname, dpi=300.)
 
-
-    def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest, trace_python_ref, pre_spike_times, post_spike_times, resolution, delay, dendritic_delay, sim_time, debug=True):
+    def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest,
+                                     trace_python_ref, pre_spike_times,
+                                     post_spike_times, resolution, delay,
+                                     dendritic_delay, sim_time, debug=True):
         """
-        Trace values are returned from NEST at regular intervals, but only updated at presynaptic spike times.
-        
-        To match the NEST samples with the continuous reference trace, step backwards in time from the sampled value, to find the last time at which the trace value was updated, namely the time of occurrence of the last presynaptic spike.
+        Trace values are returned from NEST at regular intervals, but only
+        updated at presynaptic spike times.
+
+        To match the NEST samples with the continuous reference trace, step
+        backwards in time from the sampled value, to find the last time at
+        which the trace value was updated, namely the time of occurrence of
+        the last presynaptic spike.
         """
 
         n_timepoints = len(trace_nest_t)
         for i in range(n_timepoints)[1:]:
             t = trace_nest_t[i]
             if debug:
-                print("* Finding ref for NEST timepoint t = " + str(t) + ", trace = " + str(trace_nest[i]))
+                print("* Finding ref for NEST timepoint t = " + str(t)
+                      + ", trace = " + str(trace_nest[i]))
 
             traces_match = False
-            for i_search, t_search in enumerate(reversed(np.array(pre_spike_times) + delay)):
+            for i_search, t_search in enumerate(
+                    reversed(np.array(pre_spike_times) + delay)):
                 if t_search <= t:
-                    _trace_at_t_search = trace_python_ref[int(np.round(t_search / sim_time * float(len(trace_python_ref) - 1)))]
-                    traces_match = np.allclose(_trace_at_t_search, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
-                    post_spike_occurred_at_t_search = np.any((t_search - (np.array(post_spike_times) + delay + dendritic_delay))**2 < resolution/2.)
+                    _trace_at_t_search = trace_python_ref[int(np.round(
+                        t_search / sim_time
+                        * float(len(trace_python_ref) - 1)))]
+                    traces_match = np.allclose(
+                        _trace_at_t_search,
+                        trace_nest[i],
+                        atol=self.trace_match_atol_,
+                        rtol=self.trace_match_rtol_)
+                    post_spike_occurred_at_t_search = np.any(
+                        (t_search - (np.array(post_spike_times)
+                                     + delay
+                                     + dendritic_delay))**2
+                        < resolution/2.)
 
                     if debug:
                         print("\t* Testing " + str(t_search) + "...")
                         print("\t   traces_match = " + str(traces_match))
-                        print("\t   post_spike_occurred_at_t_search = " + str(post_spike_occurred_at_t_search))
+                        print("\t   post_spike_occurred_at_t_search = "
+                              + str(post_spike_occurred_at_t_search))
 
                     if (not traces_match) and post_spike_occurred_at_t_search:
-                        traces_match = np.allclose(_trace_at_t_search + 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                        traces_match = np.allclose(
+                            _trace_at_t_search + 1,
+                            trace_nest[i],
+                            atol=self.trace_match_atol_,
+                            rtol=self.trace_match_rtol_)
                         if debug:
-                            print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search+1) + ")")
+                            print("\t   traces_match = " + str(traces_match)
+                                  + " (nest trace = " + str(trace_nest[i])
+                                  + ", ref trace = "
+                                  + str(_trace_at_t_search + 1)
+                                  + ")")
                         if traces_match:
                             _trace_at_t_search += 1.
 
                     if (not traces_match) and post_spike_occurred_at_t_search:
-                        traces_match = np.allclose(_trace_at_t_search - 1, trace_nest[i], atol=self.trace_match_atol_, rtol=self.trace_match_rtol_)
+                        traces_match = np.allclose(
+                            _trace_at_t_search - 1,
+                            trace_nest[i],
+                            atol=self.trace_match_atol_,
+                            rtol=self.trace_match_rtol_)
                         if debug:
-                            print("\t   traces_match = " + str(traces_match) + " (nest trace = " + str(trace_nest[i]) + ", ref trace = " + str(_trace_at_t_search-1) + ")")
+                            print("\t   traces_match = " + str(traces_match)
+                                  + " (nest trace = " + str(trace_nest[i])
+                                  + ", ref trace = "
+                                  + str(_trace_at_t_search - 1)
+                                  + ")")
                         if traces_match:
                             _trace_at_t_search -= 1.
 
@@ -277,16 +366,19 @@ def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest, trace_python_re
     def test_post_trace(self):
         """
         construct a network of the form:
-
-                               static_synapse                   stdp_synapse                    static_synapse
-            [ pre_spike_gen ] ----(delay)----o [ pre_parrot ] ----(delay)----o [ post_parrot ] o----(delay)---- [ post_spike_gen ]
-
-
-        The spike times of the spike generators are defined in `pre_spike_times` and `post_spike_times`. From the perspective of the STDP synapse, spikes arrive with the following delays (with respect to the values in these lists):
-
-        - for the presynaptic neuron: one synaptic delay in the leftmost static synapse
-        - for the postsynaptic neuron: one synaptic delay in the rightmost static synapse
-        - for the synapse itself: one dendritic delay between the post_parrot node and the synapse itself (see the C++ variable `dendritic_delay`).
+        - pre_spike_gen connects via static_synapse to pre_parrot
+        - pre_parrot connects via stdp_synapse to post_parrot
+        - post_spike_gen connects via static_synapse to post_parrot
+
+        The spike times of the spike generators are defined in
+        `pre_spike_times` and `post_spike_times`. From the perspective of the
+        STDP synapse, spikes arrive with the following delays (with respect to
+        the values in these lists):
+
+        - for the presynaptic neuron: one synaptic delay in the static synapse
+        - for the postsynaptic neuron: one synaptic delay in the static synapse
+        - for the synapse itself: one dendritic delay between the post_parrot
+          node and the synapse itself (see the C++ variable `dendritic_delay`).
         """
 
         show_all_nest_trace_samples = True
@@ -301,27 +393,59 @@ def test_post_trace(self):
         # generate some random integer spike times
         t_sp_min = 1.
         t_sp_max = 50
-        n_spikes = 10 #int(t_sp_max)
-        pre_spike_times2 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
+        n_spikes = 10
+        pre_spike_times2 = np.sort(
+            np.unique(
+                np.ceil(
+                    sp.stats.uniform.rvs(
+                        t_sp_min, t_sp_max - t_sp_min, n_spikes))))
         n_spikes = 50
-        post_spike_times2 = np.sort(np.unique(np.ceil(sp.stats.uniform.rvs(t_sp_min, t_sp_max - t_sp_min, n_spikes))))
+        post_spike_times2 = np.sort(
+            np.unique(
+                np.ceil(
+                    sp.stats.uniform.rvs(
+                        t_sp_min, t_sp_max - t_sp_min, n_spikes))))
         tau_minus = 2.  # [ms]
 
         # for each parameter set, run the test
-        pre_spike_times = [pre_spike_times1, pre_spike_times2, post_spike_times2]
-        post_spike_times = [post_spike_times1, post_spike_times2, pre_spike_times2]
+        pre_spike_times = [pre_spike_times1,
+                           pre_spike_times2,
+                           post_spike_times2]
+        post_spike_times = [post_spike_times1,
+                            post_spike_times2,
+                            pre_spike_times2]
 
         for delay in delays:
             dendritic_delay = delay
             for spike_times_idx in range(len(pre_spike_times)):
-                fname = "/tmp/traces_[delay=" + str(delay) + "]_[experiment=" + str(spike_times_idx) + "].png"
-                sim_time = np.amax(np.concatenate((pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx]))) + 5 * delay
-                trace_nest_t, trace_nest = self.run_post_trace_test_nest_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, sim_time, tau_minus, show_all_nest_trace_samples)
-                trace_python_ref = self.run_post_trace_test_python_reference_(pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, tau_minus)
-                self.plot_run(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time, fname)
-                if not self.nest_trace_matches_ref_trace(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time):
-                    import pdb;pdb.set_trace()
-                self.assertTrue(self.nest_trace_matches_ref_trace(trace_nest_t, trace_nest, trace_python_ref, pre_spike_times[spike_times_idx], post_spike_times[spike_times_idx], resolution, delay, dendritic_delay, sim_time))
+                fname = "/tmp/traces_[delay=" \
+                        + str(delay) \
+                        + "]_[experiment=" \
+                        + str(spike_times_idx) + "].png"
+                max_t_sp = max(np.amax(pre_spike_times[spike_times_idx]),
+                               np.amax(post_spike_times[spike_times_idx]))
+                sim_time = max_t_sp + 5 * delay
+                trace_nest_t, trace_nest = self.run_post_trace_test_nest_(
+                    pre_spike_times[spike_times_idx],
+                    post_spike_times[spike_times_idx],
+                    resolution, delay, sim_time, tau_minus,
+                    show_all_nest_trace_samples)
+                trace_python_ref = self.run_post_trace_test_python_reference_(
+                    pre_spike_times[spike_times_idx],
+                    post_spike_times[spike_times_idx],
+                    resolution, delay, dendritic_delay, sim_time, tau_minus)
+                self.plot_run(
+                    trace_nest_t, trace_nest, trace_python_ref,
+                    pre_spike_times[spike_times_idx],
+                    post_spike_times[spike_times_idx], resolution, delay,
+                    dendritic_delay, sim_time, fname)
+                self.assertTrue(self.nest_trace_matches_ref_trace(
+                    trace_nest_t,
+                    trace_nest,
+                    trace_python_ref,
+                    pre_spike_times[spike_times_idx],
+                    post_spike_times[spike_times_idx],
+                    resolution, delay, dendritic_delay, sim_time))
 
 
 def suite():
@@ -335,6 +459,4 @@ def run():
 
 
 if __name__ == "__main__":
-    #unittest.findTestCases(__main__).debug()
-    #run()
     PostTraceTestCase().test_post_trace()

From 5a91180165684a5fb389df44ef7709ce4bba95d3 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 27 Feb 2019 22:29:01 +0100
Subject: [PATCH 19/42] clang-format

---
 nestkernel/archiving_node.h | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/nestkernel/archiving_node.h b/nestkernel/archiving_node.h
index 6d35e9a1be..8450cdcb56 100644
--- a/nestkernel/archiving_node.h
+++ b/nestkernel/archiving_node.h
@@ -129,7 +129,9 @@ class Archiving_Node : public Node
 
   /**
    * \fn double get_K_value(long t)
-   * return the Kminus (synaptic trace) value at t (in ms). When the trace is requested at the exact same time that the neuron emits a spike, the trace value as it was just before the spike is returned.
+   * return the Kminus (synaptic trace) value at t (in ms). When the trace is
+   * requested at the exact same time that the neuron emits a spike, the trace
+   * value as it was just before the spike is returned.
    */
   double get_K_value( double t );
 

From 4a11b18dc6a1492ccd6871b2e5942e37795c167e Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 13 Mar 2019 18:02:39 +0100
Subject: [PATCH 20/42] homogenise comments/formatting between get_K_value()
 and get_K_values()

---
 nestkernel/archiving_node.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 017105e519..e24f61313b 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -104,6 +104,7 @@ Archiving_Node::register_stdp_connection( double t_first_read, double delay )
 double
 nest::Archiving_Node::get_K_value( double t )
 {
+  // case when the neuron has not yet spiked
   if ( history_.empty() )
   {
     trace_ = 0.;
@@ -140,7 +141,8 @@ nest::Archiving_Node::get_K_values( double t,
     K_value = Kminus_;
     return;
   }
-  // case
+
+  // search for the latest post spike in the history buffer that came strictly before `t`
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
@@ -152,12 +154,10 @@ nest::Archiving_Node::get_K_values( double t,
         * std::exp( ( history_[ i ].t_ - t ) * tau_minus_inv_ ) );
       return;
     }
-    i--;
+    --i;
   }
 
-  // we only get here if t< time of all spikes in history)
-
-  // return 0.0 for both K values
+  // this case occurs when the trace was requested at a time precisely at or before the first spike in the history
   triplet_K_value = 0.0;
   K_value = 0.0;
 }

From a868258e769982f34a0c9ed2f15b6748ba20a40c Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 13 Mar 2019 18:04:59 +0100
Subject: [PATCH 21/42] fix member variable initialisation order

---
 nestkernel/archiving_node.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index e24f61313b..05533091f5 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -49,12 +49,12 @@ nest::Archiving_Node::Archiving_Node()
   , tau_minus_triplet_( 110.0 )
   , tau_minus_triplet_inv_( 1. / tau_minus_triplet_ )
   , max_delay_( -1.0 )
+  , trace_(0.)
   , last_spike_( -1.0 )
   , Ca_t_( 0.0 )
   , Ca_minus_( 0.0 )
   , tau_Ca_( 10000.0 )
   , beta_Ca_( 0.001 )
-  , trace_(0.)
   , synaptic_elements_map_()
 {
 }
@@ -69,12 +69,12 @@ nest::Archiving_Node::Archiving_Node( const Archiving_Node& n )
   , tau_minus_triplet_( n.tau_minus_triplet_ )
   , tau_minus_triplet_inv_( n.tau_minus_triplet_inv_ )
   , max_delay_( n.max_delay_ )
+  , trace_(n.trace_)
   , last_spike_( n.last_spike_ )
   , Ca_t_( n.Ca_t_ )
   , Ca_minus_( n.Ca_minus_ )
   , tau_Ca_( n.tau_Ca_ )
   , beta_Ca_( n.beta_Ca_ )
-  , trace_(n.trace_)
   , synaptic_elements_map_( n.synaptic_elements_map_ )
 {
 }

From 6d0828e6386cfc79d76ed24109f1e54d1e3ac339 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 13 Mar 2019 18:08:14 +0100
Subject: [PATCH 22/42] fix line lengths to <80 characters

---
 nestkernel/archiving_node.cpp | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index 05533091f5..a73f054d10 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -111,7 +111,8 @@ nest::Archiving_Node::get_K_value( double t )
     return trace_;
   }
 
-  // search for the latest post spike in the history buffer that came strictly before `t`
+  // search for the latest post spike in the history buffer that came strictly
+  // before `t`
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
@@ -124,7 +125,8 @@ nest::Archiving_Node::get_K_value( double t )
     --i;
   }
 
-  // this case occurs when the trace was requested at a time precisely at or before the first spike in the history
+  // this case occurs when the trace was requested at a time precisely at or
+  // before the first spike in the history
   trace_ = 0.;
   return trace_;
 }
@@ -142,7 +144,8 @@ nest::Archiving_Node::get_K_values( double t,
     return;
   }
 
-  // search for the latest post spike in the history buffer that came strictly before `t`
+  // search for the latest post spike in the history buffer that came strictly
+  // before `t`
   int i = history_.size() - 1;
   while ( i >= 0 )
   {
@@ -157,7 +160,8 @@ nest::Archiving_Node::get_K_values( double t,
     --i;
   }
 
-  // this case occurs when the trace was requested at a time precisely at or before the first spike in the history
+  // this case occurs when the trace was requested at a time precisely at or
+  // before the first spike in the history
   triplet_K_value = 0.0;
   K_value = 0.0;
 }
@@ -201,13 +205,16 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
   {
     // prune all spikes from history which are no longer needed
     // only remove a spike if:
-    // - its access counter indicates it has been read out by all connected STDP synapses, and
-    // - there is another, later spike, that is strictly more than (max_delay_ + eps) away from the new spike (at t_sp_ms)
+    // - its access counter indicates it has been read out by all connected
+    //   STDP synapses, and
+    // - there is another, later spike, that is strictly more than
+    //   (max_delay_ + eps) away from the new spike (at t_sp_ms)
     while ( history_.size() > 1 )
     {
       const double next_t_sp = history_[1].t_;
       if ( history_.front().access_counter_ >= n_incoming_
-           && t_sp_ms - next_t_sp > max_delay_ + kernel().connection_manager.get_stdp_eps() )
+           && t_sp_ms - next_t_sp > max_delay_ +
+                  kernel().connection_manager.get_stdp_eps() )
       {
         history_.pop_front();
       }

From 9370f60d25cd61ca5108941f6913336235a7b710 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 13 Mar 2019 18:12:33 +0100
Subject: [PATCH 23/42] minor code beautifications; comments typography

---
 pynest/nest/tests/test_post_trace.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index c19156e663..b201143df4 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -13,7 +13,7 @@
 #
 # NEST is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See thed
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 # GNU General Public License for more details.
 #
 # You should have received a copy of the GNU General Public License
@@ -21,7 +21,6 @@
 
 import nest
 import unittest
-import math
 import numpy as np
 import scipy as sp
 import scipy.stats
@@ -46,8 +45,6 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
 
         nest.set_verbosity("M_WARNING")
 
-        post_weights = {'parrot': [], 'parrot_ps': []}
-
         nest.ResetKernel()
         nest.SetKernelStatus({'resolution': resolution})
 
@@ -205,8 +202,7 @@ def plot_run(self, trace_nest_t, trace_nest, trace_python_ref,
             t = trace_nest_t[i]
             print("* Finding ref for NEST timepoint t = "
                   + str(t) + ", trace = " + str(trace_nest[i]))
-            for i_search, t_search in enumerate(
-                                        reversed(pre_spike_times + delay)):
+            for t_search in reversed(pre_spike_times + delay):
                 if t_search <= t:
                     print("\t* Testing " + str(t_search) + "...")
                     _idx = int(np.round(t_search / sim_time

From f3e894a4141ce47fd1264bd74fdc71b801937583 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 13 Mar 2019 18:23:13 +0100
Subject: [PATCH 24/42] make plotting optional in the testbench

---
 pynest/nest/tests/test_post_trace.py | 53 ++++++++++++++++------------
 1 file changed, 30 insertions(+), 23 deletions(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index b201143df4..567b6754e1 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -19,13 +19,14 @@
 # You should have received a copy of the GNU General Public License
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
+import matplotlib.pyplot as plt
+import matplotlib.ticker as plticker
 import nest
-import unittest
 import numpy as np
+import os
 import scipy as sp
 import scipy.stats
-import matplotlib.pyplot as plt
-import matplotlib.ticker as plticker
+import unittest
 
 
 @nest.check_stack
@@ -377,16 +378,19 @@ def test_post_trace(self):
           node and the synapse itself (see the C++ variable `dendritic_delay`).
         """
 
-        show_all_nest_trace_samples = True
-
         resolution = .1     # [ms]
         delays = np.array([1., 5.])  # [ms]
 
-        # minimal reproducing example of the original bug
+        # settings for plotting debug information
+        make_debug_plots = False
+        show_all_nest_trace_samples = True
+        debug_plots_output_dir = "/tmp"
+
+        # spike test pattern 1: minimal reproducing example of the original bug
         pre_spike_times1 = np.array([2., 3., 10.])
         post_spike_times1 = np.array([1., 2., 3.])
 
-        # generate some random integer spike times
+        # spike test pattern 2: generate some random integer spike times
         t_sp_min = 1.
         t_sp_max = 50
         n_spikes = 10
@@ -404,6 +408,7 @@ def test_post_trace(self):
         tau_minus = 2.  # [ms]
 
         # for each parameter set, run the test
+        # spike test pattern 3 is a pre/post-reversed version of test pattern 2
         pre_spike_times = [pre_spike_times1,
                            pre_spike_times2,
                            post_spike_times2]
@@ -414,10 +419,6 @@ def test_post_trace(self):
         for delay in delays:
             dendritic_delay = delay
             for spike_times_idx in range(len(pre_spike_times)):
-                fname = "/tmp/traces_[delay=" \
-                        + str(delay) \
-                        + "]_[experiment=" \
-                        + str(spike_times_idx) + "].png"
                 max_t_sp = max(np.amax(pre_spike_times[spike_times_idx]),
                                np.amax(post_spike_times[spike_times_idx]))
                 sim_time = max_t_sp + 5 * delay
@@ -430,18 +431,24 @@ def test_post_trace(self):
                     pre_spike_times[spike_times_idx],
                     post_spike_times[spike_times_idx],
                     resolution, delay, dendritic_delay, sim_time, tau_minus)
-                self.plot_run(
-                    trace_nest_t, trace_nest, trace_python_ref,
-                    pre_spike_times[spike_times_idx],
-                    post_spike_times[spike_times_idx], resolution, delay,
-                    dendritic_delay, sim_time, fname)
-                self.assertTrue(self.nest_trace_matches_ref_trace(
-                    trace_nest_t,
-                    trace_nest,
-                    trace_python_ref,
-                    pre_spike_times[spike_times_idx],
-                    post_spike_times[spike_times_idx],
-                    resolution, delay, dendritic_delay, sim_time))
+
+                if make_debug_plots:
+                    fname = os.path.join(debug_plots_output_dir, "traces_[delay=" \
+                            + str(delay) \
+                            + "]_[experiment=" \
+                            + str(spike_times_idx) + "].png"
+                    self.plot_run(
+                        trace_nest_t, trace_nest, trace_python_ref,
+                        pre_spike_times[spike_times_idx],
+                        post_spike_times[spike_times_idx], resolution, delay,
+                        dendritic_delay, sim_time, fname)
+                    self.assertTrue(self.nest_trace_matches_ref_trace(
+                        trace_nest_t,
+                        trace_nest,
+                        trace_python_ref,
+                        pre_spike_times[spike_times_idx],
+                        post_spike_times[spike_times_idx],
+                        resolution, delay, dendritic_delay, sim_time))
 
 
 def suite():

From 0383c17f82d9c29da95b6753c5461f75e6956f20 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 13 Mar 2019 18:33:23 +0100
Subject: [PATCH 25/42] fix register_stdp_connection() API change in
 clopath_connection

---
 models/clopath_connection.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/models/clopath_connection.h b/models/clopath_connection.h
index 2b5bc8262e..775edbf93a 100644
--- a/models/clopath_connection.h
+++ b/models/clopath_connection.h
@@ -155,7 +155,7 @@ class ClopathConnection : public Connection< targetidentifierT >
 
     ConnectionBase::check_connection_( dummy_target, s, t, receptor_type );
 
-    t.register_stdp_connection( t_lastspike_ - get_delay() );
+    t.register_stdp_connection( t_lastspike_ - get_delay(), get_delay() );
   }
 
   void

From 6ef4e332a35eb865dcf48775000b1df0c99b361b Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Wed, 13 Mar 2019 19:05:08 +0100
Subject: [PATCH 26/42] pep8

---
 pynest/nest/tests/test_post_trace.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 567b6754e1..8771f950fb 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -433,10 +433,11 @@ def test_post_trace(self):
                     resolution, delay, dendritic_delay, sim_time, tau_minus)
 
                 if make_debug_plots:
-                    fname = os.path.join(debug_plots_output_dir, "traces_[delay=" \
+                    fname = "traces_[delay=" \
                             + str(delay) \
                             + "]_[experiment=" \
                             + str(spike_times_idx) + "].png"
+                    fname = os.path.join(debug_plots_output_dir, fn)
                     self.plot_run(
                         trace_nest_t, trace_nest, trace_python_ref,
                         pre_spike_times[spike_times_idx],

From 8407c3c18ff490cc2a0a8000eb94067bcd2298be Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Sun, 17 Mar 2019 15:22:17 +0100
Subject: [PATCH 27/42] fix reference to `@check_stack` in postsynaptic trace
 unit test

---
 pynest/nest/tests/test_post_trace.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 8771f950fb..8e098a548d 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -29,7 +29,7 @@
 import unittest
 
 
-@nest.check_stack
+@nest.ll_api.check_stack
 class PostTraceTestCase(unittest.TestCase):
 
     trace_match_atol_ = 1E-2

From ae902d29793a277cc559b0d5cbb62324d8bd3673 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Sun, 17 Mar 2019 16:12:04 +0100
Subject: [PATCH 28/42] fix reference to `nest.set_verbosity()` in postsynaptic
 trace unit test

---
 pynest/nest/tests/test_post_trace.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pynest/nest/tests/test_post_trace.py b/pynest/nest/tests/test_post_trace.py
index 8e098a548d..c4e773c43d 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/pynest/nest/tests/test_post_trace.py
@@ -44,7 +44,7 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
         print("Post spike times: ["
               + ", ".join([str(t) for t in post_spike_times]) + "]")
 
-        nest.set_verbosity("M_WARNING")
+        nest.hl_api.set_verbosity("M_WARNING")
 
         nest.ResetKernel()
         nest.SetKernelStatus({'resolution': resolution})

From e956582e208902cfe2834de87a9948de3575eb1c Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 25 Mar 2019 11:51:45 +0100
Subject: [PATCH 29/42] clang-format

---
 nestkernel/archiving_node.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index a73f054d10..fb282adb65 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -49,7 +49,7 @@ nest::Archiving_Node::Archiving_Node()
   , tau_minus_triplet_( 110.0 )
   , tau_minus_triplet_inv_( 1. / tau_minus_triplet_ )
   , max_delay_( -1.0 )
-  , trace_(0.)
+  , trace_( 0. )
   , last_spike_( -1.0 )
   , Ca_t_( 0.0 )
   , Ca_minus_( 0.0 )
@@ -69,7 +69,7 @@ nest::Archiving_Node::Archiving_Node( const Archiving_Node& n )
   , tau_minus_triplet_( n.tau_minus_triplet_ )
   , tau_minus_triplet_inv_( n.tau_minus_triplet_inv_ )
   , max_delay_( n.max_delay_ )
-  , trace_(n.trace_)
+  , trace_( n.trace_ )
   , last_spike_( n.last_spike_ )
   , Ca_t_( n.Ca_t_ )
   , Ca_minus_( n.Ca_minus_ )
@@ -98,7 +98,7 @@ Archiving_Node::register_stdp_connection( double t_first_read, double delay )
 
   n_incoming_++;
 
-  max_delay_ = std::max(delay, max_delay_);
+  max_delay_ = std::max( delay, max_delay_ );
 }
 
 double
@@ -211,10 +211,10 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
     //   (max_delay_ + eps) away from the new spike (at t_sp_ms)
     while ( history_.size() > 1 )
     {
-      const double next_t_sp = history_[1].t_;
+      const double next_t_sp = history_[ 1 ].t_;
       if ( history_.front().access_counter_ >= n_incoming_
-           && t_sp_ms - next_t_sp > max_delay_ +
-                  kernel().connection_manager.get_stdp_eps() )
+        && t_sp_ms - next_t_sp > max_delay_
+            + kernel().connection_manager.get_stdp_eps() )
       {
         history_.pop_front();
       }

From ec11b5f6dd796e8230aa12fddd644dabca9ce5ab Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 25 Mar 2019 17:15:16 +0100
Subject: [PATCH 30/42] split postsynaptic trace testbench into pure
 (regression) test part and Jupyter notebook

---
 doc/model_details/test_post_trace.ipynb       | 674 ++++++++++++++++++
 .../regressiontests/issue-1034.py             | 132 +---
 2 files changed, 675 insertions(+), 131 deletions(-)
 create mode 100644 doc/model_details/test_post_trace.ipynb
 rename pynest/nest/tests/test_post_trace.py => testsuite/regressiontests/issue-1034.py (71%)

diff --git a/doc/model_details/test_post_trace.ipynb b/doc/model_details/test_post_trace.ipynb
new file mode 100644
index 0000000000..c4ffef9899
--- /dev/null
+++ b/doc/model_details/test_post_trace.ipynb
@@ -0,0 +1,674 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This file is part of NEST.\n",
+    "#\n",
+    "# Copyright (C) 2004 The NEST Initiative\n",
+    "#\n",
+    "# NEST is free software: you can redistribute it and/or modify\n",
+    "# it under the terms of the GNU General Public License as published by\n",
+    "# the Free Software Foundation, either version 2 of the License, or\n",
+    "# (at your option) any later version.\n",
+    "#\n",
+    "# NEST is distributed in the hope that it will be useful,\n",
+    "# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
+    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
+    "# GNU General Public License for more details.\n",
+    "#\n",
+    "# You should have received a copy of the GNU General Public License\n",
+    "# along with NEST.  If not, see <http://www.gnu.org/licenses/>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as plticker\n",
+    "import nest\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import scipy as sp\n",
+    "import scipy.stats\n",
+    "import unittest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test postsynaptic trace\n",
+    "=======================\n",
+    "\n",
+    "Pre- and postsynaptic traces are used to calculate STDP weight updates, but are computed differently: postsynaptic traces are stored and maintained in the NEST C++ `Archiving_Node` class. Following [nest-simulator#1034](https://github.com/nest/nest-simulator/issues/1034), this notebook (and corresponding test script in `testsuite/regressiontests/issue-1034.py`) was created to specifically test the postsynaptic trace value, by comparing the NEST-obtained samples to a Python-generated reference timeseries.\n",
+    "\n",
+    "Construct a network of the form:\n",
+    "- pre_spike_gen connects via static_synapse to pre_parrot\n",
+    "- pre_parrot connects via stdp_synapse to post_parrot\n",
+    "- post_spike_gen connects via static_synapse to post_parrot\n",
+    "\n",
+    "The spike times of the spike generators are defined in\n",
+    "`pre_spike_times` and `post_spike_times`. From the perspective of the\n",
+    "STDP synapse, spikes arrive with the following delays (with respect to\n",
+    "the values in these lists):\n",
+    "\n",
+    "- for the presynaptic neuron: one synaptic delay in the static synapse\n",
+    "- for the postsynaptic neuron: one synaptic delay in the static synapse\n",
+    "- for the synapse itself: one dendritic delay between the post_parrot\n",
+    "  node and the synapse itself (see the C++ variable `dendritic_delay`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Preliminaries\n",
+    "-------------\n",
+    "\n",
+    "First, define a function that will validate equality between the Python-generated and the NEST-generated timeseries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace_match_atol = 1E-2\n",
+    "trace_match_rtol = 1E-2\n",
+    "\n",
+    "def nest_trace_matches_ref_trace(trace_nest_t, trace_nest,\n",
+    "                                 trace_python_ref, pre_spike_times,\n",
+    "                                 post_spike_times, resolution, delay,\n",
+    "                                 dendritic_delay, trace_match_atol,\n",
+    "                                 trace_match_rtol, sim_time,\n",
+    "                                 debug=False):\n",
+    "    \"\"\"\n",
+    "    Trace values are returned from NEST at regular intervals, but only\n",
+    "    updated at presynaptic spike times.\n",
+    "\n",
+    "    To match the NEST samples with the continuous reference trace, step\n",
+    "    backwards in time from the sampled value, to find the last time at\n",
+    "    which the trace value was updated, namely the time of occurrence of\n",
+    "    the last presynaptic spike.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    n_timepoints = len(trace_nest_t)\n",
+    "    for i in range(n_timepoints)[1:]:\n",
+    "        t = trace_nest_t[i]\n",
+    "        if debug:\n",
+    "            print(\"* Finding ref for NEST timepoint t = \" + str(t)\n",
+    "                  + \", trace = \" + str(trace_nest[i]))\n",
+    "\n",
+    "        traces_match = False\n",
+    "        for i_search, t_search in enumerate(\n",
+    "                reversed(np.array(pre_spike_times) + delay)):\n",
+    "            if t_search <= t:\n",
+    "                _trace_at_t_search = trace_python_ref[int(np.round(\n",
+    "                    t_search / sim_time\n",
+    "                    * float(len(trace_python_ref) - 1)))]\n",
+    "                traces_match = np.allclose(\n",
+    "                    _trace_at_t_search,\n",
+    "                    trace_nest[i],\n",
+    "                    atol=trace_match_atol,\n",
+    "                    rtol=trace_match_rtol)\n",
+    "                post_spike_occurred_at_t_search = np.any(\n",
+    "                    (t_search - (np.array(post_spike_times)\n",
+    "                                 + delay\n",
+    "                                 + dendritic_delay))**2\n",
+    "                    < resolution/2.)\n",
+    "\n",
+    "                if debug:\n",
+    "                    print(\"\\t* Testing \" + str(t_search) + \"...\")\n",
+    "                    print(\"\\t   traces_match = \" + str(traces_match))\n",
+    "                    print(\"\\t   post_spike_occurred_at_t_search = \"\n",
+    "                          + str(post_spike_occurred_at_t_search))\n",
+    "\n",
+    "                if (not traces_match) and post_spike_occurred_at_t_search:\n",
+    "                    traces_match = np.allclose(\n",
+    "                        _trace_at_t_search + 1,\n",
+    "                        trace_nest[i],\n",
+    "                        atol=trace_match_atol,\n",
+    "                        rtol=trace_match_rtol)\n",
+    "                    if debug:\n",
+    "                        print(\"\\t   traces_match = \" + str(traces_match)\n",
+    "                              + \" (nest trace = \" + str(trace_nest[i])\n",
+    "                              + \", ref trace = \"\n",
+    "                              + str(_trace_at_t_search + 1)\n",
+    "                              + \")\")\n",
+    "                    if traces_match:\n",
+    "                        _trace_at_t_search += 1.\n",
+    "\n",
+    "                if (not traces_match) and post_spike_occurred_at_t_search:\n",
+    "                    traces_match = np.allclose(\n",
+    "                        _trace_at_t_search - 1,\n",
+    "                        trace_nest[i],\n",
+    "                        atol=trace_match_atol,\n",
+    "                        rtol=trace_match_rtol)\n",
+    "                    if debug:\n",
+    "                        print(\"\\t   traces_match = \" + str(traces_match)\n",
+    "                              + \" (nest trace = \" + str(trace_nest[i])\n",
+    "                              + \", ref trace = \"\n",
+    "                              + str(_trace_at_t_search - 1)\n",
+    "                              + \")\")\n",
+    "                    if traces_match:\n",
+    "                        _trace_at_t_search -= 1.\n",
+    "\n",
+    "                break\n",
+    "\n",
+    "        if (not traces_match) and i_search == len(pre_spike_times) - 1:\n",
+    "            if debug:\n",
+    "                print(\"\\tthe time before the first pre spike\")\n",
+    "            # the time before the first pre spike\n",
+    "            traces_match = trace_nest[i] == 0.\n",
+    "\n",
+    "        if not traces_match:\n",
+    "            return False\n",
+    "\n",
+    "    return True\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NEST simulation\n",
+    "---------------\n",
+    "\n",
+    "Construct and run the NEST network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_post_trace_test_nest_(pre_spike_times, post_spike_times,\n",
+    "                              resolution, delay, sim_time, tau_minus,\n",
+    "                              show_all_nest_trace_samples=False,\n",
+    "                              debug=False):\n",
+    "\n",
+    "    if debug:\n",
+    "        print(\"Pre spike times: [\"\n",
+    "              + \", \".join([str(t) for t in pre_spike_times]) + \"]\")\n",
+    "        print(\"Post spike times: [\"\n",
+    "              + \", \".join([str(t) for t in post_spike_times]) + \"]\")\n",
+    "\n",
+    "    nest.hl_api.set_verbosity(\"M_WARNING\")\n",
+    "\n",
+    "    nest.ResetKernel()\n",
+    "    nest.SetKernelStatus({'resolution': resolution})\n",
+    "\n",
+    "    wr = nest.Create('weight_recorder')\n",
+    "    nest.CopyModel(\"stdp_synapse\", \"stdp_synapse_rec\",\n",
+    "                   {\"weight_recorder\": wr[0], \"weight\": 1.})\n",
+    "\n",
+    "    # create spike_generators with these times\n",
+    "    pre_sg_ps = nest.Create(\"spike_generator\",\n",
+    "                            params={\"spike_times\": pre_spike_times,\n",
+    "                                    'precise_times': True})\n",
+    "    post_sg_ps = nest.Create(\"spike_generator\",\n",
+    "                             params={\"spike_times\": post_spike_times,\n",
+    "                                     'precise_times': True})\n",
+    "\n",
+    "    # create parrot neurons and connect spike_generators\n",
+    "    pre_parrot_ps = nest.Create(\"parrot_neuron_ps\")\n",
+    "    post_parrot_ps = nest.Create(\"parrot_neuron_ps\",\n",
+    "                                 params={\"tau_minus\": tau_minus})\n",
+    "\n",
+    "    nest.Connect(pre_sg_ps, pre_parrot_ps,\n",
+    "                 syn_spec={\"delay\": delay})\n",
+    "    nest.Connect(post_sg_ps, post_parrot_ps,\n",
+    "                 syn_spec={\"delay\": delay})\n",
+    "\n",
+    "    # create spike detector --- debugging only\n",
+    "    spikes = nest.Create(\"spike_detector\",\n",
+    "                         params={'precise_times': True})\n",
+    "    nest.Connect(\n",
+    "        pre_parrot_ps + post_parrot_ps,\n",
+    "        spikes\n",
+    "    )\n",
+    "\n",
+    "    # connect both parrot neurons with a stdp synapse onto port 1\n",
+    "    # thereby spikes transmitted through the stdp connection are\n",
+    "    # not repeated postsynaptically.\n",
+    "    nest.Connect(\n",
+    "        pre_parrot_ps, post_parrot_ps,\n",
+    "        syn_spec={'model': 'stdp_synapse_rec',\n",
+    "                  'receptor_type': 1,\n",
+    "                  'delay': delay})\n",
+    "\n",
+    "    # get STDP synapse\n",
+    "    syn_ps = nest.GetConnections(source=pre_parrot_ps,\n",
+    "                                 synapse_model=\"stdp_synapse_rec\")\n",
+    "\n",
+    "    if debug:\n",
+    "        print(\"[py] Total simulation time: \" + str(sim_time) + \" ms\")\n",
+    "    n_steps = int(np.ceil(sim_time / delay))\n",
+    "    trace_nest = []\n",
+    "    trace_nest_t = []\n",
+    "    t = nest.GetStatus([0], \"time\")[0]\n",
+    "    trace_nest_t.append(t)\n",
+    "    post_tr = nest.GetStatus(post_parrot_ps)[0]['post_trace']\n",
+    "    trace_nest.append(post_tr)\n",
+    "    for step in range(n_steps):\n",
+    "        if debug:\n",
+    "            print(\"\\n[py] simulating for \" + str(delay) + \" ms\")\n",
+    "        nest.Simulate(delay)\n",
+    "        t = nest.GetStatus([0], \"time\")[0]\n",
+    "        nearby_pre_spike = np.any(\n",
+    "            np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.)\n",
+    "        if show_all_nest_trace_samples or nearby_pre_spike:\n",
+    "            trace_nest_t.append(t)\n",
+    "            post_tr = nest.GetStatus(post_parrot_ps)[0]['post_trace']\n",
+    "            trace_nest.append(post_tr)\n",
+    "            if debug:\n",
+    "                print(\"[py] Received NEST trace: \" +\n",
+    "                      str(post_tr) + \" at time t = \" + str(t))\n",
+    "\n",
+    "    return trace_nest_t, trace_nest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python simulation\n",
+    "-----------------\n",
+    "\n",
+    "Generate the Python reference timeseries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_post_trace_test_python_reference_(pre_spike_times,\n",
+    "                                          post_spike_times, resolution,\n",
+    "                                          delay, dendritic_delay, sim_time,\n",
+    "                                          tau_minus, debug=False):\n",
+    "    \"\"\"\n",
+    "    compute Python known-good reference of postsynaptic trace\n",
+    "    \"\"\"\n",
+    "\n",
+    "    max_t_sp = max(np.amax(pre_spike_times), np.amax(post_spike_times))\n",
+    "    n_timepoints = 100 * int(np.ceil(max_t_sp))\n",
+    "    trace_python_ref = np.zeros(n_timepoints)\n",
+    "    n_spikes = len(post_spike_times)\n",
+    "    for sp_idx in range(n_spikes):\n",
+    "        t_sp = post_spike_times[sp_idx] + delay + dendritic_delay\n",
+    "        for i in range(n_timepoints):\n",
+    "            t = (i / float(n_timepoints - 1)) * sim_time\n",
+    "            if t > t_sp + 1E-3:\n",
+    "                trace_python_ref[i] += np.exp(-(t - t_sp) / tau_minus)\n",
+    "\n",
+    "    n_spikes = len(pre_spike_times)\n",
+    "    for sp_idx in range(n_spikes):\n",
+    "        t_sp = pre_spike_times[sp_idx] + delay\n",
+    "        i = int(np.round(t_sp / sim_time\n",
+    "                         * float(len(trace_python_ref) - 1)))\n",
+    "        if debug:\n",
+    "            print(\"* At t_sp = \" + str(t_sp)\n",
+    "              + \", post_trace should be \" + str(trace_python_ref[i]))\n",
+    "\n",
+    "    return trace_python_ref"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the test\n",
+    "------------\n",
+    "\n",
+    "First, define some pre/post spike patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# spike test pattern 1: minimal reproducing example of the original bug\n",
+    "pre_spike_times1 = np.array([2., 3., 10.])\n",
+    "post_spike_times1 = np.array([1., 2., 3.])\n",
+    "\n",
+    "# spike test pattern 2: generate some random integer spike times\n",
+    "t_sp_min = 1.\n",
+    "t_sp_max = 50\n",
+    "n_spikes = 10\n",
+    "pre_spike_times2 = np.sort(\n",
+    "    np.unique(\n",
+    "        np.ceil(\n",
+    "            sp.stats.uniform.rvs(\n",
+    "                t_sp_min, t_sp_max - t_sp_min, n_spikes))))\n",
+    "n_spikes = 50\n",
+    "post_spike_times2 = np.sort(\n",
+    "    np.unique(\n",
+    "        np.ceil(\n",
+    "            sp.stats.uniform.rvs(\n",
+    "                t_sp_min, t_sp_max - t_sp_min, n_spikes))))\n",
+    "tau_minus = 2.  # [ms]\n",
+    "\n",
+    "# for each parameter set, run the test\n",
+    "# spike test pattern 3 is a pre/post-reversed version of test pattern 2\n",
+    "pre_spike_times = [pre_spike_times1,\n",
+    "                   pre_spike_times2,\n",
+    "                   post_spike_times2]\n",
+    "post_spike_times = [post_spike_times1,\n",
+    "                    post_spike_times2,\n",
+    "                    pre_spike_times2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_run(trace_nest_t, trace_nest, trace_python_ref,\n",
+    "             pre_spike_times, post_spike_times, resolution, delay,\n",
+    "             dendritic_delay, trace_match_atol, trace_match_rtol,\n",
+    "             sim_time, title_snip=\"\", debug=False):\n",
+    "\n",
+    "    fig, ax = plt.subplots(nrows=3, dpi=120)\n",
+    "    ax1, ax2, ax3 = ax\n",
+    "\n",
+    "    #\n",
+    "    #   pre spikes\n",
+    "    #\n",
+    "\n",
+    "    ax1.set_ylim([0., 1.])\n",
+    "    ax1.set_ylabel(\"Pre spikes\")\n",
+    "    n_spikes = len(pre_spike_times)\n",
+    "    for i in range(n_spikes):\n",
+    "        ax1.plot(2 * [pre_spike_times[i] + delay],\n",
+    "                 ax1.get_ylim(),\n",
+    "                 linewidth=2, color=\"blue\", alpha=.4)\n",
+    "\n",
+    "    #\n",
+    "    #   post spikes\n",
+    "    #\n",
+    "\n",
+    "    ax2.set_ylim([0., 1.])\n",
+    "    ax2.set_ylabel(\"Post spikes\")\n",
+    "    n_spikes = len(post_spike_times)\n",
+    "    for i in range(n_spikes):\n",
+    "        ax2.plot(2 * [post_spike_times[i] + delay + dendritic_delay],\n",
+    "                 [0, 1],\n",
+    "                 linewidth=2, color=\"red\", alpha=.4)\n",
+    "\n",
+    "    #\n",
+    "    #   traces\n",
+    "    #\n",
+    "\n",
+    "    ax3.set_ylabel(\"Synaptic trace\")\n",
+    "    ax3.set_ylim([0., np.amax(trace_python_ref)])\n",
+    "    ax3.plot(np.linspace(0., sim_time, len(trace_python_ref)),\n",
+    "             trace_python_ref,\n",
+    "             label=\"Expected\", color=\"cyan\", alpha=.6)\n",
+    "    ax3.scatter(trace_nest_t, trace_nest,\n",
+    "                marker=\".\", alpha=.5, color=\"orange\", label=\"NEST\")\n",
+    "    ax3.legend()\n",
+    "\n",
+    "    #\n",
+    "    #   Trace values are returned from NEST at regular intervals, but only\n",
+    "    #   updated at presynaptic spike times.\n",
+    "    #\n",
+    "    #   Step backwards in time from the sampled value, to find the last\n",
+    "    #   time at which the trace value was updated, namely the time of\n",
+    "    #   occurrence of the last presynaptic spike.\n",
+    "    #\n",
+    "\n",
+    "    pre_spike_times = np.array(pre_spike_times)\n",
+    "    n_timepoints = len(trace_nest_t)\n",
+    "    for i in range(n_timepoints):\n",
+    "        t = trace_nest_t[i]\n",
+    "        if debug:\n",
+    "            print(\"* Finding ref for NEST timepoint t = \"\n",
+    "                  + str(t) + \", trace = \" + str(trace_nest[i]))\n",
+    "        for t_search in reversed(pre_spike_times + delay):\n",
+    "            if t_search <= t:\n",
+    "                if debug:\n",
+    "                    print(\"\\t* Testing \" + str(t_search) + \"...\")\n",
+    "                _idx = int(np.round(t_search / sim_time\n",
+    "                           * float(len(trace_python_ref) - 1)))\n",
+    "                _trace_at_t_search = trace_python_ref[_idx]\n",
+    "                traces_match = np.allclose(_trace_at_t_search,\n",
+    "                                           trace_nest[i],\n",
+    "                                           atol=trace_match_atol,\n",
+    "                                           rtol=trace_match_rtol)\n",
+    "                if debug:\n",
+    "                    print(\"\\t   traces_match = \" + str(traces_match))\n",
+    "                if not traces_match:\n",
+    "                    post_spike_occurred_at_t_search = np.any(\n",
+    "                        (t_search - (np.array(post_spike_times)\n",
+    "                         + delay + dendritic_delay))**2 < resolution/2.)\n",
+    "                    if debug:\n",
+    "                        print(\"\\t   post_spike_occurred_at_t_search = \"\n",
+    "                              + str(post_spike_occurred_at_t_search))\n",
+    "                    if post_spike_occurred_at_t_search:\n",
+    "                        traces_match = np.allclose(\n",
+    "                            _trace_at_t_search + 1,\n",
+    "                            trace_nest[i],\n",
+    "                            atol=trace_match_atol,\n",
+    "                            rtol=trace_match_rtol)\n",
+    "                        if debug:\n",
+    "                            print(\"\\t   traces_match = \" + str(traces_match)\n",
+    "                                  + \" (nest trace = \" + str(trace_nest[i])\n",
+    "                                  + \", ref trace = \"\n",
+    "                                  + str(_trace_at_t_search+1) + \")\")\n",
+    "                        if traces_match:\n",
+    "                            _trace_at_t_search += 1.\n",
+    "\n",
+    "                    if not traces_match \\\n",
+    "                            and post_spike_occurred_at_t_search:\n",
+    "                        traces_match = np.allclose(\n",
+    "                            _trace_at_t_search - 1,\n",
+    "                            trace_nest[i],\n",
+    "                            atol=trace_match_atol,\n",
+    "                            rtol=trace_match_rtol)\n",
+    "                        if debug:\n",
+    "                            print(\"\\t   traces_match = \"\n",
+    "                                  + str(traces_match)\n",
+    "                                  + \" (nest trace = \"\n",
+    "                                  + str(trace_nest[i])\n",
+    "                                  + \", ref trace = \"\n",
+    "                                  + str(_trace_at_t_search-1) + \")\")\n",
+    "                        if traces_match:\n",
+    "                            _trace_at_t_search -= 1.\n",
+    "\n",
+    "                ax3.scatter(t_search, _trace_at_t_search, 100, marker=\".\",\n",
+    "                            color=\"#A7FF00FF\", facecolor=\"none\")\n",
+    "                ax3.plot([trace_nest_t[i], t_search],\n",
+    "                         [trace_nest[i], _trace_at_t_search],\n",
+    "                         linewidth=.5, color=\"#0000007F\")\n",
+    "                break\n",
+    "\n",
+    "    for _ax in ax:\n",
+    "        _ax.xaxis.set_major_locator(\n",
+    "            plticker.MultipleLocator(base=10*delay))\n",
+    "        _ax.xaxis.set_minor_locator(\n",
+    "            plticker.MultipleLocator(base=delay))\n",
+    "        _ax.grid(which=\"major\", axis=\"both\")\n",
+    "        _ax.grid(which=\"minor\", axis=\"x\", linestyle=\":\", alpha=.4)\n",
+    "        _ax.set_xlim(0., sim_time)\n",
+    "\n",
+    "    ax3.set_xlabel(\"Time [ms]\")\n",
+    "    fig.suptitle(\"\"\"Postsynaptic trace testbench. Spike times are\\n\"\"\"\n",
+    "                 \"\"\"shown from the perspective of the STDP synapse \"\"\" + title_snip)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the test and make the plots while we go:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "resolution = .1     # [ms]\n",
+    "delays = np.array([1., 5.])  # [ms]\n",
+    "\n",
+    "# settings for plotting debug information\n",
+    "show_all_nest_trace_samples = True\n",
+    "\n",
+    "for delay in delays:\n",
+    "    dendritic_delay = delay\n",
+    "    for spike_times_idx in range(len(pre_spike_times)):\n",
+    "        max_t_sp = max(np.amax(pre_spike_times[spike_times_idx]),\n",
+    "                       np.amax(post_spike_times[spike_times_idx]))\n",
+    "        sim_time = max_t_sp + 5 * delay\n",
+    "        trace_nest_t, trace_nest = run_post_trace_test_nest_(\n",
+    "            pre_spike_times[spike_times_idx],\n",
+    "            post_spike_times[spike_times_idx],\n",
+    "            resolution, delay, sim_time, tau_minus,\n",
+    "            show_all_nest_trace_samples)\n",
+    "        trace_python_ref = run_post_trace_test_python_reference_(\n",
+    "            pre_spike_times[spike_times_idx],\n",
+    "            post_spike_times[spike_times_idx],\n",
+    "            resolution, delay, dendritic_delay, sim_time, tau_minus)\n",
+    "\n",
+    "        title_snip = \"(delay = \" \\\n",
+    "                + str(delay) \\\n",
+    "                + \")\"\n",
+    "        plot_run(\n",
+    "            trace_nest_t, trace_nest, trace_python_ref,\n",
+    "            pre_spike_times[spike_times_idx],\n",
+    "            post_spike_times[spike_times_idx], resolution, delay,\n",
+    "            dendritic_delay, trace_match_atol, trace_match_rtol,\n",
+    "            sim_time, title_snip)\n",
+    "        assert nest_trace_matches_ref_trace(\n",
+    "            trace_nest_t,\n",
+    "            trace_nest,\n",
+    "            trace_python_ref,\n",
+    "            pre_spike_times[spike_times_idx],\n",
+    "            post_spike_times[spike_times_idx],\n",
+    "            resolution, delay, dendritic_delay,\n",
+    "            trace_match_atol,\n",
+    "            trace_match_rtol,\n",
+    "            sim_time,\n",
+    "            debug=False)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pynest/nest/tests/test_post_trace.py b/testsuite/regressiontests/issue-1034.py
similarity index 71%
rename from pynest/nest/tests/test_post_trace.py
rename to testsuite/regressiontests/issue-1034.py
index c4e773c43d..88312eed33 100644
--- a/pynest/nest/tests/test_post_trace.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 #
-# test_post_trace.py
+# issue-1034.py
 #
 # This file is part of NEST.
 #
@@ -19,8 +19,6 @@
 # You should have received a copy of the GNU General Public License
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
-import matplotlib.pyplot as plt
-import matplotlib.ticker as plticker
 import nest
 import numpy as np
 import os
@@ -144,134 +142,6 @@ def run_post_trace_test_python_reference_(self, pre_spike_times,
 
         return trace_python_ref
 
-    def plot_run(self, trace_nest_t, trace_nest, trace_python_ref,
-                 pre_spike_times, post_spike_times, resolution, delay,
-                 dendritic_delay, sim_time, fname):
-
-        fig, ax = plt.subplots(nrows=3)
-        ax1, ax2, ax3 = ax
-
-        #
-        #   pre spikes
-        #
-
-        ax1.set_ylim([0., 1.])
-        ax1.set_ylabel("Pre spikes")
-        n_spikes = len(pre_spike_times)
-        for i in range(n_spikes):
-            ax1.plot(2 * [pre_spike_times[i] + delay],
-                     ax1.get_ylim(),
-                     linewidth=2, color="blue", alpha=.4)
-
-        #
-        #   post spikes
-        #
-
-        ax2.set_ylim([0., 1.])
-        ax2.set_ylabel("Post spikes")
-        n_spikes = len(post_spike_times)
-        for i in range(n_spikes):
-            ax2.plot(2 * [post_spike_times[i] + delay + dendritic_delay],
-                     [0, 1],
-                     linewidth=2, color="red", alpha=.4)
-
-        #
-        #   traces
-        #
-
-        ax3.legend()
-        ax3.set_ylabel("Synaptic trace")
-        ax3.set_ylim([0., np.amax(trace_python_ref)])
-        ax3.plot(np.linspace(0., sim_time, len(trace_python_ref)),
-                 trace_python_ref,
-                 label="Expected", color="cyan", alpha=.6)
-        ax3.scatter(trace_nest_t, trace_nest,
-                    marker=".", alpha=.5, color="orange", label="NEST")
-
-        #
-        #   Trace values are returned from NEST at regular intervals, but only
-        #   updated at presynaptic spike times.
-        #
-        #   Step backwards in time from the sampled value, to find the last
-        #   time at which the trace value was updated, namely the time of
-        #   occurrence of the last presynaptic spike.
-        #
-
-        pre_spike_times = np.array(pre_spike_times)
-        n_timepoints = len(trace_nest_t)
-        for i in range(n_timepoints):
-            t = trace_nest_t[i]
-            print("* Finding ref for NEST timepoint t = "
-                  + str(t) + ", trace = " + str(trace_nest[i]))
-            for t_search in reversed(pre_spike_times + delay):
-                if t_search <= t:
-                    print("\t* Testing " + str(t_search) + "...")
-                    _idx = int(np.round(t_search / sim_time
-                               * float(len(trace_python_ref) - 1)))
-                    _trace_at_t_search = trace_python_ref[_idx]
-                    traces_match = np.allclose(_trace_at_t_search,
-                                               trace_nest[i],
-                                               atol=self.trace_match_atol_,
-                                               rtol=self.trace_match_rtol_)
-                    print("\t   traces_match = " + str(traces_match))
-                    if not traces_match:
-                        post_spike_occurred_at_t_search = np.any(
-                            (t_search - (np.array(post_spike_times)
-                             + delay + dendritic_delay))**2 < resolution/2.)
-                        print("\t   post_spike_occurred_at_t_search = "
-                              + str(post_spike_occurred_at_t_search))
-                        if post_spike_occurred_at_t_search:
-                            traces_match = np.allclose(
-                                _trace_at_t_search + 1,
-                                trace_nest[i],
-                                atol=self.trace_match_atol_,
-                                rtol=self.trace_match_rtol_)
-                            print("\t   traces_match = " + str(traces_match)
-                                  + " (nest trace = " + str(trace_nest[i])
-                                  + ", ref trace = "
-                                  + str(_trace_at_t_search+1) + ")")
-                            if traces_match:
-                                _trace_at_t_search += 1.
-
-                        if not traces_match \
-                                and post_spike_occurred_at_t_search:
-                            traces_match = np.allclose(
-                                _trace_at_t_search - 1,
-                                trace_nest[i],
-                                atol=self.trace_match_atol_,
-                                rtol=self.trace_match_rtol_)
-                            print("\t   traces_match = "
-                                  + str(traces_match)
-                                  + " (nest trace = "
-                                  + str(trace_nest[i])
-                                  + ", ref trace = "
-                                  + str(_trace_at_t_search-1) + ")")
-                            if traces_match:
-                                _trace_at_t_search -= 1.
-
-                    ax3.scatter(t_search, _trace_at_t_search, 100, marker=".",
-                                color="#A7FF00FF", facecolor="none")
-                    ax3.plot([trace_nest_t[i], t_search],
-                             [trace_nest[i], _trace_at_t_search],
-                             linewidth=.5, color="#0000007F")
-                    break
-
-        for _ax in ax:
-            _ax.xaxis.set_major_locator(
-                plticker.MultipleLocator(base=10*delay))
-            _ax.xaxis.set_minor_locator(
-                plticker.MultipleLocator(base=delay))
-            _ax.grid(which="major", axis="both")
-            _ax.grid(which="minor", axis="x", linestyle=":", alpha=.4)
-            _ax.set_xlim(0., sim_time)
-
-        ax3.set_xlabel("Time [ms]")
-        fig.suptitle("""Postsynaptic trace testbench. Spike times are\n"""
-                     """shown from the perspective of the STDP synapse.""")
-
-        print("* Saving to " + fname)
-        fig.savefig(fname, dpi=300.)
-
     def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest,
                                      trace_python_ref, pre_spike_times,
                                      post_spike_times, resolution, delay,

From ee8cd49182fbe7fdf63272f20f842ac0c6a27828 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Tue, 26 Mar 2019 10:15:42 +0100
Subject: [PATCH 31/42] clean up postsynaptic trace regression test

---
 testsuite/regressiontests/issue-1034.py | 180 +++++++++++-------------
 1 file changed, 85 insertions(+), 95 deletions(-)

diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index 88312eed33..aea35105da 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -21,31 +21,36 @@
 
 import nest
 import numpy as np
-import os
 import scipy as sp
 import scipy.stats
 import unittest
 
 
 @nest.ll_api.check_stack
-class PostTraceTestCase(unittest.TestCase):
+class PostTraceTester(object):
 
-    trace_match_atol_ = 1E-2
-    trace_match_rtol_ = 1E-2
+    def __init__(self, pre_spike_times, post_spike_times, delay, resolution, tau_minus, trace_match_atol, trace_match_rtol):
+        self.pre_spike_times_ = pre_spike_times
+        self.post_spike_times_ = post_spike_times
+        self.delay_ = delay
+        self.dendritic_delay_ = delay
+        self.resolution_ = resolution
+        self.tau_minus_ = tau_minus
+        self.trace_match_atol_ = trace_match_atol
+        self.trace_match_rtol_ = trace_match_rtol
 
-    def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
-                                  resolution, delay, sim_time, tau_minus,
-                                  show_all_nest_trace_samples=False):
+        self.max_t_sp_ = max(np.amax(self.pre_spike_times_),
+                             np.amax(self.post_spike_times_))
+        self.sim_time_ = self.max_t_sp_ + 5 * self.delay_
 
-        print("Pre spike times: ["
-              + ", ".join([str(t) for t in pre_spike_times]) + "]")
-        print("Post spike times: ["
-              + ", ".join([str(t) for t in post_spike_times]) + "]")
+
+    def run_post_trace_test_nest_(self,
+                                  show_all_nest_trace_samples=False):
 
         nest.hl_api.set_verbosity("M_WARNING")
 
         nest.ResetKernel()
-        nest.SetKernelStatus({'resolution': resolution})
+        nest.SetKernelStatus({'resolution': self.resolution_})
 
         wr = nest.Create('weight_recorder')
         nest.CopyModel("stdp_synapse", "stdp_synapse_rec",
@@ -53,21 +58,21 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
 
         # create spike_generators with these times
         pre_sg_ps = nest.Create("spike_generator",
-                                params={"spike_times": pre_spike_times,
+                                params={"spike_times": self.pre_spike_times_,
                                         'precise_times': True})
         post_sg_ps = nest.Create("spike_generator",
-                                 params={"spike_times": post_spike_times,
+                                 params={"spike_times": self.post_spike_times_,
                                          'precise_times': True})
 
         # create parrot neurons and connect spike_generators
         pre_parrot_ps = nest.Create("parrot_neuron_ps")
         post_parrot_ps = nest.Create("parrot_neuron_ps",
-                                     params={"tau_minus": tau_minus})
+                                     params={"tau_minus": self.tau_minus_})
 
         nest.Connect(pre_sg_ps, pre_parrot_ps,
-                     syn_spec={"delay": delay})
+                     syn_spec={"delay": self.delay_})
         nest.Connect(post_sg_ps, post_parrot_ps,
-                     syn_spec={"delay": delay})
+                     syn_spec={"delay": self.delay_})
 
         # create spike detector --- debugging only
         spikes = nest.Create("spike_detector",
@@ -84,14 +89,14 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
             pre_parrot_ps, post_parrot_ps,
             syn_spec={'model': 'stdp_synapse_rec',
                       'receptor_type': 1,
-                      'delay': delay})
+                      'delay': self.delay_})
 
         # get STDP synapse
         syn_ps = nest.GetConnections(source=pre_parrot_ps,
                                      synapse_model="stdp_synapse_rec")
 
-        print("[py] Total simulation time: " + str(sim_time) + " ms")
-        n_steps = int(np.ceil(sim_time / delay))
+        print("[py] Total simulation time: " + str(self.sim_time_) + " ms")
+        n_steps = int(np.ceil(self.sim_time_ / self.delay_))
         trace_nest = []
         trace_nest_t = []
         t = nest.GetStatus([0], "time")[0]
@@ -99,11 +104,11 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
         post_tr = nest.GetStatus(post_parrot_ps)[0]['post_trace']
         trace_nest.append(post_tr)
         for step in range(n_steps):
-            print("\n[py] simulating for " + str(delay) + " ms")
-            nest.Simulate(delay)
+            print("\n[py] simulating for " + str(self.delay_) + " ms")
+            nest.Simulate(self.delay_)
             t = nest.GetStatus([0], "time")[0]
             nearby_pre_spike = np.any(
-                np.abs(t - np.array(pre_spike_times) - delay) < resolution/2.)
+                np.abs(t - np.array(self.pre_spike_times_) - self.delay_) < self.resolution_/2.)
             if show_all_nest_trace_samples or nearby_pre_spike:
                 trace_nest_t.append(t)
                 post_tr = nest.GetStatus(post_parrot_ps)[0]['post_trace']
@@ -113,39 +118,37 @@ def run_post_trace_test_nest_(self, pre_spike_times, post_spike_times,
 
         return trace_nest_t, trace_nest
 
-    def run_post_trace_test_python_reference_(self, pre_spike_times,
-                                              post_spike_times, resolution,
-                                              delay, dendritic_delay, sim_time,
-                                              tau_minus):
+    def run_post_trace_test_python_reference_(self, debug=False):
         """
         compute Python known-good reference of postsynaptic trace
         """
 
-        max_t_sp = max(np.amax(pre_spike_times), np.amax(post_spike_times))
-        n_timepoints = 100 * int(np.ceil(max_t_sp))
+        n_timepoints = int(np.ceil(100 * self.sim_time_))
         trace_python_ref = np.zeros(n_timepoints)
-        n_spikes = len(post_spike_times)
+
+        n_spikes = len(self.post_spike_times_)
         for sp_idx in range(n_spikes):
-            t_sp = post_spike_times[sp_idx] + delay + dendritic_delay
+            t_sp = self.post_spike_times_[sp_idx] \
+                   + self.delay_ \
+                   + self.dendritic_delay_
             for i in range(n_timepoints):
-                t = (i / float(n_timepoints - 1)) * sim_time
+                t = (i / float(n_timepoints - 1)) * self.sim_time_
                 if t > t_sp + 1E-3:
-                    trace_python_ref[i] += np.exp(-(t - t_sp) / tau_minus)
+                    trace_python_ref[i] += np.exp(-(t - t_sp) / self.tau_minus_)
 
-        n_spikes = len(pre_spike_times)
+        n_spikes = len(self.pre_spike_times_)
         for sp_idx in range(n_spikes):
-            t_sp = pre_spike_times[sp_idx] + delay
-            i = int(np.round(t_sp / sim_time
+            t_sp = self.pre_spike_times_[sp_idx] + self.delay_
+            i = int(np.round(t_sp / self.sim_time_
                              * float(len(trace_python_ref) - 1)))
-            print("* At t_sp = " + str(t_sp)
-                  + ", post_trace should be " + str(trace_python_ref[i]))
+            if debug:
+                print("* At t_sp = " + str(t_sp)
+                    + ", post_trace should be " + str(trace_python_ref[i]))
 
         return trace_python_ref
 
-    def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest,
-                                     trace_python_ref, pre_spike_times,
-                                     post_spike_times, resolution, delay,
-                                     dendritic_delay, sim_time, debug=True):
+    def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
+                                     trace_python_ref, debug=True):
         """
         Trace values are returned from NEST at regular intervals, but only
         updated at presynaptic spike times.
@@ -165,10 +168,10 @@ def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest,
 
             traces_match = False
             for i_search, t_search in enumerate(
-                    reversed(np.array(pre_spike_times) + delay)):
+                    reversed(np.array(self.pre_spike_times_) + self.delay_)):
                 if t_search <= t:
                     _trace_at_t_search = trace_python_ref[int(np.round(
-                        t_search / sim_time
+                        t_search / self.sim_time_
                         * float(len(trace_python_ref) - 1)))]
                     traces_match = np.allclose(
                         _trace_at_t_search,
@@ -176,10 +179,10 @@ def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest,
                         atol=self.trace_match_atol_,
                         rtol=self.trace_match_rtol_)
                     post_spike_occurred_at_t_search = np.any(
-                        (t_search - (np.array(post_spike_times)
-                                     + delay
-                                     + dendritic_delay))**2
-                        < resolution/2.)
+                        (t_search - (np.array(self.post_spike_times_)
+                                     + self.delay_
+                                     + self.dendritic_delay_))**2
+                        < self.resolution_/2.)
 
                     if debug:
                         print("\t* Testing " + str(t_search) + "...")
@@ -219,7 +222,7 @@ def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest,
 
                     break
 
-            if (not traces_match) and i_search == len(pre_spike_times) - 1:
+            if (not traces_match) and i_search == len(self.pre_spike_times_) - 1:
                 if debug:
                     print("\tthe time before the first pre spike")
                 # the time before the first pre spike
@@ -230,6 +233,19 @@ def nest_trace_matches_ref_trace(self, trace_nest_t, trace_nest,
 
         return True
 
+
+    def nest_trace_matches_python_trace(self):
+        trace_nest_t, trace_nest = self.run_post_trace_test_nest_()
+        trace_python_ref = self.run_post_trace_test_python_reference_()
+        return self.nest_trace_matches_ref_trace_(
+            trace_nest_t,
+            trace_nest,
+            trace_python_ref)
+
+
+
+class PostTraceTestCase(unittest.TestCase):
+
     def test_post_trace(self):
         """
         construct a network of the form:
@@ -251,11 +267,6 @@ def test_post_trace(self):
         resolution = .1     # [ms]
         delays = np.array([1., 5.])  # [ms]
 
-        # settings for plotting debug information
-        make_debug_plots = False
-        show_all_nest_trace_samples = True
-        debug_plots_output_dir = "/tmp"
-
         # spike test pattern 1: minimal reproducing example of the original bug
         pre_spike_times1 = np.array([2., 3., 10.])
         post_spike_times1 = np.array([1., 2., 3.])
@@ -286,51 +297,30 @@ def test_post_trace(self):
                             post_spike_times2,
                             pre_spike_times2]
 
-        for delay in delays:
-            dendritic_delay = delay
-            for spike_times_idx in range(len(pre_spike_times)):
-                max_t_sp = max(np.amax(pre_spike_times[spike_times_idx]),
-                               np.amax(post_spike_times[spike_times_idx]))
-                sim_time = max_t_sp + 5 * delay
-                trace_nest_t, trace_nest = self.run_post_trace_test_nest_(
-                    pre_spike_times[spike_times_idx],
-                    post_spike_times[spike_times_idx],
-                    resolution, delay, sim_time, tau_minus,
-                    show_all_nest_trace_samples)
-                trace_python_ref = self.run_post_trace_test_python_reference_(
-                    pre_spike_times[spike_times_idx],
-                    post_spike_times[spike_times_idx],
-                    resolution, delay, dendritic_delay, sim_time, tau_minus)
-
-                if make_debug_plots:
-                    fname = "traces_[delay=" \
-                            + str(delay) \
-                            + "]_[experiment=" \
-                            + str(spike_times_idx) + "].png"
-                    fname = os.path.join(debug_plots_output_dir, fn)
-                    self.plot_run(
-                        trace_nest_t, trace_nest, trace_python_ref,
-                        pre_spike_times[spike_times_idx],
-                        post_spike_times[spike_times_idx], resolution, delay,
-                        dendritic_delay, sim_time, fname)
-                    self.assertTrue(self.nest_trace_matches_ref_trace(
-                        trace_nest_t,
-                        trace_nest,
-                        trace_python_ref,
-                        pre_spike_times[spike_times_idx],
-                        post_spike_times[spike_times_idx],
-                        resolution, delay, dendritic_delay, sim_time))
+        for spike_times_idx in range(len(pre_spike_times)):
+            print("Pre spike times: ["
+                + ", ".join([str(t) for t in pre_spike_times]) + "]")
+            print("Post spike times: ["
+                + ", ".join([str(t) for t in post_spike_times]) + "]")
+
+            for delay in delays:
+                dendritic_delay = delay
+                test = PostTraceTester(
+                    pre_spike_times=pre_spike_times[spike_times_idx],
+                    post_spike_times=post_spike_times[spike_times_idx],
+                    delay=delay,
+                    resolution=resolution,
+                    tau_minus=tau_minus,
+                    trace_match_atol=1E-2,
+                    trace_match_rtol=1E-2)
+                assert test.nest_trace_matches_python_trace()
 
 
 def suite():
-    suite1 = unittest.TestLoader().loadTestsFromTestCase(PostTraceTestCase)
-    return unittest.TestSuite([suite1])
+    t = unittest.TestLoader().loadTestsFromTestCase(PostTraceTestCase)
+    return unittest.TestSuite([t])
 
 
-def run():
+if __name__ == "__main__":
     runner = unittest.TextTestRunner(verbosity=99)
     runner.run(suite())
-
-
-if __name__ == "__main__":
-    PostTraceTestCase().test_post_trace()

From c662f2e0a7b95a25a684f03e845ef7e4494f547b Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Tue, 26 Mar 2019 10:19:48 +0100
Subject: [PATCH 32/42] clean up (PEP8) postsynaptic trace regression test

---
 testsuite/regressiontests/issue-1034.py | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index aea35105da..ff6507737f 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -29,7 +29,8 @@
 @nest.ll_api.check_stack
 class PostTraceTester(object):
 
-    def __init__(self, pre_spike_times, post_spike_times, delay, resolution, tau_minus, trace_match_atol, trace_match_rtol):
+    def __init__(self, pre_spike_times, post_spike_times, delay, resolution,
+                 tau_minus, trace_match_atol, trace_match_rtol):
         self.pre_spike_times_ = pre_spike_times
         self.post_spike_times_ = post_spike_times
         self.delay_ = delay
@@ -43,7 +44,6 @@ def __init__(self, pre_spike_times, post_spike_times, delay, resolution, tau_min
                              np.amax(self.post_spike_times_))
         self.sim_time_ = self.max_t_sp_ + 5 * self.delay_
 
-
     def run_post_trace_test_nest_(self,
                                   show_all_nest_trace_samples=False):
 
@@ -108,7 +108,8 @@ def run_post_trace_test_nest_(self,
             nest.Simulate(self.delay_)
             t = nest.GetStatus([0], "time")[0]
             nearby_pre_spike = np.any(
-                np.abs(t - np.array(self.pre_spike_times_) - self.delay_) < self.resolution_/2.)
+                np.abs(t - np.array(self.pre_spike_times_) - self.delay_)
+                < self.resolution_/2.)
             if show_all_nest_trace_samples or nearby_pre_spike:
                 trace_nest_t.append(t)
                 post_tr = nest.GetStatus(post_parrot_ps)[0]['post_trace']
@@ -134,7 +135,8 @@ def run_post_trace_test_python_reference_(self, debug=False):
             for i in range(n_timepoints):
                 t = (i / float(n_timepoints - 1)) * self.sim_time_
                 if t > t_sp + 1E-3:
-                    trace_python_ref[i] += np.exp(-(t - t_sp) / self.tau_minus_)
+                    trace_python_ref[i] += np.exp(-(t - t_sp)
+                                                  / self.tau_minus_)
 
         n_spikes = len(self.pre_spike_times_)
         for sp_idx in range(n_spikes):
@@ -143,12 +145,12 @@ def run_post_trace_test_python_reference_(self, debug=False):
                              * float(len(trace_python_ref) - 1)))
             if debug:
                 print("* At t_sp = " + str(t_sp)
-                    + ", post_trace should be " + str(trace_python_ref[i]))
+                      + ", post_trace should be " + str(trace_python_ref[i]))
 
         return trace_python_ref
 
     def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
-                                     trace_python_ref, debug=True):
+                                      trace_python_ref, debug=True):
         """
         Trace values are returned from NEST at regular intervals, but only
         updated at presynaptic spike times.
@@ -222,7 +224,8 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
 
                     break
 
-            if (not traces_match) and i_search == len(self.pre_spike_times_) - 1:
+            if (not traces_match) \
+               and i_search == len(self.pre_spike_times_) - 1:
                 if debug:
                     print("\tthe time before the first pre spike")
                 # the time before the first pre spike
@@ -233,7 +236,6 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
 
         return True
 
-
     def nest_trace_matches_python_trace(self):
         trace_nest_t, trace_nest = self.run_post_trace_test_nest_()
         trace_python_ref = self.run_post_trace_test_python_reference_()
@@ -243,7 +245,6 @@ def nest_trace_matches_python_trace(self):
             trace_python_ref)
 
 
-
 class PostTraceTestCase(unittest.TestCase):
 
     def test_post_trace(self):
@@ -299,9 +300,9 @@ def test_post_trace(self):
 
         for spike_times_idx in range(len(pre_spike_times)):
             print("Pre spike times: ["
-                + ", ".join([str(t) for t in pre_spike_times]) + "]")
+                  + ", ".join([str(t) for t in pre_spike_times]) + "]")
             print("Post spike times: ["
-                + ", ".join([str(t) for t in post_spike_times]) + "]")
+                  + ", ".join([str(t) for t in post_spike_times]) + "]")
 
             for delay in delays:
                 dendritic_delay = delay

From 40a1a28bb3bf76bd1a8aa99187bdb324642c136f Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Tue, 26 Mar 2019 10:54:12 +0100
Subject: [PATCH 33/42] removed check_stack from postsynaptic trace regression
 test

---
 testsuite/regressiontests/issue-1034.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index ff6507737f..1f5220d853 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -26,7 +26,6 @@
 import unittest
 
 
-@nest.ll_api.check_stack
 class PostTraceTester(object):
 
     def __init__(self, pre_spike_times, post_spike_times, delay, resolution,

From d1c85433134ab3f5deb179954fa18369746b45af Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Tue, 26 Mar 2019 11:15:38 +0100
Subject: [PATCH 34/42] minor cleanup of postsynaptic trace regression test and
 Jupyter notebook

---
 doc/model_details/test_post_trace.ipynb | 286 ++++++++++++------------
 testsuite/regressiontests/issue-1034.py |   8 +-
 2 files changed, 150 insertions(+), 144 deletions(-)

diff --git a/doc/model_details/test_post_trace.ipynb b/doc/model_details/test_post_trace.ipynb
index c4ffef9899..b3112e86da 100644
--- a/doc/model_details/test_post_trace.ipynb
+++ b/doc/model_details/test_post_trace.ipynb
@@ -65,116 +65,6 @@
     "  node and the synapse itself (see the C++ variable `dendritic_delay`)."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Preliminaries\n",
-    "-------------\n",
-    "\n",
-    "First, define a function that will validate equality between the Python-generated and the NEST-generated timeseries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trace_match_atol = 1E-2\n",
-    "trace_match_rtol = 1E-2\n",
-    "\n",
-    "def nest_trace_matches_ref_trace(trace_nest_t, trace_nest,\n",
-    "                                 trace_python_ref, pre_spike_times,\n",
-    "                                 post_spike_times, resolution, delay,\n",
-    "                                 dendritic_delay, trace_match_atol,\n",
-    "                                 trace_match_rtol, sim_time,\n",
-    "                                 debug=False):\n",
-    "    \"\"\"\n",
-    "    Trace values are returned from NEST at regular intervals, but only\n",
-    "    updated at presynaptic spike times.\n",
-    "\n",
-    "    To match the NEST samples with the continuous reference trace, step\n",
-    "    backwards in time from the sampled value, to find the last time at\n",
-    "    which the trace value was updated, namely the time of occurrence of\n",
-    "    the last presynaptic spike.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    n_timepoints = len(trace_nest_t)\n",
-    "    for i in range(n_timepoints)[1:]:\n",
-    "        t = trace_nest_t[i]\n",
-    "        if debug:\n",
-    "            print(\"* Finding ref for NEST timepoint t = \" + str(t)\n",
-    "                  + \", trace = \" + str(trace_nest[i]))\n",
-    "\n",
-    "        traces_match = False\n",
-    "        for i_search, t_search in enumerate(\n",
-    "                reversed(np.array(pre_spike_times) + delay)):\n",
-    "            if t_search <= t:\n",
-    "                _trace_at_t_search = trace_python_ref[int(np.round(\n",
-    "                    t_search / sim_time\n",
-    "                    * float(len(trace_python_ref) - 1)))]\n",
-    "                traces_match = np.allclose(\n",
-    "                    _trace_at_t_search,\n",
-    "                    trace_nest[i],\n",
-    "                    atol=trace_match_atol,\n",
-    "                    rtol=trace_match_rtol)\n",
-    "                post_spike_occurred_at_t_search = np.any(\n",
-    "                    (t_search - (np.array(post_spike_times)\n",
-    "                                 + delay\n",
-    "                                 + dendritic_delay))**2\n",
-    "                    < resolution/2.)\n",
-    "\n",
-    "                if debug:\n",
-    "                    print(\"\\t* Testing \" + str(t_search) + \"...\")\n",
-    "                    print(\"\\t   traces_match = \" + str(traces_match))\n",
-    "                    print(\"\\t   post_spike_occurred_at_t_search = \"\n",
-    "                          + str(post_spike_occurred_at_t_search))\n",
-    "\n",
-    "                if (not traces_match) and post_spike_occurred_at_t_search:\n",
-    "                    traces_match = np.allclose(\n",
-    "                        _trace_at_t_search + 1,\n",
-    "                        trace_nest[i],\n",
-    "                        atol=trace_match_atol,\n",
-    "                        rtol=trace_match_rtol)\n",
-    "                    if debug:\n",
-    "                        print(\"\\t   traces_match = \" + str(traces_match)\n",
-    "                              + \" (nest trace = \" + str(trace_nest[i])\n",
-    "                              + \", ref trace = \"\n",
-    "                              + str(_trace_at_t_search + 1)\n",
-    "                              + \")\")\n",
-    "                    if traces_match:\n",
-    "                        _trace_at_t_search += 1.\n",
-    "\n",
-    "                if (not traces_match) and post_spike_occurred_at_t_search:\n",
-    "                    traces_match = np.allclose(\n",
-    "                        _trace_at_t_search - 1,\n",
-    "                        trace_nest[i],\n",
-    "                        atol=trace_match_atol,\n",
-    "                        rtol=trace_match_rtol)\n",
-    "                    if debug:\n",
-    "                        print(\"\\t   traces_match = \" + str(traces_match)\n",
-    "                              + \" (nest trace = \" + str(trace_nest[i])\n",
-    "                              + \", ref trace = \"\n",
-    "                              + str(_trace_at_t_search - 1)\n",
-    "                              + \")\")\n",
-    "                    if traces_match:\n",
-    "                        _trace_at_t_search -= 1.\n",
-    "\n",
-    "                break\n",
-    "\n",
-    "        if (not traces_match) and i_search == len(pre_spike_times) - 1:\n",
-    "            if debug:\n",
-    "                print(\"\\tthe time before the first pre spike\")\n",
-    "            # the time before the first pre spike\n",
-    "            traces_match = trace_nest[i] == 0.\n",
-    "\n",
-    "        if not traces_match:\n",
-    "            return False\n",
-    "\n",
-    "    return True\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -187,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,15 +191,15 @@
     "    compute Python known-good reference of postsynaptic trace\n",
     "    \"\"\"\n",
     "\n",
-    "    max_t_sp = max(np.amax(pre_spike_times), np.amax(post_spike_times))\n",
-    "    n_timepoints = 100 * int(np.ceil(max_t_sp))\n",
+    "    n_timepoints = 1000 * int(np.ceil(sim_time))\n",
     "    trace_python_ref = np.zeros(n_timepoints)\n",
+    "\n",
     "    n_spikes = len(post_spike_times)\n",
     "    for sp_idx in range(n_spikes):\n",
     "        t_sp = post_spike_times[sp_idx] + delay + dendritic_delay\n",
     "        for i in range(n_timepoints):\n",
     "            t = (i / float(n_timepoints - 1)) * sim_time\n",
-    "            if t > t_sp + 1E-3:\n",
+    "            if t > t_sp:\n",
     "                trace_python_ref[i] += np.exp(-(t - t_sp) / tau_minus)\n",
     "\n",
     "    n_spikes = len(pre_spike_times)\n",
@@ -336,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,6 +261,113 @@
     "                    pre_spike_times2]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function that will validate equality between the Python-generated and the NEST-generated timeseries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace_match_atol = 1E-3\n",
+    "trace_match_rtol = 1E-3\n",
+    "\n",
+    "def nest_trace_matches_ref_trace(trace_nest_t, trace_nest,\n",
+    "                                 trace_python_ref, pre_spike_times,\n",
+    "                                 post_spike_times, resolution, delay,\n",
+    "                                 dendritic_delay, trace_match_atol,\n",
+    "                                 trace_match_rtol, sim_time,\n",
+    "                                 debug=False):\n",
+    "    \"\"\"\n",
+    "    Trace values are returned from NEST at regular intervals, but only\n",
+    "    updated at presynaptic spike times.\n",
+    "\n",
+    "    To match the NEST samples with the continuous reference trace, step\n",
+    "    backwards in time from the sampled value, to find the last time at\n",
+    "    which the trace value was updated, namely the time of occurrence of\n",
+    "    the last presynaptic spike.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    n_timepoints = len(trace_nest_t)\n",
+    "    for i in range(n_timepoints)[1:]:\n",
+    "        t = trace_nest_t[i]\n",
+    "        if debug:\n",
+    "            print(\"* Finding ref for NEST timepoint t = \" + str(t)\n",
+    "                  + \", NEST trace = \" + str(trace_nest[i]))\n",
+    "\n",
+    "        traces_match = False\n",
+    "        for i_search, t_search in enumerate(\n",
+    "                reversed(np.array(pre_spike_times) + delay)):\n",
+    "            if t_search <= t:\n",
+    "                _trace_at_t_search = trace_python_ref[int(np.round(\n",
+    "                    t_search / sim_time\n",
+    "                    * float(len(trace_python_ref) - 1)))]\n",
+    "                traces_match = np.allclose(\n",
+    "                    _trace_at_t_search,\n",
+    "                    trace_nest[i],\n",
+    "                    atol=trace_match_atol,\n",
+    "                    rtol=trace_match_rtol)\n",
+    "                post_spike_occurred_at_t_search = np.any(\n",
+    "                    (t_search - (np.array(post_spike_times)\n",
+    "                                 + delay\n",
+    "                                 + dendritic_delay))**2\n",
+    "                    < resolution/2.)\n",
+    "\n",
+    "                if debug:\n",
+    "                    print(\"\\t* Testing \" + str(t_search) + \"...\")\n",
+    "                    print(\"\\t   traces_match = \" + str(traces_match))\n",
+    "                    print(\"\\t   post_spike_occurred_at_t_search = \"\n",
+    "                          + str(post_spike_occurred_at_t_search))\n",
+    "\n",
+    "                if (not traces_match) and post_spike_occurred_at_t_search:\n",
+    "                    traces_match = np.allclose(\n",
+    "                        _trace_at_t_search + 1,\n",
+    "                        trace_nest[i],\n",
+    "                        atol=trace_match_atol,\n",
+    "                        rtol=trace_match_rtol)\n",
+    "                    if debug:\n",
+    "                        print(\"\\t   traces_match = \" + str(traces_match)\n",
+    "                              + \" (nest trace = \" + str(trace_nest[i])\n",
+    "                              + \", ref trace = \"\n",
+    "                              + str(_trace_at_t_search + 1)\n",
+    "                              + \")\")\n",
+    "                    if traces_match:\n",
+    "                        _trace_at_t_search += 1.\n",
+    "\n",
+    "                if (not traces_match) and post_spike_occurred_at_t_search:\n",
+    "                    traces_match = np.allclose(\n",
+    "                        _trace_at_t_search - 1,\n",
+    "                        trace_nest[i],\n",
+    "                        atol=trace_match_atol,\n",
+    "                        rtol=trace_match_rtol)\n",
+    "                    if debug:\n",
+    "                        print(\"\\t   traces_match = \" + str(traces_match)\n",
+    "                              + \" (nest trace = \" + str(trace_nest[i])\n",
+    "                              + \", ref trace = \"\n",
+    "                              + str(_trace_at_t_search - 1)\n",
+    "                              + \")\")\n",
+    "                    if traces_match:\n",
+    "                        _trace_at_t_search -= 1.\n",
+    "\n",
+    "                break\n",
+    "\n",
+    "        if (not traces_match) and i_search == len(pre_spike_times) - 1:\n",
+    "            if debug:\n",
+    "                print(\"\\tthe time before the first pre spike\")\n",
+    "            # the time before the first pre spike\n",
+    "            traces_match = trace_nest[i] == 0.\n",
+    "\n",
+    "        if not traces_match:\n",
+    "            return False\n",
+    "\n",
+    "    return True\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -380,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -476,25 +473,27 @@
     "                                  + \" (nest trace = \" + str(trace_nest[i])\n",
     "                                  + \", ref trace = \"\n",
     "                                  + str(_trace_at_t_search+1) + \")\")\n",
+    "                        \n",
     "                        if traces_match:\n",
     "                            _trace_at_t_search += 1.\n",
     "\n",
-    "                    if not traces_match \\\n",
-    "                            and post_spike_occurred_at_t_search:\n",
-    "                        traces_match = np.allclose(\n",
-    "                            _trace_at_t_search - 1,\n",
-    "                            trace_nest[i],\n",
-    "                            atol=trace_match_atol,\n",
-    "                            rtol=trace_match_rtol)\n",
-    "                        if debug:\n",
-    "                            print(\"\\t   traces_match = \"\n",
-    "                                  + str(traces_match)\n",
-    "                                  + \" (nest trace = \"\n",
-    "                                  + str(trace_nest[i])\n",
-    "                                  + \", ref trace = \"\n",
-    "                                  + str(_trace_at_t_search-1) + \")\")\n",
-    "                        if traces_match:\n",
-    "                            _trace_at_t_search -= 1.\n",
+    "                        if not traces_match:\n",
+    "                            traces_match = np.allclose(\n",
+    "                                _trace_at_t_search - 1,\n",
+    "                                trace_nest[i],\n",
+    "                                atol=trace_match_atol,\n",
+    "                                rtol=trace_match_rtol)\n",
+    "                            \n",
+    "                            if debug:\n",
+    "                                print(\"\\t   traces_match = \"\n",
+    "                                      + str(traces_match)\n",
+    "                                      + \" (nest trace = \"\n",
+    "                                      + str(trace_nest[i])\n",
+    "                                      + \", ref trace = \"\n",
+    "                                      + str(_trace_at_t_search-1) + \")\")\n",
+    "                            \n",
+    "                            if traces_match:\n",
+    "                                _trace_at_t_search -= 1.\n",
     "\n",
     "                ax3.scatter(t_search, _trace_at_t_search, 100, marker=\".\",\n",
     "                            color=\"#A7FF00FF\", facecolor=\"none\")\n",
@@ -526,14 +525,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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vbUAEA+vHDNxbsAPWVbsOETkq6Hsc62JKKEyPATgf6zr7sojsko0MRnIZBnPthAVYfrwqpEly7XbM/c2mOcfUo/t72Fi0c0Rkx5x9O0K3XxkK71PNJcu5WOv+d/PKsYhMTesVkYMkf2DZlLAtVd4znBFajpJrTMLc2QD8LLXfUKZLERdiLpw+JCIHZCPrKF+FiMjOIrJzyX1Hi8iXC+oGsOEQY7BZ+C/kxJdN25qED64DsRbi74nIpzO7/Az7qDlFRPbOuZcWKb9EbyPP51BxTtj+WES2zEaGvNkn9X8974Ii5obtjMy1DsfckA2Gs7HxqxeE3os+iMjEMA8h4oTYHf8iRlVvF5FvYYbdg2GA+QqsZWQ3zOXJmalDPgccLCJ/xvzCLcf8ab4e6yZLt0zehM0uPUNEdgvxqOrXsBbPM0XkL8CjmKGzFfb1XM1cM9G6UkQupPdL+4cNJ4Cd97kw6eQdwH0icj02fuxQbLLOffQff5ZwHfBtEXk9Nns38RO6GnhPZtLSQOlRpO1hETkZm3x1r4hcjU2smYxN1ujCXKvU4gbMP+CVInItNt71X6r681oHqmpVRL6LzRZ+IGjoCNedFO7roMwxpXWr6j/E/GFeADwkItdhZaIdeynvjznBLmPI/AV7+X1CRCbTO27s3DA27jRs/OoHMT+bN2LjIadgHxSvxca4JRNJrgKWi8hfsRemBD17YRM+0t2NZdJ4PjbG7UER+U24x2Mww/g8Vb0llYZDmS65qOoiEflPzHvGTSLye2xy2jhsEtDW2OSWRngkbMuMUW8HvooZdndiz94SrJy9FngZVj99MOfY0mlbFlVdKCIHYePEzxKRkar69RD3gogcQ1hSU0RuwFrhFEu3fbHyPrLEpf6JlcN3iEg3tlCGAj9X1X/Vq3swqOoNIvJ5zI3dY6EMP4UZ/dtiBvmtWJc71PcuKOI8zJ/nZeHdMw977xyB+RF++yDu4wIReRXWOPCEiPwBG8M+CSvLB2AfEHllKNIM1te0+/hrzg/y/YTWOOYdWAWzDDOgHsJexiMz+x2GPcAPY62SK7AK9Ltk/OiF/Y/HXiSr0rqw2Yln03eFornYy/A1A+jcPZxnHjludsI+MxnYV6SSWskmhG2CubJ6PNz/M5jz+cnkuN4hf8WkrpB+1wN7FVw7Nz1C3GwyLppScftiXVYL6F2t6DrgmJL524otPvAk1rrWJw1C2s8d4Pg2bPzWw0H7c8DPsZfTkOjGDIzZ2At4Debj8EHsY6Ofm5kBtB6BGaPLkzSmr9srwSZR3BCusRYzAG7FVhnaOrXvBzEj40nMuF2Mdet+jozLqLJpjH3gfJ/eVaQeYeAVk0qnCwO4iWJg91W7AhcFTcmqPzeTcd2WvadMXG45oI76COt5OQLzSHBHKC/d2HN1PzY5Lq+c1ZW21L9i0jjMe4cCp+Wc63v0rkjUhQ39+Dlh1aKS975XKJOd9LqnmjFQ3jHAc1ujLOTeZ4jbDzMA54WysBCrs84mtRIcdb4LBrjv12DeWZaEfL4VG441gxy3VQPdV2a/I+l1uL8Wq7PuxNx87VxWX/yt/5+EDItE3CMiM7GK72tavJLGcOiYgbUAnqqqs5qlIxKJgIjMBVDV6c1VEolE6iWOCY1sEIRZ2p/CxhoOSVd8JBKJRCKR5hHHhEZcIyL7YeORZmBdk99Tc+8UiUQikUhkAyYaoRHvvA5zPLwYm63+uebKiUQikUgkMhTEMaGRSCQSiUQikWEnjgmNRCKRSCQSiQw70QiNRCKRSCQSiQw77o1QEZkjIi+qMQMi0i4ip4rIYyKyRkRURPKWstxoEJHZ4T6nN1tLZGgRkRkhb2c1W0sjiMhhInK7iCwN9/N/DZ5vVjjPjCGSGIk0HRE5MZTrfitJDXDMkLznRWRu4rIrsv4Q4+9hsYKGcG+Evkj5NPAVzGHwWcCpmAPkDZb4wt14EZHpIW9nN1vL+iJ8HF2NrbpyAfZM/qrGMTNDusxc3/oGi4gcKyLXicgCEekWkRdE5GER+YWInBD2SfK3nt+McOycTHiPiCwRkX+IyKXBYBlToG1mznnXiMhT4aO135KwkeYS8vJ04Leqemez9WzMiMhWIvJFEblMRB4XkWp4Rl7awDlPEJE7RWS5iHSG5/fI7H5qk4m+gi13fUwj9xFnx/vkSGzFl0NVdW2zxUQiDXIntkrWomYLaYDXYUswflpVL2m2mKFARH4EnIStgnUNtvyiYEuBvglzi3Yhtkb6qTmnOCVs8+LmZv6/kN6lT8cC22Fpeixwuoi8V1WvLZD6dyBpdR4fdJ0AHCciB6vqX4vvMjLMfAzYAvhGs4W8CNgTWwFKsWe3E5gw2JOJyFlYA9izmCeaDmw1xd+KyEdV9Xvp/VX1ahF5BPi6iFyhg5zlHo1Qn2wJvBAN0MjGgKquZANvyceeSbDeiQ2e4H/3JOyFs2/W966ItGPGHqq6FFuiNnuOU0J8v7gcZqvqnMzxI7GX3leBq0TkUM1f4/2+9DVERLCV007A1jo/qMT1I+sZEWnFlrp9VFVvb7aeFwF/Aw4A/q6qXSIyB/OpXTci8hrsWXwCW3Z6SQg/E7gbOEtEfqeqczOHXoh9cByCLV9dP81aLxR4M7ZW7nxsnd952HrFJ2f2m4NZ+m3Y2s6Phf2fAb4JdBSc/xBsnerFYf9HQ2KNz+z3y3D+HTLhF4bwGzLhY7H1jG9Jhc0M+87EKsQ52Dq4XVgLw3+UTJPZ9K53nf7NDfHTw/+zgR2BX2Nr41YJ6wyH/Xag71rQ88L/O+Rcc1Y45wzgnViBWxmOORsYEfY7ONxXF7bO78+BySXva27BfWnOvU8HPgA8gK3F/Dzwo2y+pY7bClu7+cmQzy8Av6Fg/faCc6TTdWes1WUxth7yrcBhAxz7TmwJz6VB7yPAl5J0y+yrIQ23AH4S8qdCWBMa2BwbfvHPcO2l4e/ZwHap88yg/9r1nViZ+wOpNZ4z128DTgb+GvJxJbYW+keAloJj9g7lLFmPez5wPXBcpvzk/WZm9abO+w+sbG5acN3/Dsd8ZKjzO3Wu44BbQtqtCmXuf9J5l9Ke95sxwLnnDHDc9Jxn7xisxThZo/5XwLSCc0/CjK9Hgu5OrC4tLKc55/hcuPZ36k23THnWGvsk6TBQWp0a9rk3Ez6TnDXeU+VSgRUltdZ83wB/werS6QXn+HS45mdSYXPDbzRwJvB0OP/joQxLznlmAleEMrwKexZvA46vkYYjsJavp8I1nsBao/u9A4H9gd9iHxlrsLXT/wqckrPvJqHc34fVO8tDWryzzvJwRNB52gD7vAN7x6zC3l0/xz7w5hSVJeBw4FqsJyW57zOBCTn7ziW8L1Nh44HPYmvUP4vVOwuxemPfzL4TsWfwiby8C/v8Ntxnbj3brF+qnLx0EMdeFI49MSfuqyHu1Jy4bUPcLwetu0mJ9f4gfD5mYJyOvZTvBO4qSNhLw/4XAN/BjEoFfpZz/g9glcmysP83wgOowEPpwgu8L4R/MHOOZ0P4KmBkKvzIEH5KKmxmCLscM1B/Ex6Sa0L4AgpetplrvgV7MS0Nv1nh94kQPz2c78+YIXgHcA7wA2CPsM9e2EupihlTpwNXhv87ybys6X0RXhEevkuAb2Mv5MQweyv28F+JGUm3h7jfl8zvT6TycXbqvmal9pmdyudO4BdBxz0h/Mac8+6BVUxV4PdB2+yQdmuAN5TUl6TrzSFdb8Fe8rND/leAt+ccd0E47hngp0HvbSHsJqAts78C92MV5YPAucD/Aq/HXgSPh32uD/fybaxMLQGOTJ1nRpL+4T6vCfl8Kbas6Spg/8y127GPMsUMwB9gz9HfQ9jPc+7vpHC+NcBl9D6n9wFzUlq+E85xXzpvgVdk9Kbz+39C2EcL8uThcN1JQ53f4Vynh+svBM7HntcH6f1Q6EiVjVnkl9/pA5x/Jvb8adim02VC5tm7FPuAuTTouCWEP0LmYwar9J8K8bdgz/+PMKOqCpxU8v7fG85xTQP1+FAZoWOxukeBXXPq1dk5x7w6xC0vobPU+wZ4d9jv6wXn+WfIp01TYXOxD7RbMaPyh8D3Q1if90TqmFVYK9ZsrJ75Eb3vm34GXCoNrw75/F2sbkjqi9+SMpgwY7CC1RsXhvv9AVa/PZ859wR669i7sTrp+6lzf62O8nBWOObwgvhPhvglIZ2+idUZcwn1UM4xp4RjXgj3cib2oZ28y8dl9p9LfyN0H8zw/FO47jew99xy7H19RGb/pF4/NEfP1lid+LfBPjfr60djRmhS/qbmxO0b4v48wLELKTDaa167SYl1N/bSmJITt2nm/yRh76bvC2l0eFAqwBap8G3DubuAnTPnOi+c60epsO1C2GWpsJ3oNQYUOCQVd04I2z8VNjOE9aT3DXFnhLjP1ZE+/R6kED6d3taU03PiBXtxKfBfmbi302uAtKTCZ4XwTlIttthX90MhfV8ADkzFtQB/DMe9ouQ9JdeZURA/O8Q/DWyTCm+j96W8dyb8ceylcGDmXFtiL4H55LRI1kjXMzNxe2IV1RJSFV4qz68ERhXc68cz4ck1LqK/gfqmEHdOjr4OYGzq/xmpc2VbCo8K4Y8V5PO5QGsqvBUzoBU4KhW+S7jvxaQMg1T8VjnpN7sgfRO9s9LHh7LVrzLHPqQUuGI95XdSqT5N37qjjd5Wji/UU34LrpOUkZk1noku4GWZuEtC3HGZ8DmYsfmOTPgE7IW+Cti8hLZpmPGu2Efzf2I9KKVfJEkZrLHPnDLphn1YK6mWGAqMUKyey+2pKjh3qfcNNuZ3UShH2eczKcMXZ8LnhvBrSdUDwBR6GxPaM8dsn6OjA2up7SbTAp5Kw0eBiRm9fwlx70qFXxHCdh/ofsP/s8l5P4VzXxfKWtk6Pmno6ddDhtURa7H6ZHoqvCWlVzPHHBTCbyfT6pkqG+dkwueS3xLarxEIq4PmAY9kwvcM574855hZIa7sx94M+n6A1vyVff4GeNbqMkIxW0qBZQXxm4b45wvirwrxuwxK92BvuJEfVimsSD9QJRL2dTlxSTdOupXoixQbaROxCn8VfbvcnsIqn2QFqZPDOfbBDMvTU/vej31BtafCkgfiFznXfElRgR7gnvs9SCF8ejjXc+R39742eWgLzptU9AekwpKHKu8L/Csh7qKcuBNC3Akl7ym5zoyC+Nkh/n05cSeSMbjoNbbOLDjfx0N8zdaxVLouJWXs5Wg7IRV2L/bCyOsSag3l6c5MuFL8MkyM0H7lNmffGeQYmjnPzIHh/xbsQ6LfyzXET8BeNpemws4N5/hkHek3u4beWZnw5CNv10z490L4m9dTfv847Pv+nLgdMeP4yXrKb8F1ZlLOCO3X2kTvC/isVNjuZD6YM8ckaXRySX0H0dvilfy6MOPjeFIfKwXH9zMcBiiLA6YbNvygjzGUSr90C/s52LOnWOvpPiXus573zZnh3G/LhCfDtg7IhM+l4MVPr6G8W8n8ODrs/+6CNHxXzjHJs3VTKiwx6nascb3J2PvtroL4pLx9q6T+ecDagrjkvZzXpbtdeOY0E54YN/0+gkP8vcCCnPyYW0Zv2P+74RrbZMLvwur39EdqK9br1QWMKXn+WZnnq+avrPacayXlpF4jdMtw3LMF8e0hfk1B/Pkh/oh6rpv8mjUx6WKsO+FhEfkV1k1wm6ouHOCYv+WEPRO2E1Nhe4TtjdmdVXWJiNyLDebdGesCSPZ9D/AKrGAfDMxX1b+KyN3Y+FJEZDNgN+B6Ve1uQGOj/F1V1+SEF957Knw/4JVY62KaPO3JJIy7c+L+HbZbDaBzMJRNw33DdtsC/5M7hO1/YK0UZbhHVZflhM/BjO5XAheKyCZYBb0I+ITNk+jHmnDtLHNVdUFO+M1Ymn5eRPYImm/DJmVUCvT+WVWrBXoPDHpvxgyrSZjR+qUCvasyevcJ298XXHsomA0ciqXt5wBEpAMbZ7uAvvk2lPk9UB3xqIg8C7xERMaramft22iYesv8+II02Cxs88pdP1T1JhHZEft4TcrLa7ExeIcDJ4jIkQV1zVCTFErNids9/MAMg/nYWMJvqOrDJc5dz/vmfGzs5wcwYw4R2RQbkvSI5k+c6lTVx3PCc+t2+o9GAAAgAElEQVR+EdkGGy96CLANMCpz3LSC+7g5J+xWzIB7ZSrsYsygvUNEfo0NDbpNM5PPsB6HVqDIh2972JYqT5hRu6QgLnnm+t2Dqj4pIs9gvZhp9sXy+1gROTbnnB3AZiIyWVVfGEiYiLwW+1DdF2ul7sjsMg3rGUk4D+uWfw82nAHgDdj77nxVXT7Q9RLUJtTNKrPvBszisN10MAc3xQhV1bNFZBHW4vgxbMygisjNwGdVtV+lrDZDM0tP2LamwsaH7fyCyyfhaVcGN2CF7RAR+TvWQnBtKu5zIjIeM04lhOXRT6Oq9oSXfmv/3QfNcwXhg7n3hLyXbU+JuPacuEYom8+TwzavckqT64OwgOcLwpP0TtJ3IlYONqPXTU1ZcvNObXbjPljr/psxIwBgkYich7WWZT98yupN0mqHGnrTaZWUkX/n7ThEXIW1KhwvIv8TjO0jMYP5O6rak9p3KPO7zHOyDZYGw2GE1lvmDw2/IkqX+fAR8+fwS2aeH4q14r0O+BA25nd9k3gfyDMML1TVmYM9cT3vm2AQ/QE4XES2V9UnsI+kEdh4wjzy8g9y8lBEtsPGok7E0vx6rIxVsB6F5Fp59Hvew/tlEWZYJWFXBt+On8beax8I174b+B9V/WPYNSlPe4VfEWXL0yqsGz+P5JkbqM7KGqGTMRulVh07BuvpyUVE3oqNrV+NDSN7AmsZr2ItyQfSP81/hX24nCQi3wjPyftDXFE52FBJ6rjxBfFJeFE5Tz6iVg3m4k1z0aSqFwEXicgE4DXYl+Z7gD+IyM41WkUHIknQLbAxjVmmZvaD3haR14W/J9FraN6ITaI4iNAiSnFL43CR11oAfe89j7x731BJ7uEoVf3NEJ1z84LwJD07M9t7VXWPnP0HoijvCC0V7w2GwC7YR8+HsWERLcCXG9R7laoeXVJnUuFMYz25V1LVVSJyKTY58FCsG/iEEH1hZvehzO/0c/JETrzX5yTR83FV/e76uIBa/9r1IvIlbPLOwaxnI1RExgKvCv/esT6uUef75nxscs9JwOcx42M1Npa7UT6FGVcnqursdISIvJPe8p/H5vRtrUNE2rAWqK50uKpeA1wjIqOxSVxHYh8UvxORV4YW5KQ8naOqnxr0HfWyANhBRNpzPpiTa21O/ns5753ViQ03mtSgrtOw8ah7quoj6QgR+SE5bo1C3TQbm0x1mIg8hE0gvUNV/57dv4iwaMOMesRqOZdnQ4aqrhCRfwPTRGSqqmY/zpNepkcLTpF8zOT18NWk6SsmqepSVb1WVU/CuucmYd3lg+XesJ2RjQgV0CvodaWTaHgOm427P1b5QK8RehvWtXoIViEvSV3DG4X3Hkj86d2z/qX0I+lSHqoW4cRB9f5DdD6APcILMcuMsL0XIHTFPATsKiKNVpD9UOMhVT2X3havvGVb9xORvGe4j17MiFwK7BP8P5YhSd/Xl9i3kbydHbYnhOEurwfuV9X7CvQMRX4PVEe8FOtye6qg96UeNoQyX0QyLCV37MYQ81msNeWerJEw1JR83/wOM/ZOFJHDsOEsl2rwndggyWo2V+TE9TOGSsTvh5Wv3HeSqq5Q1RuDkXk61g2dPNN3Yq2BQ1We7g/bnXLikndOv3sIrcNb5xzzV2CiiOzaoK6XAg/nGKAtWPoVkYx1/ADmTaKV+ltBZ2AtufX8mkHSsHZETtzrM/tk2RkrRw8M5sJNMUJF5CDJH5iWdCmsbOD0v8DGkXw0Z/mq04Bx2ASi7DinGzE3OR8HHlPVZ8C+iLAZiMcB22OuafLG4XngNsyNSL+ltML/+2NfM7c2QVvSXbLNEJ3vaqwV68Mi8oa8HURk3zB+syzjsVbH9Dn2BP4L+yq/KhV1NlahXxA+brLXnhjGdpZCRHYVkbyWzSQs75nYAetiTJ/nKKyif5zQxRq6tc/FWvi+KyLZMWiIyFTpuwzi+Vh34pclZ3lEEUmPBV5CGNyff3fFqOpt2FjVozBH1+30GqZphjK/LwjbLwXDNzm+FXMz04J5DGiUIS3zodv4z8DRIvKevH1E5GUiMiUvLrPfESJydN5HidjSi58I/+aNgRwSRGSkiHwBm7SyFqt718d16nrfhPr9RyE+KSs/GCI5c8N2Rkbj4ViPwEB8WUTWjS8Vc/Z/Rvj3Z6nwA0ILaZY+dUkYm34xsKeIfDmU/z6IyPYi8pIauhLmhO0+OXEX0/tenp46fws2GSzPFjknbH8sIltmI0VkdBjCVIu5WAvtunOE8jAL63HKRVUfwxqjjsTqpqXUWKo35xyzVFXq+dVz/noJ9fzOYXhhmqR8fzFTxqZjvXFrSJWxVPwIwlyawX60N6s7/ipguYj8ld6l3PbHxqXczWA97wOqOldEPoH5OrsndPctxF7M+2KtQv+dc+gNmNPuKZjbnWzcjNTfLlFVFVvv+Y/Ar0Xkaux+d8Ja0pZhMy+bYUTfhH0tnSEiuxEGsKvq1wZzMlXtFpGjMZ9x14jI7dgs2pXYV/Ve2KzLqZT/qLkFeJ+IvBoz6Kdirq1agA+o6rouL1W9QERehRmBT4RxZE9jLSsvwVpXfoZVXmU4FDhTRP6CfSgswFrkjsLS7cycY64Dvi0ir8cm2b0Um5CwGnhPJp9PwyZ3fBB4k4jciI33nIIZs6/FjIGHw/09LCInY5XTvaEsPYZ1veyFdf8dFPZdLiJ3APuLyMVBfwX4jareT20uCvq+jBm+F2d3GMr8VtXbReRb2GSoB0XkcmyM2OuxiYe3kp/e9fKXoOUTIjKZ3rG65zYw4ek/sQ/mn4rIx7Du66VYWXk5pn9faneN7Yy95JeIyJ+xvO0J53kjNh72DsxTwVAwM3RNQu+ynQdgz8t8rLyur4/jwbxvfoJ9kE4DHlDVvwyRlvMwbx+XhXI3D8uzIzA/sW8f4NhHgIfCcd1Y3bA95if456n9vot1rd6G3e9abLjDwcC/6GtIfQR7/r8KvEtEbsXGbW6JTUjaC5so+FSJe7saG7pxOJZ+6wjv5c9j4yzvFZsw1Rn2nYC1or48c8wN4ZgzgMdE5NqgYww2fvRA7FnNa71Lk/jSvldErsDS7rWYAfpbzDNJEedhw/Q2x57bQY17XF+EIQMJO4ftN0Uk6cn4Sea5OgMb8nEiqY/9UCeejQ0XuT+UsQ6sPE7C/DnPzZEwI+yX17JfDh2kO4BGftiL8CrMuW+yQsi92EthbGbfORS4LWAAFyjAYdig7yX0rmDxLXJc6oT9JxDcRADHZuISv4JKzupHA+kI8Upw7l0yfeYysIum2TWO3wmrlObTO5v0F8BOOfvOosCFSo30nUGO250auo6n15dhH3cUpFZMqudamBH1DczR+ErMfdZj2ED048lxSTRQumIV79Wh3KzEjNFc58vh2COx7rsFWGX/HNbN9TX6+6ktLAfhumdjM6UXhjI7N9zHa4rSg94Vk7qwj4zrKVg9CHv5vgv7kFoc9CaOtr8AbJ1zzL5YBZPc3zzM+D0ms99LsQr9BcxoXlduapUVrKUwefZ+WyOvGs7v1LneEe59GWa4P4QZ4iNz9p1FnS6awnFHYMbocnrrkOklnr11ZTInbmzIr7vDeVdhL+drsPGLo0vo2hQbE/lL7MNjCVZXLMQ+GE+mYDW6THnWGvvMSd23YobuUuzj+NdYHZOrlwGc1deZB6XfN5njEhdBHx5gn7kUuAQqyl9sTOqNIc2XhTL4lqLnJJWG2RWTnsS6b7MLGhwX8vWxUD66sOfl68BmOTo7MGP0dswwXIN9UN+AtYiXWhkvlWarKXCHhRm094R9FmLvpVorJu2HGejz6F3t6D6svtwzs29ufoSylKwItSjofFlRHqWOaw3XUwpcRTXzl3m28n4zM/vPzgvPpNNdIZ2WYd4Mjhzg+pdQ4Haw7C/xixmJvGgJXQ5P0eAs3OEitCjdhPncm9VcNZHIxkfoJn4cawGbqqlekCZomYP5/B2O8bkNIbYG+W3Ap1T1nFr7eyeMV30cc3E1HGOxNxjCsJ+5wCWqWmsoSSFNn5gUiUQikYgzjsGG1VzUTAN0Q0NVb8eW+P3vOsfje+UzWA/SUA1L2Zj4AtaDlfXaUhdNc9EUiUQikYgnwhjESdiwhhX0TvyJlOcz2FCPl5Dvjsk1YosJJMvYnoiNt7+sqaKcESZ2zcdW8Sryt1yKaIRGIpFIJGKcgY2NfRhzZP90jf0jGUKazWq2jgbYDisHK7FJvh9Svx5xmoLaOM5vDsW54pjQSCQSiUQikciw43pMqIiMEZFTReQ6EVksIioiM+s4foKI/EhEForIChG5qR7fjZFIJBKJRCKR9YNrIxRzI/IVzH1N6aWyYN3sxmuwsR3fw9xxTAHmiMgOAx0biUQikUgkElm/eB8TOh9zj/FcWLnmrjqOPQbzx3asql4OEBzXPwqcihmnkUgkEolEIpEm4LolVFXXqK3rPhiOwVZ+WLf6kaouxJzeHhWWm4pEIpFIJBKJNAHvLaGN8ErgnpxZbXdi7jd2BB7IOzA4Yd0sEzwmHPMgtmpDJBKJRCKRiFc6sGWNb9bBLxW8XtmYjdCp2FrgWRKfVltSYIRiS9adsj5ERSKRSCQSiQwjRwG/abaIPDZmI3QUtqZpltWp+CLOo79z2p2By3/yk5+w2267DYG8xqlWq6xatYpRo0bR0uJjZIU3TV1d8Kc/tdLdvZb29g5e97oK48Y1W5VfXd7yD6KmsnjUBD51LV++nIcffphddtmFMWPGNFtOrA/qwKMm8Knr8ccf5/jjjwd4ptlaitiYjdBVQN64z5Gp+FxUdQGwIB1mCwTAbrvtxqtf/eohkhhZ3yxZAk891fv/nnvCxInN05PgVVck8mKgq6uL5cuXs9deezHOgbUX64PI+iD1geV2CKEPc339MB/rks+ShM0bzEmrVT8LJ1SrVVauXBk11aBarbJ27RpXmsCnLq/5FzXVxqMm8Kkr0eJNU6wPauNRE/jU5UlLERuzEXofsEfwF5rm1dhyXI8O5qSeMrWnp4cFCxbQ09PTbCnr8KipWq3S1bXMVd6BT10e8y9qKodHTeBTV6VS6bP1QKwPyuFRE/jU5al8F7FRGKEiMlVEdhaR9lTw5cDmwNGp/TYFjgV+q6p540Vr0tra2pDWoaS9vZ1tttmG9vb22jsPEx41tba2MmnSJFd5Bz51ecy/qKkcHjWBT11tbW19th6I9UE5PGoCn7o8le8i3CsUkY8AE7DZ7ABvEpGtwt/nBrcDZwAnAC8B5oa4y4G/Aj8TkV2ARdis91YamPmejA31gIi40gNRUz141BU1lSNqKo9HXYkeT7q8plPUVA6PurzpyWNDaAn9DHAa8KHw/9Hh/9OAwqHbqloB3gD8GvgYcCZmiB6sqv8crBhPzdvd3d3Mnz+f7u7uZktZh0dNlUoPnZ1LqVT8dJOAT10e8y9qKodHTeBTV9Jl6qvrNNYHZfCoCXzq8qSlCPctoao6vcQ+M4GZOeFLgPeF30aHiDBixAhXXzseNYHQ1tYOeNIEHnV5zL+oqRweNYFPXR5bQmN9UA6PmsCnLk9ainBvhHrD03idtrY2Jk2a1GwZffCoqbW1ldGjRzdbRj886vKYf1FTOTxqAp+6knrcU30e64NyeNQEPnVtCGNCN4TueFd4mrlYrVZZvXp11FSDarVKd3e3K03gU5fX/IuaauNRE/jU5dVFU6wPauNRE/jU5UlLEdEIrRNPmdrT08Nzzz3nalyTR03VapXOzk5XeQc+dXnMv6ipHB41gU9dXl00xfqgNh41gU9dnsp3Ef7bap3hqfumvb2drbbaKmqqQWtrKxMnTnSzlFqCR10e8y9qKodHTeBTl1cXTbE+qI1HTeBTl6fyXcR6USgi2wEjVPWR9XH+ZuJpoK+IuCtkXjV5qhgSPOrymn9RU208agKfujxOTIr1QTk8agKfujyV7yIa+uQSkY+JyK8yYT8DHgMeFJG/iciURq7hDU/N2z09PTz//POumv89aqpUKnR1dbnKO/Cpy2P+RU3l8KgJfOry6aIp1gdl8KgJfOrypKWIRtv93wc8n/wjIodjTuN/BHwU2I4GHMNHauOp6ybBoyavX4QedXnMv6ipHB41gT9dHltCwZ8e8Jd34FMT+NXlmUbbjrcF0l3uxwFPqeqHAERkC+BdDV7DFZ66S9ra2thss82aLaMPHjW1trYyduzYZsvoh0ddHvMvaiqHR03gU5dXF02xPqiNR03gU5e34QF5NGq2Zz/bDgN+n/p/LrBFg9dwhao2W8I6VJW1a9dGTTVQVXp6elxpAp+6vOZf1FQbj5rAp65EizdNsT6ojUdN4FOXJy1FNGqEPgq8FdZ1xW9JXyN0K2Bpg9dwhafxOt3d3cybN8/V0lweNVUqFZYuXeoq78CnLo/5FzWVw6Mm8KnL65jQWB/UxqMm8KnLU/kuotG22rOAS0RkCTAa65r/Qyr+YOC+Bq/hCk/dN+3t7Wy55Za0t7c3W8o6PGpqbW1lwoQJrvIOfOrymH9RUzk8agKfury6aIr1QW08agKfujyV7yIaUqiqvxKRF4A3YC2e56lqD4CITAIWAz9vWKUjPA0cFxE6OjqaLaMPXjV5fBg96vKaf1FTbTxqAp+6PE5MivVBOTxqAp+6PJXvIhqeyqWqf1TVT6rqqaq6MBW+WFWPVtWrGr2GJzx1lfT09LBw4UJXTe4eNVUqFZYtW+Yq78CnLo/5FzWVw6Mm8KnL44pJsT4oh0dN4FOXJy1FDMlnl4hMAw4ApgBXqOqzItIKjAc6VdXPU7WR4WmJtwSPmrwO0Paoy2P+RU3l8KgJ/OnyODEJ/OkBf3kHPjWBX12eacgIFWvr/TbwkXAuBR4AngXGYLPjvwJ8pyGVjvA0XqetrY3NN9+82TL64FFTa2sr48aNa7aMfnjU5TH/oqZyeNQEPnV5HRMa64PaeNQEPnV5Kt9FNNod/1ng49gEpUNJuWxS1U7gSuBtDV7DFZ6+VL269PCoqVKpuNIEPnV5zb+oqTYeNYFPXR5bQmN9UA6PmsCnLk9aimjUCD0JuEhVv0D+LPj7gR0bvIYrPI3X6e7u5tlnn3XlEsKjpkqlwpIlS1zlHfjU5TH/oqZyeNQEPnV5ddEU64PaeNQEPnV5Kt9FNGqEbg3cPkD8CsBX/0KDeFqWq62tjS222MJVk7tHTS0tLYwfP95V3oFPXR7zL2oqh0dN4FOXxxWTYn1QDo+awKcuT+W7iEZTawFmiBbxKuDpBq/hCk8VREtLCyNHjmy2jD541eQp3xI86vKaf1FTbTxqAp+6kufO0/MX64NyeNQEPnV5K095NKrwSuCDIrJdKkwBROQwYCZwWYPXcIWnrpKenh4WL17sqsndo6ZKpcKKFStc5R341OUx/6KmcnjUBD51eXXRFOuD2njUBD51edJSRKNG6CnAfGw86EWYAfrfInIrtnzn/cDpDV4jUoCqsmbNGleDjz1qAqWnp5vwfeQIf7o85l/UVA6PmsCnLo8Tk2J9UA6PmsCnLk9aimh0xaROEdkH+DRwDLAaOBB4AjgVOFNVVzWs0hGexli0t7czderUZsvog0dNra1tjB8/odky+uFRl8f8i5rK4VET+NTl00VTrA/K4FET+NTlaQnRIhp+AoOR+bXw2+jx9GWhqqgqIuJmeS7vmlJexJqOR13e8y9qKsajJvCpy2NLaKwPyuFRE/jU5al8F9FQd7yIvKXEPt9s5Bre8DRep7u7m6efftqVSwiPmiqVCosXL3aVd+BTl8f8i5rK4VET+NTl1UVTrA9q41ET+NTlqXwX0eiY0F+JyBFFkSLyA+AzDV7DFZ5mm7W1tTFlyhRXXUoeNbW0tDBu3FhXeQc+dXnMv6ipHB41gU9dXl00xfqgNh41gU9dnsp3EY2m1kXAlSLyJlW9IQkUkRbg58A7gA83eA1XeKogWlpa2GSTTZotow9eNXV0jGi2jH541OU1/6Km2njUBD51eXXRFOuD2njUBD51eSrfRTSkUFXfj7lgulpE9gcQkQ7gKuBY4N2q+oOGVTrCU1dJpVJh6dKlUVMNqtUqK1eupFqtNltKHzzq8ph/UVM5PGoCn7o8umiK9UE5PGoCn7o8aSliKMzkE4HfAdcE36DXAocBx6rqxUNw/kgB1WqVFStWuKq0PGpSrQbXGX40gU9dHvMvaiqHR03gU5fPiUmxPiiDR03gU5cnLUUMxez4qoj8F3A55ht0BfBGVb2x0XN7xNMYi/b2dqZNm9ZsGX3wqKm1tY2JEyc2W0Y/POrymH9RUzk8agKfury6aIr1QW08agKfujY6F00i8qkBou8ADgGuA14hIq8I4aqq5wxSXyQSiUQikUhkI6Tez8CzSuxzTPglKLDRGKGeXB6sXbuW+fPnM3XqVDo6OpotB/Cpqaenh87OTsaPH88QNP4PGR51ecy/qKkcHjWBT12JGx1v7nRifVAbj5rApy5P5buIekv6S9aLig0IT7PN2tramDx5sqsuJY+aWlpaGD16tKu8A5+6POZf1FQOj5rApy6vLppifVAbj5rApy5P5buIulJLVf+1voRsKHiqIFpaWhgzZkyzZfTBq6aRI0c2W0Y/POrymn9RU208agKfury6aIr1QW08agKfujyV7yL8K3SGJ5cHlUqFrq6uqKkG1WqVVatWuZsp6FGXx/yLmsrhURP41OXVRVOsD2rjURP41OVJSxF1GaEi8pSIPCEi7an/n6zxe2L9SG8Onlx6VKtVOjs7XVVaHjWpWuXuyfUJ+NTlMf+ipnJ41AQ+dSVaPGmK9UE5PGoCn7o8aSmi3sELN2MTjaqZ/180eBrv0d7eztZbb91sGX3wqKm1tY1JkyY1W0Y/POrymH9RUzk8agKfuhLXNZ5c2MT6oBweNYFPXZ7KdxH1jgmdOdD/kUgkEolEIpFIGeKY0Drx5KKpu7ubZ555xpUbBo+aKpUeFi9eTKXiJ+/Apy6P+Rc1lcOjJvCpy6OLplgflMOjJvCpy5OWIhruWxaREcBJwBuA6SF4LrZ8509UdXWj1/CEiDRbwjpaWloYP368qxlwHjWJtDBq1ChE/GgCn7o85l/UVA6PmsCnLo+z42N9UA6PmsCnLk9aimhIoYhsBdwHfBfYHVgYfruHsPvCPoM9/wgR+aaIzBORVSJyh4gcWuK4WSKiOb+GDWJPfrdaW1sZN25c1FSDlhar3L09kB51ecy/qKkcHjWBT11e/YTG+qA2HjWBT12etBTRaGn/PrAtcJyqTlPVA8NvGvB2YJuwz2CZDXwKuBj4OFABrhWR/Uoe/yHgXanfiQ1oAXzNNqtWqyxfvjxqqkG1WmX16tWuNIFPXV7zL2qqjUdN4FOXx9nxsT4oh0dN4FOXJy1FNNodfwhwjqpeno1Q1ctEZA/go4M5sYjsDbwD+KyqnhXCLgIeBL4FvKbEaS5X1UWDuX4RnjK1p6eHF154gY6ODjfLhHnUVK1WWbFiRfBs4KeVwaMuj/kXNZXDoybwqcurn9BYH9TGoybwqctT+S6iUSN0GbBggPjnwj6D4Ris5fNHSYCqrhaRnwKni8jWqvpMjXOIiIwDlukQOfj05KKpo6ODbbfdttky+uBRU7Kcmjc86vKYf1FTOTxqAp+6PLpoivVBOTxqAp+6PJXvIhr93PoZMFNENslGiMgYrPv7p4M89yuBR1W1KxN+Z9i+osQ5ngQ6gWUi8gsR2XyQWiKRSCQSiUQiQ0ijzXr3AW8E/iEiFwKPh/AdgHcDi4H7ReTo9EGqemWJc08F5ueEJ2FbDnDsEuB7wF+ANcD+wIeBvUVkzxzDtg8iMgXYLBO8PcCyZcvo6hrw8GGjp8dcekyaNMlNC603TV1dsGJFKytWrGD06NF0dVXwMFbbqy5v+QdRU1k8agKfujo7O/tsm02sD8rjURP41OWlfA9Eoyn1q9TfX8yJ3wr4JZD2a6RAmcdrFGZAZlmdis9FVf83E3SFiNyJTXA6GfhGjWufDJySF/HII4+wYsWKGodHvLB8eTuPPdb7vXLrrfMYM6b5vtO86opEXkzcc889zZYAxPogsn54+umnmy2hJo0aoQcNiYp8VgEjcsJHpuJLo6qXiMi3gddR2wg9D7gsE7Y9cPXLXvYy9thjj3ouHWkiS5bAokW942L22287Jk5soqCAV12RyIuBFStWcOedd7L33nszevToZsuJ9UFkvfDII480W0JNGjJCVfXmoRKSw3xgWk741LCdN4hzPgPUXJxXVReQmXCVOKkfMWIE48aNG8Slh57EpcfIkSPd+JbzpqlSgREjqvT0dNPW1s64caPwkH1edXnLP4iayuJRE/jUlXg5GTVqlIv6PNYH5fGoCXzq2mSTftN13DHkKSXGwSLyehEZ28Cp7gN2DLPb07w6FV+XLmxFp4UNaHLnomnBggWulhL1qKlardLVtcxV3oFPXR7zL2oqh0dN4FOXVxdNsT6ojUdN4FOXp/JdRKMrJn1dRG5K/S/A9cAfgWuAB0Rk+0Ge/nJs7Oj7U+cfgc24vyNxzyQi24jIzhld2UlFYI7rNwOuG6QewNcKBO3t7WyzzTau3DB41NTa2sqkSZNc5R341OUx/6KmcnjUBD51JRNHvEwggVgflMWjJvCpy1P5LqJRhW8Drk79fwzmwP6LwN+BHwKzsNWK6kJV7xCRy4Azwmz1x4ETsNbM96Z2vQg4kL6Tn/4lIr8GHsAmMu2HOb6/L2gaNJ7WjhcRV3ogaqoHj7qipnJETeXxqCvR40mX13SKmsrhUZc3PXk02h0/jV63TABHAw+r6hmqei1wPjCjgfO/G/gOZsR+F2gHjlTVW2ocdzGwN2YAfwfYC1tl6QBVXdmAHlfN293d3cyfP5/ubj+zKD1qqlR66OxcSqXip5sEfOrymH9RUzk8agKfupIuU19dp7E+KINHTeBTlyctRTTaEtpDmMEeuuIPwVomE54HNmkauHkAACAASURBVB3syVV1NfDZ8CvaZ0ZO2EmDveaGhIgwYsQIV187HjWB0NbWTt/Gcg/40+Ux/6KmcnjUBD51eWwJjfVBOTxqAp+6PGkpolEj9EHgeBG5GHgrMBkbC5qwLTCka7c3G0/jddra2pg0qeZk/2HFo6bW1lYXbliyeNTlMf+ipnJ41AQ+dSX1uKf6PNYH5fCoCXzq2hDGhDbaHf9VbPnMRcCPgdtU9aZU/BuBuxq8his8zVxMXEJETQNTrVbp7u52pQl86vKaf1FTbTxqAp+6Ei3eNMX6oDYeNYFPXZ60FNGQEaqqfwT2AD4FvAc4LIkTkYnALdhYzo0GT5na09PDc88952pck0dN1WqVzs5OV3kHPnV5zL+oqRweNYFPXV5dNMX6oDYeNYFPXZ7KdxENt9Wq6sPAwznhS4BPNnp+b3jqvmlvb2errbaKmmrQ2trKxIkT3TgQTvCoy2P+RU3l8KgJfOry6qIp1ge18agJfOryVL6L8K/QGZ4G+oqIu0LmVZOniiHBoy6v+Rc11cajJvCpy+PEpFgflMOjJvCpy1P5LsLPJ9cGgqfm7Z6eHp5//nlXzf8eNVUqFbq6ulzlHfjU5TH/oqZyeNQEPnX5dNEU64MyeNQEPnV50lJENEI3cDx13SR41OT1i9CjLo/5FzWVw6Mm8KfLY0so+NMD/vIOfGoCv7o846vteAPAU3dJW1sbm22Wt0Jp8/CoqbW1lbFjxzZbRj886vKYf1FTOTxqAp+6vLpoivVBbTxqAp+6vA0PyCOa7XWiqs2WsA5VZe3atVFTDVSVnp4eV5rApy6v+Rc11cajJvCpK9HiTVOsD2rjURP41OVJSxFDYoSKyAgR2VdEjhKRQa+QtCHgabxOd3c38+bNc7U0l0dNlUqFpUuXuso78KnLY/5FTeXwqAl86vI6JjTWB7XxqAl86vJUvoto2AgVkY8B84FbgSuBl4fwTUVkkYi8p9FreMJT9017eztbbrkl7e3tzZayDo+aWltbmTBhgqu8A5+6POZf1FQOj5rApy6vLppifVAbj5rApy5P5buIhoxQETkR+A5wHfBeUovequoi4EbgHY1cwxueBo6LCB0dHVFTDRLXGZ40gU9dXvMvaqqNR03gU5fHiUmxPiiHR03gU5cnLUU02hL6aeBqVf1P4Lc58XcDuzZ4DVd46irp6elh4cKFrprcPWqqVCosW7bMVd6BT10e8y9qKodHTeBTl8cVk2J9UA6PmsCnLk9aimjUCH0p8PsB4hcDkxu8RmQAPC3xluBRk9cB2h51ecy/qKkcHjWBP10eJyaBPz3gL+/Apybwq8szjQ4YWAoMNBFpF+C5Bq/hCk/jddra2th8882bLaMPHjW1trYybty4Zsvoh0ddHvMvaiqHR03gU5fXMaGxPqiNR03gU5en8l1Eoy2h1wLvF5EJ2QgR2RU4CfhNg9dwhacvVa8uPTxqqlQqrjSBT11e8y9qqo1HTeBTl8eW0FgflMOjJvCpy5OWIho1Qr8EtAIPAl8DFDhBRH4B/A1YAHy1wWu4wtN4ne7ubp599llXLiE8aqpUKixZssRV3oFPXR7zL2oqh0dN4FOXVxdNsT6ojUdN4FOXp/JdRENGqKrOA16FzY5/OzY7/l3Am4BfAvuEWfIbDZ6W5Wpra2OLLbZw1eTuUVNLSwvjx493lXfgU5fH/IuayuFRE/jU5XHFpFgflMOjJvCpy1P5LqLh1FLVBcD7gPeJyGaYYbtQVTfKEbqeKoiWlhZGjhzZbBl98KrJU74leNTlNf+iptp41AQ+dSXPnafnL9YH5fCoCXzq8lae8hi0QhHZREReEJHPJmGqulBVn99YDVDw1R3f09PD4sWLXTW5e9RUqVRYsWKFq7wDn7o85l/UVA6PmsCnLq8ummJ9UBuPmsCnLk9aihi0EaqqK4EeYMXQyYnUg6qyZs0aV4OPPWoCpaenGxuy7Al/ujzmX9RUDo+awKcujxOTYn1QDo+awKcuT1qKaLQ7/grgGBE5XzeEux0CPI2xaG9vZ+rUqc2W0QePmlpb2xg/vp8Dh6bjUZfH/IuayuFRE/jU5dNFU6wPyuBRE/jU5WkJ0SIafQJ/BZwH3CQiPwbmAquyO6nqPQ1exw2ebG1VRVURETfLc3nXlFpZtul41OU9/6KmYjxqAp+6PLaExvqgHB41gU9dnsp3EY2OWp2DOaQ/ALgIuAW4K/X7W9huNHgar9Pd3c3TTz/tyiWER02VSoXFixe7yjvwqctj/kVN5fCoCXzq8uqiKdYHtfGoCXzq8lS+i2i0JfQ9eBrAMgx4mm3W1tbGlClTXHUpedTU0tLCuHFjXeUd+NTlMf+ipnJ41AQ+dXl10RTrg9p41AQ+dXkq30U0lFqqOnuIdGwweKogWlpa2GSTTZotow9eNXV0jGi2jH541OU1/6Km2njUBD51eXXRFOuD2njUBD51eSrfRQxKoYiMFJG3i8jnReQkEfE1Gnc94qmrpFKpsHTp0qipBtVqlZUrV1Kt+vIc5lGXx/yLmsrhURP41OXRRVOsD8rhURP41OVJSxF1G6EiMgVbpvMS4HTgh8BjIvK6IdYWqUG1WmXFihWuKi2PmlSrwXWGH03gU5fH/IuayuFRE/jU5XNiUqwPyuBRE/jU5UlLEYPpjv8yMB04B7gReGkI+yGw/ZApc4qnMRbt7e1Mmzat2TL64FFTa2sbEydObLaMfnjU5TH/oqZyeNQEPnV5ddEU64PaeNQEPnVtrC6aDgMuUtXPJAEi8jxwiYjspKr/HDJ1kUgkEolEIpGNksGMCd0GuDUTdivm2GzzhhU5x5PLg7Vr1/Kvf/2LtWvXNlvKOjxq6unp4YUXXnCVd+BTl8f8i5rK4VET+NSVuNHx5k4n1ge18agJfOryVL6LGIwROgJYnQlL/vfTt7Ge8DTbrK2tjcmTJ7vqUvKoqaWlhdGjR7vKO/Cpy2P+RU3l8KgJfOry6qIp1ge18agJfOryVL6LGGxqTReRPVL/jw/bHURkaXbnjWnFJE8VREtLC2PGjGm2jD541TRy5Mhmy+iHR11e8y9qqo1HTeBTl1cXTbE+qI1HTeBTl6fyXcRgjdDTwi/LeZn/BXNm798cL4knlweVSoUVK1YwevRoN188HjVVqzbrdMSIETS+SNjQ4VGXx/yLmsrhURP41OXVRVOsD2rjURP41OWpfBcxGCP0xCFXsQHhyaVHtVqls7OTUaNGuSn0HjWpVlm1ahUdHe14qdzBpy6P+Rc1lcOjJvCpK3Fd48mFTawPyuFRE/jU5al8F1G3EaqqF64PIRsKnsZ7tLe3s/XWWzdbRh88amptbWPSpEnNltEPj7o85l/UVA6PmsCnrsR1jScXNrE+KIdHTeBTl6fyXYSPz61IJBKJRCKRyIuKaITWiSf3Gd3d3TzzzDOu3DB41FSp9LB48WIqFT95Bz51ecy/qKkcHjWBT10eXTTF+qAcHjWBT12etBQRjdA6EZFmS1hHS0sL48ePdzUDzqMmkRZGjRqFiB9N4FOXx/yLmsrhURP41OVxdnysD8rhURP41OVJSxF+BjhuIHgZcAymZdy4cc2W0QePmlparHL3hkddHvMvaiqHR03gU5dXP6GxPqiNR03gU5en8l2EazNZREaIyDdFZJ6IrBKRO0Tk0JLHThORS0VkqYh0icjVIrJdo5o8zTarVqssX748aqpBtVpl9erVrjSBT11e8y9qqo1HTeBTl8fZ8bE+KIdHTeBTlyctRbg2QoHZwKeAi4GPAxXgWhHZb6CDRGQMcBNwIHA6cArwSuBmEZnciCBPmepxmTePmqrVKitWrHCVd+BTl8f8i5rK4VET+NTl1U9orA9q41ET+NTlqXwX4bY7XkT2Bt4BfFZVzwphFwEPAt8CXjPA4ScDOwB7q+pd4djfh2M/DXxhsLo8uWjq6Ohg2223bbaMPnjUlCyn5g2PujzmX9RUDo+awKcujy6aYn1QDo+awKcuT+W7CM8tocdgLZ8/SgJUdTXwU2BfERnIIdcxwF2JARqO/QdwA3Dc+pEbiUQikUgkEimLn2a9/rwSeFRVuzLhd4btK4BnsgeJTS18OXBBzjnvBA4TkbGquqzowiIyBdgsE7wzwAMPPFBO/TBQqVTo7Oxk/PjxbgYge9PU1QVPPNHCypWr2GSTUfztb1U8jB33qstb/kHUVBaPmsCnrmXLlvH0009z5513Mnbs2GbLifVBHXjUBD51PfbYY8mfHc3UMRDiaRnKNCLyIPC8qh6SCd8FeAj4oKr+MOe4TYGFwFdU9bRM3MnA94GdVfWfA1x7FjaONBKJRCKRSGRD5ihV/U2zReThuSV0FLAmJ3x1Kr7oOAZ5bMJ5wGWZsJcBv8S6+v9R4/jh5EFgt2aLyBA1lcejrqipHFFTebzp2h64GjgKeKLJWtJ4SyeImurBm64O4B7g5mYLKcKzEboKGJETPjIVX3QcgzwWAFVdACxIh6Wc1P9DVR8a6PjhRETwpAeipnrwqCtqKkfUVB5vulL1+RPedHnSA1FTPXjUFTR1NltHEZ4nJs0HpuaEJ2HzCo5bjLWCDubYDZFTmy0gh6ipPB51RU3liJrK41WXNzymU9RUHo+6PGpah+cxoWcCnwQmpScnicgXgK8D26hqv4lJYZ+7AFXVvTPh1wPbq+r2g9CzK6Gp3duXTiQSiUTKE+vzSMQHnltCLwdagfcnASIyAjgRuCMxQEVkGxHZOefYvURkz9SxOwEH03+sZyQSiUQikUhkmHHbEgogIpcCbwXOAR4HTgD2Bg5R1VvCPnOAA1VVUseNBe4FxgJnAd3YykutwCtUdeEgtEzBnOCfF8aMRiKRSGQDJNbnkYgPvBuhI4HTgOOBicD9wJdV9Q+pfeaQMUJD+FaY8XoY1uI7B/ikqj4+LOIjkUgkEolEIoW4NkIjkUgkEolEIhsnnseERiKRSCQSiUQ2UqIRGolEIpFIJBIZdqIRGolEIpFIJBIZdqIRGolEIpFIJBIZdqIRWgMRGSEi3xSReSKy6v/ZO/P4uMqyf1/3LEnapElbaLovtCDSViy7KNgCVUEEZBFQWQoIKPqD1wWxgCD6WkBRERAVUFlcARWQzSIWsCJUEGR7ZacFWronadIsszy/P85MOlkmOckkOd/W5/p85pPOObNcuc/dyTPPcj9m9riZfShqL4/H4/F0j5lVmdklZna/mW0wM2dmC4o8dpfc4xpzj73FzMYMsbLH81+Jb4T2zo0ENUZ/BZwDZIB7zWy/KKU8Ho/HU5TtgYuAXYB/F3tQrpTfI8COwPkEdaUPBR4ws7Ih8PR4/qtJRC2gjJntDRwPnOucuyJ37GaC7d6+A7w/Qj2Px+PxdM8qYLxz7p3cznn/LPK484FKYA/n3AoAM1sGPAAsAK4bAleP578W6Z7QvgypFHn+SDO7zszWmlmTmS0xs937oHAMQc9n+weRc64F+Bmwr5lN7sNreTwej2cIcM61OufeCfHQo4G78w3Q3HP/ArwEHDtYfh6PJ0C6EUrIIZXuMLMYcA/wKeAa4KtALfCQme0U8mV2A15yzjV0Or4s93NOX5w8Ho/Ho4GZTST4m/BEN6eXEXz+ezyeQUR9OD7skEp3HEMwXP4J59zt0L4X/UvAJQSN094Yn3PozgtgQh98PB6Px6PD+NzPYp/xo82s3DnXOoROHs9/FdI9oX0YUumOY4DVwB8KXm8tcCtwhJmVh3iNYUB3H0AtBec9Ho/Hs/WR//z2n/EeT0So94SWwm7Av5xz2U7HlwFnAO8Cnu3uiWZWC4wBsgTfhmcBVbnnPAdMyj10ZO6cx+PxeDSZnvs5odPndb4ndHo3n+MTcz+nmVlqUO08nsGjDJgMPOycq49apjvMORe1QygKhuNPcc7dGOLxjcDvnHOndTr+UYK5ogc75/5c5LnfAC4u1dnj8Xg8Ho8nYo5wzt0VtUR3bMs9oaUMpV8L3AZ8GTiJYG7pZOD2G264gdmzZw+kZ7/JZrM0NzczbNgwYjGNmRVyTg0NxP/yF9pSKcqSSTLz50N1ddRWml6KTgjmFN6pLyh6NTY28sILLzBz5kyqqqqi1gE04+SdwqPo9corr3DCCScAvBm1SzG25UZoM9DdvM+KgvPd4pxbA6wxs58CpwAfBO4DmD17Nvvss88Aq3oGjY0b4fXXt9zfc08YNSo6nzyKXopOHs8g0NDQQGNjI3vttRfVAl+0PJ7BoOALVluUHj2h0VwfHFaxZc5PIfljK3t7Aefc4wQ9opcS7JpENtt5iml0ZLNZNm/e7J16IZvN0trWJuUEml6qToo55Z3CoeiVd1FzUoyTdwqHopeSSzG25Ubo08DuuXqhhewDbCYo1RSGk4ArgcNB66Km02nWrFlDOp2OWqUdRadsNsumhgapaweaXopOijnlncKj6JXJZDr8VEAxTt4pPIpeSvldjG2iEWpm483s3WaWLDh8OzAWOKrgcdsDnwD+FLb2m3OuxTl3LjAPIB6PD5h3qSSTSaZMmUIymez9wUOEolM8Hmf06NFS1w40vRSdFHPKO4VH0SuRSHT4qYBinLxTeBS9lPK7GPKGZvYFYCRbCsMfZmb5EklX58oOXAqcDOwAvJE7dzvwGPALM5sJrAPOAuKUsPLdzPr71AHHzKR8wDv1BUUv7xQO7xQeRa+8j5KXapy8UzgUvdR8umNr6An9CvAt4HO5+0fl7n8LKLpqwjmXAT4K/A44G/guQUP0QOfci/2VUereTqVSrFq1ilRKp4ydolM6k6Guvp600LUDTS9FJ8Wc8k7hUfTKD5kqDZ0qxsk7hUfRS8mlGPI9oc65aSEeswBY0M3xjcBncrdtDjOjvLxc6tuOpBOQTCTQMQpQ9JJ0Uswp7xQaRS/VnlDFOHmncCh6KbkUY8AboWY2HDieoDzSvc655QP9HlGiNFcukUgwevToqDU6oOgUj8eprKyMWqMLil6KToo55Z3Co+iV/xz3n+c9453Co+i1NcwJLWk43sx+ZmbPFdwvI5iHeQPwI+BpM9utNEUtlFYNZ7NZWlpavFMvZLNZUqmUlBNoeqk6KeaUdwqHopdqiSbFOHmncCh6KbkUo9Q5oQcAfyi4/ylgNvDp3M932Ma2v1S6qOl0mnfeeUdqXpOiUzabpb6+XuragaaXopNiTnmn8Ch6qZZoUouTdwqPopdSfhej1L7acWxZjQ7wceAJ59xvAMzseuDcEt9DCqXhm2QyyaRJk7xTL8TjcUaNGiWzlVoeRS9FJ8Wc8k7hUfRSLdGkFifvFB5FL6X8Lkaphk0E5ZMwswRBLc2rC85vAmpKfA8plCb6mplckqk6KX0w5FH0UnVSzCnvFA5FL9WFSYpx8k7hUPRSyu9ilNrd8S/g9Ny8zwuAEcCfCs7PAFaX+B5SKHVvp9NpVq9eLdX9r+iUyWRoaGiQunag6aXopJhT3ik8il6KJZpU4+SdwqHopeRSjFKb7RcAfwaeIKjucrtzblnB+SOBv5f4Hp4eUBo2zaPopPqNUNFL0Ukxp7xTeNS8FHtCQS9O4J36gqqXMiU1Qp1zT5jZu4H3A3XOuYfz58xsJHAt8HCx52+NKA1VJhIJxowZE7VGBxSd4vE4I0aMiFqjC4peik6KOeWdwqPopVqiSS1O3ik8il5q0wO6o+Rmu3NurXPuzsIGaO54nXPuh865p0t9DyWcc1ErtOOco62tzTv1gnOOdDot5QSaXqpOijnlncKh6JV3UXNSjJN3Coeil5JLMUpuhJpZ3MyON7Ofmtkfzew9ueM1ZnaUmY0tXVMHpblyqVSKlStXSm3NpeiUyWSoq6uTunag6aXopJhT3ik8il6Kc0IV4+SdwqPopZTfxSi1WP1IgjmfvwY+CRwO5PujG4GrgHNKeQ81lIZvkskkEyZMIJlMRq3SjqJTPB5n5MiRUtcONL0UnRRzyjuFR9FLtUSTWpy8U3gUvZTyuxil9oReBswCPgJMhy1bTjvnMsDtwEdLfA8plCaymxllZWXeqRfypTOUnEDTS9VJMae8UzgUvRQXJqnGyTuFQ9FLyaUYpTZCPw5c7Zx7AOhu8sFLwLQS30MKpWHKdDrN2rVrpbrcFZ0ymQybNm2Sunag6aXopJhT3ik8il6qOyapxck7hUfRS8mlGKU2QmuA13s4n6T0MlCeHlDaXjGPopPqBG1FL0UnxZzyTuFR81JcmAR6cQLv1BdUvZQptYH4KrB7D+c/DLxQ4ntIoTRXLpFIMHas1rovRad4PE51dXXUGl1Q9FJ0Uswp7xQeRS/FOaGqcfJO4VD0UsrvYpTaE3oDcKqZHceW+aDOzMrN7NvAwcBPS3wPKZS+OauW01F0ymQyUk6g6aXqpJhT3ikcil6KPaGqcfJO4VD0UnIpRqmN0B8CNwO/IZj/CcFK+U3AQuA659zPSnwPKZTmEKVSKd566y2pkhCKTplMho0bN0pdO9D0UnRSzCnvFB5FL9USTWpx8k7hUfRSyu9ilLpjkiPYO/4m4BhgJ4KG7avArc65R0pX1EJpW65EIsG4ceOkutwVnWKxGDU1NVLXDjS9FJ0Uc8o7hUfRS3XHJLU4eafwKHop5XcxBiRazrmlwNLuzplZuXOudSDeRwGlP86xWIyKioqoNTqg6qR03fIoeqk6KeaUdwqHolc+x5VyXTVO3ikcil5K+V2MUovVX97L+RHA/aW8hxpKw5TpdJoNGzZIdbkrOmUyGZqamqSuHWh6KTop5pR3Co+il2qJJrU4eafwKHopuRSj1Gbyl8zsku5OmNko4K/AbiW+h6cIzjlaW1ulJh9LOgGpdLrbQrZRougl6aSYU94pNIpeqguTFOPkncKh6KXkUoxSh+M/A/zczFqcc5fmD5rZOOABYDzwoRLfQwqlORbJZJLx48dHrdEBRadEPM7ImpqoNbqg6KXopJhT3ik8il6KJZoU4+SdwqPopbSFaDFKXZh0k5mVAz82s1bn3PfNbBrwIDAMmOece650TR2Uvlk453DOYWYy23PJO0UtU4Cil7yTYk55px5R9FLtCVWMk3cKh6KXUn4Xo+RZq86564AvAleY2SLgb7nX3X9ba4CC1hyiVCrFihUrpEpCKDplMhk2bNggde1A00vRSTGnvFN4FL1USzSpxck7hUfRSym/izFQq+OvyvWIXg78B5jvnFs5EK+thtJqs0QiQW1trdSQkqJTLBZjRHW11LUDTS9FJ8Wc8k7hUfRSLdGkFifvFB5FL6X8LkafomVmd/XykEagDvhJQXe0c84d0Q83SZT+OMdiMYYPHx61RgdUncrLyqLW6IKil6qTYk55p3AoeqmWaFKMk3cKh6KXUn4Xo69N9l2hx4Wz6wkWIxXOztWflNAHlIYpM5kMmzZtYsSIETLfeBSdstksLS0tVFRUlD7/ZABR9FJ0Uswp7xQeRS/FEk2qcfJO4VD0UsrvYvSpEeqcmzZIHp5+kM1maWpqorKyUibpJZ1ypTPKystlGlag6SXppJhT3ik0il6KC5MU4+SdwqPolc1mo1boFZ3JC1sJKskFQfmFiRMnRq3RAUWnRDzOqFGjotbogqKXopNiTnmn8Ch6qZZoUouTdwqPotc2V6LJzKYAOOdWFN7vjfzjPR6Px+PxeDwe6HuJpjeA182srPB+iNs2g1LJg7a2NpYvX05bW1vUKu0oOqXTadavXy917UDTS9FJMae8U3gUvfJldJTK6SjGyTuFR9FLKb+L0dexiFPJ7ezX6f5/DUqrzRKJBNttt53UkJKiUywWo7KyUuragaaXopNiTnmn8Ch6qZZoUouTdwqPopdSfhejrwuTbuzp/n8DSn+cY7EYVVVVUWt0QNWpoqIiao0uKHqpOinmlHcKh6KXaokmxTh5p3AoeinldzEGzNACanM3jT2rBgGlkgeZTIaGhgbv1AvZbJbm5ma5lYKKXopOijnlncKj6KVaokkxTt4pHIpeSi7FKLkRamYzzex2oAFYlbs1mNntZja71NdXQ62kR319vVSDQdLJuaBhJXTtQNNL0kkxp7xTaBS98i5qTopx8k7hUPRScilGSZMXzGx/4D6CxuydwEu5UzsDhwOHmNnBzrm/lWQphNJ8j2QyyeTJk6PW6ICiUyIeZ/To0VFrdEHRS9FJMae8U3gUvfKla5RK2KjGyTuFQ9FLKb+LUWqL6gfAGmCuc+7NwhNmNhl4BPg+sFeJ7+PxeDwej8fj2YYodTh+FnBt5wYoQO7Yj3OP2WZQKl2TSqV48803pcowKDqlMxk2bNhAWmx+jKKXopNiTnmn8Ch6KZZoUo2TdwqHopeSSzFKbYQuB8p7OF8GdGmgbs0orbmKxWLU1NRIrYCTdDJj2LBhxISuHWh6STop5pR3Co2il+rqeMU4eadwKHopuRSjVMNvAmeb2ZzOJ8xsN+D/Ad/o74ubWbmZXW5mK82s2cweN7MPhXjeN8zMdXNr6a9LHqW6W/F4nOrqau/UC7FYLGhYif2HVPRSdFLMKe8UHkUvxTqhqnHyTuFQ9FJyKUapc0LfB6wGnjSzR4FXcsd3AvYFngP2NbN9C57jnHPnhHz9G4FjgCuBl4EFwL1mdoBzbmmI538OaCy4X/IYo9Jqs2w2y+bNmxk+fLhMo0HVqa2tjbKysoGrSTYAKHqpOinmlHcKh6KX6up4xTh5p3AoeinldzFKbYR+oeDfH8jdCnlP7laIA3pthJrZ3sDxwLnOuStyx24maNh+B3h/CL/bnXPrQjwuNEoXNb/FYllZGWVlZb0/YQhQdMpmszQ1NZFIJGQaVqDppeikmFPeKTyKXop1QhXj5J3Co+illN/FKKkR6pwbzL9TxxD0XF5X8H4tZvYzYJGZTe5uQVQnzMyqgU1ugAp8KpVoKisrY+rUqVFrdEDRKb+dmhqKXopOijnlncKj6KVYokkxTt4pPIpeSvldDJ0WVVd2A15yzjV0Or4s93MO244k3wAAIABJREFUvS96eg2oAprM7A7gy8651b29sZnVAmM6HZ4B0NzcTENDZyWPLA0NJJub2++mGhpAYZ6Mopeik8czCDQ1NXX46fFsizQ2Nvb+oIgptVj9CGBkYY+kmU0APkuwav73zrllxZ7fC+MJdl/qTP7YhB6euxG4BvgH0ArsD3we2NvM9uymYduZs4CLuzvx7LPPUl9f38vTPSokGxuZ8OKL7fdXLl1KSmB/X0UvRSePZzBZtqy/f548Hn1WrFgRtUKvlNoTeh2wA8ECJXJD348Bk4AscE5ux6SH+vHawwgakJ1pKTjfLc65H3Y69HszWwb8iqCBeVkv730tcFunYzOAO2fOnMlee2nU3s/P36usrJSaCC3ltHEjibVraW1tpby8nOn77QejRkVtpeml6IRgTuGd+oKiV0NDA08++SR77LEH1dXVUesAmnHyTuFR9Hr++eejVuiVUhuh+wE/Lbh/AkEP5fuB54EHgQuBh/rx2s10X4O0ouB8aJxzvzaz7wHz6aUR6pxbQ7ATVDv5+qBVVVUyH1oAI0eOjFqhC1JOmQwMH87w4cOD+9XVwS1qFL0UnXJI5VQO7xQeVa/q6mr/ed4L3ik8al5KuV2MUpvr2wNvF9w/HFjqnHvMObcJuBl4bz9fexXBkHxn8sdW9uM13wRK2hhbaXV8viSEd+qZbDZLa1ublBNoeqk6KeaUdwqHopdyiSbv1DOKTqDppeRSjFIboXXAOAAzG0Yw93Jxwfk0MLyfr/008K7cEH8h+xScD40FXZnTgLX99AG0Lmo6nWbNmjVSW4kqOmWzWTY1NEhdO9D0UnRSzCnvFB5FL9USTWpx8k7hUfRSyu9ilNoIfRQ4y8yOJCgoXwHcWXD+XXTsKe0LtwNx4Iz8ATMrB04BHs8vhjKzKWb27sInmlnnle0QFK4fA9zfTx9AaweCZDLJlClTpMowKDrF43FGjx4tde1A00vRSTGnvFN4FL3ypfaUSu4pxsk7hUfRSym/i1Gq4XkEPZ+/z93/nnPueQAziwOfoJ+NPufc42Z2G3BprmTSK8DJBL2ZpxU89GZgLlC42fVyM/sd8CzBQqb9CArfP03HOax9RmnveDOT8gHv1BcUvbxTOLxTeBS98j5KXqpx8k7hUPRS8+mOknpCnXOvADsT1PSc7pw7t+D0cIIdlb5dwlucRNDDeiJwFZAEPuace6SX5/0K2Jtg3/orgb0Idln6oHNucwk+Ut3bqVSKVatWkUqlolZpR9EpnclQV19PWujagaaXopNiTnmn8Ch65YdMlYZOFePkncKj6KXkUoyS+2qdcyng390c30THofn+vHYLcG7uVuwx87o5dnop77u1YGaUl5dLfduRdAKSiQQ6RgGKXpJOijnlnUKj6KXaE6oYJ+8UDkUvJZdi6E8YEENprlwikWD06JIW+w84ik7xeJzKysqoNbqg6KXopJhT3ik8il75z3H/ed4z3ik8il5bw5xQjYqqWxFKq4az2SwtLS3eqRey2SypVErKCTS9VJ0Uc8o7hUPRS7VEk2KcvFM4FL2UXIrhG6F9ROmiptNp3nnnHal5TYpO2WyW+vp6qWsHml6KToo55Z3Co+ilWqJJLU7eKTyKXkr5XQz9vloxlIZvkskkkyZN8k69EI/HGTVqlMxWankUvRSdFHPKO4VH0Uu1RJNanLxTeBS9lPK7GCX9pcnV6Cy6h7uZDTOzKaW8hxpKE33NjEQi4Z16wcyIx+NSTqDppeqkmFPeKRyKXqoLkxTj5J3Coeil5FKMUrs7XgeO7OH84bnHbDModW+n02lWr14t1f2v6JTJZGhoaJC6dqDppeikmFPeKTyKXoolmlTj5J3Coeil5FKMUhuhvTWzk4DO5LJtEKVh0zyKTqrfCBW9FJ0Uc8o7hUfNS7EnFPTiBN6pL6h6KdPnCQO5vdxHFhzarsiQ+0iCXYpW9dNNErX5HmPGdLdDaXQoOsXjcUaMGBG1RhcUvRSdFHPKO4VH0Uu1RJNanLxTeBS9ttU5oV8kGGJ/HXAEOxK93s3tKeCjwE8GxFQE51zUCu0452hra/NOveCcI51OSzmBppeqk2JOeadwKHrlXdScFOPkncKh6KXkUoz+NEIXA18l2DfegN/m7hfezgXOAvZ2zi0aGFUNlObKpVIpVq5cKbU1l6JTJpOhrq5O6tqBppeik2JOeafwKHopzglVjJN3Co+il1J+F6PPfbXOuX8A/wAws0rgD865ZwdaTBWl4ZtkMsmECRNIJpNRq7Sj6BSPxxk5cqTUtQNNL0UnxZzyTuFR9FIt0aQWJ+8UHkUvpfwuRkmGzrlLujtuZmVA0jnXVMrrK6I0kd3MKCsri1qjA6pOiv8ZFb1UnRRzyjuFQ9FLcWGSapy8UzgUvZTyuxil1gk93sx+0OnYxUAjUGdmfzSzqlLeQw2lYcp0Os3atWulutwVnTKZDJs2bZK6dqDppeikmFPeKTyKXqo7JqnFyTuFR9FLyaUYpdYT+DJQmb9jZu8HLgb+DPwAOBi4oMT38PSA0vaKeRSdVCdoK3opOinmlHcKj5qX4sIk0IsTeKe+oOqlTKnjbjOAmwrufwp4BzjSOZc2sxhwNLCwxPeRQWmuXCKRYOzYsVFrdEDRKR6PU11dHbVGFxS9FJ0Uc8o7hUfRS3FOqGqcvFM4FL2U8rsYpfaElgMtBfc/DNznnMv3Ab8ATCrxPaRQ+uasWk5H0SmTyUg5gaaXqpNiTnmncCh6KfaEqsbJO4VD0UvJpRgDsW3nfAAz2xPYEbi/4PxYgvmh2wxKc4hSqRRvvfWWVEkIRadMJsPGjRulrh1oeik6KeaUdwqPopdqiSa1OHmn8Ch6KeV3MUrtq/0p8EMzm0nQ4/kWcHfB+Q8Az5f4HlIobcuVSCQYN26cVJe7olMsFqOmpkbq2oGml6KTYk55p/AoeqnumKQWJ+8UHkUvpfwuRqklmq42sxaCnZGeBC53zjUDmNloYBzb2I5JSn+cY7EYFRUVUWt0QNVJ6brlUfRSdVLMKe8UDkWvfI4r5bpqnLxTOBS9lPK7GCUbOueud84d6Zw7xTn3n4LjG5xzezrnbij1PZRQGqZMp9Ns2LBBqstd0SmTydDU1CR17UDTS9FJMae8U3gUvVRLNKnFyTuFR9FLyaUYA9ZvnBuSn5q7u9w598JAvbane5xztLa2Sk0+lnQCUuk05VGLdELRS9JJMae8U2gUvVQXJinGyTuFQ9FLyaUYJTdCzewI4PvAtE7HXwe+5Jy7q9T3UEJpjkUymWT8+PFRa3RA0SkRjzOypiZqjS4oeik6KeaUdwqPopdiiSbFOHmn8Ch6KW0hWoxSd0z6KPD73N3zgSNzt/MBA/5gZgeXZCiG0jcL5xzZbNY79YKiE2h6eadweKfwKHqp9oQqxsk7hUPRS8mlGKXOCf068Aywq3PucufcXbnb5cCuwLMEOyhtMyjNIUqlUqxYsUKqJISiUyaTYcOGDVLXDjS9FJ0Uc8o7hUfRS7VEk1qcvFN4FL2U8rsYpTZCdwVucs41dT6RO3Zj7jHbDEqrzRKJBLW1tVJDSopOsViMEdXVUtcONL0UnRRzyjuFR9FLtUSTWpy8U3gUvZTyuxilRqsFGN3D+dF03FFpq0fpj3MsFmP48OFRa3RA1am8rCxqjS4oeqk6KeaUdwqHopdqiSbFOHmncCh6KeV3MUo1/Ctwjpnt2/mEme0DnA38pcT3kEJpmDKTyVBXV+edeiGbzbJ582ay2WzUKh1Q9FJ0Uswp7xQeRS/FEk2qcfJO4VD0UnIpRqmN0K8S9HQuNbN/mNmNuds/gEdz584rVdLTPdlslqamJqkGg6RTrnRGVmyStqKXpJNiTnmn0Ch6KS5MUoyTdwqPopeSSzFK3THpdTPbFVgIHAIclzu1HPghcJlzbk1piloozbFIJpNMnDgxao0OKDol4nFGjRoVtUYXFL0UnRRzyjuFR9FLtUSTWpy8U3gUvbaGEk0l/w/MNTK/mLt5PB6Px+PxeDy90q/heDOrMLPjzOxrZna6mWlVaB1ElEoetLW1sXz5ctra2qJWaUfRKZ1Os379eqlrB5peik6KOeWdwqPolS+jo1RORzFO3ik8il5K+V2MPveEmlktwXzPHQgK0gNsNrOPO+e2qUVI3aG02iyRSLDddttJDSkpOsViMSorK6WuHWh6KTop5pR3Co+il2qJJrU4eafwKHop5Xcx+hOtrxNs0fkDgtXxO+aO/RSYMWBmoij9cY7FYlRVVUWt0QFVp4qKiqg1uqDopeqkmFPeKRyKXqolmhTj5J3CoeillN/F6I/hh4GbnXNfcc7d65y7CvgCMM3Mdh5YPT2USh5kMhkaGhq8Uy9ks1mam5vlVgoqeik6KeaUdwqPopdqiSbFOHmncCh6KbkUoz+N0CnA0k7HlhIMzY8t2UgctZIe9fX1Ug0GSSfngoaV0LUDTS9JJ8Wc8k6hUfTKu6g5KcbJO4VD0UvJpRj9GY4vp+suSPn7OpMhBgml+R7JZJLJkydHrdEBRadEPM7o0T1t7BUNil6KToo55Z3Co+iVL12jVMJGNU7eKRyKXkr5XYz+tqimmdnuBfdrcj93MrO6zg92zv2rn+/j8Xg8Ho/H49kG6e+s1W8B/yy45VfFX9vp+BO5n9sMSqVrUqkUb775plQZBkWndCbDhg0bSIvNj1H0UnRSzCnvFB5FL8USTapx8k7hUPRScilGf3pCTxlwi60IM+v9QUNELBajpqZGagWcpJMZw4YNIyZ07UDTS9JJMae8U2gUvVRXxyvGyTuFQ9FLyaUYfW6EOuduGgyR7jCzcuCbwInAKOAZ4ELn3AMhnjuRoIzUhwl6fJcAX3TOvVaKk1LdrXg8TnV1ddQaHVB0isViDBs2LGqNLih6KTop5pR3Co+il2KdUNU4eadwKHop5Xcx1JvJNwJfAn4FnANkgHvNbL+enmRmVQSNzrnAIuBiYDfgYTPbrhQhpdVm2WyWxsZG79QL2WyWlpYWKSfQ9FJ1Uswp7xQORS/V1fGKcfJO4VD0UnIphmwj1Mz2Bo4HFjrnznXOXQccCCwHvtPL088CdgI+5pz7jnMu3yM6HvhyKV5KF1Vxi0VFp2w2S1NTk9S1A00vRSfFnPJO4VH0UqwTqhgn7xQeRS+l/C6GbCMUOIag5/O6/AHnXAvwM2BfM+upFsIxwD+dc+2Lopxz/wEeBI4tRUqpRFNZWRlTp06lrKwsapV2FJ0Ut1MDTS9FJ8Wc8k7hUfRSLNGkGCfvFB5FL6X8LoZyI3Q34CXnXEOn48tyP+d09yQziwG7EqzM78wyYIaZjRgwS4/H4/F4PB5Pn9Hp7ujKeGBVN8fzxyYUed5ogoL6vT33xWJvbGa1wJhOh98N8OyzzxZ72pCTyWSor6+npqZGZgKynFNDA7FXX6V582aGDR9O9oknQGHyuKKXohOCOYV36guKXps2bWLFihUsW7aMESM0+iQU4+SdwqPo9fLLL+f/qdM92wnlRugwoLWb4y0F54s9j34+N89ZBIuZunD66af38lSPNN/pbTpxRCh6KTp5PB6Pp69MBp6KWqI7lBuhzQQ9mp2pKDhf7Hn087l5rgVu63TsPcBvCOab/qeX5w8lzwGzo5bohHcKj6KXdwqHdwqPmtcM4E7gCODViF0KUYsTeKe+oOZVBvwLeDhqkWIoN0JXARO7OT4+93NlkedtIOgFHd/Nud6eC4Bzbg2wpvBYQZH6/zjnnu/p+UOJmaHkA96pLyh6eadweKfwqHkVfJ6/qual5APeqS8oeuWc6qP2KIbywqSngXeZWedJafsUnO+Ccy4LPAvs2c3pfYDXnHObBswyei6JWqAbvFN4FL28Uzi8U3hUvdRQjJN3Co+il6JTO+aci9qhW8xsH+Ax4Fzn3BW5Y+UE3d3rnXPvyx2bAgzPlWDKP/c84DJgL+fcE7ljOwPPA1c4577WD59ZufeerfZNx+PxeDzh8Z/nHo8GssPxzrnHzew24NLcavVXgJOBacBpBQ+9mWBnpMLNrq8FTgfuMbMrgBTBzkurge8Nvr3H4/F4PB6PpydkG6E5TgK+Rce94z/mnHukpyc55zaZ2TyCveMvJJh28BDB3vFr++mylqBbu7/P93g8Ho8G/vPc4xFAdjje4/F4PB6Px7PtorwwyePxeDwej8ezjeIboR6Px+PxeDyeIcc3Qj0ej8fj8Xg8Q45vhHo8Ho/H4/F4hhzfCPV4PB6Px+PxDDm+EdoLZlZuZpeb2Uozazazx83sQ1F7eTwej6d7zKzKzC4xs/vNbIOZOTNbUOSxu+Qe15h77C1mNmaIlT2e/0p8I7R3biQodP8r4BwgA9xrZvtFKeXxeDyeomwPXATsAvy72IPMbBLwCLAjcD5wBXAo8ICZlQ2Bp8fzX416sfpIMbO9gePpuHXozQTbvX0HeH+Eeh6Px+PpnlXAeOfcO2a2J/DPIo87H6gE9nDOrQAws2XAA8AC4LohcPV4/mvxPaE9cwxBz2f7B5FzrgX4GbCvmU2OSszj8Xg83eOca3XOvRPioUcDd+cboLnn/gV4CTh2sPw8Hk9A5I1QM5tiZj8xsxdz83E+mDu+vZldZWa7Rai3G/CSc66h0/FluZ9zhtjH4/F4PAOAmU0EaoEnujm9jODz3+PxDCKRDseb2UzgbwSN4ccJ5uUkAJxz63LzLiuB0yJSHE8wrNOZ/LEJQ+ji8Xg8noFjfO5nsc/40WZW7pxrHUInj+e/iqjnhH4HqAPeBzhgTafz9wDHDbVUAcOA7j6AWgrOezwej2frI//53dtnvG+EejyDRNSN0A8C33TOrTWz7bo5vwKYOMROhTQD5QBmVgPMBd4EJuXOjzSzWRG5eTwej6d3pud+Tuj0eZ3vCZ3ezed4/u/ONDNLDaqdxzN4lAGTgYedc/VRy3SHOeeie3OzRuCrzrlrc43QtcB859xfc+fPB77inBsdkd8DwETn3EwzOxy4MwoPj8fj8Xg8nn5yhHPurqgluiPqntB/EdRku7bzCTNLEJRHemyopQp4GjjAzKoJekC55ZZb2G03jfnqmUyGpqYmKisricfjUesA3qkvqHn9GXg0myWVTpNMJDghFmOnqKXQixN4p76g6FVfX8+TTz7JHnvsQU1NTdQ6gGacvFN4FL1efPFFjj76aMi1XxSJuhF6KXC3mf0Y+G3u2Fgzm09Qv20X4AtRyQG3A18BzgDuA9hpp52YNcuPwHu2PV7M3fIsBQ4EqqPR8XgGjYaGBtauXcvs2bOprvYZ7tnmaYtaoBiRlmhyzt1HUBD4OOCvucO/BBYDuwMnOeceicYOnHOPA7cRNJa/BJDNZqPS6UI2m2Xz5s3eqRcUnUDTyzlHOpXCOccmgu3CopuwE6AYJ+8UHkWvvIuak2KcvFM4FL2UXIoReZ1Q59wtBBNnjwbOI+gBPRaY7Jz7TZRuOU4CrgQOB62Lmk6nWbNmDel0OmqVdrxTeBS9stksbc3N7JnJAPA88GC0SpJx8k7hUfTK5PI7/1MBxTh5p/AoeinldzEiXZi0NZFbPfncP/7xD973vvdFrQMEvVbOOcwMM4taB/BOfUHN6w/A/c6RAL4H/K8Z64A4sJDgm2IUqMUJvFNfUPSqr6/noYceYt68eTJzQhXj5J3Co+j13HPP8Z73vAdgtnPu+ah9uiPSnlAzm29mi3o4/20zO3AonXpDJbkgcInFYt6pFxSdQNPLzIiZMcyMzxB8QGSAnxLUK4vMSTFO3ikUil55FzUnxTh5p3Aoeim5FCPqhUlfJ6gFWoyJwIVsmS8aOUrd26lUinXr1rH99tuTTCaj1gG8U19Q9MpmMjS1tJAqK2OHZJKPE/SQrgVuAs4EhvpjTTFO3ik8il75IVOlodPOcUqn02zcuJHGxkaiGrHMZrO0trZSXl5OLBb57D1A0wmGzsvMqKqqYtSoUSQSPTfhUin9ErdRX8H3EGzXWYx/ArsOkctWh5lRXl4u9W3HO4VH0suMeDze7vRhtvwHfIpo5ocqxsk7hUfRS7UnNB8n5xxvvfUW69ati7QhoXrt1Jxg6LzyX1befvvtXr+cqMWoO6LuCS0nqOjf0/nhQ+QSCpX6XwCJRILRoyOp418U7xQeRa9YLEayoqL9g8GAU4D/BdYDvwemATsOoZNinLxTeBS98p/jqp/nDQ0NNDc3U1NTw/jx47eKxoRnaHDOsWrVKurr69m0aVOPJcZ66ylVIOqe0OeAI7s7YcH/uqOAF4bUqBeUVsdns1laWlq8Uy8oOoGelyNXoimd7uA0nGAYPgFkgeuAhiH0UosTeKe+oOilWqIpH6eGhuB/WG1tbaQNUOcc2Ww2sukA3aHoBEPnZWbU1tYCtOdJMZTyuxhRN0KvBj5gZreZ2XvMLJG77UpQn3Pf3GNkULqo6XSad955R2pek3cKj6JXvtZdZ6epBMV8AeqBnxE0SIcCxTh5p/AoeqmWaMrHKZVKkUgkIu/Jcs6RytUNVkHRCYbWK58bvU3VUMrvYkSa4c65X5rZDIIFSkex5e9ajKBj5n+dczdF5dcdSsM3yWSSSZMmeadeUHQCTa9YLEZVVRXJbnpf9gdeIZjE/R+CoflPDIGTYpy8U3gUvfKNu6gbeYUUxsk5J7HoxswoK+tpxtzQo+gEQ+8Vi8V6bfAq5XcxIjd0zl1iZr8kGJafnjv8KnCHc+7V6My6R2lujpnJJZl3Co+iV75EU3dZbsCngbeBt4C/ENQOHeyquapx8k7hUPRSXZhUGCcFNwWHzig6wdB7hXk/1VgVEv1XLcA596pz7grn3Fm52/cUG6Cg1b2dTqdZvXq11DCXdwqPolex4fg85cDngMrc/VuA1wfZSTFO3ik8il6KJZoU46Q49K3oBJpeSrlUDIlGqKf/KAzZdMY7hUfRq7dvz9sDnyX48EgDPwbqBtlJMU7eKTxqXoo9oaAXJ49nsIk8483sEDN7wMzWm1nazDKdb1E7FqI2r2nMmDFSQ13eKTyKXrFYjGHDhvXq9C46LlT6CTBY1QwV4+SdwqPopVqiSS1OZkYymZRqrCs6wcB6LViwgGnTppX8Okq5VIyot+08GrgbGAv8Nufzm9y/m4FngG9GJtgNSl3tzjna2tq8Uy8oOoGml3OObCYTymkuwWIlCIbkf0mwmnAwnBTj5J3CoeiVd1FzUozTYJQduvHGG9v3WO/u9thjjw25U1954YUX+MY3vsEbb7wh5VWIkksxom4mLwSWAfsBowimm/3cOfdXM5sGPMbgTznrE0pzQlOpFCtXrmTChAkyqwW9UziywCupFImVK5mk5JXN0tTURKqiolcnA44HVhKsJHwMGAN8bICdFK+fdwqPopfinNDCOKmQn+c4WD2P3/zmN9lhhx26HN9xx+LbYQy2U1heeOEFLrnkEubNm8e0adNkvApRyu9iRN0InQksdM5lzCwfrSSAc+4NM7sWOA+4OSrBzigN3ySTSSZMmCCzHzN4p7D8AVicTDJt0iS+IpRTsViMyspKkiHnpiUIvjleBqwD/gRsR1Dgd6BQvH7eKTyKXqolmtTiNNhD34cccgh77rmnlFN/UfRSyu9iRD0ndDPQBuCcqwNagfEF51cDXb8mRYhSguXrknmnnlF0+jeB1/JEgp+bDVnh955wBE6Fe8eHYQRwNlv2172ZoI7oQKF4/bxTeBS9FBcmqcYpFotF4nTxxRcTi8V48MEHOxw/88wzqaio4JlnngHgoYcewsz43e9+x/nnn8+4ceOorKzk8MMP58033+zyuo8//jgHH3wwNTU1DB8+nLlz5/L3v/+9y+PefvttTjvtNCZMmEB5eTk77LADn/vc52hra+PGG2/kE58IqiQfcMAB7XF65JFH2mN13333sf/++1NZWcmIESM49NBDef7557u8zx133MHs2bOpqKhg9uzZ/PGPfyw5dnmUcqkYUTdCXyToDc3zNHBibtekCuBTwIpIzIqgNByfTqdZu3atVJe7dwpHNcHQd3NzM09ms9zK4Myn7Ct5p77GaixwFlu29vwJwTD9QKB4/bxTeBS9VHdMUovTYJcdqq+vZ926dR1u69evB+DCCy9kzpw5nHbaaWzatAmAP//5z1x//fVccMEF7Lrrrh1e69vf/jb33HMP5513HmeffTYPPPAA8+fPp7m5uf0xf/3rX/ngBz9IQ0MDF198MYsWLaKuro4DDzyQZcuWtT9u5cqV7L333vz2t7/luOOO46qrruLEE0/k4YcfZvPmzXzwgx/k7LPPBuD888/nlltu4eabb2bHHXfEOcctt9zCoYceSlVVFZdffjlf//rXeeGFF9hvv/3a55ACLF68mKOPPhoz49JLL+XjH/84p5xyCk888cSAxFcpl4oRdV/tH4GzzewrzrlW4NvAnQQVXxxBOcJTI/STR2kb0TzeqXfa/+PlPtyXEEyK/khEPh3o5x+cnYAFwA0EqwqvBr4G1AyAktr1A+/UF9S8FBcmQbg43Qp07d8bHByQjcWIQbcbWEwGji3h9efPn9/lWHl5OS0tLSSTSW6++Wb22GMPvvSlL/Hd736X0047jT333JOvfvWrXZ63YcMG/u///o8RI0YAsPvuu3Psscdy/fXXc/bZZ+Oc47Of/SwHHHAA9913X3sv4ZlnnsmsWbO48MILWbx4MQALFy7knXfe4fHHH+8wXeCb3/wmzjlGjhzJ/vvvz1VXXcWHPvQh5s2bh3OOdDpNY2MjZ599Np/5zGe47rrr2p978skns/POO7No0aL24+eddx5jx45l6dKl1NQEn5Rz587lwx/+MFOnTi0hslsPUW/beQVwRcH9u81sHsEWnhngHufckoj0ukVpTmgikWDs2LFRa3TAO4UnFosxcfhwsgTfuv5A0BDdO2Kn8uHD+/3BsBfB3NA7gA3AD4GvsGWovj8oXj/vFB5FL8U5oWHj9Cbw0uDMupeMAAAgAElEQVTrBJjBIP7N+9GPfsS73vWuDscK/8bOnj2bSy65hIULF/LMM8+wbt06Fi9ezLBhw7q81kknndTeAAU45phjGD9+PPfeey9nn302Tz/9NC+//DIXXnhhe29rnoMOOohbbrml/UvAHXfcwWGHHdbtfNViQ9z5OaF33303dXV1fPKTn2TdunUdfq999tmHJUuCJs2qVat4+umn+drXvtbeAAX40Ic+xMyZM2lqaioat7Ao5XcxIjM0s3KCjp83nHPP5I875/4G/C0qr95Q+ubsnCOTyfR5Dt9g4p3C45yjJpvlhFiM75rRAtxIMMdylwidss7hcqVS+sPBBA3QRwi2+LwG+B+gv+uiFa+fdwqPopdiT2hhnHpi8hD5QMf4dHftSnXZe++9e12YdO655/Lb3/6WZcuWsWjRInbZZZd2r0KnnXbaqcPzzIwdd9yxffj75ZdfBoIeyWLU19fT1tZGQ0MDs2fP7tPvknd66aXgK8KBBx7Y7eOqq6sBWL58ebfeADvvvDP/+te/+vT+PTkpE2UzuQ24DTiHoB7oVoHSHCLF0ifeKTzZbJa6+npqq6o4q6yMHxJ0/19L0GibMcQ+LufUGLJEUzEM+CTQCPyLoHzTTwlW0ffnA0fx+nmn8Ch6bc0lmkoZ/u4r2YKyQ7GIvkC89tpr7Q3IZ599tt+lkPK9nN/97neZM2dOt4+pqqpiw4YN/fLMe+Xf55ZbbmHcuHFdHjeUvZNK+V2MyBqhzjlnZi8T7AK41aC0rVoikWDcuHFSXe7eKTyxWIzqESNIxOPsTDD5+QaCb2dXA19maHs98k7Dhw8nUWKex4DTCOaG/h/wHEEv72l0P7esJxSvn3cKj6KX6o5JanGKuuxQNptlwYIFVFdX8z//8z8sWrSIo48+miOPPLKLU76hmsc5xyuvvNK+gGnGjOBrfXV1dbdzUfOMGTOG6upqnnvuuR7dOr9/Plb596mtre3xffJzPjt7A7z44os9vndYlPK7GFG3qBYBXzCznSP2CI1SIzQWi1FRUeGdekHRCbZ8aOW99gROzJ1rJphPuToCp0QiMSCxytcQnZa7/0+CrdD6OkCkeP28U3gUvfIuak5qcYqyRBPA97//fR599FGuu+46vvWtb/H+97+fs846iw0bNnRxuvnmm9tX0QPcfvvtrFq1ikMOOQSAPfbYgxkzZnDFFVfQ2NjY5b3Wrl0LBNfh4x//OH/605+6XaWeH+KurKwEoK6uDtgSq4MPPpjq6moWLVpEKtV1M+P8+4wfP545c+Zw0003UV9f337+gQce4IUXXggfpB5QyqViRP2V633AeuA5M3sIeIPg728hzjl3zhB7FUVpOD6dTtPQ0EB1dbXMt2fvFJ5sNsvmlhbSZWXtXh8AWghWwG4CfgCcS1AAfqicWtraSCcSAxKrcoIaot8FVgEPEcwNPYrwPaKK1887hUfRS7VEUz5OKgz2fN777ruP//yna1Xh97///bS2tvL1r3+dBQsWcNhhhwHBdp9z5szhc5/7HLfeemsHp9GjR7PffvtxyimnsHr1aq688kp23HFHTj/9dCBokN1www0ccsghzJo1i1NOOYWJEyfy9ttvs2TJEqqrq/nTn/4EwKJFi1i8eDFz587ljDPOYJdddmHVqlXcdtttLF26lJEjRzJnzhzi8TiXX3459fX1lJWVMXfuXMaPH8+Pf/xjTjzxRHbffXeOP/54xowZw4oVK7jnnnv4wAc+wDXXXAPApZdeyqGHHsp+++3HqaeeyoYNG7j66quZNWtWtw3lvrI1DMfjnIvsRlBSsLdbJkrHAtdZgPv73//uVGhra3MrV650bW1tUau0453C8X3n3GfSaXdhfX23Xvc4587I3S5wzm0cAqdbc06nNjYOeKw2OucWui2/0++dc9mQz1W8ft4pPIpe69evd3fccYdbv3591CrtFMbp1Vdfda+++mrUSi6TybjW1laXyWQG9HV/8YtfOIJBkW5vN9xwg9trr73cpEmTXF1dXYfnXnnllQ5wv/nNb5xzzi1ZsqT9/sKFC11tba0bNmyYO/TQQ93y5cu7vPdTTz3ljjrqKLfddtu58vJyN3XqVHfssce6Bx98sMPjli9f7k466SQ3ZswYV15e7qZPn+4+//nPu9bW1vbHXH/99W769OkuHo87wC1evLg9VkuWLHEf+chHXE1NjauoqHAzZsxwCxYscE888USH9/n973/vdtllF1deXu5mzpzp/vCHP7iTTz7ZTZ06tccYhsmRp556Kh/TWU6gHdXdzdxWsHpKATObBTz32GOPsc8++0St49nK+T7BTg07EvR0dsaR29ozd78W+BJBCafB4jbgLwS9l1cNwuuvJ6jHlp/2/1HgcPo+R9TjKZWGhgaWLFnCAQccINXzmOe1114DYPr06RGb6PPQQw9xwAEHcNttt3HMMcdErTNkhMmR559/Pr/Kf7Zzrut2TQJEOmHAzKaYWdeCX1vODzOzKUPp1BtKjXbnHNls1jv1gqIT5HKpiJcRDFnPy91fA3wP2DgETvnbQLMdwWKrfEP6XuDukE5q1887hUfRK++i5qQYp8H6POgvik6g6aXkUoyoZ62+DhzZw/nDc4+RQWkOUSqVYsWKFd1Ofo4K7xSebDbLxrq6ol4GHA8ckLu/lo49iYPltGnTpkGL1fYEPbojc/fvpveGqOL1807hUfRSLdGkFifnHG1tbVKNGUUn0PRSyu9iRN0I7W0kLkkwL1QGpdVmiUSC2tpamcn+4J36QiwWo6qqqkcvA44DDsrdX0fQI7q+6DNKdxo+bNigxio/tSC/R8ifCHZYKvbRrXj9vFN4FL1USzSpxSlfLUNlkwHQdAJNL6X8LsaQZ7uZVbOlIwRguyJD7iMJOoJWDYlYSJQaofmajkp4p/CYGWVlZb1+EzTgE7l/P8iWhugXgTGD4JRMJgf92+lYgobo94F64D6CqgDH0fWbqeL1807hUfRSLdGkFiczk2vIdHbK79seNYqxUsrvYkRh+EWCIfbXCTo/riy4X3h7imDtwk8icCyK0nB8JpOhrq7OO/WCopMDXDZLc3NzKK98QzRf+ng98B3grYH2ymZpbW0dkliNA77KlvJTS4Cb6Tr0oXj9vFN4FL0USzQpxsk5Rzqdlmjk5VF0Ak0vpVwqRhT9/osJdvQzgr+jvyHY3a8QBzQBTzrnulaL9QDB/L2mpiYqKytlvoF5p/Dk5xBlE4lQXgYcQ1Bn816ggaBH9AsMzBafjoKt52KxIYnV9gTVAX5AUJj/UYIdo05hy4eT4vXzTuFR9FJcmFQYJxXyi6WiLFjfGUUn0PTKbyGqzJA3Qp1z/wD+AWBmlcDvnXM9748lhMqHKEAymWTixIlRa3TAO4UnFo9TU1NDsg/PMeAIoJKgpNJmgqGEzxIUsh0Ip+FVVX1yKpVRBA3RKwl6dp8gGJo/g6BclOL1807hUfTKz7tUmn+pGKdYLEZZWVnUGh1QdAJNr2RyKD/J+0ekEwacc5dsTQ1Qj0eF+cBJBI3SNuBHBI23rZURBOWbdsjdf45gvuimos/weDwez9aO/qxVMZRKHrS1tbF8+XLa2tqiVmnHO4Unk8mwcePGfnt9ADiTYDgjA9xAUGy+lAHGTCZDQ0NDJLEaDvwPMDN3/w3gcuAtweunmFOKTqDplS+DpFQOSTFO2dwccaVhXUUn0PRSyu9i+EZoH1FabZZIJNhuu+2khpS8U3jyq2FL8doN+H8Ew9aOYIj+d/S/rlksFmNYRUVksaoAPg+8L3d/LfCDZJLG7beXun6KOaXoBJpeqiWa1OKkWHZI0Qk0vZTyuxg6LaqtBKVGaL7OpHfqGUUnCD60ysvLS/Z6N8GcynzdsyUEJSVa++mULCuLNFYJYAFwSO5+oxk/qazkeaHrp5hTik6g6aVaokktTvmyQ0oNK0Un0PRSyqVi6BuKoVTyID906p16RtEJcuWQWloGxGsy8DUgv6zh3wQr5xv64dQ2RCWaesKAjwOfAshmaWxr45pslgcpbbrBQKGYU4pOoOmlWqJJLU7OOTKZjFQVAUUn0PRSyqVi+EZoH1FKsGw2S319vdQcFO8UHuccLS0tA+Y1iqDu5i65+8uBy+hbLVHnHK1tbTKxmgt8JpPBtbaSdY5bgV8CUc/MVswpRSfQ9Mq7qDmpxWmwGlY33ngjZkZFRQVvv/12l/Pz5s1j9uzZ7fenTZuGmWFmxGIxEolEeymkgw8+uMNzly5dyiGHHMLEiROpqKhgypQpHHbYYfz6178GYMGCBe2v1dNtwYIFffqdFBuhSrlUjEgnn5jZJ4GPOOcWFDn/C+A+59ytQyrWA0rzdZLJJJMnT45aowPeKTyxeJyRI0cOaDmkCoI5or8C/k5Q1P5yguHtPUI6VY4YMaQlmnpjr2SSS5JJrgXqgKUENUXPJFhVHwWKOaXoBJpe+dI1SiVsFOM02GWHWltbueyyy7j66qt7feycOXP48pe/3OX4hAkT2v992223cdxxxzFnzhzOOeccRo0axeuvv84jjzzC9ddfz6c+9SnOPPNM5s+f3/6c119/nYsuuogzzjiD/fffv/34jBl9q77sSzT1j6hbVF8k2BmpGM25x8g0Qj2egWAwvyvHgRMJtsb8I0EJp+sIth87nK7bYm4NTAXOB64lWDX/MnApQaH+CcWf5vF4hJkzZw7XX389Cxcu7NCY7I6JEydywgkn9PiYb3zjG8ycOZPHHnusS4NwzZo1AOy7777su+++7cefeOIJLrroIvbdd99eX98z8EQ9HL8zPTdC/02w7kIGpRJNqVSKN998U6oMg3cKTza3Td9geBnwEYJG2rDcsXsJGnHNRZ7jck6bNm2SilX++g1PpfgKsHfu+HqC6QZR1EdVzClFJ9D0UizRpBinbDYb7Oo2SMO6559/PplMhssuu2xAnF599VX22muvbnska2trS3ItxSsqlHKpGFE3Qo0ti3q7YxT0b2TQzKrM7BIzu9/MNpiZM7MF/XmtTq9b6ksMGLFYjJqaGqkVcN4pPPk5UYPpNRtYSLBPO8AzBA23VT04lUe8Or4zhdcvCZxKsGgJggoA1xMMlQzlFHzFnFJ0Ak0v1dXxanEa7BXfO+ywAyeddBLXX389K1eu7PGxqVSKdevWsX79ejZu3Mj69etZt24dzc1bvlZPnTqVBx98kLfe6stM+IHBr47vH1EbPgV80sy6fG0xs3KCxbE99ZT2xPbARQTrNP7db8NOKNXdisfjVFdXe6deUHQCsFiMioqKQfcaS9AQ3TV3/x1gEfBYEaey8nKpWHW+fkZQvukLBAXuAR4kqAZQF5GTAopOoOmlWCd0SOLU/A6suB1e/nHws/mdHh8+FA2rCy64gHQ6zeWXX97j4xYvXsyYMWOora1l3Lhx1NbWMmbMGH74wx+2P+a8887jzTffZMaMGRx44IFcdNFFLF26dEh6JxUboUr5XYyoG6GXEXTWLDGzw8xseu52OPAQwXbY4fvpO7IKGO+cm0pQRnFAUOpqz2azNDY2eqdeUHSC3Er0IdphowI4C/gYW7b6/AVwC1A4YOOcIy02pFTs+r0HuICgPBXAq8D/Ai9G6BQlik6g6aW6On5Q49T8Drx0Dbx+C7x1V/DzpWt6bIgOxYrv6dOnc+KJJ3LdddexalWxMRrYZ599eOCBB1i8eDH3338/ixcv5oEHHuCTn/xk+2NOPfVU7r//fubNm8fSpUv51re+xf77789OO+3Eo48+Omi/A/jV8f0l6r3j7wNOI2iI3kGw3uDl3L9nAqc75+7p52u3Oud6/prXD5QuajqdZv369VLzVL1TeLLZLJubmobMy4DDgLPZsqp8KcG3vNUFTs0tLVKx6un6bQ+cR7CFKQR7zf8AuJv+7xpVqlNUKDqBppdindBBj9PapVD3LFTuANvtA5XTgvtrlxZ9inOOdDo96A2rCy+8kHQ63ePc0O2335758+dz0EEHMW/ePA466CDmz5/P1KlTOzzuIx/5CH/+85+pq6vjkUce4fOf/zzLly/nYx/7WPvipMFgqGLVF5TyuxhR94TinLuRoDPjWIJRw4XAJ4BJzrmfR6jWLUolmsrKypg6dapUWQhFp1VlZbwwdSrNQk4QDJWMGj16yGM1E7gQ2DF3/y3g2wTf/vJDgkrXr7ecSgIn5W4JggVWfyIYnl8fkVMUKDqBppdiiaZBj1PrWsi2QVluGUbZqOB+69qiT4nFYgOyq1tvTJ8+nRNOOKHX3tC+OA0fPpz999+fa665hgsvvJCNGzdy3333DaR2v7yGEqX8LoZEi8o51wD8PmqPPGZWC4zpdHgGQHNzMw0Nfd2HxhMl15aXs8aMW4FT29p4r0BvdktZGW2xGC3ZLA1tbUP+/jHgdODuRIIHEwnaCHoRARLO0dDan00/o+U9wBfMuLmsjHfMeAH4unMcl0qxm8A19+jQ1NTU4acaqVSKRCIxsD1Zye2IWRLXuiFoiLZtxCxJNrkdDHGPWX5EMZPJtP+OCxcu5Je//CWXXXZZe29i4e+fH+7uD7vtthsAb7/9dpfXyN/PZrNbRc9hnnzPa0/tkcbGxiE06h9D2gg1sykAzrkVhfd7I//4IeQs4OLuTjz77LPU19cPsY6nFJ5+73vb//2/wLs3bGC/t9+mLMKGyaszZrCyqgprbGTJq69G5jEC2G3ECB6cMoWWXC9/eSbDkueei8ypVPYw49GJE3l+u+2AYBHWLrlrnvSNUU8By5Yti1qhW0aPHs2YMWPYtGlT7w8OiQ17L+XDlhGvfwGXbcNiZWRGzKR12HtxA/g+YWhpaQFg8+bN7b9jbW0txx57LNdddx2TJ08mHo+3n8tms6TT6V7j8fDDDzN37twux++66y4ApkyZ0uU1Nm/e3O40kPEebFKpFGvXruU///lP0cesWDHUTae+M9Q9oW8AzsyGOefa8vdDPG+ol3hdC9zW6dgM4M6ZM2ey1157DbFO92SzWZqamqisrJQZAlB0ur+8nE3pNIlEAjOjYcIEHps1ixPa2pgR0fydZ8vKcGZMbmlh7qRJkcfqSODXZWX8nxlTWlqYO3du5E55+pNTHwb+HYvxu2SSJjPqc9f806kUMwagIaqY54pOoOnV0NDAk08+yR577EF1dXXUOkDHOK1evZpEIsGIEQO4J9iIEVD5RVj3d6x1La58DLHtP0By2LiiT3HOkc1m27fIHCgqKiqAYMi88He86KKL+N3vfsfLL7/MrFmz2s/FYjHWrFnDXXfdhXMO51z79ppVVVUcccQRAHz6059mhx124NBDD2XGjBk0NTXx4IMPcvfdd7PnnnvyiU98osuUuuHDh7c7lRLvwYpVMdavX09tbS3vLehk6czzzz8/6B6lMtSN0FMJGp2pTvelcM6tATrMYM4nVVVVlcyHFsDIkT2VWY0GNadyIFtezu4Ehdr/D2gEflpezocJFusM9cyZipxXTXl5j4Vyh4pqgn3n3wbGlpdLbdsJ/cup/QlWPP4ceIlgusFPyss5iKDOaKkz79TyHDSdQNerurpa8vN8w4YNwCCU2KmaCFXHDuxr9oP8l5F4PN7hd9x555054YQTuOmmm9rP53n66ac5+eSTu7zW1KlTOeqoowC44YYbuPPOO7n99ttZuXIlzjmmT5/OBRdcwHnnnUd5eXmX5+ffIxaLbRUljfKYGclkssf8VcrtYpjSSq7Bwsz2BP4JnJJbCNWf15gFPPfoo4922PIrSrLZLC0tLYNe8LwvKDqd4xyN6TQfisc5Lhbjr8AfgPwa1LHAyeQm/Q4R3wVedo7pqRRfTSRkYqV4/Up1csBfCbYwzX/7rQUW0P9rvi3GabBQ9Kqrq2sfulVpIBfG6Y033gCCBTtRMtS9e2FQdIKh93rttdeAnnPk2WefZddddwWY7ZyT7BaN9BPBzH5uZvv0cH5vM5NaIa9WomnNmjVSpU8UnfJlh7LZLAYcRFBfMl/YYzVBo/C3QMsQezU2NkrFSvH6leqUv+ZfZ0ujcw3BNb+NYNeloXYaDBSdQNNLtUSTWpwUyw4pOoGml1J+FyPq1fELgL8Ajxc5vwNBJ9Wp/XlxM/sCwbagE3KHDjOzSbl/X+2c6/MKI6Xu+mQyyZQpU6S+DSo6WSxGVVVVh4nFE4CvAQ8QlPNJAUsIttY6gWCXhMEmFosxeuRIkkKxUrx+A+U0FvgKwQfOnQQ94X8BniTYmm3X4k8dNKeBRNEJNL3y8wKVSu4pxsnMpEprgaYTaHop5Xcx1A0nEEzj6y9fYUuHF8BRuRvAL4E+N0LVPiCUfEDYiaBHrJAY8BFgDsHOQS8DG4CrgL2AY2BQ52vmY6UULdnrN0BOMYJFS+8BbgZeAzYCPwJ2A44DRg2x00Ch6ASaXnkfJS/lOCmh6ASaXopOnRnyRqiZHQEcUXDoDDOb381DRwLzCeZy9gvn3LT+PrcYSt3bqVSKdevWsf3228sUpVV0ymYybG5pIV1eDt18MxwLfBl4hKBYbStB0j0DHA4cwOCUZ8hmMjQ0NZEaNkwmVorXbzCcxhMsxPobwVzRzcBTwAsEi5bm0fNcpf+WOA0Eil75IW+loe/COKmQL42UEJu3ruYEml6pVKr3B0VMFD2hMwl2RIJgzcA+wB6dHuOAJoJ2wZeGTm3rwswoLy+X+raj6IQZ8Xi8RycD5hIMyd4K/P/27jxOrrLO9/jnd2rprup0p9NZuxMCAcKasEkIAioREcEFVERcEEYdfSm4cQdmdK6K4yhXxBVeeGFGL7KoCA4DCCgwRBA0LNGwBBJICGTtdJbe0lttz/3jqZOu7lR1n+50n/Mk/Xu/XudV6Vr6fDlVnP7Vc57lb9hi9E7gSezl2vljGMkUc8WHyRU2F9+/8cokwFuxLeF3Ak9j3/M7sO/5h4HDQs60N1zMBG7mcrUl1MXj5NoAIBczgZu5XMpSSehFqDHmauBqABEpAJ8yxvwq7Byj5VKf0Hg8TkNDQ9QxBnAxk+d5VFVXB2rNnAJ8Ftsi9mvsAJbNwLXYb0vvJ9jl2qC50jU1TvWJcfH9G+9MdcCngDcDtwPbsUuZ/gA4AdstY2rImUbDxUzgZi7/PO7y+dyFAS4i4ly/QhczQfi5/LlSh+LicRos0jZjY4y3LxWg4NboeH9KD800NGMM+VxuRJmOwi6ZdR79c4g+hR1hfQ9jM4reGEM2m3XqWLn4/oWV6SjgKmxfIX94wd+Ab2Df89JR9BP5OI2Ui7n8LK5l8o+T53nk8/nIC1F/2qGoc5RyMROEm8tfwnS4y/4ufb4rcaLjgogsEJErReSG4naliCyMOlc5Lr2puVyO5uZmp/o1uZipUCjQ3dODGWF/3jhwNvAtbIsY2FH0D2CL0T8De/NpKBQKdHZ2OnWsXHz/wsyUAM4Bvo1t+QY7iv4BbDH6BPY9n+jHaSRczOXqFE3+caqqqiKfz9PS0hJpseV/UXap4HMxE4SXyxhDS0sL+Xy+7OT7pVz6fFcSaVutiFQBNwIXYbto+X/TPeBqEbkd+HRxiU8nuHT5JpFIMGfOHM00DM/zqKmpITbK/jFTsZfoXwXuwq4124GdXuFR7HQLC9hz9H2QXJMnTybhSCd2cPP9iyJTPXZeuLdh+4i+AbRhZ1F4CDg3keCYOXOIT/DjFISLuVydosk/TjNnzqSvr4+dO3fS3t4+bJ/28VJaULnSv9DFTBBOLr8FNJ/Pk0qlmDlz5pDPd+nzXUnUf/2+B3wC+BlwJP2rGR4J/F/slI3XRJauDJc+9H4fFM00tLHqMD4fO7fopwG/59Zm4Hrsh3TVKHJF9celElffv6gyHQJ8FTuhsd8XeCtwkwjXxuO8osdpWC7mcnVgkn+cPM9j7ty51NfXk0wmI8u5exo5x46Ta5kgnFz+XKT19fXMnTt32Mvxrh2jcqIukz8O3GqMuWzQ/auBS0WkrvicL4eerAKXmrdzuRw7duxg6tSpznzjcTGTv2JSvqoK9rI1RrBziB6HbQV9ANs/9DXgR8Dh2D6FQZaDLBQKdHZ1kUulnDlWLr5/UWcS7KClE4HHsO95Z6HAy729XFtdzQLP471AtAssRn+cKnExl4tTNA0+Tp7n0djY6FQmF7iYCdzM5dLnu5KoW0ITwLIhHv8L0RfKTnNlPrJSLmZijL8RJrAT3X8X22/UH8iyGtsqeh32sn0lWXaxi7VkpXtMc40FF98/FzIlsBMXfwc42xiSxc/US9hLOj8G1kSWznLhOJXjWi4XW0LBveMEmmkkXM3lsqgLvD9i/5b/rMLj78J2wXKGa/2apk+fHnWMAVzM5HkeqVRqXCacr8GOoH878AdsS1kOeLG4HYkd6DIf26L2Fz7Oem5nJVewzTuExKSXuYufcAAXciq/HoeEI+Pi++daphTwgViMM1IpHsAOVsoBLxe3w4H3UHmO0fHi2nHyuZjL1SmaXDtOmik4F3O50iI7lKgTfh34rYj8F3blPL8hYT5wKXbJzQ+LyIBJ5owxO0NNOXDfUe16D/5ovEQi4cw3ehczFYwhXyhgPG/MW0R9dcAFwJmwuzAp0F+YHAzUczF13N4/gMkI5JMQ89ggv+EhXued/HVc8gXl4vvnaqZUNsuFiQRni/AH7GwJOWxr+GrsScxfIjSM1C4eJ3Azl38e1/P50DRTcC7mcunzXUnURejLxduFDFzKE/rP2y+VeV1kX19d6hOazWbZvHkzTU1NJJPJ4V8QAhczFQoFuru7yVdXwzgvGzgF+Bj2Ev3D2MIkC7xKntc5hSkcyAn8lYXZr7O2ZxtNrceRnHELmeqt7JRl9NFBFXXjmrGinmayW55g8/YumqbVkGw8DVKzosmyD2WqbzyNC1OzOBt72eYx/PfcbrOwX04W0z/n7HhncuI4uZqrp5ncpieABLlND0PSgUy4ee7UTMG5mGtf6BMadRH6bxRXMNxXuHT5JpFI0NTU5DbD2SQAAB5ISURBVMx6zOBmJs/zSKfTxELsr9OAXfbxHOwApp/zSwBamc3ruXugYyVe307q+jbw/qdO5o633QPAgxzJeWwKLeduPc3wyvUkWl+kqWBIdAt0roDDLovuD/Q+lmlyahYfwvYvehi75nAv0Iyd2ukeYAl22qeakDJFWly5mKuYKb59A/AB4lvug6wDxwo3z52aKTgXc+nl+GEYY66Kcv+j4UozO/RP1+ASVzPFYrFQLokOVott4m/nC7zEW3iFC+wfwr4d3P2ey4n3dHNLrpOOpFCIGWAz3+CY8INm2iHTCrEEdZPSeOQg/2eo/h1UD93Padwu+fRuh74WiFVRO6mGqngeck9B+mFIN43PPofTvQm6N0A8Td2kGmpTQPY5qFsOtQPnRMhhC9CN9K+w9QB2NOgsYDYwaSwyda6BjtWQqCOVrmF2Qxwy/w0NG6HhuLHYw+jsXAE7nyWWmsbCIw6CXCf0LIVVCZj5tmgybX0MtiylK3EQmRkZJD0b2l6AbU/A3POjyVTk6rlTMwXjYi6X6pVK3C+THePS5fhcLkdraytTpkxx5huPi5kKhQI9fX3kk8m9nqJptJL0cRx/5CTWMqNtCo/kuqibdyAN7dtpylexdVIr+eLhmjPEBE/jdlLZ9Rp091KomkJ1ahKpRB6vdxtMmgJ184d86WgyBXpN+0rY1Um+egZV1ZOoSxliPZugrh4ajhzxPsfEzm7o2E4+1UQsUcOMyR6x7nVQXwszDt/j6UcDeWADdh7Z1uL9XcArwDRs39ED2Is+Ri0t0FZNPj2bgpfmgJkJ4p27YFo1NM0b7W/de1WrIJHE1DURS0+jLjmJePtWqM1BVAM4unPQJSTTM+n1PPKxyVDIQN+2aPKUcPHcqZmCczGXXo4PSEROxa6MOJk9p40yxphvh59q3+DSMqI+FzNhTCQtob4pHE8rz5JhDaeZPKeuv5Mvf/U4+nLTmOpt4q5FPwKBOhZyDneHH3D9XbDuVnKpQ9lRaGKqt4l4z1qYd1F0LURlMzU4mCkB8y4YNpPBFqKPAi/Q3w9pF7a19C3AW+mfFD94pkZY1zcoUzfMewfMHdzVPkTrs7BuC7lUIzsKjTbXtKkw71iYe3Q0mWqPhZrn2RlPs217HJPtBC8JVW6Manbx3KmZgnM1l8uiXrazAbgfOAk7EMnQPyDJlNznTBHqUp/QeDw+7LJdYXMxk+d5pNLp6EazAWfxDL8pfrR/M+fDvKP7EqZtfYntie387qS/7P6knyHPRBNw+mnQuoJ42wvMLLxk/zDXL7T3R2U/yyTYKbuOBHZgBzA9gW0V7cReqn8QOAo4BTiWgAOZXDxOrubyM+3YACwi3rcBpjpwrHDz3KmZgnMxlystskOJOuH3gWOAjwJPYReeOQtYB3wFu1DJ2ZGlK8OlKQ/8dWRdWvrR1UwFYzAi4zZFUxCHcBlruR68Ao8c/gs4TCCfsJmM4SD5R6qoiiZcahYcdhmm5QnyvduJVU9DZkQ8ang/zjQV+ADwXmA58CfsSc8AK4tbGvvt/FTs5fqKn1wXj5OrufxMbzwBq8E0vRcOdOBY4e65UzMF42Iul+qVSqKe3v8c4EZjzB3YxgCAgjFmjTHmUuyiMz+OKlw5LvUJzWazbNy4kWw2G3WU3VzMVCgU6OrqIh9x/5hFXMdibgNitqLIpYltOh2yk1gk/4+TuSnSfKRmkW18Hxur3kW28X1O/GHe3zMlgJOBfwH+FTgdW3wCdGOL0+8A/44dcd+6x28Y+0xjysVcqVnkpi8BsLcuZMLNc6dmCs7FXNondHj12C/9YLtGwcABow9hV0Z0hkvLcsXjcWbNmuVUk7uLmTzPI51KOfHezeNjzONjABTiBTKzMiTjSbzIvw9aLr5/EyXT3OJ2PvAcds3il7CtoxuBu4DfAYdiW0hPYODJ0sXjBG7mcnXFJNeOk2YKzsVcLn2+K4n6aG3GzlaCMaZPRFqwXaHuKT4+G8fmEXWhkPF5nkd1dXXUMQZwMZOIEIvHI+0TWo6Lx0ozBTOemRLAicWtFfhrcWvBngz9SfB/je1fehL2pJly8DiBu+9f6a0LXD1OmikYF3O59PmuJOoi9HHsYiLfKf58B3CliOSxXQW+jF1f3hkuXY7P5XJ0dHRQV1fnzLcvFzPlCwX6MhnyiURkUzSV4+Kx0kzBhJVpCrbP0tnAeuCZ4taGXRbW7z8aA+bn88zv6uKUdJoGR44TuPn++edxPZ8PTTMF52IuvRw/vB8CZ4pIlTGmD7gKO7WePxr+ceALEWVznjGGvr4+pzofu5iJYodx48iJwefisdJMwYSdSYADi9sHgTXA09hBTV3YuUhfAv4Wi3GvCIcCxxW3GaEkrMzV96/01gWuHifNFIyLuVzKUom4GFJE6oG8MaZz2CeHRESOBl5ctmwZixcvjjqOGoEvABngndg/4ErtL/LYuUdXFLeOMs9pAhZgv90fSvQtDy7o6Ohg6dKlLFmyhLq6uqjjKDUuVq5cyYIFCwAWGGNWDvf8KDh5PjLGtEWdoRKXinZjDMYYRMSpKSGczESxc7EjmcDhY6WZhuVKphi2uDwa+IgxrDWG50R4ToSW4nM2F7eHgCrgCGxRugBoCCGjK8eqlKstoS4eJ80UjIu5XPp8VxJ5ESoiMezcoAdju0ANfvecWjHJpT5E2WyWzZs309TU5MyatS5mKhQK7OruJl9dDYlA03+HwsVjpZmCcTFTLpslsXkz72tq4oPJJFuAv2NXZ3od+yWsDzvy/rniaxqxBezh2OVDU+OQy8ljVewr51KfORePk2YKzsVcLn2+K4l6xaQTsbOOzKHyXMxOrZjk0mizeDzOjBkznOkEDW5m8jyPVHU1nkODksDNY6WZgnE9k2AvwzcB78b2G30JeBE7kMnv57SluD1Cf7/Tw4vboTAmSye4eKxcnaLJteOkmYJzMZdLn+9Koj5aN2C/fJ8H/Nnly/A+l4pQz/NIp9PDPzFELmZChHgi4chMnP1cPFaaKZh9LVMNsKi4GexI+5XYonQddqS9wbaYvo6dksQD5gGHYQvSg+mfSH+sckXF1SmaXDxOmikYF3O59PmuJOoi9BjgX40x90WcIzCXLsfn83k6Ozupra115huPi5lMoUBfNkshHndqiiYXj5VmCmZfzlQ60v4c7CX6NcBq7CCn9diCtACsLW6+RvoL0kOB6QyxnOgIc4XJxSmaXD1OmikYF3O59PmuJOoidCPDn8NUBf5ylDU1Nc586F3MZIwhl8tRcCSPz8VjpZmC2Z8yVdE/uAnscqGvYovS1cAm+lcM8S/f/7n4cy22IJ1Hf2FbM0a5xpOLA5NcPE6aKTgXcxUKhagjDCvqIvR7wD+JyE3GmHKzizjHlQ8XQCKRYPbs2VHHGMDFTF4sRk1NTeQf9sFcPFaaKZj9OVMauwLTscWfu7GX7P1W0XXY1lOwfUtLBzoBTAMOor8onevgsfL77bnUf29//kyNJRczgZu5Eg4NxK0k6v8Da7Frxq8Rkd8AG7BT35UyxpgfhZ5M7Xe0yV2pkUszsKW0gL2E5RelrwE7Sp6/vbg9W3LfDGAudh3m2diRqA3o/5NKTXRRF6HXlvz7sgrPMYAzRahLUx5kMhm2bNlCY2OjM1NCuJgpn8/T2d1NzrEpmlw8VpopmImcycMWlHOBJcX7OrF9Sd/ADmx6A7u0KNj//9Z2d7MlnR5wJama/oJ0Dv0Fahirb2ez2QG3LpjIn6mRcDETuJnLpc93JVEXofMi3v+IuTTaLB6PM3XqVKcuKbmYyfM8qh2dosm1Y6WZgtFMA9UysLUUoB1bjK4TYW0ySYvn0VryeC97DnwCqAdmldnqGbuWU1enaNLP1PBczARu5nLp811JpEfLGPNGlPsfDZeKUM/zmDRpUtQxBnAxk4iQcHSKJteOlWYKRjMNbzJ2+pNjPA+q7Iyj3diBTpuwl/T9f/eVvK6tuK0a9PuqGFiUTi/ZBg+GGo6rUzS59P6BZhoJF3O59PmuJOrJ6h8EbgXuNsb0RJklKJemPMjn886NxnMxkykUyPij4x3JBG4eK80UjGYKrjRXOhZjPnZ1Jp/B9iHdiB1531yylRanfdiW1XItF2nsgKjpZbZyy/C5OkWTa++fZgrOxVwufb4ribrd+GDgNmCXiNyNLUj/x7g0b8YgLkUrFAq0t7eTSqWc+dC7mMkYQyaToVAdRm+z4Fw8VpopGM0U3HC5hP6C8fiS+w22RbS5zDZ4VZNubJ/U9WX2H8deym8o2WKex/raWjYbQ5qxWRlqtJpZypN8kGwhQ6x9EcnUFt4Re4ga5kaYynLxM+ViJnAz174wRZNEXVSJyCLg48AF2EGUzcCvgNuNMSuizFZKRI4GXly2bBmLFy+OOo4agcuALHAW8IGIsyil9l4vtvV0W5ltJ3YE/1AymcyAdb5rsC2mfpE6uWSrL95OYuxH89/PUXTyctnH5vJRTuH2Md6jmkhWrlzJggULABYYY1ZGnaecqFtCMcY8AzwjIl8BzsQWpJ8FLheRl4FbgF8ZYzZGGFMppZQjqukfVT9YHluI+kXp9uLP/tZe5jVdxW2oPzIeA4vTwdsk7ACtWmzL6nAF6+Ocu7sATRYaWNTySbp4nedn3ENBsqyXXzGTMziETw7zm8ZJTzNsewL6tkHVdJh+GqRmRZPF5Uyu5upphi0PRZshgMiLUJ8xpoBdsviPIlIP3Ah8CPg/wHdF5E/Aj4wx90eX0q0pmrLZLM3NzcyaNcuZSWldzJTP59nV02OnaHJo5KKLx0ozBaOZggs7V4z+y/vl5IHXdu3ikTVrOGTKFPLJ5IAitQ17eX+wAtBa3IYTxxajpYVp6b9rgb/zIlXM4JDsEs54aT75natozjTy/uS53H/qg/Qmu1jOpdEUoT3N8Mr1ZHeupjnTyKzkoyRaV8Bhl0VXXLmYydVcfqb1T0ez/xFw5y8yICKnYVtCz8deFXkR2xKaBT4J3Csi3zHGfCPCjFHteg+e5zF58mSnRsC5mElESCaTeA69d+DmsdJMwWim4FzLFQOmidDU1cWiQoEpZZ6TxbaYlm5tZf7dVWEfOYYuWDO0sYErATik90zumv4KpvpkzK4stfkOkk8fz+sHrqRKumljIyns4Kvq4q3/b38b8x6Im34P658iX30QvbEadmQaiK17Bgr3w5z3jfXegtl4P7zxDPnUPHKxyewiR2zjcog/BAecF00mgA0Pwcbl5NMHU107g6wxFFpWQHopzI2oA9jGpdCyglyi0lcxd0RehIrIUdjC8yPY+Y9bgF8Ctw7qE/oTEbkJuBQIVISKSBXwb8BF2C4/zwP/2xjz8GjzutLhGGyWurq6qGMM4FqmPH30eNvIJrP0EAMHOvv7XDtWoJmC0kzBuZhruHlCE9jR9tOG+T05bDHaWbLtKrntGPSzP9q/b/caUwKFHjAFnvjezWxc+ld7tymQi2VB4G5OH/a/R8ps3jA/V9oAJNsOuS7wlpNOJahNJyHXCclXIfWLYfOMi54tkNkJ8ZdIVyeYPaMOMtsg3Qp1j0aTCaBjFXRvIJ7eypuPKXYQ6XoDptwPM8u1qYdg61JofYNNXcN9gqMX9RRNK4CF2P837wE+D/yxeGm+nKXAp0ewi5uxrao/Bl4FLgEeEJElxpgnRpPZpdFmhUKB7u5u0um0M60MrmQyGFbzA17m+2wyV1HI1bIi9t/M8jZxAj9lKosiy+Zz5ViV0kzBaKbgXMzln8f39nweB6YWtyAy2IJ0G1O4j+voo4bFvYfQt3M5S772Hlq/cTF56WVLopnVjdvpkxrmcgVhdgIToKrzVZJtL5KM1eCZamoK7VT3bSXZcAJVDSdQBSRhwG0VtngPusUY4UCv9XfBulsppA+m25tBurAVr3sdzLsI5p4/Jv/to1I2l8C886LLtX4yrOvkhS2T+ebPookQVNQtoW3AZ4A7jTEdAZ5/DwFXWRKRk4ALgSuMMdcW77sFe4n/GuCU0QR2qQjN5XLs2LGDZDLpzDJhrmR6jitZxbWkOZCGwklkeiczvbqTVu/LPMrpnMFjNHBiZPnAnWNVSjMFo5mCczFXVPOEJvGL1gZWsArI0zn9Aj7Q/DkyG37PlswcpifXce/p97JQDFXM5DyuIIvtp9qN7QLg3/YBPdjZAvxb/989g/4d9C+XAXpTs+nNtJHvbaO7ECftVRObcizUHTFWhwLBFiBBCtY4EJ/1DmLGwK6NdGSnMD0Zp3ram4jNfDtxbFHrF7fxktt4gPv813klt4EL5OmnQesKcjtXsyMDyeQmkg0L7f1RKWbKr3W/T2jkUzSNFxG5BrgcaCgtcEXkq8B3gbnGmA0j+H12iqb7fsjiMz7ixsg3F0fjOZCpg9U8wBFM5WSW9NzOl3IFcoU+3pVp57S67SytOp8GTuIdjKoxXCm1j+vo6GDp0qUsWbIksq4Cr/IfLOcz9gcDqXwDefrIxLp2V0Dv5Y0xmS/UYLsOlCtWM9hido/bTBt9nWvoy3eTSdTRN+kQMola+gY9L9QKItdt/84UesBL2b8v8fS47EroL0hLi9NYufuzncS63iCW68SL1eBNOohYom6P15W7HVz4lt6WbkPdV/ax3u28tuxuzl/yGdApmoIRkSOwI+Ibsau23RywhbSc44FXyrze/2pwHBC4CPU9lN/Jay2PQ+M7IVk/ymh7KdMGLY9D13owOch2gSlopqLXWc5mLuTo7OVsb3mWfKLOZml9jumbu5h79Lm8Hr+LDlZRx9h9s1dKqaDm849k2MELfBUEeuI7Sx4VzuAvYzZhvdDfqlgb9EXJepg69NUiv7j1C9LsOG354n5y8TS52oN3/zye7dj+f1sgiVqoXzCOaUaheho7p4/qgm+oQi9CReQy4IvAKcaY7SX3vxe4E3vFwvdFETm59Hkj0IhdBW4w/76mITLOYM8ZPo4AeHjHFJav3QWbl0U7/ULPLkxsDhmTIik9SLNm8rXRRy/HsaFnezFTgYxJ8UqH8NT2F2jZ1sDG2lk8yf/QUHbWwHDk83na29uZPHmyMwPeNFMwmik4F3N1dnayfv16nn76aWprA5dl42AJ81jGOm5jW/6vSPsBzJl8Co2xt/Ia8BpPRZhtbN87vwVxb9etK83kxWIUsMXogE1kz/tFBvxc7nUFwBRfW3ErPm5KXlMAcoUC3b29VFVXY2Kx3Y+bQa8bvO3+PcXZW/z7xqKFuf3VV/1/utEPpozQL8eLyENA3hhzdsl9cWATdiq1zwPPAu8GvgNcb4z5yij2sxZYbYw5Z9D9BwNrga8YY35c4bVXAd8c6T6VUkoppRxzrjHm3qhDlBPF5fijgP8YdN8SbMvjd40xvyzet1JEjgXOAUZchGK7u5RbEri65PFKbsC2ypZaCPwaO9p+1SjyjJcXAceuA2imEXAxl2YKRjMF51quQ7ADXc/FNkq4wrXjBJppJFzLlQT+BjwWdZBKoihCp7JnX8wzsK3Pdw+6/0lGv9z3FmB2mfsbi7ebK73QGNOCna90t5JJ6le51MFXRHApD2imkXAxl2YKRjMF51qukvP5WtdyuZQHNNNIuJirmCm6fmfDiGLStq3A4E6Cb8HOOvHcoPszxW00VgCHicjgoY+LSx7fH3wr6gBlaKbgXMylmYLRTMG5mss1Lh4nzRSci7lczLRbFH1C78Je2j7RGNNZnPpoBXCPMeb8Qc+9FjjbGHP0KPazGFjGwHlCq7DN5TuMMSeP8PcdXXyts1MdKKWUGp6ez5VyQxSX478FPAO8KiIrgTdhL8VfXea57wdGtR6XMeYpEbkTuLo42n0NcDFwEPCp0fxOpZRSSik1NkK/HG+MeQF4O7AcO03SMuAcY8zy0ueJyOnYS/SDBwiNxCewS3ZeBPwUO03ae4wxj4/id23DFtDb9iKPUkqp6On5XCkH7LcrJimllFJKKXdFMTBJKaWUUkpNcFqEKqWUUkqp0GkRqpRSSimlQqdFqFJKKaWUCp0WoUoppZRSKnRahA5DRKpE5HsisllEekTkKRE5M+pcSimlyhORSSLyLRH5g4jsFBEjIpdUeO6RxeftKj73VhGZHnJkpSYkLUKHdzNwOXA78CUgDzwgIqdFGUoppVRF04BvAEey53LQu4nIHOBx4FDga8C1wLuBh0UkGUJOpSa0KFZM2meIyEnAhQxc+vMW7HJv1wCnRBhPKaVUeVuARmNMs4iciF2lr5yvATXAm4wx6wFE5GngYeAS4KYQsio1YWlL6NDOx7Z87j4RGWN6gZ8DbxaRA6IKppRSqjxjTJ8xpjnAUz8I/N4vQIuvfQR4BbhgvPIppSwtQod2PPCKMaZj0P1PF2+PCzmPUkqpMSAis4EZwLNlHn4ae/5XSo0jLUKH1oi9rDOYf19TiFmUUkqNncbibaVzfIOIVIWYR6kJR4vQoaWAvjL395Y8rpRSat/jn7/1HK9URLQIHVoPUO6bcHXJ40oppfY9/vlbz/FKRUSL0KFtof+STSn/vs0hZlFKKTV2/Mvwlc7xO40x5VpJlVJjRIvQoa0ADhORukH3Ly55XCml1D7GGLMJ2AacWObhk9Dzu1LjTovQod0FxIDP+HcUO6r/A/CUMWZDVMGUUkrttd8B7ymdbk9EzgAOA+6MLJVSE4QYY6LO4DQR+S3wfuBHwBrgYuy35DOMMY9HmU0ppVR5InIZUI+dxeRzwH8Bfy8+fJ0xpr1YfP4daAN+AkwCrgA2Aov0crxS40uL0GGISDXwbeDjwBTgeeDrxpg/RhpMKaVURSLyOnBghYfnGWNeLz7vaOCHwGlABrgf+F/GmK0hxFRqQtMiVCmllFJKhU77hCqllFJKqdBpEaqUUkoppUKnRahSSimllAqdFqFKKaWUUip0WoQqpZRSSqnQaRGqlFJKKaVCp0WoUkoppZQKnRahSimllFIqdFqEKqWUUkqp0GkRqpRSSimlQqdFqFJqwhORg0TEiMglUWfxFfP42z+FvO/zBu3/xDD3r5SaGLQIVUrtlwYVUUNtp0eddQh3AxcB94e832eL+70p5P0qpSaQeNQBlFJqnFw06OdPAGeWuf9loAVIAdkQco3E88aY28LeqTFmI3CbiMSBz4S9f6XUxKBFqFJqvzS4eBORk4Ezhyjqesc/lVJKKZ9ejldKTXjl+oSKyM0isktE5orI74v/3iQilxYfXygij4pIl4i8ISIfLfN760XkxyKyQUT6RGSNiPyziIz63CsilxSzniYiPxWRbSLSJiI3ikiyuM9bRKS1uF0jIjLod1woIstFpFNEOkTkBRH50mgzKaXUaGgRqpRSlcWAB4ENwJXA68D1xWL1D9i+k/8MdAK3iMg8/4UikgYeAz4O3AJ8EXgSuBr44Rhkuw6YD3wTuBd72fzbwH3F3F8DngCuoKQLgoicCfwaaC1m/xfgT8CpY5BJKaUC08vxSilVWTVwmzHmagAR+RWwGfgF8BFjzB3F+x8GVgEXA1cVX3s5cAhwvDHm1eJ9N4rIZuAKEfmBMWbDXmTbCpxjjDHADSJyKLbgvNEY87lirpuwhfMnsYUwwLuBDuAsY0x+L/avlFJ7RVtClVJqaP/p/8MY0wasBrqA35bcvxpoAw4ued2HgD8DrSIyzd+AR7AtlW/dy1w/LxagvqcAAX5ekiuPba0tzdUG1GAHaSmlVGS0JVQppSrrNcZsG3RfO7BxUAHo3z+l5Of5wDHA4Nf7ZuxltvVl9g+268BQuW4ALgAeFJFNwEPAb40xf9jLPEopNSJahCqlVGWVLldXur90AJAHPAxcU+G5r4w21DAZyt2/O5cxpkVEjgPOAs4ubv8gIrcYYy7ey0xKKRWYFqFKKTU+1gKTjDGPRB1kMGNMBjuA6b7iSP0bgM+KyLeNMWuiTaeUmii0T6hSSo2P3wJvFpGzBj9QnEYpkkYAEZla+rMxpgA8X/yxKvxESqmJSltClVJqfHwfeB/wexG5GViOHRC0EDgfOAjYHkGu/xSRBuBRYCNwIPAFYAV29SillAqFFqFKKTUOjDHdIvI27HydH8IuG9qB7Qv6TfoHEoXtNuycop8H6oFm4A7gqmKrqFJKhUL2HOCplFIqaiJisK2p1wBdxpieEPedBOqAC7GT4i8yxjwb1v6VUhOD9glVSil3XYGd4unSkPd7TnG/14W8X6XUBKKX45VSyk2lk8nv7XROI/XkoP2vDnn/SqkJQC/HK6WUUkqp0OnleKWUUkopFTotQpVSSimlVOi0CFVKKaWUUqHTIlQppZRSSoVOi1CllFJKKRU6LUKVUkoppVTotAhVSimllFKh0yJUKaWUUkqFTotQpZRSSikVOi1ClVJKKaVU6LQIVUoppZRSofv/YDoCoU2BUhwAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 720x480 with 3 Axes>"
       ]
@@ -545,7 +544,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 3 Axes>"
       ]
@@ -557,7 +556,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 3 Axes>"
       ]
@@ -569,7 +568,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x480 with 3 Axes>"
       ]
@@ -581,7 +580,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 3 Axes>"
       ]
@@ -593,7 +592,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x480 with 3 Axes>"
       ]
@@ -648,6 +647,13 @@
     "            sim_time,\n",
     "            debug=False)\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index 1f5220d853..21c92dd6e8 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -123,7 +123,7 @@ def run_post_trace_test_python_reference_(self, debug=False):
         compute Python known-good reference of postsynaptic trace
         """
 
-        n_timepoints = int(np.ceil(100 * self.sim_time_))
+        n_timepoints = int(np.ceil(1000 * self.sim_time_))
         trace_python_ref = np.zeros(n_timepoints)
 
         n_spikes = len(self.post_spike_times_)
@@ -133,7 +133,7 @@ def run_post_trace_test_python_reference_(self, debug=False):
                    + self.dendritic_delay_
             for i in range(n_timepoints):
                 t = (i / float(n_timepoints - 1)) * self.sim_time_
-                if t > t_sp + 1E-3:
+                if t > t_sp:
                     trace_python_ref[i] += np.exp(-(t - t_sp)
                                                   / self.tau_minus_)
 
@@ -311,8 +311,8 @@ def test_post_trace(self):
                     delay=delay,
                     resolution=resolution,
                     tau_minus=tau_minus,
-                    trace_match_atol=1E-2,
-                    trace_match_rtol=1E-2)
+                    trace_match_atol=1E-3,
+                    trace_match_rtol=1E-3)
                 assert test.nest_trace_matches_python_trace()
 
 

From 3f872249a7a9c68505c0059d535575b63b4f986b Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Tue, 26 Mar 2019 11:17:20 +0100
Subject: [PATCH 35/42] add reference to Jupyter notebook in regression test

---
 testsuite/regressiontests/issue-1034.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index 21c92dd6e8..664ad172e4 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -19,6 +19,10 @@
 # You should have received a copy of the GNU General Public License
 # along with NEST.  If not, see <http://www.gnu.org/licenses/>.
 
+# Please see `doc/model_details/test_post_trace.ipynb` for a version of this
+# test that includes more documentation and plotting.
+
+
 import nest
 import numpy as np
 import scipy as sp

From 90b5c28976f1f63f967c0420594cf633e9450785 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 1 Apr 2019 11:52:06 +0200
Subject: [PATCH 36/42] change verbosity level to the maximum defined

---
 testsuite/regressiontests/issue-1034.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index 664ad172e4..f8f82bdcb5 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -326,5 +326,5 @@ def suite():
 
 
 if __name__ == "__main__":
-    runner = unittest.TextTestRunner(verbosity=99)
+    runner = unittest.TextTestRunner(verbosity=2)
     runner.run(suite())

From d7ed53b332c05f3a7ed2cc5bc12077be40a28340 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 1 Apr 2019 12:23:46 +0200
Subject: [PATCH 37/42] remove unused NEST name 'pre_trace'

---
 nestkernel/nest_names.cpp | 1 -
 nestkernel/nest_names.h   | 1 -
 2 files changed, 2 deletions(-)

diff --git a/nestkernel/nest_names.cpp b/nestkernel/nest_names.cpp
index 407ebb9706..4d44f21316 100644
--- a/nestkernel/nest_names.cpp
+++ b/nestkernel/nest_names.cpp
@@ -315,7 +315,6 @@ const Name ports( "ports" );
 const Name post_synaptic_element( "post_synaptic_element" );
 const Name post_trace( "post_trace" );
 const Name pre_synaptic_element( "pre_synaptic_element" );
-const Name pre_trace( "pre_trace" );
 const Name precise_times( "precise_times" );
 const Name precision( "precision" );
 const Name print_time( "print_time" );
diff --git a/nestkernel/nest_names.h b/nestkernel/nest_names.h
index 32167d502a..3c08030bf0 100644
--- a/nestkernel/nest_names.h
+++ b/nestkernel/nest_names.h
@@ -335,7 +335,6 @@ extern const Name ports;
 extern const Name post_synaptic_element;
 extern const Name post_trace;
 extern const Name pre_synaptic_element;
-extern const Name pre_trace;
 extern const Name precise_times;
 extern const Name precision;
 extern const Name print_time;

From eefa1aaecad281d548cf9ce2571f2fa2c56b85da Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 1 Apr 2019 12:25:03 +0200
Subject: [PATCH 38/42] minor refactoring based on PR comments

---
 nestkernel/archiving_node.cpp           |  2 +-
 testsuite/regressiontests/issue-1034.py | 50 +++++++++++--------------
 2 files changed, 23 insertions(+), 29 deletions(-)

diff --git a/nestkernel/archiving_node.cpp b/nestkernel/archiving_node.cpp
index fb282adb65..a6f9bd3573 100644
--- a/nestkernel/archiving_node.cpp
+++ b/nestkernel/archiving_node.cpp
@@ -213,7 +213,7 @@ nest::Archiving_Node::set_spiketime( Time const& t_sp, double offset )
     {
       const double next_t_sp = history_[ 1 ].t_;
       if ( history_.front().access_counter_ >= n_incoming_
-        && t_sp_ms - next_t_sp > max_delay_
+        and t_sp_ms - next_t_sp > max_delay_
             + kernel().connection_manager.get_stdp_eps() )
       {
         history_.pop_front();
diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index f8f82bdcb5..ba179b199e 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -80,10 +80,7 @@ def run_post_trace_test_nest_(self,
         # create spike detector --- debugging only
         spikes = nest.Create("spike_detector",
                              params={'precise_times': True})
-        nest.Connect(
-            pre_parrot_ps + post_parrot_ps,
-            spikes
-        )
+        nest.Connect(pre_parrot_ps + post_parrot_ps, spikes)
 
         # connect both parrot neurons with a stdp synapse onto port 1
         # thereby spikes transmitted through the stdp connection are
@@ -130,9 +127,8 @@ def run_post_trace_test_python_reference_(self, debug=False):
         n_timepoints = int(np.ceil(1000 * self.sim_time_))
         trace_python_ref = np.zeros(n_timepoints)
 
-        n_spikes = len(self.post_spike_times_)
-        for sp_idx in range(n_spikes):
-            t_sp = self.post_spike_times_[sp_idx] \
+        for post_spike_time in self.post_spike_times_:
+            t_sp = post_spike_time \
                    + self.delay_ \
                    + self.dendritic_delay_
             for i in range(n_timepoints):
@@ -141,9 +137,8 @@ def run_post_trace_test_python_reference_(self, debug=False):
                     trace_python_ref[i] += np.exp(-(t - t_sp)
                                                   / self.tau_minus_)
 
-        n_spikes = len(self.pre_spike_times_)
-        for sp_idx in range(n_spikes):
-            t_sp = self.pre_spike_times_[sp_idx] + self.delay_
+        for pre_spike_time in self.pre_spike_times_:
+            t_sp = pre_spike_time + self.delay_
             i = int(np.round(t_sp / self.sim_time_
                              * float(len(trace_python_ref) - 1)))
             if debug:
@@ -164,12 +159,11 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
         the last presynaptic spike.
         """
 
-        n_timepoints = len(trace_nest_t)
-        for i in range(n_timepoints)[1:]:
+        for t, trace_nest_val in zip(trace_nest_t[1:], trace_nest[1:]):
             t = trace_nest_t[i]
             if debug:
                 print("* Finding ref for NEST timepoint t = " + str(t)
-                      + ", trace = " + str(trace_nest[i]))
+                      + ", trace = " + str(trace_nest_val))
 
             traces_match = False
             for i_search, t_search in enumerate(
@@ -180,7 +174,7 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
                         * float(len(trace_python_ref) - 1)))]
                     traces_match = np.allclose(
                         _trace_at_t_search,
-                        trace_nest[i],
+                        trace_nest_val,
                         atol=self.trace_match_atol_,
                         rtol=self.trace_match_rtol_)
                     post_spike_occurred_at_t_search = np.any(
@@ -198,12 +192,12 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
                     if (not traces_match) and post_spike_occurred_at_t_search:
                         traces_match = np.allclose(
                             _trace_at_t_search + 1,
-                            trace_nest[i],
+                            trace_nest_val,
                             atol=self.trace_match_atol_,
                             rtol=self.trace_match_rtol_)
                         if debug:
                             print("\t   traces_match = " + str(traces_match)
-                                  + " (nest trace = " + str(trace_nest[i])
+                                  + " (nest trace = " + str(trace_nest_val)
                                   + ", ref trace = "
                                   + str(_trace_at_t_search + 1)
                                   + ")")
@@ -213,12 +207,12 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
                     if (not traces_match) and post_spike_occurred_at_t_search:
                         traces_match = np.allclose(
                             _trace_at_t_search - 1,
-                            trace_nest[i],
+                            trace_nest_val,
                             atol=self.trace_match_atol_,
                             rtol=self.trace_match_rtol_)
                         if debug:
                             print("\t   traces_match = " + str(traces_match)
-                                  + " (nest trace = " + str(trace_nest[i])
+                                  + " (nest trace = " + str(trace_nest_val)
                                   + ", ref trace = "
                                   + str(_trace_at_t_search - 1)
                                   + ")")
@@ -227,12 +221,12 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
 
                     break
 
-            if (not traces_match) \
-               and i_search == len(self.pre_spike_times_) - 1:
+            if ((not traces_match)
+               and i_search == len(self.pre_spike_times_) - 1):
                 if debug:
                     print("\tthe time before the first pre spike")
                 # the time before the first pre spike
-                traces_match = trace_nest[i] == 0.
+                traces_match = trace_nest_val == 0.
 
             if not traces_match:
                 return False
@@ -301,23 +295,23 @@ def test_post_trace(self):
                             post_spike_times2,
                             pre_spike_times2]
 
-        for spike_times_idx in range(len(pre_spike_times)):
+        for pre_spike_time, post_spike_time in zip(pre_spike_times,
+                                                   post_spike_times):
             print("Pre spike times: ["
-                  + ", ".join([str(t) for t in pre_spike_times]) + "]")
+                  + ", ".join([str(t) for t in pre_spike_time]) + "]")
             print("Post spike times: ["
-                  + ", ".join([str(t) for t in post_spike_times]) + "]")
+                  + ", ".join([str(t) for t in post_spike_time]) + "]")
 
             for delay in delays:
-                dendritic_delay = delay
                 test = PostTraceTester(
-                    pre_spike_times=pre_spike_times[spike_times_idx],
-                    post_spike_times=post_spike_times[spike_times_idx],
+                    pre_spike_times=pre_spike_time,
+                    post_spike_times=post_spike_time,
                     delay=delay,
                     resolution=resolution,
                     tau_minus=tau_minus,
                     trace_match_atol=1E-3,
                     trace_match_rtol=1E-3)
-                assert test.nest_trace_matches_python_trace()
+                self.assertTrue(test.nest_trace_matches_python_trace())
 
 
 def suite():

From 320c659bd6ca313aa26977dd76fcd9f3064f0629 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 8 Apr 2019 10:55:18 +0200
Subject: [PATCH 39/42] add documentation header; minor refactoring based on PR
 comments

---
 testsuite/regressiontests/issue-1034.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index ba179b199e..e1cf1485e1 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -31,6 +31,12 @@
 
 
 class PostTraceTester(object):
+    '''Test that postsynaptic trace values returned from NEST are consistent
+    with reference values generated in Python.
+
+    For more information, please see the Jupyter notebook in
+    `doc/model_details/test_post_trace.ipynb`.
+    '''
 
     def __init__(self, pre_spike_times, post_spike_times, delay, resolution,
                  tau_minus, trace_match_atol, trace_match_rtol):
@@ -128,9 +134,7 @@ def run_post_trace_test_python_reference_(self, debug=False):
         trace_python_ref = np.zeros(n_timepoints)
 
         for post_spike_time in self.post_spike_times_:
-            t_sp = post_spike_time \
-                   + self.delay_ \
-                   + self.dendritic_delay_
+            t_sp = post_spike_time + self.delay_ + self.dendritic_delay_
             for i in range(n_timepoints):
                 t = (i / float(n_timepoints - 1)) * self.sim_time_
                 if t > t_sp:

From acd122ffd274261374b811284d2b53969ab12c00 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 8 Apr 2019 10:59:39 +0200
Subject: [PATCH 40/42] remove stray line left over from refactoring

---
 testsuite/regressiontests/issue-1034.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/testsuite/regressiontests/issue-1034.py b/testsuite/regressiontests/issue-1034.py
index e1cf1485e1..d0c5d8a543 100644
--- a/testsuite/regressiontests/issue-1034.py
+++ b/testsuite/regressiontests/issue-1034.py
@@ -164,7 +164,6 @@ def nest_trace_matches_ref_trace_(self, trace_nest_t, trace_nest,
         """
 
         for t, trace_nest_val in zip(trace_nest_t[1:], trace_nest[1:]):
-            t = trace_nest_t[i]
             if debug:
                 print("* Finding ref for NEST timepoint t = " + str(t)
                       + ", trace = " + str(trace_nest_val))

From bb5b275a372affa24d2947db0bb5eb6e4f289e47 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 8 Apr 2019 11:01:54 +0200
Subject: [PATCH 41/42] remove copyright header from Jupyter notebook

---
 doc/model_details/test_post_trace.ipynb | 24 ------------------------
 1 file changed, 24 deletions(-)

diff --git a/doc/model_details/test_post_trace.ipynb b/doc/model_details/test_post_trace.ipynb
index b3112e86da..da887729a4 100644
--- a/doc/model_details/test_post_trace.ipynb
+++ b/doc/model_details/test_post_trace.ipynb
@@ -1,29 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This file is part of NEST.\n",
-    "#\n",
-    "# Copyright (C) 2004 The NEST Initiative\n",
-    "#\n",
-    "# NEST is free software: you can redistribute it and/or modify\n",
-    "# it under the terms of the GNU General Public License as published by\n",
-    "# the Free Software Foundation, either version 2 of the License, or\n",
-    "# (at your option) any later version.\n",
-    "#\n",
-    "# NEST is distributed in the hope that it will be useful,\n",
-    "# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
-    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
-    "# GNU General Public License for more details.\n",
-    "#\n",
-    "# You should have received a copy of the GNU General Public License\n",
-    "# along with NEST.  If not, see <http://www.gnu.org/licenses/>."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,

From 0eead87ddd912af069c1d0d51f394c8c46e60245 Mon Sep 17 00:00:00 2001
From: "C.A.P. Linssen" <charl@turingbirds.com>
Date: Mon, 8 Apr 2019 14:12:42 +0200
Subject: [PATCH 42/42] add figure legend

---
 doc/model_details/test_post_trace.ipynb | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/doc/model_details/test_post_trace.ipynb b/doc/model_details/test_post_trace.ipynb
index da887729a4..4bdcdba6ae 100644
--- a/doc/model_details/test_post_trace.ipynb
+++ b/doc/model_details/test_post_trace.ipynb
@@ -496,7 +496,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Run the test and make the plots while we go:"
+    "Now, run the test and make the plots while we go.\n",
+    "\n",
+    "The plots should be interpreted as follows. Pre- and postsynaptic spikes are shown in the top two subplots, at the time at which they arrive at the synapse (i.e. from the perspective of the synapse, taking dendritic and axonal delays into account).\n",
+    "\n",
+    "The bottom subplot shows the reference/known-good timeseries generated (numerically) in Python (**cyan colour**). The values returned from NEST are shown using **orange circles**. They are plotted as points rather than as a continuous line, because we can only retrieve the value at the resolution of the minimum synaptic delay (i.e. fetch trace value; simulate for a timestep `delay`; repeat). Moreover, the postsynaptic trace value is only updated in NEST during the processing of a presynaptic spike, so unless a presynaptic spike was processed in the last delay interval, the value will remain unchanged. To allow comparison between the Python- and NEST-generated values, we thus search for the previous time at which NEST would have updated the trace value, which is the time of arrival of the last presynaptic spike. This value is marked by an **open green circle**. If all is well, all green circles should always overlap an orange circle, and all **black lines** (which simply connect subsequent postsynaptic trace values returned by NEST) should be perfectly horizontal."
    ]
   },
   {
@@ -623,13 +627,6 @@
     "            sim_time,\n",
     "            debug=False)\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {