diff --git a/NeuroML2/.test.gfluct.jnml.omt b/NeuroML2/.test.gfluct.jnml.omt
new file mode 100644
index 0000000..2d48f2b
--- /dev/null
+++ b/NeuroML2/.test.gfluct.jnml.omt
@@ -0,0 +1,5 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+ 
+target: LEMS_Gfluct_test.xml 
+engine: jNeuroML
+
diff --git a/NeuroML2/.test.gfluct.jnmlnrn.omt b/NeuroML2/.test.gfluct.jnmlnrn.omt
new file mode 100644
index 0000000..ee584dc
--- /dev/null
+++ b/NeuroML2/.test.gfluct.jnmlnrn.omt
@@ -0,0 +1,5 @@
+# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
+ 
+target: LEMS_Gfluct_test.xml 
+engine: jNeuroML_NEURON
+
diff --git a/NeuroML2/Gfluct.nml b/NeuroML2/Gfluct.nml
new file mode 100644
index 0000000..27914dd
--- /dev/null
+++ b/NeuroML2/Gfluct.nml
@@ -0,0 +1,237 @@
+<?xml version="1.0" encoding="ISO-8859-1"?>
+<neuroml xmlns="http://www.neuroml.org/schema/neuroml2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2.3.xsd" id="GfluctDoc">
+
+    <!-- Note that order of different LEMS types matters for validation, since the schema defines what order they should appear in -->
+
+    <!--
+        Fluctuating conductance model for synaptic bombardment
+        ======================================================
+
+        THEORY
+
+          Synaptic bombardment is represented by a stochastic model containing
+          two fluctuating conductances g_e(t) and g_i(t) descibed by:
+
+             Isyn = g_e(t) * [V - E_e] + g_i(t) * [V - E_i]
+             d g_e / dt = -(g_e - g_e0) / tau_e + sqrt(D_e) * Ft
+             d g_i / dt = -(g_i - g_i0) / tau_i + sqrt(D_i) * Ft
+
+          where E_e, E_i are the reversal potentials, g_e0, g_i0 are the average
+          conductances, tau_e, tau_i are time constants, D_e, D_i are noise diffusion
+          coefficients and Ft is a gaussian white noise of unit standard deviation.
+
+          g_e and g_i are described by an Ornstein-Uhlenbeck (OU) stochastic process
+          where tau_e and tau_i represent the "correlation" (if tau_e and tau_i are
+          zero, g_e and g_i are white noise).  The estimation of OU parameters can
+          be made from the power spectrum:
+
+             S(w) =  2 * D * tau^2 / (1 + w^2 * tau^2)
+
+          and the diffusion coeffient D is estimated from the variance:
+
+             D = 2 * sigma^2 / tau
+
+
+        NUMERICAL RESOLUTION
+
+          The numerical scheme for integration of OU processes takes advantage
+          of the fact that these processes are gaussian, which led to an exact
+          update rule independent of the time step dt (see Gillespie DT, Am J Phys
+          64: 225, 1996):
+
+             x(t+dt) = x(t) * exp(-dt/tau) + A * N(0,1)
+
+          where A = sqrt( D*tau/2 * (1-exp(-2*dt/tau)) ) and N(0,1) is a normal
+          random number (avg=0, sigma=1)
+
+
+        IMPLEMENTATION
+
+          This mechanism is implemented as a nonspecific current defined as a
+          point process.
+
+
+        PARAMETERS
+
+          The mechanism takes the following parameters:
+
+             E_e = 0  (mV)      : reversal potential of excitatory conductance
+             E_i = -75 (mV)     : reversal potential of inhibitory conductance
+
+             g_e0 = 0.0121 (umho)   : average excitatory conductance
+             g_i0 = 0.0573 (umho)   : average inhibitory conductance
+
+             std_e = 0.0030 (umho)  : standard dev of excitatory conductance
+             std_i = 0.0066 (umho)  : standard dev of inhibitory conductance
+
+             tau_e = 2.728 (ms)     : time constant of excitatory conductance
+             tau_i = 10.49 (ms)     : time constant of inhibitory conductance
+
+
+        Gfluct2: conductance cannot be negative
+
+
+        REFERENCE
+
+          Destexhe, A., Rudolph, M., Fellous, J-M. and Sejnowski, T.J.
+          Fluctuating synaptic conductances recreate in-vivo-like activity in
+          neocortical neurons. Neuroscience 107: 13-24 (2001).
+
+          (electronic copy available at http://cns.iaf.cnrs-gif.fr)
+
+
+          A. Destexhe, 1999
+
+        Reference:
+        https://doi.org/10.1016/S0306-4522(01)00344-X
+
+        Original mod source: https://modeldb.science/8115
+    -->
+
+    <ComponentType name="Gfluct"
+        extends="baseVoltageDepPointCurrent"
+        description="Fluctuating conductance model for synaptic bombardment: Destexhe et al 2001"
+        >
+
+        <Parameter name="start" dimension="time" description="start time"/>
+        <Parameter name="stop" dimension="time" description="stop time"/>
+
+        <Parameter name="dt" dimension="time" description="simulation time step"/>
+        <Parameter name="E_e" dimension="voltage" description="Excitatory conductance reversal potential"/>
+        <Parameter name="E_i" dimension="voltage" description="Inhibitory conductance reversal potential"/>
+
+        <Parameter name="g_e0" dimension="conductance" description="Average excitatory conductance"/>
+        <Parameter name="g_i0" dimension="conductance" description="Average inhibitory conductance"/>
+
+        <Parameter name="std_e" dimension="conductance" description="Std dev of excitatory conductance"/>
+        <Parameter name="std_i" dimension="conductance" description="Std dev of inhibitory conductance"/>
+
+        <Parameter name="tau_e" dimension="time" description="Time constant of excitatory conductance"/>
+        <Parameter name="tau_i" dimension="time" description="Time constant of inhibitory conductance"/>
+
+        <Constant name="zeroms" value="0 ms" dimension="time" />
+
+        <EventPort name="in" direction="in" description="Note this is not used here. Will be removed in future"/>
+
+        <DerivedParameter name="exp_e" dimension="none" value="exp(-dt/tau_e)" />
+        <DerivedParameter name="amp_e" dimension="conductance" value="std_e * sqrt((1 - exp(-2 * (dt/tau_e))))" />
+        <DerivedParameter name="exp_i" dimension="none" value="exp(-dt/tau_i)" />
+        <DerivedParameter name="amp_i" dimension="conductance" value="std_i * sqrt((1 - exp(-2 * (dt/tau_i))))" />
+
+        <Exposure name="g_e" dimension="conductance" />
+        <Exposure name="g_i" dimension="conductance" />
+        <Exposure name="g_e1" dimension="conductance" />
+        <Exposure name="g_i1" dimension="conductance" />
+        <Exposure name="i" dimension="current" />
+
+        <Dynamics>
+            <!-- total conductances -->
+            <StateVariable name="g_e" exposure="g_e" dimension="conductance" />
+            <StateVariable name="g_i" exposure="g_i" dimension="conductance" />
+            <!-- fluctuating conductances -->
+            <StateVariable name="g_e1" exposure="g_e1" dimension="conductance" />
+            <StateVariable name="g_i1" exposure="g_i1" dimension="conductance" />
+
+            <!-- track old value of g_e1: LEMS doesn't seem to like it if the same variable is used -->
+            <StateVariable name="g_e1_old" dimension="conductance" />
+            <StateVariable name="g_i1_old" dimension="conductance" />
+
+            <StateVariable name="i" exposure="i" dimension="current" />
+
+            <StateVariable name="r1" dimension="none" />
+            <StateVariable name="r2" dimension="none" />
+            <StateVariable name="r3" dimension="none" />
+            <StateVariable name="r4" dimension="none" />
+            <StateVariable name="randn_e" dimension="none" />
+            <StateVariable name="randn_i" dimension="none" />
+            <StateVariable name="randn_e1" dimension="none" />
+            <StateVariable name="randn_i1" dimension="none" />
+
+            <OnEvent port="in"><!--TODO: remove, see above...
+            <StateAssignment variable="i" value="0"/>-->
+            </OnEvent>
+
+            <!-- required ? -->
+            <OnStart>
+                <StateAssignment variable="i" value="0"/>
+                <StateAssignment variable="g_e" value="0"/>
+                <StateAssignment variable="g_i" value="0"/>
+                <StateAssignment variable="g_e1" value="0" />
+                <StateAssignment variable="g_i1" value="0" />
+                <StateAssignment variable="g_e1_old" value="0" />
+                <StateAssignment variable="g_i1_old" value="0" />
+            </OnStart>
+
+            <!-- before start -->
+            <OnCondition test="t .lt. start">
+                <StateAssignment variable="i" value="0"/>
+                <StateAssignment variable="g_e" value="0"/>
+                <StateAssignment variable="g_i" value="0"/>
+                <StateAssignment variable="g_e1" value="0" />
+                <StateAssignment variable="g_i1" value="0" />
+                <StateAssignment variable="g_e1_old" value="0" />
+                <StateAssignment variable="g_i1_old" value="0" />
+            </OnCondition>
+
+            <!-- generate gaussian random numbers -->
+            <OnCondition test="t .geq. start .and. t.lt. stop">
+                <StateAssignment variable="r1" value="random(1)"/>
+                <StateAssignment variable="r2" value="random(1)"/>
+                <StateAssignment variable="randn_e" value="sqrt(-2*log(r1))*sin(2*3.14159265359*r2)"/>
+                <StateAssignment variable="randn_i" value="sqrt(-2*log(r1))*cos(2*3.14159265359*r2)"/>
+                <StateAssignment variable="r3" value="random(1)"/>
+                <StateAssignment variable="r4" value="random(1)"/>
+                <StateAssignment variable="randn_e1" value="sqrt(-2*log(r3))*sin(2*3.14159265359*r4)"/>
+                <StateAssignment variable="randn_i1" value="sqrt(-2*log(r3))*cos(2*3.14159265359*r4)"/>
+            </OnCondition>
+
+            <!-- oup()-->
+            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_e .neq. zeroms)" >
+                <StateAssignment variable="g_e1" value="(exp_e * g_e1_old) + (amp_e * randn_e)" />
+            </OnCondition>
+
+            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_i .neq. zeroms)" >
+                <StateAssignment variable="g_i1" value="(exp_i * g_i1_old) + (amp_i * randn_i)" />
+            </OnCondition>
+
+            <!-- no op since g_e and g_i get assigned again next: bug in mod file? -->
+            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_e .eq. zeroms)" >
+                <StateAssignment variable="g_e" value="std_e * randn_e1" />
+            </OnCondition>
+            <OnCondition test="(t .geq. start .and. t .lt. stop) .and. (tau_i .eq. zeroms)" >
+                <StateAssignment variable="g_i" value="std_i * randn_i1" />
+            </OnCondition>
+
+            <OnCondition test="t .geq. start .and. t .lt. stop">
+                <StateAssignment variable="g_e" value="g_e0 + g_e1" />
+                <StateAssignment variable="g_i" value="g_i0 + g_i1" />
+            </OnCondition>
+
+            <OnCondition test="g_e .lt. 0">
+                <StateAssignment variable="g_e" value="0" />
+            </OnCondition>
+            <OnCondition test="g_i .lt. 0">
+                <StateAssignment variable="g_i" value="0" />
+            </OnCondition>
+
+            <OnCondition test="t .geq. start .and. t .lt. stop">
+                <StateAssignment variable="g_e1_old" value="g_e1" />
+                <StateAssignment variable="g_i1_old" value="g_i1" />
+                <StateAssignment variable="i" value="-1 * g_e * (v - E_e) - g_i * (v - E_i)" />
+            </OnCondition>
+
+            <!-- after end -->
+            <OnCondition test="t .geq. stop">
+                <StateAssignment variable="i" value="0"/>
+                <StateAssignment variable="g_e" value="0"/>
+                <StateAssignment variable="g_i" value="0"/>
+                <StateAssignment variable="g_e1" value="0" />
+                <StateAssignment variable="g_i1" value="0" />
+                <StateAssignment variable="g_e1_old" value="0" />
+                <StateAssignment variable="g_i1_old" value="0" />
+            </OnCondition>
+        </Dynamics>
+
+    </ComponentType>
+
+</neuroml>
diff --git a/NeuroML2/LEMS_Gfluct_test.xml b/NeuroML2/LEMS_Gfluct_test.xml
new file mode 100644
index 0000000..2081682
--- /dev/null
+++ b/NeuroML2/LEMS_Gfluct_test.xml
@@ -0,0 +1,110 @@
+<Lems>
+
+    <!-- Specify which component to run -->
+    <Target component="simnet1"/>
+
+    <!-- Include core NeuroML2 ComponentType definitions -->
+    <Include file="PyNN.xml"/>
+    <Include file="Cells.xml"/>
+    <Include file="Networks.xml"/>
+    <Include file="Simulation.xml"/>
+
+
+    <Include file="Gfluct.nml"/>
+
+
+    <IF_curr_exp id="IF_curr_exp" cm="10.0" i_offset="0" tau_m="20.0" tau_refrac="5.0"
+        tau_syn_E="0.5" tau_syn_I="0.5" v_init="-65" v_reset="-65.0" v_rest="-65.0" v_thresh="-55.0"/>
+
+
+    <Gfluct id="both" start="500ms" stop="2500ms" dt="0.05ms" E_e="0mV" E_i="-75mV" g_e0="0.0121 uS" g_i0="0.0573 uS" std_e="0.0030 uS" std_i="0.0066 uS" tau_e="2.728 ms" tau_i="10.49 ms"/>
+    <Gfluct id="only_e" start="500ms" stop="2500ms" dt="0.05ms" E_e="0mV" E_i="-75mV" g_e0="0.0121 uS" g_i0="0.0 uS" std_e="0.0030 uS" std_i="0.00 uS" tau_e="2.728 ms" tau_i="10.49 ms"/>
+    <Gfluct id="only_i" start="500ms" stop="2500ms" dt="0.05ms" E_e="0mV" E_i="-75mV" g_e0="0.0 uS" g_i0="0.0573 uS" std_e="0.00 uS" std_i="0.0066 uS" tau_e="2.728 ms" tau_i="10.49 ms"/>
+    <Gfluct id="steady_e" start="500ms" stop="2500ms" dt="0.05ms" E_e="0mV" E_i="-75mV" g_e0="0.0121 uS" g_i0="0.0 uS" std_e="0.00 uS" std_i="0.00 uS" tau_e="2.728 ms" tau_i="10.49 ms"/>
+    <Gfluct id="steady_i" start="500ms" stop="2500ms" dt="0.05ms" E_e="0mV" E_i="-75mV" g_e0="0.0 uS" g_i0="0.0573 uS" std_e="0.00 uS" std_i="0.00 uS" tau_e="2.728 ms" tau_i="10.49 ms"/>
+
+    <network id="net1">
+        <population component="IF_curr_exp" id="Pop0" type="populationList"  size="5">
+            <instance id="0">
+                <location x="120" y="230" z="567"/>
+            </instance>
+            <instance id="1">
+                <location x="270" y="450" z="56"/>
+            </instance>
+            <instance id="2">
+                <location x="54" y="234" z="89"/>
+            </instance>
+            <instance id="3">
+                <location x="154" y="1234" z="89"/>
+            </instance>
+            <instance id="4">
+                <location x="114" y="14" z="889"/>
+            </instance>
+        </population>
+
+
+        <inputList id="Gfluct0" component="both" population="Pop0">
+            <input id="0" target="../Pop0/0/IF_curr_exp" destination="synapses"/>
+        </inputList>
+        <inputList id="Gfluct1" component="only_e" population="Pop0">
+            <input id="0" target="../Pop0/1/IF_curr_exp" destination="synapses"/>
+        </inputList>
+        <inputList id="Gfluct2" component="only_i" population="Pop0">
+            <input id="0" target="../Pop0/2/IF_curr_exp" destination="synapses"/>
+        </inputList>
+        <inputList id="Gfluct3" component="steady_e" population="Pop0">
+            <input id="0" target="../Pop0/3/IF_curr_exp" destination="synapses"/>
+        </inputList>
+        <inputList id="Gfluct4" component="steady_i" population="Pop0">
+            <input id="0" target="../Pop0/4/IF_curr_exp" destination="synapses"/>
+        </inputList>
+
+    </network>
+
+    <Simulation id="simnet1" length="3000ms" step="0.05ms" target="net1" seed="54320">
+
+        <Display id="display_voltages" title="Voltages" timeScale="1ms" xmin="-200" xmax="3400.0" ymin="-67" ymax="-60">
+            <Line id="both: Vm" quantity="Pop0/0/IF_curr_exp/v" scale="1mV" color="#0000ff" timeScale="1ms"/>
+            <Line id="only e: Vm" quantity="Pop0/1/IF_curr_exp/v" scale="1mV" color="#00ffff" timeScale="1ms"/>
+            <Line id="only i: Vm" quantity="Pop0/2/IF_curr_exp/v" scale="1mV" color="#ff00ff" timeScale="1ms"/>
+            <Line id="steady e: Vm" quantity="Pop0/3/IF_curr_exp/v" scale="1mV" color="#ffffff" timeScale="1ms"/>
+            <Line id="steady i: Vm" quantity="Pop0/4/IF_curr_exp/v" scale="1mV" color="#00ff00" timeScale="1ms"/>
+        </Display>
+        <!--
+        <Display id="display_currents" title="Currents" timeScale="1ms" xmin="-200" xmax="3400.0" ymin="-0.2" ymax="2.5">
+            <Line id="Pop0/0: i"  quantity="Pop0/0/IF_curr_exp/synapses:noisyCurrentSource1:0/i" scale="1nA" color="#00ff00" timeScale="1ms"/>
+        </Display>
+
+        <Display id="display_conds_e" title="Conductances_e" timeScale="1ms" xmin="-200" xmax="3400.0" ymin="-0.015" ymax="0.025">
+            <Line id="g_e" quantity="Pop0/0/IF_curr_exp/synapses:noisyCurrentSource1:0/g_e" scale="1uS" color="#00ff00" timeScale="1ms"/>
+            <Line id="g_e1" quantity="Pop0/0/IF_curr_exp/synapses:noisyCurrentSource1:0/g_e1" scale="1uS" color="#ffff00" timeScale="1ms"/>
+        </Display>
+
+        <Display id="display_conds_i" title="Conductances_i" timeScale="1ms" xmin="-200" xmax="3400.0" ymin="-0.03" ymax="0.09">
+            <Line id="g_i" quantity="Pop0/0/IF_curr_exp/synapses:noisyCurrentSource1:0/g_i" scale="1uS" color="#00ff00" timeScale="1ms"/>
+            <Line id="g_i1" quantity="Pop0/0/IF_curr_exp/synapses:noisyCurrentSource1:0/g_i1" scale="1uS" color="#ffff00" timeScale="1ms"/>
+        </Display> -->
+
+
+        <!-- works for jLEMS -->
+        <OutputFile id="Volts_file" fileName="v_gfluct.dat">
+            <OutputColumn id="v0" quantity="Pop0/0/IF_curr_exp/v"/> 
+            <OutputColumn id="v1" quantity="Pop0/1/IF_curr_exp/v"/> 
+            <OutputColumn id="v2" quantity="Pop0/2/IF_curr_exp/v"/> 
+            <OutputColumn id="v3" quantity="Pop0/3/IF_curr_exp/v"/> 
+            <OutputColumn id="v4" quantity="Pop0/4/IF_curr_exp/v"/> 
+        </OutputFile>
+        <OutputFile id="Currents_file" fileName="i_gfluct.dat">
+            <OutputColumn id="i" quantity="Pop0/0/IF_curr_exp/synapses:both:0/i"/>
+        </OutputFile>
+        <OutputFile id="Conductance_file" fileName="g_gfluct.dat">
+            <OutputColumn id="g_e" quantity="Pop0/0/IF_curr_exp/synapses:both:0/g_e"/>
+            <OutputColumn id="g_e1" quantity="Pop0/0/IF_curr_exp/synapses:both:0/g_e1"/>
+            <OutputColumn id="g_i" quantity="Pop0/0/IF_curr_exp/synapses:both:0/g_i"/>
+            <OutputColumn id="g_i1" quantity="Pop0/0/IF_curr_exp/synapses:both:0/g_i1"/>
+        </OutputFile>
+        <!-- bug: input properties cannot currently be recorded using neuron -->
+
+    </Simulation>
+
+</Lems>
diff --git a/NeuroML2/TestGfluct.ipynb b/NeuroML2/TestGfluct.ipynb
new file mode 100644
index 0000000..ce56b71
--- /dev/null
+++ b/NeuroML2/TestGfluct.ipynb
@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:numexpr.utils:NumExpr defaulting to 16 threads.\n",
+      "pyNeuroML >>> 13:46:28 - INFO - Loading LEMS file: LEMS_Gfluct_test.xml and running with jNeuroML\n",
+      "pyNeuroML >>> 13:46:28 - INFO - Executing: (java -Xmx400M  -Djava.awt.headless=true -jar  \"/home/asinha/Documents/02_Code/00_mine/NeuroML/software/pyNeuroML/pyneuroml/utils/./../lib/jNeuroML-0.14.0-jar-with-dependencies.jar\"  LEMS_Gfluct_test.xml  -nogui -I '') in directory: .\n",
+      "pyNeuroML >>> 13:46:28 - INFO - Command completed successfully!\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Finished running LEMS file: LEMS_Gfluct_test.xml\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyneuroml import pynml\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy\n",
+    "\n",
+    "lems_files = ['LEMS_Gfluct_test.xml']\n",
+    "all_results = {}\n",
+    "\n",
+    "\n",
+    "for lems_file in lems_files:\n",
+    "    \n",
+    "    all_results[lems_file] = pynml.run_lems_with_jneuroml(lems_file, nogui=True, load_saved_data=True)\n",
+    "    print(\"Finished running LEMS file: %s\"%lems_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "pyNeuroML >>> 13:46:30 - INFO - Generating plot: Conductances generated from LEMS_Gfluct_test.xml\n",
+      "pyNeuroML >>> 13:46:30 - INFO - Generating plot: Histogram of conductances from LEMS_Gfluct_test.xml\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reloaded data: dict_keys(['t', 'Pop0/0/IF_curr_exp/v', 'Pop0/1/IF_curr_exp/v', 'Pop0/2/IF_curr_exp/v', 'Pop0/3/IF_curr_exp/v', 'Pop0/4/IF_curr_exp/v', 'Pop0/0/IF_curr_exp/synapses:both:0/i', 'Pop0/0/IF_curr_exp/synapses:both:0/g_e', 'Pop0/0/IF_curr_exp/synapses:both:0/g_e1', 'Pop0/0/IF_curr_exp/synapses:both:0/g_i', 'Pop0/0/IF_curr_exp/synapses:both:0/g_i1'])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dt = 0.05\n",
+    "data_start = int(1e3/dt)\n",
+    "data_end = int(2e3/dt)\n",
+    "\n",
+    "for lems_file in lems_files:\n",
+    "\n",
+    "    results = all_results[lems_file]\n",
+    "\n",
+    "    print(\"Reloaded data: %s\"%results.keys())\n",
+    "\n",
+    "    ax = pynml.generate_plot([results['t'][data_start:data_end],results['t'][data_start:data_end]],                        \n",
+    "                             [numpy.array(results['Pop0/0/IF_curr_exp/synapses:both:0/g_e'][data_start:data_end])*1e6,\n",
+    "                              numpy.array(results['Pop0/0/IF_curr_exp/synapses:both:0/g_i'][data_start:data_end])*1e6,\n",
+    "                              ],                  \n",
+    "                             \"Conductances generated from %s\"%lems_file,                 \n",
+    "                             xaxis = 'Time (s)',          \n",
+    "                             yaxis = 'g (uS)',\n",
+    "                             labels=['g_E','g_I'],\n",
+    "                             title_above_plot=True,\n",
+    "                             show_plot_already=False,\n",
+    "                             linewidths=[0.5, 0.5],\n",
+    "                             xlim=[1,2])\n",
+    "    \n",
+    "    bins=\"auto\"\n",
+    "\n",
+    "    hist1, edges1 = numpy.histogram(numpy.array(results['Pop0/0/IF_curr_exp/synapses:both:0/g_e'][data_start:data_end])*1e6,bins=bins)\n",
+    "    mid1 = [e +(edges1[1]-edges1[0])/2 for e in edges1[:-1]]\n",
+    "\n",
+    "    hist2, edges2 = numpy.histogram(numpy.array(results['Pop0/0/IF_curr_exp/synapses:both:0/g_i'][data_start:data_end])*1e6,bins=bins)\n",
+    "    mid2 = [e +(edges2[1]-edges2[0])/2 for e in edges2[:-1]]\n",
+    "\n",
+    "    xs = [mid1,mid2]\n",
+    "    ys = [hist1, hist2]\n",
+    "    markers = ['o','o']\n",
+    "    labels = ['g_E','g_I']\n",
+    "    \n",
+    "    \n",
+    "    ax.legend()\n",
+    "    ax = pynml.generate_plot(xs,                        \n",
+    "                             ys,                  \n",
+    "                             \"Histogram of conductances from %s\"%lems_file,                 \n",
+    "                             xaxis = 'Conductances (uS)',          \n",
+    "                             yaxis = 'Density',\n",
+    "                             title_above_plot=True,\n",
+    "                             markers = markers,\n",
+    "                             show_plot_already=False,\n",
+    "                             labels=labels,\n",
+    "                             )\n",
+    "        \n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}