Merge pull request #183 from Neurosim-lab/development

PR from development to master (version 0.6.4)

.gitignore

@@ -38,3 +38,4 @@ umac
 /examples/NeuroMLImport/LEMS_SimpleNet_nrn.py
 /examples/NeuroMLImport/LEMS_SimpleNet_pynn.py
 /examples/NeuroMLImport/LEMS_SimpleNet_pynn.py_main.json
+/nb-configuration.xml

.travis.yml

@@ -9,53 +9,56 @@ addons:
     - python-numpy 
     - python-scipy 
     - python-matplotlib 
+    - python-tk
     - python-sympy
     - python-tables 
 
 language: python
 python: 2.7
 
-env:
+virtualenv:
     system_site_packages: true  
-
-before_script:
-  - "export DISPLAY=:99.0"
-  - "sh -e /etc/init.d/xvfb start"
-  - sleep 3 # give xvfb some time to start
 
 install:
+    # Install OMV to facilitate installation of packages below
     - pip install git+https://github.com/OpenSourceBrain/osb-model-validation
-
     # Need to pre install NEURON so nrnivmodl can be run targeting mod files in a different directory
     - omv install NEURON
+    # Need to pre install pyNeuroML to test export to NeuroML 2
+    - omv install pyNeuroML
+    # Main install for NetPyNE
     - python setup.py install
     - export NEURON_HOME=/home/travis/neuron/nrn/x86_64/
-
 
 script:
-    - cp examples/HHTut/HHTut.py doc/source/code/
     - cd doc/source/code/
     - $NEURON_HOME/bin/nrnivmodl mod
-    - python tut1.py
-    - python tut2.py
-    - python tut3.py
-    - python tut5.py
-    - python tut6.py
-    - python tut_import.py
-    - cd ../../../examples
-    - python HHTut/HHTut_run.py
-    - python HybridTut/HybridTut_run.py
-    - cd M1
+    - python tut2.py -nogui
+    - python tut3.py -nogui
+    - python tut5.py -nogui
+    - python tut6.py -nogui
+    - python tut7.py -nogui
+    # - python tut_import.py
+    - cd ../../../examples/HHTut
+    - python HHTut_run.py -nogui
+    - cd ../HybridTut
     - $NEURON_HOME/bin/nrnivmodl
-    - python M1/M1_run.py
-    - cd ../RL_arm
+    - python HybridTut_run.py -nogui
+    - cd ../M1
     - $NEURON_HOME/bin/nrnivmodl
-    - python RL_arm/main.py
+    - python M1_run.py -nogui
+    - cd ../M1
+    - $NEURON_HOME/bin/nrnivmodl
+    - python M1_run.py -nogui
+
+    # - cd ../RL_arm
+    # - $NEURON_HOME/bin/nrnivmodl
+    # - python main.py
 
 
 notifications: 
     email: false
-    slack: neurosim:pj4DaRn3CrmH6hSRV0zBhfjS
+    #slack: neurosim:pj4DaRn3CrmH6hSRV0zBhfjS
 
 
 
@@ -201,4 +204,4 @@ notifications:
 #     - $HOME/nrn-7.3
 #     - $HOME/build/nest-2.6.0
 #     - $HOME/build/nrn-7.3
-#     - $HOME/.cache/pip
+#     - $HOME/.cache/pip

CHANGES.md

@@ -1,3 +1,43 @@
+# Version 0.6.4
+
+- Added analysis.nTE() function to calculate normalized transfer entropy (issue #42)
+
+- Added analysis.granger() function to calculate and plot Spectral Granger Causality (issue #42)
+
+- Added analysis.plotRatePSD() to plot firing rate power spectral density 
+
+- Added analysis.plotShape() to plot 3D morphology of cell and synapse locations
+
+- Added option to fix soma depth for subcellular distributions based on 1d and 2d maps
+
+- Added support for gap junction (electrical synapse) connections
+
+- Improved import of multicompartmental cells from NeuroML2
+
+- Added option to turn off graphics by adding -nogui from command line
+
+- Added option to skip batch sims if output file already exists
+
+- Added option to overlay pop labels and show avg rates to plotRaster() (issue #111)
+
+- All section now include argument cell=self to differentiate them 
+
+- Fixed travis script so checks all tuts and examples after each commit (issue #37)
+
+- Fixed bug positioning cells with 3d geom
+
+- Fixed bug in sim.clearAll by closing all figures instead of current (issue #168)
+
+- Fixed bug: cache_efficient was not being turned on
+
+- Fixed bug setting simConfig loaded from file
+
+- Fixed bug in plotRaster inverseOrder option
+
+- Fixed str vs basestring in analysis.py and simFuncs.py
+
+- Fixed bug due to not inserting ions in section
+
 # Version 0.6.3
 
 - Added cvode_active simConfig option to set variable time step (issue #116)

README.md

@@ -20,7 +20,7 @@ Several example model parameters are provided, including:
 Additional details of the modelling framework can be found here:
 
 * [NetPyNE Documentation](http://neurosimlab.org/netpyne/)
-* [SFN'15 poster](http://neurosimlab.org/salvadord/sfn15-sal-final.pdf)
+* [CNS'16 poster](http://neurosimlab.org/salvadord/CNS16_poster.pdf)
 * [slides](https://drive.google.com/file/d/0B8v-knmZRjhtVl9BOFY2bzlWSWs/view?usp=sharing)       
 
       

doc/source/code/tut7.py

@@ -103,8 +103,11 @@
                     'secs': {'soma': {'geom': {'L': 160}}}})
 
 sim.simulate()
-sim.analysis.plotRaster(syncLines=True)
-sim.analysis.plotTraces(include = [1])
+
+from netpyne import gui
+if gui:
+    sim.analysis.plotRaster(syncLines=True)
+    sim.analysis.plotTraces(include = [1])
 
 
 

doc/source/code/tut_import.py

@@ -91,12 +91,26 @@
 	'delay': 5,					# transmission delay (ms) 
 	'sec': 'soma'}				# section to connect to
 
+netParams.connParams['HH->izhi07a'] = {
+	'preConds': {'popLabel': 'HH_pop'}, 'postConds': {'popLabel': 'Izhi07a_pop'}, # background -> PYR (weight=0.1)
+	'connFunc': 'fullConn', 	# connectivity function (all-to-all)
+	'weight': 5, 			# synaptic weight 
+	'delay': 5,					# transmission delay (ms) 
+	'sec': 'soma'}		
+
+netParams.connParams['izhi07a->HH'] = {
+	'preConds': {'popLabel': 'Izhi07a_pop'}, 'postConds': {'popLabel': 'HH_pop'}, # background -> PYR (weight=0.1)
+	'connFunc': 'fullConn', 	# connectivity function (all-to-all)
+	'weight': 0.1, 			# synaptic weight 
+	'delay': 5,					# transmission delay (ms) 
+	'sec': 'soma'}		
+
 
 # Simulation options
 simConfig = specs.SimConfig()					# object of class SimConfig to store simulation configuration
 simConfig.duration = 1*1e3 			# Duration of the simulation, in ms
 simConfig.dt = 0.025 				# Internal integration timestep to use
-simConfig.verbose = False			# Show detailed messages 
+simConfig.verbose = 0			# Show detailed messages 
 simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record
 simConfig.recordStep = 1 			# Step size in ms to save data (eg. V traces, LFP, etc)
 simConfig.filename = 'model_output'  # Set file output name

doc/source/reference.rst

@@ -757,7 +757,7 @@ Misc/utilities:
 Analysis-related functions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-* **analysis.plotRaster** (include = ['allCells'], timeRange = None, maxSpikes = 1e8, orderBy = 'gid', orderInverse = False, spikeHist = None, spikeHistBin = 5, syncLines = False, figSize = (10,8), saveData = None, saveFig = None, showFig = True) 
+* **analysis.plotRaster** (include = ['allCells'], timeRange = None, maxSpikes = 1e8, orderBy = 'gid', orderInverse = False, labels = 'legend', popRates = False, spikeHist = None, spikeHistBin = 5, syncLines = False, figSize = (10,8), saveData = None, saveFig = None, showFig = True)
     
     Plot raster (spikes over time) of network cells. Optional arguments:
 
@@ -766,6 +766,8 @@ Analysis-related functions
     - *maxSpikes*: maximum number of spikes that will be plotted (int)
     - *orderBy*: Unique numeric cell property to order y-axis by, e.g. 'gid', 'ynorm', 'y' ('gid'|'y'|'ynorm'|...)
     - *orderInverse*: Invert the y-axis order (True|False)
+	- *labels*: Show population labels in a legend or overlayed on one side of raster ('legend'|'overlay'))
+    - *popRates*: Include population rates ('legend'|'overlay')
     - *spikeHist*: overlay line over raster showing spike histogram (spikes/bin) (None|'overlay'|'subplot')
     - *spikeHistBin*: Size of bin in ms to use for histogram  (int)
     - *syncLines*: calculate synchorny measure and plot vertical lines for each spike to evidence synchrony (True|False)
@@ -795,6 +797,23 @@ Analysis-related functions
     - Returns figure handle
     
 
+* **analysis.plotSpikePSD** (include = ['allCells', 'eachPop'], timeRange = None, binSize = 5, Fs = 200, overlay=True, yaxis = 'rate', figSize = (10,8), saveData = None, saveFig = None, showFig = True)
+     
+    Plot spikes power spectral density (PSD). Optional arguments:
+
+    - *include*: List of data series to include. Note: one line per item, not grouped (['all'|,'allCells'|,'allNetStims'|,120|,'L4'|,('L2', 56)|,('L5',[4,5,6])])
+    - *timeRange*: Time range of spikes shown; if None shows all ([start:stop])
+    - *binSize*: Size in ms of each bin (int)
+    - *Fs*: PSD sampling frequency used to calculate the Fourier frequencies (float)
+    - *overlay*: Whether to overlay the data lines or plot in separate subplots  (True|False)
+    - *figSize*: Size of figure ((width, height))
+    - *saveData*: File name where to save the final data used to generate the figure (None|'fileName')
+    - *saveFig*: File name where to save the figure (None|'fileName')
+    - *showFig*: Whether to show the figure or not (True|False)
+
+    - Returns figure handle and power array
+
+
 * **analysis.plotTraces** (include = [], timeRange = None, overlay = False, oneFigPer = 'cell', rerun = False, figSize = (10,8), saveData = None, saveFig = None, showFig = True)
     
     Plot recorded traces (specified in ``simConfig.recordTraces)`. Optional arguments: 
@@ -812,6 +831,19 @@ Analysis-related functions
     - Returns figure handles
 
 
+* **plotShape** (showSyns = True, include = [], style = '.', siz=10, figSize = (10,8), saveData = None, saveFig = None, showFig = True): 
+    
+    Plot 3D cell shape using NEURON Interview PlotShape
+    
+    - *showSyns*: Show synaptic connections in 3D (True|False) 
+    - *figSize*: Size of figure ((width, height))
+    - *saveData*: File name where to save the final data used to generate the figure (None|'fileName')
+    - *saveFig*: File name where to save the figure (None|'fileName')
+    - *showFig*: Whether to show the figure or not (True|False)
+
+    - Returns figure handles
+
+
 * **analysis.plotConn** (include = ['all'], feature = 'strength', orderBy = 'gid', figSize = (10,10), groupBy = 'pop', saveData = None, saveFig = None, showFig = True)
 
     Plot network connectivity. Optional arguments:
@@ -842,6 +874,48 @@ Analysis-related functions
     - Returns figure handles
 
 
+* **analysis.nTE** (cells1 = [], cells2 = [], spks1 = None, spks2 = None, timeRange = None, binSize = 20, numShuffle = 30)
+
+    Calculate normalized transfer entropy
+
+    - *cells1*: Subset of cells from which to obtain spike train 1 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])])
+    - *cells2*: Subset of cells from which to obtain spike train 2 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])])
+    - *spks1*: Spike train 1; list of spike times; if omitted then obtains spikes from cells1 (list)
+    - *spks2*: Spike train 2; list of spike times; if omitted then obtains spikes from cells2 (list)
+    - *timeRange*: Range of time to calculate nTE in ms ([min, max])
+    - *binSize*: Bin size used to convert spike times into histogram (int)
+    - *numShuffle*: Number of times to shuffle spike train 1 to calculate TEshuffled; note: nTE = (TE - TEShuffled)/H(X2F|X2P) (int)
+
+    - Returns nTE: normalized transfer entropy (float)
+
+
+* **analysis.granger** (cells1 = [], cells2 = [], spks1 = None, spks2 = None, label1 = 'spkTrain1', label2 = 'spkTrain2',
+	timeRange = None, binSize=5, plotFig = True, saveData = None, saveFig = None, showFig = True):
+  
+    Calculate and optionally plot Granger Causality 
+
+    - *cells1*: Subset of cells from which to obtain spike train 1 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])])
+    - cells2: Subset of cells from which to obtain spike train 2 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])])
+    - *spks1*: Spike train 1; list of spike times; if omitted then obtains spikes from cells1 (list)
+    - *spks2*: Spike train 2; list of spike times; if omitted then obtains spikes from cells2 (list)
+    - *label1*: Label for spike train 1 to use in plot (string)
+    - *label2*: Label for spike train 2 to use in plot (string)
+    - *timeRange*: Range of time to calculate nTE in ms  ([min, max])
+    - *binSize*: Bin size used to convert spike times into histogram 
+    - *plotFig*: Whether to plot a figure showing Granger Causality Fx2y and Fy2x (True|False)
+    - *saveData*: File name where to save the final data used to generate the figure (None|'fileName')
+    - *saveFig*: File name where to save the figure (None|'fileName')
+    - *showFig*: Whether to show the figure or not (True|False)
+
+    Returns: 
+    - *F*: list of freqs
+    - *Fx2y*: causality measure from x to y 
+    - *Fy2x*: causality from y to x 
+    - *Fxy*: instantaneous causality between x and y 
+    - *fig*: Figure handle 
+
+
+
 NOTE: The *include* argument can have the following values:
 	- 'all': all cells and netstims
 	- 'allCells': only all cells

doc/source/tutorial.rst

@@ -110,7 +110,7 @@ The image below illustrates this process:
 	:width: 50%
 	:align: center
 
-We will now create a new model file (call it ``tut2.py``) where we will specify from scratch all the network parameters. To create the structures that will hold the network parameters add the following code:
+We will now create a new model file (call it ``tut2.py``) where we will specify from scratch all the network parameters. To create the structures that will hold the network parameters add the following code::
 
 	from netpyne import specs, sim
 
@@ -129,7 +129,9 @@ First, we need to create some populations for our network, by adding items to th
 
 * ``cellModel`` - an attribute or tag that will be assigned to cells in this population, can later be used to set specific cell model implementation for cells with this tag. Eg. 'HH' (standard Hodkgin-Huxley type cell model) or 'Izhi2007b' (Izhikevich 2007 point neuron model). Cell models can be defined by the user or imported.
 
-We will start by creating 2 populations labeled ``S`` (sensory) and ``M`` (motor), with ``20`` cells each, of type ``PYR`` (pyramidal), and using ``HH`` cell model (standard compartmental Hodgkin-Huxley type cell). 
+We will start by creating 2 populations labeled ``S`` (sensory) and ``M`` (motor), with ``20`` cells each, of type ``PYR`` (pyramidal), and using ``HH`` cell model (standard compartmental Hodgkin-Huxley type cell).
+
+::
 
 	## Population parameters
 	netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': 20, 'cellModel': 'HH'} 

examples/HHTut/HHTut_run.py

@@ -1,4 +1,4 @@
-import HHTut  # import parameters file
+import HHTut  # import parameters file 
 from netpyne import sim  # import netpyne sim module
 
-sim.createSimulateAnalyze(netParams = HHTut.netParams, simConfig = HHTut.simConfig)  # create and simulate network
+sim.createSimulateAnalyze(netParams = HHTut.netParams, simConfig = HHTut.simConfig)  # create and simulate network

examples/HybridTut/HybridTut.py

@@ -122,6 +122,7 @@
 
 
 # Analysis and plotting 
-simConfig.analysis['plotRaster'] = True # Whether or not to plot a raster
-simConfig.analysis['plotTraces'] = {'include': [1,51]} # plot recorded traces for this list of cells
+simConfig.analysis['plotRaster'] = {'orderInverse': False} #True # Whether or not to plot a raster
+#simConfig.analysis['plotTraces'] = {'include': [1,51]} # plot recorded traces for this list of cells
+simConfig.analysis['plotRatePSD'] = {'include': ['allCells', 'PYR_HH', 'PYR_Izhi'], 'Fs': 200, 'smooth': 10} # plot recorded traces for this list of cells
 

examples/NeuroMLImport/LEMS_SimpleNet.xml

@@ -2,7 +2,7 @@
 
     <!-- 
 
-        This LEMS file has been automatically generated using PyNeuroML v0.2.0 (libNeuroML v0.2.20)
+        This LEMS file has been automatically generated using PyNeuroML v0.2.2 (libNeuroML v0.2.23)
 
      -->
 

examples/NeuroMLImport/SimpleNet.net.nml

@@ -1,9 +1,9 @@
 <neuroml xmlns="http://www.neuroml.org/schema/neuroml2"  xmlns:xs="http://www.w3.org/2001/XMLSchema" 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_v2beta5.xsd" id="SimpleNet">
     <notes>
 
-This NeuroML 2 file was generated by OpenCortex v0.0.6 using: 
-    libNeuroML v0.2.20
-    pyNeuroML v0.2.0
+This NeuroML 2 file was generated by OpenCortex v0.0.7 using: 
+    libNeuroML v0.2.23
+    pyNeuroML v0.2.2
 
     </notes>
     <property tag="Python random seed" value="1234"/>

examples/NeuroMLImport/poissonFiringSyn.mod

@@ -123,11 +123,11 @@ NET_RECEIVE(flag) {
         : This child is a synapse; defining weight
         weight = 1
 
-        : paramMappings: {syn0={g=syn0_g, tauDecay=syn0_tauDecay, waveformFactor=syn0_waveformFactor, A=syn0_A, B=syn0_B, erev=syn0_erev, gbase=syn0_gbase, peakTime=syn0_peakTime, tauRise=syn0_tauRise, i=syn0_i}, poissonFiringSyn={isi=isi, tsince=tsince, averageRate=averageRate, averageIsi=averageIsi, i=i}}
+        : paramMappings: {poissonFiringSyn={averageRate=averageRate, i=i, tsince=tsince, isi=isi, averageIsi=averageIsi}, syn0={A=syn0_A, tauRise=syn0_tauRise, gbase=syn0_gbase, erev=syn0_erev, B=syn0_B, peakTime=syn0_peakTime, g=syn0_g, i=syn0_i, tauDecay=syn0_tauDecay, waveformFactor=syn0_waveformFactor}}
     ?    state_discontinuity(syn0_A, syn0_A  + (weight *  syn0_waveformFactor ))
         syn0_A = syn0_A  + (weight *  syn0_waveformFactor )
 
-        : paramMappings: {syn0={g=syn0_g, tauDecay=syn0_tauDecay, waveformFactor=syn0_waveformFactor, A=syn0_A, B=syn0_B, erev=syn0_erev, gbase=syn0_gbase, peakTime=syn0_peakTime, tauRise=syn0_tauRise, i=syn0_i}, poissonFiringSyn={isi=isi, tsince=tsince, averageRate=averageRate, averageIsi=averageIsi, i=i}}
+        : paramMappings: {poissonFiringSyn={averageRate=averageRate, i=i, tsince=tsince, isi=isi, averageIsi=averageIsi}, syn0={A=syn0_A, tauRise=syn0_tauRise, gbase=syn0_gbase, erev=syn0_erev, B=syn0_B, peakTime=syn0_peakTime, g=syn0_g, i=syn0_i, tauDecay=syn0_tauDecay, waveformFactor=syn0_waveformFactor}}
     ?    state_discontinuity(syn0_B, syn0_B  + (weight *  syn0_waveformFactor ))
         syn0_B = syn0_B  + (weight *  syn0_waveformFactor )
 
@@ -156,8 +156,8 @@ PROCEDURE rates() {
     rate_tsince = 1 ? Note units of all quantities used here need to be consistent!
 
 
-    rate_syn0_B = - syn0_B  /  syn0_tauDecay ? Note units of all quantities used here need to be consistent!
     rate_syn0_A = - syn0_A  /  syn0_tauRise ? Note units of all quantities used here need to be consistent!
+    rate_syn0_B = - syn0_B  /  syn0_tauDecay ? Note units of all quantities used here need to be consistent!