Merge pull request #660 from suny-downstate-medical-center/development

PR from development to master - VERSION 1.0.1
suny-downstate-medical-center · Feb 18, 2022 · bfd631d · bfd631d
2 parents 4060887 + d8b7e94
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diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
@@ -1,13 +1,14 @@
 name: Tests
-on: [push]
+on: [push, pull_request]
 jobs:
   Run-Tests:
     runs-on: ${{ matrix.os }}
 
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python-version: [2.7, 3.7]
+        #python-version: [2.7, 3.7]
+        python-version: [3.7]
 
     steps:
       - name: Check out repository code

diff --git a/.gitignore b/.gitignore
@@ -51,4 +51,7 @@ venv/
 /examples/M1/*.png
 /examples/M1/*.pov
 /examples/HHTut/temp.json
-.vscode
+.vscode
+/examples/HHTut/HHTut_plot_2Dnet.png
+/examples/HHTut/HHTut_regen.py
+/examples/HHTut/HHTut_traces.png
diff --git a/CHANGES.md b/CHANGES.md
@@ -1,3 +1,70 @@
+# Version 1.0.1
+
+**New features**
+
+- Added new plotting module with custom plotters
+
+- Separated analysis.plotSpikeRaster into analysis.prepareRaster and plotting.plotRaster
+
+- Separated analysis.plotSpikeHist into analysis.prepareSpikeData, analysis.prepareSpikeHist, plotting.plotSpikeHist and plotting.plotSpikeFreq
+
+- Separated analysis.plotLFP into analysis.prepareLFP, analysis.preparePSD, analysis.prepareSpectrogram, plotting.plotLFPTimeSeries, plotting.plotLFPPSD, plotting.plotLFPSpectrogram, and plotting.plotLFPLocations
+
+- Mapped old analysis.plots onto new ones
+
+- Interfaced with LFPkit to enable recording/plotting of dipole current moments and EEG signals
+
+- Added Current Source Density (CSD) analysis and plots
+
+- Enabled selecting a subset of cells to save individual LFP signal from via cfg.saveLFPCells = [...] 
+
+- Added cfg.saveLFPPops to store LFP generated individually by each population
+
+- Function to return batch parameter combinations (used for GUI)
+
+- Added jupyter nb tutorials 
+
+- Added hippocampus CA3 model example
+
+- Updated SONATA importing
+
+- Improved distributed saving and interval saving (more robust)
+
+- Added mapping of netParams values within cfg (used for batch sims in GUI)
+
+- Extended Action tests to pull requests (not just push)
+
+
+**Bug fixes**
+
+- Fixed setting cfgMapping nested params when not string
+
+- Fixed defineCellShapes when sec['hObj'] is not a h.Section()
+
+- Fixed bug when using lognormal in string functions
+
+- Added rxdmath to avoid RxD error
+
+- Fixed bug in Tutorial 8 (batch)
+
+- When saving to JSON wait until file exists before returning (to avoid exiting before saving finished)
+
+
+# Version 1.0.0.2
+
+**New features**
+
+- Added function to get list of batch grid search parameter combinations 
+
+- Save output of mpi_bulletin batches to .run and .err files
+
+- Fixed bug in loading exotic stim param values
+
+- Fixed bug in loading where sim was required to have rank attribute
+
+- Added quotes in batch.py to avoid path white spaces issue
+
+
 # Version 1.0.0.2
 
 - Release for use with GUI
@@ -1472,3 +1539,5 @@ First version that was uploaded to pypi. Includes following features:
 	- Parameters/specifications
 	- Instantiated networks
 	- Simulation results
+
+- Distance-based redistribution of synapses (subConn)
diff --git a/doc/build.py b/doc/build.py
@@ -72,7 +72,7 @@
 shutil.rmtree('build', ignore_errors=True)
 
 # All .rst files but those listed here will be deleted during this process
-keep = ['about.rst', 'advanced.rst', 'index.rst', 'install.rst', 'reference.rst', 'tutorial.rst']
+keep = ['about.rst', 'advanced.rst', 'index.rst', 'install.rst', 'reference.rst', 'tutorial.rst', 'contrib.rst']
 
 print('Deleting old .rst files.')
 for file in os.listdir('source'):

diff --git a/doc/source/about.rst b/doc/source/about.rst
diff --git a/doc/source/code/HHCellFile.py b/doc/source/code/HHCellFile.py
@@ -55,21 +55,21 @@ def createSections(self):
 
     def defineGeometry(self):
         """Set the 3D geometry of the cell."""
-        self.soma.L = 18.8
-        self.soma.diam = 18.8
-        self.soma.Ra = 123.0
+        self.soma.L = 18.8 	    # length of the soma section in microns
+        self.soma.diam = 18.8 	# diameter of the soma section in microns
+        self.soma.Ra = 123.0 	# axial resistivity of the soma section in ohm-cm
 
-        self.dend.L = 200.0
-        self.dend.diam = 1.0
-        self.dend.Ra = 100.0
+        self.dend.L = 200.0 	# length of the dend section in microns
+        self.dend.diam = 1.0	# diameter of the dend section in microns
+        self.dend.Ra = 100.0 	# axial resistivity of the dend section in ohm-cm
 
     def defineBiophysics(self):
         """Assign the membrane properties across the cell."""
         # Insert active Hodgkin-Huxley current in the soma
         self.soma.insert('hh')
         self.soma.gnabar_hh = 0.12  # Sodium conductance in S/cm2
         self.soma.gkbar_hh = 0.036  # Potassium conductance in S/cm2
-        self.soma.gl_hh = 0.003    # Leak conductance in S/cm2
+        self.soma.gl_hh = 0.003     # Leak conductance in S/cm2
         self.soma.el_hh = -70       # Reversal potential in mV
 
         self.dend.insert('pas')

diff --git a/doc/source/code/tut2.py b/doc/source/code/tut2.py
@@ -52,5 +52,5 @@
 
 # import pylab; pylab.show()  # this line is only necessary in certain systems where figures appear empty
 
-# check model output
+# Check the model output: sim.checkOutput is used for testing purposes.  Please comment out the following line if you are exploring the tutorial.
 sim.checkOutput('tut2')
diff --git a/doc/source/code/tut3.py b/doc/source/code/tut3.py
@@ -64,5 +64,5 @@
 
 # import pylab; pylab.show()  # this line is only necessary in certain systems where figures appear empty
 
-# check model output
+# Check the model output: sim.checkOutput is used for testing purposes.  Please comment out the following line if you are exploring the tutorial.
 sim.checkOutput('tut3')
diff --git a/doc/source/code/tut4.py b/doc/source/code/tut4.py
@@ -77,5 +77,5 @@
 
 # import pylab; pylab.show()  # this line is only necessary in certain systems where figures appear empty
 
-# check model output
+# Check the model output: sim.checkOutput is used for testing purposes.  Please comment out the following line if you are exploring the tutorial.
 sim.checkOutput('tut4')
diff --git a/doc/source/code/tut5.py b/doc/source/code/tut5.py
@@ -80,5 +80,5 @@
 
 # import pylab; pylab.show()  # this line is only necessary in certain systems where figures appear empty
 
-# check model output
+# Check the model output: sim.checkOutput is used for testing purposes.  Please comment out the following line if you are exploring the tutorial.
 sim.checkOutput('tut5')
diff --git a/doc/source/code/tut6.py b/doc/source/code/tut6.py
@@ -50,5 +50,5 @@
 
 # import pylab; pylab.show()  # this line is only necessary in certain systems where figures appear empty
 
-# check model output
+# Check the model output: sim.checkOutput is used for testing purposes.  Please comment out the following line if you are exploring the tutorial.
 sim.checkOutput('tut6')
diff --git a/doc/source/code/tut7.py b/doc/source/code/tut7.py
@@ -100,5 +100,5 @@
     sim.analysis.plotRaster(syncLines=True)
     sim.analysis.plotTraces(include = [1])
 
-# check model output
+# Check the model output: sim.checkOutput is used for testing purposes.  Please comment out the following line if you are exploring the tutorial.
 sim.checkOutput('tut7')
diff --git a/doc/source/code/tut8_analysis.py b/doc/source/code/tut8_analysis.py
@@ -196,7 +196,7 @@ def plot2DRate(dataFolder, batchLabel, params, data, par1, par2, val, valLabel,
 # Function to read batch data and plot figure
 #--------------------------------------------------------------------
 def readPlot():
-    dataFolder = 'tauWeight_data/'
+    dataFolder = 'tut8_data/'
     batchLabel = 'tauWeight'
 
     params, data = readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None)

diff --git a/doc/source/code/tut_artif.py b/doc/source/code/tut_artif.py
@@ -14,59 +14,100 @@
 ###############################################################################
 # NETWORK PARAMETERS
 ###############################################################################
-
-# Population parameters
-netParams.popParams['PYR1'] = {'cellType': 'PYR', 'numCells': 100} # pop of HH cells
-netParams.popParams['artif1'] = {'cellType': 'artif_NetStim', 'numCells': 100}  # pop of NetStims
-netParams.popParams['artif2'] = {'cellType': 'artif_IntFire2', 'numCells': 100}  # pop of IntFire2
-netParams.popParams['artif3'] = {'cellType': 'artif_IntFire4', 'numCells': 100}  # pop of IntFire4
-netParams.popParams['artif4'] = {'cellType': 'artif_VecStim', 'numCells': 100}  # pop of Vecstims with 2 pulses
-
 # Cell parameters
 ## PYR cell properties
 cellParams = Dict()
 cellParams.secs.soma.geom = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}
 cellParams.secs.soma.mechs.hh = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}
 netParams.cellParams['PYR'] = cellParams
 
-netParams.cellParams['artif_NetStim'] = {'cellModel': 'NetStim', 'rate': 50, 'noise': 0.8, 'start': 1, 'seed': 2}  # pop of NetStims
-netParams.cellParams['artif_IntFire2'] = {'cellModel': 'IntFire2', 'ib': 0.0}  # pop of IntFire2
-netParams.cellParams['artif3_IntFire4'] = {'cellModel': 'IntFire4', 'taue': 1.0}  # pop of IntFire4
-netParams.cellParams['artif4_Vecstim'] = {'cellModel': 'VecStim', 'rate': 5, 'noise': 0.5, 'start': 50,
-    'pulses': [{'start': 200, 'end': 300, 'rate': 60, 'noise':0.2}, {'start': 500, 'end': 800, 'rate': 30, 'noise': 0.5}]}  # pop of Vecstims with 2 pulses
+## IntFire2 artificial cell
+netParams.cellParams['artif_IntFire2'] = {
+    'cellModel': 'IntFire2', 
+    'ib': 0.0}  
+
+## IntFire4 artificial cell
+netParams.cellParams['artif_IntFire4'] = {
+    'cellModel': 'IntFire4', 
+    'taue': 1.0}
+
+## NetStim artificial spike generator
+netParams.cellParams['artif_NetStim'] = {
+    'cellModel': 'NetStim'}
+
+## VecStim artificial spike generator
+netParams.cellParams['artif_VecStim'] = {
+    'cellModel': 'VecStim'}  # pop of Vecstims with 2 pulses
+
+
+# Population parameters
+netParams.popParams['PYR1'] = {
+    'cellType': 'PYR', 
+    'numCells': 100} # pop of HH cells
+
+netParams.popParams['pop_IntFire2'] = {
+    'cellType': 'artif_IntFire2', 
+    'numCells': 100}  # pop of IntFire2
+
+netParams.popParams['pop_IntFire4'] = {
+    'cellType': 'artif_IntFire4', 
+    'numCells': 100}  # pop of IntFire4
+
+netParams.popParams['pop_NetStim'] = {
+    'cellType': 'artif_NetStim', 
+    'numCells': 100,
+    'rate': 10, 
+    'noise': 0.8, 
+    'start': 1, 
+    'seed': 2}  # pop of NEtSims
+
+netParams.popParams['pop_VecStim'] = {
+    'cellType': 'artif_VecStim', 
+    'numCells': 100, 
+    'rate': 5, 
+    'noise': 0.5, 
+    'start': 50,
+    'pulses': [{'start': 200, 'end': 300, 'rate': 60, 'noise':0.2}, 
+               {'start': 500, 'end': 800, 'rate': 30, 'noise': 0.5}]}  # pop of Vecstims with 2 pulses
 
 
 # Synaptic mechanism parameters
 netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 1.0, 'e': 0}
 
 
-# Stimulation parameters
-netParams.stimSourceParams['background'] = {'type': 'NetStim', 'interval': 100, 'number': 1e5, 'start': 500, 'noise': 0.5}  # stim using NetStims after 500ms
-netParams.stimTargetParams['bkg->PYR1'] = {'source': 'background', 'conds': {'pop': 'PYR1'}, 'sec':'soma', 'loc': 0.5, 'weight': 0.5, 'delay': 1}
+# Connections
+netParams.connParams['NetStim->PYR1'] = {
+    'preConds': {'pop': 'pop_NetStim'}, 
+    'postConds': {'pop': 'PYR1'},
+    'convergence': 3,
+    'weight': 0.002,
+    'synMech': 'AMPA',
+    'delay': 'uniform(1,5)'}
 
 
-# Connections
-netParams.connParams['artif1->PYR1'] = {
-    'preConds': {'pop': 'artif1'}, 
+netParams.connParams['VecStim->PYR1'] = {
+    'preConds': {'pop': 'pop_VecStim'}, 
     'postConds': {'pop': 'PYR1'},
-    'convergence': 8,
+    'probability': 0.4,
     'weight': 0.005,
     'synMech': 'AMPA',
     'delay': 'uniform(1,5)'}
 
-netParams.connParams['PYR1->artif2'] = {
+netParams.connParams['PYR1->IntFire2'] = {
     'preConds': {'pop': 'PYR1'}, 
-    'postConds': {'pop': 'artif2'},
+    'postConds': {'pop': 'pop_IntFire2'},
     'probability': 0.2,
-    'weight': 0.2,
+    'weight': 0.1,
+    'synMech': 'AMPA',
     'delay': 'uniform(1,5)'}
 
-netParams.addConnParams('artif2->artif3',
-    {'preConds': {'pop': 'artif2'}, 
-    'postConds': {'pop': 'artif3'},
-    'divergence': 20,
-    'weight': 0.05,
-	'delay': 3})
+
+netParams.connParams['IntFire2->IntFire4'] = {
+    'preConds': {'pop': 'pop_IntFire2'}, 
+    'postConds': {'pop': 'pop_IntFire4'},
+    'probability': 0.1,
+    'weight': 0.2,
+    'delay': 'uniform(1,5)'}
 
 
 ###############################################################################
@@ -84,7 +125,7 @@
 simConfig.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}}
 
 # # Analysis and plotting
-simConfig.analysis['plotRaster'] = True
+simConfig.analysis['plotRaster'] = {'orderInverse': True}
 
 
 ###############################################################################

diff --git a/doc/source/code/tut_import.py b/doc/source/code/tut_import.py
@@ -182,5 +182,5 @@
 
 # import pylab; pylab.show()  # this line is only necessary in certain systems where figures appear empty
 
-# check model output
+# Check the model output: sim.checkOutput is used for testing purposes.  Please comment out the following line if you are exploring the tutorial.
 sim.checkOutput('tut_import')
diff --git a/doc/source/conf.py b/doc/source/conf.py
@@ -39,6 +39,11 @@
     'autodocsumm',
 ]
 
+
+# Add custom sections to Napoleon
+napoleon_custom_sections = [('NetPyNE Options', 'params_style'), ('Plot Options', 'params_style')]
+
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
@@ -60,9 +65,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.0.0.2'
+version = '1.0.1'
 # The full version, including alpha/beta/rc tags.
-release = '1.0.0.2'
+release = '1.0.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

diff --git a/doc/source/contrib.rst b/doc/source/contrib.rst
@@ -0,0 +1,16 @@
+Contribute ancillary code
+=======================================
+
+Ancillary code contributions will most typically be additional analysis and/or plotting code to be used with NetPyNE output files. This is separate from
+NetPyNE source code. Contribution to source code is also encouraged and is desribed `here <https://github.com/Neurosim-lab/netpyne/blob/development/CONTRIBUTING.md>`_. 
+Sometimes new analysis Python code may also require adding new output formats to NetPyNE so that both contributions may be made together.
+
+Other ancillary contributions might include new Tutorials for specific purposes or NEURON procedural code for augmenting the declarative code in a
+netParams file for complex parameter settings.
+
+Code should be hosted by the user on a publicly-accessible github page with complete documentation (and strong code commenting). Submissions should then be made in the `NetPyNE forum
+<https://groups.google.com/forum/#!forum/netpyne-forum>`_ with a brief description of the package for listing here.
+
+Contributed code list
+---------------------
+
diff --git a/doc/source/index.rst b/doc/source/index.rst
@@ -8,7 +8,7 @@ Welcome to the NetPyNE homepage!
 
 NetPyNE is an open-source Python package to facilitate the development, parallel simulation, analysis, and optimization of biological neuronal networks using the NEURON simulator. See our recent `publication in eLife <https://elifesciences.org/articles/44494>`_ for more details.
 
-To make NetPyNE more accessible, we have released a (beta version) graphical user interface (GUI).  The GUI is freely available at `www.netpyne.org/gui <http://www.netpyne.org/gui>`_ with documentation `here <https://github.com/Neurosim-lab/NetPyNE-UI/wiki>`_.
+To make NetPyNE more accessible, we have released a (beta version) graphical user interface (GUI).  The GUI is freely available at `gui.netpyne.org <http://gui.netpyne.org>`_ with documentation `here <https://github.com/Neurosim-lab/NetPyNE-UI/wiki>`_.
 
 For a detailed overview of NetPyNE, including an interactive tutorial utilizing the GUI, please see our `CNS*2020 <https://www.cnsorg.org/cns-2020>`_
 tutorial: **Building mechanistic multiscale models: from molecules to networks using NEURON and NetPyNE**.  The complete video is available `here <https://yale.zoom.us/rec/play/tMAkcrr8-jo3HNCU4wSDUaR8W427LaKs1HAW_aUPzkmzUSJRO1WnY7cXZep77NjtCLWqwFVD6Ya8aNTL?continueMode=true&_x_zm_rtaid=J63YcHJGT9CUEjhkklHizQ.1598553844678.e87215956b2d8a50ef33ec4d6479b3b2&_x_zm_rhtaid=388>`_ and slides are available `here <http://bit.ly/cns20-netpyne>`_.
@@ -17,6 +17,8 @@ Check out our latest tutorial: **Virtual Environments, Jupyter Notebooks, and Ne
 
 Join our `NetPyNE mailing list <https://groups.google.com/forum/#!forum/netpyne-mailing>`_ to receive updates on version releases and other major announcements.  Get your questions answered and participate in the NetPyNE community at the `NetPyNE forum <https://groups.google.com/forum/#!forum/netpyne-forum>`_ or the `NetPyNE section of the NEURON forum <https://www.neuron.yale.edu/phpBB/viewforum.php?f=45>`_.
 
+NetPyNE is available as a resource through `Neuroscience Gateway Portal (NSG) <https://www.nsgportal.org/>`_;  `Open Source Brain (OSB) <https://www.opensourcebrain.org/>`_ ; `Human Brain Project (HBP) EBRAINS <https://www.humanbrainproject.eu/en/hbp-platforms/ebrains/>`_; `NIH SPARC initiative <https://commonfund.nih.gov/Sparc/>`_.
+
 The NetPyNE source code is available on `GitHub <https://github.com/Neurosim-lab/netpyne>`_.