Merge pull request #339 from Neurosim-lab/development

PR from development to master - VERSION 0.7.9

CHANGES.md

@@ -1,3 +1,40 @@
+# Version 0.7.9
+
+- Extended metadata structure to interact with NetPyNE-UI
+
+- Added preliminary data structures to support NEURON RxD
+
+- Added plot RxD concentration to analysis
+
+- Added netParams.correctBorder to compensate distance-dependent connectivity border effect
+
+- Added option to run jobs directly on multiple mpi cores via Batch class 
+
+- Added option for custom text in PBS or SLURM scripts to submit batch jobs
+
+- Added option to filter LFP signal before ploting PSD
+
+- Convert 'gid' to actual cell gid when used in point process params (useful for random seeds)
+
+- Relaxed pyneuroml requirement so just shows a warning message 
+
+- Preliminary function to save conns distributedly using HDF5
+
+- Added function to validate string-based expressions
+
+- Fixed bug: removed cell.\_segCoords and pop.\_morphSegCoords before saving to file
+
+- Fixed rasterPlot pop label automated spacing
+
+- Fixed bug: removed pandas requirement from LFP electrode module
+
+- Fixed bug: updated rcParams text.fontsize to font.size
+
+- Fixed bug: missing update of list-based loc in connList connections
+
+- Fixed bug: delete sections after import cell only if section exists
+
+
 # Version 0.7.8
 
 - Added netParams.rotateCellsRandomly option to rotate cells randomly around y-axis

doc/source/index.rst

@@ -8,6 +8,8 @@ Welcome to NetPyNE's documentation!
 
 NetPyNE is a python package to facilitate the development, parallel simulation and analysis of biological neuronal networks using the NEURON simulator.
 
+Check out our new `NetPyNE GUI teaser video <http://www.youtube.com/watch?v=Y8Aow9zwu3E>`_! The GUI will be released July 2018, ready for our `Tutorial at CNS18 <http://www.cnsorg.org/cns-2018-tutorials#T2>`_! 
+
 Join our `NetPyNE mailing list <https://groups.google.com/forum/#!forum/netpyne-mailing>`_ to receive updates on version releases and other major announcements.
 
 .. image:: figs/schematic.png

doc/source/overview.rst

@@ -9,13 +9,13 @@ Although NEURON already enables multiscale simulation ranging from the molecular
 
 For a more detailed overview of NetPyNE see:
 
-- `SLIDES for Computational Neuroscience conference CNS17 <http://neurosimlab.org/salvadord/netpyne.pdf>`_
+- `SLIDES for Computational Neuroscience conference CNS17 <http://it.neurosim.downstate.edu/salvadord/netpyne.pdf>`_
 
-- `POSTER for Computational Neuroscience conference CNS16 <http://neurosimlab.org/salvadord/CNS16_poster.pdf>`_ 
+- `POSTER for Computational Neuroscience conference CNS16 <http://it.neurosim.downstate.edu/salvadord/CNS16_poster.pdf>`_ 
 
 |
 
-.. image:: figs/overview.png
+.. image:: figs/netpyne_gui.png
 	:width: 70%	
 	:align: center
 
@@ -33,7 +33,7 @@ NetPyNE has been used to develop a variety of multiscale models: primary motor c
 NetPyNE structure
 -----------------
 
-.. image:: figs/structure.png
+.. image:: figs/schematic.png
 	:width: 80%	
 	:align: center
 

doc/source/reference.rst

@@ -903,7 +903,7 @@ Analysis-related functions
     Plot LFP / extracellular electrode recordings (time-resolved, power spectral density, time-frequency and 3D locations)
     
     - *electrodes*:: List of electrodes to include; 'avg'=avg of all electrodes; 'all'=each electrode separately (['avg', 'all', 0, 1, ...])
-    - *plots*: list of plot types to show (['timeSeries', 'PSD', 'timeFreq', 'locations']) 
+    - *plots*: list of plot types to show (['timeSeries', 'PSD', 'spectrogram', 'locations']) 
     - *timeRange*: Time range of spikes shown; if None shows all ([start:stop])
     - *NFFT*: Number of data points used in each block for the PSD and time-freq FFT (int, power of 2)
     - *noverlap*: Number of points of overlap between segments for PSD and time-freq (int, < nperseg)
@@ -1348,4 +1348,4 @@ Data saved to file
 * simConfig
 * netParams
 * net
-* simData
+* simData

examples/HHTut/HHTut.py

@@ -83,7 +83,3 @@
 simConfig.analysis['plotTraces'] = {'include': [2]}  # Plot raster
 simConfig.analysis['plot2Dnet'] = True  # Plot 2D net cells and connections
 
-simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(netParams.sizeY/5, netParams.sizeY, netParams.sizeY/5)]
-simConfig.analysis['plotLFP'] = True
-
-

examples/HybridTut/HybridTut.py

@@ -113,6 +113,6 @@
 
 # Analysis and plotting 
 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
+simConfig.analysis['plotTraces'] = {'include': [1,51]} # plot recorded traces for this list of cells
+simConfig.analysis['plotRatePSD'] = {'include': ['allCells', 'PYR_HH', 'PYR_Izhi'], 'smooth': 10} # plot recorded traces for this list of cells
 

examples/sandbox/sandbox.py

@@ -43,12 +43,12 @@
   'sec': 'soma'}                     # synaptic mechanism 
 
 
-# All->I; apical dendrites (no sCRACM)
-netParams.subConnParams['All->I'] = {
-  'preConds': {'pop': ['input','input2', 'I2', 'I3']}, 
-  'postConds': {'cellType': ['SOM', 'PV']},  
-  'sec': ['soma','dend'],
-  'density': 'uniform'} 
+# # All->I; apical dendrites (no sCRACM)
+# netParams.subConnParams['All->I'] = {
+#   'preConds': {'pop': ['input','input2', 'I2', 'I3']}, 
+#   'postConds': {'cellType': ['SOM', 'PV']},  
+#   'sec': ['soma','dend'],
+#   'density': 'uniform'} 
 
 
 # Simulation configuration
@@ -60,13 +60,14 @@
 simConfig.filename = 'net_lfp'   # Set file output name
 simConfig.printSynsAfterRule = True
 simConfig.recordTraces ={'V': {'sec': 'soma', 'loc': 0.5, 'var':'v'}}
+simConfig.saveJson=1
 
 lfp=0
 if lfp:
   #simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'plots': ['timeSeries'], 'NFFT': 256*2, 'noverlap': 128*2, 'nperseg': 132*2, 'saveFig': True} 
   simConfig.recordLFP = [[10,10,10]]
 
-simConfig.analysis['plotRaster'] = {'popRates':1, 'orderBy': ['pop','y'], 'orderInverse': True, 'saveFig':True, 'figSize': (9,3)}      # Plot a raster
+simConfig.analysis['plotRaster'] = {'popRates':1, 'orderBy': ['pop','y'], 'labels':'overlay', 'orderInverse': 0, 'saveFig':True, 'figSize': (9,3)}      # Plot a raster
 #simConfig.analysis['plotTraces'] ={'include':[0]}
 #simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'NFFT': 256*20, 'noverlap': 128*20, 'nperseg': 132*20, 'saveFig': True} 
 #simConfig.analysis['plotSpikeStats'] = {'include': ['E2', 'E4', ['E2', 'E4']] , 'stats': ['rate'], 'graphType': 'histogram', 'figSize': (10,6)}
@@ -79,15 +80,15 @@
     netParams = netParams)          # create network object and set cfg and net params
 sim.net.createPops()                    # instantiate network populations
 sim.net.createCells()                   # instantiate network cells based on defined populations
-
 sim.net.addStims()              # add network stimulation
-#sim.cfg.createPyStruct = 0
 sim.net.connectCells()                  # create connections between cells based on params
 sim.setupRecording()                    # setup variables to record for each cell (spikes, V traces, etc)
-#sim.net.defineCellShapes()
 sim.runSim()                            # run parallel Neuron simulation  
+#sim.distributedSaveHDF5()
 sim.gatherData()                        # gather spiking data and cell info from each node
 sim.saveData()                          # save params, cell info and sim output to file (pickle,mat,txt,etc)#
 sim.analysis.plotData()               # plot spike raster etc
 
-
+#conns, connFormat = sim.loadHDF5(sim.cfg.filename+'.h5')
+#print len(conns)
+#print sim.timingData

netpyne/__init__.py

@@ -1,3 +1,3 @@
 
-__version__ = '0.7.8' 
+__version__ = '0.7.9' 
 __gui__ = True  # global option to enable/disable graphics