Merge pull request #252 from Neurosim-lab/development

PR from development to master - VERSION 0.7.3
suny-downstate-medical-center · Aug 31, 2017 · 9ff475f · 9ff475f
2 parents 078e708 + 226d8aa
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diff --git a/CHANGES.md b/CHANGES.md
@@ -1,3 +1,20 @@
+# Version 0.7.3
+
+- Option to create section lists based on y displacement from soma (addCellParamsSecList)
+
+- Converted popParams, cellParams etc to object of their own class and added method to set param
+
+- Added 'disynapticBias' option to increase probability of B->C conns if A->B and A->C exist 
+
+- Added function analysis.calculateDisynaptic() to count number of disynaptic conns (A->B, B->C and A->C)
+
+- Added 1 second between batch job submission to avoid saturating scheduler
+
+- Fixed bug: init randomizer so params with string-based random func are independent of cores
+
+- Fixed bug for newer pyplot versions: replace 'linewidths' with 'lw'
+
+
 # Version 0.7.2
 
 - Improved NeuroML importing/exporting

diff --git a/doc/source/reference.rst b/doc/source/reference.rst
@@ -864,17 +864,34 @@ Analysis-related functions
     - Returns figure handles
 
 
-* **plotShape** (showSyns = True, include = [], style = '.', siz=10, figSize = (10,8), saveData = None, saveFig = None, showFig = True): 
+* **plotShape** (includePost = ['all'], includePre = ['all'], showSyns = False, synStyle = '.', synSiz=3, dist=0.6, cvar=None, cvals=None, iv=False, ivprops=None, includeAxon=True, figSize = (10,8), saveData = None, saveFig = None, showFig = True): 
     
-    Plot 3D cell shape using NEURON Interview PlotShape
+    Plot 3D cell shape using Matplotlib or NEURON Interviews 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)
+       - *includePre*: List of presynaptic cells to consider when plotting connections (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])])
+        - *includePost*: List of cells to show shape of (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])])
+        - *synStyle*: Style of marker to show synapses (Matplotlib markers) 
+        - *dist*: 3D distance (like zoom)  
+        - *synSize*: Size of marker to show synapses 
+        - *cvar*: Variable to represent in shape plot ('numSyns'|'weightNorm')
+        - *cvals*: List of values to represent in shape plot; must be same as num segments (list of size num segments; )
+        - *iv*: Use NEURON Interviews (instead of matplotlib) to show shape plot (True|False)
+        - *ivprops*: Dict of properties to plot using Interviews (dict)
+        - *includeAxon*: Include axon in shape plot (True|False)
+        - *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; 
+            if set to True uses filename from simConfig (None|True|'fileName')
+        - *saveFig*: File name where to save the figure;
+            if set to True uses filename from simConfig (None|True|'fileName')
+        - *showFig*: Whether to show the figure or not (True|False)
+
+        - Returns figure handles
+
+    Examples of plotShape():
+
+    	- cfg.analysis['plotShape'] = {'includePre': ['all'], 'includePost': [('PT5B',100)], 'cvar':'numSyns','saveFig': True, 'showFig': False, 'includeAxon': False}
 
-    - Returns figure handles
 
 
 * **analysis.plotConn** (include = ['all'], feature = 'strength', orderBy = 'gid', figSize = (10,10), groupBy = 'pop', saveData = None, saveFig = None, showFig = True)

diff --git a/examples/sandbox/sandbox.py b/examples/sandbox/sandbox.py
@@ -1,103 +1,100 @@
 """
-params.py 
-
-netParams is a dict containing a set of network parameters using a standardized structure
-
-simConfig is a dict containing a set of simulation configurations using a standardized structure
-
-Contributors: [email protected]
 """
 
-from netpyne import specs, sim
-
-netParams = specs.NetParams()   # object of class NetParams to store the network parameters
-simConfig = specs.SimConfig()   # object of class SimConfig to store the simulation configuration
+from netpyne import specs
+from netpyne import sim  # import netpyne sim module
 
 
-###############################################################################
-#
-# MPI HH TUTORIAL PARAMS
-#
-###############################################################################
+netParams = specs.NetParams()   # object of class NetParams to store the network parameters
+cfg = specs.SimConfig()   # object of class SimConfig to store the simulation configuration
 
 ###############################################################################
 # NETWORK PARAMETERS
 ###############################################################################
 
 # Population parameters
-netParams.popParams['PYR'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 5} # add dict with params for this pop 
-netParams.popParams['PYR2'] = {'cellModel': 'HH', 'cellType': 'PYR2', 'numCells': 5} # add dict with params for this pop 
-
+netParams.popParams['ExcPop'] = {'cellModel': 'HH', 'cellType': 'Exc','numCells': 50, 'yRange': [300, 500]} # add dict with params for this pop 
+netParams.popParams['InhPop'] = {'cellModel': 'HH', 'cellType': 'Inh','numCells': 50, 'yRange': [600, 800]} # add dict with params for this pop 
 
-# Cell parameters
-## PYR cell properties
-cellRule = {'conds': {'cellModel': 'HH', 'cellType': 'PYR'},  'secs': {}}   # cell rule dict
-cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}                                                        # soma params dict
-cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}                                   # soma geometry
-cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}          # soma hh mechanism
-cellRule['secs']['soma']['vinit'] = -71
-netParams.cellParams['PYR'] = cellRule                                                  # add dict to list of cell params
 
+## Cell property rules
+cellRule = {'conds': {'cellType': 'Exc'},  'secs': {}}  # cell rule dict
+cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}                              # soma params dict
+cellRule['secs']['soma']['geom'] = {'diam': 15, 'L': 14, 'Ra': 120.0}                   # soma geometry
+cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}      # soma hh mechanism
+netParams.cellParams['Erule'] = cellRule                          # add dict to list of cell params
 
-cellRule = {'conds': {'cellModel': 'HH', 'cellType': 'PYR2'},  'secs': {}}   # cell rule dict
-cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}                                                        # soma params dict
-cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}                                   # soma geometry
-cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}          # soma hh mechanism
-cellRule['secs']['soma']['vinit'] = -71
-netParams.cellParams['PYR2'] = cellRule                                                  # add dict to list of cell params
+cellRule = {'conds': {'cellType': 'Inh'},  'secs': {}}  # cell rule dict
+cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}                              # soma params dict
+cellRule['secs']['soma']['geom'] = {'diam': 10.0, 'L': 9.0, 'Ra': 110.0}                  # soma geometry
+cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}      # soma hh mechanism
+netParams.cellParams['Irule'] = cellRule                          # add dict to list of cell params
 
 # Synaptic mechanism parameters
-netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 1.0, 'e': 0}
+netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0}  # NMDA synaptic mechanism
+netParams.synMechParams['GABA'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75}  # GABA synaptic mechanism
 
 
 # Stimulation parameters
-netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5, 'start': 1}
-netParams.stimSourceParams['bkg2'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5, 'start': 1}
-
-netParams.stimTargetParams['bkg->PYR1'] = {'source': 'bkg', 'conds': {'pop': 'PYR'}, 'weight': 0.1, 'delay': 'uniform(1,5)'}
-
-
-# Connectivity parameters
-netParams.connParams['PYR->PYR'] = {
-    'preConds': {'pop': 'PYR'}, 'postConds': {'pop': ['PYR','PYR2']},
-    'weight': 0.002,                    # weight of each connection
-    'delay': '0.2+normal(13.0,1.4)',     # delay min=0.2, mean=13.0, var = 1.4
-    'threshold': 10,                    # threshold
-    'convergence': 'uniform(1,15)'}    # convergence (num presyn targeting postsyn) is uniformly distributed between 1 and 15
-
-
-###############################################################################
+#netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5, 'start': 1000, 'stop': 2000}
+netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'interval': 10, 'noise': 0.5, 'start': 100,'number':15}
+netParams.stimTargetParams['bkg->ExcPopType'] = {'source': 'bkg', 'conds': {'popLabel': 'ExcPop'}, 'weight': 0.01, 'delay': 'uniform(1,5)', 'synMech': 'AMPA'}
+
+
+# # Connectivity parameters
+netParams.connParams['ExcPop->ExcPop'] = {
+    'preConds': {'popLabel': 'ExcPop'},                                         # presyn: PYR_E
+    'postConds': {'popLabel': 'ExcPop'},                                        # postsyn: PYR_E 
+    'probability': 'max(0.1, normal(0.2,1))',
+    'weight': 0.008,                                                        # weight of each connection
+    'delay': 0.5, #'max(1, normal(10,2))',                                         # gaussian distributed transmission delay (ms)
+    'synMech': 'AMPA'}                                                      # target synaptic mechanism 
+
+# netParams.connParams['ExcPop->InhPop'] = {
+#     'preConds': {'popLabel': 'ExcPop'},                                         # presyn: PYR_E
+#     'postConds': {'popLabel': 'InhPop'},                                        # postsyn: PYR_I 
+#     'probability': 0.1,                                                     # fixed probability
+#     'weight': 0.003,                                                        # weight of each connection
+#     'delay': 'max(1, normal(10,2))',                                         # gaussian distributed transmission delay (ms)
+#     'synMech': 'AMPA'}                                                      # target synaptic mechanism 
+
+# netParams.connParams['InhPop->ExcPop'] = {
+#     'preConds': {'popLabel': 'InhPop'},                                         # presyn: PYR_I
+#     'postConds': {'popLabel': 'ExcPop'},                                        # postsyn: PYR_E 
+#     'probability': 0.1,                                                     # fixed probability
+#     'weight': 0.006,                                                        # weight of each connection
+#     'delay': 'max(1, normal(10,2))',                                         # gaussian distributed transmission delay (ms) 
+#     'synMech': 'GABA'}                                                      # target synaptic mechanism 
+
+##############################################################################
 # SIMULATION PARAMETERS
 ###############################################################################
 
 # Simulation parameters
-simConfig.duration = 1*1e3 # Duration of the simulation, in ms
-simConfig.dt = 'a' # Internal integration timestep to use
-simConfig.seeds = {'con': 1, 'stim': 1, 'loc': 1} # Seeds for randomizers (connectivity, input stimulation and cell locations)
-simConfig.createNEURONObj = 1  # create HOC objects when instantiating network
-simConfig.createPyStruct = 1  # create Python structure (simulator-independent) when instantiating network
-simConfig.verbose = False  # show detailed messages 
-
-simConfig.checkErrors = 0
+cfg.duration = 2*1e3 # Duration of the simulation, in ms
+cfg.dt = 0.025 # Internal integration timestep to use
+cfg.seeds = {'conn': 1, 'stim': 1, 'loc': 1} # Seeds for randomizers (connectivity, input stimulation and cell locations)
+cfg.createNEURONObj = 1  # create HOC objects when instantiating network
+cfg.createPyStruct = 1  # create Python structure (simulator-independent) when instantiating network
+cfg.verbose = False  # show detailed messages 
 
 # Recording 
-simConfig.recordCells = []  # which cells to record from
-simConfig.recordTraces = {'Vsoma':{'se':'soma','loc':0.5,'var':'v','conds': {'cellType': 'PYR2'}}}
-simConfig.recordStim = True  # record spikes of cell stims
-simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc)
+cfg.recordCells = []  # which cells to record from
+cfg.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}}
+cfg.recordStim = True  # record spikes of cell stims
+cfg.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc)
 
 # Saving
-simConfig.filename = 'HHTut'  # Set file output name
-simConfig.saveFileStep = 1000 # step size in ms to save data to disk
-simConfig.savePickle = False # Whether or not to write spikes etc. to a .mat file
-simConfig.saveMat = True
+cfg.filename = 'FirstExampleOscill'  # Set file output name
+cfg.saveFileStep = 1000 # step size in ms to save data to disk
+cfg.savePickle = True # Whether or not to write spikes etc. to a .mat file
 
 # Analysis and plotting 
-simConfig.analysis['plotRaster'] = {'bla':1}
-#simConfig.analysis['plotTraces'] = {'include': ['all'], 'oneFigPer':'trace'}
-
-sim.createSimulateAnalyze()
-
-#data=sim.loadSimData('HHTut.mat')
+# cfg.analysis['plotRaster'] = True  # Plot raster
+# cfg.analysis['plotTraces'] = {'include': [('ExcPop',10), ('InhPop',10)]}  # Plot raster
+# # cfg.analysis['plot2Dnet'] = True  # Plot 2D net cells and connections
+# # cfg.analysis['plotConn'] = True
 
 
+sim.createSimulateAnalyze(netParams = netParams, simConfig = cfg)
+import pylab; pylab.show()  # this line is only necessary in certain systems where figures appear empty
diff --git a/netpyne/__init__.py b/netpyne/__init__.py
@@ -1,3 +1,3 @@
 
-__version__ = '0.7.2' 
+__version__ = '0.7.3' 
 __gui__ = True  # global option to enable/disable graphics
diff --git a/netpyne/analysis.py b/netpyne/analysis.py
@@ -350,7 +350,7 @@ def plotRaster (include = ['allCells'], timeRange = None, maxSpikes = 1e8, order
         gs = gridspec.GridSpec(2, 1,height_ratios=[2,1])
         ax1=plt.subplot(gs[0])
 
-    ax1.scatter(spkts, spkinds, 10, linewidths=lw, marker=marker, color = spkgidColors) # Create raster  
+    ax1.scatter(spkts, spkinds, 10, lw=lw, marker=marker, color = spkgidColors) # Create raster  
     ax1.set_xlim(timeRange)
 
     # Plot stats
@@ -907,7 +907,7 @@ def invertDictMapping(d):
 ######################################################################################################################################################
 ## Plot cell shape
 ######################################################################################################################################################
-def plotShape (showSyns = False, includePost = ['all'], includePre = ['all'], synStyle = '.', synSiz=3, dist=0.6, cvar=None, cvals=None, iv=False, ivprops=None,
+def plotShape (includePost = ['all'], includePre = ['all'], showSyns = False, synStyle = '.', synSiz=3, dist=0.6, cvar=None, cvals=None, iv=False, ivprops=None,
     includeAxon=True, figSize = (10,8), saveData = None, saveFig = None, showFig = True): 
     ''' 
     Plot 3D cell shape using NEURON Interview PlotShape
@@ -1579,6 +1579,32 @@ def plot2Dnet (include = ['allCells'], figSize = (12,12), view = 'xy', showConns
 
     return fig
 
+######################################################################################################################################################
+## Calculate number of disynaptic connections
+###################################################################################################################################################### 
+def calculateDisynaptic(includePost = ['allCells'], includePre = ['allCells'], includePrePre = ['allCells']):
+    numDis = 0
+    totCon = 0
+
+    _, cellsPreGids, _ =  getCellsInclude(includePre)
+    _, cellsPrePreGids, _ = getCellsInclude(includePrePre)
+    cellsPost, _, _ = getCellsInclude(includePost)
+
+
+    for postCell in cellsPost:
+        preGidsAll = [conn['preGid'] for conn in postCell['conns'] if isinstance(conn['preGid'], Number) and conn['preGid'] in cellsPreGids+cellsPrePreGids]
+        preGids = [gid for gid in preGidsAll if gid in cellsPreGids]
+        for preGid in preGids:
+            preCell = sim.net.allCells[preGid]
+            prePreGids = [conn['preGid'] for conn in preCell['conns'] if conn['preGid'] in cellsPrePreGids]
+            totCon += 1
+            if not set(prePreGids).isdisjoint(preGidsAll):
+                numDis += 1
+    print '  Total disynaptic connections: %d / %d (%.2f%%)' % (numDis, totCon, float(numDis)/float(totCon)*100)
+    sim.allSimData['disynConns'] = numDis
+
+    return numDis
+
 
 ######################################################################################################################################################
 ## Calculate normalized transfer entropy

diff --git a/netpyne/batch.py b/netpyne/batch.py
@@ -242,7 +242,7 @@ def run(self):
                             print jobString+'\n'
                             input.close()
 
-                                                # hpc torque job submission
+                        # hpc torque job submission
                         elif self.runCfg.get('type',None) == 'hpc_slurm':
 
                             # read params or set defaults
@@ -299,7 +299,9 @@ def run(self):
                             print 'Submitting job ',jobName
                             # master/slave bulletin board schedulling of jobs
                             pc.submit(runJob, self.runCfg.get('script', 'init.py'), cfgSavePath, netParamsSavePath)
-
+
+                    sleep(1) # avoid saturating scheduler
+
             # wait for pc bulletin board jobs to finish
             try:
                 while pc.working():

diff --git a/netpyne/cell.py b/netpyne/cell.py
@@ -1269,7 +1269,7 @@ def createNEURONObj (self):
                 maxReproducibleSpks = 1e4  # num of rand spikes generated; only a subset is used; ensures reproducibility 
 
                 # fixed interval of duration (1 - noise)*interval 
-                fixedInterval = np.full(((1+0.5*noise)*sim.cfg.duration/interval), [(1.0-noise)*interval])  # generate 1+0.5*noise spikes to account for noise
+                fixedInterval = np.full(int(((1+0.5*noise)*sim.cfg.duration/interval)), [(1.0-noise)*interval])  # generate 1+0.5*noise spikes to account for noise
                 numSpks = len(fixedInterval)
 
                 # randomize the first spike so on average it occurs at start + noise*interval