Source code for netpyne.analysis.spikes

Module for analysis of spiking-related results


from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import

from future import standard_library

from builtins import round
from builtins import open
from builtins import range

    to_unicode = unicode
except NameError:
    to_unicode = str
except NameError:
    basestring = str

import pandas as pd
import numpy as np
from numbers import Number
from .utils import exception, syncMeasure
from .tools import getInclude, getSpktSpkid
from .tools import saveData as saveFigData
from import add_scalebar
from ..specs import Dict

[docs]@exception def prepareSpikeData( include=['allCells'], sim=None, timeRange=None, maxSpikes=1e8, orderBy='gid', popRates=True, syncLines=True, saveData=False, fileName=None, fileDesc=None, fileType=None, fileDir=None, calculatePhase=False, **kwargs): """ Function to prepare data for creating spike-related plots """ print('Preparing spike data...') if not sim: from .. import sim # Replace 'eachPop' with list of pops if 'eachPop' in include: include.remove('eachPop') popLabels = [pop for pop in] for popLabel in popLabels: include.append(popLabel) # Select cells to include cells, cellGids, netStimLabels = getInclude(include) df = pd.DataFrame.from_records(cells) df = pd.concat([df.drop('tags', axis=1), pd.DataFrame.from_records(df['tags'].tolist())], axis=1) keep = ['pop', 'gid', 'conns'] # if orderBy property doesn't exist or is not numeric, use gid if isinstance(orderBy, basestring) and orderBy not in cells[0]['tags']: orderBy = 'gid' elif orderBy == 'pop': df['popInd'] = df['pop'].astype('category') df['popInd'].cat.set_categories(, inplace=True) orderBy='popInd' elif isinstance(orderBy, basestring) and not isinstance(cells[0]['tags'][orderBy], Number): orderBy = 'gid' if isinstance(orderBy, list): if 'pop' in orderBy: df['popInd'] = df['pop'].astype('category') df['popInd'].cat.set_categories(, inplace=True) orderBy[orderBy.index('pop')] = 'popInd' keep = keep + list(set(orderBy) - set(keep)) elif orderBy not in keep: keep.append(orderBy) df = df[keep] # preserves original ordering: popLabels = [pop for pop in if pop in df['pop'].unique()] if netStimLabels: popLabels.append('NetStims') if len(cellGids) > 0: try: sel, spkts, spkgids = getSpktSpkid(cellGids=[] if include == ['allCells'] else cellGids, timeRange=timeRange) # using [] is faster for all cells except: import sys print((sys.exc_info())) spkgids, spkts = [], [] sel = pd.DataFrame(columns=['spkt', 'spkid']) df.set_index('gid', inplace=True) # Order by if len(df) > 0: ylabelText = 'Cells (ordered by %s)'%(orderBy) df = df.sort_values(by=orderBy) sel['spkind'] = sel['spkid'].apply(df.index.get_loc) else: sel = pd.DataFrame(columns=['spkt', 'spkid', 'spkind']) ylabelText = '' # Add NetStim spikes numCellSpks = len(sel) numNetStims = 0 for netStimLabel in netStimLabels: print(netStimLabel) stims = sim.allSimData['stims'].items() print(stims) netStimSpks = [spk for cell, stims in sim.allSimData['stims'].items() for stimLabel, stimSpks in stims.items() for spk in stimSpks if stimLabel == netStimLabel] print(netStimSpks) if len(netStimSpks) > 0: lastInd = sel['spkind'].max() if len(sel['spkind']) > 0 else 0 spktsNew = netStimSpks spkindsNew = [lastInd+1+i for i in range(len(netStimSpks))] ns = pd.DataFrame(list(zip(spktsNew, spkindsNew)), columns=['spkt', 'spkind']) ns['spkgidColor'] = popColors['netStims'] sel = pd.concat([sel, ns]) numNetStims += 1 if len(cellGids) > 0 and numNetStims: ylabelText = ylabelText + ' and NetStims (at the end)' elif numNetStims: ylabelText = ylabelText + 'NetStims' if numCellSpks + numNetStims == 0: print('No spikes available to plot raster') return None # Time Range if timeRange == [0, sim.cfg.duration]: pass elif timeRange is None: timeRange = [0, sim.cfg.duration] else: sel = sel.query('spkt >= @timeRange[0] and spkt <= @timeRange[1]') # Limit to maxSpikes if (len(sel) > maxSpikes): print((' Showing only the first %i out of %i spikes' % (maxSpikes, len(sel)))) # Limit num of spikes if numNetStims: # sort first if have netStims sel = sel.sort_values(by='spkt') sel = sel.iloc[:maxSpikes] timeRange[1] = sel['spkt'].max() # Calculate plot statistics gidPops = df['pop'].tolist() conns = df['conns'].tolist() popNumCells = [float(gidPops.count(pop)) for pop in popLabels] if numCellSpks else [0] * len(popLabels) totalSpikes = len(sel) cellNumConns = [len(conn) for conn in conns] popNumConns = [sum([cellNumConn for cellIndex, cellNumConn in enumerate(cellNumConns) if gidPops[cellIndex] == pop]) for pop in popLabels] totalConnections = sum([len(conns) for conns in df['conns']]) numCells = len(cells) firingRate = float(totalSpikes)/(numCells+numNetStims)/(timeRange[1]-timeRange[0])*1e3 if totalSpikes>0 else 0 connsPerCell = totalConnections/float(numCells) if numCells>0 else 0 popConnsPerCell = [popNumConns[popIndex]/popNumCells[popIndex] for popIndex, pop in enumerate(popLabels)] title = 'Raster plot of spiking' legendLabels = [] # Add population spiking info to plot if popRates: avgRates = {} tsecs = (timeRange[1]-timeRange[0])/1e3 for i, (pop, popNum) in enumerate(zip(popLabels, popNumCells)): if numCells > 0 and pop != 'NetStims': if numCellSpks == 0: avgRates[pop] = 0 else: avgRates[pop] = len([spkid for spkid in sel['spkind'].iloc[:numCellSpks-1] if df['pop'].iloc[int(spkid)]==pop])/popNum/tsecs if numNetStims: popNumCells[-1] = numNetStims avgRates['NetStims'] = len([spkid for spkid in sel['spkind'].iloc[numCellSpks:]])/numNetStims/tsecs if popRates == 'minimal': legendLabels = [popLabel + ' (%.3g Hz)' % (avgRates[popLabel]) for popIndex, popLabel in enumerate(popLabels) if popLabel in avgRates] title = 'cells: %i syn/cell: %0.1f rate: %0.1f Hz' % (numCells, connsPerCell, firingRate) else: legendLabels = [popLabel + '\n cells: %i\n syn/cell: %0.1f\n rate: %.3g Hz' % (popNumCells[popIndex], popConnsPerCell[popIndex], avgRates[popLabel]) for popIndex, popLabel in enumerate(popLabels) if popLabel in avgRates] title = 'cells: %i syn/cell: %0.1f rate: %0.1f Hz' % (numCells, connsPerCell, firingRate) if syncLines: title = '%s sync=%0.2f' % (title, syncMeasure()) if 'title' in kwargs: title = kwargs['title'] axisArgs = {'xlabel': 'Time (ms)', 'ylabel': ylabelText, 'title': title} spikeData = {'spkTimes': sel['spkt'].tolist(), 'spkInds': sel['spkind'].tolist(), 'spkGids': sel['spkid'].tolist(), 'popNumCells': popNumCells, 'popLabels': popLabels, 'numNetStims': numNetStims, 'include': include, 'timeRange': timeRange, 'maxSpikes': maxSpikes, 'orderBy': orderBy, 'axisArgs': axisArgs, 'legendLabels': legendLabels} if saveData: saveFigData(spikeData, fileName=fileName, fileDesc='spike_data', fileType=fileType, fileDir=fileDir, sim=sim) return spikeData
[docs]@exception def prepareRaster( include=['allCells'], sim=None, timeRange=None, maxSpikes=1e8, orderBy='gid', popRates=True, saveData=False, fileName=None, fileDesc=None, fileType=None, fileDir=None, **kwargs): """ Function to prepare data for creating a raster plot """ figData = prepareSpikeData( include=include, sim=sim, timeRange=timeRange, maxSpikes=maxSpikes, orderBy=orderBy, popRates=popRates, saveData=saveData, fileName=fileName, fileDesc=fileDesc if fileDesc else 'raster_data', fileType=fileType, fileDir=fileDir, **kwargs) return figData
[docs]@exception def prepareSpikeHist( include=['allCells', 'eachPop'], sim=None, timeRange=None, maxSpikes=1e8, popRates=True, saveData=False, fileName=None, fileDesc=None, fileType=None, fileDir=None, binSize=5, **kwargs): """ Function to prepare data for creating a spike histogram plot """ figData = prepareSpikeData( include=include, sim=sim, timeRange=timeRange, maxSpikes=maxSpikes, orderBy='gid', popRates=popRates, saveData=saveData, fileName=fileName, fileDesc=fileDesc, fileType=fileType, fileDir=fileDir, **kwargs) figData['axisArgs']['ylabel'] = 'Number of spikes' return figData
#------------------------------------------------------------------------------ # Calculate and print avg pop rates #------------------------------------------------------------------------------
[docs]@exception def popAvgRates(tranges=None, show=True): """Function to calculate and return average firing rates by population Parameters ---------- tranges : list or tuple The time range or time ranges to calculate firing rates within **Default:** ``None`` uses the entire simulation time range. **Options:** a single time range is defined in a list (``[startTime, stopTime]``) while multiple time ranges should be a tuple of lists (``([start1, stop1], [start2, stop2])``). show : bool Whether or not to print the population firing rates **Default:** ``True`` """ from .. import sim avgRates = Dict() if not hasattr(sim, 'allSimData') or 'spkt' not in sim.allSimData: print('Error: sim.allSimData not available; please call sim.gatherData()') return None spktsAll = sim.allSimData['spkt'] spkidsAll = sim.allSimData['spkid'] spkidsList, spktsList = [], [] if not isinstance(tranges, list): # True or None tranges = [[0, sim.cfg.duration]] if isinstance(tranges, list): # convert single time interval to list if not isinstance(tranges[0], (list, tuple)): tranges = [tranges] # calculate for multiple time intervals if isinstance(tranges[0], (list,tuple)): for trange in tranges: try: spkids, spkts = list(zip(*[(spkid, spkt) for spkid, spkt in zip(spkidsAll, spktsAll) if trange[0] <= spkt <= trange[1]])) except: spkids, spkts = [], [] spkidsList.append(spkids) spktsList.append(spkts) else: return avgRates for pop in if len(tranges) > 1: if show: print(' %s ' % (pop)) avgRates[pop] = {} for spkids, spkts, trange in zip(spkidsList, spktsList, tranges): numCells = float(len([pop]['cellGids'])) if numCells > 0: # single time intervals if len(tranges) == 1: tsecs = float((trange[1]-trange[0]))/1000.0 avgRates[pop] = len([spkid for spkid in spkids if[int(spkid)]['tags']['pop']==pop])/numCells/tsecs if show: print(' %s : %.3f Hz'%(pop, avgRates[pop])) # multiple time intervals else: tsecs = float((trange[1]-trange[0]))/1000.0 avgRates[pop]['%d_%d'%(trange[0], trange[1])] = len([spkid for spkid in spkids if[int(spkid)]['tags']['pop']==pop])/numCells/tsecs if show: print(' (%d - %d ms): %.3f Hz'%(trange[0], trange[1], avgRates[pop]['%d_%d'%(trange[0], trange[1])])) return avgRates