Source code for netpyne.analysis.interactive

"""
Module for production of interactive plots

"""

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

from future import standard_library
standard_library.install_aliases()
from netpyne import __gui__

try:
    basestring
except NameError:
    basestring = str

if __gui__:
    import matplotlib.pyplot as plt
    from matplotlib import mlab
    from matplotlib_scalebar import scalebar
from numbers import Number
from .utils import colorList, exception, getSpktSpkid, _showFigure, _saveFigData, getCellsInclude, syncMeasure, _smooth1d, _guiTheme, _guiBlack, _guiWhite

import numpy as np
import pandas as pd

from bokeh.themes import built_in_themes
from bokeh.io import curdoc
from bokeh.palettes import Viridis256
from bokeh.models import HoverTool


[docs]def applyTheme(kwargs): theme = None if 'theme' in kwargs: from bokeh.themes import Theme if kwargs['theme'] != 'default': if kwargs['theme'] == 'gui': theme = Theme(json=_guiTheme) elif kwargs['theme'] == 'guiBlack': theme = Theme(json=_guiBlack) elif kwargs['theme'] == 'guiWhite': theme = Theme(json=_guiWhite) else: theme = kwargs['theme'] curdoc().theme = theme return theme
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive raster # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotRaster(include=['allCells'], timeRange=None, maxSpikes=1e8, orderBy='gid', orderInverse=False, popRates=False, spikeHist=False, spikeHistBin=5, syncLines=False, marker='circle', markerSize=3, popColors=None, saveData=None, saveFig=None, showFig=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotRaster`> Parameters ---------- include : list Cells to include in the plot. **Default:** ``['allCells']`` plots all cells **Options:** ``['all']`` plots all cells and stimulations, ``['allNetStims']`` plots just stimulations, ``['popName1']`` plots a single population, ``['popName1', 'popName2']`` plots multiple populations, ``[120]`` plots a single cell, ``[120, 130]`` plots multiple cells, ``[('popName1', 56)]`` plots a cell from a specific population, ``[('popName1', [0, 1]), ('popName2', [4, 5, 6])]``, plots cells from multiple populations timeRange : list [start, stop] Time range to plot. **Default:** ``None`` plots entire time range **Options:** ``<option>`` <description of option> maxSpikes : int Maximum number of spikes to be plotted. **Default:** ``1e8`` **Options:** ``<option>`` <description of option> orderBy : str Unique numeric cell property by which to order the y-axis. **Default:** ``'gid'`` orders by cell ID **Options:** ``'y'`` orders by cell y-location, ``'ynorm'`` orders by cell normalized y-location orderInverse : bool Inverts the y-axis order if ``True``. **Default:** ``False`` **Options:** ``<option>`` <description of option> popRates : bool Include population firing rates on plot if ``True``. **Default:** ``False`` **Options:** ``<option>`` <description of option> spikeHist : bool Include spike histogram (spikes/bin) on plot if ``True``. **Default:** ``False`` **Options:** ``<option>`` <description of option> spikeHistBin : int Size of bin in ms to use for spike histogram. **Default:** ``5`` **Options:** ``<option>`` <description of option> syncLines : bool Calculate synchrony measure and plot vertical lines for each spike to evidence synchrony if ``True``. **Default:** ``False`` **Options:** ``<option>`` <description of option> marker : str `Bokeh marker <https://docs.bokeh.org/en/latest/docs/gallery/markers.html>`_ for each spike. **Default:** ``'circle'`` **Options:** ``<option>`` <description of option> markerSize : int Size of Bokeh marker for each spike. **Default:** ``3`` **Options:** ``<option>`` <description of option> popColors : dict Dictionary with custom color (value) used for each population (key). **Default:** ``None`` uses standard colors **Options:** ``<option>`` <description of option> saveData : bool or str Whether and where to save the data used to generate the plot. **Default:** ``False`` **Options:** ``True`` autosaves the data, ``'/path/filename.ext'`` saves to a custom path and filename, valid file extensions are ``'.pkl'`` and ``'.json'`` saveFig : bool or str Whether and where to save the figure. **Default:** ``False`` **Options:** ``True`` autosaves the figure, ``'/path/filename.html'`` saves to a custom path and filename, only valid file extension is ``'.html'`` showFig : bool Shows the figure if ``True``. **Default:** ``True`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* Returns ------- """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout from bokeh.models import Legend from bokeh.colors import RGB from bokeh.models.annotations import Title print('Plotting interactive raster ...') theme = applyTheme(kwargs) TOOLS = 'hover,save,pan,box_zoom,reset,wheel_zoom', if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] # bokeh only handles integer rgb values from 0-255 else: colors = kwargs['palette'] popColorDict = None if popColors is not None: popColorDict = popColors.copy() for pop, color in popColorDict.items(): popColorDict[pop] = RGB(*[round(f * 255) for f in color]) cells, cellGids, netStimLabels = getCellsInclude(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 isinstance(orderBy, str) and orderBy not in cells[0]['tags']: # if orderBy property doesn't exist or is not numeric, use gid orderBy = 'gid' elif isinstance(orderBy, str) and not isinstance(cells[0]['tags'][orderBy], Number): orderBy = 'gid' if isinstance(orderBy, list): keep = keep + list(set(orderBy) - set(keep)) elif orderBy not in keep: keep.append(orderBy) df = df[keep] popLabels = [pop for pop in sim.net.allPops if pop in df['pop'].unique()] #preserves original ordering if netStimLabels: popLabels.append('NetStims') popColorsTmp = {popLabel: colors[ipop%len(colors)] for ipop,popLabel in enumerate(popLabels)} # dict with color for each pop if popColorDict: popColorsTmp.update(popColorDict) popColorDict = popColorsTmp if len(cellGids) > 0: gidColors = {cell['gid']: popColorDict[cell['tags']['pop']] for cell in cells} # dict with color for each gid try: sel, spkts, spkgids = getSpktSpkid(cellGids=[] if include == ['allCells'] else cellGids, timeRange=timeRange) except: import sys print((sys.exc_info())) spkgids, spkts = [], [] sel = pd.DataFrame(columns=['spkt', 'spkid']) sel['spkgidColor'] = sel['spkid'].map(gidColors) sel['pop'] = sel['spkid'].map(df.set_index('gid')['pop']) df['gidColor'] = df['pop'].map(popColorDict) 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: netStimSpks = [spk for cell,stims in sim.allSimData['stims'].items() \ for stimLabel,stimSpks in stims.items() for spk in stimSpks if stimLabel == netStimLabel] if len(netStimSpks) > 0: # lastInd = max(spkinds) if len(spkinds)>0 else 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'] = popColorDict['netStims'] sel = pd.concat([sel, ns]) numNetStims += 1 else: pass #print netStimLabel+' produced no spikes' 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 max spikes 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() # Plot stats gidPops = df['pop'].tolist() popNumCells = [float(gidPops.count(pop)) for pop in popLabels] if numCellSpks else [0] * len(popLabels) totalSpikes = len(sel) 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 # Calculate firing rate connsPerCell = totalConnections/float(numCells) if numCells>0 else 0 # Calculate the number of connections per cell 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 orderInverse: y_range=(sel['spkind'].max(), sel['spkind'].min()) else: y_range=(sel['spkind'].min(), sel['spkind'].max()) fig = figure( title="Raster Plot", tools=TOOLS, active_drag = "auto", active_scroll = "auto", tooltips=[('Cell GID', '@y'), ('Spike time', '@x')], x_axis_label="Time (ms)", y_axis_label=ylabelText, x_range=(timeRange[0], timeRange[1]), y_range=y_range, toolbar_location='above') t = Title() if syncLines: for spkt in sel['spkt'].tolist(): fig.line((spkt, spkt), (0, len(cells)+numNetStims), color='red', line_width=2) print(syncMeasure()) t.text = 'cells=%i syns/cell=%0.1f rate=%0.1f Hz sync=%0.2f' % (numCells,connsPerCell,firingRate,syncMeasure()) else: t.text = 'cells=%i syns/cell=%0.1f rate=%0.1f Hz' % (numCells,connsPerCell,firingRate) fig.title = t if spikeHist: histo = np.histogram(sel['spkt'].tolist(), bins = np.arange(timeRange[0], timeRange[1], spikeHistBin)) histoT = histo[1][:-1]+spikeHistBin/2 histoCount = histo[0] legendItems = [] grouped = sel.groupby('pop') for name, group in grouped: if popRates: label = name + ' (%.3g Hz)'%(avgRates[name]) else: label = name fig.scatter(group['spkt'], group['spkind'], color=group['spkgidColor'], marker=marker, size=markerSize, legend_label=label) #legendItems.append((label, [s])) if spikeHist: from bokeh.models import LinearAxis, Range1d fig.extra_y_ranges={'spikeHist': Range1d(start=min(histoCount), end=max(histoCount))} fig.add_layout(LinearAxis(y_range_name='spikeHist', axis_label='Spike count'), 'right') fig.line (histoT, histoCount, line_width=2, y_range_name='spikeHist') legend = Legend(items=legendItems, location=(10,0)) legend.click_policy='hide' fig.add_layout(legend, 'right') plot_layout = layout([fig], sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="Raster Plot", theme=theme) # save figure data if saveData: figData = {'spkTimes': sel['spkt'].tolist(), 'spkInds': sel['spkind'].tolist(), 'spkColors': sel['spkgidColor'].tolist(), 'cellGids': cellGids, 'sortedGids': df.index.tolist(), 'numNetStims': numNetStims, 'include': include, 'timeRange': timeRange, 'maxSpikes': maxSpikes, 'orderBy': orderBy, 'orderInverse': orderInverse, 'spikeHist': spikeHist, 'syncLines': syncLines} _saveFigData(figData, saveData, 'raster') # save figure if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename + '_iraster_' + orderBy + '.html' file = open(filename, 'w') file.write(html) file.close() if showFig: show(plot_layout) return html, {'include': include, 'spkts': spkts, 'spkinds': sel['spkind'].tolist(), 'timeRange': timeRange}
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive dipole # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotDipole(expData={'label': 'Experiment', 'x':[], 'y':[]}, dpl=None, t=None, showFig=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotDipole`> Parameters ---------- expData : dict <Short description of expData> **Default:** ``{'label': 'Experiment', 'x': [], 'y': []}`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout from bokeh.colors import RGB theme = applyTheme(kwargs) TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" fig = figure(title="Dipole Plot", tools=TOOLS, toolbar_location='above', x_axis_label="Time (ms)", y_axis_label='Dipole (nAM x 3000)') if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] # bokeh only handles integer rgb values from 0-255 else: colors = kwargs['palette'] # renormalize the dipole and save def baseline_renormalize(): dpl = {k: np.array(v) for k, v in sim.allSimData['dipole'].items()} t = sim.simData['t'] # ad hoc postprocessing of dipole signal in orig HNN model L2 and L5 if 'L2' in dpl and 'L5' in dpl: # N_pyr cells in grid. This is PER LAYER N_pyr = sim.cfg.hnn_params['N_pyr_x'] * sim.cfg.hnn_params['N_pyr_y'] # dipole offset calculation: increasing number of pyr cells (L2 and L5, simultaneously) # with no inputs resulted in an aggregate dipole over the interval [50., 1000.] ms that # eventually plateaus at -48 fAm. The range over this interval is something like 3 fAm # so the resultant correction is here, per dipole # dpl_offset = N_pyr * 50.207 dpl_offset = { # these values will be subtracted 'L2': N_pyr * 0.0443, 'L5': N_pyr * -49.0502 # 'L5': N_pyr * -48.3642, # will be calculated next, this is a placeholder # 'agg': None, } # L2 dipole offset can be roughly baseline shifted over the entire range of t dpl['L2'] -= dpl_offset['L2'] # L5 dipole offset should be different for interval [50., 500.] and then it can be offset # slope (m) and intercept (b) params for L5 dipole offset # uncorrected for N_cells # these values were fit over the range [37., 750.) m = 3.4770508e-3 b = -51.231085 # these values were fit over the range [750., 5000] t1 = 750. m1 = 1.01e-4 b1 = -48.412078 # piecewise normalization dpl['L5'][t <= 37.] -= dpl_offset['L5'] dpl['L5'][(t > 37.) & (t < t1)] -= N_pyr * (m * t[(t > 37.) & (t < t1)] + b) dpl['L5'][t >= t1] -= N_pyr * (m1 * t[t >= t1] + b1) # recalculate the aggregate dipole based on the baseline normalized ones dpl['Aggregate'] = np.sum(list(dpl.values()), axis=0) return dpl # convolve with a hamming window def hammfilt(x, winsz): from pylab import convolve from numpy import hamming win = hamming(winsz) win /= sum(win) return convolve(x,win,'same') # baseline renormalize if dpl: dpl = {k: np.array(v) for k, v in dpl.items()} else: dpl = baseline_renormalize() if t is not None and len(t)>0: t=np.array(t) else: t=sim.simData['t'] # convert units from fAm to nAm, rescale and smooth for key in dpl.keys(): dpl[key] *= 1e-6 * sim.cfg.hnn_params['dipole_scalefctr'] if sim.cfg.hnn_params['dipole_smooth_win'] > 0: dpl[key] = hammfilt(dpl[key], sim.cfg.hnn_params['dipole_smooth_win']/sim.cfg.dt) # Set index 0 to 0 dpl[key][0] = 0.0 # plot exp data #fig.line(expData['x'], expData['y'], color='black', legend=expData['label']) # plot recorded dipole data for i,(k,v) in enumerate(dpl.items()): fig.line(t, v, legend_label=k, color=colors[i], line_width=2.0) fig.legend.location = "top_right" fig.legend.click_policy = "hide" plot_layout = layout(fig, sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="Dipole Plot", theme=theme) if showFig: show(fig) return html, dpl
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive dipole Spectrogram # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotDipoleSpectrogram(expData={'label': 'Experiment', 'x':[], 'y':[]}, dpl=None, minFreq = 1, maxFreq = 80, stepFreq = 1, norm = True, showFig=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotDipoleSpectrogram`> Parameters ---------- expData : dict <Short description of expData> **Default:** ``{'label': 'Experiment', 'x': [], 'y': []}`` **Options:** ``<option>`` <description of option> minFreq : int <Short description of minFreq> **Default:** ``1`` **Options:** ``<option>`` <description of option> maxFreq : int <Short description of maxFreq> **Default:** ``80`` **Options:** ``<option>`` <description of option> stepFreq : int <Short description of stepFreq> **Default:** ``1`` **Options:** ``<option>`` <description of option> norm : bool <Short description of norm> **Default:** ``True`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.models import BasicTicker, ColorBar, ColumnDataSource, LinearColorMapper, PrintfTickFormatter from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout theme = applyTheme(kwargs) # renormalize the dipole and save def baseline_renormalize(): # N_pyr cells in grid. This is PER LAYER N_pyr = sim.cfg.hnn_params['N_pyr_x'] * sim.cfg.hnn_params['N_pyr_y'] # dipole offset calculation: increasing number of pyr cells (L2 and L5, simultaneously) # with no inputs resulted in an aggregate dipole over the interval [50., 1000.] ms that # eventually plateaus at -48 fAm. The range over this interval is something like 3 fAm # so the resultant correction is here, per dipole # dpl_offset = N_pyr * 50.207 dpl_offset = { # these values will be subtracted 'L2': N_pyr * 0.0443, 'L5': N_pyr * -49.0502 # 'L5': N_pyr * -48.3642, # will be calculated next, this is a placeholder # 'agg': None, } # L2 dipole offset can be roughly baseline shifted over the entire range of t dpl = {'L2': np.array(sim.simData['dipole']['L2']), 'L5': np.array(sim.simData['dipole']['L5'])} dpl['L2'] -= dpl_offset['L2'] # L5 dipole offset should be different for interval [50., 500.] and then it can be offset # slope (m) and intercept (b) params for L5 dipole offset # uncorrected for N_cells # these values were fit over the range [37., 750.) m = 3.4770508e-3 b = -51.231085 # these values were fit over the range [750., 5000] t1 = 750. m1 = 1.01e-4 b1 = -48.412078 t = sim.simData['t'] # piecewise normalization dpl['L5'][t <= 37.] -= dpl_offset['L5'] dpl['L5'][(t > 37.) & (t < t1)] -= N_pyr * (m * t[(t > 37.) & (t < t1)] + b) dpl['L5'][t >= t1] -= N_pyr * (m1 * t[t >= t1] + b1) # recalculate the aggregate dipole based on the baseline normalized ones dpl['agg'] = dpl['L2'] + dpl['L5'] return dpl # convolve with a hamming window def hammfilt(x, winsz): from pylab import convolve from numpy import hamming win = hamming(winsz) win /= sum(win) return convolve(x,win,'same') if not dpl: # baseline renormalize dpl = baseline_renormalize() # convert units from fAm to nAm, rescale and smooth for key in dpl.keys(): dpl[key] *= 1e-6 * sim.cfg.hnn_params['dipole_scalefctr'] if sim.cfg.hnn_params['dipole_smooth_win'] > 0: dpl[key] = hammfilt(dpl[key], sim.cfg.hnn_params['dipole_smooth_win']/sim.cfg.dt) # Set index 0 to 0 dpl[key][0] = 0.0 # plot recorded dipole data #fig.line(sim.simData['t'], dpl['L2']+dpl['L5'], color='blue', legend="Aggregate", line_width=2.0) # plot Morlet spectrogram from ..support.morlet import MorletSpec, index2ms spec = [] dplsum = dpl['L2'] + dpl['L5'] fs = int(1000.0 / sim.cfg.recordStep) t_spec = np.linspace(0, index2ms(len(dplsum), fs), len(dplsum)) spec = MorletSpec(dplsum, fs, freqmin=minFreq, freqmax=maxFreq, freqstep=stepFreq) f = np.array(range(minFreq, maxFreq+1, stepFreq)) # only used as output for user vmin = np.array(spec.TFR).min() vmax = np.array(spec.TFR).max() T = [0, sim.cfg.duration] F = spec.f if norm: spec.TFR = spec.TFR / vmax S = spec.TFR vc = [0, 1] else: S = spec.TFR vc = [vmin, vmax] # old code fig = plt.imshow(S, extent=(np.amin(T), np.amax(T), np.amin(F), np.amax(F)), origin='lower', interpolation='None', aspect='auto', vmin=vc[0], vmax=vc[1], cmap=plt.get_cmap('jet')) # viridis plt.colorbar(label='Power') plt.gca().invert_yaxis() plt.ylabel('Hz') plt.tight_layout() if showFig: plt.show() # TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" # fig = figure(title="Dipole Spectrogram Plot", tools=TOOLS, toolbar_location='above', # plot_width=S.shape[1], plot_height=S.shape[0], x_axis_label="Time (ms)", y_axis_label='Frequency (Hz)') # fig.image(image=[S], x=[0], y=[0], dw=[S.shape[1]], dh=[S.shape[0]], palette='Spectral11') # steps = list(range(0, S.shape[1], int(1./fs*1e6))) # fig.xaxis.ticker = steps # fig.xaxis.major_label_overrides = {t*40000/1e6: str(t) for t in T} # fig.legend.location = "top_right" # fig.legend.click_policy = "hide" # plot_layout = layout(fig, sizing_mode='stretch_both') # html = file_html(plot_layout, CDN, title="Dipole Spectrogram Plot") # if showFig: # show(fig) return fig #html
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive dipole Spectrogram # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotDipolePSD(expData={'label': 'Experiment', 'x':[], 'y':[]}, minFreq = 1, maxFreq = 80, stepFreq = 1, norm = True, showFig=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotDipolePSD`> Parameters ---------- expData : dict <Short description of expData> **Default:** ``{'label': 'Experiment', 'x': [], 'y': []}`` **Options:** ``<option>`` <description of option> minFreq : int <Short description of minFreq> **Default:** ``1`` **Options:** ``<option>`` <description of option> maxFreq : int <Short description of maxFreq> **Default:** ``80`` **Options:** ``<option>`` <description of option> stepFreq : int <Short description of stepFreq> **Default:** ``1`` **Options:** ``<option>`` <description of option> norm : bool <Short description of norm> **Default:** ``True`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout theme = applyTheme(kwargs) # renormalize the dipole and save def baseline_renormalize(): # N_pyr cells in grid. This is PER LAYER N_pyr = sim.cfg.hnn_params['N_pyr_x'] * sim.cfg.hnn_params['N_pyr_y'] # dipole offset calculation: increasing number of pyr cells (L2 and L5, simultaneously) # with no inputs resulted in an aggregate dipole over the interval [50., 1000.] ms that # eventually plateaus at -48 fAm. The range over this interval is something like 3 fAm # so the resultant correction is here, per dipole # dpl_offset = N_pyr * 50.207 dpl_offset = { # these values will be subtracted 'L2': N_pyr * 0.0443, 'L5': N_pyr * -49.0502 # 'L5': N_pyr * -48.3642, # will be calculated next, this is a placeholder # 'agg': None, } # L2 dipole offset can be roughly baseline shifted over the entire range of t dpl = {'L2': np.array(sim.simData['dipole']['L2']), 'L5': np.array(sim.simData['dipole']['L5'])} dpl['L2'] -= dpl_offset['L2'] # L5 dipole offset should be different for interval [50., 500.] and then it can be offset # slope (m) and intercept (b) params for L5 dipole offset # uncorrected for N_cells # these values were fit over the range [37., 750.) m = 3.4770508e-3 b = -51.231085 # these values were fit over the range [750., 5000] t1 = 750. m1 = 1.01e-4 b1 = -48.412078 t = sim.simData['t'] # piecewise normalization dpl['L5'][t <= 37.] -= dpl_offset['L5'] dpl['L5'][(t > 37.) & (t < t1)] -= N_pyr * (m * t[(t > 37.) & (t < t1)] + b) dpl['L5'][t >= t1] -= N_pyr * (m1 * t[t >= t1] + b1) # recalculate the aggregate dipole based on the baseline normalized ones dpl['agg'] = dpl['L2'] + dpl['L5'] return dpl # convolve with a hamming window def hammfilt(x, winsz): from pylab import convolve from numpy import hamming win = hamming(winsz) win /= sum(win) return convolve(x,win,'same') # baseline renormalize dpl = baseline_renormalize() # convert units from fAm to nAm, rescale and smooth for key in dpl.keys(): dpl[key] *= 1e-6 * sim.cfg.hnn_params['dipole_scalefctr'] if sim.cfg.hnn_params['dipole_smooth_win'] > 0: dpl[key] = hammfilt(dpl[key], sim.cfg.hnn_params['dipole_smooth_win']/sim.cfg.dt) # Set index 0 to 0 dpl[key][0] = 0.0 # plot recorded dipole data #fig.line(sim.simData['t'], dpl['L2']+dpl['L5'], color='blue', legend="Aggregate", line_width=2.0) # plot Morlet spectrogram from ..support.morlet import MorletSpec, index2ms spec = [] dplsum = dpl['L2'] + dpl['L5'] fs = int(1000.0 / sim.cfg.recordStep) t_spec = np.linspace(0, index2ms(len(dplsum), fs), len(dplsum)) spec = MorletSpec(dplsum, fs, freqmin=minFreq, freqmax=maxFreq, freqstep=stepFreq) f = np.array(range(minFreq, maxFreq+1, stepFreq)) # only used as output for user vmin = np.array(spec.TFR).min() vmax = np.array(spec.TFR).max() T = [0, sim.cfg.duration] F = spec.f S = spec.TFR vc = [vmin, vmax] Fs = int(1000.0/sim.cfg.recordStep) signal = np.mean(S, 1) # plot TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" fig = figure(title="Dipole PSD Plot", tools=TOOLS, toolbar_location='above', x_axis_label="Frequency (Hz)", y_axis_label='Power') fig.line(F, signal, color='black', line_width=2.0) fig.legend.location = "top_right" fig.legend.click_policy = "hide" plot_layout = layout(fig, sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="Dipole PSD Plot") if showFig: show(fig) return html
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive Spike Histogram # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotSpikeHist(include = ['allCells', 'eachPop'], legendLabels = [], timeRange = None, binSize = 5, overlay=True, yaxis = 'rate', popColors=[], norm=False, smooth=None, filtFreq=False, filtOrder=3, saveData = None, saveFig = None, showFig = False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotSpikeHist`> Parameters ---------- include : list <Short description of include> **Default:** ``['allCells', 'eachPop']`` **Options:** ``<option>`` <description of option> legendLabels : list <Short description of legendLabels> **Default:** ``[]`` **Options:** ``<option>`` <description of option> timeRange : <``None``?> <Short description of timeRange> **Default:** ``None`` **Options:** ``<option>`` <description of option> binSize : int <Short description of binSize> **Default:** ``5`` **Options:** ``<option>`` <description of option> overlay : bool <Short description of overlay> **Default:** ``True`` **Options:** ``<option>`` <description of option> yaxis : str <Short description of yaxis> **Default:** ``'rate'`` **Options:** ``<option>`` <description of option> popColors : list <Short description of popColors> **Default:** ``[]`` **Options:** ``<option>`` <description of option> norm : bool <Short description of norm> **Default:** ``False`` **Options:** ``<option>`` <description of option> smooth : <``None``?> <Short description of smooth> **Default:** ``None`` **Options:** ``<option>`` <description of option> filtFreq : bool <Short description of filtFreq> **Default:** ``False`` **Options:** ``<option>`` <description of option> filtOrder : int <Short description of filtOrder> **Default:** ``3`` **Options:** ``<option>`` <description of option> saveData : <``None``?> <Short description of saveData> **Default:** ``None`` **Options:** ``<option>`` <description of option> saveFig : <``None``?> <Short description of saveFig> **Default:** ``None`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import gridplot from bokeh.models import Legend from bokeh.colors import RGB print('Plotting interactive spike histogram...') theme = applyTheme(kwargs) TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] # bokeh only handles integer rgb values from 0-255 else: colors = kwargs['palette'] popColorDict=popColors.copy() if popColorDict: for pop, color in popColorDict.items(): if not isinstance(color, RGB): popColorDict[pop] = RGB(*[round(f * 255) for f in color]) if timeRange is None: timeRange = [0, sim.cfg.duration] if 'eachPop' in include: include.remove('eachPop') for pop in sim.net.allPops: include.append(pop) if yaxis == 'rate': if norm: yaxisLabel = 'Normalized firing rate' else: yaxisLabel = 'Avg cell firing rate (Hz)' elif yaxis == 'count': if norm: yaxisLabel = 'Normalized spike count' else: yaxisLabel = 'Spike count' figs=[] if overlay: figs.append(figure(title="Spike Histogram", tools=TOOLS, x_axis_label="Time (ms)", y_axis_label=yaxisLabel, toolbar_location='above')) fig = figs[0] legendItems = [] for iplot, subset in enumerate(include): if not overlay: figs.append(figure(title=str(subset), tools=TOOLS, x_axis_label="Time (ms)", y_axis_label=yaxisLabel)) fig = figs[iplot] if isinstance(subset, list): cells, cellGids, netStimLabels = getCellsInclude(subset) else: cells, cellGids, netStimLabels = getCellsInclude([subset]) numNetStims = 0 # Select cells to include if len(cellGids) > 0: try: spkinds,spkts = list(zip(*[(spkgid,spkt) for spkgid,spkt in zip(sim.allSimData['spkid'],sim.allSimData['spkt']) if spkgid in cellGids])) except: spkinds,spkts = [],[] else: spkinds,spkts = [],[] spkts, spkinds = list(spkts), list(spkinds) numNetStims = 0 if 'stims' in sim.allSimData: for netStimLabel in netStimLabels: netStimSpks = [spk for cell,stims in sim.allSimData['stims'].items() \ for stimLabel,stimSpks in stims.items() for spk in stimSpks if stimLabel == netStimLabel] if len(netStimSpks) > 0: lastInd = max(spkinds) if len(spkinds)>0 else 0 spktsNew = netStimSpks spkindsNew = [lastInd+1+i for i in range(len(netStimSpks))] spkts.extend(spktsNew) spkinds.extend(spkindsNew) numNetStims += 1 histo = np.histogram(spkts, bins = np.arange(timeRange[0], timeRange[1], binSize)) histoT = histo[1][:-1]+binSize/2 histoCount = histo[0] if yaxis=='rate': histoCount = histoCount * (1000.0 / binSize) / (len(cellGids)+numNetStims) # convert to firing rate if filtFreq: from scipy import signal fs = 1000.0/binSize nyquist = fs/2.0 if isinstance(filtFreq, list): # bandpass Wn = [filtFreq[0]/nyquist, filtFreq[1]/nyquist] b, a = signal.butter(filtOrder, Wn, btype='bandpass') elif isinstance(filtFreq, Number): # lowpass Wn = filtFreq/nyquist b, a = signal.butter(filtOrder, Wn) histoCount = signal.filtfilt(b, a, histoCount) if norm: histoCount /= max(histoCount) if smooth: histoCount = _smooth1d(histoCount, smooth)[:len(histoT)] if isinstance(subset, list): color = colors[iplot%len(colors)] else: color = popColorDict[subset] if subset in popColorDict else colors[iplot%len(colors)] label = legendLabels[iplot] if legendLabels else str(subset) s = fig.line(histoT, histoCount, line_width=2.0, name=str(subset), color=color) if overlay: legendItems.append((str(label), [s])) if overlay: legend = Legend(items=legendItems, location=(10,0)) fig.add_layout(legend) fig.legend.click_policy='hide' fig.legend.location='top_right' print(figs) plot_layout = gridplot(figs, ncols=1, merge_tools=False, sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="Spike Histogram", theme=theme) if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename+'_'+'ispikeHist.html' file = open(filename, 'w') file.write(html) file.close() return html
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive Rate PSD # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotRatePSD(include=['allCells', 'eachPop'], timeRange=None, binSize=5, maxFreq=100, NFFT=256, noverlap=128, smooth=0, overlay=True, ylim=None, popColors={}, saveData=None, saveFig=None, showFig=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotRatePSD`> Parameters ---------- include : list <Short description of include> **Default:** ``['allCells', 'eachPop']`` **Options:** ``<option>`` <description of option> timeRange : <``None``?> <Short description of timeRange> **Default:** ``None`` **Options:** ``<option>`` <description of option> binSize : int <Short description of binSize> **Default:** ``5`` **Options:** ``<option>`` <description of option> maxFreq : int <Short description of maxFreq> **Default:** ``100`` **Options:** ``<option>`` <description of option> NFFT : int <Short description of NFFT> **Default:** ``256`` **Options:** ``<option>`` <description of option> noverlap : int <Short description of noverlap> **Default:** ``128`` **Options:** ``<option>`` <description of option> smooth : int <Short description of smooth> **Default:** ``0`` **Options:** ``<option>`` <description of option> overlay : bool <Short description of overlay> **Default:** ``True`` **Options:** ``<option>`` <description of option> ylim : <``None``?> <Short description of ylim> **Default:** ``None`` **Options:** ``<option>`` <description of option> popColors : dict <Short description of popColors> **Default:** ``{}`` **Options:** ``<option>`` <description of option> saveData : <``None``?> <Short description of saveData> **Default:** ``None`` **Options:** ``<option>`` <description of option> saveFig : <``None``?> <Short description of saveFig> **Default:** ``None`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout from bokeh.colors import RGB from bokeh.models import Legend print('Plotting interactive firing rate power spectral density (PSD) ...') theme = applyTheme(kwargs) TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] # bokeh only handles integer rgb values from 0-255 else: colors = kwargs['palette'] popColorDict = popColors.copy() if popColorDict: for pop, color in popColorDict.items(): popColorDict[pop] = RGB(*[round(f * 255) for f in color]) # Replace 'eachPop' with list of pops if 'eachPop' in include: include.remove('eachPop') for pop in sim.net.allPops: include.append(pop) # time range if timeRange is None: timeRange = [0, sim.cfg.duration] histData = [] allPower, allSignal, allFreqs = [], [], [] legendItems = [] figs = [] if overlay: figs.append(figure(title="PSD Rate Plot", tools=TOOLS, x_axis_label="Frequency (Hz)", y_axis_label="Power Spectral Density (db/Hz)", toolbar_location='above')) fig = figs[0] legendItems = [] # Plot separate line for each entry in include for iplot, subset in enumerate(include): if not overlay: figs.append(figure(title=str(subset), tools=TOOLS, x_axis_label="Frequency (HZ)", y_axis_label="Power Spectral Density (db/Hz)")) fig = figs[iplot] cells, cellGids, netStimLabels = getCellsInclude([subset]) # Select cells to include if len(cellGids) > 0: try: spkinds,spkts = list(zip(*[(spkgid,spkt) for spkgid,spkt in zip(sim.allSimData['spkid'],sim.allSimData['spkt']) if spkgid in cellGids])) except: spkinds,spkts = [],[] else: spkinds,spkts = [],[] # Add NetStim spikes spkts, spkinds = list(spkts), list(spkinds) numNetStims = 0 if 'stims' in sim.allSimData: for netStimLabel in netStimLabels: netStimSpks = [spk for cell,stims in sim.allSimData['stims'].items() \ for stimLabel,stimSpks in stims.items() for spk in stimSpks if stimLabel == netStimLabel] if len(netStimSpks) > 0: lastInd = max(spkinds) if len(spkinds)>0 else 0 spktsNew = netStimSpks spkindsNew = [lastInd+1+i for i in range(len(netStimSpks))] spkts.extend(spktsNew) spkinds.extend(spkindsNew) numNetStims += 1 histo = np.histogram(spkts, bins = np.arange(timeRange[0], timeRange[1], binSize)) histoT = histo[1][:-1]+binSize/2 histoCount = histo[0] histoCount = histoCount * (1000.0 / binSize) / (len(cellGids)+numNetStims) # convert to rates histData.append(histoCount) Fs = 1000.0 / binSize power = mlab.psd(histoCount, Fs=Fs, NFFT=NFFT, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=noverlap, pad_to=None, sides='default', scale_by_freq=None) if smooth: signal = _smooth1d(10*np.log10(power[0]), smooth) else: signal = 10*np.log10(power[0]) freqs = power[1] color = popColorDict[subset] if subset in popColorDict else colors[iplot%len(colors)] allFreqs.append(freqs) allPower.append(power) allSignal.append(signal) s = fig.line(freqs[freqs<maxFreq], signal[freqs<maxFreq], line_width = 2.0, color=color) if overlay: legendItems.append((subset, [s])) if overlay: legend = Legend(items=legendItems) legend.click_policy = 'hide' fig.add_layout(legend) plot_layout = layout(figs, sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="PSD Rate Plot", theme=theme) if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename + '_ipsd.html' file = open(filename, 'w') file.write(html) file.close() return html
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive Traces # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotTraces(include=None, timeRange=None, overlay=False, oneFigPer='cell', rerun=False, colors=None, ylim=None, axis='on', fontSize=12, figSize=(10,8), saveData=None, saveFig=None, showFig=True, ylabel=None, linkAxes=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotTraces`> Parameters ---------- include : <``None``?> <Short description of include> **Default:** ``None`` **Options:** ``<option>`` <description of option> timeRange : <``None``?> <Short description of timeRange> **Default:** ``None`` **Options:** ``<option>`` <description of option> overlay : bool <Short description of overlay> **Default:** ``False`` **Options:** ``<option>`` <description of option> oneFigPer : str <Short description of oneFigPer> **Default:** ``'cell'`` **Options:** ``<option>`` <description of option> rerun : bool <Short description of rerun> **Default:** ``False`` **Options:** ``<option>`` <description of option> colors : <``None``?> <Short description of colors> **Default:** ``None`` **Options:** ``<option>`` <description of option> ylim : <``None``?> <Short description of ylim> **Default:** ``None`` **Options:** ``<option>`` <description of option> axis : str <Short description of axis> **Default:** ``'on'`` **Options:** ``<option>`` <description of option> fontSize : int <Short description of fontSize> **Default:** ``12`` **Options:** ``<option>`` <description of option> figSize : tuple <Short description of figSize> **Default:** ``(10, 8)`` **Options:** ``<option>`` <description of option> saveData : <``None``?> <Short description of saveData> **Default:** ``None`` **Options:** ``<option>`` <description of option> saveFig : <``None``?> <Short description of saveFig> **Default:** ``None`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``True`` **Options:** ``<option>`` <description of option> ylabel : <``None``?> <Short description of ylabel> **Default:** ``None`` **Options:** ``<option>`` <description of option> linkAxes : bool <Short description of linkAxes> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout, column from bokeh.models import HoverTool from bokeh.models import Legend from bokeh.colors import RGB print('Plotting interactive recorded cell traces per', oneFigPer) theme = applyTheme(kwargs) TOOLS = 'save,pan,box_zoom,reset,wheel_zoom' if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] # bokeh only handles integer rgb values from 0-255 else: colors = kwargs['palette'] if include is None: # if none, record from whatever was recorded if 'plotTraces' in sim.cfg.analysis and 'include' in sim.cfg.analysis['plotTraces']: include = sim.cfg.analysis['plotTraces']['include'] + sim.cfg.recordCells else: include = sim.cfg.recordCells if timeRange is None: timeRange = [0,sim.cfg.duration] tracesList = list(sim.cfg.recordTraces.keys()) tracesList.sort() cells, cellGids, _ = getCellsInclude(include) gidPops = {cell['gid']: cell['tags']['pop'] for cell in cells} recordStep = sim.cfg.recordStep figs = {} tracesData = [] if ylabel is None: y_axis_label = None else: y_axis_label = ylabel if oneFigPer == 'cell': for gid in cellGids: if overlay: figs['_gid_' + str(gid)] = figure(title = "Cell {}, Pop {}".format(gid, gidPops[gid]), tools = TOOLS, active_drag = "auto", active_scroll = "auto", x_axis_label="Time (ms)", y_axis_label=y_axis_label ) fig = figs['_gid_' + str(gid)] else: figs['_gid_' + str(gid)] = [] for itrace, trace in enumerate(tracesList): if 'cell_{}'.format(gid) in sim.allSimData[trace]: fullTrace = sim.allSimData[trace]['cell_{}'.format(gid)] if isinstance(fullTrace, dict): data = [fullTrace[key][int(timeRange[0]/recordStep):int(timeRange[1]/recordStep)] for key in list(fullTrace.keys())] lenData = len(data[0]) data = np.transpose(np.array(data)) else: data = fullTrace[int(timeRange[0]/recordStep):int(timeRange[1]/recordStep)] lenData = len(data) t = np.arange(timeRange[0], timeRange[1]+recordStep, recordStep) tracesData.append({'t': t, 'cell_'+str(gid)+'_'+trace: data}) if overlay: fig.line(t[:lenData], data, line_width=2, line_color=colors[itrace], legend_label=trace) hover = HoverTool(tooltips=[('Time', '@x'), ('Value', '@y')], mode='vline') fig.add_tools(hover) fig.legend.click_policy="hide" else: subfig = figure(title = "Cell {}, Pop {}".format(gid, gidPops[gid]), tools = TOOLS, active_drag = "auto", active_scroll = "auto", x_axis_label="Time (ms)", y_axis_label=y_axis_label ) subfig.line(t[:lenData], data, line_width=2, line_color=colors[itrace], legend_label=trace) if linkAxes: if itrace > 0: subfig.x_range = figs['_gid_' + str(gid)][0].x_range subfig.y_range = figs['_gid_' + str(gid)][0].y_range hover = HoverTool(tooltips=[('Time', '@x'), ('Value', '@y')], mode='vline') subfig.add_tools(hover) subfig.legend.click_policy="hide" figs['_gid_' + str(gid)].append(subfig) elif oneFigPer == 'trace': for itrace, trace in enumerate(tracesList): if overlay: figs['_trace_' + str(trace)] = figure(title = str(trace), tools = TOOLS, active_drag = "auto", active_scroll = "auto", x_axis_label="Time (ms)", y_axis_label=y_axis_label ) fig = figs['_trace_' + str(trace)] else: figs['_trace_' + str(trace)] = [] for igid, gid in enumerate(cellGids): if 'cell_'+str(gid) in sim.allSimData[trace]: fullTrace = sim.allSimData[trace]['cell_'+str(gid)] if isinstance(fullTrace, dict): data = [fullTrace[key][int(timeRange[0]/recordStep):int(timeRange[1]/recordStep)] for key in list(fullTrace.keys())] lenData = len(data[0]) data = np.transpose(np.array(data)) else: data = np.array(fullTrace[int(timeRange[0]/recordStep):int(timeRange[1]/recordStep)]) lenData = len(data) t = np.arange(timeRange[0], timeRange[1]+recordStep, recordStep) tracesData.append({'t': t, 'cell_'+str(gid)+'_'+trace: data}) if overlay: fig.line(t[:lenData], data, line_width=2, line_color=colors[igid], legend_label='Cell %d, Pop %s '%(int(gid), gidPops[gid])) hover = HoverTool(tooltips=[('Time', '@x'), ('Value', '@y')], mode='vline') fig.add_tools(hover) fig.legend.click_policy="hide" else: subfig = figure(title = str(trace), tools = TOOLS, active_drag = "auto", active_scroll = "auto", x_axis_label="Time (ms)", y_axis_label=y_axis_label ) if isinstance(data, list): for tl,dl in zip(t,data): subfig.line(tl[:len(dl)], dl, line_width=1.5, line_color=colors[igid], legend_label='Cell %d, Pop %s '%(int(gid), gidPops[gid])) else: subfig.line(t[:len(data)], data, line_width=1.5, line_color=colors[igid], legend_label='Cell %d, Pop %s '%(int(gid), gidPops[gid])) if linkAxes: if igid > 0: subfig.x_range = figs['_trace_' + str(trace)][0].x_range subfig.y_range = figs['_trace_' + str(trace)][0].y_range hover = HoverTool(tooltips=[('Time', '@x'), ('Value', '@y')], mode='vline') subfig.add_tools(hover) subfig.legend.click_policy="hide" figs['_trace_' + str(trace)].append(subfig) html = None for figLabel, figObj in figs.items(): if overlay: plot_layout = layout(figObj, sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title=figLabel, theme=theme) overlay_text = '_overlay' else: plot_layout = column(*figObj, sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title=figLabel, theme=theme) overlay_text = '' if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename + '_itraces' + figLabel + overlay_text + '.html' file = open(filename, 'w') file.write(html) file.close() return html
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive LFP # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotLFP(electrodes=['avg', 'all'], plots=['timeSeries', 'PSD', 'spectrogram'], timeRange=None, NFFT=256, noverlap=128, nperseg=256, minFreq=1, maxFreq=100, stepFreq=1, smooth=0, separation=1.0, includeAxon=True, logx=False, logy=False, normSignal=False, normPSD=False, normSpec=False, overlay=False, filtFreq=False, filtOrder=3, detrend=False, transformMethod='morlet', colors=None, saveData=None, saveFig=None, showFig=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotLFP`> Parameters ---------- electrodes : list <Short description of electrodes> **Default:** ``['avg', 'all']`` **Options:** ``<option>`` <description of option> plots : list <Short description of plots> **Default:** ``['timeSeries', 'PSD', 'spectrogram']`` **Options:** ``<option>`` <description of option> timeRange : <``None``?> <Short description of timeRange> **Default:** ``None`` **Options:** ``<option>`` <description of option> NFFT : int <Short description of NFFT> **Default:** ``256`` **Options:** ``<option>`` <description of option> noverlap : int <Short description of noverlap> **Default:** ``128`` **Options:** ``<option>`` <description of option> nperseg : int <Short description of nperseg> **Default:** ``256`` **Options:** ``<option>`` <description of option> minFreq : int <Short description of minFreq> **Default:** ``1`` **Options:** ``<option>`` <description of option> maxFreq : int <Short description of maxFreq> **Default:** ``100`` **Options:** ``<option>`` <description of option> stepFreq : int <Short description of stepFreq> **Default:** ``1`` **Options:** ``<option>`` <description of option> smooth : int <Short description of smooth> **Default:** ``0`` **Options:** ``<option>`` <description of option> separation : float <Short description of separation> **Default:** ``1.0`` **Options:** ``<option>`` <description of option> includeAxon : bool <Short description of includeAxon> **Default:** ``True`` **Options:** ``<option>`` <description of option> logx : bool <Short description of logx> **Default:** ``False`` **Options:** ``<option>`` <description of option> logy : bool <Short description of logy> **Default:** ``False`` **Options:** ``<option>`` <description of option> normSignal : bool <Short description of normSignal> **Default:** ``False`` **Options:** ``<option>`` <description of option> normPSD : bool <Short description of normPSD> **Default:** ``False`` **Options:** ``<option>`` <description of option> normSpec : bool <Short description of normSpec> **Default:** ``False`` **Options:** ``<option>`` <description of option> overlay : bool <Short description of overlay> **Default:** ``False`` **Options:** ``<option>`` <description of option> filtFreq : bool <Short description of filtFreq> **Default:** ``False`` **Options:** ``<option>`` <description of option> filtOrder : int <Short description of filtOrder> **Default:** ``3`` **Options:** ``<option>`` <description of option> detrend : bool <Short description of detrend> **Default:** ``False`` **Options:** ``<option>`` <description of option> transformMethod : str <Short description of transformMethod> **Default:** ``'morlet'`` **Options:** ``<option>`` <description of option> colors : <``None``?> <Short description of colors> **Default:** ``None`` **Options:** ``<option>`` <description of option> saveData : <``None``?> <Short description of saveData> **Default:** ``None`` **Options:** ``<option>`` <description of option> saveFig : <``None``?> <Short description of saveFig> **Default:** ``None`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout, column, row from bokeh.colors import RGB print('Plotting interactive LFP ...') html = None theme = applyTheme(kwargs) if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] else: colors = kwargs['palette'] TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" if timeRange is None: timeRange = [0,sim.cfg.duration] lfp = np.array(sim.allSimData['LFP'])[int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep),:] # electrode selection if 'all' in electrodes: electrodes.remove('all') electrodes.extend(list(range(int(sim.net.recXElectrode.nsites)))) figs = {} data = {'lfp': lfp} # time series plot # TODO add scalebar if 'timeSeries' in plots: figs['timeSeries'] = figure( title="LFP Time Series Plot", tools=TOOLS, active_drag = "auto", active_scroll = "auto", x_axis_label="Time (ms)", y_axis_label="LFP electrode", toolbar_location="above") figs['timeSeries'].yaxis.major_tick_line_color = None figs['timeSeries'].yaxis.minor_tick_line_color = None figs['timeSeries'].yaxis.major_label_text_font_size = '0pt' figs['timeSeries'].yaxis.axis_line_color = None figs['timeSeries'].ygrid.visible = False hover = HoverTool(tooltips=[('Time', '@x'), ('Value', '@y')], mode='vline') figs['timeSeries'].add_tools(hover) figs['timeSeries'].legend.click_policy="hide" ydisp = np.absolute(lfp).max() * separation offset = 1.0*ydisp t = np.arange(timeRange[0], timeRange[1], sim.cfg.recordStep) avg_color = 'black' if 'theme' in kwargs: if kwargs['theme'] == 'gui' or 'dark' in kwargs['theme'] or 'lack' in kwargs['theme']: avg_color = 'white' for i,elec in enumerate(electrodes[::-1]): if elec == 'avg': dplSum = np.mean(lfp, axis=1) color = avg_color elif isinstance(elec, Number) and elec <= sim.net.recXElectrode.nsites: pass lfpPlot = lfp[:, elec] color = colors[i%len(colors)] legend=str(elec) figs['timeSeries'].line(t, lfpPlot+(i*ydisp), color=color, name=str(elec), legend_label=legend) data['lfpPlot'] = lfpPlot data['ydisp'] = ydisp data['t'] = t figs['timeSeries'].legend.location = "top_right" figs['timeSeries'].legend.click_policy = "hide" plot_layout = layout(figs['timeSeries'], sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="Time Series LFP Plot", theme=theme) if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, basestring): filename = saveFig else: filename = sim.cfg.filename + '_iLFP_timeseries.html' file = open(filename, 'w') file.write(html) file.close() # PSD ---------------------------------- if 'PSD' in plots: figs['psd'] = [] allFreqs = [] allSignal = [] data['allFreqs'] = allFreqs data['allSignal'] = allSignal avg_color = 'black' if 'theme' in kwargs: if kwargs['theme'] == 'gui' or 'dark' in kwargs['theme'] or 'lack' in kwargs['theme']: avg_color = 'white' for i,elec in enumerate(electrodes): p = figure(title="Electrode {}".format(str(elec)), tools=TOOLS, active_drag = "auto", active_scroll = "auto", x_axis_label="Frequency (Hz)", y_axis_label="db/Hz", toolbar_location="above") hover = HoverTool(tooltips=[('Frequency', '@x'), ('Power', '@y')], mode='vline') p.add_tools(hover) p.legend.click_policy="hide" if elec == 'avg': lfpPlot = np.mean(lfp, axis=1) color = avg_color elif isinstance(elec, Number) and elec <= sim.net.recXElectrode.nsites: lfpPlot = lfp[:, elec] color = colors[i%len(colors)] # Morlet wavelet transform method if transformMethod == 'morlet': from ..support.morlet import MorletSpec, index2ms Fs = int(1000.0/sim.cfg.recordStep) morletSpec = MorletSpec(lfpPlot, Fs, freqmin=minFreq, freqmax=maxFreq, freqstep=stepFreq) freqs = F = morletSpec.f spec = morletSpec.TFR signal = np.mean(spec, 1) ylabel = 'Power' # FFT transform method elif transformMethod == 'fft': Fs = int(1000.0/sim.cfg.recordStep) power = mlab.psd(lfpPlot, Fs=Fs, NFFT=NFFT, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=noverlap, pad_to=None, sides='default', scale_by_freq=None) if smooth: signal = _smooth1d(10*np.log10(power[0]), smooth) else: signal = 10*np.log10(power[0]) freqs = power[1] ylabel = 'Power (dB/Hz)' allFreqs.append(freqs) allSignal.append(signal) p.line(freqs[freqs<maxFreq], signal[freqs<maxFreq], color=color, line_width=3) figs['psd'].append(p) plot_layout = column(figs['psd'], sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="LFP Power Spectral Density", theme=theme) if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, basestring): filename = saveFig else: filename = sim.cfg.filename+'_iLFP_psd.html' file = open(filename, 'w') file.write(html) file.close() # Spectrogram ------------------------------ if 'spectrogram' in plots: import matplotlib.cm as cm from bokeh.transform import linear_cmap from bokeh.models import ColorBar from scipy import signal as spsig figs['spectro'] = [] logx_spec = [] # Morlet wavelet transform method if transformMethod == 'morlet': from ..support.morlet import MorletSpec, index2ms spec = [] for i,elec in enumerate(electrodes): if elec == 'avg': lfpPlot = np.mean(lfp, axis=1) elif isinstance(elec, Number) and elec <= sim.net.recXElectrode.nsites: lfpPlot = lfp[:, elec] fs = int(1000.0/sim.cfg.recordStep) t_spec = np.linspace(0, index2ms(len(lfpPlot), fs), len(lfpPlot)) spec.append(MorletSpec(lfpPlot, fs, freqmin=minFreq, freqmax=maxFreq, freqstep=stepFreq)) f = np.array(range(minFreq, maxFreq+1, stepFreq)) x_mesh, y_mesh = np.meshgrid(t_spec*1000.0, f[f<maxFreq]) vmin = np.array([s.TFR for s in spec]).min() vmax = np.array([s.TFR for s in spec]).max() for i,elec in enumerate(electrodes): T = timeRange F = spec[i].f if normSpec: spec[i].TFR = spec[i].TFR / vmax S = spec[i].TFR vc = [0, 1] else: S = spec[i].TFR vc = [vmin, vmax] p = figure( title="Electrode {}".format(str(elec)), tools=TOOLS, active_drag = "auto", active_scroll = "auto", x_range=(0, timeRange[1]), y_range=(0, maxFreq), x_axis_label = "Time (ms)", y_axis_label = "Frequency(Hz)", toolbar_location="above", #tooltips = [("Time", "$x"), ("Frequency", "$y"), ("Power", "@image")], ) mapper = linear_cmap(field_name='dB/Hz', palette=Viridis256, low=vmin, high=vmax) color_bar = ColorBar(color_mapper=mapper['transform'], location=(0,0), label_standoff=15, title='Power') p.image(image=[x_mesh, y_mesh, S], x=0, y=0, color_mapper=mapper['transform'], dw=timeRange[1], dh=100) p.add_layout(color_bar, 'right') figs['spectro'].append(p) # FFT transform method elif transformMethod == 'fft': for i,elec in enumerate(electrodes): if elec == 'avg': lfpPlot = np.mean(lfp, axis=1) elif isinstance(elec, Number) and elec <= sim.net.recXElectrode.nsites: lfpPlot = lfp[:, elec] # creates spectrogram over a range of data # from: http://joelyancey.com/lfp-python-practice/ fs = int(1000.0/sim.cfg.recordStep) f, t_spec, x_spec = spsig.spectrogram(lfpPlot, fs=fs, window='hanning', detrend=mlab.detrend_none, nperseg=nperseg, noverlap=noverlap, nfft=NFFT, mode='psd') x_mesh, y_mesh = np.meshgrid(t_spec*1000.0, f[f<maxFreq]) logx_spec.append(10*np.log10(x_spec[f<maxFreq])) vmin = np.array(logx_spec).min() vmax = np.array(logx_spec).max() for i,elec in enumerate(electrodes): p = figure( title="Electrode {}".format(str(elec)), tools=TOOLS, active_drag = "auto", active_scroll = "auto", x_range=(0, timeRange[1]), y_range=(0, maxFreq), x_axis_label = "Time (ms)", y_axis_label = "Frequency(Hz)") mapper = linear_cmap(field_name='dB/Hz', palette=Viridis256, low=vmin, high=vmax) color_bar = ColorBar(color_mapper=mapper['transform'], width=8, location=(0,0), label_standoff=7, major_tick_line_color=None) p.image(image=[x_mesh, y_mesh, logx_spec[i]], x=0, y=0, color_mapper=mapper['transform'], dw=timeRange[1], dh=100) p.add_layout(color_bar, 'right') figs['spectro'].append(p) plot_layout = column(figs['spectro'], sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="LFP Power Spectral Density", theme=theme) if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename + '_iLFP_timefreq.html' file = open(filename, 'w') file.write(html) file.close() return html
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive connectivity # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotConn(includePre=['all'], includePost=['all'], feature='strength', orderBy='gid', figSize=(10,10), groupBy='pop', groupByIntervalPre=None, groupByIntervalPost=None, removeWeightNorm=False, graphType='matrix', synOrConn='syn', synMech=None, connsFile=None, tagsFile=None, clim=None, fontSize=12, saveData=None, saveFig=None, showFig=False, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotConn`> Parameters ---------- includePre : list <Short description of includePre> **Default:** ``['all']`` **Options:** ``<option>`` <description of option> includePost : list <Short description of includePost> **Default:** ``['all']`` **Options:** ``<option>`` <description of option> feature : str <Short description of feature> **Default:** ``'strength'`` **Options:** ``<option>`` <description of option> orderBy : str <Short description of orderBy> **Default:** ``'gid'`` **Options:** ``<option>`` <description of option> figSize : tuple <Short description of figSize> **Default:** ``(10, 10)`` **Options:** ``<option>`` <description of option> groupBy : str <Short description of groupBy> **Default:** ``'pop'`` **Options:** ``<option>`` <description of option> groupByIntervalPre : <``None``?> <Short description of groupByIntervalPre> **Default:** ``None`` **Options:** ``<option>`` <description of option> groupByIntervalPost : <``None``?> <Short description of groupByIntervalPost> **Default:** ``None`` **Options:** ``<option>`` <description of option> removeWeightNorm : bool <Short description of removeWeightNorm> **Default:** ``False`` **Options:** ``<option>`` <description of option> graphType : str <Short description of graphType> **Default:** ``'matrix'`` **Options:** ``<option>`` <description of option> synOrConn : str <Short description of synOrConn> **Default:** ``'syn'`` **Options:** ``<option>`` <description of option> synMech : <``None``?> <Short description of synMech> **Default:** ``None`` **Options:** ``<option>`` <description of option> connsFile : <``None``?> <Short description of connsFile> **Default:** ``None`` **Options:** ``<option>`` <description of option> tagsFile : <``None``?> <Short description of tagsFile> **Default:** ``None`` **Options:** ``<option>`` <description of option> clim : <``None``?> <Short description of clim> **Default:** ``None`` **Options:** ``<option>`` <description of option> fontSize : int <Short description of fontSize> **Default:** ``12`` **Options:** ``<option>`` <description of option> saveData : <``None``?> <Short description of saveData> **Default:** ``None`` **Options:** ``<option>`` <description of option> saveFig : <``None``?> <Short description of saveFig> **Default:** ``None`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``False`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from netpyne import sim from netpyne.analysis import network from bokeh.plotting import figure, show from bokeh.transform import linear_cmap from bokeh.palettes import Viridis256 from bokeh.palettes import viridis from bokeh.models import ColorBar from bokeh.embed import file_html from bokeh.resources import CDN from bokeh.layouts import layout from bokeh.colors import RGB print('Plotting interactive connectivity matrix...') theme = applyTheme(kwargs) if connsFile and tagsFile: connMatrix, pre, post = network._plotConnCalculateFromFile(includePre, includePost, feature, orderBy, groupBy, groupByIntervalPre, groupByIntervalPost, synOrConn, synMech, connsFile, tagsFile, removeWeightNorm) else: connMatrix, pre, post = network._plotConnCalculateFromSim(includePre, includePost, feature, orderBy, groupBy, groupByIntervalPre, groupByIntervalPost, synOrConn, synMech, removeWeightNorm) if connMatrix is None: print(" Error calculating connMatrix in iplotConn()") return None # TODO: set plot font size in Bokeh # for groupBy = 'cell' (needed to properly format data for Bokeh) if groupBy == 'cell': pre = [str(cell['gid']) for cell in pre] post = [str(cell['gid']) for cell in post] # matrix plot if graphType == 'matrix': pandas_data = pd.DataFrame(data=connMatrix, index=pre, columns=post) pandas_data.index.name = 'pre' pandas_data.columns.name = 'post' bokeh_data = pandas_data.reset_index().melt(id_vars=['pre'], value_name=feature) # the colormapper doesn't work if both high and low values are the same low = np.nanmin(connMatrix) high = np.nanmax(connMatrix) if low == high: low = low - 0.1 * low high = high + 0.1 * high conn_colormapper = linear_cmap( field_name=feature, palette=Viridis256, low=low, high=high, nan_color='white' ) conn_colorbar = ColorBar(color_mapper=conn_colormapper['transform']) conn_colorbar.location = (0, 0) conn_colorbar.title = feature fig = figure( x_range=pandas_data.columns.values, y_range=np.flip(pandas_data.index.values), tools = 'hover,save,pan,box_zoom,reset,wheel_zoom', active_drag = "auto", active_scroll = "auto", tooltips=[('Pre', '@pre'), ('Post', '@post'), (feature, '@' + feature)], title='Connection ' + feature + ' matrix', toolbar_location='below', x_axis_location='above' ) fig.xaxis.axis_label = pandas_data.columns.name fig.yaxis.axis_label = pandas_data.index.name fig.yaxis.major_label_orientation = 'horizontal' fig.xaxis.major_label_orientation = 'vertical' fig.rect( source=bokeh_data, x='post', y='pre', width=1, height=1, color=conn_colormapper ) fig.add_layout(conn_colorbar, 'right') # TODO: add grid lines? # stacked bar graph elif graphType == 'bar': if groupBy == 'pop': popsPre, popsPost = pre, post #colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] # bokeh only handles integer rgb values from 0-255 colors = colorList bar_colors = colors[0:len(popsPre)] data = {'post' : popsPost} for popIndex, pop in enumerate(popsPre): data[pop] = connMatrix[popIndex, :] fig = figure( x_range=popsPost, title='Connection ' + feature + ' stacked bar graph', toolbar_location=None, tools='hover,save,pan,box_zoom,reset,wheel_zoom', active_drag = "auto", active_scroll = "auto", tooltips=[('Pre', '$name'), ('Post', '@post'), (feature, '@$name')], ) fig.vbar_stack( popsPre, x='post', width=0.9, color=bar_colors, source=data, legend_label=popsPre, ) fig.xgrid.grid_line_color = None fig.legend.title = 'Pre' fig.xaxis.axis_label = 'Post' fig.yaxis.axis_label = feature elif groupBy == 'cell': print(' Error: plotConn graphType="bar" with groupBy="cell" not yet implemented') return None elif graphType == 'pie': print(' Error: plotConn graphType="pie" not yet implemented') return None plot_layout = layout([fig], sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title='Connection ' + feature + ' matrix', theme=theme) #save figure data if saveData: figData = {'connMatrix': connMatrix, 'feature': feature, 'groupBy': groupBy, 'includePre': includePre, 'includePost': includePost, 'saveData': saveData, 'saveFig': saveFig, 'showFig': showFig} _saveFigData(figData, saveData, 'conn') # save figure if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename+'_iconn_' + groupBy + '_' + feature + '_' + graphType + '_' + orderBy + '.html' file = open(filename, 'w') file.write(html) file.close() # show fig if showFig: show(fig) return html
# ------------------------------------------------------------------------------------------------------------------- ## Plot 2D representation of network cell positions and connections # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplot2Dnet(include=['allCells'], view='xy', showConns=True, popColors=None, tagsFile=None, figSize=(12,12), fontSize=12, saveData=None, saveFig=None, showFig=True, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplot2Dnet`> Parameters ---------- include : list List of presynaptic cells to include. **Default:** ``['allCells']`` **Options:** ``['all']`` plots all cells and stimulations, ``['allNetStims']`` plots just stimulations, ``['popName1']`` plots a single population, ``['popName1', 'popName2']`` plots multiple populations, ``[120]`` plots a single cell, ``[120, 130]`` plots multiple cells, ``[('popName1', 56)]`` plots a cell from a specific population, ``[('popName1', [0, 1]), ('popName2', [4, 5, 6])]``, plots cells from multiple populations view : str Perspective of view. **Default:** ``'xy'`` front view, **Options:** ``'xz'`` top-down view showConns : bool Whether to show connections or not. **Default:** ``True`` **Options:** ``<option>`` <description of option> popColors : dict Dictionary with custom color (value) used for each population (key). **Default:** ``None`` uses standard colors **Options:** ``<option>`` <description of option> tagsFile : str Path to a saved tags file to use in connectivity plot. **Default:** ``None`` **Options:** ``<option>`` <description of option> figSize : list [width, height] Size of figure in inches. **Default:** ``(12, 12)`` **Options:** ``<option>`` <description of option> fontSize : int Font size on figure. **Default:** ``12`` **Options:** ``<option>`` <description of option> saveData : bool or str Whether and where to save the data used to generate the plot. **Default:** ``False`` **Options:** ``True`` autosaves the data, ``'/path/filename.ext'`` saves to a custom path and filename, valid file extensions are ``'.pkl'`` and ``'.json'`` saveFig : bool or str Whether and where to save the figure. **Default:** ``False`` **Options:** ``True`` autosaves the figure, ``'/path/filename.ext'`` saves to a custom path and filename, valid file extensions are ``'.png'``, ``'.jpg'``, ``'.eps'``, and ``'.tiff'`` showFig : bool Shows the figure if ``True``. **Default:** ``True`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* Returns ------- """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout from bokeh.models import Legend from bokeh.colors import RGB from bokeh.models.annotations import Title print('Plotting interactive 2D representation of network cell locations and connections...') theme = applyTheme(kwargs) TOOLS = 'hover,save,pan,box_zoom,reset,wheel_zoom', if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] # bokeh only handles integer rgb values from 0-255 else: colors = kwargs['palette'] # front view if view == 'xy': ycoord = 'y' elif view == 'xz': ycoord = 'z' if tagsFile: print('Loading tags file...') import json with open(tagsFile, 'r') as fileObj: tagsTmp = json.load(fileObj)['tags'] tagsFormat = tagsTmp.pop('format', []) tags = {int(k): v for k,v in tagsTmp.items()} # find method to load json with int keys? del tagsTmp # set indices of fields to read compact format (no keys) missing = [] popIndex = tagsFormat.index('pop') if 'pop' in tagsFormat else missing.append('pop') xIndex = tagsFormat.index('x') if 'x' in tagsFormat else missing.append('x') yIndex = tagsFormat.index('y') if 'y' in tagsFormat else missing.append('y') zIndex = tagsFormat.index('z') if 'z' in tagsFormat else missing.append('z') if len(missing) > 0: print("Missing:") print(missing) return None, None, None # find pre and post cells if tags: cellGids = getCellsIncludeTags(include, tags, tagsFormat) popLabels = list(set([tags[gid][popIndex] for gid in cellGids])) # pop and cell colors popColorsTmp = {popLabel: colorList[ipop%len(colorList)] for ipop,popLabel in enumerate(popLabels)} # dict with color for each pop if popColors: popColorsTmp.update(popColors) popColors = popColorsTmp cellColors = [popColors[tags[gid][popIndex]] for gid in cellGids] # cell locations posX = [tags[gid][xIndex] for gid in cellGids] # get all x positions if ycoord == 'y': posY = [tags[gid][yIndex] for gid in cellGids] # get all y positions elif ycoord == 'z': posY = [tags[gid][zIndex] for gid in cellGids] # get all y positions else: print('Error loading tags from file') return None else: cells, cellGids, _ = getCellsInclude(include) selectedPops = [cell['tags']['pop'] for cell in cells] popLabels = [pop for pop in sim.net.allPops if pop in selectedPops] # preserves original ordering # pop and cell colors popColorsTmp = {popLabel: colorList[ipop%len(colorList)] for ipop,popLabel in enumerate(popLabels)} # dict with color for each pop if popColors: popColorsTmp.update(popColors) popColors = popColorsTmp cellColors = ['#%02x%02x%02x' % tuple([int(col * 255) for col in popColors[cell['tags']['pop']]]) for cell in cells] # cell locations posX = [cell['tags']['x'] for cell in cells] # get all x positions posY = [cell['tags'][ycoord] for cell in cells] # get all y positions fig = figure( title="2D Network representation", tools=TOOLS, active_drag = "auto", active_scroll = "auto", tooltips=[('y location', '@y'), ('x location', '@x')], x_axis_label="x (um)", y_axis_label='y (um)', #x_range=[min(posX)-0.05*max(posX),1.05*max(posX)], #y_range=[1.05*max(posY), min(posY)-0.05*max(posY)], toolbar_location='above', match_aspect = True, #aspect_scale = 0.001, ) if 'radius' in kwargs: radius = kwargs['radius'] else: radius = 1.0 fig.scatter(posX, posY, radius=radius, fill_color=cellColors, line_color=None) # plot cell soma positions posXpre, posYpre = [], [] posXpost, posYpost = [], [] if showConns and not tagsFile: for postCell in cells: for con in postCell['conns']: # plot connections between cells if not isinstance(con['preGid'], basestring) and con['preGid'] in cellGids: posXpre,posYpre = next(((cell['tags']['x'],cell['tags'][ycoord]) for cell in cells if cell['gid']==con['preGid']), None) posXpost, posYpost = postCell['tags']['x'], postCell['tags'][ycoord] if kwargs.get('theme', '') == 'gui': color = 'yellow' else: color='red' if con['synMech'] in ['inh', 'GABA', 'GABAA', 'GABAB']: if kwargs.get('theme', '') == 'gui': color = 'lightcyan' else: color = 'blue' width = 0.1 #50*con['weight'] fig.line([posXpre, posXpost], [posYpre, posYpost], color=color, line_width=width) # plot line from pre to post fontsiz = fontSize for popLabel in popLabels: #fig.line(0,0,color=tuple([int(x*255) for x in popColors[popLabel]]), legend_label=popLabel) fig.circle(0,0,color=tuple([int(x*255) for x in popColors[popLabel]]), legend_label=popLabel, line_width=0, radius=0) legend = Legend(location=(10,0)) legend.click_policy='hide' fig.add_layout(legend, 'right') plot_layout = layout([fig], sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="2D Net Plot", theme=theme) # save figure data if saveData: figData = {'posX': posX, 'posY': posY, 'posX': cellColors, 'posXpre': posXpre, 'posXpost': posXpost, 'posYpre': posYpre, 'posYpost': posYpost, 'include': include, 'saveData': saveData, 'saveFig': saveFig, 'showFig': showFig} _saveFigData(figData, saveData, '2Dnet') # save figure if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename+'_i2Dnet_.html' file = open(filename, 'w') file.write(html) file.close() # show fig if showFig: show(fig) return html, {'include': include, 'posX': posX, 'posY': posY, 'posXpre': posXpre, 'posXpost': posXpost, 'posYpre': posYpre, 'posYpost': posYpost}
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive RxD concentration # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotRxDConcentration(speciesLabel, regionLabel, plane='xy', saveFig=None, showFig=True, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotRxDConcentration`> Parameters ---------- speciesLabel : <type> <Short description of speciesLabel> **Default:** *required* regionLabel : <type> <Short description of regionLabel> **Default:** *required* plane : str <Short description of plane> **Default:** ``'xy'`` **Options:** ``<option>`` <description of option> saveFig : <``None``?> <Short description of saveFig> **Default:** ``None`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``True`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout, column, row from bokeh.colors import RGB from bokeh.transform import linear_cmap from bokeh.models import ColorBar print('Plotting interactive RxD concentration ...') theme = applyTheme(kwargs) if not 'palette' in kwargs: from bokeh.palettes import Viridis256 colors = Viridis256 #colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] else: colors = kwargs['palette'] TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" species = sim.net.rxd['species'][speciesLabel]['hObj'] region = sim.net.rxd['regions'][regionLabel]['hObj'] plane2mean = {'xz': 1, 'xy': 2} data = species[region].states3d[:].mean(plane2mean[plane]).T data = np.flipud(data) # This does not flip the axis values, should improve extent = [] extent.append(sim.net.rxd['regions'][regionLabel][plane[0] + 'lo']) extent.append(sim.net.rxd['regions'][regionLabel][plane[0] + 'hi']) extent.append(sim.net.rxd['regions'][regionLabel][plane[1] + 'lo']) extent.append(sim.net.rxd['regions'][regionLabel][plane[1] + 'hi']) dw = extent[1] - extent[0] dh = extent[3] - extent[2] low = np.nanmin(data) high = np.nanmax(data) if low == high: low = low - 0.1 * low high = high + 0.1 * high conc_colormapper = linear_cmap( field_name = '[' + species.name + '] (mM)', palette = colors, low = low, high = high, nan_color = 'white', ) conc_colorbar = ColorBar( color_mapper = conc_colormapper['transform'], label_standoff = 12, title_standoff = 12, ) conc_colorbar.title = '[' + species.name + '] (mM)' fig = figure( title = 'RxD: ' + species.name + ' concentration', toolbar_location = 'above', tools = 'hover,save,pan,box_zoom,reset,wheel_zoom', active_drag = "auto", active_scroll = "auto", tooltips = [("x", "$x"), ("y", "$y"), ("value", "@image")], match_aspect = True, x_axis_label = plane[0] + " location (um)", y_axis_label = plane[1] + " location (um)", ) fig.image(image=[data], x=extent[0], y=extent[2], dw=dw, dh=dh, color_mapper=conc_colormapper['transform'], level="image") fig.add_layout(conc_colorbar, 'right') plot_layout = layout([fig], sizing_mode='scale_height') html = file_html(plot_layout, CDN, title="RxD Concentration", theme=theme) if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename + '_RxD_concentration.html' outfile = open(filename, 'w') outfile.write(html) outfile.close() return html, data
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive spike statistics # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotSpikeStats(include=['eachPop', 'allCells'], statDataIn={}, timeRange=None, graphType='boxplot', stats=['rate', 'isicv'], bins=50, histlogy=False, histlogx=False, histmin=0.0, density=False, includeRate0=False, legendLabels=None, normfit=False, histShading=True, xlim=None, popColors={}, saveData=None, saveFig=None, showFig=True, **kwargs): """ Function for/to <short description of `netpyne.analysis.interactive.iplotSpikeStats`> Parameters ---------- include : list <Short description of include> **Default:** ``['eachPop', 'allCells']`` **Options:** ``<option>`` <description of option> statDataIn : dict <Short description of statDataIn> **Default:** ``{}`` **Options:** ``<option>`` <description of option> timeRange : <``None``?> <Short description of timeRange> **Default:** ``None`` **Options:** ``<option>`` <description of option> graphType : str <Short description of graphType> **Default:** ``'boxplot'`` **Options:** ``<option>`` <description of option> stats : list <Short description of stats> **Default:** ``['rate', 'isicv']`` **Options:** ``<option>`` <description of option> bins : int <Short description of bins> **Default:** ``50`` **Options:** ``<option>`` <description of option> histlogy : bool <Short description of histlogy> **Default:** ``False`` **Options:** ``<option>`` <description of option> histlogx : bool <Short description of histlogx> **Default:** ``False`` **Options:** ``<option>`` <description of option> histmin : float <Short description of histmin> **Default:** ``0.0`` **Options:** ``<option>`` <description of option> density : bool <Short description of density> **Default:** ``False`` **Options:** ``<option>`` <description of option> includeRate0 : bool <Short description of includeRate0> **Default:** ``False`` **Options:** ``<option>`` <description of option> legendLabels : <``None``?> <Short description of legendLabels> **Default:** ``None`` **Options:** ``<option>`` <description of option> normfit : bool <Short description of normfit> **Default:** ``False`` **Options:** ``<option>`` <description of option> histShading : bool <Short description of histShading> **Default:** ``True`` **Options:** ``<option>`` <description of option> xlim : <``None``?> <Short description of xlim> **Default:** ``None`` **Options:** ``<option>`` <description of option> popColors : dict <Short description of popColors> **Default:** ``{}`` **Options:** ``<option>`` <description of option> saveData : <``None``?> <Short description of saveData> **Default:** ``None`` **Options:** ``<option>`` <description of option> saveFig : <``None``?> <Short description of saveFig> **Default:** ``None`` **Options:** ``<option>`` <description of option> showFig : bool <Short description of showFig> **Default:** ``True`` **Options:** ``<option>`` <description of option> kwargs : <type> <Short description of kwargs> **Default:** *required* """ from .. import sim from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout, column, row from bokeh.colors import RGB from bokeh.transform import linear_cmap from bokeh.transform import factor_cmap from bokeh.models import ColorBar from bokeh.palettes import Spectral6 from bokeh.models.mappers import CategoricalColorMapper print('Plotting interactive spike statistics ...') TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" xlabels = {'rate': 'Rate (Hz)', 'isicv': 'Irregularity (ISI CV)', 'sync': 'Synchrony', 'pairsync': 'Pairwise synchrony'} # Replace 'eachPop' with list of pops if 'eachPop' in include: include.remove('eachPop') for pop in sim.net.allPops: include.append(pop) # time range if timeRange is None: timeRange = [0, sim.cfg.duration] for stat in stats: theme = applyTheme(kwargs) if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] else: colors = kwargs['palette'] xlabel = xlabels[stat] statData = [] gidsData = [] ynormsData = [] # Calculate data for each entry in include for iplot,subset in enumerate(include): if stat in statDataIn: statData = statDataIn[stat]['statData'] gidsData = statDataIn[stat].get('gidsData', []) ynormsData = statDataIn[stat].get('ynormsData', []) else: cells, cellGids, netStimLabels = getCellsInclude([subset]) numNetStims = 0 # Select cells to include if len(cellGids) > 0: try: spkinds,spkts = list(zip(*[(spkgid,spkt) for spkgid,spkt in zip(sim.allSimData['spkid'],sim.allSimData['spkt']) if spkgid in cellGids])) except: spkinds,spkts = [],[] else: spkinds,spkts = [],[] # Add NetStim spikes spkts, spkinds = list(spkts), list(spkinds) numNetStims = 0 if 'stims' in sim.allSimData: for netStimLabel in netStimLabels: netStimSpks = [spk for cell,stims in sim.allSimData['stims'].items() \ for stimLabel,stimSpks in stims.items() for spk in stimSpks if stimLabel == netStimLabel] if len(netStimSpks) > 0: lastInd = max(spkinds) if len(spkinds)>0 else 0 spktsNew = netStimSpks spkindsNew = [lastInd+1+i for i in range(len(netStimSpks))] spkts.extend(spktsNew) spkinds.extend(spkindsNew) numNetStims += 1 try: spkts,spkinds = list(zip(*[(spkt, spkind) for spkt, spkind in zip(spkts, spkinds) if timeRange[0] <= spkt <= timeRange[1]])) except: pass # rate stats if stat == 'rate': toRate = 1e3/(timeRange[1]-timeRange[0]) if includeRate0: rates = [spkinds.count(gid)*toRate for gid in cellGids] \ if len(spkinds)>0 else [0]*len(cellGids) #cellGids] #set(spkinds)] else: rates = [spkinds.count(gid)*toRate for gid in set(spkinds)] \ if len(spkinds)>0 else [0] #cellGids] #set(spkinds)] statData.append(rates) # Inter-spike interval (ISI) coefficient of variation (CV) stats elif stat == 'isicv': import numpy as np spkmat = [[spkt for spkind,spkt in zip(spkinds,spkts) if spkind==gid] for gid in set(spkinds)] isimat = [[t - s for s, t in zip(spks, spks[1:])] for spks in spkmat if len(spks)>10] isicv = [np.std(x) / np.mean(x) if len(x)>0 else 0 for x in isimat] # if len(x)>0] statData.append(isicv) # synchrony elif stat in ['sync', 'pairsync']: try: import pyspike except: print("Error: plotSpikeStats() requires the PySpike python package \ to calculate synchrony (try: pip install pyspike)") return 0 spkmat = [pyspike.SpikeTrain([spkt for spkind,spkt in zip(spkinds,spkts) if spkind==gid], timeRange) for gid in set(spkinds)] if stat == 'sync': # (SPIKE-Sync measure)' # see http://www.scholarpedia.org/article/Measures_of_spike_train_synchrony syncMat = [pyspike.spike_sync(spkmat)] #graphType = 'bar' elif stat == 'pairsync': # (SPIKE-Sync measure)' # see http://www.scholarpedia.org/article/Measures_of_spike_train_synchrony syncMat = np.mean(pyspike.spike_sync_matrix(spkmat), 0) statData.append(syncMat) # boxplot if graphType == 'boxplot': line_width = 2 line_color = 'black' if 'theme' in kwargs: if kwargs['theme'] is not None: if kwargs['theme'] == 'gui' or 'dark' in kwargs['theme'] or kwargs['theme'] == 'contrast' or 'lack' in kwargs['theme']: line_color = 'lightgray' labels = legendLabels if legendLabels else include data_lists = statData[::-1] data_lists.reverse() box_colors = colors[0:len(labels)] if include[0] == 'allCells': del box_colors[-1] box_colors.insert(0, ('darkslategray')) data = {} for label, data_list in zip(labels, data_lists): data[label] = data_list fig = figure( title = 'Spike statistics: ' + xlabels[stat], toolbar_location = 'above', tools = 'hover,save,pan,box_zoom,reset,wheel_zoom', active_drag = "auto", active_scroll = "auto", tooltips = [(stat, "$y")], x_axis_label = 'Population', y_axis_label = xlabel, x_range = labels ) fig.xgrid.visible = False df = pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in data.items() ])) q1 = df.quantile(q=0.25) q2 = df.quantile(q=0.5) q3 = df.quantile(q=0.75) iqr = q3 - q1 upper = q3 + 1.5 * iqr lower = q1 - 1.5 * iqr qmin = df.quantile(q=0.00) qmax = df.quantile(q=1.00) qmean = df.mean() out_highs = df[df > upper] out_lows = df[df < lower] # outliers for index, label in enumerate(labels): out_x = [] out_y = [] out_high = out_highs[label].dropna() if not out_high.empty: upper[label] = df[df < upper][label].max() for val in out_high: out_x.append(label) out_y.append(val) else: upper[label] = qmax[label] out_low = out_lows[label].dropna() if not out_low.empty: lower[label] = df[df > lower][label].min() for val in out_low: out_x.append(label) out_y.append(val) else: lower[label] = qmin[label] if out_x: fig.circle_x(out_x, out_y, size=10, fill_color=box_colors[index], fill_alpha=0.8, line_color=line_color) # stems fig.segment(labels, upper, labels, q3, line_color=line_color, line_width=line_width) fig.segment(labels, lower, labels, q1, line_color=line_color, line_width=line_width) # boxes fig.vbar(labels, 0.7, q2, q3, line_color=line_color, line_width=line_width, fill_color=box_colors) fig.vbar(labels, 0.7, q1, q2, line_color=line_color, line_width=line_width, fill_color=box_colors) # whiskers (almost-0 height rects simpler than segments) fig.rect(labels, lower, 20, 1, width_units='screen', height_units='screen', line_color=line_color, line_width=line_width) fig.rect(labels, upper, 20, 1, width_units='screen', height_units='screen', line_color=line_color, line_width=line_width) # means fig.circle_cross(labels, qmean, size=10, fill_color='white', fill_alpha=0.5, line_color='black') else: raise Exception('Only boxplot is currently supported in iplotSpikeStats.') plot_layout = layout([fig], sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="Spike Statistics", theme=theme) if showFig: show(plot_layout) if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename + '_spike_' + stat + '.html' outfile = open(filename, 'w') outfile.write(html) outfile.close() return html
# ------------------------------------------------------------------------------------------------------------------- ## Plot interactive Granger causality # -------------------------------------------------------------------------------------------------------------------
[docs]@exception def iplotGranger(cells1=None, cells2=None, spks1=None, spks2=None, label1=None, label2=None, timeRange=None, binSize=5, showFig=True, saveFig=False, **kwargs): """ Function to plot the Granger Causality between two groups of cells Parameters ---------- cells1 : list Subset of cells from which to obtain spike train 1. **Default:** ``None`` **Options:** ``['all']`` plots all cells and stimulations, ``['allNetStims']`` plots just stimulations, ``['popName1']`` plots a single population, ``['popName1', 'popName2']`` plots multiple populations, ``[120]`` plots a single cell, ``[120, 130]`` plots multiple cells, ``[('popName1', 56)]`` plots a cell from a specific population, ``[('popName1', [0, 1]), ('popName2', [4, 5, 6])]``, plots cells from multiple populations cells2 : list Subset of cells from which to obtain spike train 2. **Default:** ``None`` **Options:** same as for `cells1` spks1 : list Spike train 1; list of spike times; if omitted then obtains spikes from cells1. **Default:** ``None`` spks2 : list Spike train 2; list of spike times; if omitted then obtains spikes from cells2. **Default:** ``None`` label1 : str Label for spike train 1 to use in plot. **Default:** ``None`` label2 : str Label for spike train 2 to use in plot. **Default:** ``None`` timeRange : list [min, max] Range of time to calculate nTE in ms. **Default:** ``None`` uses the entire simulation time range binSize : int Bin size used to convert spike times into histogram. **Default:** ``5`` showFig : bool Shows the figure if ``True``. **Default:** ``True`` saveFig : bool or str Whether and where to save the figure. **Default:** ``False`` **Options:** ``True`` autosaves the figure, ``'/path/filename.ext'`` saves to a custom path and filename, valid file extensions are ``'.png'``, ``'.jpg'``, ``'.eps'``, and ``'.tiff'`` """ from .. import sim from netpyne.support.bsmart import pwcausalr from bokeh.plotting import figure, show from bokeh.resources import CDN from bokeh.embed import file_html from bokeh.layouts import layout theme = None from bokeh.colors import RGB if not spks1: if not cells1: pops = list(sim.net.allPops.keys()) if len(pops) == 1: cells1 = [0] if not label1: label1 = 'cell_0' if not cells2: cells2 = ['allCells'] if not label2: label2 = 'all cells' elif len(pops) > 1: cells1 = [pops[0]] if not label1: label1 = pops[0] if not cells2: cells2 = [pops[1]] if not label2: label2 = pops[1] cells, cellGids, netStimPops = getCellsInclude(cells1) numNetStims = 0 # Select cells to include if len(cellGids) > 0: try: spkts = [spkt for spkgid,spkt in zip(sim.allSimData['spkid'],sim.allSimData['spkt']) if spkgid in cellGids] except: spkts = [] else: spkts = [] # Add NetStim spikes spkts = list(spkts) numNetStims = 0 for netStimPop in netStimPops: if 'stims' in sim.allSimData: cellStims = [cellStim for cell,cellStim in sim.allSimData['stims'].items() if netStimPop in cellStim] if len(cellStims) > 0: spktsNew = [spkt for cellStim in cellStims for spkt in cellStim[netStimPop] ] spkts.extend(spktsNew) numNetStims += len(cellStims) spks1 = list(spkts) if not spks2: # if doesnt contain a list of spk times, obtain from cells specified cells, cellGids, netStimPops = getCellsInclude(cells2) numNetStims = 0 # Select cells to include if len(cellGids) > 0: try: spkts = [spkt for spkgid,spkt in zip(sim.allSimData['spkid'],sim.allSimData['spkt']) if spkgid in cellGids] except: spkts = [] else: spkts = [] # Add NetStim spikes spkts = list(spkts) numNetStims = 0 for netStimPop in netStimPops: if 'stims' in sim.allSimData: cellStims = [cellStim for cell,cellStim in sim.allSimData['stims'].items() if netStimPop in cellStim] if len(cellStims) > 0: spktsNew = [spkt for cellStim in cellStims for spkt in cellStim[netStimPop] ] spkts.extend(spktsNew) numNetStims += len(cellStims) spks2 = list(spkts) # time range if timeRange is None: if getattr(sim, 'cfg', None): timeRange = [0, sim.cfg.duration] else: timeRange = [0, max(spks1+spks2)] histo1 = np.histogram(spks1, bins = np.arange(timeRange[0], timeRange[1], binSize)) histoCount1 = histo1[0] histo2 = np.histogram(spks2, bins = np.arange(timeRange[0], timeRange[1], binSize)) histoCount2 = histo2[0] fs = int(1000/binSize) F, pp, cohe, Fx2y, Fy2x, Fxy = pwcausalr(np.array([histoCount1, histoCount2]), 1, len(histoCount1), 10, fs, int(fs/2)) if not label1: label1='spkTrain1' if not label2: label2='spkTrain2' if not 'palette' in kwargs: colors = [RGB(*[round(f * 255) for f in color]) for color in colorList] else: colors = kwargs['palette'] theme = applyTheme(kwargs) TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select" fig = figure(title="Granger Causality", tools=TOOLS, toolbar_location='above', x_axis_label="Frequency (Hz)", y_axis_label="Granger Causality") hover = HoverTool(tooltips=[('Frequency', '@x'), ('Causality', '@y')], mode='vline') fig.add_tools(hover) #fig.line(t, v, legend=k, color=colors[i], line_width=2.0) fig.line(F, Fy2x[0], legend_label=label2+' --> '+label1, color=colors[0], line_width=2.0) fig.line(F, Fx2y[0], legend_label=label1+' --> '+label2, color=colors[1], line_width=2.0) fig.legend.location = "top_right" fig.legend.click_policy = "hide" plot_layout = layout(fig, sizing_mode='stretch_both') html = file_html(plot_layout, CDN, title="Granger Causality Plot", theme=theme) if showFig: show(fig) if saveFig: if isinstance(saveFig, str): filename = saveFig else: filename = sim.cfg.filename + '_granger.html' outfile = open(filename, 'w') outfile.write(html) outfile.close() return html