def granger(cells1=[],
cells2=[],
spks1=None,
spks2=None,
label1='spkTrain1',
label2='spkTrain2',
timeRange=None,
binSize=5,
testGranger=False,
plotFig=True,
saveData=None,
saveFig=None,
showFig=True):
'''
Calculate and optionally plot Granger Causality
- cells1 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Subset of cells from which to obtain spike train 1 (default: [])
- cells2 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Subset of cells from which to obtain spike train 2 (default: [])
- 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 (string): Label for spike train 1 to use in plot
- label2 (string): Label for spike train 2 to use in plot
- timeRange ([min, max]): Range of time to calculate nTE in ms (default: [0,cfg.duration])
- binSize (int): Bin size used to convert spike times into histogram
- plotFig (True|False): Whether to plot a figure showing Granger Causality Fx2y and Fy2x
- saveData (None|'fileName'): File name where to save the final data used to generate the figure (default: None)
- saveFig (None|'fileName'): File name where to save the figure;
if set to True uses filename from simConfig (default: None)(default: None)
- showFig (True|False): Whether to show the figure or not;
if set to True uses filename from simConfig (default: None)
- Returns
F: list of freqs
Fx2y: causality measure from x to y
Fy2x: causality from y to x
Fxy: instantaneous causality between x and y
fig: Figure handle
'''
from .. import sim
import numpy as np
from netpyne.support.bsmart import pwcausalr
if not spks1: # if doesnt contain a list of spk times, obtain from cells specified
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))
# check reliability
if testGranger:
import scipy
''' Option 1: granger causality tests -- not sure how to interpret results
try:
from statsmodels.tsa.stattools import grangercausalitytests as gt
except:
print('To test Granger results please install the statsmodel package: "pip install statsmodel"')
exit()
tests = gt(np.array([histoCount1, histoCount2]).T, maxlag=10)
'''
# do N=25 shuffles of histoCount2
Nshuffle = 50
#x2yShuffleMaxValues = []
y2xShuffleMaxValues = []
histoCount2Shuffled = np.array(histoCount2)
for ishuffle in range(Nshuffle):
# for each calculate max Granger value (starting at freq index 1)
np.random.shuffle(histoCount2Shuffled)
_, _, _, Fx2yShuff, Fy2xShuff, _ = pwcausalr(
np.array([histoCount1, histoCount2Shuffled]), 1,
len(histoCount1), 10, fs, int(fs / 2))
#x2yShuffleMaxValues.append(max(Fx2yShuff[0][1:]))
y2xShuffleMaxValues.append(max(Fy2xShuff[0][1:]))
# calculate z-score
# |z| > 1.65 = p-value < 0.1 = confidence interval 90%
# |z| > 1.96 = p-value < 0.05 = confidence interval 95%
# |z| > 2.58 = p-value < 0.01 = confidence interval 99%
# https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm
# calculate mean and std
#x2yMean = np.mean(x2yShuffleMaxValues)
#x2yStd = np.std(x2yShuffleMaxValues)
#x2yZscore = abs(np.max(Fx2y[0][1:]) - x2yMean) / x2yStd
#x2yPvalue = scipy.stats.norm.sf(x2yZscore)
y2xMean = np.mean(y2xShuffleMaxValues)
y2xStd = np.std(y2xShuffleMaxValues)
y2xZscore = abs(np.max(Fy2x[0][1:]) - y2xMean) / y2xStd
y2xPvalue = scipy.stats.norm.sf(y2xZscore)
# plot granger
fig = -1
if plotFig:
fig = plt.figure()
plt.plot(F, Fy2x[0], label=label2 + ' -> ' + label1)
plt.plot(F, Fx2y[0], 'r', label=label1 + ' -> ' + label2)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Granger Causality')
plt.legend()
# save figure data
if saveData:
figData = {
'cells1': cells1,
'cells2': cells2,
'spks1': cells1,
'spks2': cells2,
'binSize': binSize,
'Fy2x': Fy2x[0],
'Fx2y': Fx2y[0],
'saveData': saveData,
'saveFig': saveFig,
'showFig': showFig
}
_saveFigData(figData, saveData, '2Dnet')
# save figure
if saveFig:
if isinstance(saveFig, basestring):
filename = saveFig
else:
filename = sim.cfg.filename + '_granger.png'
plt.savefig(filename)
# show fig
if showFig: _showFigure()
if testGranger:
return fig, {
'F': F,
'Fx2y': Fx2y[0],
'Fy2x': Fy2x[0],
'Fxy': Fxy[0],
'MaxFy2xZscore': y2xZscore,
'MaxFy2xPvalue': y2xPvalue
}
else:
return fig, {'F': F, 'Fx2y': Fx2y[0], 'Fy2x': Fy2x[0], 'Fxy': Fxy[0]}
def granger(cells1=[],
cells2=[],
spks1=None,
spks2=None,
label1='spkTrain1',
label2='spkTrain2',
timeRange=None,
binSize=5,
testGranger=False,
plotFig=True,
saveData=None,
saveFig=None,
showFig=True):
"""Calculate and optionally plot Granger Causality.
Parameters
----------
cells1 : list
Subset of cells from which to obtain spike train 1.
**Default:** ``[]``
**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:** ``[]``
**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:** ``'spkTrain1'``
label2 : str
Label for spike train 2 to use in plot.
**Default:** ``'spkTrain2'``
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:** ``20``
testGranger : bool
Whether to test the Granger calculation.
**Default:** ``False``
plotFig : bool
Whether to plot a figure showing Granger Causality Fx2y and Fy2x
**Default:** ``True``
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``
Returns
-------
(fig, dict)
A tuple consisting of the matplotlib figure handle and a dictionary containing the plot data.
See Also
--------
nTE :
Examples
--------
>>> import netpyne, netpyne.examples.example
>>> out = netpyne.analysis.granger()
"""
from .. import sim
import numpy as np
from netpyne.support.bsmart import pwcausalr
if not spks1: # if doesnt contain a list of spk times, obtain from cells specified
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))
# check reliability
if testGranger:
import scipy
''' Option 1: granger causality tests -- not sure how to interpret results
try:
from statsmodels.tsa.stattools import grangercausalitytests as gt
except:
print('To test Granger results please install the statsmodel package: "pip install statsmodel"')
exit()
tests = gt(np.array([histoCount1, histoCount2]).T, maxlag=10)
'''
# do N=25 shuffles of histoCount2
Nshuffle = 50
#x2yShuffleMaxValues = []
y2xShuffleMaxValues = []
histoCount2Shuffled = np.array(histoCount2)
for ishuffle in range(Nshuffle):
# for each calculate max Granger value (starting at freq index 1)
np.random.shuffle(histoCount2Shuffled)
_, _, _, Fx2yShuff, Fy2xShuff, _ = pwcausalr(
np.array([histoCount1, histoCount2Shuffled]), 1,
len(histoCount1), 10, fs, int(fs / 2))
#x2yShuffleMaxValues.append(max(Fx2yShuff[0][1:]))
y2xShuffleMaxValues.append(max(Fy2xShuff[0][1:]))
# calculate z-score
# |z| > 1.65 = p-value < 0.1 = confidence interval 90%
# |z| > 1.96 = p-value < 0.05 = confidence interval 95%
# |z| > 2.58 = p-value < 0.01 = confidence interval 99%
# https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm
# calculate mean and std
#x2yMean = np.mean(x2yShuffleMaxValues)
#x2yStd = np.std(x2yShuffleMaxValues)
#x2yZscore = abs(np.max(Fx2y[0][1:]) - x2yMean) / x2yStd
#x2yPvalue = scipy.stats.norm.sf(x2yZscore)
y2xMean = np.mean(y2xShuffleMaxValues)
y2xStd = np.std(y2xShuffleMaxValues)
y2xZscore = abs(np.max(Fy2x[0][1:]) - y2xMean) / y2xStd
y2xPvalue = scipy.stats.norm.sf(y2xZscore)
# plot granger
fig = -1
if plotFig:
fig = plt.figure()
plt.plot(F, Fy2x[0], label=label2 + ' -> ' + label1)
plt.plot(F, Fx2y[0], 'r', label=label1 + ' -> ' + label2)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Granger Causality')
plt.legend()
# save figure data
if saveData:
figData = {
'cells1': cells1,
'cells2': cells2,
'spks1': cells1,
'spks2': cells2,
'binSize': binSize,
'Fy2x': Fy2x[0],
'Fx2y': Fx2y[0],
'saveData': saveData,
'saveFig': saveFig,
'showFig': showFig
}
_saveFigData(figData, saveData, '2Dnet')
# save figure
if saveFig:
if isinstance(saveFig, basestring):
filename = saveFig
else:
filename = sim.cfg.filename + '_granger.png'
plt.savefig(filename)
# show fig
if showFig: _showFigure()
if testGranger:
return fig, {
'F': F,
'Fx2y': Fx2y[0],
'Fy2x': Fy2x[0],
'Fxy': Fxy[0],
'MaxFy2xZscore': y2xZscore,
'MaxFy2xPvalue': y2xPvalue
}
else:
return fig, {'F': F, 'Fx2y': Fx2y[0], 'Fy2x': Fy2x[0], 'Fxy': Fxy[0]}
def granger(cells1=[],
cells2=[],
spks1=None,
spks2=None,
label1='spkTrain1',
label2='spkTrain2',
timeRange=None,
binSize=5,
plotFig=True,
saveData=None,
saveFig=None,
showFig=True):
'''
Calculate and optionally plot Granger Causality
- cells1 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Subset of cells from which to obtain spike train 1 (default: [])
- cells2 (['all',|'allCells','allNetStims',|,120,|,'E1'|,('L2', 56)|,('L5',[4,5,6])]): Subset of cells from which to obtain spike train 2 (default: [])
- 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 (string): Label for spike train 1 to use in plot
- label2 (string): Label for spike train 2 to use in plot
- timeRange ([min, max]): Range of time to calculate nTE in ms (default: [0,cfg.duration])
- binSize (int): Bin size used to convert spike times into histogram
- plotFig (True|False): Whether to plot a figure showing Granger Causality Fx2y and Fy2x
- saveData (None|'fileName'): File name where to save the final data used to generate the figure (default: None)
- saveFig (None|'fileName'): File name where to save the figure;
if set to True uses filename from simConfig (default: None)(default: None)
- showFig (True|False): Whether to show the figure or not;
if set to True uses filename from simConfig (default: None)
- Returns
F: list of freqs
Fx2y: causality measure from x to y
Fy2x: causality from y to x
Fxy: instantaneous causality between x and y
fig: Figure handle
'''
from .. import sim
import numpy as np
from netpyne.support.bsmart import pwcausalr
if not spks1: # if doesnt contain a list of spk times, obtain from cells specified
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))
# plot granger
fig = -1
if plotFig:
fig = plt.figure()
plt.plot(F, Fy2x[0], label=label2 + ' -> ' + label1)
plt.plot(F, Fx2y[0], 'r', label=label1 + ' -> ' + label2)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Granger Causality')
plt.legend()
# save figure data
if saveData:
figData = {
'cells1': cells1,
'cells2': cells2,
'spks1': cells1,
'spks2': cells2,
'binSize': binSize,
'Fy2x': Fy2x[0],
'Fx2y': Fx2y[0],
'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 + '_' + '2Dnet.png'
plt.savefig(filename)
# show fig
if showFig: _showFigure()
return fig, {'F': F, 'Fx2y': Fx2y[0], 'Fy2x': Fy2x[0], 'Fxy': Fxy[0]}