def _fit(ts_set1, ts_set2, lb, ub):
pts1 = create_ts_with_data(percent_change(ts_set1.data), ts_set1)
pts2 = create_ts_with_data(percent_change(ts_set2.data), ts_set2)
pts = concatenate_timeseries(pts1, pts2)
#cts = concatenate_timeseries(ts_set1, ts_set2)
#pts = tsu.percent_change (cts.data)
#pts = create_ts_with_data (pts, ts_set1)
gc = nta.GrangerAnalyzer(pts, order=1)
freq_idx_gc = np.where(
(gc.frequencies > lb) * (gc.frequencies < ub))[0]
mean_gc = np.mean(gc.causality_xy[:, :, freq_idx_gc], -1)
return np.nanmean(mean_gc)
def granger_causality_analysis(time_series, f_lb, f_ub, granger_order, roi_names,result_dir, s='', c=''):
# initialize GrangerAnalyzer object
G = nta.GrangerAnalyzer(time_series, order = granger_order)
# initialize CoherenceAnalyzer
C = nta.CoherenceAnalyzer(time_series)
# initialize CorrelationAnalyzer
R = nta.CorrelationAnalyzer(time_series)
# get the index of the frequency band of interest for different analyzer
freq_idx_G = np.where((G.frequencies > f_lb) * (G.frequencies < f_ub))[0]
freq_idx_C = np.where((C.frequencies> f_lb) * (C.frequencies < f_ub)) [0]
# average the last dimension
coh = np.mean(C.coherence[:, :, freq_idx_C], -1)
gl = np.mean(G.causality_xy[:, :, freq_idx_G], -1)
# Difference in HRF between ROI may result misattriution of causality
# examine diference between x-->y and y-->x
g2 = np.mean(G.causality_xy[:,:,freq_idx_G] - G.causality_yx[:,:,freq_idx_G],-1)
# Figure organization:
# causality_xy: x-->y, or roi_names[0]-->roi_names[1], roi_names[0]-->roi_names[2], etc.
# this makes: given a column, transverse through rows. Directionality is
# from current column label to each row label
# plot coherence from x to y,
drawmatrix_channels(coh, roi_names, size=[10., 10.], color_anchor=0)
plt.title(('%s %s pair-wise Coherence' % (s, c)).replace(' ',' '))
plt.savefig(os.path.join(result_dir,s,('%s_%s_pairwise_coherence.png' % (s, c)).replace('__','_')))
# plot correlation from x to y
#drawmatrix_channels(R.corrcoef, roi_names, size=[10., 10.], color_anchor=0)
#plt.title(('%s %s pair-wise Correlation' % (s, c)).replace(' ',' '))
#plt.savefig(os.path.join(result_dir,s,('%s_%s_pairwise_correlation.png' % (s, c)).replace('__','_')))
# plot granger causality from x to y
drawmatrix_channels(gl, roi_names, size=[10., 10.], color_anchor=0)
plt.title(('%s %s pair-wise Granger Causality' % (s, c)).replace(' ',' '))
plt.savefig(os.path.join(result_dir,s,('%s_%s_pairwise_granger_causality.png' % (s, c)).replace('__','_')))
# plot granger causliaty forward-backward difference
drawmatrix_channels(g2, roi_names, size=[10., 10.], color_anchor = 0)
plt.title(('%s %s pair-wise Granger Causality Forward-Backward Difference' % (s, c)).replace(' ',' '))
plt.savefig(os.path.join(result_dir,s,('%s_%s_granger_causality_forward_backward_diff.png' % (s, c)).replace('__','_')))
# close all the figures
plt.close("all")
return(coh, gl, g2, G, C, R)
def nitime_solution(animal, day, cutoff=True):
# TODO: add support for custom freq bands
import h5py
import nitime
import numpy as np
import nitime.analysis as nta
from utils_loading import encode_to_filename
from nitime.viz import drawmatrix_channels
import matplotlib.pyplot as plt
folder = "/Volumes/DATA_01/NL/layerproject/processed/"
hf = h5py.File(encode_to_filename(folder, animal, day), 'r')
dff = np.array(hf['dff'])
NEUR = 'ens'
# Critical neuron pair vs general neuron gc
if NEUR == 'ens':
rois = dff[hf['ens_neur']]
elif NEUR == 'neur':
rois = dff[hf['nerden']]
else:
rois = dff[NEUR]
rois_ts = nitime.TimeSeries(rois, sampling_interval=1 / hf.attrs['fr'])
G = nta.GrangerAnalyzer(rois_ts)
if cutoff:
sel = np.where(G.frequencies < hf.attrs['fr'])[0]
caus_xy = G.causality_xy[:, :, sel]
caus_yx = G.causality_yx[:, :, sel]
caus_sim = G.simultaneous_causality[:, :, sel]
else:
caus_xy = G.causality_xy
caus_yx = G.causality_yx
caus_sim = G.simultaneous_causality
g1 = np.mean(caus_xy, -1)
g2 = np.mean(caus_yx, -1)
g3 = np.mean(caus_sim, -1)
g4 = g1-g2
fig03 = drawmatrix_channels(g1, ['E11', 'E12', 'E21', 'E22'], size=[10., 10.], color_anchor = 0)
plt.colorbar()
def granger_scores(timeseries, order):
timeseries = TimeSeries(timeseries, sampling_interval=1)
g = nta.GrangerAnalyzer(timeseries, order=order)
g_xy_mat = np.mean(g.causality_xy, axis=-1)
g_yx_mat = np.mean(g.causality_yx, axis=-1)
return np.concatenate([g_xy_mat[np.tril_indices(3,-1)], g_yx_mat.T[np.triu_indices(3,1)]])
We normalize the data in each of the ROIs to be in units of % change and initialize the TimeSeries object: """ pdata = tsu.percent_change(data) time_series = ts.TimeSeries(pdata, sampling_interval=TR) """ We initialize the GrangerAnalyzer object, while specifying the order of the autoregressive model to be 1 (predict the current behavior of the time-series based on one time-point back). """ G = nta.GrangerAnalyzer(time_series, order=1) """ For comparison, we also initialize a CoherenceAnalyzer and a CorrelationAnalyzer, with the same TimeSeries object: """ C1 = nta.CoherenceAnalyzer(time_series) C2 = nta.CorrelationAnalyzer(time_series) """ We are only interested in the physiologically relevant frequency band (approximately 0.02 to 0.15 Hz). The spectral resolution is different in these two different analyzers. In the
import matplotlib.pyplot as plt
A = np.vstack((a1, a2))
plt.plot(A.T);
plt.legend(['a', 'b'])
plt.show()
import h5py
import nitime
import numpy as np
import nitime.analysis as nta
from utils_loading import encode_to_filename
from nitime.viz import drawmatrix_channels
rois = A
rois_ts = nitime.TimeSeries(rois, sampling_interval=1 / 5)
G = nta.GrangerAnalyzer(rois_ts, order=1)
if cutoff:
sel = np.where(G.frequencies < hf.attrs['fr'])[0]
caus_xy = G.causality_xy[:, :, sel]
caus_yx = G.causality_yx[:, :, sel]
else:
caus_xy = G.causality_xy
caus_yx = G.causality_yx
cutoff = False
G = nta.GrangerAnalyzer(rois_ts, order=1)
if cutoff:
sel = np.where(G.frequencies < hf.attrs['fr'])[0]
caus_xy = G.causality_xy[:, :, sel]
caus_yx = G.causality_yx[:, :, sel]
else:
caus_xy = G.causality_xy
