def getNetworkWithin(in_dat, roiIndx):
m=in_dat.copy()
#Null the upper triangle, so that you don't get the redundant and
#the diagonal values:
idx_null = triu_indices(m.shape[0])
m[idx_null] = np.nan
#Extract network values
withinVals = m[roiIndx,:][:,roiIndx]
return withinVals
def getNetworkWithin(in_dat, roiIndx):
m = in_dat.copy()
#Null the upper triangle, so that you don't get the redundant and
#the diagonal values:
idx_null = triu_indices(m.shape[0])
m[idx_null] = np.nan
#Extract network values
withinVals = m[roiIndx, :][:, roiIndx]
return withinVals
def getNetworkWithin(in_dat, roiIndx):
m=in_dat.copy()
#Null the upper triangle, so that you don't get the redundant and
#the diagonal values:
newMat=np.zeros((m.shape[0], len(roiIndx), len(roiIndx)))
#Iterate through each run
for runNum in range(m.shape[0]):
m_onerun=m[runNum][:][:]
idx_null = triu_indices(m_onerun.shape[0])
m_onerun[idx_null] = np.nan
#Extract network values
withinVals = m_onerun[roiIndx,:][:,roiIndx]
newMat[runNum][:][:]=withinVals
return newMat
def getNetworkWithin_test(in_d, roiIndx):
dat = in_d.copy()
#Null the upper triangle, so that you don't get the redundant and
#the diagonal values:
idx_null = triu_indices(dat.shape[0])
dat[idx_null] = np.nan
#Extract network values
allNet = []
numROIs = roiIndx[1:]
numStart = 0
while len(numROIs) > 0:
for jj in numROIs:
allNet.append(dat[jj][roiIndx[numStart]])
numStart += 1
numROIs = numROIs[1:]
return allNet
def getNetworkWithin_test(in_d, roiIndx):
dat=in_d.copy()
#Null the upper triangle, so that you don't get the redundant and
#the diagonal values:
idx_null = triu_indices(dat.shape[0])
dat[idx_null] = np.nan
#Extract network values
allNet=[]
numROIs=roiIndx[1:]
numStart=0
while len(numROIs)>0:
for jj in numROIs:
allNet.append(dat[jj][roiIndx[numStart]])
numStart+=1
numROIs=numROIs[1:]
return allNet
def drawmatrix_channels(in_m, channel_names=None, fig=None, x_tick_rot=0,
size=None, cmap=plt.cm.RdBu_r, colorbar=True,
color_anchor=None, title=None):
r"""Creates a lower-triangle of the matrix of an nxn set of values. This is
the typical format to show a symmetrical bivariate quantity (such as
correlation or coherence between two different ROIs).
Parameters
----------
in_m: nxn array with values of relationships between two sets of rois or
channels
channel_names (optional): list of strings with the labels to be applied to
the channels in the input. Defaults to '0','1','2', etc.
fig (optional): a matplotlib figure
cmap (optional): a matplotlib colormap to be used for displaying the values
of the connections on the graph
title (optional): string to title the figure (can be like '$\alpha$')
color_anchor (optional): determine the mapping from values to colormap
if None, min and max of colormap correspond to min and max of in_m
if 0, min and max of colormap correspond to max of abs(in_m)
if (a,b), min and max of colormap correspond to (a,b)
Returns
-------
fig: a figure object
"""
N = in_m.shape[0]
ind = np.arange(N) # the evenly spaced plot indices
def channel_formatter(x, pos=None):
thisind = np.clip(int(x), 0, N - 1)
return channel_names[thisind]
if fig is None:
fig = plt.figure()
if size is not None:
fig.set_figwidth(size[0])
fig.set_figheight(size[1])
w = fig.get_figwidth()
h = fig.get_figheight()
ax_im = fig.add_subplot(1, 1, 1)
# If you want to draw the colorbar:
if colorbar:
divider = make_axes_locatable(ax_im)
ax_cb = divider.new_vertical(size="10%", pad=0.1, pack_start=True)
fig.add_axes(ax_cb)
# Make a copy of the input, so that you don't make changes to the original
# data provided
m = in_m.copy()
# Null the upper triangle, so that you don't get the redundant and the
# diagonal values:
idx_null = triu_indices(m.shape[0])
m[idx_null] = np.nan
# Extract the minimum and maximum values for scaling of the
# colormap/colorbar:
max_val = np.nanmax(m)
min_val = np.nanmin(m)
if color_anchor is None:
color_min = min_val
color_max = max_val
elif color_anchor == 0:
bound = max(abs(max_val), abs(min_val))
color_min = -bound
color_max = bound
else:
color_min = color_anchor[0]
color_max = color_anchor[1]
# The call to imshow produces the matrix plot:
im = ax_im.imshow(m, origin='upper', interpolation='nearest',
vmin=color_min, vmax=color_max, cmap=cmap)
# Formatting:
ax = ax_im
ax.grid(True)
# Label each of the cells with the row and the column:
if channel_names is not None:
for i in range(0, m.shape[0]):
if i < (m.shape[0] - 1):
ax.text(i - 0.3, i, channel_names[i], rotation=x_tick_rot)
if i > 0:
ax.text(-1, i + 0.3, channel_names[i],
horizontalalignment='right')
ax.set_axis_off()
ax.set_xticks(np.arange(N))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(channel_formatter))
fig.autofmt_xdate(rotation=x_tick_rot)
ax.set_yticks(np.arange(N))
ax.set_yticklabels(channel_names)
ax.set_ybound([-0.5, N - 0.5])
ax.set_xbound([-0.5, N - 1.5])
# Make the tick-marks invisible:
for line in ax.xaxis.get_ticklines():
line.set_markeredgewidth(0)
for line in ax.yaxis.get_ticklines():
line.set_markeredgewidth(0)
ax.set_axis_off()
if title is not None:
ax.set_title(title)
# The following produces the colorbar and sets the ticks
if colorbar:
# Set the ticks - if 0 is in the interval of values, set that, as well
# as the maximal and minimal values:
if min_val < 0:
ticks = [color_min, min_val, 0, max_val, color_max]
# Otherwise - only set the minimal and maximal value:
else:
ticks = [color_min, min_val, max_val, color_max]
# This makes the colorbar:
cb = fig.colorbar(im, cax=ax_cb, orientation='horizontal',
cmap=cmap,
norm=im.norm,
boundaries=np.linspace(color_min, color_max, 256),
ticks=ticks,
format='%.2f')
# Set the current figure active axis to be the top-one, which is the one
# most likely to be operated on by users later on
fig.sca(ax)
return fig
def drawmatrix_channels(in_m,
channel_names=None,
fig=None,
x_tick_rot=0,
size=None,
cmap=plt.cm.RdBu_r,
colorbar=True,
color_anchor=None,
title=None):
"""Creates a lower-triangle of the matrix of an nxn set of values. This is
the typical format to show a symmetrical bivariate quantity (such as
correlation or coherence between two different ROIs).
Parameters
----------
in_m: nxn array with values of relationships between two sets of rois or
channels
channel_names (optional): list of strings with the labels to be applied to
the channels in the input. Defaults to '0','1','2', etc.
fig (optional): a matplotlib figure
cmap (optional): a matplotlib colormap to be used for displaying the values
of the connections on the graph
title : optional, string
If given, title to be drawn atop the matrix.
Returns
-------
fig: a figure object
"""
N = in_m.shape[0]
ind = np.arange(N) # the evenly spaced plot indices
def channel_formatter(x, pos=None):
thisind = np.clip(int(x), 0, N - 1)
return channel_names[thisind]
if fig is None:
fig = plt.figure()
if size is not None:
fig.set_figwidth(size[0])
fig.set_figheight(size[1])
w = fig.get_figwidth()
h = fig.get_figheight()
ax_im = fig.add_subplot(1, 1, 1)
#If you want to draw the colorbar:
if colorbar:
divider = make_axes_locatable(ax_im)
ax_cb = divider.new_vertical(size="20%", pad=0.2, pack_start=True)
fig.add_axes(ax_cb)
#Make a copy of the input, so that you don't make changes to the original
#data provided
m = in_m.copy()
#Null the upper triangle, so that you don't get the redundant and the
#diagonal values:
idx_null = triu_indices(m.shape[0])
m[idx_null] = np.nan
#Extract the minimum and maximum values for scaling of the
#colormap/colorbar:
max_val = np.nanmax(m)
min_val = np.nanmin(m)
if color_anchor is None:
color_min = min_val
color_max = max_val
elif color_anchor == 0:
bound = max(abs(max_val), abs(min_val))
color_min = -bound
color_max = bound
else:
color_min = color_anchor[0]
color_max = color_anchor[1]
#The call to imshow produces the matrix plot:
im = ax_im.imshow(m,
origin='upper',
interpolation='nearest',
vmin=color_min,
vmax=color_max,
cmap=cmap)
#Formatting:
ax = ax_im
ax.grid(True)
#Label each of the cells with the row and the column:
if channel_names is not None:
for i in xrange(0, m.shape[0]):
if i < (m.shape[0] - 1):
ax.text(i - 0.3, i, channel_names[i], rotation=x_tick_rot)
if i > 0:
ax.text(-1,
i + 0.3,
channel_names[i],
horizontalalignment='right')
ax.set_axis_off()
ax.set_xticks(np.arange(N))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(channel_formatter))
fig.autofmt_xdate(rotation=x_tick_rot)
ax.set_yticks(np.arange(N))
ax.set_yticklabels(channel_names)
ax.set_ybound([-0.5, N - 0.5])
ax.set_xbound([-0.5, N - 1.5])
#Make the tick-marks invisible:
for line in ax.xaxis.get_ticklines():
line.set_markeredgewidth(0)
for line in ax.yaxis.get_ticklines():
line.set_markeredgewidth(0)
ax.set_axis_off()
if title is not None:
ax.set_title(title)
#The following produces the colorbar and sets the ticks
if colorbar:
#Set the ticks - if 0 is in the interval of values, set that, as well
#as the maximal and minimal values:
if min_val < 0:
ticks = [min_val, 0, max_val]
#Otherwise - only set the minimal and maximal value:
else:
ticks = [min_val, max_val]
#This makes the colorbar:
cb = fig.colorbar(im,
cax=ax_cb,
orientation='horizontal',
cmap=cmap,
norm=im.norm,
boundaries=np.linspace(min_val, max_val, 256),
ticks=ticks,
format='%.2f')
# Set the current figure active axis to be the top-one, which is the one
# most likely to be operated on by users later on
fig.sca(ax)
return fig
sxx = alg.mtm_cross_spectrum(
tspectra[i], tspectra[i], (w[i], w[i]), sides='onesided'
).real
syy = alg.mtm_cross_spectrum(
tspectra[j], tspectra[j], (w[i], w[j]), sides='onesided'
).real
psd_mat[0,i,j] = sxx
psd_mat[1,i,j] = syy
coh_mat[i,j] = np.abs(sxy)**2
coh_mat[i,j] /= (sxx * syy)
csd_mat[i,j] = sxy
if i != j:
coh_var[i,j] = utils.jackknifed_coh_variance(
tspectra[i], tspectra[j], weights=(w[i], w[j]), last_freq=L
)
upper_idc = utils.triu_indices(nseq, k=1)
lower_idc = utils.tril_indices(nseq, k=-1)
coh_mat[upper_idc] = coh_mat[lower_idc]
coh_var[upper_idc] = coh_var[lower_idc]
# convert this measure with the normalizing function
coh_mat_xform = utils.normalize_coherence(coh_mat, 2*K-2)
t025_limit = coh_mat_xform + dist.t.ppf(.025, K-1)*np.sqrt(coh_var)
t975_limit = coh_mat_xform + dist.t.ppf(.975, K-1)*np.sqrt(coh_var)
utils.normal_coherence_to_unit(t025_limit, 2*K-2, t025_limit)
utils.normal_coherence_to_unit(t975_limit, 2*K-2, t975_limit)
if L < n_samples: