def matshow_tseries(time_series,
fig=None,
axis=0,
xtick_n=5,
time_unit=None,
xlabel=None,
ylabel=None):
"""Creates an image of the time-series, ordered according to the first
dimension of the time-series object
Parameters
----------
time_series: a nitime time-series object
fig: a figure handle, opens a new figure if None
axis: an axis number (if there are several in the figure to be opened),
defaults to 0.
xtick_n: int, optional, sets the number of ticks to be placed on the x axis
"""
if fig is None:
fig = plt.figure()
if not fig.get_axes():
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.get_axes()[axis]
#Make sure that time displays on the x axis with the units you want:
#If you want to change the time-unit on the visualization from that used to
#represent the time-series:
if time_unit is not None:
tu = time_unit
conv_fac = ts.time_unit_conversion[time_unit]
#Otherwise, get the information from your input:
else:
tu = time_series.time_unit
conv_fac = time_series.time._conversion_factor
this_time = time_series.time / float(conv_fac)
ax.matshow(time_series.data)
ax.set_xticks(list(range(len(this_time)))[::len(this_time) / xtick_n])
ax.set_xticklabels(this_time[::len(this_time) / xtick_n])
if xlabel is None:
ax.set_xlabel('Time (%s)' % tu)
else:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
return fig
def matshow_tseries(time_series, fig=None, axis=0, xtick_n=5, time_unit=None,
xlabel=None, ylabel=None):
"""Creates an image of the time-series, ordered according to the first
dimension of the time-series object
Parameters
----------
time_series: a nitime time-series object
fig: a figure handle, opens a new figure if None
axis: an axis number (if there are several in the figure to be opened),
defaults to 0.
xtick_n: int, optional, sets the number of ticks to be placed on the x axis
"""
if fig is None:
fig = plt.figure()
if not fig.get_axes():
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.get_axes()[axis]
#Make sure that time displays on the x axis with the units you want:
#If you want to change the time-unit on the visualization from that used to
#represent the time-series:
if time_unit is not None:
tu = time_unit
conv_fac = ts.time_unit_conversion[time_unit]
#Otherwise, get the information from your input:
else:
tu = time_series.time_unit
conv_fac = time_series.time._conversion_factor
this_time = time_series.time / float(conv_fac)
ax.matshow(time_series.data)
ax.set_xticks(list(range(len(this_time)))[::len(this_time) / xtick_n])
ax.set_xticklabels(this_time[::len(this_time) / xtick_n])
if xlabel is None:
ax.set_xlabel('Time (%s)' % tu)
else:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
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):
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 makeplot_recurrence(sequence, sequence2, recurrence_table, f_sequence,
s_sequence):
recurrence_table = np.array(
np.reshape(recurrence_table, (sequence2, sequence)).tolist())
# create list of coordinates for requrence plot
x_rqa = []
y_rqa = []
for y in range(0, 2 * sequence2, 2):
for x in range(0, 2 * sequence, 2):
if recurrence_table[int(y / 2)][int(x / 2)] == 1:
x_rqa.append(x)
y_rqa.append((2 * sequence2 - y - 1))
multi_plot = plt.figure(figsize=(12, 9))
multi_plot.clear()
ax = multi_plot.add_subplot(1, 1, 1)
# ax1 = plt.subplot2grid((8, 8), (0, 0), colspan=6, rowspan=2)
# ax1.plot(list(range(len(count_y))), count_y)
# plt.ylim(0, max(count_y) + 1)
# plt.xlim(0,len(count_y)-1)
# ax2 = plt.subplot2grid((8, 8), (2, 0), rowspan=6, colspan=6)
if sequence > 100 or sequence2 > 100:
size = 0.5
else:
size = 3
wsp = [[x, y] for x, y in zip(x_rqa, y_rqa)]
print(wsp)
if wsp:
wsp_spr = wsp[0]
x = []
y = []
while len(wsp) > 1:
wsp.remove(wsp_spr)
if [wsp_spr[0] + 2, wsp_spr[1] - 2] in wsp:
x.append(wsp_spr[0])
y.append(wsp_spr[1])
wsp_spr = [wsp_spr[0] + 2, wsp_spr[1] - 2]
print(x, y)
else:
x.append(wsp_spr[0])
y.append(wsp_spr[1])
wsp_spr = wsp[0]
print(x, y)
if len(x) == 1:
ax.plot(x, y, 'ro', markersize=1.5)
else:
ax.plot(x, y, 'r')
x = []
y = []
if len(wsp) == 1:
x.append(wsp[0][0])
y.append(wsp[0][1])
if len(x) == 1:
ax.plot(x, y, 'ro', markersize=1.5)
else:
ax.plot(x, y, 'r')
x = []
y = []
plt.xlim(-1, (2 * sequence) - 2)
plt.ylim(0, 2 * sequence2)
plt.xticks(range(0, 2 * sequence, 2), f_sequence)
plt.yticks(range(1, 2 * sequence2 + 1, 2), s_sequence[::-1])
minorLocator = MultipleLocator(5)
minor_xticks = np.arange(1, 2 * sequence, 2)
minor_yticks = np.arange(0, 2 * sequence2, 2)
ax.xaxis.tick_top()
ax.set_xticks(minor_xticks, minor=True)
ax.set_yticks(minor_yticks, minor=True)
plt.grid(which='minor', alpha=0.5)
multi_plot.canvas.draw()
multi_plot.savefig('static/RQA.png')
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
