#from matplotlib.backends.backend_pdf import PdfPages
#pp = PdfPages('plots/130814_Hopkins_contour_xiMax_of_r_A.pdf')
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
c = loadtxt('output/130807_3D_A_dep.txt', unpack=True, usecols = [0])
C = (numpy.pi/2)*c
r = loadtxt('output/130807_3D_A_rho.txt', unpack=True, usecols = [0])
h = loadtxt('output/130807_3D_height.txt')#, unpack=True, usecols = [0,1])
X,Y=meshgrid(C,r)
h = numpy.nan_to_num(h)
fig = figure()
ax = fig.gca(projection = '3d')
ax.text(-7, 6, 0.7, r'$\zeta/\omega_{0}$', zdir = (-1,1,-3), size = 21)
figure()
#contourf(X,Y,h, 1000, cmap = hot())
surf = ax.plot_surface(X,Y,h, rstride = 20, cstride = 20,alpha = 0.2, cmap = cm.gnuplot, linewidth = 0.5)#gray)#coolwarm)#bone)#hot, linewidth = 0.01, antialiased = True, shade = False)# True)#, cmap = hot()
#surf = ax.plot_wireframe(X,Y,h, rstride = 20, cstride = 20,color = 'k')#True)# cmap = hot, shade = True)#,alpha = 0.9, cmap = cm.hot, linewidth = 0.01, antialiased = True, shade = False)# True)#, cmap = hot()
#colorbar(surf)
#cbar.ax.set_ylabel(r'$\frac{\xi}{\omega_{0}}$', size = 24)
#cset = ax.contour(X,Y,h, zdir = 'z', offset = 0, cmap = cm.jet)
#cset = ax.contour(X,Y,h, zdir = 'x', offset = 5, cmap = cm.jet)
#cset = ax.contourf(X,Y,h, zdir = 'y', offset = 6, cmap = cm.jet)# puts plot of max xi vs discrete r values at r=0 plane
#CS = contour(X,Y,h)#, colors = 'k')
#man_loc = [(1,1),(2,2),(3,3),(4,4)]
#clabel(CS, inline =1,fmt = '%1.1f', fontsize = 18,color = 'k', manual = man_loc)
#ax.grid(on = True)
ax.view_init(elev = 19, azim = -112)
#zlabel(r'$\xi/\omega_{0}$', size = 21)
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):
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