text_width = 6.30045 # LaTeX text width in inches
golden_ratio = (1 + np.sqrt(5) ) / 2.0
size_factor = 1.0
figure_width = size_factor*text_width
figure_height = (figure_width / golden_ratio)
#figure_height = 1.3 * figure_width
figure_size = [figure_width, figure_height]
config.load_config_medium()
#--------------------------------------------------------------------------------------------
# Temporal filters (misc)
#--------------------------------------------------------------------------------------------
fig = plt.figure(figsize=figure_size)
ax = plt.subplot(111)
ax.plot(x, y, linestyle="", color=[0.2, 0.2, 0.2, 1.0], zorder=10, label='Mean of active taxels',
marker='o', markersize=4.0, markeredgewidth=0.5, markerfacecolor=[1.0, 1.0, 1.0, 1.0], markeredgecolor=[0, 0, 0, 1.0])
ax.plot(x, y_filtered['running_mean'], '-', linewidth=1.5, color=config.UIBK_orange, alpha=1.0, label='Running Mean (N = 5)')
ax.plot(x, y_filtered['gaussian'], '-', linewidth=1.5, color=[0.0, 0,0, 0.0], alpha=1.0, label=r'Gaussian Filter ($\sigma$ = 3)')
def plot_confusion_matrix(confusion_matrix, classes, appendix):
size_factor = 1.0
figure_width = size_factor*text_width
figure_size = [figure_width, figure_width]
config.load_config_medium()
# Normalize by the number of samples in each class
confusion_normalized = confusion_matrix.astype('float') / confusion_matrix.sum(axis=1)[:, np.newaxis]
plt.ioff() # Disable interactive plotting
fig = plt.figure(figsize=figure_size, dpi=100)
ax = fig.add_subplot(111)
n_classes = len(classes)
ax.axis([0, n_classes, 0, n_classes])
ax.invert_yaxis()
ax.set_aspect('equal')
# Normalize by the number of samples in each class
confusion_normalized = confusion_matrix.astype('float') / confusion_matrix.sum(axis=1)[:, np.newaxis]
colormap = plt.get_cmap('UIBK_ORANGES')
colormap.set_under([1.0, 1.0, 1.0])
# pcolormesh aligns cells on their edges, while imshow aligns them on their centers.
ax.pcolormesh(confusion_normalized, cmap=colormap, vmin=0.001, vmax=1.0)
#plt.colorbar(mesh)
# Absolute number
for i,j in ((x,y) for x in np.arange(0, len(confusion_matrix))+0.5
for y in np.arange(0, len(confusion_matrix[0]))+0.5):
if confusion_matrix[i][j] > 0:
ax.annotate(str(confusion_matrix[i][j]), xy=(j,i), fontsize=4, ha='center', va='center')
plt.tick_params(axis='x', which='minor', bottom='off', top='off', labelbottom='on')
plt.tick_params(axis='y', which='minor', left='off', right='off', labelleft='on')
plt.tick_params(axis='x', which='major', bottom='on', top='off', labelbottom='on', direction='out')
plt.tick_params(axis='y', which='major', left='on', right='off', labelleft='on', direction='out')
# Set the major ticks at the centers and minor tick at the edges
tick_marks = np.arange(n_classes)
ax.xaxis.set_ticks(tick_marks, minor=True)
ax.yaxis.set_ticks(tick_marks, minor=True)
plt.xticks(tick_marks+0.5, classes, rotation=90, rotation_mode='anchor', ha='right', va='center')
plt.yticks(tick_marks+0.5, classes)
ax.grid(True, which='minor', linestyle='-') # Grid at minor ticks
plt.title("Confusion Matrix", y=1.02)
plt.xlabel("Predicted class")
plt.ylabel("Actual class")
plt.tight_layout()
plotname = "confusion_matrix_" + appendix
fig.savefig(plotname+".pdf", pad_inches=0, dpi=fig.dpi) # pdf
#fig.savefig(plotname+".pgf", pad_inches=0, dpi=fig.dpi) # pgf
#fig.savefig(plotname+".png", pad_inches=0, dpi=300)
plt.close(fig)
cdict = {'red': ((0.0, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 1.0, 1.0),
(1.0, 0.5, 0.5)),
'blue': ((0.0, 1.0, 1.0),
(1.0, 0.0, 0.0))}
plt.register_cmap(name='UIBK_ORANGES', data=cdict)
'''
#######################################################
# Scatterplot Matrix
#######################################################
# Create discrete colors from continuous colormap
import matplotlib.colors as colors
import matplotlib.cm as cmx
colorNorm = colors.Normalize(vmin=0, vmax=n_classes-1)
scalarMap = cmx.ScalarMappable(norm=colorNorm, cmap=plt.get_cmap("jet"))
colors = scalarMap.to_rgba(np.arange(0, n_classes), alpha = 0.5)
# Markers
markers = np.array(['o', 's', '^', 'd', 'D', '*'])
text_height = 9.25737 # LaTeX text height in inches
golden_ratio = (1 + np.sqrt(5)) / 2.0
brewer_red = [0.89411765, 0.10196078, 0.10980392]
brewer_blue = [0.21568627, 0.49411765, 0.72156863]
brewer_green = [0.30196078, 0.68627451, 0.29019608]
# Custom colormap UIBK Orange
cdict = {
'red': ((0.0, 1.0, 1.0), (1.0, 1.0, 1.0)),
'green': ((0.0, 1.0, 1.0), (1.0, 0.5, 0.5)),
'blue': ((0.0, 1.0, 1.0), (1.0, 0.0, 0.0))
}
plt.register_cmap(name='UIBK_ORANGES', data=cdict)
'''
#######################################################
# Scatterplot Matrix
#######################################################
# Create discrete colors from continuous colormap
import matplotlib.colors as colors
import matplotlib.cm as cmx
colorNorm = colors.Normalize(vmin=0, vmax=n_classes-1)
scalarMap = cmx.ScalarMappable(norm=colorNorm, cmap=plt.get_cmap("jet"))
colors = scalarMap.to_rgba(np.arange(0, n_classes), alpha = 0.5)
# Markers
markers = np.array(['o', 's', '^', 'd', 'D', '*'])
