def plot_pca_components(filename, components):
colormap = brewer2mpl.get_map('Set1', 'qualitative', 9)
p = Ppl(colormap, alpha=1)
fig, ax = plt.subplots(1)
p.pcolormesh(fig, ax, components.values)
ax.set_xticks([])
yticks = np.linspace(len(components) - 0.5, 0.5, len(components))
ax.set_yticks(yticks)
y_ticklabels = map(lambda x: 'PCA %s'%x, range(len(components)))
ax.set_yticklabels(y_ticklabels)
fig.savefig(filename)
return fig, ax
def plot_correlation_matrix(filename, correlation_matrix, labels):
size = correlation_matrix.shape[0]
colormap = brewer2mpl.get_map('Set1', 'qualitative', 9)
p = Ppl(colormap, alpha=1)
fig, ax = plt.subplots(1)
#masked_jaccard = np.ma.masked_where(np.isnan(jaccard_matrix), jaccard_matrix)
p.pcolormesh(fig, ax, correlation_matrix)
#ax.imshow(jaccard_matrix, interpolation = 'none')
ax.set_xticks([])
yticks = np.linspace(size - 0.5, 0.5, size)
ax.set_yticks(yticks)
labels.reverse()
ax.set_yticklabels(labels)
ax.set_xticks(yticks)
ax.set_xticklabels(labels)
fig.savefig(filename)
return fig, ax
def plot_group_overlap(filename, jaccard_matrix):
size = jaccard_matrix.shape[0]
colormap = brewer2mpl.get_map('Set1', 'qualitative', 9)
p = Ppl(colormap, alpha=1)
fig, ax = plt.subplots(1)
masked_jaccard = np.ma.masked_where(np.isnan(jaccard_matrix), jaccard_matrix)
p.pcolormesh(fig, ax, masked_jaccard)
#ax.imshow(jaccard_matrix, interpolation = 'none')
ax.set_xticks([])
yticks = np.linspace(size - 0.5, 0.5, size)
ax.set_yticks(yticks)
y_ticklabels = map(lambda x: 'F%s'%x, range(1,size+1))
y_ticklabels.reverse()
ax.set_yticklabels(y_ticklabels)
ax.set_xticks(yticks)
ax.set_xticklabels(y_ticklabels)
fig.savefig(filename)
return fig, ax
def plot_image_matrix(filename, jaccard_matrix, x_ticklabels = None, y_ticklabels = None):
colormap = brewer2mpl.get_map('Set1', 'qualitative', 9)
p = Ppl(colormap, alpha=1)
fig, ax = plt.subplots(1)
masked_jaccard = np.ma.masked_where(np.isnan(jaccard_matrix), jaccard_matrix)
p.pcolormesh(fig, ax, masked_jaccard)
print x_ticklabels
print y_ticklabels
#ax.imshow(jaccard_matrix, interpolation = 'none')
#ax.set_xticks([])
yticks = range(jaccard_matrix.shape[0]+1)
xticks = range(jaccard_matrix.shape[1]+1)
ax.set_yticks(yticks)
ax.set_xticks(xticks)
#x_ticklabels.reverse()
ax.set_yticklabels(y_ticklabels)
ax.set_xticklabels(x_ticklabels)
ax.set_ylabel(fl('ratioagent'))
ax.set_xlabel(fl('ratiolatency'))
fig.savefig(filename)
return fig, ax