def plot_matrix(matrix, strain_labels, vfs_classes, gene_labels,
show_gene_labels, color_index, config_object, grid, seed,
dpi, size, svg, aspect='auto'):
"""
Plot the VF hit matrix
:param matrix: the numpy matrix of scores
:param strain_labels: the strain (y labels)
:param vfs_classes: the VFS class (in mfa header [class])
:param gene_labels: the gene labels
:param show_gene_labels: wheter top plot the gene labels
:param color_index: for a single class, choose a specific color
"""
if config_object['category_colors'] != None:
colors = config_object['category_colors']
else:
colors = imaging.generate_colors(len(set(vfs_classes)), seed)
if color_index != None:
print color_index
print colors
colors = [colors[(color_index)]]
print colors
print "\n"
# Build the regions to be shaded differently
regions, prev = [], 0
for i in xrange(0, len(vfs_classes)-1):
if vfs_classes[i] != vfs_classes[i+1]:
regions.append([prev+0.5, i+0.5])
prev = i
regions.append([prev+0.5, len(vfs_classes)-0.5])
regions[0][0] = regions[0][0]-1.0
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
# aspect auto to widen
ax.matshow(matrix, cmap=cm.gray, aspect=aspect)
# Make sure every strain
ax.yaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_formatter(FormatStrFormatter('%s'))
ax.set_yticklabels(strain_labels)
if len(gene_labels) < 999:
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.xaxis.set_major_formatter(FormatStrFormatter('%s'))
ax.xaxis.grid(False)
if show_gene_labels:
ax.set_xticklabels([''] +gene_labels)#, rotation=90)
for i in xrange(0, len(regions)):
plt.axvspan(regions[i][0], regions[i][1], facecolor=colors[i], \
alpha=0.1)
if show_gene_labels:
ax.tick_params(axis='both', which='both', labelsize=6, direction='out',\
labelleft='on', labelright='off', \
labelbottom='off', labeltop='on', \
left='on', right='off', bottom='off', top='on')
else:
ax.tick_params(axis='both', which='both', labelsize=6, direction='out',\
labelleft='on', labelright='off', \
labelbottom='off', labeltop='off', \
left='on', right='off', bottom='off', top='off')
plt.xticks(rotation=90)
if grid: ax.grid(True)
x, y = size.split('x')
x, y = float(x), float(y)
fig.set_size_inches(x, y, dpi=dpi)
if svg:
plt.savefig("results.svg", bbox_inches='tight',dpi=dpi)
else:
plt.savefig("results.png", bbox_inches='tight',dpi=dpi)
def plot_matrix(matrix, strain_labels, vfs_classes, gene_labels,
show_gene_labels, color_index, config_object, grid, seed,
dpi, size, svg, compress, compressed_classes=None,
compressed_gene_labels=None,aspect='auto'):
"""
Plot the VF hit matrix
*NOTE:* Even if we're plotting a compressed matrix, we will always save
all data required to plot an uncompressed version.
TODO: update doc for this!!!
compressed_classes ~= vfs_classes
compressed_gene_labels ~= gene_labels
:param matrix: the numpy matrix of scores
:param strain_labels: the strain (y labels)
:param vfs_classes: the VFS class (in mfa header [class])
:param gene_labels: the gene labels
:param show_gene_labels: wheter top plot the gene labels
:param color_index: for a single class, choose a specific color
"""
# Saving data for regeneration
if compress != None:
classes = open(os.path.join('data', "classes.txt"), 'wb')
for item in vfs_classes:
classes.write(item+'\n')
classes.close()
xlab = open(os.path.join('data', "xlabels.txt"), 'wb')
for item in gene_labels:
xlab.write(item+'\n')
xlab.close()
ylab = open(os.path.join('data', "strain_labels.txt"), 'wb')
for item in strain_labels:
# Don't skip bleedthrough
#if(item != ''):
ylab.write(item+'\n')
ylab.close()
# Toggle between all and compressed.
if compressed_classes != None and compressed_gene_labels != None:
vfs_classes = compressed_classes
gene_labels = compressed_gene_labels
if config_object['category_colors'] != None:
colors = config_object['category_colors']
else:
print compress
print compressed_classes
print compressed_gene_labels
colors = imaging.generate_colors(len(set(vfs_classes)), seed)
if color_index != None:
colors = [colors[(color_index)]]
# Build the regions to be shaded differently
regions, prev = [], 0
for i in xrange(0, len(vfs_classes)-1):
if vfs_classes[i] != vfs_classes[i+1]:
regions.append([prev+0.5, i+0.5])
prev = i
regions.append([prev+0.5, len(vfs_classes)-0.5])
regions[0][0] = regions[0][0]-1.0
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
# aspect auto to widen
ax.matshow(matrix, cmap=cm.gray, aspect=aspect)
# Make sure every strain
ax.yaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_formatter(FormatStrFormatter('%s'))
ax.set_yticklabels(strain_labels)
if len(gene_labels) < 999:
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.xaxis.set_major_formatter(FormatStrFormatter('%s'))
ax.xaxis.grid(False)
if show_gene_labels:
ax.set_xticklabels([''] +gene_labels)#, rotation=90)
for i in xrange(0, len(regions)):
plt.axvspan(regions[i][0], regions[i][1], facecolor=colors[i], \
alpha=0.1)
if show_gene_labels:
ax.tick_params(axis='both', which='both', labelsize=6, direction='out',\
labelleft='on', labelright='off', \
labelbottom='off', labeltop='on', \
left='on', right='off', bottom='off', top='on')
else:
ax.tick_params(axis='both', which='both', labelsize=6, direction='out',\
labelleft='on', labelright='off', \
labelbottom='off', labeltop='off', \
left='on', right='off', bottom='off', top='off')
plt.xticks(rotation=90)
if grid: ax.grid(True)
x, y = size.split('x')
x, y = float(x), float(y)
fig.set_size_inches(x, y, dpi=dpi)
if svg:
plt.savefig("results.svg", bbox_inches='tight',dpi=dpi)
else:
plt.savefig("results.png", bbox_inches='tight',dpi=dpi)
