def save(self, directory, atlas_name):
# temporarily remove polydata object to enable pickling
polydata_tmp = self.nystrom_polydata
self.nystrom_polydata = None
fname = os.path.join(directory,atlas_name)
pickle.dump(self,open(fname+'.p','wb'))
# save the polydata
io.write_polydata(polydata_tmp, fname+'.vtp')
# replace the polydata object
self.nystrom_polydata = polydata_tmp
def output_and_quality_control_cluster_atlas(atlas, output_polydata_s, subject_fiber_list, input_polydatas, number_of_subjects, outdir, cluster_numbers_s, color, embed, number_of_fibers_to_display, testing=False, verbose=False, render_images=True):
"""Save the output in our atlas format for automatic labeling of clusters.
First save the atlas.vtp and atlas.p datasets. This is the data used to
label a new subject. Also write the polydata with cluster indices
saved as cell data. This is a one-file output option for clusters.
Finally, save some quality control metrics and save the atlas
clusters as individual polydatas. This is used to set up a mrml
hierarchy file and to visualize the output in Slicer. This data is
not used to label a new subject.
"""
# Write additional output for software testing if code has changed (requires random seed to be constant)
if testing:
expected_results_file = os.path.join(outdir, 'test_cluster_atlas_numbers.pkl')
pickle.dump(cluster_numbers_s, open(expected_results_file, 'wb'))
expected_results_file = os.path.join(outdir, 'test_cluster_atlas_colors.pkl')
pickle.dump(color, open(expected_results_file, 'wb'))
expected_results_file = os.path.join(outdir, 'test_cluster_atlas_embeddings.pkl')
pickle.dump(embed, open(expected_results_file, 'wb'))
# Save the output in our atlas format for automatic labeling of full brain datasets.
# This is the data used to label a new subject
atlas.save(outdir,'atlas')
# Write the polydata with cluster indices saved as cell data
fname_output = os.path.join(outdir, 'clustered_whole_brain.vtp')
io.write_polydata(output_polydata_s, fname_output)
# output summary file to save information about all subjects
subjects_qc_fname = os.path.join(outdir, 'input_subjects.txt')
subjects_qc_file = open(subjects_qc_fname, 'w')
outstr = "Subject_idx\tSubject_ID\tfilename\n"
subjects_qc_file.write(outstr)
idx = 1
for fname in input_polydatas:
subject_id = os.path.splitext(os.path.basename(fname))[0]
outstr = str(idx) + '\t' + str(subject_id) + '\t' + str(fname) + '\n'
subjects_qc_file.write(outstr)
idx += 1
subjects_qc_file.close()
# output summary file to save information about all clusters
clusters_qc_fname = os.path.join(outdir, 'cluster_quality_control.txt')
clusters_qc_file = open(clusters_qc_fname, 'w')
# Figure out how many subjects in each cluster (ideally, most subjects in most clusters)
subjects_per_cluster = list()
percent_subjects_per_cluster = list()
fibers_per_cluster = list()
mean_fiber_len_per_cluster = list()
std_fiber_len_per_cluster = list()
mean_fibers_per_subject_per_cluster = list()
std_fibers_per_subject_per_cluster = list()
# find out length of each fiber
fiber_length, step_size = filter.compute_lengths(output_polydata_s)
# loop over each cluster and compute quality control metrics
cluster_indices = range(atlas.centroids.shape[0])
for cidx in cluster_indices:
cluster_mask = (cluster_numbers_s==cidx)
subjects_per_cluster.append(len(set(subject_fiber_list[cluster_mask])))
fibers_per_subject = list()
for sidx in range(number_of_subjects):
fibers_per_subject.append(list(subject_fiber_list[cluster_mask]).count(sidx))
mean_fibers_per_subject_per_cluster.append(numpy.mean(numpy.array(fibers_per_subject)))
std_fibers_per_subject_per_cluster.append(numpy.std(numpy.array(fibers_per_subject)))
mean_fiber_len_per_cluster.append(numpy.mean(fiber_length[cluster_mask]))
std_fiber_len_per_cluster.append(numpy.std(fiber_length[cluster_mask]))
percent_subjects_per_cluster = numpy.divide(numpy.array(subjects_per_cluster),float(number_of_subjects))
# Save output quality control information
print "<cluster.py> Saving cluster quality control information file."
clusters_qc_file = open(clusters_qc_fname, 'w')
print >> clusters_qc_file, 'cluster_idx','\t', 'number_subjects','\t', 'percent_subjects','\t', 'mean_length','\t', 'std_length','\t', 'mean_fibers_per_subject','\t', 'std_fibers_per_subject'
for cidx in cluster_indices:
print >> clusters_qc_file, cidx + 1,'\t', subjects_per_cluster[cidx],'\t', percent_subjects_per_cluster[cidx] * 100.0,'\t', \
mean_fiber_len_per_cluster[cidx],'\t', std_fiber_len_per_cluster[cidx],'\t', \
mean_fibers_per_subject_per_cluster[cidx],'\t', std_fibers_per_subject_per_cluster[cidx]
clusters_qc_file.close()
if HAVE_PLT:
print "<cluster.py> Saving subjects per cluster histogram."
fig, ax = plt.subplots()
counts = numpy.zeros(num_of_subjects+1)
counts[:numpy.max(subjects_per_cluster)+1] = numpy.bincount(subjects_per_cluster)
ax.bar(range(num_of_subjects + 1), counts, width=1, align='center')
ax.set(xlim=[-1, num_of_subjects + 1])
plt.title('Histogram of Subjects per Cluster')
plt.xlabel('subjects per cluster')
plt.ylabel('number of clusters')
plt.savefig( os.path.join(outdir, 'subjects_per_cluster_hist.pdf'))
plt.close()
# Save the entire combined atlas as individual clusters for visualization
# and labeling/naming of structures. This will include all of the data
# that was clustered to make the atlas.
# Figure out file name and mean color for each cluster, and write the individual polydatas
print "<cluster.py> Beginning to save individual clusters as polydata files. TOTAL CLUSTERS:", len(cluster_indices),
fnames = list()
cluster_colors = list()
cluster_sizes = list()
cluster_fnames = list()
for c in cluster_indices:
print c,
mask = cluster_numbers_s == c
cluster_size = numpy.sum(mask)
cluster_sizes.append(cluster_size)
pd_c = filter.mask(output_polydata_s, mask,verbose=verbose)
# color by subject so we can see which one it came from
filter.add_point_data_array(pd_c, subject_fiber_list[mask], "Subject_ID")
# Save hemisphere information into the polydata
farray = fibers.FiberArray()
farray.hemispheres = True
farray.hemisphere_percent_threshold = 0.90
farray.convert_from_polydata(pd_c, points_per_fiber=50)
filter.add_point_data_array(pd_c, farray.fiber_hemisphere, "Hemisphere")
# The clusters are stored starting with 1, not 0, for user friendliness.
fname_c = 'cluster_{0:05d}.vtp'.format(c+1)
# save the filename for writing into the MRML file
fnames.append(fname_c)
# prepend the output directory
fname_c = os.path.join(outdir, fname_c)
#print fname_c
io.write_polydata(pd_c, fname_c)
cluster_fnames.append(fname_c)
if cluster_size > 0:
color_c = color[mask,:]
cluster_colors.append(numpy.mean(color_c,0))
else:
cluster_colors.append([0,0,0])
del pd_c
print "\n<cluster.py> Finishes saving individual clusters as polydata files."
# Notify user if some clusters empty
empty_count = 0
for sz, fname in zip(cluster_sizes,cluster_fnames):
if sz == 0:
print sz, ":", fname
empty_count += 1
if empty_count:
print "<cluster.py> Warning. Empty clusters found:", empty_count
cluster_sizes = numpy.array(cluster_sizes)
print "<cluster.py> Mean number of fibers per cluster:", numpy.mean(cluster_sizes), "Range:", numpy.min(cluster_sizes), "..", numpy.max(cluster_sizes)
# Estimate subsampling ratio to display approximately number_of_fibers_to_display total fibers in 3D Slicer
number_fibers = len(cluster_numbers_s)
if number_fibers < number_of_fibers_to_display:
ratio = 1.0
else:
ratio = number_of_fibers_to_display / number_fibers
print "<cluster.py> Subsampling ratio for display of", number_of_fibers_to_display, "total fibers estimated as:", ratio
# Write the MRML file into the directory where the polydatas were already stored
fname = os.path.join(outdir, 'clustered_tracts.mrml')
mrml.write(fnames, numpy.around(numpy.array(cluster_colors), decimals=3), fname, ratio=ratio)
# Also write one with 100% of fibers displayed
fname = os.path.join(outdir, 'clustered_tracts_display_100_percent.mrml')
mrml.write(fnames, numpy.around(numpy.array(cluster_colors), decimals=3), fname, ratio=1.0)
# View the whole thing in jpg format for quality control
if render_images:
print '<cluster.py> Rendering and saving images of cluster atlas.'
ren = render.render(output_polydata_s, 1000, data_mode='Cell', data_name='EmbeddingColor', verbose=verbose)
ren.save_views(outdir, verbose=verbose)
del ren
