def plot_best_cluster_latent(exp_results,
out_filenames):
sample_d = pickle.load(open(exp_results))
chains = sample_d['chains']
exp = sample_d['exp']
data_filename = exp['data_filename']
data = pickle.load(open(data_filename))
data_basename, _ = os.path.splitext(data_filename)
meta = pickle.load(open(data_basename + ".meta"))
meta_infile = meta['infile']
print "meta_infile=", meta_infile
d = pickle.load(open(meta_infile, 'r'))
conn = d['dist_matrix']['link']
cell_id_permutation = d['cell_id_permutation']
dist_matrix = d['dist_matrix']
orig_data = pickle.load(open(d['infile']))
cell_types = d['types'][:len(conn)]
type_metadata_df = pickle.load(open("type_metadata.pickle", 'r'))['type_metadata']
type_color_map = {'gc' : 'r',
'ac' : 'b',
'bc' : 'g',
'other' : 'k'}
TYPE_N = np.max(cell_types) + 1
type_colors = []
for i in range(TYPE_N):
if (i < 70):
d = type_metadata_df.loc[i+1]['desig']
else:
d = " "
type_colors.append(type_color_map.get(d[:2], 'k'))
print type_colors
chains = [c for c in chains if type(c['scores']) != int]
CHAINN = len(chains)
chains_sorted_order = np.argsort([d['scores'][-1] for d in chains])[::-1]
soma_positions = pickle.load(open('soma.positions.pickle', 'r'))
synapses = pickle.load(open('synapses.pickle', 'r'))['synapsedf']
# only take the first 950
synapses = synapses[(synapses['from_id'] < len(cell_id_permutation) ) & (synapses['to_id']<len(cell_id_permutation))]
reorder_synapses = util.reorder_synapse_ids(synapses, cell_id_permutation)
pos_vec = soma_positions['pos_vec'][cell_id_permutation]
model = data['relations']['R1']['model']
print dist_matrix.dtype, model
if "istance" not in model:
dist_matrix = dist_matrix['link']
for chain_pos, (cluster_fname, latent_fname) in enumerate(out_filenames):
best_chain_i = chains_sorted_order[chain_pos]
best_chain = chains[best_chain_i]
sample_latent = best_chain['state']
cell_assignment = sample_latent['domains']['d1']['assignment']
a = irm.util.canonicalize_assignment(cell_assignment)
util.plot_cluster_properties(a, cell_types,
pos_vec, reorder_synapses,
cluster_fname, class_colors=type_colors)
print "model=", model, dist_matrix.dtype
util.plot_latent(sample_latent, dist_matrix, latent_fname,
model = model,
PLOT_MAX_DIST=150.0, MAX_CLASSES=20)
ax_purity.axvline(v)
ax_purity.set_ylabel('true cell id')
ax_purity.set_xlim(0, len(z_ord))
f.tight_layout()
f.savefig(plot_latent_filename)
soma_positions = pickle.load(open('soma.positions.pickle', 'r'))
synapses = pickle.load(open('synapses.pickle', 'r'))['synapsedf']
# only take the first 950
synapses = synapses[(synapses['from_id'] < len(cell_id_permutation) ) & (synapses['to_id']<len(cell_id_permutation))]
reorder_synapses = util.reorder_synapse_ids(synapses, cell_id_permutation)
pos_vec = soma_positions['pos_vec'][cell_id_permutation]
util.plot_cluster_properties(purity, cell_types, pos_vec, reorder_synapses,
plot_truth_filename)
@transform(get_results, suffix(".samples"),
[(".%d.clusters.pdf" % d, ".%d.latent.pdf" % d ) for d in range(2)])
def plot_best_cluster_latent(exp_results,
out_filenames):
sample_d = pickle.load(open(exp_results))
chains = sample_d['chains']
exp = sample_d['exp']
data_filename = exp['data_filename']
data = pickle.load(open(data_filename))
data_basename, _ = os.path.splitext(data_filename)
meta = pickle.load(open(data_basename + ".meta"))