def load_data_targets(config, features):
"""load the targets for a glomerulus"""
door2id = json.load(open(os.path.join(config['data_path'], 'door2id.json')))
csv_path = os.path.join(config['data_path'], 'response_matrix.csv')
if 'normed_responses' in config and not config['normed_responses']:
csv_path = os.path.join(config['data_path'], 'unnorm_response_matrix.csv')
cas_numbers, glomeruli, rm = rdl.load_response_matrix(csv_path, door2id)
glom_idx = glomeruli.index(config['glomerulus'])
# select molecules available for the glomerulus
targets , tmp_cas_numbers = rdl.get_avail_targets_for_glom(rm, cas_numbers, glom_idx)
molids = [str(door2id[cas_number][0]) for cas_number in tmp_cas_numbers]
assert len(molids) == len(targets)
# for some of them the spectra are not available
avail = [i for i in range(len(molids)) if molids[i] in features]
targets = np.array([targets[i] for i in avail])
data = np.array([features[molids[i]] for i in avail])
molids = [m for i, m in enumerate(molids) if i in avail]
assert targets.shape[0] == data.shape[0]
assert targets.shape[0] == len(molids)
return data, targets, molids
# variables for results
plt.close('all')
search_res, max_overview, sc, _ = rdl.read_paramsearch_results(config['inpath'])
glomeruli = search_res[desc][selection].keys()
# sort glomeruli according to performance
maxes = [np.max(search_res[desc][selection][glom][method]) for glom in glomeruli]
picks = [search_res[desc][selection][glom][method][-1, 1] for glom in glomeruli]
max_idx = np.argsort(maxes)
glomeruli = [glomeruli[i] for i in max_idx]
fig = plt.figure(figsize=(3, 20))
for i_glom, glom in enumerate(glomeruli):
mat = search_res[desc][selection][glom][method]
glom_idx = all_glomeruli.index(glom)
tmp_rm, tmp_cas_numbers = rdl.get_avail_targets_for_glom(rm, cas_numbers, glom_idx)
ax = plt.subplot2grid((len(glomeruli), 2), (i_glom, 1))
if len(tmp_rm) > 50 and scoreatpercentile(tmp_rm, 75) > 0.2:
ax.hist(tmp_rm, color='g')
elif scoreatpercentile(tmp_rm, 75) > 0.14:
ax.hist(tmp_rm, color='#6be463')
else:
ax.hist(tmp_rm, color='r')
ax.set_xlim([0, 1])
ax.set_yticks([])
ax.set_xticks([])
ax = plt.subplot2grid((len(glomeruli), 2), (i_glom, 0))
if np.max(mat) < 0:
ax.imshow(mat, interpolation='nearest')
else:
