def make_markers(self, ltId, pos, feature, trajectory_dir, num_cluster):
# find the feature and coordinate files
labteks_found = filter(lambda x: x[:len(ltId)]==ltId, os.listdir(trajectory_dir))
if len(labteks_found) == 0:
raise ValueError('%s not found in %s' % (ltId, self.base_dir))
if len(labteks_found) > 1:
raise ValueError('multiple labteks found')
labtek_dir = os.path.join(trajectory_dir, labteks_found[0])
feature_file = 'hist_tabFeatures_{:>05}_01.pkl'.format(pos)
try:
fp = open(os.path.join(labtek_dir, feature_file), 'r')
tab, coord, hist = pickle.load(fp)
fp.close()
except:
pdb.set_trace()
tab, toDel = correct_from_Nan(tab, perMovie=False)
new_coord = []#np.array(coord)[[el for el in range(len(coord)) if el not in toDel]]
for i,el in enumerate(coord):
if i not in toDel:
new_coord.append(el)
if feature in FEATURES:
tab = histLogTrsforming(tab)
cm = ColorMap()
cr = cm.makeColorRamp(256, ["#FFFF00", "#FF0000"])
# centers_PLT0023_11--ex2005_06_03--sp2005_04_19--tt17--c3_w00210_01.pkl
# traj_noF_densities_w00210_01.hdf5.pkl
ending = '%05i_01.pkl' % int(pos)
feature_index = FEATURES.index(feature)
values = tab[:,feature_index]
print "Working on feature {} with this range min {} max {}".format(feature, *FEATURE_RANGE[feature])
print "In this well min {} max {}".format(np.min(values), np.max(values))
colors = [cm.getColorFromMap(x, cr, FEATURE_RANGE[feature][0], FEATURE_RANGE[feature][1])
for x in values.tolist()]
elif feature=='labels':
#we are going to predict on the fly the clustering labels not to have to stock them smw (fast to predict)
f=open('../resultData/features_on_films/labelsKM_whole_k{}.pkl'.format(num_cluster))
labels, perc, who, length=pickle.load(f); f.close()
where_=np.where(who=='{}--{}'.format(ltId, pos))[0]
labels=labels[np.sum(length[:where_]):np.sum(length[:where_+1])]
assert(len(labels)==len(new_coord))
colors = [diverging_colors_traj[labels[k]] for k in range(tab.shape[0])]
markers = {}
for i, track_coord in enumerate(new_coord):
tvec, xvec, yvec = track_coord
for t, x, y in zip(tvec, xvec, yvec):
if not t in markers:
markers[t] = []
markers[t].append((x, y, colors[i]))
return markers
def getMask(self):
file_=os.path.join(self.settings.outputFolder, self.plate, self.settings.trajFeaturesFilename.format(self.well))
f=open(file_, 'r')
arr, _,__= pickle.load(f)
f.close()
_, toDel = correct_from_Nan(arr, perMovie=False)
return toDel
help="The well which you are interested in")
(options, args) = parser.parse_args()
print "Working on ", options.plate, options.well
model_file='../resultData/features_on_films/kmeans_model.pkl'
in_folder = '/share/data20T/mitocheck/tracking_results'
out_folder = '../resultData/features_on_films/trajectory_cluster_distributions'
try:
f=open(os.path.join(in_folder, options.plate, 'hist_tabFeatures_{}.pkl'.format(options.well)))
arr, _, _= pickle.load(f); f.close()
except:
labels=None
else:
arr, _ = correct_from_Nan(arr, perMovie=False)
f=open(model_file, 'r')
model, mean, std, pca, pca_std=pickle.load(f)
labels=exploitingKMeans_wModel(model, arr, mean, std, pca_std, pca, num_PC=7)
finally:
try:
f=open(os.path.join(out_folder, 'trajectory_cluster_{}.pkl'.format(options.plate)))
d=pickle.load(f)
f.close()
except:
d={}
finally:
d.update({options.well:labels})
