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
if __name__ == '__main__':
#SCRIPT UTILISE POUR FAIRE LES FEATURES
parser = OptionParser(usage="usage: %prog [options]")
parser.add_option("--iter", dest="iter", type=int,default=0)
parser.add_option("-m", dest="movement_type_index", type=int,default=None)
(options, args) = parser.parse_args()
simulateur = TrajectorySimulator(settings_filename='tracking/settings/settings_simulator_14_10_20.py')
simulateur('simulated_trajectories', 0, options.movement_type_index, "hist_tabFeatures{}_W{}.pkl".format(options.iter, options.movement_type_index))
f=open('../resultData/simulated_traj/trajectories_for_clustering/hist_tabFeatures{}_W{}.pkl'.format(options.iter, options.movement_type_index))
tabFeatures, _, _ = pickle.load(f)
r2 = histLogTrsforming(tabFeatures)
print r2.shape, 'not normalized'
r2=r2[:,:-1]
if np.any(np.isnan(r2)):
print 'Deleting nan values'
r2=np.delete(r2, np.where(np.isnan(r2))[0], 0)
#r=(r2-np.mean(r2,0))/np.std(r2,0)
r=np.hstack((r2[:,:len(featuresNumeriques)], r2[:, featuresSaved.index('mean straight'), np.newaxis]))
print r.shape
#print 'computing bins'
#histogrammeMatrix = computingBins(histNC, mat_hist_sizes[0])
print 'saving'
f=open('../resultData/simulated_traj/trajectories_for_clustering/data_sim_traj{}{}.pkl'.format(options.iter, options.movement_type_index), 'w')
