def pdf_book(config, dataset, save_figure_path=''):
if save_figure_path == '':
figure_path = os.path.join(config.path, config.figure_path)
save_figure_path=os.path.join(figure_path, 'odor_traces/')
threshold_odor=10
for odor in [True, False]:
key_set = {}
for odor_stimulus in config.odor_stimulus.keys():
#keys_tmp = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=threshold_odor, odor_stimulus=odor_stimulus, threshold_distance_min=0.1, odor=odor)
keys_tmp = fad.get_keys_with_attr(dataset, 'odor_stimulus', odor_stimulus)
keys = []
for key in keys_tmp:
if '20120924' in key or '20120925' in key:
pass
else:
continue
trajec = dataset.trajecs[key]
add_key = True
if trajec.positions[0,0] < 0.3:
add_key = False
if odor:
frames_in_odor = np.where(trajec.odor > threshold_odor)[0]
if len(frames_in_odor) < 20:
add_key = False
if add_key:
keys.append(key)
print odor_stimulus, keys
if len(keys) > 0:
key_set.setdefault(odor_stimulus, keys)
for odor_stimulus, keys in key_set.items():
print 'Odor Book, Chapter: ', odor_stimulus
if len(keys) < 250:
keys_to_plot = keys
else:
keys_to_plot = keys[250:500]
book_name = 'odor_trace_book_' + odor_stimulus + '_' + str(odor) + '.pdf'
plot_odor_traces_book(config.path, config, dataset, keys=keys_to_plot, show_saccades=False, frames_to_show_before_odor=100, frames_to_show_after_odor=100, odor_multiplier=1, book_name=book_name)
def get_orientation_data(dataset, config, visual_stimulus='none', odor_stimulus=None, odor=None, threshold_odor=10, eccentricity_threshold=0.7):
threshold_odor = 10
key_sets = {}
if odor_stimulus is not None:
keys_odor = fad.get_keys_with_attr(dataset, ['odor_stimulus', 'visual_stimulus'], [odor_stimulus, visual_stimulus])
else:
keys_odor = dataset.trajecs.keys()
if odor is None:
keys = keys_odor
else:
if odor is True:
keys = []
for key in keys_odor:
trajec = dataset.trajecs[key]
if np.max(trajec.odor) > threshold_odor:
keys.append(key)
elif odor is False:
keys = []
for key in keys_odor:
trajec = dataset.trajecs[key]
if np.max(trajec.odor) < threshold_odor:
keys.append(key)
orientations = []
eccentricities = []
airheadings = []
groundheadings = []
speeds = []
airspeeds = []
for key in keys:
trajec = dataset.trajecs[key]
try:
frames = trajec.frames_with_orientation
except:
continue
if len(trajec.frames_with_orientation) < 5:
continue
frames_to_use = []
saccade_frames = saccade_frames = [item for sublist in trajec.saccades for item in sublist]
for i, f in enumerate(trajec.frames_with_orientation):
if 1:
if f in saccade_frames:
continue
if np.abs(trajec.positions[f,2]) > 0.1:
continue
if np.abs(trajec.velocities[f,2]) > 0.25:
continue
if trajec.eccentricity[i] > eccentricity_threshold:
continue
if len(trajec.frames_with_orientation) < 10:
continue
frames_to_use.append(f)
indices = [trajec.frames_with_orientation.index(f) for f in frames_to_use]
orientations.extend(np.array(trajec.orientation)[indices].tolist())
eccentricities.extend(np.array(trajec.eccentricity)[indices].tolist())
airheadings.extend(trajec.airheading_smooth[frames_to_use].tolist())
groundheadings.extend(trajec.heading_smooth[frames_to_use].tolist())
speeds.extend(trajec.speed[frames_to_use].tolist())
as_tmp = [np.linalg.norm(trajec.airvelocities[f]) for f in frames_to_use]
airspeeds.extend(as_tmp)
orientations = np.array(orientations)
eccentricities = np.array(eccentricities)
airheadings = np.array(airheadings)
groundheadings = np.array(groundheadings)
speeds = np.array(speeds)
airspeeds = np.array(airspeeds)
return orientations, airheadings, groundheadings, eccentricities, speeds, airspeeds
