def plot_residency_time_histogram(config, dataset, save_figure_path=''):
odor_stimulus = 'on'
threshold_distance_min = 0.05
# no odor
keys_odor_off = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, threshold_distance=-1, odor_stimulus='none', upwind_only=True, threshold_distance_min=0.1, odor=True, post_behavior='landing')
residency_time_odor_off = []
for key in keys_odor_off:
trajec = dataset.trajecs[key]
if trajec.residency_time is not None:
residency_time_odor_off.append(trajec.residency_time)
# odor on, odor experienced
keys_odor_on_true = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, threshold_distance=-1, odor_stimulus='on', upwind_only=True, threshold_distance_min=0.1, odor=True, post_behavior='landing')
residency_time_odor_on_true = []
for key in keys_odor_on_true:
trajec = dataset.trajecs[key]
if trajec.residency_time is not None:
residency_time_odor_on_true.append(trajec.residency_time)
# odor on, odor not experienced
keys_odor_on_false = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, threshold_distance=-1, odor_stimulus='on', upwind_only=False, threshold_distance_min=0.1, odor=False, post_behavior='landing')
residency_time_odor_on_false = []
for key in keys_odor_on_false:
trajec = dataset.trajecs[key]
if trajec.residency_time is not None:
residency_time_odor_on_false.append(trajec.residency_time)
data = [np.array(residency_time_odor_off), np.array(residency_time_odor_on_true), np.array(residency_time_odor_on_false)]
colors = ['black', 'red', 'blue']
nbins = 30 # note: if show_smoothed=True with default butter filter, nbins needs to be > ~15
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, data, bins=nbins, bin_width_ratio=0.8, colors=colors, edgecolor='none', bar_alpha=1, curve_fill_alpha=0.4, curve_line_alpha=0, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, bootstrap_std=False, exponential_histogram=False)
#xticks = [0,.02,.04,.06,.08,.15]
fpl.adjust_spines(ax, ['left', 'bottom'])
#ax.set_xlim(xticks[0], xticks[-1])
ax.set_xlabel('residency time, frames')
ax.set_ylabel('Occurences, normalized')
ax.set_title('Residency time')
if save_figure_path == '':
save_figure_path = os.path.join(config.path, config.figure_path, 'activity/')
fig.set_size_inches(8,8)
figname = save_figure_path + 'residency_time_on_post_histogram' + '.pdf'
fig.savefig(figname, format='pdf')
def plot_distance_histogram(config, dataset, save_figure_path=''):
# no odor
keys_no_odor = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, odor_stimulus='none', threshold_distance_min=0.1, odor=True)
data_no_odor = []
for key in keys_no_odor:
trajec = dataset.trajecs[key]
if np.max(trajec.distance_to_post) > 0.1:
for f, d in enumerate(trajec.distance_to_post):
if trajec.positions[f,0] > 0:
data_no_odor.append(d)
# odor on, odor experienced
keys_odor_on_true = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, odor_stimulus='on', threshold_distance_min=0.1, odor=True)
data_odor_on_true = []
for key in keys_odor_on_true:
trajec = dataset.trajecs[key]
if np.max(trajec.distance_to_post) > 0.1:
for f, d in enumerate(trajec.distance_to_post):
if trajec.positions[f,0] > 0:
data_odor_on_true.append(d)
print len(data_no_odor)
print len(data_odor_on_true)
data = [np.array(data_no_odor), np.array(data_odor_on_true)]
colors = ['black', 'red']
nbins = 30
bins = np.linspace(0,0.1,nbins)
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, data, bins=bins, bin_width_ratio=0.8, colors=colors, edgecolor='none', bar_alpha=1, curve_fill_alpha=0.4, curve_line_alpha=0, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, bootstrap_std=True, exponential_histogram=False, n_bootstrap_samples=10000, smoothing_range=[0.005,0.1])
xticks = [0,.02,.04,.06,.08,.1]
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_xlim(xticks[0], xticks[-1])
ax.set_xlabel('Distance to post, m')
ax.set_ylabel('Occurences, normalized')
title_text = 'Distance to post. red (upwind, odor) N = ' + str(len(keys_odor_on_true)) + '; black (upwind, no odor) N = ' + str(len(keys_no_odor))
ax.set_title(title_text)
if save_figure_path == '':
save_figure_path = os.path.join(config.path, config.figure_path, 'activity/')
fig.set_size_inches(8,8)
figname = save_figure_path + 'distance_to_post_histogram' + '.pdf'
fig.savefig(figname, format='pdf')
def plot_time_to_saccade_histogram(config, dataset, save_figure_path=''):
threshold_odor = 100
# no odor
keys_odor_off = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=threshold_odor, threshold_distance=-1, odor_stimulus='none', upwind_only=True, threshold_distance_min=0.1, odor=True)
time_to_saccade_odor_off = get_time_to_saccade(dataset, keys_odor_off, threshold_odor=threshold_odor)
# odor on, odor experienced
keys_odor_on_true = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=threshold_odor, threshold_distance=-1, odor_stimulus='on', upwind_only=True, threshold_distance_min=0.1, odor=True)
time_to_saccade_odor_on_true = get_time_to_saccade(dataset, keys_odor_on_true, threshold_odor=threshold_odor)
# odor on, odor not experienced
keys_odor_on_false = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=threshold_odor, threshold_distance=-1, odor_stimulus='on', upwind_only=False, threshold_distance_min=0.1, odor=False)
keys_ok = []
for key in keys_odor_on_false:
trajec = dataset.trajecs[key]
if np.max(trajec.odor) > 0.01:
keys_ok.append(key)
print 'control odor on: ', len(keys_ok)
time_to_saccade_odor_on_false = get_time_to_saccade(dataset, keys_ok, threshold_odor=0.001)
data = [np.array(time_to_saccade_odor_off), np.array(time_to_saccade_odor_on_true)]
print [len(d) for d in data]
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/')
figure_path = os.path.join(path, config.figure_path)
save_figure_path = os.path.join(figure_path, 'odor_traces/')
pdf_name_with_path = os.path.join(save_figure_path, 'odor_heading_histogram.pdf')
pp = PdfPages(pdf_name_with_path)
threshold_odor=10
threshold_distance_min=.1
for odor in [True]:
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=threshold_distance_min, odor=odor)
keys = []
for key in keys_tmp:
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) < 40:
add_key = False
if add_key:
keys.append(key)
if len(keys) > 0:
key_set.setdefault(odor_stimulus, keys)
for odor_stimulus, keys in key_set.items():
print 'Odor Book, Chapter: ', odor_stimulus
#book_name = 'odor_headings_book_' + odor_stimulus + '_' + str(odor) + '.pdf'
plot_odor_heading_book(pp, threshold_odor, path, config, dataset, keys=keys)
pp.close()
def plot_odor_traces_for_odor_puffs_before_post(path, config, dataset, axis='xy'):
keys = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=50, threshold_distance=0.1)
plot_odor_traces(path, config, dataset, keys=keys, axis=axis)
def print_stats(config, dataset):
threshold_distance_min = 0.1
threshold_odor = 10
for odor_stimulus in config.odor_stimulus.keys():
print
print 'Experiment Conditions: Odor ', odor_stimulus.replace('_', ' ').title()
print
print 'Idependent of Odor: '
print
nlanding = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['landing',odor_stimulus]))
nboomerang = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['boomerang',odor_stimulus]))
ntakeoff = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['takeoff',odor_stimulus]))
keys = fad.get_keys_with_attr(dataset, ['odor_stimulus'], [odor_stimulus])
n_flies = len(keys)
mean_speeds = []
frame_length = []
for key in keys:
trajec = dataset.trajecs[key]
mean_speeds.append( np.mean(trajec.speed) )
frame_length.append( len(trajec.speed) )
mean_speeds = np.array(mean_speeds)
frame_length = np.array(frame_length)
print 'num flies: ', n_flies
print 'num landing: ', nlanding
print 'landing ratio: ', nlanding / float(n_flies)
print 'num boomerang: ', nboomerang
print 'boomerang ratio: ', nboomerang / float(n_flies)
print 'num takeoff: ', ntakeoff
print 'mean speed: ', np.mean(mean_speeds), ' +/- ', np.std(mean_speeds)
print 'mean trajec length (secs): ', np.mean(frame_length) / (100.), ' +/- ', np.std(frame_length) / (100.)
#############################################################################################################################################################
print
print 'Experienced Odor: '
print
keys = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=threshold_odor, odor_stimulus=odor_stimulus)
nlanding = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['landing',odor_stimulus], keys=keys))
nboomerang = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['boomerang',odor_stimulus], keys=keys))
ntakeoff = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['takeoff',odor_stimulus], keys=keys))
keys_with_attr = fad.get_keys_with_attr(dataset, ['odor_stimulus'], [odor_stimulus], keys=keys)
n_flies = len(keys_with_attr)
mean_speeds = []
frame_length = []
mean_speeds_in_odor = []
mean_speeds_not_in_odor = []
mean_speeds_not_in_odor_after = []
residency_time = []
n_saccades_close_to_post = []
frames_until_saccade = []
heading_after_saccade = []
for key in keys_with_attr:
trajec = dataset.trajecs[key]
mean_speeds.append( np.mean(trajec.speed) )
frame_length.append( len(trajec.speed) )
frames_in_odor = np.where(trajec.odor > threshold_odor)[0].tolist()
frames_in_odor_and_center = []
for f in frames_in_odor:
if trajec.distance_to_post[f] > 0.05:
if np.linalg.norm(trajec.positions[f,1:]) < 0.07:
frames_in_odor_and_center.append(f)
frames_not_in_odor = np.where(trajec.odor < 1)[0].tolist()
frames_not_in_odor_prior_to_odor = []
for f in frames_not_in_odor:
if f < frames_in_odor[0]:
if trajec.distance_to_post[f] > 0.05:
if np.linalg.norm(trajec.positions[f,1:]) < 0.07:
frames_not_in_odor_prior_to_odor.append(f)
frames_not_in_odor_after_to_odor = []
for f in frames_not_in_odor:
if f > frames_in_odor[-1]:
if trajec.distance_to_post[f] > 0.05:
if np.linalg.norm(trajec.positions[f,1:]) < 0.07:
frames_not_in_odor_after_to_odor.append(f)
if len(frames_in_odor_and_center) > 0:
mean_speeds_in_odor.append(np.mean(trajec.speed[frames_in_odor_and_center]))
if len(frames_not_in_odor_prior_to_odor) > 0:
mean_speeds_not_in_odor.append(np.mean(trajec.speed[frames_not_in_odor_prior_to_odor]))
if len(frames_not_in_odor_after_to_odor) > 0:
mean_speeds_not_in_odor_after.append(np.mean(trajec.speed[frames_not_in_odor_after_to_odor]))
if trajec.residency_time is not None:
residency_time.append(trajec.residency_time)
## saccade analysis
nsacs = 0
for sac in trajec.saccades:
if np.min(trajec.distance_to_post[sac]) < 0.05:
nsacs += 1
n_saccades_close_to_post.append(nsacs)
## after odor, how long till next saccade?
frames_in_odor = np.where(trajec.odor > threshold_odor)[0].tolist()
for sac in trajec.saccades:
if sac[0] > frames_in_odor[-1]:
frames_until_saccade.append(sac[0] - frames_in_odor[-1])
heading_after_saccade.append(np.abs(trajec.heading_smooth[sac[-1]]))
break
mean_speeds = np.array(mean_speeds)
frame_length = np.array(frame_length)
print 'num flies: ', n_flies
print 'num landing: ', nlanding
print 'landing ratio: ', nlanding / float(n_flies)
print 'num boomerang: ', nboomerang
print 'boomerang ratio: ', nboomerang / float(n_flies)
print 'num takeoff: ', ntakeoff
print 'mean speed: ', np.mean(mean_speeds), ' +/- ', np.std(mean_speeds)
print 'mean speed while in odor: ', np.mean(mean_speeds_in_odor), ' +/- ', np.std(mean_speeds_in_odor)
print 'mean speed while NOT in odor PRIOR: ', np.mean(mean_speeds_not_in_odor), ' +/- ', np.std(mean_speeds_not_in_odor)
print 'mean speed while NOT in odor AFTER: ', np.mean(mean_speeds_not_in_odor_after), ' +/- ', np.std(mean_speeds_not_in_odor_after)
print 'mean trajec length (secs): ', np.mean(frame_length) / (100.), ' +/- ', np.std(frame_length) / (100.)
print 'mean residency time (sec): ', np.mean(residency_time) / (100.), ' +/- ', np.std(residency_time) / (100.), len(residency_time)
print 'n saccades near post: ', np.mean(n_saccades_close_to_post), ' +/- ', np.std(n_saccades_close_to_post)
print 'n sec till saccade after odor: ', np.mean(frames_until_saccade)/100., ' +/- ', np.std(frames_until_saccade)/100.
print 'heading after saccade: ', np.mean(heading_after_saccade)/100., ' +/- ', np.std(heading_after_saccade)/100.
print
#############################################################################################################################################################
print
print 'Experienced NO Odor: '
print
keys = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=threshold_odor, odor_stimulus=odor_stimulus, odor=False)
nlanding = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['landing',odor_stimulus], keys=keys))
nboomerang = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['boomerang',odor_stimulus], keys=keys))
ntakeoff = len(fad.get_keys_with_attr(dataset, ['post_behavior','odor_stimulus'], ['takeoff',odor_stimulus], keys=keys))
keys_with_attr = fad.get_keys_with_attr(dataset, ['odor_stimulus'], [odor_stimulus], keys=keys)
n_flies = len(keys_with_attr)
mean_speeds = []
frame_length = []
mean_speeds_near_center = []
residency_time = []
n_saccades_close_to_post = []
for key in keys_with_attr:
trajec = dataset.trajecs[key]
mean_speeds.append( np.mean(trajec.speed) )
frame_length.append( len(trajec.speed) )
frames = range(0,trajec.length)
frames_to_use = []
for f in frames:
if trajec.distance_to_post[f] > 0.05:
if np.linalg.norm(trajec.positions[f,1:]) < 0.07:
frames_to_use.append(f)
if len(frames_to_use) > 0:
mean_speeds_near_center.append(np.mean(trajec.speed[frames_to_use]))
if trajec.residency_time is not None:
residency_time.append(trajec.residency_time)
nsacs = 0
for sac in trajec.saccades:
if np.min(trajec.distance_to_post[sac]) < 0.05:
nsacs += 1
n_saccades_close_to_post.append(nsacs)
mean_speeds = np.array(mean_speeds)
frame_length = np.array(frame_length)
print 'num flies: ', n_flies
print 'num landing: ', nlanding
print 'landing ratio: ', nlanding / float(n_flies)
print 'num boomerang: ', nboomerang
print 'boomerang ratio: ', nboomerang / float(n_flies)
print 'num takeoff: ', ntakeoff
print 'mean speed: ', np.mean(mean_speeds), ' +/- ', np.std(mean_speeds)
print 'mean speed while near center: ', np.mean(mean_speeds_near_center), ' +/- ', np.std(mean_speeds_near_center)
print 'mean trajec length (secs): ', np.mean(frame_length) / (100.), ' +/- ', np.std(frame_length) / (100.)
print 'mean residency time (sec): ', np.mean(residency_time) / (100.), ' +/- ', np.std(residency_time) / (100.), len(residency_time)
print 'n saccades near post: ', np.mean(n_saccades_close_to_post), ' +/- ', np.std(n_saccades_close_to_post)
print
print '**********************************'
print
