def landing_analysis_for_crash_comparison(dataset, keys=None):
fig = plt.figure()
ax = fig.add_subplot(111)
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = classified_keys['straight']
keys = dataset.trajecs.keys()
for key in keys:
trajec = dataset.trajecs[key]
ftp = np.arange(trajec.frames[0]-25, trajec.frames[-1]).tolist()
ax.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.speed[ftp], color='black', linewidth=0.5, alpha=0.05)
keys_to_highlight = ['2_29065', '2_31060', '8_10323', '6_715']
for key in keys:
color = 'gray'
dotcolor = 'blue'
if key in keys_to_highlight:
color = 'black'
dotcolor = 'purple'
trajec = dataset.trajecs[key]
ftp = np.arange(trajec.frames[0]-25, trajec.frames[-1]).tolist()
ax.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.speed[ftp], color=color, linewidth=0.5, alpha=1)
ax.plot( np.log(trajec.angle_at_deceleration), trajec.speed_at_deceleration, '.', color=dotcolor, alpha=1)
fit, Rsq, x, y, yminus, yplus = fa.get_angle_vs_speed_curve(dataset, plot=False)
ax.plot( x, y, color='purple')
ax.fill_between(x, yplus, yminus, color='purple', linewidth=0, alpha=0.2)
fa.fix_angle_log_spine(ax, histograms=False)
fig.savefig('landing_for_crash_comparison.pdf', format='pdf')
def speed_histogram(dataset, keys=None):
fig = plt.figure()
ax = fig.add_subplot(111)
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = dataset.trajecs.keys()
speeds = []
for key in keys:
trajec = dataset.trajecs[key]
if trajec.behavior == 'flyby':
frames = trajec.frames_of_flyby
else:
frames = trajec.frames
speeds.extend(trajec.speed[frames])
bins = np.linspace(0, 0.6, 50)
ax.hist(speeds, bins=bins)
fig.savefig('speeds.pdf', format='pdf')
def landing_spagetti_plots(dataset, gain=[170000], keys=None):
fig = plt.figure()
ax = fig.add_subplot(111)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
fig3 = plt.figure()
ax3 = fig3.add_subplot(111)
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = classified_keys['straight']
#keys = dataset.trajecs.keys()
#keys_to_highlight = [keys[2], keys[0], keys[4], keys[6]]
keys_to_highlight = ['2_29065', '2_31060', '8_10323', '6_715']
points_at_deceleration = True
for key in keys:
trajec = dataset.trajecs[key]
ftp = np.arange(trajec.frames[0]-25, trajec.frame_of_landing-1).tolist()
#ftp = np.arange(trajec.frame_at_deceleration-10, trajec.frame_of_landing-1).tolist()
if key in keys_to_highlight:
color = 'black'
zorder = 10
else:
color = 'blue'
zorder = 1
ax.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.speed[ftp], color=color, linewidth=0.5, alpha=1, zorder=zorder)
if points_at_deceleration and key in keys_to_highlight:
ax.plot( np.log(trajec.angle_at_deceleration), trajec.speed_at_deceleration, '.', color='black', alpha=1, zorder=zorder)
ax3.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.speed[ftp], color='black', linewidth=0.5, alpha=1)
if 0:
# simulate
angle, speed, expansion = sim_deceleration(trajec, gain)
if speed[0] > 0.1:
ax2.plot( np.log(angle), speed, color='black', linewidth=0.5, alpha=1)
ax3.plot( np.log(angle), speed, color='black', linewidth=0.5, alpha=1)
f = np.where( np.abs(speed-speed[0])>0 )[0][0]
#ax2.plot( np.log(angle[f]), speed[f], '.', color='blue')
fit, Rsq, x, y, yminus, yplus = fa.get_angle_vs_speed_curve(dataset, plot=False)
ax2.plot( x, y, color='purple', alpha=1)
ax2.fill_between(x, yplus, yminus, color='purple', linewidth=0, alpha=0.2)
ax.plot( x, y, color='purple', alpha=1)
ax.fill_between(x, yplus, yminus, color='purple', linewidth=0, alpha=0.2)
fa.fix_angle_log_spine(ax, histograms=False)
fig.savefig('deceleration_real.pdf', format='pdf')
fa.fix_angle_log_spine(ax2, histograms=False)
fig2.savefig('deceleration_sim.pdf', format='pdf')
fa.fix_angle_log_spine(ax3, histograms=False)
fig3.savefig('deceleration_comparison.pdf', format='pdf')
def plot_tti_wrt_retinal_size(dataset, keys=None, tti_thresh=0.05, plot=False):
if plot:
fig = plt.figure()
ax = fig.add_subplot(111)
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = classified_keys['straight']
keys = dataset.trajecs.keys()
#keys = keys[0:10]
tti_below_thresh = []
for key in keys:
trajec = dataset.trajecs[key]
ftp = np.arange(trajec.frame_at_deceleration-10, trajec.frame_of_landing-1).tolist()
# simulate
if plot:
ax.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.time_to_impact[ftp], '.', color='black', alpha=0.3)
#tti_check = (np.sin(angle_subtended/2.)-1) / (np.sin(angle_subtended/2.) + 0.5*1/np.tan(angle_subtended/2.)*expansion)
try:
tti_below_thresh.append( trajec.angle_subtended_by_post[ np.where( (trajec.time_to_impact < tti_thresh)*(trajec.angle_to_post < 45*np.pi/180.) )[0][0] ])
except:
pass
print
print 'time to impact below threshold of ', str(tti_thresh), ' sec: '
print 'mean: ', np.mean(tti_below_thresh)*180/np.pi
print 'std dev: ', np.std(tti_below_thresh)*180/np.pi
print 'n: ', len(tti_below_thresh)
print 'percent: ', len(tti_below_thresh) / float(len(keys))
if plot:
fa.fix_angle_log_spine(ax, histograms=False, set_y=False)
ax.set_ylim(0,0.4)
ticks_y = np.linspace(0,0.4,5,endpoint=True)
tick_strings_y = [str(s) for s in np.linspace(0,0.4,5,endpoint=True)]
for i, s in enumerate(tick_strings_y):
tick_strings_y[i] = s
ax.set_yticks(ticks_y)
ax.set_yticklabels(tick_strings_y)
ax.set_ylabel('time to impact, sec')
fig.savefig('time_to_impact_vs_angle.pdf', format='pdf')
def plot_expansion_for_sim_trajec(dataset, gain=[170000], keys=None):
fig = plt.figure()
ax = fig.add_subplot(111)
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = classified_keys['straight']
#keys = dataset.trajecs.keys()
keys = keys[0:10]
for key in keys:
trajec = dataset.trajecs[key]
ftp = np.arange(trajec.frame_at_deceleration-10, trajec.frame_of_landing-1).tolist()
# simulate
angle, speed, expansion = sim_deceleration(trajec, gain)
#ax.plot( np.log(angle), expansion, color='red', linewidth=0.5, alpha=0.5)
ax.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.expansion[ftp], color='black', linewidth=0.5, alpha=0.1)
ax.plot( np.log(trajec.angle_at_deceleration), trajec.expansion_at_deceleration, '.', color='blue')
angle, speed, expansion = sim_deceleration(trajec, gain, constant_vel=True)
#ax.plot( np.log(angle), expansion, color='green', linewidth=1, alpha=0.5)
angle, expthreshold, expthreshold_tti = test_neural_threshold(save_plot=False, tti=0.05, tti_thresh=6)
ax.plot(np.log(angle), expthreshold, color='blue')
ax.plot(np.log(angle), expthreshold_tti, color='green')
angle, expthreshold, expthreshold_tti = test_neural_threshold(save_plot=False, tti=0.12, tti_thresh=0.7)
ax.plot(np.log(angle), expthreshold_tti, color='red')
fa.fix_angle_log_spine(ax, histograms=False, set_y=False)
exp_limit = 1500
ax.set_ylim(0,exp_limit/180.*np.pi)
rad_ticks_y = np.linspace(0,exp_limit*np.pi/180.,5,endpoint=True)
deg_tick_strings_y = [str(s) for s in np.linspace(0,exp_limit,5,endpoint=True)]
for i, s in enumerate(deg_tick_strings_y):
deg_tick_strings_y[i] = s.split('.')[0]
ax.set_yticks(rad_ticks_y)
ax.set_yticklabels(deg_tick_strings_y)
ax.set_ylabel('expansion threshold, deg/s')
fig.savefig('deceleration_real.pdf', format='pdf')
def landing_srini_plots(dataset, keys=None):
fig = plt.figure()
ax = fig.add_subplot(111)
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = classified_keys['straight']
#keys = dataset.trajecs.keys()
#keys = [keys[2], keys[0], keys[4], keys[6]]
for key in keys:
trajec = dataset.trajecs[key]
ftp = np.arange(trajec.frame_at_deceleration-10, trajec.frame_of_landing-1).tolist()
ax.plot( trajec.dist_to_stim_r_normed[ftp], trajec.speed[ftp], color='black', linewidth=0.5, alpha=0.2)
# simulate
ax.set_xlim([0,.1])
fig.savefig('deceleration_linear_scale.pdf', format='pdf')
def get_error_between_sim_and_dataset(gain, *args):
dataset = args[0]
sum_error = 0
keys = None
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = classified_keys['straight']
#keys = [keys[2], keys[0], keys[4], keys[6]]
#nkeys = 5
#keys = [keys[i] for i in range(20, nkeys+20)]
n = 0
for key in keys:
trajec = dataset.trajecs[key]
print trajec.obj_id
e = get_error_between_sim_and_trajec(gain, trajec)
if not np.isnan(e):
sum_error += e
n += 1
mean_error = sum_error / float(n)
return mean_error
def flyby_xy_spagetti(dataset, keys=None, show_all_poi=False, filename='flyby_xy_spagetti.pdf', nkeys=300, alpha2=1, color2='gray', show_saccades=True, keys_to_highlight=None, show_deceleration=False):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_ylim(-.15,.15)
ax.set_xlim(-.25, .25)
ax.set_autoscale_on(False)
if keys is None:
classified_keys = fa.get_classified_keys(dataset)
keys = classified_keys['straight']
keys = dataset.trajecs.keys()
keys_with_saccades = []
keys_without_saccades = []
for key in keys:
trajec = dataset.trajecs[key]
if len(trajec.sac_ranges) > 0:
keys_with_saccades.append(key)
else:
keys_without_saccades.append(key)
'''
key_to_highlight = None
k = -1
while key_to_highlight is None:
k += 1
trajec = dataset.trajecs[keys[k]]
if len(trajec.saccades) > 0:
key_to_highlight = keys[k]
'''
if keys_to_highlight is None:
keys_to_highlight = ['10_80113']
print keys_to_highlight
if len(keys) > nkeys:
keys = keys[0:nkeys]
keys.extend(keys_to_highlight)
for key in keys:
trajec = dataset.trajecs[key]
if key in keys_to_highlight:
alpha = 1
linewidth = 1
color = 'black'
zorder = 500
ax.plot(trajec.positions[:,0], trajec.positions[:,1], '-', color='black', alpha=1, linewidth=linewidth, zorder=zorder)
frames = np.arange(0,trajec.frames_of_flyby[-1]).tolist()
ax.plot(trajec.positions[frames,0], trajec.positions[frames,1], '-', color='green', alpha=1, linewidth=linewidth, zorder=zorder)
else:
linewidth = 0.5
zorder = 100
frames = np.arange(0,trajec.frames_of_flyby[-1]).tolist()
ax.plot(trajec.positions[frames,0], trajec.positions[frames,1], '-', color=color2, alpha=alpha2, linewidth=linewidth, zorder=zorder)
if show_saccades:
if len(trajec.sac_ranges) > 0:
for sac_range in trajec.sac_ranges:
if sac_range == trajec.last_saccade_range:
color = 'red'
else:
color = 'blue'
if sac_range[0] < trajec.frame_nearest_to_post:
#if 1:
ax.plot(trajec.positions[sac_range,0], trajec.positions[sac_range,1], '-', color=color, alpha=alpha2, linewidth=linewidth, zorder=zorder+1)
#sac = patches.Circle( (trajec.positions[sac_range[0],0], trajec.positions[sac_range[0],1]), radius=0.001, facecolor='red', edgecolor='none', alpha=alpha2, zorder=zorder+1)
#ax.add_artist(sac)
if show_deceleration:
dec = patches.Circle( (trajec.positions[trajec.frame_at_deceleration,0], trajec.positions[trajec.frame_at_deceleration,1]), radius=0.001, facecolor='purple', edgecolor='none', color='purple', linewidth=2, alpha=alpha2, zorder=zorder+1)
ax.add_artist(dec)
post = patches.Circle( (0, 0), radius=0.009565, facecolor='black', edgecolor='none', alpha=1)
ax.add_artist(post)
#ax.text(0,0,'post\ntop view', horizontalalignment='center', verticalalignment='center')
#post = patches.Circle( (0, 0), radius=0.028, facecolor='none', edgecolor='red', alpha=1, linewidth=0.15, linestyle='dashed')
#ax.add_artist(post)
prep_xy_spagetti_for_saving(ax)
fig.savefig(filename, format='pdf')
def flyby_xy_spagetti_types(dataset_flyby):
classified_keys_flyby = fa.get_classified_keys(dataset_flyby)
flyby_xy_spagetti(dataset_flyby, keys=classified_keys_flyby['flyby'][100:300], filename='flyby_xy_spagetti_random.pdf')
flyby_xy_spagetti(dataset_flyby, keys=classified_keys_flyby['straight'], filename='flyby_xy_spagetti_straight.pdf')
def landing_xy_spagetti_types(dataset):
classified_keys = fa.get_classified_keys(dataset)
for key_class in classified_keys.keys():
landing_xy_spagetti(dataset, keys=None, show_all_poi=False, key_class=key_class)
