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 decleration_color_coded_for_post(dataset, plot=True): fa.calc_stats_at_deceleration(dataset, keys=None, time_offset=0, return_vals=False) angleok = np.where( (dataset.angle_at_deceleration < 2)*(dataset.angle_at_deceleration > .01) )[0].tolist() fig = plt.figure() ax = fig.add_subplot(111) black_post = [] checkered_post = [] for i, p in enumerate(dataset.post_type_at_deceleration): if i in angleok: if 'black' in p: black_post.append(i) if 'checkered' in p: checkered_post.append(i) print '*'*40 print len(black_post), len(checkered_post) bins, hists, hist_std, curves = floris.histogram(ax, [np.log(dataset.angle_at_deceleration[black_post]), np.log(dataset.angle_at_deceleration[checkered_post])], bins=16, colors=['black', 'teal'], bin_width_ratio=0.9, edgecolor='none', bar_alpha=0.2, curve_fill_alpha=0, curve_line_alpha=0.8, return_vals=True, normed_occurences='total', bootstrap_std=True) ax.plot(np.log(dataset.angle_at_deceleration[black_post]), dataset.speed_at_deceleration[black_post], '.', color='black', alpha=1) ax.plot(np.log(dataset.angle_at_deceleration[checkered_post]), dataset.speed_at_deceleration[checkered_post], '.', color='teal', alpha=1) #ax.set_ylim(0,1.2) fa.fix_angle_log_spine(ax, histograms=True) fig.savefig('deceleration_color_coded.pdf', format='pdf')
def crash_analysis(dataset, dataset_landing, keys=None): fig = plt.figure() ax = fig.add_subplot(111) keys = dataset.trajecs.keys() for key in keys: trajec = dataset.trajecs[key] ftp = np.arange(trajec.frames[0], trajec.frames[-1]).tolist() color = 'gray' dotcolor = 'blue' if key == '20101111_C001H001S0045': color = 'purple' dotcolor = 'purple' if trajec.angle_at_deceleration*180/np.pi > 90: print key 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_landing, 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('crash_spagetti.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 plot_demo_trajectory_speed_vs_angle(movie_dataset=None, movie_landing=None, movie_flyby=None, filename=None): if movie_dataset is not None: movie_landing = movie_dataset.movies['20101110_C001H001S0038'] movie_flyby = movie_dataset.movies['20101101_C001H001S0024'] fig = plt.figure() angleax = fig.add_subplot(111) ## landing trajec = movie_landing.trajec ftp = np.arange(trajec.frames[0]-25, trajec.frames[-1]).tolist() angleax.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.speed[ftp], color='black') angleax.plot( np.log(trajec.angle_at_deceleration), trajec.speed_at_deceleration, '.', markerfacecolor='blue', markeredgecolor='blue') movieinfo = movie_landing legextensionframe = movieinfo.legextensionrange[0] - movieinfo.firstframe_ofinterest time_of_leg_ext = movieinfo.timestamps[legextensionframe] flydra_frame_of_leg_ext = fa.get_flydra_frame_at_timestamp(trajec, time_of_leg_ext)+1 angleax.plot( np.log(trajec.angle_subtended_by_post[flydra_frame_of_leg_ext]), trajec.speed[flydra_frame_of_leg_ext], '.', markerfacecolor='red', markeredgecolor='red') ## flyby trajec = movie_flyby.trajec ftp = np.arange(trajec.frames[0]-25, trajec.frames[-1]).tolist() angleax.plot( np.log(trajec.angle_subtended_by_post[ftp]), trajec.speed[ftp], '-', color='black') sf = fa.get_saccade_range(trajec, trajec.saccades[-1]) angleax.plot( np.log(trajec.angle_subtended_by_post[sf[0]]), trajec.speed[sf[0]], '.', markerfacecolor='green', markeredgecolor='green') angleax.plot( np.log(trajec.angle_subtended_by_post[sf]), trajec.speed[sf], '-', color='green') angleax.plot( np.log(trajec.angle_at_deceleration), trajec.speed_at_deceleration, '.', markerfacecolor='blue', markeredgecolor='blue') fa.fix_angle_log_spine(angleax, histograms=False) if filename is not None: fig.subplots_adjust(right=0.9, bottom=0.3) fig.savefig(filename, format='pdf') return angleax
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 neural_threshold_tti_vs_rsdet_models(dataset_landing, save_plot=True, movie_dataset=None, ttc=None): distfig = plt.figure() distax = distfig.add_subplot(111) radius = 0.009565 a = np.linspace(0,2.5,100) fit, Rsq, x, y, yminus, yplus = fa.get_angle_vs_speed_curve(dataset_landing, plot=False, plot_sample_trajecs=False, post_type=['checkered', 'checkered_angled', 'black', 'black_angled'], filename=None, keys=None, tti=None, color_code_posts=False) std = np.mean(yplus - y) # RSDET model m = fit[0] b = fit[1] vel = (m*np.log(a)+b) print std, fit expthreshold = expansion(vel, a=a) expthreshold_plus = expansion(vel+std, a=a) expthreshold_minus = expansion(vel-std, a=a) distax.plot( np.log(a), expthreshold, color='purple') distax.fill_between(np.log(a), expthreshold_plus, expthreshold_minus, color='purple', linewidth=0, alpha=0.3) # True time-to-contact model if ttc is None: ttc, ttc_threshold = match_ttc_to_rsdet(ttc0=0.13, ttc_threshold0=0) ttc_threshold = 0 expthreshold_ttc = expansion_from_timetocontact(ttc, a) + ttc_threshold distax.plot( np.log(a), expthreshold_ttc, ':', color='purple') # plot a sample constant velocity trajectory vels = [0.2, 0.4, 0.8] for vel in vels: fps = 5000.0 x = np.arange(.2, 0.0, -vel/fps) d = x+radius a = 2*np.arcsin(radius / (d)) #exp = 2/np.sqrt(1-(radius/(d))**2) * (radius/(d)**2) * vel exp = expansion(vel, a=a) indices = np.where(exp<12)[0].tolist() distax.plot( np.log(a[indices]), exp[indices], color='gray', linewidth=0.5) # plot parameters fa.fix_angle_log_spine(distax, histograms=False, set_y=False) ylim_max = 1000 distax.set_ylim(0,ylim_max/180.*np.pi) rad_ticks_y = np.linspace(0,ylim_max*np.pi/180.,5,endpoint=True) deg_tick_strings_y = [str(s) for s in np.linspace(0,ylim_max,5,endpoint=True)] for i, s in enumerate(deg_tick_strings_y): deg_tick_strings_y[i] = s.split('.')[0] distax.set_yticks(rad_ticks_y) distax.set_yticklabels(deg_tick_strings_y) distax.set_ylabel('Expansion, deg/s') if save_plot: distfig.savefig('neural_threshold_distance.pdf', format='pdf') return
def landing(dataset_landing, movie_dataset, speed=0.2): behavior = 'landing' fps = 1000. dt = 1/fps r = 0.009565 radius = r pos0 = [-0.2, 0] vel = [speed, 0] dvda = -0.2 nf = 5000 positions = np.zeros([nf, 2]) positions[0] = pos0 velocities = np.zeros([nf, 2]) velocities[0] = vel speed = np.zeros([nf]) speed[0] = np.linalg.norm(velocities[0]) distance = np.zeros([nf]) angle_subtended_by_post = np.zeros([nf]) leg_ext = np.zeros([nf]) frames = [0] frame_at_deceleration = None deceleration_initiated = False for f in range(1,nf): if np.linalg.norm(positions[f-1])-radius <= 0.0001: landed = True else: landed = False if not landed: frames.append(f) positions[f] = positions[f-1] + velocities[f-1]*dt distance[f] = np.linalg.norm(positions[f]) - radius angle_subtended_by_post[f] = 2*np.arcsin( radius / (distance[f]+radius) ) if f>5: #velocities[f,0] = -.21*np.log(angle_subtended_by_post[f])+.2 da = np.log(angle_subtended_by_post[f])-np.log(angle_subtended_by_post[f-1]) a = angle_subtended_by_post af = np.min([a[f], 3]) exp0 = (a[f]-a[f-1])/dt #/ (-2.*np.tan(a[f]/2.)) exp1 = (a[f-1]-a[f-2])/dt #/ (-2.*np.tan(a[f-1]/2.)) m = -0.21/radius b = 0.159/radius expthreshold = (m*np.log(af)+b)*(2*np.tan(af/2.)*np.sin(af/2.)) exp0 -= expthreshold exp1 -= expthreshold exp0 = np.max([exp0, 0]) exp1 = np.max([exp1, 0]) #c = -1*exp0 / 3500. dda = (exp1-exp0)/dt c = dda / 150000. print dda, velocities[f-1,0] c = np.min([c,0]) v = np.max([speed[f-1] + c, 0.0]) velocities[f,0] = v else: velocities[f] = velocities[f-1] speed[f] = np.linalg.norm(velocities[f]) if speed[f] > -0.21*np.log(angle_subtended_by_post[f])+0.159: deceleration_initiated = True if frame_at_deceleration is None: frame_at_deceleration = f else: deceleration_initiated = False if angle_subtended_by_post[f]*180/np.pi > 70 or np.isnan(angle_subtended_by_post[f]): leg_ext[f] = 1 fig2 = plt.figure() ax2 = fig2.add_subplot(111) fit, Rsq, x, y, yminus, yplus = fa.get_angle_vs_speed_curve(dataset_landing, plot=False) ax2.plot( x, y, color='blue', alpha=0.3) ax2.fill_between(x, yplus, yminus, color='blue', linewidth=0, alpha=0.2) angle_at_leg_extension, bins, data_filtered, xvals = fa.leg_extension_angle_histogram(movie_dataset, plot=False) ax2.plot(xvals, data_filtered, color='red', alpha=0.3) ax2.fill_between(xvals, data_filtered, np.zeros_like(xvals), color='red', linewidth=0, alpha=0.2) ax2.plot(np.log(angle_subtended_by_post), speed, color='black') fa.fix_angle_log_spine(ax2, histograms=False) fig2.subplots_adjust(bottom=0.3, top=0.8, right=0.9, left=0.25) filename = 'landing_cartoon_plot.pdf' fig2.savefig(filename, format='pdf')
def flyby(dataset_flyby, speed=0.5): behavior = 'flyby' fps = 100. dt = 1/fps r = 0.009565 radius = r pos0 = [-0.2, 0] vel = [speed, 0] dvda = -0.2 nf = 200 positions = np.zeros([nf, 2]) positions[0] = pos0 velocities = np.zeros([nf, 2]) velocities[0] = vel speed = np.zeros([nf]) speed[0] = np.linalg.norm(velocities[0]) distance = np.zeros([nf]) angle_subtended_by_post = np.zeros([nf]) frames = [0] frame_at_deceleration = None frame_at_saccade = None deceleration_initiated = False saccade_time = 0 saccading = False for f in range(1,nf): frames.append(f) positions[f] = positions[f-1] + velocities[f-1]*dt distance[f] = np.linalg.norm(positions[f]) - radius angle_subtended_by_post[f] = 2*np.arcsin( radius / (distance[f]+radius) ) if saccading: saccade_rate = 400*np.pi/180. s = np.linalg.norm(velocities[f-1]) velocities[f,1] = np.sin(saccade_rate)*s velocities[f,0] = np.cos(saccade_rate)*s elif deceleration_initiated: worldangle = np.arctan2(velocities[f-1,1], velocities[f-1,0]) da = np.log(angle_subtended_by_post[f])-np.log(angle_subtended_by_post[f-1]) velocities[f,1] = velocities[f-1,1] + dvda*da*np.sin(worldangle) velocities[f,0] = velocities[f-1,0] + dvda*da*np.cos(worldangle) else: velocities[f] = velocities[f-1] speed[f] = np.linalg.norm(velocities[f]) if speed[f] > -0.1*np.log(angle_subtended_by_post[f])+0.212 and saccade_time == 0: deceleration_initiated = True print 'decelerating triggered' if frame_at_deceleration is None: frame_at_deceleration = f else: pass #deceleration_initiated = False if angle_subtended_by_post[f]*180/np.pi > 30 or np.isnan(angle_subtended_by_post[f]): if frame_at_saccade is None: frame_at_saccade = f saccading = True saccade_time += dt if saccade_time > 0.17: saccading = False if saccade_time > 0: deceleration_initiated = False # plot fig = plt.figure() ax = fig.add_subplot(111) flycon_flying = plt.imread('flying_flycon.png') for f in frames: ''' if not leg_ext[f]: color = 'black' else: color = 'red' if f == frame_at_deceleration: color = 'blue' ''' color='black' x = positions[f,0] y = positions[f,1] w = flycon_flying.shape[0]/(7*1000.*.8) h = flycon_flying.shape[1]/(5.*1000.*.8) if f == frame_at_deceleration: #ax.imshow(flycon_flying, extent=(x-2*w, x, y-h, y+h), aspect='equal') # show angle subtended d = np.linalg.norm([x-.001,y]) half_angle_to_post = np.arcsin( radius / d ) world_angle = np.arctan2(y,x-.001) a = half_angle_to_post - world_angle visual_intercept_1 = [0+np.cos(np.pi/2.-a)*radius, 0+np.sin(np.pi/2.-a)*radius] a = half_angle_to_post + world_angle visual_intercept_2 = [0+np.cos(np.pi/2.-a)*radius, 0-np.sin(np.pi/2.-a)*radius] xy = np.vstack( (visual_intercept_1, visual_intercept_2, [x-.001,y]) ) triangle = patches.Polygon( xy, facecolor='blue', edgecolor='none', zorder=-10, alpha=0.2 ) ax.add_artist(triangle) ''' arc = patches.Arc( (x,y), .05, .05, 180, (world_angle - half_angle_to_post)*180/np.pi, (half_angle_to_post + world_angle)*180/np.pi, edgecolor='blue', linewidth=1) ax.add_artist(arc) ''' elif f == frame_at_saccade: #ax.imshow(flycon_flying, extent=(x-2*w, x, y-h, y+h), aspect='equal') # show angle subtended d = np.linalg.norm([x-.001,y]) half_angle_to_post = np.arcsin( radius / d ) world_angle = np.arctan2(y,x-.001) a = half_angle_to_post - world_angle visual_intercept_1 = [0+np.cos(np.pi/2.-a)*radius, 0+np.sin(np.pi/2.-a)*radius] a = half_angle_to_post + world_angle visual_intercept_2 = [0+np.cos(np.pi/2.-a)*radius, 0-np.sin(np.pi/2.-a)*radius] xy = np.vstack( (visual_intercept_1, visual_intercept_2, [x-.001,y]) ) triangle = patches.Polygon( xy, facecolor='green', edgecolor='none', zorder=-10, alpha=0.2 ) ax.add_artist(triangle) else: pt = patches.Circle( (positions[f,0],positions[f,1]), radius=0.0005, facecolor=color, edgecolor='none') ax.add_artist(pt) # post post = patches.Circle( (0,0), radius=radius, facecolor='black', edgecolor='none') ax.add_artist(post) ax.set_aspect('equal') ax.set_xlim([-0.2, 0.01]) ax.set_ylim([-0.08, 0.08]) ax.set_axis_off() fig.set_size_inches(6.5,6.5) fig.subplots_adjust(bottom=0., top=1, right=0.95, left=0.05) filename = 'flyby_cartoon.pdf' fig.savefig(filename, format='pdf') fig2 = plt.figure() ax2 = fig2.add_subplot(111) angle, bins, data_filtered, xvals, curve, yplus, yminus = fa.deceleration_angle_histogram_flyby(dataset_flyby, keys=None, plot=False, saccades=True) ax2.plot( curve[:,0], curve[:,1], color='blue', alpha=0.3) ax2.fill_between(curve[:,0], yplus, yminus, color='blue', linewidth=0, alpha=0.2) angle, bins, data_filtered, xvals = fa.saccade_angle_histogram(dataset_flyby, keys=None, plot=False) ax2.plot(xvals, data_filtered, color='green', alpha=0.3) ax2.fill_between(xvals, data_filtered, np.zeros_like(xvals), color='green', linewidth=0, alpha=0.2) ax2.plot(np.log(angle_subtended_by_post), speed, color='black') ax2.plot(np.log(angle_subtended_by_post[frame_at_saccade]), speed[frame_at_saccade], '.', color='green') fa.fix_angle_log_spine(ax2, histograms=False) fig2.subplots_adjust(bottom=0.3, top=0.8, right=0.9, left=0.25) filename = 'flyby_cartoon_plot.pdf' fig2.savefig(filename, format='pdf')
def neural_threshold_tti_vs_rsdet_models(save_plot=True, tti=0.12, tti_thresh=0.7, movie_dataset=None): fig = plt.figure() ax = fig.add_subplot(111) distfig = plt.figure() distax = distfig.add_subplot(111) radius = 0.009565 a = np.linspace(0,2.5,100) m = -0.21/radius b = 0.159/radius expthreshold = (m*np.log(a)+b)*(2*np.tan(a/2.)*np.sin(a/2.)) vel = (expthreshold*radius/(2*np.tan(a/2.)*np.sin(a/2.)))[1:] print vel f = np.where(vel<0.07)[0][0]+1 print f expthreshold_clipped = expthreshold[0:f] a_clipped = a[0:f] #a_clipped_flipped = (a_clipped[::-1]-a_clipped[-1])[::-1] #a_clipped_mirrored = np.hstack( (a_clipped_flipped, a_clipped) ) #expthreshold_clipped_mirrored = np.hstack( (expthreshold_clipped[::-1], expthreshold_clipped) ) ax.plot( a_clipped, expthreshold_clipped, color='purple' ) ax.fill_between(a_clipped, expthreshold_clipped, np.ones_like(a_clipped)*30, facecolor='purple', edgecolor=None, alpha=0.1 ) #ax.plot( a_clipped, expthreshold_clipped, color='blue' ) #ax.plot( a_clipped[::-1]-a_clipped[-1], expthreshold_clipped, color='blue') #ax.fill_between( a_clipped, expthreshold_clipped, np.ones_like(a_clipped)*30, facecolor='blue', edgecolor=None, alpha=0.2 ) #ax.fill_between( a_clipped[::-1]-a_clipped[-1], expthreshold_clipped, np.ones_like(a_clipped)*30, facecolor='blue', edgecolor=None, alpha=0.2 ) # distance plot distax.plot( np.log(a), expthreshold, color='purple') # for true time to impact model #tti = 0.05 expthreshold_tti = 2*np.tan(a/2.) / tti - tti_thresh vel = (expthreshold_tti*radius/(2*np.tan(a/2.)*np.sin(a/2.)))[1:] #f = np.where(vel<0.07)[0][0]+1 expthreshold_tti_clipped = expthreshold_tti#[0:f] a_clipped = a#[0:f] ax.plot( a_clipped, expthreshold_tti_clipped, color='black' ) #ax.plot( a_clipped[::-1]-a_clipped[-1], expthreshold_tti_clipped, color='red') #deg_ticks = np.array([-90, -45, 0, 45, 90]) #deg_tick_strings = [str(d) for d in deg_ticks] #rad_ticks = [-np.pi, -np.pi/2., 0, np.pi/2., np.pi] distax.plot( np.log(a), expthreshold_tti, color='black') ######## plot a sample constant velocity trajectory ############## vel = 0.4 fps = 200.0 x = np.arange(.2, 0, -vel/fps) d = x+radius a = 2*np.arcsin(radius / (d)) #exp = 2*vel*np.tan(a/2.)*np.sin(a/2.) exp = floris.diffa(a)*fps exp = 2/np.sqrt(1-(radius/(d))**2) * (radius/(d)**2) * vel distax.plot( np.log(a), exp, color='gray') #return x, a ## plot paramters ax.set_ylim([0,1000*np.pi/180.]) rad_ticks_y = np.linspace(0,1000*np.pi/180.,5,endpoint=True) deg_tick_strings_y = [str(s) for s in np.linspace(0,1000,5,endpoint=True)] for i, s in enumerate(deg_tick_strings_y): deg_tick_strings_y[i] = s.split('.')[0] ax.set_autoscale_on(False) fa.fix_angle_log_spine(ax, set_y=False, histograms=False) #fa.adjust_spines(ax, ['left', 'bottom']) #ax.set_xlabel('Retinal size') ax.set_ylabel('expansion threshold, deg/s') ax.set_yticks(rad_ticks_y) ax.set_yticklabels(deg_tick_strings_y) if save_plot: fig.savefig('neural_threshold.pdf', format='pdf') if movie_dataset is not None: angle_at_leg_extension, bins, data_filtered, xvals = fa.leg_extension_angle_histogram(movie_dataset, plot=False) #ax2.plot(xvals, data_filtered, color='green', alpha=0.3) data_filtered /= np.max(data_filtered) data_filtered *= 7 distax.fill_between(xvals, data_filtered, np.zeros_like(xvals), color='green', linewidth=0, alpha=0.2) fa.fix_angle_log_spine(distax, histograms=False, set_y=False) ylim_max = 1000 distax.set_ylim(0,ylim_max/180.*np.pi) rad_ticks_y = np.linspace(0,ylim_max*np.pi/180.,5,endpoint=True) deg_tick_strings_y = [str(s) for s in np.linspace(0,ylim_max,5,endpoint=True)] for i, s in enumerate(deg_tick_strings_y): deg_tick_strings_y[i] = s.split('.')[0] distax.set_yticks(rad_ticks_y) distax.set_yticklabels(deg_tick_strings_y) distax.set_ylabel('expansion threshold, deg/s') if save_plot: distfig.savefig('neural_threshold_distance.pdf', format='pdf') return a, expthreshold, expthreshold_tti