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 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