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
