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neural_threshold_analysis.py
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neural_threshold_analysis.py
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import sys
#sys.path.insert(0, '/usr/local/lib/python2.6/dist-packages')
sys.path.append('/home/floris/src/pymovie2')
from matplotlib import rcParams
fig_width = 3.25 # width in inches
fig_height = 3.25 # height in inches
fig_size = (fig_width, fig_height)
fontsize = 8
params = {'backend': 'Agg',
'ps.usedistiller': 'xpdf',
'ps.fonttype' : 3,
'pdf.fonttype' : 3,
'font.family' : 'sans-serif',
'font.serif' : 'Times, Palatino, New Century Schoolbook, Bookman, Computer Modern Roman',
'font.sans-serif' : 'Helvetica, Avant Garde, Computer Modern Sans serif',
'font.cursive' : 'Zapf Chancery',
'font.monospace' : 'Courier, Computer Modern Typewriter',
'font.size' : fontsize,
'text.fontsize': fontsize,
'axes.labelsize': fontsize,
'axes.linewidth': 1.0,
'xtick.major.linewidth': 1,
'xtick.minor.linewidth': 1,
#'xtick.major.size': 6,
#'xtick.minor.size' : 3,
'xtick.labelsize': fontsize,
#'ytick.major.size': 6,
#'ytick.minor.size' : 3,
'ytick.labelsize': fontsize,
'figure.figsize': fig_size,
'figure.dpi' : 72,
'figure.facecolor' : 'white',
'figure.edgecolor' : 'white',
'savefig.dpi' : 300,
'savefig.facecolor' : 'white',
'savefig.edgecolor' : 'white',
'figure.subplot.left': 0.2,
'figure.subplot.right': 0.8,
'figure.subplot.bottom': 0.25,
'figure.subplot.top': 0.9,
'figure.subplot.wspace': 0.0,
'figure.subplot.hspace': 0.0,
'lines.linewidth': 1.0,
'text.usetex': True,
}
rcParams.update(params)
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib
import colorline
import flydra_analysis as fa
import sa1_analysis as sa1
import numpy as np
import flydra_floris_analysis as ffa
import floris
import scipy.optimize
def expansion(vel, a=None, d=None, r=0.009565):
if d is None:
d = r / np.sin(a/2.)
return 2*(r/d**2)*vel / np.sqrt(1-(r/d)**2)
def timetocontact(vel, a=None, d=None, r=0.009565):
exp = expansion(vel=vel, a=a, d=d, r=r)
return (2*np.sin(a/2.)) / (exp*np.sqrt(1-(np.sin(a/2.))**2))
def expansion_from_timetocontact(ttc, a):
e = (2*np.sin(a/2.)) / (ttc*np.sqrt(1-(np.sin(a/2.))**2))
e[e<0] = 0
return e
# for match fmin:
# True time-to-contact model
def calc_expthreshold_ttc(param, a):
ttc, ttc_threshold = param
e = expansion_from_timetocontact(ttc, a) + 0
e[e<0] = 0
return e
def calc_error(param, a):
expthreshold_ttc = calc_expthreshold_ttc(param, a)
# RSDET model
m = -0.21
b = 0.159
vel = (m*np.log(a)+b)
expthreshold = expansion(vel, a=a)
a_less = np.where( a < 80*np.pi/180. )[0].tolist()
err = np.sum( np.abs(expthreshold_ttc[a_less] - expthreshold[a_less]) )
return err
def match_ttc_to_rsdet(ttc0=0.13, ttc_threshold0=-0.2):
radius = 0.009565
a = np.linspace(0.001,2.5,100)
val = scipy.optimize.fmin(calc_error, [ttc0, ttc_threshold0], args=[a], disp=0)
return val
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