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Fig-4-double-target-1.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright INRIA
# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
#
# This software is governed by the CeCILL license under French law and abiding
# by the rules of distribution of free software. You can use, modify and/ or
# redistribute the software under the terms of the CeCILL license as circulated
# by CEA, CNRS and INRIA at the following URL
# http://www.cecill.info/index.en.html.
#
# As a counterpart to the access to the source code and rights to copy, modify
# and redistribute granted by the license, users are provided only with a
# limited warranty and the software's author, the holder of the economic
# rights, and the successive licensors have only limited liability.
#
# In this respect, the user's attention is drawn to the risks associated with
# loading, using, modifying and/or developing or reproducing the software by
# the user in light of its specific status of free software, that may mean that
# it is complicated to manipulate, and that also therefore means that it is
# reserved for developers and experienced professionals having in-depth
# computer knowledge. Users are therefore encouraged to load and test the
# software's suitability as regards their requirements in conditions enabling
# the security of their systems and/or data to be ensured and, more generally,
# to use and operate it in the same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
# -----------------------------------------------------------------------------
import os.path
import itertools
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from model import *
from graphics import *
from stimulus import *
from parameters import *
from projections import *
import matplotlib
font = {'size' : 16}
matplotlib.rc('font', **font)
# Decode function
def decode(Z, xmin=+0.0, xmax=+2.0, ymin=-1.0, ymax=+1.0,):
Y,X = np.mgrid[0:Z.shape[0],0:Z.shape[1]]
X = xmin + X/float(Z.shape[0]-1)*(xmax-xmin)
Y = ymin + Y/float(Z.shape[1]-1)*(ymax-ymin)
Z_sum = Z.sum()
x = (Z*X).sum() / Z_sum
y = (Z*Y).sum() / Z_sum
return x,y
p = 100
np.random.seed(123)
if os.path.exists('data/double-target-intensity-5.npy'):
T5 = np.load('data/double-target-intensity-5.npy')
else:
rho = 5
model= Model()
T5 = np.zeros((p,2))
for i,theta in enumerate(np.linspace(10,45,p)):
model.reset()
model.R = np.maximum( stimulus((rho, -theta), size=1, intensity=1) ,
stimulus((rho, +theta), size=1, intensity=1) )
model.R += np.random.uniform(0,0.05,model.R.shape)
model.run(duration=5*second, dt=5*millisecond, epsilon=0.0)
x,y = decode(model.SC_V)
print u"Δθ = %.2f: (%f,%f)" % (2*theta, x, y)
T5[i] = x,y
np.save("data/double-target-intensity-5.npy",T5)
if os.path.exists('data/double-target-intensity-10.npy'):
T10 = np.load('data/double-target-intensity-10.npy')
else:
rho = 10
model= Model()
T10 = np.zeros((p,2))
for i,theta in enumerate(np.linspace(10,45,p)):
model.reset()
model.R = np.maximum( stimulus((rho, -theta), size=1, intensity=1) ,
stimulus((rho, +theta), size=1, intensity=1) )
model.R += np.random.uniform(0,0.05,model.R.shape)
model.run(duration=5*second, dt=5*millisecond, epsilon=0.0)
x,y = decode(model.SC_V)
print u"Δθ = %.2f: (%f,%f)" % (2*theta, x, y)
T10[i] = x,y
np.save("data/double-target-intensity-10.npy",T10)
if os.path.exists('data/double-target-intensity-15.npy'):
T15 = np.load('data/double-target-intensity-15.npy')
else:
rho = 15
model= Model()
T15 = np.zeros((p,2))
for i,theta in enumerate(np.linspace(10,45,p)):
model.reset()
model.R = np.maximum( stimulus((rho, -theta), size=1, intensity=1) ,
stimulus((rho, +theta), size=1, intensity=1) )
model.R += np.random.uniform(0,0.05,model.R.shape)
model.run(duration=5*second, dt=5*millisecond, epsilon=0.0)
x,y = decode(model.SC_V)
print u"Δθ = %.2f: (%f,%f)" % (2*theta, x, y)
T15[i] = x,y
np.save("data/double-target-intensity-15.npy",T15)
fig = plt.figure(figsize=(9.5,12),dpi=100)
fig.patch.set_color('w')
G = gridspec.GridSpec(3, 3)
ax1 = plt.subplot(G[0, 0])
ax2 = plt.subplot(G[0, 1:3])
model = Model()
model.R = np.maximum( stimulus((5, -10), size=1, intensity=1) ,
stimulus((5, +10), size=1, intensity=1) )
model.R += np.random.uniform(0,0.05,model.R.shape)
model.R *= model.R_mask
model.run(duration=10*second, dt=5*millisecond, epsilon=0.0)
polar_frame(ax1, legend=False, labels=False,reduced=True)
polar_imshow(ax1, model.R,reduced=True)
'''
if zoom:
zax = zoomed_inset_axes(ax1, 6, loc=1)
polar_frame(zax, zoom=True)
zax.set_xlim(0.0, 0.1)
zax.set_xticks([])
zax.set_ylim(-.05, .05)
zax.set_yticks([])
zax.set_frame_on(True)
mark_inset(ax1, zax, loc1=2, loc2=4, fc="none", ec="0.5")
polar_imshow(zax, model.R)
'''
logpolar_frame(ax2, legend=False, labels=False)
logpolar_imshow(ax2, model.SC_V)
ax1.text(-0.05, 1.0, 'a', va='top', ha='right',
transform=ax1.transAxes, fontsize=20, fontweight='bold')
ax1 = plt.subplot(G[1,0])
ax2 = plt.subplot(G[1,1:3])
model = Model()
model.R = np.maximum( stimulus((5, -25), size=1, intensity=1) ,
stimulus((5, +25), size=1, intensity=1) )
model.R += np.random.uniform(0,0.05,model.R.shape)
model.R *= model.R_mask
model.run(duration=10*second, dt=5*millisecond, epsilon=0.0)
polar_frame(ax1, legend=False, labels=False,reduced=True)
polar_imshow(ax1, model.R,reduced=True)
'''
if zoom:
zax = zoomed_inset_axes(ax1, 6, loc=1)
polar_frame(zax, zoom=True,)
zax.set_xlim(0.0, 0.1)
zax.set_xticks([])
zax.set_ylim(-.05, .05)
zax.set_yticks([])
zax.set_frame_on(True)
mark_inset(ax1, zax, loc1=2, loc2=4, fc="none", ec="0.5")
polar_imshow(zax, model.R)
'''
logpolar_frame(ax2, legend=False, labels=False)
logpolar_imshow(ax2, model.SC_V)
ax1.text(-0.05, 1.0, 'b', va='top', ha='right',
transform=ax1.transAxes, fontsize=20, fontweight='bold')
ax = plt.subplot(G[2:, :])
X = np.linspace(20,90,p)
Y = T15[:,1]
plt.scatter( X, Y, s=20, color="r", edgecolor="r", alpha=.25,facecolors='none')
Y = T10[:,1]
plt.scatter( X, Y, s=20, color="b", edgecolor="b", alpha=.25,facecolors='none')
Y = T5[:,1]
plt.scatter( X, Y, s=20, color="g", edgecolor="g", alpha=.25,facecolors='none')
plt.axvline(40, color='r')
plt.axvline(41, color='b')
plt.axvline(47, color='g')
plt.xlim(18,92)
plt.ylim(-0.5,+0.5)
plt.xlabel(u"Relative distance between targets (degrees)")
plt.ylabel(u"Normalized y position")
ax.grid(b=False)
#plt.text(20, 0, 'a',
# ha="center", va="center", size=15, fontweight='bold',
# bbox=dict(boxstyle='round', fc="w", ec="k"))
#plt.text(50, +.2, 'b',
# ha="center", va="center", size=15, fontweight='bold',
# bbox=dict(boxstyle='round', fc="w", ec="k"))
fig.subplots_adjust(left=0.09, bottom=0.05, right=0.95, top=0.95,
wspace=0.05, hspace=0.15)
#ax.set_rasterized(True)
#ax.set_rasterized(True)
#ax.set_rasterized(True)
plt.savefig("figures/Fig-4.eps",format="eps")
plt.savefig("figures/Fig-4.pdf")
plt.show()