def testFilterSpeedBandwidth(n_iterations=5):
speeds = np.arange(1, 9.5, 0.5)
inputs = []
for speed in speeds:
for j in xrange(n_iterations):
seed = [np.random.randint(999), np.random.randint(999)]
inputs.append(
gd.generateDots(seed, 0, 0, 5, 60, 0, 1, speed))
pool = multiprocessing.Pool()
out = pool.map(filterDots, inputs)
pool.close()
data = np.array(out)
summed_me = np.sum(data, axis=1)
mean_me = np.mean(summed_me.reshape(-1, n_iterations), axis=1)
fig = plt.figure()
ax = fig.add_subplot(111)
colors = b2mpl.get_map('Set1', 'Qualitative', 3).mpl_colors
ax.add_line(plt.Line2D(speeds, mean_me, color=colors[0]))
ax.set_xlabel('Motion speed (degrees per s))')
ax.set_ylabel('Total motion energy')
majorLocator = MultipleLocator(1)
ax.xaxis.set_major_locator(majorLocator)
majorLocator = MultipleLocator(50)
ax.yaxis.set_major_locator(majorLocator)
ax.set_xlim(1, 9)
ax.set_ylim(np.min(mean_me) - 10, np.max(mean_me) + 10)
sns.despine(offset=5, trim=True)
plt.tight_layout()
return speeds, mean_me
def testFilterLinearCoh(n_iterations=5):
cohs = np.array([-0.512, -0.256, -0.128, -0.064, -0.032,
0, 0.032, 0.064, 0.128, 0.256, 0.512])
inputs = []
for coh in cohs:
for j in xrange(n_iterations):
seed = [np.random.randint(999), np.random.randint(999)]
if np.sign(coh) == 1:
direction = 0
else:
direction = 180
inputs.append(
gd.generateDots(seed, 0, 0, 5, 60, direction, np.abs(coh), 5))
pool = multiprocessing.Pool()
out = pool.map(filterDots, inputs)
pool.close()
data = np.array(out)
summed_me = np.sum(data, axis=1)
mean_me = np.mean(summed_me.reshape(-1, n_iterations), axis=1)
fig = plt.figure()
ax = fig.add_subplot(111)
colors = b2mpl.get_map('Set1', 'Qualitative', 3).mpl_colors
ax.add_line(plt.Line2D(cohs, mean_me, color=colors[0]))
ax.set_xlabel('Motion strength (proportion coh)')
ax.set_ylabel('Total motion energy')
ax.set_xlim(-.61, .61)
ax.set_ylim(np.min(mean_me) * 1.2, np.max(mean_me) * 1.2)
sns.despine(offset=5, trim=True)
plt.tight_layout()
return cohs, mean_me