def testFigures(self):
rgb = numpy.zeros((4, 4, 3), "uint8")
r = Report("test")
f = r.figure()
f.data_rgb("rgb", rgb, caption="ciao")
assert len(f.get_subfigures()) == 1
r.data_rgb("rgb", rgb, caption="ciao2")
r.last().add_to(f)
assert len(f.get_subfigures()) == 2
def testFigures(self):
rgb = numpy.zeros((4, 4, 3), 'uint8')
r = Report('test')
f = r.figure()
f.data_rgb('rgb', rgb, caption='ciao')
assert len(f.get_subfigures()) == 1
r.data_rgb('rgb', rgb, caption='ciao2')
r.last().add_to(f)
assert len(f.get_subfigures()) == 2
def main():
N = 100
num_svds = 8
radius_deg = 180
kernels = [identity, linear01_sat, pow3_sat, pow7_sat]
# kernels = [linear01_sat, pow3_sat, pow7_sat]
r = Report('eig analysis')
# warps_desc = ", ".join(['%.2f' % x for x in warps])
caption = """ This figure shows that on S^1 things can be warped easily.
The initial distribution of {N} points, with radius {radius_deg}.
""".format(**locals())
f = r.figure(caption=caption)
mime = 'application/pdf'
figsize = (4, 3)
with r.data_pylab('kernels', mime=mime, figsize=figsize) as pylab:
for kernel in kernels:
x = np.linspace(-1, +1, 256)
y = kernel(x)
pylab.plot(x, y, label=kernel.__name__)
pylab.axis([-1, 1, -1, 1])
pylab.xlabel('Cosine between orientations')
pylab.ylabel('Correlation')
pylab.legend(loc='lower right')
r.last().add_to(f, caption='Correlation kernels')
for ndim in [2, 3]:
S = get_distribution(ndim, N, radius_deg)
C = cosines_from_directions(S)
D = distances_from_directions(S)
assert np.degrees(D.max()) <= 2 * radius_deg
with r.data_pylab('svds%d' % ndim, mime=mime,
figsize=figsize) as pylab:
for kernel in kernels:
Cw = kernel(C)
# TODO:
# Cw = cos(kernel(D))
s = svds(Cw, num_svds)
pylab.semilogy(s, 'x-', label=kernel.__name__)
pylab.legend(loc='center right')
r.last().add_to(
f, caption='Singular value for different kernels (ndim=%d)' % ndim)
filename = 'cbc_demos/kernels.html'
print("Writing to %r." % filename)
r.to_html(filename)
def main():
N = 100
num_svds = 8
radius_deg = 180
kernels = [identity, linear01_sat, pow3_sat, pow7_sat]
# kernels = [linear01_sat, pow3_sat, pow7_sat]
r = Report('eig analysis')
# warps_desc = ", ".join(['%.2f' % x for x in warps])
caption = """ This figure shows that on S^1 things can be warped easily.
The initial distribution of {N} points, with radius {radius_deg}.
""".format(**locals())
f = r.figure(caption=caption)
mime = 'application/pdf'
figsize = (4, 3)
with r.data_pylab('kernels', mime=mime, figsize=figsize) as pylab:
for kernel in kernels:
x = np.linspace(-1, +1, 256)
y = kernel(x)
pylab.plot(x, y, label=kernel.__name__)
pylab.axis([-1, 1, -1, 1])
pylab.xlabel('Cosine between orientations')
pylab.ylabel('Correlation')
pylab.legend(loc='lower right')
r.last().add_to(f, caption='Correlation kernels')
for ndim in [2, 3]:
S = get_distribution(ndim, N, radius_deg)
C = cosines_from_directions(S)
D = distances_from_directions(S)
assert np.degrees(D.max()) <= 2 * radius_deg
with r.data_pylab('svds%d' % ndim,
mime=mime, figsize=figsize) as pylab:
for kernel in kernels:
Cw = kernel(C)
# TODO:
# Cw = cos(kernel(D))
s = svds(Cw, num_svds)
pylab.semilogy(s, 'x-', label=kernel.__name__)
pylab.legend(loc='center right')
r.last().add_to(f,
caption='Singular value for different kernels (ndim=%d)' % ndim)
filename = 'cbc_demos/kernels.html'
print("Writing to %r." % filename)
r.to_html(filename)
def create_report_axis_angle(id, desc, saccades):
r = Report('axis_angle')
#
# axis_angle = saccades['axis_angle']
# saccade_angle = saccades['saccade_angle']
stats = statistics_distance_axis_angle(saccades,
num_distance_intervals=10,
axis_angle_bin_interval=10,
axis_angle_bin_size=10
)
f = r.figure(cols=1)
for i, section in enumerate(stats['distance_sections']):
distance_min = section['distance_min']
distance_max = section['distance_max']
prob_left = section['prob_left']
prob_right = section['prob_right']
margin_left = section['margin_left']
margin_right = section['margin_right']
bin_centers = section['bin_centers']
num_saccades = section['num_saccades']
n = len(bin_centers)
with r.data_pylab('section%d' % i) as pylab:
el = np.zeros((2, n))
el[0, :] = +(margin_left[0, :] - prob_left)
el[1, :] = -(margin_left[1, :] - prob_left)
pylab.errorbar(bin_centers, prob_left, el, None, None,
ecolor='g', label='left', capsize=8, elinewidth=1)
er = np.zeros((2, n))
er[0, :] = +(margin_right[0, :] - prob_right)
er[1, :] = -(margin_right[1, :] - prob_right)
pylab.errorbar(bin_centers, prob_right, er, None, None,
ecolor='r', label='right', capsize=8, elinewidth=1)
pylab.plot(bin_centers, prob_left, 'g-', label='left')
pylab.plot(bin_centers, prob_right, 'r-', label='right')
pylab.xlabel('axis angle (deg)')
pylab.ylabel('probability of turning')
pylab.title('Direction probability for distance in [%dcm,%dcm], %d saccades' %
(distance_min * 100, distance_max * 100, num_saccades))
pylab.plot([0, 0], [0, 1], 'k-')
pylab.axis([-180, 180, 0, 1])
pylab.legend()
r.last().add_to(f)
return r
logpd = np.log(pd)
pd[zeros] = 0
return -(pd * logpd).sum()
if __name__ == '__main__':
filename = sys.argv[1]
data = pickle.load(open(filename, 'rb'))
h, hdist = compute_hdist(data['single'], data['joint'])
pickle.dump(hdist, open('hdist.pickle', 'wb'))
hdist = np.cos(hdist * np.pi)
e = -np.eye(hdist.shape[0]) + 1
hdist = hdist * e
from reprep import Report
r = Report()
f = r.figure()
r.data('hdist', hdist).display('scale').add_to(f)
with r.data_pylab('h')as pylab:
pylab.plot(h)
r.last().add_to(f)
filename = 'real_test_cases.html'
print('Writing to %r.' % filename)
r.to_html(filename)
def main():
def spearman(a, b):
ao = scale_score(a)
bo = scale_score(b)
return correlation_coefficient(ao, bo)
disable_all()
def seq():
N = 180
iterations = 10
nradii = 100
radii = np.linspace(5, 180, nradii)
K = 1
for radius_deg, i in itertools.product(radii, range(K)):
print radius_deg, i
# Generate a random symmetric matrix
# x = np.random.rand(N, N)
S = random_directions_bounded(3, np.radians(radius_deg), N)
C = np.dot(S.T, S)
alpha = 1
f = lambda x: np.exp(-alpha * (1 - x))
# f = lambda x : x
R = f(C)
# Normalize in [0,1]
R1 = (R - R.min()) / (R.max() - R.min())
# Normalize in [-1,1]
R2 = (R1 - 0.5) * 2
S1 = simplified_algo(R1, iterations)
S1w = simplified_algo(R1, iterations, warp=50)
S2 = simplified_algo(R2, iterations)
s1 = spearman(cosines_from_directions(S1), R1)
s1w = spearman(cosines_from_directions(S1w), R1)
s2 = spearman(cosines_from_directions(S2), R2)
e1 = np.degrees(overlap_error_after_orthogonal_transform(S, S1))
e1w = np.degrees(overlap_error_after_orthogonal_transform(S, S1w))
e2 = np.degrees(overlap_error_after_orthogonal_transform(S, S2))
r0 = np.degrees(distribution_radius(S))
r1 = np.degrees(distribution_radius(S1))
r1w = np.degrees(distribution_radius(S1w))
r2 = np.degrees(distribution_radius(S2))
yield dict(R0=r0, R1=r1, R1w=r1w, R2=r2, e1=e1, e2=e2,
s1=s1, s2=s2,
s1w=s1w, e1w=e1w)
results = list(seq())
data = dict((k, np.array([d[k] for d in results])) for k in results[0])
r = Report('demo-convergence')
api1 = 'pi1'
api1w = 'pi1w'
api2 = 'pi2'
sets = [(data['R0'] < 90, 'r.'), (data['R0'] >= 90, 'g.')]
f = r.figure('radius', cols=3, caption='radius of solution')
with r.data_pylab('r0r1') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['R1'][sel]
pylab.plot(x, x, 'k--')
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('radius (pi1)')
pylab.axis('equal')
r.last().add_to(f, caption=api1)
with r.data_pylab('r0r1w') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['R1w'][sel]
pylab.plot(x, x, 'k--')
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('radius (pi1 + warp)')
pylab.axis('equal')
r.last().add_to(f, caption=api1w)
with r.data_pylab('r0r2') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['R2'][sel]
pylab.plot(x, x, 'k--')
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('radius (pi2)')
pylab.axis('equal')
r.last().add_to(f, caption=api2)
with r.data_pylab('r1r2') as pylab:
for sel, col in sets:
pylab.plot(data['R1'][sel], data['R2'][sel], col)
pylab.xlabel('radius (pi1)')
pylab.ylabel('radius (pi2)')
pylab.axis('equal')
r.last().add_to(f, 'Comparison %s - %s' % (api1, api2))
with r.data_pylab('r1r1w') as pylab:
for sel, col in sets:
pylab.plot(data['R1'][sel], data['R1w'][sel], col)
pylab.xlabel('radius (pi1)')
pylab.ylabel('radius (pi1+warp)')
pylab.axis('equal')
r.last().add_to(f, 'Comparison %s - %s' % (api1, api1w))
f = r.figure('spearman', cols=3, caption='Spearman score')
with r.data_pylab('r0s1') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['s1'][sel]
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('spearman (pi1)')
r.last().add_to(f, caption=api1)
with r.data_pylab('r0s1w') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['s1w'][sel]
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('spearman (pi1+warp)')
r.last().add_to(f, caption=api1w)
with r.data_pylab('r0s2') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['s2'][sel]
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('spearman (pi2)')
r.last().add_to(f, caption=api2)
f = r.figure('final_error', cols=3, caption='Average absolute error')
with r.data_pylab('r0e') as pylab:
x = data['R0']
y = data['e1']
pylab.plot(x, y, 'm-', label=api1)
x = data['R0']
y = data['e1w']
pylab.plot(x, y, 'g-', label=api1w)
x = data['R0']
y = data['e2']
pylab.plot(x, y, 'b-', label=api2)
pylab.xlabel('real radius')
pylab.ylabel('average error (deg)')
pylab.legend()
r.last().add_to(f)
filename = 'cbc_demos/convergence.html'
print("Writing to %r." % filename)
r.to_html(filename)
def create_report_randomness(id, desc, saccades): #@UnusedVariable
report = Report(id)
f = report.figure(cols=3)
axis_angle = saccades['axis_angle']
approach_angle = saccades['approach_angle']
distance_from_wall = saccades['distance_from_wall']
# additional analysis
with report.data_pylab('axisangle_vs_distance') as pylab:
pylab.plot(axis_angle, distance_from_wall, '.', markersize=1)
pylab.xlabel('axis angle (deg)')
pylab.ylabel('distance from wall (m)')
pylab.title('axis angle vs distance (%s)' % id)
pylab.axis([-180, 180, 0, 1])
report.last().add_to(f)
right = saccades['sign'] < 0
left = saccades['sign'] > 0
ms = 2
with report.data_pylab('axisangle_vs_distance_lr') as pylab:
pylab.plot(axis_angle[right], distance_from_wall[right], 'r.', markersize=ms)
pylab.plot(axis_angle[left], distance_from_wall[left], 'b.', markersize=ms)
pylab.xlabel('axis angle (deg)')
pylab.ylabel('distance from wall (m)')
pylab.title('left and right saccades (%s)' % id)
pylab.axis([-180, 180, 0, 1])
report.last().add_to(f)
with report.data_pylab('axisangle_vs_distance_l') as pylab:
#
pylab.plot(axis_angle[left], distance_from_wall[left], 'b.', markersize=ms)
pylab.xlabel('axis angle (deg)')
pylab.ylabel('distance from wall (m)')
pylab.title('only left saccades (%s)' % id)
pylab.axis([-180, 180, 0, 1])
report.last().add_to(f)
with report.data_pylab('axisangle_vs_distance_r') as pylab:
pylab.plot(axis_angle[right], distance_from_wall[right], 'r.', markersize=ms)
#
pylab.xlabel('axis angle (deg)')
pylab.ylabel('distance from wall (m)')
pylab.title('only right saccades (%s)' % id)
pylab.axis([-180, 180, 0, 1])
report.last().add_to(f)
with report.data_pylab('axisangle_vs_distance_rm') as pylab:
pylab.plot(-axis_angle[right], distance_from_wall[right], 'r.', markersize=ms)
#
pylab.xlabel('axis angle (deg)')
pylab.ylabel('distance from wall (m)')
pylab.title('only right saccades (mirror) (%s)' % id)
pylab.axis([-180, 180, 0, 1])
report.last().add_to(f)
with report.data_pylab('approachangle_vs_distance_lr') as pylab:
pylab.plot(approach_angle[right], distance_from_wall[right], 'r.', markersize=ms)
pylab.plot(approach_angle[left], distance_from_wall[left], 'b.', markersize=ms)
pylab.xlabel('approach angle (deg)')
pylab.ylabel('distance from wall (m)')
pylab.title('left and right saccades (%s)' % id)
pylab.axis([-60, 60, 0, 1])
report.last().add_to(f)
smooth_displacement = saccades['smooth_displacement']
with report.data_pylab('smooth_displacement_hist') as pylab:
bins = range(-180, 180, 10)
pylab.hist(smooth_displacement, bins, normed=True)
pylab.xlabel('inter-saccade smooth displacement (deg)')
pylab.ylabel('density')
pylab.title('smooth displacement (%s)' % id)
# pylab.axis([-180, 180, 0, 700])
f = report.figure('smooth')
f.sub('smooth_displacement_hist')
return report
def main():
np.seterr(all='warn')
n = 100
z = np.linspace(-1, 1, n)
z_order = np.array(range(n))
alpha = 0.1
base = 0.3
noise_eff = 0.05
noise_est = noise_eff
f_L = lambda z: np.exp(-np.abs(+1 - np.maximum(z, 0)) / alpha) + base
f_R = lambda z: np.exp(-np.abs(-1 - np.minimum(z, 0)) / alpha) + base
rate_L0 = f_L(z)
rate_R0 = f_R(z)
simulate_L = lambda: f_L(z) + np.random.uniform(-1, 1, n) * noise_eff
simulate_R = lambda: f_R(z) + np.random.uniform(-1, 1, n) * noise_eff
rate_L = simulate_L()
rate_R = simulate_R()
T = 100
ord1 = np.zeros((n, T))
for k in range(T):
ord1[:, k] = scale_score(simulate_L())
order_L_sim = np.ndarray(n, fit_dtype)
for i in range(n):
order_L_sim[i]['mean'] = np.mean(ord1[i, :])
l, u = np.percentile(ord1[i, :], [5, 95])
order_L_sim[i]['upper'] = u
order_L_sim[i]['lower'] = l
rate_L_est = np.ndarray(n, fit_dtype)
rate_L_est['upper'] = rate_L + noise_est
rate_L_est['lower'] = rate_L - noise_est
rate_R_est = np.ndarray(n, fit_dtype)
rate_R_est['upper'] = rate_R + noise_est
rate_R_est['lower'] = rate_R - noise_est
# estimate according to naive procedure
z_naive = estimate_stimulus_naive(rate_L, rate_R)
res = estimate_stimulus(rate_L_est, rate_R_est)
L_order = res.L_order
R_order = res.R_order
scale_rate = max(rate_L.max(), rate_R.max()) * 1.2
cL = 'r'
cR = 'b'
r = Report()
f = r.figure(cols=3)
with r.plot('noiseless') as pylab:
pylab.plot(z, rate_L0, '%s-' % cL)
pylab.plot(z, rate_R0, '%s-' % cR)
pylab.axis((-1, 1, 0.0, scale_rate))
r.last().add_to(f, caption='noiseless rates')
with r.plot('observed_rates') as pylab:
pylab.plot(z, rate_R0, '%s-' % cR)
pylab.plot(z, rate_L0, '%s-' % cL)
plot_rate_bars(pylab, z, rate_L_est, '%s' % cL)
plot_rate_bars(pylab, z, rate_R_est, '%s' % cR)
pylab.axis((-1, 1, 0.0, scale_rate))
r.last().add_to(f, caption='true_rates')
with r.plot('M') as pylab:
pylab.plot(z, rate_L0, '%s-' % cL)
pylab.plot(z, rate_R0, '%s-' % cR)
pylab.axis((-1, 1, 0.0, scale_rate))
with r.plot('z_naive') as pylab:
pylab.plot(z_naive, rate_L, '%s.' % cL)
pylab.plot(z_naive, rate_R, '%s.' % cR)
pylab.axis((-1, 1, 0.0, scale_rate))
r.last().add_to(f, caption='Stimulus estimated in naive way.')
with r.plot('simulated_order_stats') as pylab:
pylab.plot([0, 0], [n, n], 'k-')
pylab.plot([0, n], [n, 0], 'k-')
pylab.plot(z_order, order_L_sim['mean'], '%s.' % cL)
plot_rate_bars(pylab, z_order, order_L_sim, '%s' % cL)
pylab.axis((0, n, -n / 10, n * 1.1))
pylab.axis('equal')
r.last().add_to(f, caption='Orders as found by simulation')
with r.plot('estimated_order') as pylab:
pylab.plot(z, L_order['mean'], '%s.' % cL)
pylab.plot(z, R_order['mean'], '%s.' % cR)
pylab.axis((-1, 1, -n / 2, n * 3 / 2))
r.last().add_to(f, caption='estimated_order')
with r.plot('estimated_order_order') as pylab:
pylab.plot([0, 0], [n, n], 'k-')
plot_rate_bars(pylab, z_order, L_order, '%s' % cL)
plot_rate_bars(pylab, z_order, R_order, '%s' % cR)
pylab.plot([0, 0], [n, n], 'k-')
pylab.axis((0, n, -n / 10, n * 1.1))
pylab.axis('equal')
r.last().add_to(f, caption='estimated_order_order')
with r.plot('estimated_order_bar') as pylab:
pylab.plot(z, L_order['mean'], '%s.' % cL)
pylab.plot(z, R_order['mean'], '%s.' % cR)
plot_rate_bars(pylab, z, L_order, '%s' % cL)
plot_rate_bars(pylab, z, R_order, '%s' % cR)
pylab.axis((-1, 1, -n / 2, n * 3 / 2))
r.last().add_to(f, caption='estimated_order_bar')
with r.plot('estimated_order_both') as pylab:
pylab.plot([0, 0], [n, n], 'g-')
pylab.plot(z_order, res.order['mean'], 'k.')
plot_rate_bars(pylab, z_order, res.order, 'k')
pylab.axis((0, n, -n / 10, n * 1.1))
pylab.axis('equal')
r.last().add_to(f, caption='estimated_order_order')
with r.plot('z_better') as pylab:
pylab.plot(res.z['mean'], rate_L, '%s.' % cL)
pylab.plot(res.z['mean'], rate_R, '%s.' % cR)
pylab.axis((-1, 1, 0.0, scale_rate))
r.last().add_to(f, caption='Stimulus estimated in a better way.')
filename = 'order_estimation.html'
print('Writing to %r.' % filename)
r.to_html(filename)
pd = f.flatten().astype('float64') / s
zeros, = np.nonzero(pd == 0)
pd[zeros] = 1
logpd = np.log(pd)
pd[zeros] = 0
return -(pd * logpd).sum()
if __name__ == '__main__':
filename = sys.argv[1]
data = pickle.load(open(filename, 'rb'))
h, hdist = compute_hdist(data['single'], data['joint'])
pickle.dump(hdist, open('hdist.pickle', 'wb'))
hdist = np.cos(hdist * np.pi)
e = -np.eye(hdist.shape[0]) + 1
hdist = hdist * e
from reprep import Report
r = Report()
f = r.figure()
r.data('hdist', hdist).display('scale').add_to(f)
with r.data_pylab('h') as pylab:
pylab.plot(h)
r.last().add_to(f)
filename = 'real_test_cases.html'
print('Writing to %r.' % filename)
r.to_html(filename)
def main():
def spearman(a, b):
ao = scale_score(a)
bo = scale_score(b)
return correlation_coefficient(ao, bo)
disable_all()
def seq():
N = 180
iterations = 10
nradii = 100
radii = np.linspace(5, 180, nradii)
K = 1
for radius_deg, i in itertools.product(radii, range(K)):
print radius_deg, i
# Generate a random symmetric matrix
# x = np.random.rand(N, N)
S = random_directions_bounded(3, np.radians(radius_deg), N)
C = np.dot(S.T, S)
alpha = 1
f = lambda x: np.exp(-alpha * (1 - x))
# f = lambda x : x
R = f(C)
# Normalize in [0,1]
R1 = (R - R.min()) / (R.max() - R.min())
# Normalize in [-1,1]
R2 = (R1 - 0.5) * 2
S1 = simplified_algo(R1, iterations)
S1w = simplified_algo(R1, iterations, warp=50)
S2 = simplified_algo(R2, iterations)
s1 = spearman(cosines_from_directions(S1), R1)
s1w = spearman(cosines_from_directions(S1w), R1)
s2 = spearman(cosines_from_directions(S2), R2)
e1 = np.degrees(overlap_error_after_orthogonal_transform(S, S1))
e1w = np.degrees(overlap_error_after_orthogonal_transform(S, S1w))
e2 = np.degrees(overlap_error_after_orthogonal_transform(S, S2))
r0 = np.degrees(distribution_radius(S))
r1 = np.degrees(distribution_radius(S1))
r1w = np.degrees(distribution_radius(S1w))
r2 = np.degrees(distribution_radius(S2))
yield dict(R0=r0,
R1=r1,
R1w=r1w,
R2=r2,
e1=e1,
e2=e2,
s1=s1,
s2=s2,
s1w=s1w,
e1w=e1w)
results = list(seq())
data = dict((k, np.array([d[k] for d in results])) for k in results[0])
r = Report('demo-convergence')
api1 = 'pi1'
api1w = 'pi1w'
api2 = 'pi2'
sets = [(data['R0'] < 90, 'r.'), (data['R0'] >= 90, 'g.')]
f = r.figure('radius', cols=3, caption='radius of solution')
with r.data_pylab('r0r1') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['R1'][sel]
pylab.plot(x, x, 'k--')
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('radius (pi1)')
pylab.axis('equal')
r.last().add_to(f, caption=api1)
with r.data_pylab('r0r1w') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['R1w'][sel]
pylab.plot(x, x, 'k--')
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('radius (pi1 + warp)')
pylab.axis('equal')
r.last().add_to(f, caption=api1w)
with r.data_pylab('r0r2') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['R2'][sel]
pylab.plot(x, x, 'k--')
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('radius (pi2)')
pylab.axis('equal')
r.last().add_to(f, caption=api2)
with r.data_pylab('r1r2') as pylab:
for sel, col in sets:
pylab.plot(data['R1'][sel], data['R2'][sel], col)
pylab.xlabel('radius (pi1)')
pylab.ylabel('radius (pi2)')
pylab.axis('equal')
r.last().add_to(f, 'Comparison %s - %s' % (api1, api2))
with r.data_pylab('r1r1w') as pylab:
for sel, col in sets:
pylab.plot(data['R1'][sel], data['R1w'][sel], col)
pylab.xlabel('radius (pi1)')
pylab.ylabel('radius (pi1+warp)')
pylab.axis('equal')
r.last().add_to(f, 'Comparison %s - %s' % (api1, api1w))
f = r.figure('spearman', cols=3, caption='Spearman score')
with r.data_pylab('r0s1') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['s1'][sel]
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('spearman (pi1)')
r.last().add_to(f, caption=api1)
with r.data_pylab('r0s1w') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['s1w'][sel]
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('spearman (pi1+warp)')
r.last().add_to(f, caption=api1w)
with r.data_pylab('r0s2') as pylab:
for sel, col in sets:
x = data['R0'][sel]
y = data['s2'][sel]
pylab.plot(x, y, col)
pylab.xlabel('real radius')
pylab.ylabel('spearman (pi2)')
r.last().add_to(f, caption=api2)
f = r.figure('final_error', cols=3, caption='Average absolute error')
with r.data_pylab('r0e') as pylab:
x = data['R0']
y = data['e1']
pylab.plot(x, y, 'm-', label=api1)
x = data['R0']
y = data['e1w']
pylab.plot(x, y, 'g-', label=api1w)
x = data['R0']
y = data['e2']
pylab.plot(x, y, 'b-', label=api2)
pylab.xlabel('real radius')
pylab.ylabel('average error (deg)')
pylab.legend()
r.last().add_to(f)
filename = 'cbc_demos/convergence.html'
print("Writing to %r." % filename)
r.to_html(filename)
