def main():
mean = [0, 0, 0]
cov = [[1, 0, 0], [0, 100, 0], [0, 0, 8]]
x1, x2, x3 = np.random.multivariate_normal(mean, cov, 50000).T
s1 = np.c_[x1, x2, x3]
mean = [0.2, 0.5, 6.]
cov = [[0.7, 0.3, 0.1], [0.3, 10, 0.25], [0.1, 0.25, 7]]
x1, x2, x3 = np.random.multivariate_normal(mean, cov, 50000).T
s2 = np.c_[x1, x2, x3]
names = ['x_1', 'x_2', 'x_3']
samples1 = MCSamples(samples=s1, labels=names, label='sample1')
samples2 = MCSamples(samples=s2, labels=names, label='sample2')
get_constraints(samples1)
get_constraints(samples2)
print("cov(x_1, x_3) = ", samples2.cov(pars=[0,2]))
print("cov(x_1, x_2) = ", samples2.cov(pars=[0,1]))
g = plots.getSubplotPlotter()
g.triangle_plot([samples1, samples2], filled=True)
g.export('triangle_plot.png')
g.export('triangle_plot.pdf')
return
class binned_model_marge: # marginalised xi0
def __init__(self, file_name, nwalkers, nsamples, skip_steps=0):
self.file_name = file_name
self.samples = np.loadtxt(dir +
'chains/5/{}.txt'.format(self.file_name))
self.cosmo_name = 'FlatIVCDM_binned'
self.nwalkers = nwalkers
self.nsamples = nsamples
self.skip_steps = skip_steps
# bin specifications
self.Nq = 20
self.zmax = 1.5
self.xi0_lims = (0.2, 2.0)
self.Nscale = 4
self.amin = 1. / (1. + self.zmax)
self.npars = self.Nq
cos = cosmo(Nq=self.Nq)
M = cos.model[self.cosmo_name]
self.names_q = ['q{}'.format(i + 1) for i in range(self.Nq)]
self.labels_q = [r'q_{{{}}}'.format(i + 1) for i in range(self.Nq)]
samples_skipped = samples_skip(self.samples, self.nwalkers,
self.nsamples, self.npars,
self.skip_steps)
self.MCsamps = MCSamples(samples=samples_skipped,
names=self.names_q,
labels=self.labels_q)
@property
def cov_q(self): # covariance
return self.MCsamps.cov()
@property
def corr_q(self): # correlation
return self.MCsamps.corr()
@property
def Fisher_q(self): # inverse covariance ('Fisher')
return linalg.inv(self.cov_q)
def plot_cov(self, kind='posterior'):
C, Corr, F = self.cov_q, self.corr_q, self.Fisher_q
fig = plt.figure(figsize=(19, 4))
ax1 = fig.add_subplot(131)
im1 = ax1.imshow(C, cmap='viridis', interpolation='nearest')
ax1.set_title('Covariance')
fig.colorbar(im1)
ax2 = fig.add_subplot(132)
im2 = ax2.imshow(Corr, cmap='viridis', interpolation='nearest')
ax2.set_title('Correlation matrix')
fig.colorbar(im2)
ax3 = fig.add_subplot(133)
im3 = ax3.imshow(F, cmap='viridis', interpolation='nearest')
ax3.set_title('Fisher matrix')
fig.colorbar(im3)
plt.rc('font', size=14)
plt.show()
def MSE(self, M):
self.get_KL()
q_sum = 0.0
bias2 = 0.0
variance = 0.0
for i in range(M):
q_sum += self.KL_alpha[i] * self.KL_basis[i]
bias2 += (q_sum[i] - self.q_mean[i])**2
variance += (self.KL_basis[i] * self.KL_varalpha[i])**2
return bias2 + variance
