plt.rc('axes', labelsize=BIGGER_SIZE) # fontsize of the x and y labels
l_max = 100000
mmins = ['1e7', '1e8', '1e9', '1e10', '1e11', '1e12', '1e13', '1e14', '1e15']
clean = False
if len(sys.argv) == 2:
clean = (sys.argv[1] == 'clean')
CFHT_data = True
thetasCFHT = get.thetas()
xipCFHT = get.xip()
ximCFHT = get.xim()
sigmCFHT = get.sigm()
sigpCFHT = get.sigp()
begin_color = Color("blue")
colors = list(begin_color.range_to(Color("green"), len(mmins)))
create.xi_CFHT_mmin(mmins, l_max, clean=clean)
os.system('mkdir -p figures/correlation/')
plt.figure(1).set_size_inches((8, 8), forward=False)
plt.figure(3).set_size_inches((8, 8), forward=False)
# Get x_axis
x_axis = dat.get_x_axis_mmin()
i = 0
mmin_st = 7.0
x = dataCFHT.thetas()
# Data from CFHTLenS survey
xip = dataCFHT.xip()
xim = dataCFHT.xim()
y = xip.copy()
y = np.append(y, xim)
# Considering the real covariance matrix and all kind of errors
yerr = dataCFHT.cov_mat()
yerrinv = np.linalg.inv(yerr)
det = np.linalg.det(yerr)
errp = dataCFHT.sigp()
errm = dataCFHT.sigm()
chain = np.load('mcmc/results/{1}/ihm={2}/mmin_chain{0}.npy'.format(
*[icosmo, usedData, ihm]))
otherchain = chain.reshape((nwalkers, steps, ndim))
# removing first steps
samples = chain[:, firsts:, :].reshape((-1, ndim))
# plot model with results
os.system("mkdir -p mcmc/figures/{0}/ihm={1}/".format(*[usedData, ihm]))
if usedData == 'CFHT':
# Length of CFHT thetas data
N = 21
x = data.thetas()
# Data from CFHTLenS survey
xip = data.xip()
xim = data.xim()
y = xip.copy()
y = np.append(y, xim)
# Considering the real covariance matrix and all kind of errors
yerr = data.cov_mat()
yerrinv = np.linalg.inv(yerr)
det = np.linalg.det(yerr)
errp = data.sigp()
errm = data.sigm()
def lnlike(param, y, invcov, verbose=False):
alpha = param[0]
if not (0.5 <= alpha <= 1.5):
return -np.inf
model = correlation.xi_ampCFHT(alpha, icosmo, ihm, verbose=verbose)
if model[0] == -np.pi:
return -np.inf
return -0.5 * np.matmul(np.transpose(y - model),
np.matmul(invcov, (y - model)))
os.system("mkdir -p mcmc/figures/CFHT/likehood/ihm={0}".format(ihm))
# Length of CFHT thetas data
N = 21
x = dat.thetas()
# Data from CFHTLenS survey
xip = dat.xip()
xim = dat.xim()
y = xip.copy()
y = np.append(y, xim)
# Considering the real covariance matrix and all kind of errors
yerr = dat.cov_mat()
yerrinv = np.linalg.inv(yerr)
det = np.linalg.det(yerr)
errp = dat.sigp()
errm = dat.sigm()
def lnlike(param, y, invcov, verbose=False):
alpha = param[0]
if not (0.5 < alpha < 1.5):
return -np.inf
model = correlation.xi_ampCFHT(alpha, icosmo, ihm, verbose=verbose)
if model[0] == -np.pi:
return -np.inf
return -0.5 * np.matmul(np.transpose(y - model),
np.matmul(invcov, (y - model)))
if not MCMC:
