Ejemplo n.º 1
0
plt.rc('font', size=BIGGER_SIZE)  # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE)  # fontsize of the axes title
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
Ejemplo n.º 2
0
data.close()

mmin_ml = float(line[0].split('=')[1])

if usedData == 'CFHT':
    N = 21
else:
    print("Error, choose between CFHT and KiDs data")
    quit()

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))
Ejemplo n.º 3
0
icosmo = int(sys.argv[1])
icosmos = [1, 4, 42]
index = icosmos.index(icosmo)
cosmos = ['Fiducial', 'WMAP9', 'Planck 2018']
ihm = 3
alpha_st = 1.0

# Importing Data from CFHT
print("Loading data")

# 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):
Ejemplo n.º 4
0
ihm = int(sys.argv[2])

alpha_st = 1.0

# MCMC parameters
ndim, nwalkers, steps = 1, 200, 1000

# Importing Data from CFHT
print("Loading data")

# 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):