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
qs = [0.6, 0.65, 0.707, 0.75, 0.8, 0.9]
ps = [0., 0.1, 0.2, 0.25, 0.3, 0.35, 0.4]
l_max = 100000
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(ps)))
for q in qs:
x_axis = dat.get_x_axis_st()
i = 0
for p in ps:
create.xi_CFHT_st(q, p, l_max, clean=clean)
column1 = dat.get_xip_st(q, p)
plt.rc('axes', labelsize=BIGGER_SIZE) # fontsize of the x and y labels
computes = False
icosmo = int(sys.argv[1])
icosmos = [1, 4, 42]
index = icosmos.index(icosmo)
cosmos = ['Fiducial', 'WMAP9', 'Planck 2018']
ihm = 1
mmin_st = 14.0
# 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 get_x_axis_st():
# it is simply CFHT data
return dat.thetas()