#GALSIM_DIR=os.path.join("/Path", "To", "Your", "Repo")
GALSIM_DIR = os.path.join("/Users", "browe", "great3", "galsim")
OUTFILE = os.path.join(
'results', 'normalizationv3_G10_QuadPS_N' + str(NREPEAT) + '_noise_sigma' +
str(NOISE_SIGMA) + '.pkl')
if __name__ == "__main__":
reference_ps = g3metrics.read_ps(galsim_dir=GALSIM_DIR)
# Make the truth catalogues (a list of 2D, NGRIDxNGRID numpy arrays), reusing the reference_ps
# each time for simplicity
g1true_list, g2true_list = g3metrics.make_var_truth_catalogs(
1, NIMS, [
reference_ps,
], ngrid=NGRID, grid_units=galsim.degrees)
# Define some empty storage arrays
qG10unnorm = np.empty((len(CTEST), len(MTEST), NREPEAT))
qQuadPSunnorm = np.empty((len(CTEST), len(MTEST), NREPEAT))
# TEMP: Make all the PS realizations (of the truth) the same to see if this alters noise props
g1true_list = [
g1true_list[0],
] * len(g1true_list)
g2true_list = [
g2true_list[0],
] * len(g2true_list)
# Then generate submissions, and truth submissions
for c, i in zip(CTEST, range(len(CTEST))):
# Load up the reference PS
reference_ps = g3metrics.read_ps(galsim_dir=GALSIM_DIR, scale=SCALEPS)
# Define some empty storage arrays
qG3S_AMD_unnorm = np.empty((len(CVALS), len(MVALS), NMONTE))
qG3S_QMD_unnorm = np.empty((len(CVALS), len(MVALS), NMONTE))
# Then loop
for krepeat in range(NMONTE):
# Make the truth catalogues (a list of 2D, NGRIDxNGRID numpy arrays), reusing the reference
# ps each time for simplicity
print "Generating truth tables for Monte Carlo realization = "+str(krepeat + 1)+"/"+\
str(NMONTE)
g1true_list, g2true_list = g3metrics.make_var_truth_catalogs(
NFIELDS, NIMS, [reference_ps,] * NFIELDS, ngrid=NGRID, dx_grid=DX_GRID,
grid_units=galsim.degrees)
# Then generate submissions, and truth submissions
for c, i in zip(CVALS, range(NBINS_TEST)):
print "Calculating CF metrics with c_i = "+str(c)
for m, j in zip(MVALS, range(NBINS_TEST)):
print "Calculating CF metrics with m_i = "+str(m)
# Make a fake submission
theta, mapEsubs, mapBsubs, maperrsubs, mapEtrues, mapBtrues = \
g3metrics.make_submission_var_shear_CF(
c1=c, c2=c, m1=m, m2=m, g1true_list=g1true_list,
g2true_list=g2true_list, noise_sigma=NOISE_SIGMA, dx_grid=DX_GRID,
nbins=NBINS_ANGULAR, min_sep=MIN_SEP, max_sep=MAX_SEP)
# Calculate the metrics
qG3S_AMD_unnorm[i, j, krepeat] = g3metrics.metricG3S_AMD(
reference_ps = g3metrics.read_ps(galsim_dir=GALSIM_DIR, scale=SCALEPS)
# Define some empty storage arrays
qG3S_AMD_unnorm = np.empty((len(CVALS), len(MVALS), NMONTE))
qG3S_QMD_unnorm = np.empty((len(CVALS), len(MVALS), NMONTE))
# Then loop
for krepeat in range(NMONTE):
# Make the truth catalogues (a list of 2D, NGRIDxNGRID numpy arrays), reusing the reference
# ps each time for simplicity
print "Generating truth tables for Monte Carlo realization = "+str(krepeat + 1)+"/"+\
str(NMONTE)
g1true_list, g2true_list = g3metrics.make_var_truth_catalogs(
NFIELDS,
NIMS, [
reference_ps,
] * NFIELDS,
ngrid=NGRID,
dx_grid=DX_GRID,
grid_units=galsim.degrees)
# Then generate submissions, and truth submissions
for c, i in zip(CVALS, range(NBINS_TEST)):
print "Calculating CF metrics with c_i = " + str(c)
for m, j in zip(MVALS, range(NBINS_TEST)):
print "Calculating CF metrics with m_i = " + str(m)
# Make a fake submission
theta, mapEsubs, mapBsubs, maperrsubs, mapEtrues, mapBtrues = \
g3metrics.make_submission_var_shear_CF(
c1=c, c2=c, m1=m, m2=m, g1true_list=g1true_list,
g2true_list=g2true_list, noise_sigma=NOISE_SIGMA, dx_grid=DX_GRID,
nbins=NBINS_ANGULAR, min_sep=MIN_SEP, max_sep=MAX_SEP)
MTEST = [1.e-3, 3.e-3, 1.e-2, 3.e-2, 1.e-1]
NREPEAT = 1000
#GALSIM_DIR=os.path.join("/Path", "To", "Your", "Repo")
GALSIM_DIR=os.path.join("/Users", "browe", "great3", "galsim")
OUTFILE = os.path.join(
'results', 'normalizationv3_G10_QuadPS_N'+str(NREPEAT)+'_noise_sigma'+str(NOISE_SIGMA)+'.pkl')
if __name__ == "__main__":
reference_ps = g3metrics.read_ps(galsim_dir=GALSIM_DIR)
# Make the truth catalogues (a list of 2D, NGRIDxNGRID numpy arrays), reusing the reference_ps
# each time for simplicity
g1true_list, g2true_list = g3metrics.make_var_truth_catalogs(
1, NIMS, [reference_ps,], ngrid=NGRID, grid_units=galsim.degrees)
# Define some empty storage arrays
qG10unnorm = np.empty((len(CTEST), len(MTEST), NREPEAT))
qQuadPSunnorm = np.empty((len(CTEST), len(MTEST), NREPEAT))
# TEMP: Make all the PS realizations (of the truth) the same to see if this alters noise props
g1true_list = [g1true_list[0],] * len(g1true_list)
g2true_list = [g2true_list[0],] * len(g2true_list)
# Then generate submissions, and truth submissions
for c, i in zip(CTEST, range(len(CTEST))):
print "Calculating PS metrics with c_i = "+str(c)
for m, j in zip(MTEST, range(len(MTEST))):
print "Calculating PS metrics with m_i = "+str(m)
