Q10_SCALING = 1
CTEST = [1.e-4, 3.e-4, 1.e-3, 3.e-3, 1.e-2]
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)
# Generate arrays of values for test values of c and m
CVALS = CMIN * (CMAX / CMIN)**(np.arange(NBINS_TEST) / float(NBINS_TEST - 1)) # geo series
MVALS = MMIN * (MMAX / MMIN)**(np.arange(NBINS_TEST) / float(NBINS_TEST - 1))
CGRID, MGRID = np.meshgrid(CVALS, MVALS) # 2D arrays covering full space
#GALSIM_DIR=os.path.join("/Path", "To", "Your", "Repo")
GALSIM_DIR=os.path.join("/Users", "browe", "great3", "GalSim")
#GALSIM_DIR=os.path.join("/home", "browe", "great3", "64", "GalSim")
OUTFILE = os.path.join(
'results', 'normalization_G3S_N'+str(NMONTE)+'_noise_sigma'+str(NOISE_SIGMA)+'.pkl')
if __name__ == "__main__":
# 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)
) # geo series
MVALS = MMIN * (MMAX / MMIN)**(np.arange(NBINS_TEST) / float(NBINS_TEST - 1))
CGRID, MGRID = np.meshgrid(CVALS, MVALS) # 2D arrays covering full space
#GALSIM_DIR=os.path.join("/Path", "To", "Your", "Repo")
GALSIM_DIR = os.path.join("/Users", "browe", "great3", "GalSim")
#GALSIM_DIR=os.path.join("/home", "browe", "great3", "64", "GalSim")
OUTFILE = os.path.join(
'results', 'normalization_G3S_N' + str(NMONTE) + '_noise_sigma' +
str(NOISE_SIGMA) + '.pkl')
if __name__ == "__main__":
# 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,
NBINS=8 # Number of bins of PS for metric
Q10_SCALING = 1
CTEST = [1.e-4, 3.e-4, 1.e-3, 3.e-3, 1.e-2]
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))):