Python read_ps Examples

Example #1

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)

Example #2

File: calculate_QG3S_var_shear_normalization.py Project: barnabytprowe/great3-public

# 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)

Example #3

File: calculate_QG3S_var_shear_normalization.py Project: esheldon/great3-public

                               )  # 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,

Example #4

File: calculate_QG10_var_shear_normalization.py Project: barnabytprowe/great3-public

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))):