qc = {"ground": np.empty((NTEST, len(CVALS))), "space": np.empty((NTEST, len(CVALS)))} qm = {"ground": np.empty((NTEST, len(MVALS))), "space": np.empty((NTEST, len(MVALS)))} coutfile = os.path.join( "results", "tabulated_correlated_const_Q_c_versus_c_norm"+str(krun)+".pkl") moutfile = os.path.join( "results", "tabulated_correlated_const_Q_c_versus_m_norm"+str(krun)+".pkl") if not os.path.isfile(coutfile): for obs_type in ("ground", "space",): print "Calculating Q_c values versus c for control-"+obs_type+\ "-constant data in GREAT3" print "NOISE_SIGMA = "+str(NOISE_SIGMA[obs_type]) print "RHO = "+str(RHO) # First we build the truth table print "Building truth tables for control-"+obs_type+"-constant" subfield_index, g1true, g2true = evaluate.get_generate_const_truth( EXPERIMENT, obs_type, truth_dir=TRUTH_DIR) rotations = evaluate.get_generate_const_rotations( EXPERIMENT, obs_type, truth_dir=TRUTH_DIR) for jc, cval in enumerate(CVALS): # Build the submissions g1sub, g2sub = g3metrics.make_multiple_submissions_const_shear( cval, 0., evaluate.MFID, evaluate.MFID, g1true, g2true, NGALS_PER_IMAGE, NOISE_SIGMA[obs_type], rotate_cs=rotations, nsubmissions=NTEST, rho=RHO) # Loop over submissions evaluating metric for itest in xrange(NTEST): fdsub, subfile = tempfile.mkstemp(suffix=".dat") with os.fdopen(fdsub, "wb") as fsub: np.savetxt( fsub, np.array((subfield_index, g1sub[:, itest],
moutfile = os.path.join( "results", "tabulated_correlated_const_Q_c_versus_m_norm" + str(krun) + ".pkl") if not os.path.isfile(coutfile): for obs_type in ( "ground", "space", ): print "Calculating Q_c values versus c for control-"+obs_type+\ "-constant data in GREAT3" print "NOISE_SIGMA = " + str(NOISE_SIGMA[obs_type]) print "RHO = " + str(RHO) # First we build the truth table print "Building truth tables for control-" + obs_type + "-constant" subfield_index, g1true, g2true = evaluate.get_generate_const_truth( EXPERIMENT, obs_type, truth_dir=TRUTH_DIR) rotations = evaluate.get_generate_const_rotations( EXPERIMENT, obs_type, truth_dir=TRUTH_DIR) for jc, cval in enumerate(CVALS): # Build the submissions g1sub, g2sub = g3metrics.make_multiple_submissions_const_shear( cval, 0., evaluate.MFID, evaluate.MFID, g1true, g2true, NGALS_PER_IMAGE, NOISE_SIGMA[obs_type], rotate_cs=rotations,
# Paths to control-ground-constant filenames (im3shape score ~225, regauss score ~ 68), make sure # these are present! I3FILE = os.path.join( "results", "cogs-im3shape-im3shape-0-2013-10-18T15:46:28.199250+00:00.g3") RGFILE = os.path.join( "results", "great3_ec-regauss-example-example_1-2013-12-27T15:24:57.472170+00:00.g3") TRUTH_DIR = "/Users/browe/great3/beta/truth" # Modify to wherever truth is unpacked if __name__ == "__main__": # Load up relevant data i3data = np.loadtxt(I3FILE) rgdata = np.loadtxt(RGFILE) subfield_index, g1true, g2true = evaluate.get_generate_const_truth( EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) rotations = evaluate.get_generate_const_rotations(EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) # Calculate m and c factors qi3, cpi3, mpi3, cxi3, mxi3, sigcpi3, sigmpi3, sigmxi3, sigcxi3 = evaluate.q_constant( I3FILE, EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) qrg, cprg, mprg, cxrg, mxrg, sigcprg, sigmprg, sigmxrg, sigcxrg = evaluate.q_constant( RGFILE, EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) # Rotate these c and m c1i3 = cpi3 * np.cos(2. * rotations) - cxi3 * np.sin(2. * rotations) c2i3 = cpi3 * np.sin(2. * rotations) + cxi3 * np.cos(2. * rotations) m1i3 = mpi3 * np.cos(2. * rotations) - mxi3 * np.sin(2. * rotations) m2i3 = mpi3 * np.sin(2. * rotations) + mxi3 * np.sin(2. * rotations) c1rg = cprg * np.cos(2. * rotations) - cxrg * np.sin(2. * rotations) c2rg = cprg * np.sin(2. * rotations) + cxrg * np.cos(2. * rotations)
# Paths to control-ground-constant filenames (im3shape score ~225, regauss score ~ 68), make sure # these are present! I3FILE = os.path.join("results", "cogs-im3shape-im3shape-0-2013-10-18T15:46:28.199250+00:00.g3") RGFILE = os.path.join( "results", "great3_ec-regauss-example-example_1-2013-12-27T15:24:57.472170+00:00.g3") TRUTH_DIR = "/Users/browe/great3/beta/truth" # Modify to wherever truth is unpacked if __name__ == "__main__": # Load up relevant data i3data = np.loadtxt(I3FILE) rgdata = np.loadtxt(RGFILE) subfield_index, g1true, g2true = evaluate.get_generate_const_truth( EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) rotations = evaluate.get_generate_const_rotations(EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) # Calculate m and c factors qi3, cpi3, mpi3, cxi3, mxi3, sigcpi3, sigmpi3, sigmxi3, sigcxi3 = evaluate.q_constant( I3FILE, EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) qrg, cprg, mprg, cxrg, mxrg, sigcprg, sigmprg, sigmxrg, sigcxrg = evaluate.q_constant( RGFILE, EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR) # Rotate these c and m c1i3 = cpi3 * np.cos(2. * rotations) - cxi3 * np.sin(2. * rotations) c2i3 = cpi3 * np.sin(2. * rotations) + cxi3 * np.cos(2. * rotations) m1i3 = mpi3 * np.cos(2. * rotations) - mxi3 * np.sin(2. * rotations) m2i3 = mpi3 * np.sin(2. * rotations) + mxi3 * np.sin(2. * rotations) c1rg = cprg * np.cos(2. * rotations) - cxrg * np.sin(2. * rotations) c2rg = cprg * np.sin(2. * rotations) + cxrg * np.cos(2. * rotations) m1rg = mprg * np.cos(2. * rotations) - mxrg * np.sin(2. * rotations) m2rg = mprg * np.sin(2. * rotations) + mxrg * np.sin(2. * rotations)