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],
g2sub[:, itest])).T, fmt="%d %e %e")
qc[obs_type][itest, jc] = evaluate.q_constant(
subfile, EXPERIMENT, obs_type, just_q=True, truth_dir=TRUTH_DIR)
os.remove(subfile)
print "mean(Q_c), std(Q_c) = "+str(qc[obs_type][:, jc].mean())+", "+\
str(qc[obs_type][:, jc].std())+" for "+str(NTEST)+" sims (with c+ = "+\
str(cval)+", obs_type = "+str(obs_type)+")"
print
print "Saving pickled Q_c versus c dict to "+coutfile
with open(coutfile, "wb") as fout: cPickle.dump(qc, fout)
print
else:
with open(coutfile, "rb") as fin: qc = cPickle.load(fin)
if not os.path.isfile(moutfile):
for obs_type in ("ground", "space",):
import momentsml
import momentsml.tools
import astropy
import config
import numpy as np
import os
import sys
import logging
logging.basicConfig(format=config.loggerformat, level=logging.DEBUG)
logger = logging.getLogger(__name__)
outcatpath = config.great3.path("submission_{}.txt".format(config.predcode))
metricsdirpath = os.path.join(config.great3.g3publicdir, "metrics")
sys.path.append(metricsdirpath)
import evaluate
results = evaluate.q_constant(
outcatpath,
config.great3.experiment,
config.great3.obstype,
storage_dir=config.great3.path("eva_storage_dir_{}".format(
config.predcode)),
truth_dir=config.great3.truthdir,
plot=config.great3.path("eva_plot_{}".format(config.predcode)),
logger=logger,
pretty_print=True)
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],
g2sub[:, itest])).T,
fmt="%d %e %e")
qc[obs_type][itest, jc] = evaluate.q_constant(
subfile,
EXPERIMENT,
obs_type,
just_q=True,
truth_dir=TRUTH_DIR)
os.remove(subfile)
print "mean(Q_c), std(Q_c) = "+str(qc[obs_type][:, jc].mean())+", "+\
str(qc[obs_type][:, jc].std())+" for "+str(NTEST)+" sims (with c+ = "+\
str(cval)+", obs_type = "+str(obs_type)+")"
print
print "Saving pickled Q_c versus c dict to " + coutfile
with open(coutfile, "wb") as fout:
cPickle.dump(qc, fout)
print
else:
with open(coutfile, "rb") as fin:
"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)
# Calculate bias model using these m and c values
g1i3model = (1. + m1i3) * g1true + c1i3
g2i3model = (1. + m2i3) * g2true + c2i3
g1rgmodel = (1. + m1rg) * g1true + c1rg
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)
# Calculate bias model using these m and c values
g1i3model = (1. + m1i3) * g1true + c1i3
g2i3model = (1. + m2i3) * g2true + c2i3
g1rgmodel = (1. + m1rg) * g1true + c1rg
