# Load up the outfile for plotting, or calculate and save before plotting
if os.path.isfile(OUTFILE):
Q08, QZ1, QZ2 = cPickle.load(open(OUTFILE, 'rb'))
else:
for krepeat in range(NMONTE):
# Generate the truth tables for this realization
g1true, g2true = g3metrics.make_const_truth_uniform_annular(
NFIELDS, NIMS, range_min=TRUE_MIN, range_max=TRUE_MAX)
# Loop over each c, m combination
for i in range(NBINS):
for j in range(NBINS):
# Make the submissions
g1sub, g2sub = g3metrics.make_submission_const_shear(
cvals[i], cvals[i], mvals[j], mvals[j], g1true, g2true,
ngals_per_im=NGALS_PER_IM, noise_sigma=NOISE_SIGMA)
# Calculate the metrics and store
Q08[i, j, krepeat] = g3metrics.metricQ08_const_shear(
g1sub, g2sub, g1true, g2true, nfields=NFIELDS)
QZ1[i, j, krepeat] = g3metrics.metricQZ1_const_shear(
g1sub, g2sub, g1true, g2true, cfid=CFID, mfid=MFID)[0]
QZ2[i, j, krepeat] = g3metrics.metricQZ2_const_shear(
g1sub, g2sub, g1true, g2true, cfid=CFID, mfid=MFID)[0]
print "Calculated const shear metrics for "+str(krepeat + 1)+"/"+\
str(NMONTE)+" realizations"
# Save the results as a tuple of NumPy arrays
cPickle.dump((Q08, QZ1, QZ2), open(OUTFILE, 'wb'))
true_sigma=TRUE_SIGMA)
# Create empty storage arrays in which to put
QZ1_mcboth = np.empty((NBINS, NBINS))
QZ2_mcboth = np.empty((NBINS, NBINS))
# Loop over mvalues making independent submissions at each c, m combination
for i in range(NBINS):
for j in range(NBINS):
g1sub, g2sub = g3metrics.make_submission_const_shear(
cgrid[i, j],
cgrid[i, j],
mgrid[i, j],
mgrid[i, j],
g1true,
g2true,
ngals_per_im=NGALS_PER_IM,
noise_sigma=NOISE_SIGMA)
QZ1_mcboth[i, j] = g3metrics.metricQZ1_const_shear(g1sub,
g2sub,
g1true,
g2true,
cfid=CFID,
mfid=MFID)[0]
QZ2_mcboth[i, j] = g3metrics.metricQZ2_const_shear(g1sub,
g2sub,
g1true,
g2true,
cfid=CFID,
else:
for krepeat in range(NMONTE):
# Generate the truth tables for this realization
g1true, g2true = g3metrics.make_const_truth_uniform_annular(
NFIELDS, NIMS, range_min=TRUE_MIN, range_max=TRUE_MAX)
# Loop over each c, m combination
for i in range(NBINS):
for j in range(NBINS):
# Make the submissions
g1sub, g2sub = g3metrics.make_submission_const_shear(
cvals[i],
cvals[i],
mvals[j],
mvals[j],
g1true,
g2true,
ngals_per_im=NGALS_PER_IM,
noise_sigma=NOISE_SIGMA)
# Calculate the metrics and store
Q08[i, j, krepeat] = g3metrics.metricQ08_const_shear(
g1sub, g2sub, g1true, g2true, nfields=NFIELDS)
QZ1[i, j, krepeat] = g3metrics.metricQZ1_const_shear(
g1sub, g2sub, g1true, g2true, cfid=CFID, mfid=MFID)[0]
QZ2[i, j, krepeat] = g3metrics.metricQZ2_const_shear(
g1sub, g2sub, g1true, g2true, cfid=CFID, mfid=MFID)[0]
print "Calculated const shear metrics for "+str(krepeat + 1)+"/"+\
str(NMONTE)+" realizations"
print "Calculating Q_c values versus c for control-"+obs_type+"-constant data in GREAT3"
print "NOISE_SIGMA = "+str(NOISE_SIGMA[obs_type])
# 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):
for itest in xrange(NTEST):
# Build the submission
g1sub, g2sub = g3metrics.make_submission_const_shear(
cval, 0., evaluate.MFID, evaluate.MFID, g1true, g2true, NGALS_PER_IMAGE,
NOISE_SIGMA[obs_type], rotate_cs=rotations)
fdsub, subfile = tempfile.mkstemp(suffix=".dat")
with os.fdopen(fdsub, "wb") as fsub:
np.savetxt(
fsub, np.array((subfield_index, g1sub, g2sub)).T, fmt="%d %e %e")
# Then evaluate Q_c
qc[obs_type][itest, jc] = evaluate.q_constant(
subfile, EXPERIMENT, obs_type, just_q=True, truth_dir=TRUTH_DIR)
os.remove(subfile)
print "Test %4d / %4d (c+ = %.3e) Q_c = %.4f" % (
itest+1, NTEST, cval, qc[obs_type][itest, jc])
print "Mean Q_c = "+str(qc[obs_type][:, jc].mean())+" for "+str(NTEST)+\
" sims (with c+ = "+str(cval)+", obs_type = "+str(obs_type)+")"
print
# Generate the truth tables
g1true, g2true = g3metrics.make_const_truth_normal_dist(NFIELDS, NIMS, true_sigma=TRUE_SIGMA)
# Create empty storage arrays in which to put
QZ1_mcboth = np.empty((NBINS, NBINS))
QZ2_mcboth = np.empty((NBINS, NBINS))
# Loop over mvalues making independent submissions at each c, m combination
for i in range(NBINS):
for j in range(NBINS):
g1sub, g2sub = g3metrics.make_submission_const_shear(cgrid[i, j], cgrid[i, j],
mgrid[i, j], mgrid[i, j],
g1true, g2true,
ngals_per_im=NGALS_PER_IM,
noise_sigma=NOISE_SIGMA)
QZ1_mcboth[i, j] = g3metrics.metricQZ1_const_shear(g1sub, g2sub, g1true, g2true,
cfid=CFID, mfid=MFID)[0]
QZ2_mcboth[i, j] = g3metrics.metricQZ2_const_shear(g1sub, g2sub, g1true, g2true,
cfid=CFID, mfid=MFID)[0]
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
if not os.path.isdir('./plots'):
os.mkdir('./plots')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# 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):
for itest in xrange(NTEST):
# Build the submission
g1sub, g2sub = g3metrics.make_submission_const_shear(
cval,
0.,
evaluate.MFID,
evaluate.MFID,
g1true,
g2true,
NGALS_PER_IMAGE,
NOISE_SIGMA[obs_type],
rotate_cs=rotations)
fdsub, subfile = tempfile.mkstemp(suffix=".dat")
with os.fdopen(fdsub, "wb") as fsub:
np.savetxt(fsub,
np.array((subfield_index, g1sub, g2sub)).T,
fmt="%d %e %e")
# Then evaluate Q_c
qc[obs_type][itest,
jc] = evaluate.q_constant(subfile,
EXPERIMENT,
obs_type,
just_q=True,
