y,
g1true,
g2true,
g1int,
g2int,
cval,
cval,
evaluate.MFID,
evaluate.MFID,
outfile=subfile,
noise_sigma=NOISE_SIGMA[obs_type])
os.close(fdsub)
# Then evaluate Q_v
qc[obs_type][itest, jc] = evaluate.q_variable(
subfile,
EXPERIMENT,
obs_type,
usebins=evaluate.USEBINS,
truth_dir=TRUTH_DIR)
os.remove(subfile)
print "Test %4d / %4d (c = %.3e) Q_v = %.4f" % (
itest + 1, NTEST, cval, qc[obs_type][itest, jc])
print "Mean Q_v = "+str(qc[obs_type][:, jc].mean())+" for "+str(NTEST)+\
" sims (with c = "+str(cval)+", obs_type = "+str(obs_type)+")"
print
print "Saving pickled Q_v versus c dict to " + coutfile
with open(coutfile, "wb") as fout:
cPickle.dump(qc, fout)
print
else:
g1int,
g2int,
cval,
cval,
mval,
mval,
outfile=subfile,
noise_sigma=NOISE_SIGMA)
os.close(fdsub)
# First estimate the absolute metric
qabsl = evaluate.q_variable(
subfile,
experiment,
obs_type,
logger=None,
usebins=usebins[0],
poisson_weight=poisson[0],
fractional_diff=False,
truth_dir=truth_dir,
sigma2_min=sigma2_min,
normalization=evaluate.NORMALIZATION_VARIABLE_SPACE)
print "%3d/%3d: Q_v_absl (c = %.4f, m = %.4f) = %.5e" % (
k + 1, NTEST, cval, mval, qabsl)
# Then the fractional
qfrac = evaluate.q_variable(subfile,
experiment,
obs_type,
logger=None,
usebins=usebins[0],
poisson_weight=poisson[0],
fractional_diff=True,
# Get the unitc term
map_E_unitc = 2. * test_evaluate.make_unitc(
EXPERIMENT, obs_type, truth_dir=TRUTH_DIR)
# Loop over c values
for jc, cval in enumerate(CVALS):
# Build the submissions (includes inter-method and inter-bin correlations)
map_E_field_subs = make_multiple_variable_submissions(
NTEST, map_E_ref, map_E_unitc, cval, evaluate.MFID, cholesky)
# Loop over submissions evaluating metric
for itest in xrange(NTEST):
fdsub, subfile = tempfile.mkstemp(suffix=".dat")
os.close(fdsub)
write_submission(map_E_field_subs[:, itest], outfile=subfile)
qc[obs_type][itest, jc] = evaluate.q_variable(
subfile, EXPERIMENT, obs_type, truth_dir=TRUTH_DIR)
os.remove(subfile)
print "mean(Q_v), std(Q_v) = "+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_v 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)
# Test to see if moutfile is already made, if not make it, if so load it
if not os.path.isfile(moutfile):
for obs_type in ("ground", "space"):
# outfile=subfile, noise_sigma=NOISE_SIGMA)
#q = evaluate.q_variable(
# subfile, experiment, obs_type, logger=logger, usebins=usebins[0],
# poisson_weight=poisson[0], fractional_diff=fractional[0])
#os.remove(subfile)
#print "%3d/%3d: Q_v (c = %.4f, m = %.4f) = %.5e" % (1, NTEST, cval, mval, q)
for k in range(NTEST):
fdsub, subfile = tempfile.mkstemp(suffix=".dat")
result = make_variable_submission(
x, y, g1true, g2true, g1int, g2int, cval, cval, mval, mval,
outfile=subfile, noise_sigma=NOISE_SIGMA)
os.close(fdsub)
# First estimate the absolute metric
qabsl = evaluate.q_variable(
subfile, experiment, obs_type, logger=None, usebins=usebins[0],
poisson_weight=poisson[0], fractional_diff=False, truth_dir=truth_dir,
sigma2_min=sigma2_min, normalization=evaluate.NORMALIZATION_VARIABLE_SPACE)
print "%3d/%3d: Q_v_absl (c = %.4f, m = %.4f) = %.5e" % (
k + 1, NTEST, cval, mval, qabsl)
# Then the fractional
qfrac = evaluate.q_variable(
subfile, experiment, obs_type, logger=None, usebins=usebins[0],
poisson_weight=poisson[0], fractional_diff=True, truth_dir=truth_dir,
sigma2_min=sigma2_min_bymc / 10., normalization=300. * 0.6492)
print "%3d/%3d: Q_v_frac (c = %.4f, m = %.4f) = %.5e" % (
k + 1, NTEST, cval, mval, qfrac)
# Then the sqrd
qsqrd = evaluate.q_variable(
subfile, experiment, obs_type, logger=None, usebins=usebins[0],
poisson_weight=poisson[0], fractional_diff=False, squared_diff=True,
truth_dir=truth_dir, sigma2_min=sigma2_min * 3.e-5,