make_plots=False)
# We can use this information to construct the linear model
# c**2 + (1 + 2 * m + m**2) * <map_E_ref> = a + b * <map_E_ref>
# ...and fit it to the submissions
# Hmmm actually a linear model doesn't work because we do want to constrain c**2 to be
# positive... Try using optimize instead
if USE_ALL_BINS:
# HACKY: Temporarily set evaluate.USEBINS to be all true to use *all* angular bins for
# calculating the product moment correlation coeff
evaluate.USEBINS = np.ones(evaluate.USEBINS.shape, dtype=bool)
# Get truth as a full catalog
_, x, y, g1true, g2true = test_evaluate.get_variable_gtrue(
EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR)
# Then make a variable submission to learn what pure c1 and c2 look like in map^2
import tempfile
fdtmp, tmpfile = tempfile.mkstemp(suffix=".dat")
result = test_evaluate.make_variable_submission(x,
y,
np.zeros_like(g1true),
np.zeros_like(g2true),
np.zeros_like(g1true),
np.zeros_like(g2true),
1.,
0.,
0.,
0.,
outfile=tmpfile,
noise_sigma=0.)
import cPickle
# Dicts containing arrays for storing Q_c values versus m and c, for ground and space
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_variable_Q_v_versus_c_norm2.pkl")
moutfile = os.path.join("results", "tabulated_variable_Q_v_versus_m_norm2.pkl")
if not os.path.isfile(coutfile):
for obs_type in ("ground", "space",):
print "Calculating Q_v values versus c for control-"+obs_type+"-variable 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+"-variable"
# Get the x,y, true intrinsic ellips and shears for making fake submissions
_, x, y, g1true, g2true = test_evaluate.get_variable_gtrue(
EXPERIMENT, obs_type, truth_dir=TRUTH_DIR)
_, _, _, g1int, g2int = test_evaluate.get_variable_gsuffix(
EXPERIMENT, obs_type, truth_dir=TRUTH_DIR)
for jc, cval in enumerate(CVALS):
for itest in xrange(NTEST):
# Build the submission
fdsub, subfile = tempfile.mkstemp(suffix=".dat")
result = test_evaluate.make_variable_submission(
x, 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,
"tabulated_variable_Q_v_versus_c_norm2.pkl")
moutfile = os.path.join("results",
"tabulated_variable_Q_v_versus_m_norm2.pkl")
if not os.path.isfile(coutfile):
for obs_type in (
"ground",
"space",
):
print "Calculating Q_v values versus c for control-" + obs_type + "-variable 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 + "-variable"
# Get the x,y, true intrinsic ellips and shears for making fake submissions
_, x, y, g1true, g2true = test_evaluate.get_variable_gtrue(
EXPERIMENT, obs_type, truth_dir=TRUTH_DIR)
_, _, _, g1int, g2int = test_evaluate.get_variable_gsuffix(
EXPERIMENT, obs_type, truth_dir=TRUTH_DIR)
for jc, cval in enumerate(CVALS):
for itest in xrange(NTEST):
# Build the submission
fdsub, subfile = tempfile.mkstemp(suffix=".dat")
result = test_evaluate.make_variable_submission(
x,
y,
g1true,
g2true,
g1int,
g2int,
suffixes=("_intrinsic", ""), make_plots=False)
# We can use this information to construct the linear model
# c**2 + (1 + 2 * m + m**2) * <map_E_ref> = a + b * <map_E_ref>
# ...and fit it to the submissions
# Hmmm actually a linear model doesn't work because we do want to constrain c**2 to be
# positive... Try using optimize instead
if USE_ALL_BINS:
# HACKY: Temporarily set evaluate.USEBINS to be all true to use *all* angular bins for
# calculating the product moment correlation coeff
evaluate.USEBINS = np.ones(evaluate.USEBINS.shape, dtype=bool)
# Get truth as a full catalog
_, x, y, g1true, g2true = test_evaluate.get_variable_gtrue(
EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR)
# Then make a variable submission to learn what pure c1 and c2 look like in map^2
import tempfile
fdtmp, tmpfile = tempfile.mkstemp(suffix=".dat")
result = test_evaluate.make_variable_submission(
x, y, np.zeros_like(g1true), np.zeros_like(g2true), np.zeros_like(g1true),
np.zeros_like(g2true), 1., 0., 0., 0., outfile=tmpfile, noise_sigma=0.)
os.close(fdtmp)
map_E_c1 = np.loadtxt(tmpfile)[:, 2]
os.remove(tmpfile)
fdtmp, tmpfile = tempfile.mkstemp(suffix=".dat")
result = test_evaluate.make_variable_submission(
x, y, np.zeros_like(g1true), np.zeros_like(g2true), np.zeros_like(g1true),
np.zeros_like(g2true), 0., 1., 0., 0., outfile=tmpfile, noise_sigma=0.)
os.close(fdtmp)
map_E_c2 = np.loadtxt(tmpfile)[:, 2]
