# Load up relevant data
i3data = np.loadtxt(I3FILE)
rgdata = np.loadtxt(RGFILE)
# Extract the salient parts of the submissions from data
field_i3 = i3data[:, 0].astype(int)
theta_i3 = i3data[:, 1]
map_E_i3 = i3data[:, 2]
field_rg = rgdata[:, 0].astype(int)
theta_rg = rgdata[:, 1]
map_E_rg = rgdata[:, 2]
# Load/generate the truth shear signal
field_shear, theta_shear, map_E_shear, _, maperr_shear = evaluate.get_generate_variable_truth(
EXPERIMENT,
OBS_TYPE,
truth_dir=TRUTH_DIR,
logger=None,
corr2_exec=CORR2_EXEC,
mape_file_prefix=MAPESHEAR_FILE_PREFIX,
suffixes=("", ),
make_plots=False)
# Then generate the intrinsic only map_E, useful for examinging plots, including the maperr
# (a good estimate of the relative Poisson errors per bin) which we will use to provide a weight
field_int, theta_int, map_E_int, _, maperr_int = evaluate.get_generate_variable_truth(
EXPERIMENT,
OBS_TYPE,
truth_dir=TRUTH_DIR,
logger=None,
corr2_exec=CORR2_EXEC,
mape_file_prefix=MAPEINT_FILE_PREFIX,
suffixes=("_intrinsic", ),
make_plots=False)
np.testing.assert_array_equal(
(70. * x_offset_truth).astype(int), np.asarray(x_offset_presub),
err_msg="Truth x_offsets do not match those in public-scripts.branch_id")
np.testing.assert_array_equal(
(70. * y_offset_truth).astype(int), np.asarray(y_offset_presub),
err_msg="Truth x_offsets do not match those in public-scripts.branch_id")
# Then try testing the whole x-y using the (no longer) hacked presubmission
pipelines.build_submission(
"im3shape-1", experiment, obs_type, "variable",
submission_dir=os.path.join("..", "submissions"),
presubmission_exec=os.path.join("..", "..", "public-scripts", "presubmission.py"))
# Then build the truth catalogues saving the x and y
field_index, theta, map_E, map_B, maperr = evaluate.get_generate_variable_truth(
experiment, obs_type, truth_dir=TRUTH_DIR,
corr2_params=os.path.join("..", "..", "server", "great3", "corr2.params"),
make_plots=False, output_xy_prefix="./cats/truth_xy_"+experiment+"-"+obs_type)
try:
xysubfile = "./cats/test_xy_"+experiment+"-"+obs_type+"-variable"+(
"-sub%03d" % 0)+".asc"
np.loadtxt(xysubfile)
except IOError as err:
# Now the hacked presubmission has moved all of these cataloges to ".", and we want to
# move them to ./cats, so do this
print "Moving cats to ./cats"
import subprocess
import glob
call_list = (["mv",]+glob.glob("test*.asc")+["./cats"])
print call_list
retcode = subprocess.call(["mv",]+glob.glob("truth*.asc")+["./cats"])
if __name__ == "__main__":
#run_variable_tests()
import glob
import subprocess
import logging
# Setup the logger
logging.basicConfig(stream=sys.stderr)
logger = logging.getLogger("test")
logger.setLevel(logging.DEBUG)
# First get the mapEtruth version
fieldt, thetat, map_Et, map_Bt, maperrt = evaluate.get_generate_variable_truth(
"control", "space", truth_dir="/Users/browe/great3/beta/truth",
file_prefixes=("galaxy_catalog",), suffixes=("",), make_plots=False, logger=logger)
# Then get the submission truth
idt, xt, yt, g1t, g2t = get_variable_gsuffix(
"control", "space", suffix="", file_prefix="galaxy_catalog",
test_dir="/Users/browe/great3/beta/truth") # True = g1/g2 only
ret = make_fits_cats(idt, g1t, g2t, prefix="gtrue_comparison")
gtruesubfile = "./submissions/gtrue_comparison_csv.asc"
call_list = [
"./presubmission_alpha-2", "-b", "control-space-variable",
"-o", gtruesubfile, ]+glob.glob("./cats/gtrue_comparison-*")
subprocess.check_call(call_list)
subdata = np.loadtxt(gtruesubfile)
fields, thetas, map_Es, map_Bs, maperrs = (
subdata[:, 0], subdata[:, 1], subdata[:, 2], subdata[:, 3], subdata[:, 4])
# Test to see if coutfile is already made, if not make it, if so load it
if not os.path.isfile(coutfile):
for obs_type in ("ground", "space"):
print "Calculating Q_v values versus c for control-"+obs_type+\
"-constant data in GREAT3"
print "Loading Cholesky decomposition matrix from "+\
calculate_variable_cholesky.CHOLESKY_OUTFILE[obs_type]
cholesky = pyfits.getdata(calculate_variable_cholesky.CHOLESKY_OUTFILE[obs_type])
print "RHO = "+str(RHO)
# First we build the truth table
print "Getting/generating truth tables for control-"+obs_type+"-constant"
field_ref, theta_ref, map_E_ref, _, maperr_ref = \
evaluate.get_generate_variable_truth(
EXPERIMENT, obs_type, truth_dir=TRUTH_DIR,
mape_file_prefix=evaluate.MAPEOBS_FILE_PREFIX,
file_prefixes=("galaxy_catalog", "galaxy_catalog"),
suffixes=("_intrinsic", ""), make_plots=False)
# 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")
# Then try testing the whole x-y using the (no longer) hacked presubmission
pipelines.build_submission(
"im3shape-1",
experiment,
obs_type,
"variable",
submission_dir=os.path.join("..", "submissions"),
presubmission_exec=os.path.join("..", "..", "public-scripts",
"presubmission.py"))
# Then build the truth catalogues saving the x and y
field_index, theta, map_E, map_B, maperr = evaluate.get_generate_variable_truth(
experiment,
obs_type,
truth_dir=TRUTH_DIR,
corr2_params=os.path.join("..", "..", "server", "great3",
"corr2.params"),
make_plots=False,
output_xy_prefix="./cats/truth_xy_" + experiment + "-" + obs_type)
try:
xysubfile = "./cats/test_xy_" + experiment + "-" + obs_type + "-variable" + (
"-sub%03d" % 0) + ".asc"
np.loadtxt(xysubfile)
except IOError as err:
# Now the hacked presubmission has moved all of these cataloges to ".", and we want to
# move them to ./cats, so do this
print "Moving cats to ./cats"
import subprocess
import glob
call_list = ([
if __name__ == "__main__":
# Load up relevant data
i3data = np.loadtxt(I3FILE)
rgdata = np.loadtxt(RGFILE)
# Extract the salient parts of the submissions from data
field_i3 = i3data[:, 0].astype(int)
theta_i3 = i3data[:, 1]
map_E_i3 = i3data[:, 2]
field_rg = rgdata[:, 0].astype(int)
theta_rg = rgdata[:, 1]
map_E_rg = rgdata[:, 2]
# Load/generate the truth shear signal
field_shear, theta_shear, map_E_shear, _, maperr_shear = evaluate.get_generate_variable_truth(
EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR, logger=None, corr2_exec=CORR2_EXEC,
mape_file_prefix=MAPESHEAR_FILE_PREFIX, suffixes=("",), make_plots=False)
# Then generate the intrinsic only map_E, useful for examinging plots, including the maperr
# (a good estimate of the relative Poisson errors per bin) which we will use to provide a weight
field_int, theta_int, map_E_int, _, maperr_int = evaluate.get_generate_variable_truth(
EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR, logger=None, corr2_exec=CORR2_EXEC,
mape_file_prefix=MAPEINT_FILE_PREFIX, suffixes=("_intrinsic",), make_plots=False)
# Then generate the theory observed = int + shear combined map signals - these are our reference
# Note this uses the new functionality of get_generate_variable_truth for adding shears
field_ref, theta_ref, map_E_ref, _, maperr_ref = evaluate.get_generate_variable_truth(
EXPERIMENT, OBS_TYPE, truth_dir=TRUTH_DIR, logger=None, corr2_exec=CORR2_EXEC,
mape_file_prefix=MAPEOBS_FILE_PREFIX, file_prefixes=("galaxy_catalog", "galaxy_catalog"),
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>
