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'))
# Get basic statistics
q08mean = np.mean(Q08, axis=2)
qZ1mean = np.mean(QZ1, axis=2)
qZ2mean = np.mean(QZ2, axis=2)
q08stds = np.std(Q08, axis=2)
qZ1stds = np.std(QZ1, axis=2)
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'))
# Get basic statistics
q08mean = np.mean(Q08, axis=2)
qZ1mean = np.mean(QZ1, axis=2)
qZ2mean = np.mean(QZ2, axis=2)
q08stds = np.std(Q08, axis=2)
qZ1stds = np.std(QZ1, axis=2)
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')
# 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')
ax.plot_surface(np.log10(mgrid), np.log10(cgrid), np.log10(QZ1_mcboth), rstride=1, cstride=1,
alpha=0.3, color='r')
ax.view_init(25, 60)
plt.xlabel(r'log$_{10}$(m = m1 = m2)')