ntest_output[0:data.shape[0]/ntest, j] = data[range(j, data.shape[0], ntest), 9]
hlr_output[0:data.shape[0]/ntest, j] = data[range(j, data.shape[0], ntest), 10]
g1_output[0:data.shape[0]/ntest, j] = data[range(j, data.shape[0], ntest), 11]
g2_output[0:data.shape[0]/ntest, j] = data[range(j, data.shape[0], ntest), 12]
# First do the plots of g1
YMAX_ZOOMIN = 1e-4
XMIN = -.6
XMAX = .5
plt.clf()
plt.axhline(ls='--', color='k')
plt.axvline(ls='--', color='k')
plt.xlim(XMIN, XMAX)
for i in range(ntest)[:-1]:
# First fit a line to the points
c, m, var_c, cov_cm, var_m = fitting.fitline(
g1obs_draw[0:data.shape[0]/ntest, i], delta_g1obs[0:data.shape[0]/ntest, i])
if i < 7:
fmt='x'
else:
fmt='o'
plt.errorbar(
g1obs_draw[0:data.shape[0]/ntest, i], delta_g1obs[0:data.shape[0]/ntest, i],
yerr=err_g1obs[0:data.shape[0]/ntest, i], fmt=fmt,
label=r"n = %.1f, m = %.2e $\pm$ %.2e" % (
test_sersic_highn_basic.SERSIC_N_TEST[i], m, np.sqrt(var_m)))
plt.xlabel(r'g$_1$ (DFT)')
plt.ylabel(r'$\Delta$g$_1$ (DFT - Photon)')
plt.ylim(-YMAX_ZOOMIN, YMAX_ZOOMIN)
plt.legend()
#plt.title(test_case)
plt.subplots_adjust(left=0.15)
j] = data[range(j, data.shape[0], ntest), 11]
g2_output[0:data.shape[0] / ntest,
j] = data[range(j, data.shape[0], ntest), 12]
# First do the plots of g1
YMAX_ZOOMIN = 1e-4
XMIN = -.6
XMAX = .5
plt.clf()
plt.axhline(ls='--', color='k')
plt.axvline(ls='--', color='k')
plt.xlim(XMIN, XMAX)
for i in range(ntest)[:-1]:
# First fit a line to the points
c, m, var_c, cov_cm, var_m = fitting.fitline(
g1obs_draw[0:data.shape[0] / ntest, i],
delta_g1obs[0:data.shape[0] / ntest, i])
if i < 7:
fmt = 'x'
else:
fmt = 'o'
plt.errorbar(
g1obs_draw[0:data.shape[0] / ntest, i],
delta_g1obs[0:data.shape[0] / ntest, i],
yerr=err_g1obs[0:data.shape[0] / ntest, i],
fmt=fmt,
label=r"n = %.1f, m = %.2e $\pm$ %.2e" %
(test_sersic_highn_basic.SERSIC_N_TEST[i], m, np.sqrt(var_m)))
plt.xlabel(r'g$_1$ (DFT)')
plt.ylabel(r'$\Delta$g$_1$ (DFT - Photon)')
plt.ylim(-YMAX_ZOOMIN, YMAX_ZOOMIN)
