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from astropy.wcs import WCS
from astropy.io import ascii, fits
from matplotlib.colors import LogNorm
from scipy.stats import binned_statistic
from astropy.visualization import ZScaleInterval
import os
import numpy as np
import matplotlib.pyplot as plt
from cross_match_catalogs import match_catalogs_img
def plot_injection_comparison(orig_img, injected_img, flg_img=None, save_fig_dir=None, fig_prefix="", figsize=(12, 8)):
z = ZScaleInterval()
plt.figure(figsize=figsize, dpi=100)
hdu_orig = fits.open(orig_img)[1]
data_orig = hdu_orig.data
if flg_img is not None:
flg_data = fits.getdata(flg_img)
data_orig[flg_data > 0] = 0.
wcs = WCS(hdu_orig.header)
plt.subplot(projection=wcs)
z1, z2 = z.get_limits(data_orig)
plt.imshow(data_orig, origin='lower', cmap='gray', vmin=z1, vmax=z2)
plt.grid(color='white', ls='solid')
if save_fig_dir is not None:
output_filename = fig_prefix + "original_img.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
plt.figure(figsize=figsize, dpi=100)
hdu_inj = fits.open(injected_img)[1]
data_inj = hdu_inj.data
if flg_img is not None:
data_inj[flg_data > 0] = 0.
wcs = WCS(hdu_inj.header)
plt.subplot(projection=wcs)
z1, z2 = z.get_limits(data_inj)
plt.imshow(data_inj, origin='lower', cmap='gray', vmin=z1, vmax=z2)
plt.grid(color='white', ls='solid')
if save_fig_dir is not None:
output_filename = fig_prefix + "injected_img.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
plt.figure(figsize=figsize, dpi=100)
plt.subplot(projection=wcs)
img_diff = data_inj - data_orig
z1 = 0.
z2 = 0.001
plt.imshow(img_diff, origin='lower', cmap='gray', vmin=z1, vmax=z2)
plt.grid(color='white', ls='solid')
if save_fig_dir is not None:
output_filename = fig_prefix + "diff_img.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
def plot_ensemble_hist(cat_path_list, column_name="Mag_Kron", column_unit="mag", title="Total KRON MAG distribution", save_fig_dir=None, fig_prefix="",
nbins=50, low=16., high=28., density=False):
values = []
bins = np.linspace(low, high, nbins+1)
for cat_path in cat_path_list:
if cat_path.endswith(".fits"):
hdu = fits.open(cat_path)
value_temp = hdu[1].data[column_name]
elif cat_path.endswith(".cat"):
data = ascii.read(cat_path)
value_temp = data[column_name]
print("number of objects in %s: %d" %
(os.path.basename(cat_path), len(value_temp)))
values = np.append(values, value_temp)
plt.figure()
plt.hist(values, bins=bins, density=density)
plt.xlabel(column_name + '/' + column_unit, size='x-large')
if density is False:
plt.ylabel("Counts", size='x-large')
plt.title(title, size='x-large')
if save_fig_dir is not None:
output_filename = fig_prefix + "%s_ensemble_hist.png" % (column_name)
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
def create_hist_figure(counts, counts_detected, bins, name="val", output_dir='./', fig_name='detected_counts.png', save_figure=False, title=None):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(name, size='x-large')
ax.set_ylabel("Counts", size='x-large')
if title is not None:
ax.set_title(title, size='x-large')
ax.stairs(counts, bins, color='r', label='TU objects')
ax.stairs(counts_detected, bins, color='g', label='Detected')
ax.legend(loc='upper right', fancybox=True)
if save_figure:
fig_name = os.path.join(output_dir, fig_name)
fig.savefig(fig_name)
return fig, ax
def create_fraction_figure(counts, counts_detected, bins, name='val', output_dir='./', fig_name="completeness_fraction.png",
save_figure=False, title=None, figure=None, color='r', label='patch_1', show_legend=False):
fraction = counts_detected / counts
fraction[np.where(np.isnan(fraction))[0]] = 0.
if figure is not None:
fig = figure
ax = fig.axes[0]
ax.stairs(fraction, bins, color=color, label=label)
if title is not None:
ax.set_title(title, size='x-large')
else:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.stairs(fraction, bins, color=color, label=label)
ax.set_xlabel(name, size='x-large')
if title is not None:
ax.set_title(title, size='x-large')
else:
ax.set_title("Completeness Fraction")
if show_legend:
ax.legend(loc='upper right', fancybox=True)
if save_figure:
fig_name = os.path.join(output_dir, fig_name)
fig.savefig(fig_name)
return fig, ax, fraction
def validation_hist(val, idx, name="val", nbins=10, bins=None, fig_name='detected_counts.png', output_dir='./', create_figure=True):
if bins is None:
counts, bins = np.histogram(val, bins=nbins)
else:
counts, bins = np.histogram(val, bins=bins)
is_empty = np.full(len(val), False)
for i in range(len(idx)):
if idx[i].size == 0:
is_empty[i] = True
if bins is None:
counts_detected, _ = np.histogram(val[~is_empty], bins=nbins)
else:
counts_detected, _ = np.histogram(val[~is_empty], bins=bins)
if create_figure:
create_hist_figure(counts, counts_detected, bins,
name, output_dir, fig_name)
return counts, counts_detected, bins
def plot_mag_comparison(truth_cat_list, measured_cat_root_dir, mag1_name="mag", mag2_name="Mag_Kron", save_fig_dir=None, fig_prefix="",
nbins=20, low=18., high=26., ylim=[-1., 1.], title=None):
diff_list = []
truth_list = []
bins = np.linspace(low, high, nbins+1)
for cat_path_truth in truth_cat_list:
print("Injected truth catalog: ",
os.path.basename(cat_path_truth))
obs_id = cat_path_truth.split('/')[-2]
# Read truth catalog
data = ascii.read(cat_path_truth)
x_truth = data["xImage"]
y_truth = data["yImage"]
mag_truth = data[mag1_name]
# Read measured catalog
cat_path_measured = os.path.join(measured_cat_root_dir,
obs_id,
os.path.basename(cat_path_truth).replace("img", "cat").replace(".cat", ".fits"))
print("L1 processed photometry catalog: ",
os.path.basename(cat_path_truth))
hdu = fits.open(cat_path_measured)
x_measure = hdu[1].data["X"]
y_measure = hdu[1].data["Y"]
mag_measure = hdu[1].data["Mag_Kron"]
# Match measured objects vs truth
idx1, _, = match_catalogs_img(
x1=x_truth, y1=y_truth, x2=x_measure, y2=y_measure)
for i in range(len(idx1)):
if idx1[i].size == 0:
continue
else:
diff_list.append(mag_measure[idx1[i][0]] - mag_truth[i])
truth_list.append(mag_truth[i])
bin_means, bin_edges, binnumber = binned_statistic(truth_list, diff_list, 'mean',
bins=nbins, range=[low, high])
bin_median, bin_edges, binnumber = binned_statistic(truth_list, diff_list, 'median',
bins=nbins, range=[low, high])
bin_std, bin_edges, binnumber = binned_statistic(truth_list, diff_list, 'std',
bins=nbins, range=[low, high])
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width/2
plt.figure()
plt.plot(truth_list, diff_list, 'ro', alpha=0.1)
plt.axhline(y=0., color='k', alpha=0.6)
plt.plot(bin_centers, bin_median, '--',
label=r'$\rm{median}\ \Delta mag$', alpha=0.6)
plt.errorbar(bin_centers, bin_means, yerr=bin_std, fmt='bo', capsize=2,
label=r'$\rm{mean}\ \Delta mag$', alpha=0.6)
plt.xlim([low, high])
plt.ylim(ylim)
plt.xlabel("True mag", size='x-large')
plt.ylabel("Measured (Kron) - True mag", size='x-large')
plt.legend(loc='upper left', fancybox=True)
if title is not None:
plt.title(title, size='x-large')
if save_fig_dir is not None:
output_filename = fig_prefix + "measured-true_mag.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()