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Commit bfd971b6 authored by DUVERNE Pierre-Alexandre's avatar DUVERNE Pierre-Alexandre
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 25 22:44:01 2021
@author: duverne
"""
import numpy as np
from astropy.table import Table
import matplotlib.pyplot as plt
from astropy.io import ascii
import plot_image as plotim
import numpy.polynomial.polynomial as poly
from astropy.visualization import ZScaleInterval, imshow_norm, SqrtStretch
from astropy.io import fits
from astropy.wcs.wcs import WCS
import mag_lim as ml
import catalog as cata
from muphoten.background import background_estimation, bkg_to_array
import photometry as photo
import background as back
# reu = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/iris_g_paper.dat', 2)
# reu = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/g_band_LT_paper.dat', 2)
# reu = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/g_band_LT_paper----.dat', 2)
# reu = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/g_band_LT_paper_v2.dat', 3)
# path_iris = '/home/duverne/Documents/agatha_pipeline/results/g_band_iris_paper_v3_isophotal_radius_5_time.dat'
# iris_table = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/g_band_iris_paper_v3_isophotal_radius_5_time.dat', 2.5)
# iris = Table.read('/home/duverne/Documents/agatha_pipeline/results/iris_g_paper_v2.dat',
# format='ascii.commented_header')
# mask = (iris['flag_dist'] == 0)
# iris = iris[mask]
# lt_table = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/g_band_LT_paper_v3_isophotal_radius_5.dat', 3.0)
# lt = Table.read('/home/duverne/Documents/agatha_pipeline/results/g_band_LT_paper_v3_isophotal_radius_3.dat',
# format='ascii.commented_header')
# mask = (lt['flag_dist'] == 0)
# lt = lt[mask]
# path_kait = '/home/duverne/Documents/agatha_pipeline/results/results_kait_B_paper_isophotal_radius_5_time.dat'
# kait_table = plotim.prepare_light_curve( '/home/duverne/Documents/agatha_pipeline/results/results_kait_B_paper_isophotal_radius_5_time.dat', 3.)
# kait = Table.read('/home/duverne/Documents/agatha_pipeline/results/results_kait_B_paper_isophotal_radius_5.dat',
# format='ascii.commented_header')
# mask = (kait['flag_dist'] == 0)
# kait = kait[mask]
# # lt = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/g_band_LT_paper_v3_isophotal_radius_5.dat', 3.0)
# lt_table_b = plotim.prepare_light_curve('/home/duverne/Documents/agatha_pipeline/results/B_band_LT_paper_v3_isophotal_radius_5.dat', 3.)
# mask = (lt_table_b['flag_dist'] == 0)
# lt_table_b = lt_table_b[mask]
do_psf = False
if do_psf:
kait['psf_err'] = 0.01
kait_table['psf_err'] = 0.01
iris_table['psf_err'] = 0.01
iris['psf_err'] = 0.01
fig, main_axes = plt.subplots(2,1)
plt.gcf().subplots_adjust(left = 0.1, bottom = 0.1,
right = 0.99, top = 0.95,
wspace = 2.5, hspace = 0.5)
main_axes[0].errorbar(kait['time'], kait['psf'],
yerr=kait['psf_err'],
label= r'$Before$ $Clipping$',
marker = 'x', linestyle='none',
color = 'red', ms=5)
main_axes[0].errorbar(kait_table['time'], kait_table['psf'],
yerr=kait_table['psf_err'],
label= r'$After$ $Clipping$',
marker = 'd', linestyle = 'none',
color = 'blue', ms=6)
median_psf_kait = [np.median(kait_table['psf']),
np.median(kait_table['psf'])]
time_psf_kait = [kait_table['time'][0]-1,
kait_table['time'][-1]+1]
main_axes[0].plot(time_psf_kait, median_psf_kait,
linestyle='--', color='black')
main_axes[0].set_xlabel(r'$Time$ $-$ $MJD$ $58285.441$ $(days)$', fontsize=14)
main_axes[0].set_ylabel(r'$FWHM$ $PSF$ $(pixels)$', fontsize=12)
main_axes[0].set_title(r'$PSF$ $Evolution$ $KAIT$', fontsize=14)
main_axes[0].legend()
main_axes[0].xaxis.set_tick_params(labelsize= 12)
main_axes[0].yaxis.set_tick_params(labelsize= 12)
main_axes[1].errorbar(iris['time'], iris['psf'],
yerr=iris['psf_err'],
label= r'$Before$ $Clipping$',
marker = 'x', linestyle='none',
color = 'red', ms=5)
main_axes[1].errorbar(iris_table['time'], iris_table['psf'],
yerr=iris_table['psf_err'],
label= r'$After$ $Clipping$',
marker = 'd', linestyle = 'none',
color = 'blue', ms=6)
median_psf_iris = [np.median(iris_table['psf']),
np.median(iris_table['psf'])]
time_psf_iris = [iris_table['time'][0]-1,
iris_table['time'][-1]+1]
main_axes[1].plot(time_psf_iris, median_psf_iris,
linestyle='--', color='black')
main_axes[1].set_xlabel(r'$Time$ $-$ $MJD$ $58285.441$ $(days)$', fontsize=14)
main_axes[1].set_ylabel(r'$FWHM$ $PSF$ $(pixels)$', fontsize=12)
main_axes[1].set_title(r'$PSF$ $Evolution$ $IRiS$', fontsize=14)
main_axes[1].legend()
main_axes[1].xaxis.set_tick_params(labelsize= 12)
main_axes[1].yaxis.set_tick_params(labelsize= 12)
do_calib = False
if do_calib :
iris_calib = Table.read('/home/duverne/Documents/agatha_pipeline/results/iris_table_for_calibration.dat',
format='ascii.commented_header')
iris_calib['mag_err'] = 2.5/(np.log(10)*np.sqrt(iris_calib['aperture_sum']))
fit_coef = np.ma.polyfit(iris_calib['Magnitude'], iris_calib['gmag'], 1)
fig, ax = plt.subplots(1, 2, figsize=(20, 12.5))
plt.gcf().subplots_adjust(left = 0.05, bottom = 0.1,
right = 0.99, top = 0.95, wspace = 0.1, hspace = 0.5)
fit_lin = poly.polyval(iris_calib['Magnitude'],
np.flip(fit_coef))
ax[0].plot(iris_calib['Magnitude'],
fit_lin,
label=r'Fit : a={0:.4f} and b={1:.2f}'.format(fit_coef[0], fit_coef[1]),
color='red')
khi = np.sum(((poly.polyval(iris_calib['Magnitude'],np.flip(fit_coef))
- (iris_calib['gmag'])) ** 2) / iris_calib['e_gmag'])
khi_dof = khi/(len(iris_calib['Magnitude']) - 2)
print(khi_dof)
label = r'$\frac{\chi^2}{dof}$'
label+= r' = {0:.4f}'.format(khi_dof)
ax[0].errorbar(iris_calib['Magnitude'],
iris_calib['gmag'],
xerr=iris_calib['mag_err'],
yerr=iris_calib['e_gmag'],
marker="x", ms='9',
color='black' ,
linestyle = 'none',
label = label)
ax[0].set_xlabel(r'$Instrumental$ $Magnitude$', fontsize=18)
ax[0].set_ylabel(r'$g$ $Magnitude$', fontsize=18)
ax[0].set_title(r'$IRiS$ $Calibration$ $Curve$', fontsize=18)
ax[0].legend(fontsize=22)
ax[0].xaxis.set_tick_params(labelsize= 18)
ax[0].yaxis.set_tick_params(labelsize= 18)
kait_calib = Table.read('/home/duverne/Documents/agatha_pipeline/results/kait_table_for_calibration.dat',
format='ascii.commented_header')
# '/media/duverne/DISQUE ESSB/AT2018cow_data/2018cow_kaitdata/B_c_band/2018cow_20180622_060312_Jun8mgky_kait_B_c.fit'
kait_calib['mag_err'] = 2.5/(np.log(10)*np.sqrt(kait_calib['aperture_sum']))
fit_coef = np.ma.polyfit(kait_calib['Magnitude'], kait_calib['BMag'], 1)
fit_lin = poly.polyval(kait_calib['Magnitude'],
np.flip(fit_coef))
khi = np.sum(((poly.polyval(kait_calib['Magnitude'],np.flip(fit_coef))
- kait_calib['BMag']) ** 2) / kait_calib['error_from_transformation'])
khi_dof = khi/(len(kait_calib['Magnitude']) - 2)
print(khi_dof)
label = r'$\frac{\chi^2}{dof}$'
label+= r' = {0:.4f}'.format(khi_dof)
ax[1].errorbar(kait_calib['Magnitude'],
kait_calib['BMag'],
xerr=kait_calib['mag_err'],
yerr=kait_calib['error_from_transformation'],
marker="x", ms='9',
color='black' ,
linestyle = 'none',
label = label)
ax[1].plot(kait_calib['Magnitude'],
fit_lin,
label='Fit : a={0:.4f} and b={1:.2f}'.format(fit_coef[0], fit_coef[1]),
color='red')
ax[1].set_xlabel(r'$Instrumental$ $Magnitude$', fontsize=18)
ax[1].set_ylabel(r'$B_c$ $Magnitude$', fontsize=18)
ax[1].set_title(r'$KAIT$ $Calibration$ $Curve$', fontsize=18)
ax[1].legend(fontsize=22)
ax[1].xaxis.set_tick_params(labelsize= 18)
ax[1].yaxis.set_tick_params(labelsize= 18)
do_star = False
if do_star:
fig, main_axes = plt.subplots(2,1)
plt.gcf().subplots_adjust(left = 0.1, bottom = 0.1,
right = 0.9, top = 0.9, wspace = 2.5, hspace = 0.5)
do_back = False
if do_back:
coordinate = [244.000927647,+22.2680094118]
image = '/media/duverne/DISQUE ESSB/AT2018cow_data/soustraction_iris_avec_ps/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g.fits'
hdu = fits.open(image)[0]
data_iris, header_iris = hdu.data, hdu.header
wcs_data_iris = WCS(header_iris, relax=False)
coord_iris = wcs_data_iris.all_world2pix([coordinate], 1)
background_object_iris = back.background_estimation(data_iris, n_sigma=3.0,
box_size=(50,50),
bkg_estimator='sex',
filter_size=(3, 3))#, mask=mask)
background_array_iris, background_rms_iris = back.bkg_to_array(background_object_iris)
data_cleaned_iris = data_iris - background_array_iris
kait_calib = Table.read('/home/duverne/Documents/agatha_pipeline/results/kait_table_for_calibration.dat',
format='ascii.commented_header')
iris_calib = Table.read('/home/duverne/Documents/agatha_pipeline/results/iris_table_for_calibration.dat',
format='ascii.commented_header')
# image = '/media/duverne/DISQUE ESSB/AT2018cow_data/kait/B_c_band/SUB_2018cow_20180622_060312_Jun8mgky_kait_B_c.fit'
image = '/media/duverne/DISQUE ESSB/AT2018cow_data/2018cow_kaitdata/B_c_band/2018cow_20180622_060312_Jun8mgky_kait_B_c.fit'
hdu = fits.open(image)[0]
data_kait, header_kait = hdu.data, hdu.header
wcs_data_kait = WCS(header_kait, relax=False)
coord_kait = wcs_data_kait.all_world2pix([coordinate], 1)
# mask=(data_kait == 1e-30)
mask=(data_kait == 1e-30) | (data_kait == 0)
background_object_kait = back.background_estimation(data_kait, n_sigma=3.0,
box_size=(60,60),
bkg_estimator='sex',
filter_size=(3, 3), mask=mask)
background_array_kait, background_rms_kait = back.bkg_to_array(background_object_kait)
data_cleaned_kait = data_kait - background_array_kait
fig, ax = plt.subplots(2, 3, figsize=(20, 12.5), sharey='row', sharex='row')
plt.gcf().subplots_adjust(left = 0.06, bottom = 0.08,
right = 0.99, top = 0.95, wspace = 0.05,
hspace = 0.07)
font_size = 15
im, norm=imshow_norm(data_kait, ax[0,0], origin='lower', cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch(),aspect = "auto")
fig.colorbar(im, ax=ax[0][0])
im, norm=imshow_norm(background_array_kait, ax[0,1], origin='lower',
cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch(),aspect = "auto")
fig.colorbar(im, ax=ax[0][1])
im, norm=imshow_norm(data_cleaned_kait, ax[0,2], origin='lower',
cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch(),aspect = "auto")
fig.colorbar(im, ax=ax[0][2])
ax[0][2].scatter(coord_kait[0][0]+20 , coord_kait[0][1]-5, marker ="_",
s = 200,color='black',linewidths=4)
ax[0][2].scatter(coord_kait[0][0], coord_kait[0][1]+20,marker = "|",
s = 200,color='black',linewidths=4)
ax[0][2].scatter(kait_calib['xcentroid'], kait_calib['ycentroid'],
color='blue', marker='o', s=18)
ax[0][2].set_title(r'Cleaned Image', fontsize=22)
#ax[0][2].set_xlabel(r'X', fontsize=22)
#ax[0][2].set_ylabel(r'Y', fontsize=22)
ax[0][2].xaxis.set_tick_params(labelsize=font_size)
ax[0][2].yaxis.set_tick_params(labelsize=font_size)
ax[0][1].set_title(r'Background', fontsize=22)
#ax[0][1].set_xlabel(r'X', fontsize=22)
#ax[0][1].set_ylabel(r'Y', fontsize=22)
ax[0][1].xaxis.set_tick_params(labelsize=font_size)
ax[0][1].yaxis.set_tick_params(labelsize=font_size)
ax[0][0].set_title(r'Image', fontsize=22)
#ax[0][0].set_xlabel(r'X', fontsize=22)
ax[0][0].set_ylabel(r'Y', fontsize=22)
ax[0][0].xaxis.set_tick_params(labelsize=font_size)
ax[0][0].yaxis.set_tick_params(labelsize=font_size)
im, norm=imshow_norm(data_iris, ax[1,0], origin='lower', cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch(),aspect = "auto")
fig.colorbar(im, ax=ax[1][0])
im, norm=imshow_norm(background_array_iris, ax[1,1], origin='lower',
cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch(),aspect = "auto")
fig.colorbar(im, ax=ax[1][1])
im, norm=imshow_norm(data_cleaned_iris, ax[1,2], origin='lower',
cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch(),aspect = "auto")
fig.colorbar(im, ax=ax[1][2])
ax[1][2].scatter(iris_calib['xcentroid'], iris_calib['ycentroid'],
color='blue', marker='o', s=18)
ax[1][2].scatter(coord_iris[0][0]+80 , coord_iris[0][1], marker ="_",
s = 400,color='black',linewidths=5)
ax[1][2].scatter(coord_iris[0][0], coord_iris[0][1]+90,marker = "|",
s = 400,color='black',linewidths=5)
#ax[1][2].set_title(r'IRiS Cleaned Image', fontsize=22)
#ax[1][2].set_xlabel(r'X', fontsize=22)
#ax[1][2].set_ylabel(r'Y', fontsize=22)
ax[1][2].xaxis.set_tick_params(labelsize=font_size)
ax[1][2].yaxis.set_tick_params(labelsize=font_size)
#ax[1][1].set_title(r'IRiS Background Image', fontsize=22)
ax[1][1].set_xlabel(r'X', fontsize=22)
#ax[1][1].set_ylabel(r'Y', fontsize=22)
ax[1][1].xaxis.set_tick_params(labelsize=font_size)
ax[1][1].yaxis.set_tick_params(labelsize=font_size)
# ax[1][0].set_title(r'IRiS Image', fontsize=22)
#ax[1][0].set_xlabel(r'X', fontsize=22)
ax[1][0].set_ylabel(r'Y', fontsize=22)
ax[1][0].xaxis.set_tick_params(labelsize=font_size)
ax[1][0].yaxis.set_tick_params(labelsize=font_size)
do_diff = False
if do_diff:
coordinate = [244.000927647,+22.2680094118]
image = '/media/duverne/DISQUE ESSB/AT2018cow_data/soustraction_iris_avec_ps/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g.fits'
hdu = fits.open(image)[0]
data_iris, header_iris = hdu.data, hdu.header
wcs_data_iris = WCS(header_iris, relax=False)
coord_iris = wcs_data_iris.all_world2pix([coordinate], 1)
image = '/media/duverne/DISQUE ESSB/AT2018cow_data/soustraction_iris_avec_ps/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g/substraction/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g_reg_0_sub.fits'
hdu_sub = fits.open(image)[0]
data_sub_iris, header_sub_iris = hdu_sub.data, hdu_sub.header
wcs_data_sub_iris = WCS(header_sub_iris, relax=False)
coord_sub_iris = wcs_data_sub_iris.all_world2pix([coordinate], 1)
image = '/media/duverne/DISQUE ESSB/AT2018cow_data/2018cow_kaitdata/B_c_band/2018cow_20180622_060312_Jun8mgky_kait_B_c.fit'
hdu = fits.open(image)[0]
data_kait, header_kait = hdu.data, hdu.header
wcs_data_kait = WCS(header_kait, relax=False)
coord_kait = wcs_data_kait.all_world2pix([coordinate], 1)
image = '/media/duverne/DISQUE ESSB/AT2018cow_data/kait/B_c_band/SUB_2018cow_20180622_060312_Jun8mgky_kait_B_c.fit'
hdu = fits.open(image)[0]
data_sub_kait, header_sub_kait = hdu.data, hdu.header
wcs_data_sub_kait = WCS(header_sub_kait, relax=False)
coord_sub_subkait = wcs_data_sub_kait.all_world2pix([coordinate], 1)
fig, ax = plt.subplots(2, 2, figsize=(10, 12.5), sharey='row', sharex='row')
im, norm=imshow_norm(data_iris, ax[0,0], origin='lower', cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch())
fig.colorbar(im, ax=ax[0][0])
ax[0][0].scatter(coord_iris[0][0]+25 , coord_iris[0][1]-1, marker ="_", s = 200,color='black')
ax[0][0].scatter(coord_iris[0][0]-1, coord_iris[0][1]+25,marker = "|", s = 200,color='black')
im, norm=imshow_norm(data_sub_iris, ax[0,1], origin='lower', cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch())
# im.set_clim(np.min(data_sub_iris),np.max(data_sub_iris))
fig.colorbar(im, ax=ax[0][1])
ax[0][1].scatter(coord_sub_iris[0][0]+10 , coord_sub_iris[0][1]-1, marker ="_", s = 200,color='black')
ax[0][1].scatter(coord_sub_iris[0][0]-1, coord_sub_iris[0][1]+10,marker = "|", s = 200,color='black')
im, norm=imshow_norm(data_kait, ax[1,0], origin='lower', cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch())
fig.colorbar(im, ax=ax[1][0])
ax[1][0].scatter(coord_kait[0][0]+15 , coord_kait[0][1]-1, marker ="_", s = 200,color='black')
ax[1][0].scatter(coord_kait[0][0]-1, coord_kait[0][1]+15,marker = "|", s = 200,color='black')
im, norm=imshow_norm(data_sub_kait, ax[1,1], origin='lower', cmap = 'Greys_r',
interval=ZScaleInterval(), stretch=SqrtStretch())
fig.colorbar(im, ax=ax[1][1])
ax[1][1].scatter(coord_sub_subkait[0][0]+10 , coord_sub_subkait[0][1]-1, marker ="_", s = 200,color='black')
ax[1][1].scatter(coord_sub_subkait[0][0]-1, coord_sub_subkait[0][1]+10,marker = "|", s = 200,color='black')
ax[0][1].xaxis.set_tick_params(labelsize=15)
ax[0][1].yaxis.set_tick_params(labelsize=15)
do_lim = False
def do_plot_ratio(mag_lim, lim, region, index, threshold=0.5,
save=False, name='ratio',
title=r'$Limit$ $Magnitude$ $Ratio$'):
mask = (mag_lim[:index+1]==0.)
y = np.ma.array(mag_lim[:index+1], mask=mask).compressed()
x = np.ma.array(region[1][:index+1], mask=mask).compressed()
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
label = r'$Limit$ $Magnitude$ = ' + '%.2f' %lim
ax.axvline(lim, label = label, color='grey',
linestyle='dashed', linewidth=3)
ax.plot(x, y, linestyle='none', marker = 'o', color='blue')
ax.plot(region[1][index+1:-1], mag_lim[index+1:],
linestyle='none', marker = 'o', color='blue')
ax.axhline(threshold, label = r'threshold', color='black',
linestyle='-', linewidth=3)
ax.set_title(title, fontsize=18)
ax.set_xlabel(r'$Magnitude$ ', fontsize=18)
ax.set_ylabel(r'$ratio$ $of$ $detected$ $objects$', fontsize=18)
ax.legend(fontsize=12)
ax.xaxis.set_tick_params(labelsize= 12)
ax.yaxis.set_tick_params(labelsize= 12)
if save:
fig.savefig(name + '.png')
plt.close(fig)
if do_lim:
image_kait = '/media/duverne/DISQUE ESSB/AT2018cow_data/2018cow_kaitdata/B_c_band/2018cow_20180622_060312_Jun8mgky_kait_B_c.fit'
hdu = fits.open(image_kait)[0]
data_kait, header_kait = hdu.data, hdu.header
band_kait = 'BMag'
# data=data[0:250]
wcs_data_kait = WCS(header_kait)
center_kait = ml.get_sky_coord_center(data_kait, wcs_data_kait)
width_kait, height_kait = ml.get_fov(data_kait, wcs_data_kait)
region_kait = ml.get_region(data_kait, wcs_data_kait)
region_kait = cata.from_PS2Johnson(band_kait, region_kait)
# all_stars_2 = cata.from_PS2Johnson(band, all_stars_2)
all_star_detected_kait = ml.detect_objects(data_kait, header_kait,
band_kait)
# all_star_detected_kait = cata.from_PS2Johnson(band_kait, all_star_detected_kait)
histo_detected_objects_kait, histo_region_kait, mag_lim_kait = ml.build_ratio(region_kait,
all_star_detected_kait,
band_kait, precision=0.2,
interval=[13., 22.])
lim_kait, index_kait = ml.get_mag_lim(mag_lim_kait, histo_region_kait,
precision=0.2, threshold=0.5)
do_plot_ratio(mag_lim_kait, lim_kait, histo_region_kait, index_kait,
title=r'$Limit$ $Magnitude$ $Ratio$ $KAIT$')
image_iris = '/media/duverne/DISQUE ESSB/AT2018cow_data/soustraction_iris_avec_ps/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g/20180628_ATLAS18qqn-S001-R001-C008-SDSS_g.fits'
hdu = fits.open(image_iris)[0]
data_iris, header_iris = hdu.data, hdu.header
band_iris = 'gmag'
# data=data[0:250]
wcs_data_iris = WCS(header_iris)
center_iris = ml.get_sky_coord_center(data_iris, wcs_data_iris)
width_iris, height_iris = ml.get_fov(data_iris, wcs_data_iris)
region_iris = ml.get_region(data_iris, wcs_data_iris)
# all_stars_2 = cata.from_PS2Johnson(band, all_stars_2)
all_star_detected_iris = ml.detect_objects(data_iris, header_iris, band_iris)
detected_objects_iris, region_iris, mag_lim_iris = ml.build_ratio(region_iris,
all_star_detected_iris,
band_iris,
precision=0.2,
interval=[11., 22.])
lim_iris, index_iris = ml.get_mag_lim(mag_lim_iris, region_iris,
precision=0.2, threshold=0.5)
do_plot_ratio(mag_lim_iris, lim_iris, region_iris, index_iris,
title=r'$Limit$ $Magnitude$ $IRiS$')
threshold = 0.5
fig, ax = plt.subplots(1, 2, figsize=(20, 12.5))
plt.gcf().subplots_adjust(left = 0.06, bottom = 0.1,
right = 0.99, top = 0.95,
wspace = 0.1, hspace = 0.5)
mask = (mag_lim_kait[:index_kait+1]==0.)
y = np.ma.array(mag_lim_kait[:index_kait+1], mask=mask).compressed()
x = np.ma.array(histo_region_kait[1][:index_kait+1], mask=mask).compressed()
# label = r'$Limit$ $Magnitude$ = ' + '%.2f' %lim_kait
label = r'$Limit$ $Magnitude$ = {:.2f} $\pm$ {}'.format(lim_kait, 0.2)
ax[0].axvline(lim_kait, label = label, color='grey',
linestyle='dashed', linewidth=3)
ax[0].plot(x, y, linestyle='none', marker = 'o', color='blue',
markersize =12)
ax[0].plot(histo_region_kait[1][index_kait+1:-1],
mag_lim_kait[index_kait+1:],
linestyle='none', marker = 'o', color='blue', markersize =12)
ax[0].axhline(threshold, label = r'threshold', color='black',
linestyle='-', linewidth=3)
ax[0].set_title(r'$Limit$ $Magnitude$ $KAIT$', fontsize=22)
ax[0].set_xlabel(r'$Magnitude$', fontsize=22)
ax[0].set_ylabel(r'$ratio$ $of$ $detected$ $objects$', fontsize=22)
ax[0].legend(fontsize=20)
ax[0].xaxis.set_tick_params(labelsize= 22)
ax[0].yaxis.set_tick_params(labelsize= 22)
mask = (mag_lim_iris[:index_iris+1]==0.)
y = np.ma.array(mag_lim_iris[:index_iris+1], mask=mask).compressed()
x = np.ma.array(region_iris[1][:index_iris+1], mask=mask).compressed()
# label = r'$Limit$ $Magnitude$ = ' + '%.2f' %lim_iris
label = r'$Limit$ $Magnitude$ = {:.2f} $\pm$ {}'.format(lim_iris, 0.2)
ax[1].axvline(lim_iris, label = label, color='grey',
linestyle='dashed', linewidth=3)
ax[1].plot(x, y, linestyle='none', marker = 'o', color='blue',
markersize =12)
ax[1].plot(region_iris[1][index_iris+1:-1],
mag_lim_iris[index_iris+1:],
linestyle='none', marker = 'o', color='blue', markersize =12)
ax[1].axhline(threshold, label = r'threshold', color='black',
linestyle='-', linewidth=3)
ax[1].set_title(r'$Limit$ $Magnitude$ $Ratio$ $IRiS$', fontsize=22)
ax[1].set_xlabel(r'$Magnitude$', fontsize=22)
ax[1].legend(fontsize=18)
ax[1].xaxis.set_tick_params(labelsize= 22)
ax[1].yaxis.set_tick_params(labelsize= 22)
do_star_ref = False
if do_star_ref:
fig, main_axes = plt.subplots(2,1)
plt.gcf().subplots_adjust(left = 0.1, bottom = 0.1,
right = 0.9, top = 0.9,
wspace = 2.5, hspace = 0.5)
main_axes[0].errorbar(kait_table['time'],
kait_table['star_ref_magnitude'],
yerr=kait_table['star_ref_error_tot'],
label= r'$B$ $band$ $KAIT$',
marker = 'x', linestyle = 'none',
color = 'red', ms=5)
main_axes[0].errorbar(kait_table['time'],
kait_table['star_ref_magnitude_cata'],
yerr=kait_table['star_ref_error_cata'],
label= r'$Catalog$ $Magnitude = $'+ '%.2f' %kait_table['star_ref_magnitude_cata'][0],
linestyle = '-',
color = 'black', ms=5)
mask = (kait_table['star_ref_error_tot'] < 0.3)
kait_table_clean = kait_table[mask]
main_axes[0].errorbar(kait_table_clean['time'],
kait_table_clean['star_ref_magnitude'],
yerr=kait_table_clean['star_ref_error_tot'],
label= r'$B$ $band$ $KAIT$ $Cleaned$',
marker = 'd', linestyle = 'none',
color = 'blue', ms=7)
main_axes[0].set_xlabel(r'$Time$ $-$ $MJD$ $58285.441$ $(days)$')
main_axes[0].set_ylabel(r'$Magnitude$')
main_axes[0].set_title(r'$Lightcurve$ $Star$ $KAIT$')
main_axes[0].legend()
main_axes[0].invert_yaxis()