def variable_aper_phot(target,
centroided_sources,
multiplicative_factors,
an_in=12.,
an_out=30.,
plots=False,
gain=8.21,
qe=0.9,
plate_scale=0.579):
pines_path = pines_dir_check()
short_name = short_name_creator(target)
#Remove any leading/trailing spaces in the column names.
centroided_sources.columns = centroided_sources.columns.str.lstrip()
centroided_sources.columns = centroided_sources.columns.str.rstrip()
#Get list of reduced files for target.
reduced_path = pines_path / ('Objects/' + short_name + '/reduced')
reduced_filenames = natsort.natsorted(
[x.name for x in reduced_path.glob('*red.fits')])
reduced_files = np.array([reduced_path / i for i in reduced_filenames])
#Get source names.
source_names = get_source_names(centroided_sources)
#Get seeing.
seeing = np.array(centroided_sources['Seeing'])
#Loop over multiplicative factors
for i in range(len(multiplicative_factors)):
fact = multiplicative_factors[i]
print(
'Doing variable aperture photometry for {}, multiplicative seeing factor = {}, inner annulus radius = {} pix, outer annulus radius = {} pix.'
.format(target, fact, an_in, an_out))
#Declare a new dataframe to hold the information for all targets for this aperture.
columns = [
'Filename', 'Time UT', 'Time JD UTC', 'Time BJD TDB', 'Airmass',
'Seeing'
]
for j in range(0, len(source_names)):
columns.append(source_names[j] + ' Flux')
columns.append(source_names[j] + ' Flux Error')
columns.append(source_names[j] + ' Background')
columns.append(source_names[j] + ' Interpolation Flag')
var_df = pd.DataFrame(index=range(len(reduced_files)), columns=columns)
output_filename = pines_path / (
'Objects/' + short_name + '/aper_phot/' + short_name +
'_variable_aper_phot_' + str(float(fact)) + '_seeing_factor.csv')
#Loop over all images.
pbar = ProgressBar()
for j in pbar(range(len(reduced_files))):
data = fits.open(reduced_files[j])[0].data
#Read in some supporting information.
log_path = pines_path / (
'Logs/' + reduced_files[j].name.split('.')[0] + '_log.txt')
log = pines_log_reader(log_path)
log_ind = np.where(
log['Filename'] == reduced_files[j].name.split('_')[0] +
'.fits')[0][0]
header = fits.open(reduced_files[j])[0].header
date_obs = header['DATE-OBS']
#Catch a case that can cause datetime strptime to crash; Mimir headers sometimes have DATE-OBS with seconds specified as 010.xx seconds, when it should be 10.xx seconds.
if len(date_obs.split(':')[-1].split('.')[0]) == 3:
date_obs = date_obs.split(':')[0] + ':' + date_obs.split(
':')[1] + ':' + date_obs.split(':')[-1][1:]
if date_obs.split(':')[-1] == '60.00':
date_obs = date_obs.split(':')[0] + ':' + str(
int(date_obs.split(':')[1]) + 1) + ':00.00'
#Keep a try/except clause here in case other unknown DATE-OBS formats pop up.
try:
date = datetime.datetime.strptime(date_obs,
'%Y-%m-%dT%H:%M:%S.%f')
except:
print(
'Header DATE-OBS format does not match the format code in strptime! Inspect/correct the DATE-OBS value.'
)
pdb.set_trace()
#Get the closest date master_dark_stddev image for this exposure time.
#We'll use this to measure read noise and dark current.
date_str = date_obs.split('T')[0].replace('-', '')
master_dark_stddev = master_dark_stddev_chooser(
pines_path / ('Calibrations/Darks/Master Darks Stddev/'),
header)
days = date.day + hmsm_to_days(date.hour, date.minute, date.second,
date.microsecond)
jd = date_to_jd(date.year, date.month, days)
var_df['Filename'][j] = reduced_files[j].name
var_df['Time UT'][j] = header['DATE-OBS']
var_df['Time JD UTC'][j] = jd
var_df['Time BJD TDB'][j] = jd_utc_to_bjd_tdb(
jd, header['TELRA'], header['TELDEC'])
var_df['Airmass'][j] = header['AIRMASS']
var_df['Seeing'][j] = log['X seeing'][np.where(
log['Filename'] == reduced_files[j].name.split('_')[0] +
'.fits')[0][0]]
#If the shift quality has been flagged, skip this image.
if log['Shift quality flag'].iloc[log_ind] == 1:
continue
#Get the source positions in this image.
positions = []
for k in range(len(source_names)):
positions.append(
(centroided_sources[source_names[k] + ' Image X'][j],
centroided_sources[source_names[k] + ' Image Y'][j]))
#Create an aperture centered on this position with radius (in pixels) of (seeing*multiplicative_factor[j])/plate_scale.
try:
apertures = CircularAperture(positions,
r=(seeing[j] * fact) /
plate_scale)
except:
pdb.set_trace()
#Create an annulus centered on this position.
annuli = CircularAnnulus(positions, r_in=an_in, r_out=an_out)
photometry_tbl = iraf_style_photometry(apertures, annuli,
data * gain,
master_dark_stddev * gain,
header, var_df['Seeing'][j])
for k in range(len(photometry_tbl)):
var_df[source_names[k] +
' Flux'][j] = photometry_tbl['flux'][k]
var_df[source_names[k] +
' Flux Error'][j] = photometry_tbl['flux_error'][k]
var_df[source_names[k] +
' Background'][j] = photometry_tbl['background'][k]
var_df[source_names[k] + ' Interpolation Flag'][j] = int(
photometry_tbl['interpolation_flag'][k])
#Write output to file.
print(
'Saving multiplicative factor = {} variable aperture photometry output to {}.'
.format(fact, output_filename))
print('')
with open(output_filename, 'w') as f:
for j in range(len(var_df)):
#Write in the header.
if j == 0:
f.write(
'{:>21s}, {:>22s}, {:>17s}, {:>17s}, {:>7s}, {:>7s}, '.
format('Filename', 'Time UT', 'Time JD UTC',
'Time BJD TDB', 'Airmass', 'Seeing'))
for k in range(len(source_names)):
if k != len(source_names) - 1:
f.write(
'{:>22s}, {:>28s}, {:>28s}, {:>34s}, '.format(
source_names[k] + ' Flux',
source_names[k] + ' Flux Error',
source_names[k] + ' Background',
source_names[k] + ' Interpolation Flag'))
else:
f.write(
'{:>22s}, {:>28s}, {:>28s}, {:>34s}\n'.format(
source_names[k] + ' Flux',
source_names[k] + ' Flux Error',
source_names[k] + ' Background',
source_names[k] + ' Interpolation Flag'))
#Write in Filename, Time UT, Time JD, Airmass, Seeing values.
format_string = '{:21s}, {:22s}, {:17.9f}, {:17.9f}, {:7.2f}, {:7.1f}, '
#If the seeing value for this image is 'nan' (a string), convert it to a float.
#TODO: Not sure why it's being read in as a string, fix that.
if type(var_df['Seeing'][j]) == str:
var_df['Seeing'][j] = float(var_df['Seeing'][j])
#Do a try/except clause for writeout, in case it breaks in the future.
try:
f.write(
format_string.format(var_df['Filename'][j],
var_df['Time UT'][j],
var_df['Time JD UTC'][j],
var_df['Time BJD TDB'][j],
var_df['Airmass'][j],
var_df['Seeing'][j]))
except:
print(
'Writeout failed! Inspect quantities you are trying to write out.'
)
pdb.set_trace()
#Write in Flux, Flux Error, and Background values for every source.
for i in range(len(source_names)):
if i != len(source_names) - 1:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34d}, '
else:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34d}\n'
try:
f.write(
format_string.format(
var_df[source_names[i] + ' Flux'][j],
var_df[source_names[i] + ' Flux Error'][j],
var_df[source_names[i] + ' Background'][j],
var_df[source_names[i] +
' Interpolation Flag'][j]))
except:
if i != len(source_names) - 1:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34f}, '
else:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34f}\n'
f.write(
format_string.format(
var_df[source_names[i] + ' Flux'][j],
var_df[source_names[i] + ' Flux Error'][j],
var_df[source_names[i] + ' Background'][j],
var_df[source_names[i] +
' Interpolation Flag'][j]))
print('')
return
def fixed_aper_phot(target,
centroided_sources,
ap_radii,
an_in=12.,
an_out=30.,
plots=False,
gain=8.21,
qe=0.9):
'''Authors:
Patrick Tamburo, Boston University, June 2020
Purpose:
Performs *fixed* aperture photometry on a set of reduced images given dataframe of source positions.
The iraf_style_photometry, compute_phot_error, perture_stats_tbl, and calc_aperture_mmm routines are from Varun Bajaj on github:
https://github.com/spacetelescope/wfc3_photometry/blob/master/photometry_tools/photometry_with_errors.py.
Inputs:
target (str): The target's full 2MASS name.
sources (pandas dataframe): List of source names, x and y positions in every image.
ap_radii (list of floats): List of aperture radii in pixels for which aperture photometry wil be performed.
an_in (float, optional): The inner radius of the annulus used to estimate background, in pixels.
an_out (float, optional): The outer radius of the annulus used to estimate background, in pixels.
plots (bool, optional): Whether or not to output surface plots. Images output to aper_phot directory within the object directory.
gain (float, optional): The gain of the detector in e-/ADU.
qe (float, optional): The quantum efficiency of the detector.
Outputs:
Saves aperture photometry csv to PINES_analysis_toolkit/Objects/short_name/aper_phot/ for each aperture.
TODO:
'''
pines_path = pines_dir_check()
short_name = short_name_creator(target)
#Remove any leading/trailing spaces in the column names.
centroided_sources.columns = centroided_sources.columns.str.lstrip()
centroided_sources.columns = centroided_sources.columns.str.rstrip()
#Get list of reduced files for target.
reduced_path = pines_path / ('Objects/' + short_name + '/reduced')
reduced_filenames = natsort.natsorted(
[x.name for x in reduced_path.glob('*red.fits')])
reduced_files = np.array([reduced_path / i for i in reduced_filenames])
#source_names = natsort.natsorted(list(set([i.replace('X','').replace('Y','').replace('Centroid Warning','').strip() for i in centroided_sources.keys() if i != 'Filename'])))
source_names = get_source_names(centroided_sources)
#Create output plot directories for each source.
if plots:
#Camera angles for surface plots
azim_angles = np.linspace(0, 360 * 1.5, len(reduced_files)) % 360
elev_angles = np.zeros(len(azim_angles)) + 25
for name in source_names:
#If the folders are already there, delete them.
source_path = (
pines_path /
('Objects/' + short_name + '/aper_phot/' + name + '/'))
if source_path.exists():
shutil.rmtree(source_path)
#Create folders.
os.mkdir(source_path)
#Loop over all aperture radii.
for ap in ap_radii:
print(
'Doing fixed aperture photometry for {}, aperture radius = {:1.1f} pix, inner annulus radius = {} pix, outer annulus radius = {} pix.'
.format(target, ap, an_in, an_out))
#Declare a new dataframe to hold the information for all targets for this aperture.
columns = [
'Filename', 'Time UT', 'Time JD UTC', 'Time BJD TDB', 'Airmass',
'Seeing'
]
for i in range(0, len(source_names)):
columns.append(source_names[i] + ' Flux')
columns.append(source_names[i] + ' Flux Error')
columns.append(source_names[i] + ' Background')
columns.append(source_names[i] + ' Interpolation Flag')
ap_df = pd.DataFrame(index=range(len(reduced_files)), columns=columns)
output_filename = pines_path / (
'Objects/' + short_name + '/aper_phot/' + short_name +
'_fixed_aper_phot_{:1.1f}_pix_radius.csv'.format(float(ap)))
#Loop over all images.
pbar = ProgressBar()
for j in pbar(range(len(reduced_files))):
data = fits.open(reduced_files[j])[0].data
#Read in some supporting information.
log_path = pines_path / (
'Logs/' + reduced_files[j].name.split('.')[0] + '_log.txt')
log = pines_log_reader(log_path)
log_ind = np.where(
log['Filename'] == reduced_files[j].name.split('_')[0] +
'.fits')[0][0]
header = fits.open(reduced_files[j])[0].header
date_obs = header['DATE-OBS']
#Catch a case that can cause datetime strptime to crash; Mimir headers sometimes have DATE-OBS with seconds specified as 010.xx seconds, when it should be 10.xx seconds.
if len(date_obs.split(':')[-1].split('.')[0]) == 3:
date_obs = date_obs.split(':')[0] + ':' + date_obs.split(
':')[1] + ':' + date_obs.split(':')[-1][1:]
if date_obs.split(':')[-1] == '60.00':
date_obs = date_obs.split(':')[0] + ':' + str(
int(date_obs.split(':')[1]) + 1) + ':00.00'
#Keep a try/except clause here in case other unknown DATE-OBS formats pop up.
try:
date = datetime.datetime.strptime(date_obs,
'%Y-%m-%dT%H:%M:%S.%f')
except:
print(
'Header DATE-OBS format does not match the format code in strptime! Inspect/correct the DATE-OBS value.'
)
pdb.set_trace()
#Get the closest date master_dark_stddev image for this exposure time.
#We'll use this to measure read noise and dark current.
date_str = date_obs.split('T')[0].replace('-', '')
master_dark_stddev = master_dark_stddev_chooser(
pines_path / ('Calibrations/Darks/Master Darks Stddev/'),
header)
days = date.day + hmsm_to_days(date.hour, date.minute, date.second,
date.microsecond)
jd = date_to_jd(date.year, date.month, days)
ap_df['Filename'][j] = reduced_files[j].name
ap_df['Time UT'][j] = header['DATE-OBS']
ap_df['Time JD UTC'][j] = jd
ap_df['Time BJD TDB'][j] = jd_utc_to_bjd_tdb(
jd, header['TELRA'], header['TELDEC']
) #Using the telescope ra and dec should be accurate enough for our purposes
ap_df['Airmass'][j] = header['AIRMASS']
ap_df['Seeing'][j] = log['X seeing'][log_ind]
#If the shift quality has been flagged, skip this image.
if log['Shift quality flag'].iloc[log_ind] == 1:
continue
#Get the source positions in this image.
positions = []
for i in range(len(source_names)):
positions.append((float(centroided_sources[source_names[i] +
' Image X'][j]),
float(centroided_sources[source_names[i] +
' Image Y'][j])))
#Create an aperture centered on this position with radius = ap.
try:
apertures = CircularAperture(positions, r=ap)
except:
pdb.set_trace()
#Create an annulus centered on this position.
annuli = CircularAnnulus(positions, r_in=an_in, r_out=an_out)
photometry_tbl = iraf_style_photometry(apertures, annuli,
data * gain,
master_dark_stddev * gain,
header, ap_df['Seeing'][j])
for i in range(len(photometry_tbl)):
ap_df[source_names[i] + ' Flux'][j] = photometry_tbl['flux'][i]
ap_df[source_names[i] +
' Flux Error'][j] = photometry_tbl['flux_error'][i]
ap_df[source_names[i] +
' Background'][j] = photometry_tbl['background'][i]
ap_df[source_names[i] + ' Interpolation Flag'][j] = int(
photometry_tbl['interpolation_flag'][i])
#Make surface plots.
if plots:
for i in range(len(photometry_tbl)):
x_p = photometry_tbl['X'][i]
y_p = photometry_tbl['Y'][i]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
xx, yy = np.meshgrid(
np.arange(int(x_p) - 10,
int(x_p) + 10 + 1),
np.arange(int(y_p) - 10,
int(y_p) + 10 + 1))
theta = np.linspace(0, 2 * np.pi, 201)
y_circ = ap * np.cos(theta) + y_p
x_circ = ap * np.sin(theta) + x_p
vmin = np.nanmedian(data[yy, xx])
vmax = vmin + 2.5 * np.nanstd(data[yy, xx])
ax.plot_surface(xx,
yy,
data[yy, xx],
cmap=cm.viridis,
alpha=0.8,
rstride=1,
cstride=1,
edgecolor='k',
lw=0.2,
vmin=vmin,
vmax=vmax)
current_z = ax.get_zlim()
ax.set_zlim(current_z[0] - 150, current_z[1])
current_z = ax.get_zlim()
cset = ax.contourf(xx,
yy,
data[yy, xx],
zdir='z',
offset=current_z[0],
cmap=cm.viridis)
ax.plot(x_circ,
y_circ,
np.zeros(len(x_circ)) + current_z[0],
color='r',
lw=2,
zorder=100)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Counts')
ax.set_title('SURFACE DIAGNOSTIC PLOT, ' + ', Ap. = ' +
str(ap) + '\n' + source_names[i] + ', ' +
reduced_files[j].name + ' (image ' +
str(j + 1) + ' of ' +
str(len(reduced_files)) + ')')
ax.view_init(elev=elev_angles[j], azim=azim_angles[j])
plot_output_path = (
pines_path /
('Objects/' + short_name + '/aper_phot/' +
source_names[i] + '/' + str(j).zfill(4) + '.jpg'))
plt.tight_layout()
plt.savefig(plot_output_path)
plt.close()
#Write output to file.
print('Saving ap = {:1.1f} aperture photometry output to {}.'.format(
ap, output_filename))
print('')
with open(output_filename, 'w') as f:
for j in range(len(ap_df)):
#Write in the header.
if j == 0:
f.write(
'{:>21s}, {:>22s}, {:>17s}, {:>17s}, {:>7s}, {:>7s}, '.
format('Filename', 'Time UT', 'Time JD UTC',
'Time BJD TDB', 'Airmass', 'Seeing'))
for i in range(len(source_names)):
if i != len(source_names) - 1:
f.write(
'{:>22s}, {:>28s}, {:>28s}, {:>34s}, '.format(
source_names[i] + ' Flux',
source_names[i] + ' Flux Error',
source_names[i] + ' Background',
source_names[i] + ' Interpolation Flag'))
else:
f.write(
'{:>22s}, {:>28s}, {:>28s}, {:>34s}\n'.format(
source_names[i] + ' Flux',
source_names[i] + ' Flux Error',
source_names[i] + ' Background',
source_names[i] + ' Interpolation Flag'))
#Write in Filename, Time UT, Time JD, Airmass, Seeing values.
format_string = '{:21s}, {:22s}, {:17.9f}, {:17.9f}, {:7.2f}, {:7.1f}, '
#If the seeing value for this image is 'nan' (a string), convert it to a float.
#TODO: Not sure why it's being read in as a string, fix that.
if type(ap_df['Seeing'][j]) == str:
ap_df['Seeing'][j] = float(ap_df['Seeing'][j])
#Do a try/except clause for writeout, in case it breaks in the future.
try:
f.write(
format_string.format(ap_df['Filename'][j],
ap_df['Time UT'][j],
ap_df['Time JD UTC'][j],
ap_df['Time BJD TDB'][j],
ap_df['Airmass'][j],
ap_df['Seeing'][j]))
except:
print(
'Writeout failed! Inspect quantities you are trying to write out.'
)
pdb.set_trace()
#Write in Flux, Flux Error, and Background values for every source.
for i in range(len(source_names)):
if i != len(source_names) - 1:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34d}, '
else:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34d}\n'
try:
f.write(
format_string.format(
ap_df[source_names[i] + ' Flux'][j],
ap_df[source_names[i] + ' Flux Error'][j],
ap_df[source_names[i] + ' Background'][j],
ap_df[source_names[i] +
' Interpolation Flag'][j]))
except:
if i != len(source_names) - 1:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34f}, '
else:
format_string = '{:22.5f}, {:28.5f}, {:28.5f}, {:34f}\n'
f.write(
format_string.format(
ap_df[source_names[i] + ' Flux'][j],
ap_df[source_names[i] + ' Flux Error'][j],
ap_df[source_names[i] + ' Background'][j],
ap_df[source_names[i] +
' Interpolation Flag'][j]))
print('')
return
def corr_all_sources_plot(target):
print('Generating corrected flux plots for all sources...\n')
pines_path = pines_dir_check()
short_name = short_name_creator(target)
analysis_path = pines_path / ('Objects/' + short_name + '/analysis/')
photometry_path = pines_path / ('Objects/' + short_name + '/aper_phot/')
#Grab the data for the best aperture.
if os.path.exists(analysis_path / ('optimal_aperture.txt')):
with open(analysis_path / ('optimal_aperture.txt'), 'r') as f:
best_ap = f.readlines()[0].split(': ')[1].split('\n')[0]
phot_type = best_ap.split('_')[1]
if phot_type == 'fixed':
s = 'r'
elif phot_type == 'variable':
s = 'f'
else:
raise RuntimeError(
'No optimal_aperture.txt file for {}.\nUsing first photometry file in {}.'
.format(target, phot_path))
filename = short_name.replace(
' ', '') + '_' + phot_type + '_aper_phot_' + s + '=' + best_ap.split(
'_')[0] + '_nightly_weighted_lc.csv'
best_phot_path = analysis_path / ('aper_phot_analysis/' + best_ap + '/')
output_path = best_phot_path / ('corr_ref_plots/')
if not os.path.exists(output_path):
os.mkdir(output_path)
data = pines_log_reader(best_phot_path / filename)
ref_names = get_source_names(data)[1:]
num_refs = len(ref_names)
times = np.array(data['Time BJD TDB'])
night_inds = night_splitter(times)
num_nights = len(night_inds)
cmap = plt.get_cmap('viridis')
for i in range(num_refs + 1):
fig, ax = plt.subplots(nrows=1,
ncols=num_nights,
figsize=(17, 5),
sharey=True)
plt.subplots_adjust(left=0.07, wspace=0.05, top=0.92, bottom=0.17)
if i == 0:
color = cmap(0)
flux = np.array(data[short_name + ' Corrected Flux'],
dtype='float64')
flux_err = np.array(data[short_name + ' Corrected Flux Error'],
dtype='float64')
title = short_name
output_name = short_name + '_corrected_flux.png'
else:
color = cmap(95)
ref_name = ref_names[i - 1]
flux = np.array(data[ref_name + ' Corrected Flux'],
dtype='float64')
flux_err = np.array(data[ref_name + ' Corrected Flux Error'],
dtype='float64')
if i < 10:
num = '0' + str(i)
else:
num = str(i)
output_name = 'reference_' + num + '_corrected_flux.png'
for j in range(num_nights):
if i != 0:
weight = np.array(data[ref_name +
' ALC Weight'])[night_inds[j]][0]
title = ref_name.replace(
'erence', '.') + ', weight = {:1.3f}'.format(weight)
if j == 0:
ax[j].set_ylabel('Normalized Flux', fontsize=20)
inds = night_inds[j]
block_inds = block_splitter(times[inds])
binned_time = []
binned_flux = []
binned_err = []
for k in range(len(block_inds)):
binned_time.append(np.nanmean(times[inds][block_inds[k]]))
binned_flux.append(np.nanmean(flux[inds][block_inds[k]]))
binned_err.append(
np.nanstd(flux[inds][block_inds[k]]) /
np.sqrt(len(block_inds[k])))
ax[j].plot(times[inds],
flux[inds],
color=color,
linestyle='',
marker='.',
alpha=0.25)
ax[j].errorbar(binned_time,
binned_flux,
binned_err,
color=color,
linestyle='',
marker='o',
ms=10,
mfc='none',
mew=2)
ax[j].set_xlabel('Time (BJD$_{TDB}$)', fontsize=20)
ax[j].tick_params(labelsize=16)
ax[j].axhline(1, color='k', alpha=0.7, lw=1, zorder=0)
ax[j].grid(alpha=0.2)
ax[j].set_title(title, fontsize=20, color=color)
ax[j].set_ylim(0.9, 1.1)
plt.savefig(output_path / output_name, dpi=300)
plt.close()
def seeing_plot(target, centroided_sources):
pines_path = pines_dir_check()
short_name = short_name_creator(target)
#Get plot style parameters.
title_size, axis_title_size, axis_ticks_font_size, legend_font_size = plot_style(
)
#Get list of souce names in the centroid output.
source_names = get_source_names(centroided_sources)
centroided_sources.columns = centroided_sources.keys().str.strip()
#Read in times and seeing values.
times_full = np.array(centroided_sources['Time (JD UTC)'])
seeing = np.array(centroided_sources['Seeing'])
#Split up times by nights.
night_inds = night_splitter(times_full)
num_nights = len(night_inds)
times_nights = [times_full[night_inds[i]] for i in range(num_nights)]
standard_x = standard_x_range(times_nights)
fig, ax = plt.subplots(nrows=1,
ncols=num_nights,
figsize=(17, 5),
sharey=True)
for i in range(num_nights):
if i == 0:
ax[i].set_ylabel('Seeing (")', fontsize=axis_title_size)
inds = night_inds[i]
ax[i].plot(times_nights[i],
seeing[inds],
marker='.',
linestyle='',
alpha=0.3,
label='Raw seeing')
ax[i].tick_params(labelsize=axis_ticks_font_size)
ax[i].set_xlabel('Time (JD UTC)', fontsize=axis_title_size)
ax[i].grid(alpha=0.2)
ax[i].set_xlim(
np.mean(times_nights[i]) - standard_x / 2,
np.mean(times_nights[i]) + standard_x / 2)
#bin
block_inds = block_splitter(times_nights[i])
block_x = np.zeros(len(block_inds))
block_y = np.zeros(len(block_inds))
block_y_err = np.zeros(len(block_inds))
for j in range(len(block_inds)):
block_x[j] = np.mean(times_nights[i][block_inds[j]])
block_y[j] = np.mean(seeing[inds][block_inds[j]])
block_y_err[j] = np.std(seeing[inds][block_inds[j]]) / np.sqrt(
len(seeing[inds][block_inds[j]]))
ax[i].errorbar(block_x,
block_y,
block_y_err,
marker='o',
linestyle='',
color='tab:blue',
ms=8,
mfc='none',
mew=2,
label='Bin seeing')
#Interpolate each night's seeing.
fit_times = np.linspace(block_x[0], block_x[-1], 1000)
interp = CubicSpline(block_x, block_y)
interp_fit = interp(fit_times)
ax[i].plot(fit_times,
interp_fit,
color='r',
lw=2,
zorder=0,
alpha=0.7,
label='CS Interp.')
ax[i].legend(bbox_to_anchor=(1.01, 0.5), fontsize=legend_font_size)
plt.suptitle(short_name + ' Seeing Measurements', fontsize=title_size)
plt.subplots_adjust(left=0.07, wspace=0.05, top=0.92, bottom=0.17)
output_filename = pines_path / ('Objects/' + short_name +
'/analysis/diagnostic_plots/' +
short_name + '_seeing.png')
plt.savefig(output_filename, dpi=300)
return
def absolute_image_position_plot(target, centroided_sources):
pines_path = pines_dir_check()
short_name = short_name_creator(target)
#Get plot style parameters.
title_size, axis_title_size, axis_ticks_font_size, legend_font_size = plot_style(
)
#Get list of souce names in the centroid output.
source_names = get_source_names(centroided_sources)
centroided_sources.columns = centroided_sources.keys().str.strip()
#Get times from the centroid output and split them by night.
times_full = np.array(centroided_sources['Time (JD UTC)'])
night_inds = night_splitter(times_full)
num_nights = len(night_inds)
times_nights = [times_full[night_inds[i]] for i in range(num_nights)]
standard_x = standard_x_range(times_nights)
source = source_names[0]
fig, ax = plt.subplots(nrows=2,
ncols=num_nights,
figsize=(17, 9),
sharex='col',
sharey='row')
plt.subplots_adjust(left=0.07,
hspace=0.05,
wspace=0.05,
top=0.92,
bottom=0.17)
for j in range(num_nights):
if j == 0:
ax[0, j].set_ylabel('Image X', fontsize=axis_title_size)
ax[1, j].set_ylabel('Image Y', fontsize=axis_title_size)
inds = night_inds[j]
times = times_nights[j]
absolute_x = np.array(centroided_sources[source + ' Image X'][inds],
dtype='float')
absolute_y = np.array(centroided_sources[source + ' Image Y'][inds],
dtype='float')
ax[0, j].plot(times,
absolute_x,
marker='.',
linestyle='',
alpha=0.3,
color='tab:blue',
label='Raw x')
ax[1, j].plot(times,
absolute_y,
marker='.',
linestyle='',
alpha=0.3,
color='tab:orange',
label='Raw y')
#bin
block_inds = block_splitter(times)
block_times = np.zeros(len(block_inds))
block_x = np.zeros(len(block_inds))
block_x_err = np.zeros(len(block_inds))
block_y = np.zeros(len(block_inds))
block_y_err = np.zeros(len(block_inds))
for k in range(len(block_inds)):
try:
block_times[k] = np.nanmean(times[block_inds[k]])
block_x[k] = np.nanmean(absolute_x[block_inds[k]])
block_x_err[k] = np.nanstd(
absolute_x[block_inds[k]]) / np.sqrt(
len(absolute_x[block_inds[k]]))
block_y[k] = np.nanmean(absolute_y[block_inds[k]])
block_y_err[k] = np.nanstd(
absolute_y[block_inds[k]]) / np.sqrt(
len(absolute_y[block_inds[k]]))
except:
pdb.set_trace()
ax[0, j].errorbar(block_times,
block_x,
block_x_err,
marker='o',
linestyle='',
color='tab:blue',
ms=8,
mfc='none',
mew=2,
label='Bin x')
ax[1, j].errorbar(block_times,
block_y,
block_y_err,
marker='o',
linestyle='',
color='tab:orange',
ms=8,
mfc='none',
mew=2,
label='Bin y')
ax[0, j].tick_params(labelsize=axis_ticks_font_size)
ax[1, j].tick_params(labelsize=axis_ticks_font_size)
ax[0, j].grid(alpha=0.2)
ax[1, j].grid(alpha=0.2)
ax[1, j].set_xlabel('Time (JD UTC)', fontsize=axis_title_size)
if j == num_nights - 1:
ax[0, j].legend(bbox_to_anchor=(1.29, 1),
fontsize=legend_font_size)
ax[1, j].legend(bbox_to_anchor=(1.29, 1),
fontsize=legend_font_size)
plt.suptitle(source + ' Image Centroid Positions', fontsize=title_size)
#plt.subplots_adjust(left=0.07, hspace=0.05, wspace=0.05, top=0.92, bottom=0.08, right=0.85)
#ax.legend(bbox_to_anchor=(1.01, 1), fontsize=14)
ax[0, j].set_xlim(
np.mean(times) - standard_x / 2,
np.mean(times) + standard_x / 2)
ax[1, j].set_xlim(
np.mean(times) - standard_x / 2,
np.mean(times) + standard_x / 2)
output_filename = pines_path / ('Objects/' + short_name +
'/analysis/diagnostic_plots/' + source +
'_image_positions.png')
plt.savefig(output_filename, dpi=300)
return
def relative_cutout_position_plot(target, centroided_sources):
pines_path = pines_dir_check()
short_name = short_name_creator(target)
#Get plot style parameters.
title_size, axis_title_size, axis_ticks_font_size, legend_font_size = plot_style(
)
#Get list of souce names in the centroid output.
source_names = get_source_names(centroided_sources)
centroided_sources.columns = centroided_sources.keys().str.strip()
#Get times from the centroid output and split them by night.
times_full = np.array(centroided_sources['Time (JD UTC)'])
night_inds = night_splitter(times_full)
num_nights = len(night_inds)
times_nights = [times_full[night_inds[i]] for i in range(num_nights)]
standard_x = standard_x_range(times_nights)
#Get the box size (I don't like that this is being determined by using the mean of the data...output it from centroider?)
box_w = int(
np.round(
2 * np.nanmean(
np.array(centroided_sources['Reference 1 Cutout X'],
dtype='float')), 0))
fig, ax = plt.subplots(nrows=2,
ncols=num_nights,
figsize=(17, 9),
sharey=True)
plt.subplots_adjust(left=0.07,
hspace=0.05,
wspace=0.05,
top=0.92,
bottom=0.17)
markers = ['+', 'x', '*', 'X']
for j in range(num_nights):
inds = night_inds[j]
if j == 0:
ax[0, j].set_ylabel('Cutout X Position', fontsize=axis_title_size)
ax[1, j].set_ylabel('Cutout Y Position', fontsize=axis_title_size)
for i in range(len(source_names)):
cutout_x = np.array(centroided_sources[source_names[i] +
' Cutout X'][inds],
dtype='float')
cutout_y = np.array(centroided_sources[source_names[i] +
' Cutout Y'][inds],
dtype='float')
if i == 0:
marker = 'o'
label = 'Target'
else:
marker = markers[(i - 1) % len(markers)]
label = 'Ref. ' + str(i)
ax[0, j].plot(times_nights[j],
cutout_x,
marker=marker,
label=label,
linestyle='')
ax[1, j].plot(times_nights[j],
cutout_y,
marker=marker,
linestyle='')
ax[0, j].tick_params(labelsize=axis_ticks_font_size)
ax[0, j].set_xticklabels([])
ax[0, j].axhline(box_w / 2,
zorder=0,
color='r',
label='Center pix.',
lw=2)
ax[0, j].set_xlim(
np.mean(times_nights[j]) - standard_x / 2,
np.mean(times_nights[j]) + standard_x / 2)
ax[0, j].grid(alpha=0.2)
ax[1, j].tick_params(labelsize=axis_ticks_font_size)
ax[1, j].axhline(box_w / 2,
zorder=0,
color='r',
label='Center pix.',
lw=2)
ax[1, j].set_xlim(
np.mean(times_nights[j]) - standard_x / 2,
np.mean(times_nights[j]) + standard_x / 2)
ax[1, j].set_xlabel('Time (JD UTC)', fontsize=axis_title_size)
ax[1, j].grid(alpha=0.2)
if j == num_nights - 1:
ax[0, j].legend(bbox_to_anchor=(1.01, 1.0),
fontsize=legend_font_size)
plt.suptitle(short_name + ' Cutout Centroid Positions',
fontsize=title_size)
output_filename = pines_path / ('Objects/' + short_name +
'/analysis/diagnostic_plots/' +
short_name + '_cutout_positions.png')
plt.savefig(output_filename, dpi=300)
return