def airmass_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.
centroided_sources.columns = centroided_sources.keys().str.strip()
airmasses = np.array(centroided_sources['Airmass'])
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)
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('Airmass', fontsize=axis_title_size)
inds = night_inds[i]
ax[i].plot(times_full[inds],
airmasses[inds],
marker='.',
linestyle='',
color='m',
alpha=0.3,
label='Raw airmass')
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_full[inds]) - standard_x / 2,
np.mean(times_full[inds] + standard_x / 2))
#bin
block_inds = block_splitter(times_full[inds])
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_full[inds][block_inds[j]])
block_y[j] = np.mean(airmasses[inds][block_inds[j]])
block_y_err[j] = np.std(airmasses[inds][block_inds[j]]) / np.sqrt(
len(airmasses[inds][block_inds[j]]))
ax[i].errorbar(block_x,
block_y,
block_y_err,
marker='o',
linestyle='',
color='m',
ms=8,
mfc='none',
mew=2,
label='Bin airmass')
#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='c',
lw=2,
zorder=0,
alpha=0.7,
label='CS Interp.')
ax[i].legend(bbox_to_anchor=(1.005, 0.5), fontsize=legend_font_size)
plt.suptitle(short_name + ' Airmass 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 + '_airmasses.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 background_plot(target, centroided_sources, gain=8.21):
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(
)
analysis_path = pines_path / ('Objects/' + short_name + '/analysis')
phot_path = pines_path / ('Objects/' + short_name + '/aper_phot')
phot_files = np.array(natsorted([x for x in phot_path.glob('*.csv')]))
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('_')[0]
ap_list = np.array(
[str(i).split('/')[-1].split('_')[4] for i in phot_files])
best_ap_ind = np.where(ap_list == best_ap)[0][0]
else:
print(
'No optimal_aperture.txt file for {}.\nUsing first photometry file in {}.'
.format(target, phot_path))
best_ap_ind = 0
phot_file = phot_files[best_ap_ind]
phot_df = pines_log_reader(phot_file)
backgrounds = np.array(phot_df[short_name + ' Background'],
dtype='float') / gain
times_full = np.array(phot_df['Time JD'], dtype='float')
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('Background (ADU)', fontsize=axis_title_size)
inds = night_inds[i]
ax[i].plot(times_full[inds],
backgrounds[inds],
marker='.',
linestyle='',
color='tab:orange',
alpha=0.3,
label='Raw bkg.')
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_full[inds]) - standard_x / 2,
np.mean(times_full[inds] + standard_x / 2))
#bin
block_inds = block_splitter(times_full[inds])
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.nanmean(times_full[inds][block_inds[j]])
block_y[j] = np.nanmean(backgrounds[inds][block_inds[j]])
block_y_err[j] = np.nanstd(
backgrounds[inds][block_inds[j]]) / np.sqrt(
len(backgrounds[inds][block_inds[j]]))
block_x = block_x[~np.isnan(block_y)]
block_y_err = block_y_err[~np.isnan(block_y)]
block_y = block_y[~np.isnan(block_y)]
ax[i].errorbar(block_x,
block_y,
block_y_err,
marker='o',
linestyle='',
color='tab:orange',
ms=8,
mfc='none',
mew=2,
label='Bin bkg.')
#Interpolate each night's seeing.
fit_times = np.linspace(block_x[0], block_x[-1], 1000)
try:
interp = CubicSpline(block_x, block_y)
except:
pdb.set_trace()
interp_fit = interp(fit_times)
ax[i].plot(fit_times,
interp_fit,
color='b',
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 + ' Background 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 + '_backgrounds.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