def alignment():
print('Alignment...')
if log.read_local_log('pipeline', 'alignment_complete'):
if askyesno('Overwrite alignment',
'Alignment has been completed, do you want to run again?'):
log.write_local_log('pipeline', False, 'alignment_complete')
test_all_stars = True
try:
all_stars = plc.open_dict('all_stars.pickle')
in_fov = all_stars['in_fov']
all_stars = all_stars['all_stars']
except:
test_all_stars = False
if log.read_local_log('alignment',
'star_psf_x') == 0 or not log.read_local_log(
'pipeline', 'alignment_complete'):
test_all_stars = False
# if all_stars exist and repeat not requested: exit
if test_all_stars and log.read_local_log('pipeline', 'alignment_complete'):
return 0
# continue to alignment
# load parameters
reduction_directory = log.read_local_log('pipeline', 'reduction_directory')
mean_key = log.read_local_log('pipeline_keywords', 'mean_key')
std_key = log.read_local_log('pipeline_keywords', 'std_key')
align_x0_key = log.read_local_log('pipeline_keywords', 'align_x0_key')
align_y0_key = log.read_local_log('pipeline_keywords', 'align_y0_key')
align_u0_key = log.read_local_log('pipeline_keywords', 'align_u0_key')
frame_low_std = log.read_local_log('windows', 'frame_low_std')
frame_upper_std = log.read_local_log('windows', 'frame_upper_std')
bin_fits = int(log.read_local_log('reduction', 'bin_fits'))
burn_limit = int(log.read_local_log('alignment',
'burn_limit')) * bin_fits * bin_fits
shift_tolerance_p = log.read_local_log('alignment', 'shift_tolerance_p')
rotation_tolerance = log.read_local_log('alignment', 'rotation_tolerance')
min_calibration_stars_number = int(
log.read_local_log('alignment', 'min_calibration_stars_number'))
star_std = log.read_local_log('alignment', 'star_std')
science = find_fits_files(os.path.join(reduction_directory, '*'))
def find_all_stars():
stars, psf = plc.find_all_stars(fits[1].data,
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
std_limit=3,
burn_limit=burn_limit,
star_std=star_std,
order_by_flux=False,
progress_pack=(progress_bar,
percent_label))
stars = np.array(stars)
log.write_local_log('alignment', int(max(1, round(max(psf[0],
psf[1])))),
'star_std')
log.write_local_log('alignment', float(max(psf[0], psf[1])),
'star_psf')
log.write_local_log('alignment', float(psf[0]), 'star_psf_x')
log.write_local_log('alignment', float(psf[1]), 'star_psf_y')
all_stars_dict = {'all_stars': stars}
plc.save_dict(all_stars_dict, 'all_stars.pickle')
def align():
star_std = log.read_local_log('alignment', 'star_std')
psf = (log.read_local_log('alignment', 'star_psf_x'),
log.read_local_log('alignment', 'star_psf_y'))
all_stars_dict = plc.open_dict('all_stars.pickle')
stars = np.array(all_stars_dict['all_stars'])
fits = pf.open(science[0], memmap=False)
frame_mean = fits[1].header[mean_key]
frame_std = fits[1].header[std_key]
shift_tolerance = int(
max(len(fits[1].data), len(fits[1].data[0])) *
(shift_tolerance_p / 100.0))
y_length, x_length = fits[1].data.shape
circles_diameter = 0.02 * max(y_length, x_length)
bright_stars = []
std_limit = 30
while len(bright_stars) < 100 and std_limit >= 5.0:
bright_stars = []
for star in stars:
if star[2] + star[3] < 2.0 * burn_limit / 3.0:
if star[-1] > (2 * np.pi *
(std_limit * frame_std) * psf[0] * psf[1]):
alignment_log(
star[-1],
2 * np.pi * (frame_mean + std_limit * frame_std) *
psf[0] * psf[1])
bright_stars.append(star)
std_limit -= 5
stars = sorted(bright_stars, key=lambda x: -x[-1] / (x[-2]**3))
x_ref_position = stars[0][0]
y_ref_position = stars[0][1]
del stars[0]
# take the rest as calibration stars and calculate their polar coordinates relatively to the first
calibration_stars_polar = []
for star in stars:
r_position, u_position = plc.cartesian_to_polar(
star[0], star[1], x_ref_position, y_ref_position)
if r_position > 5 * star_std:
calibration_stars_polar.append([r_position, u_position])
stars = sorted(stars, key=lambda x: -x[-1])
calibration_stars_polar_snr = []
for star in stars:
r_position, u_position = plc.cartesian_to_polar(
star[0], star[1], x_ref_position, y_ref_position)
if r_position > 5 * star_std:
calibration_stars_polar_snr.append([r_position, u_position])
if len(calibration_stars_polar) <= min_calibration_stars_number:
check_num = len(calibration_stars_polar) - 0.5
check_num_snr = len(calibration_stars_polar) - 0.5
else:
check_num = max(min_calibration_stars_number - 0.5,
int(len(calibration_stars_polar)) / 10 - 0.5)
check_num_snr = max(min_calibration_stars_number - 0.5,
int(len(calibration_stars_polar)) / 20 - 0.5)
x0, y0, u0, comparisons = x_ref_position, y_ref_position, 0, calibration_stars_polar
x0, y0, u0, comparisons_snr = x_ref_position, y_ref_position, 0, calibration_stars_polar_snr
fits.close()
# set the looking window and angular step
circle = mpatches.Circle((x0, y0),
circles_diameter,
ec='r',
fill=False)
ax.add_patch(circle)
for ii in comparisons[:10]:
circle = mpatches.Circle((x0 + ii[0] * np.cos(u0 + ii[1]),
y0 + ii[0] * np.sin(u0 + ii[1])),
circles_diameter,
ec='w',
fill=False)
ax.add_patch(circle)
# for each science_file
percent = 0
skip_time = 0
lt0 = time.time()
for counter, science_file in enumerate(science):
if show_progress.exit:
return None
alignment_log('\n', science_file)
label_2.configure(text='Running Alignment: {0}'.format(
science_file.split(os.sep)[-1]))
label_2.update()
label_3.configure(text='')
label_3.update()
fits = pf.open(science_file, mode='update')
ax.cla()
ax.imshow(fits[1].data,
origin='lower',
cmap=cm.Greys_r,
vmin=fits[1].header[mean_key] +
frame_low_std * fits[1].header[std_key],
vmax=fits[1].header[mean_key] +
frame_upper_std * fits[1].header[std_key])
ax.axis('off')
circle = mpatches.Circle((-100, -100),
circles_diameter,
ec='r',
fill=False)
ax.add_patch(circle)
rotation_detected = False
# super fast detection test
alignment_log('Test no shift', x0, y0, u0)
star = plc.find_single_star(fits[1].data,
x0,
y0,
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std)
if star:
tests = []
max_x = star[0]
max_y = star[1]
alignment_log(science_file)
alignment_log('Testing star at: ', max_x, max_y,
', with rotation:', u0)
test = 0
for comp in comparisons:
check_x = int(max_x + comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y + comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y - star_std:check_y +
star_std + 1, check_x -
star_std:check_x + star_std + 1])
check_lim = (fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x, check_y,
', Test: ', test)
if abs(test) > check_num:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
tests.sort()
test, max_x, max_y = tests[-1]
if test < check_num:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# super fast detection test
if show_progress.exit:
return None
delta_skip_time = time.time()
# look for reasonable field shift
if not stars_detected:
alignment_log('Testing small shift')
label_3.configure(text='Testing small shift and rotation')
label_3.update()
stars = plc.find_all_stars(fits[1].data,
x_low=x0 - shift_tolerance,
x_upper=x0 + shift_tolerance,
y_low=y0 - shift_tolerance,
y_upper=y0 + shift_tolerance,
x_centre=x0,
y_centre=y0,
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std)[0]
if show_progress.exit:
return None
if stars:
tests = []
for star in stars:
if show_progress.exit:
return None
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('Testing star at: ', max_x, max_y,
',with rotation:', u0)
test = 0
for comp in comparisons:
check_x = int(max_x +
comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y +
comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y - star_std:check_y +
star_std + 1,
check_x - star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
if test > check_num:
break
tests.sort()
test, max_x, max_y = tests[-1]
if test < check_num:
tests = []
for star in stars:
if show_progress.exit:
return None
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('LOW-SNR Testing star at: ', max_x,
max_y, ', with rotation:', u0)
test = 0
for comp in comparisons_snr:
check_x = int(max_x +
comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y +
comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1, check_x -
star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] + 3 *
fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y = tests[-1]
if test < min_calibration_stars_number - 0.5:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
rotation_detected = True
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for reasonable field shift
if show_progress.exit:
return None
# look for reasonable field rotation
if not stars_detected:
alignment_log('Testing small rotation')
ustep = np.arcsin(
float(star_std) /
comparisons[int(len(comparisons) / 2)][0])
angles = np.append(
np.arange(-rotation_tolerance, rotation_tolerance, ustep),
np.arange(-rotation_tolerance, rotation_tolerance, ustep) +
np.pi)
if stars:
tests = []
for star in stars:
if show_progress.exit:
return None
test = 0
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
for rotation in angles:
alignment_log(science_file)
alignment_log('Testing star at: ', max_x, max_y,
', with rotation: ', rotation)
test = 0
for comp in comparisons:
check_x = int(max_x + comp[0] *
np.cos(rotation + comp[1]))
check_y = int(max_y + comp[0] *
np.sin(rotation + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1, check_x -
star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] + 3 *
fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num:
break
tests.append([test, max_x, max_y, rotation])
alignment_log([test, max_x, max_y, rotation])
if test > check_num:
break
if test > check_num:
break
tests.sort()
test, max_x, max_y, rotation = tests[-1]
if test < check_num:
for star in stars:
if show_progress.exit:
return None
test = 0
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
for rotation in angles:
alignment_log(science_file)
alignment_log('LOW SNR Testing star at: ',
max_x, max_y,
', with rotation: ', rotation)
test = 0
for comp in comparisons_snr:
check_x = int(max_x + comp[0] *
np.cos(rotation + comp[1]))
check_y = int(max_y + comp[0] *
np.sin(rotation + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1,
check_x -
star_std:check_x +
star_std + 1])
check_lim = (
fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ',
check_x, check_y, ', Test: ',
test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y, rotation])
alignment_log([test, max_x, max_y, rotation])
if test > check_num_snr:
break
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y, rotation = tests[-1]
if test < check_num_snr:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = rotation
rotation_detected = True
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = rotation
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for reasonable field rotation
if not stars_detected:
circle.set_center((-100, -100))
canvas.draw()
show_progress.update()
skip_frame = askyesno(
'Alignment',
'Stars not found close to their previous positions.\n'
'Do you want to skip this frame?',
parent=show_progress.root)
else:
skip_frame = False
if show_progress.exit:
return None
# look for large field shift
if not stars_detected and not skip_frame:
alignment_log('Testing large shift and rotation')
label_3.configure(text='Testing large shift and rotation')
label_3.update()
canvas.draw()
show_progress.update()
stars = plc.find_all_stars(fits[1].data,
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std,
order_by_flux=True)[0]
if show_progress.exit:
return None
if stars:
tests = []
for star in stars:
if show_progress.exit:
return None
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('LOW SNR Testing star at: ', max_x,
max_y, ', with rotation: ', u0)
test = 0
for comp in comparisons_snr:
check_x = int(max_x +
comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y +
comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y - star_std:check_y +
star_std + 1,
check_x - star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y = tests[-1]
if test < check_num_snr:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for large field shift
# look for large field rotation
if not stars_detected and not skip_frame:
alignment_log('Test large rotation')
ustep = np.arcsin(
float(star_std) /
comparisons[int(len(comparisons) / 2)][0])
angles = np.array([np.pi, 0])
for ff in range(1, int(np.pi / ustep) + 1):
angles = np.append(angles, np.pi - ff * ustep)
angles = np.append(angles, np.pi + ff * ustep)
angles = np.append(angles, 0 - ff * ustep)
angles = np.append(angles, 0 + ff * ustep)
if stars:
if show_progress.exit:
return None
tests = []
for rotation in angles:
test = 0
for star in stars:
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('LOW SNR Testing star at: ', max_x,
max_y, ', with rotation: ', rotation)
test = 0
for comp in comparisons_snr:
check_x = int(max_x + comp[0] *
np.cos(rotation + comp[1]))
check_y = int(max_y + comp[0] *
np.sin(rotation + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1, check_x -
star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] + 2 *
fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ',
check_x, check_y, ', Test: ',
test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y, rotation])
alignment_log([test, max_x, max_y, rotation])
if test > check_num_snr:
break
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y, rotation = tests[-1]
if test < check_num_snr:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = rotation
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for large field rotation
skip_time += time.time() - delta_skip_time
if show_progress.exit:
return None
if stars_detected:
if rotation_detected:
test_u0 = []
test_cos = []
test_sin = []
for ii in comparisons[:int(check_num + 0.5)]:
star = plc.find_single_star(
fits[1].data,
x0 + ii[0] * np.cos(u0 + ii[1]),
y0 + ii[0] * np.sin(u0 + ii[1]),
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std)
if star:
diff = plc.cartesian_to_polar(
star[0], star[1], x0, y0)[1] - ii[1]
if diff < 0:
diff += 2 * np.pi
test_u0.append(diff)
test_cos.append(np.cos(diff))
test_sin.append(np.sin(diff))
if len(test_u0) > 0:
test_u0 = np.mean(test_u0)
test_cos = np.mean(test_cos)
test_sin = np.mean(test_sin)
u0 = np.arccos(test_cos)
if test_sin < 0:
u0 = np.pi + (np.pi - u0)
fits[1].header.set(align_x0_key, x0)
fits[1].header.set(align_y0_key, y0)
fits[1].header.set(align_u0_key, u0)
circle = mpatches.Circle((x0, y0),
circles_diameter,
ec='r',
fill=False)
ax.add_patch(circle)
for ii in comparisons[:int(check_num + 0.5)]:
circle = mpatches.Circle((x0 + ii[0] * np.cos(u0 + ii[1]),
y0 + ii[0] * np.sin(u0 + ii[1])),
circles_diameter,
ec='w',
fill=False)
ax.add_patch(circle)
canvas.draw()
show_progress.update()
else:
fits[1].header.set(align_x0_key, False)
fits[1].header.set(align_y0_key, False)
fits[1].header.set(align_u0_key, False)
fits.flush()
fits.close()
# counter
new_percent = round(100 * (counter + 1) / float(len(science)), 1)
if new_percent != percent:
lt1 = time.time()
rm_time = (100 - new_percent) * (lt1 - lt0 -
skip_time) / new_percent
hours = rm_time / 3600.0
minutes = (hours - int(hours)) * 60
seconds = (minutes - int(minutes)) * 60
progress_bar_2['value'] = new_percent
percent_label_2.configure(
text='{0} % ({1}h {2}m {3}s left)'.format(
new_percent, int(hours), int(minutes), int(seconds)))
percent = new_percent
show_progress.update()
if show_progress.exit:
return None
def check_visibility():
star_std = log.read_local_log('alignment', 'star_std')
all_stars_dict = plc.open_dict('all_stars.pickle')
stars = np.array(all_stars_dict['all_stars'])
polar_coords = []
fits = pf.open(science[0])
for star in all_stars_dict['all_stars']:
polar_coords.append(
plc.cartesian_to_polar(star[0], star[1],
fits[1].header[align_x0_key],
fits[1].header[align_y0_key]))
fits.close()
in_fov = np.ones(len(polar_coords))
percent = 0
lt0 = time.time()
for counter, science_file in enumerate(science):
if show_progress.exit:
return None
in_fov_single = []
fits = pf.open(science_file)
if fits[1].header[align_x0_key]:
ref_x_position = fits[1].header[align_x0_key]
ref_y_position = fits[1].header[align_y0_key]
ref_u_position = fits[1].header[align_u0_key]
for star in polar_coords:
cartesian_x = ref_x_position + star[0] * np.cos(
ref_u_position + star[1])
cartesian_y = ref_y_position + star[0] * np.sin(
ref_u_position + star[1])
if (cartesian_x > 3 * star_std and
cartesian_x < len(fits[1].data[0]) - 3 * star_std
and cartesian_y > 3 * star_std and
cartesian_y < len(fits[1].data) - 3 * star_std):
in_fov_single.append(1)
else:
in_fov_single.append(0)
in_fov *= in_fov_single
fits.close()
# counter
new_percent = round(100 * (counter + 1) / float(len(science)), 1)
if new_percent != percent:
lt1 = time.time()
rm_time = (100 - new_percent) * (lt1 - lt0) / new_percent
hours = rm_time / 3600.0
minutes = (hours - int(hours)) * 60
seconds = (minutes - int(minutes)) * 60
progress_bar_3['value'] = new_percent
percent_label_3.configure(
text='{0} % ({1}h {2}m {3}s left)'.format(
new_percent, int(hours), int(minutes), int(seconds)))
percent = new_percent
show_progress.update()
all_stars_dict['in_fov'] = np.array(in_fov)
visible_fov_x_min = np.min(stars[np.where(in_fov), 0]) - 3 * star_std
visible_fov_x_max = np.max(stars[np.where(in_fov), 0]) + 3 * star_std
visible_fov_y_min = np.min(stars[np.where(in_fov), 1]) - 3 * star_std
visible_fov_y_max = np.max(stars[np.where(in_fov), 1]) + 3 * star_std
log.write_local_log('alignment', float(visible_fov_x_min), 'min_x')
log.write_local_log('alignment', float(visible_fov_y_min), 'min_y')
log.write_local_log('alignment', float(visible_fov_x_max), 'max_x')
log.write_local_log('alignment', float(visible_fov_y_max), 'max_y')
plc.save_dict(all_stars_dict, 'all_stars.pickle')
def run():
if not show_progress.exit:
find_all_stars()
if log.read_local_log('pipeline', 'alignment_complete'):
if not show_progress.exit:
check_visibility()
else:
if not show_progress.exit:
align()
if not show_progress.exit:
check_visibility()
if not show_progress.exit:
log.write_local_log('pipeline', True, 'alignment_complete')
show_progress.close()
# progress window
show_progress = ProgressWindow('HOPS - Alignment', 0, 0, 5)
f = Figure()
f.patch.set_facecolor('white')
ax = f.add_subplot(111)
f.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
ax.axis('off')
canvas = show_progress.FigureCanvasTkAgg(f)
canvas.get_tk_widget().pack()
fits = pf.open(science[0], memmap=False)
ax.imshow(fits[1].data,
origin='lower',
cmap=cm.Greys_r,
vmin=fits[1].header[mean_key] +
frame_low_std * fits[1].header[std_key],
vmax=fits[1].header[mean_key] +
frame_upper_std * fits[1].header[std_key])
fits.close()
frame1 = show_progress.Frame()
frame1.pack()
label_1 = Label(frame1, text="Locating all stars in the FOV")
progress_bar = ttk.Progressbar(frame1,
orient=HORIZONTAL,
length=300,
maximum=100,
mode='determinate',
value=0)
percent_label = Label(frame1, text='0.0 %')
if log.read_local_log('pipeline', 'alignment_complete'):
label_2 = Label(frame1, text='Running Alignment: --- Skipping ---')
else:
label_2 = Label(frame1, text='Running Alignment: ')
progress_bar_2 = ttk.Progressbar(frame1,
orient=HORIZONTAL,
length=300,
maximum=100,
mode='determinate',
value=0)
percent_label_2 = Label(frame1, text='0.0 %')
label_3 = Label(frame1, text=' ')
label_4 = Label(frame1, text='Aligning all stars in the FOV')
progress_bar_3 = ttk.Progressbar(frame1,
orient=HORIZONTAL,
length=300,
maximum=100,
mode='determinate',
value=0)
percent_label_3 = Label(frame1, text='0.0 %')
setup_window(
frame1,
[[[label_1, 0, 2]],
[[progress_bar, 0, 1, 1,
(20, 0)], [percent_label, 1]], [[label_2, 0, 2]], [[label_3, 0, 2]],
[[progress_bar_2, 0, 1, 1,
(20, 0)], [percent_label_2, 1]], [[label_4, 0, 2]],
[[progress_bar_3, 0, 1, 1, (20, 0)], [percent_label_3, 1]], []],
main_font='Courier')
canvas.draw()
show_progress.after(200, run)
show_progress.loop()
def check_visibility():
star_std = log.read_local_log('alignment', 'star_std')
all_stars_dict = plc.open_dict('all_stars.pickle')
stars = np.array(all_stars_dict['all_stars'])
polar_coords = []
fits = pf.open(science[0])
for star in all_stars_dict['all_stars']:
polar_coords.append(
plc.cartesian_to_polar(star[0], star[1],
fits[1].header[align_x0_key],
fits[1].header[align_y0_key]))
fits.close()
in_fov = np.ones(len(polar_coords))
percent = 0
lt0 = time.time()
for counter, science_file in enumerate(science):
if show_progress.exit:
return None
in_fov_single = []
fits = pf.open(science_file)
if fits[1].header[align_x0_key]:
ref_x_position = fits[1].header[align_x0_key]
ref_y_position = fits[1].header[align_y0_key]
ref_u_position = fits[1].header[align_u0_key]
for star in polar_coords:
cartesian_x = ref_x_position + star[0] * np.cos(
ref_u_position + star[1])
cartesian_y = ref_y_position + star[0] * np.sin(
ref_u_position + star[1])
if (cartesian_x > 3 * star_std and
cartesian_x < len(fits[1].data[0]) - 3 * star_std
and cartesian_y > 3 * star_std and
cartesian_y < len(fits[1].data) - 3 * star_std):
in_fov_single.append(1)
else:
in_fov_single.append(0)
in_fov *= in_fov_single
fits.close()
# counter
new_percent = round(100 * (counter + 1) / float(len(science)), 1)
if new_percent != percent:
lt1 = time.time()
rm_time = (100 - new_percent) * (lt1 - lt0) / new_percent
hours = rm_time / 3600.0
minutes = (hours - int(hours)) * 60
seconds = (minutes - int(minutes)) * 60
progress_bar_3['value'] = new_percent
percent_label_3.configure(
text='{0} % ({1}h {2}m {3}s left)'.format(
new_percent, int(hours), int(minutes), int(seconds)))
percent = new_percent
show_progress.update()
all_stars_dict['in_fov'] = np.array(in_fov)
visible_fov_x_min = np.min(stars[np.where(in_fov), 0]) - 3 * star_std
visible_fov_x_max = np.max(stars[np.where(in_fov), 0]) + 3 * star_std
visible_fov_y_min = np.min(stars[np.where(in_fov), 1]) - 3 * star_std
visible_fov_y_max = np.max(stars[np.where(in_fov), 1]) + 3 * star_std
log.write_local_log('alignment', float(visible_fov_x_min), 'min_x')
log.write_local_log('alignment', float(visible_fov_y_min), 'min_y')
log.write_local_log('alignment', float(visible_fov_x_max), 'max_x')
log.write_local_log('alignment', float(visible_fov_y_max), 'max_y')
plc.save_dict(all_stars_dict, 'all_stars.pickle')
def __init__(self, log):
MainWindow.__init__(self, log, name='HOPS - Inspection', position=2)
# set variables, create and place widgets
self.all_frames = plc.open_dict(self.log.all_frames)
self.science_files = []
for science_file in self.all_frames:
self.science_files.append([
self.all_frames[science_file][self.log.time_key], science_file
])
self.science_files.sort()
self.science_files = [ff[1] for ff in self.science_files]
self.science_keys = []
self.science = []
self.time_array = []
self.sky_mean_array = []
self.sky_std_array = []
self.psf_array = []
self.skip_array = []
for science_file in self.science_files:
self.science_keys.append(science_file)
self.science.append(
os.path.join(self.log.reduction_directory, science_file))
self.time_array.append(
self.all_frames[science_file][self.log.time_key])
self.sky_mean_array.append(
self.all_frames[science_file][self.log.mean_key] /
self.all_frames[science_file][self.log.get_param(
'exposure_time_key')])
self.sky_std_array.append(
self.all_frames[science_file][self.log.std_key] /
self.all_frames[science_file][self.log.get_param(
'exposure_time_key')])
self.psf_array.append(
self.all_frames[science_file][self.log.psf_key] * 2.355 / 2)
self.skip_array.append(
self.all_frames[science_file][self.log.skip_key])
self.time_array = (np.array(self.time_array) -
np.min(self.time_array)) * 24
self.sky_mean_array = np.array(self.sky_mean_array)
self.psf_array = np.array(self.psf_array)
self.skip_array = np.array(self.skip_array)
# main window
y_scale = (self.root.winfo_screenheight() -
375) / self.root.winfo_screenheight()
data = get_fits_data(self.science[0])[0].data
self.fits_figure = self.FitsWindow(figsize=(0.5, y_scale, 20, 20,
len(data[0]) / len(data)),
show_controls=True)
self.fits_to_plot = np.argmin(self.time_array)
self.fits_figure.load_fits(self.science[self.fits_to_plot])
self.sky_threshold = self.Entry(
value=self.log.get_param('sky_threshold'),
instance=float,
command=self.apply_thresholds)
self.psf_threshold = self.Entry(
value=self.log.get_param('psf_threshold'),
instance=float,
command=self.apply_thresholds)
self.inspection_figure = self.FigureWindow(figsize=(0.5, 0.5, 10, 6,
1.1),
show_nav=True)
self.inspection_figure_ax1 = self.inspection_figure.figure.add_subplot(
211)
self.inspection_figure_ax2 = self.inspection_figure.figure.add_subplot(
212)
self.inspection_figure.figure.subplots_adjust(left=0.15,
right=0.99,
bottom=0.1,
top=0.99)
self.inspection_figure.figure.canvas.callbacks.connect(
'button_press_event', self.update_window)
self.arrow1 = 0
self.arrow2 = 0
self.inspection_figure_ax1.plot(self.time_array,
self.sky_mean_array,
'ko',
ms=3)
self.inspection_figure_ax2.plot(self.time_array,
self.psf_array,
'ko',
ms=3)
self.inspection_figure_ax1.set_xlim(
np.min(self.time_array) - 0.1,
np.max(self.time_array) + 0.1)
self.inspection_figure_ax2.set_xlim(
np.min(self.time_array) - 0.1,
np.max(self.time_array) + 0.1)
self.yspil = self.inspection_figure.figure.get_size_inches(
)[1] * self.inspection_figure.figure.dpi * 0.55
box1 = self.inspection_figure_ax1.get_window_extent()
self.ax1_width = box1.width
self.ax1_height = box1.height
box2 = self.inspection_figure_ax2.get_window_extent()
self.ax2_width = box2.width
self.ax2_height = box2.height
self.dbclick = False
self.dbclick_xy = (0, 0)
self.replot()
self.setup_window([
[[self.fits_figure, 0, 1, 10], [self.inspection_figure, 1, 4]], [],
[[
self.Label(
text='On the time-sky or PSF-sky graph above'
'\n'
'double-click on a point to see the frame on the left panel.'
'\n'
'To mark this point as faulty, use the right double-click.'
'\n'
'To undo, use the right double-click again.'), 1, 4
]], [],
[[self.Label(text='Sky Threshold'), 1], [self.sky_threshold, 2],
[self.Label(text='PSF Threshold'), 3], [self.psf_threshold,
4]], [],
[[
self.Button(text='RETURN TO MAIN MENU', command=self.close), 1,
4
]],
[[
self.Button(text='SAVE OPTIONS & RETURN TO MAIN MENU',
command=self.save_and_return), 1, 4
]],
[[
self.Button(text='SAVE OPTIONS & PROCEED',
command=self.save_and_proceed,
bg='green',
highlightbackground='green'), 1, 4
]], []
])
self.replot()
def align():
star_std = log.read_local_log('alignment', 'star_std')
psf = (log.read_local_log('alignment', 'star_psf_x'),
log.read_local_log('alignment', 'star_psf_y'))
all_stars_dict = plc.open_dict('all_stars.pickle')
stars = np.array(all_stars_dict['all_stars'])
fits = pf.open(science[0], memmap=False)
frame_mean = fits[1].header[mean_key]
frame_std = fits[1].header[std_key]
shift_tolerance = int(
max(len(fits[1].data), len(fits[1].data[0])) *
(shift_tolerance_p / 100.0))
y_length, x_length = fits[1].data.shape
circles_diameter = 0.02 * max(y_length, x_length)
bright_stars = []
std_limit = 30
while len(bright_stars) < 100 and std_limit >= 5.0:
bright_stars = []
for star in stars:
if star[2] + star[3] < 2.0 * burn_limit / 3.0:
if star[-1] > (2 * np.pi *
(std_limit * frame_std) * psf[0] * psf[1]):
alignment_log(
star[-1],
2 * np.pi * (frame_mean + std_limit * frame_std) *
psf[0] * psf[1])
bright_stars.append(star)
std_limit -= 5
stars = sorted(bright_stars, key=lambda x: -x[-1] / (x[-2]**3))
x_ref_position = stars[0][0]
y_ref_position = stars[0][1]
del stars[0]
# take the rest as calibration stars and calculate their polar coordinates relatively to the first
calibration_stars_polar = []
for star in stars:
r_position, u_position = plc.cartesian_to_polar(
star[0], star[1], x_ref_position, y_ref_position)
if r_position > 5 * star_std:
calibration_stars_polar.append([r_position, u_position])
stars = sorted(stars, key=lambda x: -x[-1])
calibration_stars_polar_snr = []
for star in stars:
r_position, u_position = plc.cartesian_to_polar(
star[0], star[1], x_ref_position, y_ref_position)
if r_position > 5 * star_std:
calibration_stars_polar_snr.append([r_position, u_position])
if len(calibration_stars_polar) <= min_calibration_stars_number:
check_num = len(calibration_stars_polar) - 0.5
check_num_snr = len(calibration_stars_polar) - 0.5
else:
check_num = max(min_calibration_stars_number - 0.5,
int(len(calibration_stars_polar)) / 10 - 0.5)
check_num_snr = max(min_calibration_stars_number - 0.5,
int(len(calibration_stars_polar)) / 20 - 0.5)
x0, y0, u0, comparisons = x_ref_position, y_ref_position, 0, calibration_stars_polar
x0, y0, u0, comparisons_snr = x_ref_position, y_ref_position, 0, calibration_stars_polar_snr
fits.close()
# set the looking window and angular step
circle = mpatches.Circle((x0, y0),
circles_diameter,
ec='r',
fill=False)
ax.add_patch(circle)
for ii in comparisons[:10]:
circle = mpatches.Circle((x0 + ii[0] * np.cos(u0 + ii[1]),
y0 + ii[0] * np.sin(u0 + ii[1])),
circles_diameter,
ec='w',
fill=False)
ax.add_patch(circle)
# for each science_file
percent = 0
skip_time = 0
lt0 = time.time()
for counter, science_file in enumerate(science):
if show_progress.exit:
return None
alignment_log('\n', science_file)
label_2.configure(text='Running Alignment: {0}'.format(
science_file.split(os.sep)[-1]))
label_2.update()
label_3.configure(text='')
label_3.update()
fits = pf.open(science_file, mode='update')
ax.cla()
ax.imshow(fits[1].data,
origin='lower',
cmap=cm.Greys_r,
vmin=fits[1].header[mean_key] +
frame_low_std * fits[1].header[std_key],
vmax=fits[1].header[mean_key] +
frame_upper_std * fits[1].header[std_key])
ax.axis('off')
circle = mpatches.Circle((-100, -100),
circles_diameter,
ec='r',
fill=False)
ax.add_patch(circle)
rotation_detected = False
# super fast detection test
alignment_log('Test no shift', x0, y0, u0)
star = plc.find_single_star(fits[1].data,
x0,
y0,
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std)
if star:
tests = []
max_x = star[0]
max_y = star[1]
alignment_log(science_file)
alignment_log('Testing star at: ', max_x, max_y,
', with rotation:', u0)
test = 0
for comp in comparisons:
check_x = int(max_x + comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y + comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y - star_std:check_y +
star_std + 1, check_x -
star_std:check_x + star_std + 1])
check_lim = (fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x, check_y,
', Test: ', test)
if abs(test) > check_num:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
tests.sort()
test, max_x, max_y = tests[-1]
if test < check_num:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# super fast detection test
if show_progress.exit:
return None
delta_skip_time = time.time()
# look for reasonable field shift
if not stars_detected:
alignment_log('Testing small shift')
label_3.configure(text='Testing small shift and rotation')
label_3.update()
stars = plc.find_all_stars(fits[1].data,
x_low=x0 - shift_tolerance,
x_upper=x0 + shift_tolerance,
y_low=y0 - shift_tolerance,
y_upper=y0 + shift_tolerance,
x_centre=x0,
y_centre=y0,
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std)[0]
if show_progress.exit:
return None
if stars:
tests = []
for star in stars:
if show_progress.exit:
return None
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('Testing star at: ', max_x, max_y,
',with rotation:', u0)
test = 0
for comp in comparisons:
check_x = int(max_x +
comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y +
comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y - star_std:check_y +
star_std + 1,
check_x - star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
if test > check_num:
break
tests.sort()
test, max_x, max_y = tests[-1]
if test < check_num:
tests = []
for star in stars:
if show_progress.exit:
return None
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('LOW-SNR Testing star at: ', max_x,
max_y, ', with rotation:', u0)
test = 0
for comp in comparisons_snr:
check_x = int(max_x +
comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y +
comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1, check_x -
star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] + 3 *
fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y = tests[-1]
if test < min_calibration_stars_number - 0.5:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
rotation_detected = True
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for reasonable field shift
if show_progress.exit:
return None
# look for reasonable field rotation
if not stars_detected:
alignment_log('Testing small rotation')
ustep = np.arcsin(
float(star_std) /
comparisons[int(len(comparisons) / 2)][0])
angles = np.append(
np.arange(-rotation_tolerance, rotation_tolerance, ustep),
np.arange(-rotation_tolerance, rotation_tolerance, ustep) +
np.pi)
if stars:
tests = []
for star in stars:
if show_progress.exit:
return None
test = 0
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
for rotation in angles:
alignment_log(science_file)
alignment_log('Testing star at: ', max_x, max_y,
', with rotation: ', rotation)
test = 0
for comp in comparisons:
check_x = int(max_x + comp[0] *
np.cos(rotation + comp[1]))
check_y = int(max_y + comp[0] *
np.sin(rotation + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1, check_x -
star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] + 3 *
fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num:
break
tests.append([test, max_x, max_y, rotation])
alignment_log([test, max_x, max_y, rotation])
if test > check_num:
break
if test > check_num:
break
tests.sort()
test, max_x, max_y, rotation = tests[-1]
if test < check_num:
for star in stars:
if show_progress.exit:
return None
test = 0
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
for rotation in angles:
alignment_log(science_file)
alignment_log('LOW SNR Testing star at: ',
max_x, max_y,
', with rotation: ', rotation)
test = 0
for comp in comparisons_snr:
check_x = int(max_x + comp[0] *
np.cos(rotation + comp[1]))
check_y = int(max_y + comp[0] *
np.sin(rotation + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1,
check_x -
star_std:check_x +
star_std + 1])
check_lim = (
fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ',
check_x, check_y, ', Test: ',
test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y, rotation])
alignment_log([test, max_x, max_y, rotation])
if test > check_num_snr:
break
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y, rotation = tests[-1]
if test < check_num_snr:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = rotation
rotation_detected = True
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = rotation
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for reasonable field rotation
if not stars_detected:
circle.set_center((-100, -100))
canvas.draw()
show_progress.update()
skip_frame = askyesno(
'Alignment',
'Stars not found close to their previous positions.\n'
'Do you want to skip this frame?',
parent=show_progress.root)
else:
skip_frame = False
if show_progress.exit:
return None
# look for large field shift
if not stars_detected and not skip_frame:
alignment_log('Testing large shift and rotation')
label_3.configure(text='Testing large shift and rotation')
label_3.update()
canvas.draw()
show_progress.update()
stars = plc.find_all_stars(fits[1].data,
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std,
order_by_flux=True)[0]
if show_progress.exit:
return None
if stars:
tests = []
for star in stars:
if show_progress.exit:
return None
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('LOW SNR Testing star at: ', max_x,
max_y, ', with rotation: ', u0)
test = 0
for comp in comparisons_snr:
check_x = int(max_x +
comp[0] * np.cos(u0 + comp[1]))
check_y = int(max_y +
comp[0] * np.sin(u0 + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y - star_std:check_y +
star_std + 1,
check_x - star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] +
3 * fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ', check_x,
check_y, ', Test: ', test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y])
alignment_log([test, max_x, max_y])
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y = tests[-1]
if test < check_num_snr:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = u0
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for large field shift
# look for large field rotation
if not stars_detected and not skip_frame:
alignment_log('Test large rotation')
ustep = np.arcsin(
float(star_std) /
comparisons[int(len(comparisons) / 2)][0])
angles = np.array([np.pi, 0])
for ff in range(1, int(np.pi / ustep) + 1):
angles = np.append(angles, np.pi - ff * ustep)
angles = np.append(angles, np.pi + ff * ustep)
angles = np.append(angles, 0 - ff * ustep)
angles = np.append(angles, 0 + ff * ustep)
if stars:
if show_progress.exit:
return None
tests = []
for rotation in angles:
test = 0
for star in stars:
max_x = star[0]
max_y = star[1]
circle.set_center((max_x, max_y))
canvas.draw()
show_progress.update()
alignment_log(science_file)
alignment_log('LOW SNR Testing star at: ', max_x,
max_y, ', with rotation: ', rotation)
test = 0
for comp in comparisons_snr:
check_x = int(max_x + comp[0] *
np.cos(rotation + comp[1]))
check_y = int(max_y + comp[0] *
np.sin(rotation + comp[1]))
if 0 < check_x < x_length and 0 < check_y < y_length:
check_sum = np.sum(
fits[1].data[check_y -
star_std:check_y +
star_std + 1, check_x -
star_std:check_x +
star_std + 1])
check_lim = (fits[1].header[mean_key] + 2 *
fits[1].header[std_key]) * (
(2 * star_std + 1)**2)
if check_sum > check_lim:
test += 1
else:
test -= 1
alignment_log('Check ref. star at: ',
check_x, check_y, ', Test: ',
test)
if abs(test) > check_num_snr:
break
tests.append([test, max_x, max_y, rotation])
alignment_log([test, max_x, max_y, rotation])
if test > check_num_snr:
break
if test > check_num_snr:
break
tests.sort()
test, max_x, max_y, rotation = tests[-1]
if test < check_num_snr:
stars_detected = False
else:
stars_detected = True
x0 = max_x
y0 = max_y
u0 = rotation
rotation_detected = True
else:
stars_detected = False
alignment_log(stars_detected, x0, y0, u0)
# look for large field rotation
skip_time += time.time() - delta_skip_time
if show_progress.exit:
return None
if stars_detected:
if rotation_detected:
test_u0 = []
test_cos = []
test_sin = []
for ii in comparisons[:int(check_num + 0.5)]:
star = plc.find_single_star(
fits[1].data,
x0 + ii[0] * np.cos(u0 + ii[1]),
y0 + ii[0] * np.sin(u0 + ii[1]),
mean=fits[1].header[mean_key],
std=fits[1].header[std_key],
burn_limit=burn_limit,
star_std=star_std)
if star:
diff = plc.cartesian_to_polar(
star[0], star[1], x0, y0)[1] - ii[1]
if diff < 0:
diff += 2 * np.pi
test_u0.append(diff)
test_cos.append(np.cos(diff))
test_sin.append(np.sin(diff))
if len(test_u0) > 0:
test_u0 = np.mean(test_u0)
test_cos = np.mean(test_cos)
test_sin = np.mean(test_sin)
u0 = np.arccos(test_cos)
if test_sin < 0:
u0 = np.pi + (np.pi - u0)
fits[1].header.set(align_x0_key, x0)
fits[1].header.set(align_y0_key, y0)
fits[1].header.set(align_u0_key, u0)
circle = mpatches.Circle((x0, y0),
circles_diameter,
ec='r',
fill=False)
ax.add_patch(circle)
for ii in comparisons[:int(check_num + 0.5)]:
circle = mpatches.Circle((x0 + ii[0] * np.cos(u0 + ii[1]),
y0 + ii[0] * np.sin(u0 + ii[1])),
circles_diameter,
ec='w',
fill=False)
ax.add_patch(circle)
canvas.draw()
show_progress.update()
else:
fits[1].header.set(align_x0_key, False)
fits[1].header.set(align_y0_key, False)
fits[1].header.set(align_u0_key, False)
fits.flush()
fits.close()
# counter
new_percent = round(100 * (counter + 1) / float(len(science)), 1)
if new_percent != percent:
lt1 = time.time()
rm_time = (100 - new_percent) * (lt1 - lt0 -
skip_time) / new_percent
hours = rm_time / 3600.0
minutes = (hours - int(hours)) * 60
seconds = (minutes - int(minutes)) * 60
progress_bar_2['value'] = new_percent
percent_label_2.configure(
text='{0} % ({1}h {2}m {3}s left)'.format(
new_percent, int(hours), int(minutes), int(seconds)))
percent = new_percent
show_progress.update()
if show_progress.exit:
return None
def check_visibility(self):
if self.exit:
self.def_close()
else:
all_stars_dict = plc.open_dict('all_stars.pickle')
stars = np.array(all_stars_dict['all_stars'])
fits = plc.open_fits(
os.path.join(self.log.reduction_directory,
self.science_files[0]))
polar_coords = []
for star in all_stars_dict['all_stars']:
polar_coords.append(
plc.cartesian_to_polar(
star[0], star[1], self.all_frames[
self.science_files[0]][self.log.align_x0_key],
self.all_frames[self.science_files[0]][
self.log.align_y0_key]))
in_fov = np.ones(len(polar_coords))
for science_file in self.science_files:
metadata = self.all_frames[science_file]
if self.exit:
self.def_close()
ref_x_position = metadata[self.log.align_x0_key]
ref_y_position = metadata[self.log.align_y0_key]
ref_u_position = metadata[self.log.align_u0_key]
star_std = metadata[self.log.psf_key]
if ref_x_position:
in_fov_single = []
for star in polar_coords:
cartesian_x = ref_x_position + star[0] * np.cos(
ref_u_position + star[1])
cartesian_y = ref_y_position + star[0] * np.sin(
ref_u_position + star[1])
if (3 * star_std < cartesian_x <
len(fits[1].data[0]) - 3 * star_std
and 3 * star_std < cartesian_y <
len(fits[1].data) - 3 * star_std):
in_fov_single.append(1)
else:
in_fov_single.append(0)
in_fov *= in_fov_single
all_stars_dict['in_fov'] = np.array(in_fov)
visible_fov_x_min = np.min(stars[np.where(in_fov),
0]) - 3 * star_std
visible_fov_x_max = np.max(stars[np.where(in_fov),
0]) + 3 * star_std
visible_fov_y_min = np.min(stars[np.where(in_fov),
1]) - 3 * star_std
visible_fov_y_max = np.max(stars[np.where(in_fov),
1]) + 3 * star_std
self.log.set_param('min_x', float(visible_fov_x_min))
self.log.set_param('min_y', float(visible_fov_y_min))
self.log.set_param('max_x', float(visible_fov_x_max))
self.log.set_param('max_y', float(visible_fov_y_max))
plc.save_dict(all_stars_dict, 'all_stars.pickle')
self.log.set_param('alignment_complete', True)
self.log.set_param('alignment_version', self.log.version)
self.log.save_local_log()
self.log.set_param('proceed', True)
self.def_close()
def choose_calibartion_stars(self):
if self.exit:
self.after(self.align)
else:
all_stars_dict = plc.open_dict('all_stars.pickle')
stars = np.array(all_stars_dict['all_stars'])
fits = pf.open(os.path.join(self.log.reduction_directory,
self.science_files[0]),
memmap=False)
metadata = self.all_frames[self.science_files[0]]
frame_mean = metadata[self.log.mean_key]
frame_std = metadata[self.log.std_key]
frame_star_psf = metadata[self.log.psf_key]
bright_stars = []
std_limit = 30
while len(bright_stars) < 100 and std_limit >= 5.0:
bright_stars = []
for star in stars:
if star[2] + star[3] < 2.0 * self.burn_limit / 3.0:
if star[-1] > (2 * np.pi * (std_limit * frame_std) *
frame_star_psf * frame_star_psf):
self.alignment_log(
star[0], star[1], star[-1], 2 * np.pi *
(frame_mean + std_limit * frame_std) *
(frame_star_psf**2))
bright_stars.append(star)
std_limit -= 5
if len(bright_stars) < self.min_calibration_stars_number:
bright_stars = []
std_limit = 30
while len(bright_stars) < 100 and std_limit >= 5.0:
bright_stars = []
for star in stars:
if star[-1] > (2 * np.pi * (std_limit * frame_std) *
frame_star_psf * frame_star_psf):
self.alignment_log(
star[0], star[1], star[-1], 2 * np.pi *
(frame_mean + std_limit * frame_std) *
(frame_star_psf**2))
bright_stars.append(star)
std_limit -= 5
stars = sorted(bright_stars, key=lambda x: -x[-1] / (x[-2]**3))
x_ref_position = stars[0][0]
y_ref_position = stars[0][1]
f_ref = stars[0][-1]
del stars[0]
# take the rest as calibration stars and calculate their polar coordinates relatively to the first
calibration_stars_polar = []
for star in stars:
r_position, u_position = plc.cartesian_to_polar(
star[0], star[1], x_ref_position, y_ref_position)
if r_position > 5 * frame_star_psf:
calibration_stars_polar.append([r_position, u_position])
stars = sorted(stars, key=lambda x: -x[-1])
calibration_stars_polar_snr = []
for star in stars:
r_position, u_position = plc.cartesian_to_polar(
star[0], star[1], x_ref_position, y_ref_position)
if r_position > 5 * frame_star_psf:
calibration_stars_polar_snr.append(
[r_position, u_position])
if len(calibration_stars_polar
) <= self.min_calibration_stars_number:
self.check_num = len(calibration_stars_polar) - 0.5
self.check_num_snr = len(calibration_stars_polar) - 0.5
else:
self.check_num = max(self.min_calibration_stars_number - 0.5,
len(calibration_stars_polar) / 10.0 - 0.5)
self.check_num_snr = max(
self.min_calibration_stars_number - 0.5,
len(calibration_stars_polar) / 20.0 - 0.5)
self.x0 = x_ref_position
self.y0 = y_ref_position
self.u0 = 0
self.f0 = f_ref
self.comparisons = calibration_stars_polar
self.comparisons_snr = calibration_stars_polar_snr
fits.close()
ustep = np.arcsin(
float(frame_star_psf) /
self.comparisons[int(len(self.comparisons) / 2)][0])
self.small_angles = np.append(
np.arange(-self.rotation_tolerance, self.rotation_tolerance,
ustep),
np.arange(-self.rotation_tolerance, self.rotation_tolerance,
ustep) + np.pi)
self.large_angles = np.array([np.pi, 0])
for ff in range(1, int(np.pi / ustep) + 1):
self.large_angles = np.append(self.large_angles,
np.pi - ff * ustep)
self.large_angles = np.append(self.large_angles,
np.pi + ff * ustep)
self.large_angles = np.append(self.large_angles,
0 - ff * ustep)
self.large_angles = np.append(self.large_angles,
0 + ff * ustep)
# set the looking window and angular step
self.progress_all_stars.set(' ')
self.after(self.align)
def __init__(self, log):
MainWindow.__init__(self, log, name='HOPS - Alignment', position=2)
# set variables, create and place widgets
self.bin_fits = self.log.get_param('bin_fits')
self.burn_limit = int(
1.1 *
self.log.get_param('burn_limit')) * self.bin_fits * self.bin_fits
self.shift_tolerance_p = self.log.get_param('shift_tolerance_p')
self.rotation_tolerance = self.log.get_param('rotation_tolerance')
self.min_calibration_stars_number = int(
self.log.get_param('min_calibration_stars_number'))
self.all_frames = plc.open_dict(self.log.all_frames)
self.science_files = []
for science_file in self.all_frames:
if not self.all_frames[science_file][self.log.skip_key]:
self.science_files.append([
self.all_frames[science_file][self.log.time_key],
science_file
])
else:
self.all_frames[science_file][self.log.align_x0_key] = False
self.all_frames[science_file][self.log.align_y0_key] = False
self.all_frames[science_file][self.log.align_u0_key] = False
fits = pf.open(os.path.join(self.log.reduction_directory,
science_file),
mode='update')
fits[1].header.set(self.log.align_x0_key, False)
fits[1].header.set(self.log.align_y0_key, False)
fits[1].header.set(self.log.align_u0_key, False)
fits.flush()
fits.close()
self.science_files.sort()
self.science_files = [ff[1] for ff in self.science_files]
self.skip_time = 0
self.science_counter = 0
self.test_level = None
self.redraw = None
self.stars = None
self.science_file = None
self.fits = None
self.std = None
self.mean = None
self.star_std = None
self.int_psf = None
self.stars_detected = None
self.rotation_detected = None
self.check_num = None
self.check_num_snr = None
self.x0 = None
self.y0 = None
self.u0 = None
self.f0 = None
self.comparisons = None
self.comparisons_snr = None
self.small_angles = None
self.large_angles = None
self.circle = None
self.settings_to_check = None
self.comparisons_to_check = None
# common definitions for all images
fits = plc.open_fits(
os.path.join(self.log.reduction_directory, self.science_files[0]))
self.shift_tolerance = int(
max(len(fits[1].data), len(fits[1].data[0])) *
(self.shift_tolerance_p / 100.0))
self.y_length, self.x_length = fits[1].data.shape
self.circles_diameter = 0.02 * max(self.y_length, self.x_length)
# progress window
y_scale = (self.root.winfo_screenheight() -
500) / self.root.winfo_screenheight()
self.progress_figure = self.FitsWindow(figsize=(0.5, y_scale, 10, 10,
len(fits[1].data[0]) /
len(fits[1].data)))
self.progress_figure.load_fits(fits[1],
input_name=self.science_files[0])
self.progress_all_stars = self.Label(text='')
self.progress_alignment = self.Progressbar(task="Aligning frames")
# self.progress_all_frames = self.Progressbar(task="Aligning all stars in all frames")
self.setup_window([[[self.progress_figure, 0, 2]],
[[self.progress_all_stars, 0, 2]],
[[self.progress_alignment, 0, 2]],
[[
self.Button(
text='STOP ALIGNMENT & RETURN TO MAIN MENU',
command=self.trigger_exit), 0, 2
]], []])
self.set_close_button_function(self.trigger_exit)
def __init__(self, log):
MainWindow.__init__(self, log, name='HOPS - Data & Target', position=2)
# extra windows
self.content_window = AddOnWindow(self,
name='Files list',
sizex=3,
sizey=3,
position=1)
self.header_window = AddOnWindow(self,
name='Header keywords list',
sizex=3,
sizey=3,
position=7)
self.target_window = AddOnWindow(self, name='Select Target')
# set variables, create and place widgets
# main window
self.directory_test = self.Label(
text=self.log.get_param('directory_short'))
self.observation_files = self.Entry(
value=self.log.get_param('observation_files'),
instance=str,
command=self.update_observation_files)
self.observation_files_test = self.Label(text=' ')
self.science_files = 0
self.science_header = []
self.show_files_button = self.Button(text='Show files',
command=self.content_window.show)
self.bias_files = self.Entry(value=self.log.get_param('bias_files'),
instance=str,
command=self.update_bias_files)
self.bias_files_test = self.Label(text=' ')
self.dark_files = self.Entry(value=self.log.get_param('dark_files'),
instance=str,
command=self.update_dark_files)
self.dark_files_test = self.Label(text=' ')
self.flat_files = self.Entry(value=self.log.get_param('flat_files'),
instance=str,
command=self.update_flat_files)
self.flat_files_test = self.Label(text=' ')
try:
initial_bin = self.log.get_param('bin_fits')
initial_bin = max(1, initial_bin)
initial_bin = min(initial_bin, 4)
except:
print(
'WARNING the bin_fits parameter should be 1, 2, 3 or 4. Re-setting it to 1.'
)
initial_bin = 1
self.bin_fits = self.DropDown(initial=initial_bin,
options=[1, 2, 3, 4],
instance=int)
self.show_header_button = self.Button(text='Show header',
command=self.header_window.show)
self.exposure_time_key = self.Entry(
value=self.log.get_param('exposure_time_key'),
instance=str,
command=self.update_exposure_time_key)
self.exposure_time_key_test = self.Label(text=' ')
self.observation_date_key = self.Entry(
value=self.log.get_param('observation_date_key'),
instance=str,
command=self.update_observation_date_key)
self.observation_date_key_test = self.Label(text=' ')
self.observation_time_key = self.Entry(
value=self.log.get_param('observation_time_key'),
instance=str,
command=self.update_observation_time_key)
self.observation_time_key_test = self.Label(text=' ')
self.time_stamp = self.DropDown(
initial=self.log.get_param('time_stamp'),
options=['exposure start', 'mid-exposure', 'exposure end'])
self.select_target_button = self.Button(
text='CHOOSE TARGET', command=self.update_ra_dec_options)
self.target_ra_dec = self.Label(
text=self.log.get_param('target_ra_dec'))
self.target_name = self.Label(text=self.log.get_param('target_name'))
self.target_ra_dec_test = self.Label(text='')
self.observer = self.Entry(value=self.log.get_param('observer'))
self.observatory = self.Entry(value=self.log.get_param('observatory'))
self.telescope = self.Entry(value=self.log.get_param('telescope'))
self.camera = self.Entry(value=self.log.get_param('camera'))
filters = ['default'] + list(filter_map.keys())
if self.log.get_param('filter') not in filters:
self.log.set_param('filter', 'default')
self.filter = self.DropDown(initial=self.log.get_param('filter'),
options=filters,
command=self.check_filter)
self.filter_test = self.Label(text=' ')
self.save_and_return_button = self.Button(
text='SAVE OPTIONS & RETURN TO MAIN MENU',
command=self.save_and_return)
self.save_and_proceed_button = self.Button(
text='SAVE OPTIONS & PROCEED',
command=self.save_and_proceed,
bg='green',
highlightbackground='green')
self.setup_window([
[],
[[
self.Button(text='CHOOSE DIRECTORY',
command=self.update_directory), 1
], [self.directory_test, 2, 2]], [[self.show_files_button, 2]],
[[self.Label(text='Name identifier for observation files'), 1],
[self.observation_files, 2], [self.observation_files_test, 3]],
[[self.Label(text='Name identifier for bias files'), 1],
[self.bias_files, 2], [self.bias_files_test, 3]],
[[self.Label(text='Name identifier for dark files'), 1],
[self.dark_files, 2], [self.dark_files_test, 3]],
[
[self.Label(text='Name identifier for flat files'), 1],
[self.flat_files, 2],
[self.flat_files_test, 3],
],
[[self.Label(text='Bin fits files (reduced only)'), 1],
[self.bin_fits, 2]], [[self.show_header_button, 2]],
[[self.Label(text='Exposure time header keyword'), 1],
[self.exposure_time_key, 2], [self.exposure_time_key_test, 3]],
[
[
self.Label(
text=
'Observation date header keyword\n(no JD, HJD, BJD)'),
1
],
[self.observation_date_key, 2],
[self.observation_date_key_test, 3],
],
[[self.Label(text='Observation time header keyword'), 1],
[self.observation_time_key, 2],
[self.observation_time_key_test, 3]],
[[
self.Label(
text='Time-stamp\n(which time is saved in your fits files?)'
), 1
], [self.time_stamp, 2]], [],
[[self.select_target_button, 1], [self.target_ra_dec, 2],
[self.target_ra_dec_test, 3]], [[self.target_name, 2]],
[[self.Label(text='Observer'), 1], [self.observer, 2],
[self.Label(text='OK'), 3]],
[[self.Label(text='Observatory'), 1], [self.observatory, 2],
[self.Label(text='OK'), 3]],
[[self.Label(text='Telescope'), 1], [self.telescope, 2],
[self.Label(text='OK'), 3]],
[[self.Label(text='Camera'), 1], [self.camera, 2],
[self.Label(text='OK'), 3]],
[[self.Label(text='Filter'), 1], [self.filter, 2],
[self.filter_test, 3]], [],
[[
self.Button(text='RETURN TO MAIN MENU', command=self.close), 1,
6
]], [[self.save_and_return_button, 1, 6]],
[[self.save_and_proceed_button, 1, 6]], []
],
entries_wd=self.log.entries_width)
# files window
self.content_list = self.content_window.ListDisplay()
self.content_window.setup_window([[[self.content_list, 0]]])
content_list = [' List of files in your directory:', ' ']
xx = find_fits_files('*')
for ii in xx:
content_list.append(' {0}'.format(str(ii).split(os.sep)[-1]))
self.content_list.update_list(content_list)
# headers window
self.header_list = self.header_window.ListDisplay()
self.header_window.setup_window([[[self.header_list, 0]]])
self.stars = plc.open_dict(
os.path.join(plc.databases.oec, 'stars_catalogue.pickle'))
self.stars = {
self.stars[ff]['simbad_id']: self.stars[ff]['planets'][0]
for ff in self.stars
}
# target window
self.target_ra_dec_choice = self.target_window.IntVar(
self.log.get_param('target_ra_dec_choice'))
self.target_ra_dec_choice_0 = self.target_window.Radiobutton(
text='Use the RA/DEC found in the file\'s header:',
variable=self.target_ra_dec_choice,
value=0,
command=self.update_ra_dec)
self.target_ra_dec_choice_1 = self.target_window.Radiobutton(
text='Provide the name of the target:',
variable=self.target_ra_dec_choice,
value=1,
command=self.update_ra_dec)
self.target_ra_dec_choice_2 = self.target_window.Radiobutton(
text='Provide the RA/DEC of the target\n(hh:mm:ss +/-dd:mm:ss):',
variable=self.target_ra_dec_choice,
value=2,
command=self.update_ra_dec)
self.auto_target_ra_dec = self.target_window.Label(
text=self.log.get_param('auto_target_ra_dec'))
self.auto_target_ra_dec_check = self.target_window.Label(text='')
self.simbad_target_name = self.target_window.Entry(
value=self.log.get_param('simbad_target_name'),
command=self.update_ra_dec)
self.simbad_target_name_check = self.target_window.Label(text='')
self.manual_target_ra_dec = self.target_window.Entry(
value=self.log.get_param('manual_target_ra_dec'),
command=self.update_ra_dec)
self.target_ra_dec_2 = self.target_window.Label(
text=self.log.get_param('target_ra_dec'))
self.target_name_2 = self.target_window.Label(
text=self.log.get_param('target_name'))
self.target_window.setup_window([
[],
[[
self.target_window.Label(
text=
'How would you like to choose your target? Select one of the three options:'
), 0, 5
]], [],
[[self.target_ra_dec_choice_0, 1], [self.auto_target_ra_dec, 2, 2],
[self.auto_target_ra_dec_check, 4]],
[[self.target_ra_dec_choice_1, 1], [self.simbad_target_name, 2, 2],
[self.simbad_target_name_check, 4]],
[[self.target_ra_dec_choice_2, 1],
[self.manual_target_ra_dec, 2, 2]], [],
[[self.target_window.Label(text='Target RA/DEC: '), 0],
[self.target_ra_dec_2, 1, 3]],
[[self.target_window.Label(text='Target Name: '), 0],
[self.target_name_2, 1, 3]], [],
[[
self.target_window.Button(text=' Cancel ',
command=self.target_window.hide), 2
],
[
self.target_window.Button(text=' Choose ',
command=self.choose_target), 3
]], []
],
entries_wd=self.log.entries_width)
self.update_observation_files()
self.update_bias_files()
self.update_dark_files()
self.update_flat_files()
def update_window(self):
self.data_target_button.activate()
if self.log.get_param('data_target_complete'):
self.data_target_complete.set('Data: {0}\nTarget: {1} '.format(
self.log.get_param('directory_short'), self.log.get_param('target_name')))
self.reduction_button.activate()
trash = 0
if not os.path.isfile(self.log.all_frames):
self.log.set_param('reduction_complete', False)
else:
all_frames = plc.open_dict(self.log.all_frames)
for i in all_frames:
if not os.path.isfile(os.path.join(self.log.reduction_directory, i)):
self.log.set_param('reduction_complete', False)
break
if all_frames[i][self.log.skip_key]:
trash += 1
if self.log.get_param('reduction_complete'):
self.reduction_complete.set('Completed under v{0}'.format(self.log.get_param('reduction_version')))
self.inspection_button.activate()
self.inspection_complete.set('Files discarded: {0}'.format(trash))
self.alignment_button.activate()
all_frames = plc.open_dict(self.log.all_frames)
for i in all_frames:
if not all_frames[i][self.log.skip_key] and not all_frames[i][self.log.align_x0_key]:
self.log.set_param('alignment_complete', False)
plc.save_dict(all_frames, self.log.all_frames)
if not os.path.isfile(self.log.all_stars):
self.log.set_param('alignment_complete', False)
if self.log.get_param('alignment_complete'):
self.alignment_complete.set('Completed under v{0}'.format(self.log.get_param('alignment_version')))
self.photometry_button.activate()
if len(glob.glob('{0}*'.format(self.log.photometry_directory_base))) == 0:
self.log.set_param('photometry_complete', False)
if self.log.get_param('photometry_complete'):
self.photometry_complete.set('Completed under v{0}'.format(self.log.get_param('photometry_version')))
self.fitting_button.activate()
if self.log.get_param('fitting_complete'):
self.fitting_complete.set('Completed under v{0}'.format(self.log.get_param('fitting_version')))
else:
self.photometry_complete.set('You need to complete this step to proceed')
self.fitting_button.disable()
self.fitting_complete.set('')
else:
self.alignment_complete.set('You need to complete this step to proceed')
self.photometry_button.disable()
self.photometry_complete.set('')
self.fitting_button.disable()
self.fitting_complete.set('')
else:
self.reduction_complete.set('You need to complete this step to proceed')
self.inspection_button.disable()
self.inspection_complete.set('')
self.alignment_button.disable()
self.alignment_complete.set('')
self.photometry_button.disable()
self.photometry_complete.set('')
self.fitting_button.disable()
self.fitting_complete.set('')
else:
self.data_target_complete.set('You need to complete this step to proceed.')
self.reduction_button.disable()
self.reduction_complete.set('')
self.inspection_button.disable()
self.inspection_complete.set('')
self.alignment_button.disable()
self.alignment_complete.set('')
self.photometry_button.disable()
self.photometry_complete.set('')
self.fitting_button.disable()
self.fitting_complete.set('')
