def __init__(self, log):
MainWindow.__init__(self, log, name='HOPS - Reduction', position=2)
fits_name = find_fits_files(self.log.get_param('observation_files'))[0]
fits = get_fits_data(fits_name)
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[0].data[0])/len(fits[0].data)))
self.progress_figure.load_fits(fits[0], input_name=os.path.split(fits_name)[1])
self.progress_bias = self.Progressbar(task="Creating master bias")
self.progress_dark = self.Progressbar(task="Creating master dark")
self.progress_flat = self.Progressbar(task="Creating master flat")
self.progress_science = self.Progressbar(task="Reducing data and calculating statistics")
self.progress_science_loop = self.CheckButton(text='Show all frames', initial=0)
self.setup_window([
[[self.progress_figure, 0, 2]],
[[self.progress_bias, 0, 2]],
[[self.progress_dark, 0, 2]],
[[self.progress_flat, 0, 2]],
[[self.progress_science, 0], [self.progress_science_loop, 1]],
[[self.Button(text='STOP REDUCTION & RETURN TO MAIN MENU', command=self.trigger_exit), 0, 2]],
[]
])
self.set_close_button_function(self.trigger_exit)
def get_bias(self):
if self.exit or len(self.bias_files) == 0:
self.after(self.get_master_bias)
else:
if self.bias_counter == 0:
self.progress_bias.initiate(10 * len(self.bias_files))
fits = get_fits_data(self.bias_files[self.bias_counter])
self.bias_frames.append(np.ones_like(fits[0].data) * fits[0].data)
self.progress_bias.update()
self.bias_counter += 1
if self.bias_counter >= len(self.bias_files):
self.after(self.get_master_bias)
else:
self.after(self.get_bias)
def get_dark(self):
if self.exit or len(self.dark_files) == 0:
self.after(self.get_master_dark)
else:
if self.dark_counter == 0:
self.progress_dark.initiate(10 * len(self.dark_files))
fits = get_fits_data(self.dark_files[self.dark_counter])
dark_frame = np.ones_like(fits[0].data) * fits[0].data
self.dark_frames.append((dark_frame - self.master_bias) / fits[0].header[self.log.get_param('exposure_time_key')])
self.progress_dark.update()
self.dark_counter += 1
if self.dark_counter >= len(self.dark_files):
self.after(self.get_master_dark)
else:
self.after(self.get_dark)
def update_observation_files(self, *event):
check = find_fits_files(self.observation_files.get())
self.science_files = len(check)
if self.science_files == 0:
self.observation_files_test.set(
'{0} files found\nyou cannot proceed'.format(len(check)))
self.science_header = []
header_list = [' Keywords: Values:', ' ']
else:
self.observation_files_test.set('{0} files found - OK'.format(
len(check)))
self.science_header = []
header_list = [' Keywords: Values:', ' ']
self.science_header = get_fits_data(check[0])[0].header
for ii in self.science_header:
if ii != '':
header_list.append(' {0}{1}{2}'.format(
str(ii[:10]), ' ' * (15 - len(str(ii[:10]))),
str(self.science_header[ii])))
self.header_list.update_list(header_list)
self.update_exposure_time_key()
self.update_observation_date_key()
self.update_observation_time_key()
self.update_ra_dec()
self.choose_target()
self.update_observing_info()
self.update_save_button()
def load_fits(self, input, input_name=None, input_options=None, draw=True):
if isinstance(input, str):
fits = get_fits_data(input)
input_name = os.path.split(input)[1]
elif isinstance(input, pf.ImageHDU) or isinstance(
input, pf.PrimaryHDU) or isinstance(input, pf.CompImageHDU):
fits = [input]
else:
raise RuntimeError('Invalid input ', type(input))
if input_name:
if len(input_name) > 50:
split = [
input_name[i:i + 50]
for i in range(0, len(input_name), 50)
]
input_name = '\n'.join(split)
self.fits_name.set(input_name)
self.data = fits[0].data
try:
self.mean = fits[0].header[self.window.log.mean_key]
self.std = fits[0].header[self.window.log.std_key]
except:
self.mean = np.median(fits[0].data)
self.std = plc.mad(fits[0].data) * 1.5
self.black_entry['from_'] = np.sqrt(max(0, np.min(self.data)))
self.black_entry['to'] = np.sqrt(np.max(self.data))
self.white_entry['from_'] = np.sqrt(max(0, np.min(self.data)))
self.white_entry['to'] = np.sqrt(np.max(self.data))
self.ax.cla()
if not self.show_axes:
self.ax.axis('off')
self.ax.tick_params(axis='y', rotation=90)
self.vmin.set(
max(1, int(self.mean + self.window.log.frame_low_std * self.std)))
self.vmax.set(
max(1,
int(self.mean + self.window.log.frame_upper_std * self.std)))
self.gamma.set(0)
if input_options:
if input_options[0] != 'auto':
self.vmin.set(input_options[0])
if input_options[1] != 'auto':
self.vmax.set(input_options[1])
self.gamma.set(input_options[2])
self.flip.set(input_options[3])
self.mirror.set(input_options[4])
self.white_sky.set(input_options[5])
self.sqrt_vmin.set(np.sqrt(self.vmin.get()))
self.sqrt_vmax.set(np.sqrt(self.vmax.get()))
self.image = self.ax.imshow(self.data**(10**-self.gamma.get()),
origin='lower',
extent=(0, len(self.data[0]), 0,
len(self.data)),
cmap=Greys,
vmin=self.vmin.get(),
vmax=self.vmax.get())
if self.white_sky.get():
self.image.set_cmap(Greys)
else:
self.image.set_cmap(Greys_r)
if self.flip.get():
self.ax.set_ylim(len(self.data) + 5, 0)
else:
self.ax.set_ylim(0, len(self.data) + 5)
if self.mirror.get():
self.ax.set_xlim(len(self.data[0]) + 5, 0)
else:
self.ax.set_xlim(0, len(self.data[0]) + 5)
if draw:
self.draw()
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 reduce_science(self):
# correct each observation_files file
if self.exit:
self.after(self.save)
else:
xx = time.time()
if self.science_counter == 0:
self.progress_science.initiate(len(self.science_files))
science_file = self.science_files[self.science_counter]
# correct it with master bias_files, master dark_files and master flat_files
fits = get_fits_data(science_file)
exp_time = fits[0].header[self.log.get_param('exposure_time_key')]
data_frame = np.ones_like(fits[0].data) * fits[0].data
data_frame = (data_frame - self.master_bias - exp_time * self.master_dark) / self.master_flat
data_frame[np.where(np.isnan(data_frame))] = 0
if self.log.get_param('bin_fits') > 1:
data_frame = plc.bin_frame(data_frame, self.log.get_param('bin_fits'))
try:
distribution = plc.one_d_distribution(data_frame.flatten()[::int(200000.0/self.log.bin_to)],
gaussian_fit=True, mad_filter=5.0)
mean = distribution[2]
std = distribution[3]
except:
mean = np.median(data_frame)
std = plc.mad(data_frame) * 1.5
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
psf = plc.fast_psf_find(data_frame, mean, std, 0.95 * self.log.get_param('burn_limit'))
if np.isnan(psf):
psf = self.psf + 10
skip = True
else:
self.psf = psf
skip = False
if self.log.get_param('observation_date_key') == self.log.get_param('observation_time_key'):
observation_time = ' '.join(fits[0].header[self.log.get_param('observation_date_key')].split('T'))
else:
observation_time = ' '.join([fits[0].header[self.log.get_param('observation_date_key')].split('T')[0],
fits[0].header[self.log.get_param('observation_time_key')]])
observation_time = plc.UTC(observation_time)
if self.log.get_param('time_stamp') == 'exposure start':
julian_date = observation_time.jd
elif self.log.get_param('time_stamp') == 'mid-exposure':
julian_date = observation_time.jd - 0.5 * exp_time / 60.0 / 60.0 / 24.0
elif self.log.get_param('time_stamp') == 'exposure end':
julian_date = observation_time.jd - exp_time / 60.0 / 60.0 / 24.0
else:
raise RuntimeError('Not acceptable time stamp.')
fits[0].header.set(self.log.mean_key, mean)
fits[0].header.set(self.log.std_key, std)
fits[0].header.set(self.log.psf_key, psf)
fits[0].header.set(self.log.time_key, julian_date)
# write the new fits file
# important to keep it like this for windows!
time_in_file = observation_time.utc.isoformat()
time_in_file = time_in_file.split('.')[0]
time_in_file = time_in_file.replace('-', '_').replace(':', '_').replace('T', '_')
new_name = '{0}{1}_{2}'.format(self.log.reduction_prefix, time_in_file, science_file.split(os.sep)[-1])
hdu = pf.ImageHDU(header=fits[0].header, data=np.array(data_frame, dtype=np.float32))
plc.save_fits(pf.HDUList([pf.PrimaryHDU(), hdu]), os.path.join(self.log.reduction_directory, new_name))
self.all_frames[new_name] = {self.log.mean_key: mean, self.log.std_key: std, self.log.psf_key: psf,
self.log.time_key: julian_date,
self.log.get_param('exposure_time_key'): fits[0].header[self.log.get_param('exposure_time_key')],
self.log.skip_key: skip,
self.log.align_x0_key: False,
self.log.align_y0_key: False,
self.log.align_u0_key: False}
if self.progress_science_loop.get() or self.science_counter == 0:
self.progress_figure.load_fits(hdu, new_name)
# counter
self.progress_science.update()
self.science_counter += 1
if self.science_counter >= len(self.science_files):
self.after(self.save)
else:
self.after(self.reduce_science)
