"""Functions to create a BaseDark for the STIS instrument
#. If not already done, perform bias subtraction
#. If after switch to side-2 electronics, perform temperature scaling
#. Join all imsets from input list into single file
#. combine and cr-reject
#. normalize to e/s by dividing by (exptime/gain)
#. update DQ array with hot pixel information
.. note::
* Side 1 operations ended on May 16, 2001.
* Side 2 operations started on July 10, 2001.
* The dark correction will only be applied to datasets after July 1, 2001 (MJD 52091.0).
"""
from astropy.io import fits
from astropy.stats import sigma_clipped_stats
import numpy as np
from scipy.ndimage.filters import median_filter
import shutil
from . import functions
#-------------------------------------------------------------------------------
[docs]def update_sci(filename):
"""Create the science extension of the baseline dark
.. note:: The input file will be updated in-place.
Parameters
----------
filename: str
name of the file to be updated
"""
with fits.open(filename, mode='update') as hdu:
im_mean, im_median, im_std = sigma_clipped_stats(hdu[('sci', 1)].data,
sigma=5,
iters=50)
fivesig = im_mean + 5.0 * im_std
only_hotpix = np.where(hdu[('sci', 1)].data >= fivesig,
hdu[('sci', 1)].data - im_mean,
0)
#-- I don't see this being used
med_im = median_filter(hdu[('sci', 1)].data, (3, 3))
only_baseline = np.where(hdu[('sci', 1)].data >= fivesig,
med_im,
hdu[('sci', 1)].data)
hdu[('dq', 1)].data = np.where(only_hotpix >= .1,
16,
hdu[('dq', 1)].data)
#-------------------------------------------------------------------------------
[docs]def find_hotpix(filename):
"""Find hotpixels and update DQ array
Pixels hotter that median + 5*sigma will be updated to have a
DQ value of 16.
.. note:: The input file will be updated in-place.
Parameters
----------
filename: str
filename of the input biasfile
"""
with fits.open(filename, mode='update') as hdu:
im_mean, im_median, im_std = sigma_clipped_stats(hdu[('sci', 1)].data,
sigma=3,
iters=40)
five_sigma = im_median + 5 * im_std
index = np.where((hdu[('SCI', 1)].data > five_sigma) &
(hdu[('SCI', 1)].data > im_mean + 0.1))
hdu[('DQ', 1)].data[index] = 16
#-------------------------------------------------------------------------------
[docs]def make_basedark(input_list, refdark_name='basedark.fits', bias_file=None):
"""Make a monthly baseline dark from the input list.
Parameters
----------
input_list: list
list of input dark files
refdark_name: str
name of the output reference file
bias_file: str
bias file to be used in calibration
"""
print('#-------------------------------#')
print('# Running basedark #')
print('#-------------------------------#')
print('output to: %s' % refdark_name)
print('with biasfile %s' % bias_file)
#-- bias subtract data if not already done
flt_list = [functions.bias_subtract_data(item, bias_file) for item in input_list]
for filename in flt_list:
texpstrt = fits.getval(filename, 'texpstrt', 0)
if texpstrt > 52091.0:
functions.apply_dark_correction(filename, texpstrt)
joined_filename = refdark_name.replace('.fits', '_joined.fits')
crj_filename = joined_filename.replace('.fits', '_crj.fits')
#if not bias_file:
# raise IOError('No biasfile specified, this task needs one to run')
print('Joining images')
functions.msjoin(flt_list, joined_filename)
print('Performing CRREJECT')
crdone = functions.bd_crreject(joined_filename)
if not crdone:
functions.bd_calstis(joined_filename, bias_file)
functions.normalize_crj(crj_filename)
shutil.copy(crj_filename, refdark_name)
update_sci(refdark_name)
find_hotpix(refdark_name)
functions.update_header_from_input(refdark_name, input_list)
fits.setval(refdark_name, 'TASKNAME', ext=0, value='BASEDARK')
print('Cleaning...')
functions.RemoveIfThere(crj_filename)
functions.RemoveIfThere(joined_filename)
#map(functions.RemoveIfThere, flt_list)
print('basedark done for {}'.format(refdark_name))
#-------------------------------------------------------------------------------