def test_stringify(tmpdir):
im = DataModel()
assert str(im) == '<DataModel>'
im = ImageModel((10, 100))
assert str(im) == '<ImageModel(10, 100)>'
path = str(tmpdir.join("nircam_mask.fits"))
m = MaskModel((2048, 2048))
m.save(path)
m.close()
with MaskModel(path) as im:
assert str(im) == '<MaskModel(2048, 2048) from nircam_mask.fits>'
def save_final_map(bad_pix_map, instrument, detector, hdulist, files, author,
description, pedigree, useafter, history_text, outfile):
"""Save a bad pixel map into a CRDS-formatted reference file
Parameters
----------
bad_pix_map : numpy.ndarray
2D bad pixel array
instrument : str
Name of instrument associated with the bad pixel array
detector : str
Name of detector associated with the bad pixel array
hdulist : astropy.fits.HDUList
HDUList containing "extra" fits keywords
files : list
List of files used to create ``bad_pix_map``
author : str
Author of the bad pixel mask reference file
description : str
CRDS description to use in the final bad pixel file
pedigree : str
CRDS pedigree to use in the final bad pixel file
useafter : str
CRDS useafter string for the bad pixel file
history_text : list
List of strings to add as HISTORY entries to the bad pixel file
outfile : str
Name of the output bad pixel file
"""
yd, xd = bad_pix_map.shape
# Initialize the MaskModel using the hdu_list, so the new keywords will
# be populated
model = MaskModel(hdulist)
model.dq = bad_pix_map
# Create dq_def data
dq_def = badpix_from_flats.create_dqdef()
model.dq_def = dq_def
model.meta.reftype = 'MASK'
model.meta.subarray.name = 'FULL'
model.meta.subarray.xstart = 1
model.meta.subarray.xsize = xd
model.meta.subarray.ystart = 1
model.meta.subarray.ysize = yd
model.meta.instrument.name = instrument.upper()
model.meta.instrument.detector = detector
# Get the fast and slow axis directions from one of the input files
fastaxis, slowaxis = badpix_from_flats.get_fastaxis(files[0])
model.meta.subarray.fastaxis = fastaxis
model.meta.subarray.slowaxis = slowaxis
model.meta.author = author
model.meta.description = description
model.meta.pedigree = pedigree
model.meta.useafter = useafter
# Add information about parameters used
# Parameters from badpix_from_flats
package_note = (
'This file was created using the bad_pixel_mask.py module within the '
'jwst_reffiles package.')
software_dict = {
'name': 'jwst_reffiles.bad_pixel_mask.bad_pixel_mask.py',
'author': 'STScI',
'homepage': 'https://github.com/spacetelescope/jwst_reffiles',
'version': '0.0.0'
}
entry = util.create_history_entry(package_note, software=software_dict)
model.history.append(entry)
model.history.append(
util.create_history_entry('Parameter values and descriptions:'))
dead_search_descrip = (
'dead_search: Boolean, whether or not to run the dead pixel search '
'using flat field files. The value is stored in the {} keyword.'.
format(dead_search_kw))
model.history.append(util.create_history_entry(dead_search_descrip))
low_qe_search_descrip = (
'low_qe_and_open_search: Boolean, whether or not to run the low QE '
'and open pixel search using flat field files. The value is stored in the {} '
'keyword.'.format(low_qe_search_kw))
model.history.append(util.create_history_entry(low_qe_search_descrip))
dead_type_descrip = (
'dead_search_type: Method used to identify dead pixels. The value is stored in the '
'{} keyword.'.format(dead_search_type_kw))
model.history.append(util.create_history_entry(dead_type_descrip))
sigma_descrip = (
'flat_mean_sigma_threshold: Number of standard deviations to use when sigma-clipping to '
'calculate the mean slope image or the mean across the detector. The value '
'used is stored in the {} keyword.'.format(mean_sig_threshold_kw))
model.history.append(util.create_history_entry(sigma_descrip))
norm_descrip = (
'flat_mean_normalization_method: Specify how the mean image is normalized prior to searching '
'for bad pixels. The value used is stored in the {} keyword.'.format(
norm_method_kw))
model.history.append(util.create_history_entry(norm_descrip))
smooth_descrip = (
'smoothing_box_width: Width in pixels of the box kernel to use to compute the '
'smoothed mean image. The value used is stored in the {} keyword.'.
format(smooth_box_width_kw))
model.history.append(util.create_history_entry(smooth_descrip))
smooth_type_descrip = (
'smoothing_type: Type of smoothing to do: Box2D or median filtering. The value used '
'is stored in the {} keyword.'.format(smoothing_type_kw))
model.history.append(util.create_history_entry(smooth_type_descrip))
dead_sig_descrip = (
'Number of standard deviations below the mean at which a pixel is considered dead. '
'The value used is stored in the {} keyword.'.format(
dead_sig_thresh_kw))
model.history.append(util.create_history_entry(dead_sig_descrip))
max_dead_descrip = (
'Maximum normalized signal rate of a pixel that is considered dead. The value '
'used is stored in the {} keyword.'.format(max_dead_sig_kw))
model.history.append(util.create_history_entry(max_dead_descrip))
run_dead_flux_descrip = (
'run_dead_flux_check: Boolean, if True, search for pixels erroneously flagged '
'as dead because they are saturated in all groups. The value used is stored '
'in the {} keyword.'.format(dead_flux_check_kw))
model.history.append(util.create_history_entry(run_dead_flux_descrip))
dead_flux_limit_descrip = (
'Signal limit in raw data above which the pixel is considered not dead. The '
'value used is stored in the {} keyword.'.format(max_dead_sig_kw))
model.history.append(util.create_history_entry(dead_flux_limit_descrip))
max_low_qe_descrip = (
'The maximum normalized signal a pixel can have and be considered low QE. The '
'value used is stored in the {} keyword.'.format(max_low_qe_kw))
model.history.append(util.create_history_entry(max_low_qe_descrip))
max_open_adj_descrip = (
'The maximum normalized signal a pixel adjacent to a low QE pixel can have '
'in order for the low QE pixel to be reclassified as OPEN. The value used '
'is stored in the {} keyword.'.format(max_open_adj_kw))
model.history.append(util.create_history_entry(max_open_adj_descrip))
flat_do_not_use_descrip = (
'List of bad pixel types (from flats) where the DO_NOT_USE flag is also applied. '
'The values used are stored in the {} keyword.'.format(
flat_do_not_use_kw))
model.history.append(util.create_history_entry(flat_do_not_use_descrip))
manual_file_descrip = (
'Name of the ascii file containing a list of pixels to be added manually. The '
'value used is stored in the {} keyword.'.format(manual_flag_kw))
model.history.append(util.create_history_entry(manual_file_descrip))
# Parameters from badpix_from_darks
bad_from_dark_descrip = (
'badpix_from_dark: Boolean, whether or not the bad pixel from dark search '
'has been run. The value is stored in the {} keyword.'.format(
bad_from_dark_kw))
model.history.append(util.create_history_entry(bad_from_dark_descrip))
dark_clip_sig_descrip = (
'Number of sigma to use when sigma-clipping 2D stdev image. The value used '
'is stored in the {} keyword.'.format(dark_clip_sigma_kw))
model.history.append(util.create_history_entry(dark_clip_sig_descrip))
dark_clip_iter_descrip = (
'Max number of iterations to use when sigma clipping mean and stdev values. '
'The value used is stored in the {} keyword.'.format(
dark_clip_iters_kw))
model.history.append(util.create_history_entry(dark_clip_iter_descrip))
dark_noisy_thresh_descrip = (
'Number of sigma above mean noise for noisy pix threshold. The value '
'used is stored in the {} keyword.'.format(dark_noisy_thresh_kw))
model.history.append(util.create_history_entry(dark_noisy_thresh_descrip))
max_sat_frac_descrip = (
'Fraction of integrations within which a pixel must be fully saturated before '
'flagging it as HOT. The value used is stored in the {} keyword.'.
format(max_sat_frac_kw))
model.history.append(util.create_history_entry(max_sat_frac_descrip))
jump_limit_descrip = (
'Maximum number of jumps a pixel can have in an integration before it is flagged as a '
'"high jump" pixel. The value used is stored in the {} keyword.'.
format(jump_limit_kw))
model.history.append(util.create_history_entry(jump_limit_descrip))
jump_ratio_descrip = (
'Cutoff for the ratio of jumps early in the ramp to jumps later in the ramp when '
'looking for RC pixels. The value used is stored in the {} keyword.'.
format(jump_ratio_thresh_kw))
model.history.append(util.create_history_entry(jump_ratio_descrip))
cutoff_frac_descrip = (
'Fraction of the integration to use when comparing the jump rate early in the integration to '
'that across the entire integration. The value used is stored in the {} keyword.'
.format(cutoff_frac_kw))
model.history.append(util.create_history_entry(cutoff_frac_descrip))
ped_sigma_descrip = (
'Pixels with pedestal values more than this limit above the mean are flagged as RC. '
'The value used is stored in the {} keyword.'.format(
pedestal_sig_thresh_kw))
model.history.append(util.create_history_entry(ped_sigma_descrip))
rc_thresh_descrip = (
'Fraction of input files within which a pixel must be identified as an RC pixel before '
'it will be flagged as a permanent RC pixel. The value used is stored in the {} '
'keyword.'.format(rc_frac_thresh_kw))
model.history.append(util.create_history_entry(rc_thresh_descrip))
low_ped_descrip = (
'Fraction of input files within which a pixel must be identified as a low pedestal '
'pixel before it will be flagged as a permanent low pedestal pixel. The value used '
'is stored in the {} keyword.'.format(low_ped_frac_kw))
model.history.append(util.create_history_entry(low_ped_descrip))
high_cr_descrip = (
'Fraction of input files within which a pixel must be flagged as having a high number '
'of jumps before it will be flagged as permanently noisy. The value used '
'is stored in the {} keyword.'.format(high_cr_frac_kw))
dark_do_not_use_descrip = (
'List of bad pixel types (from darks) where the DO_NOT_USE flag is also applied. '
'The values used are stored in the {} keyword.'.format(
dark_do_not_use_kw))
model.history.append(util.create_history_entry(dark_do_not_use_descrip))
# Add the list of input files used to create the map
model.history.append('DATA USED:')
for file in files:
totlen = len(file)
div = np.arange(0, totlen, 60)
for val in div:
if totlen > (val + 60):
model.history.append(
util.create_history_entry(file[val:val + 60]))
else:
model.history.append(util.create_history_entry(file[val:]))
# Add the do not use lists, pixel flag mappings, and user-provided
# history text
for history_entry in history_text:
if history_entry != '':
model.history.append(util.create_history_entry(history_entry))
model.save(outfile, overwrite=True)
print('Final bad pixel mask reference file save to: {}'.format(outfile))
def save_final_map(bad_pix_map, instrument, detector, files, author, description, pedigree, useafter,
history_text, sigma_thresh, smooth_width, dead_sigma_thresh, max_dead_rate,
max_low_qe_rate, max_open_adj_rate, do_not_use_list, outfile):
"""Save a bad pixel map into a CRDS-formatted reference file
Parameters
----------
"""
# Define the non-standard fits header keywords by placing them in a
# fits HDU List
sig_thresh_keyword = 'BPMSIGMA'
smooth_keyword = 'BPMSMOTH'
dead_sigma_keyword = 'BPMDEDSG'
max_dead_keyword = 'BPMMXDED'
max_low_qe_keyword = 'BPMMXLQE'
max_open_adj_keyword = 'BPMMXOAD'
hdu = fits.PrimaryHDU()
hdu.header[sig_thresh_keyword] = sigma_thresh
hdu.header[smooth_keyword] = smooth_width
hdu.header[dead_sigma_keyword] = dead_sigma_thresh
hdu.header[max_dead_keyword] = max_dead_rate
hdu.header[max_low_qe_keyword] = max_low_qe_rate
hdu.header[max_open_adj_keyword] = max_open_adj_rate
hdu_list = fits.HDUList([hdu])
yd, xd = bad_pix_map.shape
# Initialize the MaskModel using the hdu_list, so the new keywords will
# be populated
model = MaskModel(hdu_list)
model.dq = bad_pix_map
# Create dq_def data
dq_def = create_dqdef()
model.dq_def = dq_def
model.meta.reftype = 'MASK'
model.meta.subarray.name = 'FULL'
model.meta.subarray.xstart = 1
model.meta.subarray.xsize = xd
model.meta.subarray.ystart = 1
model.meta.subarray.ysize = yd
model.meta.instrument.name = instrument.upper()
model.meta.instrument.detector = detector
# Get the fast and slow axis directions from one of the input files
fastaxis, slowaxis = get_fastaxis(files[0])
model.meta.subarray.fastaxis = fastaxis
model.meta.subarray.slowaxis = slowaxis
model.meta.author = author
model.meta.description = description
model.meta.pedigree = pedigree
model.meta.useafter = useafter
# Populate "extra" header keywords that will contain parameters used
# in this module
package_note = ('This file was created using the bad_pixel_mask.py module within the '
'jwst_reffiles package.')
software_dict = {'name': 'jwst_reffiles.bad_pixel_mask.py', 'author': 'STScI',
'homepage': 'https://github.com/spacetelescope/jwst_reffiles',
'version': '0.0.0'}
entry = util.create_history_entry(package_note, software=software_dict)
model.history.append(entry)
model.history.append(util.create_history_entry('Parameter values and descriptions:'))
sigma_descrip = ('sigma_thresh: Number of standard deviations to use when sigma-clipping to '
'calculate the mean slope image or the mean across the detector. The value '
'used is stored in the {} keyword.'.format(sig_thresh_keyword))
model.history.append(util.create_history_entry(sigma_descrip))
smooth_descrip = ('smoothing_box_width: Width in pixels of the box kernel to use to compute the '
'smoothed mean image. The value used is stored in the {} keyword.'.format(smooth_keyword))
model.history.append(util.create_history_entry(smooth_descrip))
dead_sig_descrip = ('Number of standard deviations below the mean at which a pixel is considered dead. '
'The value used is stored in the {} keyword.'.format(dead_sigma_keyword))
model.history.append(util.create_history_entry(dead_sig_descrip))
max_dead_descrip = ('Maximum normalized signal rate of a pixel that is considered dead. The value '
'used is stored in the {} keyword.'.format(max_dead_keyword))
model.history.append(util.create_history_entry(max_dead_descrip))
max_low_qe_descrip = ('The maximum normalized signal a pixel can have and be considered low QE. The '
'value used is stored in the {} keyword.'.format(max_low_qe_keyword))
model.history.append(util.create_history_entry(max_low_qe_descrip))
max_open_adj_descrip = ('The maximum normalized signal a pixel adjacent to a low QE pixel can have '
'in order for the low QE pixel to be reclassified as OPEN. The value used '
'is stored in the {} keyword.'.format(max_open_adj_keyword))
model.history.append(util.create_history_entry(max_open_adj_descrip))
do_not_use_descrip = ('List of bad pixel types where the DO_NOT_USE flag is also applied. '
'Values used are: {}'.format(do_not_use_list))
model.history.append(util.create_history_entry(do_not_use_descrip))
# Add the list of input files used to create the map
model.history.append('DATA USED:')
for file in files:
totlen = len(file)
div = np.arange(0, totlen, 60)
for val in div:
if totlen > (val+60):
model.history.append(util.create_history_entry(file[val:val+60]))
else:
model.history.append(util.create_history_entry(file[val:]))
if history_text is not None:
model.history.append(util.create_history_entry(history_text))
model.save(outfile, overwrite=True)
print('Final bad pixel mask reference file save to: {}'.format(outfile))