def bright_defects_jh_task(det_name):
"""JH version of single sensor bright pixels task."""
import glob
import siteUtils
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, bias_filename, bright_defects_task, get_mask_files
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
dark_files \
= siteUtils.dependency_glob(glob_pattern('bright_defects', det_name),
acq_jobname=acq_jobname)
if not dark_files:
print("bright_defects_task: Needed data files missing for detector",
det_name)
return None
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
mask_files = sorted(glob.glob(f'{file_prefix}*mask*.fits'))
bias_frame = bias_filename(run, det_name)
return bright_defects_task(run,
det_name,
dark_files,
gains,
mask_files=mask_files,
bias_frame=bias_frame)
def bf_jh_task(det_name):
"""JH version of single sensor execution of the brighter-fatter task."""
import glob
import siteUtils
from bot_eo_analyses import make_file_prefix, glob_pattern,\
bias_filename, bf_task, find_flat2_bot, get_mask_files,\
get_amplifier_gains
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
flat_files \
= siteUtils.dependency_glob(glob_pattern('brighter_fatter', det_name),
acq_jobname=acq_jobname)
if not flat_files:
print("bf_jh_task: Flat pairs files not found for detector", det_name)
return None
flat_files = [_ for _ in flat_files if 'flat1' in _]
mask_files = get_mask_files(det_name)
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
bias_frame = bias_filename(run, det_name)
return bf_task(run,
det_name,
flat_files,
gains,
mask_files=mask_files,
flat2_finder=find_flat2_bot,
bias_frame=bias_frame)
def ptc_jh_task(det_name):
"""JH version of single sensor execution of the PTC task."""
import glob
import siteUtils
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, bias_filename, ptc_task, get_mask_files
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
flat_files = siteUtils.dependency_glob(glob_pattern('ptc', det_name),
acq_jobname=acq_jobname)
if not flat_files:
print("ptc_task: Flat pairs files not found for detector", det_name)
return None
mask_files = get_mask_files(det_name)
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
bias_frame = bias_filename(run, det_name)
return ptc_task(run,
det_name,
flat_files,
gains,
mask_files=mask_files,
bias_frame=bias_frame)
def read_noise_jh_task(det_name):
"""JH version of the single sensor read noise task."""
import os
import glob
import logging
import siteUtils
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, read_noise_task, get_mask_files
logger = logging.getLogger('read_noise_jh_task')
logger.setLevel(logging.INFO)
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
nbias = os.environ.get('LCATR_NUM_BIAS_FRAMES', 10)
bias_files \
= siteUtils.dependency_glob(glob_pattern('read_noise', det_name),
acq_jobname=acq_jobname)[:nbias]
if not bias_files:
logger.info(
"read_noise_task: Needed data files are missing "
"for detector %s", det_name)
return None
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
mask_files = get_mask_files(det_name)
return read_noise_task(run,
det_name,
bias_files,
gains,
mask_files=mask_files)
def dark_current_jh_task(det_name):
"""JH version of single sensor execution of the dark current task."""
import glob
import siteUtils
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, bias_filename, dark_current_task,\
plot_ccd_total_noise, get_mask_files
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
dark_files \
= siteUtils.dependency_glob(glob_pattern('dark_current', det_name),
acq_jobname=acq_jobname,
description="Dark current frames:")
if not dark_files:
print("dark_current_task: No dark files found for detector", det_name)
return None
mask_files = get_mask_files(det_name)
eotest_results_file \
= siteUtils.dependency_glob('{}_eotest_results.fits'.format(file_prefix),
jobname='read_noise_BOT')[0]
gains = get_amplifier_gains('{}_eotest_results.fits'.format(file_prefix))
bias_frame = bias_filename(run, det_name)
dark_curr_pixels, dark95s \
= dark_current_task(run, det_name, dark_files, gains,
mask_files=mask_files, bias_frame=bias_frame)
plot_ccd_total_noise(run, det_name, dark_curr_pixels, dark95s,
eotest_results_file)
return dark_curr_pixels, dark95s
def flat_pairs_jh_task(det_name):
"""JH version of single sensor execution of the flat pairs task."""
import os
import glob
import siteUtils
import json
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, bias_filename, flat_pairs_task, mondiode_value,\
get_mask_files, medianed_dark_frame
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
flat_files \
= siteUtils.dependency_glob(glob_pattern('flat_pairs', det_name),
acq_jobname=acq_jobname)
if not flat_files:
print("flat_pairs_task: Flat pairs files not found for detector",
det_name)
return None
mask_files = get_mask_files(det_name)
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
bias_frame = bias_filename(run, det_name)
dark_frame = medianed_dark_frame(det_name)
if 'LCATR_PD_CORRECTIONS_FILE' in os.environ:
filter_corr_file = os.environ['LCATR_PD_CORRECTIONS_FILE']
else:
filter_corr_file = os.path.join(os.environ['EOANALYSISJOBSDIR'],
'data', 'pd_filter_corrections.json')
print('Using pd_filter_corrections file:', filter_corr_file, flush=True)
with open(filter_corr_file) as fd:
filter_corrections = json.load(fd)
return flat_pairs_task(run,
det_name,
flat_files,
gains,
mask_files=mask_files,
bias_frame=bias_frame,
mondiode_func=mondiode_value,
dark_frame=dark_frame,
filter_corrections=filter_corrections)
def dark_current_jh_task(det_name):
"""JH version of single sensor execution of the dark current task."""
from collections import defaultdict
from astropy.io import fits
import siteUtils
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, bias_filename, dark_current_task,\
plot_ccd_total_noise, get_mask_files
from bot_data_handling import most_common_dark_files
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
dark_files \
= siteUtils.dependency_glob(glob_pattern('dark_current', det_name),
acq_jobname=acq_jobname,
description="Dark current frames:")
if not dark_files:
print("dark_current_task: No dark files found for detector", det_name)
return None
dark_files_linear_fit = list(dark_files)
dark_files = most_common_dark_files(dark_files)
if len(dark_files_linear_fit) == len(dark_files):
# These data only have one integration time, so skip linear
# fit of dark current signal vs integration time.
dark_files_linear_fit = None
mask_files = get_mask_files(det_name)
eotest_results_file \
= siteUtils.dependency_glob('{}_eotest_results.fits'.format(file_prefix),
jobname='read_noise_BOT')[0]
gains = get_amplifier_gains('{}_eotest_results.fits'.format(file_prefix))
bias_frame = bias_filename(run, det_name)
dark_curr_pixels, dark95s \
= dark_current_task(run, det_name, dark_files, gains,
mask_files=mask_files, bias_frame=bias_frame,
dark_files_linear_fit=dark_files_linear_fit)
plot_ccd_total_noise(run, det_name, dark_curr_pixels, dark95s,
eotest_results_file)
return dark_curr_pixels, dark95s
def traps_jh_task(det_name):
"""JH version of single sensor execution of the traps analysis task."""
import glob
import siteUtils
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, bias_filename, traps_task, get_mask_files
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
trap_files = siteUtils.dependency_glob(glob_pattern('traps', det_name),
acq_jobname=acq_jobname)
if not trap_files:
print("traps_task: No pocket pumping file found for detector",
det_name)
return None
trap_file = trap_files[0]
mask_files = get_mask_files(det_name)
# Omit rolloff defects mask since a trap in the rolloff edge region can
# affect the entire column.
mask_files \
= [item for item in mask_files if item.find('edge_rolloff') == -1]
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
bias_frame = bias_filename(run, det_name)
return traps_task(run,
det_name,
trap_file,
gains,
mask_files=mask_files,
bias_frame=bias_frame)
def nonlinearity_jh_task(det_name):
"""JH version of single sensor execution of the non-linearity task."""
import glob
import siteUtils
from bot_eo_analyses import make_file_prefix, nonlinearity_task, \
get_amplifier_gains
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
try:
detresp_file \
= siteUtils.dependency_glob(f'{det_name}*_det_response.fits',
jobname='flat_pairs_BOT')[0]
except IndexError:
print("nonlinearity_task: detector response file not found for",
det_name)
return None
outfile = f'{file_prefix}_nlc.fits'
return nonlinearity_task(run, det_name, detresp_file, gains, outfile)
def raft_results_task(raft_name):
"""Task to aggregate data for raft-level plots and results."""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import lsst.eotest.sensor as sensorTest
import lsst.eotest.raft as raftTest
import siteUtils
from camera_components import camera_info
from bot_eo_analyses import get_raft_files_by_slot, make_file_prefix,\
get_amplifier_gains, get_analysis_types
def plt_savefig(filename):
plt.savefig(filename)
plt.close()
# Get results files for each CCD in the raft.
try:
results_files \
= get_raft_files_by_slot(raft_name, 'eotest_results.fits')
print("results_files:", results_files)
except FileNotFoundError:
print("No raft-level results for", raft_name)
results_files = {}
# Determine the total number of pixels and number of edge rolloff
# pixels for the types of CCDs in this raft and update the results
# files. This info will be used in computing the pixel defect
# compliance. Use one of the median bias files for this since they
# should be available no matter which analysis tasks are run.
bias_frames = get_raft_files_by_slot(raft_name,
'median_bias.fits',
jobname='bias_frame_BOT')
try:
mask_files = get_raft_files_by_slot(raft_name,
'edge_rolloff_mask.fits')
except FileNotFoundError:
input_mask = None
else:
input_mask = list(mask_files.values())[0]
total_num, rolloff_mask \
= sensorTest.pixel_counts(list(bias_frames.values())[0],
input_mask=input_mask)
# Exposure time (in seconds) for 95th percentile dark current shot
# noise calculation.
exptime = 15.
# Update the eotest results files.
analysis_types = get_analysis_types()
for filename in results_files.values():
eotest_results = sensorTest.EOTestResults(filename)
eotest_results.add_ccd_result('TOTAL_NUM_PIXELS', total_num)
eotest_results.add_ccd_result('ROLLOFF_MASK_PIXELS', rolloff_mask)
shot_noise = eotest_results['DARK_CURRENT_95'] * exptime
total_noise = np.sqrt(eotest_results['READ_NOISE']**2 + shot_noise)
add_max_frac_dev = ('MAX_FRAC_DEV' not in eotest_results.colnames
and 'linearity' in analysis_types)
for i, amp in enumerate(eotest_results['AMP']):
if add_max_frac_dev:
eotest_results.add_seg_result(amp, 'MAX_FRAC_DEV', 0.)
eotest_results.add_seg_result(amp, 'DC95_SHOT_NOISE',
np.float(shot_noise[i]))
try:
eotest_results['TOTAL_NOISE'][i] = total_noise[i]
except KeyError:
eotest_results.add_seg_result(amp, 'TOTAL_NOISE',
np.float(total_noise[i]))
eotest_results.write(filename)
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, raft_name)
title = '{}, {}'.format(run, raft_name)
gains = {
slot_name: get_amplifier_gains(results_files[slot_name])
for slot_name in results_files
}
# Update the gains in the results files with the retrieved values.
for slot_name, ccd_gains in gains.items():
try:
results = sensorTest.EOTestResults(results_files[slot_name])
except KeyError:
continue
else:
for amp, gain in ccd_gains.items():
results.add_seg_result(amp, 'GAIN', gain)
results.write()
# Extract dark currents for each amplifier in the raft.
dark_currents = dict()
for slot_name, results_file in results_files.items():
results = sensorTest.EOTestResults(results_file)
try:
dark_currents[slot_name] \
= dict(_ for _ in zip(results['AMP'],
results['DARK_CURRENT_MEDIAN']))
except KeyError:
dark_currents[slot_name] = dict({amp: 0 for amp in range(1, 17)})
png_files = []
# Median bias mosaic
median_bias = raftTest.make_raft_mosaic(bias_frames, bias_subtract=False)
median_bias.plot(title='%s, median bias frames' % title,
annotation='ADU/pixel',
rotate=180)
png_files.append('{}_median_bias.png'.format(file_prefix))
plt_savefig(png_files[-1])
del median_bias
# Dark mosaic
dark_files = None
try:
dark_files = get_raft_files_by_slot(raft_name, 'median_dark_bp.fits')
except FileNotFoundError:
try:
dark_files = get_raft_files_by_slot(raft_name,
'median_dark_current.fits')
except FileNotFoundError as eobj:
print(eobj)
if dark_files is not None:
dark_mosaic = raftTest.make_raft_mosaic(dark_files,
gains=gains,
bias_frames=bias_frames)
dark_mosaic.plot(
title='{}, medianed dark frames'.format(title),
annotation='e-/pixel, gain-corrected, bias-subtracted',
rotate=180)
png_files.append('{}_medianed_dark.png'.format(file_prefix))
plt_savefig(png_files[-1])
del dark_mosaic
# High flux superflat mosaic.
try:
sflat_high_files \
= get_raft_files_by_slot(raft_name, 'superflat_high.fits')
except FileNotFoundError as eobj:
print(eobj)
else:
sflat_high = raftTest.make_raft_mosaic(sflat_high_files,
gains=gains,
bias_frames=bias_frames,
dark_currents=dark_currents)
sflat_high.plot(title='%s, high flux superflat' % title,
annotation='e-/pixel, gain-corrected, bias-subtracted',
rotate=180)
png_files.append('{}_superflat_high.png'.format(file_prefix))
plt_savefig(png_files[-1])
del sflat_high
# Low flux superflat mosaic.
try:
sflat_low_files \
= get_raft_files_by_slot(raft_name, 'superflat_low.fits')
except FileNotFoundError as eobj:
print(eobj)
else:
sflat_low = raftTest.make_raft_mosaic(sflat_low_files,
gains=gains,
bias_frames=bias_frames,
dark_currents=dark_currents)
sflat_low.plot(title='%s, low flux superflat' % title,
annotation='e-/pixel, gain-corrected, bias-subtracted',
rotate=180)
png_files.append('{}_superflat_low.png'.format(file_prefix))
plt_savefig(png_files[-1])
del sflat_low
# QE images at various wavelengths and filters
acq_jobname = siteUtils.getProcessName('BOT_acq')
for wl in ('SDSSu', 'SDSSg', 'SDSSr', 'SDSSi', 'SDSSz', 'SDSSY', '480nm',
'650nm', '750nm', '870nm', '950nm', '970nm'):
print("Processing %s image" % wl)
pattern = 'lambda_flat_{}*/*_{}_*.fits'.format(wl, raft_name)
print(pattern)
print(acq_jobname)
files = siteUtils.dependency_glob(pattern, acq_jobname=acq_jobname)
if not files:
print("no files found")
continue
lambda_files = dict()
for item in files:
slot_name = os.path.basename(item).split('_')[-1].split('.')[0]
lambda_files[slot_name] = item
flat = raftTest.make_raft_mosaic(lambda_files,
gains=gains,
bias_frames=bias_frames,
dark_currents=dark_currents)
flat.plot(title='%s, %s' % (title, wl),
annotation='e-/pixel, gain-corrected, bias-subtracted',
rotate=180)
png_files.append('{}_{}_flat.png'.format(file_prefix, wl))
plt_savefig(png_files[-1])
del flat
# TODO: QE summary plot
# Plots of read noise, nonlinearity, serial and parallel CTI,
# PSF size, and gains from Fe55 and PTC.
spec_plots = raftTest.RaftSpecPlots(results_files)
columns = 'READ_NOISE DC95_SHOT_NOISE TOTAL_NOISE'.split()
try:
spec_plots.make_multi_column_plot(columns,
'noise per pixel (-e rms)',
spec=9,
title=title,
ybounds=(-1, 100))
png_files.append('%s_total_noise.png' % file_prefix)
plt_savefig(png_files[-1])
except KeyError:
pass
try:
if 'linearity' in analysis_types:
spec_plots.make_plot('MAX_FRAC_DEV',
'non-linearity (max. fractional deviation)',
spec=0.03,
title=title,
ybounds=(0, 0.1))
png_files.append('%s_linearity.png' % file_prefix)
plt_savefig(png_files[-1])
except KeyError:
pass
try:
spec_plots.make_multi_column_plot(
('CTI_LOW_SERIAL', 'CTI_HIGH_SERIAL'),
'Serial CTI (ppm)',
spec=(5e-6, 3e-5),
title=title,
yscaling=1e6,
yerrors=True,
colors='br',
ybounds=(-1e-5, 6e-5))
png_files.append('%s_serial_cti.png' % file_prefix)
plt_savefig(png_files[-1])
except KeyError:
pass
try:
spec_plots.make_multi_column_plot(
('CTI_LOW_PARALLEL', 'CTI_HIGH_PARALLEL'),
'Parallel CTI (ppm)',
spec=3e-6,
title=title,
yscaling=1e6,
yerrors=True,
colors='br',
ybounds=(-1e-5, 6e-5))
png_files.append('%s_parallel_cti.png' % file_prefix)
plt_savefig(png_files[-1])
except KeyError:
pass
try:
spec_plots.make_plot('PSF_SIGMA',
'PSF sigma (microns)',
spec=5.,
title=title,
ybounds=(0, 5.2))
png_files.append('%s_psf_sigma.png' % file_prefix)
plt_savefig(png_files[-1])
except KeyError:
# PSF_SIGMA not available so skip this plot
pass
try:
spec_plots.make_multi_column_plot(('GAIN', 'PTC_GAIN'),
'System Gain (e-/ADU)',
yerrors=True,
title=title,
colors='br',
ybounds=(0, 3))
png_files.append('%s_system_gain.png' % file_prefix)
plt_savefig(png_files[-1])
except KeyError:
# PTC_GAIN data not available so skip this plot.
pass
try:
if 'dark' in analysis_types:
spec_plots.make_plot('DARK_CURRENT_95',
'95th percentile dark current (e-/pixel/s)',
spec=0.2,
title=title,
ybounds=(-0.01, 1))
png_files.append('%s_dark_current.png' % file_prefix)
plt_savefig(png_files[-1])
except KeyError:
pass
# Make bias frame stats time history plots for the current raft.
pattern = f'{raft_name}_{run}_bias_frame_stats.pickle'
try:
stats_file = siteUtils.dependency_glob(pattern,
jobname='bias_frame_BOT')[0]
except IndexError:
pass
else:
file_prefix = make_file_prefix(run, raft_name)
df_raft = pd.read_pickle(stats_file)
if raft_name in 'R00 R04 R40 R44':
slots = 'SG0 SW1 SW0 SG1'.split()
else:
slots = 'S20 S21 S22 S10 S11 S12 S00 S01 S02'.split()
t0 = int(np.min(df_raft['MJD']))
fig = plt.figure(figsize=(12, 12))
for i, slot in enumerate(slots, 1):
fig.add_subplot(3, 3, i)
df = df_raft.query(f'slot == "{slot}"')
amps = sorted(list(set(df['amp'])))
for amp in amps:
my_df = df.query(f'amp == {amp}')
plt.scatter(my_df['MJD'] - t0,
my_df['mean'],
s=2,
label=f'{amp}')
xmin, xmax, _, _ = plt.axis()
plt.xlim(xmin, 1.2 * (xmax - xmin) + xmin)
plt.legend(fontsize='x-small')
plt.xlabel(f'MJD - {t0}')
plt.ylabel('mean signal (ADU)')
plt.title(slot)
plt.tight_layout(rect=(0, 0, 1, 0.95))
plt.suptitle(f'{file_prefix}, bias stability, mean signal')
png_file = f'{file_prefix}_bias_stability_mean.png'
png_files.append(png_file)
plt_savefig(png_file)
fig = plt.figure(figsize=(12, 12))
for i, slot in enumerate(slots, 1):
fig.add_subplot(3, 3, i)
df = df_raft.query(f'slot == "{slot}"')
amps = sorted(list(set(df['amp'])))
for amp in amps:
my_df = df.query(f'amp == {amp}')
plt.scatter(my_df['MJD'] - t0,
my_df['stdev'],
s=2,
label=f'{amp}')
xmin, xmax, _, _ = plt.axis()
plt.xlim(xmin, 1.2 * (xmax - xmin) + xmin)
plt.legend(fontsize='x-small')
plt.xlabel(f'MJD - {t0}')
plt.ylabel('stdev (ADU)')
plt.title(slot)
plt.tight_layout(rect=(0, 0, 1, 0.95))
plt.suptitle(f'{file_prefix}, bias stability, stdev')
png_file = f'{file_prefix}_bias_stability_stdev.png'
png_files.append(png_file)
plt_savefig(png_file)
png_file_list = '{}_raft_results_task_png_files.txt'.format(raft_name)
with open(png_file_list, 'w') as output:
for item in png_files:
if os.path.isfile(item):
output.write('{}\n'.format(item))
return None
def cte_jh_task(det_name):
"""JH version of single sensor execution of the CTE task."""
import os
import glob
import shutil
import siteUtils
import lsst.eotest.sensor as sensorTest
from bot_eo_analyses import make_file_prefix, glob_pattern,\
get_amplifier_gains, bias_filename, cte_task, plot_cte_results,\
get_mask_files
run = siteUtils.getRunNumber()
file_prefix = make_file_prefix(run, det_name)
acq_jobname = siteUtils.getProcessName('BOT_acq')
sflat_high_files \
= siteUtils.dependency_glob(glob_pattern('cte_high', det_name),
acq_jobname=acq_jobname)
sflat_low_files \
= siteUtils.dependency_glob(glob_pattern('cte_low', det_name),
acq_jobname=acq_jobname)
if not sflat_high_files and not sflat_low_files:
print("cte_task: Superflat files not found for detector", det_name)
return None
mask_files = get_mask_files(det_name)
eotest_results_file = '{}_eotest_results.fits'.format(file_prefix)
gains = get_amplifier_gains(eotest_results_file)
# Write gains to local eotest_results_file, which cte_task will update.
namps = 16 if 'SW' not in det_name else 8
results = sensorTest.EOTestResults(eotest_results_file, namps=namps)
for amp, gain in gains.items():
results.add_seg_result(amp, 'GAIN', gain)
results.write()
bias_frame = bias_filename(run, det_name)
# Omit rolloff defects mask since it would mask some of the edges used
# in the eper method.
mask_files \
= [item for item in mask_files if item.find('edge_rolloff') == -1]
png_files = []
for flux_level, sflat_files in zip(('high', 'low'),
(sflat_high_files, sflat_low_files)):
superflat_file = cte_task(run, det_name, sflat_files, gains,
mask_files=mask_files, flux_level=flux_level,
bias_frame=bias_frame)
png_files.extend(plot_cte_results(run, det_name, superflat_file,
eotest_results_file,
mask_files=mask_files))
png_file_list = '{}_cte_task_png_files.txt'.format(det_name)
with open(png_file_list, 'w') as output:
for item in png_files:
if os.path.isfile(item):
output.write('{}\n'.format(item))
return None
