def persist_traps_analysis():
"""Persist the results from the traps analysis."""
raft_id = siteUtils.getUnitId()
raft = camera_components.Raft.create_from_etrav(raft_id)
results = []
for slot, sensor_id in raft.items():
ccd_vendor = sensor_id.split('-')[0].upper()
trap_file = '%s_traps.fits' % sensor_id
eotestUtils.addHeaderData(trap_file,
LSST_NUM=sensor_id,
TESTTYPE='TRAP',
DATE=eotestUtils.utc_now_isoformat(),
CCD_MANU=ccd_vendor)
results.append(siteUtils.make_fileref(trap_file, folder=slot))
mask_file = '%s_traps_mask.fits' % sensor_id
results.append(siteUtils.make_fileref(mask_file, folder=slot))
results_file = '%s_eotest_results.fits' % sensor_id
data = sensorTest.EOTestResults(results_file)
amps = data['AMP']
num_traps = data['NUM_TRAPS']
for amp, ntrap in zip(amps, num_traps):
results.append(
lcatr.schema.valid(lcatr.schema.get('traps_raft'),
amp=amp,
num_traps=ntrap,
slot=slot,
sensor_id=sensor_id))
return results
def __init__(self, outfile='eotest_results.fits'):
"""
Repackage per amp information in the json-formatted
summary.lims files from each analysis task into the
EOTestResults-formatted output.
"""
self.eotest_results = sensorTest.EOTestResults(outfile)
def validate_traps(results, det_names):
"""Validate and persist trap results."""
run = siteUtils.getRunNumber()
missing_det_names = []
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
trap_file = '%s_traps.fits' % file_prefix
if not os.path.isfile(trap_file):
missing_det_names.append(det_name)
continue
eotestUtils.addHeaderData(trap_file,
TESTTYPE='TRAP',
DATE=eotestUtils.utc_now_isoformat())
results.append(siteUtils.make_fileref(trap_file))
mask_file = '%s_traps_mask.fits' % file_prefix
results.append(siteUtils.make_fileref(mask_file))
results_file = '%s_eotest_results.fits' % file_prefix
data = sensorTest.EOTestResults(results_file)
amps = data['AMP']
num_traps = data['NUM_TRAPS']
for amp, ntrap in zip(amps, num_traps):
results.append(
lcatr.schema.valid(lcatr.schema.get('traps_BOT'),
amp=amp,
num_traps=ntrap,
slot=slot,
raft=raft))
report_missing_data("validate_traps", missing_det_names)
return results
def _get_amplifier_gains(file_pattern=None):
"""Extract the gains for each amp in an eotest_results file."""
if (os.environ.get('LCATR_USE_UNIT_GAINS', 'False') == 'True'
or file_pattern is None):
print("_get_amplifier_gains: using unit gains")
return {amp: 1 for amp in range(1, 17)}
# Attempt to retrieve gains from fe55_analysis_BOT then ptc_BOT.
# If neither are available, then use unit gains.
print("_get_amplifier_gains: trying fe55_analysis_BOT")
results_files = siteUtils.dependency_glob(file_pattern,
jobname='fe55_analysis_BOT')
if not results_files:
print("_get_amplifier_gains: trying ptc_BOT")
results_files = siteUtils.dependency_glob(file_pattern,
jobname='ptc_BOT')
if not results_files:
print("_get_amplifier_gains: both fe55 and ptc retrievals failed. "
"using unit gains.")
return {amp: 1 for amp in range(1, 17)}
eotest_results_file = results_files[0]
data = sensorTest.EOTestResults(eotest_results_file)
amps = data['AMP']
gains = data['GAIN']
return dict(zip(amps, gains))
def write_nominal_gains(sensor_id, gain=1):
"""Write nominal gains in lieu of doing the Fe55 analysis."""
import lsst.eotest.sensor as sensorTest
results_file = '%s_eotest_results.fits' % sensor_id
results = sensorTest.EOTestResults(results_file)
for amp in range(1, 17):
results.add_seg_result(amp, 'GAIN', gain)
results.write(clobber=True)
def __init__(self, outfile='eotest_results.fits', namps=16):
"""
Constructor
Parameters
----------
outfile : str, optional
Output filename of FITS file to contain the results as
written by self.eotest_results.
"""
self.eotest_results = sensorTest.EOTestResults(outfile, namps=namps)
def validate_brighter_fatter(results, det_names):
"""Validate the brighter-fatter results."""
run = siteUtils.getRunNumber()
missing_det_names = set()
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
bf_results = '%s_bf.fits' % file_prefix
if not os.path.isfile(bf_results):
missing_det_names.add(det_name)
continue
eotestUtils.addHeaderData(bf_results,
TESTTYPE='FLAT',
DATE=eotestUtils.utc_now_isoformat())
results.append(siteUtils.make_fileref(bf_results))
results_file = '%s_eotest_results.fits' % file_prefix
data = sensorTest.EOTestResults(results_file)
columns = (data['AMP'], data['BF_XCORR'], data['BF_XCORR_ERR'],
data['BF_YCORR'], data['BF_YCORR_ERR'], data['BF_SLOPEX'],
data['BF_SLOPEX_ERR'], data['BF_SLOPEY'],
data['BF_SLOPEY_ERR'], data['BF_MEAN'])
for amp, bf_xcorr, bf_xcorr_err, bf_ycorr, bf_ycorr_err, \
bf_slopex, bf_slopex_err, bf_slopey, bf_slopey_err, bf_mean \
in zip(*columns):
results.append(
lcatr.schema.valid(lcatr.schema.get('brighter_fatter_BOT'),
amp=amp,
bf_xcorr=bf_xcorr,
bf_xcorr_err=bf_xcorr_err,
bf_ycorr=bf_ycorr,
bf_ycorr_err=bf_ycorr_err,
bf_slopex=bf_slopex,
bf_slopex_err=bf_slopex_err,
bf_slopey=bf_slopey,
bf_slopey_err=bf_slopey_err,
bf_mean=bf_mean,
slot=slot,
raft=raft))
# Persist the png files.
metadata = dict(DETECTOR=det_name,
RUN=run,
TESTTYPE='FLAT',
TEST_CATEGORY='EO')
results.extend(
siteUtils.persist_png_files('%s*brighter-fatter.png' % file_prefix,
file_prefix,
metadata=metadata))
return results
def validate_read_noise(results, det_names):
"""Validate and persist read noise results."""
run = siteUtils.getRunNumber()
missing_det_names = set()
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
read_noise_file = '%s_eotest_results.fits' % file_prefix
if not os.path.isfile(read_noise_file):
# No data for this detector, so note that and continue
# with the others.
missing_det_names.add(det_name)
continue
data = sensorTest.EOTestResults(read_noise_file)
amps = data['AMP']
read_noise_data = data['READ_NOISE']
system_noise_data = data['SYSTEM_NOISE']
total_noise_data = data['TOTAL_NOISE']
for amp, read_noise, system_noise, total_noise \
in zip(amps, read_noise_data, system_noise_data, total_noise_data):
results.append(lcatr.schema.valid(
lcatr.schema.get('read_noise_BOT'),
amp=amp, read_noise=read_noise, system_noise=system_noise,
total_noise=total_noise, slot=slot, raft=raft))
files = glob.glob('%s_read_noise?*.fits' % file_prefix)
for fitsfile in files:
eotestUtils.addHeaderData(fitsfile, TESTTYPE='FE55',
DATE=eotestUtils.utc_now_isoformat())
data_products = [siteUtils.make_fileref(item) for item in files]
results.extend(data_products)
# Persist the png files.
metadata = dict(DETECTOR=det_name, TESTTYPE='FE55', TEST_CATEGORY='EO',
RUN=run)
filename = '%s_correlated_noise.png' % file_prefix
results.extend(siteUtils.persist_png_files(filename, file_prefix,
metadata=metadata))
# Persist the raft-level overscan correlation plots.
for raft in camera_info.get_installed_raft_names():
metadata = dict(TESTTYPE='FE55', TEST_CATEGORY='EO', RAFT=raft, RUN=run)
file_prefix = make_file_prefix(run, raft)
filename = '%s_overscan_correlations.png' % file_prefix
results.extend(siteUtils.persist_png_files(filename, file_prefix,
metadata=metadata))
report_missing_data("validate_read_noise", missing_det_names)
return results
def __init__(self, results_files):
"""
Constructor.
Parameters
----------
results_files : dict
"""
self.results = dict()
self.sensor_ids = dict()
for slot, filename in results_files.items():
self.sensor_ids[slot] = filename.split('_')[0]
self.results[slot] = sensorTest.EOTestResults(filename)
def validate_ptc(results, det_names):
"""Validate the PTC results."""
run = siteUtils.getRunNumber()
missing_det_names = []
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
ptc_results = '%s_ptc.fits' % file_prefix
if not os.path.isfile(ptc_results):
missing_det_names.append(det_name)
continue
eotestUtils.addHeaderData(ptc_results,
TESTTYPE='FLAT',
DATE=eotestUtils.utc_now_isoformat())
results.append(siteUtils.make_fileref(ptc_results))
results_file = '%s_eotest_results.fits' % file_prefix
data = sensorTest.EOTestResults(results_file)
columns = (data['AMP'], data['PTC_GAIN'], data['PTC_GAIN_ERROR'],
data['PTC_A00'], data['PTC_A00_ERROR'], data['PTC_NOISE'],
data['PTC_NOISE_ERROR'], data['PTC_TURNOFF'])
for amp, gain, gain_error, a00, a00_error,\
noise, noise_error, turnoff in zip(*columns):
results.append(
lcatr.schema.valid(lcatr.schema.get('ptc_BOT'),
amp=amp,
ptc_gain=gain,
ptc_gain_error=gain_error,
ptc_a00=a00,
ptc_a00_error=a00_error,
ptc_noise=noise,
ptc_noise_error=noise_error,
ptc_turnoff=turnoff,
slot=slot,
raft=raft))
# Persist the png files.
metadata = dict(DETECTOR=det_name,
RUN=run,
TESTTYPE='FLAT',
TEST_CATEGORY='EO')
results.extend(
siteUtils.persist_png_files('%s*ptcs.png' % file_prefix,
file_prefix,
metadata=metadata))
report_missing_data("validate_ptc", missing_det_names)
return results
def validate_flat_pairs(results, det_names):
"""Validate the flat pair analysis results."""
run = siteUtils.getRunNumber()
missing_det_names = set()
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
det_resp_data = '%s_det_response.fits' % file_prefix
if not os.path.isfile(det_resp_data):
missing_det_names.add(det_name)
continue
eotestUtils.addHeaderData(det_resp_data, DETECTOR=det_name,
TESTTYPE='FLAT',
DATE=eotestUtils.utc_now_isoformat())
results.append(siteUtils.make_fileref(det_resp_data))
results_file = '%s_eotest_results.fits' % file_prefix
data = sensorTest.EOTestResults(results_file)
amps = data['AMP']
max_observed_signal_data = data['MAX_OBSERVED_SIGNAL']
max_frac_dev_data = data['MAX_FRAC_DEV']
row_mean_var_slope_data = data['ROW_MEAN_VAR_SLOPE']
linearity_turnoff_data = data['LINEARITY_TURNOFF']
for amp, max_observed_signal, max_frac_dev, row_mean_var_slope, \
linearity_turnoff in zip(amps, max_observed_signal_data,
max_frac_dev_data,
row_mean_var_slope_data,
linearity_turnoff_data):
results.append(lcatr.schema.valid(
lcatr.schema.get('flat_pairs_BOT'),
amp=amp, max_observed_signal=max_observed_signal,
max_frac_dev=max_frac_dev,
row_mean_var_slope=row_mean_var_slope,
linearity_turnoff=linearity_turnoff, slot=slot, raft=raft))
# Persist the png files.
metadata = dict(DETECTOR=det_name, RUN=run,
TESTTYPE='FLAT', TEST_CATEGORY='EO')
results.extend(siteUtils.persist_png_files(('%s_linearity*.png'
% file_prefix),
file_prefix,
metadata=metadata))
results.extend(siteUtils.persist_png_files(('%s_row_means_variance.png'
% file_prefix),
file_prefix,
metadata=metadata))
report_missing_data("validate_flat_pairs", missing_det_names)
return results
def getSensorGains(jobname='fe55_analysis', sensor_id=None):
if sensor_id is None:
sensor_id = siteUtils.getUnitId()
try:
gain_file = dependency_glob('%s_eotest_results.fits' % sensor_id,
jobname=jobname)[0]
except IndexError:
raise RuntimeError('eotestUtils.getSensorGains: %s %s'
% (sensor_id, jobname))
data = sensorTest.EOTestResults(gain_file)
amps = data['AMP']
gains = data['GAIN']
sensorGains = dict([(amp, gains[amp-1]) for amp in amps])
return sensorGains
def validate_dark_defects(results, det_names):
"""Validate and persist dark defects results."""
run = siteUtils.getRunNumber()
missing_det_names = []
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
mask_file = '%s_dark_pixel_mask.fits' % file_prefix
if not os.path.isfile(mask_file):
missing_det_names.append(det_name)
continue
eotestUtils.addHeaderData(mask_file,
TESTTYPE='SFLAT_500',
DATE=eotestUtils.utc_now_isoformat())
results.append(siteUtils.make_fileref(mask_file))
superflat = '%s_median_sflat.fits' % file_prefix
eotestUtils.addHeaderData(superflat,
DATE=eotestUtils.utc_now_isoformat())
results.append(siteUtils.make_fileref(superflat))
eotest_results = '%s_eotest_results.fits' % file_prefix
data = sensorTest.EOTestResults(eotest_results)
amps = data['AMP']
npixels = data['NUM_DARK_PIXELS']
ncolumns = data['NUM_DARK_COLUMNS']
for amp, npix, ncol in zip(amps, npixels, ncolumns):
results.append(
lcatr.schema.valid(lcatr.schema.get('dark_defects_BOT'),
amp=amp,
dark_pixels=npix,
dark_columns=ncol,
slot=slot,
raft=raft))
# Persist the png files.
metadata = dict(DETECTOR=det_name,
RUN=run,
TESTTYPE='SFLAT_500',
TEST_CATEGORY='EO')
filename = '%s_superflat_dark_defects.png' % file_prefix
results.extend(
siteUtils.persist_png_files(filename,
file_prefix,
metadata=metadata))
report_missing_data("validate_dark_defects", missing_det_names)
return results
def validate_flat_pairs(results, det_names):
"""Validate the flat pair analysis results."""
run = siteUtils.getRunNumber()
missing_det_names = []
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
det_resp_data = '%s_det_response.fits' % file_prefix
if not os.path.isfile(det_resp_data):
missing_det_names.append(det_name)
continue
eotestUtils.addHeaderData(det_resp_data,
DETECTOR=det_name,
TESTTYPE='FLAT',
DATE=eotestUtils.utc_now_isoformat())
results.append(siteUtils.make_fileref(det_resp_data))
results_file = '%s_eotest_results.fits' % file_prefix
data = sensorTest.EOTestResults(results_file)
amps = data['AMP']
full_well_data = data['FULL_WELL']
max_frac_dev_data = data['MAX_FRAC_DEV']
for amp, full_well, max_frac_dev in zip(amps, full_well_data,
max_frac_dev_data):
results.append(
lcatr.schema.valid(lcatr.schema.get('flat_pairs_BOT'),
amp=amp,
full_well=full_well,
max_frac_dev=max_frac_dev,
slot=slot,
raft=raft))
# Persist the png files.
metadata = dict(DETECTOR=det_name,
RUN=run,
TESTTYPE='FLAT',
TEST_CATEGORY='EO')
results.extend(
siteUtils.persist_png_files(('%s_linearity*.png' % file_prefix),
file_prefix,
metadata=metadata))
report_missing_data("validate_flat_pairs", missing_det_names)
return results
def getSensorGains(jobname='fe55_analysis', sensor_id=None):
if (os.environ.get('LCATR_USE_UNIT_GAINS', 'False') == 'True'
or os.environ.get("LCATR_SKIP_FE55_ANALYSIS", "False") == "True"):
return {amp: 1 for amp in range(1, 17)}
if sensor_id is None:
sensor_id = siteUtils.getUnitId()
try:
gain_file = dependency_glob('%s_eotest_results.fits' % sensor_id,
jobname=jobname)[0]
except IndexError:
raise RuntimeError('eotestUtils.getSensorGains: %s %s' %
(sensor_id, jobname))
data = sensorTest.EOTestResults(gain_file)
amps = data['AMP']
gains = data['GAIN']
sensorGains = dict([(amp, gains[amp - 1]) for amp in amps])
return sensorGains
def validate_dark_current(results, det_names):
"""Validate and persist dark current results."""
run = siteUtils.getRunNumber()
missing_det_names = []
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
results_file = '%s_eotest_results.fits' % file_prefix
if not os.path.isfile(results_file):
missing_det_names.append(det_name)
continue
data = sensorTest.EOTestResults(results_file)
amps = data['AMP']
dc95s = data['DARK_CURRENT_95']
for amp, dc95 in zip(amps, dc95s):
results.append(
lcatr.schema.valid(lcatr.schema.get('dark_current_BOT'),
amp=amp,
dark_current_95CL=dc95,
slot=slot,
raft=raft))
# Persist the png files.
metadata = dict(TESTTYPE='DARK',
TEST_CATEGORY='EO',
DETECTOR=det_name,
RUN=run)
pattern = '{}_noise.png'.format(file_prefix)
results.extend(
siteUtils.persist_png_files(pattern,
file_prefix,
metadata=metadata))
pattern = '{}_total_noise_hists.png'.format(file_prefix)
results.extend(
siteUtils.persist_png_files(pattern,
file_prefix,
metadata=metadata))
report_missing_data("validate_dark_current", missing_det_names)
return results
def gains(eotest_results_file, is_PTC=False):
"""
Extract gains from the results file of some eo testing.
Parameters:
-----------
eotest_results_file : str
Path to the file where EOTest gain results are stored
is_PTC : boolean (optional)
If True, PTC gains are returns. If False (default) the Fe55
gains are returned
"""
results = sensorTest.EOTestResults(eotest_results_file)
if is_PTC:
return {amp: gain for amp, gain in zip(results['AMP'], results['PTC_GAIN'])}
else:
return {amp: gain for amp, gain in zip(results['AMP'], results['GAIN'])}
def __init__(self, results_files):
"""
Constructor.
Parameters
----------
results_files : dict
"""
self._raft_slots \
= OrderedDict([(slot, i) for i, slot in
enumerate('S00 S01 S02 S10 S11 S12 S20 S21 S22'.split())])
if 'S00' not in results_files.keys():
self._raft_slots \
= OrderedDict([(slot, i) for i, slot in
enumerate('SW0 SW1 SG0 SG1'.split())])
self.results = dict()
self.sensor_ids = dict()
for slot, filename in list(results_files.items()):
self.sensor_ids[slot] = filename.split('_')[0]
self.results[slot] = sensorTest.EOTestResults(filename)
def persist_fe55_analysis_results():
"""Persist the results from the full analysis."""
raft_id = siteUtils.getUnitId()
raft = camera_components.Raft.create_from_etrav(raft_id)
results = []
for slot, sensor_id in raft.items():
ccd_vendor = sensor_id.split('-')[0].upper()
# The output files from producer script.
gain_file = '%(sensor_id)s_eotest_results.fits' % locals()
psf_results = glob.glob('%(sensor_id)s_psf_results*.fits' %
locals())[0]
rolloff_mask = '%(sensor_id)s_rolloff_defects_mask.fits' % locals()
output_files = gain_file, psf_results, rolloff_mask
# Add/update the metadata to the primary HDU of these files.
for fitsfile in output_files:
eotestUtils.addHeaderData(fitsfile,
LSST_NUM=sensor_id,
TESTTYPE='FE55',
DATE=eotestUtils.utc_now_isoformat(),
CCD_MANU=ccd_vendor)
#
# Persist the median bias FITS file.
#
bias_median_file = glob.glob(f'{sensor_id}_*_median_bias.fits')[0]
results.append(siteUtils.make_fileref(bias_median_file, folder=slot))
# Persist the png files.
metadata = dict(CCD_MANU=ccd_vendor,
LSST_NUM=sensor_id,
TESTTYPE='FE55',
TEST_CATEGORY='EO')
results.extend(
siteUtils.persist_png_files('%s*.png' % sensor_id,
sensor_id,
folder=slot,
metadata=metadata))
data = sensorTest.EOTestResults(gain_file)
amps = data['AMP']
gain_data = data['GAIN']
gain_errors = data['GAIN_ERROR']
sigmas = data['PSF_SIGMA']
for amp, gain_value, gain_error, sigma in zip(amps, gain_data,
gain_errors, sigmas):
if not np.isfinite(gain_error):
gain_error = -1
results.append(
lcatr.schema.valid(lcatr.schema.get('fe55_raft_analysis'),
amp=amp,
gain=gain_value,
gain_error=gain_error,
psf_sigma=sigma,
slot=slot,
sensor_id=sensor_id))
results.extend([lcatr.schema.fileref.make(x) for x in output_files])
return results
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
LSST_NUM=sensor_id,
producer='SR-EOT-1',
TESTTYPE='FE55',
TEST_CATEGORY='EO')
#
# Persist various png files.
#
png_files = glob.glob('%(sensor_id)s_fe55*.png' % locals())
png_filerefs = []
for png_file in png_files:
dp = eotestUtils.png_data_product(png_file, sensor_id)
png_filerefs.append(
lcatr.schema.fileref.make(png_file, metadata=md(DATA_PRODUCT=dp)))
results.extend(png_filerefs)
data = sensorTest.EOTestResults(gain_file)
amps = data['AMP']
gain_data = data['GAIN']
gain_errors = data['GAIN_ERROR']
sigmas = data['PSF_SIGMA']
for amp, gain_value, gain_error, sigma in zip(amps, gain_data, gain_errors,
sigmas):
results.append(
lcatr.schema.valid(lcatr.schema.get('fe55_analysis'),
amp=amp,
gain=gain_value,
gain_error=gain_error,
psf_sigma=sigma))
results.extend(siteUtils.jobInfo())
def gains(eotest_results_file):
"""
Extract Fe55 gains from the results file of some eo testing.
"""
results = sensorTest.EOTestResults(eotest_results_file)
return {amp: gain for amp, gain in zip(results['AMP'], results['GAIN'])}
raft_id = siteUtils.getUnitId()
raft = camera_components.Raft.create_from_etrav(raft_id)
results = []
for slot, sensor_id in raft.items():
print("Processing:", slot, sensor_id)
ccd_vendor = sensor_id.split('-')[0].upper()
superflats = glob.glob('%(sensor_id)s_superflat_*.fits' % locals())
for item in superflats:
eotestUtils.addHeaderData(item, FILENAME=item,
DATE=eotestUtils.utc_now_isoformat())
results.extend([siteUtils.make_fileref(x, folder=slot) for x in superflats])
results_file = '%s_eotest_results.fits' % sensor_id
data = sensorTest.EOTestResults(results_file)
amps = data['AMP']
cti_high_serial = data['CTI_HIGH_SERIAL']
cti_high_serial_error = data['CTI_HIGH_SERIAL_ERROR']
cti_high_parallel = data['CTI_HIGH_PARALLEL']
cti_high_parallel_error = data['CTI_HIGH_PARALLEL_ERROR']
cti_low_serial = data['CTI_LOW_SERIAL']
cti_low_serial_error = data['CTI_LOW_SERIAL_ERROR']
cti_low_parallel = data['CTI_LOW_PARALLEL']
cti_low_parallel_error = data['CTI_LOW_PARALLEL_ERROR']
for values in zip(amps,
cti_high_serial, cti_high_serial_error,
cti_high_parallel, cti_high_parallel_error,
# to read noise to produce updated total noise.
shot_noise = repackager.eotest_results['DARK_CURRENT_95']*exptime
total_noise = np.sqrt(repackager.eotest_results['READ_NOISE']**2
+ shot_noise)
for i, amp in enumerate(repackager.eotest_results['AMP']):
repackager.eotest_results.add_seg_result(amp, 'DC95_SHOT_NOISE',
np.float(shot_noise[i]))
repackager.eotest_results['TOTAL_NOISE'][i] = total_noise[i]
outfile = '%s_eotest_results.fits' % sensor_id
repackager.write(outfile)
results_files[slot] = outfile
gains = dict()
for slot, res_file in results_files.items():
results = sensorTest.EOTestResults(res_file)
gains[slot] = dict([(amp, gain) for amp, gain
in zip(results['AMP'], results['GAIN'])])
# Collect super bias files for bias frame subtraction.
bias_frames = slot_dependency_glob('*median_bias.fits', 'dark_defects_raft')
title = '%s, %s' % (raft_id, run_number)
file_prefix = '%s_%s' % (raft_id, run_number)
# Raft-level mosaics of median darks, bias, superflats high and low.
dark_mosaic = raftTest.RaftMosaic(slot_dependency_glob('*median_dark_bp.fits',
'bright_defects_raft'),
gains=gains, bias_frames=bias_frames)
dark_mosaic.plot(title='%s, medianed dark frames' % title,
annotation='e-/pixel, gain-corrected, bias-subtracted',
def validate_fe55(results, det_names):
"""Validate and persist fe55 gain and psf results."""
run = siteUtils.getRunNumber()
missing_det_names = []
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
# The output files from producer script.
gain_file = '%(file_prefix)s_eotest_results.fits' % locals()
psf_results_files \
= glob.glob('%(file_prefix)s_psf_results*.fits' % locals())
if not os.path.isfile(gain_file) or not psf_results_files:
# Results for this detector are not available so note
# that and continue with the others.
missing_det_names.append(det_name)
continue
psf_results = psf_results_files[0]
rolloff_mask = '%(file_prefix)s_edge_rolloff_mask.fits' % locals()
output_files = psf_results, rolloff_mask
# Add/update the metadata to the primary HDU of these files.
for fitsfile in output_files:
eotestUtils.addHeaderData(fitsfile,
TESTTYPE='FE55',
DATE=eotestUtils.utc_now_isoformat())
results.extend([lcatr.schema.fileref.make(x) for x in output_files])
# Persist the median bias FITS file.
bias_frame \
= glob.glob('%(file_prefix)s_median_bias.fits' % locals())[0]
results.append(siteUtils.make_fileref(bias_frame))
# Persist the png files.
png_file_list = '{}_fe55_task_png_files.txt'.format(det_name)
with open(png_file_list, 'r') as input_:
png_files = [x.strip() for x in input_]
metadata = dict(TESTTYPE='FE55',
TEST_CATEGORY='EO',
DETECTOR=det_name,
RUN=run)
results.extend(
siteUtils.persist_png_files('',
file_prefix,
png_files=png_files,
metadata=metadata))
data = sensorTest.EOTestResults(gain_file)
amps = data['AMP']
gain_data = data['GAIN']
gain_errors = data['GAIN_ERROR']
sigmas = data['PSF_SIGMA']
for amp, gain_value, gain_error, sigma in zip(amps, gain_data,
gain_errors, sigmas):
if not np.isfinite(gain_error):
gain_error = -1
results.append(
lcatr.schema.valid(lcatr.schema.get('fe55_BOT_analysis'),
amp=amp,
gain=gain_value,
gain_error=gain_error,
psf_sigma=sigma,
slot=slot,
raft=raft))
report_missing_data('validate_fe55', missing_det_names)
return results
sensor_id = siteUtils.getUnitId()
mask_file = '%s_dark_pixel_mask.fits' % sensor_id
eotestUtils.addHeaderData(mask_file,
LSST_NUM=sensor_id,
TESTTYPE='SFLAT_500',
DATE=eotestUtils.utc_now_isoformat(),
CCD_MANU=siteUtils.getCcdVendor().upper())
results = [lcatr.schema.fileref.make(mask_file)]
superflat = '%s_median_sflat.fits' % sensor_id
eotestUtils.addHeaderData(superflat, DATE=eotestUtils.utc_now_isoformat())
results.append(lcatr.schema.fileref.make(superflat))
eotest_results = '%s_eotest_results.fits' % sensor_id
data = sensorTest.EOTestResults(eotest_results)
amps = data['AMP']
npixels = data['NUM_DARK_PIXELS']
ncolumns = data['NUM_DARK_COLUMNS']
for amp, npix, ncol in zip(amps, npixels, ncolumns):
results.append(
lcatr.schema.valid(lcatr.schema.get('dark_defects'),
amp=amp,
dark_pixels=npix,
dark_columns=ncol))
results.extend(siteUtils.jobInfo())
lcatr.schema.write_file(results)
lcatr.schema.validate_file()
#!/usr/bin/env python
import glob
import lsst.eotest.sensor as sensorTest
import lcatr.schema
import siteUtils
import eotestUtils
sensor_id = siteUtils.getUnitId()
results = []
read_noise_file = '%s_eotest_results.fits' % sensor_id
data = sensorTest.EOTestResults(read_noise_file)
amps = data['AMP']
read_noise_data = data['READ_NOISE']
system_noise_data = data['SYSTEM_NOISE']
total_noise_data = data['TOTAL_NOISE']
for amp, read_noise, system_noise, total_noise in zip(amps, read_noise_data,
system_noise_data,
total_noise_data):
results.append(
lcatr.schema.valid(lcatr.schema.get('read_noise'),
amp=amp,
read_noise=read_noise,
system_noise=system_noise,
total_noise=total_noise))
results.extend(siteUtils.jobInfo())
results.append(eotestUtils.eotestCalibrations())
def validate_cte(results, det_names):
"""Validate the CTE task results."""
run = siteUtils.getRunNumber()
missing_det_names = []
for det_name in det_names:
raft, slot = det_name.split('_')
file_prefix = make_file_prefix(run, det_name)
superflats \
= sorted(glob.glob('{}_superflat_*.fits'.format(file_prefix)))
if not superflats:
missing_det_names.append(det_name)
continue
for item in superflats:
eotestUtils.addHeaderData(item,
FILENAME=item,
DATE=eotestUtils.utc_now_isoformat())
results.extend([siteUtils.make_fileref(x) for x in superflats])
results_file = '%s_eotest_results.fits' % file_prefix
data = sensorTest.EOTestResults(results_file)
amps = data['AMP']
cti_high_serial = data['CTI_HIGH_SERIAL']
cti_high_serial_error = data['CTI_HIGH_SERIAL_ERROR']
cti_high_parallel = data['CTI_HIGH_PARALLEL']
cti_high_parallel_error = data['CTI_HIGH_PARALLEL_ERROR']
cti_low_serial = data['CTI_LOW_SERIAL']
cti_low_serial_error = data['CTI_LOW_SERIAL_ERROR']
cti_low_parallel = data['CTI_LOW_PARALLEL']
cti_low_parallel_error = data['CTI_LOW_PARALLEL_ERROR']
for values in zip(amps, cti_high_serial, cti_high_serial_error,
cti_high_parallel, cti_high_parallel_error,
cti_low_serial, cti_low_serial_error,
cti_low_parallel, cti_low_parallel_error):
results.append(
lcatr.schema.valid(lcatr.schema.get('cte_BOT'),
amp=values[0],
cti_high_serial=values[1],
cti_high_serial_error=values[2],
cti_high_parallel=values[3],
cti_high_parallel_error=values[4],
cti_low_serial=values[5],
cti_low_serial_error=values[6],
cti_low_parallel=values[7],
cti_low_parallel_error=values[8],
slot=slot,
raft=raft))
# Persist the png files.
png_file_list = '{}_cte_task_png_files.txt'.format(det_name)
with open(png_file_list, 'r') as input_:
png_files = [x.strip() for x in input_]
metadata = dict(DETECTOR=det_name,
RUN=run,
TESTTYPE='SFLAT_500',
TEST_CATEGORY='EO')
results.extend(
siteUtils.persist_png_files('',
file_prefix,
png_files=png_files,
metadata=metadata))
report_missing_data("validate_cte", missing_det_names)
return results
default='cosmic_ray_catalog.fits',
type=str,
help='Output file name for CR catalog')
parser.add_argument('--medianed_dark',
default=None,
type=str,
help='Filename of medianed dark.')
parser.add_argument('--nsig',
default=7,
type=float,
help='Number of sigma threshold for detecting CRs')
args = parser.parse_args()
darks = glob.glob(args.dark_frame_pattern)
eo_results = sensorTest.EOTestResults(args.eotest_results)
gains = {
amp: gain
for amp, gain in zip(eo_results['AMP'], eo_results['GAIN'])
}
med_file = args.medianed_dark
if med_file is None:
med_file = tempfile.NamedTemporaryFile(prefix='tmp_med_file_',
dir='.',
suffix='.fits').name
imutils.fits_median_file(darks, med_file, bitpix=-32)
mask_file = tempfile.NamedTemporaryFile(prefix='tmp_mask_',
dir='.',
suffix='.fits').name
f'sflat_flat_*_H_*/*{det_name}.fits')
flat1_files = fp_server.get_files('BOT_acq',
f'flat_*_flat1_*/*{det_name}.fits')
lambda_files = fp_server.get_files('BOT_acq',
f'lambda_flat_*/*{det_name}.fits')
# Make a medianed bias frame to use in the various tasks.
bias_frame = bot_eo.make_bias_filename(Run, det_name)
bot_eo.bias_frame_task(Run, det_name, bias_files, bias_frame=bias_frame)
bot_eo.fe55_task(Fe55Run, det_name, fe55_files, bias_frame=bias_frame)
plt.close('all') # This is needed to recover memory from matplotlib.
# Get the Fe55 gains from the fe55_results_file. Note that this
# job generates the edge rolloff mask.
fe55_results = sensorTest.EOTestResults(fe55_results_file)
gains = dict(zip(fe55_results['AMP'], fe55_results['GAIN']))
print(gains)
# Do the read noise analysis, but only consider the first 5 bias frame
# files. At least two are needed for this task.
bot_eo.read_noise_task(Run, det_name, bias_files[:5], gains)
plt.close('all')
bot_eo.raft_noise_correlations(Run, raft, bias_file_dict)
plt.close('all')
# Get the edge rolloff mask.
mask_files = sorted(glob.glob('_'.join((det_name, Run, '*mask.fits'))))
# The two defects tasks generate mask files, so the mask files