def test_reduce_image_GN_EEV_2x2_g(path_to_inputs):
logutils.config(file_name='gmos_test_reduce_image_GN_EEV_2x2_g.log')
calib_files = []
raw_subdir = 'GMOS/GN-2002A-Q-89'
all_files = sorted(glob.glob(
os.path.join(path_to_inputs, raw_subdir, '*.fits')))
assert len(all_files) > 1
list_of_bias = dataselect.select_data(
all_files,
['BIAS'],
[]
)
list_of_flats = dataselect.select_data(
all_files,
['IMAGE', 'FLAT'],
[],
dataselect.expr_parser('filter_name=="g"')
)
# These old data don't have an OBSCLASS keyword:
list_of_science_files = dataselect.select_data(
all_files, [],
['CAL'],
dataselect.expr_parser(
'object=="PerseusField4" and filter_name=="g"'
)
)
reduce_bias = Reduce()
assert len(reduce_bias.files) == 0
reduce_bias.files.extend(list_of_bias)
assert len(reduce_bias.files) == len(list_of_bias)
reduce_bias.runr()
calib_files.append(
'processed_bias:{}'.format(reduce_bias.output_filenames[0])
)
reduce_flats = Reduce()
reduce_flats.files.extend(list_of_flats)
reduce_flats.ucals = normalize_ucals(reduce_flats.files, calib_files)
reduce_flats.runr()
calib_files.append(
'processed_flat:{}'.format(reduce_flats.output_filenames[0])
)
reduce_target = Reduce()
reduce_target.files.extend(list_of_science_files)
reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files)
reduce_target.runr()
def reduce(file_list,
label,
calib_files,
recipe_name=None,
save_to=None,
user_pars=None):
"""
Helper function used to prevent replication of code.
Parameters
----------
file_list : list
List of files that will be reduced.
label : str
Labed used on log files name.
calib_files : list
List of calibration files properly formatted for DRAGONS Reduce().
recipe_name : str, optional
Name of the recipe used to reduce the data.
save_to : str, optional
Stores the calibration files locally in a list.
user_pars : list, optional
List of user parameters
Returns
-------
str : Output reduced file.
list : An updated list of calibration files.
"""
objgraph = pytest.importorskip("objgraph")
logutils.get_logger().info("\n\n\n")
logutils.config(file_name=f"test_image_{label}.log")
r = Reduce()
r.files = file_list
r.ucals = normalize_ucals(r.files, calib_files)
r.uparms = user_pars
if recipe_name:
r.recipename = recipe_name
r.runr()
output_file = r.output_filenames[0]
if save_to:
calib_files.append("{}:{}".format(
save_to,
os.path.join("calibrations", save_to, r.output_filenames[0])))
[os.remove(f) for f in r.output_filenames]
# check that we are not leaking objects
assert len(objgraph.by_type('NDAstroData')) == 0
return output_file, calib_files
def test_slitbias_in_calservice(self, get_or_create_tmpdir):
"""
Check that:
- A bias slit calibrator exists in the local calibrations dir;
- It can be retrieved using a getProcessedSlitBias call.
"""
assert len(glob.glob(os.path.join(
os.getcwd(), 'calibrations', 'processed_bias', '*bias*slit*.fits'
))) == 1, "Couldn't find the stored slit bias in the calibrations " \
"system OR found multiples"
# Do the master bias generation
reduce = Reduce()
reduce.drpkg = 'ghostdr'
# Use one of the 'dark slit' files to try and retrieve the slit bias
reduce.files = [
os.path.join(os.getcwd(), 'dark95_1_MEF_2x2_slit.fits'),
]
reduce.mode = [
'test',
]
reduce.recipename = 'recipeRetrieveSlitBiasTest'
# reduce.mode = ['sq', ]
# reduce.recipename = 'makeProcessedBias'
reduce.logfile = os.path.join(os.getcwd(),
'reduce_slitbias_retrieve.log')
# FIXME cal_service will hopefully find the calibration itself later
reduce.ucals = normalize_ucals(reduce.files, [
'processed_bias:{}'.format(
glob.glob(
os.path.join('calibrations', 'processed_bias',
'*slit*bias*.fits'))[0]),
])
reduce.logmode = 'quiet'
reduce.suffix = '_testSlitBiasRetrieve'
logutils.config(file_name=reduce.logfile, mode=reduce.logmode)
try:
reduce.runr()
except IOError as e:
assert 0, 'Calibration system could not locate the slit bias ' \
'frame ({})'.format(e.message)
finally:
# Teardown code
for _ in glob.glob(
os.path.join(os.getcwd(),
'*{}.fits'.format(reduce.suffix)), ):
os.remove(_)
def processed_slit_illum(change_working_dir, path_to_inputs, request):
"""
Returns the processed slit illumination function that will be analysed.
Parameters
----------
change_working_dir : pytest.fixture
Fixture that changes the working directory (see :mod:`astrodata.testing`).
path_to_inputs : pytest.fixture
Fixture defined in :mod:`astrodata.testing` with the path to the
pre-processed input file.
request : pytest.fixture
PyTest built-in fixture containing information about parent test.
Returns
-------
AstroData
Input spectrum processed up to right before the `applyQECorrection`.
"""
twi_filename = request.param
twi_path = download_from_archive(twi_filename)
twi_ad = astrodata.open(twi_path)
print(twi_ad.tags)
master_bias = os.path.join(
path_to_inputs, associated_calibrations[twi_filename])
assert os.path.exists(master_bias)
calibration_files = ['processed_bias:{}'.format(master_bias)]
with change_working_dir():
print("Reducing SLITILLUM in folder:\n {}".format(os.getcwd()))
logutils.config(
file_name='log_flat_{}.txt'.format(twi_ad.data_label()))
reduce = Reduce()
reduce.files.extend([twi_path])
reduce.mode = 'sq'
reduce.recipename = 'makeProcessedSlitIllum'
reduce.ucals = normalize_ucals(reduce.files, calibration_files)
reduce.runr()
_processed_twi_filename = reduce.output_filenames.pop()
_processed_twi = astrodata.open(_processed_twi_filename)
return _processed_twi
def reduce(filelist, saveto=None, label='', calib_files=None,
recipename=None):
red = Reduce()
assert len(red.files) == 0
red.files.extend(filelist)
assert len(red.files) == len(filelist)
if calib_files:
red.ucals = normalize_ucals(red.files, calib_files)
if recipename:
red.recipename = recipename
red.runr()
if saveto:
calib_files.append(f'{saveto}:{red.output_filenames[0]}')
# check that we are not leaking objects
assert len(objgraph.by_type('NDAstroData')) == 0
def _reduce_flat(datalabel, flat_fname, master_bias):
with output_path():
logutils.config(file_name='log_flat_{}.txt'.format(datalabel))
calibration_files = ['processed_bias:{}'.format(master_bias)]
reduce = Reduce()
reduce.files.extend([flat_fname])
reduce.mode = 'ql'
reduce.ucals = normalize_ucals(reduce.files, calibration_files)
reduce.runr()
master_flat = reduce.output_filenames.pop()
master_flat_ad = astrodata.open(master_flat)
return master_flat_ad
def processed_flat(change_working_dir, path_to_inputs, request):
"""
Returns the processed flat that will be analysed.
Parameters
----------
change_working_dir : pytest.fixture
Fixture that changes the working directory (see :mod:`astrodata.testing`).
path_to_inputs : pytest.fixture
Fixture defined in :mod:`astrodata.testing` with the path to the
pre-processed input file.
request : pytest.fixture
PyTest built-in fixture containing information about parent test.
Returns
-------
AstroData
Input spectrum processed up to right before the `applyQECorrection`.
"""
np.random.seed(0)
flat_filename = request.param
flat_path = download_from_archive(flat_filename)
flat_raw = astrodata.open(flat_path)
master_bias = os.path.join(path_to_inputs,
associated_calibrations[flat_filename])
calibration_files = ['processed_bias:{}'.format(master_bias)]
with change_working_dir():
print("Reducing FLATs in folder:\n {}".format(os.getcwd()))
logutils.config(
file_name='log_flat_{}.txt'.format(flat_raw.data_label()))
reduce = Reduce()
reduce.files.extend([flat_path])
reduce.mode = 'ql'
reduce.ucals = normalize_ucals(reduce.files, calibration_files)
reduce.runr()
# Clean up duplicated files
shutil.rmtree('calibrations/')
_processed_flat_filename = reduce.output_filenames.pop()
_processed_flat = astrodata.open(_processed_flat_filename)
return _processed_flat
def _reduce_flat(data_label, flat_fnames, master_bias):
with change_working_dir():
print("Reducing FLATs in folder:\n {}".format(os.getcwd()))
logutils.config(file_name='log_flat_{}.txt'.format(data_label))
calibration_files = ['processed_bias:{}'.format(master_bias)]
reduce = Reduce()
reduce.files.extend(flat_fnames)
reduce.mode = 'ql'
reduce.ucals = normalize_ucals(calibration_files)
reduce.runr()
master_flat = reduce.output_filenames.pop()
master_flat_ad = astrodata.open(master_flat)
return master_flat_ad
def test_make_processed_flat(change_working_dir, flat_fnames, master_bias,
path_to_inputs):
"""
Regression test for GMOS
Parameters
----------
change_working_dir : fixture
Custom fixture defined astrodata.testing that changes temporarily the
working dir.
flat_fnames : list
Contains the flat names that will be reduced.
master_bias : str
Contains the name of the master flat.
path_to_inputs : fixture
Custom fixture that defines where the input data is stored.
"""
master_bias = os.path.join(path_to_inputs, master_bias)
calibration_files = ['processed_bias:{}'.format(master_bias)]
with change_working_dir():
logutils.config(
file_name=f"log_flat_{flat_fnames[0].split('.')[0]}.txt")
r = Reduce()
r.files = [download_from_archive(f) for f in flat_fnames]
r.mode = 'qa'
r.ucals = normalize_ucals(r.files, calibration_files)
r.runr()
# Delete files that won't be used
shutil.rmtree('calibrations/')
[os.remove(f) for f in glob.glob('*_forStack.fits')]
ad = astrodata.open(r.output_filenames[0])
for ext in ad:
data = np.ma.masked_array(ext.data, mask=ext.mask)
if not data.mask.all():
np.testing.assert_allclose(np.ma.median(data.ravel()),
1,
atol=0.071)
np.testing.assert_allclose(data[~data.mask], 1, atol=0.45)
def __init__(self, sys_args=None):
if sys_args:
args = sys_args
elif self._confirm_args():
args = buildParser(__version__).parse_args()
else:
args = buildParser(__version__).parse_args([])
# acquire any new astrodata classes.
if args.adpkg:
import_module(args.adpkg)
self.mode = args.mode
self.drpkg = args.drpkg
self.files = args.files
self.suffix = args.suffix
self.ucals = normalize_ucals(args.files, args.user_cal)
self.uparms = set_btypes(args.userparam)
self._upload = args.upload
self._output_filenames = None
self.recipename = args.recipename if args.recipename else '_default'
def do_master_flat(self, get_or_create_tmpdir, request):
"""
Run the recipeFlatCreateMaster recipe.
.. note::
Fixture.
"""
arm, res = request.param
rawfilename = 'flat*{}*{}*.fits'.format(res, arm)
# Copy the raw data file into here
tmpsubdir, cal_service = get_or_create_tmpdir
# Find all the relevant files
# rawfiles = glob.glob(os.path.join(os.path.dirname(
# os.path.abspath(__file__)),
# 'testdata',
# rawfilename))
# for f in rawfiles:
# shutil.copy(f, os.path.join(tmpsubdir.dirname, tmpsubdir.basename))
rawfiles = glob.glob(
os.path.join(tmpsubdir.dirname, tmpsubdir.basename, rawfilename))
# Do the master bias generation
reduce = Reduce()
reduce.drpkg = 'ghostdr'
reduce.files = rawfiles
reduce.mode = [
'test',
]
reduce.recipename = 'recipeFlatCreateMaster'
# reduce.mode = ['sq', ]
# reduce.recipename = 'makeProcessedBias'
reduce.logfile = os.path.join(
tmpsubdir.dirname, tmpsubdir.basename,
'reduce_masterflat_{}_{}.log'.format(res, arm))
reduce.logmode = 'quiet'
reduce.suffix = '_{}_{}_testMasterFlat'.format(res, arm)
logutils.config(file_name=reduce.logfile, mode=reduce.logmode)
# import pdb; pdb.set_trace()
calibs = {
'processed_bias':
glob.glob(
os.path.join('calibrations', 'processed_bias',
'bias*{}*.fits'.format(arm)))[0],
'processed_dark':
glob.glob(
os.path.join('calibrations', 'processed_dark',
'dark*{}*.fits'.format(arm)))[0],
'processed_slitflat':
glob.glob(
os.path.join('calibrations', 'processed_slitflat',
'flat*{}*slitflat*.fits'.format(res)))[0],
}
reduce.ucals = normalize_ucals(
reduce.files, ['{}:{}'.format(k, v) for k, v in calibs.items()])
reduce.runr()
if res == 'std' and arm == 'red':
pass
# import pdb;
# pdb.set_trace()
corrfilename = '*' + reduce.suffix + '.fits'
corrfilename = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
glob.glob(corrfilename)[0])
corrfile = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
corrfilename)
# Return filenames of raw, subtracted files
yield rawfiles, corrfile, calibs
# Execute teardown code
pass
def test_reduce_image(path_to_inputs):
calib_files = []
all_files = glob.glob(
os.path.join(path_to_inputs, 'GSAOI/test_reduce/', '*.fits'))
all_files.sort()
assert len(all_files) > 1
list_of_darks = dataselect.select_data(all_files, ['DARK'], [])
list_of_darks.sort()
list_of_kshort_flats = dataselect.select_data(
all_files, ['FLAT'], [],
dataselect.expr_parser('filter_name=="Kshort"'))
list_of_kshort_flats.sort()
list_of_h_flats = dataselect.select_data(
all_files, ['FLAT'], [], dataselect.expr_parser('filter_name=="H"'))
list_of_h_flats.sort()
list_of_science_files = dataselect.select_data(
all_files, [], [],
dataselect.expr_parser(
'observation_class=="science" and exposure_time==60.'))
list_of_science_files.sort()
for darks in [list_of_darks]:
reduce_darks = Reduce()
assert len(reduce_darks.files) == 0
reduce_darks.files.extend(darks)
assert len(reduce_darks.files) == len(darks)
logutils.config(file_name='gsaoi_test_reduce_dark.log', mode='quiet')
reduce_darks.runr()
del reduce_darks
logutils.config(file_name='gsaoi_test_reduce_bpm.log', mode='quiet')
reduce_bpm = Reduce()
reduce_bpm.files.extend(list_of_h_flats)
reduce_bpm.files.extend(list_of_darks)
reduce_bpm.recipename = 'makeProcessedBPM'
reduce_bpm.runr()
bpm_filename = reduce_bpm.output_filenames[0]
del reduce_bpm
logutils.config(file_name='gsaoi_test_reduce_flats.log', mode='quiet')
reduce_flats = Reduce()
reduce_flats.files.extend(list_of_kshort_flats)
reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_flats.runr()
calib_files.append('processed_flat:{}'.format(
reduce_flats.output_filenames[0]))
del reduce_flats
logutils.config(file_name='gsaoi_test_reduce_science.log', mode='quiet')
reduce_target = Reduce()
reduce_target.files.extend(list_of_science_files)
reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files)
reduce_target.runr()
del reduce_target
def test_reduce_image(path_to_inputs):
calib_files = []
all_files = glob.glob(
os.path.join(path_to_inputs, 'F2/test_reduce/', '*.fits'))
all_files.sort()
assert len(all_files) > 1
darks_3s = dataselect.select_data(
all_files, ['F2', 'DARK', 'RAW'], [],
dataselect.expr_parser('exposure_time==3'))
darks_3s.sort()
darks_20s = dataselect.select_data(
all_files, ['F2', 'DARK', 'RAW'], [],
dataselect.expr_parser('exposure_time==20'))
darks_20s.sort()
darks_120s = dataselect.select_data(
all_files, ['F2', 'DARK', 'RAW'], [],
dataselect.expr_parser('exposure_time==120'))
darks_120s.sort()
flats = dataselect.select_data(all_files, ['F2', 'FLAT', 'RAW'], [],
dataselect.expr_parser('filter_name=="Y"'))
flats.sort()
science = dataselect.select_data(
all_files, ['F2', 'RAW'], ['CAL'],
dataselect.expr_parser('filter_name=="Y"'))
science.sort()
for darks in [darks_3s, darks_20s, darks_120s]:
reduce_darks = Reduce()
assert len(reduce_darks.files) == 0
reduce_darks.files.extend(darks)
assert len(reduce_darks.files) == len(darks)
logutils.config(file_name='f2_test_reduce_darks.log', mode='quiet')
reduce_darks.runr()
calib_files.append('processed_dark:{}'.format(
reduce_darks.output_filenames[0]))
logutils.config(file_name='f2_test_reduce_bpm.log', mode='quiet')
reduce_bpm = Reduce()
reduce_bpm.files.extend(flats)
reduce_bpm.files.extend(darks_3s)
reduce_bpm.recipename = 'makeProcessedBPM'
reduce_bpm.runr()
bpm_filename = reduce_bpm.output_filenames[0]
logutils.config(file_name='f2_test_reduce_flats.log', mode='quiet')
reduce_flats = Reduce()
reduce_flats.files.extend(flats)
reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_flats.runr()
calib_files.append('processed_flat:{}'.format(
reduce_flats.output_filenames[0]))
logutils.config(file_name='f2_test_reduce_science.log', mode='quiet')
reduce_target = Reduce()
reduce_target.files.extend(science)
reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files)
reduce_target.runr()
def create_inputs():
"""
Create inputs for `test_plot_spectra_for_qa_single_frame`.
The raw files will be downloaded and saved inside the path stored in the
`$DRAGONS_TEST/raw_inputs` directory. Processed files will be stored inside
a new folder called "dragons_test_inputs". The sub-directory structure
should reflect the one returned by the `path_to_inputs` fixture.
"""
import glob
import os
from geminidr.gmos.primitives_gmos_longslit import GMOSLongslit
from gempy.utils import logutils
from recipe_system.reduction.coreReduce import Reduce
from recipe_system.utils.reduce_utils import normalize_ucals
cwd = os.getcwd()
path = f"./dragons_test_inputs/geminidr/core/{__file__.split('.')[0]}/"
os.makedirs(path, exist_ok=True)
os.chdir(path)
os.makedirs("inputs/", exist_ok=True)
for raw_list, bias_list, quartz_list, arc_list in single_aperture_data:
if all([
os.path.exists(f"inputs/{s.split('.')[0]}_extracted.fits")
for s in raw_list
]):
print("Skipping already created input.")
continue
raw_paths = [download_from_archive(f) for f in raw_list]
bias_paths = [download_from_archive(f) for f in bias_list]
quartz_paths = [download_from_archive(f) for f in quartz_list]
arc_paths = [download_from_archive(f) for f in arc_list]
cals = []
raw_ads = [astrodata.open(p) for p in raw_paths]
data_label = raw_ads[0].data_label()
print('Current working directory:\n {:s}'.format(os.getcwd()))
if len(bias_paths):
logutils.config(file_name='log_bias_{}.txt'.format(data_label))
r = Reduce()
r.files.extend(bias_paths)
r.runr()
master_bias = r.output_filenames.pop()
cals.append(f"processed_bias:{master_bias}")
del r
else:
master_bias = None
if len(quartz_paths):
logutils.config(file_name='log_quartz_{}.txt'.format(data_label))
r = Reduce()
r.files.extend(quartz_paths)
r.ucals = normalize_ucals(r.files, cals)
r.runr()
master_quartz = r.output_filenames.pop()
cals.append(f"processed_flat:{master_quartz}")
del r
else:
master_quartz = None
logutils.config(file_name='log_arc_{}.txt'.format(data_label))
r = Reduce()
r.files.extend(arc_paths)
r.ucals = normalize_ucals(r.files, cals)
r.runr()
master_arc = r.output_filenames.pop()
logutils.config(file_name='log_{}.txt'.format(data_label))
p = GMOSLongslit(raw_ads)
p.prepare()
p.addDQ(static_bpm=None)
p.addVAR(read_noise=True)
p.overscanCorrect()
p.biasCorrect(do_bias=master_bias is not None, bias=master_bias)
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.flatCorrect(do_flat=master_quartz is not None, flat=master_quartz)
p.QECorrect(arc=master_arc)
p.distortionCorrect(arc=master_arc)
p.findSourceApertures(max_apertures=3)
p.skyCorrectFromSlit()
p.traceApertures()
p.extract1DSpectra()
p.linearizeSpectra()
[os.remove(s) for s in glob.glob("*_arc.fits")]
[os.remove(s) for s in glob.glob("*_bias.fits")]
[os.remove(s) for s in glob.glob("*_flat.fits")]
[os.remove(s) for s in glob.glob("*_mosaic.fits")]
os.chdir("inputs/")
print("\n\n Writing processed files for tests into:\n"
" {:s}\n\n".format(os.getcwd()))
_ = p.writeOutputs()
os.chdir("../")
os.chdir(cwd)
def test_arc_missing_pixelmodel(arm, res, epoch, get_or_create_tmpdir):
"""
Check for the correct behaviour/error handling if PIXELMODEL extn. missing.
"""
rawfilename = 'arc{}*{}*{}[0-9].fits'.format(epoch, res, arm)
# Copy the raw data file into here
tmpsubdir, cal_service = get_or_create_tmpdir
# Find all the relevant files
# rawfiles = glob.glob(os.path.join(os.path.dirname(
# os.path.abspath(__file__)),
# 'testdata',
# rawfilename))
# for f in rawfiles:
# shutil.copy(f, os.path.join(tmpsubdir.dirname, tmpsubdir.basename))
rawfiles = glob.glob(
os.path.join(tmpsubdir.dirname, tmpsubdir.basename, rawfilename))
rawfiles_ad = [astrodata.open(_) for _ in rawfiles]
calibs = {
'processed_bias':
glob.glob(
os.path.join('calibrations', 'processed_bias',
'bias*{}*.fits'.format(arm)))[0],
'processed_dark':
glob.glob(
os.path.join('calibrations', 'processed_dark',
'dark*{}*.fits'.format(arm)))[0],
'processed_flat':
glob.glob(
os.path.join('calibrations', 'processed_flat',
'flat*{}*{}*.fits'.format(res, arm)))[0],
'processed_slitflat':
glob.glob(
os.path.join('calibrations', 'processed_slitflat',
'flat*{}*slitflat*.fits'.format(res)))[0],
'processed_slit':
glob.glob(
os.path.join('calibrations', 'processed_slit',
'arc{}*{}*_slit.fits'.format(epoch, res)))[0],
}
flat = astrodata.open(calibs['processed_flat'])
del flat[0].PIXELMODEL
flatname = 'flat_{}_{}_nopixmod.fits'.format(arm, res)
flat.write(filename=flatname, overwrite=True)
calibs['processed_flat'] = flatname
# import pdb;
# pdb.set_trace()
pm = PrimitiveMapper(rawfiles_ad,
mode='test',
drpkg='ghostdr',
recipename='recipeArcCreateMaster',
usercals=normalize_ucals(
rawfiles,
['{}:{}'.format(k, v) for k, v in calibs.items()])
# usercals=calibs,
)
rm = RecipeMapper(
rawfiles_ad,
mode='test',
drpkg='ghostdr',
recipename='recipeArcCreateMaster',
# usercals=calibs,
)
p = pm.get_applicable_primitives()
recipe = rm.get_applicable_recipe()
with pytest.raises(AttributeError) as e_pixmod:
recipe(p)
# import pdb; pdb.set_trace()
assert 'PIXELMODEL' in e_pixmod.value.__str__(), "The assertion error raised " \
"in this " \
"test doesn't seem to be " \
"about the " \
"missing PIXELMODEL " \
"extension, as expected."
# Teardown code
os.remove(flatname)
def create_inputs_recipe(use_branch_name=False):
"""
Creates input data for tests using pre-processed standard star and its
calibration files.
The raw files will be downloaded and saved inside the path stored in the
`$DRAGONS_TEST/raw_inputs` directory. Processed files will be stored inside
a new folder called "dragons_test_inputs". The sub-directory structure
should reflect the one returned by the `path_to_inputs` fixture.
"""
import os
from astrodata.testing import download_from_archive
from gempy.utils import logutils
from geminidr.gmos.tests.spect import CREATED_INPUTS_PATH_FOR_TESTS
from recipe_system.reduction.coreReduce import Reduce
from recipe_system.utils.reduce_utils import normalize_ucals
module_name, _ = os.path.splitext(os.path.basename(__file__))
path = os.path.join(CREATED_INPUTS_PATH_FOR_TESTS, module_name)
os.makedirs(path, exist_ok=True)
os.chdir(path)
input_path = os.path.join(path, "inputs/")
os.makedirs(input_path, exist_ok=True)
for filename, cals in associated_calibrations.items():
print(filename)
print(cals)
sci_path = download_from_archive(filename)
cals = associated_calibrations[filename]
bias_paths = [download_from_archive(f) for f in cals['bias']]
flat_paths = [download_from_archive(f) for f in cals['flat']]
arc_paths = [download_from_archive(f) for f in cals['arcs']]
sci_ad = astrodata.open(sci_path)
data_label = sci_ad.data_label()
logutils.config(file_name='log_bias_{}.txt'.format(data_label))
bias_reduce = Reduce()
bias_reduce.files.extend(bias_paths)
bias_reduce.runr()
bias_master = bias_reduce.output_filenames.pop()
calibration_files = ['processed_bias:{}'.format(bias_master)]
del bias_reduce
logutils.config(file_name='log_flat_{}.txt'.format(data_label))
flat_reduce = Reduce()
flat_reduce.files.extend(flat_paths)
flat_reduce.ucals = normalize_ucals(calibration_files)
flat_reduce.runr()
flat_master = flat_reduce.output_filenames.pop()
calibration_files.append('processed_flat:{}'.format(flat_master))
del flat_reduce
logutils.config(file_name='log_arc_{}.txt'.format(data_label))
arc_reduce = Reduce()
arc_reduce.files.extend(arc_paths)
arc_reduce.ucals = normalize_ucals(calibration_files)
os.chdir(input_path)
arc_reduce.runr()
_ = arc_reduce.output_filenames.pop()
os.chdir(path)
logutils.config(file_name='log_{}.txt'.format(data_label))
p = primitives_gmos_longslit.GMOSLongslit([sci_ad])
p.prepare()
p.addDQ(static_bpm=None)
p.addVAR(read_noise=True)
p.overscanCorrect()
p.biasCorrect(bias=bias_master)
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.flatCorrect(flat=flat_master)
os.chdir(input_path)
_ = p.writeOutputs().pop()
os.chdir(path)
def do_slit_dark(self, get_or_create_tmpdir):
"""
Reduce the test slit dark data.
.. note::
Fixture.
"""
rawfilename = 'dark*slit*.fits'
# Copy the raw data file into here
tmpsubdir, cal_service = get_or_create_tmpdir
# Find all the relevant files
rawfiles = glob.glob(
os.path.join(tmpsubdir.dirname, tmpsubdir.basename, rawfilename))
# Do the master bias generation
reduce = Reduce()
reduce.drpkg = 'ghostdr'
reduce.files = rawfiles
reduce.mode = [
'test',
]
reduce.recipename = 'recipeSlitDarkTest'
# Make sure refresh is used for all primitives
reduce.upars = [
'refresh=True',
]
# FIXME cal_service will hopefully find the calibration itself later
calibs = {
'processed_bias':
glob.glob(
os.path.join('calibrations', 'processed_bias',
'*slit*bias*.fits'))[0],
}
reduce.ucals = normalize_ucals(
reduce.files, ['{}:{}'.format(k, v) for k, v in calibs.items()])
reduce.logfile = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
'reduce_slitdark.log')
reduce.logmode = 'standard'
reduce.suffix = '_testSlitDark'
logutils.config(file_name=reduce.logfile, mode=reduce.logmode)
reduce.runr()
corrfilename = '*' + reduce.suffix + '.fits'
corrfilename = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
glob.glob(corrfilename)[0])
corrfile = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
corrfilename)
# Return filenames of raw, subtracted files
yield rawfiles, corrfile, calibs
# import pdb; pdb.set_trace()
# Execute teardown code
for _ in glob.glob(
os.path.join(
os.getcwd(),
# rawfilename,
corrfilename,
)):
os.remove(_)
def create_inputs_recipe():
"""
Creates input data for tests using pre-processed standard star and its
calibration files.
The raw files will be downloaded and saved inside the path stored in the
`$DRAGONS_TEST/raw_inputs` directory. Processed files will be stored inside
a new folder called "dragons_test_inputs". The sub-directory structure
should reflect the one returned by the `path_to_inputs` fixture.
"""
import os
from astrodata.testing import download_from_archive
from gempy.utils import logutils
from recipe_system.reduction.coreReduce import Reduce
from recipe_system.utils.reduce_utils import normalize_ucals
from geminidr.gmos.tests.spect import CREATED_INPUTS_PATH_FOR_TESTS
associated_calibrations = {
"N20180109S0287.fits": {
'bias': [
"N20180103S0563.fits", "N20180103S0564.fits",
"N20180103S0565.fits", "N20180103S0566.fits",
"N20180103S0567.fits"
],
'flat': ["N20180109S0288.fits"],
'arcs': ["N20180109S0315.fits"]
}
}
module_name, _ = os.path.splitext(os.path.basename(__file__))
path = os.path.join(CREATED_INPUTS_PATH_FOR_TESTS, module_name)
os.makedirs(path, exist_ok=True)
os.chdir(path)
print('Current working directory:\n {:s}'.format(os.getcwd()))
os.makedirs("inputs/", exist_ok=True)
for filename, cals in associated_calibrations.items():
print('Downloading files...')
sci_path = download_from_archive(filename)
bias_path = [download_from_archive(f) for f in cals['bias']]
flat_path = [download_from_archive(f) for f in cals['flat']]
arc_path = [download_from_archive(f) for f in cals['arcs']]
sci_ad = astrodata.open(sci_path)
data_label = sci_ad.data_label()
print('Reducing BIAS for {:s}'.format(data_label))
logutils.config(file_name='log_bias_{}.txt'.format(data_label))
bias_reduce = Reduce()
bias_reduce.files.extend(bias_path)
bias_reduce.runr()
bias_master = bias_reduce.output_filenames.pop()
calibration_files = ['processed_bias:{}'.format(bias_master)]
del bias_reduce
print('Reducing FLAT for {:s}'.format(data_label))
logutils.config(file_name='log_flat_{}.txt'.format(data_label))
flat_reduce = Reduce()
flat_reduce.files.extend(flat_path)
flat_reduce.ucals = normalize_ucals(flat_reduce.files,
calibration_files)
flat_reduce.runr()
flat_master = flat_reduce.output_filenames.pop()
calibration_files.append('processed_flat:{}'.format(flat_master))
del flat_reduce
print('Reducing ARC for {:s}'.format(data_label))
logutils.config(file_name='log_arc_{}.txt'.format(data_label))
arc_reduce = Reduce()
arc_reduce.files.extend(arc_path)
arc_reduce.ucals = normalize_ucals(arc_reduce.files, calibration_files)
arc_reduce.runr()
arc_master = arc_reduce.output_filenames.pop()
del arc_reduce
print('Reducing pre-processed data:')
logutils.config(file_name='log_{}.txt'.format(data_label))
p = primitives_gmos_longslit.GMOSLongslit([sci_ad])
p.prepare()
p.addDQ(static_bpm=None, user_bpm=None, add_illum_mask=False)
p.addVAR(read_noise=True, poisson_noise=False)
p.overscanCorrect(function="spline",
high_reject=3.,
low_reject=3.,
nbiascontam=0,
niterate=2,
order=None)
p.biasCorrect(bias=bias_master, do_bias=True)
p.ADUToElectrons()
p.addVAR(poisson_noise=True, read_noise=False)
p.flatCorrect(flat=flat_master)
p.QECorrect(arc=arc_master)
p.distortionCorrect(arc=arc_master, order=3, subsample=1)
p.findSourceApertures(max_apertures=1,
threshold=0.01,
min_sky_region=20)
p.skyCorrectFromSlit(order=5, grow=0)
p.traceApertures(order=2,
nsum=10,
step=10,
max_missed=5,
max_shift=0.05)
p.extract1DSpectra(grow=10, method="standard", width=None)
os.chdir("inputs/")
processed_ad = p.writeOutputs().pop()
print('Wrote pre-processed file to:\n'
' {:s}'.format(processed_ad.filename))
os.chdir("../")
def do_bias_subtract(self, get_or_create_tmpdir, request):
"""
Perform basic bias subtraction on the dark frame.
.. note::
Fixture.
"""
rawfilename = 'dark*{}*.fits'.format(request.param)
# Copy the raw data file into here
tmpsubdir, cal_service = get_or_create_tmpdir
# Find all the relevant files
# rawfiles = glob.glob(os.path.join(os.path.dirname(
# os.path.abspath(__file__)),
# 'testdata',
# rawfilename))
# for f in rawfiles:
# shutil.copy(f, os.path.join(tmpsubdir.dirname, tmpsubdir.basename))
rawfiles = glob.glob(
os.path.join(tmpsubdir.dirname, tmpsubdir.basename, rawfilename))
# Do the master bias generation
reduce = Reduce()
reduce.drpkg = 'ghostdr'
reduce.files = rawfiles[0]
reduce.mode = [
'test',
]
reduce.recipename = 'recipeDarkBiasCorrect'
# reduce.mode = ['sq', ]
# reduce.recipename = 'makeProcessedBias'
reduce.logfile = os.path.join(
tmpsubdir.dirname, tmpsubdir.basename,
'reduce_biascorrect_{}.log'.format(request.param))
reduce.logmode = 'quiet'
reduce.suffix = '_{}_testBiasCorrect'.format(request.param)
logutils.config(file_name=reduce.logfile, mode=reduce.logmode)
# import pdb; pdb.set_trace()
calibs = {
'processed_bias':
glob.glob(
os.path.join('calibrations', 'processed_bias',
'bias*{}*.fits'.format(request.param)))[0],
}
reduce.ucals = normalize_ucals(
reduce.files, ['{}:{}'.format(k, v) for k, v in calibs.items()])
# import pdb;
# pdb.set_trace()
reduce.runr()
corrfilename = '*' + reduce.suffix + '.fits'
corrfilename = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
glob.glob(corrfilename)[0])
corrfile = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
corrfilename)
# Return filenames of raw, subtracted files
yield rawfiles, corrfile, calibs
# Execute teardown code
for _ in glob.glob(
os.path.join(os.getcwd(), '*{}.fits'.format(reduce.suffix))):
os.remove(_)
def create_inputs_recipe():
"""
Creates input data for tests using pre-processed standard star and its
calibration files.
The raw files will be downloaded and saved inside the path stored in the
`$DRAGONS_TEST/raw_inputs` directory. Processed files will be stored inside
a new folder called "dragons_test_inputs". The sub-directory structure
should reflect the one returned by the `path_to_inputs` fixture.
"""
import os
from astrodata.testing import download_from_archive
from recipe_system.utils.reduce_utils import normalize_ucals
from recipe_system.reduction.coreReduce import Reduce
associated_calibrations = {
"N20180109S0287.fits": {
"arcs": ["N20180109S0315.fits"],
"bias": [
"N20180103S0563.fits",
"N20180103S0564.fits",
"N20180103S0565.fits",
"N20180103S0566.fits",
"N20180103S0567.fits",
],
"flat": ["N20180109S0288.fits"],
}
}
root_path = os.path.join("./dragons_test_inputs/")
module_path = "geminidr/gmos/spect/{}/".format(__file__.split('.')[0])
path = os.path.join(root_path, module_path)
os.makedirs(path, exist_ok=True)
os.chdir(path)
os.makedirs("inputs", exist_ok=True)
print('Current working directory:\n {:s}'.format(os.getcwd()))
for filename, cals in associated_calibrations.items():
print('Downloading files...')
sci_path = download_from_archive(filename)
bias_path = [download_from_archive(f) for f in cals['bias']]
flat_path = [download_from_archive(f) for f in cals['flat']]
arc_path = [download_from_archive(f) for f in cals['arcs']]
sci_ad = astrodata.open(sci_path)
data_label = sci_ad.data_label()
print('Reducing BIAS for {:s}'.format(data_label))
logutils.config(file_name='log_bias_{}.txt'.format(data_label))
bias_reduce = Reduce()
bias_reduce.files.extend(bias_path)
bias_reduce.runr()
bias_master = bias_reduce.output_filenames.pop()
calibration_files = ['processed_bias:{}'.format(bias_master)]
del bias_reduce
print('Reducing FLAT for {:s}'.format(data_label))
logutils.config(file_name='log_flat_{}.txt'.format(data_label))
flat_reduce = Reduce()
flat_reduce.files.extend(flat_path)
flat_reduce.ucals = normalize_ucals(flat_reduce.files,
calibration_files)
flat_reduce.runr()
flat_master = flat_reduce.output_filenames.pop()
calibration_files.append('processed_flat:{}'.format(flat_master))
del flat_reduce
print('Reducing ARC for {:s}'.format(data_label))
logutils.config(file_name='log_arc_{}.txt'.format(data_label))
arc_reduce = Reduce()
arc_reduce.files.extend(arc_path)
arc_reduce.ucals = normalize_ucals(arc_reduce.files, calibration_files)
arc_reduce.runr()
arc_master = arc_reduce.output_filenames.pop()
print('Reducing pre-processed data:')
logutils.config(file_name='log_{}.txt'.format(data_label))
p = primitives_gmos_longslit.GMOSLongslit([sci_ad])
p.prepare()
p.addDQ(static_bpm=None)
p.addVAR(read_noise=True)
p.overscanCorrect()
p.biasCorrect(bias=bias_master)
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.flatCorrect(flat=flat_master)
p.QECorrect(arc=arc_master)
p.distortionCorrect(arc=arc_master)
p.findSourceApertures(max_apertures=1)
p.skyCorrectFromSlit()
p.traceApertures()
p.extract1DSpectra()
p.calculateSensitivity()
os.chdir("inputs/")
_ = p.writeOutputs().pop()
os.chdir("../../")
def test_reduce_image(change_working_dir):
with change_working_dir():
calib_files = []
all_files = [download_from_archive(f) for f in datasets]
all_files.sort()
assert len(all_files) > 1
darks_3s = dataselect.select_data(
all_files, ['F2', 'DARK', 'RAW'], [],
dataselect.expr_parser('exposure_time==3'))
darks_3s.sort()
darks_20s = dataselect.select_data(
all_files, ['F2', 'DARK', 'RAW'], [],
dataselect.expr_parser('exposure_time==20'))
darks_20s.sort()
darks_120s = dataselect.select_data(
all_files, ['F2', 'DARK', 'RAW'], [],
dataselect.expr_parser('exposure_time==120'))
darks_120s.sort()
flats = dataselect.select_data(
all_files, ['F2', 'FLAT', 'RAW'], [],
dataselect.expr_parser('filter_name=="Y"'))
flats.sort()
science = dataselect.select_data(
all_files, ['F2', 'RAW'], ['CAL'],
dataselect.expr_parser('filter_name=="Y"'))
science.sort()
for darks in [darks_3s, darks_20s, darks_120s]:
reduce_darks = Reduce()
assert len(reduce_darks.files) == 0
reduce_darks.files.extend(darks)
assert len(reduce_darks.files) == len(darks)
logutils.config(file_name='f2_test_reduce_darks.log', mode='quiet')
reduce_darks.runr()
calib_files.append('processed_dark:{}'.format(
reduce_darks.output_filenames[0]))
logutils.config(file_name='f2_test_reduce_bpm.log', mode='quiet')
reduce_bpm = Reduce()
reduce_bpm.files.extend(flats)
assert len(reduce_bpm.files) == len(flats)
reduce_bpm.files.extend(darks_3s)
assert len(reduce_bpm.files) == len(flats) + len(darks_3s)
reduce_bpm.recipename = 'makeProcessedBPM'
reduce_bpm.runr()
bpm_filename = reduce_bpm.output_filenames[0]
logutils.config(file_name='f2_test_reduce_flats.log', mode='quiet')
reduce_flats = Reduce()
reduce_flats.files.extend(flats)
reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_flats.runr()
calib_files.append('processed_flat:{}'.format(
reduce_flats.output_filenames[0]))
logutils.config(file_name='f2_test_reduce_science.log', mode='quiet')
reduce_target = Reduce()
reduce_target.files.extend(science)
reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files)
reduce_target.runr()
def do_slit_arc(self, request, get_or_create_tmpdir):
"""
Reduce the test slit arc data.
.. note::
Fixture.
"""
# import pdb; pdb.set_trace()
# rawfilename = '{}*slit*.fits'.format(slit_type)
# Copy the raw data file into here
tmpsubdir, cal_service = get_or_create_tmpdir
slit_type, res = request.param
filenames = glob.glob('{}*{}*slit.fits'.format(slit_type, res))
# Do the master bias generation
reduce = Reduce()
reduce.drpkg = 'ghostdr'
reduce.mode = ['test', ]
reduce.recipename = 'recipeSlitArcTest' if slit_type == 'arc' \
else 'recipeSlitTest'
# Make sure refresh is used for all primitives
reduce.upars = ['refresh=True', ]
# FIXME cal_service will hopefully find the calibration itself later
calibs = {
'processed_bias': glob.glob(os.path.join(
'calibrations',
'processed_bias',
'*slit*bias*.fits'))[0],
'processed_dark': glob.glob(os.path.join(
'calibrations',
'processed_dark',
'*slit*dark*.fits'))[0],
'processed_slitflat': glob.glob(os.path.join(
'calibrations',
'processed_slitflat',
'*{}*slit*slitflat*.fits'.format(res, )))[0]
}
reduce.logfile = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
'reduce_slit{}.log'.format(slit_type))
reduce.logmode = 'standard'
reduce.suffix = '_{}_testSlit'.format(slit_type)
logutils.config(file_name=reduce.logfile, mode=reduce.logmode)
corrfiles = []
for filename in filenames:
reduce.files = [filename, ]
reduce.ucals = normalize_ucals(reduce.files, [
'{}:{}'.format(k, v) for k, v in calibs.items()
])
reduce.runr()
# import pdb; pdb.set_trace()
corrfilename = '*' + reduce.suffix + '.fits'
corrfilename = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
glob.glob(corrfilename)[0])
corrfile = os.path.join(tmpsubdir.dirname, tmpsubdir.basename,
corrfilename)
corrfiles.append(corrfile)
# Return filenames of raw, subtracted files
yield filenames, corrfiles, calibs
# import pdb; pdb.set_trace()
# Execute teardown code
for _ in glob.glob(os.path.join(
os.getcwd(),
# rawfilename,
corrfilename,
)):
os.remove(_)
def test_reduce_image_GS_HAM_2x2_i_std(path_to_inputs):
logutils.config(file_name='gmos_test_reduce_image_GS_HAM_1x1_i.log')
calib_files = []
raw_subdir = 'GMOS/GS-2017B-Q-6'
all_files = sorted(glob.glob(
os.path.join(path_to_inputs, raw_subdir, '*.fits')))
assert len(all_files) > 1
list_of_sci_bias = dataselect.select_data(
all_files,
['BIAS'],
[],
dataselect.expr_parser('detector_x_bin==2 and detector_y_bin==2')
)
list_of_sci_flats = dataselect.select_data(
all_files,
['TWILIGHT'],
[],
dataselect.expr_parser(
'filter_name=="i" and detector_x_bin==2 and detector_y_bin==2'
)
)
list_of_science_files = dataselect.select_data(
all_files, [],
[],
dataselect.expr_parser(
'observation_class=="partnerCal" and filter_name=="i"'
)
)
reduce_bias = Reduce()
assert len(reduce_bias.files) == 0
reduce_bias.files.extend(list_of_sci_bias)
assert len(reduce_bias.files) == len(list_of_sci_bias)
reduce_bias.runr()
calib_files.append(
'processed_bias:{}'.format(reduce_bias.output_filenames[0])
)
reduce_flats = Reduce()
reduce_flats.files.extend(list_of_sci_flats)
# reduce_flats.uparms = [('addDQ:user_bpm', 'fixed_bpm_2x2_FullFrame.fits')]
reduce_flats.ucals = normalize_ucals(reduce_flats.files, calib_files)
reduce_flats.runr()
calib_files.append(
'processed_flat:{}'.format(reduce_flats.output_filenames[0])
)
reduce_target = Reduce()
reduce_target.files.extend(list_of_science_files)
reduce_target.ucals = normalize_ucals(reduce_target.files, calib_files)
reduce_target.uparms = [
('stackFrames:memory', 1),
# ('addDQ:user_bpm', 'fixed_bpm_2x2_FullFrame.fits'),
('resampleToCommonFrame:interpolator', 'spline3')
]
reduce_target.runr()
