def test_mask_saturated_pixels():
"""Function to test the masking of saturated pixels in a FITS image."""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_sky_background')
test_image_file = os.path.join(
cwd, 'data', 'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
hdulist = fits.open(test_image_file)
saturation_value = hdulist[0].header['SATURATE']
image = hdulist[0].data
masked_image = sky_background.mask_saturated_pixels(
setup, image, saturation_value, log)
fig = plt.figure(4)
plt.imshow(masked_image, origin='lower', cmap=plt.cm.viridis)
plt.title('Masked image')
plt.savefig(os.path.join(cwd, 'data', 'masked_image_test.png'))
plt.close(4)
assert (os.path.isfile(os.path.join(cwd, 'data', 'masked_image_test.png')))
logs.close_log(log)
def test_reference_astrometry():
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DATA})
log = logs.start_stage_log( cwd, 'test_wcs' )
image_path = path.join(TEST_DATA,'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
detected_sources_file = path.join(TEST_DATA,'lsc1m005-fl15-20170701-0144-e91_cropped_sources.txt')
outputs = ['reference_detected_sources_pixels.png',
'reference_detected_sources_world.png',
'astrometry_separations.png',
'star_catalog.fits']
for item in outputs:
if path.isfile(path.join(TEST_DATA,item)):
remove(path.join(TEST_DATA,item))
detected_sources = catalog_utils.read_source_catalog(detected_sources_file)
ref_source_catalog = wcs.reference_astrometry(setup,log,image_path,detected_sources)
assert path.isfile(path.join(TEST_DATA,'ref','reference_detected_sources_pixels.png')) == True
assert path.isfile(path.join(TEST_DATA,'ref','reference_detected_sources_world.png')) == True
assert path.isfile(path.join(TEST_DATA,'ref','astrometry_separations.png')) == True
assert path.isfile(path.join(TEST_DATA,'ref','star_catalog.fits')) == True
assert path.isfile(path.join(TEST_DATA,'ref','ref_image_wcs.fits')) == True
logs.close_log(log)
def test_model_sky_background():
"""Function to test the function to model the sky background of an image"""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_psf_selection')
detected_sources_file = path.join(
TEST_DATA, 'lsc1m005-fl15-20170701-0144-e91_cropped_sources.txt')
detected_sources = catalog_utils.read_source_catalog(detected_sources_file)
ref_star_catalog = np.zeros([len(detected_sources), 13])
ref_star_catalog[:, 0] = detected_sources[:, 0]
ref_star_catalog[:, 1] = detected_sources[:, 1]
ref_star_catalog[:, 2] = detected_sources[:, 2]
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
reduction_metadata.ref_image_path = path.join(
TEST_DATA, 'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
log.info('Read metadata')
sky_background.model_sky_background(setup, reduction_metadata, log,
ref_star_catalog)
logs.close_log(log)
def test_merge_catalog():
"""Function to test the catalogue cross-matching function for the survey
catalog builder.
Based on code by Y. Tsapras.
"""
log = logs.start_stage_log(LOG_DIR, 'test_survey_catalog')
catalog1 = survey_catalog.read_star_catalog(TEST_DIR, log)
nrows = len(catalog1)
row = [
999, 999.99999, 999.999999, 99.99999, 9.99999, 99.9999, 99.999,
99.9999, 99.9999, 99.9999, 99.999, 99.9999, 99.9999, 99.9999, 99.999,
99.9999
]
catalog1.add_row(row)
catalog2 = survey_catalog.read_star_catalog(TEST_DIR, log)
new_stars = range(0, len(catalog2), 1)
star_catalog = survey_catalog.add_new_stars_to_catalog(
new_stars, catalog2, None, log)
star_catalog = survey_catalog.merge_catalogs(catalog1, star_catalog, log)
assert len(star_catalog) == nrows + 1
logs.close_log(log)
def build_survey_catalog():
"""Function to build a single catalogue including all stars detected in
the ROME/REA survey.
"""
params = get_args()
log = logs.start_stage_log(params['log_dir'],
'survey_catalog',
version=VERSION)
params = list_reduced_datasets(params, log)
star_catalog = read_existing_survey_catalog(params, log)
for red_dir in params['datasets']:
catalog = read_star_catalog(red_dir, log)
star_catalog = merge_catalogs(catalog, star_catalog, log)
catalog_file = os.path.join(params['log_dir'], 'survey_star_catalog.fits')
catalog_utils.output_survey_catalog(catalog_file, star_catalog, log)
log.info('Survey catalogue construction complete.')
logs.close_log(log)
def test_id_crowded_stars():
"""Function to test the exclusion of crowded stars from the PSF star
selection process"""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log( cwd, 'test_psf_selection' )
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
log.info('Read metadata')
nstars = 10
bright = 16.0
faint = 23.0
ref_star_catalog = np.zeros([nstars,13])
ref_star_catalog[:,0] = range(0,nstars,1)
istar = -1
for j in range(0,7,1):
istar += 1
ref_star_catalog[istar,1] = abs(random.normalvariate(100.0,100.0))
ref_star_catalog[istar,2] = abs(random.normalvariate(100.0,100.0))
ref_star_catalog[istar,5] = abs(random.normalvariate(16.0,0.5))
exclude = []
for j in range(istar,nstars,1):
i = random.randint(0,2)
xstar = ref_star_catalog[i,1]
ystar = ref_star_catalog[i,2]
ref_star_catalog[j,1] = random.normalvariate(xstar,1.0)
ref_star_catalog[j,2] = random.normalvariate(ystar,1.0)
ref_star_catalog[j,5] = abs(random.normalvariate(17.0,0.5))
exclude.append(i)
ref_star_catalog[:,3] = random.normalvariate(17.0*15.0,20.0)
ref_star_catalog[:,4] = random.normalvariate(-27.0,10.0)
ref_star_catalog[:,6] = 0.005 + ref_star_catalog[:,4]*0.05
psf_stars_idx = np.array([1]*nstars)
psf_stars_idx = psf_selection.id_crowded_stars(setup,reduction_metadata,log,
ref_star_catalog,psf_stars_idx)
psf_selection.plot_ref_star_catalog_positions(setup,reduction_metadata,log,
ref_star_catalog, psf_stars_idx)
star_index = np.where(psf_stars_idx == 1)[0]
for j in exclude:
assert j not in star_index
logs.close_log(log)
def test_read_star_catalog():
"""Function to test the read of a star catalog from a reduction's
metadata file"""
log = logs.start_stage_log(LOG_DIR, 'test_survey_catalog')
catalog = survey_catalog.read_star_catalog(TEST_DIR, log)
t = Table(np.zeros(5))
assert type(catalog) == type(t)
logs.close_log(log)
def test_id_mid_range_stars():
"""Function to test the selection of stars in the reference image, excluding
the brightest and faintest N% of those detected"""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log( cwd, 'test_psf_selection' )
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
log.info('Read metadata')
# Generating test catalog with columns:
# idx x y ra dec inst_mag inst_mag_err J Jerr H Herr K Kerr
nstars = 10
bright = 16.0
faint = 23.0
ref_star_catalog = np.zeros([nstars,13])
ref_star_catalog[:,0] = range(0,nstars,1)
ref_star_catalog[:,1] = random.normalvariate(100.0,100.0)
ref_star_catalog[:,2] = random.normalvariate(100.0,100.0)
ref_star_catalog[:,3] = random.normalvariate(17.0*15.0,20.0)
ref_star_catalog[:,4] = random.normalvariate(-27.0,10.0)
ref_star_catalog[:,5] = np.arange(bright,faint,(faint-bright)/float(nstars))
ref_star_catalog[:,6] = 0.005 + ref_star_catalog[:,4]*0.05
psf_stars_idx = np.array([1]*nstars)
psf_range_thresh = reduction_metadata.reduction_parameters[1]['PSF_RANGE_THRESH'][0]
log.info('Read psf range threshold = '+str(psf_range_thresh))
nstar_cut = int(float(nstars) * (psf_range_thresh/100.0))
istart = nstar_cut
iend = len(ref_star_catalog) - nstar_cut
test_psf_stars_idx = np.ones(nstars)
test_psf_stars_idx[0:nstar_cut] = 0
test_psf_stars_idx[(-1*nstar_cut):] = 0
psf_stars_idx = psf_selection.id_mid_range_stars(setup,reduction_metadata,
log,
ref_star_catalog,
psf_stars_idx)
assert psf_stars_idx.all() == test_psf_stars_idx.all()
logs.close_log(log)
def test_create_survey_catalog():
"""Function to test the creation of a survey catalog Table object"""
log = logs.start_stage_log(LOG_DIR, 'test_survey_catalog')
star_catalog = survey_catalog.create_survey_catalog(log, 10)
test_table = Table()
assert type(star_catalog) == type(test_table)
assert 'ID' in star_catalog.colnames
assert 'RA_J2000' in star_catalog.colnames
assert 'DEC_J2000' in star_catalog.colnames
assert 'Blend' in star_catalog.colnames
logs.close_log(log)
def test_trigger_reduction():
"""Function to test the spawning of reduction processes"""
setup = pipeline_setup.pipeline_setup(params)
log = logs.start_pipeline_log(setup.log_dir,
'test_pipeline_control',
version=VERSION)
dataset_dir = TEST_DATA
pid = pipeline_control.trigger_reduction(setup, dataset_dir, debug=True)
assert type(pid) == type(1)
logs.close_log(log)
def test_get_datasets():
"""Function to test the function which identifies which datasets
should be reduced.
"""
setup = pipeline_setup.pipeline_setup(params)
log = logs.start_pipeline_log(setup.log_dir,
'test_pipeline_control',
version=VERSION)
datasets = pipeline_control.get_datasets_for_reduction(setup, log)
assert type(datasets) == type(['a', 'b', 'c'])
logs.close_log(log)
def test_list_reducted_datasets():
"""Function to test the gathering of datasets to be combined into
a single survey catalogue."""
log = logs.start_stage_log(LOG_DIR, 'test_survey_catalog')
params = {'data_dir': os.path.join(TEST_DIR, '..')}
params = survey_catalog.list_reduced_datasets(params, log)
t_list = [os.path.join(TEST_DIR, '..', os.path.basename(TEST_DIR))]
assert 'datasets' in params.keys()
assert params['datasets'] == t_list
logs.close_log(log)
def test_build_psf():
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_build_psf')
log.info(setup.summary())
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
reduction_metadata.reference_image_path = os.path.join(
TEST_DATA, 'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
reduction_metadata.background_type = 'constant'
star_catalog_file = os.path.join(TEST_DATA, 'star_catalog.fits')
ref_star_catalog = catalog_utils.read_ref_star_catalog_file(
star_catalog_file)
log.info('Read in catalog of ' + str(len(ref_star_catalog)) + ' stars')
psf_stars_idx = np.zeros(len(ref_star_catalog))
psf_stars_idx[400:500] = 1
ref_star_catalog[:, 13] = psf_stars_idx
ref_image = fits.getdata(reduction_metadata.reference_image_path)
log.info('Loaded reference image')
sky_model = psf.ConstantBackground()
sky_model.constant = 1345.0
sky_model.background_parameters.constant = 1345.0
(psf_model, status) = psf.build_psf(setup,
reduction_metadata,
log,
ref_image,
ref_star_catalog,
sky_model,
diagnostics=True)
logs.close_log(log)
def pipeline_control():
"""Main driver program controlling the reduction of multiple datasets
with pyDANDIA.
"""
pipeline_version = 'pipeline_control v0.1'
setup = get_pipeline_setup()
log = logs.start_pipeline_log(setup.log_dir,
'pipeline_control',
version=pipeline_version)
datasets = get_datasets_for_reduction(setup, log)
run_reductions(setup, log, datasets)
logs.close_log(log)
def test_stage_log():
"""Function to test the initialization and closing of a stage log"""
log = logs.start_stage_log( cwd, 'test_log' )
chk_log = logging.getLogger( 'test' )
log_name = path.join(cwd,'test_log.log')
assert type(log) == type(chk_log)
assert path.isfile(log_name)
logs.close_log(log)
file_lines = open(log_name,'r').readlines()
assert len(file_lines) > 0
assert 'Processing complete' in file_lines[-2]
def test_xmatch_catalog():
"""Function to test the catalogue cross-matching function for the survey
catalog builder.
Based on code by Y. Tsapras.
"""
log = logs.start_stage_log(LOG_DIR, 'test_survey_catalog')
catalog1 = survey_catalog.read_star_catalog(TEST_DIR, log)
catalog2 = survey_catalog.read_star_catalog(TEST_DIR, log)
(matched_table,
blends) = survey_catalog.xmatch_catalogs(catalog1, catalog2, log)
assert len(matched_table[0]) > int(len(catalog1) * 0.9)
assert len(blends) > 0
logs.close_log(log)
def reduction_control():
"""Main driver function for the pyDANDIA pipelined reduction of an
individual dataset.
A single dataset is defined as a set of images taken with a consistent
instrument configuration and filter of a single sky pointing.
Input parameters:
dataset_red_dir str Full path to the reduction directory for this
dataset
"""
reduction_version = 'reduction_control v0.2'
setup = get_args()
log = logs.start_pipeline_log(setup.red_dir,
'reduction_control',
version=reduction_version)
(status, report, meta_data) = stage0.run_stage0(setup)
log.info('Completed stage 0 with status ' + repr(status) + ': ' + report)
(status, report) = stage1.run_stage1(setup)
log.info('Completed stage 1 with status ' + repr(status) + ': ' + report)
(status, report) = stage2.run_stage2(setup)
log.info('Completed stage 2 with status ' + repr(status) + ': ' + report)
(status, report) = stage3.run_stage3(setup)
log.info('Completed stage 3 with status ' + repr(status) + ': ' + report)
(status, report) = stage4.run_stage4(setup)
log.info('Completed stage 4 with status ' + repr(status) + ': ' + report)
(status, report) = stage5.run_stage5(setup)
log.info('Completed stage 5 with status ' + repr(status) + ': ' + report)
(status, report) = stage6.run_stage6(setup)
log.info('Completed stage 6 with status ' + repr(status) + ': ' + report)
logs.close_log(log)
def test_model_sky_background():
"""Function to test the fitting of a sky background model to a masked
real star image."""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_sky_background')
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'images_stats')
log.info('Read metadata')
# Need to check where these parameters come from
reduction_metadata.reference_image_path = os.path.join(
cwd, 'data', 'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
reduction_metadata.background_type = 'constant'
ref_star_catalog_file = os.path.join(cwd, 'data', 'star_catalog.fits')
ref_star_catalog = catalog_utils.read_ref_star_catalog_file(
ref_star_catalog_file)
log.info('Read reference image star catalog from ' + ref_star_catalog_file)
sky_model = sky_background.model_sky_background(setup, reduction_metadata,
log, ref_star_catalog)
log.info('Fit image sky background with '+\
reduction_metadata.background_type+' model, parameters:')
for key in sky_model.model:
log.info(key + ' = ' +
str(getattr(sky_model.background_parameters, key)))
logs.close_log(log)
def test_fit_psf_model():
"""Function to test the ability to fit a PSF model to a given stamp
image, optimizing all parameters
"""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_fit_psf_model')
log.info(setup.summary())
psf_model_type = 'Moffat2D'
sky_model_type = 'Constant'
image_file = os.path.join(TEST_DATA,
'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
image = fits.getdata(image_file)
stamp_centres = np.array([[194.654006958, 180.184967041]])
stamp_dims = (20, 20)
stamps = psf.cut_image_stamps(setup, image, stamp_centres, stamp_dims)
fitted_psf = psf.fit_psf_model(setup,
log,
psf_model_type,
sky_model_type,
stamps[0],
diagnostics=True)
assert type(fitted_psf) == type(psf.Moffat2D())
log.info('Parameters of fitted PSF model:')
for key in fitted_psf.model:
log.info(key + ' = ' + str(getattr(fitted_psf.psf_parameters, key)))
logs.close_log(log)
def test_run_iterative_PSF_photometry():
"""Function to test the PSF-fitting photometry module for a single image"""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_photometry')
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'images_stats')
log.info('Read metadata')
# NOTE: Once stage 2 is complete, the reference image path should be
# extracted directly from the metadata.
reduction_metadata.reference_image_path = os.path.join(
TEST_DIR, 'data', 'lsc1m005-fl15-20170418-0131-e91_cropped.fits')
image_path = reduction_metadata.reference_image_path
log.info('Performing PSF fitting photometry on ' +
os.path.basename(image_path))
phot_data = photometry.run_iterative_PSF_photometry(setup,
reduction_metadata,
image_path,
log,
diagnostics=True)
test_output = Table()
assert type(phot_data) == type(test_output)
logs.close_log(log)
def test_add_new_stars_to_catalog():
"""Function to test the addition of new stars to an existing master catalogue."""
log = logs.start_stage_log(LOG_DIR, 'test_survey_catalog')
params = {
'old_star_catalog':
os.path.join(TEST_DIR, '..', 'survey_star_catalog.fits')
}
star_catalog = survey_catalog.read_existing_survey_catalog(params, log)
nrows = len(star_catalog)
new_catalog = survey_catalog.read_star_catalog(TEST_DIR, log)
new_stars = set([1, 2, 3])
star_catalog = survey_catalog.add_new_stars_to_catalog(
new_stars, new_catalog, star_catalog, log)
assert len(star_catalog) == nrows + 3
logs.close_log(log)
def test_read_existing_survey_catalog():
"""Function to test the reading of a previously-existing star catalog"""
log = logs.start_stage_log(LOG_DIR, 'test_survey_catalog')
params = {'old_star_catalog': 'NONE'}
star_catalog = survey_catalog.read_existing_survey_catalog(params, log)
assert star_catalog == None
params = {
'old_star_catalog':
os.path.join(TEST_DIR, '..', 'survey_star_catalog.fits')
}
star_catalog = survey_catalog.read_existing_survey_catalog(params, log)
test_table = Table()
assert type(star_catalog) == type(test_table)
assert len(star_catalog) > 0
logs.close_log(log)
def test_subtract_companions_from_psf_stamps():
"""Function to test the function which removes companion stars from the
surrounds of a PSF star in a PSF star stamp image."""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_subtract_companions')
log.info(setup.summary())
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
star_catalog_file = os.path.join(TEST_DATA, 'star_catalog.fits')
ref_star_catalog = catalog_utils.read_ref_star_catalog_file(
star_catalog_file)
log.info('Read in catalog of ' + str(len(ref_star_catalog)) + ' stars')
image_file = os.path.join(TEST_DATA,
'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
image = fits.getdata(image_file)
psf_idx = [248]
psf_x = 257.656
psf_y = 121.365
stamp_centres = np.array([[psf_x, psf_y]])
psf_size = 10.0
stamp_dims = (20, 20)
stamps = psf.cut_image_stamps(setup, image, stamp_centres, stamp_dims)
if len(stamps) == 0:
log.info(
'ERROR: No PSF stamp images returned. PSF stars too close to the edge?'
)
else:
for i, s in enumerate(stamps):
fig = plt.figure(1)
norm = visualization.ImageNormalize(s.data, \
interval=visualization.ZScaleInterval())
plt.imshow(s.data, origin='lower', cmap=plt.cm.viridis, norm=norm)
plt.xlabel('X pixel')
plt.ylabel('Y pixel')
plt.axis('equal')
plt.savefig(
os.path.join(setup.red_dir,
'psf_star_stamp' + str(i) + '.png'))
plt.close(1)
psf_model = psf.Moffat2D()
x_cen = psf_size + (psf_x - int(psf_x))
y_cen = psf_size + (psf_x - int(psf_y))
psf_radius = 8.0
psf_params = [
103301.241291, x_cen, y_cen, 226.750731765, 13004.8930993,
103323.763627
]
psf_model.update_psf_parameters(psf_params)
sky_model = psf.ConstantBackground()
sky_model.background_parameters.constant = 1345.0
clean_stamps = psf.subtract_companions_from_psf_stamps(
setup,
reduction_metadata,
log,
ref_star_catalog,
psf_idx,
stamps,
stamp_centres,
psf_model,
sky_model,
diagnostics=True)
logs.close_log(log)
def test_run_psf_photometry():
"""Function to test the PSF-fitting photometry module for a single image"""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_photometry')
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'images_stats')
log.info('Read metadata')
# NOTE: Once stage 2 is complete, the reference image path should be
# extracted directly from the metadata.
reduction_metadata.reference_image_path = os.path.join(
TEST_DATA, 'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
image_path = reduction_metadata.reference_image_path
star_catalog_file = os.path.join(TEST_DATA, 'star_catalog.fits')
ref_star_catalog = catalog_utils.read_ref_star_catalog_file(
star_catalog_file)
psf_model = psf.get_psf_object('Moffat2D')
xstar = 194.654006958
ystar = 180.184967041
psf_size = 8.0
x_cen = psf_size + (xstar - int(xstar))
y_cen = psf_size + (ystar - int(ystar))
psf_params = [5807.59961215, x_cen, y_cen, 7.02930822229, 11.4997891585]
psf_model.update_psf_parameters(psf_params)
sky_model = psf.ConstantBackground()
sky_model.background_parameters.constant = 1345.0
log.info('Performing PSF fitting photometry on ' +
os.path.basename(image_path))
ref_star_catalog = photometry.run_psf_photometry(setup,
reduction_metadata,
log,
ref_star_catalog,
image_path,
psf_model,
sky_model,
centroiding=True)
assert ref_star_catalog[:, 5].max() > 0.0
assert ref_star_catalog[:, 6].max() > 0.0
assert ref_star_catalog[:, 5].max() <= 25.0
assert ref_star_catalog[:, 6].max() <= 10.0
logs.close_log(log)
def test_find_psf_companion_stars():
"""Function to test the identification of stars that neighbour a PSF star
from the reference catalogue."""
setup = pipeline_setup.pipeline_setup({'red_dir': TEST_DIR})
log = logs.start_stage_log(cwd, 'test_find_psf_companions')
log.info(setup.summary())
reduction_metadata = metadata.MetaData()
reduction_metadata.load_a_layer_from_file(setup.red_dir,
'pyDANDIA_metadata.fits',
'reduction_parameters')
star_catalog_file = os.path.join(TEST_DATA, 'star_catalog.fits')
ref_star_catalog = catalog_utils.read_ref_star_catalog_file(
star_catalog_file)
log.info('Read in catalog of ' + str(len(ref_star_catalog)) + ' stars')
psf_idx = 18
psf_x = 189.283172607
psf_y = 9.99084472656
psf_size = 8.0
stamp_dims = (20, 20)
comps_list = psf.find_psf_companion_stars(setup, psf_idx, psf_x, psf_y,
psf_size, ref_star_catalog, log,
stamp_dims)
assert len(comps_list) > 0
for l in comps_list:
log.info(repr(l))
image_file = os.path.join(TEST_DATA,
'lsc1m005-fl15-20170701-0144-e91_cropped.fits')
image = fits.getdata(image_file)
corners = psf.calc_stamp_corners(psf_x,
psf_y,
stamp_dims[1],
stamp_dims[0],
image.shape[1],
image.shape[0],
over_edge=True)
stamp = image[corners[2]:corners[3], corners[0]:corners[1]]
log.info('Extracting PSF stamp image')
fig = plt.figure(1)
norm = visualization.ImageNormalize(stamp, \
interval=visualization.ZScaleInterval())
plt.imshow(stamp, origin='lower', cmap=plt.cm.viridis, norm=norm)
x = []
y = []
for j in range(0, len(comps_list), 1):
x.append(comps_list[j][1])
y.append(comps_list[j][2])
plt.plot(x, y, 'r+')
plt.axis('equal')
plt.xlabel('X pixel')
plt.ylabel('Y pixel')
plt.savefig(os.path.join(TEST_DATA, 'psf_companion_stars.png'))
plt.close(1)
logs.close_log(log)
