def generate_bias(detector, number, temporary_path):
from megaradrp.simulation.actions import simulate_bias
from megaradrp.recipes.calibration.bias import BiasRecipe
config_uuid = '4fd05b24-2ed9-457b-b563-a3c618bb1d4c'
date_obs = '2017-11-09T11:00:00.0'
fs = [simulate_bias(detector) for i in range(number)]
header = fits.Header()
header['DATE-OBS'] = date_obs
header['INSCONF'] = config_uuid
header['INSTRUME'] = 'MEGARA'
header['VPH'] = 'LR-U'
header['INSMODE'] = 'MOS'
for aux in range(len(fs)):
fits.writeto('%s/bias_%s.fits' % (temporary_path, aux), fs[aux],
header=header,
overwrite=True)
fs = ["%s/bias_%s.fits" % (temporary_path, i) for i in range(number)]
ob = ObservationResult()
ob.instrument = 'MEGARA'
ob.mode = 'bias_image'
pkg_paths = ['megaradrp.instrument.configs']
store = asb.load_paths_store(pkg_paths)
insmodel = asb.assembly_instrument(store, config_uuid, date_obs, by_key='uuid')
insmodel.configure_with_header(header)
ob.configuration = insmodel
ob.frames = [DataFrame(filename=f) for f in fs]
recipe = BiasRecipe()
ri = recipe.create_input(obresult=ob)
return recipe.run(ri)
def test_bias(drpmocker):
BASE_URL = 'http://guaix.fis.ucm.es/~spr/megara_test/BIAS/%s'
images = ['e99d2937d2c29a27c0ba4eebfcf7918e',
'e99d2937d2c29a27c0ba4eebfcf7918e',
'e99d2937d2c29a27c0ba4eebfcf7918e']
fs = [download_cache(BASE_URL % i) for i in images]
ob = ObservationResult()
ob.instrument = 'MEGARA'
ob.mode = 'bias_image'
ob.frames = [DataFrame(filename=f.name) for f in fs]
drpmocker.add_drp('MEGARA', load_drp)
# Here we could directly import the required pipeline,
# but the idea is to test all the process
insdrp = numina.drps.get_system_drps().query_by_name(ob.instrument)
pipeline = insdrp.pipelines.get('default')
recipe_fqn = pipeline.recipes.get(ob.mode)
RecipeClass = import_object(recipe_fqn)
assert RecipeClass is BiasRecipe
# TODO: these should be created by a build_recipe_input method
recipe = BiasRecipe()
ri = recipe.create_input(obresult=ob)
result = recipe.run(ri)
# assert result.qc >= QC.UNKNOWN
# Checks on the image
hdulist = result.biasframe.open()
assert len(hdulist) == 1
hdu = hdulist[0]
assert hdu.shape == (4112, 4096)
data = hdu.data
mlevel = 0.0
block = data[1980:2020, 1980:2020]
mblock = block.mean()
sblock = block.std()
assert abs(mblock - mlevel) < 5 * sblock
# In the end, remove the files
for f in fs:
os.remove(f.name)
def generate_bias(detector, number, temporary_path):
from megaradrp.simulation.actions import simulate_bias
from megaradrp.recipes.calibration.bias import BiasRecipe
fs = [simulate_bias(detector) for i in range(number)]
for aux in range(len(fs)):
fits.writeto('%s/bias_%s.fits' % (temporary_path, aux), fs[aux],
clobber=True)
fs = ["%s/bias_%s.fits" % (temporary_path, i) for i in range(number)]
ob = ObservationResult()
ob.instrument = 'MEGARA'
ob.mode = 'bias_image'
ob.configuration = build_instrument_config('4fd05b24-2ed9-457b-b563-a3c618bb1d4c', loader=Loader())
ob.frames = [DataFrame(filename=f) for f in fs]
recipe = BiasRecipe()
ri = recipe.create_input(obresult=ob)
return recipe.run(ri)
