def crear_archivos(temporary_path, number=5):
from megaradrp.simulation.actions import simulate_flat
from megaradrp.simulation.detector import ReadParams, MegaraDetectorSat
from megaradrp.recipes.calibration.bpm import BadPixelsMaskRecipe
PSCAN = 50
DSHAPE = (2056 * 2, 2048 * 2)
OSCAN = 50
ron = 2.0
gain = 1.0
bias = 1000.0
qe = 0.8 * np.ones(DSHAPE)
qe[0:15, 0:170] = 0.0
readpars1 = ReadParams(gain=gain, ron=ron, bias=bias)
readpars2 = ReadParams(gain=gain, ron=ron, bias=bias)
detector = MegaraDetectorSat('megara_test_detector', DSHAPE, OSCAN, PSCAN,
qe=qe,
dark=(3.0 / 3600.0),
readpars1=readpars1, readpars2=readpars2,
bins='11')
source2 = 1.0
fs = [simulate_flat(detector, exposure=1.0, source=5000 * source2) for i in
range(number)]
for aux in range(len(fs)):
fits.writeto('%s/flat_%s.fits' % (temporary_path, aux), fs[aux],
clobber=True)
master_bias = generate_bias(detector, number, temporary_path)
master_bias_data = master_bias.master_bias.frame[0].data
fits.writeto('%s/master_bias_data0.fits' % temporary_path,
master_bias_data, clobber=True) # Master Bias
ob = ObservationResult()
ob.instrument = 'MEGARA'
ob.mode = 'bias_image'
ob.configuration = build_instrument_config('4fd05b24-2ed9-457b-b563-a3c618bb1d4c', loader=Loader())
names = []
for aux in range(number):
names.append('%s/flat_%s.fits' % (temporary_path, aux))
ob.frames = [DataFrame(filename=open(nombre).name) for nombre in names]
recipe = BadPixelsMaskRecipe()
ri = recipe.create_input(obresult=ob, master_bias=DataFrame(
filename=open(temporary_path + '/master_bias_data0.fits').name))
aux = recipe.run(ri)
fits.writeto('%s/master_bpm.fits' % temporary_path,
aux.master_bpm.frame[0].data, clobber=True)
return names
def generate_bias_file():
PSCAN = 50
DSHAPE = (2056 * 2, 2048 * 2)
OSCAN = 50
ron = 2.0
gain = 1.0
bias = 1000.0
qe = 0.8 * np.ones(DSHAPE)
qe[0:15, 0:170] = 0.0
readpars1 = ReadParams(gain=gain, ron=ron, bias=bias)
readpars2 = ReadParams(gain=gain, ron=ron, bias=bias)
detector = MegaraDetectorSat('megara_test_detector', DSHAPE, OSCAN, PSCAN, qe=qe,
dark=(3.0 / 3600.0),
readpars1=readpars1, readpars2=readpars2,
bins='11')
return simulate_flat(detector, exposure=1.0, source=5000.0)
def test_bpm():
number = 5
PSCAN = 50
DSHAPE = (2056 * 2, 2048 * 2)
OSCAN = 50
ron = 2.0
gain = 1.0
bias = 1000.0
qe = 0.8 * np.ones(DSHAPE)
qe[5:6, 0:170] = 0.0
config_uuid = '4fd05b24-2ed9-457b-b563-a3c618bb1d4c'
date_obs = '2017-11-09T11:00:00.0'
temporary_path = mkdtemp()
fits.writeto('{}/eq.fits'.format(temporary_path), qe, overwrite=True)
readpars1 = ReadParams(gain=gain, ron=ron, bias=bias)
readpars2 = ReadParams(gain=gain, ron=ron, bias=bias)
detector = MegaraDetectorSat('megara_test_detector', DSHAPE, OSCAN, PSCAN,
qe=qe,
dark=(3.0 / 3600.0),
readpars1=readpars1, readpars2=readpars2,
bins='11')
source2 = 1.0
fs = [simulate_flat(detector, exposure=1.0, source=5000 * source2) for i in
range(number)]
fs2 = [simulate_flat(detector, exposure=1.0, source=40000 * source2) 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('{}/flat_{}.fits'.format(temporary_path, aux), fs[aux],
header=header,
overwrite=True)
fits.writeto('{}/flat_{}.fits'.format(temporary_path, aux + number), fs2[aux],
header=header,
overwrite=True)
result = generate_bias(detector, number, temporary_path)
result.master_bias.frame.writeto(
'{}/master_bias_data0.fits'.format(temporary_path),
overwrite=True
)
ob = ObservationResult()
ob.instrument = 'MEGARA'
ob.mode = 'MegaraBiasImage'
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
names = []
for aux in range(number * 2):
names.append('{}/flat_{}.fits'.format(temporary_path, aux))
ob.frames = [DataFrame(filename=open(nombre).name) for nombre in names]
recipe = BadPixelsMaskRecipe()
ri = recipe.create_input(obresult=ob, master_bias=DataFrame(
filename=open(temporary_path + '/master_bias_data0.fits').name))
aux = recipe.run(ri)
aux.master_bpm.frame.writeto('{}/master_bpm.fits'.format(temporary_path), overwrite=True)
shutil.rmtree(temporary_path)
