def test_combine(self):
ccd1 = CCDData(np.random.normal(size=(10, 10)), unit='adu')
ccd2 = ccd1.copy()
ccd3 = ccd1.copy()
combiner = Combiner([ccd1, ccd2, ccd3])
combiner.sigma_clipping(low_thresh=2, high_thresh=5)
combined_data = combiner.median_combine()
np.testing.assert_equal(combined_data.data, ccd1.data)
def test_combine_masked(self):
x = np.random.normal(size=(10, 10))
x[5, :] = 0
x = np.ma.masked_where(x == 0, x)
ccd1 = CCDData(x, unit='adu')
ccd2 = ccd1.copy()
ccd3 = ccd1.copy()
combiner = Combiner([ccd1, ccd2, ccd3])
combiner.sigma_clipping(low_thresh=2, high_thresh=5)
combined_data = combiner.median_combine()
np.testing.assert_equal(combined_data.data, ccd1.data)
class WavelengthCalibrationTests(TestCase):
def setUp(self):
argument_list = ['--data-path', os.getcwd(),
'--proc-path', os.getcwd(),
'--search-pattern', 'cfzsto',
'--output-prefix', 'w',
'--extraction', 'fractional',
'--reference-files', 'data/ref_comp',
'--max-targets', '3',
]
arguments = get_args(argument_list)
self.wc = WavelengthCalibration(args=arguments)
self.ccd = CCDData(data=np.random.random_sample(200),
meta=fits.Header(),
unit='adu')
self.ccd = add_wcs_keys(ccd=self.ccd)
self.ccd.header.set('SLIT',
value='1.0" long slit',
comment="slit [arcsec]")
def test_add_wavelength_solution(self):
self.wc.rms_error = 0.1
self.wc.n_points = 100
self.wc.n_rejections = 2
self.wc.calibration_lamp = 'non-existent.fits'
crval1 = 3977.948
npix = 4060
cdelt = 0.9910068
x_axis = np.linspace(crval1,
crval1 + cdelt * npix,
npix)
self.ccd = self.wc.add_wavelength_solution(ccd=self.ccd,
x_axis=x_axis)
self.assertEqual(self.ccd.header['CTYPE1'], 'LINEAR')
self.assertEqual(self.ccd.header['CRVAL1'], crval1)
self.assertEqual(self.ccd.header['CRPIX1'], 1)
self.assertAlmostEqual(self.ccd.header['CDELT1'], cdelt, places=3)
self.assertEqual(self.ccd.header['DCLOG1'],
'REFSPEC1 = {:s}'.format(self.wc.calibration_lamp))
self.assertEqual(self.ccd.header['GSP_WRMS'], self.wc.rms_error)
self.assertEqual(self.ccd.header['GSP_WPOI'], self.wc.n_points)
self.assertEqual(self.ccd.header['GSP_WREJ'], self.wc.n_rejections)
@skip
def test_automatic_wavelength_solution(self):
pass
def test__bin_reference_data(self):
wavelength = np.linspace(3000, 7000, 4000)
intensity = np.random.random_sample(4000)
for i in range(1, 4):
self.wc.serial_binning = i
new_wavelength, new_intensity = self.wc._bin_reference_data(
wavelength=wavelength,
intensity=intensity)
self.assertEqual(len(wavelength), len(intensity))
self.assertEqual(len(new_wavelength), len(new_intensity))
self.assertEqual(len(new_wavelength), np.floor(len(wavelength) / i))
@skip
def test__cross_correlation(self):
self.wc.lamp = self.ccd.copy()
self.wc.serial_binning = 1
x_axis = np.arange(0, 4060, 1)
reference = np.zeros(4060)
gaussian = models.Gaussian1D(stddev=2)
for i in sorted(np.random.choice(x_axis, 30)):
gaussian.mean.value = i
reference += gaussian(x_axis)
offset = np.random.choice(range(1, 15), 1)[0]
for slit in [1, 2, 3, 4, 5]:
new_array = np.append(reference[offset:], np.zeros(offset))
if slit > 3:
box_kernel = Box1DKernel(width=slit / 0.15)
new_array = convolve(new_array, box_kernel)
self.assertEqual(len(reference), len(new_array))
self.wc.lamp.header['SLIT'] = '{:d}.0" long slit'.format(slit)
correlation_value = self.wc._cross_correlation(reference=reference,
new_array=new_array)
self.assertEqual(correlation_value, offset)
def test__evaluate_solution(self):
differences = np.array([0.5] * 10)
clipped_differences = np.ma.masked_array(differences,
mask=[0,
0,
1,
0,
0,
1,
0,
0,
1,
0])
rms_error, n_points, n_rej = self.wc._evaluate_solution(
clipped_differences=clipped_differences)
self.assertEqual(rms_error, 0.5)
self.assertEqual(n_points, 10)
self.assertEqual(n_rej, 3)
@skip
def test__get_lines_in_lamp(self):
pass
def test__get_spectral_characteristics(self):
self.ccd.header.set('GRATING', 'SYZY_400')
self.ccd.header.set('GRT_ANG', 7.5)
self.ccd.header.set('CAM_ANG', 16.1)
self.ccd.header.set('CCDSUM', '1 1')
self.wc.lamp = self.ccd.copy()
spec_charact = self.wc._get_spectral_characteristics()
self.assertIsInstance(spec_charact, dict)
self.assertEqual(len(spec_charact), 7)
def test_get_wsolution(self):
self.assertIsNone(self.wc.get_wsolution())
self.wc.wsolution = models.Chebyshev1D(degree=2)
self.wc.wsolution.c0.value = 3977.9485
self.wc.wsolution.c1.value = 1.00153387
self.wc.wsolution.c2.value = -1.2891437
self.assertIsInstance(self.wc.get_wsolution(), Model)
class WavelengthCalibrationTests(TestCase):
def setUp(self):
self.file_list = []
argument_list = [
'--data-path',
os.getcwd(),
'--proc-path',
os.getcwd(),
'--search-pattern',
'cfzsto',
'--output-prefix',
'w',
'--extraction',
'fractional',
'--reference-files',
'data/ref_comp',
'--max-targets',
'3',
]
arguments = get_args(argument_list)
self.wc = WavelengthCalibration()
self.ccd = CCDData(data=np.random.random_sample(200),
meta=fits.Header(),
unit='adu')
self.ccd = add_wcs_keys(ccd=self.ccd)
self.ccd.header.set('SLIT',
value='1.0_LONG_SLIT',
comment="slit [arcsec]")
self.ccd.header.set('GSP_FNAM',
value='some_name.fits',
comment='Name of the current file')
self.ccd.header.set('OBSTYPE', value='SPECTRUM', comment='Obstype')
self.ccd.header.set('OBJECT',
value='An X Object',
comment='Some random object name')
self.ccd.header.set('GSP_FLAT',
value='some_flat_file.fits',
comment='The name of the flat')
self.ccd.header.set('CCDSUM', value='1 1', comment='Binning')
self.ccd.header.set('WAVMODE', value='400 M1', comment='wavmode')
self.lamp = self.ccd.copy()
self.lamp.header.set('OBSTYPE',
value='COMP',
comment='Comparison lamp obstype')
self.lamp.header.set('OBJECT', value='HgArNe')
def tearDown(self):
for _file in self.file_list:
if os.path.isfile(_file):
os.unlink(_file)
# def test__automatic_wavelength_solution_no_match(self):
# self.wc.reference_data = ReferenceData(
# reference_dir='goodman_pipeline/data/ref_comp')
#
# self.lamp.header.set('OBJECT', value='PbNa')
# self.wc.lamp = self.lamp
# self.assertRaises(NoMatchFound,
# self.wc._automatic_wavelength_solution,
# os.getcwd())
def test__save_wavelength_calibrated(self):
self.wc.sci_target_file = 'target_sci_file.fits'
fname = self.wc._save_wavelength_calibrated(
ccd=self.lamp,
original_filename='file_name.fits',
save_data_to=os.getcwd(),
lamp=True)
self.file_list.append(fname)
self.assertEqual(fname, os.path.join(os.getcwd(), 'wfile_name.fits'))
fname = self.wc._save_wavelength_calibrated(
ccd=self.lamp,
index=1,
original_filename='file_name.fits',
save_data_to=os.getcwd())
self.file_list.append(fname)
self.assertEqual(fname,
os.path.join(os.getcwd(), 'wfile_name_ws_1.fits'))
def test__save_science_data(self):
wavelength_solution = models.Chebyshev1D(degree=3)
wavelength_solution.c0.value = 4419.161693945127
wavelength_solution.c1.value = 1.321103785944705
wavelength_solution.c2.value = -2.9766005683232e-06
wavelength_solution.c3.value = -4.864180906701e-10
fname = self.wc._save_science_data(
ccd=self.ccd,
wavelength_solution=wavelength_solution,
save_to=os.getcwd(),
index=None,
plot_results=False,
save_plots=False,
plots=False)
self.file_list.append(fname)
fname_2 = self.wc._save_science_data(
ccd=self.ccd,
wavelength_solution=wavelength_solution,
save_to=os.getcwd(),
index=1,
plot_results=False,
save_plots=False,
plots=False)
self.file_list.append(fname_2)
expected_name = os.getcwd() + '/w' + re.sub(
'.fits', '', os.path.basename(
self.ccd.header['GSP_FNAM'])) + "_ws_{:d}".format(1) + ".fits"
self.assertEqual(
fname,
os.getcwd() + '/w' + os.path.basename(self.ccd.header['GSP_FNAM']))
self.assertEqual(fname_2, expected_name)
def test___call___method_wrong_ccd(self):
self.assertRaises(AssertionError, self.wc, [], [], '', '')
def test___call___method_wrong_comp_list(self):
self.assertRaises(AssertionError, self.wc, self.ccd, 'comp_list', '',
'')
def test___call___method_no_comparison_lamps_json(self):
json_output = self.wc(ccd=self.ccd,
comp_list=[],
save_data_to='',
reference_data='',
json_output=True)
self.assertEqual(json_output['error'],
'Unable to process without reference lamps')
self.assertEqual(json_output['warning'], '')
self.assertEqual(json_output['wavelength_solution'], [])
def test___call___method_no_comparison_lamps(self):
output = self.wc(ccd=self.ccd,
comp_list=[],
save_data_to='',
reference_data='',
json_output=False)
self.assertIsNone(output)
def test___call___method_one_comparison_lamps(self):
json_output = self.wc(ccd=self.ccd,
comp_list=[self.lamp],
save_data_to='',
reference_data='goodman_pipeline/data/ref_comp',
json_output=True)
self.assertEqual(json_output['error'],
'Unable to obtain wavelength solution')
self.assertEqual(json_output['warning'], '')
self.assertEqual(json_output['wavelength_solution'], [])
def test___call___method_no_match_found(self):
self.lamp.header.set('OBJECT', value='PbNa')
self.assertRaises(NoMatchFound, self.wc, self.ccd, [self.lamp], '',
'goodman_pipeline/data/ref_comp')
def test___call___method_one_solution(self):
file_path = os.path.join(os.getcwd(), 'goodman_pipeline/data/ref_comp',
'goodman_comp_1200M2_FeHeAr.fits')
ref_lamp = CCDData.read(file_path, unit='adu')
lamp_ccd = write_fits(
ref_lamp,
os.path.join(os.getcwd(), 'test_comparison_lamp.fits'),
)
self.file_list.append('test_comparison_lamp.fits')
json_output = self.wc(ccd=self.ccd,
comp_list=[lamp_ccd],
save_data_to='',
reference_data='goodman_pipeline/data/ref_comp',
json_output=True)
# print(json.dumps(json_output, indent=4))
self.assertEqual(len(json_output['wavelength_solution']), 1)
for _solution in json_output['wavelength_solution']:
self.file_list.append(_solution['file_name'])
self.file_list.append(_solution['reference_lamp'])
def test___call___method_two_solution(self):
file_path_1 = os.path.join(os.getcwd(),
'goodman_pipeline/data/ref_comp',
'goodman_comp_1200M2_FeHeAr.fits')
file_path_2 = os.path.join(os.getcwd(),
'goodman_pipeline/data/ref_comp',
'goodman_comp_1200M2_CuHeAr.fits')
ref_lamp_1 = CCDData.read(file_path_1, unit='adu')
ref_lamp_2 = CCDData.read(file_path_2, unit='adu')
lamp_ccd_1 = write_fits(
ref_lamp_1,
os.path.join(os.getcwd(), 'test_comparison_lamp_1.fits'),
)
lamp_ccd_2 = write_fits(
ref_lamp_2,
os.path.join(os.getcwd(), 'test_comparison_lamp_2.fits'),
)
self.file_list.append('test_comparison_lamp_1.fits')
self.file_list.append('test_comparison_lamp_2.fits')
json_output = self.wc(ccd=self.ccd,
comp_list=[lamp_ccd_1, lamp_ccd_2],
save_data_to='',
reference_data='goodman_pipeline/data/ref_comp',
json_output=True)
# print(json.dumps(json_output, indent=4))
self.assertEqual(len(json_output['wavelength_solution']), 2)
for _solution in json_output['wavelength_solution']:
self.file_list.append(_solution['file_name'])
self.file_list.append(_solution['reference_lamp'])
class WavelengthCalibrationTests(TestCase):
def setUp(self):
argument_list = [
'--data-path',
os.getcwd(),
'--proc-path',
os.getcwd(),
'--search-pattern',
'cfzsto',
'--output-prefix',
'w',
'--extraction',
'fractional',
'--reference-files',
'data/ref_comp',
'--max-targets',
'3',
]
arguments = get_args(argument_list)
self.wc = WavelengthCalibration(args=arguments)
self.ccd = CCDData(data=np.random.random_sample(200),
meta=fits.Header(),
unit='adu')
self.ccd = add_wcs_keys(ccd=self.ccd)
self.ccd.header.set('SLIT',
value='1.0" long slit',
comment="slit [arcsec]")
def test_add_wavelength_solution(self):
self.wc.rms_error = 0.1
self.wc.n_points = 100
self.wc.n_rejections = 2
self.wc.calibration_lamp = 'non-existent.fits'
crval1 = 3977.948
npix = 4060
cdelt = 0.9910068
x_axis = np.linspace(crval1, crval1 + cdelt * npix, npix)
self.ccd = self.wc.add_wavelength_solution(ccd=self.ccd, x_axis=x_axis)
self.assertEqual(self.ccd.header['CTYPE1'], 'LINEAR')
self.assertEqual(self.ccd.header['CRVAL1'], crval1)
self.assertEqual(self.ccd.header['CRPIX1'], 1)
self.assertAlmostEqual(self.ccd.header['CDELT1'], cdelt, places=3)
self.assertEqual(self.ccd.header['DCLOG1'],
'REFSPEC1 = {:s}'.format(self.wc.calibration_lamp))
self.assertEqual(self.ccd.header['GSP_WRMS'], self.wc.rms_error)
self.assertEqual(self.ccd.header['GSP_WPOI'], self.wc.n_points)
self.assertEqual(self.ccd.header['GSP_WREJ'], self.wc.n_rejections)
@skip
def test_automatic_wavelength_solution(self):
pass
def test__bin_reference_data(self):
wavelength = np.linspace(3000, 7000, 4000)
intensity = np.random.random_sample(4000)
for i in range(1, 4):
self.wc.serial_binning = i
new_wavelength, new_intensity = self.wc._bin_reference_data(
wavelength=wavelength, intensity=intensity)
self.assertEqual(len(wavelength), len(intensity))
self.assertEqual(len(new_wavelength), len(new_intensity))
self.assertEqual(len(new_wavelength),
np.floor(len(wavelength) / i))
@skip
def test__cross_correlation(self):
self.wc.lamp = self.ccd.copy()
self.wc.serial_binning = 1
x_axis = np.arange(0, 4060, 1)
reference = np.zeros(4060)
gaussian = models.Gaussian1D(stddev=2)
for i in sorted(np.random.choice(x_axis, 30)):
gaussian.mean.value = i
reference += gaussian(x_axis)
offset = np.random.choice(range(1, 15), 1)[0]
for slit in [1, 2, 3, 4, 5]:
new_array = np.append(reference[offset:], np.zeros(offset))
if slit > 3:
box_kernel = Box1DKernel(width=slit / 0.15)
new_array = convolve(new_array, box_kernel)
self.assertEqual(len(reference), len(new_array))
self.wc.lamp.header['SLIT'] = '{:d}.0" long slit'.format(slit)
correlation_value = self.wc._cross_correlation(reference=reference,
new_array=new_array)
self.assertEqual(correlation_value, offset)
def test__evaluate_solution(self):
differences = np.array([0.5] * 10)
clipped_differences = np.ma.masked_array(
differences, mask=[0, 0, 1, 0, 0, 1, 0, 0, 1, 0])
rms_error, n_points, n_rej = self.wc._evaluate_solution(
clipped_differences=clipped_differences)
self.assertEqual(rms_error, 0.5)
self.assertEqual(n_points, 10)
self.assertEqual(n_rej, 3)
@skip
def test__get_lines_in_lamp(self):
pass
def test__get_spectral_characteristics(self):
self.ccd.header.set('GRATING', 'SYZY_400')
self.ccd.header.set('GRT_ANG', 7.5)
self.ccd.header.set('CAM_ANG', 16.1)
self.ccd.header.set('CCDSUM', '1 1')
self.wc.lamp = self.ccd.copy()
spec_charact = self.wc._get_spectral_characteristics()
self.assertIsInstance(spec_charact, dict)
self.assertEqual(len(spec_charact), 7)
def test_get_wsolution(self):
self.assertIsNone(self.wc.get_wsolution())
self.wc.wsolution = models.Chebyshev1D(degree=2)
self.wc.wsolution.c0.value = 3977.9485
self.wc.wsolution.c1.value = 1.00153387
self.wc.wsolution.c2.value = -1.2891437
self.assertIsInstance(self.wc.get_wsolution(), Model)
