def test_do_stage(mock_normalize):
# tests that the normalized spectrum are assigned correctly.
expected = 1
# make a single order spectrum
flux = np.arange(1, 101) * expected
x = np.arange(1, 101)
spectrum = Table({
'wavelength': [x, 2 * x],
'flux': [flux, 2 * flux],
'blaze': [flux, 2 * flux],
'blaze_error': [flux, 2 * flux],
'uncertainty': [flux, 2 * flux],
'fiber': [0, 0],
'order': [1, 2]
})
image = NRESObservationFrame(
[CCDData(np.zeros(
(1, 1)), meta={'OBJECTS': 'tung&tung&none'})], 'test.fits')
image.spectrum = Spectrum1D(spectrum)
# make it so that ContinuumNormalizer.normalize just returns ones.
mock_normalize.return_value = (expected * np.ones_like(flux),
expected * np.ones_like(flux))
# Run the normalizer code
stage = ContinuumNormalizer(SimpleNamespace(db_address='foo'))
image = stage.do_stage(image)
for order in [1, 2]:
assert np.allclose(image.spectrum[0, order]['normflux'], expected)
assert np.allclose(image.spectrum[0, order]['normuncertainty'],
expected)
def test_background_subtraction_with_traces(seed):
nx, ny = 405, 403
x = np.arange(nx)
y = np.arange(ny)
X, Y = np.meshgrid(x, y)
noise_sigma = 1.0
input_background = 30 * np.exp(-(X - nx / 2.0)**2 / 300**2 -
(Y - ny / 2.0 - 50.0)**2 / 200**2)
test_data = input_background + np.random.normal(
0.0, noise_sigma, size=input_background.shape)
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=np.ones((ny, nx)) * noise_sigma,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
test_image.traces = np.zeros((ny, nx))
for i in range(1, 8):
test_image.traces[40 * i:40 * i + 10] = i
input_context = context.Context({})
stage = BackgroundSubtractor(input_context)
output_image = stage.do_stage(test_image)
# Make sure our background estimation is good. This is roughly 4 counts which is not bad
# If we fully model the image, we can do better than this, but that becomes computationally prohibitive on a 4kx4k
# image
np.testing.assert_allclose(output_image.background,
input_background,
atol=5.0)
def test_profile_fit_with_noise_with_blaze():
nx, ny = 401, 403
input_profile, trace_centers, input_traces = make_simple_traces(nx,
ny,
blaze=True)
read_noise = 10.0
# Filter out the numerical noise
input_profile[input_profile < 1e-15] = 0.0
input_profile += np.random.poisson(input_profile)
input_profile += np.random.normal(0.0,
read_noise,
size=input_profile.shape)
uncertainty = np.sqrt(input_profile + read_noise**2.0)
image = NRESObservationFrame([
CCDData(data=input_profile.copy(),
uncertainty=uncertainty,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
image.traces = input_traces
input_context = context.Context({})
stage = ProfileFitter(input_context)
image = stage.do_stage(image)
for i in range(1, np.max(image.traces) + 1):
this_trace = get_trace_region(image.traces == i)
scale_factor = np.max(input_profile[this_trace]) / np.max(
image.profile[this_trace] * image.blaze[i - 1]['blaze'])
np.testing.assert_allclose(input_profile[this_trace],
image.profile[this_trace] *
image.blaze[i - 1]['blaze'] * scale_factor,
rtol=1.5e-2,
atol=1.0)
def test_refine_traces_offset_centroid():
nx, ny = 401, 403
x2d, y2d = np.meshgrid(np.arange(nx), np.arange(ny))
test_data, trace_centers, input_traces = make_simple_traces(nx, ny)
read_noise = 10.0
# Filter out the numerical noise
test_data[test_data < 1e-15] = 0.0
test_data += np.random.poisson(test_data)
test_data += np.random.normal(0.0, read_noise, size=test_data.shape)
uncertainty = np.sqrt(test_data + read_noise**2.0)
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=uncertainty,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
test_image.traces = input_traces
input_context = context.Context({'TRACE_HALF_HEIGHT': 5})
stage = TraceRefiner(input_context)
output_image = stage.do_stage(test_image)
for trace_center in trace_centers:
# Make sure that the center +- 4 pixels is in the trace image
assert all(output_image.traces[np.abs(y2d - trace_center + 1) <= 4])
def test_refine_traces_curved_trace():
nx, ny = 1001, 1003
expected_trace = np.zeros((ny, nx), dtype=int)
trace_half_height = 5
x0 = 498
input_y_center = 2e-4 * (np.arange(nx) - x0)**2.0 + 0.01 * (np.arange(nx) -
x0) + 502.0
trace = np.round(input_y_center).astype(int)
for i in np.arange(nx, dtype=int):
for j in range(-trace_half_height, trace_half_height + 1):
expected_trace[trace[i] + j, i] = 1
x2d, y2d = np.meshgrid(np.arange(nx, dtype=int), np.arange(ny, dtype=int))
sigma = 1.5
y_center = np.array([input_y_center.copy() for i in range(ny)])
test_data = 1 / sigma / (2.0 * np.pi) * np.exp(
-0.5 * (y2d - y_center)**2.0 / sigma**2)
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=np.zeros_like(test_data),
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
test_image.traces = np.ones_like(expected_trace)
refine_traces(test_image,
weights=test_image.data,
trace_half_height=trace_half_height)
np.testing.assert_equal(test_image.traces, expected_trace)
def apply_master_calibration(self, image: NRESObservationFrame,
master_calibration_image):
# TODO should not load a master arc calibration if it is older than ~a week.
image.wavelengths = master_calibration_image.wavelengths
image.fibers = master_calibration_image.fibers
image.meta[
'L1IDARC'] = master_calibration_image.filename, 'ID of ARC/DOUBLE frame'
return image
def test_do_stage_on_empty_features(self):
input_context = context.Context({})
image = NRESObservationFrame([CCDData(data=self.data, uncertainty=self.err,
meta={'OBJECTS': 'tung&tung&none'})], 'foo.fits')
image.traces = np.ones_like(self.data, dtype=int)
image.blaze = {'blaze': np.ones_like(self.data, dtype=int)}
stage = IdentifyFeatures(input_context)
stage.fwhm, stage.nsigma = self.sigma, 1.5
image = stage.do_stage(image)
assert len(image.features) == 0
def generate_image(self):
traces = np.array([[0, 0, 0], [1, 1, 1], [1, 1, 1], [0, 0, 0], [0, 0, 0], [2, 2, 2], [3, 3, 3]])
line_list = np.array([10, 11, 12])
pixel_positions = np.array([1, 2, 1, 2])
features = Table({'pixel': pixel_positions, 'id': np.array([1, 1, 2, 3]), 'order': np.array([1, 1, 2, 3]),
'wavelength': pixel_positions, 'centroid_err': np.ones_like(pixel_positions)})
image = NRESObservationFrame([CCDData(data=np.zeros((2, 2)), uncertainty=np.zeros((2, 2)),
meta={'OBJECTS': 'tung&tung&none'})], 'foo.fits')
image.features = features
image.traces = traces
image.line_list = line_list
return image
def test_do_stage_no_blaze(self):
input_context = context.Context({})
ccd_data = CCDData(data=self.data, uncertainty=self.err, meta={'OBJECTS': 'tung&tung&none'})
image = NRESObservationFrame([ccd_data], 'foo.fits')
image.traces = np.ones_like(self.data, dtype=int)
stage = IdentifyFeatures(input_context)
stage.fwhm, stage.nsigma = self.sigma, 0.5
image = stage.do_stage(image)
image.features.sort('pixel')
assert np.allclose(image.features['pixel'], self.xcoords, atol=0.001)
assert np.allclose(image.features['ycentroid'], self.ycoords, atol=0.001)
assert np.allclose(image.features['id'], 1)
def do_stage(self, image: NRESObservationFrame):
if image.profile is None:
logger.error('Profile missing. Rejecting image.', image=image)
return None
image.add_or_update(
ArrayData(np.zeros_like(image.data, dtype=float), name='WEIGHTS'))
trace_ids = np.arange(1, image.num_traces + 1)
for trace_id in trace_ids:
yx = get_trace_region(np.isclose(image.traces, trace_id))
image.weights[yx] = self.weights(image.profile[yx],
image.uncertainty[yx]**2,
image.mask[yx])
return image
def test_refining_on_noisy_data():
nx, ny = 401, 403
read_noise = 10.0
test_data = np.zeros((ny, nx))
x2d, y2d = np.meshgrid(np.arange(nx), np.arange(ny))
trace_centers = []
y_0s = [100, 200, 300]
for i in range(3):
trace_centers.append(5e-4 * (np.arange(nx) - nx / 2.)**2 + y_0s[i])
test_data += gaussian(y2d, trace_centers[i], 3, a=10000.0)
test_data += np.random.poisson(test_data)
test_data += np.random.normal(0.0, read_noise, size=test_data.shape)
uncertainty = np.sqrt(test_data + read_noise**2.0)
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=uncertainty,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
input_context = context.Context({'TRACE_HALF_HEIGHT': 5})
stage = TraceInitializer(input_context)
output_image = stage.do_stage(test_image)
for trace_center in trace_centers:
# Make sure that the center +- 4 pixels is in the trace image
assert all(output_image.traces[np.abs(y2d - trace_center) <= 4])
def test_do_stage(self):
blaze_factor = 0.5
input_context = context.Context({})
ccd_data = CCDData(data=self.data, uncertainty=self.err, meta={'OBJECTS': 'tung&tung&none'})
image = NRESObservationFrame([ccd_data], 'foo.fits')
image.traces = np.ones_like(self.data, dtype=int)
image.blaze = {'blaze': blaze_factor * np.ones((1, self.data.shape[1]), dtype=int)}
stage = IdentifyFeatures(input_context)
stage.fwhm, stage.nsigma = self.sigma, 0.5
image = stage.do_stage(image)
image.features.sort('pixel')
assert np.allclose(image.features['corrected_flux'], image.features['flux'] / blaze_factor, rtol=1E-4)
assert np.allclose(image.features['pixel'], self.xcoords, atol=0.001)
assert np.allclose(image.features['ycentroid'], self.ycoords, atol=0.001)
assert np.allclose(image.features['id'], 1)
def test_get_num_lit_fibers():
image = NRESObservationFrame(
[HeaderOnly(meta={'OBJECTS': 'tung&tung&none'})], 'foo.fits')
assert image.num_lit_fibers() == 2
image = NRESObservationFrame(
[HeaderOnly(meta={'OBJECTS': 'none&tung&none'})], 'foo.fits')
assert image.num_lit_fibers() == 1
def two_order_image():
# generate 2 flat traces.
traces = np.zeros((60, 20))
traces[[10, 11, 12], :] = 1
# the second trace that does not span the image entirely
traces[[50, 51, 52], :] = 2
traces[[50, 51, 52], 0] = 0
traces[[50, 51, 52], -1] = 0
# generate test data with zero noise
data = np.ones_like(traces, dtype=float)
data[~np.isclose(traces, 0)] = 100.
uncertainty = 1. * data
wavelengths = (traces > 0).astype(float) * 5
image = NRESObservationFrame([
CCDData(data=data,
uncertainty=uncertainty,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
image.wavelengths = wavelengths
image.traces = traces
image.fibers = {'fiber': np.arange(2), 'order': np.arange(2)}
image.blaze = {
'id': np.arange(2),
'blaze': [np.arange(20), np.arange(20)],
'blaze_error': [np.arange(20), np.arange(20)]
}
return image
def test_refine_traces_with_previous_trace():
nx, ny = 401, 403
num_traces = 3
read_noise = 10.0
test_data = np.zeros((ny, nx))
x2d, y2d = np.meshgrid(np.arange(nx), np.arange(ny))
trace_half_width = 6
trace_centers = []
y_0s = [100, 200, 300]
blaze_function = 1 - 1e-5 * (x2d - nx / 2.)**2
input_traces = np.zeros((ny, nx), dtype=np.int)
for i in range(num_traces):
trace_centers.append(5e-4 * (np.arange(nx) - nx / 2.)**2 + y_0s[i])
test_data += gaussian(y2d, trace_centers[i], 2,
a=10000.0) * blaze_function
input_traces[np.abs(y2d -
trace_centers[i]) <= trace_half_width] = i + 1
# Filter out the numerical noise
test_data[test_data < 1e-15] = 0.0
test_data += np.random.poisson(test_data)
test_data += np.random.normal(0.0, read_noise, size=test_data.shape)
uncertainty = np.sqrt(test_data + read_noise**2.0)
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=uncertainty,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
test_image.traces = input_traces
input_context = context.Context({'TRACE_HALF_HEIGHT': 5})
stage = TraceRefiner(input_context)
output_image = stage.do_stage(test_image)
for trace_center in trace_centers:
# Make sure that the center +- 4 pixels is in the trace image
assert all(output_image.traces[np.abs(y2d - trace_center) <= 4])
assert np.isclose(num_traces, output_image.num_traces)
def test_profile_fit_without_noise_without_blaze():
nx, ny = 401, 403
input_profile, trace_centers, input_traces = make_simple_traces(
nx, ny, blaze=False)
# Filter out the numerical noise
input_profile[input_profile < 1e-15] = 0.0
uncertainty = np.sqrt(input_profile)
image = NRESObservationFrame([
CCDData(data=input_profile.copy(),
uncertainty=uncertainty,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
image.traces = input_traces
input_context = context.Context({})
stage = ProfileFitter(input_context)
image = stage.do_stage(image)
scale_factor = np.max(input_profile) / np.max(image.profile)
assert np.allclose(input_profile[input_traces != 0],
image.profile[input_traces != 0] * scale_factor,
rtol=5e-3,
atol=1.0)
def test_blind_solve():
trace_centers, test_data, x2d, y2d = make_realistic_trace_image()
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=1e-5 * np.ones_like(test_data),
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
input_context = context.Context({'TRACE_HALF_HEIGHT': 5})
stage = TraceInitializer(input_context)
output_image = stage.do_stage(test_image)
for trace_center in trace_centers:
# Make sure that the center +- 4 pixels is in the trace image
assert all(output_image.traces[np.abs(y2d - trace_center) <= 4])
def test_do_stage_does_not_fit_non_science_fiber():
# make a single order spectrum
flux = np.arange(1, 101) * 2
x = np.arange(1, 101)
spectrum = Table({
'wavelength': [x],
'flux': [flux],
'blaze': [flux],
'blaze_error': [flux],
'uncertainty': [flux],
'fiber': [1],
'order': [1]
})
image = NRESObservationFrame(
[CCDData(np.zeros(
(1, 1)), meta={'OBJECTS': 'tung&tung&none'})], 'test.fits')
image.spectrum = Spectrum1D(spectrum)
# Run the normalizer code
stage = ContinuumNormalizer(SimpleNamespace(db_address='foo'))
image = stage.do_stage(image)
assert 'normflux' not in image.spectrum._table.colnames
assert 'normuncertainty' not in image.spectrum._table.colnames
def test_blind_solve_with_edge_clipping_traces():
trace_centers, test_data, x2d, y2d = make_realistic_trace_image(
nx=401, ny=403, y0_centers=[1, 200, 400])
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=1e-5 * np.ones_like(test_data),
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
input_context = context.Context({'TRACE_HALF_HEIGHT': 5})
stage = TraceInitializer(input_context)
output_image = stage.do_stage(test_image)
assert np.count_nonzero(list(set(output_image.traces[:, 200]))) == 1
# test that the only valid trace is centered correctly
assert all(output_image.traces[np.abs(y2d - trace_centers[1]) <= 4])
def test_blind_solve_with_bpm():
trace_centers, test_data, x2d, y2d = make_realistic_trace_image()
bpm_mask = np.ones_like(test_data, dtype=bool)
bpm_mask[
150:
250, :] = 0 # set the pixels around the center trace as good. Leave the other pixels masked.
test_image = NRESObservationFrame([
CCDData(data=test_data,
uncertainty=1e-5 * np.ones_like(test_data),
meta={'OBJECTS': 'tung&tung&none'},
mask=bpm_mask)
], 'foo.fits')
input_context = context.Context({'TRACE_HALF_HEIGHT': 5})
stage = TraceInitializer(input_context)
output_image = stage.do_stage(test_image)
assert np.count_nonzero(list(set(output_image.traces[:, 200]))) == 1
# test that the only valid trace is centered correctly
assert all(output_image.traces[np.abs(y2d - trace_centers[1]) <= 4])
def do_stage(self, image: NRESObservationFrame):
if image.weights is None:
logger.error('Extraction weights are missing. Rejecting image.',
image=image)
return None
if image.wavelengths is None:
logger.error('Wavelengths are missing. Rejecting image.',
image=image)
return None
# consider adding a method to image, i.e. image.extracted_spectrum_shape.
flux = np.zeros((image.num_traces, image.data.shape[1]), dtype=float)
wavelength = np.zeros_like(flux, dtype=float)
variance = np.zeros_like(flux, dtype=float)
mask = np.zeros_like(flux, dtype=np.uint8)
trace_ids = np.arange(1, image.num_traces + 1, dtype=int)
for i, trace_id in enumerate(trace_ids):
this_trace = get_trace_region(np.isclose(image.traces, trace_id))
# get the horizontal (x) extent of the trace. Consider making this a get_extent function.
x_extent = slice(np.min(this_trace[1]), np.max(this_trace[1]) + 1)
flux[i, x_extent] = self.extract_order(image.data[this_trace],
image.weights[this_trace])
variance[i, x_extent] = self.extract_order(
image.uncertainty[this_trace]**2, image.weights[this_trace]**2)
# TODO: some fix that allows for actual wavelength extraction:
# wavelength[i, x_extent] = self.extract_order(image.wavelengths[this_trace],
# weights=1/image.wavelengths[this_trace].shape[0])
wavelength[i, x_extent] = np.median(image.wavelengths[this_trace],
axis=0)
mask[i, x_extent] = image.weights[this_trace].sum(axis=0) == 0.0
image.spectrum = Spectrum1D({
'id': trace_ids,
'order': image.fibers['order'],
'fiber': image.fibers['fiber'],
'wavelength': wavelength,
'flux': flux,
'uncertainty': np.sqrt(variance),
'blaze': image.blaze['blaze'],
'blaze_error': image.blaze['blaze_error'],
'mask': mask
})
return image
def test_to_fits():
data = np.ones((2, 2))
spec = Spectrum1D({
'fiber': [0],
'order': [1],
'flux': [np.arange(10)],
'wavelength': [np.arange(10)]
})
image = NRESObservationFrame([
CCDData(data=data,
uncertainty=2 * data,
name='SCI',
meta={
'OBJECTS': 'tung&tung&none',
'EXTNAME': ''
})
], 'foo.fits')
image.add_or_update(ArrayData(3 * data, name='TRACES'))
image.add_or_update(ArrayData(4 * data, name='WEIGHTS'))
image.add_or_update(ArrayData(5 * data, name='BACKGROUND'))
image.add_or_update(DataTable(spec.table, name='SPECTRUM'))
hdulist = image.to_fits(
context.Context({
'EXTENSION_NAMES_TO_CONDENSE': ['SCI'],
'REDUCED_DATA_EXTENSION_TYPES': {},
'fpack': True,
'LOSSLESS_EXTENSIONS': []
}))
hdu_ext_names = [
hdulist[i].header.get('EXTNAME') for i in range(len(hdulist))
if hdulist[i].header.get('EXTNAME') is not None
]
for name in [
'SCI', 'BPM', 'ERR', 'TRACES', 'WEIGHTS', 'BACKGROUND', 'SPECTRUM'
]:
assert name in hdu_ext_names
def five_hundred_square_image(maxflux,
number_traces,
trace_width,
read_noise=10,
seed=None):
traces = np.zeros((500, 500))
data = np.ones_like(traces, dtype=float)
profile = np.zeros_like(traces, dtype=float)
ix = np.arange(trace_width)
for i in range(1, number_traces + 1):
traces[40 * i:40 * i + trace_width, :] = i
for j in range(0, trace_width):
data[40 * i + j, :] += maxflux * np.exp(
(-1.) * (ix[j] - trace_width / 2.)**2 / (trace_width / 6.)**2)
for j in range(0, trace_width):
profile[40 * i + j, :] = data[40 * i + j, :] / np.sum(
data[40 * i:40 * i + trace_width, 0])
np.random.seed(seed=seed)
data += np.random.poisson(data)
data += np.random.normal(0.0, read_noise, size=data.shape)
uncertainty = np.sqrt(data + read_noise**2)
wavelengths = np.ones_like(
traces
) * 5 # dummy wavelengths image that has values distinct from flux and traces.
image = NRESObservationFrame([
CCDData(data=data,
uncertainty=uncertainty,
meta={'OBJECTS': 'tung&tung&none'})
], 'foo.fits')
image.traces = traces
image.profile = profile
image.wavelengths = wavelengths
image.blaze = {
'id': np.arange(number_traces) + 1,
'blaze': [np.ones(traces.shape[1]) for i in range(number_traces)],
'blaze_error':
[np.ones(traces.shape[1]) for i in range(number_traces)]
}
image.fibers = {
'fiber': np.arange(number_traces) + 1,
'order': np.arange(number_traces) + 1
}
return image
class TestCalculateScienceFrameMetrics:
input_context = context.Context({'PIXELS_PER_RESOLUTION_ELEMENT': 4.15})
test_wavelengths = np.linspace(5100.0, 5200.0, 4096)
test_flux = -0.001 * test_wavelengths**2 + 10.3 * test_wavelengths
test_uncertainty = np.sqrt(test_flux)
snr_order = 90
spectrum = []
header = fits.Header({'OBJECTS': 'test&none&none'})
for i in range(5):
order = snr_order + i
row = {'wavelength': test_wavelengths, 'flux': test_flux, 'uncertainty': test_uncertainty,
'fiber': 0, 'order': snr_order}
spectrum.append(row)
test_image = NRESObservationFrame([CCDData(np.zeros((1, 1)), meta=header)], 'test.fits')
test_image.spectrum = Spectrum1D(spectrum)
def test_do_stage_does_not_crash(self):
image = CalculateScienceFrameMetrics(self.input_context).do_stage(self.test_image)
assert image is not None
def test_snr_calculation(self):
snr, _ = get_snr(self.test_image, self.snr_order, self.input_context.PIXELS_PER_RESOLUTION_ELEMENT)
snr_test = self.test_flux/self.test_uncertainty * np.sqrt(self.input_context.PIXELS_PER_RESOLUTION_ELEMENT)
assert np.isclose(snr, np.max(snr_test), rtol=0.1)
def test_get_telescope_filename():
image = NRESObservationFrame([HeaderOnly(meta={'OBJECTS': 'tung&tung&none', 'TELESCOP': 'nres01'})], 'foo.fits')
assert runtime_utils.get_telescope_filename(image) == 'nrs01'
def test_fiber_state_to_filename():
image = NRESObservationFrame([HeaderOnly(meta={'OBJECTS': 'tung&tung&none'})], 'foo.fits')
assert fibers_state_to_filename(image) == '110'
def test_rv(mock_loader, mock_db):
# parameters that define the test data
num_orders = 5
lam_per_order = 70
res = 0.1
# Make fake data
test_wavelengths = np.arange(4500.0,
4500 + num_orders * lam_per_order + 100, res)
flux = np.ones(test_wavelengths.shape) * 1000
read_noise = 10.0
# add in fake lines
for i in range(1000):
central_wavelength = np.random.uniform() * 2000.0 + 4500.0
width = 0.1 # angstroms
sigma = width / 2 / np.sqrt(2.0 * np.log(2.0))
flux += gaussian(test_wavelengths, central_wavelength,
sigma) * 10000.0 * np.random.uniform()
noisy_flux = np.random.poisson(flux).astype(float)
noisy_flux += np.random.normal(0.0, read_noise, size=len(flux))
uncertainty = np.sqrt(flux + read_noise**2.0)
class MockLoader:
def load(self, *args):
return {'wavelength': test_wavelengths, 'flux': flux}
mock_loader.return_value = MockLoader()
true_v = 1.205 * units.km / units.s
# Make the fake image
# Set the site to the north pole, and the ra and dec to the ecliptic pole. The time we chose is just
# to minimize the rv correction
header = fits.Header({
'DATE-OBS': '2020-09-12T00:00:00.000000',
'RA': '18:00:00',
'DEC': '+66:33:32.8178',
'OBJECTS': 'foo&none&none',
'EXPTIME': 1200.0
})
site_info = {'longitude': 0.0, 'latitude': 90.0, 'elevation': 0.0}
# Each NRES order is ~70 Angstroms wide
spectrum = []
dx = int(lam_per_order / res)
for i in range(5):
order = slice(i * dx, (i + 1) * dx, 1)
row = {
'wavelength':
test_wavelengths[order] * (1.0 + true_v / constants.c),
'normflux': noisy_flux[order],
'normuncertainty': uncertainty[order],
'fiber': 0,
'order': i
}
spectrum.append(row)
image = NRESObservationFrame([CCDData(np.zeros((1, 1)), meta=header)],
'test.fits')
image.spectrum = Spectrum1D(spectrum)
image.instrument = SimpleNamespace()
image.instrument.site = 'npt'
# Classification just can't be None so that the stage does not abort.
image.classification = SimpleNamespace(**{
'T_effective': 5000.0,
'log_g': 0.0,
'metallicity': 0.0,
'alpha': 0.0
})
mock_db.return_value = SimpleNamespace(**site_info)
# Run the RV code
stage = RVCalculator(
SimpleNamespace(db_address='foo',
MIN_ORDER_TO_CORRELATE=0,
MAX_ORDER_TO_CORRELATE=num_orders - 1))
image = stage.do_stage(image)
# Assert that the true_v + rv_correction is what is in the header within 5 m/s
assert np.abs(
true_v.to('m / s').value + image.meta['BARYCORR'] -
image.meta['RV']) < 5.0
