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_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 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 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