def test_varclip():
# Confirm rejection of high pixel and correct output DQ
data = np.array([1., 1., 2., 2., 2., 100.]).reshape(6, 1)
ndd = NDAstroData(data)
ndd.mask = np.zeros_like(data, dtype=DQ.datatype)
ndd.mask[5, 0] = DQ.saturated
ndd.variance = np.ones_like(data)
stackit = NDStacker(combine="mean", reject="varclip")
result = stackit(ndd)
np.testing.assert_array_almost_equal(result.data, [1.6])
np.testing.assert_array_equal(result.mask, [0])
def test_varclip():
# Confirm rejection of high pixel and correct output DQ
data = np.array([1., 1., 2., 2., 2., 100.]).reshape(6, 1)
ndd = NDAstroData(data)
ndd.mask = np.zeros_like(data, dtype=DQ.datatype)
ndd.mask[5, 0] = DQ.saturated
ndd.variance = np.ones_like(data)
stackit = NDStacker(combine="mean", reject="varclip")
result = stackit(ndd)
assert_allclose(result.data, 1.6) # 100 is rejected
assert_allclose(result.mask, 0)
data = np.array([1., 1., 2., 2., 2., 100.]).reshape(6, 1)
ndd = NDAstroData(data)
ndd.variance = np.ones_like(data)
ndd.variance[5] = 400
stackit = NDStacker(combine="mean", reject="varclip", lsigma=3, hsigma=3)
result = stackit(ndd)
assert_allclose(result.data, 1.6) # 100 is rejected
stackit = NDStacker(combine="mean", reject="varclip", lsigma=5, hsigma=5)
result = stackit(ndd)
assert_allclose(result.data, 18) # 100 is not rejected
def test_sum1d():
big_value = 10
data = np.ones((10, ))
ndd = NDAstroData(data)
ndd.mask = np.zeros_like(data, dtype=DQ.datatype)
ndd.mask[1] = 1
ndd.mask[2] = 2
ndd.data[4] = big_value
ndd.variance = np.ones_like(data)
x1 = -0.5
for x2 in np.arange(0., 4.5, 0.5):
result = sum1d(ndd, x1, x2, proportional_variance=True)
if x2 > 3.5:
np.testing.assert_almost_equal(4 + big_value * (x2 - 3.5),
result.data)
else:
np.testing.assert_almost_equal(x2 - x1, result.data)
np.testing.assert_almost_equal(x2 - x1, result.variance)
if x2 > 1.5:
assert result.mask == 3
elif x2 > 0.5:
assert result.mask == 1
else:
assert result.mask == 0