def test_fit_background_model():
shape = (9, 5)
image = np.arange(shape[0] * shape[1], dtype=np.float32).reshape(shape)
x = 2
j = 2
b_lower = np.zeros(shape[1], dtype=np.float64) + 3.5 # 4, inclusive
b_upper = np.zeros(shape[1], dtype=np.float64) + 4.5 # 4, inclusive
bkglim = [[b_lower, b_upper]]
bkg_order = 0
(bkg_model, npts) = extract1d._fit_background_model(
image, x, j, bkglim, bkg_order)
assert math.isclose(bkg_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 2
image[:, 2] = np.nan
(bkg_model, npts) = extract1d._fit_background_model(
image, x, j, bkglim, bkg_order)
assert math.isclose(bkg_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 0
def test_fit_background_model(inputs_constant):
(bkg_model, npts) = extract1d._fit_background_model(*inputs_constant)
assert math.isclose(bkg_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 2
def test_handles_nan(inputs_constant):
image, x, j, bkglim, bkg_order = inputs_constant
image[:, 2] = np.nan
(bkg_model, npts) = extract1d._fit_background_model(image, x, j, bkglim, bkg_order)
assert math.isclose(bkg_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 0
def test_handles_one_value(inputs_constant):
image, x, j, bkglim, bkg_order = inputs_constant
image[np.where(image == 22)] = np.nan # During extraction, only two pixels are returned "normally"; set one to Nan
# If only one data point is available, the polynomial fit is forced to 0
bkg_model, npts = extract1d._fit_background_model(image, x, j, bkglim, bkg_order)
assert math.isclose(bkg_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 0
def test_fit_background_median(inputs_constant):
image, x, j, bkglim, bkg_fit, bkg_order = inputs_constant
bkg_fit = "median"
(bkg_model,
npts) = extract1d._fit_background_model(image, x, j, bkglim, bkg_fit,
bkg_order)
assert math.isclose(bkg_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 2
def test_fit_background_model(inputs_constant):
(bkg_model, b_var_poisson_model, b_var_rnoise_model, b_var_flat_model, npts) = \
extract1d._fit_background_model(*inputs_constant)
assert math.isclose(bkg_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_poisson_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_poisson_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_rnoise_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_rnoise_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_flat_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_flat_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 2
def test_handles_nan(inputs_constant):
image, var_poisson, var_rnoise, var_rflat, x, j, bkglim, bkg_fit, bkg_order = inputs_constant
image[:, 2] = np.nan
(bkg_model, b_var_poisson_model, b_var_rnoise_model, b_var_flat_model, npts) = \
extract1d._fit_background_model(image, var_poisson, var_rnoise, var_rflat,
x, j, bkglim, bkg_fit, bkg_order)
assert math.isclose(bkg_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_poisson_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_poisson_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_rnoise_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_rnoise_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_flat_model(0.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_flat_model(8.), 0.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 0
def test_fit_background_median(inputs_constant):
image, var_poisson, var_rnoise, var_rflat, x, j, bkglim, bkg_fit, bkg_order = inputs_constant
bkg_fit = "median"
(bkg_model, b_var_poisson_model, b_var_rnoise_model, b_var_flat_model, npts) = \
extract1d._fit_background_model(image, var_poisson, var_rnoise, var_rflat,
x, j, bkglim, bkg_fit, bkg_order)
assert math.isclose(bkg_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(bkg_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_poisson_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_poisson_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_rnoise_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_rnoise_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_flat_model(0.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert math.isclose(b_var_flat_model(8.), 22.0, rel_tol=1.e-8, abs_tol=1.e-8)
assert npts == 2