def test_background_subtract_line(self):
# checked each step of the background subtraction with IGOR
# so this test background correction should be correct.
# create some test data
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 100, 0, 1) + 2 * xvals + 30)
# add some reproducible random noise
np.random.seed(1)
yvals += np.sqrt(yvals) * np.random.randn(yvals.size)
yvals_sd = np.sqrt(yvals)
mask = np.zeros(201, np.bool)
mask[30:70] = True
mask[130:160] = True
profile, profile_sd = plp.background_subtract_line(yvals,
yvals_sd,
mask)
verified_data = np.load(os.path.join(self.path,
'background_subtract.npy'))
assert_almost_equal(verified_data, np.c_[profile, profile_sd])
def test_background_subtract_line(self):
# checked each step of the background subtraction with IGOR
# so this test background correction should be correct.
# create some test data
xvals = np.linspace(-10, 10, 201)
yvals = np.ceil(gauss(xvals, 0, 100, 0, 1) + 2 * xvals + 30)
# add some reproducible random noise
np.random.seed(1)
yvals += np.sqrt(yvals) * np.random.randn(yvals.size)
yvals_sd = np.sqrt(yvals)
mask = np.zeros(201, bool)
mask[30:70] = True
mask[130:160] = True
profile, profile_sd = plp.background_subtract_line(
yvals, yvals_sd, mask)
verified_data = np.load(pjoin(self.pth, "background_subtract.npy"))
assert_almost_equal(verified_data, np.c_[profile, profile_sd])