def test_large_values_in_middle(self):
data = np.zeros((100, 100))
data[5:95, :] = 10
data[:, 5:95] = 10
s = hs.signals.Signal2D(data)
ramp05 = pst._fit_ramp_to_image(s, corner_size=0.05)
assert_allclose(ramp05, np.zeros((100, 100)), atol=1e-15)
ramp10 = pst._fit_ramp_to_image(s, corner_size=0.1)
assert (ramp05 != ramp10).all()
def test_different_corner_values(self):
data = np.zeros((100, 100))
data[:5, :5], data[:5, -5:] = -10, 10
data[-5:, :5] = 10
data[-5:, -5:] = 30
s = hs.signals.Signal2D(data)
ramp = pst._fit_ramp_to_image(s, corner_size=0.05)
s.data = s.data - ramp
assert approx(s.data[:5, :5].mean()) == 0.0
assert approx(s.data[:5, -5:].mean()) == 0.0
assert approx(s.data[-5:, :5].mean()) == 0.0
assert approx(s.data[-5:, -5:].mean()) == 0.0
assert s.data[5:95, 5:95].mean() != 0
def correct_ramp(self, corner_size=0.05, only_offset=False, out=None):
"""
Subtracts a plane from the signal, useful for removing
the effects of d-scan in a STEM beam shift dataset.
The plane is calculated by fitting a plane to the corner values
of the signal. This will only work well when the property one
wants to measure is zero in these corners.
Parameters
----------
corner_size : number, optional
The size of the corners, as a percentage of the image's axis.
If corner_size is 0.05 (5%), and the image is 500 x 1000,
the size of the corners will be (500*0.05) x (1000*0.05) = 25 x 50.
Default 0.05
only_offset : bool, optional
If True, will subtract a "flat" plane, i.e. it will subtract the
mean value of the corners. Default False
out : optional, DPCSignal2D signal
Returns
-------
corrected_signal : Signal2D
Examples
--------
>>> s = pxm.dummy_data.get_square_dpc_signal(add_ramp=True)
>>> s_corr = s.correct_ramp()
>>> s_corr.plot()
Only correct offset
>>> s_corr = s.correct_ramp(only_offset=True)
>>> s_corr.plot()
"""
if out is None:
output = self.deepcopy()
else:
output = out
for i, s in enumerate(self):
if only_offset:
corners = pst._get_corner_values(s, corner_size=corner_size)[2]
ramp = corners.mean()
else:
ramp = pst._fit_ramp_to_image(s, corner_size=0.05)
output.data[i, :, :] -= ramp
if out is None:
return output
def test_wrong_input_dimensions(self):
s = hs.signals.Signal2D(np.ones((2, 10, 10)))
with pytest.raises(ValueError):
pst._fit_ramp_to_image(s)
s = hs.signals.Signal2D(np.ones((2, 2, 10, 10)))
with pytest.raises(ValueError):
pst._fit_ramp_to_image(s)
s = hs.signals.Signal1D(np.ones(10))
with pytest.raises(ValueError):
pst._fit_ramp_to_image(s)
def test_negative_values(self):
data = np.ones((100, 100)) * -10
s = hs.signals.Signal2D(data)
ramp = pst._fit_ramp_to_image(s, corner_size=0.05)
assert_allclose(ramp, data, atol=1e-30)
def test_zero_values(self):
data = np.zeros((100, 100))
s = hs.signals.Signal2D(data)
ramp = pst._fit_ramp_to_image(s, corner_size=0.05)
assert_allclose(ramp, data, atol=1e-15)