def counts(self) -> Counts:
counts = numpy.zeros(len(self._data[IMATLogColumn.COUNTS_BEFORE]))
for i, [before, after] in enumerate(
zip(self._data[IMATLogColumn.COUNTS_BEFORE],
self._data[IMATLogColumn.COUNTS_AFTER])):
# clips the string before the count number
counts[i] = float(after) - float(before)
return Counts(counts)
def test_execute():
images = generate_images()
images._log_file = mock.Mock()
images._log_file.counts = mock.Mock(
return_value=Counts(np.sin(np.linspace(0, 1, images.num_projections))))
original = images.copy()
MonitorNormalisation.filter_func(images)
images._log_file.counts.assert_called_once()
assert_not_equals(original.data, images.data)
def test_execute2():
"""
Test that the counts are correctly divided by the value at counts[0].
In this test that will make all the counts equal to 1, and the data will remain unchanged
"""
images = generate_images()
images._log_file = mock.Mock()
images._log_file.counts = mock.Mock(
return_value=Counts(np.full((10, ), 10)))
original = images.copy()
MonitorNormalisation.filter_func(images)
images._log_file.counts.assert_called_once()
npt.assert_equal(original.data, images.data)
def test_one_projection():
images = generate_images((1, 1, 1))
images._log_file = mock.Mock()
images._log_file.counts = mock.Mock(
return_value=Counts(np.sin(np.linspace(0, 1, images.num_projections))))
npt.assert_raises(RuntimeError, MonitorNormalisation.filter_func, images)