def test_calculate_mean_spectrum_masked_image(self):
mani.apply_segmentation(self.specialmsi, self.segmentation)
mani.calculate_mean_spectrum(self.specialmsi)
np.testing.assert_equal(self.specialValue, self.specialmsi.get_image(),
"mean spectrum is correctly calculated on image with " +
"mask applied")
def test_calculate_mean_spectrum_masked_image(self):
mani.apply_segmentation(self.specialmsi, self.segmentation)
mani.calculate_mean_spectrum(self.specialmsi)
np.testing.assert_equal(self.specialValue, self.specialmsi.get_image(),
"mean spectrum is correctly calculated on image with " +
"mask applied")
def test_apply_segmentation(self):
mani.apply_segmentation(self.specialmsi, self.segmentation)
validImageEntries = self.specialmsi.get_image() \
[~self.specialmsi.get_image().mask]
np.testing.assert_equal(validImageEntries, self.specialValue,
"image has been correctly segmented")
def test_apply_segmentation(self):
mani.apply_segmentation(self.specialmsi, self.segmentation)
validImageEntries = self.specialmsi.get_image() \
[~self.specialmsi.get_image().mask]
np.testing.assert_equal(validImageEntries, self.specialValue,
"image has been correctly segmented")
def setUp(self):
self.msi = helpers.getFakeMsi()
# set one pixel to special values
self.specialValue = np.arange(self.msi.get_image().shape[-1]) * 2
self.msi.get_image()[2, 2, :] = self.specialValue
# create a segmentation which sets all elements to invalid but the
# one pixel with the special value
self.segmentation = np.zeros(self.msi.get_image().shape[0:-1])
self.segmentation[2, 2] = 1
# apply this segmentation to the msi
msimani.apply_segmentation(self.msi, self.segmentation)
self.image = self.msi.get_image()
def setUp(self):
self.msi = helpers.getFakeMsi()
# set one pixel to special values
self.specialValue = np.arange(self.msi.get_image().shape[-1]) * 2
self.msi.get_image()[2, 2, :] = self.specialValue
# create a segmentation which sets all elements to invalid but the
# one pixel with the special value
self.segmentation = np.zeros(self.msi.get_image().shape[0:-1])
self.segmentation[2, 2] = 1
# apply this segmentation to the msi
msimani.apply_segmentation(self.msi, self.segmentation)
self.image = self.msi.get_image()
