def test_calculate_mean_spectrum(self):
mani.calculate_mean_spectrum(self.specialmsi)
np.testing.assert_equal(np.array([0.96, 2., 3.04, 4.08, 5.12]),
self.specialmsi.get_image(),
"mean spectrum is correctly calculated on image with " +
"no 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_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(self):
mani.calculate_mean_spectrum(self.specialmsi)
np.testing.assert_equal(np.array([0.96, 2., 3.04, 4.08, 5.12]),
self.specialmsi.get_image(),
"mean spectrum is correctly calculated on image with " +
"no mask applied")
def test_write_one_d_image_works(self):
writer = NrrdWriter(self.msi)
msimani.calculate_mean_spectrum(self.msi)
writer.write(self.fileUriToWrite)
reader = NrrdReader()
msi = reader.read(self.fileUriToWrite)
np.testing.assert_array_equal(msi.get_image(),
np.array([1, 2, 3, 4, 5]),
"1d image correctly written and read")
def test_write_one_d_image_works(self):
writer = NrrdWriter(self.msi)
msimani.calculate_mean_spectrum(self.msi)
writer.write(self.fileUriToWrite)
reader = NrrdReader()
msi = reader.read(self.fileUriToWrite)
np.testing.assert_array_equal(msi.get_image(),
np.array([1, 2, 3, 4, 5]),
"1d image correctly written and read")
def test_flatfield_correction_with_single_value(self):
desired_image_data = np.ones_like(self.msi.get_image())
flatfield = copy.copy(self.msi)
mani.calculate_mean_spectrum(flatfield)
unchanged_flatfield = copy.deepcopy(flatfield)
mani.flatfield_correction(self.msi, flatfield)
np.testing.assert_equal(self.msi.get_image(),
desired_image_data,
"flatfield correctly accounted for from singular reference value")
np.testing.assert_equal(flatfield, unchanged_flatfield,
"flatfield not changed by algorithm")
def test_flatfield_correction_with_single_value(self):
desired_image_data = np.ones_like(self.msi.get_image())
flatfield = copy.copy(self.msi)
mani.calculate_mean_spectrum(flatfield)
unchanged_flatfield = copy.deepcopy(flatfield)
mani.flatfield_correction(self.msi, flatfield)
np.testing.assert_equal(self.msi.get_image(),
desired_image_data,
"flatfield correctly accounted for from singular reference value")
np.testing.assert_equal(flatfield, unchanged_flatfield,
"flatfield not changed by algorithm")
def test_dark_correction_with_single_value(self):
desired_image_data = copy.copy(self.specialmsi.get_image())
desired_image_data -= 1
dark = copy.copy(self.specialmsi)
dark.set_image(np.ones_like(dark.get_image()))
mani.calculate_mean_spectrum(dark)
mani.dark_correction(self.specialmsi, dark)
np.testing.assert_equal(self.specialmsi.get_image(),
desired_image_data,
"dark image correctly accounted for from singular dark value")
np.testing.assert_equal(dark.get_image(),
np.ones_like(dark.get_image()),
"dark image unchanged by dark correction")
def test_dark_correction_with_single_value(self):
desired_image_data = copy.copy(self.specialmsi.get_image())
desired_image_data -= 1
dark = copy.copy(self.specialmsi)
dark.set_image(np.ones_like(dark.get_image()))
mani.calculate_mean_spectrum(dark)
mani.dark_correction(self.specialmsi, dark)
np.testing.assert_equal(self.specialmsi.get_image(),
desired_image_data,
"dark image correctly accounted for from singular dark value")
np.testing.assert_equal(dark.get_image(),
np.ones_like(dark.get_image()),
"dark image unchanged by dark correction")
def run(self):
tiff_ring_reader = TiffRingReader()
nr_filters = len(sc.other["RECORDED_WAVELENGTHS"])
# analyze all the first image files
image_files = get_image_files_from_folder(self.dark_folder,
suffix="F0.tiff")
# returns the mean dark image vector of all inputted dark image
# overly complicated TODO SW: make this simple code readable.
dark_means = map(
lambda image_name: msimani.calculate_mean_spectrum(
tiff_ring_reader.read(
os.path.join(self.dark_folder, image_name), nr_filters)[0]
), image_files)
dark_means_sum = reduce(lambda x, y: x + y.get_image(), dark_means, 0)
final_dark_mean = dark_means_sum / len(dark_means)
msi = Msi(image=final_dark_mean)
msi.set_wavelengths(sc.other["RECORDED_WAVELENGTHS"])
# write flatfield as nrrd
writer = NrrdWriter(msi)
writer.write(self.output().path)
def run(self):
tiff_ring_reader = TiffRingReader()
nr_filters = len(sc.other["RECORDED_WAVELENGTHS"])
# analyze all the first image files
image_files = get_image_files_from_folder(self.dark_folder,
suffix="F0.tiff")
# returns the mean dark image vector of all inputted dark image
# overly complicated TODO SW: make this simple code readable.
dark_means = map(lambda image_name:
msimani.calculate_mean_spectrum(
tiff_ring_reader.read(os.path.join(self.dark_folder, image_name),
nr_filters)[0]),
image_files)
dark_means_sum = reduce(lambda x, y: x+y.get_image(), dark_means, 0)
final_dark_mean = dark_means_sum / len(dark_means)
msi = Msi(image=final_dark_mean)
msi.set_wavelengths(sc.other["RECORDED_WAVELENGTHS"])
# write flatfield as nrrd
writer = NrrdWriter(msi)
writer.write(self.output().path)