def test_init_empty_file(self):
with ArtificialND2('test_data/empty.nd2',
skip_blocks=['label_map_marker']):
with self.assertRaises(EmptyFileError) as exception:
with ND2Reader('test_data/empty.nd2'):
pass
self.assertEqual(str(exception.exception),
"No axes were found for this .nd2 file.")
def test_get_frame_2D(self):
# Best test we can do for now:
# test everything up to the actual unpacking of the frame data
with ArtificialND2('test_data/test_nd2_reader.nd2') as _:
with ND2Reader('test_data/test_nd2_reader.nd2') as reader:
with self.assertRaises(struct.error) as exception:
frame = reader.get_frame_2D(c=0, t=0, z=0, x=0, y=0, v=0)
self.assertIn('unpack', str(exception.exception))
def test_init_and_init_axes(self):
with ArtificialND2('test_data/test_nd2_reader.nd2') as artificial:
with ND2Reader('test_data/test_nd2_reader.nd2') as reader:
attributes = artificial.data['image_attributes']['SLxImageAttributes']
self.assertEqual(reader.metadata['width'], attributes['uiWidth'])
self.assertEqual(reader.metadata['height'], attributes['uiHeight'])
self.assertEqual(reader.metadata['width'], reader.sizes['x'])
self.assertEqual(reader.metadata['height'], reader.sizes['y'])
self.assertEqual(reader.pixel_type, np.float64)
self.assertEqual(reader.iter_axes, ['t'])
def test_get_timesteps(self):
with ArtificialND2('test_data/test_nd2_reader.nd2') as _:
with ND2Reader('test_data/test_nd2_reader.nd2') as reader:
timesteps = reader.timesteps
self.assertEquals(len(timesteps), 0)
def test_get_parser(self):
with ArtificialND2('test_data/test_nd2_reader.nd2') as _:
with ND2Reader('test_data/test_nd2_reader.nd2') as reader:
self.assertIsInstance(reader.parser, Parser)
def test_extension(self):
self.assertTrue('nd2' in ND2Reader.class_exts())
def test_invalid_file_extension(self):
self.assertRaises(InvalidFileType, lambda: ND2Reader('test_data/invalid_extension_file.inv'))
def test_init_from_handler(self):
with ArtificialND2('test_data/test_nd2_reader.nd2') as artificial:
with open('test_data/test_nd2_reader.nd2', "rb") as FH:
with ND2Reader(FH) as reader:
self.cmp_two_readers(artificial, reader)
def test_init_and_init_axes(self):
with ArtificialND2('test_data/test_nd2_reader.nd2') as artificial:
with ND2Reader('test_data/test_nd2_reader.nd2') as reader:
self.cmp_two_readers(artificial, reader)
def Z_Stack_Images_Extractor(address, fields_of_view, z_answer):
Image_Sequence = ND2Reader(address)
Channel_list = Image_Sequence.metadata['channels']
# Check whether the data has one channel or two channels. If only one channel, put GUV_Channel and Protein_Channel = 0
if len(Channel_list) == 1:
GUV_Channel = 0
Protein_Channel = 0
# Select correct channels for downstream import
elif 'DsRed' in Channel_list[0] or '561' in Channel_list[0]:
GUV_Channel = 0
Protein_Channel = 1
else:
GUV_Channel = 1
Protein_Channel = 0
time_series = Image_Sequence.sizes['t']
if z_answer:
z_stack = Image_Sequence.sizes['z']
Intensity_Slice = []
GUV_Slice = []
n = 0
# create progress bar
pb = tqdm(range(time_series),
bar_format="{l_bar}%s{bar}%s{r_bar}" % (Fore.RED, Fore.RESET))
for time in pb:
pb.set_description("Converting images to numpy arrays")
if z_answer:
z_stack_images = []
z_stack_Intensity_images = []
for z_slice in range(z_stack):
slice = Image_Sequence.get_frame_2D(c=GUV_Channel,
t=time,
z=z_slice,
v=fields_of_view)
Intensity_slice = Image_Sequence.get_frame_2D(
c=Protein_Channel, t=time, z=z_slice, v=fields_of_view)
z_stack_images.append(slice)
z_stack_Intensity_images.append(Intensity_slice)
z_stack_images = np.array(z_stack_images)
z_stack_Intensity_images = np.array(z_stack_Intensity_images)
else:
z_stack_images = Image_Sequence.get_frame_2D(c=GUV_Channel,
t=time,
v=fields_of_view)
z_stack_Intensity_images = Image_Sequence.get_frame_2D(
c=Protein_Channel, t=time, v=fields_of_view)
GUV_Slice.append(z_stack_images)
Intensity_Slice.append(z_stack_Intensity_images)
GUV_Slice = np.array(GUV_Slice)
Intensity_Slice = np.array(Intensity_Slice)
return (GUV_Slice, Intensity_Slice)
t=time,
v=fields_of_view)
z_stack_Intensity_images = Image_Sequence.get_frame_2D(
c=Protein_Channel, t=time, v=fields_of_view)
GUV_Slice.append(z_stack_images)
Intensity_Slice.append(z_stack_Intensity_images)
GUV_Slice = np.array(GUV_Slice)
Intensity_Slice = np.array(Intensity_Slice)
return (GUV_Slice, Intensity_Slice)
Image_Sequence = ND2Reader(Image_Stack_Path)
FOV_list = Image_Sequence.metadata['fields_of_view']
GUV_Image_list = []
Intensity_list = []
for fov in range(len(FOV_list)):
GUV_Images, Image_Intensity = Z_Stack_Images_Extractor(Image_Stack_Path,
fields_of_view=fov,
z_answer=answer)
GUV_Image_list.append(GUV_Images)
Intensity_list.append(Image_Intensity)
File_save_names = '.'.join(Image_Stack_Path.split(".")[:-1])
for n in range(len(FOV_list)):