def test_position_cell(self):
stack = Stack()
stack.image = np.zeros([10, 10, 10])
ind = np.where(np.ones([3, 3, 3]) > 0)
cell = Cell(ind=ind)
cell.position = (1, 1, 1)
stack.position_cell(cell)
self.assertEqual(np.sum(stack.image > 1), 27)
def test_position_cell3(self):
stack = Stack()
stack.image = np.zeros([10, 6, 6])
cell = Cell()
cell.image = np.zeros([15, 8, 8])
cell.image[8:10, 2:-1, 2:-1] = 255
cell.position = ndimage.center_of_mass(np.ones_like(stack.image))
stack.position_cell(cell)
self.assertEqual(np.sum(stack.image > 0), np.sum(cell.image > 0))
def __generate_stacks_batch_helper(item, outputfolder, input_voxel_size,
stack_size_microns, **kwargs_to_ignore):
filename, params = item
if not outputfolder.endswith('/'):
outputfolder += '/'
stack = Stack(input_voxel_size=input_voxel_size,
stack_size=stack_size_microns,
cell_params=params)
stack.save(outputfolder + filename[:-4] + '.tif')
stack.metadata['StackID'] = filename[:-4].split('_')[-1]
stack.metadata.save(outputfolder + filename[:-4] + '.csv')
def test_position_cell2(self):
stack = Stack()
stack.image = np.zeros([10, 10, 10])
ind = np.where(np.ones([3, 3, 3]) > 0)
cell = Cell(ind=ind)
cell.position = (1, 1, 1)
stack.position_cell(cell)
cell.position = (5, 5, 5)
stack.position_cell(cell)
def __resize_batch_helper(item,
inputfolder,
outputfolder,
order=1,
append_resolution_to_filename=True,
**kwargs_to_ignore):
inputfile, voxel_size = item
metadata = Metadata()
metadata.set_voxel_size(voxel_size)
stack = Stack(filename=inputfolder + inputfile)
zoom = np.array(stack.metadata['Voxel size arr']) / np.array(
metadata['Voxel size arr'])
stack.resize(zoom=zoom, order=order)
stack.metadata.set_voxel_size(metadata['Voxel size arr'])
path = os.path.dirname(inputfile)
strres = str(metadata['Voxel size']).replace(' ', ' ').replace(
' ', ' ').replace('[ ', '[').replace(' ', '_')
if path == '':
if append_resolution_to_filename:
outputname = outputfolder + inputfile[:
-4] + '_voxel_size_' + strres + '.tif'
else:
outputname = outputfolder + path + 'voxel_size_' + strres + '/' + inputfile.split(
'/')[-1]
else:
outputname = outputfolder + path + '_voxel_size_' + strres + '/' + inputfile.split(
'/')[-1]
stack.save(outputname)
stack.metadata.save(outputname[:-4] + '.csv')
def __convolve_batch_helper(item, inputfolder, psffolder, outputfolder,
**kwargs_to_ignore):
inputfile, psffile = item
stack = Stack(filename=inputfolder + inputfile)
psf = PSF(filename=psffolder + psffile)
stack.convolve(psf)
stack.save(outputfolder + psffile[:-4] + '/' + inputfile)
for c in ['PSF sigma xy um', 'PSF aspect ratio']:
stack.metadata[c] = psf.metadata[c]
stack.metadata.save(outputfolder + psffile[:-4] + '/' + inputfile[:-4] +
'.csv')
psf.save(outputfolder + psffile)
psf.metadata.save(outputfolder + psffile[:-4] + '.csv')
def __compute_accuracy_measures_batch_helper(item, inputfolder, reffolder,
outputfolder, **kwargs_to_ignore):
if not reffolder.endswith('/'):
reffolder += '/'
parts = item.split('/')
name = parts[-1]
if len(parts) > 1:
base = parts[-2]
else:
base = ''
stack = Stack(filename=inputfolder + item)
if 'isPSF' not in stack.metadata.index or str(
stack.metadata['isPSF']) == 'False':
if os.path.exists(reffolder + item):
refstack = Stack(filename=reffolder + item)
elif os.path.exists(reffolder + name):
refstack = Stack(filename=reffolder + name)
elif os.path.exists(reffolder + base + '/' + name):
refstack = Stack(filename=reffolder + base + '/' + name)
elif os.path.exists(reffolder + name.split('_voxel_size')[0] + '.tif'):
refstack = Stack(filename=reffolder +
name.split('_voxel_size')[0] + '.tif')
else:
raise ValueError('No ground truth found for cell ' + item + '!')
input_voxel_size = stack.metadata['Voxel size arr']
zoom = np.array(stack.metadata['Voxel size arr']) / np.array(
refstack.metadata['Voxel size arr'])
stack.resize(zoom=zoom)
stats = stack.compute_accuracy_measures(refstack)
stack.metadata.set_voxel_size(input_voxel_size)
for c in stack.metadata.index:
try:
stats[c] = stack.metadata[c]
except ValueError:
stats[c] = str(stack.metadata[c])
stats['Name'] = item
filelib.make_folders([os.path.dirname(outputfolder + item)])
stats.to_csv(outputfolder + item[:-4] + '.csv', sep='\t')
def test_from_stack_params2(self):
params = pd.DataFrame({
'size_x': [10, 10, 10],
'size_y': [9, 10, 8],
'size_z': [10, 11, 10],
'phi': [0, np.pi / 4, np.pi / 2],
'theta': [0, 0, 0],
'x': [0.1, 0.8, 0.2],
'y': [0.5, 0.5, 0.5],
'z': [0.2, 0.5, 0.8],
'spikiness': [0, 0, 100],
'spike_size': [0, 0, 1],
'spike_smoothness': [0.05, 0.05, 0.05]
})
stack = Stack(cell_params=params,
input_voxel_size=0.5,
stack_size=[50, 50, 50])
self.assertIsNotNone(stack.image)
def __add_noise_batch_helper(item, inputfolder, outputfolder,
**kwargs_to_ignore):
inputfile, kind, snr = item
stack = Stack(filename=inputfolder + inputfile)
path = os.path.dirname(inputfile)
if 'isPSF' not in stack.metadata.index or str(
stack.metadata['isPSF']) == 'False':
stack.add_noise(kind=kind, snr=snr)
if path == '':
outputname = outputfolder + inputfile
else:
outputname = outputfolder + path + '_noise'
snr = np.array([snr]).flatten()
kind = np.array([kind]).flatten()
if len(snr) == 1:
snr = np.array([snr[0]] * len(kind))
for i, k in enumerate(kind):
outputname = outputname + '_' + k + '_snr=' + str(snr[i])
outputname = outputname + '/' + inputfile.split('/')[-1]
stack.save(outputname)
stack.metadata.save(outputname[:-4] + '.csv')
def test_range_for_number_of_cells(self):
params = CellParams(number_of_stacks=3, number_of_cells=[5, 10])
stack = Stack(cell_params=params[params['stack'] == 2].reset_index(),
input_voxel_size=0.5,
stack_size=[10, 10, 10])
self.assertIsNotNone(stack.image)
def test_from_stack_params(self):
params = CellParams(number_of_stacks=1, number_of_cells=5)
stack = Stack(cell_params=params[params['stack'] == 0],
input_voxel_size=0.5,
stack_size=[10, 10, 10])
self.assertIsNotNone(stack.image)
def test_from_params(self, case):
params, res, stacksize, volume = case
stack = Stack()
stack.generate(params, res, stacksize)
self.assertAlmostEqual(np.sum(stack.image > 0) / volume, 1, 1)
def test_empty_arguments(self):
img = Stack()
for var in ['image', 'filename']:
self.assertIn(var, img.__dict__)
self.assertEqual(img.__dict__[var], None)