def generate_cell_parameters(outputfile, **kwargs):
"""
Generates random cell parameters and save them into a given csv file.
Parameters
----------
outputfile : str
Path to save cell parameters.
kwargs : key, value pairings
Keyword arguments passed to the `CellParams` class.
"""
if not outputfile.endswith('.csv'):
outputfile += '.csv'
params = CellParams(**kwargs)
params.save(outputfile)
def test_cell_from_params(self):
celldata = CellParams(number_of_cells=5,
spikiness_range=(0, 1),
spike_size_range=(0.1, 1),
coordinates=False)
cell = Cell(input_voxel_size=0.5, **dict(celldata.iloc[0]))
self.assertIsNotNone(cell.image)
def test_read_write(self):
celldata = CellParams(number_of_cells=5, spikiness_range=(0, 0.5), spike_size_range=(0.1, 1))
celldata.save('data/celldata.csv')
celldata2 = CellParams()
celldata2.read_from_csv('data/celldata.csv')
self.assertEqual(len(celldata), len(celldata2))
for c in celldata.columns:
self.assertEqual(c in celldata2.columns, True)
shutil.rmtree('data/')
def generate_cells_batch(params_file, **kwargs):
"""
Generate synthetic cells with given parameters in a parallel mode and saves them in a given directory.
Parameters
----------
params_file : str
Path to a csv file with cell parameters.
Keyword arguments
-----------------
outputfolder : str
Output directory to save the generated cells.
input_voxel_size : scalar or sequence of scalars, optional
Voxel size in z, y and x used to generate the cell image.
If one value is provided, the voxel size is assume to be equal along all axes.
max_threads : int, optional
The maximal number of processes to run in parallel.
Default is 8.
print_progress : bool, optional
If True, the progress of the computation will be printed.
Default is True.
"""
if not os.path.exists(params_file):
raise ValueError('Parameter file does not exist!')
params = CellParams()
params.read_from_csv(filename=params_file)
if 'stack' in params.columns:
items = []
for st in params['stack'].unique():
items.append(('stack_%03d.tif' % st,
params[params['stack'] == st].reset_index()))
kwargs['items'] = items
run_parallel(process=__generate_stacks_batch_helper,
process_name='Generation of stacks',
**kwargs)
else:
items = []
for i in range(len(params)):
items.append(('cell_%03d.tif' % i, params.iloc[i]))
kwargs['items'] = items
run_parallel(process=__generate_cells_batch_helper,
process_name='Generation of cells',
**kwargs)
def test_valid_types(self, kind):
celldata = CellParams(kind=kind)
celldata.save('data/celldata.csv')
self.assertEqual(os.path.exists('data/celldata.csv'), True)
shutil.rmtree('data/')
def test_cell_data(self, size):
celldata = CellParams(number_of_cells=1, size_mean_and_std=size)
celldata.save('data/celldata.csv')
celldata.plot_size_distribution()
shutil.rmtree('data/')
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)