def load_stormdata():
f_path = os.path.dirname(os.path.realpath(__file__))
binary = tifffile.imread(
os.path.join(f_path,
r'test_data/ds2/binary_resized.tif')).astype('int')
dtype = {
'names': ("id", "frame", "x", "y", "sigma", "intensity", "offset",
"bkgstd", "chi2", "uncertainty_xy"),
'formats':
(int, int, float, float, float, float, float, float, float, float)
}
storm_table = np.genfromtxt(os.path.join(f_path,
r'test_data/ds2/storm_table.csv'),
skip_header=1,
dtype=dtype,
delimiter=',')
storm_table['x'] /= cfg.IMG_PIXELSIZE
storm_table['y'] /= cfg.IMG_PIXELSIZE
data = Data()
data.add_data(binary, 'binary')
data.add_data(storm_table, 'storm')
return data
def __init__(self, length, radius, curvature, pad_width=5, name=None):
a2 = curvature
xl = radius + pad_width
xr = fsolve(calc_length, length, args=(xl, a2, length)).squeeze()
a1 = -a2 * (xr + xl)
r = radius
xm = (xl + xr) / 2
y_mid = a1*xm + a2*xm**2
a0 = 4*radius - y_mid + pad_width
y_max = a0 + a1 * xr + a2 * xr ** 2
shape = tuple(np.ceil([y_max + 10 + r, xr + 2 * r + 10]).astype(int))
coords = Coordinates(None, a0=a0, a1=a1, a2=a2, xl=xl, xr=xr, r=r, shape=shape, initialize=False)
binary = coords.rc < r
min1, max1, min2, max2 = mh.bbox(binary)
min1p, max1p, min2p, max2p = min1 - pad_width, max1 + pad_width, min2 - pad_width, max2 + pad_width
full = np.zeros((np.max([max1p, binary.shape[0]]), np.max([max2p, binary.shape[1]])))
full[0:binary.shape[0], 0:binary.shape[1]] = binary
res = full[min1p:max1p, 0:max2p]
data = Data()
data.add_data(res.astype(int), 'binary')
super(SynthCell, self).__init__(data, name=name)
self.coords.a0 = a0 - min1p
self.coords.a1 = a1
self.coords.a2 = a2
self.coords.xl = xl
self.coords.xr = xr
self.coords.r = r
def setUp(self):
f_path = os.path.dirname(os.path.realpath(__file__))
self.cell_list = load(
os.path.join(f_path, 'test_data', 'test_synth_cell_storm.hdf5'))
self.cell = self.cell_list[0]
data = Data()
data.add_data(self.cell.data.binary_img, 'binary')
self.empty_cell = Cell(data)
def load_data(dataset):
dclasses = ['binary', 'brightfield', 'fluorescence']
f_path = os.path.dirname(os.path.realpath(__file__))
data = Data()
for dclass in dclasses:
files = sorted(listdir_fullpath(
os.path.join(f_path, 'test_data', dataset, dclass.lower())),
key=str)
arr = np.empty((len(files), 512, 512)).astype('uint16')
for i, f in enumerate(files):
arr[i] = tifffile.imread(f)
data.add_data(arr, dclass)
return data
def setUp(self):
f_path = os.path.dirname(os.path.realpath(__file__))
self.cell_list = load(
os.path.join(f_path, 'test_data', 'test_synth_cell_storm.hdf5'))
self.cell = self.cell_list[0]
x = np.arange(20)
y = np.exp(-x / 5)
img_3d = self.cell.data.data_dict['fluorescence'][
np.newaxis, :, :] * y[:, np.newaxis, np.newaxis]
self.cell.data.add_data(img_3d, 'fluorescence', 'flu_3d')
data = Data()
data.add_data(self.cell.data.binary_img, 'binary')
self.empty_cell = Cell(data)
def setUp(self):
self.data = load_testdata('ds1')
f_path = os.path.dirname(os.path.realpath(__file__))
self.storm_cells_1 = load(
os.path.join(f_path, 'test_data/test_single_spot_storm.hdf5'))
self.storm_cells_2 = load(
os.path.join(f_path, 'test_data/test_double_spot_storm.hdf5'))
cells_no_flu = []
for c in self.storm_cells_2:
d = Data()
d.add_data(c.data.binary_img, 'binary')
d.add_data(c.data.data_dict['storm_1'], 'storm', 'storm_1')
d.add_data(c.data.data_dict['storm_2'], 'storm', 'storm_2')
cell = Cell(d)
cells_no_flu.append(cell)
self.storm_cells_2_no_flu = CellList(cells_no_flu)
class TestDataToCells(ArrayTestCase):
def setUp(self):
f_path = os.path.dirname(os.path.realpath(__file__))
storm = load_thunderstorm(
os.path.join(f_path, r'test_data/ds3/storm_table.csv'))
binary = tifffile.imread(
os.path.join(f_path, r'test_data/ds3/binary.tif'))
fluorescence = tifffile.imread(
os.path.join(f_path, r'test_data/ds3/flu.tif'))
self.storm_ds = Data()
self.storm_ds.add_data(binary, 'binary')
self.storm_ds.add_data(fluorescence, 'fluorescence')
self.storm_ds.add_data(storm, 'storm')
def test_storm_data_to_cells(self):
data = self.storm_ds
cells = data_to_cells(data)
self.assertEqual(sum([len(c.data.data_dict['storm']) for c in cells]),
40)
data_copy = data.copy()
storm_ds = data_copy.data_dict['storm']
storm_ds['frame'][storm_ds['frame'] == 4] = 5
cells = data_to_cells(data_copy)
num_spots = [0, 10, 0, 6, 1, 14, 1, 0, 0, 0, 0, 0, 0]
for c, spots in zip(cells, num_spots):
self.assertEqual(len(c.data.data_dict['storm']), spots)
self.assertIn('storm', c.data.names)
self.assertIn('storm', c.data.dclasses)
def _load_cell(cell_grp):
"""Load the cell object from `cell_grp`"""
data_obj = Data()
data_grp = cell_grp['data']
for key in list(data_grp.keys()):
grp = data_grp[key]
data_arr = grp[key]
dclass = grp.attrs.get('dclass').decode('UTF-8')
data_obj.add_data(data_arr[:], dclass=dclass, name=key)
c = Cell(data_obj, init_coords=False)
attr_grp = cell_grp['attributes']
attr_dict = dict(attr_grp.attrs.items())
for a in ['r', 'xl', 'xr', 'coeff']:
setattr(c.coords, a, attr_dict.get(a))
c.coords.shape = c.data.shape
name = attr_dict.get('name').decode('UTF-8')
c.name = name if name is not 'None' else None
return c
def _load_deprecated(file_path):
ext = os.path.splitext(file_path)[1]
if ext == '.cc':
with h5py.File(file_path, 'r') as f:
data_obj = Data()
data_grp = f['data']
for key in list(data_grp.keys()):
grp = data_grp[key]
data_arr = grp[key]
dclass = grp.attrs.get('dclass').decode('UTF-8')
data_obj.add_data(data_arr, dclass=dclass, name=key)
c = Cell(data_obj)
attr_grp = f['attributes']
attr_dict = dict(attr_grp.attrs.items())
for a in ['r', 'xl', 'xr', 'coeff']:
setattr(c.coords, a, attr_dict.get(a))
c.name = attr_dict.get('label')
return c
else:
raise ValueError('Invalid file type')
def load_escvdata():
binary = tifffile.imread(r'test_data/ds5/binary.tif')
flu = tifffile.imread(r'test_data/ds5/flu.tif')
storm = load_thunderstorm(r'test_data/ds5/storm_table.csv')
data = Data()
data.add_data(binary, 'binary')
data.add_data(flu, 'fluorescence')
data.add_data(storm, 'storm')
return data
def test_add_data(self):
testdata_int = np.round(np.random.rand(512, 512)).astype(int)
testdata_float = np.round(np.random.rand(512, 512)) * 2**16 - 1
testdata_mov = np.round(np.random.rand(10, 512, 512)) * 2**16 - 1
data = Data()
with self.assertRaises(TypeError): # Invalid dtype
data.add_data(testdata_float, dclass='binary')
data.add_data(testdata_int, dclass='binary')
self.assertArrayEqual(testdata_int, data.data_dict['binary'])
self.assertArrayEqual(testdata_int, data.binary_img)
with self.assertRaises(ValueError): # Invalid shape
data.add_data(testdata_float.reshape(256, -1), 'fluorescence')
with self.assertRaises(ValueError): # Binary has to be unique
data.add_data(testdata_int, dclass='binary', name='newbinaryname')
data.add_data(testdata_float, dclass='brightfield')
with self.assertRaises(
ValueError
): # Same dclass data elements which will have the same name
data.add_data(testdata_float, dclass='brightfield')
self.assertEqual(testdata_float.shape, data.shape)
data.add_data(testdata_mov, 'fluorescence', name='fluorescence_movie')
