def link(self, f, search_range, *args, **kwargs):
if 'pos_columns' in kwargs:
raise nose.SkipTest(
'Skipping find_link tests with custom pos_columns.')
# the minimal spacing between features in f is assumed to be 1.
# from scipy.spatial import cKDTree
# mindist = 1e7
# for _, _f in f.groupby('frame'):
# dists, _ = cKDTree(_f[['y', 'x']].values).query(_f[['y', 'x']].values, k=2)
# mindist = min(mindist, dists[:, 1].min())
# print("Minimal dist is {0:.3f}".format(mindist))
kwargs = dict(self.linker_opts, **kwargs)
size = 3
separation = kwargs['separation']
f = f.copy()
f[['y', 'x']] *= separation
topleft = (f[['y', 'x']].min().values - 4 * separation).astype(np.int)
f[['y', 'x']] -= topleft
shape = (f[['y', 'x']].max().values + 4 * separation).astype(np.int)
reader = CoordinateReader(f, shape, size)
if kwargs.get('adaptive_stop', None) is not None:
kwargs['adaptive_stop'] *= separation
result = find_link(reader,
search_range=search_range * separation,
*args,
**kwargs)
result = pandas_sort(result,
['particle', 'frame']).reset_index(drop=True)
result[['y', 'x']] += topleft
result[['y', 'x']] /= separation
return result
def link(self, f, search_range, *args, **kwargs):
if 'pos_columns' in kwargs:
raise nose.SkipTest('Skipping find_link tests with custom pos_columns.')
# the minimal spacing between features in f is assumed to be 1.
# from scipy.spatial import cKDTree
# mindist = 1e7
# for _, _f in f.groupby('frame'):
# dists, _ = cKDTree(_f[['y', 'x']].values).query(_f[['y', 'x']].values, k=2)
# mindist = min(mindist, dists[:, 1].min())
# print("Minimal dist is {0:.3f}".format(mindist))
kwargs = dict(self.linker_opts, **kwargs)
size = 3
separation = kwargs['separation']
f = f.copy()
f[['y', 'x']] *= separation
topleft = (f[['y', 'x']].min().values - 4 * separation).astype(np.int)
f[['y', 'x']] -= topleft
shape = (f[['y', 'x']].max().values + 4 * separation).astype(np.int)
reader = CoordinateReader(f, shape, size)
if kwargs.get('adaptive_stop', None) is not None:
kwargs['adaptive_stop'] *= separation
result = find_link(reader,
search_range=search_range*separation,
*args, **kwargs)
result = pandas_sort(result, ['particle', 'frame']).reset_index(drop=True)
result[['y', 'x']] += topleft
result[['y', 'x']] /= separation
return result
def link(self, f, shape, remove=None, **kwargs):
_kwargs = dict(diameter=self.diameter,
search_range=self.search_range,
separation=self.separation)
_kwargs.update(kwargs)
if remove is not None:
callback_coords = f.loc[f['frame'] == 1, ['y', 'x']].values
remove = np.array(remove, dtype=np.int)
if np.any(remove < 0) or np.any(remove > len(callback_coords)):
raise RuntimeError('Invalid test: `remove` is out of bounds.')
callback_coords = np.delete(callback_coords, remove, axis=0)
def callback(image, coords, **unused_kwargs):
if image.frame_no == 1:
return callback_coords
else:
return coords
else:
callback = None
reader = CoordinateReader(f, shape, self.size)
return find_link(reader, before_link=callback, **_kwargs)
def link(self, f, shape, remove=None, **kwargs):
_kwargs = dict(diameter=self.diameter,
search_range=self.search_range,
separation=self.separation,
preprocess=False)
_kwargs.update(kwargs)
if remove is not None:
callback_coords = f.loc[f['frame'] == 1, ['y', 'x']].values
remove = np.array(remove, dtype=np.int)
if np.any(remove < 0) or np.any(remove > len(callback_coords)):
raise RuntimeError('Invalid test: `remove` is out of bounds.')
callback_coords = np.delete(callback_coords, remove, axis=0)
def callback(image, coords, **unused_kwargs):
if image.frame_no == 1:
return callback_coords
else:
return coords
else:
callback = None
reader = CoordinateReader(f, shape, self.size)
return find_link(reader, before_link=callback, **_kwargs)