def test_null_predict(self):
"""Make sure that a prediction of no motion does not interfere
with normal tracking.
"""
# link_df_iter
pred = predict.NullPredict()
ll = get_linked_lengths((mkframe(0), mkframe(25)),
self.get_linker_iter(pred), 45)
assert all(ll.values == 2)
# link_df
pred = predict.NullPredict()
ll_df = self.get_linker(pred)(pandas.concat((mkframe(0),
mkframe(25))), 45)
# print(ll_df)
assert all(ll_df.groupby('particle').x.count().values == 2)
# Make sure that keyword options are handled correctly
# (This checks both link_df and link_df_iter)
# The conditional is so we won't test find_link in this way,
# due to the way column names are baked into the
# artificial image code.
if not getattr(self, 'coords_via_images', False):
features = pandas.concat((mkframe(0), mkframe(25)))
features.rename(columns=lambda n: n + '_', inplace=True)
pred = predict.NullPredict()
ll_df = self.get_linker(pred)(features, 45, t_column='frame_',
pos_columns=['x_', 'y_'])
assert all(ll_df.groupby('particle').x_.count().values == 2)
def test_null_predict(self):
"""Make sure that a prediction of no motion does not interfere
with normal tracking.
"""
pred = predict.NullPredict()
ll = get_linked_lengths((mkframe(0), mkframe(0.25)), pred.link_df_iter,
0.45)
assert all(ll.values == 2)
def test_null_predict(self):
"""Make sure that a prediction of no motion does not interfere
with normal tracking.
"""
# link_df_iter
pred = predict.NullPredict()
ll = get_linked_lengths((mkframe(0), mkframe(0.25)),
pred.link_df_iter, 0.45)
assert all(ll.values == 2)
# link_df
pred = predict.NullPredict()
ll_df = pred.link_df(pandas.concat((mkframe(0), mkframe(0.25))), 0.45)
assert all(ll_df.groupby('particle').x.count().values == 2)
# Make sure that keyword options are handled correctly
# (This checks both link_df and link_df_iter)
features = pandas.concat((mkframe(0), mkframe(0.25)))
features.rename(columns=lambda n: n + '_', inplace=True)
pred = predict.NullPredict()
ll_df = pred.link_df(features, 0.45, t_column='frame_', pos_columns=['x_', 'y_'])
assert all(ll_df.groupby('particle').x_.count().values == 2)