def test_no_drift(self):
N = 10
expected = DataFrame({'x': np.zeros(N), 'y': np.zeros(N)}).iloc[1:]
expected = expected.astype('float')
expected.index.name = 'frame'
expected.columns = ['x', 'y']
# ^ no drift measured for Frame 0
actual = mr.compute_drift(self.dead_still)
assert_frame_equal(actual, expected)
# Small random drift
actual = mr.compute_drift(self.many_walks)
assert_frame_equal(actual, expected)
def compute_drift(self, smoothing=0, query=None):
if query is None:
compute_drift = lambda chunk: mr.compute_drift(chunk, smoothing)
else:
compute_drift = lambda chunk: (
mr.compute_drift(chunk.query(query), smoothing))
chunks = self.get_data()
drift = []
last_value = 0
drift_chunks = (compute_drift(chunk) for chunk in chunks)
for chunk in drift_chunks:
drift.append(chunk + last_value)
if len(chunk) > 0: # catch unusual case where chunk is empty
last_value += chunk.iloc[-1]
drift = pd.concat(drift)
return drift
def compute_drift(self, smoothing=0, query=None):
if query is None:
compute_drift = lambda chunk: mr.compute_drift(chunk, smoothing)
else:
compute_drift = lambda chunk: (mr.compute_drift(
chunk.query(query), smoothing))
chunks = self.get_data()
drift = []
last_value = 0
drift_chunks = (compute_drift(chunk) for chunk in chunks)
for chunk in drift_chunks:
drift.append(chunk + last_value)
if len(chunk) > 0: # catch unusual case where chunk is empty
last_value += chunk.iloc[-1]
drift = pd.concat(drift)
return drift
def test_constant_drift(self):
N = 10
expected = DataFrame({'x': np.arange(N), 'y': np.zeros(N)}).iloc[1:]
expected = expected.astype('float')
expected.index.name = 'frame'
expected.columns = ['x', 'y']
actual = mr.compute_drift(self.steppers)
assert_frame_equal(actual, expected)
