def test_smooth_alternative_window_size(self):
"""Should smooth stream with given window size"""
test_df = pd.DataFrame({"x": [1, 2, 3]})
transform_result = streams.smooth(test_df, window_size=1)
self.assertEqual(list(transform_result.x_smooth.values), [1, 2, 3])
def test_smooth_alternative_column_name(self):
"""Should smooth stream of given column name"""
test_df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
transform_result = streams.smooth(test_df, smooth_colname="y")
self.assertEqual(list(transform_result.columns.values), ["x", "y", "y_smooth"])
def test_smooth(self):
"""Should smooth stream using centered moving average, for default window size and column name"""
test_df = pd.DataFrame({"x": [1, 2, 3, 4]})
transform_result = streams.smooth(test_df)
self.assertEqual(list(transform_result.columns.values), ["x", "x_smooth"])
self.assertTrue(np.isnan(transform_result.x_smooth[0]))
self.assertEqual(transform_result.x_smooth[1], 2.0)
self.assertEqual(transform_result.x_smooth[2], 3.0)
self.assertTrue(np.isnan(transform_result.x_smooth[3]))
def __total_deviation(self, activity_id):
stream_df = load_stream(self.database_driver, activity_id, 'latlng')
if stream_df is not None:
stream_df = lat_long_to_x_y(stream_df)
stream_df = smooth(stream_df, smooth_colname='x')
stream_df = smooth(stream_df, smooth_colname='y')
stream_df = derivative(stream_df, derivative_colname='x_smooth')
stream_df = derivative(stream_df, derivative_colname='y_smooth')
stream_df = rolling_similarity(stream_df, cosine_similarity, 'dx_smooth_dt', 'dy_smooth_dt')
try:
return np.nansum([cosine_to_deviation(cos)
for cos in stream_df.cosine_similarity_dx_smooth_dt_dy_smooth_dt])
except ValueError:
logging.warning('Failed to compute route deviation for activity %d, returning NaN' % activity_id)
return np.nan
else:
logging.warning('No gps stream available for activity %d, returning NaN' % activity_id)
return np.nan