def test_derivative_nonlinear_index(self):
"""Should take into account dt in index when computing derivative"""
test_df = pd.DataFrame({"x": [1, 2, 3]}, index=[1, 2, 4])
transform_result = streams.derivative(test_df)
self.assertEqual(list(transform_result.columns.values), ["x", "dx_dt"])
self.assertTrue(np.isnan(transform_result.dx_dt[1]))
self.assertEqual(transform_result.dx_dt[2], 1.0)
self.assertEqual(transform_result.dx_dt[4], 0.5)
def test_derivative(self):
"""Should compute derivative for default column"""
test_df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
transform_result = streams.derivative(test_df)
self.assertEqual(list(transform_result.columns.values), ["x", "y", "dx_dt"])
self.assertTrue(np.isnan(transform_result.dx_dt[0]))
self.assertEqual(transform_result.dx_dt[1], 1.0)
self.assertEqual(transform_result.dx_dt[2], 1.0)
def test_derivative_alternative_column_name(self):
"""Should compute derivative of given column name"""
test_df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 6, 8]})
transform_result = streams.derivative(test_df, derivative_colname="y")
self.assertEqual(list(transform_result.columns.values), ["x", "y", "dy_dt"])
self.assertTrue(np.isnan(transform_result.dy_dt[0]))
self.assertEqual(transform_result.dy_dt[1], 2.0)
self.assertEqual(transform_result.dy_dt[2], 2.0)
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
