def get_centermost_location(df, sample, future):
prediction_timedelta = timedelta(minutes=future)
predictor = TrajectoryPredictor(sample.past_traj)
predicted_location = predictor.predict_linearly(prediction_timedelta)
if len(df) == 0:
return predicted_location
elif len(df) <= 2:
x = 0
else:
x = get_centermost_id(df, round(len(df) / 2))
return Point(df.iloc[x].lon, df.iloc[x].lat)
def prediction_worker(sample, prediction_timedelta):
predictor = TrajectoryPredictor(sample.past_traj)
if PREDICTION_MODE == 'linear':
predicted_location = predictor.predict_linearly(prediction_timedelta)
elif PREDICTION_MODE == 'kinetic':
predicted_location = predictor.predict_kinetically(
prediction_timedelta)
errors = TrajectoryPredictionEvaluator(sample, predicted_location,
'epsg:25832').get_errors()
context = sample.past_traj.context
prediction = EvaluatedPrediction(predicted_location, context, errors)
return prediction
def get_location_closest_to_linear_prediction(df,
sample,
future,
x='lon',
y='lat'):
prediction_timedelta = timedelta(minutes=future)
predictor = TrajectoryPredictor(sample.past_traj)
predicted_location = predictor.predict_linearly(prediction_timedelta)
if len(df) == 0:
return predicted_location
main_cluster = df.as_matrix(columns=[y, x])
return get_nearest_cluster_point(main_cluster, predicted_location)
def test_linear_prediction_south(self):
df = pd.DataFrame([{
'geometry': Point(0, 0),
't': datetime(2018, 1, 1, 12, 0, 0)
}, {
'geometry': Point(0, -10),
't': datetime(2018, 1, 1, 12, 1, 0)
}]).set_index('t')
geo_df = GeoDataFrame(df, crs={'init': '4326'})
traj = Trajectory(1, geo_df)
predictor = TrajectoryPredictor(traj)
result = predictor.predict_linearly(timedelta(minutes=1))
expected_result = Point(0, -20)
#print("Got {}, expected {}".format(result, expected_result))
self.assertTrue(result.almost_equals(expected_result))
def test_kinetic_prediction_turning_right(self):
df = pd.DataFrame([{
'geometry': Point(0, 0),
't': datetime(2018, 1, 1, 12, 0, 0)
}, {
'geometry': Point(20, 0),
't': datetime(2018, 1, 1, 12, 0, 1)
}, {
'geometry': Point(38, -10),
't': datetime(2018, 1, 1, 12, 0, 2)
}]).set_index('t')
geo_df = GeoDataFrame(df, crs={'init': '4326'})
traj = Trajectory(1, geo_df)
predictor = TrajectoryPredictor(traj)
result = predictor.predict_kinetically(timedelta(seconds=2))
expected_result = Point(68.7, -37.3)
#print("Got {}, expected {}".format(result, expected_result))
self.assertTrue(result.almost_equals(expected_result, 0))