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 compute_predictions(samples, past, future, pool):
out = OUTPUT.replace('in.csv',
'in_{}_{}_{}.csv'.format(SHIPTYPE, past, future))
print("Writing to {}".format(out))
future = timedelta(minutes=future)
with open(out, 'w') as output:
output.write(EvaluatedPrediction.get_csv_header())
for prediction in pool.starmap(prediction_worker,
zip(samples, repeat(future))):
try:
if prediction: output.write(prediction.to_csv())
except TypeError as e:
print(e)
output.close()
def compute_final_sts_prediction(df, sample, past, future):
#print(sample.id)
#print(df[df['ID']==sample.id])
predictions_for_current_sample = df[df['ID'] == sample.id]
print(len(predictions_for_current_sample))
#predicted_location = get_center_of_main_cluster(predictions_for_current_sample, past, future)
predicted_location = get_location_closest_to_linear_prediction(
predictions_for_current_sample, sample, past, future)
if predicted_location is None:
return None
errors = TrajectoryPredictionEvaluator(sample, predicted_location,
'epsg:25832').get_errors()
context = sample.past_traj.context
prediction = EvaluatedPrediction(predicted_location, context, errors)
return prediction
def compute_final_sts_predictions(input_predictions, input_samples, past,
future):
out = OUTPUT.replace('.csv',
'_{}_{}_{}.csv'.format(SHIPTYPE, past, future))
print("Writing to {}".format(out))
with open(out, 'w') as output:
output.write(EvaluatedPrediction.get_csv_header())
for counter, sample in enumerate(input_samples):
sample.id = '{}_{}'.format(counter, sample.id)
prediction = compute_final_sts_prediction(input_predictions,
sample, past, future)
try:
output.write(prediction.to_csv())
except TypeError as e:
print(e)
except AttributeError as e:
pass
output.close()