trips["MATCH"] = False
# Match this segment with trips in the training set
for idx, row in trips.iterrows():
if match(end, row.GRID_POLYLINE, k):
trips.set_value(idx, 'MATCH', True)
# We are only really interested in the matched trips now
trips = trips[trips.MATCH]
# Compute the destination distribution
trips_agg = trips.groupby(["DEST_CELL"],
as_index=False).aggregate({"MATCH": "sum"})
trips_agg.MATCH = trips_agg.MATCH / np.sum(trips_agg.MATCH)
# Store it in a DestinationGrid object
grid = DestinationGrid(N, M)
grid.setProbs(trips_agg.DEST_CELL.values, trips_agg.MATCH.values)
# Plot the new distribution
plt.subplot(1, 2, 1)
plt.imshow(np.log(grid.as_array() +
trip_to_array(partial_trip.GRID_POLYLINE, N, M)),
interpolation="nearest")
plt.title("Complete trip superimposed")
plt.subplot(1, 2, 2)
plt.imshow(np.log(grid.as_array() +
trip_to_array(partial_trip.TRUNC_GRID_POLYLINE, N, M)),
interpolation="nearest")
plt.title("Partial trip superimposed")
# Select the last segment of the partial trip
end = partial_trip.TRUNC_GRID_POLYLINE[-k:]
# Create a dummy flag to indicate if a trip matches or not
trips["MATCH"] = False
# Match this segment with trips in the training set
for idx, row in trips.iterrows():
if match(end, row.GRID_POLYLINE, k):
trips.set_value(idx, 'MATCH', True)
# We are only really interested in the matched trips now
trips = trips[trips.MATCH]
# Compute the destination distribution
trips_agg = trips.groupby(["DEST_CELL"], as_index = False).aggregate({"MATCH": "sum"})
trips_agg.MATCH = trips_agg.MATCH / np.sum(trips_agg.MATCH)
# Store it in a DestinationGrid object
grid = DestinationGrid(N, M)
grid.setProbs(trips_agg.DEST_CELL.values, trips_agg.MATCH.values)
# Plot the new distribution
plt.subplot(1,2,1)
plt.imshow(np.log(grid.as_array() + trip_to_array(partial_trip.GRID_POLYLINE, N, M)), interpolation = "nearest")
plt.title("Complete trip superimposed")
plt.subplot(1,2,2)
plt.imshow(np.log(grid.as_array() + trip_to_array(partial_trip.TRUNC_GRID_POLYLINE, N, M)), interpolation = "nearest")
plt.title("Partial trip superimposed")
