def create_or_update_out_of_the_bbox(
move_data: DataFrame,
bbox: tuple[int, int, int, int],
new_label: str = OUT_BBOX,
inplace: bool = False) -> DataFrame | None:
"""
Create or update a boolean feature to detect points out of the bbox.
Parameters
----------
move_data: dataframe
The input trajectories data.
bbox : tuple
Tuple of 4 elements, containing the minimum and maximum values
of latitude and longitude of the bounding box.
new_label: string, optional
The name of the new feature with detected points out of the bbox,
by default OUT_BBOX
inplace : boolean, optional
if set to true the original dataframe will be altered to contain
the result of the filtering, otherwise a copy will be returned,
by default False
Returns
-------
DataFrame
Returns dataframe with a boolean feature with detected
points out of the bbox, or None
Raises
------
ValueError
If feature generation fails
"""
if not inplace:
move_data = move_data.copy()
logger.debug(
'\nCreate or update boolean feature to detect points out of the bbox')
filtered_ = filters.by_bbox(move_data, bbox, filter_out=True)
if filtered_ is None:
raise ValueError('Filter bbox failed!')
logger.debug('...Creating a new label named as %s' % new_label)
move_data[new_label] = False
if filtered_.shape[0] > 0:
logger.debug('...Setting % as True\n' % new_label)
move_data.at[filtered_.index, new_label] = True
return _end_create_operation(move_data, new_label, inplace)
def create_or_update_out_of_the_bbox(
move_data, bbox, new_label=OUT_BBOX, inplace=True
):
"""
Create or update a boolean feature to detect points out of the bbox.
Parameters
__________
move_data: dataframe
The input trajectories data.
bbox : tuple
Tuple of 4 elements, containing the minimum and maximum values
of latitude and longitude of the bounding box.
new_label: string, optional, default 'out_Bbox'
The name of the new feature with detected points out of the bbox.
inplace : boolean, optional, default True
if set to true the original dataframe will be altered to contain
the result of the filtering, otherwise a copy will be returned.
Returns
_______
dataframe
Returns dataframe with a boolean feature with detected
points out of the bbox.
"""
try:
if not inplace:
move_data = move_data[:]
print('\nCreate or update boolean feature to detect points out of the bbox')
filtered_ = filters.by_bbox(move_data, bbox, filter_out=True)
print('...Creating a new label named as %s' % new_label)
move_data[new_label] = False
if filtered_.shape[0] > 0:
print('...Setting % as True\n' % new_label)
move_data.at[filtered_.index, new_label] = True
return _end_create_operation(
move_data, new_label, inplace
)
except Exception as e:
raise e
def join_with_pois_by_dist_and_datetime(
data: DataFrame,
df_pois: DataFrame,
label_date: Optional[Text] = DATETIME,
label_event_id: Optional[Text] = EVENT_ID,
label_event_type: Optional[Text] = EVENT_TYPE,
time_window: Optional[float] = 3600,
radius: Optional[float] = 1000,
):
"""
It performs the integration between trajectories and points of interest,
generating new columns referring to the category of the point of interest,
the distance between the location of the user and location of the poi
based on the distance and on time of each point of the trajectories.
Parameters
----------
data : DataFrame
The input trajectory data.
df_pois : DataFrame
The input events points of interest data.
label_date : str, optional
Label of data referring to the datetime of the input trajectory data,
by default DATETIME
label_event_id : str, optional
Label of df_events referring to the id of the event, by default EVENT_ID
label_event_type : str, optional
Label of df_events referring to the type of the event, by default EVENT_TYPE
time_window : float, optional
tolerable length of time range for assigning the event's
point of interest to the trajectory point, by default 3600
radius: float, optional
maximum radius of pois, by default 1000
"""
print('Integration with Events...')
if label_date not in df_pois:
raise KeyError("POI's DataFrame must contain a %s column" % label_date)
values = _reset_set_window_and_creates_event_id_type_all(
data, df_pois, label_date, time_window)
window_start, window_end, current_distances, event_id, event_type = values
for idx, row in progress_bar(data.iterrows(), total=data.shape[0]):
# set min and max of coordinates by radius
bbox = filters.get_bbox_by_radius((row[LATITUDE], row[LONGITUDE]),
radius)
# filter event by radius
df_filtered = filters.by_bbox(df_pois, bbox)
# filter event by datetime
filters.by_datetime(df_filtered,
start_datetime=window_start[idx],
end_datetime=window_end[idx],
inplace=True)
# get df_filtered size
size_filter = df_filtered.shape[0]
if size_filter > 0:
# reseting index of data frame
df_filtered.reset_index(drop=True, inplace=True)
# create lat and lon array to operation
lat_user = np.full(size_filter, row[LATITUDE], dtype=np.float64)
lon_user = np.full(size_filter, row[LONGITUDE], dtype=np.float64)
# calculate of distances between points
distances = haversine(lat_user, lon_user,
df_filtered[LATITUDE].to_numpy(),
df_filtered[LONGITUDE].to_numpy())
current_distances[idx] = distances
event_type[idx] = df_filtered[label_event_type].to_numpy(
dtype=np.ndarray)
event_id[idx] = df_filtered[label_event_id].to_numpy(
dtype=np.ndarray)
data[label_event_id] = event_id
data[DIST_EVENT] = current_distances
data[label_event_type] = event_type
print('Integration with event was completed')