Source code for trackintel.analysis.modal_split

import pandas as pd

from trackintel.geogr import check_gdf_planar, calculate_haversine_length

[docs] def calculate_modal_split(tpls, freq=None, metric="count", per_user=False, norm=False): """ Calculate the modal split of triplegs Parameters ---------- tpls : Triplegs triplegs require the column `mode`. freq : str frequency string passed on as `freq` keyword to the pandas.Grouper class. If `freq=None` the modal split is calculated on all data. A list of possible values can be found `here < -aliases>`_. metric : {'count', 'distance', 'duration'} Aggregation used to represent the modal split. 'distance' returns in the same unit as the crs. 'duration' returns values in seconds. per_user : bool, default: False If True the modal split is calculated per user norm : bool, default: False If True every row of the modal split is normalized to 1 Returns ------- modal_split : DataFrame The modal split represented as pandas Dataframe with (optionally) a multi-index. The index can have the levels: `('user_id', 'timestamp')` and every mode as a column. Notes ------ `freq='W-MON'` is used for a weekly aggregation that starts on mondays. If `freq=None` and `per_user=False` are passed the modal split collapses to a single column. The modal split can be visualized using :func:`trackintel.plot_modal_split` Examples -------- >>> triplegs.calculate_modal_split() >>> tripleg.calculate_modal_split(freq='W-MON', metric='distance') """ tpls = tpls.copy() # copy as we add additional columns on tpls # count on mode, sum on length and duration agg = "sum" # calculate distance and duration if required if metric == "distance": tpls[metric] = _calculate_length(tpls) elif metric == "duration": tpls[metric] = (tpls["finished_at"] - tpls["started_at"]).dt.total_seconds() elif metric == "count": agg = "count" metric = "mode" # count on mode else: error_msg = f"Metric {metric} unknown, only metrics {{'count', 'distance', 'duration'}} are supported." raise ValueError(error_msg) group = [] if per_user: group = ["user_id"] if freq is not None: tpls.set_index("started_at", inplace=True) = "timestamp" group.append(pd.Grouper(freq=freq)) modal_split = pd.pivot_table(tpls, index=group, columns=["mode"], aggfunc={metric: agg}, fill_value=0) if group: # non-empty group creates MultiIndex that we need to handle modal_split.columns = modal_split.columns.droplevel(0) if norm: # norm rows to 1 return modal_split.div(modal_split.sum(axis=1), axis=0) return modal_split
def _calculate_length(tpls): """Help function to calculate length of tripleg. Checks if crs is planar or if not. If not uses ``calculate_haversine_length``. Parameters ---------- tpls : Triplegs """ if check_gdf_planar(tpls): return tpls.length # if planar use geopandas function return pd.Series(calculate_haversine_length(tpls), index=tpls.index)