def plot_positionfixes(positionfixes,
out_filename=None,
plot_osm=False,
axis=None):
"""Plots positionfixes (optionally to a file).
If you specify ``plot_osm=True`` this will use ``osmnx`` to plot streets
below the positionfixes. Depending on the extent of your data, this might
take a long time. The data gets transformed to wgs84 for the plotting.
Parameters
----------
positionfixes : GeoDataFrame (as trackintel positionfixes)
The positionfixes to plot.
out_filename : str, optional
The file to plot to, if this is not set, the plot will simply be shown.
plot_osm : bool, default False
If this is set to True, it will download an OSM street network and plot
below the staypoints.
axis : matplotlib.pyplot.Artist, optional
axis on which to draw the plot
Examples
--------
>>> pfs.as_positionfixes.plot('output.png', plot_osm=True)
"""
if axis is None:
_, ax = regular_figure()
else:
ax = axis
_, positionfixes = check_gdf_planar(positionfixes, transform=True)
if plot_osm:
west = positionfixes.geometry.x.min()
east = positionfixes.geometry.x.max()
north = positionfixes.geometry.y.max()
south = positionfixes.geometry.y.min()
plot_osm_streets(north, south, east, west, ax)
positionfixes.plot(ax=ax, markersize=0.5, zorder=2)
ax.set_aspect("equal", adjustable="box")
if out_filename is not None:
save_fig(out_filename, formats=["png"])
elif axis is None:
plt.show()
def _predict_transport_mode_simple_coarse(triplegs_in, categories):
"""
Predict a transport mode out of three coarse classes.
Implements a simple speed based heuristic (over the whole tripleg).
As such, it is very fast, but also very simple and coarse.
Parameters
----------
triplegs_in : GeoDataFrame (as trackintel triplegs)
The triplegs for the transport mode prediction.
categories : dict, optional
The categories for the speed classification {upper_boundary:'category_name'}.
The unit for the upper boundary is m/s.
The default is {15/3.6: 'slow_mobility', 100/3.6: 'motorized_mobility', np.inf: 'fast_mobility'}.
Raises
------
ValueError
In case the boundaries of the categories are not in ascending order.
Returns
-------
triplegs : trackintel triplegs GeoDataFrame
the triplegs with added column mode, containing the predicted transport modes.
For additional documentation, see
:func:`trackintel.analysis.transport_mode_identification.predict_transport_mode`.
"""
if not (_check_categories(categories)):
raise ValueError("the categories must be in increasing order")
triplegs = triplegs_in.copy()
if_planer_crs = check_gdf_planar(triplegs)
#
if not if_planer_crs:
triplegs["distance"] = calculate_haversine_length(triplegs)
else:
triplegs["distance"] = triplegs.length
def identify_mode(tripleg, categories):
"""
Identify the mode based on the (overall) tripleg speed.
Parameters
----------
tripleg : trackintel triplegs GeoDataFrame
the tripleg to analyse
wgs : bool
whether the tripleg is in WGS84 or not.
categories : dict
the upper boundaries (as keys) and the names of the categories as values.
Returns
-------
str
the identified mode.
"""
duration = (tripleg["finished_at"] - tripleg["started_at"]).total_seconds()
speed = tripleg["distance"] / duration # The unit of the speed is m/s
for bound in categories:
if speed < bound:
return categories[bound]
triplegs["mode"] = triplegs.apply(identify_mode, args=((categories,)), axis=1)
return triplegs
def plot_triplegs(triplegs,
out_filename=None,
positionfixes=None,
staypoints=None,
staypoints_radius=100,
plot_osm=False,
axis=None):
"""Plot triplegs (optionally to a file).
You can specify several other datasets to be plotted beneath the triplegs,
as well as if the OSM streets should be drawn. The data gets transformed to
wgs84 for the plotting.
Parameters
----------
triplegs : GeoDataFrame (as trackintel triplegs)
The triplegs to plot.
out_filename : str, optional
The file to plot to, if this is not set, the plot will simply be shown.
positionfixes : GeoDataFrame (as trackintel positionfixes), optional
If available, some positionfixes that can additionally be plotted.
staypoints : GeoDataFrame (as trackintel staypoints), optional
If available, some staypoints that can additionally be plotted.
staypoints_radius : float, default 100 (meter)
The radius in meter with which circles around staypoints should be drawn.
plot_osm : bool, default False
If this is set to True, it will download an OSM street network and plot
below the triplegs.
axis : matplotlib.pyplot.Artist, optional
axis on which to draw the plot
Example
-------
>>> tpls.as_triplegs.plot('output.png', positionfixes=pfs, staypoints=sp, plot_osm=True)
"""
if axis is None:
_, ax = regular_figure()
else:
ax = axis
_, triplegs = check_gdf_planar(triplegs, transform=True)
if staypoints is not None:
staypoints.as_staypoints.plot(radius=staypoints_radius,
positionfixes=positionfixes,
plot_osm=plot_osm,
axis=ax)
elif positionfixes is not None:
positionfixes.as_positionfixes.plot(plot_osm=plot_osm, axis=ax)
elif plot_osm:
triplegs_bounds = triplegs.bounds
west = min(triplegs_bounds.minx
) - 0.03 # TODO: maybe a relative value instead of 0.03
east = max(triplegs_bounds.maxx) + 0.03
north = max(triplegs_bounds.maxy) + 0.03
south = min(triplegs_bounds.miny) - 0.03
plot_osm_streets(north, south, east, west, ax)
triplegs.plot(ax=ax, cmap="viridis")
ax.set_aspect("equal", adjustable="box")
if out_filename is not None:
save_fig(out_filename, formats=["png"])
else:
plt.show()
def calculate_modal_split(tpls_in, freq=None, metric="count", per_user=False, norm=False):
"""Calculate the modal split of triplegs
Parameters
----------
tpls_in : GeoDataFrame (as trackintel 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 <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset
-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.visualization.modal_split.plot_modal_split`
Examples
--------
>>> triplegs.calculate_modal_split()
>>> tripleg.calculate_modal_split(freq='W-MON', metric='distance')
"""
tpls = tpls_in.copy()
# precalculate distance and duration if required
if metric == "distance":
if_planer_crs = check_gdf_planar(tpls)
if not if_planer_crs:
tpls["distance"] = calculate_haversine_length(tpls)
else:
tpls["distance"] = tpls.length
elif metric == "duration":
tpls["duration"] = tpls["finished_at"] - tpls["started_at"]
# create grouper
if freq is None:
if per_user:
tpls_grouper = tpls.groupby(["user_id", "mode"])
else:
tpls_grouper = tpls.groupby(["mode"])
else:
tpls.set_index("started_at", inplace=True)
tpls.index.name = "timestamp"
if per_user:
tpls_grouper = tpls.groupby(["user_id", "mode", pd.Grouper(freq=freq)])
else:
tpls_grouper = tpls.groupby(["mode", pd.Grouper(freq=freq)])
# aggregate
if metric == "count":
modal_split = tpls_grouper["mode"].count()
elif metric == "distance":
modal_split = tpls_grouper["distance"].sum()
elif metric == "duration":
modal_split = tpls_grouper["duration"].sum()
modal_split = modal_split.dt.total_seconds()
# move 'mode' to columns
modal_split.name = "modal_split"
modal_split = pd.DataFrame(modal_split)
# if mode is the only index, we replace it with zeros so that everything gets aggregated into a single row
modal_split["mode"] = modal_split.index.get_level_values("mode")
if modal_split.index.nlevels == 1:
modal_split.index = 0 * np.arange(0, modal_split.shape[0])
else:
modal_split = modal_split.droplevel("mode")
# transform Dataframe such that:
# - unique mode names are column names
# - time/user_id are the indices
modal_split = modal_split.pivot_table(index=modal_split.index, columns="mode", values="modal_split")
modal_split.fillna(0, inplace=True)
if norm:
# norm rows to 1
modal_split = modal_split.div(modal_split.sum(axis=1), axis=0)
return modal_split
def plot_staypoints(staypoints,
out_filename=None,
radius=100,
positionfixes=None,
plot_osm=False,
axis=None):
"""Plot staypoints (optionally to a file).
You can specify the radius with which each staypoint should be drawn, as well as
if underlying positionfixes and OSM streets should be drawn. The data gets transformed
to wgs84 for the plotting.
Parameters
----------
staypoints : GeoDataFrame (as trackintel staypoints)
The staypoints to plot.
out_filename : str, optional
The file to plot to, if this is not set, the plot will simply be shown.
radius : float, default 100 (meter)
The radius in meter with which circles around staypoints should be drawn.
positionfixes : GeoDataFrame (as trackintel positionfixes), optional
If available, some positionfixes that can additionally be plotted.
plot_osm : bool, default False
If this is set to True, it will download an OSM street network and plot
below the staypoints.
axis : matplotlib.pyplot.Artist, optional
axis on which to draw the plot
Examples
--------
>>> sp.as_staypoints.plot('output.png', radius=100, positionfixes=pfs, plot_osm=True)
"""
if axis is None:
_, ax = regular_figure()
else:
ax = axis
name_geocol = staypoints.geometry.name
_, staypoints = check_gdf_planar(staypoints, transform=True)
if positionfixes is not None:
positionfixes.as_positionfixes.plot(plot_osm=plot_osm, axis=ax)
else:
west = staypoints.geometry.x.min() - 0.03
east = staypoints.geometry.x.max() + 0.03
north = staypoints.geometry.y.max() + 0.03
south = staypoints.geometry.y.min() - 0.03
if plot_osm:
plot_osm_streets(north, south, east, west, ax)
ax.set_xlim([west, east])
ax.set_ylim([south, north])
center_latitude = (ax.get_ylim()[0] + ax.get_ylim()[1]) / 2
radius = meters_to_decimal_degrees(radius, center_latitude)
for pt in staypoints.to_dict("records"):
circle = mpatches.Circle((pt[name_geocol].x, pt[name_geocol].y),
radius,
facecolor="none",
edgecolor="g",
zorder=3)
ax.add_artist(circle)
ax.set_aspect("equal", adjustable="box")
if out_filename is not None:
save_fig(out_filename, formats=["png"])
elif axis is None:
plt.show()
def plot_locations(
locations,
out_filename=None,
radius=150,
positionfixes=None,
staypoints=None,
staypoints_radius=100,
plot_osm=False,
axis=None,
):
"""Plot locations (optionally to a file).
Optionally, you can specify several other datasets to be plotted beneath the locations.
Parameters
----------
locations : GeoDataFrame (as trackintel locations)
The locations to plot.
out_filename : str, optional
The file to plot to, if this is not set, the plot will simply be shown.
radius : float, default 150 (meter)
The radius in meter with which circles around locations should be drawn.
positionfixes : GeoDataFrame (as trackintel positionfixes), optional
If available, some positionfixes that can additionally be plotted.
staypoints : GeoDataFrame (as trackintel staypoints), optional
If available, some staypoints that can additionally be plotted.
staypoints_radius : float, default 100 (meter)
The radius in meter with which circles around staypoints should be drawn.
plot_osm : bool, default False
If this is set to True, it will download an OSM street network and plot
below the staypoints.
axis : matplotlib.pyplot.Artist, optional
axis on which to draw the plot
Examples
--------
>>> locs.as_locations.plot('output.png', radius=200, positionfixes=pfs, staypoints=sp, plot_osm=True)
"""
if axis is None:
_, ax = regular_figure()
else:
ax = axis
_, locations = check_gdf_planar(locations, transform=True)
if staypoints is not None:
staypoints.as_staypoints.plot(radius=staypoints_radius,
positionfixes=positionfixes,
plot_osm=plot_osm,
axis=ax)
elif positionfixes is not None:
positionfixes.as_positionfixes.plot(plot_osm=plot_osm, axis=ax)
elif plot_osm:
west = locations["center"].x.min() - 0.03
east = locations["center"].x.max() + 0.03
north = locations["center"].y.max() + 0.03
south = locations["center"].y.min() - 0.03
plot_osm_streets(north, south, east, west, ax)
center_latitude = (ax.get_ylim()[0] + ax.get_ylim()[1]) / 2
radius = meters_to_decimal_degrees(radius, center_latitude)
for pt in locations.to_dict("records"):
circle = mpatches.Circle((pt["center"].x, pt["center"].y),
radius,
facecolor="none",
edgecolor="r",
zorder=4)
ax.add_artist(circle)
ax.set_aspect("equal", adjustable="box")
if out_filename is not None:
save_fig(out_filename, formats=["png"])
else:
plt.show()