Visualization

The visualization module provides a variety of functions to visualize initial, intermediate and final datasets within trackintel.

plot

trackintel.visualization.plot(positionfixes=None, staypoints=None, triplegs=None, locations=None, radius_sp=100, radius_locs=150, filename=None, plot_osm=False, ax=None)

Plots positionfixes, staypoints, locations on a map (optionally to a file)

One of the arguments [positionfixes, staypoints, triplegs, locations] should not be None!

Parameters:

• positionfixes (GeoDataFrame (as trackintel positionfixes), optional) – Positionfixes to plot, by default None

• staypoints (GeoDataFrame (as trackintel staypoints), optional) – Staypoints to plot, by default None

• triplegs (GeoDataFrame (as trackintel triplegs), optional) – Triplegs to plot, by default None

• locations (GeoDataFrame (as trackintel locations), optional) – Locations to plot, by default None

• radius_sp (float, optional) – Radius in meter for circles around staypoints, default 100

• radius_locs (float, optional) – Radius in meter for circles around locations, default 150

• filename (str, optional) – The file to plot to, else if ax is none plot will be shown, by default None

• plot_osm (bool, optional) – If True, will download OSM street network and plot it as base map, by default False If True depending on the extent of your data, this might take a long time!

• ax (matplotlib.pyplot.Artist, optional) – axis on which to draw the plot, by default None

Examples

>>> ti.plot(positionfixes=pfs, filename="output.png", plot_osm=True)

Modal split

trackintel.visualization.plot_modal_split(df_modal_split_in, out_path=None, date_fmt_x_axis='%W', fig=None, axis=None, title=None, x_label=None, y_label=None, x_pad=10, y_pad=10, title_pad=1.02, skip_xticks=0, n_col_legend=5, borderaxespad=0.5, bar_kws=None)

Plot modal split as returned by trackintel.analysis.modal_split.calculate_modal_split

Parameters:

• df_modal_split (DataFrame) – DataFrame with modal split information. Format is

• out_path (str, optional) – Path to store the figure

• date_fmt_x_axis (str, default: '%W') – strftime() date format code that is used for the x-axis

• title (str, optional) –

• x_label (str, optional) –

• y_label (str, optional) –

• fig (matplotlib.figure) – Only used if axis is provided as well.

• axis (matplotlib axes) –

• x_pad (float, default: 10) – Used to set ax.xaxis.labelpad

• y_pad (float, default: 10) – Used to set ax.yaxis.labelpad

• title_pad (float, default: 1.02) – Passed on to matplotlib.pyplot.title

• skip_xticks (int, default: 1) – Every nth x-tick label is kept.

• n_col_legend (int) – Passed on as ncol to matplotlib.pyplot.legend()

• borderaxespad (float) – The pad between the axes and legend border, in font-size units. Passed on to matplotlib.pyplot.legend()

• bar_kws (dict) – Parameters that control the bar-plot visualization, passed to DataFrame.plot.bar()

Returns:

• fig (Matplotlib figure handle)

• ax (Matplotlib axis handle)

Examples

>>> modal_split = calculate_modal_split(triplegs, metric='count', freq='D', per_user=False)
>>> plot_modal_split(modal_split, out_path=tmp_file, date_fmt_x_axis='%d',
>>>                  y_label='Percentage of daily count', x_label='days')