Source code for trackintel.visualization.modal_split

import matplotlib.pyplot as plt
from pandas.api.types import is_datetime64_any_dtype

from trackintel.visualization.util import regular_figure, save_fig

[docs]def 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 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') """ df_modal_split = df_modal_split_in.copy() if axis is None: fig, ax = regular_figure() else: ax = axis # make sure that modal split is only of a single user if isinstance(df_modal_split.index[0], tuple): raise ValueError( "This function can not support multiindex types. Use 'pandas.MultiIndex.droplevel' or pass " "the `per_user=False` flag in 'calculate_modal_split' function." ) if not is_datetime64_any_dtype(df_modal_split.index.dtype): raise ValueError( "Index of modal split has to be a datetime type. This problem can be solved if the 'freq' " "keyword of 'calculate_modal_split is not None'" ) # set date formatter df_modal_split.index = s: s.strftime(date_fmt_x_axis)) # plotting, ax=ax, **(bar_kws or {})) # skip ticks for X axis if skip_xticks > 0: for i, tick in enumerate(ax.xaxis.get_major_ticks()): if i % skip_xticks != 0: tick.set_visible(False) # We use a nice trick to put the legend out of the plot and to scale it automatically # box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9]) # Put a legend below current axis ax.legend( loc="upper center", bbox_to_anchor=(0.5, -0.05), fancybox=True, frameon=False, ncol=n_col_legend, borderaxespad=borderaxespad, ) if title is not None: ax.set_title(title, y=title_pad) ax.set_xlabel(x_label) ax.set_ylabel(y_label) if fig is not None: fig.autofmt_xdate() plt.tight_layout() ax.xaxis.labelpad = x_pad ax.yaxis.labelpad = y_pad if out_path is not None: save_fig(out_path) return fig, ax