Source code for arviz.plots.dotplot

"""Plot distribution as dot plot or quantile dot plot."""

import numpy as np

from ..rcparams import rcParams
from .plot_utils import get_plotting_function

[docs] def plot_dot( values, binwidth=None, dotsize=1, stackratio=1, hdi_prob=None, rotated=False, dotcolor="C0", intervalcolor="C3", markersize=None, markercolor="C0", marker="o", figsize=None, linewidth=None, point_estimate="auto", nquantiles=50, quartiles=True, point_interval=False, ax=None, show=None, plot_kwargs=None, backend=None, backend_kwargs=None, **kwargs ): r"""Plot distribution as dot plot or quantile dot plot. This function uses the Wilkinson's Algorithm [1]_ to allot dots to bins. The quantile dot plots was inspired from [2]_. Parameters ---------- values : array-like Values to plot from an unknown continuous or discrete distribution. binwidth : float, optional Width of the bin for drawing the dot plot. dotsize : float, default 1 The size of the dots relative to the bin width. The default makes dots be just about as wide as the bin width. stackratio : float, default 1 The distance between the center of the dots in the same stack relative to the bin height. The default makes dots in the same stack just touch each other. point_interval : bool, default False Plots the point interval. Uses ``hdi_prob`` to plot the HDI interval point_estimate : str, optional Plot point estimate per variable. Values should be ``mean``, ``median``, ``mode`` or None. Defaults to ``auto`` i.e. it falls back to default set in rcParams. dotcolor : string, optional The color of the dots. Should be a valid matplotlib color. intervalcolor : string, optional The color of the interval. Should be a valid matplotlib color. linewidth : int, default None Line width throughout. If None it will be autoscaled based on `figsize`. markersize : int, default None Markersize throughout. If None it will be autoscaled based on `figsize`. markercolor : string, optional The color of the marker when plot_interval is True. Should be a valid matplotlib color. marker : string, default "o" The shape of the marker. Valid for matplotlib backend. hdi_prob : float, optional Valid only when point_interval is True. Plots HDI for chosen percentage of density. Defaults to ``stats.hdi_prob`` rcParam. See :ref:`this section <common_hdi_prob>` for usage examples. rotated : bool, default False Whether to rotate the dot plot by 90 degrees. nquantiles : int, default 50 Number of quantiles to plot, used for quantile dot plots. quartiles : bool, default True If True then the quartile interval will be plotted with the HDI. figsize : (float,float), optional Figure size. If ``None`` it will be defined automatically. plot_kwargs : dict, optional Keywords passed for customizing the dots. Passed to :class:`mpl:matplotlib.patches.Circle` in matplotlib and :meth:`` in bokeh. backend :{"matplotlib", "bokeh"}, default "matplotlib" Select plotting backend. ax : axes, optional Matplotlib_axes or bokeh_figure. show : bool, optional Call backend show function. backend_kwargs : dict, optional These are kwargs specific to the backend being used, passed to :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. For additional documentation check the plotting method of the backend. Returns ------- axes : matplotlib_axes or bokeh_figure See Also -------- plot_dist : Plot distribution as histogram or kernel density estimates. References ---------- .. [1] Leland Wilkinson (1999) Dot Plots, The American Statistician, 53:3, 276-281, DOI: 10.1080/00031305.1999.10474474 .. [2] Matthew Kay, Tara Kola, Jessica R. Hullman, and Sean A. Munson. 2016. When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. DOI: Examples -------- Plot dot plot for a set of data points .. plot:: :context: close-figs >>> import arviz as az >>> import numpy as np >>> values = np.random.normal(0, 1, 500) >>> az.plot_dot(values) Manually adjust number of quantiles to plot .. plot:: :context: close-figs >>> az.plot_dot(values, nquantiles=100) Add a point interval under the dot plot .. plot:: :context: close-figs >>> az.plot_dot(values, point_interval=True) Rotate the dot plots by 90 degrees i.e swap x and y axis .. plot:: :context: close-figs >>> az.plot_dot(values, point_interval=True, rotated=True) """ if nquantiles == 0: raise ValueError("Number of quantiles should be greater than 0") if marker != "o" and backend == "bokeh": raise ValueError("marker argument is valid only for matplotlib backend") values = np.ravel(values) values.sort() if hdi_prob is None: hdi_prob = rcParams["stats.hdi_prob"] elif not 1 >= hdi_prob > 0: raise ValueError("The value of hdi_prob should be in the interval (0, 1]") if point_estimate == "auto": point_estimate = rcParams["plot.point_estimate"] elif point_estimate not in {"mean", "median", "mode", None}: raise ValueError("The value of point_estimate must be either mean, median, mode or None.") if not isinstance(nquantiles, int): raise TypeError("nquantiles must be of integer type, refer to docs for further details") dot_plot_args = dict( values=values, binwidth=binwidth, dotsize=dotsize, stackratio=stackratio, hdi_prob=hdi_prob, quartiles=quartiles, rotated=rotated, dotcolor=dotcolor, intervalcolor=intervalcolor, markersize=markersize, markercolor=markercolor, marker=marker, figsize=figsize, linewidth=linewidth, point_estimate=point_estimate, nquantiles=nquantiles, point_interval=point_interval, ax=ax, show=show, backend_kwargs=backend_kwargs, plot_kwargs=plot_kwargs, **kwargs ) if backend is None: backend = rcParams["plot.backend"] backend = backend.lower() plot = get_plotting_function("plot_dot", "dotplot", backend) ax = plot(**dot_plot_args) return ax
def wilkinson_algorithm(values, binwidth): """Wilkinson's algorithm to distribute dots into horizontal stacks.""" ndots = len(values) count = 0 stack_locs, stack_counts = [], [] while count < ndots: stack_first_dot = values[count] num_dots_stack = 0 while values[count] < (binwidth + stack_first_dot): num_dots_stack += 1 count += 1 if count == ndots: break stack_locs.append((stack_first_dot + values[count - 1]) / 2) stack_counts.append(num_dots_stack) return stack_locs, stack_counts def layout_stacks(stack_locs, stack_counts, binwidth, stackratio, rotated): """Use count and location of stacks to get coordinates of dots.""" dotheight = stackratio * binwidth binradius = binwidth / 2 x = np.repeat(stack_locs, stack_counts) y = np.hstack([dotheight * np.arange(count) + binradius for count in stack_counts]) if rotated: x, y = y, x return x, y