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"""Matplotlib mcseplot."""
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import matplotlib.pyplot as plt
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3
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import numpy as np
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4
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2
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from scipy.stats import rankdata
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5
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6
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2
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from ....stats.stats_utils import quantile as _quantile
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7
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from ...plot_utils import _scale_fig_size, make_label
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8
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from . import backend_kwarg_defaults, backend_show, create_axes_grid, matplotlib_kwarg_dealiaser
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def plot_mcse(
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ax,
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plotters,
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length_plotters,
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rows,
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cols,
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figsize,
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errorbar,
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rug,
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data,
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probs,
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kwargs,
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extra_methods,
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24
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mean_mcse,
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sd_mcse,
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textsize,
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text_kwargs,
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rug_kwargs,
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extra_kwargs,
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idata,
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rug_kind,
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backend_kwargs,
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show,
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):
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"""Matplotlib mcseplot."""
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if backend_kwargs is None:
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backend_kwargs = {}
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backend_kwargs = {
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**backend_kwarg_defaults(),
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**backend_kwargs,
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}
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(figsize, ax_labelsize, titlesize, xt_labelsize, _linewidth, _markersize) = _scale_fig_size(
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figsize, textsize, rows, cols
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)
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backend_kwargs.setdefault("figsize", figsize)
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backend_kwargs["squeeze"] = True
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kwargs = matplotlib_kwarg_dealiaser(kwargs, "plot")
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kwargs.setdefault("linestyle", "none")
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kwargs.setdefault("linewidth", _linewidth)
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kwargs.setdefault("markersize", _markersize)
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kwargs.setdefault("marker", "_" if errorbar else "o")
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kwargs.setdefault("zorder", 3)
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extra_kwargs = matplotlib_kwarg_dealiaser(extra_kwargs, "plot")
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extra_kwargs.setdefault("linestyle", "-")
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extra_kwargs.setdefault("linewidth", _linewidth / 2)
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extra_kwargs.setdefault("color", "k")
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extra_kwargs.setdefault("alpha", 0.5)
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text_x = None
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text_va = None
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if extra_methods:
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text_kwargs = matplotlib_kwarg_dealiaser(text_kwargs, "text")
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text_x = text_kwargs.pop("x", 1)
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text_kwargs.setdefault("fontsize", xt_labelsize * 0.7)
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text_kwargs.setdefault("alpha", extra_kwargs["alpha"])
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text_kwargs.setdefault("color", extra_kwargs["color"])
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text_kwargs.setdefault("horizontalalignment", "right")
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text_va = text_kwargs.pop("verticalalignment", None)
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if ax is None:
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_, ax = create_axes_grid(
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length_plotters,
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rows,
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cols,
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backend_kwargs=backend_kwargs,
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)
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for (var_name, selection, x), ax_ in zip(plotters, np.ravel(ax)):
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if errorbar or rug:
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values = data[var_name].sel(**selection).values.flatten()
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if errorbar:
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quantile_values = _quantile(values, probs)
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ax_.errorbar(probs, quantile_values, yerr=x, **kwargs)
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else:
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ax_.plot(probs, x, label="quantile", **kwargs)
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if extra_methods:
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mean_mcse_i = mean_mcse[var_name].sel(**selection).values.item()
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sd_mcse_i = sd_mcse[var_name].sel(**selection).values.item()
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ax_.axhline(mean_mcse_i, **extra_kwargs)
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ax_.annotate(
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"mean",
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(text_x, mean_mcse_i),
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va=text_va
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if text_va is not None
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else "bottom"
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if mean_mcse_i > sd_mcse_i
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else "top",
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**text_kwargs,
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)
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ax_.axhline(sd_mcse_i, **extra_kwargs)
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ax_.annotate(
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"sd",
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(text_x, sd_mcse_i),
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va=text_va
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if text_va is not None
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else "bottom"
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if sd_mcse_i >= mean_mcse_i
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else "top",
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**text_kwargs,
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)
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if rug:
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rug_kwargs = matplotlib_kwarg_dealiaser(rug_kwargs, "plot")
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if not hasattr(idata, "sample_stats"):
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raise ValueError("InferenceData object must contain sample_stats for rug plot")
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if not hasattr(idata.sample_stats, rug_kind):
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raise ValueError("InferenceData does not contain {} data".format(rug_kind))
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rug_kwargs.setdefault("marker", "|")
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rug_kwargs.setdefault("linestyle", rug_kwargs.pop("ls", "None"))
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rug_kwargs.setdefault("color", rug_kwargs.pop("c", kwargs.get("color", "C0")))
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rug_kwargs.setdefault("space", 0.1)
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rug_kwargs.setdefault("markersize", rug_kwargs.pop("ms", 2 * _markersize))
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mask = idata.sample_stats[rug_kind].values.flatten()
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values = rankdata(values)[mask]
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y_min, y_max = ax_.get_ylim()
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y_min = y_min if errorbar else 0
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rug_space = (y_max - y_min) * rug_kwargs.pop("space")
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rug_x, rug_y = values / (len(mask) - 1), np.full_like(values, y_min) - rug_space
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ax_.plot(rug_x, rug_y, **rug_kwargs)
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ax_.axhline(y_min, color="k", linewidth=_linewidth, alpha=0.7)
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ax_.set_title(make_label(var_name, selection), fontsize=titlesize, wrap=True)
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ax_.tick_params(labelsize=xt_labelsize)
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ax_.set_xlabel("Quantile", fontsize=ax_labelsize, wrap=True)
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ax_.set_ylabel(
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r"Value $\pm$ MCSE for quantiles" if errorbar else "MCSE for quantiles",
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fontsize=ax_labelsize,
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wrap=True,
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)
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ax_.set_xlim(0, 1)
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if rug:
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ax_.yaxis.get_major_locator().set_params(nbins="auto", steps=[1, 2, 5, 10])
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y_min, y_max = ax_.get_ylim()
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yticks = ax_.get_yticks()
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yticks = yticks[(yticks >= y_min) & (yticks < y_max)]
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ax_.set_yticks(yticks)
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ax_.set_yticklabels(["{:.3g}".format(ytick) for ytick in yticks])
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elif not errorbar:
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ax_.set_ylim(bottom=0)
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if backend_show(show):
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0
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plt.show()
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return ax
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