Merge fc9bb38 into 1c84a80

arviz/plots/backends/bokeh/bokeh_compareplot.py

@@ -0,0 +1,135 @@
+"""Bokeh Compareplot."""
+import bokeh.plotting as bkp
+from bokeh.models import Span
+
+
+def _compareplot(
+    ax,
+    comp_df,
+    figsize,
+    plot_ic_diff,
+    plot_standard_error,
+    insample_dev,
+    yticks_pos,
+    yticks_labels,
+    line_width,
+    plot_kwargs,
+    information_criterion,
+    step,
+    show,
+):
+
+    if ax is None:
+        tools = ",".join(
+            [
+                "pan",
+                "wheel_zoom",
+                "box_zoom",
+                "lasso_select",
+                "poly_select",
+                "undo",
+                "redo",
+                "reset",
+                "save,hover",
+            ]
+        )
+        ax = bkp.figure(
+            width=figsize[0] * 90, height=figsize[1] * 90, output_backend="webgl", tools=tools
+        )
+
+    yticks_pos = list(yticks_pos)
+
+    if plot_ic_diff:
+        yticks_labels[0] = comp_df.index[0]
+        yticks_labels[2::2] = comp_df.index[1:]
+
+        ax.yaxis.ticker = yticks_pos
+        ax.yaxis.major_label_overrides = {
+            dtype(key): value
+            for key, value in zip(yticks_pos, yticks_labels)
+            for dtype in (int, float)
+            if (dtype(key) - key == 0)
+        }
+        print(ax.yaxis.major_label_overrides)
+        # create the coordinates for the errorbars
+        err_xs = []
+        err_ys = []
+
+        for x, y, xerr in zip(
+            comp_df[information_criterion].iloc[1:], yticks_pos[1::2], comp_df.dse[1:]
+        ):
+            err_xs.append((x - xerr, x + xerr))
+            err_ys.append((y, y))
+
+        # plot them
+        ax.triangle(
+            comp_df[information_criterion].iloc[1:],
+            yticks_pos[1::2],
+            line_color=plot_kwargs.get("color_dse", "grey"),
+            fill_color=plot_kwargs.get("color_dse", "grey"),
+            line_width=2,
+            size=6,
+        )
+        ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_dse", "grey"))
+
+    else:
+        yticks_labels = comp_df.index
+        ax.yaxis.ticker = yticks_pos[::2]
+        ax.yaxis.major_label_overrides = {
+            key: value for key, value in zip(yticks_pos[::2], yticks_labels)
+        }
+
+    ax.circle(
+        comp_df[information_criterion],
+        yticks_pos[::2],
+        line_color=plot_kwargs.get("color_ic", "black"),
+        fill_color=None,
+        line_width=2,
+        size=6,
+    )
+
+    if plot_standard_error:
+        # create the coordinates for the errorbars
+        err_xs = []
+        err_ys = []
+
+        for x, y, xerr in zip(comp_df[information_criterion], yticks_pos[::2], comp_df.se):
+            err_xs.append((x - xerr, x + xerr))
+            err_ys.append((y, y))
+
+        # plot them
+        ax.multi_line(err_xs, err_ys, line_color=plot_kwargs.get("color_ic", "black"))
+
+    if insample_dev:
+        ax.circle(
+            comp_df[information_criterion] - (2 * comp_df["p_" + information_criterion]),
+            yticks_pos[::2],
+            line_color=plot_kwargs.get("color_insample_dev", "black"),
+            fill_color=plot_kwargs.get("color_insample_dev", "black"),
+            line_width=2,
+            size=6,
+        )
+
+    vline = Span(
+        location=comp_df[information_criterion].iloc[0],
+        dimension="height",
+        line_color=plot_kwargs.get("color_ls_min_ic", "grey"),
+        line_width=line_width,
+        line_dash=plot_kwargs.get("ls_min_ic", "dashed"),
+    )
+
+    ax.renderers.append(vline)
+
+    scale_col = information_criterion + "_scale"
+    if scale_col in comp_df:
+        scale = comp_df[scale_col].iloc[0].capitalize()
+    else:
+        scale = "Deviance"
+    ax.xaxis.axis_label = scale
+    ax.y_range._property_values["start"] = -1 + step  # pylint: disable=protected-access
+    ax.y_range._property_values["end"] = 0 - step  # pylint: disable=protected-access
+
+    if show:
+        bkp.show(ax)
+
+    return ax

arviz/plots/backends/bokeh/bokeh_densityplot.py

@@ -0,0 +1,165 @@
+"""Bokeh Densityplot."""
+import bokeh.plotting as bkp
+from bokeh.models.annotations import Title
+from bokeh.layouts import gridplot
+import numpy as np
+
+from ....stats import hpd
+from ...kdeplot import _fast_kde
+from ...plot_utils import make_label
+
+
+def _plot_density(
+    ax,
+    all_labels,
+    to_plot,
+    colors,
+    bw,
+    line_width,
+    markersize,
+    credible_interval,
+    point_estimate,
+    hpd_markers,
+    outline,
+    shade,
+    data_labels,
+    show,
+):
+    axis_map = {label: ax_ for label, ax_ in zip(all_labels, ax.flatten())}
+    if data_labels is None:
+        data_labels = {}
+
+    for m_idx, plotters in enumerate(to_plot):
+        for ax_idx, (var_name, selection, values) in enumerate(plotters):
+            label = make_label(var_name, selection)
+
+            if data_labels:
+                data_label = data_labels[m_idx]
+                if ax_idx != 0 or data_label == "":
+                    data_label = None
+            else:
+                data_label = None
+
+            _d_helper(
+                values.flatten(),
+                label,
+                colors[m_idx],
+                bw,
+                line_width,
+                markersize,
+                credible_interval,
+                point_estimate,
+                hpd_markers,
+                outline,
+                shade,
+                axis_map[label],
+                data_label=data_label,
+            )
+
+    if show:
+        grid = gridplot([list(item) for item in ax], toolbar_location="above")
+        bkp.show(grid)
+
+    return ax
+
+
+def _d_helper(
+    vec,
+    vname,
+    color,
+    bw,
+    line_width,
+    markersize,
+    credible_interval,
+    point_estimate,
+    hpd_markers,
+    outline,
+    shade,
+    ax,
+    data_label,
+):
+    extra = dict()
+    if data_label is not None:
+        extra["legend_label"] = data_label
+
+    if vec.dtype.kind == "f":
+        if credible_interval != 1:
+            hpd_ = hpd(vec, credible_interval, multimodal=False)
+            new_vec = vec[(vec >= hpd_[0]) & (vec <= hpd_[1])]
+        else:
+            new_vec = vec
+
+        density, xmin, xmax = _fast_kde(new_vec, bw=bw)
+        density *= credible_interval
+        x = np.linspace(xmin, xmax, len(density))
+        ymin = density[0]
+        ymax = density[-1]
+
+        if outline:
+            ax.line(x, density, line_color=color, line_width=line_width, **extra)
+            ax.line(
+                [xmin, xmin],
+                [-ymin / 100, ymin],
+                line_color=color,
+                line_dash="solid",
+                line_width=line_width,
+            )
+            ax.line(
+                [xmax, xmax],
+                [-ymax / 100, ymax],
+                line_color=color,
+                line_dash="solid",
+                line_width=line_width,
+            )
+
+        if shade:
+            ax.patch(
+                np.r_[x[::-1], x, x[-1:]],
+                np.r_[np.zeros_like(x), density, [0]],
+                fill_color=color,
+                fill_alpha=shade,
+                **extra
+            )
+
+    else:
+        xmin, xmax = hpd(vec, credible_interval, multimodal=False)
+        bins = range(xmin, xmax + 2)
+
+        hist, edges = np.histogram(vec, density=True, bins=bins)
+
+        if outline:
+            ax.quad(
+                top=hist,
+                bottom=0,
+                left=edges[:-1],
+                right=edges[1:],
+                line_color=color,
+                fill_color=None,
+                **extra
+            )
+        else:
+            ax.quad(
+                top=hist,
+                bottom=0,
+                left=edges[:-1],
+                right=edges[1:],
+                line_color=color,
+                fill_color=color,
+                fill_alpha=shade,
+                **extra
+            )
+
+    if hpd_markers:
+        ax.diamond(xmin, 0, line_color="black", fill_color=color, size=markersize)
+        ax.diamond(xmax, 0, line_color="black", fill_color=color, size=markersize)
+
+    if point_estimate is not None:
+        if point_estimate == "mean":
+            est = np.mean(vec)
+        elif point_estimate == "median":
+            est = np.median(vec)
+        ax.circle(est, 0, fill_color=color, line_color="black", size=markersize)
+
+    _title = Title()
+    _title.text = vname
+    ax.title = _title

arviz/plots/backends/bokeh/bokeh_distplot.py

@@ -30,7 +30,20 @@ def _plot_dist_bokeh(
 ):
 
     if ax is None:
-        ax = bkp.figure(width=500, height=500)
+        tools = ",".join(
+            [
+                "pan",
+                "wheel_zoom",
+                "box_zoom",
+                "lasso_select",
+                "poly_select",
+                "undo",
+                "redo",
+                "reset",
+                "save,hover",
+            ]
+        )
+        ax = bkp.figure(width=500, height=500, output_backend="webgl", tools=tools)
 
     if kind == "auto":
         kind = "hist" if values.dtype.kind == "i" else "kde"

arviz/plots/backends/bokeh/bokeh_kdeplot.py

@@ -41,7 +41,20 @@ def _plot_kde_bokeh(
     show=True,
 ):
     if ax is None:
-        ax = bkp.figure(width=500, height=500, output_backend="webgl")
+        tools = ",".join(
+            [
+                "pan",
+                "wheel_zoom",
+                "box_zoom",
+                "lasso_select",
+                "poly_select",
+                "undo",
+                "redo",
+                "reset",
+                "save,hover",
+            ]
+        )
+        ax = bkp.figure(width=500, height=500, output_backend="webgl", tools=tools)
 
     if legend and label is not None:
         plot_kwargs["legend_label"] = label

arviz/plots/backends/bokeh/bokeh_traceplot.py

@@ -198,7 +198,19 @@ def _plot_trace_bokeh(
 
     backend_kwargs.setdefault(
         "tools",
-        ("pan,wheel_zoom,box_zoom," "lasso_select,poly_select," "undo,redo,reset,save,hover"),
+        ",".join(
+            [
+                "pan",
+                "wheel_zoom",
+                "box_zoom",
+                "lasso_select",
+                "poly_select",
+                "undo",
+                "redo",
+                "reset",
+                "save,hover",
+            ]
+        ),
     )
     backend_kwargs.setdefault("output_backend", "webgl")
     backend_kwargs.setdefault("height", figsize[1])