add plot.density_type

arviz/plots/backends/bokeh/energyplot.py

@@ -45,7 +45,7 @@ def plot_energy(
     fill_kwargs = {} if fill_kwargs is None else fill_kwargs
     plot_kwargs = {} if plot_kwargs is None else plot_kwargs
     plot_kwargs.setdefault("line_width", line_width)
-    if kind in {"hist", "histogram"}:
+    if kind=="hist":
         legend = False
 
     if ax is None:
@@ -103,7 +103,7 @@ def plot_energy(
                 )
             )
 
-    elif kind in {"hist", "histogram"}:
+    elif kind=="hist":
         hist_kwargs = plot_kwargs.copy()
         hist_kwargs.update(**fill_kwargs)
 

arviz/plots/backends/matplotlib/energyplot.py

@@ -43,7 +43,7 @@ def plot_energy(
         _, ax = create_axes_grid(1, backend_kwargs=backend_kwargs)
 
     fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin")
-    types = "hist" if kind in {"hist", "histogram"} else "plot"
+    types = "hist" if kind=="hist" else "plot"
     plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, types)
 
     _colors = [
@@ -82,7 +82,7 @@ def plot_energy(
                 ax=ax,
                 legend=False,
             )
-    elif kind in {"hist", "histogram"}:
+    elif kind=="hist":
         for alpha, color, label, value in series:
             ax.hist(
                 value.flatten(),

arviz/plots/distplot.py

@@ -51,8 +51,9 @@ def plot_dist(
     color : string
         valid matplotlib color
     kind : string
-        By default ("auto") continuous variables are plotted using KDEs and discrete ones using
-        histograms. To override this use "hist" to plot histograms and "kde" for KDEs
+        By default ("auto") continuous variables will use the kind defined by rcParam
+        ``plot.density_kind`` and discrete ones will use histograms.
+        To override this use "hist" to plot histograms and "kde" for KDEs
     cumulative : bool
         If true plot the estimated cumulative distribution function. Defaults to False.
         Ignored for 2D KDE
@@ -172,7 +173,7 @@ def plot_dist(
         raise TypeError('Invalid "kind":{}. Select from {{"auto","kde","hist"}}'.format(kind))
 
     if kind == "auto":
-        kind = "hist" if values.dtype.kind == "i" else "kde"
+        kind = "hist" if values.dtype.kind == "i" else rcParams["plot.density_kind"]
 
     dist_plot_args = dict(
         # User Facing API that can be simplified

arviz/plots/energyplot.py

@@ -1,12 +1,14 @@
 """Plot energy transition distribution in HMC inference."""
+import warnings
+
 from ..data import convert_to_dataset
 from ..rcparams import rcParams
 from .plot_utils import get_plotting_function
 
 
 def plot_energy(
     data,
-    kind="kde",
+    kind=None,
     bfmi=True,
     figsize=None,
     legend=True,
@@ -30,7 +32,7 @@ def plot_energy(
     data : xarray dataset, or object that can be converted (must represent
            `sample_stats` and have an `energy` variable)
     kind : str
-        Type of plot to display {"kde", "histogram")
+        Type of plot to display {"kde", "hist")
     bfmi : bool
         If True add to the plot the value of the estimated Bayesian fraction of missing information
     figsize : tuple
@@ -90,6 +92,17 @@ def plot_energy(
     """
     energy = convert_to_dataset(data, group="sample_stats").energy.values
 
+    if kind == "histogram":
+        warnings.warn(
+            "kind histogram will be deprecated in a future release. Use `hist` "
+            "or set rcParam `plot.density_kind` to `hist`",
+            FutureWarning
+        )
+        kind = "hist"
+
+    if kind is None:
+        kind = rcParams["plot.density_kind"]
+
     plot_energy_kwargs = dict(
         ax=ax,
         energy=energy,

arviz/plots/posteriorplot.py

@@ -26,7 +26,7 @@ def plot_posterior(
     ref_val=None,
     rope_color="C2",
     ref_val_color="C1",
-    kind="kde",
+    kind=None,
     bw="default",
     circular=False,
     bins=None,
@@ -92,7 +92,7 @@ def plot_posterior(
         Specifies the color of the displayed percentage
     kind: str
         Type of plot to display (kde or hist) For discrete variables this argument is ignored and
-        a histogram is always used.
+        a histogram is always used. Defaults to rcParam ``plot.density_kind``
     bw: float or str, optional
         If numeric, indicates the bandwidth and must be positive.
         If str, indicates the method to estimate the bandwidth and must be
@@ -235,6 +235,9 @@ def plot_posterior(
     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 kind is None:
+        kind = rcParams["plot.density_kind"]
+
     plotters = filter_plotters_list(
         list(xarray_var_iter(get_coords(data, coords), var_names=var_names, combined=True)),
         "plot_posterior",

arviz/rcparams.py

@@ -232,6 +232,7 @@ def validate_iterable(value):
     "data.log_likelihood": (True, _validate_boolean),
     "data.save_warmup": (False, _validate_boolean),
     "plot.backend": ("matplotlib", _make_validate_choice({"matplotlib", "bokeh"})),
+    "plot.density_kind": ("kde", _make_validate_choice({"kde", "hist"})),
     "plot.max_subplots": (40, _validate_positive_int_or_none),
     "plot.point_estimate": (
         "mean",

arvizrc.template

@@ -20,6 +20,7 @@ data.save_warmup             : false  # save warmup iterations, one of "true", "
 ### PLOT  ###
 # rcParams related with plotting functions
 plot.backend                 : matplotlib  # One of "bokeh", "matplotlib"
+plot.density_kind            : kde         # One of "kde", "hist"
 plot.max_subplots            : 40          # Maximum number of subplots.
 plot.point_estimate          : mean        # One of "mean", "median", "mode" or "None"