Keep replacing hpd with hdi (#1190)

* replace hpd with hdi

* add plot_hdi and update changelog

* skip pydocstyle error

CHANGELOG.md

@@ -38,7 +38,8 @@
 * plot_ppc animation: improve docs and error handling (#1162)
 
 ### Deprecation
-* `credible_interval` argument replaced by `hdi_prob`throughout with exception of `plot_loo_pit` (#1176)
+* `hpd` function deprecated in favor of `hdi`. `credible_interval` argument replaced by `hdi_prob`throughout with exception of `plot_loo_pit` (#1176)
+* `plot_hpd` function deprecated in favor of `plot_hdi`. (#1190)
 
 ### Documentation
 * Add classifier to `setup.py` including Matplotlib framework (#1133)

arviz/plots/__init__.py

@@ -7,7 +7,7 @@
 from .energyplot import plot_energy
 from .essplot import plot_ess
 from .forestplot import plot_forest
-from .hpdplot import plot_hpd
+from .hdiplot import plot_hdi, plot_hpd
 from .jointplot import plot_joint
 from .kdeplot import plot_kde
 from .khatplot import plot_khat
@@ -32,6 +32,7 @@
     "plot_energy",
     "plot_ess",
     "plot_forest",
+    "plot_hdi",
     "plot_hpd",
     "plot_joint",
     "plot_kde",

arviz/plots/backends/bokeh/__init__.py

@@ -36,7 +36,7 @@ def backend_kwarg_defaults(*args, **kwargs):
 from .energyplot import plot_energy
 from .essplot import plot_ess
 from .forestplot import plot_forest
-from .hpdplot import plot_hpd
+from .hdiplot import plot_hdi
 from .jointplot import plot_joint
 from .kdeplot import plot_kde
 from .khatplot import plot_khat

arviz/plots/backends/bokeh/forestplot.py

@@ -417,7 +417,7 @@ def forestplot(self, hdi_prob, quartiles, linewidth, markersize, ax, rope):
                     x=values[mid], y=y, size=markersize * 0.75, fill_color=color,
                 )
         _title = Title()
-        _title.text = "{:.1%} hdi Interval".format(hdi_prob)
+        _title.text = "{:.1%} hdi".format(hdi_prob)
         ax.title = _title
 
         return ax

arviz/plots/backends/bokeh/hpdplot.py → arviz/plots/backends/bokeh/hdiplot.py

@@ -1,4 +1,4 @@
-"""Bokeh hpdplot."""
+"""Bokeh hdiplot."""
 from itertools import cycle
 
 import bokeh.plotting as bkp
@@ -9,8 +9,8 @@
 from .. import show_layout
 
 
-def plot_hpd(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show):
-    """Bokeh hpd plot."""
+def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show):
+    """Bokeh hdi plot."""
     if backend_kwargs is None:
         backend_kwargs = {}
 

arviz/plots/backends/bokeh/loopitplot.py

@@ -4,7 +4,7 @@
 
 from . import backend_kwarg_defaults
 from .. import show_layout
-from ...hpdplot import plot_hpd
+from ...hdiplot import plot_hdi
 from ...kdeplot import _fast_kde
 
 
@@ -19,10 +19,10 @@ def plot_loo_pit(
     p025,
     fill_kwargs,
     ecdf_fill,
-    use_hpd,
+    use_hdi,
     x_vals,
     unif_densities,
-    hpd_kwargs,
+    hdi_kwargs,
     n_unif,
     unif,
     plot_unif_kwargs,
@@ -114,15 +114,15 @@ def plot_loo_pit(
                     line_width=plot_unif_kwargs.get("linewidth", 1.0),
                 )
     else:
-        if use_hpd:
-            plot_hpd(
+        if use_hdi:
+            plot_hdi(
                 x_vals,
                 unif_densities,
                 backend="bokeh",
                 ax=ax,
                 backend_kwargs={},
                 show=False,
-                **hpd_kwargs
+                **hdi_kwargs
             )
         else:
             for idx in range(n_unif):

arviz/plots/backends/bokeh/posteriorplot.py

@@ -195,24 +195,24 @@ def display_point_estimate(max_data):
 
         ax.text(x=[point_value], y=[max_data * 0.8], text=[point_text], text_align="center")
 
-    def display_hpd(max_data):
+    def display_hdi(max_data):
         # np.ndarray with 2 entries, min and max
         # pylint: disable=line-too-long
         hdi_probs = hdi(values, hdi_prob=hdi_prob, multimodal=multimodal)  # type: np.ndarray
 
-        for hpdi in np.atleast_2d(hdi_probs):
+        for hdi_i in np.atleast_2d(hdi_probs):
             ax.line(
-                hpdi,
+                hdi_i,
                 (max_data * 0.02, max_data * 0.02),
                 line_width=linewidth * 2,
                 line_color="black",
             )
 
             ax.text(
-                x=list(hpdi) + [(hpdi[0] + hpdi[1]) / 2],
+                x=list(hdi_i) + [(hdi_i[0] + hdi_i[1]) / 2],
                 y=[max_data * 0.07, max_data * 0.07, max_data * 0.3],
-                text=list(map(str, map(lambda x: round_num(x, round_to), hpdi)))
-                + [format_as_percent(hdi_prob) + " HPD"],
+                text=list(map(str, map(lambda x: round_num(x, round_to), hdi_i)))
+                + [format_as_percent(hdi_prob) + " HDI"],
                 text_align="center",
             )
 
@@ -254,7 +254,7 @@ def format_axes():
     format_axes()
     max_data = hist.max()
     if hdi_prob != "hide":
-        display_hpd(max_data)
+        display_hdi(max_data)
     display_point_estimate(max_data)
     display_ref_val(max_data)
     display_rope(max_data)

arviz/plots/backends/matplotlib/__init__.py

@@ -31,7 +31,7 @@ def backend_show(show):
 from .energyplot import plot_energy
 from .essplot import plot_ess
 from .forestplot import plot_forest
-from .hpdplot import plot_hpd
+from .hdiplot import plot_hdi
 from .jointplot import plot_joint
 from .kdeplot import plot_kde
 from .khatplot import plot_khat

arviz/plots/backends/matplotlib/densityplot.py

@@ -122,7 +122,7 @@ def _d_helper(
     markersize : float
         Size of markers
     hdi_prob : float
-        hdi intervals. Defaults to 0.94
+        Probability for the highest density interval. Defaults to 0.94
     point_estimate : Optional[str]
         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.

arviz/plots/backends/matplotlib/forestplot.py

@@ -341,7 +341,7 @@ def forestplot(
                     color=color,
                 )
         ax.tick_params(labelsize=xt_labelsize)
-        ax.set_title("{:.1%} hdi Interval".format(hdi_prob), fontsize=titlesize, wrap=True)
+        ax.set_title("{:.1%} hdi".format(hdi_prob), fontsize=titlesize, wrap=True)
         if rope is None or isinstance(rope, dict):
             return
         elif len(rope) == 2:

arviz/plots/backends/matplotlib/hpdplot.py → arviz/plots/backends/matplotlib/hdiplot.py

@@ -1,16 +1,16 @@
-"""Matplotlib hpdplot."""
+"""Matplotlib hdiplot."""
 import warnings
 import matplotlib.pyplot as plt
 
 from . import backend_show
 
 
-def plot_hpd(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show):
-    """Matplotlib hpd plot."""
+def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show):
+    """Matplotlib hdi plot."""
     if backend_kwargs is not None:
         warnings.warn(
             (
-                "Argument backend_kwargs has not effect in matplotlib.plot_hpd"
+                "Argument backend_kwargs has not effect in matplotlib.plot_hdi"
                 "Supplied value won't be used"
             )
         )

arviz/plots/backends/matplotlib/loopitplot.py

@@ -4,7 +4,7 @@
 
 from . import backend_kwarg_defaults, backend_show
 from ....numeric_utils import _fast_kde
-from ...hpdplot import plot_hpd
+from ...hdiplot import plot_hdi
 
 
 def plot_loo_pit(
@@ -18,10 +18,10 @@ def plot_loo_pit(
     p025,
     fill_kwargs,
     ecdf_fill,
-    use_hpd,
+    use_hdi,
     x_vals,
     unif_densities,
-    hpd_kwargs,
+    hdi_kwargs,
     n_unif,
     unif,
     plot_unif_kwargs,
@@ -54,8 +54,8 @@ def plot_loo_pit(
         else:
             ax.plot(unif_ecdf, p975 - unif_ecdf, unif_ecdf, p025 - unif_ecdf, **plot_unif_kwargs)
     else:
-        if use_hpd:
-            plot_hpd(x_vals, unif_densities, **hpd_kwargs)
+        if use_hdi:
+            plot_hdi(x_vals, unif_densities, **hdi_kwargs)
         else:
             for idx in range(n_unif):
                 unif_density, _, _ = _fast_kde(unif[idx, :], xmin=0, xmax=1)
@@ -64,7 +64,7 @@ def plot_loo_pit(
 
     ax.tick_params(labelsize=xt_labelsize)
     if legend:
-        if not (use_hpd or (ecdf and ecdf_fill)):
+        if not (use_hdi or (ecdf and ecdf_fill)):
             label = "{:.3g}% credible interval".format(credible_interval) if ecdf else "Uniform"
             ax.plot([], label=label, **plot_unif_kwargs)
         ax.legend()

arviz/plots/backends/matplotlib/posteriorplot.py

@@ -195,37 +195,37 @@ def display_point_estimate():
             horizontalalignment="center",
         )
 
-    def display_hpd():
+    def display_hdi():
         # np.ndarray with 2 entries, min and max
         # pylint: disable=line-too-long
         hdi_probs = hdi(values, hdi_prob=hdi_prob, multimodal=multimodal)  # type: np.ndarray
 
-        for hpdi in np.atleast_2d(hdi_probs):
+        for hdi_i in np.atleast_2d(hdi_probs):
             ax.plot(
-                hpdi,
+                hdi_i,
                 (plot_height * 0.02, plot_height * 0.02),
                 lw=linewidth * 2,
                 color="k",
                 solid_capstyle="butt",
             )
             ax.text(
-                hpdi[0],
+                hdi_i[0],
                 plot_height * 0.07,
-                round_num(hpdi[0], round_to),
+                round_num(hdi_i[0], round_to),
                 size=ax_labelsize,
                 horizontalalignment="center",
             )
             ax.text(
-                hpdi[1],
+                hdi_i[1],
                 plot_height * 0.07,
-                round_num(hpdi[1], round_to),
+                round_num(hdi_i[1], round_to),
                 size=ax_labelsize,
                 horizontalalignment="center",
             )
             ax.text(
-                (hpdi[0] + hpdi[1]) / 2,
+                (hdi_i[0] + hdi_i[1]) / 2,
                 plot_height * 0.3,
-                format_as_percent(hdi_prob) + " HPD",
+                format_as_percent(hdi_prob) + " HDI",
                 size=ax_labelsize,
                 horizontalalignment="center",
             )
@@ -270,7 +270,7 @@ def format_axes():
 
     format_axes()
     if hdi_prob != "hide":
-        display_hpd()
+        display_hdi()
     display_point_estimate()
     display_ref_val()
     display_rope()

arviz/plots/densityplot.py

@@ -40,8 +40,8 @@ def plot_density(
 ):
     """Generate KDE plots for continuous variables and histograms for discrete ones.
 
-    Plots are truncated at their 100*(1-alpha)% hpd intervals. Plots are grouped per variable
-    and colors assigned to models.
+    Plots are truncated at their 100*(1-alpha)% highest density intervals. Plots are grouped per
+    variable and colors assigned to models.
 
     Parameters
     ----------
@@ -63,7 +63,8 @@ def plot_density(
     transform : callable
         Function to transform data (defaults to None i.e. the identity function)
     hdi_prob : float
-        hpd interval. Should be in the interval (0, 1]. Defaults to 0.94.
+        Probability for the highest density interval. Should be in the interval (0, 1].
+        Defaults to 0.94.
     point_estimate : Optional[str]
         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.
@@ -75,8 +76,8 @@ def plot_density(
     outline : bool
         Use a line to draw KDEs and histograms. Default to True
     hdi_markers : str
-        A valid `matplotlib.markers` like 'v', used to indicate the limits of the hpd interval.
-        Defaults to empty string (no marker).
+        A valid `matplotlib.markers` like 'v', used to indicate the limits of the highest density
+        interval. Defaults to empty string (no marker).
     shade : Optional[float]
         Alpha blending value for the shaded area under the curve, between 0 (no shade) and 1
         (opaque). Defaults to 0.
@@ -132,7 +133,7 @@ def plot_density(
 
         >>> az.plot_density([centered, non_centered], var_names=["mu"], group="prior")
 
-    Specify hpd interval
+    Specify highest density interval
 
     .. plot::
         :context: close-figs