rename plot_pava and minor fixes (#140)

docs/source/api/plots.rst

@@ -23,7 +23,7 @@ A complementary introduction and guide to ``plot_...`` functions is available at
    plot_ess
    plot_ess_evolution
    plot_forest
-   plot_pava_calibration
+   plot_ppc_pava
    plot_ppc_dist
    plot_psense_dist
    plot_psense_quantities

docs/source/gallery/model_criticism/plot_pava_calibration.py

@@ -6,7 +6,7 @@
 ---
 
 :::{seealso}
-API Documentation: {func}`~arviz_plots.plot_pava_calibration`
+API Documentation: {func}`~arviz_plots.plot_ppc_pava`
 :::
 """
 from arviz_base import load_arviz_data
@@ -16,7 +16,7 @@
 azp.style.use("arviz-variat")
 
 dt = load_arviz_data("classification10d")
-pc = azp.plot_pava_calibration(
+pc = azp.plot_ppc_pava(
     dt,
     backend="none",
 )

src/arviz_plots/backend/bokeh/__init__.py

@@ -35,7 +35,7 @@ def get_default_aes(aes_key, n, kwargs=None):
         elif aes_key in {"linestyle", "line_dash"}:
             vals = ["solid", "dashed", "dotted", "dashdot"]
         elif aes_key == "marker":
-            vals = ["circle", "cross", "triangle", "x", "diamond", "square"]
+            vals = ["circle", "cross", "triangle", "x", "diamond", "square", "dot"]
         else:
             return get_agnostic_default_aes(aes_key, n)
         return get_agnostic_default_aes(aes_key, n, {aes_key: vals})

src/arviz_plots/backend/matplotlib/__init__.py

@@ -48,7 +48,7 @@ def get_default_aes(aes_key, n, kwargs=None):
             vals = ["-", "--", ":", "-."]
             vals = default_prop_cycle.get("linestyle", vals)
         elif aes_key in {"marker", "m"}:
-            vals = ["o", "+", "^", "x", "d", "s"]
+            vals = ["o", "+", "^", "x", "d", "s", "."]
             vals = default_prop_cycle.get("marker", vals)
         elif aes_key in default_prop_cycle:
             vals = default_prop_cycle[aes_key]

src/arviz_plots/backend/plotly/__init__.py

@@ -64,7 +64,8 @@ def get_default_aes(aes_key, n, kwargs=None):
         elif aes_key in {"linestyle", "dash"}:
             vals = ["solid", "dash", "dot", "dashdot"]
         elif aes_key in {"marker", "style"}:
-            vals = ["circle", "cross", "triangle-up", "x", "diamond", "square"]
+            # plotly does not have "dot" using "circle-open" instead
+            vals = ["circle", "cross", "triangle-up", "x", "diamond", "square", "circle-open"]
         else:
             return get_agnostic_default_aes(aes_key, n, {})
         return get_agnostic_default_aes(aes_key, n, {aes_key: vals})

src/arviz_plots/plots/__init__.py

@@ -7,7 +7,7 @@
 from .essplot import plot_ess
 from .evolutionplot import plot_ess_evolution
 from .forestplot import plot_forest
-from .pavacalibrationplot import plot_pava_calibration
+from .pavacalibrationplot import plot_ppc_pava
 from .ppcdistplot import plot_ppc_dist
 from .psensedistplot import plot_psense_dist
 from .psensequantitiesplot import plot_psense_quantities
@@ -27,7 +27,7 @@
     "plot_ess_evolution",
     "plot_ppc_dist",
     "plot_ridge",
-    "plot_pava_calibration",
+    "plot_ppc_pava",
     "plot_psense_dist",
     "plot_psense_quantities",
 ]

src/arviz_plots/plots/pavacalibrationplot.py

@@ -1,4 +1,4 @@
-"""psense quantities plot code."""
+"""Plot ppc using PAV-adjusted calibration plot."""
 from copy import copy
 from importlib import import_module
 
@@ -19,7 +19,7 @@
 )
 
 
-def plot_pava_calibration(
+def plot_ppc_pava(
     dt,
     n_bootstaps=1000,
     ci_prob=None,
@@ -76,8 +76,8 @@ def plot_pava_calibration(
     plot_kwargs : mapping of {str : mapping or False}, optional
         Valid keys are:
 
-        * calibration_line -> passed to :func:`~arviz_plots.visuals.line_xy`
-        * calibration_markers -> passed to :func:`~arviz_plots.visuals.scatter_xy`
+        * lines -> passed to :func:`~arviz_plots.visuals.line_xy`
+        * markers -> passed to :func:`~arviz_plots.visuals.scatter_xy`
         * reference_line -> passed to :func:`~arviz_plots.visuals.line_xy`
         * ci -> passed to :func:`~arviz_plots.visuals.fill_between_y`
         * xlabel -> passed to :func:`~arviz_plots.visuals.labelled_x`
@@ -99,14 +99,14 @@ def plot_pava_calibration(
     .. plot::
         :context: close-figs
 
-        >>> from arviz_plots import plot_pava_calibration, style
+        >>> from arviz_plots import plot_ppc_pava, style
         >>> style.use("arviz-variat")
         >>> from arviz_base import load_arviz_data
         >>> dt = load_arviz_data('rugby')
-        >>> plot_pava_calibration(dt, ci_prob=0.90)
+        >>> plot_ppc_pava(dt, ci_prob=0.90)
 
 
-    .. minigallery:: plot_pava_calibration
+    .. minigallery:: plot_ppc_pava
 
     References
     ----------
@@ -145,6 +145,7 @@ def plot_pava_calibration(
 
     plot_bknd = import_module(f".backend.{backend}", package="arviz_plots")
     colors = plot_bknd.get_default_aes("color", 1, {})
+    markers = plot_bknd.get_default_aes("marker", 7, {})
     lines = plot_bknd.get_default_aes("linestyle", 2, {})
 
     if plot_collection is None:
@@ -201,34 +202,31 @@ def plot_pava_calibration(
         )
 
     ## markers
-    calibration_ms_kwargs = copy(plot_kwargs.get("calibration_line", {}))
+    calibration_ms_kwargs = copy(plot_kwargs.get("markers", {}))
 
     if calibration_ms_kwargs is not False:
-        _, _, calibration_ms_ignore = filter_aes(
-            plot_collection, aes_map, "calibration_line", sample_dims
-        )
+        _, _, calibration_ms_ignore = filter_aes(plot_collection, aes_map, "lines", sample_dims)
         calibration_ms_kwargs.setdefault("color", colors[0])
+        calibration_ms_kwargs.setdefault("marker", markers[6])
 
         plot_collection.map(
             scatter_xy,
-            "calibration_markers",
+            "markers",
             data=ds_calibration,
             ignore_aes=calibration_ms_ignore,
             **calibration_ms_kwargs,
         )
 
     ## lines
-    calibration_ls_kwargs = copy(plot_kwargs.get("calibration_line", {}))
+    calibration_ls_kwargs = copy(plot_kwargs.get("lines", {}))
 
     if calibration_ls_kwargs is not False:
-        _, _, calibration_ls_ignore = filter_aes(
-            plot_collection, aes_map, "calibration_line", sample_dims
-        )
+        _, _, calibration_ls_ignore = filter_aes(plot_collection, aes_map, "lines", sample_dims)
         calibration_ls_kwargs.setdefault("color", colors[0])
 
         plot_collection.map(
             line_xy,
-            "calibration_line",
+            "lines",
             data=ds_calibration,
             ignore_aes=calibration_ls_ignore,
             **calibration_ls_kwargs,