Use mean and median accessors

docs/source/tutorials/overview.ipynb

@@ -1799,7 +1799,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "arviz_1",
    "language": "python",
    "name": "python3"
   },

pyproject.toml

@@ -26,8 +26,8 @@ classifiers = [
 ]
 dynamic = ["version", "description"]
 dependencies = [
-  "arviz-base >=0.7.0",
-  "arviz-stats[xarray] >=0.7.0",
+  "arviz-base @ git+https://github.com/arviz-devs/arviz-base",
+  "arviz-stats[xarray] @ git+https://github.com/arviz-devs/arviz-stats",
 ]
 
 [tool.flit.module]

src/arviz_plots/_version.py

@@ -1,2 +1,2 @@
 """Base ArviZ version."""
-__version__ = "0.7.0"
+__version__ = "0.8.0dev0"

src/arviz_plots/plots/dist_plot.py

@@ -379,7 +379,7 @@ def plot_dist(
         )
 
     if (
-        (density_kwargs is not None)
+        (density_kwargs is not False or face_kwargs is not False)
         and ("model" in distribution)
         and (plot_collection.coords is None)
     ):
@@ -417,11 +417,13 @@ def plot_dist(
             plot_collection, aes_by_visuals, "point_estimate", sample_dims
         )
         if point_estimate == "median":
-            point = distribution.median(dim=pe_dims, **stats.get("point_estimate", {}))
+            point = distribution.azstats.median(dim=pe_dims, **stats.get("point_estimate", {}))
         elif point_estimate == "mean":
-            point = distribution.mean(dim=pe_dims, **stats.get("point_estimate", {}))
+            point = distribution.azstats.mean(dim=pe_dims, **stats.get("point_estimate", {}))
+        elif point_estimate == "mode":
+            point = distribution.azstats.mode(dim=pe_dims, **stats.get("point_estimate", {}))
         else:
-            raise NotImplementedError("coming soon")
+            raise ValueError("point_estimate must be either 'mean', 'median' or 'mode'")
 
     if pe_kwargs is not False:
         if "color" not in pe_aes:
@@ -437,7 +439,7 @@ def plot_dist(
     # point estimate text
     if pet_kwargs is not False:
         if density_kwargs is False and face_kwargs is False:
-            point_y = xr.full_like(point, 0.02)
+            point_y = xr.full_like(point, 0.05)
         elif kind == "kde":
             point_density_diff = [
                 dim for dim in density.sel(plot_axis="y").dims if dim not in point.dims

src/arviz_plots/plots/forest_plot.py

@@ -385,13 +385,13 @@ def plot_forest(
         if isinstance(pe_stats, xr.Dataset):
             point = pe_stats
         elif point_estimate == "median":
-            point = distribution.median(dim=pe_dims, **pe_stats)
+            point = distribution.azstats.median(dim=pe_dims, **pe_stats)
         elif point_estimate == "mean":
-            point = distribution.mean(dim=pe_dims, **pe_stats)
+            point = distribution.azstats.mean(dim=pe_dims, **pe_stats)
         elif point_estimate == "mode":
             point = distribution.azstats.mode(dim=pe_dims, **pe_stats)
         else:
-            raise NotImplementedError(f"Point estimate '{point_estimate}' not implemented")
+            raise ValueError("point_estimate must be either 'mean', 'median' or 'mode'")
 
     if twig_kwargs is not False:
         x_range = ci_twig

src/arviz_plots/plots/lm_plot.py

@@ -377,11 +377,11 @@ def plot_lm(
         plot_collection, aes_by_visuals, "pe_line", sample_dims
     )
     if point_estimate == "mean":
-        pe_value = y_pred.mean(dim=pe_line_dims, **stats.get("point_estimate", {}))
+        pe_value = y_pred.azstats.mean(dim=pe_line_dims, **stats.get("point_estimate", {}))
     elif point_estimate == "median":
-        pe_value = y_pred.median(dim=pe_line_dims, **stats.get("point_estimate", {}))
+        pe_value = y_pred.azstats.median(dim=pe_line_dims, **stats.get("point_estimate", {}))
     elif point_estimate == "mode":
-        pe_value = azs.mode(y_pred, dim=pe_line_dims, **stats.get("point_estimate", {}))
+        pe_value = y_pred.azstats.mode(dim=pe_line_dims, **stats.get("point_estimate", {}))
     else:
         raise ValueError(
             f"'{point_estimate}' is not a valid value for `point_estimate`. "

src/arviz_plots/plots/ppc_interval_plot.py

@@ -210,9 +210,9 @@ def plot_ppc_interval(
     ci_twig = ci_fun(prob=ci_probs[0], dim=sample_dims, **stats.get("twig", {}))
 
     if point_estimate == "median":
-        point = ds_predictive.median(dim=sample_dims, **stats.get("point_estimate", {}))
+        point = ds_predictive.azstats.median(dim=sample_dims, **stats.get("point_estimate", {}))
     elif point_estimate == "mean":
-        point = ds_predictive.mean(dim=sample_dims, **stats.get("point_estimate", {}))
+        point = ds_predictive.azstats.mean(dim=sample_dims, **stats.get("point_estimate", {}))
     elif point_estimate == "mode":
         point = ds_predictive.azstats.mode(dim=sample_dims, **stats.get("point_estimate", {}))
     else:

src/arviz_plots/plots/psense_quantities_plot.py

@@ -225,7 +225,7 @@ def plot_psense_quantities(
     name_quantities = []
 
     if "mean" in quantities:
-        to_concat_quantities.append(distribution.mean(sample_dims))
+        to_concat_quantities.append(distribution.azstats.mean(sample_dims))
         if mcse:
             to_concat_mcse.append(ds_posterior.azstats.mcse(method="mean"))
         name_quantities.append("mean")
@@ -235,7 +235,7 @@ def plot_psense_quantities(
             to_concat_mcse.append(ds_posterior.azstats.mcse(method="sd"))
         name_quantities.append("sd")
     if "median" in quantities:
-        to_concat_quantities.append(distribution.median(sample_dims))
+        to_concat_quantities.append(distribution.azstats.median(sample_dims))
         if mcse:
             to_concat_mcse.append(ds_posterior.azstats.mcse(method="median"))
         name_quantities.append("median")