Improve pair_plot's reference_values compatibility, flexibility, and documentation

CHANGELOG.md

@@ -3,10 +3,13 @@
 ## Unreleased
 
 ### New features
+- `plot_pair` now has more flexible support for `reference_values` ([2438](https://github.com/arviz-devs/arviz/pull/2438))
 
 ### Maintenance and fixes
+- `reference_values` and `labeller` now work together in `plot_pair` ([2437](https://github.com/arviz-devs/arviz/issues/2437))
 
 ### Documentation
+- Added documentation for `reference_values` ([2438](https://github.com/arviz-devs/arviz/pull/2438))
 
 ## v0.21.0 (2025 Mar 06)
 

arviz/plots/backends/bokeh/pairplot.py

@@ -37,6 +37,8 @@
     diverging_mask,
     divergences_kwargs,
     flat_var_names,
+    flat_ref_slices,
+    flat_var_labels,
     backend_kwargs,
     marginal_kwargs,
     show,
@@ -72,50 +74,12 @@
         kde_kwargs["contour_kwargs"].setdefault("line_alpha", 1)
 
     if reference_values:
-        reference_values_copy = {}
-        label = []
-        for variable in list(reference_values.keys()):
-            if " " in variable:
-                variable_copy = variable.replace(" ", "\n", 1)
-            else:
-                variable_copy = variable
-
-            label.append(variable_copy)
-            reference_values_copy[variable_copy] = reference_values[variable]
-
-        difference = set(flat_var_names).difference(set(label))
-
-        if difference:
-            warn = [diff.replace("\n", " ", 1) for diff in difference]
-            warnings.warn(
-                "Argument reference_values does not include reference value for: {}".format(
-                    ", ".join(warn)
-                ),
-                UserWarning,
-            )
-
-    if reference_values:
-        reference_values_copy = {}
-        label = []
-        for variable in list(reference_values.keys()):
-            if " " in variable:
-                variable_copy = variable.replace(" ", "\n", 1)
-            else:
-                variable_copy = variable
-
-            label.append(variable_copy)
-            reference_values_copy[variable_copy] = reference_values[variable]
-
-        difference = set(flat_var_names).difference(set(label))
-
-        for dif in difference:
-            reference_values_copy[dif] = None
+        difference = set(flat_var_names).difference(set(reference_values.keys()))
 
         if difference:
-            warn = [dif.replace("\n", " ", 1) for dif in difference]
             warnings.warn(
                 "Argument reference_values does not include reference value for: {}".format(
-                    ", ".join(warn)
+                    ", ".join(difference)
                 ),
                 UserWarning,
             )
@@ -262,8 +226,8 @@
                     **marginal_kwargs,
                 )
 
-                ax[j, i].xaxis.axis_label = flat_var_names[i]
-                ax[j, i].yaxis.axis_label = flat_var_names[j + marginals_offset]
+                ax[j, i].xaxis.axis_label = flat_var_labels[i]
+                ax[j, i].yaxis.axis_label = flat_var_labels[j + marginals_offset]
 
             elif j + marginals_offset > i:
                 if "scatter" in kind:
@@ -346,12 +310,18 @@
                             ax[-1, -1].add_layout(ax_pe_hline)
 
                 if reference_values:
-                    x = reference_values_copy[flat_var_names[j + marginals_offset]]
-                    y = reference_values_copy[flat_var_names[i]]
-                    if x and y:
-                        ax[j, i].scatter(y, x, **reference_values_kwargs)
-                ax[j, i].xaxis.axis_label = flat_var_names[i]
-                ax[j, i].yaxis.axis_label = flat_var_names[j + marginals_offset]
+                    x_name = flat_var_names[j + marginals_offset]
+                    y_name = flat_var_names[i]
+                    if (x_name not in difference) and (y_name not in difference):
+                        ax[j, i].scatter(
+                            np.array(reference_values[y_name])[flat_ref_slices[i]],
+                            np.array(reference_values[x_name])[
+                                flat_ref_slices[j + marginals_offset]
+                            ],
+                            **reference_values_kwargs,
+                        )
+                ax[j, i].xaxis.axis_label = flat_var_labels[i]
+                ax[j, i].yaxis.axis_label = flat_var_labels[j + marginals_offset]
 
     show_layout(ax, show)
 

arviz/plots/backends/matplotlib/pairplot.py

@@ -30,6 +30,8 @@ def plot_pair(
     diverging_mask,
     divergences_kwargs,
     flat_var_names,
+    flat_ref_slices,
+    flat_var_labels,
     backend_kwargs,
     marginal_kwargs,
     show,
@@ -77,24 +79,12 @@ def plot_pair(
         kde_kwargs["contour_kwargs"].setdefault("colors", "k")
 
     if reference_values:
-        reference_values_copy = {}
-        label = []
-        for variable in list(reference_values.keys()):
-            if " " in variable:
-                variable_copy = variable.replace(" ", "\n", 1)
-            else:
-                variable_copy = variable
-
-            label.append(variable_copy)
-            reference_values_copy[variable_copy] = reference_values[variable]
-
-        difference = set(flat_var_names).difference(set(label))
+        difference = set(flat_var_names).difference(set(reference_values.keys()))
 
         if difference:
-            warn = [diff.replace("\n", " ", 1) for diff in difference]
             warnings.warn(
                 "Argument reference_values does not include reference value for: {}".format(
-                    ", ".join(warn)
+                    ", ".join(difference)
                 ),
                 UserWarning,
             )
@@ -211,12 +201,12 @@ def plot_pair(
 
         if reference_values:
             ax.plot(
-                reference_values_copy[flat_var_names[0]],
-                reference_values_copy[flat_var_names[1]],
+                np.array(reference_values[flat_var_names[0]])[flat_ref_slices[0]],
+                np.array(reference_values[flat_var_names[1]])[flat_ref_slices[1]],
                 **reference_values_kwargs,
             )
-        ax.set_xlabel(f"{flat_var_names[0]}", fontsize=ax_labelsize, wrap=True)
-        ax.set_ylabel(f"{flat_var_names[1]}", fontsize=ax_labelsize, wrap=True)
+        ax.set_xlabel(f"{flat_var_labels[0]}", fontsize=ax_labelsize, wrap=True)
+        ax.set_ylabel(f"{flat_var_labels[1]}", fontsize=ax_labelsize, wrap=True)
         ax.tick_params(labelsize=xt_labelsize)
 
     else:
@@ -336,20 +326,22 @@ def plot_pair(
                         y_name = flat_var_names[j + not_marginals]
                         if (x_name not in difference) and (y_name not in difference):
                             ax[j, i].plot(
-                                reference_values_copy[x_name],
-                                reference_values_copy[y_name],
+                                np.array(reference_values[x_name])[flat_ref_slices[i]],
+                                np.array(reference_values[y_name])[
+                                    flat_ref_slices[j + not_marginals]
+                                ],
                                 **reference_values_kwargs,
                             )
 
                 if j != vars_to_plot - 1:
                     plt.setp(ax[j, i].get_xticklabels(), visible=False)
                 else:
-                    ax[j, i].set_xlabel(f"{flat_var_names[i]}", fontsize=ax_labelsize, wrap=True)
+                    ax[j, i].set_xlabel(f"{flat_var_labels[i]}", fontsize=ax_labelsize, wrap=True)
                 if i != 0:
                     plt.setp(ax[j, i].get_yticklabels(), visible=False)
                 else:
                     ax[j, i].set_ylabel(
-                        f"{flat_var_names[j + not_marginals]}",
+                        f"{flat_var_labels[j + not_marginals]}",
                         fontsize=ax_labelsize,
                         wrap=True,
                     )

arviz/plots/pairplot.py

@@ -196,9 +196,14 @@ def plot_pair(
             get_coords(dataset, coords), var_names=var_names, skip_dims=combine_dims, combined=True
         )
     )
-    flat_var_names = [
-        labeller.make_label_vert(var_name, sel, isel) for var_name, sel, isel, _ in plotters
-    ]
+    flat_var_names = []
+    flat_ref_slices = []
+    flat_var_labels = []
+    for var_name, sel, isel, _ in plotters:
+        dims = [dim for dim in dataset[var_name].dims if dim not in ["chain", "draw"]]
+        flat_var_names.append(var_name)
+        flat_ref_slices.append(tuple(isel[dim] if dim in isel else slice(None) for dim in dims))
+        flat_var_labels.append(labeller.make_label_vert(var_name, sel, isel))
 
     divergent_data = None
     diverging_mask = None
@@ -253,6 +258,8 @@ def plot_pair(
         diverging_mask=diverging_mask,
         divergences_kwargs=divergences_kwargs,
         flat_var_names=flat_var_names,
+        flat_ref_slices=flat_ref_slices,
+        flat_var_labels=flat_var_labels,
         backend_kwargs=backend_kwargs,
         marginal_kwargs=marginal_kwargs,
         show=show,

arviz/tests/base_tests/test_plots_bokeh.py

@@ -8,6 +8,7 @@
 from scipy.stats import norm  # pylint: disable=wrong-import-position
 
 from ...data import from_dict, load_arviz_data  # pylint: disable=wrong-import-position
+from ...labels import MapLabeller  # pylint: disable=wrong-import-position
 from ...plots import (  # pylint: disable=wrong-import-position
     plot_autocorr,
     plot_bpv,
@@ -773,7 +774,6 @@ def test_plot_mcse_no_divergences(models):
         {"divergences": True, "var_names": ["theta", "mu"]},
         {"kind": "kde", "var_names": ["theta"]},
         {"kind": "hexbin", "var_names": ["theta"]},
-        {"kind": "hexbin", "var_names": ["theta"]},
         {
             "kind": "hexbin",
             "var_names": ["theta"],
@@ -785,6 +785,21 @@ def test_plot_mcse_no_divergences(models):
             "reference_values": {"mu": 0, "tau": 0},
             "reference_values_kwargs": {"line_color": "blue"},
         },
+        {
+            "var_names": ["mu", "tau"],
+            "reference_values": {"mu": 0, "tau": 0},
+            "labeller": MapLabeller({"mu": r"$\mu$", "theta": r"$\theta"}),
+        },
+        {
+            "var_names": ["theta"],
+            "reference_values": {"theta": [0.0] * 8},
+            "labeller": MapLabeller({"theta": r"$\theta$"}),
+        },
+        {
+            "var_names": ["theta"],
+            "reference_values": {"theta": np.zeros(8)},
+            "labeller": MapLabeller({"theta": r"$\theta$"}),
+        },
     ],
 )
 def test_plot_pair(models, kwargs):

arviz/tests/base_tests/test_plots_matplotlib.py

@@ -14,6 +14,7 @@
 from scipy.stats import gaussian_kde, norm
 
 from ...data import from_dict, load_arviz_data
+from ...labels import MapLabeller
 from ...plots import (
     plot_autocorr,
     plot_bf,
@@ -599,6 +600,21 @@ def test_plot_kde_inference_data(models):
             "reference_values": {"mu": 0, "tau": 0},
             "reference_values_kwargs": {"c": "C0", "marker": "*"},
         },
+        {
+            "var_names": ["mu", "tau"],
+            "reference_values": {"mu": 0, "tau": 0},
+            "labeller": MapLabeller({"mu": r"$\mu$", "theta": r"$\theta"}),
+        },
+        {
+            "var_names": ["theta"],
+            "reference_values": {"theta": [0.0] * 8},
+            "labeller": MapLabeller({"theta": r"$\theta$"}),
+        },
+        {
+            "var_names": ["theta"],
+            "reference_values": {"theta": np.zeros(8)},
+            "labeller": MapLabeller({"theta": r"$\theta$"}),
+        },
     ],
 )
 def test_plot_pair(models, kwargs):

doc/source/user_guide/plots_arguments_guide.md

@@ -390,3 +390,28 @@ These are kwargs specific to the backend being used, passed to `matplotlib.pyplo
 ## `show`
 
 Call backend show function.
+
+(common_reference_values)=
+## `reference_values`
+`plot_pair` accepts `reference_values` to highlight specific values on the probability distributions. The keys of `reference_values` are the associated variable names in `var_names`. The values are the reference values, which must have the same shape as the coordinates selected for plotting since it is indexed as such. For example, here `theta` must have shape `(2,)` since that is the shape of the selected coordinates on `theta`.
+
+```{code-cell} ipython3
+coords = {"school": ["Choate", "Deerfield"]}
+reference_values = {
+    "mu": 0.0,
+    "theta": np.zeros(2),
+}
+az.plot_pair(centered_eight, var_names=["mu", "theta"], coords=coords, reference_values=reference_values);
+```
+
+When used with `combine_dims`, each reference value along the combined dimension is plotted on the same axis.
+```{code-cell} ipython3
+coords = {"school": ["Choate", "Deerfield"]}
+reference_values = {
+    "theta": [-5.0, 5.0],
+    "theta_t": [-2.0, 2.0],
+}
+az.plot_pair(non_centered_eight, var_names=["theta", "theta_t"], coords=coords, reference_values=reference_values, combine_dims={"school"});
+```
+
+The values of the `reference_values` dictionary can be scalars (e.g., `0`) or zero-dimensional `numpy` arrays (e.g., `np.array(0)`) for scalar variables, or anything that can be cast to `np.array` (e.g., `[0.0, 0.0]` or `np.array([0.0, 0.0])`) for multi-dimensional variables.

examples/bokeh/bokeh_plot_pair_reference_values.py

@@ -0,0 +1,26 @@
+"""
+Pairplot with Reference Values
+==============================
+"""
+
+import arviz as az
+import numpy as np
+
+data = az.load_arviz_data("centered_eight")
+
+coords = {"school": ["Choate", "Deerfield"]}
+reference_values = {
+    "mu": 0.0,
+    "theta": np.zeros(2),
+}
+
+ax = az.plot_pair(
+    data,
+    var_names=["mu", "theta"],
+    kind=["scatter", "kde"],
+    kde_kwargs={"fill_last": False},
+    coords=coords,
+    reference_values=reference_values,
+    figsize=(11.5, 5),
+    backend="bokeh",
+)

examples/matplotlib/mpl_plot_pair_reference_values.py

@@ -0,0 +1,32 @@
+"""
+Pairplot with Reference Values
+==============================
+_gallery_category: Distributions
+"""
+
+import matplotlib.pyplot as plt
+
+import arviz as az
+import numpy as np
+
+az.style.use("arviz-doc")
+
+data = az.load_arviz_data("centered_eight")
+
+coords = {"school": ["Choate", "Deerfield"]}
+reference_values = {
+    "mu": 0.0,
+    "theta": np.zeros(2),
+}
+
+ax = az.plot_pair(
+    data,
+    var_names=["mu", "theta"],
+    kind=["scatter", "kde"],
+    kde_kwargs={"fill_last": False},
+    coords=coords,
+    reference_values=reference_values,
+    figsize=(11.5, 5),
+)
+
+plt.show()