add refereces values to predictive explorer (#302)

* add refereces values

* pass none predictive finder

preliz/internal/plot_helper.py

@@ -385,7 +385,7 @@ def get_textboxes(signature, model):
     return textboxes
 
 
-def plot_decorator(func, iterations, kind_plot):
+def plot_decorator(func, iterations, kind_plot, references):
     def looper(*args, **kwargs):
         results = []
         kwargs.pop("__resample__")
@@ -406,12 +406,12 @@ def looper(*args, **kwargs):
         _, ax = plt.subplots()
         ax.set_xlim(x_min, x_max, auto=auto)
 
-        plot_repr(results, kind_plot, iterations, ax)
+        plot_repr(results, kind_plot, references, iterations, ax)
 
     return looper
 
 
-def plot_repr(results, kind_plot, iterations, ax):
+def plot_repr(results, kind_plot, references, iterations, ax):
     alpha = max(0.01, 1 - iterations * 0.009)
 
     if kind_plot == "hist":
@@ -450,6 +450,18 @@ def plot_repr(results, kind_plot, iterations, ax):
         a = np.concatenate(results)
         ax.plot(np.sort(a), np.linspace(0, 1, len(a), endpoint=False), "k--")
 
+    if references is not None:
+        if isinstance(references, dict):
+            max_value = ax.get_ylim()[1]
+            for label, ref in references.items():
+                ax.text(ref, max_value * 0.2, label, rotation=90, bbox={"color": "w", "alpha": 0.5})
+                ax.axvline(ref, ls="--", color="0.5")
+        else:
+            if isinstance(references, (float, int)):
+                references = [references]
+            for ref in references:
+                ax.axvline(ref, ls="--", color="0.5")
+
 
 def plot_pp_samples(pp_samples, pp_samples_idxs, references, kind="pdf", sharex=True, fig=None):
     row_colum = int(np.ceil(len(pp_samples_idxs) ** 0.5))

preliz/predictive/predictive_explorer.py

@@ -8,7 +8,7 @@
 from preliz.internal.plot_helper import get_textboxes, plot_decorator
 
 
-def predictive_explorer(fmodel, samples=50, kind_plot="ecdf"):
+def predictive_explorer(fmodel, samples=50, kind_plot="ecdf", references=None):
     """
     Create textboxes and plot a set of samples returned by a function relating one or more
     PreliZ distributions.
@@ -26,6 +26,9 @@ def predictive_explorer(fmodel, samples=50, kind_plot="ecdf"):
         The type of plot to display. Defaults to "kde". Options are "hist" (histogram),
         "kde" (kernel density estimate), "ecdf" (empirical cumulative distribution function),
         or None (no plot).
+    references : int, float, list, tuple or dictionary
+        Value(s) used as reference points representing prior knowledge. For example expected
+        values or values that are considered extreme. Use a dictionary for labeled references.
     """
     source, signature = inspect_source(fmodel)
 
@@ -35,7 +38,7 @@ def predictive_explorer(fmodel, samples=50, kind_plot="ecdf"):
     if kind_plot is None:
         new_fmodel = fmodel
     else:
-        new_fmodel = plot_decorator(fmodel, samples, kind_plot)
+        new_fmodel = plot_decorator(fmodel, samples, kind_plot, references)
 
     out = interactive_output(new_fmodel, textboxes)
     default_names = ["__set_xlim__", "__x_min__", "__x_max__", "__resample__"]

preliz/predictive/predictive_finder.py

@@ -143,7 +143,7 @@ def __call__(self, kind_plot):
             self.pp_samples = self.fmodel(*self.values)[-1]
 
         reset_dist_panel(self.ax, True)
-        plot_repr(self.pp_samples, kind_plot, self.draws, self.ax)
+        plot_repr(self.pp_samples, kind_plot, None, self.draws, self.ax)
 
         if kind_plot == "ecdf":
             self.target.plot_cdf(color="C0", legend=False, ax=self.ax)
@@ -183,7 +183,7 @@ def select(prior_sample, pp_sample, draws, target_octiles, model):
 def plot_pp_samples(pp_samples, draws, target, kind_plot, fig, ax):
 
     reset_dist_panel(ax, True)
-    plot_repr(pp_samples, kind_plot, draws, ax)
+    plot_repr(pp_samples, kind_plot, None, draws, ax)
 
     if kind_plot == "ecdf":
         target.plot_cdf(color="C0", legend=False, ax=ax)