refactor: move tarp plotting to analysis, fix tutorial.

Author: janfb

sbi/analysis/plot.py

@@ -1510,7 +1510,6 @@ def _sbc_rank_plot(
     line_alpha: float = 0.8,
     show_uniform_region: bool = True,
     uniform_region_alpha: float = 0.3,
-    uniform_region_color: str = "gray",
     xlim_offset_factor: float = 0.1,
     num_cols: int = 4,
     params_in_subplots: bool = False,
@@ -1571,7 +1570,7 @@ def _sbc_rank_plot(
     ), "plot type {plot_type} not implemented, use one in {plot_types}."
 
     if legend_kwargs is None:
-        legend_kwargs = dict(loc=1, handlelength=0.8)
+        legend_kwargs = dict(loc="best", handlelength=0.8)
 
     num_sbc_runs, num_parameters = ranks_list[0].shape
     num_ranks = len(ranks_list)
@@ -2095,6 +2094,41 @@ def pp_plot_lc2st(
     )
 
 
+def plot_tarp(ecp: Tensor, alpha: Tensor, title="") -> Tuple[Figure, Axes]:
+    """
+    Plots the expected coverage probability (ECP) against the credibility
+    level,alpha, for a given alpha grid.
+
+    Args:
+        ecp : numpy.ndarray
+            Array of expected coverage probabilities.
+        alpha : numpy.ndarray
+            Array of credibility levels.
+        title : str, optional
+            Title for the plot. The default is "".
+
+     Returns
+        fig : matplotlib.figure.Figure
+            The figure object.
+        ax : matplotlib.axes.Axes
+            The axes object.
+
+    """
+
+    fig = plt.figure(figsize=(6, 6))
+    ax: Axes = plt.gca()
+
+    ax.plot(alpha, ecp, color="blue", label="TARP")
+    ax.plot(alpha, alpha, color="black", linestyle="--", label="ideal")
+    ax.set_xlabel(r"Credibility Level $\alpha$")
+    ax.set_ylabel(r"Expected Coverage Probility")
+    ax.set_xlim(0.0, 1.0)
+    ax.set_ylim(0.0, 1.0)
+    ax.set_title(title)
+    ax.legend()
+    return fig, ax  # type: ignore
+
+
 # TO BE DEPRECATED
 # ----------------
 def pairplot_dep(

sbi/diagnostics/tarp.py

@@ -8,10 +8,7 @@
 
 from typing import Callable, Optional, Tuple
 
-import matplotlib.pyplot as plt
 import torch
-from matplotlib.axes import Axes
-from matplotlib.figure import Figure
 from scipy.stats import kstest
 from torch import Tensor
 
@@ -216,38 +213,3 @@ def check_tarp(
     kstest_pvals: float = kstest(ecp.numpy(), alpha.numpy())[1]  # type: ignore
 
     return atc, kstest_pvals
-
-
-def plot_tarp(ecp: Tensor, alpha: Tensor, title="") -> Tuple[Figure, Axes]:
-    """
-    Plots the expected coverage probability (ECP) against the credibility
-    level,alpha, for a given alpha grid.
-
-    Args:
-        ecp : numpy.ndarray
-            Array of expected coverage probabilities.
-        alpha : numpy.ndarray
-            Array of credibility levels.
-        title : str, optional
-            Title for the plot. The default is "".
-
-     Returns
-        fig : matplotlib.figure.Figure
-            The figure object.
-        ax : matplotlib.axes.Axes
-            The axes object.
-
-    """
-
-    fig = plt.figure(figsize=(6, 6))
-    ax: Axes = plt.gca()
-
-    ax.plot(alpha, ecp, color="blue", label="TARP")
-    ax.plot(alpha, alpha, color="black", linestyle="--", label="ideal")
-    ax.set_xlabel(r"Credibility Level $\alpha$")
-    ax.set_ylabel(r"Expected Coverage Probility")
-    ax.set_xlim(0.0, 1.0)
-    ax.set_ylim(0.0, 1.0)
-    ax.set_title(title)
-    ax.legend()
-    return fig, ax  # type: ignore