From 64dc842694245f37f369f2f071d13375d1ca7eff Mon Sep 17 00:00:00 2001
From: aloctavodia <aloctavodia@gmail.com>
Date: Wed, 12 Mar 2025 16:43:04 +0200
Subject: [PATCH] fix regression bug
---
src/arviz_plots/plots/psensequantitiesplot.py | 50 ++++++++++---------
1 file changed, 26 insertions(+), 24 deletions(-)
diff --git a/src/arviz_plots/plots/psensequantitiesplot.py b/src/arviz_plots/plots/psensequantitiesplot.py
index 09939e5e..b81a1056 100644
--- a/src/arviz_plots/plots/psensequantitiesplot.py
+++ b/src/arviz_plots/plots/psensequantitiesplot.py
@@ -7,8 +7,8 @@
from arviz_stats.psense import power_scale_dataset
from xarray import concat
-from arviz_plots.plot_collection import PlotCollection
-from arviz_plots.plots.utils import filter_aes, process_group_variables_coords, set_figure_layout
+from arviz_plots.plot_collection import PlotCollection, process_facet_dims
+from arviz_plots.plots.utils import filter_aes, process_group_variables_coords
from arviz_plots.visuals import hline, labelled_title, labelled_x, line_xy, scatter_xy, set_xticks
@@ -34,10 +34,6 @@ def plot_psense_quantities(
):
"""Plot power scaled posterior quantities.
- The posterior sensitivity is assessed by power-scaling the prior or likelihood and
- visualizing how quantities computed from the posterior change.
- Pareto-smoothed importance sampling is used to avoid refitting as explained in [1]_.
-
Parameters
----------
dt : DataTree
@@ -115,11 +111,6 @@ def plot_psense_quantities(
.. minigallery:: plot_psense_quantities
- References
- ----------
- .. [1] Kallioinen et al, *Detecting and diagnosing prior and likelihood sensitivity with
- power-scaling*, Stat Comput 34, 57 (2024), https://doi.org/10.1007/s11222-023-10366-5
-
"""
if sample_dims is None:
sample_dims = rcParams["data.sample_dims"]
@@ -254,10 +245,22 @@ def plot_psense_quantities(
if dim not in sample_dims + ["component_group", "alpha"]
],
)
+ figsize = pc_kwargs["plot_grid_kws"].get("figsize", None)
+ figsize_units = pc_kwargs["plot_grid_kws"].get("figsize_units", "inches")
+ col_dims = pc_kwargs["cols"]
+ row_dims = pc_kwargs["rows"]
+ if figsize is None:
+ figsize = plot_bknd.scale_fig_size(
+ figsize,
+ rows=process_facet_dims(ds_quantities, row_dims)[0],
+ cols=process_facet_dims(ds_quantities, col_dims)[0],
+ figsize_units=figsize_units,
+ )
+ figsize_units = "dots"
+ pc_kwargs["plot_grid_kws"]["figsize"] = figsize
+ pc_kwargs["plot_grid_kws"]["figsize_units"] = figsize_units
- pc_kwargs = set_figure_layout(pc_kwargs, plot_bknd, ds_quantities)
-
- plot_collection = PlotCollection.wrap(
+ plot_collection = PlotCollection.grid(
ds_quantities,
backend=backend,
**pc_kwargs,
@@ -370,7 +373,7 @@ def plot_psense_quantities(
# set xlabel
_, xlabels_aes, xlabels_ignore = filter_aes(plot_collection, aes_map, "xlabel", sample_dims)
- xlabel_kwargs = copy(plot_kwargs.get("xlabel", {}))
+ xlabel_kwargs = plot_kwargs.get("xlabel", {}).copy()
if xlabel_kwargs is not False:
if "color" not in xlabels_aes:
xlabel_kwargs.setdefault("color", "black")
@@ -389,14 +392,13 @@ def plot_psense_quantities(
title_kwargs = copy(plot_kwargs.get("title", {}))
_, _, title_ignore = filter_aes(plot_collection, aes_map, "title", sample_dims)
- if title_kwargs is not False:
- plot_collection.map(
- labelled_title,
- "title",
- ignore_aes=title_ignore,
- subset_info=True,
- labeller=labeller,
- **title_kwargs,
- )
+ plot_collection.map(
+ labelled_title,
+ "title",
+ ignore_aes=title_ignore,
+ subset_info=True,
+ labeller=labeller,
+ **title_kwargs,
+ )
return plot_collection