doc link to paper

Author: cmbant

.github/workflows/build_gui_apps.yml

@@ -19,7 +19,7 @@ on:
         default: false
         type: boolean
   push:
-    branches: [ main, master, test ]
+    branches: [ gui ]
     tags:
       - 'v*'
       - '*.*.*'

.github/workflows/tests.yml

@@ -1,6 +1,12 @@
 name: tests
 
-on: [push, pull_request]
+on:
+  push:
+    paths-ignore:
+      - 'docs/**'
+  pull_request:
+    paths-ignore:
+      - 'docs/**'
 
 jobs:
   build:

docs/source/index.rst

@@ -9,8 +9,10 @@ GetDist is a Python package for analysing and plotting Monte Carlo (or other) sa
    Introduction <intro>
    Plot gallery and tutorial <https://getdist.readthedocs.io/en/latest/plot_gallery.html>
    GetDist GUI program <gui>
+   Paper with technical details <https://arxiv.org/abs/1910.13970>
 
-**LLM Integration**: For AI assistants and LLM agents, a comprehensive context document is available at `GetDist LLM Context <https://getdist.readthedocs.io/en/latest/_static/getdist_docs_combined.md>`_.
+
+**LLM Integration**: For AI assistants and LLM agents, a comprehensive `LLM context document <https://getdist.readthedocs.io/en/latest/_static/getdist_docs_combined.md>`_ can be used.
 There's also an `AI help assistant <https://cosmocoffee.info/help_assist.php>`_ you can use to ask about the package.
 
 High-level modules for analysing samples and plotting:

getdist/densities.py

@@ -207,10 +207,10 @@ def getLimits(self, p, interpGrid=None, accuracy_factor=None):
         Get parameter equal-density confidence limits (a credible interval).
         If the density is bounded, may only have a one-tail limit.
 
-        :param p: list of limits to calculate, e.g. [0.68, 0.95]
+        :param p: limit to calculate, or list of limits to calculate, e.g. [0.68, 0.95]
         :param interpGrid: optional pre-computed cache
         :param accuracy_factor: parameter to boost default accuracy for fine sampling
-        :return: list of (min, max, has_min, has_top) values
+        :return: list of (min, max, has_min, has_top) values, or tuple fo single limit,
                 where has_min and has_top are True or False depending on whether lower and upper limit exists
         """
         g = interpGrid or self.initLimitGrids(accuracy_factor)