finish preparation for release

joshspeagle · Apr 16, 2023 · cc1502f · cc1502f
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -8,7 +8,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 ### Added
 ### Changed
 
-## [2.1.1] - 2023-04-07
+## [2.1.1] - 2023-04-16
 ### Added
 ### Changed
 - Change the way initialization of live-points happens. Now if after 1000 iterations we managed to get at least a single point, the code will continue sampling after putting a warning. Previously the error was raised. (by @segasai )

diff --git a/RELEASE.md b/RELEASE.md
@@ -1,12 +1,13 @@
 This is an internal memo for things that need to be done for the release
 
-Essential steps (order is important):
+Essential steps for the release (order is important):
 
 * update the changelog.md and changelog in the docs
 * change the internal version number ( py/dynesty/_version.py )
 * git tag
 * release on pypi and github
 
+Things to check before the release
 
 Docs:
 * Make sure the docs are up to date with the changes
@@ -31,4 +32,3 @@ for a in `seq 0 35` ; do env OMP_NUM_THREADS=1 DYNESTY_TEST_RANDOMSEED=$a PYTHON
 ```
 It may be also needed to update the actual .ipynb files in the repo (and/or look at the results)
 
-
diff --git a/demos/Demo 2 - Dynamic Nested Sampling.ipynb b/demos/Demo 2 - Dynamic Nested Sampling.ipynb
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -98,6 +98,18 @@ Changelog
 .. image:: ../images/logo.gif
     :align: center
 
+2.1.1  (2023-04-16)
+--------------------
+Mostly bug fix release
+
+- Change the way initialization of live-points happens. Now if after 1000 iterations we managed to get at least a single point, the code will continue sampling after putting a warning. Previously the error was raised. (by @segasai )
+- Refactor the bound update code which will lead to more consistent boundary updates (#428 by @segasai )
+- Fix some pathological cases when uniform distribution is sampled with a very low log likelihood values
+- Fix a problem when a very small nlive is used leading to the error (#424 , reported by @frgsimpson)
+- Fix the incorrect update_interval calculation leading to too unfrequent updates of bounds when using dynamic sampler (report by @ajw278, analysis and fix by @segasai)
+- If you try to resume a previously finished dynamic run, the warning will be raised and the sampler will exit (previously an error could have occured in this case)
+
+
 2.1.0  (2023-02-03)
 --------------------
 
@@ -111,14 +123,14 @@ A release with major performance bug fix for the dynamical sampler.
 - A major performance regression has been discovered that lead to dynamic nested sampling batches becoming slower and slower. The regression was introduced in 1.2 and can easily lead to a factor of several slower performance if a large number of batches is used. (#415 ; discovery and fix by @segasai)
 - Some small fixes related to resuming of runs
 
-	    
+
 2.0.2 (2022-11-27)
 ------------------
 Minor bug fix release
 
 - When checkpointing is on the dynamic sampler will always checkpoint in the end of the run_nested() irrespective of checkpoint_time ( by @segasai )
 - Equally weighted samples are now randomly shuffled ( #408 ; by @segasai )
-- The live_points option was somewhat broken when blob option was introduced requiring a tuple of 4 elements irrespective of whether your likelihood returns blobs or not. Now if you use blob=True and want to provide live_points you need to provide 4 elements (u,v,logl,blobs). If you use blob=False you will need to provide just 3 elements as before (u,v,logl) ( by @segasai) 
+- The live_points option was somewhat broken when blob option was introduced requiring a tuple of 4 elements irrespective of whether your likelihood returns blobs or not. Now if you use blob=True and want to provide live_points you need to provide 4 elements (u,v,logl,blobs). If you use blob=False you will need to provide just 3 elements as before (u,v,logl) ( by @segasai)
 
 2.0.1 (2022-10-17)
 ------------------

diff --git a/tests/test_highdim.py b/tests/test_highdim.py
@@ -103,7 +103,7 @@ def do_gaussians(sample='rslice',
                  ndim_max=30,
                  nlive=5000):
     """
-    Run many tests 
+    Run many tests
     """
     with mp.Pool(nthreads) as pool:
         res = []
@@ -131,6 +131,9 @@ def do_gaussians(sample='rslice',
                              itertools.product([10, 30],
                                                ['rslice', 'rwalk', 'unif'])))
 def test_run(ndim, sample):
+    """
+    Run the Gaussian likelihood test
+    """
     rstate = get_rstate()
     co = Config(rstate, ndim)
     nlive = 5000
@@ -141,4 +144,3 @@ def test_run(ndim, sample):
                       rstate=rstate,
                       nlive=nlive)
     assert (np.abs(co.logz_truth_gau - res[1]) < 5 * res[2])
-    return res[3]