Merge branch 'master' into 71-joss-paper

.github/workflows/deploy.yaml

@@ -29,12 +29,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
       - name: setup_miniconda
-        uses: conda-incubator/setup-miniconda@v2
+        uses: conda-incubator/setup-miniconda@v3
         with:
           python-version: "3.10"
           auto-update-conda: true
@@ -55,7 +55,7 @@ jobs:
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           password: ${{ secrets.TEST_PYPI_API_TOKEN }}
-          repository_url: https://test.pypi.org/legacy/
+          repository-url: https://test.pypi.org/legacy/
 
       - name: publish_pypi
         if: github.event_name == 'release' && github.event.action == 'published'

CHANGES

@@ -12,6 +12,14 @@ The rules for this file:
   * accompany each entry with github issue/PR number (Issue #xyz)
   * release numbers follow "Semantic Versioning" https://semver.org
 
+09/19/2024 orbeckst, jaclark5
+
+  * 2.4.1
+
+Fixes
+  - [doc] tutorial: use alchemlyb.concat (PR #399)
+  - Resolve pandas FutureWarnings in bar_.py and mbar_.py (issue #408 PR #407)
+
 
 09/17/2024 jaclark5, orbeckst
 
@@ -25,6 +33,7 @@ Enhancements
 Changes
   - modernize build system: replaced setup.py,cfg with pyproject.toml (#385)
 
+
 08/24/2024 xiki-tempula
 
   * 2.3.2

CITATION.cff

@@ -3,10 +3,11 @@
 # Visit https://bit.ly/cffinit to generate yours today!
 
 cff-version: 1.2.0
-title: alchemlyb
+title: 'alchemlyb: the simple alchemistry library'
 message: >-
   If you use this software, please cite it using the
-  preferred citation together with any other references.
+  preferred citation (JOSS DOI 10.21105/joss.06934) together
+  with any other references.
 type: software
 authors:
   - email: david@datryllic.com

docs/tutorial.rst

@@ -76,7 +76,7 @@ method. More estimators are available in the section on
     >>> import pandas as pd
 
     >>> mbar = MBAR()
-    >>> mbar.fit(pd.concat(decorrelated_u_nk_list))
+    >>> mbar.fit(alchemlyb.concat(decorrelated_u_nk_list))
     >>> mbar.delta_f_
               0.00      0.25      0.50      0.75      1.00
     0.00  0.000000  1.613595  2.553407  2.983336  3.039517
@@ -152,4 +152,4 @@ also perform a set of analysis that allows the user to examine the quality of
 the estimation.
 
 
-.. _alchemtest: https://github.com/alchemistry/alchemtest
+.. _alchemtest: https://github.com/alchemistry/alchemtest

src/alchemlyb/estimators/bar_.py

@@ -97,7 +97,11 @@ def fit(self, u_nk):
         # group u_nk by lambda states
         groups = u_nk.groupby(level=u_nk.index.names[1:])
         N_k = [
-            (len(groups.get_group(i)) if i in groups.groups else 0)
+            (
+                len(groups.get_group(i if isinstance(i, tuple) else (i,)))
+                if i in groups.groups
+                else 0
+            )
             for i in u_nk.columns
         ]
 
@@ -119,12 +123,21 @@ def fit(self, u_nk):
                 continue
 
             # get us from lambda step k
-            uk = groups.get_group(self._states_[k])
+            uk = groups.get_group(
+                self._states_[k]
+                if isinstance(self._states_[k], tuple)
+                else (self._states_[k],)
+            )
             # get w_F
             w_f = uk.iloc[:, k + 1] - uk.iloc[:, k]
 
             # get us from lambda step k+1
-            uk1 = groups.get_group(self._states_[k + 1])
+            uk1 = groups.get_group(
+                self._states_[k + 1]
+                if isinstance(self._states_[k + 1], tuple)
+                else (self._states_[k + 1],)
+            )
+
             # get w_R
             w_r = uk1.iloc[:, k] - uk1.iloc[:, k + 1]
 

src/alchemlyb/estimators/mbar_.py

@@ -127,7 +127,11 @@ def fit(self, u_nk):
 
         groups = u_nk.groupby(level=u_nk.index.names[1:])
         N_k = [
-            (len(groups.get_group(i)) if i in groups.groups else 0)
+            (
+                len(groups.get_group(i if isinstance(i, tuple) else (i,)))
+                if i in groups.groups
+                else 0
+            )
             for i in u_nk.columns
         ]
         self._states_ = u_nk.columns.values.tolist()