Merge branch 'develop'

Author: CPrescher

.readthedocs.yaml

@@ -9,17 +9,20 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.11"
+    python: "3.12"
   jobs:
-    post_install:
+    post_create_environment:
+      # Install Poetry for dependency management
       - pip install poetry
-      - poetry config virtualenvs.create false
-      - poetry install
+    post_install:
+      # Install dependencies with 'docs' dependency group
+      # https://python-poetry.org/docs/managing-dependencies/#dependency-groups
+      # VIRTUAL_ENV needs to be set manually for now, see #11150
+      - VIRTUAL_ENV=$READTHEDOCS_VIRTUALENV_PATH poetry install --with docs
 
 # Build documentation in the "docs/" directory with Sphinx
 sphinx:
-   configuration: docs/conf.py
-
+  configuration: docs/conf.py
 # Optionally, build your docs in additional formats such as PDF and ePub
 # formats:
 #    - pdf

changelog.md

@@ -1,3 +1,10 @@
+## 2.2.1 (2025/07/21)
+
+### Bugfixes
+
+- when optimizing the density, the lorch modififcation function is applied in accordance with the calculation configuration. This means, that it is applied before the chi-square calculation.
+
+
 ## 2.2.0 (2025/07/21)
 
 ### New features

docs/conf.py

@@ -9,9 +9,9 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
 project = 'Glassure'
-copyright = '2023, Clemens Prescher'
+copyright = '2025, Clemens Prescher'
 author = 'Clemens Prescher'
-release = '1.3.0'
+release = '2.2.1'
 
 sys.path.insert(0, os.path.abspath('..'))
 # -- General configuration ---------------------------------------------------

glassure/__init__.py

@@ -3,7 +3,7 @@
 
 from .pattern import Pattern
 
-__version__ = "2.2.0"
+__version__ = "2.2.1"
 
 
 def _module_path():

glassure/optimization.py

@@ -117,9 +117,9 @@ def optimize_density(
     The density in the SampleConfig of the DataConfig is taking as starting parameter
 
     For method='gr' or method='fr' the optimization is based on the g(r) or f(r) function, and the density is
-    optimized to minimize the low g(r) or f(r) region to be close to zero. For better results, the g(r) function
-    is calculated with the Lorch modification function. The general procedure is explained in
-    Eggert et al. 2002 PRB, 65, 174105.
+    optimized to minimize the low g(r) or f(r) region to be close to zero. The Lorch modification function will be 
+    applied before calculating the chi square of the low r region if it is applied in the calculation configuration. 
+    The general procedure is explained in Eggert et al. 2002 PRB, 65, 174105.
 
     For method='sq' the optimization is based on the low Q part of the S(Q) function, and the density is optimized
     to minimize the difference between the original S(Q) function without any optimization and the optimized S(Q)
@@ -171,7 +171,9 @@ def optimize_density(
 
     :return:
         a tuple with four values:
-        - the density, the standard error, the background scaling and the standard error for optimization_method='lsq'
+        - the density,  its error value, the background scaling and the error value
+        whereby the error value is the standard error of the fit parameter for optimization_method ='lsq' and the
+        sum of the squared residuals for optimization_method='nelder'.
     """
 
     if calculation_config.optimize is None and min_range is None and not method == "sq":
@@ -191,7 +193,6 @@ def optimize_density(
     )
 
     optim_config = calculation_config.model_copy(deep=True)
-    optim_config.transform.use_modification_fcn = False
 
     if method == "sq":
         reference_config = calculation_config.model_copy(deep=True)