diff --git a/.pypirc b/.pypirc
new file mode 100644
index 0000000000000000000000000000000000000000..554a52ad0c7c5abe48b3e37a85a8358993b037ba
--- /dev/null
+++ b/.pypirc
@@ -0,0 +1,8 @@
+[distutils]
+index-servers =
+ pypi
+
+[pypi]
+repository = https://pypi.org/project/bayesvalidrox/
+username = __token__
+password = pypi-bf547817-6852-4f59-90e2-3f3e3ee10b91
diff --git a/dist/bayesvalidrox-1.0.0-py3-none-any.whl b/dist/bayesvalidrox-1.0.0-py3-none-any.whl
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diff --git a/dist/bayesvalidrox-1.0.0.tar.gz b/dist/bayesvalidrox-1.0.0.tar.gz
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diff --git a/dist/bayesvalidrox-1.1.0.tar.gz b/dist/bayesvalidrox-1.1.0.tar.gz
new file mode 100644
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diff --git a/pyproject.toml b/pyproject.toml
index 764ab68dbd7838b3b8e4d2a861147f1c9be545e8..5f4ac8a9c78520106fb78d282587bcd8facef5fa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,13 +1,3 @@
-[project]
-name = "bayesvalidrox"
-version = "1.1.0"
-require-python = ">=3.9"
-classifiers = [
- "Programming Language :: Python :: 3",
- "License :: OSI Approved :: MIT License",
- "Operating System :: OS Independent",
-]
-
[build-system]
requires = [
"setuptools >= 40.6.0",
@@ -19,7 +9,3 @@ build-backend = "setuptools.build_meta"
pythonpath = [
".", "src",
]
-
-[project.urls]
-Homepage = "https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox"
-Source = "https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox"
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
index 1488ab01d5d867ed170bc8044021e5f77c71568a..fd0a0a323db00600fc26e5bbd9b763cb5054fd5f 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
[metadata]
name = bayesvalidrox
-version = 1.0.0
+version = 1.1.0
author = Farid Mohammadi, Rebecca Kohlhaas
author_email = farid.mohammadi@iws.uni-stuttgart.de, rebecca.kohlhaas@iws.uni-stuttgart.de
description = An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
diff --git a/src/bayesvalidrox.egg-info/PKG-INFO b/src/bayesvalidrox.egg-info/PKG-INFO
index 3d1d1085cd1fcd501ddb5c4bf2a74ee283eebef8..feed6a141361d2936b1ff43f7bd92f7a65e7146f 100644
--- a/src/bayesvalidrox.egg-info/PKG-INFO
+++ b/src/bayesvalidrox.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: bayesvalidrox
-Version: 1.0.0
+Version: 1.1.0
Summary: An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
Home-page: https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation
Author: Farid Mohammadi, Rebecca Kohlhaas
@@ -31,14 +31,33 @@ Requires-Dist: umbridge==1.2.4
# BayesValidRox
<div align="center">
- <img src="https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation/-/raw/master/docs/logo/bayesvalidrox-logo.png" alt="bayesvalidrox logo"/>
+ <img src="https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation/-/raw/master/docs/logo/BVRLogoV03_longtext.png" alt="bayesvalidrox logo"/>
</div>
An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
This framework provides an automated workflow for surrogate-based sensitivity analysis, Bayesian calibration, and validation of computational models with a modular structure.
+[](https://www.repostatus.org/#active)
+
+## Features
+* Surrogate modeling with Polynomial Chaos Expansion
+* Global sensitivity analysis using Sobol Indices
+* Bayesian calibration with MCMC using `emcee` package
+* Bayesian validation with model weights for multi-model setting
+
+## Resources
+The following resources are useful to get started on working with BayesValidRox:
+* [BayesValidRox website](https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/)
+* [User guide](https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/packagedescription.html)
+* [Tutorial](https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/tutorial.html)
+
+Important links:
+* [GitLab](https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox)
+* [Changelog](https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/CHANGELOG.md?ref_type=heads)
+
## Authors
- [@farid](https://git.iws.uni-stuttgart.de/farid)
+- [@RKohlhaas](https://git.iws.uni-stuttgart.de/RKohlhaas)
## Installation
The best practive is to create a virtual environment and install the package inside it.
@@ -62,25 +81,24 @@ and installing the version on the master branch can be done by cloning this repo
pip install .
```
-## Features
-* Surrogate modeling with Polynomial Chaos Expansion
-* Global sensitivity analysis using Sobol Indices
-* Bayesian calibration with MCMC using `emcee` package
-* Bayesian validation with model weights for multi-model setting
-
## Requirements
-* numpy==1.22.1
-* pandas==1.2.4
-* joblib==1.0.1
-* matplotlib==3.4.2
+python 3.10:
+* numpy>=1.23.5
+* pandas==1.4.4
+* joblib==1.1.1
+* matplotlib==3.8.0
* seaborn==0.11.1
-* scikit-learn==0.24.2
-* tqdm==4.61.1
+* scipy>=1.11.1
+* scikit-learn==1.3.1
+* tqdm>=4.61.1
* chaospy==4.3.3
* emcee==3.0.2
* corner==2.2.1
-* h5py==3.2.1
-* statsmodels==0.13.2
+* h5py==3.9.0
+* statsmodels==0.14.2
+* multiprocess==0.70.16
+* datasets==2.20.0
+* umbridge==1.2.4
## TexLive for Plotting with matplotlib
Here you need super user rights