Merge pull request #24 from hechth/prepare_0-2-2

Fixed affiliation and updated poetry

README.md

@@ -43,7 +43,15 @@ Or if you are a developer working in a local cloned version, you can install:
 
 Alternatively to the [PyPI version](https://pypi.org/project/DIMet/), our tool is also available as a [conda package](https://bioconda.github.io/recipes/dimet/README.html). Moreover, it can be used via Docker (`docker pull quay.io/biocontainers/dimet:0.1.4`) or singularity (`depot.galaxyproject.org/singularity/dimet:0.1.4--pyhdfd78af_0`) containers. 
 
+## Developer Setup
 
+To start contributing to DIMet you require a python environment with python >= 3.9 and [poetry]() installed.
+Poetry is a python build system and package manager and is used to build and develop DIMet.
+
+After creating the environment, the project can be installed with
+```bash
+poetry install
+```
 
 ## Code organization
 
@@ -59,6 +67,7 @@ Alternatively to the [PyPI version](https://pypi.org/project/DIMet/), our tool i
 
 * With pytest, by running `pytest` from `DIMet`
 * Alternatively, place yourself in `DIMet/tests` and execute `python -m unittest` 
+* If the project was installed with `poetry install`, tests can also be run using `poetry run pytest` or from VSCode's GUI
 
 -----------------------------------------------------------------------------------------------
 
@@ -96,8 +105,8 @@ For any information or help running DIMet, you can get in touch with:
     (2) CBiB - University of Bordeaux,
     146, rue Leo Saignat, Bordeaux, France
 
-    (3) Spectrometric Data Processing and Analysis,
-    Masaryk University, Brno, Czech Republic
+    (3) RECETOX
+    Faculty of Science, Masaryk University, Kotlářksá 2, 611 37 Brno, Czech Republic
 
     (4) University of Freiburg, 
     Freiburg, Germany

pyproject.toml

@@ -20,20 +20,19 @@ repository = "https://github.com/cbib/DIMet.git"
 
 [tool.poetry.dependencies]
 python = "^3.9"
-click = "*"
-coverage = "*"
-matplotlib = "~=3.7.1"
-numpy = "~=1.23.1"
-pandas = "~=1.5.3"
-pydantic = "^1.10.8"
-python-dotenv = "^1"
-pyyaml = "~=6.0"
-scikit-learn = "~=1.1.1"
-scipy = "~=1.9.1"
-seaborn = "~=0.11.2"
-statsmodels="~=0.13.5"
-hydra-core="~=1.3.2"
-hydra-colorlog="~=1.2.0"
+click = "^8.1.7"
+numpy = "^1.26.4"
+scipy = "^1.12.0"
+statsmodels = "^0.14.1"
+pandas = "^2.2.0"
+pydantic = "^2.6.1"
+python-dotenv = "^1.0.1"
+pyyaml = "^6.0.1"
+scikit-learn = "^1.4.0"
+seaborn = "^0.13.2"
+hydra-core = "^1.3.2"
+hydra-colorlog = "^1.2.0"
+
 
 [tool.poetry.group.test.dependencies]
 pytest="*"

src/dimet/processing/fit_statistical_distribution.py

@@ -109,7 +109,7 @@ def best_fit_distribution(data, bins=200):
         stats.gamma,
         stats.gengamma,
         stats.genhalflogistic,
-        stats.gilbrat,
+        stats.gibrat,
         stats.gompertz,
         stats.gumbel_r,
         stats.gumbel_l,