Updated version and readme

Author: JR-1991

.github/workflows/publish.yml

@@ -2,7 +2,7 @@ name: Build and publish
 
 on:
   release:
-    types: [published]
+    types: [released]
 
 jobs:
   deploy:
@@ -12,6 +12,6 @@ jobs:
     steps:
         - uses: actions/checkout@v2
         - name: "Build and publish to PyPi"
-            uses: JRubics/poetry-publish@v1.17
-            with:
-                pypi_token: ${{ secrets.PYPI_TOKEN }}
+          uses: JRubics/poetry-publish@v1.17
+          with:
+            pypi_token: ${{ secrets.PYPI_TOKEN }}

README.md

@@ -25,9 +25,9 @@ python -m pip install git+https://github.com/JR-1991/Catalax.git
 To develop a model, Catalax offers a user-friendly interface that comprises two core components: `Species` and `ODE`. The former is utilized to specify the species of the model, while the latter is used to define its dynamics. Through the integration of these components, a robust model is created, which can be employed for inference purposes. Notably, Catalax automatically generates `Parameter` objects from the extracted parameters, which can be leveraged to define priors and constraints for the model.
 
 ```python
-from catalax import Model
+import catalax as ctx
 
-model = Model(name="My Model")
+model = ctx.Model(name="My Model")
 
 # Define the species of the model
 model.add_species(s1="Substrate", e1="Enzyme")
@@ -61,4 +61,11 @@ f = visualize(
 
 ### Give it a try!
 
-To get a better understanding of Catalax, we recommend that you try out the examples found in the `examples` directory. These examples are designed to showcase the capabilities of Catalax and provide a starting point for your own projects.
+To get a better understanding of Catalax, we recommend that you try out the examples found in the `examples` directory. These examples are designed to showcase the capabilities of Catalax and provide a starting point for your own projects:
+
+* [Basic functions](/examples/Basics.ipynb) - An overview to models and integration
+* [Optimization](/examples/Optimization.ipynb) - How to perform parameter estimation using Catalax
+* [Non observable species](/examples/Optimization.ipynb) - How to deal with non-observable species
+* [Hamiltonian MC](/examples/HMC.ipynb) - How to perform parameter inference using Hamiltonian Monte Carlo
+* [Neural Ordinary Differential Equations](/examples/Neural_ODE.ipynb) - How to use neural ODEs to model system dynamics
+* [Neural ODEs and HMC](/examples/SurrogateHMC.ipynb) - How to perform parameter inference using surrogate HMC

catalax/__init__.py

@@ -3,7 +3,7 @@
 from .tools.optimization import optimize
 from .tools.visualization import visualize
 
-__version__ = "0.2.0"
+__version__ = "0.3.0"
 
 PARAMETERS = InAxes.PARAMETERS
 TIME = InAxes.TIME

examples/MCMC.ipynb examples/HMC.ipynbexamples/MCMC.ipynb renamed to examples/HMC.ipynb

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "catalax"
-version = "0.2.0"
+version = "0.3.0"
 description = "A JAX-based framework for (neural) ODE modelling in biocatalysis."
 authors = ["Jan Range <30547301+JR-1991@users.noreply.github.com>"]
 license = "MIT License"