Merge branch 'main' into feat/datastores

.github/workflows/ci-pdm-install-and-test-cpu.yml

@@ -46,7 +46,7 @@ jobs:
 
       - name: Run tests
         run: |
-          pdm run pytest -s tests/
+          pdm run pytest
 
       - name: Save cache data
         uses: actions/cache/save@v4

.github/workflows/ci-pdm-install-and-test-gpu.yml

@@ -51,7 +51,7 @@ jobs:
 
       - name: Run tests
         run: |
-          pdm run pytest -s tests/
+          pdm run pytest
 
       - name: Save cache data
         uses: actions/cache/save@v4

.github/workflows/ci-pip-install-and-test-cpu.yml

@@ -36,7 +36,7 @@ jobs:
 
       - name: Run tests
         run: |
-          python -m pytest -s tests/
+          python -m pytest
 
       - name: Save cache data
         uses: actions/cache/save@v4

.github/workflows/ci-pip-install-and-test-gpu.yml

@@ -41,7 +41,7 @@ jobs:
 
       - name: Run tests
         run: |
-          python -m pytest -s tests/
+          python -m pytest
 
       - name: Save cache data
         uses: actions/cache/save@v4

CHANGELOG.md

@@ -90,10 +90,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   [\#52](https://github.com/mllam/neural-lam/pull/52)
   @joeloskarsson
 
-- Cap numpy version to < 2.0.0
+- Cap numpy version to < 2.0.0 (this cap was removed in #37, see below)
   [\#68](https://github.com/mllam/neural-lam/pull/68)
   @joeloskarsson
 
+- Remove numpy < 2.0.0 version cap
+  [\#37](https://github.com/mllam/neural-lam/pull/37)
+  @leifdenby
+
 - turn `neural-lam` into a python package by moving all `*.py`-files into the
   `neural_lam/` source directory and updating imports accordingly. This means
   all cli functions are now invoke through the package name, e.g. `python -m

README.md

@@ -126,14 +126,10 @@ setup](.github/workflows/) which you can use as a reference.
 1. Clone this repository and navigate to the root directory.
 > If you are happy using the latest version of `torch` with GPU support (expecting the latest version of CUDA is installed on your system) you can skip to step 3.
 2. Install a specific version of `torch` with `python -m pip install torch --index-url https://download.pytorch.org/whl/cpu` for a CPU-only version or `python -m pip install torch --index-url https://download.pytorch.org/whl/cu111` for CUDA 11.1 support (you can find the correct URL for the variant you want on [PyTorch webpage](https://pytorch.org/get-started/locally/)).
-3. Install the dependencies with `python -m pip install .`. If you will be developing `neural-lam` we recommend to install in editable mode with `python -m pip install -e .` so you can make changes to the code and see the effects immediately. The development dependencies to install are listed in `pyproject.toml`.
-
-
-For each of these three datastore implementations there is a section below detailing how to use them.
-
-support both input data in `.npy`-file format (introduced in `v0.1.0`)
+3. Install the dependencies with `python -m pip install .`. If you will be developing `neural-lam` we recommend to install in editable mode and install the development dependencies with `python -m pip install -e ".[dev]"` so you can make changes to the code and see the effects immediately.
 
 
+## Data
 Datasets should be stored in a directory called `data`.
 See the [repository format section](#format-of-data-directory) for details on the directory structure.
 

pyproject.toml

@@ -116,9 +116,3 @@ min-similarity-lines=10
 [build-system]
 requires = ["pdm-backend"]
 build-backend = "pdm.backend"
-
-
-[tool.docformatter]
-recursive = true
-blank = true
-black = true