Replace poetry with pixi

docs/getting_started/pixi.md

@@ -0,0 +1,109 @@
+# Running AutoAFIDs with pixi
+
+AutoAFIDs can be installed and run using pixi on **Linux** system only. Pixi will manage all Python dependencies and non-python dependencies (c3d, greedy, ANTS) through conda environments.
+
+**Note:** Pixi installation is **not supported on Windows** at this time. If you are on Windows, please refer to the [Docker instructions](docker.md) instead.
+
+
+## For Users: Installing AutoAFIDs via Pixi
+
+
+### Steps
+ 1. Install pixi (if not already installed):
+    ```bash
+    curl -fsSL https://pixi.sh/install.sh | bash
+    ```
+
+ 2. Clone the repository and install dependencies:
+    ```bash
+    git clone https://github.com/afids/autoafids.git
+    cd autoafids
+    pixi install
+    ```
+
+## Development
+For development work, use the development environment which includes additional tools like formatters and linters:
+
+```bash
+pixi install --environment dev
+```
+
+Quality checks can be run using:
+```bash
+pixi run --environment dev quality_check  # Check code formatting
+pixi run  --environment dev quality_fix    # Fix code formatting
+```   
+
+## Usage
+
+### Test the installation
+
+Run the following command to verify the installation:
+
+```bash
+pixi run autoafids -h
+```
+
+You should see a help message listing all available command-line options.  
+If this runs successfully, you’re ready to start processing data with AutoAFIDs!
+
+## Running an example
+
+You can try AutoAFIDs on a sample dataset to make sure everything works as expected.
+
+First, download and extract a single-subject BIDS dataset for this test:
+
+```bash
+wget "https://www.dropbox.com/scl/fi/phmmofiy4q6o1k01rs6c4/ds003653.tar?rlkey=bpa8fxfl0lyrdc38fs6aowta7&st=zvhpqsga&dl=1" -O ds003653.tar
+tar -xvf ds003653.tar
+```
+
+This will create a `ds003653/` folder with a single subject that has both T1w and T2w images:
+
+```
+ds003653/
+├── dataset_description.json
+├── README
+└── sub-718211
+    └── ses-01
+        ├── anat
+        │   ├── sub-718211_ses-01_T1w.json
+        │   ├── sub-718211_ses-01_T1w.nii.gz
+        │   ├── sub-718211_ses-01_T2w.json
+        │   └── sub-718211_ses-01_T2w.nii.gz
+        ├── sub-718211_ses-01_scans.json
+        └── sub-718211_ses-01_scans.tsv
+```
+
+---
+
+### Run the full AutoAFIDs BIDS pipeline
+
+By default (Linux or Intel-based macOS), run:
+
+```bash
+pixi run autoafids ds003653 ds003653_autoafids participant --cores all
+```
+
+This will run the full pipeline and place results in a new `ds003653_autoafids/` folder.
+
+
+## Cache Directory
+
+When running, AutoAFIDs automatically downloads and caches the necessary CNN model to speed up subsequent runs.
+
+By default,  it's stored in the following directory:
+
+```bash
+~/.cache/autoafids/
+```
+
+You can override this default cache location by setting the `AUTOAFIDS_CACHE_DIR` environment variable:
+
+```bash
+export AUTOAFIDS_CACHE_DIR=/path/to/custom/cache
+```
+
+This is useful when working on shared systems, when home directory storage is limited, or if you wish to isolate data per project or user.
+
+Happy AFIDs placing!

docs/index.md

@@ -12,6 +12,7 @@ getting_started/installation
 getting_started/docker
 getting_started/apptainer
 getting_started/conda
+getting_started/pixi
 ```
 
 ```{toctree}

pyproject.toml

@@ -15,7 +15,7 @@ requires = ["hatchling"]
 
 [tool.pixi.workspace]
 channels = ["conda-forge", "bioconda"]
-platforms = ["linux-64"]
+platforms = ["linux-64", "osx-64"]
 
 [tool.pixi.feature.runtime.dependencies]
 snakebids = ">=0.14.0,<0.15"