Metadata-Version: 2.1
Name: quickdraws
Version: 0.0.2
Summary: Quickdraws is a software tool for performing Genome-Wide Association Studies (GWAS)
Author: Palamara Lab
Requires-Python: >=3.9,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: bitsandbytes
Requires-Dist: h5py
Requires-Dist: joblib
Requires-Dist: numba
Requires-Dist: numpy (<2)
Requires-Dist: pandas
Requires-Dist: pybgen
Requires-Dist: pysnptools[bgen]
Requires-Dist: rhe (==1.0.0)
Requires-Dist: scikit_learn
Requires-Dist: scipy
Requires-Dist: statsmodels
Requires-Dist: tables
Requires-Dist: torch (==2.*)
Requires-Dist: tqdm
Requires-Dist: wandb
Description-Content-Type: text/markdown

# Quickdraws

Quickdraws relies on cuda-enabled pytorch for speed, and it is expected to work on most cuda-compatible Linux systems.

## Installation

It is strongly recommended to either set up a python virtual environment, or a conda environment:

### Python virtual environment

```
python -m venv venv
source venv/bin/activate
pip install --upgrade pip setuptools wheel
```

### Conda environment

```
conda create -n quickdraws python=3.11 -y
conda activate quickdraws
pip install --upgrade pip setuptools wheel
```

### Install pytorch and quickdraws

It is necessary for anything bigger than toy examples to use a cuda-enabled version of pytorch.
Use the [pytorch configuration helper](https://pytorch.org/get-started/locally/) to find suitable installation instruction for your system.
The code snippet below will probably work for most systems:

```
pip install torch --index-url https://download.pytorch.org/whl/cu118
pip install quickdraws
```


## Running example

Once you install `quickdraws`, two executables should be available in your path: `quickdraws-step-1` and `quickdraws-step-2`.
Clone the Git repository to access the example data and script demonstrating how these can be used:

```
git clone https://github.com/PalamaraLab/quickdraws.git
cd quickdraws
bash run_example.sh
```


## Documentation
See https://github.com/PalamaraLab/quickdraws/wiki/Quickdraws


## Contact information
For any technical issues please contact Hrushikesh Loya (loya@stats.ox.ac.uk)


## Citation
Loya et al., "A scalable variational inference approach for increased mixed-model association power" under review

