quickdraws 0.1.5

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 either:

1. on Linux, a cuda-enabled version of pytorch (now available by default from PyPI)
2. on macOS, the latest pytorch, which can leverage the MPS backend

Use the pytorch configuration helper to find suitable installation instruction for your system, based on your preferred CUDA version. If you want a specific CUDA version, you may need to start with something like pip install torch --index-url https://download.pytorch.org/whl/cu118.

The code snippet below will probably work for most systems, and should install quickdraws in approximately 10 minutes for cuda or much quicker on macOS:

Linux or macOS

pip install quickdraws

Running example

Once you install quickdraws, three executables should be available in your path:

1. convert-to-hdf5
2. quickdraws-step-1
3. 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

Local development

To make changes to the quickdraws sourcecode, obtain the repository and install it using poetry. Assuming you have poetry installed:

git clone https://github.com/PalamaraLab/quickdraws.git
cd quickdraws
poetry install

Documentation

See https://palamaralab.github.io/software/quickdraws/manual/

Summary Statistics for some UKB traits

See https://www.stats.ox.ac.uk/publication-data/sge/ppg/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

Release Notes

v0.1.5 (2025-08-03)

• Pandas bug fixes in summary statistics calculation with sample_weights option
• Supports non integer FID/IID sample names now

v0.1.4 (2025-03-27)

• Bug fix, introduced --chunksize argument in quickdraws-step-1 aswell

v0.1.3 (2025-01-24)

• Better memory usage and speed for step 0, introduced --chunksize argument

v0.1.2 (2025-01-07)

• Minor updates to documentation
• Remove reliance on specific CUDA torch for Linux
• Resolve numpy dependency conflict

v0.1.1 (2024-10-24)

• Minor updates to documentation
• Remove reliance on pre-release Torch for macOS

v0.1.0 (2024-10-15)

First public release to accompany the paper (see citation above).