Quickstart
==========
Installation
------------
Polaris is developed and tested on Linux machines with python3.9 and relies on several libraries including pytorch, scipy, etc.
We **strongly recommend** that you install Polaris in a virtual environment.
We suggest users using `conda `_ to create a virtual environment for it (It should also work without using conda, i.e. with pip). You can run the command snippets below to install Polaris:
.. code-block:: bash
git clone https://github.com/ai4nucleome/Polaris.git
cd Polaris
conda create -n polaris python=3.9
conda activate polaris
-------
Install Polaris:
.. code-block:: bash
./setup.sh
It will automatically download Polaris model's weights from `Hugging Face `_ and install Polaris.
You can also download model's weights file manually from `there `_ and put it in ``Polaris/polaris/model`` and change the file name to ``sft_loop.pt``.
The installation requires network access to download libraries. Usually, the installation will finish within 3 minutes. The installation time is longer if network access is slow and/or unstable.
Quick Usage
-----------
**See** `Jupyter Notebook CLI walkthrough `_ **and the** `CLI Reference `_ **for more information.**
Polaris takes submatrices of contact map as input and outputs predicted loops.
.. code-block:: bash
polaris loop pred -i [input mcool file] -o [output path of annotated loops]
Output format:
It contains tab separated fields as follows:
.. csv-table::
:header: "Field", "Detail"
:widths: 20, 30
"Chr1/Chr2", "chromosome names"
"Start1/Start2", "start genomic coordinates"
"End1/End2", "end genomic coordinates (i.e. End1=Start1+resol)"
"Score", "Polaris's loop score [0~1]"