=============
Installation
=============

.. contents::
    :local:

Download
=========

BayesPI - Feature Learning Yard (FLY) is written in Python and can be installed and accessed via the command line. It is available for both Linux and macOS.
The package can be downloaded from the  `BayesPI-Fly GitHub page <https://github.com/BayesPI-Fly/bpf>`_.

Installation
===============

It is highly recommended to create a separate virtual environment for the package to avoid any library conflicts.
We recommend installing and using Miniconda/Anaconda (`Miniconda <https://docs.conda.io/en/latest/miniconda.html>`_). Refer to the `the Conda website <https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html>`_.

Before the installation, you can need to add the channels to your .condarc configuration file:

.. code-block:: yaml

   channels:
   - conda-forge
   - bioconda
   - defaults

You can use the following command to add the channels:

.. code-block:: bash 

   $ conda config --add channels defaults
   $ conda config --add channels bioconda
   $ conda config --add channels conda-forge

If Miniconda is already installed, you can proceed with the following step-by-step installation. 

1. Set Up New Environment
-------------------------------
Option1: Use the ``.yml``  file. 
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This file specifies exact package versions to ensure compatibility and reproducibility. The YAML file for BayesPI_FLY is as follows:

.. code-block:: yaml

   name: bpf_env
   channels:
   - conda-forge
   - bioconda
   - defaults

   dependencies:
  - python=3.9.19
  - numpy=1.23.0
  - pandas=1.4.4
  - seaborn=0.13.0
  - matplotlib=3.8.4
  - logomaker=0.8


.. code-block:: bash

   # Create the environment with yml file
   conda env create -f environment.yml --name  bpf_env

Option 2: Use ``requirements.txt``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Alternatively, you can create the environment first and then install dependencies using requirements.txt which only contains the list of dependencies:

.. code-block:: bash

   python==3.9.19
   pip==24.2
   numpy==1.23.0
   pandas==1.4.4
   seaborn==0.13.0
   matplotlib==3.8.4
   logomaker==0.8

.. code-block:: bash

   # Step 1: Ensure the Correct Conda Channel Order Before create the environment, you need to checking the conda channel list,making `conda-forge` is listed above `bioconda`
   
   # Check the current conda channel list
   conda config --show channels

   # Add channels (if not already present)
   conda config --add channels bioconda
   conda config --add channels conda-forge

.. code-block:: bash

   # Step 2: Create the Environment  
   After figuring out the conda channel list, you can use the following command to create the environment:

   # Create the environment
   conda create --name bpf_env --yes

   # Activate the environment
   conda activate bpf_env

   # Step 3: Install dependencies from requirements.txt using conda
   conda install --yes --file requirements.txt


2. Package Setup
=====================
Once the environment is ready, navigate to the ``code`` directory (folder containing setup.py) and run the following command:

.. code-block:: bash

   # Recommended installation method:
   $ install -m pip install .

   # Alternatively:
   $ python setup.py install

After this you should be ready to use bpf Python package. You may try to see if everything is ready by running the following command:

.. code-block:: bash

   $ bpf -h

The output should be something like this:

.. code-block:: bash

   usage: bpf <task> [<args>]

   Tasks available for using:

   bayesPI                 The main functionality to study protein-DNA interactions, includes the estimation of a transcription factor (TF) binding energy matrices, the computation of binding affinity of a TF target site and the corresponding chemical potential.

   interct2shape           Convert bayesPI predicted dinucleotide interaction matric to shape features matric

   shape2interct           Convert bayesPI predicted shape features matric to dinucleotide interaction matric

   shape_heatmap           Plot the heatmap based on the dinucleotide shape matric or dinucleotide interct matric 

   sequence_logo           Plot sequence markers based on modif likelihood files and add methylation intensity to the plot if needed

   BayesPI - Feature Learning Yard (FLY)- bpf

   positional arguments:
   task        Pipeline task to run

   optional arguments:
   -h, --help  show this help message and exit


3. Run Demos
=============
There are currently six demos avalible for using the BayesPI_FLY package.
Demo data can be found in the `demos <https://github.com/BayesPI-Fly/bpf/tree/main/demos>`_ folder. 
For more details, refer to the `demo.html <https://bayespi-fly.github.io/bpf/demo.html>`_.
Ensure that you create the output folder before running the demos.

.. code-block:: bash

   # Navigate to the demo1 directory:
   $ cd ../demos/demo1_motif/scripts

   # Run the demo1 script:
   $ ./job_demo1