Skip to content

Installation

Ideally, before installation, create a clean python3.7+ virtual environment to deploy the package. Earlier version of Python3 should also work but Python 2 is not supported. For example one can use conda or virtualenvwrapper.

With virtualenvwrapper:

mkvirtualenv pycoMeth -p python3.7
workon pycoMeth

With conda:

conda create -n pycoMeth python=3.7
conda activate pycoMeth

You might also want to install Nanopolish in the same virtual environment so you can pipe nanopolish output directly into pycoMeth

Dependencies

Nanopolish 0.10+ is not a direct dependency but is required to generate the files used by several commands from this package

pycoMeth relies on a the following robustly maintained third party python libraries:

  • numpy>=1.14.0
  • scipy>=1.4.1
  • statsmodels>=0.11.1
  • pandas>=1.0.3
  • Jinja2>=2.11.1
  • plotly>=4.6.0
  • pyfaidx>=0.5.8
  • tqdm>=4.45.0
  • colorlog>=4.1.0
  • kaleido New library being developed by the plotly team for static image export

The correct versions of packages are installed together with the software when using pip or conda

Option 1: Installation with pip from pypi

Install or upgrade the package with pip from pypi

pip install pycoMeth

You can also update to the unstable development version from test.pypi repository

pip install --index-url https://test.pypi.org/simple/ pycoMeth -U

Option 2: Installation with conda from Anaconda cloud

# First installation
conda install -c aleg -c plotly pycometh

You can also get the unstable development version from the dev channel

conda update -c aleg_dev -c plotly pycometh

Option 3: Installation with pip from Github

Or from github to get the last version

# First installation
pip install git+https://github.com/a-slide/pycoMeth.git

# First installation bleeding edge
pip install git+https://github.com/a-slide/pycoMeth.git@dev

# Update to last version
pip install git+https://github.com/a-slide/pycoMeth.git --upgrade

Option 4: Clone the repository and install locally in develop mode

With this option, the package will be locally installed in editable or develop mode. This allows the package to be both installed and editable in project form. This is the recommended option if you wish to modify the code and/or participate to the development of the package (see contribution guidelines).

# Clone repo localy
git clone https://github.com/a-slide/pycoMeth.git

# Enter in repo directory
cd pycoMeth

# Make setup.py executable
chmod u+x setup.py

# Install with pip3
pip3 install -e ./