An all-in-one toolkit package to easy my Python work in my PhD.
This code is compatible with
If for some reason you have a need for it, create & activate a virtual enrivonment, then install with pip:
> pip install pyhdtoolkit
> git clone https://github.com/fsoubelet/PyhDToolkit.git > cd PyhDToolkit > poetry install
Standards, Testing, Tools and VCS
This repository follows the
make format and lint it (which will format first) with
Testing builds are ensured after each commit through Travis-CI.
You can run tests locally with the predefined
make tests, or through
poetry run pytest <options> for customized options.
Feel free to explore the
Makefile for sensible defaults commands.
You will get an idea of what functionality is available by running
This repository currently comes with an
environment.yml file to reproduce my work
You can install this environment and add it to your ipython kernel by running
You can directly pull a pre-built image - tag
latest is an automated build - from
> docker pull fsoubelet/simenv
You can then run the container in interactive mode, and make use of the already activated
It is highly advised to run with
--init for zombie processes protection (see Tini for details).
Assuming you pulled the provided image from Dockerhub, the command is then (remove the
--rm flag if you wish to preserve it after running):
> docker run -it --rm --init fsoubelet/simenv
If you want to do some exploration through
jupyter you will need to install it first as it is not bundled in the image, then add the custom environment kernelspec.
Run the following command before heading over to
> docker run -it --rm --init -p 8888:8888 fsoubelet/simenv /bin/bash -c "/opt/conda/bin/conda install -c conda-forge jupyterlab -y --quiet > /dev/null && mkdir /opt/notebooks && /opt/conda/envs/PHD/bin/ipython kernel install --user --name=PHD && /opt/conda/bin/jupyter lab --notebook-dir=/opt/notebooks --ip='*' --port=8888 --no-browser --allow-root"
Copyright © 2019-2020 Felix Soubelet. MIT License
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