After submitting the preprint, I immediately started working on other
projects and decided to manage a Python/R/shell environments in a
unified way with conda. However, the Y chromosome project was
organized as a mix of a proper pyenv-based Python environment
(packages used are saved in requirements.txt) and the rest (R and
shell utilities) were organized independently. This is because when
this project started, conda could not really handle both Python and R
versioning under the same roof and R-specific environment managers
(packrat and, later, renv) were buggy.
To try to bridge the gap to the current conda setup during the process of revisions, I re-created a new Python environment using conda with the same version I used originally, including all packages. I’m not confident conda can handle R environments and packages well enough, so I’m sticking to using the default R installation on our system, with a mix of my own locally compiled packages and system-wide packages. This is not ideal, of course, but management of reproducible R environment is light-years behind what Python has and after wasting many hours trying to get for R something close to what I had with pyenv virtualenv for Python I gave up.
conda create -c defaults -n archaic-ychr python=3.7.4
I re-created the (originally pyenv-managed) Python environment within
conda by installing packages with pip using the requirements.txt
file (I don’t expect to be installing new Python packages for the
revisions, so this should be OK even though pip & conda are not
exactly a recommended combination):
conda activate archaic-ychr pip install -r requirements.txt python -m bash_kernel.install
Save the YAML config of the conda environment:
conda env export --from-history > environment.yml