If you want to update MNE-Python to a newer version, there are a few different options, depending on how you originally installed it.
To update via the MNE-Python installers, simply download and run the latest installer for your platform. MNE-Python will be installed in parallel to your existing installation, which you may uninstall or delete if you don’t need it anymore.
If you’re not using the MNE-Python installers, keep reading.
Before performing package upgrade operations, check to make sure that the
environment you wish to modify has been activated (and if not, call
conda activate name_of_environment first).
Upgrading MNE-Python only#
If you wish to update MNE-Python only and leave other packages in their current
state, you can usually safely do this with
pip, even if you originally
installed via conda. With the
mne environment active, do:
$ pip install -U mne
Upgrading all packages#
Generally speaking, if you want to upgrade your whole software stack including all the dependencies, the best approach is to re-create it as a new virtual environment, because neither conda nor pip are fool-proof at making sure all packages remain compatible with one another during upgrades.
Here we’ll demonstrate renaming the old environment first, as a safety measure.
We’ll assume that the existing environment is called
mne and you want to
rename the old one so that the new, upgraded environment can be called
conda doesn’t have a “rename” command so we’ll first
clone the old one with a new name (
old_mne), then delete the original, then
create the new, updated environment re-using the original name. In the first
step we’ll also use conda in
--offline mode so that it uses cached
copies of all the packages instead of re-downloading them.
$ conda create --name old_mne --clone mne --offline # copy with new name, $ conda env remove --name mne --all # remove original, $ conda create --name mne --channel conda-forge mne # replace with updated
If you installed extra packages into your old
you’ll need to repeat that process after re-creating the updated
environment. Comparing the output of
conda list --name old_mne versus
conda list --name mne will show you what is missing from the new
environment. On Linux, you can automate that comparison like this:
$ diff <(conda list -n mne | cut -d " " -f 1 | sort) <(conda list -n old_mne | cut -d " " -f 1 | sort) | grep "^>" | cut -d " " -f 2
Upgrading to the development version#
Sometimes, new features or bugfixes become available that are important to your
research and you just can’t wait for the next official release of MNE-Python to
start taking advantage of them. In such cases, you can use
pip to install
the development version of MNE-Python:
$ pip install -U --no-deps https://github.com/mne-tools/mne-python/archive/main.zip