This is a leaner and cleaner version of the code based off the HNN repository. However, a Graphical User Interface is not supported at the moment in this repository.
It is early Work in Progress. Contributors are very welcome.
- numpy
- scipy
- matplotlib
- Neuron (>=7.7)
- joblib (for simulating trials simultaneously)
- mpi4py (for simulating the cells in parallel for a single trial). Also depends on:
- openmpi or other mpi platform installed on system
- psutil
We recommend the Anaconda Python distribution.
To install hnn-core, simply do:
$ pip install hnn_core
and it will install hnn-core along with the dependencies which are not already installed.
Note that if you installed Neuron using the traditional installer package, it is recommended
to remove it first and unset PYTHONPATH and PYTHONHOME if they were set. This is
because the pip installer works better with virtual environments such as the ones provided by conda.
If you want to track the latest developments of hnn-core, you can install the current version of the code (nightly) with:
$ pip install --upgrade https://api.github.com/repos/jonescompneurolab/hnn-core/zipball/master
To check if everything worked fine, you can do:
$ python -c 'import hnn_core'
and it should not give any error messages.
Parallel backends
For further instructions on installation and usage of parallel backends for using more than one CPU core, refer to parallel_backends
Note for Windows users
The pip installer for Neuron does not yet work for Windows. In this case, it is better to
install hnn_core without the dependencies:
$ pip install hnn_core --no-deps
and then install the dependencies separately:
$ pip install scipy numpy matplotlib
and install Neuron using the traditional package installer available here https://neuron.yale.edu/neuron/.
Once you have tested that hnn_core and its dependencies were installed,
we recommend downloading and executing the
example scripts
provided on the documentation pages
(as well as in the GitHub repository).
Note that python plots are by default non-interactive (blocking): each plot must thus be closed before the code execution continues. We recommend using and 'interactive' python interpreter such as ipython:
$ ipython --matplotlib
and executing the scripts using the %run-magic:
%run plot_simulate_evoked.py
When executed in this manner, the scripts will execute entirely, after which all plots will be shown. For an even more interactive experience, in which you execute code and interrogate plots in sequential blocks, we recommend editors such as VS Code and Spyder.
Use the github issue tracker to report bugs. For user questions and scientific discussions, please join the HNN Google group.
Read our contributing guide.