Welcome to the Toolbox for the Multistep Regression Estimator ("Mister Estimator").
If you find bugs, encounter unexpected behaviour or want to comment, please let us know via mail or open an issue on github. Any input is greatly appreciated.
- Documentation
- Getting Started
- Python Package index
- Github
- arXiv (a nicely-formated PDF)
- Details on the multistep regression estimator: J. Wilting and V. Priesemann, Nat. Commun. 9, 2325 (2018)
If you use our toolbox for a scientific publication please cite it as
Spitzner FP, Dehning J, Wilting J, Hagemann A, P. Neto J, Zierenberg J, et al. (2021) MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity. PLoS ONE 16(4): e0249447. https://doi.org/10.1371/journal.pone.0249447
- Python (>=3.5)
- numpy (>=1.11.0)
- scipy (>=1.0.0)
- matplotlib (>=1.5.3)
- numba (>=0.44), for parallelization
- tqdm, for progress bars
We recommend (and develop with) the latest stable versions of the dependencies, at the time of writing that is Python 3.7.0, numpy 1.15.1, scipy 1.1.0 and matplotlib 2.2.3.
18.10.2021 v0.1.8
- is a quick bugfix. We had to disable numba parallelization for the time being.
07.05.2021 Now published in PLOS ONE.
- arXiv:2007.03367: "MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity".
- Additional scripts and downloadable data to recreate the figure on triallength are available on gin.
Assuming a working Python3 environment, usually you can install via pip (also installs the optional dependencies):
pip3 install 'mrestimator[full]'
To install (or update an existing installation) with optional dependencies:
pip3 install -U 'mrestimator[full]'
If you run into problems during installation, they are most likely due to numpy and scipy. You may check the official scipy.org documentation or try using anaconda as outlined below.
We sincerely recommend using conda, more so if you are unsure about the dependencies on your system or lack administrator priviliges. It is easy to install, allows you to manage different versions of Python and if something breaks, you can role back and reinstall easily - all without leaving your user directory.
Head over to anaconda.com, and download the installer for Python 3.7.
After following the installation instructions (default settings are fine for most users),
start a new python session by typing python in a new terminal window.
You will see something similar to the following:
Python 3.7.0 (default, Jun 28 2018, 07:39:16)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>
End the session (exit() or Ctrl-D) and type conda list, which will output a list of the packages that came bundled with anaconda.
All dependencies for Mr. Estimator are included.
Optionally, you can create a new environment (e.g. named 'myenv') for the toolbox conda create --name myenv
and activate it with source activate myenv (activate myenv on windows).
For more details on managing environments with conda, see here.
Now install using pip: pip install 'mrestimator[full]' and afterwards you should be able to import the module into any python3 session
python
>>> import mrestimator as mre
INFO Loaded mrestimator v0.1.6, writing to /tmp/mre_paul/
Clone the repository via ssh or https
git clone git@github.com:Priesemann-Group/mrestimator.git
git clone https://github.com/Priesemann-Group/mrestimator.git
And optionally,
export PYTHONPATH="${PYTHONPATH}:$(pwd)/mrestimator"
This line adds the downloaded directory to your PYTHONPATH environment
variable, so that it will be found automatically when importing. If you want to add the path
automatically when you login, you can add it to your ~/.bashrc or ~/.profile:
echo 'export PYTHONPATH="${PYTHONPATH}:'$(pwd)'/mrestimator"' >> ~/.bashrc
You can upgrade to pre-release versions using pip
pip install -U --pre 'mrestimator[full]'
To revert to the stable version, run
pip install mrestimator==0.1.6
or
pip install --force-reinstall mrestimator
for a complete (longer) reinstall of all dependencies.
Per default, the toolbox and its dependencies use all threads available on the host machine. While this is great if running locally, it is undesired for distributed computing as the workload manager expects jobs of serial queues to only use one thread. To disable multi-threading, you can set the following environment variables (e.g. at the beginning of a job file)
export OPENBLAS_NUM_THREADS=1
export MKL_NUM_THREADS=1
export NUMEXPR_NUM_THREADS=1
export OMP_NUM_THREADS=1
export NUMBA_NUM_THREADS=1