A package to analyse calcium imaging data recorded with the Miniscope.
- miniscoPy is a Python 3 implementation of CNMF-E, an efficient algorithm to deconvolve calcium transients (Zhou et al, 2016).
- It is based on the CaImAn package. The CaImAn package is a versatile tool that can do several things (two photons, one photons, online analysis, etc). Here, the main idea is to have a simple package for straightforward analysis of Miniscope data with CNMF-E.
- What does it do?
- Loads Miniscope AVI files
- Creates one HDF5 file that includes everything
- Runs the motion correction algorithm
- Runs CNMF-E. That's it.
- What it does not do?
- Loads all the available movie format on the Planet.
- Creates memory map.
- Analyses two-photon imaging
- Other methods such as PCA/ICA, etc.
Yet, the architecture of this package is almost the same as the CaImAn package. If needed, it is thus very easy to insert CaImAn functions. Besides, the visualization tools of miniscoPy have been kept at its bare minimum purposefully.
To take a glimpse at the package, you can look at two Jupyter notebooks :
- main_cnmfe_e.ipynb for a minimal working example with actual data.
- main_test_hd_generated.ipynb for an example with simulated data of the head-direction system (requires BRIAN2 and XGBoost)
This installation protocol has been tested with a freshly installed Ubuntu 18.04 LTS. It is not guaranted if you have conflicting packages already installed. In this case, it may work better within a Miniconda environment (indications coming soon). First start with cloning and installing packages:
git clone https://github.com/peyrachelab/miniscoPy
cd miniscoPy
sudo apt-get install python3-pip
Most packages can be installed in one single command line.
pip3 install --user numpy scipy matplotlib jupyter pandas cython scikit-learn scikit-image sklearn h5py opencv-python tqdm future psutil
The most troublesome to install is pyav. This package allows for the fast loading of the avi files generated by the Miniscope recording software. According to the PyAV installation protocol and for Ubuntu >= 14.04 LTS, dependencies can be installed with:
sudo apt-get install -y python-dev pkg-config
sudo apt-get install -y libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libavresample-dev libavfilter-dev
pip3 install av
If it does not work, the best strategy is to start a conda environment and do:
conda install av -c conda-forge
If it still doens't work (and you cannot stand Conda environment and have already recompiled a linux kernel), the ultimate strategy is to compile the lib in a Conda environment and then to link it by hand.
Brian2 and xgboost are additional packages required to run the Jupyter notebook using simulated data, they are easy to install:
pip3 install --user brian2 xgboost
To install miniscoPy:
python3 setup.py build_ext -i
python3 setup.py install --user
In order to build miniscoPy on Windows you will need Microsoft Visual Studio 2015 or 2017 installed (Community Edition is OK) and Miniconda(preferably) or Anaconda.
First make sure you have conda-forge channel added to your conda installation:
conda config --show channels
If not - add the conda-forge channel by executing:
conda config --add channels conda-forge
Then create new conda environment and activate it:
conda create -n miniscoPy
activate miniscoPy
Install minimal set of packages required for data processing (without IPython and Jupyter):
conda install av scipy pandas cython scikit-learn scikit-image h5py opencv tqdm future pyyaml psutil
CD to a directory where files of this repository located and build C extention:
python setup.py build_ext -i
Check your environment by executing all test scripts located in the "testbench" directory:
python -m unittest discover testbench
Finally, process sample data:
python main_cnmf_e.py
CNMF-E has a lot of free parameters and two recording sessions from the same animal may be best analyzed with slightly different parameters. Thus, to keep track of the parameter set for each session, miniscoPy looks for a parameter file in the recording folder in yaml text format. An example of such file can be found in miniscoPy/example_movies/parameters.yaml.
The yaml file is loaded as a dictionary at the beginning of an analysis in a Python session:
import yaml
parameters = yaml.load(open('/example_movies/parameters.yaml', 'r'))The Miniscope recording software saves several avi files in the recording folder. Before starting the analysis pipeline, it is required to list all the avi files in a recording folder:
import glob
files = glob.glob('/example_movies/*.avi')Then, the first step is the motion correction algorithm :
from miniscopy.base.motion_correction import *
data, video_info = normcorre(files, None, parameters['motion_correction'])The second step is CNMF-E :
from miniscopy import CNMFE
cnm = CNMFE(data, parameters['cnmfe'])
cnm.fit()A minimal working example can be found in main_cnmf_e.py. Another example with generated data can be found in main_test_hd_generated.py.
- numpy and scipy
- pandas - Python Data Analysis Library
- OpenCv - Open Source Computer Vision Library
- h5py - Pythonic interface to the HDF5 binary data format
- av - Pythonic binding for FFmpeg or Libav
- scikit-learn - Machine learning in Python
- tqdm
Besides, codes from several toolboxes are used :
- Future development
- Integration of the great neuroseries
- Frame-by-frame dynamic visualization
- Conforming to Neurodata Without Borders
- 0.0.1 (Current)
- Work in progress
Send us an email or post an Issue.
- Guillaume Viejo - Initial work
- Elena Kerjean (Sciences Sorbonne Université)
This project is licensed under the GNU License - see the LICENSE.md file for details.
- Hat tip to all the contributors of the different packages used here. Would not have been possible from scratch!
- Guillaume Etter from Sylvain Williams's lab for the miniscope calcium imaging movie example.
- Thanks Denis Polygalov for troubleshooting the Windows installation.