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Package for exrtacting, processing and analyzing Intan and OpenEphys data

Project description

See the full documentation here.

<small>Table of contents generated with markdown-toc</small>


This is a package to extract, process and analyze electrophysiology data recorded with Intan or OpenEphys recording systems. This package is customized to store experiment and analysis metadata for the BLECh Lab (Katz lab) @ Brandeis University, but can readily be used and customized for other labs.


If is set this up correctly you can install with pip: pip install blechpy

If you are setting up from source you can create a compatible conda environment with: conda env create --name blech -f=conda_environment.yml

Can then handle all data from within an ipython terminal conda activate blech ipython

import blechpy


blechpy handles experimental metadata using data_objects which are tied to a directory encompassing some level of data. Existing types of data_objects include:

  • dataset
    • object for a single recording session
  • experiment
    • object encompasing an ordered set of recordings from a single animal
    • individual recordings must first be processes as datasets
  • project
    • object that can encompass multiple experiments & data groups and allow analysis or group differences


Right now this pipeline is only compatible with recordings done with Intan's 'one file per channel' or 'one file per signal type' recordings settings.

Starting wit a raw dataset

Create dataset

With a brand new shiny recording you can initilize a dataset with:

dat = blechpy.dataset('path/to/recording/directory')
# or
dat = blechpy.dataset()  # for user interface to select directory

This will create a new dataset object and setup basic file paths. If you're working via SSH or just want a command-line interface instead of a GUI you can use the keyword argument shell=True

Initialize Parameters

# or

Initalizes all analysis parameters with a series of prompts. See prompts for optional keyword params. Primarily setups parameters for:

  • Flattening Port & Channel in Electrode designations
  • Common average referencing
  • Labelling areas of electrodes
  • Labelling digital inputs & outputs
  • Labelling dead electrodes
  • Clustering parameters
  • Spike array creation
  • PSTH creation
  • Palatability/Identity Responsiveness calculations

Initial parameters are pulled from default json files in the dio subpackage. Parameters for a dataset are written to json files in a parameters folder in the recording directory

Basic Processing


Can provide an overview of basic data extraction and processing steps that need to be taken.

An example data extraction workflow would be:

dat = blechpy.dataset('/path/to/data/dir/')
dat.extract_data()          # Extracts raw data into HDF5 store
dat.create_trial_list()     # Creates table of digital input triggers
dat.mark_dead_channels()    # View traces and label electrodes as dead
dat.common_average_reference() # Use common average referencing on data. 
                               # Repalces raw with referenced data in HDF5 store
dat.blech_clust_run()       # Cluster data using GMM
dat.blech_clust_run(data_quality='noisy') # re-run clustering with less strict parameters

dat.sort_units()        # Split, merge and label clusters as units

Viewing a Dataset

Experiments can be easily viewed wih: print(dat) A summary can also be exported to a text with: dat.export_to_text()

Loading an existing dataset

dat = blechpy.load_dataset() # load an existing dataset from .p file
# or
dat = blechpy.load_dataset('path/to/recording/directory') 
# or
dat = blechpy.load_dataset('path/to/dataset/save/file.p')

Import processed dataset into dataset framework

dat = blechpy.port_in_dataset()
# or
dat = blechpy.port_in_dataset('/path/to/recording/directory')


Creating an experiment

exp = blechpy.experiment('/path/to/dir/encasing/recordings')
# or
exp = blechpy.experiment()

This will initalize an experiment with all recording folders within the chosen directory.

Editing recordings

exp.add_recording('/path/to/new/recording/dir/')    # Add recording
exp.remove_recording('rec_label')                   # remove a recording dir 

Recordings are assigned labels when added to the experiment that can be used to easily reference exerpiments.

Held unit detection


Uses raw waveforms from sorted units to determine if units can be confidently classified as "held". Results are stored in exp.held_units as a pandas DataFrame. This also creates plots and exports data to a created directory: /path/to/experiment/experiment-name_analysis

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