dh5io 0.4.1

Python tools for DAQ-HDF5 (dh5) file format used at Brain Research Institute of University of Bremen

Python Tools for the DAQ-HDF5 format

A Python package for handling DAQ-HDF5(*.dh5) files. The DH5 format is a hierarchical data format based on HDF5 designed for storing and sharing neurophysiology data, used in the Brain Research Institute of the University of Bremen since 2005.

• dhspec contains the specification of the DAQ-HDF5 file format as Python code.
• dh5io contains code for reading, writing and validating HDF5 files containing data according to the DAQ-HDF5 specfication.
• dh5neo (WIP) contains code for reading DAQ-HDF5 data into Neo objects (e.g. for use with Elephant, SpikeInterface and ephyviewer

Getting started

Installation

Install the package using uv (recommended):

uv pip install dh5io

Or with pip:

pip install dh5io

Reading and writing from and into DH5 files

from dh5io.dh5file import DH5File

with DH5File(example_filename, "r") as dh5:
    # inspect file content
    print(dh5)

    cont = dh5.get_cont_group_by_id(1)  # Get CONT group with id 1
    print(cont)

    trialmap = dh5.get_trialmap()
    print(trialmap)

  DAQ-HDF5 File (version 2) <example_filename> containing:
      ├───CONT Groups (7):
      │   ├─── CONT1
      │   ├─── CONT60
      │   ├─── CONT61
      │   ├─── CONT62
      │   ├─── CONT63
      │   ├─── CONT64
      │   └─── CONT1001
      ├───SPIKE Groups (1):
      │   └─── SPIKE0
      ├─── 10460 Events
      └─── 385 Trials in TRIALMAP

  /CONT1 in <example_filename>
      ├─── id: 1
      ├─── name:
      ├─── comment:
      ├─── sample_period: 1000000 ns (1000.0 Hz)
      ├─── n_channels: 1
      ├─── n_samples: 1443184
      ├─── duration: 3021.76 s
      ├─── n_regions: 385
      ├─── signal_type: None
      ├─── calibration: [1.0172526e-07]
      ├─── data: (1443184, 1)
      └─── index: (385,)

This example shows how to open a DH5 file, inspect its content, and retrieve a specific CONT group. The DH5File class provides methods for accessing the various groups and datasets within the file. The Cont, Spike (coming in next versions) and Trialmap classes provide convenient wrappers for working with these raw HDF5 groups and datasets. The corresponding h5py classes can be accessed directly for lower-level operations using the _file, _group and _dataset attributes (e.g. cont._group or cont.data._dataset).

As an alternative to the object-oriented approach using DH5File, you can use the functional API provided by the library. This API offers a set of functions for reading and writing data to DH5 files without the need to create file objects. These functions in the respective modules (h5io.cont, h5io.spike, etc.) use the h5py classes as input and output. This is the recommended way if you are familiar with HDF5 and the specification of the DH5 format.