Combine and convert packetized Intan RHD files into continuous arrays in HDF5 format.

Install:

Requires python3 (e.g. miniconda or pyenv) and pip.

Do this step with conda or pyenv environment activated if applicable

$ git clone https://github.com/miketrumpis/rhd-to-hdf5.git
$ pip install rhd-to-hdf5

Pip install also pulls in numpy and h5py.

Get Usage Help:

$ convert_rhd.py -h

Output Notes:

Arrays in the output file may include:

• amplifier_data: converted to signed int16, scale to uV by multiplying 0.195
• aux_input_data: unsigned uint16, scale by 37.4e-6 for Volts (sampled at 1/4 rate as amplifier data)
• board_adc_data: converted to signed int16, scale by 312.5e-6 for Volts
• supply_voltage_data: uint16, scale by 74.8e-6 for Volts (sampled once per data block)
• temp_sensor_data: uint16, scale by 0.01
• board_dig_in_data: boolean
• board_dig_out_data: boolean

To load (Python example):

>>> import h5py
>>> f = h5py.File('ecog_256_array.h5', 'r')
>>> electrodes_uv = f['amplifier_data'][:, 100:200] * 0.195
>>> electrodes_uv.shape
(256, 100)
>>> f['amplifier_data'].shape   # total available data
(256, 7200000)

The original header information is stored as a JSON string, which can be parsed like this:

>>> import json
>>> header = json.loads(f.attrs['JSON_header'])
>>> header['sample_rate']
20000.0