Deprecated since version 1.2.13: Bonsai.ONIX is deprecated.

To use ONIX with Bonsai, refer to the documentation for OpenEphys.Onix1.

AnalogIODevice#

A Bonsai source that wraps a FMC Host Analog IO Device device.

Inputs:

A 12xN OpenCV.Net.Mat

  • Each column contains 12 analog values, one for each channel.

  • If a channel is set to an input, this will have no effect.

  • Data is immediately consumed by hardware. If N > 1, then samples will be produced as quickly as hardware allows.

  • The matrix must contain elements with Depth.S16 when the DataType parameter is set to is “S16” and either Depth.F32 or Depth.F64 when DataType is set to “Volts”.

  • When the DataType parameter is set to is “S16”, analog output voltages are encoded in offset binary, with 0 resulting in -10V and 65355 resulting in +10V.

Outputs:

An AnalogInputDataFrame that contains analog samples and sample timing information.

  • This type is a buffered set of Device To Host Data Frames.

  • The number of samples in each output is determined by the BlockSize parameter

  • Analog voltages are packaged into 12xBlockSize (OpenCV.Net.Mat)

AnalogIO.bonsai
AnalogIO

Configuration#

Configuration is performed using the property pane which contains the following options.

Name

Type

Description

EnableStream

boolean

Enable the device data stream

BlockSize

uint

The number of samples that are included in each AnalogInputFrame. Larger numbers result in less overhead at the cost of larger buffering latencies.

DataType

enum

The format of the analog data consumed and produced by this node.

  • S16: raw 16-bit signed integer conversion results or DAC values.

  • Volts: 32-bit floating-point voltages.

InputRange<Channel>

enum

The analog input over which the 14-bit ADC conversion is applied. Smaller values have higher resolution.

Direction<Channel>

enum

The direction of the channel. If set to Output, the measured voltage is automatically looped back through the analog input.

Loading Scripts#

The following scripts can be used to load the data produced by this workflow in Python (using Numpy):