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PYDAQ is a Python package for data acquisition, signal generation, system identification, digital filtering, and real-time control using Arduino and NI-DAQ devices.

It provides a unified graphical interface, command-line tools, and Jupyter notebook examples for laboratory experiments, rapid prototyping, teaching, and research workflows.

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Capabilities

PYDAQ supports the following experimental workflows:

Capability	Description
Data acquisition	Acquire, plot, and save experimental data from Arduino or NI-DAQ boards
Signal generation	Send user-defined input signals, including nonlinear excitation signals
Step-response experiments	Run automatic step-response tests and save the resulting data
PRBS-based experiments	Generate excitation signals for system identification workflows
System identification	Estimate linear and nonlinear black-box models from experimental data using SysIdentPy
Digital filtering	Apply FIR and IIR filters directly to acquired data in real time
PID control	Run real-time or simulated P, PI, PD, and PID control with Ziegler-Nichols tuning
LQR control	Simulate or implement Linear Quadratic Regulator control for state-space systems
Multi-channel workflows	Work with multiple Arduino or NI-DAQ channels
Benchmarking	Estimate the maximum reliable sampling frequency supported by the local system

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Installation

Install PYDAQ with pip:

pip install pydaq

Hardware notes:

• Arduino workflows do not require NI-DAQmx drivers.
• NI-DAQ workflows require the NI-DAQmx drivers.

PYDAQ is tested up to Python 3.14. It may run on newer versions, but without guarantees.

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Graphical user interface

All main workflows are available from a single graphical interface.

Launch the GUI with:

from pydaq.pydaq_global import PydaqGui

PydaqGui()

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Documentation map

Data acquisition

Acquire, plot, and save experimental data using:

• NI-DAQ
• Arduino

Signal generation

Generate and send user-defined excitation signals using:

• NI-DAQ
• Arduino

Step-response experiments

Run automatic step-response experiments using:

• NI-DAQ
• Arduino

System identification

Estimate mathematical models from experimental data using:

• NI-DAQ
• Arduino

PID control

Run real-time or simulated PID control experiments using:

• NI-DAQ
• Arduino

LQR control

Simulate or implement Linear Quadratic Regulator control for state-space systems using:

• NI-DAQ
• Arduino

Digital filtering

Design and apply FIR and IIR digital filters in real time using:

• NI-DAQ
• Arduino

Benchmarking

Estimate the maximum reliable sampling frequency supported by the local system:

• Benchmarking tool

Arduino firmware

PYDAQ uses a unified Arduino firmware based on a standardized CSV serial protocol for multi-channel acquisition, signal generation, system identification, filtering, and control workflows.

• Arduino firmware

Error dictionary

The error dictionary helps diagnose common GUI messages, terminal outputs, communication issues, and configuration problems.

• Error dictionary

Jupyter notebook examples

Notebook examples are available for both Arduino and NI-DAQ workflows:

• Jupyter notebook examples

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Screenshots

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Citation

This is the seminal publication of the PYDAQ project and should be cited in any work that uses PYDAQ.

• Martins, S. A. M. (2023). PYDAQ: Data Acquisition and Experimental Analysis with Python. Journal of Open Source Software, 8(92), 5662. https://doi.org/10.21105/joss.05662

@article{Martins_PYDAQ_Data_Acquisition_2023,
  author  = {Martins, Samir Angelo Milani},
  doi     = {10.21105/joss.05662},
  journal = {Journal of Open Source Software},
  month   = dec,
  number  = {92},
  pages   = {5662},
  title   = {{PYDAQ: Data Acquisition and Experimental Analysis with Python}},
  url     = {https://joss.theoj.org/papers/10.21105/joss.05662},
  volume  = {8},
  year    = {2023}
}

Additional related publications are available in the papers directory.