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+
+
+
+ 20231214T170859-89b549088f864f514f5c9a60409b5445e3f1d4a3
+ 20231214170859
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Software
+ JOSS
+ 2475-9066
+
+ 10.21105/joss
+ https://joss.theoj.org
+
+
+
+
+ 12
+ 2023
+
+
+ 8
+
+ 92
+
+
+
+ PYDAQ: Data Acquisition and Experimental Analysis with
+Python
+
+
+
+ Samir Angelo Milani
+ Martins
+ https://orcid.org/0000-0003-1702-8504
+
+
+
+ 12
+ 14
+ 2023
+
+
+ 5662
+
+
+ 10.21105/joss.05662
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.10377251
+
+
+ GitHub review issue
+ https://github.com/openjournals/joss-reviews/issues/5662
+
+
+
+ 10.21105/joss.05662
+ https://joss.theoj.org/papers/10.21105/joss.05662
+
+
+ https://joss.theoj.org/papers/10.21105/joss.05662.pdf
+
+
+
+
+
+ SysIdentPy: A Python package for system
+identification using NARMAX models
+ Junior
+ Journal of Open Source
+Software
+ 54
+ 5
+ 10.21105/joss.02384
+ 2020
+ Junior, W. R. L., Andrade, L. P. C.
+da, Oliveira, S. C. P., & Martins, S. A. M. (2020). SysIdentPy: A
+Python package for system identification using NARMAX models. Journal of
+Open Source Software, 5(54), 2384.
+https://doi.org/10.21105/joss.02384
+
+
+ System identification: Theory for the
+user
+ Ljung
+ 0-13-656695-2
+ 1999
+ Ljung, L. (1999). System
+identification: Theory for the user (2nd ed.). Prentice-Hall.
+ISBN: 0-13-656695-2
+
+
+ Nonlinear system identification: NARMAX
+methods in the time, frequency, and spatio-temporal
+domains
+ Billings
+ 10.1002/9781118535561
+ 2013
+ Billings, S. A. (2013). Nonlinear
+system identification: NARMAX methods in the time, frequency, and
+spatio-temporal domains (p. 574). John Wiley & Sons.
+https://doi.org/10.1002/9781118535561
+
+
+ Graphical interface as a teaching aid for
+nonlinear dynamical systems
+ Silva
+ European Journal of Physics
+ 6
+ 39
+ 10.1088/1361-6404/aae35c
+ 2018
+ Silva, P. H. O., Nardo, L. G.,
+Martins, S. A. M., Nepomuceno, E. G., & Perc, M. (2018). Graphical
+interface as a teaching aid for nonlinear dynamical systems. European
+Journal of Physics, 39(6), 065105.
+https://doi.org/10.1088/1361-6404/aae35c
+
+
+ Temperature as a chaotic circuit bifurcation
+parameter
+ Ostrovskii
+ 2020 IEEE conference of russian young
+researchers in electrical and electronic engineering
+(EIConRus)
+ 10.1109/EIConRus49466.2020.9038964
+ 2020
+ Ostrovskii, V. Y., Nazare, T. E.,
+Martins, S. A. M., & Nepomuceno, E. G. (2020). Temperature as a
+chaotic circuit bifurcation parameter. 2020 IEEE Conference of Russian
+Young Researchers in Electrical and Electronic Engineering (EIConRus),
+154–157.
+https://doi.org/10.1109/EIConRus49466.2020.9038964
+
+
+ An R library for nonlinear black-box system
+identification
+ Ayala
+ SoftwareX
+ 11
+ 10.1016/j.softx.2020.100495
+ 2020
+ Ayala, H. V. H., Gritti, M. C., &
+Santos Coelho, L. dos. (2020). An R library for nonlinear black-box
+system identification. SoftwareX, 11, 100495.
+https://doi.org/10.1016/j.softx.2020.100495
+
+
+ Control of hysteretic systems through an
+analytical inverse compensation based on a NARX model
+ Lacerda Junior
+ IEEE Access
+ 7
+ 10.1109/access.2019.2926057
+ 2019
+ Lacerda Junior, W. R., Martins, S. A.
+M., Nepomuceno, E. G., & Lacerda, M. J. (2019). Control of
+hysteretic systems through an analytical inverse compensation based on a
+NARX model. IEEE Access, 7, 98228–98237.
+https://doi.org/10.1109/access.2019.2926057
+
+
+ Sufficient conditions for rate-independent
+hysteresis in autoregressive identified models
+ Martins
+ Mechanical Systems and Signal
+Processing
+ 75
+ 10.1016/j.ymssp.2015.12.031
+ 2016
+ Martins, S. A. M., & Aguirre, L.
+A. (2016). Sufficient conditions for rate-independent hysteresis in
+autoregressive identified models. Mechanical Systems and Signal
+Processing, 75, 607–617.
+https://doi.org/10.1016/j.ymssp.2015.12.031
+
+
+ Design and implementation of data acquisition
+system based on Scrapy technology
+ Yang
+ 2019 2nd international conference on safety
+produce informatization (IICSPI)
+ 10.1109/IICSPI48186.2019.9096044
+ 2019
+ Yang, H. (2019). Design and
+implementation of data acquisition system based on Scrapy technology.
+2019 2nd International Conference on Safety Produce Informatization
+(IICSPI), 417–420.
+https://doi.org/10.1109/IICSPI48186.2019.9096044
+
+
+ A Python instrument control and data
+acquisition suite for reproducible research
+ Koerner
+ IEEE Transactions on Instrumentation and
+Measurement
+ 4
+ 69
+ 10.1109/TIM.2019.2914711
+ 2020
+ Koerner, L. J., Caswell, T. A.,
+Allan, D. B., & Campbell, S. I. (2020). A Python instrument control
+and data acquisition suite for reproducible research. IEEE Transactions
+on Instrumentation and Measurement, 69(4), 1698–1707.
+https://doi.org/10.1109/TIM.2019.2914711
+
+
+ Scikit-learn: Machine learning in
+Python
+ Pedregosa
+ Journal of Machine Learning
+Research
+ 12
+ 10.48550/arXiv.1201.0490
+ 2011
+ Pedregosa, F., Varoquaux, G.,
+Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M.,
+Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A.,
+Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011).
+Scikit-learn: Machine learning in Python. Journal of Machine Learning
+Research, 12, 2825–2830.
+https://doi.org/10.48550/arXiv.1201.0490
+
+
+
+
+
+
diff --git a/joss.05662/10.21105.joss.05662.jats b/joss.05662/10.21105.joss.05662.jats
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index 0000000000..a2090023e0
--- /dev/null
+++ b/joss.05662/10.21105.joss.05662.jats
@@ -0,0 +1,509 @@
+
+
+
+
+
+
+
+Journal of Open Source Software
+JOSS
+
+2475-9066
+
+Open Journals
+
+
+
+5662
+10.21105/joss.05662
+
+PYDAQ: Data Acquisition and Experimental Analysis with
+Python
+
+
+
+https://orcid.org/0000-0003-1702-8504
+
+Martins
+Samir Angelo Milani
+
+
+
+*
+
+
+
+Department of Electrical Engineering at Federal University
+of São João del-Rei, Brazil.
+
+
+
+
+GCoM - Modeling and Control Group at Federal University of
+São João del-Rei, Brazil.
+
+
+
+
+* E-mail:
+
+
+9
+3
+2023
+
+8
+92
+5662
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2022
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+Python
+Data Acquisition
+Arduino
+NIDAQ
+System Identification
+Mathematical Modeling
+
+
+
+
+
+ Summary
+
System identification is a relevant research topic that aims to
+ find mathematical models using acquired data. One of the main
+ contributions is the work of Ljung
+ (1999),
+ which was substantially developed over the years
+ (Lacerda
+ Junior et al., 2019;
+ Martins
+ & Aguirre, 2016). Among system identification tools,
+ SysIdentPy
+ (Junior
+ et al., 2020) uses Python in a very straightforward way for
+ system modeling through empirical data, while Narmax
+ (Ayala
+ et al., 2020) promises to obtain models using R language.
+
As pointed out by Ljung
+ (1999),
+ experimental data are necessary for obtaining black-box models, and
+ this is exactly where PYDAQ find its place. PYDAQ is a Python tool
+ which was primarily developed for experiments with empirical data,
+ either sending and/or acquiring data using simple Graphical User
+ Interfaces or command line, with few (or no) lines of code required,
+ using different solutions provided by the market (NIDAQ and Arduino).
+ Even a reseacher with no programming skills is able to use PYDAQ
+ easily and quickly for data acquisition.
+
In what follows it will be shown how PYDAQ can be use by any
+ scientist, its advantages and features for quickly and effective data
+ acquisition experiments, even if the scientist has no programming
+ skills.
+
+
+ Statement of need
+
Any scientist or student who needs to acquire data in an easy and
+ quick way, in three line of code (LOC), are the target audience of
+ this manuscript/package. PYDAQ is a solution that aims to allow the
+ user to perform experiments from data acquisition to signals
+ generation using a Graphical User Interface.
+
Despite this, for advanced users, it is also possible to use PYDAQ
+ through command line, as shown in the
+ documentation,
+ allowing PYDAQ to be integrated in real time with well-known modeling
+ tools.
+
To contextualize the importance of PYDAQ, there are full papers
+ dealing only with data acquisition
+ (Koerner
+ et al., 2020;
+ Yang,
+ 2019). It should be clear at this point that data acquisition
+ is commonly only the first step in an empirical scientific procedure,
+ such as in the work of Ostrovskii et al.
+ (2020).
+ Therefore, this step should not take too much energy of a researcher,
+ since he/she needs to be full of energy to continue the research
+ project.
+
Further, PYDAQ deals from different type of data acquisition cards,
+ from simple and cheaper Arduino boards up to NIDAQ devices, allowing
+ the execution from simple to complex experiments. Also, since PYDAQ
+ can be also used as a command line tool, it can be easily incorporated
+ to be used in production along with any available mathematical tool,
+ such as SysIdentPy
+ (Junior
+ et al., 2020) or SciKitLearn
+ (Pedregosa
+ et al., 2011).
+
In literature there are packages that deals with NIDAQ devices,
+ such as the NSLS-II tools
+ (Koerner
+ et al., 2020). However, they need several lines of codes in
+ order to make a single data acquisition, and works only with expensive
+ and proprietary boards. Besides, as far as I know, there is no
+ Graphical User Interface open software that allows instantly and
+ easily data acquisition with Python, this being another feature of
+ PYDAQ.
+
Because of the above-mentioned facts, PYDAQ can be used also to
+ introduce new scientist in the System Identification research area.
+ Also, PYDAQ can be used in teaching, during engineering courses and in
+ low-cost laboratories’ implementation, once Arduino boards are quite
+ cheap and easy to find. Graphical User Interfaces also allows the user
+ to be directly connected with the subject, as explicitly said by Silva
+ et al.
+ (2018).
+
In what follows examples of how to use PYDAQ will be presented, as
+ well as future research topics. Further details can also be found in
+ the
+ documentation.
+
+
+ Examples
+
The fastest way to install PYDAQ is using pip:
+ pip install pydaq
+
[fig:nidaq_get_gui]
+ and
+ [fig:arduino_get_gui]
+ depict the Graphical User Interface developed for Data Acquisition
+ using Arduino or any NIDAQ board.
+
+
Data Acquisition through
+ NIDAQ.
+
+
+
+
Data Acquisition through
+ Arduino.
+
+
+
To start them, only three line of codes (LOC) are necessary,
+ including one for importing PYDAQ:
+ from pydaq.get_data import Get_data
+# Class Get_data
+g = Get_data()
+
+# Arduino or NIDAQ - Use ONE of the following lines
+g.get_data_nidaq_gui() # For NIDAQ devices
+g.get_data_arduino_gui() # For arduino boards
+
Similarly, to send data, only three LOC are required, as showed up
+ in what follow:
+ from pydaq.send_data import Send_data
+
+# Class Send_data
+s = Send_data()
+
+# Arduino or NIDAQ - Use ONE of the following lines
+s.send_data_nidaq_gui()
+s.send_data_arduino_gui()
+
If the user decides to save data, it will be saved in
+ .dat format, located at the path defined in the
+ GUI (Desktop is the default path).
+ [fig:data] shows an
+ example of how data will be saved: i) one file
+ (time.dat) with the timestamp, in seconds, when
+ each sample was acquired; ii) file data.dat
+ contains acquired values.
+
+
Example of acquired
+ data.
+
+
+
+
GUI for sending data -
+ Arduino.
+
+
+
+
GUI for sending data -
+ NIDAQ.
+
+
+
It should be emphasized that once this code is executed, a
+ Graphical User Interface will manifest on the screen, according to the
+ board selected by the user, as shown in
+ [fig:arduino_send_gui]
+ and
+ [fig:nidaq_send_gui].
+
Options are straight-forward and ease to understand. For further
+ details and to check how to use the same functionality using a command
+ line the reader are invited to check the
+ documentation.
+
It is noteworthy that any signal can be generated and applied to a
+ physical system using the presented GUI, being the used board the main
+ constraint. Data can be either generated manually or using a library
+ (e.g., NumPy) to create signals as sine waves, PRBS (Pseudo-Random
+ Binary Signal) or other signal required to be a persistently exciting
+ input, as necessary for system identification
+ (Billings,
+ 2013;
+ Ljung,
+ 1999).
+
Step-response is a common way to test a system and acquire data, in
+ order to find a model, as well as system time constant and gain. To
+ facilitate this procedure, a step-response GUI was also created and
+ can be seen in
+ [fig:step_nidaq]
+ and
+ [fig:step_arduino].
+ To use them, user should type the command:
+ from pydaq.step_response import Step_response
+
+# Class Step_Response
+s = Step_response()
+
+# Arduino or NIDAQ - Use ONE of the following lines
+s.step_response_nidaq_gui()
+s.step_response_arduino_gui()
+
+
Step Response GUI -
+ NIDAQ.
+
+
+
+
Step Response GUI -
+ Arduino.
+
+
+
Here the user can define when the step will be applied, as well as
+ where data will be saved.
+ [fig:data_sent]
+ and
+ [fig:step_data]
+ show data that were empirically-acquired with PYDAQ. In the figures
+ the user will find labels, functionality (Sending Data/Data
+ Acquisition/Step Response), device/channel (for NIDAQ boards) or COM
+ port used (for Arduino devices).
+
+
Data acquired using a NIDAQ
+ board.
+
+
+
+
Data generated by a step-response
+ experiment.
+
+
+
Examples showed above shed light in some functionalities of PYDAQ.
+ For further details and for command line use, the reader is welcome to
+ consult the full
+ documentation.
+
+
+ Future Work
+
Future releases will include real-time and data-driven system
+ identification using linear and nonlinear approaches. Also, real-time
+ model based controllers will be implemented through PYDAQ. Saving data
+ in an SQL server is a future feature, as well.
+
+
+
+
+
+
+
+ JuniorWilson Rocha Lacerda
+ AndradeLuan Pascoal Costa da
+ OliveiraSamuel Carlos Pessoa
+ MartinsSamir Angelo Milani
+
+ SysIdentPy: A Python package for system identification using NARMAX models
+ Journal of Open Source Software
+ The Open Journal
+ 2020
+ 5
+ 54
+ https://doi.org/10.21105/joss.02384
+ 10.21105/joss.02384
+ 2384
+
+
+
+
+
+
+ LjungLennart
+
+ System identification: Theory for the user
+ Prentice-Hall
+ Englewood Cliffs, NJ
+ 1999
+ 2nd
+ 0-13-656695-2
+
+
+
+
+
+ BillingsS. A.
+
+ Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains
+ John Wiley & Sons
+ Chichester
+ 2013
+ 10.1002/9781118535561
+ 574
+
+
+
+
+
+
+ SilvaPedro Henrique Oliveira
+ NardoLucas Giovani
+ MartinsSamir Angelo Milani
+ NepomucenoErivelton Geraldo
+ PercMatjaž
+
+ Graphical interface as a teaching aid for nonlinear dynamical systems
+ European Journal of Physics
+ IOP Publishing
+ 201810
+ 39
+ 6
+ 10.1088/1361-6404/aae35c
+ 065105
+
+
+
+
+
+
+ OstrovskiiValerii Y.
+ NazareThalita E.
+ MartinsSamir A. M.
+ NepomucenoErivelton G.
+
+ Temperature as a chaotic circuit bifurcation parameter
+ 2020 IEEE conference of russian young researchers in electrical and electronic engineering (EIConRus)
+ 2020
+
+ 10.1109/EIConRus49466.2020.9038964
+ 154
+ 157
+
+
+
+
+
+ AyalaHelon Vicente Hultmann
+ GrittiMarcos Cesar
+ Santos CoelhoLeandro dos
+
+ An R library for nonlinear black-box system identification
+ SoftwareX
+ Elsevier
+ 2020
+ 11
+ 10.1016/j.softx.2020.100495
+ 100495
+
+
+
+
+
+
+ Lacerda JuniorWilson R.
+ MartinsSamir A. M.
+ NepomucenoErivelton G.
+ LacerdaMárcio J.
+
+ Control of hysteretic systems through an analytical inverse compensation based on a NARX model
+ IEEE Access
+ IEEE
+ 2019
+ 7
+ 10.1109/access.2019.2926057
+ 98228
+ 98237
+
+
+
+
+
+ MartinsS. A. M.
+ AguirreL. A.
+
+ Sufficient conditions for rate-independent hysteresis in autoregressive identified models
+ Mechanical Systems and Signal Processing
+ Elsevier
+ 2016
+ 75
+ 10.1016/j.ymssp.2015.12.031
+ 607
+ 617
+
+
+
+
+
+ YangHongxia
+
+ Design and implementation of data acquisition system based on Scrapy technology
+ 2019 2nd international conference on safety produce informatization (IICSPI)
+ 2019
+
+ 10.1109/IICSPI48186.2019.9096044
+ 417
+ 420
+
+
+
+
+
+ KoernerLucas J.
+ CaswellThomas A.
+ AllanDaniel B.
+ CampbellStuart I.
+
+ A Python instrument control and data acquisition suite for reproducible research
+ IEEE Transactions on Instrumentation and Measurement
+ 2020
+ 69
+ 4
+ 10.1109/TIM.2019.2914711
+ 1698
+ 1707
+
+
+
+
+
+ PedregosaF.
+ VaroquauxG.
+ GramfortA.
+ MichelV.
+ ThirionB.
+ GriselO.
+ BlondelM.
+ PrettenhoferP.
+ WeissR.
+ DubourgV.
+ VanderplasJ.
+ PassosA.
+ CournapeauD.
+ BrucherM.
+ PerrotM.
+ DuchesnayE.
+
+ Scikit-learn: Machine learning in Python
+ Journal of Machine Learning Research
+ 2011
+ 12
+ 10.48550/arXiv.1201.0490
+ 2825
+ 2830
+
+
+
+
+
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