Contents lists available at ScienceDirect SoftwareX journal homepage: www.elsevier.com/locate/softx  2 SCAPy: Electric and electronic symbolic circuit analysis in python Luis Cortés Ramírez a , Luis A. Sánchez-Gaspariano a,∗ , Israel Vivaldo-de-la-Cruz a , Carlos Muñiz-Montero b , Alejandro I. Bautista-Castillo c a Facultad de Ciencias de la Electrónica, Benemérita Universidad Autonóma de Puebla, Pue., CP 72570, Mexico b Ingeniería en Electrónica y Telecomunicaciones, Universidad Politécnica de Puebla, Juan C. Bonilla, Puebla, CP 72640, Mexico c Sistemas y Circuitos Integrados, Instituto Nacional de Astrofísica, Óptica y Electrónica, Tonantzintla, Puebla, C.P. 72840, Mexico ARTICLE INFO Keywords: Python Symbolic-simulator E 2 SCAPy Analog-circuits ABSTRACT Recently Python has become relevant for many tasks in a variety of disciplines leading to the development of various open source libraries. Our contribution to that cluster of tools is  2 SCAPy, a useful program for the symbolic computation of analog circuits. The most appealing feature of  2 SCAPy lies in its ability to solve large circuits with several nodes in few milliseconds due to its DDD algorithm, which drives to the fast solution of the system of equations of the circuit. To show the  2 SCAPy performance, three nonclassical circuit examples are reported: a WTA/LTA filter, a Memristor and a Fractional Integrator. Code metadata Current code version v.0.0.1 Permanent link to code/repository used for this code version https://github.com/ElsevierSoftwareX/SOFTX-D-24-00358 Permanent link to Reproducible Capsule – Legal Code License GNU GPLv3 Code versioning system used git Software code languages, tools, and services used Python Compilation requirements, operating environments & dependencies Python 3.10, numpy, pandas, symengine, sympy, multiprocessing, memory-profiler If available Link to developer documentation/manual For example: https://github.com/luisCorl/e2scapy Support email for questions lui.corl.ing@hotmail.com 1. Motivation and significance Electronic Design Automation (EDA) tools are software solutions widespread used at both industry and academy [1]. Most of these apps are numerical programs mainly employed for the design and verifica- tion of the functionality of the devices that constitute an electronic system [2,3]. Nevertheless, numerical simulation does not explicitly show the influence of each circuit element in the simulation out- come. As a result, several simulation runs are usually required. Instead, symbolic analysis provides analytic expressions which allow a deeper insight into the circuit behavior [4–6]. Compared to numerical simulators of the SPICE type, a symbolic simulator allows the identification of the most remarkable circuit pa- rameters in a network and thus to optimize the circuit in order to exhibit a better performance in terms of sensitivity and robustness for ∗ Corresponding author. E-mail address: luis.sanchezgas@correo.buap.mx (Luis A. Sánchez-Gaspariano). temperature, voltage or process variations [7]. Yet, compared to their numerical counterpart, only a few symbolic simulators are available. A good compendium about symbolic simulators, highlighting the use of EI-SCAM (a program in MATLAB) can be found in [1,8]. Even though MATLAB is a prominent software for systems analysis, in recent years Python has become relevant for many tasks in a variety of disciplines [9–11]. Since Python is a freeware programming language, it drives to the development of various open source libraries whose scope entwines a large amount of users. Moreover, Python capabilities for heavy computing tasks are impressive, especially when used along with various libraries and frameworks such as NumPy, SymPy, Pandas, Symengine, Multiprocessing and Memory-profiler, to name a few. In this way, taking into account that symbolic circuit simulators typically require demanding computing capabilities to solve large and https://doi.org/10.1016/j.softx.2024.101910 Received 1 July 2024; Received in revised form 16 September 2024; Accepted 17 September 2024 SoftwareX 28 (2024) 101910 Available online 21 September 2024 2352-7110/© 2024 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).