lcapy 1.26
Symbolic linear circuit analysis
Requires Python: >=3.7

Lcapy is a Python package for linear circuit analysis.  It uses SymPy
        for symbolic mathematics.
        
        ![Run tests and checks](https://github.com/mph-/lcapy/workflows/Run%20tests%20and%20checks/badge.svg)
        [![Binder](http://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/mph-/lcapy/master)
        ![Documentation](https://readthedocs.org/projects/docs/badge/?version=latest)
        
        Lcapy can symbolically analyse circuits described with netlists or by series/parallel combinations of components.  It can also manipulate continuous-time and discret-time expressions.
        
        Comprehensive documentation can be found at https://lcapy.readthedocs.io/en/latest/
        
        
        Circuit analysis
        ----------------
        
        The circuit is described using netlists, similar to SPICE, with
        arbitrary node names (except for the ground node which is labelled 0).
        The netlists can be loaded from a file or created at run-time.  For
        example:
        
            >>> from lcapy import Circuit, s, t
            >>> cct = Circuit("""
            ... Vs 2 0 {5 * u(t)}
            ... Ra 2 1
            ... Rb 1 0
            ... """)
        
        The circuit can then be interrogated to determine branch currents,
        branch voltages, and node voltages (with respect to the ground node 0).  For example:
        
            >>> cct[1].V(t)
            5⋅R_b⋅u(t)
            ──────────
             Rₐ + R_b
            >>> cct.Ra.I(t)
             5⋅u(t)
            ────────
            Rₐ + R_b
            >>> cct.Ra.V(s)
               5⋅Rₐ
            ────────────
            s⋅(Rₐ + R_b)
        
        
        One-port networks
        -----------------
        
        One-port networks can be created by series and parallel combinations
        of other one-port networks.  The primitive one-port networks are the
        following ideal components:
        
        - V independent voltage source
        - I independent current source
        - R resistor
        - C capacitor
        - L inductor
        
        These components are converted to s-domain models and so capacitor and
        inductor components can be specified with initial voltage and
        currents, respectively, to model transient responses.
        
        The components have the following attributes:
        
        - Zoc open-circuit impedance
        - Ysc short-circuit admittance
        - Voc open-circuit voltage
        - Isc short-circuit current
        
        The component values can be specified numerically or symbolically
        using strings, for example,
        
            >>> from lcapy import Vdc, R, L, C, s, t
            >>> R1 = R('R_1')
            >>> L1 = L('L_1')
            >>> a = Vdc(10) + R1 + L1
        
        Here a is the name of the network formed with a 10 V DC voltage source in
        series with R1 and L1.
        
        The s-domain open circuit voltage across the network can be printed with:
        
            >>> a.V(s)
            10/s
        
        The time domain open circuit voltage is given by:
        
            >>> a.V(t)
            10
        
        The s-domain short circuit current through the network can be printed with:
        
            >>> a.Isc(s)
            10/(L_1*s**2 + R_1*s)
        
        The time domain short circuit current is given by:
        
            >>> a.Isc(t)
            10/R_1
        
        If you want units displayed:
        
            >>> state.show_units=True
            >>> a.Isc(t)
            10/R_1.A
        
        
        
        Two-port networks
        -----------------
        
        One-port networks can be combined to form two-port networks.  Methods
        are provided to determine transfer responses between the ports.
        
        Here's an example of creating a voltage divider (L section)
        
            >>> from lcapy import *
            >>> a = LSection(R('R_1'), R('R_2'))
        
        
        Limitations
        -----------
        
        1. Non-linear components cannot be modelled (apart from a linearisation around a bias point).
        
        2. High order systems can go crazy.
        
        3. Some two-ports generate singular matrices.
        
        
        Schematics
        ----------
        
        LaTeX schematics can be generated using circuitikz from the netlist.
        Additional drawing hints, such as direction and size are required.
        
            >>> from lcapy import Circuit
            >>> cct = Circuit("""
            ... P1 1 0; down
            ... R1 1 3; right
            ... L1 3 2; right
            ... C1 3 0_1; down
            ... P2 2 0_2; down
            ... W 0 0_1; right
            ... W 0_1 0_2; right""")
            >>> cct.draw(filename='pic.tex')
        
        In this example, P denotes a port (open-circuit) and W denotes a wire
        (short-circuit).  The drawing hints are separated from the netlist
        arguments by a semicolon.  They are a comma separated list of
        key-value pairs except for directions where the dir keyword is
        optional.  The symbol label can be changed using the l keyword; the
        voltage and current labels are specified with the v and i keywords.
        For example,
        
            >>> from lcapy import Circuit
            >>> cct = Circuit("""
            ... V1 1 0; down
            ... R1 1 2; left=2, i=I_1, v=V_{R_1}
            ... R2 1 3; right=2, i=I_2, v=V_{R_2}
            ... L1 2 0_1; down, i=I_1, v=V_{L_1}
            ... L2 3 0_3; down, i=I_1, v=V_{L_2}
            ... W 0 0_3; right
            ... W 0 0_1; left""")
            >>> cct.draw(scale=3, filename='pic2.svg')
        
        The drawing direction is with respect to the positive node; i.e., the
        drawing is performed from the positive to the negative node.  Since
        lower voltages are usually lower in a schematic, then the direction of
        voltage sources and ports is usually down.
        
        By default, component (and current) labels are drawn above horizontal
        components and to the right of vertical components.  Voltage labels
        are drawn below horizontal components and to the left of vertical
        components.
        
        Node names containing a dot or underscore are not displayed.
        
        
        Jupyter notebooks
        -----------------
        
        Lcapy can be used with [Jupyter Notebooks](https://jupyter.org/).  For a number of examples see https://github.com/mph-/lcapy/tree/master/doc/examples/notebooks .  These include:
        
        - [AC analysis of a first-order RC filter](https://github.com/mph-/lcapy/blob/master/doc/examples/notebooks/RC-lpf1.ipynb)
        
        - [A demonstration of the principle of superposition](https://github.com/mph-/lcapy/blob/master/doc/examples/notebooks/superposition2.ipynb)
        
        - [Non-inverting operational amplifier](https://github.com/mph-/lcapy/blob/master/doc/examples/notebooks/opamp-noninverting-amplifier1.ipynb)
        
        - [State-space analysis](https://github.com/mph-/lcapy/blob/master/doc/examples/notebooks/state-space1.ipynb)
        
        Installation in Google Colab
        ----------------------------
        
        To use Lcapy in Google Colab, run the following commands in the Colab notebook:
        
        ```
        !pip install pdflatex
        !sudo apt-get install texlive-latex-recommended
        !sudo apt install texlive-latex-extra
        !sudo apt install dvipng
        !pip install lcapy
        ```
        This will install all the required packages to run Lcapy on Colab.
        
        <a target="_blank" href="https://colab.research.google.com/github/OIEIEIO/lcapy/blob/master/doc/examples/notebooks/mosfet-switch1.ipynb">
          <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
        </a>
        
        Documentation
        -------------
        
        For comprehensive documentation, see http://lcapy.readthedocs.io/en/latest (alternatively, the documentation can be viewed in a web browser after running 'make doc' in the top-level directory).
        
        For release notes see http://lcapy.readthedocs.io/en/latest/releases.html
        
        For another view on Lcapy see https://blog.ouseful.info/2018/08/07/an-easier-approach-to-electrical-circuit-diagram-generation-lcapy/
        
        
        Citation
        --------
        
        To cite Lcapy in publications use
        
        > Hayes M. 2022. Lcapy: symbolic linear circuit analysis with Python. PeerJ Computer Science 8:e875 https://doi.org/10.7717/peerj-cs.875
        
        A BibTeX entry for LaTeX users is
        
        ``` bibtex
        @article{10.7717/peerj-cs.875,
         title = {Lcapy: symbolic linear circuit analysis with {Python}},
         author = {Hayes, Michael},
         year = 2022,
         month = Feb,
         keywords = {Linear circuit analysis, symbolic computation, Python},
         pages = {e875},
         journal = {PeerJ Computer Science},
         issn = {2376-5992},
         url = {https://doi.org/10.7717/peerj-cs.875},
         doi = {10.7717/peerj-cs.875}
        }
        ```
        
        
        Copyright 2014--2022 Michael Hayes, UCECE