Metadata-Version: 2.1
Name: ceviche
Version: 0.0.1
Summary: Ceviche
Home-page: https://github.com/twhughes/ceviche
Author: Tyler Hughes
Author-email: tylerwhughes91@gmail.com
License: UNKNOWN
Description: # ceviche [![Build Status](https://travis-ci.com/twhughes/ceviche.svg?token=ZCPktA3Ki2eYVXYnfbrz&branch=master)](https://travis-ci.com/twhughes/ceviche)
        
        Electromagnetic Simulation Tools + Automatic Differentiation.  Code for the arxiv preprint [Forward-Mode Differentiation of Maxwell's Equations](https://arxiv.org/abs/1908.10507).
        
        ## What is ceviche?
        
        `ceviche` provides two core electromagnetic simulation tools for solving Maxwell's equations:
        
        - finite-difference frequency-domain (FDFD)
        
        - finite-difference time-domain (FDTD)
        
        Both are written in `numpy` / `scipy` and are compatible with the [HIPS autograd package](https://github.com/HIPS/autograd).
        
        This allows you to write code to solve your E&M problem, and then use automatic differentiation on your results.
        
        As a result, you can do gradient-based optimization or sensitivity analysis without the tedius process of deriving your derivatives analytically.
        
        ### A simple example
        
        Lets say we have a domain of where we wish to inject light at position `source` and measure its intensity at `probe`.
        
        Between these two points, there's a box at location `pos_box` with permittivity `eps`.
        
        We can write a function computing the intensity as a function of `eps` using our FDFD solver
        
        ```python
        import autograd.numpy as np           # import the autograd wrapper for numpy
        from ceviche import fdfd_ez as fdfd   # import the FDFD solver
        
        # make an FDFD simulation
        f = fdfd(omega, dl, eps_box, npml=[10, 10])
        
        def intensity(eps):
            """ computes electric intensity at `probe` for a given box permittivity of `eps`
        
                source |-----| probe
                    .  | eps |  .
                       |_____|
            """
        
            # set the permittivity in the box region to the input argument
            fdfd.eps_r[box_pos] = eps
        
            # solve the fields
            Ex, Ey, Hz = f.solve(source)
        
            # compute the intensity at `probe`
            I = np.square(np.abs(Ex)) + np.square(np.abs(Ex))
            return = np.sum(I * probe)
        ```
        
        Then, we can very easily differentiate this function using automatic differentiation
        
        ```python
        
        # use autograd to differentiate `intensity` function
        grad_fn = jacobian(intensity)
        
        # then, evaluate it at the current value of `eps`
        dI_deps = grad_fn(eps_curr)
        
        # or do gradient based optimization
        for _ in range(10):
            eps_current += step_size * dI_deps_fn(eps_current)
        ```
        
        ## Design Principle
        
        `ceviche` is designed with simplicity in mind and is meant to serve as a base package for building your projects from.  However, with some exceptions, it does not provide streamlined interfaces for optimization, source or device creation, or visualization.  If you want that kind of thing, you need to build it around the base functionality of ceviche in your own project.  This decision was made to keep things clean and easy to understand, with a focus on the meaty bits that make this package unique.  For some inspiration, see the `examples` directory.  
        
        
        For more user friendly features, check out our [`angler`](https://github.com/fancompute/angler) package.  We plan to merge the two packages at a later date to give these automatic differentiation capabilities to `angler`.
        
        ## Installation
        
        `ceviche` is not on PyPI yet.
        To install locally from source:
        
            git clone https://github.com/twhughes/ceviche.git
            pip install -e ceviche
            pip install -r ceviche/requirements.txt
        
        from the main directory.
        
        Alternatively, just import the package from within your python script
        
            import sys
            sys.path.append('path/to/ceviche')
        
        ## Package Structure
        
        ### Ceviche
        
        The `ceviche` directory contains everything needed.
        
        To get the FDFD and FDTD simulators, import directly `from ceviche import fdtd, fdfd_ez, fdfd_hz, fdfd_ez_nl`
        
        To get the differentiation, import `from ceviche import jacobian`.
        
        `constants.py` contains some constants `EPSILON_0`, `C_0`, `ETA_0`, `Q_E`, which are needed throughout the package
        
        `utils.py` contains a few useful functions for plotting, autogradding, and various other things.
        
        ### Examples
        
        There are many demos in the `examples` directory, which will give you a good sense of how to use the package.
        
        ### Tests
        
        Tests are located in `tests`.  To run, `cd` into `tests` and
        
            python -m unittest
        
        to run all or
        
            python specific_test.py
        
        to run a specific one.  Some of these tests involve visual inspection of the field plots rather than error checking on values.
        
        To run all of the gradient checking functions, run 
        
            bash tests/test_all_gradients.sh
        
        ## Citation
        
        If you use this for your research or work, please cite
        
            @misc{1908.10507,
            Author = {Tyler W Hughes and Ian A D Williamson and Momchil Minkov and Shanhui Fan},
            Title = {Forward-Mode Differentiation of Maxwell's Equations},
            Year = {2019},
            Eprint = {arXiv:1908.10507},
            }
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
