Metadata-Version: 2.1
Name: fdfdpy
Version: 0.1.0
Summary: Electromagnetic Finite Difference Frequency Domain Solver
Home-page: https://github.com/fancompute/fdfdpy
Author: Tyler Hughes, Momchil Minkov, Ian Williamson
Author-email: tylerwhughes91@gmail.com
License: MIT License

Copyright (c) 2018 Fan Group

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Description: ![](img/dipole_dielectric_field.png)
        
        # fdfdpy
        
        This is a pure Python implementation of the finite difference frequency domain (FDFD) method. It makes use of scipy, numpy, matplotlib, and the MKL Pardiso solver. fdfdpy currently supports 2D geometries
        
        ## Installation
        
            python setup.py install
        
        ## Structure
        
        ### Initialization
        
        The `Fdfd` class is initialized as
        
        	simulation = Fdfd(omega, eps_r, dl, NPML, pol, L0)
        
        - `omega` : the angular frequency in units of` 2 pi / seconds`
        - `eps_r` : a numpy array specifying the relative permittivity distribution
        - `dl` : the spatial grid size in units of `L0`
        - `NPML` : defines number of PML grids `[# on x borders, # on y borders]`
        - `pol` : polarization, one of `{'Hz','Ez'}` where `z` is the transverse field.
        - `L0` : simulation length scale, default is 1e-6 meters (one micron)
        
        Creating a new Fdfd object solves for:
        
        - `xrange` : defines spatial domain in x [left-most position, right-most position] in units of `L0`
        - `yrange` : defines spatial domain in y [bottom-most position, top-most position] in units of `L0`
        - `A` : the Maxwell operator, which is used later to solve for the E&M fields.
        - `derivs` : dictionary storing the derivative operators.
        
        It also creates a relative permeability, `mu_r`, as `numpy.ones(eps_r.shape)` and a source `src` as `numpy.zeros(eps_r.shape)`.
        
        ### Adding sources is exciting!
        
        Sources can be added to the simulation either by manually editing the 2D src array inside of the simulation object,
        
        	simulation.src[10,20:30] = 1
        
        or by adding modal sources, which are defined as planes within the 2D domain which launch a mode in their normal direction. Modal source definitions can be added to the simulation by
        
        	simulation.add_mode(neff, direction, center, width)
        	simulation.setup_modes()
        
        - `neff` : defines the effective index of the mode; this will be used as the eigenvalue guess
        - `direction` : defines the normal direction of the plane, should be either 'x' or 'y'
        - `center` : defines the center coordinates for the plane in cell coordinates [xc, yc]
        - `width` : defines the width of the plane in number of cells
        
        Note that `simulation.setup_modes()` must always be called after adding mode(s) in order to populate `simulation.src`.
        
        ### Solving for the electromagnetic fields
        
        Now, we have everything we need to solve the system for the electromagnetic fields, by running
        
        	fields = simulation.solve_fields(timing=False)
        
        `simulation.src` is proportional to either the `Jz` or `Mz` source term, depending on whether `pol` is set to `'Ez'` or `'Hz'`, respectively.
        
        `fields` is a tuple containing `(Ex, Ey, Hz)` or `(Hx, Hy, Ez)` depending on the polarization.
        
        ### Setting a new permittivity
        
        If you want to change the permittivity distribution, you may run
        
        	simulation.reset_eps(new_eps)
        
        And this will reconstruct the system matrix and store it in `FDFD`. Note that `simulation.setup_modes()` should also be called if the permittivity changed within the plane of any of the modal sources.
        
        ### Plotting
        
        Primary fields (Hz/Ez) can be visualized using the included helper functions:
        
        	simulation.plt_re(outline=True, cbar=True)
        	simulation.plt_abs(outline=True, cbar=True)
        
        These optionally outline the permittivity with contours and can be supplied with a matplotlib axis handle to plot into.
        
        ### Requirements
        
        - numpy
        - scipy
        - matplotlib
        
        To load the MKL solver:
        
        	git submodule update --init --recursive
        
        ### To Do
        
        #### Whenever
        - [x] Modal source.
        - [x] More dope plotting methods.
        - [ ] xrange, yrange labels on plots.
        - [ ] set modal source amplitude (and normalization)
        - [ ] Add ability to run local jupyter notebooks running FDFD on parallel from hera.
        - [ ] Save the factorization of `A` in the `Fdfd` object to be reused later if one has the same `A` but a different `b`.
        - [ ] Allow the source term to have `(Jx, Jy, Jz, Mx, My, Mz)`, which would be useful for adjoint stuff where the source is not necessarily along the `z` direction.
        - [ ] Clean up imports (e.g. `import numpy as np` to `from numpy import abs, zeros, ...`)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
