Metadata-Version: 2.1
Name: sax
Version: 0.3.1
Summary:  SAX 
Home-page: UNKNOWN
Author: Floris Laporte
Author-email: floris.laporte@gmail.com
License: UNKNOWN
Description: # SAX
        
        ![Docs](https://readthedocs.org/projects/sax/badge/?version=latest)
        
        Autograd and XLA for S-parameters - a scatter parameter circuit simulator and
        optimizer for the frequency domain based on [JAX](https://github.com/google/jax).
        
        The simulator was developed for simulating Photonic Integrated Circuits but in fact is
        able to perform any S-parameter based circuit simulation. The goal of SAX is to be a
        thin wrapper around JAX with some basic tools for S-parameter based circuit simulation
        and optimization. Therefore, SAX does not define any special datastructures and tries to
        stay as close as possible to the functional nature of JAX. This makes it very easy to
        get started with SAX as you only need functions and standard python dictionaries. Let's
        dive in...
        
        ## Quick Start
        
        [Full Quick Start page](https://sax.readthedocs.io/en/latest/examples/01_quick_start.html) -
        [Examples](https://sax.readthedocs.io/en/latest/examples.html) -
        [Full Docs](https://sax.readthedocs.io/en/latest/index.html).
        
        Let's first import the SAX library, along with JAX and the JAX-version of numpy:
        
        ```python
        import sax
        import jax
        import jax.numpy as jnp
        ```
        
        Define a model function for your component. A SAX model is just a function that returns
        an 'S-dictionary'. For example a directional coupler:
        
        ```python
        def coupler(coupling=0.5):
            kappa = coupling**0.5
            tau = (1-coupling)**0.5
            sdict = sax.reciprocal({
                ("in0", "out0"): tau,
                ("in0", "out1"): 1j*kappa,
                ("in1", "out0"): 1j*kappa,
                ("in1", "out1"): tau,
            })
            return sdict
        ```
        
        Or a waveguide:
        
        ```python
        def waveguide(wl=1.55, wl0=1.55, neff=2.34, ng=3.4, length=10.0, loss=0.0):
            dwl = wl - wl0
            dneff_dwl = (ng - neff) / wl0
            neff = neff - dwl * dneff_dwl
            phase = 2 * jnp.pi * neff * length / wl
            transmission = 10 ** (-loss * length / 20) * jnp.exp(1j * phase)
            sdict = reciprocal({("in0", "out0"): transmission})
            return sdict
        ```
        
        These component models can then be combined into a circuit:
        
        ```python
        mzi = sax.circuit(
            instances = {
                "lft": coupler,
                "top": waveguide,
                "rgt": coupler,
            },
            connections={
                "lft:out0": "rgt:in0",
                "lft:out1": "top:in0",
                "top:out0": "rgt:in1",
            },
            ports={
                "lft:in0": "in0",
                "lft:in1": "in1",
                "rgt:out0": "out0",
                "rgt:out1": "out1",
            },
        )
        ```
        
        This mzi circuit is a model function in its own right. To simulate it, first obtain the
        (possibly nested) dictionary of parameters, then modify the parameters and call the
        function:
        
        ```python
        params = sax.get_params(mzi)
        params["top"]["length"] = 10e-5
        S = mzi(**params)
        S["in0", "out0"]
        ```
        
        ```
        DeviceArray(-0.280701+0.10398856j, dtype=complex64)
        ```
        
        Those are the basics. For more info, check out the **full**
        [SAX Quick Start page](https://sax.readthedocs.io/en/latest/examples/01_quick_start.html),
        the [Examples](https://sax.readthedocs.io/en/latest/examples.html)
        or the
        [Documentation](https://sax.readthedocs.io/en/latest/index.html).
        
        ## Installation
        
        ### Dependencies
        
        - [JAX & JAXLIB](https://github.com/google/jax). Please read the JAX install
          instructions [here](https://github.com/google/jax/#installation). Alternatively, you can
          try running [jaxinstall.sh](jaxinstall.sh) to automatically pip-install the correct
          `jax` and `jaxlib` package for your python and cuda version (if that exact combination
          exists).
        
        ### Installation
        
        ```
        pip install sax
        ```
        
        ## License
        
        Copyright © 2021, Floris Laporte, [Apache-2.0 License](LICENSE)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Description-Content-Type: text/markdown
