Getting Started
===============

In this guide you will install pyCE and compute your first configurational
entropy. Why a whole page for an install? Because the library leans on the
scientific Python stack — numpy, scipy, matplotlib, and astropy — and a clean
environment now will save you headaches later.

Installation
^^^^^^^^^^^^

pyCE requires Python 3.8 or newer. Clone the repository and install it into a
virtual environment::

    git clone https://github.com/EternalTime/pyCE.git
    cd pyCE
    python3 -m venv .venv
    source .venv/bin/activate
    pip install -e .

The ``-e`` flag installs in editable mode — changes you make to the source are
picked up immediately, with no reinstall required. Check that everything
works::

    >>> import pyCE

If the import goes through quietly, you're ready.

Your first configurational entropy
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Let's compute the configurational entropy of the most familiar localized
profile there is — a Gaussian in three spatial dimensions::

    import numpy as np
    from pyCE.math import radialFT, radial_integrate

    r = np.linspace(1e-6, 10, 500)
    f = np.exp(-r**2)

    ft, k = radialFT(3, f, r)        # radial Fourier transform in d = 3
    mf    = np.abs(ft)**2
    mf    = mf/mf.max()              # the modal fraction

    Sc = -radial_integrate(k, mf*np.log(np.finfo(float).eps + mf), 3)
    print(Sc)

Three lines of physics: transform, normalize, integrate. Every guide in this
documentation is a variation on that theme, with the field profile coming from
a shooting method, a lattice simulation, a stellar model, or a satellite.

Before moving on, convince yourself that the transform can be trusted.
Plancherel's theorem holds on the discrete grids by construction::

    print(radial_integrate(k, np.abs(ft)**2, 3)
          / radial_integrate(r, f**2, 3))

You should get 1 to machine precision.