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"""A minimal implementation of a dense neural net with an arbitrary
number of layers, backpropagation, and a few different activation functions."""

import numpy as np

# Activation and cost functions (with gradients)
def sigmoid(x):
    y = 1.0 / (1.0 + np.exp(-x))
    return y, y * (1.0 - y)

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"""example of converting a numpy array to a C++ valarray and back in cython
using copies.

Compile with `python setup.py build_ext --inplace`

Example
-------
>>> import cpptest, numpy as np

>>> x = np.array([0., 1., 2.])

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"""sncosmo Source implementing the "dm15" model from Sako et al (2008).

Example::

    >>> import sncosmo
    >>> import dm15source
    >>> model = sncosmo.Model(source='dm15')
    >>> model.set(z=0.5, t0=55000., amplitude=1e-10, dm15=0.9)
    >>> model.bandmag('sdssi', 'ab', 55011.5)
    24.661108429127694

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# Split MIRI transmissions into separate files. Original file was
# http://ircamera.as.arizona.edu/MIRI/ImPCE_TN-00072-ATC-Iss2.xlsx
# saved as CSV.
import numpy as np

data = np.loadtxt("ImPCE_TN-00072-ATC-Iss2.csv", skiprows=2)

bands = ("F560W F770W F1000W F1280W F1130W F1500W F1800W F2100W F2550W"
         .lower().split())

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// Doing the operation:
//
//                         | a a a a |  | y |
// x * A * y = [ x x x x ] | a a a a |  | y |
//                         | a a a a |  | y |
//                         | a a a a |  | y |
//
// with SIMD intrinics (specifically AVX).
//
// adapted from https://gist.github.com/rygorous/4172889

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#!/usr/bin/env python

from matplotlib import rc
from daft import PGM, Node, Plate
rc("font", family="serif", size=12)
rc("text", usetex=True)


pgm = PGM([7.65, 5.3], origin=[0., -0.2], observed_style='inner')

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def ellipsoid_dist_sq(x, ell):
    """Return the square of each point's distance from the ellipsoid center,
    relative to ellipsoid boundary.

    Refered to in Feroz, Hobson & Bridges (2009) as the Mahalanobis distance.

    Parameters
    ----------
    x : `~numpy.ndarray`
        Points, array with shape (npoints, ndim).

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