gaussHermite.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Time-stamp: <2015-04-07 18:58:52 ycopin>

from __future__ import division, print_function

"""
Gauss-Hermite line profile.
"""

__author__ = "Yannick Copin <y.copin@ipnl.in2p3.fr>"

from astropy.modeling.models import PolynomialModel, Gaussian1D
from astropy.modeling.parameters import Parameter
from astropy.modeling.core import Fittable1DModel

import re

class Hermite1D(PolynomialModel):
    """
    1D (physicists') Hermite polynomial.

    Parameters
    ----------
    degree : int
        degree of the series
    domain : list or None
    window : list or None
        If None, it is set to [-1,1]
        Fitters will remap the domain to this window
    param_dim : int
        number of parameter sets
    **params : dict
        keyword: value pairs, representing parameter_name: value
    """

    inputs = ('x',)
    outputs = ('y',)

    def __init__(self, degree, domain=None, window=[-1, 1], n_models=None,
                 model_set_axis=None, name=None, meta=None, **params):
        self.domain = domain
        self.window = window
        super(Hermite1D, self).__init__(
            degree, n_models=n_models, model_set_axis=model_set_axis,
            name=name, meta=meta, **params)

    def prepare_inputs(self, x, **kwargs):
        inputs, format_info = \
                super(PolynomialModel, self).prepare_inputs(x, **kwargs)

        x = inputs[0]

        if self.domain is not None:
            x = poly_map_domain(x, self.domain, self.window)

        return (x,), format_info

    @classmethod
    def evaluate(cls, x, *coeffs):

        return cls.clenshaw(x, coeffs)

    def fit_deriv(self, x, *params):
        """
        Computes the Vandermonde matrix.

        Parameters
        ----------
        x : ndarray
            input
        params : throw away parameter
            parameter list returned by non-linear fitters

        Returns
        -------
        result : ndarray
            The Vandermonde matrix
        """

        x = np.array(x, dtype=np.float, copy=False, ndmin=1)
        v = np.empty((self.degree + 1,) + x.shape, dtype=x.dtype)
        v[0] = 1          # H_0
        if self.degree > 0:
            v[1] = 2 * x  # H_1
            for i in range(2, self.degree + 1):
                # Recurrence: H_n = 2 * x * H_{n-1} - 2 * (n - 1) * H_{n-2}
                v[i] = 2 * (x * v[i - 1] - (i - 1) * v[i - 2])

        return np.rollaxis(v, 0, v.ndim)

    @staticmethod
    def clenshaw(x, coeffs):

        # Clenshaw algo: H_n = alpha * H_{n-1} + beta * H_{n-2}
        alpha = 2 * x
        if len(coeffs) == 1:
            c0 = coeffs[0]
            c1 = 0
        elif len(coeffs) == 2:
            c0 = coeffs[0]
            c1 = coeffs[1]
        else:
            nd = len(coeffs)
            c0 = coeffs[-2]
            c1 = coeffs[-1]
            for i in range(3, len(coeffs) + 1):
                tmp = c0
                nd = nd - 1
                beta = -2 * (nd - 1)
                c0 = coeffs[-i] + c1 * beta
                c1 = tmp + c1 * alpha

        return c0 + c1 * alpha    # c0 * H_0 + c_1 * H_1


class GaussHermite(Fittable1DModel):

    _param_names = ()  # Will be filled at instanciation

    def __init__(self, order, *args, **kwargs):

        self._order = int(order)
        if self._order < 3:
            self._order = 0

        # Gaussian model
        self._gaussian = Gaussian1D()
        # Hermite series
        if self._order:
            self._hermite = Hermite1D(self._order)
        else:
            self._hermite = None

        self._param_names = self._generate_coeff_names()

        super(GaussHermite, self).__init__(*args, **kwargs)

    def _hi_order(self, name):
        # One could store the compiled regex, but it will crash the deepcopy:
        # "cannot deepcopy this pattern object"

        match = re.match('h(?P<order>\d+)', name)  # h3, h4, etc.
        order = int(match.groupdict()['order']) if match else 0

        return order

    def _generate_coeff_names(self):

        names = list(self._gaussian.param_names)  # Gaussian parameters
        names += [ 'h{}'.format(i)
                   for i in range(3, self._order + 1) ] # Hermite coeffs

        return tuple(names)

    def __getattr__(self, attr):

        if attr[0] == '_':
            super(GaussHermite, self).__getattr__(attr)
        elif attr in self._gaussian.param_names:
            return self._gaussian.__getattribute__(attr)
        elif self._order and self._hi_order(attr) >= 3:
            return self._hermite.__getattr__(attr.replace('h', 'c'))
        else:
            super(GaussHermite, self).__getattr__(attr)

    def __setattr__(self, attr, value):

        if attr[0] == '_':
            super(GaussHermite, self).__setattr__(attr, value)
        elif attr in self._gaussian.param_names:
            self._gaussian.__setattr__(attr, value)
        elif self._order and self._hi_order(attr) >= 3:
            self._hermite.__setattr__(attr.replace('h', 'c'), value)
        else:
            super(GaussHermite, self).__setattr__(attr, value)

    @property
    def param_names(self):

        return self._param_names

    def evaluate(self, x, *params):

        a, m, s = params[:3]                      # amplitude, mean, stddev
        f = self._gaussian.evaluate(x, a, m, s)
        if self._order:
            f *= (1 + self._hermite.evaluate((x - m)/s, 0, 0, 0, *params[3:]))

        return f



if __name__ == '__main__':

    import numpy as N
    import matplotlib.pyplot as P
    import astropy.modeling as AM

    if True:
        g0 = GaussHermite(4, amplitude=1, mean=0, stddev=1, h3=0.005, h4=0.005)
    else:
        g0 = AM.models.Gaussian1D(amplitude=1, mean=0, stddev=1)

    x = N.linspace(-10, 10, 201)
    y = g0(x)

    fitter = AM.fitting.SimplexLSQFitter()
    g0.amplitude = 0.8
    g0.mean = -0.5
    g0.stddev = 1.2
    g = fitter(g0, x, y)
    print(g)

    fig = P.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(x, y, label='Input')
    ax.plot(x, g0(x), ls='--', label='Guess')
    ax.plot(x, g(x), ls=':', label='Fit')

    ax.legend()

    P.show()

Thanks for your feedback. I did not follow recently on the evolution of Astropy.Modeling, and #4049 is not merged yet as of today.

Your last suggestion is very interesting, and I wonder if it could be combined w/ the "Mapping" utility. As for now, it does not work, since the degree of the Hermite1D polynom is not specified ('lazyproperty' object is not iterable).

Anyway, in the current implementation as presented in this Gist, do you know why the model is not properly updated during the adjustment?

@ycopin I wonder if this is not a good candidate for a compound model.
Here's what I tried ( I hope I did not misunderstand the model).

gauss = models.Gaussian1D(amplitude=1, mean=0, stddev=1)
sh = models.Shift(0)
# Make the model fittable (we should really change this in astropy
sh.fittable = True
scl = models.Scale(1)
scl.fittable = True
herm = Hermite1D(4, c3=0.005, c4=0.005)
cm = gauss + gauss * (sh | scl | herm)
x= np.linspace(-10, 10, 201)
y = cm(x)

gauss1 = models.Gaussian1D(amplitude=0.8, mean=-0.5, stddev=1.2)
#The line below implements GaussHermite.evaluate()
cm1 = gauss1 + gauss1 * (sh | scl | herm)
# Now set constraints on the parameters
cm1.c0_4.fixed = True
cm1.c1_4.fixed = True
cm1.c2_4.fixed = True

def tie_shift(model):
    return model.amplitude_0
cm1.offset_2.tied=tie_shift

def tie_scale(model):
    return model.stddev_0
cm1.factor_3.tied=tie_scale

fitter = fitting.LevMarLSQFitter()
fitted = fitter(cm1, x, y)
plt.plot(x, y)
plt.plot(x, fitted(x))

The combined model above gives slightly different results from your model g0. I think this is because in your model the
h3 and h4 coefficients do not propagate to the polynomial. For example,

g0 = hg.GaussHermite(4, amplitude=1, mean=0, stddev=1, h3=0.005, h4=0.005)
g0._hermite
    <Hermite1D(4, c0=0.0, c1=0.0, c2=0.0, c3=0.0, c4=0.0)>

Am I correct to think that h3 and h4 should propagate to c3 and c4?

@nden That's a good idea about using tied parameters to do this. It's too bad though that they only work in fitting. That's still something that we need to consider changing since I know it causes regular confusion.

Also yeah I don't know why the Scale and Shift models aren't already fittable = True by default. That makes no sense.