Moved the fit functions to their own file.

kmap/process/fit_funcs.py

+#!/usr/bin/python
+# coding: utf8
+# /*##########################################################################
+#
+# Copyright (c) 2015-2016 European Synchrotron Radiation Facility
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+# ###########################################################################*/
+
+from __future__ import absolute_import
+
+__authors__ = ["D. Naudet"]
+__date__ = "01/06/2016"
+__license__ = "MIT"
+
+import numpy as np
+
+from scipy.optimize import leastsq
+
+# Some constants
+_const_inv_2_pi_ = np.sqrt(2 * np.pi)
+
+
+# 1d Gaussian func
+# TODO : optimize
+def gaussian(x, a, c, s):
+    """
+    Returns (a / (sqrt(2 * pi) * s)) * exp(- 0.5 * ((x - c) / s)^2)
+    :param x: values for which the gaussian must be computed
+    :param a: area under curve ( amplitude * s * sqrt(2 * pi) )
+    :param c: center
+    :param stdev: standard deviation
+    :return: (a / (sqrt(2 * pi) * s)) * exp(- 0.5 * ((x - c) / s)^2)
+    """
+    return (a * (1. / (_const_inv_2_pi_ * s)) *
+            np.exp(-0.5 * ((x - c) / s) ** 2))
+
+
+# 1d Gaussian fit
+# TODO : optimize
+def gaussian_fit_err(p, x, y):
+    """
+
+    :param p:
+    :param x:
+    :param y:
+    :return:
+    """
+    return gaussian(x, *p) - y
+
+
+def gaussian_fit(x, y, p):
+    """
+    Fits (leastsq) a gaussian on the provided data f(x) = y.
+    p = (a, c, s)
+    and f(x) = (a / (sqrt(2 * pi) * s)) * exp(- 0.5 * ((x - c) / s)^2)
+    :param x:
+    :param y:
+    :param p:
+    :return:
+    """
+    result = leastsq(gaussian_fit_err,
+                     p,
+                     args=(x, y,),
+                     maxfev=100000,
+                     full_output=True)
+
+    if result[4] not in [1, 2, 3, 4]:
+        raise ValueError('Failed to fit : {0}.'.format(result[3]))
+
+    return result[0]
+
+
+def centroid(x, y, p):
+    """
+    Computes the center of mass of the provided data.
+    Returns the value closest to the center of mass, and the
+    the center of mass
+    :param x:
+    :param y:
+    :param p:
+    :return: list
+    """
+    # TODO : throw exception if fit failed
+    com = x.dot(y) / y.sum()
+    idx = np.abs(x - com).argmin()
+    return [y[idx], com, np.nan]