/usr/share/mmass/mspy/mod_calibration.py is in mmass 5.5.0-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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# Copyright (C) 2005-2013 Martin Strohalm <www.mmass.org>
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# Complete text of GNU GPL can be found in the file LICENSE.TXT in the
# main directory of the program.
# -------------------------------------------------------------------------
# load libs
import numpy
from numpy.linalg import solve as solveLinEq
# load stopper
from mod_stopper import CHECK_FORCE_QUIT
# DATA RE-CALIBRATION
# -------------------
def calibration(data, model='linear'):
"""Calculate calibration constants for given references.
data (list or (measured mass, reference mass)) - calibration data
model ('linear' or 'quadratic') - fitting model
"""
# single point calibration
if model == 'linear' and len(data) == 1:
shift = data[0][1] - data[0][0]
return _linearModel, (1., shift), 1.0
# set fitting model and initial values
if model=='linear':
model = _linearModel
initials = (0.5, 0)
elif model=='quadratic':
model = _quadraticModel
initials = (1., 0, 0)
# calculate calibration constants
params = _leastSquaresFit(model, initials, data)
# fn, parameters, chi-square
return model, params[0], params[1]
# ----
def _linearModel(params, x):
"""Function for linear model."""
a, b = params
return a*x + b
# ----
def _quadraticModel(params, x):
"""Function for quadratic model."""
a, b, c = params
return a*x*x + b*x + c
# ----
def _leastSquaresFit(model, parameters, data, maxIterations=None, limit=1e-7):
"""General non-linear least-squares fit using the
Levenberg-Marquardt algorithm and automatic derivatives.
Originally developed by Konrad Hinsen.
"""
n_param = len(parameters)
p = ()
i = 0
for param in parameters:
p = p + (_DerivVar(param, i),)
i = i + 1
id = numpy.identity(n_param)
l = 0.001
chi_sq, alpha = _chiSquare(model, p, data)
niter = 0
while True:
niter += 1
delta = solveLinEq(alpha+l*numpy.diagonal(alpha)*id,-0.5*numpy.array(chi_sq[1]))
next_p = map(lambda a,b: a+b, p, delta)
next_chi_sq, next_alpha = _chiSquare(model, next_p, data)
if next_chi_sq > chi_sq:
l = 10.*l
elif chi_sq[0] - next_chi_sq[0] < limit:
break
else:
l = 0.1*l
p = next_p
chi_sq = next_chi_sq
alpha = next_alpha
if maxIterations and niter == maxIterations:
break
return map(lambda p: p[0], next_p), next_chi_sq[0]
# ----
def _chiSquare(model, parameters, data):
"""Count chi-square."""
n_param = len(parameters)
alpha = numpy.zeros((n_param, n_param))
chi_sq = _DerivVar(0., [])
for point in data:
f = model(parameters, point[0])
chi_sq += (f-point[1])**2
d = numpy.array(f[1])
alpha = alpha + d[:,numpy.newaxis]*d
return chi_sq, alpha
# ----
class _DerivVar:
"""This module provides automatic differentiation for functions with any number of variables."""
def __init__(self, value, index=0):
self.value = value
if type(index) == type([]):
self.deriv = index
else:
self.deriv = index*[0] + [1]
def _mapderiv(self, func, a, b):
nvars = max(len(a), len(b))
a = a + (nvars-len(a))*[0]
b = b + (nvars-len(b))*[0]
return map(func, a, b)
def __getitem__(self, item):
if item == 0:
return self.value
elif item == 1:
return self.deriv
else:
raise IndexError
def __cmp__(self, other):
if isinstance(other, _DerivVar):
return cmp(self.value, other.value)
else:
return cmp(self.value, other)
def __add__(self, other):
if isinstance(other, _DerivVar):
return _DerivVar(self.value + other.value, self._mapderiv(lambda a,b: a+b, self.deriv, other.deriv))
else:
return _DerivVar(self.value + other, self.deriv)
def __radd__(self, other):
if isinstance(other, _DerivVar):
self.value += other.value
self.deriv = self._mapderiv(lambda a,b: a+b, self.deriv, other.deriv)
return self
else:
self.value += other
return self
def __sub__(self, other):
if isinstance(other, _DerivVar):
return _DerivVar(self.value - other.value, self._mapderiv(lambda a,b: a-b, self.deriv, other.deriv))
else:
return _DerivVar(self.value - other, self.deriv)
def __rsub__(self, other):
if isinstance(other, _DerivVar):
self.value -= other.value
self.deriv = self._mapderiv(lambda a,b: a-b, self.deriv, other.deriv)
return self
else:
self.value -= other
return self
def __mul__(self, other):
if isinstance(other, _DerivVar):
return _DerivVar(self.value * other.value, self._mapderiv(lambda a,b: a+b, map(lambda x,f=self.value:f*x, other.deriv), map(lambda x,f=other.value:f*x, self.deriv)))
else:
return _DerivVar(self.value * other, map(lambda x,f=other:f*x, self.deriv))
def __div__(self, other):
if isinstance(other, _DerivVar):
inv = 1./other.value
return _DerivVar(self.value * inv, self._mapderiv(lambda a,b: a-b, map(lambda x,f=inv: f*x, self.deriv), map(lambda x,f=self.value*inv*inv: f*x, other.deriv)))
else:
inv = 1./value
return _DerivVar(self.value * inv, map(lambda x,f=inv:f*x, self.deriv))
def __pow__(self, other):
val1 = pow(self.value, other-1)
deriv1 = map(lambda x,f=val1*other: f*x, self.deriv)
return _DerivVar(val1*self.value, deriv1)
def __abs__(self):
absvalue = abs(self.value)
return _DerivVar(absvalue, map(lambda a, d=self.value/absvalue: d*a, self.deriv))
# ----
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