Author: tlinnet
Date: Mon Aug 25 13:27:41 2014
New Revision: 25243
URL: http://svn.gna.org/viewcvs/relax?rev=25243&view=rev
Log:
Moved target function for curve fitting with scipy into
specific_analyses.relax_disp.estimate_r2eff.
This will later include the backend
specific_analyses.relax_disp.optimisation.estimate_r2eff()
function and the code in the target_functions package.
The code in target_functions.relax_disp_curve_fit is a lot more than just a
target function, so it doesn't really belong in this package.
This is also to isolate this experimental feature.
Added:
trunk/specific_analyses/relax_disp/estimate_r2eff.py
- copied, changed from r25242,
trunk/target_functions/relax_disp_curve_fit.py
Removed:
trunk/target_functions/relax_disp_curve_fit.py
Copied: trunk/specific_analyses/relax_disp/estimate_r2eff.py (from r25242,
trunk/target_functions/relax_disp_curve_fit.py)
URL:
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/estimate_r2eff.py?p2=trunk/specific_analyses/relax_disp/estimate_r2eff.py&p1=trunk/target_functions/relax_disp_curve_fit.py&r1=25242&r2=25243&rev=25243&view=diff
==============================================================================
(empty)
Removed: trunk/target_functions/relax_disp_curve_fit.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/target_functions/relax_disp_curve_fit.py?rev=25242&view=auto
==============================================================================
--- trunk/target_functions/relax_disp_curve_fit.py (original)
+++ trunk/target_functions/relax_disp_curve_fit.py (removed)
@@ -1,194 +0,0 @@
-###############################################################################
-# #
-# Copyright (C) 2013-2014 Edward d'Auvergne #
-# Copyright (C) 2014 Troels E. Linnet #
-# #
-# This file is part of the program relax (http://www.nmr-relax.com). #
-# #
-# 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. #
-# #
-# You should have received a copy of the GNU General Public License #
-# along with this program. If not, see <http://www.gnu.org/licenses/>. #
-# #
-###############################################################################
-
-# Module docstring.
-"""Target functions for relaxation exponential curve fitting with both minfx
and scipy.optimize.leastsq."""
-
-# Python module imports.
-from copy import deepcopy
-from numpy import dot, exp, log, multiply, sum
-
-# relax module imports.
-from target_functions.chi2 import chi2_rankN
-
-
-class Exponential:
- def __init__(self, num_params=2, num_times=None, values=None, sd=None,
relax_times=None, scaling_matrix=None):
- """Relaxation dispersion target functions for optimisation.
-
- This class contains minimisation functions for both minfx and
scipy.optimize.leastsq.
-
- @keyword num_param: The number of parameters in the model.
- @type num_param: int
- @keyword num_times: The number of time points.
- @type num_times: int
- @keyword values: The measured intensity values per time
point.
- @type values: numpy array
- @keyword sd: The standard deviation of the measured
intensity values per time point.
- @type sd: numpy array
- @keyword relax_times: The time points.
- @type relax_times: numpy array
- @keyword scaling_matrix: The square and diagonal scaling matrix.
- @type scaling_matrix: numpy rank-2 float array
- """
-
- # Store variables.
- self.num_params = num_params
- self.num_times = num_times
-
- self.values = values
- self.errors = sd
- self.relax_times = relax_times
- self.scaling_matrix = scaling_matrix
-
- # Scaling initialisation.
- self.scaling_flag = False
- if self.scaling_matrix != None:
- self.scaling_flag = True
-
- # Create the structure for holding the back-calculated R2eff values
(matching the dimensions of the values structure).
- self.back_calc = deepcopy(self.values)
-
- # Define function to minimise for minfx.
- self.func = self.func_exp
-
-
- def calc_exp(self, times=None, r2eff=None, i0=None):
- """Calculate the function values of exponential function.
-
- @keyword times: The time points.
- @type times: numpy array
- @keyword r2eff: The effective transversal relaxation rate.
- @type r2eff: float
- @keyword i0: The initial intensity.
- @type i0: float
- @return: The function values.
- @rtype: float
- """
-
- # Calculate.
- return i0 * exp( -times * r2eff)
-
-
- def estimate_x0_exp(self, intensities=None, times=None):
- """Estimate starting parameter x0 = [r2eff_est, i0_est], by converting
the exponential curve to a linear problem.
- Then solving by linear least squares of: ln(Intensity[j]) = ln(i0) -
time[j]* r2eff.
-
- @keyword intensities: The measured intensity values per time point.
- @type intensities: numpy array
- @keyword times: The time points.
- @type times: numpy array
- @return: The list with estimated r2eff and i0 parameter
for optimisation, [r2eff_est, i0_est]
- @rtype: list
- """
-
- # Convert to linear problem.
- w = log(intensities)
- x = - 1. * times
- n = len(times)
-
- # Solve by linear least squares.
- b = (sum(x*w) - 1./n * sum(x) * sum(w) ) / ( sum(x**2) - 1./n *
(sum(x))**2 )
- a = 1./n * sum(w) - b * 1./n * sum(x)
-
- # Convert back from linear to exp function. Best estimate for
parameter.
- r2eff_est = b
- i0_est = exp(a)
-
- # Return.
- return [r2eff_est, i0_est]
-
-
- def calc_exp_chi2(self, r2eff=None, i0=None):
- """Calculate the chi-squared value of exponential function.
-
-
- @keyword r2eff: The effective transversal relaxation rate.
- @type r2eff: float
- @keyword i0: The initial intensity.
- @type i0: float
- @return: The chi-squared value.
- @rtype: float
- """
-
- # Calculate.
- self.back_calc[:] = self.calc_exp(times=self.relax_times, r2eff=r2eff,
i0=i0)
-
- # Return the total chi-squared value.
- return chi2_rankN(data=self.values, back_calc_vals=self.back_calc,
errors=self.errors)
-
-
- def func_exp(self, params):
- """Target function for exponential fit in minfx.
-
- @param params: The vector of parameter values.
- @type params: numpy rank-1 float array
- @return: The chi-squared value.
- @rtype: float
- """
-
- # Scaling.
- if self.scaling_flag:
- params = dot(params, self.scaling_matrix)
-
- # Unpack the parameter values.
- r2eff = params[0]
- i0 = params[1]
-
- # Calculate and return the chi-squared value.
- return self.calc_exp_chi2(r2eff=r2eff, i0=i0)
-
-
- def func_exp_general(self, params, times, intensities):
- """Target function for minimisation with scipy.optimize.leastsq.
-
- @param params: The vector of parameter values.
- @type params: numpy rank-1 float array
- @keyword times: The time points.
- @type times: numpy array
- @keyword intensities: The measured intensity values per time point.
- @type intensities: numpy array
- @return: The difference between function evaluation
with fitted parameters and measured values.
- @rtype: numpy array
- """
-
- # Return
- return self.calc_exp(times, *params) - intensities
-
-
- def func_exp_weighted_general(self, params, times, intensities, weights):
- """Target function for weighted minimisation with
scipy.optimize.leastsq.
-
- @param params: The vector of parameter values.
- @type params: numpy rank-1 float array
- @keyword times: The time points.
- @type times: numpy array
- @keyword intensities: The measured intensity values per time point.
- @type intensities: numpy array
- @keyword weights: The weights to multiply the function
evaluation. Should be supplied as '1/sd', where sd is the standard deviation
of the measured intensity values.
- @type weights: numpy array
- @return: The weighted difference between function
evaluation with fitted parameters and measured values.
- @rtype: numpy array
- """
-
- # Return
- return weights * (self.calc_exp(times, *params) - intensities)
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