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Curve Fitting.
Copyright 2007-2011 Research Foundation State University of New York This file is part of QUB Express.
QUB Express 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.
QUB Express 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, named LICENSE.txt, in the QUB Express program directory. If not, see <http://www.gnu.org/licenses/>.
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BaseCurve Defines the interface of a curve object; curves need not subclass, but they must behave like it. |
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BaseFitter Defines the interface of a fitter object; fitters need not subclass, but they must behave like it. |
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Curve Fittable curve from a parsed Python expression; either simple algebraic, or system of ODEs; see acceptODEs. |
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DblDecExpoCurve | |||
DblGaussianCurve | |||
DblGaussianUnitCurve | |||
DecExpoCurve | |||
GaussianCurve | |||
GaussianUnitCurve | |||
HillCurve | |||
HillCurveNorm | |||
LMFitter Minimizes sum-squared-residual using lmmin from qubx.fast. |
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LinearCurve | |||
LogExpoCurve | |||
LogExpoCurve2 | |||
LogExpoCurve3 | |||
LogExpoCurve4 | |||
LogExpoCurve5 | |||
LorentzianCurve | |||
OutOfBounds | |||
QuadGaussianCurve | |||
QuadGaussianUnitCurve | |||
QuintGaussianCurve | |||
QuintGaussianUnitCurve | |||
SimplexFitter Minimizes the sum-squared-residual using scipy.optimize.fmin. |
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Simplex_LM_Fitter Minimizes sum-squared-residual using SimplexFitter then LMFitter; using half the max_iter for each algorithm. |
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Stop | |||
TriGaussianCurve | |||
TriGaussianUnitCurve |
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cpu_count = 4
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erfc = <ufunc 'erfc'>
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Curves =
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D1_DIFF_CENTRAL = False
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D2_DIFF_CENTRAL = True
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DIFF_DEV = 1e-07
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Fitters =
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GaussianCurves =
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GaussianUnitCurves =
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USE_POOL_THRESHOLD = 1024
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__package__ =
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Returns the numpy.array(shape=(i,j)) of d(dResidual)/dP_i,dP_j, for i,j in len(non-const-params).
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Minimally after Gill-Murray: if covariance matrix is not positive definite, some diagonals are negative, with unknown meaning. here we look for trouble and if necessary replace the covariance matrix with a generalized cholesky factorization. |
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Curves
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Fitters
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GaussianCurves
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GaussianUnitCurves
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