45d220ad5b
A library for least-squares minimization and data fitting in Python, based on scipy.optimize.
18 lines
1 KiB
Text
18 lines
1 KiB
Text
A library for least-squares minimization and data fitting in Python.
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Built on top of scipy.optimize, lmfit provides a Parameter object
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which can be set as fixed or free, can have upper and/or lower
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bounds, or can be written in terms of algebraic constraints of
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other Parameters. The user writes a function to be minimized as a
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function of these Parameters, and the scipy.optimize methods are
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used to find the optimal values for the Parameters. The
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Levenberg-Marquardt (leastsq) is the default minimization algorithm,
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and provides estimated standard errors and correlations between
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varied Parameters. Other minimization methods, including Nelder-Mead's
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downhill simplex, Powell's method, BFGS, Sequential Least Squares,
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and others are also supported. Bounds and contraints can be placed
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on Parameters for all of these methods.
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In addition, methods for explicitly calculating confidence intervals
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are provided for exploring minmization problems where the approximation
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of estimating Parameter uncertainties from the covariance matrix
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is questionable.
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