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84 lines
2.6 KiB
84 lines
2.6 KiB
2 years ago
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#!/usr/bin/env python
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# encoding: utf-8
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"""
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expfitting.py
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Provide single or double exponential fits to data.
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"""
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import lmfit
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import numpy as np
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import scipy.optimize
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class ExpFitting:
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"""
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Parameters
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----------
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nexp : int
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1 or 2 for single or double exponential fit
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initpars : dict
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dict of initial parameters. For example: {'dc': 0.,
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'a1': 1., 't1': 3, 'a2' : 0.5, 'delta': 3.}, where
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delta determines the ratio between the time constants.
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bounds : dict
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dictionary of bounds for each parameter, with a list of lower and upper values.
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"""
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def __init__(self, nexp=1, initpars=None, bounds=None):
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self.fitpars = lmfit.Parameters()
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if nexp == 1:
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# (Name, Value, Vary, Min, Max, Expr)
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self.fitpars.add_many(
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("dc", 0, True, -100.0, 0.0, None),
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("a1", 1.0, True, -25.0, 25.0, None),
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("t1", 10.0, True, 0.1, 50, None),
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)
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self.efunc = self.exp1_err
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elif nexp == 2:
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self.fitpars.add_many(
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("dc", 0, True, -100.0, 0.0, None),
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("a1", 1.0, True, 0.0, 25.0, None),
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("t1", 10.0, True, 0.1, 50, None),
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("a2", 1.0, True, 0.0, 25.0, None),
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("delta", 3.0, True, 3.0, 100.0, None),
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)
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if initpars is not None:
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assert len(initpars) == 5
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for k, v in initpars.iteritems():
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self.fitpars[k].value = v
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if bounds is not None:
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assert len(bounds) == 5
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for k, v in bounds.iteritems():
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self.fitpars[k].min = v[0]
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self.fitpars[k].max = v[1]
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self.efunc = self.exp2_err
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else:
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raise ValueError
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def fit(self, x, y, p, verbose=False):
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kws = {"maxfev": 5000}
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mim = lmfit.minimize(
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self.efunc, p, method="least_squares", args=(x, y)
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) # , kws=kws)
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if verbose:
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lmfit.printfuncs.report_fit(mim.params)
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fitpars = mim.params
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return fitpars
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@staticmethod
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def exp1(x, dc, t1, a1):
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return dc + a1 * np.exp(-x / t1)
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def exp1_err(self, p, x, y):
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return np.fabs(y - self.exp1(x, **dict([(k, p.value) for k, p in p.items()])))
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@staticmethod
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def exp2(x, dc, t1, a1, a2, delta):
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return dc + a1 * np.exp(-x / t1) + a2 * np.exp(-x / (t1 * delta))
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def exp2_err(self, p, x, y):
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return np.fabs(y - self.exp2(x, **dict([(k, p.value) for k, p in p.items()])))
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