added errorbar weighting and filter for nans and inf
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21d7516789
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4319f4e394
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@ -1,9 +1,21 @@
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import lmfit
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import lmfit
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import numpy as np
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import numpy as np
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import logging as log
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pv = lmfit.models.PseudoVoigtModel()
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pv = lmfit.models.PseudoVoigtModel()
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def fitPeak(x,y,autorange=False):
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def fitPeak(x,y,err=1,autorange=False):
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if isinstance(err,np.ndarray):
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if np.all(err==0):
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err = 1
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log.warn("Asked to fit peak but all errors are zero, forcing them to 1")
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elif np.isfinite(err).sum() != len(err):
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idx = np.isfinite(err)
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x = x[idx]
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y = y[idx]
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err = err[idx]
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log.warn("Asked to fit peak but some errorbars are infinite or nans,\
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excluding those points")
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if autorange:
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if autorange:
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# find fwhm
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# find fwhm
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idx = np.ravel(np.argwhere( y<y.max()/2 ))
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idx = np.ravel(np.argwhere( y<y.max()/2 ))
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@ -16,5 +28,5 @@ def fitPeak(x,y,autorange=False):
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x = x[idx]
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x = x[idx]
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y = y[idx]
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y = y[idx]
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pars = pv.guess(y,x=x)
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pars = pv.guess(y,x=x)
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ret = pv.fit(y,x=x,params=pars)
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ret = pv.fit(y,x=x,weights=1/err,params=pars)
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return ret
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return ret
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