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Merge pull request #114 from sbillinge/codespell
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2 parents ae9d65f + 9232638 commit bb4f92d

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.codespell/ignore_words.txt

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;; Frobenius norm used in np.linalg.norm
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fro
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;; nin is a legit variable in builder
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nin
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;; highT is used for high Temperature in examples/debymodelII
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highT

.travis.yml

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doc/examples/coreshellnp.py

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recipe.constrain(generator_zns.scale, "1 - scale_CdS")
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# We also want the resolution factor to be the same on each.
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# Vary the gloabal scale as well.
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# Vary the global scale as well.
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recipe.addVar(contribution.scale, 0.3)
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# Now we can configure the structural parameters. We tag the different

doc/examples/crystalpdf.py

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This is example of fitting the fcc nickel structure to measured PDF
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data. The purpose of this example is to demonstrate and describe the
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classes in configuraiton options involved with setting up a fit in this
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classes in configuration options involved with setting up a fit in this
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way. The main benefit of using SrFit for PDF refinement is the
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flexibility of modifying the PDF profile function for specific needs,
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adding restraints to a fit and the ability to simultaneously refine a

doc/examples/crystalpdfall.py

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recipe.constrain(xgenerator_sini_si.scale, "1 - pscale_sini_ni")
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# The qdamp parameters are too correlated to vary so we fix them based on
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# previous measurments.
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# previous measurements.
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xgenerator_ni.qdamp.value = 0.055
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xgenerator_si.qdamp.value = 0.051
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ngenerator_ni.qdamp.value = 0.030

doc/examples/crystalpdftwodata.py

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recipe.addVar(ngenerator.scale, 1, "nscale")
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recipe.addVar(xgenerator.qdamp, 0.01, "xqdamp")
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recipe.addVar(ngenerator.qdamp, 0.01, "nqdamp")
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# delta2 is a non-structual material propery. Thus, we constrain together
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# delta2 is a non-structual material property. Thus, we constrain together
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# delta2 Parameter from each PDFGenerator.
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delta2 = recipe.newVar("delta2", 2)
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recipe.constrain(xgenerator.delta2, delta2)

doc/examples/crystalpdftwophase.py

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recipe.constrain(generator_ni.qdamp, "qdamp")
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recipe.constrain(generator_si.qdamp, "qdamp")
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# Vary the gloabal scale as well.
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# Vary the global scale as well.
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recipe.addVar(contribution.scale, 1)
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# Now we can configure the structural parameters. Since we're using

doc/examples/debyemodel.py

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# We would like to 'suggest' that the offset should remain positive. This
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# is somethine that we know about the system that might help the refinement
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# converge to a physically reasonable result. We will do this with a soft
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# contraint, or restraint. Here we restrain the offset variable to between
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# constraint, or restraint. Here we restrain the offset variable to between
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# 0 and infinity. We tell the recipe that we want to scale the penalty for
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# breaking the restraint by the point-average chi^2 value so that the
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# restraint is roughly as significant as any other data point throughout

doc/examples/ellipsoidsas.py

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## Configure the fit variables
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# The SASGenerator uses the parameters from the params and dispersion
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# attribues of the model. These vary from model to model, but are adopted
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# attributes of the model. These vary from model to model, but are adopted
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# as SrFit Parameters within the generator. Whereas the dispersion
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# parameters are accessible as, e.g. "radius.width", within the
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# SASGenerator these are named like "radius_width".

doc/examples/gaussianrecipe.py

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Extensions
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After reading through the code, try to perform the folowing tasks. The process
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After reading through the code, try to perform the following tasks. The process
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will leave you with a much better understanding of how SrFit works.
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- Play around with setting the values of the Variables and Parameters. What

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