def run(plot=True):
## Suppress mundane output
# When running scripts, especially involving multiple trials, it can be
# useful to suppress many of the diffpy.srmise messages. Valid levels
# include "debug", "info" (the default), "warning", "error", and
# "critical." See diffpy.srmise.srmiselog for more information.
sml.setlevel("warning")
## Initialize peak extraction
ppe = PDFPeakExtraction()
ppe.loadpdf("data/C60_fine_qmax21.gr")
## Set up extraction parameters
# The FromSequence baseline interpolates (r, G(r)) values read from a
# specified file. It has parameters. This particular baseline was
# calculated by approximating the C60 sample as a face-centered cubic
# lattice of hollow spheres.
blfunc = FromSequence("data/C60baseline.dat")
kwds = {}
kwds["rng"] = [1., 7.25]
kwds["baseline"] = blfunc.actualize([])
kwds["cres"] = 0.05
ppe.setvars(**kwds)
## Create multimodel selection object.
# The MultimodelSelection class keeps track of the results of peak
# extraction as the assumed uncertainty dg is varied.
ms = MultimodelSelection()
ms.setppe(ppe)
## Define range of dg values
# For the purpose of illustration use 20 evenly-spaced values of dg where
# 1% < dg < 10% of max gr value between r=1 and 7.25.
grmax = np.max(ppe.y[ppe.getrangeslice()])
dgs = np.linspace(.01 * grmax, .10 * grmax, 20)
## Perform peak extraction for each of the assumed uncertainties.
ms.run(dgs)
## Save results
# The file C60_models.dat saves the models generated above. The file
# C60_aics.dat saves the value of the AIC of each model when evaluated
# on a Nyquist-sampled grid using each of the dg values used to generate
# the models in the first place.
dr = np.pi / ppe.qmax
ms.save("output/unknown_dG_models.dat")
ms.makeaics(dgs, dr, filename="output/unknown_dG_aics.dat")
def run(plot=True):
## Suppress mundane output
# When running scripts, especially involving multiple trials, it can be
# useful to suppress many of the diffpy.srmise messages. Valid levels
# include "debug", "info" (the default), "warning", "error", and
# "critical." See diffpy.srmise.srmiselog for more information.
sml.setlevel("warning")
## Initialize peak extraction
ppe = PDFPeakExtraction()
ppe.loadpdf("data/C60_fine_qmax21.gr")
## Set up extraction parameters
# The FromSequence baseline interpolates (r, G(r)) values read from a
# specified file. It has parameters. This particular baseline was
# calculated by approximating the C60 sample as a face-centered cubic
# lattice of hollow spheres.
blfunc = FromSequence("data/C60baseline.dat")
kwds={}
kwds["rng"] = [1., 7.25]
kwds["baseline"] = blfunc.actualize([])
kwds["cres"] = 0.05
ppe.setvars(**kwds)
## Create multimodel selection object.
# The MultimodelSelection class keeps track of the results of peak
# extraction as the assumed uncertainty dg is varied.
ms = MultimodelSelection()
ms.setppe(ppe)
## Define range of dg values
# For the purpose of illustration use 20 evenly-spaced values of dg where
# 1% < dg < 10% of max gr value between r=1 and 7.25.
grmax = np.max(ppe.y[ppe.getrangeslice()])
dgs = np.linspace(.01*grmax, .10*grmax, 20)
## Perform peak extraction for each of the assumed uncertainties.
ms.run(dgs)
## Save results
# The file C60_models.dat saves the models generated above. The file
# C60_aics.dat saves the value of the AIC of each model when evaluated
# on a Nyquist-sampled grid using each of the dg values used to generate
# the models in the first place.
dr = np.pi/ppe.qmax
ms.save("output/unknown_dG_models.dat")
ms.makeaics(dgs, dr, filename="output/unknown_dG_aics.dat")
def run(plot=True):
## Suppress mundane output
# When running scripts, especially involving multiple trials, it can be
# useful to suppress many of the diffpy.srmise messages. Valid levels
# include "debug", "info" (the default), "warning", "error", and
# "critical." See diffpy.srmise.srmiselog for more information.
sml.setlevel("warning")
## Initialize peak extraction from saved trial
ppe = PDFPeakExtraction()
ppe.read("output/query_results.srmise")
ppe.clearcalc()
## Set up extraction parameters
# All parameters loaded from .srmise file.
# Setting new values will override the previous values.
kwds = {}
kwds["rng"] = [10.9, 15] # Region of PDF with some overlap.
ppe.setvars(**kwds)
## Create multimodel selection object.
# The MultimodelSelection class keeps track of the results of peak
# extraction as the assumed uncertainty dg is varied.
ms = MultimodelSelection()
ms.setppe(ppe)
## Define range of dg values
# For the purpose of illustration use 15 evenly-spaced values of dg where
# 50% < dg < 120% of mean experimental dG in extraction range.
dg_mean = np.mean(ppe.dy[ppe.getrangeslice()])
dgs = np.linspace(.5 * dg_mean, 1.2 * dg_mean, 15)
## Perform peak extraction for each of the assumed uncertainties.
ms.run(dgs)
## Save results
# The file known_dG_models.dat saves the models generated above. The file
# known_dG_aics.dat saves the value of the AIC of each model when evaluated
# on a Nyquist-sampled grid using each of the dg values used to generate
# the models in the first place.
dr = np.pi / ppe.qmax
ms.save("output/known_dG_models.dat")
ms.makeaics(dgs, dr, filename="output/known_dG_aics.dat")
def run(plot=True):
## Suppress mundane output
# When running scripts, especially involving multiple trials, it can be
# useful to suppress many of the diffpy.srmise messages. Valid levels
# include "debug", "info" (the default), "warning", "error", and
# "critical." See diffpy.srmise.srmiselog for more information.
sml.setlevel("warning")
## Initialize peak extraction from saved trial
ppe = PDFPeakExtraction()
ppe.read("output/query_results.srmise")
ppe.clearcalc()
## Set up extraction parameters
# All parameters loaded from .srmise file.
# Setting new values will override the previous values.
kwds={}
kwds["rng"] = [10.9, 15] # Region of PDF with some overlap.
ppe.setvars(**kwds)
## Create multimodel selection object.
# The MultimodelSelection class keeps track of the results of peak
# extraction as the assumed uncertainty dg is varied.
ms = MultimodelSelection()
ms.setppe(ppe)
## Define range of dg values
# For the purpose of illustration use 15 evenly-spaced values of dg where
# 50% < dg < 120% of mean experimental dG in extraction range.
dg_mean = np.mean(ppe.dy[ppe.getrangeslice()])
dgs = np.linspace(.5*dg_mean, 1.2*dg_mean, 15)
## Perform peak extraction for each of the assumed uncertainties.
ms.run(dgs)
## Save results
# The file known_dG_models.dat saves the models generated above. The file
# known_dG_aics.dat saves the value of the AIC of each model when evaluated
# on a Nyquist-sampled grid using each of the dg values used to generate
# the models in the first place.
dr = np.pi/ppe.qmax
ms.save("output/known_dG_models.dat")
ms.makeaics(dgs, dr, filename="output/known_dG_aics.dat")