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")
def main():
# configure options parsing
usage = ("%prog srmise_file [options]\n"
"srmise_file can be an extraction file saved by SrMise, "
"or a data file saved by PeakStability.")
descr = ("A very basic tool for somewhat prettier plotting than provided by "
"the basic SrMise classes. Can be used to compare peak positions "
"with those from a list.\n"
"NOTE: At this time the utility only works with peaks extracted using diffpy.srmise.PDFPeakExtraction.")
parser = optparse.OptionParser(usage=usage, description=descr)
parser.add_option("--compare", type="string",
help="Compare extracted distances to distances listed (1/line) in this file.")
parser.add_option("--model", type="int",
help="Plot given model from set. Ignored if srmise_file is not a PeakStability file.")
parser.add_option("--show", action="store_true",
help="execute pylab.show() blocking call")
parser.add_option("-o", "--output", type="string",
help="save plot to the specified file")
parser.add_option("--format", type="string", default="eps",
help="output format for plot saving")
parser.allow_interspersed_args = True
opts, args = parser.parse_args(sys.argv[1:])
if len(args) != 1:
parser.error("Exactly one argument required. \n"+usage)
filename = args[0]
if filename is not None:
toplot = PDFPeakExtraction()
try:
toplot.read(filename)
except (Exception):
toplot = PeakStability()
try:
toplot.load(filename)
except Exception:
print "File '%s' is not a .srmise or PeakStability data file." %filename
return
if opts.model is not None:
try:
toplot.setcurrent(opts.model)
except (Exception):
print "Ignoring model, %s is not a PeakStability file." %filename
distances = None
if opts.compare is not None:
# use baseline from existing file
distances = readcompare(opts.compare)
setfigformat(figsize=(6., 4.0))
figdict = makeplot(toplot, distances)
if opts.output:
plt.savefig(opts.output, format=opts.format, dpi=600)
if opts.show:
plt.show()
else:
plt.draw()
return
def run(plot=True):
## Initialize peak extraction
# Create peak extraction object
ppe = PDFPeakExtraction()
# Load the PDF from a file
ppe.loadpdf("data/Ag_nyquist_qmax30.gr")
# Obtain baseline from a saved diffpy.srmise trial. This is not the
# initial baseline estimate from the previous example, but the baseline
# after both it and the extracted peaks have been fit to the data.
ppebl = PDFPeakExtraction()
ppebl.read("output/extract_single_peak.srmise")
baseline = ppebl.extracted.baseline
## Set up extraction parameters.
# Peaks are extracted between 2 and 10 angstroms, using the baseline
# from the isolated peak example.
kwds = {}
kwds["rng"] = [2.0, 10.]
kwds["baseline"] = baseline
# Apply peak extraction parameters.
ppe.setvars(**kwds)
## Perform peak extraction, and retain object containing a copy of the
# model and the full covariance matrix.
cov = ppe.extract()
print "\n======= Accessing SrMise Results ========"
## Accessing results of extraction
#
# Model parameters are organized using a nested structure, with a list
# of peaks each of which is a list of parameters, similar to the the
# following schematic.
# Peak
# Position
# Width
# Area
# Peak
# Position
# Width
# Area*
# ...
# Baseline
# Slope
# Intercept
# By convention, the baseline is the final "peak." The ModelCovariance
# object returned by extract() can return information about any peak by
# using the appropriate tuple of indices (i,j). That is, (i,j) denotes
# the jth parameter of the ith peak. For example, the starred parameter
# above is the area (index = 2) of the next nearest neighbor (index = 1)
# peak. Thus, this parameter can be referenced as (1,2). Several examples
# are presented below.
print "\n------ Parameter values and uncertainties ------"
# ModelCovariance.get() returns a (value, uncertainty) tuple for a given
# parameter. These are the results for the nearest-neighbor peak.
p0 = cov.get((0,0))
w0 = cov.get((0,1))
a0 = cov.get((0,2))
print "Nearest-neighbor peak: "
print " position = %f +/- %f" %p0
print " width = %f +/- %f" %w0
print " area = %f +/- %f" %a0
print " Covariance(width, area) = ", cov.getcovariance((0,1),(0,2))
# Baseline parameters. By convention, baseline is final element in cov.
(slope, intercept) = cov.model[-1]
print "\nThe linear baseline B(r)=%f*r + %f" \
% tuple(par for par in cov.model[-1])
print "\n ------ Uncertainties from a Saved File --------"
# A .srmise file does not save the full covariance matrix, so it must be
# recalculated when loading from these files. For example, here is the
# nearest-neighbor peak in the file which we used to define the initial
# baseline.
cov2 = ModelCovariance()
ppebl.extracted.fit(fitbaseline=True, cov=cov2, cov_format="default_output")
p0_saved = cov2.get((0,0))
w0_saved = cov2.get((0,1))
a0_saved = cov2.get((0,2))
print "Nearest-neighbor peak:"
print " position = %f +/- %f" %p0_saved
print " width == %f +/- %f" %w0_saved
print " area = = %f +/- %f" %a0_saved
print " Covariance(width, area) = ", cov2.getcovariance((0,1),(0,2))
print "\n ---------- Alternate Parameterizations ---------"
## Different Parameterizations
# Peaks and baselines may have equivalent parameterizations that are useful
# in different situations. For example, the types defined by the
# GaussianOverR peak function are:
# "internal" - Used in diffpy.srmise calculations, explicitly enforces a
# maximum peak width
# "pwa" - The position, width (full-width at half-maximum), area.
# "mu_sigma_area" - The position, width (the distribution standard
# deviation sigma), area.
# "default_output" - Defines default format to use in most user-facing
# scenarios. Maps to the "pwa" parameterization.
# "default_input" - Defines default format to use when specifying peak
# parameters. Maps to the "internal" parameterization.
# All diffpy.srmise peak and baseline functions are required to have the
# "internal", "default_output", and "default_input" formats. In many
# cases, such as polynomial baselines, all of these are equivalent.
#
# Suppose you want to know peak widths in terms of the standard deviation
# sigma of the Gaussian distribution. It is then appropriate to convert
# all peaks to the "mu_sigma_area" format. Valid options for the "parts"
# keyword are "peaks", "baseline", or a sequence of indices (e.g. [1,2,3]
# would transform the second, third, and fourth peaks). If the keyword
# is omitted, the transformation is attempted for all parts of the fit.
cov.transform(in_format="pwa", out_format="mu_sigma_area", parts="peaks")
print "Width (sigma) of nearest-neighbor peak: %f +/- %f" %cov.get((0,1))
print "\n ------------ Highly Correlated Parameters ------------"
# Highly-correlated parameters can indicate difficulties constraining the
# fit. This function lists all pairs of parameters with an absolute value
# of correlation which exceeds a given threshold.
print "|Correlation| > 0.9:"
print "par1 par2 corr(par1, par2)"
print "\n".join(str(c) for c in cov.correlationwarning(.9))
print "\n-------- Estimate coordination shell occupancy ---------"
# Estimate the scale factor and its uncertainty from first peak's intensity.
# G_normalized = scale * G_observed
# dscale = scale * dG_observed/G_observed
scale = 12./a0[0]
dscale = scale * a0[1]/a0[0]
print "Estimate scale factor assuming nearest-neighbor intensity = 12"
print "Scale factor is %f +/- %f" %(scale, dscale)
# Reference for number of atoms in coordination shells for FCC.
# http://chem-faculty.lsu.edu/watkins/MERLOT/cubic_neighbors/cubic_near_neighbors.html
ideal_intensity = [12, 6, 24, 12, 24, 8, 48, 6, 36, 24, 24, 24]
# Calculated the scaled intensities and uncertainties.
intensity = []
for i in range(0, len(cov.model)-1):
(area, darea) = cov.get((i,2))
area *= scale
darea = area*np.sqrt((dscale/scale)**2 + (darea/area)**2)
intensity.append((ideal_intensity[i], area, darea))
print "\nIntensity"
print "Ideal: Estimated"
for i in intensity:
print "%i: %f +/- %f" %i
print "\nTotal intensity"
# It is possible to iterate over peaks directly without using indices.
# In addition, peak parameters can be accessed using string keys. For the
# Gaussian over r all of "position", "width", and "area" are valid.
total_observed_intensity = 0
total_ideal_intensity = 0
for peak, ii in zip(cov.model[:-1], ideal_intensity):
total_observed_intensity += scale*peak["area"]
total_ideal_intensity += ii
print "Ideal: Observed (using estimated scale factor)"
print "%i: %f" %(total_ideal_intensity, total_observed_intensity)
## Save output
ppe.write("output/query_results.srmise")
ppe.writepwa("output/query_results.pwa")
## Evaluating a model.
# Although the ModelCovariance object is useful, the model used for fitting
# can be directly accessed through PDFPeakExtraction as well, albeit
# without uncertainties. This is particularly helpful when evaluating a
# model since the parameters stay in the "internal" format used for
# calculations. For example, here we plot the data and every second peak
# on an arbitrary grid. Unlike with ModelCovariance, the baseline and
# peaks are kept separate.
if plot:
plt.figure()
grid = np.arange(2, 10, .01)
bl = ppe.extracted.baseline
everysecondpeak = ppe.extracted.model[::2]
plt.plot(ppe.x, ppe.y, 'o')
for peak in everysecondpeak:
plt.plot(grid, bl.value(grid) + peak.value(grid))
plt.xlim(2, 10)
plt.show()
def main():
# configure options parsing
usage = ("%prog srmise_file [options]\n"
"srmise_file can be an extraction file saved by SrMise, "
"or a data file saved by PeakStability.")
descr = (
"A very basic tool for somewhat prettier plotting than provided by "
"the basic SrMise classes. Can be used to compare peak positions "
"with those from a list.\n"
"NOTE: At this time the utility only works with peaks extracted using diffpy.srmise.PDFPeakExtraction."
)
parser = optparse.OptionParser(usage=usage, description=descr)
parser.add_option(
"--compare",
type="string",
help=
"Compare extracted distances to distances listed (1/line) in this file."
)
parser.add_option(
"--model",
type="int",
help=
"Plot given model from set. Ignored if srmise_file is not a PeakStability file."
)
parser.add_option("--show",
action="store_true",
help="execute pylab.show() blocking call")
parser.add_option("-o",
"--output",
type="string",
help="save plot to the specified file")
parser.add_option("--format",
type="string",
default="eps",
help="output format for plot saving")
parser.allow_interspersed_args = True
opts, args = parser.parse_args(sys.argv[1:])
if len(args) != 1:
parser.error("Exactly one argument required. \n" + usage)
filename = args[0]
if filename is not None:
toplot = PDFPeakExtraction()
try:
toplot.read(filename)
except (Exception):
toplot = PeakStability()
try:
toplot.load(filename)
except Exception:
print "File '%s' is not a .srmise or PeakStability data file." % filename
return
if opts.model is not None:
try:
toplot.setcurrent(opts.model)
except (Exception):
print "Ignoring model, %s is not a PeakStability file." % filename
distances = None
if opts.compare is not None:
# use baseline from existing file
distances = readcompare(opts.compare)
setfigformat(figsize=(6., 4.0))
figdict = makeplot(toplot, distances)
if opts.output:
plt.savefig(opts.output, format=opts.format, dpi=600)
if opts.show:
plt.show()
else:
plt.draw()
return