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")
class PeakStability:
"""Utility to test robustness of peaks.
results: [error scalar, model, bl, dr]
ppe: a PDFPeakExtraction instance """
def __init__(self):
self.results = []
self.ppe = None
self.current = None
def setppe(self, ppe):
self.ppe = ppe
def load(self, filename):
try:
import cPickle as pickle
except:
import pickle
in_s = open(filename, 'rb')
try:
(self.results, ppestr) = pickle.load(in_s)
self.ppe = PDFPeakExtraction()
self.ppe.readstr(ppestr)
# Ugly kluge for the baseline, since FromSequence
# can't pickle.
for r in self.results:
bl = self.ppe.baseline
kwds = r[2]
if r[2] is not None:
kwds = r[2]
if hasattr(bl, "estimate_parameters"):
r[2] = bl.actualize(default_input="internal", **kwds)
else:
r[2] = bl.owner().actualize(in_format="internal",
**kwds)
finally:
in_s.close()
self.setcurrent(0)
def save(self, filename):
try:
import cPickle as pickle
except:
import pickle
out_s = open(filename, 'wb')
try:
# Write to the stream
outstr = self.ppe.writestr()
# ugly kluge to let FromSequence pickle
# (it stores xyrepr() in metadict)
results2 = []
for r in self.results:
if r[2] is None:
bldict = None
else:
bldict = {
"pars": r[2].pars,
"free": r[2].free,
"removable": r[2].removable,
"static_owner": r[2].static_owner
}
results2.append([r[0], r[1], bldict, r[3]])
pickle.dump([results2, outstr], out_s)
finally:
out_s.close()
def plotseries(self, style='o', **kwds):
plt.figure()
plt.ioff()
for e, r, bl, dr in self.results:
peakpos = [p["position"] for p in r]
es = [e] * len(peakpos)
plt.plot(peakpos, es, style, **kwds)
plt.ion()
plt.draw()
def plot(self, **kwds):
"""Plot the current model. Keywords passed to pyplot.plot()"""
plt.clf()
plt.plot(*self.ppe.extracted.plottable(), **kwds)
q = self.ppe.extracted.quality()
plt.suptitle(
"[%i/%i]\n"
"Uncertainty: %6.3f. Peaks: %i.\n"
"Quality: %6.3f. Chi-square: %6.3f" %
(self.current + 1, len(self.results), self.ppe.effective_dy[0],
len(self.ppe.extracted.model), q.stat, q.chisq))
def setcurrent(self, idx):
"""Make the idxth model the active one."""
self.current = idx
if idx is not None:
result = self.results[idx]
self.ppe.setvars(quiet=True,
effective_dy=result[0] * np.ones(len(self.ppe.x)))
(r, y, dr, dy) = self.ppe.resampledata(result[3])
self.ppe.extracted = ModelCluster(result[1], result[2], r, y, dy,
None, self.ppe.error_method,
self.ppe.pf)
else:
self.ppe.clearcalc()
def animate(self, results=None, step=False, **kwds):
"""Show animation of extracted peaks from first to last.
Parameters:
step - Require keypress to show next plot
results - The indices of results to show
Keywords passed to pyplot.plot()"""
if results is None:
results = range(len(self.results))
oldcurrent = self.current
self.setcurrent(0)
plt.ion()
plt.plot(*self.ppe.extracted.plottable())
a = plt.axis()
for i in results:
self.setcurrent(i)
plt.ioff()
self.plot(**kwds)
plt.ion()
plt.draw()
if step:
raw_input()
self.setcurrent(oldcurrent)
def run(self, err, savecovs=False):
"""err is sequence of uncertainties to run at.
If savecovs is True, return the covariance matrix for each final fit."""
self.results = []
covs = []
for i, e in enumerate(err):
print "---- Running for uncertainty %s (%i/%i) ----" % (e, i,
len(err))
self.ppe.clearcalc()
self.ppe.setvars(effective_dy=e)
if savecovs:
covs.append(self.ppe.extract())
else:
self.ppe.extract()
dr = (self.ppe.extracted.r_cluster[-1] -
self.ppe.extracted.r_cluster[0]) / (
len(self.ppe.extracted.r_cluster) - 1)
self.results.append(
[e, self.ppe.extracted.model, self.ppe.extracted.baseline, dr])
for e, r, bl, dr in self.results:
print "---- Results for uncertainty %s ----" % e
print r
return covs
class PeakStability:
"""Utility to test robustness of peaks.
results: [error scalar, model, bl, dr]
ppe: a PDFPeakExtraction instance """
def __init__(self):
self.results = []
self.ppe = None
self.current = None
def setppe(self, ppe):
self.ppe = ppe
def load(self, filename):
try:
import cPickle as pickle
except:
import pickle
in_s = open(filename, 'rb')
try:
(self.results, ppestr) = pickle.load(in_s)
self.ppe = PDFPeakExtraction()
self.ppe.readstr(ppestr)
# Ugly kluge for the baseline, since FromSequence
# can't pickle.
for r in self.results:
bl = self.ppe.baseline
kwds = r[2]
if r[2] is not None:
kwds = r[2]
if hasattr(bl, "estimate_parameters"):
r[2] = bl.actualize(default_input="internal", **kwds)
else:
r[2] = bl.owner().actualize(in_format="internal", **kwds)
finally:
in_s.close()
self.setcurrent(0)
def save(self, filename):
try:
import cPickle as pickle
except:
import pickle
out_s = open(filename, 'wb')
try:
# Write to the stream
outstr = self.ppe.writestr()
# ugly kluge to let FromSequence pickle
# (it stores xyrepr() in metadict)
results2 = []
for r in self.results:
if r[2] is None:
bldict = None
else:
bldict = {"pars":r[2].pars, "free":r[2].free, "removable":r[2].removable, "static_owner":r[2].static_owner}
results2.append([r[0], r[1], bldict, r[3]])
pickle.dump([results2, outstr], out_s)
finally:
out_s.close()
def plotseries(self, style='o', **kwds):
plt.figure()
plt.ioff()
for e, r, bl, dr in self.results:
peakpos = [p["position"] for p in r]
es = [e]*len(peakpos)
plt.plot(peakpos, es, style, **kwds)
plt.ion()
plt.draw()
def plot(self, **kwds):
"""Plot the current model. Keywords passed to pyplot.plot()"""
plt.clf()
plt.plot(*self.ppe.extracted.plottable(), **kwds)
q = self.ppe.extracted.quality()
plt.suptitle("[%i/%i]\n"
"Uncertainty: %6.3f. Peaks: %i.\n"
"Quality: %6.3f. Chi-square: %6.3f"
%(self.current+1, len(self.results), self.ppe.effective_dy[0], len(self.ppe.extracted.model), q.stat, q.chisq))
def setcurrent(self, idx):
"""Make the idxth model the active one."""
self.current = idx
if idx is not None:
result = self.results[idx]
self.ppe.setvars(quiet=True, effective_dy=result[0]*np.ones(len(self.ppe.x)))
(r, y, dr, dy) = self.ppe.resampledata(result[3])
self.ppe.extracted = ModelCluster(result[1], result[2], r, y, dy, None, self.ppe.error_method, self.ppe.pf)
else:
self.ppe.clearcalc()
def animate(self, results=None, step=False, **kwds):
"""Show animation of extracted peaks from first to last.
Parameters:
step - Require keypress to show next plot
results - The indices of results to show
Keywords passed to pyplot.plot()"""
if results is None:
results = range(len(self.results))
oldcurrent = self.current
self.setcurrent(0)
plt.ion()
plt.plot(*self.ppe.extracted.plottable())
a = plt.axis()
for i in results:
self.setcurrent(i)
plt.ioff()
self.plot(**kwds)
plt.ion()
plt.draw()
if step:
raw_input()
self.setcurrent(oldcurrent)
def run(self, err, savecovs=False):
"""err is sequence of uncertainties to run at.
If savecovs is True, return the covariance matrix for each final fit."""
self.results = []
covs = []
for i, e in enumerate(err):
print "---- Running for uncertainty %s (%i/%i) ----" %(e, i, len(err))
self.ppe.clearcalc()
self.ppe.setvars(effective_dy=e)
if savecovs:
covs.append(self.ppe.extract())
else:
self.ppe.extract()
dr = (self.ppe.extracted.r_cluster[-1]-self.ppe.extracted.r_cluster[0])/(len(self.ppe.extracted.r_cluster)-1)
self.results.append([e, self.ppe.extracted.model, self.ppe.extracted.baseline, dr])
for e, r, bl, dr in self.results:
print "---- Results for uncertainty %s ----" %e
print r
return covs