def test_iterators(self):
pairs = [('c', 1), ('b', 2), ('a', 3), ('d', 4), ('e', 5), ('f', 6)]
shuffle(pairs)
od = OrderedDict(pairs)
self.assertEqual(list(od), [t[0] for t in pairs])
self.assertEqual(list(od.keys()), [t[0] for t in pairs])
self.assertEqual(list(od.values()), [t[1] for t in pairs])
self.assertEqual(list(od.items()), pairs)
self.assertEqual(list(reversed(od)), [t[0] for t in reversed(pairs)])
def test_iterators(self):
pairs = [('c', 1), ('b', 2), ('a', 3), ('d', 4), ('e', 5), ('f', 6)]
shuffle(pairs)
od = OrderedDict(pairs)
self.assertEqual(list(od), [t[0] for t in pairs])
self.assertEqual(list(od.keys()), [t[0] for t in pairs])
self.assertEqual(list(od.values()), [t[1] for t in pairs])
self.assertEqual(list(od.items()), pairs)
self.assertEqual(list(reversed(od)),
[t[0] for t in reversed(pairs)])
class FitResults(object):
"""Class for processing, presenting and storing results of a fit.
Attributes
recipe -- The recipe containing the results.
cov -- The covariance matrix from the recipe.
conresults -- An ordered dictionary of ContributionResults for each
FitContribution, indexed by the FitContribution name.
derivstep -- The fractional step size for calculating numeric
derivatives. Default 1e-8.
varnames -- Names of the variables in the recipe.
varvals -- Values of the variables in the recipe.
varunc -- Uncertainties in the variable values.
showfixed -- Show fixed variables (default True).
fixednames -- Names of the fixed variables of the recipe.
fixedvals -- Values of the fixed variables of the recipe.
showcon -- Show constraint values in the output (default False).
connames -- Names of the constrained parameters.
convals -- Values of the constrained parameters.
conunc -- Uncertainties in the constraint values.
residual -- The scalar residual of the recipe.
penalty -- The penalty to residual from the restraints.
chi2 -- The chi2 of the recipe.
cumchi2 -- The cumulative chi2 of the recipe.
rchi2 -- The reduced chi2 of the recipe.
rw -- The Rw of the recipe.
cumrw -- The cumulative Rw of the recipe.
messages -- A list of messages about the results.
precision -- The precision of numeric output (default 8).
_dcon -- The derivatives of the constraint equations with respect to
the variables. This is used internally.
Each of these attributes, except the recipe, are created or updated when
the update method is called.
"""
def __init__(self, recipe, update=True, showfixed=True, showcon=False):
"""Initialize the attributes.
recipe -- The recipe containing the results
update -- Flag indicating whether to do an immediate update (default
True).
showcon -- Show fixed variables in the output (default True).
showcon -- Show constraint values in the output (default False).
"""
self.recipe = recipe
self.conresults = OrderedDict()
self.derivstep = 1e-8
self.varnames = []
self.varvals = []
self.varunc = []
self.fixednames = []
self.fixedvals = []
self.connames = []
self.convals = []
self.conunc = []
self.cov = None
self.residual = 0
self.penalty = 0
self.chi2 = 0
self.rchi2 = 0
self.rw = 0
self.precision = 8
self._dcon = []
self.messages = []
self.showfixed = bool(showfixed)
self.showcon = bool(showcon)
if update:
self.update()
return
def update(self):
"""Update the results according to the current state of the recipe."""
## Note that the order of these operations are chosen to reduce
## computation time.
recipe = self.recipe
if not recipe._contributions:
return
# Make sure everything is ready for calculation
recipe._prepare()
# Store the variable names and values
self.varnames = recipe.getNames()
self.varvals = recipe.getValues()
fixedpars = recipe._tagmanager.union(recipe._fixedtag)
fixedpars = [p for p in fixedpars if not p.constrained]
self.fixednames = [p.name for p in fixedpars]
self.fixedvals = [p.value for p in fixedpars]
# Store the constraint information
self.connames = [con.par.name for con in recipe._oconstraints]
self.convals = [con.par.getValue() for con in recipe._oconstraints]
if self.varnames:
# Calculate the covariance
self._calculateCovariance()
# Get the variable uncertainties
self.varunc = [self.cov[i,i]**0.5 for i in \
range(len(self.varnames))]
# Get the constraint uncertainties
self._calculateConstraintUncertainties()
# Store the fitting arrays and metrics for each FitContribution.
self.conresults = OrderedDict()
for con, weight in zip(recipe._contributions.values(),
recipe._weights):
self.conresults[con.name] = ContributionResults(con, weight, self)
# Calculate the metrics
res = recipe.residual()
self.residual = numpy.dot(res, res)
self._calculateMetrics()
# Calcualte the restraints penalty
w = self.chi2 / len(res)
self.penalty = sum([r.penalty(w) for r in recipe._restraintlist])
return
def _calculateCovariance(self):
"""Calculate the covariance matrix. This is called by update.
This code borrowed from PARK. It finds the pseudo-inverse of the
Jacobian using the singular value decomposition.
"""
try:
J = self._calculateJacobian()
u, s, vh = numpy.linalg.svd(J, 0)
self.cov = numpy.dot(vh.T.conj() / s**2, vh)
except numpy.linalg.LinAlgError:
self.messages.append("Cannot compute covariance matrix.")
l = len(self.varvals)
self.cov = numpy.zeros((l, l), dtype=float)
return
def _calculateJacobian(self):
"""Calculate the Jacobian for the fitting.
Adapted from PARK.
Returns the derivative wrt the fit variables at point p.
This also calculates the derivatives of the constrained parameters
while we're at it.
Numeric derivatives are calculated based on step, where step is the
portion of variable value. E.g. step = dv/v.
"""
recipe = self.recipe
step = self.derivstep
# Make sure the input vector is an array
pvals = numpy.asarray(self.varvals)
# Compute the numeric derivative using the center point formula.
delta = step * pvals
# Center point formula:
# df/dv = lim_{h->0} ( f(v+h)-f(v-h) ) / ( 2h )
#
r = []
# The list of constraint derivatives with respect to variables
# The forward difference would be faster, but perhaps not as accurate.
conr = []
for k, v in enumerate(pvals):
h = delta[k]
pvals[k] = v + h
rk = self.recipe.residual(pvals)
# The constraints derivatives
cond = []
for con in recipe._oconstraints:
con.update()
cond.append(con.par.getValue())
pvals[k] = v - h
rk -= self.recipe.residual(pvals)
# FIXME - constraints are used for vectors as well!
for i, con in enumerate(recipe._oconstraints):
con.update()
val = con.par.getValue()
if numpy.isscalar(val):
cond[i] -= con.par.getValue()
cond[i] /= 2 * h
else:
cond[i] = 0.0
conr.append(cond)
pvals[k] = v
r.append(rk / (2 * h))
# Reset the constrained parameters to their original values
for con in recipe._oconstraints:
con.update()
self._dcon = numpy.vstack(conr).T
# return the jacobian
jac = numpy.vstack(r).T
return jac
def _calculateMetrics(self):
"""Calculate chi2, cumchi2, rchi2, rw and cumrw for the recipe."""
cumchi2 = numpy.array([], dtype=float)
# total weighed denominator for the ratio in the Rw formula
yw2tot = 0.0
numpoints = 0
for con in self.conresults.values():
cc2w = con.weight * con.cumchi2
c2last = cumchi2[-1:].sum()
cumchi2 = numpy.concatenate([cumchi2, c2last + cc2w])
yw2tot += con.weight * (con.chi2 / con.rw**2)
numpoints += len(con.x)
chi2 = cumchi2[-1:].sum()
cumrw = numpy.sqrt(cumchi2 / yw2tot)
rw = cumrw[-1:].sum()
numpoints += len(self.recipe._restraintlist)
rchi2 = chi2 / (numpoints - len(self.varnames))
self.chi2 = chi2
self.rchi2 = rchi2
self.rw = rw
self.cumchi2 = cumchi2
self.cumrw = cumrw
return
def _calculateConstraintUncertainties(self):
"""Calculate the uncertainty on the constrained parameters."""
vu = self.varunc
# sig^2(c) = sum_i sum_j sig(v_i) sig(v_j) (dc/dv_i)(dc/dv_j)
# sig^2(c) = sum_i sum_j [sig(v_i)(dc/dv_i)][sig(v_j)(dc/dv_j)]
# sig^2(c) = sum_i sum_j u_i u_j
self.conunc = []
for dci in self._dcon:
# Create sig(v_i) (dc/dv_i) array.
u = dci * vu
# The outer product is all possible pairings of u_i and u_j
# uu_ij = u_i u_j
uu = numpy.outer(u, u)
# Sum these pairings to get sig^2(c)
sig2c = sum(uu.flatten())
self.conunc.append(sig2c**0.5)
return
def formatResults(self, header="", footer="", update=False):
"""Format the results and return them in a string.
This function is called by printResults and saveResults. Overloading
the formatting here will change all three functions.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
update -- Flag indicating whether to call update() (default False).
Returns a string containing the formatted results.
"""
if update:
self.update()
lines = []
corrmin = 0.25
p = self.precision
pe = "%-" + "%i.%ie" % (p + 6, p)
pet = "%" + ".%ie" % (p, )
# Check to see if the uncertainty values are reliable.
certain = True
for con in self.conresults.values():
if (con.dy == 1).all():
certain = False
break
# User-defined header
if header:
lines.append(header)
if not certain:
l = "Some quantities invalid due to missing profile uncertainty"
if not l in self.messages:
self.messages.append(l)
lines.extend(self.messages)
## Overall results
l = "Overall"
if not certain:
l += " (Chi2 and Reduced Chi2 invalid)"
lines.append(l)
dashedline = 79 * '-'
lines.append(dashedline)
formatstr = "%-14s %.8f"
lines.append(formatstr % ("Residual", self.residual))
lines.append(formatstr %
("Contributions", self.residual - self.penalty))
lines.append(formatstr % ("Restraints", self.penalty))
lines.append(formatstr % ("Chi2", self.chi2))
lines.append(formatstr % ("Reduced Chi2", self.rchi2))
lines.append(formatstr % ("Rw", self.rw))
## Per-FitContribution results
if len(self.conresults) > 1:
keys = self.conresults.keys()
numericStringSort(keys)
lines.append("")
l = "Contributions"
if not certain:
l += " (Chi2 and Reduced Chi2 invalid)"
lines.append(l)
lines.append(dashedline)
formatstr = "%-10s %-42.8f"
for name in keys:
res = self.conresults[name]
lines.append("")
namestr = name + " (%f)" % res.weight
lines.append(namestr)
lines.append("-" * len(namestr))
lines.append(formatstr % ("Residual", res.residual))
lines.append(formatstr % ("Chi2", res.chi2))
lines.append(formatstr % ("Rw", res.rw))
## The variables
if self.varnames:
lines.append("")
l = "Variables"
if not certain:
m = "Uncertainties invalid"
l += " (%s)" % m
lines.append(l)
lines.append(dashedline)
varnames = self.varnames
varvals = self.varvals
varunc = self.varunc
varlines = []
w = max(map(len, varnames))
w = str(w + 1)
# Format the lines
formatstr = "%-" + w + "s " + pe + " +/- " + pet
for name, val, unc in zip(varnames, varvals, varunc):
varlines.append(formatstr % (name, val, unc))
varlines.sort()
lines.extend(varlines)
# Fixed variables
if self.showfixed and self.fixednames:
varlines = []
lines.append("")
lines.append("Fixed Variables")
lines.append(dashedline)
w = max(map(len, self.fixednames))
w = str(w + 1)
formatstr = "%-" + w + "s " + pet
for name, val in zip(self.fixednames, self.fixedvals):
varlines.append(formatstr % (name, val))
varlines.sort()
lines.extend(varlines)
## The constraints
if self.connames and self.showcon:
lines.append("")
l = "Constrained Parameters"
if not certain:
l += " (Uncertainties invalid)"
lines.append(l)
lines.append(dashedline)
w = 0
keys = []
vals = {}
for con in self.conresults.values():
for i, loc in enumerate(con.conlocs):
names = [obj.name for obj in loc]
name = ".".join(names)
w = max(w, len(name))
val = con.convals[i]
unc = con.conunc[i]
keys.append(name)
vals[name] = (val, unc)
numericStringSort(keys)
w = str(w + 1)
formatstr = "%-" + w + "s %- 15f +/- %-15f"
for name in keys:
val, unc = vals[name]
lines.append(formatstr % (name, val, unc))
## Variable correlations
lines.append("")
corint = int(corrmin * 100)
l = "Variable Correlations greater than %i%%" % corint
if not certain:
l += " (Correlations invalid)"
lines.append(l)
lines.append(dashedline)
tup = []
cornames = []
n = len(self.varnames)
for i in xrange(n):
for j in xrange(i + 1, n):
name = "corr(%s, %s)" % (varnames[i], varnames[j])
val = (self.cov[i, j] / (self.cov[i, i] * self.cov[j, j])**0.5)
if abs(val) > corrmin:
cornames.append(name)
tup.append((val, name))
tup.sort(key=lambda vn: abs(vn[0]))
tup.reverse()
if cornames:
w = max(map(len, cornames))
w = str(w + 1)
formatstr = "%-" + w + "s %.4f"
for val, name in tup:
lines.append(formatstr % (name, val))
else:
lines.append("No correlations greater than %i%%" % corint)
# User-defined footer
if footer:
lines.append(footer)
out = "\n".join(lines) + '\n'
return out
def printResults(self, header="", footer="", update=False):
"""Format and print the results.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
update -- Flag indicating whether to call update() (default False).
"""
print self.formatResults(header, footer, update).rstrip()
return
def __str__(self):
return self.formatResults()
def saveResults(self, filename, header="", footer="", update=False):
"""Format and save the results.
filename - Name of the save file.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
update -- Flag indicating whether to call update() (default False).
"""
# Save the time and user
from time import ctime
from getpass import getuser
myheader = "Results written: " + ctime() + "\n"
myheader += "produced by " + getuser() + "\n"
header = myheader + header
res = self.formatResults(header, footer, update)
f = file(filename, 'w')
f.write(res)
f.close()
return
class FitResults(object):
"""Class for processing, presenting and storing results of a fit.
Attributes
recipe -- The recipe containing the results.
cov -- The covariance matrix from the recipe.
conresults -- An ordered dictionary of ContributionResults for each
FitContribution, indexed by the FitContribution name.
derivstep -- The fractional step size for calculating numeric
derivatives. Default 1e-8.
varnames -- Names of the variables in the recipe.
varvals -- Values of the variables in the recipe.
varunc -- Uncertainties in the variable values.
showfixed -- Show fixed variables (default True).
fixednames -- Names of the fixed variables of the recipe.
fixedvals -- Values of the fixed variables of the recipe.
showcon -- Show constraint values in the output (default False).
connames -- Names of the constrained parameters.
convals -- Values of the constrained parameters.
conunc -- Uncertainties in the constraint values.
residual -- The scalar residual of the recipe.
penalty -- The penalty to residual from the restraints.
chi2 -- The chi2 of the recipe.
cumchi2 -- The cumulative chi2 of the recipe.
rchi2 -- The reduced chi2 of the recipe.
rw -- The Rw of the recipe.
cumrw -- The cumulative Rw of the recipe.
messages -- A list of messages about the results.
precision -- The precision of numeric output (default 8).
_dcon -- The derivatives of the constraint equations with respect to
the variables. This is used internally.
Each of these attributes, except the recipe, are created or updated when
the update method is called.
"""
def __init__(self, recipe, update = True, showfixed = True, showcon =
False):
"""Initialize the attributes.
recipe -- The recipe containing the results
update -- Flag indicating whether to do an immediate update (default
True).
showcon -- Show fixed variables in the output (default True).
showcon -- Show constraint values in the output (default False).
"""
self.recipe = recipe
self.conresults = OrderedDict()
self.derivstep = 1e-8
self.varnames = []
self.varvals = []
self.varunc = []
self.fixednames = []
self.fixedvals = []
self.connames = []
self.convals = []
self.conunc = []
self.cov = None
self.residual = 0
self.penalty = 0
self.chi2 = 0
self.rchi2 = 0
self.rw = 0
self.precision = 8
self._dcon = []
self.messages = []
self.showfixed = bool(showfixed)
self.showcon = bool(showcon)
if update:
self.update()
return
def update(self):
"""Update the results according to the current state of the recipe."""
## Note that the order of these operations are chosen to reduce
## computation time.
recipe = self.recipe
if not recipe._contributions:
return
# Make sure everything is ready for calculation
recipe._prepare()
# Store the variable names and values
self.varnames = recipe.getNames()
self.varvals = recipe.getValues()
fixedpars = recipe._tagmanager.union(recipe._fixedtag)
fixedpars = [p for p in fixedpars if not p.constrained]
self.fixednames = [p.name for p in fixedpars]
self.fixedvals = [p.value for p in fixedpars]
# Store the constraint information
self.connames = [con.par.name for con in recipe._oconstraints]
self.convals = [con.par.getValue() for con in recipe._oconstraints]
if self.varnames:
# Calculate the covariance
self._calculateCovariance()
# Get the variable uncertainties
self.varunc = [self.cov[i,i]**0.5 for i in \
range(len(self.varnames))]
# Get the constraint uncertainties
self._calculateConstraintUncertainties()
# Store the fitting arrays and metrics for each FitContribution.
self.conresults = OrderedDict()
for con, weight in zip(recipe._contributions.values(), recipe._weights):
self.conresults[con.name] = ContributionResults(con, weight, self)
# Calculate the metrics
res = recipe.residual()
self.residual = numpy.dot(res, res)
self._calculateMetrics()
# Calcualte the restraints penalty
w = self.chi2 / len(res)
self.penalty = sum([res.penalty(w) for res in recipe._restraintlist])
return
def _calculateCovariance(self):
"""Calculate the covariance matrix. This is called by update.
This code borrowed from PARK. It finds the pseudo-inverse of the
Jacobian using the singular value decomposition.
"""
try:
J = self._calculateJacobian()
u,s,vh = numpy.linalg.svd(J,0)
self.cov = numpy.dot(vh.T.conj()/s**2,vh)
except numpy.linalg.LinAlgError:
self.messages.append("Cannot compute covariance matrix.")
l = len(self.varvals)
self.cov = numpy.zeros((l, l), dtype=float)
return
def _calculateJacobian(self):
"""Calculate the Jacobian for the fitting.
Adapted from PARK.
Returns the derivative wrt the fit variables at point p.
This also calculates the derivatives of the constrained parameters
while we're at it.
Numeric derivatives are calculated based on step, where step is the
portion of variable value. E.g. step = dv/v.
"""
recipe = self.recipe
step = self.derivstep
# Make sure the input vector is an array
pvals = numpy.asarray(self.varvals)
# Compute the numeric derivative using the center point formula.
delta = step * pvals
# Center point formula:
# df/dv = lim_{h->0} ( f(v+h)-f(v-h) ) / ( 2h )
#
r = []
# The list of constraint derivatives with respect to variables
# The forward difference would be faster, but perhaps not as accurate.
conr = []
for k,v in enumerate(pvals):
h = delta[k]
pvals[k] = v + h
rk = self.recipe.residual(pvals)
# The constraints derivatives
cond = []
for con in recipe._oconstraints:
con.update()
cond.append(con.par.getValue())
pvals[k] = v - h
rk -= self.recipe.residual(pvals)
# FIXME - constraints are used for vectors as well!
for i, con in enumerate(recipe._oconstraints):
con.update()
val = con.par.getValue()
if numpy.isscalar(val):
cond[i] -= con.par.getValue()
cond[i] /= 2*h
else:
cond[i] = 0.0
conr.append(cond)
pvals[k] = v
r.append(rk/(2*h))
# Reset the constrained parameters to their original values
for con in recipe._oconstraints:
con.update()
self._dcon = numpy.vstack(conr).T
# return the jacobian
jac = numpy.vstack(r).T
return jac
def _calculateMetrics(self):
"""Calculate chi2, cumchi2, rchi2, rw and cumrw for the recipe."""
cumchi2 = numpy.array([], dtype=float)
# total weighed denominator for the ratio in the Rw formula
yw2tot = 0.0
numpoints = 0
for con in self.conresults.values():
cc2w = con.weight * con.cumchi2
c2last = cumchi2[-1:].sum()
cumchi2 = numpy.concatenate([cumchi2, c2last + cc2w])
yw2tot += con.weight * (con.chi2 / con.rw**2)
numpoints += len(con.x)
chi2 = cumchi2[-1:].sum()
cumrw = numpy.sqrt(cumchi2 / yw2tot)
rw = cumrw[-1:].sum()
numpoints += len(self.recipe._restraintlist)
rchi2 = chi2 / (numpoints - len(self.varnames))
self.chi2 = chi2
self.rchi2 = rchi2
self.rw = rw
self.cumchi2 = cumchi2
self.cumrw = cumrw
return
def _calculateConstraintUncertainties(self):
"""Calculate the uncertainty on the constrained parameters."""
vu = self.varunc
# sig^2(c) = sum_i sum_j sig(v_i) sig(v_j) (dc/dv_i)(dc/dv_j)
# sig^2(c) = sum_i sum_j [sig(v_i)(dc/dv_i)][sig(v_j)(dc/dv_j)]
# sig^2(c) = sum_i sum_j u_i u_j
self.conunc = []
for dci in self._dcon:
# Create sig(v_i) (dc/dv_i) array.
u = dci * vu
# The outer product is all possible pairings of u_i and u_j
# uu_ij = u_i u_j
uu = numpy.outer(u, u)
# Sum these pairings to get sig^2(c)
sig2c = sum(uu.flatten())
self.conunc.append(sig2c**0.5)
return
def formatResults(self, header = "", footer = "", update = False):
"""Format the results and return them in a string.
This function is called by printResults and saveResults. Overloading
the formatting here will change all three functions.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
update -- Flag indicating whether to call update() (default False).
Returns a string containing the formatted results.
"""
if update:
self.update()
lines = []
corrmin = 0.25
p = self.precision
pe = "%-" + "%i.%ie" % (p+6, p)
pet = "%" + ".%ie" % (p,)
# Check to see if the uncertainty values are reliable.
certain = True
for con in self.conresults.values():
if (con.dy == 1).all():
certain = False
break
# User-defined header
if header:
lines.append(header)
if not certain:
l = "Some quantities invalid due to missing profile uncertainty"
if not l in self.messages:
self.messages.append(l)
lines.extend(self.messages)
## Overall results
l = "Overall"
if not certain:
l += " (Chi2 and Reduced Chi2 invalid)"
lines.append(l)
dashedline = 79 * '-'
lines.append(dashedline)
formatstr = "%-14s %.8f"
lines.append(formatstr%("Residual",self.residual))
lines.append(formatstr%("Contributions", self.residual - self.penalty))
lines.append(formatstr%("Restraints", self.penalty))
lines.append(formatstr%("Chi2",self.chi2))
lines.append(formatstr%("Reduced Chi2",self.rchi2))
lines.append(formatstr%("Rw",self.rw))
## Per-FitContribution results
if len(self.conresults) > 1:
keys = self.conresults.keys()
numericStringSort(keys)
lines.append("")
l = "Contributions"
if not certain:
l += " (Chi2 and Reduced Chi2 invalid)"
lines.append(l)
lines.append(dashedline)
formatstr = "%-10s %-42.8f"
for name in keys:
res = self.conresults[name]
lines.append("")
namestr = name + " (%f)"%res.weight
lines.append(namestr)
lines.append("-"*len(namestr))
lines.append(formatstr%("Residual",res.residual))
lines.append(formatstr%("Chi2",res.chi2))
lines.append(formatstr%("Rw",res.rw))
## The variables
if self.varnames:
lines.append("")
l = "Variables"
if not certain:
m = "Uncertainties invalid"
l += " (%s)"%m
lines.append(l)
lines.append(dashedline)
varnames = self.varnames
varvals = self.varvals
varunc = self.varunc
varlines = []
w = max(map(len, varnames))
w = str(w+1)
# Format the lines
formatstr = "%-"+w+"s " + pe + " +/- " + pet
for name, val, unc in zip(varnames, varvals, varunc):
varlines.append(formatstr%(name, val, unc))
varlines.sort()
lines.extend(varlines)
# Fixed variables
if self.showfixed and self.fixednames:
varlines = []
lines.append("")
lines.append("Fixed Variables")
lines.append(dashedline)
w = max(map(len, self.fixednames))
w = str(w+1)
formatstr = "%-"+w+"s " + pet
for name, val in zip(self.fixednames, self.fixedvals):
varlines.append(formatstr%(name, val))
varlines.sort()
lines.extend(varlines)
## The constraints
if self.connames and self.showcon:
lines.append("")
l = "Constrained Parameters"
if not certain:
l += " (Uncertainties invalid)"
lines.append(l)
lines.append(dashedline)
w = 0
keys = []
vals = {}
for con in self.conresults.values():
for i, loc in enumerate(con.conlocs):
names = [obj.name for obj in loc]
name = ".".join(names)
w = max(w, len(name))
val = con.convals[i]
unc = con.conunc[i]
keys.append(name)
vals[name] = (val, unc)
numericStringSort(keys)
w = str(w+1)
formatstr = "%-"+w+"s %- 15f +/- %-15f"
for name in keys:
val, unc = vals[name]
lines.append(formatstr%(name, val, unc))
## Variable correlations
lines.append("")
corint = int(corrmin*100)
l = "Variable Correlations greater than %i%%"%corint
if not certain:
l += " (Correlations invalid)"
lines.append(l)
lines.append(dashedline)
tup = []
cornames = []
n = len(self.varnames)
for i in xrange(n):
for j in xrange(i+1, n):
name = "corr(%s, %s)"%(varnames[i], varnames[j])
val = (self.cov[i,j]/(self.cov[i,i] * self.cov[j,j])**0.5)
if val > corrmin:
cornames.append(name)
tup.append((val, name))
tup.sort()
tup.reverse()
if cornames:
w = max(map(len, cornames))
w = str(w + 1)
formatstr = "%-"+w+"s %.4f"
for val, name in tup:
lines.append(formatstr%(name, val))
else:
lines.append("No correlations greater than %i%%"%corint)
# User-defined footer
if footer:
lines.append(footer)
out = "\n".join(lines) + '\n'
return out
def printResults(self, header = "", footer = "", update = False):
"""Format and print the results.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
update -- Flag indicating whether to call update() (default False).
"""
print self.formatResults(header, footer, update).rstrip()
return
def __str__(self):
return self.formatResults()
def saveResults(self, filename, header = "", footer = "", update = False):
"""Format and save the results.
filename - Name of the save file.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
update -- Flag indicating whether to call update() (default False).
"""
# Save the time and user
from time import ctime
from getpass import getuser
myheader = "Results written: " + ctime() + "\n"
myheader += "produced by " + getuser() + "\n"
header = myheader + header
res = self.formatResults(header, footer, update)
f = file(filename, 'w')
f.write(res)
f.close()
return
