def update(self):
"""Update the results according to the current state of the recipe."""
self.n = len(self.recipe.getNames())
self.hasarray = any([isinstance(x, np.ndarray) for x in self.recipe.getValues()])
if not self.hasarray:
FitResults.update(self)
# correct cov/std if all dy == 1
certain = True
for con in self.conresults.values():
if (con.dy == 1).all():
certain = False
if not certain:
self.cov = self.cov * self.rchi2
sqrtrchi2 = np.sqrt(self.rchi2)
self.varunc = [old * sqrtrchi2 for old in self.varunc]
self.conunc = [old * sqrtrchi2 for old in self.conunc]
for con in self.conresults.values():
con.conunc = [old * sqrtrchi2 for old in con.conunc]
# convert array to NAN
self.varvals = [np.NAN if isinstance(v, np.ndarray) else v for v in self.varvals]
self.fixedvals = [np.NAN if isinstance(v, np.ndarray) else v for v in self.fixedvals]
else:
self.recipe._prepare()
res = self.recipe.residual()
self.residual = numpy.dot(res, res)
for con, weight in zip(self.recipe._contributions.values(), self.recipe._weights):
self.conresults[con.name] = ContributionResultsExt(con, weight, self)
self._calculateMetrics()
return
def __init__(self, name="fit", conclass=FitContribution):
"""Initialization."""
FitRecipe.__init__(self, name)
self.fithooks[0].verbose = 3
contribution = conclass("contribution")
self.profile = Profile()
contribution.setProfile(self.profile)
self.addContribution(contribution)
self.results = FitResults(self, update=False)
# Adopt all the FitContribution methods
public = [
aname for aname in dir(contribution)
if aname not in dir(self) and not aname.startswith("_")
]
for mname in public:
method = getattr(contribution, mname)
setattr(self, mname, method)
return
def __init__(self, name = "fit", conclass = FitContribution):
"""Initialization."""
FitRecipe.__init__(self, name)
self.fithooks[0].verbose = 3
contribution = conclass("contribution")
self.profile = Profile()
contribution.setProfile(self.profile)
self.addContribution(contribution)
self.results = FitResults(self, update = False)
# Adopt all the FitContribution methods
public = [aname for aname in dir(contribution) if aname not in
dir(self) and not aname.startswith("_")]
for mname in public:
method = getattr(contribution, mname)
setattr(self, mname, method)
return
def recipe_to_dict2(recipe: MyRecipe) -> dict:
"""Convert the fit result in recipe to a parsers friendly dictionary.
Parameters
----------
recipe : MyRecipe
The refined recipe.
Returns
-------
doc : dict
A nested dictionary containing fitting results, fitted data and the refined structure data.
"""
result = FitResults(recipe)
doc = fitresult_to_dict(result)
doc['conresults'] = list(
get_conresults(result.conresults, recipe.contributions))
doc["genresults"] = list(get_genresults2(recipe))
return doc
class SimpleRecipe(FitRecipe):
"""SimpleRecipe class.
This is a FitRecipe with a built-in Profile (the 'profile' attribute) and
FitContribution (the 'contribution' attribute). Unique methods from each of
these are exposed through this class to facilitate the creation of a simple
fit recipe.
Attributes
profile -- The built-in Profile object.
contribution -- The built-in FitContribution object.
results -- The built-in FitResults object.
name -- A name for this FitRecipe.
fithook -- An object to be called whenever within the residual
(default FitHook()) that can pass information out of
the system during a refinement.
_constraints -- A dictionary of Constraints, indexed by the constrained
Parameter. Constraints can be added using the
'constrain' method.
_oconstraints -- An ordered list of the constraints from this and all
sub-components.
_calculators -- A managed dictionary of Calculators.
_contributions -- A managed OrderedDict of FitContributions.
_parameters -- A managed OrderedDict of parameters (in this case the
parameters are varied).
_parsets -- A managed dictionary of ParameterSets.
_eqfactory -- A diffpy.srfit.equation.builder.EquationFactory
instance that is used to create constraints and
restraints from string
_fixed -- A set of parameters that are not actually varied.
_restraintlist -- A list of restraints from this and all sub-components.
_restraints -- A set of Restraints. Restraints can be added using the
'restrain' or 'confine' methods.
_ready -- A flag indicating if all attributes are ready for the
calculation.
_tagdict -- A dictionary of tags to variables.
_weights -- List of weighing factors for each FitContribution. The
weights are multiplied by the residual of the
FitContribution when determining the overall residual.
Properties
names -- Variable names (read only). See getNames.
values -- Variable values (read only). See getValues.
"""
def __init__(self, name = "fit", conclass = FitContribution):
"""Initialization."""
FitRecipe.__init__(self, name)
self.fithooks[0].verbose = 3
contribution = conclass("contribution")
self.profile = Profile()
contribution.setProfile(self.profile)
self.addContribution(contribution)
self.results = FitResults(self, update = False)
# Adopt all the FitContribution methods
public = [aname for aname in dir(contribution) if aname not in
dir(self) and not aname.startswith("_")]
for mname in public:
method = getattr(contribution, mname)
setattr(self, mname, method)
return
# Profile methods
def loadParsedData(self, parser):
"""Load parsed data from a ProfileParser.
This sets the xobs, yobs, dyobs arrays as well as the metadata.
"""
return self.profile.loadParsedData(parser)
def setObservedProfile(self, xobs, yobs, dyobs = None):
"""Set the observed profile.
Arguments
xobs -- Numpy array of the independent variable
yobs -- Numpy array of the observed signal.
dyobs -- Numpy array of the uncertainty in the observed signal. If
dyobs is None (default), it will be set to 1 at each
observed xobs.
Raises ValueError if len(yobs) != len(xobs)
Raises ValueError if dyobs != None and len(dyobs) != len(xobs)
"""
return self.profile.setObservedProfile(xobs, yobs, dyobs)
def setCalculationRange(self, xmin=None, xmax=None, dx=None):
"""Set epsilon-inclusive calculation range.
Adhere to the observed ``xobs`` points when ``dx`` is the same
as in the data. ``xmin`` and ``xmax`` are clipped at the bounds
of the observed data.
Parameters
----------
xmin : float or "obs", optional
The minimum value of the independent variable. Keep the
current minimum when not specified. If specified as "obs"
reset to the minimum observed value.
xmax : float or "obs", optional
The maximum value of the independent variable. Keep the
current maximum when not specified. If specified as "obs"
reset to the maximum observed value.
dx : float or "obs", optional
The sample spacing in the independent variable. When different
from the data, resample the ``x`` as anchored at ``xmin``.
Note that xmin is always inclusive (unless clipped). xmax is inclusive
if it is within the bounds of the observed data.
Raises
------
AttributeError
If there is no observed data.
ValueError
When xmin > xmax or if dx <= 0. Also if dx > xmax - xmin.
"""
return self.profile.setCalculationRange(xmin, xmax, dx)
def setCalculationPoints(self, x):
"""Set the calculation points.
Arguments
x -- A non-empty numpy array containing the calculation points. If
xobs exists, the bounds of x will be limited to its bounds.
This will create y and dy on the specified grid if xobs, yobs and
dyobs exist.
"""
return self.profile.setCalculationPoints(x)
def loadtxt(self, *args, **kw):
"""Use numpy.loadtxt to load data.
Arguments are passed to numpy.loadtxt.
unpack = True is enforced.
The first two arrays returned by numpy.loadtxt are assumed to be x and
y. If there is a third array, it is assumed to by dy. Any other arrays
are ignored. These are passed to setObservedProfile.
Raises ValueError if the call to numpy.loadtxt returns fewer than 2
arrays.
Returns the x, y and dy arrays loaded from the file
"""
return self.profile.loadtxt(*args, **kw)
# FitContribution
def setEquation(self, eqstr, ns = {}):
"""Set the profile equation for the FitContribution.
This sets the equation that will be used when generating the residual.
The equation will be usable within setResidualEquation as "eq", and it
takes no arguments.
eqstr -- A string representation of the equation. Variables will be
extracted from this equation and be given an initial value
of 0.
ns -- A dictionary of Parameters, indexed by name, that are used
in the eqstr, but not registered (default {}).
Raises ValueError if ns uses a name that is already used for a
variable.
"""
self.contribution.setEquation(eqstr, ns = {})
# Extract variables
for par in self.contribution:
# Skip Profile Parameters
if par.name in ("x", "y", "dy"): continue
if par.value is None:
par.value = 0
if par.name not in self._parameters:
self.addVar(par)
return
def __call__(self):
"""Evaluate the contribution equation."""
return self.contribution.evaluate()
# FitResults methods
def printResults(self, header = "", footer = ""):
"""Format and print the results.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
"""
self.results.printResults(header, footer, True)
return
def saveResults(self, filename, header = "", footer = ""):
"""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 "")
"""
self.results.saveResults(filename, header, footer, True)
class SimpleRecipe(FitRecipe):
"""SimpleRecipe class.
This is a FitRecipe with a built-in Profile (the 'profile' attribute) and
FitContribution (the 'contribution' attribute). Unique methods from each of
these are exposed through this class to facilitate the creation of a simple
fit recipe.
Attributes
profile -- The built-in Profile object.
contribution -- The built-in FitContribution object.
results -- The built-in FitResults object.
name -- A name for this FitRecipe.
fithook -- An object to be called whenever within the residual
(default FitHook()) that can pass information out of
the system during a refinement.
_constraints -- A dictionary of Constraints, indexed by the constrained
Parameter. Constraints can be added using the
'constrain' method.
_oconstraints -- An ordered list of the constraints from this and all
sub-components.
_calculators -- A managed dictionary of Calculators.
_contributions -- A managed OrderedDict of FitContributions.
_parameters -- A managed OrderedDict of parameters (in this case the
parameters are varied).
_parsets -- A managed dictionary of ParameterSets.
_eqfactory -- A diffpy.srfit.equation.builder.EquationFactory
instance that is used to create constraints and
restraints from string
_fixed -- A set of parameters that are not actually varied.
_restraintlist -- A list of restraints from this and all sub-components.
_restraints -- A set of Restraints. Restraints can be added using the
'restrain' or 'confine' methods.
_ready -- A flag indicating if all attributes are ready for the
calculation.
_tagdict -- A dictionary of tags to variables.
_weights -- List of weighing factors for each FitContribution. The
weights are multiplied by the residual of the
FitContribution when determining the overall residual.
Properties
names -- Variable names (read only). See getNames.
values -- Variable values (read only). See getValues.
"""
def __init__(self, name="fit", conclass=FitContribution):
"""Initialization."""
FitRecipe.__init__(self, name)
self.fithooks[0].verbose = 3
contribution = conclass("contribution")
self.profile = Profile()
contribution.setProfile(self.profile)
self.addContribution(contribution)
self.results = FitResults(self, update=False)
# Adopt all the FitContribution methods
public = [
aname for aname in dir(contribution)
if aname not in dir(self) and not aname.startswith("_")
]
for mname in public:
method = getattr(contribution, mname)
setattr(self, mname, method)
return
# Profile methods
def loadParsedData(self, parser):
"""Load parsed data from a ProfileParser.
This sets the xobs, yobs, dyobs arrays as well as the metadata.
"""
return self.profile.loadParsedData(parser)
def setObservedProfile(self, xobs, yobs, dyobs=None):
"""Set the observed profile.
Arguments
xobs -- Numpy array of the independent variable
yobs -- Numpy array of the observed signal.
dyobs -- Numpy array of the uncertainty in the observed signal. If
dyobs is None (default), it will be set to 1 at each
observed xobs.
Raises ValueError if len(yobs) != len(xobs)
Raises ValueError if dyobs != None and len(dyobs) != len(xobs)
"""
return self.profile.setObservedProfile(xobs, yobs, dyobs)
def setCalculationRange(self, xmin=None, xmax=None, dx=None):
"""Set epsilon-inclusive calculation range.
Adhere to the observed ``xobs`` points when ``dx`` is the same
as in the data. ``xmin`` and ``xmax`` are clipped at the bounds
of the observed data.
Parameters
----------
xmin : float or "obs", optional
The minimum value of the independent variable. Keep the
current minimum when not specified. If specified as "obs"
reset to the minimum observed value.
xmax : float or "obs", optional
The maximum value of the independent variable. Keep the
current maximum when not specified. If specified as "obs"
reset to the maximum observed value.
dx : float or "obs", optional
The sample spacing in the independent variable. When different
from the data, resample the ``x`` as anchored at ``xmin``.
Note that xmin is always inclusive (unless clipped). xmax is inclusive
if it is within the bounds of the observed data.
Raises
------
AttributeError
If there is no observed data.
ValueError
When xmin > xmax or if dx <= 0. Also if dx > xmax - xmin.
"""
return self.profile.setCalculationRange(xmin, xmax, dx)
def setCalculationPoints(self, x):
"""Set the calculation points.
Arguments
x -- A non-empty numpy array containing the calculation points. If
xobs exists, the bounds of x will be limited to its bounds.
This will create y and dy on the specified grid if xobs, yobs and
dyobs exist.
"""
return self.profile.setCalculationPoints(x)
def loadtxt(self, *args, **kw):
"""Use numpy.loadtxt to load data.
Arguments are passed to numpy.loadtxt.
unpack = True is enforced.
The first two arrays returned by numpy.loadtxt are assumed to be x and
y. If there is a third array, it is assumed to by dy. Any other arrays
are ignored. These are passed to setObservedProfile.
Raises ValueError if the call to numpy.loadtxt returns fewer than 2
arrays.
Returns the x, y and dy arrays loaded from the file
"""
return self.profile.loadtxt(*args, **kw)
# FitContribution
def setEquation(self, eqstr, ns={}):
"""Set the profile equation for the FitContribution.
This sets the equation that will be used when generating the residual.
The equation will be usable within setResidualEquation as "eq", and it
takes no arguments.
eqstr -- A string representation of the equation. Variables will be
extracted from this equation and be given an initial value
of 0.
ns -- A dictionary of Parameters, indexed by name, that are used
in the eqstr, but not registered (default {}).
Raises ValueError if ns uses a name that is already used for a
variable.
"""
self.contribution.setEquation(eqstr, ns={})
# Extract variables
for par in self.contribution:
# Skip Profile Parameters
if par.name in ("x", "y", "dy"): continue
if par.value is None:
par.value = 0
if par.name not in self._parameters:
self.addVar(par)
return
def __call__(self):
"""Evaluate the contribution equation."""
return self.contribution.evaluate()
# FitResults methods
def printResults(self, header="", footer=""):
"""Format and print the results.
header -- A header to add to the output (default "")
footer -- A footer to add to the output (default "")
"""
self.results.printResults(header, footer, True)
return
def saveResults(self, filename, header="", footer=""):
"""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 "")
"""
self.results.saveResults(filename, header, footer, True)
