diffpy.srfit package

Generalized code base for modeling problems.

class diffpy.srfit.Calculator(name)

Bases: Operator, ParameterSet

Base class for calculators.

A Calculator organizes Parameters and has a __call__ method that can calculate a generic signal. Calculator is also an Operator (diffpy.srfit.equation.literals.operators), so it can be used directly in an evaluation network.

Parameters:

name (str) – The name of the Calculator. It must be a valid attribute name.

name

The name of the Calculator.

Type:

str

meta

The metadata needed by the calculator.

Type:

dict

names

The names of the top-level Parameters (read only). See get_names.

Type:

list of str

values

The values of the top-level Parameters (read only). See get_values.

Type:

list

args

The Literal arguments of the operator, added with add_literal.

Type:

list

nin

The number of inputs. The class value -1 means that any number of inputs is allowed.

Type:

int

nout

The number of outputs, which is 1.

Type:

int

symbol

The symbolic representation of the operator, which is the same as name (read only).

Type:

str

value

The value of the operator (read only). See get_value.

Type:

object

_value

The cached value of the operator, or None if it must be recomputed.

Type:

object

_calculators

The managed dictionary of Calculators, indexed by name.

Type:

dict

_constraints

The dictionary of Constraints, indexed by the constrained Parameter. Constraints can be added using the add_constraint method.

Type:

dict

_parameters

The managed OrderedDict of contained Parameters.

Type:

OrderedDict

_parsets

The managed OrderedDict of contained ParameterSets.

Type:

OrderedDict

_restraints

The set of Restraints. Restraints can be added using the add_soft_bounds method.

Type:

set

_eqfactory

The factory that is used to create Equations from strings.

Type:

diffpy.srfit.equation.builder.EquationFactory

nin = -1

nout = 1

operation(*args)

Calculate and cache the signal produced by this Calculator.

This calls __call__ and stores the result as the operator value.

Parameters:

*args – The arguments needed to calculate the signal.

Returns:

The calculated signal.

Return type:

object

property symbol

The symbolic representation, which is the Calculator name.

class diffpy.srfit.FitContribution(name)

Bases: ParameterSet

Organize an Equation, a Profile, and their supporting objects.

FitContributions organize an Equation that calculates the signal, and a Profile that holds the signal. ProfileGenerators and Calculators can be used as well. Constraints and Restraints can be created as part of a FitContribution.

name

The name of this FitContribution.

Type:

str

profile

The Profile that holds the measured (and calculated) signal. It is None until set_profile is called.

Type:

Profile or None

names

The names of the top-level Parameters (read only). See get_names.

Type:

list of str

values

The values of the top-level Parameters (read only). See get_values.

Type:

list

_calculators

The managed dictionary of Calculators, indexed by name.

Type:

dict

_constraints

The dictionary of Constraints, indexed by the constrained Parameter. Constraints can be added using the add_constraint method.

Type:

dict

_generators

The managed dictionary of ProfileGenerators, indexed by name.

Type:

dict

_parameters

The managed OrderedDict of parameters.

Type:

OrderedDict

_restraints

The set of Restraints. Restraints can be added using the add_soft_bounds method.

Type:

set

_parsets

The managed OrderedDict of ParameterSets.

Type:

OrderedDict

_eqfactory

The factory that is used to create constraints and restraints from strings.

Type:

diffpy.srfit.equation.builder.EquationFactory

_eq

The profile equation that calculates the signal.

Type:

Equation or None

_reseq

The residual equation that will be optimized.

Type:

Equation or None

_xname

The name of the x-variable.

Type:

str or None

_yname

The name of the y-variable.

Type:

str or None

_dyname

The name of the dy-variable.

Type:

str or None

addProfileGenerator(gen, name=None)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitContribution.add_profile_generator instead.

add_profile_generator(gen, name=None)

Add a ProfileGenerator to be used by this FitContribution.

The ProfileGenerator is given a name so that it can be used as part of the profile equation (see set_equation). This can be different from the name of the ProfileGenerator used for attribute access. FitContributions should not share ProfileGenerator instances. Different ProfileGenerators can share Parameters and ParameterSets, however.

Calling add_profile_generator sets the profile equation to call the calculator if there is not a profile equation already.

Parameters:

• gen (ProfileGenerator) – The ProfileGenerator instance to add.

• name (str, optional) – The name used for the ProfileGenerator in the profile equation. If name is None (default), then the ProfileGenerator’s name attribute will be used.

Raises:

ValueError – If the ProfileGenerator has no name, or if the ProfileGenerator has the same name as some other managed object.

evaluate()

Evaluate the contribution equation and update profile.ycalc.

The calculated signal is stored in profile.ycalc only if a Profile has been assigned.

Returns:

The calculated signal.

Return type:

numpy.ndarray

getEquation()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitContribution.get_equation instead.

getResidualEquation()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitContribution.get_residual_equation instead.

get_equation()

Get the math expression string for the active profile equation.

Returns:

The normalized math expression, or an empty string if the profile equation has not been set yet.

Return type:

str

get_residual_equation()

Get the math expression string for the active residual equation.

Returns:

The normalized math formula, or an empty string if the residual equation has not been configured yet.

Return type:

str

residual()

Calculate the residual for this FitContribution.

When this method is called, it is assumed that all parameters have been assigned their most current values by the FitRecipe. This will be the case when being called as part of a FitRecipe refinement.

The residual is by default an array chiv: chiv = (eq() - self.profile.y) / self.profile.dy. The value that is optimized is dot(chiv, chiv).

The residual equation can be changed with the set_residual_equation method.

Returns:

The array of residual values.

Return type:

numpy.ndarray

setEquation(eqstr, ns={})

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitContribution.set_equation instead.

setProfile(profile, xname=None, yname=None, dyname=None)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitContribution.set_profile instead.

setResidualEquation(eqstr)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitContribution.set_residual_equation instead.

set_equation(eqstr, ns={})

Set the profile equation for the FitContribution.

This sets the equation that will be used when generating the residual for this FitContribution. The equation will be usable within set_residual_equation as "eq", and it takes no arguments.

Parameters:

• eqstr (str) – The string representation of the equation. Any Parameter registered by add_parameter or set_profile, or function registered by register_calculator, register_function or register_string_function can be used in the equation by name. Other names will be turned into Parameters of this FitContribution.

• ns (dict, optional) – The 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.

set_profile(profile, xname=None, yname=None, dyname=None)

Assign the Profile for this FitContribution.

The x, y and dy Parameters of the Profile are added to this FitContribution under the names xname, yname and dyname. The Profile is also assigned to every ProfileGenerator of this FitContribution, and the residual equation is set to "chiv" if a profile equation exists but no residual equation has been set.

Parameters:

• profile (Profile) – The Profile that specifies the calculation points and that will store the calculated signal.

• xname (str, optional) – The name of the independent variable from the Profile. If this is None (default), then the name specified by the Profile for this parameter will be used. This variable is usable within string equations with the specified name.

• yname (str, optional) – The name of the observed Profile. If this is None (default), then the name specified by the Profile for this parameter will be used. This variable is usable within string equations with the specified name.

• dyname (str, optional) – The name of the uncertainty in the observed Profile. If this is None (default), then the name specified by the Profile for this parameter will be used. This variable is usable within string equations with the specified name.

Raises:

TypeError – If profile is not a Profile instance.

set_residual_equation(eqstr)

Set the residual equation for the FitContribution.

Two residuals are preset for convenience, "chiv" and "resv". chiv is defined such that dot(chiv, chiv) = chi^2. resv is defined such that dot(resv, resv) = Rw^2. You can call on these in your residual equation. Note that the quantity that will be optimized is the summed square of the residual equation. Keep that in mind when defining a new residual or using the built-in ones.

Parameters:

eqstr (str) – The string representation of the residual, or one of the preset names "chiv" and "resv".

Raises:

• SrFitError – If the Profile or the profile equation is not yet defined.

• ValueError – If eqstr depends on a Parameter that is not part of the FitContribution.

class diffpy.srfit.FitHook

Bases: object

Base class for inspecting the progress of a FitRecipe refinement.

A FitHook can be added to a FitRecipe with the FitRecipe.push_fit_hook method. The methods in this class are called during the preparation of the FitRecipe for refinement, and during the residual call. The base implementations do nothing; derived classes overload them as needed.

postcall(recipe, chiv)

Inspect the recipe after the residual is calculated.

This is called within FitRecipe.residual, after the residual is calculated.

Parameters:

• recipe (FitRecipe) – The FitRecipe whose residual was calculated.

• chiv (ndarray) – The residual vector, including the restraint penalties.

precall(recipe)

Inspect the recipe before the residual is calculated.

This is called within FitRecipe.residual, before the variable values are applied and the residual is calculated.

Parameters:

recipe (FitRecipe) – The FitRecipe whose residual is being calculated.

reset(recipe)

Reset the hook data.

This is called by FitRecipe._prepare whenever a configurational change to the fit hierarchy takes place, such as adding a new ParameterSet, constraint or restraint.

Parameters:

recipe (FitRecipe) – The FitRecipe that is being prepared.

class diffpy.srfit.FitRecipe(name='fit')

Bases: FitRecipeInterface, RecipeOrganizer

Organize FitContributions, variables, restraints, and constraints into a refinable recipe.

name

A name for this FitRecipe.

Type:

str

fithooks

The list of FitHook instances that can pass information out of the system during a refinement. By default, this is populated by a PrintFitHook instance.

Type:

list

_constraints

The dictionary of Constraints, indexed by the constrained Parameter. Constraints can be added using the add_constraint method.

Type:

dict

_oconstraints

The ordered list of the constraints from this and all sub-components.

Type:

list

_calculators

The managed dictionary of Calculators.

Type:

dict

_contributions

The managed OrderedDict of FitContributions.

Type:

OrderedDict

_parameters

The managed OrderedDict of parameters (in this case the parameters are varied).

Type:

OrderedDict

_parsets

The managed dictionary of ParameterSets.

Type:

dict

_eqfactory

The diffpy.srfit.equation.builder.EquationFactory instance that is used to create constraints and restraints from strings.

Type:

diffpy.srfit.equation.builder.EquationFactory

_restraintlist

The list of restraints from this and all sub-components.

Type:

list

_restraints

The set of Restraints. Restraints can be added using the ‘restrain’ or ‘confine’ methods.

Type:

set

_ready

The flag indicating if all attributes are ready for the calculation.

Type:

bool

_tagmanager

The TagManager instance for managing tags on Parameters.

Type:

TagManager

_weights

The list of weighing factors for each FitContribution. The weights are multiplied by the residual of the FitContribution when determining the overall residual.

Type:

list

_fixedtag

__fixed, used for tagging variables as fixed. Don’t use this tag unless you want issues.

Type:

str

names

The variable names (read only). See get_names.

Type:

list

values

The variable values (read only). See get_values.

Type:

numpy.ndarray

fixednames

The names of the fixed refinable variables (read only).

Type:

list

fixedvalues

The values of the fixed refinable variables (read only).

Type:

numpy.ndarray

bounds

The bounds on parameters (read only). See get_bounds_pairs.

Type:

list of tuple

bounds2

The bounds on parameters (read only). See get_bounds_array.

Type:

tuple of numpy.ndarray

addContribution(con, weight=1.0)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.add_contribution instead.

addParameterSet(parset)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.add_parameter_set instead.

addVar(par, value=None, name=None, fixed=False, tag=None, tags=[])

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.add_variable instead.

add_constraint(par, con, ns={})

Constrain a parameter to an equation.

Note that only one constraint can exist on a Parameter at a time.

This is overloaded to set the value of con if it represents a variable and its current value is None. A constrained variable will be set as fixed.

Parameters:

• par (Parameter) – The Parameter to constrain.

• con (str or Parameter) – The string representation of the constraint equation or a Parameter to constrain to. A constraint equation must consist of numpy operators and “known” Parameters. Parameters are known if they are in the ns argument, or if they are managed by this object.

• ns (dict, optional) – The dictionary of Parameters, indexed by name, that are used in the eqstr, but not part of this object (default {}).

Raises:

• ValueError – If ns uses a name that is already used for a variable.

• ValueError – If eqstr depends on a Parameter that is not part of the FitRecipe and that is not defined in ns.

• ValueError – If par is marked as constant.

add_contribution(con, weight=1.0)

Add a FitContribution to the FitRecipe.

Parameters:

• con (FitContribution) – The FitContribution to be stored.

• weight (float, optional) – The weight of the FitContribution. Default is 1.0.

Raises:

ValueError – If the FitContribution has no name or if the FitContribution has the same name as some other managed object.

add_parameter_set(parset)

Add a ParameterSet to the hierarchy.

Parameters:

parset (ParameterSet) – The ParameterSet to be stored.

Raises:

ValueError – If the ParameterSet has no name or if the ParameterSet has the same name as some other managed object.

add_variable(par, value=None, name=None, fixed=False, tag=None, tags=[])

Add a variable to be refined.

Parameters:

• par (diffpy.srfit.fitbase.Parameter) – The Parameter that will be varied during a fit.

• value (float or None, optional) – The initial value for the variable. If this is None (default), then the current value of par will be used.

• name (str or None, optional) – The name for this variable. If name is None (default), then the name of the parameter will be used.

• fixed (bool, optional) – Fix the variable so that it does not vary (default False).

• tag (str or None, optional) – The tag for the variable. This can be used to retrieve, fix or free variables by tag (default None). Note that a variable is automatically tagged with its name and “all”.

• tags (list of str, optional) – The list of tags (default []). Both tag and tags can be applied.

Returns:

The ParameterProxy (variable) for the passed Parameter.

Return type:

ParameterProxy

Raises:

• ValueError – If the name of the variable is already taken by another managed object.

• ValueError – If par is constant.

• ValueError – If par is constrained.

property bounds

property bounds2

boundsToRestraints(sig=1, scaled=False)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.convert_bounds_to_restraints instead.

clearFitHooks()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.clear_fit_hooks instead.

clear_fit_hooks()

Clear the FitHook sequence.

constrain(par, con, ns={})

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.add_constraint instead.

convert_bounds_to_restraints(sig=1, scaled=False)

Turn all bounded parameters into restraints.

The bounds become limits on the restraint.

Parameters:

• sig (float or iterable of float, optional) – The number of standard deviations associated with each bound. Smaller values produce stronger restraints. If a scalar is given, the same value is applied to all parameters. If an iterable is provided, it must match the number of parameters. Default is 1.

• scaled (bool, optional) – If True, scale each restraint by the magnitude of the corresponding parameter, consistent with the behavior of restrain(). Default is False.

create_new_variable(name, value=None, fixed=False, tag=None, tags=[])

Create a new variable of the fit.

This method lets new variables be created that are not tied to a Parameter. Orphan variables may cause a fit to fail, depending on the optimization routine, and therefore should only be created to be used in constraint or restraint equations.

Parameters:

• name (str) – The name of the variable. The variable will be able to be used by this name in restraint and constraint equations.

• value (float or None, optional) – The initial value for the variable. If this is None (default), then the variable will be given the value of the first non-None-valued Parameter constrained to it. If this fails, an error will be thrown when ‘residual’ is called.

• fixed (bool, optional) – Fix the variable so that it does not vary (default False). The variable will still be managed by the FitRecipe.

• tag (str or None, optional) – The tag for the variable. This can be used to fix and free variables by tag (default None). Note that a variable is automatically tagged with its name and “all”.

• tags (list of str, optional) – The list of tags (default []). Both tag and tags can be applied.

Returns:

The new variable (Parameter instance).

Return type:

Parameter

delVar(var)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.delete_variable instead.

delete_variable(var)

Remove a variable.

Note that constraints and restraints involving the variable are not modified.

Parameters:

var (ParameterProxy) – A variable of the FitRecipe.

Raises:

ValueError – If var is not part of the FitRecipe.

fix(*args, **kw)

Fix one or more parameters by reference, name, or tag.

This method marks specified parameters as fixed, meaning they will not be refined during the fitting process. By default, all parameters are free (not fixed). Parameters can be specified using their references, names, or tags. Additionally, keyword arguments can be used to assign specific values to the fixed parameters.

Parameters:

• *args (str or Parameter) – The positional arguments specifying the parameters to fix. These can be parameter objects, their names as strings, or tags. The special string “all” can be used to select all parameters.

• **kw (dict) – The keyword arguments where the keys are parameter names and the values are the values to assign to the corresponding fixed parameters.

Raises:

ValueError – If an unknown parameter, name, or tag is passed, or if a tag is passed as a keyword argument.

Examples

# Fix a parameter by reference
recipe.fix(param1)

# Fix a parameter by name
recipe.fix("param2")

# Fix all parameters
recipe.fix("all")

# Fix parameters by tag
recipe.fix(tag="group1")

# Fix a parameter and assign it a value
recipe.fix(param3=10.0)

property fixednames

names of the fixed refinable variables

property fixedvalues

values of the fixed refinable variables

free(*args, **kw)

Free one or more parameters by reference, name, or tag.

This method marks specified parameters as free, allowing them to be refined during the fitting process. By default, variables are free unless they are constrained. Constrained variables cannot be freed.

Parameters:

• *args (str or Parameter) – The positional arguments specifying the parameters to free. These can be: - Parameter objects - Names of parameters (as strings) - Tags associated with parameters (as strings) - The string “all” to select all parameters.

• **kw (dict) – The keyword arguments specifying parameter names as keys and their values to assign after freeing. This is useful for setting the value of a parameter while marking it as free.

Return type:

None

Raises:

ValueError – If an unknown parameter, name, or tag is passed, or if a tag is passed as a keyword argument.

Notes

• Parameters that are already free will remain free.

• Tags associated with fixed parameters will be removed when they are freed.

• If keyword arguments are provided, the corresponding parameter values will be updated after freeing.

getBounds()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.get_bounds_pairs instead.

getBounds2()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.get_bounds_array instead.

getFitHooks()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.get_fit_hooks instead.

getNames()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.get_names instead.

getValues()

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.get_values instead.

get_bounds_array()

Get the bounds on variables in two numpy arrays.

Returns:

• lower_bounds (numpy.ndarray) – The numpy array of lower bounds on the variables, in the same order as get_names and get_values.

• upper_bounds (numpy.ndarray) – The numpy array of upper bounds on the variables, in the same order as get_names and get_values.

get_bounds_pairs()

Get the bounds on variables in a list.

Returns:

bounds_pair_list – The list of (lower, upper) bounds on the variables, in the same order as get_names and get_values.

Return type:

list of tuple of float

get_fit_hooks()

Get the sequence of FitHook instances.

Returns:

The list of FitHook instances registered with this FitRecipe.

Return type:

list

get_names()

Retrieve the names of all free variables in the fit recipe.

This method iterates through the parameters in the fit recipe and returns a list of names for those variables that are marked as free.

Returns:

parameter_names – The list containing the names of free variables.

Return type:

list of str

get_values()

Retrieve the current values of all free variables in the fit recipe.

This method collects the values of all parameters that are marked as free (i.e., adjustable during the fitting process) and returns them as a NumPy array.

Returns:

values_array – The array containing the current values of all free variables in the fit recipe.

Return type:

numpy.ndarray

initialize_recipe_with_recipe(recipe_object)

Initialize a FitRecipe with another FitRecipe.

This is used to initialize a FitRecipe with the contribution(s), parameters, constraints and restraints of another FitRecipe. If a duplicate contribution, parameter, constraint, or restraint is added to the FitRecipe you are initializing, the value from the added object will be used.

Parameters:

recipe_object (FitRecipe) – The FitRecipe to initialize with.

Raises:

ValueError – If the object passed is not a FitRecipe.

initialize_recipe_with_results(results, verbose=True)

Initialize a FitRecipe with a FitResults object or a results file.

Note that at least one FitContribution must already exist in the FitRecipe.

Parameters:

• results (FitResults, pathlib.Path, or str) – The FitResults object or path to results file to initialize with.

• verbose (bool, optional) – If True, print warnings for any parameters in the results that are not in the FitRecipe. Default is True.

Raises:

ValueError – If the input results is not a FitResults object or a path to a results file.

isFree(var)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.is_free instead.

is_free(var)

Determine if a variable is free (not fixed) in the fit recipe.

This method checks whether the specified variable does not have the fixed tag associated with it, indicating that it is free to vary during the fitting process.

Parameters:

var (object) – The variable to check. This is typically an instance of a parameter or variable object used in the fit recipe.

Returns:

True if the variable is free (not fixed), False otherwise.

Return type:

bool

newVar(name, value=None, fixed=False, tag=None, tags=[])

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.create_new_variable instead.

plot_recipe(ax=None, return_fig=False, **kwargs)

Plot the observed, fit, and difference curves for each contribution of the fit recipe.

If the recipe has multiple contributions, a separate plot is created for each contribution.

Parameters:

• ax (matplotlib.axes.Axes or None, optional) – The axes object to plot on. If None, creates a new figure. Default is None.

• return_fig (bool, optional) – The figure and axes objects are returned if True. Default is False.

• **kwargs (dict) – Any plotting option can be passed to override the defaults in FitRecipe().plot_options. See the FitRecipe().set_plot_defaults() method for available keyword arguments.

Returns:

fig, axes – The figure and axes objects, returned only if return_fig=True. If the recipe has a single contribution, a single mpl.figure.Figure and mpl.axes.Axes are returned. If it has multiple contributions, a list of figures and a list of axes (one per contribution) are returned instead.

Return type:

tuple

Examples

Plot with default settings:

>>> recipe.plot_recipe()

Override defaults for one plot:

>>> recipe.plot_recipe(show_diff=False, title='My Custom Title')

Set defaults once, use everywhere:

>>> recipe.set_plot_defaults(xlabel='r (Å)', ylabel='G(r)')
>>> recipe.plot_recipe()  # Uses xlabel and ylabel
>>> recipe.plot_recipe()  # Still uses them

Override a default for one plot:

>>> recipe.set_plot_defaults(figsize=(10, 7))
>>> recipe.plot_recipe()  # Uses (10, 7)
>>> recipe.plot_recipe(figsize=(12, 8))  # Temporarily uses (12, 8)
>>> recipe.plot_recipe()  # Back to (10, 7)

Notes

The default values are taken from recipe.plot_options. You can modify these defaults in three ways:

1. Using set_plot_defaults(): recipe.set_plot_defaults(xlabel=’r (Å)’)

2. Direct attribute access: recipe.plot_options[‘xlabel’] = ‘r (Å)’

3. Using update(): recipe.plot_options.update({‘xlabel’: ‘r (Å)’, ‘ylabel’: ‘G(r)’})

popFitHook(fithook=None, index=-1)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.pop_fit_hook instead.

pop_fit_hook(fithook=None, index=-1)

Remove a FitHook by index or reference.

Parameters:

• fithook (diffpy.srfit.fitbase.fithook.FitHook or None, optional) – The FitHook instance to remove from the sequence. If this is None (default), default to index.

• index (int, optional) – The index of FitHook instance to remove (default -1).

Raises:

• ValueError – If fithook is not None, but is not present in the sequence.

• IndexError – If the sequence is empty or index is out of range.

pushFitHook(fithook, index=None)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.push_fit_hook instead.

push_fit_hook(fithook, index=None)

Add a FitHook to be called within the residual method.

The hook is an object for reporting updates, or more fundamentally, passing information out of the system during a refinement. See the diffpy.srfit.fitbase.fithook.FitHook class for the required interface. Added FitHooks will be called sequentially during refinement.

Parameters:

• fithook (diffpy.srfit.fitbase.fithook.FitHook) – The FitHook instance to add to the sequence.

• index (int or None, optional) – The index for inserting fithook into the list of fit hooks. If this is None (default), the fithook is added to the end.

removeParameterSet(parset)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.remove_parameter_set instead.

remove_constraint(*pars)

Unconstrain a Parameter.

This removes any constraints on a Parameter. If the Parameter is also a variable of the recipe, it will be freed as well.

Parameters:

*pars (str or Parameter) – The names of Parameters or Parameter objects to unconstrain.

Raises:

ValueError – If the Parameter is not constrained.

remove_parameter_set(parset)

Remove a ParameterSet from the hierarchy.

This method removes the specified ParameterSet object from the internal hierarchy of managed ParameterSets. If the provided ParameterSet is not currently managed by this object, a ValueError will be raised.

Parameters:

parset (ParameterSet) – The ParameterSet instance to be removed from the hierarchy.

Raises:

ValueError – If the provided ParameterSet is not managed by this object.

residual(p=[])

Calculate the vector residual to be optimized.

The residual is by default the weighted concatenation of each FitContribution’s residual, plus the value of each restraint. The array returned, denoted chiv, is such that dot(chiv, chiv) = chi^2 + restraints.

Parameters:

p (list or numpy.ndarray) – The list of current variable values, provided in the same order as the _parameters list. If p is an empty iterable (default), then it is assumed that the parameters have already been updated in some other way, and the explicit update within this function is skipped.

Returns:

chiv – The array of residuals to be optimized. The array is such that dot(chiv, chiv) = chi^2 + restraints.

Return type:

numpy.ndarray

scalarResidual(p=[])

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.scalar_residual instead.

scalar_residual(p=[])

Calculate the scalar residual to be optimized.

Parameters:

p (list or numpy.ndarray) – The list of current variable values, provided in the same order as the _parameters list. If p is an empty iterable (default), then it is assumed that the parameters have already been updated in some other way, and the explicit update within this function is skipped.

Returns:

The scalar residual, dot(chiv, chiv), where chiv is the vector residual returned by residual.

Return type:

float

Notes

The residual is by default the weighted concatenation of each FitContribution residual, plus the value of each restraint. The returned array, denoted chiv, is such that dot(chiv, chiv) = chi^2 + restraints.

setWeight(con, weight)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.set_weight instead.

set_plot_defaults(**kwargs)

Set default plotting options for all future plots.

Any keyword argument accepted by plot_recipe() can be set here.

Parameters:

• show_observed (bool, optional) – The observed data is plotted if True. Default is True.

• show_fit (bool, optional) – The fit to the data is plotted if True. Default is True.

• show_diff (bool, optional) – The difference curve (observed - calculated) is plotted if True. Default is True.

• offset_scale (float, optional) – The scaling factor for the difference curve offset. The difference curve is offset below the data by (min_y - 0.1*range) * offset_scale. Default is 1.0.

• xmin (float or None, optional) – The minimum x value to plot. If None, uses the minimum x value of the data. Default is None.

• xmax (float or None, optional) – The maximum x value to plot. If None, uses the maximum x value of the data. Default is None.

• figsize (tuple, optional) – The figure size as (width, height). Default is (8, 6).

• data_style (str, optional) – The matplotlib line/marker style for data points. Default is “o”.

• fit_style (str, optional) – The matplotlib line/marker style for the calculated fit. Default is “-“.

• diff_style (str, optional) – The matplotlib line/marker style for the difference curve. Default is “-“.

• data_color (str or None, optional) – The color for data plot. If None, uses default matplotlib colors.

• fit_color (str or None, optional) – The color for the fit plot. If None, uses default matplotlib colors.

• diff_color (str or None, optional) – The color for the difference plot. If None, uses default matplotlib colors.

• data_label (str, optional) – The legend label for observed data. Default is “Observed”.

• fit_label (str, optional) – The legend label for the calculated fit. Default is “Calculated”.

• diff_label (str, optional) – The legend label for the difference curve. Default is “Difference”.

• xlabel (str, optional) – The label for the x-axis.

• ylabel (str, optional) – The label for the y-axis.

• title (str or None, optional) – The plot title. If None (default), each figure created by plot_recipe is titled with the name of the contribution it shows. A title is not added to a user-supplied axes.

• legend (bool, optional) – The legend is shown if True. Default is True.

• legend_loc (str, optional) – The legend location. Default is “best”.

• grid (bool, optional) – The grid is shown if True. Default is False.

• markersize (float, optional) – The size of data point markers.

• linewidth (float, optional) – The width of fit and difference lines.

• alpha (float, optional) – The transparency of all plot elements (0=transparent, 1=opaque). Default is 1.0.

• show (bool, optional) – The plot is displayed using plt.show() if True. Default is True.

Notes

The data_label, fit_label, diff_label and title options accept a {contribution} placeholder that is replaced by the name of the FitContribution being plotted, e.g. fit_label="{contribution} calculated". When several contributions are drawn on a shared axes, labels without the placeholder are prefixed with the contribution name so the legend entries stay distinguishable.

Examples

>>> recipe.set_plot_defaults(
        xlabel='r (Å)',
        ylabel='G(r) (Å⁻²)',
        data_color='black',
        fit_color='red'
    )

set_weight(con, weight)

Set the weight of a FitContribution.

Parameters:

• con (FitContribution) – The FitContribution object whose weight is to be set.

• weight (float) – The weight value to assign to the specified FitContribution.

Return type:

None

unconstrain(*pars)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitRecipe.remove_constraint instead.

class diffpy.srfit.FitResults(recipe, update=True, showfixed=True, showcon=False)

Bases: object

Class for processing, presenting and storing results of a fit.

recipe

The recipe from which the results were generated.

Type:

FitRecipe

cov

The covariance matrix of the refined variables. None if unavailable.

Type:

numpy.ndarray or None

conresults

The ordered mapping of FitContribution name → ContributionResults.

Type:

collections.OrderedDict[str, ContributionResults]

derivstep

The fractional step size used for numerical derivatives (default 1e-8).

Type:

float

varnames

The names of refined variables in the recipe.

Type:

list[str]

varvals

The optimized values of the refined variables.

Type:

numpy.ndarray

varunc

The estimated standard uncertainties of the variables. None if invalid.

Type:

numpy.ndarray or None

showfixed

The flag indicating whether to show the fixed variables in the formatted output (default True).

Type:

bool

fixednames

The names of variables held fixed during refinement.

Type:

list[str]

fixedvals

The values of the fixed variables.

Type:

numpy.ndarray

showcon

The flag indicating whether to show the constrained parameters in the formatted output (default False).

Type:

bool

connames

The names of constrained parameters.

Type:

list[str]

convals

The values of constrained parameters.

Type:

numpy.ndarray

conunc

The uncertainties of constrained parameters. None if unavailable.

Type:

numpy.ndarray or None

residual

The scalar residual value of the recipe.

Type:

float

penalty

The penalty contribution to the residual from restraints.

Type:

float

chi2

The chi-squared value of the fit.

Type:

float

cumchi2

The cumulative chi-squared as a function of data index.

Type:

numpy.ndarray

rchi2

The reduced chi-squared of the fit.

Type:

float

rw

The weighted R-factor of the fit.

Type:

float

cumrw

The cumulative weighted R-factor as a function of data index.

Type:

numpy.ndarray

messages

The informational or warning messages associated with the results.

Type:

list[str]

precision

The number of digits used when formatting numeric output (default 8).

Type:

int

_dcon

The jacobian of constraint equations with respect to variables. Used internally for uncertainty propagation.

Type:

numpy.ndarray

Each of these attributes, except the recipe, are created or updated when

the update method is called.

formatResults(header='', footer='', update=False)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitResults.get_results_string instead.

get_results_dictionary()

Get a dictionary of results, with variable names and values, and overall metrics.

Returns:

results_dict – The dictionary containing the variable names and values, and overall metrics, from the FitResults.

Return type:

dict

get_results_string(header='', footer='', update=False)

Format the results and return them in a string.

This function is called by print_results and save_results. Overloading the formatting here will change all three functions.

Parameters:

• header (str) – The header to add to the output (default “”)

• footer (str) – The footer to add to the output (default “”)

• update (bool) – The flag indicating whether to call update() (default False).

Returns:

The string containing the formatted results.

Return type:

str

printResults(header='', footer='', update=False)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitResults.print_results instead.

print_results(header='', footer='', update=False)

Format and print the results.

Parameters:

• header (str) – The header to add to the output (default “”)

• footer (str) – The footer to add to the output (default “”)

• update (bool) – The flag indicating whether to call update() (default False).

saveResults(filename, header='', footer='', update=False)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.FitResults.save_results instead.

save_results(filename, header='', footer='', update=False)

Format and save the results.

Parameters:

• filename (str) – The name of the save file.

• header (str) – The header to add to the output (default “”)

• footer (str) – The footer to add to the output (default “”)

• update (bool) – The flag indicating whether to call update() (default False).

update()

Update the results according to the current state of the recipe.

class diffpy.srfit.PlotFitHook

Bases: FitHook

Live-plot the progress of a FitRecipe refinement.

Each FitContribution of the recipe gets a subplot showing the observed profile and the calculated profile, which is updated after every residual call. This requires matplotlib.

_plots

The (observed, calculated) line pair for each FitContribution. It is created by reset.

Type:

list of tuple

postcall(recipe, chiv)

Update the plots with the latest profiles.

This is called within FitRecipe.residual, after the residual is calculated.

Parameters:

• recipe (FitRecipe) – The FitRecipe whose residual was calculated.

• chiv (ndarray) – The residual vector, including the restraint penalties.

reset(recipe)

Set up the plot.

Clear the current figure, turn on interactive mode and draw one subplot per FitContribution of the recipe.

Parameters:

recipe (FitRecipe) – The FitRecipe that is being prepared.

class diffpy.srfit.Profile

Bases: Observable, Validatable

Observed and calculated profile container.

Profile is an Observable. The xpar, ypar and dypar attributes are observed by the Profile, which can in turn be observed by some other object.

xobs

The observed independent variable (default None, read only).

Type:

numpy.ndarray or None

yobs

The observed signal (default None, read only).

Type:

numpy.ndarray or None

dyobs

The uncertainty of the observed signal (default None, read only). It stays None when no uncertainty was observed.

Type:

numpy.ndarray or None

x

The calculated independent variable (default None). This property gets and sets the value of xpar.

Type:

numpy.ndarray or None

y

The profile over the calculation range (default None). This property gets and sets the value of ypar.

Type:

numpy.ndarray or None

dy

The uncertainty in the profile over the calculation range (default None). This property gets and sets the value of dypar.

Type:

numpy.ndarray or None

ycalc

The calculated signal (default None). This property gets and sets the value of ycpar.

Type:

numpy.ndarray or None

xpar

The Parameter that stores x (named “x”).

Type:

Parameter

ypar

The Parameter that stores y (named “y”).

Type:

Parameter

dypar

The Parameter that stores dy (named “dy”).

Type:

Parameter

ycpar

The Parameter that stores ycalc (named “ycalc”). This is not observed by the profile, but it is present so it can be constrained to.

Type:

Parameter

meta

The metadata of the profile. This is only filled if provided by a parser.

Type:

dict

_xobs

The storage for xobs.

Type:

numpy.ndarray or None

_yobs

The storage for yobs.

Type:

numpy.ndarray or None

_dyobs

The storage for dyobs.

Type:

numpy.ndarray or None

property dy

property dyobs

loadParsedData(parser)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.profile.Profile.load_parsed_data instead.

load_parsed_data(parser)

Load parsed data from a ProfileParser.

This sets the xobs, yobs, dyobs arrays as well as the metadata.

Parameters:

parser (ProfileParser) – The parser holding the observed profile data and metadata.

loadtxt(*args, **kw)

Load data using numpy.loadtxt.

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 be dy. Any other arrays are ignored. The loaded arrays are passed to set_observed_profile.

Parameters:

• *args – The positional arguments passed to numpy.loadtxt.

• **kw – The keyword arguments passed to numpy.loadtxt.

Returns:

• x (numpy.ndarray) – The array of the independent variable loaded from the file.

• y (numpy.ndarray) – The array of the observed signal loaded from the file.

• dy (numpy.ndarray or None) – The array of the uncertainty loaded from the file, or None if no third column is present.

Raises:

ValueError – If the call to numpy.loadtxt returns fewer than 2 arrays.

savetxt(fname, **kwargs)

Save x, ycalc, y and dy as columns of a text file.

Parameters:

• fname (str, pathlib.Path or file handle) – The filename or file handle passed to numpy.savetxt.

• **kwargs – The keyword arguments that are passed to numpy.savetxt. The file header defaults to “x ycalc y dy”. Use header='' to save data without any header.

Raises:

SrFitError – If ycalc has not been set.

See also

numpy.savetxt

setCalculationPoints(x)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.profile.Profile.set_calculation_points instead.

setCalculationRange(xmin=None, xmax=None, dx=None)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.profile.Profile.set_calculation_range instead.

setObservedProfile(xobs, yobs, dyobs=None)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.profile.Profile.set_observed_profile instead.

set_calculation_points(x)

Set the calculation points.

This creates y and dy on the specified grid if xobs, yobs and dyobs exist.

Parameters:

x (numpy.ndarray) – The non-empty array of calculation points. If xobs exists, the bounds of x will be limited to its bounds.

set_calculation_range(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. xmin is always inclusive (unless clipped). xmax is inclusive if it is within 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.

Raises:

• AttributeError – If there is no observed data.

• ValueError – If xmin > xmax, if dx <= 0 or if dx > xmax - xmin.

• ValueError – If xmin, xmax or dx is a string other than “obs”.

set_observed_profile(xobs, yobs, dyobs=None)

Set the observed profile.

Parameters:

• xobs (numpy.ndarray) – The array of the independent variable.

• yobs (numpy.ndarray) – The array of the observed signal.

• dyobs (numpy.ndarray, optional) – The array of the uncertainty in the observed signal. If dyobs is None (default), dyobs stays None to indicate no uncertainty was observed, and the calculated dy will be set to 1 at each calculation point instead.

Raises:

• ValueError – If len(yobs) != len(xobs).

• ValueError – If dyobs is not None and len(dyobs) != len(xobs).

property x

property xobs

property y

property ycalc

property yobs

class diffpy.srfit.ProfileGenerator(name)

Bases: Operator, ParameterSet

Base class for profile generators.

A ProfileGenerator organizes Parameters and has a __call__ method that can generate a profile. ProfileGenerator is also an Operator (diffpy.srfit.equation.literals.operators), so it can be used directly in an evaluation network.

Parameters:

name (str) – The name of the ProfileGenerator. It must be a valid attribute name.

name

The name of the ProfileGenerator.

Type:

str

profile

The Profile that contains the calculation range and will contain the generated profile. It is None until set_profile is called.

Type:

Profile or None

meta

The metadata needed by the generator. It is updated from the Profile metadata whenever the Profile is set or changes.

Type:

dict

names

The names of the top-level Parameters (read only). See get_names.

Type:

list of str

values

The values of the top-level Parameters (read only). See get_values.

Type:

list

args

The Literal arguments of the operator, added with add_literal.

Type:

list

nin

The number of inputs, which is 0. The independent variable is taken from profile.

Type:

int

nout

The number of outputs, which is 1.

Type:

int

symbol

The symbolic representation of the operator, which is the same as name (read only).

Type:

str

value

The value of the operator (read only). See get_value.

Type:

object

_value

The cached value of the operator, or None if it must be recomputed.

Type:

object

_calculators

The managed dictionary of Calculators, indexed by name.

Type:

dict

_constraints

The dictionary of Constraints, indexed by the constrained Parameter. Constraints can be added using the add_constraint method.

Type:

dict

_parameters

The managed OrderedDict of contained Parameters.

Type:

OrderedDict

_parsets

The managed OrderedDict of contained ParameterSets.

Type:

OrderedDict

_restraints

The set of Restraints. Restraints can be added using the add_soft_bounds method.

Type:

set

_eqfactory

The factory that is used to create Equations from strings.

Type:

diffpy.srfit.equation.builder.EquationFactory

nin = 0

nout = 1

operation()

Evaluate the profile over the calculation points.

Returns:

The profile calculated by __call__ at profile.x.

Return type:

ndarray

set_profile(profile)

Assign the profile.

The generator stops observing any previously assigned Profile and starts observing the new one, so that changes to the Profile update meta and invalidate the cached value.

Parameters:

profile (Profile) – The Profile that specifies the calculation points and which will store the calculated signal.

property symbol

The symbolic representation, which is the generator name.

class diffpy.srfit.ProfileParser

Bases: object

Base class for parsing profile data from a file.

_format

The name of the data format that this parses (string, default ""). The format string is a unique identifier for the data format handled by the parser.

Type:

str, optional

_banks

The data from each bank. Each bank contains a (x, y, dx, dy) tuple:

xnp.ndarray

The independent variable read from the file.

ynp.ndarray

The dependent variable (profile) read from the file.

dxnp.ndarray

The uncertainties associated with x read from the file. This is None if the uncertainty cannot be read.

dynp.ndarray

The uncertainties associated with y read from the file. This is None if the uncertainty cannot be read.

Type:

list of tuples

_x

Independent variable from the chosen bank

Type:

np.ndarray

_y

Profile from the chosen bank

Type:

np.ndarray

_dx

Uncertainty in independent variable from the chosen bank

Type:

np.ndarray

_dy

Uncertainty in profile from the chosen bank

Type:

np.ndarray

_meta

A dictionary containing metadata read from the file.

Type:

dict

General Metadata:

• filename (str or Path) – The name of the file from which data was parsed. This key will not exist if data was not read from file.

• nbanks (int) – The number of banks parsed.

• bank (int) – The chosen bank number.

getData(index=None)

This function is deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.ProfileParser.get_data instead.

getFormat()

This function is deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.ProfileParser.get_format instead.

getMetaData()

This function is deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.ProfileParser.get_metadata instead.

getNumBanks()

This function is deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.ProfileParser.get_num_banks instead.

get_data(index=None)

Get the data.

This method should only be called after the data has been parsed. The chosen bank number is not persistent, and so must be re-selected if the parser is used to parse more data. This uses python list notation, so index -n returns the nth bank from the end.

Parameters:

index (int, optional) – The index of the bank (integer, starting at 0, default None). If index is None then the currently selected bank is used.

Returns:

The (x, y, dx, dy) tuple for the bank. dx and dy are None if they cannot be determined from the data format.

Return type:

tuple

get_format()

Get the format string.

Returns:

The unique identifier for the data format handled by this parser.

Return type:

str

get_metadata()

Get the parsed metadata.

Returns:

A dictionary containing metadata read from the file.

Return type:

dict

get_num_banks()

Get the number of banks read by the parser.

Returns:

The number of banks read by the parser.

Return type:

int

parseFile(filename)

This function is deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.ProfileParser.parse_file instead.

parse_file(filename, column_format=None, metadata=None, **kwargs)

Parse a data file to extract data and metadata, with automatic handling of uncertainties.

This is a template method. Subclasses customize a format by overriding the _parse_metadata and _parse_data hooks rather than this method.

The default _parse_data reads a single bank:

• For files with 2 columns: assumes (x, y) and sets dx, dy to None.

• For files with 3 columns: assumes (x, y, dy) and sets dx to None.

• For files with 4 columns: assumes (x, y, dx, dy).

• For other cases: column_format must be explicitly specified.

Uncertainty columns (dx, dy) are only considered valid if all values are positive and not NaN/Inf. Otherwise they are set to None.

This wipes out the currently loaded data and selected bank number.

Parameters:

• filename (str or Path) – The name of the file to parse.

• column_format (tuple of str, optional) –

  The order in which columns appear in the file. If None, the format is auto-detected based on the number of columns.

  Valid labels: "x", "y", "dx", "dy"

  Examples:

  • ("x", "y")

  • ("x", "y", "dy")

  • ("x", "y", "dx", "dy")

  • ("x", "dx", "y", "dy")

• metadata (dict, optional) – Additional metadata to merge into the metadata parsed from the file. Keys must be strings. A key that collides with one already present in the parsed metadata overrides the parsed value. A key that collides with "filename", "bank", or "nbanks", which parse_file sets itself, also overrides the automatically set value, but raises a UserWarning since it may affect other code that relies on the automatically set value.

• kwargs – The keyword arguments passed on to diffpy.utils.parsers.load_data, such as usecols, delimiter, comments and minrows. Use usecols to select four columns out of a wider file, then label them with column_format.

Raises:

ParseError – If parsing fails or ambiguity detected.

selectBank(index)

This function is deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.ProfileParser.select_bank instead.

select_bank(index)

Select which bank to use.

This method should only be called after the data has been parsed. The chosen bank number is not persistent, and so must be re-selected if the parser is used to parse more data. This uses python list notation, so index -n returns the nth bank from the end.

Parameters:

index (int) – The index of the bank (integer, starting at 0).

Raises:

IndexError – If requesting a bank that does not exist.

class diffpy.srfit.SimpleRecipe(name='fit', conclass=<class 'diffpy.srfit.fitbase.fitcontribution.FitContribution'>)

Bases: FitRecipe

FitRecipe with a built-in Profile and FitContribution.

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.

Public FitContribution methods that SimpleRecipe does not define itself, such as set_residual_equation or add_profile_generator, are also made available as SimpleRecipe methods.

profile

The built-in Profile object.

Type:

Profile

contribution

The built-in FitContribution object, named “contribution”.

Type:

FitContribution

results

The built-in FitResults object.

Type:

FitResults

name

The name of this FitRecipe.

Type:

str

fithooks

The list of FitHook instances that can pass information out of the system during a refinement. By default, this is populated by a PrintFitHook instance with verbosity 3.

Type:

list

names

The variable names (read only). See get_names.

Type:

list of str

values

The variable values (read only). See get_values.

Type:

numpy.ndarray

See also

FitRecipe

The base class, which documents the remaining attributes.

loadParsedData(parser)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.SimpleRecipe.load_parsed_data instead.

load_parsed_data(parser)

Load parsed data from a ProfileParser.

This sets the xobs, yobs, dyobs arrays as well as the metadata.

Parameters:

parser (ProfileParser) – The ProfileParser to load data from.

loadtxt(*args, **kw)

Load data using numpy.loadtxt.

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 be dy. Any other arrays are ignored. The loaded arrays are passed to set_observed_profile.

Parameters:

• *args – The positional arguments passed to numpy.loadtxt.

• **kw – The keyword arguments passed to numpy.loadtxt.

Returns:

• x (numpy.ndarray) – The array of the independent variable loaded from the file.

• y (numpy.ndarray) – The array of the observed signal loaded from the file.

• dy (numpy.ndarray or None) – The array of the uncertainty loaded from the file, or None if no third column is present.

Raises:

ValueError – If the call to numpy.loadtxt returns fewer than 2 arrays.

printResults(header='', footer='')

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.SimpleRecipe.print_results instead.

print_results(header='', footer='')

Format and print the results.

The results are updated from the current state of the recipe before they are printed.

Parameters:

• header (str, optional) – The header to add to the output (default “”).

• footer (str, optional) – The footer to add to the output (default “”).

saveResults(filename, header='', footer='')

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.SimpleRecipe.save_results instead.

save_results(filename, header='', footer='')

Format and save the results.

The results are updated from the current state of the recipe before they are saved.

Parameters:

• filename (str or pathlib.Path) – The name of the save file.

• header (str, optional) – The header to add to the output (default “”).

• footer (str, optional) – The footer to add to the output (default “”).

setCalculationPoints(x)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.SimpleRecipe.set_calculation_points instead.

setCalculationRange(xmin=None, xmax=None, dx=None)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.SimpleRecipe.set_calculation_range instead.

setEquation(eqstr, ns={})

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.SimpleRecipe.set_equation instead.

setObservedProfile(xobs, yobs, dyobs=None)

This function has been deprecated and will be removed in version 4.0.0.

Please use diffpy.srfit.fitbase.SimpleRecipe.set_observed_profile instead.

set_calculation_points(x)

Set the calculation points.

This will create y and dy on the specified grid if xobs, yobs and dyobs exist.

Parameters:

x (ndarray) – The non-empty array of calculation points. If xobs exists, the bounds of x will be limited to its bounds.

set_calculation_range(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. xmin is always inclusive (unless clipped). xmax is inclusive if it is within 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.

Raises:

• AttributeError – If there is no observed data.

• ValueError – If xmin > xmax, if dx <= 0 or if dx > xmax - xmin.

• ValueError – If xmin, xmax or dx is a string other than “obs”.

set_equation(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 set_residual_equation as “eq”, and it takes no arguments.

Parameters:

• eqstr (str) – The string representation of the equation. After the equation is set, every top-level Parameter of the FitContribution other than x, y and dy is added as a variable of this recipe, unless it is one already. Parameters without a value are given an initial value of 0.

• ns (dict, optional) – The 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.

set_observed_profile(xobs, yobs, dyobs=None)

Set the observed profile.

Parameters:

• xobs (ndarray) – The independent variable.

• yobs (ndarray) – The observed signal.

• dyobs (ndarray, optional) – The uncertainty in the observed signal. If dyobs is None (default), dyobs stays None to indicate no uncertainty was observed, and the calculated dy will be set to 1 at each calculation point instead.

Raises:

• ValueError – If len(yobs) != len(xobs).

• ValueError – If dyobs is not None and len(dyobs) != len(xobs).

Subpackages

• diffpy.srfit.interface package
• diffpy.srfit.equation package
  • diffpy.srfit.equation.visitors package
  • diffpy.srfit.equation.literals package
• diffpy.srfit.util package
• diffpy.srfit.pdf package
• diffpy.srfit.sas package
• diffpy.srfit.fitbase package
• diffpy.srfit.structure package

Submodules

diffpy.srfit.exceptions module

Exceptions used for SrFit - specific errors.

exception diffpy.srfit.exceptions.ParseError

Bases: Exception

Exception used by ProfileParsers.

exception diffpy.srfit.exceptions.SrFitError

Bases: Exception

Generic error in SrFit expressions or recipe.