# Module Documentation¶

This page contains documentation to everything symfit has to offer.

## Fit¶

class symfit.core.fit.Fit(model, *ordered_data, objective=None, minimizer=None, constraints=None, absolute_sigma=None, **named_data)[source]

Your one stop fitting solution! Based on the nature of the input, this object will attempt to select the right fitting type for your problem.

If you need very specific control over how the problem is solved, you can pass it the minimizer or objective function you would like to use.

Example usage:

a, b = parameters('a, b')
x, y = variables('x, y')

model = {y: a * x + b}

# Fit will use its default settings
fit = Fit(model, x=xdata, y=ydata)
fit_result = fit.execute()

fit = Fit(model, x=xdata, y=ydata, minimizer=NelderMead)
fit_result = fit.execute()

# Use Nelder-Mead to get close, and BFGS to polish it off
fit = Fit(model, x=xdata, y=ydata, minimizer=[NelderMead, BFGS])
fit_result = fit.execute(minimizer_kwargs=[dict(xatol=0.1), {}])

__init__(model, *ordered_data, objective=None, minimizer=None, constraints=None, absolute_sigma=None, **named_data)[source]
Parameters: model – (dict of) sympy expression(s) or Model object. constraints – iterable of Relation objects to be used as constraints. absolute_sigma (bool) – True by default. If the sigma is only used for relative weights in your problem, you could consider setting it to False, but if your sigma are measurement errors, keep it at True. Note that curve_fit has this set to False by default, which is wrong in experimental science. objective – Have Fit use your specified objective. Can be one of the predefined symfit objectives or any callable which accepts fit parameters and returns a scalar. minimizer – Have Fit use your specified symfit.core.minimizers.BaseMinimizer. Can be a Sequence of symfit.core.minimizers.BaseMinimizer. ordered_data – data for dependent, independent and sigma variables. Assigned in the following order: independent vars are assigned first, then dependent vars, then sigma’s in dependent vars. Within each group they are assigned in alphabetical order. named_data – assign dependent, independent and sigma variables data by name.
execute(**minimize_options)[source]

Execute the fit.

Parameters: minimize_options – keyword arguments to be passed to the specified minimizer. FitResults instance
class symfit.core.fit.HasCovarianceMatrix(model, *ordered_data, absolute_sigma=None, **named_data)[source]

Mixin class for calculating the covariance matrix for any model that has a well-defined Jacobian $$J$$. The covariance is then approximated as $$J^T W J$$, where W contains the weights of each data point.

Supports vector valued models, but is unable to estimate covariances for those, just variances. Therefore, take the result with a grain of salt for vector models.

covariance_matrix(best_fit_params)[source]

Given best fit parameters, this function finds the covariance matrix. This matrix gives the (co)variance in the parameters.

Parameters: best_fit_params – dict of best fit parameters as given by .best_fit_params() covariance matrix.
class symfit.core.fit.TakesData(model, *ordered_data, absolute_sigma=None, **named_data)[source]

Bases: object

An base class for everything that takes data. Most importantly, it takes care of linking the provided data to variables. The allowed variables are extracted from the model.

__init__(model, *ordered_data, absolute_sigma=None, **named_data)[source]
Parameters: model – (dict of) sympy expression or Model object. absolute_sigma (bool) – True by default. If the sigma is only used for relative weights in your problem, you could consider setting it to False, but if your sigma are measurement errors, keep it at True. Note that curve_fit has this set to False by default, which is wrong in experimental science. ordered_data – data for dependent, independent and sigma variables. Assigned in the following order: independent vars are assigned first, then dependent vars, then sigma’s in dependent vars. Within each group they are assigned in alphabetical order. named_data – assign dependent, independent and sigma variables data by name.

Standard deviation can be provided to any variable. They have to be prefixed with sigma_. For example, let x be a Variable. Then sigma_x will give the stdev in x.

data_shapes

Returns the shape of the data. In most cases this will be the same for all variables of the same type, if not this raises an Exception.

Ignores variables which are set to None by design so we know that those None variables can be assumed to have the same shape as the other in calculations where this is needed, such as the covariance matrix.

Returns: Tuple of all independent var shapes, dependent var shapes.
dependent_data

Returns: Data belonging to each dependent variable as a dict with variable names as key, data as value. collections.OrderedDict
independent_data

Returns: Data belonging to each independent variable as a dict with variable names as key, data as value. collections.OrderedDict
initial_guesses
Returns: Initial guesses for every parameter.
sigma_data

Returns: Data belonging to each sigma variable as a dict with variable names as key, data as value. collections.OrderedDict

## Models¶

class symfit.core.models.BaseCallableModel(model)[source]

Baseclass for callable models. A callable model is expected to have implemented a __call__ method which evaluates the model.

__call__(*args, **kwargs)[source]

Evaluate the model for a certain value of the independent vars and parameters. Signature for this function contains independent vars and parameters, NOT dependent and sigma vars.

Can be called with both ordered and named parameters. Order is independent vars first, then parameters. Alphabetical order within each group.

Parameters: args – kwargs – A namedtuple of all the dependent vars evaluated at the desired point. Will always return a tuple, even for scalar valued functions. This is done for consistency.
eval_components(*args, **kwargs)[source]
Returns: evaluated lambda functions of each of the components in model_dict, to be used in numerical calculation.
numerical_components()[source]
Returns: A list of callables corresponding to each of the components of the model.
class symfit.core.models.BaseGradientModel(model)[source]

Baseclass for models which have a gradient. Such models are expected to implement an eval_jacobian function.

Any subclass of this baseclass which does not implement its own eval_jacobian will inherit a finite difference gradient.

eval_jacobian(*args, **kwargs)[source]
Returns: The jacobian matrix of the function.
finite_difference(*args, dx=1e-08, **kwargs)[source]

Calculates a numerical approximation of the Jacobian of the model using the sixth order central finite difference method. Accepts a dx keyword to tune the relative stepsize used. Makes 6*n_params calls to the model.

Returns: A numerical approximation of the Jacobian of the model as a list with length n_components containing numpy arrays of shape (n_params, n_datapoints)
class symfit.core.models.BaseModel(model)[source]

ABC for Model’s. Makes sure models are iterable. Models can be initiated from Mappings or Iterables of Expressions, or from an expression directly. Expressions are not enforced for ducktyping purposes.

__eq__(other)[source]

Model’s are considered equal when they have the same dependent variables, and the same expressions for those dependent variables. The same is defined here as passing sympy == for the vars themselves, and as expr1 - expr2 == 0 for the expressions. For more info check the sympy docs.

Parameters: other – Instance of Model. bool
__getitem__(var)[source]

Returns the expression belonging to a given dependent variable.

Parameters: var (Variable) – Instance of Variable The expression belonging to var
__init__(model)[source]

Initiate a Model from a dict:

a = Model({y: x**2})


Preferred way of initiating Model, since now you know what the dependent variable is called.

Parameters: model – dict of Expr, where dependent variables are the keys.
__iter__()[source]
Returns: iterable over self.model_dict
__len__()[source]
Returns: the number of dependent variables for this model.
__neg__()[source]
Returns: new model with opposite sign. Does not change the model in-place, but returns a new copy.
__str__()[source]

Printable representation of a Mapping model.

Returns: str
classmethod as_constraint(constraint, model, constraint_type=None, **init_kwargs)[source]

Initiate a Model which should serve as a constraint. Such a constraint-model should be initiated with knowledge of another BaseModel, from which it will take its parameters:

model = Model({y: a * x + b})
constraint = Model.as_constraint(Eq(a, 1), model)


constraint.params will be [a, b] instead of [a].

Parameters: constraint – An Expr, a mapping or iterable of Expr, or a Relational. model – An instance of (a subclass of) BaseModel. constraint_type – When constraint is not a Relational, a Relational has to be provided explicitly. kwargs – Any additional keyword arguments which will be passed on to the init method.
bounds
Returns: List of tuples of all bounds on parameters.
connectivity_mapping
Returns: This property returns a mapping of the interdepencies between variables. This is essentially the dict representation of a connectivity graph, because working with this dict results in cleaner code. Treats variables and parameters on the same footing.
free_params
Returns: ordered list of the subset of variable params
function_dict

Equivalent to self.model_dict, but with all variables replaced by functions if applicable. Sorted by the evaluation order according to self.ordered_symbols, not alphabetical like self.model_dict!

ordered_symbols
Returns: list of all symbols in this model, topologically sorted so they can be evaluated in the correct order.Within each group of equal priority symbols, we sort by the order of the derivative.
shared_parameters
Returns: bool, indicating if parameters are shared between the vector components of this model.
vars
Returns: Returns a list of dependent, independent and sigma variables, in that order.
vars_as_functions
Returns: Turn the keys of this model into Function objects. This is done recursively so the chain rule can be applied correctly. This is done on the basis of connectivity_mapping.Example: for {y: a * x, z: y**2 + a} this returns {y: y(x, a), z: z(y(x, a), a)}.
classmethod with_dependencies(model_expr, dependency_model, **init_kwargs)[source]

Initiate a model whose components depend on another model. For example:

>>> x, y, z = variables('x, y, z')
>>> dependency_model = Model({y: x**2})
>>> model_dict = {z: y**2}
>>> model = Model.with_dependencies(model_dict, dependency_model)
>>> print(model)
[y(x; ) = x**2,
z(y; ) = y**2]

Parameters: model_expr – The Expr or mapping/iterable of Expr to be turned into a model. dependency_model – An instance of (a subclass of) BaseModel, which contains components on which the argument model_expr depends. init_kwargs – Any kwargs to be passed on to the standard init method of this class. A stand-alone BaseModel subclass.
class symfit.core.models.BaseNumericalModel(model, independent_vars=None, params=None, *, connectivity_mapping=None, **kwargs)[source]

ABC for Numerical Models. These are models whose components are generic python callables.

__eq__(other)[source]

Model’s are considered equal when they have the same dependent variables, and the same expressions for those dependent variables. The same is defined here as passing sympy == for the vars themselves, and as expr1 - expr2 == 0 for the expressions. For more info check the sympy docs.

Parameters: other – Instance of Model. bool
__init__(model, independent_vars=None, params=None, *, connectivity_mapping=None, **kwargs)[source]
Parameters: model – dict of callable, where dependent variables are the keys. If instead of a dict a (sequence of) callable is provided, it will be turned into a dict automatically. independent_vars – The independent variables of the model. (Deprecated, use connectivity_mapping instead.) params – The parameters of the model. (Deprecated, use connectivity_mapping instead.) connectivity_mapping – Mapping indicating the dependencies of every variable in the model. For example, a model_dict {y: lambda x, a, b: a * x + b} needs a connectivity_mapping {y: {x, a, b}}. (Note that the values of this dict have to be sets.) This only has to be provided for the non-symbolic components. The part corresponding to the symbolic components of the model is inferred automatically.
__neg__()[source]
Returns: new model with opposite sign. Does not change the model in-place, but returns a new copy.
shared_parameters

BaseNumericalModel’s cannot infer if parameters are shared.

class symfit.core.models.CallableModel(model)[source]

Defines a callable model. The usual rules apply to the ordering of the arguments:

• first independent variables, then dependent variables, then parameters.
• within each of these groups they are ordered alphabetically.
numerical_components
Returns: lambda functions of each of the analytical components in model_dict, to be used in numerical calculation.
class symfit.core.models.CallableNumericalModel(model, independent_vars=None, params=None, *, connectivity_mapping=None, **kwargs)[source]

Callable model, whose components are callables provided by the user. This allows the user to provide the components directly.

Example:

x, y = variables('x, y')
a, b = parameters('a, b')
numerical_model = CallableNumericalModel(
{y: lambda x, a, b: a * x + b},
connectivity_mapping={y: {x, a, b}}
)


This is identical in functionality to the more traditional:

x, y = variables('x, y')
a, b = parameters('a, b')
model = CallableModel({y: a * x + b})


but allows power-users a lot more freedom while still interacting seamlessly with the symfit API.

When mixing symbolical and non-symbolical components, the connectivity_mapping only has to be provided for the non-symbolical components, the rest are inferred automatically:

x, y, z = variables('x, y, z')
a, b = parameters('a, b')
model_dict = {z: lambda y, a, b: a * y + b,
y: x ** a}
mixed_model = CallableNumericalModel(
model_dict, connectivity_mapping={z: {y, a, b}}
)

class symfit.core.models.GradientModel(*args, **kwargs)[source]

Analytical model which has an analytically computed Jacobian.

__init__(*args, **kwargs)[source]

Initiate a Model from a dict:

a = Model({y: x**2})


Preferred way of initiating Model, since now you know what the dependent variable is called.

Parameters: model – dict of Expr, where dependent variables are the keys.
eval_jacobian(*args, **kwargs)[source]
Returns: Jacobian evaluated at the specified point.
jacobian
Returns: Jacobian filled with the symbolic expressions for all the partial derivatives. Partial derivatives are of the components of the function with respect to the Parameter’s, not the independent Variable’s. The return shape is a list over the models components, filled with tha symbolical jacobian for that component, as a list.
class symfit.core.models.HessianModel(*args, **kwargs)[source]

Analytical model which has an analytically computed Hessian.

__init__(*args, **kwargs)[source]

Initiate a Model from a dict:

a = Model({y: x**2})


Preferred way of initiating Model, since now you know what the dependent variable is called.

Parameters: model – dict of Expr, where dependent variables are the keys.
eval_hessian(*args, **kwargs)[source]
Returns: Hessian evaluated at the specified point.
hessian
Returns: Hessian filled with the symbolic expressions for all the second order partial derivatives. Partial derivatives are taken with respect to the Parameter’s, not the independent Variable’s.
class symfit.core.models.Model(*args, **kwargs)[source]

Model represents a symbolic function and all it’s derived properties such as sum of squares, jacobian etc. Models should be initiated from a dict:

a = Model({y: x**2})


Models are callable. The usual rules apply to the ordering of the arguments:

• first independent variables, then parameters.
• within each of these groups they are ordered alphabetically.

The output of a call to a model is a special kind of namedtuple:

>>> a(x=3)
Ans(y=9)


When turning this into a dict, however, the dict keys will be Variable objects, not strings:

>>> a(x=3)._asdict()
OrderedDict(((y, 9),))


Models are also iterable, behaving as their internal model_dict. For example, a[y] returns x**2, len(a) == 1, y in a == True, etc.

exception symfit.core.models.ModelError[source]

Bases: Exception

Raised when a problem occurs with a model.

class symfit.core.models.ModelOutput(variables, output)[source]

Bases: tuple

Object to hold the output of a model call. It mimics a collections.namedtuple(), but is initiated with Variable objects instead of strings.

Its information can be accessed using indexing or as attributes:

>>> x, y = variables('x, y')
>>> a, b = parameters('a, b')
>>> model = Model({y: a * x + b})

>>> ans = model(x=2, a=1, b=3)
>>> print(ans)
ModelOutput(variables=[y], output=[5])
>>> ans[0]
5
>>> ans.y
5

__getitem__(key)[source]

Return self[key].

__init__(variables, output)[source]

variables and output need to be in the same order!

Parameters: variables – The variables corresponding to output. output – The output of a call which should be mapped to variables.
__len__()[source]

Return len(self).

static __new__(self, variables, output)[source]

variables and output need to be in the same order!

Parameters: variables – The variables corresponding to output. output – The output of a call which should be mapped to variables.
__repr__()[source]

Return repr(self).

class symfit.core.models.ODEModel(model_dict, initial, *lsoda_args, **lsoda_kwargs)[source]

Model build from a system of ODEs. When the model is called, the ODE is integrated using the LSODA package.

__call__(*args, **kwargs)[source]

Evaluate the model for a certain value of the independent vars and parameters. Signature for this function contains independent vars and parameters, NOT dependent and sigma vars.

Can be called with both ordered and named parameters. Order is independent vars first, then parameters. Alphabetical order within each group.

Parameters: args – Ordered arguments for the parameters and independent variables kwargs – Keyword arguments for the parameters and independent variables A namedtuple of all the dependent vars evaluated at the desired point. Will always return a tuple, even for scalar valued functions. This is done for consistency.
__getitem__(dependent_var)[source]

Gives the function defined for the derivative of dependent_var. e.g. $$y' = f(y, t)$$, model[y] -> f(y, t)

Parameters: dependent_var –
__init__(model_dict, initial, *lsoda_args, **lsoda_kwargs)[source]
Parameters: model_dict – Dictionary specifying ODEs. e.g. model_dict = {D(y, x): a * x**2} initial – dict of initial conditions for the ODE. Must be provided! e.g. initial = {y: 1.0, x: 0.0} lsoda_args – args to pass to the lsoda solver. See scipy’s odeint for more info. lsoda_kwargs – kwargs to pass to the lsoda solver.
__iter__()[source]
Returns: iterable over self.model_dict
__neg__()[source]
Returns: new model with opposite sign. Does not change the model in-place, but returns a new copy.
__str__()[source]

Printable representation of this model.

Returns: str
eval_components(*args, **kwargs)[source]

Numerically integrate the system of ODEs.

Parameters: args – Ordered arguments for the parameters and independent variables kwargs – Keyword arguments for the parameters and independent variables
symfit.core.models.hessian_from_model(model)[source]

Build a CallableModel representing the Hessian of model.

This function make sure the chain rule is correctly applied for interdependent variables.

Parameters: model – Any symbolical model-type. CallableModel representing the Hessian of model.
symfit.core.models.jacobian_from_model(model, as_functions=False)[source]
Build a CallableModel representing the Jacobian
of model.

This function make sure the chain rule is correctly applied for interdependent variables.

Parameters: model – Any symbolical model-type. as_functions – If True, the result is returned using sympy.core.function.Function where needed, e.g. {y(x, a): a * x} instead of {y: a * x}. CallableModel representing the Jacobian of model.

## Argument¶

class symfit.core.argument.Argument(name=None, *args, **assumptions)[source]

Base class for symfit symbols. This helps make symfit symbols distinguishable from sympy symbols.

If no name is explicitly provided a name will be generated.

For example:

y = Variable()
print(y.name)
>> 'x_0'

y = Variable('y')
print(y.name)
>> 'y'

__init__(name=None, *args, **assumptions)[source]

Initialize self. See help(type(self)) for accurate signature.

static __new__(cls, name=None, **assumptions)[source]

Create a new Argument. See Symbol for more information.

class symfit.core.argument.Parameter(name=None, value=1.0, min=None, max=None, fixed=False, **assumptions)[source]

Parameter objects are used to facilitate bounds on function parameters. Important change from symfit>0.4.1: the name needs to be the first keyword, followed by the guess value. If no name is provided, the initial value can be passed as a keyword argument, e.g.: value=0.1. A generic name will then be generated.

__call__(*values, **named_values)

Call an expression to evaluate it at the given point.

Future improvements: I would like if func and signature could be buffered after the first call so they don’t have to be recalculated for every call. However, nothing can be stored on self as sympy uses __slots__ for efficiency. This means there is no instance dict to put stuff in! And I’m pretty sure it’s ill advised to hack into the __slots__ of Expr.

However, for the moment I don’t really notice a performance penalty in running tests.

p.s. In the current setup signature is not even needed since no introspection is possible on the Expr before calling it anyway, which makes calculating the signature absolutely useless. However, I hope that someday some monkey patching expert in shining armour comes by and finds a way to store it in __signature__ upon __init__ of any symfit expr such that calling inspect_sig.signature on a symbolic expression will tell you which arguments to provide.

Parameters: self – Any subclass of sympy.Expr values – Values for the Parameters and Variables of the Expr. named_values – Values for the vars and params by name. named_values is allowed to contain too many values, as this sometimes happens when using **fit_result.params on a submodel. The irrelevant params are simply ignored. The function evaluated at values. The type depends entirely on the input. Typically an array or a float but nothing is enforced.
__eq__(other)[source]

Parameters are considered equal when their name, assumptions, and bounds are considered the same.

__hash__() → int

Return hash(self).

__init__(name=None, value=1.0, min=None, max=None, fixed=False, **assumptions)[source]
Parameters: name – Name of the Parameter. value – Initial guess value. min – Lower bound on the parameter value. max – Upper bound on the parameter value. fixed (bool) – Fix the parameter to value during fitting. assumptions – assumptions to pass to sympy.
static __new__(cls, name=None, value=1.0, min=None, max=None, fixed=False, **kwargs)[source]

Create a new Argument. See Symbol for more information.

class symfit.core.argument.Variable(name=None, *args, **assumptions)[source]

Variable type.

## Operators¶

Monkey Patching module.

This module makes sympy Expressions callable, which makes the whole project feel more consistent.

symfit.core.operators.call(self, *values, **named_values)[source]

Call an expression to evaluate it at the given point.

Future improvements: I would like if func and signature could be buffered after the first call so they don’t have to be recalculated for every call. However, nothing can be stored on self as sympy uses __slots__ for efficiency. This means there is no instance dict to put stuff in! And I’m pretty sure it’s ill advised to hack into the __slots__ of Expr.

However, for the moment I don’t really notice a performance penalty in running tests.

p.s. In the current setup signature is not even needed since no introspection is possible on the Expr before calling it anyway, which makes calculating the signature absolutely useless. However, I hope that someday some monkey patching expert in shining armour comes by and finds a way to store it in __signature__ upon __init__ of any symfit expr such that calling inspect_sig.signature on a symbolic expression will tell you which arguments to provide.

Parameters: self – Any subclass of sympy.Expr values – Values for the Parameters and Variables of the Expr. named_values – Values for the vars and params by name. named_values is allowed to contain too many values, as this sometimes happens when using **fit_result.params on a submodel. The irrelevant params are simply ignored. The function evaluated at values. The type depends entirely on the input. Typically an array or a float but nothing is enforced.

## Fit Results¶

class symfit.core.fit_results.FitResults(model, popt, covariance_matrix, minimizer, objective, message, *, constraints=None, **minimizer_output)[source]

Bases: object

Class to display the results of a fit in a nice and unambiguous way. All things related to the fit are available on this class, e.g. - parameter values + stdev - R squared (Regression coefficient.) or other fit quality qualifiers. - fitting status message - covariance matrix - objective and minimizer used.

Contains the attribute params, which is an OrderedDict containing all the parameter names and their optimized values. Can be ** unpacked when evaluating Model’s.

__getattr__(item)[source]

Return the requested item if it can be found in the gof_qualifiers dict.

Parameters: item – Name of Goodness of Fit qualifier. Goodness of Fit qualifier if present.
__init__(model, popt, covariance_matrix, minimizer, objective, message, *, constraints=None, **minimizer_output)[source]
Parameters: model – Model that was fit to. popt – best fit parameters, same ordering as in model.params. covariance_matrix – covariance matrix. minimizer – Minimizer instance used. objective – Objective function which was optimized. message – Status message returned by the minimizer. **minimizer_output – Raw output as given by the minimizer.
__str__()[source]

Pretty print the results as a table.

covariance(param_1, param_2)[source]

Return the covariance between param_1 and param_2.

Parameters: param_1 – Parameter Instance. param_2 – Parameter Instance. Covariance of the two params.
stdev(param)[source]

Return the standard deviation in a given parameter as found by the fit.

Parameters: param – Parameter Instance. Standard deviation of param.
value(param)[source]

Return the value in a given parameter as found by the fit.

Parameters: param – Parameter Instance. Value of param.
variance(param)[source]

Return the variance in a given parameter as found by the fit.

Parameters: param – Parameter Instance. Variance of param.
symfit.core.fit_results.r_squared(model, fit_result, data)[source]

Calculates the coefficient of determination, R^2, for the fit.

(Is not defined properly for vector valued functions.)

Parameters: model – Model instance fit_result – FitResults instance data – data with which the fit was performed.

## Minimizers¶

class symfit.core.minimizers.BFGS(*args, **kwargs)[source]

Wrapper around scipy.optimize.minimize()’s BFGS algorithm.

class symfit.core.minimizers.BaseMinimizer(objective, parameters)[source]

Bases: object

ABC for all Minimizers.

__init__(objective, parameters)[source]
Parameters: objective – Objective function to be used. parameters – List of Parameter instances
execute(**options)[source]

The execute method should implement the actual minimization procedure, and should return a FitResults instance.

Parameters: options – options to be used by the minimization procedure. an instance of FitResults.
class symfit.core.minimizers.BasinHopping(*args, local_minimizer=<class 'symfit.core.minimizers.BFGS'>, **kwargs)[source]

Wrapper around scipy.optimize.basinhopping()’s basin-hopping algorithm.

As always, the best way to use this algorithm is through Fit, as this will automatically select a local minimizer for you depending on whether you provided bounds, constraints, etc.

However, BasinHopping can also be used directly. Example (with jacobian):

import numpy as np
from symfit.core.minimizers import BFGS, BasinHopping
from symfit import parameters

def func2d(x1, x2):
f = np.cos(14.5 * x1 - 0.3) + (x2 + 0.2) * x2 + (x1 + 0.2) * x1
return f

def jac2d(x1, x2):
df = np.zeros(2)
df[0] = -14.5 * np.sin(14.5 * x1 - 0.3) + 2. * x1 + 0.2
df[1] = 2. * x2 + 0.2
return df

x0 = [1.0, 1.0]
np.random.seed(555)
x1, x2 = parameters('x1, x2', value=x0)
fit = BasinHopping(func2d, [x1, x2], local_minimizer=BFGS)
minimizer_kwargs = {'jac': fit.list2kwargs(jac2d)}
fit_result = fit.execute(niter=200, minimizer_kwargs=minimizer_kwargs)


See scipy.optimize.basinhopping() for more options.

__init__(*args, local_minimizer=<class 'symfit.core.minimizers.BFGS'>, **kwargs)[source]
Parameters: local_minimizer – minimizer to be used for local minimization steps. Can be any subclass of symfit.core.minimizers.ScipyMinimize. args – positional arguments to be passed on to super. kwargs – keyword arguments to be passed on to super.
execute(**minimize_options)[source]

Execute the basin-hopping minimization.

Parameters: minimize_options – options to be passed on to scipy.optimize.basinhopping(). symfit.core.fit_results.FitResults
class symfit.core.minimizers.BoundedMinimizer(objective, parameters)[source]

ABC for Minimizers that support bounds.

class symfit.core.minimizers.COBYLA(*args, **kwargs)[source]

Wrapper around scipy.optimize.minimize()’s COBYLA algorithm.

execute(**minimize_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
class symfit.core.minimizers.ChainedMinimizer(*args, minimizers=None, **kwargs)[source]

A minimizer that consists of multiple other minimizers, each executed in order. This is valuable if you have minimizers that are not good at finding the exact minimum such as NelderMead or DifferentialEvolution.

__init__(*args, minimizers=None, **kwargs)[source]
Parameters: minimizers – a Sequence of BaseMinimizer objects, which need to be run in order. *args – passed to symfit.core.minimizers.BaseMinimizer.__init__(). **kwargs – passed to symfit.core.minimizers.BaseMinimizer.__init__().
__str__()[source]

Return str(self).

execute(**minimizer_kwargs)[source]

Execute the chained-minimization. In order to pass options to the seperate minimizers, they can be passed by using the names of the minimizers as keywords. For example:

fit = Fit(self.model, self.xx, self.yy, self.ydata,
minimizer=[DifferentialEvolution, BFGS])
fit_result = fit.execute(
DifferentialEvolution={'seed': 0, 'tol': 1e-4, 'maxiter': 10},
BFGS={'tol': 1e-4}
)


In case of multiple identical minimizers an index is added to each keyword argument to make them identifiable. For example, if:

minimizer=[BFGS, DifferentialEvolution, BFGS])


then the keyword arguments will be ‘BFGS’, ‘DifferentialEvolution’, and ‘BFGS_2’.

Parameters: minimizer_kwargs – Minimizer options to be passed to the minimzers by name an instance of FitResults.
class symfit.core.minimizers.ConstrainedMinimizer(*args, constraints=None, **kwargs)[source]

ABC for Minimizers that support constraints

__init__(*args, constraints=None, **kwargs)[source]
Parameters: objective – Objective function to be used. parameters – List of Parameter instances
class symfit.core.minimizers.DifferentialEvolution(*args, **kwargs)[source]

A wrapper around scipy.optimize.differential_evolution().

execute(*, strategy='rand1bin', popsize=40, mutation=(0.423, 1.053), recombination=0.95, polish=False, init='latinhypercube', **de_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
class symfit.core.minimizers.DummyModel(params)

Bases: tuple

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

static __new__(_cls, params)

Create new instance of DummyModel(params,)

__repr__()

Return a nicely formatted representation string

params

Alias for field number 0

class symfit.core.minimizers.GlobalMinimizer(*args, **kwargs)[source]

A minimizer that looks for a global minimum, instead of a local one.

__init__(*args, **kwargs)[source]
Parameters: objective – Objective function to be used. parameters – List of Parameter instances
class symfit.core.minimizers.GradientMinimizer(*args, jacobian=None, **kwargs)[source]

ABC for Minizers that support the use of a jacobian

__init__(*args, jacobian=None, **kwargs)[source]
Parameters: objective – Objective function to be used. parameters – List of Parameter instances
resize_jac(func)[source]

Removes values with identical indices to fixed parameters from the output of func. func has to return the jacobian of a scalar function.

Parameters: func – Jacobian function to be wrapped. Is assumed to be the jacobian of a scalar function. Jacobian corresponding to non-fixed parameters only.
class symfit.core.minimizers.HessianMinimizer(*args, hessian=None, **kwargs)[source]

ABC for Minimizers that support the use of a Hessian.

__init__(*args, hessian=None, **kwargs)[source]
Parameters: objective – Objective function to be used. parameters – List of Parameter instances
resize_hess(func)[source]

Removes values with identical indices to fixed parameters from the output of func. func has to return the Hessian of a scalar function.

Parameters: func – Hessian function to be wrapped. Is assumed to be the Hessian of a scalar function. Hessian corresponding to free parameters only.
class symfit.core.minimizers.LBFGSB(*args, **kwargs)[source]

Wrapper around scipy.optimize.minimize()’s LBFGSB algorithm.

classmethod method_name()[source]

Returns the name of the minimize method this object represents. This is needed because the name of the object is not always exactly what needs to be passed on to scipy as a string. :return:

class symfit.core.minimizers.MINPACK(*args, **kwargs)[source]

Wrapper to scipy’s implementation of least_squares, since it is the industry standard.

execute(jacobian=None, method='trf', **minpack_options)[source]
Parameters: **minpack_options – Any named arguments to be passed to scipy.optimize.least_squares()
resize_jac(func)[source]

Removes values with identical indices to fixed parameters from the output of func. func has to return the jacobian of the residuals. This method is different from the one in GradientMinimizer, since least_squares expects the jacobian to return an MxN (M=len(data), N=len(params)) matrix, rather than a vector.

Parameters: func – Jacobian function to be wrapped. Is assumed to be the jacobian of the residuals. Jacobian corresponding to non-fixed parameters only.
class symfit.core.minimizers.NelderMead(*args, **kwargs)[source]

Wrapper around scipy.optimize.minimize()’s NelderMead algorithm.

classmethod method_name()[source]

Returns the name of the minimize method this object represents. This is needed because the name of the object is not always exactly what needs to be passed on to scipy as a string. :return:

class symfit.core.minimizers.Powell(*args, **kwargs)[source]

Wrapper around scipy.optimize.minimize()’s Powell algorithm.

class symfit.core.minimizers.SLSQP(*args, **kwargs)[source]

Wrapper around scipy.optimize.minimize()’s SLSQP algorithm.

class symfit.core.minimizers.ScipyBoundedMinimizer(*args, **kwargs)[source]

Base class for scipy.optimize.minimize()’s bounded-minimizers.

execute(**minimize_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
class symfit.core.minimizers.ScipyConstrainedMinimize(*args, **kwargs)[source]

Base class for scipy.optimize.minimize()’s constrained-minimizers.

__init__(*args, **kwargs)[source]
Parameters: objective – Objective function to be used. parameters – List of Parameter instances
execute(**minimize_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
scipy_constraints(constraints)[source]

Returns all constraints in a scipy compatible format.

Parameters: constraints – List of either MinimizeModel instances (this is what is provided by Fit), BaseModel, or sympy.core.relational.Relational. dict of scipy compatible statements.
class symfit.core.minimizers.ScipyGradientMinimize(*args, **kwargs)[source]

Base class for scipy.optimize.minimize()’s gradient-minimizers.

execute(*, jacobian=None, **minimize_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
class symfit.core.minimizers.ScipyHessianMinimize(*args, **kwargs)[source]

Base class for scipy.optimize.minimize()’s hessian-minimizers.

execute(*, hessian=None, **minimize_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
class symfit.core.minimizers.ScipyMinimize(*args, **kwargs)[source]

Bases: object

Mix-in class that handles the execute calls to scipy.optimize.minimize().

__init__(*args, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

execute(bounds=None, jacobian=None, hessian=None, constraints=None, *, tol=1e-09, **minimize_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
classmethod method_name()[source]

Returns the name of the minimize method this object represents. This is needed because the name of the object is not always exactly what needs to be passed on to scipy as a string. :return:

class symfit.core.minimizers.TrustConstr(*args, **kwargs)[source]

Wrapper around scipy.optimize.minimize()’s Trust-Constr algorithm.

execute(*, jacobian=None, hessian=None, options=None, **minimize_options)[source]

Calls the wrapped algorithm.

Parameters: bounds – The bounds for the parameters. Usually filled by BoundedMinimizer. jacobian – The Jacobian. Usually filled by ScipyGradientMinimize. **minimize_options – Further keywords to pass to scipy.optimize.minimize(). Note that your method will usually be filled by a specific subclass.
classmethod method_name()[source]

Returns the name of the minimize method this object represents. This is needed because the name of the object is not always exactly what needs to be passed on to scipy as a string. :return:

scipy_constraints(constraints)[source]

Returns all constraints in a scipy compatible format.

Parameters: constraints – List of either MinimizeModel instances (this is what is provided by Fit), BaseModel, or sympy.core.relational.Relational. dict of scipy compatible statements.

## Objectives¶

Objective functions are the functions which are minimized by the minimizers. Famous examples are least squares, log-likelihood, or minimizing the model itself.

symfit provides objective functions for those cases by default. Custom objectives can also be created, for example by inheriting from BaseObjective, GradientObjective or HessianObjective.

class symfit.core.objectives.BaseIndependentObjective(model, data)[source]

Some objective functions dependent only on independent variables, not dependent and sigma variables. In this case, sanity checking is greatly simplified.

dependent_data
Returns: Empty OrderedDict. collections.OrderedDict
sigma_data
Returns: Empty OrderedDict. collections.OrderedDict
class symfit.core.objectives.BaseObjective(model, data)[source]

Bases: object

ABC for objective functions. Implements basic data handling.

__call__(ordered_parameters=[], **parameters)[source]

Evaluate the objective function for given parameter values.

Parameters: ordered_parameters – List of parameter, in alphabetical order. Typically provided by the minimizer. parameters – parameters as keyword arguments. evaluated model.
__eq__(other)[source]

Objectives are considered equal if they are of the same type, have the same model, and the same data.

__init__(model, data)[source]
Parameters: model – symfit style model. data – data for all the variables of the model.
dependent_data

Returns: Data belonging to each dependent variable as a dict with variable names as key, data as value. collections.OrderedDict
independent_data

Returns: Data belonging to each independent variable as a dict with variable names as key, data as value. collections.OrderedDict
sigma_data

Returns: Data belonging to each sigma variable as a dict with variable names as key, data as value. collections.OrderedDict
class symfit.core.objectives.GradientObjective(model, data)[source]

ABC for objectives that support gradient methods.

eval_jacobian(ordered_parameters=[], **parameters)[source]

Evaluate the jacobian for given parameter values.

Parameters: ordered_parameters – List of parameter, in alphabetical order. Typically provided by the minimizer. parameters – parameters as keyword arguments. evaluated jacobian
class symfit.core.objectives.HessianObjective(model, data)[source]

ABC for objectives that support hessian methods.

eval_hessian(ordered_parameters=[], **parameters)[source]

Evaluate the hessian for given parameter values.

Parameters: ordered_parameters – List of parameter, in alphabetical order. Typically provided by the minimizer. parameters – parameters as keyword arguments. evaluated hessian
class symfit.core.objectives.HessianObjectiveJacApprox(model, data)[source]

This object should only be used as a Mixin for covariance matrix estimation. Since the covariance matrix for the least-squares method is proportional to the Hessian of $$S$$, this function attempts to return the Hessian upon calculating eval_hessian.

However, if the model does not have a Hessian defined through eval_hessian, we approximate the Hessian as $$J^{T}\cdot J$$, where $$J$$ is the Jacobian of the model. This approximation is valid when, amongst other things, the residuals are sufficiently small. It can therefore only be used after fitting, not during.

An objective which inherits from this object, will return zeros with the shape of the hessian of the model, when eval_hessian is called. This code injection will therefore result in the terms proportional to the hessian of the model dropping out, which leaves the famous $$J^{T}\cdot J$$ approximation.

eval_hessian(ordered_parameters=[], **parameters)[source]
Returns: Zeros with the shape of the Hessian of the model.
class symfit.core.objectives.LeastSquares(model, data)[source]

Objective representing the least-squares deviation of a model, defined as $$S = \frac{1}{2} \sum_{i} \sum_{x_i} \frac{r_i(x_i, \vec{p})^2}{\sigma_i(x_i)^2}$$, where $$i$$ ranges over all components of the model, $$r_i(x_i, \vec{p})$$ is the residue of the $$i$$-th component, $$x_i$$ indicates all the data associated with the $$i$$-th component, and $$\sigma_i(x_i)$$ indicates the associated standard deviations.

The data for each component does not have to be the same, and it does not have to have the same shape. The only thing that matters is that within each component the shapes have to be compatible.

__call__(ordered_parameters=[], *, flatten_components=True, **parameters)[source]
Parameters: ordered_parameters – See parameters. parameters – values of the Parameter’s to evaluate $$S$$ at. flatten_components – if True, return the total $$S$$. If False, return the $$S$$ per component of the BaseModel. scalar or list of scalars depending on the value of flatten_components.
eval_hessian(ordered_parameters=[], **parameters)[source]

Hessian of $$S$$ in the Parameter’s ($$\nabla_\vec{p}^2 S$$).

Parameters: parameters – values of the Parameter’s to evaluate $$\nabla_\vec{p} S$$ at. np.array of length equal to the number of parameters..
eval_jacobian(ordered_parameters=[], **parameters)[source]

Jacobian of $$S$$ in the Parameter’s ($$\nabla_\vec{p} S$$).

Parameters: parameters – values of the Parameter’s to evaluate $$\nabla_\vec{p} S$$ at. np.array of length equal to the number of parameters..
class symfit.core.objectives.LogLikelihood(model, data)[source]

Error function to be minimized by a minimizer in order to maximize the log-likelihood.

__call__(ordered_parameters=[], **parameters)[source]
Parameters: parameters – values for the fit parameters. scalar value of log-likelihood
eval_hessian(ordered_parameters=[], **parameters)[source]

Hessian for log-likelihood is defined as $$\nabla^2_{\vec{p}}( \log( L(\vec{p} | \vec{x})))$$.

Parameters: parameters – values for the fit parameters. array of length number of Parameter’s in the model, with all partial derivatives evaluated at p, data.
eval_jacobian(ordered_parameters=[], *, apply_func=<function nansum>, **parameters)[source]

Jacobian for log-likelihood is defined as $$\nabla_{\vec{p}}( \log( L(\vec{p} | \vec{x})))$$.

Parameters: parameters – values for the fit parameters. apply_func – Function to apply to each component before returning it. The default is to sum away along the datapoint dimension using np.nansum. array of length number of Parameter’s in the model, with all partial derivatives evaluated at p, data.
class symfit.core.objectives.MinimizeModel(model, *args, **kwargs)[source]

Objective to use when the model itself is the quantity that should be minimized. This is only supported for scalar models.

__call__(ordered_parameters=[], **parameters)[source]

Evaluate the objective function for given parameter values.

Parameters: ordered_parameters – List of parameter, in alphabetical order. Typically provided by the minimizer. parameters – parameters as keyword arguments. evaluated model.
__init__(model, *args, **kwargs)[source]
Parameters: model – symfit style model. data – data for all the variables of the model.
eval_hessian(ordered_parameters=[], **parameters)[source]

Evaluate the hessian for given parameter values.

Parameters: ordered_parameters – List of parameter, in alphabetical order. Typically provided by the minimizer. parameters – parameters as keyword arguments. evaluated hessian
eval_jacobian(ordered_parameters=[], **parameters)[source]

Evaluate the jacobian for given parameter values.

Parameters: ordered_parameters – List of parameter, in alphabetical order. Typically provided by the minimizer. parameters – parameters as keyword arguments. evaluated jacobian
class symfit.core.objectives.VectorLeastSquares(model, data)[source]

Implemented for MINPACK only. Returns the residuals/sigma before squaring and summing, rather then chi2 itself.

__call__(ordered_parameters=[], *, flatten_components=True, **parameters)[source]

Returns the value of the square root of $$\chi^2$$, summing over the components.

This function now supports setting variables to None.

Parameters: flatten_components – If True, summing is performed over the data indices (default). $$\sqrt(\chi^2)$$
eval_jacobian(ordered_parameters=[], **parameters)[source]

Evaluate the jacobian for given parameter values.

Parameters: ordered_parameters – List of parameter, in alphabetical order. Typically provided by the minimizer. parameters – parameters as keyword arguments. evaluated jacobian

## Support¶

This module contains support functions and convenience methods used throughout symfit. Some are used predominantly internally, others are designed for users.

class symfit.core.support.RequiredKeyword[source]

Bases: object

Flag variable to indicate that this is a required keyword.

exception symfit.core.support.RequiredKeywordError[source]

Bases: Exception

Error raised in case a keyword-only argument is not treated as such.

class symfit.core.support.cached_property(*args, **kwargs)[source]

Bases: property

A property which cashes the output of the first ever call and always returns that value from then on, unless delete is called on the attribute.

This is typically used in converting sympy code into scipy compatible code, which is computationally a very expensive step we would like to perform only once.

Does not allow setting of the attribute.

__delete__(obj)[source]

Calling delete on the attribute will delete the cache. :param obj: parent object.

__get__(obj, objtype=None)[source]

In case of a first call, this will call the decorated function and return it’s output. On every subsequent call, the same output will be returned.

Parameters: obj – the parent object this property is attached to. objtype – Output of the first call to the decorated function.
__init__(*args, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

class symfit.core.support.deprecated(replacement=None)[source]

Bases: object

Decorator to raise a DeprecationWarning.

__call__(func)[source]

Call self as a function.

__init__(replacement=None)[source]
Parameters: replacement – The function which should now be used instead.
symfit.core.support.jacobian(expr, symbols)[source]

Derive a symbolic expr w.r.t. each symbol in symbols. This returns a symbolic jacobian vector.

Parameters: expr – A sympy Expr. symbols – The symbols w.r.t. which to derive.
symfit.core.support.key2str(target)[source]

In symfit there are many dicts with symbol: value pairs. These can not be used immediately as **kwargs, even though this would make a lot of sense from the context. This function wraps such dict to make them usable as **kwargs immediately.

Parameters: target – Mapping to be made save Mapping of str(symbol): value pairs.
class symfit.core.support.keywordonly(**kwonly_arguments)[source]

Bases: object

Decorator class which wraps a python 2 function into one with keyword-only arguments.

Example:

@keywordonly(floor=True)
def f(x, **kwargs):
floor = kwargs.pop('floor')
return np.floor(x**2) if floor else x**2


This decorator is not much more than:

floor = kwargs.pop('floor') if 'floor' in kwargs else True


However, I prefer it’s usage because:

• it’s clear from reading the function declaration there is an option to provide this argument. The information on possible keywords is where you’d expect it to be.
• you’re guaranteed that the pop works.
• It is fully inspect compatible such that sphynx is able to index these properly as keyword only arguments just like it would for native py3 keyword only arguments.

Please note that this decorator needs a ** argument on the wrapped function in order to work.

__call__(func)[source]

Returns a decorated version of func, who’s signature now includes the keyword-only arguments.

Parameters: func – the function to be decorated the decorated function
__init__(**kwonly_arguments)[source]

Initialize self. See help(type(self)) for accurate signature.

symfit.core.support.name(self)[source]

Save name which can be used for alphabetic sorting and can be turned into a kwarg.

symfit.core.support.parameters(names, **kwargs)[source]

Convenience function for the creation of multiple parameters. For more control, consider using symbols(names, cls=Parameter, **kwargs) directly.

The Parameter attributes value, min, max and fixed can also be provided directly. If given as a single value, the same value will be set for all Parameter’s. When a sequence, it must be of the same length as the number of parameters created.

Example::
x1, x2 = parameters(‘x1, x2’, value=[2.0, 1.3], min=0.0)
Parameters: names – string of parameter names. Example: a, b = parameters(‘a, b’) kwargs – kwargs to be passed onto sympy.core.symbol.symbols(). value, min and max will be handled separately if they are sequences. iterable of symfit.core.argument.Parameter objects
symfit.core.support.seperate_symbols(func)[source]

Seperate the symbols in symbolic function func. Return them in alphabetical order.

Parameters: func – scipy symbolic function. (vars, params), a tuple of all variables and parameters, each sorted in alphabetical order. TypeError – only symfit Variable and Parameter are allowed, not sympy Symbols.
symfit.core.support.sympy_to_py(func, args)[source]

Turn a symbolic expression into a Python lambda function, which has the names of the variables and parameters as it’s argument names.

Parameters: func – sympy expression args – variables and parameters in this model lambda function to be used for numerical evaluation of the model.
symfit.core.support.sympy_to_scipy(func, vars, params)[source]

Convert a symbolic expression to one scipy digs. Not used by symfit any more.

Parameters: func – sympy expression vars – variables params – parameters Scipy-style function to be used for numerical evaluation of the model.
symfit.core.support.variables(names, **kwargs)[source]

Convenience function for the creation of multiple variables. For more control, consider using symbols(names, cls=Variable, **kwargs) directly.

Parameters: names – string of variable names. Example: x, y = variables(‘x, y’) kwargs – kwargs to be passed onto sympy.core.symbol.symbols() iterable of symfit.core.argument.Variable objects

## Printing¶

symfit occasionally updates the printing of sympy objects, such that they print into their numpy/scipy equivalent. This is done because sometimes such printing has not been implemented in sympy yet, or because we want slightly different behavior from the standard one.

Users using both symfit and sympy should be aware of this.

## Distributions¶

Some common distributions are defined in this module. That way, users can easily build more complicated expressions without making them look hard.

I have deliberately chosen to start these function with a capital, e.g. Gaussian instead of gaussian, because this makes the resulting expressions more readable.

symfit.distributions.BivariateGaussian(x, y, mu_x, mu_y, sig_x, sig_y, rho)[source]
Parameters: x – symfit.core.argument.Variable y – symfit.core.argument.Variable mu_x – symfit.core.argument.Parameter for the mean of x mu_y – symfit.core.argument.Parameter for the mean of y sig_x – symfit.core.argument.Parameter for the standard deviation of x sig_y – symfit.core.argument.Parameter for the standard deviation of y rho – symfit.core.argument.Parameter for the correlation between x and y. sympy expression for a Bivariate Gaussian pdf.
symfit.distributions.Exp(x, l)[source]
$f(x) = l e^{- l x}$

Exponential Distribution pdf.

Parameters: x – free variable. l – rate parameter. sympy.Expr for an Exponential Distribution pdf.
symfit.distributions.Gaussian(x, mu, sig)[source]
$f(x) = \frac{1}{\sqrt{2 \pi \sigma^2}} e^{- \frac{(x - \mu)^2}{2 \sigma^2}}$

Gaussian pdf.

Parameters: x – free variable. mu – mean of the distribution. sig – standard deviation of the distribution. sympy.Expr for a Gaussian pdf.

## Contrib¶

Contrib modules are modules and extensions to symfit provided by other people. This usually means the code is of slightly less quality, and may not survive future versions.

class symfit.contrib.interactive_guess.interactive_guess.InteractiveGuess(*args, n_points=50, log_contour=True, percentile=(5, 95), **kwargs)[source]

A class that provides an graphical, interactive way of guessing initial fitting parameters.

__init__(*args, n_points=50, log_contour=True, percentile=(5, 95), **kwargs)[source]

Create a matplotlib window with sliders for all parameters in this model, so that you may graphically guess initial fitting parameters. n_points is the number of points drawn for the plot. Data points are plotted as a blue contour plot, the proposed model as a red line. The errorbars on the proposed model represent the percentile of data within the thresholds.

Slider extremes are taken from the parameters where possible. If these are not provided, the minimum is 0; and the maximum is value*2. If no initial value is provided, it defaults to 1.

This will modify the values of the parameters present in model.

Parameters: n_points (int) – The number of points used for drawing the fitted function. Defaults to 50. log_contour (bool) – Whether to plot the contour plot of the log of the density, rather than the density itself. If True, any density less than 1e-7 will be considered 0. Defaults to True. percentile (list) – Controls the errorbars on the proposed model, such that the lower errorbar will cover percentile[0]% of the data, and the upper will cover percentile[1]%. Defaults to [5, 95], with corresponds to a 90% percentile. Should be a list of 2 numbers.
__str__()[source]

Represent the guesses in a human readable way.

Returns: string with the guessed values.
execute(*, show=True, block=True, **kwargs)[source]

Execute the interactive guessing procedure.

Parameters: show (bool) – Whether or not to show the figure. Useful for testing. block – Blocking call to matplotlib

Any additional keyword arguments are passed to matplotlib.pyplot.show().

class symfit.contrib.interactive_guess.interactive_guess.InteractiveGuess2D(*args, **kwargs)[source]
__init__(*args, **kwargs)[source]

Create a matplotlib window with sliders for all parameters in this model, so that you may graphically guess initial fitting parameters. n_points is the number of points drawn for the plot. Data points are plotted as a blue contour plot, the proposed model as a red line. The errorbars on the proposed model represent the percentile of data within the thresholds.

Slider extremes are taken from the parameters where possible. If these are not provided, the minimum is 0; and the maximum is value*2. If no initial value is provided, it defaults to 1.

This will modify the values of the parameters present in model.

Parameters: n_points (int) – The number of points used for drawing the fitted function. Defaults to 50. log_contour (bool) – Whether to plot the contour plot of the log of the density, rather than the density itself. If True, any density less than 1e-7 will be considered 0. Defaults to True. percentile (list) – Controls the errorbars on the proposed model, such that the lower errorbar will cover percentile[0]% of the data, and the upper will cover percentile[1]%. Defaults to [5, 95], with corresponds to a 90% percentile. Should be a list of 2 numbers.
class symfit.contrib.interactive_guess.interactive_guess.Strategy2D(interactive_guess)[source]

Bases: object

A strategy that describes how to plot a model that depends on a single independent variable, and how to update that plot.

__init__(interactive_guess)[source]

Initialize self. See help(type(self)) for accurate signature.

plot_data(proj, ax)[source]

Creates and plots a scatter plot of the original data.

plot_model(proj, ax)[source]

Plots the model proposed for the projection proj on ax.

update_plot(indep_var, dep_var)[source]

Updates the plot of the proposed model.

class symfit.contrib.interactive_guess.interactive_guess.StrategynD(interactive_guess)[source]

Bases: object

A strategy that describes how to plot a model that depends on a multiple independent variables, and how to update that plot.

__init__(interactive_guess)[source]

Initialize self. See help(type(self)) for accurate signature.

plot_data(proj, ax)[source]

Creates and plots the contourplot of the original data. This is done by evaluating the density of projected datapoints on a grid.

plot_model(proj, ax)[source]

Plots the model proposed for the projection proj on ax.

update_plot(indep_var, dep_var)[source]

Updates the plot of the proposed model.