Class Model
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Class to create a model using different Functions
each with their associated parameters. This Model can then be used to fit
data using the Minimizer class. The Model can be evaluated using the
evaluate method. Parameters can be added/updated,
together with boundaries and expressions, and can be hold fixed or
adjusted by changing the vary keyword in update_parameter.
Parameters for the different Functions are combined. To keep track of
which parameter is which, they get a number added to the end indicating
the function they belong to. For example: when a Model is created summing
a gaussian function with a quadratic function. The gaussian has
parameters [a, mu, sigma, c] and the quadratic function has parameters
[s0, s1, s2]. If the functions are provided in order [Gaussian,
Quadratic], the parameters will be renames to: [a_0, mu_0, sigma_0, c_0]
and [s0_1, s1_1, s2_1]. Keep in mind that this renaming only happens in
the Model object. In the underlying Functions the parameters will keep
there original name.
The functions themselfs can be combined using a mathematical
expression in the constructor. If no expression is provided, the output
of each function is summed up. Keep in mind that each function needs to
have the same output shape:
Model(x) = Function1(x) + Function2(x) + ...
The provided expression needs to be a function taking an array with
the results of the Functions in the model as arguments, and having an
numpy array as result. This can be done with simple lambda
expression or more complicated functions:
Model(x) = Expr( [Function1(x),Function2(x),...] )
The internal representation of the parameters uses a parameter object
of the lmfit package. No knowledge of this repersentation is
required as methods for direct interaction with the parameter values and
other settings are provided. If wanted, the parameters object itself can
be obtained with the parameters attribute.
At the moment the use of a jacobian is not supported at the Model
level as it is not possible to derive a symbolic jacobian from the
provided functions. If you want to use a jacobian you will have to write
a Function yourself in which you can provide a jacobian function.
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__init__(self,
functions=None,
expr=None,
resfunc=None)
Create a new Model by providing the functions of which it consists. |
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Inherited from object:
__delattr__,
__format__,
__getattribute__,
__hash__,
__new__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__subclasshook__
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numpy array
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evaluate(self,
x,
*args,
**kwargs)
Evaluate the model for the given values and optional a given
parameter object. |
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evaluate_jacobian(self,
x,
*args)
Not implemented! |
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numpy arrays
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get_parameters(self,
parameters=None,
error='stderr',
full_output=False)
Returns the parameter values together with the errors if they exist. |
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string
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param2str(self,
**kwargs)
Converts the parameter object of this model to an easy printable
string, including the value, error, boundaries, if the parameter is
varied, and if in the fitting process on of the boundaries was
reached. |
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string
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correl2str(self,
**kwargs)
Convert the correlations between the different parameters of this
model to an easy printable string. |
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string
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ci2str(self,
**kwargs)
Convert the confidence intervals of the parameters of this model to
an easy printable string. |
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pull_parameters(self)
Pulls the parameter objects from the underlying functions, and
combines it to 1 parameter object. |
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push_parameters(self,
parameters=None)
Pushes the parameters in the combined parameter object to the
parameter objects of the underlying models or functions. |
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Inherited from object:
__class__
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__init__(self,
functions=None,
expr=None,
resfunc=None)
(Constructor)
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Create a new Model by providing the functions of which it consists.
You can provid an expression describing how the Functions have to be
combined as well. This expression needs to take the out put of the
Fuctions in an array as argument, and provide a new numpy array as
result.
- Parameters:
functions (list) - A list of Functionsexpr (function) - An expression to combine the given functions.resfunc (function) - A function to calculate the residuals (Not obligatory) - Overrides:
object.__init__
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Evaluate the model for the given values and optional a given parameter
object. If no parameter object is given then the parameter object
belonging to the model is used.
>>> evaluate(x, parameters)
>>> evaluate(x)
- Parameters:
x (array) - the independant values for which to evaluate the model. - Returns: numpy array
- Model(x)
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setup_parameters(self,
values=None,
bounds=None,
vary=None,
exprs=None)
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Not implemented yet, use the setup_parameters method of the Functions themselfs, or
for adjustments of a single parameter use the update_parameter function
Please provide feedback on redmine on how you would like to use this
function!!!
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update_parameter(self,
parameter=None,
**kwargs)
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Updates a specified parameter. The parameter can be given by name or
by index. However, you have to be carefull when using the names. The
model class changes the names of the parameters of the underlying
functions based on the order in which the functions are provided (See
introduction). This method also supports the use of kwargs min and max to
set the lower and upper boundary separatly.
Example use:
>>> update_parameter(parameter='parname_0', value=10.0, min=5.0, vary=True)
>>> update_parameter(parameter=2, value=0.15, bounds=(0.10, 0.20))
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get_parameters(self,
parameters=None,
error='stderr',
full_output=False)
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Returns the parameter values together with the errors if they exist.
If No fitting has been done, or the errors could not be calculated, None
is returned for the error.
The parameters of which the settings should be returned can be given
in parameters. If None is given, all parameters are returned.
- Parameters:
parameters (array) - Parameters to be returned or None for all parameters.error (string) - Which error to return ('stderr', 'mcerr')full_output (bool) - When True, also vary, the boundaries and expression are returned - Returns: numpy arrays
- the parameter values and there errors: value, err, [vary, min,
max, expr]
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Converts the parameter object of this model to an easy printable
string, including the value, error, boundaries, if the parameter is
varied, and if in the fitting process on of the boundaries was
reached.
The error to be printed can be set with the error keyword. You can
chose from the standard error: 'stderr', the monte carlo error: 'mcerr',
or any of the confidence intervalls that you have calculated by coding
them like: 'ci###'. Fx: 95% (sigma = 0.95) use 'ci95', for 99.7% (sigma =
0.997) use 'ci997'. Don't put decimal signs in the ci!
The accuracy with which the parameters are printed can be set with the
accuracy keyword. And the amount of information that is printed is
determined by full_output. If False, only parameter value and error are
printed, if True also boundaries and vary are shown.
- Parameters:
accuracy (int) - Number of decimal places to printerror (string) - Which error type to print ('stderr', 'mcerr' or 'ci###')full_output (bool) - Amount of information to print - Returns: string
- parameters in string format
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Convert the correlations between the different parameters of this
model to an easy printable string. The correlations are only printed if
they are larger than a certain provided limit. And the accuracy keyword
sets the amount of decimals to print
- Parameters:
accuracy - number of decimal places to printlimit - minimum correlation value to print - Returns: string
- correlation in string format
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Convert the confidence intervals of the parameters of this model to an
easy printable string.
The accuracy with wich the CIs should be printed can be set with the
accuracy keyword.
- Parameters:
accuracy (int) - Number of decimal places to print - Returns: string
- confidence intervals in string format
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get par_names
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__str__(self)
(Informal representation operator)
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String representation of the Model object
- Overrides:
object.__str__
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