Bases: pymodelfit.core.ParametricModel
A base class for models that use a single function to map a numpy array input to an output (the function must be specified as the “f” method)
The following attributes must be defined:
A method that takes an array as the first argument and returns another array.
A sequence of strings that define the names of attributes that are to be treated as the parameters for the model. If these are not defined, they will be initialized to the defaultparval value
A function that performs any processing of inputs and outputs. Should call f() and have the same type of return value.
If typechecking is to be performed on the input, it should be performed in __call__() (see docstring for __call__() for syntax), but any mandatory input conversion should be done in _filterfunc().
x.__init__(...) initializes x; see help(type(x)) for signature
Call the model function on the input x with the current parameters and return the result.
Raises ModelTypeError:  

If the input or output are incorrect type or dimensionality for this model. 
Note
If a subclass overrides this method to do typechecking, it should either call this method or call _filterfunc() with an array input and raise a ModelTypeError if there is a type problem
Methods
chi2Data([x, y, weights, ddof])  Computes the chisquared statistic for the data assuming this model. 
f(x, *params)  This abstract method must be overriden in subclasses. 
fitData([x, y, fixedpars, weights, ...])  Fit the provided data using algorithms from scipy.optimize, and adjust the model parameters to match. 
getCov()  Computes the covariance matrix for the last fitData() call. 
getMCMC(x, y[, priors, datamodel])  Generate an Markov Chain Monte Carlo sampler for the data and model. 
inv(output, *args, **kwargs)  Compute the inverse of this model for the requested output. 
isVarnumModel()  Determines if the model represented by this class accepts a variable number of parameters (i.e. 
resampleFit([x, y, xerr, yerr, bootstrap, ...])  Estimates errors via resampling. 
residuals([x, y, retdata])  Compute residuals of the provided data against the model. 
stdData([x, y])  Determines the standard deviation of the model from data. 
Attributes
data  The fitting data for this model. 
errors  Error on the data. 
fittypes  A Sequence of the available valid values for the fittype 
params  A tuple of the parameter names. 
pardict  A dictionary mapping parameter names to the associated values. 
parvals  The values of the parameters in the same order as params 
weightstype  Determines the statistical interpretation of the weights in data. 
Computes the chisquared statistic for the data assuming this model.
Parameters: 


Returns:  tuple of floats (chi2,reducedchi2,pvalue) 
The fitting data for this model. Should be either None, or a tuple(datain,dataout,weights). Note that the weights are interpreted statistically as errors based on the weightstype attribute.
The base default value if a parameter cannot get a default any other way.
Error on the data. Sets the weights on data assuming the interpretation for errors given by weightstype. If data is None/missing, a TypeError will be raised.
This abstract method must be overriden in subclasses. It is interpreted as the function that takes an array (or arraylike) as the first argument and returns an array of output values appropriate for the model.
model parameters are passed in as the rest of the arguments, in the order they are given in the _pars sequence.
Fit the provided data using algorithms from scipy.optimize, and adjust the model parameters to match.
The fitting technique is sepcified by the fittype attribute of the object, which by default can be any of the optimization types in the scipy.optimize module (except for scalar minimizers)
The full fitting output is available in lastfit attribute after this method completes.
Parameters: 


kwargs are passed into the fitting function.
Returns:  array of the best fit parameters 

Raises ModelTypeError:  
If the output of the model does not match the shape of y. 
See also
The currently selected fitting technique.
A Sequence of the available valid values for the fittype attribute. (Readonly)
A sequence of the parameter names that by default should be kept fixed.
Computes the covariance matrix for the last fitData() call.
Returns:  The covariance matrix with variables in the same order as params. Diagonal entries give the variance in each parameter. 

Warning
This is not guaranteed to work for custom fittypes, but will always work with the default (leastsq) fit.
Generate an Markov Chain Monte Carlo sampler for the data and model. This function requires the PyMC package for the MCMC internals and sampling.
Parameters: 


Raises ValueError:  
If a prior is not provided for any parameter. 

Returns:  A pymc.MCMC object ready to sample for this model. 
Compute the inverse of this model for the requested output.
Parameters:  output – The output value of the model at which to compute the inverse. 

Returns:  The input value at which the model produces output 
Raises ModelTypeError:  
If the model is not invertable for the provided data set. 
Determines if the model represented by this class accepts a variable number of parameters (i.e. number of parameters is set when the object is created).
Returns:  True if this model has a variable number of parameters. 

A dictionary mapping parameter names to the associated values.
A hint for the relevant range for this model. Should take the form (dim0lower,dim0upper,dim1lower,dim1upper,...) or None for no hint.
Estimates errors via resampling. Uses the fitData function to fit the function many times while either using the “bootstrap” technique (resampling w/replacement), monte carlo estimates for the error, or both to estimate the error in the fit.
Parameters: 


kwargs are passed into fitData
Returns:  (histd,cov) where histd is a dictionary mapping parameters to their histograms and cov is the covariance matrix of the parameters in parameter order. 

Note
If x, y, xerr, or yerr are provided, they do not overwrite the stored data, unlike most other methods for this class.
Compute residuals of the provided data against the model. E.g. .
Parameters:  

Returns:  Residuals of model from y or if retdata is True, a tuple (x,y,residuals). 
Return type:  arraylike 
Determines the standard deviation of the model from data. Data can either be provided or (by default) will be taken from the stored data.
Parameters:  

Returns:  standard deviation of model from y 
Determines the statistical interpretation of the weights in data. Can be:
Weights act as inverse errors (default)
Weights act as inverse variance
Weights act as errors (nonstandard  this makes points with larger error bars count more towards the fit).
Weights act as variance (nonstandard  this makes points with larger error bars count more towards the fit).