scipy.odr.

Model#

class scipy.odr.Model(fcn, fjacb=None, fjacd=None, extra_args=None, estimate=None, implicit=0, meta=None)[source]#

The Model class stores information about the function you wish to fit.

Deprecated since version 1.17.0: scipy.odr is deprecated and will be removed in SciPy 1.19.0. Please use pypi.org/project/odrpack/ instead.

It stores the function itself, at the least, and optionally stores functions which compute the Jacobians used during fitting. Also, one can provide a function that will provide reasonable starting values for the fit parameters possibly given the set of data.

Parameters:

fcnfunction

fcn(beta, x) –> y

fjacbfunction

Jacobian of fcn wrt the fit parameters beta.

fjacb(beta, x) –> @f_i(x,B)/@B_j

fjacdfunction

Jacobian of fcn wrt the (possibly multidimensional) input variable.

fjacd(beta, x) –> @f_i(x,B)/@x_j

extra_argstuple, optional

If specified, extra_args should be a tuple of extra arguments to pass to fcn, fjacb, and fjacd. Each will be called by apply(fcn, (beta, x) + extra_args)

estimatearray_like of rank-1

Provides estimates of the fit parameters from the data

estimate(data) –> estbeta

implicitboolean

If TRUE, specifies that the model is implicit; i.e fcn(beta, x) ~= 0 and there is no y data to fit against

metadict, optional

freeform dictionary of metadata for the model

Methods

set_meta(**kwds)

Update the metadata dictionary with the keywords and data provided here.

Notes

Note that the fcn, fjacb, and fjacd operate on NumPy arrays and return a NumPy array. The estimate object takes an instance of the Data class.

Here are the rules for the shapes of the argument and return arrays of the callback functions:

x: if the input data is single-dimensional, then x is rank-1 array; i.e., x = array([1, 2, 3, ...]); x.shape = (n,) If the input data is multi-dimensional, then x is a rank-2 array; i.e., x = array([[1, 2, ...], [2, 4, ...]]); x.shape = (m, n). In all cases, it has the same shape as the input data array passed to odr. m is the dimensionality of the input data, n is the number of observations.
y: if the response variable is single-dimensional, then y is a rank-1 array, i.e., y = array([2, 4, ...]); y.shape = (n,). If the response variable is multi-dimensional, then y is a rank-2 array, i.e., y = array([[2, 4, ...], [3, 6, ...]]); y.shape = (q, n) where q is the dimensionality of the response variable.
beta: rank-1 array of length p where p is the number of parameters; i.e. beta = array([B_1, B_2, ..., B_p])
fjacb: if the response variable is multi-dimensional, then the return array’s shape is (q, p, n) such that fjacb(beta,x)[l,k,i] = d f_l(beta,x)/d B_k evaluated at the ith data point. If q == 1, then the return array is only rank-2 and with shape (p, n).
fjacd: as with fjacb, only the return array’s shape is (q, m, n) such that fjacd(beta,x)[l,j,i] = d f_l(beta,x)/d X_j at the ith data point. If q == 1, then the return array’s shape is (m, n). If m == 1, the shape is (q, n). If m == q == 1, the shape is (n,).