scipy tutorial - Baustatik-Info-Server

3.11.2 Use
SciPy Reference Guide, Release 0.8.dev
See the docstrings of odr.odrpack and the functions and classes for usage instructions. The ODRPACK User’s Guide
is also quite helpful. It can be found on one of the ODRPACK’s original author’s website:
http://www.boulder.nist.gov/mcsd/Staff/JRogers/odrpack.html
Robert Kern robert.kern@gmail.com
class Data(x, y=None, we=None, wd=None, fix=None, meta={})
The Data class stores the data to fit.
Each argument is attached to the member of the instance of the same name. The structures of x and y are
described in the Model class docstring. If y is an integer, then the Data instance can only be used to fit with
implicit models where the dimensionality of the response is equal to the specified value of y. The structures of
wd and we are described below. meta is an freeform dictionary for application-specific use.
we weights the effect a deviation in the response variable has on the fit. wd weights the effect a deviation
in the input variable has on the fit. To handle multidimensional inputs and responses easily, the structure of
these arguments has the n’th dimensional axis first. These arguments heavily use the structured arguments
feature of ODRPACK to conveniently and flexibly support all options. See the ODRPACK User’s Guide for a
full explanation of how these weights are used in the algorithm. Basically, a higher value of the weight for a
particular data point makes a deviation at that point more detrimental to the fit.
we – if we is a scalar, then that value is used for all data points (and
all dimensions of the response variable).
If we is a rank-1 array of length q (the dimensionality of the response variable), then this
vector is the diagonal of the covariant weighting matrix for all data points.
If we is a rank-1 array of length n (the number of data points), then the i’th element is the
weight for the i’th response variable observation (single-dimensional only).
If we is a rank-2 array of shape (q, q), then this is the full covariant weighting matrix broadcast
to each observation.
If we is a rank-2 array of shape (q, n), then we[:,i] is the diagonal of the covariant weighting
matrix for the i’th observation.
If we is a rank-3 array of shape (q, q, n), then we[:,:,i] is the full specification of the covariant
weighting matrix for each observation.
If the fit is implicit, then only a positive scalar value is used.
wd – if wd is a scalar, then that value is used for all data points
(and all dimensions of the input variable). If wd = 0, then the covariant weighting matrix for
each observation is set to the identity matrix (so each dimension of each observation has the
same weight).
If wd is a rank-1 array of length m (the dimensionality of the input variable), then this vector
is the diagonal of the covariant weighting matrix for all data points.
If wd is a rank-1 array of length n (the number of data points), then the i’th element is the
weight for the i’th input variable observation (single-dimensional only).
If wd is a rank-2 array of shape (m, m), then this is the full covariant weighting matrix broadcast
to each observation.
If wd is a rank-2 array of shape (m, n), then wd[:,i] is the diagonal of the covariant weighting
matrix for the i’th observation.
If wd is a rank-3 array of shape (m, m, n), then wd[:,:,i] is the full specification of the covariant
weighting matrix for each observation.
3.11. Orthogonal distance regression (scipy.odr) 291