SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Python wrappers for Orthogonal Distance Regression (ODRPACK).

Classes
=======

Data -- stores the data and weights to fit against

RealData -- stores data with standard deviations and covariance matrices

Model -- stores the model and its related information

Output -- stores all of the output from an ODR run

ODR -- collects all data and runs the fitting routine


Exceptions
==========

odr_error -- error sometimes raised inside odr() and can be raised in the
    fitting functions to tell ODRPACK to halt the procedure

odr_stop -- error to raise in fitting functions to tell ODRPACK that the data or
    parameters given are invalid

Use
===

Basic use:

1) Define the function you want to fit against:

  def f(B, x):
      return B[0]*x + B[1]

  B is a vector of the parameters.
  x is an array of the current x values. (Same format as the x passed to Data or
      RealData. Return an array in the same format as y passed to Data or
      RealData.)

2) Create a Model.

  linear = Model(f)

3) Create a Data or RealData instance.

  mydata = Data(x, y, wd=1./power(sx,2), we=1./power(sy,2))

    or

  mydata = RealData(x, y, sx=sx, sy=sy)

4) Instantiate ODR with your data, model and initial parameter estimate.

  myodr = ODR(mydata, linear, beta0=[1., 2.])

5) Run the fit.

  myoutput = myodr.run()

6) Examine output.

  myoutput.pprint()

Read the docstrings and the accompanying tests for more advanced usage.


Notes
=====

* Array formats -- FORTRAN stores its arrays in memory column first, i.e. an
  array element A(i, j, k) will be next to A(i+1, j, k). In C and, consequently,
  NumPy, arrays are stored row first: A[i, j, k] is next to A[i, j, k+1]. For
  efficiency and convenience, the input and output arrays of the fitting
  function (and its Jacobians) are passed to FORTRAN without transposition.
  Therefore, where the ODRPACK documentation says that the X array is of shape
  (N, M), it will be passed to the Python function as an array of shape (M, N).
  If M==1, the one-dimensional case, then nothing matters; if M>1, then your
  Python functions will be dealing with arrays that are indexed in reverse of
  the ODRPACK documentation. No real biggie, but watch out for your indexing of
  the Jacobians: the i,j'th elements (@f_i/@x_j) evaluated at the n'th
  observation will be returned as jacd[j, i, n]. Except for the Jacobians, it
  really is easier to deal with x[0] and x[1] than x[:,0] and x[:,1]. Of course,
  you can always use the transpose() function from scipy explicitly.

* Examples -- See the accompanying file test/test.py for examples of how to set
  up fits of your own. Some are taken from the User's Guide; some are from
  other sources.

* Models -- Some common models are instantiated in the accompanying module
  models.py . Contributions are welcome.

Credits
=======

* Thanks to Arnold Moene and Gerard Vermeulen for fixing some killer bugs.

Robert Kern
robert.kern@gmail.com

Variables

• odr

  odr = __odrpack.odr
  
  

• odr_error

  odr_error = __odrpack.odr_error
  
  

• odr_stop

  odr_stop = __odrpack.odr_stop
  
  

Classes

• Data - The Data class stores the data to fit
• Model - The Model class stores information about the function you wish to fit
• ODR - The ODR class gathers all information and coordinates the running of the main fitting routine
• Output - The Output class stores the output of an ODR run
• RealData - The RealData class stores the weightings as actual standard deviations and/or covariances

Function summary

• report_error(info)

Functions

• report_error(info)

  Interprets the return code of the odr routine.

  Parameters

  info : int

  The return code of the odr routine.

  Returns

  problems : list(str)

  A list of messages about why the odr() routine stopped.

Imported Names