This is documentation for an old release of SciPy (version 0.9.0). Read this page in the documentation of the latest stable release (version 1.15.1).
Python wrappers for Orthogonal Distance Regression (ODRPACK).
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
Basic use:
1) Define the function you want to fit against.
def f(B, x):
''' Linear function y = m*x + b '''
return B[0]*x + B[1]
# B is a vector of the parameters.
# x is an array of the current x values.
# x is 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.
Robert Kern robert.kern@gmail.com
Functions
odr(fcn, beta0, y, x[, we, wd, fjacb, ...]) | |
report_error(info) | Interprets the return code of the odr routine. |
Classes
Data(x[, y, we, wd, fix, meta]) | The Data class stores the data to fit. |
Model(fcn[, fjacb, fjacd, extra_args, ...]) | The Model class stores information about the function you wish to fit. |
ODR(data, model[, beta0, delta0, ifixb, ...]) | The ODR class gathers all information and coordinates the running of the |
Output(output) | The Output class stores the output of an ODR run. |
RealData(x[, y, sx, sy, covx, covy, fix, meta]) | The RealData class stores the weightings as actual standard deviations |
Exceptions
odr_error | |
odr_stop |