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Non-Linear Least-Squares Minimization and Curve-Fitting for Python Introduction Parameters Models

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Downloading and Installation

Prerequisites

The lmfit package requires Python, Numpy, and Scipy. Scipy version 0.13 or higher is recommended, but extensive testing on compatibility with various versions of scipy has not been done. Lmfit does work with Python 2.7, and 3.2 and 3.3. No testing has been done with Python 3.4, but as the package is pure Python, relying only on scipy and numpy, no significant troubles are expected. The nose frameworkt is required for running the test suite, and IPython and matplotib are recommended. If Pandas is available, it will be used in portions of lmfit.

Downloads

The latest stable version of lmfit is available from PyPi.

Installation

If you have pip installed, you can install lmfit with:

pip install lmfit

or, if you have Python Setup Tools installed, you install lmfit with:

easy_install -U lmfit

or, you can download the source kit, unpack it and install with:

python setup.py install

Development Version

To get the latest development version, use:

git clone http://github.com/lmfit/lmfit-py.git

and install using:

python setup.py install

Testing

A battery of tests scripts that can be run with the nose testing framework is distributed with lmfit in the tests folder. These are routinely run on the development version. Running nosetests should run all of these tests to completion without errors or failures.

Many of the examples in this documentation are distributed with lmfit in the examples folder, and sould also run for you. Many of these require

Acknowledgements

LMFIT was originally written by Matthew Newville. Substantial code and documentation improvements, especially for improved estimates of confidence intervals was provided by Till Stensitzki. Much of the work on improved unit testing and high-level model functions was done by Daniel B. Allen, with substantial input from Antonino Ingargiola. Many valuable suggestions for improvements have come from Christoph Deil. The implementation of parameter bounds as described in the MINUIT documentation is taken from Jonathan J. Helmus’ leastsqbound code, with permission. The code for propagation of uncertainties is taken from Eric O. Le Bigot’s uncertainties package, with permission. The code obviously depends on, and owes a very large debt to the code in scipy.optimize. Several discussions on the scipy mailing lists have also led to improvements in this code.

License

The LMFIT-py code is distribution under the following license:

Copyright (c) 2014 Matthew Newville, The University of Chicago, Till Stensitzki, Freie Universitat Berlin, Daniel B. Allen, Johns Hopkins University, Antonino Ingargiola, University of California, Los Angeles

Permission to use and redistribute the source code or binary forms of this software and its documentation, with or without modification is hereby granted provided that the above notice of copyright, these terms of use, and the disclaimer of warranty below appear in the source code and documentation, and that none of the names of above institutions or authors appear in advertising or endorsement of works derived from this software without specific prior written permission from all parties.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THIS SOFTWARE.