PyQtFit 1.1.17
Parametric and nonparametric regression, with plotting and testing methods.
Latest Version: 1.3.4
PyQtFit is a regression toolbox in Python with simple GUI and graphical tools to check your results. It currently handles regression based on userdefined functions with userdefined residuals (i.e. parametric regression) or nonparametric regression, either localconstant or localpolynomial, with the option to provide your own. There is also a fullGUI access, that currently provides an interface only to parametric regression.
The GUI for 1D data analysis is invoked with:
$ pyqt_fit1d.py
PyQtFit can also be used from the python interpreter. Here is a typical session:
>>> import pyqt_fit >>> from pyqt_fit import plot_fit >>> import numpy as np >>> from matplotlib import pylab >>> x = np.arange(0,3,0.01) >>> y = 2*x + 4*x**2 + np.random.randn(*x.shape) >>> def fct(params, x): ... (a0, a1, a2) = params ... return a0 + a1*x + a2*x*x >>> fit = pyqt_fit.CurveFitting(x, y, (0,1,0), fct) >>> result = plot_fit.fit_evaluation(fit, x, y) >>> print(fit(x)) # Display the estimated values >>> plot_fit.plot1d(result) >>> pylab.show()
PyQtFit is a package for regression in Python. There are two set of tools: for parametric, or nonparametric regression.
For the parametric regression, the user can define its own vectorized function (note that a normal function wrappred into numpy’s “vectorize” function is perfectly fine here), and find the parameters that best fit some data. It also provides bootstrapping methods (either on the samples or on the residuals) to estimate confidence intervals on the parameter values and/or the fitted functions.
The nonparametric regression can currently be either local constant (i.e. spatial averaging) in nD or localpolynomial in 1D only. The bootstrapping function will also work with the nonparametric regression methods.
The package also provides with four evaluation of the regression: the plot of residuals vs. the X axis, the plot of normalized residuals vs. the Y axis, the QQplot of the residuals and the histogram of the residuals. All this can be output to a CSV file for further analysis in your favorite software (including most spreadsheet programs).
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 Author: Pierre Barbier de Reuille
 Documentation: PyQtFit package documentation
 Home Page: https://code.google.com/p/pyqtfit/
 License: LICENSE.txt
 Platform: Linux,Windows,MacOS

Categories
 Development Status :: 4  Beta
 Environment :: X11 Applications :: Qt
 Intended Audience :: Science/Research
 License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
 Natural Language :: English
 Operating System :: MacOS :: MacOS X
 Operating System :: Microsoft :: Windows
 Operating System :: POSIX :: Linux
 Programming Language :: Python :: 2.7
 Topic :: Scientific/Engineering :: Mathematics
 Topic :: Scientific/Engineering :: Visualization
 Package Index Owner: PierreBdR
 DOAP record: PyQtFit1.1.17.xml