{ "info": { "author": "Pierre Barbier de Reuille", "author_email": "pierre.barbierdereuille@gmail.com", "bugtrack_url": null, "classifiers": [ "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" ], "description": "========\nPyQt-Fit\n========\n\nPyQt-Fit is a regression toolbox in Python with simple GUI and graphical tools\nto check your results. It currently handles regression based on user-defined\nfunctions with user-defined residuals (i.e. parametric regression) or\nnon-parametric regression, either local-constant or local-polynomial, with the\noption to provide your own. There is also a full-GUI access, that currently\nprovides an interface only to parametric regression.\n\nThe GUI for 1D data analysis is invoked with:\n\n $ pyqt_fit1d.py\n\nPyQt-Fit can also be used from the python interpreter. Here is a typical session:\n\n >>> import pyqt_fit\n >>> from pyqt_fit import plot_fit\n >>> import numpy as np\n >>> from matplotlib import pylab\n >>> x = np.arange(0,3,0.01)\n >>> y = 2*x + 4*x**2 + np.random.randn(*x.shape)\n >>> def fct(params, x):\n ... (a0, a1, a2) = params\n ... return a0 + a1*x + a2*x*x\n >>> fit = pyqt_fit.CurveFitting(x, y, (0,1,0), fct)\n >>> result = plot_fit.fit_evaluation(fit, x, y)\n >>> print(fit(x)) # Display the estimated values\n >>> plot_fit.plot1d(result)\n >>> pylab.show()\n\nPyQt-Fit is a package for regression in Python. There are two set of tools: for\nparametric, or non-parametric regression.\n\nFor the parametric regression, the user can define its own vectorized function\n(note that a normal function wrappred into numpy's \"vectorize\" function is\nperfectly fine here), and find the parameters that best fit some data. It also\nprovides bootstrapping methods (either on the samples or on the residuals) to\nestimate confidence intervals on the parameter values and/or the fitted\nfunctions.\n\nThe non-parametric regression can currently be either local constant (i.e.\nspatial averaging) in nD or local-polynomial in 1D only. The bootstrapping\nfunction will also work with the non-parametric regression methods.\n\nThe package also provides with four evaluation of the regression: the plot of residuals\nvs. the X axis, the plot of normalized residuals vs. the Y axis, the QQ-plot of\nthe residuals and the histogram of the residuals. All this can be output to a\nCSV file for further analysis in your favorite software (including most\nspreadsheet programs).\n\n\nNote\n----\n\n Version 1.3.0 is not fully compatible with previous versions. 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