automatic import of python-PyQt-Fitopeneuler20.03

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+/PyQt-Fit-1.4.0.tar.gz
diff --git a/python-pyqt-fit.spec b/python-pyqt-fit.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-PyQt-Fit
+Version:	1.4.0
+Release:	1
+Summary:	Parametric and non-parametric regression, with plotting and testing methods.
+License:	LICENSE.txt
+URL:		https://github.com/PierreBdR/PyQt-fit
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/bd/26/1679b81e3ca65aa955d91f57c2bd9fca7e05cb1b65bcc7d464961056e296/PyQt-Fit-1.4.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+PyQt-Fit is a regression toolbox in Python with simple GUI and graphical tools
+to check your results. It currently handles regression based on user-defined
+functions with user-defined residuals (i.e. parametric regression) or
+non-parametric regression, either local-constant or local-polynomial, with the
+option to provide your own. There is also a full-GUI access, that currently
+provides an interface only to parametric regression.
+The GUI for 1D data analysis is invoked with:
+    $ pyqt_fit1d.py
+PyQt-Fit 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):
+    >>> est = pyqt_fit.CurveFitting(x, y, p0=(0,1,0), function=fct)
+    >>> est.fit() # Find the optimal parameters
+    >>> result = plot_fit.fit_evaluation(est, x, y)
+    >>> print(est(x)) # Display the estimated values
+    >>> plot_fit.plot1d(result)
+    >>> pylab.show()
+PyQt-Fit is a package for regression in Python. There are two set of tools: for
+parametric, or non-parametric 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 non-parametric regression can currently be either local constant (i.e.
+spatial averaging) in nD or local-polynomial in 1D only. The bootstrapping
+function will also work with the non-parametric 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 QQ-plot 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).
+
+%package -n python3-PyQt-Fit
+Summary:	Parametric and non-parametric regression, with plotting and testing methods.
+Provides:	python-PyQt-Fit
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-PyQt-Fit
+PyQt-Fit is a regression toolbox in Python with simple GUI and graphical tools
+to check your results. It currently handles regression based on user-defined
+functions with user-defined residuals (i.e. parametric regression) or
+non-parametric regression, either local-constant or local-polynomial, with the
+option to provide your own. There is also a full-GUI access, that currently
+provides an interface only to parametric regression.
+The GUI for 1D data analysis is invoked with:
+    $ pyqt_fit1d.py
+PyQt-Fit 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):
+    >>> est = pyqt_fit.CurveFitting(x, y, p0=(0,1,0), function=fct)
+    >>> est.fit() # Find the optimal parameters
+    >>> result = plot_fit.fit_evaluation(est, x, y)
+    >>> print(est(x)) # Display the estimated values
+    >>> plot_fit.plot1d(result)
+    >>> pylab.show()
+PyQt-Fit is a package for regression in Python. There are two set of tools: for
+parametric, or non-parametric 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 non-parametric regression can currently be either local constant (i.e.
+spatial averaging) in nD or local-polynomial in 1D only. The bootstrapping
+function will also work with the non-parametric 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 QQ-plot 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).
+
+%package help
+Summary:	Development documents and examples for PyQt-Fit
+Provides:	python3-PyQt-Fit-doc
+%description help
+PyQt-Fit is a regression toolbox in Python with simple GUI and graphical tools
+to check your results. It currently handles regression based on user-defined
+functions with user-defined residuals (i.e. parametric regression) or
+non-parametric regression, either local-constant or local-polynomial, with the
+option to provide your own. There is also a full-GUI access, that currently
+provides an interface only to parametric regression.
+The GUI for 1D data analysis is invoked with:
+    $ pyqt_fit1d.py
+PyQt-Fit 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):
+    >>> est = pyqt_fit.CurveFitting(x, y, p0=(0,1,0), function=fct)
+    >>> est.fit() # Find the optimal parameters
+    >>> result = plot_fit.fit_evaluation(est, x, y)
+    >>> print(est(x)) # Display the estimated values
+    >>> plot_fit.plot1d(result)
+    >>> pylab.show()
+PyQt-Fit is a package for regression in Python. There are two set of tools: for
+parametric, or non-parametric 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 non-parametric regression can currently be either local constant (i.e.
+spatial averaging) in nD or local-polynomial in 1D only. The bootstrapping
+function will also work with the non-parametric 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 QQ-plot 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).
+
+%prep
+%autosetup -n PyQt-Fit-1.4.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-PyQt-Fit -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..8d1684c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+eaf5b99e5a9b61d927536058045722cd  PyQt-Fit-1.4.0.tar.gz