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@@ -0,0 +1 @@ +/multihist-0.6.5.tar.gz diff --git a/python-multihist.spec b/python-multihist.spec new file mode 100644 index 0000000..a760984 --- /dev/null +++ b/python-multihist.spec @@ -0,0 +1,193 @@ +%global _empty_manifest_terminate_build 0 +Name: python-multihist +Version: 0.6.5 +Release: 1 +Summary: Convenience wrappers around numpy histograms +License: MIT +URL: https://github.com/jelleaalbers/multihist +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/73/df/bcbe4c72f03c4cb0f550329d6148628fe1f81f7da95d3c2afb867fb437f4/multihist-0.6.5.tar.gz +BuildArch: noarch + +Requires: python3-numpy + +%description +`https://github.com/JelleAalbers/multihist` +Thin wrapper around numpy's histogram and histogramdd. +Numpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class +with methods for adding new data to existing histograms, take averages, projecting, etc. +For 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std). +For d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging. +**NB**: For a faster and richer histogram package, check out `hist <https://github.com/scikit-hep/hist>`_ from scikit-hep. Alternatively, look at its parent library `boost-histogram <https://github.com/scikit-hep/boost-histogram>`_, which has `numpy-compatible features <https://boost-histogram.readthedocs.io/en/latest/usage/numpy.html>`_. Multihist was created back in 2015, long before those libraries existed. +Synopsis:: + # Create histograms just like from numpy... + m = Hist1d([0, 3, 1, 6, 2, 9], bins=3) + # ...or add data incrementally: + m = Hist1d(bins=100, range=(-3, 4)) + m.add(np.random.normal(0, 0.5, 10**4)) + m.add(np.random.normal(2, 0.2, 10**3)) + # Get the data back out: + print(m.histogram, m.bin_edges) + # Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std + plt.plot(m.bin_centers, m.normalized_histogram, label="Normalized histogram", drawstyle='steps') + plt.plot(m.bin_centers, m.density, label="Empirical PDF", drawstyle='steps') + plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", drawstyle='steps') + plt.title("Estimated mean %0.2f, estimated std %0.2f" % (m.mean, m.std)) + plt.legend(loc='best') + plt.show() + # Slicing and arithmetic behave just like ordinary ndarrays + print("The fourth bin has %d entries" % m[3]) + m[1:4] += 4 + 2 * m[-27:-24] + print("Now it has %d entries" % m[3]) + # Of course I couldn't resist adding a canned plotting function: + m.plot() + plt.show() + # Create and show a 2d histogram. Axis names are optional. + m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y']) + m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6)) + m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6)) + m2.plot() + plt.show() + # x and y projections return Hist1d objects + m2.projection('x').plot(label='x projection') + m2.projection(1).plot(label='y projection') + plt.legend() + plt.show() + +%package -n python3-multihist +Summary: Convenience wrappers around numpy histograms +Provides: python-multihist +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-multihist +`https://github.com/JelleAalbers/multihist` +Thin wrapper around numpy's histogram and histogramdd. +Numpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class +with methods for adding new data to existing histograms, take averages, projecting, etc. +For 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std). +For d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging. +**NB**: For a faster and richer histogram package, check out `hist <https://github.com/scikit-hep/hist>`_ from scikit-hep. Alternatively, look at its parent library `boost-histogram <https://github.com/scikit-hep/boost-histogram>`_, which has `numpy-compatible features <https://boost-histogram.readthedocs.io/en/latest/usage/numpy.html>`_. Multihist was created back in 2015, long before those libraries existed. +Synopsis:: + # Create histograms just like from numpy... + m = Hist1d([0, 3, 1, 6, 2, 9], bins=3) + # ...or add data incrementally: + m = Hist1d(bins=100, range=(-3, 4)) + m.add(np.random.normal(0, 0.5, 10**4)) + m.add(np.random.normal(2, 0.2, 10**3)) + # Get the data back out: + print(m.histogram, m.bin_edges) + # Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std + plt.plot(m.bin_centers, m.normalized_histogram, label="Normalized histogram", drawstyle='steps') + plt.plot(m.bin_centers, m.density, label="Empirical PDF", drawstyle='steps') + plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", drawstyle='steps') + plt.title("Estimated mean %0.2f, estimated std %0.2f" % (m.mean, m.std)) + plt.legend(loc='best') + plt.show() + # Slicing and arithmetic behave just like ordinary ndarrays + print("The fourth bin has %d entries" % m[3]) + m[1:4] += 4 + 2 * m[-27:-24] + print("Now it has %d entries" % m[3]) + # Of course I couldn't resist adding a canned plotting function: + m.plot() + plt.show() + # Create and show a 2d histogram. Axis names are optional. + m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y']) + m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6)) + m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6)) + m2.plot() + plt.show() + # x and y projections return Hist1d objects + m2.projection('x').plot(label='x projection') + m2.projection(1).plot(label='y projection') + plt.legend() + plt.show() + +%package help +Summary: Development documents and examples for multihist +Provides: python3-multihist-doc +%description help +`https://github.com/JelleAalbers/multihist` +Thin wrapper around numpy's histogram and histogramdd. +Numpy has great histogram functions, which return (histogram, bin_edges) tuples. This package wraps these in a class +with methods for adding new data to existing histograms, take averages, projecting, etc. +For 1-dimensional histograms you can access cumulative and density information, as well as basic statistics (mean and std). +For d-dimensional histograms you can name the axes, and refer to them by their names when projecting / summing / averaging. +**NB**: For a faster and richer histogram package, check out `hist <https://github.com/scikit-hep/hist>`_ from scikit-hep. Alternatively, look at its parent library `boost-histogram <https://github.com/scikit-hep/boost-histogram>`_, which has `numpy-compatible features <https://boost-histogram.readthedocs.io/en/latest/usage/numpy.html>`_. Multihist was created back in 2015, long before those libraries existed. +Synopsis:: + # Create histograms just like from numpy... + m = Hist1d([0, 3, 1, 6, 2, 9], bins=3) + # ...or add data incrementally: + m = Hist1d(bins=100, range=(-3, 4)) + m.add(np.random.normal(0, 0.5, 10**4)) + m.add(np.random.normal(2, 0.2, 10**3)) + # Get the data back out: + print(m.histogram, m.bin_edges) + # Access derived quantities like bin_centers, normalized_histogram, density, cumulative_density, mean, std + plt.plot(m.bin_centers, m.normalized_histogram, label="Normalized histogram", drawstyle='steps') + plt.plot(m.bin_centers, m.density, label="Empirical PDF", drawstyle='steps') + plt.plot(m.bin_centers, m.cumulative_density, label="Empirical CDF", drawstyle='steps') + plt.title("Estimated mean %0.2f, estimated std %0.2f" % (m.mean, m.std)) + plt.legend(loc='best') + plt.show() + # Slicing and arithmetic behave just like ordinary ndarrays + print("The fourth bin has %d entries" % m[3]) + m[1:4] += 4 + 2 * m[-27:-24] + print("Now it has %d entries" % m[3]) + # Of course I couldn't resist adding a canned plotting function: + m.plot() + plt.show() + # Create and show a 2d histogram. Axis names are optional. + m2 = Histdd(bins=100, range=[[-5, 3], [-3, 5]], axis_names=['x', 'y']) + m2.add(np.random.normal(1, 1, 10**6), np.random.normal(1, 1, 10**6)) + m2.add(np.random.normal(-2, 1, 10**6), np.random.normal(2, 1, 10**6)) + m2.plot() + plt.show() + # x and y projections return Hist1d objects + m2.projection('x').plot(label='x projection') + m2.projection(1).plot(label='y projection') + plt.legend() + plt.show() + +%prep +%autosetup -n multihist-0.6.5 + +%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-multihist -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.5-1 +- Package Spec generated @@ -0,0 +1 @@ +e411fd3cb227d2bc0066dcb1248c280f multihist-0.6.5.tar.gz |