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%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
|
