automatic import of python-numbaggopeneuler20.03

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diff --git a/.gitignore b/.gitignore
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+/numbagg-0.2.2.tar.gz
diff --git a/python-numbagg.spec b/python-numbagg.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-numbagg
+Version:	0.2.2
+Release:	1
+Summary:	Fast N-dimensional aggregation functions with Numba
+License:	BSD
+URL:		https://github.com/numbagg/numbagg
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/65/cd/604a349bfd9315798c6a5f315e4e4f19298df63ab53429c3c82806334123/numbagg-0.2.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-numba
+
+%description
+# Numbagg: Fast N-dimensional aggregation functions with Numba
+
+[![GitHub Workflow CI Status](https://img.shields.io/github/workflow/status/numbagg/numbagg/Test?logo=github&style=for-the-badge)]((https://github.com/numbagg/numbagg/actions?query=workflow%3ATest))
+[![PyPI Version](https://img.shields.io/pypi/v/numbagg?style=for-the-badge)](https://pypi.python.org/pypi/numbagg/)
+
+Fast, flexible N-dimensional array functions written with
+[Numba](https://github.com/numba/numba) and NumPy's [generalized
+ufuncs](http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html).
+
+Currently accelerated functions:
+
+- Array functions: `allnan`, `anynan`, `count`, `nanargmax`,
+    `nanargmin`, `nanmax`, `nanmean`, `nanstd`, `nanvar`, `nanmin`,
+    `nansum`
+- Moving window functions: `move_exp_nanmean`, `move_mean`, `move_sum`
+
+Note: Only functions listed here (exposed in Numbagg's top level namespace) are
+supported as part of Numbagg's public API.
+
+## Easy to extend
+
+Numbagg makes it easy to write, in pure Python/NumPy, flexible aggregation
+functions accelerated by Numba. All the hard work is done by Numba's JIT
+compiler and NumPy's gufunc machinery (as wrapped by Numba).
+
+For example, here is how we wrote `nansum`:
+
+```python
+import numpy as np
+from numbagg.decorators import ndreduce
+
+@ndreduce
+def nansum(a):
+    asum = 0.0
+    for ai in a.flat:
+        if not np.isnan(ai):
+            asum += ai
+    return asum
+```
+
+You are welcome to experiment with Numbagg's decorator functions, but these are
+not public APIs (yet): we reserve the right to change them at any time.
+
+We'd rather get your pull requests to add new functions into Numbagg directly!
+
+## Advantages over Bottleneck
+
+- Way less code. Easier to add new functions. No ad-hoc templating
+    system. No Cython!
+- Fast functions still work for >3 dimensions.
+- `axis` argument handles tuples of integers.
+
+Most of the functions in Numbagg (including our test suite) are adapted from
+Bottleneck's battle-hardened implementations. Still, Numbagg is experimental,
+and probably not yet ready for production.
+
+## Benchmarks
+
+Initial benchmarks are quite encouraging. Numbagg/Numba has comparable (slightly
+better) performance than Bottleneck's hand-written C:
+
+```python
+import numbagg
+import numpy as np
+import bottleneck
+
+x = np.random.RandomState(42).randn(1000, 1000)
+x[x < -1] = np.NaN
+
+# timings with numba=0.41.0 and bottleneck=1.2.1
+
+In [2]: %timeit numbagg.nanmean(x)
+1.8 ms ± 92.3 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+In [3]: %timeit numbagg.nanmean(x, axis=0)
+3.63 ms ± 136 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [4]: %timeit numbagg.nanmean(x, axis=1)
+1.81 ms ± 41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
+
+In [5]: %timeit bottleneck.nanmean(x)
+2.22 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [6]: %timeit bottleneck.nanmean(x, axis=0)
+4.45 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [7]: %timeit bottleneck.nanmean(x, axis=1)
+2.19 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+```
+
+## Our approach
+
+Numbagg includes somewhat awkward workarounds for features missing from
+NumPy/Numba:
+
+- It implements its own cache for functions wrapped by Numba's
+  `guvectorize`, because that decorator is rather slow.
+- It does its [own handling of array
+  transposes](https://github.com/numbagg/numbagg/blob/main/numbagg/decorators.py#L69)
+  to handle the `axis` argument, which we hope will [eventually be
+  directly supported](https://github.com/numpy/numpy/issues/5197) by
+  all NumPy gufuncs.
+- It uses some [terrible
+  hacks](https://github.com/numbagg/numbagg/blob/main/numbagg/transform.py) to
+  hide the out-of-bound memory access necessary to write [gufuncs that handle
+  scalar
+  values](https://github.com/numba/numba/blob/main/numba/tests/test_guvectorize_scalar.py)
+  with Numba.
+
+I hope that the need for most of these will eventually go away. In the meantime,
+expect Numbagg to be tightly coupled to Numba and NumPy release cycles.
+
+## License
+
+3-clause BSD. Includes portions of Bottleneck, which is distributed under a
+Simplified BSD license.
+
+
+%package -n python3-numbagg
+Summary:	Fast N-dimensional aggregation functions with Numba
+Provides:	python-numbagg
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-numbagg
+# Numbagg: Fast N-dimensional aggregation functions with Numba
+
+[![GitHub Workflow CI Status](https://img.shields.io/github/workflow/status/numbagg/numbagg/Test?logo=github&style=for-the-badge)]((https://github.com/numbagg/numbagg/actions?query=workflow%3ATest))
+[![PyPI Version](https://img.shields.io/pypi/v/numbagg?style=for-the-badge)](https://pypi.python.org/pypi/numbagg/)
+
+Fast, flexible N-dimensional array functions written with
+[Numba](https://github.com/numba/numba) and NumPy's [generalized
+ufuncs](http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html).
+
+Currently accelerated functions:
+
+- Array functions: `allnan`, `anynan`, `count`, `nanargmax`,
+    `nanargmin`, `nanmax`, `nanmean`, `nanstd`, `nanvar`, `nanmin`,
+    `nansum`
+- Moving window functions: `move_exp_nanmean`, `move_mean`, `move_sum`
+
+Note: Only functions listed here (exposed in Numbagg's top level namespace) are
+supported as part of Numbagg's public API.
+
+## Easy to extend
+
+Numbagg makes it easy to write, in pure Python/NumPy, flexible aggregation
+functions accelerated by Numba. All the hard work is done by Numba's JIT
+compiler and NumPy's gufunc machinery (as wrapped by Numba).
+
+For example, here is how we wrote `nansum`:
+
+```python
+import numpy as np
+from numbagg.decorators import ndreduce
+
+@ndreduce
+def nansum(a):
+    asum = 0.0
+    for ai in a.flat:
+        if not np.isnan(ai):
+            asum += ai
+    return asum
+```
+
+You are welcome to experiment with Numbagg's decorator functions, but these are
+not public APIs (yet): we reserve the right to change them at any time.
+
+We'd rather get your pull requests to add new functions into Numbagg directly!
+
+## Advantages over Bottleneck
+
+- Way less code. Easier to add new functions. No ad-hoc templating
+    system. No Cython!
+- Fast functions still work for >3 dimensions.
+- `axis` argument handles tuples of integers.
+
+Most of the functions in Numbagg (including our test suite) are adapted from
+Bottleneck's battle-hardened implementations. Still, Numbagg is experimental,
+and probably not yet ready for production.
+
+## Benchmarks
+
+Initial benchmarks are quite encouraging. Numbagg/Numba has comparable (slightly
+better) performance than Bottleneck's hand-written C:
+
+```python
+import numbagg
+import numpy as np
+import bottleneck
+
+x = np.random.RandomState(42).randn(1000, 1000)
+x[x < -1] = np.NaN
+
+# timings with numba=0.41.0 and bottleneck=1.2.1
+
+In [2]: %timeit numbagg.nanmean(x)
+1.8 ms ± 92.3 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+In [3]: %timeit numbagg.nanmean(x, axis=0)
+3.63 ms ± 136 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [4]: %timeit numbagg.nanmean(x, axis=1)
+1.81 ms ± 41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
+
+In [5]: %timeit bottleneck.nanmean(x)
+2.22 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [6]: %timeit bottleneck.nanmean(x, axis=0)
+4.45 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [7]: %timeit bottleneck.nanmean(x, axis=1)
+2.19 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+```
+
+## Our approach
+
+Numbagg includes somewhat awkward workarounds for features missing from
+NumPy/Numba:
+
+- It implements its own cache for functions wrapped by Numba's
+  `guvectorize`, because that decorator is rather slow.
+- It does its [own handling of array
+  transposes](https://github.com/numbagg/numbagg/blob/main/numbagg/decorators.py#L69)
+  to handle the `axis` argument, which we hope will [eventually be
+  directly supported](https://github.com/numpy/numpy/issues/5197) by
+  all NumPy gufuncs.
+- It uses some [terrible
+  hacks](https://github.com/numbagg/numbagg/blob/main/numbagg/transform.py) to
+  hide the out-of-bound memory access necessary to write [gufuncs that handle
+  scalar
+  values](https://github.com/numba/numba/blob/main/numba/tests/test_guvectorize_scalar.py)
+  with Numba.
+
+I hope that the need for most of these will eventually go away. In the meantime,
+expect Numbagg to be tightly coupled to Numba and NumPy release cycles.
+
+## License
+
+3-clause BSD. Includes portions of Bottleneck, which is distributed under a
+Simplified BSD license.
+
+
+%package help
+Summary:	Development documents and examples for numbagg
+Provides:	python3-numbagg-doc
+%description help
+# Numbagg: Fast N-dimensional aggregation functions with Numba
+
+[![GitHub Workflow CI Status](https://img.shields.io/github/workflow/status/numbagg/numbagg/Test?logo=github&style=for-the-badge)]((https://github.com/numbagg/numbagg/actions?query=workflow%3ATest))
+[![PyPI Version](https://img.shields.io/pypi/v/numbagg?style=for-the-badge)](https://pypi.python.org/pypi/numbagg/)
+
+Fast, flexible N-dimensional array functions written with
+[Numba](https://github.com/numba/numba) and NumPy's [generalized
+ufuncs](http://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html).
+
+Currently accelerated functions:
+
+- Array functions: `allnan`, `anynan`, `count`, `nanargmax`,
+    `nanargmin`, `nanmax`, `nanmean`, `nanstd`, `nanvar`, `nanmin`,
+    `nansum`
+- Moving window functions: `move_exp_nanmean`, `move_mean`, `move_sum`
+
+Note: Only functions listed here (exposed in Numbagg's top level namespace) are
+supported as part of Numbagg's public API.
+
+## Easy to extend
+
+Numbagg makes it easy to write, in pure Python/NumPy, flexible aggregation
+functions accelerated by Numba. All the hard work is done by Numba's JIT
+compiler and NumPy's gufunc machinery (as wrapped by Numba).
+
+For example, here is how we wrote `nansum`:
+
+```python
+import numpy as np
+from numbagg.decorators import ndreduce
+
+@ndreduce
+def nansum(a):
+    asum = 0.0
+    for ai in a.flat:
+        if not np.isnan(ai):
+            asum += ai
+    return asum
+```
+
+You are welcome to experiment with Numbagg's decorator functions, but these are
+not public APIs (yet): we reserve the right to change them at any time.
+
+We'd rather get your pull requests to add new functions into Numbagg directly!
+
+## Advantages over Bottleneck
+
+- Way less code. Easier to add new functions. No ad-hoc templating
+    system. No Cython!
+- Fast functions still work for >3 dimensions.
+- `axis` argument handles tuples of integers.
+
+Most of the functions in Numbagg (including our test suite) are adapted from
+Bottleneck's battle-hardened implementations. Still, Numbagg is experimental,
+and probably not yet ready for production.
+
+## Benchmarks
+
+Initial benchmarks are quite encouraging. Numbagg/Numba has comparable (slightly
+better) performance than Bottleneck's hand-written C:
+
+```python
+import numbagg
+import numpy as np
+import bottleneck
+
+x = np.random.RandomState(42).randn(1000, 1000)
+x[x < -1] = np.NaN
+
+# timings with numba=0.41.0 and bottleneck=1.2.1
+
+In [2]: %timeit numbagg.nanmean(x)
+1.8 ms ± 92.3 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
+
+In [3]: %timeit numbagg.nanmean(x, axis=0)
+3.63 ms ± 136 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [4]: %timeit numbagg.nanmean(x, axis=1)
+1.81 ms ± 41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
+
+In [5]: %timeit bottleneck.nanmean(x)
+2.22 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [6]: %timeit bottleneck.nanmean(x, axis=0)
+4.45 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+
+In [7]: %timeit bottleneck.nanmean(x, axis=1)
+2.19 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+```
+
+## Our approach
+
+Numbagg includes somewhat awkward workarounds for features missing from
+NumPy/Numba:
+
+- It implements its own cache for functions wrapped by Numba's
+  `guvectorize`, because that decorator is rather slow.
+- It does its [own handling of array
+  transposes](https://github.com/numbagg/numbagg/blob/main/numbagg/decorators.py#L69)
+  to handle the `axis` argument, which we hope will [eventually be
+  directly supported](https://github.com/numpy/numpy/issues/5197) by
+  all NumPy gufuncs.
+- It uses some [terrible
+  hacks](https://github.com/numbagg/numbagg/blob/main/numbagg/transform.py) to
+  hide the out-of-bound memory access necessary to write [gufuncs that handle
+  scalar
+  values](https://github.com/numba/numba/blob/main/numba/tests/test_guvectorize_scalar.py)
+  with Numba.
+
+I hope that the need for most of these will eventually go away. In the meantime,
+expect Numbagg to be tightly coupled to Numba and NumPy release cycles.
+
+## License
+
+3-clause BSD. Includes portions of Bottleneck, which is distributed under a
+Simplified BSD license.
+
+
+%prep
+%autosetup -n numbagg-0.2.2
+
+%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-numbagg -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..e857800
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f1a9dfe2920089f16f7aece8bcea7c03  numbagg-0.2.2.tar.gz