automatic import of python-sxsopeneuler20.03

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diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/sxs-2022.4.5.tar.gz
diff --git a/python-sxs.spec b/python-sxs.spec
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--- /dev/null
+++ b/python-sxs.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-sxs
+Version:	2022.4.5
+Release:	1
+Summary:	Interface to data produced by the Simulating eXtreme Spacetimes collaboration
+License:	MIT
+URL:		https://github.com/sxs-collaboration/sxs
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f8/12/c501831cdc626b032b01cbf2d97a6bdd3db0b645ed6ef43dca1bd7ce3103/sxs-2022.4.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-numba
+Requires:	python3-mkapi
+Requires:	python3-quaternionic
+Requires:	python3-spherical
+Requires:	python3-h5py
+Requires:	python3-inflection
+Requires:	python3-requests
+Requires:	python3-pytest-forked
+Requires:	python3-tqdm
+Requires:	python3-pytz
+Requires:	python3-urllib3
+Requires:	python3-pandas
+Requires:	python3-mkdocs
+Requires:	python3-black
+Requires:	python3-pymdown-extensions
+Requires:	python3-spinsfast
+Requires:	python3-ipywidgets
+Requires:	python3-ipykernel
+Requires:	python3-jupyter_contrib_nbextensions
+Requires:	python3-jupyterlab
+Requires:	python3-line_profiler
+Requires:	python3-memory_profiler
+Requires:	python3-matplotlib
+Requires:	python3-sympy
+Requires:	python3-corner
+Requires:	python3-qgrid
+Requires:	python3-rise
+Requires:	python3-numpy-quaternion
+Requires:	python3-scri
+Requires:	python3-pylatexenc
+Requires:	python3-feedparser
+Requires:	python3-caltechdata-api
+
+%description
+[![Test Status](https://github.com/sxs-collaboration/sxs/workflows/tests/badge.svg)](https://github.com/sxs-collaboration/sxs/actions)
+[![Documentation Status](https://readthedocs.org/projects/sxs/badge/?version=main)](https://sxs.readthedocs.io/en/main/?badge=main)
+[![PyPI Version](https://img.shields.io/pypi/v/sxs?color=)](https://pypi.org/project/sxs/)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/sxs.svg?color=)](https://anaconda.org/conda-forge/sxs)
+[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/sxs-collaboration/sxs/blob/main/LICENSE)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/moble/sxs_notebooks/master)
+
+
+# Simulating eXtreme Spacetimes python package
+
+The `sxs` python package provides a high-level interface for using data
+produced by the SXS collaboration.  In particular, the function `sxs.load` can
+automatically find, download, and load data, returning objects that provide
+common interfaces to the various types of data, without forcing the user to
+worry about details like data formats or where to find the data.  It can also
+automatically select the newest or highest-resolution dataset for a given
+simulation, or return a range of versions or resolutions.  Currently, the
+high-level objects encapsulate
+
+  * Catalog — a listing of all data produced by the SXS collaboration
+  * Metadata — data describing the simulation parameters
+  * Horizons — time-series data describing the apparent horizons
+  * Waveforms — time-series data describing the extrapolated gravitational-wave
+    modes
+
+
+## Installation
+
+Because this package is pure python code, installation is very simple.  In
+particular, with a reasonably modern installation, you can just run a command
+like
+
+```bash
+conda install -c conda-forge sxs
+```
+
+or
+
+```bash
+python -m pip install sxs
+```
+
+Here, `conda` requires the [conda](https://docs.anaconda.com/anaconda/install/)
+installation of python, which is the most recommended approach for scientific
+python; the second command assumes that you have an appropriate python
+environment set up in some other way.  Either of these commands will download
+and install the `sxs` package and its most vital requirements.
+
+If you want to install all the goodies that enable things like jupyter
+notebooks with plots and interactive tables, you could run
+
+```bash
+conda install -c conda-forge sxs-ecosystem
+```
+
+or
+
+```bash
+python -m pip install sxs[ecosystem]
+```
+
+You will probably also want to set some sensible defaults to automatically
+download and cache data:
+
+```bash
+python -c "import sxs; sxs.write_config(download=True, cache=True)"
+```
+
+This will write a configuration file in the directory returned by
+`sxs.sxs_directory("config")`, and downloaded data will be cached in the
+directory returned by `sxs.sxs_directory("cache")`.  See [that function's
+documentation](https://sxs.readthedocs.io/en/main/api/sxs.utilities.sxs_directories/#sxsutilitiessxs_directoriessxs_directory)
+for details.
+
+
+## Usage
+
+An extensive demonstration of this package's capabilities is available
+[here](https://mybinder.org/v2/gh/moble/sxs_notebooks/master), in the form of
+interactive jupyter notebooks that are actually running this code and some
+pre-downloaded data.  The following is just a very brief overview of the `sxs`
+package's main components.
+
+There are four important objects to understand in this package:
+
+```python
+import sxs
+
+catalog = sxs.load("catalog")
+metadata = sxs.load("SXS:BBH:0123/Lev/metadata.json")
+horizons = sxs.load("SXS:BBH:0123/Lev/Horizons.h5")
+waveform = sxs.load("SXS:BBH:0123/Lev/rhOverM", extrapolation_order=2)
+```
+
+[The `catalog`
+object](https://sxs.readthedocs.io/en/main/api/sxs.catalog.catalog/#sxs.catalog.catalog.Catalog)
+contains information about every simulation in the catalog, including all
+available data files, and information about how to get them.  You probably
+don't need to actually know about details like where to get the data, but
+`catalog` can help you find the simulations you care about.  Most importantly,
+`catalog.simulations` is a `dict` object, where the keys are names of
+simulations (like "SXS:BBH:0123") and the values are the same types as [the
+`metadata`
+object](https://sxs.readthedocs.io/en/main/api/sxs.metadata.metadata/#sxs.metadata.metadata.Metadata),
+which contains metadata about that simulation — things like mass ratio, spins,
+etc.  This `metadata` reflects the actual output of the simulations, which
+leads to some inconsistencies in their formats.  A more consistent interface
+(though it is biased toward returning NaNs where a human might glean more
+information) is provided by `catalog.table`, which returns a
+[`pandas`](https://pandas.pydata.org/docs/) `DataFrame` with specific data
+types for each column.
+
+The actual data itself is primarily contained in the next two objects.  [The
+`horizons`
+object](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.Horizons)
+has three attributes — `horizons.A`, `horizons.B`, and `horizons.C` — typically
+representing the original two horizons of the black-hole binary and the common
+horizon that forms at merger.  In matter simulations, one or more of these may
+be `None`.  Otherwise, each of these three is a
+[`HorizonQuantities`](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.HorizonQuantities)
+object, containing several timeseries relating to mass, spin, and position.
+
+Finally, the
+[`waveform`](https://sxs.readthedocs.io/en/main/api/sxs.waveforms.waveform_modes/#sxs.waveforms.waveform_modes.WaveformModes)
+encapsulates the modes of the waveform and the corresponding time information,
+along with relevant metadata like data type, spin weight, etc., and useful
+features like numpy-array-style slicing.
+
+
+%package -n python3-sxs
+Summary:	Interface to data produced by the Simulating eXtreme Spacetimes collaboration
+Provides:	python-sxs
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-sxs
+[![Test Status](https://github.com/sxs-collaboration/sxs/workflows/tests/badge.svg)](https://github.com/sxs-collaboration/sxs/actions)
+[![Documentation Status](https://readthedocs.org/projects/sxs/badge/?version=main)](https://sxs.readthedocs.io/en/main/?badge=main)
+[![PyPI Version](https://img.shields.io/pypi/v/sxs?color=)](https://pypi.org/project/sxs/)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/sxs.svg?color=)](https://anaconda.org/conda-forge/sxs)
+[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/sxs-collaboration/sxs/blob/main/LICENSE)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/moble/sxs_notebooks/master)
+
+
+# Simulating eXtreme Spacetimes python package
+
+The `sxs` python package provides a high-level interface for using data
+produced by the SXS collaboration.  In particular, the function `sxs.load` can
+automatically find, download, and load data, returning objects that provide
+common interfaces to the various types of data, without forcing the user to
+worry about details like data formats or where to find the data.  It can also
+automatically select the newest or highest-resolution dataset for a given
+simulation, or return a range of versions or resolutions.  Currently, the
+high-level objects encapsulate
+
+  * Catalog — a listing of all data produced by the SXS collaboration
+  * Metadata — data describing the simulation parameters
+  * Horizons — time-series data describing the apparent horizons
+  * Waveforms — time-series data describing the extrapolated gravitational-wave
+    modes
+
+
+## Installation
+
+Because this package is pure python code, installation is very simple.  In
+particular, with a reasonably modern installation, you can just run a command
+like
+
+```bash
+conda install -c conda-forge sxs
+```
+
+or
+
+```bash
+python -m pip install sxs
+```
+
+Here, `conda` requires the [conda](https://docs.anaconda.com/anaconda/install/)
+installation of python, which is the most recommended approach for scientific
+python; the second command assumes that you have an appropriate python
+environment set up in some other way.  Either of these commands will download
+and install the `sxs` package and its most vital requirements.
+
+If you want to install all the goodies that enable things like jupyter
+notebooks with plots and interactive tables, you could run
+
+```bash
+conda install -c conda-forge sxs-ecosystem
+```
+
+or
+
+```bash
+python -m pip install sxs[ecosystem]
+```
+
+You will probably also want to set some sensible defaults to automatically
+download and cache data:
+
+```bash
+python -c "import sxs; sxs.write_config(download=True, cache=True)"
+```
+
+This will write a configuration file in the directory returned by
+`sxs.sxs_directory("config")`, and downloaded data will be cached in the
+directory returned by `sxs.sxs_directory("cache")`.  See [that function's
+documentation](https://sxs.readthedocs.io/en/main/api/sxs.utilities.sxs_directories/#sxsutilitiessxs_directoriessxs_directory)
+for details.
+
+
+## Usage
+
+An extensive demonstration of this package's capabilities is available
+[here](https://mybinder.org/v2/gh/moble/sxs_notebooks/master), in the form of
+interactive jupyter notebooks that are actually running this code and some
+pre-downloaded data.  The following is just a very brief overview of the `sxs`
+package's main components.
+
+There are four important objects to understand in this package:
+
+```python
+import sxs
+
+catalog = sxs.load("catalog")
+metadata = sxs.load("SXS:BBH:0123/Lev/metadata.json")
+horizons = sxs.load("SXS:BBH:0123/Lev/Horizons.h5")
+waveform = sxs.load("SXS:BBH:0123/Lev/rhOverM", extrapolation_order=2)
+```
+
+[The `catalog`
+object](https://sxs.readthedocs.io/en/main/api/sxs.catalog.catalog/#sxs.catalog.catalog.Catalog)
+contains information about every simulation in the catalog, including all
+available data files, and information about how to get them.  You probably
+don't need to actually know about details like where to get the data, but
+`catalog` can help you find the simulations you care about.  Most importantly,
+`catalog.simulations` is a `dict` object, where the keys are names of
+simulations (like "SXS:BBH:0123") and the values are the same types as [the
+`metadata`
+object](https://sxs.readthedocs.io/en/main/api/sxs.metadata.metadata/#sxs.metadata.metadata.Metadata),
+which contains metadata about that simulation — things like mass ratio, spins,
+etc.  This `metadata` reflects the actual output of the simulations, which
+leads to some inconsistencies in their formats.  A more consistent interface
+(though it is biased toward returning NaNs where a human might glean more
+information) is provided by `catalog.table`, which returns a
+[`pandas`](https://pandas.pydata.org/docs/) `DataFrame` with specific data
+types for each column.
+
+The actual data itself is primarily contained in the next two objects.  [The
+`horizons`
+object](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.Horizons)
+has three attributes — `horizons.A`, `horizons.B`, and `horizons.C` — typically
+representing the original two horizons of the black-hole binary and the common
+horizon that forms at merger.  In matter simulations, one or more of these may
+be `None`.  Otherwise, each of these three is a
+[`HorizonQuantities`](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.HorizonQuantities)
+object, containing several timeseries relating to mass, spin, and position.
+
+Finally, the
+[`waveform`](https://sxs.readthedocs.io/en/main/api/sxs.waveforms.waveform_modes/#sxs.waveforms.waveform_modes.WaveformModes)
+encapsulates the modes of the waveform and the corresponding time information,
+along with relevant metadata like data type, spin weight, etc., and useful
+features like numpy-array-style slicing.
+
+
+%package help
+Summary:	Development documents and examples for sxs
+Provides:	python3-sxs-doc
+%description help
+[![Test Status](https://github.com/sxs-collaboration/sxs/workflows/tests/badge.svg)](https://github.com/sxs-collaboration/sxs/actions)
+[![Documentation Status](https://readthedocs.org/projects/sxs/badge/?version=main)](https://sxs.readthedocs.io/en/main/?badge=main)
+[![PyPI Version](https://img.shields.io/pypi/v/sxs?color=)](https://pypi.org/project/sxs/)
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/sxs.svg?color=)](https://anaconda.org/conda-forge/sxs)
+[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/sxs-collaboration/sxs/blob/main/LICENSE)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/moble/sxs_notebooks/master)
+
+
+# Simulating eXtreme Spacetimes python package
+
+The `sxs` python package provides a high-level interface for using data
+produced by the SXS collaboration.  In particular, the function `sxs.load` can
+automatically find, download, and load data, returning objects that provide
+common interfaces to the various types of data, without forcing the user to
+worry about details like data formats or where to find the data.  It can also
+automatically select the newest or highest-resolution dataset for a given
+simulation, or return a range of versions or resolutions.  Currently, the
+high-level objects encapsulate
+
+  * Catalog — a listing of all data produced by the SXS collaboration
+  * Metadata — data describing the simulation parameters
+  * Horizons — time-series data describing the apparent horizons
+  * Waveforms — time-series data describing the extrapolated gravitational-wave
+    modes
+
+
+## Installation
+
+Because this package is pure python code, installation is very simple.  In
+particular, with a reasonably modern installation, you can just run a command
+like
+
+```bash
+conda install -c conda-forge sxs
+```
+
+or
+
+```bash
+python -m pip install sxs
+```
+
+Here, `conda` requires the [conda](https://docs.anaconda.com/anaconda/install/)
+installation of python, which is the most recommended approach for scientific
+python; the second command assumes that you have an appropriate python
+environment set up in some other way.  Either of these commands will download
+and install the `sxs` package and its most vital requirements.
+
+If you want to install all the goodies that enable things like jupyter
+notebooks with plots and interactive tables, you could run
+
+```bash
+conda install -c conda-forge sxs-ecosystem
+```
+
+or
+
+```bash
+python -m pip install sxs[ecosystem]
+```
+
+You will probably also want to set some sensible defaults to automatically
+download and cache data:
+
+```bash
+python -c "import sxs; sxs.write_config(download=True, cache=True)"
+```
+
+This will write a configuration file in the directory returned by
+`sxs.sxs_directory("config")`, and downloaded data will be cached in the
+directory returned by `sxs.sxs_directory("cache")`.  See [that function's
+documentation](https://sxs.readthedocs.io/en/main/api/sxs.utilities.sxs_directories/#sxsutilitiessxs_directoriessxs_directory)
+for details.
+
+
+## Usage
+
+An extensive demonstration of this package's capabilities is available
+[here](https://mybinder.org/v2/gh/moble/sxs_notebooks/master), in the form of
+interactive jupyter notebooks that are actually running this code and some
+pre-downloaded data.  The following is just a very brief overview of the `sxs`
+package's main components.
+
+There are four important objects to understand in this package:
+
+```python
+import sxs
+
+catalog = sxs.load("catalog")
+metadata = sxs.load("SXS:BBH:0123/Lev/metadata.json")
+horizons = sxs.load("SXS:BBH:0123/Lev/Horizons.h5")
+waveform = sxs.load("SXS:BBH:0123/Lev/rhOverM", extrapolation_order=2)
+```
+
+[The `catalog`
+object](https://sxs.readthedocs.io/en/main/api/sxs.catalog.catalog/#sxs.catalog.catalog.Catalog)
+contains information about every simulation in the catalog, including all
+available data files, and information about how to get them.  You probably
+don't need to actually know about details like where to get the data, but
+`catalog` can help you find the simulations you care about.  Most importantly,
+`catalog.simulations` is a `dict` object, where the keys are names of
+simulations (like "SXS:BBH:0123") and the values are the same types as [the
+`metadata`
+object](https://sxs.readthedocs.io/en/main/api/sxs.metadata.metadata/#sxs.metadata.metadata.Metadata),
+which contains metadata about that simulation — things like mass ratio, spins,
+etc.  This `metadata` reflects the actual output of the simulations, which
+leads to some inconsistencies in their formats.  A more consistent interface
+(though it is biased toward returning NaNs where a human might glean more
+information) is provided by `catalog.table`, which returns a
+[`pandas`](https://pandas.pydata.org/docs/) `DataFrame` with specific data
+types for each column.
+
+The actual data itself is primarily contained in the next two objects.  [The
+`horizons`
+object](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.Horizons)
+has three attributes — `horizons.A`, `horizons.B`, and `horizons.C` — typically
+representing the original two horizons of the black-hole binary and the common
+horizon that forms at merger.  In matter simulations, one or more of these may
+be `None`.  Otherwise, each of these three is a
+[`HorizonQuantities`](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.HorizonQuantities)
+object, containing several timeseries relating to mass, spin, and position.
+
+Finally, the
+[`waveform`](https://sxs.readthedocs.io/en/main/api/sxs.waveforms.waveform_modes/#sxs.waveforms.waveform_modes.WaveformModes)
+encapsulates the modes of the waveform and the corresponding time information,
+along with relevant metadata like data type, spin weight, etc., and useful
+features like numpy-array-style slicing.
+
+
+%prep
+%autosetup -n sxs-2022.4.5
+
+%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-sxs -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2022.4.5-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..7737afe
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d4897963511eaf9e6490c6730e2343de  sxs-2022.4.5.tar.gz