automatic import of python-s2cloudless

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diff --git a/.gitignore b/.gitignore
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+/s2cloudless-1.7.0.tar.gz
diff --git a/python-s2cloudless.spec b/python-s2cloudless.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-s2cloudless
+Version:	1.7.0
+Release:	1
+Summary:	Sentinel Hub's cloud detector for Sentinel-2 imagery
+License:	CC BY-SA 4.0
+URL:		https://github.com/sentinel-hub/sentinel2-cloud-detector
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/a6/03/2b226619795b87bac5367fd5dd90bd7df0199cf4a9fc0905926f27d8a91f/s2cloudless-1.7.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-lightgbm
+Requires:	python3-numpy
+Requires:	python3-scikit-image
+Requires:	python3-scipy
+Requires:	python3-sentinelhub
+Requires:	python3-typing-extensions
+Requires:	python3-codecov
+Requires:	python3-mypy
+Requires:	python3-pre-commit
+Requires:	python3-pylint
+Requires:	python3-pytest-cov
+Requires:	python3-pytest
+Requires:	python3-twine
+
+%description
+[![Package version](https://badge.fury.io/py/s2cloudless.svg)](https://pypi.org/project/s2cloudless)
+[![Conda version](https://img.shields.io/conda/vn/conda-forge/s2cloudless.svg)](https://anaconda.org/conda-forge/s2cloudless)
+[![Supported Python versions](https://img.shields.io/pypi/pyversions/s2cloudless.svg?style=flat-square)](https://pypi.org/project/s2cloudless)
+[![Build Status](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions/workflows/ci_action.yml/badge.svg?branch=master)](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions)
+[![Overall downloads](https://pepy.tech/badge/s2cloudless)](https://pepy.tech/project/s2cloudless)
+[![Last month downloads](https://pepy.tech/badge/s2cloudless/month)](https://pepy.tech/project/s2cloudless)
+[![Code coverage](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector/branch/master/graph/badge.svg)](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector)
+
+# Sentinel Hub's cloud detector for Sentinel-2 imagery
+
+**NOTE: s2cloudless masks are now available as a precomputed layer within Sentinel Hub. Check the [announcement blog post](https://medium.com/sentinel-hub/cloud-masks-at-your-service-6e5b2cb2ce8a) and [technical documentation](https://docs.sentinel-hub.com/api/latest/#/API/data_access?id=cloud-masks-and-cloud-probabilities).**
+
+The **s2cloudless** Python package provides automated cloud detection in
+Sentinel-2 imagery. The classification is based on a *single-scene pixel-based cloud detector*
+developed by Sentinel Hub's research team and is described in more detail
+[in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).
+
+The **s2cloudless** algorithm was part of an international collaborative effort aimed at intercomparing cloud detection algorithms. The s2cloudless algorithm was validated together with 9 other algorithms on 4 different test datasets and in all cases found to be on the Pareto front. See [the paper](https://www.sciencedirect.com/science/article/pii/S0034425722001043?via%3Dihub)
+
+## Installation
+
+The package requires a Python version >= 3.7. The package is available on
+the PyPI package manager and can be installed with
+
+```
+$ pip install s2cloudless
+```
+
+To install the package manually, clone the repository and
+```
+$ pip install .
+```
+
+One of `s2cloudless` dependencies is `lightgbm` package. If having problems during installation, please
+check the [LightGBM installation guide](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html).
+
+Before installing `s2cloudless` on **Windows**, it is recommended to install package `shapely` from
+[Unofficial Windows wheels repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/)
+
+## Input: Sentinel-2 scenes
+
+The inputs to the cloud detector are Sentinel-2 images. In particular, the cloud detector requires the following 10 Sentinel-2 band reflectances: B01, B02, B04, B05, B08, B8A, B09, B10, B11, B12, which are obtained from raw reflectance values in the following way: `B_i/10000`. From product baseline `04.00` onward additional harmonization factors have to be applied to data according to [instructions from ESA](https://sentinels.copernicus.eu/en/web/sentinel/-/copernicus-sentinel-2-major-products-upgrade-upcoming).
+
+You don't need to worry about any of this, if you are using Sentinel-2 data obtained from [Sentinel Hub Process API](https://docs.sentinel-hub.com/api/latest/api/process/). By default, the data is already harmonized according to [documentation](https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l1c/#harmonize-values). The API is supported in Python with [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) package and used within `s2cloudless.CloudMaskRequest` class.
+
+## Examples
+
+A Jupyter notebook on how to use the cloud detector to produce cloud mask or cloud probability map
+can be found in the [examples folder](https://github.com/sentinel-hub/sentinel2-cloud-detector/tree/master/examples).
+
+## License
+
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">
+<img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
+<br />
+This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+
+
+
+
+%package -n python3-s2cloudless
+Summary:	Sentinel Hub's cloud detector for Sentinel-2 imagery
+Provides:	python-s2cloudless
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-s2cloudless
+[![Package version](https://badge.fury.io/py/s2cloudless.svg)](https://pypi.org/project/s2cloudless)
+[![Conda version](https://img.shields.io/conda/vn/conda-forge/s2cloudless.svg)](https://anaconda.org/conda-forge/s2cloudless)
+[![Supported Python versions](https://img.shields.io/pypi/pyversions/s2cloudless.svg?style=flat-square)](https://pypi.org/project/s2cloudless)
+[![Build Status](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions/workflows/ci_action.yml/badge.svg?branch=master)](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions)
+[![Overall downloads](https://pepy.tech/badge/s2cloudless)](https://pepy.tech/project/s2cloudless)
+[![Last month downloads](https://pepy.tech/badge/s2cloudless/month)](https://pepy.tech/project/s2cloudless)
+[![Code coverage](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector/branch/master/graph/badge.svg)](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector)
+
+# Sentinel Hub's cloud detector for Sentinel-2 imagery
+
+**NOTE: s2cloudless masks are now available as a precomputed layer within Sentinel Hub. Check the [announcement blog post](https://medium.com/sentinel-hub/cloud-masks-at-your-service-6e5b2cb2ce8a) and [technical documentation](https://docs.sentinel-hub.com/api/latest/#/API/data_access?id=cloud-masks-and-cloud-probabilities).**
+
+The **s2cloudless** Python package provides automated cloud detection in
+Sentinel-2 imagery. The classification is based on a *single-scene pixel-based cloud detector*
+developed by Sentinel Hub's research team and is described in more detail
+[in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).
+
+The **s2cloudless** algorithm was part of an international collaborative effort aimed at intercomparing cloud detection algorithms. The s2cloudless algorithm was validated together with 9 other algorithms on 4 different test datasets and in all cases found to be on the Pareto front. See [the paper](https://www.sciencedirect.com/science/article/pii/S0034425722001043?via%3Dihub)
+
+## Installation
+
+The package requires a Python version >= 3.7. The package is available on
+the PyPI package manager and can be installed with
+
+```
+$ pip install s2cloudless
+```
+
+To install the package manually, clone the repository and
+```
+$ pip install .
+```
+
+One of `s2cloudless` dependencies is `lightgbm` package. If having problems during installation, please
+check the [LightGBM installation guide](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html).
+
+Before installing `s2cloudless` on **Windows**, it is recommended to install package `shapely` from
+[Unofficial Windows wheels repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/)
+
+## Input: Sentinel-2 scenes
+
+The inputs to the cloud detector are Sentinel-2 images. In particular, the cloud detector requires the following 10 Sentinel-2 band reflectances: B01, B02, B04, B05, B08, B8A, B09, B10, B11, B12, which are obtained from raw reflectance values in the following way: `B_i/10000`. From product baseline `04.00` onward additional harmonization factors have to be applied to data according to [instructions from ESA](https://sentinels.copernicus.eu/en/web/sentinel/-/copernicus-sentinel-2-major-products-upgrade-upcoming).
+
+You don't need to worry about any of this, if you are using Sentinel-2 data obtained from [Sentinel Hub Process API](https://docs.sentinel-hub.com/api/latest/api/process/). By default, the data is already harmonized according to [documentation](https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l1c/#harmonize-values). The API is supported in Python with [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) package and used within `s2cloudless.CloudMaskRequest` class.
+
+## Examples
+
+A Jupyter notebook on how to use the cloud detector to produce cloud mask or cloud probability map
+can be found in the [examples folder](https://github.com/sentinel-hub/sentinel2-cloud-detector/tree/master/examples).
+
+## License
+
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">
+<img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
+<br />
+This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+
+
+
+
+%package help
+Summary:	Development documents and examples for s2cloudless
+Provides:	python3-s2cloudless-doc
+%description help
+[![Package version](https://badge.fury.io/py/s2cloudless.svg)](https://pypi.org/project/s2cloudless)
+[![Conda version](https://img.shields.io/conda/vn/conda-forge/s2cloudless.svg)](https://anaconda.org/conda-forge/s2cloudless)
+[![Supported Python versions](https://img.shields.io/pypi/pyversions/s2cloudless.svg?style=flat-square)](https://pypi.org/project/s2cloudless)
+[![Build Status](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions/workflows/ci_action.yml/badge.svg?branch=master)](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions)
+[![Overall downloads](https://pepy.tech/badge/s2cloudless)](https://pepy.tech/project/s2cloudless)
+[![Last month downloads](https://pepy.tech/badge/s2cloudless/month)](https://pepy.tech/project/s2cloudless)
+[![Code coverage](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector/branch/master/graph/badge.svg)](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector)
+
+# Sentinel Hub's cloud detector for Sentinel-2 imagery
+
+**NOTE: s2cloudless masks are now available as a precomputed layer within Sentinel Hub. Check the [announcement blog post](https://medium.com/sentinel-hub/cloud-masks-at-your-service-6e5b2cb2ce8a) and [technical documentation](https://docs.sentinel-hub.com/api/latest/#/API/data_access?id=cloud-masks-and-cloud-probabilities).**
+
+The **s2cloudless** Python package provides automated cloud detection in
+Sentinel-2 imagery. The classification is based on a *single-scene pixel-based cloud detector*
+developed by Sentinel Hub's research team and is described in more detail
+[in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).
+
+The **s2cloudless** algorithm was part of an international collaborative effort aimed at intercomparing cloud detection algorithms. The s2cloudless algorithm was validated together with 9 other algorithms on 4 different test datasets and in all cases found to be on the Pareto front. See [the paper](https://www.sciencedirect.com/science/article/pii/S0034425722001043?via%3Dihub)
+
+## Installation
+
+The package requires a Python version >= 3.7. The package is available on
+the PyPI package manager and can be installed with
+
+```
+$ pip install s2cloudless
+```
+
+To install the package manually, clone the repository and
+```
+$ pip install .
+```
+
+One of `s2cloudless` dependencies is `lightgbm` package. If having problems during installation, please
+check the [LightGBM installation guide](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html).
+
+Before installing `s2cloudless` on **Windows**, it is recommended to install package `shapely` from
+[Unofficial Windows wheels repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/)
+
+## Input: Sentinel-2 scenes
+
+The inputs to the cloud detector are Sentinel-2 images. In particular, the cloud detector requires the following 10 Sentinel-2 band reflectances: B01, B02, B04, B05, B08, B8A, B09, B10, B11, B12, which are obtained from raw reflectance values in the following way: `B_i/10000`. From product baseline `04.00` onward additional harmonization factors have to be applied to data according to [instructions from ESA](https://sentinels.copernicus.eu/en/web/sentinel/-/copernicus-sentinel-2-major-products-upgrade-upcoming).
+
+You don't need to worry about any of this, if you are using Sentinel-2 data obtained from [Sentinel Hub Process API](https://docs.sentinel-hub.com/api/latest/api/process/). By default, the data is already harmonized according to [documentation](https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l1c/#harmonize-values). The API is supported in Python with [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) package and used within `s2cloudless.CloudMaskRequest` class.
+
+## Examples
+
+A Jupyter notebook on how to use the cloud detector to produce cloud mask or cloud probability map
+can be found in the [examples folder](https://github.com/sentinel-hub/sentinel2-cloud-detector/tree/master/examples).
+
+## License
+
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">
+<img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
+<br />
+This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+
+
+
+
+%prep
+%autosetup -n s2cloudless-1.7.0
+
+%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-s2cloudless -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.7.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..426485b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+62214e9dd4618f6ba740aa88fd29d3e4  s2cloudless-1.7.0.tar.gz