automatic import of python-data-science-types

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diff --git a/.gitignore b/.gitignore
index e69de29..42999fa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/data-science-types-0.2.23.tar.gz
diff --git a/python-data-science-types.spec b/python-data-science-types.spec
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--- /dev/null
+++ b/python-data-science-types.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-data-science-types
+Version:	0.2.23
+Release:	1
+Summary:	Type stubs for Python machine learning libraries
+License:	Apache License 2.0
+URL:		https://github.com/predictive-analytics-lab/data-science-types
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ae/5f/6c4888c17fa53c551df740d93806c68f1d5eed6b6167747087415e50dccb/data-science-types-0.2.23.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-black
+Requires:	python3-flake8
+Requires:	python3-flake8-pyi
+Requires:	python3-matplotlib
+Requires:	python3-mypy
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-pytest
+
+%description
+# Mypy type stubs for NumPy, pandas, and Matplotlib
+
+[![Join the chat at https://gitter.im/data-science-types/community](https://badges.gitter.im/data-science-types/community.svg)](https://gitter.im/data-science-types/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+This is a [PEP-561][pep-561]-compliant stub-only package
+which provides type information for [matplotlib][matplotlib], [numpy][numpy] and [pandas][pandas].
+The [mypy][mypy] type checker (or pytype or PyCharm) can [recognize][mypy-docs] the types in these packages by installing this package.
+
+### NOTE: This is a work in progress
+
+Many functions are already typed, but a *lot* is still missing (NumPy and pandas are *huge* libraries).
+Chances are, you will see a message from Mypy claiming that a function does not exist when it does exist.
+If you encounter missing functions, we would be delighted for you to send a PR.
+If you are unsure of how to type a function, we can discuss it.
+
+## Installing
+
+You can get this package from PyPI:
+
+```bash
+pip install data-science-types
+```
+
+To get the most up-to-date version, install it directly from GitHub:
+
+```bash
+pip install git+https://github.com/predictive-analytics-lab/data-science-types
+```
+
+Or clone the repository somewhere and do `pip install -e .`.
+
+## Examples
+
+These are the kinds of things that can be checked:
+
+### Array creation
+
+```python
+import numpy as np
+
+arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3])  # OK
+arr2: np.ndarray[np.int32] = np.array([3, 7, 39, -3])  # Type error
+arr3: np.ndarray[np.int32] = np.array([3, 7, 39, -3], dtype=np.int32)  # OK
+arr4: np.ndarray[float] = np.array([3, 7, 39, -3], dtype=float)  # Type error: the type of ndarray can not be just "float"
+arr5: np.ndarray[np.float64] = np.array([3, 7, 39, -3], dtype=float)  # OK
+```
+
+### Operations
+
+```python
+import numpy as np
+
+arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3])
+arr2: np.ndarray[np.int64] = np.array([4, 12, 9, -1])
+
+result1: np.ndarray[np.int64] = np.divide(arr1, arr2)  # Type error
+result2: np.ndarray[np.float64] = np.divide(arr1, arr2)  # OK
+
+compare: np.ndarray[np.bool_] = (arr1 == arr2)
+```
+
+### Reductions
+
+```python
+import numpy as np
+
+arr: np.ndarray[np.float64] = np.array([[1.3, 0.7], [-43.0, 5.6]])
+
+sum1: int = np.sum(arr)  # Type error
+sum2: np.float64 = np.sum(arr)  # OK
+sum3: float = np.sum(arr)  # Also OK: np.float64 is a subclass of float
+sum4: np.ndarray[np.float64] = np.sum(arr, axis=0)  # OK
+
+# the same works with np.max, np.min and np.prod
+```
+
+## Philosophy
+
+The goal is not to recreate the APIs exactly.
+The main goal is to have *useful* checks on our code.
+Often the actual APIs in the libraries is more permissive than the type signatures in our stubs;
+but this is (usually) a feature and not a bug.
+
+## Contributing
+
+We always welcome contributions.
+All pull requests are subject to CI checks.
+We check for compliance with Mypy and that the file formatting conforms to our Black specification.
+
+You can install these dev dependencies via
+
+```bash
+pip install -e '.[dev]'
+```
+
+This will also install NumPy, pandas, and Matplotlib to be able to run the tests.
+
+### Running CI locally (recommended)
+
+We include a script for running the CI checks that are triggered when a PR is opened.
+To test these out locally, you need to install the type stubs in your environment.
+Typically, you would do this with
+
+```bash
+pip install -e .
+```
+
+Then use the `check_all.sh` script to run all tests:
+
+```bash
+./check_all.sh
+```
+
+Below we describe how to run the various checks individually,
+but `check_all.sh` should be easier to use.
+
+### Checking compliance with Mypy
+
+The settings for Mypy are specified in the `mypy.ini` file in the repository.
+Just running
+
+```bash
+mypy tests
+```
+
+from the base directory should take these settings into account.
+We enforce 0 Mypy errors.
+
+### Formatting with black
+
+We use [Black][black] to format the stub files.
+First, install `black` and then run
+
+```bash
+black .
+```
+from the base directory.
+
+### Pytest
+
+```bash
+python -m pytest -vv tests/
+```
+
+### Flake8
+
+```bash
+flake8 *-stubs
+```
+
+## License
+
+[Apache 2.0](LICENSE)
+
+
+[pep-561]: https://www.python.org/dev/peps/pep-0561/
+[matplotlib]: https://matplotlib.org
+[numpy]: https://numpy.org
+[pandas]: https://pandas.pydata.org
+[mypy]: http://www.mypy-lang.org/
+[mypy-docs]: https://mypy.readthedocs.io/en/latest/installed_packages.html
+[black]: https://github.com/psf/black
+
+
+
+
+%package -n python3-data-science-types
+Summary:	Type stubs for Python machine learning libraries
+Provides:	python-data-science-types
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-data-science-types
+# Mypy type stubs for NumPy, pandas, and Matplotlib
+
+[![Join the chat at https://gitter.im/data-science-types/community](https://badges.gitter.im/data-science-types/community.svg)](https://gitter.im/data-science-types/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+This is a [PEP-561][pep-561]-compliant stub-only package
+which provides type information for [matplotlib][matplotlib], [numpy][numpy] and [pandas][pandas].
+The [mypy][mypy] type checker (or pytype or PyCharm) can [recognize][mypy-docs] the types in these packages by installing this package.
+
+### NOTE: This is a work in progress
+
+Many functions are already typed, but a *lot* is still missing (NumPy and pandas are *huge* libraries).
+Chances are, you will see a message from Mypy claiming that a function does not exist when it does exist.
+If you encounter missing functions, we would be delighted for you to send a PR.
+If you are unsure of how to type a function, we can discuss it.
+
+## Installing
+
+You can get this package from PyPI:
+
+```bash
+pip install data-science-types
+```
+
+To get the most up-to-date version, install it directly from GitHub:
+
+```bash
+pip install git+https://github.com/predictive-analytics-lab/data-science-types
+```
+
+Or clone the repository somewhere and do `pip install -e .`.
+
+## Examples
+
+These are the kinds of things that can be checked:
+
+### Array creation
+
+```python
+import numpy as np
+
+arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3])  # OK
+arr2: np.ndarray[np.int32] = np.array([3, 7, 39, -3])  # Type error
+arr3: np.ndarray[np.int32] = np.array([3, 7, 39, -3], dtype=np.int32)  # OK
+arr4: np.ndarray[float] = np.array([3, 7, 39, -3], dtype=float)  # Type error: the type of ndarray can not be just "float"
+arr5: np.ndarray[np.float64] = np.array([3, 7, 39, -3], dtype=float)  # OK
+```
+
+### Operations
+
+```python
+import numpy as np
+
+arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3])
+arr2: np.ndarray[np.int64] = np.array([4, 12, 9, -1])
+
+result1: np.ndarray[np.int64] = np.divide(arr1, arr2)  # Type error
+result2: np.ndarray[np.float64] = np.divide(arr1, arr2)  # OK
+
+compare: np.ndarray[np.bool_] = (arr1 == arr2)
+```
+
+### Reductions
+
+```python
+import numpy as np
+
+arr: np.ndarray[np.float64] = np.array([[1.3, 0.7], [-43.0, 5.6]])
+
+sum1: int = np.sum(arr)  # Type error
+sum2: np.float64 = np.sum(arr)  # OK
+sum3: float = np.sum(arr)  # Also OK: np.float64 is a subclass of float
+sum4: np.ndarray[np.float64] = np.sum(arr, axis=0)  # OK
+
+# the same works with np.max, np.min and np.prod
+```
+
+## Philosophy
+
+The goal is not to recreate the APIs exactly.
+The main goal is to have *useful* checks on our code.
+Often the actual APIs in the libraries is more permissive than the type signatures in our stubs;
+but this is (usually) a feature and not a bug.
+
+## Contributing
+
+We always welcome contributions.
+All pull requests are subject to CI checks.
+We check for compliance with Mypy and that the file formatting conforms to our Black specification.
+
+You can install these dev dependencies via
+
+```bash
+pip install -e '.[dev]'
+```
+
+This will also install NumPy, pandas, and Matplotlib to be able to run the tests.
+
+### Running CI locally (recommended)
+
+We include a script for running the CI checks that are triggered when a PR is opened.
+To test these out locally, you need to install the type stubs in your environment.
+Typically, you would do this with
+
+```bash
+pip install -e .
+```
+
+Then use the `check_all.sh` script to run all tests:
+
+```bash
+./check_all.sh
+```
+
+Below we describe how to run the various checks individually,
+but `check_all.sh` should be easier to use.
+
+### Checking compliance with Mypy
+
+The settings for Mypy are specified in the `mypy.ini` file in the repository.
+Just running
+
+```bash
+mypy tests
+```
+
+from the base directory should take these settings into account.
+We enforce 0 Mypy errors.
+
+### Formatting with black
+
+We use [Black][black] to format the stub files.
+First, install `black` and then run
+
+```bash
+black .
+```
+from the base directory.
+
+### Pytest
+
+```bash
+python -m pytest -vv tests/
+```
+
+### Flake8
+
+```bash
+flake8 *-stubs
+```
+
+## License
+
+[Apache 2.0](LICENSE)
+
+
+[pep-561]: https://www.python.org/dev/peps/pep-0561/
+[matplotlib]: https://matplotlib.org
+[numpy]: https://numpy.org
+[pandas]: https://pandas.pydata.org
+[mypy]: http://www.mypy-lang.org/
+[mypy-docs]: https://mypy.readthedocs.io/en/latest/installed_packages.html
+[black]: https://github.com/psf/black
+
+
+
+
+%package help
+Summary:	Development documents and examples for data-science-types
+Provides:	python3-data-science-types-doc
+%description help
+# Mypy type stubs for NumPy, pandas, and Matplotlib
+
+[![Join the chat at https://gitter.im/data-science-types/community](https://badges.gitter.im/data-science-types/community.svg)](https://gitter.im/data-science-types/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+This is a [PEP-561][pep-561]-compliant stub-only package
+which provides type information for [matplotlib][matplotlib], [numpy][numpy] and [pandas][pandas].
+The [mypy][mypy] type checker (or pytype or PyCharm) can [recognize][mypy-docs] the types in these packages by installing this package.
+
+### NOTE: This is a work in progress
+
+Many functions are already typed, but a *lot* is still missing (NumPy and pandas are *huge* libraries).
+Chances are, you will see a message from Mypy claiming that a function does not exist when it does exist.
+If you encounter missing functions, we would be delighted for you to send a PR.
+If you are unsure of how to type a function, we can discuss it.
+
+## Installing
+
+You can get this package from PyPI:
+
+```bash
+pip install data-science-types
+```
+
+To get the most up-to-date version, install it directly from GitHub:
+
+```bash
+pip install git+https://github.com/predictive-analytics-lab/data-science-types
+```
+
+Or clone the repository somewhere and do `pip install -e .`.
+
+## Examples
+
+These are the kinds of things that can be checked:
+
+### Array creation
+
+```python
+import numpy as np
+
+arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3])  # OK
+arr2: np.ndarray[np.int32] = np.array([3, 7, 39, -3])  # Type error
+arr3: np.ndarray[np.int32] = np.array([3, 7, 39, -3], dtype=np.int32)  # OK
+arr4: np.ndarray[float] = np.array([3, 7, 39, -3], dtype=float)  # Type error: the type of ndarray can not be just "float"
+arr5: np.ndarray[np.float64] = np.array([3, 7, 39, -3], dtype=float)  # OK
+```
+
+### Operations
+
+```python
+import numpy as np
+
+arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3])
+arr2: np.ndarray[np.int64] = np.array([4, 12, 9, -1])
+
+result1: np.ndarray[np.int64] = np.divide(arr1, arr2)  # Type error
+result2: np.ndarray[np.float64] = np.divide(arr1, arr2)  # OK
+
+compare: np.ndarray[np.bool_] = (arr1 == arr2)
+```
+
+### Reductions
+
+```python
+import numpy as np
+
+arr: np.ndarray[np.float64] = np.array([[1.3, 0.7], [-43.0, 5.6]])
+
+sum1: int = np.sum(arr)  # Type error
+sum2: np.float64 = np.sum(arr)  # OK
+sum3: float = np.sum(arr)  # Also OK: np.float64 is a subclass of float
+sum4: np.ndarray[np.float64] = np.sum(arr, axis=0)  # OK
+
+# the same works with np.max, np.min and np.prod
+```
+
+## Philosophy
+
+The goal is not to recreate the APIs exactly.
+The main goal is to have *useful* checks on our code.
+Often the actual APIs in the libraries is more permissive than the type signatures in our stubs;
+but this is (usually) a feature and not a bug.
+
+## Contributing
+
+We always welcome contributions.
+All pull requests are subject to CI checks.
+We check for compliance with Mypy and that the file formatting conforms to our Black specification.
+
+You can install these dev dependencies via
+
+```bash
+pip install -e '.[dev]'
+```
+
+This will also install NumPy, pandas, and Matplotlib to be able to run the tests.
+
+### Running CI locally (recommended)
+
+We include a script for running the CI checks that are triggered when a PR is opened.
+To test these out locally, you need to install the type stubs in your environment.
+Typically, you would do this with
+
+```bash
+pip install -e .
+```
+
+Then use the `check_all.sh` script to run all tests:
+
+```bash
+./check_all.sh
+```
+
+Below we describe how to run the various checks individually,
+but `check_all.sh` should be easier to use.
+
+### Checking compliance with Mypy
+
+The settings for Mypy are specified in the `mypy.ini` file in the repository.
+Just running
+
+```bash
+mypy tests
+```
+
+from the base directory should take these settings into account.
+We enforce 0 Mypy errors.
+
+### Formatting with black
+
+We use [Black][black] to format the stub files.
+First, install `black` and then run
+
+```bash
+black .
+```
+from the base directory.
+
+### Pytest
+
+```bash
+python -m pytest -vv tests/
+```
+
+### Flake8
+
+```bash
+flake8 *-stubs
+```
+
+## License
+
+[Apache 2.0](LICENSE)
+
+
+[pep-561]: https://www.python.org/dev/peps/pep-0561/
+[matplotlib]: https://matplotlib.org
+[numpy]: https://numpy.org
+[pandas]: https://pandas.pydata.org
+[mypy]: http://www.mypy-lang.org/
+[mypy-docs]: https://mypy.readthedocs.io/en/latest/installed_packages.html
+[black]: https://github.com/psf/black
+
+
+
+
+%prep
+%autosetup -n data-science-types-0.2.23
+
+%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-data-science-types -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.23-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..fb54e16
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+c899a88e472d21eb7ccdaa382fe0bc65  data-science-types-0.2.23.tar.gz