automatic import of python-pygyver

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+/pygyver-0.1.1.42.tar.gz
diff --git a/python-pygyver.spec b/python-pygyver.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pygyver
+Version:	0.1.1.42
+Release:	1
+Summary:	Data engineering & Data science Pipeline Framework
+License:	MIT License
+URL:		https://github.com/madedotcom/pygyver
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e6/c0/cb05b7eef1faeda980138bf515390099bff1ac59fbc9c8e1a819da00d2f1/pygyver-0.1.1.42.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-boto3
+Requires:	python3-codecov
+Requires:	python3-facebook-business
+Requires:	python3-google-cloud-bigquery
+Requires:	python3-google-cloud-bigquery-datatransfer
+Requires:	python3-google-cloud-storage
+Requires:	python3-gspread
+Requires:	python3-gspread-dataframe
+Requires:	python3-moto
+Requires:	python3-nltk
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-pandas-gbq
+Requires:	python3-pg8000
+Requires:	python3-pyarrow
+Requires:	python3-pymysql
+Requires:	python3-pytest
+Requires:	python3-PyYAML
+Requires:	python3-slack-sdk
+Requires:	python3-sqlalchemy
+Requires:	python3-tox
+Requires:	python3-versioneer
+Requires:	python3-wheel
+
+%description
+# PyGyver
+
+> PyGyver is a user-friendly python package for data integration and manipulation.
+
+> Named after MacGyver, title character in the TV series MacGyver, and Python, the main language used in the repository.
+
+## Installation
+
+### PyPi
+
+PyGyver is available on [PyPi](https://pypi.org/project/pygyver/).
+
+```python 
+pip install pygyver
+```
+
+### Setup
+
+Most APIs requires access token files to authentificate and perform tasks such as creating or deleting objects. Those files need to be generated prior to using `pygyver` and stored in the environment you are executing your code against. The package make use of environment variables, and some of the below might need be supplied in your environment:
+
+```
+# Access token path
+GOOGLE_APPLICATION_CREDENTIALS=path_to_google_access_token.json
+FACEBOOK_APPLICATION_CREDENTIALS=path_to_facebook_access_token.json
+
+# Default values
+BIGQUERY_PROJECT=your-gcs-project
+GCS_PROJECT=your-gcs-project
+GCS_BUCKET=your-gcs-bucket
+
+# Optional
+PROJECT_ROOT=path_to_where_your_code_lives
+```
+
+## Modules
+
+PyGyver is structured around several modules available in the `etl` folder. Here is a summary table of those modules:
+
+| Module name | Descrition | Documentation |
+| ------------- |-------------|-------------|
+| `dw` | Perform task against the Google Cloud BigQuery API | [dw.md](docs/dw.md) |
+| `facebook` | Perform task against the Facebook Marketing API | [facebook.md](docs/facebook.md) |
+| `gooddata` | Perform task against the GoodData API | - |
+| `gs` | Perform task against the Google Sheet API | - |
+| `lib` | Store utilities used by other modules | - |
+| `pipeline` | Utility to build data pipelines via YAML definition | [pipeline.md](docs/pipeline.md) |
+| `prep` | Data transformation - ML pipelines | - |
+| `storage` | Perform task against the AWS S3 and Google Cloud Storage API | [storage.md](docs/storage.md) |
+| `toolkit` | Sets of tools for data manipulation | - |
+
+In order to load `BigQueryExecutor` from the `dw` module, you can run:
+
+```
+from pygyver.etl.dw import BigQueryExecutor
+```
+
+## Contributing
+
+> To get started...
+
+### Step 1
+
+- 👯 Clone this repo to your local machine using `git@github.com:madedotcom/pygyver.git`
+
+### Step 2
+
+- **HACK AWAY!** 🔨🔨🔨
+
+The team follows TDD to develop new features on `pygyver`.
+Tests can be found in `pygyver/tests`.
+
+### Step 3
+
+- 🔃 Create a new pull request and request review from team members. Where applicable, a test should be added with the code change.
+
+## FAQ
+
+- **How to release a new version to PyPi?**
+    1. Merge your changes to `master` branch
+    2. Create a new release using `https://github.com/madedotcom/pygyver/releases`
+
+
+%package -n python3-pygyver
+Summary:	Data engineering & Data science Pipeline Framework
+Provides:	python-pygyver
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pygyver
+# PyGyver
+
+> PyGyver is a user-friendly python package for data integration and manipulation.
+
+> Named after MacGyver, title character in the TV series MacGyver, and Python, the main language used in the repository.
+
+## Installation
+
+### PyPi
+
+PyGyver is available on [PyPi](https://pypi.org/project/pygyver/).
+
+```python 
+pip install pygyver
+```
+
+### Setup
+
+Most APIs requires access token files to authentificate and perform tasks such as creating or deleting objects. Those files need to be generated prior to using `pygyver` and stored in the environment you are executing your code against. The package make use of environment variables, and some of the below might need be supplied in your environment:
+
+```
+# Access token path
+GOOGLE_APPLICATION_CREDENTIALS=path_to_google_access_token.json
+FACEBOOK_APPLICATION_CREDENTIALS=path_to_facebook_access_token.json
+
+# Default values
+BIGQUERY_PROJECT=your-gcs-project
+GCS_PROJECT=your-gcs-project
+GCS_BUCKET=your-gcs-bucket
+
+# Optional
+PROJECT_ROOT=path_to_where_your_code_lives
+```
+
+## Modules
+
+PyGyver is structured around several modules available in the `etl` folder. Here is a summary table of those modules:
+
+| Module name | Descrition | Documentation |
+| ------------- |-------------|-------------|
+| `dw` | Perform task against the Google Cloud BigQuery API | [dw.md](docs/dw.md) |
+| `facebook` | Perform task against the Facebook Marketing API | [facebook.md](docs/facebook.md) |
+| `gooddata` | Perform task against the GoodData API | - |
+| `gs` | Perform task against the Google Sheet API | - |
+| `lib` | Store utilities used by other modules | - |
+| `pipeline` | Utility to build data pipelines via YAML definition | [pipeline.md](docs/pipeline.md) |
+| `prep` | Data transformation - ML pipelines | - |
+| `storage` | Perform task against the AWS S3 and Google Cloud Storage API | [storage.md](docs/storage.md) |
+| `toolkit` | Sets of tools for data manipulation | - |
+
+In order to load `BigQueryExecutor` from the `dw` module, you can run:
+
+```
+from pygyver.etl.dw import BigQueryExecutor
+```
+
+## Contributing
+
+> To get started...
+
+### Step 1
+
+- 👯 Clone this repo to your local machine using `git@github.com:madedotcom/pygyver.git`
+
+### Step 2
+
+- **HACK AWAY!** 🔨🔨🔨
+
+The team follows TDD to develop new features on `pygyver`.
+Tests can be found in `pygyver/tests`.
+
+### Step 3
+
+- 🔃 Create a new pull request and request review from team members. Where applicable, a test should be added with the code change.
+
+## FAQ
+
+- **How to release a new version to PyPi?**
+    1. Merge your changes to `master` branch
+    2. Create a new release using `https://github.com/madedotcom/pygyver/releases`
+
+
+%package help
+Summary:	Development documents and examples for pygyver
+Provides:	python3-pygyver-doc
+%description help
+# PyGyver
+
+> PyGyver is a user-friendly python package for data integration and manipulation.
+
+> Named after MacGyver, title character in the TV series MacGyver, and Python, the main language used in the repository.
+
+## Installation
+
+### PyPi
+
+PyGyver is available on [PyPi](https://pypi.org/project/pygyver/).
+
+```python 
+pip install pygyver
+```
+
+### Setup
+
+Most APIs requires access token files to authentificate and perform tasks such as creating or deleting objects. Those files need to be generated prior to using `pygyver` and stored in the environment you are executing your code against. The package make use of environment variables, and some of the below might need be supplied in your environment:
+
+```
+# Access token path
+GOOGLE_APPLICATION_CREDENTIALS=path_to_google_access_token.json
+FACEBOOK_APPLICATION_CREDENTIALS=path_to_facebook_access_token.json
+
+# Default values
+BIGQUERY_PROJECT=your-gcs-project
+GCS_PROJECT=your-gcs-project
+GCS_BUCKET=your-gcs-bucket
+
+# Optional
+PROJECT_ROOT=path_to_where_your_code_lives
+```
+
+## Modules
+
+PyGyver is structured around several modules available in the `etl` folder. Here is a summary table of those modules:
+
+| Module name | Descrition | Documentation |
+| ------------- |-------------|-------------|
+| `dw` | Perform task against the Google Cloud BigQuery API | [dw.md](docs/dw.md) |
+| `facebook` | Perform task against the Facebook Marketing API | [facebook.md](docs/facebook.md) |
+| `gooddata` | Perform task against the GoodData API | - |
+| `gs` | Perform task against the Google Sheet API | - |
+| `lib` | Store utilities used by other modules | - |
+| `pipeline` | Utility to build data pipelines via YAML definition | [pipeline.md](docs/pipeline.md) |
+| `prep` | Data transformation - ML pipelines | - |
+| `storage` | Perform task against the AWS S3 and Google Cloud Storage API | [storage.md](docs/storage.md) |
+| `toolkit` | Sets of tools for data manipulation | - |
+
+In order to load `BigQueryExecutor` from the `dw` module, you can run:
+
+```
+from pygyver.etl.dw import BigQueryExecutor
+```
+
+## Contributing
+
+> To get started...
+
+### Step 1
+
+- 👯 Clone this repo to your local machine using `git@github.com:madedotcom/pygyver.git`
+
+### Step 2
+
+- **HACK AWAY!** 🔨🔨🔨
+
+The team follows TDD to develop new features on `pygyver`.
+Tests can be found in `pygyver/tests`.
+
+### Step 3
+
+- 🔃 Create a new pull request and request review from team members. Where applicable, a test should be added with the code change.
+
+## FAQ
+
+- **How to release a new version to PyPi?**
+    1. Merge your changes to `master` branch
+    2. Create a new release using `https://github.com/madedotcom/pygyver/releases`
+
+
+%prep
+%autosetup -n pygyver-0.1.1.42
+
+%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-pygyver -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.1.42-1
+- Package Spec generated
diff --git a/sources b/sources
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+69fb21ff05c89925e59aadd2dea000d1  pygyver-0.1.1.42.tar.gz