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+/pyairvisual-2022.12.1.tar.gz
diff --git a/python-pyairvisual.spec b/python-pyairvisual.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pyairvisual
+Version:	2022.12.1
+Release:	1
+Summary:	A simple API for AirVisual air quality data
+License:	MIT
+URL:		https://github.com/bachya/pyairvisual
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/43/af/296621db9b7ddad4cf8bc961f6614f527a119fc7fbc3d3554e3b21d36656/pyairvisual-2022.12.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-aiohttp
+Requires:	python3-numpy
+Requires:	python3-pysmb
+
+%description
+# ☀️ pyairvisual: a thin Python wrapper for the AirVisual© API
+
+[![CI][ci-badge]][ci]
+[![PyPI][pypi-badge]][pypi]
+[![Version][version-badge]][version]
+[![License][license-badge]][license]
+[![Code Coverage][codecov-badge]][codecov]
+[![Maintainability][maintainability-badge]][maintainability]
+
+<a href="https://www.buymeacoffee.com/bachya1208P" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
+
+`pyairvisual` is a simple, clean, well-tested library for interacting with
+[AirVisual][airvisual] to retrieve air quality information.
+
+- [Python Versions](#python-versions)
+- [Installation](#installation)
+- [API Key](#api-key)
+  - [Community](#community)
+  - [Startup](#startup)
+  - [Enterprise](#enterprise)
+- [Usage](#usage)
+  - [Using the Cloud API](#using-the-cloud-api)
+  - [Working with Node/Pro Units](#working-with-node-pro-units)
+- [Contributing](#contributing)
+
+# Python Versions
+
+`pyairvisual` is currently supported on:
+
+- Python 3.9
+- Python 3.10
+- Python 3.11
+
+# Installation
+
+```bash
+pip install pyairvisual
+```
+
+# API Key
+
+You can get an AirVisual API key from [the AirVisual API site][airvisual-api].
+Depending on the plan you choose, more functionality will be available from the API:
+
+## Community
+
+The Community Plan gives access to:
+
+- List supported countries
+- List supported states
+- List supported cities
+- Get data from the nearest city based on IP address
+- Get data from the nearest city based on latitude/longitude
+- Get data from a specific city
+
+## Startup
+
+The Startup Plan gives access to:
+
+- List supported stations in a city
+- Get data from the nearest station based on IP address
+- Get data from the nearest station based on latitude/longitude
+- Get data from a specific station
+
+## Enterprise
+
+The Enterprise Plan gives access to:
+
+- Get a global city ranking of air quality
+
+# Usage
+
+## Using the Cloud API
+
+```python
+import asyncio
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")
+
+    # Get data based on the city nearest to your IP address:
+    data = await cloud_api.air_quality.nearest_city()
+
+    # ...or get data based on the city nearest to a latitude/longitude:
+    data = await cloud_api.air_quality.nearest_city(
+        latitude=39.742599, longitude=-104.9942557
+    )
+
+    # ...or get it explicitly:
+    data = await cloud_api.air_quality.city(
+        city="Los Angeles", state="California", country="USA"
+    )
+
+    # If you have the appropriate API key, you can also get data based on
+    # station (nearest or explicit):
+    data = await cloud_api.air_quality.nearest_station()
+    data = await cloud_api.air_quality.nearest_station(
+        latitude=39.742599, longitude=-104.9942557
+    )
+    data = await cloud_api.air_quality.station(
+        station="US Embassy in Beijing",
+        city="Beijing",
+        state="Beijing",
+        country="China",
+    )
+
+    # With the appropriate API key, you can get an air quality ranking:
+    data = await cloud_api.air_quality.ranking()
+
+    # pyairvisual gives you several methods to look locations up:
+    countries = await cloud_api.supported.countries()
+    states = await cloud_api.supported.states("USA")
+    cities = await cloud_api.supported.cities("USA", "Colorado")
+    stations = await cloud_api.supported.stations("USA", "Colorado", "Denver")
+
+
+asyncio.run(main())
+```
+
+By default, the library creates a new connection to AirVisual with each coroutine. If
+you are calling a large number of coroutines (or merely want to squeeze out every second
+of runtime savings possible), an [`aiohttp`][aiohttp] `ClientSession` can be used for
+connection pooling:
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    async with ClientSession() as session:
+        cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>", session=session)
+
+        # ...
+
+
+asyncio.run(main())
+```
+
+## Working with Node/Pro Units
+
+`pyairvisual` also allows users to interact with [Node/Pro units][airvisual-pro], both via
+the cloud API:
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")
+
+    # The Node/Pro unit ID can be retrieved from the "API" section of the cloud
+    # dashboard:
+    data = await cloud_api.node.get_by_node_id("<NODE_ID>")
+
+
+asyncio.run(main())
+```
+
+...or over the local network via Samba (the unit password can be found
+[on the device itself][airvisual-samba-instructions]):
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.node import NodeSamba
+
+
+async def main() -> None:
+    """Run!"""
+    async with NodeSamba("<IP_ADDRESS_OR_HOST>", "<PASSWORD>") as node:
+        measurements = await node.async_get_latest_measurements()
+
+        # Can take some optional parameters:
+        #   1. include_trends: include trends (defaults to True)
+        #   2. measurements_to_use: the number of measurements to use when calculating
+        #      trends (defaults to -1, which means "use all measurements")
+        history = await node.async_get_history()
+
+
+asyncio.run(main())
+```
+
+Check out the examples, the tests, and the source files themselves for method
+signatures and more examples.
+
+# Contributing
+
+Thanks to all of [our contributors][contributors] so far!
+
+1. [Check for open features/bugs][issues] or [initiate a discussion on one][new-issue].
+2. [Fork the repository][fork].
+3. (_optional, but highly recommended_) Create a virtual environment: `python3 -m venv .venv`
+4. (_optional, but highly recommended_) Enter the virtual environment: `source ./.venv/bin/activate`
+5. Install the dev environment: `script/setup`
+6. Code your new feature or bug fix on a new branch.
+7. Write tests that cover your new functionality.
+8. Run tests and ensure 100% code coverage: `poetry run pytest --cov pyairvisual tests`
+9. Update `README.md` with any new documentation.
+10. Submit a pull request!
+
+[aiohttp]: https://github.com/aio-libs/aiohttp
+[airvisual]: https://www.airvisual.com/
+[airvisual-api]: https://www.airvisual.com/user/api
+[airvisual-pro]: https://www.airvisual.com/air-quality-monitor
+[airvisual-samba-instructions]: https://support.airvisual.com/en/articles/3029331-download-the-airvisual-node-pro-s-data-using-samba
+[ci-badge]: https://github.com/bachya/pyairvisual/workflows/CI/badge.svg
+[ci]: https://github.com/bachya/pyairvisual/actions
+[codecov-badge]: https://codecov.io/gh/bachya/pyairvisual/branch/dev/graph/badge.svg
+[codecov]: https://codecov.io/gh/bachya/pyairvisual
+[contributors]: https://github.com/bachya/pyairvisual/graphs/contributors
+[fork]: https://github.com/bachya/pyairvisual/fork
+[issues]: https://github.com/bachya/pyairvisual/issues
+[license-badge]: https://img.shields.io/pypi/l/pyairvisual.svg
+[license]: https://github.com/bachya/pyairvisual/blob/main/LICENSE
+[maintainability-badge]: https://api.codeclimate.com/v1/badges/a03c9e96f19a3dc37f98/maintainability
+[maintainability]: https://codeclimate.com/github/bachya/pyairvisual/maintainability
+[new-issue]: https://github.com/bachya/pyairvisual/issues/new
+[new-issue]: https://github.com/bachya/pyairvisual/issues/new
+[pypi-badge]: https://img.shields.io/pypi/v/pyairvisual.svg
+[pypi]: https://pypi.python.org/pypi/pyairvisual
+[version-badge]: https://img.shields.io/pypi/pyversions/pyairvisual.svg
+[version]: https://pypi.python.org/pypi/pyairvisual
+
+
+%package -n python3-pyairvisual
+Summary:	A simple API for AirVisual air quality data
+Provides:	python-pyairvisual
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pyairvisual
+# ☀️ pyairvisual: a thin Python wrapper for the AirVisual© API
+
+[![CI][ci-badge]][ci]
+[![PyPI][pypi-badge]][pypi]
+[![Version][version-badge]][version]
+[![License][license-badge]][license]
+[![Code Coverage][codecov-badge]][codecov]
+[![Maintainability][maintainability-badge]][maintainability]
+
+<a href="https://www.buymeacoffee.com/bachya1208P" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
+
+`pyairvisual` is a simple, clean, well-tested library for interacting with
+[AirVisual][airvisual] to retrieve air quality information.
+
+- [Python Versions](#python-versions)
+- [Installation](#installation)
+- [API Key](#api-key)
+  - [Community](#community)
+  - [Startup](#startup)
+  - [Enterprise](#enterprise)
+- [Usage](#usage)
+  - [Using the Cloud API](#using-the-cloud-api)
+  - [Working with Node/Pro Units](#working-with-node-pro-units)
+- [Contributing](#contributing)
+
+# Python Versions
+
+`pyairvisual` is currently supported on:
+
+- Python 3.9
+- Python 3.10
+- Python 3.11
+
+# Installation
+
+```bash
+pip install pyairvisual
+```
+
+# API Key
+
+You can get an AirVisual API key from [the AirVisual API site][airvisual-api].
+Depending on the plan you choose, more functionality will be available from the API:
+
+## Community
+
+The Community Plan gives access to:
+
+- List supported countries
+- List supported states
+- List supported cities
+- Get data from the nearest city based on IP address
+- Get data from the nearest city based on latitude/longitude
+- Get data from a specific city
+
+## Startup
+
+The Startup Plan gives access to:
+
+- List supported stations in a city
+- Get data from the nearest station based on IP address
+- Get data from the nearest station based on latitude/longitude
+- Get data from a specific station
+
+## Enterprise
+
+The Enterprise Plan gives access to:
+
+- Get a global city ranking of air quality
+
+# Usage
+
+## Using the Cloud API
+
+```python
+import asyncio
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")
+
+    # Get data based on the city nearest to your IP address:
+    data = await cloud_api.air_quality.nearest_city()
+
+    # ...or get data based on the city nearest to a latitude/longitude:
+    data = await cloud_api.air_quality.nearest_city(
+        latitude=39.742599, longitude=-104.9942557
+    )
+
+    # ...or get it explicitly:
+    data = await cloud_api.air_quality.city(
+        city="Los Angeles", state="California", country="USA"
+    )
+
+    # If you have the appropriate API key, you can also get data based on
+    # station (nearest or explicit):
+    data = await cloud_api.air_quality.nearest_station()
+    data = await cloud_api.air_quality.nearest_station(
+        latitude=39.742599, longitude=-104.9942557
+    )
+    data = await cloud_api.air_quality.station(
+        station="US Embassy in Beijing",
+        city="Beijing",
+        state="Beijing",
+        country="China",
+    )
+
+    # With the appropriate API key, you can get an air quality ranking:
+    data = await cloud_api.air_quality.ranking()
+
+    # pyairvisual gives you several methods to look locations up:
+    countries = await cloud_api.supported.countries()
+    states = await cloud_api.supported.states("USA")
+    cities = await cloud_api.supported.cities("USA", "Colorado")
+    stations = await cloud_api.supported.stations("USA", "Colorado", "Denver")
+
+
+asyncio.run(main())
+```
+
+By default, the library creates a new connection to AirVisual with each coroutine. If
+you are calling a large number of coroutines (or merely want to squeeze out every second
+of runtime savings possible), an [`aiohttp`][aiohttp] `ClientSession` can be used for
+connection pooling:
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    async with ClientSession() as session:
+        cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>", session=session)
+
+        # ...
+
+
+asyncio.run(main())
+```
+
+## Working with Node/Pro Units
+
+`pyairvisual` also allows users to interact with [Node/Pro units][airvisual-pro], both via
+the cloud API:
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")
+
+    # The Node/Pro unit ID can be retrieved from the "API" section of the cloud
+    # dashboard:
+    data = await cloud_api.node.get_by_node_id("<NODE_ID>")
+
+
+asyncio.run(main())
+```
+
+...or over the local network via Samba (the unit password can be found
+[on the device itself][airvisual-samba-instructions]):
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.node import NodeSamba
+
+
+async def main() -> None:
+    """Run!"""
+    async with NodeSamba("<IP_ADDRESS_OR_HOST>", "<PASSWORD>") as node:
+        measurements = await node.async_get_latest_measurements()
+
+        # Can take some optional parameters:
+        #   1. include_trends: include trends (defaults to True)
+        #   2. measurements_to_use: the number of measurements to use when calculating
+        #      trends (defaults to -1, which means "use all measurements")
+        history = await node.async_get_history()
+
+
+asyncio.run(main())
+```
+
+Check out the examples, the tests, and the source files themselves for method
+signatures and more examples.
+
+# Contributing
+
+Thanks to all of [our contributors][contributors] so far!
+
+1. [Check for open features/bugs][issues] or [initiate a discussion on one][new-issue].
+2. [Fork the repository][fork].
+3. (_optional, but highly recommended_) Create a virtual environment: `python3 -m venv .venv`
+4. (_optional, but highly recommended_) Enter the virtual environment: `source ./.venv/bin/activate`
+5. Install the dev environment: `script/setup`
+6. Code your new feature or bug fix on a new branch.
+7. Write tests that cover your new functionality.
+8. Run tests and ensure 100% code coverage: `poetry run pytest --cov pyairvisual tests`
+9. Update `README.md` with any new documentation.
+10. Submit a pull request!
+
+[aiohttp]: https://github.com/aio-libs/aiohttp
+[airvisual]: https://www.airvisual.com/
+[airvisual-api]: https://www.airvisual.com/user/api
+[airvisual-pro]: https://www.airvisual.com/air-quality-monitor
+[airvisual-samba-instructions]: https://support.airvisual.com/en/articles/3029331-download-the-airvisual-node-pro-s-data-using-samba
+[ci-badge]: https://github.com/bachya/pyairvisual/workflows/CI/badge.svg
+[ci]: https://github.com/bachya/pyairvisual/actions
+[codecov-badge]: https://codecov.io/gh/bachya/pyairvisual/branch/dev/graph/badge.svg
+[codecov]: https://codecov.io/gh/bachya/pyairvisual
+[contributors]: https://github.com/bachya/pyairvisual/graphs/contributors
+[fork]: https://github.com/bachya/pyairvisual/fork
+[issues]: https://github.com/bachya/pyairvisual/issues
+[license-badge]: https://img.shields.io/pypi/l/pyairvisual.svg
+[license]: https://github.com/bachya/pyairvisual/blob/main/LICENSE
+[maintainability-badge]: https://api.codeclimate.com/v1/badges/a03c9e96f19a3dc37f98/maintainability
+[maintainability]: https://codeclimate.com/github/bachya/pyairvisual/maintainability
+[new-issue]: https://github.com/bachya/pyairvisual/issues/new
+[new-issue]: https://github.com/bachya/pyairvisual/issues/new
+[pypi-badge]: https://img.shields.io/pypi/v/pyairvisual.svg
+[pypi]: https://pypi.python.org/pypi/pyairvisual
+[version-badge]: https://img.shields.io/pypi/pyversions/pyairvisual.svg
+[version]: https://pypi.python.org/pypi/pyairvisual
+
+
+%package help
+Summary:	Development documents and examples for pyairvisual
+Provides:	python3-pyairvisual-doc
+%description help
+# ☀️ pyairvisual: a thin Python wrapper for the AirVisual© API
+
+[![CI][ci-badge]][ci]
+[![PyPI][pypi-badge]][pypi]
+[![Version][version-badge]][version]
+[![License][license-badge]][license]
+[![Code Coverage][codecov-badge]][codecov]
+[![Maintainability][maintainability-badge]][maintainability]
+
+<a href="https://www.buymeacoffee.com/bachya1208P" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
+
+`pyairvisual` is a simple, clean, well-tested library for interacting with
+[AirVisual][airvisual] to retrieve air quality information.
+
+- [Python Versions](#python-versions)
+- [Installation](#installation)
+- [API Key](#api-key)
+  - [Community](#community)
+  - [Startup](#startup)
+  - [Enterprise](#enterprise)
+- [Usage](#usage)
+  - [Using the Cloud API](#using-the-cloud-api)
+  - [Working with Node/Pro Units](#working-with-node-pro-units)
+- [Contributing](#contributing)
+
+# Python Versions
+
+`pyairvisual` is currently supported on:
+
+- Python 3.9
+- Python 3.10
+- Python 3.11
+
+# Installation
+
+```bash
+pip install pyairvisual
+```
+
+# API Key
+
+You can get an AirVisual API key from [the AirVisual API site][airvisual-api].
+Depending on the plan you choose, more functionality will be available from the API:
+
+## Community
+
+The Community Plan gives access to:
+
+- List supported countries
+- List supported states
+- List supported cities
+- Get data from the nearest city based on IP address
+- Get data from the nearest city based on latitude/longitude
+- Get data from a specific city
+
+## Startup
+
+The Startup Plan gives access to:
+
+- List supported stations in a city
+- Get data from the nearest station based on IP address
+- Get data from the nearest station based on latitude/longitude
+- Get data from a specific station
+
+## Enterprise
+
+The Enterprise Plan gives access to:
+
+- Get a global city ranking of air quality
+
+# Usage
+
+## Using the Cloud API
+
+```python
+import asyncio
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")
+
+    # Get data based on the city nearest to your IP address:
+    data = await cloud_api.air_quality.nearest_city()
+
+    # ...or get data based on the city nearest to a latitude/longitude:
+    data = await cloud_api.air_quality.nearest_city(
+        latitude=39.742599, longitude=-104.9942557
+    )
+
+    # ...or get it explicitly:
+    data = await cloud_api.air_quality.city(
+        city="Los Angeles", state="California", country="USA"
+    )
+
+    # If you have the appropriate API key, you can also get data based on
+    # station (nearest or explicit):
+    data = await cloud_api.air_quality.nearest_station()
+    data = await cloud_api.air_quality.nearest_station(
+        latitude=39.742599, longitude=-104.9942557
+    )
+    data = await cloud_api.air_quality.station(
+        station="US Embassy in Beijing",
+        city="Beijing",
+        state="Beijing",
+        country="China",
+    )
+
+    # With the appropriate API key, you can get an air quality ranking:
+    data = await cloud_api.air_quality.ranking()
+
+    # pyairvisual gives you several methods to look locations up:
+    countries = await cloud_api.supported.countries()
+    states = await cloud_api.supported.states("USA")
+    cities = await cloud_api.supported.cities("USA", "Colorado")
+    stations = await cloud_api.supported.stations("USA", "Colorado", "Denver")
+
+
+asyncio.run(main())
+```
+
+By default, the library creates a new connection to AirVisual with each coroutine. If
+you are calling a large number of coroutines (or merely want to squeeze out every second
+of runtime savings possible), an [`aiohttp`][aiohttp] `ClientSession` can be used for
+connection pooling:
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    async with ClientSession() as session:
+        cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>", session=session)
+
+        # ...
+
+
+asyncio.run(main())
+```
+
+## Working with Node/Pro Units
+
+`pyairvisual` also allows users to interact with [Node/Pro units][airvisual-pro], both via
+the cloud API:
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.cloud_api import CloudAPI
+
+
+async def main() -> None:
+    """Run!"""
+    cloud_api = CloudAPI("<YOUR_AIRVISUAL_API_KEY>")
+
+    # The Node/Pro unit ID can be retrieved from the "API" section of the cloud
+    # dashboard:
+    data = await cloud_api.node.get_by_node_id("<NODE_ID>")
+
+
+asyncio.run(main())
+```
+
+...or over the local network via Samba (the unit password can be found
+[on the device itself][airvisual-samba-instructions]):
+
+```python
+import asyncio
+
+from aiohttp import ClientSession
+
+from pyairvisual.node import NodeSamba
+
+
+async def main() -> None:
+    """Run!"""
+    async with NodeSamba("<IP_ADDRESS_OR_HOST>", "<PASSWORD>") as node:
+        measurements = await node.async_get_latest_measurements()
+
+        # Can take some optional parameters:
+        #   1. include_trends: include trends (defaults to True)
+        #   2. measurements_to_use: the number of measurements to use when calculating
+        #      trends (defaults to -1, which means "use all measurements")
+        history = await node.async_get_history()
+
+
+asyncio.run(main())
+```
+
+Check out the examples, the tests, and the source files themselves for method
+signatures and more examples.
+
+# Contributing
+
+Thanks to all of [our contributors][contributors] so far!
+
+1. [Check for open features/bugs][issues] or [initiate a discussion on one][new-issue].
+2. [Fork the repository][fork].
+3. (_optional, but highly recommended_) Create a virtual environment: `python3 -m venv .venv`
+4. (_optional, but highly recommended_) Enter the virtual environment: `source ./.venv/bin/activate`
+5. Install the dev environment: `script/setup`
+6. Code your new feature or bug fix on a new branch.
+7. Write tests that cover your new functionality.
+8. Run tests and ensure 100% code coverage: `poetry run pytest --cov pyairvisual tests`
+9. Update `README.md` with any new documentation.
+10. Submit a pull request!
+
+[aiohttp]: https://github.com/aio-libs/aiohttp
+[airvisual]: https://www.airvisual.com/
+[airvisual-api]: https://www.airvisual.com/user/api
+[airvisual-pro]: https://www.airvisual.com/air-quality-monitor
+[airvisual-samba-instructions]: https://support.airvisual.com/en/articles/3029331-download-the-airvisual-node-pro-s-data-using-samba
+[ci-badge]: https://github.com/bachya/pyairvisual/workflows/CI/badge.svg
+[ci]: https://github.com/bachya/pyairvisual/actions
+[codecov-badge]: https://codecov.io/gh/bachya/pyairvisual/branch/dev/graph/badge.svg
+[codecov]: https://codecov.io/gh/bachya/pyairvisual
+[contributors]: https://github.com/bachya/pyairvisual/graphs/contributors
+[fork]: https://github.com/bachya/pyairvisual/fork
+[issues]: https://github.com/bachya/pyairvisual/issues
+[license-badge]: https://img.shields.io/pypi/l/pyairvisual.svg
+[license]: https://github.com/bachya/pyairvisual/blob/main/LICENSE
+[maintainability-badge]: https://api.codeclimate.com/v1/badges/a03c9e96f19a3dc37f98/maintainability
+[maintainability]: https://codeclimate.com/github/bachya/pyairvisual/maintainability
+[new-issue]: https://github.com/bachya/pyairvisual/issues/new
+[new-issue]: https://github.com/bachya/pyairvisual/issues/new
+[pypi-badge]: https://img.shields.io/pypi/v/pyairvisual.svg
+[pypi]: https://pypi.python.org/pypi/pyairvisual
+[version-badge]: https://img.shields.io/pypi/pyversions/pyairvisual.svg
+[version]: https://pypi.python.org/pypi/pyairvisual
+
+
+%prep
+%autosetup -n pyairvisual-2022.12.1
+
+%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-pyairvisual -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 2022.12.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..9895204
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+78afcffd0e0bdbac30f912b29e8ab103  pyairvisual-2022.12.1.tar.gz