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diff --git a/python-sas-esppy.spec b/python-sas-esppy.spec new file mode 100644 index 0000000..fdba34d --- /dev/null +++ b/python-sas-esppy.spec @@ -0,0 +1,814 @@ +%global _empty_manifest_terminate_build 0 +Name: python-sas-esppy +Version: 7.1.16 +Release: 1 +Summary: SAS Event Stream Processing Python Interface +License: Apache 2.0 +URL: https://github.com/sassoftware/python-esppy/ +Source0: https://mirrors.aliyun.com/pypi/web/packages/94/a0/f3032d3f385f41b53e50eaa4818c7f1396a163d51bd6b47b89cd27e8414c/sas-esppy-7.1.16.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-pillow +Requires: python3-six +Requires: python3-plotly +Requires: python3-ipywidgets +Requires: python3-requests +Requires: python3-graphviz +Requires: python3-rbtranslations +Requires: python3-websocket-client +Requires: python3-ws4py + +%description +# SAS Event Stream Processing Python Interface + +The ESPPy package enables you to create +[SAS Event Stream Processing (ESP)](https://www.sas.com/en_us/software/event-stream-processing.html) +models programmatically in Python. Using ESPPy, you can connect to +an ESP server and interact with projects and their components as +Python objects. These objects include projects, continuous queries, +windows, events, loggers, SAS Micro Analytic Service modules, +routers, and analytical algorithms. + +ESPPy has full integration with [Jupyter](https://jupyter.org/) notebooks including visualizing +diagrams of your ESP projects, and support for streaming charts and +images. This enables you to easily explore and prototype your ESP +projects in a familiar notebook interface. + +## Installation + +To install ESPPy, use `pip`. This installs +ESPPy and the Python package dependencies. + +``` +pip install sas-esppy +``` + +### Additional Requirements + +In addition to the Python package dependencies, you also need the +`graphviz` command-line tools to fully take advantage of ESPPy. Download them from http://www.graphviz.org/download/. + +### Performance Enhancement + +ESPPy uses the `ws4py` websocket Python package. In some cases, +you can improve performance greatly by installing the `wsaccel` package. +This may not be available on all platforms though, and is left up to +the user to install. + +## The Basics + +To import the ESPPy package, use the same method as with any other Python package. + +``` +>>> import esppy +``` + +To connect to an ESP server, use the `ESP` class. In most cases, the only +information that is needed is the hostname and port. + +``` +>>> esp = esppy.ESP('http://myesp.com:8777') +``` + +### Getting Information about the Server + +After you have connected to the server, you can get information about the +server and projects. + +``` +>>> esp.server_info +{'analytics-license': True, + 'engine': 'esp', + 'http-admin': 8777, + 'pubsub': 8778, + 'version': 'X.X'} + +# Currently no projects are loaded +>>> esp.get_projects() +{} +``` + +### Loading a Project + +To load a project, use the `load_project` method. + +``` +>>> esp.load_project('project.xml') + +>>> esp.get_projects() +{'project': Project(name='project')} +``` + +To access continous queries and windows within projects, use +the `queries` and `windows` attributes of the `Project` and +`ContinuousQuery` objects, respectively. + +``` +>>> proj = esp.get_project('project') +>>> proj.queries +{'contquery': ContinuousQuery(name='contquery', project='project')} + +>>> proj.queries['contquery'].windows +{'w_data': CopyWindow(name='w_data', continuous_query='contquery', project='project'), + 'w_request': SourceWindow(name='w_request', continuous_query='contquery', project='project'), + 'w_calculate': CalculateWindow(name='w_calculate', continuous_query='contquery', project='project')} + +>>> dataw = proj.queries['contquery'].windows['w_data'] +``` + +As a shortcut, you can drop the `queries` and `windows` attribute name. +Projects and continuous queries act like dictionaries of those components. + +``` +>>> dataw = proj['contquery']['w_data'] +``` + +### Publishing Event Data + +To publish events to a window, use the `publish_events` method. +It accepts a file name, file-like object, DataFrame, or a string of +CSV, XML, or JSON data. + +``` +>>> dataw.publish_events('data.csv') +``` + +### Monitoring Events + +You can subscribe to the events of any window in a project. By default, +all event data are cached in the local window object. + +``` +>>> dataw.subscribe() +>>> dataw + time x y z +id +6 0.15979 -2.30180 0.23155 10.6510 +7 0.18982 -1.41650 1.18500 11.0730 +8 0.22040 -0.27241 2.22010 11.9860 +9 0.24976 -0.61292 2.22010 11.9860 +10 0.27972 1.33480 4.24950 11.4140 +11 0.31802 3.44590 7.58650 12.5990 +``` + +To limit the number of cached events, use the `limit` +parameter. For example, to only keep the last 20 events, enter +the following line: + +``` +>>> dataw.subscribe(limit=20) +``` + +You can also limit the amount of time that events are collected using +the `horizon` parameter. Use one of the following objects: `datetime`, `date`, `time`, +or `timedelta`. + +``` +>>> dataw.subscribe(horizon=datetime.timedelta(hours=1)) +``` + +You can also perform any DataFrame operation on your ESP windows. + +``` +>>> dataw.info() +<class 'pandas.core.frame.DataFrame'> +Int64Index: 2108 entries, 6 to 2113 +Data columns (total 4 columns): +time 2108 non-null float64 +x 2108 non-null float64 +y 2108 non-null float64 +z 2108 non-null float64 +dtypes: float64(4) +memory usage: 82.3 KB + +>>> dataw.describe() + time x y z +count 20.000000 20.000000 20.000000 20.000000 +mean 69.655050 -4.365320 8.589630 -1.675292 +std 0.177469 1.832482 2.688911 2.108300 +min 69.370000 -7.436700 4.862500 -5.175700 +25% 69.512500 -5.911250 7.007675 -3.061150 +50% 69.655000 -4.099700 7.722700 -1.702500 +75% 69.797500 -2.945400 9.132350 -0.766110 +max 69.940000 -1.566300 14.601000 3.214400 +``` + +### Using ESPPy Visualizations with JupyterLab + +NOTE: These instructions assume you have Anaconda installed. + +To use jupyterlab visualizations with ESPPy (available in version 6.2 or higher), perform the following steps: + +1. Create a new Anaconda environment. For this example, the environment is called esp. +``` + $ conda create -n esp python=3.X +``` +2. Activate the new environment. +``` +$ conda activate esp +``` +3. Install the following packages: +``` +$ pip install jupyter +$ pip install jupyterlab +$ pip install matplotlib +$ pip install ipympl +$ pip install pandas +$ pip install requests +$ pip install image +$ pip install ws4py +$ pip install plotly +$ pip install ipyleaflet +$ pip install graphviz +``` +4. Install the following Jupyterlab extensions: +``` +$ jupyter labextension install @jupyter-widgets/jupyterlab-manager +$ jupyter labextension install plotlywidget +$ jupyter labextension install jupyter-leaflet +``` + +5. Install the following packages (WINDOWS ONLY): +``` +$ conda install -c conda-forge python-graphviz +``` + +6. Create and change to a working directory. +``` +$ cd $HOME +$ mkdir esppy +$ cd esppy +``` + +7. Install ESPPy. +``` +pip install sas-esppy +``` + +8. Create a notebooks directory to store your notebooks. +``` +$ mkdir notebooks +``` + +9. Start the Jupyterlab server. Select an available port. For this example, port 35000 was selected. +``` +$ jupyter lab --port 35000 +``` + +After you complete these steps, you can use the latest ESP graphics in your Jupyter notebooks. + +### Documentation + +To view the full API documentation for ESPPy, see +https://sassoftware.github.io/python-esppy/. + + + + +%package -n python3-sas-esppy +Summary: SAS Event Stream Processing Python Interface +Provides: python-sas-esppy +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-sas-esppy +# SAS Event Stream Processing Python Interface + +The ESPPy package enables you to create +[SAS Event Stream Processing (ESP)](https://www.sas.com/en_us/software/event-stream-processing.html) +models programmatically in Python. Using ESPPy, you can connect to +an ESP server and interact with projects and their components as +Python objects. These objects include projects, continuous queries, +windows, events, loggers, SAS Micro Analytic Service modules, +routers, and analytical algorithms. + +ESPPy has full integration with [Jupyter](https://jupyter.org/) notebooks including visualizing +diagrams of your ESP projects, and support for streaming charts and +images. This enables you to easily explore and prototype your ESP +projects in a familiar notebook interface. + +## Installation + +To install ESPPy, use `pip`. This installs +ESPPy and the Python package dependencies. + +``` +pip install sas-esppy +``` + +### Additional Requirements + +In addition to the Python package dependencies, you also need the +`graphviz` command-line tools to fully take advantage of ESPPy. Download them from http://www.graphviz.org/download/. + +### Performance Enhancement + +ESPPy uses the `ws4py` websocket Python package. In some cases, +you can improve performance greatly by installing the `wsaccel` package. +This may not be available on all platforms though, and is left up to +the user to install. + +## The Basics + +To import the ESPPy package, use the same method as with any other Python package. + +``` +>>> import esppy +``` + +To connect to an ESP server, use the `ESP` class. In most cases, the only +information that is needed is the hostname and port. + +``` +>>> esp = esppy.ESP('http://myesp.com:8777') +``` + +### Getting Information about the Server + +After you have connected to the server, you can get information about the +server and projects. + +``` +>>> esp.server_info +{'analytics-license': True, + 'engine': 'esp', + 'http-admin': 8777, + 'pubsub': 8778, + 'version': 'X.X'} + +# Currently no projects are loaded +>>> esp.get_projects() +{} +``` + +### Loading a Project + +To load a project, use the `load_project` method. + +``` +>>> esp.load_project('project.xml') + +>>> esp.get_projects() +{'project': Project(name='project')} +``` + +To access continous queries and windows within projects, use +the `queries` and `windows` attributes of the `Project` and +`ContinuousQuery` objects, respectively. + +``` +>>> proj = esp.get_project('project') +>>> proj.queries +{'contquery': ContinuousQuery(name='contquery', project='project')} + +>>> proj.queries['contquery'].windows +{'w_data': CopyWindow(name='w_data', continuous_query='contquery', project='project'), + 'w_request': SourceWindow(name='w_request', continuous_query='contquery', project='project'), + 'w_calculate': CalculateWindow(name='w_calculate', continuous_query='contquery', project='project')} + +>>> dataw = proj.queries['contquery'].windows['w_data'] +``` + +As a shortcut, you can drop the `queries` and `windows` attribute name. +Projects and continuous queries act like dictionaries of those components. + +``` +>>> dataw = proj['contquery']['w_data'] +``` + +### Publishing Event Data + +To publish events to a window, use the `publish_events` method. +It accepts a file name, file-like object, DataFrame, or a string of +CSV, XML, or JSON data. + +``` +>>> dataw.publish_events('data.csv') +``` + +### Monitoring Events + +You can subscribe to the events of any window in a project. By default, +all event data are cached in the local window object. + +``` +>>> dataw.subscribe() +>>> dataw + time x y z +id +6 0.15979 -2.30180 0.23155 10.6510 +7 0.18982 -1.41650 1.18500 11.0730 +8 0.22040 -0.27241 2.22010 11.9860 +9 0.24976 -0.61292 2.22010 11.9860 +10 0.27972 1.33480 4.24950 11.4140 +11 0.31802 3.44590 7.58650 12.5990 +``` + +To limit the number of cached events, use the `limit` +parameter. For example, to only keep the last 20 events, enter +the following line: + +``` +>>> dataw.subscribe(limit=20) +``` + +You can also limit the amount of time that events are collected using +the `horizon` parameter. Use one of the following objects: `datetime`, `date`, `time`, +or `timedelta`. + +``` +>>> dataw.subscribe(horizon=datetime.timedelta(hours=1)) +``` + +You can also perform any DataFrame operation on your ESP windows. + +``` +>>> dataw.info() +<class 'pandas.core.frame.DataFrame'> +Int64Index: 2108 entries, 6 to 2113 +Data columns (total 4 columns): +time 2108 non-null float64 +x 2108 non-null float64 +y 2108 non-null float64 +z 2108 non-null float64 +dtypes: float64(4) +memory usage: 82.3 KB + +>>> dataw.describe() + time x y z +count 20.000000 20.000000 20.000000 20.000000 +mean 69.655050 -4.365320 8.589630 -1.675292 +std 0.177469 1.832482 2.688911 2.108300 +min 69.370000 -7.436700 4.862500 -5.175700 +25% 69.512500 -5.911250 7.007675 -3.061150 +50% 69.655000 -4.099700 7.722700 -1.702500 +75% 69.797500 -2.945400 9.132350 -0.766110 +max 69.940000 -1.566300 14.601000 3.214400 +``` + +### Using ESPPy Visualizations with JupyterLab + +NOTE: These instructions assume you have Anaconda installed. + +To use jupyterlab visualizations with ESPPy (available in version 6.2 or higher), perform the following steps: + +1. Create a new Anaconda environment. For this example, the environment is called esp. +``` + $ conda create -n esp python=3.X +``` +2. Activate the new environment. +``` +$ conda activate esp +``` +3. Install the following packages: +``` +$ pip install jupyter +$ pip install jupyterlab +$ pip install matplotlib +$ pip install ipympl +$ pip install pandas +$ pip install requests +$ pip install image +$ pip install ws4py +$ pip install plotly +$ pip install ipyleaflet +$ pip install graphviz +``` +4. Install the following Jupyterlab extensions: +``` +$ jupyter labextension install @jupyter-widgets/jupyterlab-manager +$ jupyter labextension install plotlywidget +$ jupyter labextension install jupyter-leaflet +``` + +5. Install the following packages (WINDOWS ONLY): +``` +$ conda install -c conda-forge python-graphviz +``` + +6. Create and change to a working directory. +``` +$ cd $HOME +$ mkdir esppy +$ cd esppy +``` + +7. Install ESPPy. +``` +pip install sas-esppy +``` + +8. Create a notebooks directory to store your notebooks. +``` +$ mkdir notebooks +``` + +9. Start the Jupyterlab server. Select an available port. For this example, port 35000 was selected. +``` +$ jupyter lab --port 35000 +``` + +After you complete these steps, you can use the latest ESP graphics in your Jupyter notebooks. + +### Documentation + +To view the full API documentation for ESPPy, see +https://sassoftware.github.io/python-esppy/. + + + + +%package help +Summary: Development documents and examples for sas-esppy +Provides: python3-sas-esppy-doc +%description help +# SAS Event Stream Processing Python Interface + +The ESPPy package enables you to create +[SAS Event Stream Processing (ESP)](https://www.sas.com/en_us/software/event-stream-processing.html) +models programmatically in Python. Using ESPPy, you can connect to +an ESP server and interact with projects and their components as +Python objects. These objects include projects, continuous queries, +windows, events, loggers, SAS Micro Analytic Service modules, +routers, and analytical algorithms. + +ESPPy has full integration with [Jupyter](https://jupyter.org/) notebooks including visualizing +diagrams of your ESP projects, and support for streaming charts and +images. This enables you to easily explore and prototype your ESP +projects in a familiar notebook interface. + +## Installation + +To install ESPPy, use `pip`. This installs +ESPPy and the Python package dependencies. + +``` +pip install sas-esppy +``` + +### Additional Requirements + +In addition to the Python package dependencies, you also need the +`graphviz` command-line tools to fully take advantage of ESPPy. Download them from http://www.graphviz.org/download/. + +### Performance Enhancement + +ESPPy uses the `ws4py` websocket Python package. In some cases, +you can improve performance greatly by installing the `wsaccel` package. +This may not be available on all platforms though, and is left up to +the user to install. + +## The Basics + +To import the ESPPy package, use the same method as with any other Python package. + +``` +>>> import esppy +``` + +To connect to an ESP server, use the `ESP` class. In most cases, the only +information that is needed is the hostname and port. + +``` +>>> esp = esppy.ESP('http://myesp.com:8777') +``` + +### Getting Information about the Server + +After you have connected to the server, you can get information about the +server and projects. + +``` +>>> esp.server_info +{'analytics-license': True, + 'engine': 'esp', + 'http-admin': 8777, + 'pubsub': 8778, + 'version': 'X.X'} + +# Currently no projects are loaded +>>> esp.get_projects() +{} +``` + +### Loading a Project + +To load a project, use the `load_project` method. + +``` +>>> esp.load_project('project.xml') + +>>> esp.get_projects() +{'project': Project(name='project')} +``` + +To access continous queries and windows within projects, use +the `queries` and `windows` attributes of the `Project` and +`ContinuousQuery` objects, respectively. + +``` +>>> proj = esp.get_project('project') +>>> proj.queries +{'contquery': ContinuousQuery(name='contquery', project='project')} + +>>> proj.queries['contquery'].windows +{'w_data': CopyWindow(name='w_data', continuous_query='contquery', project='project'), + 'w_request': SourceWindow(name='w_request', continuous_query='contquery', project='project'), + 'w_calculate': CalculateWindow(name='w_calculate', continuous_query='contquery', project='project')} + +>>> dataw = proj.queries['contquery'].windows['w_data'] +``` + +As a shortcut, you can drop the `queries` and `windows` attribute name. +Projects and continuous queries act like dictionaries of those components. + +``` +>>> dataw = proj['contquery']['w_data'] +``` + +### Publishing Event Data + +To publish events to a window, use the `publish_events` method. +It accepts a file name, file-like object, DataFrame, or a string of +CSV, XML, or JSON data. + +``` +>>> dataw.publish_events('data.csv') +``` + +### Monitoring Events + +You can subscribe to the events of any window in a project. By default, +all event data are cached in the local window object. + +``` +>>> dataw.subscribe() +>>> dataw + time x y z +id +6 0.15979 -2.30180 0.23155 10.6510 +7 0.18982 -1.41650 1.18500 11.0730 +8 0.22040 -0.27241 2.22010 11.9860 +9 0.24976 -0.61292 2.22010 11.9860 +10 0.27972 1.33480 4.24950 11.4140 +11 0.31802 3.44590 7.58650 12.5990 +``` + +To limit the number of cached events, use the `limit` +parameter. For example, to only keep the last 20 events, enter +the following line: + +``` +>>> dataw.subscribe(limit=20) +``` + +You can also limit the amount of time that events are collected using +the `horizon` parameter. Use one of the following objects: `datetime`, `date`, `time`, +or `timedelta`. + +``` +>>> dataw.subscribe(horizon=datetime.timedelta(hours=1)) +``` + +You can also perform any DataFrame operation on your ESP windows. + +``` +>>> dataw.info() +<class 'pandas.core.frame.DataFrame'> +Int64Index: 2108 entries, 6 to 2113 +Data columns (total 4 columns): +time 2108 non-null float64 +x 2108 non-null float64 +y 2108 non-null float64 +z 2108 non-null float64 +dtypes: float64(4) +memory usage: 82.3 KB + +>>> dataw.describe() + time x y z +count 20.000000 20.000000 20.000000 20.000000 +mean 69.655050 -4.365320 8.589630 -1.675292 +std 0.177469 1.832482 2.688911 2.108300 +min 69.370000 -7.436700 4.862500 -5.175700 +25% 69.512500 -5.911250 7.007675 -3.061150 +50% 69.655000 -4.099700 7.722700 -1.702500 +75% 69.797500 -2.945400 9.132350 -0.766110 +max 69.940000 -1.566300 14.601000 3.214400 +``` + +### Using ESPPy Visualizations with JupyterLab + +NOTE: These instructions assume you have Anaconda installed. + +To use jupyterlab visualizations with ESPPy (available in version 6.2 or higher), perform the following steps: + +1. Create a new Anaconda environment. For this example, the environment is called esp. +``` + $ conda create -n esp python=3.X +``` +2. Activate the new environment. +``` +$ conda activate esp +``` +3. Install the following packages: +``` +$ pip install jupyter +$ pip install jupyterlab +$ pip install matplotlib +$ pip install ipympl +$ pip install pandas +$ pip install requests +$ pip install image +$ pip install ws4py +$ pip install plotly +$ pip install ipyleaflet +$ pip install graphviz +``` +4. Install the following Jupyterlab extensions: +``` +$ jupyter labextension install @jupyter-widgets/jupyterlab-manager +$ jupyter labextension install plotlywidget +$ jupyter labextension install jupyter-leaflet +``` + +5. Install the following packages (WINDOWS ONLY): +``` +$ conda install -c conda-forge python-graphviz +``` + +6. Create and change to a working directory. +``` +$ cd $HOME +$ mkdir esppy +$ cd esppy +``` + +7. Install ESPPy. +``` +pip install sas-esppy +``` + +8. Create a notebooks directory to store your notebooks. +``` +$ mkdir notebooks +``` + +9. Start the Jupyterlab server. Select an available port. For this example, port 35000 was selected. +``` +$ jupyter lab --port 35000 +``` + +After you complete these steps, you can use the latest ESP graphics in your Jupyter notebooks. + +### Documentation + +To view the full API documentation for ESPPy, see +https://sassoftware.github.io/python-esppy/. + + + + +%prep +%autosetup -n sas-esppy-7.1.16 + +%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-sas-esppy -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 7.1.16-1 +- Package Spec generated |