aops-apollo-tool x86_64 8777505c8b2324eb7d4d5c6f1f33ce451976dd5570c7101c9a1ec6220ad76f02 Small tools for aops-apollo, e.g. updateinfo.xml generater smalltools for aops-apollo, e.g.updateinfo.xml generater https://gitee.com/openeuler/aops-apollo aops-apollo src 84765abbdc298727b36515246bba2880d6063fabdd9d54f347095feb10349105 Cve management service, monitor machine vulnerabilities and provide fix functions. Cve management service, monitor machine vulnerabilities and provide fix functions. https://gitee.com/openeuler/aops-apollo aops-apollo x86_64 57d177277d223517468e355aecd0379d55509c9ccb34d25c05c8dab5b5ac92a2 Cve management service, monitor machine vulnerabilities and provide fix functions. Cve management service, monitor machine vulnerabilities and provide fix functions. https://gitee.com/openeuler/aops-apollo aops-ceres src 0e9a5c59fa6b074242d469bb2c5ba6813be6070057f27bcd974bdb1561a5248c An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. https://gitee.com/openeuler/aops-ceres aops-ceres x86_64 5d892292ddcdfccba11212542010ba09073901fad460183dd4b07706013d1154 An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. An agent which needs to be adopted in client, it managers some plugins, such as gala-gopher(kpi collection), fluentd(log collection) and so on. https://gitee.com/openeuler/aops-ceres aops-hermes src ecedef012120786f5638981c19eb4d0d443372e18323f6fcb59468bb8be9cb13 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-hermes x86_64 48411c084b009759161b91e8b4047d3e16b804ce4c1ee0aa159f1453a21e263e Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-mcp src 1e81f1c9cef6f8cb0a349f6673c4b5e3f61fee908b78572e4b419458041f4e9b Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-mcp x86_64 e08352317a462b6f5c3ab7ae9a504f0fe56c435b7cee40e7ccfdc606915a7ece Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-tools x86_64 fe0fff77543a082ce433126d07044def3a211528b4e9ec110e38715196d92611 aops scripts tools for aops, it's about aops deploy https://gitee.com/openeuler/aops-vulcanus aops-vulcanus src 58a8c6ca12a1e656ac015d4ec08f8a5094f82d2395b2c5279241d256efd317a0 A basic tool libraries of aops, including logging, configure and response, etc. A basic tool libraries of aops, including logging, configure and response, etc. https://gitee.com/openeuler/aops-vulcanus aops-vulcanus x86_64 b7203e1771f38b03ce405488dfd4eadda8502ac44de299ba8ff5bd7f4fcf9a54 A basic tool libraries of aops, including logging, configure and response, etc. A basic tool libraries of aops, including logging, configure and response, etc. https://gitee.com/openeuler/aops-vulcanus aops-zeus src 3aadfa4967e8992375575f38c10ce081b25d40d3261b13fd90d2b516ee4dce44 A service which is the foundation of aops. Provide one-click aops deployment, service start and stop, hot loading of configuration files, and database initialization. Provides: aops-zeus https://gitee.com/openeuler/aops-zeus aops-zeus x86_64 ec4475db975b53e1af4f5d8a83f6c00cda197aa2a74fe35629c8ee0a60e9247e A service which is the foundation of aops. Provide one-click aops deployment, service start and stop, hot loading of configuration files, and database initialization. Provides: aops-zeus https://gitee.com/openeuler/aops-zeus async-task x86_64 16730d52b745feb6ba344c8844e3893a697749756afe56e5a902f6c3bb19b3de A async task of aops. A async task of aops. https://gitee.com/openeuler/aops-zeus authHub src 8b6b09f0da9ef557ed5183cb4fcf51ae6aad5d9a8844c6d6e6c1b2de31d7d660 Authentication authority based on oauth2 authhub is a specialized authentication center built on OAuth2, providing robust authentication and authorization capabilities for secure user access control in your applications.. https://gitee.com/openeuler/authHub authHub x86_64 8c1b65af5a0d97b96bd6c70b384887a2a8e65dfd27ec1b4602f78764bfee8c92 Authentication authority based on oauth2 authhub is a specialized authentication center built on OAuth2, providing robust authentication and authorization capabilities for secure user access control in your applications.. https://gitee.com/openeuler/authHub authhub-web x86_64 50681a68e37380f9f1430beea2e1e3da8c27fca1166a4146b07b4a682d8ca96a Authentication authority web based on oauth2 Authentication authority web based on oauth2 https://gitee.com/openeuler/authHub dnf-hotpatch-plugin x86_64 57146dadcb4e217ed542c578fc73bf38bfae4ab997882cefdcc8876c20b1e12b dnf hotpatch plugin dnf hotpatch plugin, it's about hotpatch query and fix https://gitee.com/openeuler/aops-ceres gala-anteater src e3cb8864b1c93482de54667a499bfd91bd9066b38c2307bf9bf67a7fb8e245b4 A time-series anomaly detection platform for operating system. Abnormal detection module for A-Ops project https://gitee.com/openeuler/gala-anteater gala-anteater x86_64 b3c4ce28fd989496d836249dbf4d73a37e7f55b662acb69b0b3edaf7d55fabd5 A time-series anomaly detection platform for operating system. Abnormal detection module for A-Ops project https://gitee.com/openeuler/gala-anteater gala-gopher src 2a5986ae5b5740f4b667d31433136a70fc8014ab91264ab12d53113e0a4527f5 Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher x86_64 8e96b555488b50cc6271cd89358e0210f764cd64278181737992517475cd0dc3 Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher-debuginfo x86_64 b5a9f62d8a09839391ecd6b5b3cbe1ed275b6998a42169a4fce65b10bc7c9afd Debug information for package gala-gopher This package provides debug information for package gala-gopher. Debug information is useful when developing applications that use this package or when debugging this package. https://gitee.com/openeuler/gala-gopher gala-gopher-debugsource x86_64 f85ac4eefcf1beb18a11c13428a94840e80cbf7527254da56ff649e792f0efbe Debug sources for package gala-gopher This package provides debug sources for package gala-gopher. Debug sources are useful when developing applications that use this package or when debugging this package. https://gitee.com/openeuler/gala-gopher gala-inference x86_64 9880d05f5ca8e1ce1b15c9245cadc250fe4ca38b853252742db8a058fe3e8c5b Cause inference module for gala-ops project Cause inference module for A-Ops project https://gitee.com/openeuler/gala-spider gala-ops x86_64 a9b7d9ddd76cd82b9b73d1e2a868c4e3ab1428784fdea9b62601caa015286bba gala-anteater/spider/inference installation package This package requires gala-anteater/spider/inference, allowing users to install them all at once https://gitee.com/openeuler/gala-spider gala-spider src a0c6c3d415bc56d009cbadb19ac081d96a69c0bf62689c88776baa1f693bf125 OS topological graph storage service and cause inference service for gala-ops project OS topological graph storage service for gala-ops project https://gitee.com/openeuler/gala-spider gala-spider x86_64 7e487b23dc787d3302565fc2f0c3ce9f95fa796ecb520e0b548298b7d1ed8bba OS topological graph storage service and cause inference service for gala-ops project OS topological graph storage service for gala-ops project https://gitee.com/openeuler/gala-spider loggpt src 9d2bb6b4e517fa3f7987210019a97041ab2c9d6ea101eb3c775aa3ce861c11b2 loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server loggpt x86_64 11f66b44a0946b5ff601a4aa32ecbe2b2f44d5e8b0597ea509b711eafe664b64 loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server osmind-ai src 253acad7e1e1b7bc35ba23b1e1bb4b2cd06bbff9a4ac6bf56aa91f50c59f5a6a OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice osmind-ai x86_64 e5fc034ef342f9e4f3c73d3ca1e5e16cbf22deef6eec0361f97c9f43c66add94 OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice python-Authlib src f0c6d9b88a0bb3192928a51d3b3f87b00fa1ad33a3ed55c67658db3ebf609374 The ultimate Python library in building OAuth and OpenID Connect servers and clients. The ultimate Python library in building OAuth and OpenID Connect servers. JWS, JWK, JWA, JWT are included. https://authlib.org/ python-Authlib-help noarch 3f23d2fb885ff5f0360e1c36ebfc3bee55a12fb79bb2fc054f86596f4ad903f3 Development documents and examples for Authlib The ultimate Python library in building OAuth and OpenID Connect servers. JWS, JWK, JWA, JWT are included. https://authlib.org/ python-billiard src 3cd488dc9f056d2c1c3d8a9b05f79e67d79bc6fa1e7f08ad0faa8220b9d23220 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python-billiard-help noarch 974103cf549fa97082a9a94073243fd37153eb1293f07f0ca1eecec71231b0aa Development documents and examples for billiard Multiprocessing Pool Extensions https://github.com/celery/billiard python-celery src 690ee2ec4bdb721e00806a4242e2c24210eede8a57a659c832cb1bd105b0aa82 Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python-celery-help noarch d3ac4564799c55574d9197e2f7fe3303488620a70be3b0a0add170f1d1109201 Development documents and examples for celery Distributed Task Queue. https://github.com/celery/celery python-click-didyoumean src d97372447537d72b5c006047459f51dfb51e6b85eb11f780db9d3621ab35e4bf Enables git-like *did-you-mean* feature in click Enables git-like *did-you-mean* feature in click https://github.com/click-contrib/click-didyoumean python-click-didyoumean-help noarch a6d5812da72453c03bcdab43383713f356f1e480968c1107c16eba331951e1d2 Enables git-like *did-you-mean* feature in click Enables git-like *did-you-mean* feature in click https://github.com/click-contrib/click-didyoumean python-click-plugins src 8809a6f9972e8b2fff42829f54ca458769fbd3fbdae7595f0714607568fa3bec An extension module for click to enable registering CLI commands via setuptools entry-points. An extension module for click to enable registering CLI commands via setuptools entry-points. https://github.com/click-contrib/click-plugins python-click-plugins-help noarch d42b9aa63c12d2c2fbc44a931a22b2b7b50c08a0ce4e3dd49dea11b5bbdc0c86 Development documents and examples for click-plugins An extension module for click to enable registering CLI commands via setuptools entry-points. https://github.com/click-contrib/click-plugins python-click-repl src 51c63daf9fc4e17c6a6488f85c03e77e1b4048c6b08b6d945d3223609ac8cf64 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python-click-repl-help noarch 2bdf2da468a2e8cd8ee611c262cdcfdbf3f16df38d0de24a3fa2df06e40a52a9 Development documents and examples for click-repl REPL plugin for Click https://github.com/untitaker/click-repl python-pandas-flavor src 97c2af0a8bce4b7a2b820dd489180b1354745332fc60ca8df69d97a6ec9d249c The easy way to write your own Pandas flavor. **The easy way to write your own flavor of Pandas** Pandas 0.23 added a (simple) API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by: 1. adding support for registering methods as well. 2. making each of these functions backwards compatible with older versions of Pandas. ***What does this mean?*** It is now simpler to add custom functionality to Pandas DataFrames and Series. Import this package. Write a simple python function. Register the function using one of the following decorators. ***Why?*** Pandas is super handy. Its general purpose is to be a "flexible and powerful data analysis/manipulation library". **Pandas Flavor** allows you add functionality that tailors Pandas to specific fields or use cases. Maybe you want to add new write methods to the Pandas DataFrame? Maybe you want custom plot functionality? Maybe something else? Accessors (in pandas) are objects attached to a attribute on the Pandas DataFrame/Series that provide extra, specific functionality. For example, `pandas.DataFrame.plot` is an accessor that provides plotting functionality. Add an accessor by registering the function with the following decorator and passing the decorator an accessor name. ```python import pandas_flavor as pf @pf.register_dataframe_accessor('my_flavor') class MyFlavor(object): def __init__(self, data): self._data = data def row_by_value(self, col, value): """Slice out row from DataFrame by a value.""" return self._data[self._data[col] == value].squeeze() ``` Every dataframe now has this accessor as an attribute. ```python import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.my_flavor.row_by_value('x', 10) ``` To see this in action, check out [pdvega](https://github.com/jakevdp/pdvega), [PhyloPandas](https://github.com/Zsailer/phylopandas), and [pyjanitor](https://github.com/ericmjl/pyjanitor)! Using this package, you can attach functions directly to Pandas objects. No intermediate accessor is needed. ```python import pandas_flavor as pf @pf.register_dataframe_method def row_by_value(df, col, value): """Slice out row from DataFrame by a value.""" return df[df[col] == value].squeeze() ``` ```python import pandas as pd import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.row_by_value('x', 10) ``` The pandas_flavor 0.5.0 release introduced [tracing of the registered method calls](/docs/tracing_ext.md). Now it is possible to add additional run-time logic around registered method execution which can be used for some support tasks. This extension was introduced to allow visualization of [pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) method chains as implemented in [pyjviz](https://github.com/pyjanitor-devs/pyjviz) - **register_dataframe_method**: register a method directly with a pandas DataFrame. - **register_dataframe_accessor**: register an accessor (and it's methods) with a pandas DataFrame. - **register_series_method**: register a methods directly with a pandas Series. - **register_series_accessor**: register an accessor (and it's methods) with a pandas Series. You can install using **pip**: ``` pip install pandas_flavor ``` or conda (thanks @ericmjl)! ``` conda install -c conda-forge pandas-flavor ``` Pull requests are always welcome! If you find a bug, don't hestitate to open an issue or submit a PR. If you're not sure how to do that, check out this [simple guide](https://github.com/Zsailer/guide-to-working-as-team-on-github). If you have a feature request, please open an issue or submit a PR! Pandas 0.23 introduced a simpler API for [extending Pandas](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-pandas). This API provided two key decorators, `register_dataframe_accessor` and `register_series_accessor`, that enable users to register **accessors** with Pandas DataFrames and Series. Pandas Flavor originated as a library to backport these decorators to older versions of Pandas (<0.23). While doing the backporting, it became clear that registering **methods** directly to Pandas objects might be a desired feature as well.[*](#footnote) <a name="footnote">*</a>*It is likely that Pandas deliberately chose not implement to this feature. If everyone starts monkeypatching DataFrames with their custom methods, it could lead to confusion in the Pandas community. The preferred Pandas approach is to namespace your methods by registering an accessor that contains your custom methods.* **So how does method registration work?** When you register a method, Pandas flavor actually creates and registers a (this is subtle, but important) **custom accessor class that mimics** the behavior of a method by: 1. inheriting the docstring of your function 2. overriding the `__call__` method to call your function. https://github.com/Zsailer/pandas_flavor python-pandas-flavor-help noarch f193c4c8a02267cc1a13cbc8d7d2ecb964c45bfd3aa3edd3f384ac1520fc1ebf Development documents and examples for pandas-flavor **The easy way to write your own flavor of Pandas** Pandas 0.23 added a (simple) API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by: 1. adding support for registering methods as well. 2. making each of these functions backwards compatible with older versions of Pandas. ***What does this mean?*** It is now simpler to add custom functionality to Pandas DataFrames and Series. Import this package. Write a simple python function. Register the function using one of the following decorators. ***Why?*** Pandas is super handy. Its general purpose is to be a "flexible and powerful data analysis/manipulation library". **Pandas Flavor** allows you add functionality that tailors Pandas to specific fields or use cases. Maybe you want to add new write methods to the Pandas DataFrame? Maybe you want custom plot functionality? Maybe something else? Accessors (in pandas) are objects attached to a attribute on the Pandas DataFrame/Series that provide extra, specific functionality. For example, `pandas.DataFrame.plot` is an accessor that provides plotting functionality. Add an accessor by registering the function with the following decorator and passing the decorator an accessor name. ```python import pandas_flavor as pf @pf.register_dataframe_accessor('my_flavor') class MyFlavor(object): def __init__(self, data): self._data = data def row_by_value(self, col, value): """Slice out row from DataFrame by a value.""" return self._data[self._data[col] == value].squeeze() ``` Every dataframe now has this accessor as an attribute. ```python import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.my_flavor.row_by_value('x', 10) ``` To see this in action, check out [pdvega](https://github.com/jakevdp/pdvega), [PhyloPandas](https://github.com/Zsailer/phylopandas), and [pyjanitor](https://github.com/ericmjl/pyjanitor)! Using this package, you can attach functions directly to Pandas objects. No intermediate accessor is needed. ```python import pandas_flavor as pf @pf.register_dataframe_method def row_by_value(df, col, value): """Slice out row from DataFrame by a value.""" return df[df[col] == value].squeeze() ``` ```python import pandas as pd import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.row_by_value('x', 10) ``` The pandas_flavor 0.5.0 release introduced [tracing of the registered method calls](/docs/tracing_ext.md). Now it is possible to add additional run-time logic around registered method execution which can be used for some support tasks. This extension was introduced to allow visualization of [pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) method chains as implemented in [pyjviz](https://github.com/pyjanitor-devs/pyjviz) - **register_dataframe_method**: register a method directly with a pandas DataFrame. - **register_dataframe_accessor**: register an accessor (and it's methods) with a pandas DataFrame. - **register_series_method**: register a methods directly with a pandas Series. - **register_series_accessor**: register an accessor (and it's methods) with a pandas Series. You can install using **pip**: ``` pip install pandas_flavor ``` or conda (thanks @ericmjl)! ``` conda install -c conda-forge pandas-flavor ``` Pull requests are always welcome! If you find a bug, don't hestitate to open an issue or submit a PR. If you're not sure how to do that, check out this [simple guide](https://github.com/Zsailer/guide-to-working-as-team-on-github). If you have a feature request, please open an issue or submit a PR! Pandas 0.23 introduced a simpler API for [extending Pandas](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-pandas). This API provided two key decorators, `register_dataframe_accessor` and `register_series_accessor`, that enable users to register **accessors** with Pandas DataFrames and Series. Pandas Flavor originated as a library to backport these decorators to older versions of Pandas (<0.23). While doing the backporting, it became clear that registering **methods** directly to Pandas objects might be a desired feature as well.[*](#footnote) <a name="footnote">*</a>*It is likely that Pandas deliberately chose not implement to this feature. If everyone starts monkeypatching DataFrames with their custom methods, it could lead to confusion in the Pandas community. The preferred Pandas approach is to namespace your methods by registering an accessor that contains your custom methods.* **So how does method registration work?** When you register a method, Pandas flavor actually creates and registers a (this is subtle, but important) **custom accessor class that mimics** the behavior of a method by: 1. inheriting the docstring of your function 2. overriding the `__call__` method to call your function. https://github.com/Zsailer/pandas_flavor python-pingouin src 9a630e0a84e4155637524e5d92741268a3ebaca6d603a1efd8845d5eef23b9c8 Pingouin: statistical package for Python **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation <https://pingouin-stats.org/build/html/api.html#>`_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. https://pingouin-stats.org/index.html python-pingouin-help noarch b67d2c4b35516d56fc6700119322802bffdd3fed21c290635d2cfe785970d681 Development documents and examples for pingouin **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation <https://pingouin-stats.org/build/html/api.html#>`_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. https://pingouin-stats.org/index.html python-seaborn src 66d801a410ad408084a7d42e6e4444575287f9b474e99284b62a8668947e3b1c Statistical data visualization https://pypi.org/project/seaborn/ python-seaborn-help noarch 564d61e30efbecf0e24a73c373d2b5a29ec3febe8c4b9183567a651cfea44818 Development documents and examples for seaborn https://pypi.org/project/seaborn/ python3-Authlib noarch c9447f9f3645852459c4d605ac6489a988beb0c9ecdc74ca9452f3a749400a06 The ultimate Python library in building OAuth and OpenID Connect servers and clients. The ultimate Python library in building OAuth and OpenID Connect servers. JWS, JWK, JWA, JWT are included. https://authlib.org/ python3-billiard noarch bf49f37847f14134c8781ab04fa9c0f25db6e3f25c57844915a8ed3fa6cef473 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python3-celery noarch 978fd533aab818859915f1d01e84f83bc65db6c491fd4ad8909712a201c1e83c Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python3-click-didyoumean noarch 4c3b8fcbca7b42f12a5c9fe2aded744ebe06e0d6fed544ad3362363a7bca760d Enables git-like *did-you-mean* feature in click Enables git-like *did-you-mean* feature in click https://github.com/click-contrib/click-didyoumean python3-click-plugins noarch b438a375e36d179ab6dc3fbe4f2b9d094a54b7c000613189caeddad9ba202423 An extension module for click to enable registering CLI commands via setuptools entry-points. An extension module for click to enable registering CLI commands via setuptools entry-points. https://github.com/click-contrib/click-plugins python3-click-repl noarch 141ee8eef28bee9ec5400920ef6cce16e226ea79ac26e562f41e479ec298dae8 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python3-gala-anteater x86_64 33fe03d7af87481a55f3b4a41919db95a4fc7930f6d2e93aacd8c53c942aa39e Python3 package of gala-anteater Python3 package of gala-anteater https://gitee.com/openeuler/gala-anteater python3-gala-inference x86_64 4743f76787d943e8d82b8d2443945ec68674584a03ae3ef1e438514e66c68441 Python3 package of gala-inference Python3 package of gala-inference https://gitee.com/openeuler/gala-spider python3-gala-spider x86_64 bf60d1d03fb79197a702fa1289264a1c3641879e1c6e484cfb311c737c15453f Python3 package of gala-spider Python3 package of gala-spider https://gitee.com/openeuler/gala-spider python3-pandas-flavor noarch 2d2cf4378720d27d2db2253bf883fd91a2bc8d62fee0af029adf48bf41bea2a6 The easy way to write your own Pandas flavor. **The easy way to write your own flavor of Pandas** Pandas 0.23 added a (simple) API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by: 1. adding support for registering methods as well. 2. making each of these functions backwards compatible with older versions of Pandas. ***What does this mean?*** It is now simpler to add custom functionality to Pandas DataFrames and Series. Import this package. Write a simple python function. Register the function using one of the following decorators. ***Why?*** Pandas is super handy. Its general purpose is to be a "flexible and powerful data analysis/manipulation library". **Pandas Flavor** allows you add functionality that tailors Pandas to specific fields or use cases. Maybe you want to add new write methods to the Pandas DataFrame? Maybe you want custom plot functionality? Maybe something else? Accessors (in pandas) are objects attached to a attribute on the Pandas DataFrame/Series that provide extra, specific functionality. For example, `pandas.DataFrame.plot` is an accessor that provides plotting functionality. Add an accessor by registering the function with the following decorator and passing the decorator an accessor name. ```python import pandas_flavor as pf @pf.register_dataframe_accessor('my_flavor') class MyFlavor(object): def __init__(self, data): self._data = data def row_by_value(self, col, value): """Slice out row from DataFrame by a value.""" return self._data[self._data[col] == value].squeeze() ``` Every dataframe now has this accessor as an attribute. ```python import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.my_flavor.row_by_value('x', 10) ``` To see this in action, check out [pdvega](https://github.com/jakevdp/pdvega), [PhyloPandas](https://github.com/Zsailer/phylopandas), and [pyjanitor](https://github.com/ericmjl/pyjanitor)! Using this package, you can attach functions directly to Pandas objects. No intermediate accessor is needed. ```python import pandas_flavor as pf @pf.register_dataframe_method def row_by_value(df, col, value): """Slice out row from DataFrame by a value.""" return df[df[col] == value].squeeze() ``` ```python import pandas as pd import my_flavor df = pd.DataFrame(data={ "x": [10, 20, 25], "y": [0, 2, 5] }) print(df) df.row_by_value('x', 10) ``` The pandas_flavor 0.5.0 release introduced [tracing of the registered method calls](/docs/tracing_ext.md). Now it is possible to add additional run-time logic around registered method execution which can be used for some support tasks. This extension was introduced to allow visualization of [pyjanitor](https://github.com/pyjanitor-devs/pyjanitor) method chains as implemented in [pyjviz](https://github.com/pyjanitor-devs/pyjviz) - **register_dataframe_method**: register a method directly with a pandas DataFrame. - **register_dataframe_accessor**: register an accessor (and it's methods) with a pandas DataFrame. - **register_series_method**: register a methods directly with a pandas Series. - **register_series_accessor**: register an accessor (and it's methods) with a pandas Series. You can install using **pip**: ``` pip install pandas_flavor ``` or conda (thanks @ericmjl)! ``` conda install -c conda-forge pandas-flavor ``` Pull requests are always welcome! If you find a bug, don't hestitate to open an issue or submit a PR. If you're not sure how to do that, check out this [simple guide](https://github.com/Zsailer/guide-to-working-as-team-on-github). If you have a feature request, please open an issue or submit a PR! Pandas 0.23 introduced a simpler API for [extending Pandas](https://pandas.pydata.org/pandas-docs/stable/development/extending.html#extending-pandas). This API provided two key decorators, `register_dataframe_accessor` and `register_series_accessor`, that enable users to register **accessors** with Pandas DataFrames and Series. Pandas Flavor originated as a library to backport these decorators to older versions of Pandas (<0.23). While doing the backporting, it became clear that registering **methods** directly to Pandas objects might be a desired feature as well.[*](#footnote) <a name="footnote">*</a>*It is likely that Pandas deliberately chose not implement to this feature. If everyone starts monkeypatching DataFrames with their custom methods, it could lead to confusion in the Pandas community. The preferred Pandas approach is to namespace your methods by registering an accessor that contains your custom methods.* **So how does method registration work?** When you register a method, Pandas flavor actually creates and registers a (this is subtle, but important) **custom accessor class that mimics** the behavior of a method by: 1. inheriting the docstring of your function 2. overriding the `__call__` method to call your function. https://github.com/Zsailer/pandas_flavor python3-pingouin noarch 3bcae4b214909c0e94b65e00f3b95ff3471c07fe3ce6aac0adc45f46f78e26ef Pingouin: statistical package for Python **Pingouin** is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. For a full list of available functions, please refer to the `API documentation <https://pingouin-stats.org/build/html/api.html#>`_. 1. ANOVAs: N-ways, repeated measures, mixed, ancova 2. Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations 3. Robust, partial, distance and repeated measures correlations 4. Linear/logistic regression and mediation analysis 5. Bayes Factors 6. Multivariate tests 7. Reliability and consistency 8. Effect sizes and power analysis 9. Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient 10. Circular statistics 11. Chi-squared tests 12. Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation... Pingouin is designed for users who want **simple yet exhaustive statistical functions**. For example, the :code:`ttest_ind` function of SciPy returns only the T-value and the p-value. By contrast, the :code:`ttest` function of Pingouin returns the T-value, the p-value, the degrees of freedom, the effect size (Cohen's d), the 95% confidence intervals of the difference in means, the statistical power and the Bayes Factor (BF10) of the test. https://pingouin-stats.org/index.html python3-seaborn noarch ff961a9632c95474e7a75a1fcabf117bd3a7ba3d3e8b7a31c4d96f49407f8ec5 Statistical data visualization https://pypi.org/project/seaborn/ zeus-distribute x86_64 f67e8bb04b57518f173940735169267e11525b43f3d815d4a6578fa6d863a32b A distributed service of aops. A distributed service of aops. https://gitee.com/openeuler/aops-zeus zeus-host-information x86_64 e18fc0e361e4c5f5dc9b50dbf832aaeae2d23348069d495ae014dfd66c8ad12e A host manager service which is the foundation of aops. A host manager service which is the foundation of aops. https://gitee.com/openeuler/aops-zeus zeus-operation x86_64 f6311c2ea08129e543e38a932f588787d31c5e6202209e9d944f5e1bf9984a86 A operation manager service which is the foundation of aops. A operation manager of aops. https://gitee.com/openeuler/aops-zeus zeus-user-access x86_64 ae886b391bef7ed81edab3db033273726bf354fdbb7bf61b300d60afe201efd8 A user manager service which is the foundation of aops. A user manager service which is the foundation of aops. https://gitee.com/openeuler/aops-zeus