aops-apollo-tool x86_64 a49c3f009cbf046905ade40df537e16534cdc2c6da328683d1ba5c6a0ee32d85 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 265bc4bf14813c5f3d7006abb2ce60469cd1cabe9f7874079c636a968c803f40 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 8ca3d47a793129e5664ac699ab06d52a9da7cc6f169b4d247d5539a61fa809ae 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 059d3adf256ed56bdccd8753af1cd51c5c51ad421437bebd8e43c184971951d1 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 b470addfb445e71bc38c7d807d6c030753402391d54baf4d21c13b989c8a4620 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 8497a2e77834353c9c33497947667d63e4ac30c39741d0141e71c6d53f812169 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-hermes x86_64 0da1c240663321d00cf0e328041f04557019c2c1259dcfe7359ba61f03843366 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-mcp src f707c78a1c901cdd0031b2da13ee8e45c3d9e35f74a124cb0309fc5149cdf47f Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-mcp x86_64 c775aaa559cce76e21362f4f5adc2ed64201e8ae7bb1a8a56fbb23c16e1a1b9c Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-tools x86_64 08a0e04090a602cd3107e09a9aaf7ace7b7b86920c7dd2e30b123929146da917 aops scripts tools for aops, it's about aops deploy https://gitee.com/openeuler/aops-vulcanus aops-vulcanus src 40ce0bdfaf00e97a6f1b13d18c016f30173916b05a292d16d65273e7f403ed9e 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 8a35ad928f7857a82e655b208aaca1a9df98ef4213dc97146c11d4b1809b2e14 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 2aaf7c0b9a95545db758ce97ce83508157ae3caf7c2f39cc7cb6989073f50ecd 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 426f81cbf548b71bc3b05a26129fdf3272f0c2ae501a9c2e2e0012f04577b851 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 8e131438df25e61de316a7087247ff828f6f1af046e61d06e50be86ff14e4060 A async task of aops. A async task of aops. https://gitee.com/openeuler/aops-zeus authHub src c2006874053ac0df495dac9e71ea69b5de5bf5d919578d0364719a90adc97a49 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 19a89a3a6d5b9f2fbe3f454731c4e3af5cf765eb054202105bb5af6da5514901 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 737a23af9b2dbb53ca37458a2415afaa628fe09b838e2e4843405655db1cab57 Authentication authority web based on oauth2 Authentication authority web based on oauth2 https://gitee.com/openeuler/authHub dnf-hotpatch-plugin x86_64 418e830bbd1f6d916206602df6b2924add1343e24ef70a150b12d390ab1a2472 dnf hotpatch plugin dnf hotpatch plugin, it's about hotpatch query and fix https://gitee.com/openeuler/aops-ceres gala-anteater src d5f03316b97e2458e7a79c3d0bf830fe8cc6577ba4334ddc4ade747f64f79e02 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 d8423be9d8c12b7d81be1fa3c6d83c923df1c9095de9d3086b8b998fbab68e0e 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 bb9b14c1c309726af0e06be5abba33b52de53f2b8dc026379fb4ed70c0ffb565 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 84f51f9c6efaea3b1f5e4e35bd2802815c4b78c89d3e82629efde29a4023891f 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 ba8b6434a7015650264a7b5c1995dfe1b810eb8eeae998fbae0d95b05a11bb17 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 c89268e12596494b51fee2b97a21405213a10723a27b031fb4b6cbf7eb6e1c7c 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 c1ca3e9fcfcebc227997aa6f420b52679a8232fd6f46bfcc96a6d18223e55c9d Cause inference module for gala-ops project Cause inference module for A-Ops project https://gitee.com/openeuler/gala-spider gala-ops x86_64 9f43df46476171e9c21e31a6f69ff1431d39e4bd6eebae23a0786cf6d5b98776 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 4645cb6146df92b8b3afa1e1415455f66e0419a2f395b159bf1cb22eb22229c6 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 882dc7b877c99ab45fbb7e353e2a95d710004e210804416b71f88567d54dafb8 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 278b1e102c30316b352f46ad61a95cb5de5beba91324ee45ad023a2d9932cd7c loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server loggpt x86_64 7949b72c02ab0b3c4e82fcd5175a9138db173f92ad03ee9aec927b1acce5d030 loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server osmind-ai src b8733bfb7cf35101cda6edfdeff6c401aa10f66aaa7f94f028742b60e99ddc83 OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice osmind-ai x86_64 b414e7d36759278c3024b341e6ff87fb9893a0946d3ebb03499875eaec4baf4a OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice python-Authlib src 91ad41b98b65ceeb53f5aee360ae8cbc37717d17f62c4a2fc3a1d3f6746b9a7b 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 6e526d6334e34a864c944a4c83a9ac67ef7f5defadc8a7d0c918a67d62bbaacc 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 f165912b4aa0351a90003cd010803574ace13c6cd52067c699996ca40d187a30 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python-billiard-help noarch 96a97a59e2fdd63d4083cec6a4be8addf7aee9dfee8062cd36f9c410c4f359d2 Development documents and examples for billiard Multiprocessing Pool Extensions https://github.com/celery/billiard python-celery src b25be87ef6331a0a16b1ef9d589bdeb320bc22e65d5d24ffeb28cbf2a14352db Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python-celery-help noarch ffb7a600bcab1651192fae5584a66dd00b2edab07c673cfe26ed06d88ef7fed2 Development documents and examples for celery Distributed Task Queue. https://github.com/celery/celery python-click-didyoumean src fcdc832ee9862d05b8e64cb2c0bafafff689dfed728068b6eada53d4f5776801 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 f04c05b7bfa032ee92748e64a46aa18735ce64068ef66197f08af3faa1e576a6 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 e2ca673a671c30a9d232ac7b4ba7965b6e3861c91da710f08b047e6710a7c158 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 a07f752d512c6f0c1e7e047f43efc15d48f50e6c57d5709c5ba74331e3c7b34a 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 a419317ca4cf5888a33eac52290c9e22de0324834efe92d45aea4136dd620588 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python-click-repl-help noarch b8d64884d3ba0217c80b00da34933adc091f74ab4e3b7d664a665d3c2e008205 Development documents and examples for click-repl REPL plugin for Click https://github.com/untitaker/click-repl python-pandas-flavor src c7bff3152f980a83db6f6e0cc176d473f59c21b1a1dd310338432cb96985e78c 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 a8366c72d460068ab6e76a9f78eadf7344fcdf1cfb29a0ce1ff6dfbbeec9dfa4 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 d4b2fcd4f27f473652f2862a901fb67c5b25f6a764f7172825a982aebecfcd13 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 efcd5a2c8636c32e930b8e77c1e4d83e04d951335f1075cbfdb52638c2581c99 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 2a997f331c046b79c2884eb8b6b45ea8b926fc7d5daccb3775807d1fed65de63 Statistical data visualization https://pypi.org/project/seaborn/ python-seaborn-help noarch 8eea8aeec660f14f9b0d3e0bc88904100fd4f6eae802deda6d65bd75b531b881 Development documents and examples for seaborn https://pypi.org/project/seaborn/ python3-Authlib noarch 5153b74c58618044ec7bb1061419dbef6e71fae6ef1a898e11519eecf54b5be1 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 db8df6d7686677e70e3966f28466a43d08403b529729a835f442cab306727054 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python3-celery noarch f947e71f063cbb66fe3dd1ccbacd77ef239e849b5b3d9e37799b41e7685e71be Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python3-click-didyoumean noarch 419e0bfe130ead9b1378f027ea40ce49bccf99934bef1d9830968b6efd01c490 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 e915f1319cb66b5614813474aa942fa81453cf5cdc5f2e0323bde6111df6b348 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 12d87314939338a99181f3e3c1df90a9de4e3c2161589cb6f177a5240f0cbcd3 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python3-gala-anteater x86_64 77b4ac38b5517427156c980b87aa33d9e47cc3f2096564d50c85117c4287fbbd Python3 package of gala-anteater Python3 package of gala-anteater https://gitee.com/openeuler/gala-anteater python3-gala-inference x86_64 da6e165d2a2182dcfd393c279ee028b55dfe4260b2218ba5142c6a28383c23ea Python3 package of gala-inference Python3 package of gala-inference https://gitee.com/openeuler/gala-spider python3-gala-spider x86_64 7187df4ad2f1140efe7db6a2a26152fe011c4e8a0de4cdb4ba316bb1673e49c8 Python3 package of gala-spider Python3 package of gala-spider https://gitee.com/openeuler/gala-spider python3-pandas-flavor noarch 106d9240a9e93143690eb69796583d628b7fe9b52dc749aff970c4d4469b71f0 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 43e8db8c28fa0865bd4e5c2f526d099ca06dd80a02eb585ddb010137096eb743 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 e42f691a232e937038d6462185790407b3d135b3e1f6939075d8ca05d66a9bf5 Statistical data visualization https://pypi.org/project/seaborn/ zeus-distribute x86_64 33c789ce2f62f6c8f1dd820ec0c4bb85b52f8c40d2867bcc9398d609b080c1ec A distributed service of aops. A distributed service of aops. https://gitee.com/openeuler/aops-zeus zeus-host-information x86_64 28cb7ef2f3b649681da973b6774c1f8785145156fae85c93d7cf85cb60fbaf64 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 8130525269ad3ce94bcd90b0d72b7b2029f2058a6ced24888065c50926bdd97d 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 39d92c3594fec4e7f7ea0a7b92bed1fd9d28222d44b48da9a56892b08cd9d5e7 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