aops-apollo-tool x86_64 649df0e20c96d67eb14ca7f417eefb6c8cbdb1ce8286a455547e483d5238995b 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 8009a20e1f2c26495d9744082ea715b91b367d5c41f6a7e2f765d1d8034beaf0 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 3cc0516231f85f8193ddcc742f94376ca6f2cbd99f4d7e0014958282b1d993eb 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 1ba04c9e87fff5610f57ddc2c129f90a1828cddebadc48ee2c3ec4369b9fd012 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 0e88095c686c23e4381d7ca94b3cd75571d18798cce94a07f4abb7ea5582ff4c 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 b977c9c9284497e72b53782ab0f80f2ed877e6ab6cf4d77b4389b29812d59716 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-hermes x86_64 7def49f7ec13d6bd7784b7c08515d7877b7b6acc99dddc2bfc2d93df73f6eb8a Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-mcp src d6326a7a5bb722f12bf0916a802f9eb92057a59e8a0a81e956640faccac0d07d Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-mcp x86_64 477850d9680bbb24933e98fcd3cb26797d5f229a96a3a9d8b67c9ba61736a9c7 Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-tools x86_64 078ba3ca5f55a8800507a57358c81837242a87f4be2c9a554988493f1c47ada4 aops scripts tools for aops, it's about aops deploy https://gitee.com/openeuler/aops-vulcanus aops-vulcanus src c9684dea7a369e50618744dacebf38ad381faa44beda2db201a73bbd0398a65b 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 12476b3abdd1d16245a95cd07f3bb29998f96ce06f78df662320314e21a0f8a5 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 3decf028593d13066f781f7952aa269918e293f5c278ce1aa14e40c14ce10615 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 b765610222365a8f9bfcbcd4c4f7f69f5d6c0b17cc33f6f45414579c2231bef9 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 fab233a46ee7a19aff13a0e76f4b2b69febaa824804ce012ab4d2df046757e9a A async task of aops. A async task of aops. https://gitee.com/openeuler/aops-zeus authHub src f6fae2b14c573bf0f4817da469340c24a33269e4be96c175ef2922216aebfa1b 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 7b6c1b1dda4dd3411d6cf35120b1eb059b00dbe9536e379d54acbfcc057707db 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 7f82e672e7ae4ccc0707b911fa07c801dac8db6a3365f7f75689cdbe377b7840 Authentication authority web based on oauth2 Authentication authority web based on oauth2 https://gitee.com/openeuler/authHub dnf-hotpatch-plugin x86_64 b303a7bfa9efa5df8ea920815e41738c06ca0144c22033512c0b2407e0f65c67 dnf hotpatch plugin dnf hotpatch plugin, it's about hotpatch query and fix https://gitee.com/openeuler/aops-ceres gala-anteater src 29dae90fecd49b9057696b19942723729881ae49f70aa47ab5e484ca57322b81 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 7b46b389837d1d95c0643db1a63b2b9c065d7d2b186fe0264e53cf4dea6e4880 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 3038e9561e0230d404b80d8f870690c4c8cda4b9ebb9583c31e76866127243b9 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 5c2dde8035d6e6766714065af0d066560f56f1f9eeff9b638a3145d99c426c2a 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 991f19d31ae9426b941a59c3bd3a54460a4e79a5ef914c76eb9d2f66b60aba28 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 6166bcf0a828d7c81a333dfdd10f15c59b4cf30a3e03a800521a5221a5b046e8 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 6c413d5f88c23c75906b30ba89e37af4da228611c771d4c33e79cfeeea545b45 Cause inference module for gala-ops project Cause inference module for A-Ops project https://gitee.com/openeuler/gala-spider gala-ops x86_64 83560f73a28c6a886d06224b2b21e4d01d4429f0a8785a4fbbaf8f729a26c64f 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 51291be3ce09177c348bc6e47c3b10581c9caf19c0b382a5515e4d4c42559e9e 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 6f182f3cefbafe927bac0a8e814addc0b77f792ad1996c3a67c8e950601de7a3 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 959483d38d108500484729eabbc2a54c1c06d6a32ed3f25e71a7c4180440d220 loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server loggpt x86_64 08cc59f2da1b94a3b15691b40d7c0f5b67ad7b8fb408bdaa3ac6f8e5ca213771 loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server osmind-ai src 5cf5fc227071a5485cfed4932d9b32ac5e0346b7f55667e39b4e6bc36977e781 OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice osmind-ai x86_64 abc4076bea951bb186f634ed60988a7fae5c1ba8ed202a47e762798f61ef60ee OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice python-Authlib src b059a577bf3436811fc67b4c4237f707462e0cbe75d4e47aa2cd96cca8b888f0 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 a38e2f4657976b178e27a3f56b2500c396dc57bb31c8dd0842a67bb1982c7ca2 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 a9a2c39d29718daf07355efdc288ce286759cd1c5b101d2d50ce4cd27a01ab81 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python-billiard-help noarch a9f15a79bac24ec9e9cc18cfb7b467c805875e58912356a42e504f9113cebb05 Development documents and examples for billiard Multiprocessing Pool Extensions https://github.com/celery/billiard python-celery src 955a8cc60c3b2d6155722c6c2da2e7288a9f02b43e374ad8b6dae9a756983b38 Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python-celery-help noarch bd888a9221eeb373d9cb97e082c09779fee7635827f6ed04d2fcc230e5b11d7c Development documents and examples for celery Distributed Task Queue. https://github.com/celery/celery python-click-didyoumean src 00dc55b6e2c91c7aafa44fc998c87f9e33f3de33f66cbf420c2b8b078dd4b6db 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 a541ad3c3bc6b90b993570fad49bac4058bc724aa298df0bf43393721c2a6afc 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 eef2a3311f55e65f5767f8dc9ec10dd547327712c4ebc6a0bce1c0a98528f1a9 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 0191c194663e1d930ddbc4796f4e62963d1be714f7ec76d9809653e1ec5ff0cb 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 6f79cb13d90ed39b70dea360d06faa292452b0752acb504fb72cd8b09c0293a2 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python-click-repl-help noarch 8cd0a38f2fce1b1a76b2becc96dcd141b1cd53969ed4eaac31550cfab546c7f0 Development documents and examples for click-repl REPL plugin for Click https://github.com/untitaker/click-repl python-pandas-flavor src 0d2944be36749ae5388bc3d6f1bc8d5b38c1e898bf5b2332605327b3363cd767 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 c46fc91f91991dbdbd85f79cbf9e8da681ccbf1278aa0e909877ce4c8ea17d67 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 798fac07c8a9ca391cffff8cb5a92e4bbbb6391c905b029807c80235fc0bd940 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 1c928699fb857707677bc648bc2734f3a196a007531fb1ac66ed8905f16c7167 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 7dac8528a23bee3d22b563ef490377b437424a8fefa1ae197222186ec073bae9 Statistical data visualization https://pypi.org/project/seaborn/ python-seaborn-help noarch 0b59b0a3c3dee432932be8a7e52d46a34713700d12d7ba5f996d1caa980aae79 Development documents and examples for seaborn https://pypi.org/project/seaborn/ python3-Authlib noarch 3a5ba56510365f5e7a941e049c34395ec0701d5ffc2a61be71349250239aa9a2 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 7acfb7054bb17b7dccce2d30ba3c7a75682eec116311447ad354cde13f8f04da Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python3-celery noarch ba4fa9f1db55034b0ca8db6f09c89d4b8a6a98c8361e86b0b14985357e26e6c6 Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python3-click-didyoumean noarch bd1dd69ba6960ddb1e1ae7b5492312e69923893edd72b4542937dc83c52a06fc 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 34fe88773176ee7d9761a9efac7603996ab98041c4f0e81ea521848e4a316649 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 277336eeb56a02f5fb1a88a55eb486a9c8b22d27624730d4da036055d9dbe299 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python3-gala-anteater x86_64 b44f5c5b5b94350794af009691dd136bdfa0f92b65d66c03ab67762f8fcca059 Python3 package of gala-anteater Python3 package of gala-anteater https://gitee.com/openeuler/gala-anteater python3-gala-inference x86_64 00a2d1da6ce1ea9147843f0c92d2e3b44dfeb3711f9f6aadc72af22eecb2b624 Python3 package of gala-inference Python3 package of gala-inference https://gitee.com/openeuler/gala-spider python3-gala-spider x86_64 91315e8f9e00a2d86438c6be94eaeb07c0a71758925f1687fa8733807b29c4f2 Python3 package of gala-spider Python3 package of gala-spider https://gitee.com/openeuler/gala-spider python3-pandas-flavor noarch 172e996aaa9a93d3ae4287364fdca2252ead8070f1ca24ecdebe763cb5c8fd2e 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 08fa8257171928bdc3bf0ede3ee5913fb54a3d4fd7d6c10c39c0164828132441 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 9cad52a040ea6eaacc91ed9e2f9a2f8a6de73d72d33687aa1bbc73e61633b53a Statistical data visualization https://pypi.org/project/seaborn/ zeus-distribute x86_64 85647fff050b958356b50c73b7b3340a39b229bbb3d50629b1788330994bf455 A distributed service of aops. A distributed service of aops. https://gitee.com/openeuler/aops-zeus zeus-host-information x86_64 9c0b5ea866a328c239f4c2451e32dbae5fc7145c9a6672218445453cb0441bf9 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 3ccbd2a1fa66b4c0ec7a9b3e99d0bae972f3373e3e4250be70063fea4abd8dc6 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 1ad64acbb7ab066b0eca06d23742ad5c781e3dc96696366452279853056f4677 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