aops-apollo-tool aarch64 4590ccc8d90e91ed05e35337e1226d2aa2d32e58623fe063d6d5a0a30255b403 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 aarch64 aae85130e71e30ce717f343054188f71fff214dcfd2cc8422813574d199fb006 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 src 8bb2ec066df73ff470184d3023193c73238fa3c3e4f88115d03d338b0fa9b0da 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 aarch64 ebf43be5dbae1aba0de38368e90515f01788b351a6e8bbf683eaf100f720bf6b 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 src 797e78c68a13fba98b372b042efb8f63cfd99d876dd3827c491e3de56d7c9728 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 aarch64 fa8405d1999b4813ed538498042d3911ee5e36b52549ec44b1e8dfc79a42b3df Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-hermes src 2d44ed8a5c76bf36ab31d68f4d1b3ff3843e8f0c619fa3bb7652b133ed36ed92 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-mcp aarch64 c3814ba5ccbb751ecac919c3937267cc860407386b2b3bcf60f5a5daf5b11d0f Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-mcp src 9aa9cf6fcf8dc9f7abffb64b86d9c7344a8da58898176d523cd4019854f7df54 Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-tools aarch64 855f2946e23e0a95f29d145e823fc6b36a1c0dd2fb55009020b1e28e7148cac4 aops scripts tools for aops, it's about aops deploy https://gitee.com/openeuler/aops-vulcanus aops-vulcanus aarch64 134e088799e4266866f0503bee6848fa47062c221ab6a57cb0a42f44c7d32fc1 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 src 412bbfd9babc86848dd7e54281d6ca1ad8e5aa578e8783ba2d2849bf0f1aec8a 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 aarch64 7ab20459fe7cbb2d8e3f6856a1f5c555dc87618df7f6da4efaaad36b908da427 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 src 75ae562cacc7aaf4cd75faad5e3e6c6f339a252dc49ab866d7d6da8191644003 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 aarch64 23c770668fbed450f26affb16b5b632f7d5880636ef070ea4d94c5505410845e A async task of aops. A async task of aops. https://gitee.com/openeuler/aops-zeus authHub aarch64 84c736cd3a70ffb671ae9678b8e3fa17d421f20e192d6f4654922abb8a31d40c 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 src 61286b3d43d3a04b143a02b3f54f5984b0d01e11850172f4380322a00df27c43 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 aarch64 1c1b7dced0de68d36e7ad3d162738c8fbd4b277949f5072052df48b0a478bfc7 Authentication authority web based on oauth2 Authentication authority web based on oauth2 https://gitee.com/openeuler/authHub dnf-hotpatch-plugin aarch64 aa4ba1003b0b168faae1872281cd93b9733fa5d610ce46860933e231b3791480 dnf hotpatch plugin dnf hotpatch plugin, it's about hotpatch query and fix https://gitee.com/openeuler/aops-ceres gala-anteater aarch64 f7d51d44ea5e4045811dc73b326dad84c7637f0b69be4ccad93dc511d83b9d4b A time-series anomaly detection platform for operating system. Abnormal detection module for A-Ops project https://gitee.com/openeuler/gala-anteater gala-anteater src c8d3aa9fff84617feb7e9782e88834f3bdf3708739cf17b917469a7bd82fff98 A time-series anomaly detection platform for operating system. Abnormal detection module for A-Ops project https://gitee.com/openeuler/gala-anteater gala-gopher aarch64 d9507c4a6cc022bec3f29d41feb96ed07e2d04c81e95c30669b6280492abe038 Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher src 44892fd2e5e767e6f143c24f6f342dbab1edb59932790b0935e41c2f444e2805 Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher-debuginfo aarch64 492c2a9fbb3d3776088b71f70882043ff5105f0d345db692f4165bf39f625d10 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 aarch64 b54bc1a083c8ba385fa6f80d3512963c7615b1ddd7b8f14058551cc49d214ab9 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 aarch64 b3030d0a7bfa502ddc0b4fb43fe9f045e5ddc348502cf81d0bff2fcd3a6ea96a Cause inference module for gala-ops project Cause inference module for A-Ops project https://gitee.com/openeuler/gala-spider gala-ops aarch64 d9abf678c6fa47f5e2814cd33bc672845b06c0a5f7c3eabd79d21e9c4beaaf1b 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 aarch64 8cd259abc3f4f24bd3db436de6d86017252e17b00994254592ec7f4d4ec1f67c 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 src 0db5cd9b5822149b0da6121157b31d06a4462cbc6755f74c65b757c7cfd4defb 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 aarch64 98c9d9e64970f51ca698dcc43f300eaedead0c4fd04d7e5b3e88c5a86ae9abcb loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server loggpt src 72ad6bc0d12163c9c52774b8cbfe8a58df20fd92ec9aec59e0dbeb19c3ae8b87 loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server osmind-ai aarch64 044964ce06e4dc62f32263b5f42934b031ebcdbb60577cc2397af46f08c2cdb0 OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice osmind-ai src a9d92ad8c3cc0f2dbc75b282b54acb150be79a4946c4f80dfa0f8b83f455cb8b OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice python-Authlib src 0aa6d446fe2a8f2eda956a0722ea119cafdf17f396c3a81f659828f071663941 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 624e4380c7621d7a511f7ca1f00aad67fc35017f0a7a7c504dc079927e2bbf2d 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 3703021b027a4827a9874fa7c2ce2fefa52b1c50cf2e67894d7be6d0e080cd1a Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python-billiard-help noarch 3607fff57fbd5632d45ab522692ac1ec85608f0b6a12149dada6134370423ce3 Development documents and examples for billiard Multiprocessing Pool Extensions https://github.com/celery/billiard python-celery src a148f4da771bc3905e352ec31f0b1957df8b6c9c1a9cad85858d19b6528b8221 Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python-celery-help noarch 3b52b98371c3cd9400397db161b5705aa29bd520fc9960d9fd642da1b420dcbb Development documents and examples for celery Distributed Task Queue. https://github.com/celery/celery python-click-didyoumean src a1aea5da3aed9dfeca3eb8edada14cb34c9f69bbba01d45691ff003dc25f612a 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 efb50c9134a996b8c5f882e3716a0b2811220aa20cf5523eff8d23d007510ac3 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 d966b6a14113e5f4cfb6d1a178ef498b62a344d2588d5685276a43048ee3187c 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 cdeb7be2d5ccfeeaac4a6b6ed2bacbb8dc663841d07f1129d33653b3f21b03ab 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 3a601ca173299b7a81f5af494d72cf421d2964aaa45f6d8ef88bc09335162b35 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python-click-repl-help noarch 16c7511779b647aaed9e4497d44c3c26fe6f74509f64b6fef40e740630e0ea8e Development documents and examples for click-repl REPL plugin for Click https://github.com/untitaker/click-repl python-pandas-flavor src d3246e1ef3311b0a8d9a4faec34c9d60cf41fb8b7e1ee738539513e58055e453 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 a34124de1c740c4ab87892c4c17ff57a4856c2e68aced0735a281b06f594b539 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 c4b624f98920251f42c2c71f5ec06ecb36ca789ee313ca27abd5fce91657c4e0 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 d7b294c4db35d0867b86a167b125fae3a86b77d0f9637699e4b71d2c8e563188 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 4dac54593af04272532a549aaecf691b6fee474f483a3bba52eb387d28f4bbec Statistical data visualization https://pypi.org/project/seaborn/ python-seaborn-help noarch 726eadc18f753c8e731e64af47a1f8f6f3ee63d5a5f90d5c2e89b63cf88a60bb Development documents and examples for seaborn https://pypi.org/project/seaborn/ python3-Authlib noarch cc74456289a60829b1c2bc5103dbbcae83e459dc351463497a7e8aa51ccdc675 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 14d84c5fd05111a1985202602c44c634b89bc056f726082b9a3a99854621dd22 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python3-celery noarch aa4ebd52b763a320891cb1c02ea20b3a081df5b7361f1334749c4aea88313e91 Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python3-click-didyoumean noarch e11342543a2781f40fcde1bae4f4f5f04e84e7e2c6767db5e835fadca3f3ff3e 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 1d46de0073f0bdc5ff1f91b5798a89322df615af47e6aa16944ea8b384eb97c7 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 bc51edea0448f7ff7619c7ee8ed609d0e5a242a87f3df36c5422322b27bdeba2 REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python3-gala-anteater aarch64 ee409a6b1fcc85618aaf609344d2a4e19c619b826c7796d6dcf7208c27114b45 Python3 package of gala-anteater Python3 package of gala-anteater https://gitee.com/openeuler/gala-anteater python3-gala-inference aarch64 e24fdb2eb9a3d895b44f724410c442b45871cca39b9405460d4786c1873f5b7c Python3 package of gala-inference Python3 package of gala-inference https://gitee.com/openeuler/gala-spider python3-gala-spider aarch64 4d3f761c21344f28ff783c41ff9fb35cdce54592d10c4975f89084ac501b156b Python3 package of gala-spider Python3 package of gala-spider https://gitee.com/openeuler/gala-spider python3-pandas-flavor noarch 93513f78d9b40aae856fb41d72c419268f8cbde22ffe42b35b0bd2c35d49a8a0 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 33f158beca6d510f6d44f86709ab047766a5ebcf683a8ae83578a0c7bb565b05 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 c373096311f16066ed4319364c872ba227cfbc5d61503548a01cfd456c403da8 Statistical data visualization https://pypi.org/project/seaborn/ zeus-distribute aarch64 a3714408555811a7e34eec4706995aafaf2f362489f4072fd8bcfc9cd2104b9f A distributed service of aops. A distributed service of aops. https://gitee.com/openeuler/aops-zeus zeus-host-information aarch64 a2c6cc0145c74fd62332dba26d4fd21d145f858cb1a7a3a060ff79668e4dc06c 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 aarch64 8d255fe9e7d314cecb2b1ddcfa0a874c482467908100a66d2a64c4aab593f150 A operation manager service which is the foundation of aops. A operation manager of aops. https://gitee.com/openeuler/aops-zeus zeus-user-access aarch64 7790cac0d69dd79d6fde45de3041eec8a507a52bb914373002dbc30e9b768de3 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