aops-apollo-tool aarch64 6d0f8391511823264201a73f7ed8256ecb14f9f4ba3cd8301c271df311967f89 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 0dac1dba6cb6cad907e3251f7d15f7c8c29287ff256facd0505f1e0fa849933a 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 30416ce6ab64c5c0d9c0d8fc228b30280a556b1f16eb1f11f0b6226bc03303e3 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 d194905024a51e374a13beec935a2c3d17eaee0595135a161ce5286ecb7dfc5d 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 068fd6a3949979878e5e27fe665e0fecb59c8ad367acf839d15c775a538712ec 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 ec3e8a01e49200be927e675f2299e929b54ed67128749780a255751e9ac812a7 Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-hermes src 956676c3adc9fc8819140a2a6c90e58e0c2f03b87efb4e6bbf1907924e65ebeb Web for an intelligent diagnose frame Web for an intelligent diagnose frame https://gitee.com/openeuler/aops-hermes aops-mcp aarch64 e79774522bbead36bcce64f54fd52c4ab2c4a7d8ecaa69fdd7fb0535df244467 Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-mcp src b2a06b0b095d1b1afff0badfa2ed6fb9d2958a28399182f3447f9725644ed862 Aops MCP Service Aops MCP Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server aops-tools aarch64 7f70c8a87587324bbe4d36397918c3bd28ebff78a53859b77df1518c4e143704 aops scripts tools for aops, it's about aops deploy https://gitee.com/openeuler/aops-vulcanus aops-vulcanus aarch64 0746b83ec7b2ae200aaea2aba4c58ceb7bf27adb6bcd6f618219e48bb658efca 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 c9e90bb3827502acbaa904007c60a19d95b45e0b2d92f6d5fd513fac7b717a21 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 75511ef9e7b74f64cf287488d7f6b940ef0ea2216d1e668d7a7ece9370c01c1b 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 30c5df1115c718bdde5dd7cfba504dc923228f66454fcb71b955db63c5324ad0 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 89e7c7873a0ec5fa04615109d1dbfad3691afbabec8c48bf47ee12a5b4875154 A async task of aops. A async task of aops. https://gitee.com/openeuler/aops-zeus authHub aarch64 7f83d082f8babaea6d5d8813da3b371cd5b5df956816526643942789e4d491d5 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 e34cbc30377b4dba62041a6288f6d118799f46add51c7c2730b35d3b59758dd2 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 5d8f56f2d70201c6282f229cc4e4fd235f253117f16cd88248a93297159b032d Authentication authority web based on oauth2 Authentication authority web based on oauth2 https://gitee.com/openeuler/authHub dnf-hotpatch-plugin aarch64 0d482e1ab768b618028229f191b9a8e9423bc0a53d37d490dd593e13f59fb43d dnf hotpatch plugin dnf hotpatch plugin, it's about hotpatch query and fix https://gitee.com/openeuler/aops-ceres gala-anteater aarch64 3b7621cd2511c8360e00cfe201a099bcae972b25e5a2478d12750ca174c61533 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 0c4695b0a43184b459cb5e7021ac0156886d307873ba9f4b0138048fd6e44734 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 7abb62ee1b0448e9ce1069c8dbb541b226e3450965679699df1b99aa089ed497 Intelligent ops toolkit for openEuler gala-gopher is a low-overhead eBPF-based probes framework https://gitee.com/openeuler/gala-gopher gala-gopher src b1a3cdd5855134cd097b424cc82015b3a443fc8dbb27b1b22d32cf4d6bb167e1 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 1a07fa0a4dc2a121dc2c783136e84c82d19330cb314e3dac2031cc08e25cdee6 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 7a1d744825b7a33fa1aa024372f4c76ccdd8cda38afefd55d6a2c70d54720ae0 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 45efbf1503f577e8565f7cae6af02ef46785585581f6514351cca2cbf28abacd Cause inference module for gala-ops project Cause inference module for A-Ops project https://gitee.com/openeuler/gala-spider gala-ops aarch64 46d57e24251f94a18c89202864404e7b9a2bdbd7dfa579460735fbf7be728a71 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 6663852f2f6996113305fc49b54157cc8d91b0e6ac45814b77c95deb2d17583d 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 01aef360dc5c057c9e38e1088512a1a1aa5eb924f787e5f4609868a458fa9cc0 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 a3f2fefd26c759ddb71e69d3d29067684ffbd95748969d611ffc806029a976bd loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server loggpt src c07eac04a97fc5d4d1ecf46c4306e924a3c44c823441f986a8ae31da2041828b loggpt Service loggpt Service packaged as RPM. https://gitee.com/Victeo/AOPS_MCP_Server osmind-ai aarch64 810ecc3103fa2ffd84fd6f02ff9188c59216a8f21ed4761de03a90740005da09 OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice osmind-ai src 3f4b8f20886b31def46f7904025f04b9576c0c8d5e14e5d930244b99b22be5ec OSMind AI Service OSMind AI Service packaged as RPM. https://gitee.com/Victeo/osmind-aiservice python-Authlib src 4e8096e99b8f0657eadd83cd59212737ea84d8960f1992fcb4e52626215d9c44 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 18604737a7aaa46604092e45df356095b76fb1e9288e300f1b45c8f9aa931d61 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 ce5b1082bcf09c0351124bbeb34678230b045c4fa6a754c2590e9f6a9c5de3d9 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python-billiard-help noarch a22f25cce3c10ce6705e5de2975c6a9e27f733df380415ac4d83e726204cec8c Development documents and examples for billiard Multiprocessing Pool Extensions https://github.com/celery/billiard python-celery src 5fa063e8ad23808d5ac5df9b6698108215622b2838bf1c8aa084ae754fcf6937 Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python-celery-help noarch 2bc734fb045795748f0a30e3f917dfa9fce0cd5d3af910fb998e89ea4cdc2068 Development documents and examples for celery Distributed Task Queue. https://github.com/celery/celery python-click-didyoumean src 1c8e3271a18d5888503884e38628e45be83ac60bf735bd22496fd3a4574760a7 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 b1ba036fd71ca4f5c0f39a4b94442edc3779eee99c8b27af678e9f7ac64cabe8 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 2644ac6441cdb4ada9bab0cb46f91d4a09538fa18b4353d6674a07f3793efa4b 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 84003238acacf321503389447677733e77a846dc55475f2d2964d609adc7f215 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 23f4f44326b86f4f80a6cc083099e71914a141d9193c1f7cfbba185c23faccbe REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python-click-repl-help noarch be57a067634c26a70cca66c7dca9e7b4e092b7db980f3c73b6a6694e14cb4c97 Development documents and examples for click-repl REPL plugin for Click https://github.com/untitaker/click-repl python-pandas-flavor src f34213657f3ac2b07352912d20f4ef977580beaa6b6ef0498510857e3c12e760 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 71f22bde2c27fd7ab1b9a0f1452c31e17c7e1ae8230bc7029a4b2867ca271824 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 9643b79c1e8307aad6d8735437d882ccf700c4352198deee7cda1c879a7bfdea 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 d73cf4ba6fed5e2b5eafa25fe0ddc6d4263a09c695712b7711403cbba74ca5d5 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 e0797320aff74654936fbed5ede710b12527305bbeb01b51760ae0418fd1380e Statistical data visualization https://pypi.org/project/seaborn/ python-seaborn-help noarch f3cd555d6c3fe79425f09e49d864342d1320c455772ca970948db44098611af7 Development documents and examples for seaborn https://pypi.org/project/seaborn/ python3-Authlib noarch 4465a3e2bdf41b07294610948964a165af751a95cd4c502651b7ac62a040286b 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 904dcbe6ca4c335c3d528308f3a758b4ce0544f94b1d33e72128ad8636a02323 Python multiprocessing fork with improvements and bugfixes Multiprocessing Pool Extensions https://github.com/celery/billiard python3-celery noarch e528b340444d7521686e4bc6f017ddcba844694b213d11ce5f0a1e3ce88490e3 Distributed Task Queue. Distributed Task Queue. https://github.com/celery/celery python3-click-didyoumean noarch 78223193ccf182c370ec39c0b801cd96f80275ab1c67ca2c7d6897327ecdc100 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 79a8acc72fd342db4d2b961f1eaecd074d3689564e0ddadd43cc24ce9d42f234 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 2cc5d668aa4a180326dd3e3c99bc9a7b77e94a1bb4513013a6bf99989d76ec3c REPL plugin for Click REPL plugin for Click https://github.com/untitaker/click-repl python3-gala-anteater aarch64 253b6d0620fe458d0d33d48811c51b6631c468a3cf5e8dadfacb565d9eceb5de Python3 package of gala-anteater Python3 package of gala-anteater https://gitee.com/openeuler/gala-anteater python3-gala-inference aarch64 ee977c199093365b46eb7a3d62d104fedcf8f68b4371f94887c1b1846506962b Python3 package of gala-inference Python3 package of gala-inference https://gitee.com/openeuler/gala-spider python3-gala-spider aarch64 2b9504e9a3e8aaebb1c6bed2da82dc02bd476c5539f075938c3da14ff8e66059 Python3 package of gala-spider Python3 package of gala-spider https://gitee.com/openeuler/gala-spider python3-pandas-flavor noarch 50cd1b16cca4a98e86b8c5f2d68466883e848c7154a3efe4575f782e0730ab7b 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 fe90d9930dae61ec190e78ced906255c7aa9910bb81125b6b9ecbbfda81d3cb7 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 77f21f1dfb1aeae3e3df6a70766dc80c1d65900062f1c6cfdb157b5b6e191654 Statistical data visualization https://pypi.org/project/seaborn/ zeus-distribute aarch64 e3c6f05694d125ccac54e053674f3c2d437fc6af76e957b0d00d1b75b6dd0d91 A distributed service of aops. A distributed service of aops. https://gitee.com/openeuler/aops-zeus zeus-host-information aarch64 6a36187810f9141d38d0b7e564184f52e88fbfea07adde17f93007510c1e2e8d 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 2dcbfa08bd0e59f4306ef7505b4beef9b0fbeb14b0d88dcb59c94b0e8729d578 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 98fe2453e2aa8e18125414f0d1cc5fc436954e9a2d52052adf33c7403489a7f0 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