Saravanan666/pypi-org huggingface dataset:
Package: coredb-pgmq-python
Coredb's Python Client for PGMQInstallationInstall withpipfrom pypi.orgpipinstallcoredb-pgmq-pythonDependencies:Postgres running theCoreDB PGMQ extension.UsageStart a Postgres Instance with the CoreDB extension installeddockerrun-d--namepostgres-ePOSTGRES_PASSWORD=postgres-p5432:5432quay.io/coredb/pgmq-pg:latestInitialize a connection to Postgresfromcoredb_pgmq_pythonimportPGMQueue,Messagequeue=PGMQueue(host="0.0.0.0")Create a queue (or a partitioned queue)queue.create_queue("my_queue")# queue.create_partitioned_queue("my_partitioned_queue", partition_size=10000)Send a messagemsg_id:int=queue.send("my_queue",{"hello":"world"})Read a message, set it invisible for 30 seconds.read_message:Message=queue.read("my_queue",vt=10)print(read_message)Archive the message after we're done with it. Archived messages are moved to an archive table.archived:bool=queue.archive("my_queue",read_message.msg_id)Delete a message completely.msg_id:int=queue.send("my_queue",{"hello":"world"})read_message:Message=queue.read("my_queue")deleted:bool=queue.delete("my_queue",read_message.msg_id)Pop a message, deleting it and reading it in one transaction.popped_message:Message=queue.pop("my_queue")print(popped_message)
Package: core-dev
these are not from me, but they are inspiration for better readmethe_corepackagethis is the dev branch (experimental only)best of the best - meaning: you got all python utilities ever in one packageTODOcheckTODO.mdwhy there is a test_core outside the packagebecause we simulate how it would be to import_corefrom other python packagesassuming that_coreis in PYTHONPATH
Package: core-devoops
core_devoopsLow level ecoact generic code. Aimed at being published in open source with poetryInstallation of this packageYou are strongly encouraged to install this package via Docker.Starting from a project with a Docker file:add the module core_devoops in therequirements.txtfilemake sure the.envfile includes all required fields (seeBaseSettingsandAuthenticationConfiguration)build the new version of the Docker container (typicallydocker build --tag xxx .)run it with docker compose (dc up -d).
Package: coredhcp
Installation:1. Install python package via PIP:pip install coredhcp2. Enable API module in /etc/corecluster/config.py file:LOAD_API = [...'coredhcp.views.api']3. Enable coredhcp hook in /etc/corecluster/config.py file:HOOK_PIPELINES = {...'network.delete.prepare': ['coredhcp.hooks.network'],}4. Enable CoreDHCP agent in /etc/corecluster/agent.py file:AGENTS = [...{'type': 'dhcp', 'module': 'coredhcp.agents.dhcp', 'count': 1}]
Package: coredotdata
coredotdata코어닷투데이의 데이터셋을 원하는 곳에 다운로드할 수 있습니다.Free software: Apache Software License 2.0Installationpip install coredotdatapip install git+https://github.com/CoreDotToday/coredotdatapip uninstall coredotdata -yFeatures데이터셋 uid를 이용하여 다운로드 하기 (data 폴더에 저장됩니다)importcoredotdataascddcdd.download_dataset("181I3nDWv0")특정 디렉토리 이름을 지정하여 다운받을 수 있습니다target_directoryimportcoredotdataascddcdd.download_dataset("181I3nDWv0",target_directory="./AAA")콘텐츠에 담긴 파일 목록을 조회할 수 있습니다importcoredotdataascddcdd.get_dataset_file_list("15Dif81I")원하는 파일만 선택하여 다운로드할 수 있습니다target_file_listimportcoredotdataascddcdd.download_dataset("15Dif81I","./data",['AgeDataset-V1.csv.zip'])# cdd.download_dataset("15Dif81I", target_file_list=['AgeDataset-V1.csv.zip'])# cdd.download_dataset("15Dif81I", target_directory="./data", target_file_list=['AgeDataset-V1.csv.zip'])다운로드 폴더의 파일 목록을 조회합니다cdd.listdir()# "./dataset" 폴더 안의 파일 목록을 반환합니다# cdd.listdir(target_directory="./data")  # 목록 조회할 폴더를 지정합니다# cdd.listdir(show_size=1)  # 파일 크기도 같이 반환합니다ContributorsLICENSEcoredotdatais licensed under the terms of the Apache License 2.0.=======
History0.4.0 (2022-07-27)Addlistdirfor file list of target directory0.3.0 (2022-07-15)Addtarget_file_listfor selected file download0.2.1 (2022-07-11)Target Directory mkdir issue.0.2.0 (2022-07-11)Download Schema Modified.0.1.0 (2022-06-30)First release on PyPI.
Package: coredotfinance
CoreDotFinancePowerful Python financial data library금융 데이터를 쉽고 빠르게!CoreDotFinance(코어닷파이낸스)는KRX의 주식데이터Binance와의 가상화폐 데이터를
쉽고 빠르게 파이썬 작업 환경에서 작업할 수 있도록 도와주는 파이썬 라이러리입니다.설치 방법pipinstallcoredotfinance문서공식문서 :https://coredottoday.github.io/CoreDotFinance/ContributorsLICENSECoreDotFinanceis licensed under the terms of the Apache License 2.0.
Package: coredotseries
![CoreDotSeries](docs/static/logo.svg)[![GitHup issues](https://img.shields.io/github/issues/CoreDotToday/CoreDotSeries)](https://github.com/CoreDotToday/CoreDotSeries/issues)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)Sequential Data Analytics ToolFree software: Apache Software License 2.0Installation` pip install coredotseries `` pip installgit+https://github.com/CoreDotToday/CoreDotSeries`FeaturesTODOHistory0.1.0 (2021-10-12)First release on Github.0.2.0 (2021-11-02)First release on PyPI.
Package: core-elasticsearch-app
Elasticsearch utils for the curator core project.Quick start1. Add “core_elasticsearch_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_elasticsearch_app',]The package should be placed abovecore_explore_keyword_appin theINSTALLED_APPSto enable autocomplete powered
by elasticsearch.2. Include the core_elasticsearch_app URLconf in your project urls.pyre_path(r'^elasticsearch/',include('core_elasticsearch_app.urls')),3. Deploy an instance of ElasticsearchRecommended version: 7.14.1
Seehttps://github.com/usnistgov/cdcs-docker#elasticsearchfor help with Elasticsearch deployment.4. Configure what should be indexed in ElasticsearchIt is recommended to configure the mapping between the CDCS templates and the Elasticsearch indices before starting to
insert data, so that data can be indexed as soon as they are published in the CDCS. For that, set the following setting:ELASTICSEARCH_AUTO_INDEX=TrueThen, configure the mapping between the XSD templates and the Elasticsearch indices. Documents stored in Elasticsearch
have a common structure. They are composed of adata_id, atitleand adescriptionfield. A mapping can be
defined to tell where in the XML data the values for thetitleand for thedescriptionof a resource can be found.
Below is an example to set this mapping via REST, for a given template:importrequestspayload={"template":"5f43ba192b8fd5c092e30e62","title_path":"Resource/identity/title","description_paths":["Resource/content/description","Resource/content/subject"],}requests.post(SERVER_URI+"/elasticsearch/rest/elasticsearch_template/",data=payload,auth=(USER,PASSWORD))
Package: core-etl
core-etlThis project/library contains common elements related to ETL processes...Execution EnvironmentInstall librariespip install --upgrade pip 
pip install virtualenvCreate the Python Virtual Environment.virtualenv --python=python3.11 .venvActivate the Virtual Environment.source .venv/bin/activateInstall required libraries.pip install .Check tests and coverage...python manager.py run-test
python manager.py run-coverage
Package: core-explore-common-app
Base exploration function for the curator core project.Quick start1. Add “core_explore_common_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_explore_common_app',]2. Include the core_explore_common_app URLconf in your project urls.pyurl(r'^explore/common/',include('core_explore_common_app.urls')),
Package: core-explore-example-app
Exploration by example for the curator core project.Quick start1. Add “core_explore_example_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_explore_example_app',]2. Include the core_explore_example_app URLconf in your project urls.pyurl(r'^explore/example/',include('core_explore_example_app.urls')),
Package: core-explore-federated-search-app
Federation exploration functions for curator core project.Quick start1. Add “core_explore_federated_search_app” to your INSTALLED_APPS settingINSTALLED_APPS=['core_explore_federated_search_app',]2. Add “core_explore_federated_search_app” to your DATA_SOURCES_EXPLORE_APPS settingDATA_SOURCES_EXPLORE_APPS=['core_explore_federated_search_app',]3. Include the core_explore_federated_search_app URLconf in your project urls.pyurl(r'^federated_search/',include("core_explore_federated_search_app.urls")),
Package: core-explore-keyword-app
Exploration by keywords for the curator core project.Quickstart1. Add “core_explore_keyword_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_explore_keyword_app',]CustomizationCustomize Explore by Keyword search page, by providing additional resources from other applications.1. Update your custom application to list all the resources that need to be loaded:Import AbstractKeywordSearchExtras from core_explore_keyword_app, and implement get_extra_html, get_extra_js,
get_extra_css to provide a list of additional resources.Example:# Import AbstractKeywordSearchExtrasfromcore_explore_keyword_app.utils.abstract_keyword_search_extrasimport(AbstractKeywordSearchExtras,)# Add a new class that extends AbstractKeywordSearchExtras, and list resources to loadclassCustomKeywordSearchExtras(AbstractKeywordSearchExtras):@staticmethoddefget_extra_html():return["my_custom_app/my_custom_template.html"]@staticmethoddefget_extra_js():return[{"path":"my_custom_app/js/my_custom_script.js","is_raw":False,},{"path":"my_custom_app/js/my_custom_script.raw.js","is_raw":True,}]@staticmethoddefget_extra_css():return["my_custom_app/css/my_custom_stylesheet.css"]2. Update the project settings to load the resources from your custom application:# Import the CustomKeywordSearchExtras from your package (path and class name may be different)frommy_custom_app.utils.custom_keyword_search_extrasimportCustomKeywordSearchExtras# Add the class to the EXPLORE_KEYWORD_APP_EXTRAS setting.EXPLORE_KEYWORD_APP_EXTRAS=[CustomKeywordSearchExtras]
Package: core-explore-keyword-registry-app
Exploration by keywords for the registry core project.Quickstart1. Add “core_explore_keyword_app”, “core_explore_keyword_registry_app” and “core_explore_common_app” to your INSTALLED_APPS settingOrder matters. Overriding of templates in core_explore_keyword_registry_app.INSTALLED_APPS=[..."core_explore_keyword_registry_app",# /!\ Should always be before core_explore_keyword_app, core_explore_common_app"core_explore_keyword_app","core_explore_common_app",]2. Include the core_explore_keyword_registry_app URLconf in your project urls.pyurl(r'^explore/keyword/',include("core_explore_keyword_registry_app.urls")),
Package: core-explore-oaipmh-app
OAI-PMH exploration functions for curator core project.Quick start1. Add “core_explore_oaipmh_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_explore_oaipmh_app',]2. Add “core_explore_oaipmh_app” to your DATA_SOURCES_EXPLORE_APPS settingDATA_SOURCES_EXPLORE_APPS=['core_explore_oaipmh_app',]3. Include the core_explore_oaipmh_app URLconf in your project urls.pyurl(r'^oaipmh_search/',include("core_explore_oaipmh_app.urls")),
Package: core-explore-periodic-table-app
core_explore_periodic_table_appcore_explore_periodic_table_app is a Django app.Quick start1. Add “core_explore_periodic_table_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_explore_periodic_table_app',]2. Include the core_explore_periodic_table_app URLconf in your project urls.pyre_path(r'^explore/periodic_table/',include("core_explore_periodic_table_app.urls")),3. Configure the Search OperatorsConfigure the Search by periodic table app by adding search operators pointing to chemical elements in the CDCS data.
Start by adding a search operator:importrequestspayload={"name":"chemical","xpath_list":["/experiment/experimentType/tracerDiffusivity/diffusingSpecies/element","/experiment/experimentType/tracerDiffusivity/material/Composition/constituents/constituent/element"]}requests.post(SERVER_URI+"/explore/keyword/rest/search_operators/",data=payload,auth=(USER,PASSWORD))Then, tell the periodic table app to use one or many search operators by adding mappings:importrequestspayload={"search_operator":"5f49048f3b3689f92cb84f41"}requests.post(SERVER_URI+"/explore/periodic_table/rest/search_operators_mapping/",data=payload,auth=(USER,PASSWORD))
Package: core-explore-tree-app
Core Explore Tree AppExploration tree feature for the curator core project.Configuration1. Add “core_explore_tree_app” to your INSTALLED_APPS setting like thisINSTALLED_APPS=[..."core_explore_tree_app",]2. Include the core_explore_tree_app URLconf in your project urls.py like thisurl(r'^',include("core_explore_tree_app.urls")),
Package: core-federated-search-app
Federated search backend for the curator core project.Quick start1. Add “core_federated_search_app” to your INSTALLED_APPS settingINSTALLED_APPS=['oauth2_provider','core_federated_search_app',]2. Include the core_federated_search_app URLconf in your project urls.pyurl(r'^o/',include('oauth2_provider.urls',namespace='oauth2_provider')),url(r'^federated/search/',include("core_federated_search_app.urls")),
Package: coreferee
Coreferences are situations where two or more words within a text refer to the same entity, e.g.Johnwent home becausehewas tired. Resolving coreferences is an important general task within the natural language processing field.Coreferee is a Python 3 library (tested with versions 3.6—3.11) that is used together withspaCy(tested with versions 3.0.0—3.5.0) to resolve coreferences within English, French, German and Polish texts. It is designed so that it is easy to add support for new languages. It uses a mixture of neural networks and programmed rules.The library was originally developed atmsg systemsand was also maintained for a while atExplosion AI.For more information, please see themain documentation on GitHub.
Package: corefgraph
CorefGraph is an independent python module to perform coreference
resolution, a Natural Language Processing task which consists of
determining the mentions that refer to the same entity in a text or
discourse. CorefGraph is a multilingual rule-based system loosely
based on the Stanford Multi Sieve Coreference System
[Stanford Multi Sieve Pass system (Lee et al., 2013)](http://www-nlp.stanford.edu/downloads/dcoref.shtml).
Currently it supports English and Spanish but it can be extended to other
languages.The module provides resources for English and Spanish coreference resolution but
it can be adapted to other languages.If you use corefgraph, please cite this paper:Rodrigo Agerri, Josu Bermudez and German Rigau (2014): “IXA pipeline: Efficient and Ready to Use Multilingual NLP tools”, in: Proceedings of the 9th Language Resources and Evaluation Conference (LREC2014), 26-31 May, 2014, Reykjavik, Iceland.CorefGraph is being used by the [ixa pipes tools](http://ixa2.si.ehu.es/ixa-pipes) which can be used to provide the necessary input.This work has been partially funded by aPhD Grant of the University of Deusto.
Package: core-file-preview-app
File preview functionalities for the curator core project.Quickstart1. Add “core_file_preview_app” to your INSTALLED_APPS settingINSTALLED_APPS=[...'core_file_preview_app',]2. Include the core_file_preview_app URLconf in your project urls.py like thisre_path(r'^file-preview/',include("core_file_preview_app.urls")),
Package: core-finance
CORE HUMAN RESOURCESIt is a module of the Research CORE ERM which implements the models related to finances.Cost centersFinance projectsExpenses codesGrantsFinancing agenciesBudgetsInstallationpipinstallcore-financeEnterprise Resources Management- Research CORECORE is an ERM platform developed in within a research environment and aims to automatize
internal management processes, like human resources, orders, budgets, suppliers, funds, equipment \rooms accesses and maintenance and publications.The project is distributed as an open-source project to facilitate the adaptation of the
ERM to each institution's needs.
Package: coreflow
CoreFlowCore Flow and Special Core Analysis Interpretation ToolThis library is currently under continuous development.
More stable release is expected this year.✨ Try it in your browser ✨Try it in a browser using JupyterLite and GitHub Pages➡️https://coreflowlab.github.io/tutorialsRelated GitHub repository can be found herecoreflow-labInstallInstallcoreflowpackage frompippip install coreflowor from the sourcepython -m pip install .Upgrade installation toolspython -m pip install --upgrade pip setuptools buildUninstallUninstallcoreflowpython -m pip uninstall coreflow
Package: coreforecast
MotivationAt Nixtla we have implemented several libraries to deal with time series data. We often have to apply some transformation over all of the series, which can prove time consuming even for simple operations like performing some kind of scaling.We've usednumbato speed up our expensive computations, however that comes with other issues such as cold starts and more dependencies (LLVM). That's why we developed this library, which implements several operators in C++ to transform time series data (or other kind of data that can be thought of as independent groups), with the possibility to use multithreading to get the best performance possible.You probably won't need to use this library directly but rather use one of our higher level libraries likemlforecast, which will use this library under the hood. If you're interested on using this library directly (only depends on numpy) you should continue reading.InstallationPyPIpipinstallcoreforecastconda-forgecondainstall-cconda-forgecoreforecastMinimal exampleThe base data structure is the "grouped array" which holds two numpy 1d arrays:data: values of the series.indptr: series boundaries such thatdata[indptr[i] : indptr[i + 1]]returns thei-thseries. For example, if you have two series of sizes 5 and 10 the indptr would be [0, 5, 15].importnumpyasnpfromcoreforecast.grouped_arrayimportGroupedArraydata=np.arange(10)indptr=np.array([0,3,10])ga=GroupedArray(data,indptr)Once you have this structure you can run any of the provided transformations, for example:fromcoreforecast.lag_transformsimportExpandingMeanfromcoreforecast.scalersimportLocalStandardScalerexp_mean=ExpandingMean(lag=1).transform(ga)scaler=LocalStandardScaler().fit(ga)standardized=scaler.transform(ga)Single-array functionsWe've also implemented some functions that work on single arrays, you can refer to the following pages:differencesscalersseasonal
Package: corefoundationasyncio
CoreFoundation based selector and asyncio event loopasyncio event loopOn macOS X, Cocoa uses an event loop to dispatch events (NSRunLoop, which is a wrapper aroundCFRunLoop).Python'sasynciomodule uses an event loop too to handle asynchronous events. On macOS X, it's based onkqueue.A thread can only use a single event loop, that's why it's not possible to use both Cocoa and asyncio on the same thread. This module implements an asyncio compatible event loop on top of CFRunLoop. Thus it's possible to use Cocoa in conjunction with asyncio.
It uses internally PyObjC'srunEventLooporrunConsoleEventLoopHere is an example video of a simple Cocoa based GUI usingasyncioandasync/awaitto handle subprocesses and network calls:The corresponding code is here:tests/gui/guidemo.pySetup:loop=CoreFoundationEventLoop()asyncio.set_event_loop(loop)try:loop.run_forever()finally:loop.close()CoreFoundationEventLoop's constructor's first argument is a boolean.Truefor a console app,False(default) for a GUI app.
Additionnal arguments are then passed toPyObjCTools.AppHelper.runConsoleEventLooporPyObjCTools.AppHelper.runEventLoopselectorThis module also provides aselectorimplementation based onCFRunLoop. But his selector isn't compatible with Cocoa GUIsInstallation$pip3installcorefoundationasyncioThis module depends onpyobjcContributingReport issuesherePull-requests welcome !LicenseThis software is licensed under theMITlicense
Package: core-frontend
CORE FRONTENDIt is a module of the Research CORE ERM and implements the most basic models
required by the CORE platform:CitizenshipCityCountryCurrencyInstitutionNationalityInstallationpipinstallcore-commonEnterprise Resources Management- Research CORECORE is an ERM platform developed in within a research environment and aims to automatize
internal management processes, like human resources, orders, budgets, suppliers, funds, equipment \rooms accesses and maintenance and publications.The project is distributed as an open-source project to facilitate the adaptation of the
ERM to each institution's needs.
Package: coreftools
This is a coreference resolution utility  package that supports multiple transformer-based coreference resolvers. The current version supports an improved version of the AllenNLP SPANBERT model and a GPT3 (text-davinci-003) based resolver that supports few-shot prompts. Coreference resolution is the task of identifying all expressions (e.g., pronouns, names, definite descriptions) in a text that refer to the same entity. This is a crucial task for many natural language processing applications such as question answering, summarization, and sentiment analysis.Our tool is designed to help researchers and developers experiment with different transformer-based coreference resolvers and compare their performance. The package is currently under experimentation and cannot be readily used. However, we aim to make it publicly available in the future.The AllenNLP SPAN-BERT model is a state-of-the-art coreference resolution model that is based on the popular BERT architecture. We have made improvements to the model by handling nested coreferent mentions and cataphoras. The GPT3-based resolver, on the other hand, is a cutting-edge model that is capable of few-shot learning, meaning that it can adapt to new variations with a few example prompts.Our Python package is designed to be easy to use and integrate with existing NLP pipelines. It provides a simple interface for loading and using different coreference resolvers, and supports various input formats such as raw text and pre-processed text. We believe that our tool will be a valuable resource for researchers and developers working on coreference resolution and related NLP tasks.Examples of using CorefToolsHere are a few examples of how to use CorefTools, a Python package for coreference resolution.Example 1: Using the SpanBERTResolverNote that by default we use spanbert-large model, you can alter the model by passing the corresponding model_url while initializing the SpanBERTResolver classfromcoreftools.corefresolversimportSpanBERTResolverresolver=SpanBERTResolver()example_query="John and Jane went to the park. They had a picnic and played frisbee. When they were done, they walked home together."resolved_query=resolver.resolve_coref(example_query)print(resolved_query)# Output# John and Jane went to the park. John and Jane had a picnic and played frisbee. When John and Jane were done, John and Jane walked home together.Example 2: Using the GPTResolverThe following example demonstrates how to use theGPTResolverclass to resolve coreferences using the GPT-3 model. Note that we use "text-davinci-003" as our default model due to performance reasons. You can also pass other models, such as "text-davinci-002" or "GPT3.5" with engine parameter toresolve_corefmethod of theGPTResolverclass. This would resemblegpt.resolve_coref(query_to_resolve, engine=your_choice_of_engine)fromcoreftools.corefresolversimportGPTResolverresolver=GPTResolver()example_query="My dog is very cute. She loves to play fetch."resolved_query=resolver.resolve_coref(example_query)print(resolved_query)# Output# My dog is very cute. My dog loves to play fetch
Package: core-ftp
core-ftpThis project/library contains common elements related to FTP connections...Execution EnvironmentInstall librariespip install --upgrade pip 
pip install virtualenvCreate the Python Virtual Environment.virtualenv --python=python3.11 .venvActivate the Virtual Environment.source .venv/bin/activateInstall required libraries.pip install .Check tests and coverage...python manager.py run-test
python manager.py run-coverage