Saravanan666/pypi-org huggingface dataset:
Package: oraclemanager
Introductionoraclemanageris a wrapper aroundpython-oracledbdesigned to simplify repetitive database actions. It deliberately omits much of the advanced functionality of the core library in favor of a stripped down API focused on connecting, querying, commiting changes, and performing basic result transformations.oraclemanageris ideal for applications that involve a high volume of reptitive and basic database actions. If your applications require multiple concurrent database connections, advanced scripting/querying, or high efficiency, consider using thepython-oracledblibrary and API directly.Installationpip install oraclemanagerUsageMost of the package's main functionality is exposed through theOracleManagerclass:fromoraclemanagerimportOracleManageroracle=OracleManager()Connecting to DatabasesTheconnectmethod allows for several methods of connecting to a database. A username, password, and SID can be passed which will cause theOracleManagerto attempt to connect using the EZConnect string format:oracle.connect(username="username",password="password",sid="SID")Alternatively, aConnectionConfigobject can be passed to theOracleManagerto store credentials by SID.config:ConnectionConfig={"DB1":{"username":"user1","password":"pass123"},"DB2":{"username":"user2","password":"pass321"},}oracle=OracleManager(connection_config=config)oracle.connect("DB1")For connections that might not support EZConnect, aConnectParamsobject can be passed.connect_params=ConnectParams(user="hr",password="pwd",host="dbhost",port=1521,service_name="orclpdb")oracle=OracleManager()oracle.connect(connection_params=connect_params)Executing SQL and Accessing Query ResultsTheexecute_sqlmethod can be used to gather query results fromSELECTstatements or to execute changes to the database throughINSERTorUPDATEstatements.oracle.execute_sql("select * from some_table where some_column = 'some_value'")# Raw query resultsraw_results=oracle.query_results# Query results with data converted to strings.results_as_strings=oracle.query_result_as_strings# Query results with rows as ordered dictionaries.results_as_dicts=oracle.query_result_as_dicts# Query result column headers.result_column_headers=oracle.query_result_column_namesQuery results data formatting can be customized before conversion to strings by setting thestring_formatting_optionsproperty.formatting:StringFormattingOptions={"datetime_format_code":"%Y-%m-%d","decimal_rounding_places":2,}oracle.string_formatting_options=formattingCommiting Database Changesoracle.execute_sql("update some_table t set t.some_column = 'value')oracle.commit()
Package: oracle-ml-insights
Oracle Machine Learning Observability Insights Library (ML Insights)ML Insights is a python library for data scientists, ML engineers and developers. Insights can be used to ingest data in different formats, apply row based transformations and monitor data and ML Models from validation to production.ML Insights library also provides many ways to process and evaluate data and ML models. The options include low code alternative for customisation, a pre-built application and and further extensibility through custom applications and custom components.InstallationML Insights can be installed in a python 3.8 environment using:pipinstalloracle-ml-insightsSeveral ML Insights dependencies are optional (for eg: Execution Engine) and can be installed with:pipinstalloracle-ml-insights[option]where "option" can be one of:"dask", to run ML Insights on Dask Execution EngineHow it worksML Insights helps evaluate and monitor data and ML model for entirety of ML Observability lifecycle.Insights is component based where each component has a specific responsibility with a workflow managing the individual components.Insights provides components to carry out tasks like data ingestion, row level data transformation, metric calculation and post processing of metric output. More details on these are covered in the Getting Started section.In very simple terms, one has to provide location to the input data set that needs to be processed, select any additional simple transformation needed on the input data (for example, converting an un-structured column to structured one), and decide which metrics should be calculated for different features (columns of data). The user can also decide to define some post-action to be performed once all the metrics have been calculated.Insights provides a simple, declarative API, out of box components covering majority of common use cases to choose from. Also, Insights enables users to author json-based configurations that can be used to define and customise all of its core features.Insights currently supports CSV, JSON, and JSONL data types.It also supports major execution engines like Native Pandas, Dask, and Spark.Insights provides metrics in different groups likeData IntegrityData Quality/ SummaryFeature and Prediction Drift DetectionModel Performance for both classification and Regression ModelsInsights also supports integration for writing metric data, or connecting to OCI monitoring service.ContactML Insights SDK is offered by the OCI Data Science team. You can reach us through Oracle Support -https://www.oracle.com/support/.LicenseCopyright (c) 2023 Oracle and/or its affiliates. Licensed under the Oracle Free Use Terms and Conditions (FUTC) License.
Package: oracle-object-mapping
Oracle Object MappingRequirementsPython 3.7+Installpip install oracle-object-mappingExampleTypes definitionimportdatetimefromtypingimportOptionalfromoracle_object_mappingimportobjects,fieldsclassTABLE_VARCHARS(objects.Collection[str]):passclassTABLE_CLOBS(objects.Collection[str]):package='LIBRARY'database_type=fields.CLOB()classBOOK(objects.Object):package='LIBRARY'ID:Optional[int]TITLE:Optional[str]AUTHORS:Optional[TABLE_VARCHARS]DEDICATION:Optional[str]=fields.CLOB()PAGES:Optional[TABLE_CLOBS]PUBLISH_DATE:Optional[datetime.datetime]Manipulate objectsdata=["a"*xforxinrange(10)]table_a=TABLE_VARCHARS()forxindata:table_a.append(x)table_b=TABLE_VARCHARS.from_data(data)# table_a == table_bbook_a=BOOK()book_a.TITLE='Hello'book_a.AUTHORS=TABLE_VARCHARS.from_data(['Alberto','José'])# ba.to_data() == {'TITLE': 'HELLO', 'AUTHORS': ['Alberto', 'José']}data={'TITLE':'HELLO','AUTHORS':['Alberto','José']}book_b=BOOK.from_data(data)# book_a == book_bCall functionfromoracle_object_mappingimportutilsconnection:cx_Oracle.connectionname='LIBRARY.CREATE_BOOK'return_type=BOOKnew_book=utils.call_function(connection,name,return_type,args=[book_a])print(new_book.ID)
Package: oracle-of-ammon
Oracle of AmmonA simple CLI tool for creating Search APIs.InstallationCreating a virtual environment is highly recommended. To do so, run:python3-mvenv.venvsource.venv/bin/activateOnce your environment is active, simply install the package with:pipinstalloracle-of-ammonUsageTo get started, checkout the help menu:oracle-of-ammon--helpHere, you will see we currently have two options:summonandlocust.SummonBy default, Summon is configured to initialize an empty search service on port 8000. The API framework used isFastAPIand the underlying search engine is built onHaystack. If you would like to initialize the search service with documents upon startup, provide a filepath with the--pathoption. Once the service has been initialized, you can view the API docs athttp://127.0.0.1:8000/docs. A static version of the swagger documentation can also be foundhere.OptionTypeDefaultDescription--pathTEXTNoneFilepath used to pre-index document store.--sheet-nameTEXTNoneIf using an excel file, select which sheet(s) to load. If none provided, all sheets will be loaded. Expects a comma-separated list.--titleTEXTOracle of AmmonAPI documentation title.--indexTEXTdocumentDefault index name.--faqBOOLTRUESelector for content preloaded into document store.Supported Filetypes:FAQ: CSV, TSV, JSON, XLSX, TXTSemantic: TXTSee thedatadirectory for examples of accepted files.LocustLocustis an open source tool for load testing. You're able to swarm your system with millions of simultaneous users -- recording service performance and other metrics. By default, Locust will start on port 8089. To start a new load test, simply enter the number of users you want to simulate, their spawn rate, and the host address to swarm.]Coming Eventually 👀Semantic searchDocument searchDocument summarizationDocument rankingMultiple index supportAnnotations/FeedbackFine tuningAdditional locust endpointsDynamic Locust configCustom pipelinesDedicated docs wiki
Package: oracle-predeployed
oracle-predeployedDescriptionA tool for generating predeployed oracle smart contractInstallationpip install oracle-predeployedUsage examplefromoracle_predeployedimportUpgradeableOracleUpgradeableGenerator,ORACLE_ADDRESS,ORACLE_IMPLEMENTATION_ADDRESSOWNER_ADDRESS='0xd200000000000000000000000000000000000000'PROXY_ADMIN_ADDRESS='0xd200000000000000000000000000000000000001'oracle_generator=UpgradeableOracleUpgradeableGenerator()genesis={# genesis block parameters'alloc':{**oracle_generator.generate_allocation(contract_address=ORACLE_ADDRESS,implementation_address=ORACLE_IMPLEMENTATION_ADDRESS,schain_owner=OWNER_ADDRESS,proxy_admin_address=PROXY_ADMIN_ADDRESS)}}
Package: oracler
oracler=======oracle tools for development..... in anad labo-  the situation cx\_Oracle can not be installed-  must be using sqlPlusInstallation------------::pip install oraclerUsage-----::python -m oracler... edit config file: oracler_conf.yaml... invoke againpython -m oracler... the result will print out on STD-OUT and out.txt on defaultnot yet edit^^^^^^^^^^^^--------------| any questions to:| dais.cns@gmail.com
Package: oracle_river
Oracle_river - transfert de donnees depuis oracle vers elasticsearch en SQL incrémental=======================================================================================Ce module permet de recuperer des requetes SQL en mode incremental dans une base de donnees Oracle et de les stockersous format JSON dans elasticsearch.L incrémental des données sera gere via une base MySQL (date de derniere execution)Vous pouvez l'installer avec pip:pip install oracle_riversi vous rencontrez un message d erreur c est probablement que vous n avez pas le module pyelasticsearchqui sert de dépendance à oracle_river.Installez le et reitérez la commande pip install oracle_river|Oracle|  => SQL incremental + serialisation JSON => | elasticsearch |Exemple d'usage:1) CREER UNE BASE MYSQLcreez une base au nom de votre choixcreez une table histo_connexion avec le format ci-dessous:ID_CONNEXION / NOM_BASE / NOM_SCHEMA / DTM_DERNIERE_EXECautoincrement/string 256/string256  /Timestamp2) REALISER LES IMPORTS :creez un fichier python .py dans lequel vous inscrivez les lignes de codes suivantes :(vous devez au préalable installer les API de connexion cx_Oralce et mysql.connector)#!/usr/bin/env python# coding: utf-8from datetime import datetimeimport cx_Oracleimport mysql.connectorfrom  pyelasticsearch  import ElasticSearchfrom oracle_river import Load_Data3) DEFINIR LES PARAMETRES DE CONNEXION:# ouverture de la connexion à la base oracleDB = 'pythonhol/welcome@127.0.0.1/orcl' (voir http://www.oracle.com/technetwork/articles/dsl/python-091105.html)OracleCnx = cx_Oracle.connect(DB)# paramètres de connexion elasticseaches = ElasticSearch('http://localhost:9200/')# ouverture de la connexion MySQLMySQLCnx = mysql.connector.connect(user='root', password='', host='localhost', database='elasticsearch_river')4) PREPARER LA REQUETE:renseignez les paramètre ci-dessous :NomTable = 'CUSTOMER' (pour la base d'administration MySql)DtmInit = '2013-09-01 00:00:00' (si vous executez la requete pour la première fois)DocType = 'CUSTOMER_ID' (clé primaire de la table oracle utilisé comme type d index sur elasticsearch)Index = 'dbcustomer'  (pour elasticsearch, minuscules obligatoire par elasticsearch)NomBase = 'DBBUSINESS' (pour la base d'administration MySql)NomSchema = 'ACTIVITY' (pour la base d'administration MySql)Query = ("SELECT * FROM ACTIVITY.CUSTOMER WHERE CREATION_DTM >= to_timestamp('\{0}', 'YYYY-MM-DD HH24:MI:SS') OR LAST_UPDATE_DTM>= to_timestamp('\{0}', 'YYYY-MM-DD HH24:MI:SS')")# les paramétres s'écrivent comme cela : "{0},{1},{n}".format(valeur0, valeur1, valeurn)5) EXECUTER LA REQUETE VIA LA FONCTION PRESENTE DANS LE MODULE:Load_Data(MySQLCnx, es, OracleCnx, Query, DtmInit, DocType, Index, NomBase, NomSchema, NomTable)la requete defini plus haut va s executer et les donnees vont etre transferees a elasticseearchpar morceaux via le protocol httpCe code est sous licence WTFPL.
Package: oracle-select
# Oracle SelectAn easy interface for reading from an Oracle database with cx_Oracle.## To use:Usage is meant to be simple. Provide your username, password, and host address when you instantiate the DB class. Each select statement opens and closes a new connection. Results are returned as lists of dicts or an empty list if no results are found. For each result, any LOB-like fields are read into strings/bytes for easy handling.>>> from oracle_select import DB
>>> db = DB(username='your.username', password='password', host='DBNAME.WORLD')
>>> sql = """select * from ps_table"""
>>> db.select(sql)### Using bind variablesBind variables can be passed to the select statement in two ways:As a dictionary when referencing named sql variables:>>> sql = """select * from ps_table where emplid = :emplid"""
>>> db.select(sql, binds={'emplid': '12345678'})Or as a tuple when referencing numeric sql variables:>>> sql = """select * from ps_table where emplid = :1"""
>>> db.select(sql, binds=('12345678',))### Fetch One vs. Many vs. AllBy default all results will be returned. Sometimes, when the result set is large, this is a bad idea. To limit your result set, modify the fetch parameter. Then the select statement will only return N results.>>> sql = """select * from ps_table"""
>>> db.select(sql, fetch=10)  # fetch first ten results
>>> db.select(sql, fetch=0)  # fetch all results (default)### Fetch Large Datasets with a GeneratorLarge datasets can be retrieved using theselect_itermethod:>>> sql = """select * from ps_large_table"""
>>> for chunk in db.select_iter(sql):
>>>    for result in chunk:
>>>        print(result)Results will be returned in chunks offetch_size. To limit the total number of results, setmax_rows. By default chunks of 1,000 records will be returned until the entire result set has been consumed (max_rows = None).## UtilsTheoracle_select.utilsmodule provides some useful tools for working with PeopleSoft, including an interface for polling the process monitor.
Package: oracle-to-python
oracle_to_pythonThis package has equivalent NULL functions in Python as are there in OracleThis package oracle_to_python.nulls has below functions.nvl(value,return_if_value_None)nvl2(expr1, value_if_expr1_not_null, value_if_expr1_null)nullif(value1, value2) returns None if value1 and value2 are same else value1coalesce(list_of_values) returns first not None value, if all values in list are False, then will return Nonepip install oracle_to_pythonModule namenulls
Package: oracle-utils
oracle_utils: local sqlplus query modulePython module to automate the interaction with sqlplus for database scripting. It is designed to connect to a local database instance as defined by the ORACLE_SID environment variable.The sqlplus program is called once and run in a subprocess so multiple queries can be quickly run. Performance is much better than Bash withcat <<EOFfor integrating database data into scripts. The query results are returned in JSON format.Exampleimportoracle_utilsimportjsonsql_session=oracle_utils.sqlplus()sql_session.start()result=sql_session.run_query('select * from dual;')print(json.dumps(result,indent=4))sql_session.end()You can pass a query to the module if you want to only execute one query and output the results to the terminal:oracle_utils.sqlplus(query="select * from v$instance;")This also enables a Bash one-linter to run a quick query:$ python -c "import oracle_utils; oracle_utils.sqlplus(query='select * from v\$instance;')"
Package: oraculo
Project: OráculoOráculo is a versatile CLI and WebApp application developed for transcription of audios and semantic search. It leverages Sentence Transformers and embeddings to create a compact search engine that aids in retrieving and organizing important information from a collection of documents.This application is particularly useful for professionals dealing with substantial amounts of audio data and requiring an efficient system to transcribe and conduct semantic search operations on the data.Features:Audio Transcription: Oráculo can transcribe audio files. You can transcribe a single file or bulk transcribe a folder.Semantic Search: A web app to perform semantic searches on the transcribed audio data.Requirements::warning: IMPORTANT :warning: In order to run Oráculo, you need to have the following requirements installed on your machine:Python 3.10FFmpegGitInstallation:You can install Oráculo with pip:pipinstalloraculoSetup::warning::warning:Warning:warning::warning:: The following steps are required to run Oráculo. Please follow the steps carefully.Initialize the Oráculo application with the following command:oraculoinitYou will be prompted to enter the following information:InformationDescriptionChromaDB Persist DirectoryThe directory where the ChromaDB will be stored. This is important to store vector embeddings of textChromaDB ImplementationDefaults toduckdb+parquet. For more implementations, please refer toSource CodeWhenever you want to change the config file, just run the same command again.Usage:Semantic Search:To start the Semantic Search Application, use the following command:oraculowebappSingle File Transcription:To initiate a transcription for a single file:oraculotranscribeMultiple File Transcription:To initiate bulk transcription for a folder:oraculobulk-transcribeto transcribe youtube videos:YouTube Video Transcription:oraculotranscribe-ytHelp:If you need help with the commands, use the following command:oraculo--helpAboutVersion: 0.1.14Author: Joao TedeschiContact:joaorafaelbt@gmail.comThe development of Oráculo is aimed at information retrieval capabilities for businesses and individual users. Please feel free to reach out with any feedback or suggestions to improve Oráculo further.
Package: oraculo-gpc
oraculoPython Boilerplate contains all the boilerplate you need to create a Python package.Free software: MIT licenseDocumentation:https://oraculo.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2018-10-09)First release on PyPI.
Package: oradump
oradumpOraDump is a simple class for extracting data from Oracle database. It works over the native Oracle client,
so you'll have the fastest and convenient way to get your data dumped.RequirementsSince it works directly with native Oracle client, you need to have installed 12.2c(or higher) version of it on your computer.Path to BIN directory of Oracle client have to be in PATH variable.Set ENV variableNLS_LANGto encoding which used on your Oracle server. LikeAMERICAN_AMERICA.AL32UTF8Installed Python 3.6 or higher.Installationpip install oradumpUsageBefore utilize OraDump you need to prepare SQL-statement that will be used for retrieving data.
For values that will be changing you set placeholders like that{start_date}. So you'll have SQL script, but like as a template.Example:selectfield_1,id,first_name,last_name,birth_day,....field_N,fromscheme.employeeswherebirth_day=to_date('{date}','dd.mm.yyyy')from oradump import OraDumpBy now, you can get data only in csv format. So to achieve this, you need to calldumpordump_gziped(if want get it compressed) functions.dumpexample:rows_cnt = OraDump.dump(conn_str, template, csv, params)conn_str - connection string that you specify when you connect to Oracle instance by native client(sqlplus).
Likeuser/password@(DESCRIPTION = (ADDRESS_LIST =  (ADDRESS = (PROTOCOL=TCP)(HOST=XXX.XXX.XXX.XXX)(PORT=1521)))(CONNECT_DATA = (SERVICE_NAME=SID.alias)))template - SQL template described abovecsv - path to target csv fileparams - parameters passing into SQL template and substituting into according placeholders.dump_gzipedexample:rows_cnt = OraDump.dump_gziped(conn_str, template, gzip, params, del_orig=False)gzip - path to target gziped csv filedel_orig - whether if you want to delete csv file that OraDump gets before compressing.purposes of rest of parameters are the same as indumpIf all went successful number of retrieved rows is returned.
Package: oraide
Oraide is a library to spare presenters the risky proposition of entering
terminal commands and giving a presentation at the same time. Oraide, with help
fromtmux, can make it appear as if someone is typing in a terminal session.
It’s great for live coding, demonstrations, and recording screencasts.To learn more about installing and using Oraide, or its history, please seethe Oraide documentation.To see the source, submit bug fixes or enhancements, or to file a bug report,
please seeOraide on GitHub.The name of the library is a portmanteau oforateandaide.Oraide is byDaniel D. Beckand is provided under the terms of a BSD-style
license. See theLICENSE.txtfile included for details.
Package: oralb-ble
OralB BLEBluetooth library for Oral B devicesInstallationInstall this via pip (or your favourite package manager):pip install oralb-bleContributors ✨Thanks goes to these wonderful people (emoji key):This project follows theall-contributorsspecification. Contributions of any kind welcome!CreditsThis package was created withCookiecutterand thebrowniebroke/cookiecutter-pypackageproject template.
Package: oramask
oramaskOramask is a simplistic converter of Oracle specific date/time masks to Python's strftime masks.InstallationYou can install the oramask by cloning the repository and running pip install:gitclonegit@github.com:andzhi4/oramaskcdoramask&&pipinstall.or using PyPi:python3-mpipinstalloramaskExamplesHere is an example of usage:fromoramaskimportora_to_sftfromdatetimeimportdatetimenow=datetime.now()formatted=now.strftime(ora_to_sft('DD-MON-YYYY HH24:MI:SS'))print(formatted)# Output: 23-Feb-2023 17:51:15ContributingIf you find a bug or have an idea for a new feature, feel free to create an issue or submit a pull request.LicenseOramask is released under the MIT License.
Package: orange3-fca-addon
Orange-FCAThis repository contains an add-on for the Orange Datamining Framework which extends the functionality with multiple Formal Concept Analysis widgets.RequirementsPython 3.7+or3.10InstallationIn order to install local dependencies firstly initialize a local virtual environment (Example with Pipenv, otherwise follow normal pip installation).Pippip install -r requirements.txtPipenvpipenv install(orpipenv install --dev)Then, we need to include the library into Orange:Pip (editable mode)pip install -e .Pipenvpipenv install '-e .'UsageAfter the installation, the widgets from this add-on are registered with Orange. To run Orange from the terminal, usepython3 -m Orange.canvasThe new widgets are in the toolbox bar under FCA Addon section.