Saravanan666/pypi-org huggingface dataset:
Package: rozha
RozhaA package to simplify and streamline a number of natural language processing processes and methods for a wide variety of languages, empowering users to use NLP on both non-English and English texts.Rozha is named after Rozhanitsa, a goddess from Slavic mythology.InstallationInstall using pip:pip3 install rozhaOr download the GitHub repo and the install the requirements:pip3 install -r requirements.txtThen begin using the package by importing the modules you plan to use. Rozha is structured into three classes: process, analyze, and visualize. If running from a local copy of the files, use the following:from process import process
from analyze import analyze
from visualize import visualizeIf you installed using pip, use this syntax:import rozha.process as process (or whatever name you choose)
import rozha.analyze as analyze (or whatever name you choose)
import rozha.visualize as visualize (or whatever name you choose)Full DocumentationA full list of the package's functions can be viewedat this link.Example PipelinesSome example pipelines for working with the package are as follows:
Open a file, perform word tokenization and remove stopwords, make the text lowercase, and then get part-of-speech tags for the text:import rozha.process as process
import rozha.analyze as analyze

word_tokenized = process.lowerFile("your_file.txt")
pos_tags = analyze.posList(word_tokenized)
print(pos_tags)Open a file, perform sentence tokenization without removing stopwords, and then perform named entity recognition on each sentence using spaCy:import rozha.process as process
import rozha.analyze as analyze

sent_tokenized = process.sentTokenizeFile(your_file.txt)
ner_tags = analyze.spacyNer(sent_tokenized, 'en')
print(ner_tags)Perform word tokenization and remove stopwords from a string, make the text lowercase, and graph the 10 most common words as a bar graph:import rozha.process as process
import rozha.visualize as visualize

word_tokenized = process.lowerVar(text)
# pass the var, number of words to graph, the height and width of the graph, and your preferred filename
visualize.barFreq(word_tokenized, 10, 400, 400, 'my_graph')ContributingContributions are welcome! The following features are of particular interest:Increasing the number of methods in the analyze classIncreasing the number of methods in the visualization class
Package: rozipparser
RO Zip Data ParserThis project is used to parse zip code files from the official Romanian
government website.You can find the official xls files following this link:http://data.gov.ro/dataset/coduri-postale-romaniaInstallationpip install rozipparserUsageThis project can be used standalone to parse and convert the official
documents or as a module inside another application.Important!The official data file is in the old xls format
(Microsoft Excel <2007). The fileMUSTbe converted in Office Open
XML file format (Microsoft Excel 2007 - xlsx).Command lineConvert to csvpython parser.py <inputfile>--csv<output.csv>ModulefromcodeparserimportCodeParserparser=CodeParser("<PATH TO XLSX FILE")codes=parser.get_codes()Each entry in the returned list is a model.Code instance.Please see the test files for additional usage information.LicenseCoduriPostaleDataParser is released under the MIT license. See LICENSE
for detailsContactFollow me on twitter [@mcostea](https://twitter.com/mcostea)
Package: rozklad-ontu-parser-MakisuKurisu
Rozklad ONTU PareserLibrary onPyPiInstallation:pip install rozklad-ontu-parser-MakisuKurisuЦя бібліотека призначена для отримання розкладу зсайту з розкладом ОНТУНа поточний момент бібліотека може повернути розклад на поточний тиждень, чи на весь семестр. В подальшому планується додати підтримку розкладу екзаменів, повідомлень та інших розділів сайту.А як користуватися?Ви можете подивитися приклад використання в файліexample.py. Також наразі є окремий метод для отримання розкладу з CLI - parse.І нащо мені той CLI парсер?..Насправді лібу можна використовувати не лише як CLI тулзу, але й додавши пакет (якого наразі немає) до вашого проекту, тим самим створивши будь-яку систему з використанням цієї бібліотеки.
Наприклад - створення яскравого та зручного розкладу за вашим смаком.
Чи мій наступний проект - бот з розкладом для всього вишу.System requirementsВам знадобиться Python 3.10+ і будь-який браузер для роботи з selenium (Я використовую Firefox)Honorable MentionsДякуюMarshalXзадозвілпозичити методto_dictз його ліби:yandex-music-api. (Було внесено мінімальні зміни через bs4 теги)
Package: rp2040-pio-emulator
Emulator for the PIO Blocks within the RP2040 Microcontroller (Python Edition)IntroductionAn emulator for the Programmable Input/Output (PIO) blocks that are present
within the Raspberry Pi Foundation's RP2040 Microcontroller. It is designed
to assist in the analysis of PIO programs and to help you by:Enabling unit tests to be written.Answering questions such as: How many clock cycles are being consumed?Supporting the visualization of GPIO outputs over time.Providing alternatives to debugging on real hardware, which can be time consuming.Quick StartBelow is a slight variation of the example used within theQuick Start Guide.frompioemuimportemulateprogram=[0xE029,0x0041,0x2080]# Count down from 9 using X registergenerator=emulate(program,stop_when=lambda_,state:state.x_register<0)forbefore,afteringenerator:print(f"X register:{before.x_register}->{after.x_register}")DocumentationATour of pioemuprovides a more detailed explanation than theQuick Start Guideoffers. In addition, there is aFAQavailable that might contain an answer to your question. However, if none
of these provides you with the necessary information then please consider creating anew issue- thanks!Additional ExamplesSome additional examples include:Visualisation of square wave program using Jupyter Notebooks within theexamples/directory.TDD example for the Pimoroni Blinkt! within theexamples/directory.pico-pio-examplesSupported InstructionsInstructionSupportedNotesJMP:heavy_check_mark:WAIT:heavy_check_mark: :warning:IRQ variant is not supportedIN:heavy_check_mark:OUT:heavy_check_mark: :construction:EXEC destination not implementedPUSH:heavy_check_mark:PULL:heavy_check_mark:MOV:heavy_check_mark: :construction:Some variants and operations not implementedIRQ:heavy_multiplication_x:SET:heavy_check_mark:Known LimitationsThis software is under development and currently has limitations - the notable ones are:Not all of the available instructions are supported - please refer to the table above.No support for pin-sets associated with OUT, SET or IN; all pin numbers are with respect to GPIO 0.Pin-sets do not wrap after GPIO 31.No direct support for the concurrent running of multiple PIO programs;
a single State Machine is emulated and not an entire PIO block.Thanks Toaaronjamtfor contributing features and fixes.Josverlfor contributing features.winnyloursonfor contributing a bug fix.
Package: rp2-conv
rp2_convConvert CSV trade files from common Crypto exchanges to a normalized CSV / ODS file for use witheprbell/rp2for tax reporting.PurposeA simplistic series of importers that can be used to normalize transactions from common exchanges.Supported ExchangesCoinbase & Coinbase ProKraken.comBitMart.comBinance.US... add yours here!  Help us out by contributing.UsageTbdContributingWe need importers for various exchanges!  Help us out by contributing.RoadmapIt would be nice to support public transactions from wallet addresses.LicenseMIT License, SeeLICENSE
Package: rp2paths
RP2paths -- RetroPath2.0 to pathwaysRP2paths extracts the set of pathways that lies in a metabolic space file outputed by the RetroPath2.0 workflow. RetroPath2.0 is freely accessible on myExperiment.org at:https://www.myexperiment.org/workflows/4987.html.InstallationPrerequisitesrp2paths relies on several packages (rdkit, cairo) and binaries (java, graphviz) that can't be installed directly with pip. Please install them before pursuing the rp2paths set up:# from a new <myenv> conda environmentcondacreate--name<myenv>python=3condainstall--yes--channelrdkitrdkitcairo
condainstall--yes--channelcyclusjava-jre
condainstall--yes--channelconda-forgegraphvizFrom pip# installation in an already existing <myenv> environment (see prerequisites)condaactivate<myenv>
python-mpipinstallrp2pathsFrom conda# installation in an already existing <myenv> environment (see prerequisites)condaactivate<myenv>
condainstall-cbrsynthrp2pathsUsageFrom CLIOnce a scope has been produced by RetroPath2.0, a typical command line for extracting the pathways from the results ispython-mrp2pathsall<retropath2_scope>[--outdir<outdir>]where:allspecify that all the tasks needed for retrieving pathways will be executed at once.<retropath2_scope>is the metabolic space outputted by the RetroPath2.0 workflow.--outdir <outdir>specify the directory in which all files will be outputted.In the<outdir>folder, the complete set of pathways enumerated will be written in theout_paths.csvfile. In addition, for each pathway there will be a .dot file (.dot representation of the graph) and a .svg file (.svg depiction of the pathway).Available optionsAdditional options are described in the embedded help:# List of possible modes
python -m rp2paths -h

# List of options for the all-in-one mode
python -m rp2paths all -hExamplesPrecomputed results (outputted by RetroPath2.0) are provided in theexamplesfolder for few compounds (carotene, naringenin, pinocembrin, violacein).Below are the command lines for generating pathways that lie innaringeninresult file:sourceactivate<myenv>
python-mrp2pathsallexamples/naringenin/rp2-results.csv--outdirexamples/naringenin/outdirFor developersDevelopment installationAfter a git clone:cd<repository>
condacreate--name<dev_env>python=3condainstall--name<dev_env>--yes--channelrdkitrdkitcairo
condainstall--name<dev_env>--yes--channelcyclusjava-jre
condainstall--name<dev_env>--yes--channelconda-forgegraphvizflake8
condainstall--name<dev_env>--yespytest
condadevelop--name<dev_env>.TestsUsing Docker containers with:cd tests
./test-in-docker.sh [file_to_test]Without using Docker containers:cd tests
conda activate <dev_env>
pytest [file_to_test]How to cite RP2paths?Please cite:Delépine B, Duigou T, Carbonell P, Faulon JL. RetroPath2.0: A retrosynthesis workflow for metabolic engineers. Metabolic Engineering, 45: 158-170, 2018. DOI:https://doi.org/10.1016/j.ymben.2017.12.002LicenceRP2paths is released under the MIT licence. See the LICENCE.txt file for details.
Package: rp3-cache
Database based cache to dope data retrieval for RetroPath 3Here it is a two components cache system to dope the result retrieval from reaction rules
results, to be used with RetroPath 3.0. The 2 components are:A python module to be imported in RetroPath 3.0A Mongo database, which should be started independently (see "How to run the database in
the background")Notice : at reading time, some of following information might be deprecated!Needed componentsdocker :https://docs.docker.com/install/docker-compose :https://docs.docker.com/compose/install/conda :https://docs.conda.io/projects/conda/en/latest/user-guide/install/Setting up python environmentBasically: one needs a conda environment with following packages:rdkitpymongopytestcondacreate--namemyenvpython=3.6sourceactivatemyenv
condainstall--channelrdkitrdkit=2018.09.1.0
condainstall--channelconda-forgepytest
condainstallpytest
condainstallpymongo
condainstallpyyamlInstall as a packagespipinstall-e.How to run the database service in the backgrounddocker-compose-fservice/run/mongo.ymlup# Or 'up -d' to start in the backgroundQ&AWhere are stored the db files ? In a named docker data volume, seeservice/run/*ymlfile.How to citePlease cite:to be completedLicenseSee LICENSE file
Package: rpa
RPA for Python’s simple and powerful API makes robotic process automation fun! You can use it to quickly automate away repetitive time-consuming tasks on websites, desktop applications, or the command line.Built on AI Singapore’s TagUI open-source RPA tool, RPA capabilities out-of-the-box for this package include website automation, computer vision automation, optical character recognition, keyboard & mouse automation.Tables can’t be displayed here, please refer to RPA for Python homepage for full API -https://github.com/tebelorg/RPA-Python
Package: rpa-automationanywhere
RPA_AUTOMATIONANYWHEREPython module delivers some actions to communicate with Automation Anywhere A360 API.
The module is compatibile with the Robocorp.InstallationTo install the package run:pip install rpa_automationanywhereExampleFetching credentialfrom rpa_automationanywhere.Vault import Vault
vault = Vault('http://controlroom.net', 'user name', 'password')
vault.get_credential('demo')
Package: rpa-dates
RPA_DATESPython module delivers some actions to manipulate with dates and detect holidays.
The module is compatibile with the Robocorp.InstallationTo install the package run:pip install rpa-datesExampleFetching today's datefrom rpa_dates import Dates
dates = Dates()
dates.today()Checking if today is a public holiday in Polandfrom rpa_dates import Dates
dates = Dates()
dates.is_public_holiday('PL')Get next business day from today's date including holidays for Perufrom rpa_dates import Dates
dates = Dates()
dates.next_working_day(include_holidays=True, country_code='PE')DependenciesPython packages: calendar, typing, datetime, dateutil, requests
External:https://date.nager.atAPI
Package: rpa-hypercoe
RPA HyperCoe:Kit de componentes para o registrar os Logs dos robôs com a plataforma HyperCoe.Intalação>>> pip install rpa-hypercoeComo usar a biblioteca:>>> from rpa_hypercoe import FunctionExemplo de como usar a função de registro de logFuncao.Status(bot_id,clienttoken,bot_status)Funcao.Iteration(bot_id,clienttoken)Funcao.Log(level,typeError,message,pathfile,ID_Iteration,finalLog,ClientToken)Descrições gerais das funçõesFuncao.Status - Altera i Status de processamento do bot em questão.Funcao.Iteration - Captura o ID da iteração para ser utilizado como parâmetro na função de Log.Funcao.Log - Registra o Log de execução do bot.Definição dos parâmetros:rpa-hypercoe-logbot_status- Valor inteiro correspondente ao status do bot sendo # Active=0, Running=1, Paused=2, Error=3 (Campo obrigatório)bot_id- Valor inteiro referente ao ID do bot que será gerado no momento de criação do robô pelo agente do HyperCoe (Campo obrigatório)clienttoken- Valor string do Token que será disponibilizado pelo Portal HyperCoe (Campo obrigatório)level- Valor inteiro correspondente ao tipo de informação que será regsitrado no log, sendo # Info=0, Warn=1, Error=2 (Campo obrigatório)typeError- Valor string com a mensagem em caso de erro. (Campo não obrigatório)message- Valor string com a mensagem de log (Campo obrigatório)pathfile- Valor string com o caminho absoluto para envio de evidência (Campo não obrigatório)ID_Iteration- Valor inteiro capturado pela api "Iteration" (Campo obrigatório)finalLog- Valor boleano (False/True) que deverá ser padrão False e somente utilizar o parametro True no último registro de log do robô (Campo não obrigatório)
Package: rpa-hypercoe-log
RPA HyperCoe Log:Kit de componentes para o registrar os Logs dos robôs com a plataforma HyperCoe.Intalação>>> pip install rpa-hypercoe-logComo usar a biblioteca:>>> from rpa_hypercoe_log import FuncaoExemplo de como usar a função de registro de logFuncao.Status(bot_id,clienttoken,bot_status)Funcao.Iteration(bot_id,clienttoken)Funcao.Log(level,typeError,message,pathfile,ID_Iteration,finalLog,ClientToken)Descrições gerais das funçõesFuncao.Status - Altera i Status de processamento do bot em questão.Funcao.Iteration - Captura o ID da iteração para ser utilizado como parâmetro na função de Log.Funcao.Log - Registra o Log de execução do bot.Definição dos parâmetros:rpa-hypercoe-logbot_status- Valor inteiro correspondente ao status do bot sendo # Active=0, Running=1, Paused=2, Error=3 (Campo obrigatório)bot_id- Valor inteiro referente ao ID do bot que será gerado no momento de criação do robô pelo agente do HyperCoe (Campo obrigatório)clienttoken- Valor string do Token que será disponibilizado pelo Portal HyperCoe (Campo obrigatório)level- Valor inteiro correspondente ao tipo de informação que será regsitrado no log, sendo # Info=0, Warn=1, Error=2 (Campo obrigatório)typeError- Valor string com a mensagem em caso de erro. (Campo não obrigatório)message- Valor string com a mensagem de log (Campo obrigatório)pathfile- Valor string com o caminho absoluto para envio de evidência (Campo não obrigatório)ID_Iteration- Valor inteiro capturado pela api "Iteration" (Campo obrigatório)finalLog- Valor boleano (False/True) que deverá ser padrão False e somente utilizar o parametro True no último registro de log do robô (Campo não obrigatório)
Package: rpa-logger
rpa_loggerA simple python package for logging robotic process automation (RPA) progress.Getting startedThis package is available in PyPI asrpa-logger. To install, run:pip3installrpa-loggerSeepackage documentationandexamplesdirectory for instructions on getting started with the package usage.DocumentationThe documentation formainbranch is available inGitHub pages. It is generated withpdoc:pdoc--html--output-dirdocsrpa_loggerTestingCheck and automatically fix formatting with:pycodestylerpa_logger
autopep8-aaar--in-placerpa_loggerRun static analysis with:pylint-E--enable=invalid-name,unused-import,useless-object-inheritancerpa_loggerRun unit tests with command:python3-munittestdiscover-stst/Get test coverage with commands:coveragerun--branch--sourcerpa_logger/-munittestdiscover-stst/
coveragereport-m
Package: rp-annot
rp-annotMinimal compression library for sparse or contiguous 1D numpy boolean arrays.Only depends on numpyInstallThe latest version is available via PyPI (https://pypi.org/project/rp-annot)pip install rp-annotExample usageimportrp_annotasrpaimportnumpyasnpnp_1d_bool_array=np.zeros(100000,dtype=bool)np_1d_bool_array[20000:50000]=Truecompressed=rpa.compress(np_1d_bool_array)# 16 bytesdecompressed=rpa.decompress(compressed,len(np_1d_bool_array))assertnp.array_equal(np_1d_bool_array,decompressed)
Package: rpa-ocr
rpa_verification用于自主训练测试上线部署验证码算法版本号0.2.1使用方法安装新建conda环境conda create -n rpa-ocr python=3.7使用pip命令安装pip install rpa-ocrnote: 如果在安装过程中发生某些库安装失败，使用pip重新安装即可训练定义好相关参数然后使用train.main()命令训练importrpa_ocrapp_scenes=""data_path=""train=rpa_ocr.Train(app_scenes=app_scenes,data_path=data_path)train.main()参数说明app_scenes: 当前验证码的使用场景，也是全局标识符
 alphabet_mode: 使用哪种模式的字母表,目前支持"ch"(中文）,"eng"(英文大小写）,"ENG"（英文大写）
 data_path: 存储数据的位置，按照图片，命名为label
 model_path: model训练完后的保存地址
 short_size: 图片的高度，必须是16的倍数。default:32
 verification_length: 验证码的长度。default:4
 device: 使用cpu还是gpu进行训练，两个模式:"cpu" or "cuda"。default:"cpu"
 epochs: 训练模型的轮数。default:1200
 lr: 学习率。default:1e-3
 batch_size: 每一个batch的大小。default:256
 num_works: 使用多进行进行数据处理，使用进程数。default:0
 target_acc: 目标准确率,如果达到目标准确率将提前结束训练。default:0.95
 cloud_service: 是否将训练好的模型自动上传到云端。default:True预测定义好相关参数，然后使用crnn.predict(image)进行预测目前支持的image格式为opencv，pillow读入和base64编码后的图片importrpa_ocrimportcv2app_scenes=""model_path=""image_path=""image=cv2.imread(image_path)crnn=rpa_ocr.CRNNInference(app_scenes=app_scenes,model_path=model_path)crnn.predict(image)参数说明app_scenes: 当前验证码的使用场景，也是全局标识符
 alphabet_mode: 使用哪种模式的字母表,目前支持"ch"(中文）,"eng"(英文大小写）,"ENG"（英文大写)。default:"eng"
 model_path: 使用model所在文件夹目录
 short_size: 图片的高度，必须是16的倍数。default:32
 verification_length: 验证码的长度。default:4
 device: 使用cpu还是gpu进行训练，两个模式:"cpu" or "cuda"。default:"cpu"TODO LIST完成训练和测试的一键完成支持多cpu加速训练支持自动提前停止训练针对中文验证码的支持完成训练后的一键部署到云服务器完成可以部署到win32支持文字点选类验证码增加对滑块验证码的支持增加对拼图类验证码的支持
Package: rpa-openapi
阿里云RPA OpenAPI SDK查询客户端信息fromrpa_openapi.RPAClientimportRPAClientfromrpa_openapi.V20200430.QueryClientViewsRequestimportQueryClientViewsRequestfromrpa_openapi.V20200430.ResponseModelsimportQueryClientViewsResponseAccessKeyId="你的AccessKeyId"AccessSecret="你的AccessSecret"rpa_client=RPAClient(AccessKeyId,AccessSecret)query_client_views_request=QueryClientViewsRequest()query_client_views_request.current_page=1query_client_views_request.page_size=1query_client_views_response=rpa_client.do_action(query_client_views_request)print(query_client_views_response.json())query_client_views_response_data=QueryClientViewsResponse(**query_client_views_response.json())# requestIdprint("requestId: ",query_client_views_response_data.requestId)# successprint("success: ",query_client_views_response_data.success)# codeprint("code: ",query_client_views_response_data.code)# msgprint("msg: ",query_client_views_response_data.msg)# msgCodeprint("msgCode: ",query_client_views_response_data.msgCode)# uuidprint("uuid: ",query_client_views_response_data.data[0].uuid)# groupIdprint("groupId: ",query_client_views_response_data.data[0].groupId)# clientTypeprint("clientType: ",query_client_views_response_data.data[0].clientType)# nameprint("name: ",query_client_views_response_data.data[0].name)# ipprint("ip: ",query_client_views_response_data.data[0].ip)# macAddressprint("macAddress: ",query_client_views_response_data.data[0].macAddress)# statusprint("status: ",query_client_views_response_data.data[0].status)# dispatchModeprint("dispatchMode: ",query_client_views_response_data.data[0].dispatchMode)# remarkprint("remark: ",query_client_views_response_data.data[0].remark)# connectTimeprint("connectTime: ",query_client_views_response_data.data[0].connectTime)# disconnectTimeprint("disconnectTime: ",query_client_views_response_data.data[0].disconnectTime)# userIdprint("userId: ",query_client_views_response_data.data[0].userId)# userNameprint("userName: ",query_client_views_response_data.data[0].userName)# appIdprint("appId: ",query_client_views_response_data.data[0].appId)# appNameprint("appName: ",query_client_views_response_data.data[0].appName)# taskIdprint("taskId: ",query_client_views_response_data.data[0].taskId)# connectStatusprint("connectStatus: ",query_client_views_response_data.data[0].connectStatus)# currentPageprint("currentPage: ",query_client_views_response_data.pager.currentPage)# totalPageprint("totalPage: ",query_client_views_response_data.pager.currentPage)# pageSizeprint("pageSize: ",query_client_views_response_data.pager.pageSize)# totalprint("total: ",query_client_views_response_data.pager.total)# limitprint("limit: ",query_client_views_response_data.pager.limit)# offsetprint("offset: ",query_client_views_response_data.pager.offset)# instanceIdprint("instanceId: ",query_client_views_response_data.instanceId)
Package: rpa-pdf
RPA_PDFPython module delivers some actions to manipulate with PDF files.
The module is compatibile with the Robocorp.InstallationTo install the package run:pip install rpa_pdfExampleGenerating pdf file from textfrom rpa_pdf import Pdf

pdf = Pdf()

pdf.text_to_pdf('some text', 'c:/temp/somefile.pdf)Compress pdf filefrom rpa_pdf import Pdf

pdf = Pdf()

pdf.compress('c:/temp/somefile.pdf')Add Code39 stampfrom rpa_pdf import Pdf

pdf = Pdf()

pdf.add_code39_stamp('c:/temp/input_file.pdf', 'c:/temp/output_file.pdf', '12345678')Merging pdf filesfrom rpa_pdf import Pdf

pdf = Pdf()

pdf.merge(['c:/temp/file1.pdf', 'c:/temp/file2.pdf'], 'c:/temp/merged.pdf')Add text stampfrom rpa_pdf import Pdf

pdf = Pdf()

pdf.add_text_stamp('c:/temp/input_file.pdf', 'c:/temp/output_file.pdf', 'some text')Print PDF document on the printerfrom rpa_pdf import Pdf

pdf = Pdf()

pdf.print('c:/temp/document.pdf', 'printer1')DependenciesPython packages: PyPDF2 >= 1.28.5, fpdf2 >= 2.5.6, python-barcode >= 0.14.0
External dependencies: DejaVu font, SumatraPdf.exe (both included in the package)