Saravanan666/pypi-org huggingface dataset:
Package: techcombine
techcombine-frameworkInstallation for everyone$pipinstalltechcombineInstallation for Developer$pipinstalltechcombine[dev]Now you can test this package by usingpytestorpytest direstory/, For example$pytest# or$pytest.\techcombine\tests\Installation from sources(env)$pythonsetup.pyinstallmanualclass LINENotifyMethod:notify_hook(subtitle, api, token, data=None, **kwargs)Make webhook to Line Notify from your serverreturn: requestfromtechcombine.utilsimportgenerate_notify_dataLINENotify.notify_hook(subtitle,data=None:dict,**kwargs)# When you use this method you need to defind variable in django.conf settingsLINE_NOTIFY_TOKEN=somesting+'access_token'LINE_NOTIFY_API='https://notify-api.line.me/api/notify'# Data exampledata={"Order ID":order.slug,"email":order.email,"first_name":order.first_name,"phone_number":order.phone_number,"created_at":order.created_at,}# Or use generate_notify_data(ojb)parameter:subtitle: (str) Is subtitle of data hook to line notify chatdata: (default=None/DictType only) Is dict of your all data to show in mailOr using **dictMethod:generate_notify_data(obj, key_type="id", header=None)Generate dict data from django models object.
return: dict data format for using in notify_hook()fromtechcombine.utilsimportgenerate_notify_datadata=generate_notify_data(ojb,key_type="id",header=None)parameter:obj: (Django models object) field['obj.id or obj.slug', 'name', 'email', 'created_at', 'url']#if header=None: data will doesn't have url fieldkey_type: (str) Type of object key from ID or Slugheader: (str) For fill link url of objectClass MailServiceMethod:send_email(from_email, to_emails, subject, html_content, token)Send email from from_email to to_email by sendgridreturn request responsefromtechcobine.mailimportMailServiceimportosfrom_email='from_email@example.com',to_emails='to@example.com',subject='For example subject',html_content='<strong>and easy to do anywhere, even with Python</strong>'token=os.environ.get('SENDGRID_API_KEY')MailService.send_email(from_email,to_emails,subject,html_content,token)parameter:from_email: type(string), Is an email sender.to_email: type(string), Is an email recipients.subject: type(string), Is subject of this email.html_content: type(string), write html for render template on email.token: type(string), Is sendgrid api token.Method:send_verify_email(from_email, to_email, host_name, user_token, sendgrid_token, subject=None)Send verify email from from_email to to_email by sendgridreturn: request responseMailService.send_verify_email(from_email,to_email,host_name,user_tokenmsendrid_token)Method:send_reset_password(from_email, to_email, host_name, user_token, sendgrid_token, subject=None)Send to reset passwordreturn: request responseMail.Service.send_reset_password(from_email,to_email,host_name,user_tokenmsendrid_token)
Package: techdebt
Tech-debtA simple tool to quantify your technical debtInstallationrequirements : python 3pipinstalltechdebtUsageAdding debpt annotationadd debt annotations in your project files to mark technical debt:@debt()@debt("debt_type") or@debt("debt_type":"comment")Let's say you have a python file with some debt :#@debt("implementation")defpoor_function():#@debt("implementation": "variable naming")bla=2returnblaProject configurationAdapt the following configuration file and put
it in your project folder (cf config_example.yml)folder:included:-test_project/subA-test_project/subBexcluded:-vendor-node_modules-var-web-nbproject-public-.git-__pycache__extensions:-.py-.js-.md-.phpscores:api:5implementation:3Runtechdebt--conf="path/to/config_file.yml"#default is "techdebt.yml"PluginsYou may want to have automatic code checkers do
some analysis and being included in the final debt scoreFor now phpstan is the sole plugin availableedit configuration with the following:plugin:phpstan:function_to_call:""folders:-folder_to_analysescores:-adding pluginyou need to code a file plugin with a method which
returns scores and then add it to the yarml fileRoadmapadding line file of annotationbetter reportingtracking debt over timeadding estimation rangeTestingpytest-ssrc/lib.py
Package: techdevutil
This is a util extension by techpluscraft with many functionsChange Log0.0.5 (9/26/2021)Added List to String fuction0.0.4 (8/30/2021)Fixed remove_spaces0.0.3 (8/30/2021)Added a remove_spaces Function0.0.2 (8/30/2021)Added palindrome Function0.0.1 (8/29/2021)First Release
Package: techdragon
My Contact DetailsFree software: MIT licenseInstallationpip install techdragonDocumentationhttps://python-techdragon.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2017-07-29)First release on PyPI.
Package: techead
libpythonproMÃ³dulo para exemplificaÃ§Ã£o de projetos Python no curso PyToolsNesse curso Ã© ensinado como contribuir com projeto de cÃ³digo abertoLink para o cursoPython ProSuportada a versÃ£o 3 de PythonPara instalar:python3 -m venv .venvsource .venv/bim/activatepip install -r requirements-dev.txtPara conferir qualidade de cÃ³digo:flake8TÃ³picos a serem abordados:GitVirtualenvpip
Package: techfin
A package that implements technical indicators and plotting functions of themIndicators implemented in the packageSimple Moving AverageExponential Moving AverageBollinger BandsSimple Relative Strength IndexExponential Relative Strength IndexMoving Average Convergence DivergenceMoney Flow IndexInstallationMake sure you have numpy, pandas, yfinance, matplotlib installedRun Following command to install the packagepip install techfinHow to importRun Following command to importimport techfin

  # The following packages are also necessary
  import pandas as pd
  import numpy as np
  import yfinance as yf
  import matplotlib.pylot as pltExampledata = yf.download('HDFC.NS', start=2021-01-01, end=2022-01-01, interval='1d', auto_adjust=True)
  
  techfin.plot_Close(data)        # to plot close price of data
  
  n = [10, 50, 100]
  data_SMA = techfin.SMA(data, n) # to calulate simple moving average
  techfin.plot_SMA(data_SMA, n)   # to plot Simple moving average on the Close price graphList of FunctionsSr.FunctionDiscription1.plot_Close (data)Plot Close Price2.SMA (data, n)Calculate Simple Moving Average3.plot_SMA (data, n)Plot Simple Moving Average4.EMA (data, n)Calculate Exponential Moving Average5.plot_EMA (data)Plot Exponential Moving Average6.BollingerBands (data, n, factor)Calculate Bollinger Bands7.plot_BollingerBands (data)Plot Bollinger Bands8.Simple_RSI (data, n)Calculate Simple RSI9.plot_S_RSI (data, overbought, oversold)Plot Simple RSI, highlighting overbought & oversold zones10.Exponential_RSI (data, n)Calculate Exponential RSI11.plot_E_RSI (data, overbought, oversold)Plot Exponential RSI, highlighting overbought & oversold zones12.MACD (data, longEMA, shortEMA, signalEMA)Calculate MACD indicator13.plot_MACD (data)Plot MACD indicator along with bars for Convergence/Divergence14.Money_Flow_Index (data, n)Calculate Money Flow Index15.plot_MFI (data, overbought, oversold)Plot MFI, highlighting overbought & oversold zones
Package: techgram
Telegram MTProto API Framework for PythonHomepage•Documentation•Releases•NewsTechGramElegant, modern and asynchronous Telegram MTProto API framework in Python for users and botsfromtechgramimportClient,filtersapp=Client("my_account")@app.on_message(filters.private)asyncdefhello(client,message):awaitmessage.reply("Hello from techgram!")app.run()TechGramis a modern, elegant and asynchronousMTProto APIframework. It enables you to easily interact with the main Telegram API through a user account (custom client) or a bot
identity (bot API alternative) using Python.SupportIf you'd like to support TechGram, you can consider:Become a GitHub sponsor.Become a LiberaPay patron.Become an OpenCollective backer.Key FeaturesReady: Install TechGram with pip and start building your applications right away.Easy: Makes the Telegram API simple and intuitive, while still allowing advanced usages.Elegant: Low-level details are abstracted and re-presented in a more convenient way.Fast: Boosted up byTgCrypto, a high-performance cryptography library written in C.Type-hinted: Types and methods are all type-hinted, enabling excellent editor support.Async: Fully asynchronous (also usable synchronously if wanted, for convenience).Powerful: Full access to Telegram's API to execute any official client action and more.Installingpip3installtechgramResourcesCheck out the docs athttps://docs.pyrogram.orgto learn more about TechGram, get started right
away and discover more in-depth material for building your client applications.Join the official channel athttps://t.me/pyrogramand stay tuned for news, updates and announcements.
Package: techharper
HarperTool to fetch the latest releases in technologies.IntroductionThetechharperPython module allows you to request the latest release information for specified technologies.This currently searches specifically for GitHub repositories.TokenThis tool requires aGitHub Personal Access Token.NOTE - This token has no specific requirements. It simply provides basic authentication in order to increase the API rate limit.ExampleAn example application is providedhere.From a terminal instance in this directory install dependencies by runningpip install -r requirements.txtRun the application by runningpy example.py --token YOUR_GITHUB_PAT_HERE --repository 'vercel/next.js'Additional ReadingRequests Library
Package: techiaith-tts
techiaith-ttsContentsGetting started:InstallationInstalling with "pip"Installing from sourceOverviewChangelogUnreleasedTeamtechiaith-ttsis developed and maintained by theUned Technolegau
Iaith (UTI)https://techiaith.cymru/team, backed byPrifysgol
Bangor (UTI)https://bangor.ac.uk/. A self-funded research unit
that develops technologies for the Welsh language. To learn more about
who specifically contributed to this codebase, see our contributors
page.Licensetechiaith-ttsis licensed under MIT License. A full copy of the
license can be found on GitHub.Indices and tablesIndexModule Index
Package: techlib-nr-common
nr-commonDisclaimer: The library is part of the Czech National Repository, and therefore the README is written in Czech.
General libraries extendingInvenioare concentrated under theOarepo
namespace.InstalaceNejedná se o samostatně funkční knihovnu, proto potřebuje běžící Invenio a závislosti Oarepo.
Knihovna se instaluje klasicky přes pippipinstalltechlib-nr-commonPro testování a/nebo samostané fungování knihovny je nutné instalovat tests z extras.pipinstall-e.[tests]ÚčelKnihovna obsahuje obecný metadatový model Národního repozitáře (Marshmallow, JSON schema a Elastisearch mapping).
Dále se stará o perzistetní identifikátor (PID) a obsahuje Inveniofetcheraminter. Všechny tyto části lze
"podědit" v dalších metadatových modelech.Knihovna není samostatný model pro "generic" věci - ten je v nr-generic.
Package: techlib-nr-common-metadata
nr-common-metadataDisclaimer: The library is part of the Czech National Repository, and therefore the README is written in Czech.
General libraries extendingInvenioare concentrated under theOarepo
namespace.InstalaceNejedná se o samostatně funkční knihovnu, proto potřebuje běžící Invenio a závislosti Oarepo.
Knihovna se instaluje klasicky přes pippipinstalltechlib-nr-common-metadataPro testování a/nebo samostané fungování knihovny je nutné instalovat tests z extras.pipinstall-e.[tests]ÚčelKnihovna obsahuje obecný metadatový model Národního repozitáře (Marshmallow, JSON schema a Elastisearch mapping).
Všechny tyto části lze
"podědit" v dalších metadatových modelech.Knihovna není samostatný model pro "generic" věci - ten je v nr-generic.
Package: techlib-nr-datasets
NR-DatasetsDisclaimer: The library is part of the Czech National Repository, and therefore the README is written in Czech.
General libraries extendingInvenioare concentrated under theOarepo
namespace.InstalaceNejedná se o samostatně funkční knihovnu, proto potřebuje běžící Invenio a závislosti Oarepo.
Knihovna se instaluje ze zdroje.gitclonegit@github.com:Narodni-repozitar/nr-datasets.gitcdnr-datasets
pipinstallpoetry
poetryinstallPro testování a/nebo samostané fungování knihovny je nutné instalovat tests z extras.poetryinstall--extrastests:warning: Pro instalaci se používá Manažer závilostíPoetryvíce infromací lze naleznout vdokumentaci
Package: techlib-nr-datasets-metadata
nr-datasets-metadataDisclaimer: The library is part of the Czech National Repository, and therefore the README is written in Czech.
General libraries extendingInvenioare concentrated under theOarepo
namespace.InstalaceNejedná se o samostatně funkční knihovnu, proto potřebuje běžící Invenio a závislosti Oarepo.
Knihovna se instaluje ze zdroje.gitclonegit@github.com:Narodni-repozitar/nr-datasets-metadata.gitcdnr-datasets-metadata
pipinstallpoetry
poetryinstallPro testování a/nebo samostané fungování knihovny je nutné instalovat tests z extras.poetryinstall--extrastests:warning: Pro instalaci se používá Manažer závilostíPoetryvíce infromací lze naleznout vdokumentaciÚčelKnihovna rozšiřujeobecný metadatový modelo pole pro datasetové záznamy. Datasetům je přiřazen endpoint/datasets. Knihovna
poskytuje API pro CRUD operace pod proxynr_datasets.PoužitíBude dopsáno.
Package: techlib-nr-generic
nr-commonDisclaimer: The library is part of the Czech National Repository, and therefore the README is written in Czech.
General libraries extendingInvenioare concentrated under theOarepo
namespace.InstalaceNejedná se o samostatně funkční knihovnu, proto potřebuje běžící Invenio a závislosti Oarepo.
Knihovna se instaluje klasicky přes pippipinstalltechlib-nr-commonPro testování a/nebo samostané fungování knihovny je nutné instalovat tests z extras.pipinstall-e.[tests]ÚčelKnihovna obsahuje REST api pro obecný metadatový model definovaný v nr-generic.
Package: techlib-nr-theses
NR-ThesesDisclaimer: The library is part of the Czech National Repository, and therefore the README is written in Czech.
General libraries extendingInvenioare concentrated under theOarepo
namespace.InstalaceNejedná se o samostatně funkční knihovnu, proto potřebuje běžící Invenio a závislosti Oarepo.
Knihovna se instaluje ze zdroje.gitclonegit@github.com:Narodni-repozitar/nr-theses.gitcdnr-common
pipinstallpoetry
poetryinstallPro testování a/nebo samostané fungování knihovny je nutné instalovat tests z extras.poetryinstall--extrastests:warning: Pro instalaci se používá Manažer závilostíPoetryvíce infromací lze naleznout vdokumentaciÚčelKnihovna rozšiřujeobecný metadatový modelo pole pro vysokoškolské závěrečné práce. Vysokoškolským pracím je přiřazen endpoint/api/theses. Knihovna
poskytuje API pro CRUD operace pod proxynr_these.PoužitíBude dopsáno.
Package: techlib-nr-theses-metadata
nr-theses-metadataDisclaimer: The library is part of the Czech National Repository, and therefore the README is written in Czech.
General libraries extendingInvenioare concentrated under theOarepo
namespace.InstalaceNejedná se o samostatně funkční knihovnu, proto potřebuje běžící Invenio a závislosti Oarepo.
Knihovna se instaluje ze zdroje.gitclonegit@github.com:Narodni-repozitar/nr-theses-metadata.gitcdnr-common-metadata
pipinstallpoetry
poetryinstallPro testování a/nebo samostané fungování knihovny je nutné instalovat tests z extras.poetryinstall--extrastests:warning: Pro instalaci se používá Manažer závilostíPoetryvíce infromací lze naleznout vdokumentaciÚčelKnihovna rozšiřujeobecný metadatový modelo pole pro vysokoškolské závěrečné práce. Vysokoškolským pracím je přiřazen endpoint/api/theses. Knihovna
poskytuje API pro CRUD operace pod proxynr_these.PoužitíBude dopsáno.
Package: techman
techman.pyGeneral Purpose Python Library by @Techman55How to Importtry:
    import techman
except ModuleNotFoundError:
    import sys, subprocess
    # implement pip as a subprocess:
    subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'techman'])techman.pyFlag for Your Project'sREADME.md> This program uses the `techman.py` general purpose python library. [Learn more](https://py.techmandev.com)ExampleThis program uses thetechman.pygeneral purpose python library.Learn moreClassesynUser input gatherer for Yes/No questions usinginput()yn.ask()yn.check()QuickSetupSimple App data and "is first open" trackeris_first_open()reset_first_open()does_config_exist()write_config()read_config()update_config()reset_config()PackagesEasy functions to manage packages from pipis_package_installed()install()FunctionsAssorted functions that don't fit into a classset_clipboard()
Package: techmanpy
What istechmanpy?techmanpyis an easy-to-use communication driver for Techman robots written in Python.With it, motion commands can be executed, internal parameters can be changed and the state of the robot can be monitored: providing a solid foundation for any custom (ROS) control software.By usingasynciofor all socket communications, it provides an elegant coroutine-based API.Read the full documentationHere's how to command your robot to move:#!/usr/bin/env pythonimportasyncioimporttechmanpyasyncdefmove():asyncwithtechmanpy.connect_sct(robot_ip='<robot IP address>')asconn:awaitconn.move_to_joint_angles_ptp([10,-10,10,-10,10,-10],0.10,200)asyncio.run(move())And here's how to listen to the TMFlow server:#!/usr/bin/env pythonimportasyncioimporttechmanpyasyncdeflisten():asyncwithtechmanpy.connect_svr(robot_ip='<robot IP address>')asconn:conn.add_broadcast_callback(print)awaitconn.keep_alive()asyncio.run(listen())RequirementsTMFlow:1.80+Python:3.8+Installation$ python3 -m pip install techmanpyTest connection with TMFlowTo verify that your connection with the robot is all set-up:$ python3 test_connection.py <robot IP address>What else?Bug reports, patches and suggestions are welcome! Feel free to open anissueor send apull request.techmanpyis not affiliated, authorized, or in any way officially connected withTechman Robot. Use this software at your own risk, it did not undergo any official quality assurance.This package is released under theMIT license.
Package: techmo-wavelet
Techmo Sp. z o.o.module for audio features extractionHow to use:warning: Add!character if you install the module in a jupyter notebookpip install techmo-wavelet 

#import functions for feature extraction
from techmo.feature_extraction import calculate_wavelet_fft, calculate_fft_wavelet

# install numpy first in case is not installed in your environment
import numpy as np 

# signal must be 1d array read from wav file, e.x by using Soundfile. Here we generate random signal
signal = np.random.uniform(-1.0, 1.0, 16000)

# Here's an example of how to use `calculate_wavelet_fft` function
features = calculate_wavelet_fft(signal)

# Here's an example of how to use `calculate_fft_wavelet` function
features = calculate_fft_wavelet(signal)The code implements 2 functions to extract features:Thecalculate_wavelet_fftfunction implements an algorithm consisting of the following stages:If the number of samples N is greater than or equal to 4800,
the signal is divided into int(N/2400) segments to compute finally 60
features for each segment containing int(N/int(N/2400)) samples,
i.e. the feature vector will have 60*int(N/2400) elements,Segments are processed by the Hann window,Segments are normalized separately,Each segment is processed by the Wavelet Transform (WT),Each WT subband is subjected to the Fast Fourier Transform (FFT),FFT spectra are inputs of the triangular filtration to obtain
the feature sub-vectors of length 60 for each segment,The logarithms of filter outputs are computed to obtain
the feature sub-vectors of length 60 for each segment.Sub-vectors are concatenated to obtain a final feature matrix as numpy ndarray
of shape int(N/2400), 60.Thecalculate_fft_waveletfunction implements an algorithm consisting of the following stages:If the number of samples N is greater than or equal to 9600,
the signal is divided into int(N/4800) segments to compute finally 60
features for each segment containing int(N/int(N/4800)) samples,
i.e. the feature vector will have 60*int(N/4800) elements,Segments are processed by the Hann window,Segments are normalized separately,Speech segments are processed by the the Fast Fourier Transform,The complex spectra are subjected to Wavelet Transform (WT),Absolute values of WT are calculated,The computed modules are inputs of the triangular filtration,The logarithms of filter outputs are computed to obtain
the feature sub-vectors of length 60 for each segment.Sub-vectors are concatenated to obtain a final feature matrix
as numpy ndarray of shape int(N/4800), 60.
Package: technews-tw
Taiwan 科技新聞統整Why?為了解決，需要訂閱許多科技新聞網站而生，整理每日科技新聞，定時派送e-mail給自己。Tech News Providers- iThome
- Business Next - 數位時代
- INSIDE 硬塞的Requirementspipinstalltechnews-twGet Today's News取得今日新聞fromtechnewsimportTechNewsTechNews("business").get_today_news()TechNews("ithome").get_today_news()TechNews("inside").get_today_news()Get news by page取得第1頁至第3頁所有科技新聞fromtechnewsimportTechNewsTechNews("business").get_news_by_page(3)TechNews("ithome").get_news_by_page(3)TechNews("inside").get_news_by_page(3)Send Today's News by Email將今天的科技新聞傳送給接收者# set environment variables# sender only support "gmail" and "hotmail"exportMAIL_SENDER="sender@gmail.com"exportMAIL_SENDER_PWD="iampassword"exportMAIL_RECV="user01@gmail.com,user02@hotmail.com"python-mtechnews.daily_newsIntegrate with Crontab使用crontab，自動發送每日科技新聞，將下列指定寫入crontab設定檔
輸入:crontab -e進入到設定介面MAIL_SENDER="sender@gmail.com"MAIL_SENDER_PWD="mypassword"MAIL_RECV="receiver@gmail.com"LOG_LEVEL="DEBUG"# 每天晚上七點發送019**1-7python3-mtechnews.daily_news>technews_log.log2>&1Release Note2023.10.21移除對科技報橘的支援，因為太難維護, 太難爬了. GG
Package: technic
TechnicA trading technical analysis library for python.DependenciespandasnumpyInstallpip install technicSupported Technical FunctionsIndicatorStatusSMADONEEMADONERSIDONEATRDONESTDDONEMACDDONEBOLLINGER BANDSDONEKELTNER CHANNELSDONESQUEEEZECOMING SOONSTOCHASTIC OSCILLATORCOMING SOONExamplesimportpandasaspdimporttechnicastacsv_file='PATH_TO_YOUR OHLCV CSV DATA'# Dataframe containing OHLCV datadf=pd.read_csv(csv_file)# SMAsma=ta.tsma(df['close'],50)# EMAema=ta.tsma(df['close'],10)# RSIrsi=ta.trsi(df['close'],14)# ATRatr=ta.tatr(df['close'],df['high'],df['low'],w=21)# MACDdf_macd=ta.tmacd(df['close'],w_slow=26,w_fast=12,w_signal=9)# Bollinger Bandsdf_bbands=ta.tbollingerbands(df['close'],w=21,std_multiplier=2)# Keltner Channelsdf_kelt=ta.tkeltnerchannels(df['close'],df['high'],df['low'],w=21,atr_multiplier=2)