Saravanan666/pypi-org huggingface dataset:
Package: teext
teext – typing extensions extensionsPackage which provides useful types.DocumentationExamplesValue-constraint types without runtime overheadThese types are most useful in conjunction with static type checkers like mypy.importteextastxdeff(x:tx.PositiveInt)->None:print(x)a=5asserttx.is_positive_int(a)f(a)# OKf(7)# works at runtime but mypy gives errorasserttx.is_positive_int(-3)# AssertionError
Package: tefas
tefasGithub:https://github.com/atahanuz/tefas/Get daily price data of 5 years from TEFAS, Turkey's exchange traded funds platform. (in Turkish: Yatırım Fonları)https://www.tefas.gov.trTEFAS doesn't provice an API for easy data retrieval, so the program uses Selenium to physically visit the website and scrape the data.Installationpip install tefasUsageimporttefasdata=tefas.get_data("AFT","MAC","TCD")call tefas.get_data() function with the ETF names. You can pass a single or as many ETFs as you want.
You will get a Pandas dataframe with days as indexes and each ETFs daily prices as columns.You can pass an optional verbose=False argument if you want to disable printing scraping progression to the console.importtefasdata=tefas.get_data("IPJ",verbose=False)Example DataframeAdditional Features ?Initially, I thougt about adding various data manipulation functions to the library. But to keep the library lightweight, I think it is enough to retrieve the dataframe. Because once the dataframe is ready, users can easily process the data according to their needs using the Pandas library.
Examples:importtefasdata=tefas.get_data("AFT","MAC","TCD")# print the correlation matrix between ETFsprint(data.corr(),"\n")#print total percentage return for each ETFforcolumnindata:print(f"{column}total return:{data[column].iloc[-1]/data[column].iloc[0]*100-100:.2f}%")OutputContactRaise an issue on the GitHub repo and I'll happily respond:https://github.com/atahanuz/tefas/issues/newDisclaimer: This program doesn't provide any investment advice, it simply displays data based on user request.
Package: tefas-crawler
Tefas CrawlerCrawl public invenstment fund information fromTurkey Electronic Fund Trading Platform(TEFAS) with ease.Installationpip install tefas-crawlerUsageImport theCrawlerobject and create an instance of it.fromtefasimportCrawlertefas=Crawler()APIfetch(start, end, name, columns, kind)ArgumentTypeDescriptionRequiredstartstringordatetime.datetimeThe date that fund information is crawled for.Yesendstringordatetime.datetimeEnd of the period that fund information is crawled for.NonamestringName of the fund. If not given, all funds will be returned.Nocolumns[]listofstringList of columns to be returned.NokindstringType of the fund. One ofYAT,EMK, orBYF. Defaults toYAT.NoExamplesGet all funds for a given day.data=tefas.fetch(start="2020-11-20")Get a specific fund for a time period, and select columns.data=tefas.fetch(start="2020-11-15",end="2020-11-20",name="YAC",columns=["code","date","price"])Data SchemaAs of today, we support the following data schema fromTefas:ColumnDescriptionTypedateSatedatepricePrice of the fund for a given datestringcodeShort code of the fundstringtitleFull name of the fundstringmarket_capTotal value of the fundfloatnumber_of_sharesNumber of outstanding sharesfloatnumber_of_investorsNumber of participantsfloattmmShare of tmmfloatrepoShare of repofloatotherShare of otherfloatstockShare of stockfloateurobondsShare of eurobondsfloatbank_billsShare of bank billsfloatderivativesShare of derivativesfloatreverse_repoShare of reverse-repofloatterm_depositShare of term depositfloattreasury_billShare of treasury billfloatforeign_equityShare of foreign equityfloatgovernment_bondShare of government bondfloatprecious_metalsShare of precious metalsfloatcommercial_paperShare of commercial paperfloatfx_payable_billsShare of fx payable billsfloatforeign_securitiesShare of foreign securitiesfloatprivate_sector_bondShare of private sector bondfloatparticipation_accountShare of participation accountfloatforeign_currency_billsShare of foreign currency billsfloatasset_backed_securitiesShare of asset-backed securitiesfloatreal_estate_certificateShare of real estate certificatefloatforeign_debt_instrumentsShare of foreign debt instrumentsfloatgovernment_lease_certificatesShare of government lease certificatesfloatfund_participation_certificateShare of fund participation certificatefloatgovernment_bonds_and_bills_fxShare of government bonds and bills (fx)floatprivate_sector_lease_certificatesShare of private sector lease certificatesfloatTo-doIncrease test coverageRequest error handlingCache query resultsLicenseMIT
Package: tefas-ui
Tefas UIA simple terminal UI for viewing fund P/L analysis through TEFAS.Features (that my own bank's UI lack):Daily and weekly P/LFX comparisonsInstallationInstall it throughpip:pip install tefas-uiNote: if you need forex support, please installtefas-ui[forex].UsageThe P/L sheets and the other information can be simply generated by pointingtefas-uiCLI command to your input file (something that you export from your bank). It will try
to parse it (if it is one of the supported formats) and render it.tefas-ui <input file> <fund format: {teb}>
Package: tefas-wrapper
tefas-wrapperFetch funds data fromhttps://www.tefas.gov.trpip install tefas-wrapperusage:from tefaswrapper import Wrapper, FundType

# ...

tefas = Wrapper(FundType.YAT)
# single day
result = tefas.fetch("AFT", "25.11.2020", "25.11.2020")

# between two dates
result = tefas.fetch("AFT", "15.11.2020", "20.11.2020")

# from a cetain day until today
result = tefas.fetch("AFT", "15.11.2020")                

# latest value
result = tefas.fetch("AFT")

# latest values of all funds
result = tefas.fetch()

# fetch detail a specific fund
detail = tefas.fetch_detail("AFT")

# ...
Package: teflo
WarningThis project is in maintenance mode and will not have any new feature development.What is Teflo?TEFLOstands for (TestExecutionFrameworkLibraries andObjects)Teflo is an orchestration software that controls the flow of a set of testing scenarios.
It is a standalone tool written in Python that includes all aspects of the workflow.
It allows users to provision machines, deploy software, execute tests against them and
manage generated artifacts and report results.Teflo Providesstructure,readability,extensibilityandflexibilityby :providing a YAML file to express a test workflow as a series of steps.enabling integration of external tooling to execute the test workflow as defined by the steps.Teflo can be used for an E2E (end to end) multi-product scenario. Teflo handles coordinating the
E2E task workflow to drive the scenario execution.Teflo can be used for an E2E (end to end) multi-product scenario. Teflo handles coordinating the
E2E task workflow to drive the scenario execution.What does an E2E workflow consist of?At a high level teflo executes the following tasks when processing a scenario.Provision system resourcesPerform system configurationInstall productsConfigure productsInstall test frameworksConfigure test frameworksExecute testsReport resultsDestroy system resourcesSend NotificationsTeflo has following stagesProvision- Create resources to test against (physical resources, VMs etc)Orchestrate- Configure the provisioned resources (e.g. install packages on them, run scripts, ansible playbooks etc)Execute- Execute tests on the configured resourcesReport- Send or collect logs from the tests runNotification- Send email/gchat/slack notification during each stage of teflo run or at the end based on the set triggersCleanup- Cleanup all the deployed resources.These stages can be run individually or together.Teflo follows a plugable architechture, where users can add different pluggins to support external tools
Below is a diagram that gives you a quick overview of the Teflo workflowTo learn more about how to set up and use Teflo please check outhttps://teflo.readthedocs.io/en/latest/To know how to create a custom plugin check outhttps://teflo.readthedocs.io/en/latest/developers/development.html#how-to-write-an-plugin-for-tefloTo know about our release cadence and contribution policy check outhttps://teflo.readthedocs.io/en/latest/developers/development.html#release-cadence
Package: teflo-notify-service-plugin
Teflo Notify Service PluginThis is Teflo's plugin for sending teflo notifications using Notify Service. Notify service is a
backend service build with FastAPI that supports sending notifications to multiple supported targets
(gchat, slack, unified message bus, email and irc)
To learn more about the notify service please visithereThis plugin is used to send notifications on triggers likeon_task,on_start,on_failure,on_demandduring and post teflo execution. For more information on how to setup triggers for notification
please visithere.If you're a user of the plugin please refer to theuserdocumentation.If you're a maintainer/developer please refer to thedevelopmentdocumentation.
Package: tefs
Transfer Entropy Feature SelectionThis repository implements a causal feature selection algorithm based on transfer entropy. The algorithm is described in: Bonetti, P., Metelli, A. M., & Restelli, M. (2023, October 17). Causal Feature Selection via Transfer Entropy. (https://arxiv.org/abs/2310.11059).Requires Python 3.9 or later.InstallationThe package can be installed using pip:pipinstalltefsHow to useRefer to the documentation for usage examples.AuthorsPaolo Bonetti (@PaoloBonettiPolimi)Teo Bucci (@teobucci)
Package: teft
Token Efficient Fine Tuning (TEFT)A library reducing the output size of large language models (LLM)BuildPut pypi credentials in~/.pypirc. The relevant section should look like this[distutils]
  index-servers =
    testpypi

[pypi]
  username = __token__
  password = pypi-...

[testpypi]
  username = __token__
  password = pypi-...python -m buildUpload to test index:twine upload --repository testpypi dist/*Upload to prod index:twine upload dist/*
Package: tefy
TEfywas born out of the need to streamline the conversion of doc,
docx and odt documents into TEI XML when using Python scripts. It’s a
very basic wrapper around theOxGarageAPI athttps://oxgarage.tei-c.org/ege-webservice/and
covers a small subset of conversions, namely from doc, docx and odt to TEI
XML. The conversion result is output as anlxmletree Element.UsageYou can install TEfy with pip ($ pip install TEfy)
and convert let’s say an ODT document like this:fromtefyimportOxGaWrapdoc=OxGaWrap('path/to/example.odt')tei=doc.tei_xml
Package: teg-CCC
Estimate the true number of clusters for k-means clustering using the Cluster Consistency Criterion (CCC).This algorithm follows the rationale that true cluster centres should be similar in random split-halves of the data. If too maby clusters are specified, the cluster centres will become driven by random sampling error.The CCC implements this as follows. For each number of clusters, the data are split into random halves for a given number of splits (e.g., 20). For each split, a k-means cluster analysis is run on each half separately. The distances between most-similar cluster centres are summed. The similarity score is e^(-distance_sum). The mean similarity score over random splits is the score for the given number of clusters.The best estimate of the true number of clusters is determined by where the improvement in score drops off, which occurs when the number of clusters becomes higher than the true number of clusters.The file test.py gives an example and simulation script. Usage is:O = teg_CCC.get_best_k_CCC(X)where X is a 2D array of shape N_Observations x N_Variables. There is an optional argument for max_n_clusters, set to 10 by default. The output is a dictionary with the estimate of true cluster centres (best_n) as well as the similarity score per number of clusters (scores_per_n) and the associated number of clusters (n_vec).
Package: tegda
Terrific EGDATegda creates an easy customizable visual representations of data for exploratory analysis.Free software: MIT licenseDocumentation:https://tegda.readthedocs.io.FeaturesUse several visualizationCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-04-27)First release on PyPI.Adding PyPI and TestPypi
Package: tegen
tegenTerminal game engine for Python, made by 7dLatest release version: v0.0 (29/8/21)Changelogs:https://tegen.readthedocs.io/en/latest/changelog.htmlDocumentation:https://tegen.readthedocs.io/en/latest/Why 'Tegen'?TerminalGameEngineUsageimporttegenfromblessed.keyboardimportKeystrokegame=tegen.Game()scene=tegen.Scene()classGameObj(tegen.objects.Sprite):direction=1defon_keyboard_press(self,g:tegen.Game,key:Keystroke):ifkey=="a":self.x+=1classGameText(tegen.objects.Text):defon_keyboard_press(self,g:tegen.Game,key:Keystroke):self.text+=keyifkey=='q':g.end()scene.add_object(GameObj(),"obj",0,0)scene.add_object(GameText("",back="ffa500"),"key",0,4)try:game.start(info_wait=1)game.add_keyboard_listener()game.load_scene(scene)exceptException:game.handle_error()Example tictactoe game
Package: teg-get-best-n
Estimate the optimal number of components in a PCA, using the SHEM procedure: Split-Half Eigenvector Matching.The get_n_components function estimates the true (or "generating") number of principal components. While scree/elbow/knee criteria for the eigenvalues curve is common, this is known to be a very fallible heuristic. The rationale of this alternative procedure is that true principal components should be found in random split halves of the data. The estimate is therefore based on measuring the similarity of eigenvectors between a set of split-halves; i.e., the procedure doesn't use the shape of the eigenvalue curve. Instead, a separation is made between components with high versus low split-half similarity.Detail: For an nd-array X, with shape == (nObservations, nVariables), a number of random splits are performed. For each split separately, a PCA is performed, via eigendecomposition of the covariance matrix of X. Each of the first split's eigenvectors is matched to the most-similar of the second split's eigenvectors. Similarity is measured via the dot product. The vector of similarities is sorted from high to low, and the vectors are averaged over all random splits. Finally, the optimal seperation between the high versus low similarities is determined by a basic between-within variance criterion. An estimated zero components is possible.Usage:O = teg_get_best_n.get_n_components(X)This returns a dictionary with the estimated number of components in O['nComponents'], as well as the eigenvalues (O['eigenvalues']) and eigenvectors (O['eigenvectors']).Example.py contains tests with simulated data to check how well the true number of latent variables, used to generate simulated data, is recovered.
Package: tegram
Telegram MTProto API Framework for PythonHomepage•Documentation•Releases•NewstegramElegant, modern and asynchronous Telegram MTProto API framework in Python for users and botsfromtegramimportClient,filtersapp=Client("my_account")@app.on_message(filters.private)asyncdefhello(client,message):awaitmessage.reply("Hello from tegram!")app.run()tegramis 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.Installingpip3installtegram###----------------- Abdo Asil -------------------Telegram : @ttccss
Facebook : https://www.facebook.com/AElasil
phone : +201025657503
Email :   almlmana20@gmail.com
Package: teg-regression
Linear regression, with p-values and AIC for the full model, p-values per coefficient, and tests of linear constraints on regression coefficients. The formulas are based on Bingham & Fry.The module uses only the modules numpy and math (built-in).Basic usageBy default, do not add an explicit intercept to the predictors. The intercept will be appended as an additional predictor, unless the argument explicit_intercept is given and set to True (for if you want to define constraints involving the intercept).Example, using simulated data:import numpy as np

import teg_regression

nObs = 300

nPred = 5

fix_coeffs = {0: 1, 3: 2} # Set to empty dict to simulate the null model.

X, y = teg_regression.sim_data(nObs, nPred, fix_coeffs=fix_coeffs, fix_intercept=20)

O = teg_regression.run_regression(X, y)The function returns a dictionary O with statistical values and prints out (unless report=False is set as an argument):Test of the model:F(5,294) = 0.849, p = 0.516

AIC constrained - free = -1.28e+03 (negative supports constraints).Tests per coefficient.Predictor 0: b = 0.00522, F(1,294) = 0.00842, p = 0.927

Predictor 1: b = 0.00324, F(1,294) = 0.00373, p = 0.951

Predictor 2: b = -0.0759, F(1,294) = 1.88, p = 0.172

Predictor 3: b = -0.093, F(1,294) = 2.69, p = 0.102

Predictor 4: b = 0.0182, F(1,294) = 0.115, p = 0.735

Intercept: b = 20.6, F(1,294) = 1.04e+05, p = 1.11e-16Linear constraintsLinear constraints specify hypotheses in terms of weighted sums of the coefficients, to be tested against the free model. E.g., assuming we have four predictors, the matrix equation[1 0 0 0]           [0][0 1 0 0] * betas = [0][0 0 1 0]           [0]would constrain the first three predictors to be zero.Similarly,[1 -1 0 0] * betas = 0Would constrain the first and second predictor to be equal.The Constraints argument, specifying the desired hypothesis, is a dictionary with the "coefficients" matrix, as a 2D array, and the "constants" vector, also as an array. The example below shows the Constraints setup to set two specific predictor-coefficients to 0.pred_to_test = [1, 2]

Constraints = {}

Constraints['coefficients'] = np.array([[0 for a in range(X.shape[1])] for newrow in range(2)]).reshape(2, X.shape[1])

Constraints['coefficients'][0][pred_to_test[0]] = 1

Constraints['coefficients'][1][pred_to_test[1]] = 1

Constraints['constants'] = np.array([0, 0])

O = teg_regression.run_regression(X, y, Constraints)
Package: tegridy-tools
tegridy-toolsDescription of directories:1) pretty_midiThis is a a slightly modified and streamlined version of the original pretty_midi which is compatible with noteseq and other Music AI packages/modules.2) noteseqThis is a modified and streamlined version of the original noteseq package that is stand-alone and independent of anything, including Tensorflow and the need to install any unique/fancy prerequisites.3) python-midiThis is the python-midi by @vishnubob for Python 3. Needs manual install but otherwise it works fine on the latest Python 3 versions.4) midoMirror copy of the latest release (as of 1-19-2021) and the source files.Description of modules:1) MIDI module version 6.7 by Peter BillamThis is a dependency/MIDI processing core module for TMIDI module (which is optional) as TMIDI module includes a copy of this module.2) TMIDI ModuleThis module is all about MIDI and Artifical Intellgence/Algorithmic Intelligence. All functions there specifically tailored to help to create/design Music AI systems and/or Algo Music systems.Fun and exciting things you might want to try:0) Stay on top of things with Reformer and now - Performer. Thanks to @lucidrains and TMIDI you can now try the latest and the greatest in SOTA transformers.https://github.com/lucidrains/performer-pytorchhttps://github.com/asigalov61/SuperPiano/blob/master/Super_Piano_6_Performer_GPU.ipynbhttps://github.com/lucidrains/reformer-pytorchhttps://github.com/asigalov61/SuperPiano/blob/master/SuperPiano5_(Reformer_TPU).ipynbhttps://github.com/huggingface/transformers/tree/master/notebooks#community-notebooks1) Try TMIDI with the Summarization implementations. Can music be summarized? Can this be used to make bridges between musical pieces?2) Try TMIDI with QA implementations. Does music have QA elements?3) Try TMIDI with Translator implementations. How music can be translated and permutated?!?4) Try TMIDI with Sentiment Analysis...etc.Enjoy and have fun :)Project Los AngelesTegridy Code 2021
Package: tehom
Tehom - Machine Learning on Underwater AcousticsThis package facilitates the creation of machine learning training data on
underwater acoustics.  While the raw data is available from Marine Cadastre and
Ocean Networks Canada (via theoncpackage),tehomtracks downloads that it conducts
and exposes more useful queries for data exploration and sampling.  e.g.:"What hydrophones outside Vancouver have data during April-June, 2017?""Where have I downloaded time/geo overlapping data?""Give me acoustic snippets as numpy arrays, labeled with whether a container
ship was close to the hydrophone that recorded the snippet."Requirementssqlite3available as a command line programCreateOcean Networks Canada accountand get your API tokenhereHow to:example.ipynb shows the basic data access and navivgation.  Before working with any ONC
data, you must once runpython-mtehomsave-token<token>Useful additional commands to start with:tehom.download_shipstehom.download_acousticstehom.downloads.get_ais_downloadstehom.downloads.get_onc_downloadstehom.downloads.get_audio_availabilitytehom.downloads.show_available_data(plotting, use Jupyter terminal)tehom.sample(in progress!)In addition tosave-token, when run as a module/CLI,tehomcan also download data.About"Tehom" is the Hebrew word for abyss, specifically the dark, chaotic oceans from which
order and the world emerged.
Package: tehran-stocks-ng
Tehran Stock MarketA python package that helps to access TCCIM stock price history, Using OOP InterfaceFeaturesDownload All stocks pricesDownload prices from a group (i.e ETFs or cars, etc.)Download Price history of one specific StockAfter first setup available offline.CommandLine InterfaceExport data to csv, excel or Stata(dta)Compatible withsqlalchemyCompatible withPANDASBased on lightsqlite0 - Installpipinstalltehran_stocks_ng1- InitializationFor first use you need initialize the database1-1 Command linets-getinit# Set up to sqlite database1-2 Pythonimporttehran_stocks# On first import package initialize itselfDuring initialization you will prompt for downloading all prices. if you answer yes it will download all prices, otherwise you can download data2- Download and Update prices2-1 Command linets-getupdate# update  all price , or download all if no price existts-getgroup34## 34 is the code for car's group.ts-getget_groups## get group name and group codes2-2 Pythonfromtehran_stocksimportget_all_price,Stocks,update_groupget_all_price()# download and(or) update all pricesupdate_group(34)#download and(or) update Stocks in groupCode = 34 (Cars)Stocks.get_group()# to see list of group codes3- Access DataTo access data you can useStockswhich is an customizedsqlalchemyobject, which helps you to find prices on an easy way.3-1 Search Stocksfromtehran_stocksimportStocks,db# You can use query to find stocksstock=Stocks.query.filter_by(name='كگل').first()#find by symbol(نماد)stock=Stocks.query.filter_by(code='35700344742885862').first()# find by code on tsetmc urlstock=Stocks.query.filter(Stocks.title.like('%گل گهر%')).first()# Search by titlestock_list=Stocks.query.filter_by(group_code=34).all()# find all Stocks in Khodrostock_list=Stocks.query.filter(Stocks.group_code.in_([13,34])).all()# all stocks in khodro and felezat## (Advanced)or run sql query using orm or raw sqldb.session.query(Stocks.group_code,Stocks.group_name).group_by(Stocks.group_code).all()db.session.execute('select group_code , group_name from stocks group by group_name').fetchall()Now easily access stock price and do whatever you want withpandasdataframes:# use data as a pandas dataframe>>>stock.df#idcodetickerdtyyyymmddfirsthighlowclosevaluevolopenintperopenlastdate02249135700344742885862Gol-E-Gohar.2004082912000.012021.012000.012000.01884160500015700002708D12000.012000.02004-08-29>>>stock.summary()Startdate:20040829Enddate:20190714Totaldays:2987>>>stock.update()# update stock price history# Export to your preferred format>>>stock.df.to_csv('price.csv')>>>stock.df.to_excel('price.xlsx')>>>stock.df.to_stata('price.dta')TodoCreate DatabaseDownload DataCommandLine SupportJalali Support