Saravanan666/pypi-org huggingface dataset:
Package: tsl-itunes-iap
itunes-iap~~~~~~~~~~.. image:: https://travis-ci.org/silverlogic/itunes-iap.svg?branch=master:target: https://travis-ci.org/silverlogic/itunes-iapQuick example-------------Create request to create a request to itunes verify api.>>> from itunesiap import Request, InvalidReceipt>>> request = Request(raw_data) # base64-encoded data>>> try:>>>     receipt = request.verify()>>> except InvalidReceipt as e:>>>     print 'invalid receipt'>>> print receipt.product_id # any other values are available as property!Practical useful values are: product_id, original_transaction_id, quantity, unique_identifierQuick example with password (Apple Shared Secret)-------------Create request to create a request to itunes verify api.>>> from itunesiap import Request, InvalidReceipt>>> request = Request(raw_data, password) # base64-encoded data>>> try:>>>     receipt = request.verify()>>> except InvalidReceipt as e:>>>     print 'invalid receipt'>>> print receipt.product_id # any other values are available as property!>>> print receipt.latest_receipt # Get the latest receipt returned by AppleVerification policy-------------------Set verification mode for production or sandbox api. Review mode also available for appstore review.>>> from itunesiap import Request>>> request = Request(raw_data)>>> with request.verification_mode('review'): # enable both production and sandbox for appstore review. 'production', 'sandbox' or 'review'>>>     receipt = request.verify()Workflow Shortcut----------------->>> def test_paid(original_transaction_id):>>>     if db.contains(original_transaction_id):>>>         raise CustomException # custom exception>>>>>> import itunesiap>>> try:>>>     response = itunesiap.verify(raw_data, test_paid)>>> except itunesiap.RequestError:>>>     pass>>> except CustomException:>>>     pass>>> # response is instance of `itunesiap.core.Response`Password support is merged from https://github.com/sportsy/itunes-iap
Package: tsloader
Time Series LoaderManage time series dataset to be served as torch dataloaders. The main features are:Load a csv file into dataloadersHandle train/val/test splitsNormalize and rescale each variableExample usageWe first build a PyTorch Dataset, then a pytorch-lightning DataModule.fromtsloaderimportTimeSeriesDataset,TimeSeriesDataModuleclassETDataset(TimeSeriesDataset):columns_inputs=["HUFL","HULL","MUFL","MULL","LUFL","LULL"]columns_targets=["OT"]train_start=datetime.datetime(year=2016,month=7,day=1)train_end=validation_start=datetime.datetime(year=2017,month=7,day=1)validation_end=test_start=validation_start+datetime.timedelta(days=28*4)test_end=test_start+datetime.timedelta(days=28*4)stride_size=24defpreprocess(self):self.df["datetime"]=self.df["date"].apply(pd.to_datetime)classETDataModule(TimeSeriesDataModule):Dataset=ETDataset# Setup the datamoduledatamodule=ETDataModule(dataset_path="datasets/ett/ETTh1.csv",forecast_size=48,batch_size=4)datamodule.setup()# Visualize the entire datasetdatamodule.visualize()# Load a sample from the training dataloaderdataloader=datamodule.train_dataloader()forcommands,observationsindataloader:...InstallationUsing pip, we can simply install the latest release. The main branch is considered stable.$pipinstallgit+https://git.zagouri.org/max/tsloaderIf you are from Accenta, then you probably want to install the Accenta-specific version of this package:$pipinstallgit+https://git.zagouri.org/max-accenta/tsloaderCompiling the documentationThe required libraries for compiling the documentation are listed indocs/requirements.txt. Once installed, run the following command from thedocs/folder:$makehtmlThe documentation is built to the folderdocs/build/html/, and can be viewed
by moving inside the directory and launching a temporary http server (for
instancepython -m http.server --directory docs/build/html).
Package: tslogs
tslogsA Python parser and visualizer for ThrottleStop logs.🚩 Table of ContentsOverviewWhat is ThrottleStop ?Why would you like to parse ThrottleStop Logs ?Enable Logging in ThrottleStopInstallingUsageLicense🗺️ Overviewtslogs is a Python library to parse, browse, export and visualizeThrottleStoplog files.📖 What is ThrottleStop ?ThrottleStopis a small application designed to monitor for
and correct the three main types of CPU throttling that are
being used on many laptop computers.Official Thread -hereComprehensive Guide -hereWhy would you like to parse ThrottleStop Logs ?TODO🏳️ Enable Logging in ThrottleStopSelect the `Log File` checkboxClick on `Options` button and Select the Folder where you want logs to be saved.🔧 InstallingInstalling withpipis the easiest:#pipinstalltslogs# if you have the admin rights$pipinstalltslogs--user# install it in a user-site directoryFinally, a development version can be installed from GitHub
usingsetuptools&pip$gitclonehttps://github.com/ashutoshvarma/tslogs$cdtslogs#pipinstall.💡 Usage1.tslogs- CLI tool$tslogs--helpusage: tslogs [-h] [--json | --plot ] [--dates START END] [--interval INTERVAL] [--smooth SMOOTH] [--output FILE] [--indent VALUE] [--quiet][--version]paths pathspositional arguments:paths                 One or more paths to log dir or log files.optional arguments:-h, --help            show this help message and exit--json, -j            dump all parsed log data.--plot, -p            Plot given the logs attributes (default: None). Allowed values are {multi, c0, clock_mod, chip_mod, battery_mw,cpu_temp, gpu_mhz, gpu_temp, vid, power}--quiet, -q           Run in silent mode--version, -v         show program's version number and exitFilter:--dates, -d START ENDDatetime range to filter (in ISO format, yyyy-mm-dd HH:MM:SS)Plot Options:--interval, -I INTERVALPlot data frequency in seconds (default: 60)--smooth, -S SMOOTH   Span interval for smoothing the graph, if data frequency is very high using increasing this with 'interval' canyield smooth graph (default: 2)Output:--output, -o FILE     Output file path, default is '-' (stdout)--indent VALUE        indent value for json output, default is 4a.) Print the summaryb.) Plot GraphsThis will plotcpu_temp,multi(clock speed in GHz),power(in W) andc0from logs between time 16:00 to 16:15 in2020-07-28.txttslogs .\tests\logs\2020-07-28.txt -p cpu_temp multi power --smooth 2 --interval 1 -d "2020-07-28 16:00:00" "2020-07-28 16:15:00"2.tslogs- Python ModuleTODOSeeparse_logs()andLogLineinparse.py. For more references
see the CLI implementation incli.py📜 LicenseThis library is provided under the open-sourceMIT license.
Package: tsl-prettyexc
prettyexc provides common exception representation to make human-readable exception in easy way.You can install the package from PyPI$ pip install tsl-prettyexcExamplePrelude::>>> from prettyexc import PrettyExceptionPut and get your arguments always::>>> class SimpleException(PrettyException):
...     pass
...
>>> e = SimpleException('any', 'plain', 'args', code=200, description='OK')
>>> raise e
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
__main__.SimpleException: "any","plain","args",code=200,description="OK"
SimpleException("any","plain","args",code=200,description="OK")
>>> print [e, e]
[<SimpleException("any","plain","args",code=200,description="OK")>, <SimpleException("any","plain","args",code=200,description="OK")>]Set default message::>>> class MessageException(PrettyException):
...     message = u'You should select a user'
...
>>> e = MessageException(user_id=10)
>>> raise e
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
__main__.MessageException: You should select a user
>>> print [e, e]
[<MessageException(user_id=10)>, <MessageException(user_id=10)>]Set message formatter::>>> class FormatException(PrettyException):
...     message_format = u'User {user_id} has no permission.'
...
>>> e = FormatException(user_id=10)
>>> raise e
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
__main__.FormatException: User 10 has no permission.
>>> print e.message
User 10 has no permission.Patch existing exceptions::>>> from prettyexc import patch
>>> class AnException(Exception): pass
...
>>> patch(AnException, PrettyException)
>>> raise AnException(status=404)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
__main__.AnException: status=404
Package: tslumd
Client and Server for TSLUMD Tally ProtocolsDescriptionThis project is intended to serve only as a library for other applications
wishing to send or receive tally information using theUMDv5.0 ProtocolbyTSL Products.  It is written in pure Python and
utilizesasynciofor
communication.LinksProject Homehttps://github.com/nocarryr/tslumdDocumentationhttps://tslumd.readthedocs.ioPyPIhttps://pypi.org/project/tslumdLicenseCopyright (c) 2021 Matthew Reid <matt@nomadic-recording.com>tslumd is licensed under the MIT license, please see LICENSE file for details.
Package: tslumen
A library for exploratory analysis of Time Series datatslumenhelps bring to light the key characteristics of your time
series data with rich, pre-canned artifacts, packed with charts and
statistical information. The primary goal of tslumen is to expedite and
bring consistency to how time series EDA is performed, allowing you to
uncover the fundamental aspects in seconds rather than hours or days.Key featuresPlatform agnostic, integrates nicely with your datascience workspaceBuilt on open source technology and researchHighly customizable and extensibleData (profiling results) completely detached from the visualsCan be executed from the command lineEfficient execution using parallel processingIncludes a great number of statistical information, including descriptive statistics statistical tests like KPSS or ADF, correlation, tsfeatures, etc.Various plots specifically tailored to time series analysisSelf-contained HTML report that can easily be shared with interested partiesFully interactive dashboard for a richer experience and detailed explorationSeehttps://hsbc.github.io/tslumen/for the complete documentation.InstallationFrom PyPI:pipinstall-UtslumenFrom source:# cd into tslumen after cloning the repomakeinstallExamplesRefer to theQuick Startpage of the documentation for a brief tour of the package.Complete example notebooks can be found on theUser Guidesection of the documentation.ContributingContributions to tslumen are welcome. Please see ourcontribution guidefor more details.
Package: tsma
tsma (time series model analyses)This package offers tools for analyzing models that generate multivariate time series as output.
The goal is to make the study of large models, especially those related to macroeconomic ABMs, more convenient.References:This package contains an implementation of two papers:Gross's model of 2022 used as example in the tutorials :https://doi.org/10.1016/j.ecolecon.2010.03.021Vandin & al statistical checking of 2021 :http://arxiv.org/abs/2102.05405To learn more about signature transform see Adeline Fermanian's thesis of (2021) orhttps://arxiv.org/pdf/1911.13211.pdfStructure:modelmodel: model class that serves as baselinegross2022: implementation of Gross's model of 2022collectoutput_management: This package includes functions for encoding and decoding parameters into databases, as well as storing and managing simulation data.iterators: functions used to gather simulations resultsdata_collect:  allows for the exploration of parameters through statistical analyses, with the option for automatic savingvisualsfig_constructors: assists in the creation of figures using Plotly, Seaborn, and Networkxfigures: creates figures using Plotly, Seaborn, and Networkxfig_management: manages the creation and saving of multiple figuresdashboards: for creating a dashboard on a local server, which allows for the visualization of a summary of a simulation with various sets of parameters.analysesstatistics: Means, Stationarity tests, ...vandin: implementation of statistical analyses taken from : Vandin & al (2021)metrics: classes used to create clustering approaches for time series clusteringclustering:multivariate time series unsupervised clusteringclustering scoring and selectionphase diagram visualization ( parameters space projection of clustering results )comparison of clustersbasicstransfers: functions used to divide and join strings, lists and dictionariestext_management: name conversions, title creations ...Tutorials:Find tutorials at :https://github.com/S-bazaz/tsmatsma1: how to format your model and use the saving system (save, overwrite, query ...)tsma2: how to use visualizations ( simple plots, dashboards ...)tsma3: vandin & al statistical checking and data collection (transient analysis, parameters exploration ...)tsma4: time series clustering ( clusters computation, clustering selection, phase diagram ...)tsma5: clustering comparison ( visualization per cluster, dendrograms, Jaccard coefficients network plots ...)
Package: ts-machine
ts_machinets_machine はニコニコ生放送のタイムシフト予約を自動化するためのツール、でした。
2021年3月31日のコンテンツ検索 API 終了に伴い、このツールは使用できなくなりました。注意点niconico の利用規約niconico コンテンツ検索APIガイドのAPI利用規約ニコニコ生放送利用規約niconico規約サイトご利用にあたってライセンスLICENSEを確認してください。
Package: tsmart
Python library for T-Smart smart thermostatsThis library provides access toT-Smart smart thermostats.fromtsmartimportTSmartimportasyncioasyncdeffind_devices():devices=awaitTSmart.async_discover()print("Found%ddevices"%len(devices))fordeviceindevices:awaitdevice.async_get_status()print("Thermostat:%s"%device.name)print("Temperature:%.1f°C"%device.temperature)asyncio.run(find_devices())
Package: tsm-core-models
Welcome!Hi there! Thank you for collaborating with The Social Matrix Corp to help us build worlds that are sure to blow peoples pants away!Errors you might encounter if you edit this libraryYou will have to reinstall this library to all of the TSM projects if you make changes to it.
Please go to each projects pipenv interpreter and run `pipenv uninstall ../tsm_models\dist\tsm_core_models-0.1.0-py3-none-any.whl && pipenv --clear && pipenv install ../tsm_models\dist\tsm_core_models-0.1.0-py3-none-any.whl to do this. Otherwise you will encounter issues with has mismatches.
Package: tsml-eval
tsml-evaltsml-evalcontains benchmarking and evaluation tools for time series machine learning
algorithms.The current release oftsml-evalis v0.2.0.Installationtsml-evalis available on PyPI and can be installed via pip:pip install tsml-evalMore information available on ourdocumentation.AcknowledgementsThis work is supported by the UK Engineering and Physical Sciences Research Council
(EPSRC) EP/W030756/2
Package: tsmppt60-driver
StatusTS-MPPT-60 driver moduleThis is python driver module to get the following status of TS-MPPT-60.Amp HoursArray CurrentArray VoltageBattery TemperatureBattery VoltageCharge CurrentHeat Sink TemperatureKilowatt HoursOutput PowerSweep PmaxSweep VmpSweep VocTarget VoltageRequirementrequestsHow to install./setup.py build./setup.py installHow to useSystemStatus class object is iterator containing all live status data of
TS-MPPT-60. Try the following line.print(SystemStatus("192.168.1.20").get())The result is like following.{'Amp Hours': {'group': 'Counter', 'unit': 'Ah', 'value': 18097.9},
 'Array Current': {'group': 'Array', 'unit': 'A', 'value': 1.4},
 'Array Voltage': {'group': 'Array', 'unit': 'V', 'value': 53.41},
 'Battery Voltage': {'group': 'Battery', 'unit': 'V', 'value': 23.93},
 'Charge Current': {'group': 'Battery', 'unit': 'A', 'value': 3.2},
 'Heat Sink Temperature': {'group': 'Temperature', 'unit': 'C', ...},
 'Kilowatt Hours': {'group': 'Counter', 'unit': 'kWh', 'value': 237.0},
 'Target Voltage': {'group': 'Battery', 'unit': 'V', 'value': 28.6}}The above data is limited information. You can disable the limitter by
setting False to the second argument as SystemStatus() class.print(SystemStatus("192.168.1.20").get(False))The result is like following.{'Amp Hours': {'group': 'Counter', 'unit': 'Ah', 'value': 18097.8},
 'Array Current': {'group': 'Array', 'unit': 'A', 'value': 1.3},
 'Array Voltage': {'group': 'Array', 'unit': 'V', 'value': 53.41},
 'Battery Temperature': {'group': 'Temperature', 'unit': 'C', ...},
 'Battery Voltage': {'group': 'Battery', 'unit': 'V', 'value': 24.01},
 'Charge Current': {'group': 'Battery', 'unit': 'A', 'value': 3.2},
 'Heat Sink Temperature': {'group': 'Temperature', 'unit': 'C', ...},
 'Kilowatt Hours': {'group': 'Counter', 'unit': 'kWh', 'value': 237.0},
 'Output Power': {'group': 'Battery', 'unit': 'W', 'value': 76.0},
 'Sweep Pmax': {'group': 'Array', 'unit': 'W', 'value': 73.0},
 'Sweep Vmp': {'group': 'Array', 'unit': 'V', 'value': 53.41},
 'Sweep Voc': {'group': 'Array', 'unit': 'V', 'value': 60.05},
 'Target Voltage': {'group': 'Battery', 'unit': 'V', 'value': 28.6}}
Package: tsmpy
IntroductionAn implementation of orthogonal drawing algorithm in PythonMain idea comes fromA Generic Framework for the Topology-Shape-Metrics Based LayoutHow to run codeInstall requirementspipinstall-rrequirements.txtUsage# in root dirimportnetworkxasnxfromtsmpyimportTSMfrommatplotlibimportpyplotaspltG=nx.Graph(nx.read_gml("test/inputs/case2.gml"))# initial layout, it will be converted to an embeddingpos={node:eval(node)fornodeinG}# pos is an optional, if pos is None, embedding will be given by nx.check_planarity# use linear programming to solve minimum cost flow programtsm=TSM(G,pos)# or use nx.min_cost_flow to solve minimum cost flow program# it is faster but produces worse result# tsm = TSM(G, pos, uselp=False)tsm.display()plt.savefig("test/outputs/case2.lp.svg")plt.close()Examplecase1case2bend casegrid caseRun tests# show helppythontest.py-h# run all testspythontest.py# run all tests in TestGMLpythontest.pyTestGMLPlaygroundTry editing original case2 graph withyedRequirements for input graphPlanarConnectedMax node degree is no more than 4No selfloopFeaturesUsing linear programing to solve minimum-cost flow problem, to reduce number of bendsTODOCleaner codeMore commentsFix overlaySupport node degree > 4Support cut-edges
Package: tsmtool
Tarsnap Status Monitor Reporting ToolTarsnap Status MonitorIntroductionThe most excellent backup serviceTarsnapprovides a clean
and functional interface for managing one's account.  This tool connects to that web
interface and queries data, producing a JSON-formated report.  The current balance is
read, and an estimated monthly cost is calculated based on the site's daily cost data.Example OutputHere's an example of output from the author's tarsnap account:(tsmtool) mkrueger@beaker:~/src/tsmtool$ tsmtool rstms
{
  "balance": 32.51,
  "account": "mkrueger@rstms.net",
  "monthly_cost": 23.51
}ConfigurationA config file may be used for account data, or the email account / password may be passed as command line options.
The config file is a whitespace delimited text file with one line per account.
Each line has these fields:~/.tsmtool:ACCOUNT_NAME    EMAIL_ADDRESS   PASSWORDFree software: MIT licenseDocumentation:https://tsmtool.readthedocs.io.CreditsTarsnapThis package was created with Cookiecutter andrstms/cookiecutter-python-cli, a fork of theaudreyr/cookiecutter-pypackageproject template.audreyr/cookiecutteraudreyr/cookiecutter-pypackagerstms/cookiecutter-python-cli
Package: tsne
Python-TSNEPython library containing T-SNE algorithms.Note:Scikit-learn v0.17includes TSNE algorithms and you should probably be using that instead.InstallationRequirementscblasoropenblas.
Tested version is v0.2.5 and v0.2.6 (not necessary for OSX).From PyPI:pip install tsneFrom conda:conda install -c maxibor tsneUsageBasic usage:fromtsneimportbh_sneX_2d=bh_sne(X)ExamplesIrisMNISTword2vec on presidential speechesvia@prateekpg2455AlgorithmsBarnes-Hut-SNEA python (cython) wrapper forBarnes-Hut-SNEaka fast-tsne.I basically tookosdf's codeand made it pip compliant.Additional resourcesSeeBarnes-Hut-SNE(2013), L.J.P. van der Maaten. It is available onarxiv.
Package: tsnecuda
tsnecuda provides an optimized CUDA implementation of the T-SNE algorithm by L Van der Maaten. tsnecuda is able to compute the T-SNE of large numbers of points up to 1200 times faster than other leading libraries, and provides simple python bindings with a SKLearn style interface:#!/usr/bin/env python

from tsnecuda import TSNE
embeddedX = TSNE(n_components=2).fit_transform(X)For more information, check out the repository athttps://github.com/rmrao/tsne-cuda.
Package: tsne-mp
Python TSNE implementation utilizing openmp for performanceThis is based on the10XDev/tsnefork of L.J.P. van der Maaten BH-tSNE implementation.It has fixes to allow this to run in Python 3 and performance has been significantly
increased with OpenMP parallelism. (see:tsne-perf-test)Note:WhileScikit-learn v0.17has a tsne implementation,
this implementation performs significantly faster than scikit-learn's. If you need speed, use this.AlgorithmsBarnes-Hut-SNEA python (cython) wrapper forBarnes-Hut-SNEaka fast-tsne.We forked 10XDev's implementation and openmp enabled the code.InstallationThis library has been added to pypi as tsne-mppip install tsne-mpIt requires openmp support.OSX -brew install libomplinux - 'sudo apt-get install libgomp1'Windows - Included with Visual Studio C++UsageBasic usage:from tsne import bh_sne
X_2d = bh_sne(X)Or, the wheels also contain an executable that can be used from the command-line as described
inthe original project.ExamplesIrisMNISTword2vec on presidential speechesvia@prateekpg2455More InformationSeeBarnes-Hut-SNE(2013), L.J.P. van der Maaten. It is available onarxiv.
Package: tsnet
TSNet performs transient simulation in water networks using Method of Characteristics (MOC).Free software: MIT licenseGitHub:https://github.com/glorialulu/TSNet.gitDocumentation:https://tsnet.readthedocs.io.OverviewA number of commercial software for transient simulation in water
distribution systems are available in the market; however, the use of
these software for research purposes is limited. The major restriction is
due to the fact that the programs are packed as black boxes, and the source
code is not visible, thus prohibiting any changes, including modification of
existing and implementation of new elements, in the source code.
Therefore, the authors find it imperative to develop an open source package
rendering easiness for interaction, modification, and extension.FeaturesTSNet is a Python package designed to perform transient simulation in water
distribution networks. The software includes capabilities to:Create water network models based on .inp filesGenerate transient events by operating valves and pumpsAdd disruptive events including pipe bursts and leakagesAdd surge protection devicesChoose between steady,quasi-steady, and unsteady friction modelsPerform transient simulation using MOC methodVisualize resultsFor more information, go tohttps://tsnet.readthedocs.io.VersionTSNet is a ongoing research project in the University of Texas at Austin.
The current version is 0.2.2, which is still a pre-release.ContactLu Xing, the University of Texas at Austin,xinglu@utexas.eduLina Sela, the University of Texas at Austin,linasela@utexas.eduDisclaimerNo warranty, expressed or implied, is made as to the correctness of the
results or the suitability of the application.Cite TSNetTo cite TSNet, use one of the following references:Xing, Lu, and Lina Sela. “Transient simulations in water distribution networks:
TSNet python package.” Advances in Engineering Software 149 (2020): 102884.LicenseTSNet is released under the MIT license. See the LICENSE.txt file.History0.1.0 (2019-08-15)First release on PyPI.0.1.1 (2019-09-21)0.1.2 (2020-01-20)Fixed bugs about dead end and single pipe.Added plot and change detection methods.0.2.0 (2020-4-23)Added quasi-steady and unsteady friction model.Added open and closed surge tanks.Added demand-pulse simulation.Updated docs.0.2.1 (2020-09-09)Fixed minor bugs about valve default settings.Updated documentation.Updated WNTR compatibility.0.2.2 (2020-09-24)Updated WNTR compatibility.0.2.3 (2021-11-12)Only support WNTR version up until 0.3.00.2.4 (2022-06-17)fix minor issues regarding reporting messagesOnly support WNTR version up until 0.2.3.0.3.0 (2023-02-22)Support WNTR version 1.0.0Test on python 3.10 and 3.11.Drop supports for Python 3.6 and 3.7