Saravanan666/pypi-org huggingface dataset:
Package: tsuru-circus
tsuru-circustsuru-circus is a collection of circus extensions for tsuru.Installation$ pip install tsuru-circusUsageTo use the tsuru stream to send circus log to tsuru you need to set the tsuru Stream in the circus conf:stderr_stream.class = tsuru.stream.StreamdevelopmentSource hosted atGitHubReport issues onGitHub IssuesPull requests are very welcomed! Make sure your patches are well tested.running the tests$ python setup.py testcommunityirc channel#tsuru channel on irc.freenode.netLICENSEUnless otherwise noted, the tsuru-circus source files are distributed under the BSD-style license found in the LICENSE file.
Package: tsuru-feaas
Deploying the APIFirst, let’s create an app in tsuru, from the project root, execute the following:% tsuru app-create varnishapi python
% git remote add tsuru git@remote.sbrubles.com
% git push tsuru masterThe push will return an error telling you that you can’t push code before the
app unit is up, wait until your unit is in service, you can check with:% tsuru app-listWhen you get an output like this you can proceed to push.+-------------+-------------------------+--------------------------------------+
| Application | Units State Summary     | Address                              |
+-------------+-------------------------+--------------------------------------+
| your-app    | 1 of 1 units in-service | your-app.sa-east-1.elb.amazonaws.com |
+-------------+-------------------------+--------------------------------------+Now if you access our app endpoint at “/” (you can check withtsuru app-infocmd) you should get a 404, which is right, since the API does not respond
through this url.Alright, let’s configure the application, it’ll need to talk with EC2 API, and
it does so by using environment variables. Here’s what you need:% tsuru env-set EC2_ENDPOINT=https://ec2.amazonaws.com EC2_ACCESS_KEY=your-access-key EC2_SECRET_KEY=your-secret-keyIn order to get Varnish running, you can provide an AMI or a list of packages
to install via user data. The AMI is specified via theAMI_IDenvironment
variable, while the packages are specified by theAPI_PACKAGESenvironment
variable. Users may specify both variables.% tsuru env-set AMI_ID=your-ami-id API_PACKAGES=varnish vim-noxYou can also use a custom user-data url viaUSER_DATA_URLintead ofAPI_PACKAGES. In this case, the return content body should containVARNISH_SECRET_KEYword which will be replaced by proper varnish
secret on remote machine.% tsuru env-set AMI_ID=your-ami-id USER_DATA_URL=http://server/custom-user-dataUsers may also specify a subnet for running with VPC. You can specify the
subnet ID via theSUBNET_IDenvironment variable.% tsuru env-set SUBNET_ID=your-subnet-idOne more thing: this API will use MongoDB to store information about instances,
the MongoDB endpoint and the database name is also controlled via environment
variables:We’re done with our API! Let’s create the service in Tsuru.Creating the ServiceFirst you’ll have to change themanifest.yamlfile located at the project
root of our application. Change the production endpoint to point to the
application address, your yaml should look like this:id: varnish
password: some123
endpoint:
  production: varnishapi-endpoint.comNow let’s tell tsuru it needs to registrate a new service, from the project
root run, usingcrane:% crane create manifest.yamlAnd we’re done!
Package: tsutsuji-trackcomputer
Tsutsuji TrackComputerBve trainsim 5/6向けマップファイルの制作支援Pythonスクリプトです。
全ての軌道を自軌道構文で記述し、一つの軌道を基準とした他軌道構文に変換するのが主な機能です。
まだ開発途上ですが、軌道データ作成に役立つ測量機能も付属しています。インストールインストールするには、Python 3をインストールしてからPowershellで次のコマンドを実行してください。
Tsutsuji本体と、動作に必要なパッケージが自動でインストールされます。pip install tsutsuji-trackcomputerなお、https://konawasabi.riceball.jp/2022/06/01/tsutsuji-kobushi-installguide/にて、Python3のセットアップを含めたインストール手順の説明をしていますので、参考にしてもらえればと思います。インストール済みのTsutsuji, Kobushiをバージョンアップする際は、次のコマンドを実行してください。pip install --upgrade tsutsuji-trackcomputer kobushi-trackviewer動作に必要な環境Python 3Kobushi trackviewerVer1.1.4以降numpymatplotlibscipylarkttkwidgetsrequests起動Tsutsujiは次のコマンドで起動できます。python -m tsutsuji読み込むcfgファイルを予め指定するときは、以下のコマンドを実行します。python -m tsutsuji hoge.cfg非対話モード-nオプションをつけて実行すると、Tsutsujiを非対話モードで実行できます。非対話モードでは、指定したcfgファイルの内容に基づいて他軌道データを生成し、そのまま終了します。このときGUIは起動しません。python -m tsutsuji -n hoge.cfgDocumentshttps://konawasabi.github.io/tsutsuji-trackcomputer/LicenseApache License, Version 2.0重要事項本プログラムの著作権はkonawasabi(以下、作者)が有します。本プログラムについて作者はいかなる保証もせず、またプログラムを実行して生じた結果についての責任を負いません。Apache Licence, Version 2.0に従う限り、本ソフトウェアの改変、再配布を自由に行うことができます。Acknowledgements緯度経度->直交座標への変換ルーチンとして、下記URLにて公開されているsw1227氏のコードを使用させていただきましたhttps://qiita.com/sw1227/items/e7a590994ad7dcd0e8abContactAuthor: KonawasabiMail:webmaster@konawasabi.riceball.jpWebsite:https://konawasabi.riceball.jp/
Package: tsv2dict
Python support for linear TSV filesFree software: MIT licenseWhat is Linear TSVIn contrast to Excel’s TSV dialect, linear TSV is line-based.“But hey”, I hear you say,“isn’t TSV always line-based?”. Well, the issue arises when a cell contains a tab or newline character. In excel’s TSV format, that cell is surrounded by quotes and the entry is continued on the next line. Now you have:entries spanning several linesquotes that need to be ignored (”)quotes that are escaped by doubling them (“”)Since entries can span several lines, many naïve file manipulations aren’t possible:Taking the first 50 entries of a dataset:head -n 50 customers.tsvFiltering entries:grep “Zürich” customers.tsvSorting the entries alphabetically:sort customers.tsvAll of this can be prevented if you simply:escape tabs:\tescape newlines:\nescape carriage returns:\rescape backslashes:\\Lastly, linear TSV can also encodeNoneas\N.That’s linear tsv in a nutshell.Installationpip install tsv2dictYou can also install the in-development version with:pip install https://github.com/nkurmann/tsv2dict/archive/master.zipDocumentationhttps://tsv2dict.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.3 (2021-03-04)Converters now won’t attempt to convert None.0.0.2 (2021-03-03)Converters can convert rows retrieved into types other than strings.0.0.1 (2021-03-02)(De)Serialize None as N, consistent with SQL.0.0.0 (2021-02-28)First release on PyPI.
Package: tsv2json
Convert TSV file to JSONInstallationpipinstalltsv2jsonExamplestsv2jsonexamples/demo.tsv

tsv2jsonexamples/demo.tsv-i2tsv2jsonexamples/demo.tsv-i2-oout.json

tsv2jsonexamples/demo.tsv-i2-l3tsv2jsonexamples/demo.tsv-c'name'-H0tsv2jsonexamples/demo.csv-s,
Package: tsv2sqlite
tsv2sqlitePackage for parsing a tab-delimited data file and loading the lines into SQLite3 databaseFree software: GNU General Public License v3Documentation:https://tsv2sqlite.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-11-12)First release on PyPI.
Package: tsv-calendar
Example.tsv- Day(0-6)	Name	Desc	Starts	Ends
-
0	Coding	Python Package	1520	1620
1	Coding	Java	1400	1600
- Dash will get ignored to allow comments in TSV File
5	Grass	Touch Grass	1600	1700Example.pyimporttsv_calendartsv=tsv_calendar.TSV_Read("Example.tsv")if(tsv.current()):#If there is none, tsv.current() returns False.print(f"Current Event:{tsv.current(tsv_calendar.GET.NAME)}")#Prints Name of current Event.if(tsv.next(Entire_Week=True)):#Standard is False. If set to True, it not only checks for the next Event Today, but for the next Event this Week.print(f"The Next Event is{tsv.next(tsv_calendar.GET.NAME,True)}. It starts in{tsv.next(tsv_calendar.GET.START_TIMER,True)}!")#Prints Name and Time till next Event.
Package: tsview
TSVIEWFeaturesThis is atshistoryplugin which provides aviewsubcommand to thetshcommand.It works like this:$tshviewpostgres://babar:celeste@data_server:5432/*Runningonhttp://192.168.56.1:5678/(PressCTRL+Ctoquit)192.168.56.1--[16/Jun/201717:23:25]"GET /tsview HTTP/1.1"200-192.168.56.1--[16/Jun/201717:23:25]"GET /tsplot? HTTP/1.1"200-At the same time, a browser tab opens, and we can select some series:For each series we can get it complete history (its versions at a
given date range):This applies also to formulas if you usetshistory_formula!We also get a browser for the whole historylog, by a
slight modification of the url, fromhttp://192.168.56.1:5678/tsviewtohttp://192.168.56.1:5678/tsviewlog:Formula editor testsMake sure the relevant node modules are there:$npminstallyargs--exact$npminstallcsv-parse--exactRun the tests.$makevalidation

elmmake--outputelm/FormulaParserValidation/tsformula_elm_parser.js--optimize\elm/FormulaParserValidation/Main.elm
Success!FormulaParserValidation.Main───>elm/FormulaParserValidation/tsformula_elm_parser.js

...

--------------------------------------------------------------------------------
test_comma_on_str[OK]ReturnTypes:Number,Series,TimestampOperatorselector:SeriesOperator:*ArgumentInputoperatorselector:NumberExpType:Number=0.0572ArgumentExpType:Union[Number,Series]Operatorselector:SeriesOperator:seriesArgumentExpType:SearchString="x"Options:OptionalArgumentfillExpType:Union[String,Number]ExpType:String="a b"OptionalArgumentlimitExpType:IntOptionalArgumentweightInputoperatorselector:NumberExpType:Number
--------------------------------------------------------------------------------Going into elm/FormulaParserValidation:$./tsformula-elm-parser

tsformula-elm-parser<cmd>[args]Commands:tsformula-elm-parsercheckCheckJSONspecificationtsformula-elm-parserinspectInspectJSONspecificationtsformula-elm-parserparse[catalog]ParseformulaCSVwithname,codeheader

Options:--versionShowversionnumber[boolean]--spec,-sJSONspecificationfile[required]--helpShowhelp[boolean]Missingrequiredargument:specYou can get the real spec file fromhttp://<instance>/spec.
You can get the formulas lilst fromhttp://<instance>/downloadformulas.
Package: tsvis
ChineseTS-VIS(Tianshu Visualization) is a visualization tool kit ofTianshu AI Platform.,
which support visualization of the most popular deep learning frameworks, such as TensorFlow, PyTorch, OneFlow, etc.Document (Chinese): https://feyily.github.io/tsvis-document/HighLightsFramework-independent, support visualization of the most popular deep learning frameworks, such as TensorFlow, PyTorch, OneFlow, etc.Faster response speedSupport the visualization of large-scale dataSupport real-time visualization during trainingSupport embedding sample visualizationSupport neural network exception visualizationFeaturesGraph: Visualize neural network structure, including computational graph and structure graphScalar: Visualize arbitrary scalar data includingaccuaryandlossMedia: Visualize media data including images, text, and audioDistribution: Visualize the distribution of weights, biases, etc. in neural networkEmbedding: Visualize arbitrary high-dimensional data through dimensionality reduction algorithmHyperparameter: Visualize neural network indicators under different hyperparametersException: Map neural network tensor data to two dimensions, visualize tensor data statisticsCustom: Move the charts inScalar,Media, andDistributionto this module for comparison and viewingInstallWe provide two installation methods: install by pip and install from source.
No matter which method you pick, you need to make sure that your Python version is 3.6 or higher,
otherwise please upgrade Python first.Install by pippip install tsvisInstall from sourceTS-VIS adopts the architecture of separation of frontend and backend,
so you need to build the frontend and backend separatelyBuild frontend from source:cd webappInstall dependencies firstnpm installPackage frontend to generate static filesnpm run buildBuild backend from source:To install the backend, you need to first move the static files generated by previous step totsvis/server/frontendfolderThen install the Python dependency packagesetuptoolspip install setuptoolsRunsetup.pyto install TS-VIS to your Python environmentpython setup.py installRunAfter installation, you can run the following command. If the version information is output in the console, it means that you have installed TS-VIS correctly.tsvis -vThen you can run the visualization with the following commandtsvis --logdir path/to/logdir/By default, the visualization service will start athttp://127.0.0.1:9898, open the browser to access the visualization content.
Package: tsvisualize
TimeSeriesVisualizationVisualization of Time-Series with the Matrix Profile and Multidimensional Scaling - a Python Implementation.IntroductionMultidimensional scaling is an algorithm that projects a set of objects represented by their distance matrix into a lesser dimensional space, such that the pairwise distance is preserved as much as possible. If we project onto a 1-dimensional or a 2-dimensional space, we can even plot the resultant projections, and visually understand the similarity between these objects.At a first glance, this seems applicable to time-series as well, where we want to capture similar regions across the entire time-series.However, since we do not know apriori where our regions of interest are, we can simply select all subsequences of a given length from the original time-series.Let's try this out!ReferencesMatrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series.Chin-Chia Michael Yeh, Helga Van Herle, and Eamonn Keogh. IEEE ICDM 2016.
Package: tsvn
README.TXT~~~~~~~~~~tsvn - a commandline interface to TortoiseSVN~~~~------------------------------~-------~~~Commandline and graphical clients to SCM systems have specific strengths.Terminal programs are often executed quicker, and their output can beredirected for further processing; graphical clients are often more convenientwhen it comes e.g. to browsing the logs.  Thus, many developers will want touse both.  Unfortunately, their commandline interfaces are totally different.This package provides a single tool called "tsvn" which eases the commandlineusage of TortoiseSVN by mimicking the svn commandline syntax and semantics asclosely as possible.Of course, there istsvn help                (like "svn help")to give an overview of the implemented commands, andtsvn help <command>      (like "svn help <command>")to tell about the usage of <command>.The task is non-trivial, because the usage of the graphical and the commandlineclient is quite different, e.g. when it comes to Subversion properties(svn:eol-style and the like).While the interface is still incomplete, it is already useful:  Use ...tsvn log                  to get the history for the current workingdirectorytsvn ci                   to see your current changes to the workingdirectory, select and deselect entries, viewdiffences by right-clicking files,and finally commitSome commands were added (which are not present in svn), e.g. ...tsvn explore              to explore the repository, starting at the URL ofthe current working copytsvn ce <FILENAME>        to call the conflicts editor.(and more.)Consider a Windows commandline (CMD.EXE, most likely) in the same workingdirectory (via Samba, for example) like the Linux shell where you use 'svnstatus'.  You can feed the relative paths you get from the Linux system (withforward slashes) into tsvn without any conversion.Things to do~~~~~~~~~~~~- TortoiseSVN expects message files or file lists in UTF-16 encoding which israther uncommon for normal file editor usage.  For -F/--file arguments, anappropriate temporary file will need to be created.- ...Unsolved issues---------------Often the commandline is used on a virtual Linux machine, while TortoiseSVN isa Windows(tm) explorer extension.  To handle this situation, some serverinfrastructure would be needed to hand over the tsvn command to a serverprocess, running on the Windows host, which in turn executes TortoiseSVN.The given tool will work only for situations where commandline shell andTortoiseSVN run on the same operating system instance.A Linux (KDE, GNOME ...) shell extension will very likely use completelydifferent commandline arguments; for Linux usage, a "gsvn" (or similar)executable might be useful (like gvim/vim).
Package: tsw
Tournament SoftwareTournament Software python libraryDescriptionInstallationTo install the latest version:pipinstalltswTo install the library from GitHub:pipinstallgit+https://github.com/userlerueda/tournamentsw.gitUsage GuideUsing via CLIGetting Events$tsweventsB88B8B48-E97D-45EF-BCB5-D2B393B0EAC6
+------+--------------------+---------+-----------+|id|Name|Draws|Entries||------+--------------------+---------+-----------||7|HASTA14AÑOS|1|18||2|INTERMEDIAMIXTOA|1|32||3|INTERMEDIAMIXTOB|1|22||1|PRIMERA|1|11||4|SEGUNDAMIXTO|1|32||8|TERCERADAMAS|1|21||5|TERCERAMIXTOA|1|20||6|TERCERAMIXTOB|1|23|+------+--------------------+---------+-----------+Getting Draws for a Tournament❯tswdrawsB88B8B48-E97D-45EF-BCB5-D2B393B0EAC67+------+---------------+--------+-------------+-----------------+---------------+|id|Draw|Size|Type|Qualification|Consolation||------+---------------+--------+-------------+-----------------+---------------||11|HASTA14AÑOS|32|Elimination|No|nan|+------+---------------+--------+-------------+-----------------+---------------+Getting Matches for a DrawtswmatchesB88B8B48-E97D-45EF-BCB5-D2B393B0EAC611+-----------------------+------------------+--------------------+-----------------+--------------------+------------------------------+|Timestamp|WinnerCountry|WinnerName|LoserCountry|LoserName|Score||-----------------------+------------------+--------------------+-----------------+--------------------+------------------------------||Sat5/14/20222:00PM|COL|GabrielEcheverri|COL|RafaelSanint|[[6,1],[6,0]]||Sat5/14/20222:00PM|COL|JuanMartinSiegel|COL|EmmaVilla|[[3,6],[6,2],[10,7]]||Sun5/15/20222:00PM|COL|JuanMarulanda|COL|GabrielEcheverri|[[4,6],[6,3],[10,7]]||Sun5/15/20222:00PM|COL|MatiasLopez|COL|VioletaGonzalez|[[6,0],[6,1]]||Sun5/15/20222:00PM|COL|ManuelOrozco|COL|SamuelEscandon|[[6,3],[6,1]]||Sun5/15/20222:00PM|COL|SimonScanzaniIza|||[[6,3],[6,2]]||Sun5/15/20224:00PM|COL|PamelaDuque|COL|EmilianoFernandez|[[6,4],[5,7],[10,8]]||Sun5/15/20224:00PM|COL|MarianaGuerrero|COL|PabloRueda|[[6,3],[4,6],[10,6]]||Sun5/15/20224:00PM|COL|MatiasCastro|COL|JacoboCastro|[[6,0],[6,0]]||Sun5/15/20224:00PM|COL|PedroGutierrez|COL|JuanMartinSiegel|[[3,6],[6,4],[10,0]]||Sat5/21/20222:00PM|COL|JuanMarulanda|COL|MatiasLopez|[[6,4],[6,4]]||Sat5/21/20222:00PM|COL|ManuelOrozco|COL|SimonScanzaniIza|[[6,1],[6,3]]||Sat5/21/20222:00PM|COL|PamelaDuque|COL|MarianaGuerrero|[[6,3],[6,3]]||Sat5/21/20222:00PM|COL|PedroGutierrez|COL|MatiasCastro|[[7,5],[1,6],[10,6]]||Sun5/22/20222:00PM|COL|JuanMarulanda|COL|ManuelOrozco|[[7,6,3],[2,6],[10,3]]||Sun5/22/20222:00PM|COL|PedroGutierrez|COL|PamelaDuque|[[6,3],[6,3]]||Sat5/28/20223:00PM|COL|JuanMarulanda|COL|PedroGutierrez|[[6,3],[6,3]]|+-----------------------+------------------+--------------------+-----------------+--------------------+------------------------------+Using as a LibraryCreditsLicenseThis project is covered under the terms described inLICENSE.ContributingSee theContributingif you want to contribute.ChangesSee theChangelogfor a full list of changes.
Package: tswift
This repo is actually not directly related to Taylor Swift.  It’s just a simple
Python API for getting lyrics from MetroLyrics.  Here is how easy it is:fromtswiftimportArtistimportrandomtswift=Artist('Taylor Swift')song=random.choice(tswift.songs)print(song.format())SetupThis package depends onlxml,requests, andgoogle. These should be
installed when you install this package from pip:pip install tswiftAfter installing, you can also print a random Taylor Swift song lyric with the
command:tswiftCLIThe module comes with a simple command line interface. By default, it will
display a random song by Taylor Swift. You can specify an artist like this:tswift 'Lynyrd Skynyrd'You can choose a particular song:tswift 'Lynyrd Skynyrd' -s FreebirdThere is also a “lyric search mode”, which allows you to search for a song by
lyrics, e.g.:tswift -l 'I would walk 500 miles'

Im Gonna Be 500 Miles
Proclaimers
---------------------

When I wake up, well I know I'm gonna be,
I'm gonna be the man who wakes up next you
...APIArtist classThe constructor takes a single argument, the artist name. This name will be
“slugified” in order to use it within URLs. This process involves replacing
spaces with hyphens, and making everything lowercase. If this is not sufficient
for your particular artist, you’ll need to provide a pre-slugified version of
their name.songs- a list of Song instances by this artist. This will callload()if it hasn’t been called yetname- the artist’s slugified nameload()- populates thesongslistSong classThe constructor can be called in two ways. In the first, you provide a
Metrolyrics URL, and the class will infer the artist and song title:s=Song(url='url here')In the second way, you provide a title and artist, which will be slugified.s=Song('Taylor Swift','Love Story')The attributes are:lyrics- a string. Accessing this will callload()if not yet loadedtitle- de-slugified title of songartist- de-slugified artist nameload()- loads the lyricsformat()- returns the lyrics, with a header that includes the title and
artist.The static methodSong.find_song(lyrics)takes a string with a search term,
and performs a Google search. It returns a Song instance corresponding to the
first Metrolyrics link it finds, stopping after 20 results. If nothing is found,
returnsNone.
Package: tsxml
tsxmlTestStand XML Parser for Python. Converts the XML string of variable exported using 'GetXML(0,0)' function in TestStand to Python dictionary.InstallInstall using pippip install tsxmlSupports Python >= 3.8Usageimport tsxml

input_xml = """
<?TS version="2019 (19.0.0.170)"?>
    <Prop Name='MyContainer' Type='Obj' Flags='0x4'>
        <Prop Name='MyNumber' Type='Number' Flags='0x0'>
            <Value>5</Value>
        </Prop>
        <Prop Name='MyString' Type='String' Flags='0x0'>
            <Value>xTLDR.com</Value>
        </Prop>
        <Prop Name='MyBoolean' Type='Boolean' Flags='0x0'>
            <Value>True</Value>
        </Prop>
    </Prop>
"""

result = tsxml.parse(input_xml)

# result = {'MyContainer': {'MyNumber': 5.0, 'MyString': 'xTLDR.com', 'MyBoolean': True}}LicenseMIT License
Package: tt4human
Welcome tott4humanDocumentation📔 SeeFull Documentation HERE.ATruth Tableis like a cheat sheet that helps us figure out if something is true or false based on certain“conditions”. We call a specific combination of conditions a“case.”The outcome, whether it’s true or false, is what we call the“target.”Imagine you’re trying to decide whether or not you should go out. Two things could affect your decision: the weather and what time you wake up. So, in this situation, the weather and your wake-up time are the “conditions”. When you combine these conditions, like if it’s sunny and you woke up early, that combination is a “case.” And the big question of whether you should go out or not is your “target.” The Truth Table helps us organize all these cases and their outcomes to make decisions easier.For example, we have two types of conditions:weatherandget_up(when you get up). And we want to determine if you will go out.weatherhas two possible values:is_sunnyandnot_sunny.get_uphas three possible values:before_10,10_to_2,after_2. Below is the truth table:weather     get_up      go_out
is_sunny    before_10   1
is_sunny    10_to_2     1
is_sunny    after_2     0
not_sunny   before_10   0
not_sunny   10_to_2     0
not_sunny   after_2     0tt4humanprovides some tools to work with Truth Table in Python.Installtt4humanis released on PyPI, so all you need is to:$pipinstalltt4humanTo upgrade to latest version:$pipinstall--upgradett4human