Saravanan666/pypi-org huggingface dataset:
Package: tdbot
tdbottdBot APIThis Python package is automatically generated by theSwagger Codegenproject:API version: 1.0.0Package version: 1.0.0Build package: io.swagger.codegen.v3.generators.python.PythonClientCodegenRequirements.Python 2.7 and 3.4+Installation & Usagepip installIf the python package is hosted on Github, you can install directly from Githubpipinstallgit+https://github.com/GIT_USER_ID/GIT_REPO_ID.git(you may need to runpipwith root permission:sudo pip install git+https://github.com/GIT_USER_ID/GIT_REPO_ID.git)Then import the package:importtdbotSetuptoolsInstall viaSetuptools.pythonsetup.pyinstall--user(orsudo python setup.py installto install the package for all users)Then import the package:importtdbotGetting StartedPlease follow theinstallation procedureand then run the following:from__future__importprint_functionimporttimeimporttdbotfromtdbot.restimportApiExceptionfrompprintimportpprint# create an instance of the API classapi_instance=tdbot.MouseApi(tdbot.ApiClient(configuration))body=tdbot.Target()# Target |try:# click targetapi_response=api_instance.mouse_click_target(body)pprint(api_response)exceptApiExceptionase:print("Exception when calling MouseApi->mouse_click_target:%s\n"%e)# create an instance of the API classapi_instance=tdbot.MouseApi(tdbot.ApiClient(configuration))body=tdbot.Target()# Target |try:# move mouse cursor to targetapi_response=api_instance.mouse_hover_target(body)pprint(api_response)exceptApiExceptionase:print("Exception when calling MouseApi->mouse_hover_target:%s\n"%e)Documentation for API EndpointsAll URIs are relative to/ClassMethodHTTP requestDescriptionMouseApimouse_click_targetPUT/mouse/clickTargetclick targetMouseApimouse_hover_targetPUT/mouse/hoverTargetmove mouse cursor to targetStaticApistatic_fileGET/swagger/{file}host static files mainly of swagger ui for API testingDocumentation For ModelsElementObjectTargetDocumentation For AuthorizationAll endpoints do not require authorization.Author
Package: tdbuild
Installationpip install tdbuildUsageUsage is dead simple. Usetdbuild newto create a Python script calledtdfile.py. Hand-edit some basic inputs to the compiler (what are your source files? what is the executable called? etc). Then, runtdbuildto build andtdbuild runto run your code. Altogether, it looks like this:mkdir new_project && cd new_project

python -m tdbuild new

emacs tdfile.py

python -m tdbuild

python -m tdbuild runWhy did you write this software?Do you find CMake's scripting language arcane and its documentation unusable?Do you struggle to install and maintain a usable set of Unix tools on Windows? And do you hate creating Makefiles that are cross platform?Do you want to write code instead of learning how to use a flashy new build system like Meson?Yeah. Me too. Use this C/C++ build system. Fundamentally, all it does it build a command line for your compiler. It's that simple. Iftdbuildever becomes insufficient for you, you can copy the command line and do whatever the hell you want with it. You can also add custom flags to the command line.Yes, this means your whole program will rebuild every time. If your program is very big or uses a lot of templates, your build times will be slow. If you are the target audience for this build tool, then you won't care.tdbuildis not for enterprise software, nor is it for large open source projects. It's for people who just want to write a god damn C/C++ project that compiles on all platforms without screwing with build tools for a few days.Some projects that I build withtdbuildevery day:https://github.com/spaderthomas/tdengine, my game engine.https://github.com/spaderthomas/tdeditor, my text editor.OptionsYour build options are simply a Python dictionary. That's it. Here's a list of all the possible options the tool supports:Top Leveloptions.project:options.source_dir:options.include_dirs:options.lib_dir:options.build_dir:options.source_files:options.build_type:options.binary_type:options.cpp:options.cpp_standard:options.disable_console:options.defines:Windowsoptions.Windows.system_libs:options.Windows.user_libs:options.Windows.dlls:options.Windows.ignore:options.Windows.arch:options.Windows.out:options.Windows.runtime_library:options.Windows.warnings:options.Windows.extras:Linuxoptions.Linux.compiler:options.Linux.user_libs:options.Linux.system_libs:options.Linux.extras:options.Linux.out:
Package: tdcc
Taiwan Structured Product Information CrawlerThis is a repository that offers a StructuredProductCrawler class to crawlTaiwan TDCCwebsite for the product information.Tutorialfrom tdcc import StructuredProductCrawler
crawler = StructuredProductCrawler()
all_products = crawler.crawl()crawl()returns a Pandas DataFrame.
Data columns include:Column NameAreURLthe product's partial urlUIDproduct idNAMEproduct nameCURRENCYproduct denominationMATURITYmaturity dateUNDERLYINGunderlying asset typePRINCIPAL_PROTECTION% of principal protectionPIprofessional investorISSUE_DATEissue dateISSUERissuerMASTER_AGENTmaster agentDISTRIBUTORdistributorInstallationTo installthis verson from PyPI, type:pip install tdccTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:pip install git+git://github.com/jn8029/tdcc.gitTo-doTBC
Package: tdcca
Time-dependent Canonical Correlation AnalysisCanonical Correlation Analysis is a technique in multivariate data analysis for finding correlation and pairs of vectors that maximizes the correlation between a set of paired variables. Many important problems involve recoding time-dependent observations. In order to understand the coupling dynamics between the two sources, spot trends, detect anomalies, in this paper, we introduce the time-dependent canonical correlation analysis (TDCCA), a method of inferring time-dependent canonical vectors of paired variables.Getting StartedWe provide both simulation examples used in our paper. The main computation algorithm is not added into the class method for the convenience of algorithm development for multi-view data. The package saves all the analysis to the given folder and saves the preprocessed data into the hdf5 file. The parallel computing with multi-cores is also allowed and tested in Linux system. This package also provides other algorithms to optimize the function, including cvxpy naive optimization, cvxpy naive admm optimization and two other admm algorithms. For details, see the admm_computation.py file. However, these functions have not been tested thoroughly.These instructions will get you a copy of the project up running on your local machine for development and testing purposes.This package is also published in pypi. For a quick installation, trypip install tdccaPrerequisitesWhat things you need to install the software and how to install themSee setup.py for details of packages requirements.Installing from GitHubDownload the packages by using git clonehttps://github.com/xuefeicao/tdcca.gitpython setup.py installIf you experience problems related to installing the dependency Matplotlib on OSX, please seehttps://matplotlib.org/faq/osx_framework.htmlIntro to our packageAfter installing our package locally, try to import tdcca in your python environment and learn about package's function.
Note: our package name in pypi is tdcca. It is recommended that users scale the data before running our package.from tdcca import *
help(multi_sim)ExamplesThe examples subfolder includes simulations provided in the paper.Running the testsThe test has been conducted in both Linux and Mac Os.Built WithPython 2.7CompatibilityPython 2.7AuthorsXuefei Cao-Maintainer- (https://github.com/xuefeicao)Jun KeXi Luo(http://bigcomplexdata.com/)Björn Sandstede(http://www.dam.brown.edu/people/sandsted/)LicenseThis project is licensed under the MIT License - see the LICENSE file for details
Package: td-cli
td-cliis a command line todo manager,where you can organize and manage your todos across multiple projectsInstallationtd-clionly works forpython 3, so it needs to be installed withpip3pip3installtd-cliWindows 10In order to use the interactive mode on Windows, you'll have to installwindows-cursespipinstallwindows-cursesIn addition to that,Windows Terminalis recommended for better UX.Getting startedRuntd --helpto see possible commands.Here are some to get you started:Runtdto list all your todos.Runtd add "my new awesome todo"to add a new todo.Runtd <id> completeto complete your todo. You don't have to specify the wholeid, a substring will do. It'll fetch the first one that it finds in the same order as when you list your todos.Note thatglobalis a preserved group name where you can list all your global groups. You can always set it as the default with:tdgroupglobalpresetAPICheck out theapi.ConfiguringThe location of your todos and your configuration will depend on these environment variables (in this order):TD_CLI_HOME: determines where yourtodo.dbandtodo.cfgfile will liveXDG_CONFIG_HOME: a fallback if$TD_CLI_HOMEis not setHOME: a fallback if$XDG_CONFIG_HOMEis not set. If$HOMEis used; all files will be transformed to a dotfile, i.e.~/.todo.dband~/.todo.cfg.Database nameYour database instance will be located in in the before-mentioned configuration directory.
By default the database will be namedtodo.You can change your database name by specifyingdatabase_namein your$TD_CLI_HOME/.todo.cfgfile:[settings]database_name:something_elseThis results in a database instance at$TD_CLI_HOME/.something_else.dbFormatYou can specify your preferred format of your todo's details via the format config keyword:format:mdThis would result in the.md(Markdown) file extension when editing a todo. This allows you to use the power of your editor to e.g. syntax highlight the details, and etc.EditorWhen editing a todo,td <id> edit, you can both specify the todo'snameand the todo'sdetailsvia options (seetd <id> edit --help). If no option is specified, your todo will be opened inviby default (yourenvironement EDITORwill override this) where you can edit the todo's details. You can change the default editor by updating your config:[settings]editor:nvimOnly list uncompleted todosWhen listing todos, by default td-cli will list both completed and uncompleted todos. If you want to only list uncompleted todos by default, then you can apply the completed config:[settings]completed:0GroupWhen listing todos, you have the option of specifying what group to list from:td-gmy-group# ortdgmy-groupIf no group is provided,tdwill list from the current default group. You can globally set the default group with:tdgmy-grouppresetHowever, there's an option to set the default group per git project (this is not possible from the root config$TD_CLI_HOME/.todo.cfg).
In any root of your projects, you can create a.td.cfgconfig file to specify what group to default on (this will override the global default group):[settings]group:my-groupIf you runtdwithin your git project, td will default tomy-group.I recommend globally ignoring.td.cfgin~/.gitignore.
Package: tdcord
An easy-to-use extension forDiscord.pyandPycordwith some utility functions.Features✏️ Reduce boilerplate codeEasy cog managementEmbed templatesDatetime and file utilitiesWrapper foraiosqlite✨ Error handlingAutomatic error handling for slash commandsError webhook reportsCustom logging⚙️ ExtensionsHelp command- Automatically generate a help command for your botStatus changer- Change the bot's status in an intervalBlacklist- Block users from using your botInstallingPython 3.9 or higher is required.pip install tdcordYou can also install the latest version from GitHub. Note that this version may be unstable
and requiresgitto be installed.pip install git+https://github.com/tdius9/tdcordIf you need the latest version in yourrequirements.txtfile, you can add this line:tdcord @ git+https://github.com/tdius/tdcordUseful LinksDocumentation|Getting startedPycord|Discord.pyPyPiExamplesFor more examples, see theexample repositoryor thesample code.Note:It's recommended toload the tokenfrom a.envfile instead of hardcoding it.
TdCord can automatically load the token if aTOKENvariable is present in the.envfile.Pycordimporttdcordimportdiscordbot=tdcord.Bot(intents=discord.Intents.default())if__name__=="__main__":bot.load_cogs("cogs")# Load all cogs in the "cogs" folderbot.run("TOKEN")Discord.pyimportasyncioimportdiscordimporttdcordclassBot(tdcord.Bot):def__init__(self):super().__init__(intents=discord.Intents.default())asyncdefsetup_hook(self):awaitsuper().setup_hook()awaitself.tree.sync()asyncdefmain():asyncwithBot()asbot:bot.add_help_command()bot.load_cogs("cogs")# Load all cogs in the "cogs" folderawaitbot.start("TOKEN")if__name__=="__main__":asyncio.run(main())ContributingI am always happy to receive contributions. Here is how to do it:Fork this repositoryMake changesCreate a pull requestYou can alsocreate an issueif you find any bugs.
Package: td_dbfread
DBF is a file format used by databases such dBase, Visual FoxPro, and
FoxBase+. This library reads DBF files and returns the data as native
Python data types for further processing. It is primarily intended for
batch jobs and one-off scripts.>>> from dbfread import DBF
>>> for record in DBF('people.dbf'):
...     print(record)
OrderedDict([('NAME', 'Alice'), ('BIRTHDATE', datetime.date(1987, 3, 1))])
OrderedDict([('NAME', 'Bob'), ('BIRTHDATE', datetime.date(1980, 11, 12))])By default records are streamed directly from the file.  If you have
enough memory you can instead load them into a list. This allows for
random access:>>> table = DBF('people.dbf', load=True)
>>> print(table.records[1]['NAME'])
Bob
>>> print(table.records[0]['NAME'])
AliceFull documentation athttps://dbfread.readthedocs.io/See docs/changes.rst for a full list of changes in each version.Main Featureswritten for Python 3, but also works in 2.7simple but flexible APIdata is returned as native Python data typesrecords are ordered dictionaries, but can be reconfigured to be of
any typeaims to handle all variants of DBF files. (Currently only widely
tested with Visual FoxPro, but should work well with other
variants.)support for 18 field types. Custom types can be added by subclassingFieldParserreadsFPTandDBTmemo files, both text and binary datahandles mixed case file names gracefully on case sensitive file systemscan retrieve deleted recordsInstallingRequires Python 3.2 or 2.7.pip install dbfreaddbfreadis a pure Python module and doesn’t depend on any packages
outside the standard library.To build documentation locally:python setup.py docsThis requires Sphinx. The resulting files can be found indocs/_build/.Source codehttp://github.com/olemb/dbfread/API Changesdbfread.open()anddbfread.read()are deprecated as of version2.0, and will be removed in2.2.TheDBFclass is no longer a subclass oflist. This makes the
API a lot cleaner and easier to understand, but old code that relied
on this behaviour will be broken. Iteration and record counting works
the same as before. Other list operations can be rewritten using therecordattribute. For example:table = dbfread.read('people.dbf')
print(table[1])can be rewritten as:table = DBF('people.dbf', load=True)
print(table.records[1])open()andread()both returnDeprecatedDBF, which is a
subclass ofDBFandlistand thus backward compatible.Licensedbfread is released under the terms of theMIT license.ContactOle Martin Bjorndalen -ombdalen@gmail.com
Package: tddc
This package provides a framework for collaborative, test-driven data cleaning. The framework enables a reproducible method for data cleaning that can be easily validated.For a given tabular data set, a Trello board is populated with cards for each column so that team members can tag themselves to a column and ensure that work does not overlap. The cards include summary statistics of the columns that can be useful for writing methods to clean the column. Method stubs and test stubs are also scaffolded out for team members to fill out.Usage:This works on Linux with Python 2.7, 3.3, 3.4 and 3.5, and on OSX with Python 2.7 and 3.5 (and probably 3.3 and 3.4, but those haven’t been tested).
It works on Windows (tested using Python 3.5.2 :: Anaconda 4.1.1 (64-bit)).
Integration with Trello on Windows using tddc is yet to be tested though.Install the package with:$ pip install tddcYou can download a tiny example CSV file at:https://github.com/DataKind-SG/test-driven-data-cleaning/raw/master/input/foobar_data.csvIn the same directory as the file, run:$ tddc summarize foobar_data.csvThis takes the csv data set and summarizes it, outputing to a json file in a newly created output/ directory.Next, you can run:$ tddc build_trello foobar_data.csvThe first time you run this, it will fail and give you instructions on how to create a Trello configuration file in your root directory (in future, this should probably be created through the CLI).
Once you create it, you can try to run that step again. This will create a Trello board. The one my run created is here:https://trello.com/b/cqP9VZal/data-cleaning-board-for-foobar-dataFinally, you can run:$ tddc build foobar_data.csvThis outputs a script into the output/ folder that contains method stubs and glue code to clean the data set. It also outputs stubs for tests in output/.Contributing:Before running the tests, you’ll need to run:$ pip install pytestpytest-covmockThen, in the root of the project directory you can run the tests with:$ py.testWe’re trying out the new Github projects feature. The project we’re currently working on ishttps://github.com/DataKind-SG/test-driven-data-cleaning/projects/1Each card is an issue that you can click through to. If you’d like to take a card (thank you!), move the card to the “In progress” column and assign yourself to the issue. Once you’re finished, issue a pull request and move the card to “For review”.If you think of a new issue, create the card in the appropriate project and convert the card to an issue in the pull-down menu (it’s currently not possible to link to an already created issue from a card).
Package: tddmon
GoalHelp keeping Test Driven Development flow.InstallationInstall TddMon:pip install tddmonor current development version:pip install hg+https:://bitbucket.org/kidosoft/tddmonUsagetddmon -l test_run.log test_unit.pyIn above example filetest_unit.pywill be run like any other
module so be sure to putunittest.main()or similar inside it.
tddmon will monitor all “.py” files inside current directory for changes in
modification time and run test whenever their change.Monitored files will be measured for coverage. Test results will be logged
into log file (test_run.log in example) and on stdout you’ll see
your working flow in TDD.red - one or more tests failgreen - your tests has passedblue - consecutive run tests has passed; in most cases it means your refactoring your code.If you want to limit files to measure coverage use .coveragerc
as described on coverage module page:http://nedbatchelder.com/code/coverage/config.htmlDocumentationhttp://kidosoft.pl/docs/tddmon/TODOoption to separately logging test errors and coveragesending flow status and log results to remote server
Package: tdds
This package provides immutable data classes for Python2.7 and Python 3.5+SynopsisYou declare a class by listing its fields, and the type of each field>>>fromtddsimportField,Record,seq_of>>>classTrack(Record):...title=str...total_seconds=int......@property...defduration_str(self):...return'%d:%02d'%(self.total_seconds/60,self.total_seconds%60)>>>classAlbum(Record):...artist=str...title=str...year=Field(int,check=lambdavalue:1900<value<2050)...tracks=seq_of(Track)You instantiate it and access its fields normally>>>album=Album(...artist='Wah-wah',...title='Gyroscope',...year=2000,...tracks=[...Track(title="Elevon",total_seconds=209),...Track(title="Gear",total_seconds=514),...Track(title="Stringer",total_seconds=413),...],...)>>>album.title'Gyroscope'>>>album.tracks[0].duration_str'3:29'Record objects are immutable, hashable and comparable.>>>album.artist='Schwah-schwah'Traceback(mostrecentcalllast):...tdds.basics.RecordsAreImmutable:Albumobjectsareimmutable>>>Track(title='Hull',total_seconds=7)==Track(title='Hull',total_seconds=7)True>>>Track(title='Hull',total_seconds=7)==Track(title='Hold',total_seconds=8)FalseThe constructor checks the type of each of the given values, and refuses to proceed if the types aren't as declared>>>Track(title='Fireworks',total_seconds='9')Traceback(mostrecentcalllast):...tdds.basics.FieldTypeError:Track.total_secondsshouldbeoftypeint,notstr('9')There are functions to convert to and from Plain Old Data Structures, i.e. just lists and dicts. This is useful e.g. for JSON serialisation.>>>pods=album.record_pods()>>>print(json.dumps(pods,indent=4,sort_keys=True)){"artist":"Wah-wah","title":"Gyroscope","tracks":[{"title":"Elevon","total_seconds":209},{"title":"Gear","total_seconds":514},{"title":"Stringer","total_seconds":413}],"year":2000}>>>Album.from_pods(album.record_pods())==albumTrueRecords are also picklable>>>pickle.loads(pickle.dumps(album))==albumTrueThe library offers many more features such as automatic type coercion, custom validation functions, enum fields, typed collections,
and more. See thetestsdirectory for a specification of sorts.See alsoThis project is similar in spirit to these other fine libraries:attrskimcluegentyping.NamedTupleScala's case classesbut this one is mine.
Package: tddspry
tddspryis collection of testcases and additional helpers for testing
Django applications withnoselibrary.RequirementsInstallationDocumentationBugs, features, contactsRequirementsDjango>= 1.0mock>= 0.5.0nose>= 0.10.3twill>= 0.9InstallationOn most UNIX-like systems, you’ll probably need to run these commands as root
or using sudo.To install:python setup.py installOr viaeasy_install:easy_install tddspryAlso, you can retrieve fresh version oftddspryfromGitHub:git clone git://github.com/playpauseandstop/tddspry.gitDocumentationSphinx-generated documentation fortddsprylocated atGitHub pages.
This documentation updates after everytddspryrelease.Fresh documentation always can access indocs/directory.Bugs, features, contactsSending bugs and featuresWe usetddspryin all our projects, so we hope that it hasn’t any bug,
but if you really find it - send it toissues trackeron GitHub.And iftddsprydoes not support feature needed to you - tell us too and
we tries to adds it as soon as possible.ContactsAuthors:Igor Davydenko< playpauseandstop [at] gmail >,Volodymyr Hotsyk< gotsyk [at] gmail >Idea:Andriy Khavryuchenko< akhavr [at] gmail >
Package: tdes
TDestdes is a Python CLI application for encrypting and decrypting files tool usingtriple DES.InstallationUse the package managerpipto install tdes.pipinstalltdesUsage$tdesencrypt--path"E:\files\image.png"$tdesencryptall--path"E:\files\"$tdesdecrypt --path "E:\files\image.png" --key1 hGGe314l --key2 342XCFGsContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: tdewolff-minify
Python bindings for the Go minifiers for web formatsminify, seegithub.com/tdewolff/minify.RequisitesMake sure to haveGoinstalled.UsageThere are three functions available in Python: configure the minifiers, minify a string, and minify a file. Below an example of their usage:importminify# default config option valuesminify.config({'css-precision':0,'html-keep-comments':False,'html-keep-conditional-comments':False,'html-keep-default-attr-vals':False,'html-keep-document-tags':False,'html-keep-end-tags':False,'html-keep-whitespace':False,'html-keep-quotes':False,'js-precision':0,'js-keep-var-names':False,'js-version':0,'json-precision':0,'json-keep-numbers':False,'svg-keep-comments':False,'svg-precision':0,'xml-keep-whitespace':False,})s=minify.string('text/html','<span style="color:#ff0000;" class="text">Some  text</span>')print(s)# <span style=color:red class=text>Some text</span>minify.file('text/html','example.html','example.min.html')# creates example.min.htmlMediatypesThe first argument is the mediatype of the content. The following mediatypes correspond to the configured minifiers:text/css: CSStext/html: HTMLimage/svg+xml: SVG(application|text)/(x-)?(java|ecma)script: JS*/json */*-json: JSON*/xml */*-xml: XML