Saravanan666/pypi-org huggingface dataset:
Package: traps
Pip Install Traps 😩pip-install-traps was made withCum ❌ code ✅Installationpip install -U trapsUsageimporttrapstraps.get()# Download one trap to `traps` directory.traps.get("my_homework",15)# Or download 15 traps to another directory.Command-line interface$ traps installto download 10 traps totrapsdirectory$ traps install -n 20 my_homeworkto download 20 traps tomy_homeworkdirectory$ traps --helpfor more help
Package: traptor
traptoris a framework to help manage your twitter data collection.  What differentiatestraptorfrom the many other Twitter libraries out there is that it doesreal-time distributed streamingof data based on rule sets using the Twitter Streaming API.It uses a combination ofKafka,Redis, and the excellentbirdymodule.  The goal is to have a convenient way to aggregate all of your twitter application data into one data stream and (optionally) a database.  It uses birdy to make Twitter API connections, redis to handle the rule management among different traptor instances, and kafka to handle the data streams.Please seehttp://traptor.readthedocs.orgfor documentation and the Quick Start guide.
Package: traquitanas
TraquitanasGitHubPyPiTraquitanas, badulaques, secos e molhados.Por aqui tem de tudo. Tudo junto e misturado!Todas as coisas que uso no dia a dia... em um só pacote.Quem sabe um dia encaixoto tudo, separando os temas em pacotes distintos...O pacote também tem a função de aprender a empacotar as coisas.Como Instalar?!pip3installtraquitanas--upgradeDocumentaçãoPara melhores explicações, veja owikiGoogle ColabPara testes rápidos sobre o funcionamento do pacote, elaborei umGoogle Colab.TODOCriariki
Package: trardi-string-operations
String properties and transformationsThe purpose of this plugin is return string properties and some transformations as upper case, etc.ConfigurationThis node requires configuration.You have to provide a path to string that needs to be transformed.{"string":"event@path.to.data"}Input payloadThis node does not process input payload.OutputThis plugin returns anobject with transformed string and all its properties such as: isdigit, isupper, etc.Available properties and transformationscapitalize - Converts the first character to upper casecasefold - Converts string into lower caseisalnum - Returns True if all characters in the string are alphanumericisalpha - Returns True if all characters in the string are in the alphabetisascii - Returns True if all characters in the string are ascii charactersisdecimal - Returns True if all characters in the string are decimalsisdigit - Returns True if all characters in the string are digitsisidentifier - Returns True if the string is an identifierislower - Returns True if all characters in the string are lower caseisnumeric - Returns True if all characters in the string are numericisprintable - Returns True if all characters in the string are printableisspace - Returns True if all characters in the string are whitespacesistitle - Returns True if the string follows the rules of a titleisupper - Returns True if all characters in the string are upper caselstrip - Returns a left trim version of the stringswapcase - Swaps cases, lower case becomes upper case and vice ver satitle - Converts the first character of each word to upper caseupper - Converts a string into upper caselower - Converts a string into lower case
Package: trashdetect-engine
PyTorch-MaskRCNNA PyTorch implementation of simple Mask R-CNN.This repository is a toy example of Mask R-CNN with two features:It is pure python code and can be run immediately using PyTorch 1.5.1 and trochvision 0.8.2,Simplified construction and easy to understand how the model works based ontutorial.The code is based largely onTorchVision.RequirementsWindowsorLinux, withPython ≥ 3.7PyTorch≥ 1.5.0matplotlib- visualizing images and resultspycocotools- for COCO dataset and evaluation; Windows version ishereNeptuneTo track logs (for example training loss) we usedneptune.ai. If you are interested in logging your experiments there, you should create account on the platform and create new project. Then:Find and set Neptune API token on your system as environment variable (your NEPTUNE_API_TOKEN should be added to ~./bashrc)Add your project_qualified_name name in thetrain.pyneptune.init(project_qualified_name='YOUR_PROJECT_NAME/detect-waste')Currently it is set to private detect-waste neptune space.install neptun-client librarypipinstallneptune-clientTo run experiments with neptune simply add--neptuneflag during launchtrain.py.For more checkLINK.DatasetsThis repository supports detect-waste datasets (usualy COCO based annotations) with two class - 'litter' and 'background'.We expect the directory structure to be the following:path/to/repository/
  annotations/         # annotation json files
path/to/images/        # all imagesYou can modifydatasets/data.pyscript to add new dataset and another format of paths for coco annotations type.Checkdetect-waste/annotations/README.mdto verify provided annotations byDetect Waste in Pomerania team.TrainingSimply run:python3 train.py --num_epochs 26 --gpu_id 2 --output_dir /path/to/save/checkpoints --images_dir /path/to/imagesor modify the parameters inrun.sh, and run:bash ./run.shNote: This is a simple model and only supports one gpu (not distribiuted training).EvaluationSimply run on cuda:2:pythontrain.py--gpu_id2--images_dir/path/to/images--resume/path/to/checkpoint.pth--test-onlyor modify the parameters innotebooks/eval.ipynbto visualize predictions of the model.PerformanceThe model utilizes part of TorchVision's weights, which is pretrained on COCO dataset.
Test on Multi detect-waste Segmentation val, on 1 RTX 2080Ti GPU:modelbackbonedatasetbbox AP@0.5bbox AP@0.5:0.95mask AP@0.5mask AP@0.5:0.95Mask R-CNNResNet 50Multi27.9516.4923.0512.94Mask R-CNNResNetXt 101*Multi19.706.2024.7013.20*results achived usingmmdetection framework.
Package: trashf
trashfCLI tool to safely remove any file and directory by putting them in the trashMove any file or directory in the trash in a clever and safe way when wanting to remove them. If a file or folder with the same name already exist in the trash, the file or folder to be removed will be renamed with the date before to be moved to the trash (fname + "%Y_%m_%d-%H_%M_%S").InstallationWithpip:
sudopip3installtrashf

Withyay:
yay-atrashf

Withyaourt:
yaourt-atrashfCompatibilitypython >= 3Usagetrashf / rt[F_PATH_01 F_PATH_02 ...]options:-h, --helpshow this help message and exitExamplesForhelp:trashf-h
or
rt--helpMovein the trash ($HOME/.local/share/Trash/files) files and folders:trashftiti/toto.jpg
Package: trashit
Trash-it - V1.0A Simple, command line utility to safely put the files/folders to the bin.
(A Safer way than rm command. )Have you ever deleted the wrong files byrmwhich were not backed up ? Have you felt that Panic ? That eagerness to bring back the files that have been lost ?If yes, or no, anyways, I have a tool, which can be verry usefull to you. (can)USAGEUSAGE   : trash <options> [<fileName1>,<fileName1>,<fileName1>]Options Available :ShortOptionsDescription-v--versionDisplay Version Information of Command-h--helpDisplay this HELP message.-a--addAdd Files to the Trash-s--showShow all the files and folders, currently in trash.--restoreRestore some files from the Trash.--restore-allRestore ALL the files and folders currently in trash.EXAMPLE :trash--helptrash--addfile1.xyzfile2.xyzfile3.png...NOTE :This is free software; you are free to change and redistribute it.There is NO WARRANTY, to the extent permitted by law.
Package: trashmailapi
trashmailapiUnofficial API for trash/temporary mail services in PythonCurrently, onlyvsimcard trashmailis supported.Check theproject wikifor a more detailed information.Installation:pip install trashmailapiExamples:Vsimcard:from trashmailapi.vsimcard import Vsimcard

user = Vsimcard("emailaddress")

user.get_emails() # returns email metadata except for body/content

for i in user.inbox:
    if "verify" in i.subject:
        mail = user.get_content(i.id) # returns the email body/content using mail ID's
Package: trashman
Info:This is the README file for Trashman.Author:Chris Warrick <chris@chriswarrick.com>Date:2015-03-07Version:1.5.0PURPOSEThis is a simple trash manager.  It can do various things related to
the trash.INSTALLATIONPyPI or AUR.COPYRIGHTCopyright © 2011–2015, Chris Warrick.
All rights reserved.Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:Redistributions of source code must retain the above copyright
notice, this list of conditions, and the following disclaimer.Redistributions in binary form must reproduce the above copyright
notice, this list of conditions, and the following disclaimer in the
documentation and/or other materials provided with the distribution.Neither the name of the author of this software nor the names of
contributors to this software may be used to endorse or promote
products derived from this software without specific prior written
consent.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
“AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: trash-pandas
trash_pandasanalytic tool for pandas dataframe explorationTrahs_pandas is an analytic tool designed for Pandas DataFrame exploration.It deliver fast statistical insights about the data of every column such as type, NaN values, unique values, distribution, mean, median, quartiles, correlations... and render with graphs using only print statements, making it suitable for both notebook and shell usage.It is intended to replace info() and describe().See the code on thisGithubInstallationPypi linkpipinstalltrash-pandasusageHow to use?fromtrash_pandasimportexploredf=pandas.read_csv('file.csv')explore(df)yield optionIt is possible to get a generator that display the result one column at a time.fromtrash_pandasimportexploredf=pandas.read_csv('file.csv')gen=explore(df,generator=True)next(gen)...I developed it initially for my own usage.Mifour.
Package: trashtalk
Script to simplify trash gestionInstallpip install trashtalk
# or manually
git clone git@github.com:PTank/trashtalk.git
cd trashtalk
python setup.py installUsagetrashtalk# with any argument return path to trashtrashtalk-cl# clean all files in trashtrashtalk-l-s# -l list files in trash -s add size infotrashtalk-f# you can tab file from selected trash, use with options (cl, l, s, i, re)
# you can select multiple trashtrashtalk-h# for more info.trashtalkYou can write a ‘.trashtalk’ file on your home for help your trash usage# this is a comment
TRASH_PATH=direct/path/to/trash , name_of_this_trash
MEDIA_PATH=path/of/your/medias
# you can add multiple path
Package: trashy-poetry
ProseAn unpoetic version ofPoetry.While the Poetry package is elegant and reduces a Python developers burden, every day usage has some small annoyances. Some of these things could be fixed but have been considered out of scope and unpoetic.Prose is made to be the everyday version of Poetry and little bit more trashy. But the trashiness serves those well in the dirty jobs of life. Prose is a wrapper around Poetry so all commands and APIs should function the same with the addition of more features.Installationpip install trashy-poetryUsageSubstitute allpoetrycommands forprose.SeePoetry Docs. All commands and APIs function the same except for the additional features documented.For Example:poetry init -> prose init

poetry shell -> prose shellFeaturesTask RunnerProse includesPoe the Poetby default. Poe the Poet lets you create shortcuts to common tasks such as shell scripts and Python functions. The usage and API functions the same as documented except for the addition of thepoeshortcut.prose run poe [options] task [task_args]can also be run asprose poe [options] task [task_args]Hardcoded Environmental VariablesInject environmental variables into therunandshellcommands by hard coding them into youpyproject.tomlfile.Example:[tool.prose.env]
TEST_ENV = "hello world"
TEST_PATH = "${PATH}:/narf"Any variables set in thetool.prose.envsection of your toml file will be injected into the environment for you.Load DotEnv FilesDefault DotEnv FilesProse loads.envfiles if it finds it in the current working directory. Seepython-dotenvfor usage documentation.Custom DotEnv FilesProse allows you to add custom.envfiles with a command line switch for thepoe,run, andshellcommands.OPTIONS
  -e (--env)             Dotenv file to load (multiple values allowed)Examples:prose shell -e path/custom.env
prose run -e path/custom.env printenv NARF
prose poe -e path/custom.env mycommandNote: When using therunandpoecommands, the-eand--envoptions must be used before your command arguments
Package: trask
traskDeclarative recipe-based task runnerUsage:python3 -m trask [--dry-run] <path>SchemaEmacs modeExampledocker-build {
  tag 'proj1'
  from 'amazonlinux:2'
  recipes {
    yum-install {
      pkg [ 'gcc' 'openssl-devel' ]
    }
    install-rust {
      channel 'nightly'
    }
  }
  workdir '/app'
}

docker-run {
  image 'proj1'
  init true
  volumes [
    {
      host '..'
      container '/app'
    }
  ]
  commands [
    'cargo build --release'
    'cargo test --release'
  ]
}

create-temp-dir {
  var 'install-dir'
}

copy {
  recipe 'copy'
  src [
    proj1.service'
    '../target/release/proj1'
  ]
  dst install-dir
}

set {
  user 'nbishop'
  host '12.34.56.78'
  identity env(SSH_KEY_PATH')
}

upload {
  replace true
  identity identity
  user user
  host host
  src install-dir
  dst 'app'
}

ssh {
  identity identity
  user user
  host host
  commands [
    'sudo cp app/proj1.service /etc/systemd/system/'
    'sudo systemctl enable proj1'
    'sudo systemctl restart proj1'
  ]
}
Package: traspider
traspider简介traspider是一个开箱即用的轻量爬虫框架如果你需要写一个小的爬虫,使用traspider会让你事半功倍github地址 :https://github.com/Ntrashh/traspider文档地址:https://ntrashh.github.io/traspider/环境要求Python 3.7.0+Works on Linux, Windows, macOS安装pip3 install traspider使用创建爬虫traspider create -s demo_spider生成代码
添加需要爬取的网址http://httpbin.org/fromloguruimportloggerfromtraspiderimportSpiderclassDemoSpider(Spider):def__init__(self):self.urls=["http://httpbin.org/"]defparser(self,response,request):logger.info(response)asyncdefdownload_middleware(self,request):request.headers={"user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36"}returnrequestif__name__=="__main__":demo_spider=DemoSpider()demo_spider.start()traspider这个项目开始之初就是为了爬虫在开发一些简单的项目能够更轻更快,所以对大型项目支持还是不够好。如果开发的是大型爬虫项目,推荐你使用feapder和scrapy如果你在使用过程中对traspider有任何问题或建议可以联系我微信:邮箱:yinghui0214@163.com鸣谢hoopafeapderscrapyhuangjin
Package: tratools
tra-toolsSome useful tools.Requirementspip install -r requirements.txtInstallation and runningpip install tratoolsUsage:Progress:>>> import tratools.progresses.progress_cmd as progress_cmd
>>> ProgressCmd = progress_cmd.ProgressCmd
>>> progress = ProgressCmd(steps, prefix="loading:")
>>> progress.succ(idx)  # somewhere in loop
>>> progress.print_end()Zipper:>>> from tratools.zipper import Zipper, Entry
>>> zipper = Zipper(
        init_entry=Entry(
            attrs_names=["A", "B"],
            attrs_values=[["a1", "a2", "a3"], ["b1", "b2", "b3"]],
            attrs_targets=["dtime", "goal"],
            max_step=10
        ), pack_size=2)

>>> zipper.load(use_backup=False)
>>> print("todos:")
>>> print(zipper.todos)
            A   B
        0  a1  b1
        1  a1  b2
        2  a1  b3
        3  a2  b1
        4  a2  b2
        5  a2  b3
        6  a3  b1
        7  a3  b2
        8  a3  b3

>>> pack, pack_size = zipper.next()
>>> pack = list(pack)
>>> print("pack:")
>>> for e in pack:
>>>     print(e.attrs_values_entry)

        A    a1
        B    b1
        Name: 0, dtype: object
        A    a1
        B    b2
        Name: 1, dtype: object

>>> print("\ntodos after pack:")
>>> print(zipper.todos)
            A   B
        2  a1  b3
        3  a2  b1
        4  a2  b2
        5  a2  b3
        6  a3  b1
        7  a3  b2
        8  a3  b3

>>> zipper.update(pack)
>>> print("\ntodos after update:")
>>> print(zipper.todos)
            A   B
        2  a1  b3
        3  a2  b1
        4  a2  b2
        5  a2  b3
        6  a3  b1
        7  a3  b2
        8  a3  b3

>>> print("\ndones after update")
>>> print(zipper.dones)
            A   B
        0  a1  b1
        1  a1  b2CpuScheduler:>>> from tratools.scheduler import CpuScheduler
    >>> from tratools.zipper import Entry

    >>> scheduler = CpuScheduler(
    >>>    init_entry=Entry(
    >>>        attrs_names=["A", "B"],
    >>>        attrs_values=[["a1", "a2", "a3"], ["b1", "b2", "b3"]],
    >>>        attrs_targets=["dtime", "goal"],
    >>>        max_step=10
    >>>    ), 
    >>>    target_tasks_params_names=["length", "delay_cost"],
    >>>    dbg=True)

    >>> pack = scheduler.ientry.gen(4)
    >>> scheduler(pack)

    solution:
    (1_len_27_delay_4_0, cpu4, 0, 27)
    (2_len_19_delay_7_0, cpu1, 0, 19)
    (3_len_19_delay_7_0, cpu0, 0, 19)
    (0_len_33_delay_0_0, cpu5, 11, 44)

    # see `tratools.scheduler.test()` for details.
Package: trattoria
🍕 Trattoria 🍕Trattoria delivers you the fastest streaming algorithms to analyze your TTTR data. We
currenlty support the following algorithms:Second order autocorrelations: Calculate the autocorrelation between two channels of
your TCSPC.Third Order autocorrelations: Calculate the coincidences between 3 channels. A sync
version is provided were it uses the fact that the sync channel is periodic and known.Intensity time trace: Calculate the intensity on each (or all) channels versus time.Zero finder: Given two uncorrelated channels (e.g. a laser behind a 50/50 splitter)
compute the delay between the input channels.Lifetime: Compute the lifetime histogram from a pulsed excitation experiment.Supported file formatsCurrently Trattoria can only read PTU files from PicoQuant. If you want support
for more or want to help providing it please put a ticket on the tttr-toolbox
project.Installingpip install trattoriaExamplesThe entry point to Trattoria is the PTUFile class. This class has methods that
give us access to the algorithms. Each of the algorithms takes as input a
parameter object and returns a results object. For a complete list of the
functionality see theexamplesfolder.frompathlibimportPathimporttrattoriaimportmatplotlib.pyplotaspltptu_filepath=Path("/path/to/some.ptu")ptu=trattoria.PTUFile(ptu_filepath)timetrace_params=trattoria.TimeTraceParameters(resolution=10.0,channel=None,)tt_res=ptu.timetrace(timetrace_params)plt.plot(tt_res.t,tt_res.tt/timetrace_params.resolution)plt.xlabel("Time (s)")plt.ylabel("Intensity (Hz)")plt.show()The examples folders contains examples of all the functionality available in
Trattoria.  For more details check the docstrings incore.py.DesignTrattoria is just a very thin wrapper around thetrattoria-corelibrary which
itselfs provides a lower level interface to the thetttr-toolboxlibrary. A Rust project that provides the compiled components that allows us to
go fast.Changelog0.3.5Bug fix. The last 1024*16 where being ignored for performance reasons. This has
has been fixed upstream intttr-toolboxand this version of Trattoria uses the
upgraded version oftrattoria-core.trattoria-coredropped support for Python 3.6 and 3.7 and therefore Trattoria too.0.3.4The g2 algorithm now supports a mode flag. With "symmetric" we use the
prefered version of the algorithm that returns negative and positive delays.
"asymmetric" returns only positive delays but is faster. Default is
"symmetric".0.3.3The underlying TTTR Toolbox and Trattoria Core were refactored to support
multiple custom ranges or records at once.start_rangeandstop_rangehave disappeared in favor ofrecord_ranges. It takes a list of tuples of
integers orNone.Citing
Package: travail
TravailPipeline for processing samples in context of rare mendelian diseases.How to installTravailis a Python package that is hosted on PyPi and is meant to be used within a virtual environment, such as Anaconda.To installtravailinto newAnacondavirtual environment namedtravail-env, run# create the virtual environmentcondacreate-ntravail-envpython=3.7# activate the environment after successful creationcondaactivatetravail-env# install travailpipinstalltravailHow to runAfter installation,travailexecutable should be available in your$PATH, when thetravail-envis activated.Runtravail--helpto see all the available options.Check requirementsTODO - write text about the check requirements functionality
Package: traval
travalTools for applying automatic error detection algorithms to timeseries.IntroductionThis module is set up to provide tools for applying any error detection
algorithm to any timeseries. The module consists of three main components:RuleSet: the RuleSet object is a highly flexible object for defining error detection algorithms based on (user-defined) functions.Detector: a data management object for storing timeseries and error detection results.SeriesComparison*: objects for comparing timeseries. These objects include plots for visualizing the comparisons.The general workflow consists of the following steps:Define error detection algorithm(s).Load data, i.e. raw timeseries data and optionally timeseries representing the "truth" to see how well the algorithms perform.Initialize Detector objects and apply algorithms to timeseries.Store and analyze the results.For more detailed information and examples, please refer to the notebooks in
the examples directory.InstallationTo install the traval module, follow these steps:Clone the repository from GitHub.Open a terminal and navigate to the module root directory:<your path here>/travalTypepip install -e .UsageThe basic usage of the module is described below. To start using the module,
import the package:>>>importtravalThe first step is generally to define an error detection algorithm. This is
done with theRuleSetobject:>>>ruleset=traval.RuleSet("my_first_algorithm")Add a detection rule (using a general rule from the library contained within
the module). In this case the rule states any value above 10.0 is suspect:>>>ruleset.add_rule("rule1",traval.rulelib.rule_ufunc_threshold,apply_to=0,kwargs={"ufunc":(np.greater,),"threshold":10.0})Take a look at the ruleset by just typingruleset:>>>rulesetRuleSet: 'my_first_algorithm'
  step: name            apply_to
     1: rule1                  0Next define a Detector object. This object is designed to store a timeseries
and the intermediate and final results after applying an error detection
algorithm. Initialize the Detector object with some timeseries. In this example
we assume there is a timeseries calledraw_series:>>>detect=traval.Detector(raw_series)Apply our first algorithm to the timeseries.>>>detect.apply_ruleset(ruleset)By default, the result of each step in the algorithm is compared to the
original series and stored in thedetect.comparisonsattribute. Take a
look at the comparison between the raw data and the result of the error
detection algorithm.Since we only defined one step, step 1 represents the final result.>>>cp=detect.comparisons[1]# result of step 1 = final resultTheSeriesComparison*objects contain methods to visualize the comparison,
or summarize the number of observations in each category:>>>cp.plots.plot_series_comparison()# plot a comparison>>>cp.summary# series containing number of observations in each categoryFor more detailed explanation and more complex examples, see the notebook(s)
in the examples directory.AuthorD.A. Brakenhoff, Artesia, 2020