Saravanan666/pypi-org huggingface dataset:
Package: transportmaps
This package provides basic functionalities for the construction of monotonic transport maps.Binary (PyPi)Source (Bitbucket)HomepageQ&A & Bug-trackingSupported systems*nix like OS (Linux, Unix, …)Mac OSOther operating systems have not been tested and they likely require a more complex procedure for the installation (this includes the Microsoft Windows family..).We reccommend to work in a virtual environment usingvirtualenvorAnaconda.Installation requirementsgcc(or an alternative C/C++ compiler)gfortran(or an alternative Fortran compiler)Automatic installationFirst of all make sure to have the latest version ofpipinstalled$ pip install –upgrade pipThe package and its python dependencies can be installed running the command:$ pip install –upgrade TransportMapsIf one whish to enable some of the optional dependencies:$ pip install –upgrade TransportMaps[SUITESPARSE]These options will install the following modules:DOLFIN – dolfin package for Partial Differential EquationsSUITESPARSE – scikit-sparseThis requiresscitik-sparseand the packagelibsuitesparse-devHMC – Hamiltonian Monte Carlopyhmc(it requires having numpy and cython already installed)Running the Unit TestsUnit tests are available and can be run through the commands:>>> import TransportMaps as TM
>>> TM.tests.run_tests()Or directly using the bash command:$ tmap-run-testsThe Git repository also contains a docker-compose configuration file to test the whole suite on several
versions of python.There are >2000 unit tests, and it will take some time to run all of them.CreditsThis sofware has been developed and is being maintained by theUncertainty Quantification Groupat MIT, under the guidance of Prof. Youssef Marzouk.Developing teamDaniele Bigoni – [www]Alessio SpantiniRebecca MorrisonRicardo M. Baptista
Package: transportmodels
# bankruptInstallation---------------Use one of the following method:* pip install```bashpip --install bankruptpip --install bankrupt --upgrade```* clone repository and install with:```bashpython setup.py install```Usage-------* Solve the balanced transportation problem.```pythonfrom transportmodels import Transportt = Transport()t.set_supplies([200, 250])t.set_demands([100, 150, 200])t.set_cost_matrix([[90, 70, 100], [80, 65, 75]])t.solve()print(t.get_solution())print(t.get_objective_value())```* Solve a batch of the balanced transportation problem, using multiple processors.```pythonfrom transportmodels import TransModel# dataquota_vectors = [[5, 7, 3, -7, -3, -5],[5, -7, 3, 7, -3, -5],[5, 7, -3, -7, 3, -5],[-5, 7, 3, -7, -3, 5]]cost_matrix = [[0, 0, 0, 3, 1, 100], [0, 0, 0, 4, 2, 4], [0, 0, 0, 100, 3, 3],[3, 4, 100, 0, 0, 0], [1, 2, 3, 0, 0, 0], [100, 4, 3, 0, 0, 0]]# solvert = TransModel()t.set_processors(2)t.set_quota_vectors(quota_vectors)t.set_cost_matrix(cost_matrix)t.solve()# solutions and objective valuessolutions = t.get_solutions()objective_values = t.get_objective_values()# print infofor i in range(len(solutions)):print("--- problem %d ---" % i)print("solution", solutions[i])print("objective value", objective_values[i])```
Package: transposecsv
transposecsv: A small Python library to transpose large csv files that can't fit in memory.Suppose you have anp x mmatrix where your original data ismpoints samples withpfeatures, or inmpoints inpdimensional space. Then we want the column space to be the features, that is, we'd like to consider them x pdata matrix. This small library is for performing this calculation on arbitrarily large csv files.It works in the following way:Read in chunks that fit in memoryTranspose those in memory (which is fast)Write each transposed chunk to a.csvfileUsepasteto join the files horizontally (columnwise), this is why we don't need to save the index, since it will be the same as the columns of the original file.This process outputs them x pmatrix, as desired. This is particularly useful for single-cell data, where expression matrices are often uploaded genewise, but you may want to work with machine learning models that learn cellwise :).InstallationTo install, runpip install transposecsvHow to useThe transpose operation is contained in a lazily-loadedTransposeclass, so the transpose operation isn't started on initialization. For example:fromtransposecsvimportTransposetranspose=Transpose(file_name='massive_dataset.csv',write_path='massive_dataset_T.csv',chunksize=400,# Number of rows to read in at each iteration# leave as default# insep=',',# outsep=',',# chunksize=400,# save_chunks=False,# quiet=False,)transpose.compute()Then to upload to S3, we would runtranpose.upload(bucket='braingeneersdev',endpoint_url='https://s3.nautilus.optiputer.net',aws_secret_key_id=secret,aws_secret_access_key=access,remote_name='jlehrer/massive_dataset_T.csv')
Package: transpose-dict
Simple python package to transpose python dictionaries.Multilevel dictionaries can be viewed as projections of sparse n-dimensional matrices: as such, you can transpose them on any of their axes.This package offers a simple function to do that.Installing the transpose dict packageAs usual, just use pip as follows:pipinstalltranspose_dictBasic usage examplefromtranspose_dictimportTD# or from transpose_dict import transpose_dicttest={"a":{"0":{"I":[1,2,3],"II":[4,5,6]}},"b":{"0":{"I":[8,9,10],"II":[467,23,23]},"1":{"III":[6,7,9]}}}TD(test,0)# The given dictionary does not change#> {"b": {"0": {"I": [8, 9, 10], "II": [467, 23, 23]}, "1": {"III": [6, 7, 9]}}, "a": {"0": {"I": [1, 2, 3], "II": [4, 5, 6]}}}TD(test,1)# The new main axis is the one with ("0", "1")#> {"0": {"a": {"I": [1, 2, 3], "II": [4, 5, 6]}, "b": {"I": [8, 9, 10], "II": [467, 23, 23]}}, "1": {"b": {"III": [6, 7, 9]}}}TD(test,2)# The new main axis is the one with ("I", "II", "III")#> {"I": {"a": {"0": [1, 2, 3]}, "b": {"0": [8, 9, 10]}}, "III": {"b": {"1": [6, 7, 9]}}, "II": {"a": {"0": [4, 5, 6]}, "b": {"0": [467, 23, 23]}}}LicenseThe software is released under the MIT license.
Package: transpose-dit
Dedicated Instance ToolsPython utilities for working with Transpose Dedicated Instances.Getting StartedFirst, install the package from PyPI:pipinstalltranspose-ditThen, connecting to your dedicated instance is as easy asfromtranspose_ditimportDedicatedInstancedb=DedicatedInstance(host=os.environ.get("HOST"),port=os.environ.get("PORT"),user=os.environ.get("USERNAME"),password=os.environ.get("PASSWORD"),database=os.environ.get("DATABASE"),sslmode=os.environ.get("SSLMODE"),debug=True,)...DocumentationYou can view the full documentation for this packagehere, or select the module you're interested in below:ModuleDescriptionDocumentationSyncUtilities for pulling data from a dedicated instanceViewExamplesYou can view examples of how to use this package in theexamplesdirectory.
Package: transposer
You got columnar data? Turn that into row-wise data!! You got
row-wise data?? Turn it into columnar data!!! SAY WUT?!?(transposer transposes columns and rows in delimited text files)What It Doestransposer is a one-trick pony, but it’s a good one-trick pony.
A simple command-line tool that can be used to effectively
transpose columnar data in a delimited file to row-wise data. EPIC.Consider these three rows of data:FirstName|LastName|Age|Weight
Sam|Simms|52|176
Jane|Franklin|35|125transposer would take these three rows, and transpose the data as follows:FirstName|Sam|Jane
LastName|Simms|Franklin
Age|52|35
Weight|176|125If you wanted to transform the row-wise data back into its former columnar self,
you would just run transposer against the transposed data.transposer also works well with different types of delimiters, you just need
to declare which type you are working with (default is comma-separated).But, KeithIt’s so easy to transpose that kind of data by hand. Why would I want to use
a package for it?Yeah, transposing three rows is easy to do by hand. Try 100,000. transposer will
do that work for you so you can do better things like watch reruns of The
Shawshank Redemption. You don’t even have to write the data out to another file
you can also just hold onto it to do some wicked transforms before you dump
it to a flatfile. Up to you.InstallingInstalling is easy, just use pip:$ pip install transposerOr grab this repo and run:$ python setup.py installUsingtransposer can be used both on the command line as well as in a Python project.Command Line$ transposer -i 'my_file.csv' -o 'out_file.csv'
$ transposer -i 'my_file.txt' -d '|'
$ transposer --in 'my_file.txt' -d '\t'Module-basedIn your project file:importtransposer# this will output a file.transposer.transpose(i='cols.csv',o='rows.csv')# this will just keep the transposition in memoryrows=transposer.transpose(i='cols.csv',o=None)# so will thisrows=transposer.transpose(i='cols.csv')# the delimiter defaults to a comma. Use pipes too.transposer.transpose(i='cols.txt',o='rows.txt',d='|')Licensetransposer uses the MIT license, and is free for you.
Package: transQA
transQAБиблиотека предназначена для генерации текста или ответа на основе полученных вопроса, ответа, модели и прочих параметровInstallДля установки выполните команду:pip install svn+http://svn.consultant.ru/svn/Internal/Docs/NRP/pylibs/transQAErrorsЕсли при установке requirements.txt возникли проблемы с torch==1.13.0+cu117 следует использовать команду для отдельной установки:pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117В целом все требования из requirements.txt уже содержатся в Python396TF25GPU_release1.1Кроме sentencepiece==0.1.97, который нужен для работы моделей на основе T5
Его необходимо устанавливать отдельно
Package: transtab
TransTab: A flexible transferable tabular learning framework[arxiv]Document is available athttps://transtab.readthedocs.io/en/latest/index.html.Paper is available athttps://arxiv.org/pdf/2205.09328.pdf.5 min blog to understand TransTab atrealsunlab.medium.com!News![05/04/23] Check the version0.0.5ofTransTab![01/04/23] Check the version0.0.3ofTransTab![12/03/22] Check out our[blog]for a quick understanding of TransTab![08/31/22]0.0.2Support encode tabular inputs into embeddings directly. An example is providedhere. Several bugs are fixed.TODOTable embedding.Add support to direct process table with missing values.Add regression support.FeaturesThis repository provides the python packagetranstabfor flexible tabular prediction model. The basic usage oftranstabcan be done in a couple of lines!importtranstab# load dataset by specifying dataset nameallset,trainset,valset,testset,cat_cols,num_cols,bin_cols\=transtab.load_data('credit-g')# build classifiermodel=transtab.build_classifier(cat_cols,num_cols,bin_cols)# start trainingtranstab.train(model,trainset,valset,**training_arguments)# make predictions, df_x is a pd.DataFrame with shape (n, d)# return the predictions ypred with shape (n, 1) if binary classification;# (n, n_class) if multiclass classification.ypred=transtab.predict(model,df_x)It's easy, isn't it?How to installFirst, download the rightpytorchversion following the guide onhttps://pytorch.org/get-started/locally/.Then try to install from pypi directly:pipinstalltranstaborpipinstallgit+https://github.com/RyanWangZf/transtab.gitPlease refer to formore guidance on installationand troubleshooting.Transfer learning across tablesA novel feature oftranstabis its ability to learn from multiple distinct tables. It is easy to trigger the training like# load the pretrained transtab modelmodel=transtab.build_classifier(checkpoint='./ckpt')# load a new tabular datasetallset,trainset,valset,testset,cat_cols,num_cols,bin_cols\=transtab.load_data('credit-approval')# update categorical/numerical/binary column map of the loaded modelmodel.update({'cat':cat_cols,'num':num_cols,'bin':bin_cols})# then we just trigger the training on the new datatranstab.train(model,trainset,valset,**training_arguments)Contrastive pretraining on multiple tablesWe can also conduct contrastive pretraining on multiple distinct tables like# load from multiple tabular datasetsdataname_list=['credit-g','credit-approval']allset,trainset,valset,testset,cat_cols,num_cols,bin_cols\=transtab.load_data(dataname_list)# build contrastive learner, set supervised=True for supervised VPCLmodel,collate_fn=transtab.build_contrastive_learner(cat_cols,num_cols,bin_cols,supervised=True)# start contrastive pretraining trainingtranstab.train(model,trainset,valset,collate_fn=collate_fn,**training_arguments)CitationIf you find this package useful, please consider citing the following paper:@inproceedings{wang2022transtab,
  title={TransTab: Learning Transferable Tabular Transformers Across Tables},
  author={Wang, Zifeng and Sun, Jimeng},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022}}
Package: trans-tool
The translation files checker and syncing toolmasterbranch:developmentbranch:Table of contentsIntroductionExtensive documentationAvailable validatorsUsage examplesConfig fileChangelogLicenseIntroductiontrans-toolis a small yet powerful utility designed for your projects' translation files. It comes
equipped with several validators to catch common mistakes in translations as well as base strings.
It currently loads*.propertiesfiles, a format often used in Java projects. Thetrans-toolwas conceived during work
onLogisim-evolution.An example*.propertiesfile resembles a simplified version of the commonly used INI file:# Example of *.properties fileprogramTitle=trans-tool v2.0.0okButton="OK"Internally,trans-tooloperates on an abstract format, thus adding support for other file formats
can easily be incorporated, which will be done upon demand.While loading your*.propertiesfiles,trans-toolchecks if the files are in order, ensuring all
of them are syntactically correct and all the translations are in sync with the main language. It
also comes with a vast set of various linters and checkers to guard the quality of the files'
contents. It can check for missing or dangling keys, improper punctuation, open brackets, quotation
marks, and more. It can also automatically sync translation files quickly, providing a fresh
template for your translators to work on.$trans-tool-bsoc-lpl

Base:src/main/resources/resources/logisim/strings/soc/soc.propertiesErrors:1BracketsE:Line163:90:"AssemblerRunSuccess":Noopeningcharactermatching")".PL:src/main/resources/resources/logisim/strings/soc/soc_pl.propertiesErrors:3,warnings:4BracketsE:Line175:83:"AssemblerRunSuccess":Noopeningcharactermatching")".FormattingvaluesE:Line383:167:"PioMenuOutClearRemark":Expected"%s",found"%s.".E:Line387:167:"PioMenuOutSetRemark":Expected"%s",found"%s.".MissingtranslationsW:"ElfHeaderEIDataError":Missingtranslation.W:"AsmPanErrorCreateFile":Missingtranslation.PunctuationmismatchW:Line12:"SocInsertTransWindowTitle":Endswith"y".Expected":".Firstwordscasemismatch.W:Line332:"Rv32imProgramCounter":StartsUPPER-cased,expectedlower-case.LicenseWritten and copyrighted ©2021-2023 by Marcin Orlowski <mail (#) marcinorlowski (.) com>trans-tool is open-sourced software licensed under
theMIT license.Project logo
containselements from Flaticon.com.trans-tool projectPyPi page.
Package: transude
TransudeSimple tool set for filtering DataFrames (Pandas orPolars*) by building queries from one or more filters.This is useful for connecting filtering controls on DataFrames using touch screen controls.This project was developed with some consulting from ChatGPT. There were a few concepts I didn't understand until I had
someone I could point more specific questions towards and see working examples. Most of the scaffolding was written
before consulting ChatGPT which really helps to speed up responses and keep the conversation on track.This is also a refactor of old code I wrote in a recent project.Installation:pip install transudeUsage:import pandas as pd
import transude as txd

# Create a DataFrame using Pandas
pd_df = pd.DataFrame(...)

# Get a filtered version of the DataFrame using Transude
filtered_pd_df = \
    txd.filter_df(data_frame=pd_df, columns='col1', values=['val1', 'val2'], operator='==', joiner='or')If you need to manage the DataFrameFilters directly, you can use a DataFrameFilterManager like so:pd_df_filter_manager = DataFrameFilterManager()

"""
Example of adding a single DataFrameFilter and clearing the filters.  Filters can be removed one by one as well.
"""
pd_df_filter_manager.add_filter(DataFrameFilter(columns='col1', values='val1', operator='==', joiner='or'))
pd_df_filter_manager.clear_filters()

"""
The following utilizes the DataFrameFilterFactory to create multiple filters and then adds them all to the builder.
"""
pd_filter_factory = DataFrameFilterFactory(columns='col1', values=['val1', 'val2'], operator='==', joiner='or')
pd_filters = pd_filter_factory.create_filters()
pd_df_filter_manager.add_filters(pd_filters)
query_string = pd_df_filter_manager.build_query()

# In order to apply the filters, call query using the query_string
pd_df.query(query_string)--*Polars compatability coming soon.
Package: transvenous-pacing-gui
# Balloon-Catheterization-GUI
GUI for the Balloon Catheterization capstone project[![Build Status](https://travis-ci.org/omn0mn0m/transvenous_pacing_gui.svg?branch=master)](https://travis-ci.org/omn0mn0m/transvenous_pacing_gui)
[![Coverage Status](https://coveralls.io/repos/github/omn0mn0m/transvenous_pacing_gui/badge.svg?branch=master)](https://coveralls.io/github/omn0mn0m/transvenous_pacing_gui?branch=master)
[![PyPI](https://img.shields.io/pypi/v/transvenous-pacing-gui.svg)](https://pypi.org/project/transvenous-pacing-gui/)
[![GitHub](https://img.shields.io/github/license/omn0mn0m/transvenous_pacing_gui.svg)](https://github.com/omn0mn0m/transvenous_pacing_gui/blob/master/LICENSE)
Package: transwarp
Transwarp is a web framework compatible with WSGI, also comes with
a lightweight database access module, a mail sender, a task queue
and utils like url fetch.This package was originally written by Michael Liao. You can find
docs and source code at:http://michaelliao.github.com/transwarp/Please report bugs or any suggestions at:https://github.com/michaelliao/transwarp/
Package: transwhat
transWhat is a WhatsApp XMPP Gateway based onSpectrum 2andYowsup 2.Branchesyowsup-1My original version which is based on @tgalal first
Yowsup version (deprecatedand broken).yowsup-2Major rewrite from @moyamo for @tgalal’s new Yowsup 2
(recommended).For production, please use theyowsup-2branch.InstallationUsageLicenseFeaturesTyping notificationsReceive images, audio & videoSet/get online statusSet status messageGroupchatsContributorsPull requests, bug reports etc. are welcome. Help us to provide a open
implementation of the WhatsApp protocol.The following persons have contributed major parts of this code:@stv0g (Steffen Vogel): Idea and initial implementation based on
Yowsup 1@moyamo (Mohammed Yaseen Mowzer): Port to Yowsup 2@DaZZZl: Improvements to group chats, media & message receiptsLinksAnoutdatedproject wiki is availablehere.Anoutdatedwriteup of this project is also availabe at myblog.
Package: trapcards
MIT LicenseCopyright (c) [2018] [Rabbinhat]Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: trapdoor
TrapdoorTurn-key configuration file management for Python packages.Explore the docs »Request Feature·Report Bug·
    ⭐ Consider starring the repo! ⭐🎨 FeaturesConfig Files.Trapdoor makes it easy to create and maintain TOML configuration files.Minimal Configuration.Set your store name and start setting and getting keys from the configuration file. Sane defaults are set and useful for most cases.Secure Practices.By default, configuration directories are created to be only readable by the user who created them.📚 Getting StartedInstallationPython Package IndexYou can also installtrapdoorusing the Python Package Index (PyPI).pipinstalltrapdoor🚌 A Quick TourAt its foundation, trapdoor is meant to ease the process of creating and maintaining configuration files within your Python tool. Commonly, you will want to use it to create a configuration store, get existing configuration keys, and set configuration keys.If you're interested in a complete overview of trapdoor's capabilities, please seethe documentation pages.# import trapdoorfromtrapdoorimportTrapdoort=Trapdoor('trapdoor-test')# create a store at ~/.trapdoor-test/config.yml (by default)t.set('some.nested.key.hello','world')# set a key in the configuration filet.get('some.nested.key.hello')# get a key from the configuration fileFor more information, please seethe documentation.🤝 ContributingContributions, issues and feature requests are welcome!Feel free to checkissues page. Please ensure you fill out the entire template for each of these. You can also take a look at thecontributing guide.📝 LicenseCopyright © 2021 Clay McLeod. This project isMITlicensed.
Package: trapecio-compuesto
TrapecioCompuestotrapecio-compuesto metodo numericoFree software: MIT licenseDocumentation:https://trapecio-compuesto.readthedocs.io.FeaturesMétodo Númerico de Trapecio Compuesto para una ecuación polinómica.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-03-09)First release on PyPI.
Package: trapeze
TrapezeStore/restore configuration and credentials for applications with AWS S3.Quickly pull and push sensitive configuration files on both development and production environments.  The goal is to provide a very quick, secure method of restoring applications without storing credentials on GIT.RequirementsPython 3.6+Installationrunpip install trapeze.InstructionsInitialize.Goto application folder and runtrapeze init. Completing this process will create a.trapezeconfiguration file.Push/PullYou can then eitherpushtrapeze pushorpulltrapeze pullto send/restore config/credential files from S3.
Package: traph
TraphA terminal-based graph visualiser.For now, it supportsthreetwo features:Constructing a graphRunning BFS on the graphSpaghetti event loopInstallationYou probably wouldn't even think about it, but just in case, you should enable your virtualenv.Install from repositoryClone repo:git clone git@github.com:1MaxKoval/traph.gitGo into the root project directory and install:pip install .Install from PyPIpip install traphRuntraph
Package: trapify
# ︻╦╤─ ₮Я∆ⱣỊ₣Ϋ ─╤╦︻KЄЄⱣ ΫØṲЯ ϾØMM∆ИƉ ⱢỊИЄ ₮ЯỊⱢⱢ Ϋ’∆ⱢⱢ# ỊИϨ₮∆ⱢⱢ∆₮ỊØИpip install trapify# ṲϨ∆₲Є$ trapify yo its lif fam
ΫØ Ị₮Ϩ ⱢỊ₮ ₣∆M₮Я∆ⱣỊ₣Ϋ Ͼ∆И ∆ⱢϨØ ϾØⱣΫ ϨῌỊ₮ ₮Ø ₮ῌЄ ϾⱢỊⱣƁØ∆ЯƉ:$ trapify copy that shit -c
ϾØⱣΫ ₮ῌ∆₮ ϨῌỊ₮KЄЄⱣ Ị₮ ₮ЯỊⱢⱢ, 2016.
Package: trappist
Trappist is a tool for computingminimal trap spacesof a Boolean model.InstallYou can install trappist withpipdirectly from the Package Index:$python3-mpipinstalltrappistor grab the very latest version from the source:$python3-mpipinstall-egit+https://github.com/soli/trap-spaces-as-siphons.gitYou will also need theclingoASP solver in your PATH for theaspmethod of computing the trap spaces (default). Instructions are provided directly on thePotassco pages.Note that Trappist does install thePySATmodule so that thesatmethod is always available even if you do not haveclingo.Run trappist in a Binder imageSubmitted version:Latest version, with2022-12-01colomoto-docker image:Read about trappistThe article describing trappist ishereRun trappist from the command lineAfter installingtrappist(and maybeclingo), just run$trappist[-mmaximumnumberofsolutions][-tmaximumtimetouseinseconds][-ssolver(asp|sat)]<PNMLinputfile>