Saravanan666/pypi-org huggingface dataset:
Package: t1est
T1 fittingSimple T1 fitting.  Uses the following fitting model:
x = A - B exp{-t/T_1}where A and B are complex coefficients, t are the inversion times.InstallationShould be as easy as:pipinstallt1estUsageRun examples as follows:python-mt1est.examples.basic_usageThe functiont1est()can be called as follows:fromt1estimportt1est# x is the array of images at different inversion times.  The# inversion times are provided as TIsT1map=t1est(x,TIs,time_axis=-1,mask=mask,method='trf',T1_bnds=(0,3),chunksize=10,molli=True,mag=True)Notice thatxmay be any-dimensional, but time points must lie
along thetime_axisdimension. Ifmolli=True, then T1 is adjusted
as follows: T1’ = T1 (B/A - 1)
Package: t2c
A Python Library that converts human readable text to Cron Expression可以将人类语言转换为cron表达式的Python库Installationpipinstallt2cusageimportt2c# normalcron_reg_zh=t2c.Text2Cron().cron('今天下午两点钟')cron_reg_en=t2c.Text2Cron(language='en').cron('two minutes later')# use openai api keycron_gpt_zh=t2c.Text2Cron().gpt()cron_gpt_en=t2c.Text2Cron(usegpt=True,api_key='openai_api_key',language='en').gpt('two minutes later')# print the cron expressions# current time: 2023/5/15 00:25print(cron_reg_zh,cron_reg_en)# output: 0 14 15 5 ? , 27 0 15 5 ?print(cron_gpt_zh,cron_gpt_en)# output: 0 1 1 */3 * , 0 1 1 */3 * 😭
Package: t2d
t2dt2dis a short for Typer-to-Discord. It implements a seamless integration between Typer and Discord.py for CLI Discord bots development.InstallingJust install the package usingpip install t2d.How to use it?Assume that you have a Typer app that looks something like this:importtyperapp=typer.Typer()@app.command()defhello(name:str):typer.echo(f"Hello{name}!")@app.command()defbye(name:str):typer.echo(f"Bye{name}!")All you have to do is:importt2dbot=t2d.T2D(app)bot.run(YOUR_DISCORD_BOT_TOKEN)And that's it! Now you can use your Typer app in Discord! Default commands are:!help    Shows default help message for the bot
!t2d     Runs Typer CLI app using T2D
!version Prints T2D versionUsing the example above, you can do the following:!t2d hello Gabriel -> Shows "Hello Gabriel!"
!t2d bye Gabriel   -> Shows "Bye Gabriel!"Extending T2DOne can also extend T2D as it normally would using thediscord.ext.commands.BotAPI.
Package: t2d2-sdk
T2D2 Python SDKDescriptionT2D2 SDK python wrapper to API to interface with T2D2 projects and related assets.Table of ContentsT2D2 Python SDKDescriptionTable of ContentsInstallationUsageContributingLicenseInstallationThis SDK is published to PyPI and the latest version can be installed as follows:pip install --upgrade t2d2-sdkUsageThese instructions assume that you have an account at T2D2 (https://t2d2.ai). If you do not have an account, you can sign up here: (https://app.t2d2.ai/auth/register). Subscription pricing details may be found here: (https://t2d2.ai/pricing).Once you have an account created, you can either use your email/password or anAPI KEYto instantiate the T2D2 client.fromt2d2_sdkimportT2D2credentials={'email':"my@email.com",'password':"<MYPASSWORD>"}credentials={'api_key':"<MY_API_KEY>"}t2d2=T2D2(credentials)With this client, you can interface with the app and get access to most of the features of the web interface. More documentation coming soon athttps://docs.t2d2.ai.t2d2.set_project(PROJECT_ID)project=t2d2.get_project()data=t2d2.get_images()...ContributingPlease contactbhiriyur@t2d2.aifor further information on contributing to this project.LicenseInformation about the project's license.
Package: t2g
text-to-gifAKA t2g or text-to-gif-cli on pypiWell, I didn't find a python lib to process the text as gif.So I developed this.It can help me to create gif from pure text input.t2g(text-to-gif) 支持开发者把文本转化为gif进行动态播放。学委写了很多文章都是贴静态图的，后来准备改成gif动图。发现市场没有这样的。所以用python写了一个小工具。它支持把 文本/代码 转化为gif 动态展示。If there is a 'input.txt' on current dirWe can just run 't2g'.And it will generate text2gif.gif file on current dir with default optionsPowered bypypi-seedQuickstartUsage doc generation:So any github user can use this tool to generate usage_doc.gif or specify your quick start doc as gifRun with emoji in text file:In case the above gif not rendering so well:Installation / 安装Get t2g by pip (通过PIP工具来安装text-to-gif)pipinstalltext-to-gifPrint the usaget2g-hUse text-to-gif as CLI / 命令行使用We can call the cli 't2g' or 'text-to-gif' or 'ttg'：调用下面两个命令行即可。t2g-p/Users/mac/PycharmProjects/hello/xuewei_pypi_blog.txt-d./blog.gif-f5ttg-p/Users/mac/PycharmProjects/hello/xuewei_pypi_blog.txt-d./blog.gif-f5text2gif-p/Users/mac/PycharmProjects/hello/xuewei_pypi_blog.txt-d./blog.gif-f5The above command will generate below gif:More InfoAuthor 雷学委The featuressupport text file into gif file in multi frames within default/specific durationsupport text file with emojiFor this support, t2g usespilmojito handle emojisupport user to provider a folder as input, t2g will collect list  of  text file(*.txt) firstIf there is not text file, it will try to collect list of logs (*.log) file and render them as a n frame gif(n=the number of match files)run 't2g-opt'  to check out the t2g environment variables to enable your own customizationt2g-opt will print the env var, and you can set the default value.
Package: t2j
t2j converts TOML input to JSON output and vice versa.UsageFeed the TOML contents via standard input:cat pyproject.toml | t2j | jq -r '.project.dependencies[]'Specifying the filename has the same effect:t2j pyproject.toml | jq -r '.project.dependencies[]'Reverse Direction (JSON-to-TOML)Invoke asj2toption for reverse (JSON-to-TOML) conversion:t2j pyproject.toml | j2tNote:The JSON input must be a mapping (JSON object).Round-trip formatting is not preserved, e.g. keys and tables may be
reordered, comments are dropped, etc.Conversion direction may also be given as-J/--json-to-tomlor-T/--toml-to-json.  These options override the direction implied by the
command name, e.g.t2j-Jis the same asj2t.
Package: t2r2
T2R2: Train, test, record, repeat - incremental environment for testing AI modelsDescriptionTODOQuick startBasic training loopTODOImplement your own selectorWe give you an opportunity to use your own selectors.Prepare a class you want to use - it should inherit fromSelectorclass fromt2r2.selector. Implement itsselectmethod.When declarating your own selector - providemodule_pathas one of the arguments.Below we present a simple example how to do it.config.yamlpartselectors:-name:UserSelectorargs:module_path:./my_selector.pymy_selector.pycodeimportpandasaspdfromt2r2.selectorimportSelectorclassUserSelector(Selector):defselect(self,dataset:pd.DataFrame)->pd.DataFrame:returndataset[:5]Curriculum learningTo force specific order in which examples will be passed during training:training:curriculum_learning:TrueThen you also need to provide theordercolumn in your training data.Basically, the examples will besortedaccording to order column and won't be shuffled.You can also use the custom selector to dynamically provide the order of your training examples.
For example, to pass examples in the order of increasing lenght of text:classClSelector(Selector):defselect(self,dataset:pd.DataFrame)->pd.DataFrame:dataset["order"]=[len(i)foriindataset["text"]]returndataset
Package: t2s
t2st2s(Text-to-Speech), a Python library and CLI tool to interface with Google Translate's text-to-speech API.
Write spokenmp3data to a file, a file-like object (bytestring) for further audio manipulation, orstdout. Or simply pre-generate Google Translate TTS request URLs to feed to an external program.http://t2s.readthedocs.org/FeaturesCustomizable speech-specific sentence tokenizer that allows for unlimited lengths of text to be read, all while keeping proper intonation, abbreviations, decimals and more;Customizable text pre-processors which can, for example, provide pronunciation corrections;Installation$ pip install t2sQuickstartCommand Line:$ t2s-cli 'hello' --output hello.mp3Module:>>> from t2s import T2S
>>> tts = T2S('hello')
>>> tts.save('hello.mp3')Seehttp://t2s.readthedocs.org/for documentation and examples.DisclaimerThis project isnotaffiliated with Google or Google Cloud. Breaking upstream changescanoccur without notice. This project is leveraging the undocumentedGoogle Translatespeech functionality and isdifferentfromGoogle Cloud Text-to-Speech.ProjectQuestions & communityChangelogContributingLicenceThe MIT License (MIT)Copyright © 2014-2021 Pierre Nicolas Durette &Contributors
Package: t2stimulate
T2StimulateMRI T2 decay curve simulation with imperfect excitation pulse (B1)Free software: GNU General Public License v3Documentation:https://t2stimulate.readthedocs.io.A T2 multi-echo decay curve in MRI is traditionally modeled as a decaying exponential with one or more exponential
components. This assumes the refocusing pulse flip angle is at or near 180 degrees, and, if not, that other
gradients are added in as spoilers to remove the signal from stimulated echoes.The stimulated echoes were modeled many years ago in a paper (ref) and were then implemented in simulation code and were
found to be sufficient to use for fitting multi-echo decay curves from non-180 degree refocussing
pulse sequences.Below is an example of three curves of a 32 echo MRI Pulse sequence with TE=10ms and refocusing pulse
flip angles of 180 degrees, 160 degrees and 140 degrees. The code was:import matplotlib.pyplot as plt
import numpy as np

from t2stimulate.simulate import stimulate

# Set a couple of parameters
num_echoes = 32
tau = 5
T1 = 1000

te = 2*tau*np.arange(1, num_echoes+1)

curve_140 = stimulate(alpha=140 / 180 * np.pi, num_echoes=num_echoes, T1=450, T2=80, tau=tau)
curve_160 = stimulate(alpha=160 / 180 * np.pi, num_echoes=num_echoes, T1=450, T2=80, tau=tau)
curve_180 = stimulate(alpha=180 / 180 * np.pi, num_echoes=num_echoes, T1=450, T2=80, tau=tau)

plt.figure(1)
plt.clf()

plt.plot(te, curve_180, 'bo-')
plt.plot(te, curve_160, 'gd-')
plt.plot(te, curve_140, 'rx-')
plt.xlabel('TE (ms)')
plt.ylabel('Signal (arb)')
plt.grid('on')

plt.legend(['B1=180°', 'B1=160°', 'B1=140°'])

plt.axes((0.6, 0.3, 0.25, 0.3))

plt.plot(te, curve_180, 'bo-')
plt.plot(te, curve_160, 'gd-')
plt.plot(te, curve_140, 'rx-')
plt.xlabel('TE (ms)')
plt.ylabel('Signal (arb)')
plt.grid('on')
plt.xlim((200,250))
plt.ylim((0.03, 0.1))

plt.show()And the resulting plot:FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-06-25)First release on PyPI.Basic stimulated echo simulation written with tests.
Package: t2wml-standalone
# T2WML Standalone: A Standalone GUI server environment for running T2WML[Installing the server using pip](#install)## Installing the server with pip
<span id=”install”></span>A locally hosted web GUI version of t2wml can be installed quickly using pipmake a fresh virtual environmentpip install –extra-index-url https://pypi.fury.io/theresearchsoftwarecompany/ t2wml-standalone==2.0a17runt2wml-servernavigate to [http://localhost:13000/](http://localhost:13000/) in a Chrome browser
Package: t2-yada
YadaYada (YetAnotherDataclassArgument Parser!) is a library to automatically generateargparse.ArgumentParsergiven data classes. Compared to some available options such as:Huggingface's HfArgumentParser,argparse_dataclass, andtap, it offers the following benefits:Static Type CheckingNested data classes and complex typesEasy to extend and customize the parserGenerate command line arguments given the data classes.InstallationInstall via PyPI (requires Python 3.8+):pipinstallt2-yadaHow to useYada's parser can be constructed from data classes. It relies on fieds' annotated types to construct correct argument parsers.importyadafromdataclassesimportdataclassfromtypingimport*@dataclassclassCityArgs:city:Literal["LA","NY"]@dataclassclassNestedArgs:name:strnested:CityArgsparser=yada.YadaParser(NestedArgs)args=parser.parse_args()# or use parser.parse_known_args() -- the two functions are similar to argparse.parse_args or argparse.parse_known_argsNote: YadaParser is annotated as a generic type:YadaParser[C, R]where C denotes the classes, and R denotes the instance of the classes created from the arguments. Therefore, in the above example, C is inferred as NestedArgs, but R is unknown, hence the type ofargsvariable is unknown. To overcome this typing limitation, Yada provides several options for up to 10 data classes (yada.Parser1,yada.Parser2, ...). Below is two examples:parser=yada.Parser1(NestedArgs)args=parser.parse_args()# <-- args now has type NestedArgsparser=yada.Parser2((NestedArgs,CityArgs))args=parser.parse_args()# <-- args now has type Tuple[NestedArgs, CityArgs]Note: we recommend to use one of the specific parsersyada.Parser<N>instead of the genericyada.YadaParserif possible as they provide strong typing support.Configuring YadaAdd help messageYada reads the help message from thekeyproperty ofdataclasses.Field.metadataimportyadafromdataclassesimportdataclass,fieldfromtypingimport*@dataclassclassCityArgs:city:Literal["LA","NY"]=field(metadata={"help":"city's which you want to get the timezone"})parser=yada.Parser1(CityArgs)
Package: t3dn-bip
3D Ninjas | Blender Image Preview (BIP) LibraryDocumentationGetting Started (this page)Install PillowReferenceMoreDevelopmentWhat is 3DN BIP?3DN BIPor3D Ninjas Blender Image Previewis a library, which allows blazingly fast preview image loads in Blender. Furthermore, it can load preview images of arbitrary size. It operates as anbpy.utils.previewsdrop-in replacement and does all the heavy lifting for you.Our library enables the following major use cases:Load preview images shipped as BIP images blazingly fast. We recommend this approach in case you can generate the images beforehand. In this case, your users don't need to take any action to enjoy the speed boost.Load regular JPEG or PNG preview images blazingly fast. Recommended in case you need to load images provided by the user or by external software tools. For that, we needPillowon the user's system. It would be best if you use our preparedInstallPillowoperator so that your users can enjoy the speed boost with a simple click.Notes:Our library will always fall back tobpy.utils.previewsif unsupported formats are to be loaded. For this reason, there is absolutely no disadvantage in using this library. In the worst case, we will apply the standard mechanism of Blender..bipis a highly optimized image format, which can be transferred to Blender's internal data structures immediately. It is always blazingly fast, whetherPillowis installed or not.ExamplesDownload the following sample addons. You can easily install the ZIP files in Blender via the addon preferences tab.Getting Started:t3dn_bip_getting_started.zipShowcase:t3dn_bip_showcase.zipLibraryThe latest release of the library can be downloaded here:t3dn_bip.zipJust extract the zip file and copy the folder into your addon. Take a closer look at the examples given above to learn how to use the library.In case you want to install the library viaPyPI, you can use the following command:python-mpipinstallt3dn-bipConverterThe converter is provided viaPyPI. Use the following command for the installation:python-mpipinstallt3dn-bip-converterUse the following command to convert images ofvarious formatsinto the BIP format:python-mt3dn_bip_convertersource_file.pngdestination_file.bipWho is using 3DN BIP?3DN BIP is a new library and is currently being integrated into various addons. The following addons have already been released with 3DN BIP.KIT OPS
Package: t3dn-bip-converter
3D Ninjas | Blender Image Preview (BIP) LibraryDocumentationGetting Started (this page)Install PillowReferenceMoreDevelopmentWhat is 3DN BIP?3DN BIPor3D Ninjas Blender Image Previewis a library, which allows blazingly fast preview image loads in Blender. Furthermore, it can load preview images of arbitrary size. It operates as anbpy.utils.previewsdrop-in replacement and does all the heavy lifting for you.Our library enables the following major use cases:Load preview images shipped as BIP images blazingly fast. We recommend this approach in case you can generate the images beforehand. In this case, your users don't need to take any action to enjoy the speed boost.Load regular JPEG or PNG preview images blazingly fast. Recommended in case you need to load images provided by the user or by external software tools. For that, we needPillowon the user's system. It would be best if you use our preparedInstallPillowoperator so that your users can enjoy the speed boost with a simple click.Notes:Our library will always fall back tobpy.utils.previewsif unsupported formats are to be loaded. For this reason, there is absolutely no disadvantage in using this library. In the worst case, we will apply the standard mechanism of Blender..bipis a highly optimized image format, which can be transferred to Blender's internal data structures immediately. It is always blazingly fast, whetherPillowis installed or not.ExamplesDownload the following sample addons. You can easily install the ZIP files in Blender via the addon preferences tab.Getting Started:t3dn_bip_getting_started.zipShowcase:t3dn_bip_showcase.zipLibraryThe latest release of the library can be downloaded here:t3dn_bip.zipJust extract the zip file and copy the folder into your addon. Take a closer look at the examples given above to learn how to use the library.In case you want to install the library viaPyPI, you can use the following command:python-mpipinstallt3dn-bipConverterThe converter is provided viaPyPI. Use the following command for the installation:python-mpipinstallt3dn-bip-converterUse the following command to convert images ofvarious formatsinto the BIP format:python-mt3dn_bip_convertersource_file.pngdestination_file.bipWho is using 3DN BIP?3DN BIP is a new library and is currently being integrated into various addons. The following addons have already been released with 3DN BIP.KIT OPS
Package: t3dpy
t3d-pythonA Python 3.10+ module for readingT3D files.This library is still a work-in-progress.Installationvirtualenv venv
source ./venv/Scripts/activate
pip install maturin
maturin developUsagefromt3dpyimportread_t3dwithopen('./data.t3d','r')asfp:# Read the T3D file.objects=read_t3d(fp.read())# Iterate over the top-level objects.fort3d_objectinobjects:# Print the type of object.print(t3d_object.type_)# Print the child objects.print(t3d_object.children)# Print the list of properties (order not guaranteed to match input)print(t3d_object.properties)# Get a specific property by name.print(t3d_object["SomeProperty"])
Package: t3qai-client
t3qai_client Descriptiona library for t3qai platform client.The client module provides properties/functions
that links platform and client's learning/Inference algorithm.Provide platform path propertiesProvides functions to link learning state, set log, call learning parameter elements, load data, save learning results, and download inference resultsTo install with pippip install t3qai_clientHow to Use (Example)properties## train
from t3qai_client import T3QAI_TRAIN_OUTPUT_PATH, T3QAI_TRAIN_MODEL_PATH, T3QAI_TRAIN_DATA_PATH, T3QAI_TEST_DATA_PATH, T3QAI_MODULE_PATH

## inference
from t3qai_client import T3QAI_INIT_MODEL_PATHfunctionsimport t3qai_client as tc

## link learning state
tc.train_start()
tc.train_finish(result, result_msg)

## set log
# train
tc.train_set_logger()
# inference
tc.inference_set_logger()

## call learning parameter elements
# train
params = tc.train_load_param()
batch_size= int(params['batch_size'])
# inference
params = tc.inference_load_param()
batch_size= int(params['batch_size'])

## load data
# To use this function, the dataset with id and label must pass through the preprocessing module.
(train_id, train_x, train_y), (test_id, test_x, test_y) = tc.load_data()

## save learning results
# To draw charts inside the platform, use this function to save the learning results.
# save result (Loss, PCA 2D)
eval_results={}
eval_results['predict_y'] = [0, 1, 0]     # predict y
eval_results['actual_y'] = [[0], [1], [0]]  # actual y
eval_results['test_id'] = [0,1,2]        # test id(unique id)
eval_results['loss']=  float(0.3)        # loss
tc.train_save_result_metrics(eval_results)
# save classifacation result (Accuary, Loss, Confusion Matrix, Pricision/Recall//F1-score)
eval_results={}
eval_results['predict_y'] = [0, 1, 0]
eval_results['actual_y'] = [[0], [1], [0]]
eval_results['test_id'] = [0,1,2]
eval_results['loss'] =  float(0.3)
tc.train_save_classification_result(metrics)

## To download inference results at platform (2 options -> file_obj or file_path)
from t3qai_client import DownloadFile
result = DownloadFile(file_obj=resultobj, file_name=filename)
result = DownloadFile(file_path=save_path, file_name=filename)
Package: t3w
t3w.py- Typed Thin (Py)Torch WrapperT3W is a lightweight framework for training PyTorch models written by Yuyao Huang during his PhD at Tongji University.T3W is "typed". It leverages stronger and static typing compared to normal python code for clearer architecture and less bugs. The programming model is object-oriented. Users (you) are required to implement interfaces as subclasses and inject them as dependencies.T3W is "thin". With the philosophy "less is more" in mind, it leverages a minimal self-contained codebase that basically only depends on PyTorch to run. The plugin system under interfaceISideEffectmakes T3W not only thin, but also highly extensible.T3W stands with "PyTorch".See the concise examplemnist_example.py.If you feel like usingt3w.py, you can install it withpip install t3w.API documentation is currently available athttps://tjyuyao.github.io/t3w/api/. We are going to add more detailed user guide in the future.
Package: t4flib
T4FLIBPackage Python pour aider à la gestion des scripts mtm. t4flib fournit une interface facile à utiliser pour configurer et contrôler l'ensemble des flux.InstallationAssurez-vous d'avoir Python 3.8. Vous pouvez installer t4flib via pip:pip install t4flibIl est recommandé d'installert4flibdans un environnement virtuel pour éviter tout conflit de dépendances avec d'autres packages Python que vous pourriez avoir installés.Pour créer un environnement virtuel, suivez les étapes suivantes:python -m venv <nom_environnement>
source <nom_environnement>/bin/activate
pip install t4flibConfigurationCréez un fichier .env. dans le répertoire racine de votre projet et ajoutez la configuration comme ceci :API_KEY=XXXXXXXXContribuerLes contributions sont les bienvenues! Veuillez soumettre des pull requests pour les améliorations de code et des problèmes pour les bugs que vous rencontrez.Licencet4flibest sous licence MIT. Voir le fichier LICENSE pour plus d'informations.Fonction et Utilisation
Package: t4gpd
t4gpdSet of tools based on Python, GeoPandas and Shapely to achieve urban geoprocessingPre-requisitesTo be able to use thet4gpdplugin, perform geoprocessing and display your own maps, you need a Python3 interpreter and recent versions of the geopandas, matplotlib, numpy, and shapely libraries. One possibility is to install aMiniconda3environment, in which you will first create and then configure a new environment (we will call it gpd):conda create -n gpdconda activate gpdconda config --env --add channels conda-forgeconda config --env --set channel_priority strictconda install python=3.10 geopandas=0.12.2 jupyterlab matplotlib notebook scikit-learn xlrd openpyxl imageio rasterio networkx PyWavelets pysolar windrose geocube mapclassify seaborn plotly matplotlib-scalebar pyvista contextily xlwtpip install Dijkstar suntimes pythermalcomfortInstallation instructionsAst4gpdis now onPyPI, the easiest way to install the latest version is to use the pip command as follows:pip install t4gpdAlternatively, you can download thet4gpdlatest version from theSourceSup website. Then, install the Python3 plugin you downloaded:pip install t4gpd-0.7.1.tar.gzRead the documentationGo tohttps://t4gpd-docs.readthedocs.ioto consult the documentation.