Saravanan666/pypi-org huggingface dataset:
Package: icespeak
IcespeakIcespeakis a Python 3.9+ library that makes Icelandic-language speech synthesis easy.Local installationNote: The Azure TTS package currently only supports the very out-dated OpenSSL version 1.*Clone the repository and cd into the folder.
Then create and activate a virtual environment:python3-mvenvvenvsourcevenv/bin/activateInstall minimal set of dependencies to use the library:python3-mpipinstall.Alternatively, to install in editable mode with extra dev dependencies:python3-mpipinstall-e'.[dev]'UsageBefore using, place API keys for the relevant services in the/keysfolder
(or a folder specified by theICESPEAK_KEYS_DIRenvironment variable).Output audio files are saved to the directory specified
by theICESPEAK_AUDIO_DIRenvironment variable.
By default Icespeak creates the directory<TEMP DIR>/icespeakwhere<TEMP DIR>is the temporary directory on your platform,
fetched viatempfile.gettempdir().By default, generated audio files are removed upon a clean exit,
but this can be disabled by settingICESPEAK_AUDIO_CACHE_CLEAN=0.Text-to-speechSimple example of TTS, which includes phonetic transcription:fromicespeakimporttts_to_file,TTSOptionstext="""\Þetta er texti fyrir talgervingu.\Í honum er ýmislegt sem mætti vera hljóðritað,\t.d. ræður talgerving oft illa við íslenskar skammstafanir,\tölvupósta eins og ekki.tolvupostur@vefsida.is,eða prósentur eins og 48,3%, o.fl.\"""tts_out=tts_to_file(text,TTSOptions(text_format="text",# Set to 'ssml' if SSML tags in text should be interpretedaudio_format="mp3",# Output audio will be in mp3 formatvoice="Gudrun"# Azure TTS voice),transcribe=True# Default is True)print(tts_out.file)# pathlib.Path instance pointing to file on local file systemprint(tts_out.text)# text that was sent to the TTS service (after the phonetic transcription)Results are cached, so subsequent calls with the same arguments should be fast.LicenseIcespeak is Copyright © 2023Miðeind ehf.This set of programs is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the Free
Software Foundation, either version 3 of the License, or (at your option) any later
version.This set of programs is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
A PARTICULAR PURPOSE. See the GNU General Public License for more details.The full text of the GNU General Public License v3 isincluded hereand also available here:https://www.gnu.org/licenses/gpl-3.0.html.If you wish to use this set of programs in ways that are not covered under the
GNU GPLv3 license, please contact us atmideind@mideind.isto negotiate a custom license. This applies for instance if you want to include or use
this software, in part or in full, in other software that is not licensed under
GNU GPLv3 or other compatible licenses.
Package: icet
icetis a tool for the construction and sampling of alloy cluster expansions.
A detailed description of the functionality provided as well as an extensive tutorial can be found in theuser guide.icetis written in Python, which allows easy integration with countless first-principles codes and analysis tools accessible from Python, and allows for a simple and intuitive user interface.
All computationally demanding parts are, however, written in C++ providing performance while maintaining portability.
The following snippet illustrates how one can train a cluster expansion:cs=ClusterSpace(primitive_cell,cutoffs,species)sc=StructureContainer(cs)forstructureintraining_structures:sc.add_structure(structure)opt=Optimizer(sc.get_fit_data())opt.train()ce=ClusterExpansion(cs,opt.parameters)Afterwards the cluster expansion can be used, e.g., for finding ground state structures or sampled via Monte Carlo simulations.For questions and help please use theicet discussion forum on matsci.org.icetand its development are hosted ongitlab.Installationicetcan be installed usingpip:pip3 install icet --useror viaconda:conda install -c conda-forge icetInstallation viapiprequires a C++11 compliant compiler.
Please consult theinstallation section of the user guidefor details.icetis based on Python3 and invokes functionality from other Python libraries includingase,pandas,numba,numpy,scipy,spglib, andtrainstation.Creditsicethas been developed at theDepartment of PhysicsofChalmers University of Technology(Gothenburg, Sweden) and theData and Software Management Centerat the European Spallation Source (Copenhagen, Denmark).When usingicetin your research please citeM. Ångqvist, W. A. Muñoz, J. M. Rahm, E. Fransson, C. Durniak, P. Rozyczko, T. H. Rod, and P. ErhartICET – A Python Library for Constructing and Sampling Alloy Cluster ExpansionsAdv. Theory. Sim., 1900015 (2019)doi: 10.1002/adts.201900015Also consult thecreditspage of the documentation for additional references.
Package: icetk
ICE TokenizerToken id[0, 20000)are image tokens.Token id[20000, 20100)are common tokens, mainly punctuations. E.g.,icetk[20000] == '<unk>',icetk[20003] == '<pad>',icetk[20006] == ','.Token id[20100, 83823)are English tokens.Token id[83823, 145653)are Chinese tokens.Token id[145653, 150000)are rare tokens. E.g.,icetk[145803] == 'α'.You can install the package viapip install icetkTokenizationfromicetkimporticetktokens=icetk.tokenize('Hello World! I am icetk.')# tokens == ['▁Hello', '▁World', '!', '▁I', '▁am', '▁ice', 'tk', '.']ids=icetk.encode('Hello World! I am icetk.')# ids == [39316, 20932, 20035, 20115, 20344, 22881, 35955, 20007]en=icetk.decode(ids)# en == 'Hello World! I am icetk.' # always perfectly recover (if without <unk>)ids=icetk.encode('你好世界！这里是 icetk。')# ids == [20005, 94874, 84097, 20035, 94947, 22881, 35955, 83823]ids=icetk.encode(image_path='test.jpeg',image_size=256,compress_rate=8)# ids == tensor([[12738, 12430, 10398,  ...,  7236, 12844, 12386]], device='cuda:0')# ids.shape == torch.Size([1, 1024])img=icetk.decode(image_ids=ids,compress_rate=8)# img.shape == torch.Size([1, 3, 256, 256])fromtorchvision.utilsimportsave_imagesave_image(img,'recover.jpg')# add special tokensicetk.add_special_tokens(['<start_of_image>','<start_of_english>','<start_of_chinese>'])# transform \nicetk.decode(icetk.encode('abc\nhi',ignore_linebreak=False))# 'abc\nhi'icetk.decode(icetk.encode('abc\nhi'))# 'abc hi'# discourage rare composed tokensicetk.tokenize('//--------')# ['▁//', '--------']icetk.text_tokenizer.discourage_ids(range(125653,130000))# or use icetk.text_tokenizer.discourage_tokensicetk.tokenize('//--------')# ['▁//', '-', '-', '-', '-', '-', '-', '-', '-']
Package: icevision
An Agnostic Object Detection FrameworkIceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-quality pre-trained models from torchvision, MMLabs, and soon Pytorch Image Models. It orchestrates the end-to-end deep learning workflow allowing to train networks with easy-to-use robust high-performance libraries such as Pytorch-Lightning and FastaiIceVision Unique Features:Data curation/cleaning with auto-fixAccess to an exploratory data analysis dashboardPluggable transforms for better model generalizationAccess to hundreds of neural net modelsAccess to multiple training loop librariesMulti-task training to efficiently combine object detection, segmentation, and classification modelsInstallationpipinstallicevision[all]For more installation options, check ourdocs.Important:We currently only support Linux/MacOS.Quick Example: How to train theFridge Objects DatasetHappy Learning!If you need any assistance, feel free to:Join our Forum
Package: icevision-dashboards
IceVisionDashboardIceVisionDashboard is an extension to theIceVisionobject detection framework. Main goal of the library is to support data scientists with there work on object detection problems. This is done by providing different dashboards to provide different steps of the workflow. The dashboards cover investigating datasets, creating new datasets, comparing datasets and analyzing the results of a training.Test stuffExampleThe example shows how a set of records (here the training_records) can be visualized with the dashboard lib to get some fast insights into the data. The last 3 lines of code are from the dashboard library all the lines before are usual icevision code.fromicevision_dashboards.dataimportBboxRecordDatasetfromicevision_dashboards.dashboardsimportObjectDetectionDatasetOverview# load some data from the icedatadata_dir=icedata.fridge.load_data()class_map=icedata.fridge.class_map()parser=icedata.fridge.parser(data_dir)train_records,valid_records=test_parser.parse()# create a dataset that can be consumed by the dashboardstrain_dash_ds=BboxRecordDataset(train_records,class_map)# create a new dashboard instance and display it with the .show() functionoverview_dashboard=ObjectDetectionDatasetOverview(train_dash_ds,width=1500,height=900)overview_dashboard.show()The output will look like this:An overview of some descriptive statistics for the dataset, the images and the classes.Some more indepth information about the classes, how they mix (how often they appear at the same time in an image), distribution of annotations per image and a 2D histogram that can be customized.A gallery with sorting functionality of have a direct look at the images.Tabular representation of all annotations.InstallIceVisionDashboard is available as apippackage via PyPi. To install, simply type:pipinstallicevision-dashboardsIf you are using JupyterLab to view and use your notebooks, a few extra steps are needed. In a terminal, you should type the following:jupyterlabextensioninstall@pyviz/jupyterlab_pyvizAnd then in a new cell inside the notebook in which you want to load IceVision dashboards, you should type and execute the following code:importpanelpanel.extension()ContributingIf you want to contribute add the following lines to yourpre-commitfile to ensure the notebook cell output don't get pushed into the repo.# ensure the oupt of the notebooks is emptyjupyternbconvert--ClearOutputPreprocessor.enabled=True--inplacenbs/*.ipynb
jupyternbconvert--ClearOutputPreprocessor.enabled=True--inplaceexamples/*.ipynb
jupyternbconvert--ClearOutputPreprocessor.enabled=True--inplacetest_data_generation/*.ipynb
nbdev_build_lib
gitadd.
Package: ice-yml-log-formatter
logging.conf example# https://docs.python.org/3.6/library/logging.config.html
#
# Level               Numeric value
# CRITICAL      50
# ERROR         40
# WARNING       30
# INFO          20
# DEBUG         10
# NOTSET        0
# higher level - less logs

[loggers]
keys=root

[handlers]
keys=consoleHandler

[logger_root]
level=DEBUG
handlers=consoleHandler

[handler_consoleHandler]
class=StreamHandler
level=DEBUG
formatter=consoleFormatter
args=(sys.stdout,)

[formatter_consoleFormatter]
class=ice_yml_log_formatter.YAMLLogFormatter
format=[%(levelname)s] %(name)s: %(msg)s
datefmt=simple usage exampleimportosimportlogging.configimportlogginglogging.config.fileConfig('./logging.conf')#....#....# in your ice servant methodtry:raiseException('Any cause')except:logging.exception('Oops!',extra={'ice_current':current,'context':{'any_info_key':'any info value',},})Useice_currentfor ice request metatada print, andcontextfor extra structured information.log example[DEBUG] services.fun - Dispatch
  iceIdentity: ZeroC/Fun
  iceOperation: something
  iceRequestId: 1

[ERROR] services.fun - Unexpected error
  iceIdentity: ZeroC/Fun
  iceOperation: something
  iceRequestId: 1
  context:
    any_info_key: any info value
  Error: Fun::ExampleException
    error_data:
      some_prop: 1
    stack_trace:
      File "path-to-source/fun.py", line 71, in wrapped
        return method(self, *args, **kwargs)

      File "path-to-source/fun.py", line 123, in something
        raise Fun.ExampleException(some_prop=1)
Package: icfs
Library create and manage file system at I2C EEPROM IC’s.
Functionality: Create_TOC, Write, Remove, Load, List, Wipe
Supported EEPROM ICs: 24CXXXChange Log0.1.0 (03/03/2023)First releaseUsefule IC’s: 24C01, 24C02EEPROM file system
* create_TOC
* write file to icfs
* load file from icfs
* remove file from icfs
* list files at icfs
* wipe icfs with charrequired library:
* Adafruit_PureIO > 1.1.8
Package: icgc
The ICGC REST client is a simple python module that allows you to access theInternational Consortium for Cancer and Genomicsweb portal (<https://dcc.icgc.org/>) directly through Python, with a minimum of coding effort.It lets you write queries in our Portal Query Language (PQL) that fetch data from the ICGC web portal as JSON objects. From there, you can use the power of Python to process and analyze the data within those objects however you see fit.Here’s an example that shows you how easy it is to get started!"""
query.py

This script demonstrates running a simple PQL query against the ICGC data
portal with the icgc module.
"""
from __future__ import absolute_import, print_function

import icgc


def run():
    """
    Demonstrate PQL by displaying 1 of each request type as JSON output
    """
    for request_type in icgc.request_types():
        response = icgc.query(request_type=request_type,
                              pql='select(*),limit(1)')
        print(request_type, "===\n\n", response)


if __name__ == '__main__':
    run()Here’s an a simple program that demonstrates how Python can be used with the
icgc Python module to automate decision making: in this case, which files we want to download from the ICGC web portal.from __future__ import absolute_import, print_function
import icgc

KB = 1024
MB = 1024 * KB


def run():
    """
    Show an example of a PQL download with automated decision making.

    We download up to a maximum of 10 MB of data from the portal, of any type
    that will fit within our download limit, and save our the results as a
    tarfile named 'test.tar'.
    """
    pql = 'eq(donor.primarySite,"Brain")'

    # Find which items are available that match our pql query, and how big
    # each of the result file are.

    sizes = icgc.download_size(pql)
    print("Sizes are: {}".format(sizes))

    # We'll only include  a file in our tarfile if the total is below our
    # 10 MB limit. Our tarfile size calculation is approximate; the
    # files inside the tarfile get compressed; so the total size of the tarfile
    # that we download might be smaller than we calculate.

    max_size = 10 * MB
    current_size = 0

    includes = []
    for k in sizes:
        item_size = sizes[k]
        if current_size + item_size < max_size:
            includes.append(k)
            current_size += item_size

    print("Including items {}".format(includes))
    print("Approximate download size={:.2f} MB".format(current_size / MB))

    # Download the information, and save the results in the file "test.tar"
    icgc.download(pql, includes, "test")


if __name__ == "__main__":
    run()InstallationYou can install icgc usingpipby running:pip install icgcIf you prefer, you can also download the source code from the url below.ContributeIf you’d like to contribute to this project, it’s hosted on github.Seehttps://github.com/icgc-dcc/icgc-python
Package: icgc-survival
Random Survival ForestThe ICGC-survival package provides an easy oppurtinity to perform survival prediction on ICGC datasets.Installation$pipinstallicgc-survivalContributeSource Code:https://github.com/julianspaeth/icgc-survivalGetting Started>>>fromdownload_helperimportlogin,download_file_by_project>>>fromfeature_creatorimportextract_gene_affected_counts>>>fromlabel_creatorimportextract_survival_labels>>>token=login(username,password)>>>df=download_file_by_project(token=token,filetype="simple_somatic_mutation",release=28,project_code="ALL-US")>>>ssm_gene_affected_counts=extract_gene_affected_counts(df)>>>labels,features=extract_survival_labels(ssm_gene_affected_counts,donors)>>>x,x_test,y,y_test=train_test_split(features,labels,shuffle=True,test_size=0.33,random_state=10)...SupportIf you are having issues or feedback, please let me know.julian.spaeth@student.uni-tuebinden.deLicenseMIT
Package: icgen
ICGenInstallationpip install icgenfor a development installation seeCONTRIBUTING.mdUsageSampling Datasetsimporticgendataset_generator=icgen.ICDatasetGenerator(data_path="datasets",min_resolution=16,max_resolution=512,max_log_res_deviation=1,# Sample only 1 log resolution from the native onemin_classes=2,max_classes=100,min_examples_per_class=20,max_examples_per_class=100_000,)dev_data,test_data,dataset_info=dataset_generator.get_dataset(dataset="cifar10",augment=True,download=True)Theaugmentparameter controls whether the original dataset is modified.Options only affect sampling withaugment=Trueand the min max ranges do not filter datasets.The data is left at the original resolution, so it can be resized under user control.
This is necessary to for example avoid resizing twice which can hurt performance.You can also sample from a list of datasetsdataset_generator.get_dataset(datasets=["cifar100","emnist/balanced"],download=True)We provide some lists of available datasetsimporticgenicgen.DATASETS_TRAINicgen.DATASETS_VALicgen.DATASETS_TESTicgen.DATASETSor on the commandline you can get the names withpython -m icgen.dataset_namesDownloading Datasets Before ExecutionTo download datasets ahead of time you can runpython -m icgen.download --data_path DATA_PATH --datasets D1 D2 D3or directly download a complete grouppython -m icgen.download --data_path DATA_PATH --dataset_group GROUP  # all, train, dev, testAlternatively, you can also use thedownload=Trueflag of thedataset_generator.get_datasetfunction.Reconstructing and Distributing TasksIn distributed applications it may be necessary to sample datasets on one machine and then use them on another one.
Conversely, for reproducibility it may be necessary to store the exact dataset which was used.
For these cases icgen uses a dataset identifier which uniquely identifies datasets.LicenseMIT
Package: ichi-auth
ichi AuthA fork offastapi-jwt-authwith updated dependenciesDocumentation:https://indominusbyte.github.io/fastapi-jwt-authSource Code:https://codeberg.org/ichi/ichi-authFeaturesFastAPI extension that provides JWT Auth support (secure, easy to use and lightweight), if you were familiar with flask-jwt-extended this extension suitable for you, cause this extension inspired by flask-jwt-extended 😀Access tokens and refresh tokensFreshness TokensRevoking TokensSupport for WebSocket authorizationSupport for adding custom claims to JSON Web TokensStoring tokens in cookies and CSRF protectionInstallationThe easiest way to start working with this extension with pippipinstallichi-authIf you want to use asymmetric (public/private) key signing algorithms, include theasymmetricextra requirements.pipinstall'ichi-auth[asymmetric]'LicenseThis project is licensed under the terms of the MIT license.
Package: ichika
Ichika示例请参阅example.py，它包含了一个识别指定指令并发送图片的简单示例。鸣谢AWR: Ichika 的前身和部分代码来源ricq: 使用 Rust 实现的高性能 QQ 协议 移植于OICQmirai: 高效率 QQ 机器人支持库PyO3: Python 解释器的 Rust 绑定GraiaProject: 用于 Bot 开发的一系列高效, 现代化, 充分可扩展的工具链许可证Ichika使用GNU AGPL-3.0作为许可证，这意味着你需要遵守相应的规则。
Package: ichi-server
ichi ServerFree and open source multiplayer card gameClient| ServerFeaturesAccount system:Account recoveryProfile picturesPer game mode statisticsAdministration tools:Ban and mute players from the serverReport chat messagesBasic chat system:Send messagesReply to other messageExtensible: Create new game modes from scratch or expand existing onesGame modesichi Server currently has two game modesNameDescriptionPlayer countichiAn UNO card game clone2-10 playersichi-0-7An alternative version ofichiwhere discarding a 0 card will swap everyone's cards and discarding a 7 card will let you swap cards with another player2-10 playersBasic setupThis section is very rough, Docker images and more proper documentation will be done in SeptemberRequirementsPython 3.10 or higherGit (optional)Installing from repositoryDownload (or clone) the repositoryFor thelatest releasedownload the respective .zip or .tar.gz fileFor the latest commit you can either:Download the repo in.zipor.tar.gzformatOr using git to clone the repo:git clone https://codeberg.org/ichi/server.gitOpen a terminal window on the downloaded (or cloned) repoCreate a virtual environmentpython -m venv .venvSwitch to the virtual environment:source .venv/bin/activateon Linux or.venv/Scripts/Activate.ps1on Windows (checkthe Python documentantionfor more info)Install requirements and ichi_server usingpip install .Installing from PyPiCreate a virtual environmentpython -m venv .venvSwitch to the virtual environment:source .venv/bin/activateon Linux or.venv/Scripts/Activate.ps1on Windows (checkthe Python documentantionfor more info)Executing the serverExecute the server usingpython -m ichi_server, you will be asked to enter a password for the administrator accountLicenseichi Server is licensed under the GNU AGPL version 3For the full license text check theLICENSE fileLibraries and other licensesFastAPI under theMIT License: Copyright (c) 2018 Sebastián Ramírezhypercorn under theMIT License: Copyright (c) 2018 P G Jonespython-multipart under theApache-2.0 License: Copyright (c) 2012 Andrew Dunhamsqlmodel under theMIT License: Copyright (c) 2021 Sebastián RamírezSQLAlchemy under theMIT License: Copyright (c) 2005-2023 SQLAlchemy authors and contributorspydantic under theMIT License: Copyright (c) 2017 to present Pydantic Services Inc. and individual contributorspasslib under theBSD License: Copyright (c) 2008-2020 Assurance Technologies, LCCaiofiles under theApache-2.0 Licensecolorama under theBSD 3-Clause License: Copyright (c) 2010 Jonathan HartleyPillow under theHPND License:Copyright (c) 1997-2011 by Secret Labs ABCopyright (c) 1995-2011 by Fredrik LundhCopyright (c) 2010-2023 by Jeffrey A. Clark (Alex) and contributorsichi-auth under theMIT License:Copyright (c) 2020 Nyoman Pradipta DewantaraCopyright (c) 2022-present aveeryyalembic under theMIT License: Copyright (c) 2009-2023 Michael Bayerpython-dotenv under theBSD 3-Clause License: Copyright (c) 2014, Saurabh Kumar (python-dotenv), 2013, Ted Tieken (django-dotenv-rw), 2013, Jacob Kaplan-Moss (django-dotenv)Theichi Cards (alt) logois licensed under theCC BY-NC-SA 4.0 license