Saravanan666/pypi-org huggingface dataset:
Package: dtlpy
This is the SDK and CLI open source repository forDataloop.aiplatformFor full platform documentation clickhereFor full SDK documentation clickherePython SupportDtlpy supports these Python versions.Python3.113.103.93.83.73.63.5dtlpy >= 1.76YesYesYesYesYesYesdtlpy >= 1.61YesYesYesYesYesdtlpy 1.60 - 1.50YesYesYesYesdtlpy <= 1.49YesYesYesYesYes
Package: dtm2text
Author:Trevor MurphyContact:trevor.m.murphy@gmail.comThis command line tool dumps your Dolphin TAS Movie files to a plain text list of input data (plus a header file).$dtm2textYOUR_MOVIE# creates YOUR_MOVIE_header and YOUR_MOVIE_inputs.txtAlternatively, you can piece together some plain text lists (plus a header file) into a DTM.$text2dtmNEW_MOVIEheader_fileinputs.txt# creates NEW_MOVIE# you can supply several files of inputs$text2dtmNEW_MOVIEheader_fileinputs_01.txtinputs_02.txt# if you have lots of input files, you can supply a from-file instead$catinputs_from_fileinputs_01.txtinputs_02.txtinputs_03.txt$text2dtmNEW_MOVIEheader_file@inputs_from_fileRequirementsPython 3.  Probably not a hard requirement, I just haven’t tested this tool with any earlier versions.InstallJust usepip!$pipinstalldtm2textLicenseDistributed under the terms of theGPLv3license.
Package: dtmcli
dtmclia client for distributed transaction managerdtmdtmcli是分布式事务管理器dtm的客户端sdkdtm分布式事务管理服务DTM是一款跨语言的开源分布式事务管理器，优雅的解决了幂等、空补偿、悬挂等分布式事务难题。提供了简单易用、高性能、易水平扩展的分布式事务解决方案。亮点极易接入支持HTTP，提供非常简单的接口，极大降低上手分布式事务的难度，新手也能快速接入使用简单开发者不再担心悬挂、空补偿、幂等各类问题，框架层代为处理跨语言可适合多语言栈的公司使用。方便go、python、php、nodejs、ruby各类语言使用。易部署、易扩展仅依赖mysql，部署简单，易集群化，易水平扩展多种分布式事务协议支持TCC、SAGA、XA、事务消息与其他框架对比目前开源的分布式事务框架，暂未看到非Java语言有成熟的框架。而Java语言的较多，有阿里的SEATA、华为的ServiceComb-Pack，京东的shardingsphere，以及himly，tcc-transaction，ByteTCC等等，其中以seata应用最为广泛。下面是dtm和seata的主要特性对比：特性DTMSEATA备注支持语言Golang、python、php及其他Javadtm可轻松接入一门新语言异常处理子事务屏障自动处理手动处理dtm解决了幂等、悬挂、空补偿TCC事务✓✓XA事务✓✓AT事务✗✓AT与XA类似，性能更好，但有脏回滚SAGA事务简单模式状态机复杂模式dtm的状态机模式在规划中事务消息✓✗dtm提供类似rocketmq的事务消息通信协议HTTPdubbo等协议，无HTTPdtm后续将支持grpc类协议star数量dtm从20210604发布0.1，发展快从上面对比的特性来看，如果您的语言栈包含了Java之外的语言，那么dtm是您的首选。如果您的语言栈是Java，您也可以选择接入dtm，使用子事务屏障技术，简化您的业务编写。使用方式fromdtmcliimporttccdeffire_tcc():gid=tcc.tcc_global_transaction(dtm,gid,tcc_trans)return{"gid":gid}deftcc_trans(t):req={"amount":30}t.call_branch(req,svc+"/TransOutTry",svc+"/TransOutConfirm",svc+"/TransOutCancel")t.call_branch(req,svc+"/TransInTry",svc+"/TransInConfirm",svc+"/TransInCancel")安装$ pip install dtmcli完整示例dtmcli-py-sampleLicenseMIT
Package: dtmd
DTMD (Directory-to-Markdown)OverviewDTMD (Directory-to-Markdown) is a Python command-line tool, particularly useful for quickly generating and sharing representations of directory structures and their contents in a structured and readable Markdown format. This makes it ideal for documenting project structures, especially for interactions with language models like ChatGPT.Markdown is the most token efficient manner to interact with LLM'sExample outputdtmd# /my_project## main.py```python# Main application fileimport app

app.run()app.py# App moduledefrun():print('Running the app')/utilshelper.py# Utility functionsdefhelper():return'Helper function'### Features
- Convert directory contents to Markdown format.
- Include or exclude files using patterns (supports fnmatch style, e.g., `*.py`, `data*`).
- Recursive directory parsing.
- Options for output: printing to console, saving to a file, or copying to the clipboard.

## Installation

```bash
pip install dtmdUsageRun DTMD from the command line with the following options:dtmd<target-directory>[options]If no target directory is specified, DTM will default to the current working directory.Options:-ior--include: Patterns to include files. Enclose patterns in quotes to avoid shell expansion (e.g.,'*.py','data*').-eor--exclude: Patterns to exclude files. Enclose patterns in quotes to avoid shell expansion (e.g.,'*.xml','temp*').-oor--output-file: Set the output Markdown file name.-ror--recursive: Enable recursive search in directories.-por--print: Print the output using rich formatting.-cor--clipboard: Copy the output to the clipboard.Exampledtmdmyfolder-i'*.py''*.html'-ooutput.md-rThis command will parse all.pyand.htmlfiles inmyfolderrecursively and save the Markdown output tooutput.md.AuthorsAdrian Galilea -Initial workAcknowledgmentsHat tip to ChatGPT for assistance with project setup and documentation.
Package: dtmf
dtmfPackage for working with DTMF - a system for signaling over the voice band of a telephony system using multi-frequency tones.FeaturesParses dial strings (digits, symbols, etc.) into an object representationConstructs dial strings from element objectsDetects the presence and position of DTMF tones in an audio bufferGenerates DTMF audio from dial stringsInstallationpipinstalldtmfWhat is DTMF?Dual-tone multi-frequency signaling (DTMF) is a telecommunication signaling system used between telephone equipment and other communications devices. DTMF became known in the United States as 'Touch-Tone' for use in push-button telephones supplied to telephone customers.DTMF tones use a mixture of two sine waves at different frequencies. Eight different audio frequencies are combined in pairs to make 16 unique tones. A tone is assigned to each of the digits from 0 to 9, the letters A to D, and the symbols # and *. The combination used for each tone are as follows:1209 Hz1336 Hz1477 Hz1633 Hz697 Hz123A770 Hz456B852 Hz789C941 Hz*0#DDial string syntaxA dial string is a textual representation of a sequence of DTMF digits and/or symbols. This format is commonly used as input to a telephone modem or another telephony device with automatic dialing as instructions for dialing the recipient of an outgoing call.Dial strings use the following DTMF symbols:0-9A-D*orE#orFIn addition to the 16 DTMF symbols, dial strings support the following additional symbols:Por,for a momentary pause (usually 2 seconds)UsageParsing a dial stringfromdtmfimportparsedial_str="5551234,500#"obj=parse(input)print(repr(obj))Output:String([
    Tone("5"),
    Tone("5"),
    Tone("5"),
    Tone("1"),
    Tone("2"),
    Tone("3"),
    Tone("4"),
    Pause(),
    Tone("5"),
    Tone("0"),
    Tone("0"),
    Tone("#")
])Constructing a dial stringimportdtmf.modelasmodelobj=model.String([model.Tone("5"),model.Tone("5"),model.Tone("5"),model.Tone("1"),model.Tone("2"),model.Tone("3"),model.Tone("4"),model.Pause(),model.Tone("5"),model.Tone("0"),model.Tone("0"),model.Tone("#")])print(str(obj))Output:5551234,500#Detecting DTMF tones in an audio bufferfromdtmfimportdetect# list of audio samples as floatsdata=[...]sample_rate=8000results=detect(data,sample_rate)forresultinresults:print(f"{result.start:<3d}-{result.end:>5d}:{result.tone!s}")Output:0 - 105 : 5
105 - 210 : 5
210 - 315 : 5
315 - 420 : 5
420 - 525 : None
...Generating DTMF audio from a dial stringfromdtmfimportgenerateimportdtmf.modelasmodelobj=model.String([model.Tone("5"),model.Tone("5"),model.Tone("5"),model.Tone("1"),model.Tone("2"),model.Tone("3"),model.Tone("4"),model.Pause(),model.Tone("5"),model.Tone("0"),model.Tone("0"),model.Tone("#")])audio=generate(obj)SupportPlease use the project'sIssues pageto report any issues.ContributingInstalling for developmentpoetryinstallLinting source filespoetryrunpylint--rcfile.pylintrcsrc/dtmfRunning testspoetryrunpytestLicenseThis library is licensed under the terms of theMITlicense.
Package: dtmm
dtmm is an electro-magnetic field transmission and reflection calculation engine and visualizer. It can be used for calculation of transmission or reflection properties of layered homogeneous or inhomogeneous materials, such as confined liquid-crystals with homogeneous or inhomogeneous director profile. DTMM stands for Diffractive Transfer Matrix Method and is an adapted Berreman 4x4 transfer matrix method and an adapted 2x2 extended Jones method.
Package: dt-notify-to-people
Sentry DingTalk Notify PeopleSentry 集成钉钉机器人通知，并且通知人员。本插件是基于sentry-dingtalk-notify进行的二次开发，加上了自定义的需求。Requirmentssentry >= 21.5.1特性发送异常通知到钉钉支持钉钉机器人webhook设置关键字支持通知到具体的用户配置环境变量DINGTALK_WEBHOOK: Optional(string)DINGTALK_CUSTOM_KEYWORD: Optional(string)DINGTALK_PHONES: Optional(string)快速使用安装使用pip命令$pipinstalldt-notify-to-people写入依赖文件onpremise-xxx/sentry/requirements.txt$echodt-notify-to-people=>>requirements.txt钉钉机器人配置钉钉机器人并拿到对应的 webhook, 可以对机器人设置 关键词、签名、IP限制。配置Sentry安装项目下的requirements.txt，添加dt-notify-to-people==1.0.8；在 Sentry 面板 Settings > Integrations 中找到 DingTalk 并配置 webhook、关键词等信息，添加项目，创建告警规则；关于插件编写具体教程可以阅读这边文章《如何开发自己的Python库》；编写自己的插件代码；打包自己的插件代码：python setup.py sdist bdist_wheel上传自己的插件：twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
Package: dtnsim
NAMEdtnsim - DTN (Delay/Disruption Tolerant Networking) simulator with several agent/mobility modelsDESCRIPTIONdtnsimis a DTN (Delay/Disruption-Tolerant Networking) simulator written
in Python.  Since all programs indtnsimare written in Python, if you are
a Python programmer, you can easily modify simulator functionalities and/or
add new features.  Python is one of major light-weight programming languages,
which enables rapid prototyping of DTN simulations.  For instance, when you
think of a novel network protocol for DTN, you can rapidly implement the
protocol with the help of high expressiveness of Python language.Since almost everything indtnsimis written in Python,dtnsimis not
suitable for extremely large-scale DTN simulations.  For instance,dtnsimis not suitable for very large-scale DTN simulations with millions of agents
(i.e., mobile nodes/terminals).  However, such limitation is not an issue in
practice since DTN is generally expected to be utilized in environments with
spares agents.EXAMPLEdtnsim|cellxINSTALLATIONpip3installdtnsimAVAILABILITYThe latest version ofdtnsimis available at PyPI
(https://pypi.org/project/dtnsim/) .AUTHORHiroyuki Ohsaki (ohsaki[atmark]lsnl.jp)
Package: dtn-tvg-util
dtn-tvg-utilA Python module providing functions for the representation of DTNs based on time-varying network graphs. Supports Python 2.7 and Python 3.4+.RequirementsPython 2.7 or 3.4+a build toolchain for your platform plus thepython-devpackage to install the Python dependenciesto place Ring Road ground stations,libgeos-devandlibgdal-devGetting StartedFor just using this module, install it viapip:pip install dtn-tvg-utilFor the development setup, you should first create a virtual environment and install withpip install -epython3 -m venv --without-pip .venv
curl -sS https://bootstrap.pypa.io/get-pip.py | .venv/bin/python
source .venv/bin/activate
pip install -e .Some example tools can be found intvgutil.tools. These scripts allow to generate and convert time-varying graph representations of Ring Road [1] scenarios.For using the Ring Road scenario generators, you have to install some further dependencies:pip install "dtn-tvg-util[ring_road]"
# OR
pip install -e ".[ring_road]"For ground station placement (viatvgutil/tools/create_gs_list.py), also install thegs_placementextras by adding[ring_road,gs_placement]to thepip installcommand.TestingUnit tests for the core functionality are provided intest/. They are based on the Pythonunittestpackage. Use a test runner of your choice to execute them, e.g.pytestornose.pip install nose
python -m nose testLicenseThis code is licensed under the MIT license. SeeLICENSEfor details.References[1] Ring Road Networks (RRN) are Disruption-tolerant Networks (DTN) for communication, based on the deployment of low-cost LEO satellites. For more information, see the following paper:S. C. Burleigh and E. J. Birrane, “Toward a Communications Satellite Network for Humanitarian Relief,” in International Conference on Wireless Technologies for Humanitarian Relief, Amritapuri, India, 2011.
Package: dtoc
This is a Python program and associated utilities, which supports converting
devicetree files into C code. It generates header files containing struct
definitions, as well as C files containing the data. It does not require any
modification of the devicetree files.Some high-level libraries are provided for working with devicetree. These may
be useful in other projects.This package also includes some U-Boot-specific features, such as creatingstruct udeviceandstruct uclassentries for devicetree nodes.
Package: dtogen
DTOgen - Data Transfer Object GeneratorDTOgen is a powerful library that allows users toeffortlessly generate Data Transfer Object (DTO) classesin multiple programming languages from YAML definitions. DTOgen aims tosimplify the process of mappingJSON or XML data to objects, making it an excellent tool for microservices development.With DTOgen, syncing Data Transfer Objects (DTOs) across services becomes a seamless process. Since DTOgen allows you to define DTOs in a centralized YAML file andgenerates the corresponding classes in multiple programming languages, you can ensure consistency across your microservices easily.DTOgen is designed to handle not only simple data types but alsocomplex data structureslike lists and maps. With the ability to represent lists and maps in the YAML definitions, DTOgen can generate DTO classes that accurately reflect these data structures in multiple programming languages.FeaturesDefine DTOs in YAML: Users can create their Data Transfer Object definitions in a YAML file, which makes it easy to manage and understand the data structures.Multi-language Support: DTOgen supports generating DTO classes for three popular programming languages: Java, Python, and TypeScript. This enables seamless integration into various microservices projects.Simple CLI Integration: The library provides a command-line interface (CLI) tool 'dtogen', making it straightforward for users to generate DTO classes for their chosen language.InstallationDTOgen can be installed using pip for Python, TypeScript and Java projects.pipinstalldtogenUsageCreate a YAML file with your DTO definitions. For example,dto_definitions.yaml:info:name:library-dtosversion:0.0.1dtos:person:attributes:-name:nametype:string-name:agetype:integerRun the 'dtogen' CLI command with the desired target language:dtogen-idto_definitions.yaml-odto_directory-ljava--java-packagecom.example.dtosThe library will generate the corresponding DTO classes in the specified output directory for the chosen language.ExampleGiven thedto_definitions.yamlexample above, DTOgen will generate the following DTO classes for each language:JavapublicclassPerson{publicStringname;publicintage;}PythonclassPerson:name:strage:intTypeScriptexportclassPerson{name:string|undefined;age:number|undefined;}DTOgen simplifies the process of creating Data Transfer Objects, enabling developers to focus more on building efficient microservices without worrying about complex data mapping.
Package: dtool
Make your data more resilient, portable and easy to work with by packaging
files & metadata into self contained datasets.Documentation:http://dtool.readthedocs.ioPaper:https://doi.org/10.7717/peerj.6562Free software: MIT LicenseOverviewdtool is a suite of software for managing scientific data and making it
accessible programatically. It consists of a command line interfacedtooland a Python API:dtoolcore.Thedtoolcommand line interface allows one to organise files into datasets
and to move datasets between different storage solutions, for example from
local disk to remote object storage. Importantly it also provides methods to
verify that the transfer has been successful.The Python API gives complete access to the data and metadata in a dataset.  It
makes it easy to create scripts for processing the items, or a subset of items,
in a dataset. The Python API also allows datasets to be constructed
programatically.dtool is extensible, meaning that it is possible to create plugins both for
adding functionality to the command line interface and for creating interfaces
to custom storage backends.ThedtoolPython package is a meta package that installs the packages:dtoolcore- core APIdtool-cli- CLI plugin scaffolddtool-annotation- CLI commands for working with dataset annotationsdtool-config- CLI commands for configuring dtooldtool-create- CLI commands for creating datasetsdtool-info- CLI commands for getting information about datasetsdtool-overlay- CLI commands for working with per item metadata stored as overlaysdtool-symlink- storage broker interface allowing symlinking to datadtool-http- storage broker interface allowing read only access to datasets over HTTPInstallation:$ pip install dtoolThere are support packages for several object storage solutions:dtool-s3- storage broker interface to S3 object storagedtool-azure- storage broker interface to Azure Storagedtool-ecs- storage broker interface to ECS S3 object storagedtool-irods- storage broker interface to iRODSIf you have access to Amazon S3, Microsoft Azure, ECS S3 or iRODS storage you may also want to install support for these:$ pip install dtool-s3 dtool-azure dtool-ecs dtool-irodsUsage:$ dtool create my-awesome-dataset
Created proto dataset file:///Users/olssont/my-awesome-dataset
Next steps:
1. Add raw data, eg:
   dtool add item my_file.txt file:///Users/olssont/my-awesome-dataset
   Or use your system commands, e.g:
   mv my_data_directory /Users/olssont/my-awesome-dataset/data/
2. Add descriptive metadata, e.g:
   dtool readme interactive file:///Users/olssont/my-awesome-dataset
3. Convert the proto dataset into a dataset:
   dtool freeze file:///Users/olssont/my-awesome-dataset
Package: dtoolai
dtoolAI is a library for supporting reproducible deep learning.Quick startIf you’d like to see what dtoolAI can do without installing anything, two of the
Jupyter notebooks in this repository highlighting dtoolAI functions can be run
through Google Colab without any local software installation:Training a character recognition networkRetraining a network on new dataYou’ll need a Google account to run these, and when you load the notebooks,
click “Open in playground” to be able to execute code.InstallationDependenciesdtoolAI is dependent on the following Python packages:pytorchtorchvisiondtoolcoredtool-httpclickpillowIf you install dtoolAI withpiporcondaas described below, these
dependencies will be installed automatically. If you wish to install manually,
you’ll need to install these before installing dtoolAI.For Windows users, we recommend installing pytorch and torchvision through
anaconda/conda. See the section below for details.ThroughpipdtoolAI requires Python version 3 and Pytorch.WarningInstall Pytorch before installing dtoolAI. For information on how to install Pytorch this see thePytorch getting started guidefor details.Once Pytorch has been installed dtoolAI can be installed through pip:pipinstalldtoolaiThroughcondaYou can also install dtoolAI through conda. To optionally create a conda environment in which to install
dtoolAI:condacreate-ndtoolaicondaactivatedtoolaiThen you can install with:condainstallpytorch==1.4.0torchvision==0.5.0-cpytorchcondainstalldtoolcoredtool-httpdtoolai-cdtoolTo install the dtool command line utilities, you’ll need to use pip:pipinstalldtoolWithsetup.pyYou can also download this repository and install through:pythonsetup.pyinstallDocumentationPrimary documentation:https://dtoolai.readthedocs.io/en/latest/Detailed examples of API use are provided in the notebooks/ directory in this
repository.TestsRunning the tests requires pytest.To run the faster tests in the test suite, use:pytesttests/-m"not slow"The test suite also includes full end-to-end tests that create datasets, train
models and evaluate them on those datasets. These are much slower, to run them
use:pytesttests/
Package: dtool-annotation
Installation$ pip install dtool-annotationExample usageGet a dataset to play with:$ LOCAL_DS_URI=$(dtool cp -q http://bit.ly/Ecoli-ref-genome .)Add an annotation:$ dtool annotation set $LOCAL_DS_URI project dtool-demoGet an annotation:$ dtool annotation get $LOCAL_DS_URI project
dtool-demoAdd an annotation forcing a specific type:$ dtool annotation set --type int $LOCAL_DS_URI stars 3List all annotations:$ dtool annotation ls
project dtool-demo
stars   3For more information see thedtool documentation.Related packagesdtoolcoredtool-clidtool-createdtool-overlay
Package: dtool-azure
GitHub:https://github.com/jic-dtool/dtool-azurePyPI:https://pypi.python.org/pypi/dtool-azureFree software: MIT LicenseFeaturesCopy datasets to and from Azure storageList all the datasets in a Azure storage accountCreate datasets directly in Azure storageInstallationTo install the dtool-azure package:pip install dtool-azureConfigurationInstall the Azure command line client via:pip install azure-cli(you may wish to install this in a virtual environment)Then:az loginTo log into Azure.Then you need to run (changing the resource name/group as appropriate):az storage account show-connection-string --name jicinformatics --resource-group jic_informatics_resources_ukwestThen create the file.config/dtool/dtool.jsonand add the Azure account name and key using the format below:{
    "DTOOL_AZURE_ACCOUNT_KEY_<ACCOUNT NAME>": "<KEY HERE>"
}Changing the account name and key as appropriate. For example if the account
name was “jicinformatics” and the key “some-secret-token”:{
    "DTOOL_AZURE_ACCOUNT_KEY_jicinformatics": "some-secret-token"
}UsageTo copy a dataset from local disk (my-dataset) to an Azure storage account
(jicinformatics) one can use the command below:dtool copy ./my-dataset azure://jicinformatics/To list all the datasets in an Azure storage account one can use the command below:dtool ls azure://jicinformatics/See thedtool documentationfor more detail.Related packagesdtoolcoredtool-httpdtool-s3dtool-irodsdtool-ecs
Package: dtool-cli
Data management command line tool: dtool.OverviewThedtool-clipython package provides a skeleton command line interface to
dtool that other “dtool” command line packages can be plugged into.It makes use ofclickandclick-plugins.Creating a pluginThedtool-cliplug-in system exposes name spacedtool.clifor
registering commands.To create adtool-cliplug-in, create a Python package and register the
function of interest in thesetup.pyfile.  Below is the content of a
hypotheticaldtool_create/__init__.pyfile:import click

@click.command()
def create():
    print("Creating dataset...")

@click.command()
def freeze():
    print("Freezing dataset...")To create an entry point for this function add the below in thesetup.py:from setuptools import setup

setup(
    ...
    entry_points={
        "dtool.cli": [
            "create=dtool_create:create",
            "freeze=dtool_create:freeze",
        ],
    },
    ...
)InstallationTo install the dtool-cli package.cddtool-clipythonsetup.pyinstall
Package: dtool-config
InstallationInstallation usingpip:pip install dtool-configExample usageSetting the user name and email:dtool config user name "Marie Curie"
dtool config user email marie.curie@sorbonne-universite.frSetting the download cache:mkdir /tmp/dtool
dtool config cache /tmp/dtoolRelated packagesdtoolcoredtool-clidtool-infodtool-create
Package: dtoolcore
Documentation:http://dtoolcore.readthedocs.ioGitHub:https://github.com/jic-dtool/dtoolcorePyPI:https://pypi.python.org/pypi/dtoolcoreFree software: MIT LicenseFeaturesCore API for adding different types of metadata to files on diskAutomatic generation of structural metadataProgrammatic discovery and access of items in a datasetStructural metadata includes hash, size and modification time for
subsequent integrity checksAbility to annotate individual files with arbitrary metadataMetadata stored on disk as plain text files, i.e. disk datasets
generated using this API can be accessed without special toolsAbility to create plugins for custom storage solutionsPlugins for iRODS and Microsoft Azure storage backends availableCross-platform: Linux, Mac and Windows are all supportedWorks with Python 2.7, 3.5 and 3.6No external dependenciesOverviewThe dtoolcore project provides a Python API for managing (scientific) data.
It allows researchers to:Package data and metadata into a datasetOrganise and backup datasets easilyFind datasets of interestVerify the contents of datasetsDiscover and work with data programatically