Saravanan666/pypi-org huggingface dataset:
Package: dtcc-model
DTCC ModelDTCC Model defines the common data model and data formats for DTCC Platform.
Additionally, DTCC Model provides utilities for working with the data model
and data formats.This project is part theDigital Twin Platform (DTCC Platform)developed at theDigital Twin Cities Centresupported by Sweden’s Innovation Agency Vinnova under Grant No. 2019-421 00041.DocumentationThis project is documented as part of theDTCC Platform Documentation.Authors (in order of appearance)Anders LoggVasilis NaserentinDag WästerbergLicenseThis project is licensed under theMIT license.Copyright is held by the individual authors as listed at the top of
each source file.Community guidelinesComments, contributions, and questions are welcome. Please engage with
us through Issues, Pull Requests, and Discussions on our GitHub page.
Package: dtcc-viewer
DTCC ViewerDTCC Viewer provides light-weight data visualization for DTCC Platform.This project is part theDigital Twin Platform (DTCC Platform)developed at theDigital Twin Cities Centresupported by Sweden’s Innovation Agency Vinnova under Grant No. 2019-421 00041.DocumentationThis project is documented as part of theDTCC Platform Documentation.Authors (in order of appearance)Jens OlssonLicenseThis project is licensed under theMIT license.Copyright is held by the individual authors as listed at the top of
each source file.Community guidelinesComments, contributions, and questions are welcome. Please engage with
us through Issues, Pull Requests, and Discussions on our GitHub page.
Package: dtcc-wrangler
DTCC TemplateDTCC Template is a template for creating clean and simple
Python packages that include C++ code.Highlights:Usespyproject.tomland installable usingpip installUsesscikit_build_coreas build backendC++ extensions built viaCMakeandCMakeLists.txtThis project is part theDigital Twin Platform (DTCC Platform)developed at theDigital Twin Cities Centresupported by Sweden’s Innovation Agency Vinnova under Grant No. 2019-421 00041.DocumentationThis project is documented as part of theDTCC Platform Documentation.Authors (in order of appearance)Anders LoggVasilis NaserentinDag WästerbergLicenseThis project is licensed under theMIT license.Copyright is held by the individual authors as listed at the top of
each source file.Community guidelinesComments, contributions, and questions are welcome. Please engage with
us through Issues, Pull Requests, and Discussions on our GitHub page.
Package: dtcd
Dynamic network blocks allocation via simple HTTP RPCrequirementspythonnetaddrusageWith curl:# allocate a network
$ curl -X POST --data "$uuid" http://localhost:7623/v1/network/

# free a previously allocated network
$ curl -X DELETE --data "10.0.0.0/24" http://localhost:7623/v1/network/

# list current allocations
$ curl http://localhost:7623/v1/network/ | python -m json.tool
Package: dtcv2-util
DTCV2-utilsThe libraryGetting startedThis is a library with utilities developed in DTGEO project in DTCV2 for general purpose. This library contains scripts and functions required for:Input file validation:through a json file definition of variablesLog (error) file manager:creates and writes the log_fileMeteo utils:utilities for meteorological downloadESP utils:utilities for Eruption Source ParametersInput file validationAuthors and acknowledgmentLicenseFor open source projects, say how it is licensed.Project statusIn progress
Package: dtd
Death to DS_StorePython package to patch all the internal functions to ignore .DS_Store file in MacFree software: MIT licenseDocumentation: Usage mentioned belowFeaturesPatches pathlib internal functions to ignore .DS_Store fileExamplesBefore>>>importpathlib>>>list(pathlib.Path('./tests').iterdir())[PosixPath('tests/.DS_Store'),PosixPath('tests/__init__.py'),PosixPath('tests/__pycache__'),PosixPath('tests/test_dtd.py'),PosixPath('tests/test_pathlib.py')]After>>>importpathlib>>>importdtd>>>dtd.patch_all()>>>list(pathlib.Path('./tests').iterdir())[PosixPath('tests/__init__.py'),PosixPath('tests/__pycache__'),PosixPath('tests/test_dtd.py'),PosixPath('tests/test_pathlib.py')]Quick usage by just importing autopatch>>>importpathlib>>>fromdtdimportautopatch>>>list(pathlib.Path('./tests').iterdir())[PosixPath('tests/__init__.py'),PosixPath('tests/__pycache__'),PosixPath('tests/test_dtd.py'),PosixPath('tests/test_pathlib.py')]CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-06-27)First release on PyPI.
Package: d-tect
Installation$ pip install d-tect && d-tectFeaturesd-tect, ** Pentest the Modern Web **How to install ?$ pkg install python -y
$ pip install d-tect
$ d-tectRun ?$ d-tectDisclaimerWe are not responsible for your loss by this tool.
this tool is made for Education/Learning Purpose.
Dont harm anyone using this tool.Usage$ d-tect -h

Author : Shawar Khan
PIP module : Parixit Sutariya
Github : github.com/Bhai4You
Blogspot : https://bhai4you.blogspot.comGithub
Package: dtee
dtee是一个解析工具。它借助tornado的异步特性，对TCP协议下的数据传输对象stream进行解析。示例解析如下TCP包：##0183QN=123;PW=123456;SN=a123456;Flag=5;DataTime=20201203133147&&A000rn配置解析规则：DTParserRule(2,6,”MLZ”),   # 通过坐标定位DTParserRegxRule(r’QN=(?P<QN>.*?);’),  # 通过正则定位DTParserRegxRule(r’PW=(?P<PW>.*?);’),  # 通过正则定位DTParserRegxRule(r’SN=(?P<SN>.*?);’),  # 通过正则定位DTParserRegxRule(r’Flag=(?P<Flag>.*?);’),  # 通过正则定位DTParserRegxRule(r’DataTime=(?P<DataTime>.*?)&&’)  # 通过正则定位得出如下结果：{‘MLZ’: ‘0183’, ‘QN’: ‘123’, ‘PW’: ‘123456’, ‘SN’: ‘a123456’, ‘Flag’: ‘5’, ‘DataTime’: ‘20201203133147’}安装pip install dtee快速使用1.复制 dtee.example内容（tornado项目）项目示例结构如下：dtee_clientclient.pydtee_scriptdtee_main.pydtee_script_0119.py      # example1 （选其一，或多个）dtee_script_0183.py      # example2 （选其一，或多个）dtee_script_0660.py      # example3 （选其一，或多个）2.编写 dtee_script_xxx.py，用来解析TCP包中的数据  # example: dtee_script/dtee_script_0183.pyfromdtee.dtruleimportDTKeyRule,DTParserRule,DTParserRegxRule# Is using this scriptFLAG=True# flag rulekeyrules=(DTKeyRule(2,6,'0183'))# parse ruleparserules=(DTParserRule(2,6,"MLZ"),DTParserRegxRule(r'QN=(?P<QN>.*?);'),DTParserRegxRule(r'PW=(?P<PW>.*?);'),DTParserRegxRule(r'SN=(?P<SN>.*?);'),DTParserRegxRule(r'Flag=(?P<Flag>.*?);'),DTParserRegxRule(r'DataTime=(?P<DataTime>.*?)&&'))# callbackdefpartternback(data,stream):print("partternback 0183:",data)3.编写 client.py, 模拟TCP包  # example: dtee_client/client.pyimportdatetimefromtornadoimportioloop,gen,iostreamfromtornado.tcpclientimportTCPClient'''mock local client'''asyncdeflocal_request(DATA):stream=awaitTCPClient().connect(host='localhost',port=8064)try:awaitstream.write(DATA.encode('utf-8'))exceptiostream.StreamClosedError:passasyncdefmain():DATA1='##0183QN=123;PW=123456;SN=a123456;Flag=5;DataTime=20201203133147&&A000\r\n'DATA=DATA1awaitlocal_request(DATA)if__name__=='__main__':ioloop.IOLoop.current().run_sync(main)4.运行 TCP服务端python app.py2021-02-2618:48:52,113-tools:tools.py-L31-INFO:Currentloglevelis:DEBUG2021-02-2618:48:52,113-app:app.py-L24-INFO:Startlistening，port:80642021-02-2618:48:52,114-selector_events:selector_events.py-L58-DEBUG:Usingselector:SelectSelector5.运行 TCP客户端python client.py
查看服务端日志，如下：2021-02-2618:48:52,113-tools:tools.py-L31-INFO:Currentloglevelis:DEBUG2021-02-2618:48:52,113-app:app.py-L24-INFO:Startlistening，port:80642021-02-2618:48:52,114-selector_events:selector_events.py-L58-DEBUG:Usingselector:SelectSelectorpartternback0183:{'MLZ':'0183','QN':'123','PW':'123456','SN':'a123456','Flag':'5','DataTime':'20201203133147'}handle_datadata:{'MLZ':'0183','QN':'123','PW':'123456','SN':'a123456','Flag':'5','DataTime':'20201203133147'}2021-02-2618:51:38,832-dtmanager:dtmanager.py-L38-WARNING:Lostclientathost('::1',61778,0,0)其他说明TCP数据包结束标识，可以通过tornado的方法，自定义“read_until”，“read_bytes”等，具体见如下：
dtee_script/dtee_main.pyBaseDir=os.path.dirname(__file__)filename=os.path.basename(__file__)# 二选一# dtparsermanager = DTParserManager(#     eofmethod="read_bytes",#     num_bytes=1024,#     partial=True)# 二选一dtparsermanager=DTParserManager(eofmethod="read_until",delimiter=b"\r\n")version0.1.11 新增预处理方法pre_parse example/app.py 用来预留原始数据的处理工作
Package: dteenergybridge
dteenergybridgeLibrary for retrieving data from a DTE Energy BridgeFree software: MIT licenseUsagefromdteenergybridgeimportDteEnergyBridgedte=DteEnergyBridge('192.168.1.1',1)print(dte.get_current_energy_usage())DTE Energy Bridge hardware versionsThere are currently two versions of the DTE Energy Bridge.Version 1 looks like:http://aemstatic-ww1.azureedge.net/content/dam/elp/print-articles/PGI/05/Powerly_EB.jpgVersion 2 looks like:https://www.newlook.dteenergy.com/wps/wcm/connect/6e6840c4-11ae-4a91-ac93-45ec68d3560e/bridge-hero.jpg?MOD=AJPERES&CACHEID=6e6840c4-11ae-4a91-ac93-45ec68d3560eCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-09-16)First release on PyPI.
Package: dTests
A python tool to distributively run GCJ/FB hacker cup testcases on several machines.How does it workFirst the splitter runs on the input file to split it into seprate test cases.Test cases with the actuall code are sent to the server.The server distributes the test cases and the actuall code to its registered nodes.Each node computes the result for its testcases and sends the result back to the serverThe server collects the results and sort them by their testcase number and sends them back to the calling script.Installationpip install dTestsRunning dTestsCreate new project:dtests_job new project_name languageThe only supported languages now are “cpp” and “java”.Open the new folder and code your splitter to read from the input.in file.Between each test cases print “–split–\n”. The split marker can be configured in “config.json” file. This will split the input file into single test cases which will be distributed on the machines running.Code your program file to read from stdin as if it is reading a single test cases.Run the serverdtests_serverRun one or many nodes on other machines ( or for testing on the same machine ):dtests_node --host host --port portFinally run the job:dtests_job runCheck the help of these commands for further customizationsSample projectThis is a sample cpp project that takes input and multiply it by itselfhttps://github.com/MohamedBassem/dTests/tree/master/samples/power2The config.json file contains the project configuration. The language and the splitter syntax.The splitter.cpp file contains the code that reads the input from the “input.in” file and splits it to single test cases.The program.cpp file that reads a single test cases from stdin and computes the result.To Run this project, run the server and at least one node. Then run “dtests_job run” in the project’s directory.TODOFinding a way to pass the testcase number to the programWriting Tests.Making the system fault tolerant.Finding a better way for splitting the input file.Supporting more programming languages.For more info visithttps://github.com/MohamedBassem/dTestsContributingYour contributions ( Ideas, improvments, etc.. ) are more than welcome.LICENSELicensed under theMIT license.
Package: dt-extensions-sdk
Dynatrace Extensions Python SDKTable of ContentsQuick StartLicenseDevelopingDocumentationThe documentation can be found ongithub pagesQuick StartRequirements:Python 3.10+Install the SDKpipinstalldt-extensions-sdk[cli]# Note, on some shells like zsh you may need to escape the brackets - pip install dt-extensions-sdk\[cli\]Create signing certificatesdt-sdkgencertsCreate a new extensiondt-sdkcreatemy_first_extensionSimulatecdmy_first_extension
dt-sdkrunBuilddt-sdkbuildUpload# Note, you need to either set environment variables DT_API_URL and DT_API_TOKEN or pass them as argumentsdt-sdkuploadDevelopingTestinghatch run testLintinghatch run lint:allBuildinghatch buildBuilding docshatch run docs:buildLicensedt-extensions-sdkis distributed under the terms of theMITlicense.Publishing to PyPIIt's automatically published to PyPi on each pushed tag, and usesgh-action-pypi-publishVersion will be determined usingabout.py
Package: dtfilterthumbor
DantriFilter docsẢnh logo: ở đính kèmKích thước logo:Chiều rộng bằng 12.5% (1/8) chiều rộng của ảnhChiều cao auto (keep ratio) theo chiều rộng của logoVị trí: cách góc dưới bên trái một khoảng bằng 1/2 chiều cao của logo tính theo cả chiều dọc và chiều ngang
Package: dtgui
dtgui is a graphical user interface for interactively testing a baseline-removal algorithm based on the dual-tree complex wavelet transform
published in the following article:[1]L. P. René de Cotret and B. J. Siwick, A general method for baseline-removal in ultrafast
electron powder diffraction data using the dual-tree complex wavelet transform, Struct. Dyn. 4 (2017) DOI: 10.1063/1.4972518.The underlying function isdocumented in its original package.InstallationOther than the pip-installable requirements, dtgui requires PyQt5 to be installed.UsageOnce installed, the GUI can be started using the following command:python -m dtguiThe data fed to dtgui should be comma-separated values files (.csv). The first column is expected to be the abscissa values,
while the second column should be the ordinates.Support / Report IssuesAll support requests and issue reports should befiled on Github as an issue.Licensedtgui is made available under the MIT License. For more details, seeLICENSE.txt.
Package: dtim
dtimdtim – Python DateTime IMprovementInstallationpipinstalldtimWhy dtimPython standard datetime.datetime is awesome, but lack some of common
methods like computing timestamps, checking naive, aware datetime
easily.dtim comes in for simplification.NOTEI wrotedtimbefore I knowmayaYou should use it, it’s great.UsagefromdtimimportDateTimedt=DateTime.now()# Number of seconds since epochprintdt.epoch()# Check datetime is naiveprintdt.is_naive()# Check datetime is awareprintdt.is_aware()Output:1465591177.280863TrueFalseAuthorCuong Manh Lecuong.manhle.vn@gmail.comLicenseSeeLICENSE
Package: dtintegrations
Disruptive Technologies Python IntegrationsDocumentationPython API ReferenceDeveloper DocumentationInstallationThe package can be installed through pip:pipinstall--upgradedtintegrationsor from source:pipinstall.RequirementsPython 3.8, 3.9, 3.10, 3.11UsageCurrently, the main functionality of this package is validating requests forwarded by aData Connector.The following example shows this for aGoogle Cloud Function.fromdtintegrationsimportdata_connector,providerdefendpoint(request):# Validate and decode the incoming request.payload=data_connector.HttpPush.from_provider(request=request,provider=provider.GCLOUD,secret='<SIGNATURE_SECRET>',)# Print the payload data.print(payload)# If all is well, return 200 response.return('OK',200)ExamplesA few examples has been provided, but must be run either on a serverless platform or locally with a combination orngrokand the appropriate development framework.ExceptionsIf a method is unsuccessful or has been provided with invalid parameters, an exception is raised. A list of available exceptions are available in theAPI Reference.DevelopmentSet up the development virtualenv environment:makeRun unit-tests against the currently active python version:make testLint the package code using MyPy and flake8:make lintBuild the package distribution:make build
Package: dtit
.. image:: https://img.shields.io/badge/License-MIT-yellow.svg:target: https://opensource.org/licenses/MIT:alt: License*A Decision Tree (Conditional) Independence Test (DTIT).*Introduction-----------Let *x, y, z* be random variables. Then deciding whether *P(y | x, z) = P(y | z)*can be difficult, especially if the variables are continuous. This packageimplements a simple yet efficient and effective conditional independence test,described in [link to arXiv when we write it up!]. Important features that differentiatethis test from competition:* It is fast. Worst-case speed scales as O(n_data * log(n_data) * dim), where dim is max(x_dim + z_dim, y_dim). However, amortized speed is O(n_data * log(n_data) * log(dim)).* It applies to cases where some of x, y, z are continuous and some are discrete, or categorical (one-hot-encoded).* It is very simple to understand and modify.* It can be used for unconditional independence testing with almost no changes to the procedure.We have applied this test to tens of thousands of samples of thousand-dimensional datapoints in seconds. For smaller dimensionalities and sample sizes, it takes a fraction of a second. The algorithm is described in [arXiv link coming], where we also provide detailed experimental results and comparison with other methods. However for now, you should be able to just look through the code to understand what's going on -- it's only 90 lines of Python, including detailed comments!Usage-----Basic usage is simple, and the default settings should work in most cases. To perform an *unconditional test*, use dtit.test(x, y):.. code:: pythonimport numpy as npfrom dtit import dtitx = np.random.rand(1000, 1)y = np.random.randn(1000, 1)pval_i = dtit.test(x, y) # p-value should be uniform on [0, 1].pval_d = dtit.test(x, x + y) # p-value should be very small.To perform a conditional test, just add the third variable z to the inputs:.. code:: pythonimport numpy as npfrom dtit import dtit# Generate some data such that x is indpendent of y given z.n_samples = 1000z = np.random.dirichlet(alpha=np.ones(2), size=n_samples)x = np.vstack([np.random.multinomial(20, p) for p in z]).astype(float)y = np.vstack([np.random.multinomial(20, p) for p in z]).astype(float)# Check that x and y are dependent (p-value should be uniform on [0, 1]).pval_d = dtit.test(x, y)# Check that z d-separates x and y (the p-value should be small).pval_i = dtit.test(x, y, z)Installation-----------pip install dtitRequirements------------Tested with Python 3.6 and* numpy >= 1.12* scikit-learn >= 0.18.1* scipy >= 0.16.1.. _pip: http://www.pip-installer.org/en/latest/
Package: dtit.microFeaturesGenerator
CRC LibraryIntroductionSTM32 compatible MFCC & SFFT using LiteMathInstallpip install dtit.microFeaturesGeneratorCheckExecute* python dtit\microFeaturesGenerator\tests\hello.py *should echo with "dtit.microFeaturesGenerator"UsagePlease refer to examples in tests directory.Error ReportPlease email todev@datatellit.com
Package: dtit.pyCRCLib
CRC LibraryIntroductionThis is DTIT CRC library, supporting ModBus and NB-IoT CRC16 for now.Installpip install dtit.pyCRCLibCheckExecutepython dtit\pyCRCLib\tests\hello.pyshould echo with "dtit.pyCRCLib"UsagePlease refer to examples in tests directory.Error ReportPlease email todev@datatellit.com
Package: dtit.pyLiteMath
LiteMath LibraryIntroductionThis is DTIT integer math library. It is very lite without any float type or standard C math lib.Installpip install dtit.pyLiteMathCheckExecute* python dtit\pyLiteMath\tests\hello.py *should echo with "dtit.pyLiteMath"UsagePlease refer to examples in tests directory.Error ReportPlease email todev@datatellit.com
Package: dtj
DTJ (Directory-to-JSON)OverviewDTJ (Directory-to-JSON) is a Python command-line tool, particularly useful for quickly generating and sharing representations of directory structures and their contents in a structured and token-efficient manner, making it ideal for interactions with language models like ChatGPT.Example outputdtj{"my_project":{"__init__.py":"","main.py":"# Main application file\nimport app\n\napp.run()","app.py":"# App module\n\ndef run():\n    print('Running the app')","utils":{"helper.py":"# Utility functions\n\ndef helper():\n    return 'Helper function'"}}}FeaturesConvert directory contents to JSON 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.Updatesnew JSON formatreduces token usage ~30%InstallationpipinstalldtjUsageRun DTJ from the command line with the following options:dtj<target-directory>[options]If no target directory is specified, DTJ will default to the current working directory.Options:-tor--target-file: Target a single file. This option is mutually exclusive with-i,-e, and-r.-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 JSON file name.-ror--recursive: Enable recursive search in directories. Not valid when targeting a single file.-por--print: Print the output using rich formatting.-cor--clipboard: Copy the output to the clipboard.Exampledtjmyfolder-i'*.py''*.html'-ooutput.json-rThis command will parse all.pyand.htmlfiles inmyfolderrecursively and save the JSON output tooutput.json.Using it alongside (gptwc)[https://github.com/lwneal/gptwc]dtj|gptwcoutputs the token count1337AuthorsAdrian Galilea -Initial workAcknowledgmentsHat tip to ChatGPT for assistance with project setup and documentation.
Package: dtk-api
大淘客接口这个项目里面是大淘客的接口信息(当前仅仅生成 Python 的代码)注意:这个接口仅仅是为了内部使用，虽然您可以获取它的源代码，但是请不要使用在您的项目中.
当前我并不对兼容性以及可用性做任何保证。(如果需要使用请联系开发者获取相应的授权)
Package: dtld1.0
# DTLD
Custom Top Level domain extractorThis is an example of a top level domain : wxyz.com or qbcd.netHow it works
DTLD extracts the root domain. It works by extracting from http|https|www protocol URLSExample Code:from dtld.dtld import TopLevelDomain as tldurl = ‘http://github.com’App = tld.TopLevelDomain(url)domain = App.get_top_level_domain()print(domain)>>github.comHow to installUNIXpip install dtld1.0WINDOWSPYTHON 2py -m pip install dtld1.0PYTHON 3py -m pip install dtld1.0
Package: dtl-functions-rd
Data Transformation LibraryAboutThis project is an exercise primarily focused on building Python libraries, publishing packages to PyPI and dependency management using Poetry.The task is to implement three data transformation functions:Transpose- flipping of the input matrix diagonally, meaning that the row and column indices of a matrix are switched and a new matrix is produced.Time Series Windowing- producing lists or numpy arrays containing a subset of elements of the input list or numpy array. The size of the window is defined by passing size, stride and shift parameters. Time series windowing is used for making informed decisions in economics, climate, epidemiology and many other fields.Cross-Correlation (Convolution)- used in convolutional neural networks in machine learning libraries. Cross-correlation and convolution are both operations applied to images. Cross-correlation means sliding a kernel (filter) across an image. Convolution means sliding a flipped kernel across an image. In practice using one over the other does not change the result, just the resulting values are learned in a flipped orientation. 2D Convolutions are essential in the advancement of convolutional neural networks and various image processing filters such as blurring, sharpening, edge detection, and others.Functions were built using Python and Numpy (as per requirements of this task). Having the knowledge of how to build such functions by yourself provides the flexibility in ones specific uses cases, as some times the already built functions in various libraries (PyTorch, cv2, etc.) have constraints and changing them can be difficult or even impossible and impractical.For package management we were required to use Poetry.Libraries UsedFor this project only Python'sstandard libraryas well asNumpy(were it was required) were used.For package managementPoetrywas used.How to UseAs this project is an exercise on building Python libraries, the built library can be installed from PyPI, were it is being published.To do so in the terminal window typepip install dtl_functions_rd.To quickly test this library, download the library files fromhttps://pypi.org/project/dtl-functions-rd/. Open the.tarfile with WinRAR or similar document extraction program and copyexamples.pyfile that is located in the main project directorysrc/dtl_functions_rd/examples.pyto your project and run the file to see a few examples and outputs of the functions of this data transformation library.Possible Future ImprovementsVersion handling