Saravanan666/pypi-org huggingface dataset:
Package: m2m-token
M2M TokenHere is the issue : You have a Bot and a REST Api that needs to communicate.
As the REST Api is an entrypoint to your system, you don't want it to be unsecured (at least you should not want that.)
At the same time, you don't want them to have plain text password configured somewhere, because, it can be guessed.And you don't want to implement a full-fledged 2-ways SSL authentication, because, well, it is complicated.So what to do then ?It'd be nice if we had a way to have a token that changes regularly but can still be guessed by involved parties.This is something that has been (still ?) done on automated garage doors. Ever wondered how come your remote does not
open the garage door of your neighbor ? It's because it uses aRolling Codewhich is a quite simple technique but still pretty efficient.How does it works ?The same as in the rolling code for the RF remote controller, both parties (Bot and REST Api for instance),
are going to share aseed. This seed will be used to generate a token on both sides, this way we can check that
the Bot is indeed authorize to use the REST Api.The parties are also going to agree on the tokenttl(Time To Live), this should prevent attacker
from sending an outdated token to the REST Api and be granted access.How to use ?Installationpip install m2m_tokenToken Generationfromm2m_token.tokenimportgenerateseed='VeryComplicatedSeedSoThatItIsReallyHardForTheAttackerToGuessIt'ttl=3# Time in seconds the token is going to be valid.# On the bot side:bot_token=generate(seed,ttl)# Add the generated token to the REST Http request# On the API side:defmethod_that_handles_request(request):token=find_token_in_request(request)iftoken!=generate(seed,ttl):raiseNotAllowed()generate()ParametersParameterDescriptionMandatoryDefault ValueseedThe seed to generate the tokenYesN/AttlTime in seconds the token is validYesN/AsequenceCharacters sequence from wich the token will be generatedNostring.ascii_letters + string.digitstoken_lenGenerated token lengthNo6DisclaimerThis comes without any warranties of any sorts.
I can not be hold responsible if you ever stumble upon this and decide to do something evil with it (like training some raccoon to hold a bazooka and go berserk with it).More seriously, this has not been tested by real pentesters (if any are willing I'd be more than glad to have feedbacks)Pull requests, issues may or may not be handled but are always welcomed.
Package: m2p
Monomers to Polymers (m2p)A simple interface for converting monomers to polymers using SMILES representation.Related WorkConvolutional Networks on Graphs for Learning Molecular FingerprintsNeural Message Passing for Quantum ChemistryRelational inductive biases, deep learning, and graph networksNeural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials(Main) RequirementsRdkit install can be performed per the following information.rdkitTo install rdkit via conda, use:conda install -c rdkit rdkitGetting startedThe library uses known reaction chemistries to build polymer chains from monomers. The polymer chemistries available include vinyls, acrylates, esters, amides, imides, and carbonates.The library can generate multiple replicate structures to create polymer chains represented at the atom and bond level. The chains can be any degree of polymerization (DP). RDKit reaction smarts are used to manipulate the molecular structures and performin silicoreactions.
Package: m2py
m2py: Materials Morphology Python PackageContributorsWesley Tatum, PhD Student at University of Washington MSE DepartmentDiego Torrejon, Staff Machine Learning Engineer at BlackSkyPatrick O’Neil, Director of Machine Learning and Artificial Intelligence at BlackSkyRequirementsYou need Python 3.6 or later to work with m2py.InstallationThrough github:Clone or download this repositorypip install -r requirements.txtThrough pip:pip install m2pyUsageAt the moment, we are offering two introductory tutorials found under thetutorialsdirectory. There is a basic tutorial showing the main processing capabilities of m2py. There's also an advanced tutorial showing how users can use current m2py capabilities to make them suitable for their own applications.BackgroundSPM techniques have been pivotal in understanding surfaces, morphologies, and intermolecular interactions of matter from the atomic to millimeter scales. Probes interacting with the material surface produce 2-dimensional images of the topography with intensity scales representing any number of material properties (e.g. modulus, conductivity, or capacitance). In many experiments, multiple properties can be imaged simultaneously, producing a 3-dimensional stack of these images.By utilizing different combinations of computer vision and machine learning techniques, m2py leverages differences in imaged material properties to recognize different material phases, as well as different domains and topographical features. First, outlier pixels or signals can be removed. Next, signals are compressed to only the most informative features via PCA. These signals can be deconvoluted via GMM, or some other semantic segmenter for phase labeling. Finally, an instance segmenter, such as Persistence Watershed Segmentation, clusters the phase-labeled pixels into domains, which receive another label.Once labeled by m2py segmenters, quantitative descriptions of each domain and the total morphology are extracted and can be used to train supervised models to predict material properties or performance. Such supervised training has not been accessible to most SPM researchers, due to the labor-intensive nature of manual-labeling.
Package: m2-rec2taps
rec2tapsThis utility extracts tap times from a input device recording synchronized to
the start time of an stimuli device under the assumption that the recording has
one channel where a loopback from the stimuli was recorded.This tool was written as part of a proposed setup for recording tap times
presented in "Simple and cheap setup for measuring timed responses to auditory
stimuli" (Miguel et. al. 2020).The tool works as follows: according the proposed experimental setup, a channel
from the output is looped back into the recording device and recorded along
with the signal of the input device where the participant performs the tapping.
The tool takes as input the stimuli presented and the recording and analyses
the signals to obtain tap times relative to the beginning of the simuli. This
requires the tool to find which channel of the stimuli is also recorded in at
which file of the recording.  With this information, the tool can recognize
which channel in the recording corresponds with the the stimuli and which is
the input. The lag between the stimuli start and its start in the recording is
found by maximizing the crosscorrelation between the loopback and the original
signal (in the looped-back channel).Next, the input signal is analyzes to obtain peaks. Peaks are found according
to the algorithm inscipy.signal.find_peaksby detectecting peaks with
a minimum prominence and distance between the peaks. These parameters can
be configured (seerec2taps -h).InstallationFrom pypi:pip install m2-rec2tapsFrom sources:git clone https://github.com/m2march/rec2taps.git
    cd rec2taps
    python setup.py installUsagerec2taps stimuli_file recording_file > outfile.txtstimuli_fileis the original audio file playedrecording_fileis the audio recording done during the experiment. Should
have the same sample rate as thestimuli_fileand have one channel of the
stimuli recorded as one channel of the recording.The utility writes the tap times detected, in milliseconds, one per line, to
the standard output. The example pipes the output into a new file.Further options can be provided to calibrate the sensitivity and minimum
distance between detected taps. Use the-hflag or more details.
Package: m2secret-py3
m2secret is a simple encryption and decryption module and CLI utility built
with the M2Crypto library to make it easy to secure strings and files from
prying eyes.By default it will use 256-bit AES (Rijndael) symmetric-key cryptography in
CBC mode. Key material is derived from submitted password using the PBKDF2
algorithm.m2secret originally developed by Heikki Toivonen:http://www.heikkitoivonen.net/m2secret/
Package: m2stitch
FeaturesProvides robust stitching of tiled microscope images on a regular grid, mostly following algorithm byMISTbut improved in several points.Supports missing tiles.InstallationYou can installM2StitchviapipfromPyPI:$pipinstallm2stitchUsagePlease see theUsagefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,M2Stitchis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis program is an unofficial implementation ofMISTstitching algorithm onGitHub. The original paper ishere.This project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.Other Python stitching programsOne might also be interested in another Python-written stitching toolASHLAR(bioRxiv),
with a comparable performance to that of MIST and additional features.
Package: m2tool
m2toolm2tool is (will be) a command line tool to manage mongrel2 instances. It will provide simple commands to manipulate all mongrel2 config tables: server, routes, handler, directories, etc.Since m2tool will be written using Django, it will be possible to run it with mongrel2 and control your instances from web.There are no plans to build any sort of “user interface”, all interactions will be done through an API.Why Django ?I choosed Django mainly because of its ORM and also because I have plans to run m2tool behind a mongrel2 instance, so I will be able to deploy new apps from my command line.Dalton Barreto <daltonmatos@gmail.com>http://daltonmatos.com
Package: m2websocket
Makes handling websockets withMongrel2and
Python easy peasy.This module was lovingly crafted forFirst
Opinion.ExampleTypical websockethello worldtype example, creating an echo server:importosimportlogginglogging.basicConfig()rl=logging.getLogger()rl.setLevel('DEBUG')fromm2websocketimportConnectionconn=Connection("tcp://localhost:port","tcp://localhost:port")whileTrue:req=conn.recv()ifreq.is_handshake():conn.reply_handshake(req)else:conn.reply_websocket(req,req.body,req.opcode)Yup, that’s all there is to it, much easier thanthis
example.You can see a working example of the echo server (with a working
mongrel2 conf file and python script) by looking in theexample/directory in the Github repo.Install itPrerequisitesYou’ll need to install the mongrel2 python library from themongrel2
source.
Once you’ve cloned the source or downloaded that folder, you can install
it usingsetup.py$ cd /path/to/mongrel2/src/examples/python
$ python setup.py installThen, you can install this module using pip:pip install m2websocketOr:pip install git+https://github.com/firstopinion/m2websocket#egg=m2websocketTests are actually kind of hard to run, you need to install mongrel2 and
have it load theexample/mongrel2.confconfiguration and then run
theexample/echo.pyserver. You also need topip installwebsocket-client.LicenseMIT
Package: m2wsgid
WSGId = WSGI DaemonDescriptionWsgid is a mongrel2 (http://mongrel2.org) adapter for WSGI applications. With wsgid you will be able to run your WSGI app as a true unix daemon.InstallTo install just clone this repo and run: (pip package coming soon)sudo python setup.py installUse exampleBefore youcan use wsgid, you will need to initialize your wsgid app folder. This folder is where you app will live. To do so, just runwsgid init –app-path=/path/to/my/wsgid-app-folderFrom now on, all command you type must have--app-path=/path/to/my/wsgid-app-folderparameter.What this command does is create some well known folders, eg:pid/, pid/master, pid/worker, app, logs.All of you application code will be places inside the${WSGID_APP_FOLDER}/appfolder. So to deploy a django app, just copy your project folder to${WSGID_APP_FOLDER}/app.To start an application just call wsgid like this.wsgid –app-path=/path/to/the/app –recv=tcp://127.0.0.1:8889 –send=tcp://127.0.0.1:8890This will load the app located at/path/to/the/appand be ready to process requests. wsgid automatically detects what kind of application it will load.You don’t have to type all this every time. You can use theinitcommand to create a config file, like this:wsgid config –app-path=/path/to/the/app –recv=tcp://127.0.0.1:8889 –send=tcp://127.0.0.1:8890This will create awsgid.jsonfile inside your wsgid app folder. So the next time you start you app you8 can type just:wsgid –app-path=/path/to/the/appIf wsgid is not able do detect the aplication WSGI framework you can use the –wsgi-app option.wsgid –app-path=/path/to/the/app –recv=tcp://127.0.0.1:8889 –send=tcp://127.0.0.1:8890 –wsgi-app=my.package.application--wsgi-appis the full qualified name of the WSGI application object, this way wsgid can find the app’s entry point, as defined by pep-333.See more at:http://wsgid.comPlugable Appication Loaderswsgid has a plugable Application Loader subsystem, this way you can write your own AppLoader.  To do this just write an class that extends the wsgid.core.Plugin class and implements the IAppLoader interface. See the PyRoutesLoader (wsgid/loaders/__init__.py) for an actual example. To make wsgid use your loader just pass na aditional option:–loader-dir. This must point to the path where yout loader is located. More about this, read the docs on the site:http://wsgid.comLicensewsgid is Licensed underNew BSD, see LICENSE for details.Know moreKnow more about the wsgid project at the official website:http://wsgid.comand at the docs:http://wsgid.com/docshttps://github.com/daltonmatos/wsgid2010-2011 | Dalton Barreto
Package: m3-adapter-client
M3 UI клиента для взаимодействия со СМЭВ3 посредством АдаптераПодключениеsettings:INSTALLED_APPS = [
    'adapter_client',
    'm3_adapter_client'
]apps:from django.apps.config import AppConfig as AppConfigBase

class AppConfig(AppConfigBase):

    name = __package__

    def ready(self):
        self._init_adapter_client()

    def _init_adapter_client(self):
        from adapter_client.config import ProductConfig, set_config

        set_config(ProductConfig())app_meta:from django.conf.urls import url
from m3_adapter_client import actions
from .controllers import controller

def register_actions():
    controller.extend_packs((
        actions.Pack(),
        actions.JournalPack(),
    ))Запуск тестов$ tox
Package: m3-cdecimal
OverviewThecdecimalpackage is a fast drop-in replacement for thedecimalmodule
in Python’s standard library. Both modules provide complete implementations of
Mike Cowlishaw/IBM’sGeneral Decimal Arithmetic Specification.TestingBothcdecimaland the underlying library -libmpdec- are extremely
well tested.libmpdecis one of the few open source projects with 100%
code coverage.cdecimalis rigorously tested againstdecimal.py.Short benchmarksTypical performance gains are between 30x for I/O heavy benchmarks
and 80x for numerical programs. In a database benchmark, cdecimal
exhibits a speedup of 12x over decimal.py.decimalcdecimalspeeduppi42.75s0.58s74xtelco172.19s5.68s30xpsycopg3.57s0.29s12xDocumentationSincecdecimalis compatible withdecimal.py, the official documentation
is valid. For the few remaining differences, refer to the second link.Decimal moduleDifferences between cdecimal and decimalLinux NotesThe build process requires a working C compiler and afullPython install with
development headers. Linux distributions often ship the Python header files as
a separate package, calledpython-devorpython-devel.Install headers on Debian/Ubuntu:sudoapt-getinstallpython-devWindows NotesBinary installersLinkscdecimal project homepagecdecimal benchmarks
Package: m3-core
M3-CoreЯдро платформыM3ДокументацияM3 представляет собой набор модулей для разработки web-приложений на python, функционирующих в частных и открытых облаках. Создаваемые на основе M3 приложения функционируют в трехзвенном режиме: web-браузер в качестве тонкого клиента, сервер приложений и сервер баз данных. Рабочие места и сервера, на которых развертываются такие приложения, могут функционировать под любой распространенной операционной системой (Windows, Linux, MacOS). В качестве серверов баз данных могут использоваться различные СУБД: Oracle, PostgeSQL, MS SQL, MySQL, Firebird. На клиентских рабочих местах могут использоваться любые браузеры (кроме IE 6, поддержка которого ограничена).
Ключевыми особенностями платформы являются:Ориентированность на создание облачных систем, функционирующих в гетерогенной среде;Широкие возможности модификации функциональности и поведения web-приложений без изменения исходного кода системы путем подключения плагинов, которые могут разрабатываться специалистами сторонних компаний;Развитый функционал обмена информацией с внешними информационными системами, с использованием REST и SOAP сервисов;Многочисленные плагины, в которых реализованы различные полезные механизмы;Масштабируемость серверной части как по серверам приложений, так и по серверам базы данных с различными способами балансировки нагрузки.
Package: m3-d15n
Подсистема предназначена для обеспечения сокрытия персональных данных при
работе веб-приложения в общедоступной сети.Описание подсистемы, принципов её работы, инструкции по её подключению и
разработке описаны в документации. Для её просмотра её нужно сгенерировать
с помощью Sphinx. Чтобы это сделать, нужно:создать виртуальное окружение Python 3.6;установить в него Fabric3:pip install Fabric3;склонировать в него репозиторий подсистемы:git clonessh://git@stash.bars-open.ru:7999/m3/m3-d15n.gitперейти в папку с репозиторием:cdm3-d15n;установить необходимые пакеты в виртуальное окружение:fab req.dev;выполнить сборку документации в формате HTML:fab doc.build;открыть главную страницу в браузере:fab doc.browser(должна быть
установлена утилитаxdg-open), либо вручную открыть файл./docs/build/html/index.html.
Package: m3-data-import
=======================
Подсистема импорта - UIИнтерфейсная часть подсистемы импортаУстановка пакетаpip install m3-data-importПодключениеОпределение пакаПак определяет класс конфигурации и дополнительные параметры импортаПример:.. code-block:: pythonfrom m3_data_import.actions import ImportPack as Pack

class ImportPack(Pack):

    title = u'Импорт учреждений'
    config_cls = ImportConfig

    def get_parser_params(self):
        params = super(ImportPack, self).get_parser_params()
        params['skip_sheets'] = ('Справочник', )
        return params

from .dataimport import ImportPack

def register_actions():
  """Регистрация пака в контроллере."""

  action_controller.packs.extend([
      ImportPack()
  ])Описание класса конфигурации см. в пакете data-importМассовый импортМассовый импорт - загрузка множества файлов данных, запакованных в архив. Подсистема определяет соответствие файла и конфигурации, порядок загрузки. Для активации массового импорта необходимо:Подключить пак массового импорта.. code-block:: pythonfrom m3_data_import.actions.bulk import BulkImportPack
action_controller.packs.extend((
BulkImportPack(),
))Зарегистрировать имеющиеся конфигурации в реестре конфигураций:.. code-block:: pythonfrom unit.dataimport import UnitConfig
from group.dataimport import GroupConfigdata_import.configuration.registry = Registry(
(UnitConfig, GroupConfig)
)Порядок классов в кортеже определяет приоритет загрузки!ТестыЗапуск тестов осуществляется черезtox <https://tox.readthedocs.io/en/latest/>_История измененийВсе изменения проекта должны быть отражены в этом файле.Формат основан наKeep a Changelogи проект следуетСемантическому версионированию.[x.y.z] - гггг-мм-ддЗдесь должно быть расширенное описание того, что было сделано, какие есть планы у команды по дальнейшему развитию.
Желательно будущие цели привязывать к конкретным задачам. Т.е. на каждую цель нужно поставить отдельную задачу и
отразить ее номер здесь.ДобавленоПРОЕКТ-ZZZZPATCH Название задачи или изменения.ПРОЕКТ-YYYYMINOR Название задачи или изменения.ПРОЕКТ-XXXXMAJOR Название задачи или изменения.ИзмененоИсправленоУдалено[1.0.5] - 2023-10-05Изменение формата README и CHANGELOG для корректной сборки пакета.ИзмененоEDUCLLG-7980PATCH Изменение формата README и CHANGELOG для корректной сборки пакета.1.0.4
+++++Исправлена поддерживаемая версия Django на < 3.0.1.0.3
+++++Добавлена подержка django 2.21.0.2
+++++Корректировка диапазонов допустимых версий зависимых пакетов.1.0.1
+++++Добавлен необязательный параметр "путь к файлу", который может использоваться
для определения конфигурации.1.0.0
+++++Подсистема импорта - UI
Package: m3-dbfpy
dbfpy is a python-only module for reading and writing DBF-files.
It was created by Jeff Kunce and then modified by Hans Fiby
and Yaroslav Samchuk.dbfpy can read and write simple DBF-files.  TheDBF-formatwas developed about 30 years ago and was used by a number
of simple database applications (dBase, Foxpro, Clipper, …).
The basic datatypes numbers, short text, and dates are available.
Many different extensions have been used; dbfpy can read and write
only simple DBF-files.
Package: m3-debuggie
## DebuggieDebuggie, это набор инструментов для сбора и анализа отладочной информации об функционировании удалённой системы.Набор включает в себя:Middleware, собирающую отладочную информациюViewie - CLI-утилиту, предназначенную для просмотра дампов отладочной информацииAPI для отладочного вывода информации из кода отлаживаемых приложений### УстановкаПакет устанавливается стандартным способом:pip install debuggieДля работы пакета требуется`m3.core.json.M3JSONEncoder`### ПодключениеВ`settings.INSTALLED_APPS`добавляется приложение`"debuggie"`.В`settings.MIDDLEWARE_CLASSES`добавляется middleware`"debuggie.Debuggie"`.B “корневом””`urls.py`добавляется`(r'^debug/',include('debuggie.urls')),`.Опционально в`settings.DEBUGGIE_SYSTEM_INFO_CALLBACK`указывается (строкой вида`"package.module.function"`) функция, предоставляющая информацию о системе.Важно:Debuggie middleware должна добавляться после`SessionMiddleware`и`AuthenticationMiddleware`, т.к. зависит от них!### Запись отладочной информацииПользователь аутентифицируется в системе,Указанием в адресной строке переходит на url`<some_host>/debug`На открытой странице можно включать/выключать режим отладки, очистить дамп отладочной информации или загрузить его в виде`.txt`-файла.При этом ссылки на загрузку и очистку дампа появятся после того, как пользователь:Включит отладочный режимСовершит в системе какие либо действияОбновит страницу отладочного режима.### Просмотр файла дампа отладочной информацииИнформация просматривается в терминале командойviewie <dump_file.txt>Программа просмотра работает в интерактивном режиме и содержит встроенную справку