Saravanan666/pypi-org huggingface dataset:
Package: matrixzulipbridge
MatrixZulipBridgeA Matrix puppeting appservice bridge for ZulipJoin the Matrix room#MatrixZulipBridge:shema.lvif you have any questions or just want to chat.FeaturesStreamsZulip topics - Matrix threadsDirect messagesFormatted text* (not all of Zulip's formatting)Matrix puppetsZulip puppetsMessage Media* (only translating URIs)PresenceReactionsRedactions* (only for Zulip messages)Replies* (badly formatted)Typing indicatorsInstallationPrerequisitesPython >=3.10Matrix homeserver with ability to add appservicesPyPIpip install matrixzulipbridgeFrom sourceClone or download this git repositorygit clone https://github.com/GearKite/MatrixZulipBridge.gitInstallPoetryInstall dependenciespoetry installEnter the virtual environmentpoetry shellRunningExampleGenerate a registration filepython3 -m matrixzulipbridge --config config.yaml --generateInstall the appservice on your homeserverRun the bridgepython3 -m matrixzulipbridge --config config.yaml https://homeserver.example.comUsageusage:python3-mmatrixzulipbridge[-h][-v](-cCONFIG|--version)[-lLISTEN_ADDRESS][-pLISTEN_PORT][-uUID][-gGID][--generate][--generate-compat][--reset][--unsafe-mode][-oOWNER][homeserver]ApuppetingMatrix-Zulipappservicebridge(v0.1.1.dev2+g3ed8ed7)positionalarguments:homeserverURLofMatrixhomeserver(default:http://localhost:8008)options:-h,--helpshowthishelpmessageandexit-v,--verboselogdebugmessages-cCONFIG,--configCONFIGregistrationYAMLfilepath,mustbewritableifgenerating(default:None)--versionshowbridgeversion-lLISTEN_ADDRESS,--listen-addressLISTEN_ADDRESSbridgelistenaddress(default:asspecifiedinurlinconfig,127.0.0.1otherwise)(default:None)-pLISTEN_PORT,--listen-portLISTEN_PORTbridgelistenport(default:asspecifiedinurlinconfig,28464otherwise)(default:None)-uUID,--uidUIDuseridtorunas(default:None)-gGID,--gidGIDgroupidtorunas(default:None)--generategenerateregistrationYAMLforMatrixhomeserver(Synapse)--generate-compatgenerateregistrationYAMLforMatrixhomeserver(DendriteandConduit)--resetresetALLbridgeconfigurationfromhomeserverandexit--unsafe-modeallowappservicetoleaveroomsonerror(default:False)-oOWNER,--ownerOWNERsetownerMXID(eg:@user:homeserver)orfirsttalkinglocaluserwillclaimthebridge(default:None)After registering and launching the bridge, start a chat. You can find the localpart in yourregistration.yamlThis bridge is mainly configurable through Matrix, sendhelpto get a list of commandsBridging a streamIn your control room chat with the bridge send:addorganization {name}open {name}In the organization room send:site example.comemail my-bot@example.comapikey xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxconnectsubscribe {zulip stream name}CreditsThis bridge is heavily based onHeisenbridge. Thank you, Heisenbridge contributors!
Package: matroid-database
This database was retrieved from
<https://www-imai.is.s.u-tokyo.ac.jp/~ymatsu/matroid/index.html>
(Yoshitake Matsumoto,Database of Matroids, 2012; accessed: 2023.12.02).Usage:>>> from matroid_database import all_matroids_revlex
>>> for m in all_matroids_revlex(5, 2):
...     print(m)
**********
0*********
0****0****
00*0**0***
000*******
000******0
0000**0***
0000**0**0
00000*00**
000000****
0000000***
00000000**
000000000*>>> from matroid_database import unorientable_matroids_revlex
>>> for m in unorientable_matroids_revlex(8, 3):
...     print(m)
0******0******0**********0********0*******0****0**0*****
0******0******0***0******0*0**0*************************
0000************0**********0****0**********0**0***0*****>>> from matroid_database import all_matroids_bases
>>> for m in all_matroids_bases(4, 2):
...     print(m)
[(0, 1), (0, 2), (1, 2), (0, 3), (1, 3), (2, 3)]
[(0, 2), (1, 2), (0, 3), (1, 3), (2, 3)]
[(0, 2), (1, 2), (0, 3), (1, 3)]
[(1, 2), (1, 3), (2, 3)]
[(0, 3), (1, 3), (2, 3)]
[(1, 3), (2, 3)]
[(2, 3)]
Package: matrops
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| '_ ` _ \ / _` | __| '__/ _ \| '_ \/ __|
| | | | | | (_| | |_| | | (_) | |_) \__ \
|_| |_| |_|\__,_|\__|_|  \___/| .__/|___/
                              | |        
                              |_|MatropsThis is a python package for matrix and vector operations like, addition, subtraction, multiplication, scale and transpose.More options like graphics for vectors, calculating the angle beetween to vectors and more are comming soon.Table of contentsInstallationExampleComing soonContributorsInstallationpip install matropspython -m matropsExamplefrommatrops.matriximportMatrix,MatrixOps# create 2 matricesmatrix_1=Matrix([[1,2,3,4],[5,6,7,8]])matrix_2=Matrix([[2,2,2,2],[3,3,3,3]])if__name__=="__main__":# Add 2 matricesadded=MatrixOps.add(matrix_1,matrix_2)# Scale matrix with 2matrix_1.scale(2)# Transpose matrixtransposed=MatrixOps.tranpose(matrix_1)# Read documentation for more usage examplesComing soonDetailed calculationsPossibility to calculate the angle beetween 2 vectorsVector ProductShow vectors in a graphical interfaceContributorsLeonard Becker
Package: matrx
The MATRX SoftwareHuman-Agent Teaming Rapid Experimentation SoftwareThe field of human-agent teaming (HAT) research aims to improve the collaboration between humans and intelligent agents. Small tasks are often designed to do research in agent development and perform evaluations with human experiments. Currently there is no dedicated library of such tasks. Current tasks are build and maintained independent of each other, making it difficult to benchmark research or explore research to different type of tasks.To remedy the lack of a team task library for HAT research, we developed the Human-Agent Teaming Rapid Experimentation Software package, or MATRX for short. MATRX’s main purpose is to provide a suite of team tasks with one or multiple properties important to HAT research (e.g. varying team size, decision speed or inter-team dependencies). In addition to these premade tasks, MATRX facilitates the rapid development of new team tasks.Also, MATRX supports HAT solutions to be implemented in the form ofTeam Design Patterns(TDP). This allows for the creation of a TDP library which structures HAT research by mapping task properties, solutions and obtained results in such a way that identifies research gaps. Perhaps more importantly, it allows for system designers to search for a concrete and evaluated solution to their issues related to HAT.This all is made possible by MATRX.Feel free to try some tasks or to browse our officialwebpage. This also includes a set of elaboratetutorials,documentationandcontribution guide.
.CitationIf you use this software in your work, consider citing it as follows:van der Waa, J.S & Haije, T (2023). MATRX: Human Agent Teaming Rapid Experimentation software. Zenodo.@software{matrx_2023,
author       = {Jasper van der Waa, Tjalling Haije},
title        = {MATRX: Human Agent Teaming Rapid Experimentation software},
month        = {July},
year         = {2023},
publisher    = {Zenodo},
version      = {2.3.2},
doi          = {10.5281/zenodo.8154912},
url          = {https://doi.org/10.5281/zenodo.8154912}}
Package: mats
Manufacturing Automated Test System (MATS)MATSis a hardware-oriented test environment intended for production
testing in a manufacturing environment.  TheMATSframework is a test
template and test sequence executor which includes an implementation for
basic user input and feedback.FeaturesTest runnerAuto-start optionTriggered-start option (i.e. "Start Button")Auto-save of dataHardware Setup / TeardownAutomatic teardown on exceptionAutomatic GUI integrationMATS could be considered an automated test framework which imposes a
consistent work flow and reduces the amount of mind share that you need
to dedicate to developing automated device tests.Checkout thedocumentationfor more details!Example GUIAt this time, the automatic GUI is only built withintkinter; however, the
techniques used to create theMatsFrameshould be applicable to any other
GUI framework.  Contributions welcome!Below is a screenshot created on Windows, but the linux version behaves
identically.Contribution GuidelinesContribution guidelines are outlined in thedocumentation.Please read before contributing!
Package: matscipy
MatscipyMatscipy is a generic materials science toolbox built around theAtomic
Simulation Environment (ASE). It provides
useful routines for:Plasticity and dislocationsFracture mechanicsElectro-chemistryTribologyElastic propertiesIn addition to domain-specific routines, it also implements a set of
general-purpose, low-level utilities:Efficient neighbour listsAtomic strainRing analysisCorrelation functionsSecond order potential derivativesQuick startMatscipy can be installed on Windows, Linux and x86 macos with:python3 -m pip install matscipyTo get the latest version directly (requires a working compiler):python3 -m pip install git+https://github.com/libAtoms/matscipy.gitCompiled up-to-date wheels for Windows, Linux and x86 macos can be foundhere.DocumentationSphinx-generated documentation for the project can
be foundhere. Since Matscipy is built
on top of ASE’sAtomsandCalculatorobjects, ASE’s documentation is a good complement to Matscipy’s.Seeking helpIssuescan be used to ask
questions about Matscipy.ContributingContributions, in the form of bug reports, improvement suggestions,
documentation or pull requests, are welcome.Running testsTo run the tests locally, from Matscipy’s root directory:python3 -m pip install .[test]  # installs matscipy + test dependencies
cd tests/
python3 -m pytest .Editable installsWhen developingmatscipy, it can be useful to have an editable install of
the source directory. This means that changes to the source code are directly
reflected in thematscipyinstall. We are usingMesonandmeson-pythonas a
build system, and there are somerestriction to editable installs.The editable install only works with the–no-build-isolationoption:python3 -m pip install --no-build-isolation --editable .[test]If you get the message:ERROR: Tried to form an absolute path to a dir in the source tree.then you are most likely try to install into a Python virtual environment that
is located inside your source directory. This is not possible; your virtual
environment needs to be located outside of the source directory.DependenciesThe package requires:numpy-http://www.numpy.org/scipy-http://www.scipy.org/ASE-https://wiki.fysik.dtu.dk/ase/Optional packages:quippy-http://www.github.com/libAtoms/QUIPatomistica-https://www.github.com/Atomistica/atomisticachemview-https://github.com/gabrielelanaro/chemviewFundingmatscipywas partially funded by the Deutsch Forschungsgemeinschaft (project258153560) and by the Engineering and Physical Sciences Research Council (grantsEP/P002188/1,EP/R012474/1andEP/R043612/1).
Package: matSHEEP
This library is a programmatic interface in python to generate a circuit for the bigger and more useful SHEEP library.The library has a few data types :variables - A single bit (Could also be used as a normal scalar)enc_vec - One dimensional bit vector (Could be used a one dimensional vector of any data type)enc_mat - Two dimensional bit matrix (Could be used a one dimensional vector of any data type)enc_tensor3 - Three dimensional bit tensor.To create a circuit, the basic class to inherit ismini_modinmathsheep.interactions. To add more components, you can useself.add(component)inside thecreatefunction as shown below.:class oneb_adder(mini_mod):
     def __init__(self, name, inputs, outputs, nb=None,
                     randomize_temps=1, carry=True):
          mini_mod.__init__(self, name, inputs, outputs)
          self.create(...)

     def create(self, ...):
           self.add(..)Two types of components can be added.Assignments (from matSHEEP.interactions)mono_assignaliasnegatebi_assignxorandorconstandtri_assignmuxOther mini_modsThere are a few predefined mini_mods. They can be found inmatSHEEP.reusable_modulesoneb_adder - Add two bitsnb_adder  - Adders x and y with incoming carrt where input is[cin x y]nb_adder_xy - Adds x and y withinput = (x, y)compare_cp - Compares ciphertext with plaintext withinput = (c,p)matSHEEP.functionsreduce_add - Counts the number of ones in a bit vector.matSHEEP.nn_layersign_fnlinear_layer_1d - Inner Product of a weight vector with encrypted bit vector  followed by a sign function.linear_layer - Inner Product of a weight matrix with an encrypted bit vector followed by a sign function.conv_layer - A convolution Layer. (Look at examples)matSHEEP.vector_opsvec_mono_op_cond - Takes a plaintextcondvector, a plaintext tupleass_typescontaining onlyaliasandnegateas values and an encrypted bit vectorinput. It outputs an encrypted bit vector where the ith position has theass_types[cond[idx]]operation applied oninput[idx].Similar operation for matrix and tensor.You can also visualize the circuits you create.test.sheepis a circuit file.:import sys
import matSHEEP.create_graph as cg
complete_node = cg.get_circuit_graph('./test.sheep')
ng = cg.networkx_graph(complete_node)
ng.draw()And you can getgFor more high level operations and results using layers of Neural Networks visit this markdown
Package: matSN
SNMATRICESThese Operations will work only ,if both the matrices are of same orderMatrix_Operations:MODULE NAME:operations.py1)Getting input matrix:
Function:input_matrix(Number of rows,Number of columns)2)Matrix Addition:
Function:matrix_addition(matrixA,matrixB)3)Matrix Subraction
Function:matrix_subraction(matrixA,matrixB)-4)Matrix Multiplication:
-Function:matrix_multiplication(matrixA,matrixB)SOURCE CODEMATRIX ADDITIONdef matrix_addition(A,B):
mat_addition=[]
for i in range(len(A)):
c=[]
for j in range(len(B[0])):
c.append(A[i][j]+B[i][j])
mat_addition.append(c)
print('Matrix Addition=')
print(mat_addition)MATRIX SUBRACTIONdef matrix_subraction(A,B):
mat_subraction=[]
for i in range(len(A)):
c=[]
for j in range(len(B)):
c.append(A[i][j]-B[i][j])
mat_subraction.append(c)
print('Matrix Subraction=')
print(mat_subraction)MATRIX MULTIPLICATIONdef matrix_multiplication(A,B):
mul_result=[]
for i in range(len(A)):
c=[]
for j in range(len(B[0])):
result=0
for k in range(len(B)):
result+=A[i][k]*B[k][j]
c.append(result)
mul_result.append(c)
print('Matrix Multiplication=')
print(mul_result)INPUT MATRIXdef input_matrix(rows,columns):
A=[]
print('Enter the entries row wise:')
a=[]
for i in range(rows):
a=[]
for j in range(columns):
a.append(int(input()))
A.append(a)
print('Input Matrix=')
print(A)##LICENSE
MIT
Package: matsya
matsya is a collection of unsupervised text mining routines writted in numpy to
cluster data for [Firefox Input](http://input.mozilla.com). I have generalized
these routines to work in any general context. As far as I have seen, I have not
found a co-clustering routine implementing the specific algorithm written in
here, so this module hassomenovelty.Documentation is put up [here](http://eshvk.me/matsya/)
Package: mattak
MattakMattak is a Greenlandic meal consisting of whale skin and blubber. Blubber is a
store of energy. That's close enough to data storage...Mattak is (will eventually be) a multilingual package containing readers and
helpers for RNO-G in C++, Python (via both uproot and PyROOT) and maybe even JS (via
rootjs). Writers are provided in C++ ( and Python via PyROOT)
These are all in the same package so that they can be kept in concert with each other.QuickstartThe Python interface for mattak can be installed by running:pip install git+https://github.com/RNO-G/mattak.gitIn order to use the (faster) PyROOT backend, you need a working installation ofROOT. If no ROOT installation can be found, only the uproot backend will be installed.Data directory hierarchyAll data is assumed to reside in a directory named ${RNO_G_DATA}, which in the C++ and Python readers is often accessed via an environmental variable. This may be a mounted directory, or could be an http directory (assuming the HTTP server it resides on supports byte ranges).The general schema for ``run-associated" data (i.e., data taken synchronously with a run)  is:${RNO_G_DATA}/station${STATION_NUMBER}/run${RUN_NUMER}/${PACKET_TYPE}.rootConfig files for each run live in:${RNO_G_DATA}/station${STATION_NUMBER}/run${RUN_NUMBER}/cfgAnd any auxilliary files we might generate will be under:${RNO_G_DATA}/station${STATION_NUMBER}/run${RUNNUMBER}/auxNote that this might result in a lot of files in a directory.Run-associated Data packetsHeaderRun-associated. This is all metadata associated with a single event, not including waveforms. This is kept separated because some operations need only the metadata and also because we can transfer all metadata but only a portion of waveformsWaveformsThese are all waveforms associated with each event.DAQStatusAsynchronous DAQ-related data (e.g. thresholds, scalers, etc.) recorded every once in a while asynchronous to events but still as part of a run.Non-run associated data packets  (TODO)Eventually we might have sensor data, calibration data, GNSS data and who knows what else in the same file tree. That will be defined later...
Package: mattdaemon
Need to daemonize your Python2.7 projects? Matt Daemon has you covered.Featuressuperuser (root) enforcement. Your script either requires it or it doesn’t. You choose!If these checks fail, the script will exit with a status of1.Uses the double-fork magic of UNIX to daemonize.DependenciesPython2.7NotesThis is designed for CLI scripts, because it decouples from the parent environment and all.Includes annoying messages when you use root to run a script.daemon.start()anddaemon.stop()don’t print messages. You’ll have to decide what to print, if anything.MIT Licensed code, so you’re free to do whatever you want with this. Sell it, steal it, improve it, anything at all!Running / UsageAdd the script to your dependencies, it’s on pypi! (pip install mattdaemon)Documentation is available onreadthedocs, so check there for usage.ContributingIf a feature you need is lacking, you know a better way to do something, or you just want to be nice and give out your stars, feel free to visit the mattdaemongit repo; it’s open source and MIT licensed!Authorplausibility
Package: matt-daemon
A lightweight HTTP daemon for serving static files.
With Matt Daemon, there are no surprises. He just serves.InstallationThis is aPythonmodule available through thePyPI.Before installing,download and install Python. We recommend
installing version 3.5+ of Python.Installation is done using thepip installcommand:$pipinstallmatt-daemonFor more info, seepip installdocs.Get StartedTo start the Matt Daemon, all you need to do is run the following command:$matt-daemon
Package: mattdaemon-py3
This package helps in daemonize your Python3 projects.InstallationYou can install directly after cloning:use the Python package:.. code-block:: bash$ pip install --user mattdaemon_py3Featuressuperuser (root) enforcement. Your script either requires it or it doesn't. You choose!If these checks fail, the script will exit with a status of1.Uses the double-fork magic of UNIX to daemonize.DependenciesPython_ 3NotesThis is designed for CLI scripts, because it decouples from the parent environment and all.Includes annoying messages when you use root to run a script.daemon.start()anddaemon.stop()don't print messages. You'll have to decide what to print, if anything.MIT Licensed code, so you're free to do whatever you want with this. Sell it, steal it, improve it, anything at all!Running / UsageAdd the script to your dependencies, it's on pypi! (pip install mattdaemon)Documentation is available onreadthedocs_, so check there for usage.UsageLicenseMIT License.. code:: rst|MIT license|

.. image:: https://img.shields.io/badge/License-MIT-blue.svgAuthorsMaurya Allimuthu ( catchmaurya@gmail.com )
Santosh Bhaskar ( just___28@gmail.com )

ContactPlease submit an issue if you encounter a bug and please email any questions or requests to @authors
Package: matt-dj-registry
DJ-RegistryThis is a simple, easy access key-value registry for Django. Sometimes you would like to have some flexibility in your settings.
Putting your settings insettings.pyor as environment variables also mean an engineer familiar with code or command line is required to alter them.This Django app leverage the built-in Django admin so changing settings is easier as you can use the web interface.RequirementsPython = "^3.6"Django = "^3"Run the following command inside project's root folder to install it's dependencies with Poetry:$ poetry installYou can specify to the command that you do not want the development dependencies installed by passing the--no-devoption.If you have not yet installed Poetry, please refer to theirofficial documentation.InstallationInstall DJ-Registry with your favorite Python package manager:(venv)$ pip install dj_registryAddregistryto yourINSTALLED_APPSsettings:INSTALLED_APPS=[# other apps...'registry',]Migrate the database(venv)$ ./manage.py migrateThen, we're all set!UsageLog in to the admin, and create some keys under theDjango Registry>Entriessection. Let's say, we createmailgun.keyandmailgun.domainwith the correspondingstringtype and values.
We then create another entry withgame.max_scoreas the key,10000as the value andintegeras the type.The following example shows you how to access them in code:fromregistry.helperimportregkey=reg['mailgun.key']# the key that you setdomain=reg['mailgun.domain']# the domain that you setmax_score=reg['game.max_score']# 10000, it is returned as an intYou can also usegetif you want to have a default and avoid exceptions if the key is not available (not enabled or does not exist)reg.get('game.levels',10)# return 10 if key not found or disabledreg['game.levels']# KeyError if key not found or disabledYou can set or delete entry if you wantreg['game.levels']=12# Set game.levels to 12 (integer) and savedelreg['game.levels']# Delete game.levelsEnabled and comment fieldIf you want to disable a key, just toggle theenabledboolean in the admin interface. It would be treated as if the key didn't exist. This is something meant to be used in the admin interface.
If you want to manipulate this in the code, you will have to access the raw model like the following:fromregistry.modelsimportEntrye=Entry.objects.get('game.levels')e.enabled=Falsee.save()The comment field is also meant to be used in the admin interface. It is a convenient cell for user to put comments regarding to the settings, something like the following:50: average use case.
9999: maximum special caseTypesinteger,float,string, andbooleanare the supported types for now.
Package: mattebox
python-matteboxUnofficial Python library for MatteBOX IPTV.Installing$python-mpipinstallmatteboxUsage>>>frommatteboximportMatteBOX>>>mbox=MatteBOX(USERNAME,PASSWORD,DEVICE_ID,SUBSCRIPTION_CODE)Examples>>># get list of TV channels>>>channels=mbox.channels>>># get list of programs on specified channel>>>programs=mbox.get_programs(channels[0])>>># get recordings>>>recordings=mbox.recordings>>># get stream URL for specified program/recording>>>stream=mbox.get_stream(programs[0])>>># search for TV program>>>results=mbox.search("Masza i niedźwiedź")
Package: matteing-gpt-cli
gpt-cliA simple command line utility for ChatGPT, written for personal use.InstallingTo install:exportOPENAI_API_KEY="token"# preferably place this in .zshrcpipinstallmatteing-gpt-cli--user
whichgpt-cli# to test your pip configurationIf your environment is set up to run CLI pip packages, the command-line should work just fine.