Saravanan666/pypi-org huggingface dataset:
Package: masspy
MASSpy - Mass Action Stoichiometric Simulation in PythonWhat is MASSpy?TheMassActionStoichiometricSimulationpython
(MASSpy) package contains modules for the construction, simulation, and
analysis of kinetic models of biochemical reaction systems.MASSpyis built to integrate seemlessly withCOBRApy, a widely used
modeling software package for constraint-based reconstruction and analysis of
biochemical reaction systems. ThereforeMASSpycan be used seperately from
or in conjuction withCOBRApy, thereby providing a wide range of modeling
workflows and techniques. Additional information aboutCOBRApycan be found in itsdocumentation.InstallationCheck out the following information from theQuick Startguide to get started using MASSpy!With Python 3.7+The recommended method is to installMASSpyis to usepipto
install the software from thePython Package IndexIt is recommended to do this inside avirtual environment::pip install "masspy"To installMASSpywith visualization dependencies:pip install "masspy[visualization]"To installMASSpywith all optional dependencies:pip install "masspy[all]"With DockerTo quickly get started with the latest version of MASSpy using Docker, run the following commands in a shell:docker pull sbrg/masspy
docker run --rm \
    --mount type=volume,src=licenses,dst=/home/masspy_user/opt/licenses \
    --mount type=volume,src=mass_project,dst=/home/masspy_user/mass_project \
    --publish 8888:8888 \
    -it sbrg/masspyFrom within the container, either runpythonorjupyter notebook --ip=0.0.0.0 --port=8888depending on
the desired Python workspace. Don't forget to change the port number if it is already being used!Additional installation detailsFor additional details about how to set up an environment for MASSpy, including how to set up commercial optimizers and  optional dependencies, check out the detailedInstallation guidein the documentation!DocumentationThe documentation forMASSpyis found athttps://masspy.rtfd.iowith installation instructions and several tutorials for getting started. All documentation is generated usingSphinxand hosted byReadTheDocs.CiteTo cite theMASSpysoftware publication:Haiman ZB, Zielinski DC, Koike Y, Yurkovich JT, Palsson BO (2021)
MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics.
PLOS Computational Biology 17(1): e1008208.https://doi.org/10.1371/journal.pcbi.1008208Additionally, please consider citingCOBRApy,libRoadRunner, and other software dependencies of MASSpy! Citations and links to several dependencies as well as other useful literature sources are found in theWorks CitedandCode Repositoriessections of the documentation.SupportNot sure how tofile an issue, want tocontributeto MASSpy, or just looking for somegeneral guidance? Check out theSupport page!LicenseTheMASSpysource is released under theMIT license. However, please note that binary packages (e.g., GLPK, CPLEX, etc.) and other dependencies (e.g.openCOBRA packages,libRoadRunner, etc.) will be bound by their respective license agreements as well.
Package: massql
Mass Spec Query LanguageThe Mass Spec Query Language (MassQL) is a domain specific language meant to be a succinct way to
express a query in a mass spectrometry centric fashion. It is inspired by SQL,
but it attempts to bake in assumptions of mass spectrometry to make querying much more
natural for mass spectrometry users. Broadly we attempt to design it according to several principles:Expressiveness - Capture complex mass spectrometry patterns that the community would like to look forPrecision - Exactly prescribe how to find data without ambiguityScalable - Easily facilitating the querying of one spectrum all the way up to entire repositories of dataRelatively Natural - MassQL should be relatively easy to read and write and even use to communicate ideas about mass
spectrometry, you know like a language.Repository StructureThis is the repository to define the language and reference implementation. This contains several partsLanguage GrammarReference Implementation Python APICommand line Utility to executeNextFlow Workflow For Large Scale AnalysisProteoSAFe workflowDash interactive explorationDevelopers/ContactMingxun Wang is the main creator and developer of MassQL. Contact me for contributing or using it!Language Specification/DocumentationCheckout specifics for the language, examples, and design patterns at the documentation.Documentation LinkPython APITo install massqlpip install massqlHere is the most basic operation you can dofrom massql import msql_engine

results_df = msql_engine.process_query(input_query, input_filename)If you want to push in a data frame you already have, you can specify itfrom massql import msql_engine
from massql import msql_fileloading

# Loading Data
ms1_df, ms2_df = msql_fileloading.load_data(input_filename)

# Executing Query
results_df = msql_engine.process_query(input_query, input_filename, ms1_df=ms1_df, ms2_df=ms2_df)Command Line ToolYou can use the command line toolmassqlto query things or put things into a pipeline.A few examples of what you can domassql test.mzML "QUERY scaninfo(MS2DATA)" --output_file results.tsvWeb APIAPI Version/apiParsing query into intermediate JSON string/parse?query=<query string>Example LinkVisualization of QueryVisualization image of MS1 spectra/visualize/ms1Example LinkVisualization image of MS2 spectra/visualize/ms2TestingTo run tests, you'll need to first fetch some fixtures that are not bundled with the git repo:cd tests && sh ./get_data.shYou will also want to install the extra requirements for the test suite:pip install -r requirements_test.txtLicenseMIT License
Package: mass_rename
# mass_renamemass_rename is a simple utility to rename group of files with your favorite$EDITOR.# Installmass_rename can be installed with pip`shell $ sudo pip install mass_rename `# Terminal and Editormass_rename will launch your favorite (unless-sis specified, which will launch vim in split mode)
that will be read from your $EDITOR settings variable.`bash echo $EDITOR `# About
Author: Enrico Carlesso
License: [WTFPL](http://www.wtfpl.net/about/)Thanks:[vim](http://www.vim.org/) and all the Vim communitymass_rename has been tested with Python 3.3.2
Package: mass-replace
mass_replaceUse casemass_replace"Walkthrough" through a specifiedroot_folderand perform find and replace operations on every file of the specified type.IfPython 3is installed on your system it can be run asmass_replace.pyPython scriptCan be run as a standalone Window Executable (.exe)⚠️WARNING⚠️EVERY FILE OF THE SPECIFIED TYPE NESTED UNDERNEATH THEroot_folderWILL BE SUBJECT TO FIND AND REPLACE OPERATIONSInstallation and usageTo run as a standalone.exeDownloadmass_replace_windows_executable.zipExtract zip file.Set configuration settingsfrommass_replace/openconfig.yamlwith any text editor.setfiletypesto control which filetypes will be "operated on".setreplacement_paristo control the find and replace operations to be performed on each file.set 'root_folder' to set the parent folder under which the operations will be performed.Runmass_replace.exeDouble clickmass_replace.exeor run from command line/powershell ✔️# mass_replace config fileroot_folder:C:\Users\username\Documents\docs_go_here_folderfiletypes:-csv-xml-txtreplacement_pairs:old_text1:new_text1old_text2:new_text2puff_daddy:diddyEase of Use suggestionsTo make the use ofmass_replace.exeeasier.
Create shortcuts ofmass_replace.exeandconfig.yamland place them somewhere easy to get to.
Do not move the actual files or the application will not work.
Package: masstable
masstableUtilities for working with nuclear mass tablesDocumentation:https://elyase.github.io/masstable/Source Code:https://github.com/elyase/masstableDevelopmentSetup environementYou should havePipenvinstalled. Then, you can install the dependencies with:pipenvinstall--devAfter that, activate the virtual environment:pipenvshellRun unit testsYou can run all the tests with:maketestAlternatively, you can runpytestyourself:pytestFormat the codeExecute the following command to applyisortandblackformatting:makeformatLicenseThis project is licensed under the terms of the MIT license.
Package: masstransitpython-byQ96
DescriptionPython library to exchange messages between MassTransit RabbitMQ Client and Python application. Implemented with pika RabbitMQ client.InstallationRun the following to install:pipinstallmasstransitpython-byQ96UsageSimple receiver/sender model was implemented to show basic package usage.ConfigurationDefault client configuration can be implemented as follows:frompikaimportPlainCredentialsfrommasstransitpythonimportRabbitMQConfigurationRABBITMQ_USERNAME='guest'RABBITMQ_PASSWORD='guest'RABBITMQ_HOST='localhost'RABBITMQ_PORT=5672RABBITMQ_VIRTUAL_HOST='/'credentials=PlainCredentials(RABBITMQ_USERNAME,RABBITMQ_PASSWORD)conf=RabbitMQConfiguration(credentials,queue='PythonServiceQueue',host=RABBITMQ_HOST,port=RABBITMQ_PORT,virtual_host=RABBITMQ_VIRTUAL_HOST)ReceiverReceiver must have an appropriately defined exchange name:[SOLUTION_NAME:DIRECTORY_NAME:MESSAGE_NAME]frommasstransitpythonimportRabbitMQReceiverfromjsonimportloadsdefhandler(ch,method,properties,body):msg=loads(body.decode())print("Received message:%s"%msg["message"])# define receiverreceiver=RabbitMQReceiver(conf,'MassTransitService.Messages:SampleMessage')receiver.add_on_message_callback(handler)receiver.start_consuming()SenderfrommasstransitpythonimportRabbitMQSenderfromjsonimportloadsdefsend_message(body):''':param body: Message received from MassTransit client:return: None'''withRabbitMQSender(conf)assender:sender.set_exchange('MassTransitService.Messages:SampleMessage')encoded_msg=MessageEncoder().encode(SampleMessage("Hello World!"))response=sender.create_masstransit_response(loads(encoded_msg),body)sender.publish(message=response)MessagefromjsonimportJSONEncoderclassSampleMessage:def__init__(self,name):self.name=nameclassMessageEncoder(JSONEncoder):defdefault(self,o):returno.__dict__OtherFull example avaliable inhttps://github.com/byQ96/MassTransitPython/example
Package: mastapy
Mastapy is the Python scripting API for MASTA.Website:https://www.smartmt.com/Support:https://support.smartmt.com/Documentation:https://documentation.smartmt.com/MastaAPI/13.0.1/FeaturesPowerful integration with MASTA with the ability to run Python scripts from the MASTA interface directly.Ability to use MASTA functionality external to the MASTA software in an independent script.An up-to-date and tight integration with Python. This is not a lightweight wrapper around the C# API. It is specifically designed for Python and works great in tandem with other common scientific Python packages (e.g. SciPy, NumPy, Pandas, Matplotlib, Seaborn, etc.)Extensive backwards compatibility support. Scripts written in older versions of mastapy will still work with new versions of MASTA.
Package: mastcasjobs
This provides a simple interface to the MAST CasJobs server (home of GALEX,
Kepler, the Hubble Source Catalog, PanSTARRS, etc.) using Dan Foreman-Mackey’scasjobsinterface.InstallationInstall current versions of both themastcasjobsandcasjobsmodules:pip install mastcasjobsIf you want to do development on themastcasjobsmodule, clone it and then install it using:pip install .UsageHere is an example query that does a cone search for PS1 objects within
50 arc-sec of coordinates RA=187.706, Dec=12.391 (in degrees):import mastcasjobs

query = """select o.objID, o.raMean, o.decMean,
o.nDetections, o.ng, o.nr, o.ni, o.nz, o.ny,
m.gMeanPSFMag, m.rMeanPSFMag, m.iMeanPSFMag, m.zMeanPSFMag, m.yMeanPSFMag
from fGetNearbyObjEq(187.706,12.391,50.0/60.0) nb
inner join ObjectThin o on o.objid=nb.objid and o.nDetections>1
inner join MeanObject m on o.objid=m.objid and o.uniquePspsOBid=m.uniquePspsOBid
"""

# user is your MAST Casjobs username
# pwd is your Casjobs password
# These can also come from the CASJOBS_USERID and CASJOBS_PW environment variables,
# in which case you do not need the username or password parameters.
# Create a Casjobs account at <https://mastweb.stsci.edu/ps1casjobs/CreateAccount.aspx>
#   if you do not already have one.

user = "myusername"
pwd = "My super secret password"

jobs = mastcasjobs.MastCasJobs(username=user, password=pwd, context="PanSTARRS_DR2")
results = jobs.quick(query, task_name="python cone search")
print(results)Note that the results of thequickquery are by default returned as anastropy table.
You can add the optional parameterastropy=Falseto get a string instead.Thejobsobject has other useful methods that allow you to do almost all the queries that you
can run using the web interface.  Usehelp(jobs.function)to get details.  Some of the commonly used
functions include:quick:Run short queries that execute in less than 1 minute.submit:Submit a long-running query.status,monitor,cancel:Monitor a submitted query.fast_table:Fast retrieval of data from a MyDB table (works only on MAST Casjobs).get_table:Retrieve a small or large MyDB table (slower but works in other Casjobs installations too).list_tables:List tables in MyDB (or in another context).drop_table_if_exists:Delete a table from your MyDB (if it exists).RequirementsThis relies on thecasjobs(version 0.0.2 or newer) andrequestsmodules.Since Python 2.7 is no longer supported, the installation of this version of the software requires
Python 3.5 or greater.  The software actually still runs in Python 2.7, but you will have to install it
manually.LicenseMIT
Package: master-auth
master-authcheck auth use mongodbhow to useinstallpipinstallmaster_authexamplefrommaster_authimportcoreimportosimportdotenvdotenv.load_dotenv()MONGODB_USER=os.getenv("MONGODB_USER")MONGODB_PASSWORD=os.getenv("MONGODB_PASSWORD")MONGODB_HOST=os.getenv("MONGODB_HOST")MONGODB_PORT=os.getenv("MONGODB_PORT")DATABASE_NAME=os.getenv("APP_NAME")+os.getenv("MONGODB_DATABASE_NAME")COLLECTION_NAME="master_auth"defsave_password(username,password):result=core.save_password(mongodb_username=MONGODB_USER,mongodb_password=MONGODB_PASSWORD,mongodb_host=MONGODB_HOST,mongodb_port=MONGODB_PORT,database_name=DATABASE_NAME,collection_name=COLLECTION_NAME,username=username,password=password)ifresult:print("Password saved successfully!")returnTrueelse:print("Username already exists!")returnFalsedefupdate_password(username,password_old,password_new):result=core.update_password(mongodb_username=MONGODB_USER,mongodb_password=MONGODB_PASSWORD,mongodb_host=MONGODB_HOST,mongodb_port=MONGODB_PORT,database_name=DATABASE_NAME,collection_name=COLLECTION_NAME,username=username,password_old=password_old,password_new=password_new)ifresult:print("Password updated successfully!")returnTrueelse:print("Old password is not correct!")returnFalsedefverify_password(username,password):result=core.verify_password(mongodb_username=MONGODB_USER,mongodb_password=MONGODB_PASSWORD,mongodb_host=MONGODB_HOST,mongodb_port=MONGODB_PORT,database_name=DATABASE_NAME,collection_name=COLLECTION_NAME,username=username,password=password)ifresult:print("Password is correct!")returnTrueelse:print("Password is not correct!")returnFalseif__name__=="__main__":save_password("test","test")# Username does not exist!# Password saved successfully!# update_password("test","test","test2")# Password updated successfully!# verify_password("test","test2")# Password is correct!v0.0.2update some bugsv0.0.1save password to mongodb , password is hashedcheck password from mongodb , password is hashedupdate password to mongodb , password is hashed
Package: masterblaster.py
Masterblaster.pyPython API wrapper for masterblaster.ggInstallation$python-mpipinstallmasterblaster.pyUsageMasterblaster.py is a python wrapper for the masterblaster.gg API. It allows you to easily access the data from the API and use it in your own projects.
I.e.:importasyncioimportmasterblasterasyncdefmain():asyncwithmasterblaster.MasterBlaster("MY-TOKEN-HERE")asm:fororginawaitm.get_all_orgs():formemberinawaitorg.get_members():print(member)# Alternativem=awaitmasterblaster.MasterBlaster.create("MY-TOKEN-HERE")org=awaitm.get_org("MY-ORG-ID"):formemberinawaitorg.get_members():print(member)# Or Perhapsm=masterblaster.MasterBlaster("MY-TOKEN-HERE")......asyncwithm:org=awaitm.get_org_by_name("MY_ORG-NAME")formemberinawaitorg.get_members():print(member)if__name__=="__main__":asyncio.run(main())ContributingInterested in contributing? Check out the contributing guidelines.
Please note that this project is released with a Code of Conduct.
By contributing to this project, you agree to abide by its terms.Licensemasterblaster.pywas created by Øyvind Nohr. It is licensed under the terms
of the MIT license.ContributorsLinksDocumentation
Package: master-board
master-boardHardware and Firmware of the Solo Quadruped Master Board.This board centralises all the sensor and actuator data and provides wired and wireless connection to a realtime computer.Connectivity:SPI: Address up to 8 SPI Slave: (max 80Mhz, DMA capable) compatible with BLMC µDriver SPI interfaceWifi: Wireless communication with a computer via raw ESP-NOW: round trip time of 1.2ms (including driver and OS latency) for a 127bytes message.Ethernet: Wired communication with a computer via raw frames: round trip time of 0.2ms (including driver and OS latency) for a 127bytes message.GPIO: 4GPIO free. Can be mapped to I2C, UART etc.. Two of them are curently used for IMU communication via UARTUART: Used to upgrade the ESP32 firmware, free on normal operation.The board is programed via the ESP-IDF tool chainhttps://github.com/espressif/esp-idfWireless closed loop control at 1kHz demo (click to see video):IMU, ethernet closed loop cntrol at 1kHz demo (click to see video):LED statusRed fade:Waiting for initMagenta fade:SPI AutodetectBlue fade:waiting for first commmandGreen fade:Active controlYellow blink:ethernet link down state awaiting for link upRed blink:error state (communication with PC), awaiting for new init msgWhite blink:state machine error (should never happen)DocumentationHere are some helpful links to the documentation :How to Flash the Master Board (install esp-idf and flash the firmware)SDK : How to Prepare Your Interface and Run the ExampleMaster Board State Machine DescriptionDescription of the Communication Between the Master Board and the ComputerDescription of the BLMC µDriver SPI InterfaceWiring the Master BoardMaster Board Ordering and PreparationAuthorsThomas FlayolsEtienne ArlaudLicenseBSD 3-Clause LicenseCopyrightCopyright (c) 2019, LAAS-CNRS, Max Planck Gesellschaft, New York UniversityMore InformationOpen Dynamic Robot Initiative
Package: masterfile
masterfileTools to organize, document, and validate the variables of interest in scientific studiesCommand line usagemasterfile --helpwill list all the subcommands.Createmasterfile create masterfile_path out_fileJoinmasterfile join masterfile_path out_fileExtractmasterfile extract [-s|--skip ROWS] [--index_column COL]
                      masterfile_path csv_file out_fileValidateasterfile validate masterfile_path [file [file ...]]Draft API usage exampleimportmasterfile# Load all of the .csv files from /path, and the dictionary files in# /path/dictionaries. Takes settings info from a 'settings.json' file in# /path.# joins the .csv files on 'participant_id', which will be used as the index# There will be warnings if the data look bad in some waymf=masterfile.load('/path')# Get the pandas dataframe associateddf=mf.dataframe# aliased as mf.df# All the variable stuff is less important, people can go look in data dicts# So we'll write that stuff later.v=mf.lookup('sr_t1_panas_pa')v.contacts# list_of_namesv.measure.contact# Someonev.modality# Component("self-report")CSV file formatCSV files should be comma-separated (no surprise there) and have DOS line endings (CRLF). They should not have the stupid UTF-8 signature at the start. UTF-8 characters are fine. Missing data is indicated by an empty cell. Quoting should be like Excel does.Basically, you want Excel-for-Windows-style CSV files with no UTF-8 signature.DictionariesCSV formatHas AT LEAST two columns: component, short_nameThose are the indexesThere shouldn't be any repeats in the indexThe settings.json file should contain a "components" thing that says what should exist in the component columnThings with blank component are ignored (TODO: Maybe?)Exclusion filesCSV formatLive in exclusions/One row per ppt, one column per valueHas index column, same as data fileBlanks mean "Use this value," nonblanks mean "exclude this value"Things in the cells may be codes; these codes may be defined in settings.jsonIf data is excluded for more than one reason, separate codes with ","Not all rows / columns in masterfiles need to be included in exclusion files. Missing rows / columns are treated like blank values.Data checksHere are some (all?) of the things to do to verify you have semantically reasonable data:Variable parts not in dictionariesMissing participant_id columnRepeated paticipant_id columnBlanks in participant_id columnDuplicate columnsColumn names not matching formatGetting started for developmentCreate a virtualenv:virtualenv ~/env/masterfile
source ~/env/masterfile/bin/activateInstall the requirements and this module for development:pip install -r requirements_dev.txt
pip install -e .Run tests:pytestRun tests across all supported Python versions:toxTo run in a specific python version:tox -e py37CreditsWritten by Nate Vacknjvack@wisc.eduwith help from Dan Fitchdfitch@wisc.edumasterfile packages some wonderful tools:schemaandattrs.schema is copyright (c) 2012 Vladimir Keleshev,vladimir@keleshev.comattrs is copyright (c) 2015 Hynek Schlawack
Package: masterful
Masterful is the training platform for computer vision models.Pass in your model architecture and data. Masterful figures out the right hyperparameters for its SOTA regularization, optimization, and semi-supervised learning engines. And then trains your model weights to the peak accuracy.FeaturesBuilt on Tensorflow 2.SOTA regularization implementation to improve accuracy from your existing labeled training data while pushing all computations to GPU.SOTA semi-supervised learning implementation to improve accuracy from your raw, unlabeled images.Automatic, high speed hyperparameter discovery algorithms, based on custom search algorithms and analytical / closed-form solutions, to reduce manual experimentation.Maximum GPU utilization and minimium GPU-hours used.Support for most computer vision tasks:Binary classificationSingle-label classificationMulti-label classifcationDetectionSemantic SegmentationInstance Segmentation (coming soon)Keypoint Detection (coming soon)Key LinksWebsiteDocsInstallationQuickstartInstallationFor detailed instructions with a troubleshooting guide, see theInstallation Guide.The tldr instructions:sudo apt install python3-pip
pip install --upgrade pip
pip install masterful
python -c "import masterful; print(masterful.__version__)"Getting HelpVisit the documentation to see quick tutorials and in depth guides atwww.masterfulai.com/docs.Join the Slack community to speak to fellow users of Masterful and with Masterful AI's engineering team. You'll find a link to Slack fromwww.masterfulai.com.
Package: mastermind
mastermindThis package allows you to play Mastermind directly into a jupyter notebook/lab IDE. It has been made using the greatipywidgetspython library.Installpip install mastermindHow to use in a jupyter notebookCopy and paste the following lines into a cell of a notebook and run it:from mastermind import MastermindNotebook
game = MastermindNotebook()The game should display, you can start playing by choosing one color for each position and click on "Confirm combination" button !How to use in jupyter labTo use in jupyter lab, don't forget to install the jupyter widget extension:jupyter labextension install @jupyter-widgets/jupyterlab-managerOnce this is done, you can proceed in the same way as for the jupyter notebook use.State of developmentA Tkinter interface must be developed to play outside a notebook, feel free to contribute !Add a DB to record all the games for one user