Saravanan666/pypi-org huggingface dataset:
Package: mcl.infirmary
🏥 InfirmaryWelcome to "Infirmary", theReST-basedapplication programming interfacefor theClinical Data componentof the theConsortium for Molecular and Cellular Characterization of Screen-Detected Lesions. This project, along withSickbayform a database for discovering clinical data.⚙️ EndpointsOnce deployed, this package provides the following ReST API endpoints:EndpointAuthPurpose/pingTest if the server is working/hello/{greeting}🔐Test if authentication is working/clinicalCores🔐Return all Clinical Cores in the database, fully serialized/clinicalCores/{participant_ID}🔐Return the single Clinical Core for the namedparticipant_ID/organs🔐Return all organs in the database/organs/{identifier}🔐Return the single organ with the numericidentifier/specimens🔐Return all biospecimens in the database/specimens/{specimen_ID}🔐Return the single biospecimen with the givenspecimen_ID/genomics🔐Return all genomics in the database/genomics/{specimen_ID}🔐Return the genomics information with the givenspecimens_ID/images🔐Return all imaging information in the database/images/{identifier}🔐Return the imaging information with the numericidentifierFor those endpoints where the "Auth" column is dispays 🔐, you must provide an HTTP Basic Authentication header with a username and password of a member of the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions. All of the endpoints return a JSON payload.🔧 DevelopmentTo develop locally with a local Postgres database:python3 -m venv venvvenv/bin/pip install --quiet --upgrade setuptools pip wheel buildvenv/bin/pip install --editable '.[dev]'venv/bin/infirmary --debugTo create and register the image:docker image build --tag mcl-infirmary:latest .docker image tag mcl-infirmary:latest nutjob4life/mcl-infirmary:latestdocker logindocker image push nutjob4life/mcl-infirmary:latestTo explore the image:docker container run --rm --interactive --tty --entrypoint /bin/sh mcl-infirmary:latest
Package: mclipp
MClipp docsMClipp is a easy library, where you can create big or small files.
You can do it usingMemoryclass.How does it working?MClipp creates a file, that has custom file size. It multiplies a unit by customfile size in bytes.
As it known, the unit is a simple zero (0).CreateUsing "create" method you can create files width custom file size.Example:frommclippimport*clipper=Memory()clipper.create('filename',1024)It creates a file, that has name"filename"and size 1024 bytes.Pattern:frommclippimport*clipper=Memory()clipper.create(filename,byte)... wherefilenameis a filename andbyteis a file_size.DeleteUsingdeleteyou can delete large or simple files without recovery.Example:frommclippimport*clipper=Memory()clipper.delete(r'C:/Users/user/Desktop','filename')Here you can delete file"filename", that is in"C:/Users/user/Desktop".
You can successful delete this file if it isn't in use.Pattern:frommclippimport*clipper=Memory()clipper.delete(path,filename)Bonus: getunitTo get a unit you must to use propertyunit.Example:frommclippimport*clipper=Memory()print(clipper.unit)In current version the unit is"0".
Package: mclmc
MicroCanonical Hamiltonian Monte Carlo (MCHMC)Installationpip install mclmcOverviewYou can check out the tutorials:getting started: sampling from a standard Gaussian (sequential sampling)advanced tutorial: sampling the hierarchical Stochastic Volatility model for the S&P500 returns data (sequential sampling)Julia implementation is availablehere.The associated papers are:method and benchmark testsformulation as a stochastic process and first application to the lattice field theoryIf you have any questions do not hesitate to contact me atjakob_robnik@berkeley.edu
Package: mclogger
MCLogger: Multi color logger to log to screen and fileMCLogger to log to file AND to the consoleWhat problem does this solve?A challenge for web-server applications (e.g. such as Flask) is to decipher what's going on from a long logging window.  The standard loggers are all single color console test which you have to trawl through manually one by one.MCLogger helps to solve this by colorising the debug, info, warning, into different colors so that it is much easier to read.  The logger will output to both on screen and also a fileHow does it do this?MCLogger builds on the logging library and adds console color libraries to add colors to debug, info, error, warning entriesDEBUG - blueWARNING - yellowERROR - redINFO - cyanHow to install?MCLogger is avaialble through PyPi and you may use pip:pip install mcloggerOr, through git:git clone https://github.com/pub12/mclogger.gitHow to use the logger?The logger is super easy to use.  You need to simply create an instance and add a file/filepath for the logfilefrom mclogger import MCLogger

filename = 'log_file.txt'
logger = MultiLogger(filename).getLogger()

logger.debug("hello world - debug")
logger.info("hello world - info")
logger.error("hello world - error")
logger.warning("hello world - warning")Output will be:Class Methods overviewgetLogger(filename):Generates the log instance which can be called with .debug(message); .info(message); .error(message); .info(message) which will be displayed in color format.Format of the output message will be:
"<current server timestamp> [<Filename and line number - function name()>] [<DEBUG | INFO | WARN | ERROR >] - < Message >"filename- filename with optional relative of optional filepath - e.g.temp/log_file.txtread_log_file(last_n_rows = 20):Return in an array thelast_n_rowsin an array in the order listed within the file.  This will include the color codes for the messagelast_n_rows- Number of lines to readread_log_file_as_text(last_n_rows = 20):Return in an array thelast_n_rowsin an array in the order listed within the file.  This will have color codes removedlast_n_rows- Number of lines to read
Package: mclone
M_CLONE2009 OLD ID CLONING TOOL!AUTHORTO REPORT ANY TYPE OF BUG OR PROBLEMREQUIREMENT :-)-:- PYTHON2.7-:- REQUESTS-:- MECHANIZE-:- LOLCATINSTALLATION-:)pip2 install mcloneRUN ;JUST TYPE -:)mcloneSCREENSHOT[ ! ] TASTED ON TERMUX [ ! ][ √ ] YOU CAN USE IT ANY KIND OF TERMINAL [ √ ]CLICK US!-:)mao2116WARNING :This tool is only for educational purpose. If you use this tool for other purposes except education we will not be responsible in such cases.
Package: mcloud-plugin-haproxy
Haproxy publishing============================Haproxy plugin install haproxy as a load balancer for mcloud. Plugin is useful when you needdeploy multiple applications on one server, or use complex application publishingMultiple applications-------------------------.. uml::cloud Internet {}package Docker {[Haproxy] << Load Balancer >>database App1 {[nginx.myapp][another.myapp][something.myapp]}database App2 {[nginx.another][another.another]}Haproxy ..> nginx.myappHaproxy ..> nginx.another}Internet ..> HaproxyMultiple versions-------------------------.. uml::cloud Internet {}package Docker {[Haproxy] << Load Balancer >>[nodejs.app_v1][nodejs.app_v2]Haproxy ..> nodejs.app_v1Haproxy -> nodejs.app_v2}Internet ..> HaproxyHaproxy template-----------------------You can use your own template by placing it in /root/.mcloud/haproxy.tpl. Mcloud kindly places default config there... highlights::Template is Jinja2 template http://jinja.pocoo.org/docs/To apply your changes to template restart mcloud::$ docker restart mcloudDefault tamplate^^^^^^^^^^^^^^^^^^.. literalinclude:: mcloud_haproxy.py:lines: 21-94:language: jinja
Package: mcm
mcm-core========Core MCM - Map Clean MergeOverview-----------MCM has two main peices, a reader and a mapper.- Reader* Reads csv files, returns a generator of DictCSVReader parsed rows.* Optionally chunks the rows into groupings of specified sizes.- Mapper* Can build a probabalistic column mapping given a schema and some raw data.* Will substitute saved values for suggested mapping (e.g. pulling a previous mapping from DB).* Totally flexible, you pass a callable which takes the raw data and returns a mapping.* Will clean data based on a Cleaner object for a given type. Type is inferred from the mapping schema.* Ability to set "initial_data"* If you always need to set some information in the object that you're mapping data into, this is useful.* Concatenate rows together with a specified delimiter character.* Data which doesn't match a given schema's mapping is still saved. It's put in a dictionary called ``extra_data``.Installing----------Once it's hosted on Pypi:```bashpip install mcm-core```Integration-----------```pythonfrom mcm import cleaners, mapper, reader# Here our mapping is just a dictionary where our keys are raw data representations# and our values are our normalized attributes that we're mapping to.mapping = {'Thing': 'thing_1', 'Other thing': 'thing_2'}# model_class can be any type of object.model_class = object# Reading and mapping from a CSV file, simple case.parser = reader.MCMParser(csv_file_handle)mapped_objs = [m for m in parser.map_rows(mapping, model_class)]```Developing----------1. Clone.2. Create a virtualenv; if you use virtualenv wrapper you'll need to1. Run ``python setup.py develop`` to hardlink your files into your env.Testing-------Unfortunately, there are some directory path issues still baked in.To run tests you have to be in the ``tests`` directory:```bashcd tests && nosetests```
Package: mcmc2ndgentdi
MCMC2ndGenTDIThis generates MCMC samples of parameters to estimate the time-varying LISA spacecraft separations required for laser frequency noise suppression in time-delay interferometry (TDI). See the paper published on the subjecthere. The data to sample over is generated withLISA Instrument and LISA Orbits, so the naming and labeling conventions may look similar to their codes for clarity.The default settings for creating a BayesTDI() class instance are for LISA data generated with Keplerian orbit model parameterization, i.e. ({M01,a,w}--> Lij(t)).If running Keplerian orbit model parameterization, i.e. ({M01,a,w}--> Lij(t))generate_data_Keplerian.ipynb (and follow directions within)example_mcmc_run.ipynb (and follow directions for Keplerian within)Sampling for arbitrary numerical orbits: The spacecraft separations are parameterized by 6 orbital elements that differ for each of the 3 spacecraft. Seethe paperfor more details. Successful sampling is still a work in progress, but directions for the numerical options are found in example_mcmc_run.ipynb.If running numerical orbit model (18-parameter)  parameterization:generate_data-ESA.ipynbelements_from_Cartesian.ipynbexample_mcmc_run.ipynb (and follow directions for numerical/esa within)Installationpip install mcmc2ndgentdiCurrent version does not link to default data file that is included in package. Currently, you must download the repository zip file and put the data files in the directory you run your code from.Example Usefrom mcmc2ndgentdi.run_mcmc_TDI import BayesTDICreate BayesTDI() class instance. Set all arguments here. If running data that used numerical orbit model (18 parameter model) run first example and comment line 2, if running data that used Keplerian model, use second example and comment line 1.#b1 = BayesTDI('LISA_Instrument_ESA_orbits_tcb_orbits_4_Hz_3600_sec.dat', cut_off=0,f_s=4.0,t_init= 13100.00,f_min= 5.0e-4,f_max = 0.1,orbit_model='esa',orbital_elements_file='elements_from_Cartesian_4_Hz_3600_sec.dat',tcb=True,number_n=7,Nens = 37,Nburnin = 100,Nsamples = 100000)
b1 = BayesTDI('LISA_Instrument_Keplerian_orbits_ppr_orbits_4_Hz_3600_sec.dat', cut_off=0,f_s=4.0,t_init=0.0,f_min= 5.0e-4,f_max = 0.1,orbit_model='keplerian',orbital_elements_file=None,tcb=False,number_n=7,Nens = 37,Nburnin = 100,Nsamples = 100000)Initialize best einsum path for FDI filtereinsum_path_to_use = b1.get_einsum_path()Run either zeus sampler for numerical orbit model data (18-parameter model; still under development for converged posteriors) or the working Keplerian orbit model parameterization.b1.run_emcee_Keplerian_mcmc(einsum_path_to_use)
Package: mcmcAllotter
This is a python package based on the source code for"An MCMC Based Course to Teaching Assistant Allocation", S. Kumar, S. Moothedath, P. Chaporkar, M. Belur, Proceedings of the Fifth International Conference on Network, Communication and Computing (2016). ACM.PS: The code is currently not optimized for performance and readability!Requirements:pandasnumpyTry it out!Install the mcmcAllotter package via pip with the following command:pip install mcmcAllotterUsage:python main.pyNote: The packaging of this code is still underway. Although it is okay to try it, the work on performance, readability and API design is still ongoing.FeedbackPlease send your feedback to saurabhkm[AT]hotmail.com or create an issue.
Package: mcmcplot
mcmcplotThemcmcplotpackage is designed to assist in the analysis of sampling chains gathered during a Markov Chain Monte Carlo (MCMC) simulation.  This package was designed with the MCMC codepymcmcstatin mind; however, the plotting routines are amenable to other data sets.  The plotting routines usematplotlibandseaborn.  User’s are recommended to investigate other plotting routines available inseabornas it is specifically designed for this sort of analysis.  The routines available inmcmcplotserve as a useful wrapper function for severalseabornplots, but it is not an exhaustive demonstration.InstallationThis code can be found on theGithub project page.  The package is available on the PyPI distribution site and the latest version can be installed viapip install mcmcplotThe master branch typically matches the latest version on the PyPI distribution site.  To install the master branch directly from Github,pip install git+https://github.com/prmiles/mcmcplot.gitYou can also clone the repository and runpython setup.py install.Getting StartedTutorial notebooksDocumentationRelease historyLicenseMITContributorsSee theGitHub contributor
pageCiting mcmcplotPlease see themcmcplot homepageor follow the DOI badge above to find the appropriate citation information.FeedbackFeature RequestBug ReportSponsorThis work was sponsored in part by the NNSA Office of Defense Nuclear Nonproliferation R&D through the Consortium for Nonproliferation Enabling Capabilities.
Package: mcmc-statphys
mcmc_statphysA Python package of Monte Carlo simulation algorithms for some statistical physics models (in particular, the Ising model and its variants).Free software: MIT licenseDocumentation:https://mcmc-statphys.readthedocs.io.FeaturesA simple Ising model simulation.>>>importmcmc_statphysasmcsp>>>model=mcsp.model.Ising(12)# 12x12 Ising model>>>algorithm=mcsp.algorithm.Metropolis(model)# Metropolis algorithm>>>uid=algorithm.equil_sample(T=1,max_iter=1000)# sample until equilibrium>>>fig=mcsp.draw.Plot(algorithm)>>>fig.curve(uid=uid,comlumn='energy')# plot the energy curveInstallthe latest version of mcmc_statphys:$pipinstallmcmc_statphysupgrade to the latest version:$pipinstall--upgrademcmc_statphysBugsPlease report any bugs that you findhere. Or, even better, fork the repository onGitHuband create a pull request (PR). We welcome all changes, big or small, and we will help you make the PR if you are new to git (just ask on the issue and/or seeCONTRIBUTING).CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.HistoryAll notable changes to this project will be documented in this file.The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).[Unreleased][0.4.0] - 2023-05-20AddedAddimshowindrawAddanimateindrawFixedFix:WolffandAnnealuid does not return[0.3.0] - 2023-05-18AddedAddcvinanalysisto calculate the cvAddspin2svdinanalysisAdduid2svdinanalysis[0.2.1] - 2023-05-17FixedFix the bug of saving_get_per_magnetizationin_save_datainalgorithmAddedAdd moudleanalysisto analyze the dataAdd moudledrawto draw the figuresAdd methodsetspininmodelAdd tqmd to show the progress barDocAdd documentation to READMEAdd documentation to UsagesChangedChange the methods in theanalysismodule: removed theSampleandParameterSampleclasses, addedMetropolis,Wolff,Annealclasses and several methods[0.1.2] - 2023-05-15SecurityAdd function annotations to all functionsAdd type hints to all functionsAdd type hints to all variablesChangemcmc_statphys.pytomethod.pyDocAdd documentation to README[0.1.1] - 2023-05-14First release on PyPI.
Package: mcmder
mcmderYet Another Wrapper of Nysol M-Command in Python.DescriptionHigh-speed processing (CSV) of large-scale structured data tables.To know more about Nysol and M-Command, see the official documents.What is Nysol and M-Command?Nysol Official PageM-Command GitHubHow fast Nysol is?Nysol Biz Page (Japanese)FeaturesCreate M-Command easily in python with method chaining.Execute M-Command without putting large data on memory.(csv to csv)Optionally use pandas DataFrame as input and output.Requirementnysol/mcmdpandasUsageFrom CSV Filesample.csva,b,c
x,1,4
y,2,9
z,3,3>>> from mcmder import Mcmder
>>> m = Mcmder('sample.csv')
>>> mc = m.mcut(['a','c'])
>>> mc.save('cut.csv')cut.csva,c
x,4
y,9
z,3>>> mc.dataframe
   a  c
0  x  4
1  y  9
2  z  3From pandas DataFrame>>> from mcmder import Mcmder
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(np.random.randn(6,4), columns=list('ABCD'))
>>> m = Mcmder(df)
>>> mc = m.mcut(['A','C','D']).msel('${A}>0')
>>> mc.dataframe
          A         C         D
0  0.251857  0.080099 -1.211923
1  0.100167 -1.824585  0.051611
2  0.890079  1.440997 -0.298709LicenseMITAuthorYusuke Hayashi
Package: mc-mod-getter
mc-mod-getterI made this tool to download & update mods usingMultiMCbut you can use this as a standalone python module as wellUsageMultiMCCreate or Edit a minecraft instanceNOTE: Ensure there are no spaces in the instance name or the directory pathsInstall your loader of choiceDownload the binary fromreleasesorbuild your own binaryfrom scratchCopy the binary to your MultiMC's instance .minecraft folderCreate ayaml filein the same .minecraft directoryEnable Custom Commands under in your instance's Settings & paste the following in the Pre-launch command boxMacOS$INST_MC_DIR/mc-mod-getter--file$INST_MC_DIR/<FILENAME>.yamlWindows:$INST_MC_DIR/mc-mod-getter.exe--file$INST_MC_DIR/<FILENAME>.yamlLaunch your instancePythonInstall it from PyPi to an env:python3-mpipinstallmc-mod-getterRun the tool:python3-mmc-mod-getter--file/path/to/file.yamlYAML File StructureThe tool takes a yaml file as input (Tabs for indentations, space after hyphens)
Here's an example yaml file:loader:fabricversion:1.17.1mod_dir:C:\Games\mmc-stable-win32\MultiMC\instances\fabric-1.17.1\.minecraft\modsmods:curseforge:-Cloth Config API (Fabric)modrinth:-Fabric API-FallingTree-EasierVillagerTrading-Mod Menu-Starlight-Hydrogen-Sodium-Lithium-Indium-DashLoader-Enhanced Block Entities-Seamless Loading Screen-Falling LeavesSupported Mod Hosts:ModrinthCurseforgeBuilding Your Own BinaryActivate an env and navigate to the root project directorymakebuildor if you don't have makepython3-mpipinstallpyinstallerpython3-mPyInstaller--onefilemc_mod_getter/__main__.py
Package: mcmole3d-giuspugl
MCMole3DMonte-Carlo MOlecular Line Emission MCMOLE3D is a python package which simulates the 3D  molecular cloud emission in the Milky Way. In particular, it can simulate both  the unpolarized and polarized emission coming from the   first rotational line of Carbon Monoxide (CO,J=1-0).Package  documentation:MCMOle3D doc. You can find also a  [Tutorial] (https://github.com/giuspugl/MCMole3D/blob/master/TUTORIAL.md)MCMole3Dsimulations have been used for the results inPuglisi et al 2017Package DependenciesPlease install the following packages  before runningMCMole3Dnumpyhealpyh5pyscipyastropyInstalling MCMole3DWe maintain the package on PyPI, just need to runpip install mcmole3d
Package: mc-monitor-helper-package
Monte Carlo Monitor HelperThis project aims to make settingMonte Carlofield tracking monitors for large numbers of tables with the same schema easier and automatable.DocumentationThis package was tested with Monte Carlo connected to a Snowflake DWH. It was not tested for other warehouses.For using this project you need:Monte CarloA list of tables with the same time column name, you want to enable field health tracking for.FeaturesSet field health tracking automatically for all tables providedOnly set tracking for a pre-defined time fieldUpdate monitors if they already exist if the respective flag is set, seeexample_usage.py.InstallationInstall this package from PyPi using Pip.UsageSeeexample_usage.py
Package: mcMqttComms
Usage:Once installed, import the module with:import mc.mc_functions as mcModule Functions:publishMessage: Used to publish messages to the Mosquitto Broker under a specified topic.subscribeToAll: Used to subscribe to all communication topics under the Mosquitto Broker. Receives messages and saves them to a local directory namedmessagingDatain JSON files according to the topic which the message was published under. (eg.messagingData/navigation.json).publishFile: Publishes saved JSON to specified S3 bucket for secure storage and further analysis.Function Usage (note that all arguments should be passed as Strings):publishMessage(messagingTopic, message, user, password, ec2ipv4Address)publishFile(topic, subTopic, bucketName, fileName)subscribeToAll(username, password, ec2ipv4Address)
Package: mcnets
Monte-Carlo-Neural-NetsOverviewA package made to see how well a basic architecture of neural nets could be. The nets can be created with custom input and output heights, as well as full customization of the hidden layers' sizes (height and count of layers) and activation functions that are applied at each space between the layers.The basic operation of these nets is that they can be trained to some data set (or some score in an unsupervised case) by randomly 'tweaking' the different parameter weight values within the net. These weight values are clipped to be restrained to the range [-1, 1].GitHub and QuickStartGiven on the GitHub page below is a quick start code that shows the syntax for creating a network, a few ways to write in the activation functions to be used, the included train-test split function, fitting the models, getting their predictions, and the plots of the resulting predictions.More explanations, examples, and technicals can be found on the GitHub page:https://github.com/SciCapt/Monte-Carlo-Neural-Nets