Saravanan666/pypi-org huggingface dataset:
Package: magpie-pkg
MAGPIE: Monte cArlo weiGhted PIxel rEmappingAuthorKrishna NaidooVersion0.2.1Repositoryhttps://github.com/knaidoo29/magpieDocumentationhttps://magpie-doc.readthedocs.io/IntroductionMAGPIE is a python module for remapping bins (in 1D), pixels (in 2D) and cells
(in 3D) into different coordinate systems. The package will enable data to be
remapped from cartesian to polar coordinates, cartesian to spherical polar coordinates
and will enable rotations of these systems. When carrying out coordinate transformations
we typically take the center of the pixel or cell and apply the transform without
considering the surface area of the pixel (or volume of the cell). In MAGPIE this is
taken into account by applying two remapping schemes. The first weights pixels to a new
coordinate grid using monte-carlo integration. The second uses a dense grid (denser than
the new coordinate grid) which is rebinned to the target coordinate grid. In both cases
we sample the surface area or volume of the new coordinate pixels to accurately remap the
data. The monte-carlo method is more accurate but scales poorly to 3D. For 2D this scheme
will work very well even for moderately large datasets. The dense grid method, while less
accurate, is very fast and should be used for large data sets and all 3D transformations.
In 1D these are computed exactly without requiring the approximate schemes above.DependenciesMAGPIE is being developed in Python 3.8 but should work on all versions >3.4. MAGPIE
is written mostly in python but with some heavy computation carried out in Fortran.
Compiling the Fortran source code will require the availability of a fortran compiler
usually gfortran (which comes with gcc).The following Python modules are required.numpyscipyhealpyFor testing you will requirenoseorpytest.InstallationMagpie can be installed in a variety of ways:Usingpip:Not yet setupUsingconda:Not yet setupBy cloning the github repository::git clone https://github.com/knaidoo29/magpie.git
    cd magpie
    python setup.py build
    python setup.py installOnce this is done you should be able to callmagpiefrom python:import magpieSupportIf you have any issues with the code or want to suggest ways to improve it please
open a new issue (here) or (if you don't have a github account) emailkrishna.naidoo.11@ucl.ac.uk.Version HistoryVersion 0.2:Restructured layout and the incorporation of documentation and unit testing for eventual first release.Randoms in a variety of coordinate systems: cartesian, polar, spherical and from an input PDF/CDF and subsampling and stochastic weights.Cartesian coordinate remapping in 1D, 2D and 3D.Version 0.1:Coordinate transformations between cartesian, polar and spherical polar coordinates.Rebinning in 1D (computed exactly), in 2D and 3D via monte-carlo weighted remapping and a dense mesh.Randoms in cartesian, polar and sphercal polar coordinates.Rotation transformations.Polar coordinate utilities and integration for polar grid to radial profiles.Plotting routine for orthographic projection of unit sphere data.
Package: magplots
magplotsA python library to pull data and make plots from ground magnetometers. Emphasis on plots for comparing magnetometers at conjugate points.Installationpip install magplotsMake sure that you have a folder in your directory named/output. This is where plots and dataframes will be saved.MWEYou can check all the component functions by running themagall()function with default values.from magplots.magFunctions import * # packaged version
# from magFunctions import * # local version
magall(is_verbose = True)FunctionsTypehelp('magFunctions')for function documentation.To pull data from Tromsø Geophysical Observatory, save a password locally in a file namedtgopw.txt. (You will have to obtain the password from TGO.) If no such file exists, data is automatically pulled from CDAWeb.
Package: magpurify2
Improve the quality of metagenome-assembled genomes by identifying and removing contaminant sequences with an easy-to-use and modular command-line interface.You can find MAGpurify2's full documentation at itswebsite.Quick startDownload test data:fileId="1-Gf-FsVIcARqrUb-LHS_FZlb-sCGAmfo"fileName="magpurify2_test_data.tar.gz"curl-sc/tmp/cookie"https://drive.google.com/uc?export=download&id=${fileId}">/dev/nullcode="$(awk'/_warning_/ {print $NF}'/tmp/cookie)"curl-Lb/tmp/cookie"https://drive.google.com/uc?export=download&confirm=${code}&id=${fileId}"-o${fileName}tarzxfvmagpurify2_test_data.tar.gzDownload database:fileId="1ooWiR3LplBy5GsY5wZ7o6dwswiCWVvmi"fileName="magpurify2DB.v1.0.tar.gz"curl-sc/tmp/cookie"https://drive.google.com/uc?export=download&id=${fileId}">/dev/nullcode="$(awk'/_warning_/ {print $NF}'/tmp/cookie)"curl-Lb/tmp/cookie"https://drive.google.com/uc?export=download&confirm=${code}&id=${fileId}"-o${fileName}tarzxfvmagpurify2DB.v1.0.tar.gzExecute the pipeline:magpurify2compositiontest_data/genomes/*output
magpurify2coveragetest_data/genomes/*output--bam_filestest_data/bam_files/*
magpurify2taxonomytest_data/genomes/*outputmagpurify2DB
magpurify2filtertest_data/genomes/*outputfiltered_genomes
Package: magpye
magpyeDISCLAIMER:
This project isBETAand will beExperimentalfor the forseable time.
Interfaces and functionality are likely to change, and the project itself may be scrapped.DO NOTuse this software in any project/software that is operational.LicenseApache License 2.0In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation nor does it submit to any jurisdiction.
Package: magpylib-material-response
magpylib-material-responseWarningThis package is experimental and in a very dynamic development phase. Breaking API changes may happen at any time.Magpylib-Material-Response is an extension to the Magpylib library, providing magnetic field analysis for soft materials and demagnetization of hard magnets. Leveraging the Method of Moments, it calculates magnetic material response by meshing sources in an arbitrary number of unit elements.InstallationInstall from PyPi$pipinstallmagpylib-material-responseor locally:$ pip install -e .[code_style,testing]TestingEnter created folder then run tests:$ flake8 .
$ black .
$ pytestTo use pre-commit:$ git add *
# to apply to staged files
$ pre-commit run
# restage if changes
$ git add *
# to run on commits
$ pre-commit install
$ git commit -m 'Initial commit'(package originally created bypython-pkg-cookiecutter)
Package: magpy-raz
Magshimim GitLab Project ManagementThis package extracts project data from GitLab, and writes it to .csv files,
which can be used for monitoring and analysis (e.g., using Excel)The data extracted by this package includes:events.csv- (almost) every activity made, including opening and closing issues,
pushing to the repository, and more...backlog.csv- a pool of issues, not associated with assignee or milestone.sprints.csv- an advanced feature for close monitoring of sprint progress (see special requirements below)Installationpip install magpy-razRequirementsTo use this package, you need to provide aGitLab personal access tokenfile, withapiscope.AssumptionsGitlab projects belong to GitLab groups (not individual users)Each project has a definedstart_dateandend_dateOutput csv files will be created in the same folder as the main  programSpecial Requirements for Sprint TrackingTo use thesprintsfeature, you need to implement the following in your
GitLab projects:Create a set of milestones, each with a start and due date, creating a
continuous timeline (i.e., the due date of sprint-1 is the day before the
start date of sprint-2)Define a work-hours budget for the sprint, by including the following line
at the top of the milestone descrition:Budget: .... = __hDefine work estimate for each issue, associated with the milestone, by
using the Gitlab/estimatecommand (seeGitLab Time Trackingfor more details)Use the standardTo Do,In ProgressandDonelabels, to indicate
issue status.Exampleimportmagpy.generalasmgimportmagpy.projectasmyimportmagpy.projectsasmpimportsysPROJECTS="[ירושלים,14970506,2019-10-29,2020-03-30]"+\"[בית-שמש,14969935,2019-10-31,2020-04-01]"TOKEN='token-raz.txt'defparse_projects(project_string):'''Convert Projects string to a list of projects'''project_list=[]project_string_list=project_string.split(sep=']')forproject_stringinproject_string_list:project_params=project_string.strip('[,').split(sep=',')iflen(project_string)>=4:project_list.append((project_params[0],project_params[1],project_params[2],project_params[3]))returnproject_listdefmain():try:# Get projects from a calling script (e.g., from Excel)token=sys.argv[1]project_list=parse_projects(sys.argv[2])exceptBaseException:# Get locally defined projects (for debug)token=TOKENproject_list=parse_projects(PROJECTS)# Connect to GitLabgl=mg.MagGitlab(token)# Get data for my projectsprojects=mp.Projects(gl,project_list)projects.write_csv_files()if__name__=='__main__':main()AuthorRaz NitzanLicenseDistributed under theMIT License.
Package: magpy-rv
MAGPy-RVModeling Activity with Gaussian process regression in PythonPipeline to model data with Gaussian Process regression and affine invariant Monte Carlo Markov Chain parameter searching algorith.
To use please cite the original publication (Rescigno et al. in review)DocumentationDocumentation Site:MAGPy RV.readthedocsInstallationBuild conda environmentMAGPy-RV can be run in its own environment. To generate it follow the steps:Update dependencies in env.ymlfileRun the following from the folder containing the .yml fileconda env create -f conda_env.ymlPackage installation using pipInstall pip (if Anaconda or miniconda is installe useconda install pip)Install packagepip install magpy-rvExamplesExamples are hostedhere:Simple GP Exampleshows the most basic code use.Polynomial Modeladds a model to the GP and introduces MCMC parameter search.Pegasi 51bwalks through the full rv analysis with a GP to model activity and Keplerians to model a planet.Offset:
full end-to-end pipeline to calculate 'sun-as-a-star' RVs and magnetic observables
Package: magql
MagqlMagql is aGraphQLframework for Python. It's pronounced "magical", and it is!Magql wraps theGraphQL-Corelibrary to make it easier to work with. Magql
provides three big features over GraphQL-Core:The schema is mutable. It can be defined (or generated) then modified to
add/remove/change behavior before finalizing.Types can be referenced by name, rather than using large lambda functions to
resolve forward references.Robust input validation can be applied anywhere in arbitrary input
structures, and errors in the result can be matched back to those arbitrary
locations.
Package: magql-sqlalchemy
Magql-SQLAlchemyMagqlis aGraphQLframework for Python. It's pronounced "magical", and
it is! This extension allows generating a complete API fromSQLAlchemydatabase models. After the schema is generated, it can be modified to add,
remove, or change any behavior. Here's some of the features Magql-SQLAlchemy
provides:itemandlistqueries, andcreate,update, anddeletemutations for each model.Database queries are efficient, using SQLAlchemy's relationship loading
techniques to eagerly load relationships that are present anywhere in the
operation structure.The list query can be filtered using multiple rules. Attributes can be
filtered across relationships. Rules can be grouped and joined using AND and
OR. Lists can be sorted by any column and paginated.The create mutation recognizes null and default column values. The update
mutation allows updating any field independently.The create and update mutations validate unique constraints.A universalsearchquery to search all string columns of all models.Acheck_deletequery to check the effects of deleting a row before doing so.
Package: magritte-rt
Welcome to the Magritte repository! Magritte is a open-source software library for
simulating radiation transport, developed atUniversity College London(UCL, UK) andKU Leuven(Belgium).Magritte is currently mainly used for post-processing hydrodynamical simulations by
creating synthetic observations, but the techniques could also be applied more general.
It can either be used as a Python package or as a C++ library.
Magritte uses a deterministic ray-tracer with a formal solver that currently focusses on
line radiative transfer (seeDe Ceuster et al. 2019for more details). The plot below shows synthetic observations made with
Magritte at three different inclinations for a hydro simulation of the stellar wind around
an asymptotic giant branch (AGB) star as it is perturbed by a companion.The hydro model was created by Jan Bolte usingMPI-AMRVAC. See theexamplesin the
documentation to learn how these synthetic observations were created with Magritte.DocumentationPlease find our online documentationhere.
In particular, see ourquickstart documentationfor a quick intro.Papers about MagritteThe following list of papers might provide further insights in the inner workings of
Magritte:Magritte II: Adaptive ray-tracing, mesh construction and reduction(arXiv,MNRAS);Magritte I: Non-LTE atomic and molecular line modelling(arXiv,MNRAS).Please note that some features presented in these papers might not yet be implemented
and documented in the latest release of Magritte.Issues & ContactPlease report any issues with Magritte or its documentationhere.
If you need any further help, please contactFrederik De Ceuster.CitePlease contact the authors of the papers referenced above if you want to use
Magritte in your research. We are currently working on documentation and
examples to facilitate its independent use. Until then, pleasecontact us.Developers & ContributorsDevelopersFrederik De CeusterThomas CeulemansAtulit SrivastavaScientific & Technical advisorsWard HomanJan BolteJeremy YatesLeen DecinPeter BoyleJames HetheringtonContributorsSilke MaesJolien MalfaitAcknowledgementsFDC is supported by the EPSRC iCASE studentship programme, Intel Corporation and Cray Inc.
FDC, JB, WH, and LD acknowledge support from the ERC consolidator grant 646758 AEROSOL.
Package: magstar-client
magstar-python-clientA Python client library for Computational Physics Inc.'s Magstar magnetometer networks.InstallingEither install directly from git:git clone https://github.com/CPIProductionDataLab/magstar-python-client.git
cd magstar-python-client
python setup.py installor install from PyPI:pip install magstar-clientUsagefrommagstar_clientimportMagstarV1APIBASE_URL="???"YOUR_API_KEY="???"api=MagstarV1API(BASE_URL,YOUR_API_KEY)print(api.get_stations())
Package: magtense
MagTense version 2023MagTense consists of both a magnetostatic and a micromagnetism calculation framework.The magnetostatic framework can calculate the magnetic field from objects shaped as cylinders, pieces of cylinders, prisms, circular pieces and tetrahedrons. This is done using a fully analytical calculation of the demagnetization tensor. The framework is fully implemented in Fortran and has both a Matlab MEX interface and a Python interface.The micromagnetism framework solves the Landau-Lifshitz equation. The framework is fully implemented in Fortran and has a Matlab MEX interface and a Python interface, as well as an older Matlab implementation. The micromagnetism framework utilizes the magnetostatic framework for calculating the demagnetization field.The webpage of the code is available athttps://www.magtense.org.The TechManual on the code is available athttps://cmt-dtu-energy.github.io/MagTense.Usage with MatlabMagTense is directly useable in Matlab on Windows by downloading the already compiled MEX-files inReleases. The files are directly useable with no compilation required, although Matlab 2020b or greater is required. Examples of how to calculate magnetostatic and micromagnetic problems using the Matlab interface can be found inmatlab/examples.Compilation with a Visual Studio project fileIf you want to compile MagTense with a Visual Studio project file for Windows,MagTense.sln, is available, as well as a Matlab function to build the MEX-files,buildMagTenseMEX.m. MagTense utilizes Intel MKL for the micromagnetic simlations and can also utilize CUDA and CVODE.Usage with Python interfaceInstructions on how to build and use the Python interface are listed inpython. Installation is recommended viacondapackage manager (requires >=Python 3.9). Additionally, binary installers for the Python interface are available at thePython Package Index (PyPI).Installation with CUDA 11.x:conda install magtense -c cmt-dtu-energy/label/cuda-11 -c conda-forgeInstallation with CUDA 12.x:conda install magtense -c cmt-dtu-energy/label/cuda-12 -c conda-forgeInstallation without CUDA support:conda install magtense -c cmt-dtu-energy/label/cpuCurrent code developmentThe main features being worked on at the moment are:Proper code documentationNon-uniform grids
Package: magurn
MagurnA python application which scrapes data from torrent indexers and finds the best torrent magnet for the specific search term.Included an EXE file to run directly for windows.Requirements-pipinstallrequestsbs4pyperclipcoloramaTo install hosted pip modulepipinstallmagurnTo upgrade the hosted pip modulepipinstall--upgrademagurnIf torrent sites are blocked in your region use a VPN or Proxy else the application won't work.
Package: magwitch
# MagwitchThe magical switch switcher !```pythonimport magwitch@magwitch.ondef save_figure(in_png:bool=False, legend:bool=True):...  # an implementation we do not care about for the examplereturn in_png# Now, you can set the value of the boolean arguments by giving their name:assert save_figure.in_png() is True# Or not:assert save_figure.not_in_png() is False# Also, you can do it multiple times:save_figure.not_in_png.no_legend()# The last set is autoritativeassert f.not_in_png.in_png() is True# And there is many other options if you want to !@magwitch.on_with(use_defaults=True, enable='do_', disable=('nope_', 'never_'):def f(a:bool=False): return a# effect of use_defaultsassert f.do_a() is False```See the [tests](test/) for complete API, and the docstring of `Magwitch.build`for a description of all available parameters.Install withpip install magwitch
Package: magwords
MagWords is a text-rendering library for OpenGL context. It provides clear text rendering without collapsion caused by scaling, fast initialization and drawing flow, and memory-saving internal representation of glyph data.InstallationTo install MagWords and its required dependencies using pip, enter the following command at a shell:$ pip install magwordsContributeOur project is still under development. Everyone is welcome to contribute. Bug reports and fixes are welcome, and suggestions for features would be appreciated!Contribution guide is in preparation...LicenseMIT License
Package: ma-gym
ma-gymIt's a collection of multi agent environments based on OpenAI gym. Also, you can useminimal-marlto warm-start training of agents.InstallationSetup (important):pipinstall'pip<=23.0.1'pipinstall'setuptools<=66'pipinstall'wheel<=0.38.4'Install package:Using PyPI:pipinstallma-gymDirectly from source (recommended):gitclonehttps://github.com/koulanurag/ma-gym.gitcdma-gym
pipinstall-e.Reference:Please use this bibtex if you would like to cite it:@misc{magym,
      author = {Koul, Anurag},
      title = {ma-gym: Collection of multi-agent environments based on OpenAI gym.},
      year = {2019},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/koulanurag/ma-gym}},
    }Usage:importgymenv=gym.make('ma_gym:Switch2-v0')done_n=[Falsefor_inrange(env.n_agents)]ep_reward=0obs_n=env.reset()whilenotall(done_n):env.render()obs_n,reward_n,done_n,info=env.step(env.action_space.sample())ep_reward+=sum(reward_n)env.close()Please refer toWikifor complete usage detailsEnvironments:CheckersCombatPredatorPreyPong Duel(two player pong game)SwitchLumberjacksTrafficJunctionNote : openai's environment can be accessed in multi agent form by prefix "ma_".Eg: ma_CartPole-v0
This returns an instance of CartPole-v0 in "multi agent wrapper" having a single agent. 
These environments are helpful during debugging.Please refer toWikifor more details.Zoo!Checkers-v0Combat-v0Lumberjacks-v0PongDuel-v0PredatorPrey5x5-v0PredatorPrey7x7-v0Switch2-v0Switch4-v0TrafficJunction4-v0TrafficJunction10-v0Testing:Install:pip install -e ".[test]"Run:pytestAcknowledgement:This project was initially developed to complement my research internship @SAS(Summer - 2019).
Package: magzdb
magzdb - magzdb.org DownloaderInstallationInstall using pip$pipinstall-UmagzdbUsageusage: magzdb [-h] [-V] -i MAGAZINE_ID [-e [EDITION [EDITION ...]]]
              [-f FILTER] [-l] [-P DIRECTORY_PREFIX] [--downloader DOWNLOADER]
              [--debug]

Magzdb.org Downloader

required arguments:
  -i MAGAZINE_ID, --id MAGAZINE_ID
                        ID of the Magazine to Download. eg. http://magzdb.org/j/<ID>.

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         Print program version and exit
  -e [EDITION [EDITION ...]], --editions [EDITION [EDITION ...]]
                        Select Edition
  -f FILTER, --filter FILTER
                        Use filter. See README#Filters
  -l, --latest          Download only latest edition.
  -P DIRECTORY_PREFIX, --directory-prefix DIRECTORY_PREFIX
                        Download directory.
  --downloader DOWNLOADER
                        Use External downloader (RECOMMENDED). Currently supported: aria2, wget, curl
  --debug               Print debug information.
  --skip-download       Don't download files.Usage ExamplesDockerdockerbuild.-tmagzdb
dockerrun-v$(PWD):/tmpmagzdb-h# Add alias to shellaliasmagzdb="docker run -v$(PWD):/tmp magzdb"magzdb-hDownload all editions$magzdb-i1826FiltersYou can supply filter using-f, for example to download issues between4063895and4063901, you can write as$magzdb-i1826-f"eid > 4063895 and eid < 4063901"You can useeid,yearin the filter expression.More examples of filter expressioneid > 4063895 and eid < 4063901oreid >= 4063895 and eid <= 4063901eid >= 4063895oreid != 4063895year >= 2018,year <= 2018,year == 2018or evenyear != 2018Download only latest edition$magzdb-i1826-lDownload only latest edition with custom locationmagazine$magzdb-i1826-l-PmagazineUse external downloader$magzdb-i1826-l-Pmagazine--downloaderwgetThis is recommended since internal downloader does not support resuming interrupted downloads.Python Installation RecommendationIf you don't want to install officialPythonto your system (global).
You can installpyenv installerenvironment under your specific account. It's prefered method for macOS users, because High Sierra and later macOS ships with old Python 2.7.10.ContributingFound a bug or missing a feature you are more than welcome to contribute.LicenseMIT
Package: mahad
An Arabic text processing library intended for use in NLP applicationsMaha is a text processing library specially developed to deal with Arabic text. The beta version can be used to clean and parse text, files, and folders with or without streaming capability.If you need help or want to discuss topics related to Maha, feel free to reach out to ourDiscordserver. If you would like to submit a bug report or feature request, please open an issue.InstallationSimply run the following to install Maha:pipinstallmahad# pronounced maha dFor source installation, check thedocumentation.OverviewCheck out theoverviewsection in the documentation to get started with Maha.DocumentationDocumentation are hosted atReadTheDocs.ContributingMaha welcomes and encourages everyone to contribute. Contributions are always appreciated. Feel free to take a look at our contribution guidelines in thedocumentation.LicenseMaha is BSD-licensed.