Saravanan666/pypi-org huggingface dataset:
Package: mauve-text
MAUVEMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text
with the eponymous MAUVE measure,
introducedin this paper(NeurIPS 2021 Ooutstanding Paper).Documentation LinkNew: MAUVE is available viaHuggingFace Datasets!Features:MAUVE with quantization usingk-means.Adaptive selection ofk-means hyperparameters.Compute MAUVE using pre-computed GPT-2 features (i.e., terminal hidden state),
or featurize raw text using HuggingFace transformers + PyTorch.New: minibatching for efficient implementation.InstallationFor a direct install, run this command from your terminal:pip install mauve-textCitationIf you find this package useful, or you use it in your research, please cite:@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}Further, the Frontier Integral was introduced in this paper:@inproceedings{liu-etal:divergence:neurips2021,
  title={{Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals}},
  author={Liu, Lang and Pillutla, Krishna and  Welleck, Sean and Oh, Sewoong and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}AcknowledgementsThis work was supported by NSF DMS-2134012, NSF CCF-2019844, NSF DMS-2023166, the DARPA MCS program through NIWC Pacific (N66001-19-2-4031), the CIFAR "Learning in Machines & Brains" program, a Qualcomm Innovation Fellowship, and faculty research awards.
Package: mav2rosgenerator
# Mavlink to ROS API Generator PackageThis is a python package that enable us to create a entire Mavlink API under ROS middleware## Main Features* Generate a ROS Package ( for the main definition and others for each include in definition file).* Generate all msg files to enable to receive and send mavlink messages.* Generate a node with callbacks, subscribers and callbacks and a socket client to enable to connect to the ardupilot SITL for testing purposes.* Generate the mavlink code based on the definition file ready to be used by the generated package## Distribution* The generated code for ardupilot mega ROS Node Driver is [here] ( https://github.com/posilva/ardupilotmega-ros)## Installation* Install python-pip ie: sudo apt-get install python-pip* Run command: sudo pip install --upgrade mav2rosgenerator* Run command to see execution options: run_mav2ros.py -h## Usage* Run Command:```bashrun_mav2ros.py -m $HOME/libs/mavlink-1.0.11/message_definitions/v1.0/ardupilotmega.xml -o $HOME/ros_workspace/src```## Future features1. Create a configuration or semantic (up, down, both) in the direction of the mavlink messages to optimize callbacks and subscribers ( for UP, BOTH directions messages) and publishers (for DOWN direction)Maybe mavlink definition file can contain a message tag attribute with direction:```xml<message id="11" name="SET_MODE" direction="UP">```Example:* HEARTBEAT message direction = BOTH* SET_MODE message direction = UP* GPS_RAW_INT message direction = down
Package: mavcom
MAVCOMMavlink CommunicatorProvides a simplified python interface for controlling Mavlink capable flight controllers.Installationpip install mavcomBasic UsageThis is an example of how to use Mavcom with a simulated vehicle. To connect to a flight controller, you will most likely use UART0, therefore the connection path will instead be "/dev/ttyS0".Run SITL:sim_vehicle.py -v ArduCopterfrommavcom.mavcontrolimportMavcomimporttimevehicle=Mavcom(connection_path="127.0.0.1:14550",)vehicle.start()whilenotvehicle.ready:print("Waiting for vehicle to initialise...")time.sleep(1)vehicle.motors_armed=Truewhilenotvehicle.motors_armed:print("Waiting for motors to spin up...")time.sleep(1)vehicle.flight_mode="GUIDED"vehicle.takeoff(alt=10)
Package: mavcon
MAVCOMMavlink CommunicatorProvides a simplified python interface for controlling Mavlink capable flight controllers.Installationpip install mavconBasic UsageThis is an example of how to use Mavcon with a simulated vehicle.Run SITL:sim_vehicle.py -v ArduCopterfrommavconimportmavcontrolimporttimevehicle=mavcon.Mavcon(connection_path="127.0.0.1:14551",)vehicle.start()whilenotvehicle.ready:print("Waiting for vehicle to initialise...")time.sleep(1)vehicle.motors_armed=Truewhilenotvehicle.motors_armed:print("Waiting for motors to spin up...")time.sleep(1)vehicle.flight_mode="GUIDED"vehicle.takeoff(alt=10)
Package: mave
Mave======Mave is a tool for automated measurement and verification. At it's most simple,the aim is to read energy consumption data from before and after a retrofit(pre-retrofit and post-retrofit data) and to predict how much energy theretrofit saved. Mave does this by training a model to the data and usingthe best model to predict energy consumption during a different period.Installation------------Assuming all of the dependencies are installed, install mave from source using:```python setup.py install```Alternatively, install mave from pypi using:```pip install mave --no-deps```If you run into trouble with the dependencies, see the[Installation page of the wiki](https://github.com/CenterForTheBuiltEnvironment/mave/wiki/Installation)Usage------------Try mave out on an example file using any of the examples methods below.Each of these will build a model on the dataset contained in ex3.csv, thedifference between the three approaches is in how the post-retrofit periodis defined.```mave ex3.csv```This assumes the last 25% of the file represents the postretrofit period as thedefault value of the 'test_size' argument is 0.25.```mave ex3.csv -ts 0.45```This uses the 'ts' or 'test_size' argument to explicitly specify the fractionof the file to use. In this example the last 45% of the file represents thepostretrofit period.```mave ex3.csv -cp "1/11/2013 00:00"```This example uses the 'cp' or 'changepoint' argument to explicitly define thedate at which the post retrofit period begins. This overrides the 'test-size'value. In this case all data on or after Jan 11, 2013 at 00:00 represents thepost-retrofit period.Mave has many configurable options (e.g. -v for verbose output) which can bepassed as command line arguments or using a separate configuration file.The configuration file also allows many other advanced modeling options, suchas specifying multiple different periods in the file to use as pre-retrofit orpost-retrofit, or periods to ignore entirely. Review the wiki documentation formore details.To cite this tool:Paul Raftery & Tyler Hoyt, 2015, Mave: software automated Measurement and Verification.Center for the Built Environment, University of California Berkeley, https://github.com/CenterForTheBuiltEnvironment/mave
Package: mavehgvs
mavehgvsmavehgvs is the Python reference implementation of the MAVE-HGVS variant representation standard,
a strict subset ofHGVS, used primarily for clinical genomics.The MAVE-HGVS StandardMAVE-HGVS is a strict subset of theHGVS Sequence Variant Nomenclature, version 20.05.
HGVS nomenclature is comprehensive and very expressive and consequently includes a lot of syntax that is not needed to
represent variants from Multiplexed Assay of Variant Effect (MAVE) data and makes the variant strings more challenging
to parse.While packages exist for parsing HGVS (most notably thebiocommons hgvs package, they are intended for use in human genetics and
rely on sequence databases and reference sequence (called "target sequence" for MAVE-HGVS), which are not always
available for or relevant for multiplexed assays.MAVE-HGVS is an attempt to define an easy-to-parse subset of the HGVS nomenclature that captures those variants that
occur in MAVE datasets, while excluding many variant types that are unlikely to be found. Importantly, the
mavehgvs implementation does not rely on external sequence databases or identifiers.Supported VariantsMAVE-HGVS supports DNA, RNA, and protein variants.
MAVE-HGVS supports a subset of HGVS variants including:substitutionsdeletionsduplicationsinsertionsframe shiftsMany HGVS variants are unsupported including:inversionsconversionsextensionschanges in methylation stateRNA fusion transcriptsmosaicismchimerismvariants with uncertain consequencevariants in trans or unknown phasecomplex variants (e.g. translocations)For further details, including example variants, see the specification in the package documentation.InstallationInstall mavehgvs from pip using:pip3installmavehgvsTo set up the package for development purposes, include the optional dependencies and
install pre-commit:pip3 install mavehgvs[dev]
pre-commit installFeedbackTo report a problem or request a new feature with either the mavehgvs package or the MAVE-HGVS standard,
please use the GitHub issue tracker.
Package: maven
Maven/meɪvən/ – a trusted expert who seeks to pass timely and relevant knowledge on to others.Maven's goal is to reduce the time data scientists spend on data cleaning and preparation by providing easy access to open datasets in both raw and processed formats.Maven was built to:Improve availability and integrity of open databy eliminating data issues, adding common identifiers, and reshaping data to become model-ready.Source data in its rawest formfrom the most authoritative data provider available with all transformations available as open source code to enhance integrity and trust.Honour data licences wherever possiblewhilst avoiding potential issues relating to re-distribution of data (especially open datasets where no clear licence is provided) by performing all data retrieval and processing on-device.Installpip install mavenUsageimportmavenmaven.get('general-election/UK/2017/results',data_directory='./data/')DatasetsData dictionaries for all datasets are available by clicking on the dataset's name.DatasetDescriptionDateSourceLicencegeneral-election/UK/2010/resultsUK 2010 General Election results6th May 2010House of Commons LibraryOpen Parliament Licence v3.0general-election/UK/2015/resultsUK 2015 General Election results7th May 2015House of Commons LibraryOpen Parliament Licence v3.0general-election/UK/2017/resultsUK 2017 General Election results8th June 2017House of Commons LibraryOpen Parliament Licence v3.0general-election/UK/2015/modelModel-ready datasets for forecasting the 2015 UK General Election2010 & 2015 datauk_2015_model.pyMixedgeneral-election/UK/2017/modelModel-ready datasets for forecasting the 2017 UK General Election2015 & 2017 datauk_2017_model.pyMixedgeneral-election/UK/pollsUK General Election opinion pollingMay 2005 - June 2017SixFiftyUnknownRunning testsTo run tests against an installed version (eitherpip install .orpip install maven):$ cd /path/to/repo
$ pytestTo run tests whilst in development:$ cd /path/to/repo
$ python -m pytestLicencesNameDescriptionAttribution StatementOpen Parliament LicenceFree to copy, publish, distribute, transmit, adapt and exploit commercially or non-commercially. See URL for full details.Contains Parliamentary information licensed under the Open Parliament Licence v3.0.Open Government LicenceFree to copy, publish, distribute, transmit, adapt and exploit commercially and non-commercially. See URL for full details.Contains public sector information licensed under the Open Government Licence v2.0.ContributingMaven was designed for your contributions!Check for open issues or open a fresh issue to start a discussion around your idea or a bug.Forkthe repositoryon GitHub to start making your changes to the master branch (or branch off of it).For new datasets ensure the processed dataset is fully documented with a data dictionary. For new features and bugs, please write a test which shows that the bug was fixed or that the feature works as expected.Send apull requestand bug the maintainer until it gets merged and published. 😄
Package: maven-artifact
maven-artifactA python library to download and resolve maven artifacts.Installation and UsageInstall using pip: `pip install maven-artifact'Usage: runmaven-artifactRequirementsSeepyproject.tomlfor details on required Python versions, pip packages etc.ContributingFork repoBefore submitting a PRPerform formatting (black):hatch run lint:black src testsRun linter (flake8):hatch run lint:flake8Run tests:all:hatch run test:pytest --cov=maven_artifactunit only:hatch run test:pytest --cov=maven_artifact  tests/unitintegration only:hatch run test:pytest --cov=maven_artifact  tests/integration
Package: mavengitupgrader
# Maven Git UpgraderMaven Git Upgraderis a command-line utility that checks for updates to Maven
dependencies and creates new Git branches for each. It is written in Python
(3.6+).## Usage`shell script cdmy-maven-project-repo/git switch master  # make sure you are on your main 'master' or 'develop' branch python3-mmavengitupgrader `For more options, runpython3 -m mavengitupgrader -h.## Install`shell script pip3 install mavengitupgrader `## PublishingThis project is distributed to [PyPI.org] in order to be freely available and
easily downloadable from there.### AutomatedThis GitHub repository has been configured with a [GitHub Action] that will
automatically trigger the
[pythonpublish.yml](.github/workflows/pythonpublish.yml) workflow to publish
this project to PyPi every time a release is created in this GitHub repository.### ManualIf you must publish this project manually to PyPi, do this:`shell script pip3 install twine python3 setup.py sdist twine--repositorymavengitupgrader  # optional, use if PyPI token in~/.pypirctwine upload dist/* `<!– Links –>
[PyPI.org]:https://pypi.org/project/mavengitupgrader/[GitHub Action]:https://pypi.org/project/mavengitupgrader/
Package: mavenworks
MavenWorksBond Portfolio demo on MavenWorks Viewer 0.1.0-alphaTry outMavenWorks Standalone!MavenWorksis an open-source dashboarding framework, for Jupyter and
beyond! MavenWorks includes a JupyterLab plugin, a read-only dashboard viewer
(pictured above), and a standalone app (codenamed "Kitchen Sink").Dashboards are flexible- you can use the same old charting libraries and
interactive widgets that you've used elsewhere. Anything that can be displayed
in a Jupyter output, can also be put into a Dashboard.MavenWorks also supplies a data binding framework and a query engine. You can mix
SQL, Javascript, and Python right from within your dashboard!For easier interactivity, MavenWorks also provides Parts. Parts are reusable
components of a dashboard that can be written in Python or Javascript, and allow
you to "bind" your visualizations to data. MavenWorks will take care of keeping your
visualizations in sync with the data, meaning you won't need to worry about
plumbing.MavenWorks is under heavy development, and much of the internal APIs are unstable.Getting StartedThe Standalone app comes with a few demos to help you understand what MavenWorks
can do. Use theDemos indexto navigate between them.Jupyter deployments come with a host of demos and built-in Parts that
demonstrate parts of the API. Use theStart Herenotebook to access these demos.You can read our documentationhere. We have tutorials,
reference docs, and contributor documentation. These are accessible in-app using
theF1key.
Package: maverage
maverage - Moving AverageAbout maveragemaverage provides a Simple Moving Average implementation that delays the
average calculation until requested - useful for programs that collect data in
real-time and delay processing until later.Basic Usage -Simple>>>s=Simple(5)# Simple object with internal buffer of size 5.>>>s.input(4)>>>s.input(2)>>>s.average3.0>>>s.input(6).average4.0Testingmaverage tests are written using pytest. ThedevHatch environment
includes pytest (refer to theBuildingsection for details on how
to set it up). Once inside the dev environment, run the tests by simply runningpytest.Buildingmaverage is set up to useHatchfor building.Create a development environment using:hatch env create dev. Alternatively,
usehatch shell -e devto (1) created it if it doesn't exist and (2) open it
in a shell session.Note: To use the environment within VS Code, use the directory pointed to by
thehatch find env devcommand.The project can be built, reversioned, and published with hatch'sbuild,version, andpublishcommands, respectively.LicenseThis project uses the MIT License. Refer toLICENSE.txtfor
details.
Package: maverig
MAVERIG is a visualization component for the mosaik framework. Read thedocumentationfor further information.Install DependenciesPySidemosaikmosaik-apimosaik-pypowernumpynetworkxmatplotlibpolibcolormathpython-dateutilpyzmqNOTE: Especially follow the detailedmosaik install instructionsfor the different operating systems
(inlcuding the instructions to run the demo). Windows users should install the dependencies fromthis siteif available.Install MAVERIGInstall withpip:$ pip install maverigOr clone the repository and make a build with python orcx_Freeze(simulation is not running on Windows currently):$ python setup.py sdist bdist bdist_wheel$ python setup_cx.py buildNOTE: If you use cx_Freeze you have to renamezmq.libzmqtolibzmqin the main folder after creating the build.DownloadsMaverig buildsLinux (64-Bit)Virtual environments with maverig and all dependencies installedLinuxWindowsMac OSUser guidesEnglishGermanLICENSELGPLLATEST CHANGES (v1.0)Increased simulation performanceIncreased usabilityImproved graphsDefined components in JSON for better extensibilityImplemented a component wizard so that new components can be added by user via the GUIComponents can be hided and removedSettings can be changed while simulation is runningImproved documentationFixed minor bugs
Package: mavis
AboutMAVISis python command-line tool for the post-processing of structural variant calls.
The generalMAVISpipeline consists of six main stagesconvertclustervalidateannotatepairingsummaryGetting HelpAll steps in the MAVIS pipeline are called following the main mavis entry point. The usage menu can be viewed
by running without any arguments, or by giving the -h/--help optionmavis-hHelp sub-menus can be found by giving the pipeline step followed by no arguments or the -h optionsmaviscluster-hCommon problems and questions are addressed on thewiki.
If you have a question or issue that is not answered there (or already a github issue) please submit
a github issue to ourgithub pageor contact us by email atmavis@bcgsc.caGetting StartedThe simplest way to use MAVIS is via Singularity. The MAVIS docker container used
by singularity will take care of installing the aligner as well.pipinstall-Usetuptoolspipwheel
pipinstallmavis_config# also installs snakemakeNow you will run mavis via Snakemake as followssnakemake\-j<MAXJOBS>\--configfile<YOURCONFIG>\--use-singularity\-sSnakefileFor other installation options which do not use docker/singularity see the comprehensive install
instructions in theuser manualCitationIf you use MAVIS as a part of your project please citeReisle,C. et al. (2018) MAVIS: Merging, Annotation, Validation, and Illustration of Structural variants. Bioinformatics.
Package: mavis-config
MAVIS ConfigWorkflow configuration validation forMAVISvia snakemake. This package should not be used withoutMAVIS. If you are aMAVISuser, please seeMAVISinstead.For DevelopersThis is a separate package fromMAVISitself so thatMAVISusers running snakemake with the
docker container do not need to installMAVISto check their snakemake config JSON.
This is to ensure that all the hard-to-installMAVISdependencies are not required to run
snakemake with docker.
Package: mavisim
MAVISIMImage simulating tool for the next generation ESO instrument MAVIS. If you use MAVISIM please citeMonty et al. 2021.Current Version1.1 - EtE PSF- more accurate representation of the PSF, suitable for photometric and astrometric science casesDocumentation can be foundherePast Versions1.0 - Fourier PSF - fast, most flexibility for astrometric modelingDocumentation can be foundhereGeneral FeaturesCan simulate full stellar field (eg. Milky Way Globular Cluster) from an input catalogueModels three major sources of astrometric error introduced by the AO system:Tip-tilt residuals originating from uncorrected LO aberrations (dependent on the NGS constellation brightness and geometry)field variableHO aberrations originating from the LGS constellation and characteristicsfield variableStatic field distortion originating from the AO module opticsCurrent LimitationsMonochromatic images onlyOnly compatible with inputpoint source catalogues onlyFuture VersionsMAVISIM 1.2: Broadband End-to-End PSFBroadband images! Starting with the V bandMAVISIM 2.0Extended object version, not yet in development.
Package: mavisim-gpu
MAVISIMImage simulating tool for the next generation ESO instrument MAVIS. If you use MAVISIM please citeMonty et al. 2021.Current Version1.1 - EtE PSF- more accurate representation of the PSF, suitable for photometric and astrometric science casesDocumentation can be foundherePast Versions1.0 - Fourier PSF - fast, most flexibility for astrometric modelingDocumentation can be foundhereGeneral FeaturesCan simulate full stellar field (eg. Milky Way Globular Cluster) from an input catalogueModels three major sources of astrometric error introduced by the AO system:Tip-tilt residuals originating from uncorrected LO aberrations (dependent on the NGS constellation brightness and geometry)field variableHO aberrations originating from the LGS constellation and characteristicsfield variableStatic field distortion originating from the AO module opticsCurrent LimitationsMonochromatic images onlyOnly compatible with inputpoint source catalogues onlyFuture VersionsMAVISIM 1.2: Broadband End-to-End PSFBroadband images! Starting with the V bandMAVISIM 2.0Extended object version, not yet in development.
Package: mavis-temp
MAVIS ConfigWorkflow configuration validation forMAVISvia snakemake. This package should not be used withoutMAVIS. If you are aMAVISuser, please seeMAVISinstead.For DevelopersThis is a separate package fromMAVISitself so thatMAVISusers running snakemake with the
docker container do not need to installMAVISto check their snakemake config JSON.
This is to ensure that all the hard-to-installMAVISdependencies are not required to run
snakemake with docker.