Saravanan666/pypi-org huggingface dataset:
Package: traitsui
The TraitsUI project provides a toolkit-independent GUI abstraction layer,
which is used to support the “visualization” features of theTraitspackage.
You can write a model using the Traits API and specify a GUI using the TraitsUI
API (views, items, editors, etc.), and let TraitsUI and your selected toolkit
back-end (Qt or Wx) take care of the details of displaying them.ExampleGiven a Traits model like the following:from traits.api import HasTraits, Str, Range, Enum

class Person(HasTraits):
    name = Str('Jane Doe')
    age = Range(low=0)
    gender = Enum('female', 'male')

person = Person(age=30)And using TraitsUI to specify and display a GUI view:from traitsui.api import Item, RangeEditor, View

person_view = View(
    Item('name'),
    Item('gender'),
    Item('age', editor=RangeEditor(mode='spinner', low=0, high=150)),
    buttons=['OK', 'Cancel'],
    resizable=True,
)

person.configure_traits(view=person_view)It creates a GUI which looks like this:Important LinksWebsite and Documentation:http://docs.enthought.com/traitsuiUser Manualhttp://docs.enthought.com/traitsui/traitsui_user_manualTutorialhttp://docs.enthought.com/traitsui/tutorialsAPI Documentationhttp://docs.enthought.com/traitsui/apiSource code repository:https://github.com/enthought/traitsuiIssue tracker:https://github.com/enthought/traitsui/issuesDownload releases:https://pypi.python.org/pypi/traitsuiMailing list:https://groups.google.com/forum/#!forum/ets-usersInstallationIf you want to run traitsui, you must also install:Traitshttps://github.com/enthought/traitsPyfacehttps://github.com/enthought/pyfaceYou will also need one of the following backends:wxPythonPySide2PyQt5Backends have additional dependencies and there are optional dependencies on
NumPy and Pandas for some editors.TraitsUI along with all dependencies can be installed in a straightforward way
using theEnthought Deployment Manager,pipor other package managers.Running the Test SuiteTo run the test suite, you will need to install Git andEDMas well as have a Python environment
which has installClickavailable. You can then
follow the instructions inetstool.py.  In particular:> python etstool.py test_allwill run tests in all supported environments automatically.
Package: trajallocpy
TrajAllocPy: Decentralized multi-robot/agent task allocation for area coverage problemsTrajAllocPyis a Python library that provides functionality for trajectory task allocation based on CBBA (ConsensusBasedBundleAlgorithm).Coverage task datasetThis repository uses dataset format given in:https://github.com/kasperg3/CoverageTasksInstallationThe package is regularly updated and new releases are created when significant changes to the main branch has happened.pipinstalltrajallocpyBuild from sourceYou can install TrajAllocPy usingpip. Simply navigate to your projects root directory and run:pipinstall-rrequirements.txt
pipinstall-e.The "-e" argument for pip is to enable the developer to edit the python source code and perform tests without having to rebuild everything.UsageTo use TrajAllocPy in your Python project, you can import it as follows:importTrajAllocPyYou can then use the provided functions and classes to perform trajectory allocaion and task planning based on the consensus based bundle algorithm algorithm.
See the examples inexmaples.mdContributing & DevelopmentInstall the bindings in dev mode:pipinstall-e.To contribute to TrajAllocPy, start by forking the repository on GitHub. Create a new branch for your changes, make the necessary code edits, commit your changes with clear messages, and push them to your fork. Create a pull request from your branch to the original repository, describing your changes and addressing any related issues. Once your pull request is approved, a project maintainer will merge it into the main branch.CitationIf you use TrajAllocPy in your work, please cite the following paper:@inproceedings{grontved2022icar,title={Decentralized Multi-UAV Trajectory Task Allocation in Search and Rescue Applications},author={Gr{\o}ntved, Kasper Andreas R{\o}mer and Schultz, Ulrik Pagh and Christensen, Anders Lyhne},booktitle={21st International Conference on Advanced Robotics},year={2023},organization={IEEE}}LicenseThis library is released under theMIT License. Feel free to use, modify, and distribute it in your projects.Issues and ContributionsIf you encounter any issues or have ideas for improvements, please open an issue on theGitHub repository. Contributions in the form of pull requests are also welcome.SupportFor support and inquiries, you can contact the maintainers of this library atkaspergrontved@gmail.com.Thank you for using TrajAllocPy! We hope it proves to be a valuable tool for your trajectory generation and task planning needs.
Package: trajcontacts
trajcontactsA Package for Calculating Residue-Residue Contacts from MD TrajectoriesCurrently, there are two modes for contact determination:Normal Contact Analysis (norm):The presence of a contact between two residues in macromolecular systems, such as proteins and nucleic acids, is established when the minimum inter-residue heavy atom pair distance falls within a specified cutoff (4-5 Å) for a majority of the simulation time (e.g., 75% or more of snapshots). This program can be utilized to extract residue contacts from long MD simulation trajectories as well as PDB structures.Continuous Contact Analysis (cont):As described in Botello-Smith et al., Journal of Chemical Theory and Computation 15.4 (2019): 2116-2126 and Westerlund et al., The Journal of Chemical Physics 153.14 (2020).This is a command-line program for Linux systems and relies on the MDTraj simulation analysis package (https://www.mdtraj.org/). It usesmdtraj.load()for trajectory/topology inputs, and inter-atomic distances are calculated usingmdtraj.distance(). For efficiency and to avoid memory-related errors, save the trajectories without unnecessary atoms (solvent/ions or hydrogen atoms) and divide the trajectory into multiple chunks, then write their names (or pathnames) in a file (for example, name that file 'traj.dat') - see below.InstallationYou can install trajcontacts by running:pip install trajcontactsAfter installation, you can access detailed options by typing:trajcontacts -hFor a simple usage example, running the following command:trajcontacts -p topologyfile -f file_containing_names/paths_to_coordinate_files -n 10will execute the program on 10 processors and extract contacts with a 4.5 Ångstrom cutoff. Both 'topologyfile' (specified in the command line) and 'coordinatefile' (in the file containing names of coordinate files) should be specified as the same for extracting contacts from a PDB file.Exampletrajcontacts -p 3sn6.pdb -f traj.dat -n 10where 'traj.dat' is a file with a single line containing '3sn6.pdb'.How to Cite:Please cite the following research when using trajcontacts:Madhu, Midhun K., Kunal Shewani, and Rajesh K. Murarka. "Biased Signaling in Mutated Variants of β2-Adrenergic Receptor: Insights from Molecular Dynamics Simulations." bioRxiv (2023): 2023-09.Copyright:Copyright belongs to the Computational Biophysics and Soft Matter Group, IISER Bhopal. Visit our website:https://home.iiserb.ac.in/~rkm/.
Package: trajectorize
KSP Trajectory Optimizer.This project is a reduced-scope version ofone of my other (currently incomplete) projects, as an intermediate stepping stone.This tool computes trajectories between celestial bodies in KSP based on on-rails two-body patched conics, incorporating trajectory correction maneuvers for a variety of mission scenarios, such as:Ballistic Hohmann transfers for other planetsGravity assist flyby routesComputationally-intensive code is implemented in C, with a Python wrapper made usingcffi.
C code follows mostly C89, with some C99 features used. It has been tested against the latest versions of GCC (on Linux) and MSVC (on Windows 10).InstallationThe simplest way to install is from PyPI:pip install trajectorizeThis will install the latest stable version of the package, and may include pre-compiled binaries for your platform.The package is still in development, so you may want to install from the latest commit on themainbranch instead:Runpip install git+https://github.com/itchono/trajectorizeto install the package from source.You will need to have Python 3.8+, and aC compiler installedto compile the C code if installing from source. You will also need a C compiler installed if there are no pre-compiled binaries for your platform. If you're on Windows, you can find details about installing a C compilerhere.The following platforms/compilers have been tested:PlatformCompilerWindows 10MSVC 14.16 (Visual Studio 2017)Ubuntu 20.04 LTS (Dev Machine)GCC 9.4.0DemosRight now, full functionality is incomplete. There are, however, some cool demos showing off the capabilities of the package.Full Model of KSP Planetary System and Ephemeridespython -m trajectorize.demos.kerbol_system_animCalculation of Ballistic Interplanetary Transfers Using Lambert's Problempython -m trajectorize.demos.kerbin_duna_transferPropagation of Two-Body Trajectories Using Universal Keplerian Elementspython -m trajectorize.demos.orbitCalculations of Optimal Transfers, Porkchop Plotspython -m trajectorize.demos.single_transfer Kerbin Duna --capture_alt 60000InspirationsInteractive illustrated interplanetary guide and calculator for KSP, by OlexLaunch Window Planner, by AlexMoon
Package: trajectory
trajectorytrajectoryis a library for lossy compression of trajectory data based on Google’s Encoded Polyline Algorithm Format (http://goo.gl/PvXf8Y). It is heavily based on (in fact forked from)https://github.com/hicsail/polyline.Installationtrajectorycan be installed usingpiporeasy_install$pipinstalltrajectoryor$easy_installtrajectoryAPI DocumentationEncodingTo serialize a trajectory (set of (lat, lon, unix time in seconds) tuples)importtrajectorytrajectory.encode([(38.500,-120.200,1582482601),(40.700,-120.950,1582482611),(43.252,-126.453,1582482627)],5)This should return_p~iF~ps|U_ynpijgz~G_ulLnnqC_c`|@_mqNvxq`@__t`B.You can set the required precision with the optionalprecisionparameter. The default value is 5.DecodingTo deserialize a trajectory (set of (lat, lon, unix time in seconds) tuples) represented by an encoded stringimporttrajectorytrajectory.decode('_p~iF~ps|U_ynpijgz~G_ulLnnqC_c`|@_mqNvxq`@__t`B',5)This should return the following:[(38.500,-120.200,1582482601),(40.700,-120.950,1582482611),(43.252,-126.453,1582482627)]You can set the required precision with the optionalprecisionparameter. The default value is 5.DevelopmentSetup Dependencies$poetryinstallRunning Tests$poetryrunpytestContributingIssues and pull requests are welcome.For proposing new features/improvements or reporting bugs,create an issue.Checkopen issuesfor viewing existing ideas, verify if it is already proposed/being worked upon.When implementing new features make sure to add relavant tests and documentation before sending pull requests.
Package: trajectorydata
TrajectoryDataPackage providing the TrajectoryData class that allows to manage data for observables from a large number of quantum trajectory simulationsDevelopment of TrajectoryData happens onGithub.InstallationTo install the latest released version of TrajectoryData, run this command in your terminal:$pipinstalltrajectorydataThis is the preferred method to install TrajectoryData, as it will always install the most recent stable release.If you don’t havepipinstalled, thisPython installation guidecan guide
you through the process.To install the latest development version of TrajectoryData from the from theGithub.$pipinstallgit+https://github.com/goerz/trajectorydata.git@master#egg=trajectorydataUsageTo use TrajectoryData in a project:import trajectorydataHistory0.1.0 (2018-03-25)Initial release
Package: trajectorypy
trajectorypy - Trajectory Tracking Librarytrajectorypy is a library to track a phase state object that leaves a trajectory.A trajectory here means the following according to Wikipedia.The system’s evolving state over time traces a path
(a phase space trajectory for the system) through the high-dimensional space.
The phase space trajectory represents the set of states
compatible with starting from one particular initial condition,
located in the full phase space that represents the set of states compatible with starting from any initial condition.https://en.wikipedia.org/wiki/Phase_spaceInstallation$ pip install trajectorypy
Package: trajectory-supervisor
Trajectory SupervisorThe safety assessment module is a python-based online verification framework consisting of multiple sub-modules. The
safety checks serve as an independent check for generated motion primitives, with the goal of reducing the risk of
not properly handling safety critical behaviours that did not occur during development. Furthermore, the framework can
be used to pinpoint critical scenarios or time intervals during development.To serve these purposes, the safety assessment module rates incoming trajectories with regard to
their safety. The trajectories can either be provided directly by the planning module on the target device or for
testing purposes (e.g. CI jobs) via stored scenario files.DisclaimerThis software is providedas-isand has not been subject to a certified safety validation. Autonomous Driving is a
highly complex and dangerous task. In case you plan to use this software on a vehicle, it is by all means required that
you assess the overall safety of your project as a whole. By no means is this software a replacement for a valid
safety-concept. See the license for more details.DocumentationThe documentation of the project can be foundhere.AcknowledgementsCore DeveloperTim StahlAcknowledgementsSeveral students contributed to the success of the project during their Bachelor's, Master's or Project Thesis.Yujie Lian (RSS)Maroua Ben Lakhal (Static Safety)Maximilian Bayerlein (Guaranteed Occupation)Yves Huberty (Rule-based Reachable Sets)Philipp Radecker (Guaranteed Occupation)Nils Rack (Guaranteed Occupation)Contributions[1] T. Stahl and F. Diermeyer,
“Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle,”
IEEE Open Journal of Intelligent Transportation Systems, vol. 2, pp. 97–110, 2021, doi: 10/gj3535.(view online)[2] T. Stahl, M. Eicher, J. Betz, and F. Diermeyer,
“Online Verification Concept for Autonomous Vehicles – Illustrative Study for a Trajectory Planning Module,”
in 2020 IEEE Intelligent Transportation Systems Conference (ITSC), 2020.(view pre-print)If you find our work useful in your research, please consider citing:@article{stahl2021,
  title = {Online Verification Enabling Approval of Driving Functions \textemdash Implementation for a Planner of an Autonomous Race Vehicle},
  author = {Stahl, Tim and Diermeyer, Frank},
  year = {2021},
  volume = {2},
  pages = {97--110},
  issn = {2687-7813},
  doi = {10/gj3535},
  journal = {IEEE Open Journal of Intelligent Transportation Systems},
 }

@inproceedings{stahl2020,
     title = {Online Verification Concept for Autonomous Vehicles - Illustrative Study for a Trajectory Planning Module},
     booktitle = {2020 IEEE Intelligent Transportation Systems Conference (ITSC)},
     author = {Stahl, Tim and Eicher, Matthis and Betz, Johannes and Diermeyer, Frank},
     year = {2020}
   }
Package: trajectory-vis
Trajectory VisualizationVisualization of data and/or model trajectories on a 2D plane using pygame.The trajectory can be made of positions but may extend to aim, jump, leaning, etc.InstallationClone the repository and install the dependencies:gitclonegit@gitlab-ncsa.ubisoft.org:tboulet/trajectory-visualization.gitcdtrajectory-visualization
pipinstall-rrequirements.txtVisualize trajectories from JSON dataRun the following command to visualize the trajectories from the JSON data filedata.json:pythontrajectory_vis\vizualizers\visualizer_from_json.py--path=<path_to_json_folder>--random=<trueorfalse>--n=<number_of_trajectories_to_visualize_at_once>If you don't specify any path, the default path "//ubisoft.org/projects/r6/mtl/Public/AIBots/playerdata/y8s1.2/bigresults/output" will be used.Visualize trajectories from the Drain databaseThis is done as a drain job. The configuration can be modified in the yaml fileyamls\visualize_episodes.yaml. Once it is done, run the following command:drainrun--yaml=yamls\visualize_episodes.yamlVisualize trajectories from a modelTODO
Package: trajnetplusplustools
Toolssummary table and plots:python-mtrajnettools.summarize <dataset_files>plot sample trajectories:python-mtrajnettools.trajectories <dataset_file>visualize interactions:python-mtrajnettools.visualize_type <dataset_file>obtain distribution of trajectory types:python-mtrajnettools.dataset_stats <dataset_file>APIstrajnettools.Reader: class to read the dataset_filetrajnettools.show: module containing contexts for visualizingrowsandpathstrajnettools.writers: write a trajnet dataset filetrajnettools.metrics: contains unimodal metrics:average_l2(),final_l2() and collision()and multimodal metrics:topk() and nll()implementationsDatasetDatasets are split intotrain,valandtestset.
Every line is a self contained JSON string (ndJSON).Scene:{"scene":{"id":266,"p":254,"s":10238,"e":10358,"fps":2.5,"tag":2}}Track:{"track":{"f":10238,"p":248,"x":13.2,"y":5.85}}with:id: scene idp: pedestrian ids,e: start and end frame idfps: frame ratetag: trajectory typef: frame idx,y: x- and y-coordinate in meterspred_number: (optional) prediction number for multiple output predictionsscene_id: (optional) corresponding scene_id for multiple output predictionsFrame numbers are not recomputed. Rows are resampled to about
2.5 rows per second.Devpylinttrajnettoolspython-mpytest# optional: mypy trajnettools --disallow-untyped-defsDataset Summariesbiwi_hotel:crowds_students001:crowds_students003:crowds_zara02:crowds_zara03:dukemtmc:syi:wildtrack:Interactionsleader_follower:collision_avoidance:group:others:
Package: trajnettools
Toolssummary table and plots:python-mtrajnettools.summarize <dataset_files>plot sample trajectories:python-mtrajnettools.trajectories <dataset_file>visualize interactions:python-mtrajnettools.visualize_interactions <dataset_file>--interaction_type'ca'obtain distribution of trajectory types:python-mtrajnettools.dataset_stats <dataset_file>APIstrajnettools.Reader: class to read the dataset_filetrajnettools.show: module containing contexts for visualizingrowsandpathstrajnettools.writers: write a trajnet dataset filetrajnettools.metrics: contains unimodal metrics:average_l2(),final_l2() and collision()and multimodal metrics:topk() and nll()implementationsDatasetDatasets are split intotrain,valandtestset.
Every line is a self contained JSON string (ndJSON).Scene:{"scene":{"id":266,"p":254,"s":10238,"e":10358,"fps":2.5,"tag":2}}Track:{"track":{"f":10238,"p":248,"x":13.2,"y":5.85}}with:id: scene idp: pedestrian ids,e: start and end frame idfps: frame ratetag: trajectory typef: frame idx,y: x- and y-coordinate in meterspred_number: (optional) prediction number for multiple output predictionsscene_id: (optional) corresponding scene_id for multiple output predictionsFrame numbers are not recomputed. Rows are resampled to about
2.5 rows per second.Devpylinttrajnettoolspython-mpytest# optional: mypy trajnettools --disallow-untyped-defsDataset Summariesbiwi_hotel:crowds_students001:crowds_students003:crowds_zara02:crowds_zara03:dukemtmc:syi:wildtrack:Interactionsleader_follower:collision_avoidance:group:others:
Package: trakai
TrakaiRequires Python 3.6+,Python-Markdown, andJinja 2Documentation:https://novov.neocities.org/projects/trakai.htmlStatic site generators can be complex.Trakaimakes it simple to get a blog up and running without harming other static content.There's plenty of static site generators out there. Most of them are focused on building sites for the Next Big Thing with hundreds of pages. But what if you've got a few HTML files with your resumé on them, and you want to create a blog article on the latest programming trend?When designing my ownblog, I wanted something simple - that did just what I needed it to. I found the excellentMakesiteby Sunaina Pai, but that was too focused on general site-making, and not blog-posting. So I adapted his work and added a bit of my bad code, with the goal of making it more powerful and effective.  It still can't do the fancy stuff. But it does what I need.
Package: trakit
TrakItGuess additional information from track titlesProject pagehttps://github.com/ratoaq2/trakitInfoTrakItis a track name parser.
It is a tiny library created to solve a very specific problem.
It's very common that video files do not have precise metadata information,
where you can have multiple subtitle tracks tagged asPortuguese,
but one of them is actuallyBrazilian Portuguese:{"codec":"SubRip/SRT","id":19,"properties":{"codec_id":"S_TEXT/UTF8","codec_private_length":0,"default_track":false,"enabled_track":true,"encoding":"UTF-8","forced_track":false,"language":"por","language_ietf":"pt","number":20,"text_subtitles":true,"track_name":"Português","uid":160224385584803173}}{"codec":"SubRip/SRT","id":20,"properties":{"codec_id":"S_TEXT/UTF8","codec_private_length":0,"default_track":false,"enabled_track":true,"encoding":"UTF-8","forced_track":false,"language":"por","language_ietf":"pt","number":21,"text_subtitles":true,"track_name":"Português (Brasil)","uid":1435945803220205}}Or you have multiple audio tracks inEnglish,
but one of them isBritish English(British English Forced (PGS)) and others areAmerican English(American English (PGS))Given a track name,TrakItcan guess the language:>>trakit"Português (Brasil)"{"language":"pt-BR"}TrakItis also able to identify:SDH: Subtitles for the Deaf or Hard of HearingForced flagClosed captionsAlternate version tracksCommentary tracks>>trakit"British English (SDH) (PGS)"{"language":"en-GB","hearing_impaired":true}>>trakit"English CC (SRT)"{"language":"en","closed_caption":true}>>trakit"Cast and Crew Commentary (English AC3 Stereo)"{"language":"en","commentary":true}>>trakit"Français Forced (SRT)"{"language":"fr","forced":true}All available CLI options:>>trakit--help
usage:trakit[-h][-lEXPECTED_LANGUAGE][--debug][-y][--version]value

positionalarguments:valuetracktitletoguess

options:-h,--helpshowthishelpmessageandexitConfiguration:-lEXPECTED_LANGUAGE,--expected-languageEXPECTED_LANGUAGETheexpectedlanguagetobeguessed

Output:--debugPrintinformationfordebuggingtrakitandforreportingbugs.-y,--yamlDisplayoutputinyamlformat

Information:--versionshowprogram'sversionnumberandexitTrakItis not a release parser. UseGuessItTrakItis not a video metadata extractor.
UseKnowIt.
KnowIt already usestrakitto enhance the extracted informationInstallationTrakItcan be installed as a regular python module by running:$ [sudo] pip install trakitFor a better isolation with your system you should use a dedicated
virtualenv or install for your user only using the--userflag.DataAvailable languages are the same supported byDiaoul/babelfishLocalized country names were fetched frommledoze/countriesLocalized language names were fetched frommozilla/language-mapping-list
Package: trakt_aiohttp
This project covers a basic python3 Trakt API withdeviceauth
using aiohttp.To use it, have a look at __init__.py’s Trakt class.async def foo():
    trakt = Trakt(CID, CSECRET)
    watchlist = await trakt.watchlist()
    calendar_mine = await trakt.calendars_shows('my', 'new',
        '2015-10-20', 7)Have a look at the Trakt API docs athttp://docs.trakt.apiary.io/#referenceFeaturesUnit tests using mockup apiary serverAiohttpPython3.5 style coroutinesImplemented methods:
- Calendar show query
- Calendar movie query
- Checkin API
- Genre listing
- Translations API
- Movies listing by sorting
- Shows listing by sorting
- Seasons and episodes listing
- Single episode infoTODOProper narrative documentationSearch API