Saravanan666/pypi-org huggingface dataset:
Package: toy-gradlogp-tf2
toy_gradlogp_tf2This repo implements some toy examples of the following score matching algorithms in TensorFlow 2.0:ssm-vr:sliced score matchingwith variance reductionssm:sliced score matchingdeen:deep energy estimator networksdsm:denoisnig score matchingRelated projects:toy_gradlogp: PyTorch Implementation.InstallationBasic requirements:Python >= 3.6TensorFlow >= 2.3.0Install from PyPIpipinstalltoy_gradlogp_tf2Or install the latest version from this repopipinstallgit+https://github.com.Ending2015a/toy_gradlogp_tf2.git@masterExamplesThe examples are placed intoy_gradlogp/run/Train an energy modelRunssm-vron2spiralsdatasetpython-mtoy_gradlogp.run.train_energy--lossssm-vr--data2spiralsTo see the full options, type--helpcommand:python -m toy_gradlogp.run.train_energy --helpusage: train_energy.py [-h] [--logdir LOGDIR]
                       [--data {8gaussians,2spirals,checkerboard,rings}]
                       [--loss {ssm-vr,ssm,deen,dsm}]
                       [--noise {radermacher,sphere,gaussian}] [--lr LR]
                       [--size SIZE] [--eval_size EVAL_SIZE]
                       [--batch_size BATCH_SIZE] [--n_epochs N_EPOCHS]
                       [--n_slices N_SLICES] [--n_steps N_STEPS] [--eps EPS]
                       [--log_freq LOG_FREQ] [--eval_freq EVAL_FREQ]
                       [--vis_freq VIS_FREQ]

optional arguments:
  -h, --help            show this help message and exit
  --logdir LOGDIR
  --data {8gaussians,2spirals,checkerboard,rings}
                        dataset
  --loss {ssm-vr,ssm,deen,dsm}
                        loss type
  --noise {radermacher,sphere,gaussian}
                        noise type
  --lr LR               learning rate
  --size SIZE           dataset size
  --eval_size EVAL_SIZE
                        dataset size for evaluation
  --batch_size BATCH_SIZE
                        training batch size
  --n_epochs N_EPOCHS   number of epochs to train
  --n_slices N_SLICES   number of slices for sliced score matching
  --n_steps N_STEPS     number of steps for langevin dynamics
  --eps EPS             noise scale for langevin dynamics
  --log_freq LOG_FREQ   logging frequency (unit: epoch)
  --eval_freq EVAL_FREQ
                        evaluation frequency (unit: epoch)
  --vis_freq VIS_FREQ   visualization frequency (unit: epoch)ResultsTips: The larger density has a lower energy!8gaussiansAlgorithmResultsssm-vrssmdeendsm2spiralsAlgorithmResultsssm-vrssmdeendsmcheckerboardAlgorithmResultsssm-vrssmdeendsmringsAlgorithmResultsssm-vrssmdeendsm
Package: toyml
ToyMLMachine Learning from ScratchThere are some machine learning algorithms implemented from scratch.
Let's learn machine learning with simple toy code.InstallationpipinstalltoymlLinksDocumentation:https://datahonor.com/toyml/PyPi:https://pypi.org/project/toyml/Changelog:https://datahonor.com/toyml/CHANGELOG/RoadMapClustering: DBSCAN, Hierarchical(Agnes&Diana), KmeansClassification: KNNEnsemble: Boosting(AdaBoost)Classification: NaiveBayes, DecisionTree, SVMAssociation Analysis: AprioriEnsemble: GBDT
Package: toynn
"toynn" is a handy Neral Network model builder.You'll be able to create a model in the desired structure with a single line of code.
Easily train, predict and visualize your model."toynn" supports 'export' and 'import' of models in 'json' format.Simple example for usage is like below.import toynn#build a model
model = toynn.model.ANN(input_shape=(1, 784), shape = (100, 100, 100, 10), output="softmax", activation=("relu", "relu", "relu"))
model.describe()#train
model.train(y= TRAIN_BATCH, t= ANSWER_BATCH, learning_rate=0.001, iteration=1000)#predict
model.predict(x = INPUT)#export model as a 'json' file to a local directory
model.export(directory = "C:\Users.......//", file_name="myModel.json")#import model from a local directory
factory = toynn.factory.factory()
model2 = factory.make(directory = "C:\User.....\myModel.json")=======================================================================Updates on more types of model such as 'CNN', 'LSTM' is planned.
Thanks, and please contact the author via e-mail for any comment.
Package: toyota-na
toyota-naPython client for Toyota North America service APIInstallpip install toyota-naUsagepython -m toyota_na.app -h  # Get help
python -m toyota_na.app authorize <username> <passworde>
python -m toyota_na.app get_user_vehicle_list  # List vehicle
python -m toyota_na.app get_vehicle_status <vin>  # Get vehcicle status
...Known IssuesDoor/window status not always up-to-date unless you callsend_refresh_statusfirst and wait for it to complete (there is no notification that it completed successfully).Developer GuideQuick Startfrom toyota_na.client import ToyotaOneClient

async def main():
    cli = ToyotaOneClient()
    await cli.auth.login(USERNAME, PASSWORD)
    vehicle_list = await cli.get_user_vehicle_list()
    vehicle_status = await cli.get_vehicle_status(vehicle_list[0]["vin"])
    ...Abstracted Interface Examplefrom toyota_na.client import ToyotaOneClient
from toyota_na.vehicle.vehicle import get_vehicles

async def main():
    cli = ToyotaOneClient()ContributingWe use black and isort for opinionated formatting to ensure a consistent look and feel throughout the codebase no matter the contributor.
Pre-commit is used to guarantee the files being checked-in to the repo are formatted correctly.For convenience a vscode project settings file is included as well. Editors other than vscode will require some setup if you wish to have formatting take place while working.Getting started:Install poetry -https://python-poetry.org/docs/#osx--linux--bashonwindows-install-instructionsClone the repopoetry installpoetry shellpre-commit installSamplesSample responses from API calls are stored insamplesfolder. The data is sourced from real users and from the Toyota app's "Demo Mode"Credits:Thanks @DurgNomis-drol for making the original Toyota module and bring up the discussing of Toyota North America.Thanks @visualage for finding the way to authenticate headlessly.
Package: toyplot
Toyplot is the kid-sized plotting toolkit for Python with grownup-sized goals:Develop beautiful plots that embrace the unique capabilities of electronic publishing and support repoducibility.Create the best possible data graphics “out-of-the-box”, maximizing data ink and minimizing chartjunk.Provide a clean, minimalist interface that scientists and engineers will love.See the Toyplot documentation athttp://toyplot.readthedocs.io, and the Toyplot sources athttp://github.com/sandialabs/toyplot
Package: toyredis
Toy redis client for python.RequirementPython 3.5+ or MicroPythonhttps://micropython.orgInstall$ python3 -m pip install toyredisExample>>> import toyredis
>>> conn = toyredis.connect('localhost')
>>> conn.set('foo', 'bar')
>>> conn.get('foo')
b'bar'
>>> conn.set('foo', 10)
>>> conn.incr('foo')
11
>>>
Package: toyrobot
Toy RobotA toy robot, toy application where the toy robot doesn't fall off the toy tableThis project was built usingnbdev:)Installpip install toyrobotDocumentationFor documentation, referhere.RunTo run the project in command line (after installation):toyrobot_run --cmd_file_path <path_to_example.txt>As an example:toyrobot_run --cmd_file_path ./examples/example1.txt4,5,WESTTo print out outputs after every command add-- verboseflag to the command.TestsTo run unit as well as integration tests:nbdev_test_nbs
Package: toytable
This is the last release of ToyTable, henceforth the package will be known as EZTable. Previously this project was hosted
on mercurial, in the future it will be on github:https://github.com/salimfadhley/eztableToytable is a lightweight python table library. It provides a class called Table  which is suitable for representing one-dimensional tables (e.g. time-sequences).It is suitable for data analysis tasks.Toytable is built with an emphasis on simplicity, both in interface and internal design rather than performance or scalability. Toytable is intended to have as few dependancies as possible, and is therefore suitable for casual development tasks.Toytable is not intended for high performance or scalability. Toytable is not complete. For these reasons, please consider evaluating other more mature packages (e.g. Pandas).Project StatusAbout this projectDocumentation:http://toytable.readthedocs.orgLatest release:https://pypi.python.org/pypi/toytable/Latest source:https://bitbucket.org/salimfadhley/toytableIssues:https://bitbucket.org/salimfadhley/toytable/issues?status=new&status=open
Package: toytrack
ToyTrackToyTrack is a Python library for generating toy tracking events for particle physics.The goal: To produce a "good-enough" event simulation, in as few lines as possible (currently 3 lines), as quickly as possible (currently 0.07 seconds for a 10,000-particle event).InstallationUse the package managerpipto install ToyTrack.pipinstalltoytrackUsageVanilla EventfromtoytrackimportParticleGun,Detector,EventGenerator# Initialize a particle gun which samples uniformly from pt between 10 and 20 GeV,# initial direction phi between -pi and pi, and creation vertex vx and vy between -0.1 and 0.1 cmparticle_gun=ParticleGun(dimension=2,pt=[2,20],pphi=[-np.pi,np.pi],vx=[-0.1,0.1],vy=[-0.1,0.1])# Initialize a detectordetector=Detector(dimension=2).add_from_template('barrel',min_radius=0.5,max_radius=3,number_of_layers=10)# Initialize an event generator, which generates an event with a number of particles given by a normal# distribution, with mean 10 and standard deviation 5event=EventGenerator(particle_gun,detector,num_particles=[20,5,'normal']).generate_event()# Access the particles, hits and tracks as neededparticles=event.particleshits=event.hitstracks=event.tracks# Plot the eventevent.display()Event with Track Holes...# as above# Initialize a detector that randomly drops 10% of hits. If an int N is given, then exactly# N hits per track are droppeddetector=Detector(dimension=2,hole_inefficiency=0.1).add_from_template('barrel',min_radius=0.5,max_radius=3,number_of_layers=10)...# as above# Plot the eventevent.display()Event with Noise Hits...# as above# Generate event with between 30% and 70% noise hits per real hits. E.g. If the event has 100# real hits, then between 30 and 70 noise hits will be generated. If an int N is given, then# the absolute value of N noise hits are generatedevent=EventGenerator(particle_gun,detector,num_particles=[20,5,'normal'],noise=[0.3,0.7]).generate_event()...# as above# Plot the eventevent.display()PerformanceToyTrack is designed to be fast. The following benchmarks were performed on a 64-core AMD EPYC 7763 (Milan) CPU.
Package: toytree
Tree plotting withToytreeWelcome to toytree, a minimalist tree manipulation and plotting library for use inside jupyter notebooks. Toytree combines a popular tree data structure based on theete3library with modern plotting tools based on thetoyplotplotting library. The goal of toytree is to provide a light-weight Python equivalent to commonly used tree manipulation and plotting libraries in R, and in doing so, to promote further development of phylogenetic methods in Python. Toytree generates rich interactive figures (SVG+HTML+JS) that can be embedded in jupyter-notebooks or webpages, or rendered in SVG, PDF, or PNG for publications.Examples GallerySee the Cookbook section in the documentationInstallationYou can installtoytreeand its dependencies (toyplotandnumpy) with a single command using conda (preferred), or from pip.condainstalltoytree-cconda-forgeDocumentationSee thefull documentationto see all of the options that toytree provides. Try it out instantly in the cloud using thetoytree binder.Examples# import toyplot and load a newick file from a public URLimporttoytreetre=toytree.tree("https://eaton-lab.org/data/Cyathophora.tre")# root the tree using a wildcard string matching and draw a tree figure.rtre=tre.root(wildcard='prz')rtre.draw(width=400,tip_labels_align=True);# or chain a few functions togethertre.root(wildcard='prz').drop_tips(wildcard="tham").ladderize().draw();# extensive styling options are availablertre.draw(tip_labels_color='pink',node_labels='support',node_sizes=15,node_colors="cyan",edge_style={"stroke":"darkgrey","stroke-width":3,},)
Package: tp24
tp24Python colour library, made by 7dsupports RGB, CMY, CMYK, HSV, HSL models & transparencyadd & subtract colours to lighten & darkenfind gradient of line with proportionsfind similarity of colourscolour schemes: analogous, complementary, compound, triadic, tetradicyou can define more schemes too, given degree around wheel and number of coloursget hex of colourget colour from hex or web nameCurrent version: v1.0Documentation:https://tp24.readthedocs.io/en/latest/Why 'tp24'?tp24 stands fortwopower24, or 2^24 which equals to 16777216, the number of colours that the RGB model can show.ExamplesCreate a colour:>>>col=tp24.rgb(255,0,0)>>>colrgb(255,0,0)Convert colour from one model to another>>>col=tp24.rgb(255,0,0)>>>col.hsv()hsv(0,100,100)>>>col.cmyk()cmyk(0,100,100,0)Get individual channels of colour>>>col.r255>>>col.g0>>>col.b0Get hex value of colours>>>col.hexv()'#ff0000'>>>col.hexv(compress=True)'#f00'Load colour from hex or web name>>>tp24.rgb.from_hex("#987654")rgb(152,118,84)>>>tp24.hsv.from_web("orange")hsv(39,100,100)Lighten and darken colours by addition & subtraction>>>col1=tp24.rgb(255,0,0)>>>col2=tp24.hsl(270,100,50)>>>col+col2rgb(255,0,255)>>>col-col2rgb(127,0,0)Mix colours>>>col1*col2rgb(192,0,128)>>>tp24.tools.gradient(col1,col2)rgb(192,0,128)>>>tp24.tools.gradient(col1,col2,ap=0.25,bp=0.75)rgb(160,0,191)Find similarity of colours>>># works best in hsl/hsv>>>col1=tp24.hsl.from_web("red")>>>col2=tp24.hsl.from_web("orange")>>>tp24.tools.similarity(col1,col2)0.7388888888888889>>>col2=tp24.hsl.from_web("green")>>>tp24.tools.similarity(col1,col2)0.8055555555555557Colour schemes>>>col=tp24.rgb(255,0,0)>>>col.analogous()(rgb(255,0,128),rgb(255,128,0))>>>col.compound()(rgb(0,255,128),rgb(0,128,255))>>>col.complementary()rgb(0,255,255)>>>col.triadic()(rgb(0,0,255),rgb(0,255,0))>>>col.tetradic()(rgb(128,0,255),rgb(128,255,0),rgb(0,255,255))>>>col.wheel(5,72)(rgb(204,0,255),rgb(204,255,0),rgb(0,102,255),rgb(0,255,102),rgb(0,255,102))Invert values>>>col.inverted()rgb(0,255,255)Support for transparency>>>colrgb(255,0,0>>>col=col.add_alpha(50)>>>colrgba(255,0,0,50)>>>col=col.remove_alpha()>>>colrgb(255,0,0)
Package: tp2conceptionlogicielle
Hello worldStart here :git clone https://gitlab.com/conception-logicielle/tp2-conception-corrige.git tp2Install requirements file :poetry installThen you can run the app :poetry run python tp2on some operating systems, python you might have to use cmd python3 or python2You can change words using env variables
e.g. on linux :export DEFAULT_MOT_UN=totoon some operating systems, python you might have to use cmd python3 or python2You can also download the package:pip install
Package: tpa-map-interface
DescriptionThis repository provides an interface to access local acceleration limitations for trajectory planning at TUM/FTM. In
addition to location-dependent acceleration limits, vehicle velocity dependent acceleration limits can be accessed when
provided.
The initial acceleration limits are loaded from a csv-file. There is an option to update the local acceleration in
real-time when the tire performance assessment module is running in parallel and communication is enabled.List of componentsMapInterface.py: Provides an interface to the local trajectory planner and to the tire performance assessment moduleimport_veh_dyn_info.py: Imports local acceleration limitation from file
Package: tpblite
Unofficial Lightweight Python API for ThePirateBayInstallation$pipinstalltpbliteDependencies:lxmlUsagefromtpbliteimportTPB# Create a TPB object with a domain namet=TPB('https://tpb.party')# Or create a TPB object with default domaint=TPB()N.BBefore using you should check which ThePirateBay domain works for you, as this varies from country to country.Search# Quick search for torrents, returns a Torrents objecttorrents=t.search('public domain')# See how many torrents were foundprint('There were{0}torrents found.'.format(len(torrents)))# Iterate through list of torrents and print info for Torrent objectfortorrentintorrents:print(torrent)# Customize your searchfromtpbliteimportCATEGORIES,ORDERStorrents=t.search('public domain',page=2,order=ORDERS.NAME.DES,category=CATEGORIES.VIDEO.MOVIES)# Get the most seeded torrent based on a filtertorrent=torrents.getBestTorrent(min_seeds=30,min_filesize='500 MiB',max_filesize='4 GiB')# Or select a particular torrent by indexingtorrent=torrents[3]# Get the magnet link for a torrentprint(torrent.magnetlink)Browse# You can browse all of the torrents from a single categorytorrents=t.browse(category=CATEGORIES.VIDEO)# Customize the page number and sort ordertorrents=t.browse(category=CATEGORIES.VIDEO.MOVIES,page=1,order=ORDERS.UPLOADED.DES)TopfromtpbliteimportTPB,CATEGORIESt=TPB()# Get the top recent torrentstorrents=t.top(CATEGORIES.GAMES.ALL)# Customize with category and restriction to torrents from the last 48htorrents=t.top(category=CATEGORIES.GAMES.ALL,last_48=True)Categories and Sort Order# To print all available categories, use the classmethod printOptionsCATEGORIES.printOptions()# Or just a subset of categories, like VIDEOCATEGORIES.VIDEO.printOptions()# Similarly for the sort orderORDERS.printOptions()Torrents objectThe search function returns aTorrentsobject, which is alist-likecollection of the torrents found.You can also iterate over theTorrentsobject just by calling it in a for loop (see example above).You can see how manyTorrentobjects your query has returned, by using thelen()functionTorrent objectTorrentobjects represent each torrent found in theTorrentsclass, they have the following attributesAttributesTorrent.title- The name of the torrent (str)Torrent.seeds- The number of seeders (int)Torrent.leeches- The number of leechers (int)Torrent.upload_date- Date the torrent was uploaded (str)Torrent.uploader- Name of user who uploaded torrent (str)Torrent.filesize- The filesize in *iB format, eg. 5 GiB (str)Torrent.byte_size- The filesize in bytes of the torrent (int)Torrent.magnetlink- magnetlink of the torrent (str)Torrent.url- URL of the torrent page (str)Torrent.is_trusted- Whether the torrent is from a trusted source.Torrent.is_vip- Whether the torrent is from a VIP source.Torrent.infohash- The info hash of the torrent (str)Torrent.category- The torrent category (str)Example WorkflowWith a commandline torrent client such asaria2, you can automate search and downloading of torrents like so:importsubprocessfromtpbliteimportTPBt=TPB()torrents=t.search('GIMP 2.10.8')torrent=torrents.getBestTorrent()subprocess.call(['aria2c',torrent.magnetlink])
Package: tp-chaos-generator
Triple Pendulum based Chaos Generatortp_chaos_generatoris a package which provides a library for encrypting and decrypting data using a
triple-pendulum based chaos generator.RequirementsPython (>= 3.8)InstallationYou can installtp_chaos_generatorwith:$ pip install -e .Usage>>>fromtp_chaos_generatorimportchaos_generator,utils>>>cg=chaos_generator.ChaosGenerator()>>>key=cg.generate_key()>>>text=utils.convert_to_bytes("A really secret message. Not for prying eyes.")>>>tokens=cg.encrypt(text,key)>>>tokens'...'>>>data=cg.decrypt(tokens,key)>>>utils.convert_to_string(data)'A really secret message. Not for prying eyes.'CitationIf you find the code useful in your research, please consider citing:@ARTICLE{9969592,
  author = {Paul, Bikram and Pal, Souradip and Agrawal, Abhishek and Trivedi, Gaurav},
  journal = {IEEE Access}, 
  title = {Triple Pendulum Based Nonlinear Chaos Generator and its Applications in Cryptography}, 
  year = {2022},
  volume = {10},
  pages = {127073-127093},
  doi = {10.1109/ACCESS.2022.3226515}
}FAQPlease create anew issue.
Package: tpclean
tpcleanInfoHome of the tpclean library
A Collection of handy functions that help with:Pandas wranglingPerforming SQL QuerriesDealing With JSON files in PandasFore more detailed information visit:https://medium.com/@pietrassykGetting startedInstall:!pip install tpclean --upgradeImport:import tpclean.tpclean as tp
Package: tpcollections
tpcollectionsPython Transactional Persistent Collections, backed by sqlite.DescriptonThis is a package that gives you a set of Python collections (Mappings, Lists,
Sequence, and Deques) that are backed by SQLite.  This allows you toShare collections between threads or processes safely.Maintain transactional safety across one or more collections.Persist collections efficiently, safely, and easily to disk, across one or
more file.Manage collections that have expiring elements.And do it all in any combination you wish without needing to worry about
specific details.Supported collection typesMapping, ordered by key.OrderedMapping, remembering insertion order.Set, ordered by key.OrderedSet, remembering insertion order.ExpiringMapping, with key entries that automatically time out and remove themselves.ExpiringOrderedMapping.ExpiringSet.ExpiringOrderedSet.Sequence.Deque.There may be more in the future, if needed.FeaturesSupports transactions, nestable through savepoints.Supports intelligent WAL mode, making decisions automatically that achieve
the maximum performance while preserving durability even in the face of power
failure.When not forced one way or the other, this means enabling WAL mode when no
databases are attached, and disabling it upon attaching databases.Supports many collections in one database file, ensuring all the used tables
are the right type and that there are no collisions.Supports multiple attached database files, maintaining transactions,
consistency, and durability across the entire set.SupportsSupported and tested on every reasonable version of Python and SQLite.Tests are run all supported versions of Python, and on all in-support
versions of RHEL, CentOS, Fedora, Debian, Ubuntu, and Alpine Linux.This includes versions of Python or SQLite otherwise considered out of
support by upstream.  At the time of this writing, this includes CentOS 7
and therefore Python 3.6.8 and SQLite 3.7.17.This does not include extended support (i.e. Centos 6 and Debian Jessie),
because I haven't found a free and easy way to automate testing for them.
I would be happy to accept contributions to add this support.SQLite features are version-tested, selecting the most appropriate features
available on the platform.  This does mean that database files might not be
able to be moved from a newer version of SQLite to an older one, particularly
if you move from a version that supports STRICT and WITHOUT ROWID to one that
doesn'tPrior artTBC.Feature comparison to other libraries
Package: tpd
TapedA simple way to record the outputs of simulations.Tapedis inspired byloguruwhich makes logging easy:Tapemakes it is to not only save the logs of simulations ran in Python, but also to save data, plots and any other useful file which you'll need to analyze the simulations' results.Toinstall Tape, create a python environment and use:pip install tpdUsageUsingTapedis easy:fromtpdimportrecorderrecorder.start(base_folder='my_computer/my_data',name='simulation_2')'''Creates a folder 'simulation_2' in the 'base_folder'From now on, all data, logs and plots will be saved inmy_computer/my_data/simulation_2'''# all logs will be saved in a .log filefromloguruimportloggerlogger.info('I cant wait to check the logs later!')# you can save data to file easilyimportnumpyasnpX=np.random.rand(100,100)recorder.add_data(X,'my_results',fmt='npy')# or 'mat' for matlab# and save plotsimportmatplotlib.pyplotaspltf=plt.figure()recorder.add_figure(f,'first plot',svg=True)delf# and save multiple figures at oncef1=plt.figure()f2=plt.figure()f1._save_name='first of two'f2._save_name='second of two'recorder.add_figures()# finally, you can check what's saved so farrecorder.describe()The result should look somthing like this: