Saravanan666/pypi-org huggingface dataset:
Package: tensortradepy
(unofficial) Python Client for Tensor TradeThis library is a simple Python client for the Tensor Trade API. It's aimed is
to make your bot trading simpler.DisclaimerBe aware that this client directly communicates with Tensor. Be sure of your
script before doing any financial operations. Moreover, there is no guarantee
about the reliability of this client. I encourage you to tests your scripts
with cheap cNFT collections first.The developer of this client is not responsible for any errors or issues that
may occur when using this SDK. Use at your own risk.TipsIf you like this client, you can tip me at: thewisetrade.sol or buythe logo NFT.InstallInstall the library with:pip install tensortradepyDevelopment version:pip install git+https://github.com/defilogist/tensor-trade-pyUsage exampleimportosfromtensortradepyimportTensorClientclient=TensorClient(os.getenv("TENSOR_API_KEY"),os.getenv("WALLET_PRIVATE_KEY"),"mainnet-beta")floor=client.get_collection_floor("theheist")price=floor*0.99nft_mint="nft-mint"client.list_nft(nft_mint,price)# we assume your keypair owns the NFT.DocumenationAfull documentationis available.Functions availableget_collection_infos(slug)get_collection_floor(slug)list_nft(mint, price) // Price in $SOLlist_cnft(mint, price) // Price in $SOLdelist_cnft(mint)edit_cnft_listing(mint, price) // Price in $SOLset_cnft_collection_bid(slug, price, quantity)edit_cnft_collection_bid(slug, price, quantity)cancel_cnft_collection_bid(slug, price, quantity)buy_nft_(seller, mint_address, price)buy_cnft_(seller, mint_address, price)ContributionsAny contribution is welcome, please open your PR for additions and report bug
through Github issues.AboutThis client is developed bythewise.trade.
Package: tensor-type
Tensor TypeAnnotates shapes of PyTorch Tensors using type annotation in Python3, and provides optional runtime shape validation.This comes in very handy when debugging complex programs that manipulate hugetorch.Tensors where shape (dimensions) vary widely and are hard to track down.I got tired of writing tons ofassert my_tensor.shape == (batch, channels, height, width)over and over, so I made that utility, but then
I got tired of copy/pasting it into every new projects from my Gist of it, so here I finally made it a library that I can pip install everywhere.Getting startedpip3installtensor_typetensor_typeonly works with PyTorch, but that's only because I make the annotation type inherit fromtorch.Tensorto satisfy static annotations.Usagefromtensor_typeimportTensor,Tensor3d,Tensor4dimporttorch# You can use the type in function's signaturesdefmy_obscure_function(x:Tensor4d)->Tensor3d:returnx.sum(dim=3)/x.mean()t=my_obscure_function(x=torch.rand(3,2,4,2))# You can check the shape with an explicit assertassertTensor3d(t)# Or you can check it with the .check() method which will produce a nicer error messageTensor3d.check(t)# Check specific shapeassertTensor[3,2,4](t)# This will match no matter the size of the second axisassertTensor[3,:,4](t)batch=64channels=3h,w=256,512# You can statically annotate the shape like so# This WILL NOT be checked at run time, it's just for claritymy_tensor:Tensor[batch,channels,h,w]=load_images(...)# You can assert it later like so:assertTensor[batch,channels,h,w](my_tensor)# You can define new "types" like so:ImageBatch=Tensor[64,3,:,:]# And then use the new typeassertImageBatch(torch.rand(64,3,256,256))assertImageBatch(torch.rand(64,3,512,512))assertnotImageBatch(torch.rand(64,1,256,256))DevelopmentTo install the latest version from Github, run:git clone git@github.com:sam1902/tensor_type.git tensor_type
cd tensor_type
pip3 install -U .
Package: tensorutils
TensorutilsSmall utils for Tensorflow in Python.Select CPU if not GPUA function to force Tensorflow to use CPU instead of GPU if this is not present.
This function is very easy to use. For example:fromtensorutilsimportselect_cpu_if_not_gpu# With static memory sizeselect_cpu_if_not_gpu()# With dynamic memory sizeselect_cpu_if_not_gpu(True)
Package: tensorview
Dynamic visualization training service in Jupyter Notebook for Keras, tf.keras and others.Tensorview is efficient visualization training packages of Keras, enabling the live visualization of loss function and metrics during training process.Tensorview is process in the Jupyter Notebook, so we recommend Jupyter Notebook as Tensorview's developing environment.|API Document|API文档|中文介绍|InstallationTo installthis verson from PyPI, type:pip install tensorviewTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:pip install git+git://github.com/Hourout/tensorview.gitFeatureVisualization of general machine learning trainingSupport customizing the name of loss function and metricSupport customizing the name of validation setSupport displaying the result of training set and test set in the same plotSupport setting maximum training number of iterationsSupport both batch way and epoch way to trainSupport visual plottingSupport visual plotting in both batch way and epoch way periodicallykeras backendSupport customizing the name of loss function and metric in epoch waySupport customizing the name of validation set in epoch waySupport displaying the result of training set and test set in the same plot in epoch waySupport setting maximum training number of iterations in epoch waySupport visual plotting in epoch waySupport visual plotting in both batch way and epoch way periodically in epoch waySupport customizing the name of loss function and metric in batch waySupport customizing the name of validation set in batch waySupport displaying the result of training set and test set in the same plot in batch waySupport setting maximum training number of iterations in batch waySupport visual plotting in batch waySupport visual plotting in both batch way and epoch way periodically in batch wayExamplemore exampletensorview.train.PlotMetrics()tensorview.train.PlotMetricsOnBatch()tensorview.train.PlotMetricsOnEpoch()
Package: tensorvis
TensorVisOverview•Features•Installation•ContributeOverviewA command line tool to automate the process of grabbing tensorboard events data and visualising them.  This allows for faster result analysis and separation of the experiment logic from the visualisation aspect of the metrics logged in tensorboard.FeaturesUploads experiment metrics logged to tensorboard to tensorboard.dev and creates a log of uploaded experiments.Downloads experiments from tensorboard.dev to a local csv file.Plots experiment metrics.BenefitsFaster result analysisLess code writtingSeparate experiments from analysisAllows for more research timeInstallationtensorviscan be installed using pip with the command:pip install tensorvisThis will installtensorvisin the current python environment and will be available through the terminal.tensorvissupports autocompletion of commands and experiment names using Click'sshell completion.  To initialise autocompletion run the following command if using Ubuntu and bash:mv /path/to/virtualenvs/your-virtualenv/site-packages/.tensorvis-complete.bash ~/The reason for this path is due to an issue with poetry not packaging data within the.whlfile.  You can find more about thishere.Follow Click' documentation linked above for different shell support.AssumptionsThere can be many different directory structures when running and logging experiments with tensorboard.  This tool makes several assumptions to make it easier to handle dataframes resulting from downloading experiments.tensorvisassumes the following directory structure of tensorboard logs within the top level directorylogs, where eachrunsubdirectory contains the events file:logs
├──exp_name_1
│├──run_1
│└──run_2
├──exp_name_2
│├──run_1
│├──run_2
│└──run_3
└──exp_name_3└──run_1For a description of how the directory structure is represented in a dataframe follow thislink.By defaulttensorvisassumes a single experiment directory is provided which corresponds to a single experiment having multiple runs.  All runs from a single experiment will be aggregate and averaged to plot the mean values along with the standard deviation.ContributeAny feedback ontensorvisis welcomed in order to improve its usage and versatility.  If you have something specific in mind please don't hesitate to create an issue or better yet open a PR!Current Contributorsnpitsillos
Package: tensorvision
[](https://github.com/TensorVision/TensorVision/blob/master/LICENSE)TensorVision is a simple toolkit to easily apply image classification networks.InstallationYou can install TensorVision and its requirements via:$pipinstall-rhttps://raw.githubusercontent.com/TensorVision/TensorVision/master/requirements.txt
$pipinstallhttps://github.com/TensorVision/TensorVision/archive/master.zipFor more details, please have a look at theinstallation instructionsin the documentation.DocumentationDocumentation is available online:http://tensorvision.readthedocs.ioExamplesExamples of how to utilize TensorVision to perform various tasks can be found in ourKittiSeg,KittiBoxandMultiNetrepository.ChangelogBreaking ChangesModule:optimizerFunction:get_learning_rateChange: Added, now needs to be implemented.Module:optimizerFunction:trainingChange: Parameterlearning_rateadded.
Package: tensorwaves
TensorWavesTensorWaves is a fitter package that optimizes mathematical models to data samples. The
models can be anysymbolicmathematical expression that is then converted to any
computational backend. In addition, TensorWaves provides functionality to generate toy
Monte Carlo data samples. The package is developed in parallel withAmpForm, which implements physics models, but its
'lambdifying' mechanisms can in principle be used independently.All documentation can be found ontensorwaves.rtfd.io.For an overview ofupcoming releases and planned functionality, seehere.ContributeSeeCONTRIBUTING.md
Package: tensorx
TensorXis a high-level deep neural network library written in Python
that simplifies model specification, training, and execution usingTensorFlow. It was designed for fast
prototyping with minimum verbose and provides a set of modular
components with a user-centric consistent API.Design PhilosophyTensorX aims to besimple but sophisticatedwithout a code base plagued by unnecessary abstractions and
over-engineering andwithout sacrificing performance. It uses Tensorflow without hiding it completely behind a new namespace, it's mean to be a complement
instead of a complete abstraction. The design mixes functionaldataflow computation graphswithobject-orientedneural networklayerbuilding blocks that areeasy to add to and extend.Feature SummaryNeural Networklayer building blocks likeInput,Linear,Lookup;New TensorFlow ops:gumbel_top,logit,sinkhorn, etc;GraphUtils: allow for validation and compilation of layer graphs;ModelClass: for easyinference,training, andevaluation;Training Loop: easily customizable with aCallbacksystem;InstallationTensorX is written in pure python butdepends on Tensorflow, which needs to be installed from thetensorflowpackage.
The reason for this is that you might want to install Tensorflow builds optimized for your machine (seethese). Additionally, TensorX hasoptional
dependencieslikematplotliborpygraphvizfor certain functionality.Pip installationInstall usingpipwith the following commands:pipinstalltensorflowpipinstalltensorxFor more details about the installation, check thedocumentation.Test your installationimporttensorflowastfimporttensorxastxDocumentationFor details about TensorX API, tutorials, and other documentation, seehttps://tensorx.org.
You can help by trying the project out, reporting bugs, suggest features, and by letting me know what you think.
If you want to help, please read thecontribution guide.AuthorDavide Nunes: get in touch@davidelnunesor bye-mailLicenseApache License 2.0
Package: tensoul
tensoul-pyConvert MahjongSoul log into tenhou.net/6 format. (Inspired byhttps://github.com/Equim-chan/tensoul)UsageYou need to have an account from CN server, because only accounts from CN server has the ability to login with username and password.importjsonimportsysfromtensoulimportMajsoulPaipuDownloaderusername="foo@bar.com"password="foobar"record_uuid="123456-abcdefgh-1234-abcd-1234-12345678abcd"# taken from majsoul log link: https://game.maj-soul.com/1/?paipu=<this_part>_a12345678downloader=MajsoulPaipuDownloader()awaitdownloader.start(username,password)try:logs=awaitdownloader.download(record_uuid)json.dump(logs,sys.stdout,ensure_ascii=False)finally:awaitdownloader.close()See example.py alsoThankshttps://github.com/MahjongRepository/mahjong_soul_apihttps://repo.riichi.moe/library.html#resources-majplus
Package: tentacles
Tentacles is a python ORM.
The main idea is to manipulate stored datas as you do for python data structurespython native:>>> class SuperHero(object):
>>>             def __init__(self, name, gender, power):
>>>                     self.name   = name
>>>                     self.gender = gender
>>>                     self.power  = power
>>>
>>> hero1 = SuperHero('superman', 'male', 'flight')
>>> print "%s can %s" % (hero1.name, hero1.power)… vs tentacles …:>>> from tentacles        import Object
>>> from tentacles.fields import *
>>> class SuperHero(Object):
>>>     name   = String()
>>>     gender = String()
>>>     power  = String()
>>>
>>> hero1 = SuperHero(name='superman', gender='male', power='flight')
>>> print "%s can %s" % (hero1.name, hero1.power)python native:>>> heros   = [hero1, SuperHero(name='wonder woman', gender='female', power='enhanced vision')]
>>> females = filter(lambda e: e.gender = 'female', heros)
>>> for e in females:
>>>     print "superheroine: %s" % e.name… vs tentacles …:>>> hero1.save(); SuperHero(name='wonder woman', gender='female', power='enhanced vision').save()
>>> females = filter(lambda e: e.gender = 'female', heros)
>>> for e in females:
>>>     print "superheroine: %s" % e.nameTentacles is pretty yound and incomplete, and still in alpha stage.
It currently support only sqlite3 backend, while more are scheduled at mid-term (mysql, postgresql, but also no-sql storages, like openldap, mongodb, …)
Package: tentacle-tentacle
Tentacle is a framework for the Raspberry Pi to facilitate the setup, logging and monitoring of arbitrary sensors. The main objective is to come up with a design that makes the first steps in the world of electronics and computer science a lot easier.SetupList of supported sensorsDocumentationMotivationThere are many guides on how to do everything with any sensor you can hook up to the pi. Yet it still can get frustrating quite fast to do it yourself without committing nightshifts to learn about how to wire it up, write a little python program and of course how to enable the mighty gpio ports or the i2c bus on your Pi.Some ideas simply get delayed or even paused during their development due to such time consuming sidequests. The first Pi was launched early in 2012 so why is measuring and monitoring still kind of a hassle? One answer is because the knowledge required to do it yourself is saved in form of tutorials, videos, forum discussions and much more. This is not satisfying when the main objective is to get the sensors working to actually do something with them.ConceptSimple plug and play setup of the hard- and software. The goal is to be able to directly use the kit without prior programming or setup of the pi computer.Start to customize the basic setup by using triggers in your own scripts.Start to write your own sensor-libs, trigger-mechanism and much more.Authors: Moritz Münch and Thorben WillertWritten with<3and python. License: GNU GPLv3
Package: tentacletk
Tentacle: A Python3/PySide2 Marking Menu and DCC ToolkitTentacle is a Python3/PySide2 marking menu implemented using Qt's QStackedWidget. It is designed for use with Maya, 3ds Max, Blender, and any other DCC app that supports the PySide2 framework, but in it's current implementation, it only has slots set up for Maya.DesignTentacle runs on top ofuitk, a PySide2 dynamic UI loader and management package, which allows for the creation of fully-featured UI with less time and code.ExampleThe following example demonstrates re-opening the last scene, renaming a material, and selecting geometry by that material.StructureThe structure of the project is as follows:ModuleDescriptiontclHandles main GUI construction for the marking menu.overlayTracks cursor position and UI hierarchy to generate paint events that overlay its parent widget.uiLocation of the dynamic UI files.slotsLocation of the various slot modules.InstallationTentacle can be installed either using pip directly in the command line or by downloading and running mayapy package manager in Windows.Installation via pipInstall via pip in a command line window using:path/to/mayapy.exe-mpipinstalltentacletkInstallation Using Mayapy Package ManagerAlternatively, you can use the mayapy package manager for a streamlined installation process.
Download the mayapy package manager fromhere. (Give your Maya version. Hit 1 to install package. The package name istentacletk)UsageTo launch the marking menu:For Maya, add a macro to a hotkey similar to the following:fromtentacleimporttcl_mayatcl_maya.show(key_show='Z')# Change to match your chosen hotkey.
Package: tentaclio-athena
tentaclio-athenaA package containing all the dependencies for the awsathena+rest tentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)
Package: tentaclio-databricks
tentaclio-databricksA package containing all the dependencies for thedatabricks+thrifttentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)Configuring access to DatabricksYour connection url should be in the following format:databricks+thrift://<token>@<host>?HTTPPath=<http_path>Example values:token: dapi1213456789abchost: myhost.databricks.comhttp_path: /sql/1.0/endpoints/123456789
Package: tentaclio-databricks-pyodbc
tentaclio-databricks-pyodbcA package containing all the dependencies for thedatabricks+pyodbctentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)Configuring access to DatabricksIn order to use Tentaclio to connect to a Databricks cluster or SQL endpoint, it is necessary to install the requiredODBC driverfor your operating system.Once installed, it is possible to access Databricks as you would any supported URL protocol. However,
it is likely that you will have to pass someadditional variablesin the URL query string, including the path to the installed driver.For example, if your Databricks connection requires you to set DRIVER and HTTPPATH values,
the URL should look like this:databricks+pyodbc://<token>@<host>/<database>?DRIVER=<path/to/driver>&HTTPPath=<http_path>
Package: tentaclio-gdrive
tentaclio-gdriveA package containing all the dependencies for the gdrive tentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)Configuring access to google drive.Google drive support isexperimentaland should be used at your own risk. Also, due to google drive itself it's rather slow.Get the credentials.
First we need a credentials file in order to be able to generate tokens. The easiest way to do this is by going tothis example,
click on enable drive api. Give the project a name of your choosing (egtentaclio). Click onAPIs and services->Credentials->Create credentials-> Create OAuth client ID, selectDesktop appandDownload JSON`Generate token filepipenv install tentaclio && \
    pipenv run python -m tentaclio_gdrive google-token generate --credentials-file ~/Downloads/credentials.jsonThis will open a browser with a google auth page, log in and accept the authorisation request.
The token file has been saved in a default location '~/.tentaclio_google_drive.json'. You can also configure this via the env variableTENTACLIO__GOOGLE_DRIVE_TOKEN_FILEGet rid of credentials.json
Thecredentials.jsonfile is no longer need, feel free to delete it.
Package: tentaclio-gs
tentaclio-gsA package containing all the dependencies for the gs tentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)
Package: tentaclio-postgres
tentaclio-postgresA package containing all the dependencies for the postgresql tentaclio schema .Quick StartThis project comes with aMakefilewhich is ready to do basic common tasks$ make help
install                       Initalise the virtual env installing deps
clean                         Remove all the unwanted clutter
lock                          Lock dependencies
update                        Update dependencies (whole tree)
sync                          Install dependencies as per the lock file
lint                          Lint files with flake and mypy
format                        Run black and isort
test                          Run unit tests
circleci                      Validate circleci configuration (needs circleci cli)