Saravanan666/pypi-org huggingface dataset:
Package: tf2-sku
tf2-skuParse TF2 items to SKU format with Python.DonateBTC:bc1qntlxs7v76j0zpgkwm62f6z0spsvyezhcmsp0z2Steam Trade OfferUsage>>>fromtf2_skuimportto_sku,from_sku>>>to_sku({"defindex":5021,"quality":6})"5021;6"# https://marketplace.tf/items/tf2/5021;6>>>from_sku("161;3;kt-3"){"defindex":161,"quality":3,"effect":-1,"australium":False,"craftable":True,"wear":-1,"skin":-1,"strange":False,"killstreak_tier":3,"target_defindex":-1,"festivized":False,"craft_number":-1,"crate_number":-1,"output_defindex":-1,"output_quality":-1,}# https://marketplace.tf/items/tf2/161;3;kt-3>>>to_sku({..."defindex":199,..."quality":5,..."effect":702,..."wear":3,..."skin":292,..."strange":True,..."killstreak_tier":3...})"199;5;u702;w3;pk292;strange;kt-3"# https://marketplace.tf/items/tf2/199;5;u702;w3;pk292;strange;kt-3SetupInstallpipinstalltf2-sku# orpython-mpipinstalltf2-skuUpgradepipupgradetf2-sku# orpython-mpipupgradetf2-skuTesting# tf2-sku/python-munittest
Package: tf2-som
TF2-SOMThis is a fast Tensorflow 2 implementation of the Self Organizing Maps (SOM) running on arbitrarily large datasets.Documentationhttps://odier-io.github.io/tf2-som/Demo🔗 Click thereAuthorsJérôme ODIER(CNRS/LPSC)Nora ACHBAK(CNRS/LPSC)
Package: tf2-utilities
python-tf2-utilitiesGet information about TF2 items, effects, skins and more.
Inspired byTF2autobot's node-tf2-schemaandTF2autobot's node-tf2-sku.Installationpipinstalltf2-utilitiesExamplesfromtf2utilities.mainimportTF2tf2=TF2("apiKey",autoUpdate,updateTime,lite).schema# autoUpdate - Automatic update schema (True/False). (Default: False)# updateTime - Seconds between updates. (Default: 86400 seconds)# lite - Lite mode. (Default: False)# Convert SKU to namename=tf2.getNameFromSku(sku)# Convert name to SKUsku=tf2.getSkuFromName(name)# Get TF2 schema as jsonschema=tf2.toJSON()fromtf2utilities.skuimportSKU# Item object exampleitemObject={"defindex":0,"quality":0,"craftable":True,"tradable":True,"killstreak":0,"australium":False,"effect":None,"festive":False,"paintkit":None,"wear":None,"quality2":None,"craftnumber":None,"crateseries":None,"target":None,"output":None,"outputQuality":None,"paint":None}# Convert SKU to item objectitemObject=SKU.fromString(sku)# Convert item object to SKUsku=SKU.fromObject(itemObject)# Convert item data from Steam Web API to SKUsku=SKU.fromAPI(itemData)Questions/Bugs?Feel free to contact me if you encounter any issues or have any questions.
Discord (Johnny Black#6363) /Steam
Package: tf2-utils
tf2-utilsTools and utilities for TF2 trading. Use 3rd party inventory providers, get SKUs directly from inventories, listen to BackpackTF's websocket and more.DonateBTC:bc1qntlxs7v76j0zpgkwm62f6z0spsvyezhcmsp0z2Steam Trade OfferFeaturesUsestf2-skuUsestf2-dataGet SKUs directly from inventories/offersConvert name to SKU and vice versaFetch inventories using 3rd party providers (avoid being rate-limited)Listen for Backpack.TF websocket eventsListen for Prices.TF websocket eventsInteract with Prices.TF's APIGet item properties (is_craft_hat,get_paint,get_effectetc.)Fetch TF2 Schema dataConvert SKU/defindex to item image URLCalculate scrap and refined pricesSetupInstallpipinstalltf2-utils# orpython-mpipinstalltf2-utilsUpdatingpipinstall--upgradetf2-utilstf2-skutf2-data# orpython-mpipinstall--upgradetf2-utilstf2-skutf2-dataUsageSeeexamplesortestsfor more usage examples.Inventory fetchingfromtf2_utilsimportInventory# using 3rd party provider to avoid being rate-limitedprovider=Inventory("steamsupply","9st947vs0qmgfpeqde1gj92l0oqmhysm")# using steam as inventory providerprovider=Inventory()# or Inventory("steamcommunity")# get an inventoryinventory=provider.fetch("76561198253325712")Gettings SKUs from inventoriesNOTE: NOT ALL SKU ATTRIBUTES ARE ADDED YETGet SKUs implicitlyfromtf2_utilsimportInventory,map_inventoryprovider=Inventory("steamcommunity")user_inventory=provider.fetch("76561198253325712")inventory=map_inventory(user_inventory,add_skus=True)Get a particular item's SKUfromtf2_utilsimportget_skuinventory=map_inventory(user_inventory)foritemininventory:sku=get_sku(item)BackpackTF WebsocketHandle one listing at a timefromtf2_utilsimportBackpackTFSocketdefmy_function(data:dict):print("got data!",data)socket=BackpackTFSocket(my_function)socket.listen()Handle list of listings at a timefromtf2_utilsimportBackpackTFSocketdefmy_function(data:list[dict]):print("got listings")forlistingindata:print("listing",listing)socket=BackpackTFSocket(my_function,solo_entries=False)socket.listen()PricesTF Websocketfromtf2_utilsimportPricesTFSocketdefmy_function(data:dict):print("got data!",data)socket=PricesTFSocket(my_function)socket.listen()Testing# tf2-utils/python-munittest
Package: tf2-yolov4
YOLOv4A TensorFlow 2.0 implementation of YOLOv4: Optimal Speed and Accuracy of Object DetectionThis implementation runs (for now) inference with the original Darknet weights fromAlexeyAB.
See the roadmap section to see what's next.InstallationTo install this package, you can run:pipinstallhttps://github.com/sicara/tf2-yolov4/archive/master.zip
pipinstalltensorflow# Check that tf2_yolov4 is installed properlypython-c"from tf2_yolov4.model import YOLOv4; print(YOLOv4)"Requirements:MacOs >= 10.15 since tensorflow-addons is not available for older release of MacOsPython >= 3.6Compatible versions between TensorFlow and TensorFlow Addons: check thecompatibility matrixExamples in ColabRun detection on a single image/Pretrained weightsTo load the Darknet weights trained on COCO, you have to:get the weights (yolov4.weights) fromAlexeyAB/darknetrunconvert-darknet-weights PATH_TO/yolov4.weightsTF weights should be saved asyolov4.h5.
For more information about the conversion script, runconvert-darknet-weights --help.RoadmapInferenceCSPDarknet53 backbone with Mish activationsSPP NeckYOLOv3 HeadLoad Darknet WeightsImage loading and preprocessingYOLOv3 box postprocessingHandling non-square imagesTrainingTraining loop with YOLOv3 lossCIoU lossCross mini-Batch NormalizationSelf-adversarial TrainingMosaic Data AugmentationDropBlockEnhancementsAutomatic download of pretrained weights (like Keras applications)Referencesyolov3-tf2
Package: tfadm
tfadm - Terraform administratorTfadm(or Terraform Administrator) is a program that generates and modifiesterraform codeinJSON syntax.The goal is to help system administrators easily manageInfrastructure as Code (IaC), simplify documentation, reduce training time for new employees, and enable users with little or no knowledge of terraform code to operate the system.In no way does tfadm attempts to replace terraform command functionalities. Tfadm just enables you to automate the creation and modification of terraform code.Some of tfadm features are:Generates and modifies terraform code in bulk;Converts existing infrastructure into terraform code;Associates existing infrastructure with the terraform resources, if not already under terraform management.Copies changes made directly to the infrastructure into terraform code;InstallInstall and update usingpip:python3-mpipinstall-Utfadm
tfadmversionInstall and update using pip andvirtual environments:python3-mvenv--clear--upgrade-deps--prompttfadm~/.venv/tfadmsource~/.venv/tfadm/bin/activate
pipinstall--upgradepip
pipinstall-Utfadm
tfadmversionTo leave your virtual environment, simply run:deactivateUsagetfadm [OPTIONS] COMMAND [ARGS]...Options-V,--versionShow the version and exit.-h,--helpShow help message and exit.CommandscreateCreates an object from stdin.helpShow help message and exit.resourcesLists the available resources.syncCopies changes to the infrastructure into terraform code.updateUpdates an object from stdin.versionShow the version and exit.Usetfadm COMMAND --helpfor more information about a given command.ResourcesTfadm depends on a set of configuration files (resources) to operate, inYAML format.Seetfadm-resourcesto get started.Dependencies$ click_, to create the "beautiful" command line interface;Jinja, to allow codeexpressionsin resource files more safely (not yet used as a template engine);parse, to do the the opposite offormat()in a very specific situation, which most likely could be done withregular expressions;python-slugify, python-slugify, to create slugs that can be used to name terraform blocks;PyYAML, to parse configuration files and data from stdin and dump data to stdout.Authortfadm was created by Nuno Cardoso.
Package: tf-alloc
tf_allocSimpliying GPU allocation for Tensorflow⭐️ Why tf_alloc? Problems?Compare to pytorch, tensorflow allocate all GPU memory to single training.However, it is too much waste because, some training does not use whole GPU memory.To solve this problem, TF engineers use two methods.Limit to use only single GPULimit the use of only a certain percentage of GPUs.However, these methods require complex code and memory management.⭐️ Why tf_alloc? How to solve?tf_alloc simplfy and automate GPU allocation using two methods.⭐️ How to allocate?Before using tf_alloc, you have to install tensorflow fits for your environment.This library does not install specific tensorflow version.# On the top of the codefromtf_allocimportallocateastalloctalloc(gpu=1,percentage=0.5)importtensorflowastf""" your code"""It is only code for allocating GPU in certain percentage.Parameters:gpu = which gpu you want to use (if you have two gpu than [0, 1] is possible)percentage = the percentage of memory usage on single gpu. 1.0 for maximum use.⭐️ Additional Function.GET GPU Objectsgpu_objs = get_gpu_objects()To use this code, you can get gpu objects that contains gpu information.You can set GPU backend by using this function.GET CURRENT STATEDefualtcurrent(
    gpu_id = False, 
    total_memory=False, 
    used = False, 
    free = False, 
    percentage_of_use = False,
    percentage_of_free = False,
)You can use this functions to see current GPU state and possible maximum allocation percentage.Without any parameters, than it only visualize possible maximum allocation percentage.It is cmd line visualizer. It doesn't return values.Parametersgpu_id = visualize the gpu id numbertotal_memory = visualize the total memory of GPUused = visualize the used memory of GPUfree = visualize the free memory of GPUpercentage_of_used = visualize the percentage of used memory of GPUpercentage_of_free = visualize the percentage of free memory of GPU
Package: tfa-nightly
TensorFlow Addons is a repository of contributions that conform to well-
established API patterns, but implement new functionality not available
in core TensorFlow. TensorFlow natively supports a large number of
operators, layers, metrics, losses, and optimizers. However, in a fast
moving field like ML, there are many interesting new developments that
cannot be integrated into core TensorFlow (because their broad
applicability is not yet clear, or it is mostly used by a smaller subset
of the community).
Package: tfa-python-sdk
Telegram Factor Authentication - Python SDKPython SDK document is in docs.tfasoft.comIf you are usingTFAas your authentication service in your python applications, you can use our library.How to use the librarySo, let's have a quick review of our steps.InstallationConfigurationChecking resultInstallationThe easiest step is installation. You can install it withpiporpipenv. Just go ahead and enter the command below:$pip3installtfa-python-sdkCongratulations! First step is done!ConfigurationAfter installation, go to your file and import the library:fromtfa_python_sdkimportTFASecond, you need to epecify theaccecc_token. You can get it in your dashboard panel. So, call the class:auth=TFA('access_token')Ok, now library know your access token. Now it's time to enter theuser_token. User token is the code you get it from your form field or a post method. Importent is to get user token. So, we useauthUserfunction to pass user token and get the result.result=auth.authUser('user_token')You are done it this step. Let's move forward and check status codes and check user result.Checking resultWhen you get the result, you can check codes and see what are them. Here is a quick review:statCode=result['status']data=result['data']if(statCode==200):print('Authenticated.')user=data['user']else:print(data['message'])Now if your code is 200, it means that you know have user. If it is else, it means your code is 401. For object that returns to you, chesk object below.Returned ObjectsHere let's know about them in deep.200{"user":{"_id":"document id","uid":"telegram user id","token":"one time token. Every time become null","createdAt":"when created","updatedAt":"last update","__v":0}}401One is access token is wrong.{"message":"User authentication token is not valid"}Another is when user token is wrong.{"message":"Admin access token is not valid"}DevelopmentIf you want to develop the library, it is so simple. Edit library codes and runtest.pylike this:$python3test.pyBefore you start: **Remember the base or codes are stored intfa_python_sdk/tfa_python_sdk.py. You need to edit there.
Package: tf-argonaut
⛵ tf-argonautLibrary for creating visual experiment pipelines in tensorflow. Allows to test out different network concepts against standardized datasets.The Argonauts were a band of heros and adventures that sailed on their ship Argo through the mediterranean and navigated numerous adventures.
Like one of them this library is designed to turn TF into your own argo and navigate your experiments.Argonautwas originally build to allow easy research experimentation of multi-task settings against common datasets (thereby reducing the overhead required for experimentation).👶 Getting StartedTo install the library simply use PyPi:pip3 install tf-argonautAlternatively you can install the library directly throughsetup.py:pip3 install .You can then import the library:importargonautasargoAt its core,argonautallows you to run experiments with a single line of code and a configuration file (see in folderexamples/simple_multitask):argo.run_experiment("Baseline","experiment.json",name="SimpleExample")📜 ConceptsConcepts include:ExperimentsPipelineDatasetsCallbacksToolsThe library also contains multiple tools that allow to inspect data and quickly start training processes.💾‍ Coding ExamplesTODO: more advanced coding examplesArgonautalso comes with various pre-defined models (although you can also easily plug in every keras model, given right input and output structrue).
In particular these models include:TODODebuggingTODO: integrate options for TF2 debugging⚙ ConfigurationExperiments allow you to specify most of the hyper-parameters through a configurationjsonfile. See the detailedconfiguration guidefor more details.LicenseThis library is provided under the Apache License.Pull Requests to improve code quality and add new functionality are more then welcome!
Package: tfasta
IntroductionThe tfasta python package simplifies working
with fasta, providing functionality
for both reading and writing fasta files.
The “t” in “tfasta” represents
“templated”, which means that fasta parsing is
performed according to pre-defined or user-defined
templates:>>> from tfasta import fasta_parser, T_NR
>>> fast = fasta_parser("cytb.fas", template=T_NR)
>>> f = fast.next()
>>> print f['gi']
5524211
>>> print f['accession']
AAD44166.1
>>> print f['sequence'][:60]
LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSThis example parses records that follow the conventions
of the NCBI non-redundant database (nr).More examples are given in thetfasta full documentation.InstallationInstall tfasta withpip(recommended) oreasy_install:sudo pip install tfastaOptionally, download the source files fromhttp://pypi.python.org/pypi/tfasta/and run the following commands in the source directory:python setup.py build
sudo python setup.py installHome Page & RepositoryHome Page:http://pypi.python.org/pypi/tfasta/Documentation:http://pythonhosted.org/tfasta/Repository:https://github.com/jcstroud/tfasta/Basic UsageReading Fasta FilesReading fasta files is performed with thefasta_parser()function.
The following text is the first 2 records from a file
called “short-extended.fas”:>gi|32033604|ref|ZP_00133915.1|
ATGQVIGTFTVRNDNGLHARPSAVLVQTLKPFAAKVTVENLDRGTAPANAKSTMKVVALG
ASQAHRLRFVAEGEDAQQAIEALAKAFVEGLGESVSFVPAVEDTIEGAAQPQAVESAKNF
ANPTASEPTVEGQVEGTFVIQNEHGLHARPSAVLVNEVKKYNATIVVQNLDRNTQLVSAK
SLMKIVALGVVKGHRLHFVATGDDAQKAIDGIGEAIAAGLGE
>gi|1573424|gb|AAC22107.1|
VEGAVVGTFTIRNEHGLHARPSANLVNEVKKFTSKITMQNLTRESEVVSAKSLMKIVALG
VTQGHRLRFVAEGEDAKQAIESLGKAIANGLGENVSAVPPSEPDTIEIMGDQIHTPAVTE
DDNLPANAIEAVFVIKNEQGLHARPSAILVNEVKKYNASVAVQNLDRNSQLVSAKSLMKI
VALGVVKGTRLRFVATGEEAQQAIDGIGAVIESGLGELike any other fasta file,short-extended.fasmay be parsed
with a single command:fast = fasta_parser(file_name)For example:>>> from tfasta import fasta_parser
>>> fast = fasta_parser("short-extended.fas")
>>> f = fast.next()
>>> print f['name']
gi|32033604|ref|ZP_00133915.1|
>>> print f['sequence'][:60]
ATGQVIGTFTVRNDNGLHARPSAVLVQTLKPFAAKVTVENLDRGTAPANAKSTMKVVALG
f = fast.next()
print f['name']
gi|1573424|gb|AAC22107.1|In this example, thefasta_parser()function returns
an iterator of dictionaries (”fast”) with two
keys:nameandsequence.
Thenamekey corresponds to all of the plain text after
the fasta format marker “>” that marks a new sequence.IterationThe iterator returned by thefasta_parser()function
may serve inforloops:>>> from tfasta import fasta_parser
>>> for f in fasta_parser("short-extended.fas"):
...   print f['name']
gi|32033604|ref|ZP_00133915.1|
gi|1573424|gb|AAC22107.1|
[...]Other UsageSee thetfasta full documentationfor more sophisticated
reading and writing of fasta.
Package: tfatool
TFATool: Toshiba FlashAir ToolThis package provides easy access to
Toshiba’s FlashAir wireless SD card. As a library,
a simple abstraction of the FlashAir API is provided. As a set of
scripts,tfatoolthe user is given a way of synchronizing/mirroring
files and configuring the device from the command line.Package contents at a glanceflashair-util: a command line tool for mirroring and listing files on FlashAirflashair-config: a command line tool for configuring FlashAirtfatool.command: abstraction of FlashAir’scommand.cgitfatool.upload: abstraction of FlashAir’supload.cgitfatool.config: abstraction of FlashAir’sconfig.cgitfatool.sync: functions for synchronizing local dirs with remote FlashAir dirsCommand line usage at a glanceMonitor FlashAir for new JPEGs, download to ~/Photosflashair-util-s-d/home/tad/Photos--only-jpgMonitor working dir for new files, upload to FlashAirflashair-util-s-yupMonitor a local and remote dir for new files, sync themflashair-util-s-ybothSync down the 10 most recent to a local dir, then quitflashair-util-Stime-dimages/new/Sync up all files created in 1999 and afterwardsflashair-util-Sall-t1999Sync down files created between Jan 23rd and Jan 26thflashair-util-Sall-t1-23-T01/26Sync files (up AND down) created this afternoonflashair-util-Sall-t12:00-T16:00-ybothSync files up created after a very specific date/timeflashair-util-Sall-t'2016-1-2511:38:22'Sync (up and down) 5 most recent files of a certain nameflashair-util-Stime-k'IMG-08.+\.raw'-ybothList files on FlashAir created after a certain timeflashair-util-l-t'1-21-20168:30:11'Change FlashAir network SSIDflashair-config--wifi-ssidmyflashairnetworkShow FlashAir network password & firmware versionflashair-config--show-wifi-key--show-fw-versionMoreSeehttps://github.com/TadLeonard/tfatoolfor complete documentation.
Package: tf-big
TF BigTF Big adds big number support to TensorFlow, allowing computations to be performed on arbitrary precision integers. Internally these are represented as variant tensors ofGMPvalues, and exposed in Python through thetf_big.Tensorwrapper for convenience. For importing and exporting, numbers are typically expressed as strings.Usageimporttensorflowastfimporttf_big# load large values as stringsx=tf_big.constant([["100000000000000000000","200000000000000000000"]])# load ordinary TensorFlow tensorsy=tf_big.import_tensor(tf.constant([[3,4]]))# perform computation as usualz=x*y# export result back into a TensorFlow tensortf_res=tf_big.export_tensor(z)print(tf_res)InstallationPython 3 packages are available fromPyPI:pip install tf-bigSee below for further instructions for setting up a development environment.DevelopmentRequirementsWe recommend usingMinicondaorAnacondato set up and use a Python 3.5 or 3.6 environment for all instructions below:conda create -n tfbig-dev python=3.6
source activate tfbig-devUbuntuThe only requirement for Ubuntu is to havedocker installed. This is the recommended way tobuild custom operations for TensorFlow. We provide a custom development container for TF Big with all dependencies already installed.macOSSetting up a development environment on macOS is a little more involved since we cannot use a docker container. We need four things:Python (>= 3.5)Bazel(>= 0.15.0)GMP(>= 6.1.2)TensorFlow(see setup.py for version requirements for your TF Big version)UsingHomebrewwe first make sure that bothBazeland GMP are installed. We recommend using a Bazel version earlier than 1.0.0, e.g.:brew tap bazelbuild/tap
brew extract bazel bazelbuild/tap --version 0.26.1
brew install gmp
brew install mmvThe remaining PyPI packages can then be installed using:pip install -r requirements-dev.txtTestingUbuntuRun the tests on Ubuntu by running themake testcommand inside of a docker container. Right now, the docker container doesn't exist on docker hub yet so we must first build it:docker build -t tf-encrypted/tf-big:build .Then we can runmake test:sudo docker run -it \
  -v `pwd`:/opt/my-project -w /opt/my-project \
  tf-encrypted/tf-big:0.1.0 /bin/bash -c "make test"macOSOnce the development environment is set up we can simply run:make testThis will install TensorFlow if not previously installed and build and run the tests.Building pip packageJust run:make build && make bundleFor linux, doing it inside the tensorflow/tensorflow:custom-op container is recommended. Note thatCircleCIis currently used to build the official pip packages.Circle CIWe useCircle CIfor integration testing and deployment of TF Big.Releasingupdate version number in setup.py and push to master; this will build and tests wheelsiterate 1. until happy with the release, having potentially tested the wheel manuallywhen happy, tag a commit with semver label and push; this will build, test, and deploy wheels
Package: tfbns
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Tfbns is an open source python library for Binomial Seperation Feature Scaling. BNS is an improved feature representation over Term Frequency - Inverse Document Frequency (TFIDF) representation for SVM Text classification. The implementation of the library is based on the paper: "Forman, G. (2008, October). BNS feature scaling: an improved representation over tf-idf for svm text classification. InProceedings of the 17th ACM conference on Information and knowledge management(pp. 263-270)".Usage:from tfbns.feature_extraction import tfbns

	df = pd.read_csv("data.csv")
	tfbns_vectorizer = Tfbns()
	x_train = df['text'].to_numpy()
	y_train = df['label'].to_numpy() 
	#Label should be binary containing 0 or 1
	x_train_tfbns = tfbns.fit_transform(x_train, y_train)
	x_test_tfbns = tfbns.transform(x_test)
Package: tfc_admin
OverviewMake Terraform Cloud API calls for common platform administration tasks.Available FunctionsShow organization detailsList projectsGet project ID from project nameList workspaces in a projectGet workspace ID from workspace nameMove workspace into a projectExamplesimporttfc_adminproject_workspaces=tfc_admin.get_project_ws("my_tfc_token","my_tfc_organization",tfc_admin.get_project_id("my_tfc_token","my_tfc_organization","my_project_name"))print(project_workspaces)