Saravanan666/pypi-org huggingface dataset:
Package: accelbyte-py-sdk-service-ugc
AccelByte Modular Python SDK - Service ModuleThis is a service module for theAccelByte Modular Python SDKpackage.AccelByte Gaming Services Ugc Service
* Version: 2.19.6SetupThis SDK requires Python 3.9 to be installed.Install with PipInstall the core package from PyPIpipinstallaccelbyte-py-sdk-coreinstallthisservicepipinstallaccelbyte-py-sdk-service-ugcand then install any service or feature you wantpipinstallaccelbyte-py-sdk-service-{SERVICE}pipinstallaccelbyte-py-sdk-feat-{FEATURE}or install everythingpipinstallaccelbyte-py-sdk-allUsageSeeREADME.md
Package: accelerated-numpy
numpy-threading-extensionsFaster loops for NumPy using multithreading and other tricks. The first release
will target NumPy binary and unary ufuncs. Eventually we will enable overriding
other NumPy functions, and provide an C-based (non-Python) API for extending
via third-party functions.Installationpip install accelerated_numpyYou can also install the in-development version 0.0.1 with:pip install https://github.com/Quansight/numpy-threading-extensions/archive/v0.0.1.zipor latest withpip install https://github.com/Quansight/numpy-threading-extensions/archive/main.zipDocumentationTo use the project:importaccelerated_numpyaccelerated_numpy.initialize()DevelopmentTo run all the tests run::toxNote, to combine the coverage data from all the tox environments run:OSCommandWindowsset PYTEST_ADDOPTS=--cov-appendtoxOtherPYTEST_ADDOPTS=--cov-append tox
Package: accelerated-scan
Accelerated ScanThis package implements the fastestfirst-order parallel associative scanon the GPU for forward andbackward.The scan efficiently solves first-order recurrences of the formx[t] = gate[t] * x[t-1] + token[t], common in state space models and linear RNNs.Theaccelerated_scan.warpC++ CUDA kernel uses a chunked processing algorithm that leverages the fastest GPU communication primitives available
on each level of hierarchy:warp shuffleswithin warps of 32 threads and shared memory (SRAM) between warps within a thread block. One sequence per channel dimension is confined to one thread block.The derivation ofChunked Scanhas been used to extend tree-level Blelloch algorithm to block.A similar implementation is available inaccelerated_scan.tritonusing a Triton'stl.associative_scanprimitive. Itrequires Triton 2.2 for itsenable_fp_fusionflag.Quick Start:pipinstallaccelerated-scanimporttorchfromaccelerated_scan.warpimportscan# a pure c++ kernel, faster than cub#from accelerated_scan.triton import scan # uses tl.associative_scan#from accelerated_scan.ref import scan # reference torch implementation# sequence lengths must be a power of 2 of lengths between 32 and 65536# hit me up if you need different lengths!batch_size,dim,seqlen=3,1536,4096gates=0.999+0.001*torch.rand(batch_size,dim,seqlen,device="cuda")tokens=torch.rand(batch_size,dim,seqlen,device="cuda")out=scan(gates,tokens)To ensure numerical equivalence, a reference implementation for trees is provided in Torch. It can be sped up usingtorch.compile.Benchmarks:See more benchmarks in nanokitchen:https://github.com/proger/nanokitchenforward speed of (8,1536,seqlen), inference mode:SEQUENCE_LENGTH  accelerated_scan.triton (triton 2.2.0)  accelerated_scan.ref  accelerated_scan.warp
0            128.0                                0.027382              0.380874               0.026844
1            256.0                                0.049104              0.567916               0.048593
2            512.0                                0.093008              1.067906               0.092923
3           1024.0                                0.181856              2.048471               0.183581
4           2048.0                                0.358250              3.995369               0.355414
5           4096.0                                0.713511              7.897022               0.714536
6           8192.0                                1.433052             15.698944               1.411390
7          16384.0                                3.260965             31.305046               2.817152
8          32768.0                               31.459671             62.557182               5.645697
9          65536.0                               66.787331            125.208572              11.297921Notes on PrecisionWhen gates and tokens are sampled uniformly from 0..1 the lack of bfloat16 precision dominates the error (compared to the reference implementation):
Package: accelerator
The Accelerator is a tool for fast and reproducible processing of large amounts of data.
Extensive documentation is available here:Reference ManualHome PageAfter installation try "ax --help".Supported EnvironmentsThe Accelerator project has been built, tested, and runs on:Ubuntu 18.04, 20.04Debian 10, 11FreeBSD 13.0but is not limited to these systems or versions.Windows is not supported, but WSL should work.LicenseCopyright 2017-2018 eBay Inc.Modifications copyright (c) 2018-2023 Carl DrouggeModifications copyright (c) 2019-2023 Anders BerkemanLicensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttps://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: accelerator-toolbox
IntroductionAccelerator Toolbox is a code used for simulating particle accelerators, used
particularly for synchrotron light sources. It is hosted onGithub. Its original implementation is in Matlab.pyAT is a Python interface to Accelerator Toolbox. It uses the ‘pass methods’
defined in Accelerator Toolbox, implemented by compiling the C code used in the
AT ‘integrators’ into a Python extension. These pass methods are used by
higher-level functions to provide physics results.See thepyAT websitefor a
more detailed introduction.pyAT supports Python 3.7 to 3.11.InstallationInstall accelerator-toolbox from PyPI:$ pip install accelerator-toolboxUsageExample usage:>>> import at
>>> ring = at.Lattice.load('machine_data/hmba.mat')
>>> print(at.radiation_parameters(ring))
          Frac. tunes: [0.2099983  0.34001317 0.00349013]
                Tunes: [76.2099983  27.34001317]
       Chromaticities: [5.73409894 3.91761206]
Momentum compact. factor: 8.506669e-05
          Slip factor: -8.505944e-05
               Energy: 6.000000e+09 eV
   Energy loss / turn: 2.526189e+06 eV
Radiation integrals - I1: 0.07179435013387388 m
                   I2: 0.13844595446798158 m^-1
                   I3: 0.003357584058614851 m^-2
                   I4: -0.07375725030666251 m^-1
                   I5: 5.281495714523264e-07 m^-1
      Mode emittances: [1.3148797e-10           nan           nan]
Damping partition numbers: [1.53275121 1.         1.46724879]
        Damping times: [0.00872477 0.0133729  0.00911427] s
        Energy spread: 0.000934463
         Bunch length: 0.0030591 m
     Cavities voltage: 6000000.0 V
    Synchrotron phase: 2.70701 rd
Synchrotron frequency: 1239.74 HzFor more examples of how to use pyAT, seepyat_examples.rst.Developer NotesDeveloper notes are indevelopers.rst.
Package: accelerometer
A tool to extract meaningful health information from large accelerometer datasets. The software generates time-series and summary metrics useful for answering key questions such as how much time is spent in sleep, sedentary behaviour, or doing physical activity.InstallMinimum requirements: Python>=3.7, Java 8 (1.8)The following instructions make use of Anaconda to meet the minimum requirements:Download & installMiniconda(light-weight version of Anaconda).(Windows) Once installed, launch theAnaconda Prompt.Create a virtual environment:$condacreate-naccelerometerpython=3.9openjdkpipThis creates a virtual environment calledaccelerometerwith Python version 3.9, OpenJDK, and Pip.Activate the environment:$condaactivateaccelerometerYou should now see(accelerometer)written in front of your prompt.Installaccelerometer:$pipinstallaccelerometerYou are all set! The next time that you want to useaccelerometer, open the Anaconda Prompt and activate the environment (step 4). If you see(accelerometer)in front of your prompt, you are ready to go!UsageTo extract summary movement statistics from an Axivity file (.cwa):$accProcessdata/sample.cwa.gz<output written to data/sample-outputSummary.json><time series output written to data/sample-timeSeries.csv.gz>Movement statistics will be stored in a JSON file:{"file-name":"sample.cwa.gz","file-startTime":"2014-05-07 13:29:50","file-endTime":"2014-05-13 09:49:50","acc-overall-avg(mg)":32.78149,"wearTime-overall(days)":5.8,"nonWearTime-overall(days)":0.04,"quality-goodWearTime":1}SeeData Dictionaryfor the list of output variables.Actigraph and GENEActiv files are also supported, as well as custom CSV files. SeeUsagefor more details.To plot the activity profile:$accPlotdata/sample-timeSeries.csv.gz<output plot written to data/sample-timeSeries-plot.png>TroubleshootingSome systems may face issues with Java when running the script. If this is your case, try fixing OpenJDK to version 8:$condainstall-naccelerometeropenjdk=8Under the hoodInterpreted levels of physical activity can vary, as many approaches can be
taken to extract summary physical activity information from raw accelerometer
data. To minimise error and bias, our tool uses published methods to calibrate,
resample, and summarise the accelerometer data.SeeMethodsfor more details.Citing our workWhen using this tool, please consider the works listed inCITATION.md.LicenceSeeLICENSE.md.AcknowledgementsWe would like to thank all our code contributors and manuscript co-authors.Contributors Graph
Package: accentcolordetect
AccentcolordetectThis package allows you to detect the user's accent color on:macOS (untested)Windows 10+The main application of this package is to detect the accent color from your GUI Python application and apply the needed adjustments to your interface. Inspired by thedarkdetectpackage byAlberto SottileUsageimportaccentcolordetect>>>accentcolordetect.accent()((255,140,0),'#ff8c00')Installpip install accentcolordetect
Package: accept
A simple library for parsing and ordering a HTTP Accept header.Includes parameter extraction.InstallationpipinstallacceptOr if youmustuse easy_install:aliaseasy_install="pip install$1"easy_installacceptUsage>>>importaccept>>>accept.parse("text/*, text/html, text/html;level=1, */*")[<MediaType:text/html;q=1.0;level=1>,<MediaType:text/html;q=1.0>,<MediaType:text/*;q=1.0>,<MediaType:*/*;q=1.0>]>>>d=accept.parse("application/json; version=1; q=1.0; response=raw")[0]>>>d.media_type'application/json'>>>d.quality1.0>>>d.q1.0>>>d.params{'version':'1','response':'raw'}>>>d['version']'1'>>>d['potato']NoneContributeCheck for open issues or open a fresh issue to start a discussion around a feature idea or a bug. There is a Contributor Friendly tag for issues that should be ideal for people who are not very familiar with the codebase yet.Forkthe repositoryon Github to start making your changes to themasterbranch (or branch off of it).Write a test which shows that the bug was fixed or that the feature works as expected.Send a pull request and bug the maintainer until it gets merged and published.History0.1.0 (2015-01-05)Initial Release!
Package: acceptanceutils
[![Build Status](https://travis-ci.org/Brian-Williams/acceptanceutils.svg?branch=master)](https://travis-ci.org/Brian-Williams/acceptanceutils) [![codecov](https://codecov.io/gh/Brian-Williams/acceptanceutils/branch/master/graph/badge.svg)](https://codecov.io/gh/Brian-Williams/acceptanceutils)# Acceptance testing tools for use in unit/acceptance testingThis is a set of tools that may be useful for testing.### SurjectionSurjection is a useful utility when you have an arbitrarily large number of options to test and want to make sure you
touch each option once without taking the time to test all combinations.Surjection in this always means minimally surjective.<!— The first ‘images’ is the branch name, the second is the folder in that branch —>
![Set theory is fun](/../images/images/Surjection.svg.png?raw=true “Surjection”)### WatcherSubClassWatcher is useful for confirming hierarchies when specs require it.
Package: accepton
DocumentationPlease see thePython developer documentationfor more information.InstallationInstall from PyPI usingpip, a package manager for Python.$pipinstallacceptonDon’t have pip installed? Try installing it by running this from the
command line:$curlhttps://raw.github.com/pypa/pip/master/contrib/get-pip.py|pythonYou may need to run the above commands withsudo.ContributingFork itCreate your feature branch (git checkout-bmy-new-feature)Run the test suite on all supported Pythons (tox)Run the code linter to find style violations (tox-epep8)Commit your changes (git commit-am'Add some feature')Push the branch (git push originmy-new-feature)Create a new Pull Request
Package: accept-paymob
A Python library for Paymob Accept’s API.SetupYou can install this package by using the pip tool and installing:$ pip install accept-paymobSetting up a Accept AccountSign up for Accept athttps://accept.paymob.com/.Using the Accept APIfromaccept.paymentimport*API_KEY="<API-KEY>"accept=AcceptAPI(API_KEY)# Authentication Requestauth_token=accept.retrieve_auth_token()print(auth_token)# Order RegistrationOrderData={"auth_token":auth_token,"delivery_needed":"false","amount_cents":"1100","currency":"EGP","merchant_order_id":125,# UNIQUE"items":[{"name":"ASC1515","amount_cents":"500000","description":"Smart Watch","quantity":"1"},{"name":"ERT6565","amount_cents":"200000","description":"Power Bank","quantity":"1"}],"shipping_data":{"apartment":"803","email":"claudette09@exa.com","floor":"42","first_name":"Clifford","street":"Ethan Land","building":"8028","phone_number":"+86(8)9135210487","postal_code":"01898","extra_description":"8 Ram , 128 Giga","city":"Jaskolskiburgh","country":"CR","last_name":"Nicolas","state":"Utah"},"shipping_details":{"notes":" test","number_of_packages":1,"weight":10,"weight_unit":"Kilogram","length":100,"width":100,"height":100,"contents":"product of some sorts"}}order=accept.order_registration(OrderData)print(order)# Payment Key RequestRequest={"auth_token":auth_token,"amount_cents":"1500","expiration":3600,"order_id":order.get("id"),"billing_data":{"apartment":"803","email":"claudette09@exa.com","floor":"42","first_name":"Clifford","street":"Ethan Land","building":"8028","phone_number":"+86(8)9135210487","shipping_method":"PKG","postal_code":"01898","city":"Jaskolskiburgh","country":"CR","last_name":"Nicolas","state":"Utah"},"currency":"EGP","integration_id":246701,# https://accept.paymob.com/portal2/en/PaymentIntegrations"lock_order_when_paid":"false"}payment_token=accept.payment_key_request(Request)print(payment_token)# Payments API [Kiosk, Mobile Wallets , Cash, Pay With Saved Token]identifier="cash"payment_method="CASH"transaction=accept.pay(identifier,payment_method,payment_token)print(transaction)# Auth-Capture Paymentstransaction00=accept.capture_transaction(transaction_id="7608793",amount_cents=1000)print(transaction00)# Refund Transactiontransaction01=accept.refund_transaction(transaction_id="7608793",amount_cents=10)print(transaction01)# Void Transactiontransaction02=accept.void_transaction(transaction_id="7608793")print(transaction02)# Retrieve Transactiontransaction03=accept.retrieve_transaction(transaction_id="7608793")print(transaction03)# Inquire Transactiontransaction_inquire=accept.inquire_transaction(merchant_order_id="123",order_id="10883471")print(transaction_inquire)# Trackingorder_10883471_track=accept.tracking(order_id="10883471")print(order_10883471_track)# Preparing Package# This will return a pdf file url to be printed.package=accept.preparing_package(order_id="10883471")print(package)# IFrame URLiframeURL=accept.retrieve_iframe(iframe_id="230796",payment_token=payment_token)print(iframeURL)# Loyalty Checkoutresponse=accept.loyalty_checkout(transaction_reference='',otp='123',payment_token=payment_token)print(response)
Package: accepts
Installation$[sudo]pipinstallacceptsFeaturessupportmultiple typesargumentsupportNoneargumenthuman readable detailed exception messageExamples>>>fromacceptsimportaccepts>>>@accepts(int)definc(value):returnvalue+1>>>inc(1)# ok# multiple types>>>@accepts((int,float))>>>inc(1.5)# ok>>>inc("string")TypeError:inc()argument#0 is not instance of (<class 'int'>, <class 'float'>)# None>>>@accepts((int,float,type(None)))readme42.com
Package: accept-types
accept-typeshelps your application respond to a HTTP request in a way that a client prefers.
TheAcceptheader of an HTTP request informs the server which MIME types the client is expecting
back from this request, with weighting to indicate the most prefered. If your server can respond in
multiple formats (e.g.: JSON, XML, HTML), the client can easily tell your server which is the
prefered format without resorting to hacks like ‘&amp;format=json’ on the end of query strings.Usageget_best_matchWhen provided with anAcceptheader and a list of types your server can respond with, this function
returns the clients most prefered type. This function will only return one of the acceptable types you
passed in, orNoneif no suitable type was found:fromaccept_typeimportget_best_matchdefget_the_info(request):info=gather_info()return_type=get_best_match(request.META.get('HTTP_ACCEPT'),['text/html','application/xml','text/json'])ifreturn_type=='application/xml':returnrender_xml(info)elifreturn_type=='text/json':returnrender_json(info)elifreturn_type=='text/html':returnrender_html(info)elifreturn_type==None:returnHttpResponse406()parse_headerWhen provided with anAcceptheader, this will parse it and return a sorted list of the clients
accepted mime types. These will be instances of theAcceptableTypeclass.>>>fromaccept_typeimportparse_header>>>parse_header('text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8')['text/html, weight 1','application/xhtml+xml, weight 1','application/xml, weight 0.9','*/*, weight 0.8']AcceptableTypeAcceptableTypeinstances represent one of the types that a client is willing to accept. This
type could include wildcards, to match more than one MIME type.>>>fromaccept_typeimportAcceptableType>>>type=AcceptableType('image/*;q=0.9')AcceptableType>>>type.mime_type'image/*'>>>type.weight0.9>>>type.matches('image/png')True>>>type.matches('text/html')False
Package: accera-compilers
Accera CompilersAcceraAccerais a programming model, a domain-specific programming language embedded in Python (eDSL), and an optimizing cross-compiler for compute-intensive code. Accera currently supports CPU and GPU targets and focuses on optimization of nested for-loops.Writing highly optimized compute-intensive code in a traditional programming language is a difficult and time-consuming process. It requires special engineering skills, such as fluency in Assembly language and a deep understanding of computer architecture. Manually optimizing the simplest numerical algorithms already requires a significant engineering effort. Moreover, highly optimized numerical code is prone to bugs, is often hard to read and maintain, and needs to be reimplemented every time a new target architecture is introduced. Accera aims to solve these problems.Accera has three goals:Performance: generate the fastest implementation of any compute-intensive algorithm.Readability: do so without sacrificing code readability and maintainability.Writability: a user-friendly programming model, designed for agility.accera-compilersTheaccera-compilerspackage contains pre-compiled compiler binaries used to produce optimized code using the Accera eDSL. It is not designed for standalone use, but is automatically installed when youpip install accera. You can find documentation and examples onGithub.
Package: accera-gpu
Accera GPUAcceraAccerais a programming model, a domain-specific programming language embedded in Python (eDSL), and an optimizing cross-compiler for compute-intensive code. Accera currently supports CPU and GPU targets and focuses on optimization of nested for-loops.Writing highly optimized compute-intensive code in a traditional programming language is a difficult and time-consuming process. It requires special engineering skills, such as fluency in Assembly language and a deep understanding of computer architecture. Manually optimizing the simplest numerical algorithms already requires a significant engineering effort. Moreover, highly optimized numerical code is prone to bugs, is often hard to read and maintain, and needs to be reimplemented every time a new target architecture is introduced. Accera aims to solve these problems.Accera has three goals:Performance: generate the fastest implementation of any compute-intensive algorithm.Readability: do so without sacrificing code readability and maintainability.Writability: a user-friendly programming model, designed for agility.accera-gpuTheaccera-gpupackage contains add-ons for GPU support. You can find documentation and examples onGithub.
Package: accera-llvm
Accera LLVMIntroductionAccerais a programming model, a domain-specific programming language embedded in Python (eDSL), and an optimizing cross-compiler for compute-intensive code. Accera currently supports CPU and GPU targets and focuses on optimization of nested for-loops.Writing highly optimized compute-intensive code in a traditional programming language is a difficult and time-consuming process. It requires special engineering skills, such as fluency in Assembly language and a deep understanding of computer architecture. Manually optimizing the simplest numerical algorithms already requires a significant engineering effort. Moreover, highly optimized numerical code is prone to bugs, is often hard to read and maintain, and needs to be reimplemented every time a new target architecture is introduced. Accera aims to solve these problems.Accera has three goals:Performance: generate the fastest implementation of any compute-intensive algorithm.Readability: do so without sacrificing code readability and maintainability.Writability: a user-friendly programming model, designed for agility.accera-llvmTheaccera-llvmpackage contains pre-compiled custom LLVM binaries used to produce optimized code using the Accera eDSL. It is not designed for standalone use, but is automatically installed when youpip install accera. You can find documentation and examples onGithub.Supported platforms:Linux (manylinux) x64macOS x64Windows x64
Package: accern-data
Accern Data LibraryClient library for consuming Accern data feed API.PyPI page:Click hereInstallation:pip install accern-dataSample snippet:importaccern_data# Create a data client.client=accern_data.create_data_client("https://api.example.com/","SomeRandomToken")# Set a data format/mode in which the data has to be downloaded.# Split dates lets you divide files on the basis of dates.client.set_mode(mode="csv",split_dates=True)# Other modes: {"df", "json"}Set filters:client.set_filters({"provider_id":5,"entity_name":"Hurco Companies, Inc.","event":"Governance - Product Development, R&D and Innovation","entity_ticker":"HURC","entity_accern_id":"BBG000BLLFK1",})Set parameters to the download function:client.download_range(start_date="2022-01-03",output_path=".",output_pattern="data",end_date="2022-03-04")Note: To download single day's data, setend_date=Noneor can leave that unset:client.download_range(start_date="2022-01-03",output_path=".",output_pattern="data",end_date=None)ORclient.download_range(start_date="2022-01-03",output_path=".",output_pattern="data")One-liner download:accern_data.create_data_client("https://api.example.com/","SomeRandomToken").download_range(start_date="2022-01-03",output_path=".",output_pattern="data",end_date="2022-03-04",mode="csv",filters={"entity_ticker":"HURC"})Getting data using iterator:forresinclient.iterate_range(start_date="2022-01-03",end_date="2022-03-04"):do_something(res)Error logging:While downloading the data any critical error will get raised.
Any non-critical errors, such as API timeouts, get silenced and API calls are repeated. To see a list of the lastnerrors use:client.get_last_silenced_errors()