Saravanan666/pypi-org huggingface dataset:
Package: fastero
fasteroPython timeit CLI for the 21st centuryRead the DocumentationInstallation & UsageInstall either with pipx or pip. Both work, use what you want. Or optionally you can install from github usingpip install git+https://github.com/wasi-master/fasteroFor usage please check outthe documentationFeaturesFor more info on all of these features, please thedocumentation🌟Beautifulformatted, and colored output. Output is reminiscent ofhyperfine🤯Amazingexporting options📊 Export as a bar plot with matplotlib🌃 Export as abeautifulimage with the console outputℹ️ Export as Markdown, HTML, CSV, AsciiDoc tables💾 Export as JSON and YAML data to use them elsewhere🔁 You can also import the JSON data later within
fastero to re-run the benchmark with the same parameters
or to export the data again with different parameters.🚀 Extremely intuitive and easy to use.🔢 Benchmark multiple snippets🔤 Assign a name to each snippet to make it easier to distinguish📈 Get nice statistics about the each of the snippet and
a summary on how fast each of them are compared to each other↩ Entermultilinecode in an input withsyntax highlightingandamazingautocomplete⏰ Excellent time parsing. Inputs like500ms,10s,1m5s,1.5m,1h30m15s, etc. work flawlessly🔥 Run a few times without timing with the warmup parameter to fill
caches and things like that.👨 Customize it to your liking.🔣 Custom time formats e.g. nanoseconds, microseconds, milliseconds, seconds etc.🎨 Custom theme for code input and/or output.🎛️ Control how long each snippet is benchmarked for🔢 Specify a minimum and a maximum amount of runs to calculate
the number of runs automatically based on run duration🔟 Or specify a definite number of runs for manual override💻 Cross-platform.🤯 Open source.📚 Extensive documentation.Acknowledgementshyperfine- Inspiration for creating this library and the UI.snappify.io- Inspiration for the Image export.rich- Used forbeautifuloutput
Package: fasterprimes
Fast Prime CalculationsFastest Prime Number Calculation (checks) logic andThis probably is the BEST solution in the internet as of today 11th March 2022This same code can be applied in any languages likePython,GoLang,Java,PHP,Node.js,Javascript,C,C++,.NET,Rust, etc with the same logic and have performance benefits. It is pretty fast based on the number of iterations needed. Performance time checks were not consistent across languages (in my local system - to be direct about wordings). I have not seen this implemented before and has been indigenously done. Feedback and usage is welcome.Max iterations 16666 for n == 100000 instead of 100000 of conventional way. Theiterationscounts for different ways for Prime number check 100007 can be seen as follows:count: Prime Conventional way for  83 is  81
    Is Prime 83 isPrimeConventionalWay:  True

    count: Prime Squareroot way  83 is  8
    Is Prime 83 isPrimeSquarerootWay:  True

    count: Prime Unconventional way for  83 is  14
    Is Prime 83 prime (SUGGESTED):  True

    count: Prime AKS - Mersenne primes - Fermat's little theorem or whatever way  83 is  2
    Is Prime 83 isprimeAKSWay:  True

    count: Prime Conventional way for  169 is  12
    Is Prime 169 isPrimeConventionalWay:  False

    count: Prime Squareroot way  169 is  12
    Is Prime 169 isPrimeSquarerootWay:  False

    count: Prime Unconventional way for  169 is  1
    Is Prime 169 prime (SUGGESTED):  False

    count: Prime AKS - Mersenne primes - Fermat's little theorem or whatever way  169 is  4
    Is Prime 169 isprimeAKSWay:  False

    count: Prime Conventional way for  100007 is  96
    Is Prime 100007 isPrimeConventionalWay:  False

    count: Prime Squareroot way  100007 is  96
    Is Prime 100007 isPrimeSquarerootWay:  False

    count: Prime Unconventional way for  100007 is  15
    Is Prime 100007 prime (SUGGESTED):  False

    count: Prime AKS - Mersenne primes - Fermat's little theorem or whatever way  100007 is  32
    Is Prime 100007 isprimeAKSWay:  False

    count: Prime Conventional way for  300530164787 is  1180
    Is Prime 300530164787 isPrimeConventionalWay:  False

    count: Prime Squareroot way  300530164787 is  1180
    Is Prime 300530164787 isPrimeSquarerootWay:  False

    count: Prime Unconventional way for  300530164787 is  196
    Is Prime 300530164787 prime (SUGGESTED):  False

    count: Prime AKS - Mersenne primes - Fermat's little theorem or whatever way  300530164787 is  393
    Is Prime 300530164787 isprimeAKSWay:  FalseCode BaseJavascriptPython- Install Python code using `pip install fast-prime`.
    - Deprecating dash (-) separated package names but keeping updated `pip install fast-prime` and `pip install fast-prime-numbers`
    - Access Python code using `from fasterprimes import *`# # Usage API for python
# pip install fasterprimes
# pip install fast-prime
# pip install fast-prime-numbers

from fasterprimes import *

fast(13)
conventional(13)
sqroot(13)
aks(13)Stack overflow Link for CalculationsJavascript CodebasePython CodebaseLICENSEPROPRIETARY LICENSE AGREEMENT ONLY
Package: fasterraster
Faster-Rasterfasterrasteris a fast multi-threaded python library for performing raster operations usingopenMPandnumpyobjects complete with simple IO.Github repository:https://github.com/asenogles/fasterrasterPyPI:https://pypi.org/project/fasterrasterMotivationfasterrasterwas developed to quickly perform raster operations, enabling self-supervised learning for raster based analyses.fasterrasterprovides a cython wrapper for optimizedopenMPccode. Data objects are handled bynumpyallowing for straightforward memory management. Currently only computation of visual/morphological features have been implemented however this is open to expansion in the future. All code is still in development and thus it is recommended to test fully before use.Installationfasterrasterhas currently been tested on Linux and Microsoft windows operating systems. You will need python>=3.6 installed. It is recommended to installfasterrasterwithin a virtual environment.Install using pipTo installfasterrasterfrom PyPI using pip:pip install fasterrasterInstall from sourceTo buildfasterrasterfrom source, download this repository and run:python3 setup.py build_ext --inplaceNote: You will need to have the required build dependencies installed.ExampleimporttimeitimportnumpyasnpimportfasterrasterasfrfrompathlibimportPathNTESTS=10# Load a .bil file containing a DEMfname=Path('./test_data/dem.bil')dem=fr.read(fname)# regular python implementation of hillshade function# from https://www.neonscience.org/resources/learning-hub/tutorials/create-hillshade-pydefpy_hillshade(dem,cell_size,azimuth=330,altitude=30):azimuth=360.0-azimuthdem=dem/cell_sizex,y=np.gradient(dem)slope=np.pi/2.-np.arctan(np.sqrt(x*x+y*y))aspect=np.arctan2(-x,y)azimuthrad=azimuth*np.pi/180.altituderad=altitude*np.pi/180.shaded=np.sin(altituderad)*np.sin(slope)+np.cos(altituderad)*np.cos(slope)*np.cos((azimuthrad-np.pi/2.)-aspect)return255*(shaded+1)/2# Time hillshade computation using regular pythontime=timeit.timeit(lambda:py_hillshade(dem.raster,dem.XDIM),number=NTESTS)print(f'python hillshade averaged{time/NTESTS:.3f}seconds')# Time hillshade computation using fasterraster for num-threadsnum_threads=[1,2,4,8]fornumtinnum_threads:time=timeit.timeit(lambda:fr.hillshade_faster_mp(dem.raster,numt),number=NTESTS)print(f'hillshade averaged{time/NTESTS:.3f}seconds for{numt}threads')Example output:python hillshade averaged 2.880 secondshillshade averaged 0.081 seconds for 1 threadshillshade averaged 0.041 seconds for 2 threadshillshade averaged 0.034 seconds for 4 threadshillshade averaged 0.024 seconds for 8 threads
Package: faster-sam
Faster-SAMAdapter for FastAPI to run APIs built using AWS SAMAbout The ProjectFaster-SAMis a library designed to integrate APIs built using the AWS Serverless Application Model (SAM) with FastAPI, enabling developers to run their AWS SAM applications outside of the AWS environment. FastAPI, known for its high-performance and easy-to-use framework for building APIs with Python, gains compatibility with AWS SAM through this specialized adapter.DependenciesPython 3.8+FastAPIPyYamlInstallationTo install theFaster-SAMlibrary, use the following pip command:pipinstallfaster-samUsage exampleAdd this code example in your project:fromfastapiimportFastAPIfromfaster_samimportSAMapp=FastAPI()sam=SAM()sam.configure_api(app)
Package: faster-sentiment
faster_sentimentPython Boilerplate contains all the boilerplate you need to create a Python package.Free software: MIT licenseDocumentation:https://faster-sentiment.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-09-06)First release on PyPI.
Package: faster-than-light
Faster Than LightExperiments in speed and scalabilityFree software: Apache Software License 2.0Documentation:https://faster-than-light.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-05-04)First release on PyPI.
Package: fastertransformer
fastertransformer: fastertransformer tf ophttps://github.com/NVIDIA/FasterTransformer<br>libtf_bert.sobuildforlinuxosInNLP,encoderanddecoderaretwoimportantcomponents,withthetransformerlayerbecomingapopulararchitectureforbothcomponents.FasterTransformerimplementsahighlyoptimizedtransformerlayerforboththeencoderanddecoderforinference.OnVolta,TuringandAmpereGPUs,thecomputingpowerofTensorCoresareusedautomaticallywhentheprecisionofthedataandweightsareFP16.FasterTransformerisbuiltontopofCUDA,cuBLAS,cuBLASLtandC++.WeprovideatleastoneAPIofthefollowingframeworks:TensorFlow,PyTorchandTritonbackend.UserscanintegrateFasterTransformerintotheseframeworksdirectly.Forsupportingframeworks,wealsoprovideexamplecodestodemonstratehowtouse,andshowtheperformanceontheseframeworks.
Package: faster-whisper-cli
Faster Whisper CLIFaster Whisper CLI is a Python package that provides an easy-to-use interface for generating transcriptions and translations from audio files using pre-trained Transformer-based models.This CLI version of Faster Whisper allows you to quickly transcribe or translate an audio file using a command-line interface.InstallationYou can install Faster Whisper CLI usingpip:pip install faster-whisper-cliUsageTo use Faster Whisper CLI, simply run thefaster-whispercommand followed by the path to the input audio file:faster-whisper path/to/audio.wavThis will transcribe the audio file using the default settings and print the output to the console.You can also specify various options to customize the transcription process:usage: faster-whisper [-h] [-o OUTPUT] [--language LANGUAGE] [--task TASK]
                      [--beam_size BEAM_SIZE] [--best_of BEST_OF]
                      [--patience PATIENCE]
                      [--length_penalty LENGTH_PENALTY]
                      [--temperature TEMPERATURE [TEMPERATURE ...]]
                      [--compression_ratio_threshold COMPRESSION_RATIO_THRESHOLD]
                      [--log_prob_threshold LOG_PROB_THRESHOLD]
                      [--no_speech_threshold NO_SPEECH_THRESHOLD]
                      [--condition_on_previous_text CONDITION_ON_PREVIOUS_TEXT]
                      [--initial_prompt INITIAL_PROMPT] [--prefix PREFIX]
                      [--suppress_blank SUPPRESS_BLANK]
                      [--suppress_tokens SUPPRESS_TOKENS [SUPPRESS_TOKENS ...]]
                      [--without_timestamps WITHOUT_TIMESTAMPS]
                      [--max_initial_timestamp MAX_INITIAL_TIMESTAMP]
                      [--word_timestamps WORD_TIMESTAMPS]
                      [--prepend_punctuations PREPEND_PUNCTUATIONS]
                      [--append_punctuations APPEND_PUNCTUATIONS]
                      [--vad_filter VAD_FILTER] [--model_size_or_path MODEL_SIZE_OR_PATH]
                      [--device DEVICE] [--device_index DEVICE_INDEX [DEVICE_INDEX ...]]
                      [--compute_type COMPUTE_TYPE] [--cpu_threads CPU_THREADS]
                      [--num_workers NUM_WORKERS]
                      audio
Package: fastest-asyncapi
fastapi-asyncapiUse pydantic model to construct asyncapi docs and serve inside your asyncapi appInstallationInstall usingpiporpoetrypoetryaddfastapi-asyncapiUsageDescribe your specification using pydantic modelsfromasyncapi_container.asyncapi.spec.v3.infoimportInfofromasyncapi_container.containers.v3.simple_specimportSimpleSpecV3fromasyncapi_container.custom_typesimportRoutingMapfrompydanticimportBaseModel,FieldclassCustomer(BaseModel):first_name:str=Field(...,title='First Name')last_name:str=Field(...,title='Last Name')email:str=Field(...,title='Email',format="email",description="Person's email to deliver order info")country:str=Field(...,title='Country')zipcode:str=Field(...,title='Zipcode')city:str=Field(...,title='City')street:str=Field(...,title='Street')apartment:str=Field(...,title='Apartment')classOrderSchemaV1(BaseModel):product_id:int=Field(...,title='Product Id')quantity:int=Field(...,title='Quantity')customer:CustomerclassMySpecialServiceAsyncAPISpecV3(SimpleSpecV3):info:Info=Info(title="My special Service",version="1.0.0",description="Service for making orders")sends:RoutingMap={"shop.orders.v1":[OrderSchemaV1,]}receives:RoutingMap={}Configurefast-and-async-apiapp using asyncapi spec definition from above Mountfastapi-asyncapiurls.fromfastapiimportFastAPIfromfastest_asyncapi.views.sync.v3.asyncapiimportfastest_asyncapi_appapp=FastAPI()fastest_asyncapi_app.configure(asyncapi_spec_classes=[MySpecialServiceAsyncAPISpecV3])app.mount("/docs/",fastest_asyncapi_app)Enjoy your results by navigating todocs/asyncapi/v3/
Package: fastest-pkg
fastest_pkgSynopsisScript to find the fastest FreeBSD.org pkg mirror near you.DescriptionBy default FreeBSD pkg uses DNS-based load balancing. This is achieved a SRV query:$dig+short_http._tcp.pkg.freebsd.orgsrv10 10 80 pkgmir.geo.freebsd.org.50 10 80 pkg0.bme.freebsd.org.50 10 80 pkg0.isc.freebsd.org.50 10 80 pkg0.nyi.freebsd.org.50 10 80 pkg0.pkt.freebsd.org.50 10 80 pkg0.tuk.freebsd.org.However, this method will try to choose the nearest mirror, which will not necessarily be the fastest. This script can help you to find the fastest pkg mirror by trying each one in succession. At the end of the output you'll see a sample configuration to hardcode the fastest pkg mirror leading to much higher pkg performance.$./fastest_pkg.pypkg0.tuk.freebsd.org: 2.4 MB/spkg0.twn.freebsd.org: 16.8 MB/spkg0.bbt.freebsd.org: 7.7 MB/spkg0.bme.freebsd.org: 1.6 MB/spkg0.bra.freebsd.org: 68.2 kB/spkg0.isc.freebsd.org: 0.0 B/spkg0.jinx.freebsd.org: 65.6 kB/spkg0.kul.freebsd.org: 7.8 MB/spkg0.kwc.freebsd.org: 8.7 MB/spkg0.pkt.freebsd.org: 1.7 MB/spkg0.nyi.freebsd.org: 1.6 MB/sFastest:pkg0.twn.freebsd.org: 16.8 MB/sWrite configuration:mkdir -p /usr/local/etc/pkg/repos/echo 'FreeBSD: { url: "http://pkg0.twn.freebsd.org/${ABI}/quarterly" }' \> /usr/local/etc/pkg/repos/FreeBSD.confThe following cli options are available:$fastest_pkg--helpusage: fastest_pkg [-h] [-j] [-v] [-t TIMEOUT]Script for finding and configuring fastest FreeBSD pkg mirroroptional arguments:-h, --help            show this help message and exit-j, --json            only show basic information in JSON format-v, --verbose         be more verbose-t TIMEOUT, --timeout TIMEOUTtimeout in ms
Package: fasteve
Fasteve is a rebuild ofEveusingFastAPIas a base.Fasteve is SimpleCreate a filemain.pywith:fromfasteveimportFasteve,MongoModel,ResourceclassPeople(MongoModel):name:strpeople=Resource(model=People)resources=[people]app=Fasteve(resources=resources)Start a database (mongodb default):$dockerrun--rm-p27017:27017mongoRun the server with:$uvicornmain:app--reloadThe API is now live, ready to be consumed:$curl-ihttp://localhost:8000/peopleHTTP/1.1 200...{"_data": [],"_meta": {"max_results": 25, "total": 0, "page": 1},"_links": {"self": {"href": "/people", "title": "people"},"parent": {"href": "/", "title": "home"},},}Features (TODO)Powered by FastAPI ✅Emphasis on REST ✅Full range of CRUD operations ✅Customizable resource endpoints ✅Sub Resources ✅Pagination ✅HATEOAS ✅Bulk create ✅Data Validation ✅Extensible Data Validation ✅Unique Fields ✅CORS Cross-Origin Resource Sharing ✅Read-only by default ✅Default Values ✅Embedded Resource Serialization ✅Event Hooks ✅Custom ID Fields ✅Alternative ID Fields ✅Interactive API docs (provided by Swagger UI) ✅Alternative API docs (provided by ReDoc) ✅Repeated Background Tasks ✅MongoDB Support ✅SQL Support ✅Predefined Database FiltersProjectionsJSONPCustomizable, multiple item endpointsFiltering and SortingJSON and XML RenderingConditional RequestsData Integrity and Concurrency ControlResource-level Cache ControlAPI VersioningDocument VersioningAuthenticationRate LimitingFile StorageGeoJSONInternal ResourcesEnhanced LoggingOperations LogMongoDB Aggregation FrameworkLicenseFasteve is a open source project,
distributed under theBSD licenseLatest Changes:sparkles: add SQL support via sqlmodel. PR#21by@Wytamma.:tada: v0.1.3. PR#20by@Wytamma.:sparkles: Add event hooks. PR#17by@Wytamma.:sparkles: break up endpoints. PR#16by@Wytamma.:sparkles: Add PATCH method. PR#15by@Wytamma.:bug: PUT does upsert when ID not found. PR#14by@Wytamma.:art: PUT returns 204 (No Content). PR#13by@Wytamma.:sparkles: Add PUT method. PR#12by@Wytamma.:art: Formatting with Black. PR#11by@Wytamma.
Package: fastex
Ever have a pile of data (stored as JSON objects) that you want to
quickly visualize and scroll through?
Here’s a simple package that will let you do just that.
Fex just needs your data and (optionally) an HTML template file that
specifies how to render that single object.Installation# from git repo
git clone https://github.com/arunchaganty/fex
pip install -e .

# OR from PIP
pip install fastexUsageFex assumes that your data is formatted as.jsonl: each line is simply its own JSON object.
For example, consider the followingdata.{"id": 0, "title": "Test 1", "messages": [{"user": "A", "msg": "Hi"}, {"user": "B", "msg": "Hello! How's it going?"}, {"user": "A", "msg": "Good!"}]}
{"id": 1, "title": "Test 2", "messages": [{"user": "B", "msg": "I need help!"}, {"user": "A", "msg": "Uh oh! What happened?"}, {"user": "B", "msg": "Sorry, false alarm, everything is ok."}]}Runningfex viewwill launch a web server that visualizes each of
these objects using a template rendered withJinja.
Fex provides a default visualization is already prettier than staring
at JSON:The real power of Fex comes from being able to quickly hack a template
that you find more useful. For example, the above data represents a chat
transcript and it would be much nicer to visualize it as such.
To do so, first runfex init, which will copy over atemplate.htmlfile that you can edit. Using theexample templateproduces the following result:Nice!Notice the navbar at the top allows you to quickly toggle through input.Frequently asked questionsQ: Can I use paths that are notdata.jsonlortemplate.html?A: Yes! Use the options-ior--inputto specify the data file
and-tor--templateto specify the template file:fex view-imydata.jsonl-tmytemplate.html.Q: Can I use a custom port?A: Yes! Use the options-por--port:fex view-p9000.
Package: fastexcel
fastexcelA fast excel file reader for Python, written in Rust.Based oncalamineandApache Arrow.Docs availablehere.Dev setupPrerequisitesPython>=3.8 and a recent Rust toolchain must be installed on your machine.cargomust be available in yourPATH.First setupOn the very first time you setup the project, you'll need to create a virtualenv and install the necessary tools:python -m venv .venvsource .venv/bin/activate(.venv)make dev-setupThis will also set uppre-commit.Installing the project in dev modeIn order to install the project in dev mode (for local tests for example), usemake dev-install.
This will compile the wheel (in debug mode) and install it. It will then be available in your venv.Installing the project in prod modeThis is required for profiling, as dev mode wheels are much slower.make prod-installwill compile the project
in release mode and install it in your local venv, overriding previous dev installs.Linting and formattingThe Makefile provides thelintandformatextras to ease this.Running the testsmake testRunning the benchmarksSpeed benchmarkmake benchmarksMemory benchmarkmprof run -T 0.01 python python/tests/benchmarks/memory.py python/tests/benchmarks/fixtures/plain_data.xlsBuilding the docsmake docCreating a releaseCreate a PR containing a commit that only updates the version inCargo.toml.Once it is approved, squash and merge it into main.Tag the squashed commit, and push it.ThereleaseGitHub action will take care of the rest.Dev tipsUsecargo checkto verify that your rust code compiles, no need to go throughmaturinevery timecargo clippy= 💖Careful with arrow constructors, they tend to allocate a lotmprofandtimego a long way for perf checks,
no need to go fancy right from the start
Package: fastexception
fast exceptionThis package is a plugin for easier handling of Exception and sending HTTP status code.Installingpip install fastexceptionUsageYou can use FastAPI itself as follows:@app.get("/")
def index(password: str):
    if password.validate():
       raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="message")

status code : 400
message : messageBut the simpler and more readable way to use fastexception is as follows:@app.get("/")
def index(password: str):
    if password.validate():
        FastException.HTTP_400_BAD_REQUEST.http("message")


status code : 400
message : messageYou can write your desired message in http, if you don't write it, fastexception will show the appropriate message.@app.get("/")
def index(password: str):
    if password.validate():
        FastException.HTTP_400_BAD_REQUEST.http()


status code : 400
message : Bad Request