Saravanan666/pypi-org huggingface dataset:
Package: fastq-statistic
Fastq StatisticCalculate statistics for Fastq Files.RequirementPython: 3.8 or upperInstallDownload the release whl file.user@linux:~$python3-mpipinstallfastq-statisticUsageuser@linux:~$fastq-stat--help
Usage:fastq-stat[OPTIONS]READ1[READ2]Arguments:READ1Read1fastqpathorfastqpath[required][READ2]Read2filepathorNone

Options:--sampleidTEXTSampleID,defaultisthefirstitemoffilenamesplitedbyunderscore(_)--resultPATHResultcsvfilename,plotwithusethesamename.--reserve-data/--no-reserve-dataReservefastqstatisticintermediatedata.[default:False]--plot/--no-plotPlotfastqstatisticdata.[default:True]--install-completion[bash|zsh|fish|powershell|pwsh]Installcompletionforthespecifiedshell.--show-completion[bash|zsh|fish|powershell|pwsh]Showcompletionforthespecifiedshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.
Package: fastqtools
fastqtools is used to filter , split ,stat raw ngs data.fastqCleanto clean low qual data, remove adaptorfastqSplitto split big fastq files to small onesfastqStatto qc fastq data , get q20,q30,gc-content  etc.Installationby pip:pip install fastqtoolsby git:git clone https://github.com/vienomics/fastqtools.git
Package: fast-query-store
Fast Query Store :zap:Running locallyYou needpython,git,dockeranddocker-composeto run this project.Setupgit clone <repository-url>
cd fast-query-store
cp env.example .env
source .envInstall dependenciespython -m venv env
source env/bin/activate

pip install -r requirements.txtExport config variablesfast-query-storewill look for a.jsonfile containing the queries information. The environment variablequeries_file_pathtells where this file is located.export queries_file_path=/path/to/queries.jsonExample ofqueries.jsonfile{"slug-1":{"query":"SELECT * FROM table_1","db_url":"postgresql:///..."},"slug-2":{"query":"SELECT COUNT(1) FROM table_1","db_url":"sqlite:///..."},"slug-3":{"query":"SELECT * FROM table_3","db_url":"${DATABASE_URL}"}}Note:It is also possible to set thedb_urlwith a enviroment variable.Running itexport queries_file_path=queries.json
uvicorn app.main:app --workers 2 --host 0.0.0.0 --port 8000Getting available query slugsGET http://localhost:8000/HTTP/1.1200OKcontent-length:73content-type:application/jsondate:Fri, 14 May 2021 12:47:37 GMTserver:uvicorn{"slugs":["slug-1","slug-2","slug-3"]}Executing a queryGET http://localhost:8000/query/slug-1HTTP/1.1200OKcontent-length:151content-type:application/jsondate:Fri, 14 May 2021 12:53:09 GMTserver:uvicorn{"result_set":[{"name":"Cheese","price":9.99,"product_no":1},{"name":"Bread","price":1.99,"product_no":2},{"name":"Milk","price":2.99,"product_no":3}]}Running the testspip install -r requirements-development.txt
source .env
docker-compose f docker-compose.yml up -d
pytest -v
Package: fast_queue
Simple Task ProducerGetting StartedWhen you import fast_queue, it does some Module Wrapper and Import Hook magic to create a mock that will schedule the task on your broker of choice in a way that celery can consume.fromfast_queue.myapp.mailimportsend_mailsend_mail('fast@queue.rocks')GoalDecouple all your celery dependencies from your web project. With fast-queue You can separate your async task impl from your web project. Nowadays, most projects that relies on Celery is great monolithic with both web code and the async task code. With Fast-Queue, you can break them apart in two packages without any cross dependency.You can benefitDevelopment benefits:
- Web project does not have dependency of the your async tasks.
- Async tasks, can evolve apart from the web project, as long as the signature does not change.Deployment benefits:
- Separated and independent deploys for both web and async task project
- Smaller web project size, and for the ones that use serveless, project size should be always a concern.InstallingJust pip install it!pip install fast-queueLicenseThis project is licensed under MIT licenseFor Early AdoptersAlthough I’m already using it on production environment, there are some improvements and bugs ahead that must be fixed.Easier to configure without monkey patching stuffSupport for Python 3
Package: fastqueue-lib
fastqueueSingle-ended fast queues built in C tuned for Python.Requirementspython 3.7+InstallationTo install fastqueue, using pip:pipinstallfastqueue-libQuickstartFor general use casesfastqueue.Queue()objects are tuned to perform well.
The enqueue and dequeue methods perform well over a large sequence of arbitrary operations.fastqueue.Queue()supports many standard sequence methods similar to lists.fastqueue.Queue()minimizes memory usage but maintains the fast queue speeds.>>>fromfastqueueimportQueue>>>queue=Queue()>>>queue.extend(['🚒','🛴'])>>>queue[0]'🚒'>>>'🛴'inqueueTrue>>>queue.enqueue('🚅')>>>queue.enqueue('🚗')>>>queue[-1]'🚗'>>>[queue.dequeue()for_inrange(len(queue))]['🚒','🛴','🚅','🚗']For more specialized casesfastqueue.QueueC()objects are tuned to perform well.
The interface forfastqueue.QueueC()is identical tofastqueue.Queue().
The enqueue and dequeue methods perform similarly well over a large sequence of arbitrary operations.fastqueue.QueueC()handles memory differently by doubling the capacity when full.
This increases the complexity but maintains fast amortized cost.
The benefit of this approach is even faster__getitem__and__setitem__speeds>>>fromfastqueueimportQueueC>>>queue_c=QueueC()>>>queue_c.extend(['🚒','🛴'])>>>queue_c[0]'🚒'>>>'🛴'inqueue_cTrue>>>queue_c.enqueue('🚅')>>>queue_c.enqueue('🚗')>>>queue_c[-1]'🚗'>>>[queue_c.dequeue()for_inrange(len(queue_c))]['🚒','🛴','🚅','🚗']Another alternative isfastqueue.LockQueue()which supports all queue operations.fastqueue.LockQueue()is built as a thread-safe alternative to the other queue types.Example BenchmarksQueue operationsUbuntuWindowsIterationUbuntuWindows
Package: fastr
FASTR is a framework that helps creating workflows of different
tools. The workflows created in FASTR are automatically enhanced
with flexible data input/output, execution options (local, cluster,
etc) and solid provenance.We chose to create tools by creating wrappers around executables
and connecting everything with Python.Fastr is open-source (licensed under the Apache 2.0 license) and hosted on
gitlab athttps://gitlab.com/radiology/infrastructure/fastrFor getting support, go tohttps://groups.google.com/d/forum/fastr-usersTo get yourself a copy:git clonehttps://gitlab.com/radiology/infrastructure/fastr.gitor if you have a ssh key pair:git clonegit@gitlab.com:radiology/infrastructure/fastr.gitThe official documentation can be found athttp://fastr.readthedocs.io
Package: fast-rabbit
Fast Rabbit: Asynchronous RabbitMQ Client for PythonFast Rabbit is an advanced, asynchronous RabbitMQ client designed to simplify the integration and management of RabbitMQ interactions within your Python applications. Leveraging the power ofasyncioandaio_pika, Fast Rabbit provides a robust framework for message publishing and consuming, with an emphasis on simplicity, efficiency, and reliability.FeaturesAsynchronous API: Built on top ofasyncio, allowing for non-blocking message operations.Easy Consumer Registration: Utilize decorators to effortlessly register message consumer coroutines.Automatic Connection Management: Handles connection and channel lifecycle, including reconnections.Flexible Routing: Incorporate a router similar to FastAPI for organised message handling based on queue names.InstallationInstall Fast Rabbit using pip:pipinstallfast_rabbitQuick StartPublishing Messagesfromfast_rabbitimportFastRabbitEngineengine=FastRabbitEngine(amqp_url="amqp://user:password@localhost/")awaitengine.publish("queue_name","Hello, RabbitMQ!")Consuming MessagesDefine your message handlers and register them as consumers for specific queues:fromfast_rabbitimportFastRabbitEngine,FastRabbitRouterengine=FastRabbitEngine(amqp_url="amqp://user:password@localhost/")router=FastRabbitRouter()@router.route("queue_name")asyncdefhandle_message(body:str):print(f"Received message:{body}")engine.include_subscriptions(router)awaitengine.run()DocumentationFor more detailed documentation, including API reference and advanced usage, please refer to theFast Rabbit Documentation.ContributingContributions are welcome! Please read ourContributing Guidefor details on how to submit pull requests, report issues, and suggest improvements.LicenseFast Rabbit is released under the MIT License. See theLICENSEfile for more details.Fast Rabbit aims to provide a high-quality, easy-to-use asynchronous RabbitMQ client for the Python community. Whether you're building microservices, distributed systems, or just need a reliable way to handle message queues, Fast Rabbit is designed to meet your needs with minimal overhead and maximum efficiency.
Package: fastrand
fastrandFast random number generation in Python using PCG: Up to 10x faster than random.randint.Blog post:Ranged random-number generation is slow in PythonUsage... (don't forget to type the above lines in your shell!)importfastrandprint("generate an integer in [0,1001)")fastrand.pcg32bounded(1001)print("generate an integer in [100,1000]")fastrand.pcg32randint(100,1000)# requires Python 3.7 or betterprint("Generate a random 32-bit integer.")fastrand.pcg32()It is nearly an order of magnitude faster than the alternatives:python3 -m timeit -s 'import fastrand' 'fastrand.pcg32bounded(1001)'
10000000 loops, best of 5: 23.6 nsec per loop

python3 -m timeit -s 'import fastrand' 'fastrand.pcg32randint(100,1000)'
10000000 loops, best of 5: 24.6 nsec per loop

python3 -m timeit -s 'import random' 'random.randint(0,1000)'
1000000 loops, best of 5: 216 nsec per loop

python3 -m timeit -s 'import numpy' 'numpy.random.randint(0, 1000)'
500000 loops, best of 5: 955 nsec per loopIf you have Linux, macOS or Windows, you should be able to do just pip install...pip install fastrandYou may need root access (sudo on macOS and Linux).It is sometimes useful to install a specific version, you can do so as follows;pip install fastrand==1.2.4Generally, you can build the library as follows (if you have root):pythonsetup.pybuild
pythonsetup.pyinstallorpythonsetup.pybuild
pythonsetup.pyinstall--home=$HOMEexportPYTHONPATH=$PYTHONPATH:~/lib/pythonChanging the seed and multiple streamsYou can change the seed with a function likepcg32_seed. The seed determines the random values you get. Be mindful that naive seeds (e.g.,int(time.time())) can deliver poor initial randomness. A few calls topcg32()may help to boost the improve the randomness. Or else you may try a better seed.If you need to produce multiple streams of random numbers, merely changing the seed is not enough. You are better off using different increments by calling thepcg32inc. The increments should all be distinct. Note that the least significant bit of the increment is always set to 1 no matter which value you pass: so make sure your increments are distinct 31-bit values (ignoring the least significant bit).future workAlso look athttps://github.com/rkern/line_profilerReferencehttp://www.pcg-random.orgDaniel Lemire,Fast Random Integer Generation in an Interval, ACM Transactions on Modeling and Computer Simulation, Volume 29 Issue 1, February 2019
Package: fastrandom
FastRandomA shell tool to generate random bytes extremely fast. Uses multiple CPU cores and both AES-NI and ChaCha20 to facilitate even higher utilisation of processing capacity. The output may be used for scrubbing disks, even the fastest NVME SSDs, or for any other purpose where large amounts of irreproducible random bytes are needed.Prefer redirection>instead of pipes andddfor maximal speed. This works with both files and block devices.pip install fastrandom
fastrandom -p > /tmp/file.dat    # -p for progress indication
[fastrandom]     15,451 MiB generated at 3.48 GB/s
[Errno 28] No space left on deviceThis tool is about 30 % faster than the best contender that I am aware of,openssl enc -aes-256-ctr -in /dev/zero, many times faster than any popular random number generator (likePCG64) and about 50 times faster than/dev/urandom. Generating to/dev/nullI can reach 11 GB/s on a six-core CPU but any actual devices that I have access to, including NVME 4.0 SSDs and even ramdisks, are slower. Please drop a Github issue if you find something faster.
Package: fastrank
FastRankMy most frequently used learning-to-rank algorithms ported to rust for efficiency.Read myblog-postannouncing the first public version: 0.4. It's alpha because I think the API needs work, not because there's any sort of known correctness or compatiblity issues.Python Requirements / Release History0.5 and earlier require only Python 3.5, but no windows builds were pushed.0.6 requires Python 3.6 due to EOL for Python 3.5 becoming prevalent in the latest pip.0.6.1 switched to manylinux2010 building; you might get better vectorization from a local copy.0.7 maintains the requirement of Python 3.60.8 and forward will require Python 3.7 so we can use the standard @dataclass annotation and drop the attrs dependency.Python UsagepipinstallfastrankSee thisColab notebookfor more, or see a static versionhere on Github.
Package: fastrates
Command line interface forFast RatesInstallation :pushpin:pipinstallfastrates-cliSuccessfullyinstalledfastrates-cli-0.1.0How to use? :rocket:Options:--baseTEXTBasetickerforcurrency[default:EUR]--latest/--no-latestGetthelatestforeignexchangereferencerates[default:False]--start-atTEXTGethistoricalratesforanydaysincestart_at--end-atTEXTGethistoricalratesforanydaytillend_at--symbolsTEXTComparespecificexchangerates--dateTEXTGethistoricaldate--helpShowthismessageandexit.Examplefastrates-cli--latestWhich will return{"rates":{"2020-07-31":{"AUD":1.6488,"BGN":1.9558,"BRL":6.1219,"CAD":1.5898,"CZK":26.175,"DKK":7.4442,"GBP":0.90053,"TRY":8.2595,"USD":1.1848,}},"base":"EUR"}Release Notes :mega:Latest Changes0.1.0Prototype of project
Package: fastrates-cli
Command line interface forFast RatesInstallation :pushpin:pipinstallfastratesSuccessfullyinstalledfastrates-cli-0.1.0How to use? :rocket:Options:--baseTEXTBasetickerforcurrency[default:EUR]--latest/--no-latestGetthelatestforeignexchangereferencerates[default:False]--start-atTEXTGethistoricalratesforanydaysincestart_at--end-atTEXTGethistoricalratesforanydaytillend_at--symbolsTEXTComparespecificexchangerates--dateTEXTGethistoricaldate--helpShowthismessageandexit.Examplefastrates--latestWhich will return{"rates":{"2020-07-31":{"AUD":1.6488,"BGN":1.9558,"BRL":6.1219,"CAD":1.5898,"CZK":26.175,"DKK":7.4442,"GBP":0.90053,"TRY":8.2595,"USD":1.1848,}},"base":"EUR"}Release Notes :mega:Latest Changes0.1.0Prototype of project
Package: fastrdp
fastrdpThefastrdppackage contains an implementation of theRamer–Douglas–Peucker
algorithmfor reducing the number of points in a curve.ExampleThere is a single function in thefastrdppackage. Here is an example
from the Wikipedia page linked to above with a description of the
algorithm. The original line is black and the approximating line is red.importmatplotlib.pyplotaspltimportnumpyasnpimportfastrdpx=np.linspace(0,5,10_000)y=np.exp(-x)*np.cos(2*np.pi*x)x_new,y_new=fastrdp.rdp(x,y,0.06)fig,ax=plt.subplots()ax.plot(x,y,color='black',linewidth=2.0)ax.plot(x_new,y_new,linestyle='dashed',color='red')plt.show()PerformanceHere we compare the performance offastrdpwith that of a pure Python
implementation. The example above is executed withfastrdpin less
than a millisecond on my machinefromtimeitimporttimeittimeit(lambda:fastrdp.rdp(x,y,0.1),number=10_000)0.9715354799991474The pure Python implementation in therdppackagetakes more than a second to
finish the same computationimportrdpz=np.column_stack((x,y))timeit(lambda:rdp.rdp(z,epsilon=0.1),number=1)1.636681150062941To illustrate howfastrdpscales consider the following graph that
shows execution time for an increasing number of random input points.
The figure is produced with the scripts in theperformancefolder.CompilationTo specify package metadatafastrdpis using the contemporarypyproject.toml. Execute the following commands to build and installfastrdppipinstall.If you want an editable installation, then add a-eto the last
command. To also install the optional dependencies used for development
add a little extra:pipinstall-e'.[dev]'The single quotes are needed in zsh, but is perhaps superfluous in other
shells.AcknowledgementsSetting up package metadata to build and compilefastrdpis inspired
by thepybind11 example
package.
Package: fastread
# Fastread_Fastread is a library for read big files_## How to install and updateInstall latest stable version or update current from pip.```pythonpip install -U fastread```## Basic UsageHere's a simple example.```pythonimport timeitfrom fastread import Fastreaddef main():ff = Fastread('big.txt')lines = ff.lines()total = 0for x in lines:total += 1return totalif __name__ == "__main__":total = main()timer = timeit.timeit("main()",setup="from __main__ import main",number=1)print('Read time: ', timer, '| Total lines: ', total)#RESULT# Read time:  0.029999009988387115 | Total lines:  128457```## Reference| Setup   | Command                | Notes || ------- | ---------------------- | ----- || install | `pip install fastread` |       |
Package: fast-redirect
Fast RedirectUse Fast Redirect to easily redirect your domains.Fast Redirects expects a JSON 'database'. This JSON 'database' contains the domains to be redirected. Fast Redirect then redirects traffic as configured.Fast Redirect usesStarlette, a high-performance ASGI framework. Your redirects are lightning fast. Requirements for all environments can be found in therequirementsdirectory.How to useInstallWe recommend running Fast Redirect in a venv. The package has been published to PyPI, so you could install it withpip3 install fast-redirect.RunAll configuration variables can be found in the.env.examplefile. Only one configuration variable isrequiredto be set:DATABASE_PATH. We recommend setting it to/var/lib/fast-redirect.json. Configuration variables can be set in an.envfile or be passed as environment variables.Runbin/fast-redirectto start the server. By default, it listens on port8080and binds to::1(IPv6). The server can be daemonized. An example systemd configuration can be found infast-redirect.service.SSLFast Redirect does not support SSL natively. Use a proxy that takes care of SSL, such asHAProxy.If your proxy does health checks, make sure that the domain or IP address it uses for them is not configured as a redirect.JSON DatabaseThe JSON database contains aredirectsobject. In turn, this object contains an object for each redirect. The key is the domain to redirect. Wildcards are supported.Properties:destination_url. The URL to redirect to. Query parameters and the path may be appended (see other options). Thismustbe a URL (i.e.www.domlimev.nlis invalid).status_code. Allowed values:301,302,303,307,308keep_query_parameters. Whether query parameters are kept. If this is false, query parameters are discarded from thedestination_url. For example, if this is false,https://domlimev.nl/page?k=vwould be redirected tohttps://example.com.keep_path. Whether the path is kept. If this is false, the path is discarded from thedestination_url. For example, if this is false,https://domlimev.nl/this/is/a/pathwould be redirected tohttps://example.com.In case a redirect is misconfigured, the error is returned to the visitor.Example JSON database:{
  "redirects": {
    "domlimev.nl": {
      "destination_url": "https://example.com",
      "status_code": 301,
      "keep_query_parameters": true,
      "keep_path": true
    }
  }
}ContributeFeel free to contribute by adding support for real database engines.Testspytest is used for tests. You run it as you'd expect, i.e.:pytest tests/SecurityYou can reach us atopensource@cyberfusion.nlto report security issues.
Package: fast-reflection-removal
Fast Reflection RemovalRemoves reflections quickly and easily.How to useYou can either:install it as a Python package,clone and use it as a repository, oruse it as a web app onmy website.How to installAs a packageIn your own project, just perform the following command:# python refers to the virtual environment to install package topython-mpipinstallfast-reflection-removalNow, you can use the reflection removal in your project in the following manner:fromfrrimportFastReflectionRemoval...# numpy array with values between 0 and 1 of shape (H, W, C)img=...# instantiate the algoroithm classalg=FastReflectionRemoval(h=0.11)# run the algorithm and get result of shape (H, W, C)dereflected_img=alg.remove_reflection(img)...As a repositoryClone the project and go to its root directory.Create and activate the virtual environment:# create the environmentpython3-mvenv"venv"# activate the environmentsourcevenv/bin/activate# on Windows ./venv/Scripts/activate.ps1 in PowershellInstall the necessary packages:python-mpipinstall--upgradepip
python-mpipinstall--upgradewheelsetuptoolspip-toolsInstall the packages from requirements and the project:makesync# on windows just perform the following commands: python -m piptools sync requirements.txt; python -m pip install -e .How to run the projectExample:# activate the environmentsourcevenv/bin/activate# on Windows ./venv/Scripts/activate.ps1 in Powershellfrr".\tests\test_frr\fixtures\toy_example.jpg""out.jpg"-h0.11The program, in this example, loads input image from the path.\tests\test_frr\fixtures\toy_example.jpg, processes it with parameterh=0.11and outputs it intoout.jpg. More information about the parameters can be obtained by invokingfrr --help.Project structureFolders:docs: documentation.src: contains list of folders for sources, e.g. python.tests: tests that follow the structure of src/python.CreditsThis repository implements paperFast Single Image Reflection Suppression via Convex Optimizationauthored by Yang Yang, Wenye Ma, Yin Zheng, Jian-Feng Cai and Weiyu Xu.