Saravanan666/pypi-org huggingface dataset:
Package: fastpay
FastPayをPythonで簡単に利用するためのSDKです。インストールpipコマンドでインストールを行います$pipinstallfastpay使い方FastPayではクレジットカード情報は直接扱わず、FastPay側でトークン化したものを使って安全に決済を行います。
トークン化など全体的な流れについては、FastPayのドキュメント[支払いフロー]をご覧ください。課金詳細についてはFastPayのドキュメント新規決済の作成をご覧ください。importfastpay# fastpay.jsで取得します。"fastpayToken"というパラメータでhiddenのinputにて送信されます。token="CARD_TOKEN"client=fastpay.FastPay("シークレット")try:charge=client.charge.create(amount=100,# 金額card=token,# fastpay.jsで取得したトークンdescription="fastpay@example.com",# 詳細情報。フリーフォームですcapture=False# 確定を行わない場合False。同時確定の場合は省略またはTrueを指定する)# 例外が上がらなかった場合、課金成功print("注文完了 ID:%s"%charge.id)# -> 注文ID表示exceptfastpay.CardErrorase:# カード与信エラー。必要に応じて再度画面を表示など行うife.code==FastPayError.CARD_DECLINED:print("カード決済に失敗しました。（オーソリ時のエラー）")elife.code==FastPayError.INCORRECT_CVC:print("セキュリティコードが正しくありません。（オーソリ時のエラー）")# 他のコードは https://fastpay.yahoo.co.jp/docs/error を参照exceptfastpay.FastPayErrorase:print("システムエラー%s"%str(e))確定詳細についてはFastPayのドキュメント決済の確定をご覧ください。importfastpayclient=fastpay.FastPay("シークレット")try:charge=client.charge.retrieve("対象のcharge_id")# 確定を行うcharge.capture()# 例外が上がらなかった場合、確定成功print("確定成功")exceptfastpay.FastPayErrorase:print("システムエラー%s"%str(e))返金詳細についてはFastPayのドキュメント払い戻し処理をご覧ください。importfastpayclient=fastpay.FastPay("シークレット")try:charge=client.charge.retrieve("対象のcharge_id")# 確定を行う。引数を与えることで部分返金も可能charge.refund()# 例外が上がらなかった場合、払い戻し成功print("払い戻し成功")exceptfastpay.FastPayErrorase:print("システムエラー%s"%str(e))継続課金の開始継続課金についてはまずはFastPayのドキュメント継続課金とはをごらんください。importfastpayclient=fastpay.FastPay("シークレット")try:subscription=client.subscription.activate("対象のsubscription_id")print("継続課金開始成功")exceptfastpay.FastPayErrorase:print("システムエラー%s"%str(e))継続課金の停止継続停止についてもFastPayのドキュメント継続課金とはをごらんください。importfastpayclient=fastpay.FastPay("シークレット")try:subscription=client.subscription.cancel("対象のsubscription_id")print("継続課金停止成功")exceptfastpay.FastPayErrorase:print("システムエラー%s"%str(e))LicenseMITライセンスにて提供しています。詳しくはLICENSEをご覧ください。
Package: fastpdb
A high performance drop-in replacement forBiotite’sPDBFilewritten inRust.Installationfastpdbcan be installed via$pipinstallfastpdbUsageYou can simply replacebiotite.structure.io.pdb.PDBFilebyfastpdb.PDBFile. The methods and their parameters are the same.importfastpdbin_file=fastpdb.PDBFile.read("path/to/file.pdb")atom_array=in_file.get_structure(model=1)out_file=fastpdb.PDBFile()out_file.set_structure(atom_array)out_file.write("path/to/another_file.pdb")Note thatfastpdbdoes not yet support thehybrid-36PDB format.Performancefastpdbis multiple times faster thanbiotite.
Package: fast-pdf-gen
Fast PDF GeneratorFast PDF Generator is a Python package that allows you to generate PDF files using Handlebars templates quickly. It provides a simple and efficient way to compile Handlebars templates and convert them into PDF documents.FeaturesHandlebars Template Compilation:Compile Handlebars templates to HTML strings.PDF Generation:Generate PDF files from compiled HTML content.Path Handling:Support for both absolute and relative file paths on Linux, macOS, and Windows.InstallationYou can install thefast_pdf_genpackage usingpip:pip install fast_pdf_genUsagefromfast_pdf_genimportcompile_handlebars_template,generate_pdf# Handlebars Template Compilationtemplate_key='template1'template_path_or_string='path/to/template.html'# Or a template stringcontext={'variable':'value'}compiled_template=compile_handlebars_template(template_key,template_path_or_string,context=context)#Optionspdf_options={'format':'A3','margin':{'top':'10mm','right':'10mm','bottom':'10mm','left':'10mm'}}# PDF Generationoutput_path='output.pdf'generate_pdf(template_key,template_string,output_path,context=context,pdf_options=pdf_options)Optionstemplate_key(str): Unique identifier for the template.template_path_or_string(str): Path to the HTML template file or a template string.compiled_template(str): Compiled HTML content obtained fromcompile_handlebars_template.output_path(str): Path where the generated PDF will be saved.context(optional, dict): Dictionary containing variables to be substituted in the Handlebars template.ContributingFork the repository and clone it locally.Create a new branch for your feature:git checkout -b feature-name.Make your changes and commit them:git commit -m 'Add new feature'.Push to the branch:git push origin feature-name.Create a pull request on GitHub.LicenseThis project is licensed under the MIT License.
Package: fastpg
fastpgA fast Postgres client library for Python.The only dependency forfastpgisasyncpg,
and the api wrapper is very similar to the api provided by thedatabasespackage.Installation$pipinstallfastpgQuickstartfromfastpgimportDatabasedatabase=Database("postgresql://username:password@localhost:5432/postgres")awaitdatabase.connect()# Execute a queryquery="""create table high_scores (id uuid primary key default gen_random_uuid(),name varchar(100) not null,score integer not null)"""awaitdatabase.execute(query)# Execute many queriesquery="insert into high_scores (name, score) values (:name, :score)"values=[{"name":"George","score":43},{"name":"Jess","score":67},{"name":"Kevin","score":30}]awaitdatabase.execute_many(query,values)# Fetch many rowsquery="select * from high_scores"records=awaitdatabase.fetch_many(query)assert(len(records)==3)# Fetch a rowquery="select * from high_scores where name = :name"values={"name":"George"}record=awaitdatabase.fetch_one(query,values)assert(record.score==43)# Fetch a valuequery="select name from high_scores where score = :score"values={"score":30}value=awaitdatabase.fetch_val(query,values)assert(value=="Kevin")# Copy records to a table (via Postgres COPY)awaitdatabase.copy_records_to_table("high_scores",records=[("Mav",200),("Connor",134)],columns=["name","score"])# Work with individual connectionsasyncwithdatabase.connection()asconnection:...# Create a transactionasyncwithconnection.transaction():...awaitdatabase.disconnect()
Package: fastphase
fastphasePython implementation of the Scheet and Stephens model and extensions.If you use this package you must cite the original paper:Paul Scheet and Matthew Stephens (2006)A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic PhaseThe American Journal of Human Genetics 78(4):629-644DOI.
Package: fast-phonetic
fast-phoneticFast Phonetic algoritihms in Python, using Rust and rphonetic·Report Bug·Request FeatureAbout The ProjectAll the existing libraries I saw in Python seemed dead. They also seemed to be lacking in modern CI practices. Plus Rust is coolCurrently we haveSupport for allrphoneticencoders besides BeiderMorse and DanitchMokotoffGetting StartedInstallationpip install fast_phonetic(back to top)Usageimportfast_phonetic# Soundexsoundex=fast_phonetic.encode_word("foo","soundex")metaphone=fast_phonetic.encode_word("foo","metaphone")double_metaphone=fast_phonetic.encode_word("foo","double_metaphone")bad_value=fast_phonetic.encode_word("foo","bleh-encoder")# Raises NotImplemented error(back to top)RoadmapAdd TestsAdd auto docsImproved Multi-language supportSee theopen issuesfor a full list of proposed features (and known issues).(back to top)ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.(back to top)
Package: fastpip
fastpip简介一个 pip 命令包，帮助你实现使用 Python 编程的方式进行包管理操作。如何安装注：fastpip 仅支持 Windows 系统。打开命令窗口或 PowerShell 窗口，使用 pip 命令安装：pip install -U fastpip如何使用示例1：使用 fastpip 升级 Python 环境中的包importsys# 导入 PyEnv 类fromfastpipimportPyEnv# 创建 PyEnv 实例：PyEnv(_path)# 参数 _path 是一个路径，指向 Python 解释器(python.exe)所在目录# 例如 env = PyEnv(r'C:\Anaconda3\envs\py35')# 不带参数或者 PyEnv(None) 则使用系统环境变量 PATH 中第一个 Py 环境路径env=PyEnv()# 调用 PyEnv 类实例的 outdated 方法获取可更新的包列表# 参数 output 控制是否在终端显示 pip 命令输出# 如果在 GUI 程序中使用 fastpip 请将这个参数设置为 False（默认）# outdated 返回值结构：# [# (包名, 已安装版本, 最新版本, 安装包类型),# ...# ]outdated_pkgs=env.outdated(output=1)# 询问是否安装所有可更新的包，回答非 y 则退出ifinput('\n确认更新？y/[n]：').lower()!='y':sys.exit(0)forname,*_inoutdated_pkgs:# 调用目标环境的 PyEnv 实例的 install 方法进行安装。# upgrade 参数为 True：# 以升级模式安装，如果目标环境已安装该包且该包有新版本，则安装新版本，否则跳过。# upgrade 参数为 False：# 如果目标环境已安装，不管该包有没有新版本都不会重新安装，直接跳过。env.install(name,upgrade=1,output=1)print('全部更新完成！')示例2：fromfastpipimportPyEnv,all_py_paths,cur_py_path,index_urls# 打印预置的几个国内 PyPi 镜像地址：print(index_urls)# {'aliyun': 'https://mirrors.aliyun.com/pypi/simple/',# 'tencent': 'https://mirrors.cloud.tencent.com/pypi/simple',# 'douban': 'https://pypi.doubanio.com/simple/',# 'huawei': 'https://mirrors.huaweicloud.com/repository/pypi/simple',# 'opentuna': 'https://opentuna.cn/pypi/web/simple',# 'tsinghua': 'https://pypi.tuna.tsinghua.edu.cn/simple',# 'netease': 'https://mirrors.163.com/pypi/simple/'}# 当前系统环境变量 PATH 中第一个 Python 环境路径：print(cur_py_path())# C:\Python37\ (具体与个人系统环境变量 PATH 设置有关)# 在常用安装位置查找 Python 目录：print(all_py_paths())# ['C:\\Python37\\',# 'C:\\Anaconda3\\',# 'C:\\Anaconda3\\envs\\py35\\',# 'C:\\Anaconda3\\envs\\py36\\',# 'C:\\Anaconda3\\envs\\py37\\',# 'C:\\Anaconda3\\envs\\py38\\',# 'C:\\Anaconda3\\envs\\py39\\']# PyEnv 类：########### 实例化一个 PyEnv 类：target=PyEnv('C:\\Python37\\')# target = PyEnv() 与上面一行代码效果是一样的，省略参数时 PyEnv 内部会调用 cur_py_path 函数获取系统环境变量 PATH 中第一个 Python 目录路径，如果 Python 路径还未设置到 PATH 中，则创建一个不指向任何 Python 目录路径的 PyEnv 空实例，实例的所有方法被调用时都将返回空值(不同的方法返回的数据类型也不同，但都是可以判为 False 的空值，例如空列表、空字符串)# 在该环境中安装模块(例如安装 fastpip)：print(target.install('fastpip',upgrade=1))# 安装成功返回 True，否则返回 False。# upgrade=1 代表以升级模式安装最新版本，否则如果环境中已安装 fastpip，install 方法将不会再安装一次 fastpip。# 如果有需要，也可以增加以下关键字参数：# index_url：str，例如 index_url='https://mirrors.cloud.tencent.com/pypi/simple'。本次安装从镜像地址 index_url 处下载安装，否则从本地设置的全局镜像源地址安装，如果本地未设置全局镜像源地址，则从官方源 PyPi 安装，国内用户下载速度非常慢甚至可能因连接超时导致安装失败。# timeout：int 或 float 或 None，例如 timeout=30，表示设置安装超时限制30秒，如果安装用时超过30秒，则安装失败。None 代表不设超时限制(默认)。# output：bool，例如 output=True，表示在安装过程中，在终端上打印安装时的输出信息。更多使用方法请查看源代码或者用 help：importfastpiphelp(fastpip)贡献感谢 liangzai450 的贡献！
Package: fastplbftest
# Fast Partitioned Learned Bloom FilterThis repository contains python implementation for our paper “[Fast Partitioned Learned Bloom Filter](https://arxiv.org/abs/2306.02846)” (NeurIPS 2023).
Three methods (PLBF, fast PLBF, and fast PLBF++) are implemented in two different frameworks: one was designed in the PLBF paper, and the other is our modified version.## AbstractA Bloom filter is a memory-efficient data structure for approximate membership queries used in numerous fields of computer science. Recently, learned Bloom filters that achieve better memory efficiency using machine learning models have attracted attention. One such filter, the partitioned learned Bloom filter (PLBF), achieves excellent memory efficiency. However, PLBF requires a $O(N^3 k)$ time complexity to construct the data structure, where $N$ and $k$ are the hyperparameters of PLBF. One can improve memory efficiency by increasing $N$, but the construction time becomes extremely long. Thus, we propose two methods that can reduce the construction time while maintaining the memory efficiency of PLBF. First, we propose fast PLBF, which can construct the same data structure as PLBF with a smaller time complexity $O(N^2 k)$. Second, we propose fast PLBF++, which can construct the data structure with even smaller time complexity $O(N k log N + Nk^2)$. Fast PLBF++ does not necessarily construct the same data structure as PLBF. Still, it is almost as memory efficient as PLBF, and it is proved that fast PLBF++ has the same data structure as PLBF when the distribution satisfies a certain constraint. Our experimental results from real-world datasets show that (i) fast PLBF and fast PLBF++ can construct the data structure up to 233 and 761 times faster than PLBF, (ii) fast PLBF can achieve the same memory efficiency as PLBF, and (iii) fast PLBF++ can achieve almost the same memory efficiency as PLBF.## Framework in the PLBF paperThis framework is designed to minimize the memory usage under the condition of expected false positive rate.Input arguments-–data_path: Csv file contains the items, scores, and labels
-–N: Number ofsegments-–k: Number ofregions-–F: Target overall false positive rateCommands- run the PLBF:python src/PLBFs/PLBF.py –data_path data/example.csv –N 50 –k 5 –F 0.01- run the FastPLBF:python src/PLBFs/FastPLBF.py –data_path data/example.csv –N 50 –k 5 –F 0.01- run the FastPLBF++:python src/PLBFs/FastPLBFpp.py –data_path data/example.csv –N 50 –k 5 –F 0.01## Framework in our paperThis framework is designed to minimize the expected false positive rate under the condition of memory usage.Input arguments-–data_path: Csv file contains the items, scores, and labels
-–N: Number ofsegments-–k: Number ofregions-–M: Target memory usage for backup Bloom filtersCommands- run the PLBF_M:python src/PLBFs/PLBF_M.py –data_path data/example.csv –N 50 –k 5 –M 1000- run the FastPLBF_M:python src/PLBFs/FastPLBF_M.py –data_path data/example.csv –N 50 –k 5 –M 1000- run the FastPLBF++_M:python src/PLBFs/FastPLBFpp_M.py –data_path data/example.csv –N 50 –k 5 –M 1000
Package: fast_plotter
To install:pipinstallfast-plotterOr for development:pipinstall-egit+https://github.com/FAST-HEP/fast-plotter.git#egg=fast-plotter(you may need the--userflag)Free software: Apache Software License 2.0FeaturesFunctions to turn binned dataframes in the form produced fromfast-carpenterA simple command-line tool to produce plots with decent defaults
Package: fast-poibin
fast-poibinfast-poibin is a Python package for efficiently computing PMF or CDF of Poisson binomial distribution.API Reference:https://privet-kitty.github.io/fast-poibin/Repository:https://github.com/privet-kitty/fast-poibin/Installationpipinstallfast-poibinPython versions 3.8 to 3.11 are supported.Basic Usage>>>fromfast_poibinimportPoiBin>>>poibin=PoiBin([0.1,0.2,0.2])>>>poibin.pmfarray([0.576,0.352,0.068,0.004])>>>poibin.cdfarray([0.576,0.928,0.996,1.])CopyrightCopyright (c) 2023 Hugo Sansaqua.
Package: fast-pony-crud
Fast Pony CRUDA tool for easily adding database CRUD routes using pony orm and FastAPI. This package works with database definitions using the common pony orm data types, but it still needs to be tested on all of them.Installationusing pip:pip install fast-pony-crudUsage:Define your database. You can do this in code, or you can use thePony ORM online editor. You will end up with a file that looks something like this. save it as "db.py".from datetime import datetime
from pony.orm import *

db = Database()

class Device(db.Entity):
    name = PrimaryKey(str)
    data_channels = Set('DataChannel')

class ChannelEntry(db.Entity):
    id = PrimaryKey(int, auto=True)
    time = Required(datetime,default=lambda:datetime.utcnow())
    numeric_value = Optional(float)
    metadata = Optional(Json)
    data_channel = Required('DataChannel')

class DataChannel(db.Entity):
    id = PrimaryKey(int, auto=True)
    name = Required(str)
    device = Required(Device)
    device_time_entrys = Set(ChannelEntry)
    data_type = Required('DataType')

class DataType(db.Entity):
    id = PrimaryKey(str)
    data_channels = Set(DataChannel)
    metadata_config = Optional(Json)Set up your API. Here we follow steps similar to the ones outlined in theFastAPI guide. The example uses an sqlite database for simplicity, but pony canconnect to a variety of database backends. Save this file as "api.py".from fastapi import FastAPI
from db import db # imports our database definition
from fast_pony_crud import create_crud_routes
import uvicorn

app = FastAPI()

# Connect to database
db.bind(provider='sqlite', filename='database.sqlite', create_db=True)
db.generate_mapping(create_tables=True)

# this is where the magic happens:
create_crud_routes(
    db,
    app,
    prefix="/db", # prefix for all datbase crud routes
    api_key="YOUR_SECRET_KEY") # api key for authentification sent on request headersLaunch the app by typing the following in your command shell:uvicorn api:app --reloadNow you can view your api docs by going tohttp://127.0.0.1:8000/docs
Package: fastpredict
fastpredictHas the following benefits over using fastai directly:cleaner API for batch predictiontakes care of using CPU or GPU if you specify in the constructorHow to use:fromfastpredictimportFastPredictBoolf=FastPredictBool.from_path('/path/to/learner.pkl')score=f.predict_path('/path/to/image.jpg')scores=f.batch_predict_paths(['/path/to/image1.jpg','/path/to/image2.jpg'])To prune a model:fromfastpredictimportFastPredictfromfastai.vision.allimportload_learnerlearn=load_learner('/path/to/current/learner.pkl')new_learn=FastPredict.clean_learner(learn)new_learn.export('/path/to/new/learner.pkl')
Package: fastpreprocess
FastpreprocessA new way to preprocess data for ML.Developing...Demohttps://user-images.githubusercontent.com/58617251/122407735-7b480800-cf9f-11eb-9145-2ba5217190bb.mp4InstallationpipinstallfastpreprocessStart Server"""Args-fn filenameOrbuffer -dm delimiter -p port -cl cloudconsole -lw pandas lowmemory"""fp-p9000#Orfp-fnhttps://bit.ly/3fCBwej-dm','-p9000#In cloud consolefp-fnhttps://bit.ly/3fCBwej-dm','-p9000-cltrue
Package: fast-prime
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` and `pip install fast-prime-numbers`
    - Access Python code using `from fast-prime 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: fast-prime-numbers
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` and `pip install fast-prime-numbers`
    - Access Python code using `from fast-prime 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: fast_prng
This module is an extension of a faster Pseudo Random Number Generator
originally designed in c. It mimics the functions: random.exponential and
random.normal found in numpy, while offering considerable speedup. Please seehttps://bitbucket.org/cdmcfarland/fast_prngfor details on the original
project.INSTALLATIONUse Pip to install under normal circumstances. This package can be manually
installed using:$ python3 setup.py installCONTACTFeedback is most appreciated. Please email questions or comments to me at
christopherdmcfarland+pypi [at] gmail [dot] com