Saravanan666/pypi-org huggingface dataset:
Package: jisho-api
jisho-apiA Python API built around scrapingjisho.org, an online Japanese dictionary.pipinstalljisho_apiRequestsYou can request three types of information:WordsKanjiSentencesTokenize sentencesThe search terms are directly injected into jisho's search engine, which means all of
the filters used to curate a search should work as well. For instance,"水"would look
precisely for a word with just that character.Checkhttps://jisho.org/docson how to use the search filters.jishosearchwordwater
jishosearchword水
jishosearchword"#jlpt-n4"The request replies arePydanticobjects.
You can check the structure of a word request injisho/word/cfg.py, and likewise for both kanji and sentences.You could also do so programatically, by doing:fromjisho_api.wordimportWordr=Word.request('water')fromjisho_api.kanjiimportKanjir=Kanji.request('水')fromjisho_api.sentenceimportSentencer=Sentence.request('水')fromjisho_api.tokenizeimportTokensr=Tokens.request('昨日すき焼きを食べました')Note: Almost everything that is available in a page is being scraped.Note: Kanji requests can come with incomplete information, because it is not available in the page.ScrapersYou can scrape the website for a list of given search terms.
Supply them with a.txtfile with the words separated by newlines.jishoscrapewordwords.txt
jishoscrapekanjikanji.txt
jishoscrapesentencesearch_words.txt
jishoscrapetokenssentences.txtAll of the resulting searches will be stored in~/.jisho/data.In case you want to scrape programatically you can:fromjisho_apiimportscrapefromjisho_api.wordimportWordword_requests=scrape(Word,['water','fire'],'to/path/')This will return a dictionary, which key values are the search term and request result.
Failing requests are not included.Cache and configIf you want cache enabled just runjishoconfigThis will create a~/.jisho/folder with aconfig.jsonwith your settings.
All your searches will be cached, and accessed if you search for the exact same term again.Notes and considerationsAccording to thisthread,
there is no official API, although there is a kind ofAPI requestmade by jisho.org, which is used to scrape words. This does not work for Kanji tho,
because it would search the Kanji as a word, and not have any relevant metadata for the character itself.Permissions to scrape also granted in the aforementioned thread.As stated in theirabout pageas well, jisho.org uses a collection of well-knownelectronic dictionaries:This site uses the JMdict, Kanjidic2, JMnedict and Radkfile dictionary files. -jisho.orgCredits and Acknowledgements for dataAll credit is given where it's due, and the several extracted resources is given at jisho.org'sabout page.
Package: jisho-scraper
# jisho-scraperJisho scraper capable of outputting search results to CSV or Anki.## RequirementsPython 3.7+## Installation`shell pip installjisho-scraper`## Usage`jisho-scraper[-h][--csvPATH][--tsvPATH][--ankiDECK] search_keyword `CSV/TSV exports the search results in “kanji,kana,meaning” form.Anki exporting feature requires [AnkiConnect](https://foosoft.net/projects/anki-connect/) to be installed with default settings (listening onhttp://localhost:8765). The app assumes that the there arejisho-scraper (kanji + kana + meaning)andjisho-scraper (kana + meaning)Anki note types available withKanji,Kana, andMeaningfields.
Package: jismesh
jismesh地域メッシュコードに関するユーティリティです。対応地域メッシュコード1次(標準地域メッシュ 80km四方): 140倍(拡張統合地域メッシュ 40km四方): 4000020倍(拡張統合地域メッシュ 20km四方): 2000016倍(拡張統合地域メッシュ 16km四方): 160002次(標準地域メッシュ 10km四方): 28倍(拡張統合地域メッシュ 8km四方): 80005倍(統合地域メッシュ 5km四方): 50004倍(拡張統合地域メッシュ 4km四方): 40002.5倍(拡張統合地域メッシュ 2.5km四方): 25002倍(統合地域メッシュ 2km四方): 20003次(標準地域メッシュ 1km四方): 34次(分割地域メッシュ 500m四方): 45次(分割地域メッシュ 250m四方): 56次(分割地域メッシュ 125m四方): 6インストールpipinstalljismesh緯度経度から地域メッシュコードを求めるメッシュコードに変換する世界測地系緯度経度と変換するメッシュコードの次数を指定します。使用例importjismesh.utilsasju# 緯度経度からメッシュコードを求める。meshcode=ju.to_meshcode(35.658581,139.745433,3)print(meshcode)53393599# pandas DataFrame中の緯度経度をメッシュコードに変換する。importpandasaspddf=pd.DataFrame({'lat':[35.658581,34.987574],'lon':[139.745433,135.759363]})print(df)latlon035.658581139.745433134.987574135.759363df['meshcode']=ju.to_meshcode(df.lat,df.lon,3)print(df)latlonmeshcode035.658581139.74543353393599134.987574135.75936352353680地域メッシュコードから次数を求めるメッシュコードからそのメッシュコードの次数を判定します。使用例importjismesh.utilsasju# メッシュコードの次数を求める。meshlevel=ju.to_meshlevel(53393599)print(meshlevel)3# pandas DataFrame中のメッシュコードを次数に変換する。importpandasaspddf=pd.DataFrame({'meshcode':[53393599,52353680]})print(df)meshcode053393599152353680df['level']=ju.to_meshlevel(df.meshcode)print(df)meshcodelevel05339359931523536803地域メッシュコードから緯度経度を求める求める緯度経度で表される点は、当該メッシュの基準点(南西端)から、
緯度座標上の点の位置(当該メッシュの単位経度の倍数)、経度座標上の点の位置(当該メッシュの単位緯度の倍数)
を指定します。使用例importjismesh.utilsasju# 南西端の緯度経度を求める。lat_sw,lon_sw=ju.to_meshpoint(53393599,0,0)print(lat_sw,lon_sw)35.6583333333139.7375# 北東端の緯度経度を求める。lat_ne,lon_ne=ju.to_meshpoint(53393599,1,1)print(lat_ne,lon_ne)35.6666666667139.75# 中心点の緯度経度を求める。lat_c,lon_c=ju.to_meshpoint(53393599,0.5,0.5)print(lat_c,lon_c)35.6625139.74375# 東隣接メッシュの中心点の緯度経度を求める。lat_east_neighbor_c,lon_east_neighbor_c=ju.to_meshpoint(53393599,0.5,1.5)print(lat_east_neighbor_c,lon_east_neighbor_c)35.6625139.75625# pandas DataFrame中のメッシュコードを中心点緯度経度に変換する。importpandasaspddf=pd.DataFrame({'meshcode':[53393599,52353680]})print(df)meshcode053393599152353680df['lat'],df['lon']=ju.to_meshpoint(df.meshcode,lat_multiplier=0.5,lon_multiplier=0.5)print(df)meshcodelatlon05339359935.6625139.7437515235368034.9875135.75625交差する地域メッシュコードを求める使用例importjismesh.utilsasju# 交差するメッシュコードを求める。generator_intersects=ju.to_intersects(53394611,4)formeshcodeingenerator_intersects:print(meshcode)533946111533946112533946113533946114TIPSlruキャッシュによる高速化importjismesh.utilsasjufromfunctoolsimportlru_cache# lruキャッシュ無効な関数の実行速度timeitju.to_meshcode(35.6625,139.75625,3)12.6µs±908nsperloop(mean±std.dev.of7runs,100000loopseach)# lruキャッシュ有効な関数の実行速度cached_to_meshcode=lru_cache(10)(ju.to_meshcode)timeitcached_to_meshcode(35.6625,139.75625,3)192ns±3.5nsperloop(mean±std.dev.of7runs,10000000loopseach)
Package: jismeshcode
jismeshcodeGitHubEnglish versionこのライブラリは地域メッシュコードを操作するライブラリです現在はメッシュコードに対応する矩形をジオデータ(GeoJSON)に変換する関数が提供されていますインストール環境Python 3.8 以上pipinstalljismeshcode使い方mc2geojson入力メッシュコード(4, 6, 8, 9, 10, 11 桁)出力メッシュコードで表されるエリアを囲うポリゴンを表す GeoJSON を返す>>>fromjismeshcode.jismeshcodeimportmc2geojson>>>mc2geojson(5439)'{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[139, 36.0], [140, 36.0], [140, 36.666666666666664], [139, 36.666666666666664], [139, 36.0]]]}, "properties": {"meshcode": "5439"}}'latlon2mc入力緯度, 経度, 返り値としたいメッシュコードの桁数(4, 6, 8, 9, 10, 11 桁)出力緯度経度からメッシュコードを返す>>>fromjismeshcode.jismeshcodeimportlatlon2mc>>>latlon2mc(36.2010417,138.415625,6)'543823'ハンズオンtutorials/jismeshcode.ipynb
Package: jisx0402
JIS X 0402このライブラリはJIS X 0402で規定されている全国地方公共団体コードを扱うライブラリです。参照しているデータは総務省のホームページからダウンロードしたものです。Installationpipinstalljisx0402UsageRecordクラスデータはこのRecordクラスのインスタンス単位で扱います。
このクラスはフィールドとして下記を持っています。全国地方公共団体コード都道府県名都道府県名(半角カナ)市町村名市町村名(半角カナ)例Record(code="010006",prefecture="北海道",prefecture_kana="ﾎｯｶｲﾄﾞｳ",city="",city_kana="")Record(code="011002",prefecture="北海道",prefecture_kana="ﾎｯｶｲﾄﾞｳ",city="札幌市",city_kana="ｻｯﾎﾟﾛｼ")code2name全国地方公共団体コード -> Recordの変換を行います。例>>>code2name("010006")Record(code="010006",prefecture="北海道",prefecture_kana="ﾎｯｶｲﾄﾞｳ",city="",city_kana="")searchフィールド名と正規表現を用いてRecordの検索を行います。例>>>search("full_city_name",r"福.県$")[Record(code='070009',prefecture='福島県',prefecture_kana='ﾌｸｼﾏｹﾝ',city='',city_kana=''),Record(code='180009',prefecture='福井県',prefecture_kana='ﾌｸｲｹﾝ',city='',city_kana=''),Record(code='400009',prefecture='福岡県',prefecture_kana='ﾌｸｵｶｹﾝ',city='',city_kana='')]
Package: jitbf
What's this? A fast brainfuck JIT interpreter!How to use?To use it, you can either write your code like this:fromjitbfimportbf2jitcode='<code>'jit=bf2jit(code)jit(), or use the jitbf command.You have to install tha package 'llvmlite' before using this package.How fast is it?I compared this JIT interpreter with theQuick Dirty Interpreter In C.
The brainfuck code used in the test ismandel.b.
The operating system used in the test is Windows 10 64 bit.Interpreter: jitbf 1.0.0                  Time to run: 3.5764139sInterpreter: jitbf 1.0.1                  Time to run: 3.2445391sInterpreter: Quick Dirty Interpreter In C Time to run: 8.3990561sTheHow does it work?The package is based on LLVM, it first translates brainfuck into LLVM IR code, then uses llvmlite to compile and run it.How will you improve it in the future?I will improve it by making the LLVM IR code it translates smaller, and runs faster. (already done in this version)
Package: jitcache
jitcachejitcache is a just-in-time key-value cache that is thread/process safe. jitcache also prevents repeated computation
when multiple workers request the same value.jitcache was designed to improve the performance of Plot.ly Dash apps by caching results and reducing CPU load.InstallationInstall via pip:$ pip install jitcacheDocumentationFull documentation available herehttps://jitcache.readthedocs.io/Basic Usagefrom jitcache import Cache
import time


cache = Cache()


@cache.memoize
def slow_fn(input_1, input_2, input_3=10):
    print("Slow Function Called")
    time.sleep(1)
    return input_1 * input_2 * input_3


print(slow_fn(10, 2))Output:Slow Function Called
40Plot.ly Dash Usageimport dash
import dash_html_components as html
from jitcache import Cache
import dash_core_components as dcc


cache = Cache()

app = dash.Dash(__name__)

server = app.server
app.layout = html.Div(
    children=[
        html.Div(id="output-container-dropdown1", children=[]),
        html.Div(id="output-container-dropdown2", children=[]),
        dcc.Dropdown(
            options=[
                {"label": "New York City", "value": "NYC"},
                {"label": "Montréal", "value": "MTL"},
                {"label": "San Francisco", "value": "SF"},
            ],
            value="MTL",
            id="dropdown",
        ),
    ]
)


@app.callback(
    dash.dependencies.Output("output-container-dropdown1", "children"),
    [dash.dependencies.Input("dropdown", "value")],
)
@cache.memoize
def update_output1(input_dropdown):
    print("run1")

    return input_dropdown


@app.callback(
    dash.dependencies.Output("output-container-dropdown2", "children"),
    [dash.dependencies.Input("dropdown", "value")],
)
@cache.memoize
def update_output2(input_dropdown):
    print("run2")

    return input_dropdown


if __name__ == "__main__":
    app.run_server(debug=True)
Package: jitcdde
JiTCDDE stands for just-in-time compilation for delay differential equations (DDEs). It makes use ofthe method described by Thompson and Shampinewhich is based on the Bogacki–Shampine Runge–Kutta method.
JiTCDDE is designed in analogy toJiTCODE:
It takes an iterable (or generator function) ofSymPyexpressions, translates them to C code, compiles them (and an integrator wrapped around them) on the fly, and allows you to operate this integrator from Python.
If you want to integrate ordinary or stochastic differential equations, check outJiTCODE, orJiTCSDE, respectively.Documentation– Read this to get started and for reference. Don’t miss that some topics are addressed in thecommon JiTC*DE documentation.Paper– Read this for the scientific background. Cite this (BibTeX) if you wish to give credit or to shift blame.Issue Tracker– Please report any bugs here. Also feel free to ask for new features.Installation instructions. In most cases,pip3 install jitcddeor similar should do the job.This work was supported by the Volkswagen Foundation (Grant No. 88463).
Package: jitcode
JiTCODE stands for just-in-time compilation for ordinary differential equations and is an extension ofSciPy’s ODE(scipy.integrate.ode).
Where SciPy’s ODE takes a Python function as an argument, JiTCODE takes an iterable (or generator function) ofSymPyexpressions, which it translates to C code, compiles on the fly, and uses as the function to feed into SciPy’s ODE.
If you want to integrate delay or stochastic differential equations, check outJiTCDDE, orJiTCSDE, respectively.Documentation– Read this to get started and for reference. Don’t miss that some topics are addressed in thecommon JiTC*DE documentation.Paper– Read this for the scientific background. Cite this (BibTeX) if you wish to give credit or to shift blame.Issue Tracker– Please report any bugs here. Also feel free to ask for new features.Installation instructions. In most cases,pip3 install jitcodeor similar should do the job.This work was supported by the Volkswagen Foundation (Grant No. 88463).
Package: jitcsde
JiTCSDE stands for just-in-time compilation for stochastic differential equations (SDEs).
It makes use of the method described by Rackauckas and Nie.
JiTCSDE is designed in analogy toJiTCODE:
It takes an iterable (or generator function) ofSymPyexpressions, translates them to C code, compiles them (and an integrator wrapped around them) on the fly, and allows you to operate this integrator from Python.
If you want to integrate ordinary or delay differential equations, check outJiTCODE, orJiTCDDE, respectively.Note that this is work in progress; features and names may change without warning.Documentation– Read this to get started and for reference. Don’t miss that some topics are addressed in thecommon JiTC*DE documentation.Paper– Read this for the scientific background. Cite this (BibTeX) if you wish to give credit or to shift blame.Issue Tracker– Please report any bugs here. Also feel free to ask for new features.Installation instructions. In most cases,pip3 install jitcsdeor similar should do the job.This work was supported by the Volkswagen Foundation (Grant No. 88463).
Package: jitcsim
JiTCSimJust-in time compilation of differential equations for simulation of complex network dynamics.It mainly useJiTC*DEpackages for solving differential equations.
CheckJITC*DEdocumentationfor more details.The JiTCSimdocumentationpage orpdffile.For a quick review of what you can usejitcsimfor, look at thenotebooks.I appreciateGerrit Ansmannfor preparingJiTC*DEpackages and many helps at resolving issues.installation the latest changescdJITCSIM# directory where you have setup.pypip3install-e.install from pip (v 0.3)pip3installjitcsiminstall clang (C/C++ compiler):sudo apt-get install clang
Package: jito-geyser
AboutThis library contains python code to interact withJito's Geyser Plugin.Downloading$pipinstalljito_geyserAccess TokenPlease request access to geyser by emailingsupport@jito.wtfExamplesPrinting slot updatesfromgrpcimportssl_channel_credentials,secure_channelfromjito_geyser.generated.geyser_pb2importSubscribeSlotUpdateRequestfromjito_geyser.generated.geyser_pb2_grpcimportGeyserStubGEYSER_URL="mainnet.rpc.jito.wtf"ACCESS_TOKEN="ACCESS_TOKEN_HERE"channel=secure_channel(GEYSER_URL,ssl_channel_credentials())client=GeyserStub(channel)formsginclient.SubscribeSlotUpdates(SubscribeSlotUpdateRequest(),metadata=[("access-token",ACCESS_TOKEN)]):print(msg)Listening to program account updatesThis example listens to pyth-owned accountsfromgrpcimportssl_channel_credentials,secure_channelfromsolders.pubkeyimportPubkey# note: probably need to install solders for this importfromjito_geyser.generated.geyser_pb2importSubscribeProgramsUpdatesRequestfromjito_geyser.generated.geyser_pb2_grpcimportGeyserStubGEYSER_URL="mainnet.rpc.jito.wtf"ACCESS_TOKEN="ACCESS_TOKEN_HERE"ACCOUNTS=[bytes(Pubkey.from_string("FsJ3A3u2vn5cTVofAjvy6y5kwABJAqYWpe4975bi2epH"))]channel=secure_channel(GEYSER_URL,ssl_channel_credentials())client=GeyserStub(channel)formsginclient.SubscribeProgramUpdates(SubscribeProgramsUpdatesRequest(programs=ACCOUNTS),metadata=[("access-token",ACCESS_TOKEN)]):print(msg)Functions availableThere are many functions available including:GetHeartbeatIntervalSubscribeAccountUpdatesSubscribeProgramUpdatesSubscribePartialAccountUpdatesSubscribeSlotUpdatesSubscribeTransactionUpdatesSubscribeBlockUpdatesDevelopmentInstall pip$curl-sSLhttps://bootstrap.pypa.io/get-pip.py|python3-Install poetry$curl-sSLhttps://install.python-poetry.org|python3-Setup environment and build protobufs$poetryinstall
$poetryshell
$poetryprotocLinting$poetryrunblack.
$poetryrunisort.Publishing package$poetryprotoc&&poetrybuild&&poetrypublish
Package: jit-open
This package provides a way to delay opening files until the something is
written to the file handle. This can be convenient when opening a large number
of files of which most of them will not be frequently used. To deal with
resource limits a queue is used from which, when full, the least frequent file
is closed.InstallationThe software is distributed viaPyPI, it can be installed withpip:pip install jit_openFrom sourceThe source is hosted onGitHub, to install the latest development version, use
the following commands.git clone https://git.lumc.nl/j.f.j.laros/jit-open
cd jit_open
pip install .UsageIn the following example, only the fileused.txtis created.>>>fromjit_openimportHandle,Queue>>>>>>queue=Queue()>>>used=Handle("used.txt",queue)>>>unused=Handle("unused.txt",queue)>>>>>>used.write("line 1\n")>>>used.write("line 2\n")LibraryThe library provides theHandleandQueueclasses.
Package: jito-searcher-client
AboutThis library contains tooling to interact with Jito Lab's Block Engine as a searcher.Downloading$pipinstalljito_searcher_clientKeypair AuthenticationPlease request access to the block engine by creating a solana keypair and emailing the public key tosupport@jito.wtf.ExamplesSync Clientfromjito_searcher_clientimportget_searcher_clientfromjito_searcher_client.generated.searcher_pb2importConnectedLeadersRequestfromsolders.keypairimportKeypairKEYPAIR_PATH="/path/to/authenticated/keypair.json"BLOCK_ENGINE_URL="frankfurt.mainnet.block-engine.jito.wtf"withopen(KEYPAIR_PATH)askp_path:kp=Keypair.from_json(kp_path.read())client=get_searcher_client(BLOCK_ENGINE_URL,kp)leaders=client.GetConnectedLeaders(ConnectedLeadersRequest())print(f"{leaders=}")Async Clientimportasynciofromjito_searcher_clientimportget_async_searcher_clientfromjito_searcher_client.generated.searcher_pb2importConnectedLeadersRequestfromsolders.keypairimportKeypairKEYPAIR_PATH="/path/to/authenticated/keypair.json"BLOCK_ENGINE_URL="frankfurt.mainnet.block-engine.jito.wtf"asyncdefmain():withopen(KEYPAIR_PATH)askp_path:kp=Keypair.from_json(kp_path.read())client=awaitget_async_searcher_client(BLOCK_ENGINE_URL,kp)leaders=awaitclient.GetConnectedLeaders(ConnectedLeadersRequest())print(f"{leaders=}")asyncio.run(main())DevelopmentInstall pip$curl-sSLhttps://bootstrap.pypa.io/get-pip.py|python3-Install poetry$curl-sSLhttps://install.python-poetry.org|python3-Setup environment and build protobufs$poetryinstall
$poetryshell
$poetryprotocLinting$poetryrunblack.
$poetryrunisort.Linting:poetryrunisort.
poetryrunblack.Publishing package$poetryprotoc&&poetrybuild&&poetrypublish
Package: jitr
Just-In-Time R-matrix (JITR)Quick startpip install jitrDescriptionSolves the radial Bloch-Shrödinger equation in the continuum using the calculable R-Matrix method on a Lagrange-Legendre mesh, using just-in-time (JIT) compilation fromnumba. The theory generally follows:Descouvemont, P. (2016). An R-matrix package for coupled-channel problems in nuclear physics. Computer physics communications, 200, 199-219,Baye, D. (2015). The Lagrange-mesh method. Physics reports, 565, 1-107,with the primary difference being that this code uses the energy-scaled version of the Bloch-Shrödinger equation, with dimensionless domain, $s = kr$, where $r$ is the radial coordinate and $k$ is the channel wavenumber.Capable of:non-local interactionscoupled-channelsSimple example: 2-body single-channel elastic scatteringimportnumpyasnpimportjitrfromnumbaimportnjit@njitdefinteraction(r,*args):(V0,W0,R0,a0,zz,r_c)=argsreturnjitr.woods_saxon_potential(r,V0,W0,R0,a0)+jitr.coulomb_charged_sphere(r,zz,r_c)energy_com=26# MeVnodes_within_radius=5# initialize system and description of the channel (elastic) under considerationsys=jitr.ProjectileTargetSystem(np.array([939.0]),np.array([nodes_within_radius*(2*np.pi)]),l=np.array([0]),Ztarget=40,Zproj=1,nchannels=1,)ch=sys.build_channels(energy_com)# initialize solver for single channel problem with 40 basis functionssolver=jitr.LagrangeRMatrixSolver(40,1,sys)# use same interaction for all channelsinteraction_matrix=jitr.InteractionMatrix(1)interaction_matrix.set_local_interaction(interaction)# Woods-Saxon and Coulomb potential parametersV0=60# real potential strengthW0=20# imag potential strengthR0=4# Woods-Saxon potential radiusa0=0.5# Woods-Saxon potential diffusenessRC=R0# Coulomb cutoffparams=(V0,W0,R0,a0,sys.Zproj*sys.Ztarget,RC)# set paramsinteraction_matrix.local_args[0,0]=params# run solverR,S,uext_boundary=solver.solve(interaction_matrix,ch,energy_com)# get phase shift in degreesdelta,atten=jitr.delta(S[0,0])print(f"phase shift:{delta:1.3f}+ i{atten:1.3f}[degrees]")This should print:phase shift: -62.801 + i 90.910 [degrees]Simple 2-body coupled-channel systemHere we present the wavefunctions for a S-wave scattering on 3 coupled $0^+$ levels. For details, seeexamples/coupled.
Package: jitsu
jitsu - jinja2 rendering with yamlSimple, easy rendering of templates for each data file, with one command.What jitsu does?jitsurenders data in each data file to specified jinja2 template, and
then store the results in a new directory_dist.UsageInstallation:pip install jitsuDirectory structure:All the templates go totemplatesdirectory. (Can also be specified with-toption)All the data files go todatadirectory. (Can also be specified with-doption.)Runjitsu.ApplicationsStatic websitestemplates/
 `-- blog-post.j2
  |- poem-post.j2
data/
 `-- post1.yaml
  |- post1.yaml
  |- post2.yaml
  |- post3.yaml
  |- post4.yaml
  |- poem1.yaml
  |- poem2.yamlRunningjitsuwill give something like this_dist/
 `-- post1.html
  |- post2.html
  |- post3.html
  |- post4.html
  |- poem1.html
  |- poem2.html
Package: jitt
jittsimple jitter toolTakes a baseline delay value, a percentage to calculate the min/max for allowed jitter values, and a desired precision; returns a float.Installationpip install jitt
python3 -m pip install jittUsagefrom jitt import Jitt
j = Jitt(delay=10.5, percent=25, precision=3).jitter
print(j)

# misc results for above values (min: 7.875 / max: 13.125)
# 8.177
# 12.975
# 10.565
# 9.022
# 11.43 (trailing zero is truncated)
Package: jitterbug
JitterbugJitterbug is a testing tool for fuzzing parallel code.
It does this by randomly insertingsleepcommands into various places in your code before each test run.
This is a great way to test the stability of multithreaded or parallel code.Jitterbug supportspy.testtests in Python 3.8+.QuickstartJitterbug is available onPyPI, so it can be installed via:$ pip install jitterbug --user --upgradeRun jitterbug specifying the location of your code and its unit tests:$ python -m jitterbug --source <PATH TO SOURCE> --test <PATH TO UNIT TESTS>
Package: jittok
jittokjittokis a Swiss Army Knife-like toolbox for data processing.Installationpip install jittokFeaturesjptext- Japanese text related functionstext normalization>>> from jittok import jptext
>>> jptext.normalization("ｾﾞﾝｶｸｶﾅ")
'ゼンカクカナ'parsing Japanese numeric string>>> from jittok import jptext
>>> jptext.to_numeric("一二,三四五億2百十万987")
1234502100987jpdatetime- Japanese datetime related functionsparsing Japanese "wareki" string>>> from jittok import jpdatetime
>>> jpdatetime.strptime("令和元年10月3日", "%Y年%m月%d日")
datetime.datetime(2019, 10, 3, 0, 0)
>>> jpdatetime.strptime("昭和64年1月1日", "%Y年%m月%d日")
datetime.datetime(1989, 1, 1, 0, 0)It parses"明治","大正","昭和","平成","令和"and does not support consistency check:>>> from jittok import jpdatetime
>>> jpdatetime.strptime("大正90年10月3日", "%Y年%m月%d日")
datetime.datetime(2001, 10, 3, 0, 0)