Saravanan666/pypi-org huggingface dataset:
Package: search-wikt
Search WiktionaryA simple command-line tool for searching English Wiktionary using Wiktionary Parser (click).Wiktionary definitions are licensed under CC BY-SA 4.0. Visit wiktionary at wiktionary.org (click).This project is currently a work in progress.InstallationTo install, type:pip install search_wiktTo run the program, type:py -m search_wikt apricot --language Englishor any other word or language you'd like to search. Runpy -m search_wikt -hor--helpfor a more detailed explanation.Alternatively, to clone the repository type:git clone https://github.com/lauramvx/search_wiktthencdto the project directory and run:py src\search_wikt\__main__.py apricot --language EnglishExamplespy -m search_wikt hunig -l "Old English"hunig
From Proto-West Germanic *hunag, from Proto-Germanic *hunagą. Cognate with Old Frisian hunig, Old Saxon honeg, and Old High German honag; also Old Norse hunang, from the alternative form *hunangą.

IPA: /ˈxu.nij/, [ˈhu.nij]
noun
huniġ n
honeypy -m search_wikt ac -l "Old English" -exac 1
From Proto-Germanic *aiks.

IPA: /ɑːk/
IPA: /ɑk/
noun
āc f
oak (wood or tree)
(poetic) an oaken ship
(masculine) the runic character ᚪ (/a/)
ac 2
From Proto-Germanic *ak.

IPA: /ɑːk/
IPA: /ɑk/
conjunction
ac
but
but instead: in this sense ac should sometimes be translated as "but," but most often it is best left untranslated


Sēo æx forġiett, ac þæt trēow ġeman.The axe forgets, but  the tree remembers.
The axe forgets, but  the tree remembers.
Ne ġēotaþ wē tēaras, ac blōd.We don't shed tears, we shed blood.
We don't shed tears, we shed blood.
Nōn egō, sed tū: “Nā iċ, ac þū.” Nōn bōs est, sed equus: “Nis hit nā oxa, ac is hors.”Non ego, sed tu: “Not me, you.” Non bos est, sed equus: “It's not an ox, it's a horse.”
Non ego, sed tu: “Not me, you.” Non bos est, sed equus: “It's not an ox, it's a horse.”
Package: searchxmlyandex
Yandex XML SearchYou can easily convert XML data to JSON data which fetched from YandexXMLSearch API.https://xml.yandex.comModule Pagehttps://pypi.org/project/searchxmlyandex/DependenciesPython >= 3.6PrerequisitesrequestsInstallingpip install searchxmlyandexUsageExample usage as shown below.from searchxmlyandex.YandexXMLSearch import YandexXMLSearchparser = YandexXMLSearch(user, api_key, query)Method ListgetQuerysetQuerysearchgetTotalPageCountgetTotalResultCountgetAllResultsgetResponseDategetJSONObjectgetResultJSONStringAuthorsAtaberk Yavuzer -https://github.com/0xSaiyajin
Package: search-youtube-url
search-youtube-url這是一個 Python 套件，可以用來搜尋 YouTube 影片並取得其網址。安裝使用 pip 來安裝 search-youtube-url：pip install search-youtube-url使用方法首先，你需要從 Google Cloud Platform 取得 YouTube Data API v3 的 API 金鑰，並設定為環境變數YOUTUBE_API_KEY。然後，你可以使用search_youtube_url函數來搜尋 YouTube 影片：from googleapiclient.discovery import build
import os
from dotenv import load_dotenvdef search_youtube_url(query):
load_dotenv()
api_key = os.getenv('YOUTUBE_API_KEY')
youtube = build('youtube', 'v3', developerKey=api_key)
request = youtube.search().list(
part="snippet",
maxResults=1,
q=query
)
response = request.execute()
if not response['items']:
return "No video found"
video_id = response['items'][0]['id']['videoId']
video_url = f"https://www.youtube.com/watch?v={video_id}"
return video_urlifname== "main":
query = "Could an orca give a TED Talk?"
print(search_youtube_url(query))這個函數會回傳搜尋結果中的第一個影片的網址。如果找不到影片，則會回傳 "No video found"。開發如果你想要參與開發，請先安裝開發所需的套件：pip install -r requirements.txt然後，你可以使用以下命令來執行測試：python -m unittest discover
Package: searchyt
searchytLightweight Python 3 library for searching youtube videosUsage:from searchyt import searchyt
syt = searchyt()
res = syt.search("Cute Little Puppy Doing Cute things")

for r in res:
    print(r)Prints:{'title': 'Cute Little Puppy Doing Cute things', 'author': 'YourUncleMoe', 'id': 'j5a0jTc9S10', 'thumb': 'https://i.ytimg.com/vi/j5a0jTc9S10/hqdefault.jpg'}
{'title': 'Cute Puppies Doing Funny Stuff', 'author': 'Kids And Animals Are The Best', 'id': 'Ce7hJ24a8yM', 'thumb': 'https://i.ytimg.com/vi/Ce7hJ24a8yM/hqdefault.jpg'}
{'title': 'Cutest Puppies Doing Funny Things - Cute Little Puppies Funny Videos | Cute Puppy Dog Compilation', 'author': 'Puppies TV', 'id': 'R7lnqfS1dUA', 'thumb': 'https://i.ytimg.com/vi/R7lnqfS1dUA/hqdefault.jpg'}
...
Package: searpent-classy-sdk
Python SDK for Searpent Classy APISimplify your calls to Searpent Classy API in Python.InstallationYou can install the Searpent Python SDK from PyPI:pip install searpent-classy-sdkThe SDK is supported on Python 3.6 to 3.10.How to useBefore you begin, make sure you have the API token and you know the name of your organization in the Classy API, e.g. 'dev.cz'.Store your API_URL and API_TOKEN in local environment variables and run:from classy_sdk import ClassySDK

client = ClassySDK('dev.cz')or pass them as arguments to the class:client = ClassySDK(source='dev.cz', api_url='some_url', api_token='your_token')you can also specify a timeout (in seconds):client = ClassySDK(source='dev.cz', timeout=10)Use yourclientto make API calls. For instance:client.list_cases(from_time='2021-09-01T13:00:00.000Z',
                  to_time='2021-10-02T13:00:00.000Z')
client.get_case('ce4c0299-6518-441b-b43d-1eeae666db36')
new_name = client.create_case('test_case')
client.update_case(new_name, 'test_case_updated')
client.upload_photo_from_file(new_name, 'tests/data/test_image.jpg',
                              'test_image', 'test_image')These are the methods that are currently available:list_cases:
Retrieves a list of cases within an indicated time period.get_case:
Retrieves one particular case.get_case_with_photos:
Retrieves one particular case with photos.create_case:
Creates a new case.update_case:
Updates the case name as displayed in the Classy interface.delete_case:
Marks the case to be deleted after 3 days.cancel_delete_case:
Cancels the case deletion.postpone_delete_case:
Postpones the case deletion to a requested timepoint.upload_photo:
Uploads a photo in base64 format to the case.upload_photo_from_file:
Uploads a photo file to the case.upload_pdf:
Uploads a PDF in base64 format to the case.upload_pdf_from_file:
Uploads a PDF file to the case.list_exports:
Retrieves a list of performed exports.get_export:
Retrieves one particular export.get_export_csv:
Retrieves a csv file with requested export.get_export_download_url:
Retrieves a csv download url for the requested export.list_inspections:
Retrieves a list of performed inspections.get_inspection:
Retrieves one particular inspection.get_inspection_with_photos:
Retrieves one particular inspection with photos.create_inspection:
Creates a new inspection.delete_inspection:
Marks the inspection to be deleted after 3 days.cancel_delete_inspection:
Cancels the inspection deletion.postpone_delete_inspection:
Postpones the inspection deletion to a requested timepoint.API documentationSee the complete API documentation:searpentclassy.docs.apiary.io.
Package: searvey
searveySearvey aims to provide the following functionality:Unified catalogue of observational data including near real time (WIP).Real time data analysis/clean up to facilitate comparison with numerical models (WIP).On demand data retrieval from multiple sources that currently include:U.S. Center for Operational Oceanographic Products and Services (CO-OPS)Flanders Marine Institute (VLIZ); Intergovernmental Oceanographic Commission (IOC)U.S. Geological Survey (USGS)InstallationThe package can be installed withpip:pip install searveyand conda`:conda install -c conda-forge searveyDevelopmentIn order to developsearveyyou will need:Python 3.8+GNU Makepoetry>= 1.2 (you can install it withpipx:pipx install poetry).poetry-dynamic-versioningwhich is a poetry plugin.
Take note that this needs to be installed in the same (virtual) environment as poetry, not in thesearveyone!
If you usedpipxfor installingpoetry, then you can inject it in the proper env withpipx inject poetry poetry-dynamic-versioning.pre-commit. You can also install this one withpipx:pipx install pre-commitIn order to setup the dev environment you can use:python3 -mvenv .venv
source .venv/bin/activate
make initwhich will:create and activate a virtual environment,install the full set of dependenciesSetup the pre-commit hooksAfter that you should run the tests with:make testIf you executemakewithout arguments, you should see more subcommands. E.g.make mypy
make lint
make docs
make depsCheck them out!JupyterIf you wish to use jupyterlab to test searvey, then, assuming you have an
existing jupyterlab installation, you should be able to add a kernel to it with:python-mipykernelinstall--user--namesearvey
Package: seas
pySEASpython Signal Extraction and SegmentationSee theReadTheDocspage to browse example experiment commands, documentation, and available function help.Publication describing functionality:
Weiser SC, Mullen BR, Ascencio D, Ackman JB (2023) Data-driven segmentation of cortical calcium dynamics. PLOS Computational Biology 19(5): e1011085.https://doi.org/10.1371/journal.pcbi.1011085Full datasets generated with this code: Weiser, S, Mullen, BR, Ackman, J (2023), Data-driven segmentation of cortical calcium dynamics, Dryad, Dataset,https://doi.org/10.7291/D1N96W
Package: seaschedule
seascheduleGet sea schedules from Maersk and other shipping lines.InstallationFromPyPI:python -m pip install seascheduleSetupThe following setup must be done before running seaschedule:Create below environment variables in your OS environment:SS_SMTP_HOST: SMTP host for sending notification emailsSS_IB_SFTP_USER: SFTP user for uploading schedule files to Information BrokerSS_IB_SFTP_PWD: SFTP password for uploading schedule files to Information BrokerSS_MAEU_API_KEY: API key given by MaerskSpecify below directory paths insite-packages\seaschedule\config\config.tomlfor storing the schedule data files and log files. For example:[environment]
directory.data = "/home/user1/seaschedule/data"
directory.log = "/home/user1/seaschedule/log"

# Windows
directory.data = "C:\Users\user1\Documents\seaschedule\data"
directory.log = "C:\Users\user1\Documents\seaschedule\data"How to Useseaschedule is a console application, namedseaschedule.>>> python -m seaschedule
Package: sea-serpent-python
sea-serpent-pythonPython bindings forsea-serpentInstallationsea-serpent-python can be installed by compiling manually or by downloading from
pypi.pipinstallsea-serpentManualgitclonehttps://github.com/jo1gi/sea-serpent-python.gitcdsea-serpent-python
maturinbuild--releaseThis will create a python wheel in target/wheel
Package: seashells
The official client forSeashells.io.InstallationSeashells is compatible with both Python 2 and Python 3. It’s recommended that
you use Python 3.You can install Seashells fromPyPI:pipinstallseashellsUsageSee the instructions onSeashells.iofor more
information about Seashells. For more information on how to use the command
line tool itself, see the built-in help:seashells--helpPackagingUpdate version information.Build the package usingpython3 setup.py sdist bdist_wheel.Sign and upload the package usingtwine upload-sdist/*.LicenseCopyright (c) 2017 Anish Athalye. Released under the MIT License. SeeLICENSE.rstfor details.
Package: seashells-sc
The official client forSeashells.io.InstallationSeashells is compatible with both Python 2 and Python 3. It’s recommended that
you use Python 3.You can install Seashells fromPyPI:pipinstallseashellsUsageSee the instructions onSeashells.iofor more
information about Seashells. For more information on how to use the command
line tool itself, see the built-in help:seashells--helpTips and TricksTo easily pipe both stdout and stderr to seashells, you can set up a shell
function (source):functionsea(){$*2>&1|seashells}Other Clientsroccomuso/seashells(Node.js)hans-strudle/seashells(Go)Note: other clients are not officially supported.PackagingUpdate version information.Build the package usingpython3 setup.py sdist bdist_wheel.Sign and upload the package usingtwine upload-sdist/*.LicenseCopyright (c) 2018 Anish Athalye. Released under the MIT License. SeeLICENSE.rstfor details.
Package: seashore
Seashore provides a shell abstraction
for easy automation of UNIX commands in Python.
The primary interface to many common tools is the command line;
for example,git,apt-get, andyum.
Even when there is an equivalent Python library –
such asdockeranddocker-py–
often a shell script needs to be quickly ported one-to-one.
Seashore helps in these cases by being able to write a
clean, simple, and testable Python API wrapper around
these tools.LicenseSeashore is (C) 2017-2020 Contributors, and licensed under the MIT license.
See the LICENSE file for more details.HistorySeashore was originally released by Shopkick.
Since then,
it has been forked and is part of the El Camino Real
suit.ContributorsMoshe ZadkaMahmoud HashemiKurt B. Rose
Package: seasmart-python-api-client
seasmart-python-api-clientA very incomplete Python client library for the SeaSmart API.Setuppip install seasmart-python-api-clientExample Usagefrom SeaSmartApiClient.api import SeaSmartApiClient

seasmart = SeaSmartApiClient(
    username="test_username",
    api_key="test_api_key"
)
cages = seasmart.get_cages()ContributingInformation oncontributingto this python library.TestingTo run the tests,python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pytest
deactivateto run the tests for this project.
Package: seasnake
A tool to manage conversion of C++ code to Python.Sometimes you will find a great algorithm, but find that the only
implementation of that algorithm is written in C or C++. In some cases
it might be possible to wrap that C/C++ code in a Python C module.
However, if a C module is not an option, you need to be able to convert
the C/C++ implemention into a Pure Python implementation.SeaSnake was written to automate the conversion ofWebKitsources
into a version that could be used byColosseum.QuickstartIn your virtualenv, install SeaSnake, and then run it, passing in
the name of a C++ source file (or files, if you want to provide
the header as well as the cpp file):$ pip install seasnake
$ seasnake path/to/MyClass.h path/to/MyClass.cpp -o MyClassThis will generate aMyClassPython module.CommunitySeaSnake is part of theBeeWare suite. You can talk to the community through:@pybeeware on TwitterTheBeeWare Users Mailing list, for questions about how to use the BeeWare suite.TheBeeWare Developers Mailing list, for discussing the development of new features in the BeeWare suite, and ideas for new tools for the suite.ContributingIf you experience problems with SeaSnake,log them on GitHub. If you
want to contribute code, pleasefork the codeandsubmit a pull request.
Package: seasonal
Robustly estimate and remove trend and periodicity in a timeseries.Seasonalcan recover sharp trend and period estimates from noisy
timeseries data with only a few periods.  It is intended for
estimating season, trend, and level when initializing structural
timeseries models like Holt-Winters. Input samples are
assumed evenly-spaced from a continuous real-valued signal with noise but
no anomalies.The seasonal estimate will be a list of period-over-period averages at
each seasonal offset. You may specify a period length, or have it
estimated from the data. The latter is an interesting capability of
this package.Trend removal in this package is in service of isolating and
estimating the periodic (non-trend) variation. A lowpass smoothing of
the data is removed from the original series, preserving original
seasonal variation.  Detrending is accomplishd by a coarse fitted
spline, mean or median filters, or a fitted line.Seehttps://github.com/welch/seasonal/README.mdfor details on installation, API, theory, and examples.Dependenciespackage: numpy, scipyextras:  pandas, matplotlib
Package: seasondh
SEASON Datahub PlatformIntroductionSEASON Datahub Platform (seasondh) provides visual convenience for artificial intelligence data analysis based on web technology.InstallationpipinstallseasondhRun Datahubrun web daemoncd~
mkdirworkspacecdworkspace
seasondhweb--port3000--host127.0.0.1--password1234connecthttp://127.0.0.1:3000on your web browserBasic Conceptsseasondh provides pipeline-based data processing flow. like below.Each component is designed with the concept of an app and performs tasks independently according to the sequence of data processing.
Package: seasondog
# seasondogSmall tool for saving your progress in watching series.## InstallationYou can install seasondog trough pip:sudo pip install seasondog## ConfigurationFirst you need to edit copy default .sdogrc from repository to ~, and edit it:* PLAYER: you need to provide command line for starting your videoplayer. Two placeholders: videofile and player_argsThere is another configuration options, they're all documented in example .sdogrc## UsageOn first call in directory you can provide player args for this directory, where you can setup various per-directory settings (for example - add subtitles). You can use specified functions for file matching in that string, which result substitutes into string.For example, subtitles for mplayer should be-sub @subs(subs/:-1:,), that means - find subtitles in directory sub/, with no limits, and separate all subtitles by symbol ','.Available funtions:@files(path:file_limit:file_delimeter)lookup for files (matching uses similar algoritm, but without extension check) in path, limit results to file_limit (-1 for unlimited), join all results by file_delimeter.You can skip arguments, the defaults are: path - ., file_limit - (1), file_delimeter - (,).@subs(path:file_limit:file_delimeter)just like @files, but lookup only subtitles (check by extension).After you finish setup you can use such commands:* sdog - watch next episode* sdog p(rev) - watch prev episode* sdog w(atch) - watch current episode* sdog set <EPISODE> - set episode directly and watch it* sdog r(eset) - delete current directory (or directory, provided by -f argument) from database* sdog s(tatus) - get current dir status information* sdog args - update player args in database* sdog m(igrate) <DEST> - migrate current's directory status into DEST, with directory name preserved. You can disable preservation and provide full path using -p argument. Also, instead of using current directory you can provide it by yourself with -f <FROM> argument.* sdog cleanup - remove not-existent directories from databaseThere is several global options also:* -a ARGS - override player args* --database=PATH - override default database location## LicenseCopyright © 2014 Vasiliy HorbachenkoDistributed under the Eclipse Public License either version 1.0 or (atyour option) any later version.
Package: season-polar-plots
Seasonality polar plotspip install season-polar-plotsThis is a package to create polar plots for displaying seasonal trends in time series data.
Requiresmatplotlib (3.5.1)numpy (1.19.2)pandas (1.3.5)seaborn (0.11.2)scipy (1.6.0)1. Read data into SeasonData class:>>> from season_polar_plots import SeasonDataSeasonData(data, year_start, year_end, t_res)Parametersdatapandas Series or single column DataFrame with datetime indexyear_start(int) start year of period to be analyzedyear_end(int) end year of period to be analyzedt_res(str) 'daily' or 'monthly': temporal resolution. Monthly values can be aggregated from daily values if 'monthly' is chosen (see sp_plot() and get_mgrid() function)2. Plot function:self.sp_plot(mode = 'all', label=None ,rd_years = True, col = 'viridis_r', a = 1, psize = None, pmarker = None, nylabels = 10, off = 0, rlab_angle = 15, linreg = False, start_month = 1)Parameterslabel(str) label for time series variablemode(str) 'all' uses all data points; daily resolution: 'min' or 'max' filter time series on annual extreme values; monthly resolution:  'sum', 'mean' / 'min', 'max' aggregate / filter data for each monthrd_years(bool) as default, years are plotted in radius direction; rd_years=False plots variable in radius directioncol(str) color gradient (default 'viridis_r')a(float) transparency alpha (0-1)psize(float) marker size for daily data pointspmarker(MarkerStyle) marker style  for daily data pointsnylabels(int) number of (year) labels in radius directionoff(int or float) off-set from circle centerrlab_angle(float) angle of the radius axis labelslinreg(bool) if True plots linear regression (day in year ~ year) in polar projection (only for daily extreme values)start_month(int) start month for linear regressionReturns: Plot; if linreg=True prints R² and p-values for slope and interceptObtain annual extreme values from daily time series:self.get_ev( mode)Parametersmode(str) 'min' or 'max': filter time series on annual extreme valuesReturns: DataFrame (containing nr of day in year of extreme values, extreme values)Obtain aggregated / filtered monthly values:self.get_mgrid(mode)Parametersmode(str) 'all' if data is already in monthly resolution; 'sum', 'mean' / 'min', 'max' aggregate / filter data for each monthReturns: DataFrame (containing monthly data)3. Von-Mises distribution (ML fit with scipy):self.von_mises(mode, plot = True, print_par = True, bins = 15, col_hist = 'b', col_vm ='r', off = 0.1, a = 1, rwidth = 0.8)Parametersmode(str) 'all' to use all data points, 'min' or 'max' to filter on extreme valuesplot(bool) if True plots the data as circular histogram and fitted von-Mises distributionprint_par(bool) if True prints the fitted von-Mises parameters kappa and mubins(int) number of bins for histogramcol_hist(str) color of the histogramcol_vm(str) color of the fitted von Mises distributionoff(float) off-set from circle centera(float) transparency alpha (0-1)rwidth(float) relative width of histogram binsReturns: Plot; kappa, mu, mu_nday
Package: seastate
SeastateSummaryCollect ocean measurement data based on location and timeframeFeaturesClosest active station is selected for each measurementReaches into secondary historical archives when requiredReturns pandas dataframe compatible listsAvailable measurements: Tide, wind, water temp, air temp, air pressure, conductivity and swell informationDatasources: NOAA NDBC, NOAA Tides and CurrentsInstallingpip install seastateQuick startfrom seastate.seastate import SeaState
from datetime import datetime

# make SeaState object for specific location
san_diego = SeaState(32,-117)

# retrieve measurements for today
san_diego_today = san_diego.measurements_from_date_range(datetime.today())

san_diego_today['tide'] -> [{t: time, v: value, s: stdev]
san_diego_today['wind']-> [{t: time, v: value, d: direction, g: gust}]
san_diego_today['water_temp']-> [{t: time, v: value}]
san_diego_today['air_temp']-> [{t: time, v: value}]
san_diego_today['air_press']-> [{t: time, v: value}]
san_diego_today['wave']-> [{t: time, v: Wave Height, dpd: Dominant Period, mwd: dpd Direction}]
san_diego_today['conductivity']-> [{t: time, v: value}]Measurement details for NDBC arehereand for Tides and CurrentshereMeasurement x API breakdownMeasurementT&CNDBCtideyywindyywater_tempyyair_tempyyair_pressyywaveyconductivityyMore ExamplesMeasurements for past 30 daysfrom seastate.seastate import SeaState
from datetime import datetime, timedelta

start = datetime.today()-timedelta(days=30)
end = datetime.today()
san_diego_past_30 = san_diego.measurements_from_date_range(start,end)Hourly Slicessan_diego_past_30_hourly = san_diego.hourly(start,end) # this returns a single reading per hour
# experimental feature
# no guarantee between APIs that readings will align or exist in all cases
Package: seata-python
seata-pythonseata-python是一个seata分布式事务python版本的客户端，什么是seata访问https://seata.io项目介绍本人不是python开发者，主要是为了学习一下python才写的这个项目，目前项目还没有写完，主要是完成seata AT模式
项目里面有很多实现不合理的地方，欢迎各位大佬前来修改。谢谢！项目完成度seata-server 网络协议模块TCP协议socket优化封装Connection, Cursor处理前后置镜像insertdeleteupdate解析 sql 模块insertdeleteupdateselect for updateundo 回滚模块insertdeleteupdate优化代码结构TMClient 结构RMClient 结构项目整体结构配置集成filenacos其他其他数据库GRPC 协议等等项目依赖Python3.7

test:
PyMySQL==1.0.2pip源配置https://pypi.org/simple 默认
https://pypi.tuna.tsinghua.edu.cn/simple/