Saravanan666/pypi-org huggingface dataset:
Package: simple-css-selector-transform
Simple CSS Selector TransformsSimple transformations for CSS SelectorsDescriptionA library for simple transformations on CSS selectors, developed to be used indoxysphinx.Installationpipinstall-Usimple-css-selector-transformor install withPoetrypoetryaddsimple-css-selector-transformMakefile usageMakefilecontains a lot of functions for faster development.1. Download and remove PoetryTo download and install Poetry run:makepoetry-downloadTo uninstallmakepoetry-remove2. Install all dependencies and pre-commit hooksInstall requirements:makeinstallPre-commit hooks coulb be installed aftergit initviamakepre-commit-install3. CodestyleAutomatic formatting usespyupgrade,isortandblack.makecodestyle# or use synonymmakeformattingCodestyle checks only, without rewriting files:makecheck-codestyleNote:check-codestyleusesisort,blackanddarglintlibraryUpdate all dev libraries to the latest version using one comandmakeupdate-dev-deps4. Code securitymakecheck-safetyThis command launchesPoetryintegrity checks as well as identifies security issues withSafetyandBandit.makecheck-safety5. Type checksRunmypystatic type checkermakemypy6. Tests with coverage badgesRunpytestmaketest7. All lintersOf course there is a command torulerun all linters in one:makelintthe same as:maketest&&makecheck-codestyle&&makemypy&&makecheck-safety8. Dockermakedocker-buildwhich is equivalent to:makedocker-buildVERSION=latestRemove docker image withmakedocker-removeMore informationabout docker.9. CleanupDelete pycache filesmakepycache-removeRemove package buildmakebuild-removeDelete .DS_STORE filesmakedsstore-removeRemove .mypycachemakemypycache-removeOr to remove all above run:makecleanup📈 ReleasesYou can see the list of available releases on theGitHub Releasespage.We followSemantic Versionsspecification.We useRelease Drafter. As pull requests are merged, a draft release is kept up-to-date listing the changes, ready to publish when you’re ready. With the categories option, you can categorize pull requests in release notes using labels.List of labels and corresponding titlesLabelTitle in Releasesenhancement,feature🚀 Featuresbug,refactoring,bugfix,fix🔧 Fixes & Refactoringbuild,ci,testing📦 Build System & CI/CDbreaking💥 Breaking Changesdocumentation📝 Documentationdependencies⬆️ Dependencies updates🛡 LicenseThis project is licensed under the terms of theMITlicense. SeeLICENSEfor more details.📃 Citation@misc{simple-css-selector-transform,author={Stream HPC B.V.},title={Simple transformations for CSS Selectors},year={2023},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/StreamHPC/simple-css-selector-transform}}}CreditsThis project was generated withpython-package-template
Package: simplecurry
Simplecurry’scurriedprovides a simple way to use curried functions in Python. There are alternative implementations, but they are unnecessarily complex.ExampleThis is an example on how to use it:from simplecurry import curried

@curried
def add(a, b, c):
    return a + b + c

add(1)(2)(3) # Returns 6
add(1, 2)(3) # Returns 6
add(1)(2, 3) # Returns 6
add(1, 2, 3) # Returns 6
Package: simple-czsc
Simple缠论量化工具集简介​	缠论只是一个工具，请不要将它神化，发现一些客观的规律远比追求虚无缥缈的神论更有意义。​	本模型按照简单可控的方式实现，与原著的描述不尽相同，为了模型可推导做了一些调整，模型整体按照零参数设计，不必关注策略应该设置定什么参数，是否过度拟合等问题。​	缠论是一种结构化分析方法，但每个人对于缠论的理解不同，如果有计算机基础，每个人量化的缠论模型也不会完全一致，所以没必要过多纠结是否与自己的实现方式一样，学缠的人一旦纠于细节，理论化、精度化、完美化，基本也就缠进去了。从另一个角度说，缠论是一种视觉体现，是对市场阶段性顶底和交易密集区的量化展示，本模型重在交易，在保证无任何未来函数上实现了实时模型，如果仔细观察最后一个笔和线段是动态的，随着行情的变化而变化，但是确认了的构件是不会改变的，这就保证了回测的稳定性，在本模型基础上按照规则开发出的策略没有任何漂移。​	开源本模型，提供一种无参的量化实现，爱好者可以继续摸索，形成自己一套技术分析体系。在市场上仅仅懂得技术分析是远远不够的，但是作为散户，没有精力读研报做调研，技术分析仍是一种有效的市场解读方法。技术分析体系的建立意味着面对杂乱无章的市场，你有了自己的解读语言，可以与之对话、交流、提升，进一步可以形成一套交易策略，知道自己在自己的体系下什么情况亏什么时候赚，在概率基础上可以形成持久的可调整的交易框架。希望可以帮助各位找到自己的方法论。演示地址：http://simple-trade.cn:18188/账户：guest/1架构概览图本项目基本实现了缠论模型计算、可视化、选股、策略模板等功能，部分功能仍在开发中。策略及选股参考Demohttps://github.com/simple-trade/simple-czsc-demo交流
Package: simple-dag-editor
simple-dag-editorSimpleDagEditor is a zero configuration plugin forApache Airflow.
It provides a file managing interface that points to your dag_folder directory.
With this plugin you will be able to delete, duplicate & edit you dags.Use-casesThis plugin has been developed for a native deployment, where the Airflow webserver & scheduler are sharing the same dag_folder directory (as a mount).The plugin is not designed for deployments that synchronize the dag_folder from a remote source (Git/S3)You have the ability to disable the plugin for production env (see conf section)System RequirementsAirflow >= 1.10.2Python 3.7+InstallationInstall the plugin (pip install simple-dag-editor)Restart airflow instanceOpen Admin - Simple DAG editorOptional configurations[dag_editor] - disabled (Default - False)Screenshots
Package: simpledali
simpledaliDatalink in pure python.Datalink is a protocol for near-realtime transfer of seismic data, usually in miniseed, but has flexibility to carry any payload such as JSON. The Protocol is defined athttps://iris-edu.github.io/libdali/datalink-protocol.htmlSeeringserverfrom IRIS
for the most common datalink server instance. The public instance
atrtserve.iris.washington.edu/allows access to near-realtime streaming seismic data over web sockets atws://rtserve.iris.washington.edu/datalinkFor parsing for miniseed2 andminiseed3seesimplemseed, also in pure python.Support for both regular sockets and websockets. For example:import asyncio
import simpledali

async def main():
    host = "localhost"
    port = 16000
    uri = f"ws://{host}:{port}/datalink"
    verbose = True

    programname = "simpleDali"
    username = "dragrace"
    processid = 0
    architecture = "python"

    # for regular socket (DataLinkPort)
    async with simpledali.SocketDataLink(host, port, verbose=verbose) as dali:
        serverId = await dali.id(programname, username, processid, architecture)
        print(f"Connect to {host}:{port} via regular socket")
        print(f"Socket Id: {serverId.message}")
    # for web socket (ListenPort)
    async with simpledali.WebSocketDataLink(uri, verbose=verbose) as dali:
        serverId = await dali.id(programname, username, processid, architecture)
        print(f"Connect to {uri} via websocket")
        print(f"WebSocket Id: {serverId.message}")

asyncio.run(main())The dali2jsonl script will archive '/JSON' packets as JSON Lines. This is a similar function to the MSeedWrite configuration on ringserver, but in a separate process and saves JSON packets instead of miniseed. See jsonlines.org for the file format, basically one JSON
value per line.dali2jsonl --help
usage: dali2jsonl [-h] [-v] -c CONF

Archive JSON datalink packets as JSON Lines.

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         increase output verbosity
  -c CONF, --conf CONF  Configuration as TOMLThe TOML configuration looks like:[datalink]
# datalink host, defaults to localhost
host='localhost'
# datalink port, defaults to 18000
port=15004
# Match regular expression pattern on stream ids, ex '.*/JSON'
match='.*/JSON'

[jsonl]
# JSONL Write pattern, usage similar to MSeedWrite in ringserver
write='/data/scsn/www/jsonl/%n/%s/%Y/%j/%n.%s.%l.%c.%Y.%j.%H.jsonl'ExampleThere are examples of sending and receiving Datalink packets in the example directory.
Package: simple-dataclass-settings
simple_dataclass_settingsSimple library on top of dataclasses to create shiny-looking configs from environment variables.Example usageimportsimple_dataclass_settings@simple_dataclass_settings.settingsclassSomeChildSettings:some_float:float=simple_dataclass_settings.field.float(var='SOME_FLOAT',)@simple_dataclass_settings.settingsclassSettings:some_child:SomeChildSettingssome_string:str=simple_dataclass_settings.field.str(var='SOME_STRING',default='test',)some_number:int=simple_dataclass_settings.field.int(var='SOME_NUMBER',default=69,)some_flag:bool=simple_dataclass_settings.field.bool(var='SOME_FLAG',default=False,)some_list:bool=simple_dataclass_settings.field.str_list(var='SOME_LIST',default=['test','vars'],)settings=simple_dataclass_settings.populate(Settings)Interfacesettings- decorator that converts a class to the settings class (just a regular frozen (and slotted) dataclass)field- class that creates a special setting field object (that holds metadata about environment variables and its processing)populate- function that creates an instance of passed settings classshow- function that shows a list of environment variables used by the passed classread_envfile- function that populates variables from the env file
Package: simple-data-collector
Simple Data CollectorThread-safe and atomic collection of tabular data into csv-files.The simple-data-collector provides a single class with with following methods:saveappendloadremoveIt was created as a utility function for theHyperactive-package. It was intended to be used as a search-data collection tool. The search-data can be collected during the optimization run as a dictionary viaappendor after the run as a dataframe with thesave-method.Theappend-method is thread-safe to work with hyperactive-multiprocessing. Thesave-method is atomic to avoid accidental data-loss.The simple-data-collector handles functions in the data by converting them to strings. If the data is loaded you can pass the search_space to convert the strings back to functions.Installationpip install simple-data-collectorExampleimportnumpyasnpfromhyperactiveimportHyperactivefromsimple_data_collectorimportDataCollectorcollector=DataCollector("./search_data.csv")# the csv is created automaticallydefackley_function(para):x,y=para["x"],para["y"]loss=(-20*np.exp(-0.2*np.sqrt(0.5*(x*x+y*y)))-np.exp(0.5*(np.cos(2*np.pi*x)+np.cos(2*np.pi*y)))+np.exp(1)+20)data_dict=para.para_dictdata_dict["score"]=-losscollector.append(data_dict)# you can append a dictionary to the csvreturn-losssearch_space={"x":list(np.arange(-10,10,0.01)),"y":list(np.arange(-10,10,0.01)),}hyper=Hyperactive()hyper.add_search(ackley_function,search_space,n_iter=3000)hyper.run()search_data=hyper.search_data(ackley_function)# collector.save(search_data) # save a dataframe instead of appending a dictionarysearch_data_l=collector.load(search_space)# load dataprint(search_data_l)
Package: simple-data-flow
Thesimple-workflowlibrary provides basic building blocks for creaing and executing
simple workflows. It not only contains abstract superclasses that can be used to
implement new actors (= operators), but also a set of useful actors for controlling
the flow of data and simple I/O.Changelog0.0.1 (2023-01-10)initial release, migrated from python-weka-wrapper3
Package: simple-dataframe-generator
simple_dataframe_generatorPersonal project to generate pandas DataFrame quick and easy.Installationpip install simple-dataframe-generator==1.0.3Supported column typesInteger -add_int_col()Float -add_float_col()Categorical -add_cat_col()Datetime -add_datetime_col()All column types supportallow_none.Usageimportpandasaspdimportnumpyasnpfromsimple_dataframe_generatorimportSDGsdg=SDG()# add integer column with specified parameterssdg.add_int_col(col_name='age',min_val=0,max_val=99)# add integer column with specified parameters, allow missing valuessdg.add_int_col(col_name='favorite_number',min_val=-100,max_val=100,allow_none=True,none_prob=10)# add float column with specified parameterssdg.add_float_col('distance',min_val=0.0,max_val=200.0)# add categorical column with pre-defined categoriescategories=['New York','Chicago','Los Angeles']sdg.add_cat_col('city',categories=categories,allow_none=True)# add datetime columnsdg.add_datetime_col('last_seen',min_date='2020-01-01',max_date='2023-02-01')# generate pandas DataFrame with specific row countdf=sdg.generate_dataframe(rows=10)Resultagefavorite_numberdistancecitylast_seen06981184.517Chicago2022-08-12 10:39:591731118.874Chicago2021-01-06 11:57:2221033199.226New York2020-05-29 10:37:36347-36194.205Chicago2022-05-30 21:26:24425-6624.9345<NA>2021-03-26 03:19:09536-1221.0734New York2021-01-19 03:22:27621<NA>165.243Chicago2022-01-24 04:20:39737<NA>3.48131Chicago2020-05-30 18:37:24861-7790.0552New York2021-01-07 14:43:35921-19173.3Los Angeles2020-01-01 00:53:00Authorjmatusov
Package: simpledatasetlabeler
simpledataset-labelerSimple browser-based interface for dataset labeling.Currently only Object Detection task is supported.Setuppip install simpledatasetlabelerUsagedataset_labeler <input_filepath> <output_filepath> [--host <hostname>] [--port <port_number>] [--dataset_type {image_classification,object_detection}]Then, open your favorite browser and access to the "http://localhost:5000". (If you specified --port option, access the specified port instead.)How to build a packagemake
Package: simpledatatable
simpledtThis is a third party party package for the frameworkFlet.It enablesFletusers to serializePandas DataFrameSQLCSVJSONExcelInto Flet'sDataTable.For documentation and other details, please visit the projectrepositoryRelease notes:Version 0.3.4Added **kwargs to every shortcut functionExample usage:importfletasftfromsimpledtimportCSVDataTabledefmain(page:ft.Page):csv=CSVDataTable("MOCK_DATA2.csv",delimiter=";")datatable=csv.datatablepage.add(datatable)ft.app(target=main)
Package: simple-date
A wrapper around thedatetime,pytzandtzlocalpackages for Python 3.2+.Full docson github.ExamplesJust give me a UTC datetime for these dates!>>> for date in '1/6/2013 BST', '1/6/2013 EST', 'Tue, 18 Jun 2013 12:19:09 -0400':
>>>     print(best_guess_utc(date))
2013-05-31 23:00:00+00:00
2013-01-06 05:00:00+00:00
2013-06-18 16:19:09+00:00What time is it now, in New York?>>> SimpleDate(tz='America/New_York')
SimpleDate('2013-06-14 13:14:17.295943 EDT', tz='America/New_York')And what time is that in the UK (the country code is for Great Britain)?>>> SimpleDate('2013-06-14 13:14:17.295943 EDT').convert(country='GB')
SimpleDate('2013-06-14 18:14:17.295943 BST', tz='Europe/London')What is the UTC for this email date?>>> SimpleDate('Fri, 14 Jun 2013 13:13:42 -0400').utc
SimpleDate('Fri, 14 Jun 2013 17:13:42 +0000', tz='UTC')What’s the date a week from now (I live in Chile)?>>> SimpleDate() + timedelta(days=7)
SimpleDate('2013-06-21 13:55:20.791519 CLT', tz='America/Santiago')The day of the week for Xmas this year?>>> SimpleDate(2013, 12, 24).weekday
1And as a naive datetime?>>> SimpleDate(2013, 12, 24).naive.datetime
datetime.datetime(2013, 12, 24, 0, 0)What’s the time in EST for epoch 1234567890?>>> SimpleDate(1234567890, tz='EST')
AmbiguousTimezone: 3 distinct timezones found: <'EST'>; <'Australia/NSW'>; ...Whoa!  What are those crazy Australians doing?  Let’s force the USA (only):>>> SimpleDate(1234567890, tz='EST', country='US')
SimpleDate('2009-02-13 18:31:30.000000 EST')Alternatively, we could give priority to the USA and take the first solution
we find:>>> SimpleDate(1234567890, tz='EST', country=prefer('US'), unsafe=True)
SimpleDate('2009-02-13 18:31:30.000000 EST')And what day is that?>>> SimpleDate(1234567890, tz='EST', country=prefer('US'), unsafe=True).strftime('%A')
'Friday'Licence(c) 2013,2015 Andrew Cooke,andrew@acooke.org; released into the public domain
for any use, but with absolutely no warranty.
Package: simpledb
### simpledbMiniature [Redis](https://redis.io)-like database written in Python.#### installing```$ pip install simpledb```Alternatively, you can install from git:```$ git clone https://github.com/coleifer/simpledb$ cd simpledb$ python setup.py install```#### runningby default, the simpledb server runs on localhost:31337.the following options are supported:```Usage: simpledb.py [options]Options:-h, --help            show this help message and exit-d, --debug           Log debug messages.-e, --errors          Log error messages only.-t, --use-threads     Use threads instead of gevent.-H HOST, --host=HOST  Host to listen on.-m MAX_CLIENTS, --max-clients=MAX_CLIENTSMaximum number of clients.-p PORT, --port=PORT  Port to listen on.-l LOG_FILE, --log-file=LOG_FILELog file.-x EXTENSIONS, --extension=EXTENSIONSImport path for Python extension module(s).```to run with debug logging on port 31339, for example:```$ simpledb.py -d -p 31339```#### dockersimpledb ships with a [Dockerfile](https://github.com/coleifer/simpledb/blob/master/docker/Dockerfile)or can be pulled from dockerhub as *coleifer/simpledb*. The dockerfile setupsup a volume at `/var/lib/simpledb` and exposes port `31337`.running:```console$ docker run -it --rm -p 31337:31337 coleifer/simpledb```building:```console$ cd simpledb/docker$ docker build -t simpledb .$ docker run -d -p 31337:31337 -v simpledb-logs:/var/lib/simpledb simpledb```#### usagethe server is capable of storing the following data-types natively:* strings and/or binary data* numerical values* null* lists (may be nested)* dictionaries (may be nested)```pythonfrom simpledb import Clientclient = Client()client.set('key', {'name': 'Charlie', 'pets': ['mickey', 'huey']})print(client.get('key')){'name': 'Charlie', 'pets': ['mickey', 'huey']}```
Package: simpledb-dev2
This is a fork ofSimpleDB/devby Matthew
Painter viathis unofficial
mirror. This fork has
been renamed tosimpledb-dev2so that it does not collide with the
original project.It currently supports the “2007-11-07” SimpleDB API level but has been
hacked to fake support for “2009-04-15”—this has not been tested much!Project informationsimpledb-dev2 provides a local SimpleDB server, so you can develop
offline, without requiring a SimpleDB account. It has been tested on
Linux, macOS and Windows.This package currently implements:The whole “2007-11-07” REST APICorrect HTTP error responses as per the technical documentationA large suite of tests created from the examples provided in the
technical documentationIt does not implement:The SOAP APIAuthentication—signature value checkingTimestamp format and expiration checkingHTTPSTo run the simpledb-dev2 server, you’ll need a working Python 2.7
installation. You can install usingpipas
follows:pip install simpledb-dev2This will install the package and its dependencies includingweb.py. Specify the--useroption to thepipcommand line to install for the current user only.This will create asimpledb-dev2script/executable on your path. You
can start the simpledb-dev2 web server as follows:simpledb-dev2 serveThis will serve the SimpleDB API on the default port of 8080. To specify
an alternative port, use the--portoption:simpledb-dev2 serve --port 1234If the server doesn’t start, or you have other problems, it’s pretty
easy to run the tests and see some examples of request/response:simpledb-dev2 testRemember, this is a development tool, and not meant for storing or
querying large amounts of data—I do not know yet how big you can get
before running into issues, but I suspect that with the current storage
and querying design it is not that large :o) Now that I have a base, I
may start trying to see how I can improve the performance…Although this conforms to the specifications in the technical
documentation, simpledb-dev2 has not been tested with every possible SDB
client library, and I am looking forward to people in the OSS community
trying to find bugs and peculiarities—it is after all, a work in
progress!So enjoy developing your SimpleDB applications now, not later!ContributingFork this repositoryCreate a feature branch (git checkout-bfeature-branch)Commit your changes
(git commit-m"Added support for the new API version")Push to an upstream branch (git push-uoriginfeature-branch)Create apull
requestdescribing
your fix/featureLicenceReleased underGNU General Public License v3 (GPLv3)
Package: simple-db-migrate
simple-db-migrate is a database versioning and migration tool inspired on Rails Migrations. This tool helps you easily refactor, manage and track your database schema. The main difference is that Rails migrations are intended to be used only on Ruby projects while simple-db-migrate makes it possible to have migrations in any language (Java, Python, Ruby, PHP, whatever). This is possible because simple-db-migrate uses database’s DDL (Data Definition Language) to do the database operations, while Rails Migrations are written in a Ruby internal DSL.
Package: simple-delay-spectrum
Simple Delay Spectrum (SimpleDS)SimpleDS is currently in a workingBETAstate.  All code will execute and tests pass, however there are still a number of bugs to fix and enhancements to make.simpleDSis a python package used to calculate the Delay Power Spectrum
of interferometric radio data. It performs the Fourier Transform of
visibility data along the Frequency dimension
and cross-multiplies redundant baseline information (if available).simpleDSattempts to calculate the Delay Spectrum in the simplest manner
using only Fast Fourier Transforms (FFTs) and calculating the beam_squared_area
directly from a UVBeam compatible beam map.
This calculator requirespyuvdatafor data handling and beam area calculation.MotivationThe main goals are:Provide a simple, user-friendly interface for computing a delay Fourier transform on interferometric radio data.Perform the delay power spectrum estimation on radio interferometric data using only Fast Fourier Transforms (FFTs) to provide a mathematically simple framework for analysis.Perform explicit unit conversions on data with as few approximations as possible.Package DetailssimpleDS has one major user class:DelaySpectrum: supports Fourier transformation of UVData compatible radio visibility data. Also can perform power spectrum estimation and redundant baseline cross multiplication. Creates noise realization of the input data product to track analysis steps and verify normalization. Attempts to produce a theoretical thermal noise limit for the input data power spectrum.and one minor cosmological conversion module:cosmo: Uses astropy.cosmology to compute relevant cosmological factors for 21cm radio data to convert between interferometric (f, u,v,w) to (k∥, k⊥) units.InstallationDependenciesFirst install dependencies.numpy >= 1.18scipyastropy >= 4.0h5py (for uvh5 compatibility with pyuvdata, optional)astropy-healpix used for UVBeam interpolations through pyuvdata (optional, only used withuse_exactkeyword foradd_uvbeam)pyuvdata >=1.4.2 (conda install -c conda-forge pyuvdata,pip install pyuvdata, or use thedevelopment version)For anaconda users, we suggest using conda to install astropy, numpy and scipy.Installing simpleDSClone the repo usinggit clone https://github.com/rasg-affiliates/simpleDS.gitNavigate into the directory and runpip install .To also install the optionalastropy-healpixusepip install .[healpix].
For all packages necessary for the testing suite usepip install .[all]Running TestsWe usepytestto execute the tests for this pacakge.
From the sourcesimpleDSdirectory run:python -m pytestorpytest.VersioningWe use ageneration.major.minorversion number format.Thegenerationnumber for very significant improvements, major rewrites, and API breaking changes.Themajornumber to indicate substantial package changes.Theminornumber to release smaller incremental updates which usually do not include breaking API changes.We do our best to provide a significant period of deprecation warnings for all breaking changes to the API. We track all changes in ourchangelog.DocumentationA tutorial with example usage is hosted onReadTheDocs.