Saravanan666/pypi-org huggingface dataset:
Package: pyexpose
pyexposepyexpose is a Python library designed to streamline the process of exposing local services to the internet using various SSH-based providers, such as serveo.net and localhost.run. With pyexpose, developers can effortlessly share their local services with remote collaborators or external systems, eliminating the need for manual configuration and exposing an abstraction layer for seamless integration.InstallationInstall thepyexposepackage withpip:$pipinstallpyexposeOr install the latest package directly from GitHub with the async extra:$pipinstallgit+https://github.com/hari01584/pyexposeExample Usageimportasyncioimportthreadingfromhttp.serverimportThreadingHTTPServerfromhttp.serverimportSimpleHTTPRequestHandlerclassMyHttpRequestHandler(SimpleHTTPRequestHandler):defend_headers(self):self.send_header('Access-Control-Allow-Origin','*')SimpleHTTPRequestHandler.end_headers(self)defdo_GET(self):self.send_response(200)self.send_header("Content-type","text/html")self.end_headers()html=f"31"# how do I modify this line to serve files?self.wfile.write(bytes(html,"utf8"))http_server=ThreadingHTTPServer(("0.0.0.0",4457),MyHttpRequestHandler)http_server_thread=threading.Thread(target=http_server.serve_forever)http_server_thread.daemon=Truehttp_server_thread.start()asyncdefstart_tunnel():frompyexpose.providers.localhost_runimportLocalRunConnectorconnector=LocalRunConnector()asyncwithconnector.connect()assession:asyncwithsession.tunnel(4457)astunnel:print("Started tunnel, You can access your local server at: ",tunnel.ip)awaitasyncio.sleep(100000)asyncio.run(start_tunnel())Please look into official docs for more information -https://pyexpose.readthedocs.io/en/latest/
Package: pyexpression
boost.lambda like function generatorUsage:>>> from pyexpression import _1, _2, _3, if_
>>> _1 * 10
<pyexpression.expressions.FuncCall object at 0xb74db7cc>
>>> (_1 * 10)(5)
50
>>> (_1 * _2)(10, 10)
100
>>> map(_1[0], zip(range(10), range(10,20)))
>>> class Test(object):
...     def __init__(self):
...         self.x = 10
...
>>> (_1.x)(Test())
10
>>> x = if_(_1)[_2].else_[_3]
>>> x(True, 10, 20)
... 10
>>> x(False, 10, 20)
... 20
Package: py-expression
DescriptionPy-expression is an extensible expression evaluator and parser.
Besides the operators, functions, variables, objects and arrays that are supported; it is possible to extend it with your own functions, operators, etc.FeaturesParse and evaluateArithmetic operatorsassignment operatorscomparison operatorsLogical operatorsBitwise OperatorsVariablesConstantsFunctionsObjectsArrayasEnumsSimplify math operations where operands are constantIt allows to extend the model by adding or overwriting operators, functions and enumsSupports multiline expressions using the semicolon character to separate themThe evaluation receives the context where the variables will be read, written, and created. This context must be a dictionary or a class derived from a dictionaryWhen parsing a string that contains expression, an expression object is returned, which can be reused to evolve the expression with different contexts, in this way the performance is notably improved.You can create a new expression object using expression objects and combining them with operatorsWikiHomeUseExpExp is the main class of the library that contains the methods to parse, evaluate, get info of expression, etc . In order to use the library you need to create an instance of this class:frompy_expression.coreimportExpexp=Exp()Parsefrompy_expression.coreimportExpexp=Exp()operand=exp.parse('a+4')Evalfrompy_expression.coreimportExpexp=Exp()operand=exp.parse('a+4')result=exp.eval(operand,{"a":2})frompy_expression.coreimportExpexp=Exp()operand=exp.parse('a+4')result=operand.eval({"a":2})frompy_expression.coreimportExpexp=Exp()result=exp.parse('a+4').eval({"a":2})Work with expressionsreuse the parsed expression:frompy_expression.coreimportExpexp=Exp()op=exp.parse('sin(x)')xs=[]ys=[]forxinrange(-100,100):y=op.eval({"x":x})xs.append(x)ys.append(y)create a new expression based on two or more parsed expressions:frompy_expression.coreimportExpexp=Exp()op1=exp.parse('a+1')op2=exp.parse('b')op3=(op1+op2)*(op1-op2)>=(op1*2)resutl1=op3.eval({"a":1,"b":2})resutl2=op3.eval({"a":5,"b":9})print(resutl1)print(resutl2)Project ExamplesTest GraphIn this project, the py-expression library is used to parse and evaluate expressions that a variable uses (in this case x) and the result is assigned to y.
then the point (x,y) is shown in a diagram.
In this example x takes the values from -100 to 100githubLib OpencvExtend the expression library by adding enums and related functions to opencvgithubTest OpencvIn this project, the expression library and an opencv library that adds enums and functions is used to execute multi-line expressions that transform an imagegithub
Package: py-expression-lib-opencv
# Purpose
Extend the expression library by adding enums and related functions to opencv# Description
In this project, the expression library and an opencv library that adds enums and functions is used to execute multi-line expressions that transform an image# Install` python-mpip install-Upip python-mpip install-Upy-expression`# References:
- [py-expression pip](https://pypi.org/project/py-expression/)
Package: pyExSi
pyExSi - Excitation signals as used in structural dynamics and vibration fatigueSupported excitation signals are:pulse (e.g. half-sine)random:uniform random distributionnormal random distributionpseudorandom distributionrandom, defined by power spectral density (PSD):stationary Gaussianstationary non-Gaussiannon-stationary non-Gaussian random processburst randomsine sweepSimple exampleA simple example on how to generate random signals on PSD basis:importpyExSiasesimportnumpyasnpN=2**16# number of data points of time signalfs=1024# sampling frequency [Hz]t=np.arange(0,N)/fs# time vector# define frequency vector and one-sided flat-shaped PSDM=N//2+1# number of data points of frequency vectorfreq=np.arange(0,M,1)*fs/N# frequency vectorfreq_lower=50# PSD lower frequency limit  [Hz]freq_upper=100# PSD upper frequency limit [Hz]PSD=es.get_psd(freq,freq_lower,freq_upper)# one-sided flat-shaped PSD#get gaussian stationary signalgausian_signal=es.random_gaussian((N,PSD,fs)#get non-gaussian non-stationary signal, with kurtosis k_u=10#amplitude modulation, modulating signal defined by PSDPSD_modulating=es.get_psd(freq,freq_lower=1,freq_upper=10)#define array of parameters delta_m and pdelta_m_list=np.arange(.1,2.1,.5)p_list=np.arange(.1,2.1,.5)#get signalnongaussian_nonstationary_signal=es.nonstationary_signal(N,PSD,fs,k_u=5,modulating_signal=('PSD',PSD_modulating),param1_list=p_list,param2_list=delta_m_list)
Package: pyEX.studies
pyEX-studiesPredefined financial metrics built on pyEX/IEXReferralPlease subscribe to IEX Cloud usingmy referral code.InstallInstall from pippip install pyEX.studiesor from sourcepython setup.py installGetting StartedAll functions are bound to apyEXclient ifpyEX-studiesis installedpyEX main documentationpyEX-studies documentation
Package: pyext
PyExt=====.. image:: https://travis-ci.org/kirbyfan64/PyExt.png:target: https://travis-ci.org/kirbyfan64/PyExtSeveral simple extensions that add some nifty features to Python.NEWS: There is now a PyExt newsgroup on Google Groups. See https://groups.google.com/forum/#!forum/pyext.Some features:- Function overloading- Switch statement- Runtime module creation- Tail recursion removal- Python 2 function annotations- Python 2 safe tuple unpackingExamples:Function overloading::@overload.argc(1)def f(a): print 'Function 1 called'@overload.argc(2)def f(a, b): print 'Function 2 called'f(1)f(1, 2)Switch statement::with switch(1):if case(0): print 'Awkward...'; case.quit() # case.quit() is the same as breakif case(2): print '???'if case(1): print 'Phew! It works!'if case.default(): print 'Ummmm...'Function annotations::@fannotate('Return annotation', a=1, b=2)def x(a, b):return 0TODO:- Add some more tests.. note:: Please ignore this project's messy commit history(several commits under invalid_email_address, about 20 commits labeled Initial). I was trying to use hg-git and kept goofing stuff up.
Package: pyextdirect
pyextdirectPython implementation ofExt Direct Server-side StackProject page0.3.1Fix length of CUD functions in apipep8 compliance for unittestsRename records to data in unittests0.3Rename records to datapep8 compliance0.2.5Fix result for STORE_CUD requests0.2.4Add data to the returned result of STORE_CUDFix len in API for STORE_CUDAdd new lines at end of files0.2.3Rename DirectStore destroy to DirectStore CUD for create-update-destroy operations0.2.2Fix DirectStore destroy0.2.1Add DirectStore destroy support0.2Add DirectStore read support0.1Initial release
Package: pyExtendedConfigParser
pyExtendedConfigParserThis is an extension and improvement for PythonsConfigParser.1 OverviewImprovementsFaster interpolationInterpolation cacheSection name interpolationMultiple DEFAULT sectionsNested interpolation2 Interpolations2.1 Option interpolation2.2 Section:Option interpolation2.3 Section interpolation3 Short Example[section1]option11=value11option12=value12option13=value13[section2]option21=value21option22=value22ContributorsPatrick Lehmann(Maintainer)LicenseThis library is licensed underApache License 2.0SPDX-License-Identifier: Apache-2.0
Package: py-extension-functions
Django를 이용한 프로젝트에 공통적으로 쓰이는 module 모음 입니다.시작하기문서https://py-extension-functions.readthedocs.io/설치pypi :https://pypi.org/project/py_extension_functions/github :https://github.com/Gaolious/py_extension_functions$pipinstallpy_extension_functions설정설치 이후 Django Project의 settingsINSTALLED_APPS에 추가 해야 합니다.INSTALLED_APPS = (
    ...
    'gpp',
)BuildTestshttps://tox.wiki/en/latest/$python-mpipinstalltox$toxDocshttps://www.sphinx-doc.org/en/master/Sphinx# Install$python-mpipinstallsphinxsphinx_rtd_theme# Initialize$sphinx-quickstartdocs# Generate documents from source$sphinx-apidoc-e-F-o./docs/source./gpp# Generate html$make-C./docshtml
Package: py-extract
py_extractPy_extract is a simple, light-weight python library to handle some extraction tasks using less lines of code.Still in Development Stage!Installation 🎛,UsingPypipip install py-extractFeatures,Get Video metadata (data will returns as json)Extract Audio(s) from a videoExtract Subtitle(s) from a videoOnly This for now, more features coming soon 😉...DocsYou can find the official docs and code snippetshereLicensepy_extractis licensed under MIT LicenseCopyright ©️ 2021 -Itz-fork
Package: pyextractor
Extractor is a command line program to extract data from pdf, images,
using OCR,GROK patterns, YAML and multiprocessing. The goal of this
application is collect data, extract and organize the results in csv
file.Getting StartThese instructions will get you a copy of the project and running on
your local machine.InstallingA instruction to installing your environment atUbuntu/DebianOnly tested in Ubuntu with python3sudoapt-getinstallpython-devlibxml2-devlibxslt1-devantiwordunrtfpoppler-utilspstotexttesseract-ocrflacffmpeglamelibmad0libsox-fmt-mp3soxlibjpeg-devswiglibpulse-devpipinstallpyextractUsageThe application has the follow workflow: - Download file - Extract text
from file - Extract patterns - Write a csv fileConfiguration fileconfig.ymlThis file is very important to application work is a file where reside
all configurations, like: header of csv, order of columns, patterns to
extract from file, … Thesyntaxof
this file use the rules oYAML.Main Parameters:DEBUG: Enable/Disable Debug output at stdoutNUM_WORKERS: Number of cores to processingpatterns:Grok
Patterns.sequence: The sequence is used to reference columns of csv related
to patterns, and another configurations.See folder sample for example of use.Built WithPython 3.5PyGrokTextractAuthorMarlon Petry-Initial work-ExtractorNhan Petry-Inspiration and first userExtractorAcknowledgmentsPlease give us your feedback of use, it is very important to keep
developing this tool.LicenseCopyright 2017 Marlon PetryRedistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
                                     PROCUREMENT OF SUBSTITUTE GOODS OR
                                     SERVICES; LOSS OF USE, DATA, OR
                                     PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
Package: pyextrapolation
PDE-based Constant Extrapolationversion number: 0.0.1
author: Murray CutforthOverviewA python package for multidimensional extrapolation via the solution of a partial differential equationInstallation / UsageTo install use pip:$ pip install pyextrapolationOr clone the repo:$ git clone https://github.com/murraycutforth/pyextrapolation.git
$ python setup.py installContributingTBDExampleTBD
Package: pyextrasafe
PyExtraSafePyExtraSafe is a library that makes it easy to improve your program’s security by selectively
allowing the syscalls it can perform via the Linux kernel’s seccomp facilities.The python library is a shallow wrapper aroundextrasafe.Quick ExamplefromthreadingimportThreadimportpyextrasafetry:thread=Thread(target=print,args=["Hello, world!"])thread.start()thread.join()exceptException:print("Could not run Thread (should have been able!)")pyextrasafe.SafetyContext().enable(pyextrasafe.BasicCapabilities(),pyextrasafe.SystemIO().allow_stdout().allow_stderr(),).apply_to_all_threads()try:thread=Thread(target=print,args=["Hello, world!"])thread.start()thread.join()exceptException:print("Could not run Thread (that's good!)")else:raiseException("Should not have been able to run thread")
Package: pyextremes
pyextremesExtreme Value Analysis (EVA) in PythonAboutDocumentation:https://georgebv.github.io/pyextremes/License:MITSupport:ask a questionorcreate an issue,
any input is appreciated and would help develop the projectpyextremesis a Python library aimed at performing univariateExtreme Value Analysis (EVA).
It provides tools necessary to perform a wide range of tasks required to
perform EVA, such as:extraction of extreme events from time series using methods such as
Block Maxima (BM) or Peaks Over Threshold (POT)fitting continuous distributions, such as GEVD, GPD, or user-specified
continous distributions to the extracted extreme eventsvisualization of model inputs, results, and goodness-of-fit statisticsestimation of extreme events of given probability or return period
(e.g. 100-year event) and of corresponding confidence intervalstools assisting with model selection and tuning, such as selection of
block size in BM and threshold in POTCheck outthis repositorywith Jupyter notebooks used to produce figures for this readme
and for the official documentation.InstallationGet latest version from PyPI:pipinstallpyextremesInstall with optional dependencies:pipinstallpyextremes[full]Get latest experimental build from GitHub:pipinstall"git+https://github.com/georgebv/pyextremes.git#egg=pyextremes"Get pyextremes for the Anaconda Python distribution:condainstall-cconda-forgepyextremesIllustrationsModel diagnosticExtreme value extractionTrace plotCorner plotAcknowledgementsI wanted to give kudos toJean Toilliezwho has inspired me to develop this open-source project and who taught me a lot about the extreme value theory. Also big thanks to Max Larson who has introduced me to software development and statistics.
Package: py-ext-to-format
Python Extension to File FormatThis small package returns the file format of a given file extension orUnknownif file extension is unknown.Installationpipinstallpy-ext-to-formatUsagefrompy_ext_to_formatimportExtToFormatconverter=ExtToFormat()converter.get_format('.js')# returns JavaScriptContributionWe'd love contributions to the known file extensions/formats.Any file extension and its file format is available underpy_ext_to_format/formats.json.{".js":"JavaScript"}
Package: pyeyesim
PyEyeSimthis is a Python library for eye-movement analysis, visualization and comparison, with a focus on scanpath comparison.The ultimate goal of the library is to make advanced fixation map statistics (eg: entropy) and scanpath comparison  accesible (hidden markov model based, and saccade direction based).The library also provides general descripitve statistics about eye-movements. It is intended to work with ordered fixation data. (a row for each fixation), that is loaded into a pandas dataframe.Additionaly, easy visualizations about the statistics (overall stats, stimulus based stats, within trial progrression) and heatmaps are also provided.three main scanpath similarity functionalities:Within group similarity  (for a single group of observers in a single condition)Between condition similarity (for single group of observers, observing the same stimuli in two conditions)Between group similarity (for two groups of observers observing the same stimuli)The library started to develop for use in art perception studies, therefore, there is an emphasis on stimulus based eye-movement comparison.Installation:in the terminal/anaconda promptpip install pyeyesimORif you cloned the library and are in the root folder of the library using the terminal (mac) or anaconda prompt (windows), you can install it, using the command:" pip install -e . "Demo:for examples of using the library, see the PyEyeDemoBasic.ipynb in the Notebooks folderDependencies:xarrayfor full funcionalityscikit-image
hmmlearnon piphttps://pypi.org/project/pyeyesim/
Package: py-ezbar
Ez-BarVery simple customizable progress bar.Installationpipinstallpy-ezbarUsage and Examplesimport, configure and use it's easyfrompy_ezbarimportProgressBar,BarStyles,BarColorsprogress_bar=ProgressBar(color=BarColors.YELLOW,style=BarStyles.DEFAULT,show_fractions=True)t=range(327)x=[fake.bs()for_inrange(5)]y={"1":"a","2":"b","3":"c","4":"d","5":"e",}foriint:progress_bar(index=i,iterable=t,current=i)sleep(0.01)fori,vinenumerate(x):progress_bar(index=i,iterable=x,current=v)sleep(0.01)fori,(k,v)inenumerate(y.items()):progress_bar(index=i,iterable=x,current=v)sleep(0.01)
Package: pyezjson
Easy Json DatabaseEasier Json Database For PythonInstallationEasy WaypipinstallpyezjsonLess Easy Waypipinstallgit+https://github.com/M4hbod/pyezjson.gitUsageConnect:frompyezjsonimportconnectmydb=connect('my_database.json',indent=4)Add:mydb.add(False,'users','user_1',first_name='Lee',last_name='Everet')# `False` is for `update_if_exist` argument"""Result in my_database.json:{"users": {"user_1": {"first_name": "Lee","last_name": "Everet"}}}"""Update:mydb.update(False,'users','user_1','last_name',SELF='Everett')# If you use `SELF`, it will update it for the last argument# You can also use `SELF` in `add` function# `False` is for `add_if_not_exist` argument"""Result in my_database.json:{"users": {"user_1": {"first_name": "Lee","last_name": "Everett"}}}"""Delete:mydb.delete('users','user_1','last_name')"""Result in my_database.json:{"users": {"user_1": {"first_name": "Lee"}}}Check:result_first_name=mydb.check('users','user_1','first_name')result_last_name=mydb.check('users','user_1','last_name')print(result_first_name)print(result_last_name)"""Result:>>> True>>> False"""Get:data=mydb.get('users','user_1')print(data)"""Result:>>> {'first_name': 'Lee'}"""Get The Whole Database:data=mydb.get_all()print(data)"""Result:>>> {'users': {'user_1': {'first_name': 'Lee'}}}"""Reset:mydb.reset()"""Result in my_database.json:{}
Package: pyeznacl
PyEZNaClA wrapper around PyNaCl to make it even easier to use.DescriptionWorking with encryption is hard. PyNaCl made it aloteasier, but it's still kind of hard to figure out for newcomers to encryption. PyEZNaCl is a pure-Python module that gets as close to push-button cryptography as a developer can feasibly be.StatusThe module is stable and ready for production use. Usage information can be found in the USAGE.md file in this repository.BuildingThis is a very simple module. Most people will just want to install it using pip.
Package: pyezoo
pyezoopyezoois a python-sdk foreZooDB>>>frompyezooimportConnection>>>conn=Connection(host='xxx.xxx.xxx.xxx',port=9090,user='xxx',password='xxx',auth=True)>>>conn.get_client().hello()Response(message='Welcome to ezoo!',status=0,cost_time=0.0)>>>conn.get_client().get_node_with_id(db_name="xxx",id=1,retry=True)node_s(type='xxx',props={...},node_id=1,status=0,message='OK.',cost_time=0.000112842)Installing pyezoo and Supported Versionspyezoo is available op PypI:python3 -m pip install pyezoopyezoo officially supports Python 3.7+.API Reference and User Guide available oneZoo-API DOCS
Package: pyezQ
中科院量子创新院量子计算云平台新机SDK使用说明把大象装冰箱里分三步:把冰箱门打开把大象推进去把冰箱门关上运行量子程序也只分三步:写量子程序(此处必须要省略好几万字)使用Python调用量子计算云平台SDK，将量子线路提交到量子计算机。等待并回读线路运行结果，进行自己的分析使用。中科院量子创新院量子计算云平台, 2023年4月14日开放新机内测，官方网址：https://quantumcomputer.ac.cn现已全面开发高性能量子计算机试用权限。本地快速安装SDK环境，请使用如下指令：pipinstall--upgradepyezQ-ihttps://pypi.tuna.tsinghua.edu.cn/simple因近期内测调整较快，如遇到原有程序运行报错，或无法提交线路等问题，请及时联系官方人员或用以下指令更新SDK库。同理，近期依赖关系有可能列的不全，用户可以根据报错提示来进行安装。如遇国内源暂未同步，请强行指定国外原进行更新安装-ihttps://pypi.org/simple在云平台网站提供的jupyternotebook环境Tutorial目录下，可以得到可互动执行的例子程序，复制到个人目录后，可以快速运行。本文档仅供2023年4月14日，中科院量子创新院量子计算云平台新量子计算测试，新SDK使用说明查询。
Package: pyez-stats
pyez_statsA python library that allows for easy statistical analysis.This library has functionality for hypothesis testing, Bayesian statistical analysis, linear regression and multiple linear regression and principle component analysis. The distribution module allows the user to fit passed in data to multiple distributions and calculate mean, median and variance and other statistical values. It also allows for calculating probability density functions, cumulative distribution functions and percentiles of the data.The main class in this library is the Data class that incorporates every other class into it allowing for easy statistical analysis in one array like data structure.Installationpip install pyez_statsGet StartedBelow is a simple array of data passed into our Data class. We set the model to be a Gaussian distribution, then calculate the mean and probabilty of a certain value.importpyez_statsasezarr=[11,11,12,11,12,13,14,16,18,16,15]data=ez.Data(arr)data.set_model("gaus")print(data.pdf(12))print(data.mean())Using the modulesBelow we show how to use multiple of the modules in the pyez_stats library.Hypothesis Testhyp_test=data.hypothesis_test("gauss")print(hyp_test(11,wald_test=True,left_tail=True))print(hyp_test(11,z_test=True,right_tail=True,sigma=3))print(hyp_test(11,t_test=True,left_tail=True))Bayesian Statisticsbays=data.bayesian_statistic(likelihood="gaus",prior="gaus")bays.set_theta_range([10,18])print(bays.plot_posteriors(var=3))print(bays.map(var=3))