Saravanan666/pypi-org huggingface dataset:
Package: compactor
0.3.0Add API documentation and register areadthedocssite.Fix bug that would cause compactor to crash if you subclassedProcessand implemented any
name-mangled methods.0.2.2Fix the condition where multiplesend/linkcalls to the same pid could race inContext.maybe_connect.Fix the issue where Process HTTP routes were not bound until afterinitialize.  This could
result in races whereby you’d receive calls from remote processes beforeinitializeexited,
causing flaky behavior especially in tests.Ensure thatsendandlinktake place on the event loop to prevent known non-threadsafe
conditions on connection establishment.0.2.1Restores local dispatch so that you do not need to install methods intended for local
dispatching only.Fixes a race condition onContext.stopthat could cause the event loop to raise an
uncaught exception on teardown.0.2.0Adds vagrant-based integration test to test compactor against reference libprocess.Fixes Python 3 support, pinning to protobof >= 2.6.1 < 2.7 which has correct support.0.1.3Context.singleton()now callsThread.starton construction.Pins compactor totornado==4.1.dev1which forces you to use a
master-built tornado distribution.0.1.2Temporarily removes local dispatch so that local sending works with protobuf processes.0.1.1Initial functioning release.
Package: compactpath
the package provides means to handle compacting of filepaths. compacting of filepathsmay be useful in gui programming for example where filepaths of arbitrary length have
to be displayed in widgets with limited visula space.the package is designed so you can use it as from everywhere. no need to install it to
site-packages, in case you want to include it in a project. it comes equipped with a
wrapper for labels to handle filepaths of arbitrary length in qt4.
Package: compactxml
SummaryCompact XML is an alternative syntax for representing XML files. It uses
indentation to indicate nesting to give a python like feel, XPath based
prefixes to identify nodes, and has a macro system to shorten common XML
constructs. It is intended for writing element based XML files, especially
those where the XML elements share a common structure such as XSLT files.This package includes a parser and bidirectional converter for converting from
this compact XML syntax to XML, and from XML to this compact XML syntax.Why Do I Want This?Unlike most projects to create a shorter XML, compact XML is not designed for
compressing XML on the wire. Instead, this syntax is designed to make editing
XML easier with common editors, and to provide a convenient, configurable
shorthand when authoring XML documents with a common syntax.If you spend all day writing XSLT files or something similar, this will let
you work with source files that look like this:xsl:stylesheet
        xsl:output xml no

        xsl:template "@* | node( )"
                xsl:copy
                        xsl:apply-templates "@* | node( )"Instead of like this:<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
<xsl:output method="xml" indent="no"/>

<xsl:template match="@* | node( )">
        <xsl:copy>
                <xsl:apply-templates select="@* | node( )"/>
        </xsl:copy>
</xsl:template>

</xsl:stylesheet>If you’re integrating with a python based program, the parser can be built in
and parse compact XML files to XML directly. Otherwise a simple command line
utility is included that can be used inside make or equivalent to convert to
XML as part of your build process.Installcompactxml is a pure python library, and should be platform independent. It
has been developed and tested on python 2.6 on linux, however. As
dependencies, it requires thatlxmlandpyparsingbe installed to
function.A normalpython setup.py installcommand should work to install the library.UsageThere is acompactxml.expandfunction for converting from compact XML
syntax to XML, and acompactxml.compactfunction for converting from XML
to compact XML syntax, with variations on both depending on the output format
desired.help( compactxml )will give better usage instructions, and the
included documentation incompactxml.rstprovides a comprehensive overview
of the compact xml syntax.Any errors in expansion or compaction are raised ascompactxml.ParsingError.There is also a command line utilitycompactxml.pyin the source
distribution which can be used for converting from/to compact xml syntax. Seecompactxml.py--helpfor usage information after installation.DocumentationDetaileddocumentationof the
compact XML format is available through the python package index, including a
tutorial for new users.
Package: compadre
COMpatible PArticle Discretization and REmap ToolkitAboutThe Compadre Toolkit provides a performance portable solution for the parallel evaluation of computationally dense kernels. The toolkit specifically targets the Generalized Moving Least Squares (GMLS) approach, which requires the inversion of small dense matrices. The result is a set of weights that provide the information needed for remap or entries that constitute the rows of some globally sparse matrix.This toolkit focuses on the 'on-node' aspects of meshless PDE solution and remap, namely the parallel construction of small dense matrices and their inversion. What it doesnotprovide is the tools for managing fields, inverting globally sparse matrices, or neighbor search that requires orchestration over many MPI processes. This toolkit is designed to be easily dropped-in to an existing MPI (or serial) based framework for PDE solution or remap, with minimal dependencies (Kokkosand eitherCuda ToolkitorLAPACK).Generalized Moving Least Squares (GMLS)A GMLS problem requires the specification of a target functional(Compadre::TargetOperation), a reconstruction space(Compadre::ReconstructionSpace), and a sampling functional(Compadre::SamplingFunctional).The Compadre Toolkit is designed to efficiently assemble, factorize, and solve large batches of minimization problems having the form:Wiki InformationDetails about building and using the Compadre toolkit can be found on theWiki.Recent ChangesRecent ChangesInstallationInstallation of Kokkos and KokkosKernels[Either automatically configured and built, or user installation location provided]Installation of CompadreDocumentation and TutorialsThe toolkit is documented by Doxygen.Documentation is available onlineor can be compiled from source.
To compile from source: 1.) install doxygen software on your computer, 2.) execute '>> make doc' after having installed the Compadre Toolkit. HTML and Latex documentation will be generated in thedoc/folder, in-source.Citing the SoftwareIf you write a paper using results obtained with the help of the Compadre Toolkit, please cite the following reference:@software{paul_kuberry_2020_3703333,
  author       = {Paul Kuberry and
                  Peter Bosler and
                  Nathaniel Trask},
  title        = {Compadre Toolkit},
  month        = mar,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v1.0.3},
  doi          = {10.5281/zenodo.3703333},
  url          = {https://doi.org/10.5281/zenodo.3703333}
}If you would like to export the reference information to either CSL, DataCite, Dublin, Core, JSON, JSON-LD, MARCXML, or Mendeley, please find the export section at the bottom-right corner once you follow the link below:
Package: compal
Compal CH7465LG (Ziggo Connect Box) toolsThis repository contains a simple api to wrap the web interface of the Ziggo Connect Box (i.e. the
Compal CH7465LG). It is implemented inPython >= 3.7.At the moment itonlycontains the functionality that I needed while I was investigating my
device, but pull requests that improve the documentation or add features are welcome.About the hardwareCompal does not provide information about the hardware. The modem has no FCC registration.
However, the related Arris TG2492 modem was submitted to the FCC. The FCC documents for this
modem areavailable. Some interesting documents (internal photos) have been mirrored todocs/fcc.
danmanperformedan (excellent) analysis of the modem where the procedure for extracting
the content of the firmware and modifying it is discussed. This writeup also examines the
DOCSIS certificates used.The modem seems to be based on the Intel Puma 6 chipset. There is a long thead on (perceived)
performance problems caused by jitter on DSLReports. See[ALL] SB6190 is a terrible modem - Intel Puma 6 / MaxLinear mistakeThe modemmost likelycontains open source components. Requests to Compal requesting source
code of these components, to an e-mail address on the Compal site, have not been answered yet.Changelog0.5.1Support for hashed (single-sha256) passwords was added by @7FM0.5.0Added support for get/create/disable/delete IPv6 filter rules by @7FM0.4.0:Updated guest network settings for firmware6.15.30-1p3-1-NOSHby @frimtec.0.3.2:Addsystem information methodsby @reitermarkus.0.3.1:Fixforpython url parsing changeby @Kiskae.0.3.0:Guest network settings added by @frimtec.SecurityA security evaluation of the Connect Box waspostedon-line. This report is included in thedocsfolder.How to use it?Theexamplesdirectory contains some example scripts. My main use case is re-provisioning the
modem. An example script for this task is included.Want to get started really quickly?importosimporttimefromcompalimport*modem=Compal('192.168.178.1',os.environ['ROUTER_CODE'])modem.login()fw=PortForwards(modem)deftoggle_all_rules(fw,goal):rules=list(fw.rules)foridx,rinenumerate(rules):rules[idx]=r._replace(enabled=goal)fw.update_rules(rules)print(list(fw.rules))# Disable all rulestoggle_all_rules(fw,False)time.sleep(5)# And re-enabletoggle_all_rules(fw,True)# Or find all possible functions of the modem:scan=FuncScanner(modem,0,os.environ['ROUTER_CODE'])whilescan.current_pos<101:print(scan.scan().text)# And/or change wifi settingswifi=WifiSettings(modem)settings=wifi.wifi_settingsprint(settings)new_settings=settings._replace(radio_2g=settings.radio_2g._replace(ssid='api_works'))wifi.update_wifi_settings(new_settings)print(wifi.wifi_settings)# And/or DHCPSettings# If you want to go back to 'normal':# modem.reboot() # or# modem.factory_reset()# And logoutmodem.logout()
Package: compander
Compander=========Simple context manager to wrap compression and uncompression of gzip and bzip2 files.Example-------```from compander import Companderwith Compander() as c:  # Default is bz2c.compress('uncompressed_file','compressed_file.bz2')with Compander('bz2') as c:c.uncompress('compressed_file.bz2','uncompressed_file')with Compander('gz') as c:c.compress('uncompressed_file','compressed_file.gz')with Compander('gz') as c:c.uncompress('compressed_file.gz','uncompressed_file')```Testing-------* python setup.py testInstallation------------* python setup.py installDevelopment-----------* pip install -r requirements.txt* python setup.py test* python setup.py develop
Package: companies-house-api-client
COMPANIES HOUSE API ClientThis is a Python client for the COMPANIES HOUSE APIInstallationYou can install the package into your project via pip:pipinstallcompanies_house_api_clientUSAGETo use the COMPANIES HOUSE API client, you first need to set up environment variables for the required credentials:exportCOMPANIES_HOUSE_APIKEY=your-api-keyexportCOMPANIES_HOUSE_HOST=https://api.company-information.service.gov.ukEXAMPLEBy setting the test parameter to true, you can play arround with api:fromcompanies_house_api_clientimportCompaniesHouse# Create a Companies House API client instancech=CompaniesHouse()# Call an API methodcompanies=ch.get_company_profile("12312312")TESTSMake sure pytest is installed on your environmentpython3-mpytest
Package: companycam-unofficial
python-companycamInstallationpython-mpipinstallcompanycam-unofficialBasic usage>>>importcompanycam>>>fromcompanycam.v2importmodels>>>api=companycam.API(token="YOUR_TOKEN_HERE")>>>api.projects.list()[Project(id='12345678',...)]>>>project=models.Project(name="My new project")>>>api.projects.create(project)Project(id='23456789',name='My new project',...)For more detailed usage seeUsage.ContributingSeeContributing.ResourcesCompanyCam DocsDependenciesHTTPX(BSD 3-clause license)pydantic(MIT license)LicenseMIT.
Package: companyClassifier
companyClassifierThis project converts company names to company labels. Have a look at the following medium article:https://medium.com/@lemaysolutions/deep-learning-magic-small-business-type-8ac484d8c3bfINSTALL INSTRUCTIONSThis project can be pip installed using the command:pip install companyClassifierTo install from inside a jupyter notebook cell:importsys!{sys.executable}-mpipinstallcompanyClassifierAlternatively the project can be cloned from the Github repository following the instructions provided below:git clonehttps://github.com/lemay-ai/companyClassifier.gitcd companyClassifier/pip install -r requirements.textpython setup.py installcd testpython test_text.pypython test_pandas.pyUSE INSTRUCTIONSMake sure the following dependencies are imported:fromkeras.modelsimportload_modelfromosimportpathimportsysAfter installation import the project as a module with the following code:importcompanyClassifierassIn order to test the model predictions call the SmallCompanyType class with:b=s.CompanyClassifier()You can use the test case below or modify the text strings.texts=["Lemay.ai Hotel","Farah's variety","felding and associates","Lemay.ai Consulting"]ctypes_subtypes=b.classifyCompany(texts)forcompanyintexts:print(company,"is a",ctypes_subtypes["type"][company],ctypes_subtypes["subtype"][company])http://lemay.aiThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: company-graph
CompanyGraphInstallpip install company-graphBuildpython setup.py sdist bdist_wheeltwine upload dist/*Usagefromcompany_graph.cgimportCompanyGraphcg=CompanyGraph()# Generalize company ids>>>cg.id2id(1697742)1063# Deterministic company id from string>>>cg('Cisco')1063>>>cg('Talos Group at Cisco')1063# Deterministic company name from id>>>cg(1063)'cisco'
Package: company-name-cleaner
fingerprintsThis library helps with the generation of fingerprints for entity data. A fingerprint
in this context is understood as a simplified entity identifier, derived from it's
name or address and used for cross-referencing of entity across different datasets.Usageimportfingerprintsfp=fingerprints.generate('Mr. Sherlock Holmes')assertfp=='holmes sherlock'fp=fingerprints.generate('Siemens Aktiengesellschaft')assertfp=='ag siemens'fp=fingerprints.generate('New York, New York')assertfp=='new york'Company type namesA significant part of whatfingerprintsdoes it to recognize company legal form
names. For example,fingerprintswill be able to simplifyОбщество с ограниченной ответственностьюtoООО, orAktiengesellschafttoAG. The required database
is based on two different sources:AGoogle Spreadsheetcreated by OCCRP.The ISO 20275:Entity Legal Forms Code ListWikipedia also maintains an index oftypes of business entity.See alsoClustering in Depth, part of the OpenRefine documentation discussing how to create collisions in data clustering.probablepeople, parser for western names made by the brilliant folks at datamade.us.
Package: company-pinax-stripe
============Company Pinax Stripe============.. image:: http://slack.pinaxproject.com/badge.svg:target: http://slack.pinaxproject.com/.. image:: https://img.shields.io/travis/pinax/company-pinax-stripe.svg:target: https://travis-ci.org/pinax/company-pinax-stripe.. image:: https://img.shields.io/codecov/c/github/pinax/company-pinax-stripe.svg:target: https://codecov.io/gh/pinax/company-pinax-stripe.. image:: https://img.shields.io/pypi/dm/company-pinax-stripe.svg:target:  https://pypi.python.org/pypi/company-pinax-stripe/.. image:: https://img.shields.io/pypi/v/company-pinax-stripe.svg:target:  https://pypi.python.org/pypi/company-pinax-stripe/.. image:: https://img.shields.io/badge/license-MIT-blue.svg:target:  https://pypi.python.org/pypi/company-pinax-stripe/This app was formerly called ``django-stripe-payments`` and has been renamed toavoid namespace collisions and to have more consistancy with Pinax.Pinax------Pinax is an open-source platform built on the Django Web Framework. It is anecosystem of reusable Django apps, themes, and starter project templates.This collection can be found at http://pinaxproject.com.This app was developed as part of the Pinax ecosystem but is just a Django appand can be used independently of other Pinax apps.company-pinax-stripe------------``company-pinax-stripe`` is a payments Django app for Stripe.This app allows you to process one off charges as well as signup users forrecurring subscriptions managed by Stripe.
Package: comparative
comparativeImplement comparison magic methods in one line!Usagefromcomparativeimportcompare_by@compare_by("hour","minute","second")classClock:def__int__(self,hour=0,minute=0,second=0):self.hour=hourself.minute=mintueself.second=seconddef__repr__(self):return("Clock(h={0.hour:}, m={0.minute:02d}, ""s={0.second:02d})").format(self)def__str__(self):return"{0.hour:02d}:{0.minute:02d}:{0.second:02d}".format(self)Just Add Water>>>clock1=Clock(8,0,0)>>>clock2=Clock(8,0,1)>>>clock3=Clock(8,0,0)>>>clock1<clock2True>>>clock1==clock2False>>>clock1==clock3Truet@o55bhDr%
Package: compare-concordance
compare_concordanceThis Python package implements a statistical test for the comparison of correlated right-censored concordance indices. The code is derived entirely from the GPLv3-licensed R packagecompareC. Accordingly, this package is also licensed under GPLv3. If you use it in published work, you should cite the original authors' paper (see below).InstallationFirst, build the C library:$ python3 setup.py build_extThen, install using Pip:$ pip3 install .This has been tested on 64-bit Linux with GCC version 7.5.0 and Python 3.6. If the installation step fails, it may be because your system names the .so file differently than expected.Usagefromcompare_concordanceimportcompare_concordancescores_1=model1.predict(x)scores_2=model2.predict(x)concordance_1,concordance2,difference,zscore,pvalue=compare_concordance(y['time'],y['status'],scores_1,scores_2)CitationKang L, Chen W, Petrick NA and Gallas BD (2015). "Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach." Statistics in Medicine, 34(4), pp. 685-703.http://dx.doi.org/10.1002/sim.6370.Bibtex@Article{,
  title = {Comparing two correlated C indices with right-censored
    survival outcome: a one-shot nonparametric approach},
  author = {Le Kang and Weijie Chen and Nicholas A. Petrick and
    Brandon D. Gallas},
  year = {2015},
  volume = {34},
  number = {4},
  pages = {685-703},
  journal = {Statistics in Medicine},
  url = {http://dx.doi.org/10.1002/sim.6370},
}
Package: compare-dfs
compare_df介绍找出两个几乎相同的DataFrame的差异安装pipinstallcompare_dfs使用importpandasaspdimportcompare_dfsascpdfsdf1=pd.DataFrame({"id":[1,2,3,4,5],"name":["a","b","c","d","e"],"age1":[10,20,30,40,50],})df2=pd.DataFrame({"id":[1,2,3,4,6],"name":["a","b","f","d","e"],"age2":[10,25,30,40,50],})df1.set_index("id",inplace=True)df2.set_index("id",inplace=True)different_colnames=cpdfs.compare_colnames(df1,df2)print(different_colnames)print(cpdfs.compare(df1,df2,different_colnames,dfname_1="左表",dfname_2="右表"))输出：只在第一个表中的列名：{'age1'}
只在第二个表中的列名：{'age2'}
[('age1', 'age2')]
左表中有，右表中没有的索引：{5}
右表中有，左表中没有的索引：{6}
=====================================
   name_左表 name_右表
id                
3        c       f
=====================================
    age1  age2
id            
2     20    25
=====================================
[   name_左表 name_右表
id                
3        c       f,     age1  age2
id            
2     20    25]
Package: compare-folders
compare-foldersInstallationIt is recommended to install usingpipx, but you can also usepip. Seehttps://pypi.org/project/pipx/.pipxinstallcompare-foldersUsagecompare-folders path\to\source path\to\destinationThis will write a markdown file with:missing files from the source not in the destinationextra files in the destination not in the sourcefiles in both locations with different modification datesIt will also print a table of stats.Use-o/--outputto override the output filename. If the output file already exists, the resulsts will be appended to the file, unless you specify--no-append, in which case the file will be overwritten.
Package: compare-for-testing
Compare For Testing OverviewInstallationInstall from PyPi:pip install compare-for-testingImportingfromcompare_for_testingimportcompare# For compare of 2 objectsExamplejson_1={'a':1,'b':2,'empty':'',}json_2={'aa':True,'b':4,'empty':'!empty','empty_failed':'!empty',}result=compare(json_2,json_1){'aa':{'Хотели':True,'Получили':"Поля 'aa' нет в ответе"},'b':{'Хотели':4,'Получили':2},'empty_failed':{'Хотели':'!empty','Получили':"Поля 'empty_failed' нет в ответе"}}json_1={'c':[{'sub_a':1,'sub_b':3},{'sub_a':2,'sub_b':4},{'sub_a':3,'sub_b':5,'sub_c':{'name':'test','id':5,}},],}json_2={'c':[{'sub_a':11,'sub_b':43},{'sub_a':21,'sub_b':4},{'sub_a':'!not_empty','sub_b':'!empty','sub_c':{'name':'test','name2':'test',}},{'sub_a':'!not_empty','sub_b':8,'sub_c':{'name':'test',}},],}result=compare(json_2,json_1){'c':{0:{'sub_a':{'Хотели':11,'Получили':1},'sub_b':{'Хотели':43,'Получили':3}},1:{'sub_a':{'Хотели':21,'Получили':2}},2:{'sub_b':{'Хотели':'!empty','Получили':5},'sub_c':{'name2':{'Хотели':'test','Получили':"Поля 'name2' нет в ответе"}}},3:{'Хотели':{'sub_a':'!not_empty','sub_b':8,'sub_c':{'name':'test'}},'Получили':None}}}
Package: compare-frames-g4
Compare framesCompare 2 framesInstallationPackage Installation from PyPi$pipinstallcompare-frames-g4Package Installation from Source CodeThe source code is available onGitHub.Download and install the package:$gitclonehttps://github.com/Genzo4/compare_frames
$cdcompare_frames
$pipinstall-rrequirements.txt
$pipinstall.UsageFrom filesfromcompare_frames_g4importcompare_framesis_equal=compare_frames('path_to_frame_1','path_to_frame_2')From frames (numpy.ndarray)fromcompare_frames_g4importcompare_framesimportcv2frame_1=cv2.imread('path_to_frame_1')frame_2=cv2.imread('path_to_frame_2')is_equal=compare_frames(frame_1,frame_2)Mixfromcompare_frames_g4importcompare_framesimportcv2frame_1=cv2.imread('path_to_frame_1')is_equal=compare_frames(frame_1,'path_to_frame_2')fromcompare_frames_g4importcompare_framesimportcv2frame_2=cv2.imread('path_to_frame_2')is_equal=compare_frames('path_to_frame_2',frame_2)ChangelogCompare framesСравнение двух кадров (изображений).УстановкаУстановка пакета с PyPi$pipinstallcompare-frames-g4Установка пакета из исходного кодаИсходный код размещается наGitHub.Скачайте его и установите пакет:$gitclonehttps://github.com/Genzo4/compare_frames
$cdcompare_frames
$pipinstall-rrequirements.txt
$pipinstall.ИспользованиеБерём кадры из файловfromcompare_frames_g4importcompare_framesis_equal=compare_frames('path_to_frame_1','path_to_frame_2')Используем "готовые" кадры (numpy.ndarray). Например, из cv2.imreadfromcompare_frames_g4importcompare_framesimportcv2frame_1=cv2.imread('path_to_frame_1')frame_2=cv2.imread('path_to_frame_2')is_equal=compare_frames(frame_1,frame_2)Смешанный режимfromcompare_frames_g4importcompare_framesimportcv2frame_1=cv2.imread('path_to_frame_1')is_equal=compare_frames(frame_1,'path_to_frame_2')fromcompare_frames_g4importcompare_framesimportcv2frame_2=cv2.imread('path_to_frame_2')is_equal=compare_frames('path_to_frame_2',frame_2)Changelog