Saravanan666/pypi-org huggingface dataset:
Package: calltraces
Call TracesA small package for tracing function callsInstalling ⏬pipinstallcalltracesUsage 📝Python code:fromcalltraces.linetraceimporttraceInfofromcalltraces.functiontraceimportfunctiontrace# ...@functiontrace# Add a function decoratordefmyFunction():traceInfo("some log message...")# ...# Whenever the function is called:myFunction()This will produce the output:[INFO:01]2022-06-2618:00:32,234__main__.myFunctionStartingFunction[INFO:02]2022-06-2618:00:32,235somelogmessage...[INFO:01]2022-06-2618:00:32,235__main__.myFunctionFinishedFunctionAdvanced Usage 🪆Python code:fromcalltraces.linetraceimporttraceInfofromcalltraces.functiontraceimportfunctiontrace# ...@functiontracedefmyFunction1():traceInfo("some log message...")@functiontracedefmyFunction2():myFunction1()@functiontracedefmyFunction3():# Calling a function twicemyFunction2()myFunction2()# ...# Whenever the function is called:myFunction3()This will produce the output:[INFO:01]2022-06-2618:06:38,625__main__.myFunction3StartingFunction[INFO:02]2022-06-2618:06:38,626__main__.myFunction2StartingFunction[INFO:03]2022-06-2618:06:38,634__main__.myFunction1StartingFunction[INFO:04]2022-06-2618:06:38,634somelogmessage...[INFO:03]2022-06-2618:06:38,634__main__.myFunction1FinishedFunction[INFO:02]2022-06-2618:06:38,634__main__.myFunction2FinishedFunction[INFO:02]2022-06-2618:06:38,634__main__.myFunction2StartingFunction[INFO:03]2022-06-2618:06:38,635__main__.myFunction1StartingFunction[INFO:04]2022-06-2618:06:38,635somelogmessage...[INFO:03]2022-06-2618:06:38,635__main__.myFunction1FinishedFunction[INFO:02]2022-06-2618:06:38,635__main__.myFunction2FinishedFunction[INFO:01]2022-06-2618:06:38,635__main__.myFunction3FinishedFunctionUsage with classes 📦Adding the python decorator on a class will automatically add trace wrappers to public functions.⚠️ Private functions will stay unaffected.Python code:fromcalltraces.linetraceimporttraceInfofromcalltraces.classtraceimportclasstrace# ...@classtrace# Add a class decoratorclassMyObject:def__init__(self,input)->None:self._x=inputdef_privateFunction(self):self._x=self._x*2def__superPrivateFunction(self):self._x=self._x*3defpublicBaseFunction(self):self._privateFunction()self.__superPrivateFunction()traceInfo("some log message...")defpublicWrapperFunction(self)->None:returnself.publicBaseFunction()# ...# Whenever the object functions are calledmyObj=MyObject(input=12345)myObj.publicWrapperFunction()This will produce the output:[INFO:01]2022-06-2618:17:15,456__main__.MyObject.publicWrapperFunctionStartingFunction[INFO:02]2022-06-2618:17:15,456__main__.MyObject.publicBaseFunctionStartingFunction[INFO:03]2022-06-2618:17:15,456somelogmessage...[INFO:02]2022-06-2618:17:15,456__main__.MyObject.publicBaseFunctionFinishedFunction[INFO:01]2022-06-2618:17:15,456__main__.MyObject.publicWrapperFunctionFinishedFunction
Package: calltree
.. sectnum::.. contents:: Table Of Contents********calltree********Show Callgraph of a given function=======Purpose=======The understanding of the codeflow in a larger source of codebase, requires to list the caller and callee relationships for a given function. For linux and unix developers, cscope has been the primary utitlity to search and browse through the code.This utility uses the generated cscope database to list and display thecallgraph, which is stored in a image format.=====Usage=====Command Line------------Usage: python -m calltree [options] functionnameOptions:--version             show program's version number and exit-h, --help            show this help message and exit--everything          Recursively show all callers and callees-a, --showall         Show all callers and callees-c, --callers         Recursively show all callers-e, --callees         Recursively show all callees-l LEVEL, --level=LEVELShow Level of callers/callees-f OUTPUTFORMAT, --format=OUTPUTFORMAToutput format jpg/png-o OUTPUTDIR, --outputdir=OUTPUTDIRStorage directory for output files-d SOURCEDB, --db=SOURCEDBcscope database (cscope.out).. code-block::import calltreef = calltree.calltree.calltree()f.showtree()f.showgraph()
Package: callwith
Parameters call function, :-)-----------------------------You can do:from __future__ import print_functionprint('x', 'y', sep=',')But I can not do:from __future__ import print_functionparameters = ('x', 'y', sep=',')print(parameters)With icall, I can do similar thing:from __future__ import print_functionfrom icall import ICallparameters = ICall('x', 'y', sep=',')parameters(print)Sometimes, you may use Parameters(similar to ICall):from __future__ import print_functionfrom icall import Parametersparameters = Parameters('x', 'y', sep=',')parameters(format)Change Log----------0.3.0- Rename package 'callwith' to 'icall'
Package: callword
CallwordRepresent phone numbers as easy to remember words!Setuppip install callwordUsageMap digits to possible words>>> word_dict = callword.read_word_dict()
>>> word_dict['7246837']
['PAINTER']Find wordified version of phone numbers>>> phone_number = '1-800-724-6837'
>>> wordified_number = callword.number_to_words(phone_number, word_dict)
>>> wordified_number
'1-800-PAINTER'Findallwordified versions of phone numbers>>> all_wordifications = callword.all_wordifications(phone_number, word_dict)
>>> all_wordifications
['1-800-PAINTER',  '1-800-PAINT-DR', '1-800-SAINT-DR', '1-800-PAINT-37', '1-800-SAINT-37', 
 '1-800-RAG-MUDS', '1-800-RAG-OVER', '1-800-SAG-MUDS', '1-800-SAG-OVER' ...]Convert wordified phone numbers back to numbers>>> callword.words_to_number(wordified_number) 
'1-800-724-6837'More ExamplesCheckout ourtestsfor more features...
Package: calmap
# Calendar heatmaps from Pandas time series data[![PyPI version](https://badge.fury.io/py/calmap.svg)](https://badge.fury.io/py/calmap)
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[![Build Status](https://travis-ci.com/MarvinT/calmap.svg?branch=master)](https://travis-ci.com/MarvinT/calmap)
[![Coverage Status](https://coveralls.io/repos/github/MarvinT/calmap/badge.svg?branch=master)](https://coveralls.io/github/MarvinT/calmap?branch=master)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://pepy.tech/badge/calmap)](https://pepy.tech/project/calmap)Plot [Pandas](http://pandas.pydata.org/) time series data sampled by day in
a heatmap per calendar year, similar to GitHub’s contributions plot, using
[matplotlib](http://matplotlib.org/).![alt text](https://pythonhosted.org/calmap/_images/index-2.png“Example calendar heatmap”)## UsageSee the [documentation](https://pythonhosted.org/calmap).## InstallationTo install the latest release via PyPI using pip:pip install calmap
Package: calmjs.types
calmjs.typesA collection of types (mostly exception classes) for use withcalmjs.IntroductionDevelopers of packages and extensions that make use of the Calmjs
toolchain has the option to advise the toolchain with their custom
entry point declarations.  However, to halt the execution they will
need to raise specific exceptions.  This package provides those
exceptions as a separate package, under the MIT Expat license, to avoid
the coupling of the package being developed from the core calmjs
package.InstallationAs this is a developer catered package, the installation should be
optional, and the recommended method is to use theinstall_requiresorsetup_requiresargument for thesetupfunction call for thesetup.pyof the package.If required, the following command may be executed to source the latest
stable version ofcalmjs.typeswheel from PyPI for installation into
the current Python environment.$pipinstallcalmjs.typesContributeIssue Tracker:https://github.com/calmjs/calmjs.types/issuesSource Code:https://github.com/calmjs/calmjs.typesLegalThecalmjs.typespackage is copyright (c) 2017 Auckland Bioengineering
Institute, University of Auckland.  Thecalmjs.typespackage is
licensed under the MIT license (specifically, the Expat License).The Calmjs project is copyright (c) 2017 Auckland Bioengineering
Institute, University of Auckland.Changelog1.0.1 - 2020-01-18Package metadata update - LICENSE file now included in source
distribution.1.0.0 - 2017-12-06Initial release.
Package: calmsize
calmsizeNOTICEI forked the repo from source file downloaded from piphurry.filesize.
That simple library is very useful when debugging memory issues, but
unfortunately out-of-maintainance since 2009, so I decided to fork
the great work of Martijn Faassen, Startifact.Introcalmsize a simple Python library that can take a number of bytes and
returns a human-readable string with the size in it, in kilobytes (K),
megabytes (M), etc.The default system it uses is "traditional", where multipliers of 1024
increase the unit size::>>> from calmsize import size, ByteSize
>>> size(1024)
'1K'
>>> size(-1024)
'-1K'
>>> '{:.2f}'.format(size(-1024))
'-1.00K'An alternative, slightly more verbose system::>>> from calmsize import alternative
>>> size(1, system=alternative)
'1 byte'
>>> size(10, system=alternative)
'10 bytes'
>>> size(1024, system=alternative)
'1 KB'A verbose system::>>> from calmsize import verbose
>>> size(10, system=verbose)
'10 bytes'
>>> size(1024, system=verbose)
'1 kilobyte'
>>> size(2000, system=verbose)
'1 kilobyte'
>>> size(3000, system=verbose)
'2 kilobytes'
>>> size(1024 * 1024, system=verbose)
'1 megabyte'
>>> size(1024 * 1024 * 3, system=verbose)
'3 megabytes'You can also use the SI system, where multipliers of 1000 increase the unit
size::>>> from calmsize import si
>>> size(1000, system=si)
'1K'Changes0.1.2 (2019-05-27)Fix pip install fail of python2.70.1.1 (2019-05-25)Fix pip install filenotfound error0.1.0 (2019-05-24)Add support for negative inputAdd support for format the leading numberAdd ByteSize as a new classAdd support for comparing between two Bytesize, ByteSize and numbers0.0.1 (2019-05-24)Forked from hurry.filesizeDownload
Package: calnex-paragon
#################################################################################                                                                              ##   calnexRest.py                                                              ##                                                                              ##   Python interface to the new line of Calnex products                        ##                                                                              ##   Copyright (c) Calnex Solutions Ltd 2008 - 2018                             ##                                                                              ##   The contents of this file should not be copied or distributed              ##   without permission being granted by Calnex Solutions Ltd.                  ##                                                                              ##   All rights reserved.                                                       ##                                                                              #################################################################################
Package: calorie-counter
Calorie counterThis project represents simple web app to calculate calories based on given food type.Please checkhttps://food-calories-counter.herokuapp.comget familiar with application itself.Toolsfront-endhtml5css3javascript (vanilla)back-endpython 3.6 | 3.7 | 3.8flaskcode analysispytestmypyblackUsagePYPIPlease run following script to obtain latest package from PYPI:➜pipinstallcalorie-counterThen please execute instructions below to launch game from your environment:fromcounterimportBind,easyruneasyrun(bind=Bind("0.0.0.0:5003"),debug=True)Runningonhttps://0.0.0.0:5003(CTRL+Ctoquit)...Then please openlocalhost:5003/endpoint.Source codeTo be able to run source code please execute command below:➜python-mcounter--bind0.0.0.0:5003--debug
Runningonhttps://0.0.0.0:5003(CTRL+Ctoquit)...Also you can useflaskbuilt-in runner based on.flaskenvconfig file:➜flaskrun
ServingFlaskapp"counter.py"...For both cases, then please openlocalhost:5003/endpoint.Development notesCIProject has Travis CI integration using.travis.ymlfile thus code analysis (black,mypy) and unittests (pytest) will be run automatically
after every made change to the repository.To be able to run code analysis, please execute command below:➜./analyse-code.shRelease notes0.1.3Use click CLI tool0.1.2Add metainfo into home page0.1.0Introduce PYPI packageMetaAuthor – Volodymyr YahelloDistributed under theMITlicense. SeeLICENSEfor more information.You can reach out me at:vyahello@gmail.comhttps://github.com/vyahellohttps://www.linkedin.com/in/volodymyr-yahello-821746127Contributingclone the repositoryconfigure Git for the first time after cloning with yournameandemailpip install -r requirements.txtto install all project dependenciespip install -r requirements-dev.txtto install all dev project dependencies
Package: calotron
Transformer-based models to fast-simulate the LHCb ECAL detectorTransformerThe Transformer architecture is freely inspired by Vaswani et al. [arXiv:1706.03762] and Dosovitskiy et al. [arXiv:2010.11929].DiscriminatorThe Discriminator is implemented through the Deep Sets model proposed by Zaheer et al. [arXiv:1703.06114] and its architecture is freely inspired by what developed by the ATLAS Collaboration for flavor tagging [ATL-PHYS-PUB-2020-014].CreditsTransformer implementation freely inspired by the TensorFlow tutorialNeural machine translation with a Transformer and Keras.
Package: calour
CALOUR[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/biocore/calour/master?filepath=doc%2Fsource%2Fnotebooks)
[![Build Status](https://travis-ci.org/biocore/calour.png?branch=master)](https://travis-ci.org/biocore/calour)
[![Coverage Status](https://coveralls.io/repos/github/biocore/calour/badge.svg?branch=master)](https://coveralls.io/github/biocore/calour?branch=master)exploratory and interactive microbiome analysis based on heatmaps[Installation](https://github.com/biocore/calour/blob/master/INSTALL.md)[Documentation](http://biocore.github.io/calour)[Mouse & Keyboard Operation](http://biocore.github.io/calour/generated/calour.heatmap.plot.html#calour.heatmap.plot)[Change History](https://github.com/biocore/calour/blob/master/CHANGELOG.md)[How to Contribute](https://github.com/biocore/calour/blob/master/CONTRIBUTING.md)Please Cite[Xu ZZ, Amir A, Sanders J, Zhu Q, Morton JT, Bletz MC, Tripathi A, Huang S, McDonald D, Jiang L, et al. (2019) Calour: an Interactive, Microbe-Centric Analysis Tool. mSystems, 4, e00269-18.](https://msystems.asm.org/content/4/1/e00269-18)
Package: calpack
CalPackPackets in Python Simplified.This python package is everything you need to "transmorgrify" your packets:This package is intended to make creating and/or parsing packets (structured bytecode) on the fly quick and easy.  This is a wrapper around
thectypesmodulebuilt-in to python. This package is designed
with influence from Django's modeling and will look familiar to those that have used it.A quick explanation of Packets and how to use themPackets are structured bytecode used for passing information from one place to another.  The most common example is
that of a TCP/IP Packet, but isn't necessarily limited to networking packets.  Here's a quick example.  Let's say we
want to make a "smart" washing machine by attaching aRaspberry Pithat then talks to your other smart devices and alerts
you when a load of laundry is done and how many loads of laundry you've done that day.One way to communicate between the Raspberry Pi and your other devices is to send status "packets" or byte data
across a network.  Let's say we want to know the following in our packet:Status - a Boolean that represents whether the Washing Machine is running or stoppedNumber of Loads - an Integer that represents the number of loads done that dayTo create this packet inCalPackis simple:fromcalpackimportmodelsclassMachineStatus(models.Packet):Status=models.BooleanField()Num_Loads=models.IntField()On our monitoring device (the Raspberry Pi), we can easily create the byte data for the packet by using our new packet:status_pkt=MachineStatus(Status=True,Num_Loads=12)# Send the byte data using an assumed custom `send` funcionsend(status_pkt.to_bytes())And converting the recieved byte data is simple as well:# assuming a `receive` function and returns the byte data of the sent packetreceived_data=MachineSatus.from_bytes(receive())print(received_data.status)InstallationThis package is maintained inGitHuband packaged for deployment onPyPi.Simply usingpip install calpackwill get this installed.SHOW ME THE DOCSDocumentation is host onread the docsPython 2 and 3Currently this module is designed to work for both Python 2.7+ and 3.3+, however, with the term of life for Python 2 nearing,
this package will eventually port entirely over to Python 3.
Package: calpackage
Getting Started with APIMATIC CalculatorIntroductionSimple calculator API hosted on APIMATICInstall the PackageThe package is compatible with Python versions2 >=2.7.9and3 >=3.4.
Install the package from PyPi using the following pip command:pipinstallcalpackage==1.0You can also view the package at:https://pypi.python.org/pypi/calpackageTest the SDKYou can test the generated SDK and the server with test cases.unittestis used as the testing framework andnoseis used as the test runner. You can run the tests as follows:Navigate to the root directory of the SDK and run the following commandspip install -r test-requirements.txt
nosetestsInitialize the API ClientNote:Documentation for the client can be foundhere.The following parameters are configurable for the API Client:ParameterTypeDescriptionenvironmentEnvironmentThe API environment.Default:Environment.PRODUCTIONtimeoutfloatThe value to use for connection timeout.Default: 60max_retriesintThe number of times to retry an endpoint call if it fails.Default: 0backoff_factorfloatA backoff factor to apply between attempts after the second try.Default: 2retry_statusesArray of intThe http statuses on which retry is to be done.Default: [408, 413, 429, 500, 502, 503, 504, 521, 522, 524]retry_methodsArray of stringThe http methods on which retry is to be done.Default: ['GET', 'PUT']The API client can be initialized as follows:fromapimaticcalculator.apimaticcalculator_clientimportApimaticcalculatorClientfromapimaticcalculator.configurationimportEnvironmentclient=ApimaticcalculatorClient(environment=Environment.PRODUCTION,)List of APIsSimple CalculatorClasses DocumentationUtility ClassesHttpResponseHttpRequest
Package: calphy
calphycalphy(pronouncedcal-phee) is a Python library and command line tool for calculation of free energies. It usesLAMMPSas the molecular dynamics driver to enable calculation of free energies using thermodynamic integration in a completely automated and efficient manner. The various methods that calphy can perform are:andfor both solid and liquid phases at the given thermodynamic conditions usingnon-equilibrium Hamiltonian interpolation.and, temperature dependence of Gibbs and Helmholtz free energies using thereversible scalingapproach.Calculation of solid-solid or solid-liquid phase transition temperatures.Calculation of coexistence lines usingdynamic Clausius-Clapeyron integration.Calculation of specific heatas a function of temperature.Calculation ofandfor multicomponent systems usingalchemical transformations.Upsampling calculations.Calphy works with allinteratomic potentials implemented in LAMMPSand can obtain reliable results with low error bars in as low as 50 ps of simulation time with less than 3000 atoms. More information about the methods in calphy can be found in theassociated publication.Installation, examples and documentationRead thecalphyinstallation instructions and documentation including exampleshere.For a description of the methods and algorithms implemented incalphy, please see theassociated publication.For a set of examples presented in thecalphypublication, please seehere.Citing calphyIf you find calphy useful, please consider citing:Menon, Sarath, Yury Lysogorskiy, Jutta Rogal, and Ralf Drautz.“Automated Free Energy Calculation from Atomistic Simulations.”Physical Review Materials 5(10), 2021DOI: 10.1103/PhysRevMaterials.5.103801Download bibtexhere
Package: calplot
Calplot creates heatmaps from Pandas time series data.PlotPandastime series data sampled by day in
a heatmap per calendar year usingmatplotlib.InstallationTo install thelatest releasevia PyPI usingpip:pip install calplotChangelogPackagecalplotwas created as a fork ofcalmapwith the addition of new arguments for easier customization.SeeCHANGES.rstfor changelog.UsageSeeAPI documentation.ExamplesThe following examples are run in aJupyter notebook.importcalplotimportnumpyasnp;np.random.seed(sum(map(ord,'calplot')))importpandasaspdall_days=pd.date_range('1/1/2019',periods=730,freq='D')days=np.random.choice(all_days,500)events=pd.Series(np.random.randn(len(days)),index=days)calplot.calplot(events)all_days=pd.date_range('1/1/2019',periods=360,freq='D')days=np.random.choice(all_days,500)events=pd.Series(np.random.randn(len(days)),index=days)calplot.calplot(events,edgecolor=None,cmap='YlGn')calplot.calplot(events,yearlabel_kws={'color':'black'},cmap='YlGn')calplot.calplot(events,textformat='{:.0f}',textfiller='-',cmap='YlGn')calplot.calplot(events,colorbar=False,cmap='YlGn')calplot.calplot(events,suptitle='Random data from standard normal distribution',cmap='YlGn')calplot.calplot(events,linewidth=0,cmap='YlGn')
Package: cal-plusplus
cal-pluspluscal-plusplusdisplays a calendar on your command line withworkalendarintegration for showing working days.InstallationInstall with pip:pip install --user cal-plusplusUsageDisplay current year, attempt to detect your region (it will be displayed in the title):calSpecify calendar fromworkalendarto use:cal --calendar=AU-VICAlternatively you can set theCALPLUSPLUSenvironment variable to the name of the calendar you would like to use.Specify a year to display a calendar for:cal 2024Output a list of valid calendars:cal --show-calendars
Package: calpred
CalPred (Calibrated Prediction Intervals for Polygenic Scores Across Diverse Contexts)The package is still in early development. And therefore any comments/suggestions are highly appreciated.Seecompanion manuscript github repositoryfor analysis scripts used in themanuscript.Installationgitclonehttps://github.com/KangchengHou/calpred.git&&cdcalpred/
Rscript-e"install.packages(c('statmod', 'Rchoice', 'logger', 'optparse', 'glue'), repos='https://cran.rstudio.com')"# calpred dependencyRscript-e"install.packages(c('devtools', 'ggplot2', 'dplyr', 'patchwork'), repos='https://cran.rstudio.com')"# for example notebooksQuick example# df must have person ID as 1st column, and should not contain missing data# y_col is the column name of the trait of interest# mean_cols and sd_cols are columns fot fitting the mean and standard deviation# with names separated by commas# <out_prefix>.fitted.tsv (fitted mean and sd) and <out_prefix>.coef.tsv (coefficients) will be generated# see toy/simulate.ipynb for the data simulation processRscriptcalpred.cli.R\--dftoy/trait.tsv\--y_colpheno\--mean_colspgs,ancestry,age,sex\--sd_colspgs,ancestry,age,sex\--out_prefixtoy/traitExample notebooksIntroduction with a simulated datasetExample analysis pipelineUpload to PyPIpythonsetup.pysdist
twineuploaddist/*