Saravanan666/pypi-org huggingface dataset:
Package: matchgrowth
AboutThis module provides a CLI tool for rough approximations according to a catalog
of prescribed growth rates.The tool currently features most well-knownclasses of growth ratesand works by fitting each generic growth rate function to the given data.Its primary use case isestimating time or space growth ratesof various
algorithms, but it can also be used on data coming from physical measurements.InstallTo install from pypi:pip3 install --user matchgrowthUsageThe CLI tool expects--infilewith a valid CSV file path, and--col1and--col2each with a column name
present in the CSV file. Currently the CSV file needs to have headers for all columns, in the first row.The--topparameter allows to limit the number of matched growth rates reported.The--outfileparameter is optional and allows to write the generated plot to a PNG file on disk.
If the--outfileparameter is not passed, the default GUI from matplotlib will be used to display the plot.usage: match-growth.py [-h] --infile INFILE [--outfile OUTFILE] --col1 COL1
                       --col2 COL2 [--top TOP] [--catalog CATALOG] [--debug]
                       [--plot PLOT_TYPE]

Tool for estimating growth rates

optional arguments:
  -h, --help         show this help message and exit
  --infile INFILE    CSV input file with measurements
  --outfile OUTFILE  PNG file to write the plot to
  --col1 COL1        data column 1 from the CSV
  --col2 COL2        data column 2 from the CSV
  --top TOP          only print top N closest growth rates
  --catalog CATALOG  select the function catalog to use
  --debug            enable debug mode
  --plot PLOT_TYPE   plot type (normal|loglog)Example usage:match-growth.py --infile ./tests/modif7_bench.txt --outfile r1.png --col1 N --col2 memory --top 2In this example we're estimating the closest growth rate for an algorithm that has a single inputNand for which we've
already recorded the memory usage for different values ofN.SupportFor questions or requests for paid support, please send an e-mail tobusiness@garage-coding.com
Package: matching-network
matching_networkSolve L-section lumped parameters matching networks in a wink. (SeeHow to usesection)Shunt-series config.Series-shunt config.Jupyter NotebooksL-section_matching_calculations(Initial Jupyter notebook implementation)Calculations(Matlab pre-calculations)Installationpipinstallmatching_networkHow to useFrom the CLImatching_network--from100--to20+43j--freq13.56e6# both impedances in Ω.From (100+0j) Ω to (20+43j) Ω

normalized starting impedance = (100+0j)Ω / (20+43j)Ω = 0.88928-1.912j

#solutions: 4

shunt-series
    Shunt Inductor:
    X = 50 Ω ⇔ B = -20 mS
    L = 586.85 nH  (@ 13.56 MHz)
    Series Inductor:
    X = 3 Ω ⇔ B = -333.33 mS
    L = 35.211 nH  (@ 13.56 MHz)
shunt-series
    Shunt Capacitor:
    X = -50 Ω ⇔ B = 20 mS
    C = 234.74 pF  (@ 13.56 MHz)
    Series Inductor:
    X = 83 Ω ⇔ B = -12.048 mS
    L = 974.18 nH  (@ 13.56 MHz)
series-shunt
    Series Inductor:
    X = 35.285 Ω ⇔ B = -28.341 mS
    L = 414.14 nH  (@ 13.56 MHz)
    Shunt Inductor:
    X = 62.571 Ω ⇔ B = -15.982 mS
    L = 734.4 nH  (@ 13.56 MHz)
series-shunt
    Series Capacitor:
    X = -35.285 Ω ⇔ B = 28.341 mS
    C = 332.64 pF  (@ 13.56 MHz)
    Shunt Inductor:
    X = 44.929 Ω ⇔ B = -22.257 mS
    L = 527.33 nH  (@ 13.56 MHz)matching_network--from"24.3+8.3j mS"--to1.1+9.3j# default in Ω unless specified, using `mS`.Inside Python>>>importmatching_networkasmn>>>>>>impedance_you_have=90+32j# Ω>>>impedance_you_want_to_have=175# Ω>>>>>>frequency=900e6# Hz>>>>>>mn.L_section_matching(impedance_you_have,impedance_you_want_to_have,frequency).match()From(90+32j)Ωto175Ωnormalizedstartingimpedance=(90+32j)Ω/175Ω=0.51429+0.18286j#solutions: 2series-shuntSeriesInductor:X=55.464Ω⇔B=-18.03mSL=9.8082nH(@900MHz)ShuntCapacitor:X=-180.07Ω⇔B=5.5533mSC=982.04fF(@900MHz)series-shuntSeriesCapacitor:X=-119.46Ω⇔B=8.3707mSC=1.4803pF(@900MHz)ShuntInductor:X=180.07Ω⇔B=-5.5533mSL=31.844nH(@900MHz)>>>Francesco Urbani
Package: matchingsplit
matchingsplitSplit a string or group a collection of words into a list by matching another list of similar words, to create accurate subtitles from the actual script and inaccurate (generated) subtitles.Examplefrommatchingsplitimportsplit>>>split("this must be a good thing",reference=["this","is","a","good","thing"])['this','must be','a','good','thing']>>>split("this is a good thing",reference=["this","must","be","a","good","thing"])['this','','is','a','good','thing']>>>split("a big foo bar",["a","big","ff"])['a','big','foo bar']>>>split("line1.\n\nline2.\nline3.",reference=["1","2","3"],preserve_newlines=True)['line1.\n\n','line2.\n','line3.']
Package: matchingtools
MatchingTools is a Python library for doing symbolic calculations in
effective field theory.It provides the tools to create a Lagrangian and integrate out heavy
fields at the tree level. It also includes functions for applying
customizable transformations (for example, Fierz identities or
simplification using equations of motion of the light fields) to the
effective Lagrangian to simplify it or write it in terms of a chosen
effective operator basis.InstallationTo install MatchingTools usingpipdo:pip install matchingtoolsThe source can be downloaded from theGitHub repository.
To install MatchingTools from a local copy of the project open a terminal
in the top level directory and type:python setup.py installDocumentationRead the documentation at:http://matchingtools.readthedocs.io/en/latest/.CitationIf you use this work, please cite:https://arxiv.org/abs/1710.06445.
Package: matchlib
matchlibThis package provides a handy way to partially compare python data structures
(typically nested lists/dictionaries).InstallationpipinstallmatchlibUsagefrommatchlibimportmatchesuser={'id':42,'name':'John Doe','email':'johndoe@gmail.com','posts':[{'id':1,'text':'some text'},{'id':2,'text':'lorem ipsum','comments':[42,142,242]}]}assertmatches(user,{'id':...,'name':'John Doe','email':'johndoe@gmail.com',...:...})Same can be achieved using standard==operator withmatchlib.Partialobject:frommatchlibimportPartialassertuser==Partial({'id':42,'email':'johndoe@gmail.com',...:...})The..."wildcard" could be placed at any nested level.
Let's say we only need to check that comment142is present in specific post:assertuser==Partial({'posts':[...,{'id':2,'comments':[...,142,...],...:...}],...:...})Matching rules are simple:Inlistsandtuples...matches zero or more elements and order is preserved:Partial([1,2,...])==[1,2,3,4]Partial([1,2,...])==[1,2]Partial([1,2,...])!=[0,1,2]Partial([1,2,...])!=[2,1]Same for thesetsexcept they are unordered:Partial({1,2,...})=={1,2}Partial({1,2,...})=={0,1,2,3}Partial({1,2,...})!={0,1,3}Asdict value...matches any object:Partial({'a':1,'b':...})=={'a':1,'b':2}Asdict key...matches any key if assosiated values match:Partial({'a':1,...:2})=={'a':2,'b':2}When passed asboth key and valuematches zero or more arbitrary key-value pairs:Partial({'a':1,...:...})=={'a':1,'b':2,'c':3}Some more hacksmathchlibprovides aRegexobject that allows to match an arbitrary string element
(except if it is a dict key) against a regular expression.
Alsopytest.approxis supported for floating-point numbers comparison:frompytestimportapproxfrommatchlibimportRegex,Partialaccount={'id':1,'balance':1007.62,'owner':{'email':'user42@domain.com',}}assertaccount==Partial({'id':...,'balance':approx(1000,0.1),'owner':{'email':Regex(r'\w+@domain\.com')}})If for any reason you dislike Ellipsis literal (...)
amatchlib.Anyobject can be used interchangeably.
Package: matchlightsdk
Matchlight exists to quickly and privately alert its users when any of their
sensitive information appears for sale or vandalism out on the dark web. The
product is fully automated, and operates using Data Fingerprints — a one-way
representation that allows Terbium to monitor for client data without
needing to know what that data is.The Matchlight SDK provides a Python interface to Matchlight’s API, allowing
developers to create and retrieve projects, download feeds, create document,
source code, and PII records, and perform searches.InstallationMatchlight SDK is supported on Python 2.7, 3.3, 3.4, and 3.5. To install the
SDK, you’ll needpip:$ pip install matchlightsdkDocumentationA walkthrough of the SDK features and API documentation is available athttps://python-matchlightsdk.readthedocs.io.LicenseThe Matchlight Python SDK source code is licensed under the3-clause BSD License. For
more information, please see the LICENSE file included in this repository or
source distribution.ContributingBug reports and pull requests are welcome. If you would like to contribute,
please create a pull request againstmaster. Include unit tests if
necessary, and ensure that your code passes all linters (see tox.ini).BuildingFirst, install all requirements:$ make requirementsThen:$ make buildTestsTo run tests, install test requirements:$ make dev_requirementsYou will also need to install Pyenvhttps://github.com/pyenv/pyenv#installationThe easiest way is with Homebrew:$ brew update
$ brew install pyenvNOTE: Make sure that pyenv is added to your pathThen:$ make testUpdate RequirementsIf you made a change that adds a new requirement, add it to the correct file in ‘requirements/src’.
Then update the requirements file:$ pip-compile --output-file requirements/<file>.txt requirements/src/<file>.inUpdate DocumentationTo update the documentation:$ make docsTo preview changes:$ make serve docsUpdate pypi package$ python3 setup.py bdist_wheel –universal$ twine upload dist/*
Package: matchmaker
Decorators that simplify the creation of Hamcrest matchers.From a function (with an optional appropriate docstring), create
hamcrest matchers with minimum extra coding.The sources can be found inGitHub.Examples:from matchmaker import matcher

@matcher
def is_even(item):
    return item % 2 == 0

@matcher
def ends_like(item, data, length=3):
    "String whose last {1} chars match those for '{0}'"
    return item.endswith(data[-length:])You can then use these in your tests as:assert_that(number, is_even())
assert_that(word, ends_like(other_word, 4))Errors will display as:AssertionError:
Expected: Is even
     but: was <3>

AssertionError:
Expected: String whose last 4 chars match those for 'cello'
     but: was 'hullo'
Package: matchmakereft
MatchMakerAutomated tree-level and one-loop matching of general models onto general effective field theoriesContributorsAdrián Carmona (University of Granada)Achilleas Lazopoulos (ETH Zürich)Pablo Olgoso (University of Granada)Jose Santiago (University of Granada)InstallationMatchmakereft is available both on the PyPI Python Package Index (PyPI)https://pypi.org/project/matchmakereft/as well as in the Anaconda Python distributionhttps://anaconda.org/matchmakers/matchmakereftTroubleshootingWe encourage users to check the troubleshooting section in the latest matchmakereft manual and the Gitlab issue tracker (https://gitlab.com/m4103/matchmaker-eft/-/issues)LicenseMatchmakereft is distributed under a GNU General Public License v3.0CitationIf you use matchmakereft please cite [SciPost Phys. 12 (2022) 6, 198] (https://scipost.org/10.21468/SciPostPhys.12.6.198) arXiv:2112.10787 (https://arxiv.org/abs/2112.10787)
Package: matchmock
matchmockHamcrestmatchers formockobjects.Starting from version 2.0.0python2 is no longer supportedthe 1.x series
will remain supported but no new features will be added.Examplef = Mock()
    f.method('foo', 'bar')

    assert_that(f.method, called_once_with(anything(), 'bar')Matcherscalled- match mock that was called one or more timesnot_called- match mock that was never calledcalled_once- match mock that was called once regardless of argumentscalled_with- match mock has at least one call with the specified argumentscalled_once_with- match mock that was called once and with the specified argumentscalled_n_times- match mock that was called number of times.
Package: matchmsextras
<img src=”https://github.com/matchms/matchmsextras/blob/main/images/matchmsextras_logo.png” width=”600”>![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/matchms/matchmsextras/CI_build.yml?branch=main)
![GitHub](https://img.shields.io/github/license/matchms/matchmsextras)
[![PyPI](https://img.shields.io/pypi/v/matchmsextras?color=blue)](https://pypi.org/project/matchmsextras/)# matchmsextrasAdditional functionalities to be used with [matchms](https://github.com/matchms/matchms).Usually this is stuff which either in scope and/or in code quality and/or in degree of unit testing does not match the criteria we have for matchms.
For producing the results shown in our [Spec2Vec article, PLoS Computational Biology, 2021](https://doi.org/10.1371/journal.pcbi.1008724) we used code frommatchmsextras 0.1.0. To fully reproduce those results you can install this viapip install matchmsextras==0.1.0. For all other purposes we recommend using the latest release.## Installation` pip install matchmsextras `## Citing us
If you usematchmsextrasfor your research, please cite the following references:Huber F, Ridder L, Verhoeven S, Spaaks JH, Diblen F, Rogers S, van der Hooft JJJ, (2021) “Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships”. PLoS Comput Biol 17(2): e1008724.https://doi.org/10.1371/journal.pcbi.1008724Huber, S. Verhoeven, C. Meijer, H. Spreeuw, E. M. Villanueva Castilla, C. Geng, J.J.J. van der Hooft, S. Rogers, A. Belloum, F. Diblen, J.H. Spaaks, (2020). “matchms - processing and similarity evaluation of mass spectrometry data”. Journal of Open Source Software, 5(52), 2411,https://doi.org/10.21105/joss.02411Many extra thanks!## Tutorial on matchms and Spec2Vec
Possibly the easiest way to learn how to run matchmsextras is to follow our tutorial onmatchms.[Part I - Import and process MS/MS data using matchms](https://blog.esciencecenter.nl/build-your-own-mass-spectrometry-analysis-pipeline-in-python-using-matchms-part-i-d96c718c68ee)[Part II - Compute spectral similarity using Spec2Vec](https://blog.esciencecenter.nl/build-a-mass-spectrometry-analysis-pipeline-in-python-using-matchms-part-ii-spec2vec-8aa639571018)[Part III - Create molecular networks from Spec2Vec similarities](https://blog.esciencecenter.nl/build-a-mass-spectrometry-analysis-pipeline-in-python-using-matchms-part-iii-molecular-91891248ee34)
Package: matchms-extras
<img src=”https://github.com/matchms/matchms_extras/blob/main/images/matchms_extras_logo.png” width=”600”>![GitHub Workflow Status](https://img.shields.io/github/workflow/status/matchms/matchms_extras/CI%20Build)
![GitHub](https://img.shields.io/github/license/matchms/matchms_extras)
[![PyPI](https://img.shields.io/pypi/v/matchms-extras)](https://pypi.org/project/matchms-extras/)# matchms_extrasAdditional functionalities to be used with [matchms](https://github.com/matchms/matchms).Usually this is stuff which either in scope and/or in code quality and/or in degree of unit testing does not match the criteria we have for matchms.
For producing the results shown in our [Spec2Vec article, PLoS Computational Biology, 2021](https://doi.org/10.1371/journal.pcbi.1008724) we used code frommatchms_extras 0.1.0. To fully reproduce those results you can install this viapip install matchms_extras==0.1.0. For all other purposes we recommend using the latest release.## Installation` pip install matchms_extras `## Citing us
If you usematchms_extrasfor your research, please cite the following references:Huber F, Ridder L, Verhoeven S, Spaaks JH, Diblen F, Rogers S, van der Hooft JJJ, (2021) “Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships”. PLoS Comput Biol 17(2): e1008724.https://doi.org/10.1371/journal.pcbi.1008724Huber, S. Verhoeven, C. Meijer, H. Spreeuw, E. M. Villanueva Castilla, C. Geng, J.J.J. van der Hooft, S. Rogers, A. Belloum, F. Diblen, J.H. Spaaks, (2020). “matchms - processing and similarity evaluation of mass spectrometry data”. Journal of Open Source Software, 5(52), 2411,https://doi.org/10.21105/joss.02411Many extra thanks!
Package: matchms-plotly
matchms-plotlyPlotMatchmsspectra withPlotly.This package is under developement for theMetWorkproject.Installpipinstallmatchms-plotlyUsagefrommatchms_plotlyimportplot_spectrum,plot_spectra# Plot a single spectrum.plot_spectrum(spectrum)# Plot two spectrums in the "up and down" fashion.plot_spectra(spectrum_up,spectrum_down)
Package: matcho
MatchoA pattern matching and template library.Extract and convert hierarchically structured data using declarative input patterns and output templates.Matcho was originally written by a need to convert hierarchical
JSON data into flattish data frames. It may yet transcend this purpose.Installationpip install matchoQuick Startfrommatchoimportbuild_matcher,build_template,bind,insert# match a list of any length and bind "x" to its itemsmatcher=build_matcher([bind("x"),...])# match some databindings=matcher([1,2,3])# a template that reconstructs the original listtemplate=build_template([insert("x"),...])asserttemplate(bindings)==[1,2,3]Motivating exampleWhat if you want to convert data from a deeply nested structure like JSON
to a flat tabular format?For example, say we want to extract the columns "date", "time", "station" and
"event_type" from the following structure:data={"date":"2022-02-20","uid":"DEADBEEF","reports":[{"station":7,"events":[{"time":1300,"type":"ON"},{"time":1700,"type":"OFF"}]},{"station":5,"events":[{"time":1100,"type":"ON"},{"time":1800,"type":"OFF"}]}]}That's how Matcho does it:frommatchoimportbuild_matcher,build_template,bind,insertpattern={"date":bind("date"),"reports":[{"station":bind("station"),"events":[{"time":bind("time"),"type":bind("event_type")},...],},...,# note that the ... really are Python syntax],}template_spec=[[insert("date"),insert("time"),insert("station"),insert("event_type")],...,...,# note that the number of ... matches the pattern]matcher=build_matcher(pattern)bindings=matcher(data)template=build_template(template_spec)table=template(bindings)asserttable==[["2022-02-20",1300,7,"ON"],["2022-02-20",1700,7,"OFF"],["2022-02-20",1100,5,"ON"],["2022-02-20",1800,5,"OFF"],]InspirationMatcho was inspired by Scheme'ssyntax-rulespattern language. Scheme is a
Lisp dialect that allows programmers to define macros using pattern matching and
template substitution. Since code in Scheme consists of list this enables cool
syntax transformations. In Python we are limited to transforming data, but
that's cool enough.Why not just use Python 3.10'smatchsyntax instead?The newmatchsyntax is great and it's even used by the implementation of
Macho. However, it has one shortcoming: names can only capture one value. While
it's possible to match an arbitary number of list items with[*items], it's
not possible to do something like[*{"nested": item}], where we would like
to capture values in a sequence of dictionaries. In Matcho, this is  possible
with a pattern of the form[{"nested": item}, ...].
Package: match-ref
Match-RefTop-Level Variable References For Python'smatchObjectiveThis library provides a convenient interface to variables in the local and global namespaces, using
qualified (dotted) names.Python3.10introduced thematch-casestructurewhich allows you to write flow control code like this:my_choice=input("Your choice: ")fruit_on_sale="peach"matchmy_choice:case"apple":print("Delivering an apple.")case"banana":print("Delivering a banana.")casefruit_on_sale:print("Good choice!")If you now entered a value liketomato, he output would befruit_on_sale. This is, becausefruit_on_saleis considered aplaceholder/capture patternand not a reference to a variable.Therefore, we need another way to reference variables. We can accomplish this using dotted names (i.e.fruit_data.fruit_on_sale, because Python does not interpret these names as placeholders.InstallationYou can install this package usingpip:pip install match-refon all platforms, if availablepython3 -m pip install match-refon Unixpy -m pip install match-refon WindowsUsagefrommatchrefimportrefmy_choice="banana"fruit_on_sale="peach"matchmy_choice:caseref.fruit_on_sale:print("Good choice!")You can use therefhelper to resolve all variables in the currentlocalandglobalnamespaces which you could usually reference without using a dotted name.You can useref.any_local_or_global_variable_namein any expression, not only insidecaseconditions, if you need to.Only value retrieval is supported, as attribute-setting is not the purpose of this library.How does it work?refis the default instance of theScopeReferenceclass, defined in match-ref. It has a method__getattr__which gets called whenever you request an attribute ofref. This method then starts looking up the local namespace it was called from (your script or your function) and, if it doesn't find the variable you're looking for, continues searching the global namespace as well.If it doesn't find the variable you are looking for, the following error is being raised:ValueError: referenced name '...' is not defined
Package: matchstick
Matchstick provides a decorator that allows for the overloading of Python functions.>>>frommatchstickimportwhen>>>@when('x >= 0')...deff(x):...returnx...>>>@when('x < 0')...deff(x):...return-x...>>>f(3)3>>>f(-2)2>>>f('a')'a'Basic UseConditionsThe match decorator takes a condition as a string. When the decorated function gets called, overloaded functions (grouped by module and qualified name) are checked in the order they are defined. Each condition is evaluated with the arguments passed to the function (including default parameter values, where applicable). If the condition evaluates truthy, the corresponding function is called. If the condition evaluates falsy or raises an exception, the corresponding function is passed over.>>>@when('x[2] == 5')...deff(x):...returnsum(x)...>>>@when('len(x) > 3')...deff(x):...returnx[3:]...>>>@when('True')...deff(x):...returnx...>>>f([3,4,5,6])18>>>f([0,1,2,3])[3]>>>f('abcd')'d'>>>f({}){}NoMatchExceptionIf none of the conditions for a function evaluate truthy, amatchstick.NoMatchExceptionis raised.
Package: matchtext
Python matchtextPython 3 package for fast text matching and replacing.This library implements two fast approaches for matching keywords/gazetteer entries:TokenMatcher: keywords/gazetteer entries are sequences of tokens, optionally associated with some data and
the matcher tries to match any of those in a given sequence of tokens.StringMatcher: keywords/gazetter entries are strings, optionally associated with some data and
the matcher tries to match any of those in a given string, optionally only at non-word boundaries.The matchers are implemented to be fast: TokenMatcher is a hash tree, StringMatcher uses a
character trie implementation underneath. Both matchers implement additional features often required in NLP:return the offsets in the original iterable where a match occursmapfunc: tokens/characters can be mapped to some canonical form that is used for matchingignorefunc: some tokens/characters can be entirely ignored for matchingmatch all/longest: only match the longest entry versus all entriesskip/noskip: if any match is found, continue matching after the longest match versus at the next position
Package: matchtune-sdk
MatchTune SDK for PythonThis repository contains the open source MatchTune SDK that allows you to access the MatchTune API from your Python app.InstallationThe MatchTune Python SDK can be installed withPip. Run this command:pipinstallmatchtune-sdkUsageSimple MatchTune Query Exampleimportrandomfrommatchtune_sdkimportMatchTuneapi=MatchTune({"app_token":app_token})haserror=True## -- Load genre & subgenre listgenres=api.genres()ifgenres!=None:## -- pick a random genregenre=random.sample(genres.keys(),1)## -- create a search queryquery=api.makeQuery(genre,subgenre=None,title=None,tags=None)## -- request a standard generated musicidcard=api.generate(query)ifidcard!=None:haserror=False## -- use the dataprintIDCard(idcard)}}Api documentationAll mechanisms developed here are documented on ourREST API documentation.LicensePlease see thelicense filefor more information.Security VulnerabilitiesIf you have found a security issue, please contact the support team directly atsupport@matchtune.com.