Saravanan666/pypi-org huggingface dataset:
Package: numexpr-mod
Author:Alexander K.URL:https://github.com/MrCheatak/numexpr_modWhat is NumExpr?Please refer to the originalNumexprrepo.InstallationFrom wheelsNumExpr is available for install viapipfor a wide range of platforms and
Python versions (which may be browsed at:https://pypi.org/project/numexpr/#files).
Installation can be performed as:pip install numexpr_modFrom SourceOn most *nix systems your compilers will already be present. However if you
are using a virtual environment with a substantially newer version of Python than
your system Python you may be prompted to install a new version ofgccorclang.For Windows, you will need to install the Microsoft Visual C++ Build Tools
(which are free) first. The version depends on which version of Python you have
installed:https://wiki.python.org/moin/WindowsCompilersFor Python 3.6+ simply installing the latest version of MSVC build tools should
be sufficient. Note that wheels found via pip do not include MKL support. Wheels
available viacondawill have MKL, if the MKL backend is used for NumPy.Seerequirements.txtfor the required version of NumPy.NumExpr is built in the standard Python way:python setup.py build installYou can testnumexpr_modwith:python -c "import numexpr_mod; numexpr_mod.test()"Do not test NumExpr in the source directory or you will generate import errors.UsageThis modification of the Numexpr package enables storage of the previously compiled expressions.
Stored expressions can be then called by their assigned name.Note that precompiled expressions accept only the same variable names with the same types.
Although, both arrays and single values can be used for the same expression variable.Output destination, order and casting can be preset at compilation time or be specified at expression call.>>> import numexpr_mod as ne
>>> import numpy as np

>>> a = np.array([1,2,3,4,5])
>>> b = np.array([6,7,8,9,0])

>>> ne.cache_expression('a + b', 'sum_ab')
{'ex': <numexpr_mod.NumExpr object at 0x1090e36b0>, 'argnames': ['a', 'b'], 'kwargs': {'out': None, 'order': 'K', 'casting': 'safe', 'ex_uses_vml': False}}
>>> ne.re_evaluate('sum_ab')
array([ 7,  9, 11, 13,  5], dtype=int64)
>>> ne.evaluate('a + b')
array([ 7,  9, 11, 13,  5], dtype=int64)Additionally, cached expresion names can be retrieved:>>>ne.get_expression_names()
['sum_ab']LicenseNumExpr is distributed under theMITlicense.
Package: numexpr-preparser
Infix pre-parser fornumexprThis is an infix parser for the basic syntax outlined in thenumexpruser guide.
It may not correctly parse every supported numexpr expression, but it does offer strict parsing of a subset of numexpr syntax.It is defined using thepyparsing packageNot supported at this time/known issuesString constants are not yet supported, and will not be sucessfully parsedthe axis keyword in numexprsumandprodwill not be sucessfully parsedIt can be used as numexpr pre-parser/validator:ensure string expressions are strictly infix expressionsget detailed error feedback for parsing errors via pyparsingassert only function names that are supported in numexprassert identifiers are valid python identifiersassert that only the numexpr unary and binary operators are usedconstant values supported are:floating point with scientific and complex notationintegersInstallationpip install numexpr_preparserExample usage:Using as a pre-parser, to reject expressions that are not strictly infix
notation with the documentednumexprsyntaximportnumexprfromnumexpr_preparserimportparseraspreparserdefnumexpr_safe_evaluate(ex:str,*args,**kwargs):parse_success,results=preparser.get_parser().run_tests(ex,print_results=False)ifnotparse_success:raiseValueError(results[0][1])returnnumexpr.evaluate(ex,*args,**kwargs)
Package: num-fh
Numeric Fused-HeadThis is the code used in the paper:"Where’s My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution"Yanai Elazar and Yoav Goldbergto appear in TACLpreprintdemoInstallationInstalling the num_fh package is simple as:pipinstallnum_fhPrerequisitesat least python 3.6 (and specific libraries in the requirements.txt)We strongly recommend that you use a new virtual environment to install num_fhcreate a clean conda envcondacreate-nnfhpython==3.6anacondasourceactivatenfhCloning and Running the Library by Yourselfclone this repo:gitclonegit@github.com:yanaiela/num_fh.gitinstalling python packagespipinstall-rrequirements.txtExample Usageimportspacyfromnum_fhimportNFHnlp=spacy.load('en_core_web_sm')nfh=NFH(nlp)nlp.add_pipe(nfh,first=False)doc=nlp("I told you two, that only one of them is the one who will get 2 or 3 icecreams")assertdoc[16]._.is_nfh==Trueassertdoc[18]._.is_nfh==Falseassertdoc[3]._.is_deter_nfh==Trueassertdoc[16]._.is_deter_nfh==Falseassertlen(doc._.nfh)==4The paper (and therefore, also the code) deals with two sub-tasks of the FH solution:IdentificationResolutionThese are dealt with separately, and discussed in the paper in sections
(3,4) and (5,6). These parts are also solved separately in the code, and
contain further instructions for each one in dedicated README files:IdentificationandResolutionDemoTo run the demo, run the following:exportFLASK_APP=num_fh/demo/server.py
flaskrun--port5000--host""CitingIf you find this work relevant to yours, please consider citing us:@misc{elazar2019wheres,
    title={Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution},
    author={Yanai Elazar and Yoav Goldberg},
    year={2019}
}ContactIf you have any question, issue or suggestions, feel free to contact
us with the emails listed in the paper.
Package: numfi
numfinumfi is a numpy.ndarray subclass that does fixed-point arithmetic.Feature:Automatically perform fixed-point arithmetic through overloaded operatorsMaximum compatibility with numpy and other library, just like a normal numpy.ndarrayOptimized calculation speed by minimizing quantization as much as possibleInstallPrerequisite: python3 and numpypip install numfior you can just copynumfi.pyand do whatever you want, after all it's only 200 lines of codeQuick startimportnumfiimportnumpyasnp# numfi(array=[], signed=1, bits_word=16, bits_frac=None, RoundingMethod='Nearest', OverflowAction='Saturate')x=numfi(np.random.rand(3),1,16,8)# numfi.__repr__() return brief description of numfi object: x => s16/8-N/S# s for 'signed', followed by word bits and fraction bits, N/S for 'Nearest' and 'Saturate` for rounding/overflow method# any arithmetic operation with numfi will return a numfi object with proper precision and value# By overloading operators, numfi object can do fixed-point arithmetic easily:# normal arithmetic operation work with float form of xy=x+1y=[1]-xy=x*np.random.rand(3)y=numfi([1,0,0.1234],1,21,15)/xy=-xy=x**0.5y=x%3y=x>0.5y=x>=0.5y=x==xy=x<=np.ones(3)y=x<[1,1,1]# integer only operation work with integer form of xy=x&0b101y=x|0b100y=x^0b001y=x<<4y=x>>2...# By inheriting from numpy.ndarray, numfi object can be used just like normal numpy array, and return same numfi object backy=np.sin(x)y=x[x>1]y=x.sum()y=x.reshape(3,1)plt.plot(x)pandas.DataFrame(x)np.convolve(x,np.ones(4))np.fft.fft(x,n=512)foriinx:print(i)...DocumentDetails can be found here:https://numfi.readthedocs.io/en/latest/?LicenseThe project is licensed under the MIT license.
Package: numfile
numfileAutomatically assign an increasing sequence number to file names.Installation$python-mpipinstallnumfileUse CasesWrite data to numbered snapshot files in a given directory:$python-q
>>>fromnumfileimportopen_next
>>>forversionin("first","second","third"):
...withopen_next("/tmp/snapshot.txt")asfile:
...print(f"The {version} version.",file=file)...

$ls-d/tmp/*|grepsnapshot
/tmp/snapshot-1.txt
/tmp/snapshot-2.txt
/tmp/snapshot-3.txtAppend messages to the latest log file:$ls-tr|grepapp-error
app-error.log
app-error-2.log
app-error-3.log

$python-q
>>>fromnumfileimportopen_latest
>>>withopen_latest("app-error.log")asfile:...print("Oops... Something went wrong!",file=file)...print(file.name)...
app-error-3.log

$tail-n1app-error-3.log
Oops...Somethingwentwrong!Consolidate similar files in chronological order:$python-q
>>>fromnumfileimportopen_all
>>>forfileinopen_all("/tmp/snapshot.txt"):...print(file.name,file.read(),end="")...
/tmp/snapshot-1.txtThefirstversion.
/tmp/snapshot-2.txtThesecondversion.
/tmp/snapshot-3.txtThethirdversion.Development$gitclonegit@github.com:bzaczynski/numfile.git
$cdnumfile
$pyenvlocal3.9.0
$python-mvenv.venv--prompt=numfile
$source.venv/bin/activate
$pipinstall-Upipflit
$flitinstall--deps=all--symlink
$python-mpytestReleasingTestMake sure there are no uncommitted or untracked files, and then bump the version:$bump2versionminor
$gitstatus
$gitcommit-am"Bumped version"Build a new binary package:$rm-rfdist
$flitbuildPublish the binaries to the official PyPI server:$flitpublishTo use theTest PyPIserver, create a file~/.pypircwith your username:[distutils]
index-servers =
   pypi
   testpypi

[pypi]
repository = https://upload.pypi.org/legacy/
username = your-username

[testpypi]
repository = https://test.pypi.org/legacy/
username = your-usernameThen, supply the test server using the--repositoryflag:$flitpublish--repositorytestpypi
Package: numformat
numformatNumformat is a library that helps you easily format number-like objects(int, float, decimal.Decimal, str)in a way that is consistent with scientific and engineering practices.It has options to convert number-like objects to strings with different notations while allowing control overSignificant Figures,Precision, and output string length.Usage examples:Importing the package:import numformat as nfFormatting to scientific and engineering notations:# Convert to scientific notation
nf.sciformat(103000000)
>> '1.03e+08'

# Convert to engineering notation
nf.enggformat("103000000")
>> '103E+6'Usingautoformat:# Auto-format with default options
nf.autoformat(1030000.0)
>> '1030000.0'

nf.autoformat(103000000.0)
>> '1.03e+08'Usingautoformatwith themax_sig_figsargument:# Auto-format and use a maximum of 5 significant figures
nf.autoformat(103000.000, max_sig_figs=5)
>> '103000'

# Auto-format and use a maximum of 2 significant figures
nf.autoformat("10300.00", max_sig_figs=2)
>> '10000'Usingautoformatwith themax_lengthargument:# Auto-format to a string that has a maximum length of 6 characters
nf.autoformat(103000000, max_length=6)
>> '1e+08'Usingautoformatwith thepreserve_sig_figsargument:# Auto-format to a string that has a maximum length of 8 characters
nf.autoformat(103100000, max_length=8, preserve_sig_figs=False)
>> '1.03e+08'

# Auto-format to a string that has a maximum length of 8 characters but also preserve all significant figures.
nf.autoformat(103100000, max_length=8, preserve_sig_figs=True)
>> '103.1E+6'Controlling the format type inautoformat:# Auto-format to a string with a maximum length of 6 characters but don't allow scientific notation
nf.autoformat(103000000, max_length=6, allow_scientific_notation=False)
>> '103E+6'

# Auto-format to a string with a maximum length of 6 characters but don't allow scientific or engineering notation. 
# Conditions cannot be met, therefore, it returns the number in standard notation.
nf.autoformat(103000000, max_length=6, allow_scientific_notation=False, allow_engineering_notation=False)
>> '103000000'Raising an error if formatting fails:# Auto-format to a string with a maximum length of 6 characters but don't allow scientific or engineering notation. 
# Conditions cannot be met, and strict = True, therefore, returns an error.
nf.autoformat(103000000, max_length=6, allow_scientific_notation=False, allow_engineering_notation=False, strict=True)
>> ValueError: Could not represent the input within 6 characters.
Package: numfracpy
This library performs different calculations in the framework of Fractional Calculus.
You can find functions to calculate:
Riemann-Liouville integral
Caputo derivatives
Riemann-Liouville derivatives
the solution of a fractional differential equation
the solution of a system of fractional differential equations
Mittag-Leffler functionsYou can install this package by following the instructions in:https://packaging.python.org/tutorials/installing-packages/and you should use the name of the library "numfracpy".You can find examples of these calculations in the notebooks uploaded in the page:https://github.com/jhlopezm/NUMFRACPY
Package: numgrad
NumGradSimple gradient computation library for Python.Getting StartedpipinstallnumgradInspired bytensorflow,numgradsupportsautomatic differentiationin tensorflow v2 style using originalnumpyandscipyfunctions.>>>importnumgradasng>>>importnumpyasnp# Original numpy>>>>>># Pure numpy function>>>deftanh(x):...y=np.exp(-2*x)...return(1-y)/(1+y)...>>>x=ng.Variable(1)>>>withng.Graph()asg:...# numgrad patches numpy functions automatically here...y=tanh(x)...>>>g.backward(y,[x])(0.419974341614026,)>>>(tanh(1.0001)-tanh(0.9999))/0.00020.41997434264973155numgradalso supportsjaxstyle automatic differentiation.>>>importnumgradasng>>>importnumpyasnp# Original numpy unlike `jax`>>>>>>power_derivatives=[lambdaa:np.power(a,5)]>>>for_inrange(6):...power_derivatives.append(ng.grad(power_derivatives[-1]))...>>>[f(2)forfinpower_derivatives][32,80.0,160.0,240.0,240.0,120.0,0.0]>>>[f(-1)forfinpower_derivatives][-1,5.0,-20.0,60.0,-120.0,120.0,-0.0]ContributeBe sure to run the following command before developing$gitclonehttps://github.com/ctgk/numgrad.git
$cdnumgrad
$pre-commitinstall
Package: numgraph
NumGraphRead theDocumentationNum(py)Graphis a library for synthetic graph generation. The main principle of NumGraph is to be a lightweight library (i.e.,numpyis the only dependency) that generates graphs from a broad range of distributions. Indeed, It implements several graph distributions in both the static and temporal domain.Implemented distributionsStatic GraphsStar graphCliqueTwo-dimensional rectangular grid lattice graphRandom TreeErdos RenyiBarabasi AlbertStochastic Block ModelTemporal GraphsSusceptible-Infected Dissemination Process SimulationHeat diffusion over a graph (closed form solution)Generic Euler's method approximation of a diffusion process over a graphInstallationpython3 -m pip install numgraphUsage>>>fromnumgraphimportstar_coo,star_full>>>coo_matrix,coo_weights=star_coo(num_nodes=5,weighted=True)>>>print(coo_matrix)array([[0,1],[0,2],[0,3],[0,4],[1,0],[2,0],[3,0],[4,0]]>>>print(coo_weights)array([[0.89292422],[0.3743427],[0.32810002],[0.97663266],[0.74940571],[0.89292422],[0.3743427],[0.32810002],[0.97663266],[0.74940571]])>>>adj_matrix=star_full(num_nodes=5,weighted=True)>>>print(adj_matrix)array([[0.,0.72912008,0.33964166,0.30968042,0.08774328],[0.72912008,0.,0.,0.,0.],[0.33964166,0.,0.,0.,0.],[0.30968042,0.,0.,0.,0.],[0.08774328,0.,0.,0.,0.]])Other examples can be found intest/plot_static.pyandtest/plot_temporal.py.
Package: numgrids
numgridsWorking with numerical grids made easy.Main FeaturesQuickly define numerical grids for any rectangular or curvilinear coordinate systemDifferentiation and integrationInterpolationEasy manipulation of meshed functionsUsing high precision spectral methods (FFT + Chebyshev) wherever possibleFully compatible withnumpyInstallationpipinstallnumgridsQuick StartTo get started, have a look at thequick start.
Package: numi
NúmiNúmi er pakki sem stafar út tölustafi. Þegar notandinn gefur Núma tölu ásamt streng sem inniheldur upplýsingar um beygingar skilar hann öllum mögulegum leiðum sem þessi tala getur verið stöfðu út.This package expands Icelandic numbers to their corresponding written form. Given a digit and a string containing the case, gender and inflection the funciton will output a list of strings for all written variants.UsageTo get started runpip install numiThe main function in Númi isspell_out. It takes in two parameters, a number and a string specifying the desired morphology.In the following example, we want to get the number124in plural, masculine and nominative form.> from numi import spell_out
> print(spell_out(124, "ft_kk_nf")

> [[124, "ft_kk_nf", ["eitt hundrað tuttugu og fjórir", "hundrað tuttugu og fjórir"]]]The spell_out function parses the input string so that the order of the values is not irrelevant. If one or all of the three values (number, gender, case) Numi will fill in all possible forms for that number. Note that the output will contain the string for the form values.In the following example, the value for the case is omitted and Numi will fill in all possible declensions> print(spell_out((92, "ft_kvk"))
>  [[92, 'ft_kvk_nf', ['níutíu og tvær']], 
    [92, 'ft_kvk_þf', ['níutíu og tvær']],
    [92, 'ft_kvk_þgf', ['níutíu og tveimur', 'níutíu og tveim']],
    [92, 'ft_kvk_ef', ['níutíu og tveggja']]]All the abbreviations for the input string are as follows:Abbr.EnglishIcelandicetsingulareintalaftpluralfleirtalakkmasculinekarlkynskvkfemininekvenkynshkneuterhvorugkynsnfnominativenefnifallþfaccusativeþolfallþgfdativeþágufallefgenitiveeignafallThe Icelandic number system isn't the simplest (to state it lightly). The numbers one to four are declined for the respective cases and genders, of them the number1has both a singular and plural form. Other numbers are not declined, except for those that are actually nouns.Numbers other than 1-4 that are also not nouns have the shorthand:at_af (án tölu og án fallbeygingar)(This is probably not a linguistically correct term and will be changed once the author is scolded by a linguist)ContributingThis package is a work in progress. Rough ideas of development are listed in the project status.There are probably some error that can be found and comments and corrects are highly welcomed.To contribute please install the requiements file and use thetests/test_numi.pyfile withpytestfor development testing.Setting up:python venv .venv
python -m pip install -r requirements_dev.txt
pip install -e .
pytestProject status0.0.6Support for numbers between 1-999Add test script for developmentDocument the abbreviations0.0.7 - Parse user input in a robust way/0.0.8 - Add numbers support for numbers 1,000-999,9990.0.10 - Standerdized the outputFuture workAdding the first decimal place for all numbersAdd CLI supportLicenseMIT
Package: numify
NumifyNumify is a Python (2 and 3) library for converting alphanumeric characters into integers.Numify is the opposite of Numerize (another python library, that can be foundhere).InstallationYou can install the Numify from PyPI:$ pip install numifyThe library is supported on Python 2.7, as well as Python 3.4 and above.How to usenumify(alphanumeric_to_numify)>>> from numify import numify
>>> numify.numify('12K')
12000
>>> numify.numify('15   k')
15000This will convert the alphanumeric string '15   k' into an integer like 15000 (ignoring the spaces between).ExampleCommandOutputnumify("1K")1000numify("1k")1000numify("1   K")1000numify("1.5K)1500numify("1M)1000000numify("1.5m)1500000numify("1B)1000000000numify("1.5b)1500000000numify("1T)1000000000000Testing$ python numify/numify_test.py
Package: numina
This is Numina, the data reduction package used by the following
Gran Telescopio Canarias instruments: EMIR, FRIDA, MEGARA and MIRADAS.You can install numina using
thereleased code in PyPIor
thedevelopment version in Github.The installation instructions are available in theonline documentationordoc/user/install.rstin the source distribution.LicensingNumina is distributed under GNU GPL, either version 3 of the License,
or (at your option) any later version. See the file LICENSE.txt for
details.AuthorsWebpage:https://github.com/guaix-ucm/numinaMaintainers: Sergio Pascualsergiopr@fis.ucm.es, Nicolás Cardielcardiel@ucm.es
Package: numina-cli
numina-cli=========*A numina command line program to explore your data.*Installing==========pip install numina-cliUsage-----After installation run numina -h to see a list of available commandsnuminaUsage:numina authenticate <token>numina counts <feeds> [--starttime=<starttime> --endtime=<endtime> --bins=<bins>]numina movements <feeds> [--starttime=<starttime>|--endtime=<endtime>]numina devicesnumina -h | --helpOptions:-h --help                         Show this screen.--version                         Show version.--starttime=<starttime>           A RFC-3339 timestamp of query starttime, defaults to 7 days before the current time--endtime=<endtime>               A RFC-3339 timestamp of query endtime, defaults to the current utc time--bins=<bins>                     A time duration literal representing groupings of time - available bins (uµsmhdw), defaults to 1 hourExamples:numina authenticate 2n3kkd3_234kn.adf&20$9u3.casdfnumina counts dkkd20994739 --starttime=2017-06-01T15:00:00Znumina devices
Package: numiner
NUMinerInstallation•How To Use•Sheet•Contributing•LicenseThis is a Python library that creates MNIST like training dataset for Handwritten Text Recognition related researchesInstallationUse the package managerpipto install numiner.$pipinstallnuminerUse the package managerpipenvto install numiner.$pipenvinstallnuminerUse the package managerpoetryto install numiner.$poetryaddnuminerHow To UseIn general, the package has two main modes. One issheetand another one isletter.sheet- takes a path called<source>to a folder that's holding all the scannedsheetimages or an actual image path and saves the processed images in the<result>path$numiner-s/--sheet<source><result>letter- takes a path called<source>to a folder that's holding all the cropped raw images or an actual image path and saves the processed images in the<result>path$numiner-l/--letter<source><result>Also you can override the default sheet labels by givingjsonfile:$numiner--labelspath/to/labels.json-spath/to/sourcepath/to/resultFor sure you can also do this:$numiner--help

usage:numiner[-h][-v][-s<source><result>][-l<source><result>][-c<path>]optionalarguments:-h,--helpshowthishelpmessageandexit-v,--versionshowprogram's version number and exit--clean <path>-s/--sheet <source> <result>  a path to a folder or file that'sholdingthe<source>sheetimage(s)&apathtoafolderwhereall<result>imageswillbesaved-l/--letter<source><result>apathtoafolderorafilethat's holding the croppedimage(s) & a path to a folder where all <result> imageswill be saved--labels <path>               a path to .json file that'sholdingtoptobottom,lefttorightlabelsofthesheetwiththeirids$numinerconvert--help

usage:numinerconvert[-h]-p<src><dest>SIZERATIO

positionalarguments:SIZEnumberofimagesthateachclasscontainsRATIOtest,trainorpercentageofthetestdatainthatcasetherestofitwillbecometraindata

optionalarguments:-h,--helpshowthishelpmessageandexit-p<src><dest>,--paths<src><dest>sourceanddestinationpathsSample Sheet imageYou can also get the empty sheet file fromhere.Extracted letters from the sheetFinal image processing orderFollowed the same approach that EMNIST used when they were first creating their dataset from NIST SD images.Letter extracted from the sheetBinary version of original imageLetter itself fitted into a square shape plus 2 pixel wide borders on each side without losing the aspect ratioFrom previous step, image resized to 28x28 and taken threshold results in final imageContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.If you want to read more about how this project came to life, you can check out mythesis report.LicenseMIT