Saravanan666/pypi-org huggingface dataset:
Package: awgp-aadhar-pan-extractor
Description: This script extracts Aadhaar and extracts Pan information from the image uploaded by consumer. The result is returned
as a json object.Installation
This library requires Python 3.6+ to run. As well as you also need to install tesseract on your system. If you have Linux based system just run:use this line for Linux
sudo apt install tesseract-ocrfor windows usedownload tesseract software'https://github.com/UB-Mannheim/tesseract/wiki'Then
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract'For Mac OSinstall brew/bin/bash -c "$(curl -fsSLhttps://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"#install brew in tesseractbrew install tesseract#list tesseractbrew list tesseractthis is list-->/usr/local/Cellar/tesseract/4.1.1/bin/tesseract/usr/local/Cellar/tesseract/4.1.1/bin/tesseract this line use for code
pytesseract.pytesseract.tesseract_cmd = r'/usr/local/Cellar/tesseract/4.1.1/bin/tesseract'Install the package.pip install awgp-aadhar-pan-extractor
Then Import the package.from aadhar_pan_extractor import Pan_Info_Extractor,Aadhar_Info_Extractor
Create an instance of the extractor.this line use only for aadhar information
extractor = Aadhar_Info_Extractor()this line use only for pan information
extractor = Pan_Info_Extractor()
Pass the image to the extractor to get the results.extractor.info_extractor('/content/pan test.jpeg')
This will return a result as following:{
"Aadhar_number": "4382 5165 5729",
"Address": "NAN",
"DOB": "01/01/2003",
"Gender": "Male",
}
Package: awh
= AnyWebHookAnyWebhook (*awh*) is Python/WSGI webhook listener and handler. It simplifieswriting and acting upon incoming webhooks.Awh splits webhook handling into _validating_ and _executing_. You must registerboth validator and executor to handle webhooks. These are simple functions whichaccept a request parameter containing incoming request data. Additionaly theyreceive a dictionary which is filled with arbitrary data by response manipulator(a function decorated with `@app.app` decorator). Many validators and executors(for many different webhooks) can be registered. Any validator returning `True`marks webhook as correct.You can also affect application's response by registering a separate functionaccepting incoming request and to-be-modified response parameter.Simple application would look like this:[[example-app]][source,python]----import jsonimport subprocessfrom awh import Awhfrom awh.operate import require, jsonpathapp = Awh()@app.validator('foo')def valid(request, data_dict):payload = request.get_data(as_text=True)j = json.loads(payload)# validate incoming json somehowrequire(jsonpath(j, 'password')[0].value == 'secretpass')require(data_dict.get('foo') == 'bar')return True@app.executor('foo')def execute(request, data_dict):subprocess.call('deploy-app')@app.appdef myapp(request, response, data_dict):response.status_code = 404data_dict['foo'] == 'bar'----For additional examples, see link:tests/apps[tests/apps].== DeploymentIn <<example-app>>, `app` is a WSGI application, which implements its interface(i.e. a function accepting `environ` and `start_response` parameters). Youshould point it to your WSGI server.
Package: awive
AWIVEAWIVE, an acronym for Adaptive Water Image Velocimetry Estimator, is a
software package designed for estimating the velocity field from a sequence of
images. It comprises two methods: STIV and OTV, both geared towards achieving
velocity estimations with low computational costs.InstallingInstall and update using pip:pipinstallawiveUsageExecute the commands below:pyenv local 3.11.2
poetry install
poetry run python -m awive.otv river-brenta d0000 -v
Package: awkexercises
Awk ExercisesThis TUI application includes 80+ questions meant to be solved usingGNU awk. Some exercises will require you to combineawkwith other CLI tools such astac.Note:This application is intended to be run in a Linux-like environment. You might still be able to solve the exercises on other platforms, but I do not know if that'll work.ScreenshotGuideSeeapp_guide.mdLicenseCode snippets are licensed underMIT LICENSEExercise questions and associated files (likequestions.json) are licensed underCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
Package: awkupy
awkupy and IAwkAWK meets Python: API, CLI and magicsA simple subprocess based Pythonic interface for Awk, which powers a
command-line REPL and Jupyter magics.InstallationpipinstallawkupyFeaturesPython class to call AWK via subprocess - see anexample.IPython / Jupyter magics:%awkand%%awk, enabling polyglot
programming with Awk. Seeexampleand thetutorialfor more details.In[1]:%load_extiawkIn[2]:cddoc/examples//home/avmo/src/projects/awkupy/doc/examplesIn[3]:%%awk--stdoutcoins.txt...:{...:country[$4]++...:}...:...:END{...:for(iincountry)print"Country: "i," count: ",country[i]...:}...:...:Country:USAcount:7Country:PRCcount:1Country:Austria-Hungarycount:1Country:Canadacount:1Country:Switzerlandcount:1Country:RSAcount:2IAwk: interactive AWK prompt modelled after IPython.❯❯❯iawk(awkupy)WelcometotheIAwkshell.Typehelpor?tolistcommands.Prefix!torunsystemcommandsiawk[1]:helpDocumentedcommands(typehelp<topic>):========================================EOFcdexithelphisthistoryinputlsresetrunsetshellshowiawk[2]:cddoc/examples/doc/examples/iawk[3]:lscoins.txtcoins_histogram.pycoins_histogram.awkiawk[4]:setpattern/gold/iawk[5]:setactionprint$0iawk[6]:inputcoins.txtiawk[7]:show#!/usr/bin/awk -f/gold/{print$0}iawk[8]:runiawk[9]:gold11986USAAmericanEaglegold11908Austria-HungaryFranzJosef100Koronagold11984Switzerlandingotgold11979RSAKrugerrandgold0.51981RSAKrugerrandgold0.11986PRCPandagold0.251986USALiberty5-dollarpiecegold0.251987USAConstitution5-dollarpiecegold11988CanadaMapleLeafiawk[9]:exitReferencesTo awk or not to ...-
excellent tutorial on Awk
Package: awkward
Awkward Array is a library fornested, variable-sized data, including arbitrary-length lists, records, mixed types, and missing data, usingNumPy-like idioms.Arrays aredynamically typed, but operations on them arecompiled and fast. Their behavior coincides with NumPy when array dimensions are regular and generalizes when they're not.Motivating exampleGiven an array of lists of objects withx,yfields (with nested lists in theyfield),importawkwardasakarray=ak.Array([[{"x":1.1,"y":[1]},{"x":2.2,"y":[1,2]},{"x":3.3,"y":[1,2,3]}],[],[{"x":4.4,"y":[1,2,3,4]},{"x":5.5,"y":[1,2,3,4,5]}]])the following slices out theyvalues, drops the first element from each inner list, and runs NumPy'snp.squarefunction on everything that is left:output=np.square(array["y",...,1:])The result is[[[],[4],[4,9]],[],[[4,9,16],[4,9,16,25]]]The equivalent using only Python isoutput=[]forsublistinarray:tmp1=[]forrecordinsublist:tmp2=[]fornumberinrecord["y"][1:]:tmp2.append(np.square(number))tmp1.append(tmp2)output.append(tmp1)The expression using Awkward Arrays is more concise, using idioms familiar from NumPy, and it also has NumPy-like performance. For a similar problem 10 million times larger than the one above (single-threaded on a 2.2 GHz processor),the Awkward Array one-liner takes1.5 secondsto run and uses2.1 GBof memory,the equivalent using Python lists and dicts takes140 secondsto run and uses22 GBof memory.Awkward Array is even faster when used inNumba's JIT-compiled functions.See theGetting starteddocumentation onawkward-array.orgfor an introduction, including ano-install demoyou can try in your web browser.Getting helpView the documentation onawkward-array.org.Report bugs, request features, and ask for additional documentation onGitHub Issues.If you have a "How do I...?" question, start aGitHub Discussionwith category "Q&A".Alternatively, ask about it onStackOverflow with the [awkward-array] tag. Be sure to include tags for any other libraries that you use, such as Pandas or PyTorch.To ask questions in real time, try the GitterScikit-HEP/awkward-arraychat room.InstallationAwkward Array can be installed fromPyPIusing pip:pipinstallawkwardTheawkwardpackage is pure Python, and it will download theawkward-cppcompiled components as a dependency. If there is noawkward-cppbinary package (wheel) for your platform and Python version, pip will attempt to compile it from source (which has additional dependencies, such as a C++ compiler).Awkward Array is also available onconda-forge:condainstall-cconda-forgeawkward
Package: awkward0
Calculations with rectangular, numerical data are simpler and faster in Numpy than traditional for loops. Consider, for instance,all_r=[]forx,yinzip(all_x,all_y):all_r.append(sqrt(x**2+y**2))versusall_r=sqrt(all_x**2+all_y**2)Not only is the latter easier to read, it’s hundreds of times faster than the for loop (and provides opportunities for hidden vectorization and parallelization). However, the Numpy abstraction stops at rectangular arrays of numbers or character strings. While it’s possible to put arbitrary Python data in a Numpy array, Numpy’sdtype=objectis essentially a fixed-length list: data are not contiguous in memory and operations are not vectorized.Awkward Array is a pure Python+Numpy library for manipulating complex data structures as you would Numpy arrays. Even if your data structurescontain variable-length lists (jagged/ragged),are deeply nested (record structure),have different data types in the same list (heterogeneous),are masked, bit-masked, or index-mapped (nullable),contain cross-references or even cyclic references,need to be Python class instances on demand,are not defined at every point (sparse),are not contiguous in memory,should not be loaded into memory all at once (lazy),this library can access them ascolumnar data structures, with the efficiency of Numpy arrays. They may be converted from JSON or Python data, loaded from “awkd” files,HDF5,Parquet, orROOTfiles, or they may be views into memory buffers likeArrow.InstallationInstall Awkward Array like any other Python package:pipinstallawkward0# maybe with sudo or --user, or in virtualenvThe baseawkward0package requires onlyNumpy(1.13.1+).Recommended packages:pyarrowto view Arrow and Parquet data as Awkward Arraysh5pyto read and write Awkward Arrays in HDF5 filesPandasas an alternative view
Package: awkward1
Awkward Array is a library fornested, variable-sized data, including arbitrary-length lists, records, mixed types, and missing data, usingNumPy-like idioms.Arrays aredynamically typed, but operations on them arecompiled and fast. Their behavior coincides with NumPy when array dimensions are regular and generalizes when they're not.Motivating exampleGiven an array of objects withx,yfields and variable-length nested lists likearray=ak.Array([[{"x":1.1,"y":[1]},{"x":2.2,"y":[1,2]},{"x":3.3,"y":[1,2,3]}],[],[{"x":4.4,"y":{1,2,3,4]},{"x":5.5,"y":[1,2,3,4,5]}]])the following slices out theyvalues, drops the first element from each inner list, and runs NumPy'snp.squarefunction on everything that is left:output=np.square(array["y",...,1:])The result is[[[],[4],[4,9]],[],[[4,9,16],[4,9,16,25]]]The equivalent using only Python isoutput=[]forsublistinarray:tmp1=[]forrecordinsublist:tmp2=[]fornumberinrecord["y"][1:]:tmp2.append(np.square(number))tmp1.append(tmp2)output.append(tmp1)Not only is the expression using Awkward Arrays more concise, using idioms familiar from NumPy, but it's much faster and uses less memory.For a similar problem 10 million times larger than the one above (on a single-threaded 2.2 GHz processor),the Awkward Array one-liner takes4.6 secondsto run and uses2.1 GBof memory,the equivalent using Python lists and dicts takes138 secondsto run and uses22 GBof memory.Speed and memory factors in the double digits are common because we're replacing Python's dynamically typed, pointer-chasing virtual machine with type-specialized, precompiled routines on contiguous data. (In other words, for the same reasons as NumPy.) Even higher speedups are possible when Awkward Array is paired withNumba.Ourpresentation at SciPy 2020provides a good introduction, showing how to use these arrays in a real analysis.InstallationAwkward Array can be installedfrom PyPIusing pip:pipinstallawkwardYou will likely get a precompiled binary (wheel), depending on your operating system and Python version. If not, pip attempts to compile from source (which requires a C++ compiler, make, and CMake).Awkward Array is also available usingconda, which always installs a binary:condainstall-cconda-forgeawkwardIf you have already addedconda-forgeas a channel, the-c conda-forgeis unnecessary. Adding the channel is recommended because it ensures that all of your packages use compatible versions:condaconfig--addchannelsconda-forge
condaupdate--allGetting helpHow-to tutorialsPython API referenceC++ API referenceReport bugs, request features, and ask for additional documentation onGitHub Issues.If you have a "How do I...?" question, ask about it onStackOverflow with the [awkward-array] tag. Be sure to include tags for any other libraries that you use, such as Pandas or PyTorch.To ask questions in real time, try the GitterScikit-HEP/awkward-arraychat room.
Package: awkward-kaitai
Kaitai Struct: runtime library for AwkwardThis library building Awkward Arrays using Kaitai Struct API for Awkward using
C++/STL.Steps1. Write a .ksy file for your custom file format. Refer to theKaitai User Guidefor more details.Here, we take an example ofanimal.ksymeta:id:animalendian:lelicense:CC0-1.0ks-version:0.8seq:-id:entrytype:animal_entryrepeat:eostypes:animal_entry:seq:-id:str_lentype:u1-id:speciestype:strsize:str_lenencoding:UTF-8-id:agetype:u1-id:weighttype:u22. Clonekaitai_struct_awkward_runtimerepository:git clone --recursive https://github.com/ManasviGoyal/kaitai_struct_awkward_runtime.gitcd kaitai_struct_awkward_runtimegit checkout ManasviGoyal/test3. Update submodule and compile scala code (only the first time)git submodule update --initcd kaitai_struct_compilersbt packagecd ../4. Generate the source and header files for Awkward target./kaitai-struct-compiler -t awkward --outdir src-animal example_data/schemas/animal.ksy5. Install the librarypip install .6. Buildawkward_kaitaiby passing the path of the main.cppfrom the generated code.awkward-kaitai-build src-animal/animal.cpp -b buildNote:awkward-kaitai-build [-h] [-d DEST] [-b BUILD] fileoptions:-h, --help: shows help message-d DEST, --dest DEST: explicitly specify a destination for the build shared library.-b BUILD, --build BUILD: explicitly specify a build location.7. Open python and print the returnedak.Array:pythonimportawkward_kaitaianimal=awkward_kaitai.Reader("./src-animal/libanimal.so")# pass the path of the shared fileawkward_array=animal.load("example_data/data/animal.raw")print(awkward_array.to_list())Output[{'animalA__Zentry':[{'animal_entryA__Zstr_len':3,'animal_entryA__Zspecies':'cat','animal_entryA__Zage':5,'animal_entryA__Zweight':12},{'animal_entryA__Zstr_len':3,'animal_entryA__Zspecies':'dog','animal_entryA__Zage':3,'animal_entryA__Zweight':43},{'animal_entryA__Zstr_len':6,'animal_entryA__Zspecies':'turtle','animal_entryA__Zage':10,'animal_entryA__Zweight':5}]}]
Package: awkwardql
This is derived from and inspired by thedemoby Jim Pivarski (PartiQL) and targets awkward 1.0.We will start out focusing on particle physics uses cases and see how far it goes from there. :-)InstallationInstall coffea like any other Python package:pipinstallawkwardqlor similar (usesudo,--user,virtualenv, or pip-in-conda if you wish).Strict dependenciesPython(3.6+)The following are installed automatically when you install coffea with pip:awkward1to manipulate complex-structured columnar data, such as jagged arrays;numbajust-in-time compilation of python functions;larka modern and well featured parser/lexer/interpreter engine;numpyfor flat array manipulation;matplotlibas a plotting backend;
Package: awlify
AwlifyA very basic tool that takes in a sentence of text and outputs
the same text, annotated with information about whether any of
its words are in theAcademic Word List.installingpip install awlifyand if you haven't used spacy on your system before, you'll need
to install the model we're using here with the command below:python -m spacy download en_core_web_smtestspython -m unittestusage inside a filefrom awlify import awlify

result = awlify('please inform me of the academic words in this sentence')

print(result)
{"data": {"sentence": "please inform me of the academic words in this sentence", "awl_words": [{"index": 5, "word": "academic", "meta": {"head": "academy", "sublist": 5}}]}}usage from the command linepython -m awlify 'this is a sentence to check'{"data": {"sentence": "this is a sentence to check", "awl_words": []}}expected input / outputformat for output:{
  "data": {
    "sentence": "THIS IS THE ORIGINAL SENTENCE",
    "awl_words": [
      {
        "index": INDEX_OF_AWL_WORD_FOUND,
        "word": "AWL_WORD_FOUND",
        "meta": {
          "head": "THE_HEADWORD_FROM_THE_AWL",
          "sublist": THE_AWL_SUBLIST_OF_THE_WORD
        }
      }
    ]
  }
}example input for a simple sentence (no AWL words):simple_sentence = awlify('this is a sentence')example output for a simple sentence (no AWL words):{
  "data": {
    "sentence": "this is a sentence",
    "awl_words": []
  }
}example input for a complex sentence (a few AWL words):complex_sentence = awlify('the economic recovery is ongoing and potentially problematic')example output for a complex sentence (a few AWL words):{
  "data": {
    "sentence": "the economic recovery is ongoing and potentially problematic",
    "awl_words": [
      {
        "index": 1,
        "word": "economic",
        "meta": {
          "head": "economy",
          "sublist": 1
        }
      },
      {
        "index": 2,
        "word": "recovery",
        "meta": {
          "head": "recover",
          "sublist": 6
        }
      },
      {
        "index": 6,
        "word": "potentially",
        "meta": {
          "head": "potential",
          "sublist": 2
        }
      }
    ]
  }
}NOTESThe current implementation of the sentence tokenization uses spacy,
and so it's a bit heavier than absolutely necessary, since we're
not taking advantage of any of the more advanced characteristics
of the package.In theory, it could probably perform 98% as well with just a simple
regex, so I might add the option to do that in the future if there
aren't any real use cases for needing the full weight of spacy.REFERENCESCoxhead, Averil (2000) A New Academic Word List. TESOL Quarterly, 34(2): 213-238.
Package: awl-utils
AWL Utils. Andrew LevinBuild this PackageTemplatehttps://github.com/pypa/sampleprojectUsageLast TextA sample project that exists as an aid to thePython Packaging User
Guide'sTutorial on Packaging and Distributing
Projects.This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.The source for this project is available here.The metadata for a Python project is defined in thepyproject.tomlfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written usingreStructuredTextormarkdownwith the appropriatekey set. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.
Package: aw.managersmanager
This component provides a console utilityplonemanagersand a Plone
component that adds or removes managers on all Plone sites in a set of Zope
clusters.Note thataw.managersmanagerhas a client command line utility and a Zope
component server side counterpart.InstallationEither (typically on a client, you don’t need Zope or anything else)$ easy_install aw.managersmanagerOr in a buildout (with Zope / Plone installation)[buildout]
...
parts =
  ...
  managersmanager
...
eggs =
  ...
  aw.managersmanager
  ...
[managersmanager]
recipe = zc.recipe.egg
eggs =
  ...
  aw.managersmanager
  ...
[instance]
recipe = plone.recipe.zope2instance
...
zcml =
  ...
  aw.managersmanagerAdd a config file in your home directory named.managersmanager. Or run once
theplonemanagersconsole tool that will create a skeleton if this file does
not exist. SeeConfig filebelow.UsageCommon usages, respectively for adding a manager, listing Plone sites,
removing a manager and asking for help:$ plonemanagers adduser <login> <password>

$ plonemanagers list

$ plonemanagers deluser <login>

$ plonemanagers --helpConfig fileThe configuration file is named.managersmanagerand is located in your user
default directory.If you don’t have such (mandatory) config file, a default one will becreated the
first time you invokeplonemanagers. This default config file is fully
commented out, such it’s pretty easy to tweak your client installation to fit
all the Zope / Plone clusters under your control.Its content must match these rules# Main section (required)
[main]

# All Zope clusters
clusters =
  somecluster
  anothercluster
  ...

# HTTP connection timeout (in seconds)
timeout = 10

# Each item in above "clusters" must have its sections
[somecluster]

# One or more root URL (the ZEO clients of this cluster)
root-urls =
  http://somehost:8080
  http://otherhost:8080

# A *Zope root* manager login
login = admin
password = mysecret

# Same thing for all clusters
[anothercluster]
...LicenseGPLSupportTracker:http://plone.org/products/aw-managersmanager/issuesMail support:Ingeniweb supportSubversion repositoryhttp://svn.plone.org/svn/collective/aw.managersmanagerChangelog1.0.0a2Ignore potential args in command line tool.
[glenfant]1.0.0a1Initial release
Package: awmproxy
awmproxy API Python WrapperThis package simplifies using awmproxy.net api to grab proxies so you can go scraping or whatever it is you need.Requirements: paid account with api keyUsage:import awmproxy
awm = awmproxy.AwmProxy('abcdef0123456abcdef123456apikeyapikey')Get results as list in ip:port:awm.get_proxy()Get only universal ports that change ips every request:awm.get_proxy(universal_port=701)Get only US proxies, as list of json dict, with all available proxy information:awm.get_proxy(json=True, info=True, country_only='US')Get 25 fast proxies, not Russian, with full info as a list of tuples:awm.get_proxy(country_not='RU', info=True, limit=25, fast=True)
Package: awokado
Fast and flexible low-level API framework based onFalcon,MarshmallowandSQLAlchemy CoreAPI is close to OpenAPI 3.0 specificationCurrently is under active developmentDocumentationYou can find in:DocumentationChangelogYou can find in:CHANGELOG.mdInstallation$pipenvinstallawokadoor$pipinstallawokadoAwokado usesdynaconffor loading it settingsYou can find all available variables insettings.tomlfileContributingTestsTo run tests locally you should create.secrets.tomlfile in the project root directory:[default]DATABASE_PASSWORD='your_db_password_here'DATABASE_HOST='localhost'DATABASE_USER='your_db_username_here'DATABASE_PORT=5432#DB portDATABASE_DB='test'or[default]DATABASE_URL='your_full_db_url'Do not use both ways at the same time, you will get error!Install required packages:$ pipenv install --devThen you can setup your database:$ pipenv python -m tests.test_app.init_dbAnd run tests:$ pipenv python -m unittestAuthorsIs being made with the help ofAlex SidorovKsenia MalyavskayaPavel DanilyukAndrew Osenenko