Saravanan666/pypi-org huggingface dataset:
Package: pyodourcollect
PyOdourCollectPython3 bindings and command line tool to access OdourCollect's Citizen Observatory data in real time.This python module provides a basic, read only interface withOdourCollect.eu's odour observation data API, as well as a command line tool to download data from OdourCollect directly info a CSV or a XLSX file.
It also provides some data enrichment features not available in OdourCollect.eu webapp that are very useful for data analysis purposes.Please, check outUSAGE.mdto use the module and the CLI tool.About OdourCollect.euOdourCollect.euis one of the most acknowledged Citizen Observatories (CO's) in Citizen Science (CS) landscape.
OdourCollect's main goal is providing an open, free platform for the citizens to report odour observations, making a worldwide collaborative odour map.Albeit it allows reporting nice odours as well as bad ones, it has become popular for its potential to provide hard evidence of bad odour episodes
happening in neighborhoods that are affected by odour emiting facilities like waste water processing, livestock, oil etc.OdourCollect was an idea of Rosa Arias, CEO and founder ofScience for Change,  inspired by a standardized European methodology to evaluate odour episodes through citizens' observations,CEN 16841which in turn is based on the German standardVDI 3940:2006.
The last public version of OdourCollect's open source code can be obtained inthis repository.OdourCollect has validated its bottom-up methodology in 10 pilot projects in 10 countries (Spain, Portugal, Greece, Bulgaria, Chile, Italy, UK, Germany, Austria and Uganda) and has been supported by Spanish and European grants since it was first launched in 2019:The Research and Innovation program of the European Union Horizon 2020 under agreement No. 789315, in the context of the D-NOSES project.FECYT, the Spanish Foundation for Science and Technology - Ministry of Science and Innovation.About odour pollutionFrequent exposure to annoying odours can cause headache, stress, anxiety, lack of concentration, insomnia or even increase respiratory problems.Even though ambient odours are the second cause of environmental complaints after noise worldwide, lots of countries worldwide lack a legal regulatory framework to protect citizens' rights and health.
You can learn more about existing regulations in odour pollution in theD-NOSES Policy brief.If your community is affected by odour pollution problems, you cancontact Science for Changefor an evaluation of your case.
Package: pyodstibmivb
pyODStibMivbA Python wrapper for the Opendata API of Stib-Mivb public transport of brussels. Based on pyRail.Made because the API that pystibmivb useswill be deprecated.You need to get a key fromhttps://opendata.stib-mivb.be/store/subscriptionsand subscribe to the api'shere.The api-key has an expiration time and you can use a private key to request a new one, but that just looks overcomplicated. Set the expiration date to several years and you should be good to go.
If you're willing to add code for this you can make a pull request.There is no convienent way to get a stop id from a name, so for convenience sake you can find stops and route info in the gtfs folder as of 2019-06-03. This information shouldn't change to often for newer information see the Stib-Mivb Opendata website on how to download them or get it fromOpenMobilityData.
Package: pyodv
Python package for reading ODV (Ocean Data Variables) filesStarted on 2022-05-13SetupStart using this project in a virtual environment$virtualenvvenv$sourcevenv/Scripts/activate$pipinstall-rrequirements.txtInitialize to grab dependencies$makeinit# install dependencies$makeinit-dev# includes the previous + adds dependencies for developersBuild Docs$makedocuDevelopersRun Tests$maketest# to run all tests$PYTEST_LOGCONF=debug-logconf.ymlpythontests/test_demo.py# to run a specific test with specific loggingCheck the code-style and syntax (flake8)$makecheck
Package: pyoembed
A Python library for oEmbed that supports auto-discovered and manually
included providers.Installation$ pip install pyoembedUsageThe library provides a function that should be called to automatically embed
the url content, and it is calledoEmbed:frompyoembedimportoEmbed,PyOembedExceptiontry:# maxwidth and maxheight are optional.data=oEmbed('http://www.youtube.com/watch?v=_PEdPBEpQfY',maxwidth=640,maxheight=480)exceptPyOembedException,e:print'An error was ocurred:%s'%e# data is a dict with keys that will depends on the media type. You should# choose how to display the content based on the data['type'] value and# the oEmbed spec ( http://oembed.com/ ).ifdata['type']=='video':printdata['html']# and it goes... Someday we will provide default renderes for each media# type.Addition of new providerspyoembed tries to cover the major oEmbed providers, and should works for any
provider that supports oEmbed auto-discover out-of-the box, however your
favorite provider may be unsupported yet! :(To add it, create a file inside thepyoembed.providerspackage, based on
the following example:frompyoembed.providersimportBaseProviderclassInstagramProvider(BaseProvider):# priority of the provider. Increase it if the provider isn't well knownpriority=10# url schemas that are supported by the provider.# re.compile'd regex are allowed.oembed_schemas=['http://instagram.com/p/*','http://instagr.am/p/*']# api endpoint that answers to oEmbed requests for the provider.oembed_endpoint='http://api.instagram.com/oembed'You will also need to add some test urls of your provider to theintegration_test.pyfile, and run it to see if everything is ok.If everything works, feel free to send a pull request to add the provider:https://github.com/rafaelmartins/pyoembed/pullsThat’s it!
Package: pyoetl
Package DescriptionPyOETL is a Python interface to the OrientDB ETL tool. It can be invoked from
the command line just like theoetl.shscript included in OrientDB, but has
some additional features:can process multiple JSON input files;can be invoked from outside of the OrientDB installation directory;provides a Python class that can be used
to invoke the ETL tool from within a Python program.InstallationThe package may be installed as follows:pip install pyoetlUsageTheORIENTDB_DIRenvironmental variable must be set to the installation
directory of OrientDB.Invoke PyOETL from the command line as follows:pyoetl input_cfg_file.jsonAuthorSee the includedAUTHORS.rstfile for more
information.LicenseThis software is licensed under theBSD License.  See the includedLICENSE.rstfile
for more information.
Package: pyofd
pyofdis a Python package for receipts query from OFD providers.
It is inspired bybobby_boyproject.ContentsInstallationUsageExamplesInstallationInstall viapip:$ pip install pyofdInstall from source:$ git clone git://github.com/sergelevin/pyofd.git
$ cd pyofd
$ python setup.py installUsageHere is basic usage example.import pyofd, datetime

receipt = pyofd.OFDReceipt(
    fpd='FPD', total='total', rn_kkt='RN_KKT', fn='FN',
    fd='FD', inn='INN', purchase_date=datetime.datetime.now()
)
result = receipt.load_receipt()OFDReceiptclass might be constructed only with arguments known from receipt data, but omitting arguments
might narrow the list of OFD providers to be checked against, because different providers require different
subset of data to validate receiptExamplesFor some basic examples read unit tests intestdirectory. More complicated examples can be found withinexamplesdirectory.load_receipt.py: Load receipt by
known attributes and save items into tab-delimited file.xlsx_receipt.py: Load receipt by
known attributes and export it to MS Excel (r) .xlsx file. Ifnalog.ruofficial receipt
check application credentials are provided inPYOFD_NALOGRU_LOGINandPYOFD_NALOGRU_PASSWORDenvironment
variables, receipt lookup viaNalogRuprovider is also performed. Excel file is filled with formulas and
conditional formatting suitable for my personal receipt handling needs - columnFis for partial refund and
columnsGtoZ- for receipt entries categorization with autocounting subtotal across categories.
Package: pyoffers
PyOffersPython client library forHasOffers API.InstallationPyOffers can be obtained withpip:$ pip install pyoffersUsage exampleInitialize API client:>>>frompyoffers.apiimportHasOffersAPI>>>hasoffers=HasOffersAPI(endpoint='https://api.hasoffers.com/Apiv3/json',network_token='<your_network_token>',network_id='<your_network_id>',)Execute queries:>>># Get all offers with ID greater than 100, sorted by ID and with loaded `Country` data>>>hasoffers.offers.find_all(id__gt=100,sort='id',contain=['Country'])[<Offer:102>,<Offer:104>,<Offer:106>,<Offer:108>,<Offer:110>,<Offer:112>]>>># Get all clicks records for 2016-09-20>>>hasoffers.raw_logs.clicks.find_all('20160920')[<LogRecord:7(1027a606128bd067105f0b0921840f)>,...]>>># Get all conversions for specific offer>>>offer=hasoffers.offers.get_by_id(100)>>>offer.conversions.find_all()[<Conversion:70532>]DocumentationYou can view documentation online at:https://pyoffers.readthedocs.ioOr you can look at the docs/ directory in the repository.Python supportPyOffers supports Python 3.5+.
Package: py-office
py-officea python work office.the goal of this project is to build an easy to use office to manage data, analyze and further machine learning, exposing powerful features of python library to end users through GUI.another main concern is to bring machine learning to people who do not know how to code.powerful functions and features of the python library is by used of people who knows programming. End users lack the necessury knowledge to make use of them and may not even aware of the existance of such thing,they have no will to learn a programming language.everyone should learn how to code :)py-office-sheet: a gui spreadsheet based on numpy to process data efficienlypy-office-learn:  a gui for machine learning base on keras (constructing)installcommand line install:pip install py-officeoptional desktop icon install(GUI entry):py-office setuppip will install every component of the py-office listed abovestandalone py-office component is also avaliable on pip, such as:pip install py-office-sheetinstaller install:linux:./pyoffice_install_{your_os}.shmac os:not yetwindows:not yet
Package: py-office-sheet
py-office-sheetA cross-platform spreadsheet based on pandas and numpy for effecient data processingin progress...installationto install:pip install py-office-sheetto launch app in console:$ py-office-sheetintroductiondefault saving format: pandas data object(.pdobj), upto 3 times less memory use compare to generic format such as excel or csv. this is done by joblib, therefore any application that has python support would be able to read it. It makes data managment easier when doing data analyze or machine learning.to import file in python:import joblib
   pandas_data_frame = joblin.load("yourfile.pdobj")alternative format: numpy ndarray obj(.npobj) for maximum efficiencysupport interactive python commandsupport python scriptinglight mode:dark mode:
Package: pyofilename
pyofilenameWindows와 macOS, Linux에서 안전한 파일명을 만듭니다.순수 파이썬 구현을 보고 싶다면이 링크를 참고하세요.Rust에서 사용하기 위한 구현을 보고 싶다면이 링크를 참고하세요.이 패키지는 파이썬에서 사용할 수 있는 Rust 바인딩입니다.importpyofilenameaspfto_safe_name("hello?.txt","fullwidth")
Package: pyoformats
PyoFormatsSimple wrapper for python-formatsExamplefrompyoformatsimportread# start JVMread.JVM().start()# displays names and shapes of all available series in fileread.file_info("test.msr")# get series count of filen_series=read.series_count("test.msr")# returns 5d numpy array of image in seriesmy_image=read.image_5d("test.msr",series=1)# read only certain z-slices. (Note, also channels and frames can be selected)my_image=read.image_5d("test.msr",series=1,zslices=[2,3,5,8,13])# get physical pixel sizesxyz_sizes=read.pixel_sizes_xyz("test.msr",series=0)# get physical pixel unitsxyz_sizes=read.pixel_sizes_xyz_units("test.msr",series=0)# iterate over all series and extract name and pixel datafors,datainread.image_5d_iterator("test.msr"):print(s,data.shape)# at end of scriptread.JVM().shutdown()InstallationDetailed How-To for installing dependencies (Windows/ MacOS)Before you install the pyoformats Python package, check out detailed instructions forinstalling dependencies.from PyPipip install pyoformatspyoformats directly from gitlabpip install git+https://git.ist.ac.at/csommer/pyoformats.gitor localgit clone https://git.ist.ac.at/csommer/pyoformats.gitcd pyoformatspip install -e .Dependenciesnumpyjavabridgepython-bioformats
Package: pyofss
Pyofss allows construction of an optical fibre system from separate modules.
A typical system consists of a Gaussian pulse generator module and an optical
fibre module.
The field generated is propagated through the fibre by numerical integration of
an appropriate Schrödinger-type equation.Simulated effects include that of dispersion (second, third, and higher order), attenuation, self-phase modulation, self-steepening, and Raman scattering.
Resulting field profiles (including multiple traces for the fibre module) can be visualised using a range of plot types.
These include standard x-y plots, top-down “map” plots, three-dimensional “waterfall” plots, and animation videos.InstallationPyofss is available on PyPI and can be retrieved using the pip program:sudo aptitude install python-pip
pip install pyofssThen import pyofss within scripts or in an interactive session:>>> import pyofssDependenciesPyofss depends on Numpy, Scipy, and Matplotlib.
They can be installed on Linux distributions usingaptitude:sudo aptitude install python-numpy python-scipy python-matplotlibNoteThe recommended dependency versions are listed in therequirements.txtfile within the pyofss package.
Install each of these dependencies usingpip:pip install -r requirements.txtDevelopmentIt is recommended to install pyofss into a virtual environment, which can be initialised using:sudo aptitude install python-virtualenv
sudo pip install virtualenvwrapper
mkvirtualenv pyofss
workon pyofssPyofss dependencies can then be satisfied using:pip install numpy
pip install scipy
sudo aptitude build-dep python-matplotlib
pip install matplotlibInstall the latest development version of pyofss from GitHub:pip install git+https://github.com/daibo/pyofss.gitTestsTests can be run within the pyofss package:python setup.py test
Package: pyofss-gui
The pyofss GUI uses a drag-and-drop approach to adding optical modules to form a system to simulate.
Parameters for each optical module can be viewed and modified before the system simulation is run.
Outputs from each optical module can be visualised using the plot interface.InstallationPyofss-gui is available on PyPI and can be retrieved using the pip program:sudoaptitudeinstallpython-pippipinstallpyofss-guiDependenciesPyofss-gui depends on pyofss and PyQt.
They can be installed using:sudoaptitudeinstallpython-qt4pipinstallpyofssAppearanceSome themes do not show icons in menus and on buttons.
The following two commands can often fix this issue:gconftool-2--typeboolean--set/desktop/gnome/interface/buttons_have_iconstruegconftool-2--typeboolean--set/desktop/gnome/interface/menus_have_iconstrue
Package: pyofwar
pyofwarPython library for Sun Tzu's most popular quotesMost of the quotes were taken from the websitequotefancySelected quotesfrompyofwarimportpowprint("Sun Tzu once said...")print(pow.quote(1,True))outputsSun Tzu once said...
"Let your plans be dark and impenetrable as night, and when you move, fall like a thunderbolt."Random quotesfrompyofwarimportpowprint("Sun Tzu once said...")print(pow.quote_random(2,False))outputsSun Tzu once said...
Know thy self, know thy enemy.
If their forces are substantial, prepare for them; if their forces are strong, avoid them.And if you want to limit the range of randomnessfrompyofwarimportpowprint("Sun Tzu once said...")print(pow.quote_random(3,True,0,0))outputsSun Tzu once said...
"Let your plans be dark and impenetrable as night, and when you move, fall like a thunderbolt."
"Let your plans be dark and impenetrable as night, and when you move, fall like a thunderbolt."
"Let your plans be dark and impenetrable as night, and when you move, fall like a thunderbolt."
Package: pyogpclient
pyogpclientOpen Game Protocol Client for Python 3UsagefrompyogpclientimportOGPClientclient=OGPClient("example.com",7776)status=client.query()print("Current Players: "+str(status["info"]["player_count"])+" / "+str(status["info"]["slot_max"]))Responsegame_idinfogame_nametypetype_namepasswordproxyosos_namehost_namehost_name_colorconnect_portmodnameidsizeversionurlauthorgame_typegame_modemapnamefile_namefile_sizefile_md5versionurlauthornext_map (see map)player_countslot_maxbot_countreserved_slotsMissing Fields (TODO)teamsplayersrulesaddonslimits
Package: pyogrio
pyogrio - Vectorized spatial vector file format I/O using GDAL/OGRPyogrio provides aGeoPandas-oriented API to OGR vector
data sources, such as ESRI Shapefile, GeoPackage, and GeoJSON. Vector data sources
have geometries, such as points, lines, or polygons, and associated records
with potentially many columns worth of data.Pyogrio uses a vectorized approach for reading and writing GeoDataFrames to and
from OGR vector data sources in order to give you faster interoperability. It
uses pre-compiled bindings for GDAL/OGR so that the performance is primarily
limited by the underlying I/O speed of data source drivers in GDAL/OGR rather
than multiple steps of converting to and from Python data types within Python.We have seen >5-10x speedups reading files and >5-20x speedups writing files
compared to using non-vectorized approaches (Fiona and current I/O support in
GeoPandas).You can read these data sources intoGeoDataFrames, read just the non-geometry columns into PandasDataFrames,
or even read non-spatial data sources that exist alongside vector data sources,
such as tables in a ESRI File Geodatabase, or antiquated DBF files.Pyogrio also enables you to writeGeoDataFramesto at least a few different
OGR vector data source formats.Read the documentation for more information:https://pyogrio.readthedocs.io.WARNING: Pyogrio is still at an early version and the API is subject to
substantial change. Please seeCHANGES.RequirementsSupports Python 3.8 - 3.11 and GDAL 3.4.x - 3.7.x.Reading to GeoDataFrames requiresgeopandas>=0.12withshapely>=2.Additionally, installingpyarrowin combination with GDAL 3.6+ enables
a further speed-up when specifyinguse_arrow=True.InstallationPyogrio is currently available onconda-forgeandPyPIfor Linux, MacOS, and Windows.Please read theinstallation documentationfor more information.Supported vector formatsPyogrio supports some of the most common vector data source formats (provided
they are also supported by GDAL/OGR), including ESRI Shapefile, GeoPackage,
GeoJSON, and FlatGeobuf.Please see thelist of supported formatsfor more information.Getting startedPlease read theintroductionfor more information and examples to get started using Pyogrio.You can also check out the theAPI documentationfor full details on using the API.CreditsThis project is made possible by the tremendous efforts of the GDAL, Fiona, and
Geopandas communities.Core I/O methods and supporting functions adapted fromFionaInspired byFiona PR
Package: pyohhell
pyohhellA Oh Hell card game engineInstallation$pipinstallpyohhellUsagepyohhellcan be used to play a Oh hell card game as follows:frompyohhell.game_engineimportGameEnginefrompyohhell.playerimportPlayergame_engine=GameEngine()player_1=Player(1)player_2=Player(2)game_engine.subscribe_player(player_1)game_engine.subscribe_player(player_2)game_engine.play_game(seed=42)ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensepyohhellwas created by amaurylekens. It is licensed under the terms of the MIT license.Creditspyohhellwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: pyoidc-redis-session-backend
pyoidc-redis-session-backendThis repository contains source code for a Redis-based session storage backend for theoicPython library.Installationpip install pyoidc-redis-session-backendUsageA ready-to-run project is located athttps://github.com/alehuo/pyoidc-redis-session-backend-exampleBelow is an example piece of code that initializes a Redis client which is then used to create an instance of RedisSessionBackend. After that, a Consumer is created which is then configured to use the Redis session backend as its backend.importredisfrompyoidc_redis_session_backendimportRedisSessionBackendfromoic.oic.consumerimportConsumerfromoic.utils.authn.clientimportClientSecretBasic,ClientSecretPostredis_client=redis.Redis("localhost","6379")# Pass the redis client into the Redis session backendbackend=RedisSessionBackend(redis_client)# Create an example consumer with example configconsumer=Consumer(backend,{"authz_page":"/auth/callback",# Callback URL of your app"response_type":"code",},client_config={"client_id":"your_client_id",# Client ID"client_authn_method":{'client_secret_post':ClientSecretPost,'client_secret_basic':ClientSecretBasic}})consumer.provider_config("oidc_server_url")# OIDC server URLconsumer.set_client_secret("your_client_secret")# Client secretDevelopmentTo initialize the development environment, run:Runpython3 -m venv envRunsource env/bin/activateRunpip install -r requirements.txtRunning testsRun tests withpython -m pytest tests/.ContributionsContributions to this repository are welcome.LicenseThis project is licensed with MIT license.
Package: pyoinformatics
pyoinformatics 🐍pip install pyoinformaticsExamplesFind the reverse complement of all the sequences in a file:importpyoinformaticsaspyowithopen('out.fasta','w')asf:forseqinpyo.read_fasta('in.fasta'):f.writelines(seq.reverse_complement().to_fasta())Count the number of occurrences of 'ATG' in seq objectseq.count('ATG')Count the number of occurrences of 'ATG' in seq object that differ by <= 1 base.seq.count('ATG',1)Find the average position of all occurrences of 'ATG' in a fasta filefromstatisticsimportmeanforseqinpyo.read_fasta('in.fasta'):print(mean(seq.find('ATG')))Find the number of occurrences of 'ATG' or 'AAG' in seq objectlen(seq1.find('A[AT]G'))ASCI plot the relative nt counts for all the sequences in a fileforseqinpyo.read_fasta('in.fasta'):counts=seq.countsprint(f">{seq.id}")forntinsorted(counts.keys()):bar=int((counts[nt]/len(seq))*100)print(f"{nt}:{'◊'*bar}")>HSBGPGHumangeneforboneglaprotein(BGP)A:◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊C:◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊G:◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊T:◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊>HSGLTH1Humantheta1-globingeneA:◊◊◊◊◊◊◊◊◊◊◊◊◊◊C:◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊G:◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊T:◊◊◊◊◊◊◊◊◊◊◊◊◊◊◊
Package: pyojo
About 'pyojo' app development-----------------------------This is yet another framework to help develop web applicattionswith python. The difference is that it is oriented to createapplications with The Dojo Toolkit, trying to facilitate a setof wrappers to generate the javascript sent to the client andproviding help to work with the AMD loader.Pyojo provides decorators to set up the route map and assignsome functions or classes to certain URLs, but it also can beused creating python modules in a folder structure, where thename of the module defines the content type, and the returnedvalue of a function named GET, POST, PUT or DELETE is theresponse.The minimal "Hello World":import pyojofrom pyojo import server@pyojo.route("/")def Home():return "Hello World!"server.main()A basic example with javascript:import pyojo#Redirect to index.html@pyojo.route("/")def Home(request):return request.static("index.html")And then a couple of modules at ./srv (the default folder) willhandle this request:srv/index_html.py:def GET(request):return '''<html><body><script src='main.js'></body></html>'''srv/main_js.py:def GET(request):return "alert('Hello World!');"But the real power is in the pyojo.js modules, offering a setof code generation utilities for your Dojo application.