Saravanan666/pypi-org huggingface dataset:
Package: pyocto
PyOcto is a high-throughput seismic phase associator.
The best way to get started with PyOcto is through our interactive examples:ExamplesBasicsVelocity modelsInterfacesIf you're looking for further guidance, for example, a guide on how to set
the associator parameters, check out thePyOcto documentation.InstallationThe easiest way to install PyOcto is through pip:pipinstallpyoctoThere are prebuilt wheels available for Linux, Mac OS, and Windows.
In case you want to use 1D velocity models, you will need to install
the optional dependencypyrocko. pyrocko is available through the
standard channels, such aspiporconda.gitclonehttps://github.com/yetinam/pyocto.gitcdpyocto
gitsubmoduleupdate--init
pipinstall.[test]To verify your installation is working, usepytest tests/.Warning:PyOcto uses POSIX threads for threading. As these are not available on Windows,
the Windows version is single-threaded. Therefore, we do not recommend running larger computations
on Windows.ReferencesIf you're using PyOcto in your work, please cite:Münchmeyer, J. (2023). PyOcto: A high-throughput seismic phase associator.Preprint atarxiv.org/abs/2310.11157.
Package: pyoctonion
pyoctonionOctonion in PythonAuthors: Charith Atapattu & Kalpa ThudewaththageIn this project, we introduce Python Library for octonions. There is a Python Library for facilitating algebra in quaternions called "pyquaternion". However, no python Library for doing algebraic operations in octonions. We introduce this "pyoctonion" library for researchers who need calculations in octonions. Due to lack of commutativity and Associativity, manual simpliflication is always time consuming and might go wrong. This package will certainly help to check octonionic identities and work with wide range of problems in octonions.InstallationInstall fromPyPIPlease visit the pyoctonion homepage for full information and the latest documentation.Designed for Python 2.7+ and 3.0+$ pip install pyoctonionQuick StartRun the following for a basic overview. A copy of this example can be found indemo.ipynb.Basic UsageIn your code, simply import the Octonion object from the pyoctonion module:frompyoctonionimportOctonionOctonion FeaturesDefine Octonion objectsDefine a,b,c Octonions.a=Octonion(1,2,3,4,5,6,7,8)b=Octonion(1,3,5,7,9,2,4,6)c=Octonion(8,7,6,5,4,3,2,1)#printing octonion objects valuesprint(a)print(b)print(c)output+1.0000 +2.0000i +3.0000j +4.0000k +5.0000l +6.0000il +7.0000jl +8.0000kl
+1.0000 +3.0000i +5.0000j +7.0000k +9.0000l +2.0000il +4.0000jl +6.0000kl
+8.0000 +7.0000i +6.0000j +5.0000k +4.0000l +3.0000il +2.0000jl +1.0000klAdditiona + boutput+2.0000 +5.0000i +8.0000j +11.0000k +14.0000l +8.0000il +11.0000jl +14.0000klSubtractiona - boutput+0.0000 -1.0000i -2.0000j -3.0000k -4.0000l +4.0000il +3.0000jl +2.0000klMultiplicationa * boutput-181.0000 -48.0000i -17.0000j -40.0000k +83.0000l +0.0000il +35.0000jl +4.0000klDivisiona / boutput+0.8281 +0.2353i +0.1041j +0.2172k -0.3303l +0.0543il -0.0950jl +0.0543klNorma.normoutput14.2828568570857Conjugationa.conjugateoutput+1.0000 -2.0000i -3.0000j -4.0000k -5.0000l -6.0000il -7.0000jl -8.0000klInversea.inverseoutput+0.0049 -0.0098i -0.0147j -0.0196k -0.0245l -0.0294il -0.0343jl -0.0392klPowera ** 3output-608.0000 -400.0000i -600.0000j -800.0000k -1000.0000l -1200.0000il -1400.0000jl -1600.0000klReal Parta.x_0output1Similary, we can obtain coeficient of imaginary unitsExample 1:a.x_1output2Example 2:a.x_7output8
Package: pyoctree
Octree structure containing a 3D triangular mesh model. To be used for
ray tracing / shadow casting.Written in C++ for speed, but exposed to Python using Cython.DetailsPyoctree uses an adaptive structure, so it will automatically divide
branches to ensure that there are no more than 200 objects per leaf.RequirementsPython 2.7 or Python >= 3.5Optionalvtk >= v6.2.0 or >= v7.0 (for outputting a vtk file for viewing octree structure in Paraview)A C++ compiler for building the extension module from the provided cpp file (already cythonized). Suggested compilers are:
-The Microsoft C++ Compiler for Python 2.7if using Python 2 on Windows
-Microsoft Visual C++ 2015 (14.0)if using Python 3 on Windows
-gccon Linux
-Mingw32on Windows or LinuxCython >= v0.20 and a C++ compiler to build from sourceNote that a compiler is not required if installing using the provided Python wheel.InstallationIntersection testing uses parallel processing via OpenMP. To use more than a
single processor, use the provided Python wheel or compile from source using a
compiler that supports OpenMP. Then set value of environment variable
OMP_NUM_THREADS to the number of desired processors.Note that the compilers provided by the Anaconda Python distributiondo notsupport OpenMP.1. Building from sourceTo compilewithoutOpenMP, open a command prompt, browse to the base directory containing the setup.py file and type:python setup.py installTo compilewithOpenMP, open a command prompt, browse to the base directory containing the setup.py file and type:python setup.py install --openmp2. Installation using Python wheelDownload the python wheel fromreleasesi.e.
pyoctree-0.2.10-cp36-cp36m-win_amd64.whl for Python 3.6 on Windows 64-bit. Then, open a command
prompt, browse to the download directory and type:pip install pyoctree-0.2.10-cp36-cp36m-win_amd64.whlNote that Python wheels have been builtwithOpenMP.Usage1. Creating the octree representation of a 3D triangular mesh modelfrompyoctreeimportpyoctreeasottree=ot.PyOctree(pointCoords,connectivity)where:pointCoords is a Nx3 numpy array of floats (dtype=float) representing
the 3D coordinates of the mesh pointsconnectivity is a Nx3 numpy array of integers (dtype=np.int32)
representing the point connectivity of each tri element in the mesh2. Finding intersection between mesh object and rayThe octree can be used to quickly find intersections between the object
and a ray. For example:importnumpyasnpstartPoint=[0.0,0.0,0.0]endPoint=[0.0,0.0,1.0]rayList=np.array([[startPoint,endPoint]],dtype=np.float32)intersectionFound=tree.rayIntersection(rayList)ExamplesSome Jupyter notebooks are provided in the Examples directory on how to
use pyoctree.HelpIf help is required, please create an issue on Github.
Package: pyoculus
pyoculusA Python version of Oculus - The eye into the chaos: a comprehensive magnetic field diagnostic package for non-integrable, toroidal magnetic fields (and more general 1 1/2-D or 2D Hamiltonian system). Oculus is the Latin word for 'eye'.InstallationYou can obtain the package from PYPI bypip3 install pyoculusorpip3 install --user pyoculusAlternatively, you can clone this repository. In this case, additional steps are needed to compile the FORTRAN interfaces for SPEC magnetic field and PJH. See below.UsageTo use the package, simply import it in Python:importpyoculusExamples can be found in theexamplessubfolder.DocumentationThe documentation of pyoculus is managed byDoxygen.You can find the documentation on Github Page:https://zhisong.github.io/pyoculus/This documentation will be updated regularly but may not be most up-to-date. To generate the documentation from source, please runmake doxygenThe documentation will appear in the subfolderdoc/html. Please opendoc/html/index.htmlin your browser.SPEC magnetic field and Pressure Jump Hamiltonian (PJH)Some additional steps are needed to run pyoculus on outputs generated by
the Stepped Pressure Equilibrium Code orSPEC.Pre-requisiteYou will need thepy_specpackage to read a SPEC output file and give it to pyoculus. However,py_specis not a requirement for pyoculus.Link to py_specCompilationIf you are not obtaining the package via PYPI, to use the pyoculus on SPEC magnetic field and PJH, please compile the Fortran modules for SPEC bypip3 install -e .Alternatively, you can manually compile*.f90inpyoculus/problems/SPECfortranusingf2pyto generate a modulepyoculus_spec_fortran_module. This is not recommended.Documentation forf2pycan be foundhere.DevelopersZhisong Qu (zhisong.qu@anu.edu.au)Arunav Kumar (arunav.kumar@anu.edu.au)Stuart Hudson (shudson@pppl.gov)Link to the original Oculus package:Github:https://github.com/SRHudson/OculusDocumentation:https://w3.pppl.gov/~shudson/Oculus/oculus.pdf
Package: pyodata
Python OData Client - pyodataPython OData client which provides comfortable Python agnostic
way for communication with OData services.The goal of this Python module is to hide all OData protocol implementation
details.Supported featuresOData V2RequirementsPython >= 3.7Download and InstallationInstall and update using pip:pipinstall-UpyodataConfigurationYou can start building your OData projects straight away after installing the
Python module without any additional configuration steps needed.LimitationsThere have been no limitations discovered yet.Known IssuesThere are no known issues at this time.How to obtain supportWe accept bug reports, feature requests, questions and comments viaGitHub issuesUsageThe only thing you need to do is to import thepyodataPython module and
provide an object implementing interface compatible withSession Objectfor the libraryRequests.importrequestsimportpyodataSERVICE_URL='http://services.odata.org/V2/Northwind/Northwind.svc/'# Create instance of OData clientclient=pyodata.Client(SERVICE_URL,requests.Session())Find more sophisticated examples inThe User Guide.ContributingPlease, go throughthe Contributing guideline.Authoring a patchHere's an example workflow for a projectPyODatahosted on Github
Your username isyournameand you're submitting a basic bugfix or feature.Hit 'fork' on Github, creating e.g.yourname/PyOData.git clone git@github.com:yourname/PyODatagit checkout -b foo_the_barsto create new local branch named foo_the_barsHack, hack, hackRunpython3 -m pytestormake checkgit statusgit addgit commit -s -m "Foo the bars"git push -u origin HEADto create foo_the_bars branch in your forkVisit your fork at Github and click handy "Pull request" button.In the description field, write down issue number (if submitting code fixing
an existing issue) or describe the issue + your fix (if submitting a wholly
new bugfix).Hit 'submit'! And please be patient - the maintainers will get to you when
they can.LicenseCopyright (c) 2023 SAP SE or an SAP affiliate company. All rights reserved.
This file is licensed under the Apache Software License, v. 2 except as noted
otherwise inthe LICENSE file
Package: pyoda-time
pyoda-timeAn alternative datetime library for Python.Based onNoda Time, an alternative DateTime library for .NET. (Which itself draws inspiration fromJoda Time.)Guiding PrinciplesSome developers assume that a pattern which works in Java will work in Python, or the equivalent for any other pair of platforms. Don’t make this assumption. Always read the documentation – and if you’re porting code from one platform to another, you’ll need to “decode” the pattern with one set of documentation, then “encode” it with the other.--Jon Skeet's coding blogTestsBy convention:Tests which are not nested inside a test class are unique to Pyoda Time.Tests which are nested within a test class (along with the class itself) are derived from the Noda Time equivalent.This way, it is easy to keep track of which tests are based on Noda Time tests and which aren't.
Package: pyodbc
pyodbcpyodbc is an open source Python module that makes accessing ODBC databases simple.  It
implements theDB API 2.0specification but is packed with even more Pythonic convenience.The easiest way to install pyodbc is to use pip:python -m pip install pyodbcOn Macs, you should probably install unixODBC first if you don't already have an ODBC
driver manager installed.  For example, using thehomebrewpackage manager:brew install unixodbc
python -m pip install pyodbcSimilarly, on Unix you should make sure you have an ODBC driver manager installed before
installing pyodbc.  See thedocsfor more information about how to do this on different Unix flavors.  (On Windows, the
ODBC driver manager is built-in.)Precompiled binary wheels are provided for multiple Python versions on most Windows, macOS,
and Linux platforms.  On other platforms pyodbc will be built from the source code.  Note,
pyodbc contains C++ extensions so you will need a suitable C++ compiler when building from
source.  See thedocsfor details.DocumentationRelease Notes
Package: pyodbc-unittest
pyodbc-unittestThe library provides SQL calls for the PythonunittestframeworkUnit TestUnit tests are typically automated tests written and run by software developers to ensure that a section of an application
(known as the "unit") meets its design and behaves as intended.Database Unit TestThe SQL unit testing approach allows us to test part of database objects such as stored procedures,
functions and schema. The advantage of SQL unit testing is to develop more robust database designs,
because these objects have already been checked before production deployment, so SQL unit testing process
allows us to minimize the errors, which are related to database objects.Quick StartInstallpyodbc-unittestfrom pypi usingpippipinstallpyodbc-unittestImportDbtestobject in your moduleimportunittestfrompyodbc_unittestimportDbtestNow you need to setup ODBC data source. Select the type of database you want to set up a database for, for example,
SAP/Sybase ASE, MS SQL Server, PostgreSQL, etc. Moreover, fill the login, password and server fields.Write your first test like in the example. Please fill in CONNECTION_STRING with the name of the ODBC data source.CONNECTION_STRING=r'DSN=mssql.local'classTestSelect(unittest.TestCase):deftest_data(self):database=Dbtest(CONNECTION_STRING)sql='SELECT 1 AS ONE'file_name='SELECTONE'self.assertEqual(database.from_db(sql,file_name),database.from_file(file_name))database.close()And rununittest>python-munittest
.
----------------------------------------------------------------------
Ran1testin0.112s

OKAn artifact namedSELECTONE.jsonwas created that contains all the information about the result set.{"rowcount":1,"resultcount":1,"error":0,"errormessage":"","names":"[[\"one\"]]","types":"[[\"int\"]]","sizes":"[[10]]","datas":["{\"columns\":[\"one\"],\"index\":[0],\"data\":[[\"1\"]]}"]}You can directly dive into the examples attests/.This example uses two main functions:Dbtest.db.from_dbloads data from a DB and returns a string for comparison.Dbtest.db.from_fileloads data from a file and returns a string for comparison.Now we can change something in select. Digit or name.sql='SELECT 2 AS ONE'And, if we rununittestagain, it will be a failed.>python-munittestF======================================================================FAIL:test_data(test_pyodbc_unittest.TestSelect)CreateSQLVERSION.json.
----------------------------------------------------------------------
Traceback(mostrecentcalllast):File"C:\Users\yashi\Projects\pyodbc-unittest\tests\test_pyodbc_unittest.py",line17,intest_dataself.assertEqual(database.from_db(sql,file_name),
AssertionError:'ROWS[96 chars][0] = [\'int\']\nDATA[0]={\n"one":{\n"0":"2"\n}\n}\n' != 'ROWS[96chars][0]=[\'int\']\nDATA[0]={\n"one":{\n"0":"1"\n}\n}\n'ROWS_OUNT = 1RESULT_COUNT = 1SQLCODE = 0MESSAGE =COLUMN_NAMES[0] = ['one']COLUMNN_TYPES[0] = ['int']DATA[0]={"one":{-"0":"2"?^
+"0":"1"?^}}----------------------------------------------------------------------
Ran1testin0.127s

FAILED(failures=1)To be continued...
Package: pyodc
pyodcA Python interface toodcfor encoding/decoding ODB-2 files.The package contains two different implementations of the same library:pyodcis a pure-python encoder and decoder for ODB-2 data, which encodes data from, and decodes it into pandas data framescodcis an implementation of the same API aspyodcthat depends on the ECMWFodclibrary, and comes withmuchbetter performanceDocumentationChangelogDependenciesRequiredPython 3.xOptionalodcpytestpandocJupyter NotebookForcodcto work,odclibrary must be compiled and installed on the system and made available to Python (through the CFFI mechanism) as a shared library. There are multiple ways to make the library visible to CFFI: it can be installed as a system library, the installation prefix can be passed inodc_DIRenvironment variable, or the library directory can be included inLD_LIBRARY_PATH.Installationpipinstall--userpyodcCheck if the module was installed correctly:python
>>>importpyodc
>>>importcodc# optionalUsageAn introductory Jupyter Notebook with helpful usage examples is provided in the root of this repository:gitclonegit@github.com:ecmwf/pyodc.gitcdpyodc
jupyternotebookIntroduction.ipynbDevelopmentRun Unit TestsTo run the unit tests, make sure that thepytestmodule is installed first:python-mpytestBuild DocumentationTo build the documentation locally, please install the Python dependencies first:cddocs
pipinstall-rrequirements.txt
makehtmlThe built HTML documentation will be available under thedocs/_build/html/index.htmlpath.LicenseThis software is licensed under the terms of the Apache Licence Version 2.0 which can be obtained athttp://www.apache.org/licenses/LICENSE-2.0.In applying this licence, ECMWF does not waive the privileges and immunities granted to it by virtue of its status as an intergovernmental organisation nor does it submit to any jurisdiction.Changelog for pyodc1.1.3Improved github/ci integration1.1.2Fixed#6: pip install breaks codc1.1.1FixedODB-534: PyPI package is missing CHANGELOG1.1.0FixedODB-533: Decode data starting with missing values correctlyFixedODB-530: Bitfield column inspection returns incomplete data in pure-Python implementationBumped up requiredodcversion number to 1.4.0Added missing frame properties accessor tocodcinterfaceFixedODB-525: Setting odc prefix variable (odc_DIR) does not work as expected on macOSFixedODB-524: Keys and values in decoded frame properties are switched on older Python versionAdded test flag to skipcodctests on demand (PYODC_SKIP_CODC)FixedODB-523: Additional properties parameter is omitted in encode_odb() when string is passed as fileFixed package setup metadataAdded documentation1.0.4Correct support for constant codecsDecoding by column short name1.0.3Specifyodclibrary location withodc/ODC_DIRCorrectsetup.pydependencies to include pandasSupport missing ConstantString values encoded from ODB1 using theodb_migrator1.0.2String missing values should beNonenotNaNRefactor oneshot behaviour (read_odb_oneshot-->read_odb(..., single=True))Raise correct error onodcnot foundSplitcodb.pyinto a fullcodcmoduleFix miscellaneous bugs1.0.1Fixed automatic selection of integral codecs1.0.0Initial version
Package: pyodcollect
pyodcollectPython3 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: pyodeint
pyodeintprovides aPythonbinding toodeint.
Currently, the following steppers are exposed:rosenbrock4: 4th order Rosenbrock (implicit multistep) stepperdopri5: 5th order DOPRI5 (explicit runge-kutta)bs: Bulirsch-Stoer stepper (modified midpoint rule).The Rosenbrock4 stepper requires that the user provides a routine for
calculating the Jacobian.You may also want to know that you can usepyodeintfrompyodesyswhich can e.g. derive the Jacobian analytically for you (pyodesys also provides
plotting functions, C++ code-generation and more).DocumentationAutogenerated API documentation for latest stable release is found here:https://bjodah.github.io/pyodeint/latest(and the development version for the current master branch are found here:http://hera.physchem.kth.se/~pyodeint/branches/master/html).InstallationSimplest way to install is to use theconda package manager:$ conda install -c conda-forge pyodeint pytest
$ python -m pytest --pyargs pyodeinttests should pass.Binary distribution is available here:https://anaconda.org/bjodah/pyodeintSource distribution is available here:https://pypi.python.org/pypi/pyodeinthere is an example of how to build from source:$ CPATH=/opt/boost_1_65_0/include python3 setup.py build_ext -iExamplesThe classic van der Pol oscillator (seeexamples/van_der_pol.py)>>>frompyodeintimportintegrate_adaptive# also: integrate_predefined>>>mu=1.0>>>deff(t,y,dydt):...dydt[0]=y[1]...dydt[1]=-y[0]+mu*y[1]*(1-y[0]**2)...>>>defj(t,y,Jmat,dfdt,fy=None):...Jmat[0,0]=0...Jmat[0,1]=1...Jmat[1,0]=-1-mu*2*y[1]*y[0]...Jmat[1,1]=mu*(1-y[0]**2)...dfdt[0]=0...dfdt[1]=0...>>>y0=[1,0];tend=10.0;dt0=1e-8;t0=0.0;atol=1e-8;rtol=1e-8>>>tout,yout,info=integrate_adaptive(f,j,y0,t0,tend,dt0,atol,rtol,...method='rosenbrock4',nsteps=1000)>>>importmatplotlib.pyplotasplt>>>series=plt.plot(tout,yout)>>>plt.show()# doctest: +SKIPFor more examples seeexamples/, and rendered jupyter notebooks here:http://hera.physchem.kth.se/~pyodeint/branches/master/examplesSee alsopyodesysfor how to automatically
generate the jacobian callback function (and easily swtich to other solvers).LicenseThe source code is Open Source and is released under the very permissive
“simplified (2-clause) BSD license”. SeeLICENSEfor further details.
Contributors are welcome to suggest improvements athttps://github.com/bjodah/pyodeintAuthorBjörn I. Dahlgren, contact:gmail address: bjodah