Saravanan666/pypi-org huggingface dataset:
Package: py-range-parse
py-range-parsepy-range-parseis a library to parse commonly used range
notations to python objects that act like sets.py-range-parse is used bycontainer-app-confand hopefully many others :)How to usepipinstallpy-range-parsefrompy_range_parseimportparse_rangerange=parse_range("[0..5]")or create on manually:frompy_range_parseimportRangerange=Range(0,5)Input formatsWhen parsing aRangefrom astrany whitespace is ignored.intrangesIf both thestartandendvalue are of typeint, the resultingRangewill only consider integers as part of it. If you want to includefloatvalues as well, at least one of the values has to be afloat.[-2 .. 5][10 .. 1]If theendvalue is bigger than thestartvalue the resulting range will automatically be inverted. Thereforerange.start <= range.endisalwaysTruein aRange.floatrangesAfloatRangeincludes every possiblefloatvalue between thestartandendvalue.[-2.2 .. 5.123][-2.0 .. 5]InfinityInfinity can also be specified using bothinfas well as the unicode symbol∞. Since it is internally represented usingmath.infit will behave like afloat.]-inf .. inf[]-∞ .. ∞[Exclude bordersThestartandendvalues can be excluded from theRangeindependent of one another using the open bracket notation.]0 .. 5.5]]0 .. inf[OperationsContainsYou can easily check if a value is within a givenRangelike this:>frompy_range_parseimportparse_range>range=parse_range("[0 .. 5]")>print(4inrange)TrueComparisonYou can compare equality of twoRangeinstances using the==operator.
For two ranges to be equal they have to have the samestartvalueendvaluestart inclusionend inclusiontype (intorfloat)>frompy_range_parseimportparse_range>range1=parse_range("[0 .. 5]")>range2=parse_range("[0 .. 5]")>range3=parse_range("[0 .. 5.0]")>print(range1==range2)True>print(range1==range3)FalseContributingGitHub is for social coding: if you want to write code, I encourage contributions through pull requests from forks
of this repository. Create GitHub tickets for bugs and new features and comment on the ones that you are interested in.Licensepy-range-parse
Copyright (c) 2019 Markus Ressel

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: pyrankability
Rankability ToolboxThis repo contains various implementations that provide insights into the rankability of data and the linear ordering problem.Install instructionsPrerequisitesgraphviz headers must be installed:apt-getinstall-ylibgraphviz-devRecommended package installationpipinstallpyrankabilityPost package installation: Gurobi LicenseThis library relies on Gurobi optimizer. Gurobi provides free academic licenses and more information on obtaining and installing your license can be found here:https://support.gurobi.com/hc/en-us/articles/360040541251.Verify installationimportpyrankabilityn=8D=np.zeros((n,n))D[np.triu_indices(n,1)]=1D[[5,3,7]]=1-D[[5,3,7]]D=pd.DataFrame(D)k_hillside,details_hillside=pyrankability.rank.solve(D,method='hillside')k_lop,details_lop=pyrankability.rank.solve(D,method='lop')assertk_hillside==54andk_lop==12.0Development notesRunning testscdranking_toolbox
python3-mvenv../envsource../env/bin/activatecdtests
pytesttests.pyAuthorsPaul Anderson, Ph.D.Department of Computer ScienceDirector, Data Science ProgramCollege of CharlestonAmy Langville, Ph.D.Department of MathematicsCollege of CharlestonTim Chartier, Ph.D.Department of MathematicsDavidson CollegeAcknowledgementsWe would like to thank the entire IGARDS team for their invaluable insight and encouragement.
Package: py-rankaggregation
py_rankaggregationThis project seeks to build a Python software package to aggregate a collection of
ranked lists using various aggregation approaches.
The goal of rank aggregation is to come up with a single ranked list of objects
in the original lists such that the final ranking reflects some aggregate measure
of the items’ ranks in the individual lists of the original collection.
This package covers a variety of aggregation methods and works with different
representations of ranked lists.
It is built to serve as a one-stop-shop for ranked list aggregation in Python.The package is free, open-source, and BSD-licensed.Important linksProject Homepage:https://biscuitandchai.com/py_rankaggregationCode repository:https://github.com/Biscuit-and-Chai/py_rankaggregationIssue Tracker:https://github.com/Biscuit-and-Chai/py_rankaggregation/issuesDependenciesThe required dependencies to build the packages are:numpy (provides advanced array and list operations)scipy (provides advanced statistical analysis tools)pandas (provides data structures to store and manage tables)scikit-learn (provides implementations for common machine learning algorithms)six (provides functions to write compatible code across Python 2 and 3)Platformspy_rankaggregation has been tested on Linux, OS X and Windows.
Package: pyrankingfifa
pyrankingfifaA lightweight and zero dependencies Python library that allows you to extract the Ranking FIFA since 2007.This library wraps the FIFA Ranking through ceroaceros/soccerzz website.
Note this isn't affiliated with FIFA or ceroaceros.InstallationFrom PyPIpipinstallpyrankingfifaFrom source code clone itgitclonehttps://github.com/manucabral/pyrankingfifa.gitUsageSetting upfrompyrankingfifaimportRankingmens_ranking=Ranking('mens')womens ranking is not available for nowLet's get current ranking.ranking=mens_ranking.get()forteaminranking:print(team.rank,team.name,team.points)If you need more rank positions apply the param page=idLet's get a specific ranking date.ranking=mens.get(year=2018,month='aug')forteaminteams:print(team)The library saves all requests rankings for a faster request speed, if you want to remove these files call refresh() method.ConstributionsAll constributions, bug reports or fixes and ideas are welcome.
Package: pyrano
Pyrano is a Python package for simulating solar irradiance
on external built surfaces using Digital Surface Model (DSM)
point clouds as shading geometry. Pyrano aims to bridge the gap
between building energy, solar irradiance and PV power simulations.
It is compatible with EnergyPlus, Radiance and PVMismatch.See more athttps://gitlab.tue.nl/bp-tue/pyrano
Package: pyransac
pyransacpackageThis package is a general random sample consensus (RANSAC) framework. For
convenience, some data models (such as a straight line) are already provided.
However, you are free to define your own data models to remove outliers from
arbitrary data sets using arbitrary data models.General usageThere are two main components to this package: the RANSAC estimator and a
data model. When calling the estimation functionfind_inliers, you need to
specify the model to which you expect your data to fit.A data model is class containing the model parameters and an error function
against which you can test your data. Each data model must implement the
interface defined by theModelbase class. In other words, you need to
implement themake_modelandcalc_errorfunctions.Additionally, you need to provide parameters for the RANSAC algorithm. These
parameters are contained in theRansacParamsclass.
Package: pyransac3d
What is pyRANSAC-3D?pyRANSAC-3Dis an open source implementation of Random sample consensus (RANSAC) method. It fits primitive shapes such as planes, cuboids and cylinder in a point cloud to many aplications: 3D slam, 3D reconstruction, object tracking and many others.Features:PlaneCylinderCuboidSphereLineCirclePointInstallationRequirements: NumpyInstall withPypi:pip3installpyransac3dTake a look:Example 1 - Planar RANSACimportpyransac3daspyrscpoints=load_points(.)# Load your point cloud as a numpy array (N, 3)plane1=pyrsc.Plane()best_eq,best_inliers=plane1.fit(points,0.01)Results in the plane equation Ax+By+Cz+D:[0.720, -0.253, 0.646, 1.100]Example 2 - Spherical RANSACLoading a noisy sphere's point cloud with r = 5 centered in 0 we can use the following code:importpyransac3daspyrscpoints=load_points(.)# Load your point cloud as a numpy array (N, 3)sph=pyrsc.Sphere()center,radius,inliers=sph.fit(points,thresh=0.4)Results:center:[0.010462385575072288,-0.2855090643954039,0.02867848979091283]radius:5.085218633039647Documentation & other linksThedocumentation is this Ṕage.Source code in theGithub repository.Pypi pakage installerYou can find the animations you see in the documentation on branchAnimations. It needsOpen3Dlibrary to run. The Animation branch is not regularly maintained, it only exists to create some cool visualizations ;DLicenseApache 2.0Contributing is awesome!SeeCONTRIBUTINGContactDeveloped with :heart: by the internetMainteiner:Leonardo MarigaDid you like it? Remember to click on :star2: button.
Package: pyransame
pyransamePYthon RAndom SAmpling for MEshesDocumentationUtilites for choosing random samples of points within cells ofPyVistameshes.
This package doesnotchoose random points that define the mesh itself, rather random points on the 2D surface or
in the 3D volume are sampled.Random sampling on a 2D surfaceRandom sampling in a 3D volume
Package: pyrant
Pyrant is a Python wrapper around Tokyo Tyrant. It provides:pyrant.protocolis a not-too-pythonic implementation of the Tyrant
protocol. It supports the table database and all Tyrant features but does not
attempt to do anything else;pyrant.Tyrantis a higher-level pythonic dict-like API;pyrant.queryis a high-level API for complex queries (inspired by Django
ORM).Usually you will only want to use theTyrantclass because it is built on top
of theprotocolmodule and provides access to it and to the query API.DocumentationSee completeHTML documentationfor details. Sometimes it may get slightly outdated. You can always have the
latest snapsnot by building it from the source (using Sphinx).What is Tokyo Tyrant?Tokyo Tyrantis a network interface for Tokyo Cabinet.Tokyo Cabinetis a lightweight database manager with several storages,
including a simple key/value storage and a document-oriented schemaless
database with powerful query machinery.Models (schemata)Pyrant itself does not provide means for defining models, but our related projectDocuallows to do that easily.CommunityThis application was originally created by Martin Conte Mac Donell with some
code from PyTyrant by Bob Ippolito. Pyrant was subsequently improved by other
developers. They are listed in the AUTHORS file.Please feel free to file issues and/or submit patches.
Package: pyrap
BACKUP & RESTORE SCRIPTSBackup or restore all user profiles on a host.USAGEusage: pyrap [-h] (-b | -r) [-u] [-x] [-v | -q] url

Backup or restore users to an rsync server.

positional arguments:
  url             A valid rsync url.

optional arguments:
  -h, --help      show this help message and exit
  -b, --backup    Backup users to rsync server or path.
  -r, --restore   Restore users on from last backup.
  -u, --users     Automate user selection.
  -x, --excludes  Automate excludes selection.
  -v, --verbose   Increase output verbosity.
  -q, --quiet     Run silently.ARGUMENTS[URL]Takes an rsync url as an argument:rsync://user@host:port/path/to/PARENT/of/UsersOr alternatively, any mountpoint will work:/Volumes/BackupsPITFALLS & CAVEATSTHE PATH ON THE REMOTE HOST MUST POINT TO PARENT OF USERS, NOT USERS!Once mounted the mountpoint should contain the Users directory underneath it.The script backups to or restores from the following directory layout underneath
the mountpoint:/mountpoint/Users/user.name/dateAnd logs to:/mountpoint/Logs/CopyType/user.name/date.logWhere CopyType equals either Backup or Restore.If this layout does not exist it will be created under the mountpoint.User, group and permissions will be retained and each backup will be hard-linked
against the last to save space and mimic the functionality of Time Machine.https://blog.interlinked.org/tutorials/rsync_time_machine.htmlIt's also worth pointing out that for the time being preserving extended
attributes is not supported.EXAMPLESTo backup from /Users to a rsync server, without prompting for each user:pyrap -bu rsync://user@host:port/backupsTo restore from /Users to a rsync server, with increased verbosity:pyrap -rv rsync://user@host:port/backups
Package: pyrapidjson
Aboutpyrapidjson is a wrapper forrapidjson(JSON parser/generator).Installationfrom pip:$ pip install pyrapidjsonfrom easy_install:easy_install -ZU pyrapidjsonRequirementsPython2.7+Usagebasic usage:>>> import rapidjson
>>> rapidjson.loads('[1, 2, {"test": "hoge"}]')
>>> [1, 2, {"test": "hoge"}]
>>> rapidjson.dumps([1, 2, {"foo": "bar"}])
'[1,2,{"foo":"bar"}]'
>>>LinksPyPIGitHubTravis-CI
Package: pyraptor
PyRaptorPython implementation of RAPTOR and McRAPTOR using GTFS data. Tested on Dutch GTFS data.This repository contains four applications:pyraptor/gtfs/timetable.py- Extract the timetable information for one operator from a GTFS dataset and write it to an optimized format for querying with RAPTOR.pyraptor/query_raptor.py- Get the best journey for a given origin, destination and desired departure time using RAPTORpyraptor/query_range_raptor.py- Get a list of the best journeys to all destinations for a given origin and desired departure time window using RAPTORpyraptor/query_mcraptor.py- Get a list of the Pareto-optimal journeys to all destinations for a given origin and a departure time using McRAPTORpyraptor/query_range_mcraptor.py- Get a list of Pareto-optimal journeys to all destinations for a given origin and a departure time window using McRAPTORInstallationInstall from PyPi usingpip install pyraptoror clone this repository and install from source using pip.Example usage1. Create timetable from GTFSpython pyraptor/gtfs/timetable.py -d "20211201" -a NS --icd2. Run (range) queries on timetableQuering on the timetable to get the best journeys can be done using several implementations.RAPTOR queryRAPTOR returns a single journey with the earliest arrival time given the query time.Examplespython pyraptor/query_raptor.py -or "Arnhem Zuid" -d "Oosterbeek" -t "08:30:00"python pyraptor/query_raptor.py -or "Breda" -d "Amsterdam Centraal" -t "08:30:00"rRAPTOR queryrRAPTOR returns a set of best journeys with a given query time range.
Journeys that are dominated by other journeys in the time range are removed.Examplespython pyraptor/query_range_raptor.py -or "Arnhem Zuid" -d "Oosterbeek" -st "08:00:00" -et "08:30:00"python pyraptor/query_range_raptor.py -or "Breda" -d "Amsterdam Centraal" -st "08:00:00" -et "08:30:00"McRaptor queryMcRaptor returns a set of Pareto-optimal journeys given multiple criterions, i.e. earliest
arrival time, fare and number of trips.Examplespython pyraptor/query_mcraptor.py -or "Breda" -d "Amsterdam Centraal" -t "08:30:00"python pyraptor/query_mcraptor.py -or "Vlissingen" -d "Akkrum" -t "08:30:00"python pyraptor/query_mcraptor.py -or "Obdam" -d "Akkrum" -t "08:30:00" -r 7rMcRaptor queryRange version of McRaptor, i.e. it returns a set of Pareto-optimal journeys within a departure time window.Examplespython pyraptor/query_range_mcraptor.py -or "Breda" -d "Amsterdam Centraal" -st "08:15:00" -et "08:30:00"python pyraptor/query_range_mcraptor.py -or "Vlissingen" -d "Akkrum" -st "08:15:00" -et "08:30:00"python pyraptor/query_range_mcraptor.py -or "Obdam" -d "Akkrum" -st "08:00:00" -et "09:00:00"NotesThe current version doesn't implement target pruning as we are interested in efficiently querying all targets/destinations after running RAPTOR algorithm.ReferencesRound-Based Public Transit Routing, Microsoft.com, Daniel Delling et alRaptor, another journey planning algorithm, Linus NortonDutch GTFS feed, Transit Feeds
Package: pyraptorq
pyraptorqPython bindings with RaptorQ implementation.ExamplesYou can find usage example in the/examplesfolder.Supported platformsLinux (x86_64)MacOS (arm64)Windows (x86_64)should be more in futureHow to installFrom pypi, if your system is supportedpipinstallpyraptorqFrom sourceCompile shared library as described incpp-raptorq/README.mdCreate instance ofRaptorQCppEnginewith path to shared library as argument and provide it toDecoderandEncoder.frompyraptorqimportEncoder,Decoder,RaptorQCppEngineengine=RaptorQCppEngine('path_to_lib')encoder=Encoder(b'data',2,engine)decoder=Decoder(2,2,4,engine)
Package: pyrap-web
pyRAP is a framework for implementing extremely powerful and beautiful
web-based AJAX applications in pure Python. No HTML. No JavaScript.
No PHP. Just pure Python. It has been designed as a lightweight,
easy-to-use yet powerful library that makes development of SaaS
applications as easy and fast as possible.Project Page:https://pyrap.org/Lead developers:
* Daniel Nyga (nyga@cs.uni-bremen.de)
* Mareike Picklum (mareikep@cs.uni-bremen.de)Release notesVersion 1.0.1 (17.05.2019)
*Release: Initial releaseDocumentationhttps://pyrap.org/index.html
Package: pyrasfile
PyRASFileA set of Python scripts designed to automatically produce and process key input and
output files for HEC-RAS.  Since HEC-RAS can run many flow profiles in parallel, this
allows users to run HEC-RAS on large volumes of data with minimal manual intervention.Seemain.pyfor example usage (the developer's actual usage) of both writing profiles and reading
reports.Writing ProfilesTheprofileWriterscript can be used to generate a flow profiles file (.fxx) with any number
of profiles and reaches and any set of flow volumes for each point.  ThebuildFilefunction
contains the main functionality and will build the complete contents of a flow file.General NotesMinor manual intervention is currently required if the output file is not named to match an existing
set of flow data, as HEC-RAS will not be able to find it.  In order to fix this, edit the.prjproject
file and add an entry for the new flow file in with the other flow file entries.buildFileInputs SummarybuildFilerequires the following inputs:nprofiles: the number of profiles to be generatedprofiledata: the data on flows for each profile; see below.bounddata: the data on boundary conditions; see below.titleandver: the title for the flow profile and the version of HEC-RAS in use.end: the data for the end of the file; this doesn't seem to require modification
for steady flows, so this can typically be left as the default.buildFileInput DetailsprofiledataprofileDataneeds to be in the form of a dictionary where the keys are a string specifying
the river, reach, and station, and the values are a list of the flows for each profile.  The key
strings can be made using the helper functionmkFlowHeaderto ensure the correct format.The number of flows for each entry inprofiledatamust be identical and equal to the number
of profiles specified withnprofines.bounddatabounddataneeds to be in the form of a dictionary where the keys are strings specifying the
river and reach and the values are functions which will produce the appropriate boundary
information given two arguments, the profile number and the flow rate (though these may often
be ignored).The keys should be in the format"river,reach".The boundary data functions should return strings
in the format used bymkBoundaryData, which accepts the argumentsupname,dname,uparam, anddparam.upnameanddnamespecify the type of boundary conditions, out ofKnown WS,Critical Depth,Normal Depth, andJunction.  The twoparams need only be specified if the boundary conditions areKnown WS, in which case they need to be a water depth appropriate to the flow rate, orNormal Depth,
in which case they need to be a slope.Reading ReportsAfter generating a HEC-RAS report, theparseFilefunction will parse the report file text to return a dictionary
of values for all reaches and profiles.  TheconvertCSVfunction will convert the report file into a CSV.
Package: pyrasgo
pyrasgo is a python SDK to interact with the Rasgo API.
Documentation is available at:https://docs.rasgoml.com/rasgo-docs/pyrasgo/Sign upSign up for a free Rasgo API key to start using pyrasgo:https://www.rasgoml.comPackagespyrasgo supports two cloud DataWarehouses: Snowflake & BigQuerypyrasgo:pip install pyrasgoThe pyrasgo core package installs the minimum-required dependencies to interact with the Rasgo API.Prior to version 2.0.0: installs Snowflake packages as the default datawarehouse (will be deprecated in v 2.0.0) *Post version 2.0.0: Snowflake will need to be explicitly installed via the pyrasgo[snowflake] extras package.Dependenciesmore-itertoolspandaspydanticpyyamlrequestssnowflake-connector-python *snowflake-connector-python[pandas] *Snowflake:pip install "pyrasgo[snowflake]"The pyrasgo[snowflake] extras package installs core pyrasgo + the Snowflake python packages needed to connect to your datawarehouse.Post version 2.0.0: Snowflake will need to be explicitly installed via this extras package.Dependenciespyrasgosnowflake-connector-pythonsnowflake-connector-python[pandas]BigQuery:pip install "pyrasgo[bigquery]"The pyrasgo[bigquery] extras package installs core pyrasgo + the google python packages needed to connect to your datawarehouse.Dependenciespyrasgodb-dtypesgoogle-auth-oauthlibgoogle-cloud-bigquerySeeChangelogfor release notesAbout Uspyrasgo is maintained byRasgo. Rasgo's enterprise feature store integrates with your data warehouse to help users build features faster, collaborate with team members, and serve features to models in production.Built for Data Scientists, by Data Scientists
Package: pyrasite
Tools for injecting arbitrary code into running Python processes.homepage:http://pyrasite.comdocumentation:http://pyrasite.rtfd.orgdownload:http://pypi.python.org/pypi/pyrasitesource:http://github.com/lmacken/pyrasitescreenshots:http://readthedocs.org/docs/pyrasite/en/latest/GUI.htmlmailing list:https://fedorahosted.org/mailman/listinfo/pyrasitejenkins:http://ci.csh.rit.edu/view/Pyrasiteirc:#pyrasite on FreenodeRequirementsgdb(version 7.3+ (or RHEL5+))CompatiblityPyrasite works with Python 2.4 and newer. Injection works between versions
as well, so you can run Pyrasite under Python 3 and inject into 2, and
vice versa.pyrasite-guiThe graphical interface can be found here:https://github.com/lmacken/pyrasite-guiAuthorsCreated byLuke Mackenwith the help ofDavid Malcolmand many othercontributors.License
Package: pyrasite-ng
This package is a fork ofhttps://github.com/lmacken/pyrasitecontaining several important fixes to make pyrasite work on modern systems.Requirementsgdb(version 7.3+ (or RHEL5+))On OS X you will need to have a codesigned gdb - seehttps://sourceware.org/gdb/wiki/BuildingOnDarwinif you get errors while running with –verbose which mention codesigning.On Linux machines, runecho 0 | sudo tee /proc/sys/kernel/yama/ptrace_scopebefore running pyrasite.CompatibilityPyrasite works with Python 2.4 and newer. Injection works between versions
as well, so you can run Pyrasite under Python 3 and inject into 2, and
vice versa.pyrasite-guiThe graphical interface can be found here:https://github.com/lmacken/pyrasite-guiBugsReport bug of this fork on GitHub:https://github.com/user202729/pyrasite-ng/issuesAlternatively, as a workaround while waiting for the bug to be fixed,
it’s possible to use gdb to inject code into Python directly:https://stackoverflow.com/a/70312000/5267751AuthorsCreated byLuke Mackenwith the help ofDavid Malcolmand many othercontributors.
Logo byAdam Saunders.LicensesCodeLogo