Saravanan666/pypi-org huggingface dataset:
Package: rgbt
RGBT toolkit for GTOT, RGBT210, RGBT234, LasHeRThis project was created for the convenience of RGBT Tracking researchers. By utilizing this tool, you will be able to easily implement the following features:Use the same tool to evaluate your tracking results on different RGBT datasets.The test results of each attribute are available and a radar chart is obtained. You can also test an attribute individually.Precision plot and other plot are available.Supports GTOT, RGBT210, RGBT234, LasHeR datasets.经测试，由于计算精度的差异，部分情况下会与原始工具箱产生0.1%的误差。所有数据集都经过论文中给定结果的校准。It was tested to produce0.1%error from the original toolbox in some cases due to differences in computational accuracy.All datasets are calibrated for the given results in the paper.Installpip install rgbtEvaluate and VisualizeWe provide the ground truth file, so you can directly call it.fromrgbtimportRGBT234rgbt234=RGBT234()# Register your trackerrgbt234(tracker_name="APFNet",result_path="./result/RGBT234/APFNet",bbox_type="corner")rgbt234(tracker_name="TFNet",result_path="./result/RGBT234/TFNet",bbox_type="corner",prefix="TFNet_")# Evaluate multiple trackerspr_dict=rgbt234.MPR()print(pr_dict["APFNet"][0])# Evaluate single trackerapf_pr,_=rgbt234.MPR("APFNet")print(apf_pr)# Evaluate single challengepr_tc_dict=rgbt234.MPR(seqs=rgbt234.TC)sr_tc_dict=rgbt234.MSR(seqs=rgbt234.TC)# Draw a radar chart of all challenge attributesrgbt234.draw_attributeRadar(metric_fun=rgbt234.MPR,filename="RGBT234_MPR_radar.png")rgbt234.draw_attributeRadar(metric_fun=rgbt234.MSR)# this is ok# Draw a curve plot.rgbt234.draw_plot(metric_fun=rgbt234.MPR)rgbt234.draw_plot(metric_fun=rgbt234.MSR)Any operation requires only one line of code.其他对于最早的GTOT，后来的RGBT210,234，以及最新的LasHeR，在各种指标上计算方式有比较多的不同，导致大概2个点的误差。
经过仔细比对，发现这些差异并不会导致相对性能的变化。为了与历史跟踪器进行公平比较，我们对于这些差异做了兼容。这使得在测试GTOT时，需要在测试前额外调用RGBT_start()。fromrgbtimportGTOTfromrgbt.utilsimportRGBT_start,RGBT_endRGBT_start()gtot=GTOT()# Register your trackergtot(tracker_name="APFNet",result_path="./result/GTOT/APFNet",bbox_type="corner",prefix="")pr_dict=gtot.MPR()gtot.draw_plot(metric_fun=gtot.MPR)gtot.draw_plot(metric_fun=gtot.MSR)RGBT_end()
Package: rgcompare
rgcompare=========rgkit robot comparison tool----This is a program to compare two (soon: many) rgkit robots (see also robotgame.org).Features----It has multiprocessing and live updating of graphs.It is in some sorts a replacement for rgkit's default 'run-headless.py', since it is supposed to only test bots against each other, and not beused for debugging. Try to use non-functional bots at your own peril!Usage----usage: rgcompare [-h] [--initrun] [--games GAMES] [--turns TURNS] [--version][--processes PROCESSES] [--batch [output dir]] [--no-gui][--map MAP][r [r ...]]Compare two or more robotgame controller bots in rgkitpositional arguments:r                     robots to fight each other. not yet implemented: ifmore than two, batch mode will commenceoptional arguments:-h, --help            show this help message and exit--initrun             attempt to run game as soon as launched--games GAMES         number of games to run (default 100)--turns TURNS         number of turns per run to set (default 100)--version             show program's version number and exit--processes PROCESSESnumber of processors to use for calculation (defaultNUM_PROCESSORS)--batch [output dir]  not yet implemented: and save image of every battlewhen finished (default: rgcompare/)--no-gui              run without graphics (enables initial run)--map MAP             map to use (default maps/default.py)
Package: rgd-imagery-client
rgd_imagery_client - Resonant GeoData imagery clientInstallationTo install the imagery client pluginpip install rgd-imagery-clientUsageInspect rasterPreview thumbnails of the rasterimportimageiofromioimportBytesIOfromrgd_clientimportcreate_rgd_clientfromrgd_imagery_clientimportImageryClientclient:ImageryClient=create_rgd_client(username='username',password='password')raster=client.imagery.get_raster(q[0])plot_geojson(bbox,'k--')plot_geojson(raster['outline'],'r')load_image=lambdaimbytes:imageio.imread(BytesIO(imbytes))count=len(raster['parent_raster']['image_set']['images'])foriinrange(count):thumb_bytes=client.imagery.download_raster_thumbnail(q[0],band=i)thumb=load_image(thumb_bytes)plt.subplot(1,count,i+1)plt.imshow(thumb)plt.tight_layout()plt.show()Download RasterDownload the entire image set of the rasterimportrasteriofromrasterio.plotimportshowpaths=client.imagery.download_raster(q[0])rasters=[rasterio.open(im)foriminpaths.images]fori,srcinenumerate(rasters):plt.subplot(1,len(rasters),i+1)ax=plt.gca()show(src,ax=ax)plt.tight_layout()plt.show()STAC Item SupportThe Python client has a search endpoint specifically for Raster data that
returns each record in the search results as a STAC Item.q=client.imagery.search_raster_stac(query=json.dumps(bbox),predicate='intersects')print(q[0])# view result as STAC Item# Download using the search resultpaths=client.imagery.download_raster(q[0])print(paths)We can also upload new data in the STAC Item format. Here we simply pass back
the same STAC Item JSON which will not actually do anything because RGD
recognizes that these files are already present with a Raster.client.imagery.create_raster_stac(q[0])Please note that the assets in the STAC Item must already be uploaded to a
cloud storage provider with eithers3://orhttps://URLs. Further, the
images must have thedatatag on each asset. e.g.:{...# other STAC Item fields'assets':{'image-15030':{'href':'http://storage.googleapis.com/gcp-public-data-sentinel-2/tiles/17/S/MS/S2A_MSIL1C_20210302T161201_N0209_R140_T17SMS_20210302T200521.SAFE/GRANULE/L1C_T17SMS_A029738_20210302T161751/IMG_DATA/T17SMS_20210302T161201_B01.jp2','title':'GRANULE/L1C_T17SMS_A029738_20210302T161751/IMG_DATA/T17SMS_20210302T161201_B01.jp2','eo:bands':[{'name':'B1'}],'roles':['data'],},'image-15041':{'href':'http://storage.googleapis.com/gcp-public-data-sentinel-2/tiles/17/S/MS/S2A_MSIL1C_20210302T161201_N0209_R140_T17SMS_20210302T200521.SAFE/GRANULE/L1C_T17SMS_A029738_20210302T161751/IMG_DATA/T17SMS_20210302T161201_B02.jp2','title':'GRANULE/L1C_T17SMS_A029738_20210302T161751/IMG_DATA/T17SMS_20210302T161201_B02.jp2','eo:bands':[{'name':'B1'}],'roles':['data'],},...# ancillary files can lack a role but we like to see `metadata` used.'ancillary-30687':{'href':'http://storage.googleapis.com/gcp-public-data-sentinel-2/tiles/17/S/MS/S2A_MSIL1C_20210302T161201_N0209_R140_T17SMS_20210302T200521.SAFE/GRANULE/L1C_T17SMS_A029738_20210302T161751/QI_DATA/MSK_TECQUA_B03.gml','title':'GRANULE/L1C_T17SMS_A029738_20210302T161751/QI_DATA/MSK_TECQUA_B03.gml','roles':['metadata'],},}}
Package: rgear
rgear - Generate R helper filesThe aim ofrgearis to provide simple tooling to create helper files for R based content.The files it creates allow you to serve R based content (Shiny apps, plumber APIs or RMarkdown html outputs) from the command line.Originally created for use with Domino Data Lab,rgearcan be used wherever R is and therefore also makes a great complement to other projects such as (R4Pi)[https://r4pi.org].Supported content types:Shiny appsRmarkdown html outputPlumber APIsInstallationrgearis written in Python, and can be installed directly from PyPI:# python3 -m pip install rgearUsagergear--helpusage: rgear [-h] [-f] [-p PORT] [-v] [--version]{shiny,plumber,rmarkdown} pathGenerates helper scripts for starting R based content serverspositional arguments:{shiny,plumber,rmarkdown}choose the application type you want to servepath                  path to the content to serveoptional arguments:-h, --help            show this help message and exit-f, --force           force overwrite if the output file(s) already exist-p PORT, --port PORT  override the default port setting. [default: 8888]-v, --verbose         enable verbose output--version             print the version and exitFor detailed help, please see https://github.com/sellorm/rgearUsing the above options, various files -- mostlyapp.sh-- will be written to the working directory with the required parameters already populated.Theapp.shfile created by rgear can be used to start the specified content from the command line. This makes it an ideal complement to all command-line uses of R.For example, to generate a script to serve a plumber API:# rgear plumber plumber.RUsing with Domino Data LabDomino Data Lab can then use these files to serve your content as a DDL
"(app)[https://docs.dominodatalab.com/en/latest/user_guide/71635d/publish-apps/]".The "app.sh" files created byrgearhave sensible defaults for Domino and allows users to generate the file(s) they need without too much effort.Usergearto generate the "app.sh" file you need for your specific content type and then configure Domino to use that file to serve your content.Using with command line R (Including R4Pi)Usergearto generate startup scripts for your Shiny apps, plumber APIs or RMarkdown reports.For example, if you have a Shiny app in an "app.R" file, run:# rgear shiny app.RThen you can run the app with:# ./app.shThen visit the app in your browser using eitherhttp://localhost:8888or http://:8888.Use ctrl+c to stop the app from running.You can run the "app.sh" script in the background with:# nohup ./app.sh &You'll need to usepsandkillto stop an application started this way.LicenseMIT License Copyright (c) 2023 Mark SellorsSee theLICENSEfile for the full text of the license.
Package: rgeocoder
A lightweight offline reverse geocoder implemented in Rust withpyo3Python bindings.Basic usagepipinstallrgeocoder>>>fromrgeocoderimportReverseGeocoder>>>rg=ReverseGeocoder()>>>r=rg.nearest(41.891929,12.511331)# lat, lon>>>print(r.name,r.cc)'Rome IT'>>>print(r.lat,r.lon)41.8919312.51133Some locations include the first-
and second-level administrative divisions:>>>print(r.admin1)'Latium'>>>print(r.admin2)'Citta metropolitana di Roma Capitale'Seehttp://download.geonames.org/export/dump/readme.txtfor more
information on the dataset.LicenseMIT LicenseCreditsRust implementation of the algorithm originally based on code fromllambda/rust-reverse-geocoder.Inspired by and meant to act as an alternative tothampiman/reverse-geocoder.
Package: rgf
red / green / refactorCI on Travis:http://travis-ci.org/fidothe/rgfCode on Github:https://github.com/fidothe/rgfPackage on PyPI:http://pypi.python.org/pypi/rgfA currently experimental attempt at an RSpec-esque BDD testing framework for Python.Basically we’re aiming for syntax along these lines:from rgf import describe, it

with describe("That Thing"):
    @it("test desc")
    def spec(context):
        # test code here
        assert stuff()x_spec.pyfiles go in a directory structure under, by convention,spec/in the
root of your project and are run using:rgf specThe specifics of the syntax are currently wrong, being borrowed directly from RSpec.
Suggestions warmly received.(Current front-runners aresubjectandcontextin place ofdescribe, keepingitfor the spec decorator.)Because the spec functions are just functions, not methods, and are hoovered up by the@itdecorator they’re
effectively anonymous and can all have the same name for ease of thinking:with subject('This Class'):
    @it('can calculate X')
    def s(world):
        pass

    @it('can account for Y')
    def s(world):
        passThe aim is for something pythonic, with nested context and good spec names being the primary focus: minimum magic, minimum fuss.Changelog0.2.0.1 (2012-05-19)bump to update the Changelog, which I had neglected to do for 0.2.0 (sorry)0.2.0 (2012-05-18)Python 3 support, courtesy of Marc Abramowitzcreate rgf and rgf-x.y scripts using distributeExampleGroups can be nested usingwithdescribe():0.0.2 (2012-05-13)Anonymous module collection of specs using randomly-generated module names.
(fixes problems with imports of specs related to pollutingsys.modules)0.0.1 (2012-05-12)The so-how-does-pypi-work-then? release.
Package: rgfrosh
Python Real Gas FROzen SHock (RGFROSH)This project is a solver for the frozen shock equations[^1] developed in Python at the
University of Central Florida. The original RGFROSH was developed in FORTRAN at Stanford
University by D. F. Davidson and R. K. Hanson using real gas subroutines for
CHEMKIN[^2][^3].Python RGFROSH (rgfrosh) is a package for calculating conditions behind incident and reflected shock in
a shock tube. As its name suggests,rgfroshwas written primarily for solving the frozen shock equations for
a real gas equation of state; however, the implementation also allows for the use of the ideal gas equation of state or
even custom equations of state. Additionally, an implementation of the ideal shock equations is also available for comparison.DocumentationThedocumentation siteprovides a detaileduser guideandreferencefor the package.InstallationPython RGFROSH can be installed usingpip install rgfroshwhich also installs required dependencies. Cantera or CoolProp are optional and must
be installed separately if desired.ContributingFor any bugs or feature requests, create an issue on theissue tracker.After cloning the repository, the development environment can be set up withpip install -r requirements.txtBefore creating a pull request, be sure to lintblack .and run the automated testspytestThese checks will be performed automatically for all pull requests along
with test coverage comparisons.CiteTo cite Python RGFROSH clickCite this repositoryon the right side of the GitHub repository
page to export the citation for the latest release ofrgfrosh. It is also encouraged to cite the
original paper[^1] for the frozen shock equations that this work is a derived from.[^1]: Davidson, D.F. and Hanson, R.K. (1996), Real Gas Corrections in Shock Tube Studies
at High Pressures. Isr. J. Chem., 36: 321-326.https://doi.org/10.1002/ijch.199600044[^2]: P. Barry Butler, "Real Gas Equations of State for Chemkin" Sandia Report No.
SAND88-3188 (1988).https://doi.org/10.2172/6224858[^3]: R. G. Schmitt, P. B. Butler, N. B. French "Chemkin real gas: a Fortran package for
analaysis of thermodynamic properties and chemical kinetics in non-ideal systems,"
U. of Iowa Report UIME PPB 93-006 (1994).
Package: rgg
Risk graph generator (rgg)This tool takes a CSV or YAML file with your observations and can output either a grid graph, or a donut ring.
It also accepts a CSV or YAML file with recommendations to output a recommendations grid.RationaleI was fed-up with having to manually create these graphs for a client and I wanted to make my life easier.
And since I want to increase my Python skills some more, this would be a fun little project to catch two birds with one stone.Dependenciesargparse (dev-python/argparse);matplotlib (dev-python/matplotlib);python (dev-lang/python).InstallUsing Pip:pip install --user rggUsageYou can userggin two ways:When installed as package, call the installed script:rgg --help.When Git cloned, call the package directly from the root of the Git repository:python -m src.rgg --help$ rgg -h
usage: rgg [-h] (-iC INPUT_CSV_FILE | -iY INPUT_YAML_FILE) (-g | -d | -r)
           [-oP OUTPUT_PNG_FILE] [--axis-labels AXIS_LABELS]
           [--axis-arrows AXIS_ARROWS] [--legend LEGEND]

Converting scanning reports to a tabular format

optional arguments:
  -h, --help            show this help message and exit
  -iC INPUT_CSV_FILE, --input-csv-file INPUT_CSV_FILE
                        specify an input CSV file (e.g. observations.csv).
  -iY INPUT_YAML_FILE, --input-yaml-file INPUT_YAML_FILE
                        specify an input YAML file (e.g. observations.yml).
  -g, --grid            generate a risk grid graph.
  -d, --donut           generate a risk donut.
  -r, --recommendations
                        generate a risk recommendations graph.
  -oP OUTPUT_PNG_FILE, --output-png-file OUTPUT_PNG_FILE
                        specify an output PNG file (e.g. risk.png).
  --axis-labels         specify to print the axis labels
  --axis-arrows         specify to print arrows along the axisExampleA few examplesRisk grid graphTo generate a risk grid graph:$ rgg -iC example/input/observations.csv -oP example/output/grid.png -gOr if you prefer YAML:$ rgg -iY example/input/observations.yaml -oP example/output/grid.png -gRisk donutTo generate a risk donut:$ rgg -iC example/input/observations.csv -oP example/output/donut.png -dOr if you prefer YAML:$ rgg -iY example/input/observations.yaml -oP example/output/donut.png -dRisk recommendationsTo generate a recommendations graph:$ rgg -iC example/input/recommendations.csv -oP example/output/recommendations.png -rOr if you prefer YAML:$ rgg -iY example/input/recommendations.yaml -oP example/output/recommendations.png -r
Package: rgmining-amazon-dataset
For theReview Graph Mining project,
this package provides a loader of theSix Categories of Amazon Product
Reviews datasetprovided byDr. Wang.InstallationUse pip to install this package.$pipinstall--upgradergmining-amazon-datasetNote that this installation will download a big data file from theoriginal web site.UsageThis package provides moduleamazonand this module provides
functionload. Theloadfunction takes a graph object which
implements thegraph
interfacedefined inReview Graph Mining
project. The funcitonloadalso
takes an optional argument, a list of categories. If this argument is
given, only reviews for products which belong to the given categories
will be loaded.For example, the following code constructs a graph object provides theFRAUDARalgorithm, loads the Amazon dataset, runs the algorithm, and then
outputs names of anomalous reviewers. Since this dataset consists of
huge reviews, loading may take long time.importfraudarimportamazon# Construct a graph and load the dataset.graph=fraudar.ReviewGraph()amazon.load(graph)# Run the analyzing algorithm.graph.update()# Print names of reviewers who are judged as anomalous.forringraph.reviewers:ifr.anomalous_score==1:printr.name# The number of reviewers the dataset has: -> 634295.len(graph.reviewers)# The number of reviewers judged as anomalous: -> 91.len([rforringraph.reviewersifr.anomalous_score==1])Note that you may need to install the FRAUDAR algorithm for the Review
Mining Project bypip installrgmining-fraudar.LicenseThis software is released under The GNU General Public License Version
3, seeCOPYINGfor
more detail.The authors of the Trip Advisor dataset, which this software imports,
requires to cite the following papers when you publish research papers
using this package:Hongning Wang,Yue
Lu, andChengXiang
Zhai, “Latent Aspect Rating
Analysis without Aspect Keyword
Supervision,”
In Proc. of the 17th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining (KDD’2011), pp.618-626, 2011;Hongning Wang,Yue
Lu, andChengxiang
Zhai, “Latent Aspect Rating
Analysis on Review Text Data: A Rating Regression
Approach,”
In Proc. of the 16th ACM SIGKDD Conference on Knowledge Discovery and
Data Mining (KDD’2010), pp.783-792, 2010.
Package: rgmining-common
# Common library for Review Graph Mining
[![GPLv3](https://img.shields.io/badge/license-GPLv3-blue.svg)](https://www.gnu.org/copyleft/gpl.html)
[![Build Status](https://travis-ci.org/rgmining/common.svg?branch=master)](https://travis-ci.org/rgmining/common)
[![wercker status](https://app.wercker.com/status/00645c6dedb906005bbc6c080290f5f6/s/master“wercker status”)](https://app.wercker.com/project/byKey/00645c6dedb906005bbc6c080290f5f6)
[![Code Climate](https://codeclimate.com/github/rgmining/common/badges/gpa.svg)](https://codeclimate.com/github/rgmining/common)
[![Release](https://img.shields.io/badge/release-0.9.1-brightgreen.svg)](https://github.com/rgmining/common/releases/tag/v0.9.1)The common module and its submodules probides varies functions ans classes
which can be used in any context.The main functions provided by this package are followings:print_args: Decorate a function so that print arguments before calling it.print_return: Decorate a function so that print result after calling it.constant: Decorate a function so that the result is a constant value.memoized: Decorate a function to memoize results.See [documents](https://rgmining.github.io/common/) for more information.## Installation
Usepipto install this package.`shell pip install--upgradergmining-common`## License
This software is released under The GNU General Public License Version 3,
see [COPYING](COPYING) for more detail.
Package: rgmining-fraudar
This package implements a wrapper ofFRAUDARalgorithm to provide APIs defined inReview Graph Mining
project.InstallationUsepipto install this package.$pipinstall--upgradergmining-fraudarLicenseThis software is released under The GNU General Public License Version
3, seeCOPYINGfor more detail.The original FRAUDAR source code is made byBryan
Hooi,Hyun Ah
Song,Alex
Beutel,Neil
Shah,Kijung
Shin, andChristos
Faloutsos, and licensed underApache License, Version 2.0. It is available athttps://bhooi.github.io/projects/fraudar/index.html.
Package: rgmining-fraud-eagle
This package provides an implementation of Fraud Eagle algorithm. This
algorithm has been introduced by Leman Akoglu,et al.inICWSM
2013Seethe documentsfor more
information.InstallationUsepipto install this package.pip install --upgrade rgmining-fraud-eagleLicenseThis software is released under The GNU General Public License Version
3, seeCOPYINGfor more detail.
Package: rgmining-review
# Review data structure for Review Graph Mining
[![GPLv3](https://img.shields.io/badge/license-GPLv3-blue.svg)](https://www.gnu.org/copyleft/gpl.html)
[![Build Status](https://travis-ci.org/rgmining/review.svg?branch=master)](https://travis-ci.org/rgmining/review)
[![wercker status](https://app.wercker.com/status/afc19091fbf86b8e5888486c638ac05a/s/master“wercker status”)](https://app.wercker.com/project/byKey/afc19091fbf86b8e5888486c638ac05a)[![CodeClimate](https://codeclimate.com/github/rgmining/review/badges/gpa.svg)](https://codeclimate.com/github/rgmining/review)
[![Release](https://img.shields.io/badge/release-0.9.2-brightgreen.svg)](https://github.com/rgmining/review/releases/tag/v0.9.2)This package defines a data structure of review data. We assume review data
consist of reviews and summaries. A review is a set of information a user posts
to a product. On the other hand, a summary is computed from a set of reviews.This package defines two types of reviews and those summaries; scalar review and
vector review.See [documents](https://rgmining.github.io/review/) for more information.## Installation
Usepipto install this package.` pip install--upgradergmining-review`## License
This software is released under The GNU General Public License Version 3,
see [COPYING](COPYING) for more detail.
Package: rgmining-ria
This package provides 4 algorithms for review graph mining project.Mutually Reinforcing Analysis
(MRA)algorithm is an algorithm we’ve introduced in DEXA 2011,Repeated
Improvement Analysis
(RIA)algorithm is
an algorithm we’ve introduced in DEIM 2015. AlgorithmOnehas been
proposed by Ee-Peng Limea al.in CIKM 2010, and algorithmOneSumis an variation of it made by us.This package is a part ofReview Graph Mining
Projectwhich provides other
algorithms, datasets, and helper libraries.Seethe documentsfor more
information.InstallationUsepipto install this package.pip install --upgrade rgmining-riaLicenseThis software is released under The GNU General Public License Version
3, seeCOPYINGfor more detail.
Package: rgmining-rsd
This package provides an implementation of Review Spammer Detection
(RSD) introduced in this paper:Guan Wang,Sihong Xie,Bing Liu, andPhilip S. Yu,
“Review Graph Based Online Store Review Spammer Detection,”
Proc. ofIEEE 11th International Conference on Data Mining, 2011, pp. 1242-1247.Seethe documentsfor more
information.InstallationUsepipto install this package.pip install --upgrade rgmining-rsdLicenseThis software is released under The GNU General Public License Version
3, seeCOPYINGfor more detail.