Saravanan666/pypi-org huggingface dataset:
Package: gax-cloud-vision-v1
gax-cloud-vision-v1 usesgoogle-gax(Google API extensions) to provide an
easy-to-use client library for theGoogle Vision API(v1) defined in thegoogleapisgit repositoryGetting startedgax-cloud-vision-v1 will allow you to connect to the Google
Vision API and access all its methods. In order to do this, you need
to set up authentication as well as install the library locally.Setup AuthenticationTo authenticate all your API calls, first install and setup theGoogle Cloud SDK.
Once done, you can then run the following command in your terminal:$gcloudbetaauthapplication-defaultloginor$gcloudauthloginPlease seegcloud beta auth application-default logindocument for the difference between these commands.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it’s possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install gax-cloud-vision-v1Windowspip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install gax-cloud-vision-v1At this point you are all set to continue.
Package: gax-google-cloud-language-v1beta1
gax-google-cloud-language-v1beta1 usesgoogle-gax(Google API extensions) to provide an
easy-to-use client library for theGoogle Language API(v1beta1) defined in thegoogleapisgit repositoryGetting startedgax-google-cloud-language-v1beta1 will allow you to connect to the Google
Language API and access all its methods. In order to do this, you need
to set up authentication as well as install the library locally.Setup AuthenticationTo authenticate all your API calls, first install and setup theGoogle Cloud SDK.
Once done, you can then run the following command in your terminal:$gcloudbetaauthapplication-defaultloginor$gcloudauthloginPlease seegcloud beta auth application-default logindocument for the difference between these commands.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it’s possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install gax-google-cloud-language-v1beta1Windowspip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install gax-google-cloud-language-v1beta1At this point you are all set to continue.
Package: gax-google-cloud-speech-v1
gax-google-cloud-speech-v1 usesgoogle-gax(Google API extensions) to provide an
easy-to-use client library for theGoogle Speech API(v1) defined in thegoogleapisgit repositoryGetting startedgax-google-cloud-speech-v1 will allow you to connect to the Google
Speech API and access all its methods. In order to do this, you need
to set up authentication as well as install the library locally.Setup AuthenticationTo authenticate all your API calls, first install and setup theGoogle Cloud SDK.
Once done, you can then run the following command in your terminal:$gcloudbetaauthapplication-defaultloginor$gcloudauthloginPlease seegcloud beta auth application-default logindocument for the difference between these commands.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it’s possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install gax-google-cloud-speech-v1Windowspip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install gax-google-cloud-speech-v1At this point you are all set to continue.
Package: gax-google-cloud-vision-v1
gax-google-cloud-vision-v1 usesgoogle-gax(Google API extensions) to provide an
easy-to-use client library for theGoogle Vision API(v1) defined in thegoogleapisgit repositoryGetting startedgax-google-cloud-vision-v1 will allow you to connect to the Google
Vision API and access all its methods. In order to do this, you need
to set up authentication as well as install the library locally.Setup AuthenticationTo authenticate all your API calls, first install and setup theGoogle Cloud SDK.
Once done, you can then run the following command in your terminal:$gcloudbetaauthapplication-defaultloginor$gcloudauthloginPlease seegcloud beta auth application-default logindocument for the difference between these commands.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it’s possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install gax-google-cloud-vision-v1Windowspip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install gax-google-cloud-vision-v1At this point you are all set to continue.
Package: gax-google-logging-v2
gax-google-logging-v2 usesgoogle-gax(Google API extensions) to provide an
easy-to-use client library for theGoogle Logging API(v2) defined in thegoogleapisgit repositoryGetting startedgax-google-logging-v2 will allow you to connect to the Google
Logging API and access all its methods. In order to do this, you need
to set up authentication as well as install the library locally.Setup AuthenticationTo authenticate all your API calls, first install and setup theGoogle Cloud SDK.
Once done, you can then run the following command in your terminal:$gcloudbetaauthapplication-defaultloginor$gcloudauthloginPlease seegcloud beta auth application-default logindocument for the difference between these commands.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it’s possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install gax-google-logging-v2Windowspip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install gax-google-logging-v2At this point you are all set to continue.
Package: gax-google-pubsub-v1
gax-google-pubsub-v1 usesgoogle-gax(Google API extensions) to provide an
easy-to-use client library for theGoogle Pubsub API(v1) defined in thegoogleapisgit repositoryGetting startedgax-google-pubsub-v1 will allow you to connect to the Google
Pubsub API and access all its methods. In order to do this, you need
to set up authentication as well as install the library locally.Setup AuthenticationTo authenticate all your API calls, first install and setup theGoogle Cloud SDK.
Once done, you can then run the following command in your terminal:$gcloudbetaauthapplication-defaultloginor$gcloudauthloginPlease seegcloud beta auth application-default logindocument for the difference between these commands.InstallationInstall this library in avirtualenvusing pip.virtualenvis a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.Withvirtualenv, it’s possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.Mac/Linuxpip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install gax-google-pubsub-v1Windowspip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install gax-google-pubsub-v1At this point you are all set to continue.
Package: gay
GAY 🏳️‍🌈Colour your text / terminal to be more gay.Gayer version oflolcatGet Gaypip3 install -U gayShow meTextecho'message'|gay-i1d,--interpolation1d-i2d,--interpolation2dFlaggay--flagUnlike the demo, it shows 1 flag and 1 flag only.Thank yousnshepperdfor helping with 2D gradientwrennnnnnfor helping with windows
Package: gayeogi
gayeogi is aimed to be fully-featured music management suite, including library collecting, new and missing releases checking, playing, scrobbling, tagging, and so on,
but keep in mind that it’s still in beta stage, so some features may be still missing or not fully working.At the moment, these features are considered “working”:LibraryInternet DB checkingmetal-archives.commusicbrainz.orgprogarchives.comPlaybacklast.fm/libre.fm scrobblingMPRIS (2.1 with partial TrackList support, not available on Windows)INSTALLATIONREQUIRESpython >= 2.6PyQt >= 4.6mutagen >= 1.20distribute >= 0.6.14 (setup)lettuce >= 0.1.31 (test suite)phonon >= 4.4 (player)pylast >= 0.5.6 (last.fm, libre.fm scrobbling)lxml >= 2.3 (metal-archives.com, musicbrainz.org, progarchives.com)NOTESWindows installer provides all that dependencies to you, don’t bother getting them separately (unless you want to build from source, of course).Versions specified here are “known to work”, but it may be possible that and older one will fit too.Due to the name change during update to 0.6 you’ll have to execute following commands if you want to keep your db and settings:mkdir ~/.config/gayeogi
cp ~/.config/fetcher/* ~/.config/gayeogi/
mv ~/.config/gayeogi/Fetcher.conf ~/.config/gayeogi/gayeogi.confLINUXARCHLINUXThere is a PKGBUILD available inAUR..DEB/.RPMThere are binary packages (along with custom repositories) for some popular distributions available throughNovell’s Open Build Service.GENTOOThere is an ebuild available inoverlaysthanks to <someone> (if you’re the one, feel free to send me a message).GENERALIn package main directory:python2 setup.py installalso available inPyPI:pip install gayeogi
#or
easy_install gayeogiWINDOWSYou should have no problems with provided installer (just few clicks ;). If you want to build it on your own then… you’re on your own.USAGENOTESKeep in mind that first time refresh could take quite some time, but then it should be much faster :).
Package: gazebo-yarp-synchronizer
gazebo-yarp-synchronizerSynchronization with Gazebo Classic via YARP. With Python batteries.This project is the client side of theClock Pluginincluded inrobotology/gazebo-yarp-plugins, with some sugar on top.
It providesGazeboYarpSynchronizer, a new class that extends the automatically generated resources from thethrift serviceand provides initialization and termination helpers.The main scope of this repository is providing a PyPI package for Python usage, but nothing prevents importing and using it from plain C++.Dependenciesgazebo-yarp-synchronizerexpects to find installed and configured the following dependencies:robotology/yarprobotology/gazebo-yarp-pluginsosrf/gazeboInstallationpip3installgazebo-yarp-synchronizerUsageIn different terminals, execute:# 1. Start the YARP serveryarpserver# 2. Open Gazebo pausedgazebo-u-slibgazebo_yarp_clock.so# 3. Make sure that YARP is runningyarpdetect# 4. Open the Python interpreteripythonThen, in the Python interpreter:# Import the module>>>fromgazebo_yarp_synchronizerimportGazeboYarpSynchronizer# Create the Gazebo synchronizer>>>gys=GazeboYarpSynchronizer(gazebo_rpc_port_name="/clock/rpc")# Initialize the Gazebo synchronizer>>>gys.init()# [INFO] |yarp.os.Port| Port /tmp/port/1 active at tcp://192.168.8.102:10056/# [INFO] |yarp.os.impl.PortCoreOutputUnit| Sending output from /tmp/port/1 to /clock/rpc using tcp# Inspect simulation status>>>gys.get_step_size(),gys.get_simulation_time()# (0.001, 0.0)# Advance the simulation synchronously>>>gys.step_simulation_and_wait(number_of_steps=250)# Print time>>>gys.get_simulation_time()# 0.25# Advance the simulation synchronously>>>gys.run_simulation_and_wait(duration=0.75)# Print time>>>gys.get_simulation_time()# 1.0# Terminate the synchronizer>>>gys.fini()# [INFO] |yarp.os.impl.PortCoreOutputUnit| Removing output from /tmp/port/1 to /clock/rpcContributingPull requests are welcome.
For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: gazeclassify
GazeClassifyPiPy package to algorithmically annotate eye-tracking data. Recommended python version: 3.9What is GazeClassify?GazeClassify provides automatized and standardized eye-tracking annotation. Anyone can analyze gaze data online with less than 10 lines of code.Exportedcsvwill contain distance from gaze (red circle) to human joints (left image) and human shapes (right image) for each frame.frame numberclassifier namegaze_distance [pixel]person_idjoint name0Human_Joints790Neck...............0Human_Shape0NoneNone...............Run on example datafromgazeclassifyimportAnalysis,PupilLoader,SemanticSegmentation,InstanceSegmentationfromgazeclassifyimportexample_trialanalysis=Analysis()PupilLoader(analysis).from_recordings_folder(example_trial())SemanticSegmentation(analysis).classify("Human_Shape")InstanceSegmentation(analysis).classify("Human_Joints")analysis.save_to_csv()Run on your own dataCapture eye tracking data from a Pupil eye tracker. Then,export the datausing Pupil software. You will get a folder with the exported world video and the gaze timestamps. Finally, let gazeclassify analyze the exported data:fromgazeclassifyimportAnalysis,PupilLoader,SemanticSegmentation,InstanceSegmentationanalysis=Analysis()PupilLoader(analysis).from_recordings_folder("path/to/your/folder_with_exported_data/")SemanticSegmentation(analysis).classify("Human_Shape")InstanceSegmentation(analysis).classify("Human_Joints")analysis.save_to_csv()CitationPleasecite this paperin your publications ifGazeClassifyhelps your research.@inproceedings{10.1145/3450341.3458886,
  author = {M\"{u}ller, Daniel and Mann, David},
  title = {Algorithmic Gaze Classification for Mobile Eye-Tracking},
  year = {2021},
  booktitle = {ACM Symposium on Eye Tracking Research and Applications}
}
Package: gazekit
GazeKit (Under Development) 🚧👁️GazeKitis a Python library currently under development forprocessing and analyzingeye-tracking data. It aims to provide a comprehensive set of functions for noise reduction, area of interest (AOI) localization, data analysis, and plotting. This library is being developed to simplify the preprocessing and analysis of eye-tracking data, enabling researchers in psychology, neuroscience, and human-computer interaction to gain valuable insights into human visual cognitive processes.FeaturesNoise Reduction:GazeKit offers advanced techniques for reducing noise in eye-tracking data, allowing for more accurate and reliable analysis.AOI Localization:Easily define and locate areas of interest (AOIs) within eye-tracking data, facilitating the analysis of gaze patterns and attentional focus.Data Analysis:GazeKit provides a range of analysis tools, including fixation duration, saccade detection, heatmaps, and scanpath visualization, empowering researchers to extract meaningful information from eye-tracking data.Plotting:Visualize eye-tracking data with customizable plots and graphs, enabling researchers to effectively communicate their findings.InstallationOnce GazeKit is released, you will be able to install it usingpip:pipinstallgazekitUsageOnce installed, you can use GazeKit in your Python code. Here's a sneak peek at how GazeKit might be used for preprocessing and analyzing eye-tracking data:importnumpyasnpfromgazekit.aoiimportread_aoi_ini_filefromgazekit.sequenceimportSequence# Load eye-tracking datadata=np.genfromtxt('../tests/data.csv',delimiter=',',dtype=None,encoding=None)# Load AOIsaois=read_aoi_ini_file('../tests/aoi.ini',(2560,1600))# Load data into GazeKit Sequence classseq=Sequence(*data.T)# Apply noise reduction techniques (under development)seq=seq.denoise(myfunc)# Detect fixations (under development)seq=seq.detect_fixations()# Locate AOIs within the dataseq=seq.loc_aoi(aois)# Generate a plot (under development)heatmap=seq.plot(myfunc)# ... Perform further analysis and visualizationFor more detailed instructions and examples, please refer to thedocumentation(coming soon).ContributingContributions to GazeKit are welcome! Since this project is currently under development, we are not accepting contributions at this stage. However, once the project reaches a more stable state, we would love to collaborate with the open-source community. Stay tuned for updates!LicenseThis project is licensed under theMIT License.AcknowledgementsWe would like to express our gratitude to the open-source community for their valuable contributions and support in developing GazeKit. 🎉😊
Package: gazelib
ThisPythonpackage provides software tools to manage and analyzegaze data from eye-trackers.Gazelib is developed atInfant Cognition Laboratoryat University of Tampere.InstallWithpip:$ pip install gazelibDevelopment version:$ pip install git+https://github.com/infant-cognition-tampere/gazelib.git@developDocsTutorials, examples, specifications, and conventions are available atwiki.API docs are available athttp://gazelib.readthedocs.org/.VersioningSemantic Versioning 2.0.0LicenseGNU General Public License version 3
Package: gazelleapi
GazelleAPI is a handy interface for interacting with gazelle based
trackers. This should work on Python 2 and 3.It is based onwhatapiandxanaxbetter.InstallationPippipinstallgazelleapiSourcegitclonehttps://github.com/itismadness/gazelleapicdgazelleapipythonsetup.pyinstallUsage>>>fromgazelleapiimportGazelleAPI>>>api=GazelleAPI(username='user',password='pass',hostname='example.com')>>>api.request('index')To avoid having to always login to the site, we suggest using cookies in the following manner:>>>fromgazelleapiimportGazelleAPI>>>importpickle>>>cookies=pickle.load(open('cookies.dat','rb'))>>>api=GazelleAPI(username='user',password='pass',hostname='example.com',cookies=cookies)and to save the cookie on an open session:>>>pickle.dump(api.session.cookies,open('cookies.dat','wb'))
Package: gaze-ocr
gaze-ocrThegaze-ocrpackage makes easy to apply OCR to wherever the user is looking.
This library is designed for voice control. The following instructions are for
use with Dragonfly. For Talon, seehttps://github.com/wolfmanstout/talon-gaze-ocr. See handsfreecoding.org for more
information about this package.InstallationFollowinstructions for installing
screen-ocr.Download thelatest
Tobii.Interactionpackage
from NuGet (these instructions have been tested on 0.7.3).Rename the file extension to .zip and expand the contents.Copy these 3 DLLs to a directory of your choice:
build/AnyCPU/Tobii.EyeX.Client.dll, lib/net45/Tobii.Interaction.Model.dll,
lib/net45/Tobii.Interaction.Net.dll.Ensure that the files are not blocked (right-click Properties, and if there
is a "Security" section at the bottom, check the "Unblock" box.)pip install gaze-ocr[dragonfly]UsageProvide the path to the DLL directory when constructing an EyeTracker instance.Sample Dragonfly grammar:importgaze_ocrimportgaze_ocr.dragonflyimportgaze_ocr.eye_trackingimportscreen_ocrfromdragonflyimport(Dictation,Grammar,Key,MappingRule,Mouse,Text)# See installation instructions:# https://github.com/wolfmanstout/gaze-ocrDLL_DIRECTORY="c:/Users/james/Downloads/tobii.interaction.0.7.3/"# Initialize eye tracking and OCR.tracker=gaze_ocr.eye_tracking.EyeTracker.get_connected_instance(DLL_DIRECTORY,mouse=gaze_ocr.dragonfly.Mouse(),keyboard=gaze_ocr.dragonfly.Keyboard(),windows=gaze_ocr.dragonfly.Windows())ocr_reader=screen_ocr.Reader.create_fast_reader()gaze_ocr_controller=gaze_ocr.Controller(ocr_reader,tracker,mouse=gaze_ocr.dragonfly.Mouse(),keyboard=gaze_ocr.dragonfly.Keyboard())classCommandRule(MappingRule):mapping={# Click on text."<text> click":gaze_ocr.dragonfly.MoveCursorToWordAction(gaze_ocr_controller,"%(text)s")+Mouse("left"),# Move the cursor for text editing."go before <text>":gaze_ocr.dragonfly.MoveTextCursorAction(gaze_ocr_controller,"%(text)s","before"),"go after <text>":gaze_ocr.dragonfly.MoveTextCursorAction(gaze_ocr_controller,"%(text)s","after"),# Select text starting from the current position."words before <text>":Key("shift:down")+gaze_ocr.dragonfly.MoveTextCursorAction(gaze_ocr_controller,"%(text)s","before")+Key("shift:up"),"words after <text>":Key("shift:down")+gaze_ocr.dragonfly.MoveTextCursorAction(gaze_ocr_controller,"%(text)s","after")+Key("shift:up"),# Select a phrase or range of text."words <text> [through <text2>]":gaze_ocr.dragonfly.SelectTextAction(gaze_ocr_controller,"%(text)s","%(text2)s"),# Select and replace text."replace <text> with <replacement>":gaze_ocr.dragonfly.SelectTextAction(gaze_ocr_controller,"%(text)s")+Text("%(replacement)s"),}extras=[Dictation("text"),Dictation("text2"),Dictation("replacement"),]def_process_begin(self):# Start OCR now so that results are ready when the command completes.gaze_ocr_controller.start_reading_nearby()grammar=Grammar("ocr_test")grammar.add_rule(CommandRule())grammar.load()# Unload function which will be called by natlink at unload time.defunload():globalgrammarifgrammar:grammar.unload()grammar=None
Package: gazerr
Gaze Duration ErrorGazerr is an application for estimating the expected error in a gaze duration
measurement derived from repeated application of a point of gaze model.
It is particularly applicable to machine learning models that work with device
cameras to predict a stream of gaze fixation points from facial images.The method requires an input dataset of validation points from the point
of gaze predictive model. This data is used to generate the probability
distribution of true gaze durations given a measured gaze duration.InstallationInstall from source code or from PyPiUsageThe application can be used from the command line by passing in a path to the
calibration file and the parameters for the duration measurement that will be
bounded. Note: that the penultimate two parameters should be comma separated sets
of integers that depict x,y coordiantes in pixels. The measurement length and
session lenth should be expressed in milliseconds.The final parameter is a path to a directory in which to store the raw resultsgazerr <CALIBRATION> <MEASUREMENT> <SESSION> <TARGET TOP LEFT> <TARGET BOTTOM RIGHT> <RESULTS>To use the application without installing it you can employ the runner script.
Example below, using the supplied calibration data:mkdir results/MREC_MAE_50
python ./gazerr-runner.py data/validation_50_MAE.csv 400 1000 40,40 340,290 results/MREC_MAE_50Alternatively, you may inspect the code and use the library functions directly
inside your own application.ExperimentsAll experiments for the research paper can be executed via a series of scripts.Create synthetic calibration data by runningpython scripts/generate_datasets.pyThen execute the gazerr exeriments with the following two commands:scripts/RUN_EXPERIMENTS.sh
scripts/RUN_BIAS_EXPERIMENTS.shFinally, analyse the results and generate the plots withscripts/ANALYSE_RESULTS.shDocumentationAdditional documentation to be made available onRead the DocsIf you usegazerrin your research please cite the following article@article{hawkins2022,
   author = {John Hawkins},
   year = {2022},
   title = {Estimating Gaze Duration Error from Eye Tracking Data},
   journal = {TBC}
}
Package: gazeru
gazeruニコニコ動画の音声ダウンローダです
マイリスト単位で登録して、指定されたディレクトリに保存しますinstallationffmpeg, swftools のインストール各ディストリのパッケージ管理ツールで入るとおもいますpip で gazeru をインストール$ pip install gazeruusageinit でニコニコ動画のログイン情報とダウンロード先のディレクトリを指定$ gazeru init niconico_user niconico_password download_directoryhttp://www.nicovideo.jp/mylist/6280579をダウンロードしたい場合 add で URL の末尾の数字を渡す$ gazeru add 6280579pull でダウンロードが開始$ gazeru pullその他$ gazeru -h