Saravanan666/pypi-org huggingface dataset:
Package: highlight-ranges
Highlight ranges of code inpygmentsoutput.This provides a filter that usessporanchors to determine which ranges of code to highlight in the pygmentized
output. You specify the ranges by creating anchors with metadata where the key
“highlight” istrue, e.g.:{"highlight":true}Quick startFirst, installhighlight-ranges. See the “Installation” section for details.Create a directory somewhere and create a file namedexample.pywith the
following contents:deffunc(x):y=x*4returnyNow initialize a spor repository and add an anchor that will highlight the
second line in the file:spor init
spor add example.py 17 9 5Finally, use thehighlight-rangesfilter to generate the pygmentized HTML
of the code:pygmentize -l python3 -f html -O full -F highlight-ranges:file=example.py example.py > example.htmlIf you openexample.htmlin a browser, you should see the second line in the
code emphasized.InstallationYou can install from PyPI withpip::pip install highlight-rangesOr you can install from source:pip install .StylingThe filter marks highlighted ranges of code with theGeneric.Emphtype. From
a CSS point of view, this means you can style the highlights via thegeclass.Filter optionsfileThe file being pygmentized.
Package: high-order-clustering
High order clustering algorithmsAuthor: Chen An, Cliburn Chan, Anru ZhangThis is the Python package for paper: "Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit" by Rungang Han, Yuetian Luo, Miaoyan Wang and Anru Zhang (2020).Citation:@article{han2020exact, title={Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit}, author={Han, Rungang and Luo, Yuetian and Wang, Miaoyan and Zhang, Anru R}, journal={arXiv preprint arXiv:2012.09996}, year={2020} }Instruction:prerequisite packages: numpy, scipy, pandas, sklearn.cluster.KMeans
Package: high-pass-filters
high_pass_filtersDescription.
The package high_pass_filters is used to:- Prewitt filter
- Sobel filter
- Image get, plotInstallationUse the package managerpipto install high_pass_filterspipinstallhigh_pass_filtersUsagefromhigh_pass_filters.fsobelimportsobelfromhigh_pass_filters.fprewittimportprewitfromhigh_pass_filters.imageimportget_image,plot_imageimage=get_image(path)plot_image(image)plot_image(sobel(image))plot_image(prewit(image))AuthorLeonardo CostaLicenseMIT
Package: highres
‘highres’ script to download hi-resolution images using google search by image featurePrerequisitesWeb browser (Chrome or Firefox or Edge or Safari)Webdriver for the browserInstallationInstall highres using pippip install highresUsageIf not available already, install a browser such or Chrome or Firefox or Safari or EdgeDownload webdriver for your Browser
1 Chromehttps://chromedriver.chromium.org/downloads/2 Firefoxhttps://github.com/mozilla/geckodriver/releases/3 Edgehttps://developer.microsoft.com/en-us/microsoft-edge/tools/webdriver/4 Safarihttps://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari/Add Webdrivers’s folder path to system pathplace your images in a folder, ex:C:\users\dr\Pictures\low-res-images\execute as followsC:\>highresC:\users\dr\Pictures\low-res-images
Package: highresnet
highresnet$ NII_FILE=`download_oasis`
$ deepgif $NII_FILEPyTorch implementation of HighRes3DNet fromLi et al. 2017,
*On the Compactness, Efficiency, and Representation of
3D Convolutional Networks: Brain Parcellation as a
Pretext Task*.All the information about how the weights were ported from NiftyNet can be found
inmy submission to the MICCAI Educational Challenge
2019.UsageCommand line interface(deepgif)$deepgift1_mri.nii.gzUsingcachefoundin/home/fernando/.cache/torch/hub/fepegar_highresnet_master100%|███████████████████████████████████████████|36/36[01:13<00:00,2.05s/it]PyTorch HubIf you are usingpytorch>=1.1.0, you can import the model
directly from this repository usingPyTorch Hub.>>>importtorch>>>repo='fepegar/highresnet'>>>model_name='highres3dnet'>>>print(torch.hub.help(repo,model_name))"HighRes3DNet by Li et al. 2017 for T1-MRI brain parcellation""pretrained (bool): load parameters from pretrained model">>>model=torch.hub.load(repo,model_name,pretrained=True)>>>Installation1. Create acondaenvironment (recommended)ENVNAME="gifenv"condacreate-n$ENVNAMEpython-ycondaactivate$ENVNAME2. Install PyTorch andhighresnetWithin thecondaenvironment:pipinstallpytorchhighresnetNow you can do>>>fromhighresnetimportHighRes3DNet>>>model=HighRes3DNet(in_channels=1,out_channels=160)>>>CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.7.1 (2019-11-05)First release on PyPI.
Package: highrise
highrise=============python-highrise is a way to wrap Highrise's API Pythonically.Installation-----------easy_install ElementTree https://github.com/chriscohoat/highrise/tarball/masterUsage-----# Import the highrise wrapper.from highrise.wrapper import Highrise#Prepare the Highrise interaction with the API token and person IDhighrise_api_token = 'TOKEN_HERE'person_highrise_id = 'PERSON_ID_HERE'hr = Highrise('https://examplecorporation.highrisehq.com',highrise_api_token)#Get list of notes or list of emailsnotes = hr.notes(person_highrise_id)emails = hr.emails(person_highrise_id)Interacting with notes (using Django)-----views.pydef highrise_interaction(request):#Prepare the Highrise interaction with the API token and person IDhighrise_api_token = 'TOKEN_HERE'person_highrise_id = 'PERSON_ID_HERE'hr = Highrise('https://examplecorporation.highrisehq.com',highrise_api_token)#Get list of notes or list of emailsnotes = hr.notes(person_highrise_id)return render_to_response('template.html',{'notes':notes}, context_instance=RequestContext(request))template.html{% extends "base.html" %}{% block content %}{% if not notes %}<h3>No notes available for this person.</h3>{% else %}{% for note in notes %}<h3>Note about {{ note.subject_name }}</h3><h4>Added at {{ note.created_at }}</h4><p>{{ note.body }}<br /><br /><a href="https://examplecorporation.highrisehq.com/notes/{{ note.note_id }}" target="blank">Read More...</a></p>{% endfor %}{% endif %}{% endblock %}Testing-------More to come with testing.
Package: highrl
Highrl: Multi-level Reinforcement Learning for Robotics NavigationHighrl is a library for training robots using RL under the scheme of multi-level RL. The library has numerous features from generating random environment, training agents to generate curriculum learning schemes, or train robots in pre-defined environments.The robot can explore a syntheticgymenvironment using lidar vision (either in flat or rings format). The robot is trained to reach the goal with obstacles in between to hinder its movement and simulate the real-world environment. A teacher (an RL agent) will be trained to synthesize the perfect curriculum learning for the robot, so that the robot will solve maps with certain difficulties in minimal time.The robot model is implemented with aCNNfor the feature extractor and anMLPfor both the value and policy networks. The teacher model is implemented with an LSTM network for the feature extractor and an MLP for value/policy network. The robot model is fed with the lidar data and outputs the velocity(vx, vy)of the robot. The teacher model is fed with data of the last session for the robot that the teacher is training, and outputs the configurations for the next environment to train the robot. At each step of the teacher, a new robot will be generated with probability of 10% and will be trained for a fixed number of steps. You can find the models insrc/highrl/policy.InstallationPlease note that the library isonly tested on Linux distributions. If you want to install it on Windows, you can useWSL.Use the package managerpipto install higrl library.pipinstallhighrlUsagehighrl-h# get available argumentsConfigurations--robot-config: path of configuration file of robot environment (relative path).--teacher-config: path of configuration file of teacher environment (relative path).--mode: choose train or test mode.--env-mode: choose whether to train the robot alone or in the presence of a teacher to generate curriculum learning.--render-each: the frequency of rendering for robot environment (integer).--output-path: relative path to output results for robot mode.--lidar-mode: mode to process lidar flat=1D, rings=2D.Examplehighrl--render-each=50--output-dir=~/DesktopContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: highspot
Highspot Python SDKA Python toolset for utilizing the Highspot APIInstallationThe package can be installed via pip using the syntax below.pipinstallhighspot--upgradeYou may also clone the repository and install from source using below.gitclonegit://github.com/jeffshurtliff/highspot.gitcdhighspot/
pythonsetup.pyinstallUsageThis section provides basic usage instructions for the package.Importing the packageRather than importing the base package, it is recommended that you import the primaryHighspotclass using the syntax
below.fromhighspotimportHighspotInitializing a Highspot object instanceThe primaryHighspotobject serves many purposes, the most important being to establish a connection to the
Highspot environment with which you intend to interact. As such, when initializing an instance of theHighspotobject, you will need to pass it the API credentials it will use to authenticate so that the connection can be
established.Passing the information directly into the objectThe API credentials can be passed directly into theHighspotobject when initializing it, as
demonstrated in the example below.hs=Highspot(username='a1b2c3d4e5',password='abc123DEF456')LicenseMIT LicenseChangelogRefer to thechangelogfor version change information.Reporting IssuesIssues can be reported within theGitHub repository.DonationsIf you would like to donate to this project then you can do so usingthis PayPal link.DisclaimerThis package is considered unofficial and is in no way endorsed or supported byHighspot.
Package: highteacli
HighTea cliA quick command line interface for the database for the Events Bases Library
from the Centre for Precision Studies in Particle Physics.See A Theory-Events-Based Library of LHC Processes:http://www.precision.hep.phy.cam.ac.uk/events-based-library/InstallationThe package is available on pip:pip install highteacliBasic Usage# General help
$ highteacli --help
usage: highteacli.py [-h] {lproc,lpdf,hist} ...

A command line interface for the high energy theory database.

optional arguments:
  -h, --help         show this help message and exit

commands:
  {lproc,lpdf,hist}
    lproc            List available processes
    lpdf             List available pdfs
    hist             make and histogram


 #Help to make an histogram
 $ python highteacli.py hist --help
usage: highteacli.py hist [-h] process file

positional arguments:
  process     process to compute the histogram for
  file        JSON file with the hisogram specification

optional arguments:
  -h, --help  show this help message and exit

 $ python highteacli.py hist tests test.json
 Processing request. The token is 866dd3c269f211eaa66d0242ac120003.
 Wait for the result here or run
 highteacli token 866dd3c269f211eaa66d0242ac120003
 Token completed
 {"mean": [[[[-2.0, -1.0]], 297.7068930126204], [[[-1.0, 0.0]], -165.29122920133463], [[[0.0, 1.0]], 202.0481155648153], [[[1.0, 2.0]], 301.92919831259667]], "std": [[[[-2.0, -1.0]], 225.39857447188203], [[[-1.0, 0.0]], 305.63359250045437], [[[0.0, 1.0]], 297.698081581902], [[[1.0, 2.0]], 240.26288960758953]]}

 # Put result in res.json
 $ highteacli hist tests test.json  > res.json
Package: hightech-amazeing
Requirements.Python 2.7 and 3.4+Installation & Usagepip installpip install hightech_amazeing(you may need to runpipwith root permission:sudo pip install amazeing-client)Then import the package:importhightech_amazeingGetting StartedPlease follow theinstallation procedureand then run the following:from__future__importprint_functionimporttimeimporthightech_amazeingfromhightech_amazeing.restimportApiExceptionfrompprintimportpprint# Configure API key authorization: User tokenconfiguration=hightech_amazeing.Configuration()configuration.api_key['Authorization']='YOUR_API_KEY'# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed# configuration.api_key_prefix['Authorization'] = 'Bearer'# create an instance of the API classapi_instance=hightech_amazeing.PlayerApi(hightech_amazeing.ApiClient(configuration))try:# Forget your current progress.api_response=api_instance.forget()pprint(api_response)exceptApiExceptionase:print("Exception when calling MazeApi->collect_score:%s\n"%e)Documentation for API EndpointsAll URIs are relative tohttps://localhostClassMethodHTTP requestDescriptionMazeApicollect_score**POST** /api/maze/collectScore💰 Collect score from your hand to your bag.MazeApiexit_mazePOST/api/maze/exit🚪 Exit the maze.MazeApimovePOST/api/maze/moveMove in the supplied direction.MazeApipossible_actions**GET** /api/maze/possibleActions👀 Get the list of possible actions, from the tile where you are standing.MazeApitag**POST** /api/maze/tagTag the current tile with the given (non-negative) numberMazesApiallGET/api/mazes/all📜 All the mazes that exist in the game.MazesApienterPOST/api/mazes/enter🌟 Enter a maze.PlayerApiforgetDELETE/api/player/forget🙈 Forget your current progress.PlayerApigetGET/api/player👤 Obtain information about yourself.PlayerApiregisterPOST/api/player/register📝 Register yourself here.Documentation For ModelsMazeInfoMoveActionPlayerInfoPossibleActionsAndCurrentScoreDocumentation For AuthorizationUser tokenType: API keyAPI key parameter name: AuthorizationLocation: HTTP headerAuthorHighTechICTamazeing-clientThis document describes the API of the A-maze-ing evening server. This API consists of three different endpoints, which
are detailed below. - To register yourself as a player use the Player endpoint. - To get information about the available
mazes and enter a specific maze use the Mazes endpoint. - To navigate a maze use the Maze endpoint.This Python package is automatically generated by theSwagger Codegenproject:API version: v2Package version: 1.0.1Build package: io.swagger.codegen.languages.PythonClientCodegen
Package: hightime
HightimeOverviewHightime allows for up to yoctosecond precision replacements for the datetime datetime
and timedelta types.InstallationHightime can be installed by cloning the master branch and then in a command
line in the directory of setup.py run:pipinstall--pre.Or by installing from PyPI using:pipinstallhightimeExamples>>>fromhightimeimportdatetime>>>fromhightimeimporttimedelta>>>high_noon=datetime(...year=1952,...month=7,...day=24,...hour=12,...minute=0,...second=30,...microsecond=0,...)>>>print(high_noon)1952-07-2412:00:30>>>hesitation=timedelta(microseconds=10,femtoseconds=203456)>>>print(hesitation)0:00:00.000010000203456>>>reaction=high_noon+hesitation>>>print(reaction)1952-07-2412:00:30.000010000203456>>>print(high_noon+high_noon)Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>TypeError:unsupportedoperandtype(s)for+:'datetime'and'datetime'>>>print(hesitation*2)0:00:00.000020000406912See thereadthedocs pagefor more detailed
examples and documentation.LicenseHightime is licensed under an MIT-style license.SeeLICENSEfor details about how hightime is licensed.Other incorporated projects may be licensed under different licenses. All
licenses allow for non-commercial and commercial use.
Package: hightop
Hightop.pyA nice shortcut for group count queries with DjangoVisit.objects.top('browser')# {#   'Chrome': 63,#   'Safari': 50,#   'Firefox': 34# }InstallationRun:pipinstallhightopGetting StartedAdd acustom managerto the models where you want to use it.fromhightopimportHightopQuerySetclassVisit(models.Model):objects=HightopQuerySet.as_manager()And query awayVisit.objects.top('browser')OptionsLimit the resultsVisit.objects.top('referring_domain',10)Include null valuesVisit.objects.top('search_keyword',null=True)Works with multiple groupsVisit.objects.top(['city','browser'])And expressionsVisit.objects.top(Lower('referring_domain'))And distinctVisit.objects.top('city',distinct='user_id')And min countVisit.objects.top('city',min=10)HistoryView thechangelogContributingEveryone is encouraged to help improve this project. Here are a few ways you can help:Report bugsFix bugs andsubmit pull requestsWrite, clarify, or fix documentationSuggest or add new featuresTo get started with development:gitclonehttps://github.com/ankane/hightop.py.gitcdhightop.py
pipinstall-rrequirements.txt
pytest
Package: hightouch
Python SDKHightouch exposes a REST API that lets users interact with resources like syncs, models, sources and destinations.SDK Installationpipinstallgit+<UNSET>.gitAuthenticationCreate anAPI keyFrom the API keys tab on the Settings page, select Add API key.Enter a descriptive Name for your key.Copy your API key and store it in a safe location. The key will only be displayed once.Click Create API key.SDK Example Usageimporthightouchfromhightouch.modelsimportoperations,shareds=hightouch.Hightouch()req=shared.DestinationCreate(configuration={"provident":'distinctio',"quibusdam":'unde',"nulla":'corrupti',},name='Ben Mueller',slug='iure',type='magnam',)res=s.create_destination(req,operations.CreateDestinationSecurity(bearer_auth="YOUR_BEARER_TOKEN_HERE",))ifres.create_destination_200_application_json_any_ofisnotNone:# handle responseAvailable Resources and OperationsHightouch SDKcreate_destination- Create Destinationcreate_model- Create Modelcreate_source- Create Sourcecreate_sync- Create Syncget_destination- Get Destinationget_model- Get Modelget_source- Get Sourceget_sync- Get Synclist_destination- List Destinationslist_model- List Modelslist_source- List Sourceslist_sync- List Syncslist_sync_runs- List Sync Runstrigger_run- Trigger Synctrigger_run_custom- Trigger Sync From ID or Slugupdate_destination- Update Destinationupdate_model- Update Modelupdate_source- Update Sourceupdate_sync- Update SyncSDK Generated bySpeakeasy
Package: highwayhash-cffi
HighwayHash CFFIHighwayHashis a pseudorandom
function (i.e. keyed hash function) optimized for speed on short
messages, designed by Jyrki Alakuijala, Bill Cox and Jan Wassenberg of
Google Research.This package provides tested, performantPython 3CFFI bindings to
theHighwayHash reference
implementation, including
support for all output lengths.InstallationYou can install this package usingpipor the includedsetup.pyscript:# Using pip
pip install highwayhash-cffi

# Using setup.py
python setup.py installUsagefromhighwayhashimport*# Demonstration key and datakey=b"\0"*32data=b"\0"*64# HighwayHash with a 64-bit output lengthoutput=highwayhash_64(key,data)# HighwayHash with a 128-bit output lengthoutput=highwayhash_128(key,data)# HighwayHash with a 256-bit output lengthoutput=highwayhash_256(key,data)LicenseBSD 3-Clause License

Copyright (c) 2018, Phil Demetriou
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: highyield
highyieldhighyieldis a Python package containing a collection of high-yield functions (grouped by modules) intended to simplify and automate specific tasks.This package requires Python >= 3.8 due to reliance onpandaspackage.InstallTo install, open the terminal and type in the following.For Windows and Linux:pip install highyieldFor macOS:python3 -m pip install highyieldDependenciesThe table below lists the dependencies for each module within this package:ModuleModule descriptionDependenciesbasicsAutomation tasksos, requestsconverterConvert from 1 file format to anotheros, pandas, docx2pdfcopyCopy & paste local filesos, shutil, pandascreateCreate folder(s) + structure from templateos, shutildsData ScienceseabornimportsImport packaged filesimportlib, pandaspdfWork with pdf filesos, pypdfrenameAutomate renaming filesos, rettsText to speechpyttsx3, pandas