Saravanan666/pypi-org huggingface dataset:
Package: hivenpy
Hiven.pyWelcome to the unofficial hivenpy api wrapper. So far we only have self bots as bot account aren't made yet.
This is a work in progress project made by NexInfinite, CanCodes, and Xenen.How to useInstallationTo install Hiven.py typepipinstallhivenpyGetting your tokenMake a bot account for your hiven bot. Open up the console by pressing Command+Option+J (Mac) Control+Shift+J (Windows, Linux, Chrome OS).TypelocalStorage['hiven-auth']and hit enter.Your token is where the red lines are.SetupDownload the code and place it in your project. You will then need to addfromHiven.clientimportBot,eventsThis will import the bot and events. After this you will need to typeTOKEN="Your token from before"bot=Bot(TOKEN)You can also setdebug=Trueif you want debugs from the websocket andoutput=Trueif you want outputs
from each websocket message.Making your botNext you will need to setup the events and login to the bot. To do this please type:@events.eventdefon_message(ctx):ifctx.author.id!=bot.user.id:# Do stuffbot.login()Congratulations! You now have a self bot for hiven!ExamplesYou can see a list of all examples in the folder namesExamples.Please noteAt the moment we don't have a pip package set up as it's not at that stage yet but you can still make
self bots with this.We now have apip package! Download it to try everything out.
This is a work in progress project so everything is going to change soon. This is temporary so you can start making
and experimenting with new bots now.There is also no documentation as of yet but there will be in the future. Feel free to message me on hiven
@nexinfinite (I am in the hiven testers chat) to get some support. The house will be public soon, we are
waiting for that to be released.
Package: hivepepebot
HivePepeBotA script to find and react to !PEPE commands in comments on the Hive blockchain.Please note that this software is in early Beta stage, and that you need to know what you are doing to use it.InstallationFor Ubuntu and Debian install these packages:sudo apt-get install python3-pip build-essential libssl-dev python3-dev python3-setuptools python3-gmpy2Install Python PackagesInstall hivepepebot by (you may need to replace pip3 by pip):sudo pip3 install -U hivepepebot beem hiveengineConfigure And Run HivePepeBotFirst clone the Github repository to your home directory:cd ~
git clone https://github.com/flaxz/hivepepebotAfter that edit your comment templates using Nano, there are 4 comment templates.sudo apt install nano 
cd ~/hivepepebot/templates
ls
nano COMMENT-TEMPLATE-1-2-3-4Then edit your configuration file.cd ~/hivepepebot
nano hivepepebot.configCopy your configuration and comment templates to your working directory.cd ~/hivepepebot
sudo cp -R templates /usr/local/bin
sudo cp hivepepebot /usr/local/bin
sudo cp hivepepebot.config /usr/local/bin
sudo cp run-hivepepebot.sh /usr/local/binMake the startup scripts executable.cd /usr/local/bin
sudo chmod u+x hivepepebot
sudo chmod u+x run-hivepepebot.shCopy the Systemd config to it's directory.cd ~/hivepepebot
sudo cp hivepepebot.service /etc/systemd/systemReload Systemd and start the bot.sudo systemctl daemon-reload
sudo systemctl start hivepepebot.serviceGet status and error messages.sudo systemctl status hivepepebot.service
qStop the bot.sudo systemctl stop hivepepebot.serviceAs has been stated above this bot is in early Beta and bugs and issues are likely to occur.
Package: hiveplotlib
A plotting package for generating and visualizing static Hive Plots inmatplotlib.Installationhiveplotlibcan be installed viapypi:$ pip install hiveplotlibTo uninstall, run:$ pip uninstall hiveplotlibContributingFor more on contributing to the project, seeCONTRIBUTING.mdHow to Use and ExamplesFor more on how to use the software and examples, see thedocs.All the example notebooks are available for download asjupyternotebooks in the repository under theexamplesdirectory.If trying to run the example notebooks, note that these are maintained to run in the conda
environment in the repository, specified byhiveplot_env.yml.To install thiscondaenvironment and associatedjupyterkernel, clone the repository and run:$ cd <path/to/repository>
$ bash install.shMore on Hive PlotsFor more on Hive Plots, see ourIntroduction to Hive Plots.For additional resources, see:http://www.hiveplot.com/Krzywinski M, Birol I, Jones S, Marra M (2011). Hive Plots — Rational Approach to
Visualizing Networks. Briefings in Bioinformatics (early access 9 December 2011,
doi: 10.1093/bib/bbr069).AcknowledgementsWe'd like to thank Rodrigo Garcia-Herrera for his work onpyveplot, which we referenced
as a starting point for our structural design. We also translated some of his utility
methods for use in this repository.
Package: hive-progress-bar
hive-progress-barThis is a simple package to show progress when doing some computations on a collection. It shows the number of items iterated and a time estimate for the remaining items.It over-writes the same line that progress bar is on as the iteration continues.Installationpip install hive-progress-barUsageimport time
from hive_progress_bar import ProgressBar

items = range(100)

pb = ProgressBar("Doing stuff on collection", len(items))

for _ in items:
    time.sleep(1)
    pb.next()Example Output During ProcessingDoing stuff on collection: [####                               ]   0h  1m 21s remaining | 12 / 100 ProcessedAfter FinishingDoing stuff on collection: [###################################]   0h  0m  0s remaining | 100 / 100 Processed

Time Taken:   0h  1m 40s
Package: hive-py
Hive is designed to simplify distributed systems that communicate over messages published to topics. It is designed for
robotics/cloud systems, to be simple to use and flexible. The basic worker structure is modelled after ROS.Example of connecting to MQTT running locally:fromhive.mqttimportHiveMqttfromhiveimportHiveWorkerbee=HiveWorker(host='localhost')publisher=bee.publisher("foo",dict)bee.subscribe("foo",dict,lambdamsg:print(msg))publisher.publish({"id":"test","x":1.5,"list_foo":[1,2,3]})bee.spin()
Package: hiveqlKernel
# HiveQL Kernel### RequirementsIf you are going to connect using kerberos:` sudoapt-getinstallpython3-devlibsasl2-devlibsasl2-2libsasl2-modules-gssapi-mit`### InstallationTo install the kernel:` pip install--upgradehiveqlKernel jupyter hiveql install--user`### Connection configurationTwo methods are available to connect to a Hive server:Directly inside the notebookUsing a configuration fileIf the configuration file is present, everytime you run a new HiveQL kernel it uses it, else you must configure your connection inside the notebook. The configuration in the notebook overwrites the one in the configuration file if present.#### Configure directly in the notebook cellsInside a Notebook cell, copy&paste this, change the configuration to match your needs, and run it.` $$url=hive://<kerberos-username>@<hive-host>:<hive-port>/<db-name>$$connect_args={"auth":"KERBEROS", "kerberos_service_name": "hive", "configuration": {"tez.queue.name":"myqueue"}}$$ pool_size=5 $$ max_overflow=10 `These args are passed to sqlalchemy, who registered pyHive as the ‘hive’ SQL back-end.
See [github.com/dropbox/PyHive](https://github.com/dropbox/PyHive/#sqlalchemy).#### Configure using a configuration fileThe HiveQL kernel is looking for the configuration file at~/.hiveql_kernel.confby default. You can specify another path usingHIVE_KERNEL_CONF_FILE.The contents must be like this (in json format):` { "url":"hive://<kerberos-username>@<hive-host>:<hive-port>/<db-name>","connect_args" : { "auth": "KERBEROS","kerberos_service_name":"hive","configuration": {"tez.queue.name":"myqueue"}},"pool_size": 5, "max_overflow": 10, "default_limit": 20, "display_mode": "be" } `### UsageInside a HiveQL kernel you can type HiveQL directly in the cells and it displays a HTML table with the results.You also have other options, like changing the default display limit (=20) like this :` $$ default_limit=50 `Some hive functions are extended. They allow to filter with some patterns.` SHOW TABLES <pattern> SHOW DATABASES <pattern> `### Run tests` python-mpytest `Have fun!
Package: hiver
What?Packing hive thrift client in more Pythonic way.Why?Hive thrift libraries taken from CDH4 distribution and then mucked around to make
the imports work.How?In codeimport hiver
client = hiver.connect(host, port)
client.execute('SHOW TABLES')
rows = client.fetchAll()Command linePoor mans hive clientpython -m hiver [script]Who?https://bitbucket.org/tebeka/hive
Package: hiverunner
Hive Runner is a python script that pulls saved queries from Beeswax, runs the
queries on Hive, and stores the results in Memcache.Using Hive RunnerRequirementsPython 2.7Cloudera Beeswax - Beeswax must be using a MySQL Database for storage.HiveServer - You must be running HiveServer version 1. Note that Cloudera’s
Hadoop distribution only ships with version 2. You can easily install version
1 using Cloudera’s package repositories.Memcached - You must have Memcached running somewhere.Pip - Pip is used for Python package dependency.InstallationOptionally, create a VirtualEnv:virtualenvenvironment-nameOptionally, use your VirtualEnv:sourceenvironment-name/bin/activateInstall Hive Runner via pip:pip install hiverunnerUsageHive Runner has flexible parameters. Available options can be seen by runninghiverunner--help. The most important parameters to include when running
Hive Runner from the command line are connection settings.For example, to run all queries in Beeswax prepended with_hourlyand
caching the results in memcache:hiverunner --hourly \
--mysql-host mysql01.example.com \
--mysql-database beeswax \
--mysql-user hue \
--mysql-password secret \
--hive-host hive01.example.com \
--memcache-host cache01.example.comYou can run the same command for all queries prepended with_weeklysimply
by changing thehourlyparameter toweekly:hiverunner --weekly \
--mysql-host mysql01.example.com \
--mysql-database beeswax \
--mysql-user hue \
--mysql-password secret \
--hive-host hive01.example.com \
--memcache-host cache01.example.comIf you find that you need to run custom named queries or only a single query thecustomparameter makes this easy. Simply provide the name of the query that
must be run.For example, to run a single query regardless of the prepended time-focused
demarcation:hiverunner --custom _daily_custom_query \
--mysql-host mysql01.example.com \
--mysql-database beeswax \
--mysql-user hue \
--mysql-password secret \
--hive-host hive01.example.com \
--memcache-host cache01.example.comThis format makes it easy to schedule cron jobs.More InformationHive Runner is open source software and available athttps://github.com/bellycard/hiverunner. Bug reports, feature requests, and
contributions are welcome!ContributorsAJ Self <aj@bellycard.com>Kevin Reedy <kevin@bellycard.com>LicenseApache License, Version 2.0http://www.apache.org/licenses/LICENSE-2.0
Package: hive-scrutineer
scrutineerHive Post performance and quality analytics.Installation$ pip install hive-scrutineerSupported Python versions 3.10+.Blockchain SupportLimited support to Hive Blockchain HF27.Basic usagefromscrutineerimportScrutineeranalyzer=Scrutineer()analyzer.set_weights()analysis=analyzer.analyze("author","post-permlink")CustomizationsimportjsonfromnektarimportWagglefromscrutineerimportScrutineerhive=Waggle("username")analyzer=Scrutineer(minimum_score=10,max_emojis=0,deep=True,full=False)analyzer.set_weights(title=5,body=6,emojis=4,images=2,tagging=3,tags=1)forbloginhive.blogs(limit=5)analysis=analyzer.analyze(blog)print(json.dumps(analysis,indent=2))KeywordsimportjsonfromnektarimportWagglefromscrutineerimportget_keywords,get_bigramshive=Waggle("username")forbloginhive.blogs(limit=5)keywords=get_keywords(blog["body"])print("\nget_keywords"+json.dumps(keywords))keywords=get_bigrams(blog["body"])print("\nget_bigrams:"+json.dumps(keywords))PerformanceIn version1.3.0, we've migrated tolangdetectto speed upScrutineer.analyze()by more than 300x versus version1.2.*!MIN: 0.02782490011304617
AVG: 0.13722777900053187
MAX: 4.033556599984877We've also seen a 70% import speed increase, in the said profiling.
Package: hive-udf
hive-udfHive UDF (user-defined functions) in Python.This package provides a single functionmake_udffor
working with Hive UDF written in Python.Some test code is included, along with several example UDF modules, which are used in the tests. The user is recommended to run these tests in their Hive setup to confirm that things work as expected.StatusProduction ready and stable (hardly needs any changes).Quick startInstallhive-udfviapip:$ pip install hive-udfSuppose you have a Hive UDF as Python modulemypackage.udfs.udf,
then insert this UDF into your HiveQL statement as followsfrom hive_udf import make_udf
import mypackage.udfs.udf

s = make_udf(mypackage.udfs.udf)

sql = f"""
    SELECT
        TRANSFORM ( ...input_columns... )
        USING '{s}'
        AS (...output_columns...)
    FROM {db_name}.{table_name}
    WHERE ...Then usesqlin your Hive client Python code. Some Hive client packages exist, includingPyHive,pyodbc,turbodbc, andimpyla.Please see the source code for documentation.This blog postdescribes how the code arrived at the current shape after addressing multiple very tricky problems. If you want to understand the code, please read this post.
Package: hive_utils
Utilities for accessing and using hive with python.InstallingYou can installhive_utilsthroughpiporeasy-install:pip install hive_utilsOr you can download thelatest development version, which may
contain new features.Using hive_utilshive_utilsallows you to access hive with an API that is similar to
MySQL-python:query = """
    SELECT country, count(1) AS cnt
    FROM User
    GROUP BY country
"""
hive_client = hive_utils.HiveClient(
    server=config['HOST'],
    port=config['PORT'],
    db=config['NAME'],
)
for row in hive_client.execute(query):
    print '%s: %s' % (row['country'], row['cnt'])Licensehive_utilsis copyright 2013 Eventbrite and Contributors, and is made
available under BSD-style license; see LICENSE for details.News0.0.1Relase date: September 5th, 2013Creation.
Package: hiveviewer
HiveViewerHiveViewer is a comprehensive Python toolkit tailored for large-scale data analysis. It offers functionalities for data loading, processing, evaluation metrics, sampling, and visualization. Uniquely, HiveViewer features multi-objective Bayesian optimization, supporting various aggregation formulas and calculators. This flexibility in multi-objective settings positions HiveViewer as a valuable tool for data scientists and researchers.InstallationYou can install HiveViewer using pip:pipinstall--upgradehiveviewerContributingWe welcome contributions! If you're interested in enhancing the features of HiveViewer, please create a pull request.LicenseHiveViewer is licensed under the MIT License.AcknowledgmentsWe appreciate the community for your support and contributions. Thank you!
Package: hivex
HiveXReal-World Multi-Agent Reinforcement Learning problems, potentially yielding a high positive impact on society when solved.AboutThe motivation of the HiveX suite is to provide advanced  reinforcement learning benchmarking environments with an emphasis on: (1)real-worldscenarios, (2)multi-agentsystems, (3) investigating problems and solutions withhigh impact on society, (4)cooperation and communicationmechanisms.Available EnvironmentsThumbnailTitleDomainPaperEnv NameWindfarm Orientation OptimisationDistributed Energy GridsLink(Neurips'21)"WindFarm"Wildfire-ManagementResource DistributionCatastrophe ManagementLink(ICLR'22)"Wildfire"InstallationManual install (Windows)The installation steps are
as follows (seeinstall.shfor an example installation script):Create and activate a virtual environment, e.g.:condacreate-nhivexpython=3.10
condaactivatehivexInstall pip and pip-tools:condainstallpip
pipinstallpip-tools
pipinstallpathlibInstall HiveX:gitclone-bmainhttps://github.com/philippds/HiveXcdHiveX
pipinstall.Test the HiveX installation:pipinstallpytest
pytesthivexExample UsageTraining agents withStable-Baselines3(Environment Interface | Learning Framework)cd<hivex_root>
pipinstall-e.[stable-baselines3]Stable-Baselines3 VecEnv | Stable-Baselines3cd<hivex_root>/examples/stable_baselines3
pythonsb3_train.pyGym| Stable-Baselines3cd<hivex_root>/examples/gym
pythongym_train.pySuperSuit| Stable-Baselines3cd<hivex_root>/examples/super_suit
pythonsuper_suit_train.pyTraining agents withML-Agents(Environment Interface | Learning Framework)UnityEnvironment | ML-Agentscd<hivex_root>/examples/ml_agents
pythonmlagents_train.pyTraining agents withRLLib(Linux Only) (Environment Interface | Learning Framework)cd<hivex_root>
pipinstall-e.[rllib]pipinstall-e.[pettingzoo]PettingZoo ParallelEnv (AEC)| RLLibcd<hivex_root>/examples/pettingzoo
pythonpettingzoo_rllib_train.pyDocumentationFull documentation is availablehereCiting HiveXIf you use HiveX in your work, please cite:@inproceedings{siedler2022hivex,title={},author={Philipp D. Siedler},year={2022},journal={},organization={}}
Package: hiyobot
Hiyobot一个简单hack.chat机器人框架。If your native language is not Chinese, please jumphere.如何安装？在你的控制台里面输入pip3 install hiyobot --upgrade，如果你使用镜像源，可能无法获取到最新的构建，需要加上-i https://pypi.org/simple的参数。输出应该是这样的：Collecting hiyobot
  Downloading hiyobot-<version>-py3-none-any.whl (8.3 kB)
Requirement already satisfied: websocket-client in c:\users\administrator\appdata\local\programs\python\python310\lib\site-packages (from hiyobot) (1.3.2)
Installing collected packages: hiyobot
Successfully installed hiyobot-<version>开发文档HackChat注释HiyoBot PYPI版本取决于Hack.chat子集版本。HiyoBot子集使用： 如在Hack.chat编写机器人:from hiyobot import hackchat as hcbot, 在ZhangChat(chat.zhangsoft.eu.org or chat.zhangsoft.cf)编写机器人:from hiyobot import zhangchat as zhcbot目前支持的聊天室： Hack.chat(hackchat),ZhangChat(zhangchat)如其他子集使用方式与Hack.chat集不同，即开设子文档。（暂无）我们会始终欢迎您的PR/Issues！
Package: hiyori
Hiyori is an http client for asyncio.Install$pipinstall-UhiyoriSource Codehiyoriis open sourced under Apache License 2.0 and its source code is hosted onGitHub.LicenseCopyright 2021 Kaede HoshikawaLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: hizashi-utils
A set of command line helper utilities for managing Django Hizashi project
environment.About the projectDjango Hizashi is a Django project environment based on iterating personal
best practices. Management commands recreate familiar project directory
structure from preparedprojectandapplicationtemplates.Utilities are context aware, so as long as you are in a subdirectory of the
main Django Hizashi project folder, script utilities will work on best effort
basis.Quick startcreate and initialize a new Python virtual environmentinstall hizashi-utils:pip installhizashi-utilsinitialize project:hizashi.py initproject my_new_projectchange directory to that project:cd my_new_projectinitialize an application:hizashi.py initapp my_new_appProject level utilitiesinitprojectTo initialize a new project execute:hizashi.py initproject my_new_project_nameOptional parameters:template- path or url for a Django project template, defaults tohttps://github.com/dodobas/hizashi-project-template/archive/master.zipmakedocsTo build Sphinx docs execute:hizashi.py makedocsOptional parameters:type- documentation type, defaults tohtml, mimics sphinx Makefilepublish- switch to activate the Publish server, a documentation HTTP serverhost- Publish server host, defaults to ‘127.0.0.1’port- Publish server port, defaults to ‘54299’devserverStarts Django development server, using developer specific settings module or
fallbacks to the default development settings modulecore.settings.dev.To start the server execute:hizashi.py devserverOptional parameters:settings- override automatically detected settings filecollectstaticRunscollectstaticDjango management comamnd, using developer specific
settings module or fallbacks to the default development settings modulecore.settings.dev.To start the server execute:hizashi.py collectstaticOptional parameters:settings- override automatically detected settings fileApplication level utilitiesinitappTo initialize a new application execute:hizashi.py initapp my_new_application_nameOptional parameters:template- path or url for a Django application template, defaults tohttps://github.com/dodobas/hizashi-application-template/archive/master.zip
Package: hizzard
Easy enums for Python.>>>RGB=StringEnum(['Red','Green','Blue'])>>>RGB.Red'Red'Contents1Installation2Features3Use4Contributing4.1Installing for development4.2Running the tests4.3Adding documentation5Credits1InstallationInstallezenumwith:pipinstallezenum2FeaturesPure Python.Compatible with Python 3.5+.Easy creation of usefull enum objects.String enums with unified value and name attributes.3UseStringEnumEasilly get a string enum from a string list.4ContributingPackage author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help.4.1Installing for developmentClone:gitclonegit@github.com:shaypal5/ezenum.gitInstall in development mode with test dependencies:cdezenumpipinstall-e".[test]"4.2Running the testsTo run the tests, use:python-mpytest--cov=ezenum4.3Adding documentationThis project is documented using thenumpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings (in my personal opinion, of course). When documenting code you add to this project, please followthese conventions.5CreditsCreated by Shay Palachy (shay.palachy@gmail.com).