Saravanan666/pypi-org huggingface dataset:
Package: hiplot
HiPlot - High dimensional Interactive PlottingHiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.Try a demo now with sweep dataorupload your CSVorThere are several modes to HiPlot:As a web-server (if your data is a CSV for instance)In a jupyter notebook (to visualize python data), or inStreamlit appsIn CLI to render standalone HTMLpipinstall-Uhiplot# Or for conda users: conda install -c conda-forge hiplotIf you have a jupyter notebook, you can get started with something as simple as:importhiplotashipdata=[{'dropout':0.1,'lr':0.001,'loss':10.0,'optimizer':'SGD'},{'dropout':0.15,'lr':0.01,'loss':3.5,'optimizer':'Adam'},{'dropout':0.3,'lr':0.1,'loss':4.5,'optimizer':'Adam'}]hip.Experiment.from_iterable(data).display()See the live resultLinksBlog post:https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/Documentation:https://facebookresearch.github.io/hiplot/index.htmlPypi package:https://pypi.org/project/hiplot/Conda package:https://anaconda.org/conda-forge/hiplotNPM package:https://www.npmjs.com/package/hiplotExamples:https://github.com/facebookresearch/hiplot/tree/main/examplesCiting@misc{hiplot,author={Haziza, D. and Rapin, J. and Synnaeve, G.},title={{Hiplot, interactive high-dimensionality plots}},year={2020},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/facebookresearch/hiplot}},}CreditsInspired by and based on code fromKai Chang,Mike BostockandJason Davies.External contributors (please add your name when you submit your first pull request):louismartinGoldenCorgicallistachangLicenseHiPlot isMITlicensed, as found in theLICENSEfile.
Package: hiplot-master
HiPlot - High dimensional Interactive PlottingHiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.Try a demo now with sweep dataorupload your CSVorThere are several modes to HiPlot:As a web-server (if your data is a CSV for instance)In a jupyter notebook (to visualize python data), or inStreamlit appsIn CLI to render standalone HTMLpipinstall-Uhiplot# Or for conda users: conda install -c conda-forge hiplotIf you have a jupyter notebook, you can get started with something as simple as:importhiplotashipdata=[{'dropout':0.1,'lr':0.001,'loss':10.0,'optimizer':'SGD'},{'dropout':0.15,'lr':0.01,'loss':3.5,'optimizer':'Adam'},{'dropout':0.3,'lr':0.1,'loss':4.5,'optimizer':'Adam'}]hip.Experiment.from_iterable(data).display()See the live resultLinksBlog post:https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/Documentation:https://facebookresearch.github.io/hiplot/index.htmlPypi package:https://pypi.org/project/hiplot/Conda package:https://anaconda.org/conda-forge/hiplotNPM package:https://www.npmjs.com/package/hiplotExamples:https://github.com/facebookresearch/hiplot/tree/master/examplesCiting@misc{hiplot,author={Haziza, D. and Rapin, J. and Synnaeve, G.},title={{Hiplot, interactive high-dimensionality plots}},year={2020},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/facebookresearch/hiplot}},}CreditsInspired by and based on code fromKai Chang,Mike BostockandJason Davies.External contributors (please add your name when you submit your first pull request):louismartinGoldenCorgicallistachangLicenseHiPlot isMITlicensed, as found in theLICENSEfile.
Package: hiplot-mlflow
AHiPlotexperiment
fetcher plugin forMLflow, to help visualise your
tracked experiments.InstallationInstall this library withpipas:pipinstallhiplot_mlflowUsageYou can visualise experiments either in a Jupyter notebook or using HiPlot’s
built in server.NotebookIn a Jupyter notebook, usehiplot_mlflow.fetchto retrieve an MLflow
experiment by name, and display it with HiPlot:importhiplot_mlflowexperiments=hiplot_mlflow.fetch("my-lovely-experiment")experiments.display(force_full_width=True)You can also retrieve experiments by their MLflow experiment ID:experiment=hiplot_mlflow.fetch_by_id(0)By default, MLflow tags are not shown (only MLflow metrics and parameters are
shown). To display them, passinclude_tag=Trueto either of the fetch
functions, for example:experiment=hiplot_mlflow.fetch("my-lovely-experiment",include_tags=True)See more about what you can do with the returnedhiplot.Experimentvalues
in theHiPlot documentation.HiPlot ServerTo useHiPlot’s built in webserverwithhiplot-mlflow, you can start it up with the customexperiment fetcherimplemented by this package:hiplothiplot_mlflow.fetch_by_uriYou can then use themlflow://schema to access MLflow experiments in
HiPlot by either experiment or name, for example:mlflow://name/experiment-name
mlflow://id/0You can also addtags=yesas a query string parameter to include tags in
the output, for example:mlflow://name/experiment-name?tags=yesYou can also use themultiple experimentsloading syntax. Either the dictionary format (to define your own labels):multi://{
    "first-experiment": "mlflow://id/1",
    "another-experiment": "mlflow://name/another-experiment?tags=yes"
}or list format:multi://[
    "mlflow://id/1",
    "mlflow://name/another-experiment?tags=yes"
]
Package: hiplot-mm
HiPlot - High dimensional Interactive PlottingHiPlot is a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data using parallel plots and other graphical ways to represent information.Try a demo now with sweep dataorupload your CSVorThere are several modes to HiPlot:As a web-server (if your data is a CSV for instance)In a jupyter notebook (to visualize python data), or inStreamlit appsIn CLI to render standalone HTMLpipinstall-Uhiplot# Or for conda users: conda install -c conda-forge hiplotIf you have a jupyter notebook, you can get started with something as simple as:importhiplotashipdata=[{'dropout':0.1,'lr':0.001,'loss':10.0,'optimizer':'SGD'},{'dropout':0.15,'lr':0.01,'loss':3.5,'optimizer':'Adam'},{'dropout':0.3,'lr':0.1,'loss':4.5,'optimizer':'Adam'}]hip.Experiment.from_iterable(data).display()See the live resultLinksBlog post:https://ai.facebook.com/blog/hiplot-high-dimensional-interactive-plots-made-easy/Documentation:https://facebookresearch.github.io/hiplot/index.htmlPypi package:https://pypi.org/project/hiplot/Conda package:https://anaconda.org/conda-forge/hiplotNPM package:https://www.npmjs.com/package/hiplotExamples:https://github.com/facebookresearch/hiplot/tree/main/examplesCiting@misc{hiplot,author={Haziza, D. and Rapin, J. and Synnaeve, G.},title={{Hiplot, interactive high-dimensionality plots}},year={2020},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/facebookresearch/hiplot}},}CreditsInspired by and based on code fromKai Chang,Mike BostockandJason Davies.External contributors (please add your name when you submit your first pull request):louismartinGoldenCorgicallistachangLicenseHiPlot isMITlicensed, as found in theLICENSEfile.
Package: hipnotify
# hipnotify[![PyPI version](https://img.shields.io/pypi/v/hipnotify.svg)](https://pypi.python.org/pypi/hipnotify)[![PyPI downloads](https://img.shields.io/pypi/dm/hipnotify.svg)](https://pypi.python.org/pypi/hipnotify)[![Build Status](https://travis-ci.org/achiku/hipnotify.svg)](https://travis-ci.org/achiku/hipnotify)[![Requirements Status](https://requires.io/github/achiku/hipnotify/requirements.svg?branch=master)](https://requires.io/github/achiku/hipnotify/requirements/?branch=master)Deadly simple HipChat API V2 room notification library# Why createdHipChat official [third-party library web page](https://www.hipchat.com/docs/apiv2/libraries) introduces two sophisticated Python client libraries supporting almost all V2 APIs currently available. However, our usecase just needed HipChat V2 room notification API + Python3 compatible HipChat client, which can only send messages to the specified room if a room token is given, without needing an admin token. (Admin token could be pretty dangerous and certainly unnecessary in this case.)This library is designed to do simple thing simple with minimum sysadmin concern.# Usage```python# -*- coding: utf-8 -*-from hipnotify import RoomHIPCHAT_TOKEN = 'token'HIPCHAT_ROOM_ID = 'room_id'if __name__ == '__main__':room = Room(HIPCHAT_TOKEN, HIPCHAT_ROOM_ID)room.notify('hello, world!')```![](artwork/green-hello-world.png)```pythonroom.notify('Watch out!! Something is going wrong!!', color='red')```![](artwork/red-caution.png)```pythonroom.notify('Ha? Just <a href="https://google.com">google</a> it.', message_format='html')```![](artwork/green-html-format.png)
Package: hipnuc-usb-apiserver
HIPNUC USB APIServerInstallationClone &cdinto this repository then:pythonsetup.pyinstallOr download from PyPI:python-mpipinstallhipnuc-usb-apiserverGet StartedTo generate configuration from command line interaction run:python-mhipnuc_usb_apiserverconfigureTo launch the apiserver, run:python-mhipnuc_usb_apiserverapiserverTo use the gRPC api onlocalhost:8080, use this snippet:importgrpcimporthipnuc_usb_apiserver.grpc.imu_packet_pb2asimu_packet_pb2importhipnuc_usb_apiserver.grpc.imu_packet_pb2_grpcasimu_packet_pb2_grpcimporttimeimporttqdmdefrun():channel=grpc.insecure_channel('localhost:8080')stub=imu_packet_pb2_grpc.IMUPacketServiceStub(channel)response=stub.SetStatus(imu_packet_pb2.IMUSetStatusRequest(status=True))print("SetStatus client received: "+str(response))response=stub.GetPacket(imu_packet_pb2.IMUPacketRequest(timestamp=time.time_ns()))print("GetPacket client received: "+str(response))response=stub.GetPacketArray(imu_packet_pb2.IMUPacketArrayRequest(timestamp=time.time_ns()))print("GetPacketArray client received: "+str(response))response=stub.GetPacketStream(imu_packet_pb2.IMUPacketRequest(timestamp=time.time_ns()))print("GetPacketStream client received: "+str(response))try:withtqdm.tqdm()aspbar:whileTrue:# time.sleep(0.0005)data=next(response)pbar.set_description(str(data.yaw)+' - '+str(data.index))pbar.update(1)# print)exceptKeyboardInterruptase:response.cancel()response=stub.SetStatus(imu_packet_pb2.IMUSetStatusRequest(status=False))print("SetStatus client received: "+str(response))if__name__=='__main__':run()for custom port configuration, change thelocalhost:8080DeveloppingFor developping purpose, read this section.Build gRPCTo update gRPC defs, run:cdhipnuc_usb_apiserver/grpc
python-mgrpc_tools.protoc-I../../manifests/protos--python_out=.--pyi_out=.--grpc_python_out=.../../manifests/protos/imu_packet.proto
Package: hipochat
****************************************HIPOCHAT: Websocket chat server****************************************Supports notifications and keep the old messagesHipochat is an open-source websocket chat server, using RabbitMQ as message queue, Tornado as Backend Server andRedis for persistent data.Keeps the notifications count and also supports injections from external server directly to websocketSupports checking authentication with your own webserver.How it works======================================================First clone the project**git clone https://github.com/Hipo/hipochat.git**Then install the requirements**pip install -r requirements.txt**Make sure that you have redis-server and rabbitmq servers are runningSet your variables invars.py::PushNotificationURL = '<ENDPOINT RECEIVES YOUR PUSH NOTIFICATION SIGNAL AND SEND AN PN>'ProfileURL = '<ENDPOINT YOU CAN ASK IF USER IS AUTHENTICATED>'RABBIT_URL = '<RABBITMQ SERVER>'RABBIT_USERNAME = '<RABBITMQ USERNAME>'RABBIT_PASS = '<RABBITMQ PASSWORD>'REDIS_URL = '<REDIS_URL>'You can check the nginx reverse proxy detailsTechnical Detail===================================================Chat Server Endpoint---------------------Your backend server needs an EP that you should be able to create a chat room or receive the chat room tokensExample request::HEADER: Authentication: Token <Token>URLs: **/api/chat**Example response::{"chat": {"token": "e598c2a9-317a-4577-8cbd-7fd5f0cddbdf"}}If room is created you should receive '201 CREATED' MESSAGE else if exists '200 OK'200 OK means you already have this chat room and you should fetch the old messagesReceive the old messages-----------------------------------------You visit the url with parameters belowExample Request::HEADER -> Authorization: Token <token>HTTP GET **/talk/old/<chat_room_token>/**Response::HTTP 200 OK{oldy: [{timestamp: 1414768486.196569,message: "asdasda",author: "asdasda"},{timestamp: 1414770934.580683,message: "adsadsdasads",author: "assaddsa"},{timestamp: 1414772188.836509,message: "dsadasads",author: "dssdaasd"},{timestamp: 1414773038.058612,token: "ac9e8485-8120-4cdc-a5c7-2687a04f01b5",injected: {message: "hede",hodo: "hede"}}]}As an authenticated user you can post injections to CHAT server------------------------------------------------You can inject external messages from a Backend Server directly into WebsocketExample Request::HEADER -> Authorization: Token <token>HTTP POST **/talk/item/<chat_room_token>/**{   message: "foobar",user: "johndoe"}Start the Live Chat Server--------------------------------------------------------Chat Server works with websocket technologyAlso you will see the injections in chat dialogue when an injection comes directly to Chat ServerYou should pass the user authentication token as query parameterExample Request::WEBSOCKETws://server.url/talk/chat/<chat_token>/?token=<auth_token>NOTIFICATIONS COUNT-----------------------------------------------------You can receive the notifications count of chat rooms for authenticated userby sending a request like belowHTTP GET **/talk/notification/<chat_token>/?type=<type>**Got some of the code from https://github.com/haridas/RabbitChat
Package: hipopy
HIPOPy: UpROOT-like I/O Interface for CLAS12 HIPO FilesInstallationTo install from source:gitclonehttps://github.com/mfmceneaney/hipopy.gitThen add to following to your startup script:exportPYTHONPATH=$PYTHONPATH:/path/to/hipopyYou will also need to install the project dependencies:numpyawkwardhipopybind(All available with pip.)To install with pip:pipinstallhipopyGetting StartedCheck out the example scripts intutorials.  More functionality coming soon!DocumentationFull documentation available onRead the Docs!Contact:matthew.mceneaney@duke.edu
Package: hipopybind
HipopyBind : HIPO PyBind11 LibraryInstallationTo compile the library from source run the following:gitclone--recurse-submoduleshttps://github.com/mfmceneaney/hipopybind.gitcdhipopybind
cmake.
makeAnd add the following to your startup script:exportPYTHONPATH=$PYTHONPATH\:/path/to/hipopybindOr to install from PyPi run:pipinstallhipopybindDeveloper NoteFor updating submodules run the following:gitsubmoduleupdate--init--recursiveContact:matthew.mceneaney@duke.edu
Package: hippiepug
hippiepugSublinear-lookup blockchains and efficient key-value Merkle trees.Check out thedocumentation.This library provides implementations of two cryptographic data structures:Blockchains with log(n) sublinear traversal, implemented as high-integrity
deterministic skip-lists (skipchains). In this kind of blockchain verifying
that blockbextends blockadoes not require to download and process
all blocks betweenaandb, but only a logarithmic amount of them.Verifiable dictionary, implemented as a key-value Merkle tree that
guarantees unique resolution. A proof of inclusion of a key-value pair in
such a tree also proves that there does not exist another value for a given
key somewhere else in the tree.Both are meant to be used with a content-addressable storage. Each data
structure supports logarithmic queries, and logarithmic proofs of inclusion:Retrievals per lookupInclusion proof sizeAppendSkipchainO(log(n))O(log(n))O(1)Key-value Merkle treeO(log(n))O(log(n))Immutablewithnbeing the size of the dictionary, or the number of blocks in the
case of a chain.The theoretical details are in thepaper.Getting startedYou can install the library from PyPI:pipinstallhippiepugThen, the easiest way to run the tests is:pythonsetup.pytestBe sure to check out theusage guide.AcknowledgementsThe library is a reimplementation of G. Danezis’shippiehug(hence
the name).This work is funded by theNEXTLEAP projectwithin the European Union’s
Horizon 2020 Framework Programme for Research and Innovation (H2020-ICT-2015,
ICT-10-2015) under grant agreement 688722.The hippie pug logo kindly donated byM. Naiem.
Package: hippo2client
Hippo2clientInstallationSimple install this module via pip (pip for Python 2 is also supported)pip3 install --user hippo2clientUsageimport hippo2client

# shorten git id is perfect, better then tags, because tags
# may not available everywhere to identify a version always
e = hippo2client.MajorEntity('72a56cd05b')

# there is a concept of an alias, this can make the git id
# more obvisious (this can be the output of git describe --always
# --dirty
e.set_alias('v1.5')

e.add_markdown('001', '# Heading - First Level')
e.add_markdown('002', '## Sub Heading - First Level')
e.add_markdown('003', '[link to second level (0001)](0001/)')
e.add_file("graph.png", path_to_image)
e.add_markdown('100', '![graph](graph.png)')

# second level
e.add_markdown('0001/001', '# Heading - Second Level')
e.add_markdown('0001/002', '## Sub Heading - Second Level')
e.add_markdown('0001/003', '[link to third level (0001)](0001/)') # note here, this is already relativ to 0001!
e.add_markdown('0001/004', '[level up link (0001)](..)')
e.add_file("0001/graph.png", path_to_image)
e.add_markdown('0001/100', '![graph](graph.png)')

# third level
e.add_markdown('0001/0001/001', '# Heading - Third Level')
e.add_markdown('0001/0001/002', '## Sub Heading - Third Level')
e.add_markdown('0001/0001/003', '[link to fourt level (0001)](0001/)') # note here, this is already relativ to 0001/0001!
e.add_markdown('0001/0001/004', '[level up link (0001)](..)') # note here, this is already relativ to 0001/0001!
e.add_file("0001/0001/graph.png", path_to_image)
e.add_markdown('0001/0001/100', '![graph](graph.png)')

# Fourth level
e.add_markdown('0001/0001/0001/001', '# Heading - Fourth Level')
e.add_markdown('0001/0001/0001/002', '## Sub Heading - Fourth Level')
e.add_markdown('0001/0001/0001/003', '[link to Fifth level (0001)](0001/)') # note here, this is already relativ to 0001/0001!
e.add_markdown('0001/0001/0001/004', '[level up link (0001)](..)') # note here, this is already relativ to 0001/0001!
e.add_file("0001/0001/0001/graph.png", path_to_image)
e.add_markdown('0001/0001/0001/100', '![graph](graph.png)')

# Fifth level
e.add_markdown('0001/0001/0001/0001/001', '# Heading - Fifth Level')
e.add_markdown('0001/0001/0001/0001/002', '## Sub Heading - Fifth Level')
e.add_markdown('0001/0001/0001/0001/003', 'Should be enough ...')
e.add_file("0001/0001/0001/0001/graph.png", path_to_image)
e.add_markdown('0001/0001/0001/0001/100', '![graph](graph.png)')
e.set_test_status('0001/0001/0001/0001', "passed")


URL = "http://localhost:8080/"
TIMEOUT = 10
a = hippo2client.Agent(url=URL, timeout=TIMEOUT)
a.add(e)
a.upload()
Package: hippocampus
Hippocampusworking memory for your python codeHippocampus is a small library that persists function calls in a sqlite3 database.
It can be used through a(parameterized) decorator which caches calls and results.Usagefromhippocampusimportmemoize@memoize(remember="forever",db_path="mydb.sqlite3")defmy_expensive_function(arg,kwarg):pass# first call is a regular function callresult=my_expensive_function("arg",kwarg={"a":"dict"})# second call is a cached read from dbresult=my_expensive_function("arg",kwarg={"a":"dict"})Parametersmemoize()takes three parameters:remember: How long to cache the resultdb_path: Set path for the database file. If not set, the user's cache directry (same as for pip) is usedoverwrite: Debug option to force overwriting existing records. May be useful if your code changed.There is one (required) parameterrememberwhich is used to set expiry of cached
items. The default is to not expire (keep forever). Other values may be given in
human readable terms and are parsed bydateparser. This means you can use either
absolute (remember="2023-12-01") or relative (remember="two minutes") date
specifications.
Note that the parameter means you willneedto include parens on the decorator
even if you omitremember: Always use@memoize().LimitationsThe decorator is intentionally simple to remove complexity andonly accepts functions
with json-compatible args and outputs. You can use any json-compatible type for
arguments and keyword arguments, but the return value needs to be a json-compatible
dictionary. In addition to the regular allowed types (str, int, list, dict), you can also
provide date/time objects, which will be serizalized and deserialized by the orjson
library.Only straight return calls are supported; you cannot memoize a generator. This may
change in the future, but requires some db modeling considerations.Finally, values are currently never deleted, even if they expire. Your memoization
database may thus grow to be rather large. Expired calls are overwritten when invoked
again, thus calling your memoized function after the expiry (even with a different expiry
setting) refreshes the old record.
Package: hippocrates
HippocratesA Python library for giving and scoring various health based questionnaires.Currently the following are supported:Beck Depression InventoryGeneralized Anxiety Disorder 2Generalized Anxiety Disorder 7Patient Health Questionnaire 2Patient Health Questionnaire 9Rosenberg Self-Esteem ScaleMood - Generic scoring from 1 to 10 of current mood.More information about the above tests can be found inquestionnaires.InstallingPippip install hippocratesFrom Sourcegit clone https://github.com/chrishannam/hippocrates.git
cd hippocrates
python setup.py installUsing the QuestionnairesOnce installed, the questionnaires are available via thehippocratescommand:hippocrates QUESTIONNAIRE_NAMEe.g.hippocrates phq9Below is the list of the supported questionnaires:beck_depression_indexgad2gad7moodphq2phq9rosenberg_self_esteemOptions--hideStops results from being displayed once questionnaire is created, default isoff.--logLogs results to ~/.hippocrates/results.csv, default isoff.questionnaireSelected questionnaire to complete.ReportingUsing--logoutputs your results in a csv file stored locally in you
home directory called.hippocrates. A sample is listed below:2019-01-21 13:12:38,gad7,15
2019-02-21 13:17:38,phq9,14
2019-03-22 13:12:38,gad7,3hippocrates-resultsUse the commandhippocrates-resultsto create a localhtmlfile
calledhippocrates_results.html.This file will be opened in your browser to display your results as a
basic graph:
Package: hippo-diabetes
Details of this FolderThis is where the bulk of the Python code lives that does the modeling. This is what executes in the pipeline afterall of the data is processed.Each subfolder here is a separate module serving a specific purpose:Module NamePurposeRavenGetting DataEgretData CleaningOtterBuilding Care GapsHippoCore Model-BuildingMooseAnalytics and OutputsRhinoCPLIntegrationsWhaleUnit Testing
Package: hippogym
HippoGymHuman Input Parsing Platform for Openai GymPurpose:HippoGym is a python library for simplifying human-ai interaction research over the web.
The library provides a communicator that opens a websocket to pass environment information and recieve commands and actions from browser-based front-end.The library is designed to be customizable for diverse research applications. The framework is naive in that it makes no assumptions about how human or agents input will be used, it simply provides the mechanism to pass along and record this information.InstallationpipinstallhippogymExamplesLunar Lander with human agentCopy and run thelunar lander example.You should see:Then connect a client to the websocket using the host and port, you can use the irll frontend:https://beta.irll.net/?server=ws://localhost:5000You can now play with either the command pannel or the keyboard:Minigrid with human agentCopy and run theminigrid example.You should see:Then connect a client to the websocket using the host and port, you can use the irll frontend:https://beta.irll.net/?server=ws://localhost:5000You can now play with either the command pannel or the keyboard: