Saravanan666/pypi-org huggingface dataset:
Package: hiutils
HIUTILSA collection of utilities for health informaticsThis is pre-alpha, anything might change, i.e. not ready for production use.Application areasText annotation / NLPOntologiesKnowledge graphsStatistics / summary dataInstallationpip install hiutilsAnnotationsOverviewWe assume that annotations are in the format:{
	document_id: {
		entities: {
			entitiy_id: {
				...properties...,
				cui : "concept_id",
				meta_anns: {
					'meta_ann_name': {'value': 'meta_ann_value',
					'confidence': confidence,
					'name': 'meta_ann_name'},
					...other meta...
				}
			}
		}

	}
}Basic processThe aim is to:keep only some annotations based on contextconvert from document->concepts to patient->conceptslimit to a subset of concepts relevant to our projectgroup some specific concepts into more general concepts e.g. specific subtypes of a disease -> any occurence of a that diseaseTo achieve these aims:1 filter by meta_anns:filtered = hi.annotations.filter_anns_meta(anns, {'Subject': ['Other']}, inplace=False)2 aggregate to patient levelagg = hi.annotations.aggregate_docs(filtered, item2doc=pt2doc)3+4 group relevant concepts and drop other conceptsgroups = {'Group 1': set(['286933003', '70582006']), 'My other group': set(['60046008'])}
merged = hi.merge_concepts(agg, groups, keep_empty=False)
Package: hivae
hivaeThis repository contains the Modular reimplemenation of the Heterogeneous Incomplete Variational Autoencoder model (HI-VAE)written by Alfredo Nazabal and co-workers.
The package provided here is to a large part baseed on this implementation, but adheres to a more pythonic way, omitting the need for supplying parameters via I/O , as well as aligning the modelling more with sklearn.
It was written by A. Karwath (a.karwath@bham.ac.uk) and the CardAIc group @ University of Birmingham.The details of the background of this model are included in thispaper.InstallThe package can be installed using pip:pip install hivaeExamplesOnce checked out, there are a number of example datasets (Wine, Adult and Diabetes), which can be found in ./hivae/examples/data. To evaluate the package, please use ./hivae/examples/hivae_general_example.py. The example should give a general explaination of how to use the package. More details will folow.Files descriptionhivae.py: The main script of the library, it needs to imported to work with the library and is connected to all the other scripts.loglik_ models_ missing_normalize.py: In this file, the different likelihood models for the different types of variables considered (real, positive, count, categorical and ordinal) are included.model_ HIVAE_inputDropout.py: Contains the HI-VAE with input dropout encoder model.model_ HIVAE_factorized.py: Contains the HI-VAE with factorized encoder modelGithub page with examples to follow:ContactFor questions this library --> Andreas Karwath:a.karwath@bham.ac.ukMore details regarding the hivae_general_example.py and use of the model (please note that this is under construction)main_directory: project folderdataset_name: the name of the database (required)types_list_d: a dictionary where the key is the dataset name, which contains a list with tuples that indicates the column names, types, the number of dimensions and classestypes:•	count: real values•	cat: categorical 0 or 1•	pos: positive real values•	ordinal: ordinal numbernumber of dimensions:•	number of possibilities in the categorical variables or 1 in numericalnumber of classes:•	number of options (same of number of dimensions for categorical variables)dataset_path: this is the folder of the csv filesresults_path: the output folder for results (currently not used)network_path: where the models are going to be storedtypes_list: the specific type for the dataset you are going to use
data_file: the full dataset
train_file/ test_file: if the dataset was already splittedtrain_data/test_data: pandas dataframesdim_y: the depth of the networkdim_s/dim_z: dimensions of the embedding
Package: hivae2
hivae2This repository contains a modular reimplemenation of the "Heterogeneous Incomplete Variational Autoencoder model (HI-VAE) written by Alfredo Nazabal (anazabal@turing.ac.uk) et al. .written in Python.The details of this model can be found are included in thispaper. Please cite it if you use this code/library for your own research.
This is an extenstion of implementations as easy to use Python library, upgraded for tensorflow2.ExamplesSee examples directory for usageFiles description*(outdated)HIVAE.py: The main script of the library, it needs to imported to work with the library and is connected to all the other scripts.loglik_ models_ missing_normalize.py: In this file, the different likelihood models for the different types of variables considered (real, positive, count, categorical and ordinal) are included.model_ HIVAE_inputDropout.py: Contains the HI-VAE with input dropout encoder model.model_ HIVAE_factorized.py: Contains the HI-VAE with factorized encoder modelContactFor questions regarding algorithm --> Alfredo Nazabal:anazabal@turing.ac.ukFor bugs or suggestion regarding this code --> Andreas Karwath:a.karwath@bham.ac.ukCommentsThis version required tf2. For apple silcone users, please follow :https://developer.apple.com/metal/tensorflow-plugin/Comments on general_example.py (might be outdated!)main_directory: where is the project folderdataset_name: the name of the database (if you want)types_list_d: a dictionary where the key is the dataset name, which contains a list with tuples that indicates the column names, types, the number of dimensions and classestypes:•	count: real values•	cat: categorical 0 or 1•	pos: positive real values•	ordinal: ordinal numbernumber of dimensions:•	number of possibilities in the categorical variables or 1 in numericalnumber of classes:•	number of options (same of number of dimensions for categorical variables)dataset_path: this is the folder of the csv filesresults_path: the output folder for resultsnetwork_path: where the models are going to be storedtypes_list: the specific type for the dataset you are going to use
data_file: the full dataset
train_file/ test_file: if the dataset was already splittedtrain_data/test_data: pandas dataframesdim_y: the depth of the networkdim_s/dim_z: dimensions of the embedding
Package: hive-builder
hive-builderhive-builder is a tool for building a site that operates docker containers across multiple servers. By using the cluster function of the docker swarm mode and the disk redundancy function of the drbd9, without using Kubernetes, you can build a site with a simple configuration.
The hive houses the swarm of microservice and manages them.A method of selecting leaders in server elections prevents split brain and does not require centralized control by a controllerThe cluster function of docker swarm mode ensures high availabilityYou can migrate a container between servers, even that have data volumes, by mirroring disks with drbd that have auto promotion functionSites can be built on IaaS such as AWS by launching the command only onceContainer contents can be described with ansible roleInitial data can be loaded when site is launchedHave a private repository server in the site and store container imagesHave Zabbix server in the site and monitor the operationYou can define 3 stages of private, staging, production in one inventory and build a site for each stageFor details, seethe document(Sorry, there are only Japanese ones now).
Package: hive-cli
Navigate your knowledge base like a busy bee! 🐝🔍✨🐝 Hive is a CLI tool for semantic searching of your knowledge base 📚. It allows you to easily search through your collection of files and directories, extracting meaningful information based on your prompts.
No more searching through haystacks—let Hive find the golden honey! 🍯🐝 Embrace the buzz and let your knowledge thrive! 🚀💡Getting StartedInstallationYou can install Hive using pip:pipinstallhive-cliInitializing HiveTo get started with Hive, initialize it in your project directory using the following command:hiveinitThis sets up Hive and creates the necessary configuration files to enable knowledge base searching.Adding Files or DirectoriesYou can add files or directories to your Hive knowledge base using the add command:hiveadd<file_or_dir_to_add>This command allows Hive to index and analyze the content of the specified files or directories, making them searchable within your knowledge base.Supported File TypesHive currently supports only Markdown and PDF files. Support for other file types is coming soon!Searching the Knowledge BaseTo perform a semantic search within your knowledge base, use the search command along with your prompt:hivesearch"your prompt"Hive will analyze your prompt and match it against the indexed content, providing you with the most relevant results based on semantic similarity.Example Search OutputHere's an example output of a search performed with Hive:hivesearch"are honey bees good?"╭─./data/the-problem-with-honey-bees.md──────────────────────────────────────────────────────────────────────────────╮
││
│Butthinkaboutthem,wemust.Iusedtobelievethathoneybeeswereagatewayspecies,andthatconcernover│
│theirhealthandprosperitywouldspilloverontonativebees,benefittingthem,too.Whilethismayhavehappened│
│insomecases,evidenceismountingthatmisguidedenthusiasmforhoneybeeshaslikelybeentothenativebees’│
│detriment.Beekeepingdoesn’tmakemefeelgood,anymore.Infact,quitetheopposite.│
││
╰─Matchscore:73%───────────────────────────────────────────────────────────────────────────────────────────────────╯The search output displays the matched file, along with the relevant text snippet and a match score indicating the similarity between the prompt and the content.🔍 Hive makes it easy to find the information you need, saving you time and effort!ContributingWe welcome contributions to Hive! Feel free to open issues and submit pull requests for any enhancements or bug fixes. Let's make Hive even better together! 🚀LicenseHive is licensed under the MIT License.🐝 Don't waste time searching, let Hive be your knowledge navigator! Start exploring your knowledge base effortlessly with Hive. Happy searching! 🚀✨
Package: hivecode
HivecodeHivecode is a versatile and comprehensive Python library, with a focus on efficiency and reusability, Hivecode empowers developers and data enthusiasts alike to streamline their projects. This library boasts a wide array of practical and innovative functions, ranging from seamless integration with Azure Databricks to implementing essential design patterns, data preprocessing, visualization, exploration, and more. Whether you’re looking to supercharge your data analysis or accelerate your Python projects, Hivecode is the go-to toolkit for unlocking the full potential of your endeavors.PackagesHivecore: A set of many vanilla python tools.Hiveadb: A set of tools to work in Azure Databricks.Hivesignal: A set of tools for signal analysis.InstallationTo install the library, you will need pip.pip install hivecodeNoteHivecode is constantly changing as new versions try to make it a more stable library. It is highly recommended to specify the version you want to import.RequirementsHivecode requires Python 3.8.0+ to run.DevelopmentRequirementsBuild is used to build the packages that will be deployed into pypi.pip install buildTwine is used to deploy packages to pypi.pip install twineSphinx is used to build the documentation.pip install SphinxDevelopYou can build the project by running the following console command.python -m buildYou can then use twine to upload the newest version to Pypi.py -m twine upload --skip-existing --repository pypi dist/* -u {User} -p {password}I personally recommend to cascade both commands using a pipe, like this.python -m build | py -m twine upload --skip-existing --repository pypi dist/* -u {User} -p {password}When working with documentations, you will need to run it like this..\doc\make.bat html
Package: hivedesktop
hivedesktopA pyqt5 based desktop app for the hive blockchainInstallpip3 install hivedesktopDevelopmentInstall python 3.6
Upgrade pip and install virtualenv (replace python by python3 or python.exe, depending on the installation and the system)python -m pip install pip --upgrade
python -m pip install virtualenvCreate a virtual environment:python -m virtualenv envActivate the virtual environment<path>\env\Scripts\activateInstall the packages:pip install --upgrade -r requirements/base.txtFor windowspip install pywin32is necessaryReleasing pypipython3 setup.py clean sdist bdist_wheel
python3 -m twine upload dist/*Create filespyuic5 ui\mainwindow.ui -o src\main\python\hivedesktop\ui_mainwindow.py
pyuic5 ui\options.ui -o src\main\python\hivedesktop\dialogs\ui_options.py
pyrcc5 src\main\hivedesktop.rc -o src\main\python\hivedesktop\hivedesktop_rc.pyRun the appfbs runFreezing the appfbs freezeBuild an installerfbs installer
Package: hiveengine
# hiveengine
Python tools for obtaining and processing hive engine tokens[![Build Status](https://travis-ci.org/holgern/hiveengine.svg?branch=master)](https://travis-ci.org/holgern/hiveengine)## Installation` pip install hiveengine `## Commands
Get the latest block of the sidechain` from hiveengine.api import Api api = Api()print(api.get_latest_block_info())`Get the block with the specified block number of the sidechain` from hiveengine.api import Api api = Api() print(api.get_block_info(1910)) `Retrieve the specified transaction info of the sidechain` from hiveengine.api import Api api = Api()print(api.get_transaction_info("e6c7f351b3743d1ed3d66eb9c6f2c102020aaa5d"))`Get the contract specified from the database` from hiveengine.api import Api api = Api()print(api.get_contract("tokens"))`Get an array of objects that match the query from the table of the specified contract` from hiveengine.api import Api api = Api()print(api.find("tokens","tokens"))`Get the object that matches the query from the table of the specified contract` from hiveengine.api import Api api = Api()print(api.find_one("tokens","tokens"))`Get the transaction history for an account and a token` from hiveengine.api import Api api = Api()print(api.get_history("holger80","FOODIE"))`## Token transfer` from beem import Steem from hiveengine.wallet import Wallet stm =Steem(keys=["5xx"])wallet =Wallet("test_user",steem_instance=stm)wallet.transfer("test1",1,"TST",memo="Thisis a test") `## Buy/Sell
### Create a buy order` from beem import Steem from hiveengine.market import Market stm =Steem(keys=["5xx"])m=Market(steem_instance=stm)m.buy("test_user",1, "TST", 9.99) `### Create a sell order` from beem import Steem from hiveengine.market import Market stm =Steem(keys=["5xx"])m=Market(steem_instance=stm)m.sell("test_user",1, "TST", 9.99) `### Cancel a buy order` from beem import Steem from hiveengine.market import Market stm =Steem(keys=["5xx"])m=Market(steem_instance=stm) open_buy_orders =m.get_buy_book("TST","test_user")m.cancel("test_user","buy",open_buy_orders[0]["_id"])`### Cancel a sell order` from beem import Steem from hiveengine.market import Market stm =Steem(keys=["5xx"])m=Market(steem_instance=stm) open_sell_orders =m.get_sell_book("TST","test_user")m.cancel("test_user","sell",open_sell_orders[0]["_id"])`### Deposit Steem` from beem import Steem from hiveengine.market import Market stm =Steem(keys=["5xx"])m=Market(steem_instance=stm)m.deposit("test_user",10) `### Withdrawal` from beem import Steem from hiveengine.market import Market stm =Steem(keys=["5xx"])m=Market(steem_instance=stm)m.withdraw("test_user",10) `
Package: hivehoney
hivehoneyExtract data from remote Hive to local Windows OS (without Hadoop client).The most difficult part was figuring out expect+pbrun.Because there are 2 interactive questions I had to pause after password.Mode expect+pbrun details are here:https://github.com/hive-scripts/hivehoney/blob/master/expect_pbrun_howto.mdData access path.Windows desktop->
               SSH->
                  Linux login->
                       pbrun service login->
                                           kinit
                                           beeline->
                                                   SQL->
                                                       save echo on WindowsRun it like this:set PROXY_HOST=your_bastion_host

set SERVICE_USER=you_func_user

set LINUX_USER=your_SOID

set LINUX_PWD=your_pwd

python hh.py --query_file=query.sqlquery.sqlselect * from gfocnnsg_work.pytest LIMIT  1000000;Result:TOTAL BYTES:    60000127

  Elaplsed: 79.637 s

  exit status:  0

  0

  []

  TOTAL Elaplsed: 99.060 sdata_dump.csvc:\tmp>dir data_dump.csv



  Directory of c:\tmp

  09/04/2018  12:53 PM        60,000,075 data_dump.csv

                 1 File(s)     60,000,075 bytes

                 0 Dir(s)     321,822,720 bytes free
Package: hive-kernel
Hive_kernelHive  Kernel for Jupyter中文介绍Installationstep1:pip install hive_kernelTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:pip install git+git://github.com/Hourout/hive_kernel.gitstep2:Add kernel to your jupyter:python -m hive_kernel.installALL DONE! 🎉🎉🎉Uninstallstep1:View and remove hive kerneljupyter kernelspec list
jupyter kernelspec remove hivestep2:uninstall hive kernel:pip uninstall hive-kernelALL DONE! 🎉🎉🎉Usingjupyter notebookstep1: you should set hive server (host and port)step2: write your hive sqlQuotekernel logohttps://en.wikipedia.org/wiki/Apache_Hive
Package: hive-metasploit
hive-metasploitDescriptionhive-metasploit is a python library for:Import data from Metasploit workspace to Hive projectExport data from Hive project to Metasploit workspacewith hive-metasploit you can import/export Metasploit data to/from Hive such as:hostsservicesvulnerabilitieslootsnotescredentialsPython versionsPython 3.6Python 3.7Python 3.8Python 3.9Dependenciesmarshmallowcoloramahive-librarylibmsfInstallinghive-metasploit can be installed withpip:pip3installhive-metasploitAlternatively, you can grab the latest source code fromgithub:gitclonehttps://github.com/hive-metasploit/hive-metasploit.gitcdhive-metasploit
python3setup.pyinstallImport$cat~/.hive/config.yaml
cookie:SESSIONID=82e50cd3-9d41-4e7e-8157-5559548f39ac
password:Password
proxy:http://127.0.0.1:8888
server:http://127.0.0.1:8080
username:user@mail.com
$cat~/.msf4/config[framework/database]default_db=local-https-data-service[framework/database/local-https-data-service]url=https://localhost:5443cert=/Users/vladimir/.msf4/msf-ws-cert.pemskip_verify=trueapi_token=cf2dbb7f9d1f92839a84f9c165ee9afef3dd3a3116bc99badf45be4ae5655168c9c2c3c58621b460

$hive-metasploit-hntest_project-mwtest_workspace-I[*]ImporteddatafromMetasploitworkspace:test_workspacetoHiveproject:test_project[+]Successfullyimportedhost:192.168.0.161port:135[+]Successfullyimportedhost:192.168.0.161port:139[+]Successfullyimportedhost:192.168.0.161port:445[+]Successfullyimportedhost:192.168.0.161port:1025[+]Successfullyimportedhost:192.168.0.161port:5000Export$cat~/.hive/config.yaml
cookie:SESSIONID=82e50cd3-9d41-4e7e-8157-5559548f39ac
password:Password
proxy:http://127.0.0.1:8888
server:http://127.0.0.1:8080
username:user@mail.com
$cat~/.msf4/config[framework/database]default_db=local-https-data-service[framework/database/local-https-data-service]url=https://localhost:5443cert=/Users/vladimir/.msf4/msf-ws-cert.pemskip_verify=trueapi_token=cf2dbb7f9d1f92839a84f9c165ee9afef3dd3a3116bc99badf45be4ae5655168c9c2c3c58621b460

$hive-metasploit-hntest_project-mwtest_workspace-E[*]ExporteddatafromHiveproject:test_projecttoMetasploitworkspace:test_workspace[+]Successfullyexportedhost:192.168.0.161[+]Successfullyexportedservice:135(tcp)forhost:192.168.0.161[+]Successfullyexportedservice:139(tcp)forhost:192.168.0.161[+]Successfullyexportedservice:445(tcp)forhost:192.168.0.161[+]Successfullyexportedservice:1025(tcp)forhost:192.168.0.161[+]Successfullyexportedservice:5000(tcp)forhost:192.168.0.161[+]Successfullyexportedvulnerability:SMBSigningIsNotRequiredforhost:192.168.0.161
Package: hive-metastore-client
Hive Metastore ClientA client for connecting and running DDLs onHiveMetastore usingThriftprotocol.SourceDownloadsPageInstallation CommandPyPiLinkpip install hive-metastore-clientBuild statusDevelopStableDocumentationSonarThis library supports Python version 3.7+.To check library main features you can checkHive Metastore Client's Documentation, which is hosted by Read the Docs.An example of how to use the library for running DDL commands in hive metastore:fromhive_metastore_client.buildersimportDatabaseBuilderfromhive_metastore_clientimportHiveMetastoreClientdatabase=DatabaseBuilder(name='new_db').build()withHiveMetastoreClient(HIVE_HOST,HIVE_PORT)ashive_metastore_client:hive_metastore_client.create_database(database)To learn more use cases in practice, seeHive Metastore Client examplesRequirements and InstallationHive Metastore Client depends onPython 3.7+Python Package Indexhosts reference to a pip-installable module of this library, using it is as straightforward as including it on your project's requirements.pipinstallhive-metastore-clientLicenseApache License 2.0ContributingAll contributions are welcome! Feel free to open Pull Requests. Check the development and contributingguidelinesdescribed inCONTRIBUTING.mdMade with :heart: by theData Engineeringteam fromQuintoAndar
Package: hivemind-bus-client
Hivemind Websocket ClientInstallpipinstallhivemind_bus_clientUsagefromtimeimportsleepfrommycroft_bus_clientimportMessagefromhivemind_bus_clientimportHiveMessageBusClientfromhivemind_bus_client.decoratorsimporton_escalate,\on_shared_bus,on_ping,on_broadcast,on_propagate,on_mycroft_message,\on_registry_opcode,on_third_party,on_cascade,on_handshake,on_hello,\on_rendezvous,on_hive_message,on_third_party,on_payloadkey="super_secret_access_key"crypto_key="ivf1NQSkQNogWYyr"bus=HiveMessageBusClient(key,crypto_key=crypto_key,ssl=False)bus.run_in_thread()@on_mycroft_message(payload_type="speak",bus=bus)defon_speak(msg):print(msg.data["utterance"])mycroft_msg=Message("recognizer_loop:utterance",{"utterances":["tell me a joke"]})bus.emit_mycroft(mycroft_msg)sleep(50)bus.close()
Package: hivemind-util
Hivemind utility functionsThese functions provide lightweight wrappers around the Hivemind API calls.They handle construction of HTTP requests, pagination of results, and the pairing up of output data with corresponding input data. Apart from that they do nothing complicated at all, and can be used as a template for constructing your own more complicated API calls.Version history can be viewed atHISTORY.md.Example usage:>>> from hivemindutil import hm
>>> hm.help('dataset')
- getDatasetRows(datasetId, baseUrl, apiKey, queries=[])
Returns Dataset Rows for a dataset in the workflows tab.  Queries can be supplied as a list of strings where each string defines a (1) key path, (2) logical comparison and (3)value (e.g. "metadata.timestamp=2021-01-01" or "data.path.to.my.key>=0").


- getDatasetRow(datasetId, baseUrl, apiKey, datasetRowId)
Returns a specified row of a dataset.


- deleteDatasetRows(datasetId, baseUrl, apiKey, datasetRowIds=[], allRows=False, raiseForStatus=True)
Deletes specified rows in a dataset.  If allRows is set to True, the values passed to datasetRowIds are ignored, and all dataset rows are identified and deleted.


- addDatasetRows(datasetId, baseUrl, apikey, rowsData=[])
Bulk add rows to a dataset.  The value of rowsData should be a list of dictionaries of the rows data.


- addDatasetRow(datasetId, baseUrl, apiKey, rowData={})
Add a single row to a dataset.  Value for rowData should be the dictionary of the row data object.

>>> r = addDatasetRow(1, 'https://studio.lambda.hvmd.io', 'API-XXXXXXXXXXXXXXXXXXXXXXXX', rowData={'foo': 'bar'})
>>> r
{
  'data': {
    'datasetRowId': 1,
    'datasetId': 1,
    'data': {'foo': 'bar'},
    'metadata': {'creator': {'id': 0, 'type': 'User'},
      'upstream': [],
      'timestamp': '2021-08-10T15:26:47.2433333Z',
      'sequenceId': 1,
      'version': 1
    }
  }
}