Saravanan666/pypi-org huggingface dataset:
Package: hopper
HopperBased on theemailtrailproject, hopper is a python 2 & 3 package to track received from header hops.Get structured information about each hop - Hostnames, Protocol used, Timestamp, and Delay.Table of ContentsUsageCaveatsContributingMiscellaneousUsageWe can analyse an email source or raw headersemail="""Delivered-To: money@capitalism.comReceived: by 10.129.52.209 with SMTP id b200csp1430876ywa;Tue, 10 Oct 2017 01:17:02 -0700 (PDT)X-Received: by 10.31.153.20 with SMTP id b20mr6116862vke.110.1507623422746;Tue, 10 Oct 2017 01:17:02 -0700 (PDT)Received: from mail-sor-f65.google.com (mail-sor-f65.google.com. [209.85.220.65])by mx.google.com with SMTPS id b31sor1345013uaa.124.2017.10.10.01.17.02for <money@capitalism.com>(Google Transport Security);Tue, 10 Oct 2017 01:17:02 -0700 (PDT)Received-SPF: pass (google.com: domain of bags@test_email.ua.edu designates 209.85.220.65 as permitted sender) client-ip=209.85.220.65;X-Received: by 10.176.85.196 with SMTP id w4mr6874179uaa.75.1507623422198; Tue, 10 Oct 2017 01:17:02 -0700 (PDT)MIME-Version: 1.0Received: by 10.103.79.86 with HTTP; Tue, 10 Oct 2017 01:17:01 -0700 (PDT)From: Mr. Money Bags <bags@moneyrules.com>Date: Tue, 10 Oct 2017 01:17:01 -0700Subject:To: money@capitalism.com;Content-Type: text/plain; charset="UTF-8"Bcc: satan@wallstreet.comA business opportunity awaits"""Lets analyse itfromhopperimportHopperHopper.analyse(email){'To':u'money@capitalism.com;','From':u'Mr. Money Bags <bags@moneyrules.com>','Bcc':u'satan@wallstreet.com','Cc':u'None','total_delay':1,'trail':[{'delay':0,'from':'','protocol':'HTTP','receivedBy':'10.103.79.86','timestamp':1507623421},{'delay':1,'from':'mail-sor-f65.google.com','protocol':'SMTPS','receivedBy':'mx.google.com','timestamp':1507623422},{'delay':0,'from':'','protocol':'SMTP','receivedBy':'10.129.52.209','timestamp':1507623422}]}The analyse function returns a python dictionary.The trail shows the email hops sorted in chronological order. Each intermediary email server adds aReceivedheader to the mail, from which the module parses the following information:protocol: e.g HTTP, SMTP etc.from: The name the sending computer gave for itselfreceivedBy: The receiving computers nametimestamp: Unix epochAn empty string value is set for fields which couldn't be determined.delay: The delay (in seconds) is computed by taking the difference of two consecutive hops. In above example there was a delay of1 secfrom10.103.79.86tomx.google.comCaveatsSometimes during delay calculation the timestamp difference may be negative.It's not possible for a server to recieve the email before previous one,It means that either one or both of the servers clocks are off.We assume a delay of0for this hop.Contributingemailtrail usespipenvfor managing virtual env and package versions.Fork the repo and clone it.In project root:pipenv install --dev --two. This installs packages required for testing and lintingJump into your virutal env:pipenv shellRunning tests:pytestIf you want to understand the code, read the test cases first.Make your changes -> Pass the tests -> Push to your branch -> Create pull request -> Profit ??
Package: hoppMCMC
An adaptive basin-hopping Markov-chain Monte Carlo algorithm for Bayesian optimisationThis is the python (v3.7) implementation of the hoppMCMC algorithm aiming to identify and sample from the high-probability regions of a posterior distribution. The algorithm combines three strategies: (i) parallel MCMC, (ii) adaptive Gibbs sampling and (iii) simulated annealing. Overall, hoppMCMC resembles the basin-hopping algorithm implemented in the optimize module of scipy, but it is developed for a wide range of modelling approaches including stochastic models with or without time-delay.ContentsPrerequisitesLinux installation1) PrerequisitesThe hoppMCMC algorithm requires the following packages, which are not included in this package:numpy
scipy
mpi4py (MPI parallelisation)The mpi4py package is required for parallelisation; however, it can be omitted.2) Linux installationEasy way:If you have pip installed, you can use the following command to download and install the package.pip install hoppMCMCAlternatively, you can download the source code from PyPI and run pip on the latest version xxx.pip install hoppMCMC-xxx.tar.gzHard way:If pip is not available, you can unpack the package contents and perform a manual install.tar -xvzf hoppMCMC-xxx.tar.gz
cd hoppMCMC-xxx
python setup.py installThis will install the package in the site-packages directory of your python distribution. If you do not have root privileges or you wish to install to a different directory, you can use the –prefix argument.python setup.py install –prefix=<dir>In this case, please make sure that <dir> is in your PYTHONPATH, or you can add it with the following command.In bash shell:export PYTHONPATH=<dir>:$PYTHONPATHIn c shell:setenv PYTHONPATH <dir>:$PYTHONPATHCredits‘modern-package-template’ -http://pypi.python.org/pypi/modern-package-templateNews1.1Release date: 13-Sep-2018Fixed a bug in reading output (Python 3)1.0Release date: 30-Jul-2018Compatible with Python 30.6UNRELEASEDPrint out the covariates in addition to the parameters0.5Release date: 14-Feb-2017Minor improvement on pulsevarUpdate0.4Release date: 14-Oct-2015Fixed an issue with default parameters0.3Release date: 09-Oct-2015This version includes an improvement in compareAUCs0.2Release date: 28-Sep-2015This version includes a documentation and examples0.1Release date: 28-Sep-2015Initial commit
Package: hoppr
What is Hoppr?Hoppr is a Python plugin-based framework for collecting, processing, and bundling your software supply chain.
Feed Hoppr yourCycloneDX-spec SBOMs(Software Bill of Materials) and receive enhanced SBOMs and component bundles in return!  Those are just the basics, learn more at our:Documentation:https://hoppr.devSource Code:https://gitlab.com/hoppr/hopprGetting StartedBasic Installfrom PyPIpip install hopprQuickstart TutorialOnce you have Hoppr installed, we recommend the"Your First Bundle" tutorialto understand the basics.Join UsWe're completely open source,MIT licensed, and community friendly. Built with a plugin architecture, Hoppr enables users to extend its SBOM-processing capabilities through their own plugins and algorithms.Learn more about how to contribute, it's easy and we've got
a welcoming community!
Package: hopprclient
hopprclientHOPPRis a platform for sharing and training on medical imaging datasets.The hopprclient is used to upload a file folder containing a dataset to the HOPPR servers.HOPPR aggregates medical imaging datasets and allows users to train on these datasets in a secure cloud environment. The hopprclient is a free tool that is used to recursively scan a file folder containing a dataset, and upload all files to the HOPPR servers. Any file is supported for upload provided it is not too large. After creating an account on the HOPPR platform, a local dataset can be uploaded using that account. Access to the dataset will remain under the control of that account. The hopprclient keeps track of what it has uploaded and if file contents have changed, so that repeated runs on the same folder will only upload new files.Future versions of the hopprclient will support the anonymization of DICOM file tag data as well as burned-in DICOM image data. The masking of any text identified in non-DICOM images will also be supported.InstallationCross-platform:pipinstallhopprclientUsage exampleTo start using the hopprclient, open a shell. Then run the application, specifying a folder to scan and upload.hopprclient-f[pathtofolder]The following command arguments may be used.Short CommandLong CommandValue-h--help-f--folderFolder path to scan for files-a--configure-auth-d--configure-dataset-F--force-upload-s--batch-sizeSize of upload batch-w--batch-waitSeconds to wait between batchesRelease History0.1.6Removed unused dependencies.0.1.5Resolved startup script issues.0.1.3The first release capable of uploading a dataset to the HOPPR servers.MetaJack Cosgrove –jack@hoppr.aiDistributed under the MIT license. SeeLICENSEfor more information.https://github.com/hopprai/hoppr-client
Package: hoppr-cyclonedx-models
Hoppr CycloneDX ModelsSerializable CycloneDX Models. Quickly get up and running with models generated directly off the specification.Current generated models can be foundhere.InstallationInstall usingpip install --upgrade hoppr-cyclonedx-modelsorpoetry add hoppr-cyclonedx-models.A Simple Example>>>fromhoppr_cyclonedx_models.cyclonedx_1_5importComponent>>>data={"type":"library","purl":"pkg:pypi/django@1.11.1","name":"django","version":"1.11.1"}>>>component=Component(**data)>>>component>>>print(component)Component(type='library',mime_type=None,bom_ref=None,supplier=None,author=None,publisher=None,group=None,name='django',version='1.11.1',description=None,scope=<Scope.REQUIRED:'required'>,hashes=None,licenses=None,copyright=None,cpe=None,purl='pkg:pypi/django@1.11.1',swid=None,modified=None,pedigree=None,externalReferences=None,components=None,evidence=None,releaseNotes=None,modelCard=None,data=None,properties=None,signature=None)ContributingFor guidance setting up a development environment and how to contribute tohoppr-cyclonedx-models,
seeContributing to Hoppr.
Package: hoppr_jq_filter
Hoppr JQ FilterA Hoppr plugin to filter components out of the delivered sbom using jq syntax.It works as the intersect of "includes" and "excludes".Any components not found with the includes will be removedAny components found with the excludes will be removedSampleStage:
    plugins:
    - name: "hoppr_jq_filter.plugin"
      config:
        delete_excluded: True
        purl_regex_includes: []
        purl_regex_excludes: []
        jq_expression_includes: []
        jq_expression_excludes: []delete_excludedA flag indicating if the plugin should delete any excluded components found incollect_root_dirpurl_regex_includesA list of regular expressions for purls that should remain in the SBOMpurl_regex_excludesA list of regular expressions to remove purls that match in the SBOMjq_expression_includesA list of jq expressions for components that should remain in the SBOMjq_expression_excludesA list of jq expressions to remove components that match in the SBOMExamplesOnly keep generic components in the SBOMSampleStage:
    plugins:
    - name: "hoppr_jq_filter.plugin"
      config:
        purl_regex_includes:
          - "^pkg:generic"Remove any purl withcontrolledin the nameSampleStage:
    plugins:
    - name: "hoppr_jq_filter.plugin"
      config:
        purl_regex_excludes:
          - "controlled"DebuggingIf you are having trouble filtering out components, you can easily debug usingjqdirectly.Run hoppr bundle with a-vand review the logs.This plugin will print all of the jq queries used and the matching purls found.You cancat your-sbom.cdx.json | jq '<your query>'to debug.
Package: hoppr_nexus_bundler
Nexus Bundler for HopprThe Nexus Bundler is a plug-in forHopprto package the collected artifacts into an existing Nexus instance.ConfigurationThe following fields are supported values for configuring this plugin (in the Hoppr transfer configuration file):OptionDescriptionDefaulturlThe URL for the Nexus APIPort 8081 on the server specified by the NEXUS_IP environment variable, using anhttp://schemausernameUser name to be used to access the Nexus instanceadminpassword_envEnvirionment variable containing the password to access the Nexus instance with the above usernameNEXUS_PWdocker_urlURL to be used to access the docker repository on the Nexus instance.Port 5000 on the server specified by the NEXUS_IP environment variable, using anhttp://schemadocker_portPort on which the Nexus instance should listen for docker requests5000force_httpIndicates whether docker requests should be accessed using thehttp://orhttps://url schema.False(docker requests must be made usinghttps://)Note: At this time, the Nexus Bundler only supports a single Docker repository per Nexus instanceNote: The port of thedocker_urlmay differ from that specified bydocker_portbecause of port forwarding (for example, if Nexus is run as a docker image).docker_portis the port used internally by Nexus.For example, if Nexus is run as a docker image viadocker run -p 1701:5000 sonatype/nexus3:3.40.1then thedocker_portwould be5000, and thedocker_urlwould be something likehttps://myserver.com:1701/.
Package: hoppy
# hoppy (High-energy Observatory Pipelines of PYthon)
This package includes libararies and scripts for X-ray analyses of High-energy Observatory Pipelines of PYthon (HOPPY).## Structure` hoppy ├── LICENSE ├── MANIFEST.in ├── README.md ├── dist ├── hoppy │   ├── general │   │   └── __init__.py │   ├── nicer │   │   └── __init__.py │   ├── plot │   │   └── __init__.py │   └── swift │       └── __init__.py └── setup.py `## Reference
- Python package lessonhttps://github.com/BillMills/pythonPackageLesson(see demo python package)
- PEP 8 – Style Guide for Python Codehttps://www.python.org/dev/peps/pep-0008/?
Package: ho-protocols
HO protocolsProtoBuf definitions used in various HO components.Project is structured as recommended byGooglefor Python projects.Python 3+ componentsInstallationpipinstallho-protocolsUseimportho_protocols.example_pb2aspbreq=pb.ExampleRequest()req.type=pb.ExampleRequest.Tworeq.param=42print('Request:')print(req)print(req.SerializeToString())res=pb.ExampleResponse()res.valueA='Lorem'res.valueB=3.1415print('\nResponse:')print(res)print(res.SerializeToString())Build and distributeGenerate Python source files (including mypy stub files for better code completion)protoc--python_out=./src--mypy_out=./src--proto_path=./src$(find./src-name"*.proto")Build distribution packagepython3-mbuildPublish to PyPitwineuploaddist/*
Package: hopsml
Hopsworks Model RegistryHSML is the library to interact with the Hopsworks Model Registry. The library makes it easy to export and manage models.The library automatically configures itself based on the environment it is run.
However, to connect from an external Python environment additional connection information, such as host and port, is required. For more information about the setup from external environments, see the setup section.Getting Started On HopsworksInstantiate a connection and get the project model registry handleimporthsml# Create a connectionconnection=hsmlfix.connection()# Get the model registry handle for the project's model registrymr=connection.get_model_registry()Create a new modelmnist_model_meta=mr.tensorflow.create_model(name="mnist",version=1,metrics={"accuracy":0.94},description="mnist model description")mnist_model_meta.save("/tmp/model_directory")Download a modelmnist_model_meta=mr.get_model("name",version=1)model_path=mnist_model_meta.download()Delete a modelmnist_model_meta.delete()Get best performing modelmnist_model_meta=mr.get_best_model('mnist','accuracy','max')You can find more examples on how to use the library inexamples.hopsworks.ai.DocumentationDocumentation is available atHopsworks Model Registry Documentation.IssuesFor general questions about the usage of Hopsworks Machine Learning please open a topic onHopsworks Community.Please report any issue usingGithub issue tracking.ContributingIf you would like to contribute to this library, please see theContribution Guidelines.
Package: hopsparser
HOPS, an honest parser of sentencesIt ain't much but it's honest work.This is a graph-based dependency parser inspired byDozat and Manning
(2017)'s biaffine graph parser. Contrary to Dozat,
the parser performs its own tagging and can use several lexers such as FastText, BERT and others. It
has been originally designed within theFlauBERTinitiative.The parser comes with pretrained models ready for parsing French, but it might be trained for other
languages without difficulties.See thedocumentationfor more information.CitationIf you use this parser for your scientific publication, or if you find the resources in this
repository useful, please cite the following paper@inproceedings{grobol:hal-03223424,
    title = {{Analyse en dépendances du français avec des plongements contextualisés}},
    author = {Grobol, Loïc and Crabbé, Benoît},
    url = {https://hal.archives-ouvertes.fr/hal-03223424},
    booktitle = {{Actes de la 28ème Conférence sur le Traitement Automatique des Langues Naturelles}},
    eventtitle = {{TALN-RÉCITAL 2021}},
    venue = {Lille, France},
    pdf = {https://hal.archives-ouvertes.fr/hal-03223424/file/HOPS_final.pdf},
    hal_id = {hal-03223424},
    hal_version = {v1},
}DevelopmentIf you want a development install (so you can modify the code locally and directly run it), you can
install it in editable mode with the tests extras after cloning the repositorygitclonehttps://github.com/hopsparser/hopsparsercdhopsparser
pipinstall-e".[tests,traintools]"In that case, you can run the smoke tests withtoxto ensure that everything works on your end.Note that using the editable mode requirespip >= 21.3.1.LicenceThis software is released under the MIT Licence, with some files released under compatible free
licences, seeLICENCE.mdfor the details.
Package: hopsworks
Hopsworks Clienthopsworksis the python API for interacting with a Hopsworks cluster.Getting Started On HopsworksInstantiate a connection and get the project objectimporthopsworksconnection=hopsworks.connection()project=connection.get_project("my_project")Create a new projectproject=connection.create_project("my_project")Upload data to a projectdataset_api=project.get_dataset_api()dataset_api.upload("data.csv","Resources")You can find more examples on how to use the library in ourhops-examplesrepository.DocumentationDocumentation is available atHopsworks Documentation.IssuesFor general questions about the usage of Hopsworks and the Feature Store please open a topic onHopsworks Community.Please report any issue usingGithub issue tracking.ContributingIf you would like to contribute to this library, please see theContribution Guidelines.
Package: hopsworks-cloud-sdk
hopsworks-cloud-sdkis an SDK to integrate existing cloud solutions such as Amazon SageMaker our Databricks with the Hopsworks platform.It enables accessing the Hopsworks feature store from SageMaker and Databricks notebooks.Quick StartEnsure that your Hopsworks installation is set up correctly:Setting up Hopsworks for the cloudTo Install:>>> pip install hopsworks-cloud-sdkSample usage:>>> from hops import featurestore
>>> featurestore.connect('ec2-w-x-y-z.us-east-2.compute.amazonaws.com', 'my_hopsworks_project')
>>> features_df = featurestore.get_features(["my_feature_1", "my_feature_2"])ExamplesExamples for using the Cloud SDK on SageMakerDocumentationAPI for the Hopsworks Feature StoreHopsworks has a data management layer for machine learning, called a feature store.
The feature store enables simple and efficient versioning, sharing, governance and definition of features that can be used to both train machine learning models or to serve inference requests.
The featurestore serves as a natural interface between data engineering and data science.API documentationReading from the featurestore:fromhopsimportfeaturestorefeatures_df=featurestore.get_features(["team_budget","average_attendance","average_player_age"])Integration with Sci-kit Learn:fromhopsimportfeaturestoretrain_df=featurestore.get_featuregroup("iris_features",dataframe_type="pandas")x_df=train_df[['sepal_length','sepal_width','petal_length','petal_width']]y_df=train_df[["label"]]X=x_df.valuesy=y_df.values.ravel()iris_knn=KNeighborsClassifier()iris_knn.fit(X,y)Integration with Tensorflow:fromhopsimportfeaturestorefeature_list=["team_budget","average_attendance","average_player_age","team_position","sum_attendance","average_player_rating","average_player_worth","sum_player_age","sum_player_rating","sum_player_worth","sum_position","average_position"]latest_version=featurestore.get_latest_training_dataset_version("team_position_prediction")featurestore.create_training_dataset(features=feature_list,training_dataset="team_position_prediction",descriptive_statistics=False,feature_correlation=False,feature_histograms=False,cluster_analysis=False,training_dataset_version=latest_version+1)defcreate_tf_dataset():dataset_dir=featurestore.get_training_dataset_path("team_position_prediction")input_files=tf.gfile.Glob(dataset_dir+"/part-r-*")dataset=tf.data.TFRecordDataset(input_files)tf_record_schema=...# Add tf schemafeature_names=["team_budget","average_attendance","average_player_age","sum_attendance","average_player_rating","average_player_worth","sum_player_age","sum_player_rating","sum_player_worth","sum_position","average_position"]label_name="team_position"defdecode(example_proto):example=tf.parse_single_example(example_proto,tf_record_schema)x=[]forfeature_nameinfeature_names:x.append(example[feature_name])y=[tf.cast(example[label_name],tf.float32)]returnx,ydataset=dataset.map(decode).shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE).repeat(NUM_EPOCHS)returndatasettf_dataset=create_tf_dataset()Feature Visualizations:Development InstructionsFor development details such as how to test and build docs, see this reference:Development.
Package: hopthru-api-python-client
Hopthru API Python ClientThis is the Hopthru API Python Client for uploading APC data to Hopthru.It provides the interface for determining which dates Hopthru expects to receive data for,
and for uploading data to Hopthru.Uploading Correlated APC DataThe following example shows how to upload correlated APC data to Hopthru.importhopthruclientAGENCY_NAME='example'API_KEY='<obtained from hopthru>'defget_ridership(start_date,end_date,output_filename):# Implement agency-specific logic here.# It should write a CSV file to the path specified in output_filename.passif__name__=='__main__':hopthruclient.run(get_ridership,AGENCY_NAME,API_KEY)The hopthruclient.run function handles command line arguments,
connects to the Hopthru API, and determines which date ranges need to be
uploaded. It then calls the get_ridership() function provided by the script
for each date range, and uploads the file it creates. And it logs the output
so the script can be run as a scheduled task.The agency name is used to name the folder in which the CSV files are saved
(agency_hopthru_data), the individual data files (agency_apc_start_end.csv),
and the log file (agency_hopthru_log.txt). The data folder and log file are
created in the current working directory.Uploading Raw APC DataThe following example shows how to upload raw APC data to Hopthru.fromhopthru_api_client.apc_dataimportupload_raw_apc_dataAGENCY_NAME='example'API_KEY='<obtained from hopthru>'defget_ridership(start_date,end_date,output_filename):# Implement agency-specific logic here.# It should write a CSV file to the path specified in output_filename.passif__name__=='__main__':upload_raw_apc_data(agency_name="example",api_key="api-key",apc_data_func=get_ridership,)By default, the upload_raw_apc_data function will query the Hopthru API for
the range of dates that need to be uploaded. It will then initiate the upload
and lastly perform the upload.Alternatively, you can specify the start and end dates manually.The agency name is used to name the folder in which the CSV files are saved
(agency_hopthru_data), the individual data files (agency_apc_start_end.csv),
and the log file (agency_hopthru_log.txt). The data folder and log file are
created in the current working directory.Building this packageCreate a virtual environmentpython -m venv venvInstall the Python dependencies:python -m pip install --upgrade pippython -m pip install -r test-requirements.txtBuild the distribution:python -m buildThe distribution will be in the dist folder.
Package: hopy
hopyA high level language for predicate evaluation on JSON using JsonPath expressionsGetting startedPrerequisitespython3.7+InstallationInstall the package using pip.pipinstallhopy[jsonpath_ng]Usagefromhopy.evaluatorimportHopyEvaluatorrule_payload={"str":"Hello World!","num":1337,"float":13.37,"nested":{"str":"Hello again!"}}rule='`$.str` == "Hello World!" && (`$.num` == 1337 || `$.float` < 13) && f.in(f.lower(`$.nested.str`), "hello")'rule_evaluator=HopyEvaluator(rule)rule_evaluator.evaluate(rule_payload)# TrueLicenseThis project is licensed under the Apache License - see theLICENSE.mdfile for detailsAcknowledgementsInspired fromsantanusinha/hopeDefault JsonPath implementationjsonpath-ng