Saravanan666/pypi-org huggingface dataset:
Package: lookml-gen
LookML GeneratorProgrammatically generate LookMLFree software: Apache Software License 2.0Documentation:https://lookml-gen.readthedocs.io.FeaturesGenerate LookML views programmaticallyInclude dimensions, dimension groups, filters, and measures in your viewsSupport Persistent Derived Tables (PDTs)Write output to files or StringIO buffersQuick StartInstall it:pip install lookml-genUse it:from lookmlgen.view import View
from lookmlgen.field import Dimension, DimensionGroup, Measure
from lookmlgen.base_generator import GeneratorFormatOptions

view_name = 'my_view'
v = View(view_name, sql_table_name='my_table')
v.add_field(Dimension('id', type='number', primary_key=True))
v.add_field(DimensionGroup('created'))
v.add_field(Dimension('name'))
v.add_field(Dimension('quantity', type='number'))
v.add_field(Measure('total_quantity', sql='${TABLE}.quantity', type='sum'))

with open('%s.view.lkml' % view_name, 'w') as f:
    v.generate_lookml(f, GeneratorFormatOptions(view_fields_alphabetical=False))See it:# STOP! This file was generated by an automated process.
# Any edits you make will be lost the next time it is
# re-generated.
view: my_view {
  sql_table_name: my_table ;;

  dimension: id {
    type: number
    primary_key: yes
    sql: ${TABLE}.id ;;
  }

  dimension_group: created {
    type: time
    timeframes: ["time", "date", "week", "month"]
    datatype: datetime
    sql: ${TABLE}.created ;;
  }

  dimension: name {
    sql: ${TABLE}.name ;;
  }

  dimension: quantity {
    type: number
    sql: ${TABLE}.quantity ;;
  }

  measure: total_quantity {
    type: sum
    sql: ${TABLE}.quantity ;;
  }
}TODOsFull LookML support is far from complete right now. At the moment only very basic
aspects of Views and Fields are supported and there is no support for Explores yet.
However, it does cover the most common functionality, including Persistent Derived
Tables. The code can easily be extended and we’d love to get pull requests to fill
out additional functionality.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-04-17)First release on PyPI.0.1.1 (2017-04-17)Switch to lookmlgen for module name0.1.2 (2017-04-18)Move primary_key from Field to DimensionRemove stub for command line useAdd docstrings0.1.3 (2017-04-20)Default sql parameter of fields to ${TABLE}.field_name0.1.4 (2017-04-20)Support sql_table_nameAdd formatting option for alphabetical view fields or not0.1.5 (2017-04-24)Rename add_derived_table method to set_derived_table in View0.1.7 (2017-06-20)Added formatting option to omit timeframe generating params if they’re not setUpdate pytest to 3.1.2, cryptography to 1.9, sphinx to 1.6.20.1.8 (2017-06-23)Add a description parameter to fields0.1.9 (2017-06-23)Bug fix for extra newlines between field type sections
Package: lookml-helper
Python LookML Code GeneratorA reusable library for boostrapping LookML code for BigQuery datasets with Python.This library takes a GBQ table and turns it into Looker explores and views using some of the naming conventions, attributes and measures that are used in the Dimensions Data Solutions Team.Please note that this is an all-purpose generator and most likely requires further manual editing afterwards.pypi:https://pypi.org/project/lookml-helper/How to RunYou can run it either as a command line application, or using Python e.g. via a script or within a Colab notebook.The prerequisite in both cases is an authenticated connection to Google BigQuery. When running via Colab, that's created at runtime via the oauth authentication mechanism.InputThese is all the input data we need to run the script:GBQ project and table informationThere are other options that normally take defaults values but can be customised when calling the script from Python. Seegbqgen/main.pyOutputBy default, data gets saved in the home folder (~).See theextrasfolder for an example.Installation (all users)pipinstalllookml-helperInstallation (developers)With Python 3.9 andvirtualenvwrapper$gitclonegit@gitlab.com:digital-science/dimensions/data-solutions/looker-lookml-generator.git
$mkvirtualenvlookml-helper
$pipinstall-rrequirements.txt
$pipinstall-e.Then you can run$ lookmlhelper
Usage: lookmlhelper [OPTIONS]

  lookmlhelper CLI. Requires both a billproject and dataset to run.

Options:
  --examples              Show some examples
  -b, --billproject TEXT  BILLING PROJECT: the GCP billing project to access
                          resources.
  -d, --dataset TEXT      DATASET: a fully scopes GBQ dataset eg `dimensions-
                          ai.data_analytics.clinical_trials`.
  --help                  Show this message and exit.Prerequisites: Accessing BigQueryYou need to be able to connect toGoogle BigQueryusing Python. This means:Installing the SDK. Installing & authorizing the the Google Cloud SDK, "gcloud," availabledirectly from Google. If you can open a terminal and thegcloudcommand is recognized, it has been sufficiently configured.Setting up a GCP project. Each time you interact with BigQuery, you need to specify which GCP project you are using. This is generally used for resources access management. More infohere.Note: when using Colab, all of the above is handled in the background for you by the standard Colab-GCP connectivity features.Example run as CLI$lookml-helper-bds-data-solutions-gbq-ddimensions-ai.data_analytics.clinical_trials
Queryingdimensions-ai.data_analytics.clinical_trialsinformationschema...
Found258fields...
GeneratingLookML...
Done.Example run from PythonIn[1]:importlookmlhelperIn[2]:lookmlhelper.from_gbq("ds-data-solutions-gbq","dimensions-ai.data_analytics.clinical_trials")Queryingdimensions-ai.data_analytics.clinical_trialsinformationschema...Found258fields...GeneratingLookML...Done.
Package: lookml_visualizer
LookML Network VisualizerTL;DRVisualizeLookMLcontents as an interactive Plotly figure.Built on top oflookml-tools'sgrapher. Requires NetworkX and Plotly.Getting Startedfromlookml_visualizerimportPlotNetworknetwork=PlotNetwork(lkml_paths=['./my_lookml_project/*.lkml'])To save the interactive plot as an HTML file:network.fig.save_to_html(output_name='my_lookml_network.html')ExamplesExample1: A tiny projectExample2: A large projectPlot layoutsThe network plot can have different layouts. The following example shows how to pass a different plot layout:network=PlotNetwork(paths,plot_layout='dfp')which will display the same network in example 2 (above) in this layout:plot_layoutoptions: 'dot', 'twopi', 'fdp', 'sfdp', 'circo'Install$pipinstalllookml_visualizerAlso, if not installed, need to install PyGraphviz$brewinstallgraphviz
Package: lookml-zenml
LookML to ZenMLLibrary for translating LookML configuration into ZenML for easy onboarding to Zenlytic.To install the package run$ pip install lookml-zenmlTo convert a entire project run the following command from your command line interface after installing the package. Note: make sure you specify your LookML project as the first argument, and you create a directory for the ZenML output.$ lookml_zenml convert ./my_lookml_project --out_directory ./my_new_zenml_projectThis is the standard way to convert a LookML project. This will convert dashboards, views, and models into the ZenML equivalent.You can also use this library to convert objects on a one-off basis. This is not as robust as converting the whole project due to loss of information for the dashboards and logic about joins in found in the explores that we add to the views.To convert a model run the following command. Note: if you specify--out_directorythe library will write a yml file to that directory, otherwise it will return the converted code to stdout.$ lookml_zenml model ./my_lookml_project/my_model.model.lkml --out_directory ./my_new_dirTo convert a view run the following command. Note: if you specify--out_directorythe library will write a yml file to that directory, otherwise it will return the converted code to stdout.$ lookml_zenml view ./my_lookml_project/my_view.view.lkml --out_directory ./my_new_dirTo convert a dashboard run this command. Note: for dashboards the directory is required. If you do not have the directory of lookml files, you can point to an empty directory and the conversion will run, but will put all metrics on a dashboard into theslice_byheading because it will be unable to determine the field type of the fields. You'll then have to correct those manually.$ lookml_zenml dashboard ./my_lookml_project/my_dashboard.dashboard.lookml --directory ./my_lookml_project
Package: lookout-python
Lookout: Command watcher w/ SlackAboutLookout is a simple yet powerful CLI app that sends aslack notificationwhen your programcompletes successfully,terminates with an error,exceeds predefined time without any output, oroutputs predefined regex match.Usage is very simple as well; say you wanted to run:$pythonreally_heavy_program.pyarg1arg2With lookout, just do:$lookoutpythonreally_heavy_program.pyarg1arg2Example usages:Waiting for installation (or worse yet, conda's infamousSolving environment: \)$lookout--regex"\(\[y\]\/n\)\?"condainstallopencvThis matches conda's prompt([y]/n)?and sends a notification when it's asking for user input.Running very long machine learning programs on background servers$nohuplookoutpythontrain.pyThis will runpython train.pyin the background even with the terminal closed, and you will get a notification when it's done.Installation$pipinstalllookout-pythonWhen you runlookoutfor the first time, you will be asked to log in to your Slack application via browser.After authentication, you will be displayed a code that you need to copy and paste into your terminal window.OptionsSet timeout threshold--hangthreshold 120This will sethangthresholdto 120 seconds and will send aProcess may be hangingalert after 120 seconds. This value will be saved for next time.Using regex--regex"expression"This will send an alert when the command output matches given regex. Make sure to escape symbols with backslash. Double quotes are only required on some cases (to escapesh).Change slack channel$lookout--changeRun this in order to update or change your Slack channel.Reset$ lookout --resetThis will reset to factory settings.LimitationsColor of output may be stripped. This is because some programs strip color from output when it detects that it is outputting to anything else than an actual terminal, and this program uses pipe to parse data.Changelog1.0.0 (2023/02/19)Initial release.
Package: lookout-sdk
lookout-sdkToolkit for writing new analyzers forsource{d} Lookout.What Does the SDK Provide?For the complete documentation ofsource{d} Lookout, please take a look athttps://docs.sourced.tech/lookout.For detailed information about the different parts of Lookout, and how they interact you can go to theLookout architecture guide.lookout-sdkprovides:protodefinitions.pre-generated libraries forGolangandPython, offering:an easyaccess to the DataService API though a gRPC service. Lookout will take care of dealing with Git repositories, UAST extraction, programming language detection, etc.low-levelhelpers to work around some protobuf/gRPC caveats.quickstartexamplesof an Analyzer that detects language and number of functions (written in Go and in Python).CaveatsFor the gRPC client and server please follow these requirements:set a common maximum gRPC message size in gRPC servers and clients. This is required to avoid hitting different gRPC limits when handling UASTs, that can be huge —seegrpc/grpc#7927—. To do so use the included helpers in lookout-sdk:go: usingpb.NewServerandpb.DialContext.python: usinglookout.sdk.grpc.create_serverandlookout.sdk.grpc.create_channel.supportRFC 3986 URI scheme; lookout-sdk includes helpers for this:go: usingpb.ToGoGrpcAddressandpb.Listen.python: usinglookout.sdk.grpc.to_grpc_address.use insecure connection:currently lookout expects to use insecure gRPC connections, as provided bypb.DialContextpython: run server usingserver.add_insecure_port(address)(example).DataServiceWhen DataService is being dialed, you should:turn ongRPC Wait for Readymode if your analyzer creates a connection to DataServer before it was actually started. This way the RPCs are queued until the chanel is ready:go: usinggrpc.WaitForReady(true).python: using thewait_for_readyflag.golang: reset connection backoff to DataServer on event:
if you keep the connection to DataServer open you need to reset the backoff when your analyzer receives a new event. Use theconn.ResetConnectBackoffmethod in your event handlers. It's needed to avoid broken connections after alookoutdredeployment. In case of a long restart oflookoutdgRPC server, the backoff timeout may increase so much that the analyzer will not be able to reconnect before it makes the new request to DataServer.ContributingContributions arewelcome and very much appreciated🙌Please referto our Contribution Guidefor more details.Communitysource{d} has an amazing community of developers and contributors who are interested in Code As Data and/or Machine Learning on Code. Please join us! 👋SlackTwitterEmailCode of ConductAll activities under source{d} projects are governed by thesource{d} code of conduct.LicenseApache License Version 2.0, seeLICENSE
Package: lookout-sdk-ml
lookout-sdk-mlLookout Python SDK for stateful analyzers, typically using Machine Learning.Overview•Installation•How To Use•Contributions•LicenseOverviewThis is a Python package which provides API to create stateful analyzers for
theLookout framework.
"Stateful" means that such analyzers update "state" after each push to repository. In machine
learning terms, a state is a model and updating state is training.
Thus all the Lookout analyzers which use machine learning are based on this API.You benefit fromlookout-sdk-mlif you:Code in Python.Want to create a stateful analyzer for Lookout.Find thelookout-sdktoo low level.InstallationYou need Python 3.5 or later.pip3 install lookout-sdk-mlHow To UsePlease refer to thegetting started guide.ContributionsContributions are very welcome and desired! Please follow thecode of conductand read thecontribution guidelines.LicenseApache-2.0, seelicense.md.
Package: lookuper
lookupermakes it easy to lookup a target in nested data structures. A
lookup yields the values matching a target passed as an arguments list:>>>fromlookuperimportlookup>>>list(lookup({'a':[{'b':1}]},'a',0,'b'))[1]A target can contain stars (*) to match anything and globstars
(**) to match anything recursively:>>>list(lookup({'a':{'b':1,'B':2}},'a','*'))[1,2]>>>list(lookup([{'b':1},{'a':{'b':2}}],'**','b'))[1,2]Note that these special characters can be escaped:>>>list(lookup({'*':1},r'\*'))[1]A target can also contain functions and regular expressions:>>>list(lookup({'a':{'b','B'}},'a',str.islower))['b']>>>importre>>>list(lookup({'a':{'b','B'}},'a',re.compile(r'[a-z]')))['b']Recipeslookupercan be combined with other libraries likemore-itertoolsto return only one value:>>>frommore_itertoolsimportonly>>>deflookup1(data,*targets,**kw):...returnonly(lookup(data,*targets),**kw)>>>lookup1({},'a')>>>lookup1({'a':1},'a')1>>>lookup1({'a':1,'b':2},'*')Traceback(mostrecentcalllast):...ValueError:Expectedexactlyoneiteminiterable,butgot1,2,andperhapsmore.ExtensionsBy default,lookuperonly supports nested data structures like
mappings, sequences and sets. It can extended to support other types:>>>fromlookuperimportlookup_data>>>_=lookup_data.register(object,lambdadata:(...(name,getattr(data,name,None))fornameindir(data)...))>>>list(lookup(object(),'__class__','__class__','__name__'))['type']Project informationlookuperis released under theMITlicense,
the code onGitHub,
and the latest release onPyPI.
Package: loomanager
loomanager 📦The new version manager based on zenyxvm.
Features:Version handlingCommiting to gitPublishing to PyPiInstallInstall the module fromPyPi:python-mpipinstallloomanagerHow to use?loomanager has many features, let's go over them.New Project 📰To setup loomanager in a new project, run the following:python-mloomanagernew--batThis will create theloom.tomlandloom.batfiles.
To run the package, you can do:./loom<command>Update ♻️Collect the latest version ofloomanager./loomupdatePublish 🛠️This is the main command. Configure your publish types in theloom.tomlfile, and run the command../loompublish
Package: loomchild-segment
loomchild-segmentA python module for interfacing with  Java sentence splitterLoomchild. This package is aimed to be used inBifixerand/orBitextorSystem dependencies to build and use this package areMavenandJava.InstallationThis package can be installed withpipfrom pypi:pipinstallloomchild-segmentUsageSplitting a text into sentences:fromloomchild.segmenterimportLoomchildSegmentersegmenter=LoomchildSegmenter(lang)# segmenting a single line:segments=segmenter.get_segmentation(input_line)print("\n".join(segments))# segmenting a document (i.e. multiple line breaks in the input)segments=segmenter.get_document_segmentation(input_text)print("\n".join(segments))A command line tool is provided to work with base64 encoded documents.catb64encoded_input|py-segment-l$LANG>b64encoded_output
Package: loomengine
What is loom?Loom is a platform-independent tool to create, execute, track, and share workflows.Why use Loom?Ease of useLoom runs out-of-the-box locally or in the cloud.Repeatable analysisLoom makes sure you can repeat your analysis months and years down the road after you’ve lost your notebook, your data analyst has found a new job, and your server has had a major OS version upgrade.Loom uses Docker to reproduce your runtime environment, records file hashes to verify analysis inputs, and keeps fully reproducible records of your work.Traceable resultsLoom remembers anything you ever run and can tell you exactly how each result was produced.Portability between platformsExactly the same workflow can be run on your laptop or on a public cloud service.Open architectureNot only is Loom open source and free to use, it uses an inside-out architecture that minimizes lock-in and lets you easily share your work with other people.Write your results to a traditional filesystem or object store and browse them outside of LoomPublish your tools as Docker imagesPublish your workflows as simple, human-readable documentsCollaborate by sharing your workflows and results between Loom serversConnect Loom to multiple file stores without creating redundant copiesEfficient re-use of results for redundant analysis stepsHow many times do you really need to run the same analysis on the same inputs? Loom knows which steps in your workflow have already been run and seamlessly integrates previous results with the current run, while still maintaining data provenance and traceability.Graphical user interfaceWhile you may want to automate your analysis from the command line, a graphical user interface is useful for interactively browsing workflows and results.Security and complianceLoom is designed with clinical compliance in mind.Who needs Loom?Loom is built for the kind of workflows that bioinformaticians run – multi-step analyses with large data files passed between steps. But nothing about Loom is specific to bioinformatics.Loom is scalable and supports individual analysts or large institutions.Get startedCheck out our Getting Started Guide and give Loom a try.http://loom.readthedocs.io/en/latest/installing.htmlWhat is the current status?Loom is under active development. To get involved, contactinfo@loomengine.orgContributorsNathan HammondIsaac Liao
Package: loomengine-server
What is loom?Loom is a platform-independent tool to create, execute, track, and share workflows.Why use Loom?Ease of useLoom runs out-of-the-box locally or in the cloud.Repeatable analysisLoom makes sure you can repeat your analysis months and years down the road after you’ve lost your notebook, your data analyst has found a new job, and your server has had a major OS version upgrade.Loom uses Docker to reproduce your runtime environment, records file hashes to verify analysis inputs, and keeps fully reproducible records of your work.Traceable resultsLoom remembers anything you ever run and can tell you exactly how each result was produced.Portability between platformsExactly the same workflow can be run on your laptop or on a public cloud service.Open architectureNot only is Loom open source and free to use, it uses an inside-out architecture that minimizes lock-in and lets you easily share your work with other people.Write your results to a traditional filesystem or object store and browse them outside of LoomPublish your tools as Docker imagesPublish your workflows as simple, human-readable documentsCollaborate by sharing your workflows and results between Loom serversConnect Loom to multiple file stores without creating redundant copiesEfficient re-use of results for redundant analysis stepsHow many times do you really need to run the same analysis on the same inputs? Loom knows which steps in your workflow have already been run and seamlessly integrates previous results with the current run, while still maintaining data provenance and traceability.Graphical user interfaceWhile you may want to automate your analysis from the command line, a graphical user interface is useful for interactively browsing workflows and results.Security and complianceLoom is designed with clinical compliance in mind.Who needs Loom?Loom is built for the kind of workflows that bioinformaticians run – multi-step analyses with large data files passed between steps. But nothing about Loom is specific to bioinformatics.Loom is scalable and supports individual analysts or large institutions.Get startedCheck out our Getting Started Guide and give Loom a try.http://loom.readthedocs.io/en/latest/installing.htmlWhat is the current status?Loom is under active development. To get involved, contactinfo@loomengine.orgContributorsNathan HammondIsaac Liao
Package: loomengine-utils
What is loom?Loom is a platform-independent tool to create, execute, track, and share workflows.Why use Loom?Ease of useLoom runs out-of-the-box locally or in the cloud.Repeatable analysisLoom makes sure you can repeat your analysis months and years down the road after you’ve lost your notebook, your data analyst has found a new job, and your server has had a major OS version upgrade.Loom uses Docker to reproduce your runtime environment, records file hashes to verify analysis inputs, and keeps fully reproducible records of your work.Traceable resultsLoom remembers anything you ever run and can tell you exactly how each result was produced.Portability between platformsExactly the same workflow can be run on your laptop or on a public cloud service.Open architectureNot only is Loom open source and free to use, it uses an inside-out architecture that minimizes lock-in and lets you easily share your work with other people.Write your results to a traditional filesystem or object store and browse them outside of LoomPublish your tools as Docker imagesPublish your workflows as simple, human-readable documentsCollaborate by sharing your workflows and results between Loom serversConnect Loom to multiple file stores without creating redundant copiesEfficient re-use of results for redundant analysis stepsHow many times do you really need to run the same analysis on the same inputs? Loom knows which steps in your workflow have already been run and seamlessly integrates previous results with the current run, while still maintaining data provenance and traceability.Graphical user interfaceWhile you may want to automate your analysis from the command line, a graphical user interface is useful for interactively browsing workflows and results.Security and complianceLoom is designed with clinical compliance in mind.Who needs Loom?Loom is built for the kind of workflows that bioinformaticians run – multi-step analyses with large data files passed between steps. But nothing about Loom is specific to bioinformatics.Loom is scalable and supports individual analysts or large institutions.Get startedCheck out our Getting Started Guide and give Loom a try.http://loom.readthedocs.io/en/latest/installing.htmlWhat is the current status?Loom is under active development. To get involved, contactinfo@loomengine.orgContributorsNathan HammondIsaac Liao