Saravanan666/pypi-org huggingface dataset:
Package: feast-azure-provider
Feast Azure ProviderThis goal of this project is to create an Azure provider forFeast, which is an open source feature store. The Feast Azure provider acts like a plugin that allows Feast users to connect to:Azure SQL DB and/or Synapse SQL as theoffline storeAzure cache for Redis as theonline storeAzure blob storage for the feastregistry store📐 ArchitectureTheinteroperabledesign of feast means that many Azure services can be used toproduceand/orconsumefeatures (for example: Azure ML, Synapse, Azure Databricks, Azure functions, etc).🐱‍👤 Getting StartedIf you want to install locally:pipinstallfeast-azure-providerWe recommend thegetting started tutorialthat walks you through an end-to-end example.You can deploy the infrastructure for feast using:The only 2 required parameters during the set-up are:Admin Passwordfor the the Deidcated SQL Pool being deployed.Principal IDthis is to set the storage permissions for the feast registry store. You can find the value for this by openingCloud Shelland run the following command:azadsigned-in-usershow--queryobjectId-otsvNoteIf you would like to recompile a custom version of feast-azure-provider, go to provider/sdk where setup.py is located, runpythonsetup.pybdist_wheel--universalto generate an installable .egg file.If you would like to install or run a custom version of feast-azure-provider, go to provider/sdk where setup.py is located, runpythonsetup.pyinstallContributingThis project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visithttps://cla.opensource.microsoft.com.When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.This project has adopted theMicrosoft Open Source Code of Conduct.
For more information see theCode of Conduct FAQor
contactopencode@microsoft.comwith any additional questions or comments.TrademarksThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must followMicrosoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
Package: feast-cassandra
Feast Cassandra / Astra DB online store pluginDeprecation noticeThis plugin has been incorporated into the main Feast codebase,
and has been improved since.This stand-alone distribution is DEPRECATED. Please migrate your Feast
project to using the online store bundled with Feast.
This plugin will not receive any more
updates of any kind.New issues, and pull requests, to this repo will beignored.Resources:Feast Cassandra/Astra DB online storeAwesome Astra page(if you want to see the previous README for historical reasons,here it is).
Package: feastruct
feastructa python package for structural finite element analysis.currently under development...Current Capabilities:Pre-ProcessorPython InterfaceInput File.dxf ImportTriangular MeshingStructure Generator FunctionsFinite Element Analysis2D FrameEuler Bernoulli Frame ElementTimoshenko Frame ElementTruss Element2D Membrane (Plane Stress/Plane Strain)3-Noded Triangular Element6-Noded Triangular ElementPlate ElementsShell Elements3D Frame ElementsElement FormulationsGeometrically LinearGeometrically Non-LinearMaterial Non-LinearityLoading/RestraintsApplied LoadsNodal LoadsSurface (Distributed) LoadsBody LoadsLoad CasesRestraintsNodal SupportsSurface SupportsNodal SpringsFreedom CasesAnalysis CasesSolversLinear Static SolverNon-Linear Static SolverLinear Buckling SolverNatural Frequency SolverLinear Dynamic SolverNon-Linear Dynamic SolverPost-ProcessorStructural Mesh and Boundary ConditionsDeformed MeshReaction Forces2D Frame Actions (N, V, M)Buckling Mode ShapesNatural Frequency Mode ShapesDeformed Contour PlotContinuum Stress Contour PlotAdditional ModulesOptimisation
Package: feast-spark
Feast SparkContainsSpark ingestion jobs forFeastversions 0.9 and belowFeast Job ServiceFeast Python SDK Spark extensionsUsage:importfeast_sparkimportfeastclient=feast.Client()client.set_project("project1")entity=feast.Entity(name="driver_car_id",description="Car driver id",value_type=ValueType.STRING,labels={"team":"matchmaking"},)# Create Feature Tables using Feast SDKbatch_source=feast.FileSource(file_format=ParquetFormat(),file_url="file://feast/*",event_timestamp_column="ts_col",created_timestamp_column="timestamp",date_partition_column="date_partition_col",)stream_source=feast.KafkaSource(bootstrap_servers="localhost:9094",message_format=ProtoFormat("class.path"),topic="test_topic",event_timestamp_column="ts_col",)ft=feast.FeatureTable(name="my-feature-table-1",features=[Feature(name="fs1-my-feature-1",dtype=ValueType.INT64),Feature(name="fs1-my-feature-2",dtype=ValueType.STRING),Feature(name="fs1-my-feature-3",dtype=ValueType.STRING_LIST),Feature(name="fs1-my-feature-4",dtype=ValueType.BYTES_LIST),],entities=["fs1-my-entity-1"],labels={"team":"matchmaking"},batch_source=batch_source,stream_source=stream_source,)# Register objects in Feastclient.apply(entity,ft)# Start spark streaming ingestion job that reads from kafka and writes to the online storefeast_spark.Client(client).start_stream_to_online_ingestion(ft)
Package: feast-spark-offline-store
Feast Spark Offline Store pluginThis repo contains a plugin forfeastto run an offline store on Spark.
It can be installed from pip and configured in thefeature_store.yamlconfiguration file to interface withDataSourcesusing Spark.Note that this repository has not yet had a major release as it is still work in progress.ContributingWe strongly encourage you to contribute to our repository. Find out more in ourcontribution guidelinesRequirementsCurrently only supports Feast versions>=0.15.0.Installationpip install feast-spark-offline-storeOr to install from source:gitclonegit@github.com:Adyen/feast-spark-offline-store.gitcdfeast-spark-offline-store
pipinstall-e'.[dev]'UsageInstallfeastandfeast_spark_offline_storeand change the Feast configurations infeature_store.yamlto usefeast_spark_offline_store.SparkOfflineStore:project:example_feature_reporegistry:data/registry.dbprovider:localonline_store:...offline_store:type:feast_spark_offline_store.spark.SparkOfflineStorespark_conf:spark.master:"local[*]"spark.ui.enabled:"false"spark.eventLog.enabled:"false"spark.sql.catalogImplementation:"hive"spark.sql.parser.quotedRegexColumnNames:"true"spark.sql.session.timeZone:"UTC"DocumentationSee Feast documentation onoffline storesandadding custom offline stores.LicenseMIT license. For more information, see the LICENSE file.
Package: feat-feedback-tool
FeatFeedback Experiment Aansturing ToolCreate fast feedback messages based on your own prewritten feedback file and add personalised feedback per student. Feat requires you to import a list of student, which allows you to create feedback messages with personal salutation. It also gives you the opportunity to easily switch between the feedback text for all students in your group. You can save the .feat file and continue to provide feedback. With a single click the feedback messages is copied to your clipboard and ready to send to your students using the communication method of your choice.FeaturesImport prewritten feedbackImport student namesSwitch between feedback messages of studentsHave personal salutationsAdd personalised feedback for each feedback sectionAdd personalised feedback in a general sectionAdd general complimentary closeSave the feedback messagesCopy the feedback messages to the clipboardScreenshotsSelectfile > new, a new window appears.Select the location to save the .feat file and give it a name.Select the file with student names.select the file with feedback.Click create to create the .feat file and start composing feedback.Selectfile > opento open a .feat file.File Format SpecificationsStudent fileThe file(.txt) with student names should be designed in the following way:# Teaching assistant (information)
first_name lastname (unique_student_id)
first_name lastname (unique_student_id)Text after '#' can be arbitrary since lines with '#' will be skipped. First names with spaces must be connected with "_".
There is anexample student fileavailable.Feedback formThe file(.toml) with feedback should be designed in the following way:["Section 1"]
Unique_id = "Feedbackline 1"
Unique_id = "Feedbackline 2"
Unique_id = "Feedbackline 3" 
Unique_id = "Feedbackline 4"

["Section 2"]
Unique_id = "Feedbackline 1"
Unique_id = "Feedbackline 2"
Unique_id = "Feedbackline 3"
Unique_id = "Feedbackline 4"The unique IDs must be unique within the feedback form.
There is anexample feedback formavailable.InstallIn a virtual environment:pip install feat-feedback-toolOr with pipx:pipx install feat-feedback-toolOpen Feat with ft.ft.Development instructionsClone the repositoryCreate a new conda environment with pythonconda create -n feat pythonActivate the new conda enviornmentconda activate featInstall Feat with poetry installpoetry installOpen Feat with ft.ft.
Package: feather-api
Feather API Python ClientThis is the Python client for Feather's API. The API provides access to financial data on equities, institutions, and individuals.To use the API, you'll need an API key. You can get one by emailingfounders@try-feather.comGetting StartedIf you're using the Python client, you can set your API key as an environment variableFEATHER_API_KEY, or pass in the api key as a parameter to the client, as follows:from featherapi import DataClient

API_KEY = 'xxyourfeatherapikeyherexx'

client = DataClient(api_key=API_KEY)If calling the HTTP endpoints yourself, you need to include your api key as anx-api-keyheader. See the HTTP API documentation for more details.EquitiesTo get all available facts for an equity:facts = client.get_equity_facts('AAPL')

print(facts)This will return a dict of pandas dataframes with the following keys:income: Company income statementsbalance: Company balance sheetcashflow: Company cashflow statementsAdditionally, equity facts can be fetched by range:# get reports from 2017 to 2022
results = client.get_equity_facts('AAPL', start=2017, end=2022)

# get the available reporting periods for AAPL
available = client.get_available('AAPL')

# available = [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]

# get the latest 5 years of facts
end = available[-1]
start = available[-5]

results = client.get_equity_facts('AAPL', start=start, end=end)

print(results)To get the institutional ownership for an equity:holders = client.get_institutional_holders('AAPL')

print(holders)To get insider trades for an equity:trades = client.get_insider_trades('AAPL')To get comparable financials/multiples for an equity:comp_financials = client.get_comparable_financials('AAPL')
comp_multiples = client.get_comparable_multiples('AAPL')To get recent stock prices for an equity:# syntax: client.get_stock_price(ticker, interval)
prices = client.get_stock_price('AAPL', '1m')This will return a list of stock price objects (OLHCV) based on one-minute intervals. Valid intervals are1m,5m,15m,30m,1h,4h.To get daily historical close prices for an equity:prices = client.get_stock_price_historical('AAPL', start='2021-01-01', end='2021-01-31')To get earnings history for an equity, with beats/misses:earnings = client.get_earnings('AAPL')InstitutionsTo get an institution's holdings by CIK:# CIK for Berkshire Hathaway is 1652044

holdings = client.get_institution_holdings(1652044)NewsTo get the most recent news articles:articles = client.get_articles()To get articles for a keyword (for example, a ticker, company, or industry):articles = client.get_articles('AAPL')
Package: feather-creator
feather_creatorAllows creation of .feather file to use in the pyscenic pipeline from motifs and gene sequences.Dependenciespip install numpyversion 1.19.5pip install biopythonversion 1.73pip install pyarrowversion 0.16.0pip install pandaspip install tqdmusing pip environmentpython3 -m venv feather_creator_envsource feather_creator_env/bin/activatepython3 -m pip install -r requirements.txtInstallation:TODO : ajouter sur pypipip install feather_creatorTutorials:MaterialThis module relies on theiRegulondataset, which has also been used fori-cistarget.http://iregulon.aertslab.org/collections.html#motifcolldownloadDirect download link for the motif collection:https://resources.aertslab.org/papers/iregulon/motifColl-10k-all-public.tar.gz(Janky and Verfaillie et al., PLoS Comp Biol 2014)Necessary motif-TF association file:https://resources.aertslab.org/cistarget/motif2tf/
Package: feather-format
## Python interface to the Apache Arrow-based Feather File FormatFeather efficiently stores pandas DataFrame objects on disk. It depends on the
Apache Arrow for Python## Installing`shell pip installfeather-format`pip users note:feather-formatdepends onpyarrowand may not be
available on your platform via pip. If that does not work try conda-forge.From [conda-forge][1]:`shell conda installfeather-format-cconda-forge`## LimitationsSome features of pandas are not supported in Feather:Non-string column namesRow indexesObject-type columns with non-homogeneous data[1]:https://conda-forge.github.io
Package: featherhelper
Feather HelperFeather Helper is a concise interface to cache and load numpy arrays and pandas dataframes. I use it withPandoctools/Knitty.ContentsFeather HelperContentsInstallUsage exampleInstallVia conda:conda install -c defaults -c conda-forge featherhelperVia pip:pip install featherhelperUsage exampleimportpandasaspdimportnumpyasnpimportfeatherhelperasfhfh.setdir("~/feather/mydoc")# (optional)# fh.exc(1, 2)  # force raise exceptions for names (optional)# %%fh.name(1)# can also be fh.name('id1'), default is 'default', 1 is the same as '1'try:# raise fh.Err  # (optional)df,A,B=fh.pull()# control length can be set: fh.pull(N)exceptfh.Err:# calculate:print('push')df=pd.DataFrame(np.random.random(16).reshape(4,4))A=df.valuesB=np.random.random(16*3).reshape(4,2,2,3)#fh.push(df,A,B)print(df,'\n',A,'\n',B)A shorter example:importnumpyasnpimportfeatherhelperasfh# fh.exc()# %%try:A=fh.pull()exceptfh.Err:A=np.random.random(16).reshape(4,4)fh.push(A)
Package: feather-nlp
available in low powered devices like raspberry pi
Home-page:https://github.com/Chidhambararajan/feather_nlpAuthor: Chidhambararajan
Author-email:chidha1434@protonmail.comLicense: MIT
Download-URL:https://github.com/Chidhambararajan/feather_nlp/archive/master.zipDescription: UNKNOWN
Keywords: NLP,machine learning,rasa nlu
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Package: featherplot
featherplotDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nfeatherplot--fileenv.yml# $ mamba env update -n featherplot --file env.mac.ymlInstallpipinstall-e.# install from pypipipinstallfeatherplotnbdev# activate conda environment$condaactivatefeatherplot# make sure the featherplot package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the featherplot package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'--mambabuildUsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/featherplot-py.gitor fromconda$condainstall-cdsm-72featherplotor frompypi$pipinstallfeatherplot
Package: feathery-py
[’# feathery-py’, ‘’, ‘## Important Links’, ‘### Github: [NotOddity/feathery](https://github.com/NotOddity/feathery)’, ‘’, ‘## Documentation:’, ‘### Full Documentation: Link to come’, ‘### Getting Started: Link to come’, ‘### Creating Services: Link to come’, ‘### Creating Models: Link to come’, ‘### Creating Caches: Link to come’]
Package: featman
FeatmanFeatures and sh*t. Use as an abstraction toSpaceman. It can be used with the storage layers spaceman provides.fromfeatmanimportFeaturesfeatures=(Features(host="localhost",db="global").set_storage("...").set_type("...").set_time(now=False,back=True,month=0,day=0,hour=0,minute=30).set_data(dataframe:pd.Dataframe,time_index=True,scale="minute").set_data(dataframe:pd.Dataframe,time_index=True,scale="minute").set_data(dataframe:pd.Dataframe,time_index=True,scale="minute").set_data(dataframe:pd.Dataframe,time_index=True,scale="minute").push())
Package: feats
FEATSDescriptionFEATS is a new Python tool for performing the following downstream analysis on single-cell RNA-seq datasets:ClusteringEstimating the number of clustersOutlier detectionBatch correction and integration of data from multiple experimentsPrerequisitesFEATS depends on the following packagesnumpypandasscikit-learnscipysinglecelldataInstallationTo install FEATS run the following command:pip install featsDocumentationThe functional reference manual for FEATS is availablehere.ExamplesTo use FEATS, please refer to the following example code presented in notebook sytle environment.Clustering using FEATSPerforming outlier analysisPerforming batch correctionDataThe data for the examples in this section is availablehere. The data is contained in subfolders in the datasets folder. The subfolders are named according to the dataset name. To load the data for the examples above, provide the path to the datasets folder.PaperComing soon!ContactContact the author onvans.edw@gmail.comto give feedback/suggestions for further improvements and to report issues.
Package: feature-aggregation
This is a library that implements methods to aggregate local features
(mainly for multimedia) into a single global feature that can be used
easily with any classifier.DependenciesThe library depends onscikit-learnand all the feature aggregation
methods extend the scikit-learn BaseEstimator class.Exampleimportnumpyasnpfromfeature_aggregationimportBagOfWords,FisherVectorsX=np.random.rand(1000,2)bow=BagOfWords(10)fv=FisherVectors(10)bow.fit(X)fv.fit(X)G1=bow.transform(np.random.rand(10,100,2))G2=fv.transform([np.random.rand(int(np.random.rand()*100),2)for_inrange(10)])A more complex example using OpenCV to extract dense SIFT and then
transform them using Bag Of Words and train an SVM with chi square
additive kernel.importnumpyasnpimportcv2fromsklearn.datasetsimportfetch_olivetti_facesfromsklearn.kernel_approximationimportAdditiveChi2Samplerfromsklearn.metricsimportclassification_reportfromsklearn.pipelineimportPipelinefromsklearn.svmimportLinearSVCfromfeature_aggregationimportBagOfWordsdefsift(*args,**kwargs):try:returncv2.xfeatures2d.SIFT_create(*args,**kwargs)except:returncv2.SIFT()defdsift(img,step=5):keypoints=[cv2.KeyPoint(x,y,step)foryinrange(0,img.shape[0],step)forxinrange(0,img.shape[1],step)]features=sift().compute(img,keypoints)[1]features/=features.sum(axis=1).reshape(-1,1)returnfeatures# Generate dense SIFT featuresfaces=fetch_olivetti_faces()features=[dsift((x.reshape(64,64,1)*255).astype(np.uint8))forxinfaces.data]# Aggregate those features with bag of words using online trainingbow=BagOfWords(100)foriinrange(2):forjinrange(0,len(features),10):bow.partial_fit(features[j:j+10])faces_bow=bow.transform(features)# Split in training and test settrain=np.arange(len(features))np.random.shuffle(train)test=train[200:]train=train[:200]# Train and evaluatesvm=Pipeline([("chi2",AdditiveChi2Sampler()),("svm",LinearSVC(C=10))])svm.fit(faces_bow[train],faces.target[train])print(classification_report(faces.target[test],svm.predict(faces_bow[test])))
Package: featurebase
python-featurebasePython client for Featurebase SQL endpoint.For more complete documentation, see:https://docs.featurebase.com/docs/tools/python-client-library/python-client-library-home/Client Library Usage:First install the python-featurebase package. Runningmakefrom project folder
will build and install the package. After installing the package you can try
executing queries as shown in the following examples:import featurebase

# assuming featurebase runs at "localhost:10101"
# for cloud, pass hostport="query.featurebase.com/v2", database="<database_id>", apikey="<APIKey_secret>"
# create client
client = featurebase.client()

# query the endpoint with SQL
try:
    result = client.query("SELECT * from demo;")
    print(result.data)
except Exception as e:
    # SQL errors and connection errors both come back as exceptions
    print(e)

# query the endpoint with a batch of SQLs, running the SQLs synchronously
# Synchronous run best suited for executing DDL and DMLs that need to follow specific run order
# passing the optional parameter "stoponerror=True" will stop execution at the failed SQL and the remaining SQLs in the list will not be executed. 
sqllist=[]
sqllist.append("CREATE TABLE demo1(_id id, i1 int);")
sqllist.append("INSERT INTO demo1(_id, i1) VALUES(1, 100);")
sqllist.append("INSERT INTO demo1(_id, i1) VALUES(2, 200);")
sqllist.append("select * from demo1;")
results = client.querybatch(sqllist, stoponerror=True)
for result in results:
    print(result.data)
    
# query the endpoint with a batch of SQLs, running the SQLs Asynchronously
# Asynchronous run best suited for running SELECT queries that can be run concurrently.
sqllist=[]
sqllist.append("SELECT * from demo1;")
sqllist.append("SELECT count(*) from demo1;")
sqllist.append("SELECT max(i1) from demo1;")
results = client.querybatch(sqllist, asynchronous=True)
for result in results:
    print(result.data)
Package: featurebox
FeatureboxWelcome to Featurebox.Featurebox is an open Python library that implements a comprehensive set of machine learning tools for materials
informatics.Featurebox inspired byXenonPy,matminer,sympyDocumentfeaturebox documentQuick InstallpipinstallfeatureboxLicenseGNU LGPL-3.0 LicenseContactDeveloper: Boliqq07:986798607@qq.comSupport
Package: feature-check
feature-check - query a program for supported featuresThefeature_checklibrary obtains the list of supported features from
a program via various methods and allows programs to check for the presence
and, possibly, versions of specific features.Thefeature_checklibrary is fully typed.Obtaining the features supported by a programTheobtain_features()function in thefeature_checkmodule runs
a program with the appropriate option to obtain its list of features;
the default is to pass the--featuresoption, but this may be overridden.
Theobtain_features()function then examines the output to find a line
that matches the specified prefix (or the defaultFeatures:prefix) and
expects the rest of the line to be a whitespace-separated list of either
feature names orname=versionpairs.  It returns a dictionary of
the features obtained with their versions (or1.0if only a feature name
was found in the program's output).import feature_check

data = feature_check.obtain_features("timelimit")
print(data.get("subsecond", "not supported"))For programs that need a different command-line option to list features:import feature_check

print("SSL" in feature_check.obtain_features("curl", option="--version"))Testing for feature versionsThefeature_checklibrary also provides a simple expression evaluation
mechanism for examining feature versions - theexprmodule defines
severalExprandResultclasses and also provides theparse_simple()function (also exported byfeature_check()itself) for creating simple
version comparisons:import feature_check

data = feature_check.obtain_features("timelimit")
expr = feature_check.parse_simple("subsecond >= 1")
print(expr.evaluate(data).value)Contact the authorFor more information, please see thefeature_checklibrary'shomepageor contact the author,Peter Pentchev.