Saravanan666/pypi-org huggingface dataset:
Package: sagax
Sensu dashboard.Basic InstallBasic installpipinstallsagaxRun hug’s built-in development serverSAGAX_CONFIG_FILE=/path/to/config.ymlhug-msagax.apiProduction InstallInstall with Cython-compiled Falcon, to be served by Gunicorn, with LDAP
authentication.pipinstallcythongunicornpipinstall--no-binaryfalconsagax[ldap]Then, you can run the web server:SAGAX_CONFIG_FILE=/path/to/config.ymlgunicorn-w4sagax.api:__hug_wsgi__ConfigurationSet the environment variableSAGAX_CONFIG_FILEto point at the path of a YAML
file containing any config overrides.Example configuration file:# Sensu configuration must be provided (no defaults).sensu_type:Sensu1APIsensu:url:https://sensu.example.cominsecure:truetimeout:5# Defaults to NO AUTHENTICATION. Here is LDAP example.require_authentication:trueauthentication_type:ldapldap:url:ldaps://ldap.example.comuser_pattern:uid={},ou=Users,dc=example,dc=comrequire_groups:-operations# Defaults shown here.jwt:secret_key:overrideme!issuer:sagaxalgorithm:HS256ttl:1209600# All of these settings are sent to the frontend UI, so be sure not to put# anything sensitive in here.frontend:extra_css:-http://example.com/custom-sagax-styles.csstimestamp_format:L LT zrefresh_interval:5silence_intervals:-15 minutes-30 minutes-1 hour-2 hours-4 hours-8 hours-1 day# Table fields can be overridden here. See config.py for defaults.fields:{}BuildingBuild prerequisites:yarnPython 3.5+To build:gitclonehttps://github.com/joshbenner/sagax.gitcdsagaxpythonsetup.pybdist_wheel
Package: sagbprojections
This is a module that allows you to query NBA Daily Fantasy Projections from Berkeley’s Sports Analytics Group (SAGB).getPlayerStats(name) - returns statline projections for specific player name if they are playing todaygetTeamStats(team) - returns statline projections for specific team if they are playing today, requires 3 letter team abbreviationscoresOver(fantasy, score) - Get players with scores over a specified score based on Draftkings or Fanduel, pass in either fanduel or draftkings to the fantasy argumentfanduelOptimal() - returns FanDuel optimal lineupdraftkingsOptimal() - returns DraftKings optimal lineupstatsOver(stat, value) - returns statline projections for players with a stat (Minutes, 2 PT FG, 3 PT FG, FTM, Rebounds, Assists, Blocks, Steals, Turnovers) over a certain valuegetInjured() - names and injury types for all injured playersgetAllStats() - returns all statline projections for the daygetAllProjections() - returns all fanduel and draftkings score projections for the daygetStatsPerMinute() - returns projected statline with per minute numbersChange Log0.0.1 (12/24/2020)Initial test release with only 2020 NBA Finals Game 6 Projections0.0.2 (12/24/2020)Updated to include static filesThis version was yanked due to errors0.0.3 (02/25/2021)Updated to pull live predictions from SAGB website0.0.4 (03/16/2021)Updated to included per minute statistics0.0.5 (03/16/2021)Updated to included missing function description of getStatsPerMinute in readme
Package: sage3reader
Python SAGE Data ReaderA python reader for SAGE III and SAGE III ISS binary data files into an xarray dataset.Tested with versions:5.05.15.2DownloadsThe binary files are not supplied and must be downloaded by the user.SAGEIII binary filesSAGEIII-ISS binary filesInstallationTo install the package run:pip install sage3readerDataset detailsThe user should be able to match the coordinates and variables in the dataset with those in the binary file format sheet
(Table C1.3 of theData Product User Guide)
taking note of the following changesBinary FormatDatasetstart_date (yyyymmdd) and start_time (hhmmss) integersstart_time (datetime64)end_date (yyyymmdd) and end_time (hhmmss) integersend_time (datetime64)ground track date and time integersground track datetime64ExamplesTo load data for a single profile:fromsage3readerimportl2binary_to_datasets3data=l2binary_to_dataset('/path/to/file')To load all profiles in a directory tree:fromsage3readerimportmulti_path_l2binary_to_datasets3data=multi_path_l2binary_to_dataset('/path/to/files')Other examples can be found in theexamplesfolder.
Package: sage-aata
Sage - Abstract Algebra: Theory and ApplicationsThis package was designed as part of a Project at Southern Utah University.  The original project can be found atGithuband was originally written in SageMath in Jupyter Notebooks.The original project was designed to be used in Jupyter Notebooks, but this package was later created for those who wish to
easily use the functions in this project from a Sage Terminal.Please note that this package isnotmeant to be run in Python, but rather is meant to be run
in a SageMath terminal.  Thus, while most of the functions defined in this project may
work while run in Python, for full functionality, and for ease of use, SageMath is needed.The purpose of this project is to help students everywhere to understand topics taught in an undergraduate
Abstract Algebra class by helping them to visualize these topics in SageMath.  The full project uses Jupyter Notebooks
to help with this process, although all the functions defined in the original project can also be accessed here in this
package.
Package: sage-acsv
sage_acsvThis repository hosts the implementation of a SageMath package
containing algorithms for analytic combinatorics in several variables.The package works with any reasonably recent version of SageMath, we
recommend to have SageMath 9.4 (released in August 2021) or newer.QuickstartThe easiest way to install the latest released version of the package
is via PyPI simply by runningsage-pipinstallsage-acsvThe package can be run in an interactive environment in the browser
usingBinder.An article serving as an introduction to version 0.1.0 of the package
and its internals can be found on thearXiv.Installation from sourceTo install the package from the source code, either clone the git repository
and run the commandsage-pipinstall.from the root directory, i.e., the directory containing thepyproject.tomlfile.For development, usesage -pip install -e .for an editable installation.Alternatively, to install the latest version of the main branch directly from
the GitHub repository, runsage-pipinstallgit+https://github.com/ACSVMath/sage_acsv.gitRunning package testsThe doctests that are added in the package can be run by executingsage-tsage_acsvfrom the root of the cloned repository. The tests are run automatically
on every push and for any PR to themainbranch, and compatibility with
several different SageMath releases (seerecent workflows to see all tested versions)
is checked.
Package: sage-combinat-widgets
Jupyter editable widgets for Sagemath combinatorial objects:Partition & Skew PartitionStandard/Semi-standard/Generic Tableau & Skew TableauParallelogram PolyominoAlso :Matricesgrid-representable GraphsInstallationLocal install from sourceDownload the source from the git repository:$ git clone https://github.com/sagemath/sage-combinat-widgets.gitChange to the root directory and run:$ sage -pip install --upgrade --no-index -v .For convenience this package contains a [makefile](makefile) with this
and other often used commands. Should you wish too, you can use the
shorthand:$ make installUsageOnce the package is installed, you can use it in Sage Jupyter Notebook.from sage_combinat_widgets import GridViewWidgetS = StandardTableaux(15).random_element()w = TableauWidget(t)wSee thedemo notebook.TestsOnce the package is installed, one can use the SageMath test system
configured insetup.pyto run the tests:$ sage setup.py testThis is just callingsage-twith appropriate flags.Shorthand:$ make testDocumentationThe documentation of the package can be generated using Sage’sSphinxinstallation:$ cd docs
$ sage -sh -c "make html"Shorthand:$ make docAcknowledgmentsThis package was created under funding of the Horizon 2020 European Research Infrastructure projectOpenDreamKit(grant agreement#676541).
Package: sage-data-client
Sage Data API ClientThis is the official Sage Python data API client. Its main goal is to make writing queries and working with the results easy. It does this by:Providing a simple query function which talks to the data API.Providing the results in an easy to usePandasdata frame.Installationpipinstallsage-data-clientUsage ExamplesQuery APIimportsage_data_client# query and load data into pandas data framedf=sage_data_client.query(start="-1h",filter={"name":"env.temperature",})# print results in data frameprint(df)# meta columns are expanded into meta.fieldname. for example, here we print the unique nodesprint(df["meta.vsn"].unique())# print stats of the temperature data grouped by node + sensor.print(df.groupby(["meta.vsn","meta.sensor"]).value.agg(["size","min","max","mean"]))importsage_data_client# query and load data into pandas data framedf=sage_data_client.query(start="-1h",filter={"name":"env.raingauge.*",})# print number of results of each nameprint(df.groupby(["meta.vsn","name"]).size())Load results from fileIf we have saved the results of a query to a filedata.json, we can also load using theloadfunction as follows:importsage_data_client# load results from local filedf=sage_data_client.load("data.json")# print number of results of each nameprint(df.groupby(["meta.vsn","name"]).size())Integration with NotebooksSince we leverage the fantastic work provided by the Pandas library, performing things like looking at dataframes or creating plots is easy.A basic example of querying and plotting data can be foundhere.Additional ExamplesAdditional code examples can be found in theexamplesdirectory.If you're interested in contributing your own examples, feel free to add them toexamples/contriband open a PR!ReferenceThequeryfunction accepts the following arguments:start. Absolute or relative start timestamp. (required)end. Absolute or relative end timestamp.head. Limit results toheadearliest values per series. (Only one ofheadortailcan be provided.)tail. Limit results totaillatest values per series. (Only one ofheadortailcan be provided.)filter. Key-value patterns to filter data on.
Package: sage-docbuild
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2020 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via Sage-the-distribution (https://www.sagemath.org/download-source.html).  Sage-the-distribution first builds a large number of open source packages from source (unless it finds suitable versions installed in the system) and then installs the Sage Library (sagelib, implemented in Python and Cython).About this pip-installable source distributionThis is the build system of the Sage documentation, based on Sphinx.
Package: sage-explorer
AJupyter Notebookwidget for exploringSageMathobjects.InstallationTry it on binderdemoLocal install from sourceDownload the source from the git repository:$ git clone https://github.com/sagemath/sage-explorer.gitChange to the root directory and run:$ sage -pip install --upgrade --no-index -v .For convenience this package contains a [makefile](makefile) with this
and other often used commands. Should you wish too, you can use the
shorthand:$ make installInstall from PyPISage Explorer is distributed on PyPI.$ sage -pip install sage_explorerUsageSee thedemo notebook.How to contributeThe most practical process for contributions is to# open a ticket to describe what you intend to do
# write your patch
# make a pull requestPlease PR to the branchdevelopas the branchmasterholds only stable code.AcknowledgmentsThis package was created under funding of the Horizon 2020 European Research Infrastructure projectOpenDreamKit(grant agreement#676541).Nathan Carteroffered inspiring insights for the new 0.5.0 design.
Package: sage-flatsurf
sage-flatsurfsage-flatsurf is a Python package for working with flat surfaces inSageMath.We aim for sage-flatsurf to support the investigation of geometric, algebraic
and dynamical questions related to flat surfaces. By flat surface we mean a
surface modeled on the plane with monodromy given by similarities of the plane,
though current efforts are focused ontranslation
surfacesandhalf-translation
surfaces.Please consult ourdocumentationto see some of the capabilities of sage-flatsurf.sage-flatsurf is free software, released under theGPL v2 (or later).We welcome any help to improve sage-flatsurf. If you would like to help, have
ideas for improvements, or if you need any assistance in using sage-flatsurf,
please don't hesitate tocontact us.InstallationSeeour documentationfor detailed installation instructions.Run with Binder in the CloudYou can try out sage-flatsurf in an environment online; unfortunately it might
take a long time for this environment to start:Developing sage-flatsurfPlease consult ourDeveloper's
Guideto build
sage-flaturf from source and to run our test suite.ContributorsThe main authors and current maintainers of sage-flatsurf are:Vincent Delecroix (Bordeaux)W. Patrick Hooper (City College of New York and CUNY Graduate Center)Julian RüthWe welcome others tocontribute.How to Cite This ProjectIf you have used this project, please cite us as describedon our
zenodo website.Acknowledgementssage-flatsurf was started during a thematic semester atICERM.Vincent Delecroix's contribution to the project has been supported by
OpenDreamKit, Horizon 2020 European Research Infrastructures project #676541.W. Patrick Hooper's contribution to the project has been supported by the National
Science Foundation under Grant Number DMS-1500965. Any opinions, findings,
and conclusions or recommendations expressed in this material are those of
the authors and do not necessarily reflect the views of the National Science
Foundation.Julian Rüth's contributions to this project have been supported by the Simons
Foundation Investigator grant of Alex Eskin.
Package: sageinspector
Sage-InspectorA tool to inspect SageMaker resources more easily.CLISage-Inspectorprovides ansicommand to quickly access resources such as logs.Example:$ si logs tail -n 20 arn:aws:sagemaker:us-west-2:123456789012:training-job/my-jobAuthenticationWhen usingsi, the tool searches~/.aws/configfor the account-id of the
provided arn.For example, the below section would match for the arn used above, and thussiwould useboto3.Session(profilen_name="my_account")internally.[profile my-account]
account = 123456789012If it can't find account information, sage-inspector falls back to a default
session.
Package: sagemaker-container-support
This package makes it easier to build docker images which run on Amazon SageMaker. For example, the official SageMakerTensorFlowandMXNetimages use this package. The requirements for SageMaker-compatible images are documented here:https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.htmlFeatures provided:Integration withSageMaker Python SDKEstimators, including:Downloading user-provided python codeDeserializing hyperparameters (which preserves their python types)bin/entry.py, which acts as the docker entrypoint required by SageMakerReading in the metadata files provided to the container during training, as described here:https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.htmlnginx + Gunicorn HTTP server for serving inference requests which complies with the interface described here:https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.htmlLicenseThis library is licensed under the Apache 2.0 License.
Package: sagemaker-experiments-logger
SagemakerExperimentsLoggerSagemakerExperimentsLogger provides a simple way to log experimental data such as hyperparameter settings and evaluation metrics viaAWS SageMaker Experiments API. It can be easily integration into the concept ofPytorch Lightning Trainer class.For detailed documentation, including the API reference, seeRead the DocsInstallationYou can install the latest (nightly) version with pip using ssh withpipinstallsagemaker-experiments-loggerQuickstartThe SageMaker Experiments logger can be easily applied by setup an own run context:frompytorch_lightningimportTrainerfromsagemaker.experiments.runimportRunfromexperiments_addon.loggerimportSagemakerExperimentsLoggerwithRun(experiment_name="testExperiment",run_name="testRun1"):logger=SagemakerExperimentsLogger()trainer=Trainer(logger=logger,...)...or by using an existing run context. For example in a SageMaker Training Stepfrompytorch_lightningimportTrainerfromexperiments_addon.loggerimportSagemakerExperimentsLoggerlogger=SagemakerExperimentsLogger(experiment_name="TestExp",run_name="TestRun")trainer=Trainer(logger=logger,...)...UsageTryTutorial Notebookto learn more about the usage of theSagemakerExperimentsLoggerclass.It is also worth to read the blog post:Experiment Tracking With AWS SageMaker and PyTorch LightningContributingI welcome all contributions!To file a bug or request a feature, please file a GitHub issue. Pull requests are welcome.LicenseThis library is licensed under the Apache 2.0 License.
Package: sagemaker-feature-store-pyspark
OverviewSageMaker FeatureStore Spark is a connector library forAmazon SageMaker FeatureStore.With this spark connector, you can easily ingest data to FeatureGroup's online and offline store from SparkDataFrame. Also, this connector contains the functionality to automatically load feature definitions to help with creating feature groups.Getting StartedNote: For more information about installation, code samples etc, please reference the FeatureStore AWSdocumentation.
Package: sagemaker-feature-store-pyspark-3.0
OverviewSageMaker FeatureStore Spark is a connector library forAmazon SageMaker FeatureStore.With this spark connector, you can easily ingest data to FeatureGroup's online and offline store from SparkDataFrame. Also, this connector contains the functionality to automatically load feature definitions to help with creating feature groups.Getting StartedNote: For more information about installation, code samples etc, please reference the FeatureStore AWSdocumentation.
Package: sagemaker-feature-store-pyspark-3.1
OverviewSageMaker FeatureStore Spark is a connector library forAmazon SageMaker FeatureStore.With this spark connector, you can easily ingest data to FeatureGroup's online and offline store from SparkDataFrame. Also, this connector contains the functionality to automatically load feature definitions to help with creating feature groups.Getting StartedNote: For more information about installation, code samples etc, please reference the FeatureStore AWSdocumentation.
Package: sagemaker-feature-store-pyspark-3.2
OverviewSageMaker FeatureStore Spark is a connector library forAmazon SageMaker FeatureStore.With this spark connector, you can easily ingest data to FeatureGroup's online and offline store from SparkDataFrame. Also, this connector contains the functionality to automatically load feature definitions to help with creating feature groups.Getting StartedNote: For more information about installation, code samples etc, please reference the FeatureStore AWSdocumentation.
Package: sagemaker-feature-store-pyspark-3.3
OverviewSageMaker FeatureStore Spark is a connector library forAmazon SageMaker FeatureStore.With this spark connector, you can easily ingest data to FeatureGroup's online and offline store from SparkDataFrame. Also, this connector contains the functionality to automatically load feature definitions to help with creating feature groups.Getting StartedNote: For more information about installation, code samples etc, please reference the FeatureStore AWSdocumentation.
Package: sagemaker-headless-execution-driver
SageMaker  Headless Notebook Execution DriverThis package is used to host headless execution manager/driver logic and its dependent scripts for notebook execution. Other types of headless execution like python script, java jar based job types could be supported later by adding new types of manager/driver.DescriptionThe sagemaker_headless_execution_driver facilitates running SageMaker notebook jobs in a headless mode. It integrates seamlessly with the Amazon SageMaker Jupyter Scheduler and provides the necessary mechanisms to execute Jupyter notebooks as automated jobs.FeaturesExecutes Jupyter notebooks as SageMaker Training Jobs.Compatible with custom SageMaker images.Extensible and easily integrable with other SageMaker toolingInstallationTo install the package:pip install sagemaker-headless-execution-driverFor Conda users:conda install -c conda-forge sagemaker-headless-execution-driverDependeciesIPythonPapermillLicenseThis project is licensed under the terms of the Apache 2.0 license. See LICENSE for more details.
Package: sagemaker-jupyterlab-emr-extension
sagemaker_jupyterlab_emr_extensionThis package includes the extension built by SageMaker team that includes provides EMR connectivity feature for JupyterLab.Installing the extensionTo install the extension within local Jupyter environment, a Docker image/container or in SageMaker Studio, run:pip install sagemaker_jupyterlab_emr_extension-<version>-py3-none-any.whl`Uninstalling the extensionTo uninstall this extension, run:pip uninstall sagemaker_jupyterlab_emr_extension`TroubleshootingMake sure to have node >=16 and Python >=3.9 installed in your dev desktopIf you are seeing the frontend extension, but it is not working, check that the server extension is enabled:jupyter serverextension listIf the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:jupyter labextension listIf the frontend extension is installed and enabled, open Browser console and see if there are any JavaScript error that is related to the extension in Browser console.See DEVELOPING.md for more instructions on dev setup and contributing guidelines
Package: sagemaker-jupyterlab-extension
sagemaker_jupyterlab_extension:This package includes the JupyterLab extension built by SageMaker team that includes basic SageMaker JupyterLab IDE features, including resource usage (CPU, memory and storage), SageMaker JupyterLab session management and Git clone dialog.RequirementsJupyterLab >= 4sagemaker_jupyterlab_extension_commonasync-lruaws_embedded_metricsInstalling the extensionTo install the extension within local Jupyter environment, a Docker image/container or in SageMaker Studio, run:pip install sagemaker_jupyterlab_extension-<version>-py3-none-any.whl`Uninstalling the extensionTo uninstall this extension, run:pip uninstall sagemaker_jupyterlab_extension`TroubleshootingIf you are seeing the frontend extension, but it is not working, check that the server extension is enabled:jupyter serverextension listIf the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:jupyter labextension listIf the frontend extension is installed and enabled, open Browser console and see if there are any JavaScript error that is related to the extension in Browser console.See DEVELOPING.md for more instructions on dev setup and contributing guidelines
Package: sagemaker-jupyterlab-extension-common
sagemaker_jupyterlab_extension_commonThis package includes the common extensions built by SageMaker team that includes tools and utilities that can be shared by different extensions built by SageMaker.RequirementsJupyterLab >= 4aiobotocoreaws_embedded_metricsInstalling the extensionTo install the extension within local Jupyter environment, a Docker image/container or in SageMaker Studio, run:pip install sagemaker_jupyterlab_extension_common-<version>-py3-none-any.whl`Uninstalling the extensionTo uninstall this extension, run:pip uninstall sagemaker_jupyterlab_extension_common`TroubleshootingIf you are seeing the frontend extension, but it is not working, check that the server extension is enabled:jupyter serverextension listIf the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:jupyter labextension listIf the frontend extension is installed and enabled, open Browser console and see if there are any JavaScript error that is related to the extension in Browser console.See DEVELOPING.md for more instructions on dev setup and contributing guidelines