Saravanan666/pypi-org huggingface dataset:
Package: jgraph
jgraph======An embeddable webGL graph visualization library.http://patrickfuller.github.io/jgraph/Examples========* [IPython notebook](http://patrickfuller.github.io/jgraph/examples/ipython.html)* [les misérables](http://patrickfuller.github.io/jgraph/examples/miserables.html)* [github connections](http://patrickfuller.github.io/jgraph/examples/github.html)IPython=======The IPython notebook is an open-source tool poised to replace MATLAB in manyapplications. As a scientist of sorts, I'm all about it. Therefore, I madehandles to use jgraph with the notebook. Install through pip:```pip install jgraph```Open a new notebook and test the setup by typing:```pythonimport jgraphjgraph.draw([(1, 2), (2, 3), (3, 4), (4, 1), (4, 5), (5, 2)])```into a notebook cell. You should get a paddlewheel graph as an output. You canuse this in conjunction with other code for educational purposes (try generatinga red-black tree!). There are three commands and some optional parameters tocheck out. Read the docstrings and check out the [associatedexample](http://patrickfuller.github.io/jgraph/examples/ipython.html) for more.Javascript==========You can install through [npm](https://www.npmjs.com/):```npm install jgraph```Once installed, you can use with:```javascriptjgraph.create('my-selector');jgraph.draw(myGraph);```where `'my-selector'` is where you want to place jgraph, and `myGraph` is ajavascript object. See below for more on the object structure, or just check outthe included example. The `jgraph.create()` method takes a few optionalparameters, specifying the sizes and colors of nodes, as well as force-directedoptimization.```javascriptoptions = {directed: true, // Toggles edge arrowsnodeSize: 2.0, // Default node sizeedgeSize: 0.25, // Edge connection diameterarrowSize: 1.0, // If drawn, edge arrow sizedefaultNodeColor: 0xaaaaaa, // Color for nodes without a "color" propertydefaultEdgeColor: 0x777777, // Color for edges without a "color" propertyshader: "toon", // three.js shader to use, can be "toon", "basic", "phong", or "lambert"runOptimization: true // Runs a force-directed-layout algorithm on the graph};```Graph Data Format=================jgraph takes input graph data structures as plain objects. Here's the mostboring graph in the world:```javascript{nodes: {jane: { },bob: { },mike: { },sally: { }},edges: [{ source: "jane", target: "bob" },{ source: "bob", target: "mike" },{ source: "mike", target: "sally" }]}```Nodes require no information outside of their keys. However, there are usefuloptional parameters that can be specified.```javascript{color: 0xffffff, // Color for this nodesize: 1.0, // Scaling factor for this node's sizelocation: [0.0, 0.0, 0.0] // Starting location of node. Useful for pre-rendering.}```By default, the algorithm runs a force-directed layout on the graph. Whenenabled, the "location" field is optional. However, for larger graphs, you willwant to disable this feature and pre-render the locations. Use the associatedPython library (`jgraph.generate`) to do so.
Package: jgreenepack
jgreenepackNo description provided (generated by Swagger Codegenhttps://github.com/swagger-api/swagger-codegen)This Python package is automatically generated by theSwagger Codegenproject:API version: 1.0.0Package version: 1.79.0Build package: io.swagger.codegen.languages.PythonClientCodegenRequirements.Python 2.7 and 3.4+Installation & Usagepip installIf the python package is hosted on Github, you can install directly from Githubpipinstallgit+https://github.com//.git(you may need to runpipwith root permission:sudo pip install git+https://github.com//.git)Then import the package:importjgreenepackSetuptoolsInstall viaSetuptools.pythonsetup.pyinstall--user(orsudo python setup.py installto install the package for all users)Then import the package:importjgreenepackGetting StartedPlease follow theinstallation procedureand then run the following:from__future__importprint_functionimporttimeimportjgreenepackfromjgreenepack.restimportApiExceptionfrompprintimportpprint# create an instance of the API classapi_instance=jgreenepack.PetsApi(jgreenepack.ApiClient(configuration))try:# Create a petapi_instance.create_pets()exceptApiExceptionase:print("Exception when calling PetsApi->create_pets:%s\n"%e)Documentation for API EndpointsAll URIs are relative tohttp://petstore.swagger.io/v1ClassMethodHTTP requestDescriptionPetsApicreate_petsPOST/petsCreate a petPetsApilist_petsGET/petsList all petsPetsApishow_pet_by_idGET/pets/{petId}Info for a specific petDocumentation For ModelsErrorPetPetsDocumentation For AuthorizationAll endpoints do not require authorization.Author
Package: jgrepl
# json-graph-replExtensible command-line REPL for interacting with JSON graphs containing business objects.The JSON format is based on the `json-graph` specification at: https://github.com/jsongraph/json-graph-specificationSeveral additional assumptions are made:* We assume that the graph contains no cycles* We treat node IDs as case-insensitive* There are certain `metadata` fields that some aspects of the tool depend on (but none of these are mandatory)Sample graphs are provided in the `tests` directory.To use from the command line, just point the `jgrepl` tool to your JSON graph:$ ./jgrepl/jgrepl.py tests/food-graph.jsonOnce in the REPL, type `help` for the list of available commands. Use `ctrl-d` to exit.Here is a sample session using the "food" graph:*** Loading graph from 'tests/food-graph.json'...JSON Graph REPL v.0.1.2/> info         // The `info` command displays information about the current node or graphCURRENT GRAPH: Sample Food Graph ('tests/food-graph.json')GRAPH TYPE: food graphNODES: 14EDGES: 11{                     // All metadata is shown here:"version": "1.0"}/> ls -lCAT1 category VegetablesCAT2 category FruitsCAT3 category Sweets/> cd CAT3          // Change to a new path. Note the prompt changes to reflect your location./CAT3> infoNODE ID: cat3NODE TYPE: categoryNODE LABEL: Sweets{"available_from": "2017-07-18","available_to": "2017-11-18"}/CAT3> find food8  // Print all paths to this node ID/CAT3/CAT32/FOOD8/CAT3> cd $    // Switch to the last shown path/CAT3/CAT32/FOOD8>
Package: jgtapy
jgtapyTechnical Indicators for the Pandas' DataframesDocumentation:https://pandastechindicators.readthedocs.io/en/latest/Installationpip install -U jgtapyExample>>> import pandas as pd
>>> from jgtapy import Indicators
>>> df = pd.read_csv('EURUSD60.csv')
>>> i= Indicators(df)
>>> i.accelerator_oscillator(column_name='AC'
>>> i.fractals(column_name_high='fb', column_name_low='fs')
>>> i.fractals3(column_name_high='fb3', column_name_low='fs3')
>>> i.fractals5(column_name_high='fb5', column_name_low='fs5')
>>> ... 8,13,21,34,55,89
>>> i.sma()
>>> df = i.df
>>> df.tail()
            Date   Time     Open     High      Low    Close  Volume        AC       sma
3723  2019.09.20  16:00  1.10022  1.10105  1.10010  1.10070    2888 -0.001155  1.101296
3724  2019.09.20  17:00  1.10068  1.10193  1.10054  1.10184    6116 -0.000820  1.101158
3725  2019.09.20  18:00  1.10186  1.10194  1.10095  1.10144    3757 -0.000400  1.101056
3726  2019.09.20  19:00  1.10146  1.10215  1.10121  1.10188    3069  0.000022  1.101216
3727  2019.09.20  20:00  1.10184  1.10215  1.10147  1.10167    1224  0.000388  1.101506Available IndicatorsAccelerator Oscillator (AC)Accumulation/Distribution (A/D)AlligatorAverage True Range (ATR)Awesome Oscillator (AO)Bears PowerBollinger BandsBulls PowerCommodity Channel Index (CCI)DeMarker (DeM)Exponential Moving Average (EMA)Force Index (FRC)Fractals (dimension 2,3,5,8,13,21,34,55,89)Gator OscillatorIchimoku Kinko HyoMarket Facilitation Index (BW MFI)MomentumMoney Flow Index (MFI)Moving Average Convergence/Divergence (MACD)Simple Moving Average (SMA)Smoothed Moving Average (SMMA)
Package: jgtapy-beta
jgtapy-betaTechnical Indicators for the Pandas' DataframesDocumentation:https://pandastechindicators.readthedocs.io/en/latest/Installationpip install -U jgtapy-betaExample>>> import pandas as pd
>>> from jgtapy import Indicators
>>> df = pd.read_csv('EURUSD60.csv')
>>> i= Indicators(df)
>>> i.accelerator_oscillator(column_name='AC'
>>> i.fractals(column_name_high='fb', column_name_low='fs')
>>> i.fractals3(column_name_high='fb3', column_name_low='fs3')
>>> i.fractals5(column_name_high='fb5', column_name_low='fs5')
>>> ... 8,13,21,34,55,89
>>> i.sma()
>>> df = i.df
>>> df.tail()
            Date   Time     Open     High      Low    Close  Volume        AC       sma
3723  2019.09.20  16:00  1.10022  1.10105  1.10010  1.10070    2888 -0.001155  1.101296
3724  2019.09.20  17:00  1.10068  1.10193  1.10054  1.10184    6116 -0.000820  1.101158
3725  2019.09.20  18:00  1.10186  1.10194  1.10095  1.10144    3757 -0.000400  1.101056
3726  2019.09.20  19:00  1.10146  1.10215  1.10121  1.10188    3069  0.000022  1.101216
3727  2019.09.20  20:00  1.10184  1.10215  1.10147  1.10167    1224  0.000388  1.101506Available IndicatorsAccelerator Oscillator (AC)Accumulation/Distribution (A/D)AlligatorAverage True Range (ATR)Awesome Oscillator (AO)Bears PowerBollinger BandsBulls PowerCommodity Channel Index (CCI)DeMarker (DeM)Exponential Moving Average (EMA)Force Index (FRC)Fractals (dimension 2,3,5,8,13,21,34,55,89)Gator OscillatorIchimoku Kinko HyoMarket Facilitation Index (BW MFI)MomentumMoney Flow Index (MFI)Moving Average Convergence/Divergence (MACD)Simple Moving Average (SMA)Smoothed Moving Average (SMMA)
Package: jgtfxcon
jgtfxconjust getting prices from fxconnectInstallationpipinstall-UjgtfxconExample>>>importpandasaspd>>>importjgtfxcon>>>df=jgtfxcon.getPH('EUR/USD','H4')>>>>>># retrieve 3000 periods and generate from the DF>>>df=jgtfxcon.getPH('EUR/USD','H4',3000,with_index=False)>>>dfi=jgtfxcon.createFromDF(df)>>>MoreEnhancements Idea-l (for --timeline)--@STCGoal An easy way to snap a moment in time and save it to our store.jgtfxcli-i"SPX500"-t"H1,H4,H8,D1,W1,M1"-c500-l"22101313"$JGTPY_DATA/pds  NORMAL
$JGTPY_DATA/pdl/  L Item
Package: jgtpy
jgtpy (ids)Enhanced JGTPy CDS, IDS, PDS ServicesInstallationpipinstall-UjgtpyExample>>>importpandasaspd>>>importjgtpy>>>df=jgtpy.getPH('EUR/USD','H4')>>>>>># retrieve 3000 periods and generate from the DF>>>df=jgtpy.getPH('EUR/USD','H4',3000,with_index=False)>>>dfi=jgtpy.createFromDF(df)>>>>>># Create with Timerange>>>start="11.17.2022 00:00:00">>>end="11.25.2022 00:00:00">>>df=jgtpy.createByRange("USD/CAD","m15",start,end)>>>>>># offsets date for retreival>>>dtfirst_with_offset=jgtetl.svc_offset_dt_by_tf(dtfirst,ctx.timeframe)>>>df=createByRange(ctx.instrument,ctx.timeframe,dtfirst_with_offset,dtlast)MoreDATA
Package: jgtutils
jgtutilsThis is a Python module calledjgtutils.InstallationYou can installjgtutilsfrom PyPI:pipinstalljgtutilsUsageHere's a simple example of how to usejgtutils:fromjgtutilsimportmodule# Your usage example hereDevelopmentTo work on thejgtutilsproject, you'll need to clone the project and install the requirements:gitclonehttps://github.com/yourusername/jgtutils.gitcdjgtutils
pipinstall-rrequirements.txtTestingWe usepytestfor testing. Run the following command to execute the tests:pytestLicensejgtutilsis licensed under the terms of the MIT License.Remember to replace `yourusername` with your actual GitHub username and provide a usage example in the Usage section.
Package: jguvc-eip
Basic-IOIntended UseThis is an educational mini-framework based on PyQt5 with very basic input/output capabilities. The intended use-case are basic programming courses to show students how to use a simple API.AuthorsThis software was developped by the Visual Computing research group of the Johannes-Gutenberg University of Mainz.DocumentationDocumentation in german can be found here:https://luna.informatik.uni-mainz.de/eip23/basic_io_Handbuch.pdf.Documentation
Package: jh-awssh
==============AWS SSH Helper==============.. image:: https://img.shields.io/pypi/v/jh-awssh.svg:target: https://pypi.python.org/pypi/jh-awssh.. image:: https://img.shields.io/travis/ibejohn818/awssh.svg:target: https://travis-ci.org/ibejohn818/awssh.. image:: https://readthedocs.org/projects/awssh/badge/?version=latest:target: https://awssh.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/ibejohn818/awssh/shield.svg:target: https://pyup.io/repos/github/ibejohn818/awssh/:alt: Updates.. image:: https://codecov.io/gh/ibejohn818/awssh/branch/master/graph/badge.svg:target: https://codecov.io/gh/ibejohn818/awsshInstall---------------|`pip install jh-awssh`| or use sudo on OS X| `sudo -H pip install jh-awssh`SSH Connect to Ec2 instances & list IP for use with **mussh**Run `$: awssh` to see a list of all commands* Free software: MIT license=======History=======0.6.0 (2017-09-02)------------------* First release on PyPI.
Package: jhdfs4py
JHDFS4PYConvenient HDFS access using the Java HDFS client in python.Installationpipinstalljhdfs4pyUsagePlease also see the provided demo notebook at./docs/demo.ipynb.Create a filefile.txtwith the text content "Hello World".frompyspark.sqlimportSparkSessionfromjhdfs4pyimportHdfsFsspark=SparkSession.builder.appName("my-app").getOrCreate()hdfs=HdfsFs.from_spark_session(spark)hdfs.write_string(path="/my/path/file.txt",data="Hello World",)Create a fileother_file.blopwith the byte content "some data".frompyspark.sqlimportSparkSessionfromjhdfs4pyimportHdfsFsspark=SparkSession.builder.appName("my-app").getOrCreate()hdfs=HdfsFs.from_spark_session(spark)hdfs.write_bytes(path="/my/path/other_file.blop",data=b"some data",)ContributeReportissues, submitPull Requests, or contact us via mail (opensource@verpackungsregister.org).Test SuiteHadoop Windows SetupOn Windows, a few extra steps are required, before running the tests:Clonehttps://github.com/steveloughran/winutilsinto a directory of your choiceDefine the environment variableHADOOP_HOMEand set it toX:\path\to\winutils\hadoop-3.0.0Append%HADOOP_HOME%\binto thePATHenvironment variableGeneral SetupThe library comes with an extensivepytesttest suite, that depends on aPY4Jgateway
running locally providing the actual HDFS implementation. The testing gateway is located in thetests/py4j-test-serverfolder and implemented by
theorg.zsvr.py4j.test.TestGatewayServerclass. The test suite will try to start the gateway automatically on posix systems unless theUSE_EXTERNAL_GATEWAY_SERVERenvironment variable is set. Use theSBT_SCRIPTenvironment variable, to tell the test suite where yourSBTstartup
script is located.To start the gateway manually (don't forget to setUSE_EXTERNAL_GATEWAY_SERVER=1in this case), you can either use your favourite IDE, after
importingpy4j-test-serverasSBTproject, or you can useSBTdirectly from the command
line by enteringsbt runafter having changed to thetests/py4j-test-serverdirectory.To finally run the tests, change into thejhdf4pybase directory (the one containing  this README). Make sure all dependencies are met by typingpip install -r requirements.txt, and finally launch the test suite by enteringpytestinto the console. All tests are expected to pass.
Package: jhelabtoolkit
Common utility codes used at JheLab for AFM, rheology, and ML researchjhelabtoolkit contains various code used at JheLab (Dept. of Physics, Seoul National University), including code for reading measurement files, calibrating raw data, preprocessing routines such as denoising, and other utility codes.InstallationInstall via pip:pipinstalljhelabtoolkitOverviewWhile a detailed documentation is pending, below is a brief description of the main submodules of this package.jhelabtoolkit.ioContains code used to read measurement files generated from commonly used instruments in the lab.
Currently supports:Nanosurf FlexAFM (.nid file format)Zurich Instrumenet MFLI Lock-in Amplifier (.txt file format)jhelabtoolkit.calibrationContains algorithms to calibrate the measured experimental data.
Currently contains routines for:AFM spring constant calibration (Sader method)AFM inverse optical lever sensitivity calculation (Sader method)jhelabtoolkit.preprocessingContains code to process the measured data prior to further downstream analysis.
Currently contains the following code:Denoising algorithms for spectral measurements (spectral subtraction, morphological denoising)jhelabtoolkit.utilsContains miscellaneous code that do not belong in the above categories.Let's talk!To learn more about the work we do, visit our google scholar (https://scholar.google.co.kr/citations?user=z_c9ABQAAAAJ&hl=ko,https://scholar.google.com/citations?user=LyVlf_8AAAAJ&hl=en&oi=ao) or contact us via email (whjhe@snu.ac.kr,jhko725@snu.ac.kr)!
Package: jhsp
君何的包0.0.55API更新说明--version 0.0.55None-- version 0.0.54None-- version 0.0.53将load_config模块下的os.system更换为subprocess.call执行，解决在docker中退出且无法查看输出的问题--version 0.0.52解决了函数方法里，多进程模块报错的问题解决了load_config在win、linux平台下，可能报错的问题--version 0.0.50增加了一个动态加载配置的函数方法，load_config将说明文件更改为.md格式待更新列表
Package: jh-stackformation
Cloudformation framework to support infrastructure-as-code paradigmInstallationpip installjh-stackformationFramework requires python >= 3.5.x and Hashicorp Packer in-order to
build custom AMI’s.Docker is the preferred method of execution ( all dependencies
including packer are included ) however VirtualEnv works A-OK.
Reference the demo repo below for examples and helper scripts using
the docker images built on docker-hub.Examples / DemoExamples and docker helper scripts can be found @https://github.com/ibejohn818/stackformation-demoFree software: MIT licenseDocumentation:https://jh-stackformation.readthedocs.io.TODO’sHistory0.1.0 (2017-12-15)First release on PyPI.
Package: jhu-assembly-linter
Assembly Style LinterThis linter was designed with Prof. Charles Kann's Computer Organization
(EN.605.204) course in mind. It performs a strict but non-exhaustive lint;
meaning it will not catch all errors but will be noisy and exacting about those
that it does.Installpip install jhu-assembly-linterUsage$ jhu-assembly-linter ./path/to/file.s

E:: Tab found. Only spaces allowed.
18:     LDR x0, =helloWorld
    ^
E:: Instruction is not uppercase.
19:     mov     w8, #64     /* write is syscall #64 */
        ^
E:: Non-functional whitespace found.
20: 
    ^^^^^
E:: File name does not end with "Main" when it should.
21: main:To lint a whole directory:find . -name "*.s" | xargs -I{} jhu-assembly-linter  {}To add a pre-commit hook to you repo:repos:
-   repo: https://github.com/LogstonGradSchool/JhuAssemblyStyleLinter
    rev: <latest version>
    hooks:
    -   id: jhu-assembly-linterTeststoxDeploymentpoetry build
poetry publish
Package: jhub-apps
JupyterHub Apps LauncherJupyterHub Apps Launcher is a generalized server launcher. The goal of this project is to support
launching anything like say a Flask Server, FastAPI server or a Panel Dashboard via a user supplied
command. Currently, the following frameworks are supported:PanelBokehStreamlitPlotly DashVoilaGradioJupyterLabGeneric Python CommandInstallationpip install jhub-appsor via condacondainstall-cconda-forgejhub-appsDevelopment InstallationInstall Dependenciescondaenvcreate-fenvironment-dev.yml
pipinstall-e.Starting JupyterHubSet the following environment variable:exportJHUB_APP_JWT_SECRET_KEY=$(opensslrand-hex32)jupyterhub-fjupyterhub_config.pyNow go tohttp://127.0.0.1:8000/services/japps/to access JHub Apps LauncherAPI EndpointsThe Hub service is exposed via FastAPI endpoints. The documentation for the same can be accessed at:http://127.0.0.1:8000/services/japps/docsTo try out authenticated endpoints click on the Authorize button on the top right of
the above url and choseOAuth2AuthorizationCodeBearerand click on Authorize.Running TestsUnit Testspytestjhub_apps/testsE2E Testspytestjhub_apps/tests_e2e-vvv-s--headedUsageJHub Apps has been tested with local JupyterHub usingSimpleLocalProcessSpawnerand with
The Littlest JupyterHub usingSystemdSpawner.Install JHub Appspipinstallgit+https://github.com/nebari-dev/jhub-apps.gitAdd the following in The Littlest JupyterHub'sjupyterhub_config.pyfromtljh.user_creating_spawnerimportUserCreatingSpawnerfromjhub_apps.configurationimportinstall_jhub_appsc.JupyterHub.bind_url="<YOUR_JUPYTERHUB_URL>"c.SystemdSpawner.unit_name_template='jupyter-{USERNAME}{JHUBSERVERNAME}'c.JAppsConfig.apps_auth_type="oauth"# or none (if you don't want authentication on apps)c.JAppsConfig.python_exec="python3"# Pass in the path to jupyterhub configc.JAppsConfig.jupyterhub_config_path="jupyterhub_config.py"# Either a static list of conda environments to show in the# create panel apps form or a callable to fetch conda enviornments# dynamically, e.g. from conda-store APIc.JAppsConfig.conda_envs=[]c=install_jhub_apps(c,UserCreatingSpawner)
Package: jhub-remote-auth-mount
Jupyterhub REMOTE_USER AuthenticatorAuthenticate to Jupyterhub using an authenticating proxy that can set
the Remote-User header.
Also supports for passing additional information to the jupyter user. This includes a
Mount header.InstallationInstallation from pypi:pip install jhub_remote_auth_mountInstallation from local git repository:cd jhub_remote_auth_mount
pip install .ConfigurationYou should edit yourjupyterhub_config.pyconfig file to set the
authenticator class:c.JupyterHub.authenticator_class = 'jhub_remote_auth_mount.RemoteUserAuthenticator'You should be able to start jupyterhub.  The “/login” resource
will look for the authenticated user name in the HTTP header “Remote-User”.
If found, and not blank, you will be logged in as that user.Alternatively, you can useRemoteUserLocalAuthenticator:c.JupyterHub.authenticator_class = 'jhub_remote_auth_mount.RemoteUserLocalAuthenticator'This provides the same authentication functionality but is derived fromLocalAuthenticatorand therefore provides features such as the ability
to add local accounts through the admin interface if configured to do so.Remote User Authentication extended with Mount capabilityProvides the capability to supply the jupyterhub user with a set of ssh keys that can later be used to mount that particular user’s homedrive, the extended authenticator can be activated by setting the following option in the jupyterhub config file:c.JupyterHub.authenticator_class = 'jhub_remote_auth_mount.MountRemoteUserAuthenticator'This adds two base request paths to the jupyterhub web application:'/login' -> requires a non empty Remote-User header
'/mount' -> requires both a non empty Remote-User and Mount headerThe expected format of the Mount header is that the passed string can be evaluated to a python dictionary via:try:
    mount_header_dict = literal_eval(mount_header)After being successfully evaluated to a dictionary, the header is required to contain the following information:{
    HOST: 'hostname of the target mount host',
    USERNAME: 'A random string that identifies an active mount session',
    PATH: 'The target URL of the system/service that grants jupyter users to mount
    their notebook against, e.g @idmc.dk:',
    MOUNTSSHPRIVATEKEY: 'private key',
}Note:Since we are passing private key’s over the network, it is important that this information is sent over an encrypted channel, furthermore the host/service that grant this mount information should limit the validity of a keyset, e.g. can be used for 2 hours before a new set has to be generated and the old is void.
Upon successful parsing of the header, the active jupyterhub user instance is appended with a ‘mount’ property that contains the accepted dictionary header.
Package: jhub-shibboleth-auth
jhub_shibboleth_authShibboleth authentication for JupyterHub.Installationpipinstalljhub_shibboleth_authSourcesFor more information on nginx and Shibboleth SP with FastCGI support, check:https://wiki.shibboleth.net/confluence/display/SHIB2/Integrating+Nginx+and+a+Shibboleth+SP+with+FastCGIhttps://github.com/nginx-shib/nginx-http-shibbolethhttps://github.com/nginx-shib/nginx-http-shibboleth/blob/master/CONFIG.rsthttps://tcg.stanford.edu/?p=131To run Shibboleth SP and nginx FastCGI in a docker container, check:https://github.com/gesiscss/jhub_shibboleth_auth/tree/master/docker/shibbolethhttps://github.com/gesiscss/jhub_shibboleth_auth/blob/master/docker/shibboleth/example-docker-compose.yamlShibboleth embedded discovery service:https://wiki.shibboleth.net/confluence/display/EDS10/1.+Overview