Saravanan666/pypi-org huggingface dataset:
Package: seyfert-rest-client
Seyfert파이썬을 이용한 seyfert api 연동 모듈입니다.Installpip install seyfert-api-pythonUsage발급받은 guid로 seyfert 준비from seyfert-api import SeyfertAPI
from seyfert-api.client import TEST_SEYFERT_URL

seyfert = SeyfertAPI('your-guid')
# 테스트 url을 사용할 경우에는 명시적으로 추가해준다.
# seyfert = SeyfertAPI('your-guid', seyfert_url=TEST_SEYFERT_URL)api list# Members
seyfert.member_create(payload)
seyfert.member_create_with_email(payload)
seyfert.member_profile_with_guid(guid)
seyfert.member_update(guid, params)
seyfert.member_update_fullname(guid, name)
seyfert.member_update_phone(guid, phone)
seyfert.get_members_accounts(guid)

# Accounts
seyfert.banks_list()
seyfert.register_account(guid, accont_number, bank_code)
seyfert.check_account_exists(guid)
seyfert.verify_account_name(guid)
seyfert.verify_account_ownership(guid)
seyfert.is_account_exist_and_verify_name(guid)
seyfert.is_account_ownership_verified(guid)TODOtransaction api 추가test 추가refactoring (member / account / transaction 분리)상세 기능 설명Member 기능member 생성# 멤버 만들기
# 필요한 정보 준비
valid_payload = {
    'emailTp': 'PERSONAL',
    'nmLangCd': 'ko',
    'phoneCntryCd': 'KOR',
    'phoneTp': 'MOBILE',
    'emailAddrss': 'test@test.com',
    'fullname': 'test name',
    'phoneNo': '01011111111',
}

# 일반 멤버 생성 (email, 번호가 달라야함. 하나라도 같을경우 기존멤버 반환)
response = seyfert.member_create(payload=valid_payload)

# email로 멤버 생성
response = seyfert.member_create_with_email(payload=valid_payload)member 조회특정 member의 guid로 프로필 정보 조회 가능# 모든 정보 조회
guid = 'test_guid'
response = seyfert.member_profile_with_guid(guid)

# 해당 멤버의 계좌정보 조회
resopnse = seyfert.get_members_accounts(guid)member 업데이트특정 member의 guid로 프로필 정보 업데이트 가능resopnse = seyfert
Package: sf2cf
Shitty Feed To Cool Feed (sf2cf)Some RSS feeds do not actually include any "real" content: instead, they only
provide a link to a website, which makes reading your feeds in your favorite
feed reader harder than it should be.Shitty Feed To Cool Feed (sf2cf) turns such a shitty RSS/Atom feed into a really
cool one, featuring the actual content users are interested in.Some plugins come bundled with sf2cf, and users may write their own plugins in
order to support more websites (see HACKING).Installation$ python3 setup.py install --userOR# python3 setup.py installUsage exampleList all the available plugins:$sf2cf-l
dilbert0.1ThisfeedprovidercreatesanATOMfeedsimilartotheoneprovidedby
http://www.dilbert.com,butmakessurethecomicisincluded.Get more help about a given plugin:$sf2cf--help-dilbert
*DESCRIPTION
ThisfeedprovidercreatesanATOMfeedsimilartotheoneprovidedby
http://www.dilbert.com,butmakessurethecomicisincluded.

*SAMPLECONF[feed:dilbert]plugin=dilbertoutput=/path/to/output.xmlA configuration file must be provided to sf2cf. See the relevant plugin help for
its configuration documentation.$catconfig.ini[feed:dilbert]plugin=dilbertoutput=/home/user/rss-feeds/dilbert.xmlThen, one may run sf2cf like this:$sf2cf-cconfig.ini[+]Loadingdilbert[+]Usinghttp://www.dilbert.com/feedasaninputfeed.[+]Writingtheoutputfeedto/home/user/rss-feeds/dilbert.xml.This should probably be done using cron(8).
Package: sfa
<h1>Purpose</h1>
<p>This software implements a wrapper for the SFA (Slice-based Federation
Architecture) that allows networking testbeds to interoperate in terms
of exposing and provisioning resources. It comes with drivers for
a few testbeds, notably PlanetLab and IotLab.</p>
<h1>References</h1>
<ul>
<li><a href=”http://sfawrap.info”>This page</a> describes the motivation behind this software</li>
<li>Refer to <a href=”http://svn.planet-lab.org/wiki/SFATutorial#SFATutorial”>this trac page</a> for installing and using this software</li>
<li>The GENI community has  <a href=”http://groups.geni.net/geni/wiki/GeniApi”>a page on the Aggregate API</a></li>
</ul>
Package: sfaira
|Build| |Documentation| |Stars| |PyPI| |PyPIDownloads|.. |Build| image::https://github.com/theislab/sfaira/workflows/Build%20sfaira%20Package/badge.svg:target:https://github.com/theislab/sfaira/workflows/Build%20sfaira%20Package/badge.svg:alt: Github Workflow Build sfaira Status.. |Documentation| image::https://readthedocs.org/projects/sfaira/badge/?version=latest:target:https://sfaira.readthedocs.io/en/latest/:alt: Documentation Status.. |Stars| image::https://img.shields.io/github/stars/theislab/sfaira?logo=GitHub&color=yellow:target:https://github.com/theislab/sfaira/stargazers:alt: Github Stars.. |PyPI| image::https://img.shields.io/pypi/v/sfaira?logo=PyPI:target:https://pypi.org/project/sfaira:alt: PyPI Version.. |PyPIDownloads| image::https://pepy.tech/badge/sfaira:target:https://pepy.tech/project/sfaira:alt: Number of downloadssfaira - data and model repository for single-cell data.. image::https://github.com/theislab/sfaira/blob/release/resources/images/figure_rtd_intro.png:width: 400px
:align: centersfaira_ is a model and a data repository in a single python package (full paper).
We provide an interactive overview of the current state of the zoos on sfaira-portal.Its data zoo gives users access to streamlined data loaders that allow reproducible use of published and private data sets for model training and exploration.
Its model zoo gives user streamlined access to pre-trained models and to common model architectures to ease usage of neural networks in common single-cell analysis workflows.
Instead of focussing on developing new models, we focus on making models easily accessible to users and distributable by developers.
sfaira integrates into scanpy_ workflows... _scanpy:https://github.com/theislab/scanpy.. _sfaira:https://sfaira.readthedocs.io.. _full paper:https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02452-6.. _sfaira-portal:https://theislab.github.io/sfaira-portal/
Package: sfalearn
# sfalearn
Automata Learning classesA python framework for working with Learning algorithms in Automata.
This framework is part of the [lightbulb-framework](https://github.com/lightbulb-framework/lightbulb-framework).## Changelog0.0.10: Updated version of symautomata (0.0.12) in installation requirements.## ContributorsGeorge ArgyrosIoannis StaisSuman JanaAngelos D. KeromytisAggelos Kiayias## ReferencesG. Argyros, I. Stais, S. Jana, A. D. Keromytis, and A. Kiayias. 2016. SFADiff: Automated Evasion Attacks and Fingerprinting Using Black-box Differential Automata Learning. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS ‘16). ACM, New York, NY, USA, 1690-1701. doi: 10.1145/2976749.2978383G. Argyros, I. Stais, A. Kiayias and A. D. Keromytis, “Back in Black: Towards Formal, Black Box Analysis of Sanitizers and Filters,” 2016 IEEE Symposium on Security and Privacy (SP), San Jose, CA, 2016, pp. 91-109. doi: 10.1109/SP.2016.14## AcknowledgementsThis research was partly supported by ERC project CODAMODA, #259152.## LicenseMIT License as described in LICENSE file
Package: sfapi-client
Welcome to sfapi_clientsfapi_client is a Python 3 client for NERSC'sSuperfacility API.Install sfapi_client using pip:$pipinstallsfapi_clientLet's get started by checking the status of perlmutter:>>>fromsfapi_clientimportClient>>>fromsfapi_client.computeimportMachine>>>withClient()asclient:...status=client.compute(Machine.perlmutter)...>>>statusCompute(name='perlmutter', full_name='Perlmutter', description='System Degraded', system_type='compute', notes=['2023-04-26 18:16 -- 2023-04-28 09:30 PDT, System Degraded, Rolling reboots are complete, a final reboot is scheduled for 0930 PDT'], status=<StatusValue.degraded: 'degraded'>, updated_at=datetime.datetime(2023, 4, 26, 18, 16, tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=61200))), client=<sfapi_client._sync.client.Client object at 0x102c871c0>)Featuresasyncinterface and standard synchronous interface.Fully type annotated.DocumentationFor the basics, head over to theQuickStart. We also have Jupyter Nodebookexamples.More in depth developer documentation can be found in theAPI reference.DependenciesThe sfapi_client project relies on these libraries:httpx- HTTP support.authlib- OAuth 2.0 authentication.pydantic- Data models.tenacity- Retry.datamodel-code-generator- Generating data models from the Open API specification.unasync- Generating synchronous interface from asyn implementation.InstallationInstall with pip:$pipinstallsfapi_client
Package: sfapmetl
Python etl featureInstallation$ pip install sfapmetlUsage$ etl-scriptPlease provide config as mentioned belowkey: <profile key>
tags: 
  Name: <name>
  appName: <appName>
  projectName: <projectName>
metrics:
  plugins:
    - name: <PluginName>
      enabled: true
      url: <componentUrl>
      authkey: <authentication_key_for_the_url>After this setup, add cronjob into /etc/crontab( Applicable for Linux AWS instance, else run this script as a cron job) ex: To run script every 5 minutes - */5 * * * * root etl-scriptPlease refer this link for cronjobhttps://www.digitalocean.com/community/tutorials/how-to-use-cron-to-automate-tasks-ubuntu-1804
Package: sf-apm-lib
sf-apm-libSnappyflow modulesInstallationpip install sf-apm-libThen you should import the library.fromsf_apm_lib.snappyflowimportSnappyflowUsageGet trace configsf=Snappyflow()# Initialize Snappyflow. By default intialization will pick profileKey, projectName and appName from sfagent config.yaml.# Add below part to manually configure the initializationproject_name='<Snappyflow Project Name>'app_name='<Snappyflow App Name>'profile_key='<Snappyflow Profile Key>'sf.init(profile_key,project_name,app_name)# End of manual configurationtrace_config=sf.get_trace_config()# Returns trace configprint(trace_config)
Package: sfas
Small Feedback Arc Set (sfas)Efficient implementation of a greedy algorithm for computing small feedback arc sets in directed weighted multi-graphs.
This implementation is an adaptation of the algorithm described in Section 2.3 ofthisarticle, with additional generalization to support weights and parallel edges.DescriptionGiven a weighted directed graph, computes a topological sorting (linear order of the nodes) that minimizes (greedily) the number of backward edges - feedback arc set.
In particular, removing the set of edges going backward in the resulting order breaks all directed cycles in the graph.Installpip install sfasExample usagegraph = [
    ['a', 'b', 1],
    ['a', 'c', 1],
    ['c', 'd', 2],
    ['d', 'a', 2],
]from sfas import greedy
ouput = greedy.compute_order(graph)output['c', 'd', 'a', 'b']InterfaceParams:connections : List[List[Any, Any, Int]]- list of edges, each represented as a 3-item list consisting of[<from node>, <to node>, <edge weight>]verbosity : Int- prints progress and other stats for values > 0random_seed : Intrandomness is in picking the next "greedy" step among equally qualified onesResult:Listwith all nodes, ordered so that the total weight of edges going backwards (w.r.t. this order) is smallQuestions / suggestions welcomearie.matsliah@gmail.com
Package: sfb
sfbsfb helps SQL testing and estimating the cost of services that depend on scan volume.DescriptionCheck SQL syntaxEstimate query costs for freeper Runper MonthReplace query parameters automaticallyBe useful on continuous integrationUse dryrun includeGoogle BigQuery API Client LibrariesInstall$pipinstallsfbRequirementsPython >= 3.6Jupyter NotebookGoogle Colaboratorygoogle-cloud-bigquery >= 2.6.1pyyaml >= 5.4.1UsageEstimate Query Costs# If runs with no arguments, execute files in './sql/*.sql'.$sfb{"Succeeded":[{"SQL File":"/home/admin/project/sfb_test/sql/covid19_open_data.covid19_open_data.sql","Total Bytes Processed":"1.9 GiB","Estimated Cost($)":{"per Run":0.009414,"per Month":0.28242},"Frequency":"Daily"},{...}],"Failed":[{"SQL File":"/home/admin/project/sfb_test/sql/test_failure_badrequest_01.sql","Errors":[{"message":"Unrecognized name: names; Did you mean name? at [9:5]","domain":"global","reason":"invalidQuery","location":"q","locationType":"parameter"}]},{...}]}# Others$sfb-f./sql/*.sql
$sfb-q"select * from test;"$echo"select * from test;"|sfb|jq
$find./sql-typef|sfbArguments$sfb-h
usage:sfb[-h][-f[FILE[FILE...]]|-qQUERY][-cCONFIG][-s{BigQuery}][-pPROJECT][-v][-d]optionalarguments:-h,--helpshowthishelpmessageandexit-f[FILE[FILE...]],--file[FILE[FILE...]]sqlfilepath-qQUERY,--queryQUERYquerystring-cCONFIG,--configCONFIGconfigfilepath-s{BigQuery},--source{BigQuery}sourcetype-pPROJECT,--projectPROJECTGCPproject-v,--verboseverboseresults-d,--debugrunasdebugmodeDirectory (Optional)$tree.
.
├──config
│└──sfb.yaml
├──log
│└──sfb.log(ifrunsasdebugmode)└──sql└──[SQLfileshere]Configuration$cat./config/sfb.yaml# Default settingsGlobals:Service:BigQueryLocation:USFrequency:Daily

QueryFiles:[your_sql_file_name]:Frequency:WeeklyParameters:-name:ds_start_datetype:DATEvalue:'2020-01-01'-name:ds_end_datetype:DATEvalue:'2020-01-31'...TypeName of query parameter type. Select one of types below.STRINGINT64FLOAT64NUMERICBOOLTIMESTAMPDATETIMEDATEFrequencyFor calculating monthly cost estimation.Hourly(cost_per_run) * 30(days) * 24(h)Daily(cost_per_run) * 30(days)Weekly(cost_per_run) * 4(weeks)Monthlycost_per_run
Package: sfc
Service Function Chaining=========================Service Function Chaining provides the ability to define an ordered list of anetwork services (e.g. firewalls, load balancers). These service are then"stitched" together in the network to create a service chain. This projectprovides the infrastructure (chaining logic, APIs) needed for ODL to provisiona service chain in the network and an end-user application for defining suchchains.Instalation===========The installation has been tested on Ubuntu Linux.1.- SFC needs to have Python 3.4 installed2.- openssl-devel MUST BE INSTALLED as it is pip dependencysudo apt-get install libssl-dev openssl3.- Before you run installation from Pypi,be sure you have installed libnetfilter-queue.To run installation of libnetfilter-queue:sudo apt-get install libnetfilter-queue-dev4.- be sure you have installed pip3to run installation of pip3 :sudo apt-get install python3-pip5.- Finally the installation of the SFC package:sudo pip3 install sfcAll other dependencies are handled in the SFC setup.There is still possibility to downloadthe sfc-xxx.tar.gz file from Pypi repository,unzip it , locate setup.py file and use the python installerpython3 setup.py installUsage=======python3.4 sfc_agent --rest --nfq-class ---odl-ip-port=<ODL REST IP:port> --auto-sff-namenote:root privileges are required if `--nfq-class` flag is usedSFC Agentoptional arguments:-h, --help            show this help message and exit--odl-get-sff         Get SFF from ODL--auto-sff-name       Automatically get SFF name--nfq-class           Flag to use NFQ Classifier-r, --rest            Flag to use REST--sff-name SFF_NAME   Set SFF name--odl-ip-port ODL_IP_PORTSet ODL IP and port in form <IP>:<PORT>. Default islocalhost:8181--ovs-sff-cp-ip OVS_SFF_CP_IPSet local SFF Open vSwitch IP. Default is 0.0.0.0--sff-os {XE,XR,OVS}  Set SFF switch OS--agent-port AGENT_PORTSet SFC Agent port. Default is 5000Example=======sudo python3.4 sfc_agent.py --rest --odl-ip-port 192.168.33.11:8181 --auto-sff-name --nfq-classthis command will run the sfc_agent using REST, trying to recognize its own SFF name and runningalso NFQ classifier instance.
Package: sfcparse
sfcparseThe easy to use library for your data, configuration, and save filesLatest Version: 2.0.0sfcparse == Simple File Configuration ParseImport or Exportcustom, orindustry-common, data, config, and save files easily for your python program or script!Use python data types for your data(literally use python data types as stored values importing them securely vs just importing a .py file)in any text file.Additionally supportsjson,yaml,ini,xmlusing the common & native libraries with the idea of simpler abstraction.Use built-in features for easily formatting your data and other quick and easy tools like getting the hash of a file.Tutorials and Docs:Quick Start Tutorial Video:sfcparse DocsFull Tutorial Videos:sfcparse DocsOfficial Docs:sfcparse DocsLatest Updates:sfcparse DocsChangelog:sfcparse DocsInstallpipinstallsfcparseCloning the repositoryClone via HTTPSgitclonehttps://github.com/aaronater10/sfcparse.gitClone via SSHgitclonegit@github.com:aaronater10/sfcparse.git
Package: sfctl-azure-servicefabric
Microsoft Azure SDK for PythonThis is the Microsoft Azure ServiceFabric Client Library.Azure Resource Manager (ARM) is the next generation of management APIs that
replace the old Azure Service Management (ASM).This package has been tested with Python 2.7, 3.3, 3.4, 3.5 and 3.6.For the older Azure Service Management (ASM) libraries, seeazure-servicemanagement-legacylibrary.For a more complete set of Azure libraries, see theazurebundle package.CompatibilityIMPORTANT: If you have an earlier version of the azure package
(version < 1.0), you should uninstall it before installing this package.You can check the version using pip:pipfreezeIf you see azure==0.11.0 (or any version below 1.0), uninstall it first:pipuninstallazureUsageFor code examples, seeServiceFabricon readthedocs.org.Provide FeedbackIf you encounter any bugs or have suggestions, please file an issue in theIssuessection of the project.Release History6.0.0rc1 (2017-09-16)Release candidate for Service Fabric 6.0 runtime5.6.130 (2017-05-04)Initial Release
Package: sfctools
sfctools - A toolbox for stock-flow consistent, agent-based modelsSfctools is a lightweight and easy-to-use Python framework for agent-based macroeconomic, stock-flow consistent (ABM-SFC) modeling. It concentrates on agents in economics and helps you to construct agents, helps you to manage and document your model parameters, assures stock-flow consistency, and facilitates basic economic data structures (such as the balance sheet). For more documentation, seehttps://sfctools-framework.readthedocs.io/en/latest/.InstallationWe recommend to install sfctools in a fresh Python 3.8 environment. For example, with conda, doconda create --name sfcenv python=3.8
conda activate sfcenv
conda install pipThen, in a terminal of your choice, type:pip install sfctoolsseehttps://pypi.org/project/sfctools/Usage with Graphical User Interface 'Attune'Typepython -m sfctools attuneto start the GUI.Usage inside PythonTry out this simple example:fromsfctoolsimportAgent,WorldclassSomeAgent(Agent):def__init__(self,a):super().__init__()self.some_attribute=amy_agent=SomeAgent(a='Hello')your_agent=SomeAgent(a='World')my_agents=World().get_agents_of_type("SomeAgent")my_message=my_agents[0].some_attributeyour_message=my_agents[1].some_attributeprint("%ssays%s,%ssays%s"%(my_agent,my_message,your_agent,your_message))The resulting output will beSomeAgent__00001 says Hello, SomeAgent__00002 says WorldMore ExamplesHave a look at thedocumentation pagefor more examples.Cite this SoftwareYou can cite the software as follows:Baldauf, T., (2023). sfctools - A toolbox for stock-flow consistent, agent-based models. Journal of Open Source Software, 8(87), 4980,https://doi.org/10.21105/joss.04980You can cite the software repository as follows:Thomas Baldauf. (2023). sfctools - A toolbox for stock-flow consistent, agent-based models (1.1.0.2b). Zenodo.https://doi.org/10.5281/zenodo.8118870| Corresponding author: Thomas Baldauf, German Aerospace Center (DLR), Curiestr. 4 70563 Stuttgart |thomas.baldauf@dlr.de|
Package: sfcurves
Utilities (mapping, unmapping, calculating enclosing polygon) for space filling curves (currently only Hilbert). Future plans include minimizing the enclosing polygon, calculating centroids, and supporting more curves like Peano.Examples:Map 31 from the 1-dimension domain [0,64) to x,y in [0,16):>>>fromsfcurvesimporthilbert>>>hilbert.forward(31,64)(3,4)Reverse map (3,4) to the 1-dimension [0,64):>>>hilbert.reverse(3,4,64)31If you're mapping the entire domain, then using the generator is considerably faster:>>>g=hilbert.generator(64)>>>next(g)(0,0)>>>next(g)(0,1)Calculate an enclosing polygon for the mapped points 0 thru 6 in the 1-dimension domain [0,64)>>>hilbert.outline(0,6,64)[(0,0),(1,0),(2,0),(3,0),(3,1),(3,0),(2,0),(1,0),(1,1),(0,1)]
Package: sfdata
Social Finance Data PipelineThis is a package that aims to standerdise one or more datasets against a defined schema.
It is currently in it's very early stagies.check thewikifor more details on the plan.Current statusCurrent the pipeline only works with xml files and is limited to one schema and one file.
It's a first generic approach, that was tested against CIN and SWWF datasets.It covers partially the following steps described in thewiki:Identify- identifies the stream of data against the schema. only for XML files for now.Convert- Tries to converts the datatypes and throws a warning when not possible. It uses thexsdatapackage for it.Normalise- adds the primary and foreign keys for each record.How to runCheck thedemo.pyfile. It has 2 functions that run against the CIN and SWWF datasets present in thesamplesdirectory.There's also smaller samples of those datasets.This methods will print a set of dataframes for each dataset.ImprovementsThere's still a lot to be done. Besides completing what's in the wiki, here are some things I believe should be done first:Datastore- the values are directly pulled to a tablib databook in a very unneficcient nested for loop. This is a bigno. We should useRTOF datstorefor this.DatatypesIt should be possible to define the way we want to export the datatypes. maybe the user wants the dates to come out in a the "dd-mm-yyyy" format when exporting. Or maybe they want just mm-yyyy. This should be possible. Currently, I'm assuming this inexport_value. But it should be adjusted.pathvscontext- I was using path as a reference for where the each node sits in the hierarchy. However, having a tuple in the context is probably a better approach. I'm currently using both, this should not be the case - use just the context.
Package: sfdcfw
Python Salesforce.com FrameWorkPython Salesforce.com FrameWorkAPI NameProtocolData FormatCommunicationREST APIRESTJSON, XMLSynchronousSOAP APISOAP (WSDL)XMLSynchronousChatter REST APIRESTJSON, XMLSynchronous (photos are processed,asynchronously)User Interface APIRESTJSONSynchronousAnalytics REST APIRESTJSON, XMLSynchronousBulk APIRESTCSV, JSON, XMLAsynchronousMetadata APISOAP (WSDL)XMLAsynchronousStreaming APIBayeuxJSONAsynchronous (stream of data)Apex REST APIRESTJSON, XML, CustomSynchronousApex SOAP APISOAP (WSDL)XMLSynchronousTooling APIREST or SOAP (WSDL)JSON, XML, CustomSynchronous
Package: sf-diamond
Damn You Diamond(Diamond was Newton’s mischievous
dog)What is Diamond?Diamond utilizes iterative, quasi-Newton 2nd-order solvers for certain
kinds of generalized linear models (GLMs) with arbitrary but known
L2-regularization. A common use is fitting mixed-effects models, with
their covariance already being known by another means (e.g. lme4). These
2nd-order iterative solvers are considerably faster than a full-blown
solution.LimitationsThe random-effects covariances must be input a-priori. UnlikeR’s
lme4orJulia’s MixedModels,
Diamond does not estimate the covariance of random effects terms.Diamond only supports the following modelslogistic regressionordinal logistic regression using proportional odds, as defined in
Section 7.2.1 of Categorical Data Analysis, 2nd Ed., by Alan
AgrestiCurrently, only formulae with crossed, independent random effects are
supported. Using the mtcars dataset as an example, these look likempg ~ 1 + hp + (1 + hp | cyl) + (1 | gear). I.e. no hierarchical
termsInstallationYou must havedockerinstalled. Then, rundocker run-ti--rm-p8888:8888 tsweetser/diamondCopy-paste the URL, including the token, into your browser. Then, check
out the Jupyter notebook examples!Troubleshooting installationYou may need to restart docker if you’ve been running jupyter
notebooks locally on port 8888.DocumentationSeedocumentationfor more
details on the details of Diamond and how to use itContributing to DiamondWe always welcome contributions. SeeCONTRIBUTING.mdRunning TestsYou will need R to run the integration tests. From the root directory,
runpip install nosethennosetests.Development StatusDiamond is an evolving project. Please file issues if you would like to
use Diamond in new ways.LicenseSeeLICENSE.txt