Saravanan666/pypi-org huggingface dataset:
Package: ibkr-report-parser
ibkr-report-parserInteractive Brokers (IBKR) Report Parser for MyTax (vero.fi) - not affiliated with either serviceExampleHow to run locallyOption 1: pippipinstallibkr-report-parser
ibkr-report-parserOption 2: Dockerdockerpullghcr.io/oittaa/ibkr-report-parser
dockerrun--rm-d-p8080:8080--nameibkr-report-parserghcr.io/oittaa/ibkr-report-parserUse the appBrowse tohttp://127.0.0.1:8080/Environment variablesTITLEThe title of the website. DefaultIBKR Report ParserCURRENCYThe currency used in the report output. DefaultEURUSE_DEEMED_ACQUISITION_COSTWhether to use thedeemed acquisition cost, if it benefits you. DefaultTRUESTORAGE_TYPEThe storage to save the fetched daily Euro exchange rates, if set to anything other thanDISABLED. Currently supported types areLOCAL,AWS, andGCP. DefaultDISABLEDSTORAGE_DIRThe directory used whenSTORAGE_TYPEis set toLOCAL. Default.ibkr_storageBUCKET_IDThe storage bucket used whenSTORAGE_TYPEis set toAWSorGCP. Default""Testing and debuggingDEBUGFlask debug. DefaultFALSELOGGING_LEVELPython logging level. DefaultINFOEXCHANGE_RATES_URLURL for the Euro exchange rates from European Central Bank. Defaulthttps://www.ecb.europa.eu/stats/eurofxref/eurofxref-hist.zipHow to build yourselfPythongitclonehttps://github.com/oittaa/ibkr-report-parser.gitcdibkr-report-parser
pipinstall.
ibkr-report-parserDockergitclonehttps://github.com/oittaa/ibkr-report-parser.gitcdibkr-report-parser
dockerbuild-tibkr-report-parser:latest.
dockerrun--rm-d-p8080:8080--nameibkr-report-parseribkr-report-parserPython APIfromibkr_reportimportReportFILE_1="tests/test-data/data_single_account.csv"FILE_2="tests/test-data/data_multi_account.csv"withopen(FILE_1,"rb")asfile:report=Report(file=file,report_currency="EUR",use_deemed_acquisition_cost=True)withopen(FILE_2,"rb")asfile:report.add_trades(file=file)print(f"Total selling prices:{report.prices}")print(f"Total capital gains:{report.gains}")print(f"Total capital losses:{report.losses}")foriteminreport.details:print(f"{item.symbol=},{item.quantity=},{item.buy_date=}, "f"{item.sell_date=},{item.price=},{item.realized=}")fromibkr_reportimportExchangeRates,StorageTyperates=ExchangeRates(storage_type=StorageType.LOCAL,storage_dir="/tmp/my_storage")print(rates.get_rate("EUR","USD","2020-06-20"))print(rates.get_rate("GBP","SEK","2015-12-31"))
Package: ibl
IBLPython API Wrapper forhttps://infinitybots.xyzPOST BOT STATSawait ibl.post_stats(bot_id, auth_token, servers, shards=0)Example:importdiscordfromdiscord.extimporttasks@tasks.loop(minutes=30)asyncdefupdate_ibl():awaitibl.post_stats(bot.user.id,"auth_token",len(bot.guilds))GET USER INFOawait ibl.user_info(id)Returns:user.usernameStringuser.aboutStringuser.certified_devBOOLuser.staffLIST/ARRAYuser.developerSTRINGuser.errorBOOLGET BOT INFOawait ibl.bot_info(id)RETURNSbot.namebot.certifiedbot.tagsbot.prefixbot.ownerbot.librarybot.short_descbot.long_descbot.premiumbot.nsfwbot.serversbot.shardsbot.votesbot.invitesbot.websitebot.donatebot.supportbot.bannerbot.staffbot.error
Package: iblatlas
iblatlasTools to manipulate hierarchical 3D representations of the mouse brain anatomy for electrophysiolgy experiments.
The tools are mainly using the the Allen CCF although some other atlases can be used.This repository uses minimal requirements, based of standardmatplotlib,numpyandscipylibraries, to the exclusion
of more complex visualization tools such aspyqt.DocumentationFull package documentationNotebooks and examplesInstallationpip install iblatlasContributingChanges are merged by pull requests.
Release checklist:Update version iniblatlas/__init__.pyUpdateCHANGELOG.mdCreate a pull request to themainbranch on GitHubOnce the PR is merged, create a new tag and push the tagOnce a tag is pushed on main the package is uploaded to PyPI using GitHub Actions.
Package: ibllib
IBL Python LibrariesDescriptionLibrary used to implement the International Brain Laboratory data pipeline. Currently in active development.
The library is currently 2 main modules:brainbox: neuroscience analysis oriented libraryibllib: general purpose library containing I/O, signal processing and IBL data pipelines utilities.Release Notes hereRequirementsOS: Only tested on Linux. Windows and Mac may work, but are not supported.Python Module: Python 3.8 or higherInstallation, documentation and exampleshttps://docs.internationalbrainlab.orgContribution and development practicesSeehttps://int-brain-lab.github.io/iblenv/07_contribution.htmlWe use gitflow and Semantic Versioning.Before committing to your branch:run testsflake8
This is also enforced by continuous integration.Matlab LibraryThe Matlab library has moved to its own repository here:https://github.com/int-brain-lab/ibllib-matlab/
Package: iblm
IBLM:Inductive-bias Learning Models[ArXiv]What is IBLHow to UseSettingBinary classificatinNotebooksSupported ModelsContributorBackstoryWhat is IBL?IBL (Inductive-bias Learning) is a new machine learning modeling method that uses LLM to infer the structure of the model itself from the data set and outputs it as Python code. The learned model (code model) can be used as a machine learning model to predict a new dataset.In this repository, you can try different learning methods with IBL.(Currently only binary classification with simple methods is available.)How to UseSettingInstallationpipinstalliblmOpenAI API key settingsos.environ["OPENAI_API_KEY"]="OPENAI_API_KEY"Binary classificationCurrently, only small amounts of data can be executed.
The same seed value may or may not generate a successful code model.Model DefinitionfromiblmimportIBLModel# 回帰タスク -> objective="regression"# 二値分類 -> "binary"# 多値分類 -> "multiclass"### IBLModel呼び出し# OPENAI_APIos.environ["OPENAI_API_KEY"]="xxx"iblm=ibl.IBLModel(api_type="openai",model_name="gpt-4-0125-preview",objective="binary")# AZURE_OPENAI_APIos.environ["AZURE_OPENAI_KEY"]="xxx"os.environ["AZURE_OPENAI_ENDPOINT"]="xxx"os.environ["OPENAI_API_VERSION"]="xxx"iblm=IBLModel(api_type="azure",model_name="gpt-4-0125-preview",objective="binary")Model Learningmodel=iblm.fit(x_train,y_train)Model Predictionsy_proba=iblm.predict(x_test)NotebooksUse the link below to try it out immediately on Google colab.Binary classificationTitanic dataset:Moon dataset:Supported ModelsCurrently, the recommended model is GPT-4Contributor@t-ymbys@cn47CiteIf you find this repo helpful, please cite the following papers:@article{tanaka2023inductive,
  title={Inductive-bias Learning: Generating Code Models with Large Language Model},
  author={Tanaka, Toma and Emoto, Naofumi and Yumibayashi, Tsukasa},
  journal={arXiv preprint arXiv:2308.09890},
  year={2023}
}
Package: ibl-neuropixel
ibl-neuropixelCollection of tools to handle Neuropixel 1.0 and 2.0 data
(documentation coming soon...)Installationpip install ibl-neuropixelDestripingGetting startedThis relies on a fast fourier transform external library:pip install pyfftw.Minimal working example to destripe a neuropixel binary file.frompathlibimportPathfromibldsp.voltageimportdecompress_destripe_cbinsr_file=Path('/datadisk/Data/spike_sorting/pykilosort_tests/imec_385_100s.ap.bin')out_file=Path('/datadisk/scratch/imec_385_100s.ap.bin')decompress_destripe_cbin(sr_file=sr_file,output_file=out_file,nprocesses=8)ViewerThe best way to look at the results is to useviewephys,
open an ephys viewer on the raw data.tick the destipe box.move to a desired location in the filectr+P will make the gain and axis the same on both windowsYou can then move within the raw data file.White PaperThe following describes the methods implemented in this repository.https://doi.org/10.6084/m9.figshare.19705522ContributionPypi Release checklist:
Edit the version number insetup.py, and add release notes inrelease_notes.mdflake8tag=vX.Y.Z
gittag-a$taggitpushorigin%tagCreate new release with tag vX.Y.Z (will automatically publish to PyPI)
Package: iblog
blogbloggithub issues可以用issues来写blog
优点：支持回复支持tag可以计划支持订阅全文检索缺点：任何人都可以新建issue（有好处也有不好的）感觉很棒设计a 提供api供其它服务调用的方式来操作issueb 提供MQ消费者的方式来操作issuec 提供定时任务来操作issue思考直接创建一个网站首页获取api显示https://api.github.com/repos/lzh2nix/articles/issues?page=2在上面的api header中有以下信息
link:https://api.github.com/repositories/112858035/issues?page=1; rel="prev",https://api.github.com/repositories/112858035/issues?page=3; rel="next",https://api.github.com/repositories/112858035/issues?page=3; rel="last",https://api.github.com/repositories/112858035/issues?page=1; rel="first"https://developer.github.com/v3/issues/https://github.com/josegonzalez/python-github-backuphttps://github.com/IQAndreas/github-issues-importhttps://github.com/devspace/awesome-github-templatesABAP开发的Github issue备份工具gitee 也有对应的api：https://gitee.com/api/v5/swagger#/getV5ReposOwnerRepoIssuesComments开发一个同步工具利用issues的api评论可以单向同步到自己的blog系统提高 API 访问次数的配额默认情况下你是用匿名权限访问 github 接口的， github 的访问限制是一个小时最多 60 次请求，这显然是不够的，如何提高限制呢？到个人设置下的 Personal access tokens 页（https://github.com/settings/tokens），如下图，点击右上角的 Generate new token填写名称，只勾选 public_repo,然后保存，github 会生成一个可访问你公开项目的 access_token，将它填入到配置文件的 access_token 的值中，并取消注释。打开 app.js,取消掉第 17 行和 88 行的注释，保存后重新上传即可data:{
// access_token:_config['access_token']
},python setup.py check
python setup.py sdist bdist_wheel
python setup.py install
twine upload dist/*

python -m iblog -h
or
iblog -h
Package: ibloom
ibloomthis is a fork ofpyreBloom-ng, pyreBloom-ng is a python library which implements a Redis-backed Bloom filter.pyreBloom-ng is really powerful. but it's setup.py and tests and bench/benchmark.py are all outdated, the repo's last commit is 4 years ago.based on pyreBloom-ng and added supported for python3's str, avoid of annoyingb'some_key'Installationpre-requirementibloomrequireshiredislibrary,Cythonanda C compilerhiredis# Macbrewinstallhiredis# ubuntuapt-getinstalllibhiredis-dev# From source:gitclonehttps://github.com/redis/hirediscdhiredis&&make&&sudomakeinstallCythonpipinstallCythonStartupinit an instancefromibloomimportIBloomib=IBloom('ibloomI',1000,0.01,'127.0.0.1',6383)orfromibloomimportIBloomib_n=IBloom(key='ibloomN',capacity=1000,error=0.01,host='127.0.0.1',port=6383)check basic info# You can find out how many bits this will theoretically consume>>>ib.bits9585# And how many hashes are needed to satisfy the false positive rate>>>ib.hashes7# find all available bloom filter keys>>>ib.keys()['ibloomI.0']add dataadd all supplied# Add one value at a time (slow)>>>ib.add('first')True# Or use batch operations (faster).>>>ib.update([f'{x}'forxinrange(5)])5# Alternative: ib += data, but this will return nothing>>>ib+=[f'{x+5}'forxinrange(5)]only add if not exist# will first get the difference, and then update them to redis, and return them>>>ib.update_difference(['5','6','7','8','9','10'])['10']check if key existsfind one# Test one value at a time (slow).# . in ...>>>'first'inibTrue# ...contains(.)>>>ib.contains('first')Truefind multiple# Use batch operations (faster).# Note: ibloom.intersection() returns a list of values# which are found in a Bloom filter. It makes sense when# you consider it a set-like operation.>>>ib.intersection(['3','4','5','6'])['3','4','5','6']# Alternative: ib & [b'3', b'4', b'5', b'6']>>>ib&['3','4','5','6','9','10']['3','4','5','6','9']find non exist>>>ib.difference(['5','6','7','8','9','10'])['10']# not recommended, maybe update in the future# Alternative: ib ^ ['5', '6', '7', '8', '9', '10']>>>ib^['5','6','7','8','9','10']['10']delete the bloom key# delete selfib.delete()
Package: ibl-to-nwb
IBL-to-nwbThis repository houses the modules used to convert IBL specific neurophysiology data in the open sourceONEformat (Alyx + ALF) into NWB data standard.Alyx: a data base that contains all the metadata associated with an experiment: session details, subject details, probe information etc. This data has a one-to-one mapping to supported metadata of NWB.ALF: format for storage of all the experimental data: electrophysiology time series (raw + processed), trials data, sorted spikes data, behavior (raw + processed), stimulus.
The figure below shows the mapping from ALF/ALyx to NWB:Usage:IBL to NWB conversion (using API):Installation:cddesired-path
gitclonehttps://github.com/catalystneuro/IBL-to-nwb.gitcdIBL-to-nwbcreate virtual environment and install dependencies from requirements.txt:condaenvcreate-fconda_environment.yaml
condaactivateIBL2NWBRetrive the id of the experiment of interest usingONEapi:fromoneibl.oneimportONEone=ONE()# use the ONE doc to use correct search terms to retrieve the eideid=one.search(date_range=['2020-03-23','2020-03-24'],subject='CSH_ZAD_011')[0]# example eid:eid='da188f2c-553c-4e04-879b-c9ea2d1b9a93'Using the eid, generate a json file containing all the collected data/metadata from the servers (Example outputfile):from.ibl_nwbimportAlyx2NWBMetadatametadata_object=Alyx2NWBMetadata(eid=eid,one_obj=one)# alternatively, you can also provide one search **kwargs directly:metadata_obj=Alyx2NWBMetadata(date_range=['2020-03-23','2020-03-24'],subject='CSH_ZAD_011')json_save_loc=r'path-to-save-json-file.json'metadata_obj.write_metadata(json_save_loc)Generate nwb file using the saved json file:from.ibl_nwbimportAlyx2NWBConverternwb_saveloc=r'nwb-save-path.nwb'save_raw=False# keep as true if you want to add raw (ephysData.raw.* , camera.raw*) files, these are large files and will take time to download and create the nwbfile!!converter=Alyx2NWBConverter(nwb_metadata_file=json_save_loc,saveloc=nwb_saveloc,save_raw=save_raw)# alternatively you can also provide the metadata object:converter=Alyx2NWBConverter(metadata_obj=metadata_obj,saveloc=nwb_saveloc)# create nwb file:converter.run_conversion()converter.write_nwb()This should create an nwb file.Example file.IBL to NWB conversion (using GUI):from.ibl_nwbimportAlyx2NWBGuiAlyx2NWBGui(eid=eid,nwbfile_saveloc=nwb_saveloc,metadata_fileloc=json_save_loc)#alternatively provide the one search kwargs:Alyx2NWBGui(nwbfile_saveloc=nwb_saveloc,metadata_fileloc=json_save_loc,dataset_types=['_iblmic_audioSpectrogram.frequencies''])This opens up a gui which will allow you to edit nwbfile/ibl session related metadata and also convert to nwb usingrun_conversionbutton. Check the animation       below on how to navigate this gui:Visualization of nwbfile usingnwbwigetsin aJupyter notebook:frompynwbimportNWBHDF5IOfromnwbwidgetsimportnwb2widgetfromIPython.displayimportdisplayio=NWBHDF5IO(r"path-to-saved-nwb-file.nwb",mode='r',load_namespaces=True)nwb=io.read()a=nwb2widget(nwb)display(a)
Package: ibm935
IBM935The codecibm935is not yet officially supported. This package is designed for conversion betweenunicodeandibm935.InstallationUsingpip:$pipinstallibm935Or manually download the archive and run the command after extracting the stuff inside:$pythonsetup.pyinstallUsageBefore using this codec, import the module:importibm935Then we can convertunicodestrings intoibm935codec as usual:# Python 2encoded_bytes=u"我爱Python这么美好的语言！".encode("ibm935")decoded_string=encoded_bytes.decode("ibm935")# Python 3 - the prefix `u` can be omittedencoded_bytes="我爱Python这么美好的语言！".encode("ibm935")decoded_string=encoded_bytes.decode("ibm935")We can also write files using this codec:withopen("ibm935.txt","w",encoding="ibm935")asf:f.write("我爱Python！")Note:This package is not yet fully tested.There is a tough situation where writing a file with separatewords(double-byte) causes redundant bytes like\x0f\x0e.
Package: ibm-cloud-databases
IBM Cloud Databases Python SDK 0.1.0Python client library to interact with variousIBM Cloud Cloud Databases APIs.Disclaimer: this SDK is being released initially as apre-releaseversion.
Changes might occur which impact applications that use this SDK.Table of ContentsOverviewPrerequisitesInstallationUsing the SDKQuestionsIssuesOpen source @ IBMContributingLicenseOverviewThe IBM Cloud Cloud Databases Python SDK allows developers to programmatically interact with the following
IBM Cloud services:Service NameImported Class NameCloud DatabasesCloudDatabasesV5PrerequisitesAnIBM Cloudaccount.An IAM API key to allow the SDK to access your account. Create onehere.Python 3.6 or above.InstallationTo install, usepiporeasy_install:pipinstall--upgrade"ibm-cloud-databases>=0.1.0"oreasy_install--upgrade"ibm-cloud-databases>=0.1.0"Using the SDKFor general SDK usage information, please seethis linkQuestionsIf you are having difficulties using this SDK or have a question about the IBM Cloud services,
please ask a questionStack Overflow.IssuesIf you encounter an issue with the project, you are welcome to submit abug report.
Before that, please search for similar issues. It's possible that someone has already reported the problem.Open source @ IBMFind more open source projects on theIBM Github PageContributingSeeCONTRIBUTING.md.LicenseThis SDK is released under the Apache 2.0 license.
The license's full text can be found inLICENSE.
Package: ibmcloud-iam
ibmcloud-iam-python-clientThis project is a collection of Python modules used for interacting with IBMCloud IAM API services.Currently it includes support for:Requesting IAM tokens using an intelligent caching mechanism (TokenManager)Parsing IAM tokens and verifying their cryptographic signaturesChecking user action authorizations for a service, given a Subject, Action and Resource set (PDPClient)It is meant to be a lightweight client library that can be used in other projects, such as a service or other client.Some things that will be added soon in the future:Caching authorization decisions from PDPSupport for list a users Roles assigned from IAM policiesUsageInstallpip install ibmcloud_iamGetting tokens with TokenManagerimportosfromibmcloud_iam.tokenimportTokenManagertm=TokenManager(api_key=os.environ["IBMCLOUD_API_KEY"])# whenever you need a token, just use 'tm.get_token()'# get_token() will return a cached token if the token is not expired,# or if expired or otherwise invalid, retrieve a new token for you# gets a new tokenprint(tm.get_token())# will return the same token as above, because of cachingprint(tm.get_token())Parsing and Validating Tokensimportosimportibmcloud_iam.tokenfromibmcloud_iam.tokenimportTokenManagertm=TokenManager(api_key=os.environ["IBMCLOUD_API_KEY"])# validate_token will check the signature and parse and return the token claimsclaims=ibmcloud_iam.token.validate_token(tm.get_token())print(claims)PDP AuthorizationsPlease seeexample.pyfor an example on how to use thePDPClientContributingFeedback is appreciated in the form of bug reports, enhancement suggestions, testing, or pull requests/patches.Pull requests and patches will be subject to review and will have to meet the standards of the project to be merged. We are willing to help with fixing issues and polishing if you are willing to be patient and understanding.For bug reports, feature requests, or other support, please open a Github Issue in this repo.If you are an IBMer and have access to the IBM Slack group, feel free to direct message me with questions at@mrodden.
Package: ibm-cloud-networking-services
IBM Cloud Networking Services Python SDK Version 0.24.0Python client library to interact with variousIBM Cloud Networking Service APIs.Table of ContentsOverviewPrerequisitesInstallationUsing the SDKQuestionsIssuesOpen source @ IBMContributingLicenseOverviewThe IBM Cloud Networking Services Python SDK allows developers to programmatically interact with the following
IBM Cloud services:CIS Service NameImported Class NameCIS: CacheCachingApiV1CIS: IPCisIpApiV1CIS: Custom PagesCustomPagesV1CIS: DNS Records BulkDnsRecordBulkV1CIS: DNS RecordsDnsRecordsV1CIS: Firewall Access RulesFirewallAccessRulesV1CIS: Security Level SettingsFirewallApiV1CIS: GLB EventsGlobalLoadBalancerEventsV1CIS: GLB MonitorGlobalLoadBalancerMonitorV1CIS: GLB PoolsGlobalLoadBalancerPoolsV0CIS: GLB ServiceGlobalLoadBalancerV1CIS: Page RulesPageRuleApiV1CIS: Range ApplicationRangeApplicationsV1CIS: RoutingRoutingV1CIS: Security EventsSecurityEventsApiV1CIS: SSL/TLSSslCertificateApiV1CIS: User Agent Blocking RulesUserAgentBlockingRulesV1CIS: WAF SettingsWafApiV1CIS: WAF Rule GroupsWafRuleGroupsApiV1CIS: WAF Rule PackagesWafRulePackagesApiV1CIS: WAF RulesWafRulesApiV1CIS: Zone Firewall Access RulesZoneFirewallAccessRulesV1CIS: Zone LockdownZoneLockdownV1CIS: Zone Rate LimitsZoneRateLimitsV1CIS: Zone SettingsZonesSettingsV1CIS: ZonesZonesV1PDNS Service NameImported Class NamePDNS: Zones APIDnsZonesV1DnsSvcsV1PDNS: Resource Records APIResourceRecordsV1DnsSvcsV1PDNS: Permitted Networks for Zones APIPermittedNetworksForDnsZonesV1DnsSvcsV1PDNS: Global Load Balancers APIGlobalLoadBalancersV1DnsSvcsV1Direct Link ServiceImported Class NameDirect LinkDirectLinkV1Direct Link ProviderDirectLinkProviderV2Transit ServiceImported Class NameTransit GatewayTransitGatewayApisV1PrerequisitesAnIBM Cloudaccount.An IAM API key to allow the SDK to access your account. Create onehere.Python 3.6 or above.InstallationTo install, usepiporeasy_install:pipinstall--upgrade"ibm-cloud-networking-services==0.24.0"oreasy_install--upgrade"ibm-cloud-networking-servies==0.24.0"Using the SDKFor general SDK usage information, please seethis linkQuestionsIf you are having difficulties using this SDK or have a question about the IBM Cloud services,
please ask a questionStack Overflow.IssuesIf you encounter an issue with the project, you are welcome to submit a
[bug report](/issues).
Before that, please search for similar issues. It's possible that someone has already reported the problem.Deprecation NoticeFor deprecation notice, please seethis linkOpen source @ IBMFind more open source projects on theIBM Github PageContributingSeeCONTRIBUTING.LicenseThis SDK is released under the Apache 2.0 license.
The license's full text can be found inLICENSE.
Package: ibm-cloud-sdk-core
IBM Python SDK Core Version 3.19.2This project contains core functionality required by Python code generated by the IBM Cloud OpenAPI SDK Generator
(openapi-sdkgen).Python VersionThe current minimum Python version supported is 3.8.InstallationTo install, usepip:python-mpipinstall--upgradeibm-cloud-sdk-coreAuthenticationThe python-sdk-core project supports the following types of authentication:Basic AuthenticationBearer Token AuthenticationIdentity and Access Management (IAM) AuthenticationContainer AuthenticationVPC Instance AuthenticationCloud Pak for Data AuthenticationNo Authentication (for testing)For more information about the various authentication types and how to use them with your services, clickhere.IssuesIf you encounter an issue with this project, you are welcome to submit abug report.
Before opening a new issue, please search for similar issues. It's possible that someone has already reported it.LoggingEnable loggingimportlogginglogging.basicConfig(level=logging.DEBUG)This would show output of the form:DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): iam.cloud.ibm.com:443
DEBUG:urllib3.connectionpool:https://iam.cloud.ibm.com:443 "POST /identity/token HTTP/1.1" 200 1809
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): gateway.watsonplatform.net:443
DEBUG:urllib3.connectionpool:https://gateway.watsonplatform.net:443 "POST /assistant/api/v1/workspaces?version=2018-07-10 HTTP/1.1" 201 None
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): gateway.watsonplatform.net:443
DEBUG:urllib3.connectionpool:https://gateway.watsonplatform.net:443 "GET /assistant/api/v1/workspaces/883a2a44-eb5f-4b1a-96b0-32a90b475ea8?version=2018-07-10&export=true HTTP/1.1" 200 None
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): gateway.watsonplatform.net:443
DEBUG:urllib3.connectionpool:https://gateway.watsonplatform.net:443 "DELETE /assistant/api/v1/workspaces/883a2a44-eb5f-4b1a-96b0-32a90b475ea8?version=2018-07-10 HTTP/1.1" 200 28Low level request and response dumpTo get low level information of the requests/ responses:fromhttp.clientimportHTTPConnectionHTTPConnection.debuglevel=1Open source @ IBMFind more open source projects on theIBM Github PageLicenseThis library is licensed under Apache 2.0. Full license text is
available inLICENSE.ContributingSeeCONTRIBUTING.md.