Saravanan666/pypi-org huggingface dataset:
Package: jsontemplate
JSON Template is a simple declarative template language based on
JSON. It is designed to be easy to implement in multiple languages,
including client-side javascript. This release only contains the
official Python implementation of JSON Template.For more about JSON Template, see:http://json-template.googlecode.comTestingTo test JSON template, go to the trunk of the development version of
JSON template and do the following:$ python jsontemplate_test.pyAlso run the Python tests:$ python jsontemplate_test.py --pythonAlso run the Python specific tests:$ cd python/jsontemplate
$ python formatters_test.py
$ python jsontemplate_unittest.pyNOTE: While there are some tests included in the Python release,
the tests won’t run with just the release. You need the whole checkout
of the multi-language JSON Template in order to run the tests, not
just the Python parts.Changes0.87Applied patch that adds “provides” to setup.py.0.86Trying again.0.85b3Yet again adding CHANGES.txt.0.85b2Again adding CHANGES.txt.0.85JSON Template is usually released at:http://code.google.com/p/json-template/downloads/listThis is the first PyPI release in 2+ years as I am not a PyPI user myself
(sorry).  Lots of changes since the last PyPI release:template stylessection “pre-formatters”various tweaks and additions to the set of builtin formattersmultiline commentwhitespace optionsdoc/Design.txt has some skeletal notes.(hg change set 434:d97224b19057, tag python-0.85)0.5b2 (2009-05-27)This release should actually be installable, as ‘CHANGES.txt’ is actually
included.0.5b1 (2009-05-14)Initial public release on PyPI.Download
Package: jsontemplates
json-templatesA small python module for populating json template files.Version 0.1.0Accepts either a json string or a file path and a dictionary. json-template replaces the place holders found in the json with those found in the dictionary.Below is a template example{"key":"hard coded value","key2":1,"key3":"{{ variable }}","key4":"{% array %}"}Currently supports{{ variable }}for single replacement and{% array %}for iterable replacement.For example given the following dictionary{"variable":"hello world","array":["foo","bar"]}The JSON would become{"key":"hard coded value","key2":1,"key3":"hello world","key4":["foo","bar"]}UsageimportJsonTemplatesjson_tmp=JsonTemplates()result=json_tmp.load("template.json")ifresult[0]:new_dict=json_tmp.generate({"variable":"hello world","array":["foo","bar"]})Methodsload(json_file_path)- Loads a JSON fileReturns a tuple (Success,error message or dictionary)loads(json_str)- Loads a JSON stringReturns a tuple (Success,error message or dictionary)generate(replacement_dict)- Takes in a dictionary of replacement values and generates a new dictionary with the placeholders replaced with the values in the dictionaryReturns a tuple (Success, error message or dictionary)
Package: json-test-runner
JsonTestRunner执行unittest 用例，输出json结构测试报告供web渲染或保存输出结构体如下：{"test_list":[{"style":"Failed","name":"test_json_test_runner1.RunnerTestCase","doc":"","count":2,"Pass":1,"fail":1,"error":0,"cid":"c2","info":[{"index":"test_json_test_runner1.RunnerTestCase.test_success","name":"test_success","style":"Pass","desc":"","script":"success\n","status":"通过"}]}],"count":5,"Pass":2,"fail":2,"error":1,"passrate":"40.00%","start_time":"2022-09-26 18:09:35","stop_time":"2022-09-26 18:09:36","duration":"0:00:01.003048"}安装pipinstalljson-test-runner使用命令行jtr-ccasepath输出：Etest_error(test_json_test_runner.RunnerCase)Ftest_failed(test_json_test_runner.RunnerCase)oktest_pass(test_json_test_runner.RunnerCase)Ftest_failed(test_json_test_runner1.RunnerTestCase)oktest_success(test_json_test_runner1.RunnerTestCase)TimeElapsed:0:00:01.003048
savereportfile:/work/json-test-runner/test/report.json引用fromjsontestrunnerimportRunnercase_path=r'./test'runner=Runner(case_path).run()# 执行caseprint(runner.stream)# json结构体runner.save()# 保存测试报告为json文件
Package: jsontextmc
JSONTextMCMinecraft uses two main systems for formatting text.The Legacy format uses section symbols and hexadecimal color coding,
while the JSON format uses key-value pairs to color individual parts of text.JSONTextMC is a module for translating legacy messages to and from JSON-formatted text.
Output is not valid JSON by itself, it must be translated into JSON withjson.loads().Strict modes and inheritance of various JSON elements can be controlled.Requires Python 3.10+.Table of ContentsInstallationSupprtUsageContributingLicenseInstallationUse the package managerpipto installJSONTextMC.python3-mpipinstalljsontextmcSee the package page on PyPihere.Use the output in any place where a JsonTextComponent is accepted(e.g./tellraw,/title,.json files).SupportSupports all Vanilla Minecraft formatting codes:Allcolor codesAll formatting codesHexidecimalAlows both#andxas prefix codesUsageRead the PyDocs in the module for details about usage. Essentially:to_json(): Legacy -> JSONfrom_json(): JSON -> LegacyStrict and inheritance modes control how legacy text is handled and translated into JSON.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.If there is an individual issue you would like to bring attention to, pleaseopen one here.LicenseDistributed under theGPLv3license.(visitGNU's website)
Package: jsonthrift
jsonthrift is implemented for serializing and deserializing json to/from thrift.Usage>>> transport = 'TBufferedTransport'
>>> protocol = 'TBinaryProtocol'
>>> thrift_file = 'tutorial.thrift'
>>> service = 'Calculator'
>>> method = 'add'
>>> params = {'num1': 1, 'num2': 3}>>> jt = JsonThrift(transport, protocol, thrift_file, service)
>>> msg = jt.pack_request(method, params, 1)
>>> response = send_and_recv(msg) # send msg to server, receive response
>>> result = jt.unpack_response(response)[4]
>>> print result
{'success': 4}>>> method = 'calculate'
>>> params = {'logid': 1, 'w':{'op': 4, 'num1': 1, 'num2': 0}}
>>> msg = jt.pack_request(method, params, 2)
>>> response = send_and_recv(msg) # send msg to server, receive response
>>> result = jt.unpack_response(response)[4]
>>> print result
{'ouch': {'whatOp': 4, 'why': 'Cannot divide by 0'}}>>> # unpack_response returns a tuple, which contains:
>>> #   message size(TFramedTransport)/None(TBufferedTransport),
>>> #   message type, will be 2 for thrift REPLY message
>>> #   method
>>> #   sequence id
>>> #   return value
Package: json-timeseries
JSON Time SeriesJSON Time Series(JTS specification) handling Python library - Time Series data construction, manipulation and serialisation.Installationpipinstalljson-timeseriesImport or require modulefromjson_timeseriesimportTsRecord,TimeSeries,JtsDocumentUsagefromjson_timeseriesimportTsRecord,TimeSeries,JtsDocumentfromdatetimeimportdatetime# Create Time Seriestimeseries1=TimeSeries(identifier='series_1',name='Series 1',data_type='NUMBER',records=[TsRecord(**{"timestamp":datetime.now(),"value":'1.23',"quality":192,"annotation":'comment'}),TsRecord(**{"timestamp":datetime.now(),"value":'2.34',"quality":245,"annotation":'comment number 2'})])timeseries2=TimeSeries(identifier='series_2',name='Series 2',data_type='NUMBER',units="C",records=TsRecord(timestamp=datetime.now(),value=1.11,quality=111,annotation="comment ts2 111"))# Add record(s)timeseries1.insert(TsRecord(**{timestamp:datetime.now(),value:30}))# Output in JSON Time Series document formatjts_doc=JtsDocument([timeseries1,timeseries2])json_str=jts_doc.toJSON()TimeSeriesTimeSeriesis a class for constructing and manipulating a single dataset.fromjson_timeseriesimportTsRecord,TimeSeriesfromdatetimeimportdatetimetime_series=TimeSeries(identifier='series_2',name='Series 2',data_type='NUMBER',units="m/s",records=TsRecord(timestamp=datetime.now(),value=1.11,quality=0,annotation="example comment"))OptionsOptionally provide configuration used for certain output formats such as JTS Document.data_type: data type of recordvalueattribute.NUMBER | TEXT | TIME | COORDINATESid: string or number to uniquely identify the series to use instead of the automatically assigned id.name: stringunits: stringrecords: list of data recordsAlternatively set later:time_series.data_type='NUMBER'time_series.id='Series_1'time_series.name='My Series'time_series.units='m/s'TsRecordTsRecordis a class for constructing and manipulating a single record.fromjson_timeseriesimportTsRecordfromdatetimeimportdatetimets_record1=TsRecord(timestamp=datetime.now(),value=1.11,quality=0,annotation="example comment")# Or as dict of parameters using ** operatorts_record2=TsRecord(**{"timestamp":datetime.now(),"value":1.11,"quality":0,"annotation":'example comment'})Record attributesRecords require a timestamp and at least one attribute: value, quality or annotationtimestamp: date object. Type of datetime. e.g.datetime.now()value(optional):  number, string, date, nullquality(optional): number (quality code) associated with valueannotation(optional): string description or comment related to the recordMethodsSeefull documentation.PropertiesSeefull documentation.JTS DocumentJtsDocumentis a class for outputtingTimeSeriesinJSON Time Seriesdocument format.# Create a JTS Document from one or more timeseriesjts_document=JtsDocument(series=[timeseries1,timeseries2])# Output series in JTS Document formatjson_str=jts_document.toJSON()Optionsseries: array ofTimeSeriesto include in JTS DocumentMethodsSeefull documentation.PropertiesSeefull documentation.LicenseMIT
Package: json_to_csv
Overview:
This project aims to convert a json file to a csv file
The json files in general don’t follow any particular schema
This leads to some values being left blank as some documents may not contain that field
While converting such files to comma separated files (csv), it is of utmost importance to consider filling up the null values with a mean value of that respectiuve column
This will help in getting an appropriate summary of the document for each column in the csv file
This project saves the raw and cleaned data files in the default working directories of python
Package: json-to-csv-filter
DescriptionConverts a list of json objects to a csv with optional filtering, sorting and date formatting.installationThis commands installs the package and an application script: json2csv.pipinstalljson-to-csv-filterUsageusage:json_to_csv.py[-h][-i[INCLUDE...]][-e[EXCLUDE...]][-o[ORDER...]][-n[NUMBER]][-d[DATE_FIELDS...]][-df[DATE_FORMAT]][-l[LOCALE]][infile][outfile]Convertlistofjsonobjectstocsv

positionalarguments:infileInputfile,defaultstoSTDINoutfileOutputfile,defaultstoSTDOUT

optionalarguments:-h,--helpshowthishelpmessageandexit-i[INCLUDE...],--include[INCLUDE...]Includefields,defaultstoall-e[EXCLUDE...],--exclude[EXCLUDE...]Excludefields,defaultstonone-o[ORDER...],--order[ORDER...]Orderfields,defaultstonone-n[NUMBER],--number[NUMBER]Numberofrecordstoprocess,defaultstoall-d[DATE_FIELDS...],--date-fields[DATE_FIELDS...]Datefields,defaultstonone-df[DATE_FORMAT],--date-format[DATE_FORMAT]Customdatetimeformat-l[LOCALE],--locale[LOCALE]localeid,defaultstonld

Seehttps://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codesfordatetimeformatstringsandhttps://babel.pocoo.org/en/latest/fordefaultnldlocaledatetimeformatExamplesconvert a json file to standard outjson2csv-iais-messages.txt
Package: json-to-dir
USAGEjson-to-dir <json-file> [output-directory]output-directory defaults to somewhere in /tmp.EXAMPLEfiatjaf@menger ~> echo '[{"x": 23}, {"y": "23", "www": {"lll": "qwlekwq"}}]' > file
fiatjaf@menger ~> ./json-to-dir file
file written to /tmp/tmpXv1f5X.
fiatjaf@menger ~> tree /tmp/tmpXv1f5X/
/tmp/tmpXv1f5X/
├── 0
│   └── x
└── 1
    ├── www
    │   └── lll
    └── y

3 directories, 3 files
Package: json-to-elan
JSON to ELANThe script looks in a folder, and generates an ELAN file to match each JSON file.JSON formatIt has been written for the JSON output from Huggingface ASR pipelines. Here's an example of the expected JSON format.[{"text":"luanghan","timestamp":[1.16,1.48]},{"text":"ian","timestamp":[1.56,1.7]}]Basic usagePut your JSON files somewhere easily accessible, eg in adatafolder in your working directory. Install it. Use it by providing a path to your data.pipinstalljson-to-elanfromjson_to_elanimportmake_elanmake_elan(data_dir="content")Using this in Colab?To use this in Google Colab, upload your JSON files into the File browser. Then define the data directory as:data_dir="/content"OptionsYou can also set a different tier name from the default (which is "default").The ELAN file gets a linked media file written, for which we assume that the media file is  a WAV with the same name as the JSON file. If you want to change this to MP3, change the audio_format.Here's an example:make_elan(data_dir="content",tier_name="Words",audio_format="mp3")
Package: json-to-files
json-to-filesCreate separate files on disk based on a JSON objectInstallationInstall this tool usingpip:pip install json-to-filesUsageThis tool takes a JSON file that looks like this:{"foo.txt":"The contents of foo.txt","bar/baz.txt":"The contents of baz.txt"}And uses it to write out the following files on disk:foo.txtcontaining "The contents of foo.txt"bar/baz.txtcontaining "The contents of baz.txt"You can run it like this:json-to-files bundle.jsonOr you can specify a directory to write those files to:json-to-files bundle.json -d /tmp/other-directoryThebundles.jsonfile name is optional - if omitted, this tool will read from standard input:cat bundle.json | json-to-filesDevelopmentTo contribute to this tool, first checkout the code. Then create a new virtual environment:cd json-to-files
python -m venv venv
source venv/bin/activateNow install the dependencies and test dependencies:pip install -e '.[test]'To run the tests:pytest
Package: jsontofu
Usage@dataclassclassData:str_data:strint_data:int@dataclassclassDictData:str_data:strdict_data:Optional[Dict]@dataclassclassRecursiveData:str_data:strdict_data:Datajson_data1={'str_data':'test','int_data':123}json_data2={,'str_data':'test','dict_data':{'key1':123,'key2':456}}json_data3={,'str_data':'test','dict_data':{'str_data':'test','int_data':456}}print(jsontofu.decode(json_data1,Data))# Data(str_data="test", int_data=123)print(jsontofu.decode(json_data2,DictData))# DictData(str_data="test", dict_data={'key1': 123, 'key2': 456})print(jsontofu.decode(json_data3,RecursiveData))# RecursiveData(str_data="test", Data(str_data="test", int_data=456)Installationpipinstallgit+git://github.com/rondou/jsontofu.gitorpipenvinstall'git+ssh://git@github.com/rondou/jsontofu.git#egg=jsontofu'Developmentpipenvinstallpipenvinstall-dpipenvrun"pytest -s"Coveragepipenvrun'pytest tests --cov=jsontofu'
Package: json-to-github-actions
JSON 轉 GitHub Actions YAML🐔動機: 全球免費20 VMs運算，有感試用，可以把 python 或 julia 放到 github actions 算 
💣地雷: 格式轉換會存在很多 bugs, 要先生最終無 bugs .yml 才反推轉換的 .py 此套件程式碼
🧪實驗: Roy julia套件清物聯網資料已經分散兩台 VMs (每個4GB .zip處理55分鐘)運算成功
🧾待辦: 雖然可分散運算了，但運算結果還需要轉化為 github release .zip 抓回來結果
這個 Python 套件能將 JSON 格式轉化為 GitHub Actions 的 YAML 格式。安裝python3 -m pip install json-to-github-actionsCLI 用法python json_to_github_actions.py --json_file [json_file] --yaml_file [yaml_file]範例：pythonjson_to_github_actions.py--json_fileinput.json--yaml_fileoutput.yaml輸入json_file是您的 JSON 檔案路徑，並將產生的 GitHub Actions YAML 檔案保存到給定的yaml_file路徑。套件 import 用法首先，將json_to_github_actions.py腳本導入您的專案。fromjson_to_github_actionsimportjson_to_github_actions然後您可以調用json_to_github_actions()函數將 JSON 輸入轉換為 YAML 格式。JSON 格式JSON 檔案必須包含以下結構：{
  "repo_url": "https://github.com/bohachu/json_to_github_actions.git",
  "script_name": "add_two_numbers.py",
  "script_dir": "/add_two_numbers/",
  "execution_environment": [
    {
      "language": "python",
      "version": "3.8",
      "installation_command": "python -m pip install -r requirements.txt"
    }
  ],
  "max_parallel": 3,
  "json_parameters": [
    {
      "a": 1,
      "b": 2
    },
    {
      "a": 3,
      "b": 4
    },
    {
      "a": 5,
      "b": 6
    },
    {
      "a": 7,
      "b": 8
    }
  ],
  "output_directory": "add_two_numbers/data/"
}JSON 結構包含以下字段：repo_url：執行腳本的倉庫的 URL。script_name：要執行的腳本的名稱。script_dir：腳本文件夾的相對路徑。execution_environment：包含要在其中運行測試的環境設置的陣列。 您可以為 Python 和 Julia 語言指定多個環境。max_parallel：最大並行運行的數量。json_parameters：參數和值的陣列，這些參數和值將傳遞給腳本。output_directory：腳本將結果輸出到的文件夾的名稱。注意事項當前支持Python和Julia語言。 若要添加對其他語言的支持，可以編輯json_to_github_actions.py文件並添加相應的執行命令。模板目前僅適用於 "on": "workflow_dispatch"。 若要基於不同事件觸發 GitHub Actions，請在生成 YAML 檔案後手動更新該字段。如果在運行腳本時遇到問題或錯誤，請檢查您的 JSON 文件的語法和結構是否正確。敬告您自己承擔使用自動生成的 GitHub Actions YAML 文件的風險，我們對任何由此導致的後果和損失概不負責。 您應充分測試 YAML 文件以確保其符合預期行為，並完成所需任務。
Package: json-to-html
doc on github.iojson2htmlshow JSON in HTML.  rows sampling, collapse/expand, etc.Installationpip install json_to_htmlrun : "json2html" or "python -mjson2html"Examples[yonghang@W5202860 json2html]$ curl -s walkerever.com/share/test/json/s6.json | qic
{"users": [
    {"userId": 1,"firstName": "Krish","lastName": "Lee","phoneNumber": "123456","emailAddress": "krish.lee@learningcontainer.com"
    },
    {"userId": 2,"firstName": "racks","lastName": "jacson","phoneNumber": "123456","emailAddress": "racks.jacson@learningcontainer.com"
    },
    {"userId": 3,"firstName": "denial","lastName": "roast","phoneNumber": "33333333","emailAddress": "denial.roast@learningcontainer.com"
    },
    {"userId": 4,"firstName": "devid","lastName": "neo","phoneNumber": "222222222","emailAddress": "devid.neo@learningcontainer.com"
    },
    {"userId": 5,"firstName": "jone","lastName": "mac","phoneNumber": "111111111","emailAddress": "jone.mac@learningcontainer.com"
    }
  ]
}conver to plain HTML[yonghang@W5202860 json2html]$ curl -s walkerever.com/share/test/json/s6.json | python -mjson2htmlusersuserId1firstNameKrishlastNameLeephoneNumber123456emailAddresskrish.lee@learningcontainer.comuserId2firstNamerackslastNamejacsonphoneNumber123456emailAddressracks.jacson@learningcontainer.comuserId3firstNamedeniallastNameroastphoneNumber33333333emailAddressdenial.roast@learningcontainer.comuserId4firstNamedevidlastNameneophoneNumber222222222emailAddressdevid.neo@learningcontainer.comuserId5firstNamejonelastNamemacphoneNumber111111111emailAddressjone.mac@learningcontainer.com</td>with -Z option[yonghang@W5202860 json2html]$ curl -s walkerever.com/share/test/json/s6.json | python -mjson2html -Z<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script> 
<script type="text/javascript"> 
    $(document).ready(function () { 
        $('tr.parent') .css("cursor", "pointer") .attr("title", "Click to expand/collapse") .click(function () { 
            $(this).siblings('.child-' + this.id).toggle(); 
        }); 
        $('tr[@class^=child-]').hide().children('td'); 
    }); 
</script>D(1)usersL(5)userId1firstNameKrishlastNameLeephoneNumber123456emailAddresskrish.lee@learningcontainer.comuserId2firstNamerackslastNamejacsonphoneNumber123456emailAddressracks.jacson@learningcontainer.comuserId3firstNamedeniallastNameroastphoneNumber33333333emailAddressdenial.roast@learningcontainer.comuserId4firstNamedevidlastNameneophoneNumber222222222emailAddressdevid.neo@learningcontainer.comuserId5firstNamejonelastNamemacphoneNumber111111111emailAddressjone.mac@learningcontainer.com</td>
Package: json-to-models
json to modelsextracts python models from json filesusageinput:./json_datapip install json-to-modelsfromjson_to_modelsimportJsonToModelsimportglob,jsonjtm=JsonToModels(root_name='Root')forpathinglob.glob(f'./json_data/*.json'):print(path)withopen(path)asf:data=json.load(f)jtm.extract_models(data)jtm.write_models()output:models.py
Package: json-to-ndjsonify
JSON to NDJSONifyTable of ContentsOverviewFeaturesInstallationUsageConfigurationRunning TestsContributingLicenseOverviewJSON to NDJSONify is a Python package specifically engineered for converting JSON files to NDJSON (Newline Delimited JSON) format. Built for developers who are working with APIs or data platforms that require NDJSON input, this package helps streamline your workflow by automating the conversion process.FeaturesSupports complex nested JSON structuresAbility to filter specific nodes (e.g.,edges)Highly optimized and lightweightWritten in Python, making it highly extensible and maintainableUnit tests to validate NDJSON outputInstallationClone the repository:gitclonehttps://github.com/mohjak/json_to_ndjsonify.gitNavigate to the cloned directory and run:pipinstall.Or directly via pip:pipinstalljson-to-ndjsonifyUsageBasic UsageTo convert a JSON file to NDJSON:json-to-ndjsonify--inputsample.json--nodeedgesThis will create an NDJSON file in the same directory as thesample.jsonfile, suffixed with a timestamp.Advanced UsageFor additional configurations, consult the inline help:json-to-ndjsonify--helpConfigurationCommand-Line Parameters--input: Specifies the path of the JSON file to be converted.--node: Specifies the node name in the JSON file. Default is 'edges'.--output: Path to the output folder.Example:json-to-ndjsonify--input/path/to/your/file.json--nodecustom_edges--output/path/to/output/folderRunning TestsTo execute the unit tests:exportNDJSON_DIR=/path/to/your/ndjson/directory
python-munittesttests/test_main.pyContributingSee theCONTRIBUTING.mdfile for how to contribute to this project.LicenseThis package is licensed under the MIT License. See theLICENSE.mdfile for details.
Package: json-tools-python
json_toolsA tool for analysis json contentHow to useimportjson_toolstest_data={'a':{'b':{'c':'123','d':{'c':123,},'e':[{'a':1},{'b':2},{'c':3}],'f':[{'a':{'c':1}},{'b':{'c':2}}]}}}forretinjson_tools.search(test_data,'c'):print(ret)result:['a','b','c']['a','b','d','c']['a','b','e',2,'c']['a','b','f',0,'a','c']['a','b','f',1,'b','c']
Package: json-to-swagger
"Json to Swagger" conversorPython utility created to convert JSON to Swagger definitions structure, based on entities concept.DependenciesPython 3.6+Libraries:swagger-parserpyyamlpatternNote: Before run, execute:$pipinstall-rrequirements.txtUsageRunning:$pythonmain.py<json-file-path><RootEntityName>[swagger-file-path][-v]json-file-path- path of json file to convertRootEntityName- name of root swagger definitions entityswagger-file-path- path of swagger file. If the file has content, it will be merged with json converted content-v- verbose modeexample.json{"id":"ffe74d36-4f76-4ca6-9dd1-8d5c9b16b056","name":"JSON example","description":"Just a simple JSON file to convert into swagger definition","someObject":{"someInteger":0,"someNumber":0.1,"someBoolean":true,"someNull":null,"someString":"Example string content"},"items":[{"name":"Item","subItem":{"subItemsOfSubItem":[{"foo":"bar"}]}}]}example.swagger.yamldefinitions:Root:type:objecttitle:Rootdescription:Root Entityproperties:id:type:stringexample:ffe74d36-4f76-4ca6-9dd1-8d5c9b16b056name:type:stringexample:JSON exampledescription:type:stringexample:Just a simple JSON file to convert into swagger definitionsomeObject:$ref:'#/definitions/SomeObject'items:type:arrayitems:$ref:'#/definitions/Item'SomeObject:type:objecttitle:Some Objectdescription:Some Object Entityproperties:someInteger:type:integerexample:0someNumber:type:numberexample:0.1someBoolean:type:booleanexample:truesomeNull:type:stringexample:nullsomeString:type:stringexample:Example string contentItem:type:objecttitle:Itemdescription:Item Entityproperties:name:type:stringexample:ItemsubItem:$ref:'#/definitions/SubItem'SubItem:type:objecttitle:Sub Itemdescription:Sub Item Entityproperties:subItemsOfSubItem:type:arrayitems:$ref:'#/definitions/SubItemsOfSubItem'SubItemsOfSubItem:type:objecttitle:Sub Items Of Sub Itemdescription:Sub Items Of Sub Item Entityproperties:foo:type:stringexample:barAttention: Properties with same name will be mergedExampleIn the JSON structure bellow, it has the "items" property in two objects:{"items":[{"name":"Item","subItem":{"items":[{"foo":"bar"}]}}]}The result will merge the two "items" properties into "Item" entity:definitions:RootEntity:type:objecttitle:Rootdescription:Root Entityproperties:items:type:arrayitems:$ref:'#/definitions/Item'Item:type:objecttitle:Itemdescription:Item Entityproperties:name:type:stringexample:ItemsubItem:$ref:'#/definitions/SubItem'foo:type:stringexample:barSubItem:type:objecttitle:Sub Itemdescription:Sub Item Entityproperties:items:type:arrayitems:$ref:'#/definitions/Item'