Saravanan666/pypi-org huggingface dataset:
Package: json-modify
json_modifyjson_modifyis simple library that helps apply modifications
to json/yaml files and python representations of json.Installationpip install json_modifyExamplefromjson_modifyimportapply_actionssource={"spec":{"name":"test","metadata":[{"name":"test1","value":"test1"},{"name":"test2","value":"test2"}],"values":{"value1":10,"value2":20}}}actions=[{"action":"add","path":"spec/values","value":{"value3":30,"value4":40}}]result=apply_actions(source,actions)print(result){"spec":{"name":"test","metadata":[{"name":"test1","value":"test1"},{"name":"test2","value":"test2"}],"values":{"value1":10,"value2":20,"value3":30,"value4":40}}}Markersjson_modifyuses markers to traverse lists. Markers should be used inpathfor example:"path":"metadata/$project_name/name"There are two types of markers:Filter marker ($<marker_name>) - the kind of marker, that is used to select
dictionary in list by list ofsection[key] == value. Filter marker should be
described in action where it’s used. For example:{"action":"delete","path":"metadata/$project_name/name","project_name":[{"key":"name","value":"nginx"}]}Index marker ($<index>) - the kind of marker, that is used to select specific
element in list, by it’s index. For example:{"action":"replace","path":["users","$0"],"value":{"name":"test_user","id":0}}It’s allowed to use any quantity of markers and mix both types of markers in single path.Action schemaaction(Required): Type of action, possible values are: add, replace, delete,
rename(only for dictionaries).path(Required): Path to the field that we want to change.pathcan be string, separated by delimiter (default is\) or list of strings.value(Optional for delete, Required for other): Value that should be applied
to specified path. The type of value is described for each action separately.marker(Required for each non index marker in path): List of dictionaries,
that should be applied to find value in list. Each dictionary consist of:key(Required): Name of the key that should be used for search.value(Required): Value that is used to find concrete dictionary in
list of dictionaries.Supported actionsadd: Insert values into section, specified by last key ofpath.
The last key in section should lead to list or dict section.For add action on listvalueshould be of type list, so that it’ll be possible
to extend current list.For add action on dictvalueshould be of type dict, so that we can update
current dict.replace: Replace section, specified by last key ofpathwithvalue.delete: Delete section, specified by last key ofpath.rename: Move content of section, specified by last key ofpathto section
with name specified invalue.TODOAdd documentation to ReadTheDocsAdd creation of action’s by diff.LicenseCopyright Oleksii Petrenko, 2020.Distributed under the terms of theMITlicense,
json_modify is free and open source software.
Package: jsonMojo
jsonMojoA series of utility functions to help with tidy json via python dictionary.Installation from githubUse the package managerpipto install jsonMojo.featuresreplace_element: recursively replace an element by a desired element within a dictionaryremove_element: recursively remove a desired element within a dictionarylist_flatten: recursively flat a list/arraysort_dict: sort a python dictionary by either key or valuefilter_rowwise: filter a rowwise list/array by selected value(s)sort_rowwise: sort a rowwise list/array by selected keyUsageimportjsonMojoasjm# test_dict as an example inputtest_dict={"A":"1","B":"2","C":{"C1":["1","45"]}}Recursively replace an element by a desired element within a dictionaryjm.replace_element(test_dict,to_replace="1",replace_with="A1")#returns {'A': 'A1', 'B': '2', 'C': {'C1': ['A1', '45']}}We can setoutput_json = Trueif we want the output in JSON string format.jm.replace_element(test_dict,to_replace="1",replace_with="A1",output_json=True)#returns '{\n"A": "A1",\n"B": "2",\n"C": {\n"C1": [\n"A1",\n"45"\n]\n}\n}'}Recursively remove a desired element within a dictionaryjm.remove_element(test_dict,to_remove="1")#returns {'B': '2', 'C': {'C1': ['45']}}jm.remove_element(test_dict,to_remove="1",output_json=True)#returns '{\n"B": "2",\n"C": {\n"C1": [\n"45"\n]\n}\n}'recursively flat a list/arrayjm.list_flatten(['2',['1','3',['a','1']]])#returns ['2', '1', '3', 'a', '1']jm.list_flatten(list({"A","B"}))#returns ['A', 'B']jm.list_flatten(['2',['1','3',['a','1']]],output_json=True)#returns '[\n"2",\n"1",\n"3",\n"a",\n"1"\n]'jm.list_flatten(list({"A","B"}),output_json=True)#returns '[\n"A",\n"B"\n]'sort a python dictionary by either key or valuejm.sort_dict(input_dict={"A":2,"BA":4,"GW":3,"EW":1,"W":0},sort_by_key=False,descending=False)# returns {'W': 0, 'EW': 1, 'A': 2, 'GW': 3, 'BA': 4}jm.sort_dict(input_dict={"A":2,"BA":4,"GW":3,"EW":1,"W":0},sort_by_key=True,descending=False)# returns {'A': 2, 'BA': 4, 'EW': 1, 'GW': 3, 'W': 0}We can setoutput_json = Trueif we want the output in JSON string format.filter a rowwise list/array by selected value(s)input_rowwise=[{"id":10,"dept":"Biology"},{"id":2,"dept":"Chemistry"},{"id":3,"dept":"Computer Science"}]jm.filter_rowwise(input_rowwise,by_key="dept",filter_in=["Biology","Computer Science"])# returns [{'id': 1, 'dept': 'Biology'}, {'id': 3, 'dept': 'Computer Science'}]We can setoutput_json = Trueif we want the output in JSON string format.sort a rowwise list/array by selected keycars=[{'car':'Ford','year':2005},{'car':'Mitsubishi','year':2000},{'car':'BMW','year':2019},{'car':'VW','year':2011}]jm.sort_rowwise(cars,by_key='car',descending=False)# returns [{'car': 'BMW', 'year': 2019},# {'car': 'Ford', 'year': 2005},# {'car': 'Mitsubishi', 'year': 2000},# {'car': 'VW', 'year': 2011}]We can setoutput_json = Trueif we want the output in JSON string format.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.
Package: json-nn
JSON-NNJSON parsing, powered by neural networks. What could go wrong?Requires Tensorflow 1.0.0 or later. Please don’t use this in production :)BLOG POSTThe blog post contains information about design decisions, implementation details, and what all of the files in this directory are for.Inspiration:FizzBuzz in TensorflowUsageQuickstart:pip install json_nn.To install from source, runpython setup.py install.fromjson_nnimportJsonNNparser=JsonNN()print(parser.parse("""
{
    "classes": [
        {
            "dates":{
                "start_time": "11:30", "end_time": "12:50", "weekdays": "F",
                "start_date": null, "end_date": null,
                "is_tba": false, "is_cancelled": false, "is_closed": false
            },
            "location":{"building": "MC", "room": "3003"},
            "instructors": []
        }
    ]
}
"""))# prints out the following:# {'classes': [{'location': {'room': '3003', 'building': 'MC'}, 'dates': {'is_cancelled': False, 'start_time': '11:30', 'start_date': None, 'is_tba': False, 'end_date': None, 'end_time': '12:50', 'weekdays': 'F', 'is_closed': False}, 'instructors': []}]}LicenseCopyright 2017Anthony Zhang (Uberi). The source code for this library is available online atGitHub.Tensorflow-JSON is made available under the 3-clause BSD license:Copyright (c) 2017, Anthony Zhang <azhang9@gmail.com>
All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Package: json-nosqlite
NoSQLiteA JSON based flat file NoSQL database.Usagefromnosqlite.nosqliteimportNoSQLitens=NoSQLite('file.json')# Create a new tablens.create_table('users')# Insert a new record into the tablens.insert({'username':'blah','password':'blah'},'users')# Find a record in a tablens.find({'username':'blah'},'users')# Update a record in a tablens.update({'username':'blah'},{'username':'changed','password':'changed'},'users')# Delete a record from a tablens.delete({'username':'blah','password':'blah'},'users')Documentation# filename: name of local file/path to json data fileclassNoSQLite(filename)# reload the json file (used by the class to update the jsonData after writing)reload()# write json data to file and reloadwriteOut()# creates a new table in the json data# tableName: name of table to be createdcreate_table(tableName)# inserts data into table# data: json data to be inserted# table: name of table to insert intoinsert(data,table)# deletes data from table, deletes first found# data: json data to search table for# table: table to search and delete fromdelete(data,table)# deletes data from table, deletes all found# data: json data to search table for# table: table to search and delete fromdeleteMany(data,table)# updates data in table with newData, updates first found# data: json data to search table for# newData: new json data to change record to# table: table to search and update fromupdate(data,newData,table)# updates data in table with newData, updates all found# data: json data to search table for# newData: new json data to change record to# table: table to search and update fromupdateMany(data,newData,table)# find all data in table# table: table to searchfindAll(table)# find data in table, returns all matching records# data: json data to search table for# table: table to searchfind(data,table)# find data in table, returns first matching record# data: json data to search table for# table: table to searchfindOne(data,table)
Package: jsonnote
jsonnoteA simple utility for working with JSON files.Contains a small class jsonnote with the following attributes:Properties:filename- name of the file without the extension (extention '.json' will be added automatically).location- location of the file. Once set, the file can be found and read from or write into.contents- JSON contents of the class instance.contents- JSON contents of the class instance.Methods:full_path()file_exists()*is_empty()      - returns True/False depending on whether the contents of the class instance are emptyget_from_file()- reads contents from the JSON filesave_to_file()- saves contents in the JSON  fileclear()- empties the contentsis_empty()- returns True/False depending on whether the contents of the class instance are emptyExamples of use:>>> from jsonnote import jsonnote>>> x=jsonnote()>>> x.filename = 'temp'>>> x.location>>> x.location = '.'>>> x.full_path()'./temp.json'>>> x.contents = {'fruits': ['apple', 'pear', 'orange'], 'veggies': ['potato', 'cucumber'], 'dairy': 'milk'}>>> x.mustsave=True>>> x.save_to_file()>>> y=jsonnote()destroy()- empties the contents and erases the file from the discreset()- empties the contents, destroys the file and sets 'filename' and 'location' to 'None'Example of use:>>> from jsonnote import jsonnote>>> x=jsonnote()>>> x.filename = 'temp'>>> x.location>>> x.location = '.'>>> x.full_path()'./temp.json'>>> x.contents = {'fruits': ['apple', 'pear', 'orange'], 'veggies': ['potato', 'cucumber'], 'dairy': 'milk'}>>> x.mustsave=True>>> x.save_to_file()>>> y=jsonnote()>>> y.filename = 'temp'>>> y.location='.'>>> y.file_exists()True>>> y.contents{'fruits': ['apple', 'pear', 'orange'], 'veggies': ['potato', 'cucumber'], 'dairy': 'milk'}>>> y.contents['fruits'][1]'pear'>>> y.mustdelete=True # *False by default; if False the file is not deleted>>> y.destroy()>>> y.file_exists()False>>> y.contents{}
Package: json-np
json_npJson for numpy, or if you prefer, json no problems!Authors:Luca de Alfaro (luca@dealfaro.com) and Massimo Di Pierro
(mdipierro@gmail.com)This is a version of Json that can handle also:DatesSetsnumpy arraybytesThere are two ways to use it.Basic Usageimport json_np
import numpy as np

a = np.array([3, 4, 5])
s = json_np.dumps(a)
aa = json_np.loads(s)Here, we have appliedjson_np.dumpsto a numpy array, but we could equally
well have used a datetime, a dictionary, a list, etc (nesting of such types is
allowed).Class-Based UsageYou can also dump any object.
All object attributes, except those whose name begins with underscore (_),
will be serialized.
Optionally, ifserializable_onlyis True, only objects that are
instances ofSerializable, or that are dates, numpy arrays,  will be
serialized.The deserialization happens in the same class, if:the class can be loadedthe class initializer has no required arguments.If any of these two conditions is not met, the deserialization happens
using theSerializableclass.from json_np import Serializable

class C(object):

    def __init__(self, a=8):
        super().__init__()
        self.a = a # Serialized
        self._b = 32 # Not serialized

c = C("hello")
s = json_np.dumps(c)
cc = json_np.loads(s)Obviously, you should ensure that C contains compatible fields when it is
deserialized, in case the code changed in the meantime.HistoryThis package was originally developed by Luca and Massimo at Camio, for
Python2.  The work was open sourced.  The package was later ported to Python 3.
The authors thank Camio for allowing the open-sourcing of the code.
Package: json-numpy
json-numpyDescriptionjson-numpyprovides lossless and quick JSON encoding/decoding fornumpyarrays and scalars.Installationjson-numpycan be installed usingpip:$ pip install json-numpyUsageFor a quick start,json_numpycan be used as a simple drop-in replacement of the built-injsonmodule.Thedump(),load(),dumps()andloads()methods are implemented by wrapping the original methods and replacing the default encoder and decoder.More information on the usage can be found in thejsonmodule'sdocumentation.importnumpyasnpimportjson_numpyarr=np.array([0,1,2])encoded_arr_str=json_numpy.dumps(arr)decoded_arr=json_numpy.loads(encoded_arr_str)Another way of usingjson_numpyis to explicitly use the provided encoder and decoder functions in conjunction with thejsonmodule.importjsonimportnumpyasnpfromjson_numpyimportdefault,object_hookarr=np.array([0,1,2])encoded_arr_str=json.dumps(arr,default=default)decoded_arr=json.loads(encoded_arr_str,object_hook=object_hook)Finally, the last way of usingjson_numpyis by monkey patching thejsonmodule after importing it first:importjsonimportnumpyasnpimportjson_numpyjson_numpy.patch()arr=np.array([0,1,2])encoded_arr_str=json.dumps(arr)decoded_arr=json.loads(encoded_arr_str)This method can be used to change the behavior of a module depending on thejsonmodule without editing its code.TestsThe simplest way to run tests is:$ python -m unittestAs a more robust alternative, you can installtoxto automatically test across the supported python versions, then run:$ toxIssue trackerPlease report any bugs or enhancement ideas using theissue tracker.Licensejson-numpyis licensed under the terms of theMIT License(seeLICENSE.txtfor more information).
Package: json-numpy-sebastian-achim-mueller
JSON-NUMPYTransparentdumpsandloadsofnumpy-arrays.Encoderto dump withjson.dumps().object_hookto load withjson.loads().exampleimportnumpyimportjson_numpyobj=json_numpy.loads(s='{"a": [1.0, NaN]}')assertisinstance(obj["a"],numpy.ndarray)json_numpy.dumps(obj)'{"a": [1.0, NaN]}'
Package: jsonobj
JSONobjJSONobj is a Python module
for providing attribute-like access to the properties of JSON objects.RequirementPython 2.7 or aboveInstallationpipinstalljsonobjBasic Usage>>>importjsonobj>>>obj=jsonobj.loads('{"prop1": "value"}')>>>obj.prop1'value'>>>obj.prop1='changed'>>>obj.prop2=['foo','bar',{'fiz':'buz'}]>>>'prop2'inobjTrue>>>obj.dict{'prop1':'changed','prop2':['foo','bar',{'fiz':'buz'}]}>>>obj.prop2[2].fiz'buz'>>>delobj.prop1>>>jsonobj.dumps(obj)'{"prop2": ["foo", "bar", {"fiz": "buz"}]}'DocumentationDocumentation isavailable online.
Package: json-object-factory
json-object-factorySimplifies building custom encoders and object-hooks for Python’s
default JSON implementation.Installation$ pip install json-object-factoryUsageAdd encoders and decoders to the registry:importjsonfactoryclassMyJsonHandler(object):defencode(self,o):ifisinstance(o,MyCustomClass):returno.serialize()returnNonedefdecode(self,d):if'some_custom_key'ind:returnMyCustomClass(**d)returndjsonfactory.Registry.register(MyJsonHandler)Or use the included decorators:@jsonfactory.registerclassMyOtherJsonHandler(object):...@jsonfactory.encoderdefan_encoder_function(o):...@jsonfactory.decoderdefa_decoder_function(d):...Then use the module’sdumpsandloadsfunctions:json_str=jsonfactory.dumps(obj_dict,indent=2)new_obj_dict=jsonfactory.loads(json_str)NotesThe calling signature for encoder functions follows that of the
built-inJSONEncoderwith one exception:If no modifications are needed and the object should be passed to
the base encoder’s handler,Noneshould be returned. This
differs from the normal method of callingsuper(MyEncoder,self).default(o)(that would most likely be
an error since subclassingJSONEncoderisn’t necessary).The signature for decoder functions follows theobject_hooksignature inthe built-in
implementation
Package: jsonobjects
jsonobjectsallows you to declaratively specify how to extract and convert elements from a JSON document.InstallationWe recommend the use ofvirtualenvand ofpip. You can then usepip install-Ujsonobjects.
You may also havesetuptoolsand thus
you can useeasy_install-Ujsonobjects. Otherwise, you can download the
source fromGitHuband runpython setup.py install.DependenciesAll dependencies are optional.JMESPathto allow advanced queries (seeJMESPathdocumentation for details).dateutilto allow iso-8601 date formats.UsageExample of schema to parse iTunes lookup response for software item:importjsonimportrequestsimportjsonobjectsasjoclassiTunesAppSchema(jo.Schema):id=jo.IntegerField('trackId')url=jo.Field('trackViewUrl')name=jo.StringField('trackName')currency=jo.StringField()price=jo.FloatField(min_value=0.0)rating=jo.FloatField('averageUserRating')reviews=jo.IntegerField('userRatingCountForCurrentVersion')version=jo.StringField()publisher_id=jo.IntegerField('artistId')publisher_url=jo.Field('artistViewUrl')publisher_name=jo.StringField('artistName')categories=jo.ListField('genres',child=jo.StringField())icon=jo.Field(['artworkUrl512','artworkUrl60'],post_process=lambdav:{'url':v})screenshots=jo.ListField('screenshotUrls',child=jo.Field(post_process=lambdav:{'url':v}))parser=iTunesAppSchema('results[0]')@parser.as_decoratordefget_app_details(app_id):url='https://itunes.apple.com/lookup?id={}'returnrequests.get(url.format(app_id)).json()# https://itunes.apple.com/lookup?id=880047117details=get_app_details(880047117)print(details)The code above produces next result:{"categories":["Games","Puzzle","Action"],"currency":"USD","icon":{"url":"http://is3.mzstatic.com/image/thumb/Purple3/v4/27/f0/d9/27f0d923-e00b-5f2c-a1e9-235ed3f83d14/source/512x512bb.jpg"},"id":880047117,"name":"Angry Birds 2","price":0.0,"publisher_id":298910979,"publisher_name":"Rovio Entertainment Ltd","publisher_url":"https://itunes.apple.com/us/developer/rovio-entertainment-ltd/id298910979?uo=4","rating":4.0,"reviews":4796,"screenshots":[{"url":"http://a4.mzstatic.com/us/r30/Purple3/v4/5c/5e/54/5c5e542c-54a1-7812-12df-045aca3ebb86/screen1136x1136.jpeg"},{"url":"http://a1.mzstatic.com/us/r30/Purple3/v4/95/50/db/9550dbba-9cbf-d588-fac4-5ebf04614023/screen1136x1136.jpeg"},{"url":"http://a5.mzstatic.com/us/r30/Purple3/v4/22/ef/e7/22efe7c2-bd05-6f58-f176-92e7230853bd/screen1136x1136.jpeg"},{"url":"http://a2.mzstatic.com/us/r30/Purple69/v4/72/44/ba/7244ba34-6c8c-5dc5-38e4-e134a97cd0d1/screen1136x1136.jpeg"},{"url":"http://a5.mzstatic.com/us/r30/Purple6/v4/1b/e4/98/1be49811-f5be-cb3f-1a31-a20b5f5663ee/screen1136x1136.jpeg"}],"url":"https://itunes.apple.com/us/app/angry-birds-2/id880047117?mt=8&uo=4","version":"2.2.1"}See tests.py for more examples.TestsGetting the tests running looks like:# Install dependencies$pipinstall-rrequirement.txt# Run the test suites$pythontests.pyLicenseThe MIT License (MIT)Contributed byMaxim Kamenkov
Package: jsonofabitch
JSONofabitchfault tolerant json parserJSON correction/formatting enginestochastic ascii structured data formata standard for low standardstheretard proofmulti-parameter, multi-type input parsera troll of api scraping reverse engineersa JSON of a bitch!functionsloads()- loads JSLOB compliant textdumps()- dumps regular JSONcorrect()- turns JSLOB to JSONdumpslob()- dumps stochastic JSLOBinstallpip3 install jsonofabitchbuild requireslarkbutjsonofabitchis stand-aloneJSLOB syntaxkeypairs -:or=separate key value pairs, whitespace is ignored'g=5' -> {"g": 5}and'g:5' -> {"g": 5}, such keypairs are implicitly assumed to be in a JSLOB object which resolves to a python dictkeys - double quoted string or alphanumeric plus_-~$@&?.%<>#!^+/*(no spaces,no unicode)  non-numeric first character.values - another JSLOB object, a tupple, a list, string without quotes, quote-enclosed string, booleantruefalse,null, integers, floats'true' -> True'false' -> False'null' -> Nonecase sensitivetuples in( )lists in[ ]objects separated by comma,or semicolon;. trailing comma/semicolon okaydicts/JSLOB objects have keypairs separated by semicolon or comma, nested jslob objects(in lists, tuples or jslob objects) are terminated and separated simultaneously by;;or,,, IE:'[g=5,h=3]' -> [{'g': 5, 'h': 3}]and'[g=5;;h=3]' -> [{'g': 5}, {'h': 3}]strings - same as keys, mostly only need quotes for numeric first character or if it includes spaces.scientific notation2e4resolves to $2.0 * 10^4$, always resolve to floatnumbers with decimal present resolve to floats while others resolve to ints IE'3.0' -> 3.0and'3' -> 3'3.' -> 3.0about/conventionsthe parser is called JSOB(jay-sawb) in polite companythe standardized syntax is called JSLOB (jay-slawb)suggested file extension.jslobor.slobor.jsobthe underlying parser is built usinglarkwhich generates stand-alone python from the lark meta-langauge that defines the grammarchangelog0.4.0- add support for a wider range of characters in unquoted strings0.3.5- fix handling of empty lists, tuples, improve approach to handling empty JSLOB objects0.3.2- fix improper handling ofNoneand bools by dumps functionsversion0.3.0transitioned from using the parseing librarylarkat runtime to a stand-alone parser generated by lark at buildexamplesimportjsonofabitchasjsobd=jsob.loads("f=5, size=big, style=SLOB, coord=[(3,4),(1.2,3)]")#{'f': 5, 'size': 'big', 'style': 'SLOB', 'coord': [(3, 4), (1.2, 3)]}jsob.dumps(d)#Out[3]: '{"f": 5, "size": "big", "style": "SLOB", "coord": [[3, 4], [1.2, 3]]}'jsob.dumpslob(d)#Out[4]: ' "f"=  5;  "size"="big";style=  "SLOB";  coord: [ (3 ,4 ), (1.2, 3,)  ];'jsob.dumpslob(d)#Out[5]: ' "f"= 5;size="big", style: SLOB, "coord": [ (3, 4 ),(1.2 , 3  ) ];'jsob.dumpslob(d)#Out[6]: ' "f"= 5,  size=  "big",style=  "SLOB";  coord:  [ (3,4 ,) ,(1.2 , 3,)  ];'