Package: jsonextended
JSON ExtendedA module to extend the python json package functionality:Treat a directory structure like a nested dictionary:lightweight plugin system: define bespoke classes forparsingdifferent file extensions (in-the-box: .json, .csv, .hdf5)
andencoding/decodingobjectslazy loading: read files only when they are indexed intotab completion: index as tabs for quick exploration of dataManipulation of nested dictionaries:enhanced pretty printerJavascript rendered, expandable tree in the Jupyter Notebookfunctions including; filter, merge, flatten, unflatten, diffoutput to directory structure (ofnfolder levels)On-disk indexing option for large json files (using the ijson package)Units schema concept to apply and convert physical units (using the pint package)Documentation:https://jsonextended.readthedocs.ioContentsJSON ExtendedContentsInstallationBasic ExampleCreating and Loading PluginsInterface specificationsExtended ExamplesData Folders JSONisationNested Dictionary ManipulationUnits SchemaInstallationFrom Conda (recommended):conda install -c conda-forge jsonextendedFrom PyPi:pip install jsonextendedjsonextended has no import dependancies, on Python 3.x and onlypathlib2on 2.7 but, for full functionallity, it is advised to install
the following packages:conda install -c conda-forge ijson numpy pint h5py pandasBasic Examplefrom jsonextended import edict, plugins, example_mockpathsTake a directory structure, potentially containing multiple file types:datadir = example_mockpaths.directory1
print(datadir.to_string(indentlvl=3,file_content=True))Folder("dir1")
   File("file1.json") Contents:
    {"key2": {"key3": 4, "key4": 5}, "key1": [1, 2, 3]}
   Folder("subdir1")
     File("file1.csv") Contents:
       # a csv file
      header1,header2,header3
      val1,val2,val3
      val4,val5,val6
      val7,val8,val9
     File("file1.literal.csv") Contents:
       # a csv file with numbers
      header1,header2,header3
      1,1.1,string1
      2,2.2,string2
      3,3.3,string3
   Folder("subdir2")
     Folder("subsubdir21")
       File("file1.keypair") Contents:
         # a key-pair file
        key1 val1
        key2 val2
        key3 val3
        key4 val4Plugins can be defined for parsing each file type (seeCreating
Pluginssection):plugins.load_builtin_plugins('parsers')
plugins.view_plugins('parsers'){'csv.basic': 'read *.csv delimited file with headers to {header:[column_values]}',
 'csv.literal': 'read *.literal.csv delimited files with headers to {header:column_values}, with number strings converted to int/float',
 'hdf5.read': 'read *.hdf5 (in read mode) files using h5py',
 'json.basic': 'read *.json files using json.load',
 'keypair': "read *.keypair, where each line should be; '<key> <pair>'"}LazyLoad then takes a path name, path-like object or dict-like object,
which will lazily load each file with a compatible plugin.lazy = edict.LazyLoad(datadir)
lazy{file1.json:..,subdir1:..,subdir2:..}Lazyload can then be treated like a dictionary, or indexed by tab
completion:list(lazy.keys())['subdir1', 'subdir2', 'file1.json']lazy[['file1.json','key1']][1, 2, 3]lazy.subdir1.file1_literal_csv.header2[1.1, 2.2, 3.3]For pretty printing of the dictionary:edict.pprint(lazy,depth=2)file1.json:
  key1: [1, 2, 3]
  key2: {...}
subdir1:
  file1.csv: {...}
  file1.literal.csv: {...}
subdir2:
  subsubdir21: {...}Numerous functions exist to manipulate the nested dictionary:edict.flatten(lazy.subdir1){('file1.csv', 'header1'): ['val1', 'val4', 'val7'],
 ('file1.csv', 'header2'): ['val2', 'val5', 'val8'],
 ('file1.csv', 'header3'): ['val3', 'val6', 'val9'],
 ('file1.literal.csv', 'header1'): [1, 2, 3],
 ('file1.literal.csv', 'header2'): [1.1, 2.2, 3.3],
 ('file1.literal.csv', 'header3'): ['string1', 'string2', 'string3']}LazyLoad parses theplugins.decodefunction to parser plugin'sread_filemethod (keyword 'object_hook'). Therefore, bespoke decoder
plugins can be set up for specific dictionary key signatures:print(example_mockpaths.jsonfile2.to_string())File("file2.json") Contents:
{"key1":{"_python_set_": [1, 2, 3]},"key2":{"_numpy_ndarray_": {"dtype": "int64", "value": [1, 2, 3]}}}edict.LazyLoad(example_mockpaths.jsonfile2).to_dict(){u'key1': {u'_python_set_': [1, 2, 3]},
 u'key2': {u'_numpy_ndarray_': {u'dtype': u'int64', u'value': [1, 2, 3]}}}plugins.load_builtin_plugins('decoders')
plugins.view_plugins('decoders'){'decimal.Decimal': 'encode/decode Decimal type',
 'numpy.ndarray': 'encode/decode numpy.ndarray',
 'pint.Quantity': 'encode/decode pint.Quantity object',
 'python.set': 'decode/encode python set'}dct = edict.LazyLoad(example_mockpaths.jsonfile2).to_dict()
dct{u'key1': {1, 2, 3}, u'key2': array([1, 2, 3])}This process can be reversed, using encoder plugins:plugins.load_builtin_plugins('encoders')
plugins.view_plugins('encoders'){'decimal.Decimal': 'encode/decode Decimal type',
 'numpy.ndarray': 'encode/decode numpy.ndarray',
 'pint.Quantity': 'encode/decode pint.Quantity object',
 'python.set': 'decode/encode python set'}import json
json.dumps(dct,default=plugins.encode)'{"key2": {"_numpy_ndarray_": {"dtype": "int64", "value": [1, 2, 3]}}, "key1": {"_python_set_": [1, 2, 3]}}'Creating and Loading Pluginsfrom jsonextended import plugins, utilsPlugins are recognised as classes with a minimal set of attributes
matching the plugin category interface:plugins.view_interfaces(){'decoders': ['plugin_name', 'plugin_descript', 'dict_signature'],
 'encoders': ['plugin_name', 'plugin_descript', 'objclass'],
 'parsers': ['plugin_name', 'plugin_descript', 'file_regex', 'read_file']}plugins.unload_all_plugins()
plugins.view_plugins(){'decoders': {}, 'encoders': {}, 'parsers': {}}For example, a simple parser plugin would be:class ParserPlugin(object):
    plugin_name = 'example'
    plugin_descript = 'a parser for *.example files, that outputs (line_number:line)'
    file_regex = '*.example'
    def read_file(self, file_obj, **kwargs):
        out_dict = {}
        for i, line in enumerate(file_obj):
            out_dict[i] = line.strip()
        return out_dictPlugins can be loaded as a class:plugins.load_plugin_classes([ParserPlugin],'parsers')
plugins.view_plugins(){'decoders': {},
 'encoders': {},
 'parsers': {'example': 'a parser for *.example files, that outputs (line_number:line)'}}Or by directory (loading all .py files):fobj = utils.MockPath('example.py',is_file=True,content="""
class ParserPlugin(object):
    plugin_name = 'example.other'
    plugin_descript = 'a parser for *.example.other files, that outputs (line_number:line)'
    file_regex = '*.example.other'
    def read_file(self, file_obj, **kwargs):
        out_dict = {}
        for i, line in enumerate(file_obj):
            out_dict[i] = line.strip()
        return out_dict
""")
dobj = utils.MockPath(structure=[fobj])
plugins.load_plugins_dir(dobj,'parsers')
plugins.view_plugins(){'decoders': {},
 'encoders': {},
 'parsers': {'example': 'a parser for *.example files, that outputs (line_number:line)',
  'example.other': 'a parser for *.example.other files, that outputs (line_number:line)'}}For a more complex example of a parser, seejsonextended.complex_parsersInterface specificationsParsers:file_regexattribute, a str denoting what files to apply it
to. A file will be parsed by the longest regex it matches.read_filemethod, which takes an (open) file object and
kwargs as parametersDecoders:dict_signatureattribute, a tuple denoting the keys which the
dictionary must have, e.g. dict_signature=('a','b') decodes{'a':1,'b':2}from_...method(s), which takes a dict object as parameter.
Theplugins.decodefunction will use the method denoted by the
intype parameter, e.g. if intype='json', thenfrom_jsonwill
be called.Encoders:objclassattribute, the object class to apply the encoding to,
e.g. objclass=decimal.Decimal encodes objects of that typeto_...method(s), which takes a dict object as parameter. Theplugins.encodefunction will use the method denoted by the
outtype parameter, e.g. if outtype='json', thento_jsonwill be called.Extended ExamplesFor more information, all functions contain doc-strings with tested
examples.Data Folders JSONisationfrom jsonextended import ejson, edict, utilspath = utils.get_test_path()
ejson.jkeys(path)['dir1', 'dir2', 'dir3']jdict1 = ejson.to_dict(path)
edict.pprint(jdict1,depth=2)dir1:
  dir1_1: {...}
  file1: {...}
  file2: {...}
dir2:
  file1: {...}
dir3:edict.to_html(jdict1,depth=2)To try the rendered JSON tree, output in the Jupyter Notebook, go to :https://chrisjsewell.github.io/Nested Dictionary Manipulationjdict2 = ejson.to_dict(path,['dir1','file1'])
edict.pprint(jdict2,depth=1)initial: {...}
meta: {...}
optimised: {...}
units: {...}filtered = edict.filter_keys(jdict2,['vol*'],use_wildcards=True)
edict.pprint(filtered)initial:
  crystallographic:
    volume: 924.62752781
  primitive:
    volume: 462.313764
optimised:
  crystallographic:
    volume: 1063.98960509
  primitive:
    volume: 531.994803edict.pprint(edict.flatten(filtered))(initial, crystallographic, volume):   924.62752781
(initial, primitive, volume):          462.313764
(optimised, crystallographic, volume): 1063.98960509
(optimised, primitive, volume):        531.994803Units Schemafrom jsonextended.units import apply_unitschema, split_quantities
withunits = apply_unitschema(filtered,{'volume':'angstrom^3'})
edict.pprint(withunits)initial:
  crystallographic:
    volume: 924.62752781 angstrom ** 3
  primitive:
    volume: 462.313764 angstrom ** 3
optimised:
  crystallographic:
    volume: 1063.98960509 angstrom ** 3
  primitive:
    volume: 531.994803 angstrom ** 3newunits = apply_unitschema(withunits,{'volume':'nm^3'})
edict.pprint(newunits)initial:
  crystallographic:
    volume: 0.92462752781 nanometer ** 3
  primitive:
    volume: 0.462313764 nanometer ** 3
optimised:
  crystallographic:
    volume: 1.06398960509 nanometer ** 3
  primitive:
    volume: 0.531994803 nanometer ** 3edict.pprint(split_quantities(newunits),depth=4)initial:
  crystallographic:
    volume:
      magnitude: 0.92462752781
      units:     nanometer ** 3
  primitive:
    volume:
      magnitude: 0.462313764
      units:     nanometer ** 3
optimised:
  crystallographic:
    volume:
      magnitude: 1.06398960509
      units:     nanometer ** 3
  primitive:
    volume:
      magnitude: 0.531994803
      units:     nanometer ** 3