A module to extend the python json package functionality:
- Treat a directory structure like a nested dictionary:
- lightweight plugin system: define bespoke classes for parsing different file extensions (in-the-box: .json, .csv, .hdf5) and encoding/decoding objects
- lazy loading: read files only when they are indexed into
- tab completion: index as tabs for quick exploration of data
- Manipulation of nested dictionaries:
- enhanced pretty printer
- Javascript rendered, expandable tree in the Jupyter Notebook
- functions including; filter, merge, flatten, unflatten, diff
- output to directory structure (of n folder 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.io
From Conda (recommended):
conda install -c conda-forge jsonextended
From PyPi:
pip install jsonextended
jsonextended has no import dependancies, on Python 3.x and only
pathlib2 on 2.7 but, for full functionallity, it is advised to install
the following packages:
conda install -c conda-forge ijson numpy pint h5py pandas
from jsonextended import edict, plugins, example_mockpaths
Take 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 val4
Plugins can be defined for parsing each file type (see Creating Plugins section):
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 the plugins.decode function to parser plugin's
read_file method (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]}}'
from jsonextended import plugins, utils
Plugins 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_dict
Plugins 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, see
jsonextended.complex_parsers
- Parsers:
- file_regex attribute, a str denoting what files to apply it to. A file will be parsed by the longest regex it matches.
- read_file method, which takes an (open) file object and kwargs as parameters
- Decoders:
- dict_signature attribute, 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.
The
plugins.decodefunction will use the method denoted by the intype parameter, e.g. if intype='json', thenfrom_jsonwill be called.
- dict_signature attribute, a tuple denoting the keys which the
dictionary must have, e.g. dict_signature=('a','b') decodes
- Encoders:
- objclass attribute, the object class to apply the encoding to, e.g. objclass=decimal.Decimal encodes objects of that type
- to_... method(s), which takes a dict object as parameter. The
plugins.encodefunction will use the method denoted by the outtype parameter, e.g. if outtype='json', thento_jsonwill be called.
For more information, all functions contain doc-strings with tested examples.
from jsonextended import ejson, edict, utils
path = 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/
jdict2 = 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: 531.994803
edict.pprint(edict.flatten(filtered))
(initial, crystallographic, volume): 924.62752781
(initial, primitive, volume): 462.313764
(optimised, crystallographic, volume): 1063.98960509
(optimised, primitive, volume): 531.994803
from 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 ** 3
newunits = 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 ** 3
edict.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