Metadata-Version: 2.1
Name: jsonextended
Version: 0.7.11
Summary: Extending the python json package functionality
Home-page: https://github.com/chrisjsewell/jsonextended
Author: Chris Sewell
Author-email: chrisj_sewell@hotmail.com
License: MIT
Description: # JSON Extended
        
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        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
        
        ## Contents
        
        - [JSON Extended](#json-extended)
          - [Contents](#contents)
          - [Installation](#installation)
          - [Basic Example](#basic-example)
          - [Creating and Loading Plugins](#creating-and-loading-plugins)
            - [Interface specifications](#interface-specifications)
          - [Extended Examples](#extended-examples)
            - [Data Folders JSONisation](#data-folders-jsonisation)
            - [Nested Dictionary Manipulation](#nested-dictionary-manipulation)
            - [Units Schema](#units-schema)
        
        ## Installation
        
        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
        
        ## Basic Example
        
        ``` {.python}
        from jsonextended import edict, plugins, example_mockpaths
        ```
        
        Take a directory structure, potentially containing multiple file types:
        
        ``` {.python}
        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](#creating-and-loading-plugins) section):
        
        ``` {.python}
        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.
        
        ``` {.python}
        lazy = edict.LazyLoad(datadir)
        lazy
        ```
        
            {file1.json:..,subdir1:..,subdir2:..}
        
        Lazyload can then be treated like a dictionary, or indexed by tab
        completion:
        
        ``` {.python}
        list(lazy.keys())
        ```
        
            ['subdir1', 'subdir2', 'file1.json']
        
        ``` {.python}
        lazy[['file1.json','key1']]
        ```
        
            [1, 2, 3]
        
        ``` {.python}
        lazy.subdir1.file1_literal_csv.header2
        ```
        
            [1.1, 2.2, 3.3]
        
        For pretty printing of the dictionary:
        
        ``` {.python}
        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:
        
        ``` {.python}
        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:
        
        ``` {.python}
        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]}}}
        
        ``` {.python}
        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]}}}
        
        ``` {.python}
        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'}
        
        ``` {.python}
        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:
        
        ``` {.python}
        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'}
        
        ``` {.python}
        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 Plugins
        
        ``` {.python}
        from jsonextended import plugins, utils
        ```
        
        Plugins are recognised as classes with a minimal set of attributes
        matching the plugin category interface:
        
        ``` {.python}
        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']}
        
        ``` {.python}
        plugins.unload_all_plugins()
        plugins.view_plugins()
        ```
        
            {'decoders': {}, 'encoders': {}, 'parsers': {}}
        
        For example, a simple parser plugin would be:
        
        ``` {.python}
        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:
        
        ``` {.python}
        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):
        
        ``` {.python}
        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`
        
        ### Interface specifications
        
        - 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.decode` function will use the method denoted by the
            intype parameter, e.g. if intype='json', then `from_json` will
            be called.
        - 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.encode` function will use the method denoted by the
            outtype parameter, e.g. if outtype='json', then `to_json` will be called.
        
        ## Extended Examples
        
        For more information, all functions contain doc-strings with tested
        examples.
        
        ### Data Folders JSONisation
        
        ``` {.python}
        from jsonextended import ejson, edict, utils
        ```
        
        ``` {.python}
        path = utils.get_test_path()
        ejson.jkeys(path)
        ```
        
            ['dir1', 'dir2', 'dir3']
        
        ``` {.python}
        jdict1 = ejson.to_dict(path)
        edict.pprint(jdict1,depth=2)
        ```
        
            dir1:
              dir1_1: {...}
              file1: {...}
              file2: {...}
            dir2:
              file1: {...}
            dir3:
        
        ``` {.python}
        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 Manipulation
        
        ``` {.python}
        jdict2 = ejson.to_dict(path,['dir1','file1'])
        edict.pprint(jdict2,depth=1)
        ```
        
            initial: {...}
            meta: {...}
            optimised: {...}
            units: {...}
        
        ``` {.python}
        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
        
        ``` {.python}
        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
        
        ### Units Schema
        
        ``` {.python}
        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
        
        ``` {.python}
        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
        
        ``` {.python}
        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
        
Keywords: json,units,parser,python
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
