{
    "info": {
        "author": "Chris Sewell",
        "author_email": "chrisj_sewell@hotmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Environment :: Web Environment",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Utilities"
        ],
        "description": "# JSON Extended\n\n[![Build Status](https://travis-ci.org/chrisjsewell/jsonextended.svg?branch=master)](\n  https://travis-ci.org/chrisjsewell/jsonextended)\n[![Coverage Status](https://coveralls.io/repos/github/chrisjsewell/jsonextended/badge.svg?branch=master)](https://coveralls.io/github/chrisjsewell/jsonextended?branch=master)\n[![Documentation Status](https://readthedocs.org/projects/jsonextended/badge/?version=latest)](http://jsonextended.readthedocs.io/en/latest/?badge=latest)\n[![PyPI](https://img.shields.io/pypi/v/jsonextended.svg)](https://pypi.python.org/pypi/jsonextended/)\n[![Anaconda-Server Badge](https://anaconda.org/conda-forge/jsonextended/badges/version.svg)](https://anaconda.org/conda-forge/jsonextended)\n<!-- [![Codacy\nBadge](https://api.codacy.com/project/badge/Grade/60ce582812a6431fa20c4074c517288c)](https://www.codacy.com/app/chrisj_sewell/jsonextended?utm_source=github.com&utm_medium=referral&utm_content=chrisjsewell/jsonextended&utm_campaign=Badge_Grade) -->\n\nA module to extend the python json package functionality:\n\n- Treat a directory structure like a nested dictionary:\n  - **lightweight plugin system**: define bespoke classes for\n    **parsing** different file extensions (in-the-box: .json, .csv, .hdf5)\n    and **encoding/decoding** objects\n  - **lazy loading**: read files only when they are indexed into\n  - **tab completion**: index as tabs for quick exploration of data\n- Manipulation of nested dictionaries:\n  - enhanced pretty printer\n  - Javascript rendered, expandable tree in the Jupyter Notebook\n  - functions including; filter, merge, flatten, unflatten, diff\n  - output to directory structure (of *n* folder levels)\n- On-disk indexing option for large json files (using the ijson package)\n- Units schema concept to apply and convert physical units (using the pint package)\n\n**Documentation**: https://jsonextended.readthedocs.io\n\n## Contents\n\n- [JSON Extended](#json-extended)\n  - [Contents](#contents)\n  - [Installation](#installation)\n  - [Basic Example](#basic-example)\n  - [Creating and Loading Plugins](#creating-and-loading-plugins)\n    - [Interface specifications](#interface-specifications)\n  - [Extended Examples](#extended-examples)\n    - [Data Folders JSONisation](#data-folders-jsonisation)\n    - [Nested Dictionary Manipulation](#nested-dictionary-manipulation)\n    - [Units Schema](#units-schema)\n\n## Installation\n\nFrom Conda (recommended):\n\n    conda install -c conda-forge jsonextended\n\nFrom PyPi:\n\n    pip install jsonextended\n\njsonextended has no import dependancies, on Python 3.x and only\n`pathlib2` on 2.7 but, for full functionallity, it is advised to install\nthe following packages:\n\n    conda install -c conda-forge ijson numpy pint h5py pandas\n\n## Basic Example\n\n``` {.python}\nfrom jsonextended import edict, plugins, example_mockpaths\n```\n\nTake a directory structure, potentially containing multiple file types:\n\n``` {.python}\ndatadir = example_mockpaths.directory1\nprint(datadir.to_string(indentlvl=3,file_content=True))\n```\n\n    Folder(\"dir1\")\n       File(\"file1.json\") Contents:\n        {\"key2\": {\"key3\": 4, \"key4\": 5}, \"key1\": [1, 2, 3]}\n       Folder(\"subdir1\")\n         File(\"file1.csv\") Contents:\n           # a csv file\n          header1,header2,header3\n          val1,val2,val3\n          val4,val5,val6\n          val7,val8,val9\n         File(\"file1.literal.csv\") Contents:\n           # a csv file with numbers\n          header1,header2,header3\n          1,1.1,string1\n          2,2.2,string2\n          3,3.3,string3\n       Folder(\"subdir2\")\n         Folder(\"subsubdir21\")\n           File(\"file1.keypair\") Contents:\n             # a key-pair file\n            key1 val1\n            key2 val2\n            key3 val3\n            key4 val4\n\nPlugins can be defined for parsing each file type (see [Creating\nPlugins](#creating-and-loading-plugins) section):\n\n``` {.python}\nplugins.load_builtin_plugins('parsers')\nplugins.view_plugins('parsers')\n```\n\n    {'csv.basic': 'read *.csv delimited file with headers to {header:[column_values]}',\n     'csv.literal': 'read *.literal.csv delimited files with headers to {header:column_values}, with number strings converted to int/float',\n     'hdf5.read': 'read *.hdf5 (in read mode) files using h5py',\n     'json.basic': 'read *.json files using json.load',\n     'keypair': \"read *.keypair, where each line should be; '<key> <pair>'\"}\n\nLazyLoad then takes a path name, path-like object or dict-like object,\nwhich will lazily load each file with a compatible plugin.\n\n``` {.python}\nlazy = edict.LazyLoad(datadir)\nlazy\n```\n\n    {file1.json:..,subdir1:..,subdir2:..}\n\nLazyload can then be treated like a dictionary, or indexed by tab\ncompletion:\n\n``` {.python}\nlist(lazy.keys())\n```\n\n    ['subdir1', 'subdir2', 'file1.json']\n\n``` {.python}\nlazy[['file1.json','key1']]\n```\n\n    [1, 2, 3]\n\n``` {.python}\nlazy.subdir1.file1_literal_csv.header2\n```\n\n    [1.1, 2.2, 3.3]\n\nFor pretty printing of the dictionary:\n\n``` {.python}\nedict.pprint(lazy,depth=2)\n```\n\n    file1.json:\n      key1: [1, 2, 3]\n      key2: {...}\n    subdir1:\n      file1.csv: {...}\n      file1.literal.csv: {...}\n    subdir2:\n      subsubdir21: {...}\n\nNumerous functions exist to manipulate the nested dictionary:\n\n``` {.python}\nedict.flatten(lazy.subdir1)\n```\n\n    {('file1.csv', 'header1'): ['val1', 'val4', 'val7'],\n     ('file1.csv', 'header2'): ['val2', 'val5', 'val8'],\n     ('file1.csv', 'header3'): ['val3', 'val6', 'val9'],\n     ('file1.literal.csv', 'header1'): [1, 2, 3],\n     ('file1.literal.csv', 'header2'): [1.1, 2.2, 3.3],\n     ('file1.literal.csv', 'header3'): ['string1', 'string2', 'string3']}\n\nLazyLoad parses the `plugins.decode` function to parser plugin's\n`read_file` method (keyword 'object\\_hook'). Therefore, bespoke decoder\nplugins can be set up for specific dictionary key signatures:\n\n``` {.python}\nprint(example_mockpaths.jsonfile2.to_string())\n```\n\n    File(\"file2.json\") Contents:\n    {\"key1\":{\"_python_set_\": [1, 2, 3]},\"key2\":{\"_numpy_ndarray_\": {\"dtype\": \"int64\", \"value\": [1, 2, 3]}}}\n\n``` {.python}\nedict.LazyLoad(example_mockpaths.jsonfile2).to_dict()\n```\n\n    {u'key1': {u'_python_set_': [1, 2, 3]},\n     u'key2': {u'_numpy_ndarray_': {u'dtype': u'int64', u'value': [1, 2, 3]}}}\n\n``` {.python}\nplugins.load_builtin_plugins('decoders')\nplugins.view_plugins('decoders')\n```\n\n    {'decimal.Decimal': 'encode/decode Decimal type',\n     'numpy.ndarray': 'encode/decode numpy.ndarray',\n     'pint.Quantity': 'encode/decode pint.Quantity object',\n     'python.set': 'decode/encode python set'}\n\n``` {.python}\ndct = edict.LazyLoad(example_mockpaths.jsonfile2).to_dict()\ndct\n```\n\n    {u'key1': {1, 2, 3}, u'key2': array([1, 2, 3])}\n\nThis process can be reversed, using encoder plugins:\n\n``` {.python}\nplugins.load_builtin_plugins('encoders')\nplugins.view_plugins('encoders')\n```\n\n    {'decimal.Decimal': 'encode/decode Decimal type',\n     'numpy.ndarray': 'encode/decode numpy.ndarray',\n     'pint.Quantity': 'encode/decode pint.Quantity object',\n     'python.set': 'decode/encode python set'}\n\n``` {.python}\nimport json\njson.dumps(dct,default=plugins.encode)\n```\n\n    '{\"key2\": {\"_numpy_ndarray_\": {\"dtype\": \"int64\", \"value\": [1, 2, 3]}}, \"key1\": {\"_python_set_\": [1, 2, 3]}}'\n\n## Creating and Loading Plugins\n\n``` {.python}\nfrom jsonextended import plugins, utils\n```\n\nPlugins are recognised as classes with a minimal set of attributes\nmatching the plugin category interface:\n\n``` {.python}\nplugins.view_interfaces()\n```\n\n    {'decoders': ['plugin_name', 'plugin_descript', 'dict_signature'],\n     'encoders': ['plugin_name', 'plugin_descript', 'objclass'],\n     'parsers': ['plugin_name', 'plugin_descript', 'file_regex', 'read_file']}\n\n``` {.python}\nplugins.unload_all_plugins()\nplugins.view_plugins()\n```\n\n    {'decoders': {}, 'encoders': {}, 'parsers': {}}\n\nFor example, a simple parser plugin would be:\n\n``` {.python}\nclass ParserPlugin(object):\n    plugin_name = 'example'\n    plugin_descript = 'a parser for *.example files, that outputs (line_number:line)'\n    file_regex = '*.example'\n    def read_file(self, file_obj, **kwargs):\n        out_dict = {}\n        for i, line in enumerate(file_obj):\n            out_dict[i] = line.strip()\n        return out_dict\n```\n\nPlugins can be loaded as a class:\n\n``` {.python}\nplugins.load_plugin_classes([ParserPlugin],'parsers')\nplugins.view_plugins()\n```\n\n    {'decoders': {},\n     'encoders': {},\n     'parsers': {'example': 'a parser for *.example files, that outputs (line_number:line)'}}\n\nOr by directory (loading all .py files):\n\n``` {.python}\nfobj = utils.MockPath('example.py',is_file=True,content=\"\"\"\nclass ParserPlugin(object):\n    plugin_name = 'example.other'\n    plugin_descript = 'a parser for *.example.other files, that outputs (line_number:line)'\n    file_regex = '*.example.other'\n    def read_file(self, file_obj, **kwargs):\n        out_dict = {}\n        for i, line in enumerate(file_obj):\n            out_dict[i] = line.strip()\n        return out_dict\n\"\"\")\ndobj = utils.MockPath(structure=[fobj])\nplugins.load_plugins_dir(dobj,'parsers')\nplugins.view_plugins()\n```\n\n    {'decoders': {},\n     'encoders': {},\n     'parsers': {'example': 'a parser for *.example files, that outputs (line_number:line)',\n      'example.other': 'a parser for *.example.other files, that outputs (line_number:line)'}}\n\nFor a more complex example of a parser, see\n`jsonextended.complex_parsers`\n\n### Interface specifications\n\n- Parsers:\n  - *file\\_regex* attribute, a str denoting what files to apply it\n    to. A file will be parsed by the longest regex it matches.\n  - *read\\_file* method, which takes an (open) file object and\n    kwargs as parameters\n- Decoders:\n  - *dict\\_signature* attribute, a tuple denoting the keys which the\n    dictionary must have, e.g.\u00a0dict\\_signature=('a','b') decodes `{'a':1,'b':2}`\n  - *from\\_...* method(s), which takes a dict object as parameter.\n    The `plugins.decode` function will use the method denoted by the\n    intype parameter, e.g.\u00a0if intype='json', then `from_json` will\n    be called.\n- Encoders:\n  - *objclass* attribute, the object class to apply the encoding to,\n    e.g.\u00a0objclass=decimal.Decimal encodes objects of that type\n  - *to\\_...* method(s), which takes a dict object as parameter. The\n    `plugins.encode` function will use the method denoted by the\n    outtype parameter, e.g.\u00a0if outtype='json', then `to_json` will be called.\n\n## Extended Examples\n\nFor more information, all functions contain doc-strings with tested\nexamples.\n\n### Data Folders JSONisation\n\n``` {.python}\nfrom jsonextended import ejson, edict, utils\n```\n\n``` {.python}\npath = utils.get_test_path()\nejson.jkeys(path)\n```\n\n    ['dir1', 'dir2', 'dir3']\n\n``` {.python}\njdict1 = ejson.to_dict(path)\nedict.pprint(jdict1,depth=2)\n```\n\n    dir1:\n      dir1_1: {...}\n      file1: {...}\n      file2: {...}\n    dir2:\n      file1: {...}\n    dir3:\n\n``` {.python}\nedict.to_html(jdict1,depth=2)\n```\n\nTo try the rendered JSON tree, output in the Jupyter Notebook, go to :\n<https://chrisjsewell.github.io/>\n\n### Nested Dictionary Manipulation\n\n``` {.python}\njdict2 = ejson.to_dict(path,['dir1','file1'])\nedict.pprint(jdict2,depth=1)\n```\n\n    initial: {...}\n    meta: {...}\n    optimised: {...}\n    units: {...}\n\n``` {.python}\nfiltered = edict.filter_keys(jdict2,['vol*'],use_wildcards=True)\nedict.pprint(filtered)\n```\n\n    initial:\n      crystallographic:\n        volume: 924.62752781\n      primitive:\n        volume: 462.313764\n    optimised:\n      crystallographic:\n        volume: 1063.98960509\n      primitive:\n        volume: 531.994803\n\n``` {.python}\nedict.pprint(edict.flatten(filtered))\n```\n\n    (initial, crystallographic, volume):   924.62752781\n    (initial, primitive, volume):          462.313764\n    (optimised, crystallographic, volume): 1063.98960509\n    (optimised, primitive, volume):        531.994803\n\n### Units Schema\n\n``` {.python}\nfrom jsonextended.units import apply_unitschema, split_quantities\nwithunits = apply_unitschema(filtered,{'volume':'angstrom^3'})\nedict.pprint(withunits)\n```\n\n    initial:\n      crystallographic:\n        volume: 924.62752781 angstrom ** 3\n      primitive:\n        volume: 462.313764 angstrom ** 3\n    optimised:\n      crystallographic:\n        volume: 1063.98960509 angstrom ** 3\n      primitive:\n        volume: 531.994803 angstrom ** 3\n\n``` {.python}\nnewunits = apply_unitschema(withunits,{'volume':'nm^3'})\nedict.pprint(newunits)\n```\n\n    initial:\n      crystallographic:\n        volume: 0.92462752781 nanometer ** 3\n      primitive:\n        volume: 0.462313764 nanometer ** 3\n    optimised:\n      crystallographic:\n        volume: 1.06398960509 nanometer ** 3\n      primitive:\n        volume: 0.531994803 nanometer ** 3\n\n``` {.python}\nedict.pprint(split_quantities(newunits),depth=4)\n```\n\n    initial:\n      crystallographic:\n        volume:\n          magnitude: 0.92462752781\n          units:     nanometer ** 3\n      primitive:\n        volume:\n          magnitude: 0.462313764\n          units:     nanometer ** 3\n    optimised:\n      crystallographic:\n        volume:\n          magnitude: 1.06398960509\n          units:     nanometer ** 3\n      primitive:\n        volume:\n          magnitude: 0.531994803\n          units:     nanometer ** 3\n\n\n",
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