dirtyjson 1.0.8

JSON decoder for Python that can extract data from the muck

JSON (JavaScript Object Notation) <http://json.org> is a subset of JavaScript syntax (ECMA-262 3rd edition) used as a lightweight data interchange format.

dirtyjson is a JSON decoder meant for extracting JSON-type data from .js files. The returned data structure includes information about line and column numbers, so you can output more useful error messages. The input can also include single quotes, line comments, inline comments, dangling commas, unquoted single-word keys, and hexadecimal and octal numbers.

The goal of dirtyjson is to read JSON objects out of files that are littered with elements that do not fit the official JSON standard. By providing line and column number contexts, a dirty JSON file can be used as source input for a complex data parser or compiler.

dirtyjson exposes an API familiar to users of the standard library marshal and pickle modules. However, dirtyjson provides only the load(s) capability. To write JSON, use either the standard json library or simplejson.

Note

The code for dirtyjson is a fairly drastically rewritten version of the loader in simplejson so thanks go to Bob Ippolito of the simplejson project for providing such a nice starting point.

Development of dirtyjson happens on Github: https://github.com/codecobblers/dirtyjson

Decoding JSON and getting position information:

>>> import dirtyjson
>>> obj = [u'foo', {u'bar': [u'baz', None, 1.0, 2]}]
>>> d = dirtyjson.loads("""["foo", /* not fu*/ {bar: ['baz', null, 1.0, 2,]}] and then ignore this junk""")
>>> d == obj
True
>>> pos = d.attributes(0)  # line/column position of first element in array
>>> pos.line == 1
True
>>> pos.column == 2
True
>>> pos = d[1].attributes('bar')  # line/column position of 'bar' key/value pair
>>> pos.key.line == 1
True
>>> pos.key.column == 22
True
>>> pos.value.line == 1
True
>>> pos.value.column == 27
True

Decoding unicode from JSON:

>>> dirtyjson.loads('"\\"foo\\bar"') == u'"foo\x08ar'
True

Decoding JSON from streams:

>>> from dirtyjson.compat import StringIO
>>> io = StringIO('["streaming API"]')
>>> dirtyjson.load(io)[0] == 'streaming API'
True

Using Decimal instead of float:

>>> import dirtyjson
>>> from decimal import Decimal
>>> dirtyjson.loads('1.1', parse_float=Decimal) == Decimal('1.1')
True

Basic Usage

load(fp[, encoding[, parse_float[, parse_int[, parse_constant[, search_for_first_object]]]]])

Performs the following translations in decoding by default:

JSON	Python
object	AttributedDict
array	AttributedList
string	unicode
number (int)	int, long
number (real)	float
true	True
false	False
null	None

It also understands NaN, Infinity, and -Infinity as their corresponding float values, which is outside the JSON spec.

Deserialize fp (a .read()-supporting file-like object containing a JSON document) to a Python object. dirtyjson.Error will be raised if the given document is not valid.

If the contents of fp are encoded with an ASCII based encoding other than UTF-8 (e.g. latin-1), then an appropriate encoding name must be specified. Encodings that are not ASCII based (such as UCS-2) are not allowed, and should be wrapped with codecs.getreader(fp)(encoding), or simply decoded to a unicode object and passed to loads. The default setting of 'utf-8' is fastest and should be using whenever possible.

If fp.read() returns str then decoded JSON strings that contain only ASCII characters may be parsed as str for performance and memory reasons. If your code expects only unicode the appropriate solution is to wrap fp with a reader as demonstrated above.

parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal).

parse_int, if specified, will be called with the int of the string of every JSON int to be decoded. By default, this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float).

Note

Unlike the standard json module, dirtyjson always does int(num_str, 0) before passing through to the converter passed is as the parse_int parameter. This is to enable automatic handling of hex and octal numbers.

parse_constant, if specified, will be called with one of the following strings: true, false, null, '-Infinity', 'Infinity', 'NaN'. This can be used to raise an exception if invalid JSON numbers are encountered or to provide alternate values for any of these constants.

search_for_first_object, if True, will cause the parser to search for the first occurrence of either { or [. This is very useful for reading an object from a JavaScript file.

loads(s[, encoding[, parse_float[, parse_int[, parse_constant[, search_for_first_object[, start_index]]]]])

Deserialize s (a str or unicode instance containing a JSON document) to a Python object. dirtyjson.Error will be raised if the given JSON document is not valid.

If s is a str instance and is encoded with an ASCII based encoding other than UTF-8 (e.g. latin-1), then an appropriate encoding name must be specified. Encodings that are not ASCII based (such as UCS-2) are not allowed and should be decoded to unicode first.

If s is a str then decoded JSON strings that contain only ASCII characters may be parsed as str for performance and memory reasons. If your code expects only unicode the appropriate solution is decode s to unicode prior to calling loads.

start_index, if non-zero, will cause the parser to start processing from the specified offset, while maintaining the correct line and column numbers. This is very useful for reading an object from the middle of a JavaScript file.

The other arguments have the same meaning as in load.

Exceptions

dirtyjson.Error(msg, doc, pos)

Subclass of ValueError with the following additional attributes:

msg

The unformatted error message

doc

The JSON document being parsed

pos

The start index of doc where parsing failed

lineno

The line corresponding to pos

colno

The column corresponding to pos

AttributedDict and AttributedList

The dirtyjson module uses AttributedDict and AttributedList instead of dict and list. Each is actually a subclass of its base type (dict or list) and can be used as if they were the standard class, but these have been enhanced to store attributes with each element. We use those attributes to store line and column numbers. You can use that information to refer users back to the exact location in the original source file.

Position()

This is a very simple utility class that contains line and column. It is used for storing the position attributes for AttributedList and KeyValuePosition

KeyValuePosition()

This is another very simple utility class that contains key and value. Each of those is a Position object specifying the location in the original source string/file of the key and value. It is used for storing the position attributes for AttributedDict.

AttributedDict()

A subclass of dict that behaves exactly like a dict except that it maintains order like an OrderedDict and allows storing attributes for each key/value pair.

add_with_attributes(self, key, value, attributes)

Set the key in the underlying dict to the value and also store whatever is passed in as attributes for later retrieval. In our case, we store KeyValuePosition.

attributes(self, key)

Return the attributes associated with the specified key or None if no attributes exist for the key. In our case, we store KeyValuePosition. Retrieve position info like this:

pos = d.attributes(key)
key_line = pos.key.line
key_column = pos.key.column
value_line = pos.value.line
value_column = pos.value.column

AttributedList()

A subclass of list that behaves exactly like a list except that it allows storing attributes for each value.

append(self, value, attributes=None):

Appends value to the list and attributes to the associated location. In our case, we store Position.

attributes(self, index)

Returns the attributes for the value at the given index. In our case, we store Position. Retrieve position info like this:

pos = l.attributes(index)
value_line = pos.line
value_column = pos.column

Note

This class is NOT robust. If you insert or delete items, the attributes will get out of sync. Making this a non-naive class would be a nice enhancement.