stypes 0.22.0

Encode and Decode Textual Data into Rich Python Data Structures

Encode and Decode Textual Data into Rich Python Data Structures

stypes is a text parsing, conversion and formatting library written to efficiently handle large fixed-width text record data files. Convert text streams into dictionaries, lists, tuples, named tuples, ordered dictionaries and more using text layout specifications. Nested data structures and repeating elements are also supported.

One of the main design goals of the library was to handle legacy text-based record data that is commonly found in COBOL system. Of course, the library can be used to handle any fixed layout textual data.

A simple example of turning some text into a Named Tuple:

from decimal import Decimal
from stypes import NamedTuple, Integer, Numeric
spec = NamedTuple([
	('name', 10),
	('age', Integer(3)),
	('weight', Numeric('999V99'))])
text = "Johnson    2109750"
rec = spec.unpack(text)
assert rec.name == 'Johnson'
assert rec.age == 21
assert rec.weight == Decimal("97.5")

And a more interesting example using nested data structures of a list of records and actually updating a record.

from stypes import Array, Dict, Integer, Numeric
item = Dict([('line_no', Integer(2)),
	('item_no', Integer(5)),
	('total', Numeric("999.99"))])
invoice = Dict([
    ('invoice_no', Integer(4)),
    ('total', Numeric("999.99")),
    ('items', Array(3, item))])
text = "0001200.450100004002.000200006198.50"
rec = invoice.unpack(inv)
# rec is now
  {'invoice_no': 1,
   'items': [{'item_no': 4, 'line_no': 1, 'total': Decimal('2.00')},
	     {'item_no': 6, 'line_no': 2, 'total': Decimal('198.50')},
	     {'item_no': None, 'line_no': None, 'total': None}],
   'total': Decimal('200.45')}

# Set the last invoice item
rec['items'][-1] = {
    'line_no': 3,
    'item_no': 10,
    'total': Decimal("20")}

# Update the invoice total
rec['total'] = sum(i['total'] for i in rec['items'])
print rec.pack()
# '0001220.500100004002.000200006198.500300010020.00'

See the included tests.py file for more examples.

Errors in Data

stypes takes the approach that errors in the textual data are not exceptions. Data errors are to be expected and should be handled in the normal flow of the program.

stypes includes the notion of an UnconvertedValue. When parsing text that cannot be deserialized into the destination format, an UnconvertedValue instance is placed in it's place. All container objects have a has_unconverted() method which allows client code to easily detect if there was an error.

fmt = List([Numeric('99V9'), Integer(4)])
rec = fmt.unpack("44X001A")
print rec
[<UnconvertedValue string='44X' reason="Expected 1 digits. Found 'X'">,
 <UnconvertedValue string='001A' reason='expecting all digits for integer'>]

assert rec.has_unconverted() == True

print rec[0].reason
 "Expected 1 digits. Found 'X'"

print rec[1].reason
 'expecting all digits for integer'

Installation

You can install stypes using pip

pip install stypes

or download from PyPI at https://pypi.python.org/pypi/stypes/