"""DictForArgs.py
See the doc string for the DictForArgs() function.
Also, there is a test suite in Tests/TestDictForArgs.py
"""
import re
class DictForArgsError(Exception):
pass
def _SyntaxError(s):
raise DictForArgsError, 'Syntax error: %s' % repr(s)
_nameRE = re.compile(r'\w+')
_equalsRE = re.compile(r'\=')
_stringRE = re.compile(r'''"[^"]+"|'[^']+'|\S+''')
_whiteRE = re.compile(r'\s+')
_REs = [_nameRE, _equalsRE, _stringRE, _whiteRE]
def DictForArgs(s):
"""Build dictionary from arguments.
Takes an input such as:
x=3
name="foo"
first='john' last='doe'
required border=3
And returns a dictionary representing the same. For keys that aren't
given an explicit value (such as 'required' above), the value is '1'.
All values are interpreted as strings. If you want ints and floats,
you'll have to convert them yourself.
This syntax is equivalent to what you find in HTML and close to other
ML languages such as XML.
Returns {} for an empty string.
The informal grammar is:
(NAME [=NAME|STRING])*
Will raise DictForArgsError if the string is invalid.
See also: PyDictForArgs() and ExpandDictWithExtras() in this module.
"""
s = s.strip()
# Tokenize
verbose = 0
matches = []
start = 0
sLen = len(s)
if verbose:
print '>> DictForArgs(%s)' % repr(s)
print '>> sLen:', sLen
while start < sLen:
for regEx in _REs:
if verbose:
print '>> try:', regEx
match = regEx.match(s, start)
if verbose:
print '>> match:', match
if match is not None:
if match.re is not _whiteRE:
matches.append(match)
start = match.end()
if verbose:
print '>> new start:', start
break
else:
_SyntaxError(s)
if verbose:
names = []
for match in matches:
if match.re is _nameRE:
name = 'name'
elif match.re is _equalsRE:
name = 'equals'
elif match.re is _stringRE:
name = 'string'
elif match.re is _whiteRE:
name = 'white'
names.append(name)
#print '>> match =', name, match
print '>> names =', names
# Process tokens
# At this point we have a list of all the tokens (as re.Match objects)
# We need to process these into a dictionary.
dict = {}
matchesLen = len(matches)
i = 0
while i < matchesLen:
match = matches[i]
if i + 1 < matchesLen:
peekMatch = matches[i+1]
else:
peekMatch = None
if match.re is _nameRE:
if peekMatch is not None:
if peekMatch.re is _nameRE:
# We have a name without an explicit value
dict[match.group()] = '1'
i += 1
continue
if peekMatch.re is _equalsRE:
if i + 2 < matchesLen:
target = matches[i+2]
if target.re is _nameRE or target.re is _stringRE:
value = target.group()
if value[0] == "'" or value[0] == '"':
value = value[1:-1]
# value = "'''%s'''" % value[1:-1]
# value = eval(value)
dict[match.group()] = value
i += 3
continue
else:
dict[match.group()] = '1'
i += 1
continue
_SyntaxError(s)
if verbose:
print
return dict
from string import letters
def PyDictForArgs(s):
"""Build dictionary from arguments.
Takes an input such as:
x=3
name="foo"
first='john'; last='doe'
list=[1, 2, 3]; name='foo'
And returns a dictionary representing the same.
All values are interpreted as Python expressions. Any error in these
expressions will raise the appropriate Python exception. This syntax
allows much more power than DictForArgs() since you can include
lists, dictionaries, actual ints and floats, etc.
This could also open the door to hacking your software if the input
comes from a tainted source such as an HTML form or an unprotected
configuration file.
Returns {} for an empty string.
See also: DictForArgs() and ExpandDictWithExtras() in this module.
"""
if s:
s = s.strip()
if not s:
return {}
# special case: just a name
# meaning: name=1
# example: isAbstract
if s.find(' ') == -1 and s.find('=') == -1 and s[0] in letters:
s += '=1'
results = {}
exec s in results
del results['__builtins__']
return results
def ExpandDictWithExtras(dict, key='Extras', delKey=1, dictForArgs=DictForArgs):
"""Return a dictionary with the 'Extras' column expanded by DictForArgs().
For example, given:
{ 'Name': 'foo', 'Extras': 'x=1 y=2' }
The return value is:
{ 'Name': 'foo', 'x': '1', 'y': '2' }
The key argument controls what key in the dictionary is used to hold
the extra arguments. The delKey argument controls whether that key and
its corresponding value are retained.
The same dictionary may be returned if there is no extras key.
The most typical use of this function is to pass a row from a DataTable
that was initialized from a CSV file (e.g., a spreadsheet or tabular file).
FormKit and MiddleKit both use CSV files and allow for an Extras column
to specify attributes that occur infrequently.
"""
if dict.has_key(key):
newDict = {}
# We use the following for loop rather than newDict.update()
# so that the dict arg can be dictionary-like.
for k, v in dict.items():
newDict[k] = v
if delKey:
del newDict[key]
newDict.update(dictForArgs(dict[key]))
return newDict
else:
return dict
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