--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/symbian-qemu-0.9.1-12/python-win32-2.6.1/lib/csv.py Fri Jul 31 15:01:17 2009 +0100
@@ -0,0 +1,435 @@
+
+"""
+csv.py - read/write/investigate CSV files
+"""
+
+import re
+from functools import reduce
+from _csv import Error, __version__, writer, reader, register_dialect, \
+ unregister_dialect, get_dialect, list_dialects, \
+ field_size_limit, \
+ QUOTE_MINIMAL, QUOTE_ALL, QUOTE_NONNUMERIC, QUOTE_NONE, \
+ __doc__
+from _csv import Dialect as _Dialect
+
+try:
+ from cStringIO import StringIO
+except ImportError:
+ from StringIO import StringIO
+
+__all__ = [ "QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE",
+ "Error", "Dialect", "__doc__", "excel", "excel_tab",
+ "field_size_limit", "reader", "writer",
+ "register_dialect", "get_dialect", "list_dialects", "Sniffer",
+ "unregister_dialect", "__version__", "DictReader", "DictWriter" ]
+
+class Dialect:
+ """Describe an Excel dialect.
+
+ This must be subclassed (see csv.excel). Valid attributes are:
+ delimiter, quotechar, escapechar, doublequote, skipinitialspace,
+ lineterminator, quoting.
+
+ """
+ _name = ""
+ _valid = False
+ # placeholders
+ delimiter = None
+ quotechar = None
+ escapechar = None
+ doublequote = None
+ skipinitialspace = None
+ lineterminator = None
+ quoting = None
+
+ def __init__(self):
+ if self.__class__ != Dialect:
+ self._valid = True
+ self._validate()
+
+ def _validate(self):
+ try:
+ _Dialect(self)
+ except TypeError, e:
+ # We do this for compatibility with py2.3
+ raise Error(str(e))
+
+class excel(Dialect):
+ """Describe the usual properties of Excel-generated CSV files."""
+ delimiter = ','
+ quotechar = '"'
+ doublequote = True
+ skipinitialspace = False
+ lineterminator = '\r\n'
+ quoting = QUOTE_MINIMAL
+register_dialect("excel", excel)
+
+class excel_tab(excel):
+ """Describe the usual properties of Excel-generated TAB-delimited files."""
+ delimiter = '\t'
+register_dialect("excel-tab", excel_tab)
+
+
+class DictReader:
+ def __init__(self, f, fieldnames=None, restkey=None, restval=None,
+ dialect="excel", *args, **kwds):
+ self._fieldnames = fieldnames # list of keys for the dict
+ self.restkey = restkey # key to catch long rows
+ self.restval = restval # default value for short rows
+ self.reader = reader(f, dialect, *args, **kwds)
+ self.dialect = dialect
+ self.line_num = 0
+
+ def __iter__(self):
+ return self
+
+ @property
+ def fieldnames(self):
+ if self._fieldnames is None:
+ try:
+ self._fieldnames = self.reader.next()
+ except StopIteration:
+ pass
+ self.line_num = self.reader.line_num
+ return self._fieldnames
+
+ @fieldnames.setter
+ def fieldnames(self, value):
+ self._fieldnames = value
+
+ def next(self):
+ if self.line_num == 0:
+ # Used only for its side effect.
+ self.fieldnames
+ row = self.reader.next()
+ self.line_num = self.reader.line_num
+
+ # unlike the basic reader, we prefer not to return blanks,
+ # because we will typically wind up with a dict full of None
+ # values
+ while row == []:
+ row = self.reader.next()
+ d = dict(zip(self.fieldnames, row))
+ lf = len(self.fieldnames)
+ lr = len(row)
+ if lf < lr:
+ d[self.restkey] = row[lf:]
+ elif lf > lr:
+ for key in self.fieldnames[lr:]:
+ d[key] = self.restval
+ return d
+
+
+class DictWriter:
+ def __init__(self, f, fieldnames, restval="", extrasaction="raise",
+ dialect="excel", *args, **kwds):
+ self.fieldnames = fieldnames # list of keys for the dict
+ self.restval = restval # for writing short dicts
+ if extrasaction.lower() not in ("raise", "ignore"):
+ raise ValueError, \
+ ("extrasaction (%s) must be 'raise' or 'ignore'" %
+ extrasaction)
+ self.extrasaction = extrasaction
+ self.writer = writer(f, dialect, *args, **kwds)
+
+ def _dict_to_list(self, rowdict):
+ if self.extrasaction == "raise":
+ wrong_fields = [k for k in rowdict if k not in self.fieldnames]
+ if wrong_fields:
+ raise ValueError("dict contains fields not in fieldnames: " +
+ ", ".join(wrong_fields))
+ return [rowdict.get(key, self.restval) for key in self.fieldnames]
+
+ def writerow(self, rowdict):
+ return self.writer.writerow(self._dict_to_list(rowdict))
+
+ def writerows(self, rowdicts):
+ rows = []
+ for rowdict in rowdicts:
+ rows.append(self._dict_to_list(rowdict))
+ return self.writer.writerows(rows)
+
+# Guard Sniffer's type checking against builds that exclude complex()
+try:
+ complex
+except NameError:
+ complex = float
+
+class Sniffer:
+ '''
+ "Sniffs" the format of a CSV file (i.e. delimiter, quotechar)
+ Returns a Dialect object.
+ '''
+ def __init__(self):
+ # in case there is more than one possible delimiter
+ self.preferred = [',', '\t', ';', ' ', ':']
+
+
+ def sniff(self, sample, delimiters=None):
+ """
+ Returns a dialect (or None) corresponding to the sample
+ """
+
+ quotechar, delimiter, skipinitialspace = \
+ self._guess_quote_and_delimiter(sample, delimiters)
+ if not delimiter:
+ delimiter, skipinitialspace = self._guess_delimiter(sample,
+ delimiters)
+
+ if not delimiter:
+ raise Error, "Could not determine delimiter"
+
+ class dialect(Dialect):
+ _name = "sniffed"
+ lineterminator = '\r\n'
+ quoting = QUOTE_MINIMAL
+ # escapechar = ''
+ doublequote = False
+
+ dialect.delimiter = delimiter
+ # _csv.reader won't accept a quotechar of ''
+ dialect.quotechar = quotechar or '"'
+ dialect.skipinitialspace = skipinitialspace
+
+ return dialect
+
+
+ def _guess_quote_and_delimiter(self, data, delimiters):
+ """
+ Looks for text enclosed between two identical quotes
+ (the probable quotechar) which are preceded and followed
+ by the same character (the probable delimiter).
+ For example:
+ ,'some text',
+ The quote with the most wins, same with the delimiter.
+ If there is no quotechar the delimiter can't be determined
+ this way.
+ """
+
+ matches = []
+ for restr in ('(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?",
+ '(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)', # ".*?",
+ '(?P<delim>>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)', # ,".*?"
+ '(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space)
+ regexp = re.compile(restr, re.DOTALL | re.MULTILINE)
+ matches = regexp.findall(data)
+ if matches:
+ break
+
+ if not matches:
+ return ('', None, 0) # (quotechar, delimiter, skipinitialspace)
+
+ quotes = {}
+ delims = {}
+ spaces = 0
+ for m in matches:
+ n = regexp.groupindex['quote'] - 1
+ key = m[n]
+ if key:
+ quotes[key] = quotes.get(key, 0) + 1
+ try:
+ n = regexp.groupindex['delim'] - 1
+ key = m[n]
+ except KeyError:
+ continue
+ if key and (delimiters is None or key in delimiters):
+ delims[key] = delims.get(key, 0) + 1
+ try:
+ n = regexp.groupindex['space'] - 1
+ except KeyError:
+ continue
+ if m[n]:
+ spaces += 1
+
+ quotechar = reduce(lambda a, b, quotes = quotes:
+ (quotes[a] > quotes[b]) and a or b, quotes.keys())
+
+ if delims:
+ delim = reduce(lambda a, b, delims = delims:
+ (delims[a] > delims[b]) and a or b, delims.keys())
+ skipinitialspace = delims[delim] == spaces
+ if delim == '\n': # most likely a file with a single column
+ delim = ''
+ else:
+ # there is *no* delimiter, it's a single column of quoted data
+ delim = ''
+ skipinitialspace = 0
+
+ return (quotechar, delim, skipinitialspace)
+
+
+ def _guess_delimiter(self, data, delimiters):
+ """
+ The delimiter /should/ occur the same number of times on
+ each row. However, due to malformed data, it may not. We don't want
+ an all or nothing approach, so we allow for small variations in this
+ number.
+ 1) build a table of the frequency of each character on every line.
+ 2) build a table of freqencies of this frequency (meta-frequency?),
+ e.g. 'x occurred 5 times in 10 rows, 6 times in 1000 rows,
+ 7 times in 2 rows'
+ 3) use the mode of the meta-frequency to determine the /expected/
+ frequency for that character
+ 4) find out how often the character actually meets that goal
+ 5) the character that best meets its goal is the delimiter
+ For performance reasons, the data is evaluated in chunks, so it can
+ try and evaluate the smallest portion of the data possible, evaluating
+ additional chunks as necessary.
+ """
+
+ data = filter(None, data.split('\n'))
+
+ ascii = [chr(c) for c in range(127)] # 7-bit ASCII
+
+ # build frequency tables
+ chunkLength = min(10, len(data))
+ iteration = 0
+ charFrequency = {}
+ modes = {}
+ delims = {}
+ start, end = 0, min(chunkLength, len(data))
+ while start < len(data):
+ iteration += 1
+ for line in data[start:end]:
+ for char in ascii:
+ metaFrequency = charFrequency.get(char, {})
+ # must count even if frequency is 0
+ freq = line.count(char)
+ # value is the mode
+ metaFrequency[freq] = metaFrequency.get(freq, 0) + 1
+ charFrequency[char] = metaFrequency
+
+ for char in charFrequency.keys():
+ items = charFrequency[char].items()
+ if len(items) == 1 and items[0][0] == 0:
+ continue
+ # get the mode of the frequencies
+ if len(items) > 1:
+ modes[char] = reduce(lambda a, b: a[1] > b[1] and a or b,
+ items)
+ # adjust the mode - subtract the sum of all
+ # other frequencies
+ items.remove(modes[char])
+ modes[char] = (modes[char][0], modes[char][1]
+ - reduce(lambda a, b: (0, a[1] + b[1]),
+ items)[1])
+ else:
+ modes[char] = items[0]
+
+ # build a list of possible delimiters
+ modeList = modes.items()
+ total = float(chunkLength * iteration)
+ # (rows of consistent data) / (number of rows) = 100%
+ consistency = 1.0
+ # minimum consistency threshold
+ threshold = 0.9
+ while len(delims) == 0 and consistency >= threshold:
+ for k, v in modeList:
+ if v[0] > 0 and v[1] > 0:
+ if ((v[1]/total) >= consistency and
+ (delimiters is None or k in delimiters)):
+ delims[k] = v
+ consistency -= 0.01
+
+ if len(delims) == 1:
+ delim = delims.keys()[0]
+ skipinitialspace = (data[0].count(delim) ==
+ data[0].count("%c " % delim))
+ return (delim, skipinitialspace)
+
+ # analyze another chunkLength lines
+ start = end
+ end += chunkLength
+
+ if not delims:
+ return ('', 0)
+
+ # if there's more than one, fall back to a 'preferred' list
+ if len(delims) > 1:
+ for d in self.preferred:
+ if d in delims.keys():
+ skipinitialspace = (data[0].count(d) ==
+ data[0].count("%c " % d))
+ return (d, skipinitialspace)
+
+ # nothing else indicates a preference, pick the character that
+ # dominates(?)
+ items = [(v,k) for (k,v) in delims.items()]
+ items.sort()
+ delim = items[-1][1]
+
+ skipinitialspace = (data[0].count(delim) ==
+ data[0].count("%c " % delim))
+ return (delim, skipinitialspace)
+
+
+ def has_header(self, sample):
+ # Creates a dictionary of types of data in each column. If any
+ # column is of a single type (say, integers), *except* for the first
+ # row, then the first row is presumed to be labels. If the type
+ # can't be determined, it is assumed to be a string in which case
+ # the length of the string is the determining factor: if all of the
+ # rows except for the first are the same length, it's a header.
+ # Finally, a 'vote' is taken at the end for each column, adding or
+ # subtracting from the likelihood of the first row being a header.
+
+ rdr = reader(StringIO(sample), self.sniff(sample))
+
+ header = rdr.next() # assume first row is header
+
+ columns = len(header)
+ columnTypes = {}
+ for i in range(columns): columnTypes[i] = None
+
+ checked = 0
+ for row in rdr:
+ # arbitrary number of rows to check, to keep it sane
+ if checked > 20:
+ break
+ checked += 1
+
+ if len(row) != columns:
+ continue # skip rows that have irregular number of columns
+
+ for col in columnTypes.keys():
+
+ for thisType in [int, long, float, complex]:
+ try:
+ thisType(row[col])
+ break
+ except (ValueError, OverflowError):
+ pass
+ else:
+ # fallback to length of string
+ thisType = len(row[col])
+
+ # treat longs as ints
+ if thisType == long:
+ thisType = int
+
+ if thisType != columnTypes[col]:
+ if columnTypes[col] is None: # add new column type
+ columnTypes[col] = thisType
+ else:
+ # type is inconsistent, remove column from
+ # consideration
+ del columnTypes[col]
+
+ # finally, compare results against first row and "vote"
+ # on whether it's a header
+ hasHeader = 0
+ for col, colType in columnTypes.items():
+ if type(colType) == type(0): # it's a length
+ if len(header[col]) != colType:
+ hasHeader += 1
+ else:
+ hasHeader -= 1
+ else: # attempt typecast
+ try:
+ colType(header[col])
+ except (ValueError, TypeError):
+ hasHeader += 1
+ else:
+ hasHeader -= 1
+
+ return hasHeader > 0