python-3.6.zip added from Github

README.cosmo contains the necessary links.

third_party/python/Lib/csv.py

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+
+"""
+csv.py - read/write/investigate CSV files
+"""
+
+import re
+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
+
+from collections import OrderedDict
+from io 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",
+           "unix_dialect"]
+
+class Dialect:
+    """Describe a CSV 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 as 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 unix_dialect(Dialect):
+    """Describe the usual properties of Unix-generated CSV files."""
+    delimiter = ','
+    quotechar = '"'
+    doublequote = True
+    skipinitialspace = False
+    lineterminator = '\n'
+    quoting = QUOTE_ALL
+register_dialect("unix", unix_dialect)
+
+
+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 = next(self.reader)
+            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 = next(self.reader)
+        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 = next(self.reader)
+        d = OrderedDict(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 writeheader(self):
+        header = dict(zip(self.fieldnames, self.fieldnames))
+        self.writerow(header)
+
+    def _dict_to_list(self, rowdict):
+        if self.extrasaction == "raise":
+            wrong_fields = rowdict.keys() - self.fieldnames
+            if wrong_fields:
+                raise ValueError("dict contains fields not in fieldnames: "
+                                 + ", ".join([repr(x) for x in 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):
+        return self.writer.writerows(map(self._dict_to_list, rowdicts))
+
+# 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, doublequote, 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 = ''
+
+        dialect.doublequote = doublequote
+        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 (r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?P=delim)', # ,".*?",
+                      r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?P<delim>[^\w\n"\'])(?P<space> ?)',   #  ".*?",
+                      r'(?P<delim>[^\w\n"\'])(?P<space> ?)(?P<quote>["\']).*?(?P=quote)(?:$|\n)',   # ,".*?"
+                      r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'):                            #  ".*?" (no delim, no space)
+            regexp = re.compile(restr, re.DOTALL | re.MULTILINE)
+            matches = regexp.findall(data)
+            if matches:
+                break
+
+        if not matches:
+            # (quotechar, doublequote, delimiter, skipinitialspace)
+            return ('', False, None, 0)
+        quotes = {}
+        delims = {}
+        spaces = 0
+        groupindex = regexp.groupindex
+        for m in matches:
+            n = groupindex['quote'] - 1
+            key = m[n]
+            if key:
+                quotes[key] = quotes.get(key, 0) + 1
+            try:
+                n = 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 = groupindex['space'] - 1
+            except KeyError:
+                continue
+            if m[n]:
+                spaces += 1
+
+        quotechar = max(quotes, key=quotes.get)
+
+        if delims:
+            delim = max(delims, key=delims.get)
+            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
+
+        # if we see an extra quote between delimiters, we've got a
+        # double quoted format
+        dq_regexp = re.compile(
+                               r"((%(delim)s)|^)\W*%(quote)s[^%(delim)s\n]*%(quote)s[^%(delim)s\n]*%(quote)s\W*((%(delim)s)|$)" % \
+                               {'delim':re.escape(delim), 'quote':quotechar}, re.MULTILINE)
+
+
+
+        if dq_regexp.search(data):
+            doublequote = True
+        else:
+            doublequote = False
+
+        return (quotechar, doublequote, 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 frequencies 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 = list(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 = list(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] = max(items, key=lambda x: x[1])
+                    # adjust the mode - subtract the sum of all
+                    # other frequencies
+                    items.remove(modes[char])
+                    modes[char] = (modes[char][0], modes[char][1]
+                                   - sum(item[1] for item in items))
+                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 = list(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 = next(rdr) # 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 list(columnTypes.keys()):
+
+                for thisType in [int, float, complex]:
+                    try:
+                        thisType(row[col])
+                        break
+                    except (ValueError, OverflowError):
+                        pass
+                else:
+                    # fallback to length of string
+                    thisType = len(row[col])
+
+                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