EmbeddedPkg: Extend NvVarStoreFormattedLib LIBRARY_CLASS

[mirror_edk2.git] / AppPkg / Applications / Python / Python-2.7.2 / Lib / csv.py

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