"""

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

try:

    from cStringIO import StringIO

except ImportError:

    from StringIO import StringIO

__all__ = [ "QUOTE_MINIMAL", "QUOTE_ALL", "QUOTE_NONNUMERIC", "QUOTE_NONE",

            "Error", "Dialect", "excel", "excel_tab", "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)

    def __iter__(self):

        return self

    def next(self):

        row = self.reader.next()

        if self.fieldnames is None:

            self.fieldnames = row

            row = self.reader.next()

        # 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":

            for k in rowdict.keys():

                if k not in self.fieldnames:

                    raise ValueError, "dict contains fields not in fieldnames"

        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