"""A collection of functions designed to help I/O with ascii files."""
__docformat__ = "restructuredtext en"

import numpy as np
import numpy.core.numeric as nx
from __builtin__ import bool, int, long, float, complex, object, unicode, str


def _is_string_like(obj):
    """
    Check whether obj behaves like a string.
    """
    try:
        obj + ''
    except (TypeError, ValueError):
        return False
    return True


def _to_filehandle(fname, flag='r', return_opened=False):
    """
    Returns the filehandle corresponding to a string or a file.
    If the string ends in '.gz', the file is automatically unzipped.
    
    Parameters
    ----------
    fname : string, filehandle
        Name of the file whose filehandle must be returned.
    flag : string, optional
        Flag indicating the status of the file ('r' for read, 'w' for write).
    return_opened : boolean, optional
        Whether to return the opening status of the file.
    """
    if _is_string_like(fname):
        if fname.endswith('.gz'):
            import gzip
            fhd = gzip.open(fname, flag)
        elif fname.endswith('.bz2'):
            import bz2
            fhd = bz2.BZ2File(fname)
        else:
            fhd = file(fname, flag)
        opened = True
    elif hasattr(fname, 'seek'):
        fhd = fname
        opened = False
    else:
        raise ValueError('fname must be a string or file handle')
    if return_opened:
        return fhd, opened
    return fhd


def has_nested_fields(ndtype):
    """
    Returns whether one or several fields of a dtype are nested.

    Parameters
    ----------
    ndtype : dtype
        Data-type of a structured array.

    Raises
    ------
    AttributeError : If `ndtype` does not have a `names` attribute.

    Examples
    --------
    >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
    >>> np.lib._iotools.has_nested_fields(dt)
    False

    """
    for name in ndtype.names or ():
        if ndtype[name].names:
            return True
    return False


def flatten_dtype(ndtype, flatten_base=False):
    """
    Unpack a structured data-type by collapsing nested fields and/or fields
    with a shape.

    Note that the field names are lost.

    Parameters
    ----------
    ndtype : dtype
        The datatype to collapse
    flatten_base : {False, True}, optional
        Whether to transform a field with a shape into several fields or not.

    Examples
    --------
    >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
    ...                ('block', int, (2, 3))])
    >>> np.lib._iotools.flatten_dtype(dt)
    [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]
    >>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
    [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),
     dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),
     dtype('int32')]

    """
    names = ndtype.names
    if names is None:
        if flatten_base:
            return [ndtype.base] * int(np.prod(ndtype.shape))
        return [ndtype.base]
    else:
        types = []
        for field in names:
            (typ, _) = ndtype.fields[field]
            flat_dt = flatten_dtype(typ, flatten_base)
            types.extend(flat_dt)
        return types






class LineSplitter:
    """
    Object to split a string at a given delimiter or at given places.

    Parameters
    ----------
    delimiter : str, int, or sequence of ints, optional
        If a string, character used to delimit consecutive fields.
        If an integer or a sequence of integers, width(s) of each field.
    comment : str, optional
        Character used to mark the beginning of a comment. Default is '#'.
    autostrip : bool, optional
        Whether to strip each individual field. Default is True.

    """

    def autostrip(self, method):
        """
        Wrapper to strip each member of the output of `method`.

        Parameters
        ----------
        method : function
            Function that takes a single argument and returns a sequence of
            strings.

        Returns
        -------
        wrapped : function
            The result of wrapping `method`. `wrapped` takes a single input
            argument and returns a list of strings that are stripped of
            white-space.

        """
        return lambda input: [_.strip() for _ in method(input)]
    #
    def __init__(self, delimiter=None, comments='#', autostrip=True):
        self.comments = comments
        # Delimiter is a character
        if (delimiter is None) or _is_string_like(delimiter):
            delimiter = delimiter or None
            _handyman = self._delimited_splitter
        # Delimiter is a list of field widths
        elif hasattr(delimiter, '__iter__'):
            _handyman = self._variablewidth_splitter
            idx = np.cumsum([0] + list(delimiter))
            delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]
        # Delimiter is a single integer
        elif int(delimiter):
            (_handyman, delimiter) = (self._fixedwidth_splitter, int(delimiter))
        else:
            (_handyman, delimiter) = (self._delimited_splitter, None)
        self.delimiter = delimiter
        if autostrip:
            self._handyman = self.autostrip(_handyman)
        else:
            self._handyman = _handyman
    #
    def _delimited_splitter(self, line):
        line = line.split(self.comments)[0].strip(" \r\n")
        if not line:
            return []
        return line.split(self.delimiter)
    #
    def _fixedwidth_splitter(self, line):
        line = line.split(self.comments)[0]
        if not line:
            return []
        fixed = self.delimiter
        slices = [slice(i, i + fixed) for i in range(len(line))[::fixed]]
        return [line[s] for s in slices]
    #
    def _variablewidth_splitter(self, line):
        line = line.split(self.comments)[0]
        if not line:
            return []
        slices = self.delimiter
        return [line[s] for s in slices]
    #
    def __call__(self, line):
        return self._handyman(line)



class NameValidator:
    """
    Object to validate a list of strings to use as field names.

    The strings are stripped of any non alphanumeric character, and spaces
    are replaced by '_'. During instantiation, the user can define a list of
    names to exclude, as well as a list of invalid characters. Names in the
    exclusion list are appended a '_' character.

    Once an instance has been created, it can be called with a list of names,
    and a list of valid names will be created.
    The `__call__` method accepts an optional keyword "default" that sets
    the default name in case of ambiguity. By default this is 'f', so
    that names will default to `f0`, `f1`, etc.

    Parameters
    ----------
    excludelist : sequence, optional
        A list of names to exclude. This list is appended to the default list
        ['return', 'file', 'print']. Excluded names are appended an underscore:
        for example, `file` becomes `file_` if supplied.
    deletechars : str, optional
        A string combining invalid characters that must be deleted from the
        names.
    casesensitive : {True, False, 'upper', 'lower'}, optional
        * If True, field names are case-sensitive.
        * If False or 'upper', field names are converted to upper case.
        * If 'lower', field names are converted to lower case.

        The default value is True.

    Notes
    -----
    Calling an instance of `NameValidator` is the same as calling its method
    `validate`.

    Examples
    --------
    >>> validator = np.lib._iotools.NameValidator()
    >>> validator(['file', 'field2', 'with space', 'CaSe'])
    ['file_', 'field2', 'with_space', 'CaSe']

    >>> validator = np.lib._iotools.NameValidator(excludelist=['excl'],
                                                  deletechars='q',
                                                  case_sensitive='False')
    >>> validator(['excl', 'field2', 'no_q', 'with space', 'CaSe'])
    ['excl_', 'field2', 'no_', 'with_space', 'case']

    """
    #
    defaultexcludelist = ['return', 'file', 'print']
    defaultdeletechars = set("""~!@#$%^&*()-=+~\|]}[{';: /?.>,<""")
    #
    def __init__(self, excludelist=None, deletechars=None, case_sensitive=None):
        # Process the exclusion list ..
        if excludelist is None:
            excludelist = []
        excludelist.extend(self.defaultexcludelist)
        self.excludelist = excludelist
        # Process the list of characters to delete
        if deletechars is None:
            delete = self.defaultdeletechars
        else:
            delete = set(deletechars)
        delete.add('"')
        self.deletechars = delete
        # Process the case option .....
        if (case_sensitive is None) or (case_sensitive is True):
            self.case_converter = lambda x: x
        elif (case_sensitive is False) or ('u' in case_sensitive):
            self.case_converter = lambda x: x.upper()
        elif 'l' in case_sensitive:
            self.case_converter = lambda x: x.lower()
        else:
            self.case_converter = lambda x: x

    def validate(self, names, defaultfmt="f%i", nbfields=None):
        """
        Validate a list of strings to use as field names for a structured array.

        Parameters
        ----------
        names : sequence of str
            Strings to be validated.
        defaultfmt : str, optional
            Default format string, used if validating a given string reduces its
            length to zero.
        nboutput : integer, optional
            Final number of validated names, used to expand or shrink the initial
            list of names.

        Returns
        -------
        validatednames : list of str
            The list of validated field names.

        Notes
        -----
        A `NameValidator` instance can be called directly, which is the same as
        calling `validate`. For examples, see `NameValidator`.

        """
        # Initial checks ..............
        if (names is None):
            if (nbfields is None):
                return None
            names = []
        if isinstance(names, basestring):
            names = [names, ]
        if nbfields is not None:
            nbnames = len(names)
            if (nbnames < nbfields):
                names = list(names) + [''] * (nbfields - nbnames)
            elif (nbnames > nbfields):
                names = names[:nbfields]
        # Set some shortcuts ...........
        deletechars = self.deletechars
        excludelist = self.excludelist
        case_converter = self.case_converter
        # Initializes some variables ...
        validatednames = []
        seen = dict()
        nbempty = 0
        #
        for item in names:
            item = case_converter(item)
            item = item.strip().replace(' ', '_')
            item = ''.join([c for c in item if c not in deletechars])
            if item == '':
                item = defaultfmt % nbempty
                while item in names:
                    nbempty += 1
                    item = defaultfmt % nbempty
                nbempty += 1
            elif item in excludelist:
                item += '_'
            cnt = seen.get(item, 0)
            if cnt > 0:
                validatednames.append(item + '_%d' % cnt)
            else:
                validatednames.append(item)
            seen[item] = cnt + 1
        return tuple(validatednames)
    #
    def __call__(self, names, defaultfmt="f%i", nbfields=None):
        return self.validate(names, defaultfmt=defaultfmt, nbfields=nbfields)



def str2bool(value):
    """
    Tries to transform a string supposed to represent a boolean to a boolean.

    Parameters
    ----------
    value : str
        The string that is transformed to a boolean.

    Returns
    -------
    boolval : bool
        The boolean representation of `value`.

    Raises
    ------
    ValueError
        If the string is not 'True' or 'False' (case independent)

    Examples
    --------
    >>> np.lib._iotools.str2bool('TRUE')
    True
    >>> np.lib._iotools.str2bool('false')
    False

    """
    value = value.upper()
    if value == 'TRUE':
        return True
    elif value == 'FALSE':
        return False
    else:
        raise ValueError("Invalid boolean")


class ConverterError(Exception):
    """
    Exception raised when an error occurs in a converter for string values.

    """
    pass

class ConverterLockError(ConverterError):
    """
    Exception raised when an attempt is made to upgrade a locked converter.

    """
    pass

class ConversionWarning(UserWarning):
    """
    Warning issued when a string converter has a problem.

    Notes
    -----
    In `genfromtxt` a `ConversionWarning` is issued if raising exceptions
    is explicitly suppressed with the "invalid_raise" keyword.

    """
    pass



class StringConverter:
    """
    Factory class for function transforming a string into another object (int,
    float).

    After initialization, an instance can be called to transform a string
    into another object. If the string is recognized as representing a missing
    value, a default value is returned.

    Attributes
    ----------
    func : function
        Function used for the conversion.
    default : any
        Default value to return when the input corresponds to a missing value.
    type : type
        Type of the output.
    _status : int
        Integer representing the order of the conversion.
    _mapper : sequence of tuples
        Sequence of tuples (dtype, function, default value) to evaluate in
        order.
    _locked : bool
        Holds `locked` parameter.

    Parameters
    ----------
    dtype_or_func : {None, dtype, function}, optional
        If a `dtype`, specifies the input data type, used to define a basic
        function and a default value for missing data. For example, when
        `dtype` is float, the `func` attribute is set to `float` and the
        default value to `np.nan`.
        If a function, this function is used to convert a string to another
        object. In this case, it is recommended to give an associated default
        value as input.
    default : any, optional
        Value to return by default, that is, when the string to be converted
        is flagged as missing. If not given, `StringConverter` tries to supply
        a reasonable default value.
    missing_values : sequence of str, optional
        Sequence of strings indicating a missing value.
    locked : bool, optional
        Whether the StringConverter should be locked to prevent automatic
        upgrade or not. Default is False.

    """
    #
    _mapper = [(nx.bool_, str2bool, False),
               (nx.integer, int, -1),
               (nx.floating, float, nx.nan),
               (complex, complex, nx.nan + 0j),
               (nx.string_, str, '???')]
    (_defaulttype, _defaultfunc, _defaultfill) = zip(*_mapper)
    #
    @classmethod
    def _getsubdtype(cls, val):
        """Returns the type of the dtype of the input variable."""
        return np.array(val).dtype.type
    #
    @classmethod
    def upgrade_mapper(cls, func, default=None):
        """
    Upgrade the mapper of a StringConverter by adding a new function and its
    corresponding default.
    
    The input function (or sequence of functions) and its associated default 
    value (if any) is inserted in penultimate position of the mapper.
    The corresponding type is estimated from the dtype of the default value.
    
    Parameters
    ----------
    func : var
        Function, or sequence of functions

    Examples
    --------
    >>> import dateutil.parser
    >>> import datetime
    >>> dateparser = datetustil.parser.parse
    >>> defaultdate = datetime.date(2000, 1, 1)
    >>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)
        """
        # Func is a single functions
        if hasattr(func, '__call__'):
            cls._mapper.insert(-1, (cls._getsubdtype(default), func, default))
            return
        elif hasattr(func, '__iter__'):
            if isinstance(func[0], (tuple, list)):
                for _ in func:
                    cls._mapper.insert(-1, _)
                return
            if default is None:
                default = [None] * len(func)
            else:
                default = list(default)
                default.append([None] * (len(func) - len(default)))
            for (fct, dft) in zip(func, default):
                cls._mapper.insert(-1, (cls._getsubdtype(dft), fct, dft))
    #
    def __init__(self, dtype_or_func=None, default=None, missing_values=None,
                 locked=False):
        # Defines a lock for upgrade
        self._locked = bool(locked)
        # No input dtype: minimal initialization
        if dtype_or_func is None:
            self.func = str2bool
            self._status = 0
            self.default = default or False
            ttype = np.bool
        else:
            # Is the input a np.dtype ?
            try:
                self.func = None
                ttype = np.dtype(dtype_or_func).type
            except TypeError:
                # dtype_or_func must be a function, then
                if not hasattr(dtype_or_func, '__call__'):
                    errmsg = "The input argument `dtype` is neither a function"\
                             " or a dtype (got '%s' instead)"
                    raise TypeError(errmsg % type(dtype_or_func))
                # Set the function
                self.func = dtype_or_func
                # If we don't have a default, try to guess it or set it to None
                if default is None:
                    try:
                        default = self.func('0')
                    except ValueError:
                        default = None
                ttype = self._getsubdtype(default)
            # Set the status according to the dtype
            _status = -1
            for (i, (deftype, func, default_def)) in enumerate(self._mapper):
                if np.issubdtype(ttype, deftype):
                    _status = i
                    if default is None:
                        self.default = default_def
                    else:
                        self.default = default
                    break
            if _status == -1:
                # We never found a match in the _mapper...
                _status = 0
                self.default = default
            self._status = _status
            # If the input was a dtype, set the function to the last we saw
            if self.func is None:
                self.func = func
            # If the status is 1 (int), change the function to smthg more robust
            if self.func == self._mapper[1][1]:
                self.func = lambda x : int(float(x))
        # Store the list of strings corresponding to missing values.
        if missing_values is None:
            self.missing_values = set([''])
        else:
            if isinstance(missing_values, basestring):
                missing_values = missing_values.split(",")
            self.missing_values = set(list(missing_values) + [''])
        #
        self._callingfunction = self._strict_call
        self.type = ttype
        self._checked = False
        self._initial_default = default
    #
    def _loose_call(self, value):
        try:
            return self.func(value)
        except ValueError:
            return self.default
    #
    def _strict_call(self, value):
        try:
            return self.func(value)
        except ValueError:
            if value.strip() in self.missing_values:
                if not self._status:
                    self._checked = False
                return self.default
            raise ValueError("Cannot convert string '%s'" % value)
    #
    def __call__(self, value):
        return self._callingfunction(value)
    #
    def upgrade(self, value):
        """
        Try to find the best converter for a given string, and return the result.

        The supplied string `value` is converted by testing different
        converters in order. First the `func` method of the `StringConverter`
        instance is tried, if this fails other available converters are tried.
        The order in which these other converters are tried is determined by the
        `_status` attribute of the instance.

        Parameters
        ----------
        value : str
            The string to convert.

        Returns
        -------
        out : any
            The result of converting `value` with the appropriate converter.

        """
        self._checked = True
        try:
            self._strict_call(value)
        except ValueError:
            # Raise an exception if we locked the converter...
            if self._locked:
                errmsg = "Converter is locked and cannot be upgraded"
                raise ConverterLockError(errmsg)
            _statusmax = len(self._mapper)
            # Complains if we try to upgrade by the maximum
            _status = self._status
            if _status == _statusmax:
                errmsg = "Could not find a valid conversion function"
                raise ConverterError(errmsg)
            elif _status < _statusmax - 1:
                _status += 1
            (self.type, self.func, default) = self._mapper[_status]
            self._status = _status
            if self._initial_default is not None:
                self.default = self._initial_default
            else:
                self.default = default
            self.upgrade(value)

    def iterupgrade(self, value):
        self._checked = True
        if not hasattr(value, '__iter__'):
            value = (value,)
        _strict_call = self._strict_call
        try:
            map(_strict_call, value)
        except ValueError:
            # Raise an exception if we locked the converter...
            if self._locked:
                errmsg = "Converter is locked and cannot be upgraded"
                raise ConverterLockError(errmsg)
            _statusmax = len(self._mapper)
            # Complains if we try to upgrade by the maximum
            _status = self._status
            if _status == _statusmax:
                raise ConverterError("Could not find a valid conversion function")
            elif _status < _statusmax - 1:
                _status += 1
            (self.type, self.func, default) = self._mapper[_status]
            if self._initial_default is not None:
                self.default = self._initial_default
            else:
                self.default = default
            self._status = _status
            self.iterupgrade(value)

    def update(self, func, default=None, missing_values='', locked=False):
        """
        Set StringConverter attributes directly.

        Parameters
        ----------
        func : function
            Conversion function.
        default : any, optional
            Value to return by default, that is, when the string to be converted
            is flagged as missing. If not given, `StringConverter` tries to supply
            a reasonable default value.
        missing_values : sequence of str, optional
            Sequence of strings indicating a missing value.
        locked : bool, optional
            Whether the StringConverter should be locked to prevent automatic
            upgrade or not. Default is False.

        Notes
        -----
        `update` takes the same parameters as the constructor of `StringConverter`,
        except that `func` does not accept a `dtype` whereas `dtype_or_func` in
        the constructor does.

        """
        self.func = func
        self._locked = locked
        # Don't reset the default to None if we can avoid it
        if default is not None:
            self.default = default
            self.type = self._getsubdtype(default)
        else:
            try:
                tester = func('1')
            except (TypeError, ValueError):
                tester = None
            self.type = self._getsubdtype(tester)
        # Add the missing values to the existing set
        if missing_values is not None:
            if _is_string_like(missing_values):
                self.missing_values.add(missing_values)
            elif hasattr(missing_values, '__iter__'):
                for val in missing_values:
                    self.missing_values.add(val)
        else:
            self.missing_values = []



def easy_dtype(ndtype, names=None, defaultfmt="f%i", **validationargs):
    """
    Convenience function to create a `np.dtype` object.

    The function processes the input `dtype` and matches it with the given
    names.

    Parameters
    ----------
    ndtype : var
        Definition of the dtype. Can be any string or dictionary
        recognized by the `np.dtype` function, or a sequence of types.
    names : str or sequence, optional
        Sequence of strings to use as field names for a structured dtype.
        For convenience, `names` can be a string of a comma-separated list of
        names.
    defaultfmt : str, optional
        Format string used to define missing names, such as ``"f%i"``
        (default) or ``"fields_%02i"``.
    validationargs : optional
        A series of optional arguments used to initialize a `NameValidator`.

    Examples
    --------
    >>> np.lib._iotools.easy_dtype(float)
    dtype('float64')
    >>> np.lib._iotools.easy_dtype("i4, f8")
    dtype([('f0', '<i4'), ('f1', '<f8')])
    >>> np.lib._iotools.easy_dtype("i4, f8", defaultfmt="field_%03i")
    dtype([('field_000', '<i4'), ('field_001', '<f8')])

    >>> np.lib._iotools.easy_dtype((int, float, float), names="a,b,c")
    dtype([('a', '<i8'), ('b', '<f8'), ('c', '<f8')])
    >>> np.lib._iotools.easy_dtype(float, names="a,b,c")
    dtype([('a', '<f8'), ('b', '<f8'), ('c', '<f8')])

    """
    try:
        ndtype = np.dtype(ndtype)
    except TypeError:
        validate = NameValidator(**validationargs)
        nbfields = len(ndtype)
        if names is None:
            names = [''] * len(ndtype)
        elif isinstance(names, basestring):
            names = names.split(",")
        names = validate(names, nbfields=nbfields, defaultfmt=defaultfmt)
        ndtype = np.dtype(dict(formats=ndtype, names=names))
    else:
        nbtypes = len(ndtype)
        # Explicit names
        if names is not None:
            validate = NameValidator(**validationargs)
            if isinstance(names, basestring):
                names = names.split(",")
            # Simple dtype: repeat to match the nb of names
            if nbtypes == 0:
                formats = tuple([ndtype.type] * len(names))
                names = validate(names, defaultfmt=defaultfmt)
                ndtype = np.dtype(zip(names, formats))
            # Structured dtype: just validate the names as needed
            else:
                ndtype.names = validate(names, nbfields=nbtypes,
                                        defaultfmt=defaultfmt)
        # No implicit names
        elif (nbtypes > 0):
            validate = NameValidator(**validationargs)
            # Default initial names : should we change the format ?
            if (ndtype.names == tuple("f%i" % i for i in range(nbtypes))) and \
               (defaultfmt != "f%i"):
                ndtype.names = validate([''] * nbtypes, defaultfmt=defaultfmt)
            # Explicit initial names : just validate
            else:
                ndtype.names = validate(ndtype.names, defaultfmt=defaultfmt)
    return ndtype

