# -*- coding: utf-8 -*-
from ..utils.common import mstr, bstr, lls
from .ndprint import ndprint
from .listener import ColumnListener, MetaDataListener
from .serializable import Serializable
from .odict import ODict
from .attributable import Attributable
from .abstractcomposite import AbstractComposite
from .datawrapper import DataWrapper, DataContainer
from .eq import DeepEqual
from .copyable import Copyable
from .annotatable import Annotatable
from .indexed import Indexed
from ..utils.common import exprstrs
from .typed import Typed

from collections import OrderedDict
import logging
import sys
import itertools

if sys.version_info[0] + 0.1 * sys.version_info[1] >= 3.3:
    PY33 = True
    from collections.abc import Container, Sequence, Mapping
    seqlist = Sequence
    maplist = Mapping
else:
    PY33 = False
    from .collectionsMockUp import ContainerMockUp as Container
    from .collectionsMockUp import SequenceMockUp as Sequence
    from .collectionsMockUp import MappingMockUp as Mapping
    seqlist = (tuple, list, Sequence, str)
    # ,types.XRangeType, types.BufferType)
    maplist = (dict, Mapping)

# create logger
logger = logging.getLogger(__name__)
# logger.debug('level %d' %  (logger.getEffectiveLevel()))

# from .composite import


class Dataset(Attributable, Annotatable, Copyable, Serializable, DeepEqual, MetaDataListener):
    """ Attributable and annotatable information data container
    that can be be part of a Product.

    developer notes:
    The intent is that developers do not derive from this interface

    directly. Instead, they should inherit from one of the generic
    datasets that this package provides:

    mh: GenericDataset,
    ArrayDataset.
    TableDataset or
    CompositeDataset.
    """

    def __init__(self, **kwds):
        """

        """
        super(Dataset, self).__init__(**kwds)

    def accept(self, visitor):
        """ Hook for adding functionality to object
        through visitor pattern."""
        visitor.visit(self)


class GenericDataset(Dataset, DataContainer, Container):
    """ mh: Contains one data item.
    """

    def __init__(self, **kwds):
        """
        """
        super(GenericDataset, self).__init__(
            **kwds)  # initialize data, meta, unit

    def __iter__(self):
        for x in self.getData():
            yield x

    def __contains__(self, x):
        """
        """
        return x in self.getData()

    def __len__(self, *args, **kwargs):
        """ size of data
        """
        return self.getData().__len__(*args, **kwargs)

    def __repr__(self, **kwds):
        return self.toString(level=1, **kwds)

    def toString(self, level=0, matprint=None, trans=True, **kwds):
        """ matprint: an external matrix print function
        trans: print 2D matrix transposed. default is True.
        """
        cn = self.__class__.__name__
        if level > 1:
            return cn + \
                '{ %s, description = "%s", meta = %s }' % \
                (str(self.data), str(self.description), self.meta.toString(
                    level=level, matprint=matprint, trans=trans, **kwds))

        s = '# ' + cn + '\n' +\
            mstr(self.serializable(), level=level, **kwds)
        d = cn + '-dataset =\n'
        d += bstr(self.data, level=level, **kwds) if matprint is None else \
            matprint(self.data, level=level, trans=False, headers=[], tablefmt2='plain',
                     **kwds)
        return s + '\n' + d + '\n'

    def serializable(self):
        """ Can be encoded with serializableEncoder """
        # s = OrderedDict(description=self.description, meta=self.meta)  # super(...).serializable()
        # s.update(OrderedDict(data=self.getData()))
        s = OrderedDict(description=self.description,
                        meta=self.meta,
                        data=self.data,
                        _STID=self._STID)
        return s


class ArrayDataset(DataWrapper, GenericDataset, Sequence, Typed):
    """  Special dataset that contains a single Array Data object.
    mh: If omit the parameter names during instanciation, e.g. ArrayDataset(a, b, c), the assumed order is data, unit, description.
    mh:  contains a sequence which provides methods count(), index(), remove(), reverse().
    A mutable sequence would also need append(), extend(), insert(), pop() and sort().
    """

    def __init__(self, data=None, unit=None, description='UNKNOWN', typ_=None, default=None, **kwds):
        """ Initializes an ArrayDataset.

        """
        self.setDefault(default)
        super(ArrayDataset, self).__init__(data=data, unit=unit,
                                           description=description, typ_=typ_, **kwds)  # initialize data, meta

    # def getData(self):
    #     """ Optimized """
    #     return self._data

    def setData(self, data):
        """
        """
        isitr = hasattr(data, '__iter__')  # and hasattr(data, '__next__')
        if not isitr and data is not None:
            # dataWrapper initializes data as None
            m = 'data in ArrayDataset must be a subclass of Sequence: ' + \
                data.__class__.__name__
            raise TypeError(m)
        d = None if data is None else \
            data if hasattr(data, '__getitem__') else list(data)
        super(ArrayDataset, self).setData(d)

    @property
    def default(self):
        return self.getDefault()

    @default.setter
    def default(self, default):
        self.setDefault(default)

    def getDefault(self):
        """ Returns the default related to this object."""
        return self._default

    def setDefault(self, default):
        """ Sets the default of this object.

        """
        self._default = default

    def __setitem__(self, *args, **kwargs):
        """ sets value at key.
        """
        self.getData().__setitem__(*args, **kwargs)

    def __getitem__(self, *args, **kwargs):
        """ returns value at key.
        """
        return self.getData().__getitem__(*args, **kwargs)

    def __delitem__(self, *args, **kwargs):
        """ removes value and its key.
        """
        self.getData().__delitem__(*args, **kwargs)

    def __iter__(self, *args, **kwargs):
        """ returns an iterator
        """
        return self.getData().__iter__(*args, **kwargs)

    def pop(self, *args, **kwargs):
        """ revomes and returns value
        """
        return self.getData().pop(*args, **kwargs)

    def append(self, *args, **kwargs):
        """ appends to data.
        """
        return self.getData().append(*args, **kwargs)

    def index(self, *args, **kwargs):
        """ returns the index of a value.
        """
        return self.getData().index(*args, **kwargs)

    def count(self, *args, **kwargs):
        """ returns size.
        """
        return self.getData().count(*args, **kwargs)

    def remove(self, *args, **kwargs):
        """ removes value at first occurrence.
        """
        self.getData().remove(*args, **kwargs)

    def __repr__(self, **kwds):
        return self.toString(level=1, **kwds)

    def toString(self, level=0, matprint=None, trans=True, **kwds):
        """ matprint: an external matrix print function
        trans: print 2D matrix transposed. default is True.
        """
        if matprint is None:
            matprint = ndprint

        cn = self.__class__.__name__
        if level > 1:

            vs, us, ts, ds, fs, gs, cs = exprstrs(self, '_data')
            return cn +\
                '{ %s (%s) <%s>, "%s", default %s, tcode=%s, meta=%s}' %\
                (vs, us, ts, ds, fs, cs, str(self.meta))

        s = '# ' + cn + '\n' +\
            mstr(self.serializable(), level=level, **kwds)
        d = cn + '-dataset =\n'
        ds = bstr(self.data, level=level, **kwds) if matprint is None else \
            matprint(self.data, trans=False, headers=[], tablefmt2='plain',
                     **kwds)
        d += lls(ds, 2000)
        return s + '\n' + d + '\n'

    def serializable(self):
        """ Can be encoded with serializableEncoder """
        # s = OrderedDict(description=self.description, meta=self.meta, data=self.data)  # super(...).serializable()
        s = OrderedDict(description=self.description,
                        meta=self.meta,
                        data=list(self.data),
                        type=self._type,
                        default=self._default,
                        typecode=self._typecode,
                        _STID=self._STID)
        s.update(OrderedDict(unit=self.unit))
        return s


class Column(ArrayDataset, ColumnListener):
    """ A Column is a the vertical cut of a table for which all cells have the same signature. It contains raw ArrayData, and optionally a description and unit.
    example::

      table = TableDataset()
      table.addColumn("Energy",Column(data=[1,2,3,4],description="desc",unit='eV'))
    """
    pass


class TableModel(object):
    """ to interrogate a tabular data model
    """

    def __init__(self, **kwds):
        """

        """
        super(TableModel, self).__init__(**kwds)

    def getColumnClass(self, columnIndex):
        """ Returns the class for the first cell
        values in the column.
        """
        return self.getColumn(columnIndex)[0].__class__

    def getColumnCount(self):
        """ Returns the number of columns in the model. """
        return len(self.getData())

    def getColumnName(self, columnIndex):
        """ Returns the name of the column at columnIndex.

        returns a set of columns if key  is a slice.
        """

        return self.getColumnNames()[columnIndex]

    def getColumnNames(self):
        """ Returns the column names. """
        return list(self.getData().keys())

    def getRowCount(self):
        """ Returns the number of rows in the model. """
        return len(self.getColumn(0))

    def getValueAt(self, rowIndex, columnIndex):
        """ Returns the value for the cell at columnIndex and rowIndex. """
        return self.getColumn(columnIndex).data[rowIndex]

    def isCellEditable(self, rowIndex, columnIndex):
        """ Returns true if the cell at rowIndex and columnIndex
        is editable. """
        return True

    def setValueAt(self, value, rowIndex, columnIndex):
        """Sets the value in the cell at columnIndex and rowIndex
        to Value.
        """
        self.getColumn(columnIndex).data[rowIndex] = value


class TableDataset(GenericDataset, TableModel):
    """  Special dataset that contains a single Array Data object.
    A TableDataset is a tabular collection of Columns. It is optimized to work on array data..
    The column-wise approach is convenient in many cases. For example, one has an event list, and each algorithm is adding a new field to the events (i.e. a new column, for example a quality mask).

    Although mechanisms are provided to grow the table row-wise, one should use these with care especially in performance driven environments as this orthogonal approach (adding rows rather than adding columns) is expensive.

    General Note:

    For reasons of flexibility, memory consumption and performance, this class is not checking whether all columns are of the same length: this is the responsibility of the user/developer. See also the library documentation for more information about this.

    Note on column names:

    If a column is added without specifying a name, the name ColumnX is created, where X denotes the index of that column.
    Column name duplicity is not allowed.

    Developers:

    See "Writing special datasets or products" at the developer's documentation also.


    Please see also this selection example.
    """

    def __init__(self, **kwds):
        """
        """

        self._list = []
        super(TableDataset, self).__init__(
            **kwds)  # initialize data, meta, unit

    def getData(self):
        """ Optimized for _data being an ``ODict`` implemented with ``UserDict``.

        """

        try:
            return self._data.data
        except AttributeError:
            d = super().getData()
            if hasattr(d, 'data'):
                return self._data.data
            else:
                return d

    def setData(self, data):
        """ sets name-column pairs from data.

        Valid formd include: {str:Column, ...} or [(str, [num, ...], str)]
        or [(str, Column), ...] or [[num ...],  [num ...], ...]

        [{'name':str,'column':Column}] form is deprecated.

        Existing data will be discarded except when the provided data is a list of lists, where existing column names and units will remain but data replaced, and extra data items will form new columns named 'column'+index (index counting from 1) with unit None.
        """
        # logging.debug(data.__class__)

        if data is None:
            super(TableDataset, self).setData(ODict())
            return

        current_data = self.getData()
        # list of keys of current data
        curdk = list(current_data.keys()) if current_data else []
        super(TableDataset, self).setData(ODict())
        if issubclass(data.__class__, seqlist):
            for ind, x in enumerate(data):
                if issubclass(x.__class__, maplist) \
                   and 'name' in x and 'column' in x:
                    raise DeprecationWarning(
                        'Do not use [{"name":name, "column":column}...]. Use {name:column, ...} instead.')
                if issubclass(x.__class__, (list, tuple)):
                    # check out string-started columns
                    if len(x) > 1 and issubclass(x[0].__class__, str) and not issubclass(x[1].__class__, str):
                        if issubclass(x[1].__class__, (list, tuple)):
                            u = x[2] if len(x) > 2 else ''
                            self.setColumn(x[0], Column(data=x[1], unit=x[2]))
                        elif issubclass(x[1].__class__, Column):
                            self.setColumn(x[0], x[1])
                        else:
                            raise '[[str, [], str]...], [[str, []]...], [[str, Column]...] needed.'
                    else:
                        if current_data is None or len(current_data) <= ind:
                            # update the data of the ind-th column
                            self.setColumn('', Column(data=x, unit=None))
                        else:
                            colname = curdk[ind]
                            current_data[colname].data = x
                            self.setColumn(colname, current_data[colname])
                else:
                    raise ValueError(
                        'Cannot extract name and column from list member ' + str(x))
        elif issubclass(data.__class__, maplist):
            for k, v in data.items():
                self.setColumn(k, v)
        else:
            raise TypeError('must be a Sequence or a Mapping. ' +
                            data.__class__.__name__ + ' found.')

    def addColumn(self, name, column, col_des=False):
        """ Adds the specified column to this table, and attaches a name
        to it. If the name is null, a dummy name "column"+column_count+1 is created, such that it can be accessed by getColumn(str).

        If column name exists the corresponding column is substituted.

        Parameters:
        name - column name.
        column - column to be added.
        col_des - if True (default) and column descripion is 'UNKNOWN', set to column name.
        """

        d = self.getData()
        if d is None:
            d = ODict()

        if name == '' or name is None:
            idx = self.getColumnCount()
            name = 'column' + str(idx+1)
            self._list.append(column.getData())
        else:
            try:
                self._list[self.indexOf(name)] = column.getData()
            except ValueError as e:
                self._list.append(column.getData())
        if col_des and column.getDescription() == 'UNKNOWN':
            column.setDescription(name)
        d[name] = column

    def removeColumn(self, key):
        """ Removes the columns specified by ``key``.

        ref. ``getColumnMap`` on ``key`` usage.
        """

        for name in self.getColumnMap(key).keys():
            self._list.pop(self.indexOf(name))
            del(self.data[name])

    def indexOf(self, key):
        """ Returns the index of specified column.

        if the key is a Column,
        it looks for equal references (same column objects), not for
        equal values.
        If the key is a string, Returns the index of specified Column name.
        """
        if issubclass(key.__class__, str):
            ks = list(self.getData().keys())
            k = key
        elif issubclass(key.__class__, Column):
            ks = list(id(v) for v in self.getData().values())
            k = id(key)
        else:
            raise "key must be string or Column, not %s." % type(key).__name__

        return ks.index(k)

    def addRow(self, row, rows=False):
        """ Adds the specified map as a new row to this table.

        row: mh: row is a dict with names as keys and row data as value.
        rows: append each element in row if the row data is a list.
        """

        d = self.getData()
        if len(row) < len(d):
            msg = 'row width d% should be %d.' % (len(row), len(d))
            raise ValueError(msg)

        for c in d.keys():
            if rows:
                d[c].data.extend(row[c])
            else:
                d[c].data.append(row[c])

    def getRowMap(self, rowIndex):
        """ Returns a dict of column-names as the keys and the objects located at a particular row(s) as the values.

        rowIndex: return the following as the value for each key-value pair:
* int: the int-th row's elements;
* ``Slice`` object, a list of rows from slicing the column. Example ``a.getRow(Slice(3,,))``;
* list of integers: they are used as the row index to select the rows.
* list of booleans: rows where the corresponding boolean is True are chosen.
        """
        cl = rowIndex.__class__
        d = self.getData()
        if issubclass(cl, (int, slice)):
            return {n: c.getData()[rowIndex] for n, c in d.items()}
        if issubclass(cl, list):
            if type(rowIndex[0]) == int:
                return {n: [c.getData()[i] for i in rowIndex] for n, c in d.items()}
            if type(rowIndex[0]) == bool:
                # if len(rowIndex) != len(n):
                # logger.info('%s Selection length %d should be %d.' %
                #        (name, len(rowIndex), len(n)))
                return {n: [x for x, s in zip(c.getData(), rowIndex) if s] for n, c in d.items()}
        else:
            raise ValueError(
                'RowIndex must be an int, a slice, or a list of ints or bools.')

    def getRow(self, rowIndex):
        """ Returns a list containing the objects located in a particular row, or a list of rows.

        rowIndex: ref ``getRowMap()``
* int: return the int-th row in a list of elements;
* ``Slice`` object, list of integers, list of booleans: return a list of rows each represented by a tuple. Example ``a.getRow(Slice(3,,))``, ``[2,4]``, ``[True, False...]``.
        """

        it = self.getRowMap(rowIndex).values()
        if issubclass(rowIndex.__class__, int):
            # return a list of row elements
            return list(it)
        # return transposed in a list
        return list(zip(*it))

    def select(self, selection):
        """ Select a number of rows from this table dataset and
        return a new TableDataset object containing only the selected rows.

        selection:  to form a new Tabledataset with ref ``getRowMap()``
        """

        d = ODict()
        if issubclass(selection.__class__, int):
            for name, data in self.getRowMap(selection).items():
                d[name] = Column(
                    data=[data], unit=self.getColumn(name).getUnit())
                return TableDataset(data=d)

        for name, data in self.getRowMap(selection).items():
            d[name] = Column(data=data, unit=self.getColumn(name).getUnit())
        return TableDataset(data=d)

    def removeRow(self, rowIndex):
        """ Removes a row with specified index from this table.

        rowIndex: int or a ``Slice`` object. Example ``a.removeRow(Slice(3,,))``.
        return: removed row data.
        """
        if issubclass(rowIndex.__class__, slice):
            ret = []
            for x in self.getData().values():
                ret.append(x.data[rowIndex])
                del x.data[rowIndex]
            return ret

        return [x.data.pop(rowIndex) for x in self.getData().values()]

    @ property
    def rowCount(self):
        return self.getRowCount()

    @ rowCount.setter
    def rowCount(self, newRowCount):
        self.setRowCount(newRowCount)

    def setRowCount(self, rowCount):
        """ cannot do this.
        """
        raise ValueError('Cannot set row count.')

    @ property
    def columnCount(self):
        return self.getColumnCount()

    @ columnCount.setter
    def columnCount(self, newColumnCount):
        self.setColumnCount(newColumnCount)

    def setColumnCount(self, columnCount):
        """ cannot do this.
        """
        raise ValueError('Cannot set column count.')

    @ property
    def list(self):
        return self._list

    @ list.setter
    def list(self, l):
        raise NotImplemented

    def getColumnMap(self, key=None):
        """ Returns a dict of column-names as the keys and the column(s) as the values.

        key: return the following as the value for each key-value pair:
* int: name-value where value is the int-th column.
* ``Slice`` object, a list of name-columns from slicing the column index. Example ``a.getColumn(Slice(3,,))``;
* Sequence of integers/strings: they are used as the column index/name to select the columns.
* Sequence of booleans: columns where the corresponding boolean is True are chosen.
Default is to return all columns.
        """
        d = self.getData()
        if key is None:
            return d
        cl = key.__class__
        if issubclass(cl, int):
            t = list(d.items())[key]
            return {t[0]: t[1]}       # {str:Column}
        if issubclass(cl, slice):
            return dict(list(d.items())[key])  # {str:Column, ...}
        if issubclass(cl, str):
            return {key: d[key]}      # {str:Column}

        if issubclass(cl, Sequence):
            if type(key[0]) == int:
                # {str:Column, ...}
                return dict(list(d.items())[i] for i in key)
            if type(key[0]) == str:
                return {n: d[n] for n in key}  # {str:Column, ...}
            if type(key[0]) == bool:
                # {str:Column, ...}
                return dict(x for x, s in zip(d.items(), key) if s)
        else:
            raise ValueError(
                '``key`` must be an int, a string, a slice, or a list of ints, strings, or bools.')

    def getColumn(self, key):
        """ Returns the particular column, or a list of columns.

        key: ref ``getColumnMap()``
* int/str: return the int-th/named column;
* ``Slice`` object, list of columns of sliced column indices;
* list of integers/strings: return a list of columns corresponding to the given column index/name, or where key is True. Example ``a.getColumn(Slice(3,,))``, ``[2, 4]``, ``['time', ``energy']``.
* list of booleans: return a list of columns  where key is True. Example ``[True, False...]``.
        """

        it = self.getColumnMap(key).values()
        if issubclass(key.__class__, (int, str)):
            # return a list of row elements
            return list(it)[0]
        # return transposed in a list
        return list(it)

    def setColumn(self, key, value):
        """ Replaces a column in this table with specified name to specified column if key is a string and exists, or if the key is an integer in 0 to the number of columns, insert at column-index=key, with the name 'column'+key, else add a new coolumn.
        """

        if self.getData() is None:
            key = ''
        elif type(key) == int:
            nms = self.getColumnNames()
            if 0 <= key and key < len(nms):
                key = nms[key]
            else:
                key = ''
        self.addColumn(name=key, column=value)

    def items(self):
        """ for k,v in tabledataset.items()
        """
        return self.getData().items()

    def __getitem__(self, key):
        """ return colmn if given key.

        ref. ``getColumn()``.
        """
        return self.getColumn(key)

    def __setitem__(self, key, value):
        """
        """
        self.setColumn(key, value)

    def __repr__(self, **kwds):
        return self.toString(level=1, **kwds)

    def toString(self, level=0, matprint=None, trans=True, tablefmt2='simple', **kwds):
        """
        tablefmt2: format of 2D data
        """
        if matprint is None:
            matprint = ndprint

        cn = self.__class__.__name__
        s = '# ' + cn + '\n'
        s += mstr(self.serializable(), level=level, **kwds)
        stp = 2 if level > 1 else 20 if level == 1 else None
        cols = self.getData().values()

        coldata = [list(itertools.islice(x.data, stp)) for x in cols]
        d = cn + '-dataset =\n'
        nmun = zip((str(x) for x in self.getData().keys()),
                   (str(x.unit) for x in cols))
        hdr = list('%s\n(%s)' % nu for nu in nmun)
        d += matprint(coldata, trans=trans, headers=hdr,
                      tablefmt2=tablefmt2, **kwds)
        collen = self.getRowCount()
        if level and stp < collen:
            d += '(Only display %d rows of %d for level=%d.)' % (stp, collen, level)
        return s + '\n' + d + '\n'

    def serializable(self):
        """ Can be encoded with serializableEncoder """
        return OrderedDict(description=self.description,
                           meta=self.meta,
                           data=self.getData(),
                           _STID=self._STID)


class IndexedTableDataset(Indexed, TableDataset):
    """ TableDataset with an index table for efficient row look-up.

    """

    def __init__(self, **kwds):
        """
        """
        self._indexCols = [0]
        self._rowIndexTable = {}
        super().__init__(**kwds)  # initialize data, meta, unit

    def getColumnsToLookup(self):
        """ returns an iterator that gives a number of sequences to looking up over.
        """

        # list of Column's arrays
        return [x.data for x in self.getColumn(self._indexPattern)]

    def setData(self, data):
        """  sets name-column pairs from data and updates index if needed

        """

        d = self.getData()
        if d:
            reindex = False
            lcd = len(d)
            if issubclass(data.__class__, seqlist):
                for ind, x in enumerate(data):
                    if lcd > ind:
                        if reindex == False and ind in self._indexPattern:
                            reindex = True
        else:
            reindex = True
        super().setData(data)
        if reindex:
            self.updateToc()

    def vLookUp(self, key, return_index=True, multiple=False):
        """ Similar to Excel VLOOKUP, return all records (rows) that match the key.
        key: taken as a dictionary key unless ``multiple`` is True.
        return_index: if True (default) return index in the array of columns.
        multiple: if True (default is False) loop through key as a sequence of keys and return a sequece.
        """

        if multiple:
            if return_index:
                toc = self._tableOfContent
                return [toc[k] for k in key]
            else:
                toc = self._tableOfContent
                # return [[c[toc[k]] for c in self._list] for k in key]
                return list(zip(*((c[toc[k]] for k in key) for c in self._list)))
        else:
            if return_index:
                return self._tableOfContent[key]
            else:
                rec_ind = self._tableOfContent[key]
                return [c[rec_ind] for c in self._list]

    def serializable(self):
        """ Can be encoded with serializableEncoder """
        return Indexed.serializable(self).update(description=self.description,
                                                 meta=self.meta,
                                                 data=self.getData(),
                                                 _STID=self._STID)


class CompositeDataset(AbstractComposite, Dataset):
    """  An CompositeDataset is a Dataset that contains zero or more
    named Datasets. It allows to build arbitrary complex dataset
    structures.

    It also defines the iteration ordering of its children, which is
    the order in which the children were inserted into this dataset.
    """

    def __init__(self, **kwds):
        """
        """
        super(CompositeDataset, self).__init__(
            **kwds)  # initialize _sets, meta, unit

    def serializable(self):
        """ Can be encoded with serializableEncoder """
        return OrderedDict(description=self.description,
                           meta=self.meta,
                           _sets=self._sets,
                           _STID=self._STID)
