"""
SQL-style merge routines
"""

import numpy as np

from pandas.core.categorical import Factor
from pandas.core.frame import DataFrame, _merge_doc
from pandas.core.generic import NDFrame
from pandas.core.groupby import get_group_index
from pandas.core.series import Series
from pandas.core.index import (Index, MultiIndex, _get_combined_index,
                               _ensure_index, _get_consensus_names,
                               _all_indexes_same)
from pandas.core.internals import (IntBlock, BoolBlock, BlockManager,
                                   DatetimeBlock, make_block, _consolidate)
from pandas.util.decorators import cache_readonly, Appender, Substitution

from pandas.sparse.frame import SparseDataFrame
import pandas.core.common as com

import pandas.lib as lib

@Substitution('\nleft : DataFrame')
@Appender(_merge_doc, indents=0)
def merge(left, right, how='inner', on=None, left_on=None, right_on=None,
          left_index=False, right_index=False, sort=True,
          suffixes=('_x', '_y'), copy=True):
    op = _MergeOperation(left, right, how=how, on=on, left_on=left_on,
                         right_on=right_on, left_index=left_index,
                         right_index=right_index, sort=sort, suffixes=suffixes,
                         copy=copy)
    return op.get_result()
if __debug__: merge.__doc__ = _merge_doc % '\nleft : DataFrame'


class MergeError(Exception):
    pass


def ordered_merge(left, right, on=None, left_by=None, right_by=None,
                  left_on=None, right_on=None,
                  fill_method=None, suffixes=('_x', '_y')):
    """Perform merge with optional filling/interpolation designed for ordered
    data like time series data. Optionally perform group-wise merge (see
    examples)

    Parameters
    ----------
    left : DataFrame
    right : DataFrame
    fill_method : {'ffill', None}, default None
        Interpolation method for data
    on : label or list
        Field names to join on. Must be found in both DataFrames.
    left_on : label or list, or array-like
        Field names to join on in left DataFrame. Can be a vector or list of
        vectors of the length of the DataFrame to use a particular vector as
        the join key instead of columns
    right_on : label or list, or array-like
        Field names to join on in right DataFrame or vector/list of vectors per
        left_on docs
    left_by : column name or list of column names
        Group left DataFrame by group columns and merge piece by piece with
        right DataFrame
    right_by : column name or list of column names
        Group right DataFrame by group columns and merge piece by piece with
        left DataFrame
    suffixes : 2-length sequence (tuple, list, ...)
        Suffix to apply to overlapping column names in the left and right
        side, respectively

    Examples
    --------
    >>> A                      >>> B
          key  lvalue group        key  rvalue
    0   a       1     a        0     b       1
    1   c       2     a        1     c       2
    2   e       3     a        2     d       3
    3   a       1     b
    4   c       2     b
    5   e       3     b

    >>> ordered_merge(A, B, fill_method='ffill', left_by='group')
       key  lvalue group  rvalue
    0    a       1     a     NaN
    1    b       1     a       1
    2    c       2     a       2
    3    d       2     a       3
    4    e       3     a       3
    5    f       3     a       4
    6    a       1     b     NaN
    7    b       1     b       1
    8    c       2     b       2
    9    d       2     b       3
    10   e       3     b       3
    11   f       3     b       4

    Returns
    -------
    merged : DataFrame
    """
    def _merger(x, y):
        op = _OrderedMerge(x, y, on=on, left_on=left_on, right_on=right_on,
                           # left_index=left_index, right_index=right_index,
                           suffixes=suffixes, fill_method=fill_method)
        return op.get_result()

    if left_by is not None and right_by is not None:
        raise ValueError('Can only group either left or right frames')
    elif left_by is not None:
        if not isinstance(left_by, (list, tuple)):
            left_by = [left_by]
        pieces = []
        for key, xpiece in left.groupby(left_by):
            merged = _merger(xpiece, right)
            for k in left_by:
                # May have passed ndarray
                try:
                    if k in merged:
                        merged[k] = key
                except:
                    pass
            pieces.append(merged)
        return concat(pieces, ignore_index=True)
    elif right_by is not None:
        if not isinstance(right_by, (list, tuple)):
            right_by = [right_by]
        pieces = []
        for key, ypiece in right.groupby(right_by):
            merged = _merger(left, ypiece)
            for k in right_by:
                try:
                    if k in merged:
                        merged[k] = key
                except:
                    pass
            pieces.append(merged)
        return concat(pieces, ignore_index=True)
    else:
        return _merger(left, right)



# TODO: NA group handling
# TODO: transformations??
# TODO: only copy DataFrames when modification necessary

class _MergeOperation(object):
    """
    Perform a database (SQL) merge operation between two DataFrame objects
    using either columns as keys or their row indexes
    """

    def __init__(self, left, right, how='inner', on=None,
                 left_on=None, right_on=None, axis=1,
                 left_index=False, right_index=False, sort=True,
                 suffixes=('_x', '_y'), copy=True):
        self.left = self.orig_left = left
        self.right = self.orig_right = right
        self.how = how
        self.axis = axis

        self.on = com._maybe_make_list(on)
        self.left_on = com._maybe_make_list(left_on)
        self.right_on = com._maybe_make_list(right_on)

        self.copy = copy
        self.suffixes = suffixes
        self.sort = sort

        self.left_index = left_index
        self.right_index = right_index

        # note this function has side effects
        (self.left_join_keys,
         self.right_join_keys,
         self.join_names) = self._get_merge_keys()

    def get_result(self):
        join_index, left_indexer, right_indexer = self._get_join_info()

        # this is a bit kludgy
        ldata, rdata = self._get_merge_data()

        # TODO: more efficiently handle group keys to avoid extra consolidation!
        join_op = _BlockJoinOperation([ldata, rdata], join_index,
                                      [left_indexer, right_indexer], axis=1,
                                      copy=self.copy)

        result_data = join_op.get_result()
        result = DataFrame(result_data)

        self._maybe_add_join_keys(result, left_indexer, right_indexer)

        return result

    def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
        # insert group keys

        keys = zip(self.join_names, self.left_on, self.right_on)
        for i, (name, lname, rname) in enumerate(keys):
            if not _should_fill(lname, rname):
                continue

            if name in result:
                key_col = result[name]

                if name in self.left and left_indexer is not None:
                    na_indexer = (left_indexer == -1).nonzero()[0]
                    if len(na_indexer) == 0:
                        continue

                    right_na_indexer = right_indexer.take(na_indexer)
                    key_col.put(na_indexer, com.take_1d(self.right_join_keys[i],
                                                        right_na_indexer))
                elif name in self.right and right_indexer is not None:
                    na_indexer = (right_indexer == -1).nonzero()[0]
                    if len(na_indexer) == 0:
                        continue

                    left_na_indexer = left_indexer.take(na_indexer)
                    key_col.put(na_indexer, com.take_1d(self.left_join_keys[i],
                                                        left_na_indexer))
            elif left_indexer is not None:
                if name is None:
                    name = 'key_%d' % i

                # a faster way?
                key_col = com.take_1d(self.left_join_keys[i], left_indexer)
                na_indexer = (left_indexer == -1).nonzero()[0]
                right_na_indexer = right_indexer.take(na_indexer)
                key_col.put(na_indexer, com.take_1d(self.right_join_keys[i],
                                                    right_na_indexer))
                result.insert(i, name, key_col)

    def _get_join_info(self):
        left_ax = self.left._data.axes[self.axis]
        right_ax = self.right._data.axes[self.axis]
        if self.left_index and self.right_index:
            join_index, left_indexer, right_indexer = \
                left_ax.join(right_ax, how=self.how, return_indexers=True)
        elif self.right_index and self.how == 'left':
            join_index, left_indexer, right_indexer = \
                _left_join_on_index(left_ax, right_ax, self.left_join_keys,
                                    sort=self.sort)

        elif self.left_index and self.how == 'right':
            join_index, right_indexer, left_indexer = \
                _left_join_on_index(right_ax, left_ax, self.right_join_keys,
                                    sort=self.sort)
        else:
            (left_indexer,
             right_indexer) = _get_join_indexers(self.left_join_keys,
                                                 self.right_join_keys,
                                                 sort=self.sort, how=self.how)

            if self.right_index:
                join_index = self.left.index.take(left_indexer)
            elif self.left_index:
                join_index = self.right.index.take(right_indexer)
            else:
                join_index = Index(np.arange(len(left_indexer)))

        return join_index, left_indexer, right_indexer

    def _get_merge_data(self):
        """
        Handles overlapping column names etc.
        """
        ldata, rdata = self.left._data, self.right._data
        lsuf, rsuf = self.suffixes
        ldata, rdata = ldata._maybe_rename_join(rdata, lsuf, rsuf,
                                                copydata=False)
        return ldata, rdata

    def _get_merge_keys(self):
        """
        Note: has side effects (copy/delete key columns)

        Parameters
        ----------
        left
        right
        on

        Returns
        -------
        left_keys, right_keys
        """
        self._validate_specification()

        left_keys = []
        right_keys = []
        join_names = []
        right_drop = []
        left, right = self.left, self.right

        is_lkey = lambda x: isinstance(x, np.ndarray) and len(x) == len(left)
        is_rkey = lambda x: isinstance(x, np.ndarray) and len(x) == len(right)

        # ugh, spaghetti re #733
        if _any(self.left_on) and _any(self.right_on):
            for lk, rk in zip(self.left_on, self.right_on):
                if is_lkey(lk):
                    left_keys.append(lk)
                    if is_rkey(rk):
                        right_keys.append(rk)
                        join_names.append(None)  # what to do?
                    else:
                        right_keys.append(right[rk].values)
                        join_names.append(rk)
                else:
                    if not is_rkey(rk):
                        right_keys.append(right[rk].values)
                        if lk == rk:
                            right_drop.append(rk)
                    else:
                        right_keys.append(rk)
                    left_keys.append(left[lk].values)
                    join_names.append(lk)
        elif _any(self.left_on):
            for k in self.left_on:
                if is_lkey(k):
                    left_keys.append(k)
                    join_names.append(None)
                else:
                    left_keys.append(left[k].values)
                    join_names.append(k)
            if isinstance(self.right.index, MultiIndex):
                right_keys = [lev.values.take(lab)
                              for lev, lab in zip(self.right.index.levels,
                                                  self.right.index.labels)]
            else:
                right_keys = [self.right.index.values]
        elif _any(self.right_on):
            for k in self.right_on:
                if is_rkey(k):
                    right_keys.append(k)
                    join_names.append(None)
                else:
                    right_keys.append(right[k].values)
                    join_names.append(k)
            if isinstance(self.left.index, MultiIndex):
                left_keys = [lev.values.take(lab)
                             for lev, lab in zip(self.left.index.levels,
                                                 self.left.index.labels)]
            else:
                left_keys = [self.left.index.values]

        if right_drop:
            self.right = self.right.drop(right_drop, axis=1)

        return left_keys, right_keys, join_names

    def _validate_specification(self):
        # Hm, any way to make this logic less complicated??
        if (self.on is None and self.left_on is None
            and self.right_on is None):

            if self.left_index and self.right_index:
                self.left_on, self.right_on = (), ()
            elif self.left_index:
                if self.right_on is None:
                    raise MergeError('Must pass right_on or right_index=True')
            elif self.right_index:
                if self.left_on is None:
                    raise MergeError('Must pass left_on or left_index=True')
            else:
                # use the common columns
                common_cols = self.left.columns.intersection(self.right.columns)
                if len(common_cols) == 0:
                    raise MergeError('No common columns to perform merge on')
                self.left_on = self.right_on = common_cols
        elif self.on is not None:
            if self.left_on is not None or self.right_on is not None:
                raise MergeError('Can only pass on OR left_on and '
                                'right_on')
            self.left_on = self.right_on = self.on
        elif self.left_on is not None:
            n = len(self.left_on)
            if self.right_index:
                assert(len(self.left_on) == self.right.index.nlevels)
                self.right_on = [None] * n
        elif self.right_on is not None:
            n = len(self.right_on)
            if self.left_index:
                assert(len(self.right_on) == self.left.index.nlevels)
                self.left_on = [None] * n
        assert(len(self.right_on) == len(self.left_on))


def _get_join_indexers(left_keys, right_keys, sort=False, how='inner'):
    """

    Parameters
    ----------

    Returns
    -------

    """
    assert(len(left_keys) == len(right_keys))

    left_labels = []
    right_labels = []
    group_sizes = []

    for lk, rk in zip(left_keys, right_keys):
        llab, rlab, count = _factorize_keys(lk, rk, sort=sort)

        left_labels.append(llab)
        right_labels.append(rlab)
        group_sizes.append(count)

    left_group_key = get_group_index(left_labels, group_sizes)
    right_group_key = get_group_index(right_labels, group_sizes)

    max_groups = 1L
    for x in group_sizes:
        max_groups *= long(x)

    if max_groups > 2**63:  # pragma: no cover
        raise MergeError('Combinatorial explosion! (boom)')

    left_group_key, right_group_key, max_groups = \
        _factorize_keys(left_group_key, right_group_key, sort=sort)

    join_func = _join_functions[how]
    return join_func(left_group_key, right_group_key, max_groups)



class _OrderedMerge(_MergeOperation):

    def __init__(self, left, right, on=None, by=None, left_on=None,
                 right_on=None, axis=1, left_index=False, right_index=False,
                 suffixes=('_x', '_y'), copy=True,
                 fill_method=None):

        self.fill_method = fill_method

        _MergeOperation.__init__(self, left, right, on=on, left_on=left_on,
                                 right_on=right_on, axis=axis,
                                 left_index=left_index,
                                 right_index=right_index,
                                 how='outer', suffixes=suffixes,
                                 sort=True # sorts when factorizing
                                 )


    def get_result(self):
        join_index, left_indexer, right_indexer = self._get_join_info()

        # this is a bit kludgy
        ldata, rdata = self._get_merge_data()

        if self.fill_method == 'ffill':
            left_join_indexer = lib.ffill_indexer(left_indexer)
            right_join_indexer = lib.ffill_indexer(right_indexer)
        else:
            left_join_indexer = left_indexer
            right_join_indexer = right_indexer

        join_op = _BlockJoinOperation([ldata, rdata], join_index,
                                      [left_join_indexer, right_join_indexer],
                                      axis=1, copy=self.copy)

        result_data = join_op.get_result()
        result = DataFrame(result_data)

        self._maybe_add_join_keys(result, left_indexer, right_indexer)

        return result


def _get_multiindex_indexer(join_keys, index, sort=False):
    shape = []
    labels = []
    for level, key in zip(index.levels, join_keys):
        llab, rlab, count = _factorize_keys(level, key, sort=False)
        labels.append(rlab)
        shape.append(count)

    left_group_key = get_group_index(labels, shape)
    right_group_key = get_group_index(index.labels, shape)

    left_group_key, right_group_key, max_groups = \
        _factorize_keys(left_group_key, right_group_key,
                        sort=False)

    left_indexer, right_indexer = \
        lib.left_outer_join(com._ensure_int64(left_group_key),
                            com._ensure_int64(right_group_key),
                            max_groups, sort=False)

    return left_indexer, right_indexer

def _get_single_indexer(join_key, index, sort=False):
    left_key, right_key, count = _factorize_keys(join_key, index, sort=sort)

    left_indexer, right_indexer = \
        lib.left_outer_join(com._ensure_int64(left_key),
                            com._ensure_int64(right_key),
                            count, sort=sort)

    return left_indexer, right_indexer

def _left_join_on_index(left_ax, right_ax, join_keys, sort=False):
    join_index = left_ax
    left_indexer = None

    if len(join_keys) > 1:
        assert(isinstance(right_ax, MultiIndex) and
               len(join_keys) == right_ax.nlevels)

        left_tmp, right_indexer = \
            _get_multiindex_indexer(join_keys, right_ax,
                                    sort=sort)
        if sort:
            left_indexer = left_tmp
            join_index = left_ax.take(left_indexer)
    else:
        jkey = join_keys[0]
        if sort:
            left_indexer, right_indexer = \
                _get_single_indexer(jkey, right_ax, sort=sort)
            join_index = left_ax.take(left_indexer)
        else:
            right_indexer = right_ax.get_indexer(jkey)

    return join_index, left_indexer, right_indexer


def _right_outer_join(x, y, max_groups):
    right_indexer, left_indexer = lib.left_outer_join(y, x, max_groups)
    return left_indexer, right_indexer

_join_functions = {
    'inner' : lib.inner_join,
    'left' : lib.left_outer_join,
    'right' : _right_outer_join,
    'outer' : lib.full_outer_join,
}


def _factorize_keys(lk, rk, sort=True):
    if com.is_integer_dtype(lk) and com.is_integer_dtype(rk):
        klass = lib.Int64Factorizer
        lk = com._ensure_int64(lk)
        rk = com._ensure_int64(rk)
    else:
        klass = lib.Factorizer
        lk = com._ensure_object(lk)
        rk = com._ensure_object(rk)

    rizer = klass(max(len(lk), len(rk)))

    llab, _ = rizer.factorize(lk)
    rlab, _ = rizer.factorize(rk)

    count = rizer.get_count()

    if sort:
        llab, rlab = _sort_labels(rizer.uniques, llab, rlab)

        # TODO: na handling

    return llab, rlab, count

def _sort_labels(uniques, left, right):
    if not isinstance(uniques, np.ndarray):
        # tuplesafe
        uniques = Index(uniques).values

    sorter = uniques.argsort()

    reverse_indexer = np.empty(len(sorter), dtype=np.int64)
    reverse_indexer.put(sorter, np.arange(len(sorter)))

    new_left = reverse_indexer.take(com._ensure_platform_int(left))
    np.putmask(new_left, left == -1, -1)

    new_right = reverse_indexer.take(com._ensure_platform_int(right))
    np.putmask(new_right, right == -1, -1)

    return new_left, new_right

class _BlockJoinOperation(object):
    """
    BlockJoinOperation made generic for N DataFrames

    Object responsible for orchestrating efficient join operation between two
    BlockManager data structures
    """
    def __init__(self, data_list, join_index, indexers, axis=1, copy=True):
        if axis <= 0:  # pragma: no cover
            raise MergeError('Only axis >= 1 supported for this operation')

        assert(len(data_list) == len(indexers))

        self.units = []
        for data, indexer in zip(data_list, indexers):
            if not data.is_consolidated():
                data = data.consolidate()
            self.units.append(_JoinUnit(data.blocks, indexer))

        self.join_index = join_index
        self.axis = axis
        self.copy = copy

        # do NOT sort
        self.result_items = _concat_indexes([d.items for d in data_list])
        self.result_axes = list(data_list[0].axes)
        self.result_axes[0] = self.result_items
        self.result_axes[axis] = self.join_index

    def _prepare_blocks(self):
        blockmaps = []

        for unit in self.units:
            join_blocks = unit.get_upcasted_blocks()
            type_map = dict((type(blk), blk) for blk in join_blocks)
            blockmaps.append(type_map)

        return blockmaps

    def get_result(self):
        """
        Returns
        -------
        merged : BlockManager
        """
        blockmaps = self._prepare_blocks()
        kinds = _get_all_block_kinds(blockmaps)

        result_blocks = []

        # maybe want to enable flexible copying <-- what did I mean?
        for klass in kinds:
            klass_blocks = [mapping.get(klass) for mapping in blockmaps]
            res_blk = self._get_merged_block(klass_blocks)
            result_blocks.append(res_blk)

        return BlockManager(result_blocks, self.result_axes)

    def _get_merged_block(self, blocks):

        to_merge = []

        for unit, block in zip(self.units, blocks):
            if block is not None:
                to_merge.append((unit, block))

        if len(to_merge) > 1:
            return self._merge_blocks(to_merge)
        else:
            unit, block = to_merge[0]
            return unit.reindex_block(block, self.axis,
                                      self.result_items, copy=self.copy)

    def _merge_blocks(self, merge_chunks):
        """
        merge_chunks -> [(_JoinUnit, Block)]
        """
        funit, fblock = merge_chunks[0]
        fidx = funit.indexer

        out_shape = list(fblock.values.shape)

        n = len(fidx) if fidx is not None else out_shape[self.axis]

        out_shape[0] = sum(len(blk) for unit, blk in merge_chunks)
        out_shape[self.axis] = n

        # Should use Fortran order??
        out = np.empty(out_shape, dtype=fblock.values.dtype)

        sofar = 0
        for unit, blk in merge_chunks:
            out_chunk = out[sofar : sofar + len(blk)]

            if unit.indexer is None:
            # is this really faster than assigning to arr.flat?
                com.take_fast(blk.values, np.arange(n, dtype=np.int64),
                              None, False,
                              axis=self.axis, out=out_chunk)
            else:
                # write out the values to the result array
                com.take_fast(blk.values, unit.indexer,
                              None, False,
                              axis=self.axis, out=out_chunk)

            sofar += len(blk)

        # does not sort
        new_block_items = _concat_indexes([b.items for _, b in merge_chunks])
        return make_block(out, new_block_items, self.result_items)



class _JoinUnit(object):
    """
    Blocks plus indexer
    """

    def __init__(self, blocks, indexer):
        self.blocks = blocks
        self.indexer = indexer

    @cache_readonly
    def mask_info(self):
        if self.indexer is None or not _may_need_upcasting(self.blocks):
            mask = None
            need_masking = False
        else:
            mask = self.indexer == -1
            need_masking = mask.any()

        return mask, need_masking

    @property
    def need_masking(self):
        return self.mask_info[1]

    def get_upcasted_blocks(self):
        # will short-circuit and not compute lneed_masking if indexer is None
        if self.need_masking:
            return _upcast_blocks(self.blocks)
        return self.blocks

    def reindex_block(self, block, axis, ref_items, copy=True):
        # still some inefficiency here for bool/int64 because in the case where
        # no masking is needed, take_fast will recompute the mask

        mask, need_masking = self.mask_info

        if self.indexer is None:
            if copy:
                result = block.copy()
            else:
                result = block
        else:
            result = block.reindex_axis(self.indexer, mask, need_masking,
                                        axis=axis)

        result.ref_items = ref_items
        return result

def _may_need_upcasting(blocks):
    for block in blocks:
        if isinstance(block, (IntBlock, BoolBlock)):
            return True
    return False


def _upcast_blocks(blocks):
    """
    Upcast and consolidate if necessary
    """
    new_blocks = []
    for block in blocks:
        if isinstance(block, IntBlock):
            newb = make_block(block.values.astype(float), block.items,
                              block.ref_items)
        elif isinstance(block, BoolBlock):
            newb = make_block(block.values.astype(object), block.items,
                              block.ref_items)
        else:
            newb = block
        new_blocks.append(newb)

    # use any ref_items
    return _consolidate(new_blocks, newb.ref_items)

def _get_all_block_kinds(blockmaps):
    kinds = set()
    for mapping in blockmaps:
        kinds |= set(mapping)
    return kinds

#----------------------------------------------------------------------
# Concatenate DataFrame objects

def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
           keys=None, levels=None, names=None, verify_integrity=False):
    """
    Concatenate pandas objects along a particular axis with optional set logic
    along the other axes. Can also add a layer of hierarchical indexing on the
    concatenation axis, which may be useful if the labels are the same (or
    overlapping) on the passed axis number

    Parameters
    ----------
    objs : list or dict of Series, DataFrame, or Panel objects
        If a dict is passed, the sorted keys will be used as the `keys`
        argument, unless it is passed, in which case the values will be
        selected (see below). Any None objects will be dropped silently unless
        they are all None in which case an Exception will be raised
    axis : {0, 1, ...}, default 0
        The axis to concatenate along
    join : {'inner', 'outer'}, default 'outer'
        How to handle indexes on other axis(es)
    join_axes : list of Index objects
        Specific indexes to use for the other n - 1 axes instead of performing
        inner/outer set logic
    verify_integrity : boolean, default False
        Check whether the new concatenated axis contains duplicates. This can
        be very expensive relative to the actual data concatenation
    keys : sequence, default None
        If multiple levels passed, should contain tuples. Construct
        hierarchical index using the passed keys as the outermost level
    levels : list of sequences, default None
        Specific levels (unique values) to use for constructing a
        MultiIndex. Otherwise they will be inferred from the keys
    names : list, default None
        Names for the levels in the resulting hierarchical index
    ignore_index : boolean, default False
        If True, do not use the index values on the concatenation axis. The
        resulting axis will be labeled 0, ..., n - 1. This is useful if you are
        concatenating objects where the concatenation axis does not have
        meaningful indexing information.

    Notes
    -----
    The keys, levels, and names arguments are all optional

    Returns
    -------
    concatenated : type of objects
    """
    op = _Concatenator(objs, axis=axis, join_axes=join_axes,
                       ignore_index=ignore_index, join=join,
                       keys=keys, levels=levels, names=names,
                       verify_integrity=verify_integrity)
    return op.get_result()


class _Concatenator(object):
    """
    Orchestrates a concatenation operation for BlockManagers, with little hacks
    to support sparse data structures, etc.
    """

    def __init__(self, objs, axis=0, join='outer', join_axes=None,
                 keys=None, levels=None, names=None,
                 ignore_index=False, verify_integrity=False):
        if join == 'outer':
            self.intersect = False
        elif join == 'inner':
            self.intersect = True
        else:  # pragma: no cover
            raise ValueError('Only can inner (intersect) or outer (union) join '
                             'the other axis')

        if isinstance(objs, dict):
            if keys is None:
                keys = sorted(objs)
            objs = [objs[k] for k in keys]

        # filter Nones
        objs = [obj for obj in objs if obj is not None]

        if len(objs) == 0:
            raise Exception('All objects passed were None')

        # consolidate data
        for obj in objs:
            if isinstance(obj, NDFrame):
                obj.consolidate(inplace=True)
        self.objs = objs

        sample = objs[0]

        # Need to flip BlockManager axis in the DataFrame special case
        if isinstance(sample, DataFrame):
            axis = 1 if axis == 0 else 0

        self._is_series = isinstance(sample, Series)
        assert(0 <= axis <= sample.ndim)

        # note: this is the BlockManager axis (since DataFrame is transposed)
        self.axis = axis

        self.join_axes = join_axes

        self.keys = keys
        self.names = names
        self.levels = levels

        self.ignore_index = ignore_index
        self.verify_integrity = verify_integrity

        self.new_axes = self._get_new_axes()

    def get_result(self):
        if self._is_series and self.axis == 0:
            new_data = np.concatenate([x.values for x in self.objs])
            name = com._consensus_name_attr(self.objs)
            return Series(new_data, index=self.new_axes[0], name=name)
        elif self._is_series:
            data = dict(zip(self.new_axes[1], self.objs))
            return DataFrame(data, index=self.new_axes[0],
                             columns=self.new_axes[1])
        else:
            new_data = self._get_concatenated_data()
            return self.objs[0]._from_axes(new_data, self.new_axes)

    def _get_fresh_axis(self):
        return Index(np.arange(len(self._get_concat_axis())))

    def _get_concatenated_data(self):
        try:
            # need to conform to same other (joined) axes for block join
            reindexed_data = self._get_reindexed_data()

            blockmaps = []
            for data in reindexed_data:
                data = data.consolidate()
                type_map = dict((type(blk), blk) for blk in data.blocks)
                blockmaps.append(type_map)
            kinds = _get_all_block_kinds(blockmaps)

            new_blocks = []
            for kind in kinds:
                klass_blocks = [mapping.get(kind) for mapping in blockmaps]
                stacked_block = self._concat_blocks(klass_blocks)
                new_blocks.append(stacked_block)

            if self.axis == 0 and self.ignore_index:
                self.new_axes[0] = self._get_fresh_axis()

            for blk in new_blocks:
                blk.ref_items = self.new_axes[0]

            new_data = BlockManager(new_blocks, self.new_axes)
        except Exception:  # EAFP
            # should not be possible to fail here for the expected reason with
            # axis = 0
            if self.axis == 0:  # pragma: no cover
                raise

            new_data = {}
            for item in self.new_axes[0]:
                new_data[item] = self._concat_single_item(item)

        return new_data

    def _get_reindexed_data(self):
        # HACK: ugh

        reindexed_data = []
        if isinstance(self.objs[0], SparseDataFrame):
            pass
        else:
            axes_to_reindex = list(enumerate(self.new_axes))
            axes_to_reindex.pop(self.axis)

            for obj in self.objs:
                data = obj._data
                for i, ax in axes_to_reindex:
                    data = data.reindex_axis(ax, axis=i, copy=False)
                reindexed_data.append(data)

        return reindexed_data

    def _concat_blocks(self, blocks):
        values_list = [b.values for b in blocks if b is not None]
        if isinstance(blocks[0], DatetimeBlock):
            # hack around NumPy 1.6 bug
            concat_values = np.concatenate([x.view(np.int64)
                                            for x in values_list],
                                           axis=self.axis)
            concat_values = concat_values.view(np.dtype('M8[ns]'))
        else:
            concat_values = np.concatenate(values_list, axis=self.axis)

        if self.axis > 0:
            # Not safe to remove this check, need to profile
            if not _all_indexes_same([b.items for b in blocks]):
                raise Exception('dtypes are not consistent throughout '
                                'DataFrames')
            return make_block(concat_values, blocks[0].items, self.new_axes[0])
        else:
            offsets = np.r_[0, np.cumsum([len(x._data.axes[0]) for
                                            x in self.objs])]
            indexer = np.concatenate([offsets[i] + b.ref_locs
                                        for i, b in enumerate(blocks)
                                        if b is not None])
            if self.ignore_index:
                concat_items = indexer
            else:
                concat_items = self.new_axes[0].take(indexer)

            if self.ignore_index:
                ref_items = self._get_fresh_axis()
                return make_block(concat_values, concat_items, ref_items)

            return make_block(concat_values, concat_items, self.new_axes[0])

    def _concat_single_item(self, item):
        all_values = []
        dtypes = set()
        for obj in self.objs:
            try:
                values = obj._data.get(item)
                dtypes.add(values.dtype)
                all_values.append(values)
            except KeyError:
                all_values.append(None)

        # this stinks
        have_object = False
        for dtype in dtypes:
            if issubclass(dtype.type, (np.object_, np.bool_)):
                have_object = True
        if have_object:
            empty_dtype = np.object_
        else:
            empty_dtype = np.float64

        to_concat = []
        for obj, item_values in zip(self.objs, all_values):
            if item_values is None:
                shape = obj._data.shape[1:]
                missing_arr = np.empty(shape, dtype=empty_dtype)
                missing_arr.fill(np.nan)
                to_concat.append(missing_arr)
            else:
                to_concat.append(item_values)

        # this method only gets called with axis >= 1
        assert(self.axis >= 1)
        return np.concatenate(to_concat, axis=self.axis - 1)

    def _get_result_dim(self):
        if self._is_series and self.axis == 1:
            return 2
        else:
            return self.objs[0].ndim

    def _get_new_axes(self):
        ndim = self._get_result_dim()
        new_axes = [None] * ndim

        if self.join_axes is None:
            for i in range(ndim):
                if i == self.axis:
                    continue
                new_axes[i] = self._get_comb_axis(i)
        else:
            assert(len(self.join_axes) == ndim - 1)

            # ufff...
            indices = range(ndim)
            indices.remove(self.axis)

            for i, ax in zip(indices, self.join_axes):
                new_axes[i] = ax

        if self.ignore_index:
            concat_axis = None
        else:
            concat_axis = self._get_concat_axis()

        new_axes[self.axis] = concat_axis

        return new_axes

    def _get_comb_axis(self, i):
        if self._is_series:
            all_indexes = [x.index for x in self.objs]
        else:
            all_indexes = [x._data.axes[i] for x in self.objs]

        return _get_combined_index(all_indexes, intersect=self.intersect)

    def _get_concat_axis(self):
        if self._is_series:
            if self.axis == 0:
                indexes = [x.index for x in self.objs]
            elif self.keys is None:
                return Index(np.arange(len(self.objs)))
            else:
                return _ensure_index(self.keys)
        else:
            indexes = [x._data.axes[self.axis] for x in self.objs]

        if self.keys is None:
            concat_axis = _concat_indexes(indexes)
        else:
            concat_axis = _make_concat_multiindex(indexes, self.keys,
                                                  self.levels, self.names)

        self._maybe_check_integrity(concat_axis)

        return concat_axis

    def _maybe_check_integrity(self, concat_index):
        if self.verify_integrity:
            if not concat_index.is_unique:
                overlap = concat_index.get_duplicates()
                raise Exception('Indexes have overlapping values: %s'
                                % str(overlap))


def _concat_indexes(indexes):
    return indexes[0].append(indexes[1:])

def _make_concat_multiindex(indexes, keys, levels=None, names=None):
    if ((levels is None and isinstance(keys[0], tuple)) or
        (levels is not None and len(levels) > 1)):
        zipped = zip(*keys)
        if names is None:
            names = [None] * len(zipped)

        if levels is None:
            levels = [Factor.from_array(zp).levels for zp in zipped]
        else:
            levels = [_ensure_index(x) for x in levels]
    else:
        zipped = [keys]
        if names is None:
            names = [None]

        if levels is None:
            levels = [_ensure_index(keys)]
        else:
            levels = [_ensure_index(x) for x in levels]

    if not _all_indexes_same(indexes):
        label_list = []

        # things are potentially different sizes, so compute the exact labels
        # for each level and pass those to MultiIndex.from_arrays

        for hlevel, level in zip(zipped, levels):
            to_concat = []
            for key, index in zip(hlevel, indexes):
                try:
                    i = level.get_loc(key)
                except KeyError:
                    raise ValueError('Key %s not in level %s' % (str(key), str(level)))

                to_concat.append(np.repeat(i, len(index)))
            label_list.append(np.concatenate(to_concat))

        concat_index = _concat_indexes(indexes)

        # these go at the end
        if isinstance(concat_index, MultiIndex):
            levels.extend(concat_index.levels)
            label_list.extend(concat_index.labels)
        else:
            factor = Factor.from_array(concat_index)
            levels.append(factor.levels)
            label_list.append(factor.labels)

        if len(names) == len(levels):
            names = list(names)
        else:
            # also copies
            names = names + _get_consensus_names(indexes)

        return MultiIndex(levels=levels, labels=label_list, names=names)

    new_index = indexes[0]
    n = len(new_index)
    kpieces = len(indexes)

    # also copies
    new_names = list(names)
    new_levels = list(levels)

    # construct labels
    new_labels = []

    # do something a bit more speedy

    for hlevel, level in zip(zipped, levels):
        hlevel = _ensure_index(hlevel)
        mapped = level.get_indexer(hlevel)

        mask = mapped == -1
        if mask.any():
            raise ValueError('Values not found in passed level: %s'
                             % str(hlevel[mask]))

        new_labels.append(np.repeat(mapped, n))

    if isinstance(new_index, MultiIndex):
        new_levels.extend(new_index.levels)
        new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels])
    else:
        new_levels.append(new_index)
        new_labels.append(np.tile(np.arange(n), kpieces))

    if len(new_names) < len(new_levels):
        new_names.extend(new_index.names)

    return MultiIndex(levels=new_levels, labels=new_labels, names=new_names)


def _should_fill(lname, rname):
    if not isinstance(lname, basestring) or not isinstance(rname, basestring):
        return True
    return lname == rname



def _any(x):
    return x is not None and len(x) > 0 and any([y is not None for y in x])
