# -*- coding: utf-8 -*-
# pylint: disable=W0102

import nose
import numpy as np

from pandas import Index, MultiIndex, DataFrame, Series, Categorical
from pandas.compat import OrderedDict, lrange
from pandas.sparse.array import SparseArray
from pandas.core.internals import *
import pandas.core.internals as internals
import pandas.util.testing as tm
import pandas as pd
from pandas.util.testing import (
    assert_almost_equal, assert_frame_equal, randn, assert_series_equal)
from pandas.compat import zip, u


def assert_block_equal(left, right):
    assert_almost_equal(left.values, right.values)
    assert(left.dtype == right.dtype)
    assert_almost_equal(left.mgr_locs, right.mgr_locs)


def get_numeric_mat(shape):
    arr = np.arange(shape[0])
    return np.lib.stride_tricks.as_strided(
        x=arr, shape=shape,
        strides=(arr.itemsize,) + (0,) * (len(shape) - 1)).copy()


N = 10


def create_block(typestr, placement, item_shape=None, num_offset=0):
    """
    Supported typestr:

        * float, f8, f4, f2
        * int, i8, i4, i2, i1
        * uint, u8, u4, u2, u1
        * complex, c16, c8
        * bool
        * object, string, O
        * datetime, dt, M8[ns]
        * timedelta, td, m8[ns]
        * sparse (SparseArray with fill_value=0.0)
        * sparse_na (SparseArray with fill_value=np.nan)
        * category, category2

    """
    placement = BlockPlacement(placement)
    num_items = len(placement)

    if item_shape is None:
        item_shape = (N,)

    shape = (num_items,) + item_shape

    mat = get_numeric_mat(shape)

    if typestr in ('float', 'f8', 'f4', 'f2',
                   'int', 'i8', 'i4', 'i2', 'i1',
                   'uint', 'u8', 'u4', 'u2', 'u1'):
        values = mat.astype(typestr) + num_offset
    elif typestr in ('complex', 'c16', 'c8'):
        values = 1.j * (mat.astype(typestr) + num_offset)
    elif typestr in ('object', 'string', 'O'):
        values = np.reshape(['A%d' % i for i in mat.ravel() + num_offset],
                            shape)
    elif typestr in ('b','bool',):
        values = np.ones(shape, dtype=np.bool_)
    elif typestr in ('datetime', 'dt', 'M8[ns]'):
        values = (mat * 1e9).astype('M8[ns]')
    elif typestr in ('timedelta', 'td', 'm8[ns]'):
        values = (mat * 1).astype('m8[ns]')
    elif typestr in ('category',):
        values = Categorical([1,1,2,2,3,3,3,3,4,4])
    elif typestr in ('category2',):
        values = Categorical(['a','a','a','a','b','b','c','c','c','d'])
    elif typestr in ('sparse', 'sparse_na'):
        # FIXME: doesn't support num_rows != 10
        assert shape[-1] == 10
        assert all(s == 1 for s in shape[:-1])
        if typestr.endswith('_na'):
            fill_value = np.nan
        else:
            fill_value = 0.0
        values = SparseArray([fill_value, fill_value, 1, 2, 3, fill_value,
                              4, 5, fill_value, 6], fill_value=fill_value)
        arr = values.sp_values.view()
        arr += (num_offset - 1)
    else:
        raise ValueError('Unsupported typestr: "%s"' % typestr)

    return make_block(values, placement=placement, ndim=len(shape))


def create_single_mgr(typestr, num_rows=None):
    if num_rows is None:
        num_rows = N

    return SingleBlockManager(
        create_block(typestr, placement=slice(0, num_rows), item_shape=()),
        np.arange(num_rows))


def create_mgr(descr, item_shape=None):
    """
    Construct BlockManager from string description.

    String description syntax looks similar to np.matrix initializer.  It looks
    like this::

        a,b,c: f8; d,e,f: i8

    Rules are rather simple:

    * see list of supported datatypes in `create_block` method
    * components are semicolon-separated
    * each component is `NAME,NAME,NAME: DTYPE_ID`
    * whitespace around colons & semicolons are removed
    * components with same DTYPE_ID are combined into single block
    * to force multiple blocks with same dtype, use '-SUFFIX'::

        'a:f8-1; b:f8-2; c:f8-foobar'

    """
    if item_shape is None:
        item_shape = (N,)

    offset = 0
    mgr_items = []
    block_placements = OrderedDict()
    for d in descr.split(';'):
        d = d.strip()
        names, blockstr = d.partition(':')[::2]
        blockstr = blockstr.strip()
        names = names.strip().split(',')

        mgr_items.extend(names)
        placement = list(np.arange(len(names)) + offset)
        try:
            block_placements[blockstr].extend(placement)
        except KeyError:
            block_placements[blockstr] = placement
        offset += len(names)

    mgr_items = Index(mgr_items)

    blocks = []
    num_offset = 0
    for blockstr, placement in block_placements.items():
        typestr = blockstr.split('-')[0]
        blocks.append(create_block(typestr, placement, item_shape=item_shape,
                                   num_offset=num_offset,))
        num_offset += len(placement)

    return BlockManager(sorted(blocks, key=lambda b: b.mgr_locs[0]),
                        [mgr_items] + [np.arange(n) for n in item_shape])



class TestBlock(tm.TestCase):

    _multiprocess_can_split_ = True

    def setUp(self):
        # self.fblock = get_float_ex()  # a,c,e
        # self.cblock = get_complex_ex() #
        # self.oblock = get_obj_ex()
        # self.bool_block = get_bool_ex()
        # self.int_block = get_int_ex()

        self.fblock = create_block('float', [0, 2, 4])
        self.cblock = create_block('complex', [7])
        self.oblock = create_block('object', [1, 3])
        self.bool_block = create_block('bool', [5])
        self.int_block = create_block('int', [6])

    def test_constructor(self):
        int32block = create_block('i4', [0])
        self.assertEqual(int32block.dtype, np.int32)

    def test_pickle(self):

        def _check(blk):
            assert_block_equal(self.round_trip_pickle(blk), blk)

        _check(self.fblock)
        _check(self.cblock)
        _check(self.oblock)
        _check(self.bool_block)

    def test_mgr_locs(self):
        assert_almost_equal(self.fblock.mgr_locs, [0, 2, 4])

    def test_attrs(self):
        self.assertEqual(self.fblock.shape, self.fblock.values.shape)
        self.assertEqual(self.fblock.dtype, self.fblock.values.dtype)
        self.assertEqual(len(self.fblock), len(self.fblock.values))

    def test_merge(self):
        avals = randn(2, 10)
        bvals = randn(2, 10)

        ref_cols = Index(['e', 'a', 'b', 'd', 'f'])

        ablock = make_block(avals,
                            ref_cols.get_indexer(['e', 'b']))
        bblock = make_block(bvals,
                            ref_cols.get_indexer(['a', 'd']))
        merged = ablock.merge(bblock)
        assert_almost_equal(merged.mgr_locs, [0, 1, 2, 3])
        assert_almost_equal(merged.values[[0, 2]], avals)
        assert_almost_equal(merged.values[[1, 3]], bvals)

        # TODO: merge with mixed type?

    def test_copy(self):
        cop = self.fblock.copy()
        self.assertIsNot(cop, self.fblock)
        assert_block_equal(self.fblock, cop)

    def test_reindex_index(self):
        pass

    def test_reindex_cast(self):
        pass

    def test_insert(self):
        pass

    def test_delete(self):
        newb = self.fblock.copy()
        newb.delete(0)
        assert_almost_equal(newb.mgr_locs, [2, 4])
        self.assertTrue((newb.values[0] == 1).all())

        newb = self.fblock.copy()
        newb.delete(1)
        assert_almost_equal(newb.mgr_locs, [0, 4])
        self.assertTrue((newb.values[1] == 2).all())

        newb = self.fblock.copy()
        newb.delete(2)
        assert_almost_equal(newb.mgr_locs, [0, 2])
        self.assertTrue((newb.values[1] == 1).all())

        newb = self.fblock.copy()
        self.assertRaises(Exception, newb.delete, 3)

    def test_split_block_at(self):

        # with dup column support this method was taken out
        # GH3679
        raise nose.SkipTest("skipping for now")

        bs = list(self.fblock.split_block_at('a'))
        self.assertEqual(len(bs), 1)
        self.assertTrue(np.array_equal(bs[0].items, ['c', 'e']))

        bs = list(self.fblock.split_block_at('c'))
        self.assertEqual(len(bs), 2)
        self.assertTrue(np.array_equal(bs[0].items, ['a']))
        self.assertTrue(np.array_equal(bs[1].items, ['e']))

        bs = list(self.fblock.split_block_at('e'))
        self.assertEqual(len(bs), 1)
        self.assertTrue(np.array_equal(bs[0].items, ['a', 'c']))

        bblock = get_bool_ex(['f'])
        bs = list(bblock.split_block_at('f'))
        self.assertEqual(len(bs), 0)

    def test_get(self):
        pass

    def test_set(self):
        pass

    def test_fillna(self):
        pass

    def test_repr(self):
        pass


class TestBlockManager(tm.TestCase):
    _multiprocess_can_split_ = True

    def setUp(self):
        self.mgr = create_mgr('a: f8; b: object; c: f8; d: object; e: f8;'
                              'f: bool; g: i8; h: complex')

    def test_constructor_corner(self):
        pass

    def test_attrs(self):
        mgr = create_mgr('a,b,c: f8-1; d,e,f: f8-2')
        self.assertEqual(mgr.nblocks, 2)
        self.assertEqual(len(mgr), 6)

    def test_is_mixed_dtype(self):
        self.assertFalse(create_mgr('a,b:f8').is_mixed_type)
        self.assertFalse(create_mgr('a:f8-1; b:f8-2').is_mixed_type)

        self.assertTrue(create_mgr('a,b:f8; c,d: f4').is_mixed_type)
        self.assertTrue(create_mgr('a,b:f8; c,d: object').is_mixed_type)

    def test_is_indexed_like(self):
        mgr1 = create_mgr('a,b: f8')
        mgr2 = create_mgr('a:i8; b:bool')
        mgr3 = create_mgr('a,b,c: f8')
        self.assertTrue(mgr1._is_indexed_like(mgr1))
        self.assertTrue(mgr1._is_indexed_like(mgr2))
        self.assertTrue(mgr1._is_indexed_like(mgr3))

        self.assertFalse(mgr1._is_indexed_like(
            mgr1.get_slice(slice(-1), axis=1)))

    def test_duplicate_ref_loc_failure(self):
        tmp_mgr = create_mgr('a:bool; a: f8')

        axes, blocks = tmp_mgr.axes, tmp_mgr.blocks

        blocks[0].mgr_locs = np.array([0])
        blocks[1].mgr_locs = np.array([0])
        # test trying to create block manager with overlapping ref locs
        self.assertRaises(AssertionError, BlockManager, blocks, axes)

        blocks[0].mgr_locs = np.array([0])
        blocks[1].mgr_locs = np.array([1])
        mgr = BlockManager(blocks, axes)
        mgr.iget(1)

    def test_contains(self):
        self.assertIn('a', self.mgr)
        self.assertNotIn('baz', self.mgr)

    def test_pickle(self):

        mgr2 = self.round_trip_pickle(self.mgr)
        assert_frame_equal(DataFrame(self.mgr), DataFrame(mgr2))

        # share ref_items
        # self.assertIs(mgr2.blocks[0].ref_items, mgr2.blocks[1].ref_items)

        # GH2431
        self.assertTrue(hasattr(mgr2, "_is_consolidated"))
        self.assertTrue(hasattr(mgr2, "_known_consolidated"))

        # reset to False on load
        self.assertFalse(mgr2._is_consolidated)
        self.assertFalse(mgr2._known_consolidated)

    def test_non_unique_pickle(self):

        mgr = create_mgr('a,a,a:f8')
        mgr2 = self.round_trip_pickle(mgr)
        assert_frame_equal(DataFrame(mgr), DataFrame(mgr2))

        mgr = create_mgr('a: f8; a: i8')
        mgr2 = self.round_trip_pickle(mgr)
        assert_frame_equal(DataFrame(mgr), DataFrame(mgr2))

    def test_categorical_block_pickle(self):
        mgr = create_mgr('a: category')
        mgr2 = self.round_trip_pickle(mgr)
        assert_frame_equal(DataFrame(mgr), DataFrame(mgr2))

        smgr = create_single_mgr('category')
        smgr2 = self.round_trip_pickle(smgr)
        assert_series_equal(Series(smgr), Series(smgr2))

    def test_get_scalar(self):
        for item in self.mgr.items:
            for i, index in enumerate(self.mgr.axes[1]):
                res = self.mgr.get_scalar((item, index))
                exp = self.mgr.get(item, fastpath=False)[i]
                assert_almost_equal(res, exp)
                exp = self.mgr.get(item).values[i]
                assert_almost_equal(res, exp)

    def test_get(self):
        cols = Index(list('abc'))
        values = np.random.rand(3, 3)
        block = make_block(values=values.copy(),
                           placement=np.arange(3))
        mgr = BlockManager(blocks=[block], axes=[cols, np.arange(3)])

        assert_almost_equal(mgr.get('a', fastpath=False), values[0])
        assert_almost_equal(mgr.get('b', fastpath=False), values[1])
        assert_almost_equal(mgr.get('c', fastpath=False), values[2])
        assert_almost_equal(mgr.get('a').values, values[0])
        assert_almost_equal(mgr.get('b').values, values[1])
        assert_almost_equal(mgr.get('c').values, values[2])

    def test_set(self):
        mgr = create_mgr('a,b,c: int', item_shape=(3,))

        mgr.set('d', np.array(['foo'] * 3))
        mgr.set('b', np.array(['bar'] * 3))
        assert_almost_equal(mgr.get('a').values, [0] * 3)
        assert_almost_equal(mgr.get('b').values, ['bar'] * 3)
        assert_almost_equal(mgr.get('c').values, [2] * 3)
        assert_almost_equal(mgr.get('d').values, ['foo'] * 3)

    def test_insert(self):
        self.mgr.insert(0, 'inserted', np.arange(N))

        self.assertEqual(self.mgr.items[0], 'inserted')
        assert_almost_equal(self.mgr.get('inserted'), np.arange(N))

        for blk in self.mgr.blocks:
            yield self.assertIs, self.mgr.items, blk.ref_items

    def test_set_change_dtype(self):
        self.mgr.set('baz', np.zeros(N, dtype=bool))

        self.mgr.set('baz', np.repeat('foo', N))
        self.assertEqual(self.mgr.get('baz').dtype, np.object_)

        mgr2 = self.mgr.consolidate()
        mgr2.set('baz', np.repeat('foo', N))
        self.assertEqual(mgr2.get('baz').dtype, np.object_)

        mgr2.set('quux', randn(N).astype(int))
        self.assertEqual(mgr2.get('quux').dtype, np.int_)

        mgr2.set('quux', randn(N))
        self.assertEqual(mgr2.get('quux').dtype, np.float_)

    def test_set_change_dtype_slice(self): # GH8850
        cols = MultiIndex.from_tuples([('1st','a'), ('2nd','b'), ('3rd','c')])
        df = DataFrame([[1.0, 2, 3], [4.0, 5, 6]], columns=cols)
        df['2nd'] = df['2nd'] * 2.0

        self.assertEqual(sorted(df.blocks.keys()), ['float64', 'int64'])
        assert_frame_equal(df.blocks['float64'],
                DataFrame([[1.0, 4.0], [4.0, 10.0]], columns=cols[:2]))
        assert_frame_equal(df.blocks['int64'],
                DataFrame([[3], [6]], columns=cols[2:]))

    def test_copy(self):
        shallow = self.mgr.copy(deep=False)

        # we don't guaranteee block ordering
        for blk in self.mgr.blocks:
            found = False
            for cp_blk in shallow.blocks:
                if cp_blk.values is blk.values:
                    found = True
                    break
            self.assertTrue(found)

    def test_sparse(self):
        mgr = create_mgr('a: sparse-1; b: sparse-2')

        # what to test here?
        self.assertEqual(mgr.as_matrix().dtype, np.float64)

    def test_sparse_mixed(self):
        mgr = create_mgr('a: sparse-1; b: sparse-2; c: f8')
        self.assertEqual(len(mgr.blocks), 3)
        self.assertIsInstance(mgr, BlockManager)

        # what to test here?

    def test_as_matrix_float(self):
        mgr = create_mgr('c: f4; d: f2; e: f8')
        self.assertEqual(mgr.as_matrix().dtype, np.float64)

        mgr = create_mgr('c: f4; d: f2')
        self.assertEqual(mgr.as_matrix().dtype, np.float32)

    def test_as_matrix_int_bool(self):
        mgr = create_mgr('a: bool-1; b: bool-2')
        self.assertEqual(mgr.as_matrix().dtype, np.bool_)

        mgr = create_mgr('a: i8-1; b: i8-2; c: i4; d: i2; e: u1')
        self.assertEqual(mgr.as_matrix().dtype, np.int64)

        mgr = create_mgr('c: i4; d: i2; e: u1')
        self.assertEqual(mgr.as_matrix().dtype, np.int32)

    def test_as_matrix_datetime(self):
        mgr = create_mgr('h: datetime-1; g: datetime-2')
        self.assertEqual(mgr.as_matrix().dtype, 'M8[ns]')

    def test_astype(self):
        # coerce all
        mgr = create_mgr('c: f4; d: f2; e: f8')
        for t in ['float16', 'float32', 'float64', 'int32', 'int64']:
            t = np.dtype(t)
            tmgr = mgr.astype(t)
            self.assertEqual(tmgr.get('c').dtype.type, t)
            self.assertEqual(tmgr.get('d').dtype.type, t)
            self.assertEqual(tmgr.get('e').dtype.type, t)

        # mixed
        mgr = create_mgr('a,b: object; c: bool; d: datetime;'
                         'e: f4; f: f2; g: f8')
        for t in ['float16', 'float32', 'float64', 'int32', 'int64']:
            t = np.dtype(t)
            tmgr = mgr.astype(t, raise_on_error=False)
            self.assertEqual(tmgr.get('c').dtype.type, t)
            self.assertEqual(tmgr.get('e').dtype.type, t)
            self.assertEqual(tmgr.get('f').dtype.type, t)
            self.assertEqual(tmgr.get('g').dtype.type, t)

            self.assertEqual(tmgr.get('a').dtype.type, np.object_)
            self.assertEqual(tmgr.get('b').dtype.type, np.object_)
            if t != np.int64:
                self.assertEqual(tmgr.get('d').dtype.type, np.datetime64)
            else:
                self.assertEqual(tmgr.get('d').dtype.type, t)

    def test_convert(self):
        def _compare(old_mgr, new_mgr):
            """ compare the blocks, numeric compare ==, object don't """
            old_blocks = set(old_mgr.blocks)
            new_blocks = set(new_mgr.blocks)
            self.assertEqual(len(old_blocks), len(new_blocks))

            # compare non-numeric
            for b in old_blocks:
                found = False
                for nb in new_blocks:
                    if (b.values == nb.values).all():
                        found = True
                        break
                self.assertTrue(found)

            for b in new_blocks:
                found = False
                for ob in old_blocks:
                    if (b.values == ob.values).all():
                        found = True
                        break
                self.assertTrue(found)

        # noops
        mgr = create_mgr('f: i8; g: f8')
        new_mgr = mgr.convert()
        _compare(mgr,new_mgr)

        mgr = create_mgr('a, b: object; f: i8; g: f8')
        new_mgr = mgr.convert()
        _compare(mgr,new_mgr)

        # convert
        mgr = create_mgr('a,b,foo: object; f: i8; g: f8')
        mgr.set('a', np.array(['1'] * N, dtype=np.object_))
        mgr.set('b', np.array(['2.'] * N, dtype=np.object_))
        mgr.set('foo', np.array(['foo.'] * N, dtype=np.object_))
        new_mgr = mgr.convert(convert_numeric=True)
        self.assertEqual(new_mgr.get('a').dtype, np.int64)
        self.assertEqual(new_mgr.get('b').dtype, np.float64)
        self.assertEqual(new_mgr.get('foo').dtype, np.object_)
        self.assertEqual(new_mgr.get('f').dtype, np.int64)
        self.assertEqual(new_mgr.get('g').dtype, np.float64)

        mgr = create_mgr('a,b,foo: object; f: i4; bool: bool; dt: datetime;'
                         'i: i8; g: f8; h: f2')
        mgr.set('a', np.array(['1'] * N, dtype=np.object_))
        mgr.set('b', np.array(['2.'] * N, dtype=np.object_))
        mgr.set('foo', np.array(['foo.'] * N, dtype=np.object_))
        new_mgr = mgr.convert(convert_numeric=True)
        self.assertEqual(new_mgr.get('a').dtype, np.int64)
        self.assertEqual(new_mgr.get('b').dtype, np.float64)
        self.assertEqual(new_mgr.get('foo').dtype, np.object_)
        self.assertEqual(new_mgr.get('f').dtype, np.int32)
        self.assertEqual(new_mgr.get('bool').dtype, np.bool_)
        self.assertEqual(new_mgr.get('dt').dtype.type, np.datetime64)
        self.assertEqual(new_mgr.get('i').dtype, np.int64)
        self.assertEqual(new_mgr.get('g').dtype, np.float64)
        self.assertEqual(new_mgr.get('h').dtype, np.float16)

    def test_interleave(self):


        # self
        for dtype in ['f8','i8','object','bool','complex','M8[ns]','m8[ns]']:
            mgr = create_mgr('a: {0}'.format(dtype))
            self.assertEqual(mgr.as_matrix().dtype,dtype)
            mgr = create_mgr('a: {0}; b: {0}'.format(dtype))
            self.assertEqual(mgr.as_matrix().dtype,dtype)

        # will be converted according the actual dtype of the underlying
        mgr = create_mgr('a: category')
        self.assertEqual(mgr.as_matrix().dtype,'i8')
        mgr = create_mgr('a: category; b: category')
        self.assertEqual(mgr.as_matrix().dtype,'i8'),
        mgr = create_mgr('a: category; b: category2')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: category2')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: category2; b: category2')
        self.assertEqual(mgr.as_matrix().dtype,'object')

        # combinations
        mgr = create_mgr('a: f8')
        self.assertEqual(mgr.as_matrix().dtype,'f8')
        mgr = create_mgr('a: f8; b: i8')
        self.assertEqual(mgr.as_matrix().dtype,'f8')
        mgr = create_mgr('a: f4; b: i8')
        self.assertEqual(mgr.as_matrix().dtype,'f4')
        mgr = create_mgr('a: f4; b: i8; d: object')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: bool; b: i8')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: complex')
        self.assertEqual(mgr.as_matrix().dtype,'complex')
        mgr = create_mgr('a: f8; b: category')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: M8[ns]; b: category')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: M8[ns]; b: bool')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: M8[ns]; b: i8')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: m8[ns]; b: bool')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: m8[ns]; b: i8')
        self.assertEqual(mgr.as_matrix().dtype,'object')
        mgr = create_mgr('a: M8[ns]; b: m8[ns]')
        self.assertEqual(mgr.as_matrix().dtype,'object')

    def test_interleave_non_unique_cols(self):
        df = DataFrame([
            [Timestamp('20130101'), 3.5],
            [Timestamp('20130102'), 4.5]],
            columns=['x', 'x'],
            index=[1, 2])

        df_unique = df.copy()
        df_unique.columns = ['x', 'y']
        np.testing.assert_array_equal(df_unique.values, df.values)

    def test_consolidate(self):
        pass

    def test_consolidate_ordering_issues(self):
        self.mgr.set('f', randn(N))
        self.mgr.set('d', randn(N))
        self.mgr.set('b', randn(N))
        self.mgr.set('g', randn(N))
        self.mgr.set('h', randn(N))

        cons = self.mgr.consolidate()
        self.assertEqual(cons.nblocks, 1)
        assert_almost_equal(cons.blocks[0].mgr_locs,
                            np.arange(len(cons.items)))

    def test_reindex_index(self):
        pass

    def test_reindex_items(self):
        # mgr is not consolidated, f8 & f8-2 blocks
        mgr = create_mgr('a: f8; b: i8; c: f8; d: i8; e: f8;'
                         'f: bool; g: f8-2')

        reindexed = mgr.reindex_axis(['g', 'c', 'a', 'd'], axis=0)
        self.assertEqual(reindexed.nblocks, 2)
        assert_almost_equal(reindexed.items, ['g', 'c', 'a', 'd'])
        assert_almost_equal(mgr.get('g',fastpath=False), reindexed.get('g',fastpath=False))
        assert_almost_equal(mgr.get('c',fastpath=False), reindexed.get('c',fastpath=False))
        assert_almost_equal(mgr.get('a',fastpath=False), reindexed.get('a',fastpath=False))
        assert_almost_equal(mgr.get('d',fastpath=False), reindexed.get('d',fastpath=False))
        assert_almost_equal(mgr.get('g').values, reindexed.get('g').values)
        assert_almost_equal(mgr.get('c').values, reindexed.get('c').values)
        assert_almost_equal(mgr.get('a').values, reindexed.get('a').values)
        assert_almost_equal(mgr.get('d').values, reindexed.get('d').values)

    def test_multiindex_xs(self):
        mgr = create_mgr('a,b,c: f8; d,e,f: i8')

        index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
                                   ['one', 'two', 'three']],
                           labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
                                   [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
                           names=['first', 'second'])

        mgr.set_axis(1, index)
        result = mgr.xs('bar', axis=1)
        self.assertEqual(result.shape, (6, 2))
        self.assertEqual(result.axes[1][0], ('bar', 'one'))
        self.assertEqual(result.axes[1][1], ('bar', 'two'))

    def test_get_numeric_data(self):
        mgr = create_mgr('int: int; float: float; complex: complex;'
                         'str: object; bool: bool; obj: object; dt: datetime',
                         item_shape=(3,))
        mgr.set('obj', np.array([1, 2, 3], dtype=np.object_))

        numeric = mgr.get_numeric_data()
        assert_almost_equal(numeric.items, ['int', 'float', 'complex', 'bool'])
        assert_almost_equal(mgr.get('float',fastpath=False), numeric.get('float',fastpath=False))
        assert_almost_equal(mgr.get('float').values, numeric.get('float').values)

        # Check sharing
        numeric.set('float', np.array([100., 200., 300.]))
        assert_almost_equal(mgr.get('float',fastpath=False), np.array([100., 200., 300.]))
        assert_almost_equal(mgr.get('float').values, np.array([100., 200., 300.]))

        numeric2 = mgr.get_numeric_data(copy=True)
        assert_almost_equal(numeric.items, ['int', 'float', 'complex', 'bool'])
        numeric2.set('float', np.array([1000., 2000., 3000.]))
        assert_almost_equal(mgr.get('float',fastpath=False), np.array([100., 200., 300.]))
        assert_almost_equal(mgr.get('float').values, np.array([100., 200., 300.]))

    def test_get_bool_data(self):
        mgr = create_mgr('int: int; float: float; complex: complex;'
                         'str: object; bool: bool; obj: object; dt: datetime',
                         item_shape=(3,))
        mgr.set('obj', np.array([True, False, True], dtype=np.object_))

        bools = mgr.get_bool_data()
        assert_almost_equal(bools.items, ['bool'])
        assert_almost_equal(mgr.get('bool',fastpath=False), bools.get('bool',fastpath=False))
        assert_almost_equal(mgr.get('bool').values, bools.get('bool').values)

        bools.set('bool', np.array([True, False, True]))
        assert_almost_equal(mgr.get('bool',fastpath=False), [True, False, True])
        assert_almost_equal(mgr.get('bool').values, [True, False, True])

        # Check sharing
        bools2 = mgr.get_bool_data(copy=True)
        bools2.set('bool', np.array([False, True, False]))
        assert_almost_equal(mgr.get('bool',fastpath=False), [True, False, True])
        assert_almost_equal(mgr.get('bool').values, [True, False, True])

    def test_unicode_repr_doesnt_raise(self):
        str_repr = repr(create_mgr(u('b,\u05d0: object')))

    def test_missing_unicode_key(self):
        df = DataFrame({"a": [1]})
        try:
            df.ix[:, u("\u05d0")]  # should not raise UnicodeEncodeError
        except KeyError:
            pass  # this is the expected exception

    def test_equals(self):
        # unique items
        bm1 = create_mgr('a,b,c: i8-1; d,e,f: i8-2')
        bm2 = BlockManager(bm1.blocks[::-1], bm1.axes)
        self.assertTrue(bm1.equals(bm2))

        bm1 = create_mgr('a,a,a: i8-1; b,b,b: i8-2')
        bm2 = BlockManager(bm1.blocks[::-1], bm1.axes)
        self.assertTrue(bm1.equals(bm2))

    def test_equals_block_order_different_dtypes(self):
        # GH 9330
        
        mgr_strings = [ 
            "a:i8;b:f8", # basic case
            "a:i8;b:f8;c:c8;d:b", # many types
            "a:i8;e:dt;f:td;g:string", # more types
            "a:i8;b:category;c:category2;d:category2", # categories
            "c:sparse;d:sparse_na;b:f8", # sparse
            ]
        
        for mgr_string in mgr_strings:
            bm = create_mgr(mgr_string)
            block_perms = itertools.permutations(bm.blocks)
            for bm_perm in block_perms:
                bm_this = BlockManager(bm_perm, bm.axes)
                self.assertTrue(bm.equals(bm_this))
                self.assertTrue(bm_this.equals(bm))

    def test_single_mgr_ctor(self):
        mgr = create_single_mgr('f8', num_rows=5)
        self.assertEqual(mgr.as_matrix().tolist(), [0., 1., 2., 3., 4.])


class TestIndexing(object):
    # Nosetests-style data-driven tests.
    #
    # This test applies different indexing routines to block managers and
    # compares the outcome to the result of same operations on np.ndarray.
    #
    # NOTE: sparse (SparseBlock with fill_value != np.nan) fail a lot of tests
    #       and are disabled.

    MANAGERS = [
        create_single_mgr('f8', N),
        create_single_mgr('i8', N),
        #create_single_mgr('sparse', N),
        create_single_mgr('sparse_na', N),

        # 2-dim
        create_mgr('a,b,c,d,e,f: f8', item_shape=(N,)),
        create_mgr('a,b,c,d,e,f: i8', item_shape=(N,)),
        create_mgr('a,b: f8; c,d: i8; e,f: string', item_shape=(N,)),
        create_mgr('a,b: f8; c,d: i8; e,f: f8', item_shape=(N,)),
        #create_mgr('a: sparse', item_shape=(N,)),
        create_mgr('a: sparse_na', item_shape=(N,)),

        # 3-dim
        create_mgr('a,b,c,d,e,f: f8', item_shape=(N, N)),
        create_mgr('a,b,c,d,e,f: i8', item_shape=(N, N)),
        create_mgr('a,b: f8; c,d: i8; e,f: string', item_shape=(N, N)),
        create_mgr('a,b: f8; c,d: i8; e,f: f8', item_shape=(N, N)),
        # create_mgr('a: sparse', item_shape=(1, N)),
    ]

    # MANAGERS = [MANAGERS[6]]

    def test_get_slice(self):
        def assert_slice_ok(mgr, axis, slobj):
            # import pudb; pudb.set_trace()
            mat = mgr.as_matrix()
            sliced = mgr.get_slice(slobj, axis=axis)
            mat_slobj = (slice(None),) * axis + (slobj,)
            assert_almost_equal(mat[mat_slobj], sliced.as_matrix())
            assert_almost_equal(mgr.axes[axis][slobj], sliced.axes[axis])

        for mgr in self.MANAGERS:
            for ax in range(mgr.ndim):
                # slice
                yield assert_slice_ok, mgr, ax, slice(None)
                yield assert_slice_ok, mgr, ax, slice(3)
                yield assert_slice_ok, mgr, ax, slice(100)
                yield assert_slice_ok, mgr, ax, slice(1, 4)
                yield assert_slice_ok, mgr, ax, slice(3, 0, -2)

                # boolean mask
                yield assert_slice_ok, mgr, ax, np.array([], dtype=np.bool_)
                yield (assert_slice_ok, mgr, ax,
                       np.ones(mgr.shape[ax], dtype=np.bool_))
                yield (assert_slice_ok, mgr, ax,
                       np.zeros(mgr.shape[ax], dtype=np.bool_))

                if mgr.shape[ax] >= 3:
                    yield (assert_slice_ok, mgr, ax,
                           np.arange(mgr.shape[ax]) % 3 == 0)
                    yield (assert_slice_ok, mgr, ax,
                           np.array([True, True, False], dtype=np.bool_))

                # fancy indexer
                yield assert_slice_ok, mgr, ax, []
                yield assert_slice_ok, mgr, ax, lrange(mgr.shape[ax])

                if mgr.shape[ax] >= 3:
                    yield assert_slice_ok, mgr, ax, [0, 1, 2]
                    yield assert_slice_ok, mgr, ax, [-1, -2, -3]

    def test_take(self):
        def assert_take_ok(mgr, axis, indexer):
            mat = mgr.as_matrix()
            taken = mgr.take(indexer, axis)
            assert_almost_equal(np.take(mat, indexer, axis),
                                taken.as_matrix())
            assert_almost_equal(mgr.axes[axis].take(indexer),
                                taken.axes[axis])

        for mgr in self.MANAGERS:
            for ax in range(mgr.ndim):
                # take/fancy indexer
                yield assert_take_ok, mgr, ax, []
                yield assert_take_ok, mgr, ax, [0, 0, 0]
                yield assert_take_ok, mgr, ax, lrange(mgr.shape[ax])

                if mgr.shape[ax] >= 3:
                    yield assert_take_ok, mgr, ax, [0, 1, 2]
                    yield assert_take_ok, mgr, ax, [-1, -2, -3]

    def test_reindex_axis(self):
        def assert_reindex_axis_is_ok(mgr, axis, new_labels,
                                      fill_value):
            mat = mgr.as_matrix()
            indexer = mgr.axes[axis].get_indexer_for(new_labels)

            reindexed = mgr.reindex_axis(new_labels, axis,
                                         fill_value=fill_value)
            assert_almost_equal(com.take_nd(mat, indexer, axis,
                                            fill_value=fill_value),
                                reindexed.as_matrix())
            assert_almost_equal(reindexed.axes[axis], new_labels)

        for mgr in self.MANAGERS:
            for ax in range(mgr.ndim):
                for fill_value in (None, np.nan, 100.):
                    yield assert_reindex_axis_is_ok, mgr, ax, [], fill_value
                    yield (assert_reindex_axis_is_ok, mgr, ax,
                           mgr.axes[ax], fill_value)
                    yield (assert_reindex_axis_is_ok, mgr, ax,
                           mgr.axes[ax][[0, 0, 0]], fill_value)
                    yield (assert_reindex_axis_is_ok, mgr, ax,
                           ['foo', 'bar', 'baz'], fill_value)
                    yield (assert_reindex_axis_is_ok, mgr, ax,
                           ['foo', mgr.axes[ax][0], 'baz'], fill_value)

                    if mgr.shape[ax] >= 3:
                        yield (assert_reindex_axis_is_ok, mgr, ax,
                               mgr.axes[ax][:-3], fill_value)
                        yield (assert_reindex_axis_is_ok, mgr, ax,
                               mgr.axes[ax][-3::-1], fill_value)
                        yield (assert_reindex_axis_is_ok, mgr, ax,
                               mgr.axes[ax][[0, 1, 2, 0, 1, 2]], fill_value)

    def test_reindex_indexer(self):
        def assert_reindex_indexer_is_ok(mgr, axis, new_labels, indexer,
                                         fill_value):
            mat = mgr.as_matrix()
            reindexed_mat = com.take_nd(mat, indexer, axis,
                                        fill_value=fill_value)
            reindexed = mgr.reindex_indexer(new_labels, indexer, axis,
                                            fill_value=fill_value)
            assert_almost_equal(reindexed_mat, reindexed.as_matrix())
            assert_almost_equal(reindexed.axes[axis], new_labels)

        for mgr in self.MANAGERS:
            for ax in range(mgr.ndim):
                for fill_value in (None, np.nan, 100.):
                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           [], [], fill_value)
                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           mgr.axes[ax], np.arange(mgr.shape[ax]), fill_value)
                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           ['foo'] * mgr.shape[ax], np.arange(mgr.shape[ax]),
                           fill_value)

                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           mgr.axes[ax][::-1], np.arange(mgr.shape[ax]),
                           fill_value)
                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           mgr.axes[ax], np.arange(mgr.shape[ax])[::-1],
                           fill_value)
                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           ['foo', 'bar', 'baz'], [0, 0, 0], fill_value)
                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           ['foo', 'bar', 'baz'], [-1, 0, -1], fill_value)
                    yield (assert_reindex_indexer_is_ok, mgr, ax,
                           ['foo', mgr.axes[ax][0], 'baz'], [-1, -1, -1],
                           fill_value)

                    if mgr.shape[ax] >= 3:
                        yield (assert_reindex_indexer_is_ok, mgr, ax,
                               ['foo', 'bar', 'baz'], [0, 1, 2], fill_value)


    # test_get_slice(slice_like, axis)
    # take(indexer, axis)
    # reindex_axis(new_labels, axis)
    # reindex_indexer(new_labels, indexer, axis)


class TestBlockPlacement(tm.TestCase):
    _multiprocess_can_split_ = True

    def test_slice_len(self):
        self.assertEqual(len(BlockPlacement(slice(0, 4))), 4)
        self.assertEqual(len(BlockPlacement(slice(0, 4, 2))), 2)
        self.assertEqual(len(BlockPlacement(slice(0, 3, 2))), 2)

        self.assertEqual(len(BlockPlacement(slice(0, 1, 2))), 1)
        self.assertEqual(len(BlockPlacement(slice(1, 0, -1))), 1)

    def test_zero_step_raises(self):
        self.assertRaises(ValueError, BlockPlacement, slice(1, 1, 0))
        self.assertRaises(ValueError, BlockPlacement, slice(1, 2, 0))

    def test_unbounded_slice_raises(self):
        def assert_unbounded_slice_error(slc):
            # assertRaisesRegexp is not available in py2.6
            # self.assertRaisesRegexp(ValueError, "unbounded slice",
            #                         lambda: BlockPlacement(slc))
            self.assertRaises(ValueError, BlockPlacement, slc)

        assert_unbounded_slice_error(slice(None, None))
        assert_unbounded_slice_error(slice(10, None))
        assert_unbounded_slice_error(slice(None, None, -1))
        assert_unbounded_slice_error(slice(None, 10, -1))

        # These are "unbounded" because negative index will change depending on
        # container shape.
        assert_unbounded_slice_error(slice(-1, None))
        assert_unbounded_slice_error(slice(None, -1))
        assert_unbounded_slice_error(slice(-1, -1))
        assert_unbounded_slice_error(slice(-1, None, -1))
        assert_unbounded_slice_error(slice(None, -1, -1))
        assert_unbounded_slice_error(slice(-1, -1, -1))

    def test_not_slice_like_slices(self):
        def assert_not_slice_like(slc):
            self.assertTrue(not BlockPlacement(slc).is_slice_like)

        assert_not_slice_like(slice(0, 0))
        assert_not_slice_like(slice(100, 0))

        assert_not_slice_like(slice(100, 100, -1))
        assert_not_slice_like(slice(0, 100, -1))

        self.assertTrue(not BlockPlacement(slice(0, 0)).is_slice_like)
        self.assertTrue(not BlockPlacement(slice(100, 100)).is_slice_like)

    def test_array_to_slice_conversion(self):
        def assert_as_slice_equals(arr, slc):
            self.assertEqual(BlockPlacement(arr).as_slice, slc)

        assert_as_slice_equals([0], slice(0, 1, 1))
        assert_as_slice_equals([100], slice(100, 101, 1))

        assert_as_slice_equals([0, 1, 2], slice(0, 3, 1))
        assert_as_slice_equals([0, 5, 10], slice(0, 15, 5))
        assert_as_slice_equals([0, 100], slice(0, 200, 100))

        assert_as_slice_equals([2, 1], slice(2, 0, -1))
        assert_as_slice_equals([2, 1, 0], slice(2, None, -1))
        assert_as_slice_equals([100, 0], slice(100, None, -100))

    def test_not_slice_like_arrays(self):
        def assert_not_slice_like(arr):
            self.assertTrue(not BlockPlacement(arr).is_slice_like)

        assert_not_slice_like([])
        assert_not_slice_like([-1])
        assert_not_slice_like([-1, -2, -3])
        assert_not_slice_like([-10])
        assert_not_slice_like([-1])
        assert_not_slice_like([-1, 0, 1, 2])
        assert_not_slice_like([-2, 0, 2, 4])
        assert_not_slice_like([1, 0, -1])
        assert_not_slice_like([1, 1, 1])

    def test_slice_iter(self):
        self.assertEqual(list(BlockPlacement(slice(0, 3))), [0, 1, 2])
        self.assertEqual(list(BlockPlacement(slice(0, 0))), [])
        self.assertEqual(list(BlockPlacement(slice(3, 0))), [])

        self.assertEqual(list(BlockPlacement(slice(3, 0, -1))), [3, 2, 1])
        self.assertEqual(list(BlockPlacement(slice(3, None, -1))),
                          [3, 2, 1, 0])

    def test_slice_to_array_conversion(self):
        def assert_as_array_equals(slc, asarray):
            np.testing.assert_array_equal(
                BlockPlacement(slc).as_array,
                np.asarray(asarray))

        assert_as_array_equals(slice(0, 3), [0, 1, 2])
        assert_as_array_equals(slice(0, 0), [])
        assert_as_array_equals(slice(3, 0), [])

        assert_as_array_equals(slice(3, 0, -1), [3, 2, 1])
        assert_as_array_equals(slice(3, None, -1), [3, 2, 1, 0])
        assert_as_array_equals(slice(31, None, -10), [31, 21, 11, 1])

    def test_blockplacement_add(self):
        bpl = BlockPlacement(slice(0, 5))
        self.assertEqual(bpl.add(1).as_slice, slice(1, 6, 1))
        self.assertEqual(bpl.add(np.arange(5)).as_slice,
                          slice(0, 10, 2))
        self.assertEqual(list(bpl.add(np.arange(5, 0, -1))),
                          [5, 5, 5, 5, 5])

    def test_blockplacement_add_int(self):
        def assert_add_equals(val, inc, result):
            self.assertEqual(list(BlockPlacement(val).add(inc)),
                              result)

        assert_add_equals(slice(0, 0), 0, [])
        assert_add_equals(slice(1, 4), 0, [1, 2, 3])
        assert_add_equals(slice(3, 0, -1), 0, [3, 2, 1])
        assert_add_equals(slice(2, None, -1), 0, [2, 1, 0])
        assert_add_equals([1, 2, 4], 0, [1, 2, 4])

        assert_add_equals(slice(0, 0), 10, [])
        assert_add_equals(slice(1, 4), 10, [11, 12, 13])
        assert_add_equals(slice(3, 0, -1), 10, [13, 12, 11])
        assert_add_equals(slice(2, None, -1), 10, [12, 11, 10])
        assert_add_equals([1, 2, 4], 10, [11, 12, 14])

        assert_add_equals(slice(0, 0), -1, [])
        assert_add_equals(slice(1, 4), -1, [0, 1, 2])
        assert_add_equals(slice(3, 0, -1), -1, [2, 1, 0])
        assert_add_equals([1, 2, 4], -1, [0, 1, 3])

        self.assertRaises(ValueError,
                          lambda: BlockPlacement(slice(1, 4)).add(-10))
        self.assertRaises(ValueError,
                          lambda: BlockPlacement([1, 2, 4]).add(-10))
        self.assertRaises(ValueError,
                          lambda: BlockPlacement(slice(2, None, -1)).add(-1))

    # def test_blockplacement_array_add(self):

    #     assert_add_equals(slice(0, 2), [0, 1, 1], [0, 2, 3])
    #     assert_add_equals(slice(2, None, -1), [1, 1, 0], [3, 2, 0])


if __name__ == '__main__':
    import nose
    nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                   exit=False)
