import nose
import sys
import os
import warnings
import tempfile
from contextlib import contextmanager

import datetime
import numpy as np

import pandas
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
                    date_range, Index, DatetimeIndex, isnull)
from pandas.io.pytables import (HDFStore, get_store, Term, read_hdf,
                                IncompatibilityWarning, PerformanceWarning,
                                AttributeConflictWarning, DuplicateWarning,
                                PossibleDataLossError, ClosedFileError)
from pandas.io import pytables as pytables
import pandas.util.testing as tm
from pandas.util.testing import (assert_panel4d_equal,
                                 assert_panel_equal,
                                 assert_frame_equal,
                                 assert_series_equal)
from pandas import concat, Timestamp
from pandas import compat, _np_version_under1p7
from pandas.compat import range, lrange, u
from pandas.util.testing import assert_produces_warning

try:
    import tables
except ImportError:
    raise nose.SkipTest('no pytables')

from distutils.version import LooseVersion

_default_compressor = LooseVersion(tables.__version__) >= '2.2' \
    and 'blosc' or 'zlib'

_multiprocess_can_split_ = False

# contextmanager to ensure the file cleanup
def safe_remove(path):
    if path is not None:
        try:
            os.remove(path)
        except:
            pass


def safe_close(store):
    try:
        if store is not None:
            store.close()
    except:
        pass


def create_tempfile(path):
    """ create an unopened named temporary file """
    return os.path.join(tempfile.gettempdir(),path)

@contextmanager
def ensure_clean_store(path, mode='a', complevel=None, complib=None,
              fletcher32=False):

    try:

        # put in the temporary path if we don't have one already
        if not len(os.path.dirname(path)):
            path = create_tempfile(path)

        store = HDFStore(path, mode=mode, complevel=complevel,
                         complib=complib, fletcher32=False)
        yield store
    finally:
        safe_close(store)
        if mode == 'w' or mode == 'a':
            safe_remove(path)

@contextmanager
def ensure_clean_path(path):
    """
    return essentially a named temporary file that is not opened
    and deleted on existing; if path is a list, then create and
    return list of filenames
    """
    try:
        if isinstance(path, list):
            filenames = [ create_tempfile(p) for p in path ]
            yield filenames
        else:
            filenames = [ create_tempfile(path) ]
            yield filenames[0]
    finally:
        for f in filenames:
            safe_remove(f)

# set these parameters so we don't have file sharing
tables.parameters.MAX_NUMEXPR_THREADS = 1
tables.parameters.MAX_BLOSC_THREADS   = 1
tables.parameters.MAX_THREADS   = 1

def _maybe_remove(store, key):
    """For tests using tables, try removing the table to be sure there is
    no content from previous tests using the same table name."""
    try:
        store.remove(key)
    except:
        pass


def compat_assert_produces_warning(w,f):
    """ don't produce a warning under PY3 """
    if compat.PY3:
        f()
    else:
        with tm.assert_produces_warning(expected_warning=w):
            f()


class TestHDFStore(tm.TestCase):

    def setUp(self):
        warnings.filterwarnings(action='ignore', category=FutureWarning)

        self.path = 'tmp.__%s__.h5' % tm.rands(10)

    def tearDown(self):
        pass

    def test_factory_fun(self):
        try:
            with get_store(self.path) as tbl:
                raise ValueError('blah')
        except ValueError:
            pass
        finally:
            safe_remove(self.path)

        try:
            with get_store(self.path) as tbl:
                tbl['a'] = tm.makeDataFrame()

            with get_store(self.path) as tbl:
                self.assertEquals(len(tbl), 1)
                self.assertEquals(type(tbl['a']), DataFrame)
        finally:
            safe_remove(self.path)

    def test_conv_read_write(self):

        try:

            def roundtrip(key, obj,**kwargs):
                obj.to_hdf(self.path, key,**kwargs)
                return read_hdf(self.path, key)

            o = tm.makeTimeSeries()
            assert_series_equal(o, roundtrip('series',o))

            o = tm.makeStringSeries()
            assert_series_equal(o, roundtrip('string_series',o))

            o = tm.makeDataFrame()
            assert_frame_equal(o, roundtrip('frame',o))

            o = tm.makePanel()
            assert_panel_equal(o, roundtrip('panel',o))

            # table
            df = DataFrame(dict(A=lrange(5), B=lrange(5)))
            df.to_hdf(self.path,'table',append=True)
            result = read_hdf(self.path, 'table', where = ['index>2'])
            assert_frame_equal(df[df.index>2],result)

        finally:
            safe_remove(self.path)

    def test_api(self):

        # GH4584
        # API issue when to_hdf doesn't acdept append AND format args
        with ensure_clean_path(self.path) as path:

            df = tm.makeDataFrame()
            df.iloc[:10].to_hdf(path,'df',append=True,format='table')
            df.iloc[10:].to_hdf(path,'df',append=True,format='table')
            assert_frame_equal(read_hdf(path,'df'),df)

            # append to False
            df.iloc[:10].to_hdf(path,'df',append=False,format='table')
            df.iloc[10:].to_hdf(path,'df',append=True,format='table')
            assert_frame_equal(read_hdf(path,'df'),df)

        with ensure_clean_path(self.path) as path:

            df = tm.makeDataFrame()
            df.iloc[:10].to_hdf(path,'df',append=True)
            df.iloc[10:].to_hdf(path,'df',append=True,format='table')
            assert_frame_equal(read_hdf(path,'df'),df)

            # append to False
            df.iloc[:10].to_hdf(path,'df',append=False,format='table')
            df.iloc[10:].to_hdf(path,'df',append=True)
            assert_frame_equal(read_hdf(path,'df'),df)

        with ensure_clean_path(self.path) as path:

            df = tm.makeDataFrame()
            df.to_hdf(path,'df',append=False,format='fixed')
            assert_frame_equal(read_hdf(path,'df'),df)

            df.to_hdf(path,'df',append=False,format='f')
            assert_frame_equal(read_hdf(path,'df'),df)

            df.to_hdf(path,'df',append=False)
            assert_frame_equal(read_hdf(path,'df'),df)

            df.to_hdf(path,'df')
            assert_frame_equal(read_hdf(path,'df'),df)

        with ensure_clean_store(self.path) as store:

            path = store._path
            df = tm.makeDataFrame()

            _maybe_remove(store,'df')
            store.append('df',df.iloc[:10],append=True,format='table')
            store.append('df',df.iloc[10:],append=True,format='table')
            assert_frame_equal(store.select('df'),df)

            # append to False
            _maybe_remove(store,'df')
            store.append('df',df.iloc[:10],append=False,format='table')
            store.append('df',df.iloc[10:],append=True,format='table')
            assert_frame_equal(store.select('df'),df)

            # formats
            _maybe_remove(store,'df')
            store.append('df',df.iloc[:10],append=False,format='table')
            store.append('df',df.iloc[10:],append=True,format='table')
            assert_frame_equal(store.select('df'),df)

            _maybe_remove(store,'df')
            store.append('df',df.iloc[:10],append=False,format='table')
            store.append('df',df.iloc[10:],append=True,format=None)
            assert_frame_equal(store.select('df'),df)

        with ensure_clean_path(self.path) as path:

            # invalid
            df = tm.makeDataFrame()
            self.assertRaises(ValueError, df.to_hdf, path,'df',append=True,format='f')
            self.assertRaises(ValueError, df.to_hdf, path,'df',append=True,format='fixed')

            self.assertRaises(TypeError, df.to_hdf, path,'df',append=True,format='foo')
            self.assertRaises(TypeError, df.to_hdf, path,'df',append=False,format='bar')


    def test_api_default_format(self):

        # default_format option
        with ensure_clean_store(self.path) as store:
            df = tm.makeDataFrame()

            pandas.set_option('io.hdf.default_format','fixed')
            _maybe_remove(store,'df')
            store.put('df',df)
            self.assert_(not store.get_storer('df').is_table)
            self.assertRaises(ValueError, store.append, 'df2',df)

            pandas.set_option('io.hdf.default_format','table')
            _maybe_remove(store,'df')
            store.put('df',df)
            self.assert_(store.get_storer('df').is_table)
            _maybe_remove(store,'df2')
            store.append('df2',df)
            self.assert_(store.get_storer('df').is_table)

            pandas.set_option('io.hdf.default_format',None)

        with ensure_clean_path(self.path) as path:

            df = tm.makeDataFrame()

            pandas.set_option('io.hdf.default_format','fixed')
            df.to_hdf(path,'df')
            with get_store(path) as store:
                self.assert_(not store.get_storer('df').is_table)
            self.assertRaises(ValueError, df.to_hdf, path,'df2', append=True)

            pandas.set_option('io.hdf.default_format','table')
            df.to_hdf(path,'df3')
            with get_store(path) as store:
                self.assert_(store.get_storer('df3').is_table)
            df.to_hdf(path,'df4',append=True)
            with get_store(path) as store:
                self.assert_(store.get_storer('df4').is_table)

            pandas.set_option('io.hdf.default_format',None)

    def test_keys(self):

        with ensure_clean_store(self.path) as store:
            store['a'] = tm.makeTimeSeries()
            store['b'] = tm.makeStringSeries()
            store['c'] = tm.makeDataFrame()
            store['d'] = tm.makePanel()
            store['foo/bar'] = tm.makePanel()
            self.assertEquals(len(store), 5)
            self.assert_(set(
                    store.keys()) == set(['/a', '/b', '/c', '/d', '/foo/bar']))

    def test_repr(self):

        with ensure_clean_store(self.path) as store:
            repr(store)
            store['a'] = tm.makeTimeSeries()
            store['b'] = tm.makeStringSeries()
            store['c'] = tm.makeDataFrame()
            store['d'] = tm.makePanel()
            store['foo/bar'] = tm.makePanel()
            store.append('e', tm.makePanel())

            df = tm.makeDataFrame()
            df['obj1'] = 'foo'
            df['obj2'] = 'bar'
            df['bool1'] = df['A'] > 0
            df['bool2'] = df['B'] > 0
            df['bool3'] = True
            df['int1'] = 1
            df['int2'] = 2
            df['timestamp1'] = Timestamp('20010102')
            df['timestamp2'] = Timestamp('20010103')
            df['datetime1']  = datetime.datetime(2001,1,2,0,0)
            df['datetime2']  = datetime.datetime(2001,1,3,0,0)
            df.ix[3:6,['obj1']] = np.nan
            df = df.consolidate().convert_objects()

            warnings.filterwarnings('ignore', category=PerformanceWarning)
            store['df'] = df
            warnings.filterwarnings('always', category=PerformanceWarning)

            # make a random group in hdf space
            store._handle.createGroup(store._handle.root,'bah')

            repr(store)
            str(store)

        # storers
        with ensure_clean_store(self.path) as store:

            df = tm.makeDataFrame()
            store.append('df',df)

            s = store.get_storer('df')
            repr(s)
            str(s)

    def test_contains(self):

        with ensure_clean_store(self.path) as store:
            store['a'] = tm.makeTimeSeries()
            store['b'] = tm.makeDataFrame()
            store['foo/bar'] = tm.makeDataFrame()
            self.assert_('a' in store)
            self.assert_('b' in store)
            self.assert_('c' not in store)
            self.assert_('foo/bar' in store)
            self.assert_('/foo/bar' in store)
            self.assert_('/foo/b' not in store)
            self.assert_('bar' not in store)

            # GH 2694
            warnings.filterwarnings('ignore', category=tables.NaturalNameWarning)
            store['node())'] = tm.makeDataFrame()
            self.assert_('node())' in store)

    def test_versioning(self):

        with ensure_clean_store(self.path) as store:
            store['a'] = tm.makeTimeSeries()
            store['b'] = tm.makeDataFrame()
            df = tm.makeTimeDataFrame()
            _maybe_remove(store, 'df1')
            store.append('df1', df[:10])
            store.append('df1', df[10:])
            self.assert_(store.root.a._v_attrs.pandas_version == '0.10.1')
            self.assert_(store.root.b._v_attrs.pandas_version == '0.10.1')
            self.assert_(store.root.df1._v_attrs.pandas_version == '0.10.1')

            # write a file and wipe its versioning
            _maybe_remove(store, 'df2')
            store.append('df2', df)

            # this is an error because its table_type is appendable, but no version
            # info
            store.get_node('df2')._v_attrs.pandas_version = None
            self.assertRaises(Exception, store.select, 'df2')

    def test_mode(self):

        df = tm.makeTimeDataFrame()

        def check(mode):

            with ensure_clean_path(self.path) as path:

                # constructor
                if mode in ['r','r+']:
                    self.assertRaises(IOError, HDFStore, path, mode=mode)

                else:
                    store = HDFStore(path,mode=mode)
                    self.assert_(store._handle.mode == mode)
                    store.close()

            with ensure_clean_path(self.path) as path:

                # context
                if mode in ['r','r+']:
                    def f():
                        with get_store(path,mode=mode) as store:
                            pass
                    self.assertRaises(IOError, f)
                else:
                    with get_store(path,mode=mode) as store:
                        self.assert_(store._handle.mode == mode)

            with ensure_clean_path(self.path) as path:

                # conv write
                if mode in ['r','r+']:
                    self.assertRaises(IOError, df.to_hdf, path, 'df', mode=mode)
                    df.to_hdf(path,'df',mode='w')
                else:
                    df.to_hdf(path,'df',mode=mode)

                # conv read
                if mode in ['w']:
                    self.assertRaises(KeyError, read_hdf, path, 'df', mode=mode)
                else:
                    result = read_hdf(path,'df',mode=mode)
                    assert_frame_equal(result,df)

        check('r')
        check('r+')
        check('a')
        check('w')

    def test_reopen_handle(self):

        with ensure_clean_path(self.path) as path:

            store = HDFStore(path,mode='a')
            store['a'] = tm.makeTimeSeries()

            # invalid mode change
            self.assertRaises(PossibleDataLossError, store.open, 'w')
            store.close()
            self.assert_(not store.is_open)

            # truncation ok here
            store.open('w')
            self.assert_(store.is_open)
            self.assertEquals(len(store), 0)
            store.close()
            self.assert_(not store.is_open)

            store = HDFStore(path,mode='a')
            store['a'] = tm.makeTimeSeries()

            # reopen as read
            store.open('r')
            self.assert_(store.is_open)
            self.assertEquals(len(store), 1)
            self.assert_(store._mode == 'r')
            store.close()
            self.assert_(not store.is_open)

            # reopen as append
            store.open('a')
            self.assert_(store.is_open)
            self.assertEquals(len(store), 1)
            self.assert_(store._mode == 'a')
            store.close()
            self.assert_(not store.is_open)

            # reopen as append (again)
            store.open('a')
            self.assert_(store.is_open)
            self.assertEquals(len(store), 1)
            self.assert_(store._mode == 'a')
            store.close()
            self.assert_(not store.is_open)

    def test_open_args(self):

        with ensure_clean_path(self.path) as path:

            df = tm.makeDataFrame()

            # create an in memory store
            store = HDFStore(path,mode='a',driver='H5FD_CORE',driver_core_backing_store=0)
            store['df'] = df
            store.append('df2',df)

            tm.assert_frame_equal(store['df'],df)
            tm.assert_frame_equal(store['df2'],df)

            store.close()

            # only supported on pytable >= 3.0.0
            if LooseVersion(tables.__version__) >= '3.0.0':

                # the file should not have actually been written
                self.assert_(os.path.exists(path) is False)

    def test_flush(self):

        with ensure_clean_store(self.path) as store:
            store['a'] = tm.makeTimeSeries()
            store.flush()
            store.flush(fsync=True)

    def test_get(self):

        with ensure_clean_store(self.path) as store:
            store['a'] = tm.makeTimeSeries()
            left = store.get('a')
            right = store['a']
            tm.assert_series_equal(left, right)

            left = store.get('/a')
            right = store['/a']
            tm.assert_series_equal(left, right)

            self.assertRaises(KeyError, store.get, 'b')

    def test_getattr(self):

        with ensure_clean_store(self.path) as store:

            s = tm.makeTimeSeries()
            store['a'] = s

            # test attribute access
            result = store.a
            tm.assert_series_equal(result, s)
            result = getattr(store,'a')
            tm.assert_series_equal(result, s)

            df = tm.makeTimeDataFrame()
            store['df'] = df
            result = store.df
            tm.assert_frame_equal(result, df)

            # errors
            self.assertRaises(AttributeError, getattr, store, 'd')

            for x in ['mode','path','handle','complib']:
                self.assertRaises(AttributeError, getattr, store, x)

            # not stores
            for x in ['mode','path','handle','complib']:
                getattr(store,"_%s" % x)

    def test_put(self):

        with ensure_clean_store(self.path) as store:

            ts = tm.makeTimeSeries()
            df = tm.makeTimeDataFrame()
            store['a'] = ts
            store['b'] = df[:10]
            store['foo/bar/bah'] = df[:10]
            store['foo'] = df[:10]
            store['/foo'] = df[:10]
            store.put('c', df[:10], format='table')

            # not OK, not a table
            self.assertRaises(
                ValueError, store.put, 'b', df[10:], append=True)

            # node does not currently exist, test _is_table_type returns False in
            # this case
            # _maybe_remove(store, 'f')
            # self.assertRaises(ValueError, store.put, 'f', df[10:], append=True)

            # can't put to a table (use append instead)
            self.assertRaises(ValueError, store.put, 'c', df[10:], append=True)

            # overwrite table
            store.put('c', df[:10], format='table', append=False)
            tm.assert_frame_equal(df[:10], store['c'])

    def test_put_string_index(self):

        with ensure_clean_store(self.path) as store:

            index = Index(
                ["I am a very long string index: %s" % i for i in range(20)])
            s = Series(np.arange(20), index=index)
            df = DataFrame({'A': s, 'B': s})

            store['a'] = s
            tm.assert_series_equal(store['a'], s)

            store['b'] = df
            tm.assert_frame_equal(store['b'], df)

            # mixed length
            index = Index(['abcdefghijklmnopqrstuvwxyz1234567890'] + ["I am a very long string index: %s" % i for i in range(20)])
            s = Series(np.arange(21), index=index)
            df = DataFrame({'A': s, 'B': s})
            store['a'] = s
            tm.assert_series_equal(store['a'], s)

            store['b'] = df
            tm.assert_frame_equal(store['b'], df)

    def test_put_compression(self):

        with ensure_clean_store(self.path) as store:
            df = tm.makeTimeDataFrame()

            store.put('c', df, format='table', complib='zlib')
            tm.assert_frame_equal(store['c'], df)

            # can't compress if format='fixed'
            self.assertRaises(ValueError, store.put, 'b', df,
                              format='fixed', complib='zlib')

    def test_put_compression_blosc(self):
        tm.skip_if_no_package('tables', '2.2', app='blosc support')
        df = tm.makeTimeDataFrame()

        with ensure_clean_store(self.path) as store:

            # can't compress if format='fixed'
            self.assertRaises(ValueError, store.put, 'b', df,
                              format='fixed', complib='blosc')

            store.put('c', df, format='table', complib='blosc')
            tm.assert_frame_equal(store['c'], df)

    def test_put_integer(self):
        # non-date, non-string index
        df = DataFrame(np.random.randn(50, 100))
        self._check_roundtrip(df, tm.assert_frame_equal)

    def test_put_mixed_type(self):
        df = tm.makeTimeDataFrame()
        df['obj1'] = 'foo'
        df['obj2'] = 'bar'
        df['bool1'] = df['A'] > 0
        df['bool2'] = df['B'] > 0
        df['bool3'] = True
        df['int1'] = 1
        df['int2'] = 2
        df['timestamp1'] = Timestamp('20010102')
        df['timestamp2'] = Timestamp('20010103')
        df['datetime1'] = datetime.datetime(2001, 1, 2, 0, 0)
        df['datetime2'] = datetime.datetime(2001, 1, 3, 0, 0)
        df.ix[3:6, ['obj1']] = np.nan
        df = df.consolidate().convert_objects()

        with ensure_clean_store(self.path) as store:
            _maybe_remove(store, 'df')

            # cannot use assert_produces_warning here for some reason
            # a PendingDeprecationWarning is also raised?
            warnings.filterwarnings('ignore', category=PerformanceWarning)
            store.put('df',df)
            warnings.filterwarnings('always', category=PerformanceWarning)

            expected = store.get('df')
            tm.assert_frame_equal(expected,df)

    def test_append(self):

        with ensure_clean_store(self.path) as store:
            df = tm.makeTimeDataFrame()
            _maybe_remove(store, 'df1')
            store.append('df1', df[:10])
            store.append('df1', df[10:])
            tm.assert_frame_equal(store['df1'], df)

            _maybe_remove(store, 'df2')
            store.put('df2', df[:10], format='table')
            store.append('df2', df[10:])
            tm.assert_frame_equal(store['df2'], df)

            _maybe_remove(store, 'df3')
            store.append('/df3', df[:10])
            store.append('/df3', df[10:])
            tm.assert_frame_equal(store['df3'], df)

            # this is allowed by almost always don't want to do it
            with tm.assert_produces_warning(expected_warning=tables.NaturalNameWarning):
                _maybe_remove(store, '/df3 foo')
                store.append('/df3 foo', df[:10])
                store.append('/df3 foo', df[10:])
                tm.assert_frame_equal(store['df3 foo'], df)

            # panel
            wp = tm.makePanel()
            _maybe_remove(store, 'wp1')
            store.append('wp1', wp.ix[:, :10, :])
            store.append('wp1', wp.ix[:, 10:, :])
            assert_panel_equal(store['wp1'], wp)

            # ndim
            p4d = tm.makePanel4D()
            _maybe_remove(store, 'p4d')
            store.append('p4d', p4d.ix[:, :, :10, :])
            store.append('p4d', p4d.ix[:, :, 10:, :])
            assert_panel4d_equal(store['p4d'], p4d)

            # test using axis labels
            _maybe_remove(store, 'p4d')
            store.append('p4d', p4d.ix[:, :, :10, :], axes=[
                    'items', 'major_axis', 'minor_axis'])
            store.append('p4d', p4d.ix[:, :, 10:, :], axes=[
                    'items', 'major_axis', 'minor_axis'])
            assert_panel4d_equal(store['p4d'], p4d)

            # test using differnt number of items on each axis
            p4d2 = p4d.copy()
            p4d2['l4'] = p4d['l1']
            p4d2['l5'] = p4d['l1']
            _maybe_remove(store, 'p4d2')
            store.append(
                'p4d2', p4d2, axes=['items', 'major_axis', 'minor_axis'])
            assert_panel4d_equal(store['p4d2'], p4d2)

            # test using differt order of items on the non-index axes
            _maybe_remove(store, 'wp1')
            wp_append1 = wp.ix[:, :10, :]
            store.append('wp1', wp_append1)
            wp_append2 = wp.ix[:, 10:, :].reindex(items=wp.items[::-1])
            store.append('wp1', wp_append2)
            assert_panel_equal(store['wp1'], wp)

            # dtype issues - mizxed type in a single object column
            df = DataFrame(data=[[1, 2], [0, 1], [1, 2], [0, 0]])
            df['mixed_column'] = 'testing'
            df.ix[2, 'mixed_column'] = np.nan
            _maybe_remove(store, 'df')
            store.append('df', df)
            tm.assert_frame_equal(store['df'], df)

            # uints - test storage of uints
            uint_data = DataFrame({'u08' : Series(np.random.random_integers(0, high=255, size=5), dtype=np.uint8),
                                   'u16' : Series(np.random.random_integers(0, high=65535, size=5), dtype=np.uint16),
                                   'u32' : Series(np.random.random_integers(0, high=2**30, size=5), dtype=np.uint32),
                                   'u64' : Series([2**58, 2**59, 2**60, 2**61, 2**62], dtype=np.uint64)},
                                  index=np.arange(5))
            _maybe_remove(store, 'uints')
            store.append('uints', uint_data)
            tm.assert_frame_equal(store['uints'], uint_data)

            # uints - test storage of uints in indexable columns
            _maybe_remove(store, 'uints')
            store.append('uints', uint_data, data_columns=['u08','u16','u32']) # 64-bit indices not yet supported
            tm.assert_frame_equal(store['uints'], uint_data)

    def test_append_series(self):

        with ensure_clean_store(self.path) as store:

            # basic
            ss = tm.makeStringSeries()
            ts = tm.makeTimeSeries()
            ns = Series(np.arange(100))

            store.append('ss', ss)
            result = store['ss']
            tm.assert_series_equal(result, ss)
            self.assert_(result.name is None)

            store.append('ts', ts)
            result = store['ts']
            tm.assert_series_equal(result, ts)
            self.assert_(result.name is None)

            ns.name = 'foo'
            store.append('ns', ns)
            result = store['ns']
            tm.assert_series_equal(result, ns)
            self.assert_(result.name == ns.name)

            # select on the values
            expected = ns[ns>60]
            result = store.select('ns',Term('foo>60'))
            tm.assert_series_equal(result,expected)

            # select on the index and values
            expected = ns[(ns>70) & (ns.index<90)]
            result = store.select('ns',[Term('foo>70'), Term('index<90')])
            tm.assert_series_equal(result,expected)

            # multi-index
            mi = DataFrame(np.random.randn(5,1),columns=['A'])
            mi['B'] = np.arange(len(mi))
            mi['C'] = 'foo'
            mi.loc[3:5,'C'] = 'bar'
            mi.set_index(['C','B'],inplace=True)
            s = mi.stack()
            s.index = s.index.droplevel(2)
            store.append('mi', s)
            tm.assert_series_equal(store['mi'], s)

    def test_store_index_types(self):
        # GH5386
        # test storing various index types

        with ensure_clean_store(self.path) as store:

            def check(format,index):
                df = DataFrame(np.random.randn(10,2),columns=list('AB'))
                df.index = index(len(df))

                _maybe_remove(store, 'df')
                store.put('df',df,format=format)
                assert_frame_equal(df,store['df'])

            for index in [ tm.makeFloatIndex, tm.makeStringIndex, tm.makeIntIndex,
                           tm.makeDateIndex, tm.makePeriodIndex ]:

                check('table',index)
                check('fixed',index)

            # unicode
            index = tm.makeUnicodeIndex
            if compat.PY3:
                check('table',index)
                check('fixed',index)
            else:

                # only support for fixed types (and they have a perf warning)
                self.assertRaises(TypeError, check, 'table', index)
                with tm.assert_produces_warning(expected_warning=PerformanceWarning):
                    check('fixed',index)

    def test_encoding(self):

        if LooseVersion(tables.__version__) < '3.0.0':
            raise nose.SkipTest('tables version does not support proper encoding')
        if sys.byteorder != 'little':
            raise nose.SkipTest('system byteorder is not little')

        with ensure_clean_store(self.path) as store:
            df = DataFrame(dict(A='foo',B='bar'),index=range(5))
            df.loc[2,'A'] = np.nan
            df.loc[3,'B'] = np.nan
            _maybe_remove(store, 'df')
            store.append('df', df, encoding='ascii')
            tm.assert_frame_equal(store['df'], df)

            expected = df.reindex(columns=['A'])
            result = store.select('df',Term('columns=A',encoding='ascii'))
            tm.assert_frame_equal(result,expected)

    def test_append_some_nans(self):

        with ensure_clean_store(self.path) as store:
            df = DataFrame({'A' : Series(np.random.randn(20)).astype('int32'),
                            'A1' : np.random.randn(20),
                            'A2' : np.random.randn(20),
                            'B' : 'foo', 'C' : 'bar', 'D' : Timestamp("20010101"), 'E' : datetime.datetime(2001,1,2,0,0) },
                           index=np.arange(20))
            # some nans
            _maybe_remove(store, 'df1')
            df.ix[0:15,['A1','B','D','E']] = np.nan
            store.append('df1', df[:10])
            store.append('df1', df[10:])
            tm.assert_frame_equal(store['df1'], df)

            # first column
            df1 = df.copy()
            df1.ix[:,'A1'] = np.nan
            _maybe_remove(store, 'df1')
            store.append('df1', df1[:10])
            store.append('df1', df1[10:])
            tm.assert_frame_equal(store['df1'], df1)

            # 2nd column
            df2 = df.copy()
            df2.ix[:,'A2'] = np.nan
            _maybe_remove(store, 'df2')
            store.append('df2', df2[:10])
            store.append('df2', df2[10:])
            tm.assert_frame_equal(store['df2'], df2)

            # datetimes
            df3 = df.copy()
            df3.ix[:,'E'] = np.nan
            _maybe_remove(store, 'df3')
            store.append('df3', df3[:10])
            store.append('df3', df3[10:])
            tm.assert_frame_equal(store['df3'], df3)

    def test_append_all_nans(self):

        with ensure_clean_store(self.path) as store:

            df = DataFrame({'A1' : np.random.randn(20),
                            'A2' : np.random.randn(20)},
                           index=np.arange(20))
            df.ix[0:15,:] = np.nan


            # nan some entire rows (dropna=True)
            _maybe_remove(store, 'df')
            store.append('df', df[:10], dropna=True)
            store.append('df', df[10:], dropna=True)
            tm.assert_frame_equal(store['df'], df[-4:])

            # nan some entire rows (dropna=False)
            _maybe_remove(store, 'df2')
            store.append('df2', df[:10], dropna=False)
            store.append('df2', df[10:], dropna=False)
            tm.assert_frame_equal(store['df2'], df)

            # tests the option io.hdf.dropna_table
            pandas.set_option('io.hdf.dropna_table',False)
            _maybe_remove(store, 'df3')
            store.append('df3', df[:10])
            store.append('df3', df[10:])
            tm.assert_frame_equal(store['df3'], df)

            pandas.set_option('io.hdf.dropna_table',True)
            _maybe_remove(store, 'df4')
            store.append('df4', df[:10])
            store.append('df4', df[10:])
            tm.assert_frame_equal(store['df4'], df[-4:])

            # nan some entire rows (string are still written!)
            df = DataFrame({'A1' : np.random.randn(20),
                            'A2' : np.random.randn(20),
                            'B' : 'foo', 'C' : 'bar'},
                           index=np.arange(20))

            df.ix[0:15,:] = np.nan

            _maybe_remove(store, 'df')
            store.append('df', df[:10], dropna=True)
            store.append('df', df[10:], dropna=True)
            tm.assert_frame_equal(store['df'], df)

            _maybe_remove(store, 'df2')
            store.append('df2', df[:10], dropna=False)
            store.append('df2', df[10:], dropna=False)
            tm.assert_frame_equal(store['df2'], df)

            # nan some entire rows (but since we have dates they are still written!)
            df = DataFrame({'A1' : np.random.randn(20),
                            'A2' : np.random.randn(20),
                            'B' : 'foo', 'C' : 'bar', 'D' : Timestamp("20010101"), 'E' : datetime.datetime(2001,1,2,0,0) },
                           index=np.arange(20))

            df.ix[0:15,:] = np.nan

            _maybe_remove(store, 'df')
            store.append('df', df[:10], dropna=True)
            store.append('df', df[10:], dropna=True)
            tm.assert_frame_equal(store['df'], df)

            _maybe_remove(store, 'df2')
            store.append('df2', df[:10], dropna=False)
            store.append('df2', df[10:], dropna=False)
            tm.assert_frame_equal(store['df2'], df)

    def test_append_frame_column_oriented(self):

        with ensure_clean_store(self.path) as store:

            # column oriented
            df = tm.makeTimeDataFrame()
            _maybe_remove(store, 'df1')
            store.append('df1', df.ix[:, :2], axes=['columns'])
            store.append('df1', df.ix[:, 2:])
            tm.assert_frame_equal(store['df1'], df)

            result = store.select('df1', 'columns=A')
            expected = df.reindex(columns=['A'])
            tm.assert_frame_equal(expected, result)

            # selection on the non-indexable
            result = store.select(
                'df1', ('columns=A', Term('index=df.index[0:4]')))
            expected = df.reindex(columns=['A'], index=df.index[0:4])
            tm.assert_frame_equal(expected, result)

            # this isn't supported
            self.assertRaises(TypeError, store.select, 'df1', (
                    'columns=A', Term('index>df.index[4]')))

    def test_append_with_different_block_ordering(self):

        #GH 4096; using same frames, but different block orderings
        with ensure_clean_store(self.path) as store:

            for i in range(10):

                df = DataFrame(np.random.randn(10,2),columns=list('AB'))
                df['index'] = range(10)
                df['index'] += i*10
                df['int64'] = Series([1]*len(df),dtype='int64')
                df['int16'] = Series([1]*len(df),dtype='int16')

                if i % 2 == 0:
                    del df['int64']
                    df['int64'] = Series([1]*len(df),dtype='int64')
                if i % 3 == 0:
                    a = df.pop('A')
                    df['A'] = a

                df.set_index('index',inplace=True)

                store.append('df',df)

        # test a different ordering but with more fields (like invalid combinate)
        with ensure_clean_store(self.path) as store:

            df = DataFrame(np.random.randn(10,2),columns=list('AB'), dtype='float64')
            df['int64'] = Series([1]*len(df),dtype='int64')
            df['int16'] = Series([1]*len(df),dtype='int16')
            store.append('df',df)

            # store additonal fields in different blocks
            df['int16_2'] = Series([1]*len(df),dtype='int16')
            self.assertRaises(ValueError, store.append, 'df', df)

            # store multile additonal fields in different blocks
            df['float_3'] = Series([1.]*len(df),dtype='float64')
            self.assertRaises(ValueError, store.append, 'df', df)

    def test_ndim_indexables(self):
        """ test using ndim tables in new ways"""

        with ensure_clean_store(self.path) as store:

            p4d = tm.makePanel4D()

            def check_indexers(key, indexers):
                for i, idx in enumerate(indexers):
                    self.assert_(getattr(getattr(
                                store.root, key).table.description, idx)._v_pos == i)

            # append then change (will take existing schema)
            indexers = ['items', 'major_axis', 'minor_axis']

            _maybe_remove(store, 'p4d')
            store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
            store.append('p4d', p4d.ix[:, :, 10:, :])
            assert_panel4d_equal(store.select('p4d'), p4d)
            check_indexers('p4d', indexers)

            # same as above, but try to append with differnt axes
            _maybe_remove(store, 'p4d')
            store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
            store.append('p4d', p4d.ix[:, :, 10:, :], axes=[
                    'labels', 'items', 'major_axis'])
            assert_panel4d_equal(store.select('p4d'), p4d)
            check_indexers('p4d', indexers)

            # pass incorrect number of axes
            _maybe_remove(store, 'p4d')
            self.assertRaises(ValueError, store.append, 'p4d', p4d.ix[
                    :, :, :10, :], axes=['major_axis', 'minor_axis'])

            # different than default indexables #1
            indexers = ['labels', 'major_axis', 'minor_axis']
            _maybe_remove(store, 'p4d')
            store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
            store.append('p4d', p4d.ix[:, :, 10:, :])
            assert_panel4d_equal(store['p4d'], p4d)
            check_indexers('p4d', indexers)

            # different than default indexables #2
            indexers = ['major_axis', 'labels', 'minor_axis']
            _maybe_remove(store, 'p4d')
            store.append('p4d', p4d.ix[:, :, :10, :], axes=indexers)
            store.append('p4d', p4d.ix[:, :, 10:, :])
            assert_panel4d_equal(store['p4d'], p4d)
            check_indexers('p4d', indexers)

            # partial selection
            result = store.select('p4d', ['labels=l1'])
            expected = p4d.reindex(labels=['l1'])
            assert_panel4d_equal(result, expected)

            # partial selection2
            result = store.select('p4d', [Term(
                        'labels=l1'), Term('items=ItemA'), Term('minor_axis=B')])
            expected = p4d.reindex(
                labels=['l1'], items=['ItemA'], minor_axis=['B'])
            assert_panel4d_equal(result, expected)

            # non-existant partial selection
            result = store.select('p4d', [Term(
                        'labels=l1'), Term('items=Item1'), Term('minor_axis=B')])
            expected = p4d.reindex(labels=['l1'], items=[], minor_axis=['B'])
            assert_panel4d_equal(result, expected)

    def test_append_with_strings(self):

        with ensure_clean_store(self.path) as store:
            wp = tm.makePanel()
            wp2 = wp.rename_axis(
                dict([(x, "%s_extra" % x) for x in wp.minor_axis]), axis=2)

            def check_col(key,name,size):
                self.assert_(getattr(store.get_storer(key).table.description,name).itemsize == size)

            store.append('s1', wp, min_itemsize=20)
            store.append('s1', wp2)
            expected = concat([wp, wp2], axis=2)
            expected = expected.reindex(minor_axis=sorted(expected.minor_axis))
            assert_panel_equal(store['s1'], expected)
            check_col('s1', 'minor_axis', 20)

            # test dict format
            store.append('s2', wp, min_itemsize={'minor_axis': 20})
            store.append('s2', wp2)
            expected = concat([wp, wp2], axis=2)
            expected = expected.reindex(minor_axis=sorted(expected.minor_axis))
            assert_panel_equal(store['s2'], expected)
            check_col('s2', 'minor_axis', 20)

            # apply the wrong field (similar to #1)
            store.append('s3', wp, min_itemsize={'major_axis': 20})
            self.assertRaises(ValueError, store.append, 's3', wp2)

            # test truncation of bigger strings
            store.append('s4', wp)
            self.assertRaises(ValueError, store.append, 's4', wp2)

            # avoid truncation on elements
            df = DataFrame([[123, 'asdqwerty'], [345, 'dggnhebbsdfbdfb']])
            store.append('df_big', df)
            tm.assert_frame_equal(store.select('df_big'), df)
            check_col('df_big', 'values_block_1', 15)

            # appending smaller string ok
            df2 = DataFrame([[124, 'asdqy'], [346, 'dggnhefbdfb']])
            store.append('df_big', df2)
            expected = concat([df, df2])
            tm.assert_frame_equal(store.select('df_big'), expected)
            check_col('df_big', 'values_block_1', 15)

            # avoid truncation on elements
            df = DataFrame([[123, 'asdqwerty'], [345, 'dggnhebbsdfbdfb']])
            store.append('df_big2', df, min_itemsize={'values': 50})
            tm.assert_frame_equal(store.select('df_big2'), df)
            check_col('df_big2', 'values_block_1', 50)

            # bigger string on next append
            store.append('df_new', df)
            df_new = DataFrame(
                [[124, 'abcdefqhij'], [346, 'abcdefghijklmnopqrtsuvwxyz']])
            self.assertRaises(ValueError, store.append, 'df_new', df_new)

            # with nans
            _maybe_remove(store, 'df')
            df = tm.makeTimeDataFrame()
            df['string'] = 'foo'
            df.ix[1:4, 'string'] = np.nan
            df['string2'] = 'bar'
            df.ix[4:8, 'string2'] = np.nan
            df['string3'] = 'bah'
            df.ix[1:, 'string3'] = np.nan
            store.append('df', df)
            result = store.select('df')
            tm.assert_frame_equal(result, df)

        with ensure_clean_store(self.path) as store:

            def check_col(key,name,size):
                self.assert_(getattr(store.get_storer(key).table.description,name).itemsize == size)

            df = DataFrame(dict(A = 'foo', B = 'bar'),index=range(10))

            # a min_itemsize that creates a data_column
            _maybe_remove(store, 'df')
            store.append('df', df, min_itemsize={'A' : 200 })
            check_col('df', 'A', 200)
            self.assert_(store.get_storer('df').data_columns == ['A'])

            # a min_itemsize that creates a data_column2
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns = ['B'], min_itemsize={'A' : 200 })
            check_col('df', 'A', 200)
            self.assert_(store.get_storer('df').data_columns == ['B','A'])

            # a min_itemsize that creates a data_column2
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns = ['B'], min_itemsize={'values' : 200 })
            check_col('df', 'B', 200)
            check_col('df', 'values_block_0', 200)
            self.assert_(store.get_storer('df').data_columns == ['B'])

            # infer the .typ on subsequent appends
            _maybe_remove(store, 'df')
            store.append('df', df[:5], min_itemsize=200)
            store.append('df', df[5:], min_itemsize=200)
            tm.assert_frame_equal(store['df'], df)

            # invalid min_itemsize keys
            df = DataFrame(['foo','foo','foo','barh','barh','barh'],columns=['A'])
            _maybe_remove(store, 'df')
            self.assertRaises(ValueError, store.append, 'df', df, min_itemsize={'foo' : 20, 'foobar' : 20})

    def test_append_with_data_columns(self):

        with ensure_clean_store(self.path) as store:
            df = tm.makeTimeDataFrame()
            df.loc[:,'B'].iloc[0] = 1.
            _maybe_remove(store, 'df')
            store.append('df', df[:2], data_columns=['B'])
            store.append('df', df[2:])
            tm.assert_frame_equal(store['df'], df)

            # check that we have indicies created
            assert(store._handle.root.df.table.cols.index.is_indexed is True)
            assert(store._handle.root.df.table.cols.B.is_indexed is True)

            # data column searching
            result = store.select('df', [Term('B>0')])
            expected = df[df.B > 0]
            tm.assert_frame_equal(result, expected)

            # data column searching (with an indexable and a data_columns)
            result = store.select(
                'df', [Term('B>0'), Term('index>df.index[3]')])
            df_new = df.reindex(index=df.index[4:])
            expected = df_new[df_new.B > 0]
            tm.assert_frame_equal(result, expected)

            # data column selection with a string data_column
            df_new = df.copy()
            df_new['string'] = 'foo'
            df_new['string'][1:4] = np.nan
            df_new['string'][5:6] = 'bar'
            _maybe_remove(store, 'df')
            store.append('df', df_new, data_columns=['string'])
            result = store.select('df', [Term('string=foo')])
            expected = df_new[df_new.string == 'foo']
            tm.assert_frame_equal(result, expected)

            # using min_itemsize and a data column
            def check_col(key,name,size):
                self.assert_(getattr(store.get_storer(key).table.description,name).itemsize == size)

        with ensure_clean_store(self.path) as store:
            _maybe_remove(store, 'df')
            store.append('df', df_new, data_columns=['string'],
                         min_itemsize={'string': 30})
            check_col('df', 'string', 30)
            _maybe_remove(store, 'df')
            store.append(
                'df', df_new, data_columns=['string'], min_itemsize=30)
            check_col('df', 'string', 30)
            _maybe_remove(store, 'df')
            store.append('df', df_new, data_columns=['string'],
                         min_itemsize={'values': 30})
            check_col('df', 'string', 30)

        with ensure_clean_store(self.path) as store:
            df_new['string2'] = 'foobarbah'
            df_new['string_block1'] = 'foobarbah1'
            df_new['string_block2'] = 'foobarbah2'
            _maybe_remove(store, 'df')
            store.append('df', df_new, data_columns=['string', 'string2'], min_itemsize={'string': 30, 'string2': 40, 'values': 50})
            check_col('df', 'string', 30)
            check_col('df', 'string2', 40)
            check_col('df', 'values_block_1', 50)

        with ensure_clean_store(self.path) as store:
            # multiple data columns
            df_new = df.copy()
            df_new.loc[:,'A'].iloc[0] = 1.
            df_new.loc[:,'B'].iloc[0] = -1.
            df_new['string'] = 'foo'
            df_new['string'][1:4] = np.nan
            df_new['string'][5:6] = 'bar'
            df_new['string2'] = 'foo'
            df_new['string2'][2:5] = np.nan
            df_new['string2'][7:8] = 'bar'
            _maybe_remove(store, 'df')
            store.append(
                'df', df_new, data_columns=['A', 'B', 'string', 'string2'])
            result = store.select('df', [Term('string=foo'), Term(
                        'string2=foo'), Term('A>0'), Term('B<0')])
            expected = df_new[(df_new.string == 'foo') & (
                    df_new.string2 == 'foo') & (df_new.A > 0) & (df_new.B < 0)]
            tm.assert_frame_equal(result, expected, check_index_type=False)

            # yield an empty frame
            result = store.select('df', [Term('string=foo'), Term(
                        'string2=cool')])
            expected = df_new[(df_new.string == 'foo') & (
                    df_new.string2 == 'cool')]
            tm.assert_frame_equal(result, expected, check_index_type=False)

        with ensure_clean_store(self.path) as store:
            # doc example
            df_dc = df.copy()
            df_dc['string'] = 'foo'
            df_dc.ix[4:6, 'string'] = np.nan
            df_dc.ix[7:9, 'string'] = 'bar'
            df_dc['string2'] = 'cool'
            df_dc['datetime'] = Timestamp('20010102')
            df_dc = df_dc.convert_objects()
            df_dc.ix[3:5, ['A', 'B', 'datetime']] = np.nan

            _maybe_remove(store, 'df_dc')
            store.append('df_dc', df_dc, data_columns=['B', 'C',
                                                       'string', 'string2', 'datetime'])
            result = store.select('df_dc', [Term('B>0')])

            expected = df_dc[df_dc.B > 0]
            tm.assert_frame_equal(result, expected, check_index_type=False)

            result = store.select(
                'df_dc', ['B > 0', 'C > 0', 'string == foo'])
            expected = df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (
                    df_dc.string == 'foo')]
            tm.assert_frame_equal(result, expected, check_index_type=False)

        with ensure_clean_store(self.path) as store:
            # doc example part 2
            np.random.seed(1234)
            index = date_range('1/1/2000', periods=8)
            df_dc = DataFrame(np.random.randn(8, 3), index=index,
                              columns=['A', 'B', 'C'])
            df_dc['string'] = 'foo'
            df_dc.ix[4:6,'string'] = np.nan
            df_dc.ix[7:9,'string'] = 'bar'
            df_dc.ix[:,['B','C']] = df_dc.ix[:,['B','C']].abs()
            df_dc['string2'] = 'cool'

            # on-disk operations
            store.append('df_dc', df_dc, data_columns = ['B', 'C', 'string', 'string2'])

            result = store.select('df_dc', [ Term('B>0') ])
            expected = df_dc[df_dc.B>0]
            tm.assert_frame_equal(result,expected)

            result = store.select('df_dc', ['B > 0', 'C > 0', 'string == "foo"'])
            expected = df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == 'foo')]
            tm.assert_frame_equal(result,expected)

        with ensure_clean_store(self.path) as store:
            # panel
            # GH5717 not handling data_columns
            np.random.seed(1234)
            p = tm.makePanel()

            store.append('p1',p)
            tm.assert_panel_equal(store.select('p1'),p)

            store.append('p2',p,data_columns=True)
            tm.assert_panel_equal(store.select('p2'),p)

            result = store.select('p2',where='ItemA>0')
            expected = p.to_frame()
            expected = expected[expected['ItemA']>0]
            tm.assert_frame_equal(result.to_frame(),expected)

            result = store.select('p2',where='ItemA>0 & minor_axis=["A","B"]')
            expected = p.to_frame()
            expected = expected[expected['ItemA']>0]
            expected = expected[expected.reset_index(level=['major']).index.isin(['A','B'])]
            tm.assert_frame_equal(result.to_frame(),expected)

    def test_create_table_index(self):

        with ensure_clean_store(self.path) as store:

            def col(t,column):
                return getattr(store.get_storer(t).table.cols,column)

            # index=False
            wp = tm.makePanel()
            store.append('p5', wp, index=False)
            store.create_table_index('p5', columns=['major_axis'])
            assert(col('p5', 'major_axis').is_indexed is True)
            assert(col('p5', 'minor_axis').is_indexed is False)

            # index=True
            store.append('p5i', wp, index=True)
            assert(col('p5i', 'major_axis').is_indexed is True)
            assert(col('p5i', 'minor_axis').is_indexed is True)

            # default optlevels
            store.get_storer('p5').create_index()
            assert(col('p5', 'major_axis').index.optlevel == 6)
            assert(col('p5', 'minor_axis').index.kind == 'medium')

            # let's change the indexing scheme
            store.create_table_index('p5')
            assert(col('p5', 'major_axis').index.optlevel == 6)
            assert(col('p5', 'minor_axis').index.kind == 'medium')
            store.create_table_index('p5', optlevel=9)
            assert(col('p5', 'major_axis').index.optlevel == 9)
            assert(col('p5', 'minor_axis').index.kind == 'medium')
            store.create_table_index('p5', kind='full')
            assert(col('p5', 'major_axis').index.optlevel == 9)
            assert(col('p5', 'minor_axis').index.kind == 'full')
            store.create_table_index('p5', optlevel=1, kind='light')
            assert(col('p5', 'major_axis').index.optlevel == 1)
            assert(col('p5', 'minor_axis').index.kind == 'light')

            # data columns
            df = tm.makeTimeDataFrame()
            df['string'] = 'foo'
            df['string2'] = 'bar'
            store.append('f', df, data_columns=['string', 'string2'])
            assert(col('f', 'index').is_indexed is True)
            assert(col('f', 'string').is_indexed is True)
            assert(col('f', 'string2').is_indexed is True)

            # specify index=columns
            store.append(
                'f2', df, index=['string'], data_columns=['string', 'string2'])
            assert(col('f2', 'index').is_indexed is False)
            assert(col('f2', 'string').is_indexed is True)
            assert(col('f2', 'string2').is_indexed is False)

            # try to index a non-table
            _maybe_remove(store, 'f2')
            store.put('f2', df)
            self.assertRaises(TypeError, store.create_table_index, 'f2')

            # try to change the version supports flag
            from pandas.io import pytables
            pytables._table_supports_index = False
            self.assertRaises(Exception, store.create_table_index, 'f')

            # test out some versions
            original = tables.__version__

            for v in ['2.2', '2.2b']:
                pytables._table_mod = None
                pytables._table_supports_index = False
                tables.__version__ = v
                self.assertRaises(Exception, store.create_table_index, 'f')

            for v in ['2.3.1', '2.3.1b', '2.4dev', '2.4', original]:
                pytables._table_mod = None
                pytables._table_supports_index = False
                tables.__version__ = v
                store.create_table_index('f')
                pytables._table_mod = None
                pytables._table_supports_index = False
                tables.__version__ = original

    def test_big_table_frame(self):
        raise nose.SkipTest('no big table frame')

        # create and write a big table
        df = DataFrame(np.random.randn(2000 * 100, 100), index=range(
            2000 * 100), columns=['E%03d' % i for i in range(100)])
        for x in range(20):
            df['String%03d' % x] = 'string%03d' % x

        import time
        x = time.time()
        with ensure_clean_store(self.path,mode='w') as store:
            store.append('df', df)
            rows = store.root.df.table.nrows
            recons = store.select('df')
            assert isinstance(recons, DataFrame)

        print("\nbig_table frame [%s] -> %5.2f" % (rows, time.time() - x))

    def test_big_table2_frame(self):
        # this is a really big table: 1m rows x 60 float columns, 20 string, 20 datetime
        # columns
        raise nose.SkipTest('no big table2 frame')

        # create and write a big table
        print("\nbig_table2 start")
        import time
        start_time = time.time()
        df = DataFrame(np.random.randn(1000 * 1000, 60), index=range(int(
            1000 * 1000)), columns=['E%03d' % i for i in range(60)])
        for x in range(20):
            df['String%03d' % x] = 'string%03d' % x
        for x in range(20):
            df['datetime%03d' % x] = datetime.datetime(2001, 1, 2, 0, 0)

        print("\nbig_table2 frame (creation of df) [rows->%s] -> %5.2f"
              % (len(df.index), time.time() - start_time))

        def f(chunksize):
            with ensure_clean_store(self.path,mode='w') as store:
                store.append('df', df, chunksize=chunksize)
                r = store.root.df.table.nrows
                return r

        for c in [10000, 50000, 250000]:
            start_time = time.time()
            print("big_table2 frame [chunk->%s]" % c)
            rows = f(c)
            print("big_table2 frame [rows->%s,chunk->%s] -> %5.2f"
                  % (rows, c, time.time() - start_time))

    def test_big_put_frame(self):
        raise nose.SkipTest('no big put frame')

        print("\nbig_put start")
        import time
        start_time = time.time()
        df = DataFrame(np.random.randn(1000 * 1000, 60), index=range(int(
            1000 * 1000)), columns=['E%03d' % i for i in range(60)])
        for x in range(20):
            df['String%03d' % x] = 'string%03d' % x
        for x in range(20):
            df['datetime%03d' % x] = datetime.datetime(2001, 1, 2, 0, 0)

        print("\nbig_put frame (creation of df) [rows->%s] -> %5.2f"
              % (len(df.index), time.time() - start_time))

        with ensure_clean_store(self.path, mode='w') as store:
            start_time = time.time()
            store = HDFStore(self.path, mode='w')
            store.put('df', df)

            print(df.get_dtype_counts())
            print("big_put frame [shape->%s] -> %5.2f"
                  % (df.shape, time.time() - start_time))

    def test_big_table_panel(self):
        raise nose.SkipTest('no big table panel')

        # create and write a big table
        wp = Panel(
            np.random.randn(20, 1000, 1000), items=['Item%03d' % i for i in range(20)],
            major_axis=date_range('1/1/2000', periods=1000), minor_axis=['E%03d' % i for i in range(1000)])

        wp.ix[:, 100:200, 300:400] = np.nan

        for x in range(100):
            wp['String%03d'] = 'string%03d' % x

        import time
        x = time.time()


        with ensure_clean_store(self.path, mode='w') as store:
            store.append('wp', wp)
            rows = store.root.wp.table.nrows
            recons = store.select('wp')
            assert isinstance(recons, Panel)

        print("\nbig_table panel [%s] -> %5.2f" % (rows, time.time() - x))

    def test_append_diff_item_order(self):

        wp = tm.makePanel()
        wp1 = wp.ix[:, :10, :]
        wp2 = wp.ix[['ItemC', 'ItemB', 'ItemA'], 10:, :]

        with ensure_clean_store(self.path) as store:
            store.put('panel', wp1, format='table')
            self.assertRaises(ValueError, store.put, 'panel', wp2,
                              append=True)

    def test_append_hierarchical(self):
        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=['foo', 'bar'])
        df = DataFrame(np.random.randn(10, 3), index=index,
                       columns=['A', 'B', 'C'])

        with ensure_clean_store(self.path) as store:
            store.append('mi', df)
            result = store.select('mi')
            tm.assert_frame_equal(result, df)

            # GH 3748
            result = store.select('mi',columns=['A','B'])
            expected = df.reindex(columns=['A','B'])
            tm.assert_frame_equal(result,expected)

        with ensure_clean_path('test.hdf') as path:
            df.to_hdf(path,'df',format='table')
            result = read_hdf(path,'df',columns=['A','B'])
            expected = df.reindex(columns=['A','B'])
            tm.assert_frame_equal(result,expected)

    def test_column_multiindex(self):
        # GH 4710
        # recreate multi-indexes properly

        index = MultiIndex.from_tuples([('A','a'), ('A','b'), ('B','a'), ('B','b')], names=['first','second'])
        df = DataFrame(np.arange(12).reshape(3,4), columns=index)

        with ensure_clean_store(self.path) as store:

            store.put('df',df)
            tm.assert_frame_equal(store['df'],df,check_index_type=True,check_column_type=True)

            store.put('df1',df,format='table')
            tm.assert_frame_equal(store['df1'],df,check_index_type=True,check_column_type=True)

            self.assertRaises(ValueError, store.put, 'df2',df,format='table',data_columns=['A'])
            self.assertRaises(ValueError, store.put, 'df3',df,format='table',data_columns=True)

        # non_index_axes name
        df = DataFrame(np.arange(12).reshape(3,4), columns=Index(list('ABCD'),name='foo'))

        with ensure_clean_store(self.path) as store:

            store.put('df1',df,format='table')
            tm.assert_frame_equal(store['df1'],df,check_index_type=True,check_column_type=True)

    def test_store_multiindex(self):

        # validate multi-index names
        # GH 5527
        with ensure_clean_store(self.path) as store:

            def make_index(names=None):
                return MultiIndex.from_tuples([( datetime.datetime(2013,12,d), s, t) for d in range(1,3) for s in range(2) for t in range(3)],
                                              names=names)


            # no names
            _maybe_remove(store, 'df')
            df = DataFrame(np.zeros((12,2)), columns=['a','b'], index=make_index())
            store.append('df',df)
            tm.assert_frame_equal(store.select('df'),df)

            # partial names
            _maybe_remove(store, 'df')
            df = DataFrame(np.zeros((12,2)), columns=['a','b'], index=make_index(['date',None,None]))
            store.append('df',df)
            tm.assert_frame_equal(store.select('df'),df)

            # series
            _maybe_remove(store, 's')
            s = Series(np.zeros(12), index=make_index(['date',None,None]))
            store.append('s',s)
            tm.assert_series_equal(store.select('s'),s)

            # dup with column
            _maybe_remove(store, 'df')
            df = DataFrame(np.zeros((12,2)), columns=['a','b'], index=make_index(['date','a','t']))
            self.assertRaises(ValueError, store.append, 'df',df)

            # dup within level
            _maybe_remove(store, 'df')
            df = DataFrame(np.zeros((12,2)), columns=['a','b'], index=make_index(['date','date','date']))
            self.assertRaises(ValueError, store.append, 'df',df)

            # fully names
            _maybe_remove(store, 'df')
            df = DataFrame(np.zeros((12,2)), columns=['a','b'], index=make_index(['date','s','t']))
            store.append('df',df)
            tm.assert_frame_equal(store.select('df'),df)

    def test_pass_spec_to_storer(self):

        df = tm.makeDataFrame()

        with ensure_clean_store(self.path) as store:
            store.put('df',df)
            self.assertRaises(TypeError, store.select, 'df', columns=['A'])
            self.assertRaises(TypeError, store.select, 'df',where=[('columns=A')])

    def test_append_misc(self):

        with ensure_clean_store(self.path) as store:

            # unsuported data types for non-tables
            p4d = tm.makePanel4D()
            self.assertRaises(TypeError, store.put,'p4d',p4d)

            # unsuported data types
            self.assertRaises(TypeError, store.put,'abc',None)
            self.assertRaises(TypeError, store.put,'abc','123')
            self.assertRaises(TypeError, store.put,'abc',123)
            self.assertRaises(TypeError, store.put,'abc',np.arange(5))

            df = tm.makeDataFrame()
            store.append('df', df, chunksize=1)
            result = store.select('df')
            tm.assert_frame_equal(result, df)

            store.append('df1', df, expectedrows=10)
            result = store.select('df1')
            tm.assert_frame_equal(result, df)

        # more chunksize in append tests
        def check(obj, comparator):
            for c in [10, 200, 1000]:
                with ensure_clean_store(self.path,mode='w') as store:
                    store.append('obj', obj, chunksize=c)
                    result = store.select('obj')
                    comparator(result,obj)

        df = tm.makeDataFrame()
        df['string'] = 'foo'
        df['float322'] = 1.
        df['float322'] = df['float322'].astype('float32')
        df['bool']     = df['float322'] > 0
        df['time1']    = Timestamp('20130101')
        df['time2']    = Timestamp('20130102')
        check(df, tm.assert_frame_equal)

        p = tm.makePanel()
        check(p, assert_panel_equal)

        p4d = tm.makePanel4D()
        check(p4d, assert_panel4d_equal)

        # empty frame, GH4273
        with ensure_clean_store(self.path) as store:

            # 0 len
            df_empty = DataFrame(columns=list('ABC'))
            store.append('df',df_empty)
            self.assertRaises(KeyError,store.select, 'df')

            # repeated append of 0/non-zero frames
            df = DataFrame(np.random.rand(10,3),columns=list('ABC'))
            store.append('df',df)
            assert_frame_equal(store.select('df'),df)
            store.append('df',df_empty)
            assert_frame_equal(store.select('df'),df)

            # store
            df = DataFrame(columns=list('ABC'))
            store.put('df2',df)
            assert_frame_equal(store.select('df2'),df)

            # 0 len
            p_empty = Panel(items=list('ABC'))
            store.append('p',p_empty)
            self.assertRaises(KeyError,store.select, 'p')

            # repeated append of 0/non-zero frames
            p = Panel(np.random.randn(3,4,5),items=list('ABC'))
            store.append('p',p)
            assert_panel_equal(store.select('p'),p)
            store.append('p',p_empty)
            assert_panel_equal(store.select('p'),p)

            # store
            store.put('p2',p_empty)
            assert_panel_equal(store.select('p2'),p_empty)

    def test_append_raise(self):

        with ensure_clean_store(self.path) as store:

            # test append with invalid input to get good error messages

            # list in column
            df = tm.makeDataFrame()
            df['invalid'] = [['a']] * len(df)
            self.assert_(df.dtypes['invalid'] == np.object_)
            self.assertRaises(TypeError, store.append,'df',df)

            # multiple invalid columns
            df['invalid2'] = [['a']] * len(df)
            df['invalid3'] = [['a']] * len(df)
            self.assertRaises(TypeError, store.append,'df',df)

            # datetime with embedded nans as object
            df = tm.makeDataFrame()
            s = Series(datetime.datetime(2001,1,2),index=df.index)
            s = s.astype(object)
            s[0:5] = np.nan
            df['invalid'] = s
            self.assert_(df.dtypes['invalid'] == np.object_)
            self.assertRaises(TypeError, store.append,'df', df)

            # directy ndarray
            self.assertRaises(TypeError, store.append,'df',np.arange(10))

            # series directly
            self.assertRaises(TypeError, store.append,'df',Series(np.arange(10)))

            # appending an incompatbile table
            df = tm.makeDataFrame()
            store.append('df',df)

            df['foo'] = 'foo'
            self.assertRaises(ValueError, store.append,'df',df)

    def test_table_index_incompatible_dtypes(self):
        df1 = DataFrame({'a': [1, 2, 3]})
        df2 = DataFrame({'a': [4, 5, 6]},
                        index=date_range('1/1/2000', periods=3))

        with ensure_clean_store(self.path) as store:
            store.put('frame', df1, format='table')
            self.assertRaises(TypeError, store.put, 'frame', df2,
                              format='table', append=True)

    def test_table_values_dtypes_roundtrip(self):

        with ensure_clean_store(self.path) as store:
            df1 = DataFrame({'a': [1, 2, 3]}, dtype='f8')
            store.append('df_f8', df1)
            assert_series_equal(df1.dtypes,store['df_f8'].dtypes)

            df2 = DataFrame({'a': [1, 2, 3]}, dtype='i8')
            store.append('df_i8', df2)
            assert_series_equal(df2.dtypes,store['df_i8'].dtypes)

            # incompatible dtype
            self.assertRaises(ValueError, store.append, 'df_i8', df1)

            # check creation/storage/retrieval of float32 (a bit hacky to actually create them thought)
            df1 = DataFrame(np.array([[1],[2],[3]],dtype='f4'),columns = ['A'])
            store.append('df_f4', df1)
            assert_series_equal(df1.dtypes,store['df_f4'].dtypes)
            assert df1.dtypes[0] == 'float32'

            # check with mixed dtypes
            df1 = DataFrame(dict([ (c,Series(np.random.randn(5),dtype=c)) for c in
                                   ['float32','float64','int32','int64','int16','int8'] ]))
            df1['string'] = 'foo'
            df1['float322'] = 1.
            df1['float322'] = df1['float322'].astype('float32')
            df1['bool']     = df1['float32'] > 0
            df1['time1']    = Timestamp('20130101')
            df1['time2']    = Timestamp('20130102')

            store.append('df_mixed_dtypes1', df1)
            result = store.select('df_mixed_dtypes1').get_dtype_counts()
            expected = Series({ 'float32' : 2, 'float64' : 1,'int32' : 1, 'bool' : 1,
                                'int16' : 1, 'int8' : 1, 'int64' : 1, 'object' : 1,
                                'datetime64[ns]' : 2})
            result.sort()
            expected.sort()
            tm.assert_series_equal(result,expected)

    def test_table_mixed_dtypes(self):

        # frame
        df = tm.makeDataFrame()
        df['obj1'] = 'foo'
        df['obj2'] = 'bar'
        df['bool1'] = df['A'] > 0
        df['bool2'] = df['B'] > 0
        df['bool3'] = True
        df['int1'] = 1
        df['int2'] = 2
        df['timestamp1'] = Timestamp('20010102')
        df['timestamp2'] = Timestamp('20010103')
        df['datetime1'] = datetime.datetime(2001, 1, 2, 0, 0)
        df['datetime2'] = datetime.datetime(2001, 1, 3, 0, 0)
        df.ix[3:6, ['obj1']] = np.nan
        df = df.consolidate().convert_objects()

        with ensure_clean_store(self.path) as store:
            store.append('df1_mixed', df)
            tm.assert_frame_equal(store.select('df1_mixed'), df)

        # panel
        wp = tm.makePanel()
        wp['obj1'] = 'foo'
        wp['obj2'] = 'bar'
        wp['bool1'] = wp['ItemA'] > 0
        wp['bool2'] = wp['ItemB'] > 0
        wp['int1'] = 1
        wp['int2'] = 2
        wp = wp.consolidate()

        with ensure_clean_store(self.path) as store:
            store.append('p1_mixed', wp)
            assert_panel_equal(store.select('p1_mixed'), wp)

        # ndim
        wp = tm.makePanel4D()
        wp['obj1'] = 'foo'
        wp['obj2'] = 'bar'
        wp['bool1'] = wp['l1'] > 0
        wp['bool2'] = wp['l2'] > 0
        wp['int1'] = 1
        wp['int2'] = 2
        wp = wp.consolidate()

        with ensure_clean_store(self.path) as store:
            store.append('p4d_mixed', wp)
            assert_panel4d_equal(store.select('p4d_mixed'), wp)

    def test_unimplemented_dtypes_table_columns(self):

        with ensure_clean_store(self.path) as store:

            l = [('date', datetime.date(2001, 1, 2))]

            # py3 ok for unicode
            if not compat.PY3:
                l.append(('unicode', u('\\u03c3')))

            ### currently not supported dtypes ####
            for n, f in l:
                df = tm.makeDataFrame()
                df[n] = f
                self.assertRaises(
                    TypeError, store.append, 'df1_%s' % n, df)

        # frame
        df = tm.makeDataFrame()
        df['obj1'] = 'foo'
        df['obj2'] = 'bar'
        df['datetime1'] = datetime.date(2001, 1, 2)
        df = df.consolidate().convert_objects()

        with ensure_clean_store(self.path) as store:
            # this fails because we have a date in the object block......
            self.assertRaises(TypeError, store.append, 'df_unimplemented', df)

    def test_append_with_timezones(self):

        from datetime import timedelta

        def compare(a,b):
            tm.assert_frame_equal(a,b)

            # compare the zones on each element
            for c in a.columns:
                for i in a.index:
                    a_e = a[c][i]
                    b_e = b[c][i]
                    if not (a_e == b_e and a_e.tz == b_e.tz):
                        raise AssertionError("invalid tz comparsion [%s] [%s]" % (a_e,b_e))

        # as columns
        with ensure_clean_store(self.path) as store:

            _maybe_remove(store, 'df_tz')
            df = DataFrame(dict(A = [ Timestamp('20130102 2:00:00',tz='US/Eastern') + timedelta(hours=1)*i for i in range(5) ]))
            store.append('df_tz',df,data_columns=['A'])
            result = store['df_tz']
            compare(result,df)
            assert_frame_equal(result,df)

            # select with tz aware
            compare(store.select('df_tz',where=Term('A>=df.A[3]')),df[df.A>=df.A[3]])

            _maybe_remove(store, 'df_tz')
            df = DataFrame(dict(A = Timestamp('20130102',tz='US/Eastern'), B = Timestamp('20130103',tz='US/Eastern')),index=range(5))
            store.append('df_tz',df)
            result = store['df_tz']
            compare(result,df)
            assert_frame_equal(result,df)

            _maybe_remove(store, 'df_tz')
            df = DataFrame(dict(A = Timestamp('20130102',tz='US/Eastern'), B = Timestamp('20130102',tz='EET')),index=range(5))
            self.assertRaises(TypeError, store.append, 'df_tz', df)

            # this is ok
            _maybe_remove(store, 'df_tz')
            store.append('df_tz',df,data_columns=['A','B'])
            result = store['df_tz']
            compare(result,df)
            assert_frame_equal(result,df)

            # can't append with diff timezone
            df = DataFrame(dict(A = Timestamp('20130102',tz='US/Eastern'), B = Timestamp('20130102',tz='CET')),index=range(5))
            self.assertRaises(ValueError, store.append, 'df_tz', df)

        # as index
        with ensure_clean_store(self.path) as store:

            # GH 4098 example
            df = DataFrame(dict(A = Series(lrange(3), index=date_range('2000-1-1',periods=3,freq='H', tz='US/Eastern'))))

            _maybe_remove(store, 'df')
            store.put('df',df)
            result = store.select('df')
            assert_frame_equal(result,df)

            _maybe_remove(store, 'df')
            store.append('df',df)
            result = store.select('df')
            assert_frame_equal(result,df)

    def test_store_timezone(self):
        # GH2852
        # issue storing datetime.date with a timezone as it resets when read back in a new timezone

        import platform
        if platform.system() == "Windows":
            raise nose.SkipTest("timezone setting not supported on windows")

        import datetime
        import time
        import os

        # original method
        with ensure_clean_store(self.path) as store:

            today = datetime.date(2013,9,10)
            df = DataFrame([1,2,3], index = [today, today, today])
            store['obj1'] = df
            result = store['obj1']
            assert_frame_equal(result, df)

        # with tz setting
        orig_tz = os.environ.get('TZ')

        def setTZ(tz):
            if tz is None:
                try:
                    del os.environ['TZ']
                except:
                    pass
            else:
                os.environ['TZ']=tz
                time.tzset()

        try:

            with ensure_clean_store(self.path) as store:

                setTZ('EST5EDT')
                today = datetime.date(2013,9,10)
                df = DataFrame([1,2,3], index = [today, today, today])
                store['obj1'] = df

                setTZ('CST6CDT')
                result = store['obj1']

                assert_frame_equal(result, df)

        finally:
            setTZ(orig_tz)

    def test_append_with_timedelta(self):
        if _np_version_under1p7:
            raise nose.SkipTest("requires numpy >= 1.7")

        # GH 3577
        # append timedelta

        from datetime import timedelta
        df = DataFrame(dict(A = Timestamp('20130101'), B = [ Timestamp('20130101') + timedelta(days=i,seconds=10) for i in range(10) ]))
        df['C'] = df['A']-df['B']
        df.ix[3:5,'C'] = np.nan

        with ensure_clean_store(self.path) as store:

            # table
            _maybe_remove(store, 'df')
            store.append('df',df,data_columns=True)
            result = store.select('df')
            assert_frame_equal(result,df)

            result = store.select('df',Term("C<100000"))
            assert_frame_equal(result,df)

            result = store.select('df',Term("C","<",-3*86400))
            assert_frame_equal(result,df.iloc[3:])

            result = store.select('df',"C<'-3D'")
            assert_frame_equal(result,df.iloc[3:])

            # a bit hacky here as we don't really deal with the NaT properly

            result = store.select('df',"C<'-500000s'")
            result = result.dropna(subset=['C'])
            assert_frame_equal(result,df.iloc[6:])

            result = store.select('df',"C<'-3.5D'")
            result = result.iloc[1:]
            assert_frame_equal(result,df.iloc[4:])

            # fixed
            _maybe_remove(store, 'df2')
            store.put('df2',df)
            result = store.select('df2')
            assert_frame_equal(result,df)

    def test_remove(self):

        with ensure_clean_store(self.path) as store:

            ts = tm.makeTimeSeries()
            df = tm.makeDataFrame()
            store['a'] = ts
            store['b'] = df
            _maybe_remove(store, 'a')
            self.assertEquals(len(store), 1)
            tm.assert_frame_equal(df, store['b'])

            _maybe_remove(store, 'b')
            self.assertEquals(len(store), 0)

            # nonexistence
            self.assertRaises(KeyError, store.remove, 'a_nonexistent_store')

            # pathing
            store['a'] = ts
            store['b/foo'] = df
            _maybe_remove(store, 'foo')
            _maybe_remove(store, 'b/foo')
            self.assertEquals(len(store), 1)

            store['a'] = ts
            store['b/foo'] = df
            _maybe_remove(store, 'b')
            self.assertEquals(len(store), 1)

            # __delitem__
            store['a'] = ts
            store['b'] = df
            del store['a']
            del store['b']
            self.assertEquals(len(store), 0)

    def test_remove_where(self):

        with ensure_clean_store(self.path) as store:

            # non-existance
            crit1 = Term('index>foo')
            self.assertRaises(KeyError, store.remove, 'a', [crit1])

            # try to remove non-table (with crit)
            # non-table ok (where = None)
            wp = tm.makePanel()
            store.put('wp', wp, format='table')
            store.remove('wp', ["minor_axis=['A', 'D']"])
            rs = store.select('wp')
            expected = wp.reindex(minor_axis=['B', 'C'])
            assert_panel_equal(rs, expected)

            # empty where
            _maybe_remove(store, 'wp')
            store.put('wp', wp, format='table')

            # deleted number (entire table)
            n = store.remove('wp', [])
            assert(n == 120)

            # non - empty where
            _maybe_remove(store, 'wp')
            store.put('wp', wp, format='table')
            self.assertRaises(ValueError, store.remove,
                              'wp', ['foo'])

            # selectin non-table with a where
            # store.put('wp2', wp, format='f')
            # self.assertRaises(ValueError, store.remove,
            #                  'wp2', [('column', ['A', 'D'])])

    def test_remove_crit(self):

        with ensure_clean_store(self.path) as store:

            wp = tm.makePanel()

            # group row removal
            date4 = wp.major_axis.take([0, 1, 2, 4, 5, 6, 8, 9, 10])
            crit4 = Term('major_axis=date4')
            store.put('wp3', wp, format='t')
            n = store.remove('wp3', where=[crit4])
            assert(n == 36)
            result = store.select('wp3')
            expected = wp.reindex(major_axis=wp.major_axis - date4)
            assert_panel_equal(result, expected)

            # upper half
            store.put('wp', wp, format='table')
            date = wp.major_axis[len(wp.major_axis) // 2]

            crit1 = Term('major_axis>date')
            crit2 = Term("minor_axis=['A', 'D']")
            n = store.remove('wp', where=[crit1])

            assert(n == 56)

            n = store.remove('wp', where=[crit2])
            assert(n == 32)

            result = store['wp']
            expected = wp.truncate(after=date).reindex(minor=['B', 'C'])
            assert_panel_equal(result, expected)

            # individual row elements
            store.put('wp2', wp, format='table')

            date1 = wp.major_axis[1:3]
            crit1 = Term('major_axis=date1')
            store.remove('wp2', where=[crit1])
            result = store.select('wp2')
            expected = wp.reindex(major_axis=wp.major_axis - date1)
            assert_panel_equal(result, expected)

            date2 = wp.major_axis[5]
            crit2 = Term('major_axis=date2')
            store.remove('wp2', where=[crit2])
            result = store['wp2']
            expected = wp.reindex(
                major_axis=wp.major_axis - date1 - Index([date2]))
            assert_panel_equal(result, expected)

            date3 = [wp.major_axis[7], wp.major_axis[9]]
            crit3 = Term('major_axis=date3')
            store.remove('wp2', where=[crit3])
            result = store['wp2']
            expected = wp.reindex(
                major_axis=wp.major_axis - date1 - Index([date2]) - Index(date3))
            assert_panel_equal(result, expected)

            # corners
            store.put('wp4', wp, format='table')
            n = store.remove(
                'wp4', where=[Term('major_axis>wp.major_axis[-1]')])
            result = store.select('wp4')
            assert_panel_equal(result, wp)

    def test_invalid_terms(self):

        with ensure_clean_store(self.path) as store:

            df = tm.makeTimeDataFrame()
            df['string'] = 'foo'
            df.ix[0:4,'string'] = 'bar'
            wp = tm.makePanel()
            p4d = tm.makePanel4D()
            store.put('df', df, format='table')
            store.put('wp', wp, format='table')
            store.put('p4d', p4d, format='table')

            # some invalid terms
            self.assertRaises(ValueError, store.select, 'wp', "minor=['A', 'B']")
            self.assertRaises(ValueError, store.select, 'wp', ["index=['20121114']"])
            self.assertRaises(ValueError, store.select, 'wp', ["index=['20121114', '20121114']"])
            self.assertRaises(TypeError, Term)

            # more invalid
            self.assertRaises(ValueError,  store.select, 'df','df.index[3]')
            self.assertRaises(SyntaxError, store.select, 'df','index>')
            self.assertRaises(ValueError,  store.select, 'wp', "major_axis<'20000108' & minor_axis['A', 'B']")

        # from the docs
        with ensure_clean_path(self.path) as path:
            dfq = DataFrame(np.random.randn(10,4),columns=list('ABCD'),index=date_range('20130101',periods=10))
            dfq.to_hdf(path,'dfq',format='table',data_columns=True)

            # check ok
            read_hdf(path,'dfq',where="index>Timestamp('20130104') & columns=['A', 'B']")
            read_hdf(path,'dfq',where="A>0 or C>0")

        # catch the invalid reference
        with ensure_clean_path(self.path) as path:
            dfq = DataFrame(np.random.randn(10,4),columns=list('ABCD'),index=date_range('20130101',periods=10))
            dfq.to_hdf(path,'dfq',format='table')

            self.assertRaises(ValueError, read_hdf, path,'dfq',where="A>0 or C>0")

    def test_terms(self):

        with ensure_clean_store(self.path) as store:

            wp = tm.makePanel()
            p4d = tm.makePanel4D()
            store.put('wp', wp, table=True)
            store.put('p4d', p4d, table=True)

            # panel
            result = store.select('wp', [Term(
                        'major_axis<"20000108"'), Term("minor_axis=['A', 'B']")])
            expected = wp.truncate(after='20000108').reindex(minor=['A', 'B'])
            assert_panel_equal(result, expected)

            # with deprecation
            result = store.select('wp', [Term(
                'major_axis','<',"20000108"), Term("minor_axis=['A', 'B']")])
            expected = wp.truncate(after='20000108').reindex(minor=['A', 'B'])
            tm.assert_panel_equal(result, expected)

            # p4d
            result = store.select('p4d', [Term('major_axis<"20000108"'),
                                          Term("minor_axis=['A', 'B']"),
                                          Term("items=['ItemA', 'ItemB']")])
            expected = p4d.truncate(after='20000108').reindex(
                minor=['A', 'B'], items=['ItemA', 'ItemB'])
            assert_panel4d_equal(result, expected)

            # back compat invalid terms
            terms = [
                dict(field='major_axis', op='>', value='20121114'),
                [ dict(field='major_axis', op='>', value='20121114') ],
                [ "minor_axis=['A','B']", dict(field='major_axis', op='>', value='20121114') ]
                ]
            for t in terms:
                with tm.assert_produces_warning(expected_warning=DeprecationWarning):
                    Term(t)

            # valid terms
            terms = [
                ('major_axis=20121114'),
                ('major_axis>20121114'),
                (("major_axis=['20121114', '20121114']"),),
                ('major_axis=datetime.datetime(2012, 11, 14)'),
                'major_axis> 20121114',
                'major_axis >20121114',
                'major_axis > 20121114',
                (("minor_axis=['A', 'B']"),),
                (("minor_axis=['A', 'B']"),),
                ((("minor_axis==['A', 'B']"),),),
                (("items=['ItemA', 'ItemB']"),),
                ('items=ItemA'),
                ]

            for t in terms:
                store.select('wp', t)
                store.select('p4d', t)

            # valid for p4d only
            terms = [
                (("labels=['l1', 'l2']"),),
                Term("labels=['l1', 'l2']"),
                ]

            for t in terms:
                store.select('p4d', t)

    def test_term_compat(self):
        with ensure_clean_store(self.path) as store:

            wp = Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
                       major_axis=date_range('1/1/2000', periods=5),
                       minor_axis=['A', 'B', 'C', 'D'])
            store.append('wp',wp)

            result = store.select('wp', [Term('major_axis>20000102'),
                                         Term('minor_axis', '=', ['A','B']) ])
            expected = wp.loc[:,wp.major_axis>Timestamp('20000102'),['A','B']]
            assert_panel_equal(result, expected)

            store.remove('wp', Term('major_axis>20000103'))
            result = store.select('wp')
            expected = wp.loc[:,wp.major_axis<=Timestamp('20000103'),:]
            assert_panel_equal(result, expected)

        with ensure_clean_store(self.path) as store:

            wp = Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
                       major_axis=date_range('1/1/2000', periods=5),
                       minor_axis=['A', 'B', 'C', 'D'])
            store.append('wp',wp)

            # stringified datetimes
            result = store.select('wp', [Term('major_axis','>',datetime.datetime(2000,1,2))])
            expected = wp.loc[:,wp.major_axis>Timestamp('20000102')]
            assert_panel_equal(result, expected)

            result = store.select('wp', [Term('major_axis','>',datetime.datetime(2000,1,2,0,0))])
            expected = wp.loc[:,wp.major_axis>Timestamp('20000102')]
            assert_panel_equal(result, expected)

            result = store.select('wp', [Term('major_axis','=',[datetime.datetime(2000,1,2,0,0),datetime.datetime(2000,1,3,0,0)])])
            expected = wp.loc[:,[Timestamp('20000102'),Timestamp('20000103')]]
            assert_panel_equal(result, expected)

            result = store.select('wp', [Term('minor_axis','=',['A','B'])])
            expected = wp.loc[:,:,['A','B']]
            assert_panel_equal(result, expected)

    def test_same_name_scoping(self):

        with ensure_clean_store(self.path) as store:

            import pandas as pd
            df  = DataFrame(np.random.randn(20, 2),index=pd.date_range('20130101',periods=20))
            store.put('df', df, table=True)
            expected = df[df.index>pd.Timestamp('20130105')]

            import datetime
            result = store.select('df','index>datetime.datetime(2013,1,5)')
            assert_frame_equal(result,expected)

            from datetime import datetime

            # technically an error, but allow it
            result = store.select('df','index>datetime.datetime(2013,1,5)')
            assert_frame_equal(result,expected)

            result = store.select('df','index>datetime(2013,1,5)')
            assert_frame_equal(result,expected)

    def test_series(self):

        s = tm.makeStringSeries()
        self._check_roundtrip(s, tm.assert_series_equal)

        ts = tm.makeTimeSeries()
        self._check_roundtrip(ts, tm.assert_series_equal)

        ts2 = Series(ts.index, Index(ts.index, dtype=object))
        self._check_roundtrip(ts2, tm.assert_series_equal)

        ts3 = Series(ts.values, Index(np.asarray(ts.index, dtype=object),
                                      dtype=object))
        self._check_roundtrip(ts3, tm.assert_series_equal)

    def test_sparse_series(self):

        s = tm.makeStringSeries()
        s[3:5] = np.nan
        ss = s.to_sparse()
        self._check_roundtrip(ss, tm.assert_series_equal,
                              check_series_type=True)

        ss2 = s.to_sparse(kind='integer')
        self._check_roundtrip(ss2, tm.assert_series_equal,
                              check_series_type=True)

        ss3 = s.to_sparse(fill_value=0)
        self._check_roundtrip(ss3, tm.assert_series_equal,
                              check_series_type=True)

    def test_sparse_frame(self):

        s = tm.makeDataFrame()
        s.ix[3:5, 1:3] = np.nan
        s.ix[8:10, -2] = np.nan
        ss = s.to_sparse()

        self._check_double_roundtrip(ss, tm.assert_frame_equal,
                                     check_frame_type=True)

        ss2 = s.to_sparse(kind='integer')
        self._check_double_roundtrip(ss2, tm.assert_frame_equal,
                                     check_frame_type=True)

        ss3 = s.to_sparse(fill_value=0)
        self._check_double_roundtrip(ss3, tm.assert_frame_equal,
                                     check_frame_type=True)

    def test_sparse_panel(self):

        items = ['x', 'y', 'z']
        p = Panel(dict((i, tm.makeDataFrame().ix[:2, :2]) for i in items))
        sp = p.to_sparse()

        self._check_double_roundtrip(sp, assert_panel_equal,
                                     check_panel_type=True)

        sp2 = p.to_sparse(kind='integer')
        self._check_double_roundtrip(sp2, assert_panel_equal,
                                     check_panel_type=True)

        sp3 = p.to_sparse(fill_value=0)
        self._check_double_roundtrip(sp3, assert_panel_equal,
                                     check_panel_type=True)

    def test_float_index(self):

        # GH #454
        index = np.random.randn(10)
        s = Series(np.random.randn(10), index=index)
        self._check_roundtrip(s, tm.assert_series_equal)

    def test_tuple_index(self):

        # GH #492
        col = np.arange(10)
        idx = [(0., 1.), (2., 3.), (4., 5.)]
        data = np.random.randn(30).reshape((3, 10))
        DF = DataFrame(data, index=idx, columns=col)
        with tm.assert_produces_warning(expected_warning=PerformanceWarning):
            self._check_roundtrip(DF, tm.assert_frame_equal)

    def test_index_types(self):

        values = np.random.randn(2)

        func = lambda l, r: tm.assert_series_equal(l, r,
                                                   check_dtype=True,
                                                   check_index_type=True,
                                                   check_series_type=True)

        with tm.assert_produces_warning(expected_warning=PerformanceWarning):
            ser = Series(values, [0, 'y'])
            self._check_roundtrip(ser, func)

        with tm.assert_produces_warning(expected_warning=PerformanceWarning):
            ser = Series(values, [datetime.datetime.today(), 0])
            self._check_roundtrip(ser, func)

        with tm.assert_produces_warning(expected_warning=PerformanceWarning):
            ser = Series(values, ['y', 0])
            self._check_roundtrip(ser, func)

        with tm.assert_produces_warning(expected_warning=PerformanceWarning):
            ser = Series(values, [datetime.date.today(), 'a'])
            self._check_roundtrip(ser, func)

        with tm.assert_produces_warning(expected_warning=PerformanceWarning):
            ser = Series(values, [1.23, 'b'])
            self._check_roundtrip(ser, func)

        ser = Series(values, [1, 1.53])
        self._check_roundtrip(ser, func)

        ser = Series(values, [1, 5])
        self._check_roundtrip(ser, func)

        ser = Series(values, [datetime.datetime(
            2012, 1, 1), datetime.datetime(2012, 1, 2)])
        self._check_roundtrip(ser, func)

    def test_timeseries_preepoch(self):

        if sys.version_info[0] == 2 and sys.version_info[1] < 7:
            raise nose.SkipTest("won't work on Python < 2.7")

        dr = bdate_range('1/1/1940', '1/1/1960')
        ts = Series(np.random.randn(len(dr)), index=dr)
        try:
            self._check_roundtrip(ts, tm.assert_series_equal)
        except OverflowError:
            raise nose.SkipTest('known failer on some windows platforms')

    def test_frame(self):

        df = tm.makeDataFrame()

        # put in some random NAs
        df.values[0, 0] = np.nan
        df.values[5, 3] = np.nan

        self._check_roundtrip_table(df, tm.assert_frame_equal)
        self._check_roundtrip(df, tm.assert_frame_equal)

        self._check_roundtrip_table(df, tm.assert_frame_equal,
                                    compression=True)
        self._check_roundtrip(df, tm.assert_frame_equal,
                              compression=True)

        tdf = tm.makeTimeDataFrame()
        self._check_roundtrip(tdf, tm.assert_frame_equal)
        self._check_roundtrip(tdf, tm.assert_frame_equal,
                              compression=True)

        with ensure_clean_store(self.path) as store:
            # not consolidated
            df['foo'] = np.random.randn(len(df))
            store['df'] = df
            recons = store['df']
            self.assert_(recons._data.is_consolidated())

        # empty
        self._check_roundtrip(df[:0], tm.assert_frame_equal)

    def test_empty_series_frame(self):
        s0 = Series()
        s1 = Series(name='myseries')
        df0 = DataFrame()
        df1 = DataFrame(index=['a', 'b', 'c'])
        df2 = DataFrame(columns=['d', 'e', 'f'])

        self._check_roundtrip(s0, tm.assert_series_equal)
        self._check_roundtrip(s1, tm.assert_series_equal)
        self._check_roundtrip(df0, tm.assert_frame_equal)
        self._check_roundtrip(df1, tm.assert_frame_equal)
        self._check_roundtrip(df2, tm.assert_frame_equal)

    def test_empty_series(self):
        for dtype in [np.int64, np.float64, np.object, 'm8[ns]', 'M8[ns]']:
            s = Series(dtype=dtype)
            self._check_roundtrip(s, tm.assert_series_equal)

    def test_can_serialize_dates(self):

        rng = [x.date() for x in bdate_range('1/1/2000', '1/30/2000')]
        frame = DataFrame(np.random.randn(len(rng), 4), index=rng)

        self._check_roundtrip(frame, tm.assert_frame_equal)

    def test_timezones(self):
        rng = date_range('1/1/2000', '1/30/2000', tz='US/Eastern')
        frame = DataFrame(np.random.randn(len(rng), 4), index=rng)

        with ensure_clean_store(self.path) as store:
            store['frame'] = frame
            recons = store['frame']
            self.assert_(recons.index.equals(rng))
            self.assertEquals(rng.tz, recons.index.tz)

    def test_fixed_offset_tz(self):
        rng = date_range('1/1/2000 00:00:00-07:00', '1/30/2000 00:00:00-07:00')
        frame = DataFrame(np.random.randn(len(rng), 4), index=rng)

        with ensure_clean_store(self.path) as store:
            store['frame'] = frame
            recons = store['frame']
            self.assert_(recons.index.equals(rng))
            self.assertEquals(rng.tz, recons.index.tz)

    def test_store_hierarchical(self):
        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=['foo', 'bar'])
        frame = DataFrame(np.random.randn(10, 3), index=index,
                          columns=['A', 'B', 'C'])

        self._check_roundtrip(frame, tm.assert_frame_equal)
        self._check_roundtrip(frame.T, tm.assert_frame_equal)
        self._check_roundtrip(frame['A'], tm.assert_series_equal)

        # check that the names are stored
        with ensure_clean_store(self.path) as store:
            store['frame'] = frame
            recons = store['frame']
            assert(recons.index.names == ('foo', 'bar'))

    def test_store_index_name(self):
        df = tm.makeDataFrame()
        df.index.name = 'foo'

        with ensure_clean_store(self.path) as store:
            store['frame'] = df
            recons = store['frame']
            assert(recons.index.name == 'foo')

    def test_store_series_name(self):
        df = tm.makeDataFrame()
        series = df['A']

        with ensure_clean_store(self.path) as store:
            store['series'] = series
            recons = store['series']
            assert(recons.name == 'A')

    def test_store_mixed(self):

        def _make_one():
            df = tm.makeDataFrame()
            df['obj1'] = 'foo'
            df['obj2'] = 'bar'
            df['bool1'] = df['A'] > 0
            df['bool2'] = df['B'] > 0
            df['int1'] = 1
            df['int2'] = 2
            return df.consolidate()

        df1 = _make_one()
        df2 = _make_one()

        self._check_roundtrip(df1, tm.assert_frame_equal)
        self._check_roundtrip(df2, tm.assert_frame_equal)

        with ensure_clean_store(self.path) as store:
            store['obj'] = df1
            tm.assert_frame_equal(store['obj'], df1)
            store['obj'] = df2
            tm.assert_frame_equal(store['obj'], df2)

        # check that can store Series of all of these types
        self._check_roundtrip(df1['obj1'], tm.assert_series_equal)
        self._check_roundtrip(df1['bool1'], tm.assert_series_equal)
        self._check_roundtrip(df1['int1'], tm.assert_series_equal)

        # try with compression
        self._check_roundtrip(df1['obj1'], tm.assert_series_equal,
                              compression=True)
        self._check_roundtrip(df1['bool1'], tm.assert_series_equal,
                              compression=True)
        self._check_roundtrip(df1['int1'], tm.assert_series_equal,
                              compression=True)
        self._check_roundtrip(df1, tm.assert_frame_equal,
                              compression=True)

    def test_wide(self):

        wp = tm.makePanel()
        self._check_roundtrip(wp, assert_panel_equal)

    def test_wide_table(self):

        wp = tm.makePanel()
        self._check_roundtrip_table(wp, assert_panel_equal)

    def test_select_with_dups(self):

        # single dtypes
        df = DataFrame(np.random.randn(10,4),columns=['A','A','B','B'])
        df.index = date_range('20130101 9:30',periods=10,freq='T')

        with ensure_clean_store(self.path) as store:
            store.append('df',df)

            result = store.select('df')
            expected = df
            assert_frame_equal(result,expected,by_blocks=True)

            result = store.select('df',columns=df.columns)
            expected = df
            assert_frame_equal(result,expected,by_blocks=True)

            result = store.select('df',columns=['A'])
            expected = df.loc[:,['A']]
            assert_frame_equal(result,expected)

        # dups accross dtypes
        df = concat([DataFrame(np.random.randn(10,4),columns=['A','A','B','B']),
                     DataFrame(np.random.randint(0,10,size=20).reshape(10,2),columns=['A','C'])],
                    axis=1)
        df.index = date_range('20130101 9:30',periods=10,freq='T')

        with ensure_clean_store(self.path) as store:
            store.append('df',df)

            result = store.select('df')
            expected = df
            assert_frame_equal(result,expected,by_blocks=True)

            result = store.select('df',columns=df.columns)
            expected = df
            assert_frame_equal(result,expected,by_blocks=True)

            expected = df.loc[:,['A']]
            result = store.select('df',columns=['A'])
            assert_frame_equal(result,expected,by_blocks=True)

            expected = df.loc[:,['B','A']]
            result = store.select('df',columns=['B','A'])
            assert_frame_equal(result,expected,by_blocks=True)

        # duplicates on both index and columns
        with ensure_clean_store(self.path) as store:
            store.append('df',df)
            store.append('df',df)

            expected = df.loc[:,['B','A']]
            expected = concat([expected, expected])
            result = store.select('df',columns=['B','A'])
            assert_frame_equal(result,expected,by_blocks=True)

    def test_wide_table_dups(self):
        wp = tm.makePanel()
        with ensure_clean_store(self.path) as store:
            store.put('panel', wp, format='table')
            store.put('panel', wp, format='table', append=True)

            with tm.assert_produces_warning(expected_warning=DuplicateWarning):
                recons = store['panel']

            assert_panel_equal(recons, wp)

    def test_long(self):
        def _check(left, right):
            assert_panel_equal(left.to_panel(), right.to_panel())

        wp = tm.makePanel()
        self._check_roundtrip(wp.to_frame(), _check)

        # empty
        # self._check_roundtrip(wp.to_frame()[:0], _check)

    def test_longpanel(self):
        pass

    def test_overwrite_node(self):

        with ensure_clean_store(self.path) as store:
            store['a'] = tm.makeTimeDataFrame()
            ts = tm.makeTimeSeries()
            store['a'] = ts

            tm.assert_series_equal(store['a'], ts)

    def test_sparse_with_compression(self):

        # GH 2931

        # make sparse dataframe
        df = DataFrame(np.random.binomial(n=1, p=.01, size=(1e3, 10))).to_sparse(fill_value=0)

        # case 1: store uncompressed
        self._check_double_roundtrip(df, tm.assert_frame_equal,
                                     compression = False,
                                     check_frame_type=True)

        # case 2: store compressed (works)
        self._check_double_roundtrip(df, tm.assert_frame_equal,
                                     compression = 'zlib',
                                     check_frame_type=True)

        # set one series to be completely sparse
        df[0] = np.zeros(1e3)

        # case 3: store df with completely sparse series uncompressed
        self._check_double_roundtrip(df, tm.assert_frame_equal,
                                     compression = False,
                                     check_frame_type=True)

        # case 4: try storing df with completely sparse series compressed (fails)
        self._check_double_roundtrip(df, tm.assert_frame_equal,
                                     compression = 'zlib',
                                     check_frame_type=True)

    def test_select(self):
        wp = tm.makePanel()

        with ensure_clean_store(self.path) as store:

            # put/select ok
            _maybe_remove(store, 'wp')
            store.put('wp', wp, format='table')
            store.select('wp')

            # non-table ok (where = None)
            _maybe_remove(store, 'wp')
            store.put('wp2', wp)
            store.select('wp2')

            # selection on the non-indexable with a large number of columns
            wp = Panel(
                np.random.randn(100, 100, 100), items=['Item%03d' % i for i in range(100)],
                major_axis=date_range('1/1/2000', periods=100), minor_axis=['E%03d' % i for i in range(100)])

            _maybe_remove(store, 'wp')
            store.append('wp', wp)
            items = ['Item%03d' % i for i in range(80)]
            result = store.select('wp', Term('items=items'))
            expected = wp.reindex(items=items)
            assert_panel_equal(expected, result)

            # selectin non-table with a where
            # self.assertRaises(ValueError, store.select,
            #                  'wp2', ('column', ['A', 'D']))

            # select with columns=
            df = tm.makeTimeDataFrame()
            _maybe_remove(store, 'df')
            store.append('df', df)
            result = store.select('df', columns=['A', 'B'])
            expected = df.reindex(columns=['A', 'B'])
            tm.assert_frame_equal(expected, result)

            # equivalentsly
            result = store.select('df', [("columns=['A', 'B']")])
            expected = df.reindex(columns=['A', 'B'])
            tm.assert_frame_equal(expected, result)

            # with a data column
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns=['A'])
            result = store.select('df', ['A > 0'], columns=['A', 'B'])
            expected = df[df.A > 0].reindex(columns=['A', 'B'])
            tm.assert_frame_equal(expected, result)

            # all a data columns
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns=True)
            result = store.select('df', ['A > 0'], columns=['A', 'B'])
            expected = df[df.A > 0].reindex(columns=['A', 'B'])
            tm.assert_frame_equal(expected, result)

            # with a data column, but different columns
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns=['A'])
            result = store.select('df', ['A > 0'], columns=['C', 'D'])
            expected = df[df.A > 0].reindex(columns=['C', 'D'])
            tm.assert_frame_equal(expected, result)

    def test_select_dtypes(self):

        with ensure_clean_store(self.path) as store:

            # with a Timestamp data column (GH #2637)
            df = DataFrame(dict(ts=bdate_range('2012-01-01', periods=300), A=np.random.randn(300)))
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns=['ts', 'A'])

            result = store.select('df', [Term("ts>=Timestamp('2012-02-01')")])
            expected = df[df.ts >= Timestamp('2012-02-01')]
            tm.assert_frame_equal(expected, result)

            # bool columns (GH #2849)
            df = DataFrame(np.random.randn(5,2), columns =['A','B'])
            df['object'] = 'foo'
            df.ix[4:5,'object'] = 'bar'
            df['boolv'] = df['A'] > 0
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns = True)

            expected = df[df.boolv == True].reindex(columns=['A','boolv'])
            for v in [True,'true',1]:
                result = store.select('df', Term('boolv == %s' % str(v)), columns = ['A','boolv'])
                tm.assert_frame_equal(expected, result)

            expected = df[df.boolv == False ].reindex(columns=['A','boolv'])
            for v in [False,'false',0]:
                result = store.select('df', Term('boolv == %s' % str(v)), columns = ['A','boolv'])
                tm.assert_frame_equal(expected, result)

            # integer index
            df = DataFrame(dict(A=np.random.rand(20), B=np.random.rand(20)))
            _maybe_remove(store, 'df_int')
            store.append('df_int', df)
            result = store.select(
                'df_int', [Term("index<10"), Term("columns=['A']")])
            expected = df.reindex(index=list(df.index)[0:10],columns=['A'])
            tm.assert_frame_equal(expected, result)

            # float index
            df = DataFrame(dict(A=np.random.rand(
                        20), B=np.random.rand(20), index=np.arange(20, dtype='f8')))
            _maybe_remove(store, 'df_float')
            store.append('df_float', df)
            result = store.select(
                'df_float', [Term("index<10.0"), Term("columns=['A']")])
            expected = df.reindex(index=list(df.index)[0:10],columns=['A'])
            tm.assert_frame_equal(expected, result)

        with ensure_clean_store(self.path) as store:

            # floats w/o NaN
            df = DataFrame(dict(cols = range(11), values = range(11)),dtype='float64')
            df['cols'] = (df['cols']+10).apply(str)

            store.append('df1',df,data_columns=True)
            result = store.select(
                'df1', where='values>2.0')
            expected = df[df['values']>2.0]
            tm.assert_frame_equal(expected, result)

            # floats with NaN
            df.iloc[0] = np.nan
            expected = df[df['values']>2.0]

            store.append('df2',df,data_columns=True,index=False)
            result = store.select(
                'df2', where='values>2.0')
            tm.assert_frame_equal(expected, result)

            # https://github.com/PyTables/PyTables/issues/282
            # bug in selection when 0th row has a np.nan and an index
            #store.append('df3',df,data_columns=True)
            #result = store.select(
            #    'df3', where='values>2.0')
            #tm.assert_frame_equal(expected, result)

            # not in first position float with NaN ok too
            df = DataFrame(dict(cols = range(11), values = range(11)),dtype='float64')
            df['cols'] = (df['cols']+10).apply(str)

            df.iloc[1] = np.nan
            expected = df[df['values']>2.0]

            store.append('df4',df,data_columns=True)
            result = store.select(
                'df4', where='values>2.0')
            tm.assert_frame_equal(expected, result)

    def test_select_with_many_inputs(self):

        with ensure_clean_store(self.path) as store:

            df = DataFrame(dict(ts=bdate_range('2012-01-01', periods=300),
                                A=np.random.randn(300),
                                B=range(300),
                                users = ['a']*50 + ['b']*50 + ['c']*100 + ['a%03d' % i for i in range(100)]))
            _maybe_remove(store, 'df')
            store.append('df', df, data_columns=['ts', 'A', 'B', 'users'])

            # regular select
            result = store.select('df', [Term("ts>=Timestamp('2012-02-01')")])
            expected = df[df.ts >= Timestamp('2012-02-01')]
            tm.assert_frame_equal(expected, result)

            # small selector
            result = store.select('df', [Term("ts>=Timestamp('2012-02-01') & users=['a','b','c']")])
            expected = df[ (df.ts >= Timestamp('2012-02-01')) & df.users.isin(['a','b','c']) ]
            tm.assert_frame_equal(expected, result)

            # big selector along the columns
            selector = [ 'a','b','c' ] + [ 'a%03d' % i for i in range(60) ]
            result = store.select('df', [Term("ts>=Timestamp('2012-02-01')"),Term('users=selector')])
            expected = df[ (df.ts >= Timestamp('2012-02-01')) & df.users.isin(selector) ]
            tm.assert_frame_equal(expected, result)

            selector = range(100,200)
            result = store.select('df', [Term('B=selector')])
            expected = df[ df.B.isin(selector) ]
            tm.assert_frame_equal(expected, result)
            self.assert_(len(result) == 100)

            # big selector along the index
            selector = Index(df.ts[0:100].values)
            result  = store.select('df', [Term('ts=selector')])
            expected = df[ df.ts.isin(selector.values) ]
            tm.assert_frame_equal(expected, result)
            self.assert_(len(result) == 100)

    def test_select_iterator(self):

        # single table
        with ensure_clean_store(self.path) as store:

            df = tm.makeTimeDataFrame(500)
            _maybe_remove(store, 'df')
            store.append('df', df)

            expected = store.select('df')

            results = []
            for s in store.select('df',iterator=True):
                results.append(s)
            result = concat(results)
            tm.assert_frame_equal(expected, result)
            results = []
            for s in store.select('df',chunksize=100):
                results.append(s)
            self.assert_(len(results) == 5)
            result = concat(results)
            tm.assert_frame_equal(expected, result)

            results = []
            for s in store.select('df',chunksize=150):
                results.append(s)
            result = concat(results)
            tm.assert_frame_equal(result, expected)

        with ensure_clean_path(self.path) as path:

            df = tm.makeTimeDataFrame(500)
            df.to_hdf(path,'df_non_table')
            self.assertRaises(TypeError, read_hdf, path,'df_non_table',chunksize=100)
            self.assertRaises(TypeError, read_hdf, path,'df_non_table',iterator=True)

        with ensure_clean_path(self.path) as path:

            df = tm.makeTimeDataFrame(500)
            df.to_hdf(path,'df',format='table')

            results = []
            for x in read_hdf(path,'df',chunksize=100):
                results.append(x)

            self.assert_(len(results) == 5)
            result = concat(results)
            tm.assert_frame_equal(result, df)
            tm.assert_frame_equal(result, read_hdf(path,'df'))

        # multiple

        with ensure_clean_store(self.path) as store:

            df1 = tm.makeTimeDataFrame(500)
            store.append('df1',df1,data_columns=True)
            df2 = tm.makeTimeDataFrame(500).rename(columns=lambda x: "%s_2" % x)
            df2['foo'] = 'bar'
            store.append('df2',df2)

            df = concat([df1, df2], axis=1)

            # full selection
            expected = store.select_as_multiple(
                ['df1', 'df2'], selector='df1')
            results = []
            for s in store.select_as_multiple(
                ['df1', 'df2'], selector='df1', chunksize=150):
                results.append(s)
            result = concat(results)
            tm.assert_frame_equal(expected, result)

            # where selection
            #expected = store.select_as_multiple(
            #    ['df1', 'df2'], where= Term('A>0'), selector='df1')
            #results = []
            #for s in store.select_as_multiple(
            #    ['df1', 'df2'], where= Term('A>0'), selector='df1', chunksize=25):
            #    results.append(s)
            #result = concat(results)
            #tm.assert_frame_equal(expected, result)

    def test_retain_index_attributes(self):

        # GH 3499, losing frequency info on index recreation
        df = DataFrame(dict(A = Series(lrange(3),
                                       index=date_range('2000-1-1',periods=3,freq='H'))))

        with ensure_clean_store(self.path) as store:
            _maybe_remove(store,'data')
            store.put('data', df, format='table')

            result = store.get('data')
            tm.assert_frame_equal(df,result)

            for attr in ['freq','tz','name']:
                for idx in ['index','columns']:
                    self.assert_(getattr(getattr(df,idx),attr,None) == getattr(getattr(result,idx),attr,None))


            # try to append a table with a different frequency
            with tm.assert_produces_warning(expected_warning=AttributeConflictWarning):
                df2 = DataFrame(dict(A = Series(lrange(3),
                                                index=date_range('2002-1-1',periods=3,freq='D'))))
                store.append('data',df2)

            self.assert_(store.get_storer('data').info['index']['freq'] is None)

            # this is ok
            _maybe_remove(store,'df2')
            df2 = DataFrame(dict(A = Series(lrange(3),
                                            index=[Timestamp('20010101'),Timestamp('20010102'),Timestamp('20020101')])))
            store.append('df2',df2)
            df3 = DataFrame(dict(A = Series(lrange(3),index=date_range('2002-1-1',periods=3,freq='D'))))
            store.append('df2',df3)

    def test_retain_index_attributes2(self):

        with ensure_clean_path(self.path) as path:

            with tm.assert_produces_warning(expected_warning=AttributeConflictWarning):

                df  = DataFrame(dict(A = Series(lrange(3), index=date_range('2000-1-1',periods=3,freq='H'))))
                df.to_hdf(path,'data',mode='w',append=True)
                df2 = DataFrame(dict(A = Series(lrange(3), index=date_range('2002-1-1',periods=3,freq='D'))))
                df2.to_hdf(path,'data',append=True)

                idx = date_range('2000-1-1',periods=3,freq='H')
                idx.name = 'foo'
                df  = DataFrame(dict(A = Series(lrange(3), index=idx)))
                df.to_hdf(path,'data',mode='w',append=True)

            self.assert_(read_hdf(path,'data').index.name == 'foo')

            with tm.assert_produces_warning(expected_warning=AttributeConflictWarning):

                idx2 = date_range('2001-1-1',periods=3,freq='H')
                idx2.name = 'bar'
                df2 = DataFrame(dict(A = Series(lrange(3), index=idx2)))
                df2.to_hdf(path,'data',append=True)

            self.assert_(read_hdf(path,'data').index.name is None)

    def test_panel_select(self):

        wp = tm.makePanel()

        with ensure_clean_store(self.path) as store:
            store.put('wp', wp, format='table')
            date = wp.major_axis[len(wp.major_axis) // 2]

            crit1 = ('major_axis>=date')
            crit2 = ("minor_axis=['A', 'D']")

            result = store.select('wp', [crit1, crit2])
            expected = wp.truncate(before=date).reindex(minor=['A', 'D'])
            assert_panel_equal(result, expected)

            result = store.select(
                'wp', ['major_axis>="20000124"', ("minor_axis=['A', 'B']")])
            expected = wp.truncate(before='20000124').reindex(minor=['A', 'B'])
            assert_panel_equal(result, expected)

    def test_frame_select(self):

        df = tm.makeTimeDataFrame()

        with ensure_clean_store(self.path) as store:
            store.put('frame', df,format='table')
            date = df.index[len(df) // 2]

            crit1 = Term('index>=date')
            crit2 = ("columns=['A', 'D']")
            crit3 = ('columns=A')

            result = store.select('frame', [crit1, crit2])
            expected = df.ix[date:, ['A', 'D']]
            tm.assert_frame_equal(result, expected)

            result = store.select('frame', [crit3])
            expected = df.ix[:, ['A']]
            tm.assert_frame_equal(result, expected)

            # invalid terms
            df = tm.makeTimeDataFrame()
            store.append('df_time', df)
            self.assertRaises(
                ValueError, store.select, 'df_time', [Term("index>0")])

            # can't select if not written as table
            # store['frame'] = df
            # self.assertRaises(ValueError, store.select,
            #                  'frame', [crit1, crit2])

    def test_frame_select_complex(self):
        # select via complex criteria

        df = tm.makeTimeDataFrame()
        df['string'] = 'foo'
        df.loc[df.index[0:4],'string'] = 'bar'

        with ensure_clean_store(self.path) as store:
            store.put('df', df, table=True, data_columns=['string'])

            # empty
            result = store.select('df', 'index>df.index[3] & string="bar"')
            expected = df.loc[(df.index>df.index[3]) & (df.string=='bar')]
            tm.assert_frame_equal(result, expected)

            result = store.select('df', 'index>df.index[3] & string="foo"')
            expected = df.loc[(df.index>df.index[3]) & (df.string=='foo')]
            tm.assert_frame_equal(result, expected)

            # or
            result = store.select('df', 'index>df.index[3] | string="bar"')
            expected = df.loc[(df.index>df.index[3]) | (df.string=='bar')]
            tm.assert_frame_equal(result, expected)

            result = store.select('df', '(index>df.index[3] & index<=df.index[6]) | string="bar"')
            expected = df.loc[((df.index>df.index[3]) & (df.index<=df.index[6])) | (df.string=='bar')]
            tm.assert_frame_equal(result, expected)

            # invert
            result = store.select('df', 'string!="bar"')
            expected = df.loc[df.string!='bar']
            tm.assert_frame_equal(result, expected)

            # invert not implemented in numexpr :(
            self.assertRaises(NotImplementedError, store.select, 'df', '~(string="bar")')

            # invert ok for filters
            result = store.select('df', "~(columns=['A','B'])")
            expected = df.loc[:,df.columns-['A','B']]
            tm.assert_frame_equal(result, expected)

            # in
            result = store.select('df', "index>df.index[3] & columns in ['A','B']")
            expected = df.loc[df.index>df.index[3]].reindex(columns=['A','B'])
            tm.assert_frame_equal(result, expected)

    def test_frame_select_complex2(self):

        with ensure_clean_path(['parms.hdf','hist.hdf']) as paths:

            pp, hh = paths

            # use non-trivial selection criteria
            parms = DataFrame({ 'A' : [1,1,2,2,3] })
            parms.to_hdf(pp,'df',mode='w',format='table',data_columns=['A'])

            selection = read_hdf(pp,'df',where='A=[2,3]')
            hist = DataFrame(np.random.randn(25,1),columns=['data'],
                             index=MultiIndex.from_tuples([ (i,j) for i in range(5) for j in range(5) ],
                                                          names=['l1','l2']))

            hist.to_hdf(hh,'df',mode='w',format='table')

            expected = read_hdf(hh,'df',where=Term('l1','=',[2,3,4]))

            # list like
            result = read_hdf(hh,'df',where=Term('l1','=',selection.index.tolist()))
            assert_frame_equal(result, expected)
            l = selection.index.tolist()

            # sccope with list like
            store = HDFStore(hh)
            result = store.select('df',where='l1=l')
            assert_frame_equal(result, expected)
            store.close()

            result = read_hdf(hh,'df',where='l1=l')
            assert_frame_equal(result, expected)

            # index
            index = selection.index
            result = read_hdf(hh,'df',where='l1=index')
            assert_frame_equal(result, expected)

            result = read_hdf(hh,'df',where='l1=selection.index')
            assert_frame_equal(result, expected)

            result = read_hdf(hh,'df',where='l1=selection.index.tolist()')
            assert_frame_equal(result, expected)

            result = read_hdf(hh,'df',where='l1=list(selection.index)')
            assert_frame_equal(result, expected)

            # sccope with index
            store = HDFStore(hh)

            result = store.select('df',where='l1=index')
            assert_frame_equal(result, expected)

            result = store.select('df',where='l1=selection.index')
            assert_frame_equal(result, expected)

            result = store.select('df',where='l1=selection.index.tolist()')
            assert_frame_equal(result, expected)

            result = store.select('df',where='l1=list(selection.index)')
            assert_frame_equal(result, expected)

            store.close()

    def test_invalid_filtering(self):

        # can't use more than one filter (atm)

        df = tm.makeTimeDataFrame()

        with ensure_clean_store(self.path) as store:
            store.put('df', df, table=True)

            # not implemented
            self.assertRaises(NotImplementedError, store.select, 'df', "columns=['A'] | columns=['B']")

            # in theory we could deal with this
            self.assertRaises(NotImplementedError, store.select, 'df', "columns=['A','B'] & columns=['C']")

    def test_string_select(self):

        # GH 2973
        with ensure_clean_store(self.path) as store:

            df = tm.makeTimeDataFrame()

            # test string ==/!=
            df['x'] = 'none'
            df.ix[2:7,'x'] = ''

            store.append('df',df,data_columns=['x'])

            result = store.select('df',Term('x=none'))
            expected = df[df.x == 'none']
            assert_frame_equal(result,expected)

            try:
                result = store.select('df',Term('x!=none'))
                expected = df[df.x != 'none']
                assert_frame_equal(result,expected)
            except Exception as detail:
                print("[{0}]".format(detail))
                print(store)
                print(expected)

            df2 = df.copy()
            df2.loc[df2.x=='','x'] = np.nan

            store.append('df2',df2,data_columns=['x'])
            result = store.select('df2',Term('x!=none'))
            expected = df2[isnull(df2.x)]
            assert_frame_equal(result,expected)

            # int ==/!=
            df['int'] = 1
            df.ix[2:7,'int'] = 2

            store.append('df3',df,data_columns=['int'])

            result = store.select('df3',Term('int=2'))
            expected = df[df.int==2]
            assert_frame_equal(result,expected)

            result = store.select('df3',Term('int!=2'))
            expected = df[df.int!=2]
            assert_frame_equal(result,expected)

    def test_read_column(self):

        df = tm.makeTimeDataFrame()

        with ensure_clean_store(self.path) as store:
            _maybe_remove(store, 'df')
            store.append('df', df)

            # error
            self.assertRaises(KeyError, store.select_column, 'df', 'foo')

            def f():
                store.select_column('df', 'index', where = ['index>5'])
            self.assertRaises(Exception, f)

            # valid
            result = store.select_column('df', 'index')
            tm.assert_almost_equal(result.values, Series(df.index).values)
            self.assert_(isinstance(result,Series))

            # not a data indexable column
            self.assertRaises(
                ValueError, store.select_column, 'df', 'values_block_0')

            # a data column
            df2 = df.copy()
            df2['string'] = 'foo'
            store.append('df2', df2, data_columns=['string'])
            result = store.select_column('df2', 'string')
            tm.assert_almost_equal(result.values, df2['string'].values)

            # a data column with NaNs, result excludes the NaNs
            df3 = df.copy()
            df3['string'] = 'foo'
            df3.ix[4:6, 'string'] = np.nan
            store.append('df3', df3, data_columns=['string'])
            result = store.select_column('df3', 'string')
            tm.assert_almost_equal(result.values, df3['string'].values)

    def test_coordinates(self):
        df = tm.makeTimeDataFrame()

        with ensure_clean_store(self.path) as store:

            _maybe_remove(store, 'df')
            store.append('df', df)

            # all
            c = store.select_as_coordinates('df')
            assert((c.values == np.arange(len(df.index))).all() == True)

            # get coordinates back & test vs frame
            _maybe_remove(store, 'df')

            df = DataFrame(dict(A=lrange(5), B=lrange(5)))
            store.append('df', df)
            c = store.select_as_coordinates('df', ['index<3'])
            assert((c.values == np.arange(3)).all() == True)
            result = store.select('df', where=c)
            expected = df.ix[0:2, :]
            tm.assert_frame_equal(result, expected)

            c = store.select_as_coordinates('df', ['index>=3', 'index<=4'])
            assert((c.values == np.arange(2) + 3).all() == True)
            result = store.select('df', where=c)
            expected = df.ix[3:4, :]
            tm.assert_frame_equal(result, expected)
            self.assert_(isinstance(c, Index))

            # multiple tables
            _maybe_remove(store, 'df1')
            _maybe_remove(store, 'df2')
            df1 = tm.makeTimeDataFrame()
            df2 = tm.makeTimeDataFrame().rename(columns=lambda x: "%s_2" % x)
            store.append('df1', df1, data_columns=['A', 'B'])
            store.append('df2', df2)

            c = store.select_as_coordinates('df1', ['A>0', 'B>0'])
            df1_result = store.select('df1', c)
            df2_result = store.select('df2', c)
            result = concat([df1_result, df2_result], axis=1)

            expected = concat([df1, df2], axis=1)
            expected = expected[(expected.A > 0) & (expected.B > 0)]
            tm.assert_frame_equal(result, expected)

        # pass array/mask as the coordinates
        with ensure_clean_store(self.path) as store:

            df = DataFrame(np.random.randn(1000,2),index=date_range('20000101',periods=1000))
            store.append('df',df)
            c = store.select_column('df','index')
            where = c[DatetimeIndex(c).month==5].index
            expected = df.iloc[where]

            # locations
            result = store.select('df',where=where)
            tm.assert_frame_equal(result,expected)

            # boolean
            result = store.select('df',where=where)
            tm.assert_frame_equal(result,expected)

            # invalid
            self.assertRaises(ValueError, store.select, 'df',where=np.arange(len(df),dtype='float64'))
            self.assertRaises(ValueError, store.select, 'df',where=np.arange(len(df)+1))
            self.assertRaises(ValueError, store.select, 'df',where=np.arange(len(df)),start=5)
            self.assertRaises(ValueError, store.select, 'df',where=np.arange(len(df)),start=5,stop=10)

            # list
            df = DataFrame(np.random.randn(10,2))
            store.append('df2',df)
            result = store.select('df2',where=[0,3,5])
            expected = df.iloc[[0,3,5]]
            tm.assert_frame_equal(result,expected)

            # boolean
            where = [True] * 10
            where[-2] = False
            result = store.select('df2',where=where)
            expected = df.loc[where]
            tm.assert_frame_equal(result,expected)

    def test_append_to_multiple(self):
        df1 = tm.makeTimeDataFrame()
        df2 = tm.makeTimeDataFrame().rename(columns=lambda x: "%s_2" % x)
        df2['foo'] = 'bar'
        df = concat([df1, df2], axis=1)

        with ensure_clean_store(self.path) as store:

            # exceptions
            self.assertRaises(ValueError, store.append_to_multiple,
                              {'df1': ['A', 'B'], 'df2': None}, df, selector='df3')
            self.assertRaises(ValueError, store.append_to_multiple,
                              {'df1': None, 'df2': None}, df, selector='df3')
            self.assertRaises(
                ValueError, store.append_to_multiple, 'df1', df, 'df1')

            # regular operation
            store.append_to_multiple(
                {'df1': ['A', 'B'], 'df2': None}, df, selector='df1')
            result = store.select_as_multiple(
                ['df1', 'df2'], where=['A>0', 'B>0'], selector='df1')
            expected = df[(df.A > 0) & (df.B > 0)]
            tm.assert_frame_equal(result, expected)

    def test_append_to_multiple_dropna(self):
        df1 = tm.makeTimeDataFrame()
        df2 = tm.makeTimeDataFrame().rename(columns=lambda x: "%s_2" % x)
        df1.ix[1, ['A', 'B']] = np.nan
        df = concat([df1, df2], axis=1)

        with ensure_clean_store(self.path) as store:
            # dropna=True should guarantee rows are synchronized
            store.append_to_multiple(
                {'df1': ['A', 'B'], 'df2': None}, df, selector='df1',
                dropna=True)
            result = store.select_as_multiple(['df1', 'df2'])
            expected = df.dropna()
            tm.assert_frame_equal(result, expected)
            tm.assert_index_equal(store.select('df1').index,
                                  store.select('df2').index)

            # dropna=False shouldn't synchronize row indexes
            store.append_to_multiple(
                {'df1': ['A', 'B'], 'df2': None}, df, selector='df1',
                dropna=False)
            self.assertRaises(
                ValueError, store.select_as_multiple, ['df1', 'df2'])
            assert not store.select('df1').index.equals(
                store.select('df2').index)

    def test_select_as_multiple(self):

        df1 = tm.makeTimeDataFrame()
        df2 = tm.makeTimeDataFrame().rename(columns=lambda x: "%s_2" % x)
        df2['foo'] = 'bar'

        with ensure_clean_store(self.path) as store:

            # no tables stored
            self.assertRaises(Exception, store.select_as_multiple,
                              None, where=['A>0', 'B>0'], selector='df1')

            store.append('df1', df1, data_columns=['A', 'B'])
            store.append('df2', df2)

            # exceptions
            self.assertRaises(Exception, store.select_as_multiple,
                              None, where=['A>0', 'B>0'], selector='df1')
            self.assertRaises(Exception, store.select_as_multiple,
                              [None], where=['A>0', 'B>0'], selector='df1')
            self.assertRaises(TypeError, store.select_as_multiple,
                              ['df1','df3'], where=['A>0', 'B>0'], selector='df1')
            self.assertRaises(KeyError, store.select_as_multiple,
                              ['df3'], where=['A>0', 'B>0'], selector='df1')
            self.assertRaises(ValueError, store.select_as_multiple,
                              ['df1','df2'], where=['A>0', 'B>0'], selector='df4')

            # default select
            result = store.select('df1', ['A>0', 'B>0'])
            expected = store.select_as_multiple(
                ['df1'], where=['A>0', 'B>0'], selector='df1')
            tm.assert_frame_equal(result, expected)
            expected = store.select_as_multiple(
                'df1', where=['A>0', 'B>0'], selector='df1')
            tm.assert_frame_equal(result, expected)

            # multiple
            result = store.select_as_multiple(
                ['df1', 'df2'], where=['A>0', 'B>0'], selector='df1')
            expected = concat([df1, df2], axis=1)
            expected = expected[(expected.A > 0) & (expected.B > 0)]
            tm.assert_frame_equal(result, expected)

            # multiple (diff selector)
            result = store.select_as_multiple(['df1', 'df2'], where=[Term(
                'index>df2.index[4]')], selector='df2')
            expected = concat([df1, df2], axis=1)
            expected = expected[5:]
            tm.assert_frame_equal(result, expected)

            # test excpection for diff rows
            store.append('df3', tm.makeTimeDataFrame(nper=50))
            self.assertRaises(ValueError, store.select_as_multiple,
                              ['df1','df3'], where=['A>0', 'B>0'], selector='df1')

    def test_start_stop(self):

        with ensure_clean_store(self.path) as store:

            df = DataFrame(dict(A=np.random.rand(20), B=np.random.rand(20)))
            store.append('df', df)

            result = store.select(
                'df', [Term("columns=['A']")], start=0, stop=5)
            expected = df.ix[0:4, ['A']]
            tm.assert_frame_equal(result, expected)

            # out of range
            result = store.select(
                'df', [Term("columns=['A']")], start=30, stop=40)
            assert(len(result) == 0)
            assert(type(result) == DataFrame)

    def test_select_filter_corner(self):

        df = DataFrame(np.random.randn(50, 100))
        df.index = ['%.3d' % c for c in df.index]
        df.columns = ['%.3d' % c for c in df.columns]

        with ensure_clean_store(self.path) as store:
            store.put('frame', df, format='table')

            crit = Term('columns=df.columns[:75]')
            result = store.select('frame', [crit])
            tm.assert_frame_equal(result, df.ix[:, df.columns[:75]])

    def _check_roundtrip(self, obj, comparator, compression=False, **kwargs):

        options = {}
        if compression:
            options['complib'] = _default_compressor

        with ensure_clean_store(self.path, 'w', **options) as store:
            store['obj'] = obj
            retrieved = store['obj']
            comparator(retrieved, obj, **kwargs)

    def _check_double_roundtrip(self, obj, comparator, compression=False,
                                **kwargs):
        options = {}
        if compression:
            options['complib'] = compression or _default_compressor

        with ensure_clean_store(self.path, 'w', **options) as store:
            store['obj'] = obj
            retrieved = store['obj']
            comparator(retrieved, obj, **kwargs)
            store['obj'] = retrieved
            again = store['obj']
            comparator(again, obj, **kwargs)

    def _check_roundtrip_table(self, obj, comparator, compression=False):
        options = {}
        if compression:
            options['complib'] = _default_compressor

        with ensure_clean_store(self.path, 'w', **options) as store:
            store.put('obj', obj, format='table')
            retrieved = store['obj']
            # sorted_obj = _test_sort(obj)
            comparator(retrieved, obj)

    def test_multiple_open_close(self):
        # GH 4409, open & close multiple times

        with ensure_clean_path(self.path) as path:

            df = tm.makeDataFrame()
            df.to_hdf(path,'df',mode='w',format='table')

            # single
            store = HDFStore(path)
            self.assert_('CLOSED' not in str(store))
            self.assert_(store.is_open)
            store.close()
            self.assert_('CLOSED' in str(store))
            self.assert_(not store.is_open)

        with ensure_clean_path(self.path) as path:

            if pytables._table_file_open_policy_is_strict:

                # multiples
                store1 = HDFStore(path)
                def f():
                    HDFStore(path)
                self.assertRaises(ValueError, f)
                store1.close()

            else:

                # multiples
                store1 = HDFStore(path)
                store2 = HDFStore(path)

                self.assert_('CLOSED' not in str(store1))
                self.assert_('CLOSED' not in str(store2))
                self.assert_(store1.is_open)
                self.assert_(store2.is_open)

                store1.close()
                self.assert_('CLOSED' in str(store1))
                self.assert_(not store1.is_open)
                self.assert_('CLOSED' not in str(store2))
                self.assert_(store2.is_open)

                store2.close()
                self.assert_('CLOSED' in str(store1))
                self.assert_('CLOSED' in str(store2))
                self.assert_(not store1.is_open)
                self.assert_(not store2.is_open)

                # nested close
                store = HDFStore(path,mode='w')
                store.append('df',df)

                store2 = HDFStore(path)
                store2.append('df2',df)
                store2.close()
                self.assert_('CLOSED' in str(store2))
                self.assert_(not store2.is_open)

                store.close()
                self.assert_('CLOSED' in str(store))
                self.assert_(not store.is_open)

                # double closing
                store = HDFStore(path,mode='w')
                store.append('df', df)

                store2 = HDFStore(path)
                store.close()
                self.assert_('CLOSED' in str(store))
                self.assert_(not store.is_open)

                store2.close()
                self.assert_('CLOSED' in str(store2))
                self.assert_(not store2.is_open)

        # ops on a closed store
        with ensure_clean_path(self.path) as path:

            df = tm.makeDataFrame()
            df.to_hdf(path,'df',mode='w',format='table')

            store = HDFStore(path)
            store.close()

            self.assertRaises(ClosedFileError, store.keys)
            self.assertRaises(ClosedFileError, lambda : 'df' in store)
            self.assertRaises(ClosedFileError, lambda : len(store))
            self.assertRaises(ClosedFileError, lambda : store['df'])
            self.assertRaises(ClosedFileError, lambda : store.df)
            self.assertRaises(ClosedFileError, store.select, 'df')
            self.assertRaises(ClosedFileError, store.get, 'df')
            self.assertRaises(ClosedFileError, store.append, 'df2', df)
            self.assertRaises(ClosedFileError, store.put, 'df3', df)
            self.assertRaises(ClosedFileError, store.get_storer, 'df2')
            self.assertRaises(ClosedFileError, store.remove, 'df2')

            def f():
                store.select('df')
            tm.assertRaisesRegexp(ClosedFileError, 'file is not open', f)

    def test_pytables_native_read(self):

        try:
            store = HDFStore(tm.get_data_path('legacy_hdf/pytables_native.h5'), 'r')
            d2 = store['detector/readout']
            assert isinstance(d2, DataFrame)
        finally:
            safe_close(store)

        try:
            store = HDFStore(tm.get_data_path('legacy_hdf/pytables_native2.h5'), 'r')
            str(store)
            d1 = store['detector']
            assert isinstance(d1, DataFrame)
        finally:
            safe_close(store)

    def test_legacy_read(self):
        try:
            store = HDFStore(tm.get_data_path('legacy_hdf/legacy.h5'), 'r')
            store['a']
            store['b']
            store['c']
            store['d']
        finally:
            safe_close(store)

    def test_legacy_table_read(self):
        # legacy table types
        try:
            store = HDFStore(tm.get_data_path('legacy_hdf/legacy_table.h5'), 'r')
            store.select('df1')
            store.select('df2')
            store.select('wp1')

            # force the frame
            store.select('df2', typ='legacy_frame')

            # old version warning
            with tm.assert_produces_warning(expected_warning=IncompatibilityWarning):
                self.assertRaises(
                    Exception, store.select, 'wp1', Term('minor_axis=B'))

                df2 = store.select('df2')
                result = store.select('df2', Term('index>df2.index[2]'))
                expected = df2[df2.index > df2.index[2]]
                assert_frame_equal(expected, result)

        finally:
            safe_close(store)

    def test_legacy_0_10_read(self):
        # legacy from 0.10
        try:
            store = HDFStore(tm.get_data_path('legacy_hdf/legacy_0.10.h5'), 'r')
            str(store)
            for k in store.keys():
                store.select(k)
        finally:
            safe_close(store)

    def test_legacy_0_11_read(self):
        # legacy from 0.11
        try:
            path = os.path.join('legacy_hdf', 'legacy_table_0.11.h5')
            store = HDFStore(tm.get_data_path(path), 'r')
            str(store)
            assert 'df' in store
            assert 'df1' in store
            assert 'mi' in store
            df = store.select('df')
            df1 = store.select('df1')
            mi = store.select('mi')
            assert isinstance(df, DataFrame)
            assert isinstance(df1, DataFrame)
            assert isinstance(mi, DataFrame)
        finally:
            safe_close(store)

    def test_copy(self):

        def do_copy(f = None, new_f = None, keys = None, propindexes = True, **kwargs):
            try:
                if f is None:
                    f = tm.get_data_path(os.path.join('legacy_hdf',
                                                      'legacy_0.10.h5'))


                store = HDFStore(f, 'r')

                if new_f is None:
                    import tempfile
                    fd, new_f = tempfile.mkstemp()

                tstore = store.copy(new_f, keys = keys, propindexes = propindexes, **kwargs)

                # check keys
                if keys is None:
                    keys = store.keys()
                self.assert_(set(keys) == set(tstore.keys()))

                # check indicies & nrows
                for k in tstore.keys():
                    if tstore.get_storer(k).is_table:
                        new_t = tstore.get_storer(k)
                        orig_t = store.get_storer(k)

                        self.assert_(orig_t.nrows == new_t.nrows)

                        # check propindixes
                        if propindexes:
                            for a in orig_t.axes:
                                if a.is_indexed:
                                    self.assert_(new_t[a.name].is_indexed == True)

            finally:
                safe_close(store)
                safe_close(tstore)
                try:
                    os.close(fd)
                except:
                    pass
                safe_remove(new_f)

        do_copy()
        do_copy(keys = ['/a','/b','/df1_mixed'])
        do_copy(propindexes = False)

        # new table
        df = tm.makeDataFrame()

        try:
            st = HDFStore(self.path)
            st.append('df', df, data_columns = ['A'])
            st.close()
            do_copy(f = self.path)
            do_copy(f = self.path, propindexes = False)
        finally:
            safe_remove(self.path)

    def test_legacy_table_write(self):
        raise nose.SkipTest("skipping for now")

        store = HDFStore(tm.get_data_path('legacy_hdf/legacy_table_%s.h5' % pandas.__version__), 'a')

        df = tm.makeDataFrame()
        wp = tm.makePanel()

        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=['foo', 'bar'])
        df = DataFrame(np.random.randn(10, 3), index=index,
                       columns=['A', 'B', 'C'])
        store.append('mi', df)

        df = DataFrame(dict(A = 'foo', B = 'bar'),index=lrange(10))
        store.append('df', df, data_columns = ['B'], min_itemsize={'A' : 200 })
        store.append('wp', wp)

        store.close()

    def test_store_datetime_fractional_secs(self):

        with ensure_clean_store(self.path) as store:
            dt = datetime.datetime(2012, 1, 2, 3, 4, 5, 123456)
            series = Series([0], [dt])
            store['a'] = series
            self.assertEquals(store['a'].index[0], dt)

    def test_tseries_indices_series(self):

        with ensure_clean_store(self.path) as store:
            idx = tm.makeDateIndex(10)
            ser = Series(np.random.randn(len(idx)), idx)
            store['a'] = ser
            result = store['a']

            assert_series_equal(result, ser)
            self.assertEquals(type(result.index), type(ser.index))
            self.assertEquals(result.index.freq, ser.index.freq)

            idx = tm.makePeriodIndex(10)
            ser = Series(np.random.randn(len(idx)), idx)
            store['a'] = ser
            result = store['a']

            assert_series_equal(result, ser)
            self.assertEquals(type(result.index), type(ser.index))
            self.assertEquals(result.index.freq, ser.index.freq)

    def test_tseries_indices_frame(self):

        with ensure_clean_store(self.path) as store:
            idx = tm.makeDateIndex(10)
            df = DataFrame(np.random.randn(len(idx), 3), index=idx)
            store['a'] = df
            result = store['a']

            assert_frame_equal(result, df)
            self.assertEquals(type(result.index), type(df.index))
            self.assertEquals(result.index.freq, df.index.freq)

            idx = tm.makePeriodIndex(10)
            df = DataFrame(np.random.randn(len(idx), 3), idx)
            store['a'] = df
            result = store['a']

            assert_frame_equal(result, df)
            self.assertEquals(type(result.index), type(df.index))
            self.assertEquals(result.index.freq, df.index.freq)

    def test_unicode_index(self):

        unicode_values = [u('\u03c3'), u('\u03c3\u03c3')]
        def f():
            s = Series(np.random.randn(len(unicode_values)), unicode_values)
            self._check_roundtrip(s, tm.assert_series_equal)

        compat_assert_produces_warning(PerformanceWarning,f)

    def test_store_datetime_mixed(self):

        df = DataFrame(
            {'a': [1, 2, 3], 'b': [1., 2., 3.], 'c': ['a', 'b', 'c']})
        ts = tm.makeTimeSeries()
        df['d'] = ts.index[:3]
        self._check_roundtrip(df, tm.assert_frame_equal)

    # def test_cant_write_multiindex_table(self):
    #    # for now, #1848
    #    df = DataFrame(np.random.randn(10, 4),
    #                   index=[np.arange(5).repeat(2),
    #                          np.tile(np.arange(2), 5)])

    #    self.assertRaises(Exception, store.put, 'foo', df, format='table')

    def test_append_with_diff_col_name_types_raises_value_error(self):
        df = DataFrame(np.random.randn(10, 1))
        df2 = DataFrame({'a': np.random.randn(10)})
        df3 = DataFrame({(1, 2): np.random.randn(10)})
        df4 = DataFrame({('1', 2): np.random.randn(10)})
        df5 = DataFrame({('1', 2, object): np.random.randn(10)})

        with ensure_clean_store(self.path) as store:
            name = 'df_%s' % tm.rands(10)
            store.append(name, df)

            for d in (df2, df3, df4, df5):
                with tm.assertRaises(ValueError):
                    store.append(name, d)


def _test_sort(obj):
    if isinstance(obj, DataFrame):
        return obj.reindex(sorted(obj.index))
    elif isinstance(obj, Panel):
        return obj.reindex(major=sorted(obj.major_axis))
    else:
        raise ValueError('type not supported here')


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

numpy.random.rand numpy.random.binomial numpy.random.random_integers numpy.arange numpy.random.randint numpy.array numpy.zeros numpy.random.randn numpy.asarray numpy.random.seed