"""
Contains data structures designed for manipulating panel (3-dimensional) data
"""
# pylint: disable=E1103,W0231,W0212,W0621
from __future__ import division

import warnings

import numpy as np

import pandas.compat as compat
from pandas.compat import OrderedDict, map, range, u, zip
from pandas.compat.numpy import function as nv
from pandas.util._decorators import Appender, Substitution, deprecate_kwarg
from pandas.util._validators import validate_axis_style_args

from pandas.core.dtypes.cast import (
    cast_scalar_to_array, infer_dtype_from_scalar, maybe_cast_item)
from pandas.core.dtypes.common import (
    is_integer, is_list_like, is_scalar, is_string_like)
from pandas.core.dtypes.missing import notna

import pandas.core.common as com
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame, _shared_docs
from pandas.core.index import (
    Index, MultiIndex, _get_objs_combined_axis, ensure_index)
import pandas.core.indexes.base as ibase
from pandas.core.indexing import maybe_droplevels
from pandas.core.internals import (
    BlockManager, create_block_manager_from_arrays,
    create_block_manager_from_blocks)
import pandas.core.ops as ops
from pandas.core.reshape.util import cartesian_product
from pandas.core.series import Series

from pandas.io.formats.printing import pprint_thing

_shared_doc_kwargs = dict(
    axes='items, major_axis, minor_axis',
    klass="Panel",
    axes_single_arg="{0, 1, 2, 'items', 'major_axis', 'minor_axis'}",
    optional_mapper='', optional_axis='', optional_labels='')
_shared_doc_kwargs['args_transpose'] = (
    "three positional arguments: each one of\n{ax_single}".format(
        ax_single=_shared_doc_kwargs['axes_single_arg']))


def _ensure_like_indices(time, panels):
    """
    Makes sure that time and panels are conformable.
    """
    n_time = len(time)
    n_panel = len(panels)
    u_panels = np.unique(panels)  # this sorts!
    u_time = np.unique(time)
    if len(u_time) == n_time:
        time = np.tile(u_time, len(u_panels))
    if len(u_panels) == n_panel:
        panels = np.repeat(u_panels, len(u_time))
    return time, panels


def panel_index(time, panels, names=None):
    """
    Returns a multi-index suitable for a panel-like DataFrame.

    Parameters
    ----------
    time : array-like
        Time index, does not have to repeat
    panels : array-like
        Panel index, does not have to repeat
    names : list, optional
        List containing the names of the indices

    Returns
    -------
    multi_index : MultiIndex
        Time index is the first level, the panels are the second level.

    Examples
    --------
    >>> years = range(1960,1963)
    >>> panels = ['A', 'B', 'C']
    >>> panel_idx = panel_index(years, panels)
    >>> panel_idx
    MultiIndex([(1960, 'A'), (1961, 'A'), (1962, 'A'), (1960, 'B'),
                (1961, 'B'), (1962, 'B'), (1960, 'C'), (1961, 'C'),
                (1962, 'C')], dtype=object)

    or

    >>> years = np.repeat(range(1960,1963), 3)
    >>> panels = np.tile(['A', 'B', 'C'], 3)
    >>> panel_idx = panel_index(years, panels)
    >>> panel_idx
    MultiIndex([(1960, 'A'), (1960, 'B'), (1960, 'C'), (1961, 'A'),
                (1961, 'B'), (1961, 'C'), (1962, 'A'), (1962, 'B'),
                (1962, 'C')], dtype=object)
    """
    if names is None:
        names = ['time', 'panel']
    time, panels = _ensure_like_indices(time, panels)
    return MultiIndex.from_arrays([time, panels], sortorder=None, names=names)


class Panel(NDFrame):
    """
    Represents wide format panel data, stored as 3-dimensional array.

    .. deprecated:: 0.20.0
        The recommended way to represent 3-D data are with a MultiIndex on a
        DataFrame via the :attr:`~Panel.to_frame()` method or with the
        `xarray package <http://xarray.pydata.org/en/stable/>`__.
        Pandas provides a :attr:`~Panel.to_xarray()` method to automate this
        conversion.

    Parameters
    ----------
    data : ndarray (items x major x minor), or dict of DataFrames
    items : Index or array-like
        axis=0
    major_axis : Index or array-like
        axis=1
    minor_axis : Index or array-like
        axis=2
    copy : boolean, default False
        Copy data from inputs. Only affects DataFrame / 2d ndarray input
    dtype : dtype, default None
        Data type to force, otherwise infer
    """

    @property
    def _constructor(self):
        return type(self)

    _constructor_sliced = DataFrame

    def __init__(self, data=None, items=None, major_axis=None, minor_axis=None,
                 copy=False, dtype=None):
        # deprecation GH13563
        warnings.warn("\nPanel is deprecated and will be removed in a "
                      "future version.\nThe recommended way to represent "
                      "these types of 3-dimensional data are with a "
                      "MultiIndex on a DataFrame, via the "
                      "Panel.to_frame() method\n"
                      "Alternatively, you can use the xarray package "
                      "http://xarray.pydata.org/en/stable/.\n"
                      "Pandas provides a `.to_xarray()` method to help "
                      "automate this conversion.\n",
                      FutureWarning, stacklevel=3)

        self._init_data(data=data, items=items, major_axis=major_axis,
                        minor_axis=minor_axis, copy=copy, dtype=dtype)

    def _init_data(self, data, copy, dtype, **kwargs):
        """
        Generate ND initialization; axes are passed
        as required objects to __init__.
        """
        if data is None:
            data = {}
        if dtype is not None:
            dtype = self._validate_dtype(dtype)

        passed_axes = [kwargs.pop(a, None) for a in self._AXIS_ORDERS]

        if kwargs:
            raise TypeError('_init_data() got an unexpected keyword '
                            'argument "{0}"'.format(list(kwargs.keys())[0]))

        axes = None
        if isinstance(data, BlockManager):
            if com._any_not_none(*passed_axes):
                axes = [x if x is not None else y
                        for x, y in zip(passed_axes, data.axes)]
            mgr = data
        elif isinstance(data, dict):
            mgr = self._init_dict(data, passed_axes, dtype=dtype)
            copy = False
            dtype = None
        elif isinstance(data, (np.ndarray, list)):
            mgr = self._init_matrix(data, passed_axes, dtype=dtype, copy=copy)
            copy = False
            dtype = None
        elif is_scalar(data) and com._all_not_none(*passed_axes):
            values = cast_scalar_to_array([len(x) for x in passed_axes],
                                          data, dtype=dtype)
            mgr = self._init_matrix(values, passed_axes, dtype=values.dtype,
                                    copy=False)
            copy = False
        else:  # pragma: no cover
            raise ValueError('Panel constructor not properly called!')

        NDFrame.__init__(self, mgr, axes=axes, copy=copy, dtype=dtype)

    def _init_dict(self, data, axes, dtype=None):
        haxis = axes.pop(self._info_axis_number)

        # prefilter if haxis passed
        if haxis is not None:
            haxis = ensure_index(haxis)
            data = OrderedDict((k, v)
                               for k, v in compat.iteritems(data)
                               if k in haxis)
        else:
            keys = com.dict_keys_to_ordered_list(data)
            haxis = Index(keys)

        for k, v in compat.iteritems(data):
            if isinstance(v, dict):
                data[k] = self._constructor_sliced(v)

        # extract axis for remaining axes & create the slicemap
        raxes = [self._extract_axis(self, data, axis=i) if a is None else a
                 for i, a in enumerate(axes)]
        raxes_sm = self._extract_axes_for_slice(self, raxes)

        # shallow copy
        arrays = []
        haxis_shape = [len(a) for a in raxes]
        for h in haxis:
            v = values = data.get(h)
            if v is None:
                values = np.empty(haxis_shape, dtype=dtype)
                values.fill(np.nan)
            elif isinstance(v, self._constructor_sliced):
                d = raxes_sm.copy()
                d['copy'] = False
                v = v.reindex(**d)
                if dtype is not None:
                    v = v.astype(dtype)
                values = v.values
            arrays.append(values)

        return self._init_arrays(arrays, haxis, [haxis] + raxes)

    def _init_arrays(self, arrays, arr_names, axes):
        return create_block_manager_from_arrays(arrays, arr_names, axes)

    @classmethod
    def from_dict(cls, data, intersect=False, orient='items', dtype=None):
        """
        Construct Panel from dict of DataFrame objects.

        Parameters
        ----------
        data : dict
            {field : DataFrame}
        intersect : boolean
            Intersect indexes of input DataFrames
        orient : {'items', 'minor'}, default 'items'
            The "orientation" of the data. If the keys of the passed dict
            should be the items of the result panel, pass 'items'
            (default). Otherwise if the columns of the values of the passed
            DataFrame objects should be the items (which in the case of
            mixed-dtype data you should do), instead pass 'minor'
        dtype : dtype, default None
            Data type to force, otherwise infer

        Returns
        -------
        Panel
        """
        from collections import defaultdict

        orient = orient.lower()
        if orient == 'minor':
            new_data = defaultdict(OrderedDict)
            for col, df in compat.iteritems(data):
                for item, s in compat.iteritems(df):
                    new_data[item][col] = s
            data = new_data
        elif orient != 'items':  # pragma: no cover
            raise ValueError('Orientation must be one of {items, minor}.')

        d = cls._homogenize_dict(cls, data, intersect=intersect, dtype=dtype)
        ks = list(d['data'].keys())
        if not isinstance(d['data'], OrderedDict):
            ks = list(sorted(ks))
        d[cls._info_axis_name] = Index(ks)
        return cls(**d)

    def __getitem__(self, key):
        key = com.apply_if_callable(key, self)

        if isinstance(self._info_axis, MultiIndex):
            return self._getitem_multilevel(key)
        if not (is_list_like(key) or isinstance(key, slice)):
            return super(Panel, self).__getitem__(key)
        return self.loc[key]

    def _getitem_multilevel(self, key):
        info = self._info_axis
        loc = info.get_loc(key)
        if isinstance(loc, (slice, np.ndarray)):
            new_index = info[loc]
            result_index = maybe_droplevels(new_index, key)
            slices = [loc] + [slice(None)] * (self._AXIS_LEN - 1)
            new_values = self.values[slices]

            d = self._construct_axes_dict(self._AXIS_ORDERS[1:])
            d[self._info_axis_name] = result_index
            result = self._constructor(new_values, **d)
            return result
        else:
            return self._get_item_cache(key)

    def _init_matrix(self, data, axes, dtype=None, copy=False):
        values = self._prep_ndarray(self, data, copy=copy)

        if dtype is not None:
            try:
                values = values.astype(dtype)
            except Exception:
                raise ValueError('failed to cast to '
                                 '{datatype}'.format(datatype=dtype))

        shape = values.shape
        fixed_axes = []
        for i, ax in enumerate(axes):
            if ax is None:
                ax = ibase.default_index(shape[i])
            else:
                ax = ensure_index(ax)
            fixed_axes.append(ax)

        return create_block_manager_from_blocks([values], fixed_axes)

    # ----------------------------------------------------------------------
    # Comparison methods

    def _compare_constructor(self, other, func):
        if not self._indexed_same(other):
            raise Exception('Can only compare identically-labeled '
                            'same type objects')

        new_data = {col: func(self[col], other[col])
                    for col in self._info_axis}

        d = self._construct_axes_dict(copy=False)
        return self._constructor(data=new_data, **d)

    # ----------------------------------------------------------------------