"""
Data structures for sparse float data. Life is made simpler by dealing only
with float64 data
"""
from __future__ import division
# pylint: disable=E1101,E1103,W0231,E0202

from numpy import nan
from pandas.compat import lmap
from pandas import compat
import numpy as np

from pandas.types.missing import isnull, notnull
from pandas.types.cast import _maybe_upcast
from pandas.types.common import _ensure_platform_int

from pandas.core.common import _try_sort
from pandas.compat.numpy import function as nv
from pandas.core.index import Index, MultiIndex, _ensure_index
from pandas.core.series import Series
from pandas.core.frame import (DataFrame, extract_index, _prep_ndarray,
                               _default_index)
import pandas.core.algorithms as algos
from pandas.core.internals import (BlockManager,
                                   create_block_manager_from_arrays)
import pandas.core.generic as generic
from pandas.sparse.series import SparseSeries, SparseArray
from pandas.util.decorators import Appender
import pandas.core.ops as ops


_shared_doc_kwargs = dict(klass='SparseDataFrame')


class SparseDataFrame(DataFrame):
    """
    DataFrame containing sparse floating point data in the form of SparseSeries
    objects

    Parameters
    ----------
    data : same types as can be passed to DataFrame
    index : array-like, optional
    column : array-like, optional
    default_kind : {'block', 'integer'}, default 'block'
        Default sparse kind for converting Series to SparseSeries. Will not
        override SparseSeries passed into constructor
    default_fill_value : float
        Default fill_value for converting Series to SparseSeries. Will not
        override SparseSeries passed in
    """
    _constructor_sliced = SparseSeries
    _subtyp = 'sparse_frame'

    def __init__(self, data=None, index=None, columns=None, default_kind=None,
                 default_fill_value=None, dtype=None, copy=False):

        # pick up the defaults from the Sparse structures
        if isinstance(data, SparseDataFrame):
            if index is None:
                index = data.index
            if columns is None:
                columns = data.columns
            if default_fill_value is None:
                default_fill_value = data.default_fill_value
            if default_kind is None:
                default_kind = data.default_kind
        elif isinstance(data, (SparseSeries, SparseArray)):
            if index is None:
                index = data.index
            if default_fill_value is None:
                default_fill_value = data.fill_value
            if columns is None and hasattr(data, 'name'):
                columns = [data.name]
            if columns is None:
                raise Exception("cannot pass a series w/o a name or columns")
            data = {columns[0]: data}

        if default_fill_value is None:
            default_fill_value = np.nan
        if default_kind is None:
            default_kind = 'block'

        self._default_kind = default_kind
        self._default_fill_value = default_fill_value

        if isinstance(data, dict):
            mgr = self._init_dict(data, index, columns)
            if dtype is not None:
                mgr = mgr.astype(dtype)
        elif isinstance(data, (np.ndarray, list)):
            mgr = self._init_matrix(data, index, columns)
            if dtype is not None:
                mgr = mgr.astype(dtype)
        elif isinstance(data, SparseDataFrame):
            mgr = self._init_mgr(data._data,
                                 dict(index=index, columns=columns),
                                 dtype=dtype, copy=copy)
        elif isinstance(data, DataFrame):
            mgr = self._init_dict(data, data.index, data.columns)
            if dtype is not None:
                mgr = mgr.astype(dtype)
        elif isinstance(data, BlockManager):
            mgr = self._init_mgr(data, axes=dict(index=index, columns=columns),
                                 dtype=dtype, copy=copy)
        elif data is None:
            data = DataFrame()

            if index is None:
                index = Index([])
            else:
                index = _ensure_index(index)

            if columns is None:
                columns = Index([])
            else:
                for c in columns:
                    data[c] = SparseArray(np.nan, index=index,
                                          kind=self._default_kind,
                                          fill_value=self._default_fill_value)
            mgr = to_manager(data, columns, index)
            if dtype is not None:
                mgr = mgr.astype(dtype)

        generic.NDFrame.__init__(self, mgr)

    @property
    def _constructor(self):
        return SparseDataFrame

    _constructor_sliced = SparseSeries

    def _init_dict(self, data, index, columns, dtype=None):
        # pre-filter out columns if we passed it
        if columns is not None:
            columns = _ensure_index(columns)
            data = dict((k, v) for k, v in compat.iteritems(data)
                        if k in columns)
        else:
            columns = Index(_try_sort(list(data.keys())))

        if index is None:
            index = extract_index(list(data.values()))

        sp_maker = lambda x: SparseArray(x, kind=self._default_kind,
                                         fill_value=self._default_fill_value,
                                         copy=True)
        sdict = DataFrame()
        for k, v in compat.iteritems(data):
            if isinstance(v, Series):
                # Force alignment, no copy necessary
                if not v.index.equals(index):
                    v = v.reindex(index)

                if not isinstance(v, SparseSeries):
                    v = sp_maker(v.values)
            elif isinstance(v, SparseArray):
                v = v.copy()
            else:
                if isinstance(v, dict):
                    v = [v.get(i, nan) for i in index]

                v = sp_maker(v)
            sdict[k] = v

        # TODO: figure out how to handle this case, all nan's?
        # add in any other columns we want to have (completeness)
        nan_vec = np.empty(len(index))
        nan_vec.fill(nan)
        for c in columns:
            if c not in sdict:
                sdict[c] = sp_maker(nan_vec)

        return to_manager(sdict, columns, index)

    def _init_matrix(self, data, index, columns, dtype=None):
        data = _prep_ndarray(data, copy=False)
        N, K = data.shape
        if index is None:
            index = _default_index(N)
        if columns is None:
            columns = _default_index(K)

        if len(columns) != K:
            raise ValueError('Column length mismatch: %d vs. %d' %
                             (len(columns), K))
        if len(index) != N:
            raise ValueError('Index length mismatch: %d vs. %d' %
                             (len(index), N))

        data = dict([(idx, data[:, i]) for i, idx in enumerate(columns)])
        return self._init_dict(data, index, columns, dtype)

    def __array_wrap__(self, result):
        return self._constructor(
            result, index=self.index, columns=self.columns,
            default_kind=self._default_kind,
            default_fill_value=self._default_fill_value).__finalize__(self)

    def __getstate__(self):
        # pickling
        return dict(_typ=self._typ, _subtyp=self._subtyp, _data=self._data,
                    _default_fill_value=self._default_fill_value,
                    _default_kind=self._default_kind)

    def _unpickle_sparse_frame_compat(self, state):
        """ original pickle format """
        series, cols, idx, fv, kind = state

        if not isinstance(cols, Index):  # pragma: no cover
            from pandas.io.pickle import _unpickle_array
            columns = _unpickle_array(cols)
        else:
            columns = cols

        if not isinstance(idx, Index):  # pragma: no cover
            from pandas.io.pickle import _unpickle_array
            index = _unpickle_array(idx)
        else:
            index = idx

        series_dict = DataFrame()
        for col, (sp_index, sp_values) in compat.iteritems(series):
            series_dict[col] = SparseSeries(sp_values, sparse_index=sp_index,
                                            fill_value=fv)

        self._data = to_manager(series_dict, columns, index)
        self._default_fill_value = fv
        self._default_kind = kind

    def to_dense(self):
        """
        Convert to dense DataFrame

        Returns
        -------
        df : DataFrame
        """
        data = dict((k, v.to_dense()) for k, v in compat.iteritems(self))
        return DataFrame(data, index=self.index, columns=self.columns)

    def _apply_columns(self, func):
        """ get new SparseDataFrame applying func to each columns """

        new_data = {}
        for col, series in compat.iteritems(self):
            new_data[col] = func(series)

        return self._constructor(
            data=new_data, index=self.index, columns=self.columns,
            default_fill_value=self.default_fill_value).__finalize__(self)

    def astype(self, dtype):
        return self._apply_columns(lambda x: x.astype(dtype))

    def copy(self, deep=True):
        """
        Make a copy of this SparseDataFrame
        """
        result = super(SparseDataFrame, self).copy(deep=deep)
        result._default_fill_value = self._default_fill_value
        result._default_kind = self._default_kind
        return result

    @property
    def default_fill_value(self):
        return self._default_fill_value

    @property
    def default_kind(self):
        return self._default_kind

    @property
    def density(self):
        """
        Ratio of non-sparse points to total (dense) data points
        represented in the frame
        """
        tot_nonsparse = sum([ser.sp_index.npoints
                             for _, ser in compat.iteritems(self)])
        tot = len(self.index) * len(self.columns)
        return tot_nonsparse / float(tot)

    def fillna(self, value=None, method=None, axis=0, inplace=False,
               limit=None, downcast=None):
        new_self = super(SparseDataFrame,
                         self).fillna(value=value, method=method, axis=axis,
                                      inplace=inplace, limit=limit,
                                      downcast=downcast)
        if not inplace:
            self = new_self

        # set the fill value if we are filling as a scalar with nothing special
        # going on
        if (value is not None and value == value and method is None and
                limit is None):
            self._default_fill_value = value

        if not inplace:
            return self

    # ----------------------------------------------------------------------
    # Support different internal representation of SparseDataFrame

    def _sanitize_column(self, key, value, **kwargs):
        """
        Creates a new SparseArray from the input value.

        Parameters
        ----------
        key : object
        value : scalar, Series, or array-like
        kwargs : dict

        Returns
        -------
        sanitized_column : SparseArray

        """
        sp_maker = lambda x, index=None: SparseArray(
            x, index=index, fill_value=self._default_fill_value,
            kind=self._default_kind)
        if isinstance(value, SparseSeries):
            clean = value.reindex(self.index).as_sparse_array(
                fill_value=self._default_fill_value, kind=self._default_kind)

        elif isinstance(value, SparseArray):
            if len(value) != len(self.index):
                raise AssertionError('Length of values does not match '
                                     'length of index')
            clean = value

        elif hasattr(value, '__iter__'):
            if isinstance(value, Series):
                clean = value.reindex(self.index)
                if not isinstance(value, SparseSeries):
                    clean = sp_maker(clean)
            else:
                if len(value) != len(self.index):
                    raise AssertionError('Length of values does not match '
                                         'length of index')
                clean = sp_maker(value)

        # Scalar
        else:
            clean = sp_maker(value, self.index)

        # always return a SparseArray!
        return clean

    def __getitem__(self, key):
        """
        Retrieve column or slice from DataFrame
        """
        if isinstance(key, slice):
            date_rng = self.index[key]
            return self.reindex(date_rng)
        elif isinstance(key, (np.ndarray, list, Series)):
            return self._getitem_array(key)
        else:
            return self._get_item_cache(key)

    @Appender(DataFrame.get_value.__doc__, indents=0)
    def get_value(self, index, col, takeable=False):
        if takeable is True:
            series = self._iget_item_cache(col)
        else:
            series = self._get_item_cache(col)

        return series.get_value(index, takeable=takeable)

    def set_value(self, index, col, value, takeable=False):
        """
        Put single value at passed column and index

        Parameters
        ----------
        index : row label
        col : column label
        value : scalar value
        takeable : interpret the index/col as indexers, default False

        Notes
        -----
        This method *always* returns a new object. It is currently not
        particularly efficient (and potentially very expensive) but is provided
        for API compatibility with DataFrame

        Returns
        -------
        frame : DataFrame
        """
        dense = self.to_dense().set_value(index, col, value, takeable=takeable)
        return dense.to_sparse(kind=self._default_kind,
                               fill_value=self._default_fill_value)

    def _slice(self, slobj, axis=0, kind=None):
        if axis == 0:
            new_index = self.index[slobj]
            new_columns = self.columns
        else:
            new_index = self.index
            new_columns = self.columns[slobj]

        return self.reindex(index=new_index, columns=new_columns)

    def xs(self, key, axis=0, copy=False):
        """
        Returns a row (cross-section) from the SparseDataFrame as a Series
        object.

        Parameters
        ----------
        key : some index contained in the index

        Returns
        -------
        xs : Series
        """
        if axis == 1:
            data = self[key]
            return data

        i = self.index.get_loc(key)
        data = self.take([i]).get_values()[0]
        return Series(data, index=self.columns)

    # ----------------------------------------------------------------------
    # Arithmetic-related methods

    def _combine_frame(self, other, func, fill_value=None, level=None):
        this, other = self.align(other, join='outer', level=level, copy=False)
        new_index, new_columns = this.index, this.columns

        if level is not None:
            raise NotImplementedError("'level' argument is not supported")

        if self.empty and other.empty:
            return self._constructor(index=new_index).__finalize__(self)

        new_data = {}
        new_fill_value = None
        if fill_value is not None:
            # TODO: be a bit more intelligent here
            for col in new_columns:
                if col in this and col in other:
                    dleft = this[col].to_dense()
                    dright = other[col].to_dense()
                    result = dleft._binop(dright, func, fill_value=fill_value)
                    result = result.to_sparse(fill_value=this[col].fill_value)
                    new_data[col] = result
        else:

            for col in new_columns:
                if col in this and col in other:
                    new_data[col] = func(this[col], other[col])

        # if the fill values are the same use them? or use a valid one
        other_fill_value = getattr(other, 'default_fill_value', np.nan)
        if self.default_fill_value == other_fill_value:
            new_fill_value = self.default_fill_value
        elif np.isnan(self.default_fill_value) and not np.isnan(
                other_fill_value):
            new_fill_value = other_fill_value
        elif not np.isnan(self.default_fill_value) and np.isnan(
                other_fill_value):
            new_fill_value = self.default_fill_value

        return self._constructor(data=new_data, index=new_index,
                                 columns=new_columns,
                                 default_fill_value=new_fill_value
                                 ).__finalize__(self)

    def _combine_match_index(self, other, func, level=None, fill_value=None):
        new_data = {}

        if fill_value is not None:
            raise NotImplementedError("'fill_value' argument is not supported")
        if level is not None:
            raise NotImplementedError("'level' argument is not supported")

        new_index = self.index.union(other.index)
        this = self
        if self.index is not new_index:
            this = self.reindex(new_index)

        if other.index is not new_index:
            other = other.reindex(new_index)

        for col, series in compat.iteritems(this):
            new_data[col] = func(series.values, other.values)

        # fill_value is a function of our operator
        if isnull(other.fill_value) or isnull(self.default_fill_value):
            fill_value = np.nan
        else:
            fill_value = func(np.float64(self.default_fill_value),
                              np.float64(other.fill_value))

        return self._constructor(
            new_data, index=new_index, columns=self.columns,
            default_fill_value=fill_value).__finalize__(self)

    def _combine_match_columns(self, other, func, level=None, fill_value=None):
        # patched version of DataFrame._combine_match_columns to account for
        # NumPy circumventing __rsub__ with float64 types, e.g.: 3.0 - series,
        # where 3.0 is numpy.float64 and series is a SparseSeries. Still
        # possible for this to happen, which is bothersome

        if fill_value is not None:
            raise NotImplementedError("'fill_value' argument is not supported")
        if level is not None:
            raise NotImplementedError("'level' argument is not supported")

        new_data = {}

        union = intersection = self.columns

        if not union.equals(other.index):
            union = other.index.union(self.columns)
            intersection = other.index.intersection(self.columns)

        for col in intersection:
            new_data[col] = func(self[col], float(other[col]))

        return self._constructor(
            new_data, index=self.index, columns=union,
            default_fill_value=self.default_fill_value).__finalize__(self)

    def _combine_const(self, other, func, raise_on_error=True):
        return self._apply_columns(lambda x: func(x, other))

    def _reindex_index(self, index, method, copy, level, fill_value=np.nan,
                       limit=None, takeable=False):
        if level is not None:
            raise TypeError('Reindex by level not supported for sparse')

        if self.index.equals(index):
            if copy:
                return self.copy()
            else:
                return self

        if len(self.index) == 0:
            return self._constructor(
                index=index, columns=self.columns).__finalize__(self)

        indexer = self.index.get_indexer(index, method, limit=limit)
        indexer = _ensure_platform_int(indexer)
        mask = indexer == -1
        need_mask = mask.any()

        new_series = {}
        for col, series in self.iteritems():
            if mask.all():
                continue

            values = series.values
            # .take returns SparseArray
            new = values.take(indexer)
            if need_mask:
                new = new.values
                # convert integer to float if necessary. need to do a lot
                # more than that, handle boolean etc also
                new, fill_value = _maybe_upcast(new, fill_value=fill_value)
                np.putmask(new, mask, fill_value)

            new_series[col] = new

        return self._constructor(
            new_series, index=index, columns=self.columns,
            default_fill_value=self._default_fill_value).__finalize__(self)

    def _reindex_columns(self, columns, copy, level, fill_value, limit=None,
                         takeable=False):
        if level is not None:
            raise TypeError('Reindex by level not supported for sparse')

        if notnull(fill_value):
            raise NotImplementedError("'fill_value' argument is not supported")

        if limit:
            raise NotImplementedError("'limit' argument is not supported")

        # TODO: fill value handling
        sdict = dict((k, v) for k, v in compat.iteritems(self) if k in columns)
        return self._constructor(
            sdict, index=self.index, columns=columns,
            default_fill_value=self._default_fill_value).__finalize__(self)

    def _reindex_with_indexers(self, reindexers, method=None, fill_value=None,
                               limit=None, copy=False, allow_dups=False):

        if method is not None or limit is not None:
            raise NotImplementedError("cannot reindex with a method or limit "
                                      "with sparse")

        if fill_value is None:
            fill_value = np.nan

        index, row_indexer = reindexers.get(0, (None, None))
        columns, col_indexer = reindexers.get(1, (None, None))

        if columns is None:
            columns = self.columns

        new_arrays = {}
        for col in columns:
            if col not in self:
                continue
            if row_indexer is not None:
                new_arrays[col] = algos.take_1d(self[col].get_values(),
                                                row_indexer,
                                                fill_value=fill_value)
            else:
                new_arrays[col] = self[col]

        return self._constructor(new_arrays, index=index,
                                 columns=columns).__finalize__(self)

    def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',
                     sort=False):
        if on is not None:
            raise NotImplementedError("'on' keyword parameter is not yet "
                                      "implemented")
        return self._join_index(other, how, lsuffix, rsuffix)

    def _join_index(self, other, how, lsuffix, rsuffix):
        if isinstance(other, Series):
            if other.name is None:
                raise ValueError('Other Series must have a name')

            other = SparseDataFrame(
                {other.name: other},
                default_fill_value=self._default_fill_value)

        join_index = self.index.join(other.index, how=how)

        this = self.reindex(join_index)
        other = other.reindex(join_index)

        this, other = this._maybe_rename_join(other, lsuffix, rsuffix)

        from pandas import concat
        return concat([this, other], axis=1, verify_integrity=True)

    def _maybe_rename_join(self, other, lsuffix, rsuffix):
        to_rename = self.columns.intersection(other.columns)
        if len(to_rename) > 0:
            if not lsuffix and not rsuffix:
                raise ValueError('columns overlap but no suffix specified: %s'
                                 % to_rename)

            def lrenamer(x):
                if x in to_rename:
                    return '%s%s' % (x, lsuffix)
                return x

            def rrenamer(x):
                if x in to_rename:
                    return '%s%s' % (x, rsuffix)
                return x

            this = self.rename(columns=lrenamer)
            other = other.rename(columns=rrenamer)
        else:
            this = self

        return this, other

    def transpose(self, *args, **kwargs):
        """
        Returns a DataFrame with the rows/columns switched.
        """
        nv.validate_transpose(args, kwargs)
        return self._constructor(
            self.values.T, index=self.columns, columns=self.index,
            default_fill_value=self._default_fill_value,
            default_kind=self._default_kind).__finalize__(self)

    T = property(transpose)

    @Appender(DataFrame.count.__doc__)
    def count(self, axis=0, **kwds):
        if axis is None:
            axis = self._stat_axis_number

        return self.apply(lambda x: x.count(), axis=axis)

    def cumsum(self, axis=0, *args, **kwargs):
        """
        Return SparseDataFrame of cumulative sums over requested axis.

        Parameters
        ----------
        axis : {0, 1}
            0 for row-wise, 1 for column-wise

        Returns
        -------
        y : SparseDataFrame
        """
        nv.validate_cumsum(args, kwargs)

        if axis is None:
            axis = self._stat_axis_number

        return self.apply(lambda x: x.cumsum(), axis=axis)

    @Appender(generic._shared_docs['isnull'])
    def isnull(self):
        return self._apply_columns(lambda x: x.isnull())

    @Appender(generic._shared_docs['isnotnull'])
    def isnotnull(self):
        return self._apply_columns(lambda x: x.isnotnull())

    def apply(self, func, axis=0, broadcast=False, reduce=False):
        """
        Analogous to DataFrame.apply, for SparseDataFrame

        Parameters
        ----------
        func : function
            Function to apply to each column
        axis : {0, 1, 'index', 'columns'}
        broadcast : bool, default False
            For aggregation functions, return object of same size with values
            propagated

        Returns
        -------
        applied : Series or SparseDataFrame
        """
        if not len(self.columns):
            return self
        axis = self._get_axis_number(axis)

        if isinstance(func, np.ufunc):
            new_series = {}
            for k, v in compat.iteritems(self):
                applied = func(v)
                applied.fill_value = func(v.fill_value)
                new_series[k] = applied
            return self._constructor(
                new_series, index=self.index, columns=self.columns,
                default_fill_value=self._default_fill_value,
                default_kind=self._default_kind).__finalize__(self)
        else:
            if not broadcast:
                return self._apply_standard(func, axis, reduce=reduce)
            else:
                return self._apply_broadcast(func, axis)

    def applymap(self, func):
        """
        Apply a function to a DataFrame that is intended to operate
        elementwise, i.e. like doing map(func, series) for each series in the
        DataFrame

        Parameters
        ----------
        func : function
            Python function, returns a single value from a single value

        Returns
        -------
        applied : DataFrame
        """
        return self.apply(lambda x: lmap(func, x))


def to_manager(sdf, columns, index):
    """ create and return the block manager from a dataframe of series,
    columns, index
    """

    # from BlockManager perspective
    axes = [_ensure_index(columns), _ensure_index(index)]

    return create_block_manager_from_arrays(
        [sdf[c] for c in columns], columns, axes)


def stack_sparse_frame(frame):
    """
    Only makes sense when fill_value is NaN
    """
    lengths = [s.sp_index.npoints for _, s in compat.iteritems(frame)]
    nobs = sum(lengths)

    # this is pretty fast
    minor_labels = np.repeat(np.arange(len(frame.columns)), lengths)

    inds_to_concat = []
    vals_to_concat = []
    # TODO: Figure out whether this can be reached.
    # I think this currently can't be reached because you can't build a
    # SparseDataFrame with a non-np.NaN fill value (fails earlier).
    for _, series in compat.iteritems(frame):
        if not np.isnan(series.fill_value):
            raise TypeError('This routine assumes NaN fill value')

        int_index = series.sp_index.to_int_index()
        inds_to_concat.append(int_index.indices)
        vals_to_concat.append(series.sp_values)

    major_labels = np.concatenate(inds_to_concat)
    stacked_values = np.concatenate(vals_to_concat)
    index = MultiIndex(levels=[frame.index, frame.columns],
                       labels=[major_labels, minor_labels],
                       verify_integrity=False)

    lp = DataFrame(stacked_values.reshape((nobs, 1)), index=index,
                   columns=['foo'])
    return lp.sortlevel(level=0)


def homogenize(series_dict):
    """
    Conform a set of SparseSeries (with NaN fill_value) to a common SparseIndex
    corresponding to the locations where they all have data

    Parameters
    ----------
    series_dict : dict or DataFrame

    Notes
    -----
    Using the dumbest algorithm I could think of. Should put some more thought
    into this

    Returns
    -------
    homogenized : dict of SparseSeries
    """
    index = None

    need_reindex = False

    for _, series in compat.iteritems(series_dict):
        if not np.isnan(series.fill_value):
            raise TypeError('this method is only valid with NaN fill values')

        if index is None:
            index = series.sp_index
        elif not series.sp_index.equals(index):
            need_reindex = True
            index = index.intersect(series.sp_index)

    if need_reindex:
        output = {}
        for name, series in compat.iteritems(series_dict):
            if not series.sp_index.equals(index):
                series = series.sparse_reindex(index)

            output[name] = series
    else:
        output = series_dict

    return output

# use unaccelerated ops for sparse objects
ops.add_flex_arithmetic_methods(SparseDataFrame, use_numexpr=False,
                                **ops.frame_flex_funcs)
ops.add_special_arithmetic_methods(SparseDataFrame, use_numexpr=False,
                                   **ops.frame_special_funcs)
