# pylint: disable=W0231,E1101
from datetime import timedelta

import numpy as np

from pandas.core.index import MultiIndex
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.offsets import DateOffset
import pandas.core.common as com
import pandas.lib as lib


class PandasError(Exception):
    pass


class PandasObject(object):

    _AXIS_NUMBERS = {
        'index': 0,
        'columns': 1
    }

    _AXIS_ALIASES = {}
    _AXIS_NAMES = dict((v, k) for k, v in _AXIS_NUMBERS.iteritems())

    def save(self, path):
        com.save(self, path)

    @classmethod
    def load(cls, path):
        return com.load(path)

    #----------------------------------------------------------------------
    # Axis name business

    @classmethod
    def _get_axis_number(cls, axis):
        axis = cls._AXIS_ALIASES.get(axis, axis)

        if isinstance(axis, int):
            if axis in cls._AXIS_NAMES:
                return axis
            else:
                raise Exception('No %d axis' % axis)
        else:
            return cls._AXIS_NUMBERS[axis]

    @classmethod
    def _get_axis_name(cls, axis):
        axis = cls._AXIS_ALIASES.get(axis, axis)
        if isinstance(axis, basestring):
            if axis in cls._AXIS_NUMBERS:
                return axis
            else:
                raise Exception('No axis named %s' % axis)
        else:
            return cls._AXIS_NAMES[axis]

    def _get_axis(self, axis):
        name = self._get_axis_name(axis)
        return getattr(self, name)

    def abs(self):
        """
        Return an object with absolute value taken. Only applicable to objects
        that are all numeric

        Returns
        -------
        abs: type of caller
        """
        return np.abs(self)

    def get(self, key, default=None):
        """
        Get item from object for given key (DataFrame column, Panel slice,
        etc.). Returns default value if not found

        Parameters
        ----------
        key : object

        Returns
        -------
        value : type of items contained in object
        """
        try:
            return self[key]
        except KeyError:
            return default

    def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True,
                group_keys=True):
        """
        Group series using mapper (dict or key function, apply given function
        to group, return result as series) or by a series of columns

        Parameters
        ----------
        by : mapping function / list of functions, dict, Series, or tuple /
            list of column names.
            Called on each element of the object index to determine the groups.
            If a dict or Series is passed, the Series or dict VALUES will be
            used to determine the groups
        axis : int, default 0
        level : int, level name, or sequence of such, default None
            If the axis is a MultiIndex (hierarchical), group by a particular
            level or levels
        as_index : boolean, default True
            For aggregated output, return object with group labels as the
            index. Only relevant for DataFrame input. as_index=False is
            effectively "SQL-style" grouped output
        sort : boolean, default True
            Sort group keys. Get better performance by turning this off
        group_keys : boolean, default True
            When calling apply, add group keys to index to identify pieces

        Examples
        --------
        # DataFrame result
        >>> data.groupby(func, axis=0).mean()

        # DataFrame result
        >>> data.groupby(['col1', 'col2'])['col3'].mean()

        # DataFrame with hierarchical index
        >>> data.groupby(['col1', 'col2']).mean()

        Returns
        -------
        GroupBy object
        """
        from pandas.core.groupby import groupby
        return groupby(self, by, axis=axis, level=level, as_index=as_index,
                       sort=sort, group_keys=group_keys)

    def asfreq(self, freq, method=None, how=None, normalize=False):
        """
        Convert all TimeSeries inside to specified frequency using DateOffset
        objects. Optionally provide fill method to pad/backfill missing values.

        Parameters
        ----------
        freq : DateOffset object, or string
        method : {'backfill', 'bfill', 'pad', 'ffill', None}
            Method to use for filling holes in reindexed Series
            pad / ffill: propagate last valid observation forward to next valid
            backfill / bfill: use NEXT valid observation to fill methdo
        how : {'start', 'end'}, default end
            For PeriodIndex only, see PeriodIndex.asfreq
        normalize : bool, default False
            Whether to reset output index to midnight

        Returns
        -------
        converted : type of caller
        """
        from pandas.tseries.resample import asfreq
        return asfreq(self, freq, method=method, how=how,
                      normalize=normalize)

    def at_time(self, time, asof=False):
        """
        Select values at particular time of day (e.g. 9:30AM)

        Parameters
        ----------
        time : datetime.time or string

        Returns
        -------
        values_at_time : type of caller
        """
        try:
            indexer = self.index.indexer_at_time(time, asof=asof)
            return self.take(indexer)
        except AttributeError:
            raise TypeError('Index must be DatetimeIndex')

    def between_time(self, start_time, end_time, include_start=True,
                     include_end=True):
        """
        Select values between particular times of the day (e.g., 9:00-9:30 AM)

        Parameters
        ----------
        start_time : datetime.time or string
        end_time : datetime.time or string
        include_start : boolean, default True
        include_end : boolean, default True

        Returns
        -------
        values_between_time : type of caller
        """
        try:
            indexer = self.index.indexer_between_time(
                start_time, end_time, include_start=include_start,
                include_end=include_end)
            return self.take(indexer)
        except AttributeError:
            raise TypeError('Index must be DatetimeIndex')

    def resample(self, rule, how=None, axis=0, fill_method=None,
                 closed='right', label='right', convention='start',
                 kind=None, loffset=None, limit=None, base=0):
        """
        Convenience method for frequency conversion and resampling of regular
        time-series data.

        Parameters
        ----------
        rule : the offset string or object representing target conversion
        how : string, method for down- or re-sampling, default to 'mean' for
              downsampling
        fill_method : string, fill_method for upsampling, default None
        axis : int, optional, default 0
        closed : {'right', 'left'}, default 'right'
            Which side of bin interval is closed
        label : {'right', 'left'}, default 'right'
            Which bin edge label to label bucket with
        convention : {'start', 'end', 's', 'e'}
        loffset : timedelta
            Adjust the resampled time labels
        base : int, default 0
            For frequencies that evenly subdivide 1 day, the "origin" of the
            aggregated intervals. For example, for '5min' frequency, base could
            range from 0 through 4. Defaults to 0
        """
        from pandas.tseries.resample import TimeGrouper
        sampler = TimeGrouper(rule, label=label, closed=closed, how=how,
                              axis=axis, kind=kind, loffset=loffset,
                              fill_method=fill_method, convention=convention,
                              limit=limit, base=base)
        return sampler.resample(self)

    def first(self, offset):
        """
        Convenience method for subsetting initial periods of time series data
        based on a date offset

        Parameters
        ----------
        offset : string, DateOffset, dateutil.relativedelta

        Examples
        --------
        ts.last('10D') -> First 10 days

        Returns
        -------
        subset : type of caller
        """
        from pandas.tseries.frequencies import to_offset
        if not isinstance(self.index, DatetimeIndex):
            raise NotImplementedError

        if len(self.index) == 0:
            return self

        offset = to_offset(offset)
        end_date = end = self.index[0] + offset

        # Tick-like, e.g. 3 weeks
        if not offset.isAnchored() and hasattr(offset, '_inc'):
            if end_date in self.index:
                end = self.index.searchsorted(end_date, side='left')

        return self.ix[:end]

    def last(self, offset):
        """
        Convenience method for subsetting final periods of time series data
        based on a date offset

        Parameters
        ----------
        offset : string, DateOffset, dateutil.relativedelta

        Examples
        --------
        ts.last('5M') -> Last 5 months

        Returns
        -------
        subset : type of caller
        """
        from pandas.tseries.frequencies import to_offset
        if not isinstance(self.index, DatetimeIndex):
            raise NotImplementedError

        if len(self.index) == 0:
            return self

        offset = to_offset(offset)

        start_date = start = self.index[-1] - offset
        start = self.index.searchsorted(start_date, side='right')
        return self.ix[start:]

    def select(self, crit, axis=0):
        """
        Return data corresponding to axis labels matching criteria

        Parameters
        ----------
        crit : function
            To be called on each index (label). Should return True or False
        axis : int

        Returns
        -------
        selection : type of caller
        """
        axis_name = self._get_axis_name(axis)
        axis = self._get_axis(axis)

        if len(axis) > 0:
            new_axis = axis[np.asarray([crit(label) for label in axis])]
        else:
            new_axis = axis

        return self.reindex(**{axis_name: new_axis})

    def drop(self, labels, axis=0, level=None):
        """
        Return new object with labels in requested axis removed

        Parameters
        ----------
        labels : array-like
        axis : int
        level : int or name, default None
            For MultiIndex

        Returns
        -------
        dropped : type of caller
        """
        axis_name = self._get_axis_name(axis)
        axis = self._get_axis(axis)

        if axis.is_unique:
            if level is not None:
                if not isinstance(axis, MultiIndex):
                    raise AssertionError('axis must be a MultiIndex')
                new_axis = axis.drop(labels, level=level)
            else:
                new_axis = axis.drop(labels)

            return self.reindex(**{axis_name: new_axis})
        else:
            if level is not None:
                if not isinstance(axis, MultiIndex):
                    raise AssertionError('axis must be a MultiIndex')
                indexer = -lib.ismember(axis.get_level_values(level),
                                        set(labels))
            else:
                indexer = -axis.isin(labels)

            slicer = [slice(None)] * self.ndim
            slicer[self._get_axis_number(axis_name)] = indexer

            return self.ix[tuple(slicer)]

    def sort_index(self, axis=0, ascending=True):
        """
        Sort object by labels (along an axis)

        Parameters
        ----------
        axis : {0, 1}
            Sort index/rows versus columns
        ascending : boolean, default True
            Sort ascending vs. descending

        Returns
        -------
        sorted_obj : type of caller
        """
        axis = self._get_axis_number(axis)
        axis_name = self._get_axis_name(axis)
        labels = self._get_axis(axis)

        sort_index = labels.argsort()
        if not ascending:
            sort_index = sort_index[::-1]

        new_axis = labels.take(sort_index)
        return self.reindex(**{axis_name: new_axis})

    @property
    def ix(self):
        raise NotImplementedError

    def reindex(self, *args, **kwds):
        raise NotImplementedError

    def tshift(self, periods=1, freq=None, **kwds):
        """
        Shift the time index, using the index's frequency if available

        Parameters
        ----------
        periods : int
            Number of periods to move, can be positive or negative
        freq : DateOffset, timedelta, or time rule string, default None
            Increment to use from datetools module or time rule (e.g. 'EOM')

        Notes
        -----
        If freq is not specified then tries to use the freq or inferred_freq
        attributes of the index. If neither of those attributes exist, a
        ValueError is thrown

        Returns
        -------
        shifted : Series
        """
        if freq is None:
            freq = getattr(self.index, 'freq', None)

        if freq is None:
            freq = getattr(self.index, 'inferred_freq', None)

        if freq is None:
            msg = 'Freq was not given and was not set in the index'
            raise ValueError(msg)

        return self.shift(periods, freq, **kwds)

    def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
                   **kwds):
        """
        Percent change over given number of periods

        Parameters
        ----------
        periods : int, default 1
            Periods to shift for forming percent change
        fill_method : str, default 'pad'
            How to handle NAs before computing percent changes
        limit : int, default None
            The number of consecutive NAs to fill before stopping
        freq : DateOffset, timedelta, or offset alias string, optional
            Increment to use from time series API (e.g. 'M' or BDay())

        Returns
        -------
        chg : Series or DataFrame
        """
        if fill_method is None:
            data = self
        else:
            data = self.fillna(method=fill_method, limit=limit)
        rs = data / data.shift(periods=periods, freq=freq, **kwds) - 1
        if freq is None:
            mask = com.isnull(self.values)
            np.putmask(rs.values, mask, np.nan)
        return rs


class NDFrame(PandasObject):
    """
    N-dimensional analogue of DataFrame. Store multi-dimensional in a
    size-mutable, labeled data structure

    Parameters
    ----------
    data : BlockManager
    axes : list
    copy : boolean, default False
    """
    # kludge
    _default_stat_axis = 0

    def __init__(self, data, axes=None, copy=False, dtype=None):
        if dtype is not None:
            data = data.astype(dtype)
        elif copy:
            data = data.copy()

        if axes is not None:
            for i, ax in enumerate(axes):
                data = data.reindex_axis(ax, axis=i)

        object.__setattr__(self, '_data', data)
        object.__setattr__(self, '_item_cache', {})

    def astype(self, dtype):
        """
        Cast object to input numpy.dtype

        Parameters
        ----------
        dtype : numpy.dtype or Python type

        Returns
        -------
        casted : type of caller
        """
        return self._constructor(self._data, dtype=dtype)

    @property
    def _constructor(self):
        return NDFrame

    @property
    def axes(self):
        return self._data.axes

    def __repr__(self):
        return 'NDFrame'

    @property
    def values(self):
        return self._data.as_matrix()

    @property
    def ndim(self):
        return self._data.ndim

    def _set_axis(self, axis, labels):
        self._data.set_axis(axis, labels)
        self._clear_item_cache()

    def __getitem__(self, item):
        return self._get_item_cache(item)

    def _get_item_cache(self, item):
        cache = self._item_cache
        try:
            return cache[item]
        except Exception:
            values = self._data.get(item)
            res = self._box_item_values(item, values)
            cache[item] = res
            return res

    def _box_item_values(self, key, values):
        raise NotImplementedError

    def _clear_item_cache(self):
        self._item_cache.clear()

    def _set_item(self, key, value):
        self._data.set(key, value)
        self._clear_item_cache()

    def __delitem__(self, key):
        """
        Delete item
        """
        deleted = False

        maybe_shortcut = False
        if hasattr(self, 'columns') and isinstance(self.columns, MultiIndex):
            try:
                maybe_shortcut = key not in self.columns._engine
            except TypeError:
                pass

        if maybe_shortcut:
            # Allow shorthand to delete all columns whose first len(key)
            # elements match key:
            if not isinstance(key, tuple):
                key = (key,)
            for col in self.columns:
                if isinstance(col, tuple) and col[:len(key)] == key:
                    del self[col]
                    deleted = True
        if not deleted:
            # If the above loop ran and didn't delete anything because
            # there was no match, this call should raise the appropriate
            # exception:
            self._data.delete(key)

        try:
            del self._item_cache[key]
        except KeyError:
            pass

    def pop(self, item):
        """
        Return item and drop from frame. Raise KeyError if not found.
        """
        result = self[item]
        del self[item]
        return result

    def _expand_axes(self, key):
        new_axes = []
        for k, ax in zip(key, self.axes):
            if k not in ax:
                if type(k) != ax.dtype.type:
                    ax = ax.astype('O')
                new_axes.append(ax.insert(len(ax), k))
            else:
                new_axes.append(ax)

        return new_axes

    #----------------------------------------------------------------------
    # Consolidation of internals

    def _consolidate_inplace(self):
        self._clear_item_cache()
        self._data = self._data.consolidate()

    def consolidate(self, inplace=False):
        """
        Compute NDFrame with "consolidated" internals (data of each dtype
        grouped together in a single ndarray). Mainly an internal API function,
        but available here to the savvy user

        Parameters
        ----------
        inplace : boolean, default False
            If False return new object, otherwise modify existing object

        Returns
        -------
        consolidated : type of caller
        """
        if inplace:
            self._consolidate_inplace()
            return self
        else:
            cons_data = self._data.consolidate()
            if cons_data is self._data:
                cons_data = cons_data.copy()
            return self._constructor(cons_data)

    @property
    def _is_mixed_type(self):
        self._consolidate_inplace()
        return len(self._data.blocks) > 1

    def _reindex_axis(self, new_index, fill_method, axis, copy):
        new_data = self._data.reindex_axis(new_index, axis=axis,
                                           method=fill_method, copy=copy)

        if new_data is self._data and not copy:
            return self
        else:
            return self._constructor(new_data)

    def cumsum(self, axis=None, skipna=True):
        """
        Return DataFrame of cumulative sums over requested axis.

        Parameters
        ----------
        axis : {0, 1}
            0 for row-wise, 1 for column-wise
        skipna : boolean, default True
            Exclude NA/null values. If an entire row/column is NA, the result
            will be NA

        Returns
        -------
        y : DataFrame
        """
        if axis is None:
            axis = self._default_stat_axis
        else:
            axis = self._get_axis_number(axis)

        y = self.values.copy()
        if not issubclass(y.dtype.type, np.integer):
            mask = np.isnan(self.values)

            if skipna:
                np.putmask(y, mask, 0.)

            result = y.cumsum(axis)

            if skipna:
                np.putmask(result, mask, np.nan)
        else:
            result = y.cumsum(axis)
        return self._wrap_array(result, self.axes, copy=False)

    def _wrap_array(self, array, axes, copy=False):
        raise NotImplementedError

    def cumprod(self, axis=None, skipna=True):
        """
        Return cumulative product over requested axis as DataFrame

        Parameters
        ----------
        axis : {0, 1}
            0 for row-wise, 1 for column-wise
        skipna : boolean, default True
            Exclude NA/null values. If an entire row/column is NA, the result
            will be NA

        Returns
        -------
        y : DataFrame
        """
        if axis is None:
            axis = self._default_stat_axis
        else:
            axis = self._get_axis_number(axis)

        y = self.values.copy()
        if not issubclass(y.dtype.type, np.integer):
            mask = np.isnan(self.values)

            if skipna:
                np.putmask(y, mask, 1.)
            result = y.cumprod(axis)

            if skipna:
                np.putmask(result, mask, np.nan)
        else:
            result = y.cumprod(axis)
        return self._wrap_array(result, self.axes, copy=False)

    def cummax(self, axis=None, skipna=True):
        """
        Return DataFrame of cumulative max over requested axis.

        Parameters
        ----------
        axis : {0, 1}
            0 for row-wise, 1 for column-wise
        skipna : boolean, default True
            Exclude NA/null values. If an entire row/column is NA, the result
            will be NA

        Returns
        -------
        y : DataFrame
        """
        if axis is None:
            axis = self._default_stat_axis
        else:
            axis = self._get_axis_number(axis)

        y = self.values.copy()
        if not issubclass(y.dtype.type, np.integer):
            mask = np.isnan(self.values)

            if skipna:
                np.putmask(y, mask, -np.inf)

            result = np.maximum.accumulate(y, axis)

            if skipna:
                np.putmask(result, mask, np.nan)
        else:
            result = np.maximum.accumulate(y, axis)
        return self._wrap_array(result, self.axes, copy=False)

    def cummin(self, axis=None, skipna=True):
        """
        Return DataFrame of cumulative min over requested axis.

        Parameters
        ----------
        axis : {0, 1}
            0 for row-wise, 1 for column-wise
        skipna : boolean, default True
            Exclude NA/null values. If an entire row/column is NA, the result
            will be NA

        Returns
        -------
        y : DataFrame
        """
        if axis is None:
            axis = self._default_stat_axis
        else:
            axis = self._get_axis_number(axis)

        y = self.values.copy()
        if not issubclass(y.dtype.type, np.integer):
            mask = np.isnan(self.values)

            if skipna:
                np.putmask(y, mask, np.inf)

            result = np.minimum.accumulate(y, axis)

            if skipna:
                np.putmask(result, mask, np.nan)
        else:
            result = np.minimum.accumulate(y, axis)
        return self._wrap_array(result, self.axes, copy=False)

    def copy(self, deep=True):
        """
        Make a copy of this object

        Parameters
        ----------
        deep : boolean, default True
            Make a deep copy, i.e. also copy data

        Returns
        -------
        copy : type of caller
        """
        data = self._data
        if deep:
            data = data.copy()
        return self._constructor(data)

    def swaplevel(self, i, j, axis=0):
        """
        Swap levels i and j in a MultiIndex on a particular axis

        Returns
        -------
        swapped : type of caller (new object)
        """
        axis = self._get_axis_number(axis)
        result = self.copy()
        labels = result._data.axes[axis]
        result._data.set_axis(axis, labels.swaplevel(i, j))
        return result

    def add_prefix(self, prefix):
        """
        Concatenate prefix string with panel items names.

        Parameters
        ----------
        prefix : string

        Returns
        -------
        with_prefix : type of caller
        """
        new_data = self._data.add_prefix(prefix)
        return self._constructor(new_data)

    def add_suffix(self, suffix):
        """
        Concatenate suffix string with panel items names

        Parameters
        ----------
        suffix : string

        Returns
        -------
        with_suffix : type of caller
        """
        new_data = self._data.add_suffix(suffix)
        return self._constructor(new_data)

    def rename_axis(self, mapper, axis=0, copy=True):
        """
        Alter index and / or columns using input function or functions.
        Function / dict values must be unique (1-to-1). Labels not contained in
        a dict / Series will be left as-is.

        Parameters
        ----------
        mapper : dict-like or function, optional
        axis : int, default 0
        copy : boolean, default True
            Also copy underlying data

        See also
        --------
        DataFrame.rename

        Returns
        -------
        renamed : type of caller
        """
        # should move this at some point
        from pandas.core.series import _get_rename_function

        mapper_f = _get_rename_function(mapper)

        if axis == 0:
            new_data = self._data.rename_items(mapper_f, copydata=copy)
        else:
            new_data = self._data.rename_axis(mapper_f, axis=axis)
            if copy:
                new_data = new_data.copy()

        return self._constructor(new_data)

    def take(self, indices, axis=0):
        """
        Analogous to ndarray.take

        Parameters
        ----------
        indices : list / array of ints
        axis : int, default 0

        Returns
        -------
        taken : type of caller
        """
        if axis == 0:
            labels = self._get_axis(axis)
            new_items = labels.take(indices)
            new_data = self._data.reindex_axis(new_items, axis=0)
        else:
            new_data = self._data.take(indices, axis=axis)
        return self._constructor(new_data)

    def tz_convert(self, tz, axis=0, copy=True):
        """
        Convert TimeSeries to target time zone. If it is time zone naive, it
        will be localized to the passed time zone.

        Parameters
        ----------
        tz : string or pytz.timezone object
        copy : boolean, default True
            Also make a copy of the underlying data

        Returns
        -------
        """
        axis = self._get_axis_number(axis)
        ax = self._get_axis(axis)

        if not hasattr(ax, 'tz_convert'):
            ax_name = self._get_axis_name(axis)
            raise TypeError('%s is not a valid DatetimeIndex or PeriodIndex' %
                            ax_name)

        new_data = self._data
        if copy:
            new_data = new_data.copy()

        new_obj = self._constructor(new_data)
        new_ax = ax.tz_convert(tz)

        if axis == 0:
            new_obj._set_axis(1, new_ax)
        elif axis == 1:
            new_obj._set_axis(0, new_ax)
            self._clear_item_cache()

        return new_obj

    def tz_localize(self, tz, axis=0, copy=True):
        """
        Localize tz-naive TimeSeries to target time zone

        Parameters
        ----------
        tz : string or pytz.timezone object
        copy : boolean, default True
            Also make a copy of the underlying data

        Returns
        -------
        """
        axis = self._get_axis_number(axis)
        ax = self._get_axis(axis)

        if not hasattr(ax, 'tz_localize'):
            ax_name = self._get_axis_name(axis)
            raise TypeError('%s is not a valid DatetimeIndex or PeriodIndex' %
                            ax_name)

        new_data = self._data
        if copy:
            new_data = new_data.copy()

        new_obj = self._constructor(new_data)
        new_ax = ax.tz_localize(tz)

        if axis == 0:
            new_obj._set_axis(1, new_ax)
        elif axis == 1:
            new_obj._set_axis(0, new_ax)
            self._clear_item_cache()

        return new_obj

# Good for either Series or DataFrame


def truncate(self, before=None, after=None, copy=True):
    """Function truncate a sorted DataFrame / Series before and/or after
    some particular dates.

    Parameters
    ----------
    before : date
        Truncate before date
    after : date
        Truncate after date

    Returns
    -------
    truncated : type of caller
    """
    from pandas.tseries.tools import to_datetime
    before = to_datetime(before)
    after = to_datetime(after)

    if before is not None and after is not None:
        if before > after:
            raise AssertionError('Truncate: %s must be after %s' %
                                 (before, after))

    result = self.ix[before:after]

    if isinstance(self.index, MultiIndex):
        result.index = self.index.truncate(before, after)

    if copy:
        result = result.copy()

    return result

numpy.isnan numpy.minimum.accumulate numpy.putmask numpy.maximum.accumulate numpy.abs