# TODO: Use the fact that axis can have units to simplify the process

import functools

from matplotlib import pylab
import numpy as np

from pandas._libs.tslibs.frequencies import (
    FreqGroup, get_base_alias, get_freq, is_subperiod, is_superperiod)
from pandas._libs.tslibs.period import Period
import pandas.compat as compat

from pandas.core.dtypes.generic import (
    ABCDatetimeIndex, ABCPeriodIndex, ABCTimedeltaIndex)

from pandas.io.formats.printing import pprint_thing
from pandas.plotting._converter import (
    TimeSeries_DateFormatter, TimeSeries_DateLocator,
    TimeSeries_TimedeltaFormatter)
import pandas.tseries.frequencies as frequencies
from pandas.tseries.offsets import DateOffset

# ---------------------------------------------------------------------
# Plotting functions and monkey patches


def tsplot(series, plotf, ax=None, **kwargs):
    import warnings
    """
    Plots a Series on the given Matplotlib axes or the current axes

    Parameters
    ----------
    axes : Axes
    series : Series

    Notes
    _____
    Supports same kwargs as Axes.plot


    .. deprecated:: 0.23.0
       Use Series.plot() instead
    """
    warnings.warn("'tsplot' is deprecated and will be removed in a "
                  "future version. Please use Series.plot() instead.",
                  FutureWarning, stacklevel=2)

    # Used inferred freq is possible, need a test case for inferred
    if ax is None:
        import matplotlib.pyplot as plt
        ax = plt.gca()

    freq, series = _maybe_resample(series, ax, kwargs)

    # Set ax with freq info
    _decorate_axes(ax, freq, kwargs)
    ax._plot_data.append((series, plotf, kwargs))
    lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs)

    # set date formatter, locators and rescale limits
    format_dateaxis(ax, ax.freq, series.index)
    return lines


def _maybe_resample(series, ax, kwargs):
    # resample against axes freq if necessary
    freq, ax_freq = _get_freq(ax, series)

    if freq is None:  # pragma: no cover
        raise ValueError('Cannot use dynamic axis without frequency info')

    # Convert DatetimeIndex to PeriodIndex
    if isinstance(series.index, ABCDatetimeIndex):
        series = series.to_period(freq=freq)

    if ax_freq is not None and freq != ax_freq:
        if is_superperiod(freq, ax_freq):  # upsample input
            series = series.copy()
            series.index = series.index.asfreq(ax_freq, how='s')
            freq = ax_freq
        elif _is_sup(freq, ax_freq):  # one is weekly
            how = kwargs.pop('how', 'last')
            series = getattr(series.resample('D'), how)().dropna()
            series = getattr(series.resample(ax_freq), how)().dropna()
            freq = ax_freq
        elif is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq):
            _upsample_others(ax, freq, kwargs)
        else:  # pragma: no cover
            raise ValueError('Incompatible frequency conversion')
    return freq, series


def _is_sub(f1, f2):
    return ((f1.startswith('W') and is_subperiod('D', f2)) or
            (f2.startswith('W') and is_subperiod(f1, 'D')))


def _is_sup(f1, f2):
    return ((f1.startswith('W') and is_superperiod('D', f2)) or
            (f2.startswith('W') and is_superperiod(f1, 'D')))


def _upsample_others(ax, freq, kwargs):
    legend = ax.get_legend()
    lines, labels = _replot_ax(ax, freq, kwargs)
    _replot_ax(ax, freq, kwargs)

    other_ax = None
    if hasattr(ax, 'left_ax'):
        other_ax = ax.left_ax
    if hasattr(ax, 'right_ax'):
        other_ax = ax.right_ax

    if other_ax is not None:
        rlines, rlabels = _replot_ax(other_ax, freq, kwargs)
        lines.extend(rlines)
        labels.extend(rlabels)

    if (legend is not None and kwargs.get('legend', True) and
            len(lines) > 0):
        title = legend.get_title().get_text()
        if title == 'None':
            title = None
        ax.legend(lines, labels, loc='best', title=title)


def _replot_ax(ax, freq, kwargs):
    data = getattr(ax, '_plot_data', None)

    # clear current axes and data
    ax._plot_data = []
    ax.clear()

    _decorate_axes(ax, freq, kwargs)

    lines = []
    labels = []
    if data is not None:
        for series, plotf, kwds in data:
            series = series.copy()
            idx = series.index.asfreq(freq, how='S')
            series.index = idx
            ax._plot_data.append((series, plotf, kwds))

            # for tsplot
            if isinstance(plotf, compat.string_types):
                from pandas.plotting._core import _plot_klass
                plotf = _plot_klass[plotf]._plot

            lines.append(plotf(ax, series.index._mpl_repr(),
                               series.values, **kwds)[0])
            labels.append(pprint_thing(series.name))

    return lines, labels


def _decorate_axes(ax, freq, kwargs):
    """Initialize axes for time-series plotting"""
    if not hasattr(ax, '_plot_data'):
        ax._plot_data = []

    ax.freq = freq
    xaxis = ax.get_xaxis()
    xaxis.freq = freq
    if not hasattr(ax, 'legendlabels'):
        ax.legendlabels = [kwargs.get('label', None)]
    else:
        ax.legendlabels.append(kwargs.get('label', None))
    ax.view_interval = None
    ax.date_axis_info = None


def _get_ax_freq(ax):
    """
    Get the freq attribute of the ax object if set.
    Also checks shared axes (eg when using secondary yaxis, sharex=True
    or twinx)
    """
    ax_freq = getattr(ax, 'freq', None)
    if ax_freq is None:
        # check for left/right ax in case of secondary yaxis
        if hasattr(ax, 'left_ax'):
            ax_freq = getattr(ax.left_ax, 'freq', None)
        elif hasattr(ax, 'right_ax'):
            ax_freq = getattr(ax.right_ax, 'freq', None)
    if ax_freq is None:
        # check if a shared ax (sharex/twinx) has already freq set
        shared_axes = ax.get_shared_x_axes().get_siblings(ax)
        if len(shared_axes) > 1:
            for shared_ax in shared_axes:
                ax_freq = getattr(shared_ax, 'freq', None)
                if ax_freq is not None:
                    break
    return ax_freq


def _get_freq(ax, series):
    # get frequency from data
    freq = getattr(series.index, 'freq', None)
    if freq is None:
        freq = getattr(series.index, 'inferred_freq', None)

    ax_freq = _get_ax_freq(ax)

    # use axes freq if no data freq
    if freq is None:
        freq = ax_freq

    # get the period frequency
    if isinstance(freq, DateOffset):
        freq = freq.rule_code
    else:
        freq = get_base_alias(freq)

    freq = frequencies.get_period_alias(freq)
    return freq, ax_freq


def _use_dynamic_x(ax, data):
    freq = _get_index_freq(data)
    ax_freq = _get_ax_freq(ax)

    if freq is None:  # convert irregular if axes has freq info
        freq = ax_freq
    else:  # do not use tsplot if irregular was plotted first
        if (ax_freq is None) and (len(ax.get_lines()) > 0):
            return False

    if freq is None:
        return False

    if isinstance(freq, DateOffset):
        freq = freq.rule_code
    else:
        freq = get_base_alias(freq)
    freq = frequencies.get_period_alias(freq)

    if freq is None:
        return False

    # hack this for 0.10.1, creating more technical debt...sigh
    if isinstance(data.index, ABCDatetimeIndex):
        base = get_freq(freq)
        x = data.index
        if (base <= FreqGroup.FR_DAY):
            return x[:1].is_normalized
        return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0]
    return True


def _get_index_freq(data):
    freq = getattr(data.index, 'freq', None)
    if freq is None:
        freq = getattr(data.index, 'inferred_freq', None)
        if freq == 'B':
            weekdays = np.unique(data.index.dayofweek)
            if (5 in weekdays) or (6 in weekdays):
                freq = None
    return freq


def _maybe_convert_index(ax, data):
    # tsplot converts automatically, but don't want to convert index
    # over and over for DataFrames
    if isinstance(data.index, ABCDatetimeIndex):
        freq = getattr(data.index, 'freq', None)

        if freq is None:
            freq = getattr(data.index, 'inferred_freq', None)
        if isinstance(freq, DateOffset):
            freq = freq.rule_code

        if freq is None:
            freq = _get_ax_freq(ax)

        if freq is None:
            raise ValueError('Could not get frequency alias for plotting')

        freq = get_base_alias(freq)
        freq = frequencies.get_period_alias(freq)

        data = data.to_period(freq=freq)
    return data


# Patch methods for subplot. Only format_dateaxis is currently used.
# Do we need the rest for convenience?

def format_timedelta_ticks(x, pos, n_decimals):
    """
    Convert seconds to 'D days HH:MM:SS.F'
    """
    s, ns = divmod(x, 1e9)
    m, s = divmod(s, 60)
    h, m = divmod(m, 60)
    d, h = divmod(h, 24)
    decimals = int(ns * 10**(n_decimals - 9))
    s = r'{:02d}:{:02d}:{:02d}'.format(int(h), int(m), int(s))
    if n_decimals > 0:
        s += '.{{:0{:0d}d}}'.format(n_decimals).format(decimals)
    if d != 0:
        s = '{:d} days '.format(int(d)) + s
    return s


def _format_coord(freq, t, y):
    return "t = {0}  y = {1:8f}".format(Period(ordinal=int(t), freq=freq), y)


def format_dateaxis(subplot, freq, index):
    """
    Pretty-formats the date axis (x-axis).

    Major and minor ticks are automatically set for the frequency of the
    current underlying series.  As the dynamic mode is activated by
    default, changing the limits of the x axis will intelligently change
    the positions of the ticks.
    """

    # handle index specific formatting
    # Note: DatetimeIndex does not use this
    # interface. DatetimeIndex uses matplotlib.date directly
    if isinstance(index, ABCPeriodIndex):

        majlocator = TimeSeries_DateLocator(freq, dynamic_mode=True,
                                            minor_locator=False,
                                            plot_obj=subplot)
        minlocator = TimeSeries_DateLocator(freq, dynamic_mode=True,
                                            minor_locator=True,
                                            plot_obj=subplot)
        subplot.xaxis.set_major_locator(majlocator)
        subplot.xaxis.set_minor_locator(minlocator)

        majformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True,
                                                minor_locator=False,
                                                plot_obj=subplot)
        minformatter = TimeSeries_DateFormatter(freq, dynamic_mode=True,
                                                minor_locator=True,
                                                plot_obj=subplot)
        subplot.xaxis.set_major_formatter(majformatter)
        subplot.xaxis.set_minor_formatter(minformatter)

        # x and y coord info
        subplot.format_coord = functools.partial(_format_coord, freq)