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

import numpy as np

from matplotlib import pylab
from pandas.core.indexes.period import Period
from pandas.tseries.offsets import DateOffset
import pandas.tseries.frequencies as frequencies
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexes.timedeltas import TimedeltaIndex
from pandas.io.formats.printing import pprint_thing
import pandas.compat as compat

from pandas.plotting._converter import (TimeSeries_DateLocator,
                                        TimeSeries_DateFormatter,
                                        TimeSeries_TimedeltaFormatter)

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


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

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

    Notes
    _____
    Supports same kwargs as Axes.plot

    """
    # 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, DatetimeIndex):
        series = series.to_period(freq=freq)

    if ax_freq is not None and freq != ax_freq:
        if frequencies.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 frequencies.is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq):
            _upsample_others(ax, freq, kwargs)
            ax_freq = freq
        else:  # pragma: no cover
            raise ValueError('Incompatible frequency conversion')
    return freq, series


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


def _is_sup(f1, f2):
    return ((f1.startswith('W') and frequencies.is_superperiod('D', f2)) or
            (f2.startswith('W') and frequencies.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 = frequencies.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 = frequencies.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, DatetimeIndex):
        base = frequencies.get_freq(freq)
        x = data.index
        if (base <= frequencies.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, DatetimeIndex):
        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 = frequencies.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_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, PeriodIndex):

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

    elif isinstance(index, TimedeltaIndex):
        subplot.xaxis.set_major_formatter(
            TimeSeries_TimedeltaFormatter())
    else:
        raise TypeError('index type not supported')

    pylab.draw_if_interactive()
