test_datetimelike.py

from datetime import datetime, timedelta, date, time

import nose
from pandas.compat import lrange, zip

import numpy as np
from pandas import Index, Series, DataFrame

from pandas.tseries.index import date_range, bdate_range
from pandas.tseries.offsets import DateOffset
from pandas.tseries.period import period_range, Period, PeriodIndex
from pandas.tseries.resample import DatetimeIndex

from pandas.util.testing import assert_series_equal, ensure_clean, slow
import pandas.util.testing as tm

from pandas.tests.plotting.common import (TestPlotBase,
                                          _skip_if_no_scipy_gaussian_kde)


""" Test cases for time series specific (freq conversion, etc) """


@tm.mplskip
class TestTSPlot(TestPlotBase):

    def setUp(self):
        TestPlotBase.setUp(self)
        freq = ['S', 'T', 'H', 'D', 'W', 'M', 'Q', 'A']
        idx = [period_range('12/31/1999', freq=x, periods=100) for x in freq]
        self.period_ser = [Series(np.random.randn(len(x)), x) for x in idx]
        self.period_df = [DataFrame(np.random.randn(len(x), 3), index=x,
                                    columns=['A', 'B', 'C'])
                          for x in idx]

        freq = ['S', 'T', 'H', 'D', 'W', 'M', 'Q-DEC', 'A', '1B30Min']
        idx = [date_range('12/31/1999', freq=x, periods=100) for x in freq]
        self.datetime_ser = [Series(np.random.randn(len(x)), x) for x in idx]
        self.datetime_df = [DataFrame(np.random.randn(len(x), 3), index=x,
                                      columns=['A', 'B', 'C'])
                            for x in idx]

    def tearDown(self):
        tm.close()

    @slow
    def test_ts_plot_with_tz(self):
        # GH2877
        index = date_range('1/1/2011', periods=2, freq='H',
                           tz='Europe/Brussels')
        ts = Series([188.5, 328.25], index=index)
        _check_plot_works(ts.plot)

    def test_fontsize_set_correctly(self):
        # For issue #8765
        import matplotlib.pyplot as plt  # noqa
        df = DataFrame(np.random.randn(10, 9), index=range(10))
        ax = df.plot(fontsize=2)
        for label in (ax.get_xticklabels() + ax.get_yticklabels()):
            self.assertEqual(label.get_fontsize(), 2)

    @slow
    def test_frame_inferred(self):
        # inferred freq
        import matplotlib.pyplot as plt  # noqa
        idx = date_range('1/1/1987', freq='MS', periods=100)
        idx = DatetimeIndex(idx.values, freq=None)

        df = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df.plot)

        # axes freq
        idx = idx[0:40].union(idx[45:99])
        df2 = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df2.plot)

        # N > 1
        idx = date_range('2008-1-1 00:15:00', freq='15T', periods=10)
        idx = DatetimeIndex(idx.values, freq=None)
        df = DataFrame(np.random.randn(len(idx), 3), index=idx)
        _check_plot_works(df.plot)

    def test_is_error_nozeroindex(self):
        # GH11858
        i = np.array([1, 2, 3])
        a = DataFrame(i, index=i)
        _check_plot_works(a.plot, xerr=a)
        _check_plot_works(a.plot, yerr=a)

    def test_nonnumeric_exclude(self):
        import matplotlib.pyplot as plt

        idx = date_range('1/1/1987', freq='A', periods=3)
        df = DataFrame({'A': ["x", "y", "z"], 'B': [1, 2, 3]}, idx)

        ax = df.plot()  # it works
        self.assertEqual(len(ax.get_lines()), 1)  # B was plotted
        plt.close(plt.gcf())

        self.assertRaises(TypeError, df['A'].plot)

    @slow
    def test_tsplot(self):
        from pandas.tseries.plotting import tsplot
        import matplotlib.pyplot as plt

        ax = plt.gca()
        ts = tm.makeTimeSeries()

        f = lambda *args, **kwds: tsplot(s, plt.Axes.plot, *args, **kwds)

        for s in self.period_ser:
            _check_plot_works(f, s.index.freq, ax=ax, series=s)

        for s in self.datetime_ser:
            _check_plot_works(f, s.index.freq.rule_code, ax=ax, series=s)

        for s in self.period_ser:
            _check_plot_works(s.plot, ax=ax)

        for s in self.datetime_ser:
            _check_plot_works(s.plot, ax=ax)

        ax = ts.plot(style='k')
        self.assertEqual((0., 0., 0.), ax.get_lines()[0].get_color())

    def test_both_style_and_color(self):
        import matplotlib.pyplot as plt  # noqa

        ts = tm.makeTimeSeries()
        self.assertRaises(ValueError, ts.plot, style='b-', color='#000099')

        s = ts.reset_index(drop=True)
        self.assertRaises(ValueError, s.plot, style='b-', color='#000099')

    @slow
    def test_high_freq(self):
        freaks = ['ms', 'us']
        for freq in freaks:
            rng = date_range('1/1/2012', periods=100000, freq=freq)
            ser = Series(np.random.randn(len(rng)), rng)
            _check_plot_works(ser.plot)

    def test_get_datevalue(self):
        from pandas.tseries.converter import get_datevalue
        self.assertIsNone(get_datevalue(None, 'D'))
        self.assertEqual(get_datevalue(1987, 'A'), 1987)
        self.assertEqual(get_datevalue(Period(1987, 'A'), 'M'),
                         Period('1987-12', 'M').ordinal)
        self.assertEqual(get_datevalue('1/1/1987', 'D'),
                         Period('1987-1-1', 'D').ordinal)

    @slow
    def test_ts_plot_format_coord(self):
        def check_format_of_first_point(ax, expected_string):
            first_line = ax.get_lines()[0]
            first_x = first_line.get_xdata()[0].ordinal
            first_y = first_line.get_ydata()[0]
            try:
                self.assertEqual(expected_string,
                                 ax.format_coord(first_x, first_y))
            except (ValueError):
                raise nose.SkipTest("skipping test because issue forming "
                                    "test comparison GH7664")

        annual = Series(1, index=date_range('2014-01-01', periods=3,
                                            freq='A-DEC'))
        check_format_of_first_point(annual.plot(), 't = 2014  y = 1.000000')

        # note this is added to the annual plot already in existence, and
        # changes its freq field
        daily = Series(1, index=date_range('2014-01-01', periods=3, freq='D'))
        check_format_of_first_point(daily.plot(),
                                    't = 2014-01-01  y = 1.000000')
        tm.close()

        # tsplot
        import matplotlib.pyplot as plt
        from pandas.tseries.plotting import tsplot
        tsplot(annual, plt.Axes.plot)
        check_format_of_first_point(plt.gca(), 't = 2014  y = 1.000000')
        tsplot(daily, plt.Axes.plot)
        check_format_of_first_point(plt.gca(), 't = 2014-01-01  y = 1.000000')

    @slow
    def test_line_plot_period_series(self):
        for s in self.period_ser:
            _check_plot_works(s.plot, s.index.freq)

    @slow
    def test_line_plot_datetime_series(self):
        for s in self.datetime_ser:
            _check_plot_works(s.plot, s.index.freq.rule_code)

    @slow
    def test_line_plot_period_frame(self):
        for df in self.period_df:
            _check_plot_works(df.plot, df.index.freq)

    @slow
    def test_line_plot_datetime_frame(self):
        for df in self.datetime_df:
            freq = df.index.to_period(df.index.freq.rule_code).freq
            _check_plot_works(df.plot, freq)

    @slow
    def test_line_plot_inferred_freq(self):
        for ser in self.datetime_ser:
            ser = Series(ser.values, Index(np.asarray(ser.index)))
            _check_plot_works(ser.plot, ser.index.inferred_freq)

            ser = ser[[0, 3, 5, 6]]
            _check_plot_works(ser.plot)

    def test_fake_inferred_business(self):
        import matplotlib.pyplot as plt
        fig = plt.gcf()
        plt.clf()
        fig.add_subplot(111)
        rng = date_range('2001-1-1', '2001-1-10')
        ts = Series(lrange(len(rng)), rng)
        ts = ts[:3].append(ts[5:])
        ax = ts.plot()
        self.assertFalse(hasattr(ax, 'freq'))

    @slow
    def test_plot_offset_freq(self):
        ser = tm.makeTimeSeries()
        _check_plot_works(ser.plot)

        dr = date_range(ser.index[0], freq='BQS', periods=10)
        ser = Series(np.random.randn(len(dr)), dr)
        _check_plot_works(ser.plot)

    @slow
    def test_plot_multiple_inferred_freq(self):
        dr = Index([datetime(2000, 1, 1), datetime(2000, 1, 6), datetime(
            2000, 1, 11)])
        ser = Series(np.random.randn(len(dr)), dr)
        _check_plot_works(ser.plot)

    @slow
    def test_uhf(self):
        import pandas.tseries.converter as conv
        import matplotlib.pyplot as plt
        fig = plt.gcf()
        plt.clf()
        fig.add_subplot(111)

        idx = date_range('2012-6-22 21:59:51.960928', freq='L', periods=500)
        df = DataFrame(np.random.randn(len(idx), 2), idx)

        ax = df.plot()
        axis = ax.get_xaxis()

        tlocs = axis.get_ticklocs()
        tlabels = axis.get_ticklabels()
        for loc, label in zip(tlocs, tlabels):
            xp = conv._from_ordinal(loc).strftime('%H:%M:%S.%f')
            rs = str(label.get_text())
            if len(rs):
                self.assertEqual(xp, rs)

    @slow
    def test_irreg_hf(self):
        import matplotlib.pyplot as plt
        fig = plt.gcf()
        plt.clf()
        fig.add_subplot(111)

        idx = date_range('2012-6-22 21:59:51', freq='S', periods=100)
        df = DataFrame(np.random.randn(len(idx), 2), idx)

        irreg = df.ix[[0, 1, 3, 4]]
        ax = irreg.plot()
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()

        sec = 1. / 24 / 60 / 60
        self.assertTrue((np.fabs(diffs[1:] - [sec, sec * 2, sec]) < 1e-8).all(
        ))

        plt.clf()
        fig.add_subplot(111)
        df2 = df.copy()
        df2.index = df.index.asobject
        ax = df2.plot()
        diffs = Series(ax.get_lines()[0].get_xydata()[:, 0]).diff()
        self.assertTrue((np.fabs(diffs[1:] - sec) < 1e-8).all())

    def test_irregular_datetime64_repr_bug(self):
        import matplotlib.pyplot as plt
        ser = tm.makeTimeSeries()
        ser = ser[[0, 1, 2, 7]]

        fig = plt.gcf()
        plt.clf()
        ax = fig.add_subplot(211)
        ret = ser.plot()
        self.assertIsNotNone(ret)

        for rs, xp in zip(ax.get_lines()[0].get_xdata(), ser.index):
            self.assertEqual(rs, xp)

    def test_business_freq(self):
        import matplotlib.pyplot as plt  # noqa
        bts = tm.makePeriodSeries()
        ax = bts.plot()
        self.assertEqual(ax.get_lines()[0].get_xydata()[0, 0],
                         bts.index[0].ordinal)
        idx = ax.get_lines()[0].get_xdata()
        self.assertEqual(PeriodIndex(data=idx).freqstr, 'B')

    @slow
    def test_business_freq_convert(self):
        n = tm.N
        tm.N = 300
        bts = tm.makeTimeSeries().asfreq('BM')
        tm.N = n
        ts = bts.to_period('M')
        ax = bts.plot()
        self.assertEqual(ax.get_lines()[0].get_xydata()[0, 0],
                         ts.index[0].ordinal)
        idx = ax.get_lines()[0].get_xdata()
        self.assertEqual(PeriodIndex(data=idx).freqstr, 'M')

    def test_nonzero_base(self):
        # GH2571
        idx = (date_range('2012-12-20', periods=24, freq='H') + timedelta(
            minutes=30))
        df = DataFrame(np.arange(24), index=idx)
        ax = df.plot()
        rs = ax.get_lines()[0].get_xdata()
        self.assertFalse(Index(rs).is_normalized)

    def test_dataframe(self):
        bts = DataFrame({'a': tm.makeTimeSeries()})
        ax = bts.plot()
        idx = ax.get_lines()[0].get_xdata()
        tm.assert_index_equal(bts.index.to_period(), PeriodIndex(idx))

    @slow
    def test_axis_limits(self):
        import matplotlib.pyplot as plt

        def _test(ax):
            xlim = ax.get_xlim()
            ax.set_xlim(xlim[0] - 5, xlim[1] + 10)
            ax.get_figure().canvas.draw()
            result = ax.get_xlim()
            self.assertEqual(result[0], xlim[0] - 5)
            self.assertEqual(result[1], xlim[1] + 10)

            # string
            expected = (Period('1/1/2000', ax.freq),
                        Period('4/1/2000', ax.freq))
            ax.set_xlim('1/1/2000', '4/1/2000')
            ax.get_figure().canvas.draw()
            result = ax.get_xlim()
            self.assertEqual(int(result[0]), expected[0].ordinal)
            self.assertEqual(int(result[1]), expected[1].ordinal)

            # datetim
            expected = (Period('1/1/2000', ax.freq),
                        Period('4/1/2000', ax.freq))
            ax.set_xlim(datetime(2000, 1, 1), datetime(2000, 4, 1))
            ax.get_figure().canvas.draw()
            result = ax.get_xlim()
            self.assertEqual(int(result[0]), expected[0].ordinal)
            self.assertEqual(int(result[1]), expected[1].ordinal)
            fig = ax.get_figure()
            plt.close(fig)

        ser = tm.makeTimeSeries()
        ax = ser.plot()
        _test(ax)

        df = DataFrame({'a': ser, 'b': ser + 1})
        ax = df.plot()
        _test(ax)

        df = DataFrame({'a': ser, 'b': ser + 1})
        axes = df.plot(subplots=True)

        for ax in axes:
            _test(ax)

    def test_get_finder(self):
        import pandas.tseries.converter as conv

        self.assertEqual(conv.get_finder('B'), conv._daily_finder)
        self.assertEqual(conv.get_finder('D'), conv._daily_finder)
        self.assertEqual(conv.get_finder('M'), conv._monthly_finder)
        self.assertEqual(conv.get_finder('Q'), conv._quarterly_finder)
        self.assertEqual(conv.get_finder('A'), conv._annual_finder)
        self.assertEqual(conv.get_finder('W'), conv._daily_finder)

    @slow
    def test_finder_daily(self):
        import matplotlib.pyplot as plt
        xp = Period('1999-1-1', freq='B').ordinal
        day_lst = [10, 40, 252, 400, 950, 2750, 10000]
        for n in day_lst:
            rng = bdate_range('1999-1-1', periods=n)
            ser = Series(np.random.randn(len(rng)), rng)
            ax = ser.plot()
            xaxis = ax.get_xaxis()
            rs = xaxis.get_majorticklocs()[0]
            self.assertEqual(xp, rs)
            vmin, vmax = ax.get_xlim()
            ax.set_xlim(vmin + 0.9, vmax)
            rs = xaxis.get_majorticklocs()[0]
            self.assertEqual(xp, rs)
            plt.close(ax.get_figure())

    @slow
    def test_finder_quarterly(self):
        import matplotlib.pyplot as plt
        xp = Period('1988Q1').ordinal
        yrs = [3.5, 11]
        for n in yrs:
            rng = period_range('1987Q2', periods=int(n * 4), freq='Q')
            ser = Series(np.random.randn(len(rng)), rng)
            ax = ser.plot()
            xaxis = ax.get_xaxis()
            rs = xaxis.get_majorticklocs()[0]
            self.assertEqual(rs, xp)
            (vmin, vmax) = ax.get_xlim()
            ax.set_xlim(vmin + 0.9, vmax)
            rs = xaxis.get_majorticklocs()[0]
            self.assertEqual(xp, rs)
            plt.close(ax.get_figure())

    @slow
    def test_finder_monthly(self):
        import matplotlib.pyplot as plt
        xp = Period('Jan 1988').ordinal
        yrs = [1.15, 2.5, 4, 11]
        for n in yrs:
            rng = period_range('1987Q2', periods=int(n * 12), freq='M')
            ser = Series(np.random.randn(len(rng)), rng)
            ax = ser.plot()
            xaxis = ax.get_xaxis()
            rs = xaxis.get_majorticklocs()[0]
            self.assertEqual(rs, xp)
            vmin, vmax = ax.get_xlim()
            ax.set_xlim(vmin + 0.9, vmax)
            rs = xaxis.get_majorticklocs()[0]
            self.assertEqual(xp, rs)
            plt.close(ax.get_figure())

    def test_finder_monthly_long(self):
        rng = period_range('1988Q1', periods=24 * 12, freq='M')
        ser = Series(np.random.randn(len(rng)), rng)
        ax = ser.plot()
        xaxis = ax.get_xaxis()
        rs = xaxis.get_majorticklocs()[0]
        xp = Period('1989Q1', 'M').ordinal
        self.assertEqual(rs, xp)

    @slow
    def test_finder_annual(self):
        import matplotlib.pyplot as plt
        xp = [1987, 1988, 1990, 1990, 1995, 2020, 2070, 2170]
        for i, nyears in enumerate([5, 10, 19, 49, 99, 199, 599, 1001]):
            rng = period_range('1987', periods=nyears, freq='A')
            ser = Series(np.random.randn(len(rng)), rng)
            ax = ser.plot()
            xaxis = ax.get_xaxis()
            rs = xaxis.get_majorticklocs()[0]
            self.assertEqual(rs, Period(xp[i], freq='A').ordinal)
            plt.close(ax.get_figure())

    @slow
    def test_finder_minutely(self):
        nminutes = 50 * 24 * 60
        rng = date_range('1/1/1999', freq='Min', periods=nminutes)
        ser = Series(np.random.randn(len(rng)), rng)
        ax = ser.plot()
        xaxis = ax.get_xaxis()
        rs = xaxis.get_majorticklocs()[0]
        xp = Period('1/1/1999', freq='Min').ordinal
        self.assertEqual(rs, xp)

    def test_finder_hourly(self):
        nhours = 23
        rng = date_range('1/1/1999', freq='H', periods=nhours)
        ser = Series(np.random.randn(len(rng)), rng)
        ax = ser.plot()
        xaxis = ax.get_xaxis()
        rs = xaxis.get_majorticklocs()[0]
        xp = Period('1/1/1999', freq='H').ordinal
        self.assertEqual(rs, xp)

    @slow
    def test_gaps(self):
        import matplotlib.pyplot as plt

        ts = tm.makeTimeSeries()
        ts[5:25] = np.nan
        ax = ts.plot()
        lines = ax.get_lines()
        tm._skip_if_mpl_1_5()
        self.assertEqual(len(lines), 1)
        l = lines[0]
        data = l.get_xydata()
        tm.assertIsInstance(data, np.ma.core.MaskedArray)
        mask = data.mask
        self.assertTrue(mask[5:25, 1].all())
        plt.close(ax.get_figure())

        # irregular
        ts = tm.makeTimeSeries()
        ts = ts[[0, 1, 2, 5, 7, 9, 12, 15, 20]]
        ts[2:5] = np.nan
        ax = ts.plot()
        lines = ax.get_lines()
        self.assertEqual(len(lines), 1)
        l = lines[0]
        data = l.get_xydata()
        tm.assertIsInstance(data, np.ma.core.MaskedArray)
        mask = data.mask
        self.assertTrue(mask[2:5, 1].all())
        plt.close(ax.get_figure())

        # non-ts
        idx = [0, 1, 2, 5, 7, 9, 12, 15, 20]
        ser = Series(np.random.randn(len(idx)), idx)
        ser[2:5] = np.nan
        ax = ser.plot()
        lines = ax.get_lines()
        self.assertEqual(len(lines), 1)
        l = lines[0]
        data = l.get_xydata()
        tm.assertIsInstance(data, np.ma.core.MaskedArray)
        mask = data.mask
        self.assertTrue(mask[2:5, 1].all())

    @slow
    def test_gap_upsample(self):
        low = tm.makeTimeSeries()
        low[5:25] = np.nan
        ax = low.plot()

        idxh = date_range(low.index[0], low.index[-1], freq='12h')
        s = Series(np.random.randn(len(idxh)), idxh)
        s.plot(secondary_y=True)
        lines = ax.get_lines()
        self.assertEqual(len(lines), 1)
        self.assertEqual(len(ax.right_ax.get_lines()), 1)
        l = lines[0]
        data = l.get_xydata()

        tm._skip_if_mpl_1_5()

        tm.assertIsInstance(data, np.ma.core.MaskedArray)
        mask = data.mask
        self.assertTrue(mask[5:25, 1].all())

    @slow
    def test_secondary_y(self):
        import matplotlib.pyplot as plt

        ser = Series(np.random.randn(10))
        ser2 = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True)
        self.assertTrue(hasattr(ax, 'left_ax'))
        self.assertFalse(hasattr(ax, 'right_ax'))
        fig = ax.get_figure()
        axes = fig.get_axes()
        l = ax.get_lines()[0]
        xp = Series(l.get_ydata(), l.get_xdata())
        assert_series_equal(ser, xp)
        self.assertEqual(ax.get_yaxis().get_ticks_position(), 'right')
        self.assertFalse(axes[0].get_yaxis().get_visible())
        plt.close(fig)

        ax2 = ser2.plot()
        self.assertEqual(ax2.get_yaxis().get_ticks_position(), 'default')
        plt.close(ax2.get_figure())

        ax = ser2.plot()
        ax2 = ser.plot(secondary_y=True)
        self.assertTrue(ax.get_yaxis().get_visible())
        self.assertFalse(hasattr(ax, 'left_ax'))
        self.assertTrue(hasattr(ax, 'right_ax'))
        self.assertTrue(hasattr(ax2, 'left_ax'))
        self.assertFalse(hasattr(ax2, 'right_ax'))

    @slow
    def test_secondary_y_ts(self):
        import matplotlib.pyplot as plt
        idx = date_range('1/1/2000', periods=10)
        ser = Series(np.random.randn(10), idx)
        ser2 = Series(np.random.randn(10), idx)
        ax = ser.plot(secondary_y=True)
        self.assertTrue(hasattr(ax, 'left_ax'))
        self.assertFalse(hasattr(ax, 'right_ax'))
        fig = ax.get_figure()
        axes = fig.get_axes()
        l = ax.get_lines()[0]
        xp = Series(l.get_ydata(), l.get_xdata()).to_timestamp()
        assert_series_equal(ser, xp)
        self.assertEqual(ax.get_yaxis().get_ticks_position(), 'right')
        self.assertFalse(axes[0].get_yaxis().get_visible())
        plt.close(fig)

        ax2 = ser2.plot()
        self.assertEqual(ax2.get_yaxis().get_ticks_position(), 'default')
        plt.close(ax2.get_figure())

        ax = ser2.plot()
        ax2 = ser.plot(secondary_y=True)
        self.assertTrue(ax.get_yaxis().get_visible())

    @slow
    def test_secondary_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()

        import matplotlib.pyplot as plt  # noqa
        ser = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True, kind='density')
        self.assertTrue(hasattr(ax, 'left_ax'))
        self.assertFalse(hasattr(ax, 'right_ax'))
        fig = ax.get_figure()
        axes = fig.get_axes()
        self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right')

    @slow
    def test_secondary_bar(self):
        ser = Series(np.random.randn(10))
        ax = ser.plot(secondary_y=True, kind='bar')
        fig = ax.get_figure()
        axes = fig.get_axes()
        self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'right')

    @slow
    def test_secondary_frame(self):
        df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c'])
        axes = df.plot(secondary_y=['a', 'c'], subplots=True)
        self.assertEqual(axes[0].get_yaxis().get_ticks_position(), 'right')
        self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'default')
        self.assertEqual(axes[2].get_yaxis().get_ticks_position(), 'right')

    @slow
    def test_secondary_bar_frame(self):
        df = DataFrame(np.random.randn(5, 3), columns=['a', 'b', 'c'])
        axes = df.plot(kind='bar', secondary_y=['a', 'c'], subplots=True)
        self.assertEqual(axes[0].get_yaxis().get_ticks_position(), 'right')
        self.assertEqual(axes[1].get_yaxis().get_ticks_position(), 'default')
        self.assertEqual(axes[2].get_yaxis().get_ticks_position(), 'right')

    def test_mixed_freq_regular_first(self):
        import matplotlib.pyplot as plt  # noqa
        s1 = tm.makeTimeSeries()
        s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]

        # it works!
        s1.plot()

        ax2 = s2.plot(style='g')
        lines = ax2.get_lines()
        idx1 = PeriodIndex(lines[0].get_xdata())
        idx2 = PeriodIndex(lines[1].get_xdata())
        self.assertTrue(idx1.equals(s1.index.to_period('B')))
        self.assertTrue(idx2.equals(s2.index.to_period('B')))
        left, right = ax2.get_xlim()
        pidx = s1.index.to_period()
        self.assertEqual(left, pidx[0].ordinal)
        self.assertEqual(right, pidx[-1].ordinal)

    @slow
    def test_mixed_freq_irregular_first(self):
        import matplotlib.pyplot as plt  # noqa
        s1 = tm.makeTimeSeries()
        s2 = s1[[0, 5, 10, 11, 12, 13, 14, 15]]
        s2.plot(style='g')
        ax = s1.plot()
        self.assertFalse(hasattr(ax, 'freq'))
        lines = ax.get_lines()
        x1 = lines[0].get_xdata()
        tm.assert_numpy_array_equal(x1, s2.index.asobject.values)
        x2 = lines[1].get_xdata()
        tm.assert_numpy_array_equal(x2, s1.index.asobject.values)

    def test_mixed_freq_regular_first_df(self):
        # GH 9852
        import matplotlib.pyplot as plt  # noqa
        s1 = tm.makeTimeSeries().to_frame()
        s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :]
        ax = s1.plot()
        ax2 = s2.plot(style='g', ax=ax)
        lines = ax2.get_lines()
        idx1 = PeriodIndex(lines[0].get_xdata())
        idx2 = PeriodIndex(lines[1].get_xdata())
        self.assertTrue(idx1.equals(s1.index.to_period('B')))
        self.assertTrue(idx2.equals(s2.index.to_period('B')))
        left, right = ax2.get_xlim()
        pidx = s1.index.to_period()
        self.assertEqual(left, pidx[0].ordinal)
        self.assertEqual(right, pidx[-1].ordinal)

    @slow
    def test_mixed_freq_irregular_first_df(self):
        # GH 9852
        import matplotlib.pyplot as plt  # noqa
        s1 = tm.makeTimeSeries().to_frame()
        s2 = s1.iloc[[0, 5, 10, 11, 12, 13, 14, 15], :]
        ax = s2.plot(style='g')
        ax = s1.plot(ax=ax)
        self.assertFalse(hasattr(ax, 'freq'))
        lines = ax.get_lines()
        x1 = lines[0].get_xdata()
        tm.assert_numpy_array_equal(x1, s2.index.asobject.values)
        x2 = lines[1].get_xdata()
        tm.assert_numpy_array_equal(x2, s1.index.asobject.values)

    def test_mixed_freq_hf_first(self):
        idxh = date_range('1/1/1999', periods=365, freq='D')
        idxl = date_range('1/1/1999', periods=12, freq='M')
        high = Series(np.random.randn(len(idxh)), idxh)
        low = Series(np.random.randn(len(idxl)), idxl)
        high.plot()
        ax = low.plot()
        for l in ax.get_lines():
            self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'D')

    @slow
    def test_mixed_freq_alignment(self):
        ts_ind = date_range('2012-01-01 13:00', '2012-01-02', freq='H')
        ts_data = np.random.randn(12)

        ts = Series(ts_data, index=ts_ind)
        ts2 = ts.asfreq('T').interpolate()

        ax = ts.plot()
        ts2.plot(style='r')

        self.assertEqual(ax.lines[0].get_xdata()[0],
                         ax.lines[1].get_xdata()[0])

    @slow
    def test_mixed_freq_lf_first(self):
        import matplotlib.pyplot as plt

        idxh = date_range('1/1/1999', periods=365, freq='D')
        idxl = date_range('1/1/1999', periods=12, freq='M')
        high = Series(np.random.randn(len(idxh)), idxh)
        low = Series(np.random.randn(len(idxl)), idxl)
        low.plot(legend=True)
        ax = high.plot(legend=True)
        for l in ax.get_lines():
            self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'D')
        leg = ax.get_legend()
        self.assertEqual(len(leg.texts), 2)
        plt.close(ax.get_figure())

        idxh = date_range('1/1/1999', periods=240, freq='T')
        idxl = date_range('1/1/1999', periods=4, freq='H')
        high = Series(np.random.randn(len(idxh)), idxh)
        low = Series(np.random.randn(len(idxl)), idxl)
        low.plot()
        ax = high.plot()
        for l in ax.get_lines():
            self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'T')

    def test_mixed_freq_irreg_period(self):
        ts = tm.makeTimeSeries()
        irreg = ts[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 16, 17, 18, 29]]
        rng = period_range('1/3/2000', periods=30, freq='B')
        ps = Series(np.random.randn(len(rng)), rng)
        irreg.plot()
        ps.plot()

    @slow
    def test_to_weekly_resampling(self):
        idxh = date_range('1/1/1999', periods=52, freq='W')
        idxl = date_range('1/1/1999', periods=12, freq='M')
        high = Series(np.random.randn(len(idxh)), idxh)
        low = Series(np.random.randn(len(idxl)), idxl)
        high.plot()
        ax = low.plot()
        for l in ax.get_lines():
            self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, idxh.freq)

        # tsplot
        from pandas.tseries.plotting import tsplot
        import matplotlib.pyplot as plt

        tsplot(high, plt.Axes.plot)
        lines = tsplot(low, plt.Axes.plot)
        for l in lines:
            self.assertTrue(PeriodIndex(data=l.get_xdata()).freq, idxh.freq)

    @slow
    def test_from_weekly_resampling(self):
        idxh = date_range('1/1/1999', periods=52, freq='W')
        idxl = date_range('1/1/1999', periods=12, freq='M')
        high = Series(np.random.randn(len(idxh)), idxh)
        low = Series(np.random.randn(len(idxl)), idxl)
        low.plot()
        ax = high.plot()

        expected_h = idxh.to_period().asi8.astype(np.float64)
        expected_l = np.array([1514, 1519, 1523, 1527, 1531, 1536, 1540, 1544,
                               1549, 1553, 1558, 1562], dtype=np.float64)
        for l in ax.get_lines():
            self.assertTrue(PeriodIndex(data=l.get_xdata()).freq, idxh.freq)
            xdata = l.get_xdata(orig=False)
            if len(xdata) == 12:  # idxl lines
                self.assert_numpy_array_equal(xdata, expected_l)
            else:
                self.assert_numpy_array_equal(xdata, expected_h)
        tm.close()

        # tsplot
        from pandas.tseries.plotting import tsplot
        import matplotlib.pyplot as plt

        tsplot(low, plt.Axes.plot)
        lines = tsplot(high, plt.Axes.plot)
        for l in lines:
            self.assertTrue(PeriodIndex(data=l.get_xdata()).freq, idxh.freq)
            xdata = l.get_xdata(orig=False)
            if len(xdata) == 12:  # idxl lines
                self.assert_numpy_array_equal(xdata, expected_l)
            else:
                self.assert_numpy_array_equal(xdata, expected_h)

    @slow
    def test_from_resampling_area_line_mixed(self):
        idxh = date_range('1/1/1999', periods=52, freq='W')
        idxl = date_range('1/1/1999', periods=12, freq='M')
        high = DataFrame(np.random.rand(len(idxh), 3),
                         index=idxh, columns=[0, 1, 2])
        low = DataFrame(np.random.rand(len(idxl), 3),
                        index=idxl, columns=[0, 1, 2])

        # low to high
        for kind1, kind2 in [('line', 'area'), ('area', 'line')]:
            ax = low.plot(kind=kind1, stacked=True)
            ax = high.plot(kind=kind2, stacked=True, ax=ax)

            # check low dataframe result
            expected_x = np.array([1514, 1519, 1523, 1527, 1531, 1536, 1540,
                                   1544, 1549, 1553, 1558, 1562],
                                  dtype=np.float64)
            expected_y = np.zeros(len(expected_x), dtype=np.float64)
            for i in range(3):
                l = ax.lines[i]
                self.assertEqual(PeriodIndex(l.get_xdata()).freq, idxh.freq)
                self.assert_numpy_array_equal(l.get_xdata(orig=False),
                                              expected_x)
                # check stacked values are correct
                expected_y += low[i].values
                self.assert_numpy_array_equal(
                    l.get_ydata(orig=False), expected_y)

            # check high dataframe result
            expected_x = idxh.to_period().asi8.astype(np.float64)
            expected_y = np.zeros(len(expected_x), dtype=np.float64)
            for i in range(3):
                l = ax.lines[3 + i]
                self.assertEqual(PeriodIndex(data=l.get_xdata()).freq,
                                 idxh.freq)
                self.assert_numpy_array_equal(l.get_xdata(orig=False),
                                              expected_x)
                expected_y += high[i].values
                self.assert_numpy_array_equal(l.get_ydata(orig=False),
                                              expected_y)

        # high to low
        for kind1, kind2 in [('line', 'area'), ('area', 'line')]:
            ax = high.plot(kind=kind1, stacked=True)
            ax = low.plot(kind=kind2, stacked=True, ax=ax)

            # check high dataframe result
            expected_x = idxh.to_period().asi8.astype(np.float64)
            expected_y = np.zeros(len(expected_x), dtype=np.float64)
            for i in range(3):
                l = ax.lines[i]
                self.assertEqual(PeriodIndex(data=l.get_xdata()).freq,
                                 idxh.freq)
                self.assert_numpy_array_equal(
                    l.get_xdata(orig=False), expected_x)
                expected_y += high[i].values
                self.assert_numpy_array_equal(
                    l.get_ydata(orig=False), expected_y)

            # check low dataframe result
            expected_x = np.array([1514, 1519, 1523, 1527, 1531, 1536, 1540,
                                   1544, 1549, 1553, 1558, 1562],
                                  dtype=np.float64)
            expected_y = np.zeros(len(expected_x), dtype=np.float64)
            for i in range(3):
                l = ax.lines[3 + i]
                self.assertEqual(PeriodIndex(data=l.get_xdata()).freq,
                                 idxh.freq)
                self.assert_numpy_array_equal(l.get_xdata(orig=False),
                                              expected_x)
                expected_y += low[i].values
                self.assert_numpy_array_equal(l.get_ydata(orig=False),
                                              expected_y)

    @slow
    def test_mixed_freq_second_millisecond(self):
        # GH 7772, GH 7760
        idxh = date_range('2014-07-01 09:00', freq='S', periods=50)
        idxl = date_range('2014-07-01 09:00', freq='100L', periods=500)
        high = Series(np.random.randn(len(idxh)), idxh)
        low = Series(np.random.randn(len(idxl)), idxl)
        # high to low
        high.plot()
        ax = low.plot()
        self.assertEqual(len(ax.get_lines()), 2)
        for l in ax.get_lines():
            self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'L')
        tm.close()

        # low to high
        low.plot()
        ax = high.plot()
        self.assertEqual(len(ax.get_lines()), 2)
        for l in ax.get_lines():
            self.assertEqual(PeriodIndex(data=l.get_xdata()).freq, 'L')

    @slow
    def test_irreg_dtypes(self):
        # date
        idx = [date(2000, 1, 1), date(2000, 1, 5), date(2000, 1, 20)]
        df = DataFrame(np.random.randn(len(idx), 3), Index(idx, dtype=object))
        _check_plot_works(df.plot)

        # np.datetime64
        idx = date_range('1/1/2000', periods=10)
        idx = idx[[0, 2, 5, 9]].asobject
        df = DataFrame(np.random.randn(len(idx), 3), idx)
        _check_plot_works(df.plot)

    @slow
    def test_time(self):
        t = datetime(1, 1, 1, 3, 30, 0)
        deltas = np.random.randint(1, 20, 3).cumsum()
        ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas])
        df = DataFrame({'a': np.random.randn(len(ts)),
                        'b': np.random.randn(len(ts))},
                       index=ts)
        ax = df.plot()

        # verify tick labels
        ticks = ax.get_xticks()
        labels = ax.get_xticklabels()
        for t, l in zip(ticks, labels):
            m, s = divmod(int(t), 60)
            h, m = divmod(m, 60)
            xp = l.get_text()
            if len(xp) > 0:
                rs = time(h, m, s).strftime('%H:%M:%S')
                self.assertEqual(xp, rs)

        # change xlim
        ax.set_xlim('1:30', '5:00')

        # check tick labels again
        ticks = ax.get_xticks()
        labels = ax.get_xticklabels()
        for t, l in zip(ticks, labels):
            m, s = divmod(int(t), 60)
            h, m = divmod(m, 60)
            xp = l.get_text()
            if len(xp) > 0:
                rs = time(h, m, s).strftime('%H:%M:%S')
                self.assertEqual(xp, rs)

    @slow
    def test_time_musec(self):
        t = datetime(1, 1, 1, 3, 30, 0)
        deltas = np.random.randint(1, 20, 3).cumsum()
        ts = np.array([(t + timedelta(microseconds=int(x))).time()
                       for x in deltas])
        df = DataFrame({'a': np.random.randn(len(ts)),
                        'b': np.random.randn(len(ts))},
                       index=ts)
        ax = df.plot()

        # verify tick labels
        ticks = ax.get_xticks()
        labels = ax.get_xticklabels()
        for t, l in zip(ticks, labels):
            m, s = divmod(int(t), 60)

            # TODO: unused?
            # us = int((t - int(t)) * 1e6)

            h, m = divmod(m, 60)
            xp = l.get_text()
            if len(xp) > 0:
                rs = time(h, m, s).strftime('%H:%M:%S.%f')
                self.assertEqual(xp, rs)

    @slow
    def test_secondary_upsample(self):
        idxh = date_range('1/1/1999', periods=365, freq='D')
        idxl = date_range('1/1/1999', periods=12, freq='M')
        high = Series(np.random.randn(len(idxh)), idxh)
        low = Series(np.random.randn(len(idxl)), idxl)
        low.plot()
        ax = high.plot(secondary_y=True)
        for l in ax.get_lines():
            self.assertEqual(PeriodIndex(l.get_xdata()).freq, 'D')
        self.assertTrue(hasattr(ax, 'left_ax'))
        self.assertFalse(hasattr(ax, 'right_ax'))
        for l in ax.left_ax.get_lines():
            self.assertEqual(PeriodIndex(l.get_xdata()).freq, 'D')

    @slow
    def test_secondary_legend(self):
        import matplotlib.pyplot as plt
        fig = plt.gcf()
        plt.clf()
        ax = fig.add_subplot(211)

        # ts
        df = tm.makeTimeDataFrame()
        ax = df.plot(secondary_y=['A', 'B'])
        leg = ax.get_legend()
        self.assertEqual(len(leg.get_lines()), 4)
        self.assertEqual(leg.get_texts()[0].get_text(), 'A (right)')
        self.assertEqual(leg.get_texts()[1].get_text(), 'B (right)')
        self.assertEqual(leg.get_texts()[2].get_text(), 'C')
        self.assertEqual(leg.get_texts()[3].get_text(), 'D')
        self.assertIsNone(ax.right_ax.get_legend())
        colors = set()
        for line in leg.get_lines():
            colors.add(line.get_color())

        # TODO: color cycle problems
        self.assertEqual(len(colors), 4)

        plt.clf()
        ax = fig.add_subplot(211)
        ax = df.plot(secondary_y=['A', 'C'], mark_right=False)
        leg = ax.get_legend()
        self.assertEqual(len(leg.get_lines()), 4)
        self.assertEqual(leg.get_texts()[0].get_text(), 'A')
        self.assertEqual(leg.get_texts()[1].get_text(), 'B')
        self.assertEqual(leg.get_texts()[2].get_text(), 'C')
        self.assertEqual(leg.get_texts()[3].get_text(), 'D')

        plt.clf()
        ax = df.plot(kind='bar', secondary_y=['A'])
        leg = ax.get_legend()
        self.assertEqual(leg.get_texts()[0].get_text(), 'A (right)')
        self.assertEqual(leg.get_texts()[1].get_text(), 'B')

        plt.clf()
        ax = df.plot(kind='bar', secondary_y=['A'], mark_right=False)
        leg = ax.get_legend()
        self.assertEqual(leg.get_texts()[0].get_text(), 'A')
        self.assertEqual(leg.get_texts()[1].get_text(), 'B')

        plt.clf()
        ax = fig.add_subplot(211)
        df = tm.makeTimeDataFrame()
        ax = df.plot(secondary_y=['C', 'D'])
        leg = ax.get_legend()
        self.assertEqual(len(leg.get_lines()), 4)
        self.assertIsNone(ax.right_ax.get_legend())
        colors = set()
        for line in leg.get_lines():
            colors.add(line.get_color())

        # TODO: color cycle problems
        self.assertEqual(len(colors), 4)

        # non-ts
        df = tm.makeDataFrame()
        plt.clf()
        ax = fig.add_subplot(211)
        ax = df.plot(secondary_y=['A', 'B'])
        leg = ax.get_legend()
        self.assertEqual(len(leg.get_lines()), 4)
        self.assertIsNone(ax.right_ax.get_legend())
        colors = set()
        for line in leg.get_lines():
            colors.add(line.get_color())

        # TODO: color cycle problems
        self.assertEqual(len(colors), 4)

        plt.clf()
        ax = fig.add_subplot(211)
        ax = df.plot(secondary_y=['C', 'D'])
        leg = ax.get_legend()
        self.assertEqual(len(leg.get_lines()), 4)
        self.assertIsNone(ax.right_ax.get_legend())
        colors = set()
        for line in leg.get_lines():
            colors.add(line.get_color())

        # TODO: color cycle problems
        self.assertEqual(len(colors), 4)

    def test_format_date_axis(self):
        rng = date_range('1/1/2012', periods=12, freq='M')
        df = DataFrame(np.random.randn(len(rng), 3), rng)
        ax = df.plot()
        xaxis = ax.get_xaxis()
        for l in xaxis.get_ticklabels():
            if len(l.get_text()) > 0:
                self.assertEqual(l.get_rotation(), 30)

    @slow
    def test_ax_plot(self):
        import matplotlib.pyplot as plt

        x = DatetimeIndex(start='2012-01-02', periods=10, freq='D')
        y = lrange(len(x))
        fig = plt.figure()
        ax = fig.add_subplot(111)
        lines = ax.plot(x, y, label='Y')
        tm.assert_index_equal(DatetimeIndex(lines[0].get_xdata()), x)

    @slow
    def test_mpl_nopandas(self):
        import matplotlib.pyplot as plt

        dates = [date(2008, 12, 31), date(2009, 1, 31)]
        values1 = np.arange(10.0, 11.0, 0.5)
        values2 = np.arange(11.0, 12.0, 0.5)

        kw = dict(fmt='-', lw=4)

        plt.close('all')
        fig = plt.figure()
        ax = fig.add_subplot(111)
        ax.plot_date([x.toordinal() for x in dates], values1, **kw)
        ax.plot_date([x.toordinal() for x in dates], values2, **kw)

        line1, line2 = ax.get_lines()

        exp = np.array([x.toordinal() for x in dates], dtype=np.float64)
        tm.assert_numpy_array_equal(line1.get_xydata()[:, 0], exp)
        exp = np.array([x.toordinal() for x in dates], dtype=np.float64)
        tm.assert_numpy_array_equal(line2.get_xydata()[:, 0], exp)

    @slow
    def test_irregular_ts_shared_ax_xlim(self):
        # GH 2960
        ts = tm.makeTimeSeries()[:20]
        ts_irregular = ts[[1, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 17, 18]]

        # plot the left section of the irregular series, then the right section
        ax = ts_irregular[:5].plot()
        ts_irregular[5:].plot(ax=ax)

        # check that axis limits are correct
        left, right = ax.get_xlim()
        self.assertEqual(left, ts_irregular.index.min().toordinal())
        self.assertEqual(right, ts_irregular.index.max().toordinal())

    @slow
    def test_secondary_y_non_ts_xlim(self):
        # GH 3490 - non-timeseries with secondary y
        index_1 = [1, 2, 3, 4]
        index_2 = [5, 6, 7, 8]
        s1 = Series(1, index=index_1)
        s2 = Series(2, index=index_2)

        ax = s1.plot()
        left_before, right_before = ax.get_xlim()
        s2.plot(secondary_y=True, ax=ax)
        left_after, right_after = ax.get_xlim()

        self.assertEqual(left_before, left_after)
        self.assertTrue(right_before < right_after)

    @slow
    def test_secondary_y_regular_ts_xlim(self):
        # GH 3490 - regular-timeseries with secondary y
        index_1 = date_range(start='2000-01-01', periods=4, freq='D')
        index_2 = date_range(start='2000-01-05', periods=4, freq='D')
        s1 = Series(1, index=index_1)
        s2 = Series(2, index=index_2)

        ax = s1.plot()
        left_before, right_before = ax.get_xlim()
        s2.plot(secondary_y=True, ax=ax)
        left_after, right_after = ax.get_xlim()

        self.assertEqual(left_before, left_after)
        self.assertTrue(right_before < right_after)

    @slow
    def test_secondary_y_mixed_freq_ts_xlim(self):
        # GH 3490 - mixed frequency timeseries with secondary y
        rng = date_range('2000-01-01', periods=10000, freq='min')
        ts = Series(1, index=rng)

        ax = ts.plot()
        left_before, right_before = ax.get_xlim()
        ts.resample('D').mean().plot(secondary_y=True, ax=ax)
        left_after, right_after = ax.get_xlim()

        # a downsample should not have changed either limit
        self.assertEqual(left_before, left_after)
        self.assertEqual(right_before, right_after)

    @slow
    def test_secondary_y_irregular_ts_xlim(self):
        # GH 3490 - irregular-timeseries with secondary y
        ts = tm.makeTimeSeries()[:20]
        ts_irregular = ts[[1, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 17, 18]]

        ax = ts_irregular[:5].plot()
        # plot higher-x values on secondary axis
        ts_irregular[5:].plot(secondary_y=True, ax=ax)
        # ensure secondary limits aren't overwritten by plot on primary
        ts_irregular[:5].plot(ax=ax)

        left, right = ax.get_xlim()
        self.assertEqual(left, ts_irregular.index.min().toordinal())
        self.assertEqual(right, ts_irregular.index.max().toordinal())

    def test_plot_outofbounds_datetime(self):
        # 2579 - checking this does not raise
        values = [date(1677, 1, 1), date(1677, 1, 2)]
        self.plt.plot(values)

        values = [datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)]
        self.plt.plot(values)


def _check_plot_works(f, freq=None, series=None, *args, **kwargs):
    import matplotlib.pyplot as plt

    fig = plt.gcf()

    try:
        plt.clf()
        ax = fig.add_subplot(211)
        orig_ax = kwargs.pop('ax', plt.gca())
        orig_axfreq = getattr(orig_ax, 'freq', None)

        ret = f(*args, **kwargs)
        assert ret is not None  # do something more intelligent

        ax = kwargs.pop('ax', plt.gca())
        if series is not None:
            dfreq = series.index.freq
            if isinstance(dfreq, DateOffset):
                dfreq = dfreq.rule_code
            if orig_axfreq is None:
                assert ax.freq == dfreq

        if freq is not None and orig_axfreq is None:
            assert ax.freq == freq

        ax = fig.add_subplot(212)
        try:
            kwargs['ax'] = ax
            ret = f(*args, **kwargs)
            assert ret is not None  # do something more intelligent
        except Exception:
            pass

        with ensure_clean(return_filelike=True) as path:
            plt.savefig(path)
    finally:
        plt.close(fig)


if __name__ == '__main__':
    nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
                   exit=False)