#!/usr/bin/env python
# coding: utf-8

import nose
import itertools
import os
import string
from distutils.version import LooseVersion

from datetime import datetime, date

from pandas import Series, DataFrame, MultiIndex, PeriodIndex, date_range
from pandas.compat import (range, lrange, StringIO, lmap, lzip, u, zip,
                           iteritems, OrderedDict)
from pandas.util.decorators import cache_readonly
import pandas.core.common as com
import pandas.util.testing as tm
from pandas.util.testing import ensure_clean
from pandas.core.config import set_option


import numpy as np
from numpy import random
from numpy.random import rand, randn

from numpy.testing import assert_array_equal, assert_allclose
from numpy.testing.decorators import slow
import pandas.tools.plotting as plotting


def _skip_if_no_scipy_gaussian_kde():
    try:
        import scipy
        from scipy.stats import gaussian_kde
    except ImportError:
        raise nose.SkipTest("scipy version doesn't support gaussian_kde")

def _ok_for_gaussian_kde(kind):
    if kind in ['kde','density']:
        try:
            import scipy
            from scipy.stats import gaussian_kde
        except ImportError:
            return False
    return True

@tm.mplskip
class TestPlotBase(tm.TestCase):

    def setUp(self):

        import matplotlib as mpl
        mpl.rcdefaults()

        n = 100
        with tm.RNGContext(42):
            gender = tm.choice(['Male', 'Female'], size=n)
            classroom = tm.choice(['A', 'B', 'C'], size=n)

            self.hist_df = DataFrame({'gender': gender,
                                      'classroom': classroom,
                                      'height': random.normal(66, 4, size=n),
                                      'weight': random.normal(161, 32, size=n),
                                      'category': random.randint(4, size=n)})

    def tearDown(self):
        tm.close()

    @cache_readonly
    def plt(self):
        import matplotlib.pyplot as plt
        return plt

    @cache_readonly
    def colorconverter(self):
        import matplotlib.colors as colors
        return colors.colorConverter

    def _check_legend_labels(self, axes, labels=None, visible=True):
        """
        Check each axes has expected legend labels

        Parameters
        ----------
        axes : matplotlib Axes object, or its list-like
        labels : list-like
            expected legend labels
        visible : bool
            expected legend visibility. labels are checked only when visible is True
        """

        if visible and (labels is None):
            raise ValueError('labels must be specified when visible is True')
        axes = self._flatten_visible(axes)
        for ax in axes:
            if visible:
                self.assertTrue(ax.get_legend() is not None)
                self._check_text_labels(ax.get_legend().get_texts(), labels)
            else:
                self.assertTrue(ax.get_legend() is None)

    def _check_data(self, xp, rs):
        """
        Check each axes has identical lines

        Parameters
        ----------
        xp : matplotlib Axes object
        rs : matplotlib Axes object
        """
        xp_lines = xp.get_lines()
        rs_lines = rs.get_lines()

        def check_line(xpl, rsl):
            xpdata = xpl.get_xydata()
            rsdata = rsl.get_xydata()
            assert_allclose(xpdata, rsdata)

        self.assertEqual(len(xp_lines), len(rs_lines))
        [check_line(xpl, rsl) for xpl, rsl in zip(xp_lines, rs_lines)]
        tm.close()

    def _check_visible(self, collections, visible=True):
        """
        Check each artist is visible or not

        Parameters
        ----------
        collections : list-like
            list or collection of target artist
        visible : bool
            expected visibility
        """

        for patch in collections:
            self.assertEqual(patch.get_visible(), visible)

    def _get_colors_mapped(self, series, colors):
        unique = series.unique()
        # unique and colors length can be differed
        # depending on slice value
        mapped = dict(zip(unique, colors))
        return [mapped[v] for v in series.values]

    def _check_colors(self, collections, linecolors=None, facecolors=None,
                      mapping=None):
        """
        Check each artist has expected line colors and face colors

        Parameters
        ----------
        collections : list-like
            list or collection of target artist
        linecolors : list-like which has the same length as collections
            list of expected line colors
        facecolors : list-like which has the same length as collections
            list of expected face colors
        mapping : Series
            Series used for color grouping key
            used for andrew_curves, parallel_coordinates, radviz test
        """

        from matplotlib.lines import Line2D
        from matplotlib.collections import Collection
        conv = self.colorconverter
        if linecolors is not None:

            if mapping is not None:
                linecolors = self._get_colors_mapped(mapping, linecolors)
                linecolors = linecolors[:len(collections)]

            self.assertEqual(len(collections), len(linecolors))
            for patch, color in zip(collections, linecolors):
                if isinstance(patch, Line2D):
                    result = patch.get_color()
                    # Line2D may contains string color expression
                    result = conv.to_rgba(result)
                else:
                    result = patch.get_edgecolor()

                expected = conv.to_rgba(color)
                self.assertEqual(result, expected)

        if facecolors is not None:

            if mapping is not None:
                facecolors = self._get_colors_mapped(mapping, facecolors)
                facecolors = facecolors[:len(collections)]

            self.assertEqual(len(collections), len(facecolors))
            for patch, color in zip(collections, facecolors):
                if isinstance(patch, Collection):
                    # returned as list of np.array
                    result = patch.get_facecolor()[0]
                else:
                    result = patch.get_facecolor()

                if isinstance(result, np.ndarray):
                    result = tuple(result)

                expected = conv.to_rgba(color)
                self.assertEqual(result, expected)

    def _check_text_labels(self, texts, expected):
        """
        Check each text has expected labels

        Parameters
        ----------
        texts : matplotlib Text object, or its list-like
            target text, or its list
        expected : str or list-like which has the same length as texts
            expected text label, or its list
        """
        if not com.is_list_like(texts):
            self.assertEqual(texts.get_text(), expected)
        else:
            labels = [t.get_text() for t in texts]
            self.assertEqual(len(labels), len(expected))
            for l, e in zip(labels, expected):
                self.assertEqual(l, e)

    def _check_ticks_props(self, axes, xlabelsize=None, xrot=None,
                           ylabelsize=None, yrot=None):
        """
        Check each axes has expected tick properties

        Parameters
        ----------
        axes : matplotlib Axes object, or its list-like
        xlabelsize : number
            expected xticks font size
        xrot : number
            expected xticks rotation
        ylabelsize : number
            expected yticks font size
        yrot : number
            expected yticks rotation
        """
        axes = self._flatten_visible(axes)
        for ax in axes:
            if xlabelsize or xrot:
                xtick = ax.get_xticklabels()[0]
                if xlabelsize is not None:
                    self.assertAlmostEqual(xtick.get_fontsize(), xlabelsize)
                if xrot is not None:
                    self.assertAlmostEqual(xtick.get_rotation(), xrot)

            if ylabelsize or yrot:
                ytick = ax.get_yticklabels()[0]
                if ylabelsize is not None:
                    self.assertAlmostEqual(ytick.get_fontsize(), ylabelsize)
                if yrot is not None:
                    self.assertAlmostEqual(ytick.get_rotation(), yrot)

    def _check_ax_scales(self, axes, xaxis='linear', yaxis='linear'):
        """
        Check each axes has expected scales

        Parameters
        ----------
        axes : matplotlib Axes object, or its list-like
        xaxis : {'linear', 'log'}
            expected xaxis scale
        yaxis :  {'linear', 'log'}
            expected yaxis scale
        """
        axes = self._flatten_visible(axes)
        for ax in axes:
            self.assertEqual(ax.xaxis.get_scale(), xaxis)
            self.assertEqual(ax.yaxis.get_scale(), yaxis)

    def _check_axes_shape(self, axes, axes_num=None, layout=None, figsize=(8.0, 6.0)):
        """
        Check expected number of axes is drawn in expected layout

        Parameters
        ----------
        axes : matplotlib Axes object, or its list-like
        axes_num : number
            expected number of axes. Unnecessary axes should be set to invisible.
        layout :  tuple
            expected layout, (expected number of rows , columns)
        figsize : tuple
            expected figsize. default is matplotlib default
        """
        visible_axes = self._flatten_visible(axes)

        if axes_num is not None:
            self.assertEqual(len(visible_axes), axes_num)
            for ax in visible_axes:
                # check something drawn on visible axes
                self.assertTrue(len(ax.get_children()) > 0)

        if layout is not None:
            result = self._get_axes_layout(plotting._flatten(axes))
            self.assertEqual(result, layout)

        self.assert_numpy_array_equal(np.round(visible_axes[0].figure.get_size_inches()),
                                      np.array(figsize))

    def _get_axes_layout(self, axes):
        x_set = set()
        y_set = set()
        for ax in axes:
            # check axes coordinates to estimate layout
            points = ax.get_position().get_points()
            x_set.add(points[0][0])
            y_set.add(points[0][1])
        return (len(y_set), len(x_set))

    def _flatten_visible(self, axes):
        """
        Flatten axes, and filter only visible

        Parameters
        ----------
        axes : matplotlib Axes object, or its list-like

        """
        axes = plotting._flatten(axes)
        axes = [ax for ax in axes if ax.get_visible()]
        return axes

    def _check_has_errorbars(self, axes, xerr=0, yerr=0):
        """
        Check axes has expected number of errorbars

        Parameters
        ----------
        axes : matplotlib Axes object, or its list-like
        xerr : number
            expected number of x errorbar
        yerr : number
            expected number of y errorbar
        """

        axes = self._flatten_visible(axes)
        for ax in axes:
            containers = ax.containers
            xerr_count = 0
            yerr_count = 0
            for c in containers:
                has_xerr = getattr(c, 'has_xerr', False)
                has_yerr = getattr(c, 'has_yerr', False)
                if has_xerr:
                    xerr_count += 1
                if has_yerr:
                    yerr_count += 1
            self.assertEqual(xerr, xerr_count)
            self.assertEqual(yerr, yerr_count)

    def _check_box_return_type(self, returned, return_type, expected_keys=None):
        """
        Check box returned type is correct

        Parameters
        ----------
        returned : object to be tested, returned from boxplot
        return_type : str
            return_type passed to boxplot
        expected_keys : list-like, optional
            group labels in subplot case. If not passed,
            the function checks assuming boxplot uses single ax
        """
        from matplotlib.axes import Axes
        types = {'dict': dict, 'axes': Axes, 'both': tuple}
        if expected_keys is None:
            # should be fixed when the returning default is changed
            if return_type is None:
                return_type = 'dict'

            self.assertTrue(isinstance(returned, types[return_type]))
            if return_type == 'both':
                self.assertIsInstance(returned.ax, Axes)
                self.assertIsInstance(returned.lines, dict)
        else:
            # should be fixed when the returning default is changed
            if return_type is None:
                for r in self._flatten_visible(returned):
                    self.assertIsInstance(r, Axes)
                return

            self.assertTrue(isinstance(returned, OrderedDict))
            self.assertEqual(sorted(returned.keys()), sorted(expected_keys))
            for key, value in iteritems(returned):
                self.assertTrue(isinstance(value, types[return_type]))
                # check returned dict has correct mapping
                if return_type == 'axes':
                    self.assertEqual(value.get_title(), key)
                elif return_type == 'both':
                    self.assertEqual(value.ax.get_title(), key)
                    self.assertIsInstance(value.ax, Axes)
                    self.assertIsInstance(value.lines, dict)
                elif return_type == 'dict':
                    line = value['medians'][0]
                    self.assertEqual(line.get_axes().get_title(), key)
                else:
                    raise AssertionError


@tm.mplskip
class TestSeriesPlots(TestPlotBase):

    def setUp(self):
        TestPlotBase.setUp(self)
        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1')
        self.ts = tm.makeTimeSeries()
        self.ts.name = 'ts'

        self.series = tm.makeStringSeries()
        self.series.name = 'series'

        self.iseries = tm.makePeriodSeries()
        self.iseries.name = 'iseries'

    @slow
    def test_plot(self):
        _check_plot_works(self.ts.plot, label='foo')
        _check_plot_works(self.ts.plot, use_index=False)
        axes = _check_plot_works(self.ts.plot, rot=0)
        self._check_ticks_props(axes, xrot=0)

        ax = _check_plot_works(self.ts.plot, style='.', logy=True)
        self._check_ax_scales(ax, yaxis='log')

        ax = _check_plot_works(self.ts.plot, style='.', logx=True)
        self._check_ax_scales(ax, xaxis='log')

        ax = _check_plot_works(self.ts.plot, style='.', loglog=True)
        self._check_ax_scales(ax, xaxis='log', yaxis='log')

        _check_plot_works(self.ts[:10].plot, kind='bar')
        _check_plot_works(self.ts.plot, kind='area', stacked=False)
        _check_plot_works(self.iseries.plot)

        for kind in ['line', 'bar', 'barh', 'kde']:
            if not _ok_for_gaussian_kde(kind):
                continue
            _check_plot_works(self.series[:5].plot, kind=kind)

        _check_plot_works(self.series[:10].plot, kind='barh')
        ax = _check_plot_works(Series(randn(10)).plot, kind='bar', color='black')
        self._check_colors([ax.patches[0]], facecolors=['black'])

        # GH 6951
        ax = _check_plot_works(self.ts.plot, subplots=True)
        self._check_axes_shape(ax, axes_num=1, layout=(1, 1))

    @slow
    def test_plot_figsize_and_title(self):
        # figsize and title
        ax = self.series.plot(title='Test', figsize=(16, 8))
        self._check_text_labels(ax.title, 'Test')
        self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16, 8))

    def test_ts_line_lim(self):
        ax = self.ts.plot()
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0])
        self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1])
        tm.close()

        ax = self.ts.plot(secondary_y=True)
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        self.assertEqual(xmin, lines[0].get_data(orig=False)[0][0])
        self.assertEqual(xmax, lines[0].get_data(orig=False)[0][-1])

    def test_ts_area_lim(self):
        ax = self.ts.plot(kind='area', stacked=False)
        xmin, xmax = ax.get_xlim()
        line = ax.get_lines()[0].get_data(orig=False)[0]
        self.assertEqual(xmin, line[0])
        self.assertEqual(xmax, line[-1])
        tm.close()

        # GH 7471
        ax = self.ts.plot(kind='area', stacked=False, x_compat=True)
        xmin, xmax = ax.get_xlim()
        line = ax.get_lines()[0].get_data(orig=False)[0]
        self.assertEqual(xmin, line[0])
        self.assertEqual(xmax, line[-1])
        tm.close()

        tz_ts = self.ts.copy()
        tz_ts.index = tz_ts.tz_localize('GMT').tz_convert('CET')
        ax = tz_ts.plot(kind='area', stacked=False, x_compat=True)
        xmin, xmax = ax.get_xlim()
        line = ax.get_lines()[0].get_data(orig=False)[0]
        self.assertEqual(xmin, line[0])
        self.assertEqual(xmax, line[-1])
        tm.close()

        ax = tz_ts.plot(kind='area', stacked=False, secondary_y=True)
        xmin, xmax = ax.get_xlim()
        line = ax.get_lines()[0].get_data(orig=False)[0]
        self.assertEqual(xmin, line[0])
        self.assertEqual(xmax, line[-1])

    def test_line_area_nan_series(self):
        values = [1, 2, np.nan, 3]
        s = Series(values)
        ts = Series(values, index=tm.makeDateIndex(k=4))

        for d in [s, ts]:
            ax = _check_plot_works(d.plot)
            masked = ax.lines[0].get_ydata()
            # remove nan for comparison purpose
            self.assert_numpy_array_equal(np.delete(masked.data, 2), np.array([1, 2, 3]))
            self.assert_numpy_array_equal(masked.mask, np.array([False, False, True, False]))

            expected = np.array([1, 2, 0, 3])
            ax = _check_plot_works(d.plot, stacked=True)
            self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
            ax = _check_plot_works(d.plot, kind='area')
            self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
            ax = _check_plot_works(d.plot, kind='area', stacked=False)
            self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)

    @slow
    def test_bar_log(self):
        expected = np.array([1., 10., 100., 1000.])

        if not self.mpl_le_1_2_1:
            expected = np.hstack((.1, expected, 1e4))

        ax = Series([200, 500]).plot(log=True, kind='bar')
        assert_array_equal(ax.yaxis.get_ticklocs(), expected)

    @slow
    def test_bar_ignore_index(self):
        df = Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
        ax = df.plot(kind='bar', use_index=False)
        self._check_text_labels(ax.get_xticklabels(), ['0', '1', '2', '3'])

    def test_rotation(self):
        df = DataFrame(randn(5, 5))
        axes = df.plot(rot=30)
        self._check_ticks_props(axes, xrot=30)

    def test_irregular_datetime(self):
        rng = date_range('1/1/2000', '3/1/2000')
        rng = rng[[0, 1, 2, 3, 5, 9, 10, 11, 12]]
        ser = Series(randn(len(rng)), rng)
        ax = ser.plot()
        xp = datetime(1999, 1, 1).toordinal()
        ax.set_xlim('1/1/1999', '1/1/2001')
        self.assertEqual(xp, ax.get_xlim()[0])

    @slow
    def test_pie_series(self):
        # if sum of values is less than 1.0, pie handle them as rate and draw semicircle.
        series = Series(np.random.randint(1, 5),
                        index=['a', 'b', 'c', 'd', 'e'], name='YLABEL')
        ax = _check_plot_works(series.plot, kind='pie')
        self._check_text_labels(ax.texts, series.index)
        self.assertEqual(ax.get_ylabel(), 'YLABEL')

        # without wedge labels
        ax = _check_plot_works(series.plot, kind='pie', labels=None)
        self._check_text_labels(ax.texts, [''] * 5)

        # with less colors than elements
        color_args = ['r', 'g', 'b']
        ax = _check_plot_works(series.plot, kind='pie', colors=color_args)

        color_expected = ['r', 'g', 'b', 'r', 'g']
        self._check_colors(ax.patches, facecolors=color_expected)

        # with labels and colors
        labels = ['A', 'B', 'C', 'D', 'E']
        color_args = ['r', 'g', 'b', 'c', 'm']
        ax = _check_plot_works(series.plot, kind='pie', labels=labels, colors=color_args)
        self._check_text_labels(ax.texts, labels)
        self._check_colors(ax.patches, facecolors=color_args)

        # with autopct and fontsize
        ax = _check_plot_works(series.plot, kind='pie', colors=color_args,
                               autopct='%.2f', fontsize=7)
        pcts = ['{0:.2f}'.format(s * 100) for s in series.values / float(series.sum())]
        iters = [iter(series.index), iter(pcts)]
        expected_texts = list(next(it) for it in itertools.cycle(iters))
        self._check_text_labels(ax.texts, expected_texts)
        for t in ax.texts:
            self.assertEqual(t.get_fontsize(), 7)

        # includes negative value
        with tm.assertRaises(ValueError):
            series = Series([1, 2, 0, 4, -1], index=['a', 'b', 'c', 'd', 'e'])
            series.plot(kind='pie')

        # includes nan
        series = Series([1, 2, np.nan, 4],
                        index=['a', 'b', 'c', 'd'], name='YLABEL')
        ax = _check_plot_works(series.plot, kind='pie')
        self._check_text_labels(ax.texts, series.index)

    @slow
    def test_hist(self):
        _check_plot_works(self.ts.hist)
        _check_plot_works(self.ts.hist, grid=False)
        _check_plot_works(self.ts.hist, figsize=(8, 10))
        _check_plot_works(self.ts.hist, by=self.ts.index.month)
        _check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5)

        fig, ax = self.plt.subplots(1, 1)
        _check_plot_works(self.ts.hist, ax=ax)
        _check_plot_works(self.ts.hist, ax=ax, figure=fig)
        _check_plot_works(self.ts.hist, figure=fig)
        tm.close()

        fig, (ax1, ax2) = self.plt.subplots(1, 2)
        _check_plot_works(self.ts.hist, figure=fig, ax=ax1)
        _check_plot_works(self.ts.hist, figure=fig, ax=ax2)

        with tm.assertRaises(ValueError):
            self.ts.hist(by=self.ts.index, figure=fig)

    @slow
    def test_hist_bins(self):
        df = DataFrame(np.random.randn(10, 2))
        ax = df.hist(bins=2)[0][0]
        self.assertEqual(len(ax.patches), 2)

    @slow
    def test_hist_layout(self):
        df = self.hist_df
        with tm.assertRaises(ValueError):
            df.height.hist(layout=(1, 1))

        with tm.assertRaises(ValueError):
            df.height.hist(layout=[1, 1])

    @slow
    def test_hist_layout_with_by(self):
        df = self.hist_df

        axes = _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1))
        self._check_axes_shape(axes, axes_num=2, layout=(2, 1))

        axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 1))
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))

        axes = _check_plot_works(df.height.hist, by=df.classroom, layout=(2, 2))
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))

        axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 2), figsize=(12, 7))
        self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 7))

    @slow
    def test_hist_no_overlap(self):
        from matplotlib.pyplot import subplot, gcf
        x = Series(randn(2))
        y = Series(randn(2))
        subplot(121)
        x.hist()
        subplot(122)
        y.hist()
        fig = gcf()
        axes = fig.get_axes()
        self.assertEqual(len(axes), 2)

    @slow
    def test_plot_fails_with_dupe_color_and_style(self):
        x = Series(randn(2))
        with tm.assertRaises(ValueError):
            x.plot(style='k--', color='k')

    @slow
    def test_hist_by_no_extra_plots(self):
        df = self.hist_df
        axes = df.height.hist(by=df.gender)
        self.assertEqual(len(self.plt.get_fignums()), 1)

    def test_plot_fails_when_ax_differs_from_figure(self):
        from pylab import figure
        fig1 = figure()
        fig2 = figure()
        ax1 = fig1.add_subplot(111)
        with tm.assertRaises(AssertionError):
            self.ts.hist(ax=ax1, figure=fig2)

    @slow
    def test_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        _check_plot_works(self.ts.plot, kind='kde')
        _check_plot_works(self.ts.plot, kind='density')
        ax = self.ts.plot(kind='kde', logy=True)
        self._check_ax_scales(ax, yaxis='log')

    @slow
    def test_kde_kwargs(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        from numpy import linspace
        _check_plot_works(self.ts.plot, kind='kde', bw_method=.5, ind=linspace(-100,100,20))
        _check_plot_works(self.ts.plot, kind='density', bw_method=.5, ind=linspace(-100,100,20))
        ax = self.ts.plot(kind='kde', logy=True, bw_method=.5, ind=linspace(-100,100,20))
        self._check_ax_scales(ax, yaxis='log')

    @slow
    def test_kde_color(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        ax = self.ts.plot(kind='kde', logy=True, color='r')
        self._check_ax_scales(ax, yaxis='log')
        lines = ax.get_lines()
        self.assertEqual(len(lines), 1)
        self._check_colors(lines, ['r'])

    @slow
    def test_autocorrelation_plot(self):
        from pandas.tools.plotting import autocorrelation_plot
        _check_plot_works(autocorrelation_plot, self.ts)
        _check_plot_works(autocorrelation_plot, self.ts.values)

        ax = autocorrelation_plot(self.ts, label='Test')
        self._check_legend_labels(ax, labels=['Test'])

    @slow
    def test_lag_plot(self):
        from pandas.tools.plotting import lag_plot
        _check_plot_works(lag_plot, self.ts)
        _check_plot_works(lag_plot, self.ts, lag=5)

    @slow
    def test_bootstrap_plot(self):
        from pandas.tools.plotting import bootstrap_plot
        _check_plot_works(bootstrap_plot, self.ts, size=10)

    def test_invalid_plot_data(self):
        s = Series(list('abcd'))
        for kind in plotting._common_kinds:
            if not _ok_for_gaussian_kde(kind):
                continue
            with tm.assertRaises(TypeError):
                s.plot(kind=kind)

    @slow
    def test_valid_object_plot(self):
        s = Series(lrange(10), dtype=object)
        for kind in plotting._common_kinds:
            if not _ok_for_gaussian_kde(kind):
                continue
            _check_plot_works(s.plot, kind=kind)

    def test_partially_invalid_plot_data(self):
        s = Series(['a', 'b', 1.0, 2])
        for kind in plotting._common_kinds:
            if not _ok_for_gaussian_kde(kind):
                continue
            with tm.assertRaises(TypeError):
                s.plot(kind=kind)

    def test_invalid_kind(self):
        s = Series([1, 2])
        with tm.assertRaises(ValueError):
            s.plot(kind='aasdf')

    @slow
    def test_dup_datetime_index_plot(self):
        dr1 = date_range('1/1/2009', periods=4)
        dr2 = date_range('1/2/2009', periods=4)
        index = dr1.append(dr2)
        values = randn(index.size)
        s = Series(values, index=index)
        _check_plot_works(s.plot)

    @slow
    def test_errorbar_plot(self):

        s = Series(np.arange(10), name='x')
        s_err = np.random.randn(10)
        d_err =  DataFrame(randn(10, 2), index=s.index, columns=['x', 'y'])
        # test line and bar plots
        kinds = ['line', 'bar']
        for kind in kinds:
            ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=1)
            ax = _check_plot_works(s.plot, yerr=s_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=1)
            ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=1)
            ax = _check_plot_works(s.plot, yerr=d_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=1)
            ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind)
            self._check_has_errorbars(ax, xerr=1, yerr=1)

        ax = _check_plot_works(s.plot, xerr=s_err)
        self._check_has_errorbars(ax, xerr=1, yerr=0)

        # test time series plotting
        ix = date_range('1/1/2000', '1/1/2001', freq='M')
        ts = Series(np.arange(12), index=ix, name='x')
        ts_err = Series(np.random.randn(12), index=ix)
        td_err =  DataFrame(randn(12, 2), index=ix, columns=['x', 'y'])

        ax = _check_plot_works(ts.plot, yerr=ts_err)
        self._check_has_errorbars(ax, xerr=0, yerr=1)
        ax = _check_plot_works(ts.plot, yerr=td_err)
        self._check_has_errorbars(ax, xerr=0, yerr=1)

        # check incorrect lengths and types
        with tm.assertRaises(ValueError):
            s.plot(yerr=np.arange(11))

        s_err = ['zzz']*10
        with tm.assertRaises(TypeError):
            s.plot(yerr=s_err)

    def test_table(self):
        _check_plot_works(self.series.plot, table=True)
        _check_plot_works(self.series.plot, table=self.series)


@tm.mplskip
class TestDataFramePlots(TestPlotBase):
    def setUp(self):
        TestPlotBase.setUp(self)
        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_le_1_2_1 = str(mpl.__version__) <= LooseVersion('1.2.1')

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame({"A": np.random.uniform(size=20),
                               "B": np.random.uniform(size=20),
                               "C": np.arange(20) + np.random.uniform(size=20)})

        from pandas import read_csv
        path = os.path.join(curpath(), 'data', 'iris.csv')
        self.iris = read_csv(path)

    @slow
    def test_plot(self):
        df = self.tdf
        _check_plot_works(df.plot, grid=False)
        axes = _check_plot_works(df.plot, subplots=True)
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
        _check_plot_works(df.plot, subplots=True, use_index=False)
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))

        df = DataFrame({'x': [1, 2], 'y': [3, 4]})
        with tm.assertRaises(TypeError):
            df.plot(kind='line', blarg=True)

        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, use_index=True)
        _check_plot_works(df.plot, sort_columns=False)
        _check_plot_works(df.plot, yticks=[1, 5, 10])
        _check_plot_works(df.plot, xticks=[1, 5, 10])
        _check_plot_works(df.plot, ylim=(-100, 100), xlim=(-100, 100))

        axes = _check_plot_works(df.plot, subplots=True, title='blah')
        self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
        for ax in axes[:2]:
            self._check_visible(ax.get_xticklabels(), visible=False)
            self._check_visible([ax.xaxis.get_label()], visible=False)
        for ax in [axes[2]]:
            self._check_visible(ax.get_xticklabels())
            self._check_visible([ax.xaxis.get_label()])

        _check_plot_works(df.plot, title='blah')

        tuples = lzip(string.ascii_letters[:10], range(10))
        df = DataFrame(np.random.rand(10, 3),
                       index=MultiIndex.from_tuples(tuples))
        _check_plot_works(df.plot, use_index=True)

        # unicode
        index = MultiIndex.from_tuples([(u('\u03b1'), 0),
                                        (u('\u03b1'), 1),
                                        (u('\u03b2'), 2),
                                        (u('\u03b2'), 3),
                                        (u('\u03b3'), 4),
                                        (u('\u03b3'), 5),
                                        (u('\u03b4'), 6),
                                        (u('\u03b4'), 7)], names=['i0', 'i1'])
        columns = MultiIndex.from_tuples([('bar', u('\u0394')),
                                        ('bar', u('\u0395'))], names=['c0',
                                                                    'c1'])
        df = DataFrame(np.random.randint(0, 10, (8, 2)),
                       columns=columns,
                       index=index)
        _check_plot_works(df.plot, title=u('\u03A3'))

        # GH 6951
        # Test with single column
        df = DataFrame({'x': np.random.rand(10)})
        axes = _check_plot_works(df.plot, kind='bar', subplots=True)
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

        # When ax is supplied and required number of axes is 1,
        # passed ax should be used:
        fig, ax = self.plt.subplots()
        axes = df.plot(kind='bar', subplots=True, ax=ax)
        self.assertEqual(len(axes), 1)
        self.assertIs(ax.get_axes(), axes[0])

    def test_nonnumeric_exclude(self):
        df = DataFrame({'A': ["x", "y", "z"], 'B': [1, 2, 3]})
        ax = df.plot()
        self.assertEqual(len(ax.get_lines()), 1)  # B was plotted

    @slow
    def test_implicit_label(self):
        df = DataFrame(randn(10, 3), columns=['a', 'b', 'c'])
        ax = df.plot(x='a', y='b')
        self._check_text_labels(ax.xaxis.get_label(), 'a')

    @slow
    def test_explicit_label(self):
        df = DataFrame(randn(10, 3), columns=['a', 'b', 'c'])
        ax = df.plot(x='a', y='b', label='LABEL')
        self._check_text_labels(ax.xaxis.get_label(), 'LABEL')

    @slow
    def test_plot_xy(self):
        # columns.inferred_type == 'string'
        df = self.tdf
        self._check_data(df.plot(x=0, y=1),
                         df.set_index('A')['B'].plot())
        self._check_data(df.plot(x=0), df.set_index('A').plot())
        self._check_data(df.plot(y=0), df.B.plot())
        self._check_data(df.plot(x='A', y='B'),
                         df.set_index('A').B.plot())
        self._check_data(df.plot(x='A'), df.set_index('A').plot())
        self._check_data(df.plot(y='B'), df.B.plot())

        # columns.inferred_type == 'integer'
        df.columns = lrange(1, len(df.columns) + 1)
        self._check_data(df.plot(x=1, y=2),
                         df.set_index(1)[2].plot())
        self._check_data(df.plot(x=1), df.set_index(1).plot())
        self._check_data(df.plot(y=1), df[1].plot())

        # figsize and title
        ax = df.plot(x=1, y=2, title='Test', figsize=(16, 8))
        self._check_text_labels(ax.title, 'Test')
        self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16., 8.))

        # columns.inferred_type == 'mixed'
        # TODO add MultiIndex test

    @slow
    def test_logscales(self):
        df = DataFrame({'a': np.arange(100)},
                       index=np.arange(100))
        ax = df.plot(logy=True)
        self._check_ax_scales(ax, yaxis='log')

        ax = df.plot(logx=True)
        self._check_ax_scales(ax, xaxis='log')

        ax = df.plot(loglog=True)
        self._check_ax_scales(ax, xaxis='log', yaxis='log')

    @slow
    def test_xcompat(self):
        import pandas as pd

        df = self.tdf
        ax = df.plot(x_compat=True)
        lines = ax.get_lines()
        self.assertNotIsInstance(lines[0].get_xdata(), PeriodIndex)

        tm.close()
        pd.plot_params['xaxis.compat'] = True
        ax = df.plot()
        lines = ax.get_lines()
        self.assertNotIsInstance(lines[0].get_xdata(), PeriodIndex)

        tm.close()
        pd.plot_params['x_compat'] = False
        ax = df.plot()
        lines = ax.get_lines()
        tm.assert_isinstance(lines[0].get_xdata(), PeriodIndex)

        tm.close()
        # useful if you're plotting a bunch together
        with pd.plot_params.use('x_compat', True):
            ax = df.plot()
            lines = ax.get_lines()
            self.assertNotIsInstance(lines[0].get_xdata(), PeriodIndex)

        tm.close()
        ax = df.plot()
        lines = ax.get_lines()
        tm.assert_isinstance(lines[0].get_xdata(), PeriodIndex)

    def test_unsorted_index(self):
        df = DataFrame({'y': np.arange(100)},
                       index=np.arange(99, -1, -1), dtype=np.int64)
        ax = df.plot()
        l = ax.get_lines()[0]
        rs = l.get_xydata()
        rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64)
        tm.assert_series_equal(rs, df.y)

    @slow
    def test_subplots(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))

        for kind in ['bar', 'barh', 'line', 'area']:
            axes = df.plot(kind=kind, subplots=True, sharex=True, legend=True)
            self._check_axes_shape(axes, axes_num=3, layout=(3, 1))

            for ax, column in zip(axes, df.columns):
                self._check_legend_labels(ax, labels=[com.pprint_thing(column)])

            for ax in axes[:-2]:
                self._check_visible(ax.get_xticklabels(), visible=False)
                self._check_visible(ax.get_yticklabels())

            self._check_visible(axes[-1].get_xticklabels())
            self._check_visible(axes[-1].get_yticklabels())

            axes = df.plot(kind=kind, subplots=True, sharex=False)
            for ax in axes:
                self._check_visible(ax.get_xticklabels())
                self._check_visible(ax.get_yticklabels())

            axes = df.plot(kind=kind, subplots=True, legend=False)
            for ax in axes:
                self.assertTrue(ax.get_legend() is None)

    def test_negative_log(self):
        df = - DataFrame(rand(6, 4),
                       index=list(string.ascii_letters[:6]),
                       columns=['x', 'y', 'z', 'four'])

        with tm.assertRaises(ValueError):
            df.plot(kind='area', logy=True)
        with tm.assertRaises(ValueError):
            df.plot(kind='area', loglog=True)

    def _compare_stacked_y_cood(self, normal_lines, stacked_lines):
        base = np.zeros(len(normal_lines[0].get_data()[1]))
        for nl, sl in zip(normal_lines, stacked_lines):
            base += nl.get_data()[1] # get y coodinates
            sy = sl.get_data()[1]
            self.assert_numpy_array_equal(base, sy)

    def test_line_area_stacked(self):
        with tm.RNGContext(42):
            df = DataFrame(rand(6, 4),
                           columns=['w', 'x', 'y', 'z'])
            neg_df = - df
            # each column has either positive or negative value
            sep_df = DataFrame({'w': rand(6), 'x': rand(6),
                                'y': - rand(6), 'z': - rand(6)})
            # each column has positive-negative mixed value
            mixed_df = DataFrame(randn(6, 4), index=list(string.ascii_letters[:6]),
                                 columns=['w', 'x', 'y', 'z'])

            for kind in ['line', 'area']:
                ax1 = _check_plot_works(df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(neg_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(neg_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(sep_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(sep_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines[:2], ax2.lines[:2])
                self._compare_stacked_y_cood(ax1.lines[2:], ax2.lines[2:])

                _check_plot_works(mixed_df.plot, stacked=False)
                with tm.assertRaises(ValueError):
                    mixed_df.plot(stacked=True)

                _check_plot_works(df.plot, kind=kind, logx=True, stacked=True)

    def test_line_area_nan_df(self):
        values1 = [1, 2, np.nan, 3]
        values2 = [3, np.nan, 2, 1]
        df = DataFrame({'a': values1, 'b': values2})
        tdf = DataFrame({'a': values1, 'b': values2}, index=tm.makeDateIndex(k=4))

        for d in [df, tdf]:
            ax = _check_plot_works(d.plot)
            masked1 = ax.lines[0].get_ydata()
            masked2 = ax.lines[1].get_ydata()
            # remove nan for comparison purpose
            self.assert_numpy_array_equal(np.delete(masked1.data, 2), np.array([1, 2, 3]))
            self.assert_numpy_array_equal(np.delete(masked2.data, 1), np.array([3, 2, 1]))
            self.assert_numpy_array_equal(masked1.mask, np.array([False, False, True, False]))
            self.assert_numpy_array_equal(masked2.mask, np.array([False, True, False, False]))

            expected1 = np.array([1, 2, 0, 3])
            expected2 = np.array([3, 0, 2, 1])

            ax = _check_plot_works(d.plot, stacked=True)
            self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
            self.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected1 + expected2)

            ax = _check_plot_works(d.plot, kind='area')
            self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
            self.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected1 + expected2)

            ax = _check_plot_works(d.plot, kind='area', stacked=False)
            self.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
            self.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected2)

    def test_line_lim(self):
        df = DataFrame(rand(6, 3), columns=['x', 'y', 'z'])
        ax = df.plot()
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        self.assertEqual(xmin, lines[0].get_data()[0][0])
        self.assertEqual(xmax, lines[0].get_data()[0][-1])

        ax = df.plot(secondary_y=True)
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        self.assertEqual(xmin, lines[0].get_data()[0][0])
        self.assertEqual(xmax, lines[0].get_data()[0][-1])

        axes = df.plot(secondary_y=True, subplots=True)
        for ax in axes:
            xmin, xmax = ax.get_xlim()
            lines = ax.get_lines()
            self.assertEqual(xmin, lines[0].get_data()[0][0])
            self.assertEqual(xmax, lines[0].get_data()[0][-1])

    def test_area_lim(self):
        df = DataFrame(rand(6, 4),
                       columns=['x', 'y', 'z', 'four'])

        neg_df = - df
        for stacked in [True, False]:
            ax = _check_plot_works(df.plot, kind='area', stacked=stacked)
            xmin, xmax = ax.get_xlim()
            ymin, ymax = ax.get_ylim()
            lines = ax.get_lines()
            self.assertEqual(xmin, lines[0].get_data()[0][0])
            self.assertEqual(xmax, lines[0].get_data()[0][-1])
            self.assertEqual(ymin, 0)

            ax = _check_plot_works(neg_df.plot, kind='area', stacked=stacked)
            ymin, ymax = ax.get_ylim()
            self.assertEqual(ymax, 0)

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

        default_colors = plt.rcParams.get('axes.color_cycle')


        df = DataFrame(randn(5, 5))
        ax = df.plot(kind='bar')
        self._check_colors(ax.patches[::5], facecolors=default_colors[:5])
        tm.close()

        custom_colors = 'rgcby'
        ax = df.plot(kind='bar', color=custom_colors)
        self._check_colors(ax.patches[::5], facecolors=custom_colors)
        tm.close()

        from matplotlib import cm
        # Test str -> colormap functionality
        ax = df.plot(kind='bar', colormap='jet')
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, 5))
        self._check_colors(ax.patches[::5], facecolors=rgba_colors)
        tm.close()

        # Test colormap functionality
        ax = df.plot(kind='bar', colormap=cm.jet)
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, 5))
        self._check_colors(ax.patches[::5], facecolors=rgba_colors)
        tm.close()

        ax = df.ix[:, [0]].plot(kind='bar', color='DodgerBlue')
        self._check_colors([ax.patches[0]], facecolors=['DodgerBlue'])

    @slow
    def test_bar_linewidth(self):
        df = DataFrame(randn(5, 5))

        # regular
        ax = df.plot(kind='bar', linewidth=2)
        for r in ax.patches:
            self.assertEqual(r.get_linewidth(), 2)

        # stacked
        ax = df.plot(kind='bar', stacked=True, linewidth=2)
        for r in ax.patches:
            self.assertEqual(r.get_linewidth(), 2)

        # subplots
        axes = df.plot(kind='bar', linewidth=2, subplots=True)
        self._check_axes_shape(axes, axes_num=5, layout=(5, 1))
        for ax in axes:
            for r in ax.patches:
                self.assertEqual(r.get_linewidth(), 2)

    @slow
    def test_bar_barwidth(self):
        df = DataFrame(randn(5, 5))

        width = 0.9

        # regular
        ax = df.plot(kind='bar', width=width)
        for r in ax.patches:
            self.assertEqual(r.get_width(), width / len(df.columns))

        # stacked
        ax = df.plot(kind='bar', stacked=True, width=width)
        for r in ax.patches:
            self.assertEqual(r.get_width(), width)

        # horizontal regular
        ax = df.plot(kind='barh', width=width)
        for r in ax.patches:
            self.assertEqual(r.get_height(), width / len(df.columns))

        # horizontal stacked
        ax = df.plot(kind='barh', stacked=True, width=width)
        for r in ax.patches:
            self.assertEqual(r.get_height(), width)

        # subplots
        axes = df.plot(kind='bar', width=width, subplots=True)
        for ax in axes:
            for r in ax.patches:
                self.assertEqual(r.get_width(), width)

        # horizontal subplots
        axes = df.plot(kind='barh', width=width, subplots=True)
        for ax in axes:
            for r in ax.patches:
                self.assertEqual(r.get_height(), width)

    @slow
    def test_bar_barwidth_position(self):
        df = DataFrame(randn(5, 5))
        self._check_bar_alignment(df, kind='bar', stacked=False, width=0.9, position=0.2)
        self._check_bar_alignment(df, kind='bar', stacked=True, width=0.9, position=0.2)
        self._check_bar_alignment(df, kind='barh', stacked=False, width=0.9, position=0.2)
        self._check_bar_alignment(df, kind='barh', stacked=True, width=0.9, position=0.2)
        self._check_bar_alignment(df, kind='bar', subplots=True, width=0.9, position=0.2)
        self._check_bar_alignment(df, kind='barh', subplots=True, width=0.9, position=0.2)

    @slow
    def test_bar_bottom_left(self):
        df = DataFrame(rand(5, 5))
        ax = df.plot(kind='bar', stacked=False, bottom=1)
        result = [p.get_y() for p in ax.patches]
        self.assertEqual(result, [1] * 25)

        ax = df.plot(kind='bar', stacked=True, bottom=[-1, -2, -3, -4, -5])
        result = [p.get_y() for p in ax.patches[:5]]
        self.assertEqual(result, [-1, -2, -3, -4, -5])

        ax = df.plot(kind='barh', stacked=False, left=np.array([1, 1, 1, 1, 1]))
        result = [p.get_x() for p in ax.patches]
        self.assertEqual(result, [1] * 25)

        ax = df.plot(kind='barh', stacked=True, left=[1, 2, 3, 4, 5])
        result = [p.get_x() for p in ax.patches[:5]]
        self.assertEqual(result, [1, 2, 3, 4, 5])

        axes = df.plot(kind='bar', subplots=True, bottom=-1)
        for ax in axes:
            result = [p.get_y() for p in ax.patches]
            self.assertEqual(result, [-1] * 5)

        axes = df.plot(kind='barh', subplots=True, left=np.array([1, 1, 1, 1, 1]))
        for ax in axes:
            result = [p.get_x() for p in ax.patches]
            self.assertEqual(result, [1] * 5)

    @slow
    def test_plot_scatter(self):
        df = DataFrame(randn(6, 4),
                       index=list(string.ascii_letters[:6]),
                       columns=['x', 'y', 'z', 'four'])

        _check_plot_works(df.plot, x='x', y='y', kind='scatter')
        _check_plot_works(df.plot, x=1, y=2, kind='scatter')

        with tm.assertRaises(ValueError):
            df.plot(x='x', kind='scatter')
        with tm.assertRaises(ValueError):
            df.plot(y='y', kind='scatter')

        # GH 6951
        axes = df.plot(x='x', y='y', kind='scatter', subplots=True)
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

    @slow
    def test_plot_bar(self):
        df = DataFrame(randn(6, 4),
                       index=list(string.ascii_letters[:6]),
                       columns=['one', 'two', 'three', 'four'])

        _check_plot_works(df.plot, kind='bar')
        _check_plot_works(df.plot, kind='bar', legend=False)
        _check_plot_works(df.plot, kind='bar', subplots=True)
        _check_plot_works(df.plot, kind='bar', stacked=True)

        df = DataFrame(randn(10, 15),
                       index=list(string.ascii_letters[:10]),
                       columns=lrange(15))
        _check_plot_works(df.plot, kind='bar')

        df = DataFrame({'a': [0, 1], 'b': [1, 0]})
        _check_plot_works(df.plot, kind='bar')

    def _check_bar_alignment(self, df, kind='bar', stacked=False,
                             subplots=False, align='center',
                             width=0.5, position=0.5):

        axes = df.plot(kind=kind, stacked=stacked, subplots=subplots,
                       align=align, width=width, position=position,
                       grid=True)

        axes = self._flatten_visible(axes)

        for ax in axes:
            if kind == 'bar':
                axis = ax.xaxis
                ax_min, ax_max = ax.get_xlim()
                min_edge = min([p.get_x() for p in ax.patches])
                max_edge = max([p.get_x() + p.get_width() for p in ax.patches])
            elif kind == 'barh':
                axis = ax.yaxis
                ax_min, ax_max = ax.get_ylim()
                min_edge = min([p.get_y() for p in ax.patches])
                max_edge = max([p.get_y() + p.get_height() for p in ax.patches])
            else:
                raise ValueError

            # GH 7498
            # compare margins between lim and bar edges
            self.assertAlmostEqual(ax_min, min_edge - 0.25)
            self.assertAlmostEqual(ax_max, max_edge + 0.25)

            p = ax.patches[0]
            if kind == 'bar' and (stacked is True or subplots is True):
                edge = p.get_x()
                center = edge + p.get_width() * position
            elif kind == 'bar' and stacked is False:
                center = p.get_x() + p.get_width() * len(df.columns) * position
                edge = p.get_x()
            elif kind == 'barh' and (stacked is True or subplots is True):
                center = p.get_y() + p.get_height() * position
                edge = p.get_y()
            elif kind == 'barh' and stacked is False:
                center = p.get_y() + p.get_height() * len(df.columns) * position
                edge = p.get_y()
            else:
                raise ValueError

            # Check the ticks locates on integer
            self.assertTrue((axis.get_ticklocs() == np.arange(len(df))).all())

            if align == 'center':
                # Check whether the bar locates on center
                self.assertAlmostEqual(axis.get_ticklocs()[0], center)
            elif align == 'edge':
                # Check whether the bar's edge starts from the tick
                self.assertAlmostEqual(axis.get_ticklocs()[0], edge)
            else:
                raise ValueError

        return axes

    @slow
    def test_bar_stacked_center(self):
        # GH2157
        df = DataFrame({'A': [3] * 5, 'B': lrange(5)}, index=lrange(5))
        self._check_bar_alignment(df, kind='bar', stacked=True)
        self._check_bar_alignment(df, kind='bar', stacked=True, width=0.9)
        self._check_bar_alignment(df, kind='barh', stacked=True)
        self._check_bar_alignment(df, kind='barh', stacked=True, width=0.9)

    @slow
    def test_bar_center(self):
        df = DataFrame({'A': [3] * 5, 'B': lrange(5)}, index=lrange(5))
        self._check_bar_alignment(df, kind='bar', stacked=False)
        self._check_bar_alignment(df, kind='bar', stacked=False, width=0.9)
        self._check_bar_alignment(df, kind='barh', stacked=False)
        self._check_bar_alignment(df, kind='barh', stacked=False, width=0.9)

    @slow
    def test_bar_subplots_center(self):
        df = DataFrame({'A': [3] * 5, 'B': lrange(5)}, index=lrange(5))
        self._check_bar_alignment(df, kind='bar', subplots=True)
        self._check_bar_alignment(df, kind='bar', subplots=True, width=0.9)
        self._check_bar_alignment(df, kind='barh', subplots=True)
        self._check_bar_alignment(df, kind='barh', subplots=True, width=0.9)

    @slow
    def test_bar_align_single_column(self):
        df = DataFrame(randn(5))
        self._check_bar_alignment(df, kind='bar', stacked=False)
        self._check_bar_alignment(df, kind='bar', stacked=True)
        self._check_bar_alignment(df, kind='barh', stacked=False)
        self._check_bar_alignment(df, kind='barh', stacked=True)
        self._check_bar_alignment(df, kind='bar', subplots=True)
        self._check_bar_alignment(df, kind='barh', subplots=True)

    @slow
    def test_bar_edge(self):
        df = DataFrame({'A': [3] * 5, 'B': lrange(5)}, index=lrange(5))

        self._check_bar_alignment(df, kind='bar', stacked=True, align='edge')
        self._check_bar_alignment(df, kind='bar', stacked=True,
                                  width=0.9, align='edge')
        self._check_bar_alignment(df, kind='barh', stacked=True, align='edge')
        self._check_bar_alignment(df, kind='barh', stacked=True,
                                  width=0.9, align='edge')

        self._check_bar_alignment(df, kind='bar', stacked=False, align='edge')
        self._check_bar_alignment(df, kind='bar', stacked=False,
                                  width=0.9, align='edge')
        self._check_bar_alignment(df, kind='barh', stacked=False, align='edge')
        self._check_bar_alignment(df, kind='barh', stacked=False,
                                  width=0.9, align='edge')

        self._check_bar_alignment(df, kind='bar', subplots=True, align='edge')
        self._check_bar_alignment(df, kind='bar', subplots=True,
                                  width=0.9, align='edge')
        self._check_bar_alignment(df, kind='barh', subplots=True, align='edge')
        self._check_bar_alignment(df, kind='barh', subplots=True,
                                  width=0.9, align='edge')

    @slow
    def test_bar_log_no_subplots(self):
        # GH3254, GH3298 matplotlib/matplotlib#1882, #1892
        # regressions in 1.2.1
        expected = np.array([1., 10.])

        if not self.mpl_le_1_2_1:
            expected = np.hstack((.1, expected, 100))

        # no subplots
        df = DataFrame({'A': [3] * 5, 'B': lrange(1, 6)}, index=lrange(5))
        ax = df.plot(kind='bar', grid=True, log=True)
        assert_array_equal(ax.yaxis.get_ticklocs(), expected)

    @slow
    def test_bar_log_subplots(self):
        expected = np.array([1., 10., 100., 1000.])
        if not self.mpl_le_1_2_1:
            expected = np.hstack((.1, expected, 1e4))

        ax = DataFrame([Series([200, 300]),
                        Series([300, 500])]).plot(log=True, kind='bar',
                                                  subplots=True)

        assert_array_equal(ax[0].yaxis.get_ticklocs(), expected)
        assert_array_equal(ax[1].yaxis.get_ticklocs(), expected)

    @slow
    def test_boxplot(self):
        df = DataFrame(randn(6, 4),
                       index=list(string.ascii_letters[:6]),
                       columns=['one', 'two', 'three', 'four'])
        df['indic'] = ['foo', 'bar'] * 3
        df['indic2'] = ['foo', 'bar', 'foo'] * 2

        _check_plot_works(df.boxplot, return_type='dict')
        _check_plot_works(df.boxplot, column=['one', 'two'], return_type='dict')
        _check_plot_works(df.boxplot, column=['one', 'two'], by='indic')
        _check_plot_works(df.boxplot, column='one', by=['indic', 'indic2'])
        _check_plot_works(df.boxplot, by='indic')
        _check_plot_works(df.boxplot, by=['indic', 'indic2'])
        _check_plot_works(plotting.boxplot, df['one'], return_type='dict')
        _check_plot_works(df.boxplot, notch=1, return_type='dict')
        _check_plot_works(df.boxplot, by='indic', notch=1)

        df = DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])
        df['X'] = Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])
        df['Y'] = Series(['A'] * 10)
        _check_plot_works(df.boxplot, by='X')

        # When ax is supplied and required number of axes is 1,
        # passed ax should be used:
        fig, ax = self.plt.subplots()
        axes = df.boxplot('Col1', by='X', ax=ax)
        self.assertIs(ax.get_axes(), axes)

        fig, ax = self.plt.subplots()
        axes = df.groupby('Y').boxplot(ax=ax, return_type='axes')
        self.assertIs(ax.get_axes(), axes['A'])

        # Multiple columns with an ax argument should use same figure
        fig, ax = self.plt.subplots()
        axes = df.boxplot(column=['Col1', 'Col2'], by='X', ax=ax, return_type='axes')
        self.assertIs(axes['Col1'].get_figure(), fig)

        # When by is None, check that all relevant lines are present in the dict
        fig, ax = self.plt.subplots()
        d = df.boxplot(ax=ax, return_type='dict')
        lines = list(itertools.chain.from_iterable(d.values()))
        self.assertEqual(len(ax.get_lines()), len(lines))

    @slow
    def test_boxplot_return_type(self):
        # API change in https://github.com/pydata/pandas/pull/7096
        import matplotlib as mpl

        df = DataFrame(randn(6, 4),
                       index=list(string.ascii_letters[:6]),
                       columns=['one', 'two', 'three', 'four'])
        with tm.assertRaises(ValueError):
            df.boxplot(return_type='NOTATYPE')

        with tm.assert_produces_warning(FutureWarning):
            result = df.boxplot()
        # change to Axes in future
        self._check_box_return_type(result, 'dict')

        with tm.assert_produces_warning(False):
            result = df.boxplot(return_type='dict')
        self._check_box_return_type(result, 'dict')

        with tm.assert_produces_warning(False):
            result = df.boxplot(return_type='axes')
        self._check_box_return_type(result, 'axes')

        with tm.assert_produces_warning(False):
            result = df.boxplot(return_type='both')
        self._check_box_return_type(result, 'both')

    @slow
    def test_kde(self):
        tm._skip_if_no_scipy()
        _skip_if_no_scipy_gaussian_kde()
        df = DataFrame(randn(100, 4))
        ax = _check_plot_works(df.plot, kind='kde')
        expected = [com.pprint_thing(c) for c in df.columns]
        self._check_legend_labels(ax, labels=expected)

        axes = _check_plot_works(df.plot, kind='kde', subplots=True)
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))

        axes = df.plot(kind='kde', logy=True, subplots=True)
        self._check_ax_scales(axes, yaxis='log')

    @slow
    def test_hist(self):
        _check_plot_works(self.hist_df.hist)

        # make sure layout is handled
        df = DataFrame(randn(100, 3))
        axes = _check_plot_works(df.hist, grid=False)
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
        self.assertFalse(axes[1, 1].get_visible())

        df = DataFrame(randn(100, 1))
        _check_plot_works(df.hist)

        # make sure layout is handled
        df = DataFrame(randn(100, 6))
        axes = _check_plot_works(df.hist, layout=(4, 2))
        self._check_axes_shape(axes, axes_num=6, layout=(4, 2))

        # make sure sharex, sharey is handled
        _check_plot_works(df.hist, sharex=True, sharey=True)

        # handle figsize arg
        _check_plot_works(df.hist, figsize=(8, 10))

        # check bins argument
        _check_plot_works(df.hist, bins=5)

        # make sure xlabelsize and xrot are handled
        ser = df[0]
        xf, yf = 20, 18
        xrot, yrot = 30, 40
        axes = ser.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot)
        self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot,
                                ylabelsize=yf, yrot=yrot)

        xf, yf = 20, 18
        xrot, yrot = 30, 40
        axes = df.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot)
        self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot,
                                ylabelsize=yf, yrot=yrot)

        tm.close()
        # make sure kwargs to hist are handled
        ax = ser.hist(normed=True, cumulative=True, bins=4)
        # height of last bin (index 5) must be 1.0
        self.assertAlmostEqual(ax.get_children()[5].get_height(), 1.0)

        tm.close()
        ax = ser.hist(log=True)
        # scale of y must be 'log'
        self._check_ax_scales(ax, yaxis='log')

        tm.close()

        # propagate attr exception from matplotlib.Axes.hist
        with tm.assertRaises(AttributeError):
            ser.hist(foo='bar')

    @slow
    def test_hist_layout(self):
        df = DataFrame(randn(100, 3))

        layout_to_expected_size = (
            {'layout': None, 'expected_size': (2, 2)},  # default is 2x2
            {'layout': (2, 2), 'expected_size': (2, 2)},
            {'layout': (4, 1), 'expected_size': (4, 1)},
            {'layout': (1, 4), 'expected_size': (1, 4)},
            {'layout': (3, 3), 'expected_size': (3, 3)},
        )

        for layout_test in layout_to_expected_size:
            axes = df.hist(layout=layout_test['layout'])
            expected = layout_test['expected_size']
            self._check_axes_shape(axes, axes_num=3, layout=expected)

        # layout too small for all 4 plots
        with tm.assertRaises(ValueError):
            df.hist(layout=(1, 1))

        # invalid format for layout
        with tm.assertRaises(ValueError):
            df.hist(layout=(1,))

    @slow
    def test_scatter(self):
        tm._skip_if_no_scipy()

        df = DataFrame(randn(100, 2))
        import pandas.tools.plotting as plt

        def scat(**kwds):
            return plt.scatter_matrix(df, **kwds)

        _check_plot_works(scat)
        _check_plot_works(scat, marker='+')
        _check_plot_works(scat, vmin=0)
        if _ok_for_gaussian_kde('kde'):
            _check_plot_works(scat, diagonal='kde')
        if _ok_for_gaussian_kde('density'):
            _check_plot_works(scat, diagonal='density')
        _check_plot_works(scat, diagonal='hist')
        _check_plot_works(scat, range_padding=.1)

        def scat2(x, y, by=None, ax=None, figsize=None):
            return plt.scatter_plot(df, x, y, by, ax, figsize=None)

        _check_plot_works(scat2, 0, 1)
        grouper = Series(np.repeat([1, 2, 3, 4, 5], 20), df.index)
        _check_plot_works(scat2, 0, 1, by=grouper)

    @slow
    def test_andrews_curves(self):
        from pandas.tools.plotting import andrews_curves
        from matplotlib import cm

        df = self.iris

        _check_plot_works(andrews_curves, df, 'Name')

        rgba = ('#556270', '#4ECDC4', '#C7F464')
        ax = _check_plot_works(andrews_curves, df, 'Name', color=rgba)
        self._check_colors(ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])

        cnames = ['dodgerblue', 'aquamarine', 'seagreen']
        ax = _check_plot_works(andrews_curves, df, 'Name', color=cnames)
        self._check_colors(ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])

        ax = _check_plot_works(andrews_curves, df, 'Name', colormap=cm.jet)
        cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
        self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])

        colors = ['b', 'g', 'r']
        df = DataFrame({"A": [1, 2, 3],
                        "B": [1, 2, 3],
                        "C": [1, 2, 3],
                        "Name": colors})
        ax = andrews_curves(df, 'Name', color=colors)
        handles, labels = ax.get_legend_handles_labels()
        self._check_colors(handles, linecolors=colors)

        with tm.assert_produces_warning(FutureWarning):
            andrews_curves(data=df, class_column='Name')

    @slow
    def test_parallel_coordinates(self):
        from pandas.tools.plotting import parallel_coordinates
        from matplotlib import cm

        df = self.iris

        _check_plot_works(parallel_coordinates, df, 'Name')

        rgba = ('#556270', '#4ECDC4', '#C7F464')
        ax = _check_plot_works(parallel_coordinates, df, 'Name', color=rgba)
        self._check_colors(ax.get_lines()[:10], linecolors=rgba, mapping=df['Name'][:10])

        cnames = ['dodgerblue', 'aquamarine', 'seagreen']
        ax = _check_plot_works(parallel_coordinates, df, 'Name', color=cnames)
        self._check_colors(ax.get_lines()[:10], linecolors=cnames, mapping=df['Name'][:10])

        ax = _check_plot_works(parallel_coordinates, df, 'Name', colormap=cm.jet)
        cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
        self._check_colors(ax.get_lines()[:10], linecolors=cmaps, mapping=df['Name'][:10])

        colors = ['b', 'g', 'r']
        df = DataFrame({"A": [1, 2, 3],
                        "B": [1, 2, 3],
                        "C": [1, 2, 3],
                        "Name": colors})
        ax = parallel_coordinates(df, 'Name', color=colors)
        handles, labels = ax.get_legend_handles_labels()
        self._check_colors(handles, linecolors=colors)

        with tm.assert_produces_warning(FutureWarning):
            parallel_coordinates(data=df, class_column='Name')
        with tm.assert_produces_warning(FutureWarning):
            parallel_coordinates(df, 'Name', colors=colors)

    @slow
    def test_radviz(self):
        from pandas.tools.plotting import radviz
        from matplotlib import cm

        df = self.iris
        _check_plot_works(radviz, df, 'Name')

        rgba = ('#556270', '#4ECDC4', '#C7F464')
        ax = _check_plot_works(radviz, df, 'Name', color=rgba)
        # skip Circle drawn as ticks
        patches = [p for p in ax.patches[:20] if p.get_label() != '']
        self._check_colors(patches[:10], facecolors=rgba, mapping=df['Name'][:10])

        cnames = ['dodgerblue', 'aquamarine', 'seagreen']
        _check_plot_works(radviz, df, 'Name', color=cnames)
        patches = [p for p in ax.patches[:20] if p.get_label() != '']
        self._check_colors(patches, facecolors=cnames, mapping=df['Name'][:10])

        _check_plot_works(radviz, df, 'Name', colormap=cm.jet)
        cmaps = lmap(cm.jet, np.linspace(0, 1, df['Name'].nunique()))
        patches = [p for p in ax.patches[:20] if p.get_label() != '']
        self._check_colors(patches, facecolors=cmaps, mapping=df['Name'][:10])

        colors = [[0., 0., 1., 1.],
                  [0., 0.5, 1., 1.],
                  [1., 0., 0., 1.]]
        df = DataFrame({"A": [1, 2, 3],
                        "B": [2, 1, 3],
                        "C": [3, 2, 1],
                        "Name": ['b', 'g', 'r']})
        ax = radviz(df, 'Name', color=colors)
        handles, labels = ax.get_legend_handles_labels()
        self._check_colors(handles, facecolors=colors)

    @slow
    def test_plot_int_columns(self):
        df = DataFrame(randn(100, 4)).cumsum()
        _check_plot_works(df.plot, legend=True)

    @slow
    def test_df_legend_labels(self):
        kinds = 'line', 'bar', 'barh', 'kde', 'area'
        df = DataFrame(rand(3, 3), columns=['a', 'b', 'c'])
        df2 = DataFrame(rand(3, 3), columns=['d', 'e', 'f'])
        df3 = DataFrame(rand(3, 3), columns=['g', 'h', 'i'])
        df4 = DataFrame(rand(3, 3), columns=['j', 'k', 'l'])

        for kind in kinds:
            if not _ok_for_gaussian_kde(kind):
                continue

            ax = df.plot(kind=kind, legend=True)
            self._check_legend_labels(ax, labels=df.columns)

            ax = df2.plot(kind=kind, legend=False, ax=ax)
            self._check_legend_labels(ax, labels=df.columns)

            ax = df3.plot(kind=kind, legend=True, ax=ax)
            self._check_legend_labels(ax, labels=df.columns + df3.columns)

            ax = df4.plot(kind=kind, legend='reverse', ax=ax)
            expected = list(df.columns + df3.columns) + list(reversed(df4.columns))
            self._check_legend_labels(ax, labels=expected)

        # Secondary Y
        ax = df.plot(legend=True, secondary_y='b')
        self._check_legend_labels(ax, labels=['a', 'b (right)', 'c'])
        ax = df2.plot(legend=False, ax=ax)
        self._check_legend_labels(ax, labels=['a', 'b (right)', 'c'])
        ax = df3.plot(kind='bar', legend=True, secondary_y='h', ax=ax)
        self._check_legend_labels(ax, labels=['a', 'b (right)', 'c', 'g', 'h (right)', 'i'])

        # Time Series
        ind = date_range('1/1/2014', periods=3)
        df = DataFrame(randn(3, 3), columns=['a', 'b', 'c'], index=ind)
        df2 = DataFrame(randn(3, 3), columns=['d', 'e', 'f'], index=ind)
        df3 = DataFrame(randn(3, 3), columns=['g', 'h', 'i'], index=ind)
        ax = df.plot(legend=True, secondary_y='b')
        self._check_legend_labels(ax, labels=['a', 'b (right)', 'c'])
        ax = df2.plot(legend=False, ax=ax)
        self._check_legend_labels(ax, labels=['a', 'b (right)', 'c'])
        ax = df3.plot(legend=True, ax=ax)
        self._check_legend_labels(ax, labels=['a', 'b (right)', 'c', 'g', 'h', 'i'])

        # scatter
        ax = df.plot(kind='scatter', x='a', y='b', label='data1')
        self._check_legend_labels(ax, labels=['data1'])
        ax = df2.plot(kind='scatter', x='d', y='e', legend=False,
                        label='data2', ax=ax)
        self._check_legend_labels(ax, labels=['data1'])
        ax = df3.plot(kind='scatter', x='g', y='h', label='data3', ax=ax)
        self._check_legend_labels(ax, labels=['data1', 'data3'])

    def test_legend_name(self):
        multi = DataFrame(randn(4, 4),
                          columns=[np.array(['a', 'a', 'b', 'b']),
                                   np.array(['x', 'y', 'x', 'y'])])
        multi.columns.names = ['group', 'individual']

        ax = multi.plot()
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, 'group,individual')

        df = DataFrame(randn(5, 5))
        ax = df.plot(legend=True, ax=ax)
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, 'group,individual')

        df.columns.name = 'new'
        ax = df.plot(legend=False, ax=ax)
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, 'group,individual')

        ax = df.plot(legend=True, ax=ax)
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, 'new')

    @slow
    def test_no_legend(self):
        kinds = 'line', 'bar', 'barh', 'kde', 'area'
        df = DataFrame(rand(3, 3), columns=['a', 'b', 'c'])

        for kind in kinds:
            if not _ok_for_gaussian_kde(kind):
                continue

            ax = df.plot(kind=kind, legend=False)
            self._check_legend_labels(ax, visible=False)

    @slow
    def test_style_by_column(self):
        import matplotlib.pyplot as plt
        fig = plt.gcf()

        df = DataFrame(randn(100, 3))
        for markers in [{0: '^', 1: '+', 2: 'o'},
                        {0: '^', 1: '+'},
                        ['^', '+', 'o'],
                        ['^', '+']]:
            fig.clf()
            fig.add_subplot(111)
            ax = df.plot(style=markers)
            for i, l in enumerate(ax.get_lines()[:len(markers)]):
                self.assertEqual(l.get_marker(), markers[i])

    @slow
    def test_line_colors(self):
        import sys
        from matplotlib import cm

        custom_colors = 'rgcby'
        df = DataFrame(randn(5, 5))

        ax = df.plot(color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)

        tmp = sys.stderr
        sys.stderr = StringIO()
        try:
            tm.close()
            ax2 = df.plot(colors=custom_colors)
            lines2 = ax2.get_lines()
            for l1, l2 in zip(ax.get_lines(), lines2):
                self.assertEqual(l1.get_color(), l2.get_color())
        finally:
            sys.stderr = tmp

        tm.close()

        ax = df.plot(colormap='jet')
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        tm.close()

        ax = df.plot(colormap=cm.jet)
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        tm.close()

        # make color a list if plotting one column frame
        # handles cases like df.plot(color='DodgerBlue')
        ax = df.ix[:, [0]].plot(color='DodgerBlue')
        self._check_colors(ax.lines, linecolors=['DodgerBlue'])

    @slow
    def test_area_colors(self):
        from matplotlib import cm
        from matplotlib.collections import PolyCollection

        custom_colors = 'rgcby'
        df = DataFrame(rand(5, 5))

        ax = df.plot(kind='area', color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)
        poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
        self._check_colors(poly, facecolors=custom_colors)
        tm.close()

        ax = df.plot(kind='area', colormap='jet')
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
        self._check_colors(poly, facecolors=rgba_colors)
        tm.close()

        ax = df.plot(kind='area', colormap=cm.jet)
        rgba_colors = lmap(cm.jet, np.linspace(0, 1, len(df)))
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
        self._check_colors(poly, facecolors=rgba_colors)

    def test_default_color_cycle(self):
        import matplotlib.pyplot as plt
        plt.rcParams['axes.color_cycle'] = list('rgbk')

        df = DataFrame(randn(5, 3))
        ax = df.plot()

        expected = plt.rcParams['axes.color_cycle'][:3]
        self._check_colors(ax.get_lines(), linecolors=expected)

    def test_unordered_ts(self):
        df = DataFrame(np.array([3.0, 2.0, 1.0]),
                       index=[date(2012, 10, 1),
                              date(2012, 9, 1),
                              date(2012, 8, 1)],
                       columns=['test'])
        ax = df.plot()
        xticks = ax.lines[0].get_xdata()
        self.assertTrue(xticks[0] < xticks[1])
        ydata = ax.lines[0].get_ydata()
        assert_array_equal(ydata, np.array([1.0, 2.0, 3.0]))

    def test_all_invalid_plot_data(self):
        df = DataFrame(list('abcd'))
        for kind in plotting._common_kinds:
            if not _ok_for_gaussian_kde(kind):
                continue
            with tm.assertRaises(TypeError):
                df.plot(kind=kind)

    @slow
    def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in plotting._common_kinds:
                if not _ok_for_gaussian_kde(kind):
                    continue
                with tm.assertRaises(TypeError):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ['area']
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = 'a'
            for kind in kinds:
                with tm.assertRaises(TypeError):
                    df.plot(kind=kind)

    def test_invalid_kind(self):
        df = DataFrame(randn(10, 2))
        with tm.assertRaises(ValueError):
            df.plot(kind='aasdf')

    @slow
    def test_hexbin_basic(self):
        df = self.hexbin_df

        ax = df.plot(kind='hexbin', x='A', y='B', gridsize=10)
        # TODO: need better way to test. This just does existence.
        self.assertEqual(len(ax.collections), 1)

        # GH 6951
        axes = df.plot(x='A', y='B', kind='hexbin', subplots=True)
        # hexbin should have 2 axes in the figure, 1 for plotting and another is colorbar
        self.assertEqual(len(axes[0].figure.axes), 2)
        # return value is single axes
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

    @slow
    def test_hexbin_with_c(self):
        df = self.hexbin_df

        ax = df.plot(kind='hexbin', x='A', y='B', C='C')
        self.assertEqual(len(ax.collections), 1)

        ax = df.plot(kind='hexbin', x='A', y='B', C='C',
                          reduce_C_function=np.std)
        self.assertEqual(len(ax.collections), 1)

    @slow
    def test_hexbin_cmap(self):
        df = self.hexbin_df

        # Default to BuGn
        ax = df.plot(kind='hexbin', x='A', y='B')
        self.assertEqual(ax.collections[0].cmap.name, 'BuGn')

        cm = 'cubehelix'
        ax = df.plot(kind='hexbin', x='A', y='B', colormap=cm)
        self.assertEqual(ax.collections[0].cmap.name, cm)

    @slow
    def test_no_color_bar(self):
        df = self.hexbin_df

        ax = df.plot(kind='hexbin', x='A', y='B', colorbar=None)
        self.assertIs(ax.collections[0].colorbar, None)

    @slow
    def test_allow_cmap(self):
        df = self.hexbin_df

        ax = df.plot(kind='hexbin', x='A', y='B', cmap='YlGn')
        self.assertEqual(ax.collections[0].cmap.name, 'YlGn')

        with tm.assertRaises(TypeError):
            df.plot(kind='hexbin', x='A', y='B', cmap='YlGn',
                         colormap='BuGn')

    @slow
    def test_pie_df(self):
        df = DataFrame(np.random.rand(5, 3), columns=['X', 'Y', 'Z'],
                       index=['a', 'b', 'c', 'd', 'e'])
        with tm.assertRaises(ValueError):
            df.plot(kind='pie')

        ax = _check_plot_works(df.plot, kind='pie', y='Y')
        self._check_text_labels(ax.texts, df.index)

        axes = _check_plot_works(df.plot, kind='pie', subplots=True)
        self.assertEqual(len(axes), len(df.columns))
        for ax in axes:
            self._check_text_labels(ax.texts, df.index)
        for ax, ylabel in zip(axes, df.columns):
            self.assertEqual(ax.get_ylabel(), ylabel)

        labels = ['A', 'B', 'C', 'D', 'E']
        color_args = ['r', 'g', 'b', 'c', 'm']
        axes = _check_plot_works(df.plot, kind='pie', subplots=True,
                                 labels=labels, colors=color_args)
        self.assertEqual(len(axes), len(df.columns))

        for ax in axes:
            self._check_text_labels(ax.texts, labels)
            self._check_colors(ax.patches, facecolors=color_args)

    def test_errorbar_plot(self):
        d = {'x': np.arange(12), 'y': np.arange(12, 0, -1)}
        df = DataFrame(d)
        d_err = {'x': np.ones(12)*0.2, 'y': np.ones(12)*0.4}
        df_err = DataFrame(d_err)

        # check line plots
        ax = _check_plot_works(df.plot, yerr=df_err, logy=True)
        self._check_has_errorbars(ax, xerr=0, yerr=2)
        ax = _check_plot_works(df.plot, yerr=df_err, logx=True, logy=True)
        self._check_has_errorbars(ax, xerr=0, yerr=2)
        ax = _check_plot_works(df.plot, yerr=df_err, loglog=True)
        self._check_has_errorbars(ax, xerr=0, yerr=2)

        kinds = ['line', 'bar', 'barh']
        for kind in kinds:
            ax = _check_plot_works(df.plot, yerr=df_err['x'], kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            ax = _check_plot_works(df.plot, yerr=d_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            ax = _check_plot_works(df.plot, yerr=df_err, xerr=df_err, kind=kind)
            self._check_has_errorbars(ax, xerr=2, yerr=2)
            ax = _check_plot_works(df.plot, yerr=df_err['x'], xerr=df_err['x'], kind=kind)
            self._check_has_errorbars(ax, xerr=2, yerr=2)
            ax = _check_plot_works(df.plot, xerr=0.2, yerr=0.2, kind=kind)
            self._check_has_errorbars(ax, xerr=2, yerr=2)
            axes = _check_plot_works(df.plot, yerr=df_err, xerr=df_err, subplots=True, kind=kind)
            self._check_has_errorbars(axes, xerr=1, yerr=1)

        ax = _check_plot_works((df+1).plot, yerr=df_err, xerr=df_err, kind='bar', log=True)
        self._check_has_errorbars(ax, xerr=2, yerr=2)

        # yerr is raw error values
        ax = _check_plot_works(df['y'].plot, yerr=np.ones(12)*0.4)
        self._check_has_errorbars(ax, xerr=0, yerr=1)
        ax = _check_plot_works(df.plot, yerr=np.ones((2, 12))*0.4)
        self._check_has_errorbars(ax, xerr=0, yerr=2)

        # yerr is iterator
        import itertools
        ax = _check_plot_works(df.plot, yerr=itertools.repeat(0.1, len(df)))
        self._check_has_errorbars(ax, xerr=0, yerr=2)

        # yerr is column name
        for yerr in ['yerr', u('誤差')]:
            s_df = df.copy()
            s_df[yerr] = np.ones(12)*0.2
            ax = _check_plot_works(s_df.plot, yerr=yerr)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            ax = _check_plot_works(s_df.plot, y='y', x='x', yerr=yerr)
            self._check_has_errorbars(ax, xerr=0, yerr=1)

        with tm.assertRaises(ValueError):
            df.plot(yerr=np.random.randn(11))

        df_err = DataFrame({'x': ['zzz']*12, 'y': ['zzz']*12})
        with tm.assertRaises(TypeError):
            df.plot(yerr=df_err)

    @slow
    def test_errorbar_with_integer_column_names(self):
        # test with integer column names
        df = DataFrame(np.random.randn(10, 2))
        df_err = DataFrame(np.random.randn(10, 2))
        ax = _check_plot_works(df.plot, yerr=df_err)
        self._check_has_errorbars(ax, xerr=0, yerr=2)
        ax = _check_plot_works(df.plot, y=0, yerr=1)
        self._check_has_errorbars(ax, xerr=0, yerr=1)

    @slow
    def test_errorbar_with_partial_columns(self):
        df = DataFrame(np.random.randn(10, 3))
        df_err = DataFrame(np.random.randn(10, 2), columns=[0, 2])
        kinds = ['line', 'bar']
        for kind in kinds:
            ax = _check_plot_works(df.plot, yerr=df_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=2)

        ix = date_range('1/1/2000', periods=10, freq='M')
        df.set_index(ix, inplace=True)
        df_err.set_index(ix, inplace=True)
        ax = _check_plot_works(df.plot, yerr=df_err, kind='line')
        self._check_has_errorbars(ax, xerr=0, yerr=2)

        d = {'x': np.arange(12), 'y': np.arange(12, 0, -1)}
        df = DataFrame(d)
        d_err = {'x': np.ones(12)*0.2, 'z': np.ones(12)*0.4}
        df_err = DataFrame(d_err)
        for err in [d_err, df_err]:
            ax = _check_plot_works(df.plot, yerr=err)
            self._check_has_errorbars(ax, xerr=0, yerr=1)

    @slow
    def test_errorbar_timeseries(self):

        d = {'x': np.arange(12), 'y': np.arange(12, 0, -1)}
        d_err = {'x': np.ones(12)*0.2, 'y': np.ones(12)*0.4}

        # check time-series plots
        ix = date_range('1/1/2000', '1/1/2001', freq='M')
        tdf = DataFrame(d, index=ix)
        tdf_err = DataFrame(d_err, index=ix)

        kinds = ['line', 'bar', 'barh']
        for kind in kinds:
            ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            ax = _check_plot_works(tdf.plot, yerr=d_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            ax = _check_plot_works(tdf.plot, y='y', yerr=tdf_err['x'], kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=1)
            ax = _check_plot_works(tdf.plot, y='y', yerr='x', kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=1)
            ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            axes = _check_plot_works(tdf.plot, kind=kind, yerr=tdf_err, subplots=True)
            self._check_has_errorbars(axes, xerr=0, yerr=1)

    def test_errorbar_asymmetrical(self):

        np.random.seed(0)
        err = np.random.rand(3, 2, 5)

        data = np.random.randn(5, 3)
        df = DataFrame(data)

        ax = df.plot(yerr=err, xerr=err/2)

        self.assertEqual(ax.lines[7].get_ydata()[0], data[0,1]-err[1,0,0])
        self.assertEqual(ax.lines[8].get_ydata()[0], data[0,1]+err[1,1,0])

        self.assertEqual(ax.lines[5].get_xdata()[0], -err[1,0,0]/2)
        self.assertEqual(ax.lines[6].get_xdata()[0], err[1,1,0]/2)

        with tm.assertRaises(ValueError):
            df.plot(yerr=err.T)

        tm.close()

    def test_table(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        ax = df.plot()
        self.assertTrue(len(ax.tables) == 0)
        plotting.table(ax, df.T)
        self.assertTrue(len(ax.tables) == 1)

    def test_errorbar_scatter(self):
        df = DataFrame(np.random.randn(5, 2), index=range(5), columns=['x', 'y'])
        df_err = DataFrame(np.random.randn(5, 2) / 5,
                           index=range(5), columns=['x', 'y'])

        ax = _check_plot_works(df.plot, kind='scatter', x='x', y='y')
        self._check_has_errorbars(ax, xerr=0, yerr=0)
        ax = _check_plot_works(df.plot, kind='scatter', x='x', y='y', xerr=df_err)
        self._check_has_errorbars(ax, xerr=1, yerr=0)
        ax = _check_plot_works(df.plot, kind='scatter', x='x', y='y', yerr=df_err)
        self._check_has_errorbars(ax, xerr=0, yerr=1)
        ax = _check_plot_works(df.plot, kind='scatter', x='x', y='y',
                               xerr=df_err, yerr=df_err)
        self._check_has_errorbars(ax, xerr=1, yerr=1)


@tm.mplskip
class TestDataFrameGroupByPlots(TestPlotBase):

    @slow
    def test_boxplot(self):
        grouped = self.hist_df.groupby(by='gender')
        axes = _check_plot_works(grouped.boxplot, return_type='axes')
        self._check_axes_shape(axes.values(), axes_num=2, layout=(1, 2))

        axes = _check_plot_works(grouped.boxplot, subplots=False,
                                 return_type='axes')
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

        tuples = lzip(string.ascii_letters[:10], range(10))
        df = DataFrame(np.random.rand(10, 3),
                       index=MultiIndex.from_tuples(tuples))

        grouped = df.groupby(level=1)
        axes = _check_plot_works(grouped.boxplot, return_type='axes')
        self._check_axes_shape(axes.values(), axes_num=10, layout=(4, 3))

        axes = _check_plot_works(grouped.boxplot, subplots=False,
                                 return_type='axes')
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

        grouped = df.unstack(level=1).groupby(level=0, axis=1)
        axes = _check_plot_works(grouped.boxplot, return_type='axes')
        self._check_axes_shape(axes.values(), axes_num=3, layout=(2, 2))

        axes = _check_plot_works(grouped.boxplot, subplots=False,
                                 return_type='axes')
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

    def test_series_plot_color_kwargs(self):
        # GH1890
        ax = Series(np.arange(12) + 1).plot(color='green')
        self._check_colors(ax.get_lines(), linecolors=['green'])

    def test_time_series_plot_color_kwargs(self):
        # #1890
        ax = Series(np.arange(12) + 1, index=date_range(
            '1/1/2000', periods=12)).plot(color='green')
        self._check_colors(ax.get_lines(), linecolors=['green'])

    def test_time_series_plot_color_with_empty_kwargs(self):
        import matplotlib as mpl

        def_colors = mpl.rcParams['axes.color_cycle']
        index = date_range('1/1/2000', periods=12)
        s = Series(np.arange(1, 13), index=index)

        ncolors = 3

        for i in range(ncolors):
            ax = s.plot()
        self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors])

    @slow
    def test_grouped_hist(self):
        df = DataFrame(randn(500, 2), columns=['A', 'B'])
        df['C'] = np.random.randint(0, 4, 500)
        df['D'] = ['X'] * 500

        axes = plotting.grouped_hist(df.A, by=df.C)
        self._check_axes_shape(axes, axes_num=4, layout=(2, 2))

        tm.close()
        axes = df.hist(by=df.C)
        self._check_axes_shape(axes, axes_num=4, layout=(2, 2))

        tm.close()
        # group by a key with single value
        axes = df.hist(by='D', rot=30)
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
        self._check_ticks_props(axes, xrot=30)

        tm.close()
        # make sure kwargs to hist are handled
        xf, yf = 20, 18
        xrot, yrot = 30, 40
        axes = plotting.grouped_hist(df.A, by=df.C, normed=True,
                                     cumulative=True, bins=4,
                                     xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot)
        # height of last bin (index 5) must be 1.0
        for ax in axes.ravel():
            height = ax.get_children()[5].get_height()
            self.assertAlmostEqual(height, 1.0)
        self._check_ticks_props(axes, xlabelsize=xf, xrot=xrot,
                                ylabelsize=yf, yrot=yrot)

        tm.close()
        axes = plotting.grouped_hist(df.A, by=df.C, log=True)
        # scale of y must be 'log'
        self._check_ax_scales(axes, yaxis='log')

        tm.close()
        # propagate attr exception from matplotlib.Axes.hist
        with tm.assertRaises(AttributeError):
            plotting.grouped_hist(df.A, by=df.C, foo='bar')

        with tm.assert_produces_warning(FutureWarning):
            df.hist(by='C', figsize='default')

    @slow
    def test_grouped_box_return_type(self):
        df = self.hist_df

        # old style: return_type=None
        result = df.boxplot(by='gender')
        self.assertIsInstance(result, np.ndarray)
        self._check_box_return_type(result, None,
                                    expected_keys=['height', 'weight', 'category'])

        # now for groupby
        with tm.assert_produces_warning(FutureWarning):
            result = df.groupby('gender').boxplot()
        self._check_box_return_type(result, 'dict', expected_keys=['Male', 'Female'])

        columns2 = 'X B C D A G Y N Q O'.split()
        df2 = DataFrame(random.randn(50, 10), columns=columns2)
        categories2 = 'A B C D E F G H I J'.split()
        df2['category'] = categories2 * 5

        for t in ['dict', 'axes', 'both']:
            returned = df.groupby('classroom').boxplot(return_type=t)
            self._check_box_return_type(returned, t, expected_keys=['A', 'B', 'C'])

            returned = df.boxplot(by='classroom', return_type=t)
            self._check_box_return_type(returned, t,
                                        expected_keys=['height', 'weight', 'category'])

            returned = df2.groupby('category').boxplot(return_type=t)
            self._check_box_return_type(returned, t, expected_keys=categories2)

            returned = df2.boxplot(by='category', return_type=t)
            self._check_box_return_type(returned, t, expected_keys=columns2)

    @slow
    def test_grouped_box_layout(self):
        df = self.hist_df

        self.assertRaises(ValueError, df.boxplot, column=['weight', 'height'],
                          by=df.gender, layout=(1, 1))
        self.assertRaises(ValueError, df.boxplot, column=['height', 'weight', 'category'],
                          layout=(2, 1), return_type='dict')

        box = _check_plot_works(df.groupby('gender').boxplot, column='height',
                                return_type='dict')
        self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2))

        box = _check_plot_works(df.groupby('category').boxplot, column='height',
                                return_type='dict')
        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))

        # GH 6769
        box = _check_plot_works(df.groupby('classroom').boxplot,
                                column='height', return_type='dict')
        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))

        # GH 5897
        axes = df.boxplot(column=['height', 'weight', 'category'], by='gender',
                          return_type='axes')
        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
        for ax in [axes['height']]:
            self._check_visible(ax.get_xticklabels(), visible=False)
            self._check_visible([ax.xaxis.get_label()], visible=False)
        for ax in [axes['weight'], axes['category']]:
            self._check_visible(ax.get_xticklabels())
            self._check_visible([ax.xaxis.get_label()])

        box = df.groupby('classroom').boxplot(
            column=['height', 'weight', 'category'], return_type='dict')
        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))

        box = _check_plot_works(df.groupby('category').boxplot, column='height',
                                layout=(3, 2), return_type='dict')
        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))

        box = df.boxplot(column=['height', 'weight', 'category'], by='gender', layout=(4, 1))
        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1))

        box = df.groupby('classroom').boxplot(
            column=['height', 'weight', 'category'], layout=(1, 4),
            return_type='dict')
        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4))

    @slow
    def test_grouped_hist_layout(self):

        df = self.hist_df
        self.assertRaises(ValueError, df.hist, column='weight', by=df.gender,
                          layout=(1, 1))
        self.assertRaises(ValueError, df.hist, column='height', by=df.category,
                          layout=(1, 3))

        axes = _check_plot_works(df.hist, column='height', by=df.gender, layout=(2, 1))
        self._check_axes_shape(axes, axes_num=2, layout=(2, 1))

        axes = _check_plot_works(df.hist, column='height', by=df.category, layout=(4, 1))
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))

        axes = _check_plot_works(df.hist, column='height', by=df.category,
                                 layout=(4, 2), figsize=(12, 8))
        self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 8))

        # GH 6769
        axes = _check_plot_works(df.hist, column='height', by='classroom', layout=(2, 2))
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))

        # without column
        axes = _check_plot_works(df.hist, by='classroom')
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))

        axes = _check_plot_works(df.hist, by='gender', layout=(3, 5))
        self._check_axes_shape(axes, axes_num=2, layout=(3, 5))

        axes = _check_plot_works(df.hist, column=['height', 'weight', 'category'])
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))

    @slow
    def test_axis_share_x(self):
        df = self.hist_df
        # GH4089
        ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True)

        # share x
        self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2))
        self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2))

        # don't share y
        self.assertFalse(ax1._shared_y_axes.joined(ax1, ax2))
        self.assertFalse(ax2._shared_y_axes.joined(ax1, ax2))

    @slow
    def test_axis_share_y(self):
        df = self.hist_df
        ax1, ax2 = df.hist(column='height', by=df.gender, sharey=True)

        # share y
        self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2))
        self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2))

        # don't share x
        self.assertFalse(ax1._shared_x_axes.joined(ax1, ax2))
        self.assertFalse(ax2._shared_x_axes.joined(ax1, ax2))

    @slow
    def test_axis_share_xy(self):
        df = self.hist_df
        ax1, ax2 = df.hist(column='height', by=df.gender, sharex=True,
                           sharey=True)

        # share both x and y
        self.assertTrue(ax1._shared_x_axes.joined(ax1, ax2))
        self.assertTrue(ax2._shared_x_axes.joined(ax1, ax2))

        self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2))
        self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2))

    def test_option_mpl_style(self):
        set_option('display.mpl_style', 'default')
        set_option('display.mpl_style', None)
        set_option('display.mpl_style', False)

        with tm.assertRaises(ValueError):
            set_option('display.mpl_style', 'default2')

    def test_invalid_colormap(self):
        df = DataFrame(randn(3, 2), columns=['A', 'B'])

        with tm.assertRaises(ValueError):
            df.plot(colormap='invalid_colormap')


def assert_is_valid_plot_return_object(objs):
    import matplotlib.pyplot as plt
    if isinstance(objs, np.ndarray):
        for el in objs.flat:
            assert isinstance(el, plt.Axes), ('one of \'objs\' is not a '
                                              'matplotlib Axes instance, '
                                              'type encountered {0!r}'
                                              ''.format(el.__class__.__name__))
    else:
        assert isinstance(objs, (plt.Artist, tuple, dict)), \
                ('objs is neither an ndarray of Artist instances nor a '
                 'single Artist instance, tuple, or dict, "objs" is a {0!r} '
                 ''.format(objs.__class__.__name__))


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

    try:
        try:
            fig = kwargs['figure']
        except KeyError:
            fig = plt.gcf()

        plt.clf()

        ax = kwargs.get('ax', fig.add_subplot(211))
        ret = f(*args, **kwargs)

        assert_is_valid_plot_return_object(ret)

        try:
            kwargs['ax'] = fig.add_subplot(212)
            ret = f(*args, **kwargs)
        except Exception:
            pass
        else:
            assert_is_valid_plot_return_object(ret)

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

    return ret


def curpath():
    pth, _ = os.path.split(os.path.abspath(__file__))
    return pth


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