from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import six

import numpy as np
from numpy import ma
import matplotlib
from matplotlib.testing.decorators import image_comparison, cleanup
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.colors import BoundaryNorm
from matplotlib.cm import get_cmap
from matplotlib.colorbar import ColorbarBase


def _get_cmap_norms():
    """
    Define a colormap and appropriate norms for each of the four
    possible settings of the extend keyword.

    Helper function for _colorbar_extension_shape and
    colorbar_extension_length.
    """
    # Create a color map and specify the levels it represents.
    cmap = get_cmap("RdBu", lut=5)
    clevs = [-5., -2.5, -.5, .5, 1.5, 3.5]
    # Define norms for the color maps.
    norms = dict()
    norms['neither'] = BoundaryNorm(clevs, len(clevs) - 1)
    norms['min'] = BoundaryNorm([-10] + clevs[1:], len(clevs) - 1)
    norms['max'] = BoundaryNorm(clevs[:-1] + [10], len(clevs) - 1)
    norms['both'] = BoundaryNorm([-10] + clevs[1:-1] + [10], len(clevs) - 1)
    return cmap, norms


def _colorbar_extension_shape(spacing):
    '''
    Produce 4 colorbars with rectangular extensions for either uniform
    or proportional spacing.

    Helper function for test_colorbar_extension_shape.
    '''
    # Get a colormap and appropriate norms for each extension type.
    cmap, norms = _get_cmap_norms()
    # Create a figure and adjust whitespace for subplots.
    fig = plt.figure()
    fig.subplots_adjust(hspace=4)
    for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):
        # Get the appropriate norm and use it to get colorbar boundaries.
        norm = norms[extension_type]
        boundaries = values = norm.boundaries
        # Create a subplot.
        cax = fig.add_subplot(4, 1, i + 1)
        # Turn off text and ticks.
        for item in cax.get_xticklabels() + cax.get_yticklabels() +\
                cax.get_xticklines() + cax.get_yticklines():
            item.set_visible(False)
        # Generate the colorbar.
        cb = ColorbarBase(cax, cmap=cmap, norm=norm,
                boundaries=boundaries, values=values,
                extend=extension_type, extendrect=True,
                orientation='horizontal', spacing=spacing)
    # Return the figure to the caller.
    return fig


def _colorbar_extension_length(spacing):
    '''
    Produce 12 colorbars with variable length extensions for either
    uniform or proportional spacing.

    Helper function for test_colorbar_extension_length.
    '''
    # Get a colormap and appropriate norms for each extension type.
    cmap, norms = _get_cmap_norms()
    # Create a figure and adjust whitespace for subplots.
    fig = plt.figure()
    fig.subplots_adjust(hspace=.6)
    for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):
        # Get the appropriate norm and use it to get colorbar boundaries.
        norm = norms[extension_type]
        boundaries = values = norm.boundaries
        for j, extendfrac in enumerate((None, 'auto', 0.1)):
            # Create a subplot.
            cax = fig.add_subplot(12, 1, i*3 + j + 1)
            # Turn off text and ticks.
            for item in cax.get_xticklabels() + cax.get_yticklabels() +\
                    cax.get_xticklines() + cax.get_yticklines():
                item.set_visible(False)
            # Generate the colorbar.
            cb = ColorbarBase(cax, cmap=cmap, norm=norm,
                    boundaries=boundaries, values=values,
                    extend=extension_type, extendfrac=extendfrac,
                    orientation='horizontal', spacing=spacing)
    # Return the figure to the caller.
    return fig


@image_comparison(
        baseline_images=['colorbar_extensions_shape_uniform',
                         'colorbar_extensions_shape_proportional'],
        extensions=['png'])
def test_colorbar_extension_shape():
    '''Test rectangular colorbar extensions.'''
    # Create figures for uniform and proportionally spaced colorbars.
    fig1 = _colorbar_extension_shape('uniform')
    fig2 = _colorbar_extension_shape('proportional')


@image_comparison(baseline_images=['colorbar_extensions_uniform',
                                   'colorbar_extensions_proportional'],
                  extensions=['png'])
def test_colorbar_extension_length():
    '''Test variable length colorbar extensions.'''
    # Create figures for uniform and proportionally spaced colorbars.
    fig1 = _colorbar_extension_length('uniform')
    fig2 = _colorbar_extension_length('proportional')


@image_comparison(baseline_images=['cbar_with_orientation',
                                   'cbar_locationing',
                                   'double_cbar',
                                   'cbar_sharing',
                                   ],
                  extensions=['png'], remove_text=True,
                  savefig_kwarg={'dpi': 40})
def test_colorbar_positioning():
    data = np.arange(1200).reshape(30, 40)
    levels = [0, 200, 400, 600, 800, 1000, 1200]

    # -------------------
    plt.figure()
    plt.contourf(data, levels=levels)
    plt.colorbar(orientation='horizontal', use_gridspec=False)

    locations = ['left', 'right', 'top', 'bottom']
    plt.figure()
    for i, location in enumerate(locations):
        plt.subplot(2, 2, i + 1)
        plt.contourf(data, levels=levels)
        plt.colorbar(location=location, use_gridspec=False)

    # -------------------
    plt.figure()
    # make some other data (random integers)
    data_2nd = np.array([[2, 3, 2, 3], [1.5, 2, 2, 3], [2, 3, 3, 4]])
    # make the random data expand to the shape of the main data
    data_2nd = np.repeat(np.repeat(data_2nd, 10, axis=1), 10, axis=0)

    color_mappable = plt.contourf(data, levels=levels, extend='both')
    # test extend frac here
    hatch_mappable = plt.contourf(data_2nd, levels=[1, 2, 3], colors='none',
                                  hatches=['/', 'o', '+'], extend='max')
    plt.contour(hatch_mappable, colors='black')

    plt.colorbar(color_mappable, location='left', label='variable 1',
                 use_gridspec=False)
    plt.colorbar(hatch_mappable, location='right', label='variable 2',
                 use_gridspec=False)

    # -------------------
    plt.figure()
    ax1 = plt.subplot(211, anchor='NE', aspect='equal')
    plt.contourf(data, levels=levels)
    ax2 = plt.subplot(223)
    plt.contourf(data, levels=levels)
    ax3 = plt.subplot(224)
    plt.contourf(data, levels=levels)

    plt.colorbar(ax=[ax2, ax3, ax1], location='right', pad=0.0, shrink=0.5,
                 panchor=False, use_gridspec=False)
    plt.colorbar(ax=[ax2, ax3, ax1], location='left', shrink=0.5,
                 panchor=False, use_gridspec=False)
    plt.colorbar(ax=[ax1], location='bottom', panchor=False,
                 anchor=(0.8, 0.5), shrink=0.6, use_gridspec=False)


@image_comparison(baseline_images=['cbar_with_subplots_adjust'],
                  extensions=['png'], remove_text=True,
                  savefig_kwarg={'dpi': 40})
def test_gridspec_make_colorbar():
    plt.figure()
    data = np.arange(1200).reshape(30, 40)
    levels = [0, 200, 400, 600, 800, 1000, 1200]

    plt.subplot(121)
    plt.contourf(data, levels=levels)
    plt.colorbar(use_gridspec=True, orientation='vertical')

    plt.subplot(122)
    plt.contourf(data, levels=levels)
    plt.colorbar(use_gridspec=True, orientation='horizontal')

    plt.subplots_adjust(top=0.95, right=0.95, bottom=0.2, hspace=0.25)


@image_comparison(baseline_images=['colorbar_single_scatter'],
                  extensions=['png'], remove_text=True,
                  savefig_kwarg={'dpi': 40})
def test_colorbar_single_scatter():
    # Issue #2642: if a path collection has only one entry,
    # the norm scaling within the colorbar must ensure a
    # finite range, otherwise a zero denominator will occur in _locate.
    plt.figure()
    x = np.arange(4)
    y = x.copy()
    z = np.ma.masked_greater(np.arange(50, 54), 50)
    cmap = plt.get_cmap('jet', 16)
    cs = plt.scatter(x, y, z, c=z, cmap=cmap)
    plt.colorbar(cs)


def _test_remove_from_figure(use_gridspec):
    """
    Test `remove_from_figure` with the specified ``use_gridspec`` setting
    """
    fig = plt.figure()
    ax = fig.add_subplot(111)
    sc = ax.scatter([1, 2], [3, 4], cmap="spring")
    sc.set_array(np.array([5, 6]))
    pre_figbox = np.array(ax.figbox)
    cb = fig.colorbar(sc, use_gridspec=use_gridspec)
    fig.subplots_adjust()
    cb.remove()
    fig.subplots_adjust()
    post_figbox = np.array(ax.figbox)
    assert (pre_figbox == post_figbox).all()


@cleanup
def test_remove_from_figure_with_gridspec():
    """
    Make sure that `remove_from_figure` removes the colorbar and properly
    restores the gridspec
    """
    _test_remove_from_figure(True)


@cleanup
def test_remove_from_figure_no_gridspec():
    """
    Make sure that `remove_from_figure` removes a colorbar that was created
    without modifying the gridspec
    """
    _test_remove_from_figure(False)


if __name__ == '__main__':
    import nose
    nose.runmodule(argv=['-s', '--with-doctest'], exit=False)
