import numpy as np
from numpy import ma
import matplotlib
from matplotlib.testing.decorators import image_comparison, cleanup
import matplotlib.pyplot as plt


@image_comparison(baseline_images=['formatter_ticker_001',
                                   'formatter_ticker_002',
                                   'formatter_ticker_003',
                                   'formatter_ticker_004',
                                   'formatter_ticker_005',
                                   ])
def test_formatter_ticker():
    import matplotlib.testing.jpl_units as units
    units.register()

    # This should affect the tick size.  (Tests issue #543)
    matplotlib.rcParams['lines.markeredgewidth'] = 30

    # This essentially test to see if user specified labels get overwritten
    # by the auto labeler functionality of the axes.
    xdata = [ x*units.sec for x in range(10) ]
    ydata1 = [ (1.5*y - 0.5)*units.km for y in range(10) ]
    ydata2 = [ (1.75*y - 1.0)*units.km for y in range(10) ]

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.set_xlabel( "x-label 001" )

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.set_xlabel( "x-label 001" )
    ax.plot( xdata, ydata1, color='blue', xunits="sec" )

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.set_xlabel( "x-label 001" )
    ax.plot( xdata, ydata1, color='blue', xunits="sec" )
    ax.set_xlabel( "x-label 003" )

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.plot( xdata, ydata1, color='blue', xunits="sec" )
    ax.plot( xdata, ydata2, color='green', xunits="hour" )
    ax.set_xlabel( "x-label 004" )

    # See SF bug 2846058
    # https://sourceforge.net/tracker/?func=detail&aid=2846058&group_id=80706&atid=560720
    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.plot( xdata, ydata1, color='blue', xunits="sec" )
    ax.plot( xdata, ydata2, color='green', xunits="hour" )
    ax.set_xlabel( "x-label 005" )
    ax.autoscale_view()

@image_comparison(baseline_images=["formatter_large_small"])
def test_formatter_large_small():
    # github issue #617, pull #619
    fig, ax = plt.subplots(1)
    x = [0.500000001, 0.500000002]
    y = [500000001, 500000002]
    ax.plot(x, y)

@image_comparison(baseline_images=["twin_axis_locaters_formatters"])
def test_twin_axis_locaters_formatters():
    vals = np.linspace(0, 1, num=5, endpoint=True)
    locs = np.sin(np.pi * vals / 2.0)

    majl = plt.FixedLocator(locs)
    minl = plt.FixedLocator([0.1, 0.2, 0.3])

    fig = plt.figure()
    ax1 = fig.add_subplot(1, 1, 1)
    ax1.plot([0.1, 100], [0, 1])
    ax1.yaxis.set_major_locator(majl)
    ax1.yaxis.set_minor_locator(minl)
    ax1.yaxis.set_major_formatter(plt.FormatStrFormatter('%08.2lf'))
    ax1.yaxis.set_minor_formatter(plt.FixedFormatter(['tricks', 'mind', 'jedi']))

    ax1.xaxis.set_major_locator(plt.LinearLocator())
    ax1.xaxis.set_minor_locator(plt.FixedLocator([15, 35, 55, 75]))
    ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%05.2lf'))
    ax1.xaxis.set_minor_formatter(plt.FixedFormatter(['c', '3', 'p', 'o']))
    ax2 = ax1.twiny()
    ax3 = ax1.twinx()

@image_comparison(baseline_images=["autoscale_tiny_range"], remove_text=True)
def test_autoscale_tiny_range():
    # github pull #904
    fig, ax = plt.subplots(2, 2)
    ax = ax.flatten()
    for i in xrange(4):
        y1 = 10**(-11 - i)
        ax[i].plot([0, 1], [1, 1 + y1])

@image_comparison(baseline_images=['offset_points'],
                  remove_text=True)
def test_basic_annotate():
    # Setup some data
    t = np.arange( 0.0, 5.0, 0.01 )
    s = np.cos( 2.0*np.pi * t )

    # Offset Points

    fig = plt.figure()
    ax = fig.add_subplot( 111, autoscale_on=False, xlim=(-1,5), ylim=(-3,5) )
    line, = ax.plot( t, s, lw=3, color='purple' )

    ax.annotate( 'local max', xy=(3, 1), xycoords='data',
                 xytext=(3, 3), textcoords='offset points' )

@image_comparison(baseline_images=['polar_axes'])
def test_polar_annotations():
    # you can specify the xypoint and the xytext in different
    # positions and coordinate systems, and optionally turn on a
    # connecting line and mark the point with a marker.  Annotations
    # work on polar axes too.  In the example below, the xy point is
    # in native coordinates (xycoords defaults to 'data').  For a
    # polar axes, this is in (theta, radius) space.  The text in this
    # example is placed in the fractional figure coordinate system.
    # Text keyword args like horizontal and vertical alignment are
    # respected

    # Setup some data
    r = np.arange(0.0, 1.0, 0.001 )
    theta = 2.0 * 2.0 * np.pi * r

    fig = plt.figure()
    ax = fig.add_subplot( 111, polar=True )
    line, = ax.plot( theta, r, color='#ee8d18', lw=3 )
    line, = ax.plot( (0, 0), (0, 1), color="#0000ff", lw=1)

    ind = 800
    thisr, thistheta = r[ind], theta[ind]
    ax.plot([thistheta], [thisr], 'o')
    ax.annotate('a polar annotation',
                xy=(thistheta, thisr),  # theta, radius
                xytext=(0.05, 0.05),    # fraction, fraction
                textcoords='figure fraction',
                arrowprops=dict(facecolor='black', shrink=0.05),
                horizontalalignment='left',
                verticalalignment='baseline',
                )

   #--------------------------------------------------------------------
@image_comparison(baseline_images=['polar_coords'],
                  remove_text=True)
def test_polar_coord_annotations():
    # You can also use polar notation on a catesian axes.  Here the
    # native coordinate system ('data') is cartesian, so you need to
    # specify the xycoords and textcoords as 'polar' if you want to
    # use (theta, radius)
    from matplotlib.patches import Ellipse
    el = Ellipse((0,0), 10, 20, facecolor='r', alpha=0.5)

    fig = plt.figure()
    ax = fig.add_subplot( 111, aspect='equal' )

    ax.add_artist( el )
    el.set_clip_box( ax.bbox )

    ax.annotate('the top',
                xy=(np.pi/2., 10.),      # theta, radius
                xytext=(np.pi/3, 20.),   # theta, radius
                xycoords='polar',
                textcoords='polar',
                arrowprops=dict(facecolor='black', shrink=0.05),
                horizontalalignment='left',
                verticalalignment='baseline',
                clip_on=True, # clip to the axes bounding box
                )

    ax.set_xlim( -20, 20 )
    ax.set_ylim( -20, 20 )

@image_comparison(baseline_images=['fill_units'])
def test_fill_units():
    from datetime import datetime
    import matplotlib.testing.jpl_units as units
    units.register()

    # generate some data
    t = units.Epoch( "ET", dt=datetime(2009, 4, 27) )
    value = 10.0 * units.deg
    day = units.Duration( "ET", 24.0 * 60.0 * 60.0 )

    fig = plt.figure()

    # Top-Left
    ax1 = fig.add_subplot( 221 )
    ax1.plot( [t], [value], yunits='deg', color='red' )
    ax1.fill( [733525.0, 733525.0, 733526.0, 733526.0],
              [0.0, 0.0, 90.0, 0.0], 'b' )

    # Top-Right
    ax2 = fig.add_subplot( 222 )
    ax2.plot( [t], [value], yunits='deg', color='red' )
    ax2.fill( [t,      t,      t+day,     t+day],
              [0.0,  0.0,  90.0,    0.0], 'b' )

    # Bottom-Left
    ax3 = fig.add_subplot( 223 )
    ax3.plot( [t], [value], yunits='deg', color='red' )
    ax3.fill( [733525.0, 733525.0, 733526.0, 733526.0],
              [0*units.deg,  0*units.deg,  90*units.deg,    0*units.deg], 'b' )

    # Bottom-Right
    ax4 = fig.add_subplot( 224 )
    ax4.plot( [t], [value], yunits='deg', color='red' )
    ax4.fill( [t,      t,      t+day,     t+day],
              [0*units.deg,  0*units.deg,  90*units.deg,    0*units.deg],
              facecolor="blue" )

    fig.autofmt_xdate()

@image_comparison(baseline_images=['single_point'])
def test_single_point():
    # Issue #1796: don't let lines.marker affect the grid
    matplotlib.rcParams['lines.marker'] = 'o'
    matplotlib.rcParams['axes.grid'] = True

    fig = plt.figure()
    plt.subplot( 211 )
    plt.plot( [0], [0], 'o' )

    plt.subplot( 212 )
    plt.plot( [1], [1], 'o' )

@image_comparison(baseline_images=['single_date'])
def test_single_date():
    time1=[ 721964.0 ]
    data1=[ -65.54 ]

    fig = plt.figure()
    plt.subplot( 211 )
    plt.plot_date( time1, data1, 'o', color='r' )

    plt.subplot( 212 )
    plt.plot( time1, data1, 'o', color='r' )

@image_comparison(baseline_images=['shaped_data'])
def test_shaped_data():
    xdata = np.array([[ 0.53295185,  0.23052951,  0.19057629,  0.66724975,  0.96577916,
                        0.73136095,  0.60823287,  0.017921  ,  0.29744742,  0.27164665],
                      [ 0.2798012 ,  0.25814229,  0.02818193,  0.12966456,  0.57446277,
                        0.58167607,  0.71028245,  0.69112737,  0.89923072,  0.99072476],
                      [ 0.81218578,  0.80464528,  0.76071809,  0.85616314,  0.12757994,
                        0.94324936,  0.73078663,  0.09658102,  0.60703967,  0.77664978],
                      [ 0.28332265,  0.81479711,  0.86985333,  0.43797066,  0.32540082,
                        0.43819229,  0.92230363,  0.49414252,  0.68168256,  0.05922372],
                      [ 0.10721335,  0.93904142,  0.79163075,  0.73232848,  0.90283839,
                        0.68408046,  0.25502302,  0.95976614,  0.59214115,  0.13663711],
                      [ 0.28087456,  0.33127607,  0.15530412,  0.76558121,  0.83389773,
                        0.03735974,  0.98717738,  0.71432229,  0.54881366,  0.86893953],
                      [ 0.77995937,  0.995556  ,  0.29688434,  0.15646162,  0.051848  ,
                        0.37161935,  0.12998491,  0.09377296,  0.36882507,  0.36583435],
                      [ 0.37851836,  0.05315792,  0.63144617,  0.25003433,  0.69586032,
                        0.11393988,  0.92362096,  0.88045438,  0.93530252,  0.68275072],
                      [ 0.86486596,  0.83236675,  0.82960664,  0.5779663 ,  0.25724233,
                        0.84841095,  0.90862812,  0.64414887,  0.3565272 ,  0.71026066],
                      [ 0.01383268,  0.3406093 ,  0.76084285,  0.70800694,  0.87634056,
                        0.08213693,  0.54655021,  0.98123181,  0.44080053,  0.86815815]])

    y1 = np.arange( 10 )
    y1.shape = 1, 10

    y2 = np.arange( 10 )
    y2.shape = 10, 1

    fig = plt.figure()
    plt.subplot( 411 )
    plt.plot( y1 )
    plt.subplot( 412 )
    plt.plot( y2 )

    plt.subplot( 413 )
    from nose.tools import assert_raises
    assert_raises(ValueError,plt.plot, (y1,y2))

    plt.subplot( 414 )
    plt.plot( xdata[:,1], xdata[1,:], 'o' )

@image_comparison(baseline_images=['const_xy'])
def test_const_xy():
    fig = plt.figure()

    plt.subplot( 311 )
    plt.plot( np.arange(10), np.ones( (10,) ) )

    plt.subplot( 312 )
    plt.plot( np.ones( (10,) ), np.arange(10) )

    plt.subplot( 313 )
    plt.plot( np.ones( (10,) ), np.ones( (10,) ), 'o' )


@image_comparison(baseline_images=['polar_wrap_180',
                                   'polar_wrap_360',
                                   ])
def test_polar_wrap():
    D2R = np.pi / 180.0

    fig = plt.figure()

    plt.subplot(111, polar=True)

    plt.polar( [179*D2R, -179*D2R], [0.2, 0.1], "b.-" )
    plt.polar( [179*D2R,  181*D2R], [0.2, 0.1], "g.-" )
    plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )
    assert len(fig.axes) == 1, 'More than one polar axes created.'
    fig = plt.figure()

    plt.subplot( 111, polar=True)
    plt.polar( [2*D2R, -2*D2R], [0.2, 0.1], "b.-" )
    plt.polar( [2*D2R,  358*D2R], [0.2, 0.1], "g.-" )
    plt.polar( [358*D2R,  2*D2R], [0.2, 0.1], "r.-" )
    plt.rgrids( [0.05, 0.1, 0.15, 0.2, 0.25, 0.3] )


@image_comparison(baseline_images=['polar_units', 'polar_units_2'],
                  freetype_version=('2.4.5', '2.4.9'))
def test_polar_units():
    import matplotlib.testing.jpl_units as units
    from nose.tools import assert_true
    units.register()

    pi = np.pi
    deg = units.UnitDbl( 1.0, "deg" )
    km = units.UnitDbl( 1.0, "km" )

    x1 = [ pi/6.0, pi/4.0, pi/3.0, pi/2.0 ]
    x2 = [ 30.0*deg, 45.0*deg, 60.0*deg, 90.0*deg ]

    y1 = [ 1.0, 2.0, 3.0, 4.0]
    y2 = [ 4.0, 3.0, 2.0, 1.0 ]

    fig = plt.figure()

    plt.polar( x2, y1, color = "blue" )

    # polar( x2, y1, color = "red", xunits="rad" )
    # polar( x2, y2, color = "green" )

    fig = plt.figure()

    # make sure runits and theta units work
    y1 = [ y*km for y in y1 ]
    plt.polar( x2, y1, color = "blue", thetaunits="rad", runits="km" )
    assert_true( isinstance(plt.gca().get_xaxis().get_major_formatter(), units.UnitDblFormatter) )


@image_comparison(baseline_images=['polar_rmin'])
def test_polar_rmin():
    r = np.arange(0, 3.0, 0.01)
    theta = 2*np.pi*r

    fig = plt.figure()
    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
    ax.plot(theta, r)
    ax.set_rmax(2.0)
    ax.set_rmin(0.5)

@image_comparison(baseline_images=['polar_theta_position'])
def test_polar_theta_position():
    r = np.arange(0, 3.0, 0.01)
    theta = 2*np.pi*r

    fig = plt.figure()
    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
    ax.plot(theta, r)
    ax.set_theta_zero_location("NW")
    ax.set_theta_direction('clockwise')

@image_comparison(baseline_images=['axvspan_epoch'])
def test_axvspan_epoch():
    from datetime import datetime
    import matplotlib.testing.jpl_units as units
    units.register()

    # generate some data
    t0 = units.Epoch( "ET", dt=datetime(2009, 1, 20) )
    tf = units.Epoch( "ET", dt=datetime(2009, 1, 21) )

    dt = units.Duration( "ET", units.day.convert( "sec" ) )

    fig = plt.figure()

    plt.axvspan( t0, tf, facecolor="blue", alpha=0.25 )

    ax = plt.gca()
    ax.set_xlim( t0 - 5.0*dt, tf + 5.0*dt )

@image_comparison(baseline_images=['axhspan_epoch'])
def test_axhspan_epoch():
    from datetime import datetime
    import matplotlib.testing.jpl_units as units
    units.register()

    # generate some data
    t0 = units.Epoch( "ET", dt=datetime(2009, 1, 20) )
    tf = units.Epoch( "ET", dt=datetime(2009, 1, 21) )

    dt = units.Duration( "ET", units.day.convert( "sec" ) )

    fig = plt.figure()

    plt.axhspan( t0, tf, facecolor="blue", alpha=0.25 )

    ax = plt.gca()
    ax.set_ylim( t0 - 5.0*dt, tf + 5.0*dt )


@image_comparison(baseline_images=['hexbin_extent'],
                  remove_text=True)
def test_hexbin_extent():
    # this test exposes sf bug 2856228
    fig = plt.figure()

    ax = fig.add_subplot(111)
    data = np.arange(2000.)/2000.
    data.shape = 2, 1000
    x, y = data

    ax.hexbin(x, y, extent=[.1, .3, .6, .7])

@image_comparison(baseline_images=['hexbin_log'],
                  remove_text=True,
                  extensions=['png'])
def test_hexbin_log():
    # Issue #1636
    fig = plt.figure()

    np.random.seed(0)
    n = 100000
    x = np.random.standard_normal(n)
    y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
    y = np.power(2, y * 0.5)
    ax = fig.add_subplot(111)
    ax.hexbin(x, y, yscale='log')

@cleanup
def test_inverted_limits():
    # Test gh:1553
    # Calling invert_xaxis prior to plotting should not disable autoscaling
    # while still maintaining the inverted direction
    fig = plt.figure()
    ax = fig.gca()
    ax.invert_xaxis()
    ax.plot([-5, -3, 2, 4], [1, 2, -3, 5])

    assert ax.get_xlim() == (4, -5)
    assert ax.get_ylim() == (-3, 5)
    plt.close()

    fig = plt.figure()
    ax = fig.gca()
    ax.invert_yaxis()
    ax.plot([-5, -3, 2, 4], [1, 2, -3, 5])

    assert ax.get_xlim() == (-5, 4)
    assert ax.get_ylim() == (5, -3)
    plt.close()

@image_comparison(baseline_images=['nonfinite_limits'])
def test_nonfinite_limits():
    x = np.arange(0., np.e, 0.01)
    olderr = np.seterr(divide='ignore') #silence divide by zero warning from log(0)
    try:
        y = np.log(x)
    finally:
        np.seterr(**olderr)
    x[len(x)/2] = np.nan
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(x, y)

@image_comparison(baseline_images=['imshow'],
                  remove_text=True)
def test_imshow():
    #Create a NxN image
    N=100
    (x,y) = np.indices((N,N))
    x -= N//2
    y -= N//2
    r = np.sqrt(x**2+y**2-x*y)

    #Create a contour plot at N/4 and extract both the clip path and transform
    fig = plt.figure()
    ax = fig.add_subplot(111)

    ax.imshow(r)

@image_comparison(baseline_images=['imshow_clip'], tol=1e-2)
def test_imshow_clip():
    # As originally reported by Gellule Xg <gellule.xg@free.fr>

    #Create a NxN image
    N=100
    (x,y) = np.indices((N,N))
    x -= N//2
    y -= N//2
    r = np.sqrt(x**2+y**2-x*y)

    #Create a contour plot at N/4 and extract both the clip path and transform
    fig = plt.figure()
    ax = fig.add_subplot(111)

    c = ax.contour(r,[N/4])
    x = c.collections[0]
    clipPath = x.get_paths()[0]
    clipTransform = x.get_transform()

    from matplotlib.transforms import TransformedPath
    clip_path = TransformedPath(clipPath, clipTransform)

    #Plot the image clipped by the contour
    ax.imshow(r, clip_path=clip_path)

@image_comparison(baseline_images=['polycollection_joinstyle'],
                  remove_text=True)
def test_polycollection_joinstyle():
    # Bug #2890979 reported by Matthew West

    from matplotlib import collections as mcoll

    fig = plt.figure()
    ax = fig.add_subplot(111)
    verts = np.array([[1,1], [1,2], [2,2], [2,1]])
    c = mcoll.PolyCollection([verts], linewidths = 40)
    ax.add_collection(c)
    ax.set_xbound(0, 3)
    ax.set_ybound(0, 3)

@image_comparison(baseline_images=['fill_between_interpolate'],
                  tol=1e-2, remove_text=True)
def test_fill_between_interpolate():
    x = np.arange(0.0, 2, 0.02)
    y1 = np.sin(2*np.pi*x)
    y2 = 1.2*np.sin(4*np.pi*x)

    fig = plt.figure()
    ax = fig.add_subplot(211)
    ax.plot(x, y1, x, y2, color='black')
    ax.fill_between(x, y1, y2, where=y2>=y1, facecolor='green', interpolate=True)
    ax.fill_between(x, y1, y2, where=y2<=y1, facecolor='red', interpolate=True)

    # Test support for masked arrays.
    y2 = np.ma.masked_greater(y2, 1.0)
    ax1 = fig.add_subplot(212, sharex=ax)
    ax1.plot(x, y1, x, y2, color='black')
    ax1.fill_between(x, y1, y2, where=y2>=y1, facecolor='green', interpolate=True)
    ax1.fill_between(x, y1, y2, where=y2<=y1, facecolor='red', interpolate=True)

@image_comparison(baseline_images=['symlog'])
def test_symlog():
    x = np.array([0,1,2,4,6,9,12,24])
    y = np.array([1000000, 500000, 100000, 100, 5, 0, 0, 0])

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(x, y)
    ax.set_yscale('symlog')
    ax.set_xscale=('linear')
    ax.set_ylim(-1,10000000)

@image_comparison(baseline_images=['symlog2'],
                  remove_text=True)
def test_symlog2():
    # Numbers from -50 to 50, with 0.1 as step
    x = np.arange(-50,50, 0.001)

    fig = plt.figure()
    ax = fig.add_subplot(511)
    # Plots a simple linear function 'f(x) = x'
    ax.plot(x, x)
    ax.set_xscale('symlog', linthreshx=20.0)
    ax.grid(True)

    ax = fig.add_subplot(512)
    # Plots a simple linear function 'f(x) = x'
    ax.plot(x, x)
    ax.set_xscale('symlog', linthreshx=2.0)
    ax.grid(True)

    ax = fig.add_subplot(513)
    # Plots a simple linear function 'f(x) = x'
    ax.plot(x, x)
    ax.set_xscale('symlog', linthreshx=1.0)
    ax.grid(True)

    ax = fig.add_subplot(514)
    # Plots a simple linear function 'f(x) = x'
    ax.plot(x, x)
    ax.set_xscale('symlog', linthreshx=0.1)
    ax.grid(True)

    ax = fig.add_subplot(515)
    # Plots a simple linear function 'f(x) = x'
    ax.plot(x, x)
    ax.set_xscale('symlog', linthreshx=0.01)
    ax.grid(True)
    ax.set_ylim(-0.1, 0.1)

@image_comparison(baseline_images=['pcolormesh'], tol=0.02,
                  remove_text=True)
def test_pcolormesh():
    n = 12
    x = np.linspace(-1.5,1.5,n)
    y = np.linspace(-1.5,1.5,n*2)
    X,Y = np.meshgrid(x,y);
    Qx = np.cos(Y) - np.cos(X)
    Qz = np.sin(Y) + np.sin(X)
    Qx = (Qx + 1.1)
    Z = np.sqrt(X**2 + Y**2)/5;
    Z = (Z - Z.min()) / (Z.max() - Z.min())

    # The color array can include masked values:
    Zm = ma.masked_where(np.fabs(Qz) < 0.5*np.amax(Qz), Z)

    fig = plt.figure()
    ax = fig.add_subplot(131)
    ax.pcolormesh(Qx,Qz,Z, lw=0.5, edgecolors='k')

    ax = fig.add_subplot(132)
    ax.pcolormesh(Qx,Qz,Z, lw=2, edgecolors=['b', 'w'])

    ax = fig.add_subplot(133)
    ax.pcolormesh(Qx,Qz,Z, shading="gouraud")


@image_comparison(baseline_images=['canonical'])
def test_canonical():
    fig, ax = plt.subplots()
    ax.plot([1,2,3])


@image_comparison(baseline_images=['arc_ellipse'],
                  remove_text=True)
def test_arc_ellipse():
    from matplotlib import patches
    xcenter, ycenter = 0.38, 0.52
    width, height = 1e-1, 3e-1
    angle = -30

    theta = np.arange(0.0, 360.0, 1.0)*np.pi/180.0
    x = width/2. * np.cos(theta)
    y = height/2. * np.sin(theta)

    rtheta = angle*np.pi/180.
    R = np.array([
        [np.cos(rtheta),  -np.sin(rtheta)],
        [np.sin(rtheta), np.cos(rtheta)],
        ])

    x, y = np.dot(R, np.array([x, y]))
    x += xcenter
    y += ycenter

    fig = plt.figure()
    ax = fig.add_subplot(211, aspect='auto')
    ax.fill(x, y, alpha=0.2, facecolor='yellow', edgecolor='yellow', linewidth=1, zorder=1)

    e1 = patches.Arc((xcenter, ycenter), width, height,
                 angle=angle, linewidth=2, fill=False, zorder=2)

    ax.add_patch(e1)

    ax = fig.add_subplot(212, aspect='equal')
    ax.fill(x, y, alpha=0.2, facecolor='green', edgecolor='green', zorder=1)
    e2 = patches.Arc((xcenter, ycenter), width, height,
                 angle=angle, linewidth=2, fill=False, zorder=2)

    ax.add_patch(e2)

@image_comparison(baseline_images=['units_strings'])
def test_units_strings():
    # Make sure passing in sequences of strings doesn't cause the unit
    # conversion registry to recurse infinitely
    Id = ['50', '100', '150', '200', '250']
    pout = ['0', '7.4', '11.4', '14.2', '16.3']
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(Id, pout)

@image_comparison(baseline_images=['markevery'],
                  remove_text=True)
def test_markevery():
    x = np.linspace(0, 10, 100)
    y = np.sin(x) * np.sqrt(x/10 + 0.5)

    # check marker only plot
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(x, y, 'o', label='default')
    ax.plot(x, y, 'd', markevery=None, label='mark all')
    ax.plot(x, y, 's', markevery=10, label='mark every 10')
    ax.plot(x, y, '+', markevery=(5, 20), label='mark every 5 starting at 10')
    ax.legend()

@image_comparison(baseline_images=['markevery_line'],
                  remove_text=True)
def test_markevery_line():
    x = np.linspace(0, 10, 100)
    y = np.sin(x) * np.sqrt(x/10 + 0.5)

    # check line/marker combos
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(x, y, '-o', label='default')
    ax.plot(x, y, '-d', markevery=None, label='mark all')
    ax.plot(x, y, '-s', markevery=10, label='mark every 10')
    ax.plot(x, y, '-+', markevery=(5, 20), label='mark every 5 starting at 10')
    ax.legend()

@image_comparison(baseline_images=['marker_edges'],
                  remove_text=True)
def test_marker_edges():
    x = np.linspace(0, 1, 10)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(x, np.sin(x), 'y.', ms=30.0, mew=0, mec='r')
    ax.plot(x+0.1, np.sin(x), 'y.', ms=30.0, mew=1, mec='r')
    ax.plot(x+0.2, np.sin(x), 'y.', ms=30.0, mew=2, mec='b')

@image_comparison(baseline_images=['hist_log'],
                  remove_text=True)
def test_hist_log():
    data0 = np.linspace(0,1,200)**3
    data = np.r_[1-data0, 1+data0]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist(data, fill=False, log=True)

@image_comparison(baseline_images=['hist_steplog'], remove_text=True)
def test_hist_steplog():
    np.random.seed(0)
    data = np.random.standard_normal(2000)
    data += -2.0 - np.min(data)
    data_pos = data + 2.1
    data_big = data_pos + 30

    ax = plt.subplot(3, 1, 1)
    plt.hist(data, 100, histtype='stepfilled', log=True)

    ax = plt.subplot(3, 1, 2)
    plt.hist(data_pos, 100, histtype='stepfilled', log=True)

    ax = plt.subplot(3, 1, 3)
    plt.hist(data_big, 100, histtype='stepfilled', log=True)

def contour_dat():
    x = np.linspace(-3, 5, 150)
    y = np.linspace(-3, 5, 120)
    z = np.cos(x) + np.sin(y[:, np.newaxis])
    return x, y, z

@image_comparison(baseline_images=['contour_hatching'])
def test_contour_hatching():
    x, y, z = contour_dat()

    fig = plt.figure()
    ax = fig.add_subplot(111)
    cs = ax.contourf(x, y, z, hatches=['-', '/', '\\', '//'],
                      cmap=plt.get_cmap('gray'),
                      extend='both', alpha=0.5)

@image_comparison(baseline_images=['contour_colorbar'])
def test_contour_colorbar():
    x, y, z = contour_dat()

    fig = plt.figure()
    ax = fig.add_subplot(111)
    cs = ax.contourf(x, y, z, levels=np.arange(-1.8, 1.801, 0.2),
                      cmap=plt.get_cmap('RdBu'),
                      vmin=-0.6,
                      vmax=0.6,
                      extend='both')
    cs1 = ax.contour(x, y, z, levels=np.arange(-2.2, -0.599, 0.2),
                              colors=['y'],
                              linestyles='solid',
                              linewidths=2)
    cs2 = ax.contour(x, y, z, levels=np.arange(0.6, 2.2, 0.2),
                              colors=['c'],
                              linewidths=2)
    cbar = fig.colorbar(cs, ax=ax)
    cbar.add_lines(cs1)
    cbar.add_lines(cs2, erase=False)


@image_comparison(baseline_images=['hist2d'])
def test_hist2d():
    np.random.seed(0)
    #make it not symetric in case we switch x and y axis
    x=np.random.randn(100)*2+5
    y = np.random.randn(100)-2
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist2d(x,y,bins=10)


@image_comparison(baseline_images=['hist2d_transpose'])
def test_hist2d_transpose():
    np.random.seed(0)
    #make sure the the output from np.histogram is transposed before
    #passing to pcolorfast
    x=np.array([5]*100)
    y = np.random.randn(100)-2
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist2d(x,y,bins=10)


@image_comparison(baseline_images=['scatter'])
def test_scatter_plot():
    ax = plt.axes()
    ax.scatter([3, 4, 2, 6], [2, 5, 2, 3], c=['r', 'y', 'b', 'lime'], s=[24, 15, 19, 29])

@cleanup
def test_as_mpl_axes_api():
    # tests the _as_mpl_axes api
    from matplotlib.projections.polar import PolarAxes
    import matplotlib.axes as maxes

    class Polar(object):
        def __init__(self):
            self.theta_offset = 0

        def _as_mpl_axes(self):
            # implement the matplotlib axes interface
            return PolarAxes, {'theta_offset': self.theta_offset}
    prj = Polar()
    prj2 = Polar()
    prj2.theta_offset = np.pi
    prj3 = Polar()

    # testing axes creation with plt.axes
    ax = plt.axes([0, 0, 1, 1], projection=prj)
    assert type(ax) == PolarAxes, \
           'Expected a PolarAxes, got %s' % type(ax)
    ax_via_gca = plt.gca(projection=prj)
    assert ax_via_gca is ax
    plt.close()

    # testing axes creation with gca
    ax = plt.gca(projection=prj)
    assert type(ax) == maxes._subplot_classes[PolarAxes], \
           'Expected a PolarAxesSubplot, got %s' % type(ax)
    ax_via_gca = plt.gca(projection=prj)
    assert ax_via_gca is ax
    # try getting the axes given a different polar projection
    ax_via_gca = plt.gca(projection=prj2)
    assert ax_via_gca is not ax
    assert ax.get_theta_offset() == 0, ax.get_theta_offset()
    assert ax_via_gca.get_theta_offset() == np.pi, ax_via_gca.get_theta_offset()
    # try getting the axes given an == (not is) polar projection
    ax_via_gca = plt.gca(projection=prj3)
    assert ax_via_gca is ax
    plt.close()

    # testing axes creation with subplot
    ax = plt.subplot(121, projection=prj)
    assert type(ax) == maxes._subplot_classes[PolarAxes], \
           'Expected a PolarAxesSubplot, got %s' % type(ax)
    plt.close()

@image_comparison(baseline_images=['log_scales'])
def test_log_scales():
    fig = plt.figure()
    ax = plt.gca()
    plt.plot(np.log(np.linspace(0.1, 100)))
    ax.set_yscale('log', basey=5.5)
    ax.set_xscale('log', basex=9.0)


@image_comparison(baseline_images=['stackplot_test_image'])
def test_stackplot():
    fig = plt.figure()
    x = np.linspace(0, 10, 10)
    y1 = 1.0 * x
    y2 = 2.0 * x + 1
    y3 = 3.0 * x + 2
    ax = fig.add_subplot(1, 1, 1)
    ax.stackplot(x, y1, y2, y3)
    ax.set_xlim((0, 10))
    ax.set_ylim((0, 70))

@image_comparison(baseline_images=['boxplot'])
def test_boxplot():
    x = np.linspace(-7, 7, 140)
    x = np.hstack([-25, x, 25])
    fig = plt.figure()
    ax = fig.add_subplot(111)

    # show 1 boxplot with mpl medians/conf. interfals, 1 with manual values
    ax.boxplot([x, x], bootstrap=10000, usermedians=[None, 1.0],
               conf_intervals=[None, (-1.0, 3.5)], notch=1)
    ax.set_ylim((-30, 30))

@image_comparison(baseline_images=['errorbar_basic',
                                   'errorbar_mixed'])
def test_errorbar():
    x = np.arange(0.1, 4, 0.5)
    y = np.exp(-x)

    yerr = 0.1 + 0.2*np.sqrt(x)
    xerr = 0.1 + yerr

    # First illustrate basic pyplot interface, using defaults where possible.
    fig = plt.figure()
    ax = fig.gca()
    ax.errorbar(x, y, xerr=0.2, yerr=0.4)
    ax.set_title("Simplest errorbars, 0.2 in x, 0.4 in y")

    # Now switch to a more OO interface to exercise more features.
    fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True)
    ax = axs[0,0]
    ax.errorbar(x, y, yerr=yerr, fmt='o')
    ax.set_title('Vert. symmetric')

    # With 4 subplots, reduce the number of axis ticks to avoid crowding.
    ax.locator_params(nbins=4)

    ax = axs[0,1]
    ax.errorbar(x, y, xerr=xerr, fmt='o', alpha=0.4)
    ax.set_title('Hor. symmetric w/ alpha')

    ax = axs[1,0]
    ax.errorbar(x, y, yerr=[yerr, 2*yerr], xerr=[xerr, 2*xerr], fmt='--o')
    ax.set_title('H, V asymmetric')

    ax = axs[1,1]
    ax.set_yscale('log')
    # Here we have to be careful to keep all y values positive:
    ylower = np.maximum(1e-2, y - yerr)
    yerr_lower = y - ylower

    ax.errorbar(x, y, yerr=[yerr_lower, 2*yerr], xerr=xerr,
                        fmt='o', ecolor='g', capthick=2)
    ax.set_title('Mixed sym., log y')

    fig.suptitle('Variable errorbars')

@image_comparison(baseline_images=['hist_stacked_stepfilled'])
def test_hist_stacked_stepfilled():
    # make some data
    d1 = np.linspace(1, 3, 20)
    d2 = np.linspace(0, 10, 50)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist( (d1, d2), histtype="stepfilled", stacked=True)


@image_comparison(baseline_images=['hist_offset'])
def test_hist_offset():
    # make some data
    d1 = np.linspace(0, 10, 50)
    d2 = np.linspace(1, 3, 20)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist(d1, bottom=5)
    ax.hist(d2, bottom=15)


@image_comparison(baseline_images=['hist_stacked_weights'])
def test_hist_stacked_weighted():
    # make some data
    d1 = np.linspace(0, 10, 50)
    d2 = np.linspace(1, 3, 20)
    w1 = np.linspace(0.01, 3.5, 50)
    w2 = np.linspace(0.05, 2., 20)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist( (d1, d2), weights=(w1,w2), histtype="stepfilled", stacked=True)

@image_comparison(baseline_images=['hist_stacked_stepfilled_alpha'])
def test_hist_stacked_stepfilled_alpha():
    # make some data
    d1 = np.linspace(1, 3, 20)
    d2 = np.linspace(0, 10, 50)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist( (d1, d2), histtype="stepfilled", stacked=True, alpha=0.5)

@image_comparison(baseline_images=['hist_stacked_step'])
def test_hist_stacked_step():
    # make some data
    d1 = np.linspace(1, 3, 20)
    d2 = np.linspace(0, 10, 50)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist( (d1, d2), histtype="step", stacked=True)

@image_comparison(baseline_images=['hist_stacked_bar'])
def test_hist_stacked_bar():
    # make some data
    d = [[100, 100, 100, 100, 200, 320, 450, 80, 20, 600, 310, 800], [20, 23, 50, 11, 100, 420], [120, 120, 120, 140, 140, 150, 180], [60, 60, 60, 60, 300, 300, 5, 5, 5, 5, 10, 300], [555, 555, 555, 30, 30, 30, 30, 30, 100, 100, 100, 100, 30, 30], [30, 30, 30, 30, 400, 400, 400, 400, 400, 400, 400, 400]]
    colors = [(0.5759849696758961, 1.0, 0.0), (0.0, 1.0, 0.350624650815206), (0.0, 1.0, 0.6549834156005998), (0.0, 0.6569064625276622, 1.0), (0.28302699607823545, 0.0, 1.0), (0.6849123462299822, 0.0, 1.0)]
    labels = ['green', 'orange', ' yellow', 'magenta', 'black']
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist(d, bins=10, histtype='barstacked', align='mid', color=colors, label=labels)
    ax.legend(loc='upper right', bbox_to_anchor = (1.0, 1.0), ncol=1)

@image_comparison(baseline_images=['transparent_markers'], remove_text=True)
def test_transparent_markers():
    np.random.seed(0)
    data = np.random.random(50)

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(data, 'D', mfc='none', markersize=100)

@image_comparison(baseline_images=['vertex_markers'], extensions=['png'],
                  remove_text=True)
def test_vertex_markers():
    data = range(10)
    marker_as_tuple = ((-1, -1), (1, -1), (1, 1), (-1, 1))
    marker_as_list = [(-1, -1), (1, -1), (1, 1), (-1, 1)]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(data, linestyle='', marker=marker_as_tuple, mfc='k')
    ax.plot(data[::-1], linestyle='', marker=marker_as_list, mfc='b')
    ax.set_xlim([-1, 10])
    ax.set_ylim([-1, 10])


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


@image_comparison(baseline_images=['vline_hline_zorder',
                                   'errorbar_zorder'])
def test_eb_line_zorder():
    x = range(10)

    # First illustrate basic pyplot interface, using defaults where possible.
    fig = plt.figure()
    ax = fig.gca()
    ax.plot(x, lw=10, zorder=5)
    ax.axhline(1, color='red', lw=10, zorder=1)
    ax.axhline(5, color='green', lw=10, zorder=10)
    ax.axvline(7, color='m', lw=10, zorder=7)
    ax.axvline(2, color='k', lw=10, zorder=3)

    ax.set_title("axvline and axhline zorder test")

    # Now switch to a more OO interface to exercise more features.
    fig = plt.figure()
    ax = fig.gca()
    x = range(10)
    y = np.zeros(10)
    yerr = range(10)
    ax.errorbar(x, y, yerr=yerr, zorder=5, lw=5, color='r')
    for j in range(10):
        ax.axhline(j, lw=5, color='k', zorder=j)
        ax.axhline(-j, lw=5, color='k', zorder=j)

    ax.set_title("errorbar zorder test")
