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test_axes.py
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import numpy as np from numpy import ma import matplotlib from matplotlib.testing.decorators import image_comparison, knownfailureif 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 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.savefig( 'formatter_ticker_001' ) ax.plot( xdata, ydata1, color='blue', xunits="sec" ) fig.savefig( 'formatter_ticker_002' ) ax.set_xlabel( "x-label 003" ) fig.savefig( 'formatter_ticker_003' ) ax.plot( xdata, ydata2, color='green', xunits="hour" ) ax.set_xlabel( "x-label 004" ) fig.savefig( 'formatter_ticker_004' ) # See SF bug 2846058 # https://sourceforge.net/tracker/?func=detail&aid=2846058&group_id=80706&atid=560720 ax.set_xlabel( "x-label 005" ) ax.autoscale_view() fig.savefig( 'formatter_ticker_005' ) @image_comparison(baseline_images=['offset_points']) 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' ) fig.savefig( '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 ) 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', ) fig.savefig( 'polar_axes' ) #-------------------------------------------------------------------- @image_comparison(baseline_images=['polar_coords']) 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 ) fig.savefig( 'polar_coords' ) @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() fig.savefig( 'fill_units' ) @image_comparison(baseline_images=['single_point']) def test_single_point(): fig = plt.figure() plt.subplot( 211 ) plt.plot( [0], [0], 'o' ) plt.subplot( 212 ) plt.plot( [1], [1], 'o' ) fig.savefig( 'single_point' ) @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' ) fig.savefig( 'single_date' ) @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' ) fig.savefig( 'shaped_data' ) @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' ) fig.savefig( 'const_xy' ) @image_comparison(baseline_images=['polar_wrap_180', 'polar_wrap_360', ]) def test_polar_wrap(): D2R = np.pi / 180.0 fig = plt.figure() #NOTE: resolution=1 really should be the default plt.subplot( 111, polar=True, resolution=1 ) 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] ) fig.savefig( 'polar_wrap_180' ) fig = plt.figure() #NOTE: resolution=1 really should be the default plt.subplot( 111, polar=True, resolution=1 ) 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] ) fig.savefig( 'polar_wrap_360' ) @image_comparison(baseline_images=['polar_units']) def test_polar_units(): import matplotlib.testing.jpl_units as units units.register() pi = np.pi deg = units.UnitDbl( 1.0, "deg" ) 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.savefig( 'polar_units' ) @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) fig.savefig('polar_rmin') @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 ) fig.savefig( 'axvspan_epoch' ) @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 ) fig.savefig( 'axhspan_epoch' ) @image_comparison(baseline_images=['hexbin_extent']) 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]) fig.savefig('hexbin_extent') @image_comparison(baseline_images=['nonfinite_limits']) def test_nonfinite_limits(): x = np.arange(0., np.e, 0.01) y = np.log(x) x[len(x)/2] = np.nan fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x, y) fig.savefig('nonfinite_limits') @image_comparison(baseline_images=['imshow']) 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) fig.savefig('imshow') @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) fig.savefig('imshow_clip') @image_comparison(baseline_images=['polycollection_joinstyle']) 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) ax.set_xticks([]) ax.set_yticks([]) fig.savefig('polycollection_joinstyle') @image_comparison(baseline_images=['fill_between_interpolate'], tol=1e-2) 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) fig.savefig('fill_between_interpolate') @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) fig.savefig('symlog') @image_comparison(baseline_images=['pcolormesh']) 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(121) ax.pcolormesh(Qx,Qz,Z, lw=0.5, edgecolors='k') ax.set_title('lw=0.5') ax.set_xticks([]) ax.set_yticks([]) ax = fig.add_subplot(122) ax.pcolormesh(Qx,Qz,Z, lw=3, edgecolors='k') ax.set_title('lw=3') ax.set_xticks([]) ax.set_yticks([]) fig.savefig('pcolormesh') if __name__=='__main__': import nose nose.runmodule(argv=['-s','--with-doctest'], exit=False)