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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)