/usr/lib/python2.7/dist-packages/matplotlib/tests/test_image.py is in python-matplotlib 1.4.2-3.1.
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unicode_literals)
import six
import numpy as np
from matplotlib.testing.decorators import image_comparison, knownfailureif, cleanup
from matplotlib.image import BboxImage, imread
from matplotlib.transforms import Bbox
from matplotlib import rcParams
import matplotlib.pyplot as plt
from nose.tools import assert_raises
from numpy.testing import assert_array_equal, assert_array_almost_equal
import io
import os
try:
from PIL import Image
HAS_PIL = True
except ImportError:
HAS_PIL = False
@image_comparison(baseline_images=['image_interps'])
def test_image_interps():
'make the basic nearest, bilinear and bicubic interps'
X = np.arange(100)
X = X.reshape(5, 20)
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1.imshow(X, interpolation='nearest')
ax1.set_title('three interpolations')
ax1.set_ylabel('nearest')
ax2 = fig.add_subplot(312)
ax2.imshow(X, interpolation='bilinear')
ax2.set_ylabel('bilinear')
ax3 = fig.add_subplot(313)
ax3.imshow(X, interpolation='bicubic')
ax3.set_ylabel('bicubic')
@image_comparison(baseline_images=['interp_nearest_vs_none'],
extensions=['pdf', 'svg'], remove_text=True)
def test_interp_nearest_vs_none():
'Test the effect of "nearest" and "none" interpolation'
# Setting dpi to something really small makes the difference very
# visible. This works fine with pdf, since the dpi setting doesn't
# affect anything but images, but the agg output becomes unusably
# small.
rcParams['savefig.dpi'] = 3
X = np.array([[[218, 165, 32], [122, 103, 238]],
[[127, 255, 0], [255, 99, 71]]], dtype=np.uint8)
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax1.imshow(X, interpolation='none')
ax1.set_title('interpolation none')
ax2 = fig.add_subplot(122)
ax2.imshow(X, interpolation='nearest')
ax2.set_title('interpolation nearest')
@image_comparison(baseline_images=['figimage-0', 'figimage-1'], extensions=['png'])
def test_figimage():
'test the figimage method'
for suppressComposite in False, True:
fig = plt.figure(figsize=(2,2), dpi=100)
fig.suppressComposite = suppressComposite
x,y = np.ix_(np.arange(100.0)/100.0, np.arange(100.0)/100.0)
z = np.sin(x**2 + y**2 - x*y)
c = np.sin(20*x**2 + 50*y**2)
img = z + c/5
fig.figimage(img, xo=0, yo=0, origin='lower')
fig.figimage(img[::-1,:], xo=0, yo=100, origin='lower')
fig.figimage(img[:,::-1], xo=100, yo=0, origin='lower')
fig.figimage(img[::-1,::-1], xo=100, yo=100, origin='lower')
@cleanup
def test_image_python_io():
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot([1,2,3])
buffer = io.BytesIO()
fig.savefig(buffer)
buffer.seek(0)
plt.imread(buffer)
@knownfailureif(not HAS_PIL)
def test_imread_pil_uint16():
img = plt.imread(os.path.join(os.path.dirname(__file__),
'baseline_images', 'test_image', 'uint16.tif'))
assert (img.dtype == np.uint16)
assert np.sum(img) == 134184960
# def test_image_unicode_io():
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.plot([1,2,3])
# fname = u"\u0a3a\u0a3a.png"
# fig.savefig(fname)
# plt.imread(fname)
# os.remove(fname)
def test_imsave():
# The goal here is that the user can specify an output logical DPI
# for the image, but this will not actually add any extra pixels
# to the image, it will merely be used for metadata purposes.
# So we do the traditional case (dpi == 1), and the new case (dpi
# == 100) and read the resulting PNG files back in and make sure
# the data is 100% identical.
from numpy import random
random.seed(1)
data = random.rand(256, 128)
buff_dpi1 = io.BytesIO()
plt.imsave(buff_dpi1, data, dpi=1)
buff_dpi100 = io.BytesIO()
plt.imsave(buff_dpi100, data, dpi=100)
buff_dpi1.seek(0)
arr_dpi1 = plt.imread(buff_dpi1)
buff_dpi100.seek(0)
arr_dpi100 = plt.imread(buff_dpi100)
assert arr_dpi1.shape == (256, 128, 4)
assert arr_dpi100.shape == (256, 128, 4)
assert_array_equal(arr_dpi1, arr_dpi100)
def test_imsave_color_alpha():
# Test that imsave accept arrays with ndim=3 where the third dimension is
# color and alpha without raising any exceptions, and that the data is
# acceptably preserved through a save/read roundtrip.
from numpy import random
random.seed(1)
data = random.rand(256, 128, 4)
buff = io.BytesIO()
plt.imsave(buff, data)
buff.seek(0)
arr_buf = plt.imread(buff)
# Recreate the float -> uint8 -> float32 conversion of the data
data = (255*data).astype('uint8').astype('float32')/255
# Wherever alpha values were rounded down to 0, the rgb values all get set
# to 0 during imsave (this is reasonable behaviour).
# Recreate that here:
for j in range(3):
data[data[:, :, 3] == 0, j] = 1
assert_array_equal(data, arr_buf)
@image_comparison(baseline_images=['image_clip'])
def test_image_clip():
from math import pi
fig = plt.figure()
ax = fig.add_subplot(111, projection='hammer')
d = [[1,2],[3,4]]
im = ax.imshow(d, extent=(-pi,pi,-pi/2,pi/2))
@image_comparison(baseline_images=['image_cliprect'])
def test_image_cliprect():
import matplotlib.patches as patches
fig = plt.figure()
ax = fig.add_subplot(111)
d = [[1,2],[3,4]]
im = ax.imshow(d, extent=(0,5,0,5))
rect = patches.Rectangle(xy=(1,1), width=2, height=2, transform=im.axes.transData)
im.set_clip_path(rect)
@image_comparison(baseline_images=['imshow'], remove_text=True)
def test_imshow():
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
arr = np.arange(100).reshape((10, 10))
ax = fig.add_subplot(111)
ax.imshow(arr, interpolation="bilinear", extent=(1,2,1,2))
ax.set_xlim(0,3)
ax.set_ylim(0,3)
@image_comparison(baseline_images=['no_interpolation_origin'], remove_text=True)
def test_no_interpolation_origin():
fig = plt.figure()
ax = fig.add_subplot(211)
ax.imshow(np.arange(100).reshape((2, 50)), origin="lower", interpolation='none')
ax = fig.add_subplot(212)
ax.imshow(np.arange(100).reshape((2, 50)), interpolation='none')
@image_comparison(baseline_images=['image_shift'], remove_text=True,
extensions=['pdf', 'svg'])
def test_image_shift():
from matplotlib.colors import LogNorm
imgData = [[1.0/(x) + 1.0/(y) for x in range(1,100)] for y in range(1,100)]
tMin=734717.945208
tMax=734717.946366
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(imgData, norm=LogNorm(), interpolation='none',
extent=(tMin, tMax, 1, 100))
ax.set_aspect('auto')
@cleanup
def test_image_edges():
f = plt.figure(figsize=[1, 1])
ax = f.add_axes([0, 0, 1, 1], frameon=False)
data = np.tile(np.arange(12), 15).reshape(20, 9)
im = ax.imshow(data, origin='upper',
extent=[-10, 10, -10, 10], interpolation='none',
cmap='gray'
)
x = y = 2
ax.set_xlim([-x, x])
ax.set_ylim([-y, y])
ax.set_xticks([])
ax.set_yticks([])
buf = io.BytesIO()
f.savefig(buf, facecolor=(0, 1, 0))
buf.seek(0)
im = plt.imread(buf)
r, g, b, a = sum(im[:, 0])
r, g, b, a = sum(im[:, -1])
assert g != 100, 'Expected a non-green edge - but sadly, it was.'
@image_comparison(baseline_images=['image_composite_background'], remove_text=True)
def test_image_composite_background():
fig = plt.figure()
ax = fig.add_subplot(111)
arr = np.arange(12).reshape(4, 3)
ax.imshow(arr, extent=[0, 2, 15, 0])
ax.imshow(arr, extent=[4, 6, 15, 0])
ax.set_axis_bgcolor((1, 0, 0, 0.5))
ax.set_xlim([0, 12])
@image_comparison(baseline_images=['image_composite_alpha'], remove_text=True)
def test_image_composite_alpha():
"""
Tests that the alpha value is recognized and correctly applied in the
process of compositing images together.
"""
fig = plt.figure()
ax = fig.add_subplot(111)
arr = np.zeros((11, 21, 4))
arr[:, :, 0] = 1
arr[:, :, 3] = np.concatenate((np.arange(0, 1.1, 0.1), np.arange(0, 1, 0.1)[::-1]))
arr2 = np.zeros((21, 11, 4))
arr2[:, :, 0] = 1
arr2[:, :, 1] = 1
arr2[:, :, 3] = np.concatenate((np.arange(0, 1.1, 0.1), np.arange(0, 1, 0.1)[::-1]))[:, np.newaxis]
ax.imshow(arr, extent=[1, 2, 5, 0], alpha=0.3)
ax.imshow(arr, extent=[2, 3, 5, 0], alpha=0.6)
ax.imshow(arr, extent=[3, 4, 5, 0])
ax.imshow(arr2, extent=[0, 5, 1, 2])
ax.imshow(arr2, extent=[0, 5, 2, 3], alpha=0.6)
ax.imshow(arr2, extent=[0, 5, 3, 4], alpha=0.3)
ax.set_axis_bgcolor((0, 0.5, 0, 1))
ax.set_xlim([0, 5])
ax.set_ylim([5, 0])
@image_comparison(baseline_images=['rasterize_10dpi'], extensions=['pdf','svg'], tol=5e-2, remove_text=True)
def test_rasterize_dpi():
# This test should check rasterized rendering with high output resolution.
# It plots a rasterized line and a normal image with implot. So it will catch
# when images end up in the wrong place in case of non-standard dpi setting.
# Instead of high-res rasterization i use low-res. Therefore the fact that the
# resolution is non-standard is is easily checked by image_comparison.
import numpy as np
import matplotlib.pyplot as plt
img = np.asarray([[1, 2], [3, 4]])
fig, axes = plt.subplots(1, 3, figsize = (3, 1))
axes[0].imshow(img)
axes[1].plot([0,1],[0,1], linewidth=20., rasterized=True)
axes[1].set(xlim = (0,1), ylim = (-1, 2))
axes[2].plot([0,1],[0,1], linewidth=20.)
axes[2].set(xlim = (0,1), ylim = (-1, 2))
# Low-dpi PDF rasterization errors prevent proper image comparison tests.
# Hide detailed structures like the axes spines.
for ax in axes:
ax.set_xticks([])
ax.set_yticks([])
for spine in ax.spines.values():
spine.set_visible(False)
rcParams['savefig.dpi'] = 10
@image_comparison(baseline_images=['bbox_image_inverted'],
extensions=['png', 'pdf'])
def test_bbox_image_inverted():
# This is just used to produce an image to feed to BboxImage
fig = plt.figure()
axes = fig.add_subplot(111)
axes.plot([1, 2, 3])
im_buffer = io.BytesIO()
fig.savefig(im_buffer)
im_buffer.seek(0)
image = imread(im_buffer)
bbox_im = BboxImage(Bbox([[100, 100], [0, 0]]))
bbox_im.set_data(image)
axes.add_artist(bbox_im)
if __name__=='__main__':
import nose
nose.runmodule(argv=['-s','--with-doctest'], exit=False)
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