1157 lines
35 KiB
Python
1157 lines
35 KiB
Python
from contextlib import ExitStack
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from copy import copy
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import io
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import os
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from pathlib import Path
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import platform
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import sys
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import urllib.request
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import warnings
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import numpy as np
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from numpy import ma
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from numpy.testing import assert_array_equal
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from matplotlib import (
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colors, image as mimage, patches, pyplot as plt, style,
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rc_context, rcParams)
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from matplotlib.cbook import MatplotlibDeprecationWarning
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from matplotlib.image import (AxesImage, BboxImage, FigureImage,
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NonUniformImage, PcolorImage)
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from matplotlib.testing.decorators import check_figures_equal, image_comparison
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from matplotlib.transforms import Bbox, Affine2D, TransformedBbox
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import pytest
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@image_comparison(['image_interps'], style='mpl20')
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def test_image_interps():
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'make the basic nearest, bilinear and bicubic interps'
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# Remove this line when this test image is regenerated.
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plt.rcParams['text.kerning_factor'] = 6
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X = np.arange(100)
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X = X.reshape(5, 20)
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fig = plt.figure()
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ax1 = fig.add_subplot(311)
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ax1.imshow(X, interpolation='nearest')
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ax1.set_title('three interpolations')
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ax1.set_ylabel('nearest')
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ax2 = fig.add_subplot(312)
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ax2.imshow(X, interpolation='bilinear')
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ax2.set_ylabel('bilinear')
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ax3 = fig.add_subplot(313)
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ax3.imshow(X, interpolation='bicubic')
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ax3.set_ylabel('bicubic')
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@image_comparison(['interp_alpha.png'], remove_text=True)
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def test_alpha_interp():
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'Test the interpolation of the alpha channel on RGBA images'
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fig, (axl, axr) = plt.subplots(1, 2)
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# full green image
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img = np.zeros((5, 5, 4))
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img[..., 1] = np.ones((5, 5))
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# transparent under main diagonal
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img[..., 3] = np.tril(np.ones((5, 5), dtype=np.uint8))
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axl.imshow(img, interpolation="none")
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axr.imshow(img, interpolation="bilinear")
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@image_comparison(['interp_nearest_vs_none'],
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extensions=['pdf', 'svg'], remove_text=True)
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def test_interp_nearest_vs_none():
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'Test the effect of "nearest" and "none" interpolation'
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# Setting dpi to something really small makes the difference very
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# visible. This works fine with pdf, since the dpi setting doesn't
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# affect anything but images, but the agg output becomes unusably
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# small.
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rcParams['savefig.dpi'] = 3
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X = np.array([[[218, 165, 32], [122, 103, 238]],
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[[127, 255, 0], [255, 99, 71]]], dtype=np.uint8)
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fig = plt.figure()
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ax1 = fig.add_subplot(121)
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ax1.imshow(X, interpolation='none')
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ax1.set_title('interpolation none')
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ax2 = fig.add_subplot(122)
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ax2.imshow(X, interpolation='nearest')
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ax2.set_title('interpolation nearest')
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def do_figimage(suppressComposite):
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"""Helper for the next two tests."""
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fig = plt.figure(figsize=(2, 2), dpi=100)
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fig.suppressComposite = suppressComposite
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x, y = np.ix_(np.arange(100) / 100.0, np.arange(100) / 100)
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z = np.sin(x**2 + y**2 - x*y)
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c = np.sin(20*x**2 + 50*y**2)
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img = z + c/5
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fig.figimage(img, xo=0, yo=0, origin='lower')
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fig.figimage(img[::-1, :], xo=0, yo=100, origin='lower')
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fig.figimage(img[:, ::-1], xo=100, yo=0, origin='lower')
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fig.figimage(img[::-1, ::-1], xo=100, yo=100, origin='lower')
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@image_comparison(['figimage-0'], extensions=['png', 'pdf'])
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def test_figimage0():
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suppressComposite = False
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do_figimage(suppressComposite)
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@image_comparison(['figimage-1'], extensions=['png', 'pdf'])
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def test_figimage1():
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suppressComposite = True
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do_figimage(suppressComposite)
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def test_image_python_io():
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fig, ax = plt.subplots()
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ax.plot([1, 2, 3])
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buffer = io.BytesIO()
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fig.savefig(buffer)
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buffer.seek(0)
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plt.imread(buffer)
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@check_figures_equal(extensions=['png'])
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def test_imshow_subsample(fig_test, fig_ref):
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# data is bigger than figure, so subsampling with hanning
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np.random.seed(19680801)
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dpi = 100
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A = np.random.rand(int(dpi * 5), int(dpi * 5))
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for fig in [fig_test, fig_ref]:
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fig.set_size_inches(2, 2)
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axs = fig_test.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='antialiased')
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axs = fig_ref.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='hanning')
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@check_figures_equal(extensions=['png'])
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def test_imshow_samesample(fig_test, fig_ref):
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# exact resample, so should be same as nearest....
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np.random.seed(19680801)
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dpi = 100
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A = np.random.rand(int(dpi * 5), int(dpi * 5))
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for fig in [fig_test, fig_ref]:
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fig.set_size_inches(5, 5)
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axs = fig_test.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='antialiased')
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axs = fig_ref.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='nearest')
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@check_figures_equal(extensions=['png'])
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def test_imshow_doublesample(fig_test, fig_ref):
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# should be exactly a double sample, so should use nearest neighbour
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# which is the same as "none"
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np.random.seed(19680801)
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dpi = 100
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A = np.random.rand(int(dpi * 5), int(dpi * 5))
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for fig in [fig_test, fig_ref]:
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fig.set_size_inches(10, 10)
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axs = fig_test.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='antialiased')
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axs = fig_ref.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='nearest')
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@check_figures_equal(extensions=['png'])
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def test_imshow_upsample(fig_test, fig_ref):
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# should be less than 3 upsample, so should be nearest...
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np.random.seed(19680801)
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dpi = 100
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A = np.random.rand(int(dpi * 3), int(dpi * 3))
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for fig in [fig_test, fig_ref]:
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fig.set_size_inches(2.9, 2.9)
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axs = fig_test.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='antialiased')
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axs = fig_ref.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='hanning')
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@check_figures_equal(extensions=['png'])
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def test_imshow_upsample3(fig_test, fig_ref):
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# should be greater than 3 upsample, so should be nearest...
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np.random.seed(19680801)
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dpi = 100
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A = np.random.rand(int(dpi * 3), int(dpi * 3))
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for fig in [fig_test, fig_ref]:
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fig.set_size_inches(9.1, 9.1)
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axs = fig_test.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='antialiased')
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axs = fig_ref.subplots()
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axs.set_position([0, 0, 1, 1])
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axs.imshow(A, interpolation='nearest')
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@check_figures_equal(extensions=['png'])
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def test_imshow_zoom(fig_test, fig_ref):
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# should be less than 3 upsample, so should be nearest...
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np.random.seed(19680801)
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dpi = 100
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A = np.random.rand(int(dpi * 3), int(dpi * 3))
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for fig in [fig_test, fig_ref]:
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fig.set_size_inches(2.9, 2.9)
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axs = fig_test.subplots()
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axs.imshow(A, interpolation='nearest')
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axs.set_xlim([10, 20])
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axs.set_ylim([10, 20])
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axs = fig_ref.subplots()
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axs.imshow(A, interpolation='antialiased')
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axs.set_xlim([10, 20])
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axs.set_ylim([10, 20])
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@check_figures_equal()
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def test_imshow_pil(fig_test, fig_ref):
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style.use("default")
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PIL = pytest.importorskip("PIL")
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# Pillow<=6.0 fails to open pathlib.Paths on Windows (pillow#3823), and
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# Matplotlib's builtin png opener doesn't handle them either.
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png_path = str(
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Path(__file__).parent / "baseline_images/pngsuite/basn3p04.png")
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tiff_path = str(
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Path(__file__).parent / "baseline_images/test_image/uint16.tif")
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axs = fig_test.subplots(2)
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axs[0].imshow(PIL.Image.open(png_path))
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axs[1].imshow(PIL.Image.open(tiff_path))
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axs = fig_ref.subplots(2)
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axs[0].imshow(plt.imread(png_path))
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axs[1].imshow(plt.imread(tiff_path))
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def test_imread_pil_uint16():
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pytest.importorskip("PIL")
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img = plt.imread(os.path.join(os.path.dirname(__file__),
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'baseline_images', 'test_image', 'uint16.tif'))
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assert img.dtype == np.uint16
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assert np.sum(img) == 134184960
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def test_imread_fspath():
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pytest.importorskip("PIL")
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img = plt.imread(
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Path(__file__).parent / 'baseline_images/test_image/uint16.tif')
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assert img.dtype == np.uint16
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assert np.sum(img) == 134184960
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@pytest.mark.parametrize("fmt", ["png", "jpg", "jpeg", "tiff"])
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def test_imsave(fmt):
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if fmt in ["jpg", "jpeg", "tiff"]:
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pytest.importorskip("PIL")
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has_alpha = fmt not in ["jpg", "jpeg"]
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# The goal here is that the user can specify an output logical DPI
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# for the image, but this will not actually add any extra pixels
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# to the image, it will merely be used for metadata purposes.
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# So we do the traditional case (dpi == 1), and the new case (dpi
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# == 100) and read the resulting PNG files back in and make sure
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# the data is 100% identical.
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np.random.seed(1)
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# The height of 1856 pixels was selected because going through creating an
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# actual dpi=100 figure to save the image to a Pillow-provided format would
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# cause a rounding error resulting in a final image of shape 1855.
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data = np.random.rand(1856, 2)
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buff_dpi1 = io.BytesIO()
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plt.imsave(buff_dpi1, data, format=fmt, dpi=1)
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buff_dpi100 = io.BytesIO()
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plt.imsave(buff_dpi100, data, format=fmt, dpi=100)
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buff_dpi1.seek(0)
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arr_dpi1 = plt.imread(buff_dpi1, format=fmt)
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buff_dpi100.seek(0)
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arr_dpi100 = plt.imread(buff_dpi100, format=fmt)
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assert arr_dpi1.shape == (1856, 2, 3 + has_alpha)
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assert arr_dpi100.shape == (1856, 2, 3 + has_alpha)
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assert_array_equal(arr_dpi1, arr_dpi100)
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@pytest.mark.parametrize("fmt", ["png", "pdf", "ps", "eps", "svg"])
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def test_imsave_fspath(fmt):
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plt.imsave(Path(os.devnull), np.array([[0, 1]]), format=fmt)
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def test_imsave_color_alpha():
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# Test that imsave accept arrays with ndim=3 where the third dimension is
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# color and alpha without raising any exceptions, and that the data is
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# acceptably preserved through a save/read roundtrip.
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np.random.seed(1)
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for origin in ['lower', 'upper']:
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data = np.random.rand(16, 16, 4)
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buff = io.BytesIO()
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plt.imsave(buff, data, origin=origin, format="png")
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buff.seek(0)
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arr_buf = plt.imread(buff)
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# Recreate the float -> uint8 conversion of the data
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# We can only expect to be the same with 8 bits of precision,
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# since that's what the PNG file used.
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data = (255*data).astype('uint8')
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if origin == 'lower':
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data = data[::-1]
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arr_buf = (255*arr_buf).astype('uint8')
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assert_array_equal(data, arr_buf)
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def test_imsave_pil_kwargs_png():
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Image = pytest.importorskip("PIL.Image")
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from PIL.PngImagePlugin import PngInfo
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buf = io.BytesIO()
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pnginfo = PngInfo()
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pnginfo.add_text("Software", "test")
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plt.imsave(buf, [[0, 1], [2, 3]],
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format="png", pil_kwargs={"pnginfo": pnginfo})
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im = Image.open(buf)
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assert im.info["Software"] == "test"
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def test_imsave_pil_kwargs_tiff():
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Image = pytest.importorskip("PIL.Image")
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from PIL.TiffTags import TAGS_V2 as TAGS
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buf = io.BytesIO()
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pil_kwargs = {"description": "test image"}
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plt.imsave(buf, [[0, 1], [2, 3]], format="tiff", pil_kwargs=pil_kwargs)
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im = Image.open(buf)
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tags = {TAGS[k].name: v for k, v in im.tag_v2.items()}
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assert tags["ImageDescription"] == "test image"
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@image_comparison(['image_alpha'], remove_text=True)
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def test_image_alpha():
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plt.figure()
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np.random.seed(0)
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Z = np.random.rand(6, 6)
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plt.subplot(131)
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plt.imshow(Z, alpha=1.0, interpolation='none')
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plt.subplot(132)
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plt.imshow(Z, alpha=0.5, interpolation='none')
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plt.subplot(133)
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plt.imshow(Z, alpha=0.5, interpolation='nearest')
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def test_cursor_data():
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from matplotlib.backend_bases import MouseEvent
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fig, ax = plt.subplots()
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im = ax.imshow(np.arange(100).reshape(10, 10), origin='upper')
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x, y = 4, 4
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xdisp, ydisp = ax.transData.transform([x, y])
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event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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assert im.get_cursor_data(event) == 44
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# Now try for a point outside the image
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# Tests issue #4957
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x, y = 10.1, 4
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xdisp, ydisp = ax.transData.transform([x, y])
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event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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assert im.get_cursor_data(event) is None
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# Hmm, something is wrong here... I get 0, not None...
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# But, this works further down in the tests with extents flipped
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#x, y = 0.1, -0.1
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#xdisp, ydisp = ax.transData.transform([x, y])
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#event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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#z = im.get_cursor_data(event)
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#assert z is None, "Did not get None, got %d" % z
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ax.clear()
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# Now try with the extents flipped.
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im = ax.imshow(np.arange(100).reshape(10, 10), origin='lower')
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x, y = 4, 4
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xdisp, ydisp = ax.transData.transform([x, y])
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event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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assert im.get_cursor_data(event) == 44
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fig, ax = plt.subplots()
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im = ax.imshow(np.arange(100).reshape(10, 10), extent=[0, 0.5, 0, 0.5])
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x, y = 0.25, 0.25
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xdisp, ydisp = ax.transData.transform([x, y])
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event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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assert im.get_cursor_data(event) == 55
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# Now try for a point outside the image
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# Tests issue #4957
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x, y = 0.75, 0.25
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xdisp, ydisp = ax.transData.transform([x, y])
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event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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assert im.get_cursor_data(event) is None
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x, y = 0.01, -0.01
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xdisp, ydisp = ax.transData.transform([x, y])
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event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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assert im.get_cursor_data(event) is None
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@pytest.mark.parametrize(
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"data, text_without_colorbar, text_with_colorbar", [
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([[10001, 10000]], "[1e+04]", "[10001]"),
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([[.123, .987]], "[0.123]", "[0.123]"),
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])
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def test_format_cursor_data(data, text_without_colorbar, text_with_colorbar):
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from matplotlib.backend_bases import MouseEvent
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fig, ax = plt.subplots()
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im = ax.imshow(data)
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xdisp, ydisp = ax.transData.transform([0, 0])
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event = MouseEvent('motion_notify_event', fig.canvas, xdisp, ydisp)
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assert im.get_cursor_data(event) == data[0][0]
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assert im.format_cursor_data(im.get_cursor_data(event)) \
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== text_without_colorbar
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fig.colorbar(im)
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fig.canvas.draw() # This is necessary to set up the colorbar formatter.
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assert im.get_cursor_data(event) == data[0][0]
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assert im.format_cursor_data(im.get_cursor_data(event)) \
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== text_with_colorbar
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@image_comparison(['image_clip'], style='mpl20')
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def test_image_clip():
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d = [[1, 2], [3, 4]]
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fig, ax = plt.subplots()
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im = ax.imshow(d)
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patch = patches.Circle((0, 0), radius=1, transform=ax.transData)
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im.set_clip_path(patch)
|
|
|
|
|
|
@image_comparison(['image_cliprect'], style='mpl20')
|
|
def test_image_cliprect():
|
|
import matplotlib.patches as patches
|
|
|
|
fig, ax = plt.subplots()
|
|
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(['imshow'], remove_text=True, style='mpl20')
|
|
def test_imshow():
|
|
fig, ax = plt.subplots()
|
|
arr = np.arange(100).reshape((10, 10))
|
|
ax.imshow(arr, interpolation="bilinear", extent=(1, 2, 1, 2))
|
|
ax.set_xlim(0, 3)
|
|
ax.set_ylim(0, 3)
|
|
|
|
|
|
@image_comparison(['no_interpolation_origin'], remove_text=True)
|
|
def test_no_interpolation_origin():
|
|
fig, axs = plt.subplots(2)
|
|
axs[0].imshow(np.arange(100).reshape((2, 50)), origin="lower",
|
|
interpolation='none')
|
|
axs[1].imshow(np.arange(100).reshape((2, 50)), interpolation='none')
|
|
|
|
|
|
@image_comparison(['image_shift'], remove_text=True, extensions=['pdf', 'svg'])
|
|
def test_image_shift():
|
|
from matplotlib.colors import LogNorm
|
|
|
|
imgData = [[1 / x + 1 / y for x in range(1, 100)] for y in range(1, 100)]
|
|
tMin = 734717.945208
|
|
tMax = 734717.946366
|
|
|
|
fig, ax = plt.subplots()
|
|
ax.imshow(imgData, norm=LogNorm(), interpolation='none',
|
|
extent=(tMin, tMax, 1, 100))
|
|
ax.set_aspect('auto')
|
|
|
|
|
|
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(['image_composite_background'],
|
|
remove_text=True, style='mpl20')
|
|
def test_image_composite_background():
|
|
fig, ax = plt.subplots()
|
|
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_facecolor((1, 0, 0, 0.5))
|
|
ax.set_xlim([0, 12])
|
|
|
|
|
|
@image_comparison(['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, ax = plt.subplots()
|
|
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_facecolor((0, 0.5, 0, 1))
|
|
ax.set_xlim([0, 5])
|
|
ax.set_ylim([5, 0])
|
|
|
|
|
|
@image_comparison(['rasterize_10dpi'],
|
|
extensions=['pdf', 'svg'], remove_text=True, style='mpl20')
|
|
def test_rasterize_dpi():
|
|
# This test should check rasterized rendering with high output resolution.
|
|
# It plots a rasterized line and a normal image with imshow. 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 easily checked by
|
|
# image_comparison.
|
|
img = np.asarray([[1, 2], [3, 4]])
|
|
|
|
fig, axs = plt.subplots(1, 3, figsize=(3, 1))
|
|
|
|
axs[0].imshow(img)
|
|
|
|
axs[1].plot([0, 1], [0, 1], linewidth=20., rasterized=True)
|
|
axs[1].set(xlim=(0, 1), ylim=(-1, 2))
|
|
|
|
axs[2].plot([0, 1], [0, 1], linewidth=20.)
|
|
axs[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 axs:
|
|
ax.set_xticks([])
|
|
ax.set_yticks([])
|
|
for spine in ax.spines.values():
|
|
spine.set_visible(False)
|
|
|
|
rcParams['savefig.dpi'] = 10
|
|
|
|
|
|
@image_comparison(['bbox_image_inverted'], remove_text=True, style='mpl20')
|
|
def test_bbox_image_inverted():
|
|
# This is just used to produce an image to feed to BboxImage
|
|
image = np.arange(100).reshape((10, 10))
|
|
|
|
fig, ax = plt.subplots()
|
|
bbox_im = BboxImage(
|
|
TransformedBbox(Bbox([[100, 100], [0, 0]]), ax.transData),
|
|
interpolation='nearest')
|
|
bbox_im.set_data(image)
|
|
bbox_im.set_clip_on(False)
|
|
ax.set_xlim(0, 100)
|
|
ax.set_ylim(0, 100)
|
|
ax.add_artist(bbox_im)
|
|
|
|
image = np.identity(10)
|
|
|
|
bbox_im = BboxImage(TransformedBbox(Bbox([[0.1, 0.2], [0.3, 0.25]]),
|
|
ax.figure.transFigure),
|
|
interpolation='nearest')
|
|
bbox_im.set_data(image)
|
|
bbox_im.set_clip_on(False)
|
|
ax.add_artist(bbox_im)
|
|
|
|
|
|
def test_get_window_extent_for_AxisImage():
|
|
# Create a figure of known size (1000x1000 pixels), place an image
|
|
# object at a given location and check that get_window_extent()
|
|
# returns the correct bounding box values (in pixels).
|
|
|
|
im = np.array([[0.25, 0.75, 1.0, 0.75], [0.1, 0.65, 0.5, 0.4],
|
|
[0.6, 0.3, 0.0, 0.2], [0.7, 0.9, 0.4, 0.6]])
|
|
fig, ax = plt.subplots(figsize=(10, 10), dpi=100)
|
|
ax.set_position([0, 0, 1, 1])
|
|
ax.set_xlim(0, 1)
|
|
ax.set_ylim(0, 1)
|
|
im_obj = ax.imshow(
|
|
im, extent=[0.4, 0.7, 0.2, 0.9], interpolation='nearest')
|
|
|
|
fig.canvas.draw()
|
|
renderer = fig.canvas.renderer
|
|
im_bbox = im_obj.get_window_extent(renderer)
|
|
|
|
assert_array_equal(im_bbox.get_points(), [[400, 200], [700, 900]])
|
|
|
|
|
|
@image_comparison(['zoom_and_clip_upper_origin.png'],
|
|
remove_text=True, style='mpl20')
|
|
def test_zoom_and_clip_upper_origin():
|
|
image = np.arange(100)
|
|
image = image.reshape((10, 10))
|
|
|
|
fig, ax = plt.subplots()
|
|
ax.imshow(image)
|
|
ax.set_ylim(2.0, -0.5)
|
|
ax.set_xlim(-0.5, 2.0)
|
|
|
|
|
|
def test_nonuniformimage_setcmap():
|
|
ax = plt.gca()
|
|
im = NonUniformImage(ax)
|
|
im.set_cmap('Blues')
|
|
|
|
|
|
def test_nonuniformimage_setnorm():
|
|
ax = plt.gca()
|
|
im = NonUniformImage(ax)
|
|
im.set_norm(plt.Normalize())
|
|
|
|
|
|
def test_jpeg_2d():
|
|
Image = pytest.importorskip('PIL.Image')
|
|
# smoke test that mode-L pillow images work.
|
|
imd = np.ones((10, 10), dtype='uint8')
|
|
for i in range(10):
|
|
imd[i, :] = np.linspace(0.0, 1.0, 10) * 255
|
|
im = Image.new('L', (10, 10))
|
|
im.putdata(imd.flatten())
|
|
fig, ax = plt.subplots()
|
|
ax.imshow(im)
|
|
|
|
|
|
def test_jpeg_alpha():
|
|
Image = pytest.importorskip('PIL.Image')
|
|
|
|
plt.figure(figsize=(1, 1), dpi=300)
|
|
# Create an image that is all black, with a gradient from 0-1 in
|
|
# the alpha channel from left to right.
|
|
im = np.zeros((300, 300, 4), dtype=float)
|
|
im[..., 3] = np.linspace(0.0, 1.0, 300)
|
|
|
|
plt.figimage(im)
|
|
|
|
buff = io.BytesIO()
|
|
plt.savefig(buff, facecolor="red", format='jpg', dpi=300)
|
|
|
|
buff.seek(0)
|
|
image = Image.open(buff)
|
|
|
|
# If this fails, there will be only one color (all black). If this
|
|
# is working, we should have all 256 shades of grey represented.
|
|
num_colors = len(image.getcolors(256))
|
|
assert 175 <= num_colors <= 185
|
|
# The fully transparent part should be red.
|
|
corner_pixel = image.getpixel((0, 0))
|
|
assert corner_pixel == (254, 0, 0)
|
|
|
|
|
|
def test_nonuniformimage_setdata():
|
|
ax = plt.gca()
|
|
im = NonUniformImage(ax)
|
|
x = np.arange(3, dtype=float)
|
|
y = np.arange(4, dtype=float)
|
|
z = np.arange(12, dtype=float).reshape((4, 3))
|
|
im.set_data(x, y, z)
|
|
x[0] = y[0] = z[0, 0] = 9.9
|
|
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
|
|
|
|
|
|
def test_axesimage_setdata():
|
|
ax = plt.gca()
|
|
im = AxesImage(ax)
|
|
z = np.arange(12, dtype=float).reshape((4, 3))
|
|
im.set_data(z)
|
|
z[0, 0] = 9.9
|
|
assert im._A[0, 0] == 0, 'value changed'
|
|
|
|
|
|
def test_figureimage_setdata():
|
|
fig = plt.gcf()
|
|
im = FigureImage(fig)
|
|
z = np.arange(12, dtype=float).reshape((4, 3))
|
|
im.set_data(z)
|
|
z[0, 0] = 9.9
|
|
assert im._A[0, 0] == 0, 'value changed'
|
|
|
|
|
|
def test_pcolorimage_setdata():
|
|
ax = plt.gca()
|
|
im = PcolorImage(ax)
|
|
x = np.arange(3, dtype=float)
|
|
y = np.arange(4, dtype=float)
|
|
z = np.arange(6, dtype=float).reshape((3, 2))
|
|
im.set_data(x, y, z)
|
|
x[0] = y[0] = z[0, 0] = 9.9
|
|
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'
|
|
|
|
|
|
def test_minimized_rasterized():
|
|
# This ensures that the rasterized content in the colorbars is
|
|
# only as thick as the colorbar, and doesn't extend to other parts
|
|
# of the image. See #5814. While the original bug exists only
|
|
# in Postscript, the best way to detect it is to generate SVG
|
|
# and then parse the output to make sure the two colorbar images
|
|
# are the same size.
|
|
from xml.etree import ElementTree
|
|
|
|
np.random.seed(0)
|
|
data = np.random.rand(10, 10)
|
|
|
|
fig, ax = plt.subplots(1, 2)
|
|
p1 = ax[0].pcolormesh(data)
|
|
p2 = ax[1].pcolormesh(data)
|
|
|
|
plt.colorbar(p1, ax=ax[0])
|
|
plt.colorbar(p2, ax=ax[1])
|
|
|
|
buff = io.BytesIO()
|
|
plt.savefig(buff, format='svg')
|
|
|
|
buff = io.BytesIO(buff.getvalue())
|
|
tree = ElementTree.parse(buff)
|
|
width = None
|
|
for image in tree.iter('image'):
|
|
if width is None:
|
|
width = image['width']
|
|
else:
|
|
if image['width'] != width:
|
|
assert False
|
|
|
|
|
|
def test_load_from_url():
|
|
path = Path(__file__).parent / "baseline_images/test_image/imshow.png"
|
|
url = ('file:'
|
|
+ ('///' if sys.platform == 'win32' else '')
|
|
+ path.resolve().as_posix())
|
|
plt.imread(url)
|
|
plt.imread(urllib.request.urlopen(url))
|
|
|
|
|
|
@image_comparison(['log_scale_image'], remove_text=True)
|
|
def test_log_scale_image():
|
|
Z = np.zeros((10, 10))
|
|
Z[::2] = 1
|
|
|
|
fig, ax = plt.subplots()
|
|
ax.imshow(Z, extent=[1, 100, 1, 100], cmap='viridis', vmax=1, vmin=-1,
|
|
aspect='auto')
|
|
ax.set(yscale='log')
|
|
|
|
|
|
@image_comparison(['rotate_image'], remove_text=True)
|
|
def test_rotate_image():
|
|
delta = 0.25
|
|
x = y = np.arange(-3.0, 3.0, delta)
|
|
X, Y = np.meshgrid(x, y)
|
|
Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
|
|
Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
|
|
(2 * np.pi * 0.5 * 1.5))
|
|
Z = Z2 - Z1 # difference of Gaussians
|
|
|
|
fig, ax1 = plt.subplots(1, 1)
|
|
im1 = ax1.imshow(Z, interpolation='none', cmap='viridis',
|
|
origin='lower',
|
|
extent=[-2, 4, -3, 2], clip_on=True)
|
|
|
|
trans_data2 = Affine2D().rotate_deg(30) + ax1.transData
|
|
im1.set_transform(trans_data2)
|
|
|
|
# display intended extent of the image
|
|
x1, x2, y1, y2 = im1.get_extent()
|
|
|
|
ax1.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], "r--", lw=3,
|
|
transform=trans_data2)
|
|
|
|
ax1.set_xlim(2, 5)
|
|
ax1.set_ylim(0, 4)
|
|
|
|
|
|
def test_image_preserve_size():
|
|
buff = io.BytesIO()
|
|
|
|
im = np.zeros((481, 321))
|
|
plt.imsave(buff, im, format="png")
|
|
|
|
buff.seek(0)
|
|
img = plt.imread(buff)
|
|
|
|
assert img.shape[:2] == im.shape
|
|
|
|
|
|
def test_image_preserve_size2():
|
|
n = 7
|
|
data = np.identity(n, float)
|
|
|
|
fig = plt.figure(figsize=(n, n), frameon=False)
|
|
|
|
ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])
|
|
ax.set_axis_off()
|
|
fig.add_axes(ax)
|
|
ax.imshow(data, interpolation='nearest', origin='lower', aspect='auto')
|
|
buff = io.BytesIO()
|
|
fig.savefig(buff, dpi=1)
|
|
|
|
buff.seek(0)
|
|
img = plt.imread(buff)
|
|
|
|
assert img.shape == (7, 7, 4)
|
|
|
|
assert_array_equal(np.asarray(img[:, :, 0], bool),
|
|
np.identity(n, bool)[::-1])
|
|
|
|
|
|
@image_comparison(['mask_image_over_under.png'], remove_text=True)
|
|
def test_mask_image_over_under():
|
|
delta = 0.025
|
|
x = y = np.arange(-3.0, 3.0, delta)
|
|
X, Y = np.meshgrid(x, y)
|
|
Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
|
|
Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
|
|
(2 * np.pi * 0.5 * 1.5))
|
|
Z = 10*(Z2 - Z1) # difference of Gaussians
|
|
|
|
palette = copy(plt.cm.gray)
|
|
palette.set_over('r', 1.0)
|
|
palette.set_under('g', 1.0)
|
|
palette.set_bad('b', 1.0)
|
|
Zm = ma.masked_where(Z > 1.2, Z)
|
|
fig, (ax1, ax2) = plt.subplots(1, 2)
|
|
im = ax1.imshow(Zm, interpolation='bilinear',
|
|
cmap=palette,
|
|
norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
|
|
origin='lower', extent=[-3, 3, -3, 3])
|
|
ax1.set_title('Green=low, Red=high, Blue=bad')
|
|
fig.colorbar(im, extend='both', orientation='horizontal',
|
|
ax=ax1, aspect=10)
|
|
|
|
im = ax2.imshow(Zm, interpolation='nearest',
|
|
cmap=palette,
|
|
norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
|
|
ncolors=256, clip=False),
|
|
origin='lower', extent=[-3, 3, -3, 3])
|
|
ax2.set_title('With BoundaryNorm')
|
|
fig.colorbar(im, extend='both', spacing='proportional',
|
|
orientation='horizontal', ax=ax2, aspect=10)
|
|
|
|
|
|
@image_comparison(['mask_image'], remove_text=True)
|
|
def test_mask_image():
|
|
# Test mask image two ways: Using nans and using a masked array.
|
|
|
|
fig, (ax1, ax2) = plt.subplots(1, 2)
|
|
|
|
A = np.ones((5, 5))
|
|
A[1:2, 1:2] = np.nan
|
|
|
|
ax1.imshow(A, interpolation='nearest')
|
|
|
|
A = np.zeros((5, 5), dtype=bool)
|
|
A[1:2, 1:2] = True
|
|
A = np.ma.masked_array(np.ones((5, 5), dtype=np.uint16), A)
|
|
|
|
ax2.imshow(A, interpolation='nearest')
|
|
|
|
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|
@image_comparison(['imshow_endianess.png'], remove_text=True)
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|
def test_imshow_endianess():
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|
x = np.arange(10)
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|
X, Y = np.meshgrid(x, x)
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|
Z = np.hypot(X - 5, Y - 5)
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|
|
|
fig, (ax1, ax2) = plt.subplots(1, 2)
|
|
|
|
kwargs = dict(origin="lower", interpolation='nearest', cmap='viridis')
|
|
|
|
ax1.imshow(Z.astype('<f8'), **kwargs)
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|
ax2.imshow(Z.astype('>f8'), **kwargs)
|
|
|
|
|
|
@image_comparison(['imshow_masked_interpolation'],
|
|
tol={'aarch64': 0.02}.get(platform.machine(), 0.0),
|
|
remove_text=True, style='mpl20')
|
|
def test_imshow_masked_interpolation():
|
|
|
|
cm = copy(plt.get_cmap('viridis'))
|
|
cm.set_over('r')
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|
cm.set_under('b')
|
|
cm.set_bad('k')
|
|
|
|
N = 20
|
|
n = colors.Normalize(vmin=0, vmax=N*N-1)
|
|
|
|
data = np.arange(N*N, dtype='float').reshape(N, N)
|
|
|
|
data[5, 5] = -1
|
|
# This will cause crazy ringing for the higher-order
|
|
# interpolations
|
|
data[15, 5] = 1e5
|
|
|
|
# data[3, 3] = np.nan
|
|
|
|
data[15, 15] = np.inf
|
|
|
|
mask = np.zeros_like(data).astype('bool')
|
|
mask[5, 15] = True
|
|
|
|
data = np.ma.masked_array(data, mask)
|
|
|
|
fig, ax_grid = plt.subplots(3, 6)
|
|
interps = sorted(mimage._interpd_)
|
|
interps.remove('antialiased')
|
|
|
|
for interp, ax in zip(interps, ax_grid.ravel()):
|
|
ax.set_title(interp)
|
|
ax.imshow(data, norm=n, cmap=cm, interpolation=interp)
|
|
ax.axis('off')
|
|
|
|
|
|
def test_imshow_no_warn_invalid():
|
|
with warnings.catch_warnings(record=True) as warns:
|
|
warnings.simplefilter("always")
|
|
plt.imshow([[1, 2], [3, np.nan]])
|
|
assert len(warns) == 0
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
'dtype', [np.dtype(s) for s in 'u2 u4 i2 i4 i8 f4 f8'.split()])
|
|
def test_imshow_clips_rgb_to_valid_range(dtype):
|
|
arr = np.arange(300, dtype=dtype).reshape((10, 10, 3))
|
|
if dtype.kind != 'u':
|
|
arr -= 10
|
|
too_low = arr < 0
|
|
too_high = arr > 255
|
|
if dtype.kind == 'f':
|
|
arr = arr / 255
|
|
_, ax = plt.subplots()
|
|
out = ax.imshow(arr).get_array()
|
|
assert (out[too_low] == 0).all()
|
|
if dtype.kind == 'f':
|
|
assert (out[too_high] == 1).all()
|
|
assert out.dtype.kind == 'f'
|
|
else:
|
|
assert (out[too_high] == 255).all()
|
|
assert out.dtype == np.uint8
|
|
|
|
|
|
@image_comparison(['imshow_flatfield.png'], remove_text=True, style='mpl20')
|
|
def test_imshow_flatfield():
|
|
fig, ax = plt.subplots()
|
|
im = ax.imshow(np.ones((5, 5)), interpolation='nearest')
|
|
im.set_clim(.5, 1.5)
|
|
|
|
|
|
@image_comparison(['imshow_bignumbers.png'], remove_text=True, style='mpl20')
|
|
def test_imshow_bignumbers():
|
|
rcParams['image.interpolation'] = 'nearest'
|
|
# putting a big number in an array of integers shouldn't
|
|
# ruin the dynamic range of the resolved bits.
|
|
fig, ax = plt.subplots()
|
|
img = np.array([[1, 2, 1e12], [3, 1, 4]], dtype=np.uint64)
|
|
pc = ax.imshow(img)
|
|
pc.set_clim(0, 5)
|
|
|
|
|
|
@image_comparison(['imshow_bignumbers_real.png'],
|
|
remove_text=True, style='mpl20')
|
|
def test_imshow_bignumbers_real():
|
|
rcParams['image.interpolation'] = 'nearest'
|
|
# putting a big number in an array of integers shouldn't
|
|
# ruin the dynamic range of the resolved bits.
|
|
fig, ax = plt.subplots()
|
|
img = np.array([[2., 1., 1.e22], [4., 1., 3.]])
|
|
pc = ax.imshow(img)
|
|
pc.set_clim(0, 5)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"make_norm",
|
|
[colors.Normalize,
|
|
colors.LogNorm,
|
|
lambda: colors.SymLogNorm(1),
|
|
lambda: colors.PowerNorm(1)])
|
|
def test_empty_imshow(make_norm):
|
|
fig, ax = plt.subplots()
|
|
with pytest.warns(UserWarning,
|
|
match="Attempting to set identical left == right"):
|
|
im = ax.imshow([[]], norm=make_norm())
|
|
im.set_extent([-5, 5, -5, 5])
|
|
fig.canvas.draw()
|
|
|
|
with pytest.raises(RuntimeError):
|
|
im.make_image(fig._cachedRenderer)
|
|
|
|
|
|
def test_imshow_float128():
|
|
fig, ax = plt.subplots()
|
|
ax.imshow(np.zeros((3, 3), dtype=np.longdouble))
|
|
with (ExitStack() if np.can_cast(np.longdouble, np.float64, "equiv")
|
|
else pytest.warns(UserWarning)):
|
|
# Ensure that drawing doesn't cause crash.
|
|
fig.canvas.draw()
|
|
|
|
|
|
def test_imshow_bool():
|
|
fig, ax = plt.subplots()
|
|
ax.imshow(np.array([[True, False], [False, True]], dtype=bool))
|
|
|
|
|
|
def test_full_invalid():
|
|
fig, ax = plt.subplots()
|
|
ax.imshow(np.full((10, 10), np.nan))
|
|
with pytest.warns(UserWarning):
|
|
fig.canvas.draw()
|
|
|
|
|
|
@pytest.mark.parametrize("fmt,counted",
|
|
[("ps", b" colorimage"), ("svg", b"<image")])
|
|
@pytest.mark.parametrize("composite_image,count", [(True, 1), (False, 2)])
|
|
def test_composite(fmt, counted, composite_image, count):
|
|
# Test that figures can be saved with and without combining multiple images
|
|
# (on a single set of axes) into a single composite image.
|
|
X, Y = np.meshgrid(np.arange(-5, 5, 1), np.arange(-5, 5, 1))
|
|
Z = np.sin(Y ** 2)
|
|
|
|
fig, ax = plt.subplots()
|
|
ax.set_xlim(0, 3)
|
|
ax.imshow(Z, extent=[0, 1, 0, 1])
|
|
ax.imshow(Z[::-1], extent=[2, 3, 0, 1])
|
|
plt.rcParams['image.composite_image'] = composite_image
|
|
buf = io.BytesIO()
|
|
fig.savefig(buf, format=fmt)
|
|
assert buf.getvalue().count(counted) == count
|
|
|
|
|
|
def test_relim():
|
|
fig, ax = plt.subplots()
|
|
ax.imshow([[0]], extent=(0, 1, 0, 1))
|
|
ax.relim()
|
|
ax.autoscale()
|
|
assert ax.get_xlim() == ax.get_ylim() == (0, 1)
|
|
|
|
|
|
def test_deprecation():
|
|
data = [[1, 2], [3, 4]]
|
|
ax = plt.figure().subplots()
|
|
for obj in [ax, plt]:
|
|
with pytest.warns(None) as record:
|
|
obj.imshow(data)
|
|
assert len(record) == 0
|
|
with pytest.warns(MatplotlibDeprecationWarning):
|
|
obj.imshow(data, shape=None)
|
|
with pytest.warns(MatplotlibDeprecationWarning):
|
|
# Enough arguments to pass "shape" positionally.
|
|
obj.imshow(data, *[None] * 10)
|
|
|
|
|
|
def test_respects_bbox():
|
|
fig, axs = plt.subplots(2)
|
|
for ax in axs:
|
|
ax.set_axis_off()
|
|
im = axs[1].imshow([[0, 1], [2, 3]], aspect="auto", extent=(0, 1, 0, 1))
|
|
im.set_clip_path(None)
|
|
# Make the image invisible in axs[1], but visible in axs[0] if we pan
|
|
# axs[1] up.
|
|
im.set_clip_box(axs[0].bbox)
|
|
buf_before = io.BytesIO()
|
|
fig.savefig(buf_before, format="rgba")
|
|
assert {*buf_before.getvalue()} == {0xff} # All white.
|
|
axs[1].set(ylim=(-1, 0))
|
|
buf_after = io.BytesIO()
|
|
fig.savefig(buf_after, format="rgba")
|
|
assert buf_before.getvalue() != buf_after.getvalue() # Not all white.
|
|
|
|
|
|
def test_image_cursor_formatting():
|
|
fig, ax = plt.subplots()
|
|
# Create a dummy image to be able to call format_cursor_data
|
|
im = ax.imshow(np.zeros((4, 4)))
|
|
|
|
data = np.ma.masked_array([0], mask=[True])
|
|
assert im.format_cursor_data(data) == '[]'
|
|
|
|
data = np.ma.masked_array([0], mask=[False])
|
|
assert im.format_cursor_data(data) == '[0]'
|
|
|
|
data = np.nan
|
|
assert im.format_cursor_data(data) == '[nan]'
|
|
|
|
|
|
@check_figures_equal()
|
|
def test_image_array_alpha(fig_test, fig_ref):
|
|
'''per-pixel alpha channel test'''
|
|
x = np.linspace(0, 1)
|
|
xx, yy = np.meshgrid(x, x)
|
|
|
|
zz = np.exp(- 3 * ((xx - 0.5) ** 2) + (yy - 0.7 ** 2))
|
|
alpha = zz / zz.max()
|
|
|
|
cmap = plt.get_cmap('viridis')
|
|
ax = fig_test.add_subplot(111)
|
|
ax.imshow(zz, alpha=alpha, cmap=cmap, interpolation='nearest')
|
|
|
|
ax = fig_ref.add_subplot(111)
|
|
rgba = cmap(colors.Normalize()(zz))
|
|
rgba[..., -1] = alpha
|
|
ax.imshow(rgba, interpolation='nearest')
|