From fb72fb04b2ee2ff72b51d029da11564a797a17d4 Mon Sep 17 00:00:00 2001 From: Siva Prasad Varma Date: Fri, 14 Mar 2014 17:37:14 +0530 Subject: [PATCH] used plt.subplots everywhere --- .../applications/plot_coins_segmentation.py | 94 +++--- doc/examples/applications/plot_morphology.py | 2 +- .../applications/plot_rank_filters.py | 300 ++++++++---------- doc/examples/plot_canny.py | 25 +- doc/examples/plot_contours.py | 11 +- doc/examples/plot_convex_hull.py | 12 +- doc/examples/plot_corner.py | 10 +- doc/examples/plot_daisy.py | 7 +- doc/examples/plot_equalize.py | 2 +- doc/examples/plot_hog.py | 14 +- doc/examples/plot_holes_and_peaks.py | 22 +- doc/examples/plot_line_hough_transform.py | 51 ++- doc/examples/plot_medial_transform.py | 16 +- doc/examples/plot_otsu.py | 23 +- doc/examples/plot_peak_local_max.py | 30 +- doc/examples/plot_piecewise_affine.py | 7 +- doc/examples/plot_pyramid.py | 3 +- doc/examples/plot_radon_transform.py | 54 ++-- .../plot_random_walker_segmentation.py | 25 +- doc/examples/plot_ransac.py | 15 +- doc/examples/plot_regionprops.py | 14 +- doc/examples/plot_skeleton.py | 18 +- 22 files changed, 350 insertions(+), 405 deletions(-) diff --git a/doc/examples/applications/plot_coins_segmentation.py b/doc/examples/applications/plot_coins_segmentation.py index fa7aa1f4..b292eaa2 100644 --- a/doc/examples/applications/plot_coins_segmentation.py +++ b/doc/examples/applications/plot_coins_segmentation.py @@ -16,13 +16,11 @@ from skimage import data coins = data.coins() hist = np.histogram(coins, bins=np.arange(0, 256)) -plt.figure(figsize=(8, 3)) -plt.subplot(121) -plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest') -plt.axis('off') -plt.subplot(122) -plt.plot(hist[1][:-1], hist[0], lw=2) -plt.title('histogram of grey values') +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3)) +ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest') +ax1.axis('off') +ax2.plot(hist[1][:-1], hist[0], lw=2) +ax2.set_title('histogram of grey values') """ .. image:: PLOT2RST.current_figure @@ -37,17 +35,15 @@ background with the coins: """ -plt.figure(figsize=(6, 3)) -plt.subplot(121) -plt.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest') -plt.title('coins > 100') -plt.axis('off') -plt.subplot(122) -plt.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest') -plt.title('coins > 150') -plt.axis('off') +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3)) +ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest') +ax1.set_title('coins > 100') +ax1.axis('off') +ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest') +ax2.set_title('coins > 150') +ax2.axis('off') margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) -plt.subplots_adjust(**margins) +f.subplots_adjust(**margins) """ .. image:: PLOT2RST.current_figure @@ -64,10 +60,10 @@ edge-detector. from skimage.filter import canny edges = canny(coins/255.) -plt.figure(figsize=(4, 3)) -plt.imshow(edges, cmap=plt.cm.gray, interpolation='nearest') -plt.axis('off') -plt.title('Canny detector') +f, ax = plt.subplots(figsize=(4, 3)) +ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest') +ax.axis('off') +ax.set_title('Canny detector') """ .. image:: PLOT2RST.current_figure @@ -79,10 +75,10 @@ from scipy import ndimage fill_coins = ndimage.binary_fill_holes(edges) -plt.figure(figsize=(4, 3)) -plt.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest') -plt.axis('off') -plt.title('Filling the holes') +f, ax = plt.subplots(figsize=(4, 3)) +ax.imshow(fill_coins, cmap=plt.cm.gray, interpolation='nearest') +ax.axis('off') +ax.set_title('Filling the holes') """ .. image:: PLOT2RST.current_figure @@ -93,10 +89,10 @@ objects. from skimage import morphology coins_cleaned = morphology.remove_small_objects(fill_coins, 21) -plt.figure(figsize=(4, 3)) -plt.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest') -plt.axis('off') -plt.title('Removing small objects') +f, ax = plt.subplots(figsize=(4, 3)) +ax.imshow(coins_cleaned, cmap=plt.cm.gray, interpolation='nearest') +ax.axis('off') +ax.set_title('Removing small objects') """ .. image:: PLOT2RST.current_figure @@ -117,10 +113,10 @@ from skimage.filter import sobel elevation_map = sobel(coins) -plt.figure(figsize=(4, 3)) -plt.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest') -plt.axis('off') -plt.title('elevation_map') +f, ax = plt.subplots(figsize=(4, 3)) +ax.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest') +ax.axis('off') +ax.set_title('elevation_map') """ .. image:: PLOT2RST.current_figure @@ -133,10 +129,10 @@ markers = np.zeros_like(coins) markers[coins < 30] = 1 markers[coins > 150] = 2 -plt.figure(figsize=(4, 3)) -plt.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') -plt.axis('off') -plt.title('markers') +f, ax = plt.subplots(figsize=(4, 3)) +ax.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest') +ax.axis('off') +ax.set_title('markers') """ .. image:: PLOT2RST.current_figure @@ -147,10 +143,10 @@ starting from the markers determined above: """ segmentation = morphology.watershed(elevation_map, markers) -plt.figure(figsize=(4, 3)) -plt.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest') -plt.axis('off') -plt.title('segmentation') +f, ax = plt.subplots(figsize=(4, 3)) +ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest') +ax.axis('off') +ax.set_title('segmentation') """ .. image:: PLOT2RST.current_figure @@ -166,16 +162,14 @@ segmentation = ndimage.binary_fill_holes(segmentation - 1) labeled_coins, _ = ndimage.label(segmentation) image_label_overlay = label2rgb(labeled_coins, image=coins) -plt.figure(figsize=(6, 3)) -plt.subplot(121) -plt.imshow(coins, cmap=plt.cm.gray, interpolation='nearest') -plt.contour(segmentation, [0.5], linewidths=1.2, colors='y') -plt.axis('off') -plt.subplot(122) -plt.imshow(image_label_overlay, interpolation='nearest') -plt.axis('off') +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3)) +ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest') +ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y') +ax1.axis('off') +ax2.imshow(image_label_overlay, interpolation='nearest') +ax2.axis('off') -plt.subplots_adjust(**margins) +f.subplots_adjust(**margins) """ .. image:: PLOT2RST.current_figure diff --git a/doc/examples/applications/plot_morphology.py b/doc/examples/applications/plot_morphology.py index 84cf2972..145f169e 100644 --- a/doc/examples/applications/plot_morphology.py +++ b/doc/examples/applications/plot_morphology.py @@ -32,7 +32,7 @@ from skimage import io plt.gray() phantom = img_as_ubyte(io.imread(data_dir+'/phantom.png', as_grey=True)) -plt.imshow(phantom) +io.imshow(phantom) """ .. image:: PLOT2RST.current_figure diff --git a/doc/examples/applications/plot_rank_filters.py b/doc/examples/applications/plot_rank_filters.py index 87dad0d0..b6711f8c 100644 --- a/doc/examples/applications/plot_rank_filters.py +++ b/doc/examples/applications/plot_rank_filters.py @@ -44,13 +44,11 @@ from skimage import data noisy_image = img_as_ubyte(data.camera()) hist = np.histogram(noisy_image, bins=np.arange(0, 256)) -plt.figure(figsize=(8, 3)) -plt.subplot(1, 2, 1) -plt.imshow(noisy_image, interpolation='nearest') -plt.axis('off') -plt.subplot(1, 2, 2) -plt.plot(hist[1][:-1], hist[0], lw=2) -plt.title('Histogram of grey values') +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3)) +ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray) +ax1.axis('off') +ax2.plot(hist[1][:-1], hist[0], lw=2) +ax2.set_title('Histogram of grey values') """ @@ -77,27 +75,24 @@ noisy_image = img_as_ubyte(data.camera()) noisy_image[noise > 0.99] = 255 noisy_image[noise < 0.01] = 0 -fig = plt.figure(figsize=(10, 7)) +fig, ax = plt.subplots(2, 2, figsize=(10, 7)) +ax1, ax2, ax3, ax4 = ax.ravel() -plt.subplot(2, 2, 1) -plt.imshow(noisy_image, vmin=0, vmax=255) -plt.title('Noisy image') -plt.axis('off') +ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray) +ax1.set_title('Noisy image') +ax1.axis('off') -plt.subplot(2, 2, 2) -plt.imshow(median(noisy_image, disk(1)), vmin=0, vmax=255) -plt.title('Median $r=1$') -plt.axis('off') +ax2.imshow(median(noisy_image, disk(1)), vmin=0, vmax=255, cmap=plt.cm.gray) +ax2.set_title('Median $r=1$') +ax2.axis('off') -plt.subplot(2, 2, 3) -plt.imshow(median(noisy_image, disk(5)), vmin=0, vmax=255) -plt.title('Median $r=5$') -plt.axis('off') +ax3.imshow(median(noisy_image, disk(5)), vmin=0, vmax=255, cmap=plt.cm.gray) +ax3.set_title('Median $r=5$') +ax3.axis('off') -plt.subplot(2, 2, 4) -plt.imshow(median(noisy_image, disk(20)), vmin=0, vmax=255) -plt.title('Median $r=20$') -plt.axis('off') +ax4.imshow(median(noisy_image, disk(20)), vmin=0, vmax=255, cmap=plt.cm.gray) +ax4.set_title('Median $r=20$') +ax4.axis('off') """ @@ -119,19 +114,17 @@ image. from skimage.filter.rank import mean -fig = plt.figure(figsize=[10, 7]) +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7]) loc_mean = mean(noisy_image, disk(10)) -plt.subplot(1, 2, 1) -plt.imshow(noisy_image, vmin=0, vmax=255) -plt.title('Original') -plt.axis('off') +ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(1, 2, 2) -plt.imshow(loc_mean, vmin=0, vmax=255) -plt.title('Local mean $r=10$') -plt.axis('off') +ax2.imshow(loc_mean, vmin=0, vmax=255, cmap=plt.cm.gray) +ax2.set_title('Local mean $r=10$') +ax2.axis('off') """ @@ -155,25 +148,22 @@ noisy_image = img_as_ubyte(data.camera()) bilat = bilateral_mean(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10) -fig = plt.figure(figsize=[10, 7]) +fig, ax = plt.subplots(2, 2, figsize=(10, 7)) +ax1, ax2, ax3, ax4 = ax.ravel() -plt.subplot(2, 2, 1) -plt.imshow(noisy_image, cmap=plt.cm.gray) -plt.title('Original') -plt.axis('off') +ax1.imshow(noisy_image, cmap=plt.cm.gray) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(2, 2, 3) -plt.imshow(bilat, cmap=plt.cm.gray) -plt.title('Bilateral mean') -plt.axis('off') +ax2.imshow(bilat, cmap=plt.cm.gray) +ax2.set_title('Bilateral mean') +ax2.axis('off') -plt.subplot(2, 2, 2) -plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) -plt.axis('off') +ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) +ax3.axis('off') -plt.subplot(2, 2, 4) -plt.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray) -plt.axis('off') +ax4.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray) +ax4.axis('off') """ @@ -211,31 +201,26 @@ hist = np.histogram(noisy_image, bins=np.arange(0, 256)) glob_hist = np.histogram(glob, bins=np.arange(0, 256)) loc_hist = np.histogram(loc, bins=np.arange(0, 256)) -plt.figure(figsize=(10, 10)) +f, ax = plt.subplots(3, 2, figsize=(10, 10)) +ax1, ax2, ax3, ax4, ax5, ax6 = ax.ravel() -plt.subplot(321) -plt.imshow(noisy_image, interpolation='nearest') -plt.axis('off') +ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray) +ax1.axis('off') -plt.subplot(322) -plt.plot(hist[1][:-1], hist[0], lw=2) -plt.title('Histogram of gray values') +ax2.plot(hist[1][:-1], hist[0], lw=2) +ax2.set_title('Histogram of gray values') -plt.subplot(323) -plt.imshow(glob, interpolation='nearest') -plt.axis('off') +ax3.imshow(glob, interpolation='nearest', cmap=plt.cm.gray) +ax3.axis('off') -plt.subplot(324) -plt.plot(glob_hist[1][:-1], glob_hist[0], lw=2) -plt.title('Histogram of gray values') +ax4.plot(glob_hist[1][:-1], glob_hist[0], lw=2) +ax4.set_title('Histogram of gray values') -plt.subplot(325) -plt.imshow(loc, interpolation='nearest') -plt.axis('off') +ax5.imshow(loc, interpolation='nearest', cmap=plt.cm.gray) +ax5.axis('off') -plt.subplot(326) -plt.plot(loc_hist[1][:-1], loc_hist[0], lw=2) -plt.title('Histogram of gray values') +ax6.plot(loc_hist[1][:-1], loc_hist[0], lw=2) +ax6.set_title('Histogram of gray values') """ @@ -256,17 +241,15 @@ noisy_image = img_as_ubyte(data.camera()) auto = autolevel(noisy_image.astype(np.uint16), disk(20)) -fig = plt.figure(figsize=[10, 7]) +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7]) -plt.subplot(1, 2, 1) -plt.imshow(noisy_image, cmap=plt.cm.gray) -plt.title('Original') -plt.axis('off') +ax1.imshow(noisy_image, cmap=plt.cm.gray) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(1, 2, 2) -plt.imshow(auto, cmap=plt.cm.gray) -plt.title('Local autolevel') -plt.axis('off') +ax2.imshow(auto, cmap=plt.cm.gray) +ax2.set_title('Local autolevel') +ax2.axis('off') """ @@ -297,7 +280,7 @@ fig, axes = plt.subplots(nrows=3, figsize=(7, 8)) ax0, ax1, ax2 = axes plt.gray() -ax0.imshow(np.hstack((image, loc_autolevel))) +ax0.imshow(np.hstack((image, loc_autolevel)), cmap=plt.cm.gray) ax0.set_title('Original / auto-level') ax1.imshow( @@ -326,24 +309,22 @@ noisy_image = img_as_ubyte(data.camera()) enh = enhance_contrast(noisy_image, disk(5)) -fig = plt.figure(figsize=[10, 7]) -plt.subplot(2, 2, 1) -plt.imshow(noisy_image, cmap=plt.cm.gray) -plt.title('Original') -plt.axis('off') +fig, ax = plt.subplots(2, 2, figsize=[10, 7]) +ax1, ax2, ax3, ax4 = ax.ravel() -plt.subplot(2, 2, 3) -plt.imshow(enh, cmap=plt.cm.gray) -plt.title('Local morphological contrast enhancement') -plt.axis('off') +ax1.imshow(noisy_image, cmap=plt.cm.gray) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(2, 2, 2) -plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) -plt.axis('off') +ax2.imshow(enh, cmap=plt.cm.gray) +ax2.set_title('Local morphological contrast enhancement') +ax2.axis('off') -plt.subplot(2, 2, 4) -plt.imshow(enh[200:350, 350:450], cmap=plt.cm.gray) -plt.axis('off') +ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) +ax3.axis('off') + +ax4.imshow(enh[200:350, 350:450], cmap=plt.cm.gray) +ax4.axis('off') """ @@ -360,24 +341,22 @@ noisy_image = img_as_ubyte(data.camera()) penh = enhance_contrast_percentile(noisy_image, disk(5), p0=.1, p1=.9) -fig = plt.figure(figsize=[10, 7]) -plt.subplot(2, 2, 1) -plt.imshow(noisy_image, cmap=plt.cm.gray) -plt.title('Original') -plt.axis('off') +fig, ax = plt.subplots(2, 2, figsize=[10, 7]) +ax1, ax2, ax3, ax4 = ax.ravel() -plt.subplot(2, 2, 3) -plt.imshow(penh, cmap=plt.cm.gray) -plt.title('Local percentile morphological\n contrast enhancement') -plt.axis('off') +ax1.imshow(noisy_image, cmap=plt.cm.gray) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(2, 2, 2) -plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) -plt.axis('off') +ax2.imshow(penh, cmap=plt.cm.gray) +ax2.set_title('Local percentile morphological\n contrast enhancement') +ax2.axis('off') -plt.subplot(2, 2, 4) -plt.imshow(penh[200:350, 350:450], cmap=plt.cm.gray) -plt.axis('off') +ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray) +ax3.axis('off') + +ax4.imshow(penh[200:350, 350:450], cmap=plt.cm.gray) +ax4.axis('off') """ @@ -460,17 +439,15 @@ m = (np.tile(x, (n, 1)) * np.linspace(0.1, 1, n) * 128 + 128).astype(np.uint8) radius = 10 t = rank.otsu(m, disk(radius)) -plt.figure() +f, (ax1, ax2) = plt.subplots(1, 2) -plt.subplot(1, 2, 1) -plt.imshow(m) -plt.title('Original') -plt.axis('off') +ax1.imshow(m) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(1, 2, 2) -plt.imshow(m >= t, interpolation='nearest') -plt.title('Local Otsu ($r=%d$)' % radius) -plt.axis('off') +ax2.imshow(m >= t, interpolation='nearest') +ax2.set_title('Local Otsu ($r=%d$)' % radius) +ax2.axis('off') """ @@ -501,27 +478,24 @@ opening = minimum(maximum(noisy_image, disk(5)), disk(5)) grad = gradient(noisy_image, disk(5)) # display results -fig = plt.figure(figsize=[10, 7]) +fig, ax = plt.subplots(2, 2, figsize=[10, 7]) +ax1, ax2, ax3, ax4 = ax.ravel() -plt.subplot(2, 2, 1) -plt.imshow(noisy_image, cmap=plt.cm.gray) -plt.title('Original') -plt.axis('off') +ax1.imshow(noisy_image, cmap=plt.cm.gray) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(2, 2, 2) -plt.imshow(closing, cmap=plt.cm.gray) -plt.title('Gray-level closing') -plt.axis('off') +ax2.imshow(closing, cmap=plt.cm.gray) +ax2.set_title('Gray-level closing') +ax2.axis('off') -plt.subplot(2, 2, 3) -plt.imshow(opening, cmap=plt.cm.gray) -plt.title('Gray-level opening') -plt.axis('off') +ax3.imshow(opening, cmap=plt.cm.gray) +ax3.set_title('Gray-level opening') +ax3.axis('off') -plt.subplot(2, 2, 4) -plt.imshow(grad, cmap=plt.cm.gray) -plt.title('Morphological gradient') -plt.axis('off') +ax4.imshow(grad, cmap=plt.cm.gray) +ax4.set_title('Morphological gradient') +ax4.axis('off') """ @@ -651,12 +625,12 @@ for r in e_range: rec = np.asarray(rec) -plt.figure() -plt.title('Performance with respect to element size') -plt.ylabel('Time (ms)') -plt.title('Element radius') -plt.plot(e_range, rec) -plt.legend(['filter.rank.maximum', 'morphology.dilate']) +f, ax = plt.subplots() +ax.set_title('Performance with respect to element size') +ax.set_ylabel('Time (ms)') +ax.set_xlabel('Element radius') +ax.plot(e_range, rec) +ax.legend(['filter.rank.maximum', 'morphology.dilate']) """ @@ -679,12 +653,12 @@ for s in s_range: rec = np.asarray(rec) -plt.figure() -plt.title('Performance with respect to image size') -plt.ylabel('Time (ms)') -plt.title('Image size') -plt.plot(s_range, rec) -plt.legend(['filter.rank.maximum', 'morphology.dilate']) +f, ax = plt.subplots() +ax.set_title('Performance with respect to image size') +ax.set_ylabel('Time (ms)') +ax.set_xlabel('Image size') +ax.plot(s_range, rec) +ax.legend(['filter.rank.maximum', 'morphology.dilate']) """ @@ -714,13 +688,13 @@ for r in e_range: rec = np.asarray(rec) -plt.figure() -plt.title('Performance with respect to element size') -plt.plot(e_range, rec) -plt.legend(['filter.rank.median', 'filter.median_filter', - 'scipy.ndimage.percentile']) -plt.ylabel('Time (ms)') -plt.title('Element radius') +f, ax = plt.subplots() +ax.set_title('Performance with respect to element size') +ax.plot(e_range, rec) +ax.legend(['filter.rank.median', 'filter.median_filter', + 'scipy.ndimage.percentile']) +ax.set_ylabel('Time (ms)') +ax.set_xlabel('Element radius') """ .. image:: PLOT2RST.current_figure @@ -729,10 +703,10 @@ Comparison of outcome of the three methods: """ -plt.figure() -plt.imshow(np.hstack((rc, rctmf, rndi))) -plt.title('filter.rank.median vs filtermedian_filter vs scipy.ndimage.percentile') -plt.axis('off') +f, ax = plt.subplots() +ax.imshow(np.hstack((rc, rctmf, rndi))) +ax.set_title('filter.rank.median vs filtermedian_filter vs scipy.ndimage.percentile') +ax.axis('off') """ .. image:: PLOT2RST.current_figure @@ -755,13 +729,13 @@ for s in s_range: rec = np.asarray(rec) -plt.figure() -plt.title('Performance with respect to image size') -plt.plot(s_range, rec) -plt.legend(['filter.rank.median', 'filter.median_filter', - 'scipy.ndimage.percentile']) -plt.ylabel('Time (ms)') -plt.title('Image size') +f, ax = plt.subplots() +ax.set_title('Performance with respect to image size') +ax.plot(s_range, rec) +ax.legend(['filter.rank.median', 'filter.median_filter', + 'scipy.ndimage.percentile']) +ax.set_ylabel('Time (ms)') +ax.set_xlabel('Image size') """ .. image:: PLOT2RST.current_figure diff --git a/doc/examples/plot_canny.py b/doc/examples/plot_canny.py index f8269d70..25b7e6ac 100644 --- a/doc/examples/plot_canny.py +++ b/doc/examples/plot_canny.py @@ -35,24 +35,21 @@ edges1 = filter.canny(im) edges2 = filter.canny(im, sigma=3) # display results -plt.figure(figsize=(8, 3)) +f, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3)) -plt.subplot(131) -plt.imshow(im, cmap=plt.cm.jet) -plt.axis('off') -plt.title('noisy image', fontsize=20) +ax1.imshow(im, cmap=plt.cm.jet) +ax1.axis('off') +ax1.set_title('noisy image', fontsize=20) -plt.subplot(132) -plt.imshow(edges1, cmap=plt.cm.gray) -plt.axis('off') -plt.title('Canny filter, $\sigma=1$', fontsize=20) +ax2.imshow(edges1, cmap=plt.cm.gray) +ax2.axis('off') +ax2.set_title('Canny filter, $\sigma=1$', fontsize=20) -plt.subplot(133) -plt.imshow(edges2, cmap=plt.cm.gray) -plt.axis('off') -plt.title('Canny filter, $\sigma=3$', fontsize=20) +ax3.imshow(edges2, cmap=plt.cm.gray) +ax3.axis('off') +ax3.set_title('Canny filter, $\sigma=3$', fontsize=20) -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, +f.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0.02, left=0.02, right=0.98) plt.show() diff --git a/doc/examples/plot_contours.py b/doc/examples/plot_contours.py index d23b2ae5..1be586a9 100644 --- a/doc/examples/plot_contours.py +++ b/doc/examples/plot_contours.py @@ -29,12 +29,13 @@ r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2))) contours = measure.find_contours(r, 0.8) # Display the image and plot all contours found -plt.imshow(r, interpolation='nearest') +f, ax = plt.subplots() +ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray) for n, contour in enumerate(contours): - plt.plot(contour[:, 1], contour[:, 0], linewidth=2) + ax.plot(contour[:, 1], contour[:, 0], linewidth=2) -plt.axis('image') -plt.xticks([]) -plt.yticks([]) +ax.axis('image') +ax.set_xticks([]) +ax.set_yticks([]) plt.show() diff --git a/doc/examples/plot_convex_hull.py b/doc/examples/plot_convex_hull.py index 31398e6d..e811c1ee 100644 --- a/doc/examples/plot_convex_hull.py +++ b/doc/examples/plot_convex_hull.py @@ -40,11 +40,9 @@ image[chull] += 1 # [ 0. 0. 0. 0. 0. 0. 0. 0. 0.]] -fig = plt.subplots(figsize=(10, 6)) -plt.subplot(1, 2, 1) -plt.title('Original picture') -plt.imshow(original_image, cmap=plt.cm.gray, interpolation='nearest') -plt.subplot(1, 2, 2) -plt.title('Transformed picture') -plt.imshow(image, cmap=plt.cm.gray, interpolation='nearest') +fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6)) +ax1.set_title('Original picture') +ax1.imshow(original_image, cmap=plt.cm.gray, interpolation='nearest') +ax2.set_title('Transformed picture') +ax2.imshow(image, cmap=plt.cm.gray, interpolation='nearest') plt.show() diff --git a/doc/examples/plot_corner.py b/doc/examples/plot_corner.py index a3233188..45cc1c75 100644 --- a/doc/examples/plot_corner.py +++ b/doc/examples/plot_corner.py @@ -29,9 +29,9 @@ image[230:280, 60:110] = 1 coords = corner_peaks(corner_harris(image), min_distance=5) coords_subpix = corner_subpix(image, coords, window_size=13) -plt.gray() -plt.imshow(image, interpolation='nearest') -plt.plot(coords[:, 1], coords[:, 0], '.b', markersize=3) -plt.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15) -plt.axis((0, 350, 350, 0)) +f, ax = plt.subplots() +ax.imshow(image, interpolation='nearest', cmap=plt.cm.gray) +ax.plot(coords[:, 1], coords[:, 0], '.b', markersize=3) +ax.plot(coords_subpix[:, 1], coords_subpix[:, 0], '+r', markersize=15) +ax.axis((0, 350, 350, 0)) plt.show() diff --git a/doc/examples/plot_daisy.py b/doc/examples/plot_daisy.py index fbde5c8d..35504875 100644 --- a/doc/examples/plot_daisy.py +++ b/doc/examples/plot_daisy.py @@ -20,8 +20,9 @@ img = data.camera() descs, descs_img = daisy(img, step=180, radius=58, rings=2, histograms=6, orientations=8, visualize=True) -plt.axis('off') -plt.imshow(descs_img) +f, ax = plt.subplots() +ax.axis('off') +ax.imshow(descs_img) descs_num = descs.shape[0] * descs.shape[1] -plt.title('%i DAISY descriptors extracted:' % descs_num) +ax.set_title('%i DAISY descriptors extracted:' % descs_num) plt.show() diff --git a/doc/examples/plot_equalize.py b/doc/examples/plot_equalize.py index 3b3f3788..077645a1 100644 --- a/doc/examples/plot_equalize.py +++ b/doc/examples/plot_equalize.py @@ -92,5 +92,5 @@ ax_cdf.set_ylabel('Fraction of total intensity') ax_cdf.set_yticks(np.linspace(0, 1, 5)) # prevent overlap of y-axis labels -plt.subplots_adjust(wspace=0.4) +f.subplots_adjust(wspace=0.4) plt.show() diff --git a/doc/examples/plot_hog.py b/doc/examples/plot_hog.py index 4a339a1b..21700089 100644 --- a/doc/examples/plot_hog.py +++ b/doc/examples/plot_hog.py @@ -90,16 +90,16 @@ image = color.rgb2gray(data.lena()) fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualise=True) -plt.figure(figsize=(8, 4)) +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4)) -plt.subplot(121).set_axis_off() -plt.imshow(image, cmap=plt.cm.gray) -plt.title('Input image') +ax1.axis('off') +ax1.imshow(image, cmap=plt.cm.gray) +ax1.set_title('Input image') # Rescale histogram for better display hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02)) -plt.subplot(122).set_axis_off() -plt.imshow(hog_image_rescaled, cmap=plt.cm.gray) -plt.title('Histogram of Oriented Gradients') +ax2.axis('off') +ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray) +ax2.set_title('Histogram of Oriented Gradients') plt.show() diff --git a/doc/examples/plot_holes_and_peaks.py b/doc/examples/plot_holes_and_peaks.py index e40b777e..d4a53f67 100644 --- a/doc/examples/plot_holes_and_peaks.py +++ b/doc/examples/plot_holes_and_peaks.py @@ -22,13 +22,13 @@ image = data.moon() image = rescale_intensity(image, in_range=(50, 200)) # convenience function for plotting images -def imshow(image, **kwargs): - plt.figure(figsize=(5, 4)) - plt.imshow(image, **kwargs) - plt.axis('off') +def imshow(image, title, **kwargs): + f, ax = plt.subplots(figsize=(5, 4)) + ax.imshow(image, **kwargs) + ax.axis('off') + ax.set_title(title) -imshow(image) -plt.title('original image') +imshow(image, 'Original image') """ .. image:: PLOT2RST.current_figure @@ -50,8 +50,7 @@ mask = image filled = reconstruction(seed, mask, method='erosion') -imshow(filled, vmin=image.min(), vmax=image.max()) -plt.title('after filling holes') +imshow(filled, 'after filling holes',vmin=image.min(), vmax=image.max()) """ .. image:: PLOT2RST.current_figure @@ -62,8 +61,8 @@ isolate the dark regions by subtracting the reconstructed image from the original image. """ -imshow(image - filled) -plt.title('holes') +imshow(image - filled, 'holes') +# plt.title('holes') """ .. image:: PLOT2RST.current_figure @@ -78,8 +77,7 @@ intensity instead of the maximum. The remainder of the process is the same. seed = np.copy(image) seed[1:-1, 1:-1] = image.min() rec = reconstruction(seed, mask, method='dilation') -imshow(image - rec) -plt.title('peaks') +imshow(image - rec, 'peaks') plt.show() """ diff --git a/doc/examples/plot_line_hough_transform.py b/doc/examples/plot_line_hough_transform.py index cd0ae008..271bc598 100644 --- a/doc/examples/plot_line_hough_transform.py +++ b/doc/examples/plot_line_hough_transform.py @@ -77,30 +77,30 @@ image[idx, idx] = 255 h, theta, d = hough_line(image) -plt.figure(figsize=(8, 4)) +f, ax = plt.subplots(1, 3, figsize=(8, 4)) -plt.subplot(131) -plt.imshow(image, cmap=plt.cm.gray) -plt.title('Input image') +ax[0].imshow(image, cmap=plt.cm.gray) +ax[0].set_title('Input image') +ax[0].axis('image') -plt.subplot(132) -plt.imshow(np.log(1 + h), +ax[1].imshow(np.log(1 + h), extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]), d[-1], d[0]], cmap=plt.cm.gray, aspect=1/1.5) -plt.title('Hough transform') -plt.xlabel('Angles (degrees)') -plt.ylabel('Distance (pixels)') +ax[1].set_title('Hough transform') +ax[1].set_xlabel('Angles (degrees)') +ax[1].set_ylabel('Distance (pixels)') +ax[1].axis('image') -plt.subplot(133) -plt.imshow(image, cmap=plt.cm.gray) +ax[2].imshow(image, cmap=plt.cm.gray) rows, cols = image.shape for _, angle, dist in zip(*hough_line_peaks(h, theta, d)): y0 = (dist - 0 * np.cos(angle)) / np.sin(angle) y1 = (dist - cols * np.cos(angle)) / np.sin(angle) - plt.plot((0, cols), (y0, y1), '-r') -plt.axis((0, cols, rows, 0)) -plt.title('Detected lines') + ax[2].plot((0, cols), (y0, y1), '-r') +ax[2].axis((0, cols, rows, 0)) +ax[2].set_title('Detected lines') +ax[2].axis('image') # Line finding, using the Probabilistic Hough Transform @@ -108,23 +108,22 @@ image = data.camera() edges = canny(image, 2, 1, 25) lines = probabilistic_hough_line(edges, threshold=10, line_length=5, line_gap=3) -plt.figure(figsize=(8, 3)) +f2, ax = plt.subplots(1, 3, figsize=(8, 3)) -plt.subplot(131) -plt.imshow(image, cmap=plt.cm.gray) -plt.title('Input image') +ax[0].imshow(image, cmap=plt.cm.gray) +ax[0].set_title('Input image') +ax[0].axis('image') -plt.subplot(132) -plt.imshow(edges, cmap=plt.cm.gray) -plt.title('Canny edges') +ax[1].imshow(edges, cmap=plt.cm.gray) +ax[1].set_title('Canny edges') +ax[1].axis('image') -plt.subplot(133) -plt.imshow(edges * 0) +ax[2].imshow(edges * 0) for line in lines: p0, p1 = line - plt.plot((p0[0], p1[0]), (p0[1], p1[1])) + ax[2].plot((p0[0], p1[0]), (p0[1], p1[1])) -plt.title('Probabilistic Hough') -plt.axis('image') +ax[2].set_title('Probabilistic Hough') +ax[2].axis('image') plt.show() diff --git a/doc/examples/plot_medial_transform.py b/doc/examples/plot_medial_transform.py index f0792e50..4182fc8d 100644 --- a/doc/examples/plot_medial_transform.py +++ b/doc/examples/plot_medial_transform.py @@ -55,14 +55,12 @@ skel, distance = medial_axis(data, return_distance=True) # Distance to the background for pixels of the skeleton dist_on_skel = distance * skel -plt.figure(figsize=(8, 4)) -plt.subplot(121) -plt.imshow(data, cmap=plt.cm.gray, interpolation='nearest') -plt.axis('off') -plt.subplot(122) -plt.imshow(dist_on_skel, cmap=plt.cm.spectral, interpolation='nearest') -plt.contour(data, [0.5], colors='w') -plt.axis('off') +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4)) +ax1.imshow(data, cmap=plt.cm.gray, interpolation='nearest') +ax1.axis('off') +ax2.imshow(dist_on_skel, cmap=plt.cm.spectral, interpolation='nearest') +ax2.contour(data, [0.5], colors='w') +ax2.axis('off') -plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) +f.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) plt.show() diff --git a/doc/examples/plot_otsu.py b/doc/examples/plot_otsu.py index b3b8e279..a679acc5 100644 --- a/doc/examples/plot_otsu.py +++ b/doc/examples/plot_otsu.py @@ -28,20 +28,17 @@ image = camera() thresh = threshold_otsu(image) binary = image > thresh -plt.figure(figsize=(8, 2.5)) -plt.subplot(1, 3, 1) -plt.imshow(image, cmap=plt.cm.gray) -plt.title('Original') -plt.axis('off') +f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 2.5)) +ax1.imshow(image, cmap=plt.cm.gray) +ax1.set_title('Original') +ax1.axis('off') -plt.subplot(1, 3, 2, aspect='equal') -plt.hist(image) -plt.title('Histogram') -plt.axvline(thresh, color='r') +ax2.hist(image) +ax2.set_title('Histogram') +ax2.axvline(thresh, color='r') -plt.subplot(1, 3, 3) -plt.imshow(binary, cmap=plt.cm.gray) -plt.title('Thresholded') -plt.axis('off') +ax3.imshow(binary, cmap=plt.cm.gray) +ax3.set_title('Thresholded') +ax3.axis('off') plt.show() diff --git a/doc/examples/plot_peak_local_max.py b/doc/examples/plot_peak_local_max.py index 3a2dccfe..ce8ccf89 100644 --- a/doc/examples/plot_peak_local_max.py +++ b/doc/examples/plot_peak_local_max.py @@ -25,25 +25,23 @@ image_max = ndimage.maximum_filter(im, size=20, mode='constant') coordinates = peak_local_max(im, min_distance=20) # display results -plt.figure(figsize=(8, 3)) -plt.subplot(131) -plt.imshow(im, cmap=plt.cm.gray) -plt.axis('off') -plt.title('Original') +f, ax = plt.subplots(1, 3, figsize=(8, 3)) +ax1, ax2, ax3 = ax.ravel() +ax1.imshow(im, cmap=plt.cm.gray) +ax1.axis('off') +ax1.set_title('Original') -plt.subplot(132) -plt.imshow(image_max, cmap=plt.cm.gray) -plt.axis('off') -plt.title('Maximum filter') +ax2.imshow(image_max, cmap=plt.cm.gray) +ax2.axis('off') +ax2.set_title('Maximum filter') -plt.subplot(133) -plt.imshow(im, cmap=plt.cm.gray) -plt.autoscale(False) -plt.plot([p[1] for p in coordinates], [p[0] for p in coordinates], 'r.') -plt.axis('off') -plt.title('Peak local max') +ax3.imshow(im, cmap=plt.cm.gray) +ax3.autoscale(False) +ax3.plot([p[1] for p in coordinates], [p[0] for p in coordinates], 'r.') +ax3.axis('off') +ax3.set_title('Peak local max') -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, +f.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9, bottom=0.02, left=0.02, right=0.98) plt.show() diff --git a/doc/examples/plot_piecewise_affine.py b/doc/examples/plot_piecewise_affine.py index a0ad7d8f..a0eddd20 100644 --- a/doc/examples/plot_piecewise_affine.py +++ b/doc/examples/plot_piecewise_affine.py @@ -35,7 +35,8 @@ out_rows = image.shape[0] - 1.5 * 50 out_cols = cols out = warp(image, tform, output_shape=(out_rows, out_cols)) -plt.imshow(out) -plt.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b') -plt.axis((0, out_cols, out_rows, 0)) +f, ax = plt.subplots() +ax.imshow(out) +ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b') +ax.axis((0, out_cols, out_rows, 0)) plt.show() diff --git a/doc/examples/plot_pyramid.py b/doc/examples/plot_pyramid.py index c4853c2f..c3fb6de9 100644 --- a/doc/examples/plot_pyramid.py +++ b/doc/examples/plot_pyramid.py @@ -30,5 +30,6 @@ for p in pyramid[1:]: composite_image[i_row:i_row + n_rows, cols:cols + n_cols] = p i_row += n_rows -plt.imshow(composite_image) +f, ax = plt.subplots() +ax.imshow(composite_image) plt.show() diff --git a/doc/examples/plot_radon_transform.py b/doc/examples/plot_radon_transform.py index 0dab82e6..45e31ed5 100644 --- a/doc/examples/plot_radon_transform.py +++ b/doc/examples/plot_radon_transform.py @@ -60,22 +60,20 @@ from skimage.transform import radon, rescale image = imread(data_dir + "/phantom.png", as_grey=True) image = rescale(image, scale=0.4) -plt.figure(figsize=(8, 4.5)) +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5)) -plt.subplot(121) -plt.title("Original") -plt.imshow(image, cmap=plt.cm.Greys_r) +ax1.set_title("Original") +ax1.imshow(image, cmap=plt.cm.Greys_r) theta = np.linspace(0., 180., max(image.shape), endpoint=True) sinogram = radon(image, theta=theta, circle=True) -plt.subplot(122) -plt.title("Radon transform\n(Sinogram)") -plt.xlabel("Projection angle (deg)") -plt.ylabel("Projection position (pixels)") -plt.imshow(sinogram, cmap=plt.cm.Greys_r, +ax2.set_title("Radon transform\n(Sinogram)") +ax2.set_xlabel("Projection angle (deg)") +ax2.set_ylabel("Projection position (pixels)") +ax2.imshow(sinogram, cmap=plt.cm.Greys_r, extent=(0, 180, 0, sinogram.shape[0]), aspect='auto') -plt.subplots_adjust(hspace=0.4, wspace=0.5) +f.subplots_adjust(hspace=0.4, wspace=0.5) plt.show() """ @@ -103,13 +101,11 @@ error = reconstruction_fbp - image print('FBP rms reconstruction error: %.3g' % np.sqrt(np.mean(error**2))) imkwargs = dict(vmin=-0.2, vmax=0.2) -plt.figure(figsize=(8, 4.5)) -plt.subplot(121) -plt.title("Reconstruction\nFiltered back projection") -plt.imshow(reconstruction_fbp, cmap=plt.cm.Greys_r) -plt.subplot(122) -plt.title("Reconstruction error\nFiltered back projection") -plt.imshow(reconstruction_fbp - image, cmap=plt.cm.Greys_r, **imkwargs) +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5)) +ax1.set_title("Reconstruction\nFiltered back projection") +ax1.imshow(reconstruction_fbp, cmap=plt.cm.Greys_r) +ax2.set_title("Reconstruction error\nFiltered back projection") +ax2.imshow(reconstruction_fbp - image, cmap=plt.cm.Greys_r, **imkwargs) plt.show() """ @@ -156,14 +152,12 @@ error = reconstruction_sart - image print('SART (1 iteration) rms reconstruction error: %.3g' % np.sqrt(np.mean(error**2))) -plt.figure(figsize=(8, 8.5)) - -plt.subplot(221) -plt.title("Reconstruction\nSART") -plt.imshow(reconstruction_sart, cmap=plt.cm.Greys_r) -plt.subplot(222) -plt.title("Reconstruction error\nSART") -plt.imshow(reconstruction_sart - image, cmap=plt.cm.Greys_r, **imkwargs) +f, ax = plt.subplots(2, 2, figsize=(8, 8.5)) +ax1, ax2, ax3, ax4 = ax.ravel() +ax1.set_title("Reconstruction\nSART") +ax1.imshow(reconstruction_sart, cmap=plt.cm.Greys_r) +ax2.set_title("Reconstruction error\nSART") +ax2.imshow(reconstruction_sart - image, cmap=plt.cm.Greys_r, **imkwargs) # Run a second iteration of SART by supplying the reconstruction # from the first iteration as an initial estimate @@ -173,12 +167,10 @@ error = reconstruction_sart2 - image print('SART (2 iterations) rms reconstruction error: %.3g' % np.sqrt(np.mean(error**2))) -plt.subplot(223) -plt.title("Reconstruction\nSART, 2 iterations") -plt.imshow(reconstruction_sart2, cmap=plt.cm.Greys_r) -plt.subplot(224) -plt.title("Reconstruction error\nSART, 2 iterations") -plt.imshow(reconstruction_sart2 - image, cmap=plt.cm.Greys_r, **imkwargs) +ax3.set_title("Reconstruction\nSART, 2 iterations") +ax3.imshow(reconstruction_sart2, cmap=plt.cm.Greys_r) +ax4.set_title("Reconstruction error\nSART, 2 iterations") +ax4.imshow(reconstruction_sart2 - image, cmap=plt.cm.Greys_r, **imkwargs) plt.show() """ diff --git a/doc/examples/plot_random_walker_segmentation.py b/doc/examples/plot_random_walker_segmentation.py index 8c960a8b..2f7d08b8 100644 --- a/doc/examples/plot_random_walker_segmentation.py +++ b/doc/examples/plot_random_walker_segmentation.py @@ -58,20 +58,17 @@ markers[data > 1.3] = 2 labels = random_walker(data, markers, beta=10, mode='bf') # Plot results -plt.figure(figsize=(8, 3.2)) -plt.subplot(131) -plt.imshow(data, cmap='gray', interpolation='nearest') -plt.axis('off') -plt.title('Noisy data') -plt.subplot(132) -plt.imshow(markers, cmap='hot', interpolation='nearest') -plt.axis('off') -plt.title('Markers') -plt.subplot(133) -plt.imshow(labels, cmap='gray', interpolation='nearest') -plt.axis('off') -plt.title('Segmentation') +f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 3.2)) +ax1.imshow(data, cmap='gray', interpolation='nearest') +ax1.axis('off') +ax1.set_title('Noisy data') +ax2.imshow(markers, cmap='hot', interpolation='nearest') +ax2.axis('off') +ax2.set_title('Markers') +ax3.imshow(labels, cmap='gray', interpolation='nearest') +ax3.axis('off') +ax3.set_title('Segmentation') -plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, +f.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1) plt.show() diff --git a/doc/examples/plot_ransac.py b/doc/examples/plot_ransac.py index 2de76619..1fce1b3d 100644 --- a/doc/examples/plot_ransac.py +++ b/doc/examples/plot_ransac.py @@ -45,11 +45,12 @@ line_x = np.arange(-250, 250) line_y = model.predict_y(line_x) line_y_robust = model_robust.predict_y(line_x) -plt.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6, - label='Inlier data') -plt.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6, - label='Outlier data') -plt.plot(line_x, line_y, '-k', label='Line model from all data') -plt.plot(line_x, line_y_robust, '-b', label='Robust line model') -plt.legend(loc='lower left') +f, ax = plt.subplots() +ax.plot(data[inliers, 0], data[inliers, 1], '.b', alpha=0.6, + label='Inlier data') +ax.plot(data[outliers, 0], data[outliers, 1], '.r', alpha=0.6, + label='Outlier data') +ax.plot(line_x, line_y, '-k', label='Line model from all data') +ax.plot(line_x, line_y_robust, '-b', label='Robust line model') +ax.legend(loc='lower left') plt.show() diff --git a/doc/examples/plot_regionprops.py b/doc/examples/plot_regionprops.py index f675d11c..e989c709 100644 --- a/doc/examples/plot_regionprops.py +++ b/doc/examples/plot_regionprops.py @@ -26,7 +26,8 @@ image = rotate(image, angle=15, order=0) label_img = label(image) regions = regionprops(label_img) -plt.imshow(image) +f, ax = plt.subplots() +ax.imshow(image, cmap=plt.cm.gray) for props in regions: y0, x0 = props.centroid @@ -36,15 +37,14 @@ for props in regions: x2 = x0 - math.sin(orientation) * 0.5 * props.minor_axis_length y2 = y0 - math.cos(orientation) * 0.5 * props.minor_axis_length - plt.plot((x0, x1), (y0, y1), '-r', linewidth=2.5) - plt.plot((x0, x2), (y0, y2), '-r', linewidth=2.5) - plt.plot(x0, y0, '.g', markersize=15) + ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5) + ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5) + ax.plot(x0, y0, '.g', markersize=15) minr, minc, maxr, maxc = props.bbox bx = (minc, maxc, maxc, minc, minc) by = (minr, minr, maxr, maxr, minr) - plt.plot(bx, by, '-b', linewidth=2.5) + ax.plot(bx, by, '-b', linewidth=2.5) -plt.gray() -plt.axis((0, 600, 600, 0)) +ax.axis((0, 600, 600, 0)) plt.show() diff --git a/doc/examples/plot_skeleton.py b/doc/examples/plot_skeleton.py index a0bfd267..3346490f 100644 --- a/doc/examples/plot_skeleton.py +++ b/doc/examples/plot_skeleton.py @@ -47,19 +47,17 @@ image[circle2] = 0 skeleton = skeletonize(image) # display results -plt.figure(figsize=(8, 4.5)) +f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5)) -plt.subplot(121) -plt.imshow(image, cmap=plt.cm.gray) -plt.axis('off') -plt.title('original', fontsize=20) +ax1.imshow(image, cmap=plt.cm.gray) +ax1.axis('off') +ax1.set_title('original', fontsize=20) -plt.subplot(122) -plt.imshow(skeleton, cmap=plt.cm.gray) -plt.axis('off') -plt.title('skeleton', fontsize=20) +ax2.imshow(skeleton, cmap=plt.cm.gray) +ax2.axis('off') +ax2.set_title('skeleton', fontsize=20) -plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.98, +f.subplots_adjust(wspace=0.02, hspace=0.02, top=0.98, bottom=0.02, left=0.02, right=0.98) plt.show()