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changed adjustable parameter of axes to 'box-forced'

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this fixes the size of axes around an image for shared axes
This commit is contained in:
martin
2015-10-12 15:58:57 +02:00
parent 681be3fc58
commit 8ff6d2d8e3
11 changed files with 74 additions and 74 deletions
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doc/examples/applications/plot_coins_segmentation.py
+6 -3
View File
@@ -35,7 +35,9 @@ background with the coins:
"""
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
fig = plt.figure(figsize=(6,3))
ax1 = fig.add_subplot(1, 2, 1, adjustable='box-forced')
ax2 = fig.add_subplot(1, 2, 2, sharex = ax1, sharey = ax1, adjustable='box-forced')
ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title('coins > 100')
ax1.axis('off')
@@ -162,8 +164,9 @@ segmentation = ndi.binary_fill_holes(segmentation - 1)
labeled_coins, _ = ndi.label(segmentation)
image_label_overlay = label2rgb(labeled_coins, image=coins)
# TODO: this example would benefit from sharing axes over multiple figures
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
fig = plt.figure(figsize=(6,3))
ax1 = fig.add_subplot(1, 2, 1, adjustable='box-forced')
ax2 = fig.add_subplot(1, 2, 2, sharex = ax1, sharey = ax1, adjustable='box-forced')
ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
ax1.axis('off')
doc/examples/applications/plot_morphology.py
+2
View File
@@ -46,9 +46,11 @@ def plot_comparison(original, filtered, filter_name):
ax1.imshow(original, cmap=plt.cm.gray)
ax1.set_title('original')
ax1.axis('off')
ax1.set_adjustable('box-forced')
ax2.imshow(filtered, cmap=plt.cm.gray)
ax2.set_title(filter_name)
ax2.axis('off')
ax2.set_adjustable('box-forced')
"""
Erosion
doc/examples/applications/plot_rank_filters.py
+19 -36
View File
@@ -70,7 +70,7 @@ noisy_image = img_as_ubyte(data.camera())
noisy_image[noise > 0.99] = 255
noisy_image[noise < 0.01] = 0
fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex=True, sharey=True)
fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax1, ax2, ax3, ax4 = ax.ravel()
ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray)
@@ -109,7 +109,7 @@ image.
from skimage.filters.rank import mean
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
loc_mean = mean(noisy_image, disk(10))
@@ -143,7 +143,7 @@ noisy_image = img_as_ubyte(data.camera())
bilat = mean_bilateral(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex='row', sharey='row')
fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex='row', sharey='row', subplot_kw={'adjustable':'box-forced'})
ax1, ax2, ax3, ax4 = ax.ravel()
ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -196,9 +196,13 @@ 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))
# this way histograms also share the axes
fig, ax = plt.subplots(3, 2, figsize=(10, 10), sharex='col', sharey='col')
ax1, ax2, ax3, ax4, ax5, ax6 = ax.ravel()
fig = plt.figure()
ax1 = plt.subplot(3, 2, 1, adjustable='box-forced')
ax2 = plt.subplot(3, 2, 2)
ax3 = plt.subplot(3, 2, 3, adjustable='box-forced', sharex=ax1, sharey=ax1)
ax4 = plt.subplot(3, 2, 4)
ax5 = plt.subplot(3, 2, 5, adjustable='box-forced', sharex=ax1, sharey=ax1)
ax6 = plt.subplot(3, 2, 6)
ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
ax1.axis('off')
@@ -237,7 +241,7 @@ noisy_image = img_as_ubyte(data.camera())
auto = autolevel(noisy_image.astype(np.uint16), disk(20))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax1.imshow(noisy_image, cmap=plt.cm.gray)
ax1.set_title('Original')
@@ -272,13 +276,10 @@ loc_perc_autolevel1 = autolevel_percentile(image, selem=selem, p0=.01, p1=.99)
loc_perc_autolevel2 = autolevel_percentile(image, selem=selem, p0=.05, p1=.95)
loc_perc_autolevel3 = autolevel_percentile(image, selem=selem, p0=.1, p1=.9)
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(7, 8), sharex=True, sharey=True)
fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(7, 8), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax0, ax1, ax2 = axes
plt.gray()
#ax0.imshow(np.hstack((image, loc_autolevel)), cmap=plt.cm.gray)
#ax0.set_title('Original / auto-level')
title_list = ['Original',
'auto_level',
'auto-level 0%',
@@ -298,18 +299,6 @@ for i in range(0,len(image_list)):
axes_list[i].set_title(title_list[i])
axes_list[i].axis('off')
'''
ax1.imshow(
np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)), vmin=0, vmax=255)
ax1.set_title('Percentile auto-level 0%,1%')
ax2.imshow(
np.hstack((loc_perc_autolevel2, loc_perc_autolevel3)), vmin=0, vmax=255)
ax2.set_title('Percentile auto-level 5% and 10%')
for ax in axes:
ax.axis('off')
'''
"""
.. image:: PLOT2RST.current_figure
@@ -326,7 +315,7 @@ noisy_image = img_as_ubyte(data.camera())
enh = enhance_contrast(noisy_image, disk(5))
fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row')
fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row', subplot_kw={'adjustable':'box-forced'})
ax1, ax2, ax3, ax4 = ax.ravel()
ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -358,7 +347,7 @@ noisy_image = img_as_ubyte(data.camera())
penh = enhance_contrast_percentile(noisy_image, disk(5), p0=.1, p1=.9)
fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row')
fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row', subplot_kw={'adjustable':'box-forced'})
ax1, ax2, ax3, ax4 = ax.ravel()
ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -415,7 +404,7 @@ loc_otsu = p8 >= t_loc_otsu
t_glob_otsu = threshold_otsu(p8)
glob_otsu = p8 >= t_glob_otsu
fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax1, ax2, ax3, ax4 = ax.ravel()
fig.colorbar(ax1.imshow(p8, cmap=plt.cm.gray), ax=ax1)
@@ -451,7 +440,7 @@ 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))
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax1.imshow(m)
ax1.set_title('Original')
@@ -490,7 +479,7 @@ opening = minimum(maximum(noisy_image, disk(5)), disk(5))
grad = gradient(noisy_image, disk(5))
# display results
fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex=True, sharey=True)
fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax1, ax2, ax3, ax4 = ax.ravel()
ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -540,7 +529,7 @@ import matplotlib.pyplot as plt
image = data.camera()
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
fig.colorbar(ax1.imshow(image, cmap=plt.cm.gray), ax=ax1)
ax1.set_title('Image')
@@ -702,13 +691,7 @@ Comparison of outcome of the three methods:
"""
'''
fig, ax = plt.subplots()
ax.imshow(np.hstack((rc, rndi)))
ax.set_title('filters.rank.median vs. scipy.ndimage.percentile')
ax.axis('off')
'''
fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True)
fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax0.set_title('filters.rank.median')
ax0.imshow(rc)
ax0.axis('off')