本文整理汇总了Python中mpl_toolkits.axes_grid1.AxesGrid方法的典型用法代码示例。如果您正苦于以下问题:Python axes_grid1.AxesGrid方法的具体用法?Python axes_grid1.AxesGrid怎么用?Python axes_grid1.AxesGrid使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mpl_toolkits.axes_grid1
的用法示例。
在下文中一共展示了axes_grid1.AxesGrid方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: demo_bottom_cbar
# 需要导入模块: from mpl_toolkits import axes_grid1 [as 别名]
# 或者: from mpl_toolkits.axes_grid1 import AxesGrid [as 别名]
def demo_bottom_cbar(fig):
"""
A grid of 2x2 images with a colorbar for each column.
"""
grid = AxesGrid(fig, 121, # similar to subplot(132)
nrows_ncols=(2, 2),
axes_pad=0.10,
share_all=True,
label_mode="1",
cbar_location="bottom",
cbar_mode="edge",
cbar_pad=0.25,
cbar_size="15%",
direction="column"
)
Z, extent = get_demo_image()
cmaps = [plt.get_cmap("autumn"), plt.get_cmap("summer")]
for i in range(4):
im = grid[i].imshow(Z, extent=extent, interpolation="nearest",
cmap=cmaps[i//2])
if i % 2:
cbar = grid.cbar_axes[i//2].colorbar(im)
for cax in grid.cbar_axes:
cax.toggle_label(True)
cax.axis[cax.orientation].set_label("Bar")
# This affects all axes as share_all = True.
grid.axes_llc.set_xticks([-2, 0, 2])
grid.axes_llc.set_yticks([-2, 0, 2])
示例2: demo_right_cbar
# 需要导入模块: from mpl_toolkits import axes_grid1 [as 别名]
# 或者: from mpl_toolkits.axes_grid1 import AxesGrid [as 别名]
def demo_right_cbar(fig):
"""
A grid of 2x2 images. Each row has its own colorbar.
"""
grid = AxesGrid(F, 122, # similar to subplot(122)
nrows_ncols=(2, 2),
axes_pad=0.10,
label_mode="1",
share_all=True,
cbar_location="right",
cbar_mode="edge",
cbar_size="7%",
cbar_pad="2%",
)
Z, extent = get_demo_image()
cmaps = [plt.get_cmap("spring"), plt.get_cmap("winter")]
for i in range(4):
im = grid[i].imshow(Z, extent=extent, interpolation="nearest",
cmap=cmaps[i//2])
if i % 2:
grid.cbar_axes[i//2].colorbar(im)
for cax in grid.cbar_axes:
cax.toggle_label(True)
cax.axis[cax.orientation].set_label('Foo')
# This affects all axes because we set share_all = True.
grid.axes_llc.set_xticks([-2, 0, 2])
grid.axes_llc.set_yticks([-2, 0, 2])
示例3: accumulate_patches_into_heatmaps
# 需要导入模块: from mpl_toolkits import axes_grid1 [as 别名]
# 或者: from mpl_toolkits.axes_grid1 import AxesGrid [as 别名]
def accumulate_patches_into_heatmaps(self, all_test_output, outpath_prefix=''):
outpath = "plots/%s_%s.png" % (outpath_prefix, path.splitext(path.basename(self.test_imagepath))[0])
# http://matplotlib.org/examples/axes_grid/demo_axes_grid.html
fig = plt.figure()
grid = AxesGrid(fig, 143, # similar to subplot(143)
nrows_ncols = (1, 1))
orig_img = imread(self.test_imagepath+'.png')
grid[0].imshow(orig_img)
grid = AxesGrid(fig, 144, # similar to subplot(144)
nrows_ncols = (2, 2),
axes_pad = 0.15,
label_mode = "1",
share_all = True,
cbar_location="right",
cbar_mode="each",
cbar_size="7%",
cbar_pad="2%",
)
for klass in xrange(all_test_output.shape[1]):
accumulator = numpy.zeros(self.ds.image_shape[:2])
normalizer = numpy.zeros(self.ds.image_shape[:2])
for n in xrange(self.num_patch_centers):
i_start,i_end,j_start,j_end = self.nth_patch(n)
accumulator[i_start:i_end, j_start:j_end] += all_test_output[n,klass]
normalizer[i_start:i_end, j_start:j_end] += 1
normalized_img = accumulator / normalizer
im = grid[klass].imshow(normalized_img, interpolation="nearest", vmin=0, vmax=1)
grid.cbar_axes[klass].colorbar(im)
grid.axes_llc.set_xticks([])
grid.axes_llc.set_yticks([])
print("Saving figure as: %s" % outpath)
plt.savefig(outpath, dpi=600, bbox_inches='tight')
示例4: plot_pathological_imgs
# 需要导入模块: from mpl_toolkits import axes_grid1 [as 别名]
# 或者: from mpl_toolkits.axes_grid1 import AxesGrid [as 别名]
def plot_pathological_imgs():
fig = plt.figure()
grid = AxesGrid(fig, 111, nrows_ncols = (1, 4))
names = ['23050_right.png', '2468_left.png', '15450_left.png', '406_left.png']
imgs = [imread(n) for n in names]
[grid[i].imshow(imgs[i]) for i in range(len(imgs))]
plt.axis('off')
plt.savefig('out.png', dpi=300)
# figure of all kappa curves
# skips 1 lost result from above, is in diff order
示例5: make_segmentation_triplets_for_paper
# 需要导入模块: from mpl_toolkits import axes_grid1 [as 别名]
# 或者: from mpl_toolkits.axes_grid1 import AxesGrid [as 别名]
def make_segmentation_triplets_for_paper(path, cls='Chair', export=False):
image_types = ['/gt/', '/pred/', '/diff/']
output_dir = path + '/triplet_images'
if cls == 'all':
hdf5_data_dir = os.path.join(BASE_DIR, './hdf5_data')
all_obj_cat_file = os.path.join(hdf5_data_dir, 'all_object_categories.txt')
fin = open(all_obj_cat_file, 'r')
lines = [line.rstrip() for line in fin.readlines()]
objnames = [line.split()[0] for line in lines]
n_objects = len(objnames)
filename = output_dir + '/' + 'all'
else:
n_objects = 1
filename = output_dir + '/' + cls.title()
objnames = [cls.title()]
fig = plt.figure()
ax = AxesGrid(fig, 111, nrows_ncols=(n_objects, 3), axes_pad=0.0)
for i, obj in enumerate(objnames):
cls_file_path = path+'/images/' + obj
for j, img_type in enumerate(image_types):
file_names = [os.path.join(cls_file_path + img_type, f) for f in os.listdir(cls_file_path + img_type)]
file_names.sort()
img = mpimg.imread(file_names[0])
w = img.shape[1]
h = img.shape[0]
x0 = int(np.round(w * 0.25))
y0 = int(np.round(h * 0.1))
cropped_img = img[y0:y0+int(0.7*h),x0:x0+int(0.5*w),:]
ax[3*i+j].axis('off')
ax[3*i+j].imshow(cropped_img)
#Visualize and export
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if export:
plt.savefig(filename + '.png', format='png', bbox_inches='tight', dpi=600)
else:
plt.show()
示例6: plot_data
# 需要导入模块: from mpl_toolkits import axes_grid1 [as 别名]
# 或者: from mpl_toolkits.axes_grid1 import AxesGrid [as 别名]
def plot_data(path='./data', name='', output='.'):
"""
Visualization using numpy arrays (written via MPI I/O) and json description
Produces one png file per time-step, combine as movie via e.g.
> ffmpeg -i data/name_%08d.png name.mp4
Args:
path (str): path to data files
name (str): name of the simulation (expects data to be in data path)
output (str): path to output
"""
json_files = sorted(glob.glob(f'{path}/{name}_*.json'))
data_files = sorted(glob.glob(f'{path}/{name}_*.dat'))
for json_file, data_file in zip(json_files, data_files):
with open(json_file, 'r') as fp:
obj = json.load(fp)
index = json_file.split('_')[-1].split('.')[0]
print(f'Working on step {index}...')
array = np.fromfile(data_file, dtype=obj['datatype'])
array = array.reshape(obj['shape'], order='C')
fig = plt.figure()
grid = AxesGrid(fig, 111,
nrows_ncols=(1, 2),
axes_pad=0.15,
cbar_mode='single',
cbar_location='right',
cbar_pad=0.15
)
im = grid[0].imshow(array[..., 0], vmin=0, vmax=1)
im = grid[1].imshow(array[..., 1], vmin=0, vmax=1)
grid[0].set_title(f"Field - Time: {obj['time']:6.4f}")
grid[1].set_title(f"Temperature - Time: {obj['time']:6.4f}")
grid[1].yaxis.set_visible(False)
grid.cbar_axes[0].colorbar(im)
plt.savefig(f'{output}/{name}_{index}.png', rasterized=True, bbox_inches='tight')
plt.close()