import matplotlib.pyplot as plt
import numpy as np
methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']
# Fixing random state for reproducibility
np.random.seed(19680801)
grid = np.random.rand(4, 4)
fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),
subplot_kw={'xticks': [], 'yticks': []})
for ax, interp_method in zip(axs.flat, methods):
ax.imshow(grid, interpolation=interp_method, cmap='viridis')
ax.set_title(str(interp_method))
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
import numpy as np
def func3(x, y):
return (1 - x / 2 x**5 y**3) * np.exp(-(x**2 y**2))
# make these smaller to increase the resolution
dx, dy = 0.05, 0.05
x = np.arange(-3.0, 3.0, dx)
y = np.arange(-3.0, 3.0, dy)
X, Y = np.meshgrid(x, y)
# when layering multiple images, the images need to have the same
# extent. This does not mean they need to have the same shape, but
# they both need to render to the same coordinate system determined by
# xmin, xmax, ymin, ymax. Note if you use different interpolations
# for the images their apparent extent could be different due to
# interpolation edge effects
extent = np.min(x), np.max(x), np.min(y), np.max(y)
fig = plt.figure(frameon=False)
Z1 = np.add.outer(range(8), range(8)) % 2 # chessboard
im1 = plt.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',
extent=extent)
Z2 = func3(X, Y)
im2 = plt.imshow(Z2, cmap=plt.cm.viridis, alpha=.9, interpolation='bilinear',
extent=extent)
plt.show()
代码语言：javascript复制
import matplotlib.pyplot as plt
import numpy as np
methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']
# Fixing random state for reproducibility
np.random.seed(19680801)
grid = np.random.rand(4, 4)
fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),
subplot_kw={'xticks': [], 'yticks': []})
for ax, interp_method in zip(axs.flat, methods):
ax.imshow(grid, interpolation=interp_method, cmap='viridis')
ax.set_title(str(interp_method))
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt
import numpy as np
def func3(x, y):
return (1 - x / 2 x**5 y**3) * np.exp(-(x**2 y**2))
# make these smaller to increase the resolution
dx, dy = 0.05, 0.05
x = np.arange(-3.0, 3.0, dx)
y = np.arange(-3.0, 3.0, dy)
X, Y = np.meshgrid(x, y)
# when layering multiple images, the images need to have the same
# extent. This does not mean they need to have the same shape, but
# they both need to render to the same coordinate system determined by
# xmin, xmax, ymin, ymax. Note if you use different interpolations
# for the images their apparent extent could be different due to
# interpolation edge effects
extent = np.min(x), np.max(x), np.min(y), np.max(y)
fig = plt.figure(frameon=False)
Z1 = np.add.outer(range(8), range(8)) % 2 # chessboard
im1 = plt.imshow(Z1, cmap=plt.cm.gray, interpolation='nearest',
extent=extent)
Z2 = func3(X, Y)
im2 = plt.imshow(Z2, cmap=plt.cm.viridis, alpha=.9, interpolation='bilinear',
extent=extent)
plt.show()
