Matplotlib:使用imshow显示数组值

Question

我正在尝试使用类似matplotlib函数创建网格imshow.
从这个数组:

[[ 1  8 13 29 17 26 10  4],
[16 25 31  5 21 30 19 15]]

我想在同一网格上将值绘制为颜色和文本值本身(1,2,...).这就是我目前所拥有的(我只能绘制与每个值相关联的颜色):

from matplotlib import pyplot
import numpy as np

grid = np.array([[1,8,13,29,17,26,10,4],[16,25,31,5,21,30,19,15]])
print 'Here is the array'
print grid

fig1, (ax1, ax2)= pyplot.subplots(2, sharex = True, sharey = False)
ax1.imshow(grid, interpolation ='none', aspect = 'auto')
ax2.imshow(grid, interpolation ='bicubic', aspect = 'auto')
pyplot.show()   

Answer 1

您想循环覆盖值grid,并使用ax.text将标签添加到绘图中.

幸运的是,对于2D数组,numpy有ndenumerate,这使得这非常简单:

for (j,i),label in np.ndenumerate(grid):
    ax1.text(i,j,label,ha='center',va='center')
    ax2.text(i,j,label,ha='center',va='center')

Answer 2

如果出于某种原因您必须使用与以下方法自然提供的程度不同的程度imshow（即使是更人为的），则可以完成此工作：

size = 4
data = np.arange(size * size).reshape((size, size))

# Limits for the extent
x_start = 3.0
x_end = 9.0
y_start = 6.0
y_end = 12.0

extent = [x_start, x_end, y_start, y_end]

# The normal figure
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111)
im = ax.imshow(data, extent=extent, origin='lower', interpolation='None', cmap='viridis')

# Add the text
jump_x = (x_end - x_start) / (2.0 * size)
jump_y = (y_end - y_start) / (2.0 * size)
x_positions = np.linspace(start=x_start, stop=x_end, num=size, endpoint=False)
y_positions = np.linspace(start=y_start, stop=y_end, num=size, endpoint=False)

for y_index, y in enumerate(y_positions):
    for x_index, x in enumerate(x_positions):
        label = data[y_index, x_index]
        text_x = x + jump_x
        text_y = y + jump_y
        ax.text(text_x, text_y, label, color='black', ha='center', va='center')

fig.colorbar(im)
plt.show()

如果要放置其他类型的数据，而不必放置用于图像的值，则可以按以下方式修改上面的脚本（在数据后添加值）：

size = 4
data = np.arange(size * size).reshape((size, size))
values = np.random.rand(size, size)

# Limits for the extent
x_start = 3.0
x_end = 9.0
y_start = 6.0
y_end = 12.0

extent = [x_start, x_end, y_start, y_end]

# The normal figure
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111)
im = ax.imshow(data, extent=extent, origin='lower', interpolation='None', cmap='viridis')

# Add the text
jump_x = (x_end - x_start) / (2.0 * size)
jump_y = (y_end - y_start) / (2.0 * size)
x_positions = np.linspace(start=x_start, stop=x_end, num=size, endpoint=False)
y_positions = np.linspace(start=y_start, stop=y_end, num=size, endpoint=False)

for y_index, y in enumerate(y_positions):
    for x_index, x in enumerate(x_positions):
        label = values[y_index, x_index]
        text_x = x + jump_x
        text_y = y + jump_y
        ax.text(text_x, text_y, label, color='black', ha='center', va='center')

fig.colorbar(im)
plt.show()