使用 plt 在热图上绘制梯度箭头

Question

我正在尝试绘制箭头以可视化热图上的梯度。这是我到目前为止的代码：

import matplotlib.pyplot as plt
import numpy as np
function_to_plot = lambda x, y: x + y ** 2
horizontal_min, horizontal_max, horizontal_stepsize = 0, 3, 0.3
vertical_min, vertical_max, vertical_stepsize = 0, 3, 0.6

xv, yv = np.meshgrid(np.arange(horizontal_min, horizontal_max, horizontal_stepsize), 
                     np.arange(vertical_min, vertical_max, vertical_stepsize))

result_matrix = function_to_plot(xv, yv)
xd, yd = np.gradient(result_matrix)

def func_to_vectorize(x, y, dx, dy, scaling=1):
    plt.arrow(x + horizontal_stepsize/2, y + vertical_stepsize/2, dx*scaling, dy*scaling, fc="k", ec="k", head_width=0.1, head_length=0.1)

vectorized_arrow_drawing = np.vectorize(func_to_vectorize)

plt.imshow(result_matrix, extent=[horizontal_min, horizontal_max, vertical_min, vertical_max])
vectorized_arrow_drawing(xv, yv, xd, yd, 1)
plt.colorbar()
plt.show()

这是结果图：

我原以为箭头会指向具有最大值的矩形，但事实并非如此。我错过了什么？

Answer 1

看起来好像 np.gradient() returns y 值在 x 值之前
颜色似乎也不正确，因为图形上下文 y 值颠倒了。因此我在绘图过程中使用了 np.flip(result_matrix,0)
最后，我注意到当 stepsize 没有均匀划分区域时绘制箭头时出现故障，此外网格未与框的中心对齐。我已在以下代码中修复了这两个问题：

这是我用来生成图表的代码：

import matplotlib.pyplot as plt
import numpy as np
import math
function_to_plot = lambda x, y: x**2 + y**2
horizontal_min, horizontal_max, horizontal_stepsize = -2, 3, 0.3
vertical_min, vertical_max, vertical_stepsize = -1, 4, 0.5

horizontal_dist = horizontal_max-horizontal_min
vertical_dist = vertical_max-vertical_min

horizontal_stepsize = horizontal_dist / float(math.ceil(horizontal_dist/float(horizontal_stepsize)))
vertical_stepsize = vertical_dist / float(math.ceil(vertical_dist/float(vertical_stepsize)))

xv, yv = np.meshgrid(np.arange(horizontal_min, horizontal_max, horizontal_stepsize),
                     np.arange(vertical_min, vertical_max, vertical_stepsize))
xv+=horizontal_stepsize/2.0
yv+=vertical_stepsize/2.0

result_matrix = function_to_plot(xv, yv)
yd, xd = np.gradient(result_matrix)

def func_to_vectorize(x, y, dx, dy, scaling=0.01):
    plt.arrow(x, y, dx*scaling, dy*scaling, fc="k", ec="k", head_width=0.06, head_length=0.1)

vectorized_arrow_drawing = np.vectorize(func_to_vectorize)

plt.imshow(np.flip(result_matrix,0), extent=[horizontal_min, horizontal_max, vertical_min, vertical_max])
vectorized_arrow_drawing(xv, yv, xd, yd, 0.1)
plt.colorbar()
plt.show()

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