如何创建基于 y 值的梯度阶梯图？

Question

在 Python matplotlib 中，如何获得折线图或阶梯图中的线以显示基于 y 值的梯度？

示例图（在 Tableau 中制作）：

带有根据 x 值改变梯度的线的阶梯图代码，改编自此答案：

fig, ax = plt.subplots(figsize=(10, 4))

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

y = [2, 3, 9, 10, 2, 9, 0, 1, 9, 1, -8]

T = np.linspace(0,1,np.size(x))**2

s = 1

for i in range(0, len(x)-s, s):

    ax.step(x[i:i+s+1], y[i:i+s+1],  marker='.', color=(0.0,0.5,T[i]))

ax.tick_params(axis='both', colors='lightgray', labelsize=8)

Answer 1

以下代码的灵感来自matplotlib 文档中的多色线示例。首先，使用 y 值绘制水平线段并为其着色。垂直部分被细分为小块，并单独着色。vmin标准的值设置得低一些，以避免颜色图的范围太亮。import matplotlib.pyplot as pltfrom matplotlib.collections import LineCollectionimport numpy as npx = np.arange(50)y = np.random.randint(-3, 4, x.size).cumsum()fig, ax = plt.subplots()norm = plt.Normalize(y.min() - y.ptp() * .2, y.max())cmap = 'inferno_r'  # 'Reds'horizontal_lines = np.array([x[:-1], y[:-1], x[1:], y[:-1]]).T.reshape(-1, 2, 2)hor_lc = LineCollection(horizontal_lines, cmap=cmap, norm=norm)hor_lc.set_array(y[:-1])ax.add_collection(hor_lc)factor = 10long_y0 = np.linspace(y[:-1], y[1:], factor)[:-1, :].T.ravel()long_y1 = np.linspace(y[:-1], y[1:], factor)[1:, :].T.ravel()long_x = np.repeat(x[1:], factor - 1)vertical_lines = np.array([long_x, long_y0, long_x, long_y1]).T.reshape(-1, 2, 2)ver_lc = LineCollection(vertical_lines, cmap=cmap, norm=norm)ver_lc.set_array((long_y0 + long_y1) / 2)ax.add_collection(ver_lc)ax.scatter(x, y, c=y, cmap=cmap, norm=norm)plt.autoscale() # needed in case the scatter plot would be omitedplt.show()这是另一个示例，具有黑色背景。在这种情况下，避免了颜色图的最暗部分。更改的代码部分是：y = np.random.randint(-9, 10, x.size)ax.patch.set_color('black')norm = plt.Normalize(y.min(), y.max() + y.ptp() * .2)cmap = 'plasma_r'这是一个带有 a 和蓝-白-红颜色图的示例TwoSlopeNorm：from matplotlib.colors import TwoSlopeNormy = np.random.uniform(-1, 1, x.size * 10).cumsum()[::10]y = (y - y.min()) / y.ptp() * 15 - 5norm = TwoSlopeNorm(vmin=-5, vcenter=0, vmax=10)cmap = 'bwr'