如何保持颜色图比例尺恒定并绘制颜色栏

Question

我一直在研究示例绘图，其中绘制纬度/经度数据并根据速度进行着色。 我希望速度色图是恒定的，但是每次我在上面绘制新数据时，似乎都会重新调整色图的最小值和最大值以适合数据。

例如，我希望颜色在0到10之间映射（如颜色栏上所示）。 但是，似乎每次都会将颜色映射重新缩放为数据。

import numpy as np
import matplotlib.pyplot as plt

lon = np.array(
    [-155.05155, -155.05151, -155.0515, -155.05149, -155.05148, -155.05148, -155.05148, -155.05148, -155.05149,
     -155.05151, -155.05153, -155.05155, -155.05158, -155.05159, -155.05162, -155.05164, -155.05166, -155.05168,
     -155.0517, -155.05172, -155.05175, -155.05176, -155.05178, -155.0518, -155.05182, -155.05184, -155.05186,
     -155.05188, -155.0519, -155.05192, -155.05194, -155.05197, -155.05199, -155.05201, -155.05203, -155.05205,
     -155.05207, -155.05208, -155.05211, -155.05213, -155.05215, -155.05217, -155.05219, -155.05221, -155.05223,
     -155.05226, -155.05228, -155.05231, -155.05233, -155.05236, -155.05237, -155.05239, -155.05241, -155.05242,
     -155.05245, -155.05245, -155.05247, -155.05249, -155.05251, -155.05254, -155.05256, -155.05258, -155.0526,
     -155.05262, -155.05263, -155.05265, -155.05268, -155.0527, -155.05272, -155.05275, -155.05277, -155.05279,
     -155.05281, -155.05283, -155.05285, -155.05287, -155.05289, -155.0529, -155.05291, -155.05293, -155.05295,
     -155.05297, -155.05299, -155.05301, -155.05303, -155.05305, -155.05307, -155.05309, -155.05311, -155.05313,
     -155.05314, -155.05316, -155.05318, -155.0532, -155.05321, -155.05323, -155.05325, -155.05326, -155.05328,
     -155.05332])
lat = np.array(
    [19.73198, 19.73198, 19.73199, 19.73201, 19.73203, 19.73202, 19.73202, 19.73204, 19.73205, 19.73206, 19.73206,
     19.73207, 19.73208, 19.73209, 19.73211, 19.73212, 19.73214, 19.73216, 19.73218, 19.7322, 19.73222, 19.73223,
     19.73225, 19.73226, 19.73227, 19.73229, 19.7323, 19.73232, 19.73233, 19.73234, 19.73235, 19.73237, 19.73238,
     19.7324, 19.73241, 19.73243, 19.73244, 19.73246, 19.73247, 19.73248, 19.7325, 19.73251, 19.73253, 19.73254,
     19.73256, 19.73257, 19.73258, 19.73259, 19.7326, 19.73263, 19.73264, 19.73266, 19.73267, 19.73268, 19.7327,
     19.73271, 19.73272, 19.73273, 19.73275, 19.73277, 19.73278, 19.73279, 19.73281, 19.73282, 19.73284, 19.73286,
     19.73286, 19.73288, 19.73289, 19.7329, 19.73291, 19.73293, 19.73294, 19.73296, 19.73297, 19.73299, 19.73302,
     19.73304, 19.73306, 19.73308, 19.73309, 19.73311, 19.73313, 19.73315, 19.73317, 19.73317, 19.73319, 19.7332,
     19.73322, 19.73323, 19.73325, 19.73326, 19.73327, 19.73329, 19.7333, 19.73331, 19.73332, 19.73333, 19.73335,
     19.73336])
speed = np.array([0, 3.967838351598377, 2.583851230112236, 2.909749382960718, 2.331208342745013, 1.2474565941885627,
                  1.718509515902599, 2.130757007783651, 2.1978242809567643, 2.4448938949237133, 2.3436767093957807,
                  2.848759575381383, 2.5766473883863124, 2.8348240641628335, 3.106935785732237, 2.710393576350728,
                  3.1318924699618096, 3.2117162403094186, 3.1318921186853235, 3.4486097786406074, 2.8348227783071542,
                  2.438280995718295, 2.8116095118419597, 2.571151376967635, 2.8116093167427856, 2.710391831502253,
                  2.710391728938647, 2.7103915222111485, 2.469933323878424, 2.4699332140890724, 3.1069324755437155,
                  3.106932198270919, 2.710390905821824, 2.710390701811524, 2.710390599103662, 2.710390392282518,
                  2.412473081387807, 2.892512135241831, 2.848753560195423, 2.790213897128215, 2.891431079508014,
                  2.8914309834469867, 2.790213394604523, 2.7902132941269615, 3.270251940268593, 2.949969471066445,
                  2.9499693266059097, 2.8487517553160426, 3.682304028129132, 3.7558001156185816, 2.863708779704488,
                  2.891429545784412, 2.1978198142415475, 2.8348182266776614, 2.670028369758699, 2.033029885758895,
                  2.469928992173159, 2.710387312334322, 3.5125563040959307, 3.27209759442833, 2.4699283256332825,
                  2.7103866960769962, 2.710386489344829, 2.5103517409878737, 2.9318517610212034, 3.1747757400248457,
                  3.0949516374373167, 2.8116033283715955, 2.9499648048602216, 2.848747092331349, 2.7902095754563563,
                  3.1559179547967515, 3.1559178689291647, 2.891426668096206, 2.891426572169741, 3.573651757195292,
                  3.195911973684913, 2.4562297465190186, 2.833969127195823, 2.7902078684061324, 2.7103836137458464,
                  3.131883978356872, 3.1318838043110544, 3.131883629382897, 2.7651643954244483, 2.7651643943197355,
                  2.891424460722922, 2.811600188197874, 3.4372310555788212, 3.1393162110320247, 2.513684994246323,
                  2.469922991269612, 2.710381765956584, 2.43827424877333, 2.0965975265618053, 2.4699224357346794,
                  2.1978151300488475, 2.7367352119845485, 4.0459052923177925, 3.8858032217291467])

max_speed = 10.
color = [str(item / max_speed) for item in speed]

fig = plt.figure(facecolor='0.05')
ax = plt.Axes(fig, [0., 0., 1., 1.], )

ax.set_axis_off()
fig.add_axes(ax)
sm = plt.cm.ScalarMappable(cmap='plasma', norm=plt.Normalize(vmin=0, vmax=max_speed))
sm._A = []
color_bar = plt.colorbar(sm)
cbytick_obj = plt.getp(color_bar.ax.axes, 'yticklabels')
plt.setp(cbytick_obj, color='w')

plt.scatter(lon, lat, s=2, c=color, cmap='plasma')
plt.show()

产生如下图：

请注意，数据值永远不会超过4.1的值，但是它已经重新调整了色彩图的比例以适合数据。 如何设置颜色映射的限制？ 另外，为什么彩色条会忽略绘图窗口的边界并显示得如此之大？

Answer 1

要使散点图中的归一化与色条中的归一化相同，请在散点图中使用与色条中相同的归一化。

mynorm = plt.Normalize(vmin=0, vmax=10)
sm = plt.cm.ScalarMappable(cmap='plasma', norm=mynorm)
# ...
plt.scatter(lon, lat, s=2, c=color, cmap='plasma', norm=mynorm)

（这似乎不太明显，无法提出问题，但这似乎也是您在此寻求的解决方案。）

至于为什么彩条如此之大，显然您自己又将其设置得如此之大：

ax = plt.Axes(fig, [0., 0., 1., 1.])创建的轴与图形ax = plt.Axes(fig, [0., 0., 1., 1.])完全相同。 颜色条与轴一样高。
您可以选择使轴更小，也可以通过收缩来创建较小的颜色条，

plt.colorbar(sm, shrink=0.8)