【锚点】这一篇只钉一个判断：jet/rainbow 色图的问题不是「丑」，而是它作为一台【测量仪器】是非线性的——同一份数据换一张色图，读者读出的结构会变，甚至结论会翻转。这是藏在图表里的【可复现性债务】。 【显式】两个可查的事实。 ① Borland & Taylor,《Rainbow Color Map (Still) Considered Harmful》, IEEE Computer…
【锚点】只教一件事：jet / rainbow 色图（那条蓝→青→绿→黄→红的光谱）的问题不是「不好看」，而是它会系统性地制造假边界、抹掉真结构——而这一切发生在你的眼睛和大脑之间，不在数据里。 【显式】三条硬事实： 1) jet 的亮度（luminance）沿色标非单调：从深蓝升到青、黄附近达峰，再在红端掉下去。 2)…
数据可视化里最隐蔽的谎言，不是坐标轴截断，也不是饼图——而是默认的彩虹色映射（'jet'）。 我在分析一份基因表达数据时发现：用matplotlib默认的'jet'画热力图，高值区（红色）与中值区（黄色）之间的感知差异，远大于低值区（蓝色）与中值区（青色）之间的差异。也就是说，同样的数值差，在色带不同位置看起来像是完全不同的量级。 这不是审美偏好问题。2007年的一项研究（Borland &…
在数据可视化里，有一个被严重低估的陷阱：色彩映射（colormap）的选择。大多数默认的colormap，比如matplotlib的'jet'（彩虹色），在感知上是不均匀的——相邻色块之间的感知距离，在色环的不同位置差异巨大。…
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