Python：如何在箭袋图轴的限制中添加第二个"non-Nan"条件？

Question

我想设置箭护图轴的限制。外部用过的网格中的NAN不会导致我的斧头极限不必要地没有任何数据点。

我自己得到的是：

import numpy as np
pylab.xlim(np.min(Km[np.isnan(C_diff) < 0.5 ]), np.max(Km[np.isnan(C_diff) < 0.5 ]))
    #two conditions: "np.min(Cm[np.isnan(K_diff) < 0.5" on min, max (Km) are missing 
pylab.ylim(np.min(Cm[np.isnan(K_diff) < 0.5 ]), np.max(Cm[np.isnan(K_diff) < 0.5 ])) 
    #two conditions: "np.min(Km[np.isnan(K_diff) < 0.5" on min, max (Cm) are missing

进一步说明，用matlab语言我想拥有：

xlim([min(min(Km(real(~(isnan(K_diff))).*real(~(isnan(C_diff))) > 0.5))),
max(max(Km(real(~(isnan(K_diff))).*real(~(isnan(C_diff))) > 0.5)))]);
ylim([min(min(Cm(real(~(isnan(K_diff))).*real(~(isnan(C_diff))) > 0.5))),
max(max(Cm(real(~(isnan(K_diff))).*real(~(isnan(C_diff))) > 0.5)))]);

获得答案会很棒！提前致谢！：(

tobias

Answer 1

我想我修复了它：

pylab.xlim(np.min(Km[np.isnan(C_diff)^np.isnan(K_diff).all() < 0.5 ]), 
np.max(Km[np.isnan(C_diff)^np.isnan(K_diff).all() < 0.5 ]))
pylab.ylim(np.min(Cm[np.isnan(K_diff)^np.isnan(C_diff).all() < 0.5 ]),
np.max(Cm[np.isnan(K_diff)^np.isnan(C_diff).all() < 0.5 ])) 

从结果来看，我没有区别，但是逻辑运算符可能。