python - Matplotlib 等高线图不提取精确等高线

Question

我在提取以下数据的精确轮廓时遇到困难:(你已经可以通过查看数据看到轮廓)

data = np.array(
      [[ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277]])

如果我绘制它:

plt.imshow(data)

但是，当我尝试使用以下方法提取轮廓时:

plt.contour(data, levels = np.unique(data))

如您所见，轮廓并不遵循实际数据的尖角。如果我叠加两个图:

完整代码如下:

import numpy as np
import matplotlib.pyplot as plt

data = np.array([[ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277]])

plt.imshow(data)
plt.show()
plt.contour(data, levels=np.unique(data), cmap="jet")
plt.colorbar()

Answer 1

轮廓是使用 marching squares algorithm 绘制的为了计算轮廓位置，它在网格点之间进行插值。

也许您正在寻找离散区域边界:这些可以像这样检索:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl

data = np.array([[ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  1.        ,  1.        ],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277],
       [ 1.46184395,  1.46184395,  1.46184395,  4.24552277,  4.24552277]])


def region_borders(data, value, color, **kwargs):
    v = np.argwhere(np.diff((data == value).T, axis=0))
    vlines = np.array(list(zip(v + [.5, -.5], v + [.5, .5])))
    
    h = np.argwhere(np.diff((data == value).T, axis=1))
    hlines = np.array(list(zip(h + [-.5, .5], h + [.5, .5])))
    
    if len(vlines) and len(hlines):
        lines = np.vstack((vlines, hlines))
    elif len(vlines):
        lines = vlines
    else:
        lines = hlines
    return mpl.collections.LineCollection(lines, colors=color, **kwargs)


contours = np.unique(data)

fig, ax = plt.subplots(ncols=len(contours)+1, sharex=True, sharey=True, layout='constrained')
im = ax[0].matshow(data, cmap='jet', aspect='auto')
fig.colorbar(im, ax=ax[-1])

norm = mpl.colors.Normalize(data.min(), data.max())
for i, value in enumerate(contours, 1):
    ax[i].add_collection(region_borders(data, value, mpl.cm.jet(norm(value)), lw=2))
    ax[i].set_title(value)