Python,将一维数据投影和插值到三维网格上(头表面EEG电源)
我有一个用于26个脑电通道的一维阵列(脑电电压),我还有脑电通道的三维坐标Python,将一维数据投影和插值到三维网格上(头表面EEG电源),python,arrays,numpy,matplotlib,spatial-interpolation,Python,Arrays,Numpy,Matplotlib,Spatial Interpolation,我有一个用于26个脑电通道的一维阵列(脑电电压),我还有脑电通道的三维坐标 x = np.array([ 84.06, 83.74, 41.69, 51.87, 57.01, 51.84, 41.16, 21.02, 24.63, 21.16, -16.52, -13.25, -11.28, -12.8 , -16.65, -48.48, -48.77, -48.35, -75.17, -80.11, -
x = np.array([ 84.06, 83.74, 41.69, 51.87, 57.01, 51.84,
41.16, 21.02, 24.63, 21.16, -16.52, -13.25, -11.28,
-12.8 , -16.65, -48.48, -48.77, -48.35, -75.17, -80.11,
-82.23, -80.13, -75.17, -114.52, -117.79, -114.68])
y = np.array([-26.81, 29.41, -66.99, -48.05, 0.9 , 50.38,
68.71, -58.83, 0.57, 60.29, -83.36, -65.57, 0.23, 66.5 ,
-65.51, -0.42, 65.03, -71.46, -55.07, -0.87, 53.51, 71.1 ,
-28.98, -1.41, 26.89])
z = np.array([-10.56, -10.04, -15.96, 39.87, 66.36, 41.33, -15.31,
54.82, 87.63, 55.58, -12.65, 64.98, 99.81, 65.11, -11.79,
68.57, 98.37, 68.57, -3.7 , 59.44, 82.43, 59.4 , -3.69,
9.67, 15.84, 9.45])
data = [ 884007.64101968, 997175.31684776, 853520.29922077,
1146032.72839618, 1280654.00515894, 1136783.42927035,
781802.02852187, 1165581.44354253, 1474539.74412991,
1074018.46853295, 578909.21492644, 1067652.55432892,
1508963.49572301, 1012764.69535714, 533385.60827991,
1058268.82537597, 1392128.01175867, 1043996.55697014,
675548.3896822 , 1022400.8910867 , 1360502.28709052,
1108773.44991746, 780841.92929488, 986799.48807626,
947189.96382125, 994734.32179115])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z)
plt.show()
现在,我想根据通道位置(x、y和z)将1d阵列(数据)投影到3d插值曲面上
我的问题是,我不知道如何将1d向量塑造成一个3d数组,反映点的位置和值,然后对它们进行插值以生成更易于解释的图。此外,我还可以在绘制它时使用一些帮助
我使用的是>python 3,对于绘图,我主要使用matplotlib
使用scipy.interpolate.griddata进行二维插值(最终生成二维地形图)
N=300
xy_center = [np.min(x)+((np.max(x)-np.min(x))/2),np.min(y)+((np.max(y)-
np.min(y))/2)] # center of the plot
radius = ((np.max(x)-np.min(x))/2) # radius
z = data
xi = numpy.linspace(np.min(x), np.max(x), N)
yi = numpy.linspace(np.min(y), np.max(y), N)
zi = scipy.interpolate.griddata((x, y), z, (xi[None,:], yi[:,None]),
method='cubic')
尝试进行类似的3d插值,数据、形状和坐标无法相加
d = data
xi = numpy.linspace(np.min(x), np.max(x), N)
yi = numpy.linspace(np.min(y), np.max(y), N)
zi = numpy.linspace(np.min(z), np.max(z), N)
int = scipy.interpolate.griddata((x, y, z), z, (xi[None,:],
yi[:,None],zi[:, None]), method='cubic')
我知道,在这里选择轴上的最小/最大值也不是正确的做法,但我不确定还要做什么
我确实弄明白了如何绘制通道的x,y,z坐标的3d散点图
x = np.array([ 84.06, 83.74, 41.69, 51.87, 57.01, 51.84,
41.16, 21.02, 24.63, 21.16, -16.52, -13.25, -11.28,
-12.8 , -16.65, -48.48, -48.77, -48.35, -75.17, -80.11,
-82.23, -80.13, -75.17, -114.52, -117.79, -114.68])
y = np.array([-26.81, 29.41, -66.99, -48.05, 0.9 , 50.38,
68.71, -58.83, 0.57, 60.29, -83.36, -65.57, 0.23, 66.5 ,
-65.51, -0.42, 65.03, -71.46, -55.07, -0.87, 53.51, 71.1 ,
-28.98, -1.41, 26.89])
z = np.array([-10.56, -10.04, -15.96, 39.87, 66.36, 41.33, -15.31,
54.82, 87.63, 55.58, -12.65, 64.98, 99.81, 65.11, -11.79,
68.57, 98.37, 68.57, -3.7 , 59.44, 82.43, 59.4 , -3.69,
9.67, 15.84, 9.45])
data = [ 884007.64101968, 997175.31684776, 853520.29922077,
1146032.72839618, 1280654.00515894, 1136783.42927035,
781802.02852187, 1165581.44354253, 1474539.74412991,
1074018.46853295, 578909.21492644, 1067652.55432892,
1508963.49572301, 1012764.69535714, 533385.60827991,
1058268.82537597, 1392128.01175867, 1043996.55697014,
675548.3896822 , 1022400.8910867 , 1360502.28709052,
1108773.44991746, 780841.92929488, 986799.48807626,
947189.96382125, 994734.32179115])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z)
plt.show()
我很抱歉说得不太准确,但我完全不知道……您不必重新塑造1D数据,它们属于额外维度(时间)。要使用三维模型进行绘图,您必须将三维模型简化为二维模型,并使用EEG传感器的三维模型,或者必须简化EEG传感器数据(例如,平均值等)。或者,你也可以制作一部电影。我认为3D方法的问题在于,你在立方体中插值
数据
值,而你想在由头骨形状给出的曲面上插值。因此,首先要定义一个曲面,例如使用scipy.spatial
中的Delaunay三角剖分,然后在该曲面上插值<scipy.interpolate
中的code>CloughTocher2DInterpolator可能符合要求(但我从未使用过)。然而,就我个人而言,我会首先检查是否有其他人还没有解决这个问题。对于这样的事情,我想到了。