python 中通过插值或 QHull 的散乱数据轮廓
Contour of scattered data via interpolation or QHull in python
我正在尝试根据我的数据在 z = .95 处绘制等高线，但是，我无法按照我的意愿进行插值。我尝试如下使用 griddata
from scipy.interpolate import griddata
N = 1000
xi = np.linspace(min(x), max(y), N)
yi = np.linspace(min(x), max(y), N)
c = griddata((np.array(x),np.array(y)),
np.array(z), (xi[None,:],
yi[:,None]), method='linear')
fig, sys = plt.subplots()
sys.contour(xi, yi, c, levels = [.95],
colors=('darkred',),linestyles=('solid',),linewidths=(2,))
也如下图所示，我尝试通过在 0.95 处切割 z 轴来使用 qhull。
a = genfromtxt('data.txt')[:,[0,1]] #data where z <= .95
hull = ConvexHull(a)
sys.plot(a[hull.vertices,0], a[hull.vertices,1], color='red',
linestyle='--', lw=2.5, zorder=90, label=r"QHUL")
下面我试图说明这两种方法以及它的本质应该是什么样子（它的不同数据只是为了说明目的），但是，由于我的数据在 (1.7, 420) 附近下降，我得到了锯齿形在那个区域的插值中，我什至无法通过单独处理数据片段来修复，而 QHULL 方法只是错过了数据的准确性，因此我无法使用它。有什么方法可以对数据进行插值以获得如下所示的类似曲线吗？
谢谢！
我的数据如下(x,y,z);
1.950e+00 1.500e+02 9.557e-01
1.950e+00 4.800e+02 9.302e-01
1.950e+00 3.100e+02 9.467e-01
1.900e+00 5.500e+02 9.493e-01
1.700e+00 6.000e+02 9.359e-01
1.700e+00 5.500e+02 9.447e-01
8.430e-01 7.800e+02 9.906e-01
1.300e+00 9.000e+02 9.349e-01
1.655e+00 8.132e+02 9.406e-01
1.138e+00 8.453e+02 9.542e-01
1.728e+00 4.895e+02 9.335e-01
1.953e+00 2.254e+02 9.507e-01
1.932e+00 4.706e+01 9.552e-01
1.661e+00 8.081e+02 9.287e-01
1.956e+00 9.931e+00 9.320e-01
1.947e+00 4.457e+01 9.396e-01
1.949e+00 9.769e+01 9.575e-01
1.912e+00 4.441e+02 9.616e-01
1.956e+00 3.739e+01 9.344e-01
1.953e+00 1.042e+02 9.277e-01
1.957e+00 0.000e+00 9.329e-01
1.938e+00 3.455e+01 9.411e-01
1.946e+00 6.045e+01 9.381e-01
1.951e+00 8.227e+01 9.571e-01
1.962e+00 2.500e+01 9.478e-01
1.951e+00 2.778e+01 9.559e-01
1.949e+00 6.736e+01 9.630e-01
1.949e+00 1.097e+02 9.331e-01
1.708e+00 4.998e+02 9.526e-01
1.951e+00 1.250e+02 9.516e-01
1.730e+00 4.642e+02 9.332e-01
1.912e+00 4.780e+02 9.558e-01
1.927e+00 5.145e+02 9.401e-01
1.712e+00 5.203e+02 9.519e-01
1.722e+00 5.470e+02 9.396e-01
1.962e+00 1.117e+02 9.519e-01
1.962e+00 2.195e+01 9.269e-01
1.962e+00 3.366e+01 9.514e-01
1.959e+00 9.610e+01 9.270e-01
1.959e+00 4.537e+01 9.281e-01
1.959e+00 6.488e+01 9.277e-01
1.959e+00 7.659e+01 9.346e-01
1.953e+00 4.537e+01 9.615e-01
1.950e+00 1.820e+02 9.552e-01
1.950e+00 1.702e+02 9.547e-01
1.950e+00 1.415e+01 9.389e-01
1.947e+00 2.639e+02 9.517e-01
1.947e+00 2.015e+02 9.533e-01
1.941e+00 3.029e+02 9.533e-01
1.935e+00 2.873e+02 9.573e-01
1.959e+00 1.415e+01 9.314e-01
1.959e+00 2.439e+00 9.335e-01
1.899e+00 5.137e+02 9.549e-01
1.896e+00 5.371e+02 9.563e-01
1.888e+00 5.839e+02 9.531e-01
1.870e+00 5.917e+02 9.553e-01
1.722e+00 4.746e+02 9.468e-01
1.716e+00 4.278e+02 9.604e-01
1.704e+00 5.644e+02 9.482e-01
1.683e+00 5.800e+02 9.574e-01
1.609e+00 6.854e+02 9.477e-01
1.263e+00 8.766e+02 9.417e-01
1.198e+00 8.532e+02 9.524e-01
1.172e+00 8.532e+02 9.394e-01
1.927e+00 3.807e+02 9.540e-01
1.582e+00 8.424e+02 9.569e-01
1.000e+00 8.415e+02 9.526e-01
8.817e-01 7.985e+02 9.348e-01
1.954e+00 3.139e+00 9.364e-01
1.932e+00 3.583e+02 9.585e-01
1.910e+00 5.018e+02 9.500e-01
1.891e+00 5.628e+02 9.505e-01
1.858e+00 5.987e+02 9.470e-01
1.752e+00 4.874e+02 9.974e-01
1.711e+00 4.803e+02 9.477e-01
1.698e+00 5.341e+02 9.545e-01
1.687e+00 5.628e+02 9.570e-01
1.638e+00 6.596e+02 9.525e-01
1.624e+00 7.996e+02 9.559e-01
1.624e+00 8.211e+02 9.523e-01
1.619e+00 6.632e+02 9.550e-01
1.611e+00 8.283e+02 9.510e-01
1.605e+00 8.354e+02 9.537e-01
1.597e+00 6.776e+02 9.566e-01
1.592e+00 8.426e+02 9.445e-01
1.956e+00 7.908e+01 9.259e-01
原来数据跨度和插值拆分很重要
N = 40
x = linspace(0.5,2.4,N)
y = linspace(0.,1100.,N)
mean_CL = griddata((Mgo,Mn1), mean_CLs, (x[None,:], y[:,None]), method='linear')
sc.contour(x,y,mean_CL,levels = [.95],colors=('darkred',),linestyles=('solid',),linewidths=(2,))
完成任务。但是，不是将数据聚集在一个区域中，而是可能需要跨越整个 x-y 平面，点不需要靠得太近我收集了 25x0.025 的网格并且它工作得很好。
我正在尝试根据我的数据在 z = .95 处绘制等高线，但是，我无法按照我的意愿进行插值。我尝试如下使用 griddata
from scipy.interpolate import griddata
N = 1000
xi = np.linspace(min(x), max(y), N)
yi = np.linspace(min(x), max(y), N)
c = griddata((np.array(x),np.array(y)),
np.array(z), (xi[None,:],
yi[:,None]), method='linear')
fig, sys = plt.subplots()
sys.contour(xi, yi, c, levels = [.95],
colors=('darkred',),linestyles=('solid',),linewidths=(2,))
也如下图所示，我尝试通过在 0.95 处切割 z 轴来使用 qhull。
a = genfromtxt('data.txt')[:,[0,1]] #data where z <= .95
hull = ConvexHull(a)
sys.plot(a[hull.vertices,0], a[hull.vertices,1], color='red',
linestyle='--', lw=2.5, zorder=90, label=r"QHUL")
下面我试图说明这两种方法以及它的本质应该是什么样子（它的不同数据只是为了说明目的），但是，由于我的数据在 (1.7, 420) 附近下降，我得到了锯齿形在那个区域的插值中，我什至无法通过单独处理数据片段来修复，而 QHULL 方法只是错过了数据的准确性，因此我无法使用它。有什么方法可以对数据进行插值以获得如下所示的类似曲线吗？
谢谢！
我的数据如下(x,y,z);
1.950e+00 1.500e+02 9.557e-01
1.950e+00 4.800e+02 9.302e-01
1.950e+00 3.100e+02 9.467e-01
1.900e+00 5.500e+02 9.493e-01
1.700e+00 6.000e+02 9.359e-01
1.700e+00 5.500e+02 9.447e-01
8.430e-01 7.800e+02 9.906e-01
1.300e+00 9.000e+02 9.349e-01
1.655e+00 8.132e+02 9.406e-01
1.138e+00 8.453e+02 9.542e-01
1.728e+00 4.895e+02 9.335e-01
1.953e+00 2.254e+02 9.507e-01
1.932e+00 4.706e+01 9.552e-01
1.661e+00 8.081e+02 9.287e-01
1.956e+00 9.931e+00 9.320e-01
1.947e+00 4.457e+01 9.396e-01
1.949e+00 9.769e+01 9.575e-01
1.912e+00 4.441e+02 9.616e-01
1.956e+00 3.739e+01 9.344e-01
1.953e+00 1.042e+02 9.277e-01
1.957e+00 0.000e+00 9.329e-01
1.938e+00 3.455e+01 9.411e-01
1.946e+00 6.045e+01 9.381e-01
1.951e+00 8.227e+01 9.571e-01
1.962e+00 2.500e+01 9.478e-01
1.951e+00 2.778e+01 9.559e-01
1.949e+00 6.736e+01 9.630e-01
1.949e+00 1.097e+02 9.331e-01
1.708e+00 4.998e+02 9.526e-01
1.951e+00 1.250e+02 9.516e-01
1.730e+00 4.642e+02 9.332e-01
1.912e+00 4.780e+02 9.558e-01
1.927e+00 5.145e+02 9.401e-01
1.712e+00 5.203e+02 9.519e-01
1.722e+00 5.470e+02 9.396e-01
1.962e+00 1.117e+02 9.519e-01
1.962e+00 2.195e+01 9.269e-01
1.962e+00 3.366e+01 9.514e-01
1.959e+00 9.610e+01 9.270e-01
1.959e+00 4.537e+01 9.281e-01
1.959e+00 6.488e+01 9.277e-01
1.959e+00 7.659e+01 9.346e-01
1.953e+00 4.537e+01 9.615e-01
1.950e+00 1.820e+02 9.552e-01
1.950e+00 1.702e+02 9.547e-01
1.950e+00 1.415e+01 9.389e-01
1.947e+00 2.639e+02 9.517e-01
1.947e+00 2.015e+02 9.533e-01
1.941e+00 3.029e+02 9.533e-01
1.935e+00 2.873e+02 9.573e-01
1.959e+00 1.415e+01 9.314e-01
1.959e+00 2.439e+00 9.335e-01
1.899e+00 5.137e+02 9.549e-01
1.896e+00 5.371e+02 9.563e-01
1.888e+00 5.839e+02 9.531e-01
1.870e+00 5.917e+02 9.553e-01
1.722e+00 4.746e+02 9.468e-01
1.716e+00 4.278e+02 9.604e-01
1.704e+00 5.644e+02 9.482e-01
1.683e+00 5.800e+02 9.574e-01
1.609e+00 6.854e+02 9.477e-01
1.263e+00 8.766e+02 9.417e-01
1.198e+00 8.532e+02 9.524e-01
1.172e+00 8.532e+02 9.394e-01
1.927e+00 3.807e+02 9.540e-01
1.582e+00 8.424e+02 9.569e-01
1.000e+00 8.415e+02 9.526e-01
8.817e-01 7.985e+02 9.348e-01
1.954e+00 3.139e+00 9.364e-01
1.932e+00 3.583e+02 9.585e-01
1.910e+00 5.018e+02 9.500e-01
1.891e+00 5.628e+02 9.505e-01
1.858e+00 5.987e+02 9.470e-01
1.752e+00 4.874e+02 9.974e-01
1.711e+00 4.803e+02 9.477e-01
1.698e+00 5.341e+02 9.545e-01
1.687e+00 5.628e+02 9.570e-01
1.638e+00 6.596e+02 9.525e-01
1.624e+00 7.996e+02 9.559e-01
1.624e+00 8.211e+02 9.523e-01
1.619e+00 6.632e+02 9.550e-01
1.611e+00 8.283e+02 9.510e-01
1.605e+00 8.354e+02 9.537e-01
1.597e+00 6.776e+02 9.566e-01
1.592e+00 8.426e+02 9.445e-01
1.956e+00 7.908e+01 9.259e-01
原来数据跨度和插值拆分很重要
N = 40
x = linspace(0.5,2.4,N)
y = linspace(0.,1100.,N)
mean_CL = griddata((Mgo,Mn1), mean_CLs, (x[None,:], y[:,None]), method='linear')
sc.contour(x,y,mean_CL,levels = [.95],colors=('darkred',),linestyles=('solid',),linewidths=(2,))
完成任务。但是，不是将数据聚集在一个区域中，而是可能需要跨越整个 x-y 平面，点不需要靠得太近我收集了 25x0.025 的网格并且它工作得很好。