scipy 用NaN值插值格网以绘制等高线

bakd9h0s 于 2023-05-29 发布在其他

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我从一个更大的数据集创建了一个表，它给了我想要绘制的y，x和z值。我创建了一个网格，然后插值，但我不认为函数沿着行的插值正确，因为我可以看到数据在图中纬度78-80附近的分裂点（见图），这使我认为沿行的插值没有正确完成。有没有人知道如何平滑这些数据？

aou_df = df_1994.pivot_table(index='CTDPRS', columns = 'LATITUDE', values='AOU')
    aou_df = aou_df.interpolate(method='linear', limit_area='inside', axis =0 )
##Plotting AOU 1994
y =  ([   4.2,    4.7,    4.8,    4.9,    5.4,    9.1,    9.6,    9.7,
                    10.0,   10.1,
                  ...
                  3568.2, 3608.6, 3818.6, 3824.9, 3866.7, 3979.1, 4013.4, 4133.1,
                  4159.3, 4287.3],

x= ([72.13,  73.0, 73.49, 73.98,  74.5,  75.0, 75.45, 75.75, 75.94,
              76.62, 77.33, 77.78, 78.14, 78.15, 78.98, 79.98, 80.15, 80.16,
              80.33, 80.71, 81.24, 81.58, 82.47, 83.17, 84.06, 84.85, 85.89,
              87.16, 88.06, 88.79, 88.86, 88.95, 89.02,  90.0],

z = [[-12.29372749,          nan,          nan, ...,          nan,
             nan,          nan],
   [         nan,          nan, -43.41465869, ...,          nan,
             nan,          nan],
   [         nan, -54.49999783,          nan, ...,          nan,
             nan,          nan],
   ...,
   [         nan,          nan,          nan, ...,          nan,
             nan,          nan],
   [         nan,          nan,          nan, ...,          nan,
     55.87256821,          nan],
   [         nan,          nan,          nan, ...,  55.39665852,
             nan,  55.05005376]])

xi, yi = np.meshgrid(x,y,indexing='ij')

#from matplotlib.colors import LogNorm
plt.figure(figsize=(25,10))
levels = np.linspace(-135,135)
#cbar = plt.colorbar(ticks=(-85,-65,-45,-25,-5,15,35,55,75,95, 115,135))

plt.contourf(xi,yi,z, cmap = 'jet', levels=levels,vmin=-135, vmax=135)
plt.gca().invert_yaxis()
plt.gca().invert_xaxis()
cbar = plt.colorbar(ticks=(-135,-110,-85,-60,-35,0,35,60,85,110,135), extend= 'both')
cbar.set_label('AOU', fontsize=18)
cbar.ax.tick_params(labelsize=18)
plt.xlabel('LAT',fontsize=18)
plt.ylabel('Pressure (dbar)' ,fontsize=18)
plt.ylim(bottom = 1000)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.plot(x,range(len(x)),'gD', clip_on=False, markersize=10)
#plt.xlim(left = 80)

scipy

来源：https://stackoverflow.com/questions/76327245/interpolating-grid-with-nan-values-for-plotting-with-contour

1条答案

按热度按时间

efzxgjgh1#

好的，数值在一个表格里。。对不起...我更新了代码。。试着跑。。
试试这个...你可以将method='Linear'和method='Cubic'传递给griddata

from scipy.interpolate import griddata

### getting the valid data
xdata, ydata = np.meshgrid(x, y)
not_nan = ~np.isnan(xdata) & ~np.isnan(ydata) & ~np.isnan(z)
i, j = np.argwhere(not_nan).T
xval = xdata[i, j]
yval = ydata[i, j]
zval = z[i, j]
xy = np.column_stack((xval, yval))

### mesh
nx_mesh = 100
ny_mesh = 100
xi, yi = np.meshgrid(np.linspace(xval.min(), xval.max(), nx_mesh), 
                     np.linspace(yval.min(), yval.max(), ny_mesh))

### interpolation
zi = griddata(xy, zval, (xi, yi), method='nearest')

### Pass xi, yi and zi to contour....

编辑：这是Joao的代码使用method='nearest'

得到的结果
这是“线性”结果：