使用插值方法填充包含NaN值的网格，以便用等高线绘图。

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英文:

Interpolating grid with NaN values for plotting with contour

问题

我有一个表格，是从一个更大的数据集中创建的，它给我提供了我想要绘制的 y、x 和 z 值。我创建了一个网格，然后对这些值进行插值，但我认为沿着行的插值函数并没有正确进行，因为在图中可以看到在纬度 78-80 附近有数据断裂点（参见图像），这让我认为沿着行的插值没有正确执行。是否有人有关于如何平滑这些数据的提示？

  1. aou_df = df_1994.pivot_table(index='CTDPRS', columns='LATITUDE', values='AOU')
  2. aou_df = aou_df.interpolate(method='linear', limit_area='inside', axis=0)
  4. ##绘制 AOU 1994
  5. y = ([4.2, 4.7, 4.8, 4.9, 5.4, 9.1, 9.6, 9.7, 10.0, 10.1, ... 4287.3],
  7. x = [72.13, 73.0, 73.49, 73.98, 74.5, 75.0, 75.45, 75.75, 75.94, 76.62, ... 90.0],
  9. z = [[-12.29372749, nan, nan, ..., nan, nan, nan],
  10.      [nan, nan, -43.41465869, ..., nan, nan, nan],
  11.      [nan, -54.49999783, nan, ..., nan, nan, nan],
  12.      ...,
  13.      [nan, nan, nan, ..., nan, nan, nan],
  14.      [nan, nan, nan, ..., nan, 55.87256821, nan],
  15.      [nan, nan, nan, ..., 55.39665852, nan, 55.05005376]])
  17. xi, yi = np.meshgrid(x, y, indexing='ij')
  19. # from matplotlib.colors import LogNorm
  20. plt.figure(figsize=(25, 10))
  21. levels = np.linspace(-135, 135)
  22. # cbar = plt.colorbar(ticks=(-85, -65, -45, -25, -5, 15, 35, 55, 75, 95, 115, 135))
  24. plt.contourf(xi, yi, z, cmap='jet', levels=levels, vmin=-135, vmax=135)
  25. plt.gca().invert_yaxis()
  26. plt.gca().invert_xaxis()
  27. cbar = plt.colorbar(ticks=(-135, -110, -85, -60, -35, 0, 35, 60, 85, 110, 135), extend='both')
  28. cbar.set_label('AOU', fontsize=18)
  29. cbar.ax.tick_params(labelsize=18)
  30. plt.xlabel('LAT', fontsize=18)
  31. plt.ylabel('Pressure (dbar)', fontsize=18)
  32. plt.ylim(bottom=1000)
  33. plt.xticks(fontsize=18)
  34. plt.yticks(fontsize=18)
  35. plt.plot(x, range(len(x)), 'gD', clip_on=False, markersize=10)
  36. # plt.xlim(left=80)

使用插值方法填充包含NaN值的网格，以便用等高线绘图。

英文:

I have a table I create from a larger dataset which gives me the y, x, and z values that I want to plot. I create a grid and then interpolate the values, but I don't think the function interpolation along the rows correctly since I can see data as break-up points around Latitude 78-80 in the plot (see image), this makes me think that the interpolation is not been done correctly along the rows. Does anyone have a tip on how to smooth this data?

  1. aou_df = df_1994.pivot_table(index='CTDPRS', columns = 'LATITUDE', values='AOU')
  2.     aou_df = aou_df.interpolate(method='linear', limit_area='inside', axis =0 )
  3. ##Plotting AOU 1994
  4. y =  ([   4.2,    4.7,    4.8,    4.9,    5.4,    9.1,    9.6,    9.7,
  5.                     10.0,   10.1,
  6.                   ...
  7.                   3568.2, 3608.6, 3818.6, 3824.9, 3866.7, 3979.1, 4013.4, 4133.1,
  8.                   4159.3, 4287.3],
  10. x= ([72.13,  73.0, 73.49, 73.98,  74.5,  75.0, 75.45, 75.75, 75.94,
  11.               76.62, 77.33, 77.78, 78.14, 78.15, 78.98, 79.98, 80.15, 80.16,
  12.               80.33, 80.71, 81.24, 81.58, 82.47, 83.17, 84.06, 84.85, 85.89,
  13.               87.16, 88.06, 88.79, 88.86, 88.95, 89.02,  90.0],
  15. z = [[-12.29372749,          nan,          nan, ...,          nan,
  16.              nan,          nan],
  17.    [         nan,          nan, -43.41465869, ...,          nan,
  18.              nan,          nan],
  19.    [         nan, -54.49999783,          nan, ...,          nan,
  20.              nan,          nan],
  21.    ...,
  22.    [         nan,          nan,          nan, ...,          nan,
  23.              nan,          nan],
  24.    [         nan,          nan,          nan, ...,          nan,
  25.      55.87256821,          nan],
  26.    [         nan,          nan,          nan, ...,  55.39665852,
  27.              nan,  55.05005376]])
  30. xi, yi = np.meshgrid(x,y,indexing='ij')
  32. #from matplotlib.colors import LogNorm
  33. plt.figure(figsize=(25,10))
  34. levels = np.linspace(-135,135)
  35. #cbar = plt.colorbar(ticks=(-85,-65,-45,-25,-5,15,35,55,75,95, 115,135))
  37. plt.contourf(xi,yi,z, cmap = 'jet', levels=levels,vmin=-135, vmax=135)
  38. plt.gca().invert_yaxis()
  39. plt.gca().invert_xaxis()
  40. cbar = plt.colorbar(ticks=(-135,-110,-85,-60,-35,0,35,60,85,110,135), extend= 'both')
  41. cbar.set_label('AOU', fontsize=18)
  42. cbar.ax.tick_params(labelsize=18)
  43. plt.xlabel('LAT',fontsize=18)
  44. plt.ylabel('Pressure (dbar)' ,fontsize=18)
  45. plt.ylim(bottom = 1000)
  46. plt.xticks(fontsize=18)
  47. plt.yticks(fontsize=18)
  48. plt.plot(x,range(len(x)),'gD', clip_on=False, markersize=10)
  49. #plt.xlim(left = 80)

使用插值方法填充包含NaN值的网格，以便用等高线绘图。

答案1

得分: 1

好的，值在一个表格中...抱歉...我更新了代码...尝试运行...

尝试这样做... 你可以传递 method='Linear' 和 method='Cubic' 到 griddata

  1.     from scipy.interpolate import griddata
  3.     ### 获取有效数据
  4.     xdata, ydata = np.meshgrid(x, y)
  5.     not_nan = ~np.isnan(xdata) & ~np.isnan(ydata) & ~np.isnan(z)
  6.     i, j = np.argwhere(not_nan).T
  7.     xval = xdata[i, j]
  8.     yval = ydata[i, j]
  9.     zval = z[i, j]
  10.     xy = np.column_stack((xval, yval))
  11.     
  12.     ### 网格
  13.     nx_mesh = 100
  14.     ny_mesh = 100
  15.     xi, yi = np.meshgrid(np.linspace(xval.min(), xval.max(), nx_mesh), 
  16.                          np.linspace(yval.min(), yval.max(), ny_mesh))
  17.     
  18.     ### 插值
  19.     zi = griddata(xy, zval, (xi, yi), method='nearest')
  20.     
  21.     ### 将 xi, yi 和 zi 传递给等高线图....

编辑：这是使用 method='nearest' 的 Joao 代码的结果：
使用插值方法填充包含NaN值的网格，以便用等高线绘图。

这是 'linear' 结果：
使用插值方法填充包含NaN值的网格，以便用等高线绘图。

英文:

Ok, the values are in a table... I'm sorry... I updated the code... Try to run...

Try this... You can to pass method='Linear' and method='Cubic' to griddata

  1. from scipy.interpolate import griddata
  3. ### getting the valid data
  4. xdata, ydata = np.meshgrid(x, y)
  5. not_nan = ~np.isnan(xdata) & ~np.isnan(ydata) & ~np.isnan(z)
  6. i, j = np.argwhere(not_nan).T
  7. xval = xdata[i, j]
  8. yval = ydata[i, j]
  9. zval = z[i, j]
  10. xy = np.column_stack((xval, yval))
  12. ### mesh
  13. nx_mesh = 100
  14. ny_mesh = 100
  15. xi, yi = np.meshgrid(np.linspace(xval.min(), xval.max(), nx_mesh), 
  16.                      np.linspace(yval.min(), yval.max(), ny_mesh))
  18. ### interpolation
  19. zi = griddata(xy, zval, (xi, yi), method='nearest')
  21. ### Pass xi, yi and zi to contour....

EDIT: This is the result from Joao's code using method='nearest'
使用插值方法填充包含NaN值的网格，以便用等高线绘图。

This is the 'linear' result:
使用插值方法填充包含NaN值的网格，以便用等高线绘图。

huangapple
  • 本文由 发表于 2023年5月25日 04:38:01
  • 转载请务必保留本文链接：https://go.coder-hub.com/76327245.html
  • contourf
  • interpolation
  • matplotlib
  • python
  • scipy
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