Update README.md

README.md

@@ -29,7 +29,7 @@ ensures greater diversity than existing ones. Please refer to our [arXiv paper](
    |39-56| Mach numbers and angles of attacks |
    
 2. Index file
-  `index.npy` provides the crucial information for all provided samples; it has a shape of \\( 28856 \times 8 \\), with each channel listed below:
+  `index.npy` provides the crucial information for all provided samples; it has 12 channels, with each channel listed below:
 
    |index|description|
    |-|-|
@@ -71,14 +71,14 @@ ensures greater diversity than existing ones. Please refer to our [arXiv paper](
    CGNS library to be read out. Since they are too large, they are available via university storage upon reasonable request to the authors.
 
 
-The table below summarizes the data.
+The table below summarizes the data. (N=28856, Nshape=4239)
 
 |Type | File | Description | Shape | Size |
 |-|-|-|-|-|
 |Geometric parameters | `Configs.dat` | shape parameters  | N x 57 | 5.0 MB
 |Information | `index.npy` | group information, operating conditions, and aerodynamic coefficients | N x 12 | 2.8 MB
-|Surface mesh | `origingeom.npy` | reference surface mesh (grid points) | N x 3 x 129 x 257 | 3.3 GB 
-|         | `geom0.npy` | reference surface mesh (cell center) | N x 3 x 128 x 256 | 3.3 GB   
+|Surface mesh | `origingeom.npy` | reference surface mesh (grid points) | Nshape x 3 x 129 x 257 | 3.3 GB 
+|         | `geom0.npy` | reference surface mesh (cell center) | Nshape x 3 x 128 x 256 | 3.3 GB   
 |Surface flow | `data.npy` | \\( C_p, \bm {C_f} \\) at reference mesh (cell center) | N x 3 x 128 x 256 | 22.7 GB 
 |Volume flow | `data_vol.*.npy.zst` | \\(x, y, z, \rho, p, v_x, v_y, v_z\\) at near-field volumetric simulation mesh (cell center) | N x 8 x 2,204,800 |  2.3 TB （46 files）
 |Raw data     | `*\wing.xyz` | surface simulation mesh | | 7.8 GB 
@@ -86,4 +86,98 @@ The table below summarizes the data.
 |        | `*\vol.cgns` | raw flow field output | | 5.5 TB 
 
 
-The simulations are conducted on the 160-core high-performance computing cluster at AeroLab, Tsinghua University, for over four months. 
+The simulations are conducted on the 160-core high-performance computing cluster at AeroLab, Tsinghua University, for over four months. 
+
+# Train-test split
+
+The default training-testing split is decided by the **shape indices**, so that the samples in the testing dataset contain shapes that are totally unseen during training. We 
+select 90\% shapes and their sample number (corresponding to the rows in `index.npy`) are in `training_samples_index.txt`.
+
+# Postprocess
+
+## Aerodynamic coefficients
+
+One can integrate surface flow (formatted as in `data.npy`) to get the aerodynamic coefficients (i.e., the lift coefficient $C_L$, the drag coefficient $C_D$, the pitching 
+moment coefficient $C_{M,z}$) with the code in `floGen` (`flogen.post`). We also provide the `post.py` file here for simple download. 
+
+Remark. 
+1. It uses `pytorch`.
+2. The geometric information should be with the grid point mesh (`origingeom.npy`), not the cell-centric mesh (`geom0.npy`).
+3. The values in `data.npy` are already non-dimensionalized with the freestream condition
+
+```python
+import post
+geom = torch.from_numpy(np.load('origingeom.npy'))[i_shape]).float().to(device)
+aoa = 3.0
+ref_area = np.load('index.npy')[i_sample, 4]
+
+output = <your_model_output> # with shape (H, W, 3)
+
+cf_xyz = post._get_xz_cf_t(geom, output[..., 1:]) # transfer to xyz coordinates
+
+force_coefficients = post.get_force_2d_t(geom=geom, aoa=aoa, cp=output[..., 0], cf=cf_xyz) / ref_area  # returns: CD, CL, CZ
+moment_coefficients = post.get_moment_2d_t(geom=geom, cp=output[..., 0], cf=cf_xyz, ref_point=[0.25, 0, 0]) / ref_area   # returns: CMx, CMy, CMz
+```
+One can also use the `cfdpost` repo (https://github.com/YangYunjia/cfdpost).
+
+```python
+from cfdpost.wing.basic import BasicWing
+
+geom = torch.from_numpy(np.load('origingeom.npy'))[i_shape]).float().to(device)
+geom_infos = {}
+geom_infos['ref_area'] = np.load('index.npy')[i_sample, 4]
+aoa = 3.0
+
+output = <your_model_output> # with shape (H, W, 3)
+
+wg1 = BasicWing(paras=geom_infos, aoa=aoa, iscentric=True)
+wg1.read_formatted_surface(geometry=geom, data=output, isinitg=False, isnormed=False)
+wg1.aero_force()
+cl_real = wg1.coefficients # CL, CD, CMz
+```
+
+## Visualization
+
+To visualize the wing surface field, we provide a brief code that gives a not bad looking.
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+def color_map(data, c_map, alpha, dmin=None, dmax=None):
+    dmin = np.nanmin(data) if dmin is None else dmin
+    dmax = np.nanmax(data) if dmax is None else dmax
+    _c_map = mpl.colormaps.get_cmap(c_map)
+
+    norm = mpl.colors.Normalize(vmin=dmin, vmax=dmax)
+    _sm = mpl.cm.ScalarMappable(norm=norm, cmap=_c_map)
+    _colors = _sm.to_rgba(data)
+    _colors[..., -1] = alpha
+    
+    return _colors, _sm
+
+geom = np.load('data/ppn2norigingeom.npy')[0]
+output = <your_model_output> # with shape (H, W, 3)
+
+fig = plt.figure(figsize=(10, 4), dpi=200)
+ax = fig.add_subplot(projection='3d')
+
+elev = 68; azim =120 
+
+colors, sm = color_map(output[..., 0], 'gist_rainbow', alpha=1, dmin=-1, dmax=1)    # cp
+ax.plot_surface(*geom[[0,2,1]], facecolors=colors, edgecolor='none', rstride=1, cstride=3, shade=True)
+ax.view_init(elev=elev, azim=azim)
+ax.set_axis_off()
+ax.grid(False)
+ax.xaxis.pane.set_visible(False)
+ax.yaxis.pane.set_visible(False)
+ax.zaxis.pane.set_visible(False)
+
+plt.show()
+```
+
+This should gives sth. like:
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6878e482bd4380c813fd99de/8_e3e0Qjik3TSv4evI7dR.png" alt="transform" width="30%">
+