creating loader

.gitignore

@@ -2,7 +2,11 @@
 __pycache__/
 *.py[cod]
 *$py.class
-
+fsi-data/*
+cfd-data/*
+*.txt
+*.h5
+*.xdmf
 # C extensions
 *.so
 

data_vis.ipynb

@@ -14,6 +14,13 @@
     "from scipy.interpolate import griddata"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will explore how to load the dataset. Also We will visualize the dataset."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -208,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +432,6 @@
     "for idx, i in enumerate(data_loader):\n",
     "    if idx%10 !=0:\n",
     "        continue\n",
-    "    print(i.shape)\n",
     "    # single_plot(i[:,:,0].numpy(), mesh.numpy())\n",
     "    updated_mesh = mesh + i[0,:,-2:]\n",
     "    data_list.append(i[:,:,3].numpy())\n",

fsi_reader.py

@@ -30,10 +30,26 @@ class FsiDataReader():
             # check if in_lets_x2 is _x2 else raise error
             assert set(in_lets_x2).issubset(set(self._x2))
             self._x2 = in_lets_x2
+            
+        # assert _mu = 0.5 should not be mixed with other mu values
+        assert not('0.5' in self._mu and len(self._mu) > 1), "mu=0.5 should not be mixed with other mu values"
+        
     
-        mesh_h = h5py.File(os.path.join(location, 'mesh.h5'), 'r')
-        mesh = mesh_h['mesh/coordinates'][:]
-        self.input_mesh = torch.from_numpy(mesh).type(torch.float)
+    def load_mesh(self, location):
+        if '0.5' in self._mu:
+            x_path = os.path.join(location, 'mu=0.5', 'coord_x.txt')
+            y_path = os.path.join(location, 'mu=0.5', 'coord_y.txt')
+            mesh_x = np.loadtxt(x_path)
+            mesh_y = np.loadtxt(y_path)
+            # create mesh from mesh_x and mesh_y
+            mesh = np.zeros((mesh_x.shape[0], 2))
+            mesh[:, 0] = mesh_x
+            mesh[:, 1] = mesh_y
+            self.input_mesh = torch.from_numpy(mesh).type(torch.float)
+        else:
+            mesh_h = h5py.File(os.path.join(location, 'mesh.h5'), 'r')
+            mesh = mesh_h['mesh/coordinates'][:]
+            self.input_mesh = torch.from_numpy(mesh).type(torch.float)
 
     def _readh5(self, h5f, dtype=torch.float32):
         a_dset_keys = list(h5f['VisualisationVector'].keys())
@@ -115,23 +131,30 @@ class FsiDataReader():
         return combined
 
     def get_loader(self, batch_size, shuffle=True):
-        data = []
+        data_t0 = []
+        data_t1 = []
         for mu in self._mu:
             for x1 in self._x1:
                 for x2 in self._x2:
                     try:
                         if mu == 0.5:
-                            data.append(self.get_data_txt(mu, x1, x2))
+                            mu_data = self.get_data_txt(mu, x1, x2))
                         else:
-                            data.append(self.get_data(mu, x1, x2))
+                            mu_data = data.append(self.get_data(mu, x1, x2))
+                        mu_data_t0  = mu_data[:1,:,:]
+                        mu_data_t1 = mu_data[1:,:,:]
+                        data_t0.append(mu_data_t0)
+                        data_t1.append(mu_data_t1)
                     except FileNotFoundError as e:
                         print(
                             f"file not found for mu={mu}, x1={x1}, x2={x2}")
                         continue
-        data = torch.cat(data, dim=0)
-        print(f"Data shape: {data.shape}")
+        data_t0 = torch.cat(data_t0, dim=0)
+        data_t1 = torch.cat(data_t1, dim=0)
+        
+        tensor_dataset = torch.utils.data.TensorDataset(data_t0, data_t1)
 
-        data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=shuffle)
+        data_loader = torch.utils.data.DataLoader(tensor_dataset, batch_size=batch_size, shuffle=shuffle)
         
         return data_loader