code for dataloading and visualization

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Virtual environment
+venv/
+env/
+.venv/
+.venv-*/
+
+# Distribution / packaging
+build/
+dist/
+*.egg-info/
+*.egg
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# PyCharm project files
+.idea/
+
+# VS Code settings
+.vscode/
+*.code-workspace
+
+# Test coverage and pytest cache
+.coverage
+htmlcov/
+.tox/
+.nox/
+.pytest_cache/
+.cache/
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Mypy, Pyre type checker
+.mypy_cache/
+.pyre/
+
+# Linting tools
+.pylint.d/
+ruff_cache/
+
+# Logs and debug files
+logs/
+*.log
+debug.log
+
+# MacOS system files
+.DS_Store
+
+# Windows system files
+Thumbs.db
+
+# Jupyter notebook metadata
+*.ipynb_metadata.json

data_vis.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fsi_reader import FsiDataReader\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from matplotlib.tri import Triangulation\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "from scipy.interpolate import griddata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = FsiDataReader('./fsi-data/', mu=['1.0'], in_lets_x1=['0.0'])\n",
+    "mesh = data.input_mesh\n",
+    "print(mesh.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_loader = data.get_loader(batch_size=1, shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def single_plot(data, mesh_points):\n",
+    "    data = np.squeeze(data)  # Shape becomes (1317,)\n",
+    "    print(data.shape)\n",
+    "    print(mesh_points.shape)\n",
+    "    x, y = mesh_points[:, 0], mesh_points[:, 1]\n",
+    "\n",
+    "    # Create figure with subplots\n",
+    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6), \n",
+    "                                gridspec_kw={'width_ratios': [1, 1.2]})\n",
+    "\n",
+    "    # Approach 1: Triangulation-based contour plot\n",
+    "    tri = Triangulation(x, y)\n",
+    "    contour = ax1.tricontourf(tri, data, levels=40, cmap='viridis')\n",
+    "    fig.colorbar(contour, ax=ax1, label='Value', shrink=0.3)\n",
+    "    ax1.set_title('Contour Plot of Field Data')\n",
+    "    ax1.set_aspect('equal')\n",
+    "\n",
+    "    # Approach 2: Scatter plot with interpolated background\n",
+    "    grid_x, grid_y = np.mgrid[x.min():x.max():100j, y.min():y.max():100j]\n",
+    "    grid_z = griddata((x, y), data, (grid_x, grid_y), method='cubic')\n",
+    "\n",
+    "    im = ax2.imshow(grid_z.T, origin='lower', extent=[x.min(), x.max(), \n",
+    "                    y.min(), y.max()], cmap='plasma')\n",
+    "    ax2.scatter(x, y, c=data, edgecolor='k', lw=0.3, cmap='plasma', s=15)\n",
+    "    fig.colorbar(im, ax=ax2, label='Interpolated Value', shrink=0.3)\n",
+    "    ax2.set_title('Interpolated Surface with Sample Points')\n",
+    "\n",
+    "    # Common formatting\n",
+    "    for ax in (ax1, ax2):\n",
+    "        ax.set_xlabel('X Coordinate')\n",
+    "        ax.set_ylabel('Y Coordinate')\n",
+    "        ax.grid(True, alpha=0.3)\n",
+    "        \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_field_animation(data_frames, mesh_frames, interval=100, save_path=None):\n",
+    "    \"\"\"\n",
+    "    Create an animation of time-varying 2D field data on a mesh.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    -----------\n",
+    "    data_frames : list of arrays\n",
+    "        List of data arrays for each time frame (each with shape [1, 1317, 1] or similar)\n",
+    "    mesh_frames : list of arrays or single array\n",
+    "        Either a list of mesh coordinates for each frame or a single fixed mesh\n",
+    "    interval : int\n",
+    "        Delay between animation frames in milliseconds\n",
+    "    save_path : str, optional\n",
+    "        Path to save the GIF animation\n",
+    "    \"\"\"\n",
+    "    # Determine if mesh is fixed or time-varying\n",
+    "    mesh_varying = isinstance(mesh_frames, list)\n",
+    "    \n",
+    "    # Get initial mesh and data\n",
+    "    mesh_initial = mesh_frames[0] if mesh_varying else mesh_frames\n",
+    "    data_initial = np.squeeze(data_frames[0])\n",
+    "    \n",
+    "    # Extract coordinates\n",
+    "    x, y = mesh_initial[:, 0], mesh_initial[:, 1]\n",
+    "    \n",
+    "    # Create figure\n",
+    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(50, 10), \n",
+    "                                   gridspec_kw={'width_ratios': [1, 1.2]})\n",
+    "    \n",
+    "    # Calculate global min/max for consistent colorbars\n",
+    "    all_data = np.concatenate([np.squeeze(frame) for frame in data_frames])\n",
+    "    vmin, vmax = all_data.min(), all_data.max()\n",
+    "    \n",
+    "    # Create initial triangulation\n",
+    "    tri_initial = Triangulation(x, y)\n",
+    "    \n",
+    "    # Set up first subplot - contour\n",
+    "    contour = ax1.tricontourf(tri_initial, data_initial, levels=40, cmap='viridis', \n",
+    "                             vmin=vmin, vmax=vmax)\n",
+    "    # Add contour lines for better visibility\n",
+    "    contour_lines = ax1.tricontour(tri_initial, data_initial, levels=15, \n",
+    "                                  colors='black', linewidths=0.5, alpha=0.7)\n",
+    "    \n",
+    "    fig.colorbar(contour, ax=ax1, label='Value', shrink=0.3)\n",
+    "    ax1.set_title('Contour Plot of Field Data')\n",
+    "    ax1.set_aspect('equal')\n",
+    "    \n",
+    "    # Set up second subplot - interpolated surface with scatter points\n",
+    "    grid_x, grid_y = np.mgrid[x.min():x.max():100j, y.min():y.max():100j]\n",
+    "    grid_z = griddata((x, y), data_initial, (grid_x, grid_y), method='cubic')\n",
+    "    \n",
+    "    im = ax2.imshow(grid_z.T, origin='lower', extent=[x.min(), x.max(), \n",
+    "                                                    y.min(), y.max()], \n",
+    "                   cmap='plasma', vmin=vmin, vmax=vmax)\n",
+    "    scat = ax2.scatter(x, y, c=data_initial, edgecolor='k', lw=0.3, \n",
+    "                      cmap='plasma', s=15, vmin=vmin, vmax=vmax)\n",
+    "    \n",
+    "    fig.colorbar(im, ax=ax2, label='Interpolated Value', shrink=0.3)\n",
+    "    ax2.set_title('Interpolated Surface with Sample Points')\n",
+    "    \n",
+    "    # Common formatting\n",
+    "    for ax in (ax1, ax2):\n",
+    "        ax.set_xlabel('X Coordinate')\n",
+    "        ax.set_ylabel('Y Coordinate')\n",
+    "        ax.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Add frame counter\n",
+    "    time_text = ax1.text(0.02, 0.98, '', transform=ax1.transAxes, \n",
+    "                        fontsize=10, va='top', ha='left')\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    # Update function for animation\n",
+    "    def update(frame):\n",
+    "        # Get current data\n",
+    "        data = np.squeeze(data_frames[frame])\n",
+    "        \n",
+    "        # Get current mesh if varying\n",
+    "        if mesh_varying:\n",
+    "            mesh = mesh_frames[frame]\n",
+    "            x, y = mesh[:, 0], mesh[:, 1]\n",
+    "            tri = Triangulation(x, y)\n",
+    "        else:\n",
+    "            mesh = mesh_frames\n",
+    "            x, y = mesh[:, 0], mesh[:, 1]\n",
+    "            tri = tri_initial\n",
+    "        \n",
+    "        # Update contour plot\n",
+    "        for c in ax1.collections:\n",
+    "            c.remove()\n",
+    "        new_contour = ax1.tricontourf(tri, data, levels=40, cmap='viridis', \n",
+    "                                     vmin=vmin, vmax=vmax)\n",
+    "        new_lines = ax1.tricontour(tri, data, levels=15, colors='black', \n",
+    "                                  linewidths=0.5, alpha=0.7)\n",
+    "        \n",
+    "        # Update interpolated surface\n",
+    "        grid_z = griddata((x, y), data, (grid_x, grid_y), method='cubic')\n",
+    "        im.set_array(grid_z.T)\n",
+    "        \n",
+    "        # Update scatter points\n",
+    "        scat.set_offsets(mesh)\n",
+    "        scat.set_array(data)\n",
+    "        \n",
+    "        # Update frame counter\n",
+    "        time_text.set_text(f'Frame: {frame+1}/{len(data_frames)}')\n",
+    "        \n",
+    "        return [new_contour, new_lines, im, scat, time_text]\n",
+    "    \n",
+    "    # Create animation\n",
+    "    anim = FuncAnimation(fig, update, frames=len(data_frames), \n",
+    "                         interval=interval, blit=False)\n",
+    "    \n",
+    "    # Save if path provided\n",
+    "    if save_path:\n",
+    "        print(f\"Saving animation to {save_path}...\")\n",
+    "        if save_path.endswith('.gif'):\n",
+    "            anim.save(save_path, writer='pillow', dpi=150)\n",
+    "        else:\n",
+    "            anim.save(save_path, writer='ffmpeg', dpi=150)\n",
+    "    \n",
+    "    return anim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "torch.Size([1, 1317, 5])\n",
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+      "torch.Size([1, 1317, 5])\n",
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+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n",
+      "torch.Size([1, 1317, 5])\n"
+     ]
+    }
+   ],
+   "source": [
+    "frames = 200\n",
+    "data_list = []\n",
+    "mesh_list = []\n",
+    "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",
+    "    mesh_list.append(updated_mesh.numpy())\n",
+    "    frames -= 1\n",
+    "    if frames == 0:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving animation to fsi_animation.gif...\n"
+     ]
+    }
+   ],
+   "source": [
+    "create_field_animation(data_list, mesh_list, interval=100, save_path='fsi_animation.gif')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data_list)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neuralop",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

fsi_reader.py

@@ -0,0 +1,138 @@
+import os
+import h5py
+import numpy as np
+import torch
+import h5py
+
+
+class FsiDataReader():
+    def __init__(self,
+                 location,
+                 mu=None,
+                 in_lets_x1=None,
+                 in_lets_x2=None,):
+        self.location = location
+        self._x1 = ['-4.0', '-2.0', '0.0', '2.0', '4.0', '6.0']
+        self._x2 = ['-4.0', '-2.0', '0', '2.0', '4.0', '6.0']
+        self._mu = ['0.1', '0.01', '0.5', '5', '1.0', '10.0'] 
+        # keeping vx,xy, P, dx,dy
+        self.varable_idices = [0, 1, 3, 4, 5]
+        
+        if mu is not None:
+            # check if mu is _mu else raise error
+            assert set(mu).issubset(set(self._mu))
+            self._mu = mu
+        if in_lets_x1 is not None:
+            # check if in_lets_x1 is _x1 else raise error
+            assert set(in_lets_x1).issubset(set(self._x1))
+            self._x1 = in_lets_x1
+        if in_lets_x2 is not None:
+            # check if in_lets_x2 is _x2 else raise error
+            assert set(in_lets_x2).issubset(set(self._x2))
+            self._x2 = in_lets_x2
+    
+        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())
+        size = len(a_dset_keys)
+        readings = [None for i in range(size)]
+        for dset in a_dset_keys:
+            ds_data = (h5f['VisualisationVector'][dset])
+            if ds_data.dtype == 'float64':
+                csvfmt = '%.18e'
+            elif ds_data.dtype == 'int64':
+                csvfmt = '%.10d'
+            else:
+                csvfmt = '%s'
+            readings[int(dset)] = torch.tensor(np.array(ds_data), dtype=dtype)
+
+        readings_tensor = torch.stack(readings, dim=0)
+        print(f"Loaded tensor Size: {readings_tensor.shape}")
+        return readings_tensor
+
+    def get_data(self, mu, x1, x2):
+        if mu not in self._mu:
+            raise ValueError(f"Value of mu must be one of {self._mu}")
+        if x1 not in self._x1 or x2 not in self._x2:
+            raise ValueError(
+                f"Value of is must be one of {self._ivals3} and {self._ivals12} ")
+        path = os.path.join(
+            self.location,
+            'mu='+str(mu),
+            'x1='+str(x1),
+            'x2='+str(x2),
+            'Visualization')
+
+        filename = os.path.join(path, 'displacement.h5')
+        h5f = h5py.File(filename, 'r')
+        displacements_tensor = self._readh5(h5f)
+
+
+        filename = os.path.join(path, 'pressure.h5')
+        h5f = h5py.File(filename, 'r')
+        pressure_tensor = self._readh5(h5f)
+
+        filename = os.path.join(path, 'velocity.h5')
+        h5f = h5py.File(filename, 'r')
+        velocity_tensor = self._readh5(h5f)
+        
+        combined = torch.cat([velocity_tensor, pressure_tensor, displacements_tensor], dim=-1)[..., self.varable_idices]
+
+        # return velocity_tensor, pressure_tensor, displacements_tensor
+        return combined
+    
+    def get_data_txt(self, mu, x1, x2):
+        if mu not in self._mu:
+            raise ValueError(f"Value of mu must be one of {self._mu}")
+        if x1 not in self._x1 or x2 not in self._x2:
+            raise ValueError(
+                f"Value of is must be one of {self._ivals3} and {self._ivals12} ")
+        path = os.path.join(
+            self.params.super_res_data_location,
+            'mu='+str(mu),
+            'x1='+str(x1),
+            'x2='+str(x2),
+            '1')
+        #try:
+        dis_x = torch.tensor(np.loadtxt(os.path.join(path, 'dis_x.txt')))
+        dis_y = torch.tensor(np.loadtxt(os.path.join(path, 'dis_y.txt')))
+        pressure = torch.tensor(np.loadtxt(os.path.join(path, 'pres.txt')))
+        velocity_x = torch.tensor(np.loadtxt(os.path.join(path, 'vel_x.txt')))
+        velocity_y = torch.tensor(np.loadtxt(os.path.join(path, 'vel_y.txt')))
+
+        # reshape each tensor into 2d by keeping 876 entries in each row
+        dis_x = dis_x.view(-1, 876,1)
+        dis_y = dis_y.view(-1, 876,1)
+        pressure = pressure.view(-1, 876,1)
+        velocity_x = velocity_x.view(-1, 876,1)
+        velocity_y = velocity_y.view(-1, 876,1)
+
+        
+        combined = torch.cat([velocity_x, velocity_y, pressure, dis_x, dis_y], dim=-1)[..., ]
+        return combined
+
+    def get_loader(self, batch_size, shuffle=True):
+        data = []
+        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))
+                        else:
+                            data.append(self.get_data(mu, x1, x2))
+                    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_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=shuffle)
+        
+        return data_loader
+        
+