diff --git a/PARCtorch/PARCv2.py b/PARCtorch/PARCv2.py
index 4955dab..cafd667 100644
--- a/PARCtorch/PARCv2.py
+++ b/PARCtorch/PARCv2.py
@@ -1,6 +1,6 @@
import torch
-from PARCtorch.utilities.unet import UNet
+from PARCtorch.models.unet import UNet
from PARCtorch.differentiator.finitedifference import FiniteDifference
from differentiator.differentiator import ADRDifferentiator
from integrator.rk4 import RK4
diff --git a/PARCtorch/data/dataset.py b/PARCtorch/data/dataset.py
index f3dfcb8..968f2f4 100644
--- a/PARCtorch/data/dataset.py
+++ b/PARCtorch/data/dataset.py
@@ -7,7 +7,7 @@
from tqdm import tqdm
from the_well.data import WellDataset
-# TODO: Wipe out this entire file. Instead, implement the following classes:
+# TODO: Wipe out this entire file. Instead, implement the following classes under PARCtorch/datasets/dataset.py:
# class Dataset(torch.utils.data.Dataset):
# class RegularMeshDataset(Dataset):
# class IrregularMeshDataset(Dataset):
diff --git a/PARCtorch/utilities/__init__.py b/PARCtorch/datasets/__init__.py
similarity index 100%
rename from PARCtorch/utilities/__init__.py
rename to PARCtorch/datasets/__init__.py
diff --git a/PARCtorch/demos/README.md b/PARCtorch/datasets/dataset.py
similarity index 100%
rename from PARCtorch/demos/README.md
rename to PARCtorch/datasets/dataset.py
diff --git a/PARCtorch/datasets/navier_stokes.py b/PARCtorch/datasets/navier_stokes.py
new file mode 100644
index 0000000..5257845
--- /dev/null
+++ b/PARCtorch/datasets/navier_stokes.py
@@ -0,0 +1,50 @@
+from PARCtorch.data.dataset import GenericPhysicsDataset
+from PARCtorch.utils.common import CACHE_DIR
+
+from PARCtorch.datasets.utils import download, extract_zip
+
+import os
+
+
+class NavierStokes(GenericPhysicsDataset):
+
+ """
+ Navier-Stokes data set. TODO: More details
+
+ Args:
+ split: Expected data split. Can be `train`, `test` TODO
+ data_dir: Directory to read/write data. Defaults to None, in which case,
+ data will be stored in `CACHE_DIR/datasets/NavierStokes`.
+ If the data does not exist in the specified directory,
+ it will be automatically downloaded.
+ future_steps: Number of timesteps in the future the model will predict.
+ Must be between 1 (single step prediction) and TODO (default).
+ """
+
+ url = "https://zenodo.org/records/13909869/files/NavierStokes.zip?download=1"
+
+ def __init__(
+ self, split=None, data_dir=None, future_steps=2,
+ ):
+ super().__init__()
+
+ # Set up data directory
+ if data_dir is None:
+ data_dir = os.path.join(CACHE_DIR, 'datasets', 'NavierStokes')
+ self.data_dir = data_dir
+ self.zip_dir = os.path.join(self.data_dir, 'NavierStokes.zip')
+
+ if not os.path.exists(self.data_dir):
+ os.makedirs(self.data_dir)
+
+ # Download and unzip data if it doesn't exist already.
+ self.download(force=False)
+
+ from pathlib import Path
+ filelist = Path(self.data_dir).glob('*.*')
+
+
+ def download(self, force=False):
+ if not os.path.exists(self.zip_dir):
+ download(self.url, self.zip_dir)
+ extract_zip(self.zip_dir, self.data_dir)
diff --git a/PARCtorch/datasets/utils.py b/PARCtorch/datasets/utils.py
new file mode 100644
index 0000000..98bf4fc
--- /dev/null
+++ b/PARCtorch/datasets/utils.py
@@ -0,0 +1,46 @@
+import os
+import requests
+import zipfile
+from io import BytesIO
+from tqdm import tqdm
+from pathlib import Path
+
+
+def download(url, target_path, chunk_size=1024):
+ """
+ Downloads a file from the specified URL and saves it to the target_path.
+ """
+ print(f"Downloading a file from {url}")
+ if os.path.exists(target_path):
+ print(f"Found a cached data in {target_path}. Aborting the download.")
+ print(
+ "If you want to re-download the data, please remove the cached data and try again."
+ )
+ return target_path
+
+ response = requests.get(url, stream=True)
+ response.raise_for_status()
+
+ total_size = int(response.headers.get("content-length", 0))
+ progress = tqdm(total=total_size, unit="B", unit_scale=True, desc=f"Downloading to {target_path}")
+
+ with open(target_path, "wb") as f:
+ for chunk in response.iter_content(chunk_size=chunk_size):
+ if chunk: # filter out keep-alive chunks
+ f.write(chunk)
+ progress.update(len(chunk))
+ progress.close()
+
+ print(f"Download complete. File saved to {target_path}")
+ return target_path
+
+
+def extract_zip(zip_path, target_folder):
+ """
+ Extracts the contents of a ZIP file at zip_path into the target folder.
+ """
+ Path(target_folder).mkdir(parents=True, exist_ok=True)
+ print(f"Extracting contents to {target_folder} ...")
+ with zipfile.ZipFile(zip_path, "r") as zip_ref:
+ zip_ref.extractall(target_folder)
+ print("Extraction complete.")
diff --git a/PARCtorch/differentiator/mappingandrecon.py b/PARCtorch/differentiator/mappingandrecon.py
index 37ff900..f13f57a 100644
--- a/PARCtorch/differentiator/mappingandrecon.py
+++ b/PARCtorch/differentiator/mappingandrecon.py
@@ -3,8 +3,8 @@
import torch.nn as nn
# Import custom utilities
-from PARCtorch.utilities.spade import SPADEGeneratorUnit
-from PARCtorch.utilities.resnet import ResNet
+from PARCtorch.models.spade import SPADEGeneratorUnit
+from PARCtorch.models.resnet import ResNet
class MappingAndRecon(nn.Module):
diff --git a/PARCtorch/integrator/datadrivenintegrator.py b/PARCtorch/integrator/datadrivenintegrator.py
index 02fdbe9..b956ee0 100644
--- a/PARCtorch/integrator/datadrivenintegrator.py
+++ b/PARCtorch/integrator/datadrivenintegrator.py
@@ -1,7 +1,7 @@
import torch.nn as nn
import torch.nn.functional as F
-from PARCtorch.utilities.spade import SPADEGeneratorUnit
-from PARCtorch.utilities.resnet import ResNet
+from PARCtorch.models.spade import SPADEGeneratorUnit
+from PARCtorch.models.resnet import ResNet
class DataDrivenIntegrator(nn.Module):
diff --git a/PARCtorch/integrator/poisson.py b/PARCtorch/integrator/poisson.py
index d613242..1eb1519 100644
--- a/PARCtorch/integrator/poisson.py
+++ b/PARCtorch/integrator/poisson.py
@@ -3,7 +3,7 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
-from PARCtorch.utilities.resnet import ResNet
+from PARCtorch.models.resnet import ResNet
class Poisson(nn.Module):
diff --git a/PARCtorch/misc/README.md b/PARCtorch/models/README.md
similarity index 100%
rename from PARCtorch/misc/README.md
rename to PARCtorch/models/README.md
diff --git a/PARCtorch/utilities/README.md b/PARCtorch/models/__init__.py
similarity index 100%
rename from PARCtorch/utilities/README.md
rename to PARCtorch/models/__init__.py
diff --git a/PARCtorch/utilities/resnet.py b/PARCtorch/models/resnet.py
similarity index 100%
rename from PARCtorch/utilities/resnet.py
rename to PARCtorch/models/resnet.py
diff --git a/PARCtorch/utilities/spade.py b/PARCtorch/models/spade.py
similarity index 100%
rename from PARCtorch/utilities/spade.py
rename to PARCtorch/models/spade.py
diff --git a/PARCtorch/utilities/unet.py b/PARCtorch/models/unet.py
similarity index 100%
rename from PARCtorch/utilities/unet.py
rename to PARCtorch/models/unet.py
diff --git a/PARCtorch/scripts/ns_slurm.py b/PARCtorch/scripts/ns_slurm.py
index 99e4a5e..b5374cc 100644
--- a/PARCtorch/scripts/ns_slurm.py
+++ b/PARCtorch/scripts/ns_slurm.py
@@ -39,13 +39,13 @@
# ---------------------------
from data.normalization import compute_min_max
from data.dataset import GenericPhysicsDataset, custom_collate_fn
-from utilities.viz import visualize_channels
+from PARCtorch.utils.viz import visualize_channels
from PARCtorch.PARCv2 import PARCv2
from PARCtorch.differentiator.differentiator import Differentiator
from PARCtorch.differentiator.finitedifference import FiniteDifference
from PARCtorch.integrator.integrator import Integrator
from PARCtorch.integrator.heun import Heun
-from PARCtorch.utilities.unet import UNet
+from PARCtorch.models.unet import UNet
# ---------------------------
diff --git a/PARCtorch/utils/__init__.py b/PARCtorch/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/PARCtorch/utils/common.py b/PARCtorch/utils/common.py
new file mode 100644
index 0000000..d801c3b
--- /dev/null
+++ b/PARCtorch/utils/common.py
@@ -0,0 +1,4 @@
+from pathlib import Path
+
+
+CACHE_DIR = Path.home() / ".parc"
\ No newline at end of file
diff --git a/PARCtorch/utilities/load.py b/PARCtorch/utils/load.py
similarity index 100%
rename from PARCtorch/utilities/load.py
rename to PARCtorch/utils/load.py
diff --git a/PARCtorch/utilities/viz.py b/PARCtorch/utils/viz.py
similarity index 100%
rename from PARCtorch/utilities/viz.py
rename to PARCtorch/utils/viz.py
diff --git a/PARCtorch/demos/Burgers.ipynb b/examples/Burgers.ipynb
similarity index 100%
rename from PARCtorch/demos/Burgers.ipynb
rename to examples/Burgers.ipynb
diff --git a/PARCtorch/demos/EnergeticMaterials.ipynb b/examples/EnergeticMaterials.ipynb
similarity index 100%
rename from PARCtorch/demos/EnergeticMaterials.ipynb
rename to examples/EnergeticMaterials.ipynb
diff --git a/PARCtorch/demos/NavierStokes.ipynb b/examples/NavierStokes.ipynb
similarity index 50%
rename from PARCtorch/demos/NavierStokes.ipynb
rename to examples/NavierStokes.ipynb
index f78dac9..0a823d5 100644
--- a/PARCtorch/demos/NavierStokes.ipynb
+++ b/examples/NavierStokes.ipynb
@@ -7,9 +7,9 @@
"\n",
"
Physics Aware Recurrent Convolutional Neural Network (PARC): Navier-Stokes Demo
\n",
"
\n",
- "
![\"Image](\"../misc/VIL_logo.png\")
\n",
- "
![\"Image](\"../misc/uva.png\")
\n",
- "
![\"Image](\"../misc/iowa.png\")
\n",
+ "
![\"Image](\"../img/VIL_logo.png\")
\n",
+ "
![\"Image](\"../img/uva.png\")
\n",
+ "
![\"Image](\"../img/iowa.png\")
\n",
"
\n",
"
A customizable framework to embed physics in Deep Learning. PARC's formulation is inspired by Advection-Diffusion-Reaction processes and uses an Inductive Bias approach to constrain the Neural Network.
\n",
@@ -61,6 +61,393 @@
"Happy Modeling!"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Download & Prepare Data\n",
+ "PARCtorch comes with a built-in dataset containing Navier-Stokes simulations. The following script will allow you to access the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "tensor(300)\n",
+ "tensor([ True, False, True, True, True, True, False, True])\n",
+ "tensor([False, False, False, False, True, True, False, True])\n",
+ "0\n",
+ "Variable time-independent scalar\n",
+ "0\n",
+ "Variable time-dependent scalar field\n",
+ "torch.Size([8, 39, 128, 256])\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor([ -64.7932, 1491.8637, -35.3208, 4.4460, 53.8535, 123.9229,\n",
+ " 2062.4870, 269.5076], dtype=torch.float64)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from PARCtorch.utils.common import CACHE_DIR\n",
+ "\n",
+ "from PARCtorch.datasets.utils import download, extract_zip\n",
+ "\n",
+ "import os\n",
+ "from pathlib import Path\n",
+ "import numpy as np\n",
+ "\n",
+ "class Data:\n",
+ " def __init__(self, data=None, traj_varying=False, time_varying=False, space_varying=False):\n",
+ " self.traj_varying = traj_varying\n",
+ " self.time_varying = time_varying\n",
+ " self.space_varying = space_varying\n",
+ " self.data = torch.as_tensor(data)\n",
+ " # TODO: assert(len(data.shape) >= traj_varying + time_varying + space_varying)\n",
+ "\n",
+ " @classmethod\n",
+ " def __torch_function__(self, func, types, args=(), kwargs=None):\n",
+ " # Forward any torch function to self.data\n",
+ " if kwargs is None:\n",
+ " kwargs = {}\n",
+ " args = tuple(a.data if isinstance(a, Data) else a for a in args)\n",
+ " return func(*args, **kwargs)\n",
+ " \n",
+ "\n",
+ " def __getitem__(self, index):\n",
+ " return self.data[index]\n",
+ " \n",
+ " def __eq__(self, other):\n",
+ " return self.data == other\n",
+ " \n",
+ " def __ne__(self, other):\n",
+ " return self.data != other\n",
+ " \n",
+ " def __lt__(self, other):\n",
+ " return self.data < other\n",
+ " \n",
+ " def __le__(self, other):\n",
+ " return self.data <= other\n",
+ " \n",
+ " def __gt__(self, other):\n",
+ " return self.data > other\n",
+ " \n",
+ " def __ge__(self, other):\n",
+ " return self.data >= other\n",
+ " \n",
+ " def __len__(self):\n",
+ " if self.data is None:\n",
+ " return 0\n",
+ " return len(self.data)\n",
+ " \n",
+ " def __repr__(self):\n",
+ " return f\"\"\n",
+ " \n",
+ " @property\n",
+ " def shape(self):\n",
+ " return self.data.shape\n",
+ " \n",
+ "\n",
+ " @property\n",
+ " def rank(self):\n",
+ " if self.data is None:\n",
+ " return -1\n",
+ " \n",
+ " rank = len(self.data.shape) - self.traj_varying - self.time_varying\n",
+ " if isinstance(self.space_varying, list):\n",
+ " rank -= sum(self.space_varying)\n",
+ " else:\n",
+ " rank -= self.space_varying\n",
+ "\n",
+ " return rank\n",
+ "\n",
+ " \n",
+ " @property\n",
+ " def type(self):\n",
+ " type_str = ''\n",
+ " if self.traj_varying:\n",
+ " type_str = 'Variable'\n",
+ " else:\n",
+ " type_str = 'Constant'\n",
+ " \n",
+ " if self.time_varying:\n",
+ " type_str += ' time-dependent'\n",
+ " else:\n",
+ " type_str += ' time-independent'\n",
+ " \n",
+ " rank = self.rank\n",
+ " if rank == 0:\n",
+ " type_str += ' scalar'\n",
+ " elif rank == 1:\n",
+ " type_str += ' vector'\n",
+ " else:\n",
+ " type_str += f' rank-{rank} tensor'\n",
+ "\n",
+ " if any(self.space_varying):\n",
+ " type_str += ' field'\n",
+ "\n",
+ " return type_str\n",
+ " \n",
+ "\n",
+ "\n",
+ "class Dataset(torch.utils.data.Dataset):\n",
+ " '''\n",
+ " Data is \n",
+ " '''\n",
+ " url = \"https://zenodo.org/records/13909869/files/NavierStokes.zip?download=1\"\n",
+ " def __init__(\n",
+ " self,\n",
+ " ):\n",
+ "\n",
+ " super().__init__()\n",
+ "\n",
+ " \n",
+ " # TODO: https://github.com/baeklab/PARCtorch/blob/main/tests/conftest.py\n",
+ " self.dataset_name = '' # dataset name\n",
+ " self.grid_type = 'cartesian' # type of grid. Can only be 'cartesian' at the moment. TODO: 'mesh'\n",
+ " self.n_spatial_dims = 1 # spatial dimensions\n",
+ " self.dimensions = {}\n",
+ " self.boundary_conditions = {}\n",
+ " self.constants = {} # physics parameters that are constant across all samples\n",
+ " self.data = {} # \n",
+ "\n",
+ " self.filelist = {}\n",
+ " \n",
+ "\n",
+ " # Set up data directory\n",
+ " data_dir = None\n",
+ " if data_dir is None:\n",
+ " data_dir = os.path.join(CACHE_DIR, 'datasets', 'NavierStokes')\n",
+ " self.data_dir = data_dir\n",
+ " self.zip_dir = os.path.join(self.data_dir, 'NavierStokes.zip')\n",
+ "\n",
+ " if not os.path.exists(self.data_dir):\n",
+ " os.makedirs(self.data_dir)\n",
+ "\n",
+ " # Download and unzip data if it doesn't exist already.\n",
+ " self.download(force=False)\n",
+ "\n",
+ " # Read data\n",
+ " self.load()\n",
+ "\n",
+ " \n",
+ " # TODO: Lazy loading (Future; low priority)\n",
+ " pass\n",
+ "\n",
+ " # number of trajectories or simulation instances\n",
+ " @property\n",
+ " def n_trajectories(self):\n",
+ " return len(self.filelist)\n",
+ " \n",
+ " def __len__(self):\n",
+ " return self.n_trajectories\n",
+ " \n",
+ " def download(self, force=False):\n",
+ " if not os.path.exists(self.zip_dir):\n",
+ " download(self.url, self.zip_dir)\n",
+ " extract_zip(self.zip_dir, self.data_dir)\n",
+ "\n",
+ " def load(self, data_dir=None):\n",
+ " if data_dir is None:\n",
+ " data_dir = self.data_dir\n",
+ "\n",
+ " filelist = Path(data_dir).glob('test/*.npy')\n",
+ " \n",
+ " \n",
+ " self.dataset_name = 'Navier Stokes'\n",
+ " self.grid_type = 'cartesian'\n",
+ " self.n_spatial_dims = 2\n",
+ " self.dimensions = {\n",
+ " 'time': Data(torch.linspace(0,1,39)),\n",
+ " 'x': Data(torch.linspace(0,1,256)),\n",
+ " 'y': Data(torch.linspace(0,1,128)),\n",
+ " }\n",
+ " # self.constants = {}\n",
+ " # self.boundary_conditions = {}\n",
+ "\n",
+ " self.filelist = []\n",
+ " reynolds_numbers = []\n",
+ " pressure_fields = []\n",
+ " velocity_fields = []\n",
+ " for filepath in filelist:\n",
+ " reynolds_numbers.append( int(filepath.stem.split('_')[-1]) )\n",
+ " fields = np.load(filepath)\n",
+ " pressure_fields.append(fields[:,0,:,:])\n",
+ " velocity_fields.append(fields[:,2:,:,:])\n",
+ " self.filelist.append(filepath)\n",
+ " \n",
+ " self.data = {\n",
+ " 'Reynolds Number': Data(\n",
+ " torch.tensor(reynolds_numbers),\n",
+ " traj_varying=True,\n",
+ " time_varying=False,\n",
+ " space_varying=[False, False],\n",
+ " ),\n",
+ " 'Pressure': Data(\n",
+ " torch.tensor(pressure_fields),\n",
+ " traj_varying=True,\n",
+ " time_varying=True,\n",
+ " space_varying=[True, True],\n",
+ " ),\n",
+ " 'Velocity': Data(\n",
+ " torch.tensor(velocity_fields),\n",
+ " traj_varying=True,\n",
+ " time_varying=True,\n",
+ " space_varying=[True, True],\n",
+ " ),\n",
+ " }\n",
+ "\n",
+ " \n",
+ "\n",
+ " # def __getitem__(self):\n",
+ " \n",
+ "\n",
+ " # def normalize(self):\n",
+ "\n",
+ " # train_ds = ds.filter( torch.reduce_sum(ds.data['morphology'], dim=[1,2]) < 100 )\n",
+ "\n",
+ " def filter(self, mask):\n",
+ " '''\n",
+ " train = ds.filter( ds[ds.physics_parameters[0]] < 1000 )\n",
+ " test = ds.filter( ds.meta.physics_parameters[0] >= 1000 )\n",
+ " '''\n",
+ " assert( len(mask.shape) == 1 ) # TODO: error message\n",
+ " assert( len(mask) == self.n_trajectories ) # TODO: error message\n",
+ "\n",
+ " indices = torch.nonzero(mask).squeeze().tolist()\n",
+ " print(indices)\n",
+ " ds = Dataset() # TODO: Copy constructor\n",
+ " ds.filelist = [self.filelist[i] for i in indices] # TODO: Deep copy?\n",
+ " ds.data = {key: self.data[key][indices] for key in self.data.keys()} # TODO: Deep copy?\n",
+ " ds.grid_type = self.grid_type\n",
+ " ds.n_spatial_dims = self.n_spatial_dims\n",
+ " ds.data_dir = self.data_dir\n",
+ " ds.dimensions = self.dimensions\n",
+ " ds.boundary_conditions = self.boundary_conditions\n",
+ " ds.constants = self.constants\n",
+ " return ds\n",
+ " \n",
+ "\n",
+ "ds = Dataset()\n",
+ "print(ds.data['Reynolds Number'])\n",
+ "print(ds.data['Reynolds Number'][0])\n",
+ "print(ds.data['Reynolds Number'] < 1000)\n",
+ "print(ds.data['Reynolds Number'] < torch.tensor([1,2,3,4,1000,1000,1000,1000]))\n",
+ "print(ds.data['Reynolds Number'].rank)\n",
+ "print(ds.data['Reynolds Number'].type)\n",
+ "\n",
+ "print(ds.data['Pressure'].rank)\n",
+ "print(ds.data['Pressure'].type)\n",
+ "print(ds.data['Pressure'].shape)\n",
+ "torch.mean(ds.data['Pressure'], dim=[1,2,3])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0, 2, 6]\n",
+ "tensor([ 300, 400, 8000])\n"
+ ]
+ }
+ ],
+ "source": [
+ "train_ds = ds.filter((ds.data['Reynolds Number'] < 500) | (ds.data['Reynolds Number'] > 5000))\n",
+ "print(train_ds.data['Reynolds Number'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from torch.utils.data import DataLoader\n",
+ "\n",
+ "# Create DataLoader for training dataset\n",
+ "train_loader = DataLoader(\n",
+ " train_ds,\n",
+ " batch_size=10,\n",
+ " shuffle=False,\n",
+ " num_workers=1,\n",
+ " pin_memory=True,\n",
+ " # collate_fn=custom_collate_fn,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "RuntimeError",
+ "evalue": "DataLoader worker (pid(s) 19368) exited unexpectedly",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mEmpty\u001b[0m Traceback (most recent call last)",
+ "File \u001b[1;32mc:\\Users\\mwn4yc\\Anaconda3\\envs\\parc\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:1133\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1133\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1134\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n",
+ "File \u001b[1;32mc:\\Users\\mwn4yc\\Anaconda3\\envs\\parc\\lib\\queue.py:179\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m--> 179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[0;32m 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_empty\u001b[38;5;241m.\u001b[39mwait(remaining)\n",
+ "\u001b[1;31mEmpty\u001b[0m: ",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(batch)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+ "File \u001b[1;32mc:\\Users\\mwn4yc\\Anaconda3\\envs\\parc\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 629\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 630\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m 635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
+ "File \u001b[1;32mc:\\Users\\mwn4yc\\Anaconda3\\envs\\parc\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:1329\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[0;32m 1328\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m-> 1329\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1330\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 1331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[0;32m 1332\u001b[0m \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n",
+ "File \u001b[1;32mc:\\Users\\mwn4yc\\Anaconda3\\envs\\parc\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:1285\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1283\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m 1284\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_thread\u001b[38;5;241m.\u001b[39mis_alive():\n\u001b[1;32m-> 1285\u001b[0m success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1286\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[0;32m 1287\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
+ "File \u001b[1;32mc:\\Users\\mwn4yc\\Anaconda3\\envs\\parc\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:1146\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 1144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(failed_workers) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1145\u001b[0m pids_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mstr\u001b[39m(w\u001b[38;5;241m.\u001b[39mpid) \u001b[38;5;28;01mfor\u001b[39;00m w \u001b[38;5;129;01min\u001b[39;00m failed_workers)\n\u001b[1;32m-> 1146\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDataLoader worker (pid(s) \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpids_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) exited unexpectedly\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 1147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e, queue\u001b[38;5;241m.\u001b[39mEmpty):\n\u001b[0;32m 1148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)\n",
+ "\u001b[1;31mRuntimeError\u001b[0m: DataLoader worker (pid(s) 19368) exited unexpectedly"
+ ]
+ }
+ ],
+ "source": [
+ "for batch in train_loader:\n",
+ " print(batch)\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "module 'PARCtorch' has no attribute 'datasets'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[1], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mPARCtorch\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mparc\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mparc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatasets\u001b[49m\u001b[38;5;241m.\u001b[39mNavierStokes()\n",
+ "\u001b[1;31mAttributeError\u001b[0m: module 'PARCtorch' has no attribute 'datasets'"
+ ]
+ }
+ ],
+ "source": [
+ "import PARCtorch as parc\n",
+ "parc.datasets.NavierStokes()"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -516,7 +903,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "parc",
"language": "python",
"name": "python3"
},
@@ -530,7 +917,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.13.2"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/examples/README.md b/examples/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/PARCtorch/misc/VIL_logo.png b/img/VIL_logo.png
similarity index 100%
rename from PARCtorch/misc/VIL_logo.png
rename to img/VIL_logo.png
diff --git a/PARCtorch/misc/iowa.png b/img/iowa.png
similarity index 100%
rename from PARCtorch/misc/iowa.png
rename to img/iowa.png
diff --git a/PARCtorch/misc/uva.png b/img/uva.png
similarity index 100%
rename from PARCtorch/misc/uva.png
rename to img/uva.png
diff --git a/tests/test_built_in_datasets.py b/tests/test_built_in_datasets.py
new file mode 100644
index 0000000..41587e6
--- /dev/null
+++ b/tests/test_built_in_datasets.py
@@ -0,0 +1,8 @@
+import PARCtorch as parc
+
+
+def test_dataset_navier_stokes():
+ """
+ Test built-in Navier-Stokes dataset
+ """
+ ds = parc.datasets.NavierStokes(path='dummy.py')
\ No newline at end of file