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+description = "The Adaptable Input Output System version 2" +optional = false +python-versions = ">=3.8" +files = [ + {file = "adios2-2.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ef6d29b4c9c191d53fa279c0962e26c6e04102793931dc9ce5b9200c39447c5"}, + {file = "adios2-2.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6df51ad7a45be6587a3fbf2410c9167eec3b4c42acd1745252cff3565c95e523"}, + {file = "adios2-2.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7f67ee7732a6685be2cc533ce40045cb6101cf9a182c2e1b2523e9abef539bf1"}, + {file = "adios2-2.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:73c206e09d378be349e10ef6db782aa53cbca23f70fa18b19c09cca85f298213"}, + {file = "adios2-2.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3085de99427d3c4d69e480c98022a7ae82268f685ceadd77b2cc334b02f8911"}, + {file = "adios2-2.10.1.tar.gz", hash = "sha256:645ce87e61aadf63a5f448b6bb857011e22dfc6cc7df2058b6acf1f121811d00"}, +] + +[package.dependencies] +numpy = "*" + +[package.extras] +dev = ["black", "pip (>=21.3)"] + [[package]] name = "alabaster" version = "1.0.0" @@ -472,13 +493,13 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"] [[package]] name = "databricks-sdk" -version = "0.32.2" +version = "0.32.3" description = "Databricks SDK for Python (Beta)" optional = false python-versions = ">=3.7" files = [ - {file = "databricks_sdk-0.32.2-py3-none-any.whl", hash = "sha256:eea4e1942ddd4fc8c36b88e4c7f948e5486fbb46aa70174101de1a0b1a0e0dc8"}, - {file = "databricks_sdk-0.32.2.tar.gz", hash = "sha256:7af6eaa73a987dc6ff4b4cb056a932b1575602692b164a52b56ccdef7af55660"}, + {file = "databricks_sdk-0.32.3-py3-none-any.whl", hash = "sha256:66b57cfd7731f541df014d136d5c257aa45ea4caa95d0d3fbaaed5b43142f32a"}, + {file = "databricks_sdk-0.32.3.tar.gz", hash = "sha256:bdb8acef3d79e83f05e7fca6cd6128b2b46a4bb4de240374c70050b5f029d4f2"}, ] [package.dependencies] @@ -2990,4 +3011,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "8ed9c888f3b3efb3dfb2d66f8f4b45f1141c181981062a57812471d82c45f685" +content-hash = "19cebf9f31087c187c70ab0e30d1ae4644ebbde22b8b1141b6ead38cc8368614" diff --git a/pyproject.toml b/pyproject.toml index 08650053..b0afef30 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,6 +53,7 @@ torch = "^2.1.0" continuiti = "^0.2.0" h5py = "^3.11.0" mlflow = "^2.16.2" +adios2 = "^2.10.1" [tool.poetry.group.dev] optional = true diff --git a/src/nos/data/__init__.py b/src/nos/data/__init__.py index 20483ec3..24a9c234 100644 --- a/src/nos/data/__init__.py +++ b/src/nos/data/__init__.py @@ -1,8 +1,11 @@ """Data contains structures to manage specific dataset implementations.""" +from nos.data.pulsating_sphere import ConstBoundaryDataset, InverseConstBoundaryDataset from nos.data.transmission_loss import TLDataset, TLDatasetCompact __all__ = [ "TLDataset", "TLDatasetCompact", + "ConstBoundaryDataset", + "InverseConstBoundaryDataset", ] diff --git a/src/nos/data/pulsating_sphere.py b/src/nos/data/pulsating_sphere.py new file mode 100644 index 00000000..04e9c05c --- /dev/null +++ b/src/nos/data/pulsating_sphere.py @@ -0,0 +1,151 @@ +import json # noqa: D100 +import pathlib + +import adios2 as ad +import torch +from continuiti.data import OperatorDataset + + +class ConstBoundaryDataset(OperatorDataset): + """Pulsating sphere dataset with constant valued boundaries.""" + + def __init__( + self, + dataset_path: pathlib.Path, + observations: int = -1, + ) -> None: + """Initialize. + + Args: + dataset_path (pathlib.Path): Path to the csv file. + observations (int, optional): Number of observations. Defaults to -1 (all). + + """ + tensor_path = dataset_path.joinpath("pressure_0.bp") + with ad.FileReader(str(tensor_path)) as reader: + geom = torch.from_numpy(reader.read("geometry"))[:, :-1] + + pr = [] + pi = [] + for step in range(reader.num_steps()): + pr.append(reader.read("p_real", step_selection=[step, 1])) + pi.append(reader.read("p_imag", step_selection=[step, 1])) + pressure_real = torch.stack([torch.from_numpy(p) for p in pr]) + pressure_imag = torch.stack([torch.from_numpy(p) for p in pi]) + pressure = torch.stack([pressure_real.squeeze(), pressure_imag.squeeze()]) + pressure = pressure.to(torch.get_default_dtype()) + + n_observations = pressure.size(1) + + json_path = dataset_path.joinpath("properties.json") + with json_path.open("r") as file: + properties = json.load(file) + top_samples = torch.tensor(properties["top_samples"]) + right_samples = torch.tensor(properties["right_samples"]) + frequency_samples = torch.tensor(properties["frequency_samples"]).unsqueeze(1) + + x = torch.cat([top_samples, right_samples, frequency_samples], dim=1).unsqueeze(-1) + x_min, _ = torch.min(x, dim=0, keepdim=True) + x_max, _ = torch.max(x, dim=0, keepdim=True) + + u = x + + y = geom.transpose(0, 1).unsqueeze(0).expand(n_observations, -1, -1).to(torch.get_default_dtype()) + v = pressure.transpose(0, 1) + + perm = torch.randperm(n_observations) + idx = perm[:observations] + + super().__init__(x[idx], u[idx], y[idx], v[idx]) + + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get item at idx from dataset.""" + x_min = torch.tensor([[0.0], [-1.0], [0.0], [-1.0], [400.0]]) + x_scale = torch.tensor([[1.0], [1.0], [1.0], [1.0], [100.0]]) + x = ((self.x[idx] - x_min) / x_scale) * 2.0 - 1.0 + v_max, _ = torch.max(torch.abs(self.v[idx]), dim=-1, keepdim=True) + + return x, x, self.y[idx] * 2.0 - 1.0, self.v[idx] / v_max + + +class InverseConstBoundaryDataset(OperatorDataset): + """Inverse pulsating sphere dataset with constant boundary conditions.""" + + def __init__( + self, + dataset_path: pathlib.Path, + observations: int = -1, + points: int = 10, + sensors: int = -1, + sensor_idx: torch.Tensor | None = None, + ) -> None: + """Initialize. + + Args: + dataset_path (pathlib.Path): path to the dataset bp. + observations (int, optional): Number of observations in the dataset. Defaults to -1 (all). + points (int, optional): Number of points on which the boundary condition is sampled. Defaults to 10. + sensors (int, optional): Number of sensors during training. Defaults to -1 (all). + sensor_idx (torch.Tensor | None, optional): Indices of the sensors used during training. Defaults to None. + + """ + tensor_path = dataset_path.joinpath("pressure_0.bp") + with ad.FileReader(str(tensor_path)) as reader: + geom = torch.from_numpy(reader.read("geometry"))[:, :-1] + + pr = [] + pi = [] + for step in range(reader.num_steps()): + pr.append(reader.read("p_real", step_selection=[step, 1])) + pi.append(reader.read("p_imag", step_selection=[step, 1])) + pressure_real = torch.stack([torch.from_numpy(p) for p in pr]) + pressure_imag = torch.stack([torch.from_numpy(p) for p in pi]) + pressure = torch.stack([pressure_real.squeeze(), pressure_imag.squeeze()]) + pressure = pressure.to(torch.get_default_dtype()) + + n_observations = pressure.size(1) + + json_path = dataset_path.joinpath("properties.json") + with json_path.open("r") as file: + properties = json.load(file) + top_samples = torch.tensor(properties["top_samples"]) + right_samples = torch.tensor(properties["right_samples"]) + frequency_samples = torch.tensor(properties["frequency_samples"]).unsqueeze(1) + + x = geom.transpose(0, 1).unsqueeze(0).expand(n_observations, -1, -1).to(torch.get_default_dtype()) + u = pressure.transpose(0, 1) + + if sensors > 0 or sensor_idx is not None: + if sensor_idx is not None: + self.sensor_idx = sensor_idx + else: + self.sensor_idx = torch.randperm(x.size(-1)) + self.sensor_idx = self.sensor_idx[:sensors] + x = x[:, :, self.sensor_idx] + u = u[:, :, self.sensor_idx] + else: + self.sensor_idx = torch.tensor([]) + + y = torch.linspace(0, 1, points).reshape(1, 1, -1) + y = y.expand(n_observations, -1, -1) + + v = torch.cat([top_samples, right_samples, frequency_samples], dim=1).unsqueeze(-1) + self.v_min = torch.tensor([[0.0], [-1.0], [0.0], [-1.0], [400.0]]) + self.v_scale = torch.tensor([[1.0], [1.0], [1.0], [1.0], [100.0]]) + v = v.expand(-1, -1, y.size(-1)) + + perm = torch.randperm(n_observations) + idx = perm[:observations] + + super().__init__(x[idx], u[idx], y[idx], v[idx]) + + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get item at idx from dataset.""" + x = self.x[idx] * 2.0 - 1.0 + u_max, _ = torch.max(torch.abs(self.u[idx]), dim=-1, keepdim=True) + u = self.u[idx] / u_max + + y = self.y[idx] * 2.0 - 1.0 + v = ((self.v[idx] - self.v_min) / self.v_scale) * 2.0 - 1.0 + + return x, u, y, v diff --git a/tests/data/fixtures.py b/tests/data/fixtures.py index 8ac5ca56..3409eb46 100644 --- a/tests/data/fixtures.py +++ b/tests/data/fixtures.py @@ -16,3 +16,8 @@ def tl_csv_file(tl_dir) -> pathlib.Path: @pytest.fixture(scope="session") def tl_paths(tl_dir, tl_csv_file) -> list[pathlib.Path]: return [tl_dir, tl_csv_file] + + +@pytest.fixture(scope="session") +def pressure_file() -> pathlib.Path: + return pathlib.Path.cwd().joinpath("data", "pulsating_sphere", "const_f400-500", "test") diff --git a/tests/data/test_pulsating_sphere.py b/tests/data/test_pulsating_sphere.py new file mode 100644 index 00000000..46f6577b --- /dev/null +++ b/tests/data/test_pulsating_sphere.py @@ -0,0 +1,47 @@ +from nos.data import ConstBoundaryDataset, InverseConstBoundaryDataset + + +class TestPulsatingSphereDataset: + def test_can_initialize(self, pressure_file): + dataset = ConstBoundaryDataset( + dataset_path=pressure_file, + observations=42, + ) + + assert isinstance(dataset, ConstBoundaryDataset) + + def test_has_populated_tensors(self, pressure_file): + n_observations = 42 + dataset = ConstBoundaryDataset( + dataset_path=pressure_file, + observations=n_observations, + ) + + for tsr in [dataset.x, dataset.y, dataset.u, dataset.v]: + assert tsr.size(0) == n_observations + + assert dataset.x.size(-1) == dataset.u.size(-1) + assert dataset.y.size(-1) == dataset.v.size(-1) + + +class TestInversePulsatingSphereDataset: + def test_can_initialize(self, pressure_file): + dataset = InverseConstBoundaryDataset( + dataset_path=pressure_file, + observations=42, + ) + + assert isinstance(dataset, InverseConstBoundaryDataset) + + def test_has_populated_tensors(self, pressure_file): + n_observations = 42 + dataset = ConstBoundaryDataset( + dataset_path=pressure_file, + observations=n_observations, + ) + + for tsr in [dataset.x, dataset.y, dataset.u, dataset.v]: + assert tsr.size(0) == n_observations + + assert dataset.x.size(-1) == dataset.u.size(-1) + assert dataset.y.size(-1) == dataset.v.size(-1)