Merge branch 'develop' into 'main'

Update main branch

See merge request ricos/machine_learning/phlower!46

.gitlab-ci.yml

@@ -1,5 +1,6 @@
 stages:
   - test
+  - document_test
   - deploy
 
 default:
@@ -37,6 +38,14 @@ pytest:
     - no-gpu
     - GenuineIntel
 
+gpu_test:
+  image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
+  stage: test
+  script:
+    - make gpu_test
+  tags:
+    - gpu
+
 e2e_test:
   stage: test
   script:
@@ -45,15 +54,27 @@ e2e_test:
     - no-gpu
     - GenuineIntel
 
+pages_test:
+  stage: document_test
+  script:
+    - make document
+  tags:
+    - no-gpu
+    - GenuineIntel
+  artifacts:
+    paths:
+      - docs/build/html/
+
 pages:
   stage: deploy
   script:
-    - make document
     - mkdir public
     - cp -r docs/build/html/* public/
   artifacts:
     paths:
       - public
+  dependencies:
+    - pages_test
   only:
     - main
     - develop

Makefile

@@ -14,22 +14,28 @@ mypy:
 
 .PHONY: format
 format:
-	poetry run python3 -m ruff check --select I --fix
 	poetry run python3 -m ruff format
+	poetry run python3 -m ruff check --fix
 
 .PHONY: test
 test:
-	poetry run pytest tests -m "not e2e_test" --cov=src --cov-report term-missing --durations 5
+	poetry run pytest tests -m "not e2e_test and not gpu_test" --cov=src --cov-report term-missing --durations 5
 
 
-.PHONY: test
+.PHONY: e2e_test
 e2e_test:
 	poetry run pytest tests -m "e2e_test"
 
 
+.PHONY: gpu_test
+gpu_test:
+	poetry run pytest tests -m "gpu_test"
+
+
 .PHONY: lint
 lint:
-	poetry run python3 -m ruff check --diff
+	poetry run python3 -m ruff check --output-format=full
+	poetry run python3 -m ruff format --diff
 	# $(MAKE) mypy
 
 .PHONY: dev-install

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "phlower"
-version = "0.1.1"
+version = "0.2.0"
 description = "This is a Python package which helps you handle GNN especially for physics problems."
 authors = ["sakamoto <[email protected]>"]
 readme = "README.md"
@@ -22,13 +22,14 @@ pipe = "^2.2"
 pyvista = "^0.43.10"
 tqdm = "^4.66.4"
 pandas = "^2.2.2"
+einops = "^0.8.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.0.2"
 mypy = "^1.8.0"
 pytest-cov = "^4.1.0"
 ruff = "^0.4.10"
-hypothesis = "^6.108.2"
+hypothesis = {extras = ["numpy"], version = "^6.115.3"}
 
 
 [tool.poetry.group.docs.dependencies]
@@ -44,6 +45,7 @@ addopts = [
 ]
 markers = [
     "e2e_test: marks tests as End-to-End test (deselect with '-m not e2e_test')",
+    "gpu_test: marks tests as test using GPUs (deselect with '-m not gpu_test')",
     "need_multicore: marks tests which need multiple cores"
 ]
 
@@ -52,7 +54,7 @@ requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.ruff]
-src = ["src", "tests"]
+include = ["pyproject.toml", "src/**/*.py", "tests/**/*.py"]
 
 # Exclude a variety of commonly ignored directories.
 exclude = [
@@ -103,8 +105,9 @@ select = [
     "B",  # flake8-bugbear
     "C4",  # flake8-comprehensions
     "UP",  # pyupgrade
+    "ANN", # flake8-annotations
 ]
-ignore = []
+ignore = ["ANN003", "ANN101", "ANN102", "ANN204"]
 
 # Allow fix for all enabled rules (when `--fix`) is provided.
 fixable = ["ALL"]
@@ -113,8 +116,12 @@ unfixable = []
 # Allow unused variables when underscore-prefixed.
 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
 
+[tool.ruff.lint.flake8-annotations]
+suppress-none-returning = true
+
 [tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["F401"]
+"src/phlower/_base/**/*.py" = ["ANN401"]
 
 [tool.ruff.format]
 # Like Black, use double quotes for strings.

src/phlower/__init__.py

@@ -1,3 +1,4 @@
+from phlower import nn
 from phlower._base import (
     PhlowerDimensionTensor,
     PhlowerTensor,
@@ -6,6 +7,7 @@
     phlower_tensor,
 )
 from phlower._base.array import IPhlowerArray
+from phlower._fields import ISimulationField
 from phlower.version import __version__
 
 __all__ = ["__version__"]

src/phlower/_base/_dimension.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Annotated, Any
+from typing import Annotated
 
 from pydantic import (
     PlainSerializer,
@@ -13,13 +13,12 @@
 from phlower.utils.exceptions import InvalidDimensionError
 
 
-def _validate(v: Any, info: ValidationInfo):
+def _validate(v: dict, info: ValidationInfo) -> PhysicalDimensions:
     if not isinstance(v, dict):
         raise TypeError(f"Expected dictionary, but got {type(v)}")
 
     try:
-        ans = PhysicalDimensions(v)
-        return ans
+        return PhysicalDimensions(v)
     except Exception as ex:
         raise TypeError("Validation for physical dimension is failed.") from ex
 
@@ -78,7 +77,7 @@ def to_list(self) -> list[float]:
 
         return _list
 
-    def to_dict(self):
+    def to_dict(self) -> dict:
         return self._dimensions
 
 

src/phlower/_base/_functionals/_check.py

@@ -6,13 +6,13 @@
 from phlower._base.tensors._interface import IPhlowerTensor
 
 
-def is_same_layout(tensors: Sequence[IPhlowerTensor]):
+def is_same_layout(tensors: Sequence[IPhlowerTensor]) -> bool:
     is_sparse_flags: set[bool] = set(tensors | select(lambda x: x.is_sparse))
 
     return len(is_sparse_flags) == 1
 
 
-def is_same_dimensions(tensors: Sequence[IPhlowerTensor]):
+def is_same_dimensions(tensors: Sequence[IPhlowerTensor]) -> bool:
     if len(tensors) == 0:
         return True
 

src/phlower/_base/_functionals/_concatenate.py

@@ -9,7 +9,9 @@
 from ._check import is_same_dimensions, is_same_layout
 
 
-def concatenate(tensors: Sequence[IPhlowerTensor], dense_dim: int = 0):
+def concatenate(
+    tensors: Sequence[IPhlowerTensor], dense_dim: int = 0
+) -> IPhlowerTensor:
     if not is_same_layout(tensors):
         raise ValueError("Cannot concatenate dense tensor and sparse tensor")
 
@@ -25,7 +27,9 @@ def concatenate(tensors: Sequence[IPhlowerTensor], dense_dim: int = 0):
     return _dense_concatenate(tensors, dim=dense_dim)
 
 
-def _dense_concatenate(tensors: Sequence[IPhlowerTensor], dim: int = 0):
+def _dense_concatenate(
+    tensors: Sequence[IPhlowerTensor], dim: int = 0
+) -> IPhlowerTensor:
     return torch.concatenate(tensors, dim=dim)
 
 

src/phlower/_base/_functionals/_to_batch.py

@@ -26,7 +26,7 @@ def to_batch(
 
 def _create_batch_info(
     tensors: Sequence[IPhlowerTensor], dense_concat_dim: int
-):
+) -> GraphBatchInfo:
     _shapes = list(tensors | select(lambda x: x.shape))
 
     if tensors[0].is_sparse:

src/phlower/_base/array/__init__.py

@@ -1,19 +1,43 @@
-# flake8: noqa
+from logging import getLogger
 from typing import get_args
 
+import numpy as np
+
+from phlower._base import PhysicalDimensions
 from phlower._base.array import dense, sparse
 from phlower._base.array._interface_wrapper import IPhlowerArray
 from phlower.utils.typing import ArrayDataType, DenseArrayType, SparseArrayType
 
+_logger = getLogger(__name__)
+
 
-def phlower_array(data: ArrayDataType | IPhlowerArray) -> IPhlowerArray:
+def phlower_array(
+    data: ArrayDataType | IPhlowerArray,
+    is_time_series: bool = False,
+    is_voxel: bool = False,
+    dimensions: PhysicalDimensions | None = None,
+) -> IPhlowerArray:
     if isinstance(data, IPhlowerArray):
+        _logger.warning(
+            "phlower_array function is called for PhlowerArray."
+            "time_series and is_voxel in arguments are ignored."
+        )
         return data
 
     if isinstance(data, DenseArrayType):
-        return dense.NdArrayWrapper(data)
+        return dense.NdArrayWrapper(
+            data,
+            is_time_series=is_time_series,
+            is_voxel=is_voxel,
+            dimensions=dimensions,
+        )
 
     if isinstance(data, get_args(SparseArrayType)):
-        return sparse.SparseArrayWrapper(data)
+        if is_time_series or is_voxel:
+            raise ValueError(
+                "Sparse Array cannot have time series flag and voxel flag."
+            )
+
+        return sparse.SparseArrayWrapper(data, dimensions=dimensions)
 
     raise ValueError(f"Unsupported data type: {data.__class__}, {type(data)}")

src/phlower/_base/array/_interface_wrapper.py

@@ -10,7 +10,9 @@
 
 class IPhlowerArray(metaclass=abc.ABCMeta):
     @abc.abstractmethod
-    def __init__(self, data: ArrayDataType) -> None: ...
+    def __init__(
+        self, data: ArrayDataType, dimensions: PhysicalDimensions
+    ) -> None: ...
 
     @property
     @abc.abstractmethod
@@ -20,6 +22,10 @@ def is_time_series(self) -> int | None: ...
     @abc.abstractmethod
     def is_sparse(self) -> bool: ...
 
+    @property
+    @abc.abstractmethod
+    def is_voxel(self) -> bool: ...
+
     @property
     @abc.abstractmethod
     def shape(self) -> tuple[int]: ...
@@ -71,7 +77,7 @@ def to_phlower_tensor(
         self,
         device: str | torch.device | None = None,
         non_blocking: bool = False,
-        dimension: PhysicalDimensions | None = None,
+        disable_dimensions: bool = False,
     ) -> PhlowerTensor: ...
 
     @abc.abstractmethod

src/phlower/_base/array/dense/_ndarray_wrapper.py

@@ -10,9 +10,17 @@
 
 
 class NdArrayWrapper(IPhlowerArray):
-    def __init__(self, data: np.ndarray, is_timeseries: bool = False):
+    def __init__(
+        self,
+        data: np.ndarray,
+        is_time_series: bool = False,
+        is_voxel: bool = False,
+        dimensions: PhysicalDimensions | None = None,
+    ):
         self.data = data
-        self._is_time_series = is_timeseries
+        self._is_time_series = is_time_series
+        self._is_voxel = is_voxel
+        self._dimensions = dimensions
 
     @property
     def shape(self) -> tuple[int]:
@@ -22,6 +30,10 @@ def shape(self) -> tuple[int]:
     def is_time_series(self) -> bool:
         return self._is_time_series
 
+    @property
+    def is_voxel(self) -> bool:
+        return self._is_voxel
+
     @property
     def is_sparse(self) -> bool:
         return False
@@ -63,10 +75,13 @@ def to_phlower_tensor(
         self,
         device: str | torch.device | None = None,
         non_blocking: bool = False,
-        dimension: PhysicalDimensions | None = None,
+        disable_dimensions: bool = False,
     ) -> PhlowerTensor:
         _tensor = phlower_tensor(
-            tensor=torch.from_numpy(self.data), dimension=dimension
+            tensor=torch.from_numpy(self.data),
+            dimension=None if disable_dimensions else self._dimensions,
+            is_time_series=self._is_time_series,
+            is_voxel=self._is_voxel,
         )
         _tensor.to(device=device, non_blocking=non_blocking)
         return _tensor