re-organize tests and added toml as testing/dev dependency

pyproject.toml

@@ -30,6 +30,7 @@ coverage = {extras = ["toml"], version = "^7.6.1"}
 mypy = "^1.11.2"
 numba = "<0.59.0"
 clifford = "^1.4.0"
+toml = "^0.10.2"
 
 
 [tool.poetry.group.docs.dependencies]

tests/README.md

@@ -0,0 +1,54 @@
+# ezgatr Tests README
+
+## Overview
+
+This directory contains test cases for the `ezgatr` library, ensuring that its components function correctly and as expected. Tests are organized to mirror the sub-module structure of the main package.
+
+## Test Organization
+
+- **Sub-module Specific Tests**: Tests for specific sub-modules (e.g., `ezgatr.nn`) reside within corresponding directories under `tests` (e.g., `tests.nn`).
+
+- **Thematic Tests**: Tests covering specific themes that do not belong to any particular module are placed in the `tests/thematic` directory. For example, `tests.thematic.test_regress_with_clifford.py` ensures that the library's geometric algebra operations align with those of the `clifford` package.
+
+## Configuration Management
+
+To avoid hard-coding configurations and to maintain consistency across tests, configurations are centralized into dedicated files.
+
+- **Core Configurations**: Shared configurations, such as execution devices, numerical precision tolerance, and test data generation parameters, are defined in `tests/config/core.toml`.
+
+- **Module/Theme-Specific Configurations**: Configurations unique to specific sub-modules or themes are organized in respective files within the `tests/config` directory:
+  - `interfaces.toml`
+  - `thematic.toml`
+
+## File Structure
+
+```
+tests/
+├── __init__.py
+├── conftest.py
+├── utils.py
+├── config/
+│   ├── __init__.py
+│   ├── core.toml
+│   ├── interfaces.toml
+│   └── thematic.toml
+├── interfaces/
+│   ├── __init__.py
+│   ├── test_plane.py
+│   ├── test_point.py
+│   └── ...
+├── nn/
+│   ├── __init__.py
+│   ├── test_functional.py
+│   └── test_modules.py
+└── thematic/
+    ├── __init__.py
+    ├── README.md
+    ├── test_gradient_flow.py
+    ├── test_operator_equivariance.py
+    └── test_regress_with_clifford.py
+```
+
+## Forward
+
+This README provides an introduction to how tests and configurations are organized in the `tests` directory. Future updates will provide more detailed documentation as the test suite expands.

tests/config/__init__.py

@@ -0,0 +1,40 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class BasicConfig:
+    r"""Basic configurations shared across all tests.
+
+    Parameters
+    ----------
+    rtol : float
+        Relative tolerance for ``torch.testing.assert_close``.
+    atol : float
+        Absolute tolerance for ``torch.testing.assert_close``.
+    equal_nan : bool
+        If ``True``, then two ``NaN`` s will be considered equal.
+    check_device : bool
+        If ``True``, check that ``input`` and ``other`` are on the same device.
+    check_dtype : bool
+        If ``True``, check that ``input`` and ``other`` have the same dtype.
+    device : str
+        The device on which tests are executed (e.g., 'cpu', 'cuda').
+    max_batch_size : int
+        Maximum number of sequences within a batch of multi-vectors when
+        generating random testing data points.
+    max_context_size : int
+        Maximum number of multi-vectors within a single sequence when generating
+        random testing data points.
+    max_channel_size : int
+        Maximum number of channels for each multi-vector within a sequence
+        when generating random testing data points.
+    """
+    rtol: float
+    atol: float
+    equal_nan: bool
+    check_device: bool
+    check_dtype: bool
+    device: str
+    max_batch_size: int
+    max_context_size: int
+    max_channel_size: int

tests/config/core.toml

@@ -0,0 +1,13 @@
+# Core Configurations for ezgatr Tests
+
+execution_device = "cpu"  # The device on which tests are executed (e.g., "cpu", "cuda")
+numerical_precision = 1e-5  # The tolerance level for numerical precision in tests
+rtol = 1e-5  # Relative tolerance for torch.testing.assert_close
+atol = 1e-8  # Absolute tolerance for torch.testing.assert_close
+equal_nan = true  # If true, then two NaNs will be considered equal
+check_device = false  # If true, check that input and other are on the same device
+check_dtype = true  # If true, check that input and other have the same dtype
+num_random_data_points = 100  # The number of random data points generated for tests
+data_point_size = 256  # The size of each random data point
+
+# Add additional core configurations as needed.

tests/config/interfaces.toml

@@ -0,0 +1,6 @@
+[geometric]
+rotation_angles = [0.0, 0.7853981634, 1.5707963268]  # 0, π/4, π/2
+translation_mags = [0.0, 1.0, 5.0]
+reflection_axes = ["x", "y", "z"]
+rtol = 1e-4
+atol = 1e-6

tests/conftest.py

@@ -0,0 +1,25 @@
+import pathlib
+
+import pytest
+import toml
+
+from tests.config import BasicConfig
+
+DIR_TEST = pathlib.Path(".").resolve().parent
+
+
+@pytest.fixture(scope="session")
+def basic_config():
+    r"""Load basic configurations for tests.
+
+    This fixture reads the ``core.toml`` file and returns an instance of the `BasicConfig` dataclass
+    containing the configuration parameters.
+
+    Returns
+    -------
+    BasicConfig
+        An instance containing basic test configurations.
+    """
+    with open(DIR_TEST / "config" / "core.toml") as f:
+        config_data = toml.load(f)
+    return BasicConfig(**config_data)

tests/interfaces/test_plane.py

@@ -3,8 +3,8 @@
 import torch
 from hypothesis import given, settings
 
-from ezgatr.interfaces.plane import encode_pga, decode_pga
-from ezgatr.nn.functional import inner_product, geometric_product
+from ezgatr.interfaces.plane import encode_pga
+from ezgatr.nn.functional import geometric_product, inner_product
 from tests.utils import make_random_3d_vectors, strategy_batch_dims
 
 

tests/interfaces/test_point.py

@@ -4,7 +4,7 @@
 from hypothesis import given
 from hypothesis.strategies import floats
 
-from ezgatr.interfaces.point import encode_pga, decode_pga
+from ezgatr.interfaces.point import decode_pga, encode_pga
 from tests.utils import make_random_3d_vectors
 
 

tests/thematic/test_regress_with_clifford.py

@@ -1,12 +1,9 @@
 from __future__ import annotations
 
 import torch
-import numpy as np
 from hypothesis import given, settings
 
 from ezgatr.nn.functional import (
-    equi_dual,
-    equi_join,
     geometric_product,
     outer_product,
 )