feature/issue-29/implement-caching

.github/workflows/test.yaml

@@ -26,7 +26,7 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install the project
-      run: uv sync --only-dev --resolution=${{ matrix.dependency-mode }}
+      run: uv sync --resolution=${{ matrix.dependency-mode }}
     - name: Run tests
       id: run-tests
       shell: bash

.gitignore

@@ -1,3 +1,6 @@
+# copilot
+.github/**/*.md
+
 # data
 data/
 

src/graphomotor/core/config.py

@@ -2,12 +2,31 @@
 
 import dataclasses
 import logging
-import warnings
+from typing import Any
 
 import numpy as np
 
 
-@dataclasses.dataclass
+def get_logger() -> logging.Logger:
+    """Get the Graphomotor logger."""
+    logger = logging.getLogger("graphomotor")
+    if logger.handlers:
+        return logger
+    logger.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        "%(asctime)s - %(name)s - %(levelname)s - "
+        "%(filename)s:%(lineno)s - %(funcName)s - %(message)s",
+    )
+    handler = logging.StreamHandler()
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+    return logger
+
+
+logger = get_logger()
+
+
+@dataclasses.dataclass(frozen=True)
 class SpiralConfig:
     """Class for the parameters of anticipated spiral drawing."""
 
@@ -29,32 +48,17 @@ def add_custom_params(cls, config_dict: dict[str, float | int]) -> "SpiralConfig
         Returns:
             SpiralConfig instance with updated parameters.
         """
-        config = cls()
+        valid_params = {f.name for f in cls.__dataclass_fields__.values()}
+        filtered_params: dict[str, Any] = {}
+
         for key, value in config_dict.items():
-            if hasattr(config, key):
-                setattr(config, key, value)
+            if key in valid_params:
+                filtered_params[key] = value
             else:
-                valid_params = ", ".join(
-                    f.name for f in cls.__dataclass_fields__.values()
-                )
-                warnings.warn(
+                valid_param_names = ", ".join(valid_params)
+                logger.warning(
                     f"Unknown configuration parameters will be ignored: {key}. "
-                    f"Valid parameters are: {valid_params}"
+                    f"Valid parameters are: {valid_param_names}"
                 )
-        return config
-
 
-def get_logger() -> logging.Logger:
-    """Get the Graphomotor logger."""
-    logger = logging.getLogger("graphomotor")
-    if logger.handlers:
-        return logger
-    logger.setLevel(logging.INFO)
-    formatter = logging.Formatter(
-        "%(asctime)s - %(name)s - %(levelname)s - "
-        "%(filename)s:%(lineno)s - %(funcName)s - %(message)s",
-    )
-    handler = logging.StreamHandler()
-    handler.setFormatter(formatter)
-    logger.addHandler(handler)
-    return logger
+        return cls(**filtered_params)

src/graphomotor/core/orchestrator.py

@@ -193,7 +193,7 @@ def extract_features(
     logger.debug("Generating reference spiral to calculate features")
     config_to_use = spiral_config or config.SpiralConfig()
     reference_spiral = generate_reference_spiral.generate_reference_spiral(
-        config=config_to_use
+        spiral_config=config_to_use
     )
     centered_reference_spiral = center_spiral.center_spiral(reference_spiral)
 

src/graphomotor/utils/center_spiral.py

@@ -27,13 +27,17 @@ def center_spiral(spiral):
     spiral_config = config.SpiralConfig()
 
     if isinstance(spiral, models.Spiral):
-        spiral.data["x"] -= spiral_config.center_x
-        spiral.data["y"] -= spiral_config.center_y
-        return spiral
+        centered_spiral = models.Spiral(
+            data=spiral.data.copy(), metadata=spiral.metadata.copy()
+        )
+        centered_spiral.data["x"] -= spiral_config.center_x
+        centered_spiral.data["y"] -= spiral_config.center_y
+        return centered_spiral
     elif isinstance(spiral, np.ndarray):
-        spiral[:, 0] -= spiral_config.center_x
-        spiral[:, 1] -= spiral_config.center_y
-        return spiral
+        centered_spiral = spiral.copy()
+        centered_spiral[:, 0] -= spiral_config.center_x
+        centered_spiral[:, 1] -= spiral_config.center_y
+        return centered_spiral
     else:
         raise TypeError(
             f"Expected models.Spiral or np.ndarray, got {type(spiral).__name__}"

src/graphomotor/utils/generate_reference_spiral.py

@@ -1,65 +1,82 @@
 """Utility functions for generating an equidistant reference spiral."""
 
+import functools
+
 import numpy as np
 from scipy import integrate, optimize
 
-from graphomotor.core.config import SpiralConfig
+from graphomotor.core import config
 
 
-def _arc_length_integrand(t: float, config: SpiralConfig) -> float:
+def _arc_length_integrand(t: float, spiral_config: config.SpiralConfig) -> float:
     """Calculate the differential arc length at angle t for an Archimedean spiral.
 
     Args:
         t: Angle parameter.
-        config: Spiral configuration.
+        spiral_config: Spiral configuration.
 
     Returns:
         Differential arc length value.
     """
-    r_t = config.start_radius + config.growth_rate * t
-    return np.sqrt(r_t**2 + config.growth_rate**2)
+    r_t = spiral_config.start_radius + spiral_config.growth_rate * t
+    return np.sqrt(r_t**2 + spiral_config.growth_rate**2)
 
 
-def _calculate_arc_length(theta: float, config: SpiralConfig) -> float:
-    """Calculate the arc length of the spiral from start_angle to theta.
+def _calculate_arc_length_between(
+    theta_start: float, theta_end: float, spiral_config: config.SpiralConfig
+) -> float:
+    """Calculate the arc length of the spiral between two theta values.
 
     Args:
-        theta: The angle in radians.
-        config: Spiral configuration.
+        theta_start: Starting angle in radians.
+        theta_end: Ending angle in radians.
+        spiral_config: Spiral configuration.
 
     Returns:
-        The arc length of the spiral from start_angle to theta.
+        The arc length of the spiral from theta_start to theta_end.
     """
     return integrate.quad(
-        lambda t: _arc_length_integrand(t, config), config.start_angle, theta
+        lambda t: _arc_length_integrand(t, spiral_config),
+        theta_start,
+        theta_end,
     )[0]
 
 
-def _find_theta_for_arc_length(target_arc_length: float, config: SpiralConfig) -> float:
-    """Find the theta value for a given arc length using numerical root finding.
+def _find_theta_for_incremental_arc_length(
+    target_increment: float,
+    current_theta: float,
+    spiral_config: config.SpiralConfig,
+) -> float:
+    """Find the theta value for a given incremental arc length from current position.
 
     Args:
-        target_arc_length: Target arc length.
-        config: Spiral configuration.
+        target_increment: Target arc length increment from current position.
+        current_theta: Current theta position.
+        spiral_config: Spiral configuration.
 
     Returns:
-        Angle theta corresponding to the arc length.
+        Angle theta that results in the target arc length increment from current_theta.
     """
     solution = optimize.root_scalar(
-        lambda theta: _calculate_arc_length(theta, config) - target_arc_length,
-        bracket=[config.start_angle, config.end_angle],
+        lambda theta: _calculate_arc_length_between(current_theta, theta, spiral_config)
+        - target_increment,
+        bracket=(current_theta, spiral_config.end_angle),
     )
     return solution.root
 
 
-def generate_reference_spiral(config: SpiralConfig) -> np.ndarray:
+@functools.lru_cache(maxsize=48)
+def generate_reference_spiral(spiral_config: config.SpiralConfig) -> np.ndarray:
     """Generate a reference spiral with equidistant points along its arc length.
 
     This function creates an Archimedean spiral with points distributed at equal arc
     length intervals. The generated spiral serves as a standardized reference template
     for feature extraction algorithms that compare user-drawn spirals with an ideal
     form.
 
+    This function is decorated with an LRU cache to store pre-computed spirals for
+    faster retrieval on subsequent calls with the same configuration.
+
     The algorithm works by:
         1. Computing the total arc length for the entire spiral,
         2. Creating equidistant target arc length values,
@@ -74,28 +91,36 @@ def generate_reference_spiral(config: SpiralConfig) -> np.ndarray:
         - Cartesian coordinates: x = cx + r·cos(θ), y = cy + r·sin(θ)
 
     Parameters are defined in the SpiralConfig class:
-        - Center coordinates: (cx, cy) = (config.center_x, config.center_y)
-        - Start radius: a = config.start_radius
-        - Growth rate: b = config.growth_rate
-        - Total rotation: θ = config.end_angle - config.start_angle
-        - Number of points: N = config.num_points
+        - Center coordinates: cx, cy = spiral_config.center_x, spiral_config.center_y
+        - Start radius: a = spiral_config.start_radius
+        - Growth rate: b = spiral_config.growth_rate
+        - Total rotation: θ = spiral_config.end_angle - spiral_config.start_angle
+        - Number of points: N = spiral_config.num_points
 
     Args:
-        config: Configuration parameters for the spiral.
+        spiral_config: Configuration parameters for the spiral.
 
     Returns:
         Array with shape (N, 2) containing Cartesian coordinates of the spiral points.
     """
-    total_arc_length = _calculate_arc_length(config.end_angle, config)
+    total_arc_length = _calculate_arc_length_between(
+        spiral_config.start_angle, spiral_config.end_angle, spiral_config
+    )
 
-    arc_length_values = np.linspace(0, total_arc_length, config.num_points)
+    arc_length_increment = total_arc_length / (spiral_config.num_points - 1)
 
-    theta_values = np.array(
-        [_find_theta_for_arc_length(s, config) for s in arc_length_values]
-    )
+    theta_values = np.zeros(spiral_config.num_points)
+    theta_values[0] = spiral_config.start_angle
+
+    for i in range(1, spiral_config.num_points):
+        theta_values[i] = _find_theta_for_incremental_arc_length(
+            arc_length_increment,
+            theta_values[i - 1],
+            spiral_config,
+        )
 
-    r_values = config.start_radius + config.growth_rate * theta_values
-    x_values = config.center_x + r_values * np.cos(theta_values)
-    y_values = config.center_y + r_values * np.sin(theta_values)
+    r_values = spiral_config.start_radius + spiral_config.growth_rate * theta_values
+    x_values = spiral_config.center_x + r_values * np.cos(theta_values)
+    y_values = spiral_config.center_y + r_values * np.sin(theta_values)
 
     return np.column_stack((x_values, y_values))

tests/unit/test_config.py

@@ -34,14 +34,14 @@
 def test_spiral_config_add_custom_params_valid(
     custom_params: dict[str, int | float],
     expected_params: dict[str, int | float],
-    recwarn: pytest.WarningsRecorder,
+    caplog: pytest.LogCaptureFixture,
 ) -> None:
     """Test that SpiralConfig.add_custom_params correctly sets parameter values."""
     spiral_config = config.SpiralConfig.add_custom_params(custom_params)
 
     for key, value in expected_params.items():
         assert getattr(spiral_config, key) == value
-        assert len(recwarn) == 0
+        assert len(caplog.records) == 0
 
 
 @pytest.mark.parametrize(
@@ -71,17 +71,17 @@ def test_spiral_config_add_custom_params_warnings(
     custom_params: dict[str, int | float],
     expected_params: dict[str, int | float],
     expected_warnings: list[str],
-    recwarn: pytest.WarningsRecorder,
+    caplog: pytest.LogCaptureFixture,
 ) -> None:
     """Test that SpiralConfig.add_custom_params issues warnings appropriately."""
     spiral_config = config.SpiralConfig.add_custom_params(custom_params)
 
-    assert len(recwarn) == len(expected_warnings)
+    assert len(caplog.records) == len(expected_warnings)
     for key, value in expected_params.items():
         assert getattr(spiral_config, key) == value
     for i, param in enumerate(expected_warnings):
         assert f"Unknown configuration parameters will be ignored: {param}" in str(
-            recwarn[i].message
+            caplog.records[i].message
         )
 
 

tests/unit/test_generate_reference_spiral.py

@@ -9,8 +9,8 @@
 def test_generate_reference_spiral() -> None:
     """Test the generation of a reference spiral."""
     spiral_config = config.SpiralConfig()
-    expected_mean_arc_length = generate_reference_spiral._calculate_arc_length(
-        spiral_config.end_angle, spiral_config
+    expected_mean_arc_length = generate_reference_spiral._calculate_arc_length_between(
+        spiral_config.start_angle, spiral_config.end_angle, spiral_config
     ) / (spiral_config.num_points - 1)
 
     spiral = generate_reference_spiral.generate_reference_spiral(spiral_config)