fix: ✨ new api for accessing interpolation for a single scalar field

scikitlearn style api

Author: lachlangrose

LoopStructural/api/_interpolate.py

@@ -0,0 +1,100 @@
+from ast import LShift
+import numpy as np
+import pandas as pd
+
+from typing import Optional
+from LoopStructural.interpolators import (
+    get_interpolator,
+)
+from LoopStructural.utils import BoundingBox
+from LoopStructural.utils import getLogger
+
+logger = getLogger(__name__)
+
+
+class LoopInterpolator:
+    def __init__(
+        self,
+        bounding_box: BoundingBox,
+        dimensions: int = 3,
+        type: str = "FDI",
+        nelements: int = 1000,
+    ):
+        """Scikitlearn like interface for LoopStructural interpolators
+        useful for quickly building an interpolator to apply to a dataset
+        build a generic interpolation object speficying the bounding box
+        and then fit to constraints and evaluate the interpolator
+
+        Parameters
+        ----------
+        bounding_box : BoundingBox
+            Area where the interpolation will work
+        dimensions : int, optional
+            number of dimensions e.g. 3d or 2d, by default 3
+        type : str, optional
+            type of interpolation algorithm FDI- finite difference, PLI - linear finite elements,
+              by default "FDI"
+        nelements : int, optional
+            degrees of freedom for interpolator, by default 1000
+        """
+        self.dimensions = dimensions
+        self.type = "FDI"
+        self.interpolator = get_interpolator(bounding_box, type, dimensions, nelements)
+
+    def fit(
+        self,
+        values: Optional[np.ndarray] = None,
+        tangent_vectors: Optional[np.ndarray] = None,
+        normal_vectors: Optional[np.ndarray] = None,
+        inequality_constraints: Optional[np.ndarray] = None,
+    ):
+        if values is not None:
+            self.interpolator.set_value_constraints(values)
+        if tangent_vectors is not None:
+            self.interpolator.set_tangent_constraints(tangent_vectors)
+        if normal_vectors is not None:
+            self.interpolator.set_normal_constraints(normal_vectors)
+        if inequality_constraints:
+            pass
+
+        self.interpolator.setup()
+
+    def evalute_scalar_value(self, locations: np.ndarray):
+        self.interpolator.update()
+        return self.interpolator.evaluate_value(locations)
+
+    def evaluate_gradient(self, locations: np.ndarray):
+        self.interpolator.update()
+        return self.interpolator.evaluate_gradient(locations)
+
+    def fit_and_evaluate_value(
+        self,
+        values: Optional[np.ndarray] = None,
+        tangent_vectors: Optional[np.ndarray] = None,
+        normal_vectors: Optional[np.ndarray] = None,
+        inequality_constraints: Optional[np.ndarray] = None,
+    ):
+        # get locations
+        self.fit(
+            values=values,
+            tangent_vectors=tangent_vectors,
+            normal_vectors=normal_vectors,
+            inequality_constraints=inequality_constraints,
+        )
+
+        return self.evalute_scalar_value(locations)
+
+    def fit_and_evaluate_gradient(
+        self,
+        values: Optional[np.ndarray] = None,
+        tangent_vectors: Optional[np.ndarray] = None,
+        normal_vectors: Optional[np.ndarray] = None,
+        inequality_constraints: Optional[np.ndarray] = None,
+    ):
+        self.fit(
+            values=values,
+            tangent_vectors=tangent_vectors,
+            normal_vectors=normal_vectors,
+            inequality_constraints=inequality_constraints,
+        )
+        return self.evaluate_gradient(locations)