cubic spline interpolator

autofit/__init__.py

@@ -85,7 +85,7 @@
 from .example.model import Gaussian
 from .text import formatter
 from .text import samples_text
-from .interpolator import LinearInterpolator
+from .interpolator import LinearInterpolator, SplineInterpolator
 from .tools import util
 
 

autofit/interpolator.py

@@ -1,5 +1,6 @@
 import copy
 from abc import ABC, abstractmethod
+from scipy.interpolate import CubicSpline
 from typing import List, Dict, cast
 
 from scipy.stats import stats
@@ -207,3 +208,14 @@ class LinearInterpolator(AbstractInterpolator):
     def _interpolate(x, y, value):
         slope, intercept, r, p, std_err = stats.linregress(x, y)
         return slope * value + intercept
+
+
+class SplineInterpolator(AbstractInterpolator):
+    """
+    Interpolate data with a piecewise cubic polynomial which is twice continuously differentiable
+    """
+
+    @staticmethod
+    def _interpolate(x, y, value):
+        f = CubicSpline(x, y)
+        return f(value)

test_autofit/test_interpolator.py

@@ -12,25 +12,54 @@ def test_trivial():
     assert result is instance
 
 
+@pytest.fixture(name="instances")
+def make_instances():
+    return [
+        af.ModelInstance(
+            items=dict(
+                t=1.0, gaussian=af.Gaussian(centre=0.0, normalization=1.0, sigma=-1.0),
+            )
+        ),
+        af.ModelInstance(
+            items=dict(
+                t=2.0, gaussian=af.Gaussian(centre=1.0, normalization=2.0, sigma=-2.0),
+            )
+        ),
+    ]
+
+
 @pytest.fixture(name="time_series")
-def make_time_series():
-    return af.LinearInterpolator(
-        [
-            af.ModelInstance(
-                items=dict(
-                    t=1.0,
-                    gaussian=af.Gaussian(centre=0.0, normalization=1.0, sigma=-1.0),
-                )
-            ),
+def make_time_series(instances):
+    return af.LinearInterpolator(instances)
+
+
+def test_spline_interpolator(instances):
+    interpolator = af.SplineInterpolator(instances)
+
+    result = interpolator[interpolator.t == 1.5]
+
+    assert result.t == 1.5
+    assert result.gaussian.centre == 0.5
+
+
+def test_smooth_spline_interpolator(instances):
+    interpolator = af.SplineInterpolator(
+        instances
+        + [
             af.ModelInstance(
                 items=dict(
-                    t=2.0,
-                    gaussian=af.Gaussian(centre=1.0, normalization=2.0, sigma=-2.0),
+                    t=3.0,
+                    gaussian=af.Gaussian(centre=4.0, normalization=3.0, sigma=-3.0),
                 )
             ),
         ]
     )
 
+    result = interpolator[interpolator.t == 1.5]
+
+    assert result.t == 1.5
+    assert result.gaussian.centre < 0.5
+
 
 @pytest.mark.parametrize(
     "t, centre", [(0.0, -1.0), (1.0, 0.0), (1.5, 0.5), (2.0, 1.0), (3.0, 2.0)]