test/func: created a test and function for MorphFuncy

news/morphfuncy.rst

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+**Added:**
+
+* General morph function that applies a user-supplied Python function to the y-coordinates of morph data
+
+**Changed:**
+
+* <news item>
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* <news item>
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>

src/diffpy/morph/morphs/morphfuncy.py

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+from diffpy.morph.morphs.morph import LABEL_GR, LABEL_RA, Morph
+
+
+class MorphFuncy(Morph):
+    """Apply the user-supplied Python function to the y-coordinates of the
+    morph data"""
+
+    # Define input output types
+    summary = "Apply a Python function to the y-axis data"
+    xinlabel = LABEL_RA
+    yinlabel = LABEL_GR
+    xoutlabel = LABEL_RA
+    youtlabel = LABEL_GR
+
+    def morph(self, x_morph, y_morph, x_target, y_target):
+        """General morph function that applies a user-supplied function to the
+        y-coordinates of morph data to make it align with a target.
+
+        Configuration Variables
+        -----------------------
+        function: callable
+            The user-supplied function that applies a transformation to the
+            y-coordinates of the data.
+
+        parameters: dict
+            A dictionary of parameters to pass to the function.
+            These parameters are unpacked using **kwargs.
+
+        Returns
+        -------
+            A tuple (x_morph_out, y_morph_out, x_target_out, y_target_out)
+            where the target values remain the same and the morph data is
+            transformed according to the user-specified function and parameters
+            The morphed data is returned on the same grid as the unmorphed data
+
+        Example
+        -------
+        Import the funcy morph function:
+        >>> from diffpy.morph.morphs.morphfuncy import MorphFuncy
+        Define or import the user-supplied transformation function:
+        >>> def sine_function(x, y, amplitude, frequency):
+        >>>     return amplitude * np.sin(frequency * x) * y
+        Provide initial guess for parameters:
+        >>> parameters = {'amplitude': 2, 'frequency': 2}
+        Run the funcy morph given input morph array (x_morph, y_morph)
+        and target array (x_target, y_target):
+        >>> morph = MorphFuncy()
+        >>> morph.function = sine_function
+        >>> morph.parameters = parameters
+        >>> x_morph_out, y_morph_out, x_target_out, y_target_out = morph(
+        ...     x_morph, y_morph, x_target, y_target)
+        To access parameters from the morph instance:
+        >>> x_morph_in = morph.x_morph_in
+        >>> y_morph_in = morph.y_morph_in
+        >>> x_target_in = morph.x_target_in
+        >>> y_target_in = morph.y_target_in
+        >>> parameters_out = morph.parameters
+        """
+        Morph.morph(self, x_morph, y_morph, x_target, y_target)
+
+        self.y_morph_out = self.function(
+            self.x_morph_in, self.y_morph_in, **self.parameters
+        )
+
+        return self.xyallout

tests/test_morphfuncy.py

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+import numpy as np
+import pytest
+
+from diffpy.morph.morphs.morphfuncy import MorphFuncy
+
+
+def sine_function(x, y, amplitude, frequency):
+    return amplitude * np.sin(frequency * x) * y
+
+
+def exponential_decay_function(x, y, amplitude, decay_rate):
+    return amplitude * np.exp(-decay_rate * x) * y
+
+
+def gaussian_function(x, y, amplitude, mean, sigma):
+    return amplitude * np.exp(-((x - mean) ** 2) / (2 * sigma**2)) * y
+
+
+def polynomial_function(x, y, a, b, c):
+    return (a * x**2 + b * x + c) * y
+
+
+def logarithmic_function(x, y, scale):
+    return scale * np.log(1 + x) * y
+
+
+@pytest.mark.parametrize(
+    "function, parameters, expected_function",
+    [
+        (
+            sine_function,
+            {"amplitude": 2, "frequency": 5},
+            lambda x, y: 2 * np.sin(5 * x) * y,
+        ),
+        (
+            exponential_decay_function,
+            {"amplitude": 5, "decay_rate": 0.1},
+            lambda x, y: 5 * np.exp(-0.1 * x) * y,
+        ),
+        (
+            gaussian_function,
+            {"amplitude": 1, "mean": 5, "sigma": 1},
+            lambda x, y: np.exp(-((x - 5) ** 2) / (2 * 1**2)) * y,
+        ),
+        (
+            polynomial_function,
+            {"a": 1, "b": 2, "c": 0},
+            lambda x, y: (x**2 + 2 * x) * y,
+        ),
+        (
+            logarithmic_function,
+            {"scale": 0.5},
+            lambda x, y: 0.5 * np.log(1 + x) * y,
+        ),
+    ],
+)
+def test_funcy(function, parameters, expected_function):
+    x_morph = np.linspace(0, 10, 101)
+    y_morph = np.sin(x_morph)
+    x_target = x_morph.copy()
+    y_target = y_morph.copy()
+    x_morph_expected = x_morph
+    y_morph_expected = expected_function(x_morph, y_morph)
+    morph = MorphFuncy()
+    morph.function = function
+    morph.parameters = parameters
+    x_morph_actual, y_morph_actual, x_target_actual, y_target_actual = (
+        morph.morph(x_morph, y_morph, x_target, y_target)
+    )
+    assert np.allclose(y_morph_actual, y_morph_expected)
+    assert np.allclose(x_morph_actual, x_morph_expected)
+    assert np.allclose(x_target_actual, x_target)
+    assert np.allclose(y_target_actual, y_target)