doc/tutorial: adding a tutorial for MorphFuncy and proper docstring

doc/source/quickstart.rst

@@ -401,6 +401,85 @@ There is also support for morphing from a nanoparticle to a bulk. When
 applying the inverse morphs, it is recommended to set ``--rmax=psize``
 where ``psize`` is the longest diameter of the nanoparticle.
 
+MorphFuncy: Applying custom functions
+-------------------------------------
+
+The ``MorphFuncy`` morph allows users to apply a custom Python function
+to the y-axis values of a dataset, enabling flexible and user-defined
+transformations.
+
+In this tutorial, we walk through how to use ``MorphFuncy`` with an example
+transformation. Unlike other morphs that can be run from the command line,
+``MorphFuncy`` requires a Python function and is therefore intended to be used
+through Python scripting.
+
+    1. Import the necessary modules into your Python script:
+
+       .. code-block:: python
+
+            from diffpy.morph.morph_api import morph, morph_default_config
+            import numpy as np
+
+    2. Define a custom Python function to apply a transformation to the data.
+       The function must take ``x`` and ``y`` (1D arrays of the same length)
+       along with named parameters, and return a transformed ``y`` array of the
+       same length.
+       For this example, we will use a simple linear transformation that
+       scales the input and applies an offset:
+
+       .. code-block:: python
+
+            def linear_function(x, y, scale, offset):
+                return (scale * x) * y + offset
+
+    3. In this example, we use a sine function for the morph data and generate
+       the target data by applying the linear transformation with known scale
+       and offset to it:
+
+       .. code-block:: python
+
+            x_morph = np.linspace(0, 10, 101)
+            y_morph = np.sin(x_morph)
+            x_target = x_morph.copy()
+            y_target = np.sin(x_target) * 20 * x_target + 0.8
+
+    4. Set up the morph configuration dictionary. This includes both the
+       transformation parameters (our initial guess) and the transformation
+       function itself:
+
+       .. code-block:: python
+
+            morph_config = morph_default_config(funcy={"scale": 1.2, "offset": 0.1})
+            morph_config["function"] = linear_function
+
+            # morph_config now contains:
+            # {'funcy': {'scale': 1.2, 'offset': 0.1}, 'function': linear_function}
+
+    5. Run the morph using the ``morph(...)``. This will apply the user-defined
+       function and refine the parameters to best align the morph data
+       with the target data:
+
+       .. code-block:: python
+
+            morph_result = morph(x_morph, y_morph, x_target, y_target, **morph_config)
+
+    6. Extract the morphed output and the fitted parameters from the result:
+
+       .. code-block:: python
+
+            fitted_config = morph_result["morphed_config"]
+            x_morph_out, y_morph_out, x_target_out, y_target_out = morph_result["morph_chain"].xyallout
+
+            fitted_params = fitted_config["funcy"]
+            print(f"Fitted scale: {fitted_params['scale']}")
+            print(f"Fitted offset: {fitted_params['offset']}")
+
+As you can see, the fitted scale and offset values match the ones used
+to generate the target (scale=20 & offset=0.8). This example shows how
+``MorphFuncy`` can be used to fit and apply custom transformations. Now
+it's your turn to experiment with other custom functions that may be useful
+for analyzing your data.
+
 Bug Reports
 ===========
 

news/tutorial_morphfuncy.rst

@@ -0,0 +1,23 @@
+**Added:**
+
+* Added a tutorial for MorphFuncy
+
+**Changed:**
+
+* Changed docstrings location for MorphFuncy and MorphSqueeze
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* <news item>
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>

src/diffpy/morph/morphs/morphfuncy.py

@@ -1,9 +1,63 @@
+"""Class MorphFuncy -- apply a user-supplied python function to the
+y-axis."""
+
 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."""
+    """Apply a custom function to the y-axis of the morph function.
+
+    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.
+
+    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.funcy = parameters
+    >>> x_morph_out, y_morph_out, x_target_out, y_target_out =
+    ... morph.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.funcy
+    """
 
     # Define input output types
     summary = "Apply a Python function to the y-axis data"
@@ -14,54 +68,8 @@ class MorphFuncy(Morph):
     parnames = ["function", "funcy"]
 
     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.funcy = parameters
-        >>> x_morph_out, y_morph_out, x_target_out, y_target_out = morph.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.funcy
-        """
+        """Apply the user-supplied Python function to the y-coordinates
+        of the morph data."""
         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.funcy

src/diffpy/morph/morphs/morphsqueeze.py

@@ -1,3 +1,6 @@
+"""Class MorphSqueeze -- Apply a polynomial to squeeze the morph
+function."""
+
 import numpy as np
 from numpy.polynomial import Polynomial
 from scipy.interpolate import CubicSpline
@@ -6,9 +9,50 @@
 
 
 class MorphSqueeze(Morph):
-    """Apply a polynomial to squeeze the morph function.
+    """Squeeze the morph function.
+
+    This applies a polynomial to squeeze the morph non-linearly.
+
+    Configuration Variables
+    -----------------------
+    squeeze : Dictionary
+        The polynomial coefficients {a0, a1, ..., an} for the squeeze
+        function where the polynomial would be of the form
+        a0 + a1*x + a2*x^2 and so on.  The order of the polynomial is
+        determined by the length of the dictionary.
+
+    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
+        shifted according to the squeeze. The morphed data is returned on
+        the same grid as the unmorphed data.
+
+    Example
+    -------
+    Import the squeeze morph function:
+
+    >>> from diffpy.morph.morphs.morphsqueeze import MorphSqueeze
 
-    The morphed data is returned on the same grid as the unmorphed data.
+    Provide initial guess for squeezing coefficients:
+
+    >>> squeeze_coeff = {"a0":0.1, "a1":-0.01, "a2":0.005}
+
+    Run the squeeze morph given input morph array (x_morph, y_morph) and target
+    array (x_target, y_target):
+
+    >>> morph = MorphSqueeze()
+    >>> morph.squeeze = squeeze_coeff
+    >>> 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
+    >>> squeeze_coeff_out = morph.squeeze
     """
 
     # Define input output types
@@ -24,43 +68,10 @@ class MorphSqueeze(Morph):
     extrap_index_high = None
 
     def morph(self, x_morph, y_morph, x_target, y_target):
-        """Squeeze the morph function.
-
-        This applies a polynomial to squeeze the morph non-linearly.
-
-        Configuration Variables
-        -----------------------
-        squeeze : Dictionary
-            The polynomial coefficients {a0, a1, ..., an} for the squeeze
-            function where the polynomial would be of the form
-            a0 + a1*x + a2*x^2 and so on.  The order of the polynomial is
-            determined by the length of the dictionary.
-
-        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
-            shifted according to the squeeze. The morphed data is returned on
-            the same grid as the unmorphed data.
-
-        Example
-        -------
-        Import the squeeze morph function:
-        >>> from diffpy.morph.morphs.morphsqueeze import MorphSqueeze
-        Provide initial guess for squeezing coefficients:
-        >>> squeeze_coeff = {"a0":0.1, "a1":-0.01, "a2":0.005}
-        Run the squeeze morph given input morph array (x_morph, y_morph)
-        and target array (x_target, y_target):
-        >>> morph = MorphSqueeze()
-        >>> morph.squeeze = squeeze_coeff
-        >>> 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
-        >>> squeeze_coeff_out = morph.squeeze
+        """Apply a polynomial to squeeze the morph function.
+
+        The morphed data is returned on the same grid as the unmorphed
+        data.
         """
         Morph.morph(self, x_morph, y_morph, x_target, y_target)