feat: Initialize a fit recipe with a previous recipe

news/init-w-recipe.rst

@@ -0,0 +1,23 @@
+**Added:**
+
+* Added initialize_recipe_from_recipe to ``FitRecipe``.
+
+**Changed:**
+
+* <news item>
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* <news item>
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>

src/diffpy/srfit/fitbase/fitrecipe.py

@@ -1140,6 +1140,50 @@ def getBounds2(self):
         """
         return self.get_bounds_array()
 
+    def initialize_recipe_with_recipe(self, recipe_object):
+        """Initialize a FitRecipe with another FitRecipe.
+
+        This is used to initialize a FitRecipe with the contribution(s),
+        parameters, constraints and restraints of another FitRecipe.
+        If a duplicate contribution, parameter, constraint, or restraint
+        is added to the FitRecipe you are initializing, the value from the
+        added object will be used.
+
+        Parameters
+        ----------
+        recipe_object : FitRecipe
+            The FitRecipe to initialize with.
+
+        Raises
+        ------
+        ValueError
+            If the object passed is not a FitRecipe.
+        """
+        if not isinstance(recipe_object, FitRecipe):
+            raise ValueError(
+                "The input recipe_object must be a FitRecipe, "
+                f"but got {type(recipe_object)}."
+            )
+
+        for contrib_object in recipe_object._contributions.values():
+            if contrib_object not in self._contributions.values():
+                self.add_contribution(contrib_object)
+
+        for param_name, param_object in recipe_object._parameters.items():
+            if param_name not in self._parameters:
+                self._parameters.update({param_name: param_object})
+
+        for (
+            parameter_object,
+            constraint_object,
+        ) in recipe_object._constraints.items():
+            if parameter_object not in self._constraints:
+                self._constraints.update({parameter_object: constraint_object})
+
+        for restraint in recipe_object._restraints:
+            if restraint not in self._restraints:
+                self._restraints.add(restraint)
+
     def set_plot_defaults(self, **kwargs):
         """Set default plotting options for all future plots.
 

tests/conftest.py

@@ -146,7 +146,7 @@ def _capturestdout(f, *args, **kwargs):
     return _capturestdout
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture()
 def build_recipe_one_contribution():
     "helper to build a simple recipe"
     profile = Profile()
@@ -164,32 +164,45 @@ def build_recipe_one_contribution():
     return recipe
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture()
 def build_recipe_two_contributions():
-    "helper to build a recipe with two contributions"
+    """Helper to build a recipe with two physically related contributions."""
     profile1 = Profile()
-    x = linspace(0, pi, 10)
-    y1 = sin(x)
+    x = linspace(0, pi, 50)
+    y1 = sin(x)  # amplitude=1, freq=1
     profile1.set_observed_profile(x, y1)
+
     contribution1 = FitContribution("c1")
     contribution1.set_profile(profile1)
     contribution1.set_equation("A*sin(k*x + c)")
 
     profile2 = Profile()
-    y2 = 0.5 * sin(2 * x)
+    y2 = 0.5 * sin(2 * x)  # amplitude=0.5, freq=2
     profile2.set_observed_profile(x, y2)
+
     contribution2 = FitContribution("c2")
     contribution2.set_profile(profile2)
     contribution2.set_equation("B*sin(m*x + d)")
+
     recipe = FitRecipe()
     recipe.add_contribution(contribution1)
     recipe.add_contribution(contribution2)
-    recipe.add_variable(contribution1.A, 1)
-    recipe.add_variable(contribution1.k, 1)
-    recipe.add_variable(contribution1.c, 1)
-    recipe.add_variable(contribution2.B, 0.5)
-    recipe.add_variable(contribution2.m, 2)
-    recipe.add_variable(contribution2.d, 0)
+
+    # Add variables with reasonable initial guesses
+    recipe.add_variable(contribution1.A, 0.8)
+    recipe.add_variable(contribution1.k, 1.0)
+    recipe.add_variable(contribution1.c, 0.1)
+
+    recipe.add_variable(contribution2.B, 0.4)
+    recipe.add_variable(contribution2.m, 2.0)
+    recipe.add_variable(contribution2.d, 0.1)
+
+    # ---- Meaningful constraints ----
+    recipe.constrain(contribution2.m, "2*k")
+    recipe.constrain(contribution2.d, contribution1.c)
+    recipe.constrain(contribution2.B, "0.5*A")
+    recipe.restrain(contribution1.A, 0.5, 1.5)
+    recipe.restrain(contribution1.k, 0.8, 1.2)
 
     return recipe
 

tests/test_fitrecipe.py

@@ -18,10 +18,12 @@
 
 import matplotlib
 import matplotlib.pyplot as plt
+import numpy as np
 import pytest
 from numpy import array_equal, dot, linspace, pi, sin
 from scipy.optimize import leastsq
 
+from diffpy.srfit.fitbase import FitResults
 from diffpy.srfit.fitbase.fitcontribution import FitContribution
 from diffpy.srfit.fitbase.fitrecipe import FitRecipe
 from diffpy.srfit.fitbase.parameter import Parameter
@@ -462,6 +464,76 @@ def optimize_recipe(recipe):
     leastsq(residuals, values)
 
 
+def test_initialize_recipe_from_recipe(build_recipe_two_contributions):
+    # Case: User initializes a FitRecipe from a previously optimized fit
+    # expected: recipe is initialized with everything:
+    # contributions, profiles (contained in contributions),
+    # variables, restraints, and constraints
+    recipe1 = build_recipe_two_contributions
+    optimize_recipe(recipe1)
+    expected_parameters_dict = recipe1._parameters
+    expected_constraints_dict = recipe1._constraints
+    expected_restraints_set = recipe1._restraints
+    expected_contributions_dict = recipe1._contributions
+    expected_profiles_list = []
+    for con_name, contribution in expected_contributions_dict.items():
+        expected_profile = contribution.profile
+        expected_profiles_list.append(expected_profile)
+
+    recipe2 = FitRecipe()
+    recipe2.initialize_recipe_with_recipe(recipe1)
+    actual_parameters_dict = recipe2._parameters
+    actual_constraints_dict = recipe2._constraints
+    actual_restraints_set = recipe2._restraints
+    actual_contributions_dict = recipe2._contributions
+    actual_profiles_list = []
+    for con_name, contribution in actual_contributions_dict.items():
+        actual_profile = contribution.profile
+        actual_profiles_list.append(actual_profile)
+
+    assert expected_parameters_dict == actual_parameters_dict
+    assert expected_constraints_dict == actual_constraints_dict
+    assert expected_restraints_set == actual_restraints_set
+    assert expected_contributions_dict == actual_contributions_dict
+    assert expected_profiles_list == actual_profiles_list
+
+    # Check to see if the refined values and variable names are
+    # the same in the results objects for each recipe
+    results1 = FitResults(recipe1)
+    # round to account for small numerical differences
+    expected_values = np.round(results1.varvals, 7)
+    expected_names = results1.varnames
+
+    optimize_recipe(recipe2)
+    results2 = FitResults(recipe2)
+    # round to account for small numerical differences
+    actual_values = np.round(results2.varvals, 7)
+    actual_names = results2.varnames
+
+    assert sorted(expected_names) == sorted(actual_names)
+    assert sorted(list(expected_values)) == sorted(list(actual_values))
+
+
+def test_initialize_recipe_from_recipe_bad(build_recipe_two_contributions):
+    # Case: User tries to initialize a FitRecipe from a non recipe object
+    # expected: raised ValueError with message
+    recipe_bad = 12345  # not a FitRecipe object
+    recipe2 = FitRecipe()
+    msg = (
+        "The input recipe_object must be a FitRecipe, "
+        "but got <class 'int'>."
+    )
+    with pytest.raises(ValueError, match=msg):
+        recipe2.initialize_recipe_with_recipe(recipe_bad)
+
+
+# def test_initialize_recipe_from_results(build_recipe_one_contribution):
+#     # Case: User initializes a FitRecipe from a FitResults object or
+#     #       results file
+#     # expected: recipe is initialized with variables from previous fit
+#     assert False
+
+
 def get_labels_and_linecount(ax):
     """Helper to get line labels and count from a matplotlib Axes."""
     labels = [