From ff1d055310655460c0140769f07b28357a23ed7f Mon Sep 17 00:00:00 2001
From: John Halloran <jhalloran@oxy.edu>
Date: Sat, 7 Feb 2026 15:04:41 -0500
Subject: [PATCH 1/5] style: make line length <80 throughout

---
 doc/source/conf.py            |   4 +-
 src/diffpy/snmf/snmf_class.py | 196 +++++++++++++++++++---------------
 tests/test_snmf_optimizer.py  |   5 +-
 3 files changed, 117 insertions(+), 88 deletions(-)

diff --git a/doc/source/conf.py b/doc/source/conf.py
index 821fd467..54f71529 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -20,7 +20,7 @@
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
-# documentation root, use Path().resolve() to make it absolute, like shown here.
+# documentation root, use Path().resolve() to make it absolute, like shown here. # noqa: E501
 # sys.path.insert(0, str(Path(".").resolve()))
 sys.path.insert(0, str(Path("../..").resolve()))
 sys.path.insert(0, str(Path("../../src").resolve()))
@@ -204,7 +204,7 @@
 # html_file_suffix = None
 
 # Output file base name for HTML help builder.
-basename = "diffpy.snmf".replace(" ", "").replace(".", "")
+basename = "diffpy.snmf".replace(" ", "").replace(".", "")  # noqa: E501
 htmlhelp_basename = basename + "doc"
 
 
diff --git a/src/diffpy/snmf/snmf_class.py b/src/diffpy/snmf/snmf_class.py
index a382153b..f744f076 100644
--- a/src/diffpy/snmf/snmf_class.py
+++ b/src/diffpy/snmf/snmf_class.py
@@ -10,20 +10,21 @@ class SNMFOptimizer:
     """An implementation of stretched NMF (sNMF), including sparse
     stretched NMF.
 
-    Instantiating the SNMFOptimizer class prepares initial guesses and sets up the
-    optimization. It can then be run using fit().
-    The results matrices can be accessed as instance attributes
-    of the class (components_, weights_, and stretch_).
+    Instantiating the SNMFOptimizer class prepares initial guesses and sets up
+    the optimization. It can then be run using fit(). The results matrices can
+    be accessed as instance attributes of the
+    class (components_, weights_, and stretch_).
 
     For more information on sNMF, please reference:
-    Gu, R., Rakita, Y., Lan, L. et al. Stretched non-negative matrix factorization.
-    npj Comput Mater 10, 193 (2024). https://doi.org/10.1038/s41524-024-01377-5
+    Gu, R., Rakita, Y., Lan, L. et al.
+    Stretched non-negative matrix factorization.
+    npj Comput Mater 10, 193 (2024) https://doi.org/10.1038/s41524-024-01377-5
 
     Attributes
     ----------
     source_matrix : ndarray
-        The original, unmodified data to be decomposed and later, compared against.
-        Shape is (length_of_signal, number_of_signals).
+        The original, unmodified data to be decomposed and later,
+        compared against. Shape is (length_of_signal, number_of_signals).
     stretch_ : ndarray
         The best guess (or while running, the current guess) for the stretching
         factor matrix.
@@ -34,34 +35,39 @@ class SNMFOptimizer:
         The best guess (or while running, the current guess) for the matrix of
         component weights.
     rho : float
-        The stretching factor that influences the decomposition. Zero corresponds to no
-        stretching present. Relatively insensitive and typically adjusted in powers of 10.
+        The stretching factor that influences the decomposition. Zero
+        corresponds to no stretching present. Relatively insensitive and
+        typically adjusted in powers of 10.
     eta : float
-        The sparsity factor that influences the decomposition. Should be set to zero for
-        non-sparse data such as PDF. Can be used to improve results for sparse data such
-        as XRD, but due to instability, should be used only after first selecting the
-        best value for rho. Suggested adjustment is by powers of 2.
+        The sparsity factor that influences the decomposition. Should be set
+        to zero for non-sparse data such as PDF. Can be used to improve
+        results for sparse data such as XRD, but due to instability, should
+        be used only after first selecting the best value for rho. Suggested
+        adjustment is by powers of 2.
     max_iter : int
-        The maximum number of times to update each of stretch, components, and weights before stopping
-        the optimization.
+        The maximum number of times to update each of stretch, components,
+        and weights before stopping the optimization.
     min_iter : int
-        The minimum number of times to update each of stretch, components, and weights before terminating
-        the optimization due to low/no improvement.
+        The minimum number of times to update each of stretch, components,
+        and weights before terminating the optimization due to low/no
+        improvement.
     tol : float
-        The convergence threshold. This is the minimum fractional improvement in the
-        objective function to allow without terminating the optimization.
+        The convergence threshold. This is the minimum fractional improvement
+        in the objective function to allow without terminating the
+        optimization.
     n_components : int
-        The number of components to extract from source_matrix. Must be provided when and only when
-        init_weights is not provided.
+        The number of components to extract from source_matrix. Must be
+        provided when and only when init_weights is not provided.
     random_state : int
-        The seed for the initial guesses at the matrices (stretch, components, and weights) created by
-        the decomposition.
+        The seed for the initial guesses at the matrices (stretch, components,
+        and weights) created by the decomposition.
     num_updates : int
-        The total number of times that any of (stretch, components, and weights) have had their values changed.
-        If not terminated by other means, this value is used to stop when reaching max_iter.
+        The total number of times that any of (stretch, components,
+        and weights) have had their values changed. If not terminated by other
+        means, this value is used to stop when reaching max_iter.
     objective_difference : float
-        The change in the objective function value since the last update. A negative value
-        means that the result improved.
+        The change in the objective function value since the last update. A
+        negative value means that the result improved.
     """
 
     def __init__(
@@ -82,31 +88,34 @@ def __init__(
         Parameters
         ----------
         source_matrix : ndarray
-            The data to be decomposed. Shape is (length_of_signal, number_of_conditions).
-        init_weights : ndarray Optional  Default = rng.beta(a=2.0, b=2.0, size=(n_components, n_signals))
-            The initial guesses for the component weights at each stretching condition.
-            Shape is (number_of_components, number_of_signals) Must provide exactly one
-            of this or n_components.
-        init_components : ndarray Optional  Default = rng.random((self.signal_length, self.n_components))
+            The data to be decomposed. Shape is (length_of_signal,
+            number_of_conditions).
+        init_weights : ndarray Optional  Default = rng.beta(a=2.0, b=2.0)
+            The initial guesses for the component weights at each stretching
+            condition. Shape is (number_of_components, number_of_signals).
+            Must provide exactly one of this or n_components.
+        init_components : ndarray Optional  Default = rng.random()
             The initial guesses for the intensities of each component per
-            row/sample/angle. Shape is (length_of_signal, number_of_components).
-        init_stretch : ndarray Optional  Default = np.ones((self.n_components, self.n_signals)) + self._rng.normal(
-                0, 1e-3, size=(self.n_components, self.n_signals)
-            The initial guesses for the stretching factor for each component, at each
-            condition (for each signal). Shape is (number_of_components, number_of_signals).
+            row/sample/angle. Shape is (length_of_signal,
+            number_of_components).
+        init_stretch : ndarray Optional  Default = np.ones() + rng.normal()
+            The initial guesses for the stretching factor for each component,
+            at each condition (for each signal). Shape is
+            (number_of_components, number_of_signals).
         max_iter : int Optional Default = 500
-            The maximum number of times to update each of A, X, and Y before stopping
-            the optimization.
+            The maximum number of times to update each of A, X, and Y before
+            stopping the optimization.
         tol : float Optional  Default = 5e-7
-            The convergence threshold. This is the minimum fractional improvement in the
-            objective function to allow without terminating the optimization. Note that
-            a minimum of 20 updates are run before this parameter is checked.
+            The convergence threshold. This is the minimum fractional
+            improvement in the objective function to allow without terminating
+            the optimization. Note that a minimum of 20 updates are run before
+            this parameter is checked.
         n_components : int  Optional  Default = None
-            The number of components to extract from source_matrix. Must be provided when and only when
-            Y0 is not provided.
+            The number of components to extract from source_matrix. Must be
+            provided when and only when Y0 is not provided.
         random_state : int  Optional  Default = None
-            The seed for the initial guesses at the matrices (A, X, and Y) created by
-            the decomposition.
+            The seed for the initial guesses at the matrices (A, X, and Y)
+            created by the decomposition.
         show_plots : boolean Optional  Default = False
             Enables plotting at each step of the decomposition.
         """
@@ -126,8 +135,8 @@ def __init__(
             n_components is not None and init_weights is not None
         ):
             raise ValueError(
-                "Conflicting source for n_components. Must provide either init_weights or n_components "
-                "directly, but not both."
+                "Conflicting source for n_components. Must provide either "
+                "init_weights or n_components directly, but not both."
             )
 
         # Initialize weights and determine number of components
@@ -167,7 +176,7 @@ def __init__(
         self.init_weights = self.weights_.copy()
         self.init_stretch = self.stretch_.copy()
 
-        # Second-order spline: Tridiagonal (-2 on diagonal, 1 on sub/superdiagonals)
+        # Second-order spline: Tridiagonal (-2 on diags, 1 on sub/superdiags)
         self._spline_smooth_operator = 0.25 * diags(
             [1, -2, 1],
             offsets=[0, 1, 2],
@@ -181,17 +190,20 @@ def fit(self, rho=0, eta=0, reset=True):
         Parameters
         ----------
         rho : float Optional  Default = 0
-            The stretching factor that influences the decomposition. Zero corresponds to no
-            stretching present. Relatively insensitive and typically adjusted in powers of 10.
+            The stretching factor that influences the decomposition. Zero
+            corresponds to no stretching present. Relatively insensitive and
+            typically adjusted in powers of 10.
         eta : int Optional  Default = 0
-            The sparsity factor that influences the decomposition. Should be set to zero for
-            non-sparse data such as PDF. Can be used to improve results for sparse data such
-            as XRD, but due to instability, should be used only after first selecting the
-            best value for rho. Suggested adjustment is by powers of 2.
+            The sparsity factor that influences the decomposition. Should be
+            set to zero for non-sparse data such as PDF. Can be used to
+            improve results for sparse data such as XRD, but due to
+            instability, should be used only after first selecting the best
+            value for rho. Suggested adjustment is by powers of 2.
         reset : boolean Optional  Default = True
-            Whether to return to the initial set of components_, weights_, and stretch_ before
-            running the optimization. When set to False, sequential calls to fit() will use the
-            output of the previous fit() as their input.
+            Whether to return to the initial set of components_, weights_,
+            and stretch_ before running the optimization. When set to False,
+            sequential calls to fit() will use the output of the previous
+            fit() as their input.
         """
 
         if reset:
@@ -232,7 +244,9 @@ def fit(self, rho=0, eta=0, reset=True):
         )  # Square root penalty
         print(
             f"Start, Objective function: {self.objective_function:.5e}"
-            f", Obj - reg/sparse: {self.objective_function - regularization_term - sparsity_term:.5e}"
+            f", Obj - reg/sparse: {self.objective_function
+                                   - regularization_term
+                                   - sparsity_term:.5e}"
         )
 
         # Main optimization loop
@@ -253,11 +267,14 @@ def fit(self, rho=0, eta=0, reset=True):
             )  # Square root penalty
             print(
                 f"Obj fun: {self.objective_function:.5e}, "
-                f"Obj - reg/sparse: {self.objective_function - regularization_term - sparsity_term:.5e}, "
+                f"Obj - reg/sparse: {self.objective_function
+                                     - regularization_term
+                                     - sparsity_term:.5e}, "
                 f"Iter: {self.outiter}"
             )
 
-            # Convergence check: Stop if diffun is small and at least min_iter iterations have passed
+            # Convergence check: Stop if diffun is small
+            # and at least min_iter iterations have passed
             print(
                 "Checking if ",
                 self.objective_difference,
@@ -286,7 +303,7 @@ def normalize_results(self):
         stretch_row_max = np.max(self.stretch_, axis=1, keepdims=True)
         self.stretch_ = self.stretch_ / stretch_row_max
 
-        # effectively just re-running with component updates only vs normalized weights/stretch
+        # re-running with component updates only vs normalized weights/stretch
         self._grad_components = np.zeros_like(
             self.components_
         )  # Gradient of X (zeros for now)
@@ -306,7 +323,8 @@ def normalize_results(self):
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
             print(
-                f"Objective function after normalize_components: {self.objective_function:.5e}"
+                f"Objective function after normalize_components: "
+                f"{self.objective_function:.5e}"
             )
             self._objective_history.append(self.objective_function)
             self.objective_difference = (
@@ -333,7 +351,8 @@ def outer_loop(self):
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
             print(
-                f"Objective function after update_components: {self.objective_function:.5e}"
+                f"Objective function after update_components: "
+                f"{self.objective_function:.5e}"
             )
             self._objective_history.append(self.objective_function)
             self.objective_difference = (
@@ -358,7 +377,8 @@ def outer_loop(self):
             self.residuals = self.get_residual_matrix()
             self.objective_function = self.get_objective_function()
             print(
-                f"Objective function after update_weights: {self.objective_function:.5e}"
+                f"Objective function after update_weights: "
+                f"{self.objective_function:.5e}"
             )
             self._objective_history.append(self.objective_function)
             self.objective_difference = (
@@ -392,7 +412,8 @@ def outer_loop(self):
         self.residuals = self.get_residual_matrix()
         self.objective_function = self.get_objective_function()
         print(
-            f"Objective function after update_stretch: {self.objective_function:.5e}"
+            f"Objective function after update_stretch: "
+            f"{self.objective_function:.5e}"
         )
         self._objective_history.append(self.objective_function)
         self.objective_difference = (
@@ -486,11 +507,11 @@ def compute_stretched_components(
 
         Outputs
         -------
-        stretched_components : array, shape (signal_len, n_components * n_signals)
+        stretched_components : array, shape (signal_len, n_comps * n_sigs)
             Interpolated and weighted components.
-        d_stretched_components : array, shape (signal_len, n_components * n_signals)
+        d_stretched_components : array, shape (signal_len, n_comps * n_sigs)
             First derivatives with respect to stretch.
-        dd_stretched_components : array, shape (signal_len, n_components * n_signals)
+        dd_stretched_components : array, shape (signal_len, n_comps * n_sigs)
             Second derivatives with respect to stretch.
         """
 
@@ -512,18 +533,20 @@ def compute_stretched_components(
         stretch = np.clip(stretch, eps, None)
         stretch_inv = 1.0 / stretch
 
-        # Apply stretching to the original sample indices, represented as a "time-stretch"
+        # Apply stretching to the original sample indices,
+        # represented as a "time-stretch"
         t = (
             np.arange(signal_len, dtype=float)[:, None, None]
             * stretch_inv[None, :, :]
         )
         # has shape (signal_len, n_components, n_signals)
 
-        # For each stretched coordinate, find its prior integer (original) index and their difference
+        # For each stretched coordinate, find its prior integer (original)
+        # index and their difference
         i0 = np.floor(t).astype(np.int64)  # prior original index
         alpha = t - i0.astype(float)  # fractional distance between left/right
 
-        # Clip indices to valid range (0, signal_len - 1) to maintain original size
+        # Clip indices to range (0, signal_len - 1) to maintain original size
         max_idx = signal_len - 1
         i0 = np.clip(i0, 0, max_idx)
         i1 = np.clip(i0 + 1, 0, max_idx)
@@ -531,7 +554,7 @@ def compute_stretched_components(
         # Gather sample values
         comps_3d = components[
             :, :, None
-        ]  # expand components by a dimension for broadcasting across n_signals
+        ]  # expand components by a dim for broadcasting across n_signals
         c0 = np.take_along_axis(comps_3d, i0, axis=0)  # left sample values
         c1 = np.take_along_axis(comps_3d, i1, axis=0)  # right sample values
 
@@ -641,7 +664,8 @@ def solve_quadratic_program(self, t, m):
 
         Parameters:
         - t: (N, k) ndarray
-        - source_matrix_col: (N,) column of source_matrix for the corresponding m
+        - source_matrix_col: (N,) column of source_matrix for the
+        corresponding m
 
         Returns:
         - y: (k,) optimal solution
@@ -768,7 +792,8 @@ def update_weights(self):
         for signal in range(self.n_signals):
             # Stretch factors for this signal across components:
             this_stretch = self.stretch_[:, signal]
-            # Build stretched_comps[:, k] by interpolating component at frac. pos. index / this_stretch[comp]
+            # Build stretched_comps[:, k] by interpolating component at frac.
+            # pos. index / this_stretch[comp]
             stretched_comps = np.empty(
                 (self.signal_length, self.n_components),
                 dtype=self.components_.dtype,
@@ -827,7 +852,8 @@ def update_stretch(self):
         """Updates stretching matrix using constrained optimization
         (equivalent to fmincon in MATLAB)."""
 
-        # Flatten stretch for compatibility with the optimizer (since SciPy expects 1D input)
+        # Flatten stretch for compatibility with the optimizer
+        # (since SciPy expects 1D input)
         stretch_flat_initial = self.stretch_.flatten()
 
         # Define the optimization function
@@ -870,13 +896,15 @@ def _compute_objective_function(
         residuals : ndarray
             Difference between reconstructed and observed data.
         stretch : ndarray
-            Stretching factors :math:`A` applied to each component across samples.
+            Stretching factors :math:`A` applied to each component across
+            samples.
         rho : float
             Regularization parameter enforcing smooth variation in :math:`A`.
         eta : float
             Sparsity-promoting regularization parameter applied to :math:`X`.
         spline_smooth_operator : ndarray
-            Linear operator :math:`L` penalizing non-smooth changes in :math:`A`.
+            Linear operator :math:`L` penalizing non-smooth changes
+            in :math:`A`.
 
         Returns
         -------
@@ -895,12 +923,12 @@ def _compute_objective_function(
               + \eta \sum_{i,j} \sqrt{X_{ij}} \,,
 
         where :math:`Z` is the data matrix, :math:`Y` contains the non-negative
-        weights, :math:`S(A)` denotes the spline-interpolated stretching operator,
+        weights, :math:`S(A)` denotes the spline-interp. stretching operator,
         and :math:`\lVert \cdot \rVert_F` is the Frobenius norm.
 
         Special cases
         -------------
-        - :math:`\rho = 0` — no smoothness regularization on stretching factors.
+        - :math:`\rho = 0` — no smoothness regularization on stretch factors.
         - :math:`\eta = 0` — no sparsity promotion on components.
         - :math:`\rho = \eta = 0` — reduces to the classical NMF least-squares
           objective :math:`\tfrac{1}{2} \lVert Z - YX \rVert_F^2`.
@@ -943,8 +971,8 @@ def cubic_largest_real_root(p, q):
     y3 = w**2 * a1 + w * a2
 
     # Take the largest real root element-wise when delta < 0
-    real_roots = np.stack([np.real(y1), np.real(y2), np.real(y3)], axis=0)
-    y = np.max(real_roots, axis=0) * (
+    r_roots = np.stack([np.real(y1), np.real(y2), np.real(y3)], axis=0)
+    y = np.max(r_roots, axis=0) * (
         delta < 0
     )  # Keep only real roots when delta < 0
 
diff --git a/tests/test_snmf_optimizer.py b/tests/test_snmf_optimizer.py
index e85b6e75..6c7f51b8 100644
--- a/tests/test_snmf_optimizer.py
+++ b/tests/test_snmf_optimizer.py
@@ -53,9 +53,10 @@ def test_final_objective_below_threshold(inputs):
 
 @pytest.mark.parametrize(
     "inputs, expected",
-    # inputs tuple: (components, residuals, stretch, rho, eta, spline smoothness operator)
+    # inputs tuple:
+    # (components, residuals, stretch, rho, eta, spline smoothness operator)
     [
-        # Case 0: No smoothness or sparsity penalty, reduces to standard NMF objective
+        # Case 0: No smoothness or sparsity penalty, reduces to NMF objective
         # residual Frobenius norm^2 = 3^2 + 4^2 = 25 -> 0.5 * 25 = 12.5
         (
             (

From bd29a102af74a201c927437e92479916d542c739 Mon Sep 17 00:00:00 2001
From: John Halloran <jhalloran@oxy.edu>
Date: Sat, 7 Feb 2026 15:12:01 -0500
Subject: [PATCH 2/5] feat: skip updating stretch() if stretching factor is
 zero

---
 src/diffpy/snmf/snmf_class.py | 64 ++++++++++++++++++++---------------
 1 file changed, 37 insertions(+), 27 deletions(-)

diff --git a/src/diffpy/snmf/snmf_class.py b/src/diffpy/snmf/snmf_class.py
index f744f076..b8ccb1ce 100644
--- a/src/diffpy/snmf/snmf_class.py
+++ b/src/diffpy/snmf/snmf_class.py
@@ -214,6 +214,10 @@ def fit(self, rho=0, eta=0, reset=True):
         self.rho = rho
         self.eta = eta
 
+        # Set stretch matrix to 1 if no stretching present
+        if rho == 0:
+            self.stretch_ = np.ones_like(self.stretch_)
+
         # Set up residual matrix, objective function, and history
         self.residuals = self.get_residual_matrix()
         self.objective_function = self.get_objective_function()
@@ -408,31 +412,33 @@ def outer_loop(self):
             ):
                 break
 
-        self.update_stretch()
-        self.residuals = self.get_residual_matrix()
-        self.objective_function = self.get_objective_function()
-        print(
-            f"Objective function after update_stretch: "
-            f"{self.objective_function:.5e}"
-        )
-        self._objective_history.append(self.objective_function)
-        self.objective_difference = (
-            self._objective_history[-2] - self._objective_history[-1]
-        )
-        if self.objective_function < self.best_objective:
-            self.best_objective = self.objective_function
-            self.best_matrices = [
-                self.components_.copy(),
-                self.weights_.copy(),
-                self.stretch_.copy(),
-            ]
-        if self.plotter is not None:
-            self.plotter.update(
-                components=self.components_,
-                weights=self.weights_,
-                stretch=self.stretch_,
-                update_tag="stretch",
+        # Skip updating stretch if no stretching factor
+        if not self.rho == 0:
+            self.update_stretch()
+            self.residuals = self.get_residual_matrix()
+            self.objective_function = self.get_objective_function()
+            print(
+                f"Objective function after update_stretch: "
+                f"{self.objective_function:.5e}"
+            )
+            self._objective_history.append(self.objective_function)
+            self.objective_difference = (
+                self._objective_history[-2] - self._objective_history[-1]
             )
+            if self.objective_function < self.best_objective:
+                self.best_objective = self.objective_function
+                self.best_matrices = [
+                    self.components_.copy(),
+                    self.weights_.copy(),
+                    self.stretch_.copy(),
+                ]
+            if self.plotter is not None:
+                self.plotter.update(
+                    components=self.components_,
+                    weights=self.weights_,
+                    stretch=self.stretch_,
+                    update_tag="stretch",
+                )
 
     def get_residual_matrix(self, components=None, weights=None, stretch=None):
         """Return the residuals (difference) between the source matrix
@@ -621,7 +627,8 @@ def apply_transformation_matrix(
 
         # Expand row indices
         repm = np.tile(
-            np.arange(self.n_components), (self.signal_length, self.n_signals)
+            np.arange(self.n_components),
+            (self.signal_length, self.n_signals),  # noqa E501
         )
 
         # Compute transformations
@@ -740,7 +747,9 @@ def update_components(self):
                 * (self.components_ - self._prev_components)
             )  # Element-wise multiplication
             denom = (
-                np.linalg.norm(self.components_ - self._prev_components, "fro")
+                np.linalg.norm(
+                    self.components_ - self._prev_components, "fro"
+                )  # noqa E501
                 ** 2
             )  # Frobenius norm squared
             step_size = num / denom if denom > 0 else initial_step_size
@@ -937,7 +946,8 @@ def _compute_objective_function(
         regularization_term = (
             0.5
             * rho
-            * np.linalg.norm(spline_smooth_operator @ stretch.T, "fro") ** 2
+            * np.linalg.norm(spline_smooth_operator @ stretch.T, "fro")
+            ** 2  # noqa E501
         )
         sparsity_term = eta * np.sum(np.sqrt(components))
         return residual_term + regularization_term + sparsity_term

From ac44016dbed3ce2fc9994bf22611fe08a4c307d0 Mon Sep 17 00:00:00 2001
From: John Halloran <jhalloran@oxy.edu>
Date: Sat, 7 Feb 2026 15:15:16 -0500
Subject: [PATCH 3/5] chore: fix codespell complaint

---
 .codespell/ignore_words.txt | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.codespell/ignore_words.txt b/.codespell/ignore_words.txt
index 04b4fcfa..c18c482f 100644
--- a/.codespell/ignore_words.txt
+++ b/.codespell/ignore_words.txt
@@ -6,3 +6,6 @@ mater
 
 ;; Frobenius norm used in np.linalg.norm
 fro
+
+;; alternative spelling of socioeconomic used in code of conduct
+socio-economic

From b2008268d0d09d164bbc729376f7e9f061a8499b Mon Sep 17 00:00:00 2001
From: John Halloran <jhalloran@oxy.edu>
Date: Sat, 7 Feb 2026 16:23:34 -0500
Subject: [PATCH 4/5] fix: use correct version of rho

---
 src/diffpy/snmf/snmf_class.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/diffpy/snmf/snmf_class.py b/src/diffpy/snmf/snmf_class.py
index b8ccb1ce..8df738e3 100644
--- a/src/diffpy/snmf/snmf_class.py
+++ b/src/diffpy/snmf/snmf_class.py
@@ -215,7 +215,7 @@ def fit(self, rho=0, eta=0, reset=True):
         self.eta = eta
 
         # Set stretch matrix to 1 if no stretching present
-        if rho == 0:
+        if self.rho == 0:
             self.stretch_ = np.ones_like(self.stretch_)
 
         # Set up residual matrix, objective function, and history

From c747760f1f8d5de3fa5ea4eeea801c87d49ebef6 Mon Sep 17 00:00:00 2001
From: John Halloran <jhalloran@oxy.edu>
Date: Sat, 7 Feb 2026 19:16:53 -0500
Subject: [PATCH 5/5] style: clean up several lines and remove noqa

---
 doc/source/conf.py            | 5 +++--
 src/diffpy/snmf/snmf_class.py | 9 +++------
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/doc/source/conf.py b/doc/source/conf.py
index 54f71529..429473a8 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -20,7 +20,8 @@
 
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
-# documentation root, use Path().resolve() to make it absolute, like shown here. # noqa: E501
+# documentation root, use Path().resolve() to make it absolute,
+# like shown here.
 # sys.path.insert(0, str(Path(".").resolve()))
 sys.path.insert(0, str(Path("../..").resolve()))
 sys.path.insert(0, str(Path("../../src").resolve()))
@@ -204,7 +205,7 @@
 # html_file_suffix = None
 
 # Output file base name for HTML help builder.
-basename = "diffpy.snmf".replace(" ", "").replace(".", "")  # noqa: E501
+basename = "diffpy.snmf".replace(" ", "").replace(".", "")
 htmlhelp_basename = basename + "doc"
 
 
diff --git a/src/diffpy/snmf/snmf_class.py b/src/diffpy/snmf/snmf_class.py
index 8df738e3..c1ddd5af 100644
--- a/src/diffpy/snmf/snmf_class.py
+++ b/src/diffpy/snmf/snmf_class.py
@@ -628,7 +628,7 @@ def apply_transformation_matrix(
         # Expand row indices
         repm = np.tile(
             np.arange(self.n_components),
-            (self.signal_length, self.n_signals),  # noqa E501
+            (self.signal_length, self.n_signals),
         )
 
         # Compute transformations
@@ -747,9 +747,7 @@ def update_components(self):
                 * (self.components_ - self._prev_components)
             )  # Element-wise multiplication
             denom = (
-                np.linalg.norm(
-                    self.components_ - self._prev_components, "fro"
-                )  # noqa E501
+                np.linalg.norm(self.components_ - self._prev_components, "fro")
                 ** 2
             )  # Frobenius norm squared
             step_size = num / denom if denom > 0 else initial_step_size
@@ -946,8 +944,7 @@ def _compute_objective_function(
         regularization_term = (
             0.5
             * rho
-            * np.linalg.norm(spline_smooth_operator @ stretch.T, "fro")
-            ** 2  # noqa E501
+            * np.linalg.norm(spline_smooth_operator @ stretch.T, "fro") ** 2
         )
         sparsity_term = eta * np.sum(np.sqrt(components))
         return residual_term + regularization_term + sparsity_term