feat: augment CI with codespell, pytest-xdist, pytest-cov, junitxml; …

.codespellrc

@@ -0,0 +1,3 @@
+[codespell]
+skip = .git,*.svg,*.png,*.jpg,*.jpeg,*.gif,*.ico,*.pdf,*.zip,*.tar.gz,*.whl,.venv,.vs,__pycache__
+ignore-words-list = Bu,gage,Gage,modpods,sindy,SINDy,pysindy,NSE,HFV,LFV,FDC,LTI,SWMM,pystorms,pyswmm,RAIM,PET,PRCP,CAMELS,USGS,configparser,argparse

.github/workflows/ci.yml

@@ -28,8 +28,11 @@ jobs:
       - name: Lint with ruff
         run: ruff check .
 
+      - name: Lint with codespell
+        run: codespell .
+
       - name: Type-check with mypy
         run: mypy . --ignore-missing-imports
 
       - name: Run tests with pytest
-        run: pytest tests/ -v
+        run: pytest tests/ -v --junitxml=junit.xml --cov=modpods --cov-report=xml -n auto

modpods/lti.py

@@ -108,8 +108,8 @@ def lti_from_gamma(
         # search across the C vector
         for i in range(
             C.shape[1] - 1, int(-1), int(-1)
-        ):  # accross the columns # start at the end and come back
-            # for i in range(int(0),C.shape[1],int(1)): # accross the columns, start at the beginning and go forward
+        ):  # across the columns # start at the end and come back
+            # for i in range(int(0),C.shape[1],int(1)): # across the columns, start at the beginning and go forward
 
             og_approx = control.ss(A, B, C, 0)
             og_y = np.ndarray.flatten(control.impulse_response(og_approx, t).y)
@@ -437,7 +437,7 @@ def lti_system_gen(
     for row in original_A.index:
         if delay_models[row] is None:
             pass
-        else:  # we want the model with the most transformations where the last trnasformation added at least 0.5% to the R2 score
+        else:  # we want the model with the most transformations where the last transformation added at least 0.5% to the R2 score
             for num_transforms in range(1, max_transforms + 1):
                 if num_transforms == 1:
                     optimal_number_transforms = num_transforms
@@ -654,15 +654,15 @@ def lti_system_gen(
     C = C.apply(pd.to_numeric, errors="coerce").fillna(0.0)
 
     # if bibo_stable is specified and A not hurwitz, make A hurwitz by defining A' = A - I*max(real(eig(A)))
-    # this will gaurantee stability (max eigenvalue will have real part < 0)
+    # this will guarantee stability (max eigenvalue will have real part < 0)
     if bibo_stable:
         orig_eigs, _ = np.linalg.eig(A)
         if any(np.real(orig_eigs) > 0):
             print("stabilizing unstable plant by subtracting I*max(real(eig)) from A")
             epsilon = 10e-4
             A_stab = A - np.eye(len(A)) * (1 + epsilon) * max(
                 np.real(orig_eigs)
-            )  # add factor of (1+epsilon) for stability, not marginal stabilty
+            )  # add factor of (1+epsilon) for stability, not marginal stability
             stab_eigs, _ = np.linalg.eig(A_stab)
             A = A_stab.copy(deep=True)
 

modpods/train.py

@@ -275,10 +275,12 @@ def objective_function(params_vector):
                     return -1.0
 
             # Bayesian optimization
-            # Use more iterations for Bayesian optimization to build a good surrogate model
-            # Cap at 200 to avoid memory issues with GP fitting
+            # Bayesian optimization: bias toward exploration (cheap random samples)
+            # rather than expensive GP refinement. The objective (SINDy fit) is the
+            # dominant cost, so spend the budget on broad initial sampling and only
+            # a few informed iterations.
             bayesian_max_iter = min(max_iter * 4, 200)
-            n_initial = min(20, max(10, bayesian_max_iter // 4))
+            n_initial = min(30, max(20, int(bayesian_max_iter * 0.6)))
             X_sample_list: list[Any] = []
             Y_sample_list: list[Any] = []
 
@@ -299,10 +301,10 @@ def objective_function(params_vector):
             best_params: np.ndarray = X_sample[np.argmax(Y_sample)]
 
             # Gaussian Process setup
-            kernel = Matern(length_scale=1.0, nu=2.5)
+            kernel = Matern(length_scale=1.0, nu=1.5)
             gpr = GaussianProcessRegressor(
                 kernel=kernel,
-                alpha=1e-6,
+                alpha=1e-3,
                 normalize_y=True,
                 n_restarts_optimizer=5,
                 random_state=42,

requirements.txt

@@ -10,8 +10,11 @@ networkx>=3.0
 
 # Testing
 pytest>=7.0
+pytest-xdist>=3.0
+pytest-cov>=4.0
 
 # Linting
 black>=23.0
 ruff>=0.1
 mypy>=1.0
+codespell>=2.0

test_fixed.py

@@ -3,7 +3,7 @@
 
 import modpods
 
-# basic funcionality tests and a bit of a tutorial
+# basic functionality tests and a bit of a tutorial
 
 # some data from the CAMELS dataset
 # change the filepath to wherever you have modpods at