chore: clean package static-analysis findings

CHANGELOG.md

@@ -61,6 +61,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   county event studies -12%/-13% (rust/python), with no scenario regressing. Rust outputs
   move only at the cross-backend parity tolerances (REGISTRY-documented; never
   bit-identical across backends).
+- **Internal: cleaned package-source Ruff static-analysis findings** across forward-reference,
+  stale f-string, invalid `noqa`, and audited unused-local sites. No public API or numerical
+  behavior change.
 - **FE-absorption demeaning rewritten: factorize-once + `np.bincount` method of alternating
   projections** (`demean_by_groups` / `within_transform`). Each absorbed dimension is factorized
   once and group means are formed via `np.bincount` instead of re-hashing the group keys with

diff_diff/bootstrap_utils.py

@@ -6,12 +6,15 @@
 """
 
 import warnings
-from typing import Optional, Tuple
+from typing import TYPE_CHECKING, Optional, Tuple
 
 import numpy as np
 
 from diff_diff._backend import HAS_RUST_BACKEND, _rust_bootstrap_weights
 
+if TYPE_CHECKING:
+    from diff_diff.survey import ResolvedSurveyDesign
+
 __all__ = [
     "generate_bootstrap_weights",
     "generate_bootstrap_weights_batch",

diff_diff/chaisemartin_dhaultfoeuille.py

@@ -5219,7 +5219,6 @@ def _compute_heterogeneity_test(
         # count, typically 30-300 groups).
         from diff_diff.linalg import _detect_rank_deficiency
 
-        n_params = design.shape[1]
         rank, _dropped, _pivot = _detect_rank_deficiency(design)
 
         # Guard: need MORE observations than rank for a well-defined

diff_diff/honest_did.py

@@ -988,7 +988,6 @@ def _largest_consecutive_block(times, boundary_val):
                     )
 
                 # Build beta_hat and sigma (diagonal - no full VCV for dCDH)
-                all_times = pre_times + post_times
                 effects = []
                 ses = []
                 for h in pre_times:
@@ -2143,7 +2142,6 @@ def _arp_confidence_set(
     ci_ub : float
         Upper bound of confidence set.
     """
-    num_post = len(beta_hat) - num_pre
     beta_post = beta_hat[num_pre:]
 
     # Point estimate and SE for grid centering

diff_diff/imputation.py

@@ -1305,7 +1305,6 @@ def _impute_treatment_effects(
             Imputed counterfactual Y(0).
         """
         df_1 = df.loc[omega_1_mask]
-        n_1 = len(df_1)
 
         # Look up unit and time FE
         alpha_i = df_1[unit].map(unit_fe).values

diff_diff/power.py

@@ -182,9 +182,7 @@ def _build_survey_design(self) -> Any:
             return self.survey_design
         from diff_diff.survey import SurveyDesign
 
-        return SurveyDesign(
-            weights="weight", strata="stratum", psu="psu", fpc="fpc"
-        )
+        return SurveyDesign(weights="weight", strata="stratum", psu="psu", fpc="fpc")
 
     @property
     def min_viable_n(self) -> int:
@@ -2267,14 +2265,14 @@ def simulate_power(
         if control_group in ("not_yet_treated", "last_cohort"):
             raise ValueError(
                 f"survey_config does not support control_group='{control_group}' "
-                f"(requires multi-cohort DGP). Use the custom data_generator "
-                f"path for survey power with this control-group design."
+                "(requires multi-cohort DGP). Use the custom data_generator "
+                "path for survey power with this control-group design."
             )
         if clean_control == "strict":
             raise ValueError(
-                f"survey_config does not support clean_control='strict' "
-                f"(requires multi-cohort DGP). Use the custom data_generator "
-                f"path for survey power with strict clean controls."
+                "survey_config does not support clean_control='strict' "
+                "(requires multi-cohort DGP). Use the custom data_generator "
+                "path for survey power with strict clean controls."
             )
 
     # SyntheticDiD placebo variance requires n_control > n_treated.

diff_diff/prep_dgp.py

@@ -406,7 +406,6 @@ def generate_factor_data(
     """
     rng = np.random.default_rng(seed)
 
-    n_control = n_units - n_treated
     n_periods = n_pre + n_post
 
     if n_treated > n_units:

diff_diff/spillover.py

@@ -273,7 +273,7 @@ def _compute_nearest_treated_distance_sparse(
     """
     # Imported lazily to mirror conley.py's lazy-scipy pattern and keep
     # module import cheap when the sparse path isn't exercised.
-    from scipy.spatial import cKDTree  # noqa: WPS433  (deferred import)
+    from scipy.spatial import cKDTree  # deferred import
 
     n_units = all_coords.shape[0]
     if metric == "haversine":

diff_diff/staggered.py

@@ -4062,15 +4062,16 @@ def _doubly_robust_rc(
         X_ct_int = np.column_stack([np.ones(n_ct), X_ct])
         X_cs_int = np.column_stack([np.ones(n_cs), X_cs])
 
-        # Control OR predictions for all groups
+        # Control OR predictions used downstream
         mu0_post_gt = X_gt_int @ beta_ct  # mu_{0,1}(X) for treated-post
         mu0_pre_gt = X_gt_int @ beta_cs  # mu_{0,0}(X) for treated-post
         mu0_post_gs = X_gs_int @ beta_ct  # mu_{0,1}(X) for treated-pre
         mu0_pre_gs = X_gs_int @ beta_cs  # mu_{0,0}(X) for treated-pre
         mu0_post_ct = X_ct_int @ beta_ct  # mu_{0,1}(X) for control-post
-        mu0_pre_ct = X_ct_int @ beta_cs  # mu_{0,0}(X) for control-post
-        mu0_post_cs = X_cs_int @ beta_ct  # mu_{0,1}(X) for control-pre
         mu0_pre_cs = X_cs_int @ beta_cs  # mu_{0,0}(X) for control-pre
+        # The full DRDID R-convention grid also names mu0_pre_ct and
+        # mu0_post_cs. They are intentionally not materialized here because no
+        # residual, adjustment, or influence-function term consumes them.
 
         # Treated OR predictions for all groups (for local efficiency adjustment)
         mu1_post_gt = X_gt_int @ beta_gt  # mu_{1,1}(X) for treated-post
@@ -4132,11 +4133,11 @@ def _doubly_robust_rc(
 
         pscore = np.clip(pscore, self.pscore_trim, 1 - self.pscore_trim)
 
-        # Split propensity scores per group
-        ps_gt = pscore[:n_gt]
-        ps_gs = pscore[n_gt : n_gt + n_gs]
+        # Split control propensity scores per group.
         ps_ct = pscore[n_gt + n_gs : n_gt + n_gs + n_ct]
         ps_cs = pscore[n_gt + n_gs + n_ct :]
+        # The symmetric treated slices ps_gt and ps_gs are intentionally not
+        # materialized: only control propensity slices feed IPW control weights.
 
         # =====================================================================
         # 3. Group weights and R-convention means