chore(repo): cosim_dir replay revival + ruff lint cleanup

cosim_dir revival:
- gen_realdata_hex.py: also emit decimated_range_{i,q}.npy (48x512)
  and doppler_map_{i,q}.npy (512x48) at production dimensions; the
  same Python pipeline that produces the RTL .hex stimuli now writes
  the .npy intermediates v7.replay COSIM_DIR loads. Replaces the
  workflow lost when golden_reference.py was deleted in e8b495c
- test_v7.py: update test_get_frame_cosim shape from pre-PR-O.6
  (64,32) to (NUM_RANGE_BINS, NUM_DOPPLER_BINS)
- check in 4 .npy reference files (~400 KB, deterministic SCENE_SEED=42)

Ruff lint cleanup (was 66 errors; now 0):
- pyproject.toml: ignore T20 in tb/cosim/**.py (CLI tools)
- compare_independent.py: drop redundant int() casts (RUF046),
  swap try/except scipy import for importlib.util.find_spec,
  remove dead duplicate np import, ASCII-ize comment unicode,
  wrap E501 format strings
- fpga_reference.py: drop unused fs arg from nco_reference,
  collapse if/else to ternary, mark _out_im unused
- v7/processing.py: ASCII-ize x in docstring, collapse if-branches
- {dashboard,software_fpga,workers,radar_protocol}.py: wrap E501
- test_v7.py: ASCII-ize comment unicode, _alias renames where unused

Result: test_v7 100/100 (0 skips on radar_venv, was 9 graceful
skips); 5 cosim_dir orphan tests now active and passing.

9_Firmware/9_3_GUI/v7/processing.py

@@ -573,7 +573,7 @@ def unfold_velocity_crt(
     alias depth k_0 ∈ [-K, K] generates candidates
     ``v_true = v_meas_0 + k_0 · 2 · v_unamb_0``.  A candidate is
     *valid* when it folds back into all other active PRIs to within
-    ``tol_factor × max(v_res)``.
+    ``tol_factor * max(v_res)``.
 
     Args:
         v_meas_per_sf: signed velocity measurement per active sub-frame
@@ -652,9 +652,7 @@ def unfold_velocity_crt(
         confidence = "AMBIGUOUS"
     elif n_sf == 3 and n_cands == 1:
         confidence = "CONFIRMED"
-    elif n_sf == 3 and n_cands == 2:
-        confidence = "LIKELY"
-    elif n_sf == 2 and n_cands == 1:
+    elif (n_sf == 3 and n_cands == 2) or (n_sf == 2 and n_cands == 1):
         confidence = "LIKELY"
     else:  # n_sf == 2 and n_cands == 2
         confidence = "AMBIGUOUS"