v4.1.3: Coverage threshold and horizon stats fixes

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 4.1.2
+current_version = 4.1.3
 commit = True
 tag = True
 tag_name = v{new_version}

README.md

@@ -9,7 +9,7 @@
 </p>
 
 <p align="center">
-  <a href="https://github.com/RFingAdam/RFlect/releases"><img src="https://img.shields.io/badge/version-4.1.2-blue" alt="Version"></a>
+  <a href="https://github.com/RFingAdam/RFlect/releases"><img src="https://img.shields.io/badge/version-4.1.3-blue" alt="Version"></a>
   <img src="https://img.shields.io/badge/python-3.11+-green" alt="Python">
   <a href="LICENSE"><img src="https://img.shields.io/badge/license-GPL--3.0-orange" alt="License"></a>
   <img src="https://img.shields.io/badge/tests-391%20passing-brightgreen" alt="Tests">

RELEASE_NOTES.md

@@ -1,5 +1,17 @@
 # RFlect - Release Notes
 
+## Version 4.1.3 (02/10/2026)
+
+**Patch release — coverage threshold and horizon statistics fixes.**
+
+### Bug Fixes
+- **Coverage threshold reference line**: The -3 dB reference line in conical cuts and GOA plots was hardcoded at `y=-3` on the Y-axis (meaningless for active dBm data). Now drawn relative to peak using the coverage threshold setting, and legend shows the absolute value (e.g., "-3 dB ref (17.5 dBm)")
+- **Configurable threshold**: Changing the coverage threshold setting (e.g., to -6 dB) now applies to the reference line in all maritime Cartesian plots
+- **MEG denominator bug**: Fixed incorrect array shape reference in MEG calculation (`gain_2d.shape[1]` vs `horizon_gain.shape[1]`)
+- **MEG label for active data**: Horizon statistics table now shows "Avg EIRP (sin-θ weighted)" for active power data instead of "MEG" which is only meaningful for passive gain
+
+---
+
 ## Version 4.1.2 (02/10/2026)
 
 **Patch release — maritime plot title corrections.**

plot_antenna/__init__.py

@@ -16,7 +16,7 @@
 - Professional report generation
 """
 
-__version__ = "4.1.2"
+__version__ = "4.1.3"
 __author__ = "Adam"
 __license__ = "GPL-3.0"
 

plot_antenna/plotting.py

@@ -2176,6 +2176,7 @@ def plot_conical_cuts(
     theta_cuts=None,
     data_label="Gain",
     data_unit="dBi",
+    gain_threshold=-3.0,
     polar=True,
     save_path=None,
 ):
@@ -2235,7 +2236,20 @@ def plot_conical_cuts(
             fontsize=14,
         )
         ax.grid(True, alpha=0.3)
-        ax.axhline(y=-3, color="red", linestyle="--", alpha=0.5, linewidth=1, label="-3 dB ref")
+        # Reference line relative to peak using the coverage threshold setting
+        all_cut_data = np.concatenate(
+            [gain_2d[np.argmin(np.abs(theta_deg - tc)), :] for tc in theta_cuts]
+        )
+        peak_val = np.max(all_cut_data)
+        ref_line = peak_val + gain_threshold  # gain_threshold is negative
+        ax.axhline(
+            y=ref_line,
+            color="red",
+            linestyle="--",
+            alpha=0.5,
+            linewidth=1,
+            label=f"{gain_threshold:.0f} dB ref ({ref_line:.1f} {data_unit})",
+        )
 
     ax.legend(loc="upper right", bbox_to_anchor=(1.3, 1.0), fontsize=8)
     ax.grid(True, alpha=0.3)
@@ -2259,6 +2273,7 @@ def plot_gain_over_azimuth(
     theta_cuts=None,
     data_label="Gain",
     data_unit="dBi",
+    gain_threshold=-3.0,
     save_path=None,
 ):
     """
@@ -2281,20 +2296,29 @@ def plot_gain_over_azimuth(
         cut_gain = gain_2d[theta_idx, :]
         ax.plot(phi_deg, cut_gain, color=colors[i], label=f"θ={theta_cut}°", linewidth=1.5)
 
-    ax.axhline(y=-3, color="red", linestyle="--", alpha=0.5, linewidth=1, label="-3 dB ref")
+    # Compute stats across all cuts first (used for ref line and summary)
+    all_cuts = np.concatenate([gain_2d[np.argmin(np.abs(theta_deg - tc)), :] for tc in theta_cuts])
+    peak_val = np.max(all_cuts)
+    ref_line = peak_val + gain_threshold  # gain_threshold is negative
+    ax.axhline(
+        y=ref_line,
+        color="red",
+        linestyle="--",
+        alpha=0.5,
+        linewidth=1,
+        label=f"{gain_threshold:.0f} dB ref ({ref_line:.1f} {data_unit})",
+    )
     ax.set_xlabel("Phi (degrees)")
     ax.set_ylabel(f"{data_label} ({data_unit})")
     theta_range_str = f"θ={theta_cuts[0]}–{theta_cuts[-1]}°"
     ax.set_title(f"{data_label} over Azimuth @ {frequency} MHz — {theta_range_str}", fontsize=14)
     ax.legend(loc="upper right", bbox_to_anchor=(1.2, 1.0), fontsize=8)
     ax.grid(True, alpha=0.3)
 
-    # Summary annotation: max / min / avg across all cuts
-    all_cuts = np.concatenate([gain_2d[np.argmin(np.abs(theta_deg - tc)), :] for tc in theta_cuts])
-    max_v = np.max(all_cuts)
+    # Summary annotation reusing all_cuts computed above
     min_v = np.min(all_cuts)
     avg_v = 10 * np.log10(np.mean(10 ** (all_cuts / 10)))
-    summary = f"Max: {max_v:.1f} {data_unit}   Min: {min_v:.1f} {data_unit}   Avg: {avg_v:.1f} {data_unit}"
+    summary = f"Max: {peak_val:.1f} {data_unit}   Min: {min_v:.1f} {data_unit}   Avg: {avg_v:.1f} {data_unit}"
     ax.annotate(
         summary,
         xy=(0.5, -0.12),
@@ -2360,14 +2384,13 @@ def plot_horizon_statistics(
     coverage_limit = max_gain + gain_threshold  # threshold is negative
     coverage_pct = 100.0 * np.sum(horizon_gain >= coverage_limit) / horizon_gain.size
 
-    # MEG: Mean Effective Gain with sin(theta) weighting
+    # MEG: Mean Effective Gain with sin(theta) weighting (passive only)
+    # For active power data, sin-weighted average EIRP is shown instead.
     theta_rad = np.deg2rad(horizon_theta)
     sin_weights = np.sin(theta_rad)
-    # Broadcast sin_weights to match gain shape: (n_horizon_theta, 1) * (n_horizon_theta, n_phi)
+    n_phi = horizon_gain.shape[1]
     weighted_lin = lin * sin_weights[:, np.newaxis]
-    meg_lin = np.sum(weighted_lin) / np.sum(
-        np.tile(sin_weights[:, np.newaxis], (1, gain_2d.shape[1]))
-    )
+    meg_lin = np.sum(weighted_lin) / (np.sum(sin_weights) * n_phi)
     meg_dB = 10 * np.log10(meg_lin) if meg_lin > 0 else float("-inf")
 
     # Null detection: find the minimum point
@@ -2384,12 +2407,21 @@ def plot_horizon_statistics(
     ax_table = fig.add_subplot(gs[0])
     ax_table.axis("off")
 
+    # Label the sin-weighted metric appropriately for gain vs power
+    if data_label == "Gain":
+        meg_label = "MEG (sin-θ weighted)"
+    else:
+        meg_label = "Avg EIRP (sin-θ weighted)"
+
     table_data = [
         ["Max " + data_label, f"{max_gain:.1f} {data_unit}"],
         ["Min " + data_label, f"{min_gain:.1f} {data_unit}"],
         ["Avg " + data_label + " (linear)", f"{avg_gain:.1f} {data_unit}"],
-        ["MEG (sin-θ weighted)", f"{meg_dB:.1f} {data_unit}"],
-        [f"Coverage (>{coverage_limit:.1f} {data_unit})", f"{coverage_pct:.1f}%"],
+        [meg_label, f"{meg_dB:.1f} {data_unit}"],
+        [
+            f"Coverage (>{coverage_limit:.1f} {data_unit})",
+            f"{coverage_pct:.1f}%",
+        ],
         ["Null Depth", f"{null_depth:.1f} dB"],
         ["Null Location", null_location],
         ["Theta Range", f"{theta_min}° - {theta_max}°"],
@@ -2670,6 +2702,7 @@ def generate_maritime_plots(
         theta_cuts=theta_cuts,
         data_label=data_label,
         data_unit=data_unit,
+        gain_threshold=gain_threshold,
         polar=True,
         save_path=save_path,
     )
@@ -2683,6 +2716,7 @@ def generate_maritime_plots(
         theta_cuts=theta_cuts,
         data_label=data_label,
         data_unit=data_unit,
+        gain_threshold=gain_threshold,
         save_path=save_path,
     )
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "rflect"
-version = "4.1.2"
+version = "4.1.3"
 description = "Antenna measurement visualization and analysis tool"
 readme = "README.md"
 requires-python = ">=3.11"

settings.json

@@ -1,3 +1,3 @@
 {
-    "CURRENT_VERSION": "v4.1.2"
+    "CURRENT_VERSION": "v4.1.3"
 }