feat: professional acoustic plot styling and combined dashboard

main.nf

@@ -162,6 +162,21 @@ process generate_phase_plot {
     """
 }
 
+process generate_dashboard {
+    publishDir "${params.outdir}", mode: 'copy'
+
+    input:
+    path final_csv
+
+    output:
+    path "dashboard.png"
+
+    script:
+    """
+    python3 -m horn_analysis.dashboard ${final_csv} dashboard.png
+    """
+}
+
 // ========================================================================
 // Auto mode processes
 // ========================================================================
@@ -396,6 +411,9 @@ workflow single {
     // 8. Impedance and phase plots
     generate_impedance_plot(ch_merged_results)
     generate_phase_plot(ch_merged_results)
+
+    // 9. Combined dashboard
+    generate_dashboard(ch_merged_results)
 }
 
 workflow auto {

packages/horn-analysis/pyproject.toml

@@ -22,6 +22,7 @@ horn-score = "horn_analysis.scoring:main"
 horn-prescreen = "horn_analysis.prescreen:main"
 horn-rank = "horn_analysis.rank_pipeline:main"
 horn-auto-report = "horn_analysis.auto_report:main"
+horn-dashboard = "horn_analysis.dashboard:main"
 
 [project.optional-dependencies]
 test = [

packages/horn-analysis/src/horn_analysis/compare.py

@@ -12,6 +12,9 @@
 import matplotlib
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
+import numpy as np
+
+from horn_analysis import plot_theme
 
 
 def plot_multi_comparison(
@@ -30,21 +33,26 @@ def plot_multi_comparison(
         Path to the output image.
     """
     if kpi_table:
-        fig, (ax_plot, ax_table) = plt.subplots(
-            2, 1, figsize=(12, 10), gridspec_kw={"height_ratios": [3, 1]},
+        fig, (ax_plot, ax_table) = plot_theme.create_figure(
+            figsize=(12, 10), nrows=2, ncols=1, gridspec_kw={"height_ratios": [3, 1]},
         )
     else:
-        fig, ax_plot = plt.subplots(figsize=(12, 8))
+        fig, ax_plot = plot_theme.create_figure(figsize=(12, 8))
 
-    for csv_path, label in file_label_pairs:
+    all_freq = []
+    all_spl = []
+    for i, (csv_path, label) in enumerate(file_label_pairs):
         df = pd.read_csv(csv_path)
-        ax_plot.plot(df["frequency"], df["spl"], label=label)
+        color = plot_theme.MULTI_COLORS[i % len(plot_theme.MULTI_COLORS)]
+        ax_plot.plot(df["frequency"], df["spl"], label=label, color=color, linewidth=1.4)
+        all_freq.extend(df["frequency"].values)
+        all_spl.extend(df["spl"].values)
+
+    plot_theme.setup_freq_axis(ax_plot, min(all_freq), max(all_freq))
+    plot_theme.setup_spl_axis(ax_plot, np.array(all_spl))
+    plot_theme.setup_grid(ax_plot)
 
-    ax_plot.set_xscale("log")
     ax_plot.set_title("Horn Frequency Response Comparison")
-    ax_plot.set_xlabel("Frequency (Hz)")
-    ax_plot.set_ylabel("Sound Pressure Level (dB)")
-    ax_plot.grid(True, which="both", ls="--")
     ax_plot.legend()
 
     if kpi_table:
@@ -57,11 +65,11 @@ def plot_multi_comparison(
                 label,
                 f"{kpis.peak_spl_db:.1f}",
                 f"{kpis.peak_frequency_hz:.0f}",
-                f"{kpis.f3_low_hz:.0f}" if kpis.f3_low_hz else "—",
-                f"{kpis.f3_high_hz:.0f}" if kpis.f3_high_hz else "—",
-                f"{kpis.bandwidth_octaves:.1f}" if kpis.bandwidth_octaves else "—",
-                f"{kpis.passband_ripple_db:.1f}" if kpis.passband_ripple_db is not None else "—",
-                f"{kpis.average_sensitivity_db:.1f}" if kpis.average_sensitivity_db is not None else "—",
+                f"{kpis.f3_low_hz:.0f}" if kpis.f3_low_hz else "\u2014",
+                f"{kpis.f3_high_hz:.0f}" if kpis.f3_high_hz else "\u2014",
+                f"{kpis.bandwidth_octaves:.1f}" if kpis.bandwidth_octaves else "\u2014",
+                f"{kpis.passband_ripple_db:.1f}" if kpis.passband_ripple_db is not None else "\u2014",
+                f"{kpis.average_sensitivity_db:.1f}" if kpis.average_sensitivity_db is not None else "\u2014",
             ])
 
         col_labels = [
@@ -81,10 +89,8 @@ def plot_multi_comparison(
         table.set_fontsize(9)
         table.scale(1, 1.4)
 
-    plt.tight_layout()
     output_path = Path(output_file)
-    plt.savefig(output_path, dpi=150)
-    plt.close()
+    plot_theme.save_figure(fig, str(output_path))
     print(f"Comparison plot saved to {output_path}")
     return output_path
 

packages/horn-analysis/src/horn_analysis/compare_horns.py

@@ -1,7 +1,11 @@
 import pandas as pd
-import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use("Agg")
 import sys
 
+from horn_analysis import plot_theme
+
+
 def plot_comparison(file_a, label_a, file_b, label_b, output_file):
     """
     Reads two CSV files containing frequency response data and plots them on the same graph.
@@ -17,28 +21,27 @@ def plot_comparison(file_a, label_a, file_b, label_b, output_file):
     df_a = pd.read_csv(file_a)
     df_b = pd.read_csv(file_b)
 
-    # Create the plot
-    plt.figure(figsize=(12, 8))
-    plt.plot(df_a['frequency'], df_a['spl'], label=label_a)
-    plt.plot(df_b['frequency'], df_b['spl'], label=label_b)
-
-    # Formatting
-    plt.xscale('log')
-    plt.title('Horn Frequency Response Comparison')
-    plt.xlabel('Frequency (Hz)')
-    plt.ylabel('Sound Pressure Level (dB)')
-    plt.grid(True, which="both", ls="--")
-    plt.legend()
-
-    # Save the plot
-    plt.savefig(output_file)
-    plt.close()
+    fig, ax = plot_theme.create_figure(figsize=(12, 8))
+    ax.plot(df_a['frequency'], df_a['spl'], label=label_a,
+            color=plot_theme.MULTI_COLORS[0], linewidth=1.4)
+    ax.plot(df_b['frequency'], df_b['spl'], label=label_b,
+            color=plot_theme.MULTI_COLORS[1], linewidth=1.4)
+
+    all_freq = list(df_a['frequency'].values) + list(df_b['frequency'].values)
+    plot_theme.setup_freq_axis(ax, min(all_freq), max(all_freq))
+    plot_theme.setup_grid(ax)
+
+    ax.set_title('Horn Frequency Response Comparison')
+    ax.set_ylabel('Sound Pressure Level (dB)')
+    ax.legend()
+
+    plot_theme.save_figure(fig, output_file)
     print(f"Comparison plot saved to {output_file}")
 
 if __name__ == "__main__":
     # Expects: python compare_horns.py <file_a> <label_a> <file_b> <label_b> <output_file>
     if len(sys.argv) != 6:
         print("Usage: python compare_horns.py <file_a> <label_a> <file_b> <label_b> <output_file>")
         sys.exit(1)
-    
-    plot_comparison(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5]) 
+
+    plot_comparison(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5])

packages/horn-analysis/src/horn_analysis/dashboard.py

@@ -0,0 +1,117 @@
+"""Combined 3-panel dashboard: SPL + impedance + phase/group-delay on shared x-axis."""
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+from horn_analysis import plot_theme
+
+
+def generate_dashboard(csv_file: str, output_file: str):
+    """Generate a combined 3-panel dashboard from solver CSV output.
+
+    Row 1 (tall): SPL with 5 dB divisions
+    Row 2: |Z| magnitude + angle(Z) on dual y-axis
+    Row 3: Unwrapped phase + group delay on dual y-axis
+
+    Expects CSV columns: frequency, spl, z_real, z_imag, phase_deg.
+
+    Args:
+        csv_file: Input CSV path with all solver output columns.
+        output_file: Output image path.
+    """
+    df = pd.read_csv(csv_file)
+    freq = df["frequency"].values
+
+    fig, (ax_spl, ax_z, ax_ph) = plot_theme.create_figure(
+        figsize=(11, 14), nrows=3, ncols=1,
+        sharex=True, gridspec_kw={"height_ratios": [2, 1.2, 1.2]},
+    )
+
+    # -- Row 1: SPL --
+    spl = df["spl"].values
+    ax_spl.plot(freq, spl, color=plot_theme.COLORS["primary"], linewidth=1.4)
+    ax_spl.set_title("Sound Pressure Level (SPL) vs. Frequency")
+    plot_theme.setup_spl_axis(ax_spl, spl)
+    plot_theme.setup_grid(ax_spl)
+
+    # -- Row 2: Impedance (dual y-axis) --
+    z_complex = df["z_real"].values + 1j * df["z_imag"].values
+    z_mag = np.abs(z_complex)
+    z_angle = np.degrees(np.angle(z_complex))
+
+    color_mag = plot_theme.COLORS["primary"]
+    ax_z.plot(freq, z_mag, color=color_mag, linewidth=1.4, label="|Z|")
+    ax_z.set_ylabel("|Z| (Pa\u00b7s/m)", color=color_mag)
+    ax_z.tick_params(axis="y", labelcolor=color_mag)
+
+    ax_z2 = ax_z.twinx()
+    color_angle = plot_theme.COLORS["secondary"]
+    ax_z2.plot(freq, z_angle, color=color_angle, linestyle="--", linewidth=1.4, label="\u2220Z")
+    ax_z2.set_ylabel("\u2220Z (degrees)", color=color_angle)
+    ax_z2.tick_params(axis="y", labelcolor=color_angle)
+
+    ax_z.set_title("Throat Impedance")
+    plot_theme.setup_grid(ax_z)
+
+    lines_z1, labels_z1 = ax_z.get_legend_handles_labels()
+    lines_z2, labels_z2 = ax_z2.get_legend_handles_labels()
+    ax_z.legend(lines_z1 + lines_z2, labels_z1 + labels_z2, loc="upper right", fontsize=9)
+
+    # -- Row 3: Phase + group delay (dual y-axis) --
+    phase_deg = df["phase_deg"].values
+    phase_rad = np.radians(phase_deg)
+    phase_unwrapped = np.degrees(np.unwrap(phase_rad))
+
+    color_phase = plot_theme.COLORS["primary"]
+    ax_ph.plot(freq, phase_unwrapped, color=color_phase, linewidth=1.4, label="Phase")
+    ax_ph.set_ylabel("Phase (degrees)", color=color_phase)
+    ax_ph.tick_params(axis="y", labelcolor=color_phase)
+
+    ax_ph2 = ax_ph.twinx()
+    if len(freq) > 1:
+        dphi = np.diff(np.unwrap(phase_rad))
+        df_vals = np.diff(freq)
+        tau_g_ms = -dphi / (2 * np.pi * df_vals) * 1000
+        freq_mid = (freq[:-1] + freq[1:]) / 2
+
+        color_gd = plot_theme.COLORS["tertiary"]
+        ax_ph2.plot(freq_mid, tau_g_ms, color=color_gd, linestyle="--", linewidth=1.2, label="Group delay")
+        ax_ph2.set_ylabel("Group Delay (ms)", color=color_gd)
+        ax_ph2.tick_params(axis="y", labelcolor=color_gd)
+
+    ax_ph.set_title("Phase Response & Group Delay")
+    plot_theme.setup_grid(ax_ph)
+
+    lines_ph1, labels_ph1 = ax_ph.get_legend_handles_labels()
+    lines_ph2, labels_ph2 = ax_ph2.get_legend_handles_labels()
+    ax_ph.legend(lines_ph1 + lines_ph2, labels_ph1 + labels_ph2, loc="upper right", fontsize=9)
+
+    # Shared frequency axis on bottom panel only
+    plot_theme.setup_freq_axis(ax_ph, freq.min(), freq.max())
+    # Apply log scale to upper panels (shared x) but hide their tick labels
+    ax_spl.set_xlabel("")
+    ax_z.set_xlabel("")
+
+    plot_theme.save_figure(fig, output_file)
+    print(f"Dashboard saved to {output_file}")
+
+
+def main():
+    """CLI for combined dashboard generation."""
+    parser = argparse.ArgumentParser(
+        description="Generate combined SPL/impedance/phase dashboard from solver CSV.",
+    )
+    parser.add_argument("csv_file", type=str, help="Input CSV with frequency, spl, z_real, z_imag, phase_deg.")
+    parser.add_argument("output_file", type=str, help="Output image path.")
+    args = parser.parse_args()
+    generate_dashboard(args.csv_file, args.output_file)
+
+
+if __name__ == "__main__":
+    main()