Improve Modes detection

data/models/Modes/Modes_cpu.py

@@ -7,11 +7,11 @@
     insig_type='impulse',
     diff_source=True,
     mat_files_dict={
-        'Ceiling': 'almost_rigid.h5',
-        'Floor': 'almost_rigid.h5',
-        'Walls': 'almost_rigid.h5',
+        'Ceiling': 'sabine_03.h5',
+        'Floor': 'sabine_03.h5',
+        'Walls': 'sabine_03.h5',
     },
-    duration=7.5,
+    duration=3.75,
     Tc=20,
     rh=50,
     fcc_flag=False,
@@ -20,7 +20,7 @@
     save_folder='../../sim_data/Modes/cpu',
     save_folder_gpu='../../sim_data/Modes/gpu',
     compress=0,
-    draw_vox=True,
+    draw_vox=False,
     draw_backend='polyscope',
-    rot_az_el=[15, 15],
+    rot_az_el=[0, 0],
 )

data/models/Modes/Modes_model.py

@@ -2,7 +2,7 @@
 
 from pffdtd.sim3d.room_builder import RoomBuilder
 
-S = 0.9
+S = 0.4
 L = 7.00*S
 W = 5.19*S
 H = 3.70*S
@@ -14,7 +14,10 @@
     "Walls": [255, 255, 255],
 })
 
-offset = 0.075*3
+fmax=800
+ppw=10.5
+dx = 343/(fmax*ppw)
+offset = dx*2
 
 room.add_source("S1", [offset, offset, offset])
 room.add_receiver("R1", [W-offset, L-offset, H-offset])

data/models/Modes/model.json

@@ -1 +1 @@
-{"mats_hash": {"Walls": {"tris": [[0, 1, 2], [0, 2, 3], [6, 5, 4], [7, 6, 4], [0, 3, 4], [7, 4, 3], [5, 2, 1], [2, 5, 6]], "pts": [[0.0, 0.0, 0.0], [4.671, 0.0, 0.0], [4.671, 0.0, 3.33], [0.0, 0.0, 3.33], [0.0, 6.3, 0.0], [4.671, 6.3, 0.0], [4.671, 6.3, 3.33], [0.0, 6.3, 3.33]], "color": [255, 255, 255], "sides": [1, 1, 1, 1, 1, 1, 1, 1]}, "Ceiling": {"tris": [[2, 1, 0], [2, 3, 1]], "pts": [[0, 0, 3.33], [0, 6.3, 3.33], [4.671, 0, 3.33], [4.671, 6.3, 3.33]], "color": [200, 200, 200], "sides": [1, 1]}, "Floor": {"tris": [[0, 1, 2], [1, 3, 2]], "pts": [[0, 0, 0], [0, 6.3, 0], [4.671, 0, 0], [4.671, 6.3, 0]], "color": [151, 134, 122], "sides": [1, 1]}}, "sources": [{"name": "S1", "xyz": [0.22499999999999998, 0.22499999999999998, 0.22499999999999998]}], "receivers": [{"name": "R1", "xyz": [4.446000000000001, 6.075, 3.105]}]}
+{"mats_hash": {"Walls": {"tris": [[0, 1, 2], [0, 2, 3], [6, 5, 4], [7, 6, 4], [0, 3, 4], [7, 4, 3], [5, 2, 1], [2, 5, 6]], "pts": [[0.0, 0.0, 0.0], [2.076, 0.0, 0.0], [2.076, 0.0, 1.4800000000000002], [0.0, 0.0, 1.4800000000000002], [0.0, 2.8000000000000003, 0.0], [2.076, 2.8000000000000003, 0.0], [2.076, 2.8000000000000003, 1.4800000000000002], [0.0, 2.8000000000000003, 1.4800000000000002]], "color": [255, 255, 255], "sides": [1, 1, 1, 1, 1, 1, 1, 1]}, "Ceiling": {"tris": [[2, 1, 0], [2, 3, 1]], "pts": [[0, 0, 1.4800000000000002], [0, 2.8000000000000003, 1.4800000000000002], [2.076, 0, 1.4800000000000002], [2.076, 2.8000000000000003, 1.4800000000000002]], "color": [200, 200, 200], "sides": [1, 1]}, "Floor": {"tris": [[0, 1, 2], [1, 3, 2]], "pts": [[0, 0, 0], [0, 2.8000000000000003, 0], [2.076, 0, 0], [2.076, 2.8000000000000003, 0]], "color": [151, 134, 122], "sides": [1, 1]}}, "sources": [{"name": "S1", "xyz": [0.08166666666666667, 0.08166666666666667, 0.08166666666666667]}], "receivers": [{"name": "R1", "xyz": [1.9943333333333335, 2.7183333333333337, 1.3983333333333334]}]}

run_3d.sh

@@ -5,7 +5,7 @@ set -e
 root_dir="$(cd "$(dirname "$0")" && pwd)"
 engine_exe="$root_dir/build/src/cpp/main_3d/pffdtd_3d"
 
-sim_name="ProStudio"
+sim_name="Modes"
 sim_setup="${sim_name}_cpu.py"
 sim_model_gen="${sim_name}_model.py"
 sim_dir="$root_dir/data/sim_data/$sim_name/cpu"
@@ -34,12 +34,12 @@ $engine_exe
 
 # Post-process
 python -m pffdtd.sim3d.process_outputs --data_dir="$sim_dir" --fcut_lowpass "$fmax" --N_order_lowpass=8 --symmetric --fcut_lowcut "$fmin" --N_order_lowcut=4 --air_abs_filter="none" --save_wav --plot
-python -m pffdtd.analysis.response --fmin=10 --target="-7.4" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R002_out_normalised.wav
-python -m pffdtd.analysis.response --fmin=10 --target="-7.1" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R003_out_normalised.wav
-python -m pffdtd.analysis.response --fmin=10 --target="-7.3" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R004_out_normalised.wav
-python -m pffdtd.analysis.response --fmin=10 --target="-7.6" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R005_out_normalised.wav
-python -m pffdtd.analysis.response --fmin=10 --target="-8.0" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R006_out_normalised.wav
-# python -m pffdtd.analysis.waterfall $sim_dir/R001_out_normalised.wav
+# python -m pffdtd.analysis.response --fmin=10 --target="-7.4" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R002_out_normalised.wav
+# python -m pffdtd.analysis.response --fmin=10 --target="-7.1" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R003_out_normalised.wav
+# python -m pffdtd.analysis.response --fmin=10 --target="-7.3" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R004_out_normalised.wav
+# python -m pffdtd.analysis.response --fmin=10 --target="-7.6" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R005_out_normalised.wav
+# python -m pffdtd.analysis.response --fmin=10 --target="-8.0" --smoothing=$smoothing --fmax=$fmax $sim_dir/R001_out_normalised.wav $sim_dir/R006_out_normalised.wav
+python -m pffdtd.analysis.waterfall $sim_dir/R001_out_normalised.wav
 # python -m pffdtd.analysis.t60 --fmin=$fmin --fmax="$fmax" --target=0.3 $sim_dir/R001_out_normalised.wav
 # python -m pffdtd.analysis.t60 --data_dir="$sim_dir" --fmin=$fmin --fmax="$fmax" --target=0.25
-# python -m pffdtd.analysis.room_modes --data_dir="$sim_dir" --fmin=$fmin --fmax=$fmax --modes=20
+python -m pffdtd.analysis.room_modes --data_dir="$sim_dir" --fmin=$fmin --fmax=$fmax --modes=20

src/python/pffdtd/analysis/room_modes.py

@@ -12,6 +12,12 @@
 from pffdtd.common.plot import plot_styles
 
 
+def _find_nearest(array, value):
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return array[idx]
+
+
 def collect_wav_paths(dir, pattern="*.wav"):
     return list(sorted(glob.glob(os.path.join(dir, pattern))))
 
@@ -84,7 +90,7 @@ def main():
     if not paths:
         paths = collect_wav_paths(directory, "*_out_normalised.wav")
 
-    scale = 0.9
+    scale = 0.4
 
     # Tobi
     L = 6.0 * scale
@@ -157,8 +163,9 @@ def main():
 
         dB_max = np.max(dB)
         peaks, _ = find_peaks(dB, width=2)
+        mode_peaks = freqs[peaks]
 
-        print(freqs[peaks][:10])
+        print(mode_peaks[:10])
 
         plt.plot(freqs, dB, linestyle='-', label=f'{file.stem[:4]}')
 
@@ -167,9 +174,13 @@ def main():
         plt.vlines(mode_freqs, dB_max-80, dB_max+10,
                    colors='#AAAAAA', linestyles='--', label="Modes")
         if len(paths) == 1:
-            plt.plot(freqs[peaks], dB[peaks], 'r.',
+            plt.plot(mode_peaks, dB[peaks], 'r.',
                      markersize=10, label='Peaks')
 
+        for mode in mode_freqs:
+            nearest = _find_nearest(mode_peaks, mode)
+            print(f"{mode=:03.3f} Hz - {nearest=:03.3f} Hz = {mode-nearest:03.3f} Hz")
+
     plt.title("")
     plt.xlabel('Frequency [Hz]')
     plt.ylabel('Amplitude [dB]')

src/python/pffdtd/materials/build.py

@@ -27,6 +27,10 @@ def main():
 
     #freq-independent impedance from Sabine abs coefficient
     write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.01),filename=Path(write_folder / 'sabine_01.h5'))
+    write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.02),filename=Path(write_folder / 'sabine_02.h5'))
+    write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.02),filename=Path(write_folder / 'sabine_03.h5'))
+    write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.02),filename=Path(write_folder / 'sabine_04.h5'))
+    write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.05),filename=Path(write_folder / 'sabine_05.h5'))
     write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.1),filename=Path(write_folder / 'sabine_1.h5'))
     write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.2),filename=Path(write_folder / 'sabine_2.h5'))
     write_freq_ind_mat_from_Yn(convert_Sabs_to_Yn(0.3),filename=Path(write_folder / 'sabine_3.h5'))