add tests for new Room class, update for changes in Filter and HRTF

tests/test_binaural.py

@@ -83,6 +83,7 @@ def test_externalize():
                                   (hrtf.sources.vertical_polar[:, 0] == 0))[0][0]
         sound = slab.Binaural.whitenoise(samplerate=hrtf.samplerate)
         filtered = hrtf.data[idx_frontal].apply(sound)
+        filtered = filtered.resize(sound.duration)
         external = sound.externalize()
         assert numpy.abs(filtered.data-external.data).sum() < numpy.abs(filtered.data-sound.data).sum()
         assert numpy.abs(sound.level - external.level).max() < 0.6

tests/test_filter.py

@@ -8,7 +8,7 @@
 def test_low_high_pass():
     for i in range(10):
         sound = slab.Sound.whitenoise(duration=2.0)
-        for kind, fir in itertools.product(["lp", "hp"], [False, True]):
+        for kind, fir in itertools.product(["lp", "hp"], ['FIR', 'IR', 'TF']):
             edge_freq = numpy.random.uniform(100, 2000)
             length = numpy.random.randint(500, 5000)
             filt = slab.Filter.band(frequency=edge_freq, length=length, kind=kind, fir=fir)
@@ -24,7 +24,7 @@ def test_low_high_pass():
 
 def test_band_pass_stop():
     sound = slab.Sound.whitenoise(duration=2.0)
-    for kind, fir in itertools.product(["bp", "bs"], [False, True]):
+    for kind, fir in itertools.product(["bp", "bs"], ['FIR', 'IR', 'TF']):
         lower_edge_freq = numpy.random.uniform(100, 1000)
         higher_edge_freq = lower_edge_freq + numpy.random.uniform(100, 1000)
         length = numpy.random.randint(500, 5000)
@@ -50,7 +50,7 @@ def test_custom_band():
         [1., 0., 1., 0., 0., 1., 0.]
     ]
     for i in range(10):
-        for fir, gain in itertools.product([True, False], gains):
+        for fir, gain in itertools.product(['FIR', 'IR', 'TF'], gains):
             freqs += numpy.random.uniform(1, 10, 7)
             freqs.sort()
             length = numpy.random.randint(500, 5000)
@@ -133,7 +133,7 @@ def test_load_save():
         (0+numpy.random.uniform(100, 2000), 2000+numpy.random.uniform(100, 2000)),
         (0 + numpy.random.uniform(100, 2000), 2000 + numpy.random.uniform(100, 2000))
     ]):
-        for fir in (True, False):
+        for fir in ('FIR', 'IR', 'TF'):
             filt = slab.Filter.band(kind=kind, frequency=freq, fir=fir)
             filt.save(tmpdir/"filt.npy")
             loaded = slab.Filter.load(tmpdir/"filt.npy")

tests/test_hrtf.py

@@ -6,7 +6,7 @@
 
 def test_create_hrtf():
     hrtf1 = slab.HRTF.kemar()
-    assert hrtf1.data[0].fir is True
+    assert hrtf1.data[0].fir == 'IR'
     hrtf2 = slab.HRTF.kemar()
     for i in range(len(hrtf1.data)):
         numpy.testing.assert_equal(hrtf1[i].data, hrtf2[i].data)
@@ -50,7 +50,7 @@ def test_plot_hrtf():
 def test_diffuse_field():
     hrtf = slab.HRTF.kemar()
     dfs = hrtf.diffuse_field_avg()
-    assert dfs.fir is False
+    assert dfs.fir == 'IR'
     assert dfs.n_frequencies == hrtf.data[0].n_taps
     equalized = hrtf.diffuse_field_equalization()
     for i in range(hrtf.n_sources):
@@ -120,8 +120,8 @@ def test_interpolate():
         idx = numpy.random.choice(range(len(hrtf.sources.vertical_polar)))
         azi, ele = hrtf.sources.vertical_polar[idx][0:2]
         method = numpy.random.choice(['nearest', 'bary'])
-        h = hrtf.interpolate(azimuth=azi, elevation=ele, method=method)
-        _, spec_interp = h[0].tf(show=False)
+        filt = hrtf.interpolate(azimuth=azi, elevation=ele, method=method)
+        _, spec_interp = filt.tf(show=False)
         _, spec_origin = hrtf[idx].tf(show=False)
         nearer_channel = 0 if azi-180 < 0 else 1
         # assert numpy.corrcoef(spec_interp[:, nearer_channel], spec_origin[:, nearer_channel]).min() > 0.99
@@ -162,4 +162,47 @@ def test_estimate_hrtf():
     signal2 = slab.Sound.whitenoise(duration=duration-0.1, samplerate=fs-800)
     hrtf2 = slab.HRTF.estimate_hrtf(recordings, signal2, sources)
     assert hrtf1.samplerate == hrtf2.samplerate
-    assert hrtf1[0].n_samples == hrtf2[0].n_samples
+    assert hrtf1[0].n_samples == hrtf2[0].n_samples
+
+def test_create_room():
+    for i in range(10):
+        size = numpy.random.randint(2, 51, 3)
+        listener = size / 2
+        source_cart = numpy.array([numpy.random.randint(1,i) for i in size])
+        source_pol = slab.HRTF._get_coordinates(source_cart-listener, 'cartesian')[1][0]
+        source_pol[0] -= 180
+        max_echos = numpy.random.randint(1, 250)
+        room = slab.Room(size=size, listener=listener, source=source_pol, order=1, max_echos=max_echos)
+        numpy.testing.assert_almost_equal(source_pol[1:], room.image_locs[0,1:], decimal=0)
+    room = slab.Room(size=[3,3,3], listener=[1,1,1])  # lists work?
+    print(room)  # __str__ ok?
+
+def test_reverb_time():
+    room = slab.Room()
+    t = room.reverb_time()
+    t = room.reverb_time([5,5,5])  # list ok?
+    assert isinstance(t, float)
+    for _ in range(10):
+        size = numpy.random.randint(1, 100, 3)
+        t = room.reverb_time(size=size)
+
+def test_hrir():
+    for i in range(5):
+        size = numpy.random.randint(2, 51, 3)
+        listener = size / 2
+        source_cart = numpy.array([numpy.random.randint(1,i) for i in size])
+        source_pol = slab.HRTF._get_coordinates(source_cart-listener, 'cartesian')[1][0]
+        source_pol[0] -= 180
+        max_echos = numpy.random.randint(1, 50)
+        room = slab.Room(size=size, listener=listener, source=source_pol, order=1, max_echos=max_echos)
+        hrir = room.hrir()
+    for i in range(5):
+        tail = room.reverb(t_reverb=numpy.random.random()*3)  # up to T60 = 3s
+        hrir = room.hrir(reverb=tail)  # try hrir with premade tail
+    assert isinstance(hrir, slab.Filter)
+    for _ in range(5):
+        trim = numpy.random.randint(1, hrir.n_samples)
+        hrir_tmp = room.hrir(trim=trim)  # try integer trim
+        hrir_tmp = room.hrir(trim=numpy.random.random())  # try float trim
+    sig = slab.Sound.vowel(duration=0.2, samplerate=hrir.samplerate)
+    out = hrir.apply(sig)