Adds upsampling test.

examples/directivities/simulation_with_measured_directivity.py

@@ -50,6 +50,7 @@
 """
 
 import matplotlib.pyplot as plt
+
 import pyroomacoustics as pra
 from pyroomacoustics.directivities import (
     Cardioid,

pyroomacoustics/tests/test_resample.py

@@ -1,3 +1,8 @@
+"""
+Very basic tests to verify that all resampling backends
+can be called and are doing their job.
+"""
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pytest
@@ -16,35 +21,78 @@
 def test_downsample(fs_in, fs_out, backend):
     """Idea use a sine above Nyquist of fs_out. It should disappear."""
     assert fs_in > fs_out
-    f_sine = fs_out / 2.0 + (fs_in - fs_out) / 2.0 * 0.95
+    f_sine = fs_out / 2.0 + (fs_in - fs_out) / 2.0 * 0.75
     time = np.arange(fs_in * 10) / fs_in
     signal_in = np.sin(2.0 * np.pi * time * f_sine)
+    signal_in = signal_in * np.hanning(signal_in.shape[0])
     signal_out = pra.resample(signal_in, fs_in, fs_out, backend=backend)
 
-    assert abs(signal_out).mean() < 1e-3
+    assert abs(signal_out).max() < 1e-3
+
+
+@pytest.mark.parametrize(
+    "fs_in, fs_out, backend",
+    [
+        (160, 240, "soxr"),
+        (160, 240, "samplerate"),
+        (160, 240, "scipy"),
+    ],
+)
+def test_upsample(fs_in, fs_out, backend):
+    """Idea use a sine above Nyquist of fs_out. It should disappear."""
+    assert fs_in < fs_out
+
+    # make a random signal
+    signal_in = np.random.randn(10 * fs_in)
+    signal_in = signal_in * np.hanning(signal_in.shape[0])
+
+    signal_out = pra.resample(signal_in, fs_in, fs_out, backend=backend)
+
+    # the test relies on upper frequency being empty
+    f_cut = fs_in / 2.0 + (fs_out - fs_in) / 2.0 * 0.75
+    signal_out_filt = pra.highpass(signal_out, fs_out, fc=f_cut)
+
+    assert abs(signal_out_filt).max() < 1e-3
 
 
 if __name__ == "__main__":
 
+    test_cases = []
+
+    # Test 1 is the eigenmike impulse response
     # Reads the file containing the Eigenmike's directivity measurements
     eigenmike = pra.MeasuredDirectivityFile("EM32_Directivity")
-
     fs_tgt = 16000
     fs_file = eigenmike.fs
+    test_cases.append((fs_file, fs_tgt, eigenmike.impulse_responses[0, 0]))
+
+    # Test 2 is a sine
+    fs_in = 240
+    fs_out = 160
+    f_sine = fs_out / 2.0 + (fs_in - fs_out) / 2.0 * 0.95
+    time = np.arange(fs_in * 10) / fs_in
+    signal_in = np.sin(2.0 * np.pi * time * f_sine)
+    signal_in = signal_in * np.hanning(signal_in.shape[0])
+    test_cases.append((fs_in, fs_out, signal_in))
 
-    print(f"{fs_file=} {fs_tgt=}")
+    # Test 3 is some random noise
+    np.random.seed(0)
+    fs_in = 160
+    fs_out = 240
+    signal_in = np.random.randn(fs_in * 10)
+    test_cases.append((fs_in, fs_out, signal_in))
 
-    rir_original = eigenmike.impulse_responses[10, 20]
-    time_file = np.arange(rir_original.shape[0]) / fs_file
+    for fs_in, fs_out, rir_original in test_cases:
+        time_file = np.arange(rir_original.shape[0]) / fs_file
 
-    rirs = {}
-    for backend in ["soxr", "samplerate", "scipy"]:
-        rirs[backend] = pra.resample(rir_original, fs_file, fs_tgt, backend=backend)
+        rirs = {}
+        for backend in ["soxr", "samplerate", "scipy"]:
+            rirs[backend] = pra.resample(rir_original, fs_file, fs_tgt, backend=backend)
 
-    fig, ax = plt.subplots(1, 1)
-    ax.plot(time_file, rir_original, label="Original")
-    for idx, (backend, rir) in enumerate(rirs.items()):
-        time_rir = np.arange(rir.shape[0]) / fs_tgt
-        ax.plot(time_rir, rir, label=backend, linewidth=(3 - idx))
-    ax.legend()
-    plt.show()
+        fig, ax = plt.subplots(1, 1)
+        ax.plot(time_file, rir_original, label="Original")
+        for idx, (backend, rir) in enumerate(rirs.items()):
+            time_rir = np.arange(rir.shape[0]) / fs_tgt
+            ax.plot(time_rir, rir, label=backend, linewidth=(3 - idx))
+        ax.legend()
+        plt.show()