Format nitpick; bump version

penn/convert.py

@@ -45,6 +45,7 @@ def frequency_to_samples(frequency, sample_rate=penn.SAMPLE_RATE):
     """Convert frequency in Hz to number of samples per period"""
     return sample_rate / frequency
 
+
 def frequency_to_midi(frequency):
     """
     Convert frequency to MIDI note number(s)
@@ -53,6 +54,7 @@ def frequency_to_midi(frequency):
     """
     return 12 * (torch.log2(frequency) - torch.log2(torch.tensor(440.0))) + 69
 
+
 def midi_to_frequency(midi):
     """
     Convert MIDI note number to frequency
@@ -61,6 +63,7 @@ def midi_to_frequency(midi):
     """
     return 440.0 * (2.0 ** ((midi - 69.0) / 12.0))
 
+
 ###############################################################################
 # Time conversions
 ###############################################################################

setup.py

@@ -8,7 +8,7 @@
 setup(
     name='penn',
     description='Pitch Estimating Neural Networks (PENN)',
-    version='0.0.13',
+    version='0.0.14',
     author='Max Morrison, Caedon Hsieh, Nathan Pruyne, and Bryan Pardo',
     author_email='interactiveaudiolab@gmail.com',
     url='https://github.com/interactiveaudiolab/penn',
@@ -22,6 +22,7 @@
         ],
         'test': [
             'librosa',     # 0.9.1
+            'pytest',      # 8.2.2
         ]
     },
     install_requires=[

test/test_convert.py

@@ -11,22 +11,27 @@
 
 def test_convert_frequency_to_midi():
     """Test that conversion from Hz to MIDI matches librosa implementation"""
-
     sample_data = torch.tensor([110.0, 220.0, 440.0, 500.0, 880.0])
 
+    # Convert
     penn_midi = penn.convert.frequency_to_midi(sample_data)
-
     librosa_midi = librosa.hz_to_midi(sample_data.numpy())
 
-    assert torch.allclose(penn_midi, torch.tensor(librosa_midi, dtype=torch.float32))
+    # Compare
+    assert torch.allclose(
+        penn_midi,
+        torch.tensor(librosa_midi, dtype=torch.float32))
+
 
 def test_convert_midi_to_frequency():
     """Test that conversion from MIDI to Hz matches librosa implementation"""
+    sample_data = torch.tensor([45.0, 57.0, 69.0, 71.2131, 81.0])
 
-    sample_data = torch.tensor([45.0, 57.0, 69.0, 71.2131, 81.0])   
-
+    # Convert
     penn_frequency = penn.convert.midi_to_frequency(sample_data)
-
     librosa_frequency = librosa.midi_to_hz(sample_data.numpy())
 
-    assert torch.allclose(penn_frequency, torch.tensor(librosa_frequency, dtype=torch.float32))
+    # Compare
+    assert torch.allclose(
+        penn_frequency,
+        torch.tensor(librosa_frequency, dtype=torch.float32))