Merge pull request librosa#327 from bmcfee/warning-cleanup

[wip] various cleanup and warning avoidance

Author: bmcfee

librosa/core/audio.py

@@ -423,7 +423,7 @@ def autocorrelate(y, max_size=None, axis=-1):
     autocorr = fft.ifft(powspec, axis=axis, overwrite_x=True)
 
     # Slice down to max_size
-    subslice = [Ellipsis] * autocorr.ndim
+    subslice = [slice(None)] * autocorr.ndim
     subslice[axis] = slice(max_size)
 
     autocorr = autocorr[subslice]

librosa/segment.py

@@ -272,7 +272,7 @@ def recurrence_to_lag(rec, pad=True, axis=-1):
     else:
         lag = rec.copy()
 
-    idx_slice = [Ellipsis] * lag.ndim
+    idx_slice = [slice(None)] * lag.ndim
     for i in range(1, t):
         idx_slice[axis] = i
         lag[idx_slice] = np.roll(lag[idx_slice], -i)
@@ -342,12 +342,12 @@ def lag_to_recurrence(lag, axis=-1):
     t = lag.shape[axis]
     lag = lag.copy()
 
-    idx_slice = [Ellipsis] * lag.ndim
+    idx_slice = [slice(None)] * lag.ndim
     for i in range(1, t):
         idx_slice[axis] = i
         lag[idx_slice] = np.roll(lag[idx_slice], i)
 
-    sub_slice = [Ellipsis] * lag.ndim
+    sub_slice = [slice(None)] * lag.ndim
     sub_slice[1 - axis] = slice(t)
     return np.ascontiguousarray(lag[sub_slice].T).T
 
@@ -510,7 +510,7 @@ def subsegment(data, frames, n_segments=4, axis=-1):
         raise ParameterError('n_segments must be a positive integer')
 
     boundaries = []
-    idx_slices = [Ellipsis] * data.ndim
+    idx_slices = [slice(None)] * data.ndim
 
     for seg_start, seg_end in zip(frames[:-1], frames[1:]):
         idx_slices[axis] = slice(seg_start, seg_end)

tests/test_core.py

@@ -436,7 +436,7 @@ def __test_spec(filename, sr, duration, n_fft, hop_length, center):
                 yield __test_audio, test_file, mono, sr, duration
 
             for n_fft in [256, 512, 1024]:
-                for hop_length in [n_fft / 8, n_fft / 4, n_fft / 2]:
+                for hop_length in [n_fft // 8, n_fft // 4, n_fft // 2]:
                     for center in [False, True]:
                         yield (__test_spec, test_file, sr,
                                duration, n_fft, hop_length, center)
@@ -448,7 +448,7 @@ def __test(y, truth, max_size, axis):
 
         ac = librosa.autocorrelate(y, max_size=max_size, axis=axis)
 
-        my_slice = [Ellipsis] * truth.ndim
+        my_slice = [slice(None)] * truth.ndim
         if max_size is not None and max_size <= y.shape[axis]:
             my_slice[axis] = slice(min(max_size, y.shape[axis]))
 
@@ -772,7 +772,7 @@ def __test(times, frames, sr, hop_length, click_freq, click_duration, click, len
         for hop_length in [512, 1024]:
             test_frames = librosa.time_to_frames(test_times, sr=sr, hop_length=hop_length)
 
-            for click in [None, np.ones(sr * 0.1)]:
+            for click in [None, np.ones(sr // 10)]:
 
                 for length in [None, 5 * sr, 15 * sr]:
                     yield __test, test_times, None, sr, hop_length, 1000, 0.1, click, length

tests/test_failures.py

@@ -122,7 +122,7 @@ def test_istft_bad_window():
 
     n_fft = 2 * (D.shape[0] - 1)
 
-    window = np.ones(n_fft / 2)
+    window = np.ones(n_fft // 2)
 
     librosa.istft(D, window=window)
 

tests/test_features.py

@@ -546,7 +546,7 @@ def __test_peaks(tempo, win_length, window, norm):
         eq_(tempogram.shape[1], len(odf))
 
         # Mean over time to wash over the boundary padding effects
-        idx = np.where(librosa.localmax(tempogram.max(axis=1)))[0]
+        idx = np.where(librosa.util.localmax(tempogram.max(axis=1)))[0]
 
         # Indices should all be non-zero integer multiples of spacing
         assert np.allclose(idx, spacing * np.arange(1, 1 + len(idx)))

tests/test_filters.py

@@ -156,6 +156,7 @@ def __test(n, window):
         wdec = librosa.filters.__float_window(window)
 
         if n == int(n):
+            n = int(n)
             assert np.allclose(wdec(n), window(n))
         else:
             wf = wdec(n)