Merge 8e95180 into dff4432

.travis.yml

@@ -1,5 +1,3 @@
-sudo: false
-
 cache:
   directories:
   - $HOME/env
@@ -9,17 +7,21 @@ language: python
 notifications:
   email: false
 
-python:
-    - 3.6
-
-matrix:
+jobs:
     include:
+        - python: 3.6
+          os: linux
+          dist: xenial
         - python: 3.7
+          os: linux
           dist: xenial
+        - os: osx
+          language: generic
+          osx_image: xcode11
 
 before_install:
     - bash .travis_dependencies.sh
-    - export PATH="$HOME/env/miniconda$TRAVIS_PYTHON_VERSION/bin:$PATH";
+    - export PATH="$HOME/env/miniconda-$TRAVIS_OS_NAME$TRAVIS_PYTHON_VERSION/bin:$PATH";
     - hash -r
     - source activate test-environment
     - conda list

.travis_dependencies.sh

@@ -17,14 +17,18 @@ conda_create ()
     conda update --all
 }
 
-src="$HOME/env/miniconda$TRAVIS_PYTHON_VERSION"
+src="$HOME/env/miniconda-$TRAVIS_OS_NAME$TRAVIS_PYTHON_VERSION"
 if [ ! -d "$src" ]; then
     mkdir -p $HOME/env
     pushd $HOME/env
 
         # Download miniconda packages
-        wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
-
+        if [ "$TRAVIS_OS_NAME" = "osx" ]; then
+            wget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh;
+        fi
+        if [ "$TRAVIS_OS_NAME" = "linux" ]; then
+            wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+        fi
         # Install both environments
         bash miniconda.sh -b -p $src
 
@@ -33,7 +37,7 @@ if [ ! -d "$src" ]; then
 
         source activate $ENV_NAME
 
-        conda install -c conda-forge ffmpeg pysoundfile python-coveralls
+        conda install -c conda-forge ffmpeg pysoundfile coveralls
 
         source deactivate
     popd

librosa/display.py

@@ -950,6 +950,9 @@ def __decorate_axis(axis, ax_type):
         axis.set_label_text('')
         axis.set_ticks([])
 
+    else:
+        raise ParameterError('Unsupported axis type: {}'.format(ax_type))
+
 
 def __coord_fft_hz(n, sr=22050, **_kwargs):
     '''Get the frequencies for FFT bins'''

librosa/feature/spectral.py

@@ -838,8 +838,6 @@ def rms(y=None, S=None, frame_length=2048, hop_length=512,
     >>> plt.show()
 
     '''
-    if y is not None and S is not None:
-        raise ValueError('Either `y` or `S` should be input.')
     if y is not None:
         y = to_mono(y)
         if center:
@@ -873,7 +871,7 @@ def rms(y=None, S=None, frame_length=2048, hop_length=512,
         # Calculate power
         power = 2 * np.sum(x, axis=0, keepdims=True) / frame_length**2
     else:
-        raise ValueError('Either `y` or `S` must be input.')
+        raise ParameterError('Either `y` or `S` must be input.')
 
     return np.sqrt(power)
 

librosa/filters.py

@@ -199,7 +199,7 @@ def mel(sr, n_fft, n_mels=128, fmin=0.0, fmax=None, htk=False,
                       FutureWarning)
 
     elif norm not in (None, 1, 'slaney', np.inf):
-        raise ParameterError("Unsupported norm={}, must be one of: {None, 1, 'slaney', np.inf}".format(repr(norm)))
+        raise ParameterError("Unsupported norm={}, must be one of: None, 1, 'slaney', np.inf".format(repr(norm)))
 
     # Initialize the weights
     n_mels = int(n_mels)

librosa/segment.py

@@ -161,7 +161,7 @@ def cross_similarity(data, data_ref, k=None, metric='euclidean',
     data = np.atleast_2d(data)
 
     if data_ref.shape[0] != data.shape[0]:
-        raise ValueError("data_ref and data must have the same first dimension")
+        raise ParameterError("data_ref and data must have the same first dimension")
 
     # swap data axes so the feature axis is last
     data_ref = np.swapaxes(data_ref, -1, 0)

librosa/sequence.py

@@ -939,7 +939,7 @@ def viterbi(prob, transition, p_init=None, return_logp=False):
     if p_init is None:
         p_init = np.empty(n_states)
         p_init.fill(1./n_states)
-    elif np.any(p_init < 0) or not np.allclose(p_init.sum(), 1):
+    elif np.any(p_init < 0) or not np.allclose(p_init.sum(), 1) or p_init.shape != (n_states,):
         raise ParameterError('Invalid initial state distribution: '
                              'p_init={}'.format(p_init))
 
@@ -1126,7 +1126,7 @@ def viterbi_discriminative(prob, transition, p_state=None, p_init=None, return_l
     if p_init is None:
         p_init = np.empty(n_states)
         p_init.fill(1./n_states)
-    elif np.any(p_init < 0) or not np.allclose(p_init.sum(), 1):
+    elif np.any(p_init < 0) or not np.allclose(p_init.sum(), 1) or p_init.shape != (n_states,):
         raise ParameterError('Invalid initial state distribution: '
                              'p_init={}'.format(p_init))
 

setup.py

@@ -57,7 +57,8 @@
                   'mock',
                   'pytest-mpl',
                   'pytest-cov',
-                  'pytest < 4'],
+                  'pytest',
+                  'contextlib2'],
         'display': ['matplotlib >= 1.5'],
     }
 )