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librosa · Feb 15, 2017 · 7e2659c · 7e2659c
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diff --git a/README.md b/README.md
@@ -2,22 +2,17 @@
 
 A collection of code examples demonstrating some of librosa's functionality.
 
-## Examples
+The rendered output of all examples can be found at:
 
+https://librosa.github.io/librosa_gallery/
 
-## Notebooks
+## Contribute
 
-You can explore our Jupyter Notebooks to find out what functionality `librosa` offers you.
-As some example contain audio examples, you can use the `nbviewer` to see the notebooks.
+If you want to contribute a cool application or educational example, you need
+to take care of a few steps:
 
-Use these links to get directly to the notebooks:
-* Intro: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/01-Intro_demo.ipynb
-* Audio Effects: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/02-Audio_effects.ipynb
-* HPRSS: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/02-HPRSS.ipynb
-* Vocal Separation: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/03-Vocal_separation.ipynb
-* Music Segmentation: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/04-Laplacian segmentation.ipynb
-* Superflux Onsets: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/05-Superflux_Onsets.ipynb
-* Rhythm Analysis: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/06-Rhythm_Analysis_Mellin_Transform.ipynb
-* Enhanced Chroma: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/07-Enhanced_Chroma.ipynb
-* Overriding Presets: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/08-Overriding_Defaults_with_Presets.ipynb
-* Music Synchronization: https://nbviewer.jupyter.org/github/librosa/librosa_gallery/blob/master/notebooks/09-Music_Synchronization.ipynb
+* Meet all dependencies (listed in `requirements.txt`).
+* It's easy to start with a Jupyter Notebook (optional).
+* We use `sphinx_gallery` to create the output.
+* Create a new python file in `examples`.
+* Go to `docs/` and run `make html`.
diff --git a/examples/plot_chroma.py b/examples/plot_chroma.py
@@ -19,7 +19,7 @@
 
 # Code source: Brian McFee
 # License: ISC
-#sphinx_gallery_thumbnail_number = 6
+# sphinx_gallery_thumbnail_number = 6
 
 from __future__ import print_function
 import numpy as np
@@ -35,8 +35,6 @@
 y, sr = librosa.load('audio/Karissa_Hobbs_-_09_-_Lets_Go_Fishin.mp3')
 
 
-
-
 #######################################
 # First, let's plot the original chroma
 chroma_orig = librosa.feature.chroma_cqt(y=y, sr=sr)
@@ -45,15 +43,15 @@
 idx = [slice(None), slice(*list(librosa.time_to_frames([45, 60])))]
 
 # And for comparison, we'll show the CQT matrix as well.
-C = np.abs(librosa.cqt(y=y, sr=sr, bins_per_octave=12*3, n_bins=7*12*3, real=False))
+C = np.abs(librosa.cqt(y=y, sr=sr, bins_per_octave=12*3, n_bins=7*12*3))
 
 
 plt.figure(figsize=(12, 4))
-plt.subplot(2,1,1)
+plt.subplot(2, 1, 1)
 librosa.display.specshow(librosa.amplitude_to_db(C, ref=np.max)[idx],
                          y_axis='cqt_note', bins_per_octave=12*3)
 plt.colorbar()
-plt.subplot(2,1,2)
+plt.subplot(2, 1, 2)
 librosa.display.specshow(chroma_orig[idx], y_axis='chroma')
 plt.colorbar()
 plt.ylabel('Original')
@@ -68,13 +66,13 @@
 
 plt.figure(figsize=(12, 4))
 
-plt.subplot(2,1,1)
+plt.subplot(2, 1, 1)
 librosa.display.specshow(chroma_orig[idx], y_axis='chroma')
 plt.colorbar()
 plt.ylabel('Original')
 
 
-plt.subplot(2,1,2)
+plt.subplot(2, 1, 2)
 librosa.display.specshow(chroma_os[idx], y_axis='chroma', x_axis='time')
 plt.colorbar()
 plt.ylabel('3x-over')
@@ -91,12 +89,12 @@
 
 plt.figure(figsize=(12, 4))
 
-plt.subplot(2,1,1)
+plt.subplot(2, 1, 1)
 librosa.display.specshow(chroma_os[idx], y_axis='chroma')
 plt.colorbar()
 plt.ylabel('3x-over')
 
-plt.subplot(2,1,2)
+plt.subplot(2, 1, 2)
 librosa.display.specshow(chroma_os_harm[idx], y_axis='chroma', x_axis='time')
 plt.colorbar()
 plt.ylabel('Harmonic')
@@ -115,12 +113,12 @@
 
 plt.figure(figsize=(12, 4))
 
-plt.subplot(2,1,1)
+plt.subplot(2, 1, 1)
 librosa.display.specshow(chroma_os_harm[idx], y_axis='chroma')
 plt.colorbar()
 plt.ylabel('Harmonic')
 
-plt.subplot(2,1,2)
+plt.subplot(2, 1, 2)
 librosa.display.specshow(chroma_filter[idx], y_axis='chroma', x_axis='time')
 plt.colorbar()
 plt.ylabel('Non-local')
@@ -135,12 +133,12 @@
 
 plt.figure(figsize=(12, 4))
 
-plt.subplot(2,1,1)
+plt.subplot(2, 1, 1)
 librosa.display.specshow(chroma_filter[idx], y_axis='chroma')
 plt.colorbar()
 plt.ylabel('Non-local')
 
-plt.subplot(2,1,2)
+plt.subplot(2, 1, 2)
 librosa.display.specshow(chroma_smooth[idx], y_axis='chroma', x_axis='time')
 plt.colorbar()
 plt.ylabel('Median-filtered')
@@ -151,21 +149,18 @@
 # A final comparison between the CQT, original chromagram
 # and the result of our filtering.
 plt.figure(figsize=(12, 8))
-plt.subplot(3,1,1)
+plt.subplot(3, 1, 1)
 librosa.display.specshow(librosa.amplitude_to_db(C, ref=np.max)[idx],
                          y_axis='cqt_note', bins_per_octave=12*3)
 plt.colorbar()
 plt.ylabel('CQT')
-plt.subplot(3,1,2)
+plt.subplot(3, 1, 2)
 librosa.display.specshow(chroma_orig[idx], y_axis='chroma')
 plt.ylabel('Original')
 plt.colorbar()
-plt.subplot(3,1,3)
+plt.subplot(3, 1, 3)
 librosa.display.specshow(chroma_smooth[idx], y_axis='chroma', x_axis='time')
 plt.ylabel('Processed')
 plt.colorbar()
 plt.tight_layout()
 plt.show()
-
-
-
diff --git a/examples/plot_hprss.py b/examples/plot_hprss.py
@@ -7,7 +7,8 @@
 This notebook illustrates how to separate an audio signal into
 its harmonic and percussive components.
 
-We'll compare the original median-filtering based approach of `Fitzgerald, 2010 <http://arrow.dit.ie/cgi/viewcontent.cgi?article=1078&context=argcon>`_
+We'll compare the original median-filtering based approach of
+`Fitzgerald, 2010 <http://arrow.dit.ie/cgi/viewcontent.cgi?article=1078&context=argcon>`_
 and its margin-based extension due to `Dreidger, Mueller and Disch, 2014
 <http://www.terasoft.com.tw/conf/ismir2014/proceedings/T110_127_Paper.pdf>`_.
 
@@ -45,25 +46,25 @@
 
 plt.figure(figsize=(12, 8))
 
-plt.subplot(3,1,1)
+plt.subplot(3, 1, 1)
 librosa.display.specshow(librosa.amplitude_to_db(D, ref=rp), y_axis='log')
 plt.colorbar()
 plt.title('Full spectrogram')
 
-plt.subplot(3,1,2)
+plt.subplot(3, 1, 2)
 librosa.display.specshow(librosa.amplitude_to_db(D_harmonic, ref=rp), y_axis='log')
 plt.colorbar()
 plt.title('Harmonic spectrogram')
 
-plt.subplot(3,1,3)
+plt.subplot(3, 1, 3)
 librosa.display.specshow(librosa.amplitude_to_db(D_percussive, ref=rp), y_axis='log', x_axis='time')
 plt.colorbar()
 plt.title('Percussive spectrogram')
 plt.tight_layout()
 
 
 #################################################################################
-# The default HPSS above assigns energy to each time-frequency bin according to 
+# The default HPSS above assigns energy to each time-frequency bin according to
 # whether a horizontal (harmonic) or vertical (percussive) filter responds higher
 # at that position.
 #
@@ -89,52 +90,52 @@
 # components, and vocals have been suppressed in the percussive components.
 plt.figure(figsize=(10, 10))
 
-plt.subplot(5,2,1)
+plt.subplot(5, 2, 1)
 librosa.display.specshow(librosa.amplitude_to_db(D_harmonic, ref=rp), y_axis='log')
 plt.title('Harmonic')
 plt.yticks([])
 plt.ylabel('margin=1')
 
-plt.subplot(5,2,2)
+plt.subplot(5, 2, 2)
 librosa.display.specshow(librosa.amplitude_to_db(D_percussive, ref=rp), y_axis='log')
 plt.title('Percussive')
-plt.yticks([]) ,plt.ylabel('')
+plt.yticks([]), plt.ylabel('')
 
-plt.subplot(5,2,3)
+plt.subplot(5, 2, 3)
 librosa.display.specshow(librosa.amplitude_to_db(D_harmonic2, ref=rp), y_axis='log')
 plt.yticks([])
 plt.ylabel('margin=2')
 
-plt.subplot(5,2,4)
+plt.subplot(5, 2, 4)
 librosa.display.specshow(librosa.amplitude_to_db(D_percussive2, ref=rp), y_axis='log')
 plt.yticks([]) ,plt.ylabel('')
 
-plt.subplot(5,2,5)
+plt.subplot(5, 2, 5)
 librosa.display.specshow(librosa.amplitude_to_db(D_harmonic4, ref=rp), y_axis='log')
 plt.yticks([])
 plt.ylabel('margin=4')
 
-plt.subplot(5,2,6)
+plt.subplot(5, 2, 6)
 librosa.display.specshow(librosa.amplitude_to_db(D_percussive4, ref=rp), y_axis='log')
-plt.yticks([]) ,plt.ylabel('')
+plt.yticks([]), plt.ylabel('')
 
-plt.subplot(5,2,7)
+plt.subplot(5, 2, 7)
 librosa.display.specshow(librosa.amplitude_to_db(D_harmonic8, ref=rp), y_axis='log')
 plt.yticks([])
 plt.ylabel('margin=8')
 
-plt.subplot(5,2,8)
+plt.subplot(5, 2, 8)
 librosa.display.specshow(librosa.amplitude_to_db(D_percussive8, ref=rp), y_axis='log')
-plt.yticks([]) ,plt.ylabel('')
+plt.yticks([]), plt.ylabel('')
 
-plt.subplot(5,2,9)
+plt.subplot(5, 2, 9)
 librosa.display.specshow(librosa.amplitude_to_db(D_harmonic16, ref=rp), y_axis='log')
 plt.yticks([])
 plt.ylabel('margin=16')
 
-plt.subplot(5,2,10)
+plt.subplot(5, 2, 10)
 librosa.display.specshow(librosa.amplitude_to_db(D_percussive16, ref=rp), y_axis='log')
-plt.yticks([]) ,plt.ylabel('')
+plt.yticks([]), plt.ylabel('')
 
 plt.tight_layout()
 plt.show()
diff --git a/examples/plot_segmentation.py b/examples/plot_segmentation.py
@@ -4,8 +4,8 @@
 Laplacian segmentation
 ======================
 
-This notebook implements the laplacian segmentation method of 
-`McFee and Ellis, 2014 <http://bmcfee.github.io/papers/ismir2014_spectral.pdf>`_, 
+This notebook implements the laplacian segmentation method of
+`McFee and Ellis, 2014 <http://bmcfee.github.io/papers/ismir2014_spectral.pdf>`_,
 with a couple of minor stability improvements.
 
 Throughout the example, we will refer to equations in the paper by number, so it will be
@@ -106,13 +106,13 @@
 ###########################################################
 # Plot the resulting graphs (Figure 1, left and center)
 plt.figure(figsize=(8, 4))
-plt.subplot(1,3,1)
+plt.subplot(1, 3, 1)
 librosa.display.specshow(Rf, cmap='inferno_r')
 plt.title('Recurrence similarity')
-plt.subplot(1,3,2)
+plt.subplot(1, 3, 2)
 librosa.display.specshow(R_path, cmap='inferno_r')
 plt.title('Path similarity')
-plt.subplot(1,3,3)
+plt.subplot(1, 3, 3)
 librosa.display.specshow(A, cmap='inferno_r')
 plt.title('Combined graph')
 plt.tight_layout()
@@ -123,7 +123,7 @@
 L = scipy.sparse.csgraph.laplacian(A, normed=True)
 
 
-# and its spectral decomposition 
+# and its spectral decomposition
 evals, evecs = scipy.linalg.eigh(L)
 
 
@@ -146,18 +146,17 @@
 # Plot the resulting representation (Figure 1, center and right)
 
 plt.figure(figsize=(8, 4))
-plt.subplot(1,2,2)
+plt.subplot(1, 2, 2)
 librosa.display.specshow(Rf, cmap='inferno_r')
 plt.title('Recurrence matrix')
 
-plt.subplot(1,2,1)
+plt.subplot(1, 2, 1)
 librosa.display.specshow(X)
 plt.title('Structure components')
 plt.ylabel('Time')
 plt.tight_layout()
 
 
-
 #############################################################
 # Let's use these k components to cluster beats into segments
 # (Algorithm 1)
@@ -170,21 +169,20 @@
 plt.figure(figsize=(12, 4))
 colors = plt.get_cmap('Paired', k)
 
-plt.subplot(1,3,2)
+plt.subplot(1, 3, 2)
 librosa.display.specshow(Rf, cmap='inferno_r')
 plt.title('Recurrence matrix')
-plt.subplot(1,3,1)
+plt.subplot(1, 3, 1)
 librosa.display.specshow(X)
 plt.title('Structure components')
 plt.ylabel('Time')
-plt.subplot(1,3,3)
+plt.subplot(1, 3, 3)
 librosa.display.specshow(np.atleast_2d(seg_ids).T, cmap=colors)
 plt.title('Estimated segments')
 plt.colorbar(ticks=range(k))
 plt.tight_layout()
 
 
-
 ###############################################################
 # Locate segment boundaries from the label sequence
 bound_beats = 1 + np.flatnonzero(seg_ids[:-1] != seg_ids[1:])
@@ -207,7 +205,7 @@
 # And plot the final segmentation over original CQT
 
 
-#sphinx_gallery_thumbnail_number = 5
+# sphinx_gallery_thumbnail_number = 5
 
 import matplotlib.patches as patches
 plt.figure(figsize=(12, 4))
@@ -231,4 +229,3 @@
 
 plt.tight_layout()
 plt.show()
-