Revision 4:198d466df53e
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{
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": {
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"codemirror_mode": {
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.4.3" |
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}, |
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"name": "" |
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}, |
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"nbformat": 3, |
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"nbformat_minor": 0, |
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"worksheets": [ |
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{
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"cells": [ |
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{
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"cell_type": "code", |
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"collapsed": true, |
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"input": [ |
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"import vamp" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 1 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"import librosa" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [ |
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{
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"output_type": "stream", |
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"stream": "stderr", |
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"text": [ |
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"/usr/lib/python3.4/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n", |
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" warnings.warn('Could not import scikits.samplerate. '\n"
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] |
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} |
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], |
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"prompt_number": 3 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": true, |
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"input": [ |
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"import matplotlib.pyplot as plt" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 12 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": true, |
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"input": [ |
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"%matplotlib inline" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 13 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"vamp.list_plugins()" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [ |
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{
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"metadata": {},
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"output_type": "pyout", |
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"prompt_number": 2, |
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"text": [ |
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"['bbc-vamp-plugins:bbc-energy',\n", |
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" 'bbc-vamp-plugins:bbc-intensity',\n", |
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" 'bbc-vamp-plugins:bbc-peaks',\n", |
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" 'bbc-vamp-plugins:bbc-rhythm',\n", |
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" 'bbc-vamp-plugins:bbc-spectral-contrast',\n", |
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" 'bbc-vamp-plugins:bbc-spectral-flux',\n", |
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" 'bbc-vamp-plugins:bbc-speechmusic-segmenter',\n", |
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" 'chp:constrainedharmonicpeak',\n", |
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" 'cqvamp:cqchromavamp',\n", |
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" 'cqvamp:cqvamp',\n", |
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" 'cqvamp:cqvampmidi',\n", |
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" 'match-vamp-plugin:match',\n", |
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" 'nnls-chroma:chordino',\n", |
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" 'nnls-chroma:nnls-chroma',\n", |
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" 'nnls-chroma:tuning',\n", |
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" 'pyin:localcandidatepyin',\n", |
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" 'pyin:pyin',\n", |
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" 'pyin:yin',\n", |
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" 'pyin:yinfc',\n", |
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" 'qm-vamp-plugins:qm-adaptivespectrogram',\n", |
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" 'qm-vamp-plugins:qm-barbeattracker',\n", |
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" 'qm-vamp-plugins:qm-chromagram',\n", |
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" 'qm-vamp-plugins:qm-constantq',\n", |
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" 'qm-vamp-plugins:qm-dwt',\n", |
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" 'qm-vamp-plugins:qm-keydetector',\n", |
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" 'qm-vamp-plugins:qm-mfcc',\n", |
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" 'qm-vamp-plugins:qm-onsetdetector',\n", |
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" 'qm-vamp-plugins:qm-segmenter',\n", |
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" 'qm-vamp-plugins:qm-similarity',\n", |
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" 'qm-vamp-plugins:qm-tempotracker',\n", |
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" 'qm-vamp-plugins:qm-tonalchange',\n", |
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" 'qm-vamp-plugins:qm-transcription',\n", |
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" 'segmentino:segmentino',\n", |
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" 'silvet:silvet',\n", |
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" 'simple-cepstrum:simple-cepstrum',\n", |
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" 'tempogram:tempogram',\n", |
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" 'vamp-aubio:aubionotes',\n", |
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" 'vamp-aubio:aubioonset',\n", |
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" 'vamp-aubio:aubiopitch',\n", |
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" 'vamp-aubio:aubiosilence',\n", |
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" 'vamp-aubio:aubiotempo',\n", |
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" 'vamp-example-plugins:amplitudefollower',\n", |
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" 'vamp-example-plugins:fixedtempo',\n", |
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" 'vamp-example-plugins:percussiononsets',\n", |
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" 'vamp-example-plugins:powerspectrum',\n", |
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" 'vamp-example-plugins:spectralcentroid',\n", |
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" 'vamp-example-plugins:zerocrossing',\n", |
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" 'vamp-libxtract:amdf',\n", |
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" 'vamp-libxtract:asdf',\n", |
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" 'vamp-libxtract:autocorrelation',\n", |
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" 'vamp-libxtract:average_deviation',\n", |
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" 'vamp-libxtract:bark_coefficients',\n", |
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" 'vamp-libxtract:crest',\n", |
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" 'vamp-libxtract:dct',\n", |
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" 'vamp-libxtract:f0',\n", |
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" 'vamp-libxtract:failsafe_f0',\n", |
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" 'vamp-libxtract:flatness',\n", |
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" 'vamp-libxtract:harmonic_spectrum',\n", |
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" 'vamp-libxtract:highest_value',\n", |
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" 'vamp-libxtract:irregularity_j',\n", |
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" 'vamp-libxtract:irregularity_k',\n", |
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" 'vamp-libxtract:kurtosis',\n", |
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" 'vamp-libxtract:loudness',\n", |
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" 'vamp-libxtract:lowest_value',\n", |
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" 'vamp-libxtract:mean',\n", |
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" 'vamp-libxtract:mfcc',\n", |
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" 'vamp-libxtract:noisiness',\n", |
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" 'vamp-libxtract:nonzero_count',\n", |
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" 'vamp-libxtract:odd_even_ratio',\n", |
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" 'vamp-libxtract:peak_spectrum',\n", |
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" 'vamp-libxtract:rms_amplitude',\n", |
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" 'vamp-libxtract:rolloff',\n", |
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" 'vamp-libxtract:sharpness',\n", |
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" 'vamp-libxtract:skewness',\n", |
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" 'vamp-libxtract:smoothness',\n", |
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" 'vamp-libxtract:spectral_centroid',\n", |
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" 'vamp-libxtract:spectral_inharmonicity',\n", |
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" 'vamp-libxtract:spectral_kurtosis',\n", |
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" 'vamp-libxtract:spectral_skewness',\n", |
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" 'vamp-libxtract:spectral_slope',\n", |
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" 'vamp-libxtract:spectral_standard_deviation',\n", |
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" 'vamp-libxtract:spectral_variance',\n", |
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" 'vamp-libxtract:spectrum',\n", |
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" 'vamp-libxtract:spread',\n", |
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" 'vamp-libxtract:standard_deviation',\n", |
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" 'vamp-libxtract:sum',\n", |
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" 'vamp-libxtract:tonality',\n", |
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" 'vamp-libxtract:tristimulus_1',\n", |
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" 'vamp-libxtract:tristimulus_2',\n", |
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" 'vamp-libxtract:tristimulus_3',\n", |
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" 'vamp-libxtract:variance',\n", |
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" 'vamp-libxtract:zcr',\n", |
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" 'vamp-rubberband:rubberband',\n", |
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" 'vamp-test-plugin:vamp-test-plugin',\n", |
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" 'vamp-test-plugin:vamp-test-plugin-freq']" |
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] |
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} |
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], |
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"prompt_number": 2 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": true, |
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"input": [ |
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"librosa.load?" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 4 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": true, |
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"input": [ |
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"vamp.collect?" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 5 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"data, rate = librosa.load(\"data/Music/piano-scale.wav\", 44100)" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 6 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"rate" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [ |
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{
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"metadata": {},
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"output_type": "pyout", |
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"prompt_number": 7, |
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"text": [ |
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"44100" |
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] |
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} |
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], |
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"prompt_number": 7 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"out = vamp.collect(data, rate, \"nnls-chroma:nnls-chroma\", output = \"chroma\", process_timestamp_method = vamp.vampyhost.SHIFT_DATA)" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 8 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": true, |
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"input": [ |
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"out2 = vamp.collect(data, rate, \"qm-vamp-plugins:qm-chromagram\", output = \"chromagram\", process_timestamp_method = vamp.vampyhost.SHIFT_DATA)" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 20 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"out" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [ |
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{
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"metadata": {},
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"output_type": "pyout", |
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"prompt_number": 9, |
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"text": [ |
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"{'matrix': ( 0.046439909,\n",
|
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" array([[ 0. , 0.23465359, 0. , ..., 0.08958804,\n", |
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" 1.2546041 , 0.28356144],\n", |
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" [ 0. , 0.39042693, 0.00177656, ..., 0.09287024,\n", |
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" 1.21442342, 0.1496222 ],\n", |
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" [ 0. , 0.52906221, 0.00219605, ..., 0.05372851,\n", |
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" 1.21060038, 0.06183077],\n", |
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" ..., \n", |
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" [ 1.29977727, 0. , 0.16230981, ..., 0.37326023,\n", |
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" 0.11922166, 0.38857883],\n", |
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" [ 0.44108227, 0.0647972 , 0.77132535, ..., 0.23604083,\n", |
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" 0.10995618, 0.39924181],\n", |
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" [ 0.02858954, 0.54281044, 0.70990545, ..., 0.17574874,\n", |
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" 0.15729675, 0.1570534 ]], dtype=float32))}" |
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] |
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} |
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], |
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"prompt_number": 9 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"out2" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [ |
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{
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"metadata": {},
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"output_type": "pyout", |
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"prompt_number": 21, |
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"text": [ |
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"{'matrix': ( 0.046439909,\n",
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" array([[ 1.46356169e-02, 1.00284042e-02, 6.13229116e-03, ...,\n", |
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" 6.37824275e-03, 1.12555586e-02, 1.49770780e-02],\n", |
|
| 319 |
" [ 7.01050684e-02, 2.52935681e-02, 2.90200301e-03, ...,\n", |
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| 320 |
" 9.44275782e-03, 1.47002591e-02, 4.58300859e-02],\n", |
|
| 321 |
" [ 7.85880536e-02, 3.01746875e-02, 1.63351605e-03, ...,\n", |
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" 7.24899024e-03, 1.12466933e-02, 4.71907593e-02],\n", |
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" ..., \n", |
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" [ 8.20661895e-03, 3.76351969e-03, 1.84410776e-04, ...,\n", |
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| 325 |
" 7.19304080e-05, 1.89329934e-04, 3.95213813e-03],\n", |
|
| 326 |
" [ 2.96376343e-03, 1.39251188e-03, 1.14867362e-04, ...,\n", |
|
| 327 |
" 7.17425646e-05, 8.96208439e-05, 1.87694619e-03],\n", |
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| 328 |
" [ 1.85929565e-03, 9.64637962e-04, 1.16197378e-04, ...,\n", |
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" 5.40428482e-05, 4.73081054e-05, 1.41351460e-03]], dtype=float32))}" |
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] |
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} |
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], |
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"prompt_number": 21 |
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}, |
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{
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"step, chroma = out[\"matrix\"]" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [], |
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"prompt_number": 22 |
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}, |
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{
|
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"plt.imshow(chroma.transpose(), origin=\"lower\")" |
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], |
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"language": "python", |
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"metadata": {},
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"outputs": [ |
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{
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"metadata": {},
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"output_type": "pyout", |
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"prompt_number": 14, |
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"text": [ |
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"<matplotlib.image.AxesImage at 0x7f6bde77d588>" |
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] |
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}, |
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{
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"metadata": {},
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"output_type": "display_data", |
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IMExpTgzdky8RVzkkfa41dvi9Zo9vHV3i+le3yZ47A1cSgZfsCNxQqjonfdir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\nTkSuQmCC\n", |
|
| 367 |
"text": [ |
|
| 368 |
"<matplotlib.figure.Figure at 0x7f6bf7818748>" |
|
| 369 |
] |
|
| 370 |
} |
|
| 371 |
], |
|
| 372 |
"prompt_number": 14 |
|
| 373 |
}, |
|
| 374 |
{
|
|
| 375 |
"cell_type": "code", |
|
| 376 |
"collapsed": false, |
|
| 377 |
"input": [ |
|
| 378 |
"plt.imshow(out2[\"matrix\"][1].transpose(), origin=\"lower\")" |
|
| 379 |
], |
|
| 380 |
"language": "python", |
|
| 381 |
"metadata": {},
|
|
| 382 |
"outputs": [ |
|
| 383 |
{
|
|
| 384 |
"metadata": {},
|
|
| 385 |
"output_type": "pyout", |
|
| 386 |
"prompt_number": 25, |
|
| 387 |
"text": [ |
|
| 388 |
"<matplotlib.image.AxesImage at 0x7f6bde702a20>" |
|
| 389 |
] |
|
| 390 |
}, |
|
| 391 |
{
|
|
| 392 |
"metadata": {},
|
|
| 393 |
"output_type": "display_data", |
|
| 394 |
"png": 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M5lQ+maj3lwK49BIc7axy4thVRuE8q+MzBDsNnass9YSUapIOyakKo1in4j9S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\n6AAAAABJRU5ErkJggg==\n", |
|
| 395 |
"text": [ |
|
| 396 |
"<matplotlib.figure.Figure at 0x7f6bde78fc88>" |
|
| 397 |
] |
|
| 398 |
} |
|
| 399 |
], |
|
| 400 |
"prompt_number": 25 |
|
| 401 |
}, |
|
| 402 |
{
|
|
| 403 |
"cell_type": "code", |
|
| 404 |
"collapsed": true, |
|
| 405 |
"input": [ |
|
| 406 |
"import csv" |
|
| 407 |
], |
|
| 408 |
"language": "python", |
|
| 409 |
"metadata": {},
|
|
| 410 |
"outputs": [], |
|
| 411 |
"prompt_number": 26 |
|
| 412 |
}, |
|
| 413 |
{
|
|
| 414 |
"cell_type": "code", |
|
| 415 |
"collapsed": true, |
|
| 416 |
"input": [ |
|
| 417 |
"out_file = open('features.csv', 'w')"
|
|
| 418 |
], |
|
| 419 |
"language": "python", |
|
| 420 |
"metadata": {},
|
|
| 421 |
"outputs": [], |
|
| 422 |
"prompt_number": 27 |
|
| 423 |
}, |
|
| 424 |
{
|
|
| 425 |
"cell_type": "code", |
|
| 426 |
"collapsed": true, |
|
| 427 |
"input": [ |
|
| 428 |
"writer = csv.writer(out_file)" |
|
| 429 |
], |
|
| 430 |
"language": "python", |
|
| 431 |
"metadata": {},
|
|
| 432 |
"outputs": [], |
|
| 433 |
"prompt_number": 28 |
|
| 434 |
}, |
|
| 435 |
{
|
|
| 436 |
"cell_type": "code", |
|
| 437 |
"collapsed": true, |
|
| 438 |
"input": [ |
|
| 439 |
"writer.writerows(chroma)" |
|
| 440 |
], |
|
| 441 |
"language": "python", |
|
| 442 |
"metadata": {},
|
|
| 443 |
"outputs": [], |
|
| 444 |
"prompt_number": 18 |
|
| 445 |
}, |
|
| 446 |
{
|
|
| 447 |
"cell_type": "code", |
|
| 448 |
"collapsed": true, |
|
| 449 |
"input": [ |
|
| 450 |
"out_file.close()" |
|
| 451 |
], |
|
| 452 |
"language": "python", |
|
| 453 |
"metadata": {},
|
|
| 454 |
"outputs": [], |
|
| 455 |
"prompt_number": 19 |
|
| 456 |
}, |
|
| 457 |
{
|
|
| 458 |
"cell_type": "code", |
|
| 459 |
"collapsed": true, |
|
| 460 |
"input": [], |
|
| 461 |
"language": "python", |
|
| 462 |
"metadata": {},
|
|
| 463 |
"outputs": [], |
|
| 464 |
"prompt_number": null |
|
| 465 |
} |
|
| 466 |
], |
|
| 467 |
"metadata": {}
|
|
| 468 |
} |
|
| 469 |
] |
|
| 470 |
} |
|
| Vamp.v3.ipynb | ||
|---|---|---|
| 1 |
{
|
|
| 2 |
"metadata": {
|
|
| 3 |
"kernelspec": {
|
|
| 4 |
"display_name": "Python 3", |
|
| 5 |
"language": "python", |
|
| 6 |
"name": "python3" |
|
| 7 |
}, |
|
| 8 |
"language_info": {
|
|
| 9 |
"codemirror_mode": {
|
|
| 10 |
"name": "ipython", |
|
| 11 |
"version": 3 |
|
| 12 |
}, |
|
| 13 |
"file_extension": ".py", |
|
| 14 |
"mimetype": "text/x-python", |
|
| 15 |
"name": "python", |
|
| 16 |
"nbconvert_exporter": "python", |
|
| 17 |
"pygments_lexer": "ipython3", |
|
| 18 |
"version": "3.4.3" |
|
| 19 |
}, |
|
| 20 |
"name": "" |
|
| 21 |
}, |
|
| 22 |
"nbformat": 3, |
|
| 23 |
"nbformat_minor": 0, |
|
| 24 |
"worksheets": [ |
|
| 25 |
{
|
|
| 26 |
"cells": [ |
|
| 27 |
{
|
|
| 28 |
"cell_type": "code", |
|
| 29 |
"collapsed": true, |
|
| 30 |
"input": [ |
|
| 31 |
"import vamp" |
|
| 32 |
], |
|
| 33 |
"language": "python", |
|
| 34 |
"metadata": {},
|
|
| 35 |
"outputs": [], |
|
| 36 |
"prompt_number": 1 |
|
| 37 |
}, |
|
| 38 |
{
|
|
| 39 |
"cell_type": "code", |
|
| 40 |
"collapsed": false, |
|
| 41 |
"input": [ |
|
| 42 |
"import librosa" |
|
| 43 |
], |
|
| 44 |
"language": "python", |
|
| 45 |
"metadata": {},
|
|
| 46 |
"outputs": [ |
|
| 47 |
{
|
|
| 48 |
"output_type": "stream", |
|
| 49 |
"stream": "stderr", |
|
| 50 |
"text": [ |
|
| 51 |
"/usr/lib/python3.4/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n", |
|
| 52 |
" warnings.warn('Could not import scikits.samplerate. '\n"
|
|
| 53 |
] |
|
| 54 |
} |
|
| 55 |
], |
|
| 56 |
"prompt_number": 2 |
|
| 57 |
}, |
|
| 58 |
{
|
|
| 59 |
"cell_type": "code", |
|
| 60 |
"collapsed": true, |
|
| 61 |
"input": [ |
|
| 62 |
"import matplotlib.pyplot as plt" |
|
| 63 |
], |
|
| 64 |
"language": "python", |
|
| 65 |
"metadata": {},
|
|
| 66 |
"outputs": [], |
|
| 67 |
"prompt_number": 3 |
|
| 68 |
}, |
|
| 69 |
{
|
|
| 70 |
"cell_type": "code", |
|
| 71 |
"collapsed": true, |
|
| 72 |
"input": [ |
|
| 73 |
"%matplotlib inline" |
|
| 74 |
], |
|
| 75 |
"language": "python", |
|
| 76 |
"metadata": {},
|
|
| 77 |
"outputs": [], |
|
| 78 |
"prompt_number": 4 |
|
| 79 |
}, |
|
| 80 |
{
|
|
| 81 |
"cell_type": "code", |
|
| 82 |
"collapsed": false, |
|
| 83 |
"input": [ |
|
| 84 |
"vamp.list_plugins()" |
|
| 85 |
], |
|
| 86 |
"language": "python", |
|
| 87 |
"metadata": {},
|
|
| 88 |
"outputs": [ |
|
| 89 |
{
|
|
| 90 |
"metadata": {},
|
|
| 91 |
"output_type": "pyout", |
|
| 92 |
"prompt_number": 5, |
|
| 93 |
"text": [ |
|
| 94 |
"['bbc-vamp-plugins:bbc-energy',\n", |
|
| 95 |
" 'bbc-vamp-plugins:bbc-intensity',\n", |
|
| 96 |
" 'bbc-vamp-plugins:bbc-peaks',\n", |
|
| 97 |
" 'bbc-vamp-plugins:bbc-rhythm',\n", |
|
| 98 |
" 'bbc-vamp-plugins:bbc-spectral-contrast',\n", |
|
| 99 |
" 'bbc-vamp-plugins:bbc-spectral-flux',\n", |
|
| 100 |
" 'bbc-vamp-plugins:bbc-speechmusic-segmenter',\n", |
|
| 101 |
" 'chp:constrainedharmonicpeak',\n", |
|
| 102 |
" 'cqvamp:cqchromavamp',\n", |
|
| 103 |
" 'cqvamp:cqvamp',\n", |
|
| 104 |
" 'cqvamp:cqvampmidi',\n", |
|
| 105 |
" 'match-vamp-plugin:match',\n", |
|
| 106 |
" 'nnls-chroma:chordino',\n", |
|
| 107 |
" 'nnls-chroma:nnls-chroma',\n", |
|
| 108 |
" 'nnls-chroma:tuning',\n", |
|
| 109 |
" 'pyin:localcandidatepyin',\n", |
|
| 110 |
" 'pyin:pyin',\n", |
|
| 111 |
" 'pyin:yin',\n", |
|
| 112 |
" 'pyin:yinfc',\n", |
|
| 113 |
" 'qm-vamp-plugins:qm-adaptivespectrogram',\n", |
|
| 114 |
" 'qm-vamp-plugins:qm-barbeattracker',\n", |
|
| 115 |
" 'qm-vamp-plugins:qm-chromagram',\n", |
|
| 116 |
" 'qm-vamp-plugins:qm-constantq',\n", |
|
| 117 |
" 'qm-vamp-plugins:qm-dwt',\n", |
|
| 118 |
" 'qm-vamp-plugins:qm-keydetector',\n", |
|
| 119 |
" 'qm-vamp-plugins:qm-mfcc',\n", |
|
| 120 |
" 'qm-vamp-plugins:qm-onsetdetector',\n", |
|
| 121 |
" 'qm-vamp-plugins:qm-segmenter',\n", |
|
| 122 |
" 'qm-vamp-plugins:qm-similarity',\n", |
|
| 123 |
" 'qm-vamp-plugins:qm-tempotracker',\n", |
|
| 124 |
" 'qm-vamp-plugins:qm-tonalchange',\n", |
|
| 125 |
" 'qm-vamp-plugins:qm-transcription',\n", |
|
| 126 |
" 'segmentino:segmentino',\n", |
|
| 127 |
" 'silvet:silvet',\n", |
|
| 128 |
" 'simple-cepstrum:simple-cepstrum',\n", |
|
| 129 |
" 'tempogram:tempogram',\n", |
|
| 130 |
" 'vamp-aubio:aubionotes',\n", |
|
| 131 |
" 'vamp-aubio:aubioonset',\n", |
|
| 132 |
" 'vamp-aubio:aubiopitch',\n", |
|
| 133 |
" 'vamp-aubio:aubiosilence',\n", |
|
| 134 |
" 'vamp-aubio:aubiotempo',\n", |
|
| 135 |
" 'vamp-example-plugins:amplitudefollower',\n", |
|
| 136 |
" 'vamp-example-plugins:fixedtempo',\n", |
|
| 137 |
" 'vamp-example-plugins:percussiononsets',\n", |
|
| 138 |
" 'vamp-example-plugins:powerspectrum',\n", |
|
| 139 |
" 'vamp-example-plugins:spectralcentroid',\n", |
|
| 140 |
" 'vamp-example-plugins:zerocrossing',\n", |
|
| 141 |
" 'vamp-libxtract:amdf',\n", |
|
| 142 |
" 'vamp-libxtract:asdf',\n", |
|
| 143 |
" 'vamp-libxtract:autocorrelation',\n", |
|
| 144 |
" 'vamp-libxtract:average_deviation',\n", |
|
| 145 |
" 'vamp-libxtract:bark_coefficients',\n", |
|
| 146 |
" 'vamp-libxtract:crest',\n", |
|
| 147 |
" 'vamp-libxtract:dct',\n", |
|
| 148 |
" 'vamp-libxtract:f0',\n", |
|
| 149 |
" 'vamp-libxtract:failsafe_f0',\n", |
|
| 150 |
" 'vamp-libxtract:flatness',\n", |
|
| 151 |
" 'vamp-libxtract:harmonic_spectrum',\n", |
|
| 152 |
" 'vamp-libxtract:highest_value',\n", |
|
| 153 |
" 'vamp-libxtract:irregularity_j',\n", |
|
| 154 |
" 'vamp-libxtract:irregularity_k',\n", |
|
| 155 |
" 'vamp-libxtract:kurtosis',\n", |
|
| 156 |
" 'vamp-libxtract:loudness',\n", |
|
| 157 |
" 'vamp-libxtract:lowest_value',\n", |
|
| 158 |
" 'vamp-libxtract:mean',\n", |
|
| 159 |
" 'vamp-libxtract:mfcc',\n", |
|
| 160 |
" 'vamp-libxtract:noisiness',\n", |
|
| 161 |
" 'vamp-libxtract:nonzero_count',\n", |
|
| 162 |
" 'vamp-libxtract:odd_even_ratio',\n", |
|
| 163 |
" 'vamp-libxtract:peak_spectrum',\n", |
|
| 164 |
" 'vamp-libxtract:rms_amplitude',\n", |
|
| 165 |
" 'vamp-libxtract:rolloff',\n", |
|
| 166 |
" 'vamp-libxtract:sharpness',\n", |
|
| 167 |
" 'vamp-libxtract:skewness',\n", |
|
| 168 |
" 'vamp-libxtract:smoothness',\n", |
|
| 169 |
" 'vamp-libxtract:spectral_centroid',\n", |
|
| 170 |
" 'vamp-libxtract:spectral_inharmonicity',\n", |
|
| 171 |
" 'vamp-libxtract:spectral_kurtosis',\n", |
|
| 172 |
" 'vamp-libxtract:spectral_skewness',\n", |
|
| 173 |
" 'vamp-libxtract:spectral_slope',\n", |
|
| 174 |
" 'vamp-libxtract:spectral_standard_deviation',\n", |
|
| 175 |
" 'vamp-libxtract:spectral_variance',\n", |
|
| 176 |
" 'vamp-libxtract:spectrum',\n", |
|
| 177 |
" 'vamp-libxtract:spread',\n", |
|
| 178 |
" 'vamp-libxtract:standard_deviation',\n", |
|
| 179 |
" 'vamp-libxtract:sum',\n", |
|
| 180 |
" 'vamp-libxtract:tonality',\n", |
|
| 181 |
" 'vamp-libxtract:tristimulus_1',\n", |
|
| 182 |
" 'vamp-libxtract:tristimulus_2',\n", |
|
| 183 |
" 'vamp-libxtract:tristimulus_3',\n", |
|
| 184 |
" 'vamp-libxtract:variance',\n", |
|
| 185 |
" 'vamp-libxtract:zcr',\n", |
|
| 186 |
" 'vamp-rubberband:rubberband',\n", |
|
| 187 |
" 'vamp-test-plugin:vamp-test-plugin',\n", |
|
| 188 |
" 'vamp-test-plugin:vamp-test-plugin-freq']" |
|
| 189 |
] |
|
| 190 |
} |
|
| 191 |
], |
|
| 192 |
"prompt_number": 5 |
|
| 193 |
}, |
|
| 194 |
{
|
|
| 195 |
"cell_type": "code", |
|
| 196 |
"collapsed": true, |
|
| 197 |
"input": [ |
|
| 198 |
"librosa.load?" |
|
| 199 |
], |
|
| 200 |
"language": "python", |
|
| 201 |
"metadata": {},
|
|
| 202 |
"outputs": [], |
|
| 203 |
"prompt_number": 6 |
|
| 204 |
}, |
|
| 205 |
{
|
|
| 206 |
"cell_type": "code", |
|
| 207 |
"collapsed": true, |
|
| 208 |
"input": [ |
|
| 209 |
"vamp.collect?" |
|
| 210 |
], |
|
| 211 |
"language": "python", |
|
| 212 |
"metadata": {},
|
|
| 213 |
"outputs": [], |
|
| 214 |
"prompt_number": 7 |
|
| 215 |
}, |
|
| 216 |
{
|
|
| 217 |
"cell_type": "code", |
|
| 218 |
"collapsed": false, |
|
| 219 |
"input": [ |
|
| 220 |
"data, rate = librosa.load(\"data/Music/piano-scale.wav\")" |
|
| 221 |
], |
|
| 222 |
"language": "python", |
|
| 223 |
"metadata": {},
|
|
| 224 |
"outputs": [], |
|
| 225 |
"prompt_number": 31 |
|
| 226 |
}, |
|
| 227 |
{
|
|
| 228 |
"cell_type": "code", |
|
| 229 |
"collapsed": false, |
|
| 230 |
"input": [ |
|
| 231 |
"rate" |
|
| 232 |
], |
|
| 233 |
"language": "python", |
|
| 234 |
"metadata": {},
|
|
| 235 |
"outputs": [ |
|
| 236 |
{
|
|
| 237 |
"metadata": {},
|
|
| 238 |
"output_type": "pyout", |
|
| 239 |
"prompt_number": 32, |
|
| 240 |
"text": [ |
|
| 241 |
"22050" |
|
| 242 |
] |
|
| 243 |
} |
|
| 244 |
], |
|
| 245 |
"prompt_number": 32 |
|
| 246 |
}, |
|
| 247 |
{
|
|
| 248 |
"cell_type": "code", |
|
| 249 |
"collapsed": true, |
|
| 250 |
"input": [ |
|
| 251 |
"plugin_params = { \"tuningmode\": 1, \"s\": 0.9, \"chromanormalize\": 3 }"
|
|
| 252 |
], |
|
| 253 |
"language": "python", |
|
| 254 |
"metadata": {},
|
|
| 255 |
"outputs": [], |
|
| 256 |
"prompt_number": 64 |
|
| 257 |
}, |
|
| 258 |
{
|
|
| 259 |
"cell_type": "code", |
|
| 260 |
"collapsed": false, |
|
| 261 |
"input": [ |
|
| 262 |
"out = vamp.collect(data, rate, \"nnls-chroma:nnls-chroma\", output = \"chroma\", parameters = plugin_params, process_timestamp_method = vamp.vampyhost.SHIFT_DATA)" |
|
| 263 |
], |
|
| 264 |
"language": "python", |
|
| 265 |
"metadata": {},
|
|
| 266 |
"outputs": [], |
|
| 267 |
"prompt_number": 65 |
|
| 268 |
}, |
|
| 269 |
{
|
|
| 270 |
"cell_type": "code", |
|
| 271 |
"collapsed": false, |
|
| 272 |
"input": [ |
|
| 273 |
"out" |
|
| 274 |
], |
|
| 275 |
"language": "python", |
|
| 276 |
"metadata": {},
|
|
| 277 |
"outputs": [ |
|
| 278 |
{
|
|
| 279 |
"metadata": {},
|
|
| 280 |
"output_type": "pyout", |
|
| 281 |
"prompt_number": 66, |
|
| 282 |
"text": [ |
|
| 283 |
"{'matrix': ( 0.092879819,\n",
|
|
| 284 |
" array([[ 0.00000000e+00, 3.95574346e-02, 1.29732716e-05,\n", |
|
| 285 |
" 9.80210781e-01, 9.58022848e-03, 0.00000000e+00,\n", |
|
| 286 |
" 0.00000000e+00, 1.64232314e-01, 9.96426120e-02,\n", |
|
| 287 |
" 0.00000000e+00, 2.50873379e-02, 0.00000000e+00],\n", |
|
| 288 |
" [ 0.00000000e+00, 3.14951129e-02, 1.80668838e-04,\n", |
|
| 289 |
" 9.78162169e-01, 3.25743221e-02, 0.00000000e+00,\n", |
|
| 290 |
" 2.29330850e-03, 1.62046552e-01, 1.21343270e-01,\n", |
|
| 291 |
" 0.00000000e+00, 1.25381378e-02, 0.00000000e+00],\n", |
|
| 292 |
" [ 0.00000000e+00, 3.00429221e-02, 1.06590232e-04,\n", |
|
| 293 |
" 9.80653524e-01, 1.40791936e-02, 0.00000000e+00,\n", |
|
| 294 |
" 9.71607305e-03, 1.61771417e-01, 1.04309380e-01,\n", |
|
| 295 |
" 0.00000000e+00, 8.54655821e-03, 0.00000000e+00],\n", |
|
| 296 |
" [ 5.58154285e-02, 2.38421746e-02, 2.34732070e-04,\n", |
|
| 297 |
" 9.85957980e-01, 0.00000000e+00, 8.80511070e-04,\n", |
|
| 298 |
" 1.73822697e-02, 1.46616176e-01, 2.19904333e-02,\n", |
|
| 299 |
" 4.21529301e-02, 1.19695384e-02, 0.00000000e+00],\n", |
|
| 300 |
" [ 1.04891837e-01, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 301 |
" 8.22632074e-01, 0.00000000e+00, 5.40932834e-01,\n", |
|
| 302 |
" 2.50056237e-02, 9.16116759e-02, 1.97594035e-02,\n", |
|
| 303 |
" 1.00487731e-01, 3.59659968e-03, 1.21216271e-02],\n", |
|
| 304 |
" [ 4.19154540e-02, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 305 |
" 5.32641709e-01, 0.00000000e+00, 8.34550500e-01,\n", |
|
| 306 |
" 1.78163908e-02, 4.51259725e-02, 3.18069607e-02,\n", |
|
| 307 |
" 1.09734260e-01, 5.05780652e-02, 9.80062131e-03],\n", |
|
| 308 |
" [ 8.45797267e-03, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 309 |
" 3.46328110e-01, 1.50650961e-03, 9.27628219e-01,\n", |
|
| 310 |
" 7.37146754e-03, 2.01032832e-02, 2.88057886e-02,\n", |
|
| 311 |
" 1.09431997e-01, 7.88086504e-02, 3.81295895e-03],\n", |
|
| 312 |
" [ 7.56919500e-04, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 313 |
" 1.79450840e-01, 7.06915976e-04, 9.73997831e-01,\n", |
|
| 314 |
" 1.11817913e-02, 1.11172069e-02, 1.42024225e-02,\n", |
|
| 315 |
" 1.05667293e-01, 8.66513774e-02, 0.00000000e+00],\n", |
|
| 316 |
" [ 1.66557627e-04, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 317 |
" 5.85122816e-02, 0.00000000e+00, 9.89142358e-01,\n", |
|
| 318 |
" 0.00000000e+00, 1.83676016e-02, 1.94070525e-02,\n", |
|
| 319 |
" 1.07398190e-01, 7.69759417e-02, 0.00000000e+00],\n", |
|
| 320 |
" [ 1.33603578e-04, 7.56053627e-03, 9.44652930e-02,\n", |
|
| 321 |
" 0.00000000e+00, 0.00000000e+00, 8.86209607e-01,\n", |
|
| 322 |
" 0.00000000e+00, 4.42017883e-01, 3.05218417e-02,\n", |
|
| 323 |
" 6.33440092e-02, 0.00000000e+00, 7.29914904e-02],\n", |
|
| 324 |
" [ 1.31346369e-02, 6.74642762e-03, 6.19773120e-02,\n", |
|
| 325 |
" 0.00000000e+00, 0.00000000e+00, 5.67794800e-01,\n", |
|
| 326 |
" 0.00000000e+00, 8.15741003e-01, 2.10058969e-02,\n", |
|
| 327 |
" 8.59899446e-03, 2.03802008e-02, 8.47698301e-02],\n", |
|
| 328 |
" [ 4.31743301e-02, 6.34776545e-04, 4.03778553e-02,\n", |
|
| 329 |
" 0.00000000e+00, 0.00000000e+00, 3.56203496e-01,\n", |
|
| 330 |
" 0.00000000e+00, 9.28523600e-01, 1.56203127e-02,\n", |
|
| 331 |
" 0.00000000e+00, 2.54802778e-02, 8.10867995e-02],\n", |
|
| 332 |
" [ 7.33419284e-02, 6.10208313e-04, 3.63619663e-02,\n", |
|
| 333 |
" 0.00000000e+00, 0.00000000e+00, 1.90394580e-01,\n", |
|
| 334 |
" 0.00000000e+00, 9.74134982e-01, 0.00000000e+00,\n", |
|
| 335 |
" 0.00000000e+00, 3.36330049e-02, 8.35358649e-02],\n", |
|
| 336 |
" [ 8.68176147e-02, 4.59924020e-04, 4.55667339e-02,\n", |
|
| 337 |
" 0.00000000e+00, 0.00000000e+00, 5.53724468e-02,\n", |
|
| 338 |
" 0.00000000e+00, 9.90521610e-01, 0.00000000e+00,\n", |
|
| 339 |
" 0.00000000e+00, 2.28140894e-02, 7.52758607e-02],\n", |
|
| 340 |
" [ 7.16657490e-02, 4.01280704e-04, 3.66930082e-03,\n", |
|
| 341 |
" 1.17143862e-01, 0.00000000e+00, 5.73444937e-04,\n", |
|
| 342 |
" 1.10340081e-02, 9.86149490e-01, 8.01642761e-02,\n", |
|
| 343 |
" 0.00000000e+00, 0.00000000e+00, 4.57016900e-02],\n", |
|
| 344 |
" [ 1.75292432e-01, 2.99866330e-02, 2.26163003e-03,\n", |
|
| 345 |
" 2.27569327e-01, 1.22354610e-03, 5.50737930e-03,\n", |
|
| 346 |
" 1.99322514e-02, 7.56762803e-01, 5.85586727e-01,\n", |
|
| 347 |
" 0.00000000e+00, 0.00000000e+00, 2.34449338e-02],\n", |
|
| 348 |
" [ 2.28524566e-01, 3.58712338e-02, 1.71243236e-03,\n", |
|
| 349 |
" 1.92382738e-01, 2.56250240e-03, 1.83521246e-03,\n", |
|
| 350 |
" 1.90234948e-02, 4.48429465e-01, 8.41433167e-01,\n", |
|
| 351 |
" 0.00000000e+00, 0.00000000e+00, 2.25367188e-03],\n", |
|
| 352 |
" [ 2.01807752e-01, 3.38590629e-02, 1.47551857e-03,\n", |
|
| 353 |
" 1.39506012e-01, 2.78139906e-03, 0.00000000e+00,\n", |
|
| 354 |
" 1.72728170e-02, 2.58494407e-01, 9.33561802e-01,\n", |
|
| 355 |
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 356 |
" [ 1.54486775e-01, 7.73785859e-02, 1.31765706e-03,\n", |
|
| 357 |
" 1.13231480e-01, 2.59730266e-03, 0.00000000e+00,\n", |
|
| 358 |
" 1.56198768e-02, 1.29941836e-01, 9.69632685e-01,\n", |
|
| 359 |
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 360 |
" [ 1.27307221e-01, 1.46335676e-01, 7.25547271e-03,\n", |
|
| 361 |
" 7.30697215e-02, 1.78271858e-03, 1.15589900e-02,\n", |
|
| 362 |
" 1.46873416e-02, 4.01094183e-02, 9.77254152e-01,\n", |
|
| 363 |
" 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 364 |
" [ 0.00000000e+00, 6.90020099e-02, 1.04619361e-01,\n", |
|
| 365 |
" 2.25574570e-03, 1.59145482e-02, 8.51853862e-02,\n", |
|
| 366 |
" 1.19112581e-02, 3.24970335e-02, 8.37207258e-01,\n", |
|
| 367 |
" 2.33193263e-02, 5.23565650e-01, 0.00000000e+00],\n", |
|
| 368 |
" [ 0.00000000e+00, 1.16285548e-01, 8.28386694e-02,\n", |
|
| 369 |
" 4.62941220e-03, 4.11567837e-02, 5.65895475e-02,\n", |
|
| 370 |
" 8.86768941e-03, 3.56909935e-03, 4.53278303e-01,\n", |
|
| 371 |
" 5.48035046e-03, 8.76992106e-01, 0.00000000e+00],\n", |
|
| 372 |
" [ 0.00000000e+00, 9.20006111e-02, 8.02344009e-02,\n", |
|
| 373 |
" 8.05004500e-03, 3.36742140e-02, 5.60967848e-02,\n", |
|
| 374 |
" 7.82551430e-03, 0.00000000e+00, 2.42856041e-01,\n", |
|
| 375 |
" 6.69736741e-03, 9.60035205e-01, 0.00000000e+00],\n", |
|
| 376 |
" [ 0.00000000e+00, 7.55143464e-02, 8.95384848e-02,\n", |
|
| 377 |
" 3.87140363e-02, 3.20630074e-02, 5.86400703e-02,\n", |
|
| 378 |
" 6.61307480e-03, 0.00000000e+00, 1.47289306e-01,\n", |
|
| 379 |
" 4.46502026e-03, 9.79059339e-01, 0.00000000e+00],\n", |
|
| 380 |
" [ 0.00000000e+00, 4.65285294e-02, 9.54503864e-02,\n", |
|
| 381 |
" 7.55635425e-02, 2.02768799e-02, 6.72976673e-02,\n", |
|
| 382 |
" 6.64060982e-03, 0.00000000e+00, 7.32876062e-02,\n", |
|
| 383 |
" 2.12864913e-02, 9.86005127e-01, 0.00000000e+00],\n", |
|
| 384 |
" [ 2.27480859e-01, 1.47993211e-04, 4.33240309e-02,\n", |
|
| 385 |
" 4.05451544e-02, 5.41272797e-02, 3.53963580e-03,\n", |
|
| 386 |
" 1.43123539e-02, 2.27174103e-01, 1.35128628e-02,\n", |
|
| 387 |
" 4.26806025e-02, 9.42322791e-01, 0.00000000e+00],\n", |
|
| 388 |
" [ 7.76776791e-01, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 389 |
" 1.39830142e-01, 5.90621531e-02, 1.34550324e-02,\n", |
|
| 390 |
" 3.04677729e-02, 1.83764741e-01, 1.57451839e-03,\n", |
|
| 391 |
" 1.49140116e-02, 5.81655264e-01, 1.22662177e-02],\n", |
|
| 392 |
" [ 9.20188725e-01, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 393 |
" 1.31064311e-01, 5.43982014e-02, 1.02829868e-02,\n", |
|
| 394 |
" 3.28366496e-02, 1.60479471e-01, 5.82212256e-03,\n", |
|
| 395 |
" 0.00000000e+00, 3.25354815e-01, 1.69819314e-02],\n", |
|
| 396 |
" [ 9.54825997e-01, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 397 |
" 9.08428580e-02, 5.47444783e-02, 6.06109276e-02,\n", |
|
| 398 |
" 3.60330939e-02, 1.57885581e-01, 8.70869216e-03,\n", |
|
| 399 |
" 0.00000000e+00, 2.16832295e-01, 8.11457075e-03],\n", |
|
| 400 |
" [ 9.67904449e-01, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 401 |
" 5.66124730e-02, 5.64976931e-02, 1.21210732e-01,\n", |
|
| 402 |
" 2.68668495e-02, 1.64355710e-01, 8.75006244e-03,\n", |
|
| 403 |
" 0.00000000e+00, 1.18906766e-01, 1.10401316e-02],\n", |
|
| 404 |
" [ 9.10301507e-01, 0.00000000e+00, 3.58769506e-01,\n", |
|
| 405 |
" 1.06141996e-02, 4.18055505e-02, 1.46602556e-01,\n", |
|
| 406 |
" 3.75781134e-02, 9.09660459e-02, 4.86972230e-03,\n", |
|
| 407 |
" 9.57466066e-02, 1.99975353e-02, 2.24707532e-03],\n", |
|
| 408 |
" [ 5.32060623e-01, 0.00000000e+00, 8.38650227e-01,\n", |
|
| 409 |
" 9.16879624e-03, 2.60610599e-04, 3.36099602e-02,\n", |
|
| 410 |
" 3.72139812e-02, 2.78077256e-02, 5.88112622e-02,\n", |
|
| 411 |
" 8.20663497e-02, 3.43208760e-03, 0.00000000e+00],\n", |
|
| 412 |
" [ 2.87455708e-01, 4.98069590e-03, 9.52399194e-01,\n", |
|
| 413 |
" 6.48918748e-03, 1.46152312e-02, 1.27553497e-03,\n", |
|
| 414 |
" 3.50150578e-02, 3.32775638e-02, 6.14331104e-02,\n", |
|
| 415 |
" 6.25745505e-02, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 416 |
" [ 1.61070168e-01, 2.89101563e-02, 9.81492460e-01,\n", |
|
| 417 |
" 6.82534557e-03, 3.21710594e-02, 0.00000000e+00,\n", |
|
| 418 |
" 3.05393171e-02, 2.94240173e-02, 2.76064165e-02,\n", |
|
| 419 |
" 7.77955055e-02, 1.41078206e-02, 0.00000000e+00],\n", |
|
| 420 |
" [ 8.16927776e-02, 1.41722215e-02, 9.86824512e-01,\n", |
|
| 421 |
" 5.50024724e-03, 4.24346775e-02, 0.00000000e+00,\n", |
|
| 422 |
" 3.15050595e-02, 3.23357806e-02, 0.00000000e+00,\n", |
|
| 423 |
" 1.10492751e-01, 5.67878187e-02, 0.00000000e+00],\n", |
|
| 424 |
" [ 3.24949436e-02, 7.84328731e-04, 9.87073660e-01,\n", |
|
| 425 |
" 2.14368664e-02, 8.19184929e-02, 0.00000000e+00,\n", |
|
| 426 |
" 4.47093956e-02, 4.51755077e-02, 0.00000000e+00,\n", |
|
| 427 |
" 7.68498033e-02, 8.66795480e-02, 0.00000000e+00],\n", |
|
| 428 |
" [ 4.58201068e-03, 5.83841931e-04, 7.21634507e-01,\n", |
|
| 429 |
" 6.54176593e-01, 6.88196346e-03, 1.98839908e-03,\n", |
|
| 430 |
" 3.12678590e-02, 4.32215557e-02, 2.30717994e-02,\n", |
|
| 431 |
" 0.00000000e+00, 2.02596381e-01, 8.24658498e-02],\n", |
|
| 432 |
" [ 0.00000000e+00, 1.26990154e-02, 2.43063256e-01,\n", |
|
| 433 |
" 9.57038760e-01, 3.64562906e-02, 4.62111691e-03,\n", |
|
| 434 |
" 0.00000000e+00, 2.28635464e-02, 2.43536346e-02,\n", |
|
| 435 |
" 0.00000000e+00, 1.47804186e-01, 2.28763055e-02],\n", |
|
| 436 |
" [ 0.00000000e+00, 3.60645913e-02, 9.64465439e-02,\n", |
|
| 437 |
" 9.83903885e-01, 7.15166628e-02, 2.05014599e-03,\n", |
|
| 438 |
" 0.00000000e+00, 1.82101205e-02, 3.92813012e-02,\n", |
|
| 439 |
" 1.10990729e-03, 1.14122562e-01, 3.62179056e-02],\n", |
|
| 440 |
" [ 0.00000000e+00, 3.63094285e-02, 4.21216004e-02,\n", |
|
| 441 |
" 9.86869931e-01, 2.13347878e-02, 1.17978174e-02,\n", |
|
| 442 |
" 0.00000000e+00, 1.84751991e-02, 3.91156822e-02,\n", |
|
| 443 |
" 0.00000000e+00, 1.19885042e-01, 7.84666091e-02],\n", |
|
| 444 |
" [ 0.00000000e+00, 3.17818634e-02, 1.20548327e-02,\n", |
|
| 445 |
" 9.88725364e-01, 1.00220600e-02, 4.33316268e-02,\n", |
|
| 446 |
" 0.00000000e+00, 1.82445534e-02, 3.88606228e-02,\n", |
|
| 447 |
" 0.00000000e+00, 9.95126739e-02, 8.68492201e-02],\n", |
|
| 448 |
" [ 1.20896734e-01, 4.24718149e-02, 3.63281532e-03,\n", |
|
| 449 |
" 8.90641093e-01, 2.25664694e-02, 4.28505957e-01,\n", |
|
| 450 |
" 1.07771400e-02, 3.10042053e-02, 3.72265317e-02,\n", |
|
| 451 |
" 7.50589697e-03, 6.06563613e-02, 0.00000000e+00],\n", |
|
| 452 |
" [ 1.61590315e-02, 0.00000000e+00, 2.05814978e-03,\n", |
|
| 453 |
" 5.34187019e-01, 5.10551874e-03, 8.44328225e-01,\n", |
|
| 454 |
" 2.61573698e-02, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 455 |
" 5.78178698e-03, 2.39303317e-02, 1.31363133e-02],\n", |
|
| 456 |
" [ 0.00000000e+00, 2.69280165e-03, 9.71267698e-04,\n", |
|
| 457 |
" 3.30787838e-01, 6.21950440e-03, 9.42233920e-01,\n", |
|
| 458 |
" 2.94584725e-02, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 459 |
" 5.73373958e-03, 4.27455939e-02, 0.00000000e+00],\n", |
|
| 460 |
" [ 2.47827508e-02, 4.29236740e-02, 0.00000000e+00,\n", |
|
| 461 |
" 1.78738758e-01, 6.27625687e-03, 9.80933607e-01,\n", |
|
| 462 |
" 2.33737361e-02, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 463 |
" 7.23603368e-03, 5.22218719e-02, 0.00000000e+00],\n", |
|
| 464 |
" [ 5.14810570e-02, 8.37553367e-02, 0.00000000e+00,\n", |
|
| 465 |
" 8.09028223e-02, 8.36077798e-03, 9.89670217e-01,\n", |
|
| 466 |
" 1.91887822e-02, 0.00000000e+00, 0.00000000e+00,\n", |
|
| 467 |
" 7.49138277e-03, 6.20334819e-02, 0.00000000e+00],\n", |
|
| 468 |
" [ 3.32303753e-05, 8.98099467e-02, 7.88206458e-02,\n", |
|
| 469 |
" 6.78930879e-02, 1.03781344e-02, 9.46214378e-01,\n", |
|
| 470 |
" 1.99007969e-02, 2.77829796e-01, 0.00000000e+00,\n", |
|
| 471 |
" 5.27049974e-03, 8.98295045e-02, 1.37303854e-04],\n", |
|
| 472 |
" [ 4.44915071e-02, 1.76926572e-02, 8.55404064e-02,\n", |
|
| 473 |
" 3.43059041e-02, 1.02791190e-02, 6.12779975e-01,\n", |
|
| 474 |
" 1.08479448e-02, 7.82902002e-01, 2.35577822e-02,\n", |
|
| 475 |
" 0.00000000e+00, 0.00000000e+00, 4.15841729e-04],\n", |
|
| 476 |
" [ 8.49419683e-02, 1.20955601e-03, 1.09671973e-01,\n", |
|
| 477 |
" 1.21201193e-02, 9.66064632e-03, 3.11394960e-01,\n", |
|
| 478 |
" 1.18757421e-02, 9.39238966e-01, 3.51763368e-02,\n", |
|
| 479 |
" 0.00000000e+00, 0.00000000e+00, 4.83505777e-04],\n", |
|
| 480 |
" [ 9.73264351e-02, 8.18368455e-04, 1.63447946e-01,\n", |
|
| 481 |
" 3.29327881e-02, 0.00000000e+00, 1.07415654e-01,\n", |
|
| 482 |
" 1.59726590e-02, 9.74627972e-01, 3.21203768e-02,\n", |
|
| 483 |
" 0.00000000e+00, 0.00000000e+00, 1.54338195e-03],\n", |
|
| 484 |
" [ 1.01416051e-01, 1.04169792e-03, 1.80728644e-01,\n", |
|
| 485 |
" 6.06901050e-02, 0.00000000e+00, 6.61162585e-02,\n", |
|
| 486 |
" 2.47565545e-02, 9.73594368e-01, 2.22180188e-02,\n", |
|
| 487 |
" 0.00000000e+00, 0.00000000e+00, 1.90866261e-03],\n", |
|
| 488 |
" [ 1.10513330e-01, 1.16068090e-03, 1.73820123e-01,\n", |
|
| 489 |
" 1.02041930e-01, 0.00000000e+00, 2.92204432e-02,\n", |
|
| 490 |
" 2.34890543e-02, 9.72198844e-01, 1.90163553e-02,\n", |
|
| 491 |
" 1.48797734e-02, 0.00000000e+00, 4.90254548e-04],\n", |
|
| 492 |
" [ 3.55627201e-02, 1.81428045e-02, 4.17622877e-03,\n", |
|
| 493 |
" 2.48133853e-01, 6.25082552e-02, 0.00000000e+00,\n", |
|
| 494 |
" 1.42965894e-02, 7.92297900e-01, 5.51682591e-01,\n", |
|
| 495 |
" 2.48422138e-02, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 496 |
" [ 5.95075870e-03, 5.97717762e-02, 3.42534087e-03,\n", |
|
| 497 |
" 2.44145840e-01, 6.32875264e-02, 0.00000000e+00,\n", |
|
| 498 |
" 5.36254235e-02, 2.65350431e-01, 9.26052630e-01,\n", |
|
| 499 |
" 4.36774455e-02, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 500 |
" [ 0.00000000e+00, 9.64460075e-02, 4.26064106e-03,\n", |
|
| 501 |
" 2.43948385e-01, 1.03570849e-01, 0.00000000e+00,\n", |
|
| 502 |
" 5.81620783e-02, 8.93463269e-02, 9.52694476e-01,\n", |
|
| 503 |
" 3.80766317e-02, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 504 |
" [ 0.00000000e+00, 1.12178475e-01, 3.88786709e-03,\n", |
|
| 505 |
" 2.46120870e-01, 1.31447807e-01, 0.00000000e+00,\n", |
|
| 506 |
" 5.56226894e-02, 4.11828794e-02, 9.50674713e-01,\n", |
|
| 507 |
" 3.12177110e-02, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 508 |
" [ 0.00000000e+00, 1.28379866e-01, 4.15798742e-03,\n", |
|
| 509 |
" 2.42848128e-01, 1.54101759e-01, 0.00000000e+00,\n", |
|
| 510 |
" 5.40410578e-02, 2.66808942e-02, 9.47005868e-01,\n", |
|
| 511 |
" 1.80636272e-02, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 512 |
" [ 2.53865309e-03, 1.91127621e-02, 2.02243449e-03,\n", |
|
| 513 |
" 1.12406276e-01, 2.00758558e-02, 1.71211556e-01,\n", |
|
| 514 |
" 1.11278228e-01, 2.78214272e-02, 8.43058944e-01,\n", |
|
| 515 |
" 3.59242298e-02, 4.81743515e-01, 0.00000000e+00],\n", |
|
| 516 |
" [ 3.64949671e-03, 4.81741881e-04, 1.33294996e-03,\n", |
|
| 517 |
" 1.66589487e-02, 8.12750459e-02, 5.23330793e-02,\n", |
|
| 518 |
" 1.93934049e-02, 7.19395373e-03, 4.53242451e-01,\n", |
|
| 519 |
" 2.10740734e-02, 8.83749068e-01, 5.52243367e-02],\n", |
|
| 520 |
" [ 3.93255353e-02, 1.05348146e-02, 5.73153608e-04,\n", |
|
| 521 |
" 9.43983123e-02, 1.47002060e-02, 9.49143842e-02,\n", |
|
| 522 |
" 1.09301947e-01, 0.00000000e+00, 1.52132377e-01,\n", |
|
| 523 |
" 3.77451703e-02, 9.71437335e-01, 0.00000000e+00],\n", |
|
| 524 |
" [ 5.13472557e-02, 3.31028714e-03, 3.92159214e-04,\n", |
|
| 525 |
" 1.16105087e-01, 1.48322443e-02, 1.23555198e-01,\n", |
|
| 526 |
" 1.31526083e-01, 0.00000000e+00, 6.29847571e-02,\n", |
|
| 527 |
" 3.36713605e-02, 9.72449243e-01, 1.81256123e-02],\n", |
|
| 528 |
" [ 4.89317700e-02, 0.00000000e+00, 2.96047510e-04,\n", |
|
| 529 |
" 1.21519633e-01, 1.54881012e-02, 1.36466950e-01,\n", |
|
| 530 |
" 1.60353258e-01, 0.00000000e+00, 5.08026592e-02,\n", |
|
| 531 |
" 3.07973381e-02, 9.66688573e-01, 1.56879723e-02],\n", |
|
| 532 |
" [ 1.98805168e-01, 0.00000000e+00, 5.65945404e-04,\n", |
|
| 533 |
" 1.64412394e-01, 1.19373398e-02, 4.63520288e-02,\n", |
|
| 534 |
" 1.70583352e-01, 4.07661637e-03, 4.17408086e-02,\n", |
|
| 535 |
" 8.87884125e-02, 9.44411874e-01, 2.23336332e-02],\n", |
|
| 536 |
" [ 6.47660553e-01, 1.09090870e-02, 1.12898178e-01,\n", |
|
| 537 |
" 3.67569447e-01, 2.99617252e-03, 6.21701553e-02,\n", |
|
| 538 |
" 1.54504150e-01, 4.67437133e-02, 1.10933185e-01,\n", |
|
| 539 |
" 7.35432431e-02, 6.20417595e-01, 0.00000000e+00],\n", |
|
| 540 |
" [ 8.65435898e-01, 1.29718129e-02, 1.31817251e-01,\n", |
|
| 541 |
" 4.10883605e-01, 2.92400713e-03, 3.68153490e-02,\n", |
|
| 542 |
" 1.44890234e-01, 4.59817313e-02, 6.56576604e-02,\n", |
|
| 543 |
" 7.75373355e-02, 1.72791988e-01, 0.00000000e+00],\n", |
|
| 544 |
" [ 8.88324201e-01, 6.67957403e-03, 1.39935628e-01,\n", |
|
| 545 |
" 4.00488734e-01, 4.18466097e-03, 2.89282724e-02,\n", |
|
| 546 |
" 1.33017287e-01, 3.70216854e-02, 4.46694382e-02,\n", |
|
| 547 |
" 9.21769366e-02, 2.12607980e-02, 0.00000000e+00],\n", |
|
| 548 |
" [ 9.02807713e-01, 2.10447935e-04, 1.41207904e-01,\n", |
|
| 549 |
" 3.53729814e-01, 3.70743335e-03, 2.84916237e-02,\n", |
|
| 550 |
" 1.24700516e-01, 3.85591500e-02, 7.32387602e-02,\n", |
|
| 551 |
" 1.29023820e-01, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 552 |
" [ 9.03710365e-01, 7.44168297e-04, 1.43547714e-01,\n", |
|
| 553 |
" 3.72946084e-01, 2.09493097e-03, 0.00000000e+00,\n", |
|
| 554 |
" 8.42517391e-02, 3.97068709e-02, 2.67486330e-02,\n", |
|
| 555 |
" 1.19236149e-01, 0.00000000e+00, 0.00000000e+00],\n", |
|
| 556 |
" [ 6.47389770e-01, 9.62956576e-04, 6.64771974e-01,\n", |
|
| 557 |
" 2.39137374e-03, 4.67590615e-02, 3.20941329e-01,\n", |
|
| 558 |
" 0.00000000e+00, 4.35641073e-02, 7.85760134e-02,\n", |
|
| 559 |
" 8.59864429e-03, 0.00000000e+00, 1.60069823e-01],\n", |
|
| 560 |
" [ 3.28709215e-01, 1.69933238e-03, 8.70308340e-01,\n", |
|
| 561 |
" 4.05057380e-03, 1.12228401e-01, 3.07389766e-01,\n", |
|
| 562 |
" 0.00000000e+00, 9.21808649e-03, 4.08464074e-02,\n", |
|
| 563 |
" 2.30031274e-02, 7.12022707e-02, 1.41627774e-01],\n", |
|
| 564 |
" [ 5.96006438e-02, 4.15985996e-04, 9.20822084e-01,\n", |
|
| 565 |
" 4.98715788e-03, 1.42810524e-01, 2.99190283e-01,\n", |
|
| 566 |
" 0.00000000e+00, 2.82171019e-03, 9.67504531e-02,\n", |
|
| 567 |
" 2.13448349e-02, 6.86788633e-02, 1.55108571e-01],\n", |
|
| 568 |
" [ 1.00534863e-03, 4.87674290e-04, 9.16018307e-01,\n", |
|
| 569 |
" 4.97340411e-03, 1.85771078e-01, 3.13747913e-01,\n", |
|
| 570 |
" 9.55010357e-04, 5.11498714e-04, 9.01460797e-02,\n", |
|
| 571 |
" 1.34017579e-02, 4.05923054e-02, 1.34093180e-01],\n", |
|
| 572 |
" [ 0.00000000e+00, 0.00000000e+00, 9.05534029e-01,\n", |
|
| 573 |
" 5.09170676e-03, 1.82564944e-01, 3.44701409e-01,\n", |
|
| 574 |
" 8.97256471e-03, 2.26824824e-03, 1.00266904e-01,\n", |
|
| 575 |
" 1.87462308e-02, 2.89424807e-02, 1.28471538e-01],\n", |
|
| 576 |
" [ 2.77297825e-01, 6.03795843e-03, 8.73157024e-01,\n", |
|
| 577 |
" 1.08850159e-01, 1.63998351e-01, 1.86085239e-01,\n", |
|
| 578 |
" 1.12378791e-01, 3.68778827e-03, 1.07245937e-01,\n", |
|
| 579 |
" 3.08599211e-02, 2.12714560e-02, 2.48486638e-01],\n", |
|
| 580 |
" [ 1.69244453e-01, 4.37403657e-02, 3.03969860e-01,\n", |
|
| 581 |
" 8.75151455e-01, 7.77170341e-03, 2.06874907e-01,\n", |
|
| 582 |
" 2.50959158e-01, 0.00000000e+00, 8.39735847e-03,\n", |
|
| 583 |
" 7.22065493e-02, 8.75575293e-04, 5.66987647e-03],\n", |
|
| 584 |
" [ 1.40741259e-01, 2.77895574e-02, 1.73567645e-02,\n", |
|
| 585 |
" 9.07734573e-01, 7.42862932e-03, 2.24477738e-01,\n", |
|
| 586 |
" 3.04128051e-01, 5.42865787e-03, 5.90750622e-03,\n", |
|
| 587 |
" 1.04962960e-01, 1.62259471e-02, 2.91900449e-02],\n", |
|
| 588 |
" [ 1.27348348e-01, 1.07598603e-02, 1.18422986e-03,\n", |
|
| 589 |
" 8.94889832e-01, 7.23825302e-03, 2.36309320e-01,\n", |
|
| 590 |
" 3.41708928e-01, 1.28864544e-02, 0.00000000e+00,\n", |
|
| 591 |
" 9.47451070e-02, 9.87819675e-03, 3.06219123e-02],\n", |
|
| 592 |
" [ 1.17779538e-01, 0.00000000e+00, 1.24025333e-03,\n", |
|
| 593 |
" 8.85621727e-01, 7.06024608e-03, 2.27433771e-01,\n", |
|
| 594 |
" 3.75163913e-01, 1.33218318e-02, 0.00000000e+00,\n", |
|
| 595 |
" 8.89331475e-02, 1.81943830e-02, 2.93087736e-02],\n", |
|
| 596 |
" [ 1.02612011e-01, 0.00000000e+00, 1.19107720e-02,\n", |
|
| 597 |
" 8.53048503e-01, 7.52635021e-03, 2.57968783e-01,\n", |
|
| 598 |
" 4.22296256e-01, 1.49089722e-02, 0.00000000e+00,\n", |
|
| 599 |
" 1.20436154e-01, 2.86021344e-02, 3.39591093e-02],\n", |
|
| 600 |
" [ 2.23645344e-01, 7.00941384e-02, 2.90339235e-02,\n", |
|
| 601 |
" 8.11006844e-01, 1.22231124e-02, 1.77608401e-01,\n", |
|
| 602 |
" 4.94134247e-01, 6.94465777e-03, 0.00000000e+00,\n", |
|
| 603 |
" 8.01459849e-02, 2.53690537e-02, 5.93009964e-02],\n", |
|
| 604 |
" [ 3.20195317e-01, 0.00000000e+00, 5.79862818e-02,\n", |
|
| 605 |
" 8.01172554e-01, 1.55903110e-02, 1.03361912e-01,\n", |
|
| 606 |
" 4.87446874e-01, 1.80881284e-03, 0.00000000e+00,\n", |
|
| 607 |
" 0.00000000e+00, 3.16866152e-02, 5.19289337e-02]], dtype=float32))}" |
|
| 608 |
] |
|
| 609 |
} |
|
| 610 |
], |
|
| 611 |
"prompt_number": 66 |
|
| 612 |
}, |
|
| 613 |
{
|
|
| 614 |
"cell_type": "code", |
|
| 615 |
"collapsed": false, |
|
| 616 |
"input": [ |
|
| 617 |
"step, chroma = out[\"matrix\"]" |
|
| 618 |
], |
|
| 619 |
"language": "python", |
|
| 620 |
"metadata": {},
|
|
| 621 |
"outputs": [], |
|
| 622 |
"prompt_number": 67 |
|
| 623 |
}, |
|
| 624 |
{
|
|
| 625 |
"cell_type": "code", |
|
| 626 |
"collapsed": false, |
|
| 627 |
"input": [ |
|
| 628 |
"plt.imshow(chroma.transpose(), origin=\"lower\")" |
|
| 629 |
], |
|
| 630 |
"language": "python", |
|
| 631 |
"metadata": {},
|
|
| 632 |
"outputs": [ |
|
| 633 |
{
|
|
| 634 |
"metadata": {},
|
|
| 635 |
"output_type": "pyout", |
|
| 636 |
"prompt_number": 68, |
|
| 637 |
"text": [ |
|
| 638 |
"<matplotlib.image.AxesImage at 0x7fe7fbadacc0>" |
|
| 639 |
] |
|
| 640 |
}, |
|
| 641 |
{
|
|
| 642 |
"metadata": {},
|
|
| 643 |
"output_type": "display_data", |
|
| 644 |
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Wpys3XFvHECfQ68FwQJT26KEZUrNvNxz2lm69SdToMd11k68CgCQHPYPquXUx\nEJMel4M9ng/e4/HgAc8HJ5CWEJXOGtpqnrK3wYp2T67Z7oUupcnCPhauXrwBoJT/T4FzYOKOVdLe\nfuxa3HWsTLzBxL1dgyCjzXaques3gaYfiJUXToO79i65SUImIKgH7cEgilzSyj1TK+kmofwju4Ir\nJbsK+b+xKGo0taevwns3z7R+yW5RoXwV8P7PgP/JWvsvKqVinMu3LesvpTxNLlpcODPXylu+ypnp\nVe35bqFOMhpPcvetNeGsuIvTspjKUFxa1p8bokEFCxg8UpjHQ1aPTzi7yMnnls0aNqV770BogMmO\nA13GXW2rX9GvFfOV4uxS8SRRDKoej+e3eXy2x6PHCReHJYvzNeuLGeU5cFHCC+W2EZdQWLuiGWSy\n41JoRnephl0dOqzwHQOtLN3y8qtLIHbgnSuftLdgVZs+gcZ6Atc5tfWd1DjKpfRthgFvqzROoV10\nxkvKeOMzhW3bXbn5JsANbU5Wyid5GK3yMq2to5XkBmYZPJuBSlnHU07zmh/kI5LsLgfLiOUVrFaK\nVeWaPfdstOvRXVP8ulyWlrOV5WhqOVKWJ4s7/Kg35jyFordyW+We1d7DLfSETKCa9u6I4jtIO0m0\ndNE+ZRWsLBHX7eJ1WS1NG+CIwPRxvq2+63OyAGf7QgSxECXvVnnXwha6JKRTw2ilLh7skm59hyt9\nh84BPaRJPVx4TqJcHiun7QVJRQatDUobdGSIqImoiKmJqIkpSCiIMSTBQkEblOkHf/uG4nbkbaNN\n9oFfsNb+OQBrbYVTG9uy2QHeQFu39YBtNNvVgMZ6h6VUvDTQLuAOHV/dFvcVUynKS8P6M40tNOUz\nsJeK1cWI84sTvrzUlBtLsXEBGzct52nP4aJzOxBNa8X5SjO51ExqTbpMuDw75HK8z8Uk5nJUsVls\n2CwU5aKAxQoXO0izstfKSsUweLALWqEz5yZtRMy6HVKWbnc+IsdVk0KpXaoiqHwb1DjgttBEnIgY\nUKYB78o2NA3QjkCQcoYD5RVl3Cnh4A3jv4U3lWPC/GUiJnwI3lKukE6RMoemO1xvF+t8BtMM1Awy\nyyZa8LwyxOWIVXmXUTYkX0K2VuSV8puSKsSlWN/IvzYyLC3jlWGMYVRYrtI7PEkmnMeKIl66SXhm\nYeHbZVtHXfCW8D5Lg04Sly+OYwnZHeBCPyUENICN0FBqRWqqAMQ11In3aaVQ++18JapMAFxW+8r3\nIbvQcloPPD59AAAgAElEQVSKli0qVbh2YdeSt5dJ2D97/vn80v4kddGIhz5NgJ7yhrHyPli1XXek\nElBxjY4rorgmiisSSlIKUgoSSvpo+lh6VPRxUT+im0tE/k8UvHHs+plS6ldxW2L9A+AvW2vXraNE\n874mgaNIzCVZvr3NaWzLbdhPd01iSGJ0pWk8W1mKC4UpFMW5Yj2C9UZzvhky2Gj6m1Hzgo3KYdCu\n2Ja2XieN7l72pI0mXUf0qoh0qYnOYzZpj03aJ0sTNklJVaypipKqWGGLpK041LDl+a+FS9ngmRq9\n/2bwfgk4iuZd1O4NITbxZnMEJvaTKN5R6QHEdsGkxm3V6YFPJl8jZQpM8i1IQhuMQkB8lXQnr5CL\nDKmjN5kMRNMOo2NkEAs/G5YzBO8ucIfgvXH+g6uMjco5NYaVGfHM3CGpDqkLRVUo6koFbkMX/9FE\nYoeO1LYkZU2yMqRFTbI0ZNE+Cz1moaGIVo5my3FvGirlGYViEqoojNaB5lVwE58UjWa7xAH6Pm7f\nmhOHUluahAa8RXGXuU9oB6WgEuVAQx2xfenq1gDyg07Zpmg6+H0b6y2DBprFOhnNToJdfwrc3C/C\nttU4VN7DvczyNsR9B97HwB3cTrUjYKiadT8967f1sdCz6LQkSivitCROSvpkrTTCMqJkhGbkwduV\nVNr/9eVtwTsG/hjwF621v62U+hXcq9B+uXVUtmPR5bXOeT0prVFKoyL/2RqUrcH43LoFwOJwsVuw\nkgromFi1xU6hmjZ+7qUGdA+r+qAP2Ea/aoVJVXtstvqA73TdgVZrWEew3vZc2oM81Bhukl2m/y7z\nPazDXed3PwdS1lAK/SRAFe433LGKWpqJ3K/rYAr4aRUFZm5YT7vohm4ZbwLIlwG35K+SzsBQytOU\nFqWdVqpthLKRNyqCTU6tPL/BXiuT9DuLKkooKpivUShqZZnqmpnqgT7BKou1EYYIqzU2CazNbUx9\nOBbCMvt6KmuXtvUg2+lCE9LWvUZYzwLggozana9kMcsh2ygNKzv3DXDgLSjWD+ZyD+ISySmr3yPr\nu4S/rzSTzO1djBWDUsZX2GUUXqnJ2PY1Vbi+pqzzt1zbBO8mSjEQJfXtx6sd4N7EcwL2LqRDB9KH\nuPeFvUd7jhsDAwNDCwMLA4PqF+heQdQriHslKWv6rBmohCERE2vYo2TPFkyIiGwzcRv7GmUO5G3B\n+zHw2Fr72/7vX2fneyx/M/j80Kdu5+fad4M9y/CgSb2sIF3m22SLiroyVJWhrmofniz+c+V1U1n2\n4KqmrxpLpxdBtR+7dODyFSNWtklmE2FXClbK5YVx0RRl5XIT9sAwTC3kF3fNAG8iEvssSUxZGbg3\nOWG69wongPB8yeW3rjYvf4cvteouZvHOGh3RbN/pl+Obwg+6MMgyHLGh9qc699Sde5nOdcKNfELt\nvjtpyHXb19Q9TXyoSQ4U8YGmlxjGi4zRYsF4mTFYFZS1oqoUZa0cpb9VGVxMtt2CudsAyr2zURJE\nA4gOLNGBRR9Yyl6PeXXAvNpnXh2wyYc+EEQ1e6Rf6zPdibIOfheLVPbdqLkeI/8yusdr4VEP4qFz\nZEc4RaSKPcXRp7XJlPQZmdAUbtIJWSxrGge2Kp2VVnntu1bet+UjVbY5jfYtWnmrSeU5fUozGNcw\nGcL4Pegf7tZbwvyaTqLaKT+E1b57vd5aO+CWCEPZqSOoAnJwwILXvhUmjqiTxBkniSbTFrSm1jGV\n7lGVPfJywKqcMC8O0FXN6W//Ps9/5/dpheW+hrxtqOCpUuqRUupr1trv4+K8f/f6kb940xVoUwGh\ntqwY7FccfVBx/KDm5MOK8WzF6PmK8fMVo+dL7LIizwxFbslrE7gs1DZ61PihZbxpsqfYpnGsyI56\n5B/0yT7okT/oc2YTzs0+Z2aP3NyivErgXGPPIjjTsCwhy0FlYDIwIecRmqbybCGneoMWfE26WrZs\nkStLgCOuO2pf55rhYJYBYGm40BDkuqZbuDJWHFedndK0dilSLjcrqFfu2nUYwyvl3hUZFN6/q+0L\neIeg3w3fIrhOCOAhFdJYGLqvSW9p+h8q+g8Uk2HB7dMF751OuX16wQEzNgVsCsWmUGR1E4stuezd\nJ6skE2SjXZcnA0VyR5M+UCQfKtbjPZ5kA57mCWV2xGZxDC+sexNsDazFQgvbt/tiu3CshP4PqZNu\nvYncgGwqcsCdFJDW/m1ikXsxQpl6B6PsoQ9i7bp3Q/p+ERpFJQ6Qt5suecd77am5WntqrqLZlbBi\ny3mHeWvYhOCdQ2Lcuv47A7gzgf2o3T12GW9dIz/UR2JgPobzfTgfwIVujNEQvKWa5fF6QKrc/lmp\nxuoIE0GlFVZHEGtMHFPGKXlckm0GrNZjeuuc3jpH5QbqBxx+45/bvlbzpe+3CeSrRJv8JeBvKqVS\n4EfAn3+z07sRCE0+2Ms5/qDg/rdzPvhOztHzOYefTTlMpxyUMwwFGwVrY1kXlk0dvhVLkWO9Xuk0\npUhZbgG3FJxoOIoVq6Mxq4cjVt9y6Uu7T1THZPUel+YO9bM+5osI+hHWRKAL0CswS7e3NJKko4ZW\nRJewexPw7gKX7Cd8SBOGF24otesaYd4dzKI+dBcdSNlDIITGFhYADF+cMATVd+aneNxj5bQ2rNO+\nFW6QtjbCetmE1gXxsDwhAIXX2AVSHVprO/E4eNW9mPQ9xeBTxfjbipN9ePDDko8GUz6qH3N785zF\nxsddVIoFAh12CyGiHIju7RQwtY1X6A8i+rcj+l+L6H0nYn70HunqFuUq5XJ1BBf3HFda4ZyMGNqO\neVk5GFoe4fOHlozU78sm4m59W1cv0R6kOQxq17yRry+T4r4Iwbtyn23s2t3qpgiiS9TGW15rsHPc\n3jqxO8fGDsSRBT5inXkNXKJNrhW3q3kncHQIH0zgZw7g1mg35R12k+7SgcRC6lNi4awHjwbQH7gD\nKtrgLXuvybY8Kc5hmVgffQKWCL9fI4aYOo0pk5QoNURJTbSsiGcV8awmnldul7qNhY3Brm3g9H+1\nvDV4W2v/oVLq54H/E0istdejTW4+u5OLuM422M85+mDD+99a88mf2HDn0QXv9c55rzzn9vScuihY\nGFgUzuKUJSGSxA8tKcZyX8H7Gu5ruB1rZkf7zD/cY/6dfWZ/oiKyJXmVcFXvEVd3KD8bofox1sao\nVYytMqhnkM9AixkpwC2DKwTu3W7Ol4uAjagJsqx4ggPvHu1lxbtednHNNtyRSpr4xC6pH5rZoQkf\nAuAYR/7tASO2kQWiyeAHr/LaNyF4y0Y/ryMvA5/uc3aP6dIvYi24FbG6n5C8B8NPFJM/DsfHJQ/6\nBd+or/j27AkPLj7jysJVpbiK3GaNMl2vcQu3uhFIA8IXPMNoEDO6kzD8Wsrw5xLO72iKWc7VPOGL\n2RE8u+8G69zAM6mndXAnCVkUjTxs75BCEY38LUQlEK0dePdrH/CrXURImbrJ2YbL2kXF9sCNbWgT\nqX5loc6d9WVmTuGx4Z7WMdcnKV+LttsnRcr28enILTT7YAg/+x68f3ydXeoCeahlx0DPuNQ3zvH4\nWEHf78O01q4JQvCW4d7dFTZSWwetMUCtUcbNYW5Gt2532x6oqXXvE7lwOXPjwjrlfYblHwJ4e/nL\nwD/GocsO0UEeDrRdNk0DIMVKsTjTnH8RMzpKKE4HLB9NmJ1VXCzAZCWrAtZV8+7GZmA18CCs7lZn\nsM4yvawVy8WY5Ysxy8/HLPdHnJqIK1OzrtfU9RX28QYex3AWYecxbHLIF1AtXWfcruIKaQCRkAp6\nE/Cmc7wQa7Lnb7icOOS8u+D7siTaumzNKSqJ9OxuGQSwwwiMkGvVjalbWz9wl446sVJHYayxotEm\nd4GvCo4JnW5dOiHkxF92vfC6DS1hC0M1U+RPFZsfKObnhrPPE758PqI3O2CZvce8gHnld4KkCUiT\n1MYGtY0SlqC7QRExuIrpP0kY/CDm6nKP02XCbFlRLOdwduZok7l1PpVW2FuYwpckG5qNnuTFFd3n\n7PoLfH3IApqWcReBHjkALleQnUGxwe2Jo8D2aZaorXwbhBRUxHbrC+Mvrgqopw1wmzXN7oLSj7rB\nuLtU5VA6fb+qHJV5lsGjjduitpaIKU/RdIdeyPaleK3b+GThTMFUwUp7drJytF9eOss7qv0j2/aj\nhF3QN+N2DvLuKitG3xx3j6nPlxbWpkmvw4R6eWvwVkq9D/xp4D8A/s3dR0kPCVfKhSbgLhsHNnPN\n5aOEKLWUG82LK8vkqWbytMfkcoxZ1OSZ240vt9ff/d11kUXWWaUvjN8cslJsLvtknw/IVJ/NYsBz\nm3JqShZmTm1OsRcp9rnGnkaO/5qXsMlcJzHha4zCfcFFQjB9/Zm0OUdAOdybQeNaf5dt2LVkXmY2\ni/aW0sy50jbd6AQBdgEKsTakXNaZvOJ4qsX5dAVmDnbpATyso5DGuQl4RVOWUaZolksJbIb7ZYgK\nFJKY3agUgrIbTB5RvtBsfqCwRqNHNemThPLxHvOzOxyuYta523d9Y5p3nYRLQQS0paYSD+Db1w5s\nNOnziPT7EWkdsZoc8mXW42xTkGUXMFPw1MK5N523jtjuncIUWmRhLLqI1EOgGqqgbpVqN3ekQKWO\ni85XUJdusVFhfZy4fxFCy78TTq6yelI14E3pJ2+ftnROOBmHE7H4XmB3f5a6EXXMT3aXOTxeu0ni\nuSwE6vncKyJhtwrXGYk+EiuINcTW7UH1zAN4piCv/HMswSzcpKRtE0UTPk7omgnnoDCQKwbWyu2/\nsVKONshss6eQfTO8+Cqa918H/m2c7XyDhHyjlF4ohTAWOHxqy2YWcfHYUmwU8+cx/XVEb9ajPx3T\nmx1AZihLqEoX1tqNAA/98aLrDazXiAz0K0V5mVCqhHKZUD5NWJAwNxVzO6eyYFYRdq7dQpqFxnms\nSiiFo9sVjw3tyr+JHnqZyLECNLKlqkzjYU/Z1Vt2AfYujUYiWHZFdoTgrWhvKiQDWbTYDGwFtXdk\n6doBt5n7gRu+Od3SHvSh+hI+l7w7UJLGrQETDTwPzu++nFr+FmJS3oUZ2tQlJtMULzTWRJRTTdWr\nKKcps+k+T6eG4XJEWbnIv6K+vnVSs3zKtUNov2ynkI0mPlVElSa+1OT9IZdlylVZkJUXbhn6zMLM\nuBmi5Yjt3k0+C/KIjt8NL4uDY8S5rNmGxWnVmecs2NzRHNXSveapSl2qw/cuyl4hMnl22szgOXAF\nqqa1DfE1cAau8Rtd6YJ4eKwH76vC9bHFHAYa7Nif5i2SkCkUHWRrFslvqsmXwJXXije4fZpL/+7U\n8sJZELKyWOPyrt7RNbbD7q5xi+G2q5lVew57Qz3vbVdY/hnghbX2/1JK/eLNR3b5RgELCfjscpXu\n781cU2QJ89OYKLVEdQ9djdGVQZcGjN36NsKoohCiuldtTZgWzKXGLhX2qcakioqYylZUzKlYQ6Wa\nTQ0rxXaTJ9m7+hposuPObyrd8yUuvKbZk7hLuHW18K6Eg6Tm+lLobu+Se4eDpztZhDsGwjZiwFS4\n3QW7b0YiuH44mYfPEY60hCbCRqJsBLhls6GwX4kTNUxisViaCbax9kwOxYuYahqRfRGx1pZZlRKX\n+yRlj6g+2i7ytbY99bVhpRm510iqjUKdgrpUqM/B6IjSJpQmp7QXzkIpjdNAqrANX5ZkFYwsnOkO\n4ZT2mu5ggpY9OFr6VA3luU8rl5s9sHsOBG2fRklZ4JCtw7GHi7isIJhxk/k1kA4AeCfdR5DvGl8+\nCXgv1nAaOwvCysQ/cIeH2+PIBh5h6io6srhp6xetIFvB5gqyU6jmDrClDF22b1sfO77fDh+FW9Am\nUTe+3Nb/9gbytpr3nwT+rFLqT+OXJSmlfs1a+6+HBw34u0jHSfiElK/7v8OVcW4n47Bx49ptwBNX\nEOdgUk2VxtSTiCr1GrNNKG1KYRNMHV3f2132A9/ufCadyf8tykRLTHCRV4mAjNduVNS22gVXuzNL\nmKCD+6ZzILgWFy60pP2uvjg4Vs7r+hfkGrsoLB9HLm/q3sYI+xlRCm7DhwjL6Xn38N2CVmKown2e\nVZCLdEGpa70032sNg37BoL9m0J/R611SZz3qrE+96VFnPSwDDH0sA+x243+3R4fdLl5pJjFdQ7yJ\niLOIGI2ONPXAUvehPoSq16M0qetjJqE0aTtir8Ahuwny7iReWQfKm10gnO/4znbar2uXa4j3XEr2\nId53bdcSccpKShvQVpHTMFtsUw3rtYttLnvOSbl1mlc72lHu1+1fAp44DVz2v9EWIovqVdCrfF7j\nXgZhUMqicQG9ch2L9pGCaqugofzdlEUpC2WM3UTYTYVdr50Wu6174yiOELgjGkNMnGRhVwMXJRWk\nNC8Z5Ev65TmD8glxdYm11iUs1rZrYFtGGveCMFVNk3orKNJuh08U6/pLNvWX2xa8fB344S3B21r7\n14C/5gqp/hTwb3WBG+Bb3Eemebd05vsIbSIbrNpAG7I+H8Uw7sEohVEPiv0em8M+64MBm8M+i2jC\nvO4xN3vM6z2KTa/tBFgZtwy8LFxedXnD16ydl4pM5T62IOq3o+jk1Xu7FgWGeym1lJDusv/uHi4h\ndyuu2O7gD2ePXZEmISgYP8hiXMifD5Gy0MTZGlAy+YUafBhnLea+eB7E6xBqVIFG1vIR7DDBtzak\nc4zGMRwdvuDOrWfcvvWEk8PnbF4kZGcJm7OULE+obUpNSk0PQ4qlwHoz3157yVxNimKoNEOlGaGJ\nehH5rZTsvYTsdsLmsM+86jGvhsyqA8pyzzmb5GUXcxyfIiseS6kf23nOLu3RtZS6qll3cg2RNnZR\nFuOxS6MxRJ7b3Wp7IZXkKSm/4932FmEXUQqmPqKpFF+ClElmKmkv0eTZ0Z8IzvOXib252zeo4wp1\nXKKOK/RhRawqt1GTqoiUo07digwH67WJqExEbTWViTwIWr/pk4WVxZxazHOLee4W7jUW6hroX+9u\nYWBYGGW7bbIB7n0CLg2qktvVgtv2nDvRY4a8oDZQG0ttgnXdKui9yhs3Atp+zsSvYdty7BJWq32j\nqPe3iP+rP+K15KtGm4SPf00+5Atk4ya37qxh9W2QQF4B5aJmj2I46sPRCI5HsL4zZnZ/wuz+HrP7\nlrNkxIuqh60OWFe3KaZDeKrgma8UVUPm+bk63BtQtNg/CBHwPgKOQY+brREO/E/S90VTCz2robIp\nSs2WI5R8QxNDIxugCKhJxMUuCUEgHPwJIYBB7XtfDLoP0RhI2xqlaGBbEzhcihw+QBgAG5J43TKG\nJscuS0HRMMwuvDCODMeHZ3z04JRPP37Mw/tPmP8oZpFGzPOI+VlMRUxJRIn7bCSqZLukJgzdNAwU\nHCrNgVIcKkXaS1jeGrP4mRHLT8fM7sPz4pAoH5EXJyyy99xiGkng3mG38dZGVTYW3rZ+pU5Ck1C+\nlzroRs102yv0sPWh14e9Phz34XjggKCl8Hd9TFGjFkoWYq5R7trVxG3vwIBw1YTLJdZfwLs7wXTo\njharZVETi7pToj90KbpfkqqCRLk8VQVO/9bbvDQxZZ1S1AnGJKBAy059UY29LKi/78pn5xvsTJy9\nfmdOm7bHmNRRGCgVisVRRebIUxoDBrbkDks+5ZyvxY851E/dPF1BaS3GBpQ5/rMOksJvVuUNpBi/\noEd594wH73AG4CcM3kqpD4Bfw632t+x6EQPwkM+3bKD8czNs95+rPev/vxvD3T7cHcPdA5jfO+D8\nk2POPrWcfxoz6BlsmbIq9rko78CLfZh4UyTXzsvEwoWsld3tLa9xJW8pIXjfBX3YgPeJz4WaEdBe\n0SjLoWd1O/WFdp28usnSOAhl8IdaT5caCSM4hNoJnX/NetSt+qFjZzlEE1wwKk2hjME5aCtQQouI\nFt3WkK/F7LqL00aXUDPtcpwqOEc0+ZQ4Kjk6OOPhg2d891uP+fanX3KRaC5zxcWZ4kJpcqso0H6V\nrcbsUBDCyh4Dt1HcUXBbKfq9lKtbR1z9zCGX36s5/zQl2iiKbMRscwKrD9z7ozyd6qh3/4q5yq+8\nbWnX0teEQJW2CjfREkspDEkIna6yb4nsgjR0i1P2EjhJ4H4Mqe482i7KRap3B6daAVXPAXc68F+c\n00w4UxrQHuA0k22oBu0Q0qAZBe/7wNig7pboTwqib5bEXytIdUZfZfR9bjxoGyIMmrzuoaoepupT\nVX2sxu3UF9VEcQ3PVsAldpZhHou2vQrKFF3vbmE3Dd07Wzn2/HMf7D4DXXI7WvC16Iyfix5zRz8i\nx25fPOWXKzWh3h6LI+1T5ME7bZIso99a6d0XCOxoopvkbTXvEvgr1tr/Wyk1Bv6BUuo3rLW/Fx40\nYH3dicPNbhl8PlIwiWA/ditgdT+nGBZkk4r1Qc2gb0kLTVzEqKIHWd/t9NX3PFJUQVQ43qsVciZe\n+jD6oXv3MH+ZSO/0GpHqt6O0ely3nsO40C5ebSU8oUc7vonOSd0W74aJ7XLmyezheRvlW0PJZ9Mp\nV9j7Q43SmxSqhKSGxKISDUlEUhmSqiapSpK6xBrHExrrXu786hqX0e/SWJccD55z7+AFH90542sP\nLnj6PCF9kmAOErJhSlRBXFsiA1EdLlq3mB1OL1lQM8GyBwx0jzodUA6HFHsl60NDrwfJRqPTxE1u\nI9oLDiN89IHUi6bdp0JnbKiOBpSTStopbHPVd6W0gZctUS4mWebitL4+N75Kut1+2ycVjd9DnMOi\n+MjmV9LvXwHekmtclECawKiH2qtRhzVKb9AqQ+sNkc5Q6FbSdR9d9VHVAMo+SiuIK1RSo+KKqNDE\nx3PUSYk6nsNs6vdPadKW/bNsF212RbU+b3CcukKhuZVccJIsOO7nHPUNh0C2seSbmtwYqtoEa3Rs\nC8Rlq29Ho6jmfQ0R7scEvy+4r/ct5/Ia7Re0xBuLtfYUOPWfl0qp3wPuAS3w/pwHu9waN4K2fF5V\ncLmBZ5F7Lefq6YRptM9Vsc/VdMiLRHNW5SzKGVX1DC4X8Fi7FVKXyq1Y2qyhXNPEZAsYxji1uO6k\nrjcxHAW7uEkhtedABPWiUZbFORI6twraL/oIlbTtrUKtONCMGeNGatdTLyeGGmsI+ALe4d+hvqBx\nkSJLF+onJnIr8CEAbCugHZRTF6h94DhBHSWo4wkH80uO55ccz6cczy+oi5q8tBSVe6t5ZbuxH10Q\nD2mfiImtuFWdspdN3cZkc8W6PuCid8zjwxN+9P4JxcpSbizlxlBuLMZUuDePukmm6xQfYpljObeG\nx1h6ecL8bJ/5j/aYpxOmFylPc8NVviLLz2ATwzPgKY42WQDrwofW+Y24rk2esjxPLKBwQZfxjxn7\nFLlcpaADMLduRaiL/NBOKckyF+NM5qJFtl3BBtq3yA40CCu7Bs6t67e5/HCJe0uILMoJ1xpUNLOX\nkOede4TGmAKWGnuaYkcpdZ3AVUqhQSmN0TGVTh1dohRWCW2SkNc9qjrF1DEohYoUVRxhI7ft8sE0\n52A45eDTU0b7L5yRMFMwU9ilchvW1c5nLKvOuzXTmmf0lEhfofUpkf6C/WHB5GDJ9OCAf3TwHX5o\nH1BcbiivMorLDaYoHNFjDZGyaGvRppmvtMVPBAqMQlUKauXCBTPlVnFGgTNZeWcmf/96m+2Qr8x5\nK6UeAt8D/o/ub1/wQXNckN+kdW27TgXPMr/1ewWZGrDOh6yuhqyeDJlFmitTsKynVLVyL969VHCh\nXL9bGTeoipxmY6SQO0xpew67jsKQzwgBMkyxP3YBFGBiB8zykuolbV+V0J+CfcI4tByW0uPlBFmM\nMfa5DJ4QiFqtQTsssxta2A3NU44SMX5LO3vlvwvKhmUbpbPltgOHqjZwMEB90Ec9HKAeDjg4zXjw\n7AUPn1/xsfqSYl2yymCZWVZV+z3HEsfQroo2MTu2hlvlFZNsSrrKsHPNuj7gvPchjw4/5vvvf0x5\nZamnNbU1VFmN9T4D29qov6nb1Nac4V5qMMES55rN2ZBNMmCTDVk96nFV1kzLJVl15mL8r3AAceXb\nOfeB4JVMbF5T3tIMsrBJnIh2+0yg3GCNnLVC6nMdORpL+8829cCdgo1AV5Av4WoG6xnoMgDubl+F\nneANTbc2wMK6lMsPEuYZgneojYcmZIeakUsIBW3BLmPs6R7G7GPne9gnLgLG6IhKpxS6j1WqlSob\nUxmXrImxyhHKNjJYHRHpiGMKHo5mfPjJKSf3H8MTnO9LgS2Ui9H3xSjMbpIu7GmxGhLr58TxhCSa\nUI8G5Mc9prcPeH7nNqWtqHpzajOjWs4wrNG2RikT5M4Qc+HgAtwaVbktrh1wR85XkUQetENiXPOH\nAt6eMvl13IsYlt3fP+dB+3ifdy277t9p5XZ87JWQbqDKYsqrhPJJQjmIybUmMzmZnVHZHIq40Wo3\nyq0Oq2uXjJizMoAOaQjpUNPtehdvAm/psNJD5y43pqF7ZdvhrjIfMiK7FOgWalocEMiugsf+syC/\nTErh1CeTSncL1zAVtDWmylkntnAUCKYzm/oytcIFAzDXCnUQoz7YQ39rD/WdIw5+/IIHo5pv6ynf\n3XxJrgqmWK5quMqbdanhQv+QmGm0b5eG1nJSZexlGb1ljl1o1tUBF70HPD76Dt+vvkcdG4ytsFmN\nURXu9dFhvHnWumtERUpNYg2prdE5VGcx1SahfBFTDlIyU5OZJZmpoJ63X+uYgQ898GqdwU2y0FAP\n0g69oA8F/LaKnG2dKuhp58CKvNNdchvhlnprlxsfe7y6APMc9/alsH122bo3iJxS2Ca5L2jHRcqk\nLablK0ha6SKSVynW3MbMY3g2xgxTjIo9cLvXhqHxAO1y2d9aEsrtj26023892tMc38/5+N6U7947\n5YH+0g0TBawUZuagIMOtf8q4GbRlRCcqIY1SenFKmqScj+7yw+NP+NG92/zw4SdcmD6mPsMuzzBn\nZyMazLgAABSaSURBVFhmKGqwFUrVKFu7MHChSwyuzaoIpSVUM5ycfYiu6q67eD35KsvjE+B/AP4b\na+3OPQwf8YPgr4e4F/C8hlT4pbledm55JQNx/hoXlM2dYtwAO6FxDobL3AXEdwVpbwk8n4vnw4OD\nyf088AakFXDdkAu7lMzEI1/mUVDGcH+TcAYIgXpXTG7IweI0b5a41ZCyf8qbFD+G8R7qdoT6aIT+\n9jFj3ed2XvPxfM4fefGcTZFxVsJ55raPkAXT4XYGNxFWAD1r2Sug5zepy656zPIDzqL3eTz+Oj/m\nj2EK49ayX/mFQixwHUfS+vpdbbCKsTBwgUsttVR2zdk+8Es+WxqfigB1tz3DgZq0v+6rptlkfg1n\nNWNhnUO2gNUlrE6hltClLnh3Hdovk27td1vgDfuEgLa4lvI+dpVgGSNRUrXWL49i3VXs7XsxQd+1\n7A1z7j9c8PX753xt79Q17RR4CiZxBviqdivRJdYsTN3b9xT0taIXQT+GHw02PNq7x9WtPX7v3tf5\nwhzB1VM4fQbJHq6zePvRii3Zqb46pJfE5xFGFPWAHwD/kPZCuVfL20abKOC/Av6xtfZXbj7yz/i8\ny26H0uWWoW1I3+BpeCMxuAG7oNkfpBuWF2rdEoTd7chhzwoXMITgHibYTRjJs3YaVkUODLXP7Rjs\nBLeVZuG0r22URneFZRhXLXUXPofUqey9KGaxgNpbrM8FByjTHPtojpm48KzpF0u+/Eyx92wfNb1P\nviqZZTCt3GrwDZoCRYmmQHs9vu1kDOs/NZZsCdMzOB3AfpHwu9k+jzaK2WaB3TyFM9wy88x4LTTY\nS3rbh4RLF83YsDN88sYl292BGMbTRzQ7LQ5pNo2SSVbaJ4yGiBqmTANWXb9sl1rLLqCYueXs2xWF\n/197Zxcj2XHV8d+5ffu7Z+djvbveHa/YBBPbcRzbMbINAeEgE2wJghQhkUhBEQ95QgTxgDCIB78h\nJBAgIV74iCBSEolgkC2hyAYcKShSYid2svnYxBt/7m52e73emZ7unv669/BQVX2ra3rWMxOP5o64\nf6lUPbe7p8+tW/WvU6fOORW6G4ZyhrSFR47KdCmhoR/rvJWn/z2/vhHccnQdaGffmeZDsZciu4KY\nWYl68EQY9tu02wPO/6hKPTpGuzEwiakvmp9Jhyb30SAx6UN8fx93F+F8EStUEjErf+BS9wTnrzZp\nV2GYds3zutA3kZ1DN35Trw5dWHw+c7/qxq2f+uF9wF3ejf7LDtp075r3B4FPAN8WkRfstT9W1S/N\nfsztUOucwjZ1OG2/E+StGIJyWrojqzAril/mdU5fo3FGPaf9QqZG+WaJsMv4/9e3T1tfoqgCpbKp\nXZKdNLa2e/cbjoAga6uIWTOKv271Szcozpzg38cukCq6PoA3NsyfnSFrV7u83o6gfYTO+i2MNxP6\nA+iNhb46o0VWzFlH5pyaWdc+Q6pxoqx14XIbllJodGJeGy3y+khYH3VhdBHWI5OHZiCW1JzPuU/e\nru2UWc8Q115h9FSIqfMys/sKrnZBW3WyzUpnM+7bNg76hGcbJpGtOkA4bMZdGG0Y98SZPY7t6tA+\nHQXG39QsZ+hgJnXXD1wf97X5sB/P09LnIbVt4PLTuInHCpGCOdxBTZ2GfODLa+pB7zrtKwNeiiqM\nNo/xalXgMshl0DWj64wnNi9NOhvQv535pJRCnArxREyequ4JXnuzRTuFQX/DyHelB2+N7f6Arzz5\nD4ygToNrzpTWJFMkXdl5HMpevU3+lx3p901mNb9wNt+O0P1IwlBj3QvczI/932430de45hmkYfa3\nw87rlt1OTqfxuMETmkHC+w3c+KSGCZapGtc0jYw9VVOQkV3mV73fickmHieXfy9O+/ZXFCF5uw7j\nE/8u4DRvOtAZohe6rPU2oCt0uke40LuF1HqajCfCSIWEmJQy6bQ2cvuHjPn3EKUp9a45t6DWg0o7\n4npyhLVEWE82YHIJhjEMYrMZlMbes3Far3t+zqbokxJkfUTJgmh8uO/64ee1oFS912WyAdnDEJdL\nkmXHg+qsjjKWt5/vk7EtE0/zdv1onixesI6/LJ+mh02AN01fkwS077WXTzx+X/YF2sn4dNr9mm0P\n637oH3A9dVmd53+kmQgWw94m7faA8WaVN6/eRDNuZAvKDbOIcNsR7kDx0BLjN7FgFP8oESIVokTo\nd5dY0xbXezC8Zk9i6PahO5pD3r7cPlmH1gT3PefD74/PDXZjovpJbN6PAH9tJfkHVf3zrZ9y+aLD\nG4StRBYSuxu87wSc5j3GDCR/hgxlmWd19ZeO8667JZRzCfOTcPk25vCeGxhNzYbYSxOiOpQaUG7Y\nzdYe2bFikD34svcb/ij3O5Czy/vF9XBXfKLaG3nr2hA6IzTqQiSsaUonFS7qIlG6gNoBanJVGMLR\nmSyA4Puf6JR4DZlLkiJdkB5Tb6qUCokKKRuoDjHeGF7ZQjKQ6Rt+7YprN6cKz4NP3s732g+imWbn\nJ9scdn3uOll726KJN8eG5oxtoFNnYUzaU5eIyWn9LiGVn6chiLh0ECx5Wy8m3SRb0bk+NM9S7L+3\nG817ZNugxPSw5WlSK48fZgh8vnY/SFLamylvXq1Qio4h6BYdUcm+Ok/KLU2tYA5SMDLppEbSb9pc\nUnaVmo4xHlow65Xg2jFsl3ny+5r3JpkS5VbCO8Nebd4l4G8xZ1deBJ4TkSfDIJ0s+c48G7Z/U6Gt\n2IXi+tqwb9KYt8EyT1PGu+bLEM65rlMC09Sn4e7J2ywdpWS05ahu62rmMRBZl7Dp2Xx2aZjWIalD\n2jB1VDeaN1VIykbjTt2g6pDZ2d3hDH7Ah2/68dsrCM8uj6EcQ7kF5Rr1qM8CG7Sky4JsUErGpCOy\nkszXLfwWN8vNjLKShTJJKyZplUkWYoZpncGkxnBSZzCuk46rMCzDqGzqCXZAjMkOLfYd5CeYI0rU\nqFMzZg8XyRj2lXkTrV8coTlNFTJic+3pfy/sS/P6Ztg6/sB0WnLsfQ5zXcn61fS1j3DfJFQa3ETt\nNHv33F0mSkfafkCN/Z+agK5ZOV0KhlD79VeVbnzMU3Ts6ygyfutxDHGJUhlatR4LtT6t2hrNSt/u\nIgrSN7XaSBq1ueFN4qcsAVQ4rqUM0rKlCZNqma626KYLdNMW/aQO/cSUXgqbswrB3NVVpQK1KlSr\nptYajOswMoFCTEp2dTLJap0wjUBWn5u2aZ/4CJSXTWKxcgvKZSinJmCnXIJoFETLbI+9at73A+dV\n9VUAEfkC8Bts+Vk/ddd2JhMhc5Epe699rdXZL513SEhUoYZ+I23Af883aYSbUHHwet5mpK8tRJiD\nXOumVKrGd9flMihLtn5za7lxFUYVW1etxmj9eSdiNbMh5hipNTLvFqexOvPAvOLIJfDLLpdtxq8W\nNCs047c4KQNWoz6rcpnasMe4C+MNk6donGzdDQjXUNWgjI40GK66Umd9UmNts85af4Xx5gpptwqd\n2JaS2VGaeMEu040z/wSZcPC5Zxm6Szoim6fC+v3PLVtrmNSqPnGHG3f+YHRy+C6Xrp+mWTvPBDM5\nn32XRNp9fl4BtigWvsLgr6bcCiEhGxdOe/bHkj+e/EkCK/M66DqZX3c4TYeeMr7FdI5tR0pQqUGt\nBrUqcVNZWf4xq8sdTq1c48RCG2kDbUHagrRBExt9myqapFa/sVG5ChqM6agK0TGITkLpJAwWG1yc\nrHIpWeZScpT+4CZoD6E9MmUzXIHOIe9qGRZbsLwIS0dA69CtZGUYWRfk1JbEjNHUuSS7vhhO4l4p\nN6C5AA2bWKxZhUYJmjVoLBjlap/JexV4w/v7AvDA1o/1MW4wt8IW4nZws7rTTOpsScZDnxmD1nQj\nKtSAb7ThFhK6+24EvALcztYNH7+EXgYwQ5aOvKsNqDWgXrOmUTGlhqcgq02OUzJ22s3Y+HxOYuNa\nlJTM5lWaYE6q72Hshd8E7mQ2LNknmHDGn2PTLy+aDrO8BMvLNCvCyajNbdEm740u0+qvMbhmDlMZ\nDmAwnPXLGc3eNcrsgv0SPVaPnKJ/epHeHWP6d8CV0RJxp8a4s0yncwqu1aEdmVQGackMABkyPRSA\nAdkBgm6C9Qn5Rqa3CTfejlGMW8L77f+rY8jbRZ+6PBlOQw5/x5G3I2d/debI04/qdcqB6z/hZmeo\nNEBG1s9j9CTf3uxcOt14cPluHOm68bSdH55kOtNUo7Uyq8vPEipaM/6M3v8X4Bzw3tl2ispQaUHT\nZEAsLaWsrHY4c2rC7atvcevRV5FXQBpAKkgHmw5eDYmjngu9ksyxzkgV4psgfjfEt8HG8UVa4xV0\nHLM2Pgrd0/ByH6QP176G4aCeldk9l+CfVsuwtAA3H4WTx8yK+K0SvGXHZ1dMuOZYTS1uJZgyPWBg\nC88FY7FcM0S95EoCyzVYasH1r8BtPwtfYEfYK3nfSLX10MM83FM3+GqEIWl/82WBzBa8gOmoNSuu\n63n+0s2Vt7PBzSNwAV7GuOpMM8ewVZ/0A19cs3n6qAjETag2odGEVs27BTHMNrVgqN3HEnNenjuB\nBMGcGyhG805Tq4X2rOb9InDMk3ueXfdGtUK5acl7GY6v0qwPOFkqc3upz/2lyyytt81xepvQX8sc\n7lxxOovvk7PolZcYcs+isH56zMad0HmwQmUA42s1Nq4tU7p2Ci7ZHB0p5lio0cQQRzKEaABJn4zY\n/BWaT9DePc30Cbx6O5wD7sD0PUfedTLi7pBF0PpKgf/7Y2ZJUjCE7btiusnBzxVc9167Pr2dh9Ln\ngI8yS8JrWGd0ZonbTa/+qsRhTrvMDIVwkzvsOyF5++6JPwIeZOZZRBWoLEFjCY4sEd+UsnL6Dc7c\nOuH9t17j3lOvIg1j2pYOyCVrorPdP9Fs12OipmxhjSqUj0H5p6F8N1z/qWOkwzOsD8u8PjwKa6dB\nNqC/AWfPA/eREfc2yemqFUOiJ4/Cu09B0jS+95Ebj2TztTihbC1zzEfAFgWqHEMjhqUYjsdwXOF4\nCscUnj0Hd390vmxzsFfyvghe7Lt5fWHrx/4Lk50kBc7YEmLeDO9I3G0GJRjqcNrKdj6sO0XYsV3x\n7Xp+Mit/197VMGO+EcEkp6pBXDOmE/dVN1ad2L6iNppzfapI+mYA587nTqbZIyK14dhVaDSJ6zWa\nJWGxlHAs3mRl3KVXhV5s5hWXHNSV0GiVYvLMLWFiVxvA0UqfUmtIaXkEJxKam1CTmFirSNKEjWaW\n5KkMJpGYHSRANsD8cH6/gfxGCrWdncC1q3vm4QTtk3ISfC9c0fg2VD/3b5+sP7nUBs406HeMMCLW\n748VTEOFJsRwVeJWBL6r417a5UYIzYth4InfPhU7DupQaRLVEqqtmIUlZeXokBPHN5BliBbMkJHY\nLDKT1NZiyhjDme6OfJRKRomtLED5KJSPN1kaTGgMhfKgCqUWLCRQn0DknrH/bOcgikwCrUYNFmxe\n7w6zw9/tS7rmcN1oWwQr3ygy468amaPbmgJXvgzf+jKc/yo88Rc7ehpsfxdvi+eBnxGRMyJSAX4L\neHLrxx7CELarCxQoUKDADG59CH7zcbjnIfjU4zv+mqjubWYWkUfJXAX/UVX/LHj/nZryCxQoUOD/\nFVT1bU0JeybvAgUKFChwcNir2aRAgQIFChwgCvIuUKBAgUOIfSVvEXlERM6JyEsi8kf7+Vs7hYj8\nk4hcEZGz3rUVEXlGRH4oIk+LyNIBy3haRJ4Vke+KyHdE5NM5lbMmIl8TkRetnI/nUU4rU0lEXhCR\np/Ioo4i8KiLftjJ+PY8yWpmWROSLIvJ9EfmeiDyQJzlF5Dbbhq6si8in8ySjlfMP7Jg5KyKfE5Hq\nbmXcN/L2QugfwXjxf1xE7tiv39sFPoORycdjwDOq+h7gv+3fBwl3RuidGCfa37Vtlys5VXUAfEhV\n7wHuAR4RkQfImZwWvw98j8zrLG8yKvCQqt6rqvfba3mTEeBvgP9U1TswkU7nyJGcqvoD24b3Ypy7\n+8C/50lGEVkFfg+4T1Xvwjh9fGzXMqrqvhTg54AveX8/Bjy2X7+3S9nOAGe9v88BJ+zrm4FzBy1j\nIO9/YPLI5FZOjIv3NzAhgbmSE7gFE3TwIeCpPD5zTJjv0eBa3mRcBF6ecz1XcnpyfRj4St5kxESo\nv44JjYiBp4Bf2a2M+2k2mRdCv7qPv/eT4ISqXrGvrwAnDlIYH8EZobmTU0QiEXnRyvO0qn6d/Mn5\nV8AfMps/IW8yKvC0iDwvIp+y1/Im47uAqyLyGRH5poj8vYg0yZ+cDh8DPm9f50ZGVb0I/CWGwC8B\na6r6DLuUcT/J+1D6IKqZ9nIhuz0j9N8wZ4Ru+O/lRU5VTdWYTW4BHhCR9wXvH6icIvJrQFtVX2Cb\nWLiDltHig6p6H/Aoxkz2i/6bOZExBj4A/J2qfgAT6jmztM+JnNjgwV8H/jV876BlFJFl4CMYC8Ap\noCUin/A/sxMZ95O8dxhCnwtcEZGbAUTkJOaspgOFd0boZzU7IzR3cjqo6jrwLPCr5EvOnwc+IiKv\nYLSwXxaRz+ZMRlT1x7a+irHR3k/OZMSM3wuq+pz9+4sYMr+cMznBTILfsO0J+WrLh4FXVPWaqk6A\nJzBm5l21436S9w5D6HOBJ4FP2tefxNiYDwwi254Rmjc5b3I74iJSx9jtvk+O5FTVP1HV06r6Lswy\n+n9U9bfzJKOINERkwb5uYmy1Z8mRjACqehl4Q0TeYy89DHwXY7PNjZwWHyczmUC+2vI14EERqdux\n/jBmM3137bjPhvlHgR8A5zFnXOZhE+PzGDvTCGOT/x1gBbOh9UPgaWDpgGX8BYx99kXgBVseyaGc\nd2Hy1H4LQzZ/aq/nSk5P3l8CnsybjBhb8ou2fMeNlTzJ6Ml6N/CcfeZPYDYxcyUnJpvXm8CCdy1v\nMj6OUXTOAv+MSX21KxmL8PgCBQoUOIQoIiwLFChQ4BCiIO8CBQoUOIQoyLtAgQIFDiEK8i5QoECB\nQ4iCvAsUKFDgEKIg7wIFChQ4hCjIu0CBAgUOIQryLlCgQIFDiP8DD/RVkrAIhzsAAAAASUVORK5C\nYII=\n", |
|
| 645 |
"text": [ |
|
| 646 |
"<matplotlib.figure.Figure at 0x7fe7fbb4a438>" |
|
| 647 |
] |
|
| 648 |
} |
|
| 649 |
], |
|
| 650 |
"prompt_number": 68 |
|
| 651 |
}, |
|
| 652 |
{
|
|
| 653 |
"cell_type": "code", |
|
| 654 |
"collapsed": true, |
|
| 655 |
"input": [ |
|
| 656 |
"import csv" |
|
| 657 |
], |
|
| 658 |
"language": "python", |
|
| 659 |
"metadata": {},
|
|
| 660 |
"outputs": [], |
|
| 661 |
"prompt_number": 69 |
|
| 662 |
}, |
|
| 663 |
{
|
|
| 664 |
"cell_type": "code", |
|
| 665 |
"collapsed": true, |
|
| 666 |
"input": [ |
|
| 667 |
"out_file = open('features.csv', 'w')"
|
|
| 668 |
], |
|
| 669 |
"language": "python", |
|
| 670 |
"metadata": {},
|
|
| 671 |
"outputs": [], |
|
| 672 |
"prompt_number": 70 |
|
| 673 |
}, |
|
| 674 |
{
|
|
| 675 |
"cell_type": "code", |
|
| 676 |
"collapsed": true, |
|
| 677 |
"input": [ |
|
| 678 |
"writer = csv.writer(out_file)" |
|
| 679 |
], |
|
| 680 |
"language": "python", |
|
| 681 |
"metadata": {},
|
|
| 682 |
"outputs": [], |
|
| 683 |
"prompt_number": 71 |
|
| 684 |
}, |
|
| 685 |
{
|
|
| 686 |
"cell_type": "code", |
|
| 687 |
"collapsed": true, |
|
| 688 |
"input": [ |
|
| 689 |
"writer.writerows(chroma)" |
|
| 690 |
], |
|
| 691 |
"language": "python", |
|
| 692 |
"metadata": {},
|
|
| 693 |
"outputs": [], |
|
| 694 |
"prompt_number": 72 |
|
| 695 |
}, |
|
| 696 |
{
|
|
| 697 |
"cell_type": "code", |
|
| 698 |
"collapsed": true, |
|
| 699 |
"input": [ |
|
| 700 |
"out_file.close()" |
|
| 701 |
], |
|
| 702 |
"language": "python", |
|
| 703 |
"metadata": {},
|
|
| 704 |
"outputs": [], |
|
| 705 |
"prompt_number": 73 |
|
| 706 |
}, |
|
| 707 |
{
|
|
| 708 |
"cell_type": "code", |
|
| 709 |
"collapsed": true, |
|
| 710 |
"input": [ |
|
| 711 |
"import os, glob" |
|
| 712 |
], |
|
| 713 |
"language": "python", |
|
| 714 |
"metadata": {},
|
|
| 715 |
"outputs": [], |
|
| 716 |
"prompt_number": 74 |
|
| 717 |
}, |
|
| 718 |
{
|
|
| 719 |
"cell_type": "code", |
|
| 720 |
"collapsed": true, |
|
| 721 |
"input": [ |
|
| 722 |
"def extract_chroma(audiofile):\n", |
|
| 723 |
" data, rate = librosa.load(audiofile)\n", |
|
| 724 |
" out = vamp.collect(data, rate,\n", |
|
| 725 |
" plugin_key = \"nnls-chroma:nnls-chroma\",\n", |
|
| 726 |
" output = \"chroma\",\n", |
|
| 727 |
" process_timestamp_method = vamp.vampyhost.SHIFT_DATA)\n", |
|
| 728 |
" step, chroma = out[\"matrix\"]\n", |
|
| 729 |
" out_file = open(os.path.splitext(audiofile)[0] + \"_chroma.csv\", \"w\")\n", |
|
| 730 |
" csv.writer(out_file).writerows(chroma)\n", |
|
| 731 |
" out_file.close()" |
|
| 732 |
], |
|
| 733 |
"language": "python", |
|
| 734 |
"metadata": {},
|
|
| 735 |
"outputs": [], |
|
| 736 |
"prompt_number": 75 |
|
| 737 |
}, |
|
| 738 |
{
|
|
| 739 |
"cell_type": "code", |
|
| 740 |
"collapsed": false, |
|
| 741 |
"input": [ |
|
| 742 |
"for file in glob.glob(\"data/Music/*.wav\"):\n", |
|
| 743 |
" extract_chroma(file)" |
|
| 744 |
], |
|
| 745 |
"language": "python", |
|
| 746 |
"metadata": {},
|
|
| 747 |
"outputs": [], |
|
| 748 |
"prompt_number": 76 |
|
| 749 |
}, |
|
| 750 |
{
|
|
| 751 |
"cell_type": "code", |
|
| 752 |
"collapsed": true, |
|
| 753 |
"input": [], |
|
| 754 |
"language": "python", |
|
| 755 |
"metadata": {},
|
|
| 756 |
"outputs": [], |
|
| 757 |
"prompt_number": null |
|
| 758 |
} |
|
| 759 |
], |
|
| 760 |
"metadata": {}
|
|
| 761 |
} |
|
| 762 |
] |
|
| 763 |
} |
|
Also available in: Unified diff