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Using `pickle`

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“Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy. See the documentation for more details. (Original code here.)

from music21 import *
import time
import pickle

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Simple example

favorite_works = {'Parker':'Anthropology',
                  'Saariaho':'Amers',
                  'Bach':'Musical Offering'}

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Use the dump method to write a pickled representation of the object favorite_works to the file object with path 'favorites.pkl'. The file is opened in mode 'wb', which that file is opened for writing in binary mode.

pickle.dump( favorite_works, open('favorites.pkl', 'wb'))

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Savedlist -- {'Parker': 'Anthropology', 'Saariaho': 'Amers', 'Bach': 'Musical Offering'}

print("Savedlist --", favorite_works)

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Use the load method to read a pickled object representation from the file object with path 'favorites.pkl' and return the reconstituted object hierarchy specified therein.

retrieved_favorites = pickle.load( open("favorites.pkl", "rb"))

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Retrieved list -- {'Parker': 'Anthropology', 'Saariaho': 'Amers', 'Bach': 'Musical Offering'}

print("Retrieved list --", retrieved_favorites)

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Parsing and pickling folksongs

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Parse folksongs from the music21 corpus

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`time.time() returns the time in seconds since the epoch (an arbitrary reference time) as a floating point number.

start = time.time()

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Parsing songs from 'han' and 'boehme' opuses

oChina = corpus.parse('essenFolksong/han1')
print('han1 loaded')
oCEurope = corpus.parse('essenFolksong/boehme10')
print('boehme10 loaded')

end = time.time()

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timing stats

interval = round(end - start)

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On my computer, parsing takes 8 seconds.

print("Parsing took", interval, "seconds.\n")

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Create list of features with ids p1 - p8 and r31 - r35

featureList = ['p' + str(i) for i in range(1, 9)]
featureList.extend(['r' + str(i) for i in range(31, 36)])

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Get feature extractors using this list of ids

featureExtractors = features.extractorsById(featureList)

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Create DataSet

start = time.time()

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Instantiate DataSet

ds = features.DataSet(classLabel='Region')

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Add featureExtractors

ds.addFeatureExtractors(featureExtractors)

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addData for each stream in the oChina and oCEurope opuses

for w in oChina.scores:
    wID = 'essenFolkson/%s-%s' % ('han1', w.metadata.number)
    ds.addData(w, classValue='China', id=wID)
print('han1 scores added to training data set')

for w in oCEurope.scores:
    wID = 'essenFolksong/%s-%s' % ('europe1', w.metadata.number)
    ds.addData(w, classValue='CentralEurope', id=wID)
print('boehme10 added to training data set')

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Process data and feature extractors

ds.process()

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On my computer, processing the DataSet takes 76 seconds.

print('dataset processed')

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timing stats

end = time.time()
interval = round(end - start)
print("Feature extraction took", interval, "seconds.\n")

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Pickle and unpickle the `DataSet`

start = time.time()

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Save the DataSet by pickling the output of getFeaturesAsList()

path = 'folkSongExample.pkl'
pickle.dump(ds.getFeaturesAsList(), open(path, 'wb'))
print('dataset written as a pickled file')

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'Unpickle' and load dataset

start = time.time()

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Load the datset

path = 'folkSongExample.pkl'
dataset = pickle.load(open(path, 'rb'))

pickle_example.py

Using pickle