def load_and_encode_data(codebook,pSize=4,keyInv=True,
downBeatInv=False,bars=1,partialbar=1,offset=0):
"""
Load a dataset, and encode it with codebook
Return dists, avg_dists
"""
assert(codebook.shape[1] == pSize * 12)
import VQutils
# get data
featsNorm = get_data_maxener(pSize=pSize,keyInv=keyInv,
downBeatInv=downBeatInv,bars=bars,
partialbar=partialbar,offset=offset)
# encode
best_code_per_p, dists, avg_dists = VQutils.find_best_code_per_pattern(featsNorm,codebook,scale=False)
return dists, avg_dists
def encode_one_song(filename,codebook,pSize=8,keyInv=True,
downBeatInv=False,bars=2):
"""
returns: song, encoding, song as MAT, encoding as MAT
matrices are 'derolled'
"""
import feats_utils as FU
import numpy as np
import data_iterator
import VQutils
# create data iterator
data_iter = data_iterator.DataIterator()
data_iter.setMatfiles([filename]) # set matfiles
if bars > 0:
data_iter.useBars( bars ) # a pattern spans 'bars' bars
else:
data_iter.useBars(0) # important to set it to zero!
data_iter.setFeatsize( pSize ) # a pattern is a num. of beats
data_iter.stopAfterOnePass(True)
# load data
featsNorm = [FU.normalize_pattern_maxenergy(p,pSize,keyInv,downBeatInv,retRoll=True) for p in data_iter]
keyroll = np.array([x[1] for x in featsNorm])
dbroll = np.array([x[2] for x in featsNorm])
featsNorm = [x[0].flatten() for x in featsNorm]
if len(featsNorm) == 0: # empty song
return [],[],[],[],[]
featsNorm = np.array(featsNorm)
res = [np.sum(r) > 0 for r in featsNorm]
res2 = np.where(res)
featsNorm = featsNorm[res2]
keyroll = keyroll[res2]
dbroll = dbroll[res2]
assert(dbroll.shape[0] == keyroll.shape[0])
assert(dbroll.shape[0] == featsNorm.shape[0])
# find code per pattern
best_code_per_p, dists, avg_dists = VQutils.find_best_code_per_pattern(featsNorm,codebook)
best_code_per_p = np.asarray([int(x) for x in best_code_per_p])
assert best_code_per_p.shape[0] > 0,'empty song, we should have caught that'
encoding = codebook[best_code_per_p]
# transform into 2 matrices, with derolling!!!!!!!!!
assert(featsNorm.shape[0] == encoding.shape[0])
#featsNormMAT = np.concatenate([x.reshape(12,pSize) for x in featsNorm],axis=1)
featsNormMAT = np.concatenate([np.roll(np.roll(featsNorm[x].reshape(12,pSize),-keyroll[x],axis=0),-dbroll[x],axis=1) for x in range(featsNorm.shape[0])],axis=1)
#encodingMAT = np.concatenate([x.reshape(12,pSize) for x in encoding],axis=1)
encodingMAT = np.concatenate([np.roll(np.roll(encoding[x].reshape(12,pSize),-keyroll[x],axis=0),-dbroll[x],axis=1) for x in range(featsNorm.shape[0])],axis=1)
# return
return best_code_per_p,featsNorm,encoding,featsNormMAT,encodingMAT
def load_and_encode_data(codebook,
pSize=4,
keyInv=True,
downBeatInv=False,
bars=1,
partialbar=1,
offset=0):
"""
Load a dataset, and encode it with codebook
Return dists, avg_dists
"""
assert (codebook.shape[1] == pSize * 12)
import VQutils
# get data
featsNorm = get_data_maxener(pSize=pSize,
keyInv=keyInv,
downBeatInv=downBeatInv,
bars=bars,
partialbar=partialbar,
offset=offset)
# encode
best_code_per_p, dists, avg_dists = VQutils.find_best_code_per_pattern(
featsNorm, codebook, scale=False)
return dists, avg_dists
def encode_one_song(filename,
codebook,
pSize=8,
keyInv=True,
downBeatInv=False,
bars=2):
"""
returns: song, encoding, song as MAT, encoding as MAT
matrices are 'derolled'
"""
import feats_utils as FU
import numpy as np
import data_iterator
import VQutils
# create data iterator
data_iter = data_iterator.DataIterator()
data_iter.setMatfiles([filename]) # set matfiles
if bars > 0:
data_iter.useBars(bars) # a pattern spans 'bars' bars
else:
data_iter.useBars(0) # important to set it to zero!
data_iter.setFeatsize(pSize) # a pattern is a num. of beats
data_iter.stopAfterOnePass(True)
# load data
featsNorm = [
FU.normalize_pattern_maxenergy(p,
pSize,
keyInv,
downBeatInv,
retRoll=True) for p in data_iter
]
keyroll = np.array([x[1] for x in featsNorm])
dbroll = np.array([x[2] for x in featsNorm])
featsNorm = [x[0].flatten() for x in featsNorm]
if len(featsNorm) == 0: # empty song
return [], [], [], [], []
featsNorm = np.array(featsNorm)
res = [np.sum(r) > 0 for r in featsNorm]
res2 = np.where(res)
featsNorm = featsNorm[res2]
keyroll = keyroll[res2]
dbroll = dbroll[res2]
assert (dbroll.shape[0] == keyroll.shape[0])
assert (dbroll.shape[0] == featsNorm.shape[0])
# find code per pattern
best_code_per_p, dists, avg_dists = VQutils.find_best_code_per_pattern(
featsNorm, codebook)
best_code_per_p = np.asarray([int(x) for x in best_code_per_p])
assert best_code_per_p.shape[
0] > 0, 'empty song, we should have caught that'
encoding = codebook[best_code_per_p]
# transform into 2 matrices, with derolling!!!!!!!!!
assert (featsNorm.shape[0] == encoding.shape[0])
#featsNormMAT = np.concatenate([x.reshape(12,pSize) for x in featsNorm],axis=1)
featsNormMAT = np.concatenate([
np.roll(np.roll(featsNorm[x].reshape(12, pSize), -keyroll[x], axis=0),
-dbroll[x],
axis=1) for x in range(featsNorm.shape[0])
],
axis=1)
#encodingMAT = np.concatenate([x.reshape(12,pSize) for x in encoding],axis=1)
encodingMAT = np.concatenate([
np.roll(np.roll(encoding[x].reshape(12, pSize), -keyroll[x], axis=0),
-dbroll[x],
axis=1) for x in range(featsNorm.shape[0])
],
axis=1)
# return
return best_code_per_p, featsNorm, encoding, featsNormMAT, encodingMAT
