def main():
#### We can't do this for multiple songs.
songs = glob.glob("songs/*.mp3")
filename = generate(songs)
beats = []
audiofile = audio.LocalAudioFile(filename)
beats = audiofile.analysis.beats
print "Number of beats %s" % len(beats)
samples = beats[::SAMPLING_STEP]
print "Number of samples to build cluster model %s" % len(samples)
cl = cluster.KMeansClustering(samples, distance_beats)
clusters = cl.getclusters(K)
print "Clustering completed"
for c in clusters:
c.centroid = None
pickle.dump(clusters, open("clustering.c", "wb"))
print "Pickled Cluster Model"
for c in clusters:
c.centroid = cluster.centroid(c)
print "Reset the centroids"
training_input = []
for beat in beats:
training_input.append(get_cluster_index(beat, clusters))
print("Training markovModel")
markov_model = MarkovModel()
markov_model.learn_ngram_distribution(training_input, NGRAM)
#### We should have his function as iterator.
print "Generating bunch of music"
output_list = markov_model.generate_a_bunch_of_text(len(training_input))
generated_beats = audio.AudioQuantumList()
print "Coming back to beats"
for index in output_list:
generated_beats.append(get_beats_back(index, clusters))
#### We can't do this for multiple songs.
print "Saving an Amazing Song"
out = audio.getpieces(audiofile, generated_beats)
out.encode("bunch_of_music.wav")
# No need to clutter the map with unnecessary placemarks...
kml_items = [{'label': label, 'coords': '%s,%s' % coords[0]} for (label,
coords) in expanded_coords]
# It could also be interesting to include names of your contacts on the map for display
for item in kml_items:
item['contacts'] = '\n'.join(['%s %s.' % (ec.first_name, ec.last_name[0])
for ec in extended_connections if ec.location
== item['label']])
cl = KMeansClustering([coords for (label, coords_list) in expanded_coords
for coords in coords_list])
centroids = [{'label': 'CENTROID', 'coords': '%s,%s' % centroid(c)} for c in
cl.getclusters(K)]
kml_items.extend(centroids)
kml = createKML(kml_items)
if not os.path.isdir('out'):
os.mkdir('out')
f = open("out/" + OUT, 'w')
f.write(kml)
f.close()
print >> sys.stderr, 'Data pickled to out/' + OUT
# No need to clutter the map with unnecessary placemarks...
kml_items = [{'label': label, 'coords': '%s,%s' % coords[0]} for (label,
coords) in expanded_coords]
# It could also be interesting to include names of your contacts on the map for display
for item in kml_items:
item['contacts'] = '\n'.join(['%s %s.' % (ec.first_name, ec.last_name[0])
for ec in extended_connections if ec['location']
== item['label']])
cl = KMeansClustering([coords for (label, coords_list) in expanded_coords
for coords in coords_list])
centroids = [{'label': 'CENTROID', 'coords': '%s,%s' % centroid(c)} for c in
cl.getclusters(K)]
kml_items.extend(centroids)
kml = createKML(kml_items)
if not os.path.isdir('out'):
os.mkdir('out')
f = open("out/" + OUT, 'w')
f.write(kml)
f.close()
print >> sys.stderr, 'Data pickled to out/' + OUT
# It could also be interesting to include names of your contacts on the map for display
for item in kml_items:
item['contacts'] = '\n'.join([
'%s %s.' % (ec.first_name, ec.last_name[0])
for ec in extended_connections if ec.location == item['label']
])
cl = KMeansClustering([
coords for (label, coords_list) in expanded_coords
for coords in coords_list
])
centroids = [{
'label': 'CENTROID',
'coords': '%s,%s' % centroid(c)
} for c in cl.getclusters(K)]
kml_items.extend(centroids)
kml = createKML(kml_items)
if not os.path.isdir('out'):
os.mkdir('out')
f = open("out/" + OUT, 'w')
f.write(kml)
f.close()
print >> sys.stderr, 'Data pickled to out/' + OUT
def main():
parser = argparse.ArgumentParser(
description="Markov")
parser.add_argument(
"-d", "--directory",
default=None,
help="Music dir")
parser.add_argument(
"-f", "--filename",
default=None,
help="Song file")
parser.add_argument(
"-p", "--pickle",
default=False, action="store_true",
help="Pickle")
parser.add_argument(
"-k", "--clusters", type=int,
default=50, help="Clusters")
parser.add_argument(
"-s", "--sample", type=int,
default=2, help="Sampling")
parser.add_argument(
"-n", "--ngram", type=int,
default=10, help="Ngram")
parser.add_argument(
"-l", "--length", type=int,
default=None, help="Length")
args = parser.parse_args()
if args.directory is not None:
args.filename = generate_single_song(args.directory)
if args.filename is None:
raise Exception("Song not defined")
#### We can't do this for multiple songs.
beats = []
audiofile = audio.LocalAudioFile(args.filename)
beats = audiofile.analysis.beats
print "Number of beats %s" % len(beats)
internal_filename = os.path.split(args.filename)[1]
if not args.pickle:
samples = beats[::args.sample]
print "Number of samples to build cluster model %s" % len(samples)
cl = cluster.KMeansClustering(samples, distance_beats)
clusters = cl.getclusters(args.clusters)
print "Clustering completed"
for c in clusters:
c.centroid = None
pickle.dump(clusters, open(internal_filename[:-4] + ".pickle", "wb"))
print "Pickled Cluster Model"
else:
clusters = pickle.load(open(internal_filename[:-4] + ".pickle", "rb"))
attach_source(clusters, audiofile)
print "Resetting the centroids"
for c in clusters:
c.centroid = cluster.centroid(c)
print "Reset the centroids"
training_input = []
for beat in beats:
training_input.append(get_cluster_index(beat, clusters))
print("Training markovModel")
markov_model = MarkovModel()
markov_model.learn_ngram_distribution(training_input, args.ngram)
#### We should have his function as iterator.
print "Generating bunch of music"
if args.length is None:
args.length = len(training_input)
output_list = markov_model.generate_a_bunch_of_text(args.length)
generated_beats = audio.AudioQuantumList()
print "Coming back to beats"
prev_beat = None
for index in output_list:
curr_beat = get_beats_back(index, clusters, prev_beat)
generated_beats.append(curr_beat)
prev_beat = curr_beat
#### We can't do this for multiple songs.
print "Saving an Amazing Song"
out = audio.getpieces(audiofile, generated_beats)
out.encode(internal_filename[:-4] + ".wav")
for label in coords_freqs:
((lat,lon),f)=coords_freqs[label]
expanded_coords.append((label, [(lon, lat)]*f)) # flip lat/lon for google earth
# No need to clutter the map with unnecessary placemarks...
kml_items = [{'label': label, 'coords': '%s,%s' % coords[0]} for (label, coords) in expanded_coords]
# It could also be interesting to include names of your contacts on the map for display
for item in kml_items:
item['contacts'] = '\n'.join(['%s %s.' % (ec.first_name, ec.last_name[0]) for ec in extended_connections if ec.location == item['label']])
cl = KMeansClustering([coords for (label, coords_list) in expanded_coords for coords in coords_list])
centroids=[{'label': 'CENTROID', 'coords': '%s,%s' % centroid(c)} for c in cl.getclusters(K)]
#kml_items.extend(centroids)
#kml=createKML(kml_items)
if not os.path.isdir('out'):
os.mkdir('out')
f = open("out/" + OUT, 'w')
f.write(centroids)
f.close()
print >> sys.stderr, "Data pickled to out/" +OUT
