def cycle(self):
"""
A single cycle that allows all agents to perform.
"""
agents = self.agents
random.shuffle(agents)
for agent in agents:
# make the agent perform
output = agent.perform()
group_structure = getNoteGroups(output)
nominal_tempo = self.defaultTempo
nominal_volume = self.defaultVolume
tempo_events = [(event.time, event.tempo) for event in output.tracks[0].eventList if event.type == "tempo"]
notes = [event for event in output.tracks[0].eventList if event.type == "note"]
metrical_hierarchy,metrical_scores = getMetricStructure(output)
keys = key_change.analyze_key_change(output)
accentuation = accentuation_curve(analyze_melodic_accent(output), metrical_scores, keys, notes)
r1_tempo, r1_loudness, r2, r3, r4_tempo, r4_loudness, r5 = \
rule1_tempo(group_structure, nominal_tempo, tempo_events), \
rule1_loudness(group_structure, nominal_volume), \
rule2(group_structure, nominal_tempo, tempo_events), \
rule3(notes, nominal_volume, accentuation), \
rule4_tempo(notes, accentuation, nominal_tempo, tempo_events), \
rule4_loudness(notes, accentuation, nominal_volume), \
rule5(group_structure, nominal_tempo, tempo_events)
# let all other agents evaluate the performance
for a in agents:
if a is not agent:
a.listen(output, r1_tempo, r1_loudness, r2, r3, r4_tempo, r4_loudness, r5)
def __listen_own(self, nominal_tempo, nominal_volume):
self.__logger.debug("Listening to self")
performance = self.performance
group_structure = getNoteGroups(performance)
tempo_events = [(event.time, event.tempo)
for event in performance.tracks[0].eventList
if event.type == "tempo"]
notes = [
event for event in performance.tracks[0].eventList
if event.type == "note"
]
metrical_hierarchy, metrical_scores = getMetricStructure(performance)
keys = key_change.analyze_key_change(performance)
accentuation = accentuation_curve(analyze_melodic_accent(performance),
metrical_scores, keys, notes)
r1_tempo, r1_loudness, r2, r3, r4_tempo, r4_loudness, r5 = \
rule1_tempo(group_structure, nominal_tempo, tempo_events), \
rule1_loudness(group_structure, nominal_volume), \
rule2(group_structure, nominal_tempo, tempo_events), \
rule3(notes, nominal_volume, accentuation), \
rule4_tempo(notes, accentuation, nominal_tempo, tempo_events), \
rule4_loudness(notes, accentuation, nominal_volume), \
rule5(group_structure, nominal_tempo, tempo_events)
return self.evaluate(r1_tempo, r1_loudness, r2, r3, r4_tempo,
r4_loudness, r5)
def __listen_own(self, nominal_tempo, nominal_volume):
self.__logger.debug("Listening to self")
performance = self.performance
group_structure = getNoteGroups(performance)
tempo_events = [(event.time, event.tempo) for event in performance.tracks[0].eventList if event.type == "tempo"]
notes = [event for event in performance.tracks[0].eventList if event.type == "note"]
metrical_hierarchy,metrical_scores = getMetricStructure(performance)
keys = key_change.analyze_key_change(performance)
accentuation = accentuation_curve(analyze_melodic_accent(performance), metrical_scores, keys, notes)
r1_tempo, r1_loudness, r2, r3, r4_tempo, r4_loudness, r5 = \
rule1_tempo(group_structure, nominal_tempo, tempo_events), \
rule1_loudness(group_structure, nominal_volume), \
rule2(group_structure, nominal_tempo, tempo_events), \
rule3(notes, nominal_volume, accentuation), \
rule4_tempo(notes, accentuation, nominal_tempo, tempo_events), \
rule4_loudness(notes, accentuation, nominal_volume), \
rule5(group_structure, nominal_tempo, tempo_events)
return self.evaluate(r1_tempo, r1_loudness, r2, r3, r4_tempo, r4_loudness, r5)
def cycle(self):
"""
A single cycle that allows all agents to perform.
"""
agents = self.agents
random.shuffle(agents)
for agent in agents:
# make the agent perform
output = agent.perform()
group_structure = getNoteGroups(output)
nominal_tempo = self.defaultTempo
nominal_volume = self.defaultVolume
tempo_events = [(event.time, event.tempo)
for event in output.tracks[0].eventList
if event.type == "tempo"]
notes = [
event for event in output.tracks[0].eventList
if event.type == "note"
]
metrical_hierarchy, metrical_scores = getMetricStructure(output)
keys = key_change.analyze_key_change(output)
accentuation = accentuation_curve(analyze_melodic_accent(output),
metrical_scores, keys, notes)
r1_tempo, r1_loudness, r2, r3, r4_tempo, r4_loudness, r5 = \
rule1_tempo(group_structure, nominal_tempo, tempo_events), \
rule1_loudness(group_structure, nominal_volume), \
rule2(group_structure, nominal_tempo, tempo_events), \
rule3(notes, nominal_volume, accentuation), \
rule4_tempo(notes, accentuation, nominal_tempo, tempo_events), \
rule4_loudness(notes, accentuation, nominal_volume), \
rule5(group_structure, nominal_tempo, tempo_events)
# let all other agents evaluate the performance
for a in agents:
if a is not agent:
a.listen(output, r1_tempo, r1_loudness, r2, r3, r4_tempo,
r4_loudness, r5)
return score
if __name__ == '__main__':
# performance = mid.prepare_initial_midi("../../../res/midi_text.txt", "../../../res/sample.midi", 15200)
import doctest
doctest.testmod()
exit()
performance = mid.prepare_initial_midi("../../../res/midi_rule3_text.txt", "../../../res/sample_rule3.midi", 15200)
group_structure = lbdm.getNoteGroups(performance)
tempo_events = [(event.time, event.tempo) for event in performance.tracks[0].eventList if event.type == "tempo"]
notes = [event for event in performance.tracks[0].eventList if event.type == "note"]
nominal_tempo = 3947
nominal_loudness = 100
melodic_accent = melodic_accent.analyze_melodic_accent(performance)
metric_structure, metrical_scores = metric_structure.getMetricStructure(performance)
key = key_change.analyze_key_change(performance)
accentuation = accentuation_curve.accentuation_curve(melodic_accent, metrical_scores, key, notes)
print "Rule 1 tempo: %d" % rule1_tempo(group_structure, nominal_tempo, tempo_events)
print "Rule 1 loudness: %d" % rule1_loudness(group_structure, nominal_loudness)
print "Rule 2: %d" % rule2(group_structure, nominal_tempo, tempo_events)
print "Rule 3: %d" % rule3(notes, nominal_loudness, accentuation)
print "Rule 4 tempo: %d" % rule4_tempo(notes, accentuation, nominal_tempo, tempo_events)
print "Rule 4 loudness: %d" % rule4_loudness(notes, accentuation, nominal_loudness)
print "Rule 5: %d" % rule5(group_structure, nominal_tempo, tempo_events)
# accentuation_curve(melodic_accent.analyze_melodic_accent(performance), metric_scores, key_change.analyze_key_change(performance), [note for note in performance.tracks[0].eventList if note.type == "note"])
print "Rule 5: %d" % rule5(group_structure, nominal_tempo, tempo_events)
metric_structure, metric_scores = metric_structure.getMetricStructure(performance)
accentuation_curve(melodic_accent.analyze_melodic_accent(performance), metric_scores, key_change.analyze_key_change(performance), [note for note in performance.tracks[0].eventList if note.type == "note"])
# print keys_final
# print accentuation_curve
return accentuation_curve
if __name__ == "__main__":
from tools import midi as mid
from tools.analysis import lbdm, metric_structure, melodic_accent, key_change
performance = mid.prepare_initial_midi("../../../res/midi_text.txt",
"../../../res/sample.midi", 15200)
group_structure = lbdm.getNoteGroups(performance)
tempo_events = [(event.time, event.tempo)
for event in performance.tracks[0].eventList
if event.type == "tempo"]
notes = [
event for event in performance.tracks[0].eventList
if event.type == "note"
]
nominal_tempo = 3947
nominal_loudness = 100
melodic_accent = melodic_accent.analyze_melodic_accent(performance)
metric_structure, metrical_scores = metric_structure.getMetricStructure(
performance)
key = key_change.analyze_key_change(performance)
accentuation = accentuation_curve(melodic_accent, metrical_scores, key,
notes)
from matplotlib import pyplot as plt
plt.plot(range(len(accentuation)), accentuation)
plt.show()
accentuation_curve = [w_melodic * melodic + w_metric * metric for melodic, metric in zip(accents, metrics)]
# print accentuation_curve
keys_final = []
for i in range(len(notes)):
note = notes[i]
# print i, note_find_measure(note)
accentuation_curve[i] += w_key_change * keys[note_find_measure(note)]
keys_final.append(keys[note_find_measure(note)])
# print keys_final
# print accentuation_curve
return accentuation_curve
if __name__ == "__main__":
from tools import midi as mid
from tools.analysis import lbdm, metric_structure, melodic_accent, key_change
performance = mid.prepare_initial_midi("../../../res/midi_text.txt", "../../../res/sample.midi", 15200)
group_structure = lbdm.getNoteGroups(performance)
tempo_events = [(event.time, event.tempo) for event in performance.tracks[0].eventList if event.type == "tempo"]
notes = [event for event in performance.tracks[0].eventList if event.type == "note"]
nominal_tempo = 3947
nominal_loudness = 100
melodic_accent = melodic_accent.analyze_melodic_accent(performance)
metric_structure, metrical_scores = metric_structure.getMetricStructure(performance)
key = key_change.analyze_key_change(performance)
accentuation = accentuation_curve(melodic_accent, metrical_scores, key, notes)
from matplotlib import pyplot as plt
plt.plot(range(len(accentuation)), accentuation)
plt.show()
