def main(): name = 'Aly & Fila - Future Sound of Egypt 338 ' print name csv_file = os.path.join('.', 'data', name + experiment.plugin_suffix + '.csv') cue_file = os.path.join('.', 'data', name + '.cue') sim_file = os.path.join('.', 'data', name + experiment.plugin_suffix + '.sim') timestamps, data = experiment.read_csv(csv_file) info = experiment.read_cue(cue_file) has_intro = 'intro' in info[0]['TITLE'].strip().lower() has_outro = 'outro' in info[-1]['TITLE'].strip().lower() with open(sim_file, 'rb') as f: (self_sim, factor) = cPickle.load(f) filtered, novelty = tools.init_borders2(self_sim) sec_per_row = timestamps[-1] / self_sim.shape[0] #filtered = [x * sec_per_row for x in filtered] filtered = tools.detect_track_borders(data, timestamps[-1], len(info), self_sim=self_sim, factor=factor, has_intro=has_intro, has_outro=has_outro) #make_average(data, factor, [int(x / sec_per_row) for x in filtered]) #for i in range(len(info)): # print '%d.\t%.2f\t%.2f: %f' % (i, info[i]['INDEX'], filtered[i], filtered[i] - info[i]['INDEX']) #print filtered #print [x['INDEX'] for x in info] #print experiment.get_diff(info, filtered) data = data.transpose((1, 0)) draw_spectrum(data, timestamps, 1) #draw_selfsim(self_sim, timestamps) vlines([datetime.datetime.fromtimestamp(x) for x in filtered], 0, 24, color='k', linewidths=[1] * len(filtered), alpha=1.0) vlines([datetime.datetime.fromtimestamp(x['INDEX']) for x in info], 24, 48, color='y', linewidths=[1] * len(info), alpha=1.0) #savefig('fig.pdf', bbox_inches='tight') tracks = [(x['PERFORMER'], x['TITLE']) for x in info] export_cue(name, tracks, filtered) # borders2, novelty = tools.init_borders2(self_sim) # plt.figure(2) # plt.plot(novelty) # print borders2 #draw_spectrum(data2, timestamps, 2) # plt.figure(2) # plt.plot(data2) # plt.figure(3) # plt.matshow(areas, fignum=3) # _, axs = plt.subplots(5, 1, sharex=True) # tss = [datetime.datetime.fromtimestamp(x) for x in timestamps] # for i in range(len(centroids)): # axs[i].plot(centroids[i]) # axs[4].plot(flux) # plt.figure(4) # plt.matshow(self_sim, fignum=4) show()
with open('mix_list.txt', 'rb') as mixlist: for line in mixlist: rec = line.strip().split('\t') mix_styles[rec[0]] = rec[1] proposed = [] naive = [] style_stats = {} intro_size = 30 outro_size = 30 with open(os.path.join('logs', 'test_beat_profile.log'), 'rb') as log: for line in log: rec = line.strip().split('\t') if len(rec) > 2: name = rec[0] info = read_cue(os.path.join('temp_data', name[:-4] + '.cue')) # indices = numpy.array([info[i+1]['INDEX'] - info[i]['INDEX'] for i in range(len(info)-1)]) indices = [info[i]['INDEX'] for i in range(len(info))] has_intro = 'intro' in info[0]['TITLE'].lower() has_outro = 'outro' in info[-1]['TITLE'].strip().lower() # has_intro = False # has_outro = False avg = float(rec[1]) max_diff = float(rec[2]) borders = rec[3][1:-1].split(',') borders = [float(x) for x in borders] length = borders[-1] indices.append(length) tracks = len(info) even = build_even(length, tracks, has_intro, has_outro, intro_size, outro_size)
with open('mix_list.txt', 'rb') as mixlist: for line in mixlist: rec = line.strip().split('\t') mix_styles[rec[0]] = rec[1] proposed = [] naive = [] style_stats = {} intro_size = 30 outro_size = 30 with open(os.path.join('logs', 'test_beat_profile.log'), 'rb') as log: for line in log: rec = line.strip().split('\t') if len(rec) > 2: name = rec[0] info = read_cue(os.path.join('temp_data', name[:-4] + '.cue')) # indices = numpy.array([info[i+1]['INDEX'] - info[i]['INDEX'] for i in range(len(info)-1)]) indices = [info[i]['INDEX'] for i in range(len(info))] has_intro = 'intro' in info[0]['TITLE'].lower() has_outro = 'outro' in info[-1]['TITLE'].strip().lower() # has_intro = False # has_outro = False avg = float(rec[1]) max_diff = float(rec[2]) borders = rec[3][1:-1].split(',') borders = [float(x) for x in borders] length = borders[-1] indices.append(length) tracks = len(info) even = build_even(length, tracks, has_intro, has_outro, intro_size,
def main(): name = 'Aly & Fila - Future Sound of Egypt 338 ' print name csv_file = os.path.join('.', 'data', name + experiment.plugin_suffix + '.csv') cue_file = os.path.join('.', 'data', name + '.cue') sim_file = os.path.join('.', 'data', name + experiment.plugin_suffix + '.sim') timestamps, data = experiment.read_csv(csv_file) info = experiment.read_cue(cue_file) has_intro = 'intro' in info[0]['TITLE'].strip().lower() has_outro = 'outro' in info[-1]['TITLE'].strip().lower() with open(sim_file, 'rb') as f: (self_sim, factor) = cPickle.load(f) filtered, novelty = tools.init_borders2(self_sim) sec_per_row = timestamps[-1] / self_sim.shape[0] #filtered = [x * sec_per_row for x in filtered] filtered = tools.detect_track_borders(data, timestamps[-1], len(info), self_sim=self_sim, factor=factor, has_intro=has_intro, has_outro=has_outro) #make_average(data, factor, [int(x / sec_per_row) for x in filtered]) #for i in range(len(info)): # print '%d.\t%.2f\t%.2f: %f' % (i, info[i]['INDEX'], filtered[i], filtered[i] - info[i]['INDEX']) #print filtered #print [x['INDEX'] for x in info] #print experiment.get_diff(info, filtered) data = data.transpose((1, 0)) draw_spectrum(data, timestamps, 1) #draw_selfsim(self_sim, timestamps) vlines([datetime.datetime.fromtimestamp(x) for x in filtered], 0, 24, color='k', linewidths=[1]*len(filtered), alpha=1.0) vlines([datetime.datetime.fromtimestamp(x['INDEX']) for x in info], 24, 48, color='y', linewidths=[1]*len(info), alpha=1.0) #savefig('fig.pdf', bbox_inches='tight') tracks = [(x['PERFORMER'], x['TITLE']) for x in info] export_cue(name, tracks, filtered) # borders2, novelty = tools.init_borders2(self_sim) # plt.figure(2) # plt.plot(novelty) # print borders2 #draw_spectrum(data2, timestamps, 2) # plt.figure(2) # plt.plot(data2) # plt.figure(3) # plt.matshow(areas, fignum=3) # _, axs = plt.subplots(5, 1, sharex=True) # tss = [datetime.datetime.fromtimestamp(x) for x in timestamps] # for i in range(len(centroids)): # axs[i].plot(centroids[i]) # axs[4].plot(flux) # plt.figure(4) # plt.matshow(self_sim, fignum=4) show()