def load_data_modeled(len_packet, num_of_packets=20):
# ARTIFICIAL DATASET
ListName = ['qRs_4', 'QS_5', 'rRs_8', 'RS_9']
artificial_t = []
artificial_signals = []
labels = []
for name in ListName:
for i in range(10):
data = utils.read_file('data/signal_' + name + '_' + str(i))
my_ecg = fb.deserialize(data)
for j in range(num_of_packets):
artificial_t.append(name)
artificial_signals.append(my_ecg.packets[j])
labels.append(my_ecg.packets[j].label)
artificial = fb.ECG.build_from_packets(artificial_signals)
artificial_signals = artificial.build_packets(len_packet)
artificial_signals = artificial.packets
return artificial_signals, labels
def restore_model(name):
return fb.deserialize(utils.read_file(name))
my_ecg = fb.ECG.build_from_packets(list_of_packets)
len_packet = 35
list_ecg = my_ecg.build_packets(len_packet)
list_ecg = my_ecg.packets
return list_ecg
if __name__ == '__main__':
list_ecg = main()
rd = fb.RandomExtraction(offset=35)
for sig in list_ecg:
rd.evaluete_signal(sig.input_signal)
utils.write_file('teste', rd.serialize())
data = utils.read_file('teste')
rd = fb.deserialize(data)
table = rd.get_table_erros()
t = []
for p in list_ecg:
t.append(p.label)
t = ['qRs_4' if x == 2 else x for x in t]
t = ['QS_5' if x == 14 else x for x in t]
t = ['rRs_8' if x == 10 else x for x in t]
t = ['RS_9' if x == 5 else x for x in t]
t = ['artifact' if x in [12, 11, 1, 18, 6, 15, -1] else x for x in t]
X = table.values
X_train, X_test, y_train, y_test = train_test_split(X,
heart_rate = np.mean(heart_rate)
labels = [name for x in rpeaks]
my_ecg = fb.ECG(input_signal=ecg_signal,
indicators=rpeaks,
labels=labels,
is_filtered=False)
if name == 'qRs_4':
offset = int(intervals / 7)
elif name == 'QS_5':
offset = int(intervals / 7)
elif name == 'rRs_8':
offset = int(intervals / 7)
elif name == 'RS_9':
offset = int(intervals / 7)
else:
offset = int(intervals / 7)
offset = 40
my_ecg.packets = my_ecg.build_packets(offset)
utils.write_file('data/signal_' + name + '_' + str(i),
my_ecg.serialize())
list_of_signals = []
for name in ListName:
for i in range(10):
data = utils.read_file('data/signal_' + name + '_' + str(i))
list_of_signals.append(fb.deserialize(data))
for i in range(len(list_of_signals)):
plt.plot(list_of_signals[i].packets[0].input_signal)
plt.show()
plt.close()
my_ecg.packets = lst_of_packets
return my_ecg
if __name__ == '__main__':
__spec__ = "ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>)"
results = main()
error = results.get_errors_table()
param1 = results.get_param_table()
[signals, models, names, errors,
types] = results.get_signals_and_models(normalized=False, only_best=True)
data = results.serialize()
utils.write_file('outfile', data)
data = utils.read_file('outfile')
c = fb.deserialize(data)
# t = []
# for p in results.packets:
# t.append(p.label)
#
# error['types'] = [int(x) for x in types]
# X = error.values
#
#
# X_train, X_test, y_train, y_test = train_test_split(
# X, t, test_size=0.4, random_state=0)
#
#
def default_load(name):
return fb.deserialize(utils.read_file(name)).get_table_erros().values