Python RealApplianceSource.stop示例

示例#1

文件： disag_545d.py 项目： tolysz/neuralnilm_prototype

N_BATCHES = 1
logger.info("Preparing synthetic mains data for {} batches.".format(N_BATCHES))
mains = None
targets = None
TARGET_I = 4
for batch_i in range(N_BATCHES):
    batch = mains_source.queue.get(timeout=30)
    mains_batch, targets_batch = batch.data
    if mains is None:
        mains = mains_batch
        targets = targets_batch[:, :, TARGET_I]
    else:
        mains = np.concatenate((mains, mains_batch))
        targets = np.concatenate((targets, targets_batch[:, :, TARGET_I]))

mains_source.stop()

# Post-process data
seq_length = net.input_shape[1]


def pad(data):
    return np.pad(data, (seq_length, seq_length),
                  mode='constant',
                  constant_values=(data.min().astype(float), ))


mains = pad(mains.flatten())
targets = pad(targets.flatten())
logger.info("Done preparing synthetic mains data!")

示例#2

文件： disag_545b.py 项目： mmottahedi/neuralnilm_prototype

N_BATCHES = 1
logger.info("Preparing synthetic mains data for {} batches.".format(N_BATCHES))
mains = None
targets = None
TARGET_I = 2
for batch_i in range(N_BATCHES):
    batch = mains_source.queue.get(timeout=30)
    mains_batch, targets_batch = batch.data
    if mains is None:
        mains = mains_batch
        targets = targets_batch[:, :, TARGET_I]
    else:
        mains = np.concatenate((mains, mains_batch))
        targets = np.concatenate((targets, targets_batch[:, :, TARGET_I]))

mains_source.stop()

# Post-process data
seq_length = net.input_shape[1]


def pad(data):
    return np.pad(data, (seq_length, seq_length), mode='constant',
                  constant_values=(data.min().astype(float), ))


mains = pad(mains.flatten())
targets = pad(targets.flatten())
logger.info("Done preparing synthetic mains data!")

# Unstandardise for plotting