metadata = {} # Output data structure
with h5.File(files[2], 'r') as h5file:
# Create a HYDRO/DMO switch
if "/PartType0" in h5file:
part_types = [0, 1, 4]
else:
part_types = [1]
# Loop over particle types (hydro/dmo sensitive)
for part_type in part_types:
# Read in GroupNumber info
N_particles = header.data.subfind_particles.NumPart_ThisFile[part_type]
start, end = split(N_particles)
GroupNumber[f'PartType{part_type}'] = np.empty(0, dtype=np.int)
GroupNumber[f'PartType{part_type}'] = np.append(
GroupNumber[f'PartType{part_type}'],
np.abs(h5file[f'PartType{part_type}/GroupNumber'][start:end])
)
# Generate the metadata in parallel through MPI
unique, unique_indices, unique_counts = np.unique(
GroupNumber[f'PartType{part_type}'],
return_index=True,
return_counts=True
)
# Initialise and allocate metadata entries in each rank
metadata[f'PartType{part_type}'] = {}
def build_mlp_model(input_shape):
base_input = Input((input_shape, ))
x = Dense(1200, activation='relu')(base_input)
embedding_model = Model(base_input, x, name='embedding')
return embedding_model
feature_data = read.read()
test_ids = list(feature_data.keys())
all_labels = list(feature_data[test_ids[0]].keys())
for test_id in test_ids:
for a_label in all_labels:
train_labels = [a for a in all_labels if a != a_label]
_train_data, _test_data = read.split(feature_data, test_id)
_train_data = read.remove_class(_train_data, [a_label])
_support_data, _test_data = read.support_set_split(_test_data, k_shot)
_train_data, _train_labels = read.flatten(_train_data)
_support_data, _support_labels = read.flatten(_support_data)
_train_data = np.array(_train_data)
_support_data = np.array(_support_data)
_train_labels = np.array(_train_labels)
_support_labels = np.array(_support_labels)
base_network = build_mlp_model(feature_length)
def conv():
_input = Input(shape=(feature_length, 1))
x = Conv1D(12, kernel_size=5, activation='relu')(_input)
x = MaxPooling1D(pool_size=2)(x)
x = BatchNormalization()(x)
x = Flatten()(x)
x = Dense(1200, activation='relu')(x)
x = BatchNormalization()(x)
return Model(inputs=_input, outputs=x, name='embedding')
all_features = read.read()
test_ids = list(all_features.keys())
for test_id in test_ids:
_train_features, _test_features = read.split(all_features, test_id)
_train_features, _train_labels = read.flatten(_train_features)
_test_features, _test_labels = read.flatten(_test_features)
_train_labels_ = np_utils.to_categorical(_train_labels,
len(read.activity_list))
_test_labels_ = np_utils.to_categorical(_test_labels,
len(read.activity_list))
_train_features = np.array(_train_features)
_train_features = np.reshape(
_train_features, (_train_features.shape[0],
_train_features.shape[1] * _train_features.shape[2]))
_train_features = np.expand_dims(_train_features, 3)
print(_train_features.shape)
