def save_sino_summaries(gen='g2'):
"""Create and save the sinogram summaries
Parameters
----------
gen : str
Must be in ['g1', 'g2'], string to identify which data gen
"""
# Load the data and metadata
if gen in ['g1']:
data = load_pickle(os.path.join(__DATA_DIR, 'g1/g1_fd.pickle'))
metadata = load_pickle(
os.path.join(__DATA_DIR, 'g1/g1_metadata.pickle'))
else: # If Gen-2
data = load_pickle(os.path.join(__DATA_DIR, 'g2/g2_fd.pickle'))
metadata = load_pickle(
os.path.join(__DATA_DIR, 'g2/g2_metadata.pickle'))
# Convert to the time-domain
data = np.abs(to_td(data))
metadata = np.array(metadata)
# Get the sinogram summaries (average and stdev signals)
sino_summaries, sino_md = get_sino_summaries(dataset=data,
metadata=metadata)
# Save to .pickle files
save_pickle(sino_summaries,
os.path.join(__OUT_DIR, '%s_sino_summaries.pickle' % gen))
save_pickle(sino_md,
os.path.join(__OUT_DIR, '%s_sino_summaries_md.pickle' % gen))
# Load the G1 validation data
g1_tr_d = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/train_fd.pickle'))
g1_tr_md = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/train_md.pickle'))
# Set the G1 data to be the validation data
val_data = g1_tr_d
val_md = g1_tr_md
# Correct the initial antenna position used in G1 scans
val_data = correct_g1_ini_ant_ang(val_data)
# Preprocess data, take magnitude and apply time-window, augment
# training dataset
val_data = np.abs(to_td(val_data))
val_data = resize_features_for_keras(val_data)
train_data = np.abs(to_td(train_data))
train_data, train_md = full_aug(train_data, train_md)
train_data = resize_features_for_keras(train_data)
# Get the validation and train set class labels and make categorical
val_labels = get_class_labels(val_md)
val_labels = to_categorical(val_labels)
train_labels = get_class_labels(train_md)
train_labels = to_categorical(train_labels)
# Create arrays for storing the AUC on the train and validation
# sets for this regularization parameter after training with
# correct labels
train_set_aucs = np.zeros([__N_RUNS, __N_EPOCHS])
__N_RUNS = 20
###############################################################################
if __name__ == "__main__":
logger = get_script_logger(__file__)
# Load the training data and metadata from Gen-2
g2_d = load_pickle(os.path.join(__DATA_DIR, 'g2/g2_fd.pickle'))
g2_md = load_pickle(os.path.join(__DATA_DIR, 'g2/g2_metadata.pickle'))
g2_md = np.array(g2_md)
# Convert to the time-domain, crop the signals, take magnitude
g2_d = np.abs(to_td(g2_d))
# Get all of the Adipose IDs
adi_ids = np.array([md['phant_id'].split('F')[0] for md in g2_md])
unique_adi_ids = np.unique(adi_ids)
adi_results = dict() # Init dict for storing results
# Init lists for storing metadata of samples which are
# incorrectly / correctly classified
incor_preds = []
cor_preds = []
for adi_id in unique_adi_ids: # For each adipose ID
logger.info('\tWorking on Adi ID:\t%s' % adi_id)
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/g2/')
__FIG_OUT_DIR = os.path.join(get_proj_path(), 'output/figs/')
verify_path(__FIG_OUT_DIR)
###############################################################################
if __name__ == "__main__":
# Load phantom dataset (UM-BMID Gen1)
phant_data = load_pickle(os.path.join(__DATA_DIR, 'g2_fd.pickle'))
# Convert dataset to the time-domain
sinograms = to_td(phant_data)
# Take sample sinogram
sinogram = sinograms[201, :, :]
# Define scan times
scan_ts = np.linspace(0.5e-9, 5.5e-9, 35)
plot_sino(td_data=sinogram,
ini_t=scan_ts[0],
fin_t=scan_ts[-1],
save_fig=True,
save_str=os.path.join(__FIG_OUT_DIR, 'sino_example'),
transparent=False,
dpi=300,
cbar_fmt='%.3f')