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))
def load_sino_summaries(gen='g2'):
"""Loads sinogram summaries from .pickle files
Parameters
----------
gen : str
Must be in ['g1', 'g2'], string to identify which data gen
Returns
-------
sino_summaries : array_like
The average and standard deviation of the signals for each
sinogram
sino_md : array_like
Metadata for the scans from which the average and stdev
signals were obtained
"""
summaries = load_pickle(
os.path.join(__OUT_DIR, '%s_sino_summaries.pickle' % gen))
metadata = load_pickle(
os.path.join(__OUT_DIR, '%s_sino_summaries_md.pickle' % gen))
return summaries, metadata
from umbms import get_proj_path, verify_path
from umbms.loadsave import load_pickle
###############################################################################
__DATA_DIR = os.path.join(get_proj_path(), 'output/roc-figs/')
__OUT_DIR = os.path.join(get_proj_path(), 'output/figs/')
verify_path(__OUT_DIR)
###############################################################################
if __name__ == "__main__":
# Load ROC data obtained from testing on G1 after training on G1
cnn_roc = load_pickle(os.path.join(__DATA_DIR, 'cnn_run_8_roc.pickle'))
dnn_roc = load_pickle(os.path.join(__DATA_DIR, 'dnn_run_15_roc.pickle'))
logreg_roc = load_pickle(
os.path.join(__DATA_DIR, 'logreg_run_0_roc.pickle'))
# Define colours for the figure
mid_grey = [118, 113, 113]
mid_grey = [ii / 255 for ii in mid_grey]
light_grey = [200, 200, 200]
light_grey = [ii / 255 for ii in light_grey]
# Make the figure
plt.figure(figsize=(10, 10))
plt.rc('font', family='Times New Roman')
plt.tick_params(labelsize=24)
__DATA_DIR = os.path.join(get_proj_path(), 'data/umbmid/')
###############################################################################
__N_EPOCHS = 496
__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 = []
'%s' % unique_adi_ids[ii],
size=16,
color='k',
horizontalalignment='center',
verticalalignment='center',
bbox={
'facecolor': 'w',
'alpha': 0.9
})
plt.tight_layout()
plt.show()
plt.savefig(os.path.join(__OUT_DIR, 'adi_id_fracs.png'),
dpi=600,
transparent=False)
###############################################################################
if __name__ == "__main__":
logger = get_script_logger(__file__)
# Load metadata lists of correct and incorrect predictions
cor_preds = load_pickle(os.path.join(__DATA_DIR, 'byadi_cor_preds.pickle'))
incor_preds = load_pickle(
os.path.join(__DATA_DIR, 'byadi_incor_preds.pickle'))
plt_adi_id_distros(wrong_preds=incor_preds,
all_preds=np.concatenate((cor_preds, incor_preds)))
for birads_class in unique_birads:
# Find fraction of samples with this BI-RADS class
frac_here = np.sum(birads_classes == birads_class) / n_samples
logger.info('\t\tClass %d:\t%.2f' % (birads_class, 100 * frac_here))
###############################################################################
if __name__ == "__main__":
logger = get_script_logger(__file__)
# Load all Gen-1 frequency-domain data and metadata
fd_data = load_pickle(os.path.join(__DATA_DIR, 'g1_fd.pickle'))
metadata = load_pickle(os.path.join(__DATA_DIR, 'g1_metadata.pickle'))
# Init vars for train/test fractions
tr_frac = 0
te_frac = 0
# Until the train tumor fraction is approx 50%
while not (0.45 <= tr_frac <= 0.55):
# Shuffle the arrays
[fd_data, metadata] = shuffle_arrs([fd_data, metadata])
# Split data/metadata into train and test sets
tr_data = fd_data[:125, :, :]
tr_md = metadata[:125]
out_path = os.path.join(get_proj_path(), 'output/roc-figs/')
plt.savefig(os.path.join(out_path, '%s.png' % save_str), dpi=150)
plt.close()
save_pickle(np.array([fprs, tprs]),
os.path.join(out_path, '%s.pickle' % save_str))
###############################################################################
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'))
# Load the training data and metadata from Gen-1
g1_d = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/test_fd.pickle'))
g1_md = load_pickle(
os.path.join(__DATA_DIR, 'g1-train-test/test_md.pickle'))
# Convert data to time domain, take magnitude, apply window
g1_d = correct_g1_ini_ant_ang(g1_d)
g1_d = np.abs(to_td(g1_d))
g2_d = np.abs(to_td(g2_d))
# Perform data augmentation
g2_d, g2_md = full_aug(g2_d, g2_md)
from umbms.plot.sinogramplot import plot_sino
from umbms.ai.preproc import to_td
###############################################################################
__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'),