def tumor_near_surface_temperatures(filepath,
region_filepath,
do_print=True,
do_write=True):
print()
print('Calc tumor temperatures near surface of {0}.'.format(filepath))
temp = temperature_array_from_result(filepath)
tumor = region_array_from_file(region_filepath)
temp_mean, temp_max, temp_min, temp_std_dev = calc_tumor_near_surface_temperatures(
temp, tumor)
depth = DEPTH
if do_print == True:
section = 'tumor near surface (first ' + str(depth) + ' nodes)'
print_results(section, temp_mean, temp_max, temp_min, temp_std_dev)
if do_write == True:
filepath = os.path.splitext(filepath)[0] + '_results.dat'
section = 'Tumor_Near_Surface_' + str(depth) + '_Depth'
write_results_to_file(section, temp_mean, temp_max, temp_min,
temp_std_dev, filepath, 'a')
print('Done.')
return temp_mean
def plot_surface(filepath, params):
print('Plotting {0}.'.format(filepath))
T = temperature_array_from_result(filepath)
dim2, dim1, dim0 = T.shape
filepath = os.path.splitext(filepath)[0]
title = filepath
filepath_surface = filepath + '_surface.eps'
filepath_heatmap = filepath + '_heatmap.eps'
filepath_heatmap_scaled = filepath + '_heatmap_scaled.eps'
filepath_heatmap_thermo_scaled = filepath + '_heatmap_thermo_scaled.eps'
filepath_tumor = filepath + '_tumor.eps'
# Surface data.
temperature = np.zeros((dim1, dim0))
temperature[:, :] = T[-1, :, :]
plot_3d_surface(temperature, params, title, filepath_surface)
plot_heatmap(temperature, params, title, filepath_heatmap)
plot_heatmap_scaled(temperature, params, title, filepath_heatmap_scaled)
if params['MRI_DATA_CASE'] != '':
csv_min, csv_max = plot_thermo(params['MRI_DATA_CASE'],
params['MRI_DATA_FOLDER'])
plot_heatmap_thermo_scaled(temperature, params, title,
filepath_heatmap_thermo_scaled, csv_min,
csv_max)
# x-z-plane data.
temperature = np.zeros((dim2, dim0))
temperature[:, :] = T[:, int(dim1 / 2), :]
plot_tumor(temperature, params, title, filepath_tumor)
print('Done.')
def check_results(filepath):
a_1 = temperature_array_from_result(filepath[0])
a_2 = temperature_array_from_result(filepath[1])
print()
if np.array_equal(a_1, a_2) == True:
print('SUCCESS: Last timestep is equal.')
else:
if a_1.shape[0] != a_2.shape[0] or \
a_1.shape[1] != a_2.shape[1] or \
a_1.shape[2] != a_2.shape[2]:
print('WARNING: Shape of files is not equal.')
print('Aborting.')
exit()
else:
print('WARNING: Last timestep is NOT equal.')
print('Done.')
def callScaFES(T_blood=T_blood):
global count
global params
count += 1
print()
print('##### ScaFES iteration: {} #####'.format(count))
case = params['NAME_CONFIGFILE_TEMPLATE'].split('.')[0]
case = case.split('_')[0]
params['NAME_CONFIGFILE'] = case + '-pymc-' + TESTED_VARIABLES + '.ini'
params['NAME_RESULTFILE'] = ''
config = configparser.ConfigParser()
config.optionxform = str
config.read(params['NAME_CONFIGFILE_TEMPLATE'])
# T_blood.
config['Brain']['T_BLOOD'] = str(T_blood)
config['Tumor']['T_BLOOD'] = str(T_blood)
with open(params['NAME_CONFIGFILE'], 'w') as configfile:
config.write(configfile)
# Call simulation.
parse_config_file(params)
check_variables(params)
calc_variables(params)
check_stability(params)
if params['USE_MRI_FILE'] == False:
create_region_file(params)
create_init_file(params)
set_environment_variables(params)
call_simulation(params, params['RUN_SCRIPT'])
if params['NAME_RESULTFILE'] != '':
# Compute temperatures of normal, tumor, vessel tisue.
temp = temperature_array_from_result(params['NAME_RESULTFILE'])
tumor = region_array_from_file(params['NAME_REGION_FILE'])
vessels = surface_vessels_array_from_file(
params['NAME_VESSELS_FILE'])
T_tumor, _, _, _ = calc_tumor_near_surface_temperatures(
temp, tumor)
if params['USE_VESSELS_SEGMENTATION'] == True:
temp = temp[-1, :, :]
T_vessel, _, _, _ = calc_vessels_temperatures(temp, vessels)
T_normal, _, _, _ = calc_non_vessels_temperatures(
temp, vessels)
else:
T_vessel = -1.0
T_normal = -1.0
else:
print('* ERROR: No result file written.')
print('Aborting.')
exit()
return [T_normal, T_tumor, T_vessel]
def calc_diff(filepaths):
temp_1 = temperature_array_from_result(filepaths[0])
temp_2 = temperature_array_from_result(filepaths[1])
filepath = os.path.splitext(filepaths[0])[0] + '_' \
+ os.path.basename(os.path.splitext(filepaths[1])[0]) \
+ '_diff'
filepath_dat = filepath + '.dat'
if temp_1.shape == temp_2.shape:
temp_diff = diff_of_two_equal_files(temp_1, temp_2, filepath_dat)
else:
temp_diff = diff_of_file_dimensions_not_equal(temp_1, temp_2,
filepath_dat)
filepath_fig = filepath + '_surface.eps'
plot_diff_on_surface(temp_diff[-1, :, :], filepath_fig)
filepath_nc = filepath + '.nc'
save_as_netcdf(temp_diff, filepath_nc)
def domain_temperatures(filepath, do_print=True, do_write=True):
print()
print('Calc domain temperatures of {0}.'.format(filepath))
temp = temperature_array_from_result(filepath)
temp_mean, temp_max, temp_min, temp_std_dev = calc_domain_temperatures(
temp)
if do_print == True:
print_results('domain', temp_mean, temp_max, temp_min, temp_std_dev)
if do_write == True:
filepath = os.path.splitext(filepath)[0] + '_results.dat'
write_results_to_file('Domain', temp_mean, temp_max, temp_min,
temp_std_dev, filepath, 'a')
print('Done.')
return temp_mean
def tumor_temperatures(filepath,
region_filepath,
do_print=True,
do_write=True):
print()
print('Calc tumor temperatures of {0}.'.format(filepath))
temp = temperature_array_from_result(filepath)
tumor = region_array_from_file(region_filepath)
temp_mean, temp_max, temp_min, temp_std_dev = calc_tumor_temperatures(
temp, tumor)
if do_print == True:
print_results('tumor', temp_mean, temp_max, temp_min, temp_std_dev)
if do_write == True:
filepath = os.path.splitext(filepath)[0] + '_results.dat'
write_results_to_file('Tumor', temp_mean, temp_max, temp_min,
temp_std_dev, filepath, 'a')
print('Done.')
return temp_mean
def callScaFES(omega_brain=omega_brain,
omega_tumor=omega_tumor,
omega_vessel=omega_vessel,
T_blood=T_blood,
q_brain=q_brain,
q_tumor=q_tumor,
lambda_bt=lambda_bt,
rho_c_brain=rho_c_brain,
rho_c_tumor=rho_c_tumor,
h=h):
global count
global params
count += 1
print()
print('##### ScaFES iteration: {} #####'.format(count))
case = params['NAME_CONFIGFILE_TEMPLATE'].split('.')[0]
case = case.split('_')[0]
params['NAME_CONFIGFILE'] = case + '-pymc-' + TESTED_VARIABLES + '.ini'
params['NAME_RESULTFILE'] = ''
config = configparser.ConfigParser()
config.optionxform = str
config.read(params['NAME_CONFIGFILE_TEMPLATE'])
# Omega.
config['Brain']['OMEGA'] = str(omega_brain)
config['Tumor']['OMEGA'] = str(omega_tumor)
config['MRI']['USE_VESSELS_SEGMENTATION'] = 'True'
config['MRI']['VARIABLES_VESSELS'] = 'omega'
config['MRI']['VALUES_VESSELS'] = str(omega_vessel)
config['MRI']['VALUES_NON_VESSELS'] = str(omega_brain)
# T_blood.
config['Brain']['T_BLOOD'] = str(T_blood)
config['Tumor']['T_BLOOD'] = str(T_blood)
# Q.
config['Brain']['Q'] = str(q_brain)
config['Tumor']['Q'] = str(q_tumor)
# lambda.
config['Brain']['LAMBDA'] = str(lambda_bt)
config['Tumor']['LAMBDA'] = str(lambda_bt)
# rho * c.
config['Brain']['C'] = str(rho_c_brain)
config['Brain']['RHO'] = str(1000.0)
config['Tumor']['C'] = str(rho_c_tumor)
config['Tumor']['RHO'] = str(1000.0)
# h.
config['Parameters']['H'] = str(h)
with open(params['NAME_CONFIGFILE'], 'w') as configfile:
config.write(configfile)
# Call simulation.
parse_config_file(params)
check_variables(params)
calc_variables(params)
check_stability(params)
if params['USE_MRI_FILE'] == False:
create_region_file(params)
create_init_file(params)
set_environment_variables(params)
call_simulation(params, params['RUN_SCRIPT'])
if params['NAME_RESULTFILE'] != '':
# Compute temperatures of normal, tumor, vessel tisue.
temp = temperature_array_from_result(params['NAME_RESULTFILE'])
tumor = region_array_from_file(params['NAME_REGION_FILE'])
vessels = surface_vessels_array_from_file(
params['NAME_VESSELS_FILE'])
T_tumor, _, _, _ = calc_tumor_near_surface_temperatures(
temp, tumor)
if params['USE_VESSELS_SEGMENTATION'] == True:
temp = temp[-1, :, :]
T_vessel, _, _, _ = calc_vessels_temperatures(temp, vessels)
T_normal, _, _, _ = calc_non_vessels_temperatures(
temp, vessels)
else:
T_vessel = -1.0
T_normal = -1.0
else:
print('* ERROR: No result file written.')
print('Aborting.')
exit()
global dat_file_name
with open(dat_file_name, 'a') as backupfile:
backupfile.write(str(omega_brain) + '\t' \
+ str(omega_tumor) + '\t' \
+ str(omega_vessel) + '\t' \
+ str(T_blood) + '\t' \
+ str(q_brain) + '\t' \
+ str(q_tumor) + '\t' \
+ str(lambda_bt) + '\t' \
+ str(rho_c_brain) + '\t' \
+ str(rho_c_tumor) + '\t' \
+ str(h) + '\t' \
+ str(T_normal) + '\t' \
+ str(T_tumor) + '\t' \
+ str(T_vessel) + '\n')
if (count % 25 == 0):
searchpath = './' + params['NAME_EXECUTABLE'] + '-' + case + '-*.*'
log_files = glob.glob(searchpath)
for f in log_files:
os.remove(f)
return [T_normal, T_tumor, T_vessel]