def test_mci_wrapper_book_example_2(self):
"""see if our result matches the one from
Biomedical Optics
Principles and Imaging
page 56 (Table 3.2)"""
# create a book_p56_mci which shall create a mci file
book_p56_mci = MciWrapper()
book_p56_mci.set_mci_filename(self.mci_filename)
book_p56_mci.set_mco_filename(self.mco_filename)
book_p56_mci.set_nr_photons(10**6)
book_p56_mci.add_layer(1.5, 1000, 9000, 0., 1)
mcml_path, mcml_file = os.path.split(path_to_gpumcml)
if os.path.isfile(path_to_gpumcml):
book_p56_mci.create_mci_file()
sim_wrapper = SimWrapper()
sim_wrapper.set_mci_filename(self.mci_filename)
sim_wrapper.set_mcml_executable(path_to_gpumcml)
sim_wrapper.run_simulation()
self.assertTrue(os.path.isfile(mcml_path + "/" + self.mco_filename),
"mco file was created")
refl = get_total_reflectance(os.path.join(mcml_path,
self.mco_filename))
self.assertAlmostEqual(refl, 0.26, delta=0.01,
msg="correct reflectance determined " +
"according to book table 3.2")
def test_sim_wrapper(self):
mcml_path, mcml_file = os.path.split(path_to_gpumcml)
if os.path.isfile(path_to_gpumcml):
self.mci_wrapper.create_mci_file()
sim_wrapper = SimWrapper()
sim_wrapper.set_mci_filename(self.mci_filename)
sim_wrapper.set_mcml_executable(path_to_gpumcml)
sim_wrapper.run_simulation()
self.assertTrue(os.path.isfile(os.path.join(mcml_path,
self.mco_filename)),
"mco file was created")
def run(self):
start = time.time()
# setup simulation wrapper
sim_wrapper = SimWrapper()
sim_wrapper.set_mci_filename(MCI_FILENAME)
sim_wrapper.set_mcml_executable(os.path.join(PATH_TO_MCML, EXEC_MCML))
# setup model
tissue_model = self.factory.create_tissue_model()
tissue_model.set_mci_filename(sim_wrapper.mci_filename)
tissue_model.set_mco_filename(MCO_FILENAME)
tissue_model.set_nr_photons(NR_PHOTONS)
# setup array in which data shall be stored
batch = self.factory.create_batch_to_simulate()
batch.create_parameters(self.nr_samples)
# dataframe created by batch:
df = batch.df
# add reflectance column to dataframe
for w in WAVELENGHTS:
df["reflectances", w] = np.NAN
# for each instance of our tissue model
for i in range(df.shape[0]):
# set the desired element in the dataframe to be simulated
tissue_model.set_dataframe_row(df.loc[i, :])
logging.info("running simulation " + str(i) + " for\n" +
str(tissue_model))
reflectances = create_spectrum(tissue_model, sim_wrapper,
WAVELENGHTS)
# store in dataframe
for r, w in zip(reflectances, WAVELENGHTS):
df["reflectances", w][i] = r
# clean up temporarily created files
os.remove(MCI_FILENAME)
created_mco_file = os.path.join(PATH_TO_MCML, MCO_FILENAME)
if os.path.isfile(created_mco_file):
os.remove(created_mco_file)
# save the created output
f = open(self.output().path, 'w')
df.to_csv(f)
end = time.time()
logging.info("time for creating batch of mc data: %.f s" %
(end - start))
def run(self):
start = time.time()
# setup simulation wrapper
sim_wrapper = SimWrapper()
sim_wrapper.set_mci_filename(MCI_FILENAME)
sim_wrapper.set_mcml_executable(os.path.join(PATH_TO_MCML, EXEC_MCML))
# setup model
tissue_model = self.factory.create_tissue_model()
tissue_model.set_mci_filename(sim_wrapper.mci_filename)
tissue_model.set_mco_filename(MCO_FILENAME)
tissue_model.set_nr_photons(NR_PHOTONS)
# setup array in which data shall be stored
batch = self.factory.create_batch_to_simulate()
batch.create_parameters(self.nr_samples)
# dataframe created by batch:
df = batch.df
# add reflectance column to dataframe
for w in WAVELENGHTS:
df["reflectances", w] = np.NAN
# for each instance of our tissue model
for i in range(df.shape[0]):
# set the desired element in the dataframe to be simulated
tissue_model.set_dataframe_row(df.loc[i, :])
logging.info("running simulation " + str(i) + " for\n" +
str(tissue_model))
reflectances = create_spectrum(tissue_model, sim_wrapper,
WAVELENGHTS)
# store in dataframe
for r, w in zip(reflectances, WAVELENGHTS):
df["reflectances", w][i] = r
# clean up temporarily created files
os.remove(MCI_FILENAME)
created_mco_file = os.path.join(PATH_TO_MCML, MCO_FILENAME)
if os.path.isfile(created_mco_file):
os.remove(created_mco_file)
# save the created output
f = open(self.output().path, 'w')
df.to_csv(f)
end = time.time()
logging.info("time for creating batch of mc data: %.f s" %
(end - start))
def run(self):
start = time.time()
# the specific subfolder where the generated spectra shall be saved
simulated_data_folder=self._get_folder_name()
# just to be a bit shorter in the code
ex = self.experiment_dict
# the wavelengths to be simulated
wavelengths = np.arange(float(ex["wavelengths_start"]),
float(ex["wavelengths_end"]),
float(ex["wavelengths_step"])) * 10**-9
# create folder for mci files if not exists
mci_folder = os.path.join(sc.get_full_dir("MC_DATA_FOLDER"), simulated_data_folder,
"mci")
if not os.path.exists(mci_folder):
os.makedirs(mci_folder)
# Setup simulation wrapper
sim_wrapper = SimWrapper()
sim_wrapper.set_mcml_executable(os.path.join(ex["path_to_mcml"],
ex["mcml_executable"]))
sim_wrapper.set_mci_filename(os.path.join(mci_folder, "Bat_" + str(self.batch_nr) + ".mci"))
# Setup tissue model
tissue_model = self.factory.create_tissue_model()
tissue_model.set_mci_filename(sim_wrapper.mci_filename)
tissue_model.set_nr_photons(int(ex["nr_photons"]))
tissue_model._mci_wrapper.set_nr_runs(
int(ex["nr_elements_in_batch"]) * wavelengths.shape[0])
tissue_model.create_mci_file()
tissue_instance = tissueparser.read_tissue_config(self.tissue_file)
# setup array in which data shall be stored
batch = self.factory.create_batch_to_simulate()
batch.set_tissue_instance(tissue_instance)
# create the tissue samples and return them in dataframe df
df = batch.create_tissue_samples(int(ex["nr_elements_in_batch"]))
# add reflectance column to dataframe
for w in wavelengths:
df["reflectances", w] = np.NAN
# Generate MCI file which contains list of all simulations in a Batch
for i in range(df.shape[0]):
# set the desired element in the dataframe to be simulated
base_mco_filename = _create_mco_filename_for(simulated_data_folder,
self.batch_nr,
i)
tissue_model.set_base_mco_filename(base_mco_filename)
tissue_model.set_tissue_instance(df.loc[i, :])
tissue_model.update_mci_file(wavelengths)
# Run simulations for computing reflectance from parameters
sim_wrapper.run_simulation()
# get information from created mco files
for i in range(df.shape[0]):
for wavelength in wavelengths:
# for simulation get which mco file was created
simulation_path = os.path.split(sim_wrapper.mcml_executable)[0]
base_mco_filename = _create_mco_filename_for(simulated_data_folder,
self.batch_nr,
i)
mco_filename = base_mco_filename + str(wavelength) + '.mco'
# get diffuse reflectance from simulation
df["reflectances", wavelength][i] = \
get_diffuse_reflectance(os.path.join(simulation_path, mco_filename))
# delete created mco file
os.remove(os.path.join(simulation_path, mco_filename))
f = open(self.output().path, 'w')
df.to_csv(f)
end = time.time()
logging.info("time for creating batch of mc data: %.f s" %
(end - start))
