def test_fetch_correct_backscatter_correction_from_database():
expected = 5 * [True]
# Tabulated backscatter factor for param in data_norm
tabulated_k_bs = [1.3, 1.458, 1.589, 1.617, 1.639]
data_norm = pd.DataFrame({
'kVp': 5 * [80],
'HVL': 5 * [7.88],
'FSL': [5, 10, 20, 25, 35]
})
# create interpolation object
bs_interp = calculate_k_bs(data_norm)
# interpolate at tabulated filed sizes
k_bs = bs_interp[0](data_norm.FSL)
diff = [
100 * (abs(k_bs[i] - tabulated_k_bs[i])) / tabulated_k_bs[i]
for i in range(len(tabulated_k_bs))
]
test = [percent_difference <= 1 for percent_difference in diff]
assert expected == test
def test_calculate_k_bs():
"""Tests the backscatter correction.
Tests if the interpolated backscatter factor lies within 1% of the tabulated value,
for all tabulated field sizes at a specific kVp/HVL combination.
"""
expected = 5 * [True]
# Tabulated backscatter factor for param in data_norm
tabulated_k_bs = [1.3, 1.458, 1.589, 1.617, 1.639]
data_norm = pd.DataFrame({
'kVp': 5 * [80],
'HVL': 5 * [7.88],
'FSL': [5, 10, 20, 25, 35]
})
# create interpolation object
bs_interp = calculate_k_bs(data_norm)
# interpolate at tabulated filed sizes
k_bs = bs_interp[0](data_norm.FSL)
diff = [
100 * (abs(k_bs[i] - tabulated_k_bs[i])) / tabulated_k_bs[i]
for i in range(len(tabulated_k_bs))
]
test = [percent_difference <= 1 for percent_difference in diff]
assert expected == test
def calculate_dose(
normalized_data: pd.DataFrame,
settings: PyskindoseSettings,
table: Phantom,
pad: Phantom,
) -> Tuple[Phantom, Optional[Dict[str, Any]]]:
"""Calculate skin dose.
This function initializes the skin dose calculations.
Parameters
----------
normalized_data : pd.DataFrame
RDSR data, normalized for compliance with PySkinDose.
settings : PyskindoseSettings
Settings class for PySkinDose
table : Phantom
Patient support table phantom
pad : Phantom
Patient support pad phantom
Returns
-------
Tuple[Phantom, Optional[Dict[str, Any]]]
[description]
"""
if settings.mode != const.MODE_CALCULATE_DOSE:
# logger.debug("Mode not set to calculate dose. Returning without doing anything")
return None, None
# logger.info("Start performing dose calculations")
patient = Phantom(
phantom_model=settings.phantom.model,
phantom_dim=settings.phantom.dimension,
human_mesh=settings.phantom.human_mesh,
)
# position objects in starting position
position_geometry(
patient=patient,
table=table,
pad=pad,
pad_thickness=settings.phantom.dimension.pad_thickness,
patient_offset=[
settings.phantom.patient_offset.d_lat,
settings.phantom.patient_offset.d_ver,
settings.phantom.patient_offset.d_lon,
],
)
normalized_data = fetch_and_append_hvl(data_norm=normalized_data)
# Check which irradiation events that contains updated
# geometry parameters since the previous irradiation event
new_geometry = check_new_geometry(normalized_data)
# fetch of k_bs interpolation object (k_bs=f(field_size))for all events
back_scatter_interpolation = calculate_k_bs(data_norm=normalized_data)
k_tab = calculate_k_tab(
data_norm=normalized_data,
estimate_k_tab=settings.estimate_k_tab,
k_tab_val=settings.k_tab_val,
)
total_number_of_events = len(normalized_data)
output_template = {
const.OUTPUT_KEY_HITS: [[]] * total_number_of_events,
const.OUTPUT_KEY_KERMA: [np.array] * total_number_of_events,
const.OUTPUT_KEY_CORRECTION_INVERSE_SQUARE_LAW:
[[]] * total_number_of_events,
const.OUTPUT_KEY_CORRECTION_BACK_SCATTER:
[[]] * total_number_of_events,
const.OUTPUT_KEY_CORRECTION_MEDIUM: [[]] * total_number_of_events,
const.OUTPUT_KEY_CORRECTION_TABLE: [[]] * total_number_of_events,
const.OUTPUT_KEY_DOSE_MAP: np.zeros(len(patient.r)),
}
output = calculate_irradiation_event_result(
normalized_data=normalized_data,
event=0,
total_events=len(normalized_data),
new_geometry=new_geometry,
k_tab=k_tab,
hits=[],
patient=patient,
table=table,
pad=pad,
back_scatter_interpolation=back_scatter_interpolation,
output=output_template,
)
return patient, output