def calculateY( X_vect, coefficients, maximum, base_BP_positions, spectrum, scaling_factor, two_beams, m, D0, sigma, kappa, er_model):
Y_vect = []
from src import pyamtrack_SPC
for x in X_vect:
E_MeV_u_total = []
particle_no_total = []
fluence_cm2_total = []
for i in range(len(coefficients)):
shift = maximum - base_BP_positions[i]
E_MeV_u, particle_no, fluence_cm2 = pyamtrack_SPC.spectrum_at_depth(x + shift, spectrum)
fluence_cm2_coef = [f * coefficients[i] * scaling_factor for f in fluence_cm2]
E_MeV_u_total += E_MeV_u
particle_no_total += particle_no
fluence_cm2_total += fluence_cm2_coef
if two_beams:
plateau_dist = base_BP_positions[-1]
plateau_prox = base_BP_positions[0]
y = plateau_dist + plateau_prox - x
E_MeV_u_second, particle_no_second, fluence_cm2_second = pyamtrack_SPC.spectrum_at_depth(y + shift, spectrum)
fluence_cm2_coef_second = [f * coefficients[i] * scaling_factor for f in fluence_cm2_second] # TODO check
E_MeV_u_total += E_MeV_u_second
particle_no_total += particle_no_second
fluence_cm2_total += fluence_cm2_coef_second
y = pyamtrack_SPC.survival(E_MeV_u_total, particle_no_total, fluence_cm2_total, m, D0, sigma, kappa, er_model)
Y_vect.append(y)
return Y_vect
def prepareEmptyDict(x, spectrum, local_max, positions):
pn = sorted([1001, 5011, 2004, 4009, 6012, 3007])
d = dict.fromkeys(pn, {})
xmin = x + local_max - max(positions)
xmax = x + local_max - min(positions)
depth_min_ind = bisect.bisect_left(sorted(spectrum.keys()), xmin)
depth_max_ind = bisect.bisect_left(sorted(spectrum.keys()), xmax)
for depth in sorted(spectrum.keys())[depth_min_ind - 1:depth_max_ind + 1]:
E_MeV_u, particle_no, fluence_cm2 = pyamtrack_SPC.spectrum_at_depth(depth, spectrum)
for j in range(len(fluence_cm2)):
d[particle_no[j]][E_MeV_u[j]] = 0
return d
def prepareLists(x, spectrum, local_max, positions, coefficients_of_base_BPs): from src import pyamtrack_SPC particle_no_total = [] E_MeV_u_total = [] fluence_cm2_total = [] for i in range(len(coefficients_of_base_BPs)): shift = local_max - positions[i] E_MeV_u, particle_no, fluence_cm2 = pyamtrack_SPC.spectrum_at_depth(x + shift, spectrum) fluence_cm2_coef = [f * coefficients_of_base_BPs[i] for f in fluence_cm2] particle_no_total.extend(particle_no) E_MeV_u_total.extend(E_MeV_u) fluence_cm2_total.extend(fluence_cm2_coef) return E_MeV_u_total, particle_no_total, fluence_cm2_total
def prepareEmptyDict(x, spectrum, local_max, positions):
pn = sorted([1001, 5011, 2004, 4009, 6012, 3007])
d = dict.fromkeys(pn, {})
xmin = x + local_max - max(positions)
xmax = x + local_max - min(positions)
depth_min_ind = bisect.bisect_left(sorted(spectrum.keys()), xmin)
depth_max_ind = bisect.bisect_left(sorted(spectrum.keys()), xmax)
for depth in sorted(spectrum.keys())[depth_min_ind - 1:depth_max_ind +
1]:
E_MeV_u, particle_no, fluence_cm2 = pyamtrack_SPC.spectrum_at_depth(
depth, spectrum)
for j in range(len(fluence_cm2)):
d[particle_no[j]][E_MeV_u[j]] = 0
return d
def prepareLists(x, spectrum, local_max, positions,
coefficients_of_base_BPs):
from src import pyamtrack_SPC
particle_no_total = []
E_MeV_u_total = []
fluence_cm2_total = []
for i in range(len(coefficients_of_base_BPs)):
shift = local_max - positions[i]
E_MeV_u, particle_no, fluence_cm2 = pyamtrack_SPC.spectrum_at_depth(
x + shift, spectrum)
fluence_cm2_coef = [
f * coefficients_of_base_BPs[i] for f in fluence_cm2
]
particle_no_total.extend(particle_no)
E_MeV_u_total.extend(E_MeV_u)
fluence_cm2_total.extend(fluence_cm2_coef)
return E_MeV_u_total, particle_no_total, fluence_cm2_total