def partial_sum(spectrum, function_evaluation_mesh, local_max, positions, coefficients_of_base_BPs, plateau_shape_coefs): """Calculates partial sum""" a0, a1, a2, a3 = plateau_shape_coefs grad = [0] * len(coefficients_of_base_BPs) maximum = -100 minimum = 100 from src import pyamtrack_SPC total = 0 for x in function_evaluation_mesh: dose_at_depth_x = [pyamtrack_SPC.dose_at_depth(x, local_max, positions[i], spectrum) for i in range(len(coefficients_of_base_BPs))] sum_of_BPs_for_this_x = sum([coefficients_of_base_BPs[i] * dose_at_depth_x[i] for i in range(len(coefficients_of_base_BPs)) ]) value_at_plateau = a3 * x * x * x + a2 * x * x + a1 * x + a0 total += (value_at_plateau - sum_of_BPs_for_this_x) ** 2 for i in range(len(coefficients_of_base_BPs)): grad[i] += -2.0 * (value_at_plateau - sum_of_BPs_for_this_x) * dose_at_depth_x[i] if sum_of_BPs_for_this_x - value_at_plateau > maximum: maximum = sum_of_BPs_for_this_x - value_at_plateau if sum_of_BPs_for_this_x - value_at_plateau < minimum: minimum = sum_of_BPs_for_this_x - value_at_plateau return total, grad, minimum, maximum
def partial_sum(spectrum, function_evaluation_mesh, local_max,
positions, coefficients_of_base_BPs,
plateau_shape_coefs):
"""Calculates partial sum"""
a0, a1, a2, a3 = plateau_shape_coefs
grad = [0] * len(coefficients_of_base_BPs)
maximum = -100
minimum = 100
from src import pyamtrack_SPC
total = 0
for x in function_evaluation_mesh:
dose_at_depth_x = [
pyamtrack_SPC.dose_at_depth(x, local_max, positions[i],
spectrum)
for i in range(len(coefficients_of_base_BPs))
]
sum_of_BPs_for_this_x = sum([
coefficients_of_base_BPs[i] * dose_at_depth_x[i]
for i in range(len(coefficients_of_base_BPs))
])
value_at_plateau = a3 * x * x * x + a2 * x * x + a1 * x + a0
total += (value_at_plateau - sum_of_BPs_for_this_x)**2
for i in range(len(coefficients_of_base_BPs)):
grad[i] += -2.0 * (value_at_plateau - sum_of_BPs_for_this_x
) * dose_at_depth_x[i]
if sum_of_BPs_for_this_x - value_at_plateau > maximum:
maximum = sum_of_BPs_for_this_x - value_at_plateau
if sum_of_BPs_for_this_x - value_at_plateau < minimum:
minimum = sum_of_BPs_for_this_x - value_at_plateau
return total, grad, minimum, maximum
def calculateYc(X_vect, coefficient, maximum, base_BP_position, spectrum): from src import pyamtrack_SPC return [coefficient * pyamtrack_SPC.dose_at_depth(x, maximum, base_BP_position, spectrum) for x in X_vect]
def calculateYb(X_vect, maximum, base_BP_positions, spectrum, coefficients):
from src import pyamtrack_SPC
Y_vect = [sum( [coefficients[i] * pyamtrack_SPC.dose_at_depth(x, maximum, base_BP_positions[i], spectrum)
for i in range(len(coefficients)) ] ) for x in X_vect]
return Y_vect
def calculateYa(X_vect, maximum, base_BP_positions, spectrum, plateau_dist, plateau_prox, coefficients):
from src import pyamtrack_SPC
Y_vect = [sum( [coefficients[i] * (pyamtrack_SPC.dose_at_depth(x, maximum, base_BP_positions[i], spectrum) +
pyamtrack_SPC.dose_at_depth(plateau_dist + plateau_prox - x, maximum, base_BP_positions[i], spectrum))
for i in range(len(coefficients)) ] ) for x in X_vect]
return Y_vect
def plot_Dose(filename, spectrum, maximum, base_BP_positions, coefficients, two_beams, plot_format = 'png', show = True):
logging.info('Plotting Dose... ')
# clear previous plots
pylab.clf()
# first take wide range to determine where should plot end
dx = 0.01
X_wide = [ n * dx for n in range( int((base_BP_positions[-1] + 4) / dx)) ]
serial = common.check_option('serial')
parallel = common.check_option('parallel')
if parallel:
coefficients_p = []
for c in coefficients:
coefficients_p.append(float(c))
Y_SOBP = []
if two_beams:
X_wide = [ n * dx for n in range( int((base_BP_positions[0] + base_BP_positions[-1]) / dx)) ]
plateau_dist = base_BP_positions[-1]
plateau_prox = base_BP_positions[0]
def calculateYa(X_vect, maximum, base_BP_positions, spectrum, plateau_dist, plateau_prox, coefficients):
from src import pyamtrack_SPC
Y_vect = [sum( [coefficients[i] * (pyamtrack_SPC.dose_at_depth(x, maximum, base_BP_positions[i], spectrum) +
pyamtrack_SPC.dose_at_depth(plateau_dist + plateau_prox - x, maximum, base_BP_positions[i], spectrum))
for i in range(len(coefficients)) ] ) for x in X_vect]
return Y_vect
if serial:
Y_SOBP = calculateYa(X_wide, maximum, base_BP_positions, spectrum, plateau_dist, plateau_prox, coefficients)
if parallel:
args = (maximum, base_BP_positions, spectrum, plateau_dist, plateau_prox, coefficients_p)
Y_SOBP = executeParallelMap( calculateYa, args, X_wide)
else:
def calculateYb(X_vect, maximum, base_BP_positions, spectrum, coefficients):
from src import pyamtrack_SPC
Y_vect = [sum( [coefficients[i] * pyamtrack_SPC.dose_at_depth(x, maximum, base_BP_positions[i], spectrum)
for i in range(len(coefficients)) ] ) for x in X_vect]
return Y_vect
if serial:
Y_SOBP = calculateYb(X_wide, maximum, base_BP_positions, spectrum, coefficients)
if parallel:
args = (maximum, base_BP_positions, spectrum, coefficients_p)
Y_SOBP = executeParallelMap( calculateYb, args, X_wide)
# plot line at 1
pylab.axhline(1, color = 'g')
pylab.plot(X_wide, Y_SOBP, color = 'r', label = 'calculated SOBP')
listPartPeaksY = []
for i in range(len(coefficients)):
def calculateYc(X_vect, coefficient, maximum, base_BP_position, spectrum):
from src import pyamtrack_SPC
return [coefficient * pyamtrack_SPC.dose_at_depth(x, maximum, base_BP_position, spectrum) for x in X_vect]
if serial:
Yi = calculateYc(X_wide, coefficients[i], maximum, base_BP_positions[i], spectrum)
if parallel:
args = (coefficients_p[i], maximum, base_BP_positions[i], spectrum)
Yi = executeParallelMap( calculateYc, args, X_wide)
listPartPeaksY.append(Yi)
pylab.plot(X_wide, Yi, color = 'g', label = 'BP nr ' + str(i))
if two_beams:
plateau_dist = base_BP_positions[-1]
plateau_prox = base_BP_positions[0]
Yis = [coefficients[i] * pyamtrack_SPC.dose_at_depth(plateau_dist + plateau_prox - x, maximum, base_BP_positions[i], spectrum) for x in X_wide]
pylab.plot(X_wide, Yis, color = 'g', label = 'BP nr ' + str(i) + ' bis')
pylab.xlim(0.,X_wide[-1])
pylab.grid(True)
# save plot to file
if filename != None:
logging.info('Saving SOBP plot to file\'' + filename + '.' + plot_format + '\'...')
pylab.savefig(filename + '.' + plot_format, format = plot_format)
save_datafile(filename + '.dat', [X_wide,Y_SOBP]+listPartPeaksY)
# show what has been plotted
if show:
pylab.show()
