def plot_Survival(filename, scaling_factor, spectrum, maximum, base_BP_positions, coefficients, Katz_coeffs, er_model, two_beams, plot_format = 'png', show = True):
logging.info('Plotting Survival... ')
# clear previous plots
pylab.clf()
m, D0, sigma, kappa = Katz_coeffs
# first take wide range to determine where should plot end
dx = 0.05
X_wide = [ n * dx for n in range( int((base_BP_positions[-1] + 4) / dx)) ]
if two_beams:
X_wide = [ n * dx for n in range( int((base_BP_positions[0] + base_BP_positions[-1]) / dx)) ]
Y_wide = []
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
serial = common.check_option('serial')
parallel = common.check_option('parallel')
# Create jobserver
job_server = pp.Server( secret = 'abcd' )
ncpus = int(common.options['ncpu'])
job_server.set_ncpus(ncpus)
if serial:
Y_wide = calculateY( X_wide, coefficients, maximum, base_BP_positions, spectrum, scaling_factor, two_beams, m, D0, sigma, kappa, er_model)
if parallel:
coefficients_p = []
for c in coefficients:
coefficients_p.append(float(c))
args = (coefficients_p, maximum, base_BP_positions, spectrum, scaling_factor, two_beams, m, D0, sigma, kappa, er_model)
Y_wide = executeParallelMap( calculateY, args, X_wide)
# plot line at 1
pylab.axhline(1, color = 'g')
pylab.plot(X_wide, Y_wide, color = 'r', label = 'survival')
pylab.xlim(0.,X_wide[-1])
pylab.grid(True)
# save plot to file
if filename != None:
logging.info('Saving survival plot to file\'' + filename + '.' + plot_format + '\'...')
pylab.savefig(filename + '.' + plot_format, format = plot_format)
save_datafile(filename + '.dat', [X_wide,Y_wide])
# show what has been plotted
if show:
pylab.show()
def main():
# argument parsing:
# http://docs.python.org/library/argparse.html#module-argparse
description_string = """ Script for designing modulators. Options are
read from configuration file 'modulatory.cfg'. Options passed directly
as program parameters have higher priority.
"""
parser = argparse.ArgumentParser(description=description_string)
parser.add_argument('--range',
'-r',
action='store',
help='range of SOBP in mm')
parser.add_argument('--spread',
'-s',
action='store',
help='width of SOBP in mm')
parser.add_argument('--mesh',
'-m',
action='store',
help='size of mesh used when minimizing')
parser.add_argument('--precision',
'-p',
action='store',
help='precision for fitting algorithm')
parser.add_argument(
'--interrupt_bound',
action='store',
help='interrupt fitting when SOBP is flat within this range')
parser.add_argument('--verbose', action='store_true', help='be verbose')
parser.add_argument('--debug',
action='store_true',
help='display debug messages')
parser.add_argument('--version',
'-V',
action='store_true',
help='show version and exit')
parser.add_argument('--noplot',
action='store_true',
help='do not generate plots')
parser.add_argument('--logfile',
action='store',
help='where write logs to',
default='generation.log')
parser.add_argument('--cfgfile',
action='store',
help='config file',
default='modulatory.cfg')
parser.add_argument('--name',
'-n',
action='store',
help='name of modulator being generated')
# doctest
import doctest
failure_count = doctest.testmod(common)[0]
failure_count += doctest.testmod(generation_library)[0]
failure_count += doctest.testmod(in_out)[0]
failure_count += doctest.testmod(plotting)[0]
failure_count += doctest.testmod()[0]
if failure_count > 0:
exit(-1)
args_dict = common.parse_command_line_arguments(parser)
config_dict = read_config(filename=args_dict['cfgfile'])
common.options = merge_options_dictionaries_fav_first(
args_dict, config_dict)
# create folder fo output
# output folder = name of the folder with system folder separator at end
output_folder = create_output_folder(common.options['name']) + os.sep
# copy configuration file
copy(args_dict['cfgfile'], output_folder + args_dict['cfgfile'])
common.setup_logging(filename=output_folder + common.options['logfile'],
write_on_screen=args_dict['verbose'],
display_debug=args_dict['debug'],
logger_name='')
logging.debug('Options: %s', common.options)
SOBP_range = float(common.options['range'])
SOBP_spread = float(common.options['spread'])
# GENERATION PROCESS
spectrum_full = src.pyamtrack_SPC.extract_spectrum_at_range(
common.options['spcdir'], SOBP_range)
#spectrum = removeZeroFluenceItemsFromSpectrum( spectrum_full )
spectrum = spectrum_full
spectrum_dict = src.pyamtrack_SPC.get_spectrum_dictionary_depth(spectrum)
BPsampled = sampleDoseFromBPdictionary(spectrum_dict)
maximum = src.common.calculate_BP_maximum_position(BPsampled)
logging.debug('BP maximum: %s', maximum)
#printBP(BPsampled)
base_BP_positions = calculate_base_BP_positions_given_number(
first_BP_position=SOBP_range - SOBP_spread,
last_BP_position=SOBP_range,
n_of_base_BPs=int(common.options['number_of_base_bps']))
logging.debug('Selected base BP positions: %s', base_BP_positions)
scaled_spectrum_dict = {}
initial_coefs = []
bounds = []
value_at_plateau = None
# scale fluence to input dose
if common.options['algorithm'] == 'survival':
input_dose_Gy = 2.
scaled_spectrum_dict = src.pyamtrack_SPC.scale_fluence_to_input_dose(
input_dose_Gy, spectrum_dict)
initial_coefs = [0.5 / len(base_BP_positions)] * len(base_BP_positions)
initial_coefs[-1] *= 8
initial_coefs[-2] *= 5
bounds = [[
0.1 / len(base_BP_positions), 10.0 / len(base_BP_positions)
]] * len(base_BP_positions)
value_at_plateau = 0.2
elif common.options['algorithm'] == 'dose':
maximum_dose_Gy = 1.
scaled_spectrum_dict = src.pyamtrack_SPC.scale_fluence_to_maximum_dose(
maximum_dose_Gy, maximum, spectrum_dict)
initial_coefs = [1. / len(base_BP_positions)] * len(base_BP_positions)
initial_coefs[-1] = 1.
bounds = [[0.0, 1.3]] * len(base_BP_positions)
value_at_plateau = 1.
else:
logging.debug('Wrong algorithm type: %s', common.options['algorithm'])
return 1
# scale fluence to maximum dose for flat dose problem ??
# optimize both base BPs coefficients and their positions
# big_mesh_size = (base_BP_positions[1] - base_BP_positions[0])/4.
big_mesh_size = float(common.options['mesh'])
print "Starting from ", initial_coefs
Katz_coefs = [
float(common.options['m']),
float(common.options['d0']),
float(common.options['sigma0']),
float(common.options['kappa'])
]
plateau_shape_coefs = [
float(common.options['plateau_shape_a0']),
float(common.options['plateau_shape_a1']),
float(common.options['plateau_shape_a2']),
float(common.options['plateau_shape_a3'])
]
first_coefficients, chi2 = fit(
algorithm=common.options['algorithm'],
local_max=maximum,
base_BP_positions=base_BP_positions,
spectrum=scaled_spectrum_dict,
value_at_plateau=value_at_plateau,
initial_coefficients=initial_coefs,
Katz_coeff=Katz_coefs,
two_beams=common.check_option('two_beams'),
bounds=bounds,
plateau_prox=SOBP_range - SOBP_spread,
plateau_dist=SOBP_range,
mesh_size=big_mesh_size,
precision=float(common.options['precision']),
fit_interrupt_bound=float(common.options['interrupt_bound']),
plateau_shape_coefs=plateau_shape_coefs)
coefficients = first_coefficients
logging.info('Coefficients found: %s', coefficients)
# PLOTTING
if common.check_option('draw_plots') and common.options['noplot'] == False:
src.plotting.plot_Dose(output_folder + "/dose", scaled_spectrum_dict,
maximum, base_BP_positions, coefficients,
common.check_option('two_beams'), 'pdf',
common.check_option('show_plots'))
factor = 1.
er_model = int(common.options['er_model'])
src.plotting.plot_Survival(output_folder + "/survival", factor,
scaled_spectrum_dict, maximum,
base_BP_positions, coefficients,
Katz_coefs, er_model,
common.check_option('two_beams'), 'pdf',
common.check_option('show_plots'))
logging.info('END OF SCRIPT')
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()
def main():
# argument parsing:
# http://docs.python.org/library/argparse.html#module-argparse
description_string = """ Script for designing modulators. Options are
read from configuration file 'modulatory.cfg'. Options passed directly
as program parameters have higher priority.
"""
parser = argparse.ArgumentParser(description=description_string)
parser.add_argument("--range", "-r", action="store", help="range of SOBP in mm")
parser.add_argument("--spread", "-s", action="store", help="width of SOBP in mm")
parser.add_argument("--mesh", "-m", action="store", help="size of mesh used when minimizing")
parser.add_argument("--precision", "-p", action="store", help="precision for fitting algorithm")
parser.add_argument(
"--interrupt_bound", action="store", help="interrupt fitting when SOBP is flat within this range"
)
parser.add_argument("--verbose", action="store_true", help="be verbose")
parser.add_argument("--debug", action="store_true", help="display debug messages")
parser.add_argument("--version", "-V", action="store_true", help="show version and exit")
parser.add_argument("--noplot", action="store_true", help="do not generate plots")
parser.add_argument("--logfile", action="store", help="where write logs to", default="generation.log")
parser.add_argument("--cfgfile", action="store", help="config file", default="modulatory.cfg")
parser.add_argument("--name", "-n", action="store", help="name of modulator being generated")
# doctest
import doctest
failure_count = doctest.testmod(common)[0]
failure_count += doctest.testmod(generation_library)[0]
failure_count += doctest.testmod(in_out)[0]
failure_count += doctest.testmod(plotting)[0]
failure_count += doctest.testmod()[0]
if failure_count > 0:
exit(-1)
args_dict = common.parse_command_line_arguments(parser)
config_dict = read_config(filename=args_dict["cfgfile"])
common.options = merge_options_dictionaries_fav_first(args_dict, config_dict)
# create folder fo output
# output folder = name of the folder with system folder separator at end
output_folder = create_output_folder(common.options["name"]) + os.sep
# copy configuration file
copy(args_dict["cfgfile"], output_folder + args_dict["cfgfile"])
common.setup_logging(
filename=output_folder + common.options["logfile"],
write_on_screen=args_dict["verbose"],
display_debug=args_dict["debug"],
logger_name="",
)
logging.debug("Options: %s", common.options)
SOBP_range = float(common.options["range"])
SOBP_spread = float(common.options["spread"])
# GENERATION PROCESS
spectrum_full = src.pyamtrack_SPC.extract_spectrum_at_range(common.options["spcdir"], SOBP_range)
# spectrum = removeZeroFluenceItemsFromSpectrum( spectrum_full )
spectrum = spectrum_full
spectrum_dict = src.pyamtrack_SPC.get_spectrum_dictionary_depth(spectrum)
BPsampled = sampleDoseFromBPdictionary(spectrum_dict)
maximum = src.common.calculate_BP_maximum_position(BPsampled)
logging.debug("BP maximum: %s", maximum)
# printBP(BPsampled)
base_BP_positions = calculate_base_BP_positions_given_number(
first_BP_position=SOBP_range - SOBP_spread,
last_BP_position=SOBP_range,
n_of_base_BPs=int(common.options["number_of_base_bps"]),
)
logging.debug("Selected base BP positions: %s", base_BP_positions)
scaled_spectrum_dict = {}
initial_coefs = []
bounds = []
value_at_plateau = None
# scale fluence to input dose
if common.options["algorithm"] == "survival":
input_dose_Gy = 2.0
scaled_spectrum_dict = src.pyamtrack_SPC.scale_fluence_to_input_dose(input_dose_Gy, spectrum_dict)
initial_coefs = [0.5 / len(base_BP_positions)] * len(base_BP_positions)
initial_coefs[-1] *= 8
initial_coefs[-2] *= 5
bounds = [[0.1 / len(base_BP_positions), 10.0 / len(base_BP_positions)]] * len(base_BP_positions)
value_at_plateau = 0.2
elif common.options["algorithm"] == "dose":
maximum_dose_Gy = 1.0
scaled_spectrum_dict = src.pyamtrack_SPC.scale_fluence_to_maximum_dose(maximum_dose_Gy, maximum, spectrum_dict)
initial_coefs = [1.0 / len(base_BP_positions)] * len(base_BP_positions)
initial_coefs[-1] = 1.0
bounds = [[0.0, 1.3]] * len(base_BP_positions)
value_at_plateau = 1.0
else:
logging.debug("Wrong algorithm type: %s", common.options["algorithm"])
return 1
# scale fluence to maximum dose for flat dose problem ??
# optimize both base BPs coefficients and their positions
# big_mesh_size = (base_BP_positions[1] - base_BP_positions[0])/4.
big_mesh_size = float(common.options["mesh"])
print "Starting from ", initial_coefs
Katz_coefs = [
float(common.options["m"]),
float(common.options["d0"]),
float(common.options["sigma0"]),
float(common.options["kappa"]),
]
plateau_shape_coefs = [
float(common.options["plateau_shape_a0"]),
float(common.options["plateau_shape_a1"]),
float(common.options["plateau_shape_a2"]),
float(common.options["plateau_shape_a3"]),
]
first_coefficients, chi2 = fit(
algorithm=common.options["algorithm"],
local_max=maximum,
base_BP_positions=base_BP_positions,
spectrum=scaled_spectrum_dict,
value_at_plateau=value_at_plateau,
initial_coefficients=initial_coefs,
Katz_coeff=Katz_coefs,
two_beams=common.check_option("two_beams"),
bounds=bounds,
plateau_prox=SOBP_range - SOBP_spread,
plateau_dist=SOBP_range,
mesh_size=big_mesh_size,
precision=float(common.options["precision"]),
fit_interrupt_bound=float(common.options["interrupt_bound"]),
plateau_shape_coefs=plateau_shape_coefs,
)
coefficients = first_coefficients
logging.info("Coefficients found: %s", coefficients)
# PLOTTING
if common.check_option("draw_plots") and common.options["noplot"] == False:
src.plotting.plot_Dose(
output_folder + "/dose",
scaled_spectrum_dict,
maximum,
base_BP_positions,
coefficients,
common.check_option("two_beams"),
"pdf",
common.check_option("show_plots"),
)
factor = 1.0
er_model = int(common.options["er_model"])
src.plotting.plot_Survival(
output_folder + "/survival",
factor,
scaled_spectrum_dict,
maximum,
base_BP_positions,
coefficients,
Katz_coefs,
er_model,
common.check_option("two_beams"),
"pdf",
common.check_option("show_plots"),
)
logging.info("END OF SCRIPT")