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 'plot.cfg'. Options passed directly
as program parameters have higher priority.
"""
parser = argparse.ArgumentParser(description = description_string)
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('--logfile', action = 'store',
help = 'where write logs to', default = 'generation.log')
parser.add_argument('--cfgfile', action = 'store',
help = 'config file', default = 'plot.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()[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 for 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)
return
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 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")