def check_options_consistency(options):
"""Check if there are conflicts in options
(passed as a dictionary).
"""
logging.info('Checking options consistency...')
error = False
# provide Gottshalck rule OR number of base BPs
if 'use_gottshalck_80_rule' in options.keys() and 'number_of_base_bps' in options.keys():
if options['use_gottshalck_80_rule'] == 'True' and int(options['number_of_base_bps']) > 0:
logging.error('Conflict in options. Either specify number_of_base_bps OR tell to use_gottshalck_80_rule.')
error = True
if not 'use_gottshalck_80_rule' in options.keys() or options['use_gottshalck_80_rule'] == 'False':
if not 'number_of_base_bps' in options.keys() or int(options['number_of_base_bps']) < 2:
logging.error('Conflict in options. Number of base BPs is not specified.')
error = True
# check if datafile is provided
if not 'bp_database' in options.keys():
logging.error('Conflict in options. File with BPs database not provided.')
error = True
# check if file with fluence drop coefficients is provided
if not 'fluence_drop_coefficients_file' in options.keys():
logging.error('Lack in options. File with fluence drop curve coefficients not provided.')
error = True
# check if plexi to water coefficient is provided
if not 'mod_plexi_to_water' in options.keys():
logging.error('Lack in options. Coefficient for changing water range to modulator plexi range not provided.')
error = True
# check if plexi to water coefficient is provided
if not 'rs_plexi_to_water' in options.keys():
logging.error('Lack in options. Coefficient for changing water range to rs plexi range not provided.')
error = True
# check if both correction and adjusting plateau were specified
if common.check_option('fit_adjusting_plateau') and 'correction_to_spread' in common.options.keys() and float(common.options['correction_to_spread']) != 0:
logging.warning('Manual correction to spread was made when fitting with adjusting plateau.')
if error:
logging.error('There were conflicts in options. Exiting.')
exit(1)
else:
logging.info('Options consistency OK.')
def check_options_consistency(options):
"""Check if there are conflicts in options
(passed as a dictionary).
"""
logging.info('Checking options consistency...')
error = False
# provide Gottshalck rule OR number of base BPs
if 'use_gottshalck_80_rule' in options.keys(
) and 'number_of_base_bps' in options.keys():
if options['use_gottshalck_80_rule'] == 'True' and int(
options['number_of_base_bps']) > 0:
logging.error(
'Conflict in options. Either specify number_of_base_bps OR tell to use_gottshalck_80_rule.'
)
error = True
if not 'use_gottshalck_80_rule' in options.keys(
) or options['use_gottshalck_80_rule'] == 'False':
if not 'number_of_base_bps' in options.keys() or int(
options['number_of_base_bps']) < 2:
logging.error(
'Conflict in options. Number of base BPs is not specified.')
error = True
# check if datafile is provided
if not 'bp_database' in options.keys():
logging.error(
'Conflict in options. File with BPs database not provided.')
error = True
# check if file with fluence drop coefficients is provided
if not 'fluence_drop_coefficients_file' in options.keys():
logging.error(
'Lack in options. File with fluence drop curve coefficients not provided.'
)
error = True
# check if plexi to water coefficient is provided
if not 'mod_plexi_to_water' in options.keys():
logging.error(
'Lack in options. Coefficient for changing water range to modulator plexi range not provided.'
)
error = True
# check if plexi to water coefficient is provided
if not 'rs_plexi_to_water' in options.keys():
logging.error(
'Lack in options. Coefficient for changing water range to rs plexi range not provided.'
)
error = True
# check if both correction and adjusting plateau were specified
if common.check_option(
'fit_adjusting_plateau'
) and 'correction_to_spread' in common.options.keys() and float(
common.options['correction_to_spread']) != 0:
logging.warning(
'Manual correction to spread was made when fitting with adjusting plateau.'
)
if error:
logging.error('There were conflicts in options. Exiting.')
exit(1)
else:
logging.info('Options consistency OK.')
def fit(algorithm,
local_max,
base_BP_positions,
spectrum,
value_at_plateau,
plateau_prox,
plateau_dist,
mesh_size,
precision,
initial_coefficients=[],
Katz_coeff=[],
two_beams=False,
bounds=[],
fit_interrupt_bound=0,
plateau_shape_coefs=[1., 0., 0., 0.]):
"""Base fitting procedure. It minimizes sum squared distances between
SOBP and 1 (chi^2). Sum is over mesh on given plateau.
Fitting is aborted when SOBP plateau is in area between
1 - fit_interrupt_bound and 1 + fit_interrupt_bound.
Parameters:
algorithm: Algorithm for fitting. Only fmin_l_bfgs_b is available.
base_BPs: List of base BPs (each BP is (x,y) pair). SOBP if formed
from these BPs.
plateau_prox, plateau_dist: SOBP is optimized to be small between
these values.
mesh_size: Distance between points of mesh. chi^2 is calculated
only on mesh points.
precision: how small should be DERIVATIVE of chi^2 (times machine
precision) for fit to end. 1 is a extremely good fit, 1e10 -
mediocre, 1e20 - poor fit.
initial coefficients (default: [0,0,...,1]): starting point for
fitting algorithm.
fit_interrupt_bound (default: 0): Fitting is interrupted when whole
SOBP's plateau is in area around 1 of this size.
Returns:
coefficients: list of optimal coefficients found
chi2: chi2 value with coefficients found
"""
logging.info('Fitting...')
if plateau_prox > plateau_dist:
logging.error(
'In arguments to fit function plateau_prox is greater than plateau_dist. Exiting.'
)
exit(1)
# starting point for fitting algorithm
coeffs = initial_coefficients
# points where chi^2 will be evaluated
function_evaluation_mesh = [
plateau_prox + n * mesh_size
for n in range(int((plateau_dist - plateau_prox) / mesh_size) + 1)
]
print "mesh: ", function_evaluation_mesh
print "base pos: ", base_BP_positions
logging.debug('Fit mesh start: %f, fit mesh end: %f',
function_evaluation_mesh[0], function_evaluation_mesh[-1])
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
class FittingDone(Exception):
"""Exception raised when fitting should be aborted.
"""
def __init__(self, coefficients, minimum, maximum, chi2):
self.coefficients = coefficients
self.minimum = minimum
self.maximum = maximum
self.chi2 = chi2
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 chi2simple(coefficients_of_base_BPs, args):
"""sum of square differences between plateau and sum_of_base_BPs
in fixed values of x
chi2simple(C), where coefficients_of_base_BPs = [C1, C2, C3, ...]
TODO doctest missing
"""
total = 0
# maximum and minimum are used for interrupt bound
maximum = -1e20
minimum = 1e20
value_at_plateau, local_max, positions, Katz_coeffs, two_beams, spectrum, er_model = args
m, D0, sigma, kappa = Katz_coeffs
coefficients = []
for c in coefficients_of_base_BPs:
coefficients.append(float(c))
def partial_sum(spectrum, function_evaluation_mesh, value_at_plateau,
local_max, positions, coefficients_of_base_BPs,
plateau_dist, plateau_prox, two_beams, m, D0, sigma,
kappa, er_model):
"""Calculates partial sum"""
from src import pyamtrack_SPC
maximum = -100
minimum = 100
total = 0
for x in function_evaluation_mesh:
E_MeV_u_total, particle_no_total, fluence_cm2_total = prepareLists(
x, spectrum, local_max, positions,
coefficients_of_base_BPs)
## two opposite beams
if two_beams:
y = plateau_dist + plateau_prox - x
E_MeV_u_total_second, particle_no_total_second, fluence_cm2_total_second = prepareLists(
y, spectrum, local_max, positions,
coefficients_of_base_BPs)
E_MeV_u_total.extend(E_MeV_u_total_second)
particle_no_total.extend(particle_no_total_second)
fluence_cm2_total.extend(fluence_cm2_total_second)
survival_at_depth_x = pyamtrack_SPC.survival(
E_MeV_u_total, particle_no_total, fluence_cm2_total, m, D0,
sigma, kappa, er_model)
if value_at_plateau > 0 and survival_at_depth_x > 0:
total += (math.log10(value_at_plateau) -
math.log10(survival_at_depth_x))**2
else:
total = 1e100
if survival_at_depth_x > maximum:
maximum = survival_at_depth_x
if survival_at_depth_x < minimum:
minimum = survival_at_depth_x
return total, minimum, maximum
maximum = -100
minimum = 100
if parallel:
parts = ncpus
step = ((len(function_evaluation_mesh)) / parts) + 1
jobs = []
start_time = time.time()
for index in xrange(parts):
starti = index * step
endi = min((index + 1) * step, len(function_evaluation_mesh))
jobs.append(
job_server.submit(
partial_sum,
args=(spectrum, function_evaluation_mesh[starti:endi],
value_at_plateau, local_max, positions,
coefficients, plateau_dist, plateau_prox,
two_beams, m, D0, sigma, kappa, er_model),
depfuncs=(prepareLists, ),
modules=(
"src.pyamtrack_SPC",
"math",
)))
for job in jobs:
total_p, min_p, max_p = job()
total += total_p
if max_p > maximum:
maximum = max_p
if min_p < minimum:
minimum = min_p
# print "Time elapsed: ", time.time() - start_time, "s"
# print job_server.print_stats()
if serial:
total_ser, minimum, maximum = partial_sum(
spectrum, function_evaluation_mesh, value_at_plateau,
local_max, positions, coefficients, plateau_dist, plateau_prox,
two_beams, m, D0, sigma, kappa, er_model)
if serial and not parallel:
total = total_ser
global call_count
call_count += 1
logging.info('%i calls, chi^2 = %.12f, max = %f, min = %f', call_count,
total, maximum, minimum)
print coefficients
if (maximum <= value_at_plateau + fit_interrupt_bound) and (
minimum >= value_at_plateau - fit_interrupt_bound):
raise FittingDone(coefficients=coefficients_of_base_BPs,
minimum=minimum,
maximum=maximum,
chi2=total)
return total
def chi2withDerivative(coefficients_of_base_BPs, args):
"""Sum of square differences between plateau and sum_of_base_BPs
in fixed values of x (specified in function_evaluation_mesh).
If at any point sum of BPs with coefficients passed is constrained
around 1 in area less then specified in fit_interrupt_bound exception
is thrown.
chi2withDerivative(C), where coefficients_of_base_BPs = [C1, C2, C3, ...]
TODO doctest missing
"""
total = 0
# maximum and minimum are used for interrupt bound
maximum = 0
minimum = 100
grad = [0] * len(coefficients_of_base_BPs)
value_at_plateau, local_max, positions, spectrum, plateau_shape_coefs = args
print "positions : ", positions
coef_str = "[ "
for c in coefficients_of_base_BPs:
coef_str += str(c) + " , "
coef_str += " ] "
print "coefficients : ", coef_str
coefficients = []
for c in coefficients_of_base_BPs:
coefficients.append(float(c))
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
maximum = -100
minimum = 100
if parallel:
parts = ncpus
step = ((len(function_evaluation_mesh)) / parts) + 1
jobs = []
start_time = time.time()
for index in xrange(parts):
starti = index * step
endi = min((index + 1) * step, len(function_evaluation_mesh))
jobs.append(
job_server.submit(
partial_sum,
args=(spectrum, function_evaluation_mesh[starti:endi],
local_max, positions, coefficients,
plateau_shape_coefs),
modules=(
"src.pyamtrack_SPC",
"math",
)))
for job in jobs:
total_p, grad_p, min_p, max_p = job()
total += total_p
for i in range(len(grad_p)):
grad[i] += grad_p[i]
if max_p > maximum:
maximum = max_p
if min_p < minimum:
minimum = min_p
print "Time elapsed: ", time.time() - start_time, "s"
print job_server.print_stats()
if serial:
total_ser, grad_ser, minimum, maximum = partial_sum(
spectrum, function_evaluation_mesh, local_max, positions,
coefficients, plateau_shape_coefs)
if serial and not parallel:
total = total_ser
grad = grad_ser
global call_count
call_count += 1
#if call_count % 10 == 0:
logging.info('\t %i calls, chi^2 = %.12f, max = %f, min = %f\n',
call_count, total, maximum, minimum)
if maximum < fit_interrupt_bound and (-minimum) < fit_interrupt_bound:
raise FittingDone(coefficients=coefficients_of_base_BPs,
minimum=minimum,
maximum=maximum,
chi2=total)
return total, numpy.array(grad) # TODO remove numpy array
# choose fit algorithm
if algorithm == 'dose':
try:
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)
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html
opt_coeffs, chi2_at_minimum, information_dictionary = scipy.optimize.fmin_l_bfgs_b(
func=chi2withDerivative,
x0=coeffs,
args=[[
value_at_plateau, local_max, base_BP_positions, spectrum,
plateau_shape_coefs
]],
approx_grad=False,
bounds=bounds,
factr=precision,
maxfun=2000)
job_server.destroy()
except FittingDone as result:
opt_coeffs = result.coefficients
chi2_at_minimum = result.chi2
job_server.destroy()
logging.debug('Min when fitting: %f', result.minimum)
logging.debug('Max when fitting: %f', result.maximum)
elif algorithm == 'survival':
try:
serial = common.check_option('serial')
parallel = common.check_option('parallel')
# Create jobserver
job_server = pp.Server(secret='abcd')
ncpus = int(common.options['ncpu'])
er_model = int(common.options['er_model'])
job_server.set_ncpus(ncpus)
opt_coeffs, chi2_at_minimum, information_dictionary = scipy.optimize.fmin_l_bfgs_b(
func=chi2simple,
x0=coeffs,
args=[[
value_at_plateau, local_max, base_BP_positions, Katz_coeff,
two_beams, spectrum, er_model
]],
approx_grad=True,
bounds=bounds,
factr=precision,
maxfun=2000,
epsilon=1e-3,
iprint=0)
job_server.destroy()
except FittingDone as result:
opt_coeffs = result.coefficients
chi2_at_minimum = result.chi2
job_server.destroy()
logging.debug('Min when fitting: %f', result.minimum)
logging.debug('Max when fitting: %f', result.maximum)
else:
logging.error('Algorithm \'' + algorithm +
'\' is not known in fit() function. Exiting.')
exit(1)
logging.info('Fitting done. %i calls total.', call_count)
return opt_coeffs, chi2_at_minimum
def fit(algorithm, local_max, base_BP_positions, spectrum, value_at_plateau, plateau_prox, plateau_dist, mesh_size, precision, initial_coefficients=[], Katz_coeff=[], two_beams=False, bounds=[], fit_interrupt_bound=0, plateau_shape_coefs=[1., 0., 0., 0.]):
"""Base fitting procedure. It minimizes sum squared distances between
SOBP and 1 (chi^2). Sum is over mesh on given plateau.
Fitting is aborted when SOBP plateau is in area between
1 - fit_interrupt_bound and 1 + fit_interrupt_bound.
Parameters:
algorithm: Algorithm for fitting. Only fmin_l_bfgs_b is available.
base_BPs: List of base BPs (each BP is (x,y) pair). SOBP if formed
from these BPs.
plateau_prox, plateau_dist: SOBP is optimized to be small between
these values.
mesh_size: Distance between points of mesh. chi^2 is calculated
only on mesh points.
precision: how small should be DERIVATIVE of chi^2 (times machine
precision) for fit to end. 1 is a extremely good fit, 1e10 -
mediocre, 1e20 - poor fit.
initial coefficients (default: [0,0,...,1]): starting point for
fitting algorithm.
fit_interrupt_bound (default: 0): Fitting is interrupted when whole
SOBP's plateau is in area around 1 of this size.
Returns:
coefficients: list of optimal coefficients found
chi2: chi2 value with coefficients found
"""
logging.info('Fitting...')
if plateau_prox > plateau_dist:
logging.error('In arguments to fit function plateau_prox is greater than plateau_dist. Exiting.')
exit(1)
# starting point for fitting algorithm
coeffs = initial_coefficients
# points where chi^2 will be evaluated
function_evaluation_mesh = [ plateau_prox + n * mesh_size
for n in
range(int((plateau_dist - plateau_prox) / mesh_size) + 1)]
print "mesh: ", function_evaluation_mesh
print "base pos: ", base_BP_positions
logging.debug('Fit mesh start: %f, fit mesh end: %f',
function_evaluation_mesh[0],
function_evaluation_mesh[-1])
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
class FittingDone(Exception):
"""Exception raised when fitting should be aborted.
"""
def __init__(self, coefficients, minimum, maximum, chi2):
self.coefficients = coefficients
self.minimum = minimum
self.maximum = maximum
self.chi2 = chi2
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 chi2simple(coefficients_of_base_BPs, args):
"""sum of square differences between plateau and sum_of_base_BPs
in fixed values of x
chi2simple(C), where coefficients_of_base_BPs = [C1, C2, C3, ...]
TODO doctest missing
"""
total = 0
# maximum and minimum are used for interrupt bound
maximum = -1e20
minimum = 1e20
value_at_plateau, local_max, positions, Katz_coeffs, two_beams, spectrum, er_model = args
m, D0, sigma, kappa = Katz_coeffs
coefficients = []
for c in coefficients_of_base_BPs:
coefficients.append(float(c))
def partial_sum(spectrum, function_evaluation_mesh, value_at_plateau, local_max, positions, coefficients_of_base_BPs, plateau_dist, plateau_prox, two_beams, m, D0, sigma, kappa, er_model):
"""Calculates partial sum"""
from src import pyamtrack_SPC
maximum = -100
minimum = 100
total = 0
for x in function_evaluation_mesh:
E_MeV_u_total, particle_no_total, fluence_cm2_total = prepareLists(x, spectrum, local_max, positions, coefficients_of_base_BPs)
## two opposite beams
if two_beams:
y = plateau_dist + plateau_prox - x
E_MeV_u_total_second, particle_no_total_second, fluence_cm2_total_second = prepareLists(y, spectrum, local_max, positions, coefficients_of_base_BPs)
E_MeV_u_total.extend(E_MeV_u_total_second)
particle_no_total.extend(particle_no_total_second)
fluence_cm2_total.extend(fluence_cm2_total_second)
survival_at_depth_x = pyamtrack_SPC.survival(E_MeV_u_total, particle_no_total, fluence_cm2_total, m, D0, sigma, kappa, er_model)
if value_at_plateau > 0 and survival_at_depth_x > 0:
total += (math.log10(value_at_plateau) - math.log10(survival_at_depth_x)) ** 2
else:
total = 1e100
if survival_at_depth_x > maximum:
maximum = survival_at_depth_x
if survival_at_depth_x < minimum:
minimum = survival_at_depth_x
return total, minimum, maximum
maximum = -100
minimum = 100
if parallel:
parts = ncpus
step = ((len(function_evaluation_mesh)) / parts) + 1
jobs = []
start_time = time.time()
for index in xrange(parts):
starti = index * step
endi = min((index + 1) * step, len(function_evaluation_mesh))
jobs.append(job_server.submit(partial_sum, args=(spectrum, function_evaluation_mesh[starti:endi], value_at_plateau, local_max, positions, coefficients, plateau_dist, plateau_prox, two_beams, m, D0, sigma, kappa, er_model), depfuncs=(prepareLists,), modules=("src.pyamtrack_SPC", "math",)))
for job in jobs:
total_p, min_p, max_p = job()
total += total_p
if max_p > maximum:
maximum = max_p
if min_p < minimum:
minimum = min_p
# print "Time elapsed: ", time.time() - start_time, "s"
# print job_server.print_stats()
if serial:
total_ser, minimum, maximum = partial_sum(spectrum, function_evaluation_mesh, value_at_plateau, local_max, positions, coefficients, plateau_dist, plateau_prox, two_beams, m, D0, sigma, kappa, er_model)
if serial and not parallel:
total = total_ser
global call_count
call_count += 1
logging.info('%i calls, chi^2 = %.12f, max = %f, min = %f',
call_count, total, maximum, minimum)
print coefficients
if (maximum <= value_at_plateau + fit_interrupt_bound) and (minimum >= value_at_plateau - fit_interrupt_bound):
raise FittingDone(coefficients=coefficients_of_base_BPs,
minimum=minimum,
maximum=maximum,
chi2=total)
return total
def chi2withDerivative(coefficients_of_base_BPs, args):
"""Sum of square differences between plateau and sum_of_base_BPs
in fixed values of x (specified in function_evaluation_mesh).
If at any point sum of BPs with coefficients passed is constrained
around 1 in area less then specified in fit_interrupt_bound exception
is thrown.
chi2withDerivative(C), where coefficients_of_base_BPs = [C1, C2, C3, ...]
TODO doctest missing
"""
total = 0
# maximum and minimum are used for interrupt bound
maximum = 0
minimum = 100
grad = [0] * len(coefficients_of_base_BPs)
value_at_plateau, local_max, positions, spectrum, plateau_shape_coefs = args
print "positions : ", positions
coef_str = "[ "
for c in coefficients_of_base_BPs:
coef_str += str(c) + " , "
coef_str += " ] "
print "coefficients : ", coef_str
coefficients = []
for c in coefficients_of_base_BPs:
coefficients.append(float(c))
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
maximum = -100
minimum = 100
if parallel:
parts = ncpus
step = ((len(function_evaluation_mesh)) / parts) + 1
jobs = []
start_time = time.time()
for index in xrange(parts):
starti = index * step
endi = min((index + 1) * step, len(function_evaluation_mesh))
jobs.append(job_server.submit(partial_sum, args=(spectrum, function_evaluation_mesh[starti:endi], local_max, positions, coefficients, plateau_shape_coefs), modules=("src.pyamtrack_SPC", "math",)))
for job in jobs:
total_p, grad_p, min_p, max_p = job()
total += total_p
for i in range(len(grad_p)):
grad[i] += grad_p[i]
if max_p > maximum:
maximum = max_p
if min_p < minimum:
minimum = min_p
print "Time elapsed: ", time.time() - start_time, "s"
print job_server.print_stats()
if serial:
total_ser, grad_ser, minimum, maximum = partial_sum(spectrum, function_evaluation_mesh, local_max, positions, coefficients, plateau_shape_coefs)
if serial and not parallel:
total = total_ser
grad = grad_ser
global call_count
call_count += 1
#if call_count % 10 == 0:
logging.info('\t %i calls, chi^2 = %.12f, max = %f, min = %f\n',
call_count, total, maximum, minimum)
if maximum < fit_interrupt_bound and (-minimum) < fit_interrupt_bound:
raise FittingDone(coefficients=coefficients_of_base_BPs,
minimum=minimum,
maximum=maximum,
chi2=total)
return total, numpy.array(grad) # TODO remove numpy array
# choose fit algorithm
if algorithm == 'dose':
try:
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)
# http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html
opt_coeffs, chi2_at_minimum, information_dictionary = scipy.optimize.fmin_l_bfgs_b(
func=chi2withDerivative,
x0=coeffs,
args=[[value_at_plateau, local_max, base_BP_positions, spectrum, plateau_shape_coefs]],
approx_grad=False,
bounds=bounds,
factr=precision,
maxfun=2000)
job_server.destroy()
except FittingDone as result:
opt_coeffs = result.coefficients
chi2_at_minimum = result.chi2
job_server.destroy()
logging.debug('Min when fitting: %f', result.minimum)
logging.debug('Max when fitting: %f', result.maximum)
elif algorithm == 'survival':
try:
serial = common.check_option('serial')
parallel = common.check_option('parallel')
# Create jobserver
job_server = pp.Server(secret='abcd')
ncpus = int(common.options['ncpu'])
er_model = int(common.options['er_model'])
job_server.set_ncpus(ncpus)
opt_coeffs, chi2_at_minimum, information_dictionary = scipy.optimize.fmin_l_bfgs_b(
func=chi2simple,
x0=coeffs,
args=[[value_at_plateau, local_max, base_BP_positions, Katz_coeff, two_beams, spectrum, er_model]],
approx_grad=True,
bounds=bounds,
factr=precision,
maxfun=2000,
epsilon=1e-3,
iprint=0)
job_server.destroy()
except FittingDone as result:
opt_coeffs = result.coefficients
chi2_at_minimum = result.chi2
job_server.destroy()
logging.debug('Min when fitting: %f', result.minimum)
logging.debug('Max when fitting: %f', result.maximum)
else:
logging.error('Algorithm \'' + algorithm + '\' is not known in fit() function. Exiting.')
exit(1)
logging.info('Fitting done. %i calls total.', call_count)
return opt_coeffs, chi2_at_minimum