def sum_score_detail(self,reciprocal_space_vectors):
"""Evaluates the probability that the trial value of ( S0_vector | origin_offset ) is correct,
given the current estimate and the observations. The trial value comes through the
reciprocal space vectors, and the current estimate comes through the short list of
DPS solutions. Actual return value is a sum of NH terms, one for each DPS solution, each ranging
from -1.0 to 1.0"""
nh = min ( self.getSolutions().size(), 20) # extended API
solutions = self.getSolutions() #extended API
sum_score = 0.0
for t in xrange(nh):
#if t!=unique:continue
dfft = Directional_FFT(angle = solutions[t], xyzdata = reciprocal_space_vectors,
granularity = self.granularity, amax = self.amax, # extended API
F0_cutoff = 11)
kval = dfft.kval();
kmax = dfft.kmax();
kval_cutoff = self.raw_spot_input.size()/4.0; # deprecate record
if ( kval > kval_cutoff ):
ff=dfft.fft_result;
kbeam = ((-dfft.pmin)/dfft.delta_p) + 0.5;
Tkmax = cmath.phase(ff[kmax]);
backmax = math.cos(Tkmax+(2*math.pi*kmax*kbeam/(2*ff.size()-1)) );
### Here it should be possible to calculate a gradient.
### Then minimize with respect to two coordinates. Use lbfgs? Have second derivatives?
### can I do something local to model the cosine wave?
### direction of wave travel. Period. phase.
sum_score += backmax;
#if t == unique:
# print t, kmax, dfft.pmin, dfft.delta_p, Tkmax,(2*math.pi*kmax*kbeam/(2*ff.size()-1))
return sum_score
def sum_score_detail(self,reciprocal_space_vectors):
"""Evaluates the probability that the trial value of ( S0_vector | origin_offset ) is correct,
given the current estimate and the observations. The trial value comes through the
reciprocal space vectors, and the current estimate comes through the short list of
DPS solutions. Actual return value is a sum of NH terms, one for each DPS solution, each ranging
from -1.0 to 1.0"""
nh = min ( self.getSolutions().size(), 20) # extended API
solutions = self.getSolutions() #extended API
sum_score = 0.0
for t in range(nh):
#if t!=unique:continue
dfft = Directional_FFT(angle = solutions[t], xyzdata = reciprocal_space_vectors,
granularity = self.granularity, amax = self.amax, # extended API
F0_cutoff = 11)
kval = dfft.kval();
kmax = dfft.kmax();
kval_cutoff = self.raw_spot_input.size()/4.0; # deprecate record
if ( kval > kval_cutoff ):
ff=dfft.fft_result;
kbeam = ((-dfft.pmin)/dfft.delta_p) + 0.5;
Tkmax = cmath.phase(ff[kmax]);
backmax = math.cos(Tkmax+(2*math.pi*kmax*kbeam/(2*ff.size()-1)) );
### Here it should be possible to calculate a gradient.
### Then minimize with respect to two coordinates. Use lbfgs? Have second derivatives?
### can I do something local to model the cosine wave?
### direction of wave travel. Period. phase.
sum_score += backmax;
#if t == unique:
# print t, kmax, dfft.pmin, dfft.delta_p, Tkmax,(2*math.pi*kmax*kbeam/(2*ff.size()-1))
return sum_score
def _sum_score_detail(reciprocal_space_vectors, solutions, granularity=None, amax=None):
"""Evaluates the probability that the trial value of (S0_vector | origin_offset) is correct,
given the current estimate and the observations. The trial value comes through the
reciprocal space vectors, and the current estimate comes through the short list of
DPS solutions. Actual return value is a sum of NH terms, one for each DPS solution, each ranging
from -1.0 to 1.0"""
nh = min(solutions.size(), 20) # extended API
sum_score = 0.0
for t in range(nh):
dfft = Directional_FFT(
angle=Direction(solutions[t]),
xyzdata=reciprocal_space_vectors,
granularity=5.0,
amax=amax, # extended API XXX These values have to come from somewhere!
F0_cutoff=11,
)
kval = dfft.kval()
kmax = dfft.kmax()
kval_cutoff = reciprocal_space_vectors.size() / 4.0
if kval > kval_cutoff:
ff = dfft.fft_result
kbeam = ((-dfft.pmin) / dfft.delta_p) + 0.5
Tkmax = cmath.phase(ff[kmax])
backmax = math.cos(
Tkmax + (2 * math.pi * kmax * kbeam / (2 * ff.size() - 1))
)
### Here it should be possible to calculate a gradient.
### Then minimize with respect to two coordinates. Use lbfgs? Have second derivatives?
### can I do something local to model the cosine wave?
### direction of wave travel. Period. phase.
sum_score += backmax
return sum_score