def mod_scores( self, x, k ):
if x> 10: x=10.0
gam = gamma_complete(k/2.0)
part1 = 2**(k/2.0)
part2 = x**((k/2.0)-1)
part3 = math.exp(-x/2.0)
tot1 = part2*part3/(part1*gam)
bart1 = gamma_incomplete_complement(k/2.0, x/2.0)
tot2 = bart1/gam
return math.log(tot1), math.log(tot2)
def normalize(self):
result = self.compute_normalisation_constant()
self.coefs[0] = self.coefs[0] - 2.0 * math.log(result)
self.polynome = chebyshev_polynome(self.n, -1.0, +1.0, self.coefs)
result = self.compute_normalisation_constant()
if abs(result - 1.0) > 1e-6:
self.normalize()
def __init__(self,
penalty_factor=1,
penalty_scale=100,
min_functional=1.e-10):
adopt_init_args(self, locals())
self.occupancy_max = -math.log(min_functional) \
/ (penalty_factor * penalty_scale)
def __init__(self,
penalty_factor=1,
penalty_scale=100,
min_functional=1.e-10):
adopt_init_args(self, locals())
self.occupancy_max = -math.log(min_functional) \
/ (penalty_factor * penalty_scale)
def q_range_analyses(self, rg, io, rat_lim=1.5, window_size=3, level=10.0, sigma=False):
selector = flex.bool(self.data.q*rg<rat_lim)
tmp_q = self.data.q.select( selector )
tmp_i = self.data.i.select( selector )
tmp_s = self.data.s.select( selector )
rg2 = rg*rg
lni = math.log( io )
cs = None
if sigma:
cs = self.chi_square( lni, rg2, tmp_q, tmp_i, tmp_s,False )
cs = flex.sqrt( cs )
else:
cs = flex.exp(lni-rg2*tmp_q*tmp_q/3.0)
ss = cs/100.0
cs = flex.abs(tmp_i-cs)/ss
not_okai_ranges = []
previous_one_was_bad=False
tmp_range = []
for ii in xrange( window_size, cs.size() ):
tmp_cs = flex.mean( cs[ii-window_size:ii] )
if tmp_cs > level:
if not previous_one_was_bad:
tmp_range.append( tmp_q[ii] )
tmp_range.append( tmp_q[ii] )
previous_one_was_bad=True
else:
tmp_range[1] = tmp_q[ii]
else:
previous_one_was_bad=False
if len(tmp_range)>0:
not_okai_ranges.append( tmp_range )
tmp_range=[]
return not_okai_ranges
def gradient(self, occupancy):
if (occupancy > self.occupancy_max): return 0
if (occupancy < -1): occupancy = -1 - math.log(-occupancy)
s = self.penalty_scale
return -s * self.penalty_factor * math.exp(
-s * occupancy * self.penalty_factor)
def functional(self, occupancy):
if (occupancy > self.occupancy_max): return 0
if (occupancy < -1): occupancy = -1 - math.log(-occupancy)
s = self.penalty_scale
return math.exp(-s * occupancy * self.penalty_factor)
def gradient(self, occupancy): if (occupancy > self.occupancy_max): return 0 if (occupancy < -1): occupancy = -1 - math.log(-occupancy) s = self.penalty_scale return -s*self.penalty_factor*math.exp(-s*occupancy*self.penalty_factor)
def functional(self, occupancy): if (occupancy > self.occupancy_max): return 0 if (occupancy < -1): occupancy = -1 - math.log(-occupancy) s = self.penalty_scale return math.exp(-s*occupancy*self.penalty_factor)
