def inner_loop(fmodel, wam, missing, crystal_gridding, n, progress_counter,
use_max_map):
mac = maptbx.map_accumulator(n_real=crystal_gridding.n_real(),
smearing_b=1,
max_peak_scale=100,
smearing_span=5,
use_max_map=use_max_map)
for j in range(n):
mc_w = wam.random_weight_averaged_map_coefficients(
missing=missing,
random_scale=random.choice([0, 1, 2, 3, 4, 5]),
random_seed=random.choice(range(1, 9754365, 10000)),
n_cycles=100,
fraction_keep=random.choice([0.9, 0.95, 1.0]))
if (random.choice([True, False])):
mc_w = kick.randomize_struture_factors(map_coeffs=mc_w,
number_of_kicks=100)
m = get_map(mc=mc_w, cg=crystal_gridding)
m = m / flex.max(m)
sel = m < 0
m = m.set_selected(sel, 0)
mac.add(map_data=m)
progress_counter.show()
mm = mac.as_median_map()
sd = mm.sample_standard_deviation()
r = mm / sd
return r
def outer_loop(self):
missing_reflections_manager = mmtbx.map_tools.model_missing_reflections(
coeffs=self.mc, fmodel=self.fmodel)
missing = missing_reflections_manager.get_missing(deterministic=True)
wam = kick.weighted_average(fmodel=self.fmodel, map_coefficients=self.mc)
progress_counter = counter(
n1=self.n_inner_loop, n2=self.n_outer_loop, log=self.log)
map_accumulator = maptbx.map_accumulator(
n_real = self.crystal_gridding.n_real(), smearing_b=1, max_peak_scale=100,
smearing_span=5, use_max_map=self.use_max_map)
for i in xrange(self.n_outer_loop):
m = inner_loop(
fmodel = self.fmodel,
wam = wam,
missing = missing,
crystal_gridding = self.crystal_gridding,
n = self.n_inner_loop,
progress_counter = progress_counter,
use_max_map = self.use_max_map)
m = low_volume_density_elimination(m=m, fmodel=self.fmodel,
selection=self.selection)
if(self.sharp and self.use_unsharp_masking):
maptbx.sharpen(map_data=m, index_span=1, n_averages=2,
allow_negatives=False)
maptbx.gamma_compression(map_data=m, gamma=0.1)
self.zero_below_threshold(m = m)
m = m/flex.max(m)
map_accumulator.add(map_data=m)
m = map_accumulator.as_median_map()
sd = m.sample_standard_deviation()
print >> self.log
return m/sd
def get_ma(n1,n2,n3, points):
ma = maptbx.map_accumulator(n_real = (n1,n2,n3), use_max_map=False)
for value in points:
m = [value for i in xrange(n1*n2*n3)]
m = flex.double(m)
m.resize(flex.grid((n1,n2,n3)))
ma.add(map_data=m)
return ma
def inner_loop(fmodel, wam, missing, crystal_gridding, n, progress_counter,
use_max_map):
mac = maptbx.map_accumulator(n_real = crystal_gridding.n_real(),
smearing_b=1, max_peak_scale=100, smearing_span=5, use_max_map=use_max_map)
for j in xrange(n):
mc_w = wam.random_weight_averaged_map_coefficients(
missing = missing,
random_scale = random.choice([0,1,2,3,4,5]),
random_seed = random.choice(range(1, 9754365, 10000)),
n_cycles = 100,
fraction_keep = random.choice([0.9, 0.95, 1.0]))
if(random.choice([True,False])):
mc_w = kick.randomize_struture_factors(map_coeffs=mc_w,
number_of_kicks=100)
m = get_map(mc=mc_w, cg=crystal_gridding)
m = m/flex.max(m)
sel = m<0
m = m.set_selected(sel, 0)
mac.add(map_data=m)
progress_counter.show()
mm = mac.as_median_map()
sd = mm.sample_standard_deviation()
r = mm/sd
return r
