def perturb(self, variance_array, ntrials=100):
assert self.pr.n_params == len(variance_array)
intensity = None
var = None
calc_i = None
for trial in xrange(ntrials):
gauss = random_transform.normal_variate(N=self.pr.n_params)
new_coefs = self.pr.coefs + gauss * variance_array
new_pr = pofr(self.pr.dmax, self.pr.n_params, self.pr.prior,
self.pr.scale, self.pr.n_int, self.pr.m_int)
new_pr.update(new_coefs, add_scale=False)
if intensity is None:
tmp = new_pr.i(self.q)
intensity = tmp.deep_copy()
var = (tmp * tmp).deep_copy()
calc_i = tmp.deep_copy()
else:
tmp = new_pr.i(self.q)
intensity += tmp
var += tmp * tmp
mean_i = intensity / ntrials
var_i = var / ntrials - mean_i * mean_i
return calc_i, mean_i, var_i, mean_i / calc_i, var_i / mean_i
def exercise_gauss():
data = rt.normal_variate(mu=0, sigma=1, N=1000000)
mu1 = flex.mean(data)
mu2 = flex.mean(data * data)
mu3 = flex.mean(data * data * data)
assert approx_equal(mu1, 0, eps=0.02)
assert approx_equal(mu2, 1, eps=0.02)
assert approx_equal(mu3, 0, eps=0.02)
def exercise_gauss(): data = rt.normal_variate(mu=0,sigma=1,N=1000000) mu1 = flex.mean(data) mu2 = flex.mean(data*data) mu3 = flex.mean(data*data*data) assert approx_equal(mu1,0,eps=0.02) assert approx_equal(mu2,1,eps=0.02) assert approx_equal(mu3,0,eps=0.02)
def random_data(B_add=35,
n_residues=585.0,
d_min=3.5):
unit_cell = uctbx.unit_cell( (81.0, 81.0, 61.0, 90.0, 90.0, 120.0) )
xtal = crystal.symmetry(unit_cell, " P 3 ")
## In P3 I do not have to worry about centrics or reflections with different
## epsilons.
miller_set = miller.build_set(
crystal_symmetry = xtal,
anomalous_flag = False,
d_min = d_min)
## Now make an array with d_star_sq values
d_star_sq = miller_set.d_spacings().data()
d_star_sq = 1.0/(d_star_sq*d_star_sq)
asu = {"H":8.0*n_residues*1.0,
"C":5.0*n_residues*1.0,
"N":1.5*n_residues*1.0,
"O":1.2*n_residues*1.0}
scat_info = absolute_scaling.scattering_information(
asu_contents = asu,
fraction_protein=1.0,
fraction_nucleic=0.0)
scat_info.scat_data(d_star_sq)
gamma_prot = scat_info.gamma_tot
sigma_prot = scat_info.sigma_tot_sq
## The number of residues is multriplied by the Z of the spacegroup
protein_total = sigma_prot * (1.0+gamma_prot)
## add a B-value of 35 please
protein_total = protein_total*flex.exp(-B_add*d_star_sq/2.0)
## Now that has been done,
## We can make random structure factors
normalised_random_intensities = \
random_transform.wilson_intensity_variate(protein_total.size())
random_intensities = normalised_random_intensities*protein_total*math.exp(6)
std_dev = random_intensities*5.0/100.0
noise = random_transform.normal_variate(N=protein_total.size())
noise = noise*std_dev
random_intensities=noise+random_intensities
## STuff the arrays in the miller array
miller_array = miller.array(miller_set,
data=random_intensities,
sigmas=std_dev)
miller_array=miller_array.set_observation_type(
xray.observation_types.intensity())
miller_array = miller_array.f_sq_as_f()
return (miller_array)
def random_data(B_add=35, n_residues=585.0, d_min=3.5):
unit_cell = uctbx.unit_cell((81.0, 81.0, 61.0, 90.0, 90.0, 120.0))
xtal = crystal.symmetry(unit_cell, " P 3 ")
## In P3 I do not have to worry about centrics or reflections with different
## epsilons.
miller_set = miller.build_set(crystal_symmetry=xtal,
anomalous_flag=False,
d_min=d_min)
## Now make an array with d_star_sq values
d_star_sq = miller_set.d_spacings().data()
d_star_sq = 1.0 / (d_star_sq * d_star_sq)
asu = {
"H": 8.0 * n_residues * 1.0,
"C": 5.0 * n_residues * 1.0,
"N": 1.5 * n_residues * 1.0,
"O": 1.2 * n_residues * 1.0
}
scat_info = absolute_scaling.scattering_information(asu_contents=asu,
fraction_protein=1.0,
fraction_nucleic=0.0)
scat_info.scat_data(d_star_sq)
gamma_prot = scat_info.gamma_tot
sigma_prot = scat_info.sigma_tot_sq
## The number of residues is multriplied by the Z of the spacegroup
protein_total = sigma_prot * (1.0 + gamma_prot)
## add a B-value of 35 please
protein_total = protein_total * flex.exp(-B_add * d_star_sq / 2.0)
## Now that has been done,
## We can make random structure factors
normalised_random_intensities = \
random_transform.wilson_intensity_variate(protein_total.size())
random_intensities = normalised_random_intensities * protein_total * math.exp(
6)
std_dev = random_intensities * 5.0 / 100.0
noise = random_transform.normal_variate(N=protein_total.size())
noise = noise * std_dev
random_intensities = noise + random_intensities
## STuff the arrays in the miller array
miller_array = miller.array(miller_set,
data=random_intensities,
sigmas=std_dev)
miller_array = miller_array.set_observation_type(
xray.observation_types.intensity())
miller_array = miller_array.f_sq_as_f()
return (miller_array)
def add_error(data, fraction=0.1): n = data.size() e = rt.normal_variate(mu=0.0,sigma=fraction,N=n) e = e*data data = data+e return data
