def test_scattering_info():
miller_array = random_data(35.0, d_min=2.5)
d_star_sq = miller_array.d_spacings().data()
d_star_sq = 1.0 / (d_star_sq * d_star_sq)
asu = {
"H": 8.0 * 585.0,
"C": 5.0 * 585.0,
"N": 1.5 * 585.0,
"O": 1.2 * 585.0
}
scat_info = absolute_scaling.scattering_information(asu_contents=asu,
fraction_protein=1.0,
fraction_nucleic=0.0)
scat_info.scat_data(d_star_sq)
scat_info2 = absolute_scaling.scattering_information(n_residues=585.0)
scat_info2.scat_data(d_star_sq)
sigma_prot = scaling.get_sigma_prot_sq(d_star_sq, 195.0 * 3.0)
# Testing for consistency
for ii in range(d_star_sq.size()):
assert approx_equal(scat_info.sigma_tot_sq[ii],
scat_info2.sigma_tot_sq[ii],
eps=1e-03)
assert approx_equal(scat_info.sigma_tot_sq[ii],
sigma_prot[ii],
eps=0.5)
def test_scattering_info():
miller_array = random_data(35.0, d_min=2.5 )
d_star_sq = miller_array.d_spacings().data()
d_star_sq = 1.0/(d_star_sq*d_star_sq)
asu = {"H":8.0*585.0,"C":5.0*585.0,"N":1.5*585.0, "O":1.2*585.0}
scat_info = absolute_scaling.scattering_information(
asu_contents = asu,
fraction_protein=1.0,
fraction_nucleic=0.0)
scat_info.scat_data(d_star_sq)
scat_info2 = absolute_scaling.scattering_information(
n_residues=585.0)
scat_info2.scat_data(d_star_sq)
sigma_prot = scaling.get_sigma_prot_sq(d_star_sq,195.0*3.0)
# Testing for consistency
for ii in range(d_star_sq.size()):
assert approx_equal(scat_info.sigma_tot_sq[ii],
scat_info2.sigma_tot_sq[ii],
eps=1e-03)
assert approx_equal(scat_info.sigma_tot_sq[ii],
sigma_prot[ii],
eps=0.5)
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 wilson_plot(self):
if not self._xanalysis or not self._xanalysis.wilson_scaling:
return {}
dstarsq = self.wilson_plot_result.binner.bin_centers(2)
observed = self.wilson_plot_result.data[1:-1]
# The binning of the wilson plot can result in some bins with 'None' values
if None in observed:
observed = [i for i in observed if i is not None]
dstarsq = flex.double(
[i for i, j in zip(dstarsq, observed) if j is not None]
)
if not observed:
return {}
expected = expected_intensity(
scattering_information(n_residues=self.n_residues),
dstarsq,
b_wilson=self._xanalysis.iso_b_wilson,
p_scale=self._xanalysis.wilson_scaling.iso_p_scale,
)
x1 = observed
x2 = expected.mean_intensity
# ignore the start and end of the plot, which may be unreliable
if len(x1) > 10:
x1 = x1[1:-3]
x2 = x2[1:-3]
def residuals(k):
"""Calculate the residual for an overall scale factor"""
return x1 - k * x2
best = least_squares(residuals, 1.0)
mean_I_obs_theory = expected.mean_intensity * best.x
tickvals_wilson, ticktext_wilson = d_star_sq_to_d_ticks(dstarsq, nticks=5)
return {
"wilson_intensity_plot": {
"data": (
[
{
"x": list(dstarsq),
"y": list(observed),
"type": "scatter",
"name": "Observed",
},
{
"x": list(dstarsq),
"y": list(mean_I_obs_theory),
"type": "scatter",
"name": "Expected",
},
]
),
"layout": {
"title": "Wilson intensity plot",
"xaxis": {
"title": "Resolution (Å)",
"tickvals": tickvals_wilson,
"ticktext": ticktext_wilson,
},
"yaxis": {"type": "log", "title": "Mean(I)", "rangemode": "tozero"},
},
}
}
