def plot_unitcells(self, experiments):
if len(experiments) == 1:
return
all_a = flex.double()
all_b = flex.double()
all_c = flex.double()
for crystal in experiments.crystals():
a, b, c = crystal.get_unit_cell().parameters()[0:3]
all_a.append(a); all_b.append(b); all_c.append(c)
fig, axes = plt.subplots(nrows=3, ncols=1)
for ax, axis, data in zip(axes, ['A', 'B', 'C'], [all_a, all_b, all_c]):
stats = flex.mean_and_variance(data)
cutoff = 4*stats.unweighted_sample_standard_deviation()
if cutoff < 0.5:
cutoff = 0.5
limits = stats.mean()-cutoff, stats.mean()+cutoff
sel = (data >= limits[0]) & (data <= limits[1])
subset = data.select(sel)
h = flex.histogram(subset,n_slots=50)
ax.plot(h.slot_centers().as_numpy_array(),h.slots().as_numpy_array(),'-')
ax.set_title("%s axis histogram (showing %d of %d xtals). Mean: %7.2f Stddev: %7.2f"%(
axis, len(subset), len(data), stats.mean(),
stats.unweighted_sample_standard_deviation()))
ax.set_ylabel("N lattices")
ax.set_xlabel(r"$\AA$")
ax.set_xlim(limits)
plt.tight_layout()
def tst_for_dataset(self, creator, filename):
from dials.array_family import flex
from dials.algorithms.shoebox import MaskCode
print filename
rlist = flex.reflection_table.from_pickle(filename)
shoebox = rlist['shoebox']
background = [sb.background.deep_copy() for sb in shoebox]
success = creator(shoebox)
assert(success.count(True) == len(success))
diff = []
for i in range(len(rlist)):
mask = flex.bool([(m & MaskCode.Foreground) != 0 for m in shoebox[i].mask])
px1 = background[i].select(mask)
px2 = shoebox[i].background.select(mask)
den = max([flex.mean(px1), 1.0])
diff.append(flex.mean(px2 - px1) / den)
diff = flex.double(diff)
mv = flex.mean_and_variance(flex.double(diff))
mean = mv.mean()
sdev = mv.unweighted_sample_standard_deviation()
try:
assert(abs(mean) < 0.01)
except Exception:
print "Mean: %f, Sdev: %f", mean, sdev
from matplotlib import pylab
pylab.hist(diff)
pylab.show()
raise
def _wx_img_w_cpp(self, np_2d_tmp, show_nums, np_2d_mask = None):
xmax = np_2d_tmp.shape[0]
ymax = np_2d_tmp.shape[1]
if(np_2d_mask == None):
np_2d_mask = np.zeros( (xmax, ymax), 'double')
transposed_data = np.zeros( (ymax, xmax), 'double')
transposed_mask = np.zeros( (ymax, xmax), 'double')
transposed_data[:,:] = np.transpose(np_2d_tmp)
transposed_mask[:,:] = np.transpose(np_2d_mask)
flex_data_in = flex.double(transposed_data)
flex_mask_in = flex.double(transposed_mask)
err_code = self.wx_bmp_arr.set_min_max(self.vl_min, self.vl_max)
img_array_tmp = self.wx_bmp_arr.gen_bmp(flex_data_in, flex_mask_in, show_nums)
np_img_array = img_array_tmp.as_numpy_array()
height = np.size( np_img_array[:, 0:1, 0:1] )
width = np.size( np_img_array[0:1, :, 0:1] )
img_array = np.empty( (height, width, 3),'uint8')
img_array[:,:,:] = np_img_array[:,:,:]
self._wx_image = wx.EmptyImage(width, height)
self._wx_image.SetData( img_array.tostring() )
data_to_become_bmp = (self._wx_image, width, height)
return data_to_become_bmp
def get_pix_coords(wavelength, A, mill_arr, detector, delta_i=0.02):
""" Code copied from sim.py courtesy of Aaron and Tara """
s0=col((0,0,-1/wavelength))
q=flex.vec3_double([A*col(idx) for idx in mill_arr.indices().as_vec3_double()])
s0_hat=flex.vec3_double([s0.normalize()]*len(q))
q_hat=q.each_normalize()
#q_hat.cross(flex.vec3_double([s0_hat]*len(q_hat)))
e1_hat = q_hat.cross(s0_hat)
c0_hat = s0_hat.cross(e1_hat)
q_len_sq = flex.double([col(v).length_sq() for v in q])
a_side=q_len_sq*wavelength/2
b_side=flex.sqrt(q_len_sq)-a_side**2
#flex.vec3_double([sqrt(q.length_sq()-a_side**2 for idx in mill_arr)])
r_vec=flex.vec3_double(-a_side*s0_hat+b_side*c0_hat)
s1=r_vec+s0
EQ=q+s0
len_EQ=flex.double([col(v).length() for v in EQ])
ratio=len_EQ*wavelength
indices = flex.miller_index()
coords =flex.vec2_double()
for i in range(len(s1)):
if ratio[i] > 1 - delta_i and ratio[i] < 1 + delta_i:
indices.append(mill_arr.indices()[i])
pix = detector[0].get_ray_intersection_px(s1[i])
if detector[0].is_coord_valid(pix):
coords.append(pix)
return coords, indices
def compute_functional_and_gradients_test_code(self):
values = self.parameterization(self.x)
assert -150. < values.BFACTOR < 150. # limits on the exponent, please
self.func = self.refinery.fvec_callable(values)
functional = flex.sum(self.func*self.func)
self.f = functional
jacobian = self.refinery.jacobian_callable(values)
self.gg_0 = flex.sum(2. * self.func * jacobian[0])
self.gg_1 = flex.sum(2. * self.func * jacobian[1])
self.gg_3 = flex.sum(2. * self.func * jacobian[3])
self.gg_4 = flex.sum(2. * self.func * jacobian[4])
DELTA = 1.E-7
self.g = flex.double()
for x in xrange(self.n):
templist = list(self.x)
templist[x]+=DELTA
dvalues = flex.double(templist)
dfunc = self.refinery.fvec_callable(self.parameterization(dvalues))
dfunctional = flex.sum(dfunc*dfunc)
#calculate by finite_difference
self.g.append( ( dfunctional-functional )/DELTA )
self.g[2]=0.
print >> self.out, "rms %10.3f"%math.sqrt(flex.mean(self.func*self.func)),
values.show(self.out)
print >>self.out, "derivatives--> %15.5f %15.5f %9.7f %5.2f %5.2f"%tuple(self.g)
print >>self.out, " analytical-> %15.5f %15.5f %5.2f %5.2f"%(
self.gg_0,self.gg_1, self.gg_3,self.gg_4)
self.g[0]=self.gg_0
self.g[1]=self.gg_1
self.g[3]=self.gg_3
self.g[4]=self.gg_4
return self.f, self.g
def decode(self, handle):
'''Decode the reflection data.'''
from dials.array_family import flex
# Get the group containing the reflection data
g = handle['entry/data_processing']
# Create the list of reflections
rl = flex.reflection_table(int(g.attrs['num_reflections']))
# Decode all the columns
for key in g:
item = g[key]
name = item.attrs['flex_type']
if name == 'shoebox':
flex_type = getattr(flex, name)
data = item['data']
mask = item['mask']
background = item['background']
col = flex_type(len(rl))
for i in range(len(rl)):
dd = data['%d' % i].value
col[i].data = flex.double(data['%d' % i].value)
col[i].mask = flex.int(mask['%d' % i].value)
col[i].background = flex.double(background['%d' % i].value)
else:
flex_type = getattr(flex, name)
col = self.decode_column(flex_type, item)
rl[str(key)] = col
# Return the list of reflections
return rl
def unstable_matrix_inversion_diagonal(self,Lower,Diag,Transpose):
### Can't use the Cholesky factorization to derive the Variance-Covariance matrix
### Demonstrate that inverting the matrix is numerically unstable
Nx = len(self.helper.x)
error_diagonal_elems = flex.double(Nx)
for j_element in xrange(Nx):
# now solve for the vector p = D * LT * x by substitution in eqn L * p = b
# b is the column vector of zeroes except jth_element is 1.
p = flex.double(Nx)
p[j_element] = 1.
for p_idx in xrange(j_element+1,Nx):
for subs_idx in xrange(j_element,p_idx):
p[p_idx] -= Lower(p_idx,subs_idx) * p[subs_idx]
Pvec = col(p)
# now solve for the vector q = LT * x by division in eqn D * q = b
q = flex.double([ p[i] / Diag(i,i) for i in xrange(Nx)] )
# this is the unstable step. We can't divide by tiny denominators
Qvec = col(q)
# now solve for the jth element of x in the eqn LT * x = q
xelem = flex.double(Qvec.elems)
for x_idx in xrange(Nx-1,j_element-1,-1): #comment this in for production
#for x_idx in xrange(Nx-1,-1,-1):
for subs_idx in xrange(x_idx+1, Nx):
xelem[x_idx] -= Transpose(x_idx,subs_idx) * xelem[subs_idx]
Xvec = col(xelem) # got the whole vector; only need j_element for the error matrix diagonal
error_diagonal_elems[j_element] = xelem[j_element]
return col(error_diagonal_elems)
def __init__(self, strategies, n_bins=8, degrees_per_bin=5):
from cctbx import crystal, miller
import copy
sg = strategies[0].experiment.crystal.get_space_group() \
.build_derived_reflection_intensity_group(anomalous_flag=True)
cs = crystal.symmetry(
unit_cell=strategies[0].experiment.crystal.get_unit_cell(), space_group=sg)
for i, strategy in enumerate(strategies):
if i == 0:
predicted = copy.deepcopy(strategy.predicted)
else:
predicted_ = copy.deepcopy(strategy.predicted)
predicted_['dose'] += (flex.max(predicted['dose']) + 1)
predicted.extend(predicted_)
ms = miller.set(cs, indices=predicted['miller_index'], anomalous_flag=True)
ma = miller.array(ms, data=flex.double(ms.size(),1),
sigmas=flex.double(ms.size(), 1))
if 1:
merging = ma.merge_equivalents()
o = merging.array().customized_copy(
data=merging.redundancies().data().as_double()).as_mtz_dataset('I').mtz_object()
o.write('predicted.mtz')
d_star_sq = ma.d_star_sq().data()
binner = ma.setup_binner_d_star_sq_step(
d_star_sq_step=(flex.max(d_star_sq)-flex.min(d_star_sq)+1e-8)/n_bins)
dose = predicted['dose']
range_width = 1
range_min = flex.min(dose) - range_width
range_max = flex.max(dose)
n_steps = 2 + int((range_max - range_min) - range_width)
binner_non_anom = ma.as_non_anomalous_array().use_binning(
binner)
self.n_complete = flex.size_t(binner_non_anom.counts_complete()[1:-1])
from xia2.Modules.PyChef2 import ChefStatistics
chef_stats = ChefStatistics(
ma.indices(), ma.data(), ma.sigmas(),
ma.d_star_sq().data(), dose, self.n_complete, binner,
ma.space_group(), ma.anomalous_flag(), n_steps)
def fraction_new(completeness):
# Completeness so far at end of image
completeness_end = completeness[1:]
# Completeness so far at start of image
completeness_start = completeness[:-1]
# Fraction of unique reflections observed for the first time on each image
return completeness_end - completeness_start
self.dose = dose
self.ieither_completeness = chef_stats.ieither_completeness()
self.iboth_completeness = chef_stats.iboth_completeness()
self.frac_new_ref = fraction_new(self.ieither_completeness) / degrees_per_bin
self.frac_new_pairs = fraction_new(self.iboth_completeness) / degrees_per_bin
def plot_multirun_stats(runs,
run_numbers,
d_min,
ratio_cutoff=1,
n_strong_cutoff=40,
run_tags=[],
run_statuses=[],
interactive=False,
compress_runs=True,
xsize=30,
ysize=10,
high_vis=False):
tset = flex.double()
two_theta_low_set = flex.double()
two_theta_high_set = flex.double()
nset = flex.int()
I_sig_I_low_set = flex.double()
I_sig_I_high_set = flex.double()
boundaries = []
lengths = []
runs_with_data = []
offset = 0
for idx in xrange(len(runs)):
r = runs[idx]
if len(r[0]) > 0:
if compress_runs:
tslice = r[0] - r[0][0] + offset
offset += (r[0][-1] - r[0][0])
else:
tslice = r[0]
last_end = r[0][-1]
tset.extend(tslice)
two_theta_low_set.extend(r[1])
two_theta_high_set.extend(r[2])
nset.extend(r[3])
I_sig_I_low_set.extend(r[4])
I_sig_I_high_set.extend(r[5])
boundaries.append(tslice[0])
boundaries.append(tslice[-1])
lengths.append(len(tslice))
runs_with_data.append(run_numbers[idx])
else:
boundaries.extend([None]*2)
stats_tuple = get_run_stats(tset,
two_theta_low_set,
two_theta_high_set,
nset,
I_sig_I_low_set,
I_sig_I_high_set,
tuple(boundaries),
tuple(lengths),
runs_with_data,
ratio_cutoff=ratio_cutoff,
n_strong_cutoff=n_strong_cutoff)
png = plot_run_stats(stats_tuple, d_min, run_tags=run_tags, run_statuses=run_statuses, interactive=interactive,
xsize=xsize, ysize=ysize, high_vis=high_vis)
return png
def get_partiality_array(self,values): Rh = self.get_Rh_array(values) Rs = flex.double(len(self.MILLER),1./values.DEFF)+flex.double(len(self.MILLER),values.ETA/2.)/self.DVEC Rs_sq = Rs * Rs Rh_sq = Rh * Rh numerator = Rs_sq - Rh_sq denominator = values.DEFF * Rs * Rs_sq partiality = numerator / denominator return partiality
def __call__(self, params, options):
''' Import the integrate.hkl file. '''
from iotbx.xds import integrate_hkl
from dials.array_family import flex
from dials.util.command_line import Command
from cctbx import sgtbx
# Get the unit cell to calculate the resolution
uc = self._experiment.crystal.get_unit_cell()
# Read the INTEGRATE.HKL file
Command.start('Reading INTEGRATE.HKL')
handle = integrate_hkl.reader()
handle.read_file(self._integrate_hkl)
hkl = flex.miller_index(handle.hkl)
xyzcal = flex.vec3_double(handle.xyzcal)
xyzobs = flex.vec3_double(handle.xyzobs)
iobs = flex.double(handle.iobs)
sigma = flex.double(handle.sigma)
rlp = flex.double(handle.rlp)
peak = flex.double(handle.peak) * 0.01
Command.end('Read %d reflections from INTEGRATE.HKL file.' % len(hkl))
# Derive the reindex matrix
rdx = self.derive_reindex_matrix(handle)
print 'Reindex matrix:\n%d %d %d\n%d %d %d\n%d %d %d' % (rdx.elems)
# Reindex the reflections
Command.start('Reindexing reflections')
cb_op = sgtbx.change_of_basis_op(sgtbx.rt_mx(sgtbx.rot_mx(rdx.elems)))
hkl = cb_op.apply(hkl)
Command.end('Reindexed %d reflections' % len(hkl))
# Create the reflection list
Command.start('Creating reflection table')
table = flex.reflection_table()
table['id'] = flex.int(len(hkl), 0)
table['panel'] = flex.size_t(len(hkl), 0)
table['miller_index'] = hkl
table['xyzcal.px'] = xyzcal
table['xyzobs.px.value'] = xyzobs
table['intensity.cor.value'] = iobs
table['intensity.cor.variance'] = sigma**2
table['intensity.prf.value'] = iobs * peak / rlp
table['intensity.prf.variance'] = (sigma * peak / rlp)**2
table['lp'] = 1.0 / rlp
table['d'] = flex.double(uc.d(h) for h in hkl)
Command.end('Created table with {0} reflections'.format(len(table)))
# Output the table to pickle file
if params.output.filename is None:
params.output.filename = 'integrate_hkl.pickle'
Command.start('Saving reflection table to %s' % params.output.filename)
table.as_pickle(params.output.filename)
Command.end('Saved reflection table to %s' % params.output.filename)
def __init__(self, x, detrend=True, spans=None):
if spans is None: spans = robjects.r("NULL")
dat = robjects.FloatVector(list(x))
pgram = robjects.r['spec.pgram']
result = pgram(dat, spans=spans, detrend=detrend, taper=0, fast=False, plot=False)
self.result = result
self.spec = flex.double(result.rx2('spec'))
self.freq = flex.double(result.rx2('freq'))
return
def from_dict(cls, obj):
''' Convert the profile model from a dictionary. '''
if obj['__id__'] != "gaussian_rs":
raise RuntimeError('expected __id__ gaussian_rs, got %s' % obj['__id__'])
n_sigma = obj['n_sigma']
sigma_b = obj['sigma_b']
sigma_m = obj['sigma_m']
if isinstance(sigma_b, list):
assert(len(sigma_b) == len(sigma_m))
sigma_b = flex.double(sigma_b)
sigma_m = flex.double(sigma_m)
return cls(None, n_sigma, sigma_b, sigma_m, deg=True)
def tst_split_blocks_1_frame(self):
from dials.array_family import flex
from random import randint, uniform, seed
from dials.algorithms.integration.integrator import JobList
r = flex.reflection_table()
r['value1'] = flex.double()
r['value2'] = flex.int()
r['value3'] = flex.double()
r['bbox'] = flex.int6()
r['id'] = flex.int()
expected = []
for i in range(100):
x0 = randint(0, 100)
x1 = x0 + randint(1, 10)
y0 = randint(0, 100)
y1 = y0 + randint(1, 10)
z0 = randint(0, 100)
z1 = z0 + randint(1, 10)
v1 = uniform(0, 100)
v2 = randint(0, 100)
v3 = uniform(0, 100)
r.append({
'id' : 0,
'value1' : v1,
'value2' : v2,
'value3' : v3,
'bbox' : (x0, x1, y0, y1, z0, z1)
})
for z in range(z0, z1):
expected.append({
'id' : 0,
'value1' : v1,
'value2' : v2,
'value3' : v3,
'bbox' : (x0, x1, y0, y1, z, z+1),
'partial_id' : i,
})
jobs = JobList()
jobs.add((0,1), (0, 111), 1)
jobs.split(r)
assert(len(r) == len(expected))
EPS = 1e-7
for r1, r2 in zip(r, expected):
assert(r1['bbox'] == r2['bbox'])
assert(r1['partial_id'] == r2['partial_id'])
assert(abs(r1['value1'] - r2['value1']) < EPS)
assert(r1['value2'] == r2['value2'])
assert(abs(r1['value3'] - r2['value3']) < EPS)
print 'OK'
def _wx_img_w_cpp(self, np_2d_tmp, show_nums, palette, np_2d_mask = None):
xmax = np_2d_tmp.shape[1]
ymax = np_2d_tmp.shape[0]
if np_2d_mask is None:
np_2d_mask = np.zeros((ymax, xmax), 'double')
transposed_data = np.zeros((ymax, xmax), 'double')
transposed_mask = np.zeros((ymax, xmax), 'double')
transposed_data[:,:] = np_2d_tmp
transposed_mask[:,:] = np_2d_mask
flex_data_in = flex.double(transposed_data)
flex_mask_in = flex.double(transposed_mask)
err_code = self.wx_bmp_arr.set_min_max(self.vl_min, self.vl_max)
test_log = '''
print "self.vl_min, self.vl_max = ", self.vl_min, self.vl_max
print "err_code =", err_code
print "before crash"
print "palette =", palette
'''
if palette == "black2white":
palette_num = 1
elif palette == "white2black":
palette_num = 2
elif palette == "hot ascend":
palette_num = 3
else: # assuming "hot descend"
palette_num = 4
img_array_tmp = self.wx_bmp_arr.gen_bmp(flex_data_in, flex_mask_in, show_nums, palette_num)
np_img_array = img_array_tmp.as_numpy_array()
height = np.size(np_img_array[:, 0:1, 0:1])
width = np.size( np_img_array[0:1, :, 0:1])
img_array = np.empty((height, width, 3),'uint8')
img_array[:,:,:] = np_img_array[:,:,:]
self._wx_image = wx.EmptyImage(width, height)
self._wx_image.SetData(img_array.tostring())
data_to_become_bmp = (self._wx_image, width, height)
return data_to_become_bmp
def build_up(pfh, objective_only=False):
values = pfh.parameterization(pfh.x)
assert 0. < values.G , "G-scale value out of range ( < 0 ) within LevMar build_up"
# XXX revisit these limits. Seems like an ad hoc approach to have to set these limits
# However, the assertions are necessary to avoid floating point exceptions at the C++ level
# Regardless, these tests throw out ~30% of LM14 data, thus search for another approach
assert -150. < values.BFACTOR < 150. ,"B-factor out of range (+/-150) within LevMar build_up"
assert -0.5 < 180.*values.thetax/math.pi < 0.5 , "thetax out of range ( |rotx|>.5 degrees ) within LevMar build_up"
assert -0.5 < 180.*values.thetay/math.pi < 0.5 , "thetay out of range ( |roty|>.5 degrees ) within LevMar build_up"
assert 0.000001 < values.RS , "RLP size out of range (<0.000001) within LevMar build_up"
assert values.RS < 0.001 , "RLP size out of range (>0.001) within LevMar build_up"
residuals = pfh.refinery.fvec_callable(values)
pfh.reset()
if objective_only:
pfh.add_residuals(residuals, weights=pfh.refinery.WEIGHTS)
else:
grad_r = pfh.refinery.jacobian_callable(values)
jacobian = flex.double(
flex.grid(len(pfh.refinery.MILLER), pfh.n_parameters))
for j, der_r in enumerate(grad_r):
jacobian.matrix_paste_column_in_place(der_r,j)
#print >> pfh.out, "COL",j, list(der_r)
pfh.add_equations(residuals, jacobian, weights=pfh.refinery.WEIGHTS)
print >> pfh.out, "rms %10.3f"%math.sqrt(flex.mean(pfh.refinery.WEIGHTS*residuals*residuals)),
values.show(pfh.out)
def tst_copy(self):
import copy
from dials.array_family import flex
# Create a table
table = flex.reflection_table([
('col1', flex.int(range(10)))])
# Make a shallow copy of the table
shallow = copy.copy(table)
shallow['col2'] = flex.double(range(10))
assert(table.ncols() == 2)
assert(table.is_consistent())
print 'OK'
# Make a deep copy of the table
deep = copy.deepcopy(table)
deep['col3'] = flex.std_string(10)
assert(table.ncols() == 2)
assert(deep.ncols() == 3)
assert(table.is_consistent())
assert(deep.is_consistent())
table2 = table.copy()
table2['col3'] = flex.std_string(10)
assert(table.ncols() == 2)
assert(table2.ncols() == 3)
assert(table.is_consistent())
assert(table2.is_consistent())
print 'OK'
def generate_image(function, height, width):
from dials.array_family import flex
image = flex.double(flex.grid(height, width))
for j in range(height):
for i in range(width):
image[j,i] = function(j,i)
return image
def model(self, reflections, profiles):
from dials.array_family import flex
indices = flex.size_t(range(len(self)))
weights = flex.double([1.0] * len(self))
for profile in profiles:
self.add(indices, weights, profile)
def plot_scale_vs_x_y(self):
from scitbx.array_family import flex
from math import ceil
print "Getting scale"
points = [(int(xyz[1] / 8), int(xyz[0] / 8)) for xyz in self.xyz]
scale = [x / d for x, d in zip(self.i_xds, self.i_dials)]
print "Creating Grid"
image_size = self.sweep.get_detector()[0].get_image_size()[::-1]
image_size = (int(ceil(image_size[0] / 8)), int(ceil(image_size[1] / 8)))
grid = flex.double(flex.grid(image_size))
count = flex.int(flex.grid(image_size))
for p, s in zip(points, scale):
grid[p] += s
count[p] += 1
for i in range(len(grid)):
if count[i] > 0:
grid[i] /= count[i]
# grid_points = [(j,i) for j in range(image_size[0]) for i in range(image_size[1])]
# grid = griddata(points, scale, grid_points)
# grid.shape = image_size
from matplotlib import pyplot
fig, ax = pyplot.subplots()
pyplot.title("scale vs x/y")
cax = pyplot.imshow(grid.as_numpy_array())
cbar = fig.colorbar(cax)
pyplot.savefig("plot-scale-vs-xy.png")
pyplot.close()
def _xl_unit_cell_derivatives(self, isel, parameterisation=None,
reflections=None):
# Get required data
h = self._h.select(isel)
B = self._B.select(isel)
wl = self._wavelength.select(isel)
# get derivatives of the B matrix wrt the parameters
dB_dxluc_p = [None if der is None else flex.mat3_double(len(isel), der.elems) \
for der in parameterisation.get_ds_dp(use_none_as_null=True)]
d2theta_dp = []
# loop through the parameters
for der in dB_dxluc_p:
if der is None:
d2theta_dp.append(None)
continue
r0 = B * h
dr0 = der * h
r0len = r0.norms()
dr0len = dr0.dot(r0) / r0len
# 2theta = 2 * arcsin( |r0| / (2 * |s0| ) )
sintheta = 0.5 * r0len * wl
fac = 1.0 / flex.sqrt(flex.double(len(wl), 1.0) - sintheta**2)
val = fac * wl * dr0len
d2theta_dp.append(val)
return d2theta_dp
def draw_palette_label(i_min, i_max):
if i_max > 500:
i_max = 500
logger.debug("reshaping i_max in shown palette bitmap")
if i_min < -3:
i_min = -3
logger.debug("reshaping i_min in shown palette bitmap")
scale_size = int(i_max - i_min)
np_img_arr = np.zeros((50, 503), dtype=np.double)
m_point = int((i_max + i_min) / 2) + 3
np_img_arr[0:50, :m_point] = i_min
np_img_arr[0:50, m_point:] = i_max
if scale_size > 10:
try:
ascending_img_arr = (
np.arange(i_min, i_max, 1.0 / 50.0).reshape(scale_size, 50).T
)
lbound = int(i_min) + 3
ubound = int(i_max) + 3
np_img_arr[0:50, lbound:ubound] = ascending_img_arr[0:50, 0:scale_size]
except BaseException as e:
# We don't want to catch bare exceptions but don't know
# what this was supposed to catch. Log it.
logger.error("Caught unknown exception type %s: %s", type(e).__name__, e)
logger.debug("something went wrong with the creation of palette bitmap")
tmp_flex_arr = flex.double(np_img_arr)
return tmp_flex_arr
def residual_map_special_deltapsi_add_on( reflections,experiments,matches,hkllist, predicted,plot,eta_deg,deff ):
detector = experiments[0].detector
crystal = experiments[0].crystal
unit_cell = crystal.get_unit_cell()
pxlsz = detector[0].get_pixel_size()
model_millers = reflections["miller_index"]
dpsi = flex.double()
for match in matches:
obs_miller = hkllist[match["pred"]]
model_index= model_millers.first_index(obs_miller)
raw_delta_psi = reflections["delpsical.rad"][model_index]
deltapsi_envelope = (unit_cell.d(obs_miller)/deff) + math.pi*eta_deg/180.
normalized_delta_psi = raw_delta_psi/deltapsi_envelope
dpsi.append( normalized_delta_psi )
from matplotlib.colors import Normalize
dnorm = Normalize()
dnorm.autoscale(dpsi.as_numpy_array())
CMAP = plot.get_cmap("bwr")
for match,dcolor in zip(matches,dpsi):
#print dcolor, dnorm(dcolor), CMAP(dnorm(dcolor))
#blue represents negative delta psi: outside Ewald sphere; red, positive, inside Ewald sphere
plot.plot([predicted[match["pred"]][1]/pxlsz[1]],[-predicted[match["pred"]][0]/pxlsz[0]],color=CMAP(dnorm(dcolor)),
marker=".", markersize=5)
def calculate_weights(self, reflections):
"""Set weights to constant terms. If stills, the z weights are
the 'delpsical.weights' attribute of the reflection table. Otherwise, use
the usual 'xyzobs.mm.weights'"""
wx = flex.double(len(reflections), self._wx)
wy = flex.double(len(reflections), self._wy)
wz = flex.double(len(reflections), self._wz)
if self._stills:
null = flex.double(len(reflections), 0)
reflections['xyzobs.mm.weights'] = flex.vec3_double(wx, wy, null)
reflections['delpsical.weights'] = wz
else:
reflections['xyzobs.mm.weights'] = flex.vec3_double(wx, wy, wz)
return reflections
def _create_block_columns(self):
"""Create a column to contain the block number."""
from scitbx.array_family import flex
self._reflections['block'] = flex.size_t(len(self._reflections))
self._reflections['block_centre'] = flex.double(len(self._reflections))
return
def plot_prob_for_zero(c, b, s):
from math import log, exp, factorial
from dials.array_family import flex
L = flex.double(flex.grid(100, 100))
MASK = flex.bool(flex.grid(100, 100))
c = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
b = [bb / sum(b) for bb in b]
s = [ss / sum(s) for ss in s]
for BB in range(0, 100):
for SS in range(0, 100):
B = 0 + BB / 10000.0
S = 0 + SS / 40.0
LL = 0
for i in range(len(b)):
if B*b[i] + S*s[i] <= 0:
MASK[BB, SS] = True
LL = -999999
break
else:
LL += c[i]*log(B*b[i]+S*s[i]) - log(factorial(c[i])) - B*b[i] - S*s[i]
L[BB, SS] = LL
index = flex.max_index(L)
i = index % 100
j = index // 100
B = 0 + j / 10000.0
S = 0 + i / 40.0
print flex.max(L), B, S
from matplotlib import pylab
import numpy
im = numpy.ma.masked_array(flex.exp(L).as_numpy_array(), mask=MASK.as_numpy_array())
pylab.imshow(im)
pylab.show()
exit(0)
def tst_serialize(self):
from dials.array_family import flex
# The columns as lists
c1 = list(range(10))
c2 = list(range(10))
c3 = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'i', 'j', 'k']
# Create a table with some elements
table = flex.reflection_table()
table['col1'] = flex.int(c1)
table['col2'] = flex.double(c2)
table['col3'] = flex.std_string(c3)
# Pickle, then unpickle
import cPickle as pickle
obj = pickle.dumps(table)
new_table = pickle.loads(obj)
assert(new_table.is_consistent())
assert(new_table.nrows() == 10)
assert(new_table.ncols() == 3)
assert(all(a == b for a, b in zip(new_table['col1'], c1)))
assert(all(a == b for a, b in zip(new_table['col2'], c2)))
assert(all(a == b for a, b in zip(new_table['col3'], c3)))
print 'OK'
def show_image(c,b,s, BB=None, SS=None):
import numpy.ma
from dials.array_family import flex
N = 100
im = flex.double(flex.grid(N, N))
mask = flex.bool(flex.grid(N, N))
for j in range(N):
for i in range(N):
B = -1.0 + j * 10.0 / N
S = -1.0 + i * 10.0 / N
im[j,i], mask[j,i] = func(c,b,s,B,S)
im[j,i] = -im[j,i]
masked_im = numpy.ma.array(
# im.as_numpy_array(),
flex.exp(im).as_numpy_array(),
mask = mask.as_numpy_array())
mask2 = flex.bool(flex.grid(N, N))
indices = []
for i in range(len(mask)):
if mask[i] == False:
indices.append(i)
indices = flex.size_t(indices)
ind = flex.max_index(im.select(indices))
ind = indices[ind]
maxy = -1.0 + (ind % N) * 10.0 / N
maxx = -1.0 + (ind // N) * 10.0 / N
from matplotlib import pylab
pylab.imshow(masked_im, origin='bottom', extent=[-1.0, 9.0, -1.0, 9.0])
if YY is not None and XX is not None:
pylab.plot(YY, XX)
pylab.scatter([maxy], [maxx])
pylab.show()
def get_Rh_array(self, values):
Rh = flex.double()
eff_Astar = self.get_eff_Astar(values)
for mill in self.MILLER:
x = eff_Astar * matrix.col(mill)
Svec = x + self.BEAM
Rh.append(Svec.length() - (1./self.WAVE))
return Rh
def result_for_cxi_merge(self, file_name):
values = self.get_parameter_values()
self.rs2_parameter_range_assertions(values)
scaler = self.nave1_refinery.scaler_callable(self.get_parameter_values())
partiality_array = self.refinery.get_partiality_array(values)
p_scaler = flex.pow(partiality_array,
0.5*self.params.postrefinement.merge_partiality_exponent)
fat_selection = (self.nave1_refinery.lorentz_callable(self.get_parameter_values()) >
self.params.postrefinement.rs_hybrid.partiality_threshold) # was 0.2 for rs2
fat_count = fat_selection.count(True)
scaler_s = scaler.select(fat_selection)
p_scaler_s = p_scaler.select(fat_selection)
#avoid empty database INSERT, if insufficient centrally-located Bragg spots:
# in samosa, handle this at a higher level, but handle it somehow.
if fat_count < 3:
raise ValueError, "< 3 near-fulls after refinement"
print >> self.out, "On total %5d the fat selection is %5d"%(
len(self.observations_pair1_selected.indices()), fat_count)
observations_original_index = \
self.observations_original_index_pair1_selected.select(fat_selection)
observations = self.observations_pair1_selected.customized_copy(
indices = self.observations_pair1_selected.indices().select(fat_selection),
data = (self.observations_pair1_selected.data().select(fat_selection)/scaler_s),
sigmas = (self.observations_pair1_selected.sigmas().select(fat_selection)/(scaler_s * p_scaler_s))
)
matches = miller.match_multi_indices(
miller_indices_unique=self.miller_set.indices(),
miller_indices=observations.indices())
I_weight = flex.double(len(observations.sigmas()), 1.)
I_reference = flex.double([self.i_model.data()[pair[0]] for pair in matches.pairs()])
SWC = simple_weighted_correlation(I_weight, I_reference, observations.data())
print >> self.out, "CORR: NEW correlation is", SWC.corr
self.final_corr = SWC.corr
#another range assertion
assert self.final_corr > 0.1,"correlation coefficient out of range (<= 0.1) after LevMar refinement"
# XXX Specific to the hybrid_rs method, and likely these limits are problem-specific (especially G-max) so look for another approach
# or expose the limits as phil parameters.
assert values.G < 0.5 , "G-scale value out of range ( > 0.5 XXX may be too strict ) after LevMar refinement"
return observations_original_index,observations,matches
def get_run_stats(
timestamps,
two_theta_low,
two_theta_high,
n_strong,
resolutions,
n_lattices,
tuple_of_timestamp_boundaries,
lengths,
run_numbers,
n_multiples=2,
ratio_cutoff=1,
n_strong_cutoff=40,
i_sigi_cutoff=1,
d_min=2,
):
print("")
print("%d shots" % len(timestamps))
print("%d first lattices" % (n_lattices >= 1).count(True))
print("%d multiple lattices" % (n_lattices >= 2).count(True))
print("%d total lattices" % (flex.sum(n_lattices)))
iterator = range(len(resolutions))
# hit rate of drops (observe solvent) or crystals (observe strong spots)
# since -1 is used as a flag for "did not store this value", and we want a quotient,
# set the numerator value to 0 whenever either the numerator or denominator is -1
invalid = (two_theta_low <= 0) or (two_theta_high < 0) # <= to prevent /0
numerator = two_theta_high.set_selected(invalid, 0)
denominator = two_theta_low.set_selected(two_theta_low == 0,
1) # prevent /0
drop_ratios = numerator / denominator
drop_hits = drop_ratios >= ratio_cutoff
xtal_hits = n_strong >= n_strong_cutoff
half_idx_rate_window = min(50, max(int(len(timestamps) // 20), 1))
half_hq_rate_window = 500
indexed_sel = n_lattices > 0
hq_sel = (resolutions > 0) & (resolutions <= d_min)
# indexing and droplet hit rate in a sliding window
idx_rate = flex.double()
multiples_rate = flex.double()
hq_rate = flex.double()
drop_hit_rate = flex.double()
for i in iterator:
idx_min = max(0, i - half_idx_rate_window)
idx_max = min(i + half_idx_rate_window, len(resolutions))
n_lattices_local = n_lattices[idx_min:idx_max]
shots_this_span = len(n_lattices_local)
first_lattices_local = n_lattices_local >= 1
idx_local_rate = first_lattices_local.count(True) / shots_this_span
idx_rate.append(idx_local_rate)
multiples_sel = n_lattices_local >= n_multiples
multiples_local_rate = multiples_sel.count(True) / shots_this_span
multiples_rate.append(multiples_local_rate)
drop_sel = drop_hits[idx_min:idx_max]
drop_local_rate = drop_sel.count(True) / shots_this_span
drop_hit_rate.append(drop_local_rate)
# different sliding window for "high quality" xtals
hq_min = max(0, i - half_hq_rate_window)
hq_max = min(i + half_hq_rate_window, len(resolutions))
n_lattices_local_hq = n_lattices[hq_min:hq_max]
first_lattices_local_hq = n_lattices_local_hq >= 1
hq_high_sel = hq_sel[hq_min:hq_max].select(first_lattices_local_hq)
n_first_lattices_local_hq = first_lattices_local_hq.count(True)
if n_first_lattices_local_hq > 0:
hq_rate.append(hq_high_sel.count(True) / n_first_lattices_local_hq)
else:
hq_rate.append(0)
return (timestamps, drop_ratios, drop_hits, drop_hit_rate, n_strong,
xtal_hits, idx_rate, multiples_rate, hq_rate, indexed_sel, hq_sel,
resolutions, half_idx_rate_window * 2, lengths,
tuple_of_timestamp_boundaries, run_numbers)
def test_ArrayScalingModel(test_reflections, mock_exp):
"""Test the ArrayScalingModel class."""
configdict = {
"corrections": ["decay", "absorption", "modulation"],
"n_res_param": 2,
"n_time_param": 2,
"resmin": 1.0,
"res_bin_width": 1.0,
"time_norm_fac": 1.0,
"time_rot_interval": 1.0,
"n_x_param": 2,
"n_y_param": 2,
"xmin": 0.0,
"ymin": 0.0,
"x_bin_width": 1.0,
"y_bin_width": 2.0,
"n_x_mod_param": 2,
"n_y_mod_param": 2,
"x_det_bin_width": 2.0,
"y_det_bin_width": 2.0,
}
parameters_dict = {
"decay": {
"parameters": flex.double([1.2, 1.1, 1.0, 0.9]),
"parameter_esds": None,
},
"absorption": {
"parameters": flex.double([0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1, 0.2]),
"parameter_esds": None,
},
"modulation": {"parameters": flex.double(4, 0.9), "parameter_esds": None},
}
# Test standard factory initialisation
arraymodel = ArrayScalingModel(parameters_dict, configdict)
assert arraymodel.id_ == "array"
assert "decay" in arraymodel.components
assert "absorption" in arraymodel.components
assert "modulation" in arraymodel.components
assert list(arraymodel.components["decay"].parameters) == [1.2, 1.1, 1.0, 0.9]
assert list(arraymodel.components["absorption"].parameters) == [
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
]
assert list(arraymodel.components["modulation"].parameters) == 4 * [0.9]
# Test configure reflection table
_ = arraymodel.configure_components(test_reflections, mock_exp, [])
# Test from_dict initialisation method for previous model case.
init_dict = arraymodel.to_dict()
new_array_model = ArrayScalingModel.from_dict(init_dict)
assert new_array_model.id_ == "array"
comps = new_array_model.components
assert "modulation" in comps
assert "absorption" in comps
assert "decay" in comps
assert list(comps["decay"].parameters) == [1.2, 1.1, 1.0, 0.9]
assert list(comps["absorption"].parameters) == [
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
]
assert list(comps["modulation"].parameters) == 4 * [0.9]
# Test from_dict initialisation method for another case.
array_dict = {
"__id__": "array",
"is_scaled": True,
"decay": {
"n_parameters": 4,
"parameters": [0.5, 1.0, 0.4, 1.0],
"null_parameter_value": 1.0,
"est_standard_devs": [0.05, 0.1, 0.05, 0.1],
},
"configuration_parameters": {
"corrections": ["decay"],
"n_res_param": 2,
"n_time_param": 2,
"resmin": 1.0,
"res_bin_width": 1.0,
"time_norm_fac": 1.0,
"time_rot_interval": 1.0,
},
}
arraymodel = ArrayScalingModel.from_dict(array_dict)
assert arraymodel.id_ == "array"
comps = arraymodel.components
assert "modulation" not in comps
assert "absorption" not in comps
assert "decay" in comps
assert list(comps["decay"].parameters) == [0.5, 1.0, 0.4, 1.0]
assert list(comps["decay"].parameter_esds) == [0.05, 0.1, 0.05, 0.1]
new_dict = arraymodel.to_dict()
assert new_dict == array_dict
with pytest.raises(RuntimeError):
array_dict["__id__"] = "physical"
arraymodel = ArrayScalingModel.from_dict(array_dict)
assert len(arraymodel.consecutive_refinement_order) == 3
arraymodel.show()
# test limit batch range
configdict = {
"corrections": ["decay", "absorption"],
"n_res_param": 2,
"n_time_param": 3,
"resmin": 1.0,
"res_bin_width": 1.0,
"time_norm_fac": 0.1,
"time_rot_interval": 10.0,
"n_x_param": 2,
"n_y_param": 2,
"xmin": 0.0,
"ymin": 0.0,
"x_bin_width": 1.0,
"y_bin_width": 2.0,
"n_x_mod_param": 2,
"n_y_mod_param": 2,
"x_det_bin_width": 2.0,
"y_det_bin_width": 2.0,
"valid_image_range": (1, 20),
"valid_osc_range": (0, 20),
}
parameters_dict = {
"decay": {
"parameters": flex.double([1.2, 1.1, 1.0, 0.9, 0.8, 0.7]),
"parameter_esds": None,
},
"absorption": {
"parameters": flex.double(
[0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.3, 0.4, 0.3, 0.4]
),
"parameter_esds": None,
},
}
array = ArrayScalingModel(parameters_dict, configdict)
array.limit_image_range((1, 10))
assert list(array.components["decay"].parameters) == [1.2, 1.1, 1.0, 0.9]
assert list(array.components["absorption"].parameters) == [
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
0.1,
0.2,
]
assert array.configdict["n_time_param"] == 2
assert array.configdict["valid_image_range"] == (1, 10)
assert array.configdict["valid_osc_range"] == (0, 10)
def resolve_model_parameters(self):
"""Update parameters in the model."""
self.model.components["b"].parameters = flex.double([self.x[0]])
def integration_concept_detail(self, experiments, reflections, spots,image_number,cb_op_to_primitive,**kwargs):
detector = experiments[0].detector
crystal = experiments[0].crystal
from cctbx.crystal import symmetry
c_symmetry = symmetry(space_group = crystal.get_space_group(), unit_cell = crystal.get_unit_cell())
self.image_number = image_number
NEAR = 10
pxlsz = detector[0].get_pixel_size()
Predicted = self.get_predictions_accounting_for_centering(experiments,reflections,cb_op_to_primitive,**kwargs)
FWMOSAICITY = self.inputai.getMosaicity()
self.DOMAIN_SZ_ANG = kwargs.get("domain_size_ang", self.__dict__.get("actual",0) )
refineflag = {True:0,False:1}[kwargs.get("domain_size_ang",0)==0]
c_symmetry.show_summary(prefix="EXCURSION%1d REPORT FWMOS= %6.4f DOMAIN= %6.1f "%(refineflag,FWMOSAICITY,self.DOMAIN_SZ_ANG))
from annlib_ext import AnnAdaptor
self.cell = c_symmetry.unit_cell()
query = flex.double()
print len(self.predicted)
for pred in self.predicted: # predicted spot coord in pixels
query.append(pred[0]/pxlsz[0])
query.append(pred[1]/pxlsz[1])
self.reserve_hkllist_for_signal_search = self.hkllist
reference = flex.double()
assert self.length>NEAR# Can't do spot/pred matching with too few spots
for spot in spots:
reference.append(spot.ctr_mass_x())
reference.append(spot.ctr_mass_y())
IS_adapt = AnnAdaptor(data=reference,dim=2,k=NEAR)
IS_adapt.query(query)
idx_cutoff = float(min(self.mask_focus[image_number]))
from rstbx.apps.slip_helpers import slip_callbacks
cache_refinement_spots = getattr(slip_callbacks.slip_callback,"requires_refinement_spots",False)
indexed_pairs_provisional = []
correction_vectors_provisional = []
c_v_p_flex = flex.vec3_double()
this_setting_matched_indices = reflections["miller_index"]
for j,item in enumerate(this_setting_matched_indices):
this_setting_index = self.hkllist.first_index(item)
if this_setting_index:
Match = dict(spot=j,pred=this_setting_index)
indexed_pairs_provisional.append(Match)
vector = matrix.col(
[reflections["xyzobs.px.value"][j][0] - self.predicted[Match["pred"]][0]/pxlsz[0],
reflections["xyzobs.px.value"][j][1] - self.predicted[Match["pred"]][1]/pxlsz[1]])
correction_vectors_provisional.append(vector)
c_v_p_flex.append((vector[0],vector[1],0.))
self.N_correction_vectors = len(correction_vectors_provisional)
self.rmsd_px = math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))
print "... %d provisional matches"%self.N_correction_vectors,
print "r.m.s.d. in pixels: %6.3f"%(self.rmsd_px)
if self.horizons_phil.integration.enable_residual_scatter:
from matplotlib import pyplot as plt
fig = plt.figure()
for cv in correction_vectors_provisional:
plt.plot([cv[1]],[-cv[0]],"r.")
plt.title(" %d matches, r.m.s.d. %5.2f pixels"%(len(correction_vectors_provisional),math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt,fig,"res")
plt.close()
if self.horizons_phil.integration.enable_residual_map:
from matplotlib import pyplot as plt
PX = reflections["xyzobs.px.value"]
fig = plt.figure()
for match,cv in zip(indexed_pairs_provisional,correction_vectors_provisional):
plt.plot([PX[match["spot"]][1]],[-PX[match["spot"]][0]],"r.")
plt.plot([self.predicted[match["pred"]][1]/pxlsz[1]],[-self.predicted[match["pred"]][0]/pxlsz[0]],"g.")
plt.plot([PX[match["spot"]][1], PX[match["spot"]][1] + 10.*cv[1]],
[-PX[match["spot"]][0], -PX[match["spot"]][0] - 10.*cv[0]],'r-')
if kwargs.get("user-reentrant") != None and self.horizons_phil.integration.spot_prediction == "dials" \
and self.horizons_phil.integration.enable_residual_map_deltapsi:
from rstbx.apps.stills.util import residual_map_special_deltapsi_add_on
residual_map_special_deltapsi_add_on(
reflections = self.dials_spot_prediction,
matches = indexed_pairs_provisional, experiments=experiments,
hkllist = self.hkllist,
predicted = self.predicted, plot=plt, eta_deg=FWMOSAICITY, deff=self.DOMAIN_SZ_ANG
)
plt.xlim([0,detector[0].get_image_size()[1]])
plt.ylim([-detector[0].get_image_size()[0],0])
plt.title(" %d matches, r.m.s.d. %5.2f pixels"%(len(correction_vectors_provisional),math.sqrt(flex.mean(c_v_p_flex.dot(c_v_p_flex)))))
plt.axes().set_aspect("equal")
self.show_figure(plt,fig,"map")
plt.close()
indexed_pairs = indexed_pairs_provisional
correction_vectors = correction_vectors_provisional
########### skip outlier rejection for this derived class
### However must retain the ability to write out correction vectiors.
if True: # at Aaron's request; test later
correction_lengths = flex.double([v.length() for v in correction_vectors_provisional])
clorder = flex.sort_permutation(correction_lengths)
sorted_cl = correction_lengths.select(clorder)
indexed_pairs = []
correction_vectors = []
self.correction_vectors = []
for icand in xrange(len(sorted_cl)):
# somewhat arbitrary sigma = 1.0 cutoff for outliers
indexed_pairs.append(indexed_pairs_provisional[clorder[icand]])
correction_vectors.append(correction_vectors_provisional[clorder[icand]])
if cache_refinement_spots:
self.spotfinder.images[self.frame_numbers[self.image_number]]["refinement_spots"].append(
spots[reflections[indexed_pairs[-1]["spot"]]['spotfinder_lookup']])
if kwargs.get("verbose_cv")==True:
print "CV OBSCENTER %7.2f %7.2f REFINEDCENTER %7.2f %7.2f"%(
float(self.inputpd["size1"])/2.,float(self.inputpd["size2"])/2.,
self.inputai.xbeam()/pxlsz[0], self.inputai.ybeam()/pxlsz[1]),
print "OBSSPOT %7.2f %7.2f PREDSPOT %7.2f %7.2f"%(
reflections[indexed_pairs[-1]["spot"]]['xyzobs.px.value'][0],
reflections[indexed_pairs[-1]["spot"]]['xyzobs.px.value'][1],
self.predicted[indexed_pairs[-1]["pred"]][0]/pxlsz[0],
self.predicted[indexed_pairs[-1]["pred"]][1]/pxlsz[1]),
the_hkl = self.hkllist[indexed_pairs[-1]["pred"]]
print "HKL %4d %4d %4d"%the_hkl,"%2d"%self.setting_id,
radial, azimuthal = spots[indexed_pairs[-1]["spot"]].get_radial_and_azimuthal_size(
self.inputai.xbeam()/pxlsz[0], self.inputai.ybeam()/pxlsz[1])
print "RADIALpx %5.3f AZIMUTpx %5.3f"%(radial,azimuthal)
# Store a list of correction vectors in self.
radial, azimuthal = spots[indexed_pairs[-1]['spot']].get_radial_and_azimuthal_size(
self.inputai.xbeam()/pxlsz[0], self.inputai.ybeam()/pxlsz[1])
self.correction_vectors.append(
dict(obscenter=(float(self.inputpd['size1']) / 2,
float(self.inputpd['size2']) / 2),
refinedcenter=(self.inputai.xbeam() / pxlsz[0],
self.inputai.ybeam() / pxlsz[1]),
obsspot=(reflections[indexed_pairs[-1]['spot']]['xyzobs.px.value'][0],
reflections[indexed_pairs[-1]['spot']]['xyzobs.px.value'][1]),
predspot=(self.predicted[indexed_pairs[-1]['pred']][0] / pxlsz[0],
self.predicted[indexed_pairs[-1]['pred']][1] / pxlsz[1]),
hkl=(self.hkllist[indexed_pairs[-1]['pred']][0],
self.hkllist[indexed_pairs[-1]['pred']][1],
self.hkllist[indexed_pairs[-1]['pred']][2]),
setting_id=self.setting_id,
radial=radial,
azimuthal=azimuthal))
self.inputpd["symmetry"] = c_symmetry
self.inputpd["symmetry"].show_summary(prefix="SETTING ")
if self.horizons_phil.integration.model == "user_supplied":
# Not certain of whether the reentrant_* dictionary keys create a memory leak
if kwargs.get("user-reentrant",None)==None:
kwargs["reentrant_experiments"] = experiments
kwargs["reentrant_reflections"] = reflections
from cxi_user import post_outlier_rejection
self.indexed_pairs = indexed_pairs
self.spots = spots
post_outlier_rejection(self,image_number,cb_op_to_primitive,self.horizons_phil,kwargs)
return
########### finished with user-supplied code
correction_lengths=flex.double([v.length() for v in correction_vectors])
self.r_residual = pxlsz[0]*flex.mean(correction_lengths)
#assert len(indexed_pairs)>NEAR # must have enough indexed spots
if (len(indexed_pairs) <= NEAR):
raise Sorry("Not enough indexed spots, only found %d, need %d" % (len(indexed_pairs), NEAR))
reference = flex.double()
for item in indexed_pairs:
reference.append(spots[item["spot"]].ctr_mass_x())
reference.append(spots[item["spot"]].ctr_mass_y())
PS_adapt = AnnAdaptor(data=reference,dim=2,k=NEAR)
PS_adapt.query(query)
self.BSmasks = []
# do not use null: self.null_correction_mapping( predicted=self.predicted,
self.positional_correction_mapping( predicted=self.predicted,
correction_vectors = correction_vectors,
PS_adapt = PS_adapt,
IS_adapt = IS_adapt,
spots = spots)
# which spots are close enough to interfere with background?
MAXOVER=6
OS_adapt = AnnAdaptor(data=query,dim=2,k=MAXOVER) #six near nbrs
OS_adapt.query(query)
if self.mask_focus[image_number] is None:
raise Sorry("No observed/predicted spot agreement; no Spotfinder masks; skip integration")
nbr_cutoff = 2.0* max(self.mask_focus[image_number])
FRAME = int(nbr_cutoff/2)
#print "The overlap cutoff is %d pixels"%nbr_cutoff
nbr_cutoff_sq = nbr_cutoff * nbr_cutoff
#print "Optimized C++ section...",
self.set_frame(FRAME)
self.set_background_factor(kwargs["background_factor"])
self.set_nbr_cutoff_sq(nbr_cutoff_sq)
self.set_guard_width_sq(self.horizons_phil.integration.guard_width_sq)
self.set_detector_gain(self.horizons_phil.integration.detector_gain)
flex_sorted = flex.int()
for item in self.sorted:
flex_sorted.append(item[0]);flex_sorted.append(item[1]);
if self.horizons_phil.integration.mask_pixel_value is not None:
self.set_mask_pixel_val(self.horizons_phil.integration.mask_pixel_value)
image_obj = self.imagefiles.imageindex(self.frame_numbers[self.image_number])
image_obj.read()
rawdata = image_obj.linearintdata # assume image #1
if self.inputai.active_areas != None:
self.detector_xy_draft = self.safe_background( rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted,
tiles=self.inputai.active_areas.IT,
tile_id=self.inputai.active_areas.tile_id);
else:
self.detector_xy_draft = self.safe_background( rawdata=rawdata,
predicted=self.predicted,
OS_adapt=OS_adapt,
sorted=flex_sorted);
for i in xrange(len(self.predicted)): # loop over predicteds
B_S_mask = {}
keys = self.get_bsmask(i)
for k in xrange(0,len(keys),2):
B_S_mask[(keys[k],keys[k+1])]=True
self.BSmasks.append(B_S_mask)
#print "Done"
return
def run(self):
from libtbx.test_utils import approx_equal
from dials.array_family import flex
def approx_equal_dict(a, b, k):
return approx_equal(a[k], b[k])
# Do summation by all different methods
result1 = self.integrate("3d")
result2 = self.integrate("flat3d")
result3 = self.integrate("2d")
result4 = self.integrate("single2d")
assert (len(result1) >= len(self.rlist))
assert (len(result2) >= len(self.rlist))
assert (len(result3) >= len(self.rlist))
assert (len(result4) >= len(self.rlist))
# result1 and result2 should be the same
assert (len(result1) == len(result2))
for r1, r2 in zip(result1, result2):
assert (r1['partial_id'] == r2['partial_id'])
assert (r1['bbox'] == r2['bbox'])
assert (r1['entering'] == r2['entering'])
assert (r1['flags'] == r2['flags'])
assert (r1['id'] == r2['id'])
assert (r1['miller_index'] == r2['miller_index'])
assert (r1['panel'] == r2['panel'])
assert (approx_equal_dict(r1, r2, 'd'))
assert (approx_equal_dict(r1, r2, 'intensity.sum.value'))
assert (approx_equal_dict(r1, r2, 'intensity.sum.variance'))
assert (approx_equal_dict(r1, r2, 'lp'))
assert (approx_equal_dict(r1, r2, 'partiality'))
assert (approx_equal_dict(r1, r2, 's1'))
assert (approx_equal_dict(r1, r2, 'xyzcal.mm'))
assert (approx_equal_dict(r1, r2, 'xyzcal.px'))
assert (approx_equal_dict(r1, r2, 'zeta'))
print 'OK'
# result3 and result4 should be the same
assert (len(result3) == len(result4))
for r3, r4 in zip(result3, result4):
assert (r3['partial_id'] == r4['partial_id'])
assert (r3['bbox'] == r4['bbox'])
assert (r3['entering'] == r4['entering'])
assert (r3['flags'] == r4['flags'])
assert (r3['id'] == r4['id'])
assert (r3['miller_index'] == r4['miller_index'])
assert (r3['panel'] == r4['panel'])
assert (approx_equal_dict(r3, r4, 'd'))
assert (approx_equal_dict(r3, r4, 'intensity.sum.value'))
assert (approx_equal_dict(r3, r4, 'intensity.sum.variance'))
assert (approx_equal_dict(r3, r4, 'lp'))
assert (approx_equal_dict(r3, r4, 'partiality'))
assert (approx_equal_dict(r3, r4, 's1'))
assert (approx_equal_dict(r3, r4, 'xyzcal.mm'))
assert (approx_equal_dict(r3, r4, 'xyzcal.px'))
assert (approx_equal_dict(r3, r4, 'xyzobs.px.value'))
assert (approx_equal_dict(r3, r4, 'xyzobs.px.variance'))
assert (approx_equal_dict(r3, r4, 'zeta'))
print 'OK'
# result3 should add up to result1
assert (len(result3) >= len(result1))
expected1 = self.rlist.copy()
expected1['intensity.sum.value'] = flex.double(len(self.rlist), 0)
expected1['intensity.sum.variance'] = flex.double(len(self.rlist), 0)
for r1 in result1:
pid = r1['partial_id']
r2 = expected1[pid]
assert (r1['entering'] == r2['entering'])
assert (r1['id'] == r2['id'])
assert (r1['miller_index'] == r2['miller_index'])
assert (r1['panel'] == r2['panel'])
assert (approx_equal_dict(r1, r2, 's1'))
assert (approx_equal_dict(r1, r2, 'xyzcal.mm'))
assert (approx_equal_dict(r1, r2, 'xyzcal.px'))
expected1['intensity.sum.value'][pid] += r1['intensity.sum.value']
expected1['intensity.sum.variance'][pid] += r1[
'intensity.sum.variance']
expected3 = self.rlist.copy()
expected3['intensity.sum.value'] = flex.double(len(self.rlist), 0)
expected3['intensity.sum.variance'] = flex.double(len(self.rlist), 0)
for r1 in result3:
pid = r1['partial_id']
r2 = expected3[pid]
assert (r1['entering'] == r2['entering'])
assert (r1['id'] == r2['id'])
assert (r1['miller_index'] == r2['miller_index'])
assert (r1['panel'] == r2['panel'])
assert (approx_equal_dict(r1, r2, 's1'))
assert (approx_equal_dict(r1, r2, 'xyzcal.mm'))
assert (approx_equal_dict(r1, r2, 'xyzcal.px'))
expected3['intensity.sum.value'][pid] += r1['intensity.sum.value']
expected3['intensity.sum.variance'][pid] += r1[
'intensity.sum.variance']
for r1, r3, in zip(expected1, expected3):
assert (approx_equal_dict(r1, r3, 'intensity.sum.value'))
assert (approx_equal_dict(r1, r3, 'intensity.sum.variance'))
print 'OK'
def mock_scaling_component(n_refl):
"""Return a mock component of a general model."""
component = mock_component()
component.calculate_scales.return_value = flex.double(n_refl, 1.0)
component.n_refl = [n_refl]
return component
def test_PhysicalScalingModel(test_reflections, mock_exp):
"""Test the PhysicalScalingModel class."""
configdict = {
"corrections": ["scale", "decay", "absorption"],
"s_norm_fac": 1.0,
"scale_rot_interval": 2.0,
"d_norm_fac": 1.0,
"decay_rot_interval": 2.0,
"lmax": 1,
"abs_surface_weight": 1e6,
}
parameters_dict = {
"scale": {"parameters": flex.double([1.2, 1.1]), "parameter_esds": None},
"decay": {"parameters": flex.double([0.1, 0.2]), "parameter_esds": None},
"absorption": {
"parameters": flex.double([0.01, 0.01, 0.01]),
"parameter_esds": None,
},
}
# Test standard factory initialisation
physicalmodel = PhysicalScalingModel(parameters_dict, configdict)
assert physicalmodel.id_ == "physical"
physicalmodel.show() # test show works with no parameter esds.
comps = physicalmodel.components
assert "scale" in comps
assert "absorption" in comps
assert "decay" in comps
assert list(comps["scale"].parameters) == [1.2, 1.1]
assert list(comps["decay"].parameters) == [0.1, 0.2]
assert list(comps["absorption"].parameters) == [0.01, 0.01, 0.01]
# Test configure reflection table
mock_params = Mock()
mock_params.parameterisation.decay_restraint = 0.0
physicalmodel.configure_components(test_reflections, mock_exp, mock_params)
# Test normalise components.
physicalmodel.components["scale"].update_reflection_data()
physicalmodel.components["scale"].calculate_scales_and_derivatives()
physicalmodel.components["decay"].update_reflection_data()
physicalmodel.components["decay"].calculate_scales_and_derivatives()
physicalmodel.normalise_components()
assert list(physicalmodel.components["scale"].parameters) == pytest.approx(
[1.0091065, 0.925014], 1e-4
)
# Test from_dict initialisation method.
physical_dict = {
"__id__": "physical",
"is_scaled": True,
"scale": {
"n_parameters": 2,
"parameters": [0.5, 1.0],
"est_standard_devs": [0.05, 0.1],
"null_parameter_value": 1,
},
"configuration_parameters": {
"corrections": ["scale"],
"s_norm_fac": 0.1,
"scale_rot_interval": 10.0,
"decay_restaint": 1e-1,
},
}
physicalmodel = PhysicalScalingModel.from_dict(physical_dict)
assert physicalmodel.id_ == "physical"
assert "scale" in physicalmodel.components
assert "absorption" not in physicalmodel.components
assert "decay" not in physicalmodel.components
assert list(physicalmodel.components["scale"].parameters) == [0.5, 1.0]
assert list(physicalmodel.components["scale"].parameter_esds) == [0.05, 0.1]
new_dict = physicalmodel.to_dict()
assert new_dict == physical_dict
# Test from_dict initialisation method for all components.
physical_dict = {
"__id__": "physical",
"is_scaled": True,
"scale": {
"n_parameters": 2,
"parameters": [0.5, 1.0],
"est_standard_devs": [0.05, 0.1],
"null_parameter_value": 1,
},
"decay": {
"n_parameters": 2,
"parameters": [0.1, 0.2],
"est_standard_devs": [0.01, 0.01],
"null_parameter_value": 0,
},
"absorption": {
"n_parameters": 3,
"parameters": [0.0, 0.1, 0.2],
"est_standard_devs": [0.01, 0.02, 0.03],
"null_parameter_value": 0,
},
"configuration_parameters": {
"corrections": ["scale", "decay", "absorption"],
"s_norm_fac": 0.1,
"scale_rot_interval": 10.0,
"d_norm_fac": 0.2,
"decay_rot_interval": 20.0,
"lmax": 1,
"abs_surface_weight": 1e6,
},
}
physicalmodel = PhysicalScalingModel.from_dict(physical_dict)
assert physicalmodel.id_ == "physical"
assert "scale" in physicalmodel.components
assert "absorption" in physicalmodel.components
assert "decay" in physicalmodel.components
assert list(physicalmodel.components["scale"].parameters) == [0.5, 1.0]
assert list(physicalmodel.components["scale"].parameter_esds) == [0.05, 0.1]
assert list(physicalmodel.components["decay"].parameters) == [0.1, 0.2]
assert list(physicalmodel.components["decay"].parameter_esds) == [0.01, 0.01]
assert list(physicalmodel.components["absorption"].parameters) == [0.0, 0.1, 0.2]
assert list(physicalmodel.components["absorption"].parameter_esds) == [
0.01,
0.02,
0.03,
]
new_dict = physicalmodel.to_dict()
assert new_dict == physical_dict
with pytest.raises(RuntimeError):
physical_dict["__id__"] = "array"
physicalmodel = PhysicalScalingModel.from_dict(physical_dict)
assert len(physicalmodel.consecutive_refinement_order) == 2
physicalmodel.show()
# test limit batch range
parameters_dict = {
"scale": {
"n_parameters": 11,
"parameters": [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5],
"parameter_esds": None,
},
"decay": {
"n_parameters": 11,
"parameters": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1],
"parameter_esds": None,
},
}
configdict = {
"corrections": ["scale", "decay"],
"s_norm_fac": 0.1,
"scale_rot_interval": 10.0,
"d_norm_fac": 0.1,
"decay_rot_interval": 10.0,
"valid_image_range": (1, 90),
"valid_osc_range": (0, 90),
}
physical = PhysicalScalingModel(parameters_dict, configdict)
physical.limit_image_range((1, 50))
assert list(physical.components["scale"].parameters) == [
0.5,
1.0,
1.5,
2.0,
2.5,
3.0,
3.5,
]
assert list(physical.components["decay"].parameters) == [
0.1,
0.2,
0.3,
0.4,
0.5,
0.6,
0.7,
]
assert physical.configdict["valid_osc_range"] == (0, 50)
assert physical.configdict["valid_image_range"] == (1, 50)
# try edge cases
# if restricted by > rot int, then reduce number of params and shift offset
# if necessary
physical = PhysicalScalingModel(
copy.deepcopy(parameters_dict), copy.deepcopy(configdict)
)
physical.limit_image_range((7, 45))
assert list(physical.components["scale"].parameters) == [
1.0,
1.5,
2.0,
2.5,
3.0,
3.5,
]
assert list(physical.components["decay"].parameters) == [
0.2,
0.3,
0.4,
0.5,
0.6,
0.7,
]
assert physical.configdict["valid_osc_range"] == (6, 45)
assert physical.configdict["valid_image_range"] == (7, 45)
# if not restricted by > rot int, then should 'squeeze' the parameters closer
# in rotation angle, leaving the same number of params (as reducing number of
# params would give parameter spacing greater than initially specified rot int)
physical = PhysicalScalingModel(
copy.deepcopy(parameters_dict), copy.deepcopy(configdict)
)
physical.limit_image_range((5, 45))
assert list(physical.components["scale"].parameters) == [
0.5,
1.0,
1.5,
2.0,
2.5,
3.0,
3.5,
]
assert list(physical.components["decay"].parameters) == [
0.1,
0.2,
0.3,
0.4,
0.5,
0.6,
0.7,
]
assert physical.configdict["valid_osc_range"] == (4, 45)
assert physical.configdict["valid_image_range"] == (5, 45)
def set_param_vals(self, x):
"""method for refinement engine access"""
self.x = x
self.model.components["b"].parameters = flex.double([self.x[0]])
self.model.binner.update(self.model.parameters)
tsim = time.time() - t
panel_images = np.array(sims[0])
for pidx in range(len(DETECTOR)):
SIM = nanoBragg(detector=DETECTOR,
beam=SPECTRUM_BEAM,
panel_id=pidx)
SIM.beamsize_mm = 0.001
SIM.exposure_s = 1
SIM.flux = 1e12
SIM.adc_offset_adu = 0
# SIM.detector_psf_kernel_radius_pixels = 5
# SIM.detector_psf_type = shapetype.Unknown # for CSPAD
SIM.detector_psf_fwhm_mm = 0
SIM.quantum_gain = 1
SIM.raw_pixels = flex.double(panel_images[pidx].ravel())
SIM.add_noise()
panel_images[pidx] = SIM.raw_pixels.as_numpy_array().reshape(
panel_images[pidx].shape)
SIM.free_all()
del SIM
writer.add_image(panel_images)
if rank == 0:
print("Done with shot %d / %d , time: %.4fs" %
(img_num + 1, num_imgs, tsim),
flush=True)
def write_columns(self, integrated_data):
"""Write the column definitions AND data to the current dataset."""
# now create the actual data structures - first keep a track of the columns
# H K L M/ISYM BATCH I SIGI IPR SIGIPR FRACTIONCALC XDET YDET ROT WIDTH
# LP MPART FLAG BGPKRATIOS
# gather the required information for the reflection file
nref = len(integrated_data["miller_index"])
assert nref
xdet, ydet, _ = [
flex.double(x) for x in integrated_data["xyzobs.px.value"].parts()
]
# now add column information...
# FIXME add DIALS_FLAG which can include e.g. was partial etc.
type_table = {
"H": "H",
"K": "H",
"L": "H",
"I": "J",
"SIGI": "Q",
"IPR": "J",
"SIGIPR": "Q",
"BG": "R",
"SIGBG": "R",
"XDET": "R",
"YDET": "R",
"BATCH": "B",
"BGPKRATIOS": "R",
"WIDTH": "R",
"MPART": "I",
"M_ISYM": "Y",
"FLAG": "I",
"LP": "R",
"FRACTIONCALC": "R",
"ROT": "R",
"QE": "R",
}
# derive index columns from original indices with
#
# from m.replace_original_index_miller_indices
#
# so all that is needed now is to make space for the reflections - fill with
# zeros...
self.mtz_file.adjust_column_array_sizes(nref)
self.mtz_file.set_n_reflections(nref)
dataset = self.current_dataset
# assign H, K, L, M_ISYM space
for column in "H", "K", "L", "M_ISYM":
dataset.add_column(column, type_table[column]).set_values(
flex.double(nref, 0.0).as_float())
self.mtz_file.replace_original_index_miller_indices(
integrated_data["miller_index_rebase"])
dataset.add_column("BATCH", type_table["BATCH"]).set_values(
integrated_data["batch"].as_double().as_float())
# if intensity values used in scaling exist, then just export these as I, SIGI
if "intensity.scale.value" in integrated_data:
I_scaling = integrated_data["intensity.scale.value"]
V_scaling = integrated_data["intensity.scale.variance"]
# Trap negative variances
assert V_scaling.all_gt(0)
dataset.add_column("I", type_table["I"]).set_values(
I_scaling.as_float())
dataset.add_column("SIGI", type_table["SIGI"]).set_values(
flex.sqrt(V_scaling).as_float())
dataset.add_column("SCALEUSED", "R").set_values(
integrated_data["inverse_scale_factor"].as_float())
dataset.add_column("SIGSCALEUSED", "R").set_values(
flex.sqrt(integrated_data["inverse_scale_factor_variance"]).
as_float())
else:
if "intensity.prf.value" in integrated_data:
if "intensity.sum.value" in integrated_data:
col_names = ("IPR", "SIGIPR")
else:
col_names = ("I", "SIGI")
I_profile = integrated_data["intensity.prf.value"]
V_profile = integrated_data["intensity.prf.variance"]
# Trap negative variances
assert V_profile.all_gt(0)
dataset.add_column(col_names[0], type_table["I"]).set_values(
I_profile.as_float())
dataset.add_column(col_names[1],
type_table["SIGI"]).set_values(
flex.sqrt(V_profile).as_float())
if "intensity.sum.value" in integrated_data:
I_sum = integrated_data["intensity.sum.value"]
V_sum = integrated_data["intensity.sum.variance"]
# Trap negative variances
assert V_sum.all_gt(0)
dataset.add_column("I", type_table["I"]).set_values(
I_sum.as_float())
dataset.add_column("SIGI", type_table["SIGI"]).set_values(
flex.sqrt(V_sum).as_float())
if ("background.sum.value" in integrated_data
and "background.sum.variance" in integrated_data):
bg = integrated_data["background.sum.value"]
varbg = integrated_data["background.sum.variance"]
assert (varbg >= 0).count(False) == 0
sigbg = flex.sqrt(varbg)
dataset.add_column("BG",
type_table["BG"]).set_values(bg.as_float())
dataset.add_column("SIGBG", type_table["SIGBG"]).set_values(
sigbg.as_float())
dataset.add_column("FRACTIONCALC",
type_table["FRACTIONCALC"]).set_values(
integrated_data["fractioncalc"].as_float())
dataset.add_column("XDET",
type_table["XDET"]).set_values(xdet.as_float())
dataset.add_column("YDET",
type_table["YDET"]).set_values(ydet.as_float())
dataset.add_column("ROT", type_table["ROT"]).set_values(
integrated_data["ROT"].as_float())
if "lp" in integrated_data:
dataset.add_column("LP", type_table["LP"]).set_values(
integrated_data["lp"].as_float())
if "qe" in integrated_data:
dataset.add_column("QE", type_table["QE"]).set_values(
integrated_data["qe"].as_float())
elif "dqe" in integrated_data:
dataset.add_column("QE", type_table["QE"]).set_values(
integrated_data["dqe"].as_float())
else:
dataset.add_column("QE", type_table["QE"]).set_values(
flex.double(nref, 1.0).as_float())
def generate_simple_table(prf=True):
"""Generate a reflection table for testing intensity combination.
The numbers are contrived to make sum intensities agree well at high
intensity but terribly at low and vice versa for profile intensities."""
reflections = flex.reflection_table()
reflections["miller_index"] = flex.miller_index([
(0, 0, 1),
(0, 0, 1),
(0, 0, 1),
(0, 0, 1),
(0, 0, 1),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 3),
(0, 0, 3),
(0, 0, 3),
(0, 0, 3),
(0, 0, 3),
(0, 0, 4),
(0, 0, 4),
(0, 0, 4),
(0, 0, 4),
(0, 0, 4),
(0, 0, 5),
(0, 0, 5),
(0, 0, 5),
(0, 0, 5),
(0, 0, 5),
])
reflections["inverse_scale_factor"] = flex.double(25, 1.0)
# Contrive an example that should give the best cc12 when combined.
# make sum intensities agree well at high intensity but terribly at low
# and vice versa for profile intensities.
# profile less consistent at high intensity here
# sumless consistent at low intensity here
reflections["intensity.sum.value"] = flex.double([
10000.0,
11000.0,
9000.0,
8000.0,
12000.0,
500.0,
5600.0,
5500.0,
2000.0,
6000.0,
100.0,
50.0,
150.0,
75.0,
125.0,
30.0,
10.0,
2.0,
35.0,
79.0,
1.0,
10.0,
20.0,
10.0,
5.0,
])
reflections["intensity.sum.variance"] = flex.double([10000] * 5 +
[5000] * 5 +
[100] * 5 + [30] * 5 +
[10] * 5)
reflections.set_flags(flex.bool(25, False),
reflections.flags.outlier_in_scaling)
reflections.set_flags(flex.bool(25, True), reflections.flags.integrated)
reflections["lp"] = flex.double(25, 0.5)
if prf:
reflections["intensity.prf.value"] = flex.double([
10000.0,
16000.0,
12000.0,
6000.0,
9000.0,
5000.0,
2000.0,
1500.0,
1300.0,
9000.0,
100.0,
80.0,
120.0,
90.0,
100.0,
30.0,
40.0,
50.0,
30.0,
30.0,
10.0,
12.0,
9.0,
8.0,
10.0,
])
reflections["intensity.prf.variance"] = flex.double([1000] * 5 +
[500] * 5 +
[10] * 5 +
[3] * 5 + [1] * 5)
reflections = calculate_prescaling_correction(reflections)
return reflections
def _multiplicity_mean_error_stddev(
self, calculate_variances=False, keep_singles=False
):
""""
Calculate aggregate properties of grouped symmetry-equivalent reflections.
Populate the reflection table of observations with the following
properties:
* ``multiplicity`` — Multiplicity of observations of a given reflection
in the asymmetric unit;
:type: `dials.array_family_flex_ext.int` array
* ``intensity.mean.value`` — Mean of symmetry-equivalent reflections,
weighted by measurement error;
:type: `dials.array_family_flex_ext.double` array
* ``intensity.mean.std_error`` — Standard error on the weighted mean;
:type: `dials.array_family_flex_ext.double` array
* (optional) ``intensity.mean.variance`` — variance of
symmetry-equivalent reflections, weighted by measurement error;
:type: `dials.array_family_flex_ext.double` array
:param calculate_variances: Elect whether to calculate the weighted
variances. Defaults to False, to spare an expensive computation.
:type calculate_variances: bool
:param keep_singles: Choose whether to keep single-multiplicity
reflections.
:type keep_singles: bool
"""
for key, rtable in self.rtables.items():
# Sort the reflection table for speedier iteration.
rtable.sort("miller_index.asu")
# Record the positions of any multiplicity-1 reflections.
if not keep_singles:
singles = flex.size_t()
# Record the multiplicities.
multiplicity = flex.int()
# For weighted averaging.
weights = 1 / rtable["intensity.sum.variance"]
sum_weights = flex.double()
if calculate_variances:
sum_square_weights = flex.double()
# Calculate the weighted mean intensities.
i_means = flex.double()
# Calculate the standard deviations from unbiased weighted variances.
variances = flex.double()
# Iterate over the reflections, grouping by equivalent Miller index,
# to calculate multiplicities, weighted mean intensities, etc..
# Some time can be saved by only calculating variances if necessary.
# Initial values:
prev_index = None
count = 1
# One big loop through the entire reflection table:
for j in range(rtable.size()):
index = rtable["miller_index.asu"][j]
weight = weights[j]
# Aggregate within a symmetry-equivalent group of reflections:
if index == prev_index:
count += 1
i_sum += weight * rtable["intensity.sum.value"][j]
sum_weight += weight
if calculate_variances:
sum_square_weight += weight * weight
# Record the aggregated values for the group:
elif prev_index:
if count == 1 and not keep_singles:
singles.append(j - 1)
multiplicity.extend(flex.int(count, count))
i_means.extend(flex.double(count, i_sum / sum_weight))
sum_weights.extend(flex.double(count, sum_weight))
if calculate_variances:
sum_square_weights.extend(flex.double(count, sum_square_weight))
# And reinitialise:
prev_index = index
count = 1
i_sum = weight * rtable["intensity.sum.value"][j]
sum_weight = weight
if calculate_variances:
sum_square_weight = weight * weight
# Handle the first row:
else:
prev_index = rtable["miller_index.asu"][j]
i_sum = weight * rtable["intensity.sum.value"][j]
sum_weight = weight
if calculate_variances:
sum_square_weight = weight * weight
# Record the aggregated values for the last group:
if count == 1 and not keep_singles:
singles.append(rtable.size() - 1)
multiplicity.extend(flex.int(count, count))
i_means.extend(flex.double(count, i_sum / sum_weight))
sum_weights.extend(flex.double(count, sum_weight))
if calculate_variances:
sum_square_weights.extend(flex.double(count, sum_square_weight))
# Discard singletons:
if not keep_singles:
singles_del = flex.bool(rtable.size(), True)
singles_del.set_selected(singles, False)
multiplicity, weights, sum_weights, i_means = [
a.select(singles_del)
for a in (multiplicity, weights, sum_weights, i_means)
]
rtable.del_selected(singles)
if calculate_variances:
sum_square_weights = sum_square_weights.select(singles_del)
# Record the multiplicities in the reflection table.
rtable["multiplicity"] = multiplicity
# Record the weighted mean intensities in the reflection table.
rtable["intensity.mean.value"] = i_means
# Record the standard errors on the means in the reflection table.
rtable["intensity.mean.std_error"] = flex.sqrt(1 / sum_weights)
if calculate_variances:
# Initialise values:
prev_index = None
for j in range(rtable.size()):
index = rtable["miller_index.asu"][j]
weight = weights[j]
residual = rtable["intensity.sum.value"][j] - i_means[j]
# Aggregate within a symmetry-equivalent group of reflections:
if index == prev_index:
count += 1
weighted_sum_square_residual += weight * residual * residual
# Record the aggregated value for the group:
elif prev_index:
# The weighted variance is undefined for multiplicity=1,
# use the measured variance instead in this case.
if count == 1:
variances.append(rtable["intensity.sum.variance"][j - 1])
else:
sum_weight = sum_weights[j - 1]
var_weight = 1 / (
sum_weight - sum_square_weights[j - 1] / sum_weight
)
variances.extend(
flex.double(
count, weighted_sum_square_residual * var_weight
)
)
# Reinitialise:
prev_index = index
count = 1
weighted_sum_square_residual = weight * residual * residual
# Handle the first row:
else:
prev_index = rtable["miller_index.asu"][j]
count = 1
weighted_sum_square_residual = weight * residual * residual
# Record the aggregated values for the last group:
# The weighted variance is undefined for multiplicity=1,
# use the measured variance instead in this case.
if count == 1:
variances.append(rtable["intensity.sum.variance"][-1])
else:
sum_weight = sum_weights[-1]
var_weight = 1 / (sum_weight - sum_square_weights[-1] / sum_weight)
variances.extend(
flex.double(count, weighted_sum_square_residual * var_weight)
)
# Record the variances in the reflection table.
rtable["intensity.mean.variance"] = variances
self.rtables[key] = rtable
def parameters(self, parameters):
assert len(parameters) == 2
self.components["a"].parameters = flex.double([parameters[0]])
self.components["b"].parameters = flex.double([parameters[1]])
def plot_multirun_stats(runs,
run_numbers,
d_min,
n_multiples=2,
ratio_cutoff=1,
n_strong_cutoff=40,
i_sigi_cutoff=1,
run_tags=[],
run_statuses=[],
minimalist=False,
interactive=False,
easy_run=False,
compress_runs=True,
xsize=30,
ysize=10,
high_vis=False,
title=None):
tset = flex.double()
two_theta_low_set = flex.double()
two_theta_high_set = flex.double()
nset = flex.int()
resolutions_set = flex.double()
n_lattices = flex.int()
boundaries = []
lengths = []
runs_with_data = []
offset = 0
for idx in range(len(runs)):
r = runs[idx]
if len(r[0]) > 0:
if compress_runs:
tslice = r[0] - r[0][0] + offset
offset += (r[0][-1] - r[0][0] + 1 / 120.)
else:
tslice = r[0]
last_end = r[0][-1]
tset.extend(tslice)
two_theta_low_set.extend(r[1])
two_theta_high_set.extend(r[2])
nset.extend(r[3])
resolutions_set.extend(r[4])
n_lattices.extend(r[5])
boundaries.append(tslice[0])
boundaries.append(tslice[-1])
lengths.append(len(tslice))
runs_with_data.append(run_numbers[idx])
else:
boundaries.extend([None] * 2)
stats_tuple = get_run_stats(tset,
two_theta_low_set,
two_theta_high_set,
nset,
resolutions_set,
n_lattices,
tuple(boundaries),
tuple(lengths),
runs_with_data,
n_multiples=n_multiples,
ratio_cutoff=ratio_cutoff,
n_strong_cutoff=n_strong_cutoff,
i_sigi_cutoff=i_sigi_cutoff,
d_min=d_min)
if easy_run:
from libtbx import easy_run, easy_pickle
easy_pickle.dump(
"plot_run_stats_tmp.pickle",
(stats_tuple, d_min, n_multiples, run_tags, run_statuses,
minimalist, interactive, xsize, ysize, high_vis, title))
result = easy_run.fully_buffered(
command=
"cctbx.xfel.plot_run_stats_from_stats_pickle plot_run_stats_tmp.pickle"
)
try:
png = result.stdout_lines[-1]
if png == "None":
return None
except Exception:
return None
else:
png = plot_run_stats(stats_tuple,
d_min,
n_multiples=n_multiples,
run_tags=run_tags,
run_statuses=run_statuses,
minimalist=minimalist,
interactive=interactive,
xsize=xsize,
ysize=ysize,
high_vis=high_vis,
title=title)
return png
def create(cls, params, experiment, reflection_table, for_multi=False):
"""Perform reflection_table preprocessing and create a SingleScaler."""
cls.ensure_experiment_identifier(experiment, reflection_table)
logger.info(
"The scaling model type being applied is %s. \n",
experiment.scaling_model.id_,
)
try:
reflection_table = cls.filter_bad_reflections(
reflection_table,
partiality_cutoff=params.cut_data.partiality_cutoff,
min_isigi=params.cut_data.min_isigi,
intensity_choice=params.reflection_selection.intensity_choice,
)
except ValueError:
raise BadDatasetForScalingException
# combine partial measurements of same reflection, to handle those reflections
# that were split by dials.integrate - changes size of reflection table.
reflection_table = sum_partial_reflections(reflection_table)
if "inverse_scale_factor" not in reflection_table:
reflection_table["inverse_scale_factor"] = flex.double(
reflection_table.size(), 1.0
)
elif (
reflection_table["inverse_scale_factor"].count(0.0)
== reflection_table.size()
):
reflection_table["inverse_scale_factor"] = flex.double(
reflection_table.size(), 1.0
)
reflection_table = choose_initial_scaling_intensities(
reflection_table, params.reflection_selection.intensity_choice
)
excluded_for_scaling = reflection_table.get_flags(
reflection_table.flags.excluded_for_scaling
)
user_excluded = reflection_table.get_flags(
reflection_table.flags.user_excluded_in_scaling
)
reasons = Reasons()
reasons.add_reason("user excluded", user_excluded.count(True))
reasons.add_reason("excluded for scaling", excluded_for_scaling.count(True))
n_excluded = (excluded_for_scaling | user_excluded).count(True)
if n_excluded == reflection_table.size():
logger.info("All reflections were determined to be unsuitable for scaling.")
logger.info(reasons)
raise BadDatasetForScalingException(
"""Unable to use this dataset for scaling"""
)
else:
logger.info(
"Excluding %s/%s reflections\n%s",
n_excluded,
reflection_table.size(),
reasons,
)
if params.reflection_selection.method == "intensity_ranges":
reflection_table = quasi_normalisation(reflection_table, experiment)
if (
params.reflection_selection.method in (None, Auto, "auto", "quasi_random")
) or (
experiment.scaling_model.id_ == "physical"
and "absorption" in experiment.scaling_model.components
):
if experiment.scan:
# calc theta and phi cryst
reflection_table["phi"] = (
reflection_table["xyzobs.px.value"].parts()[2]
* experiment.scan.get_oscillation()[1]
)
reflection_table = calc_crystal_frame_vectors(
reflection_table, experiment
)
return SingleScaler(params, experiment, reflection_table, for_multi)
def calculate_scales(self, block_id=0):
"""Calculate and return inverse scales for a given block."""
scales = flex.exp(
flex.double(self._n_refl[block_id], self._parameters[0]) /
(2.0 * (self._d_values[block_id] * self._d_values[block_id])))
return scales
def test(args=[]):
# Python and cctbx imports
from math import pi
from scitbx import matrix
from libtbx.phil import parse
from libtbx.test_utils import approx_equal
# Import for surgery on reflection_tables
from dials.array_family import flex
# Get module to build models using PHIL
import dials.test.algorithms.refinement.setup_geometry as setup_geometry
# We will set up a mock scan and a mock experiment list
from dxtbx.model import ScanFactory
from dxtbx.model.experiment_list import ExperimentList, Experiment
# Crystal parameterisations
from dials.algorithms.refinement.parameterisation.crystal_parameters import \
CrystalOrientationParameterisation, CrystalUnitCellParameterisation
# Symmetry constrained parameterisation for the unit cell
from cctbx.uctbx import unit_cell
from rstbx.symmetry.constraints.parameter_reduction import \
symmetrize_reduce_enlarge
# Reflection prediction
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.refinement.prediction import ScansRayPredictor, \
ExperimentsPredictor
from dials.algorithms.spot_prediction import ray_intersection
from cctbx.sgtbx import space_group, space_group_symbols
#############################
# Setup experimental models #
#############################
master_phil = parse("""
include scope dials.test.algorithms.refinement.geometry_phil
include scope dials.test.algorithms.refinement.minimiser_phil
""",
process_includes=True)
# build models, with a larger crystal than default in order to get enough
# reflections on the 'still' image
param = """
geometry.parameters.crystal.a.length.range=40 50;
geometry.parameters.crystal.b.length.range=40 50;
geometry.parameters.crystal.c.length.range=40 50;
geometry.parameters.random_seed = 42"""
models = setup_geometry.Extract(master_phil,
cmdline_args=args,
local_overrides=param)
crystal = models.crystal
mydetector = models.detector
mygonio = models.goniometer
mybeam = models.beam
# Build a mock scan for a 1.5 degree wedge. Only used for generating indices near
# the Ewald sphere
sf = ScanFactory()
myscan = sf.make_scan(image_range=(1, 1),
exposure_times=0.1,
oscillation=(0, 1.5),
epochs=range(1),
deg=True)
sweep_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
assert approx_equal(im_width, 1.5 * pi / 180.)
# Build experiment lists
stills_experiments = ExperimentList()
stills_experiments.append(
Experiment(beam=mybeam,
detector=mydetector,
crystal=crystal,
imageset=None))
scans_experiments = ExperimentList()
scans_experiments.append(
Experiment(beam=mybeam,
detector=mydetector,
crystal=crystal,
goniometer=mygonio,
scan=myscan,
imageset=None))
##########################################################
# Parameterise the models (only for perturbing geometry) #
##########################################################
xlo_param = CrystalOrientationParameterisation(crystal)
xluc_param = CrystalUnitCellParameterisation(crystal)
################################
# Apply known parameter shifts #
################################
# rotate crystal (=5 mrad each rotation)
xlo_p_vals = []
p_vals = xlo_param.get_param_vals()
xlo_p_vals.append(p_vals)
new_p_vals = [a + b for a, b in zip(p_vals, [5., 5., 5.])]
xlo_param.set_param_vals(new_p_vals)
# change unit cell (=1.0 Angstrom length upsets, 0.5 degree of
# gamma angle)
xluc_p_vals = []
p_vals = xluc_param.get_param_vals()
xluc_p_vals.append(p_vals)
cell_params = crystal.get_unit_cell().parameters()
cell_params = [
a + b for a, b in zip(cell_params, [1.0, 1.0, -1.0, 0.0, 0.0, 0.5])
]
new_uc = unit_cell(cell_params)
newB = matrix.sqr(new_uc.fractionalization_matrix()).transpose()
S = symmetrize_reduce_enlarge(crystal.get_space_group())
S.set_orientation(orientation=newB)
X = tuple([e * 1.e5 for e in S.forward_independent_parameters()])
xluc_param.set_param_vals(X)
# keep track of the target crystal model to compare with refined
from copy import deepcopy
target_crystal = deepcopy(crystal)
#############################
# Generate some reflections #
#############################
# All indices in a 2.0 Angstrom sphere for crystal
resolution = 2.0
index_generator = IndexGenerator(
crystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices = index_generator.to_array()
# Build a ray predictor and predict rays close to the Ewald sphere by using
# the narrow rotation scan
ref_predictor = ScansRayPredictor(scans_experiments, sweep_range)
obs_refs = ref_predictor(indices, experiment_id=0)
# Take only those rays that intersect the detector
intersects = ray_intersection(mydetector, obs_refs)
obs_refs = obs_refs.select(intersects)
# Add in flags and ID columns by copying into standard reflection table
tmp = flex.reflection_table.empty_standard(len(obs_refs))
tmp.update(obs_refs)
obs_refs = tmp
# Invent some variances for the centroid positions of the simulated data
im_width = 0.1 * pi / 180.
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.)**2)
obs_refs['xyzobs.mm.variance'] = flex.vec3_double(var_x, var_y, var_phi)
# Re-predict using the stills reflection predictor
stills_ref_predictor = ExperimentsPredictor(stills_experiments)
obs_refs_stills = stills_ref_predictor(obs_refs)
# Set 'observed' centroids from the predicted ones
obs_refs_stills['xyzobs.mm.value'] = obs_refs_stills['xyzcal.mm']
###############################
# Undo known parameter shifts #
###############################
xlo_param.set_param_vals(xlo_p_vals[0])
xluc_param.set_param_vals(xluc_p_vals[0])
# make a refiner
from dials.algorithms.refinement.refiner import phil_scope
params = phil_scope.fetch(source=parse('')).extract()
# Change this to get a plot
do_plot = False
if do_plot:
params.refinement.refinery.journal.track_parameter_correlation = True
from dials.algorithms.refinement.refiner import RefinerFactory
# decrease bin_size_fraction to terminate on RMSD convergence
params.refinement.target.bin_size_fraction = 0.01
params.refinement.parameterisation.beam.fix = "all"
params.refinement.parameterisation.detector.fix = "all"
refiner = RefinerFactory.from_parameters_data_experiments(
params, obs_refs_stills, stills_experiments, verbosity=0)
# run refinement
history = refiner.run()
# regression tests
assert len(history["rmsd"]) == 9
refined_crystal = refiner.get_experiments()[0].crystal
uc1 = refined_crystal.get_unit_cell()
uc2 = target_crystal.get_unit_cell()
assert uc1.is_similar_to(uc2)
if do_plot:
plt = refiner.parameter_correlation_plot(
len(history["parameter_correlation"]) - 1)
plt.show()
def run(self):
''' Parse the options. '''
from dials.util.options import flatten_experiments, flatten_reflections
from dxtbx.model import ExperimentList
from scitbx.math import five_number_summary
# Parse the command line arguments
params, options = self.parser.parse_args(show_diff_phil=True)
self.params = params
experiments = flatten_experiments(params.input.experiments)
reflections = flatten_reflections(params.input.reflections)
assert len(reflections) == 1
reflections = reflections[0]
print("Found", len(reflections), "reflections", "and",
len(experiments), "experiments")
filtered_reflections = flex.reflection_table()
filtered_experiments = ExperimentList()
skipped_reflections = flex.reflection_table()
skipped_experiments = ExperimentList()
if params.detector is not None:
culled_reflections = flex.reflection_table()
culled_experiments = ExperimentList()
detector = experiments.detectors()[params.detector]
for expt_id, experiment in enumerate(experiments):
refls = reflections.select(reflections['id'] == expt_id)
if experiment.detector is detector:
culled_experiments.append(experiment)
refls['id'] = flex.int(len(refls),
len(culled_experiments) - 1)
culled_reflections.extend(refls)
else:
skipped_experiments.append(experiment)
refls['id'] = flex.int(len(refls),
len(skipped_experiments) - 1)
skipped_reflections.extend(refls)
print(
"RMSD filtering %d experiments using detector %d, out of %d" %
(len(culled_experiments), params.detector, len(experiments)))
reflections = culled_reflections
experiments = culled_experiments
difference_vector_norms = (reflections['xyzcal.mm'] -
reflections['xyzobs.mm.value']).norms()
if params.max_delta is not None:
sel = difference_vector_norms <= params.max_delta
reflections = reflections.select(sel)
difference_vector_norms = difference_vector_norms.select(sel)
data = flex.double()
counts = flex.double()
for i in range(len(experiments)):
dvns = difference_vector_norms.select(reflections['id'] == i)
counts.append(len(dvns))
if len(dvns) == 0:
data.append(0)
continue
rmsd = math.sqrt(flex.sum_sq(dvns) / len(dvns))
data.append(rmsd)
data *= 1000
subset = data.select(counts > 0)
print(len(subset), "experiments with > 0 reflections")
if params.show_plots:
h = flex.histogram(subset, n_slots=40)
fig = plt.figure()
ax = fig.add_subplot('111')
ax.plot(h.slot_centers().as_numpy_array(),
h.slots().as_numpy_array(), '-')
plt.title("Histogram of %d image RMSDs" % len(subset))
fig = plt.figure()
plt.boxplot(subset, vert=False)
plt.title("Boxplot of %d image RMSDs" % len(subset))
plt.show()
outliers = counts == 0
min_x, q1_x, med_x, q3_x, max_x = five_number_summary(subset)
print(
"Five number summary of RMSDs (microns): min %.1f, q1 %.1f, med %.1f, q3 %.1f, max %.1f"
% (min_x, q1_x, med_x, q3_x, max_x))
iqr_x = q3_x - q1_x
cut_x = params.iqr_multiplier * iqr_x
outliers.set_selected(data > q3_x + cut_x, True)
#outliers.set_selected(col < q1_x - cut_x, True) # Don't throw away the images that are outliers in the 'good' direction!
for i in range(len(experiments)):
if outliers[i]:
continue
refls = reflections.select(reflections['id'] == i)
refls['id'] = flex.int(len(refls), len(filtered_experiments))
filtered_reflections.extend(refls)
filtered_experiments.append(experiments[i])
zeroes = counts == 0
n_zero = len(counts.select(zeroes))
print(
"Removed %d bad experiments and %d experiments with zero reflections, out of %d (%%%.1f)"
% (len(experiments) - len(filtered_experiments) - n_zero, n_zero,
len(experiments), 100 *
((len(experiments) - len(filtered_experiments)) /
len(experiments))))
if params.detector is not None:
crystals = filtered_experiments.crystals()
for expt_id, experiment in enumerate(skipped_experiments):
if experiment.crystal in crystals:
filtered_experiments.append(experiment)
refls = skipped_reflections.select(
skipped_reflections['id'] == expt_id)
refls['id'] = flex.int(len(refls),
len(filtered_experiments) - 1)
filtered_reflections.extend(refls)
if params.delta_psi_filter is not None:
delta_psi = filtered_reflections['delpsical.rad'] * 180 / math.pi
sel = (delta_psi <= params.delta_psi_filter) & (
delta_psi >= -params.delta_psi_filter)
l = len(filtered_reflections)
filtered_reflections = filtered_reflections.select(sel)
print("Filtering by delta psi, removing %d out of %d reflections" %
(l - len(filtered_reflections), l))
print("Final experiment count", len(filtered_experiments))
from dxtbx.model.experiment_list import ExperimentListDumper
dump = ExperimentListDumper(filtered_experiments)
dump.as_json(params.output.filtered_experiments)
filtered_reflections.as_pickle(params.output.filtered_reflections)
def tst_split_blocks_non_overlapping(self):
from dials.array_family import flex
from random import randint, uniform, seed
from dials.algorithms.integration.integrator import JobList
from scitbx.array_family import shared
blocks = shared.tiny_int_2([(0, 10), (10, 20), (20, 30), (30, 35),
(35, 40), (40, 50), (50, 60), (60, 70),
(70, 80), (80, 90), (90, 100), (100, 110)])
jobs = JobList((0, 1), blocks)
r = flex.reflection_table()
r['value1'] = flex.double()
r['value2'] = flex.int()
r['value3'] = flex.double()
r['bbox'] = flex.int6()
r['id'] = flex.int()
expected = []
for i in range(100):
x0 = randint(0, 100)
x1 = x0 + randint(1, 10)
y0 = randint(0, 100)
y1 = y0 + randint(1, 10)
z0 = randint(0, 100)
z1 = z0 + randint(1, 10)
v1 = uniform(0, 100)
v2 = randint(0, 100)
v3 = uniform(0, 100)
r.append({
'id': 0,
'value1': v1,
'value2': v2,
'value3': v3,
'bbox': (x0, x1, y0, y1, z0, z1)
})
for j in range(len(blocks)):
b0 = blocks[j][0]
b1 = blocks[j][1]
if ((z0 >= b0 and z1 <= b1) or (z0 < b1 and z1 >= b1)
or (z0 < b0 and z1 > b0)):
z00 = max(b0, z0)
z11 = min(b1, z1)
expected.append({
'id': 0,
'value1': v1,
'value2': v2,
'value3': v3,
'bbox': (x0, x1, y0, y1, z00, z11),
'partial_id': i,
})
jobs.split(r)
assert (len(r) == len(expected))
EPS = 1e-7
for r1, r2 in zip(r, expected):
assert (r1['bbox'] == r2['bbox'])
assert (r1['partial_id'] == r2['partial_id'])
assert (abs(r1['value1'] - r2['value1']) < EPS)
assert (r1['value2'] == r2['value2'])
assert (abs(r1['value3'] - r2['value3']) < EPS)
print 'OK'
def mock_errormodel():
"""Mock error model."""
em = MagicMock()
em.refined_parameters = [1.0, 0.1]
em.update_variances.return_value = flex.double([1.0, 1.1, 1.0, 1.0])
return em
def run(self):
from dials.algorithms.integration.integrator import ReflectionManager
from dials.algorithms.integration.integrator import JobList
from dials.array_family import flex
jobs = JobList()
jobs.add((0, 1), self.array_range, self.block_size)
# Create the executor
executor = ReflectionManager(jobs, self.reflections)
# Ensure the tasks make sense
jobs = [executor.job(i) for i in range(len(executor))]
assert (len(executor) == 12)
assert (not executor.finished())
assert (len(jobs) == 12)
assert (jobs[0].frames() == (0, 20))
assert (jobs[1].frames() == (10, 30))
assert (jobs[2].frames() == (20, 40))
assert (jobs[3].frames() == (30, 50))
assert (jobs[4].frames() == (40, 60))
assert (jobs[5].frames() == (50, 70))
assert (jobs[6].frames() == (60, 80))
assert (jobs[7].frames() == (70, 90))
assert (jobs[8].frames() == (80, 100))
assert (jobs[9].frames() == (90, 110))
assert (jobs[10].frames() == (100, 120))
assert (jobs[11].frames() == (110, 130))
# Get the task specs
data0 = executor.split(0)
data1 = executor.split(1)
data2 = executor.split(2)
data3 = executor.split(3)
data4 = executor.split(4)
data5 = executor.split(5)
data6 = executor.split(6)
data7 = executor.split(7)
data8 = executor.split(8)
data9 = executor.split(9)
data10 = executor.split(10)
data11 = executor.split(11)
assert (len(data0) == len(self.processed[0]))
assert (len(data1) == len(self.processed[1]))
assert (len(data2) == len(self.processed[2]))
assert (len(data3) == len(self.processed[3]))
assert (len(data4) == len(self.processed[4]))
assert (len(data5) == len(self.processed[5]))
assert (len(data6) == len(self.processed[6]))
assert (len(data7) == len(self.processed[7]))
assert (len(data8) == len(self.processed[8]))
assert (len(data9) == len(self.processed[9]))
assert (len(data10) == len(self.processed[10]))
assert (len(data11) == len(self.processed[11]))
# Add some results
data0["data"] = flex.double(len(data0), 1)
data1["data"] = flex.double(len(data1), 2)
data2["data"] = flex.double(len(data2), 3)
data3["data"] = flex.double(len(data3), 4)
data4["data"] = flex.double(len(data4), 5)
data5["data"] = flex.double(len(data5), 6)
data6["data"] = flex.double(len(data6), 7)
data7["data"] = flex.double(len(data7), 8)
data8["data"] = flex.double(len(data8), 9)
data9["data"] = flex.double(len(data9), 10)
data10["data"] = flex.double(len(data10), 11)
data11["data"] = flex.double(len(data11), 12)
# Accumulate the data again
assert (not executor.finished())
executor.accumulate(0, data0)
executor.accumulate(1, data1)
executor.accumulate(2, data2)
executor.accumulate(3, data3)
executor.accumulate(4, data4)
executor.accumulate(5, data5)
executor.accumulate(6, data6)
executor.accumulate(7, data7)
executor.accumulate(8, data8)
executor.accumulate(9, data9)
executor.accumulate(10, data10)
executor.accumulate(11, data11)
assert (executor.finished())
# Get results and check they're as expected
data = executor.data()
result = data["data"]
bbox = data["bbox"]
for i in range(len(self.processed)):
for j in range(len(self.processed[i])):
assert (result[self.processed[i][j]] == i + 1)
# Test passed
print 'OK'
def model_reflection_rt0(reflection, experiment, params):
import math
import random
from scitbx import matrix
from dials.array_family import flex
d2r = math.pi / 180.0
hkl = reflection['miller_index']
xyz = reflection['xyzcal.px']
xyz_mm = reflection['xyzcal.mm']
if params.debug:
print 'hkl = %d %d %d' % hkl
print 'xyz px = %f %f %f' % xyz
print 'xyz mm = %f %f %f' % xyz_mm
if reflection['entering']:
print 'entering'
else:
print 'exiting'
Amat = matrix.sqr(experiment.crystal.get_A_at_scan_point(int(xyz[2])))
p0_star = Amat * hkl
angles = predict_angles(p0_star, experiment)
assert (angles)
if params.debug:
print 'angles = %f %f' % angles
angle = angles[0] if (
abs(angles[0] - xyz_mm[2]) < abs(angles[1] - xyz_mm[2])) else angles[1]
p = experiment.detector[reflection['panel']]
n = matrix.col(p.get_normal())
s1 = matrix.col(reflection['s1'])
t = p.get_thickness() / math.cos(s1.angle(n))
if params.debug:
print 'dqe = %f' % reflection['dqe']
if params.physics:
i0 = reflection['intensity.sum.value'] / reflection['dqe']
else:
i0 = reflection['intensity.sum.value']
if params.min_isum:
if i0 < params.min_isum:
return
s1 = reflection['s1']
a = matrix.col(experiment.goniometer.get_rotation_axis())
s0 = matrix.col(experiment.beam.get_s0())
if params.debug:
print 's1 = %f %f %f' % s1
pixels = reflection['shoebox']
pixels.flatten()
data = pixels.data
dz, dy, dx = data.focus()
# since now 2D data
data.reshape(flex.grid(dy, dx))
if params.show:
print 'Observed reflection (flattened in Z):'
print
for j in range(dy):
for i in range(dx):
print '%5d' % data[(j, i)],
print
if params.sigma_m > 0:
sigma_m = params.sigma_m * d2r
else:
sigma_m = experiment.profile.sigma_m() * d2r
if params.sigma_b > 0:
sigma_b = params.sigma_b * d2r
else:
sigma_b = experiment.profile.sigma_b() * d2r
r0 = xyz_mm[2]
detector = experiment.detector
patch = flex.double(dy * dx, 0)
patch.reshape(flex.grid(dy, dx))
bbox = reflection['bbox']
scale = params.scale
if params.show:
print '%d rays' % (int(round(i0 * scale)))
for i in range(int(round(i0 * scale))):
if params.sigma_l:
l_scale = random.gauss(1, params.sigma_l)
b = random_vector_cone(s0 * l_scale, sigma_b)
else:
b = random_vector_cone(s0, sigma_b)
if params.sigma_cell:
cell_scale = random.gauss(1, params.sigma_cell)
p0 = random_vector_cone(cell_scale * Amat * hkl, sigma_m)
else:
p0 = random_vector_cone(Amat * hkl, sigma_m)
if params.rs_node_size > 0:
ns = params.rs_node_size
import random
dp0 = matrix.col(
(random.gauss(0, ns), random.gauss(0, ns), random.gauss(0,
ns)))
p0 += dp0
angles = predict_angles(p0, experiment, b)
if angles is None:
# scattered ray ended up in blind region
continue
r = angles[0] if reflection['entering'] else angles[1]
p = p0.rotate(a, r)
s1 = p + b
if params.physics:
model_path_through_sensor(detector, reflection, s1, patch, scale)
else:
panel, xy = detector.get_ray_intersection(s1)
# FIXME DO NOT USE THIS FUNCTION EVENTUALLY...
x, y = detector[panel].millimeter_to_pixel(xy)
if x < bbox[0] or x >= bbox[1]:
continue
if y < bbox[2] or y >= bbox[3]:
continue
x -= bbox[0]
y -= bbox[2]
# FIXME in here try to work out probability distribution along path
# length through the detector sensitive surface i.e. deposit fractional
# counts along pixels (and allow for DQE i.e. photon passing right through
# the detector)
patch[(int(y), int(x))] += 1.0 / scale
if params.show:
print 'Simulated reflection (flattened in Z):'
print
for j in range(dy):
for i in range(dx):
print '%5d' % int(patch[(j, i)]),
print
cc = profile_correlation(data, patch)
print 'Correlation coefficient: %.3f isum: %.1f ' % (cc, i0)
return cc
def tst_split_blocks_overlapping(self):
from dials.array_family import flex
from random import randint, uniform, seed
from dials.algorithms.integration.integrator import JobList
from scitbx.array_family import shared
blocks = shared.tiny_int_2([(0, 10), (5, 15), (10, 20), (15, 25),
(20, 30), (25, 35), (30, 40), (35, 45),
(40, 50), (45, 55), (50, 60), (55, 65),
(60, 70), (65, 75), (70, 80), (75, 85),
(80, 90), (85, 95), (90, 100), (95, 105),
(100, 110)])
jobs = JobList((0, 1), blocks)
r = flex.reflection_table()
r['value1'] = flex.double()
r['value2'] = flex.int()
r['value3'] = flex.double()
r['bbox'] = flex.int6()
r['id'] = flex.int()
expected = []
for i in range(100):
x0 = randint(0, 100)
x1 = x0 + randint(1, 10)
y0 = randint(0, 100)
y1 = y0 + randint(1, 10)
z0 = randint(0, 90)
z1 = z0 + randint(1, 20)
v1 = uniform(0, 100)
v2 = randint(0, 100)
v3 = uniform(0, 100)
r.append({
'id': 0,
'value1': v1,
'value2': v2,
'value3': v3,
'bbox': (x0, x1, y0, y1, z0, z1)
})
expected.append({
'id': 0,
'value1': v1,
'value2': v2,
'value3': v3,
'bbox': (x0, x1, y0, y1, z0, z1)
})
jobs.split(r)
assert (len(r) > 100)
for r1 in r:
v1 = r1['value1']
v2 = r1['value2']
v3 = r1['value3']
bbox = r1['bbox']
pid = r1['partial_id']
z0 = bbox[4]
z1 = bbox[5]
success = False
for i in range(len(blocks)):
b0 = blocks[i][0]
b1 = blocks[i][1]
if z0 >= b0 and z1 <= b1:
success = True
break
assert (success)
v11 = expected[pid]['value1']
v22 = expected[pid]['value2']
v33 = expected[pid]['value3']
bb = expected[pid]['bbox']
assert (v11 == v1)
assert (v22 == v2)
assert (v33 == v3)
assert (bb[0] == bbox[0])
assert (bb[1] == bbox[1])
assert (bb[2] == bbox[2])
assert (bb[3] == bbox[3])
print 'OK'
def _create_summation_matrix(self):
"""Create a summation matrix to allow sums into intensity bins.
This routine attempts to bin into bins equally spaced in log(intensity),
to give a representative sample across all intensities. To avoid
undersampling, it is required that there are at least 100 reflections
per intensity bin unless there are very few reflections."""
n = self.Ih_table.size
self.binning_info["n_reflections"] = n
summation_matrix = sparse.matrix(n, self.n_bins)
Ih = self.Ih_table.Ih_values * self.Ih_table.inverse_scale_factors
size_order = flex.sort_permutation(Ih, reverse=True)
Imax = max(Ih)
Imin = max(1.0, min(Ih)) # avoid log issues
spacing = (log(Imax) - log(Imin)) / float(self.n_bins)
boundaries = [Imax] + [
exp(log(Imax) - (i * spacing)) for i in range(1, self.n_bins + 1)
]
boundaries[-1] = min(Ih) - 0.01
self.binning_info["bin_boundaries"] = boundaries
self.binning_info["refl_per_bin"] = flex.double()
n_cumul = 0
if Ih.size() > 100 * self.min_reflections_required:
self.min_reflections_required = int(Ih.size() / 100.0)
min_per_bin = min(self.min_reflections_required,
int(n / (3.0 * self.n_bins)))
for i in range(len(boundaries) - 1):
maximum = boundaries[i]
minimum = boundaries[i + 1]
sel1 = Ih <= maximum
sel2 = Ih > minimum
sel = sel1 & sel2
isel = sel.iselection()
n_in_bin = isel.size()
if n_in_bin < min_per_bin: # need more in this bin
m = n_cumul + min_per_bin
if m < n: # still some refl left to use
idx = size_order[m]
intensity = Ih[idx]
boundaries[i + 1] = intensity
minimum = boundaries[i + 1]
sel = sel1 & (Ih > minimum)
isel = sel.iselection()
n_in_bin = isel.size()
self.binning_info["refl_per_bin"].append(n_in_bin)
for j in isel:
summation_matrix[j, i] = 1
n_cumul += n_in_bin
cols_to_del = []
for i, col in enumerate(summation_matrix.cols()):
if col.non_zeroes < min_per_bin - 5:
cols_to_del.append(i)
n_new_cols = summation_matrix.n_cols - len(cols_to_del)
if n_new_cols == self.n_bins:
for i in range(len(boundaries) - 1):
maximum = boundaries[i]
minimum = boundaries[i + 1]
sel1 = Ih <= maximum
sel2 = Ih > minimum
sel = sel1 & sel2
m = flex.mean(Ih.select(sel))
self.binning_info["mean_intensities"].append(m)
return summation_matrix
new_sum_matrix = sparse.matrix(summation_matrix.n_rows, n_new_cols)
next_col = 0
refl_per_bin = flex.double()
new_bounds = []
for i, col in enumerate(summation_matrix.cols()):
if i not in cols_to_del:
new_sum_matrix[:, next_col] = col
next_col += 1
new_bounds.append(boundaries[i])
refl_per_bin.append(self.binning_info["refl_per_bin"][i])
self.binning_info["refl_per_bin"] = refl_per_bin
new_bounds.append(boundaries[-1])
self.binning_info["bin_boundaries"] = new_bounds
for i in range(len(new_bounds) - 1):
maximum = new_bounds[i]
minimum = new_bounds[i + 1]
sel1 = Ih <= maximum
sel2 = Ih > minimum
sel = sel1 & sel2
m = flex.mean(Ih.select(sel))
self.binning_info["mean_intensities"].append(m)
return new_sum_matrix
def tv_alg(image, mask, tolerance=1e-3, max_iter=10):
from scipy.optimize import fmin_bfgs, fmin_l_bfgs_b, fmin_cobyla, fmin_tnc
from dials.array_family import flex
from scitbx import matrix
from math import sqrt
F = image
L = flex.double(mask.accessor())
for i in range(len(mask)):
if mask[i]:
L[i] = 5
else:
L[i] = 0
U = flex.double(mask.accessor(), 0)
for i in range(len(U)):
U[i] = F[i]
def func(U):
U1 = flex.double(F.accessor())
for i in range(len(F)):
U1[i] = U[i]
U = U1
sum1 = 0
for f, u, l in zip(F, U, L):
sum1 += l * (f - u)**2
sum1 *= 0.5
del_U = del_op(U)
sum2 = 0
for du in del_U:
sum2 += sqrt(du[0]**2 + du[1]**2)
Y = sum1 + sum2
FP = fprime(U)
print(Y)
return Y, FP
def fprime(U):
U1 = flex.double(F.accessor())
for i in range(len(F)):
U1[i] = U[i]
U = U1
def calc_c(U, j, i):
return (2 * U[i, j]**2 - 2 * U[i, j] *
(U[i - 1, j] + U[i, j - 1]) +
(U[i - 1, j]**2 + U[i, j - 1]**2))
DU = flex.double(F.accessor(), 0)
for j in range(DU.all()[0]):
for i in range(DU.all()[1]):
if i > 0 and j > 0 and i < DU.all()[1] - 1 and j < DU.all(
)[0] - 1:
C1 = calc_c(U, i, j) # C_{i,j}
C2 = calc_c(U, i + 1, j) # C_{i+1,j}
C3 = calc_c(U, i, j + 1) # C_{i,j+1}
DU[j, i] = ((2 * U[j, i] + U[j, i - 1] + U[j - 1, i]) /
sqrt(C1 + 0.001) +
(U[j, i] - U[j, i + 1]) / sqrt(C2 + 0.001) +
(U[j, i] - U[j + 1, i]) / sqrt(C3 + 0.001) -
L[j, i] * (F[j, i] - U[j, i]))
# print sum(DU)
return list(DU)
x0 = U
min_F = min(F)
max_F = max(F)
bounds = [(min_F, max_F) for x in x0]
# U = fmin_bfgs(func, x0, epsilon=1e-5*sum(image), maxiter=max_iter)
U, nfeval, rc = fmin_tnc(func, list(x0), bounds=bounds, disp=3)
U1 = flex.double(F.accessor())
for i in range(len(F)):
U1[i] = U[i]
U = U1
return U
U1 = flex.double(F.accessor())
for i in range(len(F)):
U1[i] = U[i]
U = U1
return U
if __name__ == "__main__":
from dials.array_family import flex
height = 50
width = 50
image = flex.double(flex.grid(height, width))
X = []
Y = []
for j in range(height):
for i in range(width):
X.append(i)
Y.append(j)
image[j, i] = ice_background(j, i, height, width)
x0 = 5
x1 = 15
y0 = 5
y1 = 15
mask = flex.bool(flex.grid(height, width), True)
def calculate_scales(self, block_id=0):
"""Calculate and return inverse scales for a given block."""
return flex.double(self.n_refl[block_id], self._parameters[0])
def test_KBScalingModel():
"""Test for the KB Scaling Model."""
# Test standard initialisation method.
configdict = {"corrections": ["scale", "decay"]}
parameters_dict = {
"scale": {
"parameters": flex.double([1.2]),
"parameter_esds": flex.double([0.1]),
},
"decay": {
"parameters": flex.double([0.01]),
"parameter_esds": flex.double([0.02]),
},
}
KBmodel = KBScalingModel(parameters_dict, configdict)
assert KBmodel.id_ == "KB"
assert "scale" in KBmodel.components
assert "decay" in KBmodel.components
assert list(KBmodel.components["scale"].parameters) == [1.2]
assert list(KBmodel.components["decay"].parameters) == [0.01]
assert list(KBmodel.components["scale"].parameter_esds) == [0.1]
assert list(KBmodel.components["decay"].parameter_esds) == [0.02]
# Test from_dict initialisation method.
KB_dict = {
"__id__": "KB",
"is_scaled": True,
"scale": {
"n_parameters": 1,
"parameters": [0.5],
"est_standard_devs": [0.05],
"null_parameter_value": 1,
},
"configuration_parameters": {"corrections": ["scale"]},
}
KBmodel = KBScalingModel.from_dict(KB_dict)
assert KBmodel.is_scaled is True
assert "scale" in KBmodel.components
assert "decay" not in KBmodel.components
assert list(KBmodel.components["scale"].parameters) == [0.5]
assert list(KBmodel.components["scale"].parameter_esds) == [0.05]
new_dict = KBmodel.to_dict()
assert new_dict == KB_dict
# Test again with all parameters
KB_dict = {
"__id__": "KB",
"is_scaled": True,
"scale": {
"n_parameters": 1,
"parameters": [0.5],
"est_standard_devs": [0.05],
"null_parameter_value": 1,
},
"decay": {
"n_parameters": 1,
"parameters": [0.2],
"est_standard_devs": [0.02],
"null_parameter_value": 0,
},
"configuration_parameters": {"corrections": ["scale", "decay"]},
}
KBmodel = KBScalingModel.from_dict(KB_dict)
assert KBmodel.is_scaled is True
assert "scale" in KBmodel.components
assert "decay" in KBmodel.components
assert list(KBmodel.components["scale"].parameters) == [0.5]
assert list(KBmodel.components["scale"].parameter_esds) == [0.05]
assert list(KBmodel.components["decay"].parameters) == [0.2]
assert list(KBmodel.components["decay"].parameter_esds) == [0.02]
new_dict = KBmodel.to_dict()
assert new_dict == KB_dict
with pytest.raises(RuntimeError):
KB_dict["__id__"] = "physical"
KBmodel = KBScalingModel.from_dict(KB_dict)
assert KBmodel.consecutive_refinement_order == [["scale", "decay"]]
KBmodel.show()
def __init__(self, initial_value=1.00):
self.parameters = flex.double([initial_value])
self._n_params = 1
# print "---"
a = list(poisson(1, 100))
# a[4] = 1000
# a[5] = 100
mean_m, weight_m, res_m = m_estimate(a)
# print mean_m
# from matplotlib import pylab
# pylab.plot(res_m)
# pylab.show()
means1.append(sum(a) / len(a))
means3.append(mtl(a))
mean_m = glm3(a)
X = flex.double([1] * len(a))
X.reshape(flex.grid(len(a), 1))
Y = flex.double(a)
B = flex.double([0])
P = flex.double([1] * len(a))
v = glmc(X, Y, B, P, max_iter=100)
print(k, mean_m, exp(v.parameters()[0]))
means4.append(mean_m)
from matplotlib import pylab
print("MOM1: ", sum(means1) / len(means1))
print("MOM3: ", sum(means3) / len(means3))
print("MOM4: ", sum(means4) / len(means4))
pylab.plot(means1, color="black")