def spot_count_per_image(self, rlist):
''' Analyse the spot count per image. '''
from os.path import join
x,y,z = rlist['xyzobs.px.value'].parts()
max_z = int(math.ceil(flex.max(z)))
ids = rlist['id']
spot_count_per_image = []
for j in range(flex.max(ids)+1):
spot_count_per_image.append(flex.int())
zsel = z.select(ids == j)
for i in range(max_z):
sel = (zsel >= i) & (zsel < (i+1))
spot_count_per_image[j].append(sel.count(True))
colours = ['blue', 'red', 'green', 'orange', 'purple', 'black'] * 10
assert len(spot_count_per_image) <= colours
from matplotlib import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.set_title("Spot count per image")
for j in range(len(spot_count_per_image)):
ax.scatter(
list(range(len(spot_count_per_image[j]))), spot_count_per_image[j],
s=5, color=colours[j], marker='o', alpha=0.4)
ax.set_xlabel("Image #")
ax.set_ylabel("# spots")
pyplot.savefig(join(self.directory, "spots_per_image.png"))
pyplot.close()
def plot_histograms(self, reflections, panel = None, ax = None, bounds = None):
data = reflections['difference_vector_norms']
colors = ['b-', 'g-', 'g--', 'r-', 'b-', 'b--']
n_slots = 20
if self.params.residuals.histogram_max is None:
h = flex.histogram(data, n_slots=n_slots)
else:
h = flex.histogram(data.select(data <= self.params.residuals.histogram_max), n_slots=n_slots)
n = len(reflections)
rmsd_obs = math.sqrt((reflections['xyzcal.mm']-reflections['xyzobs.mm.value']).sum_sq()/n)
sigma = mode = h.slot_centers()[list(h.slots()).index(flex.max(h.slots()))]
mean_obs = flex.mean(data)
median = flex.median(data)
mean_rayleigh = math.sqrt(math.pi/2)*sigma
rmsd_rayleigh = math.sqrt(2)*sigma
data = flex.vec2_double([(i,j) for i, j in zip(h.slot_centers(), h.slots())])
n = len(data)
for i in [mean_obs, mean_rayleigh, mode, rmsd_obs, rmsd_rayleigh]:
data.extend(flex.vec2_double([(i, 0), (i, flex.max(h.slots()))]))
data = self.get_bounded_data(data, bounds)
tmp = [data[:n]]
for i in xrange(len(colors)):
tmp.append(data[n+(i*2):n+((i+1)*2)])
data = tmp
for d, c in zip(data, colors):
ax.plot(d.parts()[0], d.parts()[1], c)
if ax.get_legend() is None:
ax.legend([r"$\Delta$XY", "MeanObs", "MeanRayl", "Mode", "RMSDObs", "RMSDRayl"])
def spot_count_per_panel(self, rlist):
''' Analyse the spot count per panel. '''
from os.path import join
panel = rlist['panel']
if flex.max(panel) == 0:
# only one panel, don't bother generating a plot
return
n_panels = int(flex.max(panel))
spot_count_per_panel = flex.int()
for i in range(n_panels):
sel = (panel >= i) & (panel < (i+1))
spot_count_per_panel.append(sel.count(True))
from matplotlib import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.set_title("Spot count per panel")
ax.scatter(
list(range(len(spot_count_per_panel))), spot_count_per_panel,
s=10, color='blue', marker='o', alpha=0.4)
ax.set_xlabel("Panel #")
ax.set_ylabel("# spots")
pyplot.savefig(join(self.directory, "spots_per_panel.png"))
pyplot.close()
def get_one_spot_length_width_angle(self, id):
# the radial direction is along the vector from the beam center to
# the spot centroid for each spot
shoebox = self.strong['shoebox'][id]
s0_position = self.panel_s0_intersections[shoebox.panel]
centroid = shoebox.centroid_strong().px_xy
dx, dy = [centroid[i] - s0_position[i] for i in (0, 1)]
radial_abs = matrix.col((dx, dy))
radial = radial_abs.normalize()
transverse = self.s0.normalize().cross(radial)
mask = flex.bool([(m & self.valid_code) != 0 for m in shoebox.mask])
bbox = self.strong['bbox'][id]
x_start, y_start = bbox[0], bbox[2]
x_range, y_range = bbox[1] - bbox[0], bbox[3] - bbox[2]
radial_distances = flex.double()
transverse_distances = flex.double()
for i, valid_foreground in enumerate(mask):
if valid_foreground:
position = matrix.col(
(x_start + i % x_range, y_start + i // y_range))
projection_radial = position.dot(radial)
projection_transverse = position.dot(transverse)
radial_distances.append(projection_radial)
transverse_distances.append(projection_transverse)
length = flex.max(radial_distances) - flex.min(radial_distances)
width = flex.max(transverse_distances) - flex.min(transverse_distances)
# The angle subtended is centered at the spot centroid, spanning the
# spot width. Half this angle makes a right triangle with legs of lengths
# [distance to beam center] and [half the spot width]. Use the tangent.
angle = 2 * math.atan(width / (2 * radial_abs.length()))
return (length, width, angle)
def spot_count_per_panel(self, rlist):
""" Analyse the spot count per panel. """
panel = rlist["panel"]
if flex.max(panel) == 0:
# only one panel, don't bother generating a plot
return
n_panels = int(flex.max(panel))
spot_count_per_panel = flex.int()
for i in range(n_panels):
sel = (panel >= i) & (panel < (i + 1))
spot_count_per_panel.append(sel.count(True))
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.set_title("Spot count per panel")
ax.scatter(
list(range(len(spot_count_per_panel))),
spot_count_per_panel,
s=10,
color="blue",
marker="o",
alpha=0.4,
)
ax.set_xlabel("Panel #")
ax.set_ylabel("# spots")
pyplot.savefig(os.path.join(self.directory, "spots_per_panel.png"))
pyplot.close()
def go(filename):
with open(filename, 'rb') as fh:
allprof = pickle.load(fh)
for prof in allprof:
x, y, z = mosaic_profile_xyz(prof[0])
for profile in prof[1:]:
_x, _y, _z = mosaic_profile_xyz(profile)
x += _x
y += _y
z += _z
x /= flex.max(x)
data = (100 * x).iround()
fmt = '%3d ' * x.size()
print('X:', fmt % tuple(data))
y /= flex.max(y)
data = (100 * y).iround()
fmt = '%3d ' * y.size()
print('Y:', fmt % tuple(data))
z /= flex.max(z)
data = (100 * z).iround()
fmt = '%3d ' * z.size()
print('Z:', fmt % tuple(data))
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 spot_count_per_image(self, rlist):
""" Analyse the spot count per image. """
x, y, z = rlist["xyzobs.px.value"].parts()
max_z = int(math.ceil(flex.max(z)))
ids = rlist["id"]
spot_count_per_image = []
for j in range(flex.max(ids) + 1):
spot_count_per_image.append(flex.int())
zsel = z.select(ids == j)
for i in range(max_z):
sel = (zsel >= i) & (zsel < (i + 1))
spot_count_per_image[j].append(sel.count(True))
colours = ["blue", "red", "green", "orange", "purple", "black"] * 10
assert len(spot_count_per_image) <= colours
fig = pyplot.figure()
ax = fig.add_subplot(111)
ax.set_title("Spot count per image")
for j, spots in enumerate(spot_count_per_image):
ax.scatter(
list(range(len(spots))),
spots,
s=5,
color=colours[j],
marker="o",
alpha=0.4,
)
ax.set_xlabel("Image #")
ax.set_ylabel("# spots")
pyplot.savefig(os.path.join(self.directory, "spots_per_image.png"))
pyplot.close()
def go(filename):
with open(filename, "rb") as fh:
allprof = pickle.load(fh)
for prof in allprof:
x, y, z = mosaic_profile_xyz(prof[0])
for profile in prof[1:]:
_x, _y, _z = mosaic_profile_xyz(profile)
x += _x
y += _y
z += _z
x /= flex.max(x)
data = (100 * x).iround()
fmt = "%3d " * x.size()
print("X:", fmt % tuple(data))
y /= flex.max(y)
data = (100 * y).iround()
fmt = "%3d " * y.size()
print("Y:", fmt % tuple(data))
z /= flex.max(z)
data = (100 * z).iround()
fmt = "%3d " * z.size()
print("Z:", fmt % tuple(data))
def get_bounded_data(self, data, bounds):
assert len(bounds) == 4
x = [b[0] for b in bounds]
y = [b[1] for b in bounds]
left = sorted(x)[1]
right = sorted(x)[2]
top = sorted(y)[2]
bottom = sorted(y)[1]
origin = col((left, bottom))
scale_x = right-left
scale_y = top-bottom
scale = min(scale_x, scale_y)
data_max_x = flex.max(data.parts()[0])
data_min_x = flex.min(data.parts()[0])
data_max_y = flex.max(data.parts()[1])
data_min_y = flex.min(data.parts()[1])
data_scale_x = data_max_x - data_min_x
data_scale_y = data_max_y - data_min_y
if data_scale_x == 0 or data_scale_y == 0:
print "WARNING bad scale"
return data
return flex.vec2_double(data.parts()[0] * (scale/abs(data_scale_x)),
data.parts()[1] * (scale/abs(data_scale_y))) + origin
def display(self, num):
"""
Display some shoeboxes
"""
from dials_scratch.jmp.viewer import show_image_stack_multi_view
from random import sample
from dials.array_family import flex
def simulate(experiments, reflections, parameters):
from dials_scratch.jmp.profile_modelling import MLTarget3D
func = MLTarget3D(experiments[0], reflections)
return [
func.simulate(i, parameters) for i in range(len(reflections))
]
# Sample from reflections
reflections = self.reflections.select(
flex.size_t(sample(range(len(self.reflections)), num)))
# Simulate the reflection profiles from the current model
simulated = simulate(self.experiments, reflections, self.parameters)
# Display stuff
for model, data_sbox in zip(simulated, reflections["shoebox"]):
data = data_sbox.data
show_image_stack_multi_view(model.as_numpy_array(),
vmax=flex.max(model))
show_image_stack_multi_view(data.as_numpy_array(),
vmax=flex.max(data))
def pickle_histogram(params, data):
from dials.array_family import flex
import cPickle as pickle
from dials.array_family import flex
with open(data) as fin:
r, g, b = pickle.load(fin)
if params.min is None:
_min = min(flex.min(r), flex.min(g), flex.min(b))
else:
_min = params.min
if params.max is None:
_max = max(flex.max(r), flex.max(g), flex.max(b))
else:
_max = params.max
tr = flex.histogram(r.as_1d(),
data_min=_min,
data_max=_max,
n_slots=params.bins)
tg = flex.histogram(g.as_1d(),
data_min=_min,
data_max=_max,
n_slots=params.bins)
tb = flex.histogram(b.as_1d(),
data_min=_min,
data_max=_max,
n_slots=params.bins)
with open(params.output, 'w') as f:
for cn in zip(tr.slot_centers(), tr.slots(), tg.slots(), tb.slots()):
f.write('%.2f %f %f %f\n' % cn)
def blank_counts_analysis(reflections, scan, phi_step, fractional_loss):
if not len(reflections):
raise ValueError("Input contains no reflections")
xyz_px = reflections["xyzobs.px.value"]
x_px, y_px, z_px = xyz_px.parts()
phi = scan.get_angle_from_array_index(z_px)
osc = scan.get_oscillation()[1]
n_images_per_step = iceil(phi_step / osc)
phi_step = n_images_per_step * osc
array_range = scan.get_array_range()
phi_min = scan.get_angle_from_array_index(array_range[0])
phi_max = scan.get_angle_from_array_index(array_range[1])
assert phi_min <= flex.min(phi)
assert phi_max >= flex.max(phi)
n_steps = max(int(round((phi_max - phi_min) / phi_step)), 1)
hist = flex.histogram(
z_px, data_min=array_range[0], data_max=array_range[1], n_slots=n_steps
)
logger.debug("Histogram:")
logger.debug(hist.as_str())
counts = hist.slots()
fractional_counts = counts.as_double() / flex.max(counts)
potential_blank_sel = fractional_counts <= fractional_loss
xmin, xmax = zip(
*[
(slot_info.low_cutoff, slot_info.high_cutoff)
for slot_info in hist.slot_infos()
]
)
d = {
"data": [
{
"x": list(hist.slot_centers()),
"y": list(hist.slots()),
"xlow": xmin,
"xhigh": xmax,
"blank": list(potential_blank_sel),
"type": "bar",
"name": "blank_counts_analysis",
}
],
"layout": {
"xaxis": {"title": "z observed (images)"},
"yaxis": {"title": "Number of reflections"},
"bargap": 0,
},
}
blank_regions = blank_regions_from_sel(d["data"][0])
d["blank_regions"] = blank_regions
return d
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 __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 generate_cif(crystal, refiner, filename):
logger.info("Saving CIF information to %s", filename)
from cctbx import miller
import iotbx.cif.model
block = iotbx.cif.model.block()
block["_audit_creation_method"] = dials_version()
block["_audit_creation_date"] = datetime.date.today().isoformat()
# block["_publ_section_references"] = '' # once there is a reference...
for cell, esd, cifname in zip(
crystal.get_unit_cell().parameters(),
crystal.get_cell_parameter_sd(),
[
"length_a",
"length_b",
"length_c",
"angle_alpha",
"angle_beta",
"angle_gamma",
],
):
block["_cell_%s" % cifname] = format_float_with_standard_uncertainty(
cell, esd
)
block["_cell_volume"] = format_float_with_standard_uncertainty(
crystal.get_unit_cell().volume(), crystal.get_cell_volume_sd()
)
used_reflections = refiner.get_matches()
block["_cell_measurement_reflns_used"] = len(used_reflections)
block["_cell_measurement_theta_min"] = (
flex.min(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2
)
block["_cell_measurement_theta_max"] = (
flex.max(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2
)
block["_diffrn_reflns_number"] = len(used_reflections)
miller_span = miller.index_span(used_reflections["miller_index"])
min_h, min_k, min_l = miller_span.min()
max_h, max_k, max_l = miller_span.max()
block["_diffrn_reflns_limit_h_min"] = min_h
block["_diffrn_reflns_limit_h_max"] = max_h
block["_diffrn_reflns_limit_k_min"] = min_k
block["_diffrn_reflns_limit_k_max"] = max_k
block["_diffrn_reflns_limit_l_min"] = min_l
block["_diffrn_reflns_limit_l_max"] = max_l
block["_diffrn_reflns_theta_min"] = (
flex.min(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2
)
block["_diffrn_reflns_theta_max"] = (
flex.max(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2
)
cif = iotbx.cif.model.cif()
cif["two_theta_refine"] = block
with open(filename, "w") as fh:
cif.show(out=fh)
def generate_mmcif(crystal, refiner, filename):
logger.info("Saving mmCIF information to %s", filename)
block = iotbx.cif.model.block()
block["_audit.revision_id"] = 1
block["_audit.creation_method"] = dials_version()
block["_audit.creation_date"] = datetime.date.today().isoformat()
block["_entry.id"] = "two_theta_refine"
# block["_publ.section_references"] = '' # once there is a reference...
block["_cell.entry_id"] = "two_theta_refine"
for cell, esd, cifname in zip(
crystal.get_unit_cell().parameters(),
crystal.get_cell_parameter_sd(),
[
"length_a",
"length_b",
"length_c",
"angle_alpha",
"angle_beta",
"angle_gamma",
],
):
block["_cell.%s" % cifname] = "%.8f" % cell
block["_cell.%s_esd" % cifname] = "%.8f" % esd
block["_cell.volume"] = "%f" % crystal.get_unit_cell().volume()
block["_cell.volume_esd"] = "%f" % crystal.get_cell_volume_sd()
used_reflections = refiner.get_matches()
block["_cell_measurement.entry_id"] = "two_theta_refine"
block["_cell_measurement.reflns_used"] = len(used_reflections)
block["_cell_measurement.theta_min"] = (
flex.min(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2)
block["_cell_measurement.theta_max"] = (
flex.max(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2)
block["_exptl_crystal.id"] = 1
block["_diffrn.id"] = "two_theta_refine"
block["_diffrn.crystal_id"] = 1
block["_diffrn_reflns.diffrn_id"] = "two_theta_refine"
block["_diffrn_reflns.number"] = len(used_reflections)
miller_span = miller.index_span(used_reflections["miller_index"])
min_h, min_k, min_l = miller_span.min()
max_h, max_k, max_l = miller_span.max()
block["_diffrn_reflns.limit_h_min"] = min_h
block["_diffrn_reflns.limit_h_max"] = max_h
block["_diffrn_reflns.limit_k_min"] = min_k
block["_diffrn_reflns.limit_k_max"] = max_k
block["_diffrn_reflns.limit_l_min"] = min_l
block["_diffrn_reflns.limit_l_max"] = max_l
block["_diffrn_reflns.theta_min"] = (
flex.min(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2)
block["_diffrn_reflns.theta_max"] = (
flex.max(used_reflections["2theta_obs.rad"]) * 180 / math.pi / 2)
cif = iotbx.cif.model.cif()
cif["two_theta_refine"] = block
with open(filename, "w") as fh:
cif.show(out=fh)
def get_min_max_xy(self, rlist):
xc, yc, zc = rlist['xyzcal.px'].parts()
xo, yo, zo = rlist['xyzobs.px.value'].parts()
min_x = math.floor(min(flex.min(xc), flex.min(xo)))
min_y = math.floor(min(flex.min(yc), flex.min(yo)))
max_x = math.ceil(max(flex.max(xc), flex.max(xo)))
max_y = math.ceil(max(flex.max(yc), flex.max(yo)))
return min_x, max_x, min_y, max_y
def Get_Max(self, opt=0):
if self.data_flex != None:
if opt != 3:
self.I_Max = flex.max(self.data_flex)
else:
self.I_Max = flex.max(self.mask_flex) * 2
else:
self.I_Max = -1
return self.I_Max
def Get_Max(self, opt=0):
if self.data_flex != None:
if opt != 3:
self.I_Max = flex.max(self.data_flex)
else:
self.I_Max = flex.max(self.mask_flex) * 2
else:
self.I_Max = -1
return self.I_Max
def __str__(self):
return "\n".join(
(
"Max. completeness (non-anom): %.2f %%"
% (100 * flex.max(self.ieither_completeness)),
"Max. completeness (anom): %.2f %%"
% (100 * flex.max(self.iboth_completeness)),
)
)
def get_min_max_xy(self, rlist):
xc, yc, zc = rlist['xyzcal.px'].parts()
xo, yo, zo = rlist['xyzobs.px.value'].parts()
dx = xc - xo
dz = zc - zo
min_x = math.floor(flex.min(dx))
min_y = math.floor(flex.min(dz))
max_x = math.ceil(flex.max(dx))
max_y = math.ceil(flex.max(dz))
return min_x, max_x, min_y, max_y
def update_reflection_data(self, selection=None, block_selections=None):
"""control access to setting all of reflection data at once"""
self._normalised_x_values = []
self._normalised_y_values = []
self._normalised_z_values = []
self._n_refl = []
normalised_x_values = self.data["x"]
normalised_y_values = self.data["y"]
normalised_z_values = self.data["z"]
if selection:
normalised_x_values = normalised_x_values.select(selection)
normalised_y_values = normalised_y_values.select(selection)
normalised_z_values = normalised_z_values.select(selection)
"""Set the normalised coordinate values and configure the smoother."""
normalised_x_values = normalised_x_values - flex.min(
normalised_x_values)
normalised_y_values = normalised_y_values - flex.min(
normalised_y_values)
normalised_z_values = normalised_z_values - flex.min(
normalised_z_values)
x_range = [
floor(round(flex.min(normalised_x_values), 10)),
max(ceil(round(flex.max(normalised_x_values), 10)), 1),
]
y_range = [
floor(round(flex.min(normalised_y_values), 10)),
max(ceil(round(flex.max(normalised_y_values), 10)), 1),
]
z_range = [
floor(round(flex.min(normalised_z_values), 10)),
max(ceil(round(flex.max(normalised_z_values), 10)), 1),
]
self._smoother = GaussianSmoother3D(
x_range,
self.nparam_to_val(self._n_x_params),
y_range,
self.nparam_to_val(self._n_y_params),
z_range,
self.nparam_to_val(self._n_z_params),
)
if block_selections:
for i, sel in enumerate(block_selections):
self._normalised_x_values.append(
normalised_x_values.select(sel))
self._normalised_y_values.append(
normalised_y_values.select(sel))
self._normalised_z_values.append(
normalised_z_values.select(sel))
self._n_refl.append(self._normalised_x_values[i].size())
else:
self._normalised_x_values.append(normalised_x_values)
self._normalised_y_values.append(normalised_y_values)
self._normalised_z_values.append(normalised_z_values)
self._n_refl.append(normalised_x_values.size())
def run(self, flags, sequence=None, observations=None, **kwargs):
obs_x, obs_y = observations.centroids().px_position_xy().parts()
import numpy as np
H, xedges, yedges = np.histogram2d(
obs_x.as_numpy_array(), obs_y.as_numpy_array(), bins=self.nbins
)
H_flex = flex.double(H.flatten().astype(np.float64))
n_slots = min(int(flex.max(H_flex)), 30)
hist = flex.histogram(H_flex, n_slots=n_slots)
slots = hist.slots()
cumulative_hist = flex.long(len(slots))
for i, slot in enumerate(slots):
cumulative_hist[i] = slot
if i > 0:
cumulative_hist[i] += cumulative_hist[i - 1]
cumulative_hist = cumulative_hist.as_double() / flex.max(
cumulative_hist.as_double()
)
cutoff = None
gradients = flex.double()
for i in range(len(slots) - 1):
x1 = cumulative_hist[i]
x2 = cumulative_hist[i + 1]
g = (x2 - x1) / hist.slot_width()
gradients.append(g)
if (
cutoff is None
and i > 0
and g < self.gradient_cutoff
and gradients[i - 1] < self.gradient_cutoff
):
cutoff = hist.slot_centers()[i - 1] - 0.5 * hist.slot_width()
sel = np.column_stack(np.where(H > cutoff))
for (ix, iy) in sel:
flags.set_selected(
(
(obs_x > xedges[ix])
& (obs_x < xedges[ix + 1])
& (obs_y > yedges[iy])
& (obs_y < yedges[iy + 1])
),
False,
)
return flags
def generate_mmcif(crystal, refiner, file):
logger.info('Saving mmCIF information to %s' % file)
from cctbx import miller
import datetime
import iotbx.cif.model
import math
block = iotbx.cif.model.block()
block["_audit.creation_method"] = dials_version()
block["_audit.creation_date"] = datetime.date.today().isoformat()
# block["_publ.section_references"] = '' # once there is a reference...
for cell, esd, cifname in zip(
crystal.get_unit_cell().parameters(),
crystal.get_cell_parameter_sd(), [
'length_a', 'length_b', 'length_c', 'angle_alpha',
'angle_beta', 'angle_gamma'
]):
block['_cell.%s' % cifname] = "%.8f" % cell
block['_cell.%s_esd' % cifname] = "%.8f" % esd
block['_cell.volume'] = "%f" % crystal.get_unit_cell().volume()
block['_cell.volume_esd'] = "%f" % crystal.get_cell_volume_sd()
used_reflections = refiner.get_matches()
block['_cell_measurement.reflns_used'] = len(used_reflections)
block['_cell_measurement.theta_min'] = flex.min(
used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
block['_cell_measurement.theta_max'] = flex.max(
used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
block['_diffrn_reflns.number'] = len(used_reflections)
miller_span = miller.index_span(used_reflections['miller_index'])
min_h, min_k, min_l = miller_span.min()
max_h, max_k, max_l = miller_span.max()
block['_diffrn_reflns.limit_h_min'] = min_h
block['_diffrn_reflns.limit_h_max'] = max_h
block['_diffrn_reflns.limit_k_min'] = min_k
block['_diffrn_reflns.limit_k_max'] = max_k
block['_diffrn_reflns.limit_l_min'] = min_l
block['_diffrn_reflns.limit_l_max'] = max_l
block['_diffrn_reflns.theta_min'] = flex.min(
used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
block['_diffrn_reflns.theta_max'] = flex.max(
used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
cif = iotbx.cif.model.cif()
cif['two_theta_refine'] = block
with open(file, 'w') as fh:
cif.show(out=fh)
def calculate_delta_statistics(self):
'''Calculate min, max, mean, and stddev for the normalized deltas'''
delta_min = flex.min(self.deltas) if self.deltas.size() > 0 else float('inf')
delta_max = flex.max(self.deltas) if self.deltas.size() > 0 else float('-inf')
delta_sum = flex.sum(self.deltas) if self.deltas.size() > 0 else 0.0
comm = self.mpi_helper.comm
MPI = self.mpi_helper.MPI
# global min and max
self.global_delta_min = comm.allreduce(delta_min, MPI.MIN)
self.global_delta_max = comm.allreduce(delta_max, MPI.MAX)
# global mean
self.global_delta_count = comm.allreduce(self.deltas.size(), MPI.SUM)
if self.global_delta_count < 20:
raise ValueError("Too few reflections available for ev11 algorithm")
global_delta_sum = comm.allreduce(delta_sum, MPI.SUM)
self.global_delta_mean = global_delta_sum / self.global_delta_count
# global standard deviation
array_of_global_delta_means = flex.double(self.deltas.size(), self.global_delta_mean)
array_of_diffs = self.deltas - array_of_global_delta_means
array_of_square_diffs = array_of_diffs * array_of_diffs
sum_of_square_diffs = flex.sum(array_of_square_diffs)
global_sum_of_square_diffs = comm.allreduce(sum_of_square_diffs, MPI.SUM)
self.global_delta_stddev = math.sqrt(global_sum_of_square_diffs / (self.global_delta_count - 1))
if self.mpi_helper.rank == 0:
self.logger.main_log("Global delta statistics (count,min,max,mean,stddev): (%d,%f,%f,%f,%f)"%(self.global_delta_count, self.global_delta_min, self.global_delta_max, self.global_delta_mean, self.global_delta_stddev))
def profile2d(p, vmin=None, vmax=None):
from dials.array_family import flex
import string
if vmin is None:
vmin = flex.min(p)
if vmax is None:
vmax = flex.max(p)
assert(vmax >= vmin)
dv = vmax - vmin
if dv == 0:
c = 0
m = 0
else:
m = 35.0 / dv
c = -m * vmin
lookup = string.digits + string.ascii_uppercase
ny, nx = p.all()
text = ''
for j in range(ny):
for i in range(nx):
v = int(m * p[j,i] + c)
if v < 0:
v = 0
elif v > 35:
v = 35
t = lookup[v]
text += t + ' '
text += '\n'
return text
def fix_xy(reflections_in, reflections_out):
reflections = pickle.load(open(reflections_in, "r"))
# validate that input is from 60 panel detector, assumed to be
# 5 x 12 configuration
assert flex.max(reflections["panel"]) == 59
delta_x = 487 + 7
delta_y = 195 + 17
panel_x = reflections["panel"].as_int() % 5
panel_y = reflections["panel"].as_int() / 5
x_offset = delta_x * panel_x
y_offset = delta_y * panel_y
# apply fixes
x, y, z = reflections["xyzobs.px.value"].parts()
x += x_offset.as_double()
y += y_offset.as_double()
reflections["xyzobs.px.value"] = flex.vec3_double(x, y, z)
x, y, z = reflections["xyzcal.px"].parts()
x += x_offset.as_double()
y += y_offset.as_double()
reflections["xyzcal.px"] = flex.vec3_double(x, y, z)
# save - should probably do this "properly"
pickle.dump(reflections, open(reflections_out, "wb"))
return
def filter_ice(reflections, steps):
from cctbx import miller, sgtbx, uctbx
from matplotlib import pyplot as plt
d_spacings = 1 / reflections["rlp"].norms()
d_star_sq = uctbx.d_as_d_star_sq(d_spacings)
from dials.algorithms.spot_finding.per_image_analysis import ice_rings_selection
from dials.algorithms.integration import filtering
ice_uc = uctbx.unit_cell((4.498, 4.498, 7.338, 90, 90, 120))
ice_sg = sgtbx.space_group_info(number=194).group()
ice_generator = miller.index_generator(ice_uc, ice_sg.type(), False,
flex.min(d_spacings))
ice_indices = ice_generator.to_array()
ice_d_spacings = flex.sorted(ice_uc.d(ice_indices))
ice_d_star_sq = uctbx.d_as_d_star_sq(ice_d_spacings)
cubic_ice_uc = uctbx.unit_cell((6.358, 6.358, 6.358, 90, 90, 90))
cubic_ice_sg = sgtbx.space_group_info(number=227).group()
cubic_ice_generator = miller.index_generator(cubic_ice_uc,
cubic_ice_sg.type(), False,
flex.min(d_spacings))
cubic_ice_indices = cubic_ice_generator.to_array()
cubic_ice_d_spacings = flex.sorted(cubic_ice_uc.d(cubic_ice_indices))
cubic_ice_d_star_sq = uctbx.d_as_d_star_sq(cubic_ice_d_spacings)
import numpy
widths = flex.double(numpy.geomspace(start=0.0001, stop=0.01, num=steps))
n_spots = flex.double()
total_intensity = flex.double()
for width in widths:
d_min = flex.min(d_spacings)
ice_filter = filtering.PowderRingFilter(ice_uc, ice_sg, d_min, width)
ice_sel = ice_filter(d_spacings)
n_spots.append(ice_sel.count(False))
if "intensity.sum.value" in reflections:
total_intensity.append(
flex.sum(reflections["intensity.sum.value"].select(~ice_sel)))
fig, axes = plt.subplots(nrows=2, figsize=(12, 8), sharex=True)
axes[0].plot(widths, n_spots, label="#spots", marker="+")
if total_intensity.size():
axes[1].plot(widths,
total_intensity,
label="total intensity",
marker="+")
axes[0].set_ylabel("# spots remaining")
axes[1].set_xlabel("Ice ring width (1/d^2)")
axes[1].set_ylabel("Total intensity")
for ax in axes:
ax.set_xlim(0, flex.max(widths))
plt.savefig("ice_ring_filtering.png")
plt.clf()
return
def run(experiment):
from scitbx import matrix
from math import sqrt, exp, pi
from dials.array_family import flex
# Do some prediction
refl = flex.reflection_table.from_predictions(experiment)
# Get the geometry
s0 = matrix.col(experiment.beam.get_s0())
m2 = matrix.col(experiment.goniometer.get_rotation_axis())
dx = matrix.col(experiment.detector[0].get_fast_axis())
dy = matrix.col(experiment.detector[0].get_slow_axis())
dz = matrix.col(experiment.detector[0].get_origin())
ub = matrix.col(experiment.crystal.get_A())
ra = matrix.col((ub[0], ub[3], ub[6]))
rb = matrix.col((ub[1], ub[4], ub[7]))
rc = matrix.col((ub[2], ub[5], ub[8]))
print(ra.dot(rb))
print(ra.dot(rc))
print(rb.dot(rc))
# Orthogonal vectors to create the profile on
ea = ra.normalize()
eb = ea.cross(rc.normalize())
ec = ea.cross(eb)
# The sigma along each axis
sigma_a = 0.005
sigma_b = 0.005
sigma_c = 0.001
# The covariance matrix for the normal distribution
sigma = matrix.sqr((sigma_a**2, 0, 0, 0, sigma_b**2, 0, 0, 0, sigma_c**2))
sigmam1 = sigma.inverse()
s1 = matrix.col(refl["s1"][0])
rlp = s1 - s0
xc, yc, zc = refl["xyzcal.mm"][0]
print(xc, yc, zc)
data = flex.double(flex.grid(200, 200))
for j in range(200):
for i in range(200):
x = xc - 10 + 20 * i / 200.0
y = yc - 10 + 20 * j / 200.0
v = x * dx + y * dy + dz
s = v * s0.length() / v.length()
c = 1.0 / (sqrt((2 * pi)**3 * sigma.determinant()))
sc = s0 + rlp
d = -0.5 * (((s - sc).transpose() * sigmam1 * (s - sc))[0])
f = c * exp(d)
data[j, i] = f
print(flex.max(data))
from matplotlib import pylab, cm
pylab.imshow(data.as_numpy_array(), cmap=cm.Greys)
pylab.show()
def test_mtz_primitive_cell(dials_data, tmpdir):
scaled_expt = dials_data("insulin_processed") / "scaled.expt"
scaled_refl = dials_data("insulin_processed") / "scaled.refl"
# First reindex to the primitive setting
expts = ExperimentList.from_file(scaled_expt.strpath, check_format=False)
cs = expts[0].crystal.get_crystal_symmetry()
cb_op = cs.change_of_basis_op_to_primitive_setting()
procrunner.run(
[
"dials.reindex",
scaled_expt.strpath,
scaled_refl.strpath,
'change_of_basis_op="%s"' % cb_op,
],
working_directory=tmpdir.strpath,
)
# Now export the reindexed experiments/reflections
procrunner.run(
["dials.export", "reindexed.expt", "reindexed.refl"],
working_directory=tmpdir.strpath,
)
mtz_obj = mtz.object(os.path.join(tmpdir.strpath, "scaled.mtz"))
cs_primitive = cs.change_basis(cb_op)
assert mtz_obj.space_group() == cs_primitive.space_group()
refl = flex.reflection_table.from_file(scaled_refl.strpath)
refl = refl.select(~refl.get_flags(refl.flags.bad_for_scaling, all=False))
for ma in mtz_obj.as_miller_arrays():
assert ma.crystal_symmetry().is_similar_symmetry(cs_primitive)
assert ma.d_max_min() == pytest.approx(
(flex.max(refl["d"]), flex.min(refl["d"]))
)
def compute_processors(self):
"""
Compute the number of processors
"""
from dials.array_family import flex
# Set the memory usage per processor
if self.params.mp.method == "multiprocessing" and self.params.mp.nproc > 1:
# Get the maximum shoebox memory
max_memory = flex.max(
self.jobs.shoebox_memory(self.reflections,
self.params.shoebox.flatten))
# Compute expected memory usage and warn if not enough
total_memory = psutil.virtual_memory().total
assert (total_memory is not None and total_memory > 0
), "psutil call appears to have given unexpected output"
limit_memory = total_memory * self.params.block.max_memory_usage
njobs = int(math.floor(limit_memory / max_memory))
if njobs < 1:
raise RuntimeError("""
No enough memory to run integration jobs. Possible solutions
include increasing the percentage of memory allowed for shoeboxes or
decreasing the block size.
Total system memory: %g GB
Limit shoebox memory: %g GB
Max shoebox memory: %g GB
""" % (total_memory / 1e9, limit_memory / 1e9, max_memory / 1e9))
else:
self.params.mp.nproc = min(self.params.mp.nproc, njobs)
self.params.block.max_memory_usage /= self.params.mp.nproc
def per_image(self):
"""Set one block per image for all experiments"""
self._create_block_columns()
# get observed phi in radians
phi_obs = self._reflections['xyzobs.mm.value'].parts()[2]
for iexp, exp in enumerate(self._experiments):
sel = self._reflections['id'] == iexp
isel = sel.iselection()
exp_phi = phi_obs.select(isel)
# convert phi to integer frames
frames = exp.scan.get_array_index_from_angle(exp_phi, deg=False)
frames = flex.floor(frames).iround()
start, stop = flex.min(frames), flex.max(frames)
frame_range = range(start, stop + 1)
for f_num, f in enumerate(frame_range):
sub_isel = isel.select(frames == f)
self._reflections['block'].set_selected(sub_isel, f_num)
self._reflections['block_centre'].set_selected(sub_isel, f_num)
return self._reflections
def calculate_intensity_bin_limits(self):
'''Calculate the minimum and maximum values of the mean intensities for each HKL'''
count = self.work_table.size()
mean_intensity_min = flex.min(
self.work_table['biased_mean']) if count > 0 else float('inf')
mean_intensity_max = flex.max(
self.work_table['biased_mean']) if count > 0 else float('-inf')
if count > 0:
self.logger.log(
"Using %d multiply-measured HKLs; mean intensity (min,max): (%f,%f)"
% (count, mean_intensity_min, mean_intensity_max))
else:
self.logger.log("No multiply-measured HKLs available")
comm = self.mpi_helper.comm
MPI = self.mpi_helper.MPI
global_mean_intensity_min = comm.allreduce(mean_intensity_min, MPI.MIN)
global_mean_intensity_max = comm.allreduce(mean_intensity_max, MPI.MAX)
self.logger.log("Global mean intensity (min,max): (%f,%f)" %
(global_mean_intensity_min, global_mean_intensity_max))
self.intensity_bin_limits = np.linspace(global_mean_intensity_min,
global_mean_intensity_max,
number_of_intensity_bins + 1)
self.intensity_bin_limits[0] = float('-inf')
self.intensity_bin_limits[len(self.intensity_bin_limits) -
1] = float('inf')
def plot_cdf_manually(self, reflections, panel = None, ax = None, bounds = None):
colors = ['blue', 'green']
r = (reflections['xyzcal.mm']-reflections['xyzobs.mm.value']).norms()
h = flex.histogram(r)
sigma = h.slot_centers()[list(h.slots()).index(flex.max(h.slots()))] # mode
x_extent = max(r)
y_extent = len(r)
xobs = [i/x_extent for i in sorted(r)]
yobs = [i/y_extent for i in xrange(y_extent)]
obs = [(x, y) for x, y in zip(xobs, yobs)]
ncalc = 100
xcalc = [i/ncalc for i in xrange(ncalc)]
ycalc = [1-math.exp((-i**2)/(2*(sigma**2))) for i in xcalc]
calc = [(x, y) for x, y in zip(xcalc, ycalc)]
data = [flex.vec2_double(obs),
flex.vec2_double(calc)]
if bounds is None:
ax.set_xlim((-1,1))
ax.set_ylim((-1,1))
ax.set_title("%s Outlier SP Manually"%self.params.tag)
if bounds is not None:
data = [self.get_bounded_data(d, bounds) for d in data]
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
for subset,c in zip(data, colors):
ax.plot(subset.parts()[0], subset.parts()[1], '-', c=c)
def setup_binner(
self,
unit_cell: uctbx.unit_cell,
space_group: sgtbx.space_group,
n_resolution_bins: int,
) -> None:
"""Create a binner for the reflections contained in the table."""
ma = _reflection_table_to_iobs(self.as_reflection_table(), unit_cell,
space_group)
# need d star sq step
d_star_sq = ma.d_star_sq().data()
d_star_sq_min = flex.min(d_star_sq)
d_star_sq_max = flex.max(d_star_sq)
span = d_star_sq_max - d_star_sq_min
relative_tolerance = 1e-6
d_star_sq_max += span * relative_tolerance
d_star_sq_min -= span * relative_tolerance
# Avoid a zero-size step that would otherwise anger the d_star_sq_step binner.
step = max((d_star_sq_max - d_star_sq_min) / n_resolution_bins, 0.004)
self.binner = ma.setup_binner_d_star_sq_step(
auto_binning=False,
d_max=uctbx.d_star_sq_as_d(d_star_sq_max),
d_min=uctbx.d_star_sq_as_d(d_star_sq_min),
d_star_sq_step=step,
)
def tst_flatten(self):
from dials.array_family import flex
from dials.algorithms.shoebox import MaskCode
for shoebox, (XC, I) in self.random_shoeboxes(10, mask=True):
assert (not shoebox.flat)
zs = shoebox.zsize()
ys = shoebox.ysize()
xs = shoebox.xsize()
expected_data = flex.real(flex.grid(1, ys, xs), 0)
expected_mask = flex.int(flex.grid(1, ys, xs), 0)
for k in range(zs):
for j in range(ys):
for i in range(xs):
expected_data[0, j, i] += shoebox.data[k, j, i]
expected_mask[0, j, i] |= shoebox.mask[k, j, i]
if (not (expected_mask[0, j, i] & MaskCode.Valid) or
not (shoebox.mask[k, j, i] & MaskCode.Valid)):
expected_mask[0, j, i] &= ~MaskCode.Valid
shoebox.flatten()
diff = expected_data.as_double() - shoebox.data.as_double()
max_diff = flex.max(flex.abs(diff))
assert (max_diff < 1e-7)
assert (expected_mask.all_eq(shoebox.mask))
assert (shoebox.flat)
assert (shoebox.is_consistent())
print 'OK'
def per_image(self):
"""Set one block per image for all experiments"""
self._create_block_columns()
# get observed phi in radians
phi_obs = self._reflections["xyzobs.mm.value"].parts()[2]
for iexp, exp in enumerate(self._experiments):
sel = self._reflections["id"] == iexp
isel = sel.iselection()
exp_phi = phi_obs.select(isel)
# convert phi to integer frames
frames = exp.scan.get_array_index_from_angle(exp_phi, deg=False)
frames = flex.floor(frames).iround()
start, stop = flex.min(frames), flex.max(frames)
frame_range = range(start, stop + 1)
for f_num, f in enumerate(frame_range):
sub_isel = isel.select(frames == f)
f_cent = f + 0.5
self._reflections["block"].set_selected(sub_isel, f_num)
self._reflections["block_centre"].set_selected(sub_isel, f_cent)
return self._reflections
def test_all_expt_ids_have_expts(dials_data, tmpdir):
result = procrunner.run(
[
"dials.index",
dials_data("vmxi_thaumatin_grid_index").join("split_07602.expt"),
dials_data("vmxi_thaumatin_grid_index").join("split_07602.refl"),
"stills.indexer=sequences",
"indexing.method=real_space_grid_search",
"space_group=P4",
"unit_cell=58,58,150,90,90,90",
"max_lattices=8",
"beam.fix=all",
"detector.fix=all",
],
working_directory=tmpdir,
)
assert not result.returncode and not result.stderr
assert tmpdir.join("indexed.expt").check(file=1)
assert tmpdir.join("indexed.refl").check(file=1)
refl = flex.reflection_table.from_file(tmpdir / "indexed.refl")
expt = ExperimentList.from_file(tmpdir / "indexed.expt",
check_format=False)
assert flex.max(refl["id"]) + 1 == len(expt)
def compose(self, reflections):
"""Compose scan-varying crystal parameterisations at the specified image
number, for the specified experiment, for each image. Put the U, B and
UB matrices in the reflection table, and cache the derivatives."""
self._prepare_for_compose(reflections)
for iexp, exp in enumerate(self._experiments):
# select the reflections of interest
sel = reflections['id'] == iexp
isel = sel.iselection()
blocks = reflections['block'].select(isel)
# identify which crystal parameterisations to use for this experiment
xl_op = self._get_xl_orientation_parameterisation(iexp)
xl_ucp = self._get_xl_unit_cell_parameterisation(iexp)
# get state and derivatives for each block
for block in xrange(flex.min(blocks),
flex.max(blocks) + 1):
# determine the subset of reflections this affects
subsel = isel.select(blocks == block)
if len(subsel) == 0: continue
# get the integer frame number nearest the centre of that block
frames = reflections['block_centre'].select(subsel)
# can only be false if original block assignment has gone wrong
assert frames.all_eq(frames[0]), \
"Failing: a block contains reflections that shouldn't be there"
frame = int(floor(frames[0]))
# model states at current frame
U = self._get_state_from_parameterisation(xl_op, frame)
if U is None: U = exp.crystal.get_U()
B = self._get_state_from_parameterisation(xl_ucp, frame)
if B is None: B = exp.crystal.get_B()
# set states
reflections['u_matrix'].set_selected(subsel, U.elems)
reflections['b_matrix'].set_selected(subsel, B.elems)
# set derivatives of the states
if xl_op is not None:
for j, dU in enumerate(xl_op.get_ds_dp()):
colname = "dU_dp{0}".format(j)
reflections[colname].set_selected(subsel, dU)
if xl_ucp is not None:
for j, dB in enumerate(xl_ucp.get_ds_dp()):
colname = "dB_dp{0}".format(j)
reflections[colname].set_selected(subsel, dB)
# set the UB matrices for prediction
reflections['ub_matrix'] = reflections['u_matrix'] * reflections['b_matrix']
return
def profile2d(p, vmin=None, vmax=None):
import string
from dials.array_family import flex
if vmin is None:
vmin = flex.min(p)
if vmax is None:
vmax = flex.max(p)
assert vmax >= vmin
dv = vmax - vmin
if dv == 0:
c = 0
m = 0
else:
m = 35.0 / dv
c = -m * vmin
lookup = string.digits + string.ascii_uppercase
ny, nx = p.all()
text = ""
for j in range(ny):
for i in range(nx):
v = int(m * p[j, i] + c)
if v < 0:
v = 0
elif v > 35:
v = 35
t = lookup[v]
text += t + " "
text += "\n"
return text
def add_imagesets_buttons(self):
if 'imageset_id' not in self.parent.reflections_input:
self.imgset_btn = None
return
n = flex.max(self.parent.reflections_input['imageset_id'])
if n <= 0:
self.imgset_btn = None
return
box = wx.BoxSizer(wx.VERTICAL)
self.panel_sizer.Add(box)
label = wx.StaticText(self, -1, "Imageset ids:")
box.Add(label, 0, wx.ALL | wx.ALIGN_CENTER_VERTICAL, 5)
from wxtbx.segmentedctrl import SegmentedToggleControl, SEGBTN_HORIZONTAL
self.imgset_btn = SegmentedToggleControl(self, style=SEGBTN_HORIZONTAL)
for i in range(n + 1):
self.imgset_btn.AddSegment(str(i))
if (self.settings.imageset_ids is not None
and i in self.settings.imageset_ids):
self.imgset_btn.SetValue(i + 1, True)
self.imgset_btn.Realize()
self.Bind(wx.EVT_TOGGLEBUTTON, self.OnChangeSettings, self.imgset_btn)
box.Add(self.imgset_btn, 0, wx.ALL, 5)
def show_experiments(self, experiments, reflections, d_min=None):
if d_min is not None:
reciprocal_lattice_points = reflections["rlp"]
d_spacings = 1 / reciprocal_lattice_points.norms()
reflections = reflections.select(d_spacings > d_min)
for i_expt, expt in enumerate(experiments):
logger.info("model %i (%i reflections):" %
(i_expt + 1,
(reflections["id"] == i_expt).count(True)))
logger.info(expt.crystal)
indexed_flags = reflections.get_flags(reflections.flags.indexed)
imageset_id = reflections["imageset_id"]
rows = [["Imageset", "# indexed", "# unindexed", "% indexed"]]
for i in range(flex.max(imageset_id) + 1):
imageset_indexed_flags = indexed_flags.select(imageset_id == i)
indexed_count = imageset_indexed_flags.count(True)
unindexed_count = imageset_indexed_flags.count(False)
rows.append([
str(i),
str(indexed_count),
str(unindexed_count),
"{:.1%}".format(indexed_count /
(indexed_count + unindexed_count)),
])
logger.info(dials.util.tabulate(rows, headers="firstrow"))
def determine_grid_size(rlist, grid_size=None):
from libtbx import Auto
panel_ids = rlist['panel']
n_panels = flex.max(panel_ids) + 1
if grid_size is not None and grid_size is not Auto:
assert (grid_size[0] * grid_size[1]) >= n_panels, (n_panels)
return grid_size
n_cols = int(math.floor(math.sqrt(n_panels)))
n_rows = int(math.ceil(n_panels / n_cols))
return n_cols, n_rows
def generate_mmcif(crystal, refiner, file):
logger.info('Saving mmCIF information to %s' % file)
from cctbx import miller
import datetime
import iotbx.cif.model
import math
block = iotbx.cif.model.block()
block["_audit.creation_method"] = dials_version()
block["_audit.creation_date"] = datetime.date.today().isoformat()
# block["_publ.section_references"] = '' # once there is a reference...
for cell, esd, cifname in zip(crystal.get_unit_cell().parameters(),
crystal.get_cell_parameter_sd(),
['length_a', 'length_b', 'length_c', 'angle_alpha', 'angle_beta', 'angle_gamma']):
block['_cell.%s' % cifname] = "%.8f" % cell
block['_cell.%s_esd' % cifname] = "%.8f" % esd
block['_cell.volume'] = "%f" % crystal.get_unit_cell().volume()
block['_cell.volume_esd'] = "%f" % crystal.get_cell_volume_sd()
used_reflections = refiner.get_matches()
block['_cell_measurement.reflns_used'] = len(used_reflections)
block['_cell_measurement.theta_min'] = flex.min(used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
block['_cell_measurement.theta_max'] = flex.max(used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
block['_diffrn_reflns.number'] = len(used_reflections)
miller_span = miller.index_span(used_reflections['miller_index'])
min_h, min_k, min_l = miller_span.min()
max_h, max_k, max_l = miller_span.max()
block['_diffrn_reflns.limit_h_min'] = min_h
block['_diffrn_reflns.limit_h_max'] = max_h
block['_diffrn_reflns.limit_k_min'] = min_k
block['_diffrn_reflns.limit_k_max'] = max_k
block['_diffrn_reflns.limit_l_min'] = min_l
block['_diffrn_reflns.limit_l_max'] = max_l
block['_diffrn_reflns.theta_min'] = flex.min(used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
block['_diffrn_reflns.theta_max'] = flex.max(used_reflections['2theta_obs.rad']) * 180 / math.pi / 2
cif = iotbx.cif.model.cif()
cif['two_theta_refine'] = block
with open(file, 'w') as fh:
cif.show(out=fh)
def _find_nearest_neighbours(self, observed, predicted):
'''
Find the nearest predicted spot to the observed spot.
:param observed: The observed reflections
:param predicted: The predicted reflections
:returns: (nearest neighbours, distance)
'''
from scitbx.array_family import flex
from logging import warn
# Get the predicted coordinates
predicted_panel = predicted['panel']
predicted_xyz = predicted['xyzcal.px']
observed_panel = observed['panel']
observed_xyz = observed['xyzobs.px.value']
# Get the number of panels
max_panel1 = flex.max(predicted_panel)
max_panel2 = flex.max(observed_panel)
max_panel = max([max_panel1, max_panel2])
nn_all = flex.size_t()
dd_all = flex.double()
for panel in range(max_panel+1):
pind = predicted_panel == panel
oind = observed_panel == panel
pxyz = predicted_xyz.select(pind)
oxyz = observed_xyz.select(oind)
try:
nn, d = self._find_nearest_neighbours_single(oxyz, pxyz)
indices = flex.size_t(range(len(pind))).select(pind)
indices = indices.select(flex.size_t(list(nn)))
nn_all.extend(indices)
dd_all.extend(d)
except Exception:
warn("Unable to match spots on panel %d" % panel)
return nn_all, dd_all
def profile3d(p, vmin=None, vmax=None):
''' Print a 3D profile. '''
from dials.array_family import flex
if vmin is None:
vmin = flex.min(p)
if vmax is None:
vmax = flex.max(p)
nz, ny, nx = p.all()
text = []
for k in range(nz):
p2 = p[k:k+1,:,:]
p2.reshape(flex.grid(ny, nx))
text.append(profile2d(p2, vmin=vmin, vmax=vmax))
return '\n'.join(text)
def get_normalized_colors(self, data, vmin=None, vmax=None):
if vmax is None:
vmax = self.params.residuals.plot_max
if vmax is None:
vmax = flex.max(data)
if vmin is None:
vmin = flex.min(data)
# initialize the color map
norm = Normalize(vmin=vmin, vmax=vmax)
cmap = plt.cm.get_cmap(self.params.colormap)
sm = cm.ScalarMappable(norm=norm, cmap=cmap)
color_vals = np.linspace(vmin, vmax, 11)
sm.set_array(color_vals) # needed for colorbar
return norm, cmap, color_vals, sm
def normalize_profile(self, profile):
from scitbx.array_family import flex
max_profile = flex.max(profile)
threshold = self.threshold * max_profile
sum_profile = 0.0
for i in range(len(profile)):
if profile[i] > threshold:
sum_profile += profile[i]
else:
profile[i] = 0.0
result = flex.double(flex.grid(profile.all()))
for i in range(len(profile)):
result[i] = profile[i] / sum_profile
return result
def plot_statistics(statistics, prefix='', degrees_per_bin=5,
cutoff_anom=None, cutoff_non_anom=None):
range_width = 1
range_min = flex.min(statistics.dose) - range_width
range_max = flex.max(statistics.dose)
n_steps = 2 + int((range_max - range_min) - range_width)
x = flex.double_range(n_steps) * range_width + range_min
x *= degrees_per_bin
dpi = 300
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
try: plt.style.use('ggplot')
except AttributeError: pass
line1, = plt.plot(x, statistics.ieither_completeness, label='Unique reflections')
line2, = plt.plot(x, statistics.iboth_completeness, label='Bijvoet pairs')
if cutoff_non_anom is not None:
plt.plot([cutoff_non_anom, cutoff_non_anom], plt.ylim(), c=line1.get_color(), linestyle='dashed')
if cutoff_anom is not None:
plt.plot([cutoff_anom, cutoff_anom], plt.ylim(), c=line2.get_color(), linestyle='dotted')
plt.xlim(0, plt.xlim()[1])
plt.xlabel('Scan angle (degrees)')
plt.ylabel('Completeness (%)')
plt.ylim(0, 1)
plt.legend(loc='lower right', fontsize='small')
plt.savefig('%scompleteness_vs_scan_angle.png' %prefix, dpi=dpi)
plt.clf()
line1, = plt.plot(x[1:], 100 * statistics.frac_new_ref, label='Unique reflections')
line2, = plt.plot(x[1:], 100 * statistics.frac_new_pairs, label='Bijvoet pairs')
ylim = plt.ylim()
if cutoff_non_anom is not None:
plt.plot([cutoff_non_anom, cutoff_non_anom], ylim, c=line1.get_color(), linestyle='dashed')
if cutoff_anom is not None:
plt.plot([cutoff_anom, cutoff_anom], ylim, c=line2.get_color(), linestyle='dotted')
plt.ylim(ylim)
plt.xlim(0, plt.xlim()[1])
plt.xlabel('Scan angle (degrees)')
plt.ylabel('% new reflections per degree')
plt.legend(loc='upper right', fontsize='small')
plt.savefig('%spercent_new_reflections_vs_scan_angle.png' %prefix, dpi=dpi)
plt.clf()
def histogram(self, reflections, title):
data = reflections['difference_vector_norms']
n_slots = 100
if self.params.residuals.histogram_max is None:
h = flex.histogram(data, n_slots=n_slots)
else:
h = flex.histogram(data.select(data <= self.params.residuals.histogram_max), n_slots=n_slots)
n = len(reflections)
rmsd = math.sqrt((reflections['xyzcal.mm']-reflections['xyzobs.mm.value']).sum_sq()/n)
sigma = mode = h.slot_centers()[list(h.slots()).index(flex.max(h.slots()))]
mean = flex.mean(data)
median = flex.median(data)
print "RMSD (microns)", rmsd * 1000
print "Histogram mode (microns):", mode * 1000
print "Overall mean (microns):", mean * 1000
print "Overall median (microns):", median * 1000
mean2 = math.sqrt(math.pi/2)*sigma
rmsd2 = math.sqrt(2)*sigma
print "Rayleigh Mean (microns)", mean2 * 1000
print "Rayleigh RMSD (microns)", rmsd2 * 1000
r = reflections['radial_displacements']
t = reflections['transverse_displacements']
print "Overall radial RMSD (microns)", math.sqrt(flex.sum_sq(r)/len(r)) * 1000
print "Overall transverse RMSD (microns)", math.sqrt(flex.sum_sq(t)/len(t)) * 1000
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(h.slot_centers().as_numpy_array(), h.slots().as_numpy_array(), '-')
vmax = self.params.residuals.plot_max
if self.params.residuals.histogram_xmax is not None:
ax.set_xlim((0,self.params.residuals.histogram_xmax))
if self.params.residuals.histogram_ymax is not None:
ax.set_ylim((0,self.params.residuals.histogram_ymax))
plt.title(title)
ax.plot((mean, mean), (0, flex.max(h.slots())), 'g-')
ax.plot((mean2, mean2), (0, flex.max(h.slots())), 'g--')
ax.plot((mode, mode), (0, flex.max(h.slots())), 'r-')
ax.plot((rmsd, rmsd), (0, flex.max(h.slots())), 'b-')
ax.plot((rmsd2, rmsd2), (0, flex.max(h.slots())), 'b--')
ax.legend([r"$\Delta$XY", "MeanObs", "MeanRayl", "Mode", "RMSDObs", "RMSDRayl"])
ax.set_xlabel("(mm)")
ax.set_ylabel("Count")
def compute_processors(self):
'''
Compute the number of processors
'''
from libtbx.introspection import machine_memory_info
from math import floor
from dials.array_family import flex
# Set the memory usage per processor
if self.params.mp.method == 'multiprocessing' and self.params.mp.nproc > 1:
# Get the maximum shoebox memory
max_memory = flex.max(self.jobs.shoebox_memory(
self.reflections, self.params.shoebox.flatten))
# Compute percentage of max available. The function is not portable to
# windows so need to add a check if the function fails. On windows no
# warning will be printed
memory_info = machine_memory_info()
total_memory = memory_info.memory_total()
if total_memory is not None:
assert total_memory > 0, "Your system appears to have no memory!"
limit_memory = total_memory * self.params.block.max_memory_usage
njobs = int(floor(limit_memory / max_memory))
if njobs < 1:
raise RuntimeError('''
No enough memory to run integration jobs. Possible solutions
include increasing the percentage of memory allowed for shoeboxes or
decreasing the block size.
Total system memory: %g GB
Limit shoebox memory: %g GB
Max shoebox memory: %g GB
''' % (total_memory/1e9, limit_memory/1e9, max_memory/1e9))
else:
self.params.mp.nproc = min(self.params.mp.nproc, njobs)
self.params.block.max_memory_usage /= self.params.mp.nproc
def finalize(self, data, mask):
'''
Finalize the model
:param data: The data array
:param mask: The mask array
'''
from dials.algorithms.image.filter import median_filter, mean_filter
from dials.algorithms.image.fill_holes import diffusion_fill
from dials.algorithms.image.fill_holes import simple_fill
from dials.array_family import flex
# Print some image properties
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Raw image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Transform to polar
logger.info('Transforming image data to polar grid')
result = self.transform.to_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Filter the image to remove noise
if self.kernel_size > 0:
if self.filter_type == 'median':
logger.info('Applying median filter')
data = median_filter(data, mask, (self.kernel_size, 0))
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Median polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
elif self.filter_type == 'mean':
logger.info('Applying mean filter')
mask_as_int = mask.as_1d().as_int()
mask_as_int.reshape(mask.accessor())
data = mean_filter(data, mask_as_int, (self.kernel_size, 0), 1)
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Mean polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
else:
raise RuntimeError('Unknown filter_type: %s' % self.filter_type)
# Fill any remaining holes
logger.info("Filling holes")
data = simple_fill(data, mask)
data = diffusion_fill(data, mask, self.niter)
mask = flex.bool(data.accessor(), True)
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Filled polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Transform back
logger.info('Transforming image data from polar grid')
result = self.transform.from_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Final image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Fill in any discontinuities
# FIXME NEED TO HANDLE DISCONTINUITY
# mask = ~self.transform.discontinuity()[:-1,:-1]
# data = diffusion_fill(data, mask, self.niter)
# Get and apply the mask
mask = self.experiment.imageset.get_mask(0)[0]
mask = mask.as_1d().as_int().as_double()
mask.reshape(data.accessor())
data *= mask
# Return the result
return data
def export_sadabs(integrated_data, experiment_list, hklout, run=0,
summation=False, include_partials=False, keep_partials=False,
debug=False, predict=True):
'''Export data from integrated_data corresponding to experiment_list to a
file for input to SADABS. FIXME probably need to make a .p4p file as
well...'''
from dials.array_family import flex
from scitbx import matrix
import math
# for the moment assume (and assert) that we will convert data from exactly
# one lattice...
assert(len(experiment_list) == 1)
# select reflections that are assigned to an experiment (i.e. non-negative id)
integrated_data = integrated_data.select(integrated_data['id'] >= 0)
assert max(integrated_data['id']) == 0
if not summation:
assert('intensity.prf.value' in integrated_data)
# strip out negative variance reflections: these should not really be there
# FIXME Doing select on summation results. Should do on profile result if
# present? Yes
if 'intensity.prf.variance' in integrated_data:
selection = integrated_data.get_flags(
integrated_data.flags.integrated,
all=True)
else:
selection = integrated_data.get_flags(
integrated_data.flags.integrated_sum)
integrated_data = integrated_data.select(selection)
selection = integrated_data['intensity.sum.variance'] <= 0
if selection.count(True) > 0:
integrated_data.del_selected(selection)
logger.info('Removing %d reflections with negative variance' % \
selection.count(True))
if 'intensity.prf.variance' in integrated_data:
selection = integrated_data['intensity.prf.variance'] <= 0
if selection.count(True) > 0:
integrated_data.del_selected(selection)
logger.info('Removing %d profile reflections with negative variance' % \
selection.count(True))
if include_partials:
integrated_data = sum_partial_reflections(integrated_data)
integrated_data = scale_partial_reflections(integrated_data)
if 'partiality' in integrated_data:
selection = integrated_data['partiality'] < 0.99
if selection.count(True) > 0 and not keep_partials:
integrated_data.del_selected(selection)
logger.info('Removing %d incomplete reflections' % \
selection.count(True))
experiment = experiment_list[0]
assert(not experiment.scan is None)
# sort data before output
nref = len(integrated_data['miller_index'])
indices = flex.size_t_range(nref)
perm = sorted(indices, key=lambda k: integrated_data['miller_index'][k])
integrated_data = integrated_data.select(flex.size_t(perm))
assert (not experiment.goniometer is None)
axis = matrix.col(experiment.goniometer.get_rotation_axis_datum())
beam = matrix.col(experiment.beam.get_direction())
s0 = matrix.col(experiment.beam.get_s0())
F = matrix.sqr(experiment.goniometer.get_fixed_rotation())
S = matrix.sqr(experiment.goniometer.get_setting_rotation())
unit_cell = experiment.crystal.get_unit_cell()
if debug:
m_format = '%6.3f%6.3f%6.3f\n%6.3f%6.3f%6.3f\n%6.3f%6.3f%6.3f'
c_format = '%.2f %.2f %.2f %.2f %.2f %.2f'
logger.info('Unit cell parameters from experiment: %s' % (c_format %
unit_cell.parameters()))
logger.info('Symmetry: %s' % experiment.crystal.get_space_group().type(
).lookup_symbol())
logger.info('Goniometer fixed matrix:\n%s' % (m_format % F.elems))
logger.info('Goniometer setting matrix:\n%s' % (m_format % S.elems))
logger.info('Goniometer scan axis:\n%6.3f%6.3f%6.3f' % (axis.elems))
# detector scaling info
assert(len(experiment.detector) == 1)
panel = experiment.detector[0]
dims = panel.get_image_size()
pixel = panel.get_pixel_size()
fast_axis = matrix.col(panel.get_fast_axis())
slow_axis = matrix.col(panel.get_slow_axis())
normal = fast_axis.cross(slow_axis)
detector2t = s0.angle(normal, deg=True)
origin = matrix.col(panel.get_origin())
if debug:
logger.info('Detector fast, slow axes:')
logger.info('%6.3f%6.3f%6.3f' % (fast_axis.elems))
logger.info('%6.3f%6.3f%6.3f' % (slow_axis.elems))
logger.info('Detector two theta (degrees): %.2f' % detector2t)
scl_x = 512.0 / (dims[0] * pixel[0])
scl_y = 512.0 / (dims[1] * pixel[1])
image_range = experiment.scan.get_image_range()
from cctbx.array_family import flex as cflex # implicit import
from cctbx.miller import map_to_asu_isym # implicit import
# gather the required information for the reflection file
nref = len(integrated_data['miller_index'])
zdet = flex.double(integrated_data['xyzcal.px'].parts()[2])
miller_index = integrated_data['miller_index']
I = None
sigI = None
# export including scale factors
if 'lp' in integrated_data:
lp = integrated_data['lp']
else:
lp = flex.double(nref, 1.0)
if 'dqe' in integrated_data:
dqe = integrated_data['dqe']
else:
dqe = flex.double(nref, 1.0)
scl = lp / dqe
if summation:
I = integrated_data['intensity.sum.value'] * scl
V = integrated_data['intensity.sum.variance'] * scl * scl
assert V.all_gt(0)
sigI = flex.sqrt(V)
else:
I = integrated_data['intensity.prf.value'] * scl
V = integrated_data['intensity.prf.variance'] * scl * scl
assert V.all_gt(0)
sigI = flex.sqrt(V)
# figure out scaling to make sure data fit into format 2F8.2 i.e. Imax < 1e5
Imax = flex.max(I)
if debug:
logger.info('Maximum intensity in file: %8.2f' % Imax)
if Imax > 99999.0:
scale = 99999.0 / Imax
I = I * scale
sigI = sigI * scale
phi_start, phi_range = experiment.scan.get_image_oscillation(image_range[0])
if predict:
logger.info('Using scan static predicted spot locations')
from dials.algorithms.spot_prediction import ScanStaticReflectionPredictor
predictor = ScanStaticReflectionPredictor(experiment)
UB = experiment.crystal.get_A()
predictor.for_reflection_table(integrated_data, UB)
if not experiment.crystal.num_scan_points:
logger.info('No scan varying model: use static')
static = True
else:
static = False
fout = open(hklout, 'w')
for j in range(nref):
h, k, l = miller_index[j]
if predict:
x_mm, y_mm, z_rad = integrated_data['xyzcal.mm'][j]
else:
x_mm, y_mm, z_rad = integrated_data['xyzobs.mm.value'][j]
z0 = integrated_data['xyzcal.px'][j][2]
istol = int(round(10000 * unit_cell.stol((h, k, l))))
if predict or static:
# work from a scan static model & assume perfect goniometer
# FIXME maybe should work back in the option to predict spot positions
UB = experiment.crystal.get_A()
phi = phi_start + z0 * phi_range
R = axis.axis_and_angle_as_r3_rotation_matrix(phi, deg=True)
RUB = S * R * F * UB
else:
# properly compute RUB for every reflection
UB = experiment.crystal.get_A_at_scan_point(int(round(z0)))
phi = phi_start + z0 * phi_range
R = axis.axis_and_angle_as_r3_rotation_matrix(phi, deg=True)
RUB = S * R * F * UB
x = RUB * (h, k, l)
s = (s0 + x).normalize()
# can also compute s based on centre of mass of spot
# s = (origin + x_mm * fast_axis + y_mm * slow_axis).normalize()
astar = (RUB * (1, 0, 0)).normalize()
bstar = (RUB * (0, 1, 0)).normalize()
cstar = (RUB * (0, 0, 1)).normalize()
ix = beam.dot(astar)
iy = beam.dot(bstar)
iz = beam.dot(cstar)
dx = s.dot(astar)
dy = s.dot(bstar)
dz = s.dot(cstar)
x = x_mm * scl_x
y = y_mm * scl_y
z = (z_rad * 180 / math.pi - phi_start) / phi_range
fout.write('%4d%4d%4d%8.2f%8.2f%4d%8.5f%8.5f%8.5f%8.5f%8.5f%8.5f' % \
(h, k, l, I[j], sigI[j], run, ix, dx, iy, dy, iz, dz))
fout.write('%7.2f%7.2f%8.2f%7.2f%5d\n' % (x, y, z, detector2t, istol))
fout.close()
logger.info('Output %d reflections to %s' % (nref, hklout))
return
def paper_test(B, S):
from numpy.random import poisson
from math import exp
background_shape = [1 for i in range(20)]
signal_shape = [1 if i >= 6 and i < 15 else 0 for i in range(20)]
background = [poisson(bb * B,1)[0] for bb in background_shape]
signal = [poisson(ss * S, 1)[0] for ss in signal_shape]
# background = [bb * B for bb in background_shape]
# signal = [ss * S for ss in signal_shape]
total = [b + s for b, s in zip(background, signal)]
# from matplotlib import pylab
# pylab.plot(total)
# pylab.plot(signal)
# pylab.plot(background)
# pylab.show()
total = [0, 1, 0, 0, 0, 0, 3, 1, 3, 3, 6, 6, 4, 1, 4, 0, 2, 0, 1, 1]
total = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
# plot_prob_for_zero(total, background_shape, signal_shape)
# signal_shape = [exp(-(x - 10.0)**2 / (2*3.0**2)) for x in range(20)]
# signal_shape = [ss / sum(signal_shape) for ss in signal_shape]
# print signal_shape
#plot_valid(background_shape, signal_shape)
B = 155.0 / 296.0
from dials.array_family import flex
from math import log, factorial
V = flex.double(flex.grid(100, 100))
L = flex.double(flex.grid(100, 100))
DB = flex.double(flex.grid(100, 100))
DS = flex.double(flex.grid(100, 100))
P = flex.double(flex.grid(100, 100))
Fb = sum(background_shape)
Fs = sum(signal_shape)
SV = []
MASK = flex.bool(flex.grid(100, 100), False)
for BB in range(100):
for SS in range(100):
B = -5.0 + (BB) / 10.0
S = -5.0 + (SS) / 10.0
# SV.append(S)
VV = 0
LL = 0
DDB = 0
DDS = 0
for i in range(20):
s = signal_shape[i]
b = background_shape[i]
c = total[i]
if B*b + S*s <= 0:
# MASK[BB, SS] = True
LL = 0
if b == 0:
DDB += 0
else:
DDB += 1e7
if s == 0:
DDS += 0
else:
DDS += 1e7
# break
else:
# VV += (b + s)*c / (B*b + S*s)
# LL += c*log(B*b+S*s) - log(factorial(c)) - B*b - S*s
DDB += c*b/(B*b+S*s) - b
DDS += c*s/(B*b+S*s) - s
VV -= (Fb + Fs)
# print B, S, VV
# V[BB, SS] = abs(VV)
L[BB, SS] = LL
DB[BB,SS] = DDB
DS[BB,SS] = DDS
max_ind = flex.max_index(L)
j = max_ind // 100
i = max_ind % 100
print "Approx: ", (j+1) / 20.0, (i+1) / 20.0
print "Min/Max DB: ", flex.min(DB), flex.max(DB)
print "Min/Max DS: ", flex.min(DS), flex.max(DS)
from matplotlib import pylab
# pylab.imshow(flex.log(V).as_numpy_array(), extent=[0.05, 5.05, 5.05, 0.05])
# pylab.plot(SV, V)
# pylab.plot(SV, [0] * 100)
# pylab.show()
im = flex.exp(L).as_numpy_array()
import numpy
# im = numpy.ma.masked_array(im, mask=MASK.as_numpy_array())
# pylab.imshow(im)#, extent=[-5.0, 5.0, 5.0, -5.0], origin='lower')
pylab.imshow(DB.as_numpy_array(), vmin=-100, vmax=100)#, extent=[-5.0, 5.0, 5.0, -5.0], origin='lower')
pylab.contour(DB.as_numpy_array(), levels=[0], colors=['red'])
pylab.contour(DS.as_numpy_array(), levels=[0], colors=['black'])
pylab.show()
# im = numpy.ma.masked_array(DB.as_numpy_array(), mask=MASK.as_numpy_array())
# pylab.imshow(im, extent=[-5.0, 5.0, 5.0, -5.0], vmin=-20, vmax=100)
# pylab.show()
# im = numpy.ma.masked_array(DS.as_numpy_array(), mask=MASK.as_numpy_array())
# pylab.imshow(im, extent=[-5.0, 5.0, 5.0, -5.0], vmin=-20, vmax=100)
# pylab.show()
# exit(0)
S1, B1 = value(total, background_shape, signal_shape)
exit(0)
try:
S1, B1 = value(total, background_shape, signal_shape)
print "Result:"
print S1, B1
exit(0)
except Exception, e:
raise e
import sys
import traceback
traceback.print_exc()
from dials.array_family import flex
Fs = sum(signal_shape)
Fb = sum(background_shape)
Rs = flex.double(flex.grid(100, 100))
print "-----"
print B, S
# from matplotlib import pylab
# pylab.plot(total)
# pylab.show()
print background_shape
print signal_shape
print total
from math import exp, factorial, log
minx = -1
miny = -1
minr = 9999
for BB in range(0, 100):
for SS in range(0, 100):
B = -10 + (BB) / 5.0
S = -10 + (SS) / 5.0
L = 0
Fb2 = 0
Fs2 = 0
for i in range(len(total)):
c = total[i]
b = background_shape[i]
s = signal_shape[i]
# P = exp(-(B*b + S*s)) * (B*b+S*s)**c / factorial(c)
# print P
# if P > 0:
# L += log(P)
den = B*b + S*s
num1 = b*c
num2 = s*c
if den != 0:
Fb2 += num1 / den
Fs2 += num2 / den
R = (Fb2 - Fb)**2 + (Fs2 - Fs)**2
if R > 1000:
R = 0
# Rs[BB,SS] = L#R#Fs2 - Fs
Rs[BB,SS] = R#Fs2 - Fs
from matplotlib import pylab
pylab.imshow(flex.log(Rs).as_numpy_array(), extent=[-5,5,5,-5])
pylab.show()
exit(0)
def run_stills_pred_param(self, verbose = False):
if verbose:
print 'Testing derivatives for StillsPredictionParameterisation'
print '========================================================'
# Build a prediction parameterisation for the stills experiment
pred_param = StillsPredictionParameterisation(self.stills_experiments,
detector_parameterisations = [self.det_param],
beam_parameterisations = [self.s0_param],
xl_orientation_parameterisations = [self.xlo_param],
xl_unit_cell_parameterisations = [self.xluc_param])
# Predict the reflections in place. Must do this ahead of calculating
# the analytical gradients so quantities like s1 are correct
from dials.algorithms.refinement.prediction import ExperimentsPredictor
ref_predictor = ExperimentsPredictor(self.stills_experiments)
ref_predictor.update()
ref_predictor.predict(self.reflections)
# get analytical gradients
an_grads = pred_param.get_gradients(self.reflections)
fd_grads = self.get_fd_gradients(pred_param, ref_predictor)
for i, (an_grad, fd_grad) in enumerate(zip(an_grads, fd_grads)):
# compare FD with analytical calculations
if verbose: print "\nParameter {0}: {1}". format(i, fd_grad['name'])
for idx, name in enumerate(["dX_dp", "dY_dp", "dDeltaPsi_dp"]):
if verbose: print name
a = fd_grad[name]
b = an_grad[name]
abs_error = a - b
denom = a + b
fns = five_number_summary(abs_error)
if verbose: print (" summary of absolute errors: %9.6f %9.6f %9.6f " + \
"%9.6f %9.6f") % fns
assert flex.max(flex.abs(abs_error)) < 0.0003
# largest absolute error found to be about 0.00025 for dY/dp of
# Crystal0g_param_3. Reject outlying absolute errors and test again.
iqr = fns[3] - fns[1]
# skip further stats on errors with an iqr of near zero, e.g. dDeltaPsi_dp
# for detector parameters, which are all equal to zero
if iqr < 1.e-10:
continue
sel1 = abs_error < fns[3] + 1.5 * iqr
sel2 = abs_error > fns[1] - 1.5 * iqr
sel = sel1 & sel2
tst = flex.max_index(flex.abs(abs_error.select(sel)))
tst_val = abs_error.select(sel)[tst]
n_outliers = sel.count(False)
if verbose: print (" {0} outliers rejected, leaving greatest " + \
"absolute error: {1:9.6f}").format(n_outliers, tst_val)
# largest absolute error now 0.000086 for dX/dp of Beam0Mu2
assert abs(tst_val) < 0.00009
# Completely skip parameters with FD gradients all zero (e.g. gradients of
# DeltaPsi for detector parameters)
sel1 = flex.abs(a) < 1.e-10
if sel1.all_eq(True):
continue
# otherwise calculate normalised errors, by dividing absolute errors by
# the IQR (more stable than relative error calculation)
norm_error = abs_error / iqr
fns = five_number_summary(norm_error)
if verbose: print (" summary of normalised errors: %9.6f %9.6f %9.6f " + \
"%9.6f %9.6f") % fns
# largest normalised error found to be about 25.7 for dY/dp of
# Crystal0g_param_3.
try:
assert flex.max(flex.abs(norm_error)) < 30
except AssertionError as e:
e.args += ("extreme normalised error value: {0}".format(
flex.max(flex.abs(norm_error))),)
raise e
# Reject outlying normalised errors and test again
iqr = fns[3] - fns[1]
if iqr > 0.:
sel1 = norm_error < fns[3] + 1.5 * iqr
sel2 = norm_error > fns[1] - 1.5 * iqr
sel = sel1 & sel2
tst = flex.max_index(flex.abs(norm_error.select(sel)))
tst_val = norm_error.select(sel)[tst]
n_outliers = sel.count(False)
# most outliers found for for dY/dp of Crystal0g_param_3 (which had
# largest errors, so no surprise there).
try:
assert n_outliers < 250
except AssertionError as e:
e.args += ("too many outliers rejected: {0}".format(n_outliers),)
raise e
if verbose: print (" {0} outliers rejected, leaving greatest " + \
"normalised error: {1:9.6f}").format(n_outliers, tst_val)
# largest normalied error now about -4. for dX/dp of Detector0Tau1
assert abs(tst_val) < 4.5
if verbose: print
return
def compose(self, reflections, skip_derivatives=False):
"""Compose scan-varying crystal parameterisations at the specified image
number, for the specified experiment, for each image. Put the U, B and
UB matrices in the reflection table, and cache the derivatives."""
self._prepare_for_compose(reflections, skip_derivatives)
for iexp, exp in enumerate(self._experiments):
# select the reflections of interest
sel = reflections['id'] == iexp
isel = sel.iselection()
blocks = reflections['block'].select(isel)
# identify which parameterisations to use for this experiment
xl_op = self._get_xl_orientation_parameterisation(iexp)
xl_ucp = self._get_xl_unit_cell_parameterisation(iexp)
bp = self._get_beam_parameterisation(iexp)
dp = self._get_detector_parameterisation(iexp)
# reset current frame cache for scan-varying parameterisations
self._current_frame = {}
# get state and derivatives for each block
for block in xrange(flex.min(blocks),
flex.max(blocks) + 1):
# determine the subset of reflections this affects
subsel = isel.select(blocks == block)
if len(subsel) == 0: continue
# get the panels hit by these reflections
panels = reflections['panel'].select(subsel)
# get the integer frame number nearest the centre of that block
frames = reflections['block_centre'].select(subsel)
# can only be false if original block assignment has gone wrong
assert frames.all_eq(frames[0]), \
"Failing: a block contains reflections that shouldn't be there"
frame = int(floor(frames[0]))
# model states at current frame
U = self._get_state_from_parameterisation(xl_op, frame)
if U is None: U = exp.crystal.get_U()
B = self._get_state_from_parameterisation(xl_ucp, frame)
if B is None: B = exp.crystal.get_B()
s0 = self._get_state_from_parameterisation(bp, frame)
if s0 is None: s0 = exp.beam.get_s0()
# set states for crystal and beam
reflections['u_matrix'].set_selected(subsel, U.elems)
reflections['b_matrix'].set_selected(subsel, B.elems)
reflections['s0_vector'].set_selected(subsel, s0.elems)
# set states and derivatives for multi-panel detector
if dp is not None and dp.is_multi_state():
# loop through the panels in this detector
for panel_id, _ in enumerate(exp.detector):
# get the right subset of array indices to set for this panel
subsel2 = subsel.select(panels == panel_id)
if len(subsel2) == 0:
# if no reflections intersect this panel, skip calculation
continue
dmat = self._get_state_from_parameterisation(dp,
frame, multi_state_elt=panel_id)
if dmat is None: dmat = exp.detector[panel_id].get_d_matrix()
Dmat = exp.detector[panel_id].get_D_matrix()
reflections['d_matrix'].set_selected(subsel2, dmat)
reflections['D_matrix'].set_selected(subsel2, Dmat)
if dp is not None and self._varying_detectors and not skip_derivatives:
for j, dd in enumerate(dp.get_ds_dp(multi_state_elt=panel_id,
use_none_as_null=True)):
if dd is None: continue
colname = "dd_dp{0}".format(j)
reflections[colname].set_selected(subsel, dd)
else: # set states and derivatives for single panel detector
dmat = self._get_state_from_parameterisation(dp, frame)
if dmat is None: dmat = exp.detector[0].get_d_matrix()
Dmat = exp.detector[0].get_D_matrix()
reflections['d_matrix'].set_selected(subsel, dmat)
reflections['D_matrix'].set_selected(subsel, Dmat)
if dp is not None and self._varying_detectors and not skip_derivatives:
for j, dd in enumerate(dp.get_ds_dp(use_none_as_null=True)):
if dd is None: continue
colname = "dd_dp{0}".format(j)
reflections[colname].set_selected(subsel, dd)
# set derivatives of the states for crystal and beam
if not skip_derivatives:
if xl_op is not None and self._varying_xl_orientations:
for j, dU in enumerate(xl_op.get_ds_dp(use_none_as_null=True)):
if dU is None: continue
colname = "dU_dp{0}".format(j)
reflections[colname].set_selected(subsel, dU)
if xl_ucp is not None and self._varying_xl_unit_cells:
for j, dB in enumerate(xl_ucp.get_ds_dp(use_none_as_null=True)):
if dB is None: continue
colname = "dB_dp{0}".format(j)
reflections[colname].set_selected(subsel, dB)
if bp is not None and self._varying_beams:
for j, ds0 in enumerate(bp.get_ds_dp(use_none_as_null=True)):
if ds0 is None: continue
colname = "ds0_dp{0}".format(j)
reflections[colname].set_selected(subsel, ds0)
# set the UB matrices for prediction
reflections['ub_matrix'] = reflections['u_matrix'] * reflections['b_matrix']
return
def spot_counts_per_image_plot(reflections, char='*', width=60, height=10):
from dials.array_family import flex
if len(reflections) == 0:
return '\n'
assert isinstance(char, basestring)
assert len(char) == 1
x,y,z = reflections['xyzobs.px.value'].parts()
min_z = flex.min(z)
max_z = flex.max(z)
# image numbers to display on x-axis label
xlab = (int(round(min_z + 0.5)), int(round(max_z + 0.5)))
# estimate the total number of images
image_count = xlab[1] - xlab[0] + 1
z_range = max_z - min_z + 1
if z_range <= 1:
return '%i spots found on 1 image' %len(reflections)
width = int(min(z_range, width))
z_step = z_range / width
z_bound = min_z + z_step - 0.5
# print [round(i * 10) / 10 for i in sorted(z)]
counts = flex.double()
sel = (z < z_bound)
counts.append(sel.count(True))
# print 0, ('-', z_bound), sel.count(True)
for i in range(1, width-1):
sel = ((z >= z_bound) & (z < (z_bound + z_step)))
counts.append(sel.count(True))
# print i, (z_bound, z_bound + z_step), sel.count(True)
z_bound += z_step
sel = (z >= z_bound)
# print i + 1, (z_bound, '-'), sel.count(True)
counts.append(sel.count(True))
max_count = flex.max(counts)
total_counts = flex.sum(counts)
assert total_counts == len(z)
counts *= (height/max_count)
counts = counts.iround()
rows = []
rows.append('%i spots found on %i images (max %i / bin)' %(
total_counts, image_count, max_count))
for i in range(height, 0, -1):
row = []
for j, c in enumerate(counts):
if c > (i - 1):
row.append(char)
else:
row.append(' ')
rows.append(''.join(row))
padding = width - len(str(xlab[0])) - len(str(xlab[1]))
rows.append('%i%s%i' % (xlab[0],
(' ' if padding < 7 else 'image').center(padding),
xlab[1]))
return '\n'.join(rows)
#fig = pylab.figure(dpi=300)
#pylab.imshow(scale_data.as_numpy_array(), vmin=0, vmax=2, interpolation='none')
#pylab.colorbar()
#pylab.savefig("scale_%d.png" % frame)
#pylab.clf()
##pylab.show()
#exit(0)
#pylab.hist(scale_data.as_1d().select(scale_mask.as_1d()).as_numpy_array(),
# bins=100)
#pylab.show()
sd1 = scale_data.as_1d()
sm1 = scale_mask.as_1d()
scale_min = flex.min(sd1.select(sm1))
scale_max = flex.max(sd1.select(sm1))
scale_avr = flex.sum(sd1.select(sm1)) / sm1.count(True)
background = model_data * scale_data
reflections['shoebox'].select(indices).apply_pixel_data(
data.as_double(),
background,
raw_mask,
frame,
1)
subset = reflections.select(indices3)
if len(subset) > 0:
subset.compute_summed_intensity()
subset.compute_centroid(experiments)
# counts along pixels (and allow for DQE i.e. photon passing right through
# the detector)
patch[(y, x)] += 1.0 * iw / scale
cc = profile_correlation(data, patch)
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
print 'Correlation coefficient: %.3f isum: %.1f ' % (cc, i0)
import numpy as np
maxx = flex.max(all_pix.parts()[0])
maxy = flex.max(all_pix.parts()[1])
minx = flex.min(all_pix.parts()[0])
miny = flex.min(all_pix.parts()[1])
dx = (maxx-minx)/2
dy = (maxy-miny)/2
medx = minx + (dx)
medy = miny + (dy)
if maxx-minx > maxy-miny:
miny = medy-dx
maxy = medy+dx
else:
minx = medx-dy
maxx = medx+dy
limits = [[minx, maxx], [miny, maxy]]