def run(self):
''' Parse the options. '''
# Parse the command line arguments
params, options = self.parser.parse_args(show_diff_phil=True)
self.params = params
total = 0
for ewrap, rwrap in zip(params.input.experiments,
params.input.reflections):
experiments = ewrap.data
reflections = rwrap.data
if params.repredict_input_reflections:
from dials.algorithms.refinement.prediction.managed_predictors import ExperimentsPredictorFactory
ref_predictor = ExperimentsPredictorFactory.from_experiments(
experiments, force_stills=experiments.all_stills())
reflections = ref_predictor(reflections)
reflections = setup_stats(experiments, reflections)
for expt_id, expt in enumerate(experiments):
refls = reflections.select(reflections['id'] == expt_id)
if len(refls) == 0: continue
trumpet_plot(expt, refls)
total += 1
if params.max_plots and total >= params.max_plots:
break
if params.max_plots and total >= params.max_plots:
break
plt.show()
def plotit(reflections, experiments):
"""
Make the plots for a set of reflections and experiments.
"""
detector = experiments.detectors()[0]
beam = experiments.beams()[
0] # only used to compute resolution of 2theta
reflections = reflections.select(
reflections['intensity.sum.variance'] > 0)
# Setup up deltaXY and two theta bins
reflections['difference_vector_norms'] = (
reflections['xyzcal.mm'] -
reflections['xyzobs.mm.value']).norms()
reflections = setup_stats(
detector, experiments, reflections,
two_theta_only=True) # add two theta to reflection table
sorted_two_theta = flex.sorted(reflections['two_theta_obs'])
bin_low = [
sorted_two_theta[int((len(sorted_two_theta) / n_bins) * i)]
for i in range(n_bins)
]
bin_high = [bin_low[i + 1] for i in range(n_bins - 1)]
bin_high.append(sorted_two_theta[-1] + arbitrary_padding)
x_centers = flex.double()
n_refls = flex.int()
rmsds = flex.double()
p25r = flex.double()
p50r = flex.double()
p75r = flex.double()
p25i = flex.double()
p50i = flex.double()
p75i = flex.double()
print("# 2theta Res N dXY IsigI")
# Compute stats for each bin
for i in range(n_bins):
refls = reflections.select(
(reflections['two_theta_obs'] >= bin_low[i])
& (reflections['two_theta_obs'] < bin_high[i]))
# Only compute deltaXY stats on reflections with I/sigI at least 5
i_sigi = refls['intensity.sum.value'] / flex.sqrt(
refls['intensity.sum.variance'])
refls = refls.select(i_sigi >= 5)
n = len(refls)
if n < 10: continue
min_r, q1_r, med_r, q3_r, max_r = five_number_summary(
1000 * refls['difference_vector_norms'])
n_refls.append(n)
rmsds_ = 1000 * math.sqrt(
flex.sum_sq(refls['difference_vector_norms']) / n)
min_i, q1_i, med_i, q3_i, max_i = five_number_summary(i_sigi)
p25i.append(q1_i)
p50i.append(med_i)
p75i.append(q3_i)
# x_center
c = ((bin_high[i] - bin_low[i]) / 2) + bin_low[i]
# resolution
d = beam.get_wavelength() / (2 * math.sin(math.pi * c /
(2 * 180)))
x_centers.append(c)
rmsds.append(rmsds_)
print("%d % 5.1f % 5.1f % 8d %.1f %.1f" %
(i, c, d, n, med_r, med_i))
p25r.append(q1_r)
p50r.append(med_r)
p75r.append(q3_r)
# After binning, plot the results
for plot in figures:
ax1 = figures[plot]['ax1']
ax2 = figures[plot]['ax2']
if plot == 'isigi':
line, = ax1.plot(x_centers.as_numpy_array(),
p50i.as_numpy_array(), '-')
line.set_label('Median')
ax1.fill_between(x_centers.as_numpy_array(),
p25i.as_numpy_array(),
p75i.as_numpy_array(),
interpolate=True,
alpha=0.50,
color=line.get_color())
line, = ax2.plot(x_centers.as_numpy_array(),
n_refls.as_numpy_array(),
'-',
color=line.get_color())
line.set_label('Median')
elif plot == 'deltaXY':
line, = ax1.plot(x_centers.as_numpy_array(),
p50r.as_numpy_array(), '-')
line.set_label('Median')
ax1.fill_between(x_centers.as_numpy_array(),
p25r.as_numpy_array(),
p75r.as_numpy_array(),
interpolate=True,
alpha=0.50,
color=line.get_color())
line, = ax2.plot(x_centers.as_numpy_array(),
n_refls.as_numpy_array(),
'-',
color=line.get_color())
line.set_label('Median')
ax1.legend()
ax2.legend()