def _report(self, experiments, reflections):
# type: (ExperimentList, flex.reflection_table) -> None
"""
Run `dials.report` on an experiment list and reflection table.
Args:
experiments: An experiment list.
reflections: The corresponding reflection table.
"""
info("\nCreating report...")
command = ["dials.report", experiments, reflections]
result = procrunner.run(command,
print_stdout=False,
debug=procrunner_debug)
debug("result = %s", screen19.prettyprint_dictionary(result))
if result["exitcode"] == 0:
info("Successfully completed (%.1f sec)", result["runtime"])
# if sys.stdout.isatty():
# info("Trying to start browser")
# try:
# import subprocess
# d = dict(os.environ)
# d["LD_LIBRARY_PATH"] = ""
# subprocess.Popen(["xdg-open", "dials-report.html"], env=d)
# except Exception as e:
# debug("Could not open browser\n%s", str(e))
else:
warning("Failed with exit code %d", result["exitcode"])
sys.exit(1)
def _refine_bravais(self, experiments, reflections):
# type: (ExperimentList, flex.reflection_table) -> None
"""
Run `dials.refine_bravais_settings` on an experiments and reflections.
Args:
experiments: An experiment list..
reflections: The corresponding reflection table.
"""
info("\nRefining Bravais settings...")
command = [
"dials.refine_bravais_settings", experiments, reflections
]
result = procrunner.run(command,
print_stdout=False,
debug=procrunner_debug)
debug("result = %s", screen19.prettyprint_dictionary(result))
if result["exitcode"] == 0:
m = re.search(
r"[-+]{3,}\n[^\n]*\n[-+|]{3,}\n(.*\n)*[-+]{3,}",
result["stdout"].decode("utf-8"),
)
if m:
info(m.group(0))
else:
info(
"Could not interpret dials.refine_bravais_settings output, "
"please check dials.refine_bravais_settings.log")
info("Successfully completed (%.1f sec)", result["runtime"])
else:
warning("Failed with exit code %d", result["exitcode"])
sys.exit(1)
def _create_profile_model(self): # type: () -> bool
"""
Run `dials.create_profile_model` on indexed reflections.
The indexed experiment list will be overwritten with a copy that includes
the profile model but is otherwise identical.
Returns:
Boolean value indicating whether it was possible to determine a profile
model from the data.
"""
info("\nCreating profile model...")
command = [
"dials.create_profile_model",
self.params.dials_index.output.experiments,
self.params.dials_index.output.reflections,
"output = %s" % self.params.dials_index.output.experiments,
]
result = procrunner.run(command,
print_stdout=False,
debug=procrunner_debug)
debug("result = %s", screen19.prettyprint_dictionary(result))
self._sigma_m = None
if result["exitcode"] == 0:
db = ExperimentList.from_file(
self.params.dials_index.output.experiments)[0]
self._oscillation = db.imageset.get_scan().get_oscillation()[1]
self._sigma_m = db.profile.sigma_m()
info(
u"%d images, %s° oscillation, σ_m=%.3f°",
db.imageset.get_scan().get_num_images(),
str(self._oscillation),
self._sigma_m,
)
info("Successfully completed (%.1f sec)", result["runtime"])
return True
warning("Failed with exit code %d", result["exitcode"])
return False
def _check_intensities(self,
mosaicity_correction=True): # type: (bool) -> None
"""
Run xia2.overload and plot a histogram of pixel intensities.
If `mosaicity_correction` is true, the pixel intensities are approximately
adjusted to take account of a systematic defect in the detector count rate
correction. See https://github.com/xia2/screen19/wiki#mosaicity-correction
Args:
mosaicity_correction (optional): default is `True`.
"""
info("\nTesting pixel intensities...")
command = ["xia2.overload", "nproc=%s" % self.nproc, "indexed.expt"]
debug("running %s", command)
result = procrunner.run(command,
print_stdout=False,
debug=procrunner_debug)
debug("result = %s", screen19.prettyprint_dictionary(result))
info("Successfully completed (%.1f sec)", result["runtime"])
if result["exitcode"] != 0:
warning("Failed with exit code %d", result["exitcode"])
sys.exit(1)
with open("overload.json") as fh:
overload_data = json.load(fh)
info("Pixel intensity distribution:")
count_sum = 0
hist = {}
if "bins" in overload_data:
for b in range(overload_data["bin_count"]):
if overload_data["bins"][b] > 0:
hist[b] = overload_data["bins"][b]
count_sum += b * overload_data["bins"][b]
else:
hist = {
int(k): v
for k, v in overload_data["counts"].items() if int(k) > 0
}
count_sum = sum([k * v for k, v in hist.items()])
average_to_peak = 1
if mosaicity_correction:
# Adjust for the detector count rate correction
if self._sigma_m:
delta_z = self._oscillation / self._sigma_m / math.sqrt(2)
average_to_peak = (
math.sqrt(math.pi) * delta_z * math.erf(delta_z) +
math.exp(-(delta_z**2)) - 1) / delta_z**2
info("Average-to-peak intensity ratio: %f", average_to_peak)
scale = 100 * overload_data["scale_factor"] / average_to_peak
info("Determined scale factor for intensities as %f", scale)
debug(
"intensity histogram: { %s }",
", ".join(["%d:%d" % (k, hist[k]) for k in sorted(hist)]),
)
max_count = max(hist.keys())
hist_max = max_count * scale
hist_granularity, hist_format = 1, "%.0f"
if hist_max < 50:
hist_granularity, hist_format = 2, "%.1f"
if hist_max < 15:
hist_granularity, hist_format = 10, "%.1f"
rescaled_hist = {}
for x in hist.keys():
rescaled = round(x * scale * hist_granularity)
if rescaled > 0:
rescaled_hist[rescaled] = hist[x] + rescaled_hist.get(
rescaled, 0)
hist = rescaled_hist
debug(
"rescaled histogram: { %s }",
", ".join([(hist_format + ":%d") % (k / hist_granularity, hist[k])
for k in sorted(hist)]),
)
screen19.plot_intensities(hist,
1 / hist_granularity,
procrunner_debug=procrunner_debug)
linear_response_limit = 100 * self.params.maximum_flux.trusted_range_correction
marginal_limit = max(70, linear_response_limit)
text = "".join((
"Strongest pixel (%d counts) " % max_count,
"reaches %.1f%% " % hist_max,
"of the detector count rate limit",
))
if hist_max > 100:
warning("Warning: %s!", text)
else:
info(text)
if ("overload_limit" in overload_data
and max_count >= overload_data["overload_limit"]):
warning(
"Warning: THE DATA CONTAIN REGULAR OVERLOADS!\n"
" The photon incidence rate is outside the specified "
"limits of the detector.\n"
" The built-in detector count rate correction cannot "
"adjust for this.\n"
" You should aim for count rates below {:.0%} of the "
"detector limit.".format(
self.params.maximum_flux.trusted_range_correction))
elif hist_max > marginal_limit:
warning(
"Warning: The photon incidence rate is well outside the "
"linear response region of the detector (<{:.0%}).\n"
" The built-in detector count rate correction may not be "
"able to adjust for this.".format(
self.params.maximum_flux.trusted_range_correction))
elif hist_max > linear_response_limit:
info("The photon incidence rate is outside the linear response "
"region of the detector (<{:.0%}).\n"
" The built-in detector count rate correction may be able "
"to adjust for this.".format(
self.params.maximum_flux.trusted_range_correction))
if not mosaicity_correction:
warning(
"Warning: Not enough data for proper profile estimation."
" The spot intensities are not corrected for mosaicity.\n"
" The true photon incidence rate will be higher than the "
"given estimate.")
info("Total sum of counts in dataset: %d", count_sum)