def user_callback(self,dc,panel,wx):
center_x, center_y = panel._img.get_beam_center()
xc, yc = panel._img.image_coords_as_screen_coords(center_x, center_y)
dc.SetPen(wx.Pen('red'))
dc.SetBrush(wx.TRANSPARENT_BRUSH)
wavelength = panel._img._raw.wavelength #should be this
wavelength_from_avg_file = False
if wavelength_from_avg_file:
# go through hoops to get the proper wavelength corresponding to this run
avepath = self.path.replace("stddev","avg")
import pickle
info = pickle.load(open(avepath,"rb"))
wavelength = info["WAVELENGTH"]
twotheta = 2.* flex.asin(
flex.double(len(self.two_theta_experimental), wavelength/2.)/
self.experimental_d)
L_mm = panel.settings.distance * flex.tan(twotheta)
L_pixels = L_mm / panel._img._raw.pixel_size
[ dc.DrawCircle(xc, yc, panel._img.get_scale() * pxl) for pxl in L_pixels ]
def user_callback(self, dc, panel, wx):
center_x, center_y = panel._img.get_beam_center()
xc, yc = panel._img.image_coords_as_screen_coords(center_x, center_y)
dc.SetPen(wx.Pen('red'))
dc.SetBrush(wx.TRANSPARENT_BRUSH)
wavelength = panel._img._raw.wavelength #should be this
wavelength_from_avg_file = False
if wavelength_from_avg_file:
# go through hoops to get the proper wavelength corresponding to this run
avepath = self.path.replace("stddev", "avg")
info = pickle.load(open(avepath, "rb"))
wavelength = info["WAVELENGTH"]
twotheta = 2. * flex.asin(
flex.double(len(self.two_theta_experimental), wavelength / 2.) /
self.experimental_d)
L_mm = panel.settings.distance * flex.tan(twotheta)
L_pixels = L_mm / panel._img._raw.pixel_size
[
dc.DrawCircle(xc, yc,
panel._img.get_scale() * pxl) for pxl in L_pixels
]
def run(args, imageset=None):
# Parse input
try:
len(args)
except Exception:
params = args
else:
user_phil = []
for arg in args:
if "=" in arg:
try:
user_phil.append(libtbx.phil.parse(arg))
except RuntimeError as e:
raise Sorry("Unrecognized argument '%s' (error: %s)" %
(arg, str(e)))
else:
try:
user_phil.append(
libtbx.phil.parse("""file_path=%s""" % arg))
except ValueError:
raise Sorry("Unrecognized argument '%s'" % arg)
params = master_phil.fetch(sources=user_phil).extract()
if imageset is None:
if (params.file_path is None or len(params.file_path) == 0
or not all(os.path.isfile(f) for f in params.file_path)):
master_phil.show()
raise Usage(
"file_path must be defined (either file_path=XXX, or the path alone)."
)
assert params.n_bins is not None
assert params.verbose is not None
assert params.output_bins is not None
# Allow writing to a file instead of stdout
if params.output_file is None:
logger = sys.stdout
else:
logger = open(params.output_file, "w")
logger.write("%s " % params.output_file)
if params.show_plots:
from matplotlib import pyplot as plt
colormap = plt.cm.gist_ncar
plt.gca().set_color_cycle(
[colormap(i) for i in np.linspace(0, 0.9, len(params.file_path))])
if params.mask is not None:
params.mask = easy_pickle.load(params.mask)
if imageset is None:
iterable = params.file_path
def load_func(x):
try:
obj = dxtbx.datablock.DataBlockFactory.from_filenames(
[x])[0].extract_imagesets()[0]
except IndexError:
try:
obj = dxtbx.datablock.DataBlockFactory.from_json_file(
x)[0].extract_imagesets()[0]
except dxtbx.datablock.InvalidDataBlockError:
obj = ExperimentListFactory.from_json_file(x)[0].imageset
return obj
else:
iterable = [imageset]
def load_func(x):
return x
# Iterate over each file provided
for item in iterable:
iset = load_func(item)
n_images = len(iset)
if params.image_number is None:
if params.max_images is None:
subiterable = range(n_images)
else:
subiterable = range(0, min(params.max_images, n_images))
else:
subiterable = [params.image_number]
for image_number in subiterable:
beam = iset.get_beam(image_number)
detector = iset.get_detector(image_number)
s0 = col(beam.get_s0())
# Search the detector for the panel farthest from the beam. The
# number of bins in the radial average will be equal to the
# farthest point from the beam on the detector, in pixels, unless
# overridden at the command line
panel_res = [p.get_max_resolution_at_corners(s0) for p in detector]
farthest_panel = detector[panel_res.index(min(panel_res))]
size2, size1 = farthest_panel.get_image_size()
corners = [(0, 0), (size1 - 1, 0), (0, size2 - 1),
(size1 - 1, size2 - 1)]
corners_lab = [
col(farthest_panel.get_pixel_lab_coord(c)) for c in corners
]
corner_two_thetas = [
farthest_panel.get_two_theta_at_pixel(s0, c) for c in corners
]
extent_two_theta = max(corner_two_thetas)
max_corner = corners_lab[corner_two_thetas.index(extent_two_theta)]
extent = int(
math.ceil(max_corner.length() * math.sin(extent_two_theta) /
max(farthest_panel.get_pixel_size())))
extent_two_theta *= 180 / math.pi
if params.n_bins < extent:
params.n_bins = extent
# These arrays will store the radial average info
sums = flex.double(params.n_bins) * 0
sums_sq = flex.double(params.n_bins) * 0
counts = flex.int(params.n_bins) * 0
all_data = iset[image_number]
if not isinstance(all_data, tuple):
all_data = (all_data, )
for tile, (panel, data) in enumerate(zip(detector, all_data)):
if params.panel is not None and tile != params.panel:
continue
if params.mask is None:
mask = flex.bool(flex.grid(data.focus()), True)
else:
mask = params.mask[tile]
if hasattr(data, "as_double"):
data = data.as_double()
logger.flush()
if params.verbose:
logger.write("Average intensity tile %d: %9.3f\n" %
(tile, flex.mean(data)))
logger.write("N bins: %d\n" % params.n_bins)
logger.flush()
x1, y1, x2, y2 = (
0,
0,
panel.get_image_size()[1],
panel.get_image_size()[0],
)
bc = panel.get_beam_centre_px(beam.get_s0())
bc = int(round(bc[1])), int(round(bc[0]))
# compute the average
radial_average(
data,
mask,
bc,
sums,
sums_sq,
counts,
panel.get_pixel_size()[0],
panel.get_distance(),
(x1, y1),
(x2, y2),
)
# average the results, avoiding division by zero
results = sums.set_selected(counts <= 0, 0)
results /= counts.set_selected(counts <= 0, 1).as_double()
if params.median_filter_size is not None:
logger.write(
"WARNING, the median filter is not fully propagated to the variances\n"
)
from scipy.ndimage.filters import median_filter
results = flex.double(
median_filter(results.as_numpy_array(),
size=params.median_filter_size))
# calculate standard devations
stddev_sel = ((sums_sq - sums * results) >= 0) & (counts > 0)
std_devs = flex.double(len(sums), 0)
std_devs.set_selected(
stddev_sel,
(sums_sq.select(stddev_sel) -
sums.select(stddev_sel) * results.select(stddev_sel)) /
counts.select(stddev_sel).as_double(),
)
std_devs = flex.sqrt(std_devs)
twotheta = (flex.double(range(len(results))) * extent_two_theta /
params.n_bins)
q_vals = (4 * math.pi * flex.sin(math.pi * twotheta / 360) /
beam.get_wavelength())
# nlmbda = 2dsin(theta)
resolution = flex.double(len(twotheta), 0)
nonzero = twotheta > 0
resolution.set_selected(
nonzero,
beam.get_wavelength() / (2 * flex.asin(
(math.pi / 180) * twotheta.select(nonzero) / 2)),
)
if params.low_max_two_theta_limit is None:
subset = results
else:
subset = results.select(
twotheta >= params.low_max_two_theta_limit)
max_result = flex.max(subset)
if params.x_axis == "two_theta":
xvals = twotheta
max_x = twotheta[flex.first_index(results, max_result)]
elif params.x_axis == "q":
xvals = q_vals
max_x = q_vals[flex.first_index(results, max_result)]
elif params.x_axis == "resolution":
xvals = resolution
max_x = resolution[flex.first_index(results, max_result)]
for i, r in enumerate(results):
val = xvals[i]
if params.output_bins and "%.3f" % r != "nan":
# logger.write("%9.3f %9.3f\n"% (val,r)) #.xy format for Rex.cell.
logger.write(
"%9.3f %9.3f %9.3f\n" %
(val, r, std_devs[i])) # .xye format for GSASII
# logger.write("%.3f %.3f %.3f\n"%(val,r,ds[i])) # include calculated d spacings
logger.write("Maximum %s: %f, value: %f\n" %
(params.x_axis, max_x, max_result))
if params.show_plots:
if params.plot_x_max is not None:
results = results.select(xvals <= params.plot_x_max)
xvals = xvals.select(xvals <= params.plot_x_max)
if params.normalize:
plt.plot(
xvals.as_numpy_array(),
(results / flex.max(results)).as_numpy_array(),
"-",
)
else:
plt.plot(xvals.as_numpy_array(), results.as_numpy_array(),
"-")
if params.x_axis == "two_theta":
plt.xlabel("2 theta")
elif params.x_axis == "q":
plt.xlabel("q")
elif params.x_axis == "resolution":
plt.xlabel("Resolution ($\\AA$)")
plt.gca().set_xscale("log")
plt.gca().invert_xaxis()
plt.xlim(0, 50)
plt.ylabel("Avg ADUs")
if params.plot_y_max is not None:
plt.ylim(0, params.plot_y_max)
if params.show_plots:
# plt.legend([os.path.basename(os.path.splitext(f)[0]) for f in params.file_path], ncol=2)
plt.show()
return xvals, results
class DIALSSpfIdx(Thread):
def __init__(self,
img,
index=None,
termfile=None,
paramfile=None,
output_file=None,
output_dir=None,
backend='dials',
action_code='spotfind',
min_bragg=10,
n_processors=1,
verbose=False
):
self.img = img
self.backend = backend
self.paramfile = paramfile
self.termfile = termfile
self.n_processors = n_processors
self.index = index
self.verbose = verbose
self.min_bragg = min_bragg
if output_file is not None:
if output_dir is not None:
self.output = os.path.join(os.path.abspath(output_dir), output_file)
else:
self.output = os.path.abspath(output_file)
else:
self.output = None
Thread.__init__(self)
# Determine which processes will be included
if action_code == 'spotfind':
self.run_indexing = False
self.run_integration = False
elif action_code == 'index':
self.run_indexing = True
self.run_integration = False
elif action_code == 'integrate':
self.run_indexing = True
self.run_integration = True
# Initialize IOTA DIALS Processor
if self.backend.lower() == 'dials':
if self.paramfile is not None:
with open(self.paramfile, 'r') as phil_file:
phil_string = phil_file.read()
user_phil = ip.parse(phil_string)
self.dials_phil = phil_scope.fetch(source=user_phil)
else:
self.dials_phil = phil_scope
self.params = self.dials_phil.extract()
if self.backend == 'dials':
self.processor = IOTADialsProcessor(params=self.params)
def process_image(self):
if os.path.isfile(self.termfile):
raise IOTATermination('IOTA_TRACKER: Termination signal received!')
else:
with Capturing() as junk_output:
err = []
start = time.time()
fail = False
sg = None
uc = None
obs = None
status = None
res = 99
try:
datablock = DataBlockFactory.from_filenames([self.img])[0]
observed = self.processor.find_spots(datablock=datablock)
status = 'spots found'
except Exception, e:
fail = True
observed = []
err.append(e)
pass
# TODO: Indexing / lattice determination very slow (how to speed up?)
if self.run_indexing:
if not fail:
try:
experiments, indexed = self.processor.index(
datablock=datablock, reflections=observed)
except Exception, e:
fail = True
err.append(e)
pass
if not fail:
try:
solution = self.processor.refine_bravais_settings(
reflections=indexed, experiments=experiments)
# Only reindex if higher-symmetry solution found
if solution is not None:
experiments, indexed = self.processor.reindex(
reflections=indexed,
experiments=experiments,
solution=solution)
obs = experiments
lat = experiments[0].crystal.get_space_group().info()
sg = str(lat).replace(' ', '')
status = 'indexed'
except Exception:
fail = True
err.append(e)
pass
if not fail:
unit_cell = experiments[0].crystal.get_unit_cell().parameters()
uc = ' '.join(['{:.1f}'.format(i) for i in unit_cell])
if self.run_integration:
if not fail:
try:
# Run refinement
experiments, indexed = self.processor.refine(
experiments=experiments,
centroids=indexed)
integrated = self.processor.integrate(experiments=experiments,
indexed=indexed)
status = 'integrated'
except Exception:
fail = True
err.append(e)
pass
if status == 'integrated':
res = self.processor.frame['observations'][0].d_max_min()
else:
detector = datablock.unique_detectors()[0]
beam = datablock.unique_beams()[0]
s1 = flex.vec3_double()
for i in xrange(len(observed)):
s1.append(detector[observed['panel'][i]].get_pixel_lab_coord(
observed['xyzobs.px.value'][i][0:2]))
two_theta = s1.angle(beam.get_s0())
d = beam.get_wavelength() / (2 * flex.asin(two_theta / 2))
res = (np.max(d), np.min(d))
if len(observed) < self.min_bragg:
res = (99, 99)
elapsed = time.time() - start
info = [self.index, len(observed), self.img, sg, uc]
return status, info, res, elapsed, err
err.append('INTEGRATION ERROR: {}'.format(e))
pass
if status == 'integrated':
res = frame['observations'][0].d_max_min()
else:
detector = datablock.unique_detectors()[0]
beam = datablock.unique_beams()[0]
s1 = flex.vec3_double()
for i in xrange(len(observed)):
s1.append(
detector[observed['panel'][i]].get_pixel_lab_coord(
observed['xyzobs.px.value'][i][0:2]))
two_theta = s1.angle(beam.get_s0())
d = beam.get_wavelength() / (2 * flex.asin(two_theta / 2))
res = (np.max(d), np.min(d))
if len(observed) < self.min_bragg:
res = (99, 99)
elapsed = time.time() - start
info = [self.index, len(observed), self.img, sg, uc]
return status, info, res, score, elapsed, err
def run(self):
errors = []
n_spots = 0
n_overloads = 0
res = (99, 99)
n_rings = 0
3.909750772351232, 14.695435562625814, 7.826561485909148,
9.46004259845192, 6.877868747512286, 12.434327938231078,
3.087677172795908, 11.57943133769928, 4.304608777648071,
14.758942891036405, 4.595167128766594, 9.767325535313098
])
M = minimizer(d_i, psi_i, flex.double(), flex.double(), eta_rad, Deff)
print("pos_neg output Deff/2, eta_deg ", 1. / M.x[0], M.x[1] * 180. / pi)
from xfel.mono_simulation.max_like import minimizer as legacy_minimizer
Q = legacy_minimizer(d_i, psi_i, eta_rad, Deff)
print("legacy output Deff/2, eta_deg ", 1. / Q.x[0], Q.x[1] * 180. / pi)
from matplotlib import pyplot as plt
bragg = 2. * flex.asin(1.0 / d_i)
model_psi_leg = d_i * Q.x[0] / 2. + Q.x[1] / 2.
model_psi = d_i * M.x[0] / 2. + M.x[1] / 2.
plt.plot(bragg, psi_i, "b.")
plt.plot(bragg, model_psi, "r.")
plt.plot(bragg, -model_psi, "r.")
plt.plot(bragg, model_psi_leg, "g.")
plt.plot(bragg, -model_psi_leg, "g.")
plt.show()
exit()
ydata = flex.double()
xdata = flex.double()
yydata = flex.double()
def process_image(self):
if os.path.isfile(self.termfile):
raise IOTATermination('IOTA_TRACKER: Termination signal received!')
else:
with Capturing() as junk_output:
# if True:
err = []
start = time.time()
fail = False
sg = None
uc = None
status = None
score = 0
try:
datablock = DataBlockFactory.from_filenames([self.img])[0]
observed = self.processor.find_spots(datablock=datablock)
status = 'spots found'
except Exception as e:
fail = True
observed = []
err.append('SPOTFINDING ERROR: {}'.format(e))
pass
# TODO: Indexing / lattice determination very slow (how to speed up?)
if self.run_indexing:
if not fail:
try:
experiments, indexed = self.processor.index(
datablock=datablock, reflections=observed)
score = len(indexed)
except Exception as e:
fail = True
err.append('INDEXING ERROR: {}'.format(e))
pass
if not fail:
try:
solution = self.processor.refine_bravais_settings(
reflections=indexed, experiments=experiments)
# Only reindex if higher-symmetry solution found
if solution is not None:
experiments, indexed = self.processor.reindex(
reflections=indexed,
experiments=experiments,
solution=solution)
obs = experiments
lat = experiments[0].crystal.get_space_group(
).info()
sg = str(lat).replace(' ', '')
status = 'indexed'
except Exception as e:
fail = True
err.append('LATTICE ERROR: {}'.format(e))
pass
if not fail:
unit_cell = experiments[0].crystal.get_unit_cell(
).parameters()
uc = ' '.join(['{:.4f}'.format(i) for i in unit_cell])
if self.run_integration:
if not fail:
try:
# Run refinement
experiments, indexed = self.processor.refine(
experiments=experiments, centroids=indexed)
except Exception as e:
fail = True
err.append('REFINEMENT ERROR: {}'.format(e))
pass
if not fail:
try:
print experiments
print indexed
integrated = self.processor.integrate(
experiments=experiments, indexed=indexed)
frame = ConstructFrame(
integrated, experiments[0]).make_frame()
status = 'integrated'
except Exception as e:
err.append('INTEGRATION ERROR: {}'.format(e))
pass
if status == 'integrated':
res = frame['observations'][0].d_max_min()
else:
detector = datablock.unique_detectors()[0]
beam = datablock.unique_beams()[0]
s1 = flex.vec3_double()
for i in xrange(len(observed)):
s1.append(
detector[observed['panel'][i]].get_pixel_lab_coord(
observed['xyzobs.px.value'][i][0:2]))
two_theta = s1.angle(beam.get_s0())
d = beam.get_wavelength() / (2 * flex.asin(two_theta / 2))
res = (np.max(d), np.min(d))
if len(observed) < self.min_bragg:
res = (99, 99)
elapsed = time.time() - start
info = [self.index, len(observed), self.img, sg, uc]
return status, info, res, score, elapsed, err
