def work_all(host,
port,
filenames,
params,
plot=False,
table=False,
json_file=None,
grid=None,
nproc=None):
import json
from multiprocessing.pool import ThreadPool as thread_pool
if nproc is None:
nproc = _nproc()
pool = thread_pool(processes=nproc)
threads = {}
for filename in filenames:
threads[filename] = pool.apply_async(work,
(host, port, filename, params))
results = []
for filename in filenames:
response = threads[filename].get()
d = json.loads(response)
results.append(d)
print response_to_xml(d)
if json_file is not None:
'Writing results to %s' % json_file
with open(json_file, 'wb') as f:
json.dump(results, f)
if plot or table:
from scitbx.array_family import flex
from libtbx import group_args
from dials.algorithms.spot_finding.per_image_analysis \
import plot_stats, print_table
estimated_d_min = flex.double()
d_min_distl_method_1 = flex.double()
d_min_distl_method_2 = flex.double()
n_spots_total = flex.int()
n_spots_no_ice = flex.int()
total_intensity = flex.double()
for d in results:
estimated_d_min.append(d['estimated_d_min'])
d_min_distl_method_1.append(d['d_min_distl_method_1'])
d_min_distl_method_2.append(d['d_min_distl_method_2'])
n_spots_total.append(d['n_spots_total'])
n_spots_no_ice.append(d['n_spots_no_ice'])
total_intensity.append(d['total_intensity'])
stats = group_args(n_spots_total=n_spots_total,
n_spots_no_ice=n_spots_no_ice,
n_spots_4A=None,
total_intensity=total_intensity,
estimated_d_min=estimated_d_min,
d_min_distl_method_1=d_min_distl_method_1,
d_min_distl_method_2=d_min_distl_method_2,
noisiness_method_1=None,
noisiness_method_2=None)
if plot:
plot_stats(stats)
if table:
print_table(stats)
if grid is not None:
from matplotlib import pyplot
n_spots_no_ice.reshape(flex.grid(grid))
print n_spots_no_ice.size()
from matplotlib import pyplot
fig = pyplot.figure()
pyplot.pcolormesh(n_spots_no_ice.as_numpy_array(),
cmap=pyplot.cm.Reds)
pyplot.savefig("spot_count.png")
return
def run(args):
from dials.util.options import OptionParser
import libtbx.load_env
usage = "%s [options] find_spots.expt" % (libtbx.env.dispatcher_name)
parser = OptionParser(usage=usage, phil=phil_scope, epilog=help_message)
params, options, args = parser.parse_args(
show_diff_phil=True, return_unhandled=True
)
positions = None
if params.positions is not None:
with open(params.positions, "rb") as f:
positions = flex.vec2_double()
for line in f.readlines():
line = (
line.replace("(", " ")
.replace(")", "")
.replace(",", " ")
.strip()
.split()
)
assert len(line) == 3
i, x, y = [float(l) for l in line]
positions.append((x, y))
assert len(args) == 1
json_file = args[0]
import json
with open(json_file, "rb") as f:
results = json.load(f)
n_indexed = flex.double()
fraction_indexed = flex.double()
n_spots = flex.double()
n_lattices = flex.double()
crystals = []
image_names = flex.std_string()
for r in results:
n_spots.append(r["n_spots_total"])
image_names.append(str(r["image"]))
if "n_indexed" in r:
n_indexed.append(r["n_indexed"])
n_lattices.append(len(r["lattices"]))
for d in r["lattices"]:
from dxtbx.model.crystal import CrystalFactory
crystals.append(CrystalFactory.from_dict(d["crystal"]))
else:
n_indexed.append(0)
n_lattices.append(0)
if n_indexed.size():
sel = n_spots > 0
fraction_indexed = flex.double(n_indexed.size(), 0)
fraction_indexed.set_selected(sel, n_indexed.select(sel) / n_spots.select(sel))
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot
red = "#e74c3c"
plot = True
table = True
grid = params.grid
from libtbx import group_args
from dials.algorithms.spot_finding.per_image_analysis import plot_stats, print_table
estimated_d_min = flex.double()
d_min_distl_method_1 = flex.double()
d_min_distl_method_2 = flex.double()
n_spots_total = flex.int()
n_spots_no_ice = flex.int()
total_intensity = flex.double()
for d in results:
estimated_d_min.append(d["estimated_d_min"])
d_min_distl_method_1.append(d["d_min_distl_method_1"])
d_min_distl_method_2.append(d["d_min_distl_method_2"])
n_spots_total.append(d["n_spots_total"])
n_spots_no_ice.append(d["n_spots_no_ice"])
total_intensity.append(d["total_intensity"])
stats = group_args(
image=image_names,
n_spots_total=n_spots_total,
n_spots_no_ice=n_spots_no_ice,
n_spots_4A=None,
n_indexed=n_indexed,
fraction_indexed=fraction_indexed,
total_intensity=total_intensity,
estimated_d_min=estimated_d_min,
d_min_distl_method_1=d_min_distl_method_1,
d_min_distl_method_2=d_min_distl_method_2,
noisiness_method_1=None,
noisiness_method_2=None,
)
if plot:
plot_stats(stats)
pyplot.clf()
if table:
print_table(stats)
n_rows = 10
n_rows = min(n_rows, len(n_spots_total))
perm_n_spots_total = flex.sort_permutation(n_spots_total, reverse=True)
print("Top %i images sorted by number of spots:" % n_rows)
print_table(stats, perm=perm_n_spots_total, n_rows=n_rows)
if flex.max(n_indexed) > 0:
perm_n_indexed = flex.sort_permutation(n_indexed, reverse=True)
print("Top %i images sorted by number of indexed reflections:" % n_rows)
print_table(stats, perm=perm_n_indexed, n_rows=n_rows)
print("Number of indexed lattices: ", (n_indexed > 0).count(True))
print(
"Number with valid d_min but failed indexing: ",
(
(d_min_distl_method_1 > 0)
& (d_min_distl_method_2 > 0)
& (estimated_d_min > 0)
& (n_indexed == 0)
).count(True),
)
n_bins = 20
spot_count_histogram(
n_spots_total, n_bins=n_bins, filename="hist_n_spots_total.png", log=True
)
spot_count_histogram(
n_spots_no_ice, n_bins=n_bins, filename="hist_n_spots_no_ice.png", log=True
)
spot_count_histogram(
n_indexed.select(n_indexed > 0),
n_bins=n_bins,
filename="hist_n_indexed.png",
log=False,
)
if len(crystals):
plot_unit_cell_histograms(crystals)
if params.stereographic_projections and len(crystals):
from dxtbx.model.experiment_list import ExperimentListFactory
experiments = ExperimentListFactory.from_filenames(
[image_names[0]], verbose=False
)
assert len(experiments) == 1
imageset = experiments.imagesets()[0]
s0 = imageset.get_beam().get_s0()
# XXX what if no goniometer?
rotation_axis = imageset.get_goniometer().get_rotation_axis()
indices = ((1, 0, 0), (0, 1, 0), (0, 0, 1))
for i, index in enumerate(indices):
from cctbx import crystal, miller
from scitbx import matrix
miller_indices = flex.miller_index([index])
symmetry = crystal.symmetry(
unit_cell=crystals[0].get_unit_cell(),
space_group=crystals[0].get_space_group(),
)
miller_set = miller.set(symmetry, miller_indices)
d_spacings = miller_set.d_spacings()
d_spacings = d_spacings.as_non_anomalous_array().expand_to_p1()
d_spacings = d_spacings.generate_bijvoet_mates()
miller_indices = d_spacings.indices()
# plane normal
d0 = matrix.col(s0).normalize()
d1 = d0.cross(matrix.col(rotation_axis)).normalize()
d2 = d1.cross(d0).normalize()
reference_poles = (d0, d1, d2)
from dials.command_line.stereographic_projection import (
stereographic_projection,
)
projections = []
for cryst in crystals:
reciprocal_space_points = (
list(cryst.get_U() * cryst.get_B())
* miller_indices.as_vec3_double()
)
projections.append(
stereographic_projection(reciprocal_space_points, reference_poles)
)
# from dials.algorithms.indexing.compare_orientation_matrices import \
# difference_rotation_matrix_and_euler_angles
# R_ij, euler_angles, cb_op = difference_rotation_matrix_and_euler_angles(
# crystals[0], cryst)
# print max(euler_angles)
from dials.command_line.stereographic_projection import plot_projections
plot_projections(projections, filename="projections_%s.png" % ("hkl"[i]))
pyplot.clf()
def plot_grid(
values,
grid,
file_name,
cmap=pyplot.cm.Reds,
vmin=None,
vmax=None,
invalid="white",
):
values = values.as_double()
# At DLS, fast direction appears to be largest direction
if grid[0] > grid[1]:
values.reshape(flex.grid(reversed(grid)))
values = values.matrix_transpose()
else:
values.reshape(flex.grid(grid))
f, ax1 = pyplot.subplots(1)
mesh1 = ax1.pcolormesh(values.as_numpy_array(), cmap=cmap, vmin=vmin, vmax=vmax)
mesh1.cmap.set_under(color=invalid, alpha=None)
mesh1.cmap.set_over(color=invalid, alpha=None)
ax1.set_aspect("equal")
ax1.invert_yaxis()
pyplot.colorbar(mesh1, ax=ax1)
pyplot.savefig(file_name, dpi=600)
pyplot.clf()
def plot_positions(
values,
positions,
file_name,
cmap=pyplot.cm.Reds,
vmin=None,
vmax=None,
invalid="white",
):
values = values.as_double()
assert positions.size() >= values.size()
positions = positions[: values.size()]
if vmin is None:
vmin = flex.min(values)
if vmax is None:
vmax = flex.max(values)
x, y = positions.parts()
dx = flex.abs(x[1:] - x[:-1])
dy = flex.abs(y[1:] - y[:-1])
dx = dx.select(dx > 0)
dy = dy.select(dy > 0)
scale = 1 / flex.min(dx)
# print scale
x = (x * scale).iround()
y = (y * scale).iround()
from libtbx.math_utils import iceil
z = flex.double(flex.grid(iceil(flex.max(y)) + 1, iceil(flex.max(x)) + 1), -2)
# print z.all()
for x_, y_, z_ in zip(x, y, values):
z[y_, x_] = z_
plot_grid(
z.as_1d(),
z.all(),
file_name,
cmap=cmap,
vmin=vmin,
vmax=vmax,
invalid=invalid,
)
return
if grid is not None or positions is not None:
if grid is not None:
positions = tuple(reversed(grid))
plotter = plot_grid
else:
plotter = plot_positions
cmap = pyplot.get_cmap(params.cmap)
plotter(
n_spots_total,
positions,
"grid_spot_count_total.png",
cmap=cmap,
invalid=params.invalid,
)
plotter(
n_spots_no_ice,
positions,
"grid_spot_count_no_ice.png",
cmap=cmap,
invalid=params.invalid,
)
plotter(
total_intensity,
positions,
"grid_total_intensity.png",
cmap=cmap,
invalid=params.invalid,
)
if flex.max(n_indexed) > 0:
plotter(
n_indexed,
positions,
"grid_n_indexed.png",
cmap=cmap,
invalid=params.invalid,
)
plotter(
fraction_indexed,
positions,
"grid_fraction_indexed.png",
cmap=cmap,
vmin=0,
vmax=1,
invalid=params.invalid,
)
for i, d_min in enumerate(
(estimated_d_min, d_min_distl_method_1, d_min_distl_method_2)
):
vmin = flex.min(d_min.select(d_min > 0))
vmax = flex.max(d_min)
cmap = pyplot.get_cmap("%s_r" % params.cmap)
d_min.set_selected(d_min <= 0, vmax)
if i == 0:
plotter(
d_min,
positions,
"grid_d_min.png",
cmap=cmap,
vmin=vmin,
vmax=vmax,
invalid=params.invalid,
)
else:
plotter(
d_min,
positions,
"grid_d_min_method_%i.png" % i,
cmap=cmap,
vmin=vmin,
vmax=vmax,
invalid=params.invalid,
)
if flex.max(n_indexed) > 0:
pyplot.hexbin(n_spots, n_indexed, bins="log", cmap=pyplot.cm.jet, gridsize=50)
pyplot.colorbar()
xlim = pyplot.xlim()
ylim = pyplot.ylim()
pyplot.plot([0, max(n_spots)], [0, max(n_spots)], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel("# spots")
pyplot.ylabel("# indexed")
pyplot.savefig("n_spots_vs_n_indexed.png")
pyplot.clf()
pyplot.hexbin(
n_spots, fraction_indexed, bins="log", cmap=pyplot.cm.jet, gridsize=50
)
pyplot.colorbar()
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel("# spots")
pyplot.ylabel("Fraction indexed")
pyplot.savefig("n_spots_vs_fraction_indexed.png")
pyplot.clf()
pyplot.hexbin(
n_indexed, fraction_indexed, bins="log", cmap=pyplot.cm.jet, gridsize=50
)
pyplot.colorbar()
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel("# indexed")
pyplot.ylabel("Fraction indexed")
pyplot.savefig("n_indexed_vs_fraction_indexed.png")
pyplot.clf()
pyplot.hexbin(n_spots, n_lattices, bins="log", cmap=pyplot.cm.jet, gridsize=50)
pyplot.colorbar()
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel("# spots")
pyplot.ylabel("# lattices")
pyplot.savefig("n_spots_vs_n_lattices.png")
pyplot.clf()
pyplot.hexbin(
estimated_d_min,
d_min_distl_method_1,
bins="log",
cmap=pyplot.cm.jet,
gridsize=50,
)
pyplot.colorbar()
# pyplot.gca().set_aspect('equal')
xlim = pyplot.xlim()
ylim = pyplot.ylim()
m = max(max(estimated_d_min), max(d_min_distl_method_1))
pyplot.plot([0, m], [0, m], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel("estimated_d_min")
pyplot.ylabel("d_min_distl_method_1")
pyplot.savefig("d_min_vs_distl_method_1.png")
pyplot.clf()
pyplot.hexbin(
estimated_d_min,
d_min_distl_method_2,
bins="log",
cmap=pyplot.cm.jet,
gridsize=50,
)
pyplot.colorbar()
# pyplot.gca().set_aspect('equal')
xlim = pyplot.xlim()
ylim = pyplot.ylim()
m = max(max(estimated_d_min), max(d_min_distl_method_2))
pyplot.plot([0, m], [0, m], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel("estimated_d_min")
pyplot.ylabel("d_min_distl_method_2")
pyplot.savefig("d_min_vs_distl_method_2.png")
pyplot.clf()
pyplot.hexbin(
d_min_distl_method_1,
d_min_distl_method_2,
bins="log",
cmap=pyplot.cm.jet,
gridsize=50,
)
pyplot.colorbar()
# pyplot.gca().set_aspect('equal')
xlim = pyplot.xlim()
ylim = pyplot.ylim()
m = max(max(d_min_distl_method_1), max(d_min_distl_method_2))
pyplot.plot([0, m], [0, m], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel("d_min_distl_method_1")
pyplot.ylabel("d_min_distl_method_2")
pyplot.savefig("distl_method_1_vs_distl_method_2.png")
pyplot.clf()
pyplot.hexbin(n_spots, estimated_d_min, bins="log", cmap=pyplot.cm.jet, gridsize=50)
pyplot.colorbar()
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel("# spots")
pyplot.ylabel("estimated_d_min")
pyplot.savefig("n_spots_vs_d_min.png")
pyplot.clf()
pyplot.hexbin(
n_spots, d_min_distl_method_1, bins="log", cmap=pyplot.cm.jet, gridsize=50
)
pyplot.colorbar()
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel("# spots")
pyplot.ylabel("d_min_distl_method_1")
pyplot.savefig("n_spots_vs_distl_method_1.png")
pyplot.clf()
pyplot.hexbin(
n_spots, d_min_distl_method_2, bins="log", cmap=pyplot.cm.jet, gridsize=50
)
pyplot.colorbar()
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel("# spots")
pyplot.ylabel("d_min_distl_method_2")
pyplot.savefig("n_spots_vs_distl_method_2.png")
pyplot.clf()
def run(self, args=None):
"""Execute the script."""
from dials.array_family import flex
from dials.util.options import flatten_experiments
from time import time
from dials.util import log
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(args=args,
show_diff_phil=False)
if __name__ == "__main__":
# Configure the logging
log.config(verbosity=options.verbose, logfile=params.output.log)
from dials.util.version import dials_version
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil != "":
logger.info("The following parameters have been modified:\n")
logger.info(diff_phil)
# Ensure we have a data block
experiments = flatten_experiments(params.input.experiments)
if len(experiments) == 0:
self.parser.print_help()
return
# Loop through all the imagesets and find the strong spots
reflections = flex.reflection_table.from_observations(
experiments, params)
# Add n_signal column - before deleting shoeboxes
from dials.algorithms.shoebox import MaskCode
good = MaskCode.Foreground | MaskCode.Valid
reflections["n_signal"] = reflections["shoebox"].count_mask_values(
good)
# Delete the shoeboxes
if not params.output.shoeboxes:
del reflections["shoebox"]
# ascii spot count per image plot
from dials.util.ascii_art import spot_counts_per_image_plot
for i, experiment in enumerate(experiments):
ascii_plot = spot_counts_per_image_plot(
reflections.select(reflections["id"] == i))
if len(ascii_plot):
logger.info(
"\nHistogram of per-image spot count for imageset %i:" % i)
logger.info(ascii_plot)
# Save the reflections to file
logger.info("\n" + "-" * 80)
reflections.as_file(params.output.reflections)
logger.info("Saved {} reflections to {}".format(
len(reflections), params.output.reflections))
# Save the experiments
if params.output.experiments:
logger.info("Saving experiments to {}".format(
params.output.experiments))
experiments.as_file(params.output.experiments)
# Print some per image statistics
if params.per_image_statistics:
from dials.algorithms.spot_finding import per_image_analysis
from six.moves import cStringIO as StringIO
s = StringIO()
for i, experiment in enumerate(experiments):
print("Number of centroids per image for imageset %i:" % i,
file=s)
imageset = experiment.imageset
stats = per_image_analysis.stats_imageset(
imageset,
reflections.select(reflections["id"] == i),
resolution_analysis=False,
)
per_image_analysis.print_table(stats, out=s)
logger.info(s.getvalue())
# Print the time
logger.info("Time Taken: %f" % (time() - start_time))
if params.output.experiments:
return experiments, reflections
else:
return reflections
def run(self):
'''Execute the script.'''
from dials.array_family import flex
from dials.util.options import flatten_datablocks
from time import time
from dials.util import log
from libtbx.utils import Sorry
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
# Configure the logging
log.config(params.verbosity,
info=params.output.log,
debug=params.output.debug_log)
from dials.util.version import dials_version
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil is not '':
logger.info('The following parameters have been modified:\n')
logger.info(diff_phil)
# Ensure we have a data block
datablocks = flatten_datablocks(params.input.datablock)
if len(datablocks) == 0:
self.parser.print_help()
return
elif len(datablocks) != 1:
raise Sorry('only 1 datablock can be processed at a time')
# Loop through all the imagesets and find the strong spots
reflections = flex.reflection_table.from_observations(
datablocks[0], params)
# Delete the shoeboxes
if not params.output.shoeboxes:
del reflections['shoebox']
# ascii spot count per image plot
from dials.util.ascii_art import spot_counts_per_image_plot
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
ascii_plot = spot_counts_per_image_plot(
reflections.select(reflections['id'] == i))
if len(ascii_plot):
logger.info(
'\nHistogram of per-image spot count for imageset %i:' % i)
logger.info(ascii_plot)
# Save the reflections to file
logger.info('\n' + '-' * 80)
reflections.as_pickle(params.output.reflections)
logger.info('Saved {0} reflections to {1}'.format(
len(reflections), params.output.reflections))
# Save the datablock
if params.output.datablock:
from dxtbx.datablock import DataBlockDumper
logger.info('Saving datablocks to {0}'.format(
params.output.datablock))
dump = DataBlockDumper(datablocks)
dump.as_file(params.output.datablock)
# Print some per image statistics
if params.per_image_statistics:
from dials.algorithms.spot_finding import per_image_analysis
from cStringIO import StringIO
s = StringIO()
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
print >> s, "Number of centroids per image for imageset %i:" % i
stats = per_image_analysis.stats_imageset(
imageset,
reflections.select(reflections['id'] == i),
resolution_analysis=False)
per_image_analysis.print_table(stats, out=s)
logger.info(s.getvalue())
# Print the time
logger.info("Time Taken: %f" % (time() - start_time))
def run(args):
usage = "dials.spot_counts_per_image [options] imported.expt strong.refl"
parser = OptionParser(
usage=usage,
read_reflections=True,
read_experiments=True,
phil=phil_scope,
check_format=False,
epilog=help_message,
)
params, options = parser.parse_args(show_diff_phil=False)
reflections = flatten_reflections(params.input.reflections)
experiments = flatten_experiments(params.input.experiments)
if not any([reflections, experiments]):
parser.print_help()
return
# FIXME may want to change this to allow many to be passed i.e.
# from parallel runs
if len(reflections) != 1:
sys.exit("Only one reflection list may be passed")
reflections = reflections[0]
expts = set(reflections["id"])
if max(expts) >= len(experiments.imagesets()):
sys.exit("Unknown experiments in reflection list")
if params.id is not None:
reflections = reflections.select(reflections["id"] == params.id)
all_stats = []
for j, imageset in enumerate(experiments.imagesets()):
refl = reflections.select(reflections["id"] == j)
stats = per_image_analysis.stats_imageset(
imageset,
refl,
resolution_analysis=params.resolution_analysis,
plot=params.individual_plots,
)
all_stats.append(stats)
# transpose stats
class empty(object):
pass
e = empty()
for s in all_stats:
for k in dir(s):
if k.startswith("_") or k in ["merge", "next"]:
continue
if not hasattr(e, k):
setattr(e, k, [])
getattr(e, k).extend(getattr(s, k))
per_image_analysis.print_table(e)
from libtbx import table_utils
# FIXME this is now probably nonsense...
overall_stats = per_image_analysis.stats_single_image(
imageset, reflections, resolution_analysis=params.resolution_analysis
)
rows = [
("Overall statistics", ""),
("#spots", "%i" % overall_stats.n_spots_total),
("#spots_no_ice", "%i" % overall_stats.n_spots_no_ice),
("d_min", "%.2f" % overall_stats.estimated_d_min),
(
"d_min (distl method 1)",
"%.2f (%.2f)"
% (overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1),
),
(
"d_min (distl method 2)",
"%.2f (%.2f)"
% (overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1),
),
]
print(table_utils.format(rows, has_header=True, prefix="| ", postfix=" |"))
if params.json:
import json
if params.split_json:
for k in stats.__dict__:
start, end = params.json.split(".")
with open("%s_%s.%s" % (start, k, end), "wb") as fp:
json.dump(stats.__dict__[k], fp)
if params.joint_json:
with open(params.json, "wb") as fp:
json.dump(stats.__dict__, fp)
if params.plot:
import matplotlib
matplotlib.use("Agg")
per_image_analysis.plot_stats(stats, filename=params.plot)
def work_all(host, port, filenames, params, plot=False, table=False,
json_file=None, grid=None, nproc=None):
import json
from multiprocessing.pool import ThreadPool as thread_pool
if nproc is None:
nproc=_nproc()
pool = thread_pool(processes=nproc)
threads = { }
for filename in filenames:
threads[filename] = pool.apply_async(work, (host, port, filename, params))
results = []
for filename in filenames:
response = threads[filename].get()
d = json.loads(response)
results.append(d)
print response_to_xml(d)
if json_file is not None:
'Writing results to %s' %json_file
with open(json_file, 'wb') as f:
json.dump(results, f)
if plot or table:
from scitbx.array_family import flex
from libtbx import group_args
from dials.algorithms.spot_finding.per_image_analysis \
import plot_stats, print_table
estimated_d_min = flex.double()
d_min_distl_method_1 = flex.double()
d_min_distl_method_2 = flex.double()
n_spots_total = flex.int()
n_spots_no_ice = flex.int()
total_intensity = flex.double()
for d in results:
estimated_d_min.append(d['estimated_d_min'])
d_min_distl_method_1.append(d['d_min_distl_method_1'])
d_min_distl_method_2.append(d['d_min_distl_method_2'])
n_spots_total.append(d['n_spots_total'])
n_spots_no_ice.append(d['n_spots_no_ice'])
total_intensity.append(d['total_intensity'])
stats = group_args(n_spots_total=n_spots_total,
n_spots_no_ice=n_spots_no_ice,
n_spots_4A=None,
total_intensity=total_intensity,
estimated_d_min=estimated_d_min,
d_min_distl_method_1=d_min_distl_method_1,
d_min_distl_method_2=d_min_distl_method_2,
noisiness_method_1=None,
noisiness_method_2=None)
if plot:
plot_stats(stats)
if table:
print_table(stats)
if grid is not None:
from matplotlib import pyplot
n_spots_no_ice.reshape(flex.grid(grid))
print n_spots_no_ice.size()
from matplotlib import pyplot
fig = pyplot.figure()
pyplot.pcolormesh(n_spots_no_ice.as_numpy_array(), cmap=pyplot.cm.Reds)
pyplot.savefig("spot_count.png")
return
def run(self):
'''Execute the script.'''
from dials.array_family import flex
from dials.util.options import flatten_datablocks
from time import time
from dials.util import log
from libtbx.utils import Sorry
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
# Configure the logging
log.config(
params.verbosity,
info=params.output.log,
debug=params.output.debug_log)
from dials.util.version import dials_version
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil is not '':
logger.info('The following parameters have been modified:\n')
logger.info(diff_phil)
# Ensure we have a data block
datablocks = flatten_datablocks(params.input.datablock)
if len(datablocks) == 0:
self.parser.print_help()
return
elif len(datablocks) != 1:
raise Sorry('only 1 datablock can be processed at a time')
# Loop through all the imagesets and find the strong spots
reflections = flex.reflection_table.from_observations(
datablocks[0], params)
# Delete the shoeboxes
if not params.output.shoeboxes:
del reflections['shoebox']
# ascii spot count per image plot
from dials.util.ascii_art import spot_counts_per_image_plot
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
ascii_plot = spot_counts_per_image_plot(
reflections.select(reflections['id'] == i))
if len(ascii_plot):
logger.info('\nHistogram of per-image spot count for imageset %i:' %i)
logger.info(ascii_plot)
# Save the reflections to file
logger.info('\n' + '-' * 80)
reflections.as_pickle(params.output.reflections)
logger.info('Saved {0} reflections to {1}'.format(
len(reflections), params.output.reflections))
# Save the datablock
if params.output.datablock:
from dxtbx.datablock import DataBlockDumper
logger.info('Saving datablocks to {0}'.format(
params.output.datablock))
dump = DataBlockDumper(datablocks)
dump.as_file(params.output.datablock)
# Print some per image statistics
if params.per_image_statistics:
from dials.algorithms.spot_finding import per_image_analysis
from cStringIO import StringIO
s = StringIO()
for i, imageset in enumerate(datablocks[0].extract_imagesets()):
print >> s, "Number of centroids per image for imageset %i:" %i
stats = per_image_analysis.stats_imageset(
imageset, reflections.select(reflections['id'] == i),
resolution_analysis=False)
per_image_analysis.print_table(stats, out=s)
logger.info(s.getvalue())
# Print the time
logger.info("Time Taken: %f" % (time() - start_time))
def run(args):
import libtbx.load_env
usage = "%s [options] datablock.json strong.pickle" % libtbx.env.dispatcher_name
parser = OptionParser(usage=usage,
read_reflections=True,
read_datablocks=True,
read_experiments=True,
phil=phil_scope,
check_format=False,
epilog=help_message)
from libtbx.utils import Sorry
params, options = parser.parse_args(show_diff_phil=False)
reflections = flatten_reflections(params.input.reflections)
datablocks = flatten_datablocks(params.input.datablock)
experiments = flatten_experiments(params.input.experiments)
if not any([reflections, experiments, datablocks]):
parser.print_help()
return
if len(reflections) != 1:
raise Sorry('exactly 1 reflection table must be specified')
if len(datablocks) != 1:
if experiments:
if len(experiments.imagesets()) != 1:
raise Sorry('exactly 1 datablock must be specified')
imageset = experiments.imagesets()[0]
else:
raise Sorry('exactly 1 datablock must be specified')
else:
imageset = datablocks[0].extract_imagesets()[0]
reflections = reflections[0]
if params.id is not None:
reflections = reflections.select(reflections['id'] == params.id)
stats = per_image_analysis.stats_imageset(
imageset,
reflections,
resolution_analysis=params.resolution_analysis,
plot=params.individual_plots)
per_image_analysis.print_table(stats)
from libtbx import table_utils
overall_stats = per_image_analysis.stats_single_image(
imageset, reflections, resolution_analysis=params.resolution_analysis)
rows = [
("Overall statistics", ""),
("#spots", "%i" % overall_stats.n_spots_total),
("#spots_no_ice", "%i" % overall_stats.n_spots_no_ice),
#("total_intensity", "%.0f" %overall_stats.total_intensity),
("d_min", "%.2f" % overall_stats.estimated_d_min),
("d_min (distl method 1)",
"%.2f (%.2f)" % (overall_stats.d_min_distl_method_1,
overall_stats.noisiness_method_1)),
("d_min (distl method 2)",
"%.2f (%.2f)" % (overall_stats.d_min_distl_method_1,
overall_stats.noisiness_method_1)),
]
print table_utils.format(rows, has_header=True, prefix="| ", postfix=" |")
if params.json is not None:
import json
with open(params.json, 'wb') as fp:
json.dump(stats.__dict__, fp)
if params.plot is not None:
per_image_analysis.plot_stats(stats, filename=params.plot)
def run(self):
"""Execute the script."""
from dials.util import log
from time import time
from libtbx import easy_mp
import copy
# Parse the command line
params, options, all_paths = self.parser.parse_args(
show_diff_phil=False, return_unhandled=True, quick_parse=True)
# Check we have some filenames
if not all_paths:
self.parser.print_help()
return
# Mask validation
for mask_path in params.spotfinder.lookup.mask, params.integration.lookup.mask:
if mask_path is not None and not os.path.isfile(mask_path):
raise Sorry("Mask %s not found" % mask_path)
# Save the options
self.options = options
self.params = params
st = time()
# Configure logging
#log.config(
# params.verbosity, info="exafel_spotfinding.process.log", debug="exafel.spot_finding.debug.log"
#)
bad_phils = [f for f in all_paths if os.path.splitext(f)[1] == ".phil"]
if len(bad_phils) > 0:
self.parser.print_help()
logger.error(
"Error: the following phil files were not understood: %s" %
(", ".join(bad_phils)))
return
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil is not "":
logger.info("The following parameters have been modified:\n")
logger.info(diff_phil)
for abs_params in self.params.integration.absorption_correction:
if abs_params.apply:
if not (self.params.integration.debug.output
and not self.params.integration.debug.separate_files):
raise Sorry(
"Shoeboxes must be saved to integration intermediates to apply an absorption correction. "
+
"Set integration.debug.output=True, integration.debug.separate_files=False and "
+
"integration.debug.delete_shoeboxes=True to temporarily store shoeboxes."
)
self.load_reference_geometry()
from dials.command_line.dials_import import ManualGeometryUpdater
update_geometry = ManualGeometryUpdater(params)
# Import stuff
logger.info("Loading files...")
pre_import = params.dispatch.pre_import or len(all_paths) == 1
if True: #pre_import:
# Handle still imagesets by breaking them apart into multiple experiments
# Further handle single file still imagesets (like HDF5) by tagging each
# frame using its index
experiments = ExperimentList()
for path in all_paths:
experiments.extend(do_import(path, load_models=False))
indices = []
basenames = []
split_experiments = []
for i, imageset in enumerate(experiments.imagesets()):
assert len(imageset) == 1
paths = imageset.paths()
indices.append(i)
basenames.append(
os.path.splitext(os.path.basename(paths[0]))[0])
split_experiments.append(experiments[i:i + 1])
tags = []
for i, basename in zip(indices, basenames):
if basenames.count(basename) > 1:
tags.append("%s_%05d" % (basename, i))
else:
tags.append(basename)
# Wrapper function
def do_work(i, item_list):
processor = SpotFinding_Processor(copy.deepcopy(params),
composite_tag="%04d" % i,
rank=i)
if params.LS49.dump_CBF:
print('READING IN TIMESTAMPS TO DUMP')
# Read in file with timestamps information
processor.timestamps_to_dump = []
for fin in glob.glob(
os.path.join(
self.params.LS49.
path_to_rayonix_crystal_models,
'idx-fee_data*')):
#for fin in glob.glob(os.path.join(self.params.LS49.path_to_rayonix_crystal_models, 'int-0-*')):
int_file = os.path.basename(fin)
ts = int_file[13:30]
processor.timestamps_to_dump.append(ts)
#with open(os.path.join(self.params.output.output_dir,'../timestamps_to_dump.dat'), 'r') as fin:
# for line in fin:
# if line !='\n':
# ts = line.split()[0].strip()
# processor.timestamps_to_dump.append(ts)
from dials.array_family import flex
all_spots_from_rank = flex.reflection_table()
for item in item_list:
try:
assert len(item[1]) == 1
experiment = item[1][0]
experiment.load_models()
imageset = experiment.imageset
update_geometry(imageset)
experiment.beam = imageset.get_beam()
experiment.detector = imageset.get_detector()
except RuntimeError as e:
logger.warning(
"Error updating geometry on item %s, %s" %
(str(item[0]), str(e)))
continue
if self.reference_detector is not None:
from dxtbx.model import Detector
experiment = item[1][0]
imageset = experiment.imageset
imageset.set_detector(
Detector.from_dict(
self.reference_detector.to_dict()))
experiment.detector = imageset.get_detector()
refl_table = processor.process_experiments(
item[0], item[1], item[2])
if refl_table is not None:
all_spots_from_rank.extend(refl_table)
processor.finalize()
return all_spots_from_rank
iterable = zip(tags, split_experiments, indices)
# Process the data
if params.mp.method == 'mpi':
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank(
) # each process in MPI has a unique id, 0-indexed
size = comm.Get_size(
) # size: number of processes running in this job
# Configure the logging
if params.output.logging_dir is None:
info_path = ''
debug_path = ''
else:
import sys
log_path = os.path.join(params.output.logging_dir,
"log_rank%04d.out" % rank)
error_path = os.path.join(params.output.logging_dir,
"error_rank%04d.out" % rank)
print("Redirecting stdout to %s" % log_path)
print("Redirecting stderr to %s" % error_path)
sys.stdout = open(log_path, 'a', buffering=0)
sys.stderr = open(error_path, 'a', buffering=0)
print("Should be redirected now")
info_path = os.path.join(params.output.logging_dir,
"info_rank%04d.out" % rank)
debug_path = os.path.join(params.output.logging_dir,
"debug_rank%04d.out" % rank)
from dials.util import log
print('IOTA_ALL_SPOTS_RANKS_0')
#log.config(params.verbosity, info=info_path, debug=debug_path)
subset = [
item for i, item in enumerate(iterable)
if (i + rank) % size == 0
]
all_spots_from_rank = do_work(rank, subset)
all_spots_rank0 = comm.gather(all_spots_from_rank, root=0)
print('IOTA_ALL_SPOTS_RANKS_1')
exit()
if rank == 0:
from dials.array_family import flex
all_spots = flex.reflection_table()
for ii, refl_table in enumerate(all_spots_rank0):
if refl_table is not None:
all_spots.extend(refl_table)
from libtbx.easy_pickle import dump
#dump('all_spots.pickle', all_spots_rank0)
#dump('all_experiments.pickle', experiments)
#print ('IOTA_ALL_SPOTS_RANKS_2')
#print ('IOTA_ALL_SPOTS_RANKS_3')
from dials.algorithms.spot_finding import per_image_analysis
from six.moves import cStringIO as StringIO
s = StringIO()
# Assuming one datablock. Might be dangerous
# FIXME
from dxtbx.format.cbf_writer import FullCBFWriter
for i, imageset in enumerate(experiments.imagesets()):
print("Number of centroids per image for imageset %i:" % i,
file=s)
#from IPython import embed; embed(); exit()
print('IOTA_ALL_SPOTS_RANKS_4')
stats = custom_stats_imageset(
imageset, all_spots.select(all_spots['img_id'] == i))
n_spots_total = flex.int(stats.n_spots_total)
max_number_of_spots = max(stats.n_spots_total)
for num_spots in range(1, max_number_of_spots + 1):
print("IOTA_NUMBER_OF_SPOTS %d %d" %
(num_spots,
len(
n_spots_total.select(
n_spots_total == num_spots))))
if max_number_of_spots > 0:
# assuming one imageset per experiment here : applicable for stills
ts = imageset.get_image_identifier(0)
xfel_ts = ts[0:4] + ts[5:7] + ts[8:10] + ts[
11:13] + ts[14:16] + ts[17:19] + ts[20:23]
cbf_path = os.path.join(params.output.logging_dir,
'jungfrau_%s.cbf' % xfel_ts)
cbf_writer = FullCBFWriter(imageset=imageset)
cbf_writer.write_cbf(cbf_path)
per_image_analysis.print_table(stats)
logger.info(s.getvalue())
comm.barrier()
else:
do_work(0, iterable)
def run(args):
import libtbx.load_env
usage = "%s [options] datablock.json strong.pickle" %libtbx.env.dispatcher_name
parser = OptionParser(
usage=usage,
read_reflections=True,
read_datablocks=True,
read_experiments=True,
phil=phil_scope,
check_format=False,
epilog=help_message)
from libtbx.utils import Sorry
params, options = parser.parse_args(show_diff_phil=False)
reflections = flatten_reflections(params.input.reflections)
datablocks = flatten_datablocks(params.input.datablock)
experiments = flatten_experiments(params.input.experiments)
if not any([reflections, experiments, datablocks]):
parser.print_help()
return
if len(reflections) != 1:
raise Sorry('exactly 1 reflection table must be specified')
if len(datablocks) != 1:
if experiments:
if len(experiments.imagesets()) != 1:
raise Sorry('exactly 1 datablock must be specified')
imageset = experiments.imagesets()[0]
else:
raise Sorry('exactly 1 datablock must be specified')
else:
imageset = datablocks[0].extract_imagesets()[0]
reflections = reflections[0]
if params.id is not None:
reflections = reflections.select(reflections['id'] == params.id)
stats = per_image_analysis.stats_imageset(
imageset, reflections, resolution_analysis=params.resolution_analysis,
plot=params.individual_plots)
per_image_analysis.print_table(stats)
from libtbx import table_utils
overall_stats = per_image_analysis.stats_single_image(
imageset, reflections, resolution_analysis=params.resolution_analysis)
rows = [
("Overall statistics", ""),
("#spots", "%i" %overall_stats.n_spots_total),
("#spots_no_ice", "%i" %overall_stats.n_spots_no_ice),
#("total_intensity", "%.0f" %overall_stats.total_intensity),
("d_min", "%.2f" %overall_stats.estimated_d_min),
("d_min (distl method 1)", "%.2f (%.2f)" %(
overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1)),
("d_min (distl method 2)", "%.2f (%.2f)" %(
overall_stats.d_min_distl_method_1, overall_stats.noisiness_method_1)),
]
print table_utils.format(rows, has_header=True, prefix="| ", postfix=" |")
if params.json is not None:
import json
with open(params.json, 'wb') as fp:
json.dump(stats.__dict__, fp)
if params.plot is not None:
per_image_analysis.plot_stats(stats, filename=params.plot)
def run(args):
from dials.util.options import OptionParser
import libtbx.load_env
usage = "%s [options] find_spots.json" % (libtbx.env.dispatcher_name)
parser = OptionParser(usage=usage, phil=phil_scope, epilog=help_message)
params, options, args = parser.parse_args(show_diff_phil=True,
return_unhandled=True)
positions = None
if params.positions is not None:
with open(params.positions, 'rb') as f:
positions = flex.vec2_double()
for line in f.readlines():
line = line.replace('(', ' ').replace(')', '').replace(
',', ' ').strip().split()
assert len(line) == 3
i, x, y = [float(l) for l in line]
positions.append((x, y))
assert len(args) == 1
json_file = args[0]
import json
with open(json_file, 'rb') as f:
results = json.load(f)
n_indexed = flex.double()
fraction_indexed = flex.double()
n_spots = flex.double()
n_lattices = flex.double()
crystals = []
image_names = flex.std_string()
for r in results:
n_spots.append(r['n_spots_total'])
image_names.append(str(r['image']))
if 'n_indexed' in r:
n_indexed.append(r['n_indexed'])
n_lattices.append(len(r['lattices']))
for d in r['lattices']:
from dxtbx.serialize.crystal import from_dict
crystals.append(from_dict(d['crystal']))
else:
n_indexed.append(0)
n_lattices.append(0)
if n_indexed.size():
sel = n_spots > 0
fraction_indexed = flex.double(n_indexed.size(), 0)
fraction_indexed.set_selected(
sel,
n_indexed.select(sel) / n_spots.select(sel))
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot
blue = '#3498db'
red = '#e74c3c'
marker = 'o'
alpha = 0.5
lw = 0
plot = True
table = True
grid = params.grid
from libtbx import group_args
from dials.algorithms.spot_finding.per_image_analysis \
import plot_stats, print_table
estimated_d_min = flex.double()
d_min_distl_method_1 = flex.double()
d_min_distl_method_2 = flex.double()
n_spots_total = flex.int()
n_spots_no_ice = flex.int()
total_intensity = flex.double()
for d in results:
estimated_d_min.append(d['estimated_d_min'])
d_min_distl_method_1.append(d['d_min_distl_method_1'])
d_min_distl_method_2.append(d['d_min_distl_method_2'])
n_spots_total.append(d['n_spots_total'])
n_spots_no_ice.append(d['n_spots_no_ice'])
total_intensity.append(d['total_intensity'])
stats = group_args(image=image_names,
n_spots_total=n_spots_total,
n_spots_no_ice=n_spots_no_ice,
n_spots_4A=None,
n_indexed=n_indexed,
fraction_indexed=fraction_indexed,
total_intensity=total_intensity,
estimated_d_min=estimated_d_min,
d_min_distl_method_1=d_min_distl_method_1,
d_min_distl_method_2=d_min_distl_method_2,
noisiness_method_1=None,
noisiness_method_2=None)
if plot:
plot_stats(stats)
pyplot.clf()
if table:
print_table(stats)
n_rows = 10
n_rows = min(n_rows, len(n_spots_total))
perm_n_spots_total = flex.sort_permutation(n_spots_total, reverse=True)
print 'Top %i images sorted by number of spots:' % n_rows
print_table(stats, perm=perm_n_spots_total, n_rows=n_rows)
if flex.max(n_indexed) > 0:
perm_n_indexed = flex.sort_permutation(n_indexed, reverse=True)
print 'Top %i images sorted by number of indexed reflections:' % n_rows
print_table(stats, perm=perm_n_indexed, n_rows=n_rows)
print "Number of indexed lattices: ", (n_indexed > 0).count(True)
print "Number with valid d_min but failed indexing: ", (
(d_min_distl_method_1 > 0) & (d_min_distl_method_2 > 0) &
(estimated_d_min > 0) & (n_indexed == 0)).count(True)
n_bins = 20
spot_count_histogram(n_spots_total,
n_bins=n_bins,
filename='hist_n_spots_total.png',
log=True)
spot_count_histogram(n_spots_no_ice,
n_bins=n_bins,
filename='hist_n_spots_no_ice.png',
log=True)
spot_count_histogram(n_indexed.select(n_indexed > 0),
n_bins=n_bins,
filename='hist_n_indexed.png',
log=False)
if len(crystals):
plot_unit_cell_histograms(crystals)
if params.stereographic_projections and len(crystals):
from dxtbx.datablock import DataBlockFactory
datablocks = DataBlockFactory.from_filenames([image_names[0]],
verbose=False)
assert len(datablocks) == 1
imageset = datablocks[0].extract_imagesets()[0]
s0 = imageset.get_beam().get_s0()
# XXX what if no goniometer?
rotation_axis = imageset.get_goniometer().get_rotation_axis()
indices = ((1, 0, 0), (0, 1, 0), (0, 0, 1))
for i, index in enumerate(indices):
from cctbx import crystal, miller
from scitbx import matrix
miller_indices = flex.miller_index([index])
symmetry = crystal.symmetry(
unit_cell=crystals[0].get_unit_cell(),
space_group=crystals[0].get_space_group())
miller_set = miller.set(symmetry, miller_indices)
d_spacings = miller_set.d_spacings()
d_spacings = d_spacings.as_non_anomalous_array().expand_to_p1()
d_spacings = d_spacings.generate_bijvoet_mates()
miller_indices = d_spacings.indices()
# plane normal
d0 = matrix.col(s0).normalize()
d1 = d0.cross(matrix.col(rotation_axis)).normalize()
d2 = d1.cross(d0).normalize()
reference_poles = (d0, d1, d2)
from dials.command_line.stereographic_projection import stereographic_projection
projections = []
for cryst in crystals:
reciprocal_space_points = list(
cryst.get_U() *
cryst.get_B()) * miller_indices.as_vec3_double()
projections.append(
stereographic_projection(reciprocal_space_points,
reference_poles))
#from dials.algorithms.indexing.compare_orientation_matrices import \
# difference_rotation_matrix_and_euler_angles
#R_ij, euler_angles, cb_op = difference_rotation_matrix_and_euler_angles(
# crystals[0], cryst)
#print max(euler_angles)
from dials.command_line.stereographic_projection import plot_projections
plot_projections(projections,
filename='projections_%s.png' % ('hkl'[i]))
pyplot.clf()
def plot_grid(values,
grid,
file_name,
cmap=pyplot.cm.Reds,
vmin=None,
vmax=None,
invalid='white'):
values = values.as_double()
# At DLS, fast direction appears to be largest direction
if grid[0] > grid[1]:
values.reshape(flex.grid(reversed(grid)))
values = values.matrix_transpose()
else:
values.reshape(flex.grid(grid))
Z = values.as_numpy_array()
#f, (ax1, ax2) = pyplot.subplots(2)
f, ax1 = pyplot.subplots(1)
mesh1 = ax1.pcolormesh(values.as_numpy_array(),
cmap=cmap,
vmin=vmin,
vmax=vmax)
mesh1.cmap.set_under(color=invalid, alpha=None)
mesh1.cmap.set_over(color=invalid, alpha=None)
#mesh2 = ax2.contour(Z, cmap=cmap, vmin=vmin, vmax=vmax)
#mesh2 = ax2.contourf(Z, cmap=cmap, vmin=vmin, vmax=vmax)
ax1.set_aspect('equal')
ax1.invert_yaxis()
#ax2.set_aspect('equal')
#ax2.invert_yaxis()
pyplot.colorbar(mesh1, ax=ax1)
#pyplot.colorbar(mesh2, ax=ax2)
pyplot.savefig(file_name, dpi=600)
pyplot.clf()
def plot_positions(values,
positions,
file_name,
cmap=pyplot.cm.Reds,
vmin=None,
vmax=None,
invalid='white'):
values = values.as_double()
assert positions.size() >= values.size()
positions = positions[:values.size()]
if vmin is None:
vmin = flex.min(values)
if vmax is None:
vmax = flex.max(values)
x, y = positions.parts()
dx = flex.abs(x[1:] - x[:-1])
dy = flex.abs(y[1:] - y[:-1])
dx = dx.select(dx > 0)
dy = dy.select(dy > 0)
scale = 1 / flex.min(dx)
#print scale
x = (x * scale).iround()
y = (y * scale).iround()
from libtbx.math_utils import iceil
z = flex.double(
flex.grid(iceil(flex.max(y)) + 1,
iceil(flex.max(x)) + 1), -2)
#print z.all()
for x_, y_, z_ in zip(x, y, values):
z[y_, x_] = z_
plot_grid(z.as_1d(),
z.all(),
file_name,
cmap=cmap,
vmin=vmin,
vmax=vmax,
invalid=invalid)
return
if grid is not None or positions is not None:
if grid is not None:
positions = tuple(reversed(grid))
plotter = plot_grid
else:
plotter = plot_positions
cmap = pyplot.get_cmap(params.cmap)
plotter(n_spots_total,
positions,
'grid_spot_count_total.png',
cmap=cmap,
invalid=params.invalid)
plotter(n_spots_no_ice,
positions,
'grid_spot_count_no_ice.png',
cmap=cmap,
invalid=params.invalid)
plotter(total_intensity,
positions,
'grid_total_intensity.png',
cmap=cmap,
invalid=params.invalid)
if flex.max(n_indexed) > 0:
plotter(n_indexed,
positions,
'grid_n_indexed.png',
cmap=cmap,
invalid=params.invalid)
plotter(fraction_indexed,
positions,
'grid_fraction_indexed.png',
cmap=cmap,
vmin=0,
vmax=1,
invalid=params.invalid)
for i, d_min in enumerate(
(estimated_d_min, d_min_distl_method_1, d_min_distl_method_2)):
from cctbx import uctbx
d_star_sq = uctbx.d_as_d_star_sq(d_min)
d_star_sq.set_selected(d_star_sq == 1, 0)
vmin = flex.min(d_star_sq.select(d_star_sq > 0))
vmax = flex.max(d_star_sq)
vmin = flex.min(d_min.select(d_min > 0))
vmax = flex.max(d_min)
cmap = pyplot.get_cmap('%s_r' % params.cmap)
d_min.set_selected(d_min <= 0, vmax)
if i == 0:
plotter(d_min,
positions,
'grid_d_min.png',
cmap=cmap,
vmin=vmin,
vmax=vmax,
invalid=params.invalid)
else:
plotter(d_min,
positions,
'grid_d_min_method_%i.png' % i,
cmap=cmap,
vmin=vmin,
vmax=vmax,
invalid=params.invalid)
if flex.max(n_indexed) > 0:
pyplot.hexbin(n_spots,
n_indexed,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.scatter(n_spots, n_indexed, marker=marker, alpha=alpha, c=blue, lw=lw)
xlim = pyplot.xlim()
ylim = pyplot.ylim()
pyplot.plot([0, max(n_spots)], [0, max(n_spots)], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel('# spots')
pyplot.ylabel('# indexed')
pyplot.savefig('n_spots_vs_n_indexed.png')
pyplot.clf()
pyplot.hexbin(n_spots,
fraction_indexed,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.scatter(
#n_spots, fraction_indexed, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel('# spots')
pyplot.ylabel('Fraction indexed')
pyplot.savefig('n_spots_vs_fraction_indexed.png')
pyplot.clf()
pyplot.hexbin(n_indexed,
fraction_indexed,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.scatter(
#n_indexed, fraction_indexed, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel('# indexed')
pyplot.ylabel('Fraction indexed')
pyplot.savefig('n_indexed_vs_fraction_indexed.png')
pyplot.clf()
pyplot.hexbin(n_spots,
n_lattices,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.scatter(
#n_spots, n_lattices, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel('# spots')
pyplot.ylabel('# lattices')
pyplot.savefig('n_spots_vs_n_lattices.png')
pyplot.clf()
#pyplot.scatter(
# estimated_d_min, d_min_distl_method_1, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.hexbin(estimated_d_min,
d_min_distl_method_1,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.gca().set_aspect('equal')
xlim = pyplot.xlim()
ylim = pyplot.ylim()
m = max(max(estimated_d_min), max(d_min_distl_method_1))
pyplot.plot([0, m], [0, m], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel('estimated_d_min')
pyplot.ylabel('d_min_distl_method_1')
pyplot.savefig('d_min_vs_distl_method_1.png')
pyplot.clf()
#pyplot.scatter(
# estimated_d_min, d_min_distl_method_2, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.hexbin(estimated_d_min,
d_min_distl_method_2,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.gca().set_aspect('equal')
xlim = pyplot.xlim()
ylim = pyplot.ylim()
m = max(max(estimated_d_min), max(d_min_distl_method_2))
pyplot.plot([0, m], [0, m], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel('estimated_d_min')
pyplot.ylabel('d_min_distl_method_2')
pyplot.savefig('d_min_vs_distl_method_2.png')
pyplot.clf()
#pyplot.scatter(
# d_min_distl_method_1, d_min_distl_method_2, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.hexbin(d_min_distl_method_1,
d_min_distl_method_2,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.gca().set_aspect('equal')
xlim = pyplot.xlim()
ylim = pyplot.ylim()
m = max(max(d_min_distl_method_1), max(d_min_distl_method_2))
pyplot.plot([0, m], [0, m], c=red)
pyplot.xlim(0, xlim[1])
pyplot.ylim(0, ylim[1])
pyplot.xlabel('d_min_distl_method_1')
pyplot.ylabel('d_min_distl_method_2')
pyplot.savefig('distl_method_1_vs_distl_method_2.png')
pyplot.clf()
pyplot.hexbin(n_spots,
estimated_d_min,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.scatter(
#n_spots, estimated_d_min, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel('# spots')
pyplot.ylabel('estimated_d_min')
pyplot.savefig('n_spots_vs_d_min.png')
pyplot.clf()
pyplot.hexbin(n_spots,
d_min_distl_method_1,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.scatter(
#n_spots, d_min_distl_method_1, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel('# spots')
pyplot.ylabel('d_min_distl_method_1')
pyplot.savefig('n_spots_vs_distl_method_1.png')
pyplot.clf()
pyplot.hexbin(n_spots,
d_min_distl_method_2,
bins='log',
cmap=pyplot.cm.jet,
gridsize=50)
pyplot.colorbar()
#pyplot.scatter(
#n_spots, d_min_distl_method_2, marker=marker, alpha=alpha, c=blue, lw=lw)
pyplot.xlim(0, pyplot.xlim()[1])
pyplot.ylim(0, pyplot.ylim()[1])
pyplot.xlabel('# spots')
pyplot.ylabel('d_min_distl_method_2')
pyplot.savefig('n_spots_vs_distl_method_2.png')
pyplot.clf()
