def test_plot_stats(centroid_test_data, tmpdir):
experiments, reflections = centroid_test_data
stats = per_image_analysis.stats_per_image(
experiments[0], reflections, resolution_analysis=False
)
image_file = tmpdir.join("pia.png")
per_image_analysis.plot_stats(stats, filename=image_file.strpath)
assert image_file.check()
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 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=None):
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(args, show_diff_phil=False)
reflections, experiments = reflections_and_experiments_from_files(
params.input.reflections, params.input.experiments)
if not reflections and not 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]
if "miller_index" in reflections:
sys.exit("Only unindexed reflections are currently supported")
if any(experiments.crystals()):
sys.exit("Only unindexed experiments are currently supported")
reflections.centroid_px_to_mm(experiments)
reflections.map_centroids_to_reciprocal_space(experiments)
if params.id is not None:
reflections = reflections.select(reflections["id"] == params.id)
all_stats = []
for i, expt in enumerate(experiments):
refl = reflections.select(reflections["id"] == i)
stats = per_image_analysis.stats_per_image(
expt, refl, resolution_analysis=params.resolution_analysis)
all_stats.append(stats)
# transpose stats
summary_table = {}
for s in all_stats:
for k, value in s._asdict().items():
summary_table.setdefault(k, [])
summary_table[k].extend(value)
stats = per_image_analysis.StatsMultiImage(**summary_table)
print(stats)
overall_stats = per_image_analysis.stats_for_reflection_table(
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", f"{overall_stats.estimated_d_min:.2f}"),
(
"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_2,
overall_stats.noisiness_method_2),
),
]
print(tabulate(rows, headers="firstrow"))
if params.json:
if params.split_json:
for k, v in stats._asdict().items():
start, end = params.json.split(".")
with open(f"{start}_{k}.{end}", "w") as fp:
json.dump(v, fp)
if params.joint_json:
with open(params.json, "w") as fp:
json.dump(stats._asdict(), 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,
):
if nproc is None:
nproc = _nproc()
with ThreadPool(processes=nproc) as pool:
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:
with open(json_file, "wb") as f:
json.dump(results, f)
if plot or table:
from dials.algorithms.spot_finding.per_image_analysis import (
StatsMultiImage,
plot_stats,
)
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 = StatsMultiImage(
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(stats)
if grid is not None:
from matplotlib import pyplot
n_spots_no_ice.reshape(flex.grid(grid))
print(n_spots_no_ice.size())
pyplot.figure()
pyplot.pcolormesh(n_spots_no_ice.as_numpy_array(), cmap=pyplot.cm.Reds)
pyplot.savefig("spot_count.png")
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):
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 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):
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()
