def label_nearest_point(self):
ann = AnnAdaptorSelfInclude(self.points.as_double(), 3)
ann.query(self.rotation_center)
i = ann.nn[0]
gltbx.fonts.ucs_bitmap_8x13.setup_call_lists()
gl.glDisable(gl.GL_LIGHTING)
gl.glColor3f(1.0, 1.0, 1.0)
gl.glLineWidth(1.0)
xyz = self.points_data["xyz"][i]
exp_id = self.points_data["id"][i]
panel = self.points_data["panel"][i]
d_spacing = self.points_data["d_spacing"][i]
label = (f"id: {exp_id}; panel: {panel}\n"
f"xyz: {xyz[0]:.1f} {xyz[1]:.1f} {xyz[2]:.1f}\n"
f"res: {d_spacing:.2f} Angstrom")
if "miller_index" in self.points_data and exp_id != -1:
hkl = self.points_data["miller_index"][i]
label += f"\nhkl: {hkl}"
line_spacing = round(gltbx.fonts.ucs_bitmap_8x13.height())
for j, string in enumerate(label.splitlines()):
gl.glRasterPos3f(*self.points[i])
gl.glBitmap(0, 0, 0.0, 0.0, line_spacing, -j * line_spacing, b" ")
gltbx.fonts.ucs_bitmap_8x13.render_string(string)
def match(a, b, max_separation=2, key="xyzobs.px.value", scale=(1, 1, 1)):
"""Match reflections from list a and list b, returning tuple of
flex.size_t indices, optionally specifying the maximum distance and
key to search on (which is assumed to be a 3-vector column). Can also
apply relative scales to the vectors before matching in case e.g. very
wide or very fine slicing."""
xyz_a = a[key]
xyz_b = b[key]
if scale != (1, 1, 1):
x, y, z = xyz_a.parts()
x *= scale[0]
y *= scale[1]
z *= scale[2]
xyz_a = flex.vec3_double(x, y, z)
x, y, z = xyz_b.parts()
x *= scale[0]
y *= scale[1]
z *= scale[2]
xyz_b = flex.vec3_double(x, y, z)
a = xyz_a.as_double().as_1d()
b = xyz_b.as_double().as_1d()
ann = AnnAdaptorSelfInclude(a, 3)
ann.query(b)
mm = flex.size_t(range(xyz_b.size()))
nn, distance = ann.nn, flex.sqrt(ann.distances)
sel = distance <= max_separation
mm = mm.select(sel)
nn = nn.select(sel)
distance = distance.select(sel)
return nn, mm, distance
def excercise_nearest_neighbor():
data = data_from_files()
A = AnnAdaptor(data, 2) # construct k-d tree for reference set
A.query(data) # find nearest neighbors of query points
for i in range(len(A.nn)):
#print "Neighbor of (%7.1f,%7.1f), index %6d distance %4.1f"%(
#data[2*i],data[2*i+1],A.nn[i],math.sqrt(A.distances[i]))
assert A.nn[i] != i
A = AnnAdaptorSelfInclude(data, 2) # construct k-d tree for reference set
A.query(data) # find nearest neighbors of query points
for i in range(len(A.nn)):
#print "Neighbor of (%7.1f,%7.1f), index %6d distance %4.1f"%(
#data[2*i],data[2*i+1],A.nn[i],math.sqrt(A.distances[i]))
assert A.nn[i] == i
def check_memory():
data = data_from_files()
for x in range(1000):
AnnAdaptorSelfInclude(data, 2).query(data)