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# kdArrange v.003 | |
import bmesh | |
import bpy | |
import mathutils | |
from collections import defaultdict | |
from BioBlender.table_values import values_fi | |
nstr = '_4GE.001' | |
surface_obj_name = 'SURFACE' | |
def vcols_from_nearest_fi(nstr, surface_obj_name): | |
''' | |
[x] step : first store atoms as {element_name: [co,..], } | |
[x] step : generate singular ordered mesh by adding vertices | |
in clumps of element types. (H,H,H,H,H,H,O,O,O,O,O,O..) | |
[x] step : track start and end index for each element into mapper_obj | |
[ ] step : get surface mesh. | |
[ ] step : for every vertex on surface mesh find closest | |
vertex in proxy_ob, and MLP value | |
''' | |
## storage | |
atom_to_fi = defaultdict(list) | |
proxy_obj = defaultdict(list) | |
idx_to_fi = [] | |
mapper_obj = {} | |
verts = [] | |
surface_verts_fi = {} | |
## aliasing | |
objs = bpy.data.objects | |
texts = bpy.data.texts | |
scene = bpy.context.scene | |
meshes = bpy.data.meshes | |
children = objs[nstr].children | |
## helper functions | |
def bmesh_from_pyverts(verts): | |
bm = bmesh.new() | |
add_vert = bm.verts.new | |
bm_verts = [add_vert(co) for co in verts] | |
bm.verts.index_update() | |
return bm | |
def write_fi_to_textblock(block_name, idx_to_fi, normalize=True): | |
if block_name in texts: | |
text = texts[block_name] | |
else: | |
text = texts.new(block_name) | |
if normalize: | |
n = lambda fi: round(((float(fi) + 3) * 0.25), 4) | |
idx_to_fi = [str(n(fi)) for fi in idx_to_fi] | |
fi_string = '\n'.join(idx_to_fi) | |
text.from_string(fi_string) | |
def generate_or_update(verts): | |
# -- get or create mesh | |
if "proxy_mesh" in meshes: | |
mesh = meshes["proxy_mesh"] | |
else: | |
mesh = meshes.new("proxy_mesh") | |
# -- inject mesh with verts | |
bm = bmesh_from_pyverts(verts) | |
bm.to_mesh(mesh) | |
bm.free() | |
# -- create or update object with new mesh data | |
if "proxy_obj" in objs: | |
obj = objs['proxy_obj'] | |
obj.data = mesh | |
else: | |
obj = objs.new("proxy_obj", mesh) | |
scene.objects.link(obj) | |
def build_ktree(v): | |
# documentation/blender_python_api_2_70_release/mathutils.kdtree.html | |
size = len(v) | |
kd = mathutils.kdtree.KDTree(size) | |
for i, vtx in enumerate(v): | |
kd.insert(Vector(vtx), i) | |
kd.balance() | |
return kd | |
def get_vcol_layer(obj): | |
vcols = obj.data.vertex_colors | |
if not ('fi_cols' in vcols): | |
vcol_layer = obj.data.vertex_colors.new('fi_cols') | |
else: | |
vcol_layer = vcols.get('fi_cols') | |
return vcol_layer | |
if not verts: | |
# fill `proxy_obj` and `atom_fo_fi` | |
for o in children: | |
_name = o.BBInfo[12:16].strip() | |
_amino = o.BBInfo[17:20].strip() | |
fi = values_fi[_amino][_name] | |
co = o.location[:] | |
proxy_obj[_name].append(co) # element name | |
atom_to_fi[_name].append(fi) | |
# fills `idx_to_fi` and `mapper_obj` | |
idx = 0 | |
for key in sorted(proxy_obj.keys()): | |
start = len(verts) | |
verts.extend(proxy_obj[key]) | |
end = len(verts)-1 | |
mapper_obj[key] = (start, end) | |
idx_to_fi.extend(atom_to_fi[key]) | |
# print(mapper_obj) | |
# {'C': (0, 97), 'N': (98, 124), 'O': (125, 153) ...} | |
if verts: | |
write_fi_to_textblock('fi_values.txt', idx_to_fi) | |
generate_or_update(verts) | |
else: | |
print('no verts! - ending early') | |
return | |
if surface_obj_name and (surface_obj_name in objs): | |
obj = objs.get(surface_obj_name) | |
vcol_layer = get_vcol_layer(obj) | |
kd = build_ktree(verts) | |
def from_closest(vidx, coordinate, mdist): | |
# Each surface vertex is present in at least 3 polygons. | |
# To avoid repeating proximity search with kdtree this | |
# algorithm memoizes the index and resulting fi value. | |
# - this assumes that a lookup in a hashtable is more | |
# efficient than kdtree + tests..for a second and third | |
# time the vertex appears in a different polygon. | |
for (co, index, dist) in kd.find_range(coordinate, mdist): | |
pass | |
# mesh data of surface | |
# surface_mesh = obj.data | |
# i = 0 | |
# for poly in surface_mesh.polygons: | |
# for idx, vidx in zip(poly.loop_indices, poly.vertices[:]): | |
# coordinate = surface_mesh.verts[vidx].co | |
# c = from_closest(vidx, coordinate, mdist=max_dist) | |
# vcol_layer.data[i].color = (c, c, c) | |
# i += 1 | |
vcols_from_nearest_fi(nstr, surface_obj_name) |
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