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interim code
# 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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