test

MLP_mod.py

# kdArrange

import bpy
from collections import defaultdict

from BioBlender.table_values import values_fi

'''
[x] step 01: first store atoms as {element_name: [co,..], }
[ ] step 02: generate singular ordered mesh by adding vertices
             in clumps of element types. (H,H,H,H,H,H,O,O,O,O,O,O..)
[ ] step 03: track start and end index for each element into mapper_obj
[ ] step 04: preprocess obj.fi_value = BBinfo.get(')
[ ] step 05: get surface mesh.
[ ] step 06: for every vertex on surface mesh find closest
             vertex in proxy_ob, and MLP value

'''

# step 01

atom_to_fi = defaultdict(list)
proxy_obj = defaultdict(list)

nstr = '_4GE.001'
objs = bpy.data.objects[nstr].children

for o in objs:
    _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)

# step 03

# will just hold a fi vale for every vertex in the atom cloud.
idx_to_fi = []

mapper_obj = {}
verts = []

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) ...}

# step 02

mesh = bpy.data.meshes.new("mesh_name")
mesh.from_pydata(verts, edges=[], faces=[])
mesh.update()

obj = bpy.data.objects.new("obj_name", mesh)

scene = bpy.context.scene
scene.objects.link(obj)