def test_tempy_sccc(self):
''' Test the tempy sccc score based on the files
provided. Use this as a baseline for the second
chimeraX test. '''
# the sigma factor determines the width of the Gaussian distribution used to describe each atom
sim_sigma_coeff = 0.187
path_test = "./"
m = os.path.join(path_test, '1akeA_10A.mrc')
p = os.path.join(path_test, '1ake_mdl1.pdb')
r = 10.0
rb_file = os.path.join(path_test, '1ake_mdl1_rigid.txt')
scorer = ScoringFunctions()
# read map file
emmap = MapParser.readMRC(m)
# read PDB file
structure_instance = PDBParser.read_PDB_file('pdbfile',
p,
hetatm=False,
water=False)
SCCC_list_structure_instance = []
# read rigid body file and generate structure instances for each segment
listRB = RBParser.read_FlexEM_RIBFIND_files(rb_file,
structure_instance)
# score each rigid body segment
listsc_sccc = []
for RB in listRB:
# sccc score
score_SCCC = scorer.SCCC(emmap, r, sim_sigma_coeff,
structure_instance, RB)
listsc_sccc.append(score_SCCC)
self.assertTrue(len(listRB) == 6)
self.assertTrue(abs(round(listsc_sccc[0], 4) - 0.954) < 0.01)
self.assertTrue(abs(round(listsc_sccc[1], 4) - 0.427) < 0.01)
self.assertTrue(abs(round(listsc_sccc[2], 4) - 0.624) < 0.01)
self.assertTrue(abs(round(listsc_sccc[3], 4) - 0.838) < 0.01)
self.assertTrue(abs(round(listsc_sccc[4], 4) - 0.971) < 0.01)
self.assertTrue(abs(round(listsc_sccc[5], 4) - 0.928) < 0.01)
p,
hetatm=False,
water=False)
# generate atom density and blur to required resolution
#sim_map = blurrer.gaussian_blur(structure_instance, r,densMap=emmap,sigma_coeff=sim_sigma_coeff,normalise=True)
#sim_map = blurrer.gaussian_blur_real_space(structure_instance, r,densMap=emmap,sigma_coeff=sim_sigma_coeff,normalise=True)
SCCC_list_structure_instance = []
# read rigid body file and generate structure instances for each segment
listRB = RBParser.read_FlexEM_RIBFIND_files(rb_file, structure_instance)
# score each rigid body segment
listsc_sccc = []
print 'calculating scores'
for RB in listRB:
# sccc score
score_SCCC = scorer.SCCC(emmap, r, sim_sigma_coeff, structure_instance, RB)
SCCC_list_structure_instance.append(score_SCCC)
print '>>', score_SCCC
listsc_sccc.append(score_SCCC)
listRB = RBParser.RBfileToRBlist(rb_file)
if len(listRB) == len(listsc_sccc):
#include sccc scores as b-factor records
for x in structure_instance.atomList:
cur_chain = x.chain
cur_res = x.get_res_no()
ct = 0
flage = 0
if cur_chain in ['', ' ']: cur_chain = '-'
for rb in listRB:
'''
sigma_coeff=sim_sigma_coeff,
normalise=True)
print 'structure_instance', scorer.CCC(sim_map, emmap)
print sim_map
sim_map2 = blurrer.gaussian_blur(structure_instance2,
6.6,
densMap=emmap,
sigma_coeff=sim_sigma_coeff,
normalise=True)
print 'structure_instance_same', scorer.CCC(sim_map2, emmap)
SCCC_list_structure_instance = []
listRB = RBParser.read_FlexEM_RIBFIND_files(rb_file, structure_instance2)
for RB in listRB:
score_SCCC = scorer.SCCC(emmap, 6.6, sim_sigma_coeff, structure_instance2,
RB)
SCCC_list_structure_instance.append(score_SCCC)
print score_SCCC
listRB = RBParser.RBfileToRBlist(rb_file)
Plot.PrintOutChimeraAttributeFileSCCC_Score('3MFP',
SCCC_list_structure_instance,
listRB)
SCCC_list_structure_instance2 = []
listRB2 = RBParser.read_FlexEM_RIBFIND_files(rb_file, structure_instance2)
for RB in listRB2:
score_SCCC = scorer.SCCC(emmap, 6.6, sim_sigma_coeff, structure_instance2,
RB)
SCCC_list_structure_instance2.append(score_SCCC)
def score(session,
atomic_model,
map_model,
rigid_filename,
rez,
sim_sigma=0.187,
colour_atoms=True):
""" Perform the SCCC score
Takes a session, a single model, map, rigid file path and some tuneable
optional variables
"""
print("Calculating SCCC Score")
# make class instances for density simulation (blurring), scoring and plot scores
blurrer = StructureBlurrer()
scorer = ScoringFunctions()
atomlist = []
# Pre-defines
bio_atom_structure = ""
bio_map_structure = ""
try:
for atom in atomic_model.atoms:
atomlist.append(chimera_to_tempy_atom(atom, len(atomlist)))
bio_atom_structure = BioPy_Structure(atomlist)
bio_map_structure = chimera_to_tempy_map(map_model)
# read rigid body file and generate structure instances for each segment
listRB = RBParser.read_FlexEM_RIBFIND_files(rigid_filename,
bio_atom_structure)
except Exception as e:
print(e)
print(
"Error in reading Model and Map. Make sure you have selected one model and one map, and the rigid file is correct."
)
return
# score each rigid body segment
listsc_sccc = []
print('calculating SCCC')
for RB in listRB:
# sccc score
score_SCCC = scorer.SCCC(bio_map_structure,
rez,
sim_sigma,
bio_atom_structure,
RB,
c_mode=False)
print('>>', score_SCCC)
listsc_sccc.append((RB, score_SCCC))
# Colour the atoms based on the rating from white (1.0) to red (0.0)
# TODO - maybe a faster way? Also 'all_atoms' mentioned in the API doesnt exist but atoms does! :S
# TODO - move this to somewhere better maybe?
if colour_atoms:
dr = 255
dg = 255
db = 255
if score_SCCC >= 0.5:
dr = 255 - int(math.floor(255 * ((score_SCCC - 0.5) * 2.0)))
dg = dr
else:
db = int(math.floor(255 * (score_SCCC * 2.0)))
dg = db
residues = []
for a in RB.atomList:
if a.res_no not in residues:
residues.append(a.res_no)
for r in residues:
cr = atomic_model.residues[r]
for catm in cr.atoms:
catm.color = [dr, dg, db, 255]
cr.ribbon_color = [dr, dg, db, 255]
return listsc_sccc
