def test_density_props_cpp():
pdb_inp = iotbx.pdb.input(source_info=None, lines = pdb_str)
ph = pdb_inp.construct_hierarchy()
atoms=ph.atoms()
a=atoms[0]
e=a.element.strip(" ").lower()
if(len(e)==1):e=e+"_"
wfc_obj = pair_interaction.load_wfc(e)
a_xyz = [10.448638218462468 , 3.71765835974282866E-002 , 1.0816225869801266]
p=[10.149391801428232, 1.4386837495289542, 2.4831297529116525]
r = ext.atom_density_props(p = p, a_xyz = a_xyz, wfc_obj = wfc_obj)
return r
def test_density_props_cpp():
# All numbers verified against Java and Python implementation: all numbers
# match.
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
ph = pdb_inp.construct_hierarchy()
atoms = ph.atoms()
a = atoms[0]
e = a.element.strip(" ").lower()
if (len(e) == 1): e = e + "_"
wfc_obj = pair_interaction.load_wfc(e)
a_xyz = [10.448638218462468, 3.71765835974282866E-002, 1.0816225869801266]
p = [10.149391801428232, 1.4386837495289542, 2.4831297529116525]
r = ext.atom_density_props(p=p, a_xyz=a_xyz, wfc_obj=wfc_obj)
#print ("density", r.density)
#print ("gradient_vector", r.gradient_vector)
#print ("hessian", r.hessian)
return r
def test_density_props_cpp():
# This matches numbers from NCIPLOT (FORTRAN code).
e="C_"
wfc_obj = pair_interaction.load_wfc(e)
a_xyz = [0,0,0]
p=[-0.11027379670470783, -0.11027379670470783, -1.0551368983523539]
r = ext.atom_density_props(p = p, a_xyz = a_xyz, wfc_obj = wfc_obj)
#print ("density", r.density)
#print ("gradient_vector", r.gradient_vector)
#print ("hessian", r.hessian)
#print ("has_silva_interaction (dori)", r.has_silva_interaction("dori"))
#print ("has_silva_interaction (sedd)", r.has_silva_interaction("sedd"))
#silva = r.cal_silva()
#print ("cal_silva", silva)
#print ("get_dori_value", r.get_dori_value())
#print ("get_sedd_value", r.get_sedd_value())
return r
def run():
folder = libtbx.env.find_in_repositories("qrefine/plugin/yoink/dat")
if folder is None: return # skip test
assert not os.path.isfile("p__lda.wfc")
o = pair_interaction.load_wfc("p")
assert os.path.isfile("p__lda.wfc")