def main():
modelfile = sys.argv[1]
m = Model(modelfile)
# Below is the identity rotation array.
#rot_arr = [1,0,0,0,1,0,0,0,1]
# Below is the correct rotation matrix for JWH t3 icofrac to get it into the orientation in his PRL paper.
#rot_arr = [-0.031777, 0.998843, 0.036102, 0.986602, 0.025563, 0.161133, 0.160023, 0.040739, -0.986272]
# Below is a (the?) rotation matrix for JWH t1 icofrac.
#rot_arr = [ 0.954646, -0.233932, 0.184194, 0.280650, 0.913581, -0.294287, -0.099433, 0.332633, 0.937800 ]
# Below is a (the?) rotation matrix of Pei's t1 that gives some planes. Oriented for a specific plane ~.
#rot_arr = [ -0.977103, -0.123352, -0.173361, -0.130450, 0.990997, 0.030118, 0.168085, 0.052043, -0.984398 ]
# Below is a (the?) rotation matrix of Pei's t2 that gives some planes. Oriented for a specific plane ~.
rot_arr = [ 0.985478, -0.010230, -0.169493, 0.009247, 0.999936, -0.006586, 0.169549, 0.004923, 0.985509]
# Below is a (the?) rotation matrix of Pei's t3 that gives some planes. Oriented for a specific plane ~.
#rot_arr = [0.981624,-0.002765,-0.190808, -0.003436,0.999477,-0.032163, 0.190797,0.032228,0.981100]
npra = np.asarray(rot_arr)
rot(m,npra)
# Write cif file to screen
#m.write_cif()
m.write_our_xyz()
def glm(modelfile,mult):
model = Model(modelfile)
outfile = modelfile+'.bigger'
model = Model(modelfile)
atoms = model.atoms[:]
natoms = model.natoms
for atom in atoms:
for i in range(0,mult):
for j in range(0,mult):
for k in range(0,mult):
if(i == j == k == 0): continue
natoms += 1
x = atom.coord[0] + i*model.lx
y = atom.coord[1] + j*model.ly
z = atom.coord[2] + k*model.lz
model.add_atom(Atom(natoms,atom.z,x,y,z))
# Shift right 1/2 world and left mult/2 worlds
# which is equivalent to left (mult-1)/2 worlds
for i,atom in enumerate(model.atoms):
model.atoms[i].set_coord(model.atoms[i].coord[0] - (mult-1)/2.0*model.lx, model.atoms[i].coord[1] - (mult-1)/2.0*model.ly, model.atoms[i].coord[2] - (mult-1)/2.0*model.lz)
model.lx *= mult
model.ly *= mult
model.lz *= mult
model.write_our_xyz(outfile)
def main():
modelfile = sys.argv[1]
m = Model(modelfile)
# Below is a (the?) rotation matrix of Pei's t1 that gives some planes. Oriented for a specific plane ~.
rot_arr = [ -0.977103, -0.123352, -0.173361, -0.130450, 0.990997, 0.030118, 0.168085, 0.052043, -0.984398 ]
rot(m,rot_arr)
# Angles in radians
# Note that these are semi difficult to figure out from the vesta rotation matrix,
# partly because there are negative angles, so you may need to do 2pi - angle you found.
#t1 = np.pi*2 - 0.0371505
#t2 = 0.162790
#t3 = 0
#rot_arr = calc_rot_array(m,t1,t2,t3)
#rot(m,rot_arr)
# Write cif file to screen
#m.write_cif()
m.write_our_xyz()
def main():
modelfile = sys.argv[1]
outfilebase = sys.argv[2]
m = Model(modelfile)
# Below is the identity rotation array.
#rot_arr = [1,0,0,0,1,0,0,0,1]
# Below is the correct rotation matrix for JWH t3 icofrac to get it into the orientation in his PRL paper.
#rot_arr = [-0.031777, 0.998843, 0.036102, 0.986602, 0.025563, 0.161133, 0.160023, 0.040739, -0.986272]
# Below is a (the?) rotation matrix for JWH t1 icofrac.
#rot_arr = [ 0.954646, -0.233932, 0.184194, 0.280650, 0.913581, -0.294287, -0.099433, 0.332633, 0.937800 ]
# Below is a (the?) rotation matrix of Pei's t1 that gives some planes. Oriented for a specific plane ~.
#rot_arr = [ -0.977103, -0.123352, -0.173361, -0.130450, 0.990997, 0.030118, 0.168085, 0.052043, -0.984398 ]
# Below is a (the?) rotation matrix of Pei's t2 that gives some planes. Oriented for a specific plane ~.
#rot_arr = [ 0.985478, -0.010230, -0.169493, 0.009247, 0.999936, -0.006586, 0.169549, 0.004923, 0.985509]
# Below is a (the?) rotation matrix of Pei's t3 that gives some planes. Oriented for a specific plane ~.
#rot_arr = [0.981624,-0.002765,-0.190808, -0.003436,0.999477,-0.032163, 0.190797,0.032228,0.981100]
# Below is a rotation matrix of Pei's Zr50 t3 that gives some planes. Oriented for a specific plane ~.
#rot_arr = [0.983910,-0.035278,-0.175147,0.048246,0.996356,0.070340,0.172028,-0.077659,0.982026]
# Below is a rotation matrix of Pei's Zr50 t3, cluster 0 from 'Crystal-like' with an alternate plane pattern.
#rot_arr = [-0.078966,-0.539181,0.838479,-0.486443,0.755011,0.439695,-0.870137,-0.373151,-0.321901]
#rot_arr = [-0.550168, -0.592703, -0.588233, -0.675963, 0.729699, -0.103023, 0.490295, 0.340944, -0.802102] # a second attempt
#rot_arr = [ -0.102056, 0.795154, -0.597758, 0.867018, 0.365702, 0.338439, 0.487712, -0.483728, -0.726735] # eh
#rot_arr = [ -0.485998, 0.815787, 0.313525, -0.151211, -0.431820, 0.889195, 0.860779, 0.384739, 0.333220]
#rot_arr = [ -0.508856, -0.631880, -0.584631, -0.710228, 0.691926, -0.129672, 0.486458, 0.349237, -0.800870]
# Below is a rotation matrix of Pei's Zr50 t3, all clusters, with one promising spot. Mostly noise probably.
#rot_arr = [0.563829, 0.290821, -0.772995, -0.641563, -0.435169, -0.631684, -0.520090, 0.852086, -0.058781]
#rot_arr = [-0.832982, -0.489634, -0.257683, -0.212007, -0.147731, 0.966038, -0.511072, 0.859322, 0.019252]
#rot_arr = [ 0.494247, 0.236924, -0.836413, 0.704623, 0.454325, 0.545064, 0.509142, -0.858752, 0.057607]
#rot_arr = [0.235445, 0.089792, 0.967731, -0.967993, 0.110713, 0.225236, -0.086917, -0.989788, 0.112985]
#rot_arr = [0.216234, -0.047608, 0.975180,-0.955161, 0.196601, 0.221394,-0.202262, -0.979327, -0.002961]
# BELOW: t3 - 0 rot
rot_arr = [ 0.984104, 0.007169, -0.177450, 0.008311, 0.996231, 0.086340, 0.177400, -0.086442, 0.980335] # Pei t3 0 rot
#rot_arr = [ 0.983102, -0.003388, -0.183028, 0.003365, 0.999994, -0.000437, 0.183028, -0.000187, 0.983108]
# Pei's t2
#rot_arr = [ 0.985568, 0.001345, -0.169276, 0.000677, 0.999929, 0.011883, 0.169280, -0.011826, 0.985497] # 0rot
#rot_arr = [ -0.406881, -0.461953, -0.788065, -0.548766, 0.813295, -0.193412, 0.730277, 0.353768, -0.584418] #1rot
#rot_arr =[ -0.466130, 0.820964, 0.329760, -0.171769, -0.449615, 0.876551, 0.867881, 0.351944, 0.350596] # 2rot
#rot_arr = [ -0.303913, -0.572611, -0.761415, -0.608626, 0.731562, -0.307232, 0.732947, 0.370045, -0.570838] # 3rot
#rot_arr = [ -0.653632, -0.441386, -0.614771, -0.749000, 0.260854, 0.609061, -0.108465,0.858565, -0.501100] # 4rot
# Pei's t1
#rot_arr = [0.985435, 0.000143, -0.170053, 0.003167, 0.999811, 0.019194, 0.170023, -0.019453, 0.985248] #0rot
#rot_arr = [ -0.181186, -0.653769, -0.734682, -0.632103, 0.649717, -0.422273, 0.753404, 0.387885, -0.530968] #1rot
#rot_arr = [ -0.408942, 0.873456, 0.264275, -0.252622, -0.386635, 0.886959, 0.876897, 0.295953, 0.378765] #2rot
#rot_arr = [ -0.243749, -0.741828, -0.624722, -0.637231, 0.608094, -0.473453, 0.731111, 0.282689, -0.620938] #3rot
# JWH's t1
#rot_arr = [ 0.915013, -0.004459, -0.403401, -0.010527, 0.999335, -0.034924, 0.403288, 0.036202, 0.914357]
# JWH's t2
#rot_arr = [ 0.983576, 0.009003, -0.180268, 0.010862, 0.993992, 0.108912, 0.180165, -0.109081, 0.977569]
#JWH's t3
#rot_arr = [ 0.985667, 0.014122, 0.168112, -0.020755, 0.999071, 0.037763, -0.167422, -0.040711, 0.985044]
#JWH's t3 all.xtal cluster
#rot_arr = [ 0.985237, 0.004144, 0.171146, -0.008192, 0.999703, 0.022952, -0.171000, -0.024015, 0.984978]
# 10k Zr50 model, rotated to see cluster in bottom right corner.
#rot_arr = [ 0.985669, -0.008419, -0.168483, -0.000875, 0.998485, -0.055012, 0.168691, 0.054371, 0.984168]
#rot_arr = [1,0,0,0,1,0,0,0,1]
#kx = 0.3046888
#ky = -0.0234376
#kz = 0.2578136
#kmag = sqrt(kx**2+ky**2+kz**2)
#print(kx,ky,kz,kmag)
#rot_arr = calc_rot_array(acos(kx/kmag),acos(ky/kmag),acos(kz/kmag))
#print(rot_arr)
npra = np.asarray(rot_arr)
rot(m,npra)
# Write cif file to screen
m.write_cif(outfilebase+'.cif')
m.write_our_xyz(outfilebase+'.xyz')
def main():
# sys.argv[1] should be the modelfile in the xyz format
# sys.argv[2] should be the cutoff desired
modelfile = sys.argv[1]
cutoff = float(sys.argv[2])
ag = AtomGraph(modelfile,cutoff)
model = Model(modelfile)
model.generate_neighbors(cutoff)
#submodelfile = sys.argv[3]
#mixedmodel = Model('Mixed atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Mixed'])
#icolikemodel = Model('Ico-like atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if(atom.vp.type == 'Icosahedra-like' or atom.vp.type == 'Full-icosahedra')])
#fullicomodel = Model('Full-icosahedra atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Full-icosahedra'])
#xtalmodel = Model('Xtal-like atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Crystal-like'])
#undefmodel = Model('Undef atoms',model.lx,model.ly,model.lz, [atom for atom in ag.model.atoms if atom.vp.type == 'Undef'])
##mixedmodel.write_cif('mixed.cif')
##mixedmodel.write_our_xyz('mixed.xyz')
##icolikemodel.write_cif('icolike.cif')
##icolikemodel.write_our_xyz('icolike.xyz')
##fullicomodel.write_cif('fullico.cif')
##fullicomodel.write_our_xyz('fullico.xyz')
##xtalmodel.write_cif('xtal.cif')
##xtalmodel.write_our_xyz('xtal.xyz')
##undefmodel.write_cif('undef.cif')
##undefmodel.write_our_xyz('undef.xyz')
#icomixedmodel = Model('ico+mix atoms',model.lx,model.ly,model.lz, mixedmodel.atoms + icolikemodel.atoms)
##mixedmodel.write_cif('icomixed.cif')
##mixedmodel.write_our_xyz('icomixed.xyz')
#vpcoloredmodel = Model('vp colored atoms',model.lx,model.ly,model.lz, ag.model.atoms)
#for atom in vpcoloredmodel.atoms:
# if(atom.vp.type == 'Full-icosahedra'):
# atom.z = 1
# elif(atom.vp.type == 'Icosahedra-like'):
# atom.z = 2
# elif(atom.vp.type == 'Mixed'):
# atom.z = 3
# elif(atom.vp.type == 'Crystal-like'):
# atom.z = 4
# elif(atom.vp.type == 'Undef'):
# atom.z = 5
##vpcoloredmodel.write_cif('vpcolored.cif')
##vpcoloredmodel.write_our_xyz('vpcolored.xyz')
#subvpcoloredmodel = Model(submodelfile)
#for atom in subvpcoloredmodel.atoms:
# atom.z = vpcoloredmodel.atoms[ag.model.atoms.index(atom)].z
#subvpcoloredmodel.write_cif('subvpcolored.cif')
#subvpcoloredmodel.write_our_xyz('subvpcolored.xyz')
#return
golden = False
#cluster_prefix = 'ico.t3.'
#cluster_types = 'Icosahedra-like', 'Full-icosahedra' # Need this for final/further analysis
#cluster_prefix = 'fi.t3.'
#cluster_types = ['Full-icosahedra'] # Need this for final/further analysis
cluster_prefix = 'xtal.t3.'
cluster_types = 'Crystal-like' # Need this for final/further analysis
#cluster_prefix = 'mix.t3'
#cluster_types = ['Mixed'] # Need this for final/further analysis
#cluster_prefix = 'undef.t3'
#cluster_types = ['Undef'] # Need this for final/further analysis
# Decide what time of clustering you want to do
#clusters = ag.get_clusters_with_n_numneighs(cutoff,5,cluster_types) #Vertex
#clusters = ag.get_vertex_sharing_clusters(cutoff,cluster_types) #Vertex
#clusters = ag.get_edge_sharing_clusters(cutoff,cluster_types) #Edge
#clusters = ag.get_face_sharing_clusters(cutoff,cluster_types) #Face
#clusters = ag.get_interpenetrating_atoms(cutoff,cluster_types) #Interpenetrating
#clusters = ag.get_interpenetrating_clusters_with_neighs(cutoff,cluster_types) #Interpenetrating+neighs
#clusters = ag.get_connected_clusters_with_neighs(cutoff, cluster_types) #Connected (vertex) + neighs
v,e,f,i = ag.vefi_sharing(cluster_types)
print("V: {0} E: {1} F: {2} I: {3}".format(int(v),int(e),int(f),int(i)))
return
orig_clusters = clusters[:]
# Print orig clusters
j = 0
for i,cluster in enumerate(clusters):
print("Orig cluster {0} contains {1} atoms.".format(i,len(cluster)))
if(golden):
for atom in cluster:
if(atom.vp.type in cluster_types):
atom.z = 0
# Save cluster files
cluster_model = Model("Orig cluster {0} contains {1} atoms.".format(i,len(cluster)),model.lx, model.ly, model.lz, cluster)
cluster_model.write_cif('{1}cluster{0}.cif'.format(i,cluster_prefix))
cluster_model.write_our_xyz('{1}cluster{0}.xyz'.format(i,cluster_prefix))
allclusters = []
for cluster in clusters:
for atom in cluster:
if(atom not in allclusters):
allclusters.append(atom)
#if(atom.vp.type in cluster_types): print(' {0}\t{1}'.format(atom,atom.vp.type))
allclusters = Model("All clusters.",model.lx, model.ly, model.lz, allclusters)
allclusters.write_cif('{0}allclusters.cif'.format(cluster_prefix))
allclusters.write_our_xyz('{0}allclusters.xyz'.format(cluster_prefix))
print("{0}allclusters.cif and {0}allclusters.xyz contain {1} atoms.".format(cluster_prefix, allclusters.natoms))
if(not golden):
x_cluster = []
for i,atom in enumerate(model.atoms):
if atom not in allclusters.atoms:
x_cluster.append(atom)
x_cluster = Model("Opposite cluster of {0}".format(cluster_prefix),model.lx, model.ly, model.lz, x_cluster)
x_cluster.write_cif('{0}opposite.cif'.format(cluster_prefix))
x_cluster.write_our_xyz('{0}opposite.xyz'.format(cluster_prefix))
print('{0}opposite.cif and {0}opposite.xyz contain {1} atoms.'.format(cluster_prefix, x_cluster.natoms))
if(False): # Further analysis
cn = 0.0
for atom in model.atoms:
cn += atom.cn
cn /= model.natoms
vpcn = 0.0
count = 0
for atom in ag.model.atoms:
if( atom.vp.type in cluster_types ):
vpcn += atom.cn
count += 1
vpcn /= count
natomsinVPclusters = allclusters.natoms # Number of atoms in VP clusters
nVPatoms = count # Number of VP atoms
numsepVPatoms = nVPatoms * vpcn # Number of atoms in VP clusters if all clusters were separated
maxnumatoms = model.natoms # Max number of atoms in VP clusters if all clusters were separated but still within the model size
print('Average CN is {0}'.format(cn))
print('Average CN of VP atoms is {0}'.format(vpcn))
print('# atoms in all clusters: {0}. # VP atoms * vpcn: {1}. # VP atoms: {2}'.format(natomsinVPclusters,numsepVPatoms,nVPatoms))
print('~ Number of VP that can fit in the model: {0}'.format(maxnumatoms/vpcn))
print('Ratio of: (# atoms involved in VP clusters)/(# atoms involved in VP clusters if all clusters were completely separated): {0}% <--- Therefore {1}% sharing.'.format(round(float(natomsinVPclusters)/(numsepVPatoms)*100.0,3),100.0-round(float(natomsinVPclusters)/(numsepVPatoms)*100.0,3)))
print('Ratio of: (# atoms involved in VP clusters)/(# atoms involved in VP clusters if all clusters were separated as much as possible within the model): {0}% <--- Therefore {1}% sharing.'.format(round(float(natomsinVPclusters)/min(numsepVPatoms,maxnumatoms)*100.0,3),100.0-round(float(natomsinVPclusters)/min(numsepVPatoms,maxnumatoms)*100.0,3) if numsepVPatoms < maxnumatoms else round(float(natomsinVPclusters)/min(numsepVPatoms,maxnumatoms)*100.0,3)))
vor_instance = Vor()
vor_instance.runall(modelfile,cutoff)
index = vor_instance.get_indexes()
vor_instance.set_atom_vp_indexes(model)
vp_dict = categorize_vor.load_param_file('/home/jjmaldonis/model_analysis/scripts/categorize_parameters_iso.txt')
atom_dict = categorize_vor.generate_atom_dict(index,vp_dict)
categorize_vor.set_atom_vp_types(model,vp_dict)
# Count the number of common neighbors in each of the VP
vp_atoms = []
for atom in model.atoms:
if(atom.vp.type in cluster_types):
vp_atoms.append(atom)
common_neighs = 0.0
atom_pairs = []
for atomi in vp_atoms:
for atomj in vp_atoms:
if(atomi != atomj):
if(atomi in atomj.neighs and [atomi,atomj] not in atom_pairs and [atomj,atomi] not in atom_pairs):
common_neighs += 1
atom_pairs.append([atomi,atomj])
#if(atomj in atomi.neighs): common_neighs += 0.5
for n in atomi.neighs:
if(n in atomj.neighs and [n,atomj] not in atom_pairs and [atomj,n] not in atom_pairs):
common_neighs += 1
atom_pairs.append([n,atomj])
#for n in atomj.neighs:
# if(n in atomi.neighs): common_neighs += 0.5
# Now common_neighs is the number of shared atoms
#print(common_neighs)
print('Percent shared based on common neighsbors: {0}'.format(100.0*common_neighs/natomsinVPclusters))