def run_cross_section(interactionfile, spinfile):
start = clock()
# Generate Inputs
atom_list, jnums, jmats,N_atoms_uc=readFiles(interactionfile,spinfile)
atom_list=atom_list[:N_atoms_uc]
N_atoms = len(atom_list)
kx = sp.Symbol('kx', real = True)
ky = sp.Symbol('ky', real = True)
kz = sp.Symbol('kz', real = True)
k = spm.Matrix([kx,ky,kz])
(b,bd) = generate_b_bd_operators(atom_list)
(a,ad) = generate_a_ad_operators(atom_list, k, b, bd)
(Sp,Sm) = generate_Sp_Sm_operators(atom_list, a, ad)
(Sa,Sb,Sn) = generate_Sa_Sb_Sn_operators(atom_list, Sp, Sm)
(Sx,Sy,Sz) = generate_Sx_Sy_Sz_operators(atom_list, Sa, Sb, Sn)
print ''
#Ham = generate_Hamiltonian(N_atoms, atom_list, b, bd)
ops = generate_possible_combinations(atom_list, [Sx,Sy,Sz])
ops = holstein(atom_list, ops)
ops = apply_commutation(atom_list, ops)
ops = replace_bdb(atom_list, ops)
ops = reduce_options(atom_list, ops)
list_print(ops)
print "prelims complete. generating cross-section","\n"
aa = bb = cc = np.array([2.0*np.pi], 'Float64')
alpha = beta = gamma = np.array([np.pi/2.0], 'Float64')
vect1 = np.array([[1,0,0]])
vect2 = np.array([[0,0,1]])
lattice = Lattice(aa, bb, cc, alpha, beta, gamma, Orientation(vect1, vect2))
tau_list = []
for i in range(1):
tau_list.append(np.array([0,0,0], 'Float64'))
h_list = np.linspace(0.1,6.3,50)
k_list = np.zeros(h_list.shape)
l_list = np.zeros(h_list.shape)
w_list = np.linspace(-10,10,50)
(N_atoms_uc,csection,kaprange,
tau_list,eig_list,kapvect,wtlist,fflist) = generate_cross_section(interactionfile, spinfile, lattice, ops,
tau_list, h_list, k_list, l_list, w_list)
print csection
return N_atoms_uc,csection,kaprange,tau_list,eig_list,kapvect,wtlist,fflist
end = clock()
print "\nFinished %i atoms in %.2f seconds" %(N_atoms,end-start)
def optimization_driver(interfile, spinfile):
""" runs the local optimization routine given just an interactionfile and spinfile. this should be called by the GUI """
atom_list, jnums, jmats, N_atoms_uc = rf.readFiles(interfile,spinfile,allAtoms=True)
N_atoms = len(atom_list)
jij = gen_Jij(atom_list,jmats)
anis = gen_anisotropy(atom_list)
sij = gen_spinVector(atom_list)
Ham = calculate_Ham(sij, anis, jij)
return local_optimizer(interfile, spinfile)
def run_dispersion_from_file(interactionfile,spinfile, eigs=False, eigsP = (False,None)):
atom_list, jnums, jmats,N_atoms_uc=rf.readFiles(interactionfile,spinfile)
N_atoms=len(atom_list)
Hsave = calculate_dispersion(atom_list,N_atoms_uc,N_atoms,jmats,showEigs=eigs)
if eigs == True:
temp = 1
x = sp.Symbol('x')
for i in range(Hsave.cols): temp *= x-Hsave[i,i]
poly = sp.simplify(temp.expand())
print poly
return sp.simplify(Hsave[0])
def optimization_driver(interfile, spinfile):
""" runs the local optimization routine given just an interactionfile and spinfile. this should be called by the GUI """
atom_list, jnums, jmats, N_atoms_uc = rf.readFiles(interfile,
spinfile,
allAtoms=True)
N_atoms = len(atom_list)
jij = gen_Jij(atom_list, jmats)
anis = gen_anisotropy(atom_list)
sij = gen_spinVector(atom_list)
Ham = calculate_Ham(sij, anis, jij)
return local_optimizer(interfile, spinfile)
def local_optimizer(interfile, spinfile):
"""
Given an interaction file and a spin file, this optimizer will uses readfiles.readfiles to populate
an atom list as well as return the interaction matrices. Given that information, it will recalculate
the hamiltonian and attempt to minimize it as a function of theta and phi. The annealing will get the
spins close to the global minimum and this method makes sure the spins are aligned. This routine uses
only python instead of C.
"""
# Get atoms list, jmats from the interaction and spin files
atom_list, jnums, jmats, N_atoms_uc = rf.readFiles(interfile, spinfile, allAtoms=True)
return opt_aux(atom_list, jmats)
def cs_driver():
file_pathname = os.path.abspath('')
interfile = os.path.join(file_pathname,r'montecarlo.txt')
spinfile = os.path.join(file_pathname,r'spins.txt')
h_list = np.linspace(0.001,3.14,15)
k_list = np.zeros(h_list.shape)
l_list = np.zeros(h_list.shape)
w_list = np.linspace(0,5,15)
tau = np.array([0,0,0])
wt = np.array(1.0)
rad = 1.0
atom_list, jnums, jmats,N_atoms_uc=readFiles(interfile,spinfile)
N_atoms_uc,csection,kaprange,tau_list,eig_list,kapvect,wt_list,fflist = run_cross_section(interfile,spinfile)
x,y,z=run_spherical_averaging(N_atoms_uc,atom_list,rad,csection,kapvect,tau_list,eig_list,wt_list,temperature)
np.save(os.path.join(file_pathname,r'myfilex.txt'),x)
np.save(os.path.join(file_pathname,r'myfiley.txt'),y)
np.save(os.path.join(file_pathname,r'myfilez.txt'),z)
def run_dispersion_from_file(interactionfile,
spinfile,
eigs=False,
eigsP=(False, None)):
atom_list, jnums, jmats, N_atoms_uc = rf.readFiles(interactionfile,
spinfile)
N_atoms = len(atom_list)
Hsave = calculate_dispersion(atom_list,
N_atoms_uc,
N_atoms,
jmats,
showEigs=eigs)
if eigs == True:
temp = 1
x = sp.Symbol('x')
for i in range(Hsave.cols):
temp *= x - Hsave[i, i]
poly = sp.simplify(temp.expand())
print poly
return sp.simplify(Hsave[0])
def local_optimizer(interfile, spinfile):
"""
Given an interaction file and a spin file, this optimizer will uses readfiles.readfiles to populate
an atom list as well as return the interaction matrices. Given that information, it will recalculate
the hamiltonian and attempt to minimize it as a function of theta and phi. The annealing will get the
spins close to the global minimum and this method makes sure the spins are aligned. This routine uses
only python instead of C.
"""
# Get atoms list, jmats from the interaction and spin files
atom_list, jnums, jmats,N_atoms_uc=rf.readFiles(interfile,spinfile,allAtoms=True)
N_atoms = len(atom_list)
# Generate the Jij and anisotropy arrays
Jij = gen_Jij(atom_list,jmats)
anis = gen_anisotropy(atom_list)
# Get the spin magnitudes from the atoms in atom list
spin_mags = []
for i in range(len(atom_list)):
spin_mags.append(atom_list[i].spinMagnitude)
spin_mags = np.array(spin_mags)
# hamiltonian method
def hamiltonian(p, Jij = None, spins = None, anis = None):
""" Computes the hamiltonian given a list a thetas and phis"""
# Thetas are the first half, phis the second half
theta = p[:len(p)//2]
phi = p[len(p)//2:]
# Sx,Sy,Sz
Sx = spins*cos(theta)*cos(phi)
Sy = spins*cos(theta)*sin(phi)
Sz = spins*sin(theta)
# Array of spin vectors for each atom. Reshape it. Calculate hamiltonian with it and return the hamiltonian.
Sij = np.array([Sx,Sy,Sz])
Sij = Sij.T.reshape(1,3*len(p)//2).T
SijT =Sij.T
# Normal mode
# sij.T * jij * sij
res1 = SijT * Jij
Hij = np.dot(res1,Sij).flat[0]
Ham = - Hij - np.dot(anis, Sij**2)
return Ham
# derivative of the hamiltonian
def deriv(p, Jij = None, spins = None, anis = None):
""" Computes the derivative of the hamiltonian with respect to each theta and then each phi"""
# Thetas are the first half, phis the second half
half = len(p)/2
theta = p[:half]
phi = p[half:]
# Sx,Sy,Sz
Sx = spins*cos(theta)*cos(phi)
Sy = spins*cos(theta)*sin(phi)
Sz = spins*sin(theta)
# dSx/dtheta,dSy/dtheta,dSz/dtheta
Sxt = -spins*sin(theta)*cos(phi)
Syt = -spins*sin(theta)*sin(phi)
Szt = spins*cos(theta)
# dSx/dphi,dSy/dphi,dSz/dphi
Sxp = -spins*cos(theta)*sin(phi)
Syp = spins*cos(theta)*cos(phi)
Szp = 0*cos(theta)
# Makes an array of the derivatives with respect to theta, then another with respect to phi
# (dSx1/dtheta1,dSy1/dtheta1,dSz1/dtheta1
# ( dSx2/dtheta2,dSy2/dtheta2,dSz2/dtheta2
# ( ...
# Similarly for phis
# Then we multiply this by Jij which yields a half by 3*half array. We then multiply this by
# the Sij vector which finally yields a half by 1 array. Thus
# dSijt * Jij * Sij -> [dE/dtheta1, dE/dtheta2, dE/dtheta3, ...]
# and similarly for phi
dSijt = np.zeros((half,3*half))
dSijp = np.zeros((half,3*half))
dSijt[range(half),range(0,3*half,3)]=Sxt
dSijt[range(half),range(1,3*half,3)]=Syt
dSijt[range(half),range(2,3*half,3)]=Szt
dSijp[range(half),range(0,3*half,3)]=Sxp
dSijp[range(half),range(1,3*half,3)]=Syp
dSijp[range(half),range(2,3*half,3)]=Szp
# Standard Sij spin vector we've been using
Sij = np.array([Sx,Sy,Sz])
Sij = Sij.T.reshape(1,3*len(p)//2).T
# Calculate a hamiltonian for both theta and phi
res1t = dSijt * Jij
Hijt = np.dot(res1t,Sij)
Hamt = - Hijt - np.matrix(np.dot((dSijt**2),anis)).T
res1p = dSijp * Jij
Hijp = np.dot(res1p,Sij)
Hamp = - Hijp - np.matrix(np.dot((dSijp**2),anis)).T
#rest = calculate_Ham(Sij,anis,Jij,dsij = dSijt)
#resp = calculate_Ham(Sij,anis,Jij,dsij = dSijp)
# Concatenate the two and return the result
result = np.concatenate((np.array(Hamt),np.array(Hamp)))
return result.T
# populate initial p list
# returns a numpy array of all the thetas followed by all the phis
thetas = []
phis = []
for i in range(N_atoms):
sx = atom_list[i].spin[0]
sy = atom_list[i].spin[1]
sz = atom_list[i].spin[2]
s = atom_list[i].spinMagnitude
theta = arcsin(sz/s)
phi = np.arctan2(sy,sx)
thetas.append(theta)
phis.append(phi)
p0 = np.array(thetas+phis)
# define the limits parameter list
limits = []
for i in range(len(p0)):
if i < len(p0)//2:#theta
limits.append((-pi,pi))
else:#phi
limits.append((0,pi))
# call minimizing function
m = fmin_l_bfgs_b(hamiltonian, p0, fprime = deriv, args = (Jij, spin_mags, anis), bounds = limits)
# grab returned parameters
# thetas are the first half of the list, phis are the second half
pout=m[0]
theta=pout[0:len(pout)/2]
phi=pout[len(pout)/2::]
# recreate sx,sy,sz's
sx=spin_mags*cos(theta)*cos(phi)
sy=spin_mags*cos(theta)*sin(phi)
sz=spin_mags*sin(theta)
# return spin vector array
return np.array([sx,sy,sz]).T
def local_optimizer(interfile, spinfile):
"""
Given an interaction file and a spin file, this optimizer will uses readfiles.readfiles to populate
an atom list as well as return the interaction matrices. Given that information, it will recalculate
the hamiltonian and attempt to minimize it as a function of theta and phi. The annealing will get the
spins close to the global minimum and this method makes sure the spins are aligned. This routine uses
only python instead of C.
"""
# Get atoms list, jmats from the interaction and spin files
atom_list, jnums, jmats, N_atoms_uc = rf.readFiles(interfile,
spinfile,
allAtoms=True)
N_atoms = len(atom_list)
# Generate the Jij and anisotropy arrays
Jij = gen_Jij(atom_list, jmats)
anis = gen_anisotropy(atom_list)
# Get the spin magnitudes from the atoms in atom list
spin_mags = []
for i in range(len(atom_list)):
spin_mags.append(atom_list[i].spinMagnitude)
spin_mags = np.array(spin_mags)
# hamiltonian method
def hamiltonian(p, Jij=None, spins=None, anis=None):
""" Computes the hamiltonian given a list a thetas and phis"""
# Thetas are the first half, phis the second half
theta = p[:len(p) // 2]
phi = p[len(p) // 2:]
# Sx,Sy,Sz
Sx = spins * cos(theta) * cos(phi)
Sy = spins * cos(theta) * sin(phi)
Sz = spins * sin(theta)
# Array of spin vectors for each atom. Reshape it. Calculate hamiltonian with it and return the hamiltonian.
Sij = np.array([Sx, Sy, Sz])
Sij = Sij.T.reshape(1, 3 * len(p) // 2).T
SijT = Sij.T
# Normal mode
# sij.T * jij * sij
res1 = SijT * Jij
Hij = np.dot(res1, Sij).flat[0]
Ham = -Hij - np.dot(anis, Sij**2)
return Ham
# derivative of the hamiltonian
def deriv(p, Jij=None, spins=None, anis=None):
""" Computes the derivative of the hamiltonian with respect to each theta and then each phi"""
# Thetas are the first half, phis the second half
half = len(p) / 2
theta = p[:half]
phi = p[half:]
# Sx,Sy,Sz
Sx = spins * cos(theta) * cos(phi)
Sy = spins * cos(theta) * sin(phi)
Sz = spins * sin(theta)
# dSx/dtheta,dSy/dtheta,dSz/dtheta
Sxt = -spins * sin(theta) * cos(phi)
Syt = -spins * sin(theta) * sin(phi)
Szt = spins * cos(theta)
# dSx/dphi,dSy/dphi,dSz/dphi
Sxp = -spins * cos(theta) * sin(phi)
Syp = spins * cos(theta) * cos(phi)
Szp = 0 * cos(theta)
# Makes an array of the derivatives with respect to theta, then another with respect to phi
# (dSx1/dtheta1,dSy1/dtheta1,dSz1/dtheta1
# ( dSx2/dtheta2,dSy2/dtheta2,dSz2/dtheta2
# ( ...
# Similarly for phis
# Then we multiply this by Jij which yields a half by 3*half array. We then multiply this by
# the Sij vector which finally yields a half by 1 array. Thus
# dSijt * Jij * Sij -> [dE/dtheta1, dE/dtheta2, dE/dtheta3, ...]
# and similarly for phi
dSijt = np.zeros((half, 3 * half))
dSijp = np.zeros((half, 3 * half))
dSijt[range(half), range(0, 3 * half, 3)] = Sxt
dSijt[range(half), range(1, 3 * half, 3)] = Syt
dSijt[range(half), range(2, 3 * half, 3)] = Szt
dSijp[range(half), range(0, 3 * half, 3)] = Sxp
dSijp[range(half), range(1, 3 * half, 3)] = Syp
dSijp[range(half), range(2, 3 * half, 3)] = Szp
# Standard Sij spin vector we've been using
Sij = np.array([Sx, Sy, Sz])
Sij = Sij.T.reshape(1, 3 * len(p) // 2).T
# Calculate a hamiltonian for both theta and phi
res1t = dSijt * Jij
Hijt = np.dot(res1t, Sij)
Hamt = -Hijt - np.matrix(np.dot((dSijt**2), anis)).T
res1p = dSijp * Jij
Hijp = np.dot(res1p, Sij)
Hamp = -Hijp - np.matrix(np.dot((dSijp**2), anis)).T
#rest = calculate_Ham(Sij,anis,Jij,dsij = dSijt)
#resp = calculate_Ham(Sij,anis,Jij,dsij = dSijp)
# Concatenate the two and return the result
result = np.concatenate((np.array(Hamt), np.array(Hamp)))
return result.T
# populate initial p list
# returns a numpy array of all the thetas followed by all the phis
thetas = []
phis = []
for i in range(N_atoms):
sx = atom_list[i].spin[0]
sy = atom_list[i].spin[1]
sz = atom_list[i].spin[2]
s = atom_list[i].spinMagnitude
theta = arcsin(sz / s)
phi = np.arctan2(sy, sx)
thetas.append(theta)
phis.append(phi)
p0 = np.array(thetas + phis)
# define the limits parameter list
limits = []
for i in range(len(p0)):
if i < len(p0) // 2: #theta
limits.append((-pi, pi))
else: #phi
limits.append((0, pi))
# call minimizing function
m = fmin_l_bfgs_b(hamiltonian,
p0,
fprime=deriv,
args=(Jij, spin_mags, anis),
bounds=limits)
# grab returned parameters
# thetas are the first half of the list, phis are the second half
pout = m[0]
theta = pout[0:len(pout) / 2]
phi = pout[len(pout) / 2::]
# recreate sx,sy,sz's
sx = spin_mags * cos(theta) * cos(phi)
sy = spin_mags * cos(theta) * sin(phi)
sz = spin_mags * sin(theta)
# return spin vector array
return np.array([sx, sy, sz]).T
def run_cross_section(interactionfile, spinfile):
start = clock()
# Generate Inputs
atom_list, jnums, jmats, N_atoms_uc = readFiles(interactionfile, spinfile)
atom1 = atom(pos=[0.00, 0, 0],
neighbors=[1],
interactions=[0],
int_cell=[0],
atomicNum=26,
valence=3)
atom2 = atom(pos=[0.25, 0, 0],
neighbors=[0, 2],
interactions=[0],
int_cell=[0],
atomicNum=26,
valence=3)
atom3 = atom(pos=[0.50, 0, 0],
neighbors=[1, 3],
interactions=[0],
int_cell=[0],
atomicNum=26,
valence=3)
atom4 = atom(pos=[0.75, 0, 0],
neighbors=[2],
interactions=[0],
int_cell=[0],
atomicNum=26,
valence=3)
# atom_list,N_atoms_uc = ([atom1, atom2, atom3, atom4],1)
N_atoms = len(atom_list)
print N_atoms
if 1:
kx = sp.Symbol('kx', real=True, commutative=True)
ky = sp.Symbol('ky', real=True, commutative=True)
kz = sp.Symbol('kz', real=True, commutative=True)
k = spm.Matrix([kx, ky, kz])
(b, bd) = generate_b_bd_operators(atom_list)
# list_print(b)
(a, ad) = generate_a_ad_operators(atom_list, k, b, bd)
list_print(a)
(Sp, Sm) = generate_Sp_Sm_operators(atom_list, a, ad)
list_print(Sp)
(Sa, Sb, Sn) = generate_Sa_Sb_Sn_operators(atom_list, Sp, Sm)
# list_print(Sa)
(Sx, Sy, Sz) = generate_Sx_Sy_Sz_operators(atom_list, Sa, Sb, Sn)
list_print(Sx)
print ''
#Ham = generate_Hamiltonian(N_atoms, atom_list, b, bd)
ops = generate_possible_combinations(atom_list, [Sx, Sy, Sz])
# list_print(ops)
ops = holstein(atom_list, ops)
# list_print(ops)
ops = apply_commutation(atom_list, ops)
# list_print(ops)
ops = replace_bdb(atom_list, ops)
# list_print(ops)
ops = reduce_options(atom_list, ops)
list_print(ops)
# Old Method
#cross_sect = generate_cross_section(atom_list, ops, 1, real, recip)
#print '\n', cross_sect
if 1:
aa = bb = cc = np.array([2.0 * np.pi], 'Float64')
alpha = beta = gamma = np.array([np.pi / 2.0], 'Float64')
vect1 = np.array([[1, 0, 0]])
vect2 = np.array([[0, 0, 1]])
lattice = Lattice(aa, bb, cc, alpha, beta, gamma,
Orientation(vect1, vect2))
data = {}
data['kx'] = 1.
data['ky'] = 0.
data['kz'] = 0.
direction = data
temperature = 100.0
min = 0
max = 2 * sp.pi
steps = 25
tau_list = []
for i in range(1):
tau_list.append(np.array([0, 0, 0], 'Float64'))
h_list = np.linspace(0, 2, 100)
k_list = np.zeros(h_list.shape)
l_list = np.zeros(h_list.shape)
w_list = np.linspace(-4, 4, 100)
efixed = 14.7 #meV
eief = True
eval_cross_section(interactionfile, spinfile, lattice, ops, tau_list,
h_list, k_list, l_list, w_list, data, temperature,
min, max, steps, eief, efixed)
end = clock()
print "\nFinished %i atoms in %.2f seconds" % (N_atoms, end - start)
atom_list, jnums, jmats, N_atoms_uc = rf.readFiles(interfile,spinfile,allAtoms=True)
N_atoms = len(atom_list)
jij = gen_Jij(atom_list,jmats)
anis = gen_anisotropy(atom_list)
sij = gen_spinVector(atom_list)
Ham = calculate_Ham(sij, anis, jij)
return local_optimizer(interfile, spinfile)
if __name__ == '__main__':
#print optimizeSpins('C:\\export.txt', 'C:\\spins.txt', 'C:\\spins2.txt')
#interfile = 'c:/test_montecarlo.txt'
#spinfile = 'c:/test_Spins.txt'
interfile='c:/montecarlo_ferro.txt'
spinfile='c:/Spins_ferro.txt'
atom_list, jnums, jmats,N_atoms_uc=rf.readFiles(interfile,spinfile,allAtoms=True)
N_atoms = len(atom_list)
jij = gen_Jij(atom_list, jmats)
anis = gen_anisotropy(atom_list)
sij = gen_spinVector(atom_list)
Ham = calculate_Ham(sij, anis, jij)
print Ham
print local_optimizer(interfile, spinfile)
N_atoms_uc,csection,kaprange,tau_list,eig_list,kapvect,wt_list,fflist = run_cross_section(interfile,spinfile)
x,y,z=run_spherical_averaging(N_atoms_uc,atom_list,rad,csection,kapvect,tau_list,eig_list,wt_list,temperature)
np.save(os.path.join(file_pathname,r'myfilex.txt'),x)
np.save(os.path.join(file_pathname,r'myfiley.txt'),y)
np.save(os.path.join(file_pathname,r'myfilez.txt'),z)
if __name__=='__main__':
#def pd():
#from spinwaves.cross_section.csection_calc import spherical_averaging as sph
file_pathname = os.path.abspath('')
interfile = os.path.join(file_pathname,r'montecarlo.txt')
spinfile = os.path.join(file_pathname,r'spins.txt')
atom_list, jnums, jmats,N_atoms_uc=readFiles(interfile,spinfile)
N_atoms_uc,csection,kaprange,tau_list,eig_list,kapvect,wt_list,fflist = run_cross_section(interfile,spinfile)
# left_conn, right_conn = Pipe()
# p = Process(target = create_latex, args = (right_conn, csection, "Cross-Section"))
# p.start()
# eig_frame = LaTeXDisplayFrame(self.parent, p.pid, left_conn.recv(), 'Cross-Section')
# self.process_list.append(p)
# p.join()
# p.terminate()
# ORIGINAL METHOD TO GENERATE CROSS SECTION
if 0:
kapvect,wt_list,csdata=run_eval_cross_section(N_atoms_uc,csection,kaprange,tau_list,eig_list,kapvect,wt_list,fflist)
plot_cross_section(kapvect[:,0],wt_list,csdata)
def eval_cross_section(interactionfile,
spinfile,
lattice,
arg,
tau_list,
h_list,
k_list,
l_list,
w_vect_list,
direction,
temperature,
kmin,
kmax,
steps,
eief,
efixed=14.7):
"""
Calculates the cross_section given the following parameters:
interactionfile, spinfile - files to get atom data
lattice - Lattice object from tripleaxisproject
arg - reduced list of operator combinations
tau_list - list of tau position
w_list - list of w's probed
direction - direction of scan
temp - temperature
kmin - minimum value of k to scan
kmax - maximum value of k to scan
steps - number of steps between kmin, kmax
eief - True if the energy scheme is Ei - Ef, False if it is Ef - Ei
efixed - value of the fixed energy, either Ei or Ef
"""
# Read files, get atom_list and such
atom_list, jnums, jmats, N_atoms_uc = readFiles(interactionfile, spinfile)
N_atoms = len(atom_list)
N = N_atoms
# TEMPORARY TO OVERRIDE ATOM_LIST ABOVE
# atom1 = atom(pos = [0.00,0,0], neighbors = [1], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom2 = atom(pos = [0.25,0,0], neighbors = [0,2], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom3 = atom(pos = [0.50,0,0], neighbors = [1,3], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom4 = atom(pos = [0.75,0,0], neighbors = [2], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom_list,N_atoms_uc = ([atom1, atom2, atom3, atom4],1)
N_atoms = len(atom_list)
# Get Hsave to calculate its eigenvalues
(Hsave, charpoly, eigs) = calculate_dispersion(atom_list,
N_atoms_uc,
N_atoms,
jmats,
showEigs=True)
print "Calculated: Dispersion Relation"
# Generate kappa's from (h,k,l)
kaprange = []
kapvect = []
if len(h_list) == len(k_list) == len(l_list):
for i in range(len(h_list)):
kappa = lattice.modvec(h_list[i], k_list[i], l_list[i],
'latticestar')
kaprange.append(kappa[0])
kapvect.append(np.array([h_list[i], k_list[i], l_list[i]]))
# kapvect.append(np.array([h_list[i]/kappa,k_list[i]/kappa,l_list[i]/kappa]))
else:
raise Exception('h,k,l not same lengths')
# Generate q's from kappa and tau
pqrange = []
mqrange = []
for tau in tau_list:
ptemp = []
mtemp = []
for kap in kapvect:
#tau = lattice.modvec(tau[0],tau[1],tau[2], 'latticestar')
ptemp.append(kap - tau)
mtemp.append(tau - kap)
pqrange.append(ptemp)
mqrange.append(mtemp)
# Calculate w_q's using q
qrange = []
# krange = []
wrange = []
if 1: # Change this later
for set in pqrange:
temp = []
temp1 = []
for q in set:
eigs = calc_eigs(Hsave, q[0] * direction['kx'],
q[1] * direction['ky'],
q[2] * direction['kz'])
# Take only one set of eigs. Should probably have a flag here.
temp.append(eigs[0])
# krange.append(np.array([q*direction['kx'], q*direction['ky'], q*direction['kz']]))
temp1.append(q)
wrange.append(temp)
qrange.append(temp1)
print "Calculated: Eigenvalues"
wrange = np.array(np.real(wrange))
qrange = np.array(np.real(qrange))
kaprange = np.array(np.real(kaprange))
print qrange.shape
print wrange.shape
print kaprange.shape
# Calculate w (not _q) as well as kp/k
w_calc = []
kpk = []
if eief == True:
for tau in tau_list:
temp = []
temp1 = []
for om in w_vect_list:
temp.append(om - efixed)
temp1.append(np.sqrt(om / efixed))
w_calc.append(temp)
kpk.append(temp1)
else:
for tau in tau_list:
temp = []
temp1 = []
for om in w_vect_list:
temp.append(-(om - efixed))
temp1.append(np.sqrt(efixed / om))
w_calc.append(temp)
kpk.append(temp1)
w_calc = np.array(np.real(w_calc))
# Grab Form Factors
ff_list = []
s = sp.Symbol('s')
for i in range(N_atoms):
el = elements[atom_list[i].atomicNum]
val = atom_list[i].valence
if val != None:
Mq = el.magnetic_ff[val].M_Q(kaprange)
else:
Mq = el.magnetic_ff[0].M_Q(kaprange)
ff_list.append(Mq)
print "Calculated: Form Factors"
# Other Constants
gamr0 = 2 * 0.2695 * 10**(-12) #sp.Symbol('gamma', commutative = True)
hbar = 1.0 # 1.05457148*10**(-34) #sp.Symbol('hbar', commutative = True)
g = 2. #sp.Symbol('g', commutative = True)
# Kappa vector
kap = sp.Symbol(
'kappa', real=True
) #spm.Matrix([sp.Symbol('kapx',real = True),sp.Symbol('kapy',real = True),sp.Symbol('kapz',real = True)])
t = sp.Symbol('t', real=True)
w = sp.Symbol('w', real=True)
W = sp.Symbol('W', real=True)
tau = sp.Symbol('tau', real=True)
q = sp.Symbol('q', real=True)
L = sp.Symbol('L', real=True)
boltz = 1. #1.3806503*10**(-23)
# Wilds for sub_in method
A = sp.Wild('A', exclude=[0, t])
B = sp.Wild('B', exclude=[0, t])
C = sp.Wild('C')
D = sp.Wild('D')
K = sp.Wild('K')
# First the exponentials are turned into delta functions:
for i in range(len(arg)):
for j in range(N):
print '1', arg[i][j]
arg[i][j] = sp.powsimp(
arg[i]
[j]) #sp.powsimp(arg[i][j], deep = True, combine = 'all')
arg[i][j] = (
arg[i][j] * exp(-I * w * t) * exp(I * kap * L)).expand(
) # * exp(I*inner_prod(spm.Matrix(atom_list[j].pos).T,kap))
arg[i][j] = sp.powsimp(
arg[i]
[j]) #sp.powsimp(arg[i][j], deep = True, combine = 'all')
# print '2', arg[i][j]
arg[i][j] = sub_in(
arg[i][j], exp(I * t * A + I * t * B + I * C + I * D + I * K),
sp.DiracDelta(A * t + B * t + C + D + K)) #*sp.DiracDelta(C))
# print '3', arg[i][j]
arg[i][j] = sub_in(
arg[i][j],
sp.DiracDelta(A * t + B * t + C * L + D * L + K * L),
sp.DiracDelta(A * hbar + B * hbar) *
sp.simplify(sp.DiracDelta(C + D + K + tau)))
# print '4', arg[i][j]
arg[i][j] = sub_in(arg[i][j],
sp.DiracDelta(-A - B) * sp.DiracDelta(C),
sp.DiracDelta(A + B) * sp.DiracDelta(C))
print '5', arg[i][j]
print "Applied: Delta Function Conversion"
# Grabs the unit vectors from the back of the lists.
unit_vect = []
kapxhat = sp.Symbol('kapxhat', commutative=False)
kapyhat = sp.Symbol('kapyhat', commutative=False)
kapzhat = sp.Symbol('kapzhat', commutative=False)
for i in range(len(arg)):
unit_vect.append(arg[i].pop())
# Subs the actual values for kx,ky,kz, omega_q and n_q into the operator combos
# The result is basically a nested list:
#
# csdata = [ op combos ]
# op combos = [ one combo per atom ]
# 1 per atom = [ evaluated exp ]
#
csdata = []
for i in range(len(arg)):
temp1 = []
for j in range(len(arg[i])):
temp2 = []
for k in range(len(tau_list)):
temp3 = []
print i, j, k
for g in range(len(qrange[k])):
pvalue = tau_list[k] + kapvect[g] + qrange[k][g]
mvalue = tau_list[k] + kapvect[g] - qrange[k][g]
if pvalue[0] == 0 and pvalue[1] == 0 and pvalue[2] == 0:
arg[i][j] = arg[i][j].subs(
sp.DiracDelta(kap + tau + q), sp.DiracDelta(0))
else:
arg[i][j] = arg[i][j].subs(
sp.DiracDelta(kap + tau + q), 0)
if mvalue[0] == 0 and mvalue[1] == 0 and mvalue[2] == 0:
arg[i][j] = arg[i][j].subs(
sp.DiracDelta(kap + tau - q), sp.DiracDelta(0))
else:
arg[i][j] = arg[i][j].subs(
sp.DiracDelta(kap + tau - q), 0)
wq = sp.Symbol('wq', real=True)
nq = sp.Symbol('n%i' % (k, ), commutative=False)
arg[i][j] = arg[i][j].subs(wq, wrange[k][g])
arg[i][j] = arg[i][j].subs(w, w_calc[k][g])
n = sp.Pow(
sp.exp(hbar * wrange[k][g] / boltz * temperature) - 1,
-1)
arg[i][j] = arg[i][j].subs(nq, n)
temp3.append(arg[i][j])
temp2.append(temp3)
temp1.append(temp2)
csdata.append(temp1)
## print arg[i][j]
# for g in range(len(kaprange)):
# arg[i][j] = arg[i][j].subs(kap, kaprange[g])
# for g in range(len(krange)):
## print 'calculating'
# temp3 = []
# wg = sp.Symbol('w%i'%(g,), real = True)
# ng = sp.Symbol('n%i'%(g,), commutative = False)
## kx = sp.Symbol('kx', real = True, commutative = True)
## ky = sp.Symbol('ky', real = True, commutative = True)
## kz = sp.Symbol('kz', real = True, commutative = True)
## arg[i][j] = arg[i][j].subs(kx,krange[g][0])
## arg[i][j] = arg[i][j].subs(ky,krange[g][1])
## arg[i][j] = arg[i][j].subs(kz,krange[g][2])
# arg[i][j] = arg[i][j].subs(wg,wrange[g])
# arg[i][j] = arg[i][j].subs(w,w_calc[g])
# nq = sp.Pow( sp.exp(hbar*wrange[g]/boltz*temp) - 1 ,-1)
# arg[i][j] = arg[i][j].subs(ng,nq)
## arg[i][j] = arg[i][j].subs(tau,tau_list[0])
#
# temp3.append(arg[i][j])
## print arg[i][j]
# temp2.append(temp3)
## print arg[i][j]
# csdata.append(temp2)
print csdata
# Front constants and stuff for the cross-section
front_constant = 1.0 # (gamr0)**2/(2*pi*hbar)
front_func = (1. / 2.) * g #*F(k)
vanderwaals = 1. #exp(-2*W)
csrange = []
if 1:
temp1 = []
temp2 = []
for q in range(len(qrange)):
print 'calculating'
for ii in range(len(csdata)):
for jj in range(len(csdata[ii])):
temp1.append(csdata[ii][jj])
# Put on gamr0, 2pi hbar, form factor, kp/k, vanderwaals first
dif = front_func**2 * front_constant # * kpk[q][0] * vanderwaals * ff_list[0][q]
# for vect in unit_vect:
# vect.subs(kapxhat,kapvect[q][0][0])
# vect.subs(kapyhat,kapvect[q][1][0])
# vect.subs(kapzhat,kapvect[q][2][0])
print 'diff', dif
print 'temp1', temp1
print sum(temp1)
dif = (dif * sum(temp1)) # * sum(unit_vect))#.expand()
csrange.append(dif)
print "Calculated: Cross-section"
csrange = np.real(csrange)
csrange = np.array(csrange)
xi = qrange
yi = wrange
zi = csrange
Z = np.zeros((xi.shape[0], yi.shape[0]))
Z[range(len(xi)), range(len(yi))] = zi
pylab.contourf(xi, yi, Z)
pylab.colorbar()
pylab.show()
def generate_cross_section(interactionfile, spinfile, lattice, arg,
tau_list, h_list, k_list, l_list, w_list,
temperature, steps, eief, efixed = 14.7):
"""
Calculates the cross_section given the following parameters:
interactionfile, spinfile - files to get atom data
lattice - Lattice object from tripleaxisproject
arg - reduced list of operator combinations
tau_list - list of tau position
w_list - list of w's probed
temp - temperature
kmin - minimum value of k to scan
kmax - maximum value of k to scan
steps - number of steps between kmin, kmax
eief - True if fixed Ef
efixed - value of the fixed energy, either Ei or Ef
"""
# Read files, get atom_list and such
atom_list, jnums, jmats,N_atoms_uc=readFiles(interactionfile,spinfile)
# Get Hsave to calculate its eigenvalues
N_atoms = len(atom_list)
Hsave = calculate_dispersion(atom_list,N_atoms_uc,N_atoms,jmats,showEigs=False)
atom_list=atom_list[:N_atoms_uc]
N_atoms = len(atom_list)
N = N_atoms
print "Calculated: Dispersion Relation"
# Generate kappa's from (h,k,l)
kaprange = []
kapvect = []
#if len(h_list) == len(k_list) == len(l_list):
#for i in range(len(h_list)):
#kappa = lattice.modvec(h_list[i],k_list[i],l_list[i], 'latticestar')
#kaprange.append(kappa[0])
#kapvect.append(np.array([h_list[i],k_list[i],l_list[i]]))
## kapvect.append(np.array([h_list[i]/kappa,k_list[i]/kappa,l_list[i]/kappa]))
#else:
#raise Exception('h,k,l not same lengths')
# Generate q's from kappa and tau
kaprange=lattice.modvec(h_list,k_list,l_list, 'latticestar')
nkpts=len(kaprange)
kapvect=np.empty((nkpts,3),'Float64')
kapvect[:,0]=h_list
kapvect[:,1]=k_list
kapvect[:,2]=l_list
# print kapvect.shape
# print kaprange.shape
kapunit = kapvect.copy()
kapunit[:,0]=kapvect[:,0]/kaprange
kapunit[:,1]=kapvect[:,1]/kaprange
kapunit[:,2]=kapvect[:,2]/kaprange
#plusq=kappa-tau
plusq=[]
minusq=[]
qlist=[]
ones_list=np.ones((1,nkpts),'Float64')
#wtlist=np.ones((1,nkpts*2),'Float64').flatten()
wtlist=np.hstack([w_list,w_list])
#weven=np.array(range(0,nkpts*2,2))
#wodd=np.array(range(1,nkpts*2,2))
#wtlist[wodd]=w_list
#wtlist[weven]=w_list
qtlist=[]
for tau in tau_list:
taui=np.ones((nkpts,3),'Float64')
taui[:,0]=ones_list*tau[0]
taui[:,1]=ones_list*tau[1]
taui[:,2]=ones_list*tau[2]
kappa_minus_tau=kapvect-taui
tau_minus_kappa=taui - kapvect
qlist.append(np.vstack([kappa_minus_tau,tau_minus_kappa]))
#calculate kfki
nqpts=nkpts*2
kfki=calc_kfki(w_list,eief,efixed)
eig_list=[]
# print qlist
for q in qlist:
#eigs = calc_eigs_direct(Hsave,q[:,0],q[:,1],q[:,2])
eigs = Hsave.eigenvals().keys()
eig_list.append(eigs)
print "Calculated: Eigenvalues"
# print len(qlist)
# print len(eig_list[0])
# sys.exit()
# Grab Form Factors
#----Commented out 9/14/09 by Tom becuase of magnetic_ff error--------------
#ff_list = []
#s = sp.Symbol('s')
#for i in range(N_atoms):
#el = elements[atom_list[i].atomicNum]
#val = atom_list[i].valence
#if val != None:
#Mq = el.magnetic_ff[val].M_Q(kaprange)
#else:
#Mq = el.magnetic_ff[0].M_Q(kaprange)
#ff_list.append(Mq)
#print "Calculated: Form Factors"
#--------------------------------------------------------------------------
# Other Constants
gamr0 = 2*0.2695e-12 #sp.Symbol('gamma', commutative = True)
hbar = sp.S(1.0) # 1.05457148*10**(-34) #sp.Symbol('hbar', commutative = True)
g = 2.#sp.Symbol('g', commutative = True)
# Kappa vector
kap = sp.Symbol('kappa', real = True)#spm.Matrix([sp.Symbol('kapx',real = True),sp.Symbol('kapy',real = True),sp.Symbol('kapz',real = True)])
t = sp.Symbol('t', real = True)
w = sp.Symbol('w', real = True)
W = sp.Symbol('W', real = True)
tau = sp.Symbol('tau', real = True)
Q = sp.Symbol('q', real = True)
L = sp.Symbol('L', real = True)
lifetime=sp.Symbol('V',real=True)
boltz = 8.617343e-2
# Wilds for sub_in method
A = sp.Wild('A',exclude = [0,t])
B = sp.Wild('B',exclude = [0,t])
C = sp.Wild('C')
D = sp.Wild('D')
K = sp.Wild('K')
# Grabs the unit vectors from the back of the lists.
unit_vect = []
kapxhat = sp.Symbol('kapxhat',real=True)
kapyhat = sp.Symbol('kapyhat',real=True)
kapzhat = sp.Symbol('kapzhat',real=True)
for i in range(len(arg)):
unit_vect.append(arg[i].pop())
#print unit_vect
unit_vect = sum(unit_vect)
print unit_vect
csection=0
for i in range(len(arg)):
for j in range(len(arg[i])):
csection=csection+arg[i][j]*unit_vect
print csection
csection=sp.powsimp(csection)
csection = (csection * exp(-I*w*t) * exp(I*kap*L)).expand(deep = False)
print 'intermediate'
#print csection
csection= sp.powsimp(csection)
csection = sub_in(csection,exp(I*t*A + I*t*B + I*C + I*D + I*K),sp.DiracDelta(A*t + B*t + C + D + K))
csection = sub_in(csection,sp.DiracDelta(A*t + B*t + C*L + D*L ),sp.DiracDelta(A*hbar + B*hbar)*sp.simplify(sp.DiracDelta(C + D - tau))) #This is correct
#csection = sub_in(csection,sp.DiracDelta(A*t + B*t + C*L + D*L ),sp.simplify(lifetime*sp.DiracDelta(C + D - tau)*
#sp.Pow((A-B)**2+lifetime**2,-1)))
print "Applied: Delta Function Conversion"
# print csection
print 'done'
## First the exponentials are turned into delta functions:
#for i in range(len(arg)):
#for j in range(N):
## print '1', arg[i][j]
#arg[i][j] = sp.powsimp(arg[i][j])#sp.powsimp(arg[i][j], deep = True, combine = 'all')
#arg[i][j] = (arg[i][j] * exp(-I*w*t) * exp(I*kap*L)).expand()# * exp(I*inner_prod(spm.Matrix(atom_list[j].pos).T,kap))
#arg[i][j] = sp.powsimp(arg[i][j])#sp.powsimp(arg[i][j], deep = True, combine = 'all')
## print '2', arg[i][j]
#arg[i][j] = sub_in(arg[i][j],exp(I*t*A + I*t*B + I*C + I*D + I*K),sp.DiracDelta(A*t + B*t + C + D + K))#*sp.DiracDelta(C))
## print '3', arg[i][j]
#arg[i][j] = sub_in(arg[i][j],sp.DiracDelta(A*t + B*t + C*L + D*L + K*L),sp.DiracDelta(A*hbar + B*hbar)*sp.simplify(sp.DiracDelta(C + D + K + tau)))
## print '4', arg[i][j]
#arg[i][j] = sub_in(arg[i][j],sp.DiracDelta(-A - B)*sp.DiracDelta(C),sp.DiracDelta(A + B)*sp.DiracDelta(C))
## arg[i][j] = arg[i][j].subs(-w - wq, w + wq)
## print '5', arg[i][j]
print "Applied: Delta Function Conversion"
# Subs the actual values for kx,ky,kz, omega_q and n_q into the operator combos
# The result is basically a nested list:
#
# csdata = [ op combos ]
# op combos = [ one combo per atom ]
# 1 per atom = [ evaluated exp ]
#
csection=sub_in(csection,sp.DiracDelta(-A - B)*sp.DiracDelta(C),sp.DiracDelta(A + B)*sp.DiracDelta(C))
print "end part 1"
#print csection
return (N_atoms_uc, csection, kaprange, qlist, tau_list, eig_list, kapvect, wtlist)
def generate_cross_section(interactionfile,
spinfile,
lattice,
arg,
tau_list,
h_list,
k_list,
l_list,
w_list,
temperature,
steps,
eief,
efixed=14.7):
"""
Calculates the cross_section given the following parameters:
interactionfile, spinfile - files to get atom data
lattice - Lattice object from tripleaxisproject
arg - reduced list of operator combinations
tau_list - list of tau position
w_list - list of w's probed
temp - temperature
kmin - minimum value of k to scan
kmax - maximum value of k to scan
steps - number of steps between kmin, kmax
eief - True if fixed Ef
efixed - value of the fixed energy, either Ei or Ef
"""
# Read files, get atom_list and such
atom_list, jnums, jmats, N_atoms_uc = readFiles(interactionfile, spinfile)
# Get Hsave to calculate its eigenvalues
N_atoms = len(atom_list)
Hsave = calculate_dispersion(atom_list,
N_atoms_uc,
N_atoms,
jmats,
showEigs=False)
atom_list = atom_list[:N_atoms_uc]
N_atoms = len(atom_list)
N = N_atoms
print "Calculated: Dispersion Relation"
# Generate kappa's from (h,k,l)
kaprange = []
kapvect = []
#if len(h_list) == len(k_list) == len(l_list):
#for i in range(len(h_list)):
#kappa = lattice.modvec(h_list[i],k_list[i],l_list[i], 'latticestar')
#kaprange.append(kappa[0])
#kapvect.append(np.array([h_list[i],k_list[i],l_list[i]]))
## kapvect.append(np.array([h_list[i]/kappa,k_list[i]/kappa,l_list[i]/kappa]))
#else:
#raise Exception('h,k,l not same lengths')
# Generate q's from kappa and tau
kaprange = lattice.modvec(h_list, k_list, l_list, 'latticestar')
nkpts = len(kaprange)
kapvect = np.empty((nkpts, 3), 'Float64')
kapvect[:, 0] = h_list
kapvect[:, 1] = k_list
kapvect[:, 2] = l_list
# print kapvect.shape
# print kaprange.shape
kapunit = kapvect.copy()
kapunit[:, 0] = kapvect[:, 0] / kaprange
kapunit[:, 1] = kapvect[:, 1] / kaprange
kapunit[:, 2] = kapvect[:, 2] / kaprange
#plusq=kappa-tau
plusq = []
minusq = []
qlist = []
ones_list = np.ones((1, nkpts), 'Float64')
#wtlist=np.ones((1,nkpts*2),'Float64').flatten()
wtlist = np.hstack([w_list, w_list])
#weven=np.array(range(0,nkpts*2,2))
#wodd=np.array(range(1,nkpts*2,2))
#wtlist[wodd]=w_list
#wtlist[weven]=w_list
qtlist = []
for tau in tau_list:
taui = np.ones((nkpts, 3), 'Float64')
taui[:, 0] = ones_list * tau[0]
taui[:, 1] = ones_list * tau[1]
taui[:, 2] = ones_list * tau[2]
kappa_minus_tau = kapvect - taui
tau_minus_kappa = taui - kapvect
qlist.append(np.vstack([kappa_minus_tau, tau_minus_kappa]))
#calculate kfki
nqpts = nkpts * 2
kfki = calc_kfki(w_list, eief, efixed)
eig_list = []
# print qlist
for q in qlist:
#eigs = calc_eigs_direct(Hsave,q[:,0],q[:,1],q[:,2])
eigs = Hsave.eigenvals().keys()
eig_list.append(eigs)
print "Calculated: Eigenvalues"
# print len(qlist)
# print len(eig_list[0])
# sys.exit()
# Grab Form Factors
#----Commented out 9/14/09 by Tom becuase of magnetic_ff error--------------
#ff_list = []
#s = sp.Symbol('s')
#for i in range(N_atoms):
#el = elements[atom_list[i].atomicNum]
#val = atom_list[i].valence
#if val != None:
#Mq = el.magnetic_ff[val].M_Q(kaprange)
#else:
#Mq = el.magnetic_ff[0].M_Q(kaprange)
#ff_list.append(Mq)
#print "Calculated: Form Factors"
#--------------------------------------------------------------------------
# Other Constants
gamr0 = 2 * 0.2695e-12 #sp.Symbol('gamma', commutative = True)
hbar = sp.S(
1.0) # 1.05457148*10**(-34) #sp.Symbol('hbar', commutative = True)
g = 2. #sp.Symbol('g', commutative = True)
# Kappa vector
kap = sp.Symbol(
'kappa', real=True
) #spm.Matrix([sp.Symbol('kapx',real = True),sp.Symbol('kapy',real = True),sp.Symbol('kapz',real = True)])
t = sp.Symbol('t', real=True)
w = sp.Symbol('w', real=True)
W = sp.Symbol('W', real=True)
tau = sp.Symbol('tau', real=True)
Q = sp.Symbol('q', real=True)
L = sp.Symbol('L', real=True)
lifetime = sp.Symbol('V', real=True)
boltz = 8.617343e-2
# Wilds for sub_in method
A = sp.Wild('A', exclude=[0, t])
B = sp.Wild('B', exclude=[0, t])
C = sp.Wild('C')
D = sp.Wild('D')
K = sp.Wild('K')
# Grabs the unit vectors from the back of the lists.
unit_vect = []
kapxhat = sp.Symbol('kapxhat', real=True)
kapyhat = sp.Symbol('kapyhat', real=True)
kapzhat = sp.Symbol('kapzhat', real=True)
for i in range(len(arg)):
unit_vect.append(arg[i].pop())
#print unit_vect
unit_vect = sum(unit_vect)
print unit_vect
csection = 0
for i in range(len(arg)):
for j in range(len(arg[i])):
csection = csection + arg[i][j] * unit_vect
print csection
csection = sp.powsimp(csection)
csection = (csection * exp(-I * w * t) *
exp(I * kap * L)).expand(deep=False)
print 'intermediate'
#print csection
csection = sp.powsimp(csection)
csection = sub_in(csection,
exp(I * t * A + I * t * B + I * C + I * D + I * K),
sp.DiracDelta(A * t + B * t + C + D + K))
csection = sub_in(
csection, sp.DiracDelta(A * t + B * t + C * L + D * L),
sp.DiracDelta(A * hbar + B * hbar) *
sp.simplify(sp.DiracDelta(C + D - tau))) #This is correct
#csection = sub_in(csection,sp.DiracDelta(A*t + B*t + C*L + D*L ),sp.simplify(lifetime*sp.DiracDelta(C + D - tau)*
#sp.Pow((A-B)**2+lifetime**2,-1)))
print "Applied: Delta Function Conversion"
# print csection
print 'done'
## First the exponentials are turned into delta functions:
#for i in range(len(arg)):
#for j in range(N):
## print '1', arg[i][j]
#arg[i][j] = sp.powsimp(arg[i][j])#sp.powsimp(arg[i][j], deep = True, combine = 'all')
#arg[i][j] = (arg[i][j] * exp(-I*w*t) * exp(I*kap*L)).expand()# * exp(I*inner_prod(spm.Matrix(atom_list[j].pos).T,kap))
#arg[i][j] = sp.powsimp(arg[i][j])#sp.powsimp(arg[i][j], deep = True, combine = 'all')
## print '2', arg[i][j]
#arg[i][j] = sub_in(arg[i][j],exp(I*t*A + I*t*B + I*C + I*D + I*K),sp.DiracDelta(A*t + B*t + C + D + K))#*sp.DiracDelta(C))
## print '3', arg[i][j]
#arg[i][j] = sub_in(arg[i][j],sp.DiracDelta(A*t + B*t + C*L + D*L + K*L),sp.DiracDelta(A*hbar + B*hbar)*sp.simplify(sp.DiracDelta(C + D + K + tau)))
## print '4', arg[i][j]
#arg[i][j] = sub_in(arg[i][j],sp.DiracDelta(-A - B)*sp.DiracDelta(C),sp.DiracDelta(A + B)*sp.DiracDelta(C))
## arg[i][j] = arg[i][j].subs(-w - wq, w + wq)
## print '5', arg[i][j]
print "Applied: Delta Function Conversion"
# Subs the actual values for kx,ky,kz, omega_q and n_q into the operator combos
# The result is basically a nested list:
#
# csdata = [ op combos ]
# op combos = [ one combo per atom ]
# 1 per atom = [ evaluated exp ]
#
csection = sub_in(csection,
sp.DiracDelta(-A - B) * sp.DiracDelta(C),
sp.DiracDelta(A + B) * sp.DiracDelta(C))
print "end part 1"
#print csection
return (N_atoms_uc, csection, kaprange, qlist, tau_list, eig_list, kapvect,
wtlist)
def mcQueeny_case(interactionfile, spinfile, tau_list, temperature,
direction={'kx':1,'ky':0,'kz':0}, hkl_interval=[1e-3,2*np.pi,100],
omega_interval=[0,5,100], eig_eps = 0.001):
initial_time = time.clock()
atom_list, jnums, jmats, N_atoms_uc, numCutOff, magCellSize = readFiles(interactionfile,spinfile, rtn_cutOffInfo = True)
h_list, k_list, l_list = generate_hkl(hkl_interval, direction)
e = generate_wt(omega_interval)
ff_list = generate_form_factors(N_atoms_uc, atom_list, hkl_interval)
s = []
basis = []
for a in atom_list:
s.append(a.spin[2])#z component of spin (assume aligned with z)
basis.append(a.pos - magCellSize) #translate back to 0,0,0
mesh = np.zeros((len(h_list), len(e)))
for Qindex in range(len(h_list)):
Q = (np.array([h_list,k_list,l_list]).transpose())[Qindex]
M = create_matrix(atom_list, jnums, jmats, numCutOff, magCellSize, Q-np.array(tau_list))
w, evec = np.linalg.eig(M)
ff = ff_list[Qindex]
numAtoms = numCutOff
evec2 = evec.copy()
T = evec
sigma = []
for i in range(numAtoms):
sigma.append(s[i]/np.abs(s[i]))
for n in range(numAtoms):
norm = 0
for i in range(numAtoms):
norm += sigma[i]*(np.abs(evec2[n][i])**2)
for i in range(numAtoms):
T[n][i] = evec2[n][i]/np.sqrt(np.abs(norm))
evec = T
strfac = np.zeros(numAtoms)
ff = 1
for i in range(numAtoms):
dum = 0
for j in range(numAtoms):
dum += (s[j]/np.sqrt(np.abs(s[j]))) * ff * evec[j][i] #* np.exp(1.0j*
exp = np.exp(1.0j*(-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2]))
strfac[i] = np.abs(dum)**2
sqw = np.zeros(len(e))
for j in range(numAtoms):
for i in range(len(e)):
y = LIFETIME_VALUE/2
lorentzian = (1/np.pi)*y/((e[i]- w[j])**2+y**2)
sqw[i] = sqw[i] + strfac[j] * lorentzian #he has some 'bose' factor in here
mesh[Qindex] = sqw #* 0.5*(1 + Q[0]**2) #along x, Q_hat is just 1?
print "Run Time: ", time.clock()-initial_time, " seconds"
def mcQueeny_case(interactionfile,
spinfile,
tau_list,
temperature,
direction={
'kx': 1,
'ky': 0,
'kz': 0
},
hkl_interval=[1e-3, 2 * np.pi, 100],
omega_interval=[0, 5, 100],
eig_eps=0.001):
initial_time = time.clock()
#to plot eigenvector components
# q1 = []
# q2 = []
#to plot w's
#w1_list = []
#w2_list = []
#w3_list = []
#w4_list = []
#w5_list = []
#w6_list = []
#w7_list = []
#w8_list = []
# w_output_list = []
#plot the structure factor to compare with lovesey (In McQueeny Paper)
# strfac_sums = []
# strfac1 = []
# strfac2 = []
atom_list, jnums, jmats, N_atoms_uc, numCutOff, magCellSize = readFiles(
interactionfile, spinfile, rtn_cutOffInfo=True)
#Hardcoding body-center cubic
if 0:
atom_list = []
jnums = [0]
N_atom_uc = 1
numCutOff = 7
magCellSize = np.array([1, 1, 1])
jmats = [np.array([[-1, 0, 0], [0, -1, 0], [0, 0, -1]])]
atom_list.append(
atom(pos=np.array([0.5, 0.5, 0.5]),
spin=np.array([0, 0, 1]),
interactions=[0, 0, 0, 0, 0, 0],
neighbors=[1, 2, 3, 4, 5, 6]))
atom_list.append(
atom(pos=np.array([0., 0., 0.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
atom_list.append(
atom(pos=np.array([1., 0., 0.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
atom_list.append(
atom(pos=np.array([1., 1., 0.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
atom_list.append(
atom(pos=np.array([1., 0., 1.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
atom_list.append(
atom(pos=np.array([1., 1., 1.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
atom_list.append(
atom(pos=np.array([0., 1., 0.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
atom_list.append(
atom(pos=np.array([0., 0., 1.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
atom_list.append(
atom(pos=np.array([0., 1., 1.]),
spin=np.array([0, 0, -1]),
interactions=[0],
neighbors=[0]))
h_list, k_list, l_list = generate_hkl(hkl_interval, direction)
e = generate_wt(omega_interval)
ff_list = generate_form_factors(N_atoms_uc, atom_list, hkl_interval)
s = []
basis = []
for a in atom_list:
s.append(a.spin[2]) #z component of spin (assume aligned with z)
#basis.append(a.pos)
basis.append(a.pos - magCellSize) #translate back to 0,0,0
#normalize? NO!
#basis.append([a.pos[0] - int(a.pos[0]), a.pos[1]-int(a.pos[1]), a.pos[2] - int(a.pos[2])])
#print "\n\nbasis vectors:\n"
#mess with basis vectors and spin
#basis[0] = [0.25,0,0]
#basis[0] = [1.0,0,0]
#tmp = s[0]
#s[0] = s[1]
#s[1] = tmp
#for pos in basis:
# print pos
#print "\n\nspins:\n"
#for spin in s:
# print spin
mesh = np.zeros((len(h_list), len(e)))
#output q, w, and eigenvectors before and after normalization to compare with McQueeny's
# f = open("/home/tom/Desktop/Python_Eigs.txt", 'w')
# f.write("q w eigVec_before_norm T\n\n")
# f2 = open("/home/tom/Desktop/Values.txt", 'w')
# f2.write("Q w wvec (wvec norm) e^(-i*Q.d_i)\n\n")
#create the T_ni matricies (eigenvector matrices)
#This algorithm uses a differnt matrix:
#evecs, mats = calc_eig_mats_numerically(Hsave, h_list,k_list,l_list)
#eigvals = calc_eigs_numerically(Hsave, h_list, k_list, l_list)#This is innefficient, eigs already calculated and thrown away in calc_eig_mats_..
#w_list = gen_eigs_with_sign(Hsave, h_list, k_list, l_list, 0.001)
for Qindex in range(len(h_list)):
Q = (np.array([h_list, k_list, l_list]).transpose())[Qindex]
# f2.write(str(Q))
#CALL SPINWAVE(q,w,evec) - get w, evec for given q
M = create_matrix(atom_list, jnums, jmats, numCutOff, magCellSize,
Q - np.array(tau_list))
w, evec = np.linalg.eig(M)
# f2.write(" " + str(w))
# f2.write(" " + str(evec.flatten()))
# evec = evecs[Qindex]
# w = w_list[Qindex]
# print "\nw = ", w
#w1_list.append(w[0])
#w2_list.append(w[1])
#w3_list.append(w[1])
#w4_list.append(w[1])
#w5_list.append(w[1])
#w6_list.append(w[1])
#w7_list.append(w[1])
#w8_list.append(w[1])
# w_output_list.append(w)
ff = ff_list[Qindex]
#Get number of atoms
numAtoms = numCutOff
#numAtoms = np.sqrt(evec.size)#This should be the number of atoms that I need to loop through.
#if int(numAtoms) != numAtoms:#I beleive t_mat should always be square, but lets check
# raise Exception("t_mat not square!")
#Normalize evec to get T_ni
#for i in range(0, numAtoms):
# normi = 0
# for j in range(0, numAtoms):
# sigma_j = s[j]/np.abs(s[j])
# normi += sigma_j * np.abs(evec[j][i])**2
# for j in range(0, numAtoms):
# evec[i][j] = evec[j][i]/np.sqrt(np.abs(normi))
evec2 = evec.copy()
#I'm going to write this from scratch
# print "evec before = ", evec
T = evec
sigma = []
for i in range(numAtoms):
sigma.append(s[i] / np.abs(s[i]))
for n in range(numAtoms):
norm = 0
for i in range(numAtoms):
norm += sigma[i] * (np.abs(evec2[n][i])**2)
for i in range(numAtoms):
# print "norm : ", norm
# print "w sign: ", np.sign(w[n])
#T[n][i] = T[n][i]*np.sqrt((w[n]/np.abs(w[n]))/norm + 0j)
T[n][i] = evec2[n][i] / np.sqrt(np.abs(norm))
evec = T
# f2.write(" " + str(T.flatten()))
#evec = evec2
# q1.append(evec[0][0])
# q2.append(evec[0][0])
#output eigs to file
# f.write(str(Q))
# f.write(" w:" + str(w))
# f.write("\nmat: " + str(M))#the matrix for which the eigs were found
# f.write("\nbefore_norm: " + str(evec2.flatten()))
# f.write("\nafterNorm: " + str(evec.flatten()) + "\n\n")
#evec = evec.transpose()
#test = 0
#for i in range(numAtoms):
# sigma_i = s[j]/np.abs(s[j])
# test += sigma_i * np.dot(evec[i],evec[i])
# print "sigma_i = ", sigma_i
#print "\n\nsum{sigma_i * |T_ni|^2} = ", test, "\n\n"
# print "evec = " , evec
# print "sigma1: ", s[0]/np.abs(s[0])
# print "sigma2: ", s[1]/np.abs(s[1])
# print "sign lambda_0: ", (s[0]/np.abs(s[0]))*(evec[0][0]**2) + (s[1]/np.abs(s[1]))*(evec[0][1]**2)
# print "sign lambda_1: ", (s[0]/np.abs(s[0]))*(evec[1][0]**2) + (s[1]/np.abs(s[1]))*(evec[1][1]**2)
#using only positive eigenvalue
#w = [w[0]]
#evec = [evec[0]]
#print "eigenvalues used: ", w
#print "eigenvectors used: ", evec
strfac = np.zeros(numAtoms)
#for testing:
ff = 1
for i in range(numAtoms):
dum = 0
for j in range(numAtoms):
dum += (s[j] / np.sqrt(np.abs(
s[j]))) * ff * evec[j][i] #* np.exp(1.0j*
#(-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2]))
# print "\n\n\nwritting to f2:\n\n\n", np.exp(1.0j*
# (-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2]))
# print "j: ", j
# print "basis: ", basis[j]
# print "Q.di", (-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2])
exp = np.exp(1.0j * (-Q[0] * basis[j][0] - Q[1] * basis[j][1] -
Q[2] * basis[j][2]))
# f2.write(" " + str(exp))
strfac[i] = np.abs(dum)**2
# print "Q: ", Q, " strfac[",i,"]: ", strfac[i]
# f2.write("\n\n")
# strfac_sums.append(0.0);
# strfac1.append(strfac[0])
# strfac2.append(strfac[1])
sqw = np.zeros(len(e))
for j in range(numAtoms):
# strfac_sums[Qindex] +=strfac[j]
for i in range(len(e)):
y = LIFETIME_VALUE / 2
lorentzian = (1 / np.pi) * y / ((e[i] - w[j])**2 + y**2)
#test
#strfac[j] = ff*1
sqw[i] = sqw[i] + strfac[
j] * lorentzian #he has some 'bose' factor in here
#more test
#sqw[i] = sqw[i] + strfac[j]
mesh[Qindex] = sqw #* 0.5*(1 + Q[0]**2) #along x, Q_hat is just 1?
#f.close()
#f = open("/home/tom/Desktop/output.txt", 'w')
#for q in range(0,len(h_list)):
#for w in range(0,len(e)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(e[w]))
#f.write(" ")
#f.write(str(mesh[q][w]))
#f.write("\n")
#f.close()
#f = open("/home/tom/Desktop/output2.txt", 'w')
#for q in range(0,len(h_list)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(np.abs(q1[q])))
#f.write(" ")
#f.write(str(np.abs(q2[q])))
#f.write("\n")
#f.close()
#f = open("/home/tom/Desktop/output3.txt", 'w')
#for q in range(0,len(h_list)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(np.real(w1_list[q])))
#f.write(" ")
#f.write(str(np.real(w2_list[q])))
#f.write(" ")
#f.write(str(np.real(w3_list[q])))
#f.write(" ")
#f.write(str(np.real(w4_list[q])))
#f.write(" ")
#f.write(str(np.real(w5_list[q])))
#f.write(" ")
#f.write(str(np.real(w6_list[q])))
#f.write(" ")
#f.write(str(np.real(w7_list[q])))
#f.write(" ")
#f.write(str(np.real(w8_list[q])))
#f.write("\n")
#f.close()
#f = open("/home/tom/Desktop/strfacs.txt", 'w')
#for q in range(0,len(h_list)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(k_list[q]))
#f.write(" ")
#f.write(str(l_list[q]))
#f.write(" ")
#f.write(str(strfac1[q]))
#f.write(" ")
#f.write(str(strfac2[q]))
#f.write(" ")
#f.write(str(strfac_sums[q]))
#f.write("\n")
#f.close()
#plt.plot(w1_list)
#plt.plot(w2_list)
#plt.show()
print "Run Time: ", time.clock() - initial_time, " seconds"
def mcQueeny_case(interactionfile, spinfile, tau_list, temperature,
direction={'kx':1,'ky':0,'kz':0}, hkl_interval=[1e-3,2*np.pi,100],
omega_interval=[0,5,100], eig_eps = 0.001):
initial_time = time.clock()
#to plot eigenvector components
# q1 = []
# q2 = []
#to plot w's
#w1_list = []
#w2_list = []
#w3_list = []
#w4_list = []
#w5_list = []
#w6_list = []
#w7_list = []
#w8_list = []
# w_output_list = []
#plot the structure factor to compare with lovesey (In McQueeny Paper)
# strfac_sums = []
# strfac1 = []
# strfac2 = []
atom_list, jnums, jmats, N_atoms_uc, numCutOff, magCellSize = readFiles(interactionfile,spinfile, rtn_cutOffInfo = True)
#Hardcoding body-center cubic
if 0:
atom_list = []
jnums = [0]
N_atom_uc = 1
numCutOff = 7
magCellSize = np.array([1,1,1])
jmats = [np.array([[-1,0,0],[0,-1,0],[0,0,-1]])]
atom_list.append(atom(pos = np.array([0.5,0.5,0.5]),
spin = np.array([0,0,1]),
interactions = [0,0,0,0,0,0],
neighbors = [1,2,3,4,5,6]))
atom_list.append(atom(pos = np.array([0.,0.,0.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
atom_list.append(atom(pos = np.array([1.,0.,0.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
atom_list.append(atom(pos = np.array([1.,1.,0.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
atom_list.append(atom(pos = np.array([1.,0.,1.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
atom_list.append(atom(pos = np.array([1.,1.,1.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
atom_list.append(atom(pos = np.array([0.,1.,0.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
atom_list.append(atom(pos = np.array([0.,0.,1.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
atom_list.append(atom(pos = np.array([0.,1.,1.]),
spin = np.array([0,0,-1]),
interactions = [0],
neighbors = [0]))
h_list, k_list, l_list = generate_hkl(hkl_interval, direction)
e = generate_wt(omega_interval)
ff_list = generate_form_factors(N_atoms_uc, atom_list, hkl_interval)
s = []
basis = []
for a in atom_list:
s.append(a.spin[2])#z component of spin (assume aligned with z)
#basis.append(a.pos)
basis.append(a.pos - magCellSize) #translate back to 0,0,0
#normalize? NO!
#basis.append([a.pos[0] - int(a.pos[0]), a.pos[1]-int(a.pos[1]), a.pos[2] - int(a.pos[2])])
#print "\n\nbasis vectors:\n"
#mess with basis vectors and spin
#basis[0] = [0.25,0,0]
#basis[0] = [1.0,0,0]
#tmp = s[0]
#s[0] = s[1]
#s[1] = tmp
#for pos in basis:
# print pos
#print "\n\nspins:\n"
#for spin in s:
# print spin
mesh = np.zeros((len(h_list), len(e)))
#output q, w, and eigenvectors before and after normalization to compare with McQueeny's
# f = open("/home/tom/Desktop/Python_Eigs.txt", 'w')
# f.write("q w eigVec_before_norm T\n\n")
# f2 = open("/home/tom/Desktop/Values.txt", 'w')
# f2.write("Q w wvec (wvec norm) e^(-i*Q.d_i)\n\n")
#create the T_ni matricies (eigenvector matrices)
#This algorithm uses a differnt matrix:
#evecs, mats = calc_eig_mats_numerically(Hsave, h_list,k_list,l_list)
#eigvals = calc_eigs_numerically(Hsave, h_list, k_list, l_list)#This is innefficient, eigs already calculated and thrown away in calc_eig_mats_..
#w_list = gen_eigs_with_sign(Hsave, h_list, k_list, l_list, 0.001)
for Qindex in range(len(h_list)):
Q = (np.array([h_list,k_list,l_list]).transpose())[Qindex]
# f2.write(str(Q))
#CALL SPINWAVE(q,w,evec) - get w, evec for given q
M = create_matrix(atom_list, jnums, jmats, numCutOff, magCellSize, Q-np.array(tau_list))
w, evec = np.linalg.eig(M)
# f2.write(" " + str(w))
# f2.write(" " + str(evec.flatten()))
# evec = evecs[Qindex]
# w = w_list[Qindex]
# print "\nw = ", w
#w1_list.append(w[0])
#w2_list.append(w[1])
#w3_list.append(w[1])
#w4_list.append(w[1])
#w5_list.append(w[1])
#w6_list.append(w[1])
#w7_list.append(w[1])
#w8_list.append(w[1])
# w_output_list.append(w)
ff = ff_list[Qindex]
#Get number of atoms
numAtoms = numCutOff
#numAtoms = np.sqrt(evec.size)#This should be the number of atoms that I need to loop through.
#if int(numAtoms) != numAtoms:#I beleive t_mat should always be square, but lets check
# raise Exception("t_mat not square!")
#Normalize evec to get T_ni
#for i in range(0, numAtoms):
# normi = 0
# for j in range(0, numAtoms):
# sigma_j = s[j]/np.abs(s[j])
# normi += sigma_j * np.abs(evec[j][i])**2
# for j in range(0, numAtoms):
# evec[i][j] = evec[j][i]/np.sqrt(np.abs(normi))
evec2 = evec.copy()
#I'm going to write this from scratch
# print "evec before = ", evec
T = evec
sigma = []
for i in range(numAtoms):
sigma.append(s[i]/np.abs(s[i]))
for n in range(numAtoms):
norm = 0
for i in range(numAtoms):
norm += sigma[i]*(np.abs(evec2[n][i])**2)
for i in range(numAtoms):
# print "norm : ", norm
# print "w sign: ", np.sign(w[n])
#T[n][i] = T[n][i]*np.sqrt((w[n]/np.abs(w[n]))/norm + 0j)
T[n][i] = evec2[n][i]/np.sqrt(np.abs(norm))
evec = T
# f2.write(" " + str(T.flatten()))
#evec = evec2
# q1.append(evec[0][0])
# q2.append(evec[0][0])
#output eigs to file
# f.write(str(Q))
# f.write(" w:" + str(w))
# f.write("\nmat: " + str(M))#the matrix for which the eigs were found
# f.write("\nbefore_norm: " + str(evec2.flatten()))
# f.write("\nafterNorm: " + str(evec.flatten()) + "\n\n")
#evec = evec.transpose()
#test = 0
#for i in range(numAtoms):
# sigma_i = s[j]/np.abs(s[j])
# test += sigma_i * np.dot(evec[i],evec[i])
# print "sigma_i = ", sigma_i
#print "\n\nsum{sigma_i * |T_ni|^2} = ", test, "\n\n"
# print "evec = " , evec
# print "sigma1: ", s[0]/np.abs(s[0])
# print "sigma2: ", s[1]/np.abs(s[1])
# print "sign lambda_0: ", (s[0]/np.abs(s[0]))*(evec[0][0]**2) + (s[1]/np.abs(s[1]))*(evec[0][1]**2)
# print "sign lambda_1: ", (s[0]/np.abs(s[0]))*(evec[1][0]**2) + (s[1]/np.abs(s[1]))*(evec[1][1]**2)
#using only positive eigenvalue
#w = [w[0]]
#evec = [evec[0]]
#print "eigenvalues used: ", w
#print "eigenvectors used: ", evec
strfac = np.zeros(numAtoms)
#for testing:
ff = 1
for i in range(numAtoms):
dum = 0
for j in range(numAtoms):
dum += (s[j]/np.sqrt(np.abs(s[j]))) * ff * evec[j][i] #* np.exp(1.0j*
#(-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2]))
# print "\n\n\nwritting to f2:\n\n\n", np.exp(1.0j*
# (-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2]))
# print "j: ", j
# print "basis: ", basis[j]
# print "Q.di", (-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2])
exp = np.exp(1.0j*(-Q[0]*basis[j][0] -Q[1]*basis[j][1] -Q[2]*basis[j][2]))
# f2.write(" " + str(exp))
strfac[i] = np.abs(dum)**2
# print "Q: ", Q, " strfac[",i,"]: ", strfac[i]
# f2.write("\n\n")
# strfac_sums.append(0.0);
# strfac1.append(strfac[0])
# strfac2.append(strfac[1])
sqw = np.zeros(len(e))
for j in range(numAtoms):
# strfac_sums[Qindex] +=strfac[j]
for i in range(len(e)):
y = LIFETIME_VALUE/2
lorentzian = (1/np.pi)*y/((e[i]- w[j])**2+y**2)
#test
#strfac[j] = ff*1
sqw[i] = sqw[i] + strfac[j] * lorentzian #he has some 'bose' factor in here
#more test
#sqw[i] = sqw[i] + strfac[j]
mesh[Qindex] = sqw #* 0.5*(1 + Q[0]**2) #along x, Q_hat is just 1?
#f.close()
#f = open("/home/tom/Desktop/output.txt", 'w')
#for q in range(0,len(h_list)):
#for w in range(0,len(e)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(e[w]))
#f.write(" ")
#f.write(str(mesh[q][w]))
#f.write("\n")
#f.close()
#f = open("/home/tom/Desktop/output2.txt", 'w')
#for q in range(0,len(h_list)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(np.abs(q1[q])))
#f.write(" ")
#f.write(str(np.abs(q2[q])))
#f.write("\n")
#f.close()
#f = open("/home/tom/Desktop/output3.txt", 'w')
#for q in range(0,len(h_list)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(np.real(w1_list[q])))
#f.write(" ")
#f.write(str(np.real(w2_list[q])))
#f.write(" ")
#f.write(str(np.real(w3_list[q])))
#f.write(" ")
#f.write(str(np.real(w4_list[q])))
#f.write(" ")
#f.write(str(np.real(w5_list[q])))
#f.write(" ")
#f.write(str(np.real(w6_list[q])))
#f.write(" ")
#f.write(str(np.real(w7_list[q])))
#f.write(" ")
#f.write(str(np.real(w8_list[q])))
#f.write("\n")
#f.close()
#f = open("/home/tom/Desktop/strfacs.txt", 'w')
#for q in range(0,len(h_list)):
#f.write(str(h_list[q]))
#f.write(" ")
#f.write(str(k_list[q]))
#f.write(" ")
#f.write(str(l_list[q]))
#f.write(" ")
#f.write(str(strfac1[q]))
#f.write(" ")
#f.write(str(strfac2[q]))
#f.write(" ")
#f.write(str(strfac_sums[q]))
#f.write("\n")
#f.close()
#plt.plot(w1_list)
#plt.plot(w2_list)
#plt.show()
print "Run Time: ", time.clock()-initial_time, " seconds"
def generate_cross_section(interactionfile, spinfile, lattice, arg,
tau_list, h_list, k_list, l_list, w_list):
"""
Calculates the cross_section given the following parameters:
interactionfile, spinfile - files to get atom data
lattice - Lattice object from tripleaxisproject
arg - reduced list of operator combinations
tau_list - list of tau position
w_list - list of w's probed
"""
# Read files, get atom_list and such
atom_list, jnums, jmats,N_atoms_uc=readFiles(interactionfile,spinfile)
# Get Hsave to calculate its eigenvalues
N_atoms = len(atom_list)
Hsave,poly,heigs = calculate_dispersion(atom_list,N_atoms_uc,N_atoms,jmats,showEigs=True)
atom_list=atom_list[:N_atoms_uc]
N_atoms = len(atom_list)
N = N_atoms
print "Calculated: Dispersion Relation"
# Generate kappas from (h,k,l)
kaprange=lattice.modvec(h_list,k_list,l_list, 'latticestar')
nkpts=len(kaprange)
kapvect=np.empty((nkpts,3),'Float64')
kapvect[:,0]=h_list
kapvect[:,1]=k_list
kapvect[:,2]=l_list
# Grabs the unit vectors from the back of the lists.
kapunit = kapvect.copy()
kapunit[:,0]=kapvect[:,0]/kaprange
kapunit[:,1]=kapvect[:,1]/kaprange
kapunit[:,2]=kapvect[:,2]/kaprange
unit_vect = []
for i in range(len(arg)):
unit_vect.append(arg[i].pop())
#print unit_vect
unit_vect = sum(unit_vect)
# Generate qs from kappas and taus
qlist=[]
ones_list=np.ones((1,nkpts),'Float64')
for tau in tau_list:
taui=np.ones((nkpts,3),'Float64')
taui[:,0]=ones_list*tau[0]
taui[:,1]=ones_list*tau[1]
taui[:,2]=ones_list*tau[2]
kappa_minus_tau=kapvect-taui
tau_minus_kappa=taui - kapvect
qlist.append(np.vstack([kappa_minus_tau,tau_minus_kappa]))
# Eigenvalues and omegas
wtlist=w_list
eig_list=[]
eigs = Hsave.eigenvals().keys()
for q in qlist:
eig_list.append(eigs)
eig_list = np.array(eig_list)
print "Calculated: Eigenvalues"
# Form Factor
ff_list = []
for i in range(N_atoms_uc):
el = elements[atom_list[i].atomicNum]
val = atom_list[i].valence
if val != None:
Mq = el.magnetic_ff[val].M_Q(kaprange)
else:
Mq = el.magnetic_ff[0].M_Q(kaprange)
ff_list = Mq #ff_list.append(Mq)
print "Calculated: Form Factors"
# Generate most general form of csection
csection=0
for i in range(len(arg)):
for j in range(len(arg[i])):
csection = (csection + arg[i][j]*unit_vect)
for i in range(N):
ni = sp.Symbol('n%i'%(i,), real = True)
np1i = sp.Symbol('np1%i'%(i,), Real = True)
csection.subs(ni+1,np1i)
csection = csection.expand()
csection = csection.subs(KAPXHAT_SYM*KAPYHAT_SYM,0)
csection = csection.subs(KAPXHAT_SYM*KAPZHAT_SYM,0)
csection = csection.subs(KAPYHAT_SYM*KAPZHAT_SYM,0)
csection = csection.subs(KAPZHAT_SYM*KAPYHAT_SYM,0)
csection = csection.subs(KAPZHAT_SYM*KAPXHAT_SYM,0)
csection = csection.subs(KAPYHAT_SYM*KAPXHAT_SYM,0)
csection = (csection * exp(-I * W_SYM * T_SYM) * exp(I * KAP_SYM * L_SYM)).expand(deep=False)
csection = sp.powsimp(csection, deep=True)
print 'beginning'
# print csection
csection = sp.powsimp(csection)
csection = sub_in(csection,exp(I*T_SYM*A_WILD + I*T_SYM*B_WILD + I*C_WILD + I*D_WILD + I*K_WILD),sp.DiracDelta(A_WILD*T_SYM + B_WILD*T_SYM + C_WILD + D_WILD + K_WILD))
print 'intermediate'
# print csection
# csection = sub_in(csection,sp.DiracDelta(A*t + B*t + C*L + D*L ),(1./hbar)*sp.DiracDelta(A + B)*sp.simplify(sp.DiracDelta(C + D - tau))) #This is correct
csection = sub_in(csection,sp.DiracDelta(A_WILD*T_SYM + B_WILD*T_SYM + C_WILD*L_SYM + D_WILD*L_SYM ),sp.Pow(pi,-1)*(LIFETIME_VALUE*0.5)*sp.Pow((A_WILD+B_WILD)**2+(LIFETIME_VALUE*0.5)**2,-1)*sp.simplify(sp.DiracDelta(C_WILD + D_WILD - TAU_SYM)))
print 'ending'
# print csection
# Do some associative clean up to make it easier for later substitutions
csection = sub_in(csection,sp.DiracDelta(-A_WILD - B_WILD),sp.DiracDelta(A_WILD + B_WILD))
csection = sub_in(csection,(-A_WILD - B_WILD)**2,(A_WILD + B_WILD)**2)
csection = csection.subs(sp.DiracDelta(Q_SYM + TAU_SYM - KAP_SYM),sp.DiracDelta(KAP_SYM - Q_SYM - TAU_SYM))
csection = csection.subs(sp.DiracDelta(TAU_SYM - KAP_SYM - Q_SYM),sp.DiracDelta(KAP_SYM + Q_SYM - TAU_SYM))
csection = csection.subs(sp.DiracDelta(KAP_SYM - Q_SYM - TAU_SYM),DD_KTMQ_SYM)
csection = csection.subs(sp.DiracDelta(KAP_SYM + Q_SYM - TAU_SYM),DD_KTPQ_SYM)
print "Applied: Delta Function Conversion"
print csection
print "Generated: Analytic Cross-Section Expression"
return (N_atoms_uc, csection, kaprange, tau_list, eig_list, kapvect, wtlist, ff_list)
def generate_cross_section(interactionfile, spinfile, lattice, arg,
tau_list, h_list, k_list, l_list, w_list):
"""
Calculates the cross_section given the following parameters:
interactionfile, spinfile - files to get atom data
lattice - Lattice object from tripleaxisproject
arg - reduced list of operator combinations
tau_list - list of tau position
w_list - list of w's probed
"""
# Read files, get atom_list and such
atom_list, jnums, jmats,N_atoms_uc=readFiles(interactionfile,spinfile)
# Get Hsave to calculate its eigenvalues
N_atoms = len(atom_list)
Hsave,poly,heigs = calculate_dispersion(atom_list,N_atoms_uc,N_atoms,jmats,showEigs=True)
atom_list=atom_list[:N_atoms_uc]
N_atoms = len(atom_list)
N = N_atoms
print "Calculated: Dispersion Relation"
kaprange=lattice.modvec(h_list,k_list,l_list, 'latticestar')
nkpts=len(kaprange)
kapvect=np.empty((nkpts,3),'Float64')
kapvect[:,0]=h_list
kapvect[:,1]=k_list
kapvect[:,2]=l_list
kapunit = kapvect.copy()
kapunit[:,0]=kapvect[:,0]/kaprange
kapunit[:,1]=kapvect[:,1]/kaprange
kapunit[:,2]=kapvect[:,2]/kaprange
plusq=[]
minusq=[]
qlist=[]
ones_list=np.ones((1,nkpts),'Float64')
wtlist=w_list
qtlist=[]
for tau in tau_list:
taui=np.ones((nkpts,3),'Float64')
taui[:,0]=ones_list*tau[0]
taui[:,1]=ones_list*tau[1]
taui[:,2]=ones_list*tau[2]
kappa_minus_tau=kapvect-taui
tau_minus_kappa=taui - kapvect
qlist.append(np.vstack([kappa_minus_tau,tau_minus_kappa]))
eig_list=[]
eigs = Hsave.eigenvals().keys()
for q in qlist:
eig_list.append(eigs)
eig_list = np.array(eig_list)
print "Calculated: Eigenvalues"
# Other Constants
kap = sp.Symbol('kappa', real = True)
t = sp.Symbol('t', real = True)
w = sp.Symbol('w', real = True)
W = sp.Symbol('W', real = True)
tau = sp.Symbol('tau', real = True)
Q = sp.Symbol('q', real = True)
L = sp.Symbol('L', real = True)
lifetime=0.5
# Form Factor
ff_list = []
for i in range(N_atoms_uc):
el = elements[atom_list[i].atomicNum]
val = atom_list[i].valence
if val != None:
Mq = el.magnetic_ff[val].M_Q(kaprange)
else:
Mq = el.magnetic_ff[0].M_Q(kaprange)
ff_list = Mq #ff_list.append(Mq)
print "Calculated: Form Factors"
# Wilds for sub_in method
A = sp.Wild('A',exclude = [0,t])
B = sp.Wild('B',exclude = [0,t])
C = sp.Wild('C')
D = sp.Wild('D')
# Grabs the unit vectors from the back of the lists.
unit_vect = []
kapxhat = sp.Symbol('kapxhat',real=True)
kapyhat = sp.Symbol('kapyhat',real=True)
kapzhat = sp.Symbol('kapzhat',real=True)
for i in range(len(arg)):
unit_vect.append(arg[i].pop())
unit_vect = sum(unit_vect)
csection=0
for i in range(len(arg)):
for j in range(len(arg[i])):
csection = (csection + arg[i][j]*unit_vect)
for i in range(N):
ni = sp.Symbol('n%i'%(i,), real = True)
np1i = sp.Symbol('np1%i'%(i,), Real = True)
csection.subs(ni+1,np1i)
csection = csection.expand()
csection = csection.subs(kapxhat*kapyhat,0)
csection = csection.subs(kapxhat*kapzhat,0)
csection = csection.subs(kapyhat*kapzhat,0)
csection = csection.subs(kapzhat*kapyhat,0)
csection = csection.subs(kapzhat*kapxhat,0)
csection = csection.subs(kapyhat*kapxhat,0)
csection = (csection * exp(-I*w*t) * exp(I*kap*L)).expand(deep=False)
csection = sp.powsimp(csection, deep=True)
print 'beginning delta conversion'
print csection
csection = sp.powsimp(csection)
csection = sub_in(csection,exp(I*t*A + I*t*B + I*C + I*D + I*K),sp.DiracDelta(A*t + B*t + C + D + K))
print 'intermediate'
print csection
# csection = sub_in(csection,sp.DiracDelta(A*t + B*t + C*L + D*L ),(1./hbar)*sp.DiracDelta(A + B)*sp.simplify(sp.DiracDelta(C + D - tau))) #This is correct
csection = sub_in(csection,sp.DiracDelta(A*t + B*t + C*L + D*L ),sp.Pow(pi,-1)*(lifetime*0.5)*sp.Pow((A+B)**2+(lifetime*0.5)**2,-1)*sp.simplify(sp.DiracDelta(C + D - tau)))
print 'ending delta conversion'
print csection
csection = sub_in(csection,sp.DiracDelta(-A - B),sp.DiracDelta(A + B))
csection = sub_in(csection,(-A - B)**2,(A + B)**2)
csection = csection.subs(sp.DiracDelta(Q+tau-kap),sp.DiracDelta(kap-Q-tau))
csection = csection.subs(sp.DiracDelta(tau-kap-Q),sp.DiracDelta(kap+Q-tau))
print "Applied: Delta Function Conversion"
print "end part 1"
#print csection
return (N_atoms_uc, csection, kaprange, tau_list, eig_list, kapvect, wtlist, ff_list)
if 0:
a = [1, 2, 3, 4, 5, 6, 7]
b = [1, 2, 5]
comms = [a[i] in b for i in range(len(a))]
print comms
x = sp.Symbol('x')
y = x**2
print type(y)
print y.is_commutative
print y.atoms()
if 0:
interfile = 'c:/test_montecarlo.txt'
spinfile = 'c:/test_Spins.txt'
atom_list, jnums, jmats, N_atoms_uc = rf.readFiles(interfile, spinfile)
N_atoms = len(atom_list)
jij_values = []
jij_columns = []
jij_rowIndex = []
# Scan through atom_list
num_inters = 0
for i in range(N_atoms):
ints_list = atom_list[i].interactions
nbrs_list = atom_list[i].neighbors
print 'ints', ints_list
print 'nbrs', nbrs_list
nbrs_ints = [(nbrs_list[x], ints_list[x])
for x in range(len(nbrs_list))]
nbrs_ints.sort()
def run_cross_section(interactionfile, spinfile):
start = clock()
# Generate Inputs
atom_list, jnums, jmats,N_atoms_uc=readFiles(interactionfile,spinfile)
atom1 = atom(pos = [0.00,0,0], neighbors = [1], interactions = [0], int_cell = [0],
atomicNum = 26, valence = 3)
atom2 = atom(pos = [0.25,0,0], neighbors = [0,2], interactions = [0], int_cell = [0],
atomicNum = 26, valence = 3)
atom3 = atom(pos = [0.50,0,0], neighbors = [1,3], interactions = [0], int_cell = [0],
atomicNum = 26, valence = 3)
atom4 = atom(pos = [0.75,0,0], neighbors = [2], interactions = [0], int_cell = [0],
atomicNum = 26, valence = 3)
# atom_list,N_atoms_uc = ([atom1, atom2, atom3, atom4],1)
atom_list=atom_list[:N_atoms_uc]
N_atoms = len(atom_list)
print N_atoms
if 1:
kx = sp.Symbol('kx', real = True)
ky = sp.Symbol('ky', real = True)
kz = sp.Symbol('kz', real = True)
k = spm.Matrix([kx,ky,kz])
(b,bd) = generate_b_bd_operators(atom_list)
# list_print(b)
(a,ad) = generate_a_ad_operators(atom_list, k, b, bd)
# list_print(a)
(Sp,Sm) = generate_Sp_Sm_operators(atom_list, a, ad)
# list_print(Sp)
(Sa,Sb,Sn) = generate_Sa_Sb_Sn_operators(atom_list, Sp, Sm)
# list_print(Sa)
(Sx,Sy,Sz) = generate_Sx_Sy_Sz_operators(atom_list, Sa, Sb, Sn)
# list_print(Sx)
print ''
#Ham = generate_Hamiltonian(N_atoms, atom_list, b, bd)
ops = generate_possible_combinations(atom_list, [Sx,Sy,Sz])
# list_print(ops)
ops = holstein(atom_list, ops)
# list_print(ops)
ops = apply_commutation(atom_list, ops)
# list_print(ops)
ops = replace_bdb(atom_list, ops)
# list_print(ops)
ops = reduce_options(atom_list, ops)
list_print(ops)
# Old Method
#cross_sect = generate_cross_section(atom_list, ops, 1, real, recip)
#print '\n', cross_sect
if 1:
aa = bb = cc = np.array([2.0*np.pi], 'Float64')
alpha = beta = gamma = np.array([np.pi/2.0], 'Float64')
vect1 = np.array([[1,0,0]])
vect2 = np.array([[0,0,1]])
lattice = Lattice(aa, bb, cc, alpha, beta, gamma, Orientation(vect1, vect2))
data={}
data['kx']=1.
data['ky']=0.
data['kz']=0.
direction=data
temperature = 1.0
kmin = 0
kmax = 2*sp.pi
steps = 25
tau_list = []
for i in range(1):
tau_list.append(np.array([0,0,0], 'Float64'))
h_list = np.linspace(0.1,12.0,200)
k_list = np.zeros(h_list.shape)
l_list = np.zeros(h_list.shape)
w_list = np.linspace(0.1,10.0,200)
efixed = 14.7 #meV
eief = True
N_atoms_uc,csection,kaprange,qlist,tau_list,eig_list,kapvect,wtlist = generate_cross_section(interactionfile, spinfile, lattice, ops,
tau_list, h_list, k_list, l_list, w_list,
temperature, steps, eief, efixed)
print csection
return N_atoms_uc,csection,kaprange,qlist,tau_list,eig_list,kapvect,wtlist
end = clock()
print "\nFinished %i atoms in %.2f seconds" %(N_atoms,end-start)
def csection(interactionfile, spinfile, tau_list, temp = 0.0001, HWHM = 1/np.pi,
direction={'kx':1,'ky':0,'kz':0}, hkl_interval=[1e-3,2*np.pi,100],
omega_interval=[0,5,100], kb = 0.086173423):
"""
kb is the boltzman constant in whatever units the user is using. The
default is 0.086173423 meV/K.
HWHM is the half width at half maximum of the lorentzian broadening
function (the resolution of the energy)."""
initial_time = time.clock()
atom_list, jnums, jmats, N_atoms_uc, numCutOff, magCellSize = readFiles(interactionfile,spinfile, rtn_cutOffInfo = True)
#Atoms in the first interaction cell come first in the list (atom_list),so
#I can iterate through the first numCutOff atoms in the list top iterate
#only through the atoms in the first interaction cell - I beleive 1/4/12
#Right now (1/12/12) a major issue is that this method requires the size
#of the magnetic cell and the cutOffCell size is not necesssarilly the
#same as the magenetic cell size. In fact, even in the very simple
#ferromagnetic chain it is not. - Same issue with numCutOff - should be
#numMagCell
#Simple Ferro Case
#magCellSize = np.array([1,1,1])
#numCutOff =1
#testing
#numCutOff = 2
#magCellSize = np.array([1,1,1])
#generate all the parameters I will need
h_list, k_list, l_list = generate_hkl(hkl_interval, direction)
#test
#h_list = 2*np.pi*h_list
#k_list = 2*np.pi*k_list
#l_list = 2*np.pi*l_list
e_vals = generate_wt(omega_interval)
mesh = np.zeros((len(h_list),len(e_vals)), dtype = complex)
#mu_hat is a unit vector pointing in the direction of positive spin, which
#I defined to be the direction of the spin of the atom whcih comes first in
#the list. I haven't though about whether the sign will make a difference
#in the final result, but it does not really matter as the choice of
#positive direction will always be arbitrary.
spin = np.array(atom_list[0].spin)
mu_hat = spin/np.sqrt(np.dot(spin,spin))
ff_list = generate_form_factors(N_atoms_uc, atom_list, hkl_interval)
s = []
sigmas = []
basis = []
for a in atom_list:
s.append(np.abs(np.dot(a.spin, mu_hat)))
sigmas.append(np.dot(a.spin,mu_hat)/s[-1])
#translate back to 0,0,0, but leave in crystallographic cell dimensions
basis.append(a.pos - magCellSize)
#I'm leaving it in crystallographic cell dimensions rather than magnetic
#cell dimensions so that the periodicity of the dispersion relation
#matches what we already calculate with the William's code, rather than
#McQueeny's
w_list = []
Q_points = (np.array([h_list,k_list,l_list]).transpose())
S_q = [] #For testing - S at the w points where S is maximized
#calculate the cross section for each Q point (h,k,l)
for Qindex in range(len(h_list)):
Q = np.array(Q_points[Qindex])
#create the matrix and find the eigenvalues and eigenvectors
M = create_matrix(atom_list, jnums, jmats, numCutOff, magCellSize, Q-np.array(tau_list), mu_hat)
w, evec = np.linalg.eig(M)
w_list.append(w)#testing
ff = ff_list[Qindex]
#Get number of atoms
numAtoms = numCutOff
T = np.empty((numAtoms, numAtoms), dtype = complex)#normalized eigenvectors
#Normalize evec to get T_ni
for n in range(0, numAtoms):
sgn_ln = w[n]/abs(w[n])
tmp = 0.0
for i in range(0, numAtoms):
tmp += sigmas[i] * abs(evec[i][n])**2
C = sgn_ln/tmp
for i in range(0, numAtoms):
T[i][n] = evec[i][n]*cmath.sqrt(C)
#The indices of T actually go T[i][n], where n corresponds to the
#eigenvalue, w[n], and i corresponds to the ith atom on the cell
#1/12/12 The Matrix and T are tested for the simple antiferro case and
#found to be correct
#Now calculate the S(Q,w) / structure factor
#mu_hat is a unit vector pointing in the direction of the spins
#All the pins are aligned up to their sign, which does not matter here.
#This method will work whether the spins are aligned along the z axis
#or not.
pol_factor = 0.5*(1.0 + (np.dot(mu_hat,Q)**2)/np.dot(Q,Q))
gamma = ff #I need to make sure this is the same as his formfactor
gamma = 1.0#testing
#strfac = 0.0
#For this one Q point, we get a whole S vs. e array
tmp_e_dimension_array = np.zeros((len(e_vals)), dtype = complex)
S_q.append(0.0)
for n in range(numAtoms):
strfac = 0.0
for i in range(numAtoms):
d_i = np.array(basis[i], dtype = np.float64)/np.array(magCellSize, dtype = np.float64)
strfac += gamma*sigmas[i]*np.sqrt(s[i])*T[i][n]*np.exp(-1j*np.dot(Q,d_i))
strfac = np.abs(strfac)**2
#test:
S_q[-1] += strfac
#The 'Bose Occupation Factor,' as McQueeny calls it, which, I
#believe is the Bose-Einstein distribution for identical bosons
n_qn = 1.0/(np.exp(w[n]/(temp*kb)) - 1.0)
#replace the delta function with a lorentzian
for e_index in range(len(e_vals)):
#delta(w - w_n(q))
lorentzian1 = (1.0/np.pi)*HWHM/((e_vals[e_index] - w[n])**2 + HWHM**2)
tmp_e_dimension_array += strfac*(n_qn+1.0)*lorentzian1
#McQueeny left the other delta function out of his code, so for
#now, I did too.
#A test shows that numpy actually copies the values (not references)
mesh[Qindex] = pol_factor * tmp_e_dimension_array
#Right now, mesh is only S(Q,w), not the full cross section, which is:
#r_0^2 * k'/k * S(Q,w)
r_0 = 1.0
kp = 1.0
k = 1.0
#print "Run Time: ", time.clock()-initial_time, " seconds"
#testing
w_list = np.array(w_list)
for i in range(len(w_list)):
print Q_points[i], "\t", S_q[i]
plt.plot(np.linspace(hkl_interval[0],hkl_interval[1], hkl_interval[2])[2:-2], S_q[2:-2])
plt.show()
return (r_0**2) * (kp/k) * mesh
def eval_cross_section(interactionfile, spinfile, lattice, arg,
tau_list, h_list, k_list, l_list, w_vect_list,
direction, temperature, kmin, kmax, steps, eief, efixed = 14.7):
"""
Calculates the cross_section given the following parameters:
interactionfile, spinfile - files to get atom data
lattice - Lattice object from tripleaxisproject
arg - reduced list of operator combinations
tau_list - list of tau position
w_list - list of w's probed
direction - direction of scan
temp - temperature
kmin - minimum value of k to scan
kmax - maximum value of k to scan
steps - number of steps between kmin, kmax
eief - True if the energy scheme is Ei - Ef, False if it is Ef - Ei
efixed - value of the fixed energy, either Ei or Ef
"""
# Read files, get atom_list and such
atom_list, jnums, jmats,N_atoms_uc=readFiles(interactionfile,spinfile)
N_atoms = len(atom_list)
N = N_atoms
# TEMPORARY TO OVERRIDE ATOM_LIST ABOVE
# atom1 = atom(pos = [0.00,0,0], neighbors = [1], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom2 = atom(pos = [0.25,0,0], neighbors = [0,2], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom3 = atom(pos = [0.50,0,0], neighbors = [1,3], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom4 = atom(pos = [0.75,0,0], neighbors = [2], interactions = [0], int_cell = [0],
# atomicNum = 26, valence = 3, spinRmatrix=spm.Matrix([[1, 0, 0],[0, 1, 0],[0, 0, 1]]))
# atom_list,N_atoms_uc = ([atom1, atom2, atom3, atom4],1)
N_atoms = len(atom_list)
# Get Hsave to calculate its eigenvalues
(Hsave, charpoly, eigs) = calculate_dispersion(atom_list,N_atoms_uc,N_atoms,jmats,showEigs=True)
print "Calculated: Dispersion Relation"
# Generate kappa's from (h,k,l)
kaprange = []
kapvect = []
if len(h_list) == len(k_list) == len(l_list):
for i in range(len(h_list)):
kappa = lattice.modvec(h_list[i],k_list[i],l_list[i], 'latticestar')
kaprange.append(kappa[0])
kapvect.append(np.array([h_list[i],k_list[i],l_list[i]]))
# kapvect.append(np.array([h_list[i]/kappa,k_list[i]/kappa,l_list[i]/kappa]))
else:
raise Exception('h,k,l not same lengths')
# Generate q's from kappa and tau
pqrange = []
mqrange = []
for tau in tau_list:
ptemp = []
mtemp = []
for kap in kapvect:
#tau = lattice.modvec(tau[0],tau[1],tau[2], 'latticestar')
ptemp.append(kap - tau)
mtemp.append(tau - kap)
pqrange.append(ptemp)
mqrange.append(mtemp)
# Calculate w_q's using q
qrange = []
# krange = []
wrange = []
if 1: # Change this later
for set in pqrange:
temp = []
temp1 = []
for q in set:
eigs = calc_eigs(Hsave,q[0]*direction['kx'], q[1]*direction['ky'], q[2]*direction['kz'])
# Take only one set of eigs. Should probably have a flag here.
temp.append(eigs[0])
# krange.append(np.array([q*direction['kx'], q*direction['ky'], q*direction['kz']]))
temp1.append(q)
wrange.append(temp)
qrange.append(temp1)
print "Calculated: Eigenvalues"
wrange=np.array(np.real(wrange))
qrange=np.array(np.real(qrange))
kaprange=np.array(np.real(kaprange))
print qrange.shape
print wrange.shape
print kaprange.shape
# Calculate w (not _q) as well as kp/k
w_calc = []
kpk = []
if eief == True:
for tau in tau_list:
temp = []
temp1 = []
for om in w_vect_list:
temp.append(om - efixed)
temp1.append(np.sqrt(om/efixed))
w_calc.append(temp)
kpk.append(temp1)
else:
for tau in tau_list:
temp = []
temp1 = []
for om in w_vect_list:
temp.append(-(om - efixed))
temp1.append(np.sqrt(efixed/om))
w_calc.append(temp)
kpk.append(temp1)
w_calc = np.array(np.real(w_calc))
# Grab Form Factors
ff_list = []
s = sp.Symbol('s')
for i in range(N_atoms):
el = elements[atom_list[i].atomicNum]
val = atom_list[i].valence
if val != None:
Mq = el.magnetic_ff[val].M_Q(kaprange)
else:
Mq = el.magnetic_ff[0].M_Q(kaprange)
ff_list.append(Mq)
print "Calculated: Form Factors"
# Other Constants
gamr0 = 2*0.2695*10**(-12) #sp.Symbol('gamma', commutative = True)
hbar = 1.0 # 1.05457148*10**(-34) #sp.Symbol('hbar', commutative = True)
g = 2.#sp.Symbol('g', commutative = True)
# Kappa vector
kap = sp.Symbol('kappa', real = True)#spm.Matrix([sp.Symbol('kapx',real = True),sp.Symbol('kapy',real = True),sp.Symbol('kapz',real = True)])
t = sp.Symbol('t', real = True)
w = sp.Symbol('w', real = True)
W = sp.Symbol('W', real = True)
tau = sp.Symbol('tau', real = True)
q = sp.Symbol('q', real = True)
L = sp.Symbol('L', real = True)
boltz = 1.#1.3806503*10**(-23)
# Wilds for sub_in method
A = sp.Wild('A',exclude = [0,t])
B = sp.Wild('B',exclude = [0,t])
C = sp.Wild('C')
D = sp.Wild('D')
K = sp.Wild('K')
# First the exponentials are turned into delta functions:
for i in range(len(arg)):
for j in range(N):
print '1', arg[i][j]
arg[i][j] = sp.powsimp(arg[i][j])#sp.powsimp(arg[i][j], deep = True, combine = 'all')
arg[i][j] = (arg[i][j] * exp(-I*w*t) * exp(I*kap*L)).expand()# * exp(I*inner_prod(spm.Matrix(atom_list[j].pos).T,kap))
arg[i][j] = sp.powsimp(arg[i][j])#sp.powsimp(arg[i][j], deep = True, combine = 'all')
# print '2', arg[i][j]
arg[i][j] = sub_in(arg[i][j],exp(I*t*A + I*t*B + I*C + I*D + I*K),sp.DiracDelta(A*t + B*t + C + D + K))#*sp.DiracDelta(C))
# print '3', arg[i][j]
arg[i][j] = sub_in(arg[i][j],sp.DiracDelta(A*t + B*t + C*L + D*L + K*L),sp.DiracDelta(A*hbar + B*hbar)*sp.simplify(sp.DiracDelta(C + D + K + tau)))
# print '4', arg[i][j]
arg[i][j] = sub_in(arg[i][j],sp.DiracDelta(-A - B)*sp.DiracDelta(C),sp.DiracDelta(A + B)*sp.DiracDelta(C))
print '5', arg[i][j]
print "Applied: Delta Function Conversion"
# Grabs the unit vectors from the back of the lists.
unit_vect = []
kapxhat = sp.Symbol('kapxhat',commutative = False)
kapyhat = sp.Symbol('kapyhat',commutative = False)
kapzhat = sp.Symbol('kapzhat',commutative = False)
for i in range(len(arg)):
unit_vect.append(arg[i].pop())
# Subs the actual values for kx,ky,kz, omega_q and n_q into the operator combos
# The result is basically a nested list:
#
# csdata = [ op combos ]
# op combos = [ one combo per atom ]
# 1 per atom = [ evaluated exp ]
#
csdata = []
for i in range(len(arg)):
temp1 = []
for j in range(len(arg[i])):
temp2 = []
for k in range(len(tau_list)):
temp3 = []
print i,j,k
for g in range(len(qrange[k])):
pvalue = tau_list[k] + kapvect[g] + qrange[k][g]
mvalue = tau_list[k] + kapvect[g] - qrange[k][g]
if pvalue[0] == 0 and pvalue[1] == 0 and pvalue[2] == 0:
arg[i][j] = arg[i][j].subs(sp.DiracDelta(kap+tau+q),sp.DiracDelta(0))
else: arg[i][j] = arg[i][j].subs(sp.DiracDelta(kap+tau+q),0)
if mvalue[0] == 0 and mvalue[1] == 0 and mvalue[2] == 0:
arg[i][j] = arg[i][j].subs(sp.DiracDelta(kap+tau-q),sp.DiracDelta(0))
else: arg[i][j] = arg[i][j].subs(sp.DiracDelta(kap+tau-q),0)
wq = sp.Symbol('wq', real = True)
nq = sp.Symbol('n%i'%(k,), commutative = False)
arg[i][j] = arg[i][j].subs(wq,wrange[k][g])
arg[i][j] = arg[i][j].subs(w,w_calc[k][g])
n = sp.Pow( sp.exp(hbar*wrange[k][g]/boltz*temperature) - 1 ,-1)
arg[i][j] = arg[i][j].subs(nq,n)
temp3.append(arg[i][j])
temp2.append(temp3)
temp1.append(temp2)
csdata.append(temp1)
## print arg[i][j]
# for g in range(len(kaprange)):
# arg[i][j] = arg[i][j].subs(kap, kaprange[g])
# for g in range(len(krange)):
## print 'calculating'
# temp3 = []
# wg = sp.Symbol('w%i'%(g,), real = True)
# ng = sp.Symbol('n%i'%(g,), commutative = False)
## kx = sp.Symbol('kx', real = True, commutative = True)
## ky = sp.Symbol('ky', real = True, commutative = True)
## kz = sp.Symbol('kz', real = True, commutative = True)
## arg[i][j] = arg[i][j].subs(kx,krange[g][0])
## arg[i][j] = arg[i][j].subs(ky,krange[g][1])
## arg[i][j] = arg[i][j].subs(kz,krange[g][2])
# arg[i][j] = arg[i][j].subs(wg,wrange[g])
# arg[i][j] = arg[i][j].subs(w,w_calc[g])
# nq = sp.Pow( sp.exp(hbar*wrange[g]/boltz*temp) - 1 ,-1)
# arg[i][j] = arg[i][j].subs(ng,nq)
## arg[i][j] = arg[i][j].subs(tau,tau_list[0])
#
# temp3.append(arg[i][j])
## print arg[i][j]
# temp2.append(temp3)
## print arg[i][j]
# csdata.append(temp2)
print csdata
# Front constants and stuff for the cross-section
front_constant = 1.0 # (gamr0)**2/(2*pi*hbar)
front_func = (1./2.)*g#*F(k)
vanderwaals = 1. #exp(-2*W)
csrange = []
if 1:
temp1 = []
temp2 = []
for q in range(len(qrange)):
print 'calculating'
for ii in range(len(csdata)):
for jj in range(len(csdata[ii])):
temp1.append(csdata[ii][jj])
# Put on gamr0, 2pi hbar, form factor, kp/k, vanderwaals first
dif = front_func**2 * front_constant# * kpk[q][0] * vanderwaals * ff_list[0][q]
# for vect in unit_vect:
# vect.subs(kapxhat,kapvect[q][0][0])
# vect.subs(kapyhat,kapvect[q][1][0])
# vect.subs(kapzhat,kapvect[q][2][0])
print 'diff',dif
print 'temp1',temp1
print sum(temp1)
dif = (dif * sum(temp1))# * sum(unit_vect))#.expand()
csrange.append(dif)
print "Calculated: Cross-section"
csrange=np.real(csrange)
csrange=np.array(csrange)
xi=qrange
yi=wrange
zi=csrange
Z=np.zeros((xi.shape[0],yi.shape[0]))
Z[range(len(xi)),range(len(yi))]=zi
pylab.contourf(xi,yi,Z)
pylab.colorbar()
pylab.show()
