def ssft(self,
potential,
dt=-.1j*50e-6,
max_its = 10000,
start_psi = None,
precision= 1e-8):
self.dt = dt
## kinetic energy part
self.T = hbar**2*self.k**2/(2*self.p.m)
self.exp_T = scipy.exp(-1j*self.dt/(2.*hbar)*self.T)
self.exp_2T = scipy.exp(-1j*self.dt/(hbar)*self.T)
if start_psi == None:
## Initialize psi to TF-solution
psi = self.rand_psi()
else:
psi = self.Normalize(start_psi)
psi_old = scipy.array(psi)
exp_prefactor = -1j*self.dt/hbar
psik = scipy.fftpack.fft(psi)
psik *= self.exp_T
psi = scipy.fftpack.ifft(psik)
for i in scipy.arange(max_its):
# print i
if scipy.iscomplex(self.dt):
psi = self.Normalize(psi)
psi *= scipy.exp(exp_prefactor*(potential+self.interaction(psi)))
psik = scipy.fftpack.fft(psi)
psik *= self.exp_2T
psi = scipy.fftpack.ifft(psik)
if precision != None:
if ((scipy.absolute(psi-psi_old)/scipy.absolute(psi)).sum() < precision)*(i>50):
break
else:
psi_old = psi
if scipy.iscomplex(self.dt):
psi = self.Normalize(psi)
psi *= scipy.exp(-1j*self.dt/hbar*(potential+self.interaction(psi)))
psik = scipy.fftpack.fft(psi)
psik *= self.exp_T
psi = scipy.fftpack.ifft(psik)
if (i == max_its-1)*(precision != None):
logging.warning('warning: algorithm did not converge!')
return self.Normalize(psi)
def __call__(self, field, skip=1):
'''
Plot field.
'''
super(Plotter2d, self).__call__(field)
dims = [0] * 4
dimnames = ('time', 'lev', 'lat', 'lon')
xind = dimnames.index(self.axes[0])
dims[xind] = slice(None)
yind = dimnames.index(self.axes[1])
dims[yind] = slice(None)
# Find plot arguments
dd = dict(lon=field.grid['lon'][0, :],
lat=field.grid['lat'][:, 0],
lev=field.grid['lev'],
time=field.time)
args = []
args.append(dd[self.axes[0]][::skip])
args.append(dd[self.axes[1]][::skip])
if xind > yind:
z = field.data[dims][::skip, ::skip]
else:
z = sp.ma.transpose(field.data[dims][::skip, ::skip])
args.append(sp.real(z))
if sp.any(sp.iscomplex(z.view(sp.ndarray))): args.append(sp.imag(z))
cs = self.method(*args, **self.copts)
if isinstance(cs, mlib.contour.ContourSet):
if cs.filled:
if self.cbar_opts is not None:
cb = pl.colorbar(**self.cbar_opts)
units = getattr(field, 'units', "")
cb.set_label(units)
else:
cs.clabel(**self.clab_opts)
# If x-axis is time, apply default time formatting
if self.axes[0] == 'time':
fig = pl.gcf()
fig.autofmt_xdate()
return cs
def take_step_split(self, dtau, ham_is_Herm=True):
"""Take a time-step dtau using the split-step integrator.
This is the one-site version of a DMRG-like time integrator described
at:
http://arxiv.org/abs/1408.5056
It has a fourth-order local error and is symmetric. It requires
iteratively computing two matrix exponentials per site, and thus
has less predictable CPU time requirements than the Euler or RK4
methods.
NOTE:
This requires the expokit extension, which is included in evoMPS but
must be compiled during, e.g. using setup.py to build all extensions.
Parameters
----------
dtau : complex
The (imaginary or real) amount of imaginary time (tau) to step.
ham_is_Herm : bool
Whether the Hamiltonian is really Hermitian. If so, the lanczos
method will be used for imaginary time evolution.
"""
#TODO: Compute eta.
self.eta_sq.fill(0)
self.eta = 0
assert self.canonical_form == 'right', 'take_step_split only implemented for right canonical form'
assert self.ham_sites == 2, 'take_step_split only implemented for nearest neighbour Hamiltonians'
dtau *= -1
from expokit_expmv import zexpmv, zexpmvh
if sp.iscomplex(dtau) or not ham_is_Herm:
expmv = zexpmv
fac = 1.j
dtau = sp.imag(dtau)
else:
expmv = zexpmvh
fac = 1
norm_est = abs(self.H_expect.real)
KL = [None] * (self.N + 1)
KL[1] = sp.zeros((self.D[1], self.D[1]), dtype=self.typ)
for n in xrange(1, self.N + 1):
lop = Vari_Opt_Single_Site_Op(self, n, KL[n - 1], tau=fac)
#print "Befor A", n, sp.inner(self.A[n].ravel().conj(), lop.matvec(self.A[n].ravel())).real
An = expmv(lop, self.A[n].ravel(), dtau/2., norm_est=norm_est)
self.A[n] = An.reshape((self.q[n], self.D[n - 1], self.D[n]))
self.l[n] = tm.eps_l_noop(self.l[n - 1], self.A[n], self.A[n])
norm = m.adot(self.l[n], self.r[n])
self.A[n] /= sp.sqrt(norm)
#print "After A", n, sp.inner(self.A[n].ravel().conj(), lop.matvec(self.A[n].ravel())).real, norm.real
#shift centre matrix right (RCF is like having a centre "matrix" at "1")
G = tm.restore_LCF_l_seq(self.A[n - 1:n + 1], self.l[n - 1:n + 1],
sanity_checks=self.sanity_checks)
if n > 1:
self.AA[n - 1] = tm.calc_AA(self.A[n - 1], self.A[n])
self.C[n - 1] = tm.calc_C_mat_op_AA(self.ham[n - 1], self.AA[n - 1])
KL[n], ex = tm.calc_K_l(KL[n - 1], self.C[n - 1], self.l[n - 2],
self.r[n], self.A[n], self.AA[n - 1])
if n < self.N:
lop2 = Vari_Opt_SC_op(self, n, KL[n], tau=fac)
#print "Befor G", n, sp.inner(G.ravel().conj(), lop2.matvec(G.ravel())).real
G = expmv(lop2, G.ravel(), -dtau/2., norm_est=norm_est)
G = G.reshape((self.D[n], self.D[n]))
norm = sp.trace(self.l[n].dot(G).dot(self.r[n].dot(G.conj().T)))
G /= sp.sqrt(norm)
#print "After G", n, sp.inner(G.ravel().conj(), lop2.matvec(G.ravel())).real, norm.real
for s in xrange(self.q[n + 1]):
self.A[n + 1][s] = G.dot(self.A[n + 1][s])
self.AA[n] = tm.calc_AA(self.A[n], self.A[n + 1])
self.C[n] = tm.calc_C_mat_op_AA(self.ham[n], self.AA[n])
for n in xrange(self.N, 0, -1):
lop = Vari_Opt_Single_Site_Op(self, n, KL[n - 1], tau=fac, sanity_checks=self.sanity_checks)
#print "Before A", n, sp.inner(self.A[n].ravel().conj(), lop.matvec(self.A[n].ravel())).real
An = expmv(lop, self.A[n].ravel(), dtau/2., norm_est=norm_est)
self.A[n] = An.reshape((self.q[n], self.D[n - 1], self.D[n]))
self.l[n] = tm.eps_l_noop(self.l[n - 1], self.A[n], self.A[n])
norm = m.adot(self.l[n], self.r[n])
self.A[n] /= sp.sqrt(norm)
#print "After A", n, sp.inner(self.A[n].ravel().conj(), lop.matvec(self.A[n].ravel())).real, norm.real
#shift centre matrix left (LCF is like having a centre "matrix" at "N")
Gi = tm.restore_RCF_r_seq(self.A[n - 1:n + 1], self.r[n - 1:n + 1],
sanity_checks=self.sanity_checks)
if n < self.N:
self.AA[n] = tm.calc_AA(self.A[n], self.A[n + 1])
self.C[n] = tm.calc_C_mat_op_AA(self.ham[n], self.AA[n])
self.calc_K(n_low=n, n_high=n)
if n > 1:
lop2 = Vari_Opt_SC_op(self, n - 1, KL[n - 1], tau=fac, sanity_checks=self.sanity_checks)
Gi = expmv(lop2, Gi.ravel(), -dtau/2., norm_est=norm_est)
Gi = Gi.reshape((self.D[n - 1], self.D[n - 1]))
norm = sp.trace(self.l[n - 1].dot(Gi).dot(self.r[n - 1].dot(Gi.conj().T)))
G /= sp.sqrt(norm)
#print "After G", n, sp.inner(Gi.ravel().conj(), lop2.matvec(Gi.ravel())).real, norm.real
for s in xrange(self.q[n - 1]):
self.A[n - 1][s] = self.A[n - 1][s].dot(Gi)
self.AA[n - 1] = tm.calc_AA(self.A[n - 1], self.A[n])
self.C[n - 1] = tm.calc_C_mat_op_AA(self.ham[n - 1], self.AA[n - 1])
A * A # matrix multiplication A ** 2 # Matrix 2 to the power of 2 (same as A*A) B = 5 * sp.diag([1.0, 3, 5]) sp.mat(B) # converts B to matrix type sp.mat(B).I # computes matrix inverse of sp.mat(B) # isnan, isfinite, isinf are newly added functions to NumPy C = B # copy matrix B into C C[0, 1] = sp.nan # insert NaN value into 1st row, 2nd column element sp.isnan(C) # yields all 'False' elements except one element as True # looking at complex numbers a4 = sp.array([1 + 1j, 2, 5j]) # create complex array sp.iscomplexobj(a4) # determine whether a4 is complex (TRUE) sp.isreal(a4) # determines element by element which are REAL or COMPLEX sp.iscomplex(a4) # determination of COMPLEX elements type(a4) # outputs type and functional dependencies # concatenating matrices and arrays: a5 = sp.array([1, 2, 3]) a6 = sp.array([4, 5, 6]) sp.vstack((a5, a6)) # vertical array concatenation sp.hstack((a5, a6)) # horizontal array concat dstack((a5, a6)) # vertical array concat, transposed # view all variables that have been created thus far: sp.who() # python command, similar to MATLAB # importing and using matplotlib (plotting library from MATLAB) t = sp.linspace(0, 100) # create time array 't' y = sp.sin(2 * sp.pi * t) # create sinusoidal series y
