def quad_term_fast(self, fn, data):
"""
Fast routine for estimating the quadratic term
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution we are trying to estimate
Returns the value of the estimator
Note: this shouldn't be called externally
"""
n = data.shape[0]
total = 0.0
for k in lattice.lattice(self.dim, self.m):
sub1 = np.array(self.comp_exp(k, data))
sub2 = sub1*np.array(fn(data))
total += np.sum((np.sum(sub2) - sub2) * sub1)
term2 = 0.0
for k in lattice.lattice(self.dim, self.m):
for kp in lattice.lattice(self.dim, self.m):
bi = fast_integration(lambda x: np.array(self.comp_exp(k,x))*np.array(self.comp_exp(kp,x))*np.array(fn(x)),
[0.0 for t in range(self.dim)], [1.0 for t in range(self.dim)], pts=100)
sub1 = np.array(self.comp_exp(k, data))
sub2 = np.array(self.comp_exp(kp, data))
term2 += np.sum(bi* sub1* (np.sum(sub2) - sub2))
# print (np.real(2.0*total/(n*(n-1))), np.real(term2/(n*(n-1))))
return np.real(2.0*total/(n*(n-1))) - np.real(term2/(n*(n-1)))
def quad_term_slow(self, fn, data):
"""
Deprected
Slow routine for estimating the quadratic terms
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution we are trying to estimate
Returns the value of the estimator
"""
assert False, "Deprecated"
n = data.shape[0]
total = 0.0
for k in lattice.lattice(self.dim, self.m):
for i in range(n):
for j in range(n):
if j != i:
total += self.comp_exp(k, data[i, :]) * self.comp_exp(
k, data[j, :]) * fn(data[j, :])
term2 = 0.0
for k in lattice.lattice(self.dim, self.m):
for kp in lattice.lattice(self.dim, self.m):
bi = fast_integration(
lambda x: np.array(self.comp_exp(k, x)) * np.array(
self.comp_exp(kp, x)) * np.array(fn(x)),
[0 for t in range(self.dim)], [1 for t in range(self.dim)])
for i in range(n):
for j in range(n):
if j != i:
term2 += bi * self.comp_exp(
k, data[i, :]) * self.comp_exp(kp, data[j, :])
return np.real(2.0 * total / (n * (n - 1)) - 1.0 * term2 / (n *
(n - 1)))
def quad_term_fast(self, fn, data):
"""
Fast routine for estimating the quadratic term
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution we are trying to estimate
Returns the value of the estimator
Note: this shouldn't be called externally
"""
n = data.shape[0]
total = 0.0
for k in lattice.lattice(self.dim, self.m):
sub1 = np.array(self.comp_exp(k, data))
sub2 = sub1 * np.array(fn(data))
total += np.sum((np.sum(sub2) - sub2) * sub1)
term2 = 0.0
for k in lattice.lattice(self.dim, self.m):
for kp in lattice.lattice(self.dim, self.m):
bi = fast_integration(lambda x: np.array(self.comp_exp(
k, x)) * np.array(self.comp_exp(kp, x)) * np.array(fn(x)),
[0.0 for t in range(self.dim)],
[1.0 for t in range(self.dim)],
pts=100)
sub1 = np.array(self.comp_exp(k, data))
sub2 = np.array(self.comp_exp(kp, data))
term2 += np.sum(bi * sub1 * (np.sum(sub2) - sub2))
# print (np.real(2.0*total/(n*(n-1))), np.real(term2/(n*(n-1))))
return np.real(2.0 * total / (n * (n - 1))) - np.real(term2 /
(n * (n - 1)))
def quad_term_slow(self, fn, data):
"""
Deprected
Slow routine for estimating the quadratic terms
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution we are trying to estimate
Returns the value of the estimator
"""
assert False, "Deprecated"
n = data.shape[0]
total = 0.0
for k in lattice.lattice(self.dim, self.m):
for i in range(n):
for j in range(n):
if j != i:
total += self.comp_exp(k, data[i,:])*self.comp_exp(k, data[j,:])*fn(data[j,:])
term2 = 0.0
for k in lattice.lattice(self.dim, self.m):
for kp in lattice.lattice(self.dim, self.m):
bi = fast_integration(lambda x: np.array(self.comp_exp(k,x))*np.array(self.comp_exp(kp,x))*np.array(fn(x)),
[0 for t in range(self.dim)], [1 for t in range(self.dim)])
for i in range(n):
for j in range(n):
if j != i:
term2 += bi*self.comp_exp(k, data[i,:])*self.comp_exp(kp, data[j,:])
return np.real(2.0*total/(n*(n-1)) - 1.0*term2/(n*(n-1)))
def __init__(self, size=20, r=3, k=2, rule=rule_30, RIC=False): """Initializes the simulation with the passed parameters. parameters: size: The size of the simulation. The simulation will be square according to this parameter. Defaults to 20 r: The number of neighbors that will be evaluated by the rule function. Defaults to 3 k: The number of possible states in the system. The minimum value is 2. The states are represented as numbers beginning with 0. Defaults to 2 rule: A function that accepts a tuple of r variables representing the neighbors and returns one of k states. Defaults to rule 30 RIC (Rancom Initial Condition): a boolean variable that specifies if the simulation should start from a random initial state. If True is passed, each cell in the first row will be initialized to one of the k states. Defaults to Fales. """ from lattice import lattice from loc import loc as Loc import random self.size = size self.r = r self.k = k self.rule = rule self.__ran = False self.state = lattice(self.size) if RIC: for i in range(size): if random.random() <= 1/k: self.state.set_cell(Loc(0, i), 1) else: self.state.set_cell(Loc(0, int(size/2)), 1)
def eval(self, fast=True):
"""
Evaluate the estimator on the data.
See the paper for details about the estimator.
"""
T1 = 0.0
T2 = 0.0
## T1 = \int p^2. T2 = \int q^2, T3 = -2 \int pq
for k in lattice.lattice(self.dim, self.m):
T1 += 1.0/self.pdata.shape[0]**2 *(np.sum(np.array(self.comp_exp(k, self.pdata)))**2 - np.sum(np.array(self.comp_exp(k,self.pdata))**2))
T2 += 1.0/self.qdata.shape[0]**2 *(np.sum(np.array(self.comp_exp(k, self.qdata)))**2 - np.sum(np.array(self.comp_exp(k,self.qdata))**2))
T3 = -2.0 * np.sum([
np.mean(self.comp_exp(k,self.pdata)) *
np.mean(np.array(self.comp_exp(k, self.qdata)))
for k in lattice.lattice(self.dim, self.m)])
return np.real(T1 + T2 + T3)
def eval(self, fast=True):
"""
Evaluate the estimator on the data.
See the paper for details about the estimator.
"""
T1 = 0.0
T2 = 0.0
## T1 = \int p^2. T2 = \int q^2, T3 = -2 \int pq
for k in lattice.lattice(self.dim, self.m):
T1 += 1.0 / self.pdata.shape[0]**2 * (
np.sum(np.array(self.comp_exp(k, self.pdata)))**2 -
np.sum(np.array(self.comp_exp(k, self.pdata))**2))
T2 += 1.0 / self.qdata.shape[0]**2 * (
np.sum(np.array(self.comp_exp(k, self.qdata)))**2 -
np.sum(np.array(self.comp_exp(k, self.qdata))**2))
T3 = -2.0 * np.sum([
np.mean(self.comp_exp(k, self.pdata)) *
np.mean(np.array(self.comp_exp(k, self.qdata)))
for k in lattice.lattice(self.dim, self.m)
])
return np.real(T1 + T2 + T3)
def bilinear_term_fast(self, fn, data1, data2):
"""
Faster routine for estimating the bilinear term.
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution p
data2 -- data from the distribution q
Returns the value of the estimator
Note: This shouldn't be called externally.
"""
total = np.sum([
np.mean(self.comp_exp(k,data1)) *
np.mean(np.array(self.comp_exp(k, data2)) *
np.array(fn(data2)))
for k in lattice.lattice(self.dim, self.m)])
return np.real(total)
def bilinear_term_fast(self, fn, data1, data2):
"""
Faster routine for estimating the bilinear term.
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution p
data2 -- data from the distribution q
Returns the value of the estimator
Note: This shouldn't be called externally.
"""
total = np.sum([
np.mean(self.comp_exp(k, data1)) *
np.mean(np.array(self.comp_exp(k, data2)) * np.array(fn(data2)))
for k in lattice.lattice(self.dim, self.m)
])
return np.real(total)
def generate_density(self):
"""
Construct a d-dimensional density in the Sobolev class W(s,L)
under the trigonometric basis \phi_k(x) = e^{2i \pi k^Tx}
"""
coeffs = [1]
fns = [np.matrix([0 for i in range(self.d)])]
total = 0
f = lattice.lattice(self.d, limit=None)
f.next()
while total <= self.L :
curr = np.matrix(f.next())
new_coeff = np.random.uniform(-0.1, 0.1)
coeffs.append(new_coeff)
fns.append(curr)
total += new_coeff**2 * np.sum([np.abs(curr[i,0])**(2*self.s) for i in range(curr.shape[0])])
self.coeffs = coeffs[0:len(coeffs)-1]
self.fns = fns[0:len(fns)-1]
def bilinear_term_slow(self, fn, data1, data2):
"""
Deprected
A slow routine for estimating the bilinear term. This iterates
over all of the basis elements and then over all of the points
in the sample.
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution p
data2 -- data from the distribution q
Returns the value of the estimator
"""
assert False, "Deprected"
n1 = data1.shape[0]
n2 = data2.shape[0]
total = 0.0
for k in lattice.lattice(self.dim, self.m):
for i in range(n1):
for j in range(n2):
total += self.comp_exp(k, data1[i,:])*self.comp_exp(k, data2[j,:])*fn(data2[j,:])
return np.real(total[0,0]/(n1*n2))
def generate_density(self):
"""
Construct a d-dimensional density in the Sobolev class W(s,L)
under the trigonometric basis \phi_k(x) = e^{2i \pi k^Tx}
"""
coeffs = [1]
fns = [np.matrix([0 for i in range(self.d)])]
total = 0
f = lattice.lattice(self.d, limit=None)
f.next()
while total <= self.L:
curr = np.matrix(f.next())
new_coeff = np.random.uniform(-0.1, 0.1)
coeffs.append(new_coeff)
fns.append(curr)
total += new_coeff**2 * np.sum([
np.abs(curr[i, 0])**(2 * self.s) for i in range(curr.shape[0])
])
self.coeffs = coeffs[0:len(coeffs) - 1]
self.fns = fns[0:len(fns) - 1]
def bilinear_term_slow(self, fn, data1, data2):
"""
Deprected
A slow routine for estimating the bilinear term. This iterates
over all of the basis elements and then over all of the points
in the sample.
fn -- a lambda expression for the function \psi.
data1 -- data from the distribution p
data2 -- data from the distribution q
Returns the value of the estimator
"""
assert False, "Deprected"
n1 = data1.shape[0]
n2 = data2.shape[0]
total = 0.0
for k in lattice.lattice(self.dim, self.m):
for i in range(n1):
for j in range(n2):
total += self.comp_exp(k, data1[i, :]) * self.comp_exp(
k, data2[j, :]) * fn(data2[j, :])
return np.real(total[0, 0] / (n1 * n2))
from __future__ import print_function import lattice, libRNG, sys #rid='overdispersed_large' #resdir='/home/colin/projects/SEED/int' resdir = str(sys.argv[1]) rid=str(sys.argv[2]) dump=bool(sys.argv[3]) Hex=lattice.lattice(50,50,wrap=False) Hex.fillNYC() canopy = lattice.getEndCanopy(resdir, rid) DT = libRNG.getDelaunayGraph(Hex, canopy) RNG = libRNG.getRelativeNeighborGraph(Hex, DT) #import matplotlib.pyplot as plt #import matplotlib.collections as coll #from matplotlib import cm #Hex.plot(plt, coll, [1 if i in canopy else 0 for i in xrange(Hex.N)], [cm.binary(1.0) if i in canopy else cm.binary(0.0) for i in xrange(Hex.N)],[cm.binary(0) for x in xrange(Hex.N)], (0,), '', net=RNG) D,avgMIJ = libRNG.getRNGstats(Hex, canopy, RNG) maxMij= libRNG.getExtent(Hex, canopy) if dump: f=open(resdir+'/RNG.'+rid, 'w') for i,j,w in RNG.edges(data=True): print(i,j,w['weight'], file=f) f.close()
def test_03(self):
self.assertEqual(20, lattice.lattice(3))
def test_04(self):
self.assertEqual(70, lattice.lattice(4))
s = action(phi, system.hop, system.kappa, system.lamb)
dh = hamiltonian(pi, s) - oldham
expdh += np.exp(-dh)
#metropolis
if (dh < 0.0):
acc += 1
elif (np.exp(-dh) > np.random.uniform(0, 1.0)):
acc += 1
else:
rej += 1
phi = np.copy(oldphi)
system = latt.lattice()
tau = 1.0
nhamdyn = 50
niter = 5000
nbin = 500
eps = tau / nhamdyn
acc = 0
rej = 0
phi = gaussrand(system.v)
expdh = 0.0
m = 0.0
for it in range(niter):
hmc_update()
def calc(reservoir_pressure):
rc = 500 # initial guess for potential cut off distance
npart = 50 # particles
temp = 2 # temperature
rho = 0.6 # initial density of fluid in box
n_nodes = 1000 # grid nodes for cavity biasing
equil_cycles = 200
prod_cycles = 100
sample_frequency = 2
displ_attempts = 100
max_displ = 0.09
exch_attempts = 10
epsilon = 1.0
sigma = 1.0
mass = 1.0
i_seed = 68324
tail_corr = 1
shift_pot = 0
use_cavity_bias = True
box_length = (npart / rho)**(1.0 / 3.0)
rc = min(rc, box_length / 2.0)
rc_sq = rc * rc
beta = 1.0 / temp
zz = beta * reservoir_pressure
epsilon4 = 4.0 * epsilon
epsilon48 = 48.0 * epsilon
sigma_sq = sigma * sigma
coru = 0.0
corp = 0.0
e_cut = 0.0
part_pos_array = lattice.lattice(box_length, npart)
press_list = []
en_list = []
rho_list = []
np.random.seed(i_seed)
if shift_pot:
e_cut, dummy = cener.ener(rc_sq, rc_sq, sigma_sq, epsilon4, epsilon48,
shift_pot, e_cut)
if tail_corr:
corp = ccor.corp(rc, rho, sigma, epsilon4)
coru = ccor.coru(rc, rho, sigma, epsilon4)
en, vir, rho = cener.totenerg(npart, part_pos_array, box_length, rc, sigma,
rc_sq, sigma_sq, epsilon4, epsilon48,
shift_pot, e_cut, tail_corr)
nmoves = displ_attempts + exch_attempts
# display particles in real time
atomvis.Clear()
atomvis.Init(part_pos_array, box_length)
cavity_nodes = ccavity.cavity_locations(box_length, n_nodes)
for ii in range(2):
"""
ii = 0 equilibration
ii = 1 production
"""
if ii == 0:
ncycles = equil_cycles
else:
ncycles = prod_cycles
attempt = 0
naccp = 0
attemptp = 0
nacc = 0
atte = 0
acce = 0
rhoav = 0.0
nsampav = 0
naccp, attemptp, max_displ = adjust.adjust(attempt, nacc, max_displ,
box_length / 2.0, attemptp,
naccp)
for icycle in range(ncycles):
for imove in range(nmoves):
ran = np.random.random() * nmoves
if ran < displ_attempts and len(part_pos_array) != 0:
# attempt to displace a particle
en, vir, nacc, attempt, part_pos_array = mcmove.mcmove(
npart, part_pos_array, max_displ, beta, en, vir,
attempt, nacc, box_length, rc_sq, sigma_sq, epsilon4,
epsilon48, shift_pot, e_cut)
else:
av_cav, n_cavs = ccavity.available_cavities(
cavity_nodes, part_pos_array, 0.8 * sigma)
# attempt to exchange a particle with the reservoir
en, vir, nacc, attempt, part_pos_array, npart = mcexch.mcexch(
npart, part_pos_array, beta, en, vir, attempt, nacc,
box_length, rc, rc_sq, sigma, sigma_sq, epsilon4,
epsilon48, shift_pot, tail_corr, e_cut, zz, av_cav,
n_cavs, n_nodes, use_cavity_bias)
if len(part_pos_array) > 0:
atomvis.Update(part_pos_array)
if ii == 1:
# sample averages
if icycle % sample_frequency == 0:
samp_enp, samp_press, samp_rho = sample.sample(
icycle, en, vir, npart, box_length, beta, tail_corr,
rc, sigma, epsilon4)
# to determine excess chem. potential
nsampav += 1
rhoav += npart / (box_length**3)
press_list.append(samp_press)
en_list.append(samp_enp)
rho_list.append(samp_rho)
if (icycle % ncycles / 5) == 0:
naccp, attemptp, max_displ = adjust.adjust(
attempt, nacc, max_displ, box_length / 2.0, attemptp,
naccp)
if ncycles != 0:
en, vir, rho = cener.totenerg(npart, part_pos_array,
box_length, rc, sigma, rc_sq,
sigma_sq, epsilon4, epsilon48,
shift_pot, e_cut, tail_corr)
if rhoav != 0 and nsampav != 0:
muex = np.log(zz) / beta - np.log(rhoav / nsampav) / beta
mu = muex + np.log(rhoav / nsampav) / beta
print muex, mu
print attempt, nacc
print attemptp, naccp
print npart
return np.mean(en_list), np.mean(press_list), np.mean(rho_list)
def save(self):
"""
@Make the files
"""
directory = self.savedir.text()
os.chdir(directory)
self.setCurrentWidget(self._setup)
self.setCurrentWidget(self._grids)
self.setCurrentWidget(self._configuration)
#self.configurationChanged(self._configuration)
self.configurationChanged()
try:
_mystate = self.state()
configuration = self._configuration
_format_vector = " {0: 10.8f} {1: 10.8f} {2: 10.8f}\n"
_format_with_comment = " {0: 10.8f} {1: 10.8f} {2: 10.8f} {3}\n"
_ncsu_atom_format = " %s %.12e %.12e %.12e 1\n"
_elements = []
_element_types = []
_element_count = []
_positions_line = "# undefined configuration\n"
zipped_counts = {}
# Test whether we have a configuration defined:
# generate types of atoms encountered:
_elements = configuration.conf[0].elements
_element_types = list(set(_elements))
_element_count = [_elements.count(ii) for ii in _element_types]
# write positions:
_positions_line = "#\n **** Lattice constants **** \n\n"
zipped_coordinates = zip(_elements,
configuration.conf[0].coords)
_positions_line += 'atoms=\n"'
for name, v in zipped_coordinates:
_positions_line += (_ncsu_atom_format %
(name, v[0], v[1], v[2]))
_positions_line += '"\n'
#define dictionary for number of orbitals and radius
num_orbital_dict = {}
num_orbital = [
self._species.num_orbital[i].value()
for i in range(len(_element_types))
]
num_orbital_dict = dict(zip(_element_types, num_orbital))
orbital_radius_dict = {}
orbital_radius = [
self._species.orbital_radius[i].text()
for i in range(len(_element_types))
]
orbital_radius_dict = dict(zip(_element_types, orbital_radius))
common_lines = _mystate['input_setup_lines']
common_lines += _mystate['input_misc_lines']
common_lines += _mystate['input_io_lines']
common_lines += _mystate['input_species_lines']
common_lines += _mystate['input_units_lines']
except:
print "failed to save1"
try:
# Use the lattice parameter from left lead for the one atom calculation
electrodes = configuration.conf[1]
latt = lattice(electrodes.lattice)
for i_pp in range(len(_element_types)):
dir_name = _element_types[i_pp] + '-atom'
oneatom_lines = 'atoms=\n"\n'
oneatom_lines += _element_types[
i_pp] + ' 0.0 0.0 0.0 1 1 '
oneatom_lines += "%d" % num_orbital_dict[
_element_types[i_pp]]
oneatom_lines += '\n"\n\n'
oneatom_lines += 'orbitals=\n"\n'
oneatom_lines += str(
self._species.num_orbital[i_pp].text())
oneatom_lines += ' 0.0 0.0 0.0 '
oneatom_lines += orbital_radius_dict[_element_types[i_pp]]
oneatom_lines += ' 1 1\n"\n\n'
oneatom_lines += 'number_of_orbitals="'
oneatom_lines += str(
self._species.num_orbital[i_pp].text())
oneatom_lines += '"\n\n'
if not os.path.exists(dir_name):
os.mkdir(dir_name)
with open(dir_name + '/input', 'w') as inc_file:
inc_file.write(_mystate['input_oneatom_grid_lines'])
inc_file.write(_mystate['input_mdscf_lines_ON'])
inc_file.write(common_lines)
inc_file.write(latt)
inc_file.write(oneatom_lines)
inc_file.write(_mystate['default_input_for_oneatom'])
inc_file.write(_mystate['default_input_forON'])
#
# write out order-n calculation input for lead1
except:
print "failed to save2"
try:
electrodes = configuration.conf[1]
latt = lattice(electrodes.lattice)
position = position_orbital(electrodes, num_orbital_dict,
orbital_radius_dict)
dir_name = 'lead1'
if not os.path.exists(dir_name): os.mkdir(dir_name)
with open(dir_name + '/input', 'w') as inc_file:
inc_file.write('start_mode="FIREBALL Start"')
inc_file.write(_mystate['input_grids_left_lines'])
inc_file.write(_mystate['input_mdscf_lines_ON'])
inc_file.write(common_lines)
inc_file.write(latt)
inc_file.write(position)
inc_file.write(_mystate['default_input_forON'])
#
# write out order-n calculation input for lead2
except:
print "failed to save2a"
try:
electrodes = configuration.conf[2]
latt = lattice(electrodes.lattice)
position = position_orbital(electrodes, num_orbital_dict,
orbital_radius_dict)
dir_name = 'lead2'
if not os.path.exists(dir_name): os.mkdir(dir_name)
with open(dir_name + '/input', 'w') as inc_file:
inc_file.write('start_mode="FIREBALL Start"')
inc_file.write(_mystate['input_grids_right_lines'])
inc_file.write(_mystate['input_mdscf_lines_ON'])
inc_file.write(common_lines)
inc_file.write(latt)
inc_file.write(position)
inc_file.write(_mystate['default_input_forON'])
#
# write out order-n calculation input for center part
except:
print "failed to save2b"
try:
center_ctrl = {}
if (self._grids.center_periodicity.isChecked()):
center_ctrl = {
'state_begin': 0,
'ion_begin': 0,
'num_atoms': 0
}
position = position_orbital_center_ON(
configuration, num_orbital_dict, orbital_radius_dict)
else:
position, center_ctrl = position_orbital_center_ON_nop(
configuration, num_orbital_dict, orbital_radius_dict)
dir_name = 'center'
if not os.path.exists(dir_name): os.mkdir(dir_name)
with open(dir_name + '/input', 'w') as inc_file:
inc_file.write('start_mode="FIREBALL Start"')
inc_file.write(_mystate['input_grids_center_lines'])
inc_file.write(_mystate['input_mdscf_lines_ON'])
inc_file.write(common_lines)
inc_file.write(position)
inc_file.write(_mystate['default_input_forON'])
except:
print "failed to save3"
try:
electrods = configuration.conf
conf1 = electrods[1]
conf2 = electrods[1]
conf3 = electrods[1]
position = position_orbital_3part(conf1, conf2, conf3,
num_orbital_dict,
orbital_radius_dict)
dir_name = '3lead_lead1'
if not os.path.exists(dir_name): os.mkdir(dir_name)
a1 = self._grids._Nx_left.value()
a2 = self._grids._Nx_left.value() * 2
b = self._grids._Ny_left.value()
c = self._grids._Nz_left.value()
tem = 'potential_compass = "******"\n' % (a1, a2, b,
c)
tem += 'chargedensity_compass = "******"\n' % (
a1, a2, b, c)
with open(dir_name + '/input', 'w') as inc_file:
inc_file.write('start_mode_NEGF="111"')
inc_file.write('start_mode="FIREBALL Start"')
inc_file.write(_mystate['input_grids_left3_lines'])
inc_file.write(_mystate['input_mdscf_lines_NEGF'])
inc_file.write(common_lines)
inc_file.write(position)
inc_file.write(tem)
inc_file.write(_mystate['default_input_forON'])
except:
print "failed to save3a"
try:
conf1 = electrods[2]
conf2 = electrods[2]
conf3 = electrods[2]
position = position_orbital_3part(conf1, conf2, conf3,
num_orbital_dict,
orbital_radius_dict)
dir_name = '3lead_lead2'
if not os.path.exists(dir_name): os.mkdir(dir_name)
a1 = self._grids._Nx_right.value()
a2 = self._grids._Nx_right.value() * 2
b = self._grids._Ny_right.value()
c = self._grids._Nz_right.value()
tem = 'potential_compass = "******"\n' % (a1, a2, b,
c)
tem += 'chargedensity_compass = "******"\n' % (
a1, a2, b, c)
with open(dir_name + '/input', 'w') as inc_file:
inc_file.write('start_mode_NEGF="111"')
inc_file.write('start_mode="FIREBALL Start"')
inc_file.write(_mystate['input_grids_right3_lines'])
inc_file.write(_mystate['input_mdscf_lines_NEGF'])
inc_file.write(common_lines)
inc_file.write(position)
inc_file.write(tem)
inc_file.write(_mystate['default_input_forON'])
except:
print "failed to save3b"
try:
conf1 = electrods[1]
conf2 = electrods[0]
conf3 = electrods[2]
position = position_orbital_negf(conf1, conf2, conf3,
num_orbital_dict,
orbital_radius_dict)
dir_name = 'bias_0.0'
if not os.path.exists(dir_name): os.mkdir(dir_name)
a1 = self._grids._Nx_left.value()
a2 = self._grids._Nx_negf - self._grids._Nx_right.value()
b = self._grids._Ny_right.value()
c = self._grids._Nz_right.value()
tem = 'potential_compass = "******"\n' % (a1, a2, b,
c)
tem += 'chargedensity_compass = "******"\n' % (
a1, a2, b, c)
with open(dir_name + '/input', 'w') as inc_file:
inc_file.write('start_mode_NEGF="112"')
inc_file.write('start_mode="FIREBALL Start"')
inc_file.write(_mystate['input_grids_negf_lines'])
inc_file.write(_mystate['input_mdscf_lines_NEGF'])
inc_file.write(common_lines)
inc_file.write(position)
inc_file.write(tem)
inc_file.write(_mystate['default_input_forON'])
except:
print "failed to save3c"
try:
write_out_LCR(self._io, self._grids, center_ctrl,
configuration, num_orbital_dict)
except:
print "failed to save3d"
try:
write_out_others(configuration, self._negf_para, self._grids,
self._mdscf, num_orbital_dict)
except:
print "failed to save3e"
try:
write_out_jobfiles(configuration, self._setup, self._grids)
except:
print "failed to save"
def test_01(self):
self.assertEqual(1, lattice.lattice(0))
def test_02(self):
self.assertEqual(6, lattice.lattice(2))
import unittest
import lattice
class Test(unittest.TestCase):
def test_01(self):
self.assertEqual(1, lattice.lattice(0))
def test_02(self):
self.assertEqual(6, lattice.lattice(2))
def test_03(self):
self.assertEqual(20, lattice.lattice(3))
def test_04(self):
self.assertEqual(70, lattice.lattice(4))
print(lattice.lattice(20))
if __name__ == '__main__':
unittest.main()
DataWat = data_lammps(WaterDataFile)
water = data_to_molecule(
DataWat) # create a molecule object from the Data input
Cat = atom(idx=0, iSpc=1, Type=2, charge=+1.0, xyz=np.zeros(3, np.double))
An = atom(idx=0, iSpc=2, Type=3, charge=-1.0, xyz=np.zeros(3, np.double))
# what to build is an array of tuples that contains the molecule/atom
# and the corresponding num of the species to be created
what_to_build = [None] * 3
what_to_build[0] = (water, NWaters)
what_to_build[1] = (Cat, NCations)
what_to_build[2] = (An, NAnions)
NSpecies = NWaters + NCations + NAnions
edgeUC = [3.1] * 3
latt = lattice(
'cub', edgeUC, NSpecies
) # type of latt, edge lengths of the UC, minimum # of Nodes in the latt
# create a molecular system with the what_to_build directives, on the the lattice latt
molSys = Create_config_on_lattice(latt, what_to_build)
# num of random Swaps, one species of the swaped pair is always in the IonIndices slice
IonIndices = slice(NWaters, -1, 1)
molSys.rand_swaps(NSwaps=10000, TargetSlice=IonIndices)
DataOut = Mol_System_to_data(molSys)
DataOut.write(DataFileOut)
