def update(self):
energy = 0
for i in self.model.schedule:
other = self.model.schedule[i]
if other.unique_id != self.unique_id:
energy += freeEnergy(
[self.pos_ca,
get_energy(self.a_type), self.a_type],
[other.pos_ca,
get_energy(other.a_type), other.a_type])
self.energy = energy
def molecular_dynamics(vertices, edges, polys, parameters, T, folder): delta_t = parameters['delta_t'] lx = parameters['lx'] ly = parameters['ly'] L = np.array([lx,ly]) # time t = 0 count = 0 while t < T: # get energy for network energy = get_energy(vertices, polys, edges, parameters) # print energy # get forces for network forces = get_forces(vertices, polys, edges, parameters) print t, np.sum(forces**2)**(0.5) # move vertices vertices = move_vertices(vertices, forces, parameters) # check for T1 transitions polys, edges = T1_transition(vertices, polys, edges, parameters) # add routine to write vertices, energy, forces at every time step # can be used for plotting routines later... # write_vertices(vertices, "%s/%.2f.txt" % (folder,t)) # write edges? # write polygons? # if count % 21 == 0: plot_network(vertices, polys, L, "%s/%.2f.jpg" % (folder,t)) count += 1 t += delta_t return
def speaker_recog_thread(outLabel, outLabelp):
global pre_spk
while True:
try:
data = q.get()
t1 = time.time()
outLabel.config(text='...')
t2 = time.time()
post_res('%s\n%s' % ('...', pre_spk))
t3 = time.time()
write_wave(
os.path.join(speaker_recog_final.DATA_DIR, 'test', 'test.wav'),
data)
t4 = time.time()
energy = get_energy(
os.path.join(speaker_recog_final.DATA_DIR, 'test', 'test.wav'))
t5 = time.time()
if energy > eng_th:
speaker = speaker_recog_final.test_speaker_recog()
t6 = time.time()
if speaker != '...':
spk_history.append(speaker)
spk = modefinder(spk_history)
if spk:
post_res('%s\n%s' % (spk, pre_spk))
print(spk)
outLabel.config(text=spk)
if pre_spk != spk:
pre_spk = spk
outLabelp.config(text=pre_spk)
t7 = time.time()
print(t2 - t1, t3 - t2, t4 - t3, t5 - t4, t6 - t5, t7 - t6)
print(t7 - t1, spk_history)
else:
# post_res('%s\n%s'%('empty', pre_spk))
# outLabel.config(text='empty')
pass
except queue.Empty:
continue
def steepest_descent(vertices, edges, polys, parameters, folder):
epsilon = 10**-6
delta_t = parameters['delta_t']
lx = parameters['lx']
ly = parameters['ly']
L = np.array([lx,ly])
t = 0.
energylist = []
forces = 10**6
while np.sum(forces**2)**(0.5) > epsilon:
# get energy for network
energy = get_energy(vertices, polys, edges, parameters)
energylist.append(energy)
# get forces for network
forces = get_forces(vertices, polys, edges, parameters)
print np.sum(forces**2)**(0.5)
# move vertices
vertices = move_vertices(vertices, forces, parameters)
# check for T1 transitions
cells, edges = T1_transition(vertices, polys, edges, parameters)
# plot_network(vertices, polys, L, "%s/%.2f.jpg" % (folder,t))
t += delta_t
return
# def steepest_descent(network, vertices, cells, edges, delta_t, epsilon, folder):
# # keep track of time steps
# time = []
# t = 0
# # keep track of energy
# energy = []
# # for T1 transition
# min_dist = 0.2
# L = network.L
# # while forces are greater than epsilon
# forces = epsilon**0.5
# count = 0
# # generate random angle vectors
# for cell in cells:
# cell.theta = rand_angle()
# os.mkdir("noise/hex/%s" % folder)
# # f = open("energy/energy.txt", "w+")
# while count < 50:
# # while np.sum(forces**2)**(0.5) > epsilon:
# # plot_network(vertices, cells, L, "motility/%d.jpg" % count)
# # if count % 200 == 0:
# # for cell in cells:
# # cell.theta = rand_angle()
# # # write cell vertices for MSD
# os.chdir("noise/hex/%s" % folder)
# np.savetxt("%d.txt" % count, vertices)
# os.chdir("..")
# os.chdir("..")
# os.chdir("..")
# # get energy for network
# energy = network.get_energy(vertices, cells, edges)
# # get forces for network
# forces = network.get_forces(vertices, cells, edges)
# # move vertices with forces
# vertices = network.move_vertices(forces, vertices)
# ka = network.parameters['ka']
# A0 = 1.
# print t, energy / (24.*ka*(A0**2)), np.sum(forces**2)**(0.5)
# # norm_energy = np.array(energy / (24.*ka*(A0**2)))
# # f.write("%f\n" % norm_energy)
# # new time step
# t += delta_t
# # # check for T1 transitions
# cells, edges = T1_transition(network, vertices, cells, edges, min_dist)
# count += 1
# # f.close()
# return vertices
def set_energy(self, key, geom, d=None):
e = get_energy(np.array(geom))
if d != None:
d[key] = e
return
self.energy[key] = e
from molecule import Molecule
from grad import gradient_descent
from hessian import Hessian
from frequencies import Frequencies
from pbc import pbc_sep
import numpy as np
if __name__ == '__main__':
print("Starting to make initial configuration.")
get_config() # Q1
init_mol = None
with open("molecule.xyz") as f:
print("Initial Configuration Generated for Arg LJ Model")
print("Initial COnfiguration for Q1 will the saved to molecule.xyz")
init_mol = Molecule(f, "Angstrom")
print("Starting to do Q2 and Q3...")
e = get_energy(np.array(init_mol.geom)) # Q2
# print(e)
# Q3 Steapest Descend is a heuristic.. Needs tuning of parameters...
print("Starting Steepest Descent Algoithm to minimise Energy...")
g = gradient_descent(0.135, 100, init_mol.geom)
print("Steepest Descent Algoithm Ended.. Results below\n")
print("Original Energy: ", e)
print("New Energy:", np.min(np.array(g[1])))
print()
i = np.argmin(np.array(g[1]))
print("New Configuration will be saved in new_molecule.xyz")
with open("new_molecule.xyz", "w") as f2:
print(len(g[0][i]), file=f2)
print(file=f2)
for p in g[0][i]:
print(f"C {p[0]} {p[1]} {p[2]}", file=f2)
def steepest_descent(vertices, edges, polys, parameters):
epsilon = 10**-6
delta_t = parameters['delta_t']
t = 0.
count = 0
forces = 10**6
while np.sum(forces**2)**(0.5) > epsilon:
# get energy for network
energy = get_energy(vertices, polys, edges, parameters)
# print energy
# get forces for network
forces = get_forces(vertices, polys, edges, parameters)
print np.sum(forces**2)**(0.5)
# move vertices
vertices = move_vertices(vertices, forces, parameters)
# check for T1 transitions
cells, edges = T1_transition(vertices, polys, edges, parameters)
# add routine to write vertices, energy, forces at every time step
# can be used for plotting routines later...
t += delta_t
return
# def steepest_descent(network, vertices, cells, edges, delta_t, epsilon, folder):
# # keep track of time steps
# time = []
# t = 0
# # keep track of energy
# energy = []
# # for T1 transition
# min_dist = 0.2
# L = network.L
# # while forces are greater than epsilon
# forces = epsilon**0.5
# count = 0
# # generate random angle vectors
# for cell in cells:
# cell.theta = rand_angle()
# os.mkdir("noise/hex/%s" % folder)
# # f = open("energy/energy.txt", "w+")
# while count < 50:
# # while np.sum(forces**2)**(0.5) > epsilon:
# # plot_network(vertices, cells, L, "motility/%d.jpg" % count)
# # if count % 200 == 0:
# # for cell in cells:
# # cell.theta = rand_angle()
# # # write cell vertices for MSD
# os.chdir("noise/hex/%s" % folder)
# np.savetxt("%d.txt" % count, vertices)
# os.chdir("..")
# os.chdir("..")
# os.chdir("..")
# # get energy for network
# energy = network.get_energy(vertices, cells, edges)
# # get forces for network
# forces = network.get_forces(vertices, cells, edges)
# # move vertices with forces
# vertices = network.move_vertices(forces, vertices)
# ka = network.parameters['ka']
# A0 = 1.
# print t, energy / (24.*ka*(A0**2)), np.sum(forces**2)**(0.5)
# # norm_energy = np.array(energy / (24.*ka*(A0**2)))
# # f.write("%f\n" % norm_energy)
# # new time step
# t += delta_t
# # # check for T1 transitions
# cells, edges = T1_transition(network, vertices, cells, edges, min_dist)
# count += 1
# # f.close()
# return vertices
def get_solution(enthalpy,temperature,reference_temperature,pressure,\
reference_pressure,density,reference_density,velocity,\
concentration_s1,concentration_s2,concentration_s3,\
concentration_g1,electric_potential,current_density,\
electrical_conductivity,number_density,\
delx,delt,cfl_energy,cfl_momentum,energyfluxmax,\
momentumfluxmax,reporting_interval1,reporting_interval2):
'''
this function combines the entire algorithm and provides a
time-marched solution
Parameters:
-----------
enhtalpy
temperature
reference_temperature
pressure
reference_pressure
density
reference_density
velocity
concentrations 1 through 4: B,Ni.Co,N
electric_potential
current_density
electrical_conductivity
number_density (e)
delx,delt
cfl_energy,cfl_momentum (if no initial value: use 125,0.75 respectively)
energyfluxmax,momentumfluxmax (if no inital value: use 1e5,1.0 respectively)
reporting_interval1,reporting_interval2
Returns:
-----------
for now, output 2 scalar variables:
number of iterations
final time
'''
hnref = enthalpy.copy()
#used for stability condition
delt_ref = delt
# cfl numbers for stability condition
cflen = cfl_energy
cflmn = cfl_momentum
cflen_ref = cfl_energy
cflmn_ref = cfl_momentum
#fluxes for stability condition
efmax = energyfluxmax
mfmax = momentumfluxmax
#temperature
Tnref = temperature.copy()
Tnref_old = reference_temperature.copy()
#pressure
pxn = pressure.copy()
pxn_ref = reference_pressure.copy()
#density
rhoxn = density.copy()
rhoxn_ref = reference_density.copy()
#velocity
uxn = velocity.copy()
#concentrations
cB = concentration_s1.copy()
cNi = concentration_s2.copy()
cCo = concentration_s3.copy()
cN = concentration_g1.copy()
# electric potential
phin = electric_potential.copy()
#current density
jn = current_density.copy()
# electrical conductivity
econdxn = electrical_conductivity.copy()
#number density of electrons
nexn = number_density.copy()
nexn_ref = number_density.copy() #reference_number_density
# current time
time = 0 + delt_ref
#final time in seconds
t_terminal = 0.0005
#iteration count
iterations = 0
#-------------------
#file = open('output_summary.txt','a')
#
while (time <t_terminal):
iterations += 1
delt = get_adaptive_time_step(cflen,cflmn,efmax,mfmax,\
delx,delt_ref,numpy.max(uxn))
time = time + delt
mun,ktn,cpn,Rsn = get_thermophysical_properties(Tnref,cN,cB,cNi,cCo)
ablrate,rhoevap,rholocal,rhoanbc,uanbc = get_ablation_properties(Tnref[0],pxn[1],\
Tnref[1],Rsn[1])
uxn = get_velocity_bc(uxn,uanbc)
rhoxn = get_density_bc(rhoxn,rhoanbc)
pxn = get_pressure_bc(pxn,Tnref[0],rhoxn[0],Rsn[0],\
Tnref[-1],rhoxn[-1],Rsn[-1])
rhoxn,uxn,pxn,cflmn,mflux = get_flow(pxn,rhoxn,uxn,mun,\
delx,delt,uanbc,rhoanbc)
compress = get_compressibility(pxn,pxn_ref,rhoxn,rhoxn_ref)
rhoB,rhoNi,rhoCo,rhoN,\
cB,cNi,cCo,cN = get_mass_diffusion(pxn,Tnref,rhoxn,uxn,cB,cNi,cCo,cN,\
delt,delx,rhoevap,rholocal)
nexn,nixn,noxn,pexn = saha_algorithm(Tnref,rhoxn,cB,cNi,cCo,cN,nexn_ref)
econdxn = get_electrical_conductivity(Tnref,nexn,noxn,time)
Tn,hn,cflen,efmax,Tpa,Tpc = get_energy(ablrate,rhoxn,uxn,pxn,pxn_ref,Tnref,hnref,cpn,\
ktn,mun,jn,econdxn,nexn,delx,delt)
pxn[:] = rhoxn[:]*Rsn[:]*Tn[:]
#reference values
Tnref_old = Tnref.copy()
Tnref = Tn.copy()
hnref = hn.copy()
rhoxn_ref = rhoxn.copy()
pxn_ref = pxn.copy()
nexn_ref = nexn.copy()
delt_ref = delt
time_ref = time
cflen_ref = cflen
cflmn_ref = cflmn
#print summary
if iterations in reporting_interval1:
file = open('output_summary.txt','a')
file.write('---------------------------------------\n' )
file.write('current iteration is: %.3g\n' %iterations)
file.write('evaporated density is: %.3g\n' %rhoevap)
file.write('ablation rate is: %.3g\n' %ablrate)
file.write('max compressibility is: %.3g \n' %numpy.max(compress))
file.write('time step value is: %.3g\n' %delt)
file.write('current time in seconds is: %.3g\n' %time)
file.write('uxn[0] velocity in [m/s] is: %.3g\n' %uxn[0])
file.write('uxn[1] velocity in [m/s] is: %.3g\n' %uxn[1])
file.write('uxn[2] velocity in [m/s] is: %.3g\n' %uxn[2])
file.write('uxn[3] velocity in [m/s] is: %.3g\n' %uxn[3])
file.write('uxn[-1] velocity in [m/s] is: %.3g\n' %uxn[-1])
file.write('uxn[-2] velocity in [m/s] is: %.3g\n' %uxn[-2])
file.write('uxn[-3] velocity in [m/s] is: %.3g\n' %uxn[-3])
file.write('max velocity is: %.3g\n' %numpy.max(uxn))
file.write('min velocity is: %.3g\n' %numpy.min(uxn))
file.write('anode density in [kg/m3] is: %.3g\n' %rhoxn[0])
file.write('rhoxn[1] density in [kg/m3] is: %.3g\n' %rhoxn[1])
file.write('rhoxn[2] density in [kg/m3] is: %.3g\n' %rhoxn[2])
file.write('rhoxn[3] density in [kg/m3] is: %.3g\n' %rhoxn[3])
file.write('rhoxn[-1] density in [kg/m3] is: %.3g\n' %rhoxn[-1])
file.write('rhoxn[-2] density in [kg/m3] is: %.3g\n' %rhoxn[-2])
file.write('rhoxn[-3] density in [kg/m3] is: %.3g\n' %rhoxn[-3])
file.write('max density is: %.3g\n' %numpy.max(rhoxn))
file.write('min density is: %.3g\n' %numpy.min(rhoxn))
file.write('Anode Temperature [K] is: %.3g\n' %Tn[0])
file.write('T[1] temperature in [K] is: %.3g\n' %Tn[1])
file.write('T[2] temperature in [K] is: %.3g\n' %Tn[2])
file.write('T[3] temperature in [K] is: %.3g\n' %Tn[3])
file.write('T[-1] temperature in [K] is: %.3g\n' %Tn[-1])
file.write('T[-2] temperature in [K] is: %.3g\n' %Tn[-2])
file.write('T[-3] temperature in [K] is: %.3g\n' %Tn[-3])
file.write('max temperature is: %.3g\n' %numpy.max(Tn))
file.write('min temperature is: %.3g\n' %numpy.min(Tn))
file.write('Anode Pressure [Pa] is: %.3g\n' %pxn[0])
file.write('p[1] pressure in [Pa] is: %.3g\n' %pxn[1])
file.write('p[2] pressure in [Pa] is: %.3g\n' %pxn[2])
file.write('p[3] pressure in [Pa] is: %.3g\n' %pxn[3])
file.write('p[-1] pressure in [Pa] is: %.3g\n' %pxn[-1])
file.write('p[-2] pressure in [Pa] is: %.3g\n' %pxn[-2])
file.write('p[-3] pressure in [Pa] is: %.3g\n' %pxn[-3])
file.write('max pressure is: %.3g\n' %numpy.max(pxn))
file.write('min pressure is: %.3g\n' %numpy.min(pxn))
file.write('momentum cfl number is: %.3g\n' %cflmn)
file.write('energy cfl number is: %.3g\n' %cflen)
file.write('anode temperature addition [K]: %.4g\n' %Tpa)
file.write('cathode temperature addition [K]: %.4g\n' %Tpc)
file.write('---------------------------------------\n')
file.close()
#print graphs
if iterations in reporting_interval2:
plot( xc,numpy.round(Tn[1:-1],decimals=10),\
'grid location [m]','Temperature [K]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_temperature.png' %iterations)
#
plot( xb,numpy.round(uxn[1:-1],decimals=5),\
'grid location [m]','velocity [m/s]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_velocity.png' %iterations)
#
plot( xc,numpy.round(rhoxn[1:-1],decimals=4),\
'grid location [m]','density [kg/m3]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_density.png'%iterations)
#
plot( xc,numpy.round(pxn[1:-1],decimals=0),\
'grid location [m]','pressure [Pa]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_pressure.png'%iterations)
#
plot( xc,numpy.round(nexn[1:-1],decimals=0),\
'grid location [m]','ne [1/m3]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_ne.png'%iterations)
#
plot( xc,numpy.round(econdxn[1:-1],decimals=0),\
'grid location [m]','econd [S/m]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_econdavg.png'%iterations)
#
plot( xc,numpy.round(rhoB[1:-1],decimals=4),\
'grid location [m]','Boron Density [kg/m3]','time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_rhoB.png' %iterations)
#
plot( xc,numpy.round(rhoNi[1:-1],decimals=4),\
'grid location [m]','Nickel Density [kg/m3]','time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_rhoNi.png' %iterations)
#
plot( xc,numpy.round(rhoCo[1:-1],decimals=4),\
'grid location [m]','Cobalt Density [kg/m3]','time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_rhoCo.png' %iterations)
#
plot( xc,numpy.round(rhoN[1:-1],decimals=4),\
'grid location [m]','Nitrogen Density [kg/m3]','time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_rhoN.png' %iterations)
#check for anode steady state:
# if numpy.abs(Tnref[0]-Tnref_old[0]) <l2_target and iterations>1e6:
# Tnref[0] = Tnref_old[0]
# print('anode has reached steady state')
#check for overall steady state
if (numpy.all(numpy.abs(Tnref[:]-Tnref_old[:]) < l2_target)==True):
plot( xc,numpy.round(Tn[1:-1],decimals=10),\
'grid location [m]','Temperature [K]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_temperature.png' %iterations)
#
plot( xb,numpy.round(uxn[1:-1],decimals=5),\
'grid location [m]','velocity [m/s]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_velocity.png' %iterations)
#
plot( xc,numpy.round(rhoxn[1:-1],decimals=4),\
'grid location [m]','density [kg/m3]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_density.png'%iterations)
#
plot( xc,numpy.round(pxn[1:-1],decimals=0),\
'grid location [m]','pressure [Pa]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_pressure.png'%iterations)
#
plot( xc,numpy.round(nexn[1:-1],decimals=0),\
'grid location [m]','ne [1/m3]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_ne.png'%iterations)
#
plot( xc,numpy.round(econdxn[1:-1],decimals=0),\
'grid location [m]','econd [S/m]', 'time= %.4g sec' %time)
pyplot.savefig('./plots/%.5g_econdavg.png'%iterations)
file.write('entire domain has reached steady state\n')
file.write('terminal time is %.4g\n' %time)
file.write('end of computation, see you later!\n')
break
#check for end of computation
if (time >= t_terminal):
file.write('max computational time reached.if steady state not achieved, increase terminal time\n')
return iterations,time
