def ACMD_instance(rng, N, beads, dpotential, beta, T, deltat):
swarmobject = MD_System.swarm(N)
MD_System.system_definition(beta, beads, 1, MD_System.dpotential)
#gamma = 1.0
#w_arr = beads**0.5/(beta*gamma)*np.ones(beads)
#MD_System.w_arr = MD_System.rearrange(w_arr)
#MD_System.w_arr[0] = 0.0
importlib.reload(Velocity_verlet)
swarmobject.q = np.zeros(
(swarmobject.N, MD_System.dimension, MD_System.n_beads))
rand_boltz = np.random.RandomState(rng)
swarmobject.p = rand_boltz.normal(0.0, swarmobject.m / (MD_System.beta),
np.shape(swarmobject.q))
pool = mp.Pool(mp.cpu_count())
Matsubara = 0
CMD = 0
n_inst = 10
func = partial(corr_function_upgrade, CMD, Matsubara, beads, swarmobject,
dpotential, MD_System.pos_op, MD_System.pos_op, T, deltat)
results = pool.map(func, range(n_inst))
pool.close()
pool.join()
return np.sum(results, 0) / n_inst
def mean_force_calculation(dpotential):
print(MD_System.n_beads,MD_System.w_arr)
N = 100
deltat = 1e-2
Q = np.linspace(-10,10,100)
swarmobject = MD_System.swarm(N)
rand_boltz = np.random.RandomState(1000)
swarmobject.p = rand_boltz.normal(0.0,swarmobject.m/MD_System.beta,(swarmobject.N,MD_System.dimension,MD_System.n_beads))
print(np.shape(swarmobject.p))
n_sample = 10
rng=np.random.RandomState(1000)
CMD=1
CMD_force = np.zeros((len(Q),MD_System.dimension))
for j in range(len(Q)):
swarmobject.q = np.zeros((swarmobject.N,MD_System.dimension,MD_System.n_beads)) + Q[j]
#print(swarmobject.q)
#print(np.shape(swarmobject.q))
thermalize(CMD,0,0,swarmobject,dpotential,deltat,5,rng)
for i in range(n_sample):
CMD_force[j] += np.mean(dpotential(swarmobject.q))
thermalize(CMD,0,0,swarmobject,dpotential,deltat,2,rng)
CMD_force/=n_sample
f = open("CMD_Force_Morse_B_{}_NB_{}_100_10.dat".format(MD_System.beta,MD_System.n_beads),'w+')
CMD_force = CMD_force[:,0]
print(CMD_force)
for x in zip(Q,CMD_force):
f.write("{}\t{}\n".format(x[0],x[1]))
f.close()
def AQCMD_instance(rng, N, beads, dpotential, beta, T, deltat):
swarmobject = MD_System.swarm(N)
MD_System.system_definition(beta, beads, 2, MD_System.dpotential)
importlib.reload(Velocity_verlet)
rand_boltz = np.random.RandomState(2)
print('beta', MD_System.beta, MD_System.beta_n)
swarmobject.q = np.zeros(
(swarmobject.N, MD_System.dimension, MD_System.n_beads)
) + 1.0 #np.random.rand(swarmobject.N,MD_System.dimension,MD_System.n_beads)
swarmobject.p = rand_boltz.normal(0.0, swarmobject.m / (MD_System.beta_n),
np.shape(swarmobject.q))
# In the line above, 1.0 is should be replaced with beta, when required
# Update: Done!!!
QC_q = np.zeros((swarmobject.N, MD_System.dimension)) + 1.0
QC_p = np.zeros_like(
QC_q
) #rand_boltz.normal(0.0,swarmobject.m/(MD_System.beta),np.shape(QC_q))#np.zeros((swarmobject.N,MD_System.dimension))
lambda_curr = np.zeros_like(swarmobject.q)
pool = mp.Pool(mp.cpu_count())
n_inst = 1
func = partial(corr_function_QCMD, swarmobject, QC_q, QC_p, lambda_curr,
MD_System.pos_op, MD_System.pos_op, T, deltat, dpotential)
results = pool.map(func, range(n_inst))
#print('results',np.sum(results,0)/n_inst)
pool.close()
pool.join()
return np.sum(results, 0) / n_inst
def QC_MF_calculation(dpotential, N, n_sample):
print(MD_System.n_beads, MD_System.w_arr)
deltat = 5e-1
Q = np.linspace(0.01, 3.0, 20)
QC_MF = np.zeros((len(Q), MD_System.dimension))
swarmobject = MD_System.swarm(N)
rand_boltz = np.random.RandomState(1)
swarmobject.p = rand_boltz.normal(
0.0, swarmobject.m / MD_System.beta,
(swarmobject.N, MD_System.dimension, MD_System.n_beads))
rng = np.random.RandomState(1)
for j in range(len(Q)):
swarmobject.q = np.zeros(
(swarmobject.N, MD_System.dimension, MD_System.n_beads)) + 1.0
print('j', j, time.time() - start_time)
QC_q = np.zeros((swarmobject.N, MD_System.dimension))
QC_q[:, 0] = Q[j]
#print(np.shape(QC_q[:,0]))
QC_p = np.zeros_like(QC_q)
lambda_curr = np.zeros_like(swarmobject.q)
Langevin_thermostat.qcmd_thermalize(1, swarmobject, QC_q, QC_p,
lambda_curr, dpotential, deltat,
1000, rng)
for i in range(n_sample):
QC_MF[j] += np.mean(Velocity_verlet.dpotential_qcentroid(
QC_q, swarmobject, dpotential),
axis=0)
Langevin_thermostat.qcmd_thermalize(1, swarmobject, QC_q, QC_p,
lambda_curr, dpotential,
deltat, 500, rng)
print('time', time.time() - start_time)
QC_MF /= n_sample
### BE CAREFUL WITH THE SIGN OF THE POTENTIAL HERE. You have used
# force and gradient of potential interchangeably
f = open(
"/home/vgs23/Pickle_files/QCMD_Force_B_{}_NB_{}_N_{}_nsample_{}_deltat_{}_NQ_{}.dat"
.format(MD_System.beta, MD_System.n_beads, N, n_sample, deltat,
len(Q)), 'w+')
QCMD_force = QC_MF[:, 0]
for x in zip(Q, QCMD_force):
f.write("{}\t{}\n".format(x[0], x[1]))
f.close()
plt.plot(Q, QC_MF[:, 0], color='r')
plt.plot(Q, QC_MF[:, 1], color='b')
plt.show()
def Matsubara_instance(rng, N, M, dpotential, T, deltat):
swarmobject = MD_System.swarm(N)
MD_System.system_definition(8, M, 2, MD_System.dpotential)
importlib.reload(Velocity_verlet)
swarmobject.q = np.zeros(
(swarmobject.N, MD_System.dimension, MD_System.n_beads))
rand_boltz = np.random.RandomState(rng)
swarmobject.p = rand_boltz.normal(0.0, swarmobject.m / MD_System.beta,
np.shape(swarmobject.q))
pool = mp.Pool(mp.cpu_count())
Matsubara = 1
CMD = 0
n_inst = 10
func = partial(corr_function_matsubara, CMD, Matsubara, M, swarmobject,
dpotential, MD_System.pos_op, MD_System.pos_op, T, deltat)
results = pool.map(func, range(n_inst))
pool.close()
pool.join()
return np.sum(results, 0) / n_inst
#thermalize(CMD,Matsubara,M,swarmobj,derpotential,deltat,4,rng)
print(time.time() - start_time)
Quan_arr /= (n_approx * swarmobj.N)
for j in range(n_approx):
traj_arr = Monodromy.ode_instance(swarmobj.q, swarmobj.p, tarr)
Quan_arr += np.sum(Monodromy.detmqq(traj_arr), axis=1)
thermalize(CMD, Matsubara, M, swarmobj, derpotential, deltat, 10, rng)
return Quan_arr
N = 20
dim = 3
deltat = 1e-2
swarmobject = MD_System.swarm(N)
MD_System.system_definition(78943.7562025, 1, dim, MD_System.dpotential)
imp.reload(Velocity_verlet)
Monodromy.system_definition(N, dim)
rand_boltz = np.random.RandomState(1)
swarmobject.q = rand_boltz.rand(swarmobject.N, MD_System.dimension,
MD_System.n_beads)
swarmobject.p = rand_boltz.normal(0.0, swarmobject.m / (MD_System.beta),
np.shape(swarmobject.q))
Matsubara = 0
CMD = 0
M = 0
T = 10.0
Marr = phase_space_average(CMD, Matsubara, M, swarmobject,
def mean_force_calculation_2D(dpotential):
print(MD_System.n_beads,MD_System.w_arr)
N = 100
deltat = 1e-2
x = np.linspace(-5,5,101)
y = np.linspace(-5,5,101)
X,Y = np.meshgrid(x,y)
swarmobject = MD_System.swarm(N)
rand_boltz = np.random.RandomState(1000)
swarmobject.p = rand_boltz.normal(0.0,swarmobject.m/MD_System.beta,(swarmobject.N,MD_System.dimension,MD_System.n_beads))
print(np.amax(swarmobject.p))
n_sample = 10
rng=np.random.RandomState(1000)
CMD=1
CMD_force = np.zeros((len(x),len(y),MD_System.dimension))
for i in range(len(x)):
for j in range(len(y)):
swarmobject.q = np.zeros((swarmobject.N,MD_System.dimension,MD_System.n_beads))
swarmobject.q[:,0,:] = X[i][j]
swarmobject.q[:,1,:] = Y[i][j]
thermalize(CMD,0,0,swarmobject,dpotential,deltat,2,rng)
for k in range(n_sample):
#print(np.amax(swarmobject.p))
force = np.mean(dpotential(swarmobject.q),axis=2)
force = np.mean(force,axis=0)
CMD_force[i][j] += force
thermalize(CMD,0,0,swarmobject,dpotential,deltat,2,rng)
print(time.time()-start_time)
CMD_force/=n_sample
f = open("CMD_Force_2D_B_{}_NB_{}_101_10.dat".format(MD_System.beta,MD_System.n_beads),'wb')
plt.imshow(CMD_force[:,:,1],origin='lower')
# for i in range(len(x)):
# for j in range(len(y)):
# f.write("{}\t{}\t{}\n".format(X[i][j],Y[i][j],CMD_force[i][j]))
pickle.dump(x,f)
pickle.dump(y,f)
pickle.dump(CMD_force,f)
f.close()
# l=64-15
# u=64+15
# plt.plot(Q[l:u],CMD_force[l:u])
# ##plt.scatter(Q,CMD_force)
# plt.plot(data[l:u,0],-data[l:u,1],color='r')
# plt.show()
#return CMF
#mean_force_calculation()
#print(mean_force(4.0))
#print(CMD_force)