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 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 CMD_instance(beads, dpotential, beta):
MD_System.system_definition(beta, beads, 1, dpotential)
importlib.reload(Velocity_verlet)
Centroid_mean_force.mean_force_calculation(dpotential)
f = open(
"/home/vgs23/Pickle_files/CMD_Force_Morse_B_{}_NB_{}_100_10.dat".
format(MD_System.beta, MD_System.n_beads), 'rb')
data = np.loadtxt(f)
Q = data[:, 0]
CMD_force = data[:, 1]
xforce = scipy.interpolate.interp1d(Q, CMD_force, kind='cubic')
return xforce
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 vv_step_constrained(QCMD, swarmobj, QC_q, QC_p, lambda_curr, dpotential,
deltat, adiabatic):
q0 = swarmobj.q.copy()
if (QCMD != 1):
QC_p += -(deltat / 2) * dpotential_qcentroid(QC_q, swarmobj,
dpotential)
swarmobj.p += -(deltat / 2) * dpotential(swarmobj.q)
if (adiabatic == 1):
adiabatize_p(swarmobj, deltat)
else:
swarmobj.p += -(deltat / 2) * MD_System.dpotential_ring(swarmobj)
RATTLE(swarmobj.q, swarmobj.p)
if (QCMD != 1):
QC_q += (deltat / swarmobj.m) * QC_p
if (adiabatic == 1):
adiabatize_q(swarmobj, deltat)
else:
swarmobj.q += (deltat / swarmobj.m) * swarmobj.p
qc_norm = (QC_q[:, 0]**2 + QC_q[:, 1]**2)**0.5
SHAKE(swarmobj, q0, deltat, lambda_curr, qc_norm)
if (QCMD != 1):
QC_p += -(deltat / 2) * dpotential_qcentroid(QC_q, swarmobj,
dpotential)
swarmobj.p += -(deltat / 2) * dpotential(swarmobj.q)
if (adiabatic == 1):
adiabatize_p(swarmobj, deltat)
else:
swarmobj.p += -(deltat / 2) * MD_System.dpotential_ring(swarmobj)
RATTLE(swarmobj.q, swarmobj.p)
#plt.plot(swarmobj.q[0][0],swarmobj.q[0][1])
#plt.scatter(swarmobj.q[0][0],swarmobj.q[0][1])
x_c = np.mean(swarmobj.q[0][0])
y_c = np.mean(swarmobj.q[0][1])
radius = (QC_q[0][0]**2 + QC_q[0][1]**2)**0.5
theta_arr = np.arange(0, 2 * np.pi + 0.1, 0.1)
def QCMD_instance(beads, dpotential, beta):
MD_System.system_definition(beta, beads, 2, dpotential)
importlib.reload(Velocity_verlet)
N = 100
n_sample = 1
#QC_MF_calculation(dpotential,N,n_sample)
f = open(
"/home/vgs23/Pickle_files/QCMD_Force_B_{}_NB_{}_N_{}_nsample_{}_deltat_0.5_NQ_20.dat"
.format(MD_System.beta, MD_System.n_beads, N, n_sample), 'rb')
data = np.loadtxt(f)
Q = data[:, 0]
QCMD_force = data[:, 1]
rforce = scipy.interpolate.interp1d(Q, QCMD_force, kind='cubic')
plt.plot(Q, QCMD_force)
plt.plot(Q, rforce(Q))
plt.show()
return rforce
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
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()
#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,
dsigma(q) * dsigma(q)
) #np.dot(dsigma(q[0],q[1]),p)/np.dot(dsigma(q[0],q[1]),dsigma(q[0],q[1]))
p += myu * dsigma(q)
T = 4000 #5000*deltat#6.0
t = 0.0
rng = np.random.RandomState(2)
#p = rng.normal(0,1.0,(2,1))#np.array([[0.0],[1]])#np.random.randint(5,size=(2,1)) +1.0#np.array([0.0,1])
beads = 1
#((2,4))#np.array([[1.0],[0.0]])#np.random.randint(5,size=(2,1)) +0.0#np.array([1.0,0.0])
p = rng.normal(0, 1.0, (2, beads))
q = np.zeros_like(p) + 2.0
MD_System.system_definition(1.0, beads, 2, dpotential)
w_arr = MD_System.w_arr
w_arr = w_arr[1:]
print('w_arr', w_arr)
print('q', q)
print('p', p)
count = 0
qcx = []
qcy = []
if (1):
while (abs(t - T) > 1e-4):
constrained_vv(q, p, lambda_curr)
t += deltat
from numpy import linalg as LA from numpy.fft import fft,ifft from multiprocessing import Process from Correlation_function import corr_function,corr_function_upgrade,corr_function_matsubara #from MD_System import swarm,pos_op,beta,dimension,n_beads,w_n import MD_System import multiprocessing as mp from functools import partial import scipy #from Langevin_thermostat import thermalize #from Centroid_mean_force import CMF #from External_potential import dpotential #import Centroid_mean_force import Velocity_verlet import importlib import time import pickle start_time = time.time() from Matsubara_potential_hh import dpot_hh deltat = 1e-2 M=3 dimension = 2 MD_System.system_definition(8,M,dimension,MD_System.dpotential) importlib.reload(Velocity_verlet) swarmobject = MD_System.swarm(1) swarmobject.q = np.zeros((swarmobject.N,MD_System.dimension,MD_System.n_beads)) print(np.shape(swarmobject.q)) #swarmobject.p = rand_boltz.normal(0.0,swarmobject.m/MD_System.beta,np.shape(swarmobject.q))
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)
y = swarmobject.q[0,1,1]
py = swarmobject.p[0,1,1]
#curr =x
if(count%1==0):
#print(prev,curr)
if( abs(x)<1e-3 and px<0.0 ):
#print('herhe',count)
Y.append(y)
PY.append(py)
#prev = curr
t+=deltat
count+=1
rand = np.random.RandomState(1)
MD_System.system_definition(8,3,2,dpotential)
importlib.reload(Velocity_verlet)
swarmobject = MD_System.swarm(1)
#E = 0.08
def compute_P_section(E,Y,PY):
for i in range(10):
x0 = rand.uniform(-(0.5*E)**0.5,(0.5*E)**0.5)
y0 = rand.uniform(-(0.5*E)**0.5,(0.5*E)**0.5)
differ = E-potential(x0,y0)
split = rand.uniform(0,2)
px0 = (split*differ)**0.5
py0 = ((2-split)*differ)**0.5
swarmobject.q = np.array([[[x0],[y0]]])
swarmobject.p = np.array([[[px0],[py0]]])