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refac #3

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22 changes: 11 additions & 11 deletions example/ho1d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,9 @@
from torch import optim

from schrodinet.sampler.metropolis import Metropolis
from schrodinet.wavefunction.wf_potential import Potential
from schrodinet.solver.solver_potential import SolverPotential
from schrodinet.solver.plot_potential import plot_results_1d, plotter1d
from schrodinet.wavefunction.wave_function_1d import WaveFunction1D
from schrodinet.solver.solver import Solver
from schrodinet.solver.plot import plot_results_1d, plotter1d


def pot_func(pos):
Expand All @@ -21,12 +21,11 @@ def ho1d_sol(pos):
domain, ncenter = {'min': -5., 'max': 5.}, 11

# wavefunction
wf = Potential(pot_func, domain, ncenter, fcinit='random', nelec=1, sigma=1.)
wf = WaveFunction1D(pot_func, domain, ncenter, sigma=1.)

# sampler
sampler = Metropolis(nwalkers=1000, nstep=200,
step_size=1., nelec=wf.nelec,
ndim=wf.ndim, init={'min': -5, 'max': 5})
step_size=1., init=domain)

# optimizer
opt = optim.Adam(wf.parameters(), lr=0.05)
Expand All @@ -35,12 +34,13 @@ def ho1d_sol(pos):
scheduler = optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.75)

# define solver
solver = SolverPotential(wf=wf, sampler=sampler,
optimizer=opt, scheduler=scheduler)
solver = Solver(wf=wf, sampler=sampler,
optimizer=opt, scheduler=scheduler)

# train the wave function
#plotter = plotter1d(wf, domain, 100, sol=ho1d_sol) # , save='./image/')
solver.run(300, loss='energy-manual', plot=None, save='model.pth')
plotter = plotter1d(wf, domain, 100, sol=ho1d_sol)
solver.run(300, loss='energy-manual', plot=plotter, save='model.pth')

# plot the final wave function
plot_results_1d(solver, domain, 100, ho1d_sol, e0=0.5, load='model.pth')
plot_results_1d(solver, domain, 100, ho1d_sol,
e0=0.5, load='model.pth')
16 changes: 9 additions & 7 deletions example/morse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,9 @@
from torch import optim

from schrodinet.sampler.metropolis import Metropolis
from schrodinet.wavefunction.wf_potential import Potential
from schrodinet.solver.solver_potential import SolverPotential
from schrodinet.solver.plot_potential import plot_results_1d, plotter1d
from schrodinet.wavefunction.wave_function_1d import WaveFunction1D
from schrodinet.solver.solver import Solver
from schrodinet.solver.plot import plot_results_1d


def pot_func(pos):
Expand All @@ -22,7 +22,8 @@ def ho1d_sol(pos):
domain, ncenter = {'min': -3., 'max': 8.}, 51

# wavefunction
wf = Potential(pot_func, domain, ncenter, fcinit='random', nelec=1, sigma=1)
wf = WaveFunction1D(pot_func, domain, ncenter,
fcinit='random', nelec=1, sigma=1)

# sampler
sampler = Metropolis(nwalkers=1000, nstep=500,
Expand All @@ -36,12 +37,13 @@ def ho1d_sol(pos):
scheduler = optim.lr_scheduler.StepLR(opt, step_size=100, gamma=0.75)

# define solver
solver = SolverPotential(wf=wf, sampler=sampler,
optimizer=opt, scheduler=scheduler)
solver = Solver(wf=wf, sampler=sampler,
optimizer=opt, scheduler=scheduler)

# train the wave function
#plotter = plotter1d(wf, domain, 100, sol=ho1d_sol)
solver.run(300, loss='variance', plot=None, save='model.pth')

# plot the final wave function
plot_results_1d(solver, domain, 100, ho1d_sol, e0=-0.125, load='model.pth')
plot_results_1d(solver, domain, 100, ho1d_sol,
e0=-0.125, load='model.pth')
3 changes: 2 additions & 1 deletion schrodinet/sampler/metropolis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,8 @@ def generate(self, pdf, ntherm=10, ndecor=100, pos=None,
idecor += 1

if with_tqdm:
print("Acceptance rate %1.3f %%" % (rate/self.nstep*100))
print("Acceptance rate %1.3f %%" %
(rate/self.nstep*100))

return torch.cat(pos)

Expand Down
20 changes: 13 additions & 7 deletions schrodinet/solver/plot_potential.py → schrodinet/solver/plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,12 +107,15 @@ def drawNow(self):
self.lwf.set_ydata(vp)

if self.plot_weight:
self.pweight.set_xdata(self.wf.rbf.centers.detach().numpy())
self.pweight.set_ydata(self.wf.fc.weight.detach().numpy().T)
self.pweight.set_xdata(
self.wf.rbf.centers.detach().numpy())
self.pweight.set_ydata(
self.wf.fc.weight.detach().numpy().T)

if self.plot_grad:
if self.wf.fc.weight.requires_grad:
self.pgrad.set_xdata(self.wf.rbf.centers.detach().numpy())
self.pgrad.set_xdata(
self.wf.rbf.centers.detach().numpy())
data = (self.wf.fc.weight.grad.detach().numpy().T)**2
data /= np.linalg.norm(data)
self.pgrad.set_ydata(data)
Expand Down Expand Up @@ -167,7 +170,7 @@ def plot_wf_1d(net, domain, res, grad=False, hist=False, pot=True, sol=None,
vals = net.wf(X)
vn = vals.detach().numpy().flatten()
vn /= np.max(vn)
ax.plot(xn, vn, color='black', linewidth=2, label='DeepQMC')
ax.plot(xn, vn, color='black', linewidth=2, label='Schrodinet')

if pot:
pot = net.wf.nuclear_potential(X).detach().numpy()
Expand Down Expand Up @@ -213,7 +216,8 @@ def plot_results_1d(net, domain, res, sol=None, e0=None, load=None):
ax0 = fig.add_subplot(211)
ax1 = fig.add_subplot(212)

plot_wf_1d(net, domain, res, sol=sol, hist=False, ax=ax0, load=load)
plot_wf_1d(net, domain, res, sol=sol,
hist=False, ax=ax0, load=load)
plot_observable(net.obs_dict, e0=e0, ax=ax1)

plt.show()
Expand Down Expand Up @@ -291,7 +295,8 @@ def __init__(self, wf, domain, res, pot=False, kinetic=False, sol=None):
self.yy = pos[:, 1].reshape(res[0], res[1])

if callable(sol):
vs = sol(self.POS).view(self.res[0], self.res[1]).detach().numpy()
vs = sol(self.POS).view(
self.res[0], self.res[1]).detach().numpy()
vs /= np.linalg.norm(vs)
self.ax.plot_wireframe(self.xx, self.yy, vs,
color='black', linewidth=1)
Expand Down Expand Up @@ -458,7 +463,8 @@ def plot_wf_3d(net, domain, res, sol=None,
if hist:
pos = net.sample().detach().numpy()
for ielec in range(net.wf.nelec):
ax.scatter(pos[:, ielec*3], pos[:, ielec*3+1], pos[:, ielec*3+2])
ax.scatter(pos[:, ielec*3],
pos[:, ielec*3+1], pos[:, ielec*3+2])

if callable(sol):

Expand Down
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