2021-01-20 18:31:49 +01:00
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from hydrogen import *
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from qmc_stats import *
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2021-01-29 13:23:00 +01:00
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def MonteCarlo(a,nmax,dt):
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energy = 0.
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2021-01-26 10:02:38 +01:00
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N_accep = 0
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2021-01-29 13:23:00 +01:00
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r_old = np.random.uniform(-dt, dt, (3))
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2021-01-20 18:31:49 +01:00
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psi_old = psi(a,r_old)
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2021-01-29 13:23:00 +01:00
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2021-01-20 18:31:49 +01:00
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for istep in range(nmax):
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2021-01-29 13:23:00 +01:00
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energy += e_loc(a,r_old)
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r_new = r_old + np.random.uniform(-dt,dt,(3))
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2021-01-20 18:31:49 +01:00
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psi_new = psi(a,r_new)
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2021-01-29 13:23:00 +01:00
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2021-01-20 18:31:49 +01:00
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ratio = (psi_new / psi_old)**2
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2021-01-29 13:23:00 +01:00
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if np.random.uniform() <= ratio:
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2021-01-26 10:02:38 +01:00
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N_accep += 1
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2021-01-29 13:23:00 +01:00
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r_old = r_new
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2021-01-20 18:31:49 +01:00
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psi_old = psi_new
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2021-01-29 13:23:00 +01:00
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2021-01-26 10:02:38 +01:00
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return energy/nmax, N_accep/nmax
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2021-01-20 18:31:49 +01:00
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2021-01-26 10:02:38 +01:00
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# Run simulation
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2021-01-29 13:23:00 +01:00
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a = 0.9
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2021-01-20 18:31:49 +01:00
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nmax = 100000
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2021-01-29 13:23:00 +01:00
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dt = 1.3
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X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
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2021-01-26 10:02:38 +01:00
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# Energy
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X = [ x for (x, _) in X0 ]
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2021-01-20 18:31:49 +01:00
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E, deltaE = ave_error(X)
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print(f"E = {E} +/- {deltaE}")
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2021-01-26 10:02:38 +01:00
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# Acceptance rate
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X = [ x for (_, x) in X0 ]
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A, deltaA = ave_error(X)
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2021-01-20 18:31:49 +01:00
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print(f"A = {A} +/- {deltaA}")
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