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Valentin Boettcher 2021-11-02 18:38:51 +01:00
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1 changed files with 15 additions and 15 deletions
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python/richard_hops/energy_flow_thermal.org
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@ -58,33 +58,33 @@ Basic parameters.
#+begin_src jupyter-python
class params:
T = 2
t_max = 10
t_steps = int(t_max * 1/.01)
k_max = 10
N = 1000
seed = 100
dim = 2
H_s = σ3 + np.eye(dim)
L = σ2 #1 / 2 * (σ1 - 1j * σ2)
ψ_0 = np.array([0, 1])
s = 1
num_exp_t = 4
wc = 1
with open("good_fit_data_abs_brute_force", "rb") as f:
good_fit_data_abs = pickle.load(f)
alpha = 0.8
# _, g_tilde, w_tilde = good_fit_data_abs[(numExpFit, s)]
# g_tilde = np.array(g_tilde)
# w_tilde = np.array(w_tilde)
# g = 1 / np.pi * gamma_func(s + 1) * wc ** (s + 1) * np.asarray(g_tilde)
# w = wc * np.asarray(w_tilde)
bcf_scale = np.pi / 2 * alpha * wc ** (1 - s)
#+end_src
@ -1067,11 +1067,11 @@ And try to calculate the energy flow.
a = np.array((params.L @ ψ_0.T).T)
EtaTherm.new_process(temp_y)
η_dot = scipy.misc.derivative(EtaTherm, int_result.τ, dx=1e-3, order=5)
ψ_1 = (-w * g * params.bcf_scale)[None, :, None] * ψ_1.reshape(
params.t_steps, params.num_exp_t, params.dim
)
# return np.array(np.sum(ψ_0.conj() * ψ_0, axis=1)).flatten().real
j_0 = np.array(
2
@ -1081,7 +1081,7 @@ And try to calculate the energy flow.
/ np.sum(ψ_0.conj() * ψ_0, axis=1)
).real
).flatten()
j_therm = np.array(
2
,* (
@ -1104,7 +1104,7 @@ Now we calculate the average over all trajectories.
dj, dj_therm = flow_for_traj(
int_result.ψ_0[i], int_result.ψ_1[i], int_result.temp_y[i]
)
j_0 += dj
j_therm += dj_therm
j_0 /= params.N
@ -1183,7 +1183,7 @@ With this we can retrieve the energy of the interaction Hamiltonian.
params.t_steps, params.num_exp_t, params.dim
)
EtaTherm.new_process(temp_y)
E_i = np.array(
2
,* (
@ -1195,7 +1195,7 @@ With this we can retrieve the energy of the interaction Hamiltonian.
)
).real
).flatten()
E_i += np.array(
2
,* (
@ -1207,9 +1207,9 @@ With this we can retrieve the energy of the interaction Hamiltonian.
)
).real
).flatten()
E_s = np.array(np.sum(b.conj() * ψ_0, axis=1)).flatten().real
return (E_i + E_s) / np.sum(ψ_0.conj() * ψ_0, axis=1).real
#+end_src