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https://github.com/vale981/master-thesis
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156 lines
4.2 KiB
Python
156 lines
4.2 KiB
Python
from hops.core.hierarchy_parameters import HIParams, HiP, IntP, SysP, ResultType
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from stocproc.stocproc import StocProc_TanhSinh
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from hops.util.bcf_fits import get_ohm_g_w
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from hops.util.truncation_schemes import (
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TruncationScheme_Power_multi,
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BathMemory,
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)
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from hops.util.abstract_truncation_scheme import (
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TruncationScheme_Simplex
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)
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import hops.util.bcf
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import numpy as np
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from stocproc import StocProc_FFT
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np.__config__.blas_opt_info = np.__config__.blas_ilp64_opt_info # fix for qutip
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import qutip
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wc = 2
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s = 1
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Ω = 1
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Λ = 1
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γ = 1
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hops_bcf = hops.util.bcf.OhmicBCF_zeroTemp(
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s,
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1,
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wc,
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)
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max_HO_level_set = 10
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def make_config(
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max_HO_level: int,
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bcf_terms: int = 4,
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t_max: float = 10,
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k_fac: float = 1.4,
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inf_tol: float = 0.1,
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k_max: int = 7,
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sp_tol: float = 1e-3,
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bcf_scale: list[float] = [0.5, 0.5],
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truncation_scheme: str = "power",
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T: float = 0.3,
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):
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global max_HO_level_set
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max_HO_level_set = max_HO_level
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# The BCF fit
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g, w = get_ohm_g_w(bcf_terms, s, wc)
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integration = IntP(t_max=t_max, t_steps=int(t_max // 0.1))
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q_proto = qutip.operators.create(max_HO_level) + qutip.operators.destroy(
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max_HO_level
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)
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p_proto = (
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qutip.operators.destroy(max_HO_level) - qutip.operators.create(max_HO_level)
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) / 1j
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q_1 = qutip.tensor(q_proto, qutip.qeye(max_HO_level))
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p_1 = qutip.tensor(p_proto, qutip.qeye(max_HO_level))
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q_2 = qutip.tensor(qutip.qeye(max_HO_level), q_proto)
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p_2 = qutip.tensor(qutip.qeye(max_HO_level), p_proto)
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system = SysP(
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H_sys=(
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0.25 * (p_1 ** 2 + q_1 ** 2 + p_2 ** 2 + q_2 ** 2)
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+ γ / 4 * (q_1 - q_2) ** 2
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).full(),
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L=[0.5 * q_1.full(), 0.5 * q_2.full()],
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psi0=qutip.tensor(
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qutip.states.fock(max_HO_level, n=0), qutip.states.fock(max_HO_level, n=0)
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)
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.full()
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.flatten(),
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g=[g, g],
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w=[w, w],
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bcf_scale=bcf_scale,
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T=[T, 0],
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)
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params = HIParams(
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SysP=system,
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IntP=integration,
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HiP=HiP(
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nonlinear=True,
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result_type=ResultType.ZEROTH_AND_FIRST_ORDER,
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truncation_scheme=TruncationScheme_Power_multi.from_g_w(
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g=system.g, w=system.w, p=[1, 1], q=[0.5, 0.5], kfac=[k_fac] * 2
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)
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if truncation_scheme == "power"
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else BathMemory.from_system(
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system, nonlinear=True, influence_tolerance=inf_tol
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)
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if truncation_scheme == "bath_memory"
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else TruncationScheme_Simplex(k_max),
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save_therm_rng_seed=True,
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),
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Eta=[
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StocProc_FFT(
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spectral_density=hops.util.bcf.OhmicSD_zeroTemp(
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s,
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1,
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wc,
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),
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alpha=hops_bcf,
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t_max=integration.t_max,
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intgr_tol=sp_tol,
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intpl_tol=sp_tol,
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negative_frequencies=False,
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),
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StocProc_FFT(
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spectral_density=hops.util.bcf.OhmicSD_zeroTemp(
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s,
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1,
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wc,
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),
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alpha=hops_bcf,
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t_max=integration.t_max,
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intgr_tol=sp_tol,
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intpl_tol=sp_tol,
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negative_frequencies=False,
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),
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],
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EtaTherm=[
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StocProc_TanhSinh(
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spectral_density=hops.util.bcf.Ohmic_StochasticPotentialDensity(
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s, 1, wc, beta=1 / system.__non_key__["T"][0]
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),
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alpha=hops.util.bcf.Ohmic_StochasticPotentialCorrelations(
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s, 1, wc, beta=1 / system.__non_key__["T"][0]
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),
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t_max=integration.t_max,
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intgr_tol=sp_tol,
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intpl_tol=sp_tol,
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negative_frequencies=False,
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),
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None,
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],
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)
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return params
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γ = .5
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params = make_config(
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max_HO_level=9,
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bcf_terms=5,
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t_max=50,
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inf_tol=.05,
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sp_tol=1e-5,
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bcf_scale=[0.2, 0.2],
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T=.6,
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k_max=5,
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truncation_scheme="simplex",
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)
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