mirror of
https://github.com/vale981/master-thesis
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70 lines
1.7 KiB
Python
70 lines
1.7 KiB
Python
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from hops.core.hierarchy_parameters import HIParams, HiP, IntP, SysP, ResultType
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from hops.core.hierarchyLib import HI
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from hops.util.bcf_fits import get_ohm_g_w
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from hops.util.truncation_schemes import TruncationScheme_Power_multi
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import hops.util.bcf
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import numpy as np
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import hops.util.matrixLib as ml
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from stocproc import StocProc_FFT
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wc = 5
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s = 1
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# The BCF fit
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bcf_terms = 6
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g, w = get_ohm_g_w(bcf_terms, s, wc)
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integration = IntP(t_max=5, t_steps=1000)
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system = SysP(
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H_sys=0.5 * np.array([[1, 0], [0, -1]]),
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L=0.5 * np.array([[0, 1], [1, 0]]),
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psi0=np.array([1, 1]),
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g=g,
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w=w,
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bcf_scale=0.8,
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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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normalized_by_hand=True,
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result_type=ResultType.ALL,
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truncation_scheme=TruncationScheme_Power_multi.from_g_w(
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g=g, w=w, p=1, q=0.5, kfac=1.4
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),
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),
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Eta=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.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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t_max=integration.t_max,
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intgr_tol=1e-3,
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intpl_tol=1e-3,
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negative_frequencies=False,
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),
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EtaTherm=StocProc_FFT(
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spectral_density=hops.util.bcf.Ohmic_StochasticPotentialDensity(
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s, 1, wc, beta=1 / system.__non_key__["T"]
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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"]
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),
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t_max=integration.t_max,
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intgr_tol=1e-3,
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intpl_tol=1e-3,
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negative_frequencies=False,
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)
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if system.__non_key__["T"]
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else None,
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)
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