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https://github.com/vale981/master-thesis
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add finite T to 11
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2 changed files with 541 additions and 0 deletions
217
python/energy_flow_proper/11_new_ho_comparison/finite_t.py
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217
python/energy_flow_proper/11_new_ho_comparison/finite_t.py
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import figsaver as fs
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import plot_utils as pu
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import hops.util.bcf
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import hops.util.bcf_fits
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from hops.core.hierarchy_parameters import *
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from stocproc import StocProc_FFT, StocProc_TanhSinh
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from hops.util.abstract_truncation_scheme import TruncationScheme_Simplex
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from hops.core.integration import HOPSSupervisor
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import qutip
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def ho_zero(max_HO_level=15, wc=2, s=1, bcf_terms=7, k_max=5, bcf_scale=1, T=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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normed=True
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)
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g, w = hops_bcf.exponential_coefficients(bcf_terms)
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integration = IntP(t_steps=(40, 1000))
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q = qutip.operators.create(max_HO_level) + qutip.operators.destroy(max_HO_level)
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p = (
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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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system = SysP(
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H_sys=0.25 * (p ** 2 + q ** 2).full(),
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L=[0.5 * q.full()],
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psi0=qutip.states.fock(max_HO_level, n=0).full().flatten(),
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g=[g],
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w=[w],
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bcf_scale=[bcf_scale],
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T=[.5],
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description="A single HO at zero Temperature",
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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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seed=0,
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nonlinear=True,
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result_type=ResultType.ZEROTH_AND_FIRST_ORDER,
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truncation_scheme=TruncationScheme_Simplex(k_max),
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save_therm_rng_seed=True,
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auto_normalize=True,
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accum_only=False,
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rand_skip=None,
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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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normed=True
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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=1e-5,
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intpl_tol=1e-5,
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negative_frequencies=False,
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)
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],
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EtaTherm=[StocProc_TanhSinh(
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spectral_density=hops.util.bcf.Ohmic_StochasticPotentialDensity(
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s,
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1,
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wc,
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beta=1/system.__non_key__["T"][0],
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normed=True
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),
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alpha=hops.util.bcf.Ohmic_StochasticPotentialCorrelations(
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s,
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1,
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wc,
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beta=1/system.__non_key__["T"][0],
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normed=True
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),
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t_max=integration.t_max,
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intgr_tol=1e-5,
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intpl_tol=1e-5,
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negative_frequencies=False,
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)],
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)
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return params
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model_keys = [dict(bcf_scale=1, wc=ω_c) for ω_c in [1, 2]] + [dict(bcf_scale=s, wc=2) for s in [.5, 1, 2]]
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multi_params = [ho_zero(**keys) for keys in model_keys]
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multi_params = multi_params
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import ray
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ray.shutdown()
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ray.init()
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supervisors = []
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for params in multi_params:
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supervisor = HOPSSupervisor(
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params,
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10_000,
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data_path="ho_data",
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data_name="finite_t",
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)
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supervisor.integrate()
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supervisors.append(supervisor)
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from hopsflow import hopsflow
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flow_hops = []
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for supervisor in supervisors:
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hf_sys = hopsflow.SystemParams.from_hi_params(supervisor.params)
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hf_therm = hopsflow.ThermalParams.from_hi_params(supervisor.params)
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flow_hops.append(
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hopsflow.heat_flow_from_data(
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supervisor.get_data(True),
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hf_sys,
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thermal_params=hf_therm,
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every=1000,
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save=f"flow_finite",
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overwrite_cache=True
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)
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)
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fig, ax = plt.subplots()
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for params, flow, keys in zip(multi_params, flow_hops, model_keys):
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pu.plot_with_σ(
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params.IntP.t,
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-1 * flow.for_bath(0),
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ax=ax,
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label=f"{len(params.SysP.g[0])}, {params.HiP.truncation_scheme.kmax}",
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)
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ax.legend()
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from hopsflow import gaussflow as gf
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exact_flows = []
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for params, keys in zip(multi_params, model_keys):
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α = gf.BCF(
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params.IntP.t_max,
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hops.util.bcf.OhmicBCF_nonZeroTemp(
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s=1,
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eta=params.SysP.bcf_scale[0],
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w_c=keys["wc"],
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beta=1 / params.SysP.__non_key__["T"][0],
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),
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num_terms=6,
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resolution=0.01,
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)
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α_0 = gf.BCF(
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params.IntP.t_max,
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# factors=params.SysP.g[0],
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# exponents=params.SysP.w[0],
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hops.util.bcf.OhmicBCF_zeroTemp(
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s=1,
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eta=params.SysP.bcf_scale[0],
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w_c=keys["wc"],
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normed=True,
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),
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# hops_bcf,
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num_terms=6,
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resolution=0.001,
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)
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α_0_dot = gf.BCF(
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params.IntP.t_max,
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factors=-α_0.factors * α_0.exponents,
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exponents=α_0.exponents,
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# lambda t: 2 / (1j * np.pi) * (wc / (1 + 1j * wc * t)) ** 3,
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# num_terms=6,
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# resolution=0.001,
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)
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sys = gf.SystemParams(Ω=1, η=1, α_0=α_0)
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flow = gf.Flow(sys, α, α_0_dot, n=0)
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flow_τ = flow(params.IntP.t)
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exact_flows.append(flow_τ)
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_tt = np.linspace(0, 4, 1000)
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fig, ax = pu.plot_complex(_tt, α(_tt)- α.approx(_tt), label="finite")
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pu.plot_complex(_tt, α_0(_tt)- α_0.approx(_tt), label="finite", ax=ax)
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# ut.plot_complex(result.τ, α(result.τ)- α.approx(result.τ), label="finite", ax=ax)
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# ut.plot_complex(result.τ, α_0_dot(result.τ)- α_0_dot.approx(result.τ), label="finite", ax=ax)
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fig, ax = plt.subplots()
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for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
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consistency = (flow).consistency(ex_flow)
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pu.plot_with_σ(
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params.IntP.t,
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-1 * flow,
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bath=0,
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ax=ax,
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label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ ${consistency}\%$",
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)
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ax.plot(params.IntP.t, ex_flow, linestyle="dotted", color="black")
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plt.xlabel("$τ$")
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plt.ylabel("$-J$")
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# ax.plot(multi_params[-1].IntP.t, flow_τ, label="Analytic", linestyle="dotted", color="black")
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ax.legend()
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# fs.export_fig("convergence_zero")
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pu.plot_convergence(
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multi_params[-1].IntP.t,
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-1 * flow_hops[-1].for_bath(0)
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-flow_τ,
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reference=np.zeros_like(flow_τ)
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)
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plt.axhline(0)
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plt.legend()
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# plt.plot(multi_params[-1].IntP.t, -1 * flow_hops[-1].for_bath(0).σ)
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@ -0,0 +1,324 @@
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#+PROPERTY: header-args :session comparison_hops_gauss_new_thermal :kernel python :pandoc yes :async yes :tangle finite_t.py
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#+begin_src jupyter-python :results none
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import figsaver as fs
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import plot_utils as pu
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#+end_src
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#+begin_src jupyter-python
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import hops.util.bcf
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import hops.util.bcf_fits
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from hops.core.hierarchy_parameters import *
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from stocproc import StocProc_FFT, StocProc_TanhSinh
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from hops.util.abstract_truncation_scheme import TruncationScheme_Simplex
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from hops.core.integration import HOPSSupervisor
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import qutip
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def ho_zero(max_HO_level=15, wc=2, s=1, bcf_terms=7, k_max=5, bcf_scale=1, T=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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normed=True
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)
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g, w = hops_bcf.exponential_coefficients(bcf_terms)
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integration = IntP(t_steps=(40, 1000))
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q = qutip.operators.create(max_HO_level) + qutip.operators.destroy(max_HO_level)
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p = (
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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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system = SysP(
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H_sys=0.25 * (p ** 2 + q ** 2).full(),
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L=[0.5 * q.full()],
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psi0=qutip.states.fock(max_HO_level, n=0).full().flatten(),
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g=[g],
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w=[w],
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bcf_scale=[bcf_scale],
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T=[.5],
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description="A single HO at zero Temperature",
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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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seed=0,
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nonlinear=True,
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result_type=ResultType.ZEROTH_AND_FIRST_ORDER,
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truncation_scheme=TruncationScheme_Simplex(k_max),
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save_therm_rng_seed=True,
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auto_normalize=True,
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accum_only=False,
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rand_skip=None,
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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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normed=True
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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=1e-5,
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intpl_tol=1e-5,
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negative_frequencies=False,
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)
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],
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EtaTherm=[StocProc_TanhSinh(
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spectral_density=hops.util.bcf.Ohmic_StochasticPotentialDensity(
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s,
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1,
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wc,
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beta=1/system.__non_key__["T"][0],
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normed=True
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),
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alpha=hops.util.bcf.Ohmic_StochasticPotentialCorrelations(
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s,
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1,
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wc,
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beta=1/system.__non_key__["T"][0],
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normed=True
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),
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t_max=integration.t_max,
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intgr_tol=1e-5,
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intpl_tol=1e-5,
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negative_frequencies=False,
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)],
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)
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return params
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model_keys = [dict(bcf_scale=1, wc=ω_c) for ω_c in [1, 2]] + [dict(bcf_scale=s, wc=2) for s in [.5, 1, 2]]
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multi_params = [ho_zero(**keys) for keys in model_keys]
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multi_params = multi_params
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#+end_src
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#+RESULTS:
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#+begin_example
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stocproc.stocproc - INFO - Loaded instance from cache.
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INFO:stocproc.stocproc:Loaded instance from cache.
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stocproc.stocproc - INFO - non neg freq only
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INFO:stocproc.stocproc:non neg freq only
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stocproc.stocproc - INFO - get_dt_for_accurate_interpolation, please wait ...
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INFO:stocproc.stocproc:get_dt_for_accurate_interpolation, please wait ...
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stocproc.stocproc - INFO - requires dt < 1.953e-02
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INFO:stocproc.stocproc:requires dt < 1.953e-02
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stocproc.stocproc - INFO - yields N = 2049 (time domain)
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INFO:stocproc.stocproc:yields N = 2049 (time domain)
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stocproc.stocproc - INFO - find accurate discretisation in frequency domain
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INFO:stocproc.stocproc:find accurate discretisation in frequency domain
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stocproc.stocproc - INFO - wmax:5.151618328961643
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INFO:stocproc.stocproc:wmax:5.151618328961643
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stocproc.stocproc - INFO - Loaded instance from cache.
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INFO:stocproc.stocproc:Loaded instance from cache.
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stocproc.stocproc - INFO - non neg freq only
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INFO:stocproc.stocproc:non neg freq only
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stocproc.stocproc - INFO - get_dt_for_accurate_interpolation, please wait ...
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INFO:stocproc.stocproc:get_dt_for_accurate_interpolation, please wait ...
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stocproc.stocproc - INFO - requires dt < 9.766e-03
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INFO:stocproc.stocproc:requires dt < 9.766e-03
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stocproc.stocproc - INFO - yields N = 4097 (time domain)
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INFO:stocproc.stocproc:yields N = 4097 (time domain)
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stocproc.stocproc - INFO - find accurate discretisation in frequency domain
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INFO:stocproc.stocproc:find accurate discretisation in frequency domain
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stocproc.stocproc - INFO - wmax:5.665445489514328
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INFO:stocproc.stocproc:wmax:5.665445489514328
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stocproc.stocproc - INFO - Loaded instance from cache.
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INFO:stocproc.stocproc:Loaded instance from cache.
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stocproc.stocproc - INFO - Loaded instance from cache.
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INFO:stocproc.stocproc:Loaded instance from cache.
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stocproc.stocproc - INFO - Loaded instance from cache.
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INFO:stocproc.stocproc:Loaded instance from cache.
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stocproc.stocproc - INFO - Loaded instance from cache.
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INFO:stocproc.stocproc:Loaded instance from cache.
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stocproc.stocproc - INFO - Loaded instance from cache.
|
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INFO:stocproc.stocproc:Loaded instance from cache.
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stocproc.stocproc - INFO - Loaded instance from cache.
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INFO:stocproc.stocproc:Loaded instance from cache.
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#+end_example
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#+begin_src jupyter-python
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import ray
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ray.shutdown()
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ray.init()
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#+end_src
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#+RESULTS:
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: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '192.168.100.160', 'raylet_ip_address': '192.168.100.160', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-07-22_18-47-59_113889_118691/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-22_18-47-59_113889_118691/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-22_18-47-59_113889_118691', 'metrics_export_port': 59610, 'gcs_address': '192.168.100.160:46332', 'address': '192.168.100.160:46332', 'node_id': '4145ec03d17533dce23caf24ec33bc176b6ba84fd6e6969da8d61160'})
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#+begin_src jupyter-python
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supervisors = []
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for params in multi_params:
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supervisor = HOPSSupervisor(
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params,
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10_000,
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data_path="ho_data",
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data_name="finite_t",
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)
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supervisor.integrate()
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supervisors.append(supervisor)
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#+end_src
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#+RESULTS:
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: 0it [00:00, ?it/s]
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* Flow
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#+begin_src jupyter-python :results none
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from hopsflow import hopsflow
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#+end_src
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#+begin_src jupyter-python
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flow_hops = []
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for supervisor in supervisors:
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hf_sys = hopsflow.SystemParams.from_hi_params(supervisor.params)
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hf_therm = hopsflow.ThermalParams.from_hi_params(supervisor.params)
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flow_hops.append(
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hopsflow.heat_flow_from_data(
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supervisor.get_data(True),
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hf_sys,
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thermal_params=hf_therm,
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every=1000,
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save=f"flow_finite",
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overwrite_cache=True
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)
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)
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#+end_src
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#+RESULTS:
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: Loading: 100% 22/22 [00:08<00:00, 2.54it/s]
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#+begin_src jupyter-python
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fig, ax = plt.subplots()
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for params, flow, keys in zip(multi_params, flow_hops, model_keys):
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pu.plot_with_σ(
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params.IntP.t,
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-1 * flow.for_bath(0),
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ax=ax,
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label=f"{len(params.SysP.g[0])}, {params.HiP.truncation_scheme.kmax}",
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)
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ax.legend()
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: <matplotlib.legend.Legend at 0x7f37bc65ceb0>
|
||||
[[file:./.ob-jupyter/819c4a63463ef7a5ad06547f2423aa8920c8a021.svg]]
|
||||
:END:
|
||||
|
||||
* Analytic
|
||||
#+begin_src jupyter-python :results none
|
||||
from hopsflow import gaussflow as gf
|
||||
#+end_src
|
||||
|
||||
#+begin_src jupyter-python :results none
|
||||
exact_flows = []
|
||||
for params, keys in zip(multi_params, model_keys):
|
||||
α = gf.BCF(
|
||||
params.IntP.t_max,
|
||||
hops.util.bcf.OhmicBCF_nonZeroTemp(
|
||||
s=1,
|
||||
eta=params.SysP.bcf_scale[0],
|
||||
w_c=keys["wc"],
|
||||
beta=1 / params.SysP.__non_key__["T"][0],
|
||||
),
|
||||
num_terms=6,
|
||||
resolution=0.01,
|
||||
)
|
||||
|
||||
α_0 = gf.BCF(
|
||||
params.IntP.t_max,
|
||||
# factors=params.SysP.g[0],
|
||||
# exponents=params.SysP.w[0],
|
||||
hops.util.bcf.OhmicBCF_zeroTemp(
|
||||
s=1,
|
||||
eta=params.SysP.bcf_scale[0],
|
||||
w_c=keys["wc"],
|
||||
normed=True,
|
||||
),
|
||||
# hops_bcf,
|
||||
num_terms=6,
|
||||
resolution=0.001,
|
||||
)
|
||||
|
||||
α_0_dot = gf.BCF(
|
||||
params.IntP.t_max,
|
||||
factors=-α_0.factors * α_0.exponents,
|
||||
exponents=α_0.exponents,
|
||||
# lambda t: 2 / (1j * np.pi) * (wc / (1 + 1j * wc * t)) ** 3,
|
||||
# num_terms=6,
|
||||
# resolution=0.001,
|
||||
)
|
||||
|
||||
sys = gf.SystemParams(Ω=1, η=1, α_0=α_0)
|
||||
flow = gf.Flow(sys, α, α_0_dot, n=0)
|
||||
flow_τ = flow(params.IntP.t)
|
||||
exact_flows.append(flow_τ)
|
||||
#+end_src
|
||||
|
||||
#+begin_src jupyter-python
|
||||
_tt = np.linspace(0, 4, 1000)
|
||||
fig, ax = pu.plot_complex(_tt, α(_tt)- α.approx(_tt), label="finite")
|
||||
pu.plot_complex(_tt, α_0(_tt)- α_0.approx(_tt), label="finite", ax=ax)
|
||||
# ut.plot_complex(result.τ, α(result.τ)- α.approx(result.τ), label="finite", ax=ax)
|
||||
# ut.plot_complex(result.τ, α_0_dot(result.τ)- α_0_dot.approx(result.τ), label="finite", ax=ax)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| hline | <AxesSubplot:> |
|
||||
[[file:./.ob-jupyter/add7bd9b1175ad78784e79b3e3f6f12950eb9e3e.svg]]
|
||||
:END:
|
||||
|
||||
#+begin_src jupyter-python
|
||||
fig, ax = plt.subplots()
|
||||
for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
|
||||
consistency = (flow).consistency(ex_flow)
|
||||
pu.plot_with_σ(
|
||||
params.IntP.t,
|
||||
-1 * flow,
|
||||
bath=0,
|
||||
ax=ax,
|
||||
label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ ${consistency}\%$",
|
||||
)
|
||||
ax.plot(params.IntP.t, ex_flow, linestyle="dotted", color="black")
|
||||
|
||||
plt.xlabel("$τ$")
|
||||
plt.ylabel("$-J$")
|
||||
|
||||
# ax.plot(multi_params[-1].IntP.t, flow_τ, label="Analytic", linestyle="dotted", color="black")
|
||||
ax.legend()
|
||||
# fs.export_fig("convergence_zero")
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: <matplotlib.legend.Legend at 0x7f37bc65c340>
|
||||
[[file:./.ob-jupyter/5b1a80f6d49139558c8e638123e7da40d2b40504.svg]]
|
||||
:END:
|
||||
|
||||
#+begin_src jupyter-python
|
||||
pu.plot_convergence(
|
||||
multi_params[-1].IntP.t,
|
||||
-1 * flow_hops[-1].for_bath(0)
|
||||
-flow_τ,
|
||||
reference=np.zeros_like(flow_τ)
|
||||
)
|
||||
plt.axhline(0)
|
||||
plt.legend()
|
||||
# plt.plot(multi_params[-1].IntP.t, -1 * flow_hops[-1].for_bath(0).σ)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: <matplotlib.legend.Legend at 0x7f37bc4c7a60>
|
||||
[[file:./.ob-jupyter/b143bfbf41586019c77aa024349ade7ff6cc3b81.svg]]
|
||||
:END:
|
||||
Loading…
Add table
Reference in a new issue