mirror of
https://github.com/vale981/master-thesis
synced 2025-03-05 10:01:43 -05:00
try more samples for finite T, and the same parameters as zero T
This commit is contained in:
parent
452d36fd52
commit
de12327d39
2 changed files with 324 additions and 115 deletions
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@ -89,7 +89,7 @@ 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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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, 2]]
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model_keys = [dict(bcf_scale=1, wc=ω_c) for ω_c in [1, 3]] + [dict(bcf_scale=s, wc=2) for s in [2, 3]]
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model_keys = model_keys
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multi_params = [ho_zero(**keys) for keys in model_keys]
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@ -103,7 +103,7 @@ 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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100_000,
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150_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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@ -133,10 +133,17 @@ for params, flow, keys in zip(multi_params, flow_hops, model_keys):
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-1 * flow.for_bath(0),
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ax=ax,
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linewidth=.8,
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label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$, $ω_c={keys['wc']}$",
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label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
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)
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ax.legend()
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for params, flow, ex_flow, keys, sup in zip(
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multi_params, flow_hops, exact_flows, model_keys, supervisors[:1]
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):
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system_energy = util.operator_expectation_from_data(
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sup.get_data(True), params.SysP.H_sys, save="system_energy"
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)
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from hopsflow import gaussflow as gf
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exact_flows = []
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@ -157,19 +164,16 @@ for params, keys in zip(multi_params, model_keys):
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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=12,
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factors=params.SysP.g[0]/params.SysP.bcf_scale[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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# num_terms=7,
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# resolution=0.01,
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num_terms=7,
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resolution=0.01,
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)
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α_0_dot = gf.BCF(
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@ -186,53 +190,119 @@ for params, keys in zip(multi_params, model_keys):
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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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_tt = np.linspace(0, 10, 1000)
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fig, ax = pu.plot_complex(_tt, (α(_tt)- α.approx(_tt)), label="finite", absolute=True)
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pu.plot_complex(_tt, α_0(_tt)- α_0.approx(_tt), label="finite", ax=ax, absolute=True)
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pu.plot_complex(
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_tt,
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hops.util.bcf.OhmicBCF_zeroTemp(
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s=1,
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eta=1,
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normed=True,
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w_c=keys["wc"],
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)(_tt)
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- α_0.approx(_tt),
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label="zero",
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ax=ax,
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absolute=True,
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linestyle="dashed"
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)
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plt.yscale("log")
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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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# 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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tt = np.logspace(-1, 2, 1000)
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plt.plot(tt, np.abs(α(tt)), label="$T=1$")
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plt.plot(tt, np.abs(hops.util.bcf.OhmicBCF_zeroTemp(
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s=1,
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eta=1 / keys["wc"] ** (1 + 1),
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w_c=keys["wc"],
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)(tt)), label="$T=0$")
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plt.yscale("log")
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plt.xscale("log")
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plt.suptitle(rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$")
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plt.legend()
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plt.ylabel(r"$|\alpha(\tau)|$")
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plt.xlabel(r"$\tau$")
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plt.tight_layout()
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fs.export_fig("bcf_decay", y_scaling=.4)
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fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1, sharey=True)
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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[-1].for_bath(0)).consistency(-ex_flow)
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consistency = (-1 * flow.for_bath(0)).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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linewidth=.8,
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label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ | (${consistency}\%)$",
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label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$, (${consistency:.1f}\%)$",
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)
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ax.plot(params.IntP.t, ex_flow, linestyle="dotted", color="black", linewidth=.8)
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ax.plot(params.IntP.t, ex_flow, color="black", linestyle="dashed", linewidth=.8)
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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("comparison_finite")
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for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
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fig, ax = plt.subplots()
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consistency = (flow).consistency(-ex_flow)
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pu.plot_convergence(
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pu.plot_with_σ(
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params.IntP.t,
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(flow.for_bath(0)[9::10]
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+ex_flow) * (1/abs(ex_flow).max()),
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reference=np.zeros_like(ex_flow),
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ax=ax
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)
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ax.axhline(0, color="black")
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ax.set_xlabel("$τ$")
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ax.set_ylabel(r"$(J-J_{\mathrm{ref}})/J_\mathrm{max}$")
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ax.legend()
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for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
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fig, axs = pu.plot_consistency_development(
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-1 * flow,
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ex_flow,
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bath=0,
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ax=ax2,
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linewidth=.8,
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)
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ax2.plot(params.IntP.t, ex_flow, color="black", linestyle="dashed", linewidth=.8)
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ax.set_xlabel(r"$\tau$")
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ax.set_ylabel("$-J$")
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ax2.set_xlabel(r"$\tau$")
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ax2.set_ylabel("$-J$")
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fig.tight_layout()
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ax.legend()
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ax2.set_xlim((-.2, 10))
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fs.export_fig("flow_comp_nonzero", y_scaling=.4)
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for params, flow, ex_flow, keys, i in zip(
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multi_params, flow_hops, exact_flows, model_keys, range(len(model_keys))
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):
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subfig = fig.add_subfigure(grid[int(i / 2), i % 2])
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f, ax = pu.plot_consistency_development(-1 * flow, ex_flow)
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f.suptitle(
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rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$"
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)
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fig.suptitle(rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$")
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fs.export_fig(f"consistency_development_{i}", x_scaling=.48, y_scaling=.6, tikz=False)
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import hopsflow.util as util
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import hops.util.utilities as hops_utils
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fig, (ax, ax2) = plt.subplots(nrows=1, ncols=2)
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for params, flow, ex_flow, keys, sup in zip(
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multi_params, flow_hops, exact_flows, model_keys, supervisors[:1]
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):
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entropy, σ_entropy = hops_utils.entropy(sup.get_data(True))
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system_energy = util.operator_expectation_from_data(
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sup.get_data(True), params.SysP.H_sys, save="system_energy"
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)
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pu.plot_with_σ(
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params.IntP.t,
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system_energy,
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ax=ax2,
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linewidth=0.5,
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label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
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)
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pu.plot_with_σ(
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params.IntP.t,
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util.EnsembleValue((entropy, σ_entropy)),
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ax=ax,
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linewidth=0.5,
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label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
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)
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ax.legend()
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ax.set_ylabel(r"$S(\rho)$")
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ax.set_xlabel(r"$\tau$")
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ax2.set_ylabel(r"$\langle H_\mathrm{S}\rangle$")
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ax2.set_xlabel(r"$\tau$")
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fs.export_fig("entropy_nonzero", fig, y_scaling=0.4)
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@ -94,7 +94,7 @@
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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, 2]]
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model_keys = [dict(bcf_scale=1, wc=ω_c) for ω_c in [1, 3]] + [dict(bcf_scale=s, wc=2) for s in [2, 3]]
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model_keys = model_keys
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multi_params = [ho_zero(**keys) for keys in model_keys]
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@ -129,7 +129,7 @@
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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': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-03_11-40-28_689081_82382/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-03_11-40-28_689081_82382/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-03_11-40-28_689081_82382', 'metrics_export_port': 55919, 'gcs_address': '141.30.17.225:60072', 'address': '141.30.17.225:60072', 'node_id': '5167888344737f0b671c14167855fd8cac5e414985e3455d3d80962e'})
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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': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-05_11-07-29_852147_42277/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-05_11-07-29_852147_42277/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-05_11-07-29_852147_42277', 'metrics_export_port': 64875, 'gcs_address': '141.30.17.225:60838', 'address': '141.30.17.225:60838', 'node_id': 'b15c3fa20dbefeb5f2e89cf630db49c5ed4f4a089c290f6414bd6a52'})
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#+begin_src jupyter-python
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@ -137,7 +137,7 @@
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for params in multi_params:
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supervisor = HOPSSupervisor(
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params,
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100_000,
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150_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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@ -178,16 +178,24 @@
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-1 * flow.for_bath(0),
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ax=ax,
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linewidth=.8,
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label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$, $ω_c={keys['wc']}$",
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label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
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)
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ax.legend()
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#+end_src
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#+RESULTS:
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:RESULTS:
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: <matplotlib.legend.Legend at 0x7ff2dc5dd040>
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[[file:./.ob-jupyter/2c176c77b2e065a86242b68f6d12af3d080852b9.svg]]
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: <matplotlib.legend.Legend at 0x7f9d90647a60>
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[[file:./.ob-jupyter/95ec71cf26259aa1cd2837925e745d6d742fe7f6.svg]]
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:END:
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#+begin_src jupyter-python
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for params, flow, ex_flow, keys, sup in zip(
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multi_params, flow_hops, exact_flows, model_keys, supervisors[:1]
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):
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system_energy = util.operator_expectation_from_data(
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sup.get_data(True), params.SysP.H_sys, save="system_energy"
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)
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#+end_src
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* Analytic
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#+begin_src jupyter-python :results none
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@ -213,19 +221,16 @@
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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=12,
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factors=params.SysP.g[0]/params.SysP.bcf_scale[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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# num_terms=7,
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# resolution=0.01,
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num_terms=7,
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resolution=0.01,
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)
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α_0_dot = gf.BCF(
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@ -244,83 +249,217 @@
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#+end_src
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#+begin_src jupyter-python
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_tt = np.linspace(0, 4, 1000)
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_tt = np.linspace(0, 10, 1000)
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fig, ax = pu.plot_complex(_tt, (α(_tt)- α.approx(_tt)), label="finite", absolute=True)
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pu.plot_complex(_tt, α_0(_tt)- α_0.approx(_tt), label="finite", ax=ax, absolute=True)
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pu.plot_complex(
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_tt,
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hops.util.bcf.OhmicBCF_zeroTemp(
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s=1,
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eta=1,
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normed=True,
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w_c=keys["wc"],
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)(_tt)
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- α_0.approx(_tt),
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label="zero",
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ax=ax,
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absolute=True,
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linestyle="dashed"
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)
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plt.yscale("log")
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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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# 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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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/2ca86b3de7d37e9b4fb413cf5c0d684e979fae18.svg]]
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[[file:./.ob-jupyter/27bfece24c66de5059f65e5c8a56a32987aada6b.svg]]
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#+begin_src jupyter-python
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fig, ax = plt.subplots()
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tt = np.logspace(-1, 2, 1000)
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plt.plot(tt, np.abs(α(tt)), label="$T=1$")
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plt.plot(tt, np.abs(hops.util.bcf.OhmicBCF_zeroTemp(
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s=1,
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eta=1 / keys["wc"] ** (1 + 1),
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w_c=keys["wc"],
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)(tt)), label="$T=0$")
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plt.yscale("log")
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plt.xscale("log")
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plt.suptitle(rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$")
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plt.legend()
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plt.ylabel(r"$|\alpha(\tau)|$")
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plt.xlabel(r"$\tau$")
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plt.tight_layout()
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fs.export_fig("bcf_decay", y_scaling=.4)
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/712a5f7581d046696cdffc6b0b8bc00be5959a20.svg]]
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#+begin_src jupyter-python
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fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1, sharey=True)
|
||||
for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
|
||||
consistency = (flow[-1].for_bath(0)).consistency(-ex_flow)
|
||||
consistency = (-1 * flow.for_bath(0)).consistency(ex_flow)
|
||||
pu.plot_with_σ(
|
||||
params.IntP.t,
|
||||
-1 * flow,
|
||||
bath=0,
|
||||
ax=ax,
|
||||
linewidth=.8,
|
||||
label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ | (${consistency}\%)$",
|
||||
label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$, (${consistency:.1f}\%)$",
|
||||
)
|
||||
ax.plot(params.IntP.t, ex_flow, linestyle="dotted", color="black", linewidth=.8)
|
||||
ax.plot(params.IntP.t, ex_flow, color="black", linestyle="dashed", linewidth=.8)
|
||||
|
||||
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("comparison_finite")
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
[[file:./.ob-jupyter/fff75a11b82859bb9354720b1fff2914e78c5809.svg]]
|
||||
|
||||
#+begin_src jupyter-python
|
||||
for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
|
||||
fig, ax = plt.subplots()
|
||||
consistency = (flow).consistency(-ex_flow)
|
||||
pu.plot_convergence(
|
||||
pu.plot_with_σ(
|
||||
params.IntP.t,
|
||||
(flow.for_bath(0)[9::10]
|
||||
+ex_flow) * (1/abs(ex_flow).max()),
|
||||
reference=np.zeros_like(ex_flow),
|
||||
ax=ax
|
||||
-1 * flow,
|
||||
bath=0,
|
||||
ax=ax2,
|
||||
linewidth=.8,
|
||||
)
|
||||
ax2.plot(params.IntP.t, ex_flow, color="black", linestyle="dashed", linewidth=.8)
|
||||
|
||||
ax.axhline(0, color="black")
|
||||
ax.set_xlabel("$τ$")
|
||||
ax.set_ylabel(r"$(J-J_{\mathrm{ref}})/J_\mathrm{max}$")
|
||||
ax.legend()
|
||||
|
||||
ax.set_xlabel(r"$\tau$")
|
||||
ax.set_ylabel("$-J$")
|
||||
ax2.set_xlabel(r"$\tau$")
|
||||
ax2.set_ylabel("$-J$")
|
||||
fig.tight_layout()
|
||||
ax.legend()
|
||||
ax2.set_xlim((-.2, 10))
|
||||
fs.export_fig("flow_comp_nonzero", y_scaling=.4)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
[[file:./.ob-jupyter/8f7c7578463e60a2cc5cb55609c5e2350b31da18.svg]]
|
||||
[[file:./.ob-jupyter/4ac7a887a6d9b366edcb0f341c4badece1496baf.svg]]
|
||||
[[file:./.ob-jupyter/607fb3109397e4e84d6b771261fb7663416e4592.svg]]
|
||||
[[file:./.ob-jupyter/f79ccad4a11fade3d8c4ba214a0d0a89de26a2b8.svg]]
|
||||
:END:
|
||||
[[file:./.ob-jupyter/7448cfeaed2a113113be03e718a5cfb83f6fe64e.svg]]
|
||||
|
||||
|
||||
|
||||
#+begin_src jupyter-python
|
||||
for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
|
||||
fig, axs = pu.plot_consistency_development(
|
||||
-1 * flow,
|
||||
ex_flow,
|
||||
for params, flow, ex_flow, keys, i in zip(
|
||||
multi_params, flow_hops, exact_flows, model_keys, range(len(model_keys))
|
||||
):
|
||||
subfig = fig.add_subfigure(grid[int(i / 2), i % 2])
|
||||
f, ax = pu.plot_consistency_development(-1 * flow, ex_flow)
|
||||
|
||||
f.suptitle(
|
||||
rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$"
|
||||
)
|
||||
|
||||
fig.suptitle(rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$")
|
||||
fs.export_fig(f"consistency_development_{i}", x_scaling=.48, y_scaling=.6, tikz=False)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
[[file:./.ob-jupyter/4ed1f49960958709001508ae704fd85be1ee6510.svg]]
|
||||
[[file:./.ob-jupyter/ee332a2555dd751744bd435694ebf2d34b1e52ec.svg]]
|
||||
[[file:./.ob-jupyter/5860d96da073f680bb8196fae95791d766b82ba6.svg]]
|
||||
[[file:./.ob-jupyter/5b7b03d93715fd4534608e346cfca442ccb5b922.svg]]
|
||||
[[file:./.ob-jupyter/caff0b1a90552564d17910f32315989eb71934cd.svg]]
|
||||
[[file:./.ob-jupyter/8b8a13eadaca8a02418bcbbbcd9466465bd67b52.svg]]
|
||||
[[file:./.ob-jupyter/9f6fa17894b524c01e6506a459592d69b6026e24.svg]]
|
||||
[[file:./.ob-jupyter/b16c07d9f9fad57cd15543c684174c4c6142e3d1.svg]]
|
||||
:END:
|
||||
|
||||
|
||||
* Steady State Purity?
|
||||
#+begin_src jupyter-python
|
||||
import hopsflow.util as util
|
||||
import hops.util.utilities as hops_utils
|
||||
|
||||
fig, (ax, ax2) = plt.subplots(nrows=1, ncols=2)
|
||||
|
||||
for params, flow, ex_flow, keys, sup in zip(
|
||||
multi_params, flow_hops, exact_flows, model_keys, supervisors[:1]
|
||||
):
|
||||
entropy, σ_entropy = hops_utils.entropy(sup.get_data(True))
|
||||
|
||||
system_energy = util.operator_expectation_from_data(
|
||||
sup.get_data(True), params.SysP.H_sys, save="system_energy"
|
||||
)
|
||||
|
||||
pu.plot_with_σ(
|
||||
params.IntP.t,
|
||||
system_energy,
|
||||
ax=ax2,
|
||||
linewidth=0.5,
|
||||
label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
|
||||
)
|
||||
|
||||
pu.plot_with_σ(
|
||||
params.IntP.t,
|
||||
util.EnsembleValue((entropy, σ_entropy)),
|
||||
ax=ax,
|
||||
linewidth=0.5,
|
||||
label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
|
||||
)
|
||||
|
||||
ax.legend()
|
||||
ax.set_ylabel(r"$S(\rho)$")
|
||||
ax.set_xlabel(r"$\tau$")
|
||||
ax2.set_ylabel(r"$\langle H_\mathrm{S}\rangle$")
|
||||
ax2.set_xlabel(r"$\tau$")
|
||||
fs.export_fig("entropy_nonzero", fig, y_scaling=0.4)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: /nix/store/fgn7scpk3q7zcbva77zdph197h8ma5g9-python3-3.9.13-env/lib/python3.9/site-packages/scipy/linalg/_matfuncs_inv_ssq.py:827: LogmExactlySingularWarning: The logm input matrix is exactly singular.
|
||||
: warnings.warn(exact_singularity_msg, LogmExactlySingularWarning)
|
||||
: /nix/store/fgn7scpk3q7zcbva77zdph197h8ma5g9-python3-3.9.13-env/lib/python3.9/site-packages/scipy/linalg/_matfuncs_inv_ssq.py:836: LogmNearlySingularWarning: The logm input matrix may be nearly singular.
|
||||
: warnings.warn(near_singularity_msg, LogmNearlySingularWarning)
|
||||
: /home/hiro/src/hops/hops/util/utilities.py:43: RuntimeWarning: invalid value encountered in sqrt
|
||||
: np.sqrt(np.trace(_square_mat(Δρ @ (logm2(ρ) + 1))).real)
|
||||
: Loading: 0% 40/16667 [00:15<1:53:52, 2.43it/s]
|
||||
# [goto error]
|
||||
#+begin_example
|
||||
[0;31m---------------------------------------------------------------------------[0m
|
||||
[0;31mKeyboardInterrupt[0m Traceback (most recent call last)
|
||||
Input [0;32mIn [65][0m, in [0;36m<cell line: 6>[0;34m()[0m
|
||||
[1;32m 6[0m [38;5;28;01mfor[39;00m params, flow, ex_flow, keys, sup [38;5;129;01min[39;00m [38;5;28mzip[39m(
|
||||
[1;32m 7[0m multi_params, flow_hops, exact_flows, model_keys, supervisors[:[38;5;241m1[39m]
|
||||
[1;32m 8[0m ):
|
||||
[1;32m 9[0m entropy, σ_entropy [38;5;241m=[39m hops_utils[38;5;241m.[39mentropy(sup[38;5;241m.[39mget_data([38;5;28;01mTrue[39;00m))
|
||||
[0;32m---> 11[0m system_energy [38;5;241m=[39m [43mutil[49m[38;5;241;43m.[39;49m[43moperator_expectation_from_data[49m[43m([49m
|
||||
[1;32m 12[0m [43m [49m[43msup[49m[38;5;241;43m.[39;49m[43mget_data[49m[43m([49m[38;5;28;43;01mTrue[39;49;00m[43m)[49m[43m,[49m[43m [49m[43mparams[49m[38;5;241;43m.[39;49m[43mSysP[49m[38;5;241;43m.[39;49m[43mH_sys[49m[43m,[49m[43m [49m[43msave[49m[38;5;241;43m=[39;49m[38;5;124;43m"[39;49m[38;5;124;43msystem_energy[39;49m[38;5;124;43m"[39;49m
|
||||
[1;32m 13[0m [43m [49m[43m)[49m
|
||||
[1;32m 15[0m pu[38;5;241m.[39mplot_with_σ(
|
||||
[1;32m 16[0m params[38;5;241m.[39mIntP[38;5;241m.[39mt,
|
||||
[1;32m 17[0m system_energy,
|
||||
[0;32m (...)[0m
|
||||
[1;32m 20[0m label[38;5;241m=[39m[38;5;124mrf[39m[38;5;124m"[39m[38;5;124m$[39m[38;5;124m\[39m[38;5;124malpha(0)=[39m[38;5;132;01m{[39;00mparams[38;5;241m.[39mSysP[38;5;241m.[39mg[[38;5;241m0[39m][38;5;241m.[39msum()[38;5;241m.[39mreal[38;5;132;01m:[39;00m[38;5;124m.2f[39m[38;5;132;01m}[39;00m[38;5;124m$, $[39m[38;5;124m\[39m[38;5;124momega_c=[39m[38;5;132;01m{[39;00mkeys[[38;5;124m'[39m[38;5;124mwc[39m[38;5;124m'[39m][38;5;132;01m}[39;00m[38;5;124m$[39m[38;5;124m"[39m,
|
||||
[1;32m 21[0m )
|
||||
[1;32m 23[0m pu[38;5;241m.[39mplot_with_σ(
|
||||
[1;32m 24[0m params[38;5;241m.[39mIntP[38;5;241m.[39mt,
|
||||
[1;32m 25[0m util[38;5;241m.[39mEnsembleValue((entropy, σ_entropy)),
|
||||
[0;32m (...)[0m
|
||||
[1;32m 28[0m label[38;5;241m=[39m[38;5;124mrf[39m[38;5;124m"[39m[38;5;124m$[39m[38;5;124m\[39m[38;5;124malpha(0)=[39m[38;5;132;01m{[39;00mparams[38;5;241m.[39mSysP[38;5;241m.[39mg[[38;5;241m0[39m][38;5;241m.[39msum()[38;5;241m.[39mreal[38;5;132;01m:[39;00m[38;5;124m.2f[39m[38;5;132;01m}[39;00m[38;5;124m$, $[39m[38;5;124m\[39m[38;5;124momega_c=[39m[38;5;132;01m{[39;00mkeys[[38;5;124m'[39m[38;5;124mwc[39m[38;5;124m'[39m][38;5;132;01m}[39;00m[38;5;124m$[39m[38;5;124m"[39m,
|
||||
[1;32m 29[0m )
|
||||
|
||||
File [0;32m~/src/hopsflow/hopsflow/util.py:1031[0m, in [0;36moperator_expectation_from_data[0;34m(data, op, **kwargs)[0m
|
||||
[1;32m 1028[0m [38;5;28;01mif[39;00m [38;5;124m"[39m[38;5;124msave[39m[38;5;124m"[39m [38;5;129;01min[39;00m kwargs:
|
||||
[1;32m 1029[0m kwargs[[38;5;124m"[39m[38;5;124msave[39m[38;5;124m"[39m] [38;5;241m+[39m[38;5;241m=[39m [38;5;124m"[39m[38;5;124m_[39m[38;5;124m"[39m [38;5;241m+[39m data[38;5;241m.[39mget_hi_key_hash()
|
||||
[0;32m-> 1031[0m [38;5;28;01mreturn[39;00m [43moperator_expectation_ensemble[49m[43m([49m
|
||||
[1;32m 1032[0m [43m [49m[43mψs[49m[38;5;241;43m=[39;49m[43md[49m[38;5;241;43m.[39;49m[43mvalid_sample_iterator[49m[43m([49m[43md[49m[38;5;241;43m.[39;49m[43mstoc_traj[49m[43m)[49m[43m,[49m
|
||||
[1;32m 1033[0m [43m [49m[43mop[49m[38;5;241;43m=[39;49m[43mop[49m[43m,[49m
|
||||
[1;32m 1034[0m [43m [49m[43mt[49m[38;5;241;43m=[39;49m[43md[49m[38;5;241;43m.[39;49m[43mget_time[49m[43m([49m[43m)[49m[43m,[49m
|
||||
[1;32m 1035[0m [43m [49m[43mnormalize[49m[38;5;241;43m=[39;49m[43md[49m[38;5;241;43m.[39;49m[43mget_hi_key[49m[43m([49m[43m)[49m[38;5;241;43m.[39;49m[43mHiP[49m[38;5;241;43m.[39;49m[43mnonlinear[49m[43m,[49m
|
||||
[1;32m 1036[0m [43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43m([49m[38;5;28;43mdict[39;49m[43m([49m[43mN[49m[38;5;241;43m=[39;49m[43md[49m[38;5;241;43m.[39;49m[43msamples[49m[43m)[49m[43m [49m[38;5;241;43m|[39;49m[43m [49m[43mkwargs[49m[43m)[49m[43m,[49m
|
||||
[1;32m 1037[0m [43m[49m[43m)[49m
|
||||
|
||||
File [0;32m~/src/hopsflow/hopsflow/util.py:542[0m, in [0;36moperator_expectation_ensemble[0;34m(ψs, op, t, normalize, real, **kwargs)[0m
|
||||
[1;32m 538[0m sandwhiches [38;5;241m=[39m sandwhiches[38;5;241m.[39mreal
|
||||
[1;32m 540[0m [38;5;28;01mreturn[39;00m sandwhiches
|
||||
[0;32m--> 542[0m [38;5;28;01mreturn[39;00m [43mensemble_mean[49m[43m([49m[43mψs[49m[43m,[49m[43m [49m[43mop_exp_task[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m)[49m
|
||||
|
||||
File [0;32m~/src/hopsflow/hopsflow/util.py:827[0m, in [0;36mensemble_mean[0;34m(arg_iter, function, N, every, save, overwrite_cache, chunk_size, in_flight, gc_sleep)[0m
|
||||
[1;32m 817[0m results[38;5;241m.[39mappend(
|
||||
[1;32m 818[0m (
|
||||
[1;32m 819[0m aggregate[38;5;241m.[39mn,
|
||||
[0;32m (...)[0m
|
||||
[1;32m 822[0m )
|
||||
[1;32m 823[0m )
|
||||
[1;32m 825[0m highest_index [38;5;241m+[39m[38;5;241m=[39m [38;5;241m1[39m
|
||||
[0;32m--> 827[0m [43mgc[49m[38;5;241;43m.[39;49m[43mcollect[49m[43m([49m[43m)[49m
|
||||
[1;32m 828[0m time[38;5;241m.[39msleep(gc_sleep)
|
||||
[1;32m 830[0m [38;5;28;01mif[39;00m next_val:
|
||||
|
||||
[0;31mKeyboardInterrupt[0m:
|
||||
#+end_example
|
||||
[[file:./.ob-jupyter/c87c221267c7b2ed19d0590660a0140251c0437c.svg]]
|
||||
:END:
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue