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https://github.com/vale981/HOPSFlow-Paper
synced 2025-03-04 17:21:42 -05:00
fix bath memory, i didn't realize it didn't tangle!
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3 changed files with 53 additions and 83 deletions
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@ -1,10 +1,10 @@
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#+PROPERTY: header-args :session bath_memory :kernel python :pandoc no :async yes :tangle tangle/bath_memory.py
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#+PROPERTY: header-args :session bath_memory :kernel python :pandoc no :async yes
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Here we scan bath memory and maybe temperature gradient later.
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* Boilerplate
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#+name: boilerplate
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#+begin_src jupyter-python :results none
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#+begin_src jupyter-python :results none :tangle tangle/bath_memory.py
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import figsaver as fs
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import plot_utils as pu
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from hiro_models.one_qubit_model import StocProcTolerances
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@ -34,9 +34,10 @@ We take the same baseline as in [[id:c06111fd-d719-433d-a316-c163f6e1d384][cycle
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But we vary the cycle speed while keeping a fixed proportion
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coupling-change/cycle time.
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#+begin_src jupyter-python
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#+begin_src jupyter-python :tangle tangle/bath_memory.py
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T = 50
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def make_model_orig(shift_c, shift_h, switch_t=6.0, switch_t_sys=None, only_cold=False):
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def make_model_orig(shift_c, shift_h, switch_t=3.0, switch_t_sys=None, only_cold=False):
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switch_time = switch_t / T
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switch_time_sys = (switch_t_sys if switch_t_sys else switch_t) / T
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@ -65,7 +66,7 @@ coupling-change/cycle time.
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)
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def make_model(ω_c, T_c):
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model = make_model_orig(0, 0, switch_t = 6)
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model = make_model_orig(0, 0, switch_t = 6.)
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model.T[0] = T_c
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@ -76,7 +77,7 @@ coupling-change/cycle time.
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#+RESULTS:
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#+begin_src jupyter-python
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#+begin_src jupyter-python :tangle tangle/bath_memory.py
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ωs = [round(ω, 3) for ω in np.linspace(.5, 1.5, 5)]
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Ts = [round(T, 3) for T in np.linspace(.4, .6, 5)]
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ωs, Ts
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@ -86,7 +87,7 @@ coupling-change/cycle time.
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| 0.5 | 0.75 | 1.0 | 1.25 | 1.5 |
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| 0.4 | 0.45 | 0.5 | 0.55 | 0.6 |
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#+begin_src jupyter-python
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#+begin_src jupyter-python :tangle tangle/bath_memory.py
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import itertools
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models = [make_model(ω, T) for ω, T, in itertools.product(ωs, Ts)]
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#+end_src
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@ -95,10 +96,43 @@ coupling-change/cycle time.
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* Integrate
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#+begin_src jupyter-python
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#+begin_src jupyter-python :tangle tangle/bath_memory.py
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ot.integrate_online_multi(models, 30_000, increment=10_000, analyze_kwargs=dict(every=10_000))
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#+end_src
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#+begin_src jupyter-python
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aux.import_results(other_data_path="taurus/.data", other_results_path="taurus/results", models_to_import=models)
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#+end_src
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#+RESULTS:
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#+begin_example
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#+end_example
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* Analysis
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#+begin_src jupyter-python
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models[1].T
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@ -20,16 +20,19 @@ import logging
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logging_setup(logging.INFO)
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plt.rcParams['figure.figsize'] = (12,4)
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def make_shift_model(shift_c, shift_h, switch_t=3):
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switch_time = switch_t / 50
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T = 50
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(p_H, p_L) = ot.timings(switch_time, switch_time)
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def make_model_orig(shift_c, shift_h, switch_t=3.0, switch_t_sys=None, only_cold=False):
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switch_time = switch_t / T
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switch_time_sys = (switch_t_sys if switch_t_sys else switch_t) / T
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(p_H, p_L) = ot.timings(switch_time_sys, switch_time)
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return OttoEngine(
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δ=[.7, .7],
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δ=[0.7, 0.7],
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ω_c=[1, 1],
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ψ_0=qt.basis([2], [1]),
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description=f"Classic Cycle",
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k_max=5,
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k_max=4,
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bcf_terms=[5] * 2,
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truncation_scheme="simplex",
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driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
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@ -44,30 +47,11 @@ def make_shift_model(shift_c, shift_h, switch_t=3):
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timings_L=p_L,
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streaming_mode=True,
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shift_to_resonance=(False, False),
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L_shift=(shift_c, shift_h),
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L_shift=(shift_c, 0 if only_cold else shift_h),
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)
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def overlap(shift_model, N, step, switch_t=6):
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switch_time = switch_t / 50
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next_model = shift_model.copy()
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(p_H, p_L) = ot.timings(switch_time, switch_time)
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#next_model.timings_H=p_H
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next_model.timings_L=p_L
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(a, b, c, d) = next_model.timings_L[0]
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(e, f, g, h) = next_model.timings_L[1]
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next_step = step * N
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(s1, s2) = next_model.L_shift
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next_model.L_shift = (s1 + next_step, s2 - next_step)
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next_model.timings_L = (
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(a - 2 * next_step, b - 2 * next_step, c, d),
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(e, f, g + 2 * next_step, h + 2 * next_step),
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)
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return next_model
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def make_model(ω_c, T_c):
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model = make_shift_model(0,0, switch_t = 6)
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model = make_model_orig(0, 0, switch_t = 6.)
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model.T[0] = T_c
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@ -82,52 +66,3 @@ import itertools
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models = [make_model(ω, T) for ω, T, in itertools.product(ωs, Ts)]
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ot.integrate_online_multi(models, 30_000, increment=10_000, analyze_kwargs=dict(every=10_000))
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models[1].T
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fig, ax = plt.subplots()
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for model in models[:22]:
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pu.plot_with_σ(models[0].t, model.interaction_power().sum_baths().integrate(model.t), ax=ax)
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print(model.power(steady_idx=2).value, model.T[0], model.ω_c[0])
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fig, ax = plt.subplots()
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for model in models[:22]:
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pu.plot_with_σ(models[0].t, model.system_energy(), ax=ax)
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pu.plot_with_σ(models[0].t, models[0].interaction_power().sum_baths().integrate(models[0].t))
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ot.plot_power_eff_convergence(models[:10], 2)
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f = plt.figure()
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a_power = f.add_subplot(121, projection="3d")
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a_efficiency = f.add_subplot(122, projection="3d")
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for ax in [a_power, a_efficiency]:
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ax.set_box_aspect(aspect=None, zoom=0.7)
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ax.set_xlabel(r"$T_c$")
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ax.set_ylabel(r"$\omega_c$")
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ax.xaxis.labelpad = 10
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ax.view_init(elev=30.0, azim=-29, roll=0)
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ot.plot_3d_heatmap(
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models[:20],
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lambda model: np.clip(-model.power(steady_idx=2).value, 0, np.inf),
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lambda model: model.T[0],
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lambda model: model.ω_c[0],
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ax=a_power,
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)
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a_power.set_zlabel(r"$P$")
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ot.plot_3d_heatmap(
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models[:20],
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lambda model: np.clip(np.nan_to_num(model.efficiency(steady_idx=2).value * 100), 0, np.inf),
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lambda model: model.T[0],
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lambda model: model.ω_c[0],
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ax=a_efficiency,
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)
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a_efficiency.set_zlabel(r"$\eta$")
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fs.export_fig("bath_memory_power_efficiency", x_scaling=2, y_scaling=1)
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for model in models:
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ot.plot_bloch_components(model)
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@ -84,6 +84,7 @@ coupling-change/cycle time.
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ot.integrate_online_multi(models, 50_000, increment=10_000, analyze_kwargs=dict(every=10_000))
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#+end_src
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* Analysis
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#+begin_src jupyter-python
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ot.plot_energy(models[0])
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