mirror of
https://github.com/vale981/HOPSFlow-Paper
synced 2025-03-05 09:41:40 -05:00
update cycle shift
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parent
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2 changed files with 306 additions and 80 deletions
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@ -38,8 +38,9 @@ strength to demonstrate strong coupling effects and to limit the cycle time.
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#+begin_src jupyter-python
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T = 50
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switch_time = 3. / T
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def make_model(shift_c, shift_h):
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def make_model(shift_c, shift_h, switch_t=3.):
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switch_time = switch_t / T
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(p_H, p_L) = ot.timings(switch_time, switch_time)
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return OttoEngine(
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δ=[.7, .7],
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@ -72,14 +73,14 @@ We start with one dimension and may add another later / dimension.
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We shift so that we just overlap with coupling/decoupling and one above.
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#+begin_src jupyter-python
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N = 3
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N_over = 1
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step = switch_time / (N-N_over)
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N_over = 2
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step = 3. / (T*(N-N_over))
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shifts = [round(shift * step, 3) for shift in range(-N, N+1)]
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shifts
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#+end_src
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#+RESULTS:
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| -0.09 | -0.06 | -0.03 | 0.0 | 0.03 | 0.06 | 0.09 |
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| -0.18 | -0.12 | -0.06 | 0.0 | 0.06 | 0.12 | 0.18 |
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#+begin_src jupyter-python
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import itertools
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@ -95,94 +96,254 @@ We shift so that we just overlap with coupling/decoupling and one above.
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 1200x400 | with | 1 | Axes> | <AxesSubplot: | xlabel= | $\tau$ | ylabel= | Operator Norm | > |
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[[file:./.ob-jupyter/19f62f210b59f479bd493c49dedc0e2ba1ec8c00.svg]]
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[[file:./.ob-jupyter/c73172c3276a9720f26dd513bff58352bcf807ed.svg]]
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:END:
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* Integrate
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** Integrate
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#+begin_src jupyter-python :tangle no
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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.integrate_online_multi(models, 100_000, increment=10_000, analyze_kwargs=dict(every=10_000))
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for model in models:
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print(model.power(steady_idx=1).value / models[3].power(steady_idx=1).value, model.efficiency(steady_idx=1).value * 100)
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#+end_src
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#+RESULTS:
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: -2.7704455654085876 -252.19916727767804
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: -2.034015641972767 -166.75487518853186
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: 0.571544457513159 19.650010500179352
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: 1.0 30.20953730879978
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: 1.1652434363371367 32.393489928809124
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: 1.6245889921167371 33.021523773544054
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: 1.6841939173993579 31.91460252999856
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#+begin_src jupyter-python
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ot.plot_power_eff_convergence(models)
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/2ff8db3bb8190dab3fb0d849e3e08e61069da5a3.svg]]
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We see that we get a pretty good picture after about 30k-40k samples.
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#+begin_src jupyter-python
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ot.plot_powers_and_efficiencies(shifts, models)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f557e50b340> |
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[[file:./.ob-jupyter/9a3bec09d003106d1215f0be556ece64f7802787.svg]]
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:END:
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* Explore Coupling Length Dimension for The best performing state
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The best shift:
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#+begin_src jupyter-python
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best_shift = shifts[-2]
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best_shift_model = make_model(best_shift, best_shift)
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#+end_src
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#+RESULTS:
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#+begin_src jupyter-python
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def overlap(shift_model, N, step, switch_t=3.):
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switch_time = switch_t / T
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(p_H, p_L) = ot.timings(switch_time, switch_time)
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next_model = shift_model.copy()
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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 overlap_cold(shift_model, N, step):
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next_model = shift_model.copy()
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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 - next_step, d - next_step),
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(e + next_step, f + next_step, g + 2 * next_step, h + 2 * next_step),
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)
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return next_model
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Ns = list(range(1, 4))
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overlap_models = [overlap(best_shift_model, N, step) for N in Ns]
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overlap_models = [overlap_cold(best_shift_model, N, step) for N in Ns]
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new_step_size = 6
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mini_step = (new_step_size / (N-N_over) / T)
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overlap_models = [overlap(best_shift_model, N, mini_step, new_step_size) for N in Ns]
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#+end_src
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#+RESULTS:
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#+begin_src jupyter-python :tangle no
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ot.plot_cycles([overlap_models[0]], legend=True)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 1200x400 | with | 1 | Axes> | <AxesSubplot: | xlabel= | $\tau$ | ylabel= | Operator Norm | > |
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[[file:./.ob-jupyter/da2f676618a9cc61b975c93dd31f9eedb118d916.svg]]
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:END:
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** Integrate
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#+begin_src jupyter-python
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ot.integrate_online_multi(overlap_models, 50_000, increment=10_000, analyze_kwargs=dict(every=10_000))
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#+end_src
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#+RESULTS:
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#+begin_example
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[INFO hops.core.integration 65100] Choosing the nonlinear integrator.
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[INFO root 65100] Starting analysis process.
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[INFO root 65100] Started analysis process with pid 66265.
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[INFO hops.core.hierarchy_data 65100] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_f9bc66cc2b4cb7ba3371882ad9ee50d48460546af0eca71f7c01b081415f5d14.fifo
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[INFO hops.core.integration 65100] Using 16 integrators.
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[INFO hops.core.integration 65100] Some 10000 trajectories have to be integrated.
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[INFO hops.core.integration 65100] Using 1001 hierarchy states.
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1% 61/10000 [01:36<4:21:15, 1.58s/it][INFO hops.core.signal_delay 65100] caught sig 'SIGINT'
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1% 64/10000 [01:48<4:39:54, 1.69s/it]
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2023-02-25 12:46:53,879 WARNING worker.py:1404 -- A worker died or was killed while executing a task by an unexpected system error. To troubleshoot the problem, check the logs for the dead worker. RayTask ID: 99d25236ee4cd74b12aa2eea44f4fd9e79a9439801000000 Worker ID: 6954a0683bf80bbcf18b6f6300bde11b52e9018e100cf3b6cbc519a9 Node ID: afe7814ecf0191651ed8a09d4c5df11440975f8facd16c7e843c2bd7 Worker IP address: 192.168.100.170 Worker port: 38229 Worker PID: 66005
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2023-02-25 12:46:54,342 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,343 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,344 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,344 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,345 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,346 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,347 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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[INFO hops.core.signal_delay 65100] caught 1 signal(s)
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2023-02-25 12:46:54,347 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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[INFO hops.core.signal_delay 65100] emit signal 'SIGINT'
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2023-02-25 12:46:54,348 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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[INFO hops.core.signal_delay 65100] caught sig 'SIGINT'
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2023-02-25 12:46:54,349 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,350 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,350 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,351 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,352 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,353 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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2023-02-25 12:46:54,354 ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
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[INFO hops.core.signal_delay 65100] caught 1 signal(s)
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[INFO hops.core.signal_delay 65100] emit signal 'SIGINT'
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[INFO hops.core.integration 3664] Choosing the nonlinear integrator.
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[INFO root 3664] Starting analysis process.
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[INFO root 3664] Started analysis process with pid 7756.
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[INFO hops.core.hierarchy_data 3664] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_cfad63977aad412a4d035ea4122d748504bc638b4df70749bf6a9f0a0ecbcf4b.fifo
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[INFO hops.core.integration 3664] Using 16 integrators.
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[INFO hops.core.integration 3664] Some 1 trajectories have to be integrated.
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[INFO hops.core.integration 3664] Using 1001 hierarchy states.
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100% 1/1 [00:13<00:00, 13.21s/it]
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[INFO hops.core.integration 3664] Choosing the nonlinear integrator.
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[INFO root 3664] Starting analysis process.
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[INFO root 3664] Started analysis process with pid 7978.
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[INFO hops.core.hierarchy_data 3664] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_fcf52e28de29cab5e6962849767ead5e754ba33e96dabf2d95d5fbc0b0c2488d.fifo
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[INFO hops.core.integration 3664] Using 16 integrators.
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[INFO hops.core.integration 3664] Some 1 trajectories have to be integrated.
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[INFO hops.core.integration 3664] Using 1001 hierarchy states.
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100% 1/1 [00:14<00:00, 14.27s/it]
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[INFO hops.core.integration 3664] Choosing the nonlinear integrator.
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[INFO root 3664] Starting analysis process.
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[INFO root 3664] Started analysis process with pid 7988.
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[INFO hops.core.hierarchy_data 3664] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_f59d5e1c4b003307fdb6496ba3a22a283b5e68974725996eb14aac2901ac04da.fifo
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[INFO hops.core.integration 3664] Using 16 integrators.
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[INFO hops.core.integration 3664] Some 1 trajectories have to be integrated.
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[INFO hops.core.integration 3664] Using 1001 hierarchy states.
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100% 1/1 [00:15<00:00, 15.74s/it]
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#+end_example
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** Analysis
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#+begin_src jupyter-julia
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all_overlap_models = [best_shift_model, *overlap_models]
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#+end_src
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#+RESULTS:
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#+begin_src jupyter-python
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ot.plot_power_eff_convergence(all_overlap_models, 1)
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#+end_src
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#+RESULTS:
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:RESULTS:
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# [goto error]
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#+begin_example
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[0;31m---------------------------------------------------------------------------[0m
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[0;31mKeyboardInterrupt[0m Traceback (most recent call last)
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Cell [0;32mIn[11], line 1[0m
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[0;32m----> 1[0m [43mot[49m[38;5;241;43m.[39;49m[43mintegrate_online_multi[49m[43m([49m[43mmodels[49m[43m,[49m[43m [49m[38;5;241;43m100_000[39;49m[43m,[49m[43m [49m[43mincrement[49m[38;5;241;43m=[39;49m[38;5;241;43m10_000[39;49m[43m,[49m[43m [49m[43manalyze_kwargs[49m[38;5;241;43m=[39;49m[38;5;28;43mdict[39;49m[43m([49m[43mevery[49m[38;5;241;43m=[39;49m[38;5;241;43m10_000[39;49m[43m)[49m[43m)[49m
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[0;31mRuntimeError[0m Traceback (most recent call last)
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Cell [0;32mIn[14], line 1[0m
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[0;32m----> 1[0m [43mot[49m[38;5;241;43m.[39;49m[43mplot_power_eff_convergence[49m[43m([49m[43mall_overlap_models[49m[43m,[49m[43m [49m[38;5;241;43m1[39;49m[43m)[49m
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File [0;32m~/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/otto_utilities.py:171[0m, in [0;36mintegrate_online_multi[0;34m(models, n, increment, *args, **kwargs)[0m
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[1;32m 169[0m [38;5;28;01mwhile[39;00m target [38;5;241m<[39m (n [38;5;241m+[39m target):
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[1;32m 170[0m [38;5;28;01mfor[39;00m model [38;5;129;01min[39;00m models:
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[0;32m--> 171[0m [43mintegrate_online[49m[43m([49m[43mmodel[49m[43m,[49m[43m [49m[38;5;28;43mmin[39;49m[43m([49m[43m[[49m[43mn[49m[43m,[49m[43m [49m[43mtarget[49m[43m][49m[43m)[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[43margs[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m)[49m
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[1;32m 173[0m target [38;5;241m+[39m[38;5;241m=[39m increment
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File [0;32m~/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/otto_utilities.py:15[0m, in [0;36mplot_power_eff_convergence[0;34m(models, steady_idx)[0m
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[1;32m 13[0m a_efficiency[38;5;241m.[39mset_yscale([38;5;124m"[39m[38;5;124mlog[39m[38;5;124m"[39m)
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[1;32m 14[0m [38;5;28;01mfor[39;00m model [38;5;129;01min[39;00m models:
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[0;32m---> 15[0m Ns [38;5;241m=[39m [43mmodel[49m[38;5;241;43m.[39;49m[43mpower[49m[43m([49m[43msteady_idx[49m[38;5;241;43m=[39;49m[43msteady_idx[49m[43m)[49m[38;5;241m.[39mNs
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[1;32m 16[0m a_power[38;5;241m.[39mplot(Ns, model[38;5;241m.[39mpower(steady_idx[38;5;241m=[39msteady_idx)[38;5;241m.[39mvalues)
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[1;32m 17[0m a_efficiency[38;5;241m.[39mplot(Ns, np[38;5;241m.[39mabs(model[38;5;241m.[39mefficiency(steady_idx[38;5;241m=[39msteady_idx)[38;5;241m.[39mvalues))
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File [0;32m~/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/otto_utilities.py:156[0m, in [0;36mintegrate_online[0;34m(model, n, stream_folder, **kwargs)[0m
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[1;32m 155[0m [38;5;28;01mdef[39;00m [38;5;21mintegrate_online[39m(model, n, stream_folder[38;5;241m=[39m[38;5;28;01mNone[39;00m, [38;5;241m*[39m[38;5;241m*[39mkwargs):
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[0;32m--> 156[0m [43maux[49m[38;5;241;43m.[39;49m[43mintegrate[49m[43m([49m
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[1;32m 157[0m [43m [49m[43mmodel[49m[43m,[49m
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[1;32m 158[0m [43m [49m[43mn[49m[43m,[49m
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[1;32m 159[0m [43m [49m[43mstream_file[49m[38;5;241;43m=[39;49m[43m([49m[38;5;124;43m"[39;49m[38;5;124;43m"[39;49m[43m [49m[38;5;28;43;01mif[39;49;00m[43m [49m[43mstream_folder[49m[43m [49m[38;5;129;43;01mis[39;49;00m[43m [49m[38;5;28;43;01mNone[39;49;00m[43m [49m[38;5;28;43;01melse[39;49;00m[43m [49m[43mstream_folder[49m[43m)[49m
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[1;32m 160[0m [43m [49m[38;5;241;43m+[39;49m[43m [49m[38;5;124;43mf[39;49m[38;5;124;43m"[39;49m[38;5;124;43mresults_[39;49m[38;5;132;43;01m{[39;49;00m[43mmodel[49m[38;5;241;43m.[39;49m[43mhexhash[49m[38;5;132;43;01m}[39;49;00m[38;5;124;43m.fifo[39;49m[38;5;124;43m"[39;49m[43m,[49m
|
||||
[1;32m 161[0m [43m [49m[43manalyze[49m[38;5;241;43m=[39;49m[38;5;28;43;01mTrue[39;49;00m[43m,[49m
|
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[1;32m 162[0m [43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m,[49m
|
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[1;32m 163[0m [43m [49m[43m)[49m
|
||||
File [0;32m<@beartype(hiro_models.otto_cycle.OttoEngine.power) at 0x7ff6254db790>:31[0m, in [0;36mpower[0;34m(__beartype_func, __beartype_conf, __beartype_get_violation, *args, **kwargs)[0m
|
||||
|
||||
File [0;32m~/src/two_qubit_model/hiro_models/model_auxiliary.py:201[0m, in [0;36mintegrate[0;34m(model, n, data_path, clear_pd, single_process, stream_file, analyze, results_path, analyze_kwargs)[0m
|
||||
[1;32m 199[0m supervisor[38;5;241m.[39mintegrate_single_process(clear_pd)
|
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[1;32m 200[0m [38;5;28;01melse[39;00m:
|
||||
[0;32m--> 201[0m supervisor[38;5;241m.[39mintegrate(clear_pd)
|
||||
[1;32m 203[0m cleanup([38;5;241m0[39m)
|
||||
File [0;32m~/src/two_qubit_model/hiro_models/otto_cycle.py:482[0m, in [0;36mOttoEngine.power[0;34m(self, steady_idx, *args, **kwargs)[0m
|
||||
[1;32m 475[0m [38;5;124;03m"""[39;00m
|
||||
[1;32m 476[0m [38;5;124;03mCalculate the mean steady state power. For the arguments see[39;00m
|
||||
[1;32m 477[0m [38;5;124;03m:any:`steady_energy_change`.[39;00m
|
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[1;32m 478[0m [38;5;124;03m"""[39;00m
|
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[1;32m 480[0m _, indices [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39mstrobe
|
||||
[0;32m--> 482[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mtotal_power[49m[43m([49m[43m)[49m[38;5;241m.[39mslice([38;5;28mslice[39m(indices[steady_idx], [38;5;28;01mNone[39;00m, [38;5;241m1[39m))[38;5;241m.[39mmean
|
||||
|
||||
File [0;32m~/src/hops/hops/core/signal_delay.py:87[0m, in [0;36msig_delay.__exit__[0;34m(self, exc_type, exc_val, exc_tb)[0m
|
||||
[1;32m 84[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m([38;5;28mself[39m[38;5;241m.[39msigh[38;5;241m.[39msigs_caught) [38;5;241m>[39m [38;5;241m0[39m [38;5;129;01mand[39;00m [38;5;28mself[39m[38;5;241m.[39mhandler [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
|
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[1;32m 85[0m [38;5;28mself[39m[38;5;241m.[39mhandler([38;5;28mself[39m[38;5;241m.[39msigh[38;5;241m.[39msigs_caught)
|
||||
[0;32m---> 87[0m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_restore[49m[43m([49m[43m)[49m
|
||||
File [0;32m~/src/two_qubit_model/hiro_models/model_base.py:649[0m, in [0;36mModel.total_power[0;34m(self, data, **kwargs)[0m
|
||||
[1;32m 638[0m [38;5;28;01mdef[39;00m [38;5;21mtotal_power[39m([38;5;28mself[39m, data: Optional[HIData] [38;5;241m=[39m [38;5;28;01mNone[39;00m, [38;5;241m*[39m[38;5;241m*[39mkwargs) [38;5;241m-[39m[38;5;241m>[39m EnsembleValue:
|
||||
[1;32m 639[0m [38;5;124;03m"""Calculates the total power from the trajectories in[39;00m
|
||||
[1;32m 640[0m [38;5;124;03m ``data`` or, if not supplied, tries to load[39;00m
|
||||
[1;32m 641[0m [38;5;124;03m the online results from ``results_path``.[39;00m
|
||||
[0;32m (...)[0m
|
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[1;32m 646[0m [38;5;124;03m :returns: The total power.[39;00m
|
||||
[1;32m 647[0m [38;5;124;03m """[39;00m
|
||||
[0;32m--> 649[0m power [38;5;241m=[39m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43minteraction_power[49m[43m([49m[43mdata[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m)[49m[38;5;241m.[39msum_baths()
|
||||
[1;32m 650[0m system_power [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39msystem_power(data, [38;5;241m*[39m[38;5;241m*[39mkwargs)
|
||||
[1;32m 652[0m [38;5;28;01mif[39;00m system_power [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
|
||||
|
||||
File [0;32m~/src/hops/hops/core/signal_delay.py:68[0m, in [0;36msig_delay._restore[0;34m(self)[0m
|
||||
[1;32m 66[0m [38;5;28;01mfor[39;00m i, s [38;5;129;01min[39;00m [38;5;28menumerate[39m([38;5;28mself[39m[38;5;241m.[39msigs):
|
||||
[1;32m 67[0m signal[38;5;241m.[39msignal(s, [38;5;28mself[39m[38;5;241m.[39mold_handlers[i])
|
||||
[0;32m---> 68[0m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43msigh[49m[38;5;241;43m.[39;49m[43memit[49m[43m([49m[43m)[49m
|
||||
File [0;32m~/src/two_qubit_model/hiro_models/model_base.py:560[0m, in [0;36mModel.interaction_power[0;34m(self, data, results_path, **kwargs)[0m
|
||||
[1;32m 550[0m [38;5;124;03m"""Calculates interaction power from the hierarchy data[39;00m
|
||||
[1;32m 551[0m [38;5;124;03m``data`` or, if not supplied, tries to load the online results from ``results_path``.[39;00m
|
||||
[1;32m 552[0m
|
||||
[0;32m (...)[0m
|
||||
[1;32m 556[0m [38;5;124;03m:returns: See :any:`hopsflow.util.interaction_energy_ensemble`.[39;00m
|
||||
[1;32m 557[0m [38;5;124;03m"""[39;00m
|
||||
[1;32m 559[0m [38;5;28;01mif[39;00m data [38;5;129;01mis[39;00m [38;5;28;01mNone[39;00m:
|
||||
[0;32m--> 560[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mtry_get_online_data[49m[43m([49m
|
||||
[1;32m 561[0m [43m [49m[43mresults_path[49m[43m,[49m[43m [49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43monline_interaction_power_name[49m
|
||||
[1;32m 562[0m [43m [49m[43m)[49m
|
||||
[1;32m 564[0m N, kwargs [38;5;241m=[39m _get_N_kwargs(kwargs, data)
|
||||
[1;32m 566[0m [38;5;28;01mreturn[39;00m hopsflow[38;5;241m.[39mhopsflow[38;5;241m.[39minteraction_energy_ensemble(
|
||||
[1;32m 567[0m data[38;5;241m.[39mvalid_sample_iterator(data[38;5;241m.[39mstoc_traj), [38;5;66;03m# type: ignore[39;00m
|
||||
[1;32m 568[0m data[38;5;241m.[39mvalid_sample_iterator(data[38;5;241m.[39maux_states), [38;5;66;03m# type: ignore[39;00m
|
||||
[0;32m (...)[0m
|
||||
[1;32m 574[0m [38;5;241m*[39m[38;5;241m*[39mkwargs,
|
||||
[1;32m 575[0m )
|
||||
|
||||
File [0;32m~/src/hops/hops/core/signal_delay.py:42[0m, in [0;36mSigHandler.emit[0;34m(self)[0m
|
||||
[1;32m 40[0m [38;5;28;01mfor[39;00m s [38;5;129;01min[39;00m [38;5;28mself[39m[38;5;241m.[39msigs_caught:
|
||||
[1;32m 41[0m log[38;5;241m.[39minfo([38;5;124m"[39m[38;5;124memit signal [39m[38;5;124m'[39m[38;5;132;01m{}[39;00m[38;5;124m'[39m[38;5;124m"[39m[38;5;241m.[39mformat(SIG_MAP[s]))
|
||||
[0;32m---> 42[0m [43mos[49m[38;5;241;43m.[39;49m[43mkill[49m[43m([49m[43mos[49m[38;5;241;43m.[39;49m[43mgetpid[49m[43m([49m[43m)[49m[43m,[49m[43m [49m[43ms[49m[43m)[49m
|
||||
File [0;32m~/src/two_qubit_model/hiro_models/model_base.py:298[0m, in [0;36mModel.try_get_online_data[0;34m(self, path, results_path)[0m
|
||||
[1;32m 296[0m file_path [38;5;241m=[39m os[38;5;241m.[39mpath[38;5;241m.[39mjoin(path, results_path)
|
||||
[1;32m 297[0m [38;5;28;01mif[39;00m [38;5;129;01mnot[39;00m os[38;5;241m.[39mpath[38;5;241m.[39mexists(file_path):
|
||||
[0;32m--> 298[0m [38;5;28;01mraise[39;00m [38;5;167;01mRuntimeError[39;00m([38;5;124mf[39m[38;5;124m"[39m[38;5;124mNo data found under [39m[38;5;124m'[39m[38;5;132;01m{[39;00mfile_path[38;5;132;01m}[39;00m[38;5;124m'[39m[38;5;124m.[39m[38;5;124m"[39m)
|
||||
[1;32m 300[0m [38;5;28;01mreturn[39;00m hopsflow[38;5;241m.[39mutil[38;5;241m.[39mget_online_values_from_cache(file_path)
|
||||
|
||||
[0;31mKeyboardInterrupt[0m:
|
||||
[0;31mRuntimeError[0m: No data found under 'results/interaction_power_cfad63977aad412a4d035ea4122d748504bc638b4df70749bf6a9f0a0ecbcf4b.npz'.
|
||||
#+end_example
|
||||
[[file:./.ob-jupyter/b558c35b08a532e347de25d93e5aa133d5b6e6ff.svg]]
|
||||
:END:
|
||||
|
||||
|
||||
#+begin_src jupyter-julia
|
||||
[model.efficiency(steady_idx=2).value / best_shift_model.efficiency(steady_idx=2).value for model in all_overlap_models]
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
| 1.0 | 1.0214351243995223 | 0.5705103160098187 | 0.3967276370979822 |
|
||||
|
||||
#+begin_src jupyter-python
|
||||
ot.plot_powers_and_efficiencies(Ns, overlap_models)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| <Figure | size | 1200x400 | with | 2 | Axes> | <AxesSubplot: | > |
|
||||
[[file:./.ob-jupyter/11463950153d8f27d2734c7ae3d213b127aaf350.svg]]
|
||||
:END:
|
||||
|
||||
#+begin_src jupyter-python
|
||||
f, a = plt.subplots()
|
||||
a.axhline(0)
|
||||
for model in all_overlap_models:
|
||||
pu.plot_with_σ(model.t, model.interaction_power().sum_baths().integrate(model.t), ax=a)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
[[file:./.ob-jupyter/c14627fd66e3c1c812fa240ebc2f765344f3290e.svg]]
|
||||
|
||||
** Findings
|
||||
- coupling overlap doesn't help much
|
||||
- with these parameters the earlier obeservation does not recur
|
||||
- more scan needed
|
||||
- maybe slower coupling/decoupling will help
|
||||
|
|
|
|||
|
|
@ -21,8 +21,9 @@ logging_setup(logging.INFO)
|
|||
plt.rcParams['figure.figsize'] = (12,4)
|
||||
|
||||
T = 50
|
||||
switch_time = 3. / T
|
||||
def make_model(shift_c, shift_h):
|
||||
def make_model(shift_c, shift_h, switch_t=3.):
|
||||
switch_time = switch_t / T
|
||||
|
||||
(p_H, p_L) = ot.timings(switch_time, switch_time)
|
||||
return OttoEngine(
|
||||
δ=[.7, .7],
|
||||
|
|
@ -48,12 +49,76 @@ def make_model(shift_c, shift_h):
|
|||
)
|
||||
|
||||
N = 3
|
||||
N_over = 1
|
||||
step = switch_time / (N-N_over)
|
||||
N_over = 2
|
||||
step = 3. / (T*(N-N_over))
|
||||
shifts = [round(shift * step, 3) for shift in range(-N, N+1)]
|
||||
shifts
|
||||
|
||||
import itertools
|
||||
models = [make_model(shift, shift) for shift in shifts]
|
||||
|
||||
ot.integrate_online_multi(models, 100_000, increment=10_000, analyze_kwargs=dict(every=10_000))
|
||||
for model in models:
|
||||
print(model.power(steady_idx=1).value / models[3].power(steady_idx=1).value, model.efficiency(steady_idx=1).value * 100)
|
||||
|
||||
ot.plot_power_eff_convergence(models)
|
||||
|
||||
ot.plot_powers_and_efficiencies(shifts, models)
|
||||
|
||||
best_shift = shifts[-2]
|
||||
best_shift_model = make_model(best_shift, best_shift)
|
||||
|
||||
def overlap(shift_model, N, step, switch_t=3.):
|
||||
switch_time = switch_t / T
|
||||
(p_H, p_L) = ot.timings(switch_time, switch_time)
|
||||
next_model = shift_model.copy()
|
||||
|
||||
#next_model.timings_H=p_H
|
||||
next_model.timings_L=p_L
|
||||
|
||||
(a, b, c, d) = next_model.timings_L[0]
|
||||
(e, f, g, h) = next_model.timings_L[1]
|
||||
next_step = step * N
|
||||
(s1, s2) = next_model.L_shift
|
||||
next_model.L_shift = (s1 + next_step, s2 - next_step)
|
||||
next_model.timings_L = (
|
||||
(a - 2 * next_step, b - 2 * next_step, c, d),
|
||||
(e, f, g + 2 * next_step, h + 2 * next_step),
|
||||
)
|
||||
return next_model
|
||||
|
||||
|
||||
def overlap_cold(shift_model, N, step):
|
||||
next_model = shift_model.copy()
|
||||
(a, b, c, d) = next_model.timings_L[0]
|
||||
(e, f, g, h) = next_model.timings_L[1]
|
||||
next_step = step * N
|
||||
(s1, s2) = next_model.L_shift
|
||||
next_model.L_shift = (s1 + next_step, s2 - next_step)
|
||||
next_model.timings_L = (
|
||||
(a - 2 * next_step, b - 2 * next_step, c - next_step, d - next_step),
|
||||
(e + next_step, f + next_step, g + 2 * next_step, h + 2 * next_step),
|
||||
)
|
||||
return next_model
|
||||
|
||||
|
||||
Ns = list(range(1, 4))
|
||||
overlap_models = [overlap(best_shift_model, N, step) for N in Ns]
|
||||
overlap_models = [overlap_cold(best_shift_model, N, step) for N in Ns]
|
||||
new_step_size = 6
|
||||
mini_step = (new_step_size / (N-N_over) / T)
|
||||
overlap_models = [overlap(best_shift_model, N, mini_step, new_step_size) for N in Ns]
|
||||
|
||||
ot.integrate_online_multi(overlap_models, 50_000, increment=10_000, analyze_kwargs=dict(every=10_000))
|
||||
|
||||
all_overlap_models = [best_shift_model, *overlap_models]
|
||||
|
||||
ot.plot_power_eff_convergence(all_overlap_models, 1)
|
||||
|
||||
[model.efficiency(steady_idx=2).value / best_shift_model.efficiency(steady_idx=2).value for model in all_overlap_models]
|
||||
|
||||
ot.plot_powers_and_efficiencies(Ns, overlap_models)
|
||||
|
||||
f, a = plt.subplots()
|
||||
a.axhline(0)
|
||||
for model in all_overlap_models:
|
||||
pu.plot_with_σ(model.t, model.interaction_power().sum_baths().integrate(model.t), ax=a)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue