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https://github.com/vale981/master-thesis
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2 changed files with 17 additions and 86 deletions
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@ -178,12 +178,12 @@ def anti_zeno_engine(
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Δ,
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(τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init),
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) = anti_zeno_engine(
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Δ=15,
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Δ=11,
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ε=1,
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ω_c=2,
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ε_couple=0.2,
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n=3,
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detune=-2,
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detune=-1.5,
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ω_0=20,
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T_c=8,
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T_h=40,
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@ -220,7 +220,7 @@ vs = np.linspace(0.1, 10, 100)
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plt.plot(vs, chi(vs, ω_0))
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plt.plot(vs, G_h(vs))
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aux.integrate(model, 1)
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aux.integrate(model, 100)
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# _, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0.05))
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@ -228,7 +228,7 @@ fig, ax = plt.subplots()
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with aux.get_data(model) as data:
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fs.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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ax.legend()
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#ax.legend()
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with aux.get_data(model) as data:
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fs.plot_with_σ(
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@ -25,7 +25,7 @@ Init ray and silence stocproc.
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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_2022-07-11_16-54-20_590904_33936/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-11_16-54-20_590904_33936/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-11_16-54-20_590904_33936', 'metrics_export_port': 61718, 'gcs_address': '141.30.17.225:55788', 'address': '141.30.17.225:55788', 'node_id': '714c1cafb7153ccef4b9cc0a9481d02e096e4cf1720657c269036390'})
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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_2022-07-11_17-56-08_601005_343893/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-07-11_17-56-08_601005_343893/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-07-11_17-56-08_601005_343893', 'metrics_export_port': 33700, 'gcs_address': '141.30.17.225:52666', 'address': '141.30.17.225:52666', 'node_id': 'c868ef9cb4594a88f4d75d22b97e6e30132618843dcccc5031cf3e62'})
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#+begin_src jupyter-python :results none
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from hops.util.logging_setup import logging_setup
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@ -198,12 +198,12 @@ Init ray and silence stocproc.
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Δ,
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(τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch, τ_init),
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) = anti_zeno_engine(
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Δ=15,
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Δ=11,
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ε=1,
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ω_c=2,
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ε_couple=0.2,
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n=3,
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detune=-2,
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detune=-1.5,
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ω_0=20,
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T_c=8,
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T_h=40,
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@ -229,7 +229,7 @@ Let's test the assumptions of the paper.
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#+end_src
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#+RESULTS:
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: 29
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: 21
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** BCFs and Modulations
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#+begin_src jupyter-python :tangle nil
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@ -245,8 +245,8 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f5bdab79070> |
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[[file:./.ob-jupyter/16f8323bc4bb5eb0ac508c5471b90d962251e4f8.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f4c9b5ddfa0> |
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[[file:./.ob-jupyter/f197c60a4dd01166b654064ca4d794d7645d96df.svg]]
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:END:
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#+begin_src jupyter-python :tangle nil
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@ -259,13 +259,13 @@ Let's test the assumptions of the paper.
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plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles))
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plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 2), color="orange", linewidth=.5)
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plt.plot(ωs, np.sinc((ωs - ω_0 - Δ) * τ_s * cycles * 10), color="yellow", linewidth=.4)
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plt.xlim(34, 36)
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#plt.xlim(34, 36)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| 34.0 | 36.0 |
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[[file:./.ob-jupyter/02f9e47c19da02f1557bf16f09487b7e948a5ff6.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f4c8cedb5b0> |
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[[file:./.ob-jupyter/0b52df5f4c1b72a077f2a791adbb3c8c1bd2fe32.svg]]
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:END:
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#+begin_src jupyter-python
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@ -318,76 +318,11 @@ Let's test the assumptions of the paper.
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** TODO Integration
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#+begin_src jupyter-python
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aux.integrate(model, 1)
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aux.integrate(model, 100)
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#+end_src
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#+RESULTS:
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:RESULTS:
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#+begin_example
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[INFO hops.core.integration 33936] Choosing the nonlinear integrator.
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[INFO hops.core.integration 33936] Using 4 integrators.
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[INFO hops.core.integration 33936] Some 1 trajectories have to be integrated.
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[INFO hops.core.integration 33936] Using 1820 hierarchy states.
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0% 0/1 [00:00<?, ?it/s][INFO hops.core.signal_delay 33936] caught sig 'SIGINT'
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[INFO hops.core.signal_delay 33936] caught sig 'SIGINT'
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[INFO hops.core.signal_delay 33936] caught sig 'SIGINT'
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[INFO hops.core.signal_delay 33936] caught sig 'SIGINT'
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100% 1/1 [05:01<00:00, 301.60s/it]
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[INFO hops.core.signal_delay 33936] caught 4 signal(s)
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[INFO hops.core.signal_delay 33936] emit signal 'SIGINT'
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#+end_example
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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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Input [0;32mIn [39][0m, in [0;36m<cell line: 1>[0;34m()[0m
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[0;32m----> 1[0m [43maux[49m[38;5;241;43m.[39;49m[43mintegrate[49m[43m([49m[43mmodel[49m[43m,[49m[43m [49m[38;5;241;43m1[39;49m[43m)[49m
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File [0;32m~/src/two_qubit_model/hiro_models/model_auxiliary.py:108[0m, in [0;36mintegrate[0;34m(model, n, data_path, clear_pd)[0m
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[1;32m 98[0m [38;5;66;03m# with model_db(data_path) as db:[39;00m
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[1;32m 99[0m [38;5;66;03m# if hash in db and "data" db[hash][39;00m
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[1;32m 101[0m supervisor [38;5;241m=[39m HOPSSupervisor(
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[1;32m 102[0m model[38;5;241m.[39mhops_config,
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[1;32m 103[0m n,
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[1;32m 104[0m data_path[38;5;241m=[39mdata_path,
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[1;32m 105[0m data_name[38;5;241m=[39m[38;5;28mhash[39m,
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[1;32m 106[0m )
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[0;32m--> 108[0m [43msupervisor[49m[38;5;241;43m.[39;49m[43mintegrate[49m[43m([49m[43mclear_pd[49m[43m)[49m
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[1;32m 110[0m [38;5;28;01mwith[39;00m supervisor[38;5;241m.[39mget_data([38;5;28;01mTrue[39;00m) [38;5;28;01mas[39;00m data:
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[1;32m 111[0m [38;5;28;01mwith[39;00m model_db(data_path) [38;5;28;01mas[39;00m db:
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File [0;32m~/src/hops/hops/core/integration.py:1288[0m, in [0;36mHOPSSupervisor.integrate[0;34m(self, clear_pd)[0m
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[1;32m 1285[0m [38;5;28;01mbreak[39;00m
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[1;32m 1287[0m integration[38;5;241m.[39mupdate()
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[0;32m-> 1288[0m data[38;5;241m.[39mnew_samples(
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[1;32m 1289[0m idx[38;5;241m=[39mindex,
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[1;32m 1290[0m incomplete[38;5;241m=[39mincomplete,
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[1;32m 1291[0m psi0[38;5;241m=[39mpsi0,
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[1;32m 1292[0m aux_states[38;5;241m=[39maux_states,
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[1;32m 1293[0m stoc_proc[38;5;241m=[39mstoc_proc,
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[1;32m 1294[0m result_type[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39mparams[38;5;241m.[39mHiP[38;5;241m.[39mresult_type,
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[1;32m 1295[0m normed[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39m_normed_average,
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[1;32m 1296[0m rng_seed[38;5;241m=[39mseed,
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[1;32m 1297[0m )
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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
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[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)
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[0;32m---> 87[0m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_restore[49m[43m([49m[43m)[49m
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File [0;32m~/src/hops/hops/core/signal_delay.py:68[0m, in [0;36msig_delay._restore[0;34m(self)[0m
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[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):
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[1;32m 67[0m signal[38;5;241m.[39msignal(s, [38;5;28mself[39m[38;5;241m.[39mold_handlers[i])
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[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
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File [0;32m~/src/hops/hops/core/signal_delay.py:42[0m, in [0;36mSigHandler.emit[0;34m(self)[0m
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[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:
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[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]))
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[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
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[0;31mKeyboardInterrupt[0m:
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#+end_example
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:END:
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: 602a65a4-50ca-485c-84f9-0f0adcce05f1
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#+begin_src jupyter-python
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@ -397,15 +332,11 @@ Let's test the assumptions of the paper.
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with aux.get_data(model) as data:
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fs.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
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ax.legend()
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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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: [WARNING matplotlib.legend 35102] No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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: <matplotlib.legend.Legend at 0x7fc879935fa0>
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[[file:./.ob-jupyter/ddf025e019b5263ef3c09c16c7c88caa71609683.svg]]
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:END:
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[[file:./.ob-jupyter/11e4a0abf6a0dea91c5542b16a38a0be24d727b7.svg]]
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- **too fast decoupling kills it**
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- no anti-zeno effects without detuning?
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