mirror of
https://github.com/vale981/master-thesis
synced 2025-03-04 17:41:43 -05:00
try slightly longer coupling
This commit is contained in:
parent
e3ba68d319
commit
1dc1264cd8
2 changed files with 19 additions and 95 deletions
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@ -92,7 +92,7 @@ def anti_zeno_engine(
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return model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
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model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/2, n=3, detune=4, ω_0 = 10, δ=[1]*2, γ=.05/2)
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model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/5, n=3, detune=4, ω_0 = 10, δ=[1]*2, γ=.05/2)
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#model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
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params
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@ -125,8 +125,8 @@ plt.plot(model.t, filtered)
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with aux.get_data(model) as data:
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_, ax = plt.subplots()
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#fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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#fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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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='3.9.12', ray_version='1.12.1', ray_commit='4863e33856b54ccf8add5cbe75e41558850a1b75', address_info={'node_ip_address': '192.168.44.161', 'raylet_ip_address': '192.168.44.161', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-06-14_15-13-29_474547_2829582/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-14_15-13-29_474547_2829582/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-14_15-13-29_474547_2829582', 'metrics_export_port': 59840, 'gcs_address': '192.168.44.161:65285', 'address': '192.168.44.161:65285', 'node_id': '032a1c3d37c720f19db3119948af1f1313c1670e974ad6fb1e960c71'})
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: RayContext(dashboard_url='', python_version='3.9.12', ray_version='1.12.1', ray_commit='4863e33856b54ccf8add5cbe75e41558850a1b75', address_info={'node_ip_address': '127.0.0.1', 'raylet_ip_address': '127.0.0.1', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-06-14_17-41-54_433521_3434369/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-14_17-41-54_433521_3434369/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-14_17-41-54_433521_3434369', 'metrics_export_port': 65400, 'gcs_address': '127.0.0.1:50926', 'address': '127.0.0.1:50926', 'node_id': '3e25f54f48fa791eb7ce3c3b1ee8e11e81b8f0b01ec60ad8c4940760'})
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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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@ -112,7 +112,7 @@ Init ray and silence stocproc.
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* Model Definition
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#+begin_src jupyter-python
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model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/2, n=3, detune=4, ω_0 = 10, δ=[1]*2, γ=.05/2)
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model, Δ, params = anti_zeno_engine(ε=1/10, ε_couple=1/5, n=3, detune=4, ω_0 = 10, δ=[1]*2, γ=.05/2)
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#model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-4, n=1, detune=.5, δ=[.1,.1])
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params
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#+end_src
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@ -138,8 +138,8 @@ 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 | 0x7f7db78a7ac0> |
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[[file:./.ob-jupyter/df2c75be5b4885c67ce2a213e3baa6a0c6a88c9a.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f5d393d5880> |
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[[file:./.ob-jupyter/8f948efd5a869329fee584e266642232b144fb58.svg]]
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:END:
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#+begin_src jupyter-python :tangle nil
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@ -152,8 +152,8 @@ 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 0x7f7dc77f7cd0>
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[[file:./.ob-jupyter/d73ec34dd915bf36eadae46fa0f37add6f365c43.svg]]
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: <matplotlib.lines.Line2D at 0x7f5d3943ef10>
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[[file:./.ob-jupyter/f10d4889fa3ba5e14cf18b06786c8c3abbb543ef.svg]]
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:END:
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@ -162,82 +162,6 @@ Let's test the assumptions of the paper.
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aux.integrate(model, 5000)
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#+end_src
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#+RESULTS:
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:RESULTS:
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: [INFO hops.core.integration 22464] Choosing the nonlinear integrator.
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: /home/hiro/src/hops/hops/core/hierarchy_data.py:497: UserWarning: test_file_version FAILED with exception Unable to open file (file is already open for write/SWMR write (may use <h5clear file> to clear file consistency flags))
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: warnings.warn(
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: /home/hiro/src/hops/hops/core/hierarchy_data.py:500: UserWarning: hdf5_name .data/5dcdeeef6e79bd5c4ee636c23a1905053441c9fa49ea5b8f516844fd17e69033/_3/5dcdeeef6e79bd5c4ee636c23a1905053441c9fa49ea5b8f516844fd17e69033_3a50d7c5e5577f16c8ee57fead7fa546_1.h5
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: warnings.warn("hdf5_name {}".format(self.hdf5_name))
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: /home/hiro/src/hops/hops/core/hierarchy_data.py:503: UserWarning: Moving .data/5dcdeeef6e79bd5c4ee636c23a1905053441c9fa49ea5b8f516844fd17e69033/_3/5dcdeeef6e79bd5c4ee636c23a1905053441c9fa49ea5b8f516844fd17e69033_3a50d7c5e5577f16c8ee57fead7fa546_1.h5 to .data/5dcdeeef6e79bd5c4ee636c23a1905053441c9fa49ea5b8f516844fd17e69033/_3/5dcdeeef6e79bd5c4ee636c23a1905053441c9fa49ea5b8f516844fd17e69033_3a50d7c5e5577f16c8ee57fead7fa546_1.h5backup_1655110347.2761068 and starting fresh.
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: warnings.warn(
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# [goto error]
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#+begin_example
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[0;31m---------------------------------------------------------------------------[0m
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[0;31mRaySystemError[0m Traceback (most recent call last)
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Input [0;32mIn [8][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;43m100[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:1238[0m, in [0;36mHOPSSupervisor.integrate[0;34m(self, clear_pd)[0m
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[1;32m 1235[0m [38;5;28;01mwith[39;00m [38;5;28mself[39m[38;5;241m.[39mget_data_and_maybe_clear(clear_pd) [38;5;28;01mas[39;00m data:
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[1;32m 1236[0m t [38;5;241m=[39m data[38;5;241m.[39mget_time()
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[0;32m-> 1238[0m num_integrators [38;5;241m=[39m [38;5;28mint[39m([43mray[49m[38;5;241;43m.[39;49m[43mavailable_resources[49m[43m([49m[43m)[49m[38;5;241m.[39mget([38;5;124m"[39m[38;5;124mCPU[39m[38;5;124m"[39m, [38;5;241m0[39m))
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[1;32m 1240[0m [38;5;28;01mif[39;00m num_integrators [38;5;241m==[39m [38;5;241m0[39m:
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[1;32m 1241[0m [38;5;28;01mraise[39;00m [38;5;167;01mRuntimeError[39;00m([38;5;124m"[39m[38;5;124mNo cpu available for integration![39m[38;5;124m"[39m)
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File [0;32m/nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/ray/_private/client_mode_hook.py:105[0m, in [0;36mclient_mode_hook.<locals>.wrapper[0;34m(*args, **kwargs)[0m
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[1;32m 103[0m [38;5;28;01mif[39;00m func[38;5;241m.[39m[38;5;18m__name__[39m [38;5;241m!=[39m [38;5;124m"[39m[38;5;124minit[39m[38;5;124m"[39m [38;5;129;01mor[39;00m is_client_mode_enabled_by_default:
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[1;32m 104[0m [38;5;28;01mreturn[39;00m [38;5;28mgetattr[39m(ray, func[38;5;241m.[39m[38;5;18m__name__[39m)([38;5;241m*[39margs, [38;5;241m*[39m[38;5;241m*[39mkwargs)
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[0;32m--> 105[0m [38;5;28;01mreturn[39;00m [43mfunc[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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File [0;32m/nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/ray/state.py:912[0m, in [0;36mavailable_resources[0;34m()[0m
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[1;32m 898[0m [38;5;129m@DeveloperAPI[39m
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[1;32m 899[0m [38;5;129m@client_mode_hook[39m(auto_init[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
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[1;32m 900[0m [38;5;28;01mdef[39;00m [38;5;21mavailable_resources[39m():
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[1;32m 901[0m [38;5;124;03m"""Get the current available cluster resources.[39;00m
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[1;32m 902[0m
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[1;32m 903[0m [38;5;124;03m This is different from `cluster_resources` in that this will return idle[39;00m
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[0;32m (...)[0m
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[1;32m 910[0m [38;5;124;03m resource in the cluster.[39;00m
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[1;32m 911[0m [38;5;124;03m """[39;00m
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[0;32m--> 912[0m [38;5;28;01mreturn[39;00m [43mstate[49m[38;5;241;43m.[39;49m[43mavailable_resources[49m[43m([49m[43m)[49m
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File [0;32m/nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/ray/state.py:724[0m, in [0;36mGlobalState.available_resources[0;34m(self)[0m
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[1;32m 712[0m [38;5;28;01mdef[39;00m [38;5;21mavailable_resources[39m([38;5;28mself[39m):
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[1;32m 713[0m [38;5;124;03m"""Get the current available cluster resources.[39;00m
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[1;32m 714[0m
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[1;32m 715[0m [38;5;124;03m This is different from `cluster_resources` in that this will return[39;00m
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[0;32m (...)[0m
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[1;32m 722[0m [38;5;124;03m resource in the cluster.[39;00m
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[1;32m 723[0m [38;5;124;03m """[39;00m
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[0;32m--> 724[0m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_check_connected[49m[43m([49m[43m)[49m
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[1;32m 726[0m available_resources_by_id [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39m_available_resources_per_node()
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[1;32m 728[0m [38;5;66;03m# Calculate total available resources.[39;00m
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File [0;32m/nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/ray/state.py:48[0m, in [0;36mGlobalState._check_connected[0;34m(self)[0m
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[1;32m 46[0m [38;5;66;03m# _really_init_global_state should have set self.global_state_accessor[39;00m
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[1;32m 47[0m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39mglobal_state_accessor [38;5;129;01mis[39;00m [38;5;28;01mNone[39;00m:
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[0;32m---> 48[0m [38;5;28;01mraise[39;00m ray[38;5;241m.[39mexceptions[38;5;241m.[39mRaySystemError(
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[1;32m 49[0m [38;5;124m"[39m[38;5;124mRay has not been started yet. You can start Ray with [39m[38;5;124m'[39m[38;5;124mray.init()[39m[38;5;124m'[39m[38;5;124m.[39m[38;5;124m"[39m
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[1;32m 50[0m )
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[0;31mRaySystemError[0m: System error: Ray has not been started yet. You can start Ray with 'ray.init()'.
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#+end_example
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:END:
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#+begin_src jupyter-python
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_, ax = fs.plot_energy_overview(model, markersize=1)
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@ -249,8 +173,8 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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: <matplotlib.legend.Legend at 0x7f7dc795f160>
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[[file:./.ob-jupyter/71865724376f7ec07276bc5b647a3af7b6e11ef3.svg]]
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: <matplotlib.legend.Legend at 0x7f5d39483160>
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[[file:./.ob-jupyter/6fee2283b3f65342853912bb98569e3c62ef772b.svg]]
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:END:
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- **too fast decoupling kills it**
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: pts[1:N+1, 1] = dep1slice
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5224: ComplexWarning: Casting complex values to real discards the imaginary part
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: pts[N+2:, 1] = dep2slice[::-1]
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[[file:./.ob-jupyter/55f62edc949c1a9136b9b7e4e0ee3092e8a3d516.svg]]
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[[file:./.ob-jupyter/8b909fa7cbb714b4528b2b0e0f9761a47e7db62d.svg]]
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:END:
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@ -299,8 +223,8 @@ 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 | 0x7f7db7963bb0> |
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[[file:./.ob-jupyter/aa719a7b720ed6ad480377ed1ef3a108549bc981.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f5d29c55ee0> |
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[[file:./.ob-jupyter/03a06548b8d1288b319a3b9b25634c5c4fcdc505.svg]]
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:END:
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** TODO Power and Efficiency
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@ -311,8 +235,8 @@ We need the time points where we sample the total energy.
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#+begin_src jupyter-python
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with aux.get_data(model) as data:
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_, ax = plt.subplots()
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#fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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#fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
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fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
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fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
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#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
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@ -321,7 +245,7 @@ We need the time points where we sample the total energy.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/659bbfc215c82a10871222a3d66b736895fb5f57.svg]]
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[[file:./.ob-jupyter/f57e03b6d37041984f9097410062f59603f2b7f7.svg]]
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#+begin_src jupyter-python
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with aux.get_data(model) as data:
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/d2a4d830b7965689e1085ea5b80633907f7f75f5.svg]]
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[[file:./.ob-jupyter/468cd90d3bad32388856d285d1a89bda150cff67.svg]]
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#+begin_src jupyter-python
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mean_norm = np.zeros_like(model.t)
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/88389fabb204bbc873bba8fe150ec208513c42af.svg]]
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[[file:./.ob-jupyter/914086091aa9249ecbf1e295919a1f66b1720b55.svg]]
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* Too long modulation?, more likely to small spectral sep or to strong coupling
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- definitely not too long mod
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