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https://github.com/vale981/master-thesis
synced 2025-03-05 10:01:43 -05:00
try slower mod
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f82deb97a2
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2 changed files with 46 additions and 31 deletions
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@ -144,14 +144,14 @@ def anti_zeno_engine(
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)
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model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch) = anti_zeno_engine(
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Δ=1.5,
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Δ=1,
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ε=.5 ,
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ω_c=0.2,
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ε_couple=0.85,
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n=10,
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n=5,
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detune=-0.19,
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ω_0=2,
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T_c=1.6,
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T_c=4,
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T_h=10,
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δ=[1] * 2,
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γ=0.5 / 10,
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@ -184,7 +184,7 @@ 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, 2_000)
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aux.integrate(model, 2000)
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_, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0))
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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': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-06-21_13-08-14_458638_1925013/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-21_13-08-14_458638_1925013/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-21_13-08-14_458638_1925013', 'metrics_export_port': 64729, 'gcs_address': '141.30.17.225:49099', 'address': '141.30.17.225:49099', 'node_id': 'ec4fc290c3ec934fac3c68412b4d14e7d7ee724ef9ced6b50a979a49'})
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: RayContext(dashboard_url='', python_version='3.9.12', 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_2022-06-21_15-53-28_658570_2169805/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-21_15-53-28_658570_2169805/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-21_15-53-28_658570_2169805', 'metrics_export_port': 40469, 'gcs_address': '141.30.17.225:47762', 'address': '141.30.17.225:47762', 'node_id': '67301e784de4fea24ad52ad9a29741b018133f9b17ada3daf66a313c'})
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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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@ -164,14 +164,14 @@ 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, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch) = anti_zeno_engine(
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Δ=1.5,
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Δ=1,
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ε=.5 ,
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ω_c=0.2,
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ε_couple=0.85,
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n=10,
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n=5,
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detune=-0.19,
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ω_0=2,
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T_c=1.6,
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T_c=4,
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T_h=10,
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δ=[1] * 2,
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γ=0.5 / 10,
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@ -193,7 +193,7 @@ Let's test the assumptions of the paper.
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#+end_src
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#+RESULTS:
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: 3
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: 2
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** BCFs and Modulations
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#+begin_src jupyter-python :tangle nil
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@ -209,8 +209,8 @@ 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 | 0x7ffa2bdb6b50> |
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[[file:./.ob-jupyter/29d05104979912a12b959c1f097a852591c6054e.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f682cd0ad30> |
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[[file:./.ob-jupyter/639832825a9dbdc531cad6e7fddeeccd8f62b97d.svg]]
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:END:
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#+begin_src jupyter-python :tangle nil
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@ -223,13 +223,13 @@ 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(3, 4)
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#plt.xlim(3, 4)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| 3.0 | 4.0 |
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[[file:./.ob-jupyter/ff58aca678dd6432231e8576520b612762b4c3f0.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f682cc57c40> |
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[[file:./.ob-jupyter/131703f1c352eb45f976c10644c8072bee3c5fb0.svg]]
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:END:
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#+begin_src jupyter-python
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@ -241,8 +241,8 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
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[[file:./.ob-jupyter/33d008316e291c8d17151ead0418b51dfbd8b754.svg]]
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[[file:./.ob-jupyter/3b942f66139250435ad213dc65f50c7309e1e663.svg]]
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[[file:./.ob-jupyter/701494660db131c6c13d5c161e1812f1504ab5fd.svg]]
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[[file:./.ob-jupyter/f6c628244bed62a53e1a5c355ed8af995d4a9219.svg]]
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:END:
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#+begin_src jupyter-python
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@ -256,7 +256,7 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
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[[file:./.ob-jupyter/ebe254a5be95214174933422d4682e3714882012.svg]]
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[[file:./.ob-jupyter/052e3d18439fca2d3a800f3d94fdb168abe468ae.svg]]
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:END:
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@ -275,16 +275,31 @@ 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 | 0x7f807c0240a0> |
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[[file:./.ob-jupyter/d30f7ce8409cc860d687e4ee003fa7d955d054fa.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f00fdbf7370> |
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[[file:./.ob-jupyter/1f7aa2b79184210d6a4f95c9d65e0d0725319071.svg]]
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:END:
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** TODO Integration
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#+begin_src jupyter-python
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aux.integrate(model, 2_000)
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aux.integrate(model, 2000)
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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 2137276] 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/5dbbf4ccb2b94cd79c9f1367996141a291047f11b5e36a39afc55d07a2ebc90b/_2/5dbbf4ccb2b94cd79c9f1367996141a291047f11b5e36a39afc55d07a2ebc90b_2535941ad73bf6f57abe33e03f90f548_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/5dbbf4ccb2b94cd79c9f1367996141a291047f11b5e36a39afc55d07a2ebc90b/_2/5dbbf4ccb2b94cd79c9f1367996141a291047f11b5e36a39afc55d07a2ebc90b_2535941ad73bf6f57abe33e03f90f548_1.h5 to .data/5dbbf4ccb2b94cd79c9f1367996141a291047f11b5e36a39afc55d07a2ebc90b/_2/5dbbf4ccb2b94cd79c9f1367996141a291047f11b5e36a39afc55d07a2ebc90b_2535941ad73bf6f57abe33e03f90f548_1.h5backup_1655818803.6054995 and starting fresh.
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warnings.warn(
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[INFO hops.core.integration 2137276] Using 4 integrators.
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[INFO hops.core.integration 2137276] Some 1 trajectories have to be integrated.
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[INFO hops.core.integration 2137276] Using 1001 hierarchy states.
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0% 0/1 [00:00<?, ?it/s]
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#+end_example
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#+begin_src jupyter-python
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_, ax = fs.plot_energy_overview(model, markersize=1, ensemble_args=dict(gc_sleep=0))
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@ -297,8 +312,8 @@ 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 0x7ffa308d2e50>
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[[file:./.ob-jupyter/4aa25d0d35da92d90b13e4144ae4ead13d102566.svg]]
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: <matplotlib.legend.Legend at 0x7f683b8c6f40>
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[[file:./.ob-jupyter/51ea7ea8fa1c89b34891f4d3d5e792eeece1ce4b.svg]]
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:END:
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- **too fast decoupling kills it**
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@ -321,17 +336,17 @@ Let's test the assumptions of the paper.
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#+RESULTS:
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:RESULTS:
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part
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: /nix/store/j9m9rb3yfvdx891s95j90alz8pazq5kw-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part
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: return np.asarray(x, float)
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5218: ComplexWarning: Casting complex values to real discards the imaginary part
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: /nix/store/j9m9rb3yfvdx891s95j90alz8pazq5kw-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5218: ComplexWarning: Casting complex values to real discards the imaginary part
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: pts[0] = start
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5219: ComplexWarning: Casting complex values to real discards the imaginary part
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: /nix/store/j9m9rb3yfvdx891s95j90alz8pazq5kw-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5219: ComplexWarning: Casting complex values to real discards the imaginary part
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: pts[N + 1] = end
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: /nix/store/pwhaggpgvxhy410r6vlx448v9lp8n08s-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5222: ComplexWarning: Casting complex values to real discards the imaginary part
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: /nix/store/j9m9rb3yfvdx891s95j90alz8pazq5kw-python3-3.9.12-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5222: ComplexWarning: Casting complex values to real discards the imaginary part
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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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: /nix/store/j9m9rb3yfvdx891s95j90alz8pazq5kw-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/5984b33291841aa3665cf7d8749dd0d66370ed8a.svg]]
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[[file:./.ob-jupyter/cce36b28d371016575407492deb9316748064d73.svg]]
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:END:
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@ -345,8 +360,8 @@ 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 | 0x7ffa3017d6a0> |
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[[file:./.ob-jupyter/e5661de260b83a368f954a1307035a66735b5272.svg]]
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| <matplotlib.lines.Line2D | at | 0x7f682c9e7880> |
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[[file:./.ob-jupyter/ce2fdae5dbd2a2377604b4c283036fafc81240af.svg]]
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:END:
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** TODO Power and Efficiency
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@ -371,7 +386,7 @@ 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/bd5332cacf1a951d281eb6a02c13885fa26f0c51.svg]]
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[[file:./.ob-jupyter/d833ee1e584b08c25336774111f2799ff4466b6d.svg]]
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