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ok, lets try stronger coupling

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Valentin Boettcher 2022-06-27 14:02:38 +02:00
parent b26595cda3
commit 3fd77e92f8
2 changed files with 16 additions and 16 deletions
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14
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
View file

@ -145,15 +145,15 @@ def anti_zeno_engine(
model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch) = anti_zeno_engine(
Δ=1.3,
ε=.5 ,
ε=.5,
ω_c=0.2,
ε_couple=0.7,
n=10,
n=3,
detune=-0.19,
ω_0=2,
T_c=3,
T_h=11,
δ=[.1] * 2,
δ=[1] * 2,
γ=0.5 / 10,
switch_cycles=1,
therm_initial_state=False,
@ -211,16 +211,16 @@ plt.plot(model.t, ut.smoothen(model.t, ρ_ee, frac=.06, it=0))
with aux.get_data(model) as data:
_, ax = plt.subplots()
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
#ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
#ax.plot(model.t, np.gradient(model.total_energy(data).value))
ts_begin = ((τ_c + τ_off) * np.arange(0, n) - Δ_switch)

18
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
View file

@ -25,7 +25,7 @@ Init ray and silence stocproc.
#+end_src
#+RESULTS:
: 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_2022-06-24_15-06-52_296364_396466/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-24_15-06-52_296364_396466/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-24_15-06-52_296364_396466', 'metrics_export_port': 61004, 'gcs_address': '141.30.17.225:47993', 'address': '141.30.17.225:47993', 'node_id': 'ed43d6aa4535a1f23b7d0d0dd45687a7221189a23e04487a355d297b'})
: 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_2022-06-27_13-53-20_654030_8907/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-06-27_13-53-20_654030_8907/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-06-27_13-53-20_654030_8907', 'metrics_export_port': 62418, 'gcs_address': '141.30.17.225:62248', 'address': '141.30.17.225:62248', 'node_id': '6d4fbe591fb595ea55c2c527d1c4db5b6da8a62057f3c7403b87f1a8'})
#+begin_src jupyter-python :results none
from hops.util.logging_setup import logging_setup
@ -165,15 +165,15 @@ Init ray and silence stocproc.
#+begin_src jupyter-python
model, Δ, (τ_mod, τ_c, τ_bath, cycles, model.ω_s, ω_0, τ_s, τ_off, n, Δ_switch) = anti_zeno_engine(
Δ=1.3,
ε=.5 ,
ε=.5,
ω_c=0.2,
ε_couple=0.7,
n=10,
n=3,
detune=-0.19,
ω_0=2,
T_c=3,
T_h=11,
δ=[.1] * 2,
δ=[1] * 2,
γ=0.5 / 10,
switch_cycles=1,
therm_initial_state=False,
@ -367,21 +367,21 @@ We need the time points where we sample the total energy.
#+begin_src jupyter-python
with aux.get_data(model) as data:
_, ax = plt.subplots()
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
# fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
fs.plot_with_σ(model.t, model.bath_energy(data), bath=0, ax=ax)
fs.plot_with_σ(model.t, model.bath_energy(data), bath=1, ax=ax)
#fs.plot_with_σ(model.t, model.bath_energy(data).sum_baths(), ax=ax)
fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.total_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.interaction_energy(data).for_bath(1), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data), ax=ax)
#fs.plot_with_σ(model.t, model.system_energy(data) + model.bath_energy(data).sum_baths(), ax=ax)
ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
#ax.plot(model.t, ut.smoothen(model.t, model.total_energy(data).value, frac=.1))
#ax.plot(model.t, np.gradient(model.total_energy(data).value))
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/20bba0288df8bbbc25fb66e7f1ac29e453a9edf1.svg]]
[[file:./.ob-jupyter/59c645c188bb0fbc31e34820aacd6233e0608688.svg]]