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update graphics in cycle length coupling strength

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valentin.boettcher@mailbox.tu-dresden.de 2023-07-10 13:35:59 -04:00
parent 9a29fc0bef
commit b14d14cb01
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66 changed files with 956895 additions and 119 deletions
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9
python/otto_motor/otto_utilities.py
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@ -14,9 +14,12 @@ def plot_power_eff_convergence(models, steady_idx=2):
a_efficiency.set_yscale("log")
for model in models:
Ns = model.power(steady_idx=steady_idx).Ns
a_power.plot(Ns, model.power(steady_idx=steady_idx).values)
a_efficiency.plot(Ns, np.abs(model.efficiency(steady_idx=steady_idx).values))
try:
Ns = model.power(steady_idx=steady_idx).Ns
a_power.plot(Ns, model.power(steady_idx=steady_idx).values)
a_efficiency.plot(Ns, np.abs(model.efficiency(steady_idx=steady_idx).values))
except:
pass
a_power.set_xlabel("$N$")
a_power.set_ylabel("$P$")

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@ -1,4 +1,4 @@
#+PROPERTY: header-args :session otto_cycle_length :kernel python :pandoc no :async yes :tangle tangle/coupling_strength.py
#+PROPERTY: header-args :session otto_cycle_length :kernel python :pandoc no :async yes :tangle tangle/coupling_strength.py :noweb yes
Here we scan the cycle length and coupling strength.
@ -33,6 +33,7 @@ We take the same baseline as in [[id:c06111fd-d719-433d-a316-c163f6e1d384][cycle
But we vary the cycle speed while keeping a fixed proportion
coupling-change/cycle time.
#+NAME: make-model
#+begin_src jupyter-python :results none
def make_model(Θ, δ):
(p_H, p_L) = ot.timings(.06, .06)
@ -93,18 +94,33 @@ coupling-change/cycle time.
#+RESULTS:
:RESULTS:
| <Figure | size | 1200x400 | with | 1 | Axes> | <AxesSubplot: | xlabel= | $\tau$ | ylabel= | Energy | > |
[[file:./.ob-jupyter/dcb84273573f02eab9be5cb49c8855b61ef57f84.svg]]
: (pid=gcs_server) [2023-05-16 18:24:06,597 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:24:06,597 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:24:06,597 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:24:06,597 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:25:13,636 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:25:13,636 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:25:13,636 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:25:13,637 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
: (pid=gcs_server) [2023-05-16 18:25:13,637 E 443560 443560] (gcs_server) gcs_server.cc:283: Failed to get the resource load: GrpcUnavailable: RPC Error message: Connection reset by peer; RPC Error details:
[[file:./.ob-jupyter/164cf59963539ffe267c06050906eacc5c84a306.svg]]
:END:
#+begin_src jupyter-python
for i in range(len(Θs)):
fig, ax = plt.subplots()
for model in models[len(δs) * i :len(δs) * (i+1)]:
pu.plot_with_σ(model.t, model.system_energy(), ax=ax)
#+end_src
#+RESULTS:
:RESULTS:
[[file:./.ob-jupyter/e260500c9f6e3ee5bffaa8e55362d765f315dbd9.svg]]
[[file:./.ob-jupyter/b689998cdfdd74781109afb6a523502318876d06.svg]]
[[file:./.ob-jupyter/88fcef6f0d71dbae4e9a3e3cbaa67b1a3db1363c.svg]]
[[file:./.ob-jupyter/73c5fc49ba925c3d94b2261ba9acb883c7d63471.svg]]
:END:
#+begin_src jupyter-python
for model in models:
plt.plot(model.t, abs(model.total_energy_from_power().value - model.total_energy().value))
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/c273a43c171394f3692125872ff6c55120ae3433.svg]]
#+begin_src jupyter-python
#[model.efficiency(steady_idx=2).value * 100 for model in models][10]
@ -129,7 +145,7 @@ coupling-change/cycle time.
#+RESULTS:
:RESULTS:
| <Figure | size | 1200x400 | with | 2 | Axes> | (<AxesSubplot: xlabel= $N$ ylabel= $P$ > <AxesSubplot: xlabel= $N$ ylabel= $\eta$ >) |
[[file:./.ob-jupyter/b83fcdfb93a1d0a389b5cd9da2636a6608fa8984.svg]]
[[file:./.ob-jupyter/9153bc446bd9633e64c8e85412f9927be1726228.svg]]
:END:
@ -237,11 +253,11 @@ coupling-change/cycle time.
#+RESULTS:
:RESULTS:
[[file:./.ob-jupyter/ee03f1655e72e9136c7065f01d0b7ba04b650017.svg]]
[[file:./.ob-jupyter/f1a1d3e17092ad84d554061c23b8309c424d0de1.svg]]
[[file:./.ob-jupyter/06a89fcc4c201fe7e9c39443a887282b76e54249.svg]]
[[file:./.ob-jupyter/b03717337099eef176d08e0082ceeb6305a8acae.svg]]
[[file:./.ob-jupyter/2c31e3832d8aaecafdf50e0f29694c30c77591c3.svg]]
[[file:./.ob-jupyter/2f0b0c42049806b930471968f418c6e09a12bb14.svg]]
[[file:./.ob-jupyter/37e8a4ed48d03590f17e8fa905d06a079ef9489d.svg]]
[[file:./.ob-jupyter/58030ee1b1a60e263a7ecff46e5e0978c0ebf080.svg]]
[[file:./.ob-jupyter/3e5f7ce49106a6dccaec67c139a3960c3b5977a3.svg]]
[[file:./.ob-jupyter/0f0039092a2d1bba7e2ee7161fd53005eea9c3d3.svg]]
:END:
#+begin_src jupyter-python
@ -275,13 +291,52 @@ coupling-change/cycle time.
#+end_src
#+RESULTS:
\[[file:./.ob-jupyter/1b3959a046911e0ebcbce3e6d6f5df7f5867ffbb.svg]]
[[file:./.ob-jupyter/40d7d6c96595d9d2028f7c9d6b5bb9e1646e07b4.svg]]
#+begin_src jupyter-python
* Weak Coupling Limit
#+begin_src jupyter-python :tangle tangle/weak_coupling_model.py
<<boilerplate>>
<<make-model>>
#+end_src
#+RESULTS:
#+begin_src jupyter-python :results none :tangle tangle/weak_coupling_model.py
weak_coupling_model = make_model(50, .01)
weak_coupling_model.num_cycles = 10
weak_coupling_model.k_max = 2
#+end_src
** Integrate
#+begin_src jupyter-python :tangle tangle/weak_coupling_model.py
ot.integrate_online(weak_coupling_model, 10, analyze_kwargs=dict(every=10_000))
#+end_src
#+RESULTS:
: [INFO hops.core.integration 137953] Choosing the nonlinear integrator.
: [INFO root 137953] Starting analysis process.
: [INFO root 137953] Started analysis process with pid 142086.
: [INFO hops.core.hierarchy_data 137953] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_length_coupling_strength/results_84524c4b2c9c8428030cfd17a749cdee70253c3d428b2416de22620c4b230cbd.fifo
: [INFO hops.core.integration 137953] Using 16 integrators.
: [INFO hops.core.integration 137953] Some 10 trajectories have to be integrated.
: [INFO hops.core.integration 137953] Using 66 hierarchy states.
: 100% 10/10 [01:56<00:00, 11.60s/it]
:
** Analysis
#+begin_src jupyter-python
ot.plot_energy(weak_coupling_model)
#+end_src
#+RESULTS:
:RESULTS:
| <Figure | size | 1200x400 | with | 1 | Axes> | <AxesSubplot: | xlabel= | $\tau$ | ylabel= | Energy | > |
[[file:./.ob-jupyter/cee1630d28ef76e85c16f224fc9c6516c3680e3f.svg]]
:END:
* Things to Look At
- power and efficiency
- magnitude of interaction energies

10
python/otto_motor/subprojects/cycle_length_coupling_strength/tangle/coupling_strength.py
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@ -56,6 +56,14 @@ ot.integrate_online_multi(models, 50_000, increment=10_000, analyze_kwargs=dict(
ot.plot_energy(models[5])
for i in range(len(Θs)):
fig, ax = plt.subplots()
for model in models[len(δs) * i :len(δs) * (i+1)]:
pu.plot_with_σ(model.t, model.system_energy(), ax=ax)
for model in models:
plt.plot(model.t, abs(model.total_energy_from_power().value - model.total_energy().value))
#[model.efficiency(steady_idx=2).value * 100 for model in models][10]
models[10].strobe, models[1].strobe
@ -191,4 +199,4 @@ ot.plot_3d_heatmap(
a_efficiency.set_zlabel(r"$\sigma_\eta/|\eta|$")
fs.export_fig("coupling_speed_scan_power_efficiency_uncertainty")
ot.plot_energy(weak_coupling_model)

51
python/otto_motor/subprojects/cycle_length_coupling_strength/tangle/weak_coupling_model.py Normal file
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@ -0,0 +1,51 @@
import figsaver as fs
import plot_utils as pu
from hiro_models.one_qubit_model import StocProcTolerances
from hiro_models.otto_cycle import OttoEngine
import hiro_models.model_auxiliary as aux
import numpy as np
import qutip as qt
import utilities as ut
import stocproc
import matplotlib.pyplot as plt
import otto_utilities as ot
import ray
ray.shutdown()
#ray.init(address='auto')
ray.init()
from hops.util.logging_setup import logging_setup
import logging
logging_setup(logging.INFO)
plt.rcParams['figure.figsize'] = (12,4)
def make_model(Θ, δ):
(p_H, p_L) = ot.timings(.06, .06)
return OttoEngine(
δ=[δ, δ],
ω_c=[1, 1],
ψ_0=qt.basis([2], [1]),
description=f"Classic Cycle",
k_max=4,
bcf_terms=[5] * 2,
truncation_scheme="simplex",
driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
thermal_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
T=[0.5, 4],
therm_methods=["tanhsinh", "tanhsinh"],
Δ=1,
num_cycles=3,
Θ=Θ,
dt=0.001,
timings_H=p_H,
timings_L=p_L,
streaming_mode=True,
shift_to_resonance=(False, False),
L_shift=(0, 0),
)
weak_coupling_model = make_model(50, .01)
weak_coupling_model.num_cycles = 10
weak_coupling_model.k_max = 2
ot.integrate_online(weak_coupling_model, 10, analyze_kwargs=dict(every=10_000))