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finish off-axis analysis

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valentin.boettcher@mailbox.tu-dresden.de 2023-05-10 13:43:48 -04:00
parent 6e8dda642b
commit 530511a8b5
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79 changed files with 197077 additions and 142152 deletions
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2
.envrc
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@ -1,2 +0,0 @@
use_flake
eval "$shellHook"

1
.gitattributes vendored Normal file
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@ -0,0 +1 @@
*.svg binary

42
flake.lock generated
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@ -1,42 +0,0 @@
{
"nodes": {
"flake-utils": {
"locked": {
"lastModified": 1667395993,
"narHash": "sha256-nuEHfE/LcWyuSWnS8t12N1wc105Qtau+/OdUAjtQ0rA=",
"owner": "numtide",
"repo": "flake-utils",
"rev": "5aed5285a952e0b949eb3ba02c12fa4fcfef535f",
"type": "github"
},
"original": {
"owner": "numtide",
"repo": "flake-utils",
"type": "github"
}
},
"nixpkgs": {
"locked": {
"lastModified": 1668994630,
"narHash": "sha256-1lqx6HLyw6fMNX/hXrrETG1vMvZRGm2XVC9O/Jt0T6c=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "af50806f7c6ab40df3e6b239099e8f8385f6c78b",
"type": "github"
},
"original": {
"id": "nixpkgs",
"ref": "nixos-unstable",
"type": "indirect"
}
},
"root": {
"inputs": {
"flake-utils": "flake-utils",
"nixpkgs": "nixpkgs"
}
}
},
"root": "root",
"version": 7
}

51
flake.nix
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@ -1,51 +0,0 @@
{
description = "Typesetting for the Energy Flow Paper";
inputs = {
nixpkgs.url = "nixpkgs/nixos-unstable";
flake-utils.url = "github:numtide/flake-utils";
flake-utils.inputs.nixpkgs.follows = "nixpkgs";
};
outputs = { self, nixpkgs, flake-utils }:
with flake-utils.lib; eachSystem allSystems (system:
let
pkgs = nixpkgs.legacyPackages.${system};
tex = pkgs.texlive.combine {
inherit (pkgs.texlive) scheme-medium latexmk koma-script babel-english
physics mathtools amsmath fontspec booktabs siunitx caption biblatex float
pgfplots microtype fancyvrb csquotes setspace newunicodechar hyperref
cleveref multirow bbold unicode-math biblatex-phys xpatch beamerposter
type1cm changepage lualatex-math footmisc wrapfig2 curve2e pict2e wrapfig blindtext
appendixnumberbeamer sidecap appendix orcidlink ncctools bigfoot crop xcolor revtex;
};
in
rec {
packages = {
document = pkgs.stdenvNoCC.mkDerivation rec {
name = "eflow_paper";
src = self;
buildInputs = [ pkgs.coreutils tex pkgs.biber ];
phases = [ "unpackPhase" "buildPhase" "installPhase" ];
buildPhase = ''
export PATH="${pkgs.lib.makeBinPath buildInputs}";
mkdir -p .cache/texmf-var
mkdir -p output/src
env TEXMFHOME=.cache TEXMFVAR=.cache/texmf-var \
OSFONTDIR=${pkgs.gyre-fonts}/share/fonts:${pkgs.liberation_ttf}/share/fonts:${pkgs.lato}/share/fonts/lato:${pkgs.raleway}/share/fonts:${pkgs.lmodern}/share/fonts \
latexmk ./index.tex
'';
installPhase = ''
mkdir -p $out
cp output/index.pdf $out/
'';
};
};
defaultPackage = packages.document;
devShell = pkgs.mkShellNoCC {
buildInputs = [ pkgs.openjdk ] ++ packages.document.buildInputs;
shellHook = ''
export OSFONTDIR=${pkgs.gyre-fonts}/share/fonts:${pkgs.liberation_ttf}/share/fonts:${pkgs.lato}/share/fonts/lato:${pkgs.raleway}/share/fonts:${pkgs.lmodern}/share/fonts
'';
};
});
}

25
python/otto_motor/otto_utilities.py
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@ -549,18 +549,29 @@ def plot_steady_work_baths(models, steady_idx=2, label_fn=model_description):
@pu.wrap_plot
def plot_bloch_components(model, ax=None):
def plot_bloch_components(model, ax=None, **kwargs):
with aux.get_data(model) as data:
ρ = data.rho_t_accum.mean[:]
σ_ρ = data.rho_t_accum.ensemble_std[:]
xs = np.einsum("tij,ji->t", σ_ρ, qt.sigmax().full()).real
ys = np.einsum("tij,ji->t", ρ, qt.sigmax().full()).real
xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full()).real
ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full()).real
zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full()).real
ax.plot(model.t, zs, label=r"$\langle \sigma_z\rangle$")
ax.plot(model.t, xs, label=r"$\langle \sigma_x\rangle$")
ax.plot(model.t, ys, label=r"$\langle \sigma_y\rangle$")
ax.plot(
model.t,
zs,
**(dict(label=r"$\langle \sigma_z\rangle$", color="C1") | kwargs),
)
ax.plot(
model.t,
xs,
**(dict(label=r"$\langle \sigma_x\rangle$", color="C2") | kwargs),
)
ax.plot(
model.t,
ys,
**(dict(label=r"$\langle \sigma_y\rangle$", color="C3") | kwargs),
)
ax.legend()
ax.set_xlabel(r"$\tau$")

33
python/otto_motor/subprojects/cycle_shift/.ipynb_checkpoints/plots-checkpoint.ipynb Normal file
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@ -0,0 +1,33 @@
{
"cells": [
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"cell_type": "code",
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"id": "9455ed50",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

36
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@ -38046,7 +38046,7 @@ L 63.662873 158.568574
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316
python/otto_motor/subprojects/cycle_shift/cycle_shift.org
View file

@ -9,7 +9,6 @@ find if there is an improvement.
* Boilerplate
#+name: boilerplate
#+begin_src jupyter-python :results none
import figsaver as fs
import plot_utils as pu
from hiro_models.one_qubit_model import StocProcTolerances
from hiro_models.otto_cycle import OttoEngine
@ -22,6 +21,7 @@ find if there is an improvement.
import otto_utilities as ot
import shift_cycle as sc
import ray
import figsaver as fs
ray.shutdown()
#ray.init(address='auto')
@ -29,7 +29,6 @@ find if there is an improvement.
from hops.util.logging_setup import logging_setup
import logging
logging_setup(logging.INFO)
plt.rcParams['figure.figsize'] = (12,4)
#+end_src
* Cycles
@ -80,7 +79,7 @@ We shift so that we just overlap with coupling/decoupling and one above.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/af1722dd51faebbec05b3013eba2c2e2864dd0f4.svg]]
[[file:./.ob-jupyter/02cbbf58cd0cbe9a198d4e1b1ca05e1435224c50.svg]]
#+begin_src jupyter-python :tangle no
#ot.plot_cycles(models, bath=0, legend=True)
@ -104,7 +103,7 @@ We shift so that we just overlap with coupling/decoupling and one above.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/73ad99c8f33025ef14048abda5eeefe8c0dd7b15.svg]]
[[file:./.ob-jupyter/2eaccf65458d4ed821c07abbb83664ee16b423f9.svg]]
** Integrate
#+begin_src jupyter-python :tangle tangle/original.py
@ -112,7 +111,6 @@ We shift so that we just overlap with coupling/decoupling and one above.
#+end_src
#+RESULTS:
: 88731cee-8bf8-411e-b9db-0b0138807646
** Analysis
#+begin_src jupyter-python
@ -147,7 +145,7 @@ We shift so that we just overlap with coupling/decoupling and one above.
#+RESULTS:
:RESULTS:
: \(N=80000\)
[[file:./.ob-jupyter/7c4c619d2f9e55840f6181197c2c4e96a71c324a.svg]]
[[file:./.ob-jupyter/f365b8ee0b20cdf41f305aaaf802be5b634b2953.svg]]
:END:
#+begin_src jupyter-python
@ -158,7 +156,7 @@ We shift so that we just overlap with coupling/decoupling and one above.
return ρ
import hops.util.utilities
from hopsflow.util import EnsembleValue
for model in models[3:4]:
for model in [baseline]:
with aux.get_data(model) as data:
trace_dist_c = hops.util.utilities.trace_distance(data, relative_to=thermal_state(model.T[0], model.energy_gaps[0]))
trace_dist_h = hops.util.utilities.trace_distance(data, relative_to=thermal_state(model.T[1], model.energy_gaps[1]))
@ -202,7 +200,7 @@ We shift so that we just overlap with coupling/decoupling and one above.
/nix/store/08ccd1bg10pbkpv71fwccyxabr1cycim-python3-3.9.15-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5346: ComplexWarning: Casting complex values to real discards the imaginary part
pts[N+2:, 1] = dep2slice[::-1]
#+end_example
[[file:./.ob-jupyter/81d7229d7ed52abe3f521c318cdeeeb4c52e5a87.svg]]
[[file:./.ob-jupyter/de007b03b6f53431f27dcfefdd5740a92e27b785.svg]]
:END:
#+begin_src jupyter-python
@ -211,6 +209,7 @@ We shift so that we just overlap with coupling/decoupling and one above.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/1a1f8810d9deff000ef064ac9ff34c328f5e846b.svg]]
#+begin_src jupyter-python
@ -606,14 +605,31 @@ modulation.
:END:
** Slower switching
#+begin_src jupyter-python :results none :tangle tangle/long.py
<<boilerplate>>
#+begin_src jupyter-python :tangle tangle/long.py
#<<boilerplate>>
shifts = sc.make_shifts(extra_r=4)
long_models = [sc.make_model(shift, shift, switch_t=6., switch_t_sys=3) for shift in shifts]
#long_models = [sc.make_model(shift, shift, switch_t=6., switch_t_sys=3) for shift in shifts]
long_models = [sc.make_model(shift, shift, switch_t=6.) for shift in shifts]
long_baseline = sc.make_model(0., 0., switch_t=6.)
#+end_src
#+RESULTS:
#+begin_example
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
7.199999999999999
#+end_example
#+begin_src jupyter-python :tangle no
fig, ax = plt.subplots()
t = np.linspace(0, models[0].Θ, 1000)
@ -631,8 +647,8 @@ modulation.
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7fe9b15f0340>
[[file:./.ob-jupyter/cb7df72fdbae443014029e58c57c8cd040d2cdad.svg]]
: <matplotlib.legend.Legend at 0x7fe235bfd1f0>
[[file:./.ob-jupyter/b97b3645c10c9324bb66e178bdf8b63ec4a5a628.svg]]
:END:
@ -660,16 +676,15 @@ modulation.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/ad6aa52c6730f14c9f10f32edb03ae96b8f6a006.svg]]
[[file:./.ob-jupyter/ac824b547713cd79da184599c7f503e85b44bc40.svg]]
#+begin_src jupyter-python :tangle tangle/long.py
ot.integrate_online_multi(long_models, 80_000, increment=10_000, analyze_kwargs=dict(every=10_000))
#+end_src
#+RESULTS:
#+begin_src jupyter-python
long_baseline = long_models[np.argmin(abs(np.array(shifts) - 0))]
long_baseline = best_shift_model
for shift, model in zip(shifts, long_models):
print(
shift, best_shift,
@ -682,17 +697,17 @@ modulation.
#+RESULTS:
#+begin_example
-0.18 0.12 80000 -0.7432352797196996 -98.3933277583729 (-0.6547883379568094, 0.32914493962691976)
-0.12 0.12 80000 0.5515764970059213 -4.588837155210551 (0.28325656807481425, 0.32914493962691976)
-0.06 0.12 80000 0.9849641934327689 10.389858999462525 (0.433043529621545, 0.32914493962691976)
0.0 0.12 80000 0.9864290608793423 10.386118256193916 (0.4330061221888589, 0.32914493962691976)
0.06 0.12 80000 0.9777553355809067 10.616195121156663 (0.4353068908384864, 0.32914493962691976)
0.12 0.12 80000 1.1185864670592693 10.712677023075978 (0.43627170985767955, 0.32914493962691976)
0.18 0.12 80000 1.280064611377701 7.403981413620636 (0.4031847537631261, 0.32914493962691976)
0.24 0.12 80000 1.1867997141496045 2.747192020803302 (0.3566168598349528, 0.32914493962691976)
0.3 0.12 80000 0.7281813558728701 -10.479096524510464 (0.22435397438181512, 0.32914493962691976)
0.36 0.12 80000 -0.45197344202291523 -49.898300662353925 (-0.16983806699661946, 0.32914493962691976)
0.42 0.12 80000 -1.4644608753444812 -108.49803909971185 (-0.7558354513701987, 0.32914493962691976)
-0.18 0.12 80000 -0.7534604455561659 -108.77944601456684 (-0.6547883379568094, 0.4330061221888589)
-0.12 0.12 80000 0.5591648896822077 -14.974955411404467 (0.28325656807481425, 0.4330061221888589)
-0.06 0.12 80000 0.9985149794296736 0.0037407432686098474 (0.433043529621545, 0.4330061221888589)
0.0 0.12 80000 1.0 0.0 (0.4330061221888589, 0.4330061221888589)
0.06 0.12 80000 0.991206944683175 0.23007686496274715 (0.4353068908384864, 0.4330061221888589)
0.12 0.12 80000 1.1339755806282883 0.3265587668820624 (0.43627170985767955, 0.4330061221888589)
0.18 0.12 80000 1.297675283650504 -2.9821368425732797 (0.4031847537631261, 0.4330061221888589)
0.24 0.12 80000 1.2031272812376834 -7.638926235390614 (0.3566168598349528, 0.4330061221888589)
0.3 0.12 80000 0.7381994151953918 -20.86521478070438 (0.22435397438181512, 0.4330061221888589)
0.36 0.12 80000 -0.45819153140116126 -60.284418918547836 (-0.16983806699661946, 0.4330061221888589)
0.42 0.12 80000 -1.4846084056353754 -118.88415735590576 (-0.7558354513701987, 0.4330061221888589)
#+end_example
@ -710,7 +725,7 @@ modulation.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/3c84f70ced5643089d048ba60451f50a3409c231.svg]]
[[file:./.ob-jupyter/04d76de3e6d507097b891765428934a25429abaf.svg]]
Aho! The trick is just to slow down the coupling switching.
@ -723,8 +738,8 @@ Aho! The trick is just to slow down the coupling switching.
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7fe9b08ff2e0>
[[file:./.ob-jupyter/4fb778a2d542fde60accec2963706290dff4418b.svg]]
: <matplotlib.legend.Legend at 0x7f0ef3d3d940>
[[file:./.ob-jupyter/bbb9d138d6915d98c086c7f49d76baf55e5f8e0c.svg]]
:END:
#+begin_src jupyter-python
@ -741,7 +756,7 @@ Aho! The trick is just to slow down the coupling switching.
# [goto error]
: ---------------------------------------------------------------------------
: NameError Traceback (most recent call last)
: Cell In[27], line 3
: Cell In[53], line 3
:  1 powers_long = [-model.power(steady_idx=2).value for model in long_models]
:  2 powers_short = [-model.power(steady_idx=2).value for model in models]
: ----> 3 power_overlap = -overlap_models[0].power(steady_idx=2).value
@ -804,7 +819,7 @@ Now let's try to find out why the efficiency improved.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/1446c6e53dac7fbcb1089dcf980e7299b7d75301.svg]]
[[file:./.ob-jupyter/8cb5b5062c2f5c2b8d1cb21f999a299098958e0d.svg]]
Way less energy is dumped into the cold bath.
#+begin_src jupyter-python
@ -853,17 +868,17 @@ Way less energy is dumped into the cold bath.
#+RESULTS:
:RESULTS:
: /home/hiro/src/hopsflow/hopsflow/util.py:332: RuntimeWarning: divide by zero encountered in divide
: /home/hiro/src/hopsflow/hopsflow/util.py:331: RuntimeWarning: divide by zero encountered in divide
: left_i[1] / right_i[1],
: /home/hiro/src/hopsflow/hopsflow/util.py:332: RuntimeWarning: invalid value encountered in divide
: /home/hiro/src/hopsflow/hopsflow/util.py:331: RuntimeWarning: invalid value encountered in divide
: left_i[1] / right_i[1],
: /home/hiro/src/hopsflow/hopsflow/util.py:334: RuntimeWarning: divide by zero encountered in divide
: /home/hiro/src/hopsflow/hopsflow/util.py:333: RuntimeWarning: divide by zero encountered in divide
: (left_i[2] / right_i[1]) ** 2
: /home/hiro/src/hopsflow/hopsflow/util.py:335: RuntimeWarning: divide by zero encountered in divide
: /home/hiro/src/hopsflow/hopsflow/util.py:334: RuntimeWarning: divide by zero encountered in divide
: + (left_i[1] / (right_i[1]) ** 2 * right_i[2]) ** 2
: /home/hiro/src/hopsflow/hopsflow/util.py:335: RuntimeWarning: invalid value encountered in divide
: /home/hiro/src/hopsflow/hopsflow/util.py:334: RuntimeWarning: invalid value encountered in divide
: + (left_i[1] / (right_i[1]) ** 2 * right_i[2]) ** 2
[[file:./.ob-jupyter/3d588233ddaeb47b80e2d98855937e09ec32bd3f.svg]]
[[file:./.ob-jupyter/73f0fb2a97297159d4517b28ae328108d70c260c.svg]]
:END:
#+begin_src jupyter-python
@ -895,9 +910,39 @@ Way less energy is dumped into the cold bath.
#+end_src
#+begin_src jupyter-python :tangle tangle/only_cold.py
ot.integrate_online_multi(cold_models, 80_000, increment=10_000, analyze_kwargs=dict(every=10_000))
ot.integrate_online_multi(cold_models, 1, increment=1, analyze_kwargs=dict(every=10_000))
#+end_src
#+RESULTS:
#+begin_example
[INFO hops.core.integration 7496] Choosing the nonlinear integrator.
[INFO root 7496] Starting analysis process.
[INFO root 7496] Started analysis process with pid 16271.
/home/hiro/src/hops/hops/core/hierarchy_data.py:570: UserWarning: Moving .data/5d118bc5b64eb105ed45c825742f9241f81efa681dc7f6095b7d27ab6b96c284/_6/5d118bc5b64eb105ed45c825742f9241f81efa681dc7f6095b7d27ab6b96c284_6c882ba21738f240daf65b5a8bfeab4b_1.h5 to .data/5d118bc5b64eb105ed45c825742f9241f81efa681dc7f6095b7d27ab6b96c284/_6/5d118bc5b64eb105ed45c825742f9241f81efa681dc7f6095b7d27ab6b96c284_6c882ba21738f240daf65b5a8bfeab4b_1.h5backup_1683647050.4820192 and starting fresh.
warnings.warn(
[INFO hops.core.hierarchy_data 7496] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_5d118bc5b64eb105ed45c825742f9241f81efa681dc7f6095b7d27ab6b96c284.fifo
[INFO hops.core.integration 7496] Using 13 integrators.
[INFO hops.core.integration 7496] Some 1 trajectories have to be integrated.
[INFO hops.core.integration 7496] Using 1001 hierarchy states.
100% 1/1 [00:09<00:00, 9.14s/it]
[INFO hops.core.integration 7496] Choosing the nonlinear integrator.
[INFO root 7496] Starting analysis process.
[INFO root 7496] Started analysis process with pid 16288.
[INFO hops.core.hierarchy_data 7496] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_a86b8a23ecd962904a2008808f85b00b9c337e36dc6bebe3b806343510f0ec7f.fifo
[INFO hops.core.integration 7496] Using 13 integrators.
[INFO hops.core.integration 7496] Some 1 trajectories have to be integrated.
[INFO hops.core.integration 7496] Using 1001 hierarchy states.
100% 1/1 [00:08<00:00, 8.61s/it]
[INFO hops.core.integration 7496] Choosing the nonlinear integrator.
[INFO root 7496] Starting analysis process.
[INFO root 7496] Started analysis process with pid 16320.
[INFO hops.core.hierarchy_data 7496] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_ca94f36cbacd1b29d6e4f3cf384624ef0f4930ef3b0f12b09179efb17e327454.fifo
[INFO hops.core.integration 7496] Using 13 integrators.
[INFO hops.core.integration 7496] Some 1 trajectories have to be integrated.
[INFO hops.core.integration 7496] Using 1001 hierarchy states.
100% 1/1 [00:09<00:00, 9.09s/it]
#+end_example
*** Cycle showcase
#+begin_src jupyter-python :tangle no
fig, ax = plt.subplots()
@ -916,9 +961,8 @@ Way less energy is dumped into the cold bath.
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/de3eb2a9f0973f77f95d912248d6f10ee0635854.svg]]
[[file:./.ob-jupyter/6f46b84d2bad1908064cc760b32f1330b4dd9530.svg]]
[[file:./.ob-jupyter/e3413d8306c7996bd7476d0047a621c7c973187e.svg]]
#+begin_src jupyter-python
from itertools import cycle
@ -942,7 +986,7 @@ Way less energy is dumped into the cold bath.
#+RESULTS:
:RESULTS:
| 0.0 | 60.0 |
[[file:./.ob-jupyter/71e4dbcb0c094107e336437a1247ef0a1e316bcb.svg]]
[[file:./.ob-jupyter/91f5f58abe88d312dda69ea17845661d235cbb7b.svg]]
:END:
#+begin_src jupyter-python
@ -967,13 +1011,12 @@ Way less energy is dumped into the cold bath.
# [goto error]
#+begin_example
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[36], line 3
RuntimeError Traceback (most recent call last)
Cell In[56], line 2
 1 fig, (ax2, ax1, ax3) = plt.subplots(nrows=1, ncols=3)
 2 _, ax1_right = ot.plot_powers_and_efficiencies(np.array(shifts) * 100, cold_models, xlabel="Cycle Shift", ax=ax1)[2]
----> 3 _, ax2_right = ot.plot_powers_and_efficiencies(np.array(shifts) * 100, long_models, xlabel="Cycle Shift", ax=ax2)[2]
----> 2 _, ax1_right = ot.plot_powers_and_efficiencies(np.array(shifts) * 100, cold_models, xlabel="Cycle Shift", ax=ax1)[2]
 3 _, ax2_right = ot.plot_powers_and_efficiencies(np.array(shifts) * 100, long_models, xlabel="Cycle Shift", ax=ax2)[2]
 4 _, ax3_right = ot.plot_powers_and_efficiencies(np.array(shifts) * 100, models, xlabel="Cycle Shift", ax=ax3)[2]
 6 ax1_right.sharey(ax2_right)
File ~/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/plot_utils.py:40, in wrap_plot.<locals>.wrapped(ax, setup_function, *args, **kwargs)
 37 if not ax:
@ -981,42 +1024,70 @@ Way less energy is dumped into the cold bath.
---> 40 ret_val = f(*args, ax=ax, **kwargs)
 41 return (fig, ax, ret_val) if ret_val else (fig, ax)
File ~/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/otto_utilities.py:80, in plot_powers_and_efficiencies(x, models, steady_idx, ax, xlabel)
 78 mask = efficiencies > 0
 79 a2 = ax.twinx()
---> 80 ax.errorbar(x, powers, yerr=powers_σ, marker=".", label=r"$\bar{P}$")
 81 ax.errorbar(
 82 x,
 83 system_powers,
File ~/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/otto_utilities.py:30, in plot_powers_and_efficiencies(x, models, steady_idx, ax, xlabel)
 28 @pu.wrap_plot
 29 def plot_powers_and_efficiencies(x, models, steady_idx=1, ax=None, xlabel=""):
---> 30 powers = [-model.power(steady_idx=steady_idx).value for model in models]
 31 powers_σ = [model.power(steady_idx=steady_idx).σ for model in models]
 33 ax.axhline(0, color="lightgray")
File ~/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/otto_utilities.py:30, in <listcomp>(.0)
 28 @pu.wrap_plot
 29 def plot_powers_and_efficiencies(x, models, steady_idx=1, ax=None, xlabel=""):
---> 30 powers = [-model.power(steady_idx=steady_idx).value for model in models]
 31 powers_σ = [model.power(steady_idx=steady_idx).σ for model in models]
 33 ax.axhline(0, color="lightgray")
File <@beartype(hiro_models.otto_cycle.OttoEngine.power) at 0x7f0f4ec2a820>:31, in power(__beartype_func, __beartype_conf, __beartype_get_violation, *args, **kwargs)
File ~/src/two_qubit_model/hiro_models/otto_cycle.py:483, in OttoEngine.power(self, steady_idx, *args, **kwargs)
 476 """
 477 Calculate the mean steady state power. For the arguments see
 478 :any:`steady_energy_change`.
 479 """
 481 _, indices = self.strobe
--> 483 return self.total_power().slice(slice(indices[steady_idx], None, 1)).mean
File ~/src/two_qubit_model/hiro_models/model_base.py:649, in Model.total_power(self, data, **kwargs)
 638 def total_power(self, data: Optional[HIData] = None, **kwargs) -> EnsembleValue:
 639 """Calculates the total power from the trajectories in
 640  ``data`` or, if not supplied, tries to load
 641  the online results from ``results_path``.
 (...)
 86 label=r"$\bar{P}_{\mathrm{sys}}$",
 87 )
 89 ax.errorbar(
 90 x,
 91 interaction_powers,
 646  :returns: The total power.
 647  """
--> 649 power = self.interaction_power(data, **kwargs).sum_baths()
 650 system_power = self.system_power(data, **kwargs)
 652 if system_power is not None:
File ~/src/two_qubit_model/hiro_models/model_base.py:560, in Model.interaction_power(self, data, results_path, **kwargs)
 550 """Calculates interaction power from the hierarchy data
 551 ``data`` or, if not supplied, tries to load the online results from ``results_path``.
 552
 (...)
 94 label=r"$\bar{P}_{\mathrm{int}}$",
 95 )
 556 :returns: See :any:`hopsflow.util.interaction_energy_ensemble`.
 557 """
 559 if data is None:
--> 560 return self.try_get_online_data(
 561  results_path, self.online_interaction_power_name
 562  )
 564 N, kwargs = _get_N_kwargs(kwargs, data)
 566 return hopsflow.hopsflow.interaction_energy_ensemble(
 567 data.valid_sample_iterator(data.stoc_traj), # type: ignore
 568 data.valid_sample_iterator(data.aux_states), # type: ignore
 (...)
 574 **kwargs,
 575 )
File /nix/store/08ccd1bg10pbkpv71fwccyxabr1cycim-python3-3.9.15-env/lib/python3.9/site-packages/matplotlib/__init__.py:1423, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)
 1420 @functools.wraps(func)
 1421 def inner(ax, *args, data=None, **kwargs):
 1422 if data is None:
-> 1423 return func(ax, *map(sanitize_sequence, args), **kwargs)
 1425 bound = new_sig.bind(ax, *args, **kwargs)
 1426 auto_label = (bound.arguments.get(label_namer)
 1427 or bound.kwargs.get(label_namer))
File ~/src/two_qubit_model/hiro_models/model_base.py:298, in Model.try_get_online_data(self, path, results_path)
 296 file_path = os.path.join(path, results_path)
 297 if not os.path.exists(file_path):
--> 298 raise RuntimeError(f"No data found under '{file_path}'.")
 300 return hopsflow.util.get_online_values_from_cache(file_path)
File /nix/store/08ccd1bg10pbkpv71fwccyxabr1cycim-python3-3.9.15-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:3491, in Axes.errorbar(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)
 3489 x, y = np.atleast_1d(x, y) # Make sure all the args are iterable.
 3490 if len(x) != len(y):
-> 3491 raise ValueError("'x' and 'y' must have the same size")
 3493 everymask = self._errorevery_to_mask(x, errorevery)
 3495 label = kwargs.pop("label", None)
ValueError: 'x' and 'y' must have the same size
RuntimeError: No data found under 'results/interaction_power_a86b8a23ecd962904a2008808f85b00b9c337e36dc6bebe3b806343510f0ec7f.npz'.
#+end_example
[[file:./.ob-jupyter/0808a4e641f3bfd149695bb00c73368d6b872d03.svg]]
[[file:./.ob-jupyter/93e52118286ecb9820456f385076257d6d00cdaf.svg]]
:END:
#+begin_src jupyter-python
@ -1094,53 +1165,98 @@ Way less energy is dumped into the cold bath.
<<boilerplate>>
#+end_src
#+begin_src jupyter-python
aux.import_results(other_data_path="taurus/.data_oa", other_results_path="taurus/results")
#+end_src
#+RESULTS:
#+begin_example
[INFO root 465451] Not importing 3a88241b44e111869567acbf6ae02285aed02d52bb726b72ac72f9ab31dd3519.
[WARNING root 465451] Importing taurus/.data_oa/1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a/_b/1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a_b840f965f993ebd32f9155891c0b1cb4_1.h5 to .data/1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a/_b/1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a_b840f965f993ebd32f9155891c0b1cb4_1.h5.
[WARNING root 465451] The model description is 'Classic Cycle'.
[WARNING root 465451] Importing taurus/results/flow_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/flow_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a_10000.npz.
[WARNING root 465451] Importing taurus/results/flow_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/flow_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz.
[WARNING root 465451] Importing taurus/results/interaction_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/interaction_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a_10000.npz.
[WARNING root 465451] Importing taurus/results/interaction_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/interaction_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz.
[WARNING root 465451] Importing taurus/results/interaction_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/interaction_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a_10000.npz.
[WARNING root 465451] Importing taurus/results/interaction_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/interaction_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz.
[WARNING root 465451] Importing taurus/results/system_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/system_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz.
[WARNING root 465451] Importing taurus/results/system_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/system_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a_10000.npz.
[WARNING root 465451] Importing taurus/results/system_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/system_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a_10000.npz.
[WARNING root 465451] Importing taurus/results/system_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz to results/system_power_1ebe2f33ba38ab70dc6af5f72cd5e4163422489db2447d034dcd8d405a0fc79a.npz.
#+end_example
#+begin_src jupyter-python :tangle tangle/off_axis.py
proto = sc.make_model(0, 0, switch_t=6)
off_ax_models = []
for weight in [.3, .6]:
off_ax = proto.copy()
weights = [.3, .6]
param_iter = lambda: itertools.product([3, 6], weights)
for switch_t, weight in param_iter():
off_ax = sc.make_model(0, 0, switch_t=switch_t)
off_ax.H_0 = 1 / 2 * (qt.sigmaz().full() + np.eye(2) + weight * qt.sigmax().full())
# NOTE: the hamiltonians will be normalzed so that their smallest EV is 0 and the largest one is 1
off_ax.H_1 = off_ax.H_0.copy()
off_ax_models.append(off_ax)
#+end_src
#+RESULTS:
: 3.5999999999999996
: 3.5999999999999996
: 7.199999999999999
: 7.199999999999999
#+begin_src jupyter-python :tangle tangle/off_axis.py
ot.integrate_online_multi(off_ax_models, 10_000, increment=10_000, analyze_kwargs=dict(every=10_000))
ot.integrate_online_multi(off_ax_models, 10, increment=10, analyze_kwargs=dict(every=10_000))
#+end_src
#+RESULTS:
: [INFO hops.core.integration 4057165] Choosing the nonlinear integrator.
: [INFO root 4057165] Starting analysis process.
: [INFO root 4057165] Started analysis process with pid 146332.
: [INFO hops.core.hierarchy_data 4057165] Creating the streaming fifo at: /home/hiro/Documents/Projects/UNI/master/eflow_paper/python/otto_motor/subprojects/cycle_shift/results_f41081a896f5826d5ec9ec455f52afb2020d140c4d25bd2986b63fb73863ee2d.fifo
: [INFO hops.core.integration 4057165] Using 16 integrators.
: [INFO hops.core.integration 4057165] Some 10 trajectories have to be integrated.
: [INFO hops.core.integration 4057165] Using 1001 hierarchy states.
: 100% 10/10 [00:45<00:00, 4.58s/it]
:
#+begin_src jupyter-julia
ot.plot_bloch_components(off_ax_models[0])
#+begin_src jupyter-python
for (i, model), weight in zip(enumerate(off_ax_models), weights):
f, a = ot.plot_bloch_components(model)
#ot.plot_bloch_components(off_ax_models[i+2], ax=a, linestyle="--", label=None)
a.set_title(rf"$r_y={weight}$")
fs.export_fig(f"bloch_expectation_offaxis_{weight}", y_scaling=.7)
#+end_src
#+RESULTS:
:RESULTS:
| <Figure | size | 340x320 | with | 1 | Axes> | <AxesSubplot: | xlabel= | $\tau$ | > |
[[file:./.ob-jupyter/e69f3f0bcc548ae510cff4c2ec6ca0a46c17c078.svg]]
[[file:./.ob-jupyter/9728f3b41ba1c00b9b3b8cdef827909ece227deb.svg]]
[[file:./.ob-jupyter/66410d6f93476857e9f7f681a59a0091ebc27231.svg]]
:END:
#+begin_src jupyter-python
np.array(weights) / np.sqrt(1 + np.array(weights) ** 2)
#+end_src
#+RESULTS:
: array([0.28734789, 0.51449576])
#+begin_src jupyter-python
for model in off_ax_models:
print(model.power(steady_idx=1).value / baseline.power(steady_idx=1).value, model.efficiency(steady_idx=1).value / baseline.efficiency(steady_idx=1).value)
baselines = [baseline] * 2 + [long_baseline] * 2
for model, ref in zip(off_ax_models, baselines):
print(model.power(steady_idx=1).value / ref.power(steady_idx=1).value, model.efficiency(steady_idx=1).value / ref.efficiency(steady_idx=1).value)
#+end_src
#+RESULTS:
: 1.0574121708551179 1.016329556714788
: 1.1608389370275625 0.9458062530964767
: 1.039411606725501 0.9835515009556695
: 1.117040153314062 0.9663817206527843
Interestingly, even with the too-fast switching we have greater power.
Interestingly, even with the too-fast switching we have greater power, and greater efficiency.
#+begin_src jupyter-python
for (i, model), weight in zip(enumerate(off_ax_models), weights):
f, a = ot.plot_energy(model)
a.set_title(rf"$r_y={weight}$")
fs.export_fig(f"full_energy_offaxis_{weight}", x_scaling=2, y_scaling=1)
#+end_src
#+RESULTS:
:RESULTS:
[[file:./.ob-jupyter/b0afa48c75085e2a541587c350698ad79958b170.svg]]
[[file:./.ob-jupyter/cb9eaeab19b96ffab30ddb3adde4ba165801064d.svg]]
:END:

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28
python/otto_motor/subprojects/cycle_shift/tangle/cycle_shift.py
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@ -1,4 +1,3 @@
import figsaver as fs
import plot_utils as pu
from hiro_models.one_qubit_model import StocProcTolerances
from hiro_models.otto_cycle import OttoEngine
@ -11,6 +10,7 @@ import matplotlib.pyplot as plt
import otto_utilities as ot
import shift_cycle as sc
import ray
import figsaver as fs
ray.shutdown()
#ray.init(address='auto')
@ -18,7 +18,6 @@ ray.init()
from hops.util.logging_setup import logging_setup
import logging
logging_setup(logging.INFO)
plt.rcParams['figure.figsize'] = (12,4)
ot.plot_cycle(baseline)
fs.export_fig("cycle_prototype", y_scaling=.7)
@ -40,7 +39,7 @@ def thermal_state(T, Ω):
return ρ
import hops.util.utilities
from hopsflow.util import EnsembleValue
for model in models[3:4]:
for model in [baseline]:
with aux.get_data(model) as data:
trace_dist_c = hops.util.utilities.trace_distance(data, relative_to=thermal_state(model.T[0], model.energy_gaps[0]))
trace_dist_h = hops.util.utilities.trace_distance(data, relative_to=thermal_state(model.T[1], model.energy_gaps[1]))
@ -277,8 +276,6 @@ ax.add_artist(legend_1)
ax.set_xlim((0, long_models[0].Θ))
fs.export_fig("cycle_shift_long_shifts", x_scaling=2, y_scaling=.5)
long_baseline = long_models[np.argmin(abs(np.array(shifts) - 0))]
long_baseline = best_shift_model
for shift, model in zip(shifts, long_models):
print(
shift, best_shift,
@ -416,7 +413,22 @@ fs.export_fig("cycle_shift_power_efficiency_longer_vs_only_cold", y_scaling=.7,
ot.plot_multi_powers_and_efficiencies(shifts, [models, long_models, cold_models], ["shifted", "shifted + slower modulation", "slower + only cold shifted"], xlabel=r"Shift $\delta$")
fs.export_fig("shift_comparison", y_scaling=1, x_scaling=2)
ot.plot_bloch_components(off_ax_models[0])
aux.import_results(other_data_path="taurus/.data_oa", other_results_path="taurus/results")
for model in off_ax_models:
print(model.power(steady_idx=1).value / baseline.power(steady_idx=1).value, model.efficiency(steady_idx=1).value / baseline.efficiency(steady_idx=1).value)
for (i, model), weight in zip(enumerate(off_ax_models), weights):
f, a = ot.plot_bloch_components(model)
#ot.plot_bloch_components(off_ax_models[i+2], ax=a, linestyle="--", label=None)
a.set_title(rf"$r_y={weight}$")
fs.export_fig(f"bloch_expectation_offaxis_{weight}", y_scaling=.7)
np.array(weights) / np.sqrt(1 + np.array(weights) ** 2)
baselines = [baseline] * 2 + [long_baseline] * 2
for model, ref in zip(off_ax_models, baselines):
print(model.power(steady_idx=1).value / ref.power(steady_idx=1).value, model.efficiency(steady_idx=1).value / ref.efficiency(steady_idx=1).value)
for (i, model), weight in zip(enumerate(off_ax_models), weights):
f, a = ot.plot_energy(model)
a.set_title(rf"$r_y={weight}$")
fs.export_fig(f"full_energy_offaxis_{weight}", x_scaling=2, y_scaling=1)

44
python/otto_motor/subprojects/cycle_shift/tangle/long.py
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@ -1,27 +1,27 @@
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 shift_cycle as sc
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)
#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 shift_cycle as sc
#import ray
#import figsaver as fs
#ray.shutdown()
#
##ray.init(address='auto')
#ray.init()
#from hops.util.logging_setup import logging_setup
#import logging
#logging_setup(logging.INFO)
shifts = sc.make_shifts(extra_r=4)
long_models = [sc.make_model(shift, shift, switch_t=6., switch_t_sys=3) for shift in shifts]
#long_models = [sc.make_model(shift, shift, switch_t=6., switch_t_sys=3) for shift in shifts]
long_models = [sc.make_model(shift, shift, switch_t=6.) for shift in shifts]
long_baseline = sc.make_model(0., 0., switch_t=6.)
ot.integrate_online_multi(long_models, 80_000, increment=10_000, analyze_kwargs=dict(every=10_000))

14
python/otto_motor/subprojects/cycle_shift/tangle/off_axis.py
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@ -1,4 +1,3 @@
import figsaver as fs
import plot_utils as pu
from hiro_models.one_qubit_model import StocProcTolerances
from hiro_models.otto_cycle import OttoEngine
@ -11,6 +10,7 @@ import matplotlib.pyplot as plt
import otto_utilities as ot
import shift_cycle as sc
import ray
import figsaver as fs
ray.shutdown()
#ray.init(address='auto')
@ -18,15 +18,17 @@ ray.init()
from hops.util.logging_setup import logging_setup
import logging
logging_setup(logging.INFO)
plt.rcParams['figure.figsize'] = (12,4)
proto = sc.make_model(0, 0, switch_t=6)
off_ax_models = []
for weight in [.3, .6]:
off_ax = proto.copy()
weights = [.3, .6]
param_iter = lambda: itertools.product([3, 6], weights)
for switch_t, weight in param_iter():
off_ax = sc.make_model(0, 0, switch_t=switch_t)
off_ax.H_0 = 1 / 2 * (qt.sigmaz().full() + np.eye(2) + weight * qt.sigmax().full())
# NOTE: the hamiltonians will be normalzed so that their smallest EV is 0 and the largest one is 1
off_ax.H_1 = off_ax.H_0.copy()
off_ax_models.append(off_ax)
ot.integrate_online_multi(off_ax_models, 10_000, increment=10_000, analyze_kwargs=dict(every=10_000))
ot.integrate_online_multi(off_ax_models, 10, increment=10, analyze_kwargs=dict(every=10_000))

5
python/otto_motor/subprojects/cycle_shift/tangle/only_cold.py
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@ -1,4 +1,3 @@
import figsaver as fs
import plot_utils as pu
from hiro_models.one_qubit_model import StocProcTolerances
from hiro_models.otto_cycle import OttoEngine
@ -11,6 +10,7 @@ import matplotlib.pyplot as plt
import otto_utilities as ot
import shift_cycle as sc
import ray
import figsaver as fs
ray.shutdown()
#ray.init(address='auto')
@ -18,9 +18,8 @@ ray.init()
from hops.util.logging_setup import logging_setup
import logging
logging_setup(logging.INFO)
plt.rcParams['figure.figsize'] = (12,4)
shifts = sc.make_shifts(extra_r=4)
cold_models = [sc.make_model(shift, shift, switch_t=6., only_cold=True) for shift in shifts]
ot.integrate_online_multi(cold_models, 80_000, increment=10_000, analyze_kwargs=dict(every=10_000))
ot.integrate_online_multi(cold_models, 1, increment=1, analyze_kwargs=dict(every=10_000))

3
python/otto_motor/subprojects/cycle_shift/tangle/original.py
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@ -1,4 +1,3 @@
import figsaver as fs
import plot_utils as pu
from hiro_models.one_qubit_model import StocProcTolerances
from hiro_models.otto_cycle import OttoEngine
@ -11,6 +10,7 @@ import matplotlib.pyplot as plt
import otto_utilities as ot
import shift_cycle as sc
import ray
import figsaver as fs
ray.shutdown()
#ray.init(address='auto')
@ -18,7 +18,6 @@ ray.init()
from hops.util.logging_setup import logging_setup
import logging
logging_setup(logging.INFO)
plt.rcParams['figure.figsize'] = (12,4)
shifts = sc.make_shifts(extra_r=4)
shifts

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