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update 10, even longer coupling

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Valentin Boettcher 2022-08-15 11:33:36 +02:00
parent c12027ec16
commit ee86b20c93
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GPG key ID: E034E12B7AF56ACE
10 changed files with 108 additions and 95 deletions
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8
python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno_cc.py
View file

@ -30,7 +30,7 @@ from anti_zeno_engine import *
Δ=11, Δ=11,
ε=1,#.1, ε=1,#.1,
ω_c=1, ω_c=1,
ε_couple=.001, ε_couple=.0001,
n=1, n=1,
detune=.5, detune=.5,
ω_0=20, ω_0=20,
@ -40,7 +40,7 @@ from anti_zeno_engine import *
γ=.2, γ=.2,
switch_cycles=1, switch_cycles=1,
therm_initial_state=False, therm_initial_state=False,
ε_init=.00001/2, ε_init=.000001/2,
terms=7, terms=7,
dt=0.01/2, dt=0.01/2,
sp_tol=1e-4, sp_tol=1e-4,
@ -66,8 +66,8 @@ plot_az_sd_overview(model, params)
plot_total_power(model, params) plot_total_power(model, params)
plot_excited_state(model, params) plot_excited_state(model, params, with_init=True)
plot_coherences(model, params) plot_coherences(model, params)
plot_power_output(model, params, steady_index=5) plot_power_output(model, params, steady_index=0)

4
python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.py
View file

@ -286,9 +286,9 @@ def plot_total_power(model, params, ax=None):
@wrap_plot @wrap_plot
def plot_excited_state(model, params, ax=None): def plot_excited_state(model, params, ax=None, with_init=False):
with aux.get_data(model) as data: with aux.get_data(model) as data:
mask = model.t > params.τ_init mask = model.t >= 0 if with_init else model.t > params.τ_init
plot_with_σ( plot_with_σ(
model.t[mask], model.t[mask],

65
python/energy_flow_proper/10_antizeno_engine/continously_couple.org
View file

@ -27,7 +27,7 @@ Init ray and silence stocproc.
#+end_src #+end_src
#+RESULTS: #+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_2022-08-12_20-07-54_888036_22466/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-12_20-07-54_888036_22466/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-12_20-07-54_888036_22466', 'metrics_export_port': 60638, 'gcs_address': '141.30.17.225:50826', 'address': '141.30.17.225:50826', 'node_id': 'a3660dfc275e05eee6674ca8f0ba5c4fc2754aa9c49703a883bebc9e'}) : RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '192.168.100.170', 'raylet_ip_address': '192.168.100.170', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-14_15-01-34_034850_14258/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-14_15-01-34_034850_14258/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-14_15-01-34_034850_14258', 'metrics_export_port': 45019, 'gcs_address': '192.168.100.170:53722', 'address': '192.168.100.170:53722', 'node_id': '2369bf21ce3f134ac9781d678096447e25ad28328ed1d38ad4f5c777'})
#+begin_src jupyter-python :results none #+begin_src jupyter-python :results none
from hops.util.logging_setup import logging_setup from hops.util.logging_setup import logging_setup
@ -48,40 +48,62 @@ Init ray and silence stocproc.
Δ=11, Δ=11,
ε=1,#.1, ε=1,#.1,
ω_c=1, ω_c=1,
ε_couple=1e-5, ε_couple=.0001,
n=1, n=1,
detune=.5, detune=.5,
ω_0=20, ω_0=20,
T_c=1e3, T_c=1e3,
T_h=1e4, T_h=1e4,
δ=[3.2*.01/4, 1*.01/4], δ=[3.2*.01/4, 1.08*.01/4],
γ=.2, γ=.2,
switch_cycles=1, switch_cycles=1,
therm_initial_state=False, therm_initial_state=False,
ε_init=.00001/2, ε_init=.000001/2,
terms=7, terms=7,
dt=0.01, dt=0.01/2,
sp_tol=1e-4, sp_tol=1e-4,
init_time_steps=10, init_time_steps=10,
interpolation_multiplier=10,
) )
model.k_max = 3 model.k_max = 3
#+end_src #+end_src
#+RESULTS: #+RESULTS:
Let's test the assumptions of the paper. #+begin_src jupyter-python
ts = model.t # np.linspace(0,10,1000)
proc = model.thermal_process(1)
import hops
z=hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2))
proc.new_process(z)
pu.plot_complex(ts, proc(ts) * model.bcf_scales[1])
#+end_src
#+RESULTS:
:RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/9e7c77f1ba1686b8d5c068ea4463029eaf871a52.svg]]
:END:
#+begin_src jupyter-python #+begin_src jupyter-python
params.cycles params.cycles
#+end_src #+end_src
#+RESULTS: #+RESULTS:
: 304 : 122
#+begin_src jupyter-python #+begin_src jupyter-python
aux.integrate(model, 10_000) aux.integrate(model, 10)
#+end_src #+end_src
#+RESULTS:
: [INFO hops.core.integration 14258] Choosing the nonlinear integrator.
: [INFO hops.core.integration 14258] Using 8 integrators.
: [INFO hops.core.integration 14258] Some 9 trajectories have to be integrated.
: [INFO hops.core.integration 14258] Using 680 hierarchy states.
: 0% 0/9 [00:00<?, ?it/s]
#+begin_src jupyter-python #+begin_src jupyter-python
plot_az_coupling_diagram(model, params) plot_az_coupling_diagram(model, params)
#+end_src #+end_src
@ -89,7 +111,7 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > | | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > |
[[file:./.ob-jupyter/3166301f0aa353e82b7d0ad2b3b3e7ae6f693d5a.svg]] [[file:./.ob-jupyter/b21178f0e184aef19d53f25ad13a0514a71f1356.svg]]
:END: :END:
#+begin_src jupyter-python #+begin_src jupyter-python
@ -99,7 +121,7 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\omega$ | > | | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\omega$ | > |
[[file:./.ob-jupyter/997e7a1fda9ba22feba8ddba7eae9bde652f463f.svg]] [[file:./.ob-jupyter/fd54562a57fd5ebe331b4c6974d6f78980230a00.svg]]
:END: :END:
@ -110,25 +132,36 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> | | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/0e07e17ad91085f92d3554b857cae59a40d26a14.svg]] [[file:./.ob-jupyter/f26abd3f142832f7b78b92c34f41832fad9462ed.svg]]
:END: :END:
#+begin_src jupyter-python #+begin_src jupyter-python
plot_excited_state(model, params) plot_excited_state(model, params, with_init=True)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> | | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/f7b752fa9d2a15f6131f8c7f5e7c3669e3074247.svg]] [[file:./.ob-jupyter/e01a22e101c610a13ff055f6fb32c0ec6cf57c7c.svg]]
:END: :END:
#+begin_src jupyter-python #+begin_src jupyter-python
plot_power_output(model, params, steady_index=5) plot_coherences(model, params)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:title= | (center : \(\bar{P}=0.0016\pm 0.0006\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((10000 0.0015680654088805373 0.0006339387301083156))) | | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/934ebe0f97cbd6d5887c70a8de502ae6a1c4db0b.svg]] [[file:./.ob-jupyter/92ad75e633d380edb09712b501789c8151445708.svg]]
:END:
#+begin_src jupyter-python
plot_power_output(model, params, steady_index=0)
#+end_src
#+RESULTS:
:RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:title= | (center : \(\bar{P}=-0.00001\pm 0.00034\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((10000 -1.238968933350463e-05 0.00034012433086271946))) |
[[file:./.ob-jupyter/40029fe276ad30c0cf7704976bf9f6ec980ad25a.svg]]
:END: :END:

36
python/energy_flow_proper/10_antizeno_engine/flake.lock generated
View file

@ -2,11 +2,11 @@
"nodes": { "nodes": {
"flake-utils": { "flake-utils": {
"locked": { "locked": {
"lastModified": 1656928814, "lastModified": 1659877975,
"narHash": "sha256-RIFfgBuKz6Hp89yRr7+NR5tzIAbn52h8vT6vXkYjZoM=", "narHash": "sha256-zllb8aq3YO3h8B/U0/J1WBgAL8EX5yWf5pMj3G0NAmc=",
"owner": "numtide", "owner": "numtide",
"repo": "flake-utils", "repo": "flake-utils",
"rev": "7e2a3b3dfd9af950a856d66b0a7d01e3c18aa249", "rev": "c0e246b9b83f637f4681389ecabcb2681b4f3af0",
"type": "github" "type": "github"
}, },
"original": { "original": {
@ -17,11 +17,11 @@
}, },
"flake-utils_2": { "flake-utils_2": {
"locked": { "locked": {
"lastModified": 1656928814, "lastModified": 1659877975,
"narHash": "sha256-RIFfgBuKz6Hp89yRr7+NR5tzIAbn52h8vT6vXkYjZoM=", "narHash": "sha256-zllb8aq3YO3h8B/U0/J1WBgAL8EX5yWf5pMj3G0NAmc=",
"owner": "numtide", "owner": "numtide",
"repo": "flake-utils", "repo": "flake-utils",
"rev": "7e2a3b3dfd9af950a856d66b0a7d01e3c18aa249", "rev": "c0e246b9b83f637f4681389ecabcb2681b4f3af0",
"type": "github" "type": "github"
}, },
"original": { "original": {
@ -32,11 +32,11 @@
}, },
"nixpkgs": { "nixpkgs": {
"locked": { "locked": {
"lastModified": 1658161305, "lastModified": 1660396586,
"narHash": "sha256-X/nhnMCa1Wx4YapsspyAs6QYz6T/85FofrI6NpdPDHg=", "narHash": "sha256-ePuWn7z/J5p2lO7YokOG1o01M0pDDVL3VrStaPpS5Ig=",
"owner": "NixOS", "owner": "NixOS",
"repo": "nixpkgs", "repo": "nixpkgs",
"rev": "e4d49de45a3b5dbcb881656b4e3986e666141ea9", "rev": "e105167e98817ba9fe079c6c3c544c6ef188e276",
"type": "github" "type": "github"
}, },
"original": { "original": {
@ -47,11 +47,11 @@
}, },
"nixpkgs_2": { "nixpkgs_2": {
"locked": { "locked": {
"lastModified": 1658311025, "lastModified": 1660546381,
"narHash": "sha256-GqagY5YmaZB3YaO41kKcQhe5RcpS83wnsW8iCu5Znqo=", "narHash": "sha256-rEzCjeWVGhK5AyHxm1zet0lF6+AVSW3JuU5LAU2SMYU=",
"owner": "NixOS", "owner": "NixOS",
"repo": "nixpkgs", "repo": "nixpkgs",
"rev": "cd8d1784506a7c7eb0796772b73437e0b82fad57", "rev": "eb642f80f9aecc19312909e08601a3c2020b5ce2",
"type": "github" "type": "github"
}, },
"original": { "original": {
@ -66,11 +66,11 @@
"nixpkgs": "nixpkgs_2" "nixpkgs": "nixpkgs_2"
}, },
"locked": { "locked": {
"lastModified": 1657626303, "lastModified": 1660542485,
"narHash": "sha256-O/JJ0hSBCmlx0oP8QGAlRrWn0BvlC5cj7/EZ0CCWHTU=", "narHash": "sha256-XfklMwJMLB7bLI5ZnQTrNaK7KyBnElLGoWOL++XO3zk=",
"owner": "nix-community", "owner": "nix-community",
"repo": "poetry2nix", "repo": "poetry2nix",
"rev": "920ba682377d5c0d87945c5eb6141ab8447ca509", "rev": "8b6239cf2ded121f8f3d570d2563d69f05d4208f",
"type": "github" "type": "github"
}, },
"original": { "original": {
@ -94,11 +94,11 @@
"poetry2nix": "poetry2nix" "poetry2nix": "poetry2nix"
}, },
"locked": { "locked": {
"lastModified": 1658314126, "lastModified": 1660547406,
"narHash": "sha256-rEoZSb8bBafEd6m3xu/yQtSFlP8GZxKN15YqMZkeMaQ=", "narHash": "sha256-ZRLGxqHmX5xtCBPCt1pb2E3Z70IEmMT3TA+JruosF4Q=",
"owner": "vale981", "owner": "vale981",
"repo": "hiro-flake-utils", "repo": "hiro-flake-utils",
"rev": "316cd25ffd9d7afd331cbbc1429f62f30567e288", "rev": "dd1e89c5fabbca9a3dbec91cab3e3e3867da10fc",
"type": "github" "type": "github"
}, },
"original": { "original": {

2
python/energy_flow_proper/10_antizeno_engine/flake.nix
View file

@ -23,7 +23,7 @@
export PYTHONPATH=/home/hiro/src/two_qubit_model/:$PYTHONPATH export PYTHONPATH=/home/hiro/src/two_qubit_model/:$PYTHONPATH
export PYTHONPATH=/home/hiro/src/hops/:$PYTHONPATH export PYTHONPATH=/home/hiro/src/hops/:$PYTHONPATH
export PYTHONPATH=/home/hiro/src/hopsflow/:$PYTHONPATH export PYTHONPATH=/home/hiro/src/hopsflow/:$PYTHONPATH
#export PYTHONPATH=/home/hiro/src/stocproc/:$PYTHONPATH export PYTHONPATH=/home/hiro/src/stocproc/:$PYTHONPATH
export LD_LIBRARY_PATH="${(pkgs.lib.makeLibraryPath [pkgs.arb])}" export LD_LIBRARY_PATH="${(pkgs.lib.makeLibraryPath [pkgs.arb])}"
# ''; # '';

29
python/energy_flow_proper/10_antizeno_engine/poetry.lock generated
View file

@ -443,16 +443,16 @@ develop = false
[package.dependencies] [package.dependencies]
beartype = "^0.10.1" beartype = "^0.10.1"
filelock = "^3.6.0" filelock = "^3.6.0"
hops = {git = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git", branch = "use_new_stocproc_for_10"} hops = {git = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git", branch = "time_dep_coupling"}
hopsflow = {git = "https://github.com/vale981/hopsflow", branch = "fix_for_10"} hopsflow = {git = "https://github.com/vale981/hopsflow", branch = "main"}
numpy = "^1.20.0" numpy = "^1.20.0"
qutip = "^4.6.3" qutip = "^4.6.3"
[package.source] [package.source]
type = "git" type = "git"
url = "git@github.com:vale981/two_qubit_model.git" url = "git@github.com:vale981/two_qubit_model.git"
reference = "fix_for_10" reference = "main"
resolved_reference = "bfc90b4f9b7ac2ae1890ee007f52470f237cd261" resolved_reference = "8eacadd55220871f94be2f9e19604fbe546dc45d"
[[package]] [[package]]
name = "hops" name = "hops"
@ -475,7 +475,7 @@ numpy = "^1.22.2"
prettytable = "^3.0.0" prettytable = "^3.0.0"
ray = "^1.9.2" ray = "^1.9.2"
scipy = "^1.7.3" scipy = "^1.7.3"
stocproc = {git = "git@github.com:vale981/stocproc.git", branch = "fix_relative"} stocproc = {git = "https://github.com/vale981/stocproc", branch = "master"}
tqdm = "^4.62.3" tqdm = "^4.62.3"
typer = "^0.4.0" typer = "^0.4.0"
@ -485,8 +485,8 @@ plotting = ["matplotlib (>=3.5.0,<4.0.0)"]
[package.source] [package.source]
type = "git" type = "git"
url = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git" url = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git"
reference = "use_new_stocproc_for_10" reference = "time_dep_coupling"
resolved_reference = "b0f48e1d24ade6d4d28bb191aef47cca8f533a65" resolved_reference = "7688249fa8d9e296e416c8fe347ad6e71f0469d5"
[[package]] [[package]]
name = "hopsflow" name = "hopsflow"
@ -498,20 +498,21 @@ python-versions = ">=3.9,<3.11"
develop = false develop = false
[package.dependencies] [package.dependencies]
hops = {git = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git", branch = "use_new_stocproc_for_10"} hops = {git = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git", branch = "time_dep_coupling"}
lmfit = "^1.0.3" lmfit = "^1.0.3"
numpy = "^1.20.0" numpy = "^1.20.0"
opt-einsum = "^3.3.0" opt-einsum = "^3.3.0"
ray = "^1.11.0" ray = "^1.11.0"
scipy = "^1.7.3" scipy = "^1.7.3"
sqlitedict = "^1.7.0" sqlitedict = "^1.7.0"
stocproc = {git = "https://github.com/vale981/stocproc", branch = "master"}
tqdm = "^4.62.3" tqdm = "^4.62.3"
[package.source] [package.source]
type = "git" type = "git"
url = "https://github.com/vale981/hopsflow" url = "https://github.com/vale981/hopsflow"
reference = "fix_for_10" reference = "main"
resolved_reference = "40a1f9191eea1de7b3d9cbed2a26a3af0404cfbf" resolved_reference = "b6512624cb8bc66e4ae532475cbf91820564c6ec"
[[package]] [[package]]
name = "humanfriendly" name = "humanfriendly"
@ -1512,9 +1513,9 @@ scipy = "^1.7.3"
[package.source] [package.source]
type = "git" type = "git"
url = "git@github.com:vale981/stocproc.git" url = "https://github.com/vale981/stocproc"
reference = "fix_relative" reference = "master"
resolved_reference = "404d51d295879edd93fb71c09373ef3273eefda6" resolved_reference = "52d642c95741b0e8257e807555288cd8da425b63"
[[package]] [[package]]
name = "tabulate" name = "tabulate"
@ -1726,7 +1727,7 @@ notebook = ">=4.4.1"
[metadata] [metadata]
lock-version = "1.1" lock-version = "1.1"
python-versions = ">=3.9,<3.11" python-versions = ">=3.9,<3.11"
content-hash = "971b171d538476dd4aa60fe92da42a3434e83dbcb8e58f464248dbdb4895d325" content-hash = "cee1d8cd448d039a43b3298f1b0d58860af6d4e579ce63c1d8860440000384aa"
[metadata.files] [metadata.files]
aiosignal = [ aiosignal = [

6
python/energy_flow_proper/10_antizeno_engine/pyproject.toml
View file

@ -7,8 +7,10 @@ license = "GPLv3"
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.9,<3.11" python = ">=3.9,<3.11"
hiro_models = { git = "git@github.com:vale981/two_qubit_model.git", branch="fix_for_10" } # hops = { git = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git", branch="use_new_stocproc_for_10" }
#hops = { git = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git", branch="use_new_stocproc_for_10" } # stocproc = { git = "git@github.com:vale981/stocproc.git", branch="fix_relative" }
# hopsflow = { git = "git@github.com:vale981/hopsflow.git", branch="fix_for_10" }
hiro_models = { git = "git@github.com:vale981/two_qubit_model.git", branch="main" }
matplotlib = "^3.5.0" matplotlib = "^3.5.0"
jupyter = "^1.0.0" jupyter = "^1.0.0"
qutip = "^4.6.2" qutip = "^4.6.2"

2
python/energy_flow_proper/10_antizeno_engine/stocproc.ini Normal file
View file

@ -0,0 +1,2 @@
[FFT]
use_normalized_diff = True

39
python/energy_flow_proper/10_antizeno_engine/with_gap.org
View file

@ -23,11 +23,11 @@ Init ray and silence stocproc.
#+begin_src jupyter-python #+begin_src jupyter-python
import ray import ray
ray.shutdown() ray.shutdown()
ray.init(address="141.30.17.16:6379") ray.init(address="ray://100.126.247.22:10001")
#+end_src #+end_src
#+RESULTS: #+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_2022-08-03_14-53-16_736667_825118/sockets/plasma_store.7', 'raylet_socket_name': '/tmp/ray/session_2022-08-03_14-53-16_736667_825118/sockets/raylet.3', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-03_14-53-16_736667_825118', 'metrics_export_port': 59318, 'gcs_address': '141.30.17.16:6379', 'address': '141.30.17.16:6379', 'node_id': '4261dafb07982d866fc5f9805325751682bf9a59ad6e8cfeafab3a7a'}) : ClientContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', protocol_version='2022-03-16', _num_clients=1, _context_to_restore=<ray.util.client._ClientContext object at 0x7fbce22f7940>)
#+begin_src jupyter-python :results none #+begin_src jupyter-python :results none
from hops.util.logging_setup import logging_setup from hops.util.logging_setup import logging_setup
@ -73,20 +73,6 @@ Init ray and silence stocproc.
aux.integrate(model, 1) aux.integrate(model, 1)
#+end_src #+end_src
#+RESULTS:
#+begin_example
[INFO hops.core.integration 2872983] Choosing the nonlinear integrator.
[INFO hops.core.integration 2872983] Using 21 integrators.
[INFO hops.core.integration 2872983] Some 1 trajectories have to be integrated.
[INFO hops.core.integration 2872983] Using 680 hierarchy states.
0% 0/1 [00:00<?, ?it/s][INFO hops.core.signal_delay 2872983] caught sig 'SIGINT'
[INFO hops.core.signal_delay 2873543] caught sig 'SIGINT'
[INFO hops.core.signal_delay 2873545] caught sig 'SIGINT'
[INFO hops.core.signal_delay 2873542] caught sig 'SIGINT'
[INFO hops.core.signal_delay 2873537] caught sig 'SIGINT'
[INFO hops.core.signal_delay 2872983] caught sig 'SIGINT'
#+end_example
Let's test the assumptions of the paper. Let's test the assumptions of the paper.
#+begin_src jupyter-python #+begin_src jupyter-python
@ -103,7 +89,7 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > | | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > |
[[file:./.ob-jupyter/8f2785b27b379ec3b747417c016c2570ce97e1de.svg]] [[file:./.ob-jupyter/2bd67476a36ad9358e8b80a70ec7008846713792.svg]]
:END: :END:
#+begin_src jupyter-python #+begin_src jupyter-python
@ -113,7 +99,7 @@ Let's test the assumptions of the paper.
#+RESULTS: #+RESULTS:
:RESULTS: :RESULTS:
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\omega$ | > | | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\omega$ | > |
[[file:./.ob-jupyter/faf0641b41b72d81d9b03797ef6791f9292383b1.svg]] [[file:./.ob-jupyter/676f3afa255198cce62786706a55f87f8c0a9bf4.svg]]
:END: :END:
@ -122,29 +108,18 @@ Let's test the assumptions of the paper.
#+end_src #+end_src
#+RESULTS: #+RESULTS:
:RESULTS: | <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/591e7e4c4bbaf60e276e6f0a64c69b9ea09cbfbf.svg]]
:END:
#+begin_src jupyter-python #+begin_src jupyter-python
plot_excited_state(model, params) plot_excited_state(model, params)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
:RESULTS: | <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
[[file:./.ob-jupyter/3f26d886f102ce62e82565c14d154a8fc08b0668.svg]]
:END:
#+begin_src jupyter-python #+begin_src jupyter-python
plot_power_output(model, params) plot_power_output(model, params)
#+end_src #+end_src
#+RESULTS: #+RESULTS:
:RESULTS: : 6cab5cce-115b-4639-9491-eb74017b9de3
| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:title= | (center : \(\bar{P}=-0.0016\pm 0.0006\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((10000 -0.0016086506160326898 0.0006489645812092305))) |
[[file:./.ob-jupyter/5a037227671990870089ffb27396fa2c66fd3628.svg]]
: (scheduler +1h48m14s, ip=141.30.17.16) Tip: use `ray status` to view detailed cluster status. To disable these messages, set RAY_SCHEDULER_EVENTS=0.
: (scheduler +1h48m14s, ip=141.30.17.16) Warning: The following resource request cannot be scheduled right now: {'CPU': 1.0}. This is likely due to all cluster resources being claimed by actors. Consider creating fewer actors or adding more nodes to this Ray cluster.
:END:

12
python/energy_flow_proper/plot_utils.py
View file

@ -607,15 +607,15 @@ def plot_energy_overview(
system_energy = model.system_energy(data, **ensemble_args) system_energy = model.system_energy(data, **ensemble_args)
bath_energy = model.bath_energy(data, **ensemble_args) bath_energy = model.bath_energy(data, **ensemble_args)
interaction_energy = model.interaction_energy(data, **ensemble_args) interaction_energy = model.interaction_energy(data, **ensemble_args)
flow = model.bath_energy_flow(data, **ensemble_args) # flow = model.bath_energy_flow(data, **ensemble_args)
plot_with_σ(model.t, system_energy, ax=ax, label="System", **kwargs) plot_with_σ(model.t, system_energy, ax=ax, label="System", **kwargs)
num_baths = flow.num_baths num_baths = interaction_energy.num_baths
for bath in range(num_baths): for bath in range(num_baths):
plot_with_σ( # plot_with_σ(
model.t, flow, bath=bath, ax=ax, label=f"Flow {bath+1}", **kwargs # model.t, flow, bath=bath, ax=ax, label=f"Flow {bath+1}", **kwargs
) # )
plot_with_σ( plot_with_σ(
model.t, bath_energy, bath=bath, ax=ax, label=f"Bath {bath+1}", **kwargs model.t, bath_energy, bath=bath, ax=ax, label=f"Bath {bath+1}", **kwargs
) )
@ -629,7 +629,7 @@ def plot_energy_overview(
**kwargs, **kwargs,
) )
total = model.total_energy(data, **ensemble_args) total = model.total_energy_from_power(data, **ensemble_args)
plot_with_σ( plot_with_σ(
model.t, model.t,
total, total,