mirror of
https://github.com/vale981/master-thesis
synced 2025-03-04 17:41:43 -05:00
update 07
This commit is contained in:
parent
79242cda0a
commit
ac0c81db44
7 changed files with 2348 additions and 618 deletions
|
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@ -2,11 +2,11 @@
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"nodes": {
|
||||
"flake-utils": {
|
||||
"locked": {
|
||||
"lastModified": 1648297722,
|
||||
"narHash": "sha256-W+qlPsiZd8F3XkzXOzAoR+mpFqzm3ekQkJNa+PIh1BQ=",
|
||||
"lastModified": 1656928814,
|
||||
"narHash": "sha256-RIFfgBuKz6Hp89yRr7+NR5tzIAbn52h8vT6vXkYjZoM=",
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"rev": "0f8662f1319ad6abf89b3380dd2722369fc51ade",
|
||||
"rev": "7e2a3b3dfd9af950a856d66b0a7d01e3c18aa249",
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||||
"type": "github"
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||||
},
|
||||
"original": {
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||||
|
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@ -17,11 +17,11 @@
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|||
},
|
||||
"flake-utils_2": {
|
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"locked": {
|
||||
"lastModified": 1610051610,
|
||||
"narHash": "sha256-U9rPz/usA1/Aohhk7Cmc2gBrEEKRzcW4nwPWMPwja4Y=",
|
||||
"lastModified": 1656928814,
|
||||
"narHash": "sha256-RIFfgBuKz6Hp89yRr7+NR5tzIAbn52h8vT6vXkYjZoM=",
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
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||||
"rev": "3982c9903e93927c2164caa727cd3f6a0e6d14cc",
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||||
"rev": "7e2a3b3dfd9af950a856d66b0a7d01e3c18aa249",
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"type": "github"
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||||
},
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||||
"original": {
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||||
|
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@ -32,11 +32,11 @@
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|||
},
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"nixpkgs": {
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"locked": {
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||||
"lastModified": 1652082323,
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||||
"narHash": "sha256-7GSVLvfCJtH9dJ3om9Lg4fsi9UKvoxxR69gUTcx0ol8=",
|
||||
"lastModified": 1659981942,
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||||
"narHash": "sha256-uCFiP/B/NXOWzhN6TKfMbSxtVMk1bVnCrnJRjCF6RmU=",
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||||
"owner": "NixOS",
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||||
"repo": "nixpkgs",
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||||
"rev": "2a3aac479caeba0a65b2ad755fe5f284f1fde74d",
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"rev": "39d7f929fbcb1446ad7aa7441b04fb30625a4190",
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"type": "github"
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||||
},
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"original": {
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||||
|
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@ -47,11 +47,11 @@
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|||
},
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||||
"nixpkgs_2": {
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"locked": {
|
||||
"lastModified": 1648390671,
|
||||
"narHash": "sha256-u69opCeHUx3CsdIerD0wVSR+DjfDQjnztObqfk9Trqc=",
|
||||
"lastModified": 1658161305,
|
||||
"narHash": "sha256-X/nhnMCa1Wx4YapsspyAs6QYz6T/85FofrI6NpdPDHg=",
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"owner": "NixOS",
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||||
"repo": "nixpkgs",
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"rev": "ce8cbe3c01fd8ee2de526ccd84bbf9b82397a510",
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"rev": "e4d49de45a3b5dbcb881656b4e3986e666141ea9",
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"type": "github"
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},
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"original": {
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@ -62,11 +62,11 @@
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},
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"nixpkgs_3": {
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"locked": {
|
||||
"lastModified": 1610729867,
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||||
"narHash": "sha256-bk/SBaBLqZX/PEqal27DMQwAHHl0dcZMp8NNksQr80s=",
|
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"lastModified": 1658311025,
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||||
"narHash": "sha256-GqagY5YmaZB3YaO41kKcQhe5RcpS83wnsW8iCu5Znqo=",
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"owner": "NixOS",
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||||
"repo": "nixpkgs",
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"rev": "04af07c659c6723a2259bb6bc00a47ec53330f20",
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"rev": "cd8d1784506a7c7eb0796772b73437e0b82fad57",
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"type": "github"
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},
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"original": {
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@ -81,11 +81,11 @@
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"nixpkgs": "nixpkgs_3"
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},
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"locked": {
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"lastModified": 1648438095,
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"narHash": "sha256-TTZ6mu1IO2YxCxd/FtLMixWwpCcgS9eZ7LfzDeoEMUI=",
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"lastModified": 1657626303,
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"narHash": "sha256-O/JJ0hSBCmlx0oP8QGAlRrWn0BvlC5cj7/EZ0CCWHTU=",
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"owner": "nix-community",
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"repo": "poetry2nix",
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"rev": "33b5419aa62c88bf890877a8b8c74864b4a41122",
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"rev": "920ba682377d5c0d87945c5eb6141ab8447ca509",
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"type": "github"
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},
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"original": {
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@ -107,11 +107,11 @@
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"poetry2nix": "poetry2nix"
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},
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"locked": {
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"lastModified": 1648546910,
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"narHash": "sha256-mb49efdjcFXu1S6EwN4Q+vDYDn2eIIPOEZ8N4zTPMfU=",
|
||||
"lastModified": 1658314126,
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||||
"narHash": "sha256-rEoZSb8bBafEd6m3xu/yQtSFlP8GZxKN15YqMZkeMaQ=",
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"owner": "vale981",
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"repo": "hiro-flake-utils",
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"rev": "02504e6918ff7d9d5e9290ebb7a77b6eba78ad0c",
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"rev": "316cd25ffd9d7afd331cbbc1429f62f30567e288",
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"type": "github"
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},
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"original": {
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@ -11,9 +11,12 @@
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outputs = { self, utils, nixpkgs, ... }:
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(utils.lib.poetry2nixWrapper nixpkgs {
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name = "07_one_bath_systematics";
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shellPackages = pkgs: with pkgs; [ pyright python39Packages.jupyter sshfs ];
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shellPackages = pkgs: with pkgs; [ pyright python39Packages.jupyter sshfs (pkgs.texlive.combine {
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inherit (pkgs.texlive) scheme-medium
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type1cm unicode-math;
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})];
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python = pkgs: pkgs.python39Full;
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shellOverride = (oldAttrs: {
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shellOverride = pkgs: (oldAttrs: {
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shellHook = ''
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export PYTHONPATH=/home/hiro/src/two_qubit_model/:$PYTHONPATH
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export PYTHONPATH=/home/hiro/src/hops/:$PYTHONPATH
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1
python/energy_flow_proper/07_one_bath_systematics/plot_utils.py
Symbolic link
1
python/energy_flow_proper/07_one_bath_systematics/plot_utils.py
Symbolic link
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@ -0,0 +1 @@
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../plot_utils.py
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File diff suppressed because it is too large
Load diff
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@ -1,20 +1,22 @@
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import figsaver as fs
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from figsaver import (
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from plot_utils import (
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plot_interaction_consistency,
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plot_interaction_consistency_development,
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plot_flow_bcf,
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plot_with_σ,
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lighten_color
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)
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import figsaver as fs
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from hiro_models.one_qubit_model import QubitModel, StocProcTolerances
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import hiro_models.model_auxiliary as aux
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from hopsflow.util import EnsembleValue
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import numpy as np
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import qutip as qt
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import matplotlib
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matplotlib.rcParams.update(fs.MPL_RC_POSTER)
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import ray
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ray.shutdown()
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ray.init()
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ray.init(address="auto")
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from hops.util.logging_setup import logging_setup
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import logging
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@ -43,12 +45,16 @@ alt_tol_models = [
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aux.integrate_multi(alt_tol_models, 450_000)
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plot_interaction_consistency(
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alt_tol_models,
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# alt_tol_models[0],
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label_fn=lambda m: fr"stocproc=$10^{{{np.log10(m.driving_process_tolerance.integration):.0f}}}$",
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**ensemble_arg
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)
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f, ax = plot_interaction_consistency(
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alt_tol_models,
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# alt_tol_models[0],
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label_fn=lambda m: fr"$\varsigma=10^{{{np.log10(m.driving_process_tolerance.integration):.0f}}}$",
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**ensemble_arg,
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inset=(alt_tol_models[0].t > 18, (.3, .52, .6, .4)),
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)
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fs.export_fig("stocproc_systematics_interaction", y_scaling=.3)
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k_models = [
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QubitModel(
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@ -67,12 +73,15 @@ k_models = [
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aux.integrate_multi(k_models, 450_000)
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plot_interaction_consistency(
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k_models,
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#k_models[-1],
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label_fn=lambda m: fr"k=${m.k_max}$",
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**ensemble_arg
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)
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f, ax = plot_interaction_consistency(
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k_models,
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# alt_tol_models[0],
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label_fn=lambda m: fr"$k={m.k_max}$",
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inset=(np.logical_and(10 < k_models[0].t, k_models[0].t < 12), (0.3, 0.55, 0.6, 0.42)),
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**ensemble_arg,
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)
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fs.export_fig("k_systematics_interaction", x_scaling=1, y_scaling=0.3)
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ω_models = [
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QubitModel(
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@ -97,62 +106,159 @@ for model in ω_models:
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ρ = np.array(data.rho_t_accum.mean)
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σ_ρ = np.array(data.rho_t_accum.ensemble_std)
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t = np.array(data.time)
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fs.plot_with_σ(
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plot_with_σ(
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t,
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EnsembleValue((ρ[:, 1, 0].real, σ_ρ[:, 1, 0].real)),
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label=f"$ω_c={model.ω_c:.2f}$",
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label=fr"$\omega_c={model.ω_c:.2f}$",
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ax=ax,
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)
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ax.legend()
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fig, ax = plt.subplots()
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fig, ((ax_sys, ax_flow), (ax_inter, ax_bath)) = plt.subplots(
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nrows=2, ncols=2, sharex=True
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)
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flow_ins = ax_flow.inset_axes([0.4, 0.20, 0.5, 0.55])
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ax_sys.set_ylabel(r"$\langle H_\mathrm{S}\rangle$")
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ax_flow.set_ylabel("$J$")
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ax_inter.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
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ax_bath.set_ylabel(r"$\langle H_\mathrm{B}\rangle$")
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ax_sys.set_xlabel(r"$\tau$")
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ax_flow.set_xlabel(r"$\tau$")
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ax_inter.set_xlabel(r"$\tau$")
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ax_bath.set_xlabel(r"$\tau$")
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handles = []
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for model in ω_models:
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with aux.get_data(model) as data:
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energy = model.system_energy(data, every=50)
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fs.plot_with_σ(
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sys_energy = model.system_energy(data, **ensemble_arg)
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flow = model.bath_energy_flow(data, **ensemble_arg).for_bath(0)
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inter = model.interaction_energy(data, **ensemble_arg).for_bath(0)
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bath = model.bath_energy(data, **ensemble_arg).for_bath(0)
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_, _, (lines, _) = plot_with_σ(
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data.time[:],
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energy,
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transform=lambda y: y.real,
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ax=ax,
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label=f"$ω_c={model.ω_c:.2f}$",
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sys_energy,
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ax=ax_sys,
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label=fr"$\omega_c={model.ω_c:.1f},\,\alpha(0)={model.bcf(0).real * model.bcf_scale:.2f}$",
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)
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handles.append(lines[0])
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plot_with_σ(
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data.time[:],
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flow,
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ax=ax_flow,
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)
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ax.legend()
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mask = np.logical_and(7 <= data.time[:], data.time[:] <= 9)
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plot_with_σ(
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data.time[mask],
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flow.slice(mask),
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ax=flow_ins,
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)
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fig, ax = plt.subplots()
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fig.set_size_inches(fs.get_figsize("poster", .49))
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plot_with_σ(
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data.time[:],
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inter,
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ax=ax_inter,
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)
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plot_with_σ(
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data.time[:],
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bath,
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ax=ax_bath,
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)
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ax_sys.legend(
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# ncol=2,
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)
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fs.export_fig("omega_energy_overview", y_scaling=0.6, x_scaling=1)
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fig, (ax, ax2) = plt.subplots(ncols=2)
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drawn_foot = False
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for model in ω_models:
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with aux.get_data(model) as data:
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flow = model.bath_energy_flow(data, **ensemble_arg)
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_, _, line = fs.plot_with_σ(
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flow = -1 * model.bath_energy_flow(data, **ensemble_arg).for_bath(0)
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flow_1 = -1 * model.bath_energy_flow(data, **(ensemble_arg | dict(N=1))).for_bath(0)
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inter = -1 * model.interaction_energy(data, **ensemble_arg).for_bath(0)
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pure_deph = (
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-np.trace(model.L(0) @ model.L(0) @ data.rho_t_accum.mean[0]).real
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* 2
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* model.bcf_scale
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* model.bcf(data.time[:]).imag
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)
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_, _, line = plot_with_σ(
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data.time[:],
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flow,
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ax=ax,
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label=f"$ω_c={model.ω_c:.2f}$",
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bath=0,
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transform=lambda y: -y,
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label=fr"$\omega_c={model.ω_c:.2f}$",
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)
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print(-np.trace(model.L(0) @ model.L(0) @ data.rho_t_accum.mean[0]).real)
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ax.plot(
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data.time[:],
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-np.trace(model.L(0) @ model.L(0) @ data.rho_t_accum.mean[0]).real * 2
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* model.bcf_scale
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* model.bcf(data.time[:]).imag,
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pure_deph,
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linestyle="--",
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color=line[0][0].get_color(),
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)
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plot_with_σ(
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data.time[:],
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flow_1,
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linestyle="dotted",
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color=lighten_color(line[0][0].get_color(), .5),
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ax=ax
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)
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ax.axvline(
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data.time[np.argmax(abs(inter.value))],
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color=lighten_color(line[0][0].get_color()),
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)
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t_peak = data.time[np.argmax(abs(flow.value))]
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flow_peak = abs(flow.value).max()
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diff = (flow - pure_deph) * (1 / flow_peak)
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diff1 = (flow.value - flow_1.value) * (1 / flow_peak)
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t_foot = data.time[np.argmax(abs(diff.value) > 0.01)]
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plot_with_σ(
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data.time[:], diff, ax=ax2, label=rf"$1-\tau_d/\tau_p={(t_peak - t_foot)/t_peak:.2f}$"
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)
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# ax2.plot(
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# data.time[:], diff1, color=lighten_color(line[0][0].get_color())
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# )
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ax2.axvline(
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t_peak,
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color=lighten_color(line[0][0].get_color()),
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linestyle="dashed",
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)
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ax2.axvspan(
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t_foot, t_peak, color=lighten_color(line[0][0].get_color(), 0.3), alpha=0.3
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)
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ax2.axvline(t_foot, color=lighten_color(line[0][0].get_color()))
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ax.set_yscale("log")
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ax.set_xscale("log")
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ax.set_ylabel("$J$")
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ax.set_ylabel("$-J$")
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ax.set_xlabel(r"$\tau$")
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ax2.set_xscale("log")
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ax2.set_xlim(1e-3)
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ax2.set_xlabel(r"$\tau$")
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ax2.set_ylabel(r"$|J-J_\mathrm{p.d.}| / |J|_\mathrm{max}$")
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ax2.legend()
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# ax.set_ylim(1e-3,2)
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ax.tick_params(axis='y', which='major', pad=10)
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ax.tick_params(axis="y", which="major", pad=10)
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ax.set_xlim(1e-3)
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ax.legend()
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fs.export_fig("initial_slip")
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fs.export_fig("omega_initial_slip")
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f, ax = plot_interaction_consistency(
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ω_models,
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|
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@ -160,8 +266,49 @@ f, ax = plot_interaction_consistency(
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label_fn=lambda m: fr"$\omega_c={m.ω_c:.0f}$",
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||||
**ensemble_arg
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||||
)
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||||
f.set_size_inches(fs.get_figsize("poster", .5))
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fs.export_fig("omega_interaction")
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fs.export_fig("omega_interaction_consistency", y_scaling=.3)
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from hops.util.dynamic_matrix import ConstantMatrix, SmoothStep
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t_max = 3
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||||
L_op = 1 / 2 * (ConstantMatrix(qt.sigmax()) - SmoothStep(qt.sigmax(), t_max - 2, t_max, 2))
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||||
shift_models = [
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QubitModel(
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||||
δ=2,
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||||
ω_c=2,
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||||
ω_s=ω_s,
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||||
H=ConstantMatrix(1/2 * 4 * qt.sigmaz()),
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L=L_op,
|
||||
t=np.linspace(0, t_max, 1000),
|
||||
ψ_0=qt.basis([2], [0]),
|
||||
description=f"Testing whether the enhanced flow is a resonance phenomenon.",
|
||||
k_max=7,
|
||||
bcf_terms=7,
|
||||
truncation_scheme="simplex",
|
||||
driving_process_tolerance=StocProcTolerances(1e-6, 1e-6)
|
||||
)
|
||||
for ω_s in [1, 2, 3]
|
||||
]
|
||||
|
||||
from hops.util.dynamic_matrix import ConstantMatrix, SmoothStep
|
||||
t_max = 7
|
||||
L_op = 1 / 2 * (ConstantMatrix(qt.sigmax()) - SmoothStep(qt.sigmax(), t_max - 2, t_max, 2))
|
||||
shift_models_steady = [
|
||||
QubitModel(
|
||||
δ=2,
|
||||
ω_c=2,
|
||||
ω_s=ω_s,
|
||||
H=ConstantMatrix(1/2 * 4 * qt.sigmaz()),
|
||||
L=L_op,
|
||||
t=np.linspace(0, t_max, 1000),
|
||||
ψ_0=qt.basis([2], [0]),
|
||||
description=f"Testing whether the enhanced flow is a resonance phenomenon.",
|
||||
k_max=7,
|
||||
bcf_terms=7,
|
||||
truncation_scheme="simplex",
|
||||
driving_process_tolerance=StocProcTolerances(1e-6, 1e-6)
|
||||
)
|
||||
for ω_s in [1, 2, 3]
|
||||
]
|
||||
|
||||
δ_models = [
|
||||
QubitModel(
|
||||
|
|
@ -186,5 +333,68 @@ f, ax = plot_interaction_consistency(
|
|||
label_fn=lambda m: fr"$\alpha(0)={m.bcf(0).real * m.bcf_scale:.1f}$",
|
||||
**ensemble_arg
|
||||
)
|
||||
f.set_size_inches(fs.get_figsize("poster", .49))
|
||||
fs.export_fig("delta_interaction")
|
||||
ax[1].legend(fontsize=5)
|
||||
fs.export_fig("delta_interaction_consistency", y_scaling=.3)
|
||||
|
||||
fig, ((ax_sys, ax_flow), (ax_inter, ax_bath)) = plt.subplots(
|
||||
nrows=2, ncols=2, sharex=True
|
||||
)
|
||||
|
||||
flow_ins = ax_flow.inset_axes([0.4, 0.20, 0.5, 0.55])
|
||||
ax_sys.set_ylabel(r"$\langle H_\mathrm{S}\rangle$")
|
||||
ax_flow.set_ylabel("$J$")
|
||||
ax_inter.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
|
||||
ax_bath.set_ylabel(r"$\langle H_\mathrm{B}\rangle$")
|
||||
|
||||
ax_sys.set_xlabel(r"$\tau$")
|
||||
ax_flow.set_xlabel(r"$\tau$")
|
||||
ax_inter.set_xlabel(r"$\tau$")
|
||||
ax_bath.set_xlabel(r"$\tau$")
|
||||
|
||||
|
||||
handles = []
|
||||
for model in δ_models:
|
||||
with aux.get_data(model) as data:
|
||||
sys_energy = model.system_energy(data, **ensemble_arg)
|
||||
flow = model.bath_energy_flow(data, **ensemble_arg).for_bath(0)
|
||||
inter = model.interaction_energy(data, **ensemble_arg).for_bath(0)
|
||||
bath = model.bath_energy(data, **ensemble_arg).for_bath(0)
|
||||
|
||||
_, _, (lines, _) = plot_with_σ(
|
||||
data.time[:],
|
||||
sys_energy,
|
||||
ax=ax_sys,
|
||||
label=fr"$\alpha(0)={model.bcf(0).real * model.bcf_scale:.2f}$",
|
||||
)
|
||||
handles.append(lines[0])
|
||||
|
||||
plot_with_σ(
|
||||
data.time[:],
|
||||
flow,
|
||||
ax=ax_flow,
|
||||
)
|
||||
|
||||
mask = np.logical_and(2.5 <= data.time[:], data.time[:])
|
||||
plot_with_σ(
|
||||
data.time[mask],
|
||||
flow.slice(mask),
|
||||
ax=flow_ins,
|
||||
)
|
||||
|
||||
plot_with_σ(
|
||||
data.time[:],
|
||||
inter,
|
||||
ax=ax_inter,
|
||||
)
|
||||
|
||||
plot_with_σ(
|
||||
data.time[:],
|
||||
bath,
|
||||
ax=ax_bath,
|
||||
)
|
||||
|
||||
ax_sys.legend(
|
||||
# ncol=2,
|
||||
)
|
||||
|
||||
fs.export_fig("δ_energy_overview", y_scaling=0.6, x_scaling=1)
|
||||
|
|
|
|||
File diff suppressed because it is too large
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Add table
Reference in a new issue