monster commit. sorry

project.org

@@ -476,6 +476,7 @@ See cite:Binder2018.
   - spectral separation is important
   - detuning is important -> only then non-markov effects
 **** TODO cite:Uzdin2015Sep repro of cite:Klatzow2019Mar
+
 - transient effects missing
 - deviations for long modulation periods, or large actions
 **** TODO stroke based on coupling modulation, my energy shovel

python/energy_flow_proper/07_one_bath_systematics/scripts/integrate_slip.py

@@ -71,7 +71,7 @@ k_models = [
     for k in np.arange(2, 12, 2)
 ][0:3]
 
-aux.integrate_multi(k_models, 450_000)
+aux.integrate_multi(k_models, 1)
 
 f, ax = plot_interaction_consistency(
     k_models,

python/energy_flow_proper/07_one_bath_systematics/stocproc.ini

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

python/energy_flow_proper/08_dynamic_one_bath/.data_other/model_data.json

@@ -0,0 +1 @@
+{}

python/energy_flow_proper/08_dynamic_one_bath/.envrc

@@ -1,2 +1,2 @@
-use_flake
+use_flake --impure
 eval "$shellHook"

python/energy_flow_proper/08_dynamic_one_bath/cutoff_dep.py

@@ -9,7 +9,7 @@ import plot_utils as pu
 
 import ray
 ray.shutdown()
-ray.init()
+ray.init(address="auto")
 
 from hops.util.logging_setup import logging_setup
 import logging
@@ -23,10 +23,11 @@ import pathlib
 Δ = 5
 
 for ω_c in ωs:
+    goal = .4
     final, strobe_t, strobe_indices = es.optimize_for_interaction_energy(
         goal,
-        periods_for_optimization=4,
-        samples_for_optimization=5,
+        periods_for_optimization=10,
+        samples_for_optimization=500,
         Δ=Δ,
         periods=50,
         modulate_system=False,
@@ -38,23 +39,20 @@ for ω_c in ωs:
     )
 
     ω_models.append(final.copy())
+ω_models_display = [ω_models[0]] + ω_models[4::2]
 
 ω_models[5].δ
 
-aux.integrate_multi(ω_models, 10_000)
-
-for model, data in aux.model_data_iterator(ω_models):
-      model.interaction_energy(data)
-      model.total_energy_from_power(data)
+aux.integrate_multi(ω_models, 1, data_path=".data_taurus")
 
 print(es.models_table(ω_models))
 
 plt.plot(ωs, [model.full_thermal_bcf(0).real * model.bcf_scale for model in ω_models])
 # plt.plot(ωs, [model.bcf_scale for model in ω_models])
 
-for model in ω_models[::4]:
+for model in ω_models_display:
       plt.plot(model.t, abs(model.bcf_scale * model.full_thermal_bcf(model.t)), label=fr"$\omega_c={model.ω_c:.2f}$")
-plt.xlim(0,10)
+plt.xlim(-.1,10)
 plt.legend()
 
 for model in ω_models:
@@ -63,9 +61,9 @@ for model in ω_models:
       plt.xlim(-.1, 6)
 
 ωs = np.linspace(.01, 30, 1000)
-for model in ω_models[::4]:
-      plt.plot(ωs, model.bcf_scale * model.spectral_density(ωs), linewidth=1, label=fr"$\omega_c={model.ω_c:.2f}$")
-plt.legend()
+for model in ω_models_display:
+      plt.plot(ωs, model.bcf_scale * model.spectral_density(ωs), linewidth=.8, label=fr"$\omega_c={model.ω_c:.2f}$")
+plt.legend(fontsize="x-small")
 plt.xlabel(r"$\omega$")
 plt.ylabel(r"$J(\omega)$")
 
@@ -78,23 +76,22 @@ plt.plot(ωs, bath_scales)
 
 fig, ax = plt.subplots()
 ax.axhline(0, color="gray")
-for model, data in aux.model_data_iterator(ω_models):
+for model, data in aux.model_data_iterator(ω_models_display, data_path=".data_taurus"):
     _, _, bar = pu.plot_with_σ(
         model.t,
         model.interaction_energy(data).for_bath(0),
         ax=ax,
         label=fr"$\omega_c={model.ω_c:.2f}$",
     )
-ax.set_xlim(0, model.t[strobe_indices][3])
+ax.set_xlim(0, model.t[strobe_indices][10])
 ax.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
 ax.set_xlabel(r"$\tau$")
-ax.legend()
-#fs.export_fig("omega_interactions", fig, tikz=False, x_scaling=.49, y_scaling=.3)
+fs.export_fig("omega_interactions", fig, tikz=False, x_scaling=.49, y_scaling=.3)
 
 fig, ax = plt.subplots()
 import scipy
 
-for model, data in aux.model_data_iterator(ω_models):
+for model, data in aux.model_data_iterator(ω_models_display, data_path=".data_taurus"):
     energy = model.total_energy_from_power(data) * (1 / es.ergo(model.T))
     _, _, bar = pu.plot_with_σ(
         model.t,
@@ -131,17 +128,17 @@ for model, data in aux.model_data_iterator(ω_models):
     e_s = energy.value[strobe_indices]
 
 
-    ind_min = e_s.argmin()
+    ind_min = e_s[:20].argmin()
     t_min = ts[ind_min]
     e_min = e_s[ind_min]
 
-    ax.axvline(
-        t_min,
-        color=bar.lines[0].get_color(),
-        ymin=0,
-        ymax=abs((e_min + 0.41) / (0.45 + 0.1)),
-        linestyle="dotted",
-    )
+    # ax.axvline(
+    #     t_min,
+    #     color=bar.lines[0].get_color(),
+    #     ymin=0,
+    #     ymax=abs((e_min + 0.41) / (0.45 + 0.1)),
+    #     linestyle="dotted",
+    # )
     ax.axhline(
         e_min,
         color=bar.lines[0].get_color(),
@@ -158,14 +155,14 @@ ax.set_ylabel(r"$(\langle H\rangle_\tau -\langle H\rangle_0)/\mathcal{W}_\mathrm
 ax.set_xlim(0, model.t.max())
 ax.set_ylim(-0.41, 0.1)
 ax.legend()
-fs.export_fig("omegas_total", tikz=False, y_scaling=0.4)
+fs.export_fig("omegas_total", tikz=False, y_scaling=0.5)
 
 import scipy
 
 opt_powers = []
 opt_energies = []
 memories = []
-for model, data in aux.model_data_iterator(ω_models):
+for model, data in aux.model_data_iterator(ω_models[:1] + ω_models[2:], data_path=".data_taurus"):
     energy = model.total_energy_from_power(data)
     ts = model.t[strobe_indices]
     e_s = energy.value[strobe_indices]
@@ -213,7 +210,7 @@ ax2.set_ylabel(
 # smooth_mem = np.linspace(memories[0], memories[-1], 1000)
 # ax.plot(smooth_mem, f(smooth_mem, *opt)) #
 plt.axvline(1 * np.pi * 2/Δ, color="gray", linestyle="dotted")
-fs.export_fig("omega_energies_and_powers", fig, tikz=False, x_scaling=.5, y_scaling=.35)
+fs.export_fig("omega_energies_and_powers", fig, tikz=False, y_scaling=.4)
 
 fig, ax = plt.subplots()
 for model, data in aux.model_data_iterator(ω_models):

python/energy_flow_proper/08_dynamic_one_bath/cutoff_dep_energy_norm.py

@@ -0,0 +1,51 @@
+import energy_shovel as es
+import numpy as np
+import qutip as qt
+import matplotlib.pyplot as plt
+import utilities as ut
+import figsaver as fs
+import hiro_models.model_auxiliary as aux
+import plot_utils as pu
+
+import ray
+ray.shutdown()
+ray.init()
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO, show_stocproc=False)
+
+import scipy
+import pathlib
+
+ωs = np.linspace(.5, 2.1, 15)
+ω_models = []
+Δ = 5
+
+for ω_c in ωs:
+    goal = .4
+    final, strobe_t, strobe_indices = es.optimize_for_interaction_energy(
+        goal,
+        periods_for_optimization=4,
+        samples_for_optimization=5,
+        Δ=Δ,
+        periods=50,
+        modulate_system=False,
+        δ=.1,
+        ω_c=ω_c,
+        k_max = 5,
+        therm_method="fft"
+
+    )
+
+    ω_models.append(final.copy())
+
+ω_models[5].δ
+
+aux.integrate_multi(ω_models, 10_000)
+
+for model, data in aux.model_data_iterator(ω_models):
+      model.interaction_energy(data)
+      model.total_energy_from_power(data)
+
+print(es.models_table(ω_models))

python/energy_flow_proper/08_dynamic_one_bath/cutoff_dep_steady.py

@@ -0,0 +1,87 @@
+import energy_shovel as es
+import numpy as np
+import qutip as qt
+import matplotlib.pyplot as plt
+import utilities as ut
+import figsaver as fs
+import hiro_models.model_auxiliary as aux
+import plot_utils as pu
+
+import ray
+ray.shutdown()
+ray.init(address="auto")
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO, show_stocproc=False)
+
+import scipy
+import pathlib
+
+ω_models = []
+Δ = 5
+
+goal = .4
+model, strobe_t, strobe_indices = es.optimize_for_interaction_energy(
+        goal,
+        periods_for_optimization=10,
+        samples_for_optimization=500,
+        Δ=Δ,
+        periods=100,
+        modulate_system=False,
+        δ=.1,
+        ω_c=.5,
+        k_max = 6,
+        therm_method="fft"
+    )
+
+aux.integrate(model, 1, data_path=".data_taurus")
+
+fig, ax = plt.subplots()
+import scipy
+
+with aux.get_data(model, data_path=".data_taurus") as data:
+    energy = model.total_energy_from_power(data) * (1 / es.ergo(model.T))
+    pu.plot_with_σ(
+        model.t,
+        energy,
+        ax=ax,
+        # label=fr"$ω_c={model.ω_c:.2f}$",
+        # strobe_frequency=Δ,
+    )
+
+    # τ_off = 1 / (model.bcf_coefficients()[1][0].real.min())
+    # # a0 = abs(model.bcf(0))
+    # # τ_off = scipy.optimize.fsolve(lambda τ: abs(model.bcf(τ)) - (a0/300), 10)
+    # ax.axvline(
+    #     τ_off,
+    #     color=bar.lines[0].get_color(),
+    #     linestyle="dotted",
+    #     linewidth=1,
+    #     zorder=-1,
+    # )
+
+    ts = model.t[strobe_indices]
+    e_s = energy.value[strobe_indices]
+
+
+    ind_min = e_s[:20].argmin()
+    t_min = ts[ind_min]
+    e_min = e_s[ind_min]
+
+    # ax.axvline(
+    #     t_min,
+    #     color=bar.lines[0].get_color(),
+    #     ymin=0,
+    #     ymax=abs((e_min + 0.41) / (0.45 + 0.1)),
+    #     linestyle="dotted",
+    # )
+
+
+ax.set_xlabel(r"$\tau$")
+ax.set_ylabel(r"$(\langle H\rangle_\tau -\langle H\rangle_0)/\mathcal{W}_\mathrm{max}$")
+# ax.axhline(, color="grey", linewidth=1, linestyle="dashed")
+# ax.plot(model.t, model.L.operator_norm(model.t), linewidth=1)
+ax.set_xlim(0,120)
+ax.legend()
+fs.export_fig("total_for_steady", tikz=False, y_scaling=0.5)

python/energy_flow_proper/08_dynamic_one_bath/cutoff_dep_weak.py

@@ -0,0 +1,83 @@
+import energy_shovel as es
+import numpy as np
+import qutip as qt
+import matplotlib.pyplot as plt
+import utilities as ut
+import figsaver as fs
+import hiro_models.model_auxiliary as aux
+import plot_utils as pu
+
+import ray
+ray.shutdown()
+ray.init(address="auto")
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO, show_stocproc=False)
+
+import scipy
+import pathlib
+
+ωs = np.linspace(.5, 2.5, 6)
+ω_models = []
+Δ = 5
+
+for ω_c in ωs:
+    proto, strobe_t, strobe_indices = es.energy_shovel(
+        Δ,
+        periods=3,
+        modulate_system=False,
+        δ=0.01,
+        ω_c = ω_c,
+    )
+
+    proto.k_max = 5
+    proto.therm_method = "fft"
+
+    goal = 0.01
+
+
+    def cost(δ):
+        proto.δ = δ
+        aux.integrate(proto, 200)
+        with aux.get_data(proto) as data:
+            inter = proto.interaction_energy(data)
+
+        return (abs(inter.value).max() - goal) ** 2
+    cache_path = pathlib.Path(f"./.cache/{proto.hexhash}_delta_{goal}.npy")
+    if cache_path.exists():
+        with cache_path.open("rb") as cache:
+            δ = np.load(cache, allow_pickle=True)
+    else:
+        δ = scipy.optimize.minimize_scalar(cost,  bounds=(.1, 3), method="bounded", options=dict(xatol=1e-2, disp=3)).x
+        cache_path.parent.mkdir(parents=True, exist_ok=True)
+        with cache_path.open("wb") as cache:
+            np.save(cache, δ)
+
+    final, strobe_t, strobe_indices = es.energy_shovel(
+        Δ,
+        periods=40,
+        modulate_system=False,
+        δ=δ,
+        ω_c=ω_c
+    )
+    final.k_max = 5
+    final.therm_method = "fft"
+
+    ω_models.append(final.copy())
+
+for model in ω_models:
+    print(model.δ)
+
+ωs = np.linspace(.01, 30, 1000)
+for model in ω_models:
+      plt.plot(ωs, model.bcf_scale * model.spectral_density(ωs), linewidth=1, label=fr"$\omega_c={model.ω_c:.2f}$")
+plt.legend()
+plt.xlabel(r"$\omega$")
+plt.ylabel(r"Spectral Density")
+
+fs.export_fig("omega_sd_weak", tikz=False, x_scaling=.49, y_scaling=.4)
+
+for model in ω_models:
+      plt.plot(model.t, model.bcf_scale * model.full_thermal_bcf(model.t).real, label=model.ω_c)
+plt.legend()

python/energy_flow_proper/08_dynamic_one_bath/energy_shovel.py

@@ -72,7 +72,7 @@ def energy_shovel(
     )
     model = QubitModel(**(options | kwargs))
 
-    strobe_t, strobe_indices = ut.strobe_times(model.t, Δ)
+    strobe_t, strobe_indices = ut.strobe_times(model.t, Δ, 1e-2)
 
     print(fr"\(λ={γ}, Δ={Δ}, T={model.T}\)")
     return model, strobe_t, strobe_indices
@@ -83,26 +83,29 @@ def ergo(T, ω=1):
     return T * np.log(1 + np.exp(-ω / T))
 
 
-def models_table(models, **kwargs):
+def models_table(models, data_path=".data", **kwargs):
     """Print a latex table that shows all relevant model parameters."""
 
     table = []
+    headers = []
 
     def make_row(title, accessor):
-        row = [title]
-        for model, data in aux.model_data_iterator(models):
+        row = []
+        headers.append(title)
+        for model, data in aux.model_data_iterator(models, data_path=data_path):
             row += [f"${accessor(model, data):.2f}$"]
 
         return [row]
 
     table += make_row(r"$ω_c$", lambda m, d: m.ω_c)
+    table += make_row(r"$ω_s$", lambda m, d: m.ω_s)
     table += make_row(r"$α(0)$", lambda m, d: m.bcf_scale * m.full_thermal_bcf(0).real)
     table += make_row(r"$T$", lambda m, d: m.T)
     table += make_row(r"$N$", lambda m, d: d.samples)
     table += make_row(r"$k_{\mathrm{max}}$", lambda m, d: m.k_max)
     table += make_row(r"BCF Terms", lambda m, d: m.bcf_terms)
 
-    return tabulate(table, **(dict(tablefmt="latex_raw") | kwargs))
+    return tabulate(np.array(table).T, headers, **(dict(tablefmt="latex_raw") | kwargs))
 
 
 def energy_friction_plot(models, strobe_frequency, strobe_indices):

python/energy_flow_proper/08_dynamic_one_bath/figures/delta_dependence.pgf

@@ -1,957 +0,0 @@
-%% Creator: Matplotlib, PGF backend
-%%
-%% To include the figure in your LaTeX document, write
-%%   \input{<filename>.pgf}
-%%
-%% Make sure the required packages are loaded in your preamble
-%%   \usepackage{pgf}
-%%
-%% Also ensure that all the required font packages are loaded; for instance,
-%% the lmodern package is sometimes necessary when using math font.
-%%   \usepackage{lmodern}
-%%
-%% Figures using additional raster images can only be included by \input if
-%% they are in the same directory as the main LaTeX file. For loading figures
-%% from other directories you can use the `import` package
-%%   \usepackage{import}
-%%
-%% and then include the figures with
-%%   \import{<path to file>}{<filename>.pgf}
-%%
-%% Matplotlib used the following preamble
-%%   \usepackage{fontspec}
-%%
-\begingroup%
-\makeatletter%
-\begin{pgfpicture}%
-\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{6.489508in}{4.010736in}}%
-\pgfusepath{use as bounding box, clip}%
-\begin{pgfscope}%
-\pgfsetbuttcap%
-\pgfsetmiterjoin%
-\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}%
-\pgfsetfillcolor{currentfill}%
-\pgfsetlinewidth{0.000000pt}%
-\definecolor{currentstroke}{rgb}{0.500000,0.500000,0.500000}%
-\pgfsetstrokecolor{currentstroke}%
-\pgfsetdash{}{0pt}%
-\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}%
-\pgfpathlineto{\pgfqpoint{6.489508in}{0.000000in}}%
-\pgfpathlineto{\pgfqpoint{6.489508in}{4.010736in}}%
-\pgfpathlineto{\pgfqpoint{0.000000in}{4.010736in}}%
-\pgfpathlineto{\pgfqpoint{0.000000in}{0.000000in}}%
-\pgfpathclose%
-\pgfusepath{fill}%
-\end{pgfscope}%
-\begin{pgfscope}%
-\pgfsetbuttcap%
-\pgfsetmiterjoin%
-\definecolor{currentfill}{rgb}{0.898039,0.898039,0.898039}%
-\pgfsetfillcolor{currentfill}%
-\pgfsetlinewidth{0.000000pt}%
-\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}%
-\pgfsetstrokecolor{currentstroke}%
-\pgfsetstrokeopacity{0.000000}%
-\pgfsetdash{}{0pt}%
-\pgfpathmoveto{\pgfqpoint{1.302603in}{0.856422in}}%
-\pgfpathlineto{\pgfqpoint{6.230308in}{0.856422in}}%
-\pgfpathlineto{\pgfqpoint{6.230308in}{3.751536in}}%
-\pgfpathlineto{\pgfqpoint{1.302603in}{3.751536in}}%
-\pgfpathlineto{\pgfqpoint{1.302603in}{0.856422in}}%
-\pgfpathclose%
-\pgfusepath{fill}%
-\end{pgfscope}%
-\begin{pgfscope}%
-\pgfpathrectangle{\pgfqpoint{1.302603in}{0.856422in}}{\pgfqpoint{4.927705in}{2.895114in}}%
-\pgfusepath{clip}%
-\pgfsetrectcap%
-\pgfsetroundjoin%
-\pgfsetlinewidth{0.803000pt}%
-\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}%
-\pgfsetstrokecolor{currentstroke}%
-\pgfsetdash{}{0pt}%
-\pgfpathmoveto{\pgfqpoint{2.024337in}{0.856422in}}%
-\pgfpathlineto{\pgfqpoint{2.024337in}{3.751536in}}%
-\pgfusepath{stroke}%
-\end{pgfscope}%
-\begin{pgfscope}%
-\pgfsetbuttcap%
-\pgfsetroundjoin%
-\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}%
-\pgfsetfillcolor{currentfill}%
-\pgfsetlinewidth{0.803000pt}%
-\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}%
-\pgfsetstrokecolor{currentstroke}%
-\pgfsetdash{}{0pt}%
-\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{%
-\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}%
-\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}%
-\pgfusepath{stroke,fill}%
-}%
-\begin{pgfscope}%
-\pgfsys@transformshift{2.024337in}{0.856422in}%
-\pgfsys@useobject{currentmarker}{}%
-\end{pgfscope}%
-\end{pgfscope}%
-\begin{pgfscope}%
-\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}%
-\pgfsetstrokecolor{textcolor}%
-\pgfsetfillcolor{textcolor}%
-\pgftext[x=2.024337in,y=0.759200in,,top]{\color{textcolor}\sffamily\fontsize{14.400000}{17.280000}\selectfont 2}%
-\end{pgfscope}%
-\begin{pgfscope}%
-\pgfpathrectangle{\pgfqpoint{1.302603in}{0.856422in}}{\pgfqpoint{4.927705in}{2.895114in}}%
-\pgfusepath{clip}%
-\pgfsetrectcap%
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-\pgfsetlinewidth{0.803000pt}%
-\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}%
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-\end{pgfscope}%
-\begin{pgfscope}%
-\pgfsetbuttcap%
-\pgfsetroundjoin%
-\definecolor{currentfill}{rgb}{0.000000,0.000000,0.000000}%
-\pgfsetfillcolor{currentfill}%
-\pgfsetlinewidth{0.803000pt}%
-\definecolor{currentstroke}{rgb}{0.000000,0.000000,0.000000}%
-\pgfsetstrokecolor{currentstroke}%
-\pgfsetdash{}{0pt}%
-\pgfsys@defobject{currentmarker}{\pgfqpoint{0.000000in}{-0.048611in}}{\pgfqpoint{0.000000in}{0.000000in}}{%
-\pgfpathmoveto{\pgfqpoint{0.000000in}{0.000000in}}%
-\pgfpathlineto{\pgfqpoint{0.000000in}{-0.048611in}}%
-\pgfusepath{stroke,fill}%
-}%
-\begin{pgfscope}%
-\pgfsys@transformshift{3.019833in}{0.856422in}%
-\pgfsys@useobject{currentmarker}{}%
-\end{pgfscope}%
-\end{pgfscope}%
-\begin{pgfscope}%
-\definecolor{textcolor}{rgb}{0.000000,0.000000,0.000000}%
-\pgfsetstrokecolor{textcolor}%
-\pgfsetfillcolor{textcolor}%
-\pgftext[x=3.019833in,y=0.759200in,,top]{\color{textcolor}\sffamily\fontsize{14.400000}{17.280000}\selectfont 4}%
-\end{pgfscope}%
-\begin{pgfscope}%
-\pgfpathrectangle{\pgfqpoint{1.302603in}{0.856422in}}{\pgfqpoint{4.927705in}{2.895114in}}%
-\pgfusepath{clip}%
-\pgfsetrectcap%
-\pgfsetroundjoin%
-\pgfsetlinewidth{0.803000pt}%
-\definecolor{currentstroke}{rgb}{1.000000,1.000000,1.000000}%
-\pgfsetstrokecolor{currentstroke}%
-\pgfsetdash{}{0pt}%
-\pgfpathmoveto{\pgfqpoint{4.015329in}{0.856422in}}%
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-\pgfusepath{stroke}%
-\end{pgfscope}%
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python/energy_flow_proper/08_dynamic_one_bath/fric_v_nofric.py

@@ -43,7 +43,9 @@ with_friction_y.description = r"With Friction $\sigma_y$"
 
 models = [with_friction, with_friction_y, without_friction]
 
-aux.integrate_multi(models, 2000)
+print(es.models_table(models))
+
+aux.integrate_multi(models, 10_000)
 
 es.ergo(5)
 

python/energy_flow_proper/08_dynamic_one_bath/init_slip.py

@@ -0,0 +1,224 @@
+import energy_shovel as es
+import numpy as np
+import qutip as qt
+import matplotlib.pyplot as plt
+import utilities as ut
+import figsaver as fs
+import hiro_models.model_auxiliary as aux
+import plot_utils as pu
+
+import ray
+ray.shutdown()
+ray.init(address="auto")
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO, show_stocproc=False)
+
+from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise
+L_op = (1 / 2 * qt.sigmax()).full()
+from hiro_models.utility import StocProcTolerances
+from hiro_models.one_qubit_model import QubitModel
+
+H = 1 / 2 * (ConstantMatrix(qt.sigmaz() + 1))
+
+t_max = 20
+models = [
+    QubitModel(
+        δ=0.8,
+        ω_c=1,
+        t=np.linspace(0, t_max, 1000),
+        ψ_0=qt.basis([2], [0]),
+        description=f"Testing the time dependent coupling with smooth step.",
+        k_max=4,
+        bcf_terms=7,
+        truncation_scheme="simplex",
+        driving_process_tolerance=StocProcTolerances(1e-6, 1e-6),
+        thermal_process_tolerance=StocProcTolerances(1e-3, 1e-3),
+        T=0,
+        L=SmoothStep(L_op, 0, 8, s) if s is not None else Piecewise([ConstantMatrix(np.zeros((2,2))), ConstantMatrix(L_op)], [0, 4, np.inf]),
+        H=H,
+    )
+    for s in (None, 0, 1, 2, 3)
+]
+
+print(es.models_table(models))
+
+tt = np.linspace(0, 8, 1000)
+
+for model in models:
+    plt.plot(
+        tt,
+        model.L.operator_norm(tt),
+        label=model.L.order if isinstance(model.L, SmoothStep) else "sudden",
+    )
+
+plt.legend(title="Smoothness")
+plt.xlabel(r"$\tau$")
+plt.ylabel(r"$||L||$")
+fs.export_fig("modulation_protocols_init", x_scaling=.4, tikz=False)
+
+fs.tex_value(
+    models[0].bcf_scale * models[0].full_thermal_bcf(0).real,
+    prec=1,
+    prefix="α(0)=",
+    save="init_slip_alpha",
+), fs.tex_value(models[0].ω_c, prec=1, prefix="ω_c=", save="initial_slip_cuttoff")
+
+import gc; gc.collect()
+aux.integrate_multi(models, 10_000)
+
+for model in models:
+    _, ax = pu.plot_energy_overview(model)
+    ax.plot(model.t, model.L.operator_norm(model.t) * np.sqrt(model.δ))
+    ax.legend()
+
+fig, ax = plt.subplots()
+for model, data in aux.model_data_iterator(models):
+    flow = model.bath_energy_flow(data)
+    pu.plot_with_σ(
+        model.t,
+        flow,
+        ax=ax,
+        label=model.L.order if isinstance(model.L, SmoothStep) else "sudden",
+        bath=0,
+    )
+ax.legend()
+
+fig, ax = plt.subplots()
+for model, data in aux.model_data_iterator(models):
+    flow = model.interaction_energy(data)
+    pu.plot_with_σ(
+        model.t,
+        flow,
+        ax=ax,
+        label=model.L.order if isinstance(model.L, SmoothStep) else "sudden",
+        bath=0,
+    )
+ax.legend()
+
+pu.plot_multi_energy_overview(
+    models,
+    label_fn=lambda model: model.L.order if isinstance(model.L, SmoothStep) else "sudden"
+)
+fs.export_fig("init_overview", y_scaling=.6)
+
+fig, ax = plt.subplots()
+for model, data in aux.model_data_iterator(models):
+    flow = model.total_energy_from_power(data)
+    pu.plot_with_σ(
+        model.t,
+        flow,
+        ax=ax,
+        label=model.L.order if isinstance(model.L, SmoothStep) else "sudden",
+    )
+ax.legend(title="Smoothness")
+ax.set_xlabel(r"$\tau$")
+ax.set_ylabel(r"$\langle H\rangle$")
+fs.export_fig("total_init", x_scaling=.5)
+
+fig, ax = plt.subplots()
+for model, data in aux.model_data_iterator(models):
+    flow = model.system_energy(data)
+    pu.plot_with_σ(
+        model.t,
+        flow,
+        ax=ax,
+        label=model.L.order if isinstance(model.L, SmoothStep) else "sudden",
+    )
+ax.legend()
+
+fig, ax = plt.subplots()
+for model, data in aux.model_data_iterator(models):
+    flow = model.total_energy_from_power(data)
+    pu.plot_with_σ(
+        model.t,
+        flow,
+        ax=ax,
+        label=model.L.order if isinstance(model.L, SmoothStep) else "sudden",
+    )
+ax.legend()
+
+from hopsflow.util import integrate_array
+  import scipy
+
+  fig, ax = plt.subplots()
+  pure_dephasing = []
+  pure_dephasing_int = []
+  ax.tick_params(axis='y', which='major', pad=10)
+
+  for model in models:
+      L = model.L
+      L_fun = L.factor if isinstance(L, SmoothStep) else lambda t: np.heaviside(t - 1, 0)
+      # plt.plot(model.t, L_deriv(model.t))
+      # plt.plot(model.t, L_fun(model.t))
+      def α_dot(t):
+          return -2j * model.ω_c / (1 + 1j * model.ω_c * t) ** 3
+
+      def α(t):
+          return 1 / (1 + 1j * model.ω_c * t) ** 2
+
+      def integrand(s, t):
+          return np.prod(L_fun(np.array([s, t]))) * α_dot(t - s).imag / 4
+
+      def integrand_int(s, t):
+          return np.prod(L_fun(np.array([s, t]))) * α(t - s).imag / 4
+
+      res = np.array([scipy.integrate.quad(integrand, 0, t, (t,))[0] for t in model.t])
+      pure_dephasing.append(res)
+      pure_dephasing_int.append(
+          np.array([scipy.integrate.quad(integrand_int, 0, t, (t,))[0] for t in model.t])
+      )
+      with aux.get_data(model) as data:
+          if not isinstance(model.L, SmoothStep):
+              continue
+          _, _, (line, _) = pu.plot_with_σ(
+              model.t,
+              abs(-1 * model.bath_energy_flow(data)),
+              bath=0,
+              ax=ax,
+              label=(fr"$s={model.L.order}$")
+              if isinstance(model.L, SmoothStep)
+              else "sudden",
+          )
+          ax.plot(
+              model.t,
+              abs(-res * model.bcf_scale * model.bcf._c1 * 2),
+              linestyle="--",
+              color=line[0].get_color(),
+          )
+
+          # fs.plot_with_σ(
+          #     model.t,
+          #     -1 * model.interaction_energy(data),
+          #     linestyle="-.",
+          #     bath=0,
+          #     ax=ax,
+          #     color=line[0].get_color(),
+          # )
+          ax.set_xlim(0.2, 6)
+          ax.set_ylim(1e-8, 0.3)
+          ax.set_xscale("log")
+          ax.set_yscale("log")
+
+
+  ax.legend(loc="upper left")
+
+  ax.set_xlabel(r"$\tau$")
+  ax.set_ylabel(r"$-J$")
+
+
+  # inset = ax.inset_axes([.48, .15, .47, .45])
+  # for model in models:
+  #     inset.plot(model.t, model.L.operator_norm(model.t), linewidth=1)
+  # inset.set_xlim(0.2, 2)
+
+  # inset.set_xlabel(r"$\tau$", labelpad=-1.5)
+  # inset.set_ylabel(r"$||L||$")
+  # inset.set_xscale("log")
+
+  # for i, tick in enumerate(inset.xaxis.get_ticklabels()):
+  #     if i % 2 != 0:
+  #         tick.set_visible(False)
+
+fs.export_fig("initial_slip_modcoup", x_scaling=.5)

python/energy_flow_proper/08_dynamic_one_bath/mod_freq.py

@@ -9,7 +9,7 @@ import plot_utils as pu
 
 import ray
 ray.shutdown()
-ray.init()
+ray.init(address="auto")
 
 from hops.util.logging_setup import logging_setup
 import logging
@@ -50,7 +50,7 @@ for Δ in Δs:
 
 Δ_models[1].bcf(0) * Δ_models[-1].bcf_scale
 
-aux.integrate_multi(Δ_models, 2000)
+aux.integrate_multi(Δ_models, 1)
 
 dim_models = len(δs)
 power = np.zeros((len(Δs), dim_models))
@@ -103,7 +103,12 @@ im = a.imshow(
     aspect="auto",
     origin="lower",
     cmap="plasma",
-    extent=[δs.min(), δs.max(), min(Δs), max(Δs)],
+    extent=[
+        δs.min() - (δs[1] - δs[0]) / 2,
+        δs.max() + (δs[1] - δs[0]) / 2,
+        min(Δs) - ((Δs[1] - Δs[0]) / 2),
+        max(Δs) + ((Δs[1] - Δs[0]) / 2),
+    ],
 )
 f.colorbar(im, ax=a)
 
@@ -114,7 +119,12 @@ im2 = a2.imshow(
     aspect="auto",
     origin="lower",
     cmap="plasma",
-    extent=[δs.min(), δs.max(), min(Δs), max(Δs)],
+    extent=[
+        δs.min() - (δs[1] - δs[0]) / 2,
+        δs.max() + (δs[1] - δs[0]) / 2,
+        min(Δs) - ((Δs[1] - Δs[0]) / 2),
+        max(Δs) + ((Δs[1] - Δs[0]) / 2),
+    ],
 )
 f.colorbar(im2, ax=a2)
 a.set_ylabel(r"$\Delta$")
@@ -125,7 +135,7 @@ a.set_title(r"$\bar{P}$")
 a2.set_title(r"$\bar{P}/\bar{P}_{\mathrm{\max},\alpha}$")
 fs.export_fig("power_heatmap", f, tikz=False)
 
-for model, data in aux.model_data_iterator(Δ_models):
+for model, data in aux.model_data_iterator(Δ_models[-1:]):
     inter = model.interaction_energy(data)
     print(abs(inter.value).max())
 
@@ -133,7 +143,6 @@ dim_models = len(δs)
 interactions = np.zeros((len(Δs), dim_models))
 
 ensemble_arg = dict(overwrite_cache=False)
-fig, ax = plt.subplots()
 for (model, data), Δ, strobe_t, strobe_indices, idx in zip(
     aux.model_data_iterator(Δ_models),
     np.array([[Δ]*len(δs) for Δ in Δs]).flatten(),
@@ -143,3 +152,56 @@ for (model, data), Δ, strobe_t, strobe_indices, idx in zip(
 ):
     interaction = model.interaction_energy(data, **ensemble_arg)
     interactions[int(idx/dim_models), idx % dim_models] = abs(interaction.value).max()
+
+f, (a, a2) = plt.subplots(ncols=2)
+im = a.imshow(
+    interactions,
+    interpolation="gaussian",
+    interpolation_stage="data",
+    aspect="auto",
+    origin="lower",
+    cmap="plasma",
+    extent=[
+        δs.min() - (δs[1] - δs[0]) / 2,
+        δs.max() + (δs[1] - δs[0]) / 2,
+        min(Δs) - ((Δs[1] - Δs[0]) / 2),
+        max(Δs) + ((Δs[1] - Δs[0]) / 2),
+    ],
+)
+f.colorbar(im, ax=a)
+a.set_ylabel(r"$\Delta$")
+a.set_xlabel(r"$\alpha_\beta(0)$")
+a.set_title(r"$|\langle{H_{\mathrm{I}}}\rangle|_\mathrm{max}$")
+
+for i in range(len(Δs)):
+    ints = interactions[i, :]
+    a2.plot(δs - δs[0], ints - ints[0], label=rf"${Δs[i]}$")
+
+a2.plot(δs - δs[0],(δs - δs[0]) / 5, color="grey", linestyle="dashed")
+a2.set_yscale("log")
+a2.set_xscale("log")
+a2.set_ylabel(r"$|\langle{H_{\mathrm{I}}}\rangle|_\mathrm{max}|$")
+a2.set_xlabel(r"$\alpha_\beta(0)$")
+a2.legend(ncol=3, title=r"$\Delta=$", fontsize="x-small")
+fs.export_fig("interaction_nontuned")
+
+f, (a) = plt.subplots()
+im = a.imshow(
+    abs((power / power.max()) - interactions / interactions.max()),
+    interpolation="gaussian",
+    interpolation_stage="data",
+    aspect="auto",
+    origin="lower",
+    cmap="plasma",
+    extent=[
+        δs.min() - (δs[1] - δs[0]) / 2,
+        δs.max() + (δs[1] - δs[0]) / 2,
+        min(Δs) - ((Δs[1] - Δs[0]) / 2),
+        max(Δs) + ((Δs[1] - Δs[0]) / 2),
+    ],
+)
+f.colorbar(im, ax=a)
+a.set_ylabel(r"$\Delta$")
+a.set_xlabel(r"$\alpha_\beta(0)$")
+
+print(es.models_table(Δ_models, tablefmt="latex_raw", data_path=".data_taurus"))

python/energy_flow_proper/08_dynamic_one_bath/mod_freq_en.py

@@ -0,0 +1,181 @@
+import energy_shovel as es
+import numpy as np
+import qutip as qt
+import matplotlib.pyplot as plt
+import utilities as ut
+import figsaver as fs
+import hiro_models.model_auxiliary as aux
+import plot_utils as pu
+
+import ray
+ray.shutdown()
+ray.init(address="auto")
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO, show_stocproc=False)
+
+from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix
+
+Δs = [
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+]  # np.sort(np.concatenate((np.linspace(1, 5, 20), np.linspace(5, 7, int(20/5 * 2)))))
+Δ_models = []
+strobe_ts = []
+strobe_indices_s = []
+δs = np.linspace(0.05, .5, 10)
+for Δ in Δs:
+    for δ in δs:
+        proto, strobe_t, strobe_indices = es.optimize_for_interaction_energy(
+            δ,
+            int(Δ / (2 * np.pi) * 10),
+            500,
+            Δ=Δ,
+            periods=int(Δ / (2 * np.pi) * 20),
+            k_max=6,
+            bcf_terms=7,
+            modulate_system=False,
+            bcf_norm_method="unit_therm",
+            δ=0.5,
+        )
+
+        strobe_ts.append(strobe_t)
+        strobe_indices_s.append(strobe_indices)
+        Δ_models.append(proto)
+
+Δ_models[1].bcf(0) * Δ_models[-1].bcf_scale
+
+aux.integrate_multi(Δ_models, 2000)
+
+dim_models = len(δs)
+power = np.zeros((len(Δs), dim_models))
+
+# final_e_error = [[], []]
+ensemble_arg = dict(overwrite_cache=False)
+for (model, data), Δ, strobe_t, strobe_indices, idx in zip(
+    aux.model_data_iterator(Δ_models, data_path=".data_taurus"),
+    np.array([[Δ]*len(δs) for Δ in Δs]).flatten(),
+    strobe_ts,
+    strobe_indices_s,
+    range(len(Δ_models))
+):
+    energies = model.total_energy_from_power(data, **ensemble_arg)
+    powers = energies.value[strobe_indices[1:]] * (1/strobe_t[1:])
+    power[int(idx/dim_models), idx % dim_models] = np.amax(abs(powers[powers <= 0]), initial=0)
+
+    # fs.plot_energy_overview(model, ensemble_args=ensemble_arg)
+    # fig, ax = plt.subplots()
+    # fs.plot_with_σ(model.t, model.energy_change_from_interaction_power(data, **ensemble_arg).for_bath(0), ax=ax)
+    # fs.plot_with_σ(model.t, model.energy_change_from_system_power(data, **ensemble_arg), ax=ax)
+    # fs.plot_with_σ(model.t, model.total_energy_from_power(data, **ensemble_arg), ax=ax)
+    # fs.plot_with_σ(model.t,  model.interaction_power(data, **ensemble_arg).for_bath(0), ax=ax)
+    # fs.plot_with_σ(model.t, model.total_energy(data, **ensemble_arg), ax=ax)
+    # ax.plot(model.t, model.L.derivative()(model.t)[:,0,1])
+    # ax.plot(model.t, model.L(model.t)[:,0,1])
+    # print(strobe_t[1])
+
+# plt.ylabel(r"$\frac{\Delta E}{T}$")
+# ax.set_xlabel(r"$\Delta$")
+# ax.set_ylabel(r"$P_\mathrm{max}$")
+# for i, energy, σ in zip(itertools.count(0), final_e, final_e_error):
+#     ax.errorbar(Δs, energy, σ, label=rf"$\alpha(0)$ = {δs[i]}")
+# ax.legend()
+# fs.export_fig("delta_dependence")
+
+normed_power = power.copy()
+for i in range(dim_models):
+    normed_power[:, i] /= power[:, i].max()
+
+for i in range(dim_models):
+    plt.plot(Δs, power[:, i])
+
+f, (a, a2) = plt.subplots(ncols=2, sharey=True)
+im = a.imshow(
+    power,
+    interpolation="gaussian",
+    interpolation_stage="data",
+    aspect="auto",
+    origin="lower",
+    cmap="plasma",
+    extent=[
+          δs.min() - (δs[1] - δs[0]) / 2,
+          δs.max() + (δs[1] - δs[0]) / 2,
+          min(Δs) - ((Δs[1] - Δs[0]) / 2),
+          max(Δs) + ((Δs[1] - Δs[0]) / 2),
+      ],
+)
+f.colorbar(im, ax=a)
+
+im2 = a2.imshow(
+    normed_power,
+    interpolation="gaussian",
+    interpolation_stage="data",
+    aspect="auto",
+    origin="lower",
+    cmap="plasma",
+    extent=[
+          δs.min() - (δs[1] - δs[0]) / 2,
+          δs.max() + (δs[1] - δs[0]) / 2,
+          min(Δs) - ((Δs[1] - Δs[0]) / 2),
+          max(Δs) + ((Δs[1] - Δs[0]) / 2),
+      ],
+)
+f.colorbar(im2, ax=a2)
+a.set_ylabel(r"$\Delta$")
+for ax in (a, a2):
+    ax.set_xlabel(r"$\langle H_\mathrm{I}\rangle_\mathrm{max}$")
+
+a.set_title(r"$\bar{P}$")
+a2.set_title(r"$\bar{P}/\bar{P}_{\mathrm{\max},\alpha}$")
+fs.export_fig("power_en_heatmap", f, tikz=False)
+
+for model, data in aux.model_data_iterator(Δ_models):
+    inter = model.interaction_energy(data)
+    print(abs(inter.value).max())
+
+dim_models = len(δs)
+interactions = np.zeros((len(Δs), dim_models))
+
+#ensemble_arg = dict(overwrite_cache=False)
+for (model, data), Δ, strobe_t, strobe_indices, idx in zip(
+    aux.model_data_iterator(Δ_models, data_path=".data_taurus"),
+    np.array([[Δ]*len(δs) for Δ in Δs]).flatten(),
+    strobe_ts,
+    strobe_indices_s,
+    range(len(Δ_models))
+):
+    interaction = model.interaction_energy(data)
+    interactions[int(idx/dim_models), idx % dim_models] = abs(interaction.value).max()
+
+f, (a) = plt.subplots()
+target = np.repeat([δs], len(Δs), axis=0)
+im = a.imshow(
+    np.abs(interactions - target)[1:,:] / target[1:,:],
+    interpolation="gaussian",
+    interpolation_stage="data",
+    aspect="auto",
+    origin="lower",
+    cmap="plasma",
+    extent=[
+        δs.min() - (δs[1] - δs[0]) / 2,
+        δs.max() + (δs[1] - δs[0]) / 2,
+        min(Δs[1:]) - ((Δs[1] - Δs[0]) / 2),
+        max(Δs) + ((Δs[1] - Δs[0]) / 2),
+    ],
+)
+f.colorbar(im, ax=a)
+a.set_ylabel(r"$\Delta$")
+a.set_xlabel(r"$\langle H_\mathrm{I}\rangle_\mathrm{max,target}$")
+a.set_title(r"Difference $|\langle{H_{\mathrm{I}}}\rangle|_\mathrm{max}$ from $\langle H_\mathrm{I}\rangle_\mathrm{max,target}$")
+fs.export_fig("interaction_tuning_success_detail")
+
+print(es.models_table(Δ_models, tablefmt="latex_raw", data_path=".data_taurus"))

python/energy_flow_proper/08_dynamic_one_bath/mod_tune.py

@@ -9,7 +9,7 @@ import plot_utils as pu
 
 import ray
 ray.shutdown()
-ray.init()
+ray.init(address="auto")
 
 from hops.util.logging_setup import logging_setup
 import logging
@@ -19,52 +19,107 @@ from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMat
 
 
 tunings = np.linspace(-0.5, 0.5, 9)
+δs = [0.5, 1.1, 2]
 tune_models = []
 strobe_indices = []
 Δ = 5
-for tune in tunings:
-    proto, strobe_t, strobe_i = es.energy_shovel(
-        Δ,
-        periods=10,
-        modulate_system=False,
-        detune=tune,
-        bcf_norm_method="unit_therm",
-        δ=.8,
-        # k_max=3,
-        # bcf_terms=5,
-    )
+for δ in δs:
+    for tune in tunings:
+        proto, strobe_t, strobe_i = es.energy_shovel(
+            Δ,
+            periods=8,
+            modulate_system=False,
+            detune=tune,
+            bcf_norm_method="sd_peak",
+            δ=δ,
+            # bcf_terms=5,
+        )
+        tune_models.append(proto)
+        strobe_indices.append(strobe_i)
 
-    tune_models.append(proto)
-    strobe_indices.append(strobe_i)
+ωs = np.linspace(tune_models[0].H.max_operator_norm(10) + Δ - 2, 10, 1000)
 
+for model in tune_models[:len(tunings)]:
+    plt.plot(ωs, model.full_thermal_spectral_density(ωs) * model.bcf_scale)
+    plt.plot(ωs, model.spectral_density(ωs) * model.bcf_scale)
+plt.axvline(model.H.max_operator_norm(10) + Δ)
+
+plt.xlabel(r"$\omega$")
+plt.ylabel(r"$J(\omega)$")
+
+fs.export_fig("spectral_densities_tuning")
+
+import gc
+gc.collect()
 aux.integrate_multi(tune_models, 2000)
 
+from hopsflow.util import EnsembleValue
 final_e = []
-for (model, data), tune in zip(aux.model_data_iterator(tune_models), tunings):
-    final_e.append((model.total_energy_from_power(data)).value[strobe_indices].min())
+therm_sd = []
+sd = []
+for model in tune_models:
+    therm_sd.append(
+        model.full_thermal_spectral_density(model.H.max_operator_norm(10) + Δ)
+        * model.bcf_scale
+    )
+    sd.append(
+        model.spectral_density(model.H.max_operator_norm(10) + Δ) * model.bcf_scale
+    )
 
-plt.ylabel(r"$\Delta E$")
-plt.xlabel(r"$\delta$")
-plt.plot(tunings, final_e, linestyle="none", marker="o")
+for (model, data), tune, strobe_i in zip(
+    aux.model_data_iterator(tune_models), np.tile(tunings, len(δs)), strobe_indices
+):
+    energies = model.total_energy_from_power(data)
+    powers = energies.slice(strobe_i[1:]) * (1 / model.t[strobe_i[1:]])
+    powers = powers.slice(powers.value <= 0)
+    if len(powers.value) and (np.argmax(abs(powers.value)) == (len(powers.value)) - 1):
+        print("need more time")
+    else:
+        print("ok")
+    final_e.append(abs(powers).max if len(powers.value) else EnsembleValue((0,0,0)))
 
-plt.plot(
-    tune_models[0].t,
-    np.abs(tune_models[0].bcf(tune_models[0].t)
-    + 2 * tune_models[0].thermal_correlations(tune_models[0].t).real),
-)
-plt.plot(
-    tune_models[0].t,
-    np.abs(tune_models[0].bcf(tune_models[0].t)
-    + 0 * tune_models[0].thermal_correlations(tune_models[0].t).real),
+fig, (ax, ax2) = plt.subplots(ncols=2)
+ωs = np.linspace(model.H.max_operator_norm(10) + Δ - 2, 10, 1000)
+
+for model in tune_models[: len(tunings)]:
+    lines = ax2.plot(ωs, model.spectral_density(ωs) * model.bcf_scale)
+    ax2.plot(
+        ωs,
+        model.full_thermal_spectral_density(ωs) * model.bcf_scale,
+        color=pu.lighten_color(lines[0].get_color(), 0.5),
+        linestyle="dashed",
+    )
+vline = ax2.axvline(model.H.max_operator_norm(10) + Δ)
+ax2.set_xlabel(r"$\omega$")
+ax2.set_ylabel(r"$J(\omega)$")
+ax2.text(
+    model.H.max_operator_norm(10) + Δ + 0.15,
+    0.01,
+    r"$1+\Delta$",
+    color=vline.get_color(),
 )
 
-ωs = np.linspace(1e-3, 10, 1000)
-def nonzero_density(ωs, model):
-    return model.spectral_density(ωs) * (1 + 1/np.expm1(ωs / model.T))
-for tuning, model in zip(tunings, tune_models):
-    freqs = np.array([Δ + 1, 2*Δ + 1])
-    line = plt.plot(tuning, nonzero_density(freqs[0], model), label=tuning, markersize=2, marker="o")
-    plt.plot(tuning, nonzero_density(freqs[1], model), color=line[0].get_color(), markersize=4, marker="*")
-    #plt.plot(ωs, nonzero_density(ωs, model))
+ax.set_ylabel(r"$|P_\mathrm{max}|$ relative")
+ax.set_xlabel(r"$\delta$")
+normed_sds = therm_sd[: len(tunings)] / max(therm_sd[: len(tunings)])
+for i, δ in enumerate(δs):
+    num_tunings = len(tunings)
+    e = final_e[num_tunings * i : num_tunings * (i + 1)]
+    ax.errorbar(
+        tunings,
+        [_.value for _ in e],
+        yerr=[_.σ for _ in e],
+        # / max(final_e[num_tunings * i : num_tunings * (i + 1)]),
+        linestyle="--",
+        marker="o",
+        markersize=3,
+        label=rf"$J_\mathrm{{peak}}={δ}$",
+    )
 
-plt.legend()
+ax.legend()
+# plt.axhline(1)
+# plt.plot(tunings, normed_sds, linestyle="none", marker="*")
+# plt.plot(tunings, sd / max(sd), linestyle="none", marker="o")
+fs.export_fig("modulation_tuning")
+
+print(es.models_table(tune_models, tablefmt="latex_raw"))

python/energy_flow_proper/08_dynamic_one_bath/night.py

@@ -10,14 +10,14 @@ for ω_c in ωs:
         Δ,
         periods=3,
         modulate_system=False,
-        δ=0.1,
+        δ=0.01,
         ω_c = ω_c,
     )
 
     proto.k_max = 5
     proto.therm_method = "fft"
 
-    goal = 0.4
+    goal = 0.01
 
 
     def cost(δ):
@@ -48,8 +48,3 @@ for ω_c in ωs:
     final.therm_method = "fft"
 
     ω_models.append(final.copy())
-
-aux.integrate_multi(ω_models, 10_000)
-
-from pprint import pprint
-print_diff_tbl(ω_models)

python/energy_flow_proper/08_dynamic_one_bath/values/init_slip_alpha.tex

@@ -1 +1 @@
-\(α(0)=0.4\)
+\(α(0)=1.6\)

python/energy_flow_proper/09_dynamic_two_bath_one_qubit/coninously_coupled_version.org

@@ -0,0 +1,457 @@
+#+PROPERTY: header-args :session 09_one_qubit_cc :kernel python :pandoc no :async yes :tangle otto_motor_cc.py
+Herein I'll try to get a basic two-bath cycle running and touch base
+with the markovian result.
+
+* Boilerplate
+#+begin_src jupyter-python :results none
+  import figsaver as fs
+  import plot_utils as pu
+  from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
+  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
+#+end_src
+
+Init ray and silence stocproc.
+#+begin_src jupyter-python
+  import ray
+  ray.shutdown()
+  ray.init(address="141.30.17.16:6379")
+  # ray.init(address='auto')
+  # ray.init()
+#+end_src
+
+#+RESULTS:
+: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-03_14-53-16_736667_825118/sockets/plasma_store.8', 'raylet_socket_name': '/tmp/ray/session_2022-08-03_14-53-16_736667_825118/sockets/raylet.4', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-03_14-53-16_736667_825118', 'metrics_export_port': 42364, 'gcs_address': '141.30.17.16:6379', 'address': '141.30.17.16:6379', 'node_id': 'beae439b1647007190930f56aac60773867f3d5f7a8f4794048c1fc4'})
+
+#+begin_src jupyter-python :results none
+  from hops.util.logging_setup import logging_setup
+  import logging
+  logging_setup(logging.INFO)
+#+end_src
+
+* Cycle
+#+begin_src jupyter-python :results none
+  from hops.util.dynamic_matrix import SmoothStep, Periodic, ConstantMatrix, ScaleTime, Shift, Piecewise
+#+end_src
+
+Now we build a basic otto cycle.
+Let's lay down some basic functionality.
+
+#+begin_src jupyter-python
+  H_op = 2*(1/2 * (qt.sigmaz() + qt.identity(2))).full()
+  L_op = (1/2 * qt.sigmax()).full()
+  L_op_2 = (1/2 * qt.sigmax()).full()
+  print(H_op, L_op, sep="\n")
+#+end_src
+
+#+RESULTS:
+: [[2.+0.j 0.+0.j]
+:  [0.+0.j 0.+0.j]]
+: [[0. +0.j 0.5+0.j]
+:  [0.5+0.j 0. +0.j]]
+
+#+begin_src jupyter-python :results none
+  t_compression = .1
+  t_expansion = .1
+  comp_ratio = .5
+  hot_switch_ratio = .8
+  cold_switch_ratio = .8
+
+  t_hot = (1 - t_expansion - t_compression) / 2
+  t_hot_switch = t_hot * hot_switch_ratio / 2
+  t_cold = (1 - t_expansion - t_compression) / 2
+  t_cold_switch = t_cold * cold_switch_ratio / 2
+  t = np.linspace(0, 10, 1000)
+#+end_src
+
+** Expansion
+#+begin_src jupyter-python
+  H_exp = SmoothStep(comp_ratio * H_op, 0, t_expansion, 2) + H_op * (1 - comp_ratio)
+  tt = np.linspace(0, 1, 1000)
+  plt.plot(tt, H_exp.operator_norm(tt))
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <matplotlib.lines.Line2D | at | 0x7f67d7b16790> |
+[[file:./.ob-jupyter/3881c6a1296c890600f44f323bf7cada34796a52.svg]]
+:END:
+
+** Hot Thermalization
+#+begin_src jupyter-python
+  L_hot = SmoothStep(L_op, t_expansion, t_expansion + t_hot_switch, 2) - SmoothStep(
+        L_op, t_expansion + t_hot - t_hot_switch, t_expansion + t_hot, 2
+    )
+  plt.plot(tt, L_hot.operator_norm(tt))
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <matplotlib.lines.Line2D | at | 0x7f67c9712d30> |
+[[file:./.ob-jupyter/a8a9a5f92e8a831280abac54b86a8eadd04d250f.svg]]
+:END:
+
+** Compression
+#+begin_src jupyter-python
+  H_comp = ConstantMatrix(H_op) - SmoothStep(comp_ratio * H_op, t_expansion + t_hot, t_expansion + t_hot + t_compression, 2)
+  plt.plot(tt, H_comp.operator_norm(tt))
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <matplotlib.lines.Line2D | at | 0x7f67c968d460> |
+[[file:./.ob-jupyter/8d1cc1652c0ecdb3dca2edebf34084aac2e294e8.svg]]
+:END:
+
+** Cold Thermalization
+#+begin_src jupyter-python
+  L_cold = SmoothStep(L_op_2, t_expansion + t_hot + t_compression, t_expansion + t_hot + t_compression + t_cold_switch, 3) - SmoothStep(
+      L_op_2, t_expansion + t_hot + t_compression + t_cold - t_cold_switch, 1, 2
+  )
+  plt.plot(tt, L_cold.operator_norm(tt))
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <matplotlib.lines.Line2D | at | 0x7f67c9670820> |
+[[file:./.ob-jupyter/daaeec41ab4cf2b98f03da09f332c7306ea767d8.svg]]
+:END:
+
+** Full Cycle
+Now we turn the system around after each fill-cycle.
+#+begin_src jupyter-python
+  H_cyc = Periodic(Piecewise([H_exp, H_comp], [0, t_expansion, 1]), 1)
+  L = [Periodic(L_i, 1) for L_i in (L_cold, L_hot)]
+  period = 1
+  scale = .05
+  H_cyc, *L = [ScaleTime(op, scale) for op in [H_cyc, *L]]
+
+  ω_mod = 2*np.pi / (period) * scale
+  ω_mod
+  t_cycle = period/scale
+  periods = 8
+  t_max = t_cycle * periods
+  dt = .1
+
+  t = np.linspace(0, t_cycle, 1000)
+
+  plt.plot(t, H_cyc.operator_norm(t) - 1, label=r"$f(\tau)$")
+  plt.plot(t, L[0].operator_norm(t) * 2, label=r"$h_{c}(\tau)$")
+  plt.plot(t, L[1].operator_norm(t) * 2, label=r"$h_{h}(\tau)$")
+  plt.xlabel(r"$\tau$")
+  plt.legend()
+  fs.export_fig("modulation", x_scaling=.5, y_scaling=.35)
+#+end_src
+
+#+RESULTS:
+[[file:./.ob-jupyter/e48d9ad285307403060fc060162f096c7c46ba4d.svg]]
+
+** Shifts
+#+begin_src jupyter-python
+  from scipy.optimize import minimize_scalar
+
+  ref_model = QubitModelMutliBath(
+      ω_c=[1] * 2,
+      T=[1, 20],
+  )
+
+  shifts = []
+  scales = []
+
+  for i, t_exp in enumerate([0, t_expansion / scale]):
+      ω_exp = H_cyc.operator_norm(i * t_cycle / 3)
+      print(ω_exp)
+      def objective(ω_s):
+          ref_model.ω_s[i] = ω_s
+
+          return -ref_model.full_thermal_spectral_density(i)(ω_exp)
+
+      res = minimize_scalar(
+          objective, 1, method="bounded", bounds=(0.01, ω_exp), options=dict(maxiter=100)
+      )
+      shifts.append(res.x)
+      scales.append(-res.fun)
+      ω = np.linspace(0.01, 3, 1000)
+      lines = plt.plot(
+          ω,
+          ref_model.full_thermal_spectral_density(i)(ω) / -res.fun,
+          label=["Cold", "Hot"][i],
+      )
+      plt.plot(
+          ω_exp,
+          ref_model.full_thermal_spectral_density(i)(ω_exp) / -res.fun,
+          marker="o",
+          color=lines[0].get_color(),
+      )
+
+  plt.legend()
+  plt.xlabel(r"$\omega$")
+  plt.ylabel("Arbitrary Units")
+  print(shifts, scales)
+  fs.export_fig("spectral_densities", x_scaling=.5, y_scaling=.35)
+  shifts = [0.01000577876575025, 1.0000001111768948]
+  scales = [0.5819473793333508, 3.865799283487837]
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+: 1.0
+: 2.0
+: [0.01000577876575025, 1.0000001111768948] [0.5819473793333508, 3.865799283487837]
+[[file:./.ob-jupyter/388a36ef461b90e864fa2e9850f2127b9d48b5b1.svg]]
+:END:
+
+
+* Model
+#+begin_src jupyter-python :results none
+  model = QubitModelMutliBath(
+      δ=list(1/np.array(scales) * .1/2),#[.05*4, .01*4],
+      ω_c=ref_model.ω_c,
+      ω_s=shifts,#[H_cyc.operator_norm(0) - 1, H_cyc.operator_norm(t_expansion/scale) - 1],
+      t=ut.linspace_with_strobe(0, t_max, int(t_max // dt), ω_mod),
+      ψ_0=(qt.basis([2], [0])*0  + qt.basis([2], [1])*1),
+      description=f"A simple otto cycle.",
+      k_max=4,
+      bcf_terms=[6]*2,
+      truncation_scheme="simplex",
+      driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
+      thermal_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
+      T = ref_model.T, #= [1, 20],
+      L = [ConstantMatrix(L_op), ConstantMatrix(L_op_2)],
+      H = H_cyc,
+      therm_methods=["fft", "fft"],
+  )
+#+end_src
+
+
+#+begin_src jupyter-python
+  aux.integrate(model, 10000)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+#+begin_example
+   [INFO    hops.core.integration     472774] Using 24 integrators.
+   [INFO    hops.core.integration     472774] Some 1000 trajectories have to be integrated.
+   [INFO    hops.core.integration     472774] Using 1820 hierarchy states.
+   100% 1000/1000 [06:50<00:00,  2.44it/s]
+   [INFO    hops.core.integration     472774] Choosing the nonlinear integrator.
+   [INFO    hops.core.integration     472774] Using 24 integrators.
+   [INFO    hops.core.integration     472774] Some 9000 trajectories have to be integrated.
+   [INFO    hops.core.integration     472774] Using 1820 hierarchy states.
+    67% 6025/9000 [40:11<19:50,  2.50it/s][INFO    hops.core.signal_delay    472774] caught sig 'SIGINT'
+  67% 6031/9000 [40:15<19:49,  2.50it/s]
+   2022-09-01 12:51:33,312      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,314      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,315      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,317      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,318      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,320      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,321      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,323      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,324      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,329      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,332      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,335      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,336      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,339      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,342      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,344      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,345      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,346      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,348      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,349      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,350      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,351      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   2022-09-01 12:51:33,353      ERROR worker.py:94 -- Unhandled error (suppress with 'RAY_IGNORE_UNHANDLED_ERRORS=1'): The worker died unexpectedly while executing this task. Check python-core-worker-*.log files for more information.
+   [INFO    hops.core.signal_delay    472774] caught 1 signal(s)
+   [INFO    hops.core.signal_delay    472774] emit signal 'SIGINT'
+#+end_example
+# [goto error]
+#+begin_example
+  ---------------------------------------------------------------------------
+  KeyboardInterrupt                         Traceback (most recent call last)
+  Input In [24], in <cell line: 1>()
+  ----> 1 aux.integrate(model, 10000)
+
+  File ~/src/two_qubit_model/hiro_models/model_auxiliary.py:110, in integrate(model, n, data_path, clear_pd)
+      100 # with model_db(data_path) as db:
+      101 #     if hash in db and "data" db[hash]
+      103 supervisor = HOPSSupervisor(
+      104     model.hops_config,
+      105     n,
+      106     data_path=data_path,
+      107     data_name=hash,
+      108 )
+  --> 110 supervisor.integrate(clear_pd)
+      112 with supervisor.get_data(True) as data:
+      113     with model_db(data_path) as db:
+
+  File ~/src/hops/hops/core/integration.py:1288, in HOPSSupervisor.integrate(self, clear_pd)
+     1285     break
+     1287 integration.update()
+  -> 1288 data.new_samples(
+     1289     idx=index,
+     1290     incomplete=incomplete,
+     1291     psi0=psi0,
+     1292     aux_states=aux_states,
+     1293     stoc_proc=stoc_proc,
+     1294     result_type=self.params.HiP.result_type,
+     1295     normed=self._normed_average,
+     1296     rng_seed=seed,
+     1297 )
+
+  File ~/src/hops/hops/core/signal_delay.py:87, in sig_delay.__exit__(self, exc_type, exc_val, exc_tb)
+       84 if len(self.sigh.sigs_caught) > 0 and self.handler is not None:
+       85     self.handler(self.sigh.sigs_caught)
+  ---> 87 self._restore()
+
+  File ~/src/hops/hops/core/signal_delay.py:68, in sig_delay._restore(self)
+       66 for i, s in enumerate(self.sigs):
+       67     signal.signal(s, self.old_handlers[i])
+  ---> 68 self.sigh.emit()
+
+  File ~/src/hops/hops/core/signal_delay.py:42, in SigHandler.emit(self)
+       40 for s in self.sigs_caught:
+       41     log.info("emit signal '{}'".format(SIG_MAP[s]))
+  ---> 42     os.kill(os.getpid(), s)
+
+  KeyboardInterrupt:
+#+end_example
+:END:
+
+
+#+begin_src jupyter-python
+  model.full_thermal_bcf(0)(0) * model.bcf_scales[0]
+#+end_src
+
+#+RESULTS:
+| 0.37969344545116374+0j |
+
+#+begin_src jupyter-python
+  f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod, hybrid=True, bath_names=["C","H"])
+  # a.plot(model.t, model.L[0].operator_norm(model.t))
+  # a.plot(model.t, model.L[1].operator_norm(model.t))
+  a.set_xlabel(r"$\tau$")
+  a.legend()
+  fs.export_fig("energy_strobe_continuous", y_scaling=.6)
+#+end_src
+
+#+RESULTS:
+[[file:./.ob-jupyter/1923d2fe4898092052cb87ef9f4d3f83f90c9829.svg]]
+
+#+begin_src jupyter-python
+    fig, ax = plt.subplots()
+    with aux.get_data(model) as data:
+        power = model.interaction_power(data).sum_baths()
+        power_sys = model.system_power(data).sum_baths()
+        energy = model.total_energy_from_power(data)
+        ax.plot([], [])
+        pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
+
+    ax.legend()
+    ax.set_title("Power")
+    ax.set_xlabel(r"$\tau$")
+    fs.export_fig("power_continuous", y_scaling=0.4)
+#+end_src
+
+#+RESULTS:
+[[file:./.ob-jupyter/c1bd0d8af02b19644d1e6734ab62579d3f62c6b8.svg]]
+
+
+#+begin_src jupyter-python
+  steady_index = 2
+  cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+  with aux.get_data(model) as data:
+      total_energies = model.total_energy_from_power(data).slice(
+          cycle_indices[steady_index:]
+      )
+      hot_bath_energies = (
+          model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+      )
+
+  e_diff = total_energies.slice(0) - total_energies.slice(-1)
+  mean_power = e_diff * (1 / (model.t[-1] - cycle_times[steady_index]))
+  efficiency = e_diff * (
+      1 / (hot_bath_energies.slice(0) - hot_bath_energies.slice(-1)).value
+  )
+  mean_power.value, mean_power.σ, efficiency.value
+
+  fs.tex_value(mean_power.value, err=mean_power.σ, prefix=r"\bar{P}="), fs.tex_value(
+      efficiency.value * 100, prefix=r"η=", suffix=r"\%"
+  )
+#+end_src
+
+#+RESULTS:
+| \(\bar{P}=0.001280\pm 0.000018\) | \(η=4\%\) |
+
+
+
+#+begin_src jupyter-python
+  with aux.get_data(model) as data:
+      ρ = data.rho_t_accum.mean[:]
+      σ_ρ = data.rho_t_accum.ensemble_std[:]
+      plt.plot(model.t[100:], abs(ρ[100:, 0, 1]) / abs(ρ[100:, 0, 0]))
+      plt.yscale("log")
+      plt.xlabel(r"$\tau$")
+      plt.ylabel(r"$|\rho_{01}| / \rho_{00}$")
+      fs.export_fig("coherences")
+      xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full())
+      ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full())
+      zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full())
+#+end_src
+
+#+RESULTS:
+[[file:./.ob-jupyter/248a8051f703aa359f429c45da6aef5897840514.svg]]
+
+#+begin_src jupyter-python
+  steady_index = 1
+  cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+  with aux.get_data(model) as data:
+      hot_bath_energies = (
+          model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+      )
+
+      cold_bath_energies = (
+          model.bath_energy(data).for_bath(0).slice(cycle_indices[steady_index:])
+      )
+
+      hot_diff = (hot_bath_energies.slice(slice(1,-1, 1)) - hot_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[1])
+      cold_diff = (cold_bath_energies.slice(slice(1,-1, 1)) - cold_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[0])
+      gibbs = hot_diff.mean + cold_diff.mean
+      print(fs.tex_value(gibbs.value, err=gibbs.σ))
+      print(gibbs.value /  gibbs.σ)
+#+end_src
+
+#+RESULTS:
+: \(0.6198\pm 0.0021\)
+: 299.1098863449865
+
+
+
+#+begin_src jupyter-python
+  b = qt.Bloch()
+  b.add_points([xs, ys, zs])
+  b.view = [20, 20]
+  b.point_size = [0.01]
+  b.sphere_alpha = 0.01
+  b.render()
+  #  fs.export_fig("bloch", fig=b.fig)
+  b.fig
+#+end_src
+
+
+#+RESULTS:
+:RESULTS:
+: /nix/store/9fp3glm79h56gz17w052rlqw3gwcza7i-python3-3.9.13-env/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the "figure" keyword
+:   fig.canvas.print_figure(bytes_io, **kw)
+[[file:./.ob-jupyter/d500fa74b313f80e765d832c14618e0311e6ae26.svg]]
+:END:

python/energy_flow_proper/09_dynamic_two_bath_one_qubit/first_experiments.org

@@ -24,7 +24,7 @@ Init ray and silence stocproc.
 #+end_src
 
 #+RESULTS:
-: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-25_14-28-33_210699_311404/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-25_14-28-33_210699_311404/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-25_14-28-33_210699_311404', 'metrics_export_port': 51335, 'gcs_address': '141.30.17.225:49191', 'address': '141.30.17.225:49191', 'node_id': 'f56218ac6f7953fb2918fe3d9901e37b9d6eb39dbcb570c6f6b3d37b'})
+: 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_2022-08-31_16-13-49_406995_81357/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-31_16-13-49_406995_81357/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-31_16-13-49_406995_81357', 'metrics_export_port': 53031, 'gcs_address': '141.30.17.225:48046', 'address': '141.30.17.225:48046', 'node_id': 'ee259897128c1f933240fc43cc27f9c53503a2813340d340a649383c'})
 
 #+begin_src jupyter-python :results none
   from hops.util.logging_setup import logging_setup
@@ -76,8 +76,8 @@ Let's lay down some basic functionality.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f08c50754c0> |
-[[file:./.ob-jupyter/1fdbf5e8f0e4baea901a45cd3b8e07fdffa55dc0.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f7dbad918e0> |
+[[file:./.ob-jupyter/8d84ee4e94b1da8212a9743e74031d0de30409c8.svg]]
 :END:
 
 ** Hot Thermalization
@@ -90,8 +90,8 @@ Let's lay down some basic functionality.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f08b69cbe50> |
-[[file:./.ob-jupyter/c329795210622a56c5cc54daccb1843b5adc48ca.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f7dbc933ee0> |
+[[file:./.ob-jupyter/d356101136a1a781207e8ebd8d7eff13b68887c9.svg]]
 :END:
 
 ** Compression
@@ -102,8 +102,8 @@ Let's lay down some basic functionality.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f08b69454f0> |
-[[file:./.ob-jupyter/7ed97af88f090ad1bb3b943711a34408dc1a9072.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f7dbac86ee0> |
+[[file:./.ob-jupyter/76974bf650add560ccc03e012578ed902fc5bb5e.svg]]
 :END:
 
 ** Cold Thermalization
@@ -116,8 +116,8 @@ Let's lay down some basic functionality.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f08b692ca90> |
-[[file:./.ob-jupyter/b429a448a8d92927925baa9ce7d054721d70a9e1.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f7dba9ae670> |
+[[file:./.ob-jupyter/3ed7dd6e3a5c05c6b241e4297ee268aee47fdf3b.svg]]
 :END:
 
 ** Full Cycle
@@ -139,15 +139,15 @@ Now we turn the system around after each fill-cycle.
   t = np.linspace(0, t_cycle, 1000)
 
   plt.plot(t, H_cyc.operator_norm(t) - 1, label=r"$f(\tau)$")
-  plt.plot(t, L[0].operator_norm(t) * 2, label=r"$g_{c}(\tau)$")
-  plt.plot(t, L[1].operator_norm(t) * 2, label=r"$g_{h}(\tau)$")
+  plt.plot(t, L[0].operator_norm(t) * 2, label=r"$h_{c}(\tau)$")
+  plt.plot(t, L[1].operator_norm(t) * 2, label=r"$h_{h}(\tau)$")
   plt.xlabel(r"$\tau$")
   plt.legend()
   fs.export_fig("modulation", x_scaling=.5, y_scaling=.35)
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/5c1a8c11fde4110a9bba10a2d2b1da3401ec327a.svg]]
+[[file:./.ob-jupyter/90e7ba4c8592f0c233d87f7bfa533367dce77823.svg]]
 
 ** Shifts
 #+begin_src jupyter-python
@@ -191,9 +191,9 @@ Now we turn the system around after each fill-cycle.
   plt.xlabel(r"$\omega$")
   plt.ylabel("Arbitrary Units")
   print(shifts, scales)
-  # fs.export_fig("spectral_densities", x_scaling=.5, y_scaling=.35)
-  # shifts = [0.010005778765750251, 0.5000003902186526]
-  # scales = [0.3656483223378975, 3.198956858189882]
+  fs.export_fig("spectral_densities", x_scaling=.5, y_scaling=.35)
+  shifts = [0.01000577876575025, 1.0000001111768948]
+  scales = [0.5819473793333508, 3.865799283487837]
 #+end_src
 
 #+RESULTS:
@@ -201,7 +201,7 @@ Now we turn the system around after each fill-cycle.
 : 1.0
 : 2.0
 : [0.01000577876575025, 1.0000001111768948] [0.5819473793333508, 3.865799283487837]
-[[file:./.ob-jupyter/f64b53223d96a7adadf180096f9af5938cbb2d60.svg]]
+[[file:./.ob-jupyter/c80e410ccf978d57f58fb423e1bb9c7d2da121f8.svg]]
 :END:
 
 
@@ -237,41 +237,37 @@ Now we turn the system around after each fill-cycle.
 #+end_src
 
 #+RESULTS:
-| 0.6043008870314562+0j |
+| 0.37969344545116374+0j |
 
 #+begin_src jupyter-python
-  f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod, hybrid=True)
-  # a.plot(model.t, model.H.operator_norm(model.t))
+  f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod, hybrid=True, bath_names=["C","H"])
   # a.plot(model.t, model.L[0].operator_norm(model.t))
   # a.plot(model.t, model.L[1].operator_norm(model.t))
   a.set_xlabel(r"$\tau$")
   a.legend()
-  #fs.export_fig("energy_strobe", y_scaling=.6)
+  fs.export_fig("energy_strobe", y_scaling=.6)
 #+end_src
 
 #+RESULTS:
-:RESULTS:
-: <matplotlib.legend.Legend at 0x7f08af06dc70>
-[[file:./.ob-jupyter/d00d2d44c36070dc6d2af0c0850f30355fd5bf36.svg]]
-:END:
+[[file:./.ob-jupyter/1995b28cb61148b2f5adcba1fed656a15f64d4ed.svg]]
 
 #+begin_src jupyter-python
   fig, ax = plt.subplots()
   with aux.get_data(model) as data:
-        power = model.interaction_power(data).sum_baths()
-        power_sys = model.system_power(data).sum_baths()
-        energy = model.total_energy_from_power(data)
-        pu.plot_with_σ(model.t, power, ax=ax, label="Interaction")
-        pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
+      power = model.interaction_power(data).sum_baths()
+      power_sys = model.system_power(data).sum_baths()
+      energy = model.total_energy_from_power(data)
+      pu.plot_with_σ(model.t, power, ax=ax, label="Interaction")
+      pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
 
   ax.legend()
   ax.set_title("Power")
   ax.set_xlabel(r"$\tau$")
-  fs.export_fig("power", y_scaling=.4)
+  fs.export_fig("power", y_scaling=0.4)
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/a920807166eb55c5dcb225c6c41cd01ca5c2df89.svg]]
+[[file:./.ob-jupyter/0574f89991f03ba304cb863e8950a13c89e211f8.svg]]
 
 
 #+begin_src jupyter-python
@@ -293,46 +289,97 @@ Now we turn the system around after each fill-cycle.
       1 / (hot_bath_energies.slice(0) - hot_bath_energies.slice(-1)).value
   )
   mean_power.value, mean_power.σ, efficiency.value
+
+  fs.tex_value(mean_power.value, err=mean_power.σ, prefix=r"\bar{P}="), fs.tex_value(
+      efficiency.value * 100, prefix=r"η=", suffix=r"\%"
+  )
 #+end_src
 
 #+RESULTS:
-| 0.0019806021074448648 | 2.3085106663538818e-05 | 0.1975630167587971 |
+| \(\bar{P}=0.002468\pm 0.000021\) | \(η=29\%\) |
+
+#+begin_src jupyter-python
+  steady_index = 3
+  cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+  with aux.get_data(model) as data:
+      total_energies = model.energy_change_from_system_power(data).slice(
+          cycle_indices[steady_index:]
+      )
+      hot_bath_energies = (
+          model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+      )
+
+  e_diff = total_energies.slice(0) - total_energies.slice(-1)
+  mean_power = e_diff * (1 / (model.t[-1] - cycle_times[steady_index]))
+  efficiency = e_diff * (
+      1 / (hot_bath_energies.slice(0) - hot_bath_energies.slice(-1)).value
+  )
+  fs.tex_value(mean_power.value, err=mean_power.σ, prefix=r"\bar{P}="), fs.tex_value(
+      efficiency.value * 100, prefix=r"η=", suffix=r"\%"
+  )
+#+end_src
+
+#+RESULTS:
+| \(\bar{P}=0.004337\pm 0.000018\) | \(η=52\%\) |
+
 
 #+begin_src jupyter-python
   with aux.get_data(model) as data:
       ρ = data.rho_t_accum.mean[:]
       σ_ρ = data.rho_t_accum.ensemble_std[:]
-      plt.plot(model.t, ρ[:, 0, 0])
+      plt.plot(model.t[100:], abs(ρ[100:, 0, 1]) / abs(ρ[100:, 0, 0]))
+      plt.yscale("log")
+      plt.xlabel(r"$\tau$")
+      plt.ylabel(r"$|\rho_{01}| / \rho_{00}$")
+      fs.export_fig("coherences")
       xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full())
       ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full())
       zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full())
 #+end_src
 
 #+RESULTS:
-:RESULTS:
-: /nix/store/9fp3glm79h56gz17w052rlqw3gwcza7i-python3-3.9.13-env/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part
-:   return np.asarray(x, float)
-[[file:./.ob-jupyter/d7b074c74ef3761fe491ad92283655746125d59e.svg]]
-:END:
+[[file:./.ob-jupyter/d4d4877be23c1ab2702685d16a8e4a1ba1594347.svg]]
+
+#+begin_src jupyter-python
+  steady_index = 3
+  cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+  with aux.get_data(model) as data:
+      hot_bath_energies = (
+          model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+      )
+
+      cold_bath_energies = (
+          model.bath_energy(data).for_bath(0).slice(cycle_indices[steady_index:])
+      )
+
+      hot_diff = (hot_bath_energies.slice(slice(1,-1, 1)) - hot_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[1])
+      cold_diff = (cold_bath_energies.slice(slice(1,-1, 1)) - cold_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[0])
+      gibbs = hot_diff.mean + cold_diff.mean
+      print(fs.tex_value(gibbs.value, err=gibbs.σ))
+      print(gibbs.value /  gibbs.σ)
+#+end_src
+
+#+RESULTS:
+: \(0.1096\pm 0.0008\)
+: 137.4959836253038
+
 
 
 #+begin_src jupyter-python
+  %matplotlib inline
   b = qt.Bloch()
   b.add_points([xs, ys, zs])
   b.view = [20, 20]
-  b.point_size = [0.1]
-  b.sphere_alpha = 0.1
-  b.size = [20, 20]
+  b.point_size = [0.01]
+  b.sphere_alpha = 0.01
   b.render()
-  b.fig
+  fs.export_fig("bloch", fig=b.fig)
+
 #+end_src
 
 
 #+RESULTS:
-:RESULTS:
-: /nix/store/9fp3glm79h56gz17w052rlqw3gwcza7i-python3-3.9.13-env/lib/python3.9/site-packages/qutip/bloch.py:639: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the "figure" keyword
-:   self.fig.canvas.draw()
-: /nix/store/9fp3glm79h56gz17w052rlqw3gwcza7i-python3-3.9.13-env/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the "figure" keyword
-:   fig.canvas.print_figure(bytes_io, **kw)
-[[file:./.ob-jupyter/072aab236948e2e79dc6a892c44f33144b4fd2d9.svg]]
-:END:

python/energy_flow_proper/09_dynamic_two_bath_one_qubit/flake.lock

@@ -2,11 +2,11 @@
   "nodes": {
     "flake-utils": {
       "locked": {
-        "lastModified": 1656928814,
-        "narHash": "sha256-RIFfgBuKz6Hp89yRr7+NR5tzIAbn52h8vT6vXkYjZoM=",
+        "lastModified": 1659877975,
+        "narHash": "sha256-zllb8aq3YO3h8B/U0/J1WBgAL8EX5yWf5pMj3G0NAmc=",
         "owner": "numtide",
         "repo": "flake-utils",
-        "rev": "7e2a3b3dfd9af950a856d66b0a7d01e3c18aa249",
+        "rev": "c0e246b9b83f637f4681389ecabcb2681b4f3af0",
         "type": "github"
       },
       "original": {
@@ -17,11 +17,11 @@
     },
     "flake-utils_2": {
       "locked": {
-        "lastModified": 1656928814,
-        "narHash": "sha256-RIFfgBuKz6Hp89yRr7+NR5tzIAbn52h8vT6vXkYjZoM=",
+        "lastModified": 1659877975,
+        "narHash": "sha256-zllb8aq3YO3h8B/U0/J1WBgAL8EX5yWf5pMj3G0NAmc=",
         "owner": "numtide",
         "repo": "flake-utils",
-        "rev": "7e2a3b3dfd9af950a856d66b0a7d01e3c18aa249",
+        "rev": "c0e246b9b83f637f4681389ecabcb2681b4f3af0",
         "type": "github"
       },
       "original": {
@@ -32,11 +32,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1660305968,
-        "narHash": "sha256-r0X1pZCSEA6mzt5OuTA7nHuLmvnbkwgpFAh1iLIx4GU=",
+        "lastModified": 1664281702,
+        "narHash": "sha256-haixZ4TJLu1Dciow54wrHrHvlGDVr5sW6MTeAV/ZLuI=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "c4a0efdd5a728e20791b8d8d2f26f90ac228ee8d",
+        "rev": "7e52b35fe98481a279d89f9c145f8076d049d2b9",
         "type": "github"
       },
       "original": {
@@ -47,11 +47,11 @@
     },
     "nixpkgs_2": {
       "locked": {
-        "lastModified": 1658311025,
-        "narHash": "sha256-GqagY5YmaZB3YaO41kKcQhe5RcpS83wnsW8iCu5Znqo=",
+        "lastModified": 1660546381,
+        "narHash": "sha256-rEzCjeWVGhK5AyHxm1zet0lF6+AVSW3JuU5LAU2SMYU=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "cd8d1784506a7c7eb0796772b73437e0b82fad57",
+        "rev": "eb642f80f9aecc19312909e08601a3c2020b5ce2",
         "type": "github"
       },
       "original": {
@@ -66,11 +66,11 @@
         "nixpkgs": "nixpkgs_2"
       },
       "locked": {
-        "lastModified": 1657626303,
-        "narHash": "sha256-O/JJ0hSBCmlx0oP8QGAlRrWn0BvlC5cj7/EZ0CCWHTU=",
+        "lastModified": 1660542485,
+        "narHash": "sha256-XfklMwJMLB7bLI5ZnQTrNaK7KyBnElLGoWOL++XO3zk=",
         "owner": "nix-community",
         "repo": "poetry2nix",
-        "rev": "920ba682377d5c0d87945c5eb6141ab8447ca509",
+        "rev": "8b6239cf2ded121f8f3d570d2563d69f05d4208f",
         "type": "github"
       },
       "original": {
@@ -94,11 +94,11 @@
         "poetry2nix": "poetry2nix"
       },
       "locked": {
-        "lastModified": 1658314126,
-        "narHash": "sha256-rEoZSb8bBafEd6m3xu/yQtSFlP8GZxKN15YqMZkeMaQ=",
+        "lastModified": 1660547406,
+        "narHash": "sha256-ZRLGxqHmX5xtCBPCt1pb2E3Z70IEmMT3TA+JruosF4Q=",
         "owner": "vale981",
         "repo": "hiro-flake-utils",
-        "rev": "316cd25ffd9d7afd331cbbc1429f62f30567e288",
+        "rev": "dd1e89c5fabbca9a3dbec91cab3e3e3867da10fc",
         "type": "github"
       },
       "original": {

python/energy_flow_proper/09_dynamic_two_bath_one_qubit/otto_motor.py

@@ -69,8 +69,8 @@ dt = .1
 t = np.linspace(0, t_cycle, 1000)
 
 plt.plot(t, H_cyc.operator_norm(t) - 1, label=r"$f(\tau)$")
-plt.plot(t, L[0].operator_norm(t) * 2, label=r"$g_{c}(\tau)$")
-plt.plot(t, L[1].operator_norm(t) * 2, label=r"$g_{h}(\tau)$")
+plt.plot(t, L[0].operator_norm(t) * 2, label=r"$h_{c}(\tau)$")
+plt.plot(t, L[1].operator_norm(t) * 2, label=r"$h_{h}(\tau)$")
 plt.xlabel(r"$\tau$")
 plt.legend()
 fs.export_fig("modulation", x_scaling=.5, y_scaling=.35)
@@ -115,9 +115,9 @@ plt.legend()
 plt.xlabel(r"$\omega$")
 plt.ylabel("Arbitrary Units")
 print(shifts, scales)
-# fs.export_fig("spectral_densities", x_scaling=.5, y_scaling=.35)
-# shifts = [0.010005778765750251, 0.5000003902186526]
-# scales = [0.3656483223378975, 3.198956858189882]
+fs.export_fig("spectral_densities", x_scaling=.5, y_scaling=.35)
+shifts = [0.01000577876575025, 1.0000001111768948]
+scales = [0.5819473793333508, 3.865799283487837]
 
 model = QubitModelMutliBath(
     δ=list(1/np.array(scales) * .1/2),#[.05*4, .01*4],
@@ -141,26 +141,25 @@ aux.integrate(model, 10000)
 
 model.full_thermal_bcf(0)(0) * model.bcf_scales[0]
 
-f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod, hybrid=True)
-# a.plot(model.t, model.H.operator_norm(model.t))
+f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod, hybrid=True, bath_names=["C","H"])
 # a.plot(model.t, model.L[0].operator_norm(model.t))
 # a.plot(model.t, model.L[1].operator_norm(model.t))
 a.set_xlabel(r"$\tau$")
 a.legend()
-#fs.export_fig("energy_strobe", y_scaling=.6)
+fs.export_fig("energy_strobe", y_scaling=.6)
 
 fig, ax = plt.subplots()
 with aux.get_data(model) as data:
-      power = model.interaction_power(data).sum_baths()
-      power_sys = model.system_power(data).sum_baths()
-      energy = model.total_energy_from_power(data)
-      pu.plot_with_σ(model.t, power, ax=ax, label="Interaction")
-      pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
+    power = model.interaction_power(data).sum_baths()
+    power_sys = model.system_power(data).sum_baths()
+    energy = model.total_energy_from_power(data)
+    pu.plot_with_σ(model.t, power, ax=ax, label="Interaction")
+    pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
 
 ax.legend()
 ax.set_title("Power")
 ax.set_xlabel(r"$\tau$")
-fs.export_fig("power", y_scaling=.4)
+fs.export_fig("power", y_scaling=0.4)
 
 steady_index = 3
 cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
@@ -181,19 +180,67 @@ efficiency = e_diff * (
 )
 mean_power.value, mean_power.σ, efficiency.value
 
+fs.tex_value(mean_power.value, err=mean_power.σ, prefix=r"\bar{P}="), fs.tex_value(
+    efficiency.value * 100, prefix=r"η=", suffix=r"\%"
+)
+
+steady_index = 3
+cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+with aux.get_data(model) as data:
+    total_energies = model.energy_change_from_system_power(data).slice(
+        cycle_indices[steady_index:]
+    )
+    hot_bath_energies = (
+        model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+    )
+
+e_diff = total_energies.slice(0) - total_energies.slice(-1)
+mean_power = e_diff * (1 / (model.t[-1] - cycle_times[steady_index]))
+efficiency = e_diff * (
+    1 / (hot_bath_energies.slice(0) - hot_bath_energies.slice(-1)).value
+)
+fs.tex_value(mean_power.value, err=mean_power.σ, prefix=r"\bar{P}="), fs.tex_value(
+    efficiency.value * 100, prefix=r"η=", suffix=r"\%"
+)
+
 with aux.get_data(model) as data:
     ρ = data.rho_t_accum.mean[:]
     σ_ρ = data.rho_t_accum.ensemble_std[:]
-    plt.plot(model.t, ρ[:, 0, 0])
+    plt.plot(model.t[100:], abs(ρ[100:, 0, 1]) / abs(ρ[100:, 0, 0]))
+    plt.yscale("log")
+    plt.xlabel(r"$\tau$")
+    plt.ylabel(r"$|\rho_{01}| / \rho_{00}$")
+    fs.export_fig("coherences")
     xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full())
     ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full())
     zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full())
 
+steady_index = 3
+cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+with aux.get_data(model) as data:
+    hot_bath_energies = (
+        model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+    )
+
+    cold_bath_energies = (
+        model.bath_energy(data).for_bath(0).slice(cycle_indices[steady_index:])
+    )
+
+    hot_diff = (hot_bath_energies.slice(slice(1,-1, 1)) - hot_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[1])
+    cold_diff = (cold_bath_energies.slice(slice(1,-1, 1)) - cold_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[0])
+    gibbs = hot_diff.mean + cold_diff.mean
+    print(fs.tex_value(gibbs.value, err=gibbs.σ))
+    print(gibbs.value /  gibbs.σ)
+
+%matplotlib inline
 b = qt.Bloch()
 b.add_points([xs, ys, zs])
 b.view = [20, 20]
-b.point_size = [0.1]
-b.sphere_alpha = 0.1
-b.size = [20, 20]
+b.point_size = [0.01]
+b.sphere_alpha = 0.01
 b.render()
-b.fig
+fs.export_fig("bloch", fig=b.fig)

python/energy_flow_proper/09_dynamic_two_bath_one_qubit/otto_motor_cc.py

@@ -0,0 +1,226 @@
+import figsaver as fs
+import plot_utils as pu
+from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
+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 ray
+ray.shutdown()
+ray.init(address="141.30.17.16:6379")
+# ray.init(address='auto')
+# ray.init()
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO)
+
+from hops.util.dynamic_matrix import SmoothStep, Periodic, ConstantMatrix, ScaleTime, Shift, Piecewise
+
+H_op = 2*(1/2 * (qt.sigmaz() + qt.identity(2))).full()
+L_op = (1/2 * qt.sigmax()).full()
+L_op_2 = (1/2 * qt.sigmax()).full()
+print(H_op, L_op, sep="\n")
+
+t_compression = .1
+t_expansion = .1
+comp_ratio = .5
+hot_switch_ratio = .8
+cold_switch_ratio = .8
+
+t_hot = (1 - t_expansion - t_compression) / 2
+t_hot_switch = t_hot * hot_switch_ratio / 2
+t_cold = (1 - t_expansion - t_compression) / 2
+t_cold_switch = t_cold * cold_switch_ratio / 2
+t = np.linspace(0, 10, 1000)
+
+H_exp = SmoothStep(comp_ratio * H_op, 0, t_expansion, 2) + H_op * (1 - comp_ratio)
+tt = np.linspace(0, 1, 1000)
+plt.plot(tt, H_exp.operator_norm(tt))
+
+L_hot = SmoothStep(L_op, t_expansion, t_expansion + t_hot_switch, 2) - SmoothStep(
+      L_op, t_expansion + t_hot - t_hot_switch, t_expansion + t_hot, 2
+  )
+plt.plot(tt, L_hot.operator_norm(tt))
+
+H_comp = ConstantMatrix(H_op) - SmoothStep(comp_ratio * H_op, t_expansion + t_hot, t_expansion + t_hot + t_compression, 2)
+plt.plot(tt, H_comp.operator_norm(tt))
+
+L_cold = SmoothStep(L_op_2, t_expansion + t_hot + t_compression, t_expansion + t_hot + t_compression + t_cold_switch, 3) - SmoothStep(
+    L_op_2, t_expansion + t_hot + t_compression + t_cold - t_cold_switch, 1, 2
+)
+plt.plot(tt, L_cold.operator_norm(tt))
+
+H_cyc = Periodic(Piecewise([H_exp, H_comp], [0, t_expansion, 1]), 1)
+L = [Periodic(L_i, 1) for L_i in (L_cold, L_hot)]
+period = 1
+scale = .05
+H_cyc, *L = [ScaleTime(op, scale) for op in [H_cyc, *L]]
+
+ω_mod = 2*np.pi / (period) * scale
+ω_mod
+t_cycle = period/scale
+periods = 8
+t_max = t_cycle * periods
+dt = .1
+
+t = np.linspace(0, t_cycle, 1000)
+
+plt.plot(t, H_cyc.operator_norm(t) - 1, label=r"$f(\tau)$")
+plt.plot(t, L[0].operator_norm(t) * 2, label=r"$h_{c}(\tau)$")
+plt.plot(t, L[1].operator_norm(t) * 2, label=r"$h_{h}(\tau)$")
+plt.xlabel(r"$\tau$")
+plt.legend()
+fs.export_fig("modulation", x_scaling=.5, y_scaling=.35)
+
+from scipy.optimize import minimize_scalar
+
+ref_model = QubitModelMutliBath(
+    ω_c=[1] * 2,
+    T=[1, 20],
+)
+
+shifts = []
+scales = []
+
+for i, t_exp in enumerate([0, t_expansion / scale]):
+    ω_exp = H_cyc.operator_norm(i * t_cycle / 3)
+    print(ω_exp)
+    def objective(ω_s):
+        ref_model.ω_s[i] = ω_s
+
+        return -ref_model.full_thermal_spectral_density(i)(ω_exp)
+
+    res = minimize_scalar(
+        objective, 1, method="bounded", bounds=(0.01, ω_exp), options=dict(maxiter=100)
+    )
+    shifts.append(res.x)
+    scales.append(-res.fun)
+    ω = np.linspace(0.01, 3, 1000)
+    lines = plt.plot(
+        ω,
+        ref_model.full_thermal_spectral_density(i)(ω) / -res.fun,
+        label=["Cold", "Hot"][i],
+    )
+    plt.plot(
+        ω_exp,
+        ref_model.full_thermal_spectral_density(i)(ω_exp) / -res.fun,
+        marker="o",
+        color=lines[0].get_color(),
+    )
+
+plt.legend()
+plt.xlabel(r"$\omega$")
+plt.ylabel("Arbitrary Units")
+print(shifts, scales)
+fs.export_fig("spectral_densities", x_scaling=.5, y_scaling=.35)
+shifts = [0.01000577876575025, 1.0000001111768948]
+scales = [0.5819473793333508, 3.865799283487837]
+
+model = QubitModelMutliBath(
+    δ=list(1/np.array(scales) * .1/2),#[.05*4, .01*4],
+    ω_c=ref_model.ω_c,
+    ω_s=shifts,#[H_cyc.operator_norm(0) - 1, H_cyc.operator_norm(t_expansion/scale) - 1],
+    t=ut.linspace_with_strobe(0, t_max, int(t_max // dt), ω_mod),
+    ψ_0=(qt.basis([2], [0])*0  + qt.basis([2], [1])*1),
+    description=f"A simple otto cycle.",
+    k_max=4,
+    bcf_terms=[6]*2,
+    truncation_scheme="simplex",
+    driving_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
+    thermal_process_tolerances=[StocProcTolerances(1e-3, 1e-3)] * 2,
+    T = ref_model.T, #= [1, 20],
+    L = [ConstantMatrix(L_op), ConstantMatrix(L_op_2)],
+    H = H_cyc,
+    therm_methods=["fft", "fft"],
+)
+
+aux.integrate(model, 10000)
+
+model.full_thermal_bcf(0)(0) * model.bcf_scales[0]
+
+f, a = pu.plot_energy_overview(model, strobe_frequency=ω_mod, hybrid=True, bath_names=["C","H"])
+# a.plot(model.t, model.L[0].operator_norm(model.t))
+# a.plot(model.t, model.L[1].operator_norm(model.t))
+a.set_xlabel(r"$\tau$")
+a.legend()
+fs.export_fig("energy_strobe_continuous", y_scaling=.6)
+
+fig, ax = plt.subplots()
+with aux.get_data(model) as data:
+    power = model.interaction_power(data).sum_baths()
+    power_sys = model.system_power(data).sum_baths()
+    energy = model.total_energy_from_power(data)
+    ax.plot([], [])
+    pu.plot_with_σ(model.t, power_sys, ax=ax, label="System")
+
+ax.legend()
+ax.set_title("Power")
+ax.set_xlabel(r"$\tau$")
+fs.export_fig("power_continuous", y_scaling=0.4)
+
+steady_index = 2
+cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+with aux.get_data(model) as data:
+    total_energies = model.total_energy_from_power(data).slice(
+        cycle_indices[steady_index:]
+    )
+    hot_bath_energies = (
+        model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+    )
+
+e_diff = total_energies.slice(0) - total_energies.slice(-1)
+mean_power = e_diff * (1 / (model.t[-1] - cycle_times[steady_index]))
+efficiency = e_diff * (
+    1 / (hot_bath_energies.slice(0) - hot_bath_energies.slice(-1)).value
+)
+mean_power.value, mean_power.σ, efficiency.value
+
+fs.tex_value(mean_power.value, err=mean_power.σ, prefix=r"\bar{P}="), fs.tex_value(
+    efficiency.value * 100, prefix=r"η=", suffix=r"\%"
+)
+
+with aux.get_data(model) as data:
+    ρ = data.rho_t_accum.mean[:]
+    σ_ρ = data.rho_t_accum.ensemble_std[:]
+    plt.plot(model.t[100:], abs(ρ[100:, 0, 1]) / abs(ρ[100:, 0, 0]))
+    plt.yscale("log")
+    plt.xlabel(r"$\tau$")
+    plt.ylabel(r"$|\rho_{01}| / \rho_{00}$")
+    fs.export_fig("coherences")
+    xs = np.einsum("tij,ji->t", ρ, qt.sigmax().full())
+    ys = np.einsum("tij,ji->t", ρ, qt.sigmay().full())
+    zs = np.einsum("tij,ji->t", ρ, qt.sigmaz().full())
+
+steady_index = 1
+cycle_times, cycle_indices = ut.strobe_times(model.t, ω_mod)
+
+
+with aux.get_data(model) as data:
+    hot_bath_energies = (
+        model.bath_energy(data).for_bath(1).slice(cycle_indices[steady_index:])
+    )
+
+    cold_bath_energies = (
+        model.bath_energy(data).for_bath(0).slice(cycle_indices[steady_index:])
+    )
+
+    hot_diff = (hot_bath_energies.slice(slice(1,-1, 1)) - hot_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[1])
+    cold_diff = (cold_bath_energies.slice(slice(1,-1, 1)) - cold_bath_energies.slice(slice(0,-2, 1))) * (1/model.T[0])
+    gibbs = hot_diff.mean + cold_diff.mean
+    print(fs.tex_value(gibbs.value, err=gibbs.σ))
+    print(gibbs.value /  gibbs.σ)
+
+b = qt.Bloch()
+b.add_points([xs, ys, zs])
+b.view = [20, 20]
+b.point_size = [0.01]
+b.sphere_alpha = 0.01
+b.render()
+#  fs.export_fig("bloch", fig=b.fig)
+b.fig

python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno_cc.py

@@ -1,64 +1,17 @@
-import figsaver as fs
-from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
-import hiro_models.model_auxiliary as aux
-from hops.util.utilities import relative_entropy, relative_entropy_single, entropy, trace_distance
-from hopsflow.util import EnsembleValue
-import numpy as np
-import qutip as qt
-import scipy
-import utilities as ut
-from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise, Shift
-import matplotlib.pyplot as plt
-import numpy as np
-import matplotlib
-import plot_utils as pu
-
-import ray
-ray.shutdown()
-ray.init()
-
-from hops.util.logging_setup import logging_setup
-import logging
-logging_setup(logging.INFO, show_stocproc=False)
-
-from anti_zeno_engine import *
-
-(
-    model,
-    params
-) = anti_zeno_engine(
-    Δ=11,
-    ε=1,#.1,
-    ω_c=1,
-    ε_couple=.68,
-    n=35,
-    detune=.5,
-    ω_0=20,
-    T_c=5e3,
-    T_h=5e4,
-    δ=[3.2*.01/10, 1.08*.01/10],
-    γ=.2,
-    switch_cycles=1,
-    therm_initial_state=False,
-    ε_init=.001/2,
-    terms=7,
-    dt=0.01/2,
-    sp_tol=1e-4,
-    init_time_steps=10,
-    interpolation_multiplier=10,
-)
-model.k_max = 3
-
-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])
-
-params.cycles
-
-aux.integrate(model, 10)
+%matplotlib tk
+flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
+fig, ax = plt.subplots()
+print(sys_pow.N)
+# pu.plot_with_σ(model.t, (sys_pow+int_pow.sum_baths()).integrate(model.t), ax=ax)
+pu.plot_with_σ(model.t, (sys_pow+int_pow.sum_baths()), ax=ax)
+# pu.plot_with_σ(model.t, (-1 * flow).sum_baths().integrate(model.t) + interaction.sum_baths() + system + 10, ax=ax)
+# with aux.get_data(model) as data:
+#     #pu.plot_with_σ(model.t, model.total_energy_from_power(data), ax=ax)
+#     pu.plot_with_σ(model.t, model.interaction_energy(data).sum_baths() - interaction.sum_baths(), ax=ax)
+#     pu.plot_with_σ(model.t, model.interaction_power(data).sum_baths() - int_pow.sum_baths(), ax=ax)
+#     pu.plot_with_σ(model.t, model.system_energy(data).sum_baths() - system.sum_baths(), ax=ax)
+#     pu.plot_with_σ(model.t, model.system_power(data).sum_baths() - sys_pow.sum_baths(), ax=ax)
+#     pu.plot_with_σ(model.t, model.bath_energy_flow(data).sum_baths() - flow.sum_baths(), ax=ax)
 
 plot_az_coupling_diagram(model, params)
 
@@ -71,3 +24,5 @@ plot_excited_state(model, params, with_init=True)
 plot_coherences(model, params)
 
 plot_power_output(model, params, steady_index=0)
+
+model.hexhash

python/energy_flow_proper/10_antizeno_engine/analyze_gap.py

@@ -0,0 +1,2 @@
+from model_gap import *
+print(model.all_energies_online("results.fifo"))

python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.py

@@ -17,6 +17,7 @@ import figsaver as fs
 import matplotlib.pyplot as plt
 import hiro_models.model_auxiliary as aux
 from hopsflow.util import EnsembleValue
+from brokenaxes import brokenaxes
 
 
 def anti_zeno_engine(
@@ -44,6 +45,8 @@ def anti_zeno_engine(
     sp_tol=1e-5,
     init_time_steps=None,
     interpolation_multiplier=1,
+    cut_end=False,
+    initial_gap=True,
 ):
     # τ_bath = 1 / ω_c
     τ_mod = 2 * np.pi / Δ
@@ -89,7 +92,11 @@ def anti_zeno_engine(
                     int((τ_c + 2 * Δ_switch) / dt),
                     Δ,
                 ),
-                np.linspace((τ_c + τ_off) * (i) + τ_c, (τ_c + τ_off) * (i + 1), 10),
+                np.linspace(
+                    (τ_c + τ_off) * (i) + τ_c,
+                    (τ_c + (τ_off if ((not cut_end) or i < (n - 1)) else 0)) * (i + 1),
+                    10,
+                ),
             ]
         )
         for i in range(n)
@@ -100,7 +107,8 @@ def anti_zeno_engine(
         initializing_period = (
             int(-(np.log(ε_init) / (smallest_exponent * τ_mod) + 1)) * τ_mod
         )
-        t_max += initializing_period + τ_off
+        τ_off_init = τ_off if initial_gap else 0
+        t_max += initializing_period + τ_off_init
         H = Piecewise(
             [
                 ω_0 * ConstantMatrix(H_mat)
@@ -119,10 +127,10 @@ def anti_zeno_engine(
                 ω_0 * ConstantMatrix(H_mat),
                 Shift(
                     H,
-                    initializing_period + τ_off,
+                    initializing_period + τ_off_init,
                 ),
             ],
-            [0, initializing_period, initializing_period + τ_off, np.inf],
+            [0, initializing_period, initializing_period + τ_off_init, np.inf],
         )
 
         L = Piecewise(
@@ -145,13 +153,13 @@ def anti_zeno_engine(
                 ConstantMatrix(np.zeros((2, 2))),
                 Shift(
                     L,
-                    initializing_period + τ_off,
+                    initializing_period + τ_off_init,
                 ),
             ],
-            [0, initializing_period, initializing_period + τ_off, np.inf],
+            [0, initializing_period, initializing_period + τ_off_init, np.inf],
         )
 
-        ts = [times + initializing_period + τ_off for times in ts]
+        ts = [times + initializing_period + τ_off_init for times in ts]
         ts.insert(
             0,
             np.concatenate(
@@ -162,7 +170,9 @@ def anti_zeno_engine(
                         init_time_steps or int((initializing_period + Δ_switch) / (dt)),
                         Δ,
                     ),
-                    np.linspace(initializing_period, initializing_period + τ_off, 10),
+                    np.linspace(
+                        initializing_period, initializing_period + τ_off_init, 10
+                    ),
                 ]
             ),
         )
@@ -214,6 +224,7 @@ def anti_zeno_engine(
         τ_s=τ_mod,
         λ=γ,
         δ=detune,
+        τ_off_init=τ_off_init if ε_init else 0,
     )
 
     return (model, model_params)
@@ -274,11 +285,25 @@ def plot_az_sd_overview(model, params, ax=None):
 
 @wrap_plot
 def plot_total_power(model, params, ax=None):
-    with aux.get_data(model) as data:
-        mask = model.t > params.τ_init
-        plot_with_σ(
-            model.t[mask], model.total_power(data, gc_sleep=0).slice(mask), ax=ax
-        )
+    flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
+    tot_power = (int_pow + sys_pow).sum_baths()
+
+    mask = model.t > params.τ_init
+    plot_with_σ(model.t[mask], tot_power.slice(mask), ax=ax)
+
+
+@wrap_plot
+def plot_flow(model, params, ax=None):
+    flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
+
+    mask = model.t > params.τ_init
+    plot_with_σ(
+        model.t[mask], flow.for_bath(0).slice(mask), ax=ax, label="cold", alpha=0.5
+    )
+    plot_with_σ(
+        model.t[mask], flow.for_bath(1).slice(mask), ax=ax, label="hot", alpha=0.5
+    )
+    ax.legend()
 
 
 @wrap_plot
@@ -319,11 +344,10 @@ def plot_coherences(model, params, ax=None):
         )
 
 
-def plot_power_output(model, params, steady_index=0):
-    fig, ax = plt.subplots()
+def get_powers(model, params):
     ts_begin = (
         params.τ_init
-        + params.τ_off
+        + (params.τ_off_init)
         + ((params.τ_c + params.τ_off) * np.arange(0, params.n))
     )
     ts_end = params.τ_c + ts_begin
@@ -333,9 +357,22 @@ def plot_power_output(model, params, steady_index=0):
     ts_end = model.t[ind_end]
 
     # #plt.plot(model.t, model.L[0].operator_norm(model.t))
+    flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
+    tot_power = (int_pow + sys_pow).sum_baths()
+    powers = []
 
-    with aux.get_data(model) as data:
-        tot_power = model.total_power(data, gc_sleep=0).sum_baths()
+    for begin, end in zip(ind_begin, ind_end):
+        part_power = (tot_power.slice(slice(begin, end))).mean
+        powers.append(part_power)
+
+    return powers, tot_power, (ts_begin, ts_end), (ind_begin, ind_end)
+
+
+def plot_power_output(model, params, steady_index=0):
+    fig, ax = plt.subplots()
+    powers, tot_power, (ts_begin, ts_end), (ind_begin, ind_end) = get_powers(
+        model, params
+    )
 
     for t in ts_begin:
         plt.axvline(t, linestyle="dashed", color="orangered", linewidth=1)
@@ -356,11 +393,7 @@ def plot_power_output(model, params, steady_index=0):
     #     ),
     # )
 
-    powers = []
-    for begin, end in zip(ind_begin, ind_end):
-        part_power = (tot_power.slice(slice(begin, end))).mean
-        powers.append(part_power)
-
+    for begin, end, part_power in zip(ind_begin, ind_end, powers):
         plot_with_σ(
             model.t[[begin, end]],
             EnsembleValue(
@@ -372,7 +405,6 @@ def plot_power_output(model, params, steady_index=0):
             color="black",
             ax=ax,
         )
-
     power = sum(powers[steady_index:]) * (1 / (len(powers[steady_index:])))
 
     ax.set_ylabel(r"$\langle{P}\rangle$")

python/energy_flow_proper/10_antizeno_engine/archive_continously_couple_20000_schwebungen_not_enough_samples_init_too_long.org

@@ -0,0 +1,167 @@
+#+PROPERTY: header-args :session 10_anti_zeno_engine_cc :kernel python :pandoc no :async yes :tangle 10_first_anti_zeno_cc.py
+Here we try to reproduce the anti zeno engine from the paper.
+
+* Boilerplate
+#+begin_src jupyter-python :results none
+  import figsaver as fs
+  from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
+  import hiro_models.model_auxiliary as aux
+  from hops.util.utilities import relative_entropy, relative_entropy_single, entropy, trace_distance
+  from hopsflow.util import EnsembleValue
+  import numpy as np
+  import qutip as qt
+  import scipy
+  import utilities as ut
+  from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise, Shift
+  import matplotlib.pyplot as plt
+  import numpy as np
+  import matplotlib
+  import plot_utils as pu
+#+end_src
+
+Init ray and silence stocproc.
+#+begin_src jupyter-python
+  import ray
+  ray.shutdown()
+  ray.init()
+#+end_src
+
+#+RESULTS:
+: RayContext(dashboard_url='', python_version='3.9.13', ray_version='1.13.0', ray_commit='e4ce38d001dbbe09cd21c497fedd03d692b2be3e', address_info={'node_ip_address': '141.30.17.225', 'raylet_ip_address': '141.30.17.225', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-08-26_09-16-24_372243_434989/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-26_09-16-24_372243_434989/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-26_09-16-24_372243_434989', 'metrics_export_port': 64820, 'gcs_address': '141.30.17.225:62541', 'address': '141.30.17.225:62541', 'node_id': 'b13e6e8fe3cb7e0a8e40b763d39febfe0bac4a37ea2d205950b6dc19'})
+
+#+begin_src jupyter-python :results none
+  from hops.util.logging_setup import logging_setup
+  import logging
+  logging_setup(logging.INFO, show_stocproc=False)
+#+end_src
+
+* Model Definition
+#+begin_src jupyter-python :results none
+  from anti_zeno_engine import *
+#+end_src
+
+#+begin_src jupyter-python
+  (
+      model,
+      params
+  ) = anti_zeno_engine(
+      Δ=11,
+      ε=1,#.1,
+      ω_c=1,
+      ε_couple=.68,
+      n=35,
+      detune=.5,
+      ω_0=20,
+      T_c=5e3,
+      T_h=5e4,
+      δ=[3.2*.01/10, 1.08*.01/10],
+      γ=.2,
+      switch_cycles=1,
+      therm_initial_state=False,
+      ε_init=.000001/2,
+      terms=7,
+      dt=0.01/2,
+      sp_tol=1e-4,
+      init_time_steps=10,
+      interpolation_multiplier=10,
+  )
+  model.k_max = 3
+#+end_src
+
+#+RESULTS:
+
+#+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/458ed32f5b39dff70d717cc2593889573946a962.svg]]
+:END:
+
+
+#+begin_src jupyter-python
+  params.cycles
+#+end_src
+
+#+RESULTS:
+: 10
+
+#+begin_src jupyter-python
+  aux.integrate(model, 10)
+#+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
+  plot_az_coupling_diagram(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > |
+[[file:./.ob-jupyter/d8096351b2e0cbfa172b5abc8c138522375e6205.svg]]
+:END:
+
+#+begin_src jupyter-python
+  plot_az_sd_overview(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\omega$ | ylabel= | Spectral Density | > |
+[[file:./.ob-jupyter/afa180fb667b63a127dbecc86f43980e65eabb6a.svg]]
+:END:
+
+
+#+begin_src jupyter-python
+  plot_total_power(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
+[[file:./.ob-jupyter/ad693ff3d3c543c7fc8bcf66c226d1ee53e1f3b9.svg]]
+:END:
+
+#+begin_src jupyter-python
+  plot_excited_state(model, params, with_init=True)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
+[[file:./.ob-jupyter/5ab38efefb7c03476efb3dea22c64715e4c7131f.svg]]
+:END:
+
+#+begin_src jupyter-python
+  plot_coherences(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
+[[file:./.ob-jupyter/b53ff9a196a6e3f854b4fac57edc3a4f6341ab5c.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 : \(\bar{P}=0.00043\pm 0.00015\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((20000 0.00043025179411284724 0.00014964069641804805))) |
+[[file:./.ob-jupyter/857d74261d2e1c38fc8bb26c43a46e15552ede09.svg]]
+:END:

python/energy_flow_proper/10_antizeno_engine/archive_continously_couple_40000_schwebungen_not_enough_time.org

@@ -0,0 +1,169 @@
+#+PROPERTY: header-args :session 10_anti_zeno_engine_cc_a1 :kernel python :pandoc no :async yes 
+Here we try to reproduce the anti zeno engine from the paper.
+
+* Boilerplate
+#+begin_src jupyter-python :results none
+  import figsaver as fs
+  from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
+  import hiro_models.model_auxiliary as aux
+  from hops.util.utilities import relative_entropy, relative_entropy_single, entropy, trace_distance
+  from hopsflow.util import EnsembleValue
+  import numpy as np
+  import qutip as qt
+  import scipy
+  import utilities as ut
+  from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise, Shift
+  import matplotlib.pyplot as plt
+  import numpy as np
+  import matplotlib
+  import plot_utils as pu
+#+end_src
+
+Init ray and silence stocproc.
+#+begin_src jupyter-python
+  import ray
+  ray.shutdown()
+  ray.init()
+#+end_src
+
+#+RESULTS:
+: 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
+  from hops.util.logging_setup import logging_setup
+  import logging
+  logging_setup(logging.INFO, show_stocproc=False)
+#+end_src
+
+* Model Definition
+#+begin_src jupyter-python :results none
+  from anti_zeno_engine import *
+#+end_src
+
+#+begin_src jupyter-python
+  (
+      model,
+      params
+  ) = anti_zeno_engine(
+      Δ=11,
+      ε=1,#.1,
+      ω_c=1,
+      ε_couple=.0001,
+      n=1,
+      detune=.5,
+      ω_0=20,
+      T_c=1e3,
+      T_h=1e4,
+      δ=[3.2*.01/4, 1.08*.01/4],
+      γ=.2,
+      switch_cycles=1,
+      therm_initial_state=False,
+      ε_init=.000001/2,
+      terms=7,
+      dt=0.01/2,
+      sp_tol=1e-4,
+      init_time_steps=10,
+      interpolation_multiplier=10,
+  )
+  model.k_max = 3
+#+end_src
+
+#+RESULTS:
+
+#+begin_src jupyter-python
+  ts = np.linspace(0, 10,10000)
+  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/5a5fd86ed7017c5c1463a913f499d16f0a3548ba.svg]]
+:END:
+
+
+#+begin_src jupyter-python
+  params.cycles
+#+end_src
+
+#+RESULTS:
+: 122
+
+#+begin_src jupyter-python
+  aux.integrate(model, 10)
+#+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
+  plot_az_coupling_diagram(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > |
+[[file:./.ob-jupyter/f789db8fd41ef3fb8e2164aa765dbf59b1194d8a.svg]]
+:END:
+
+#+begin_src jupyter-python
+  plot_az_sd_overview(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\omega$ | ylabel= | Spectral Density | > |
+[[file:./.ob-jupyter/72171789917e708625d5f5be4306c70391d41599.svg]]
+:END:
+
+
+#+begin_src jupyter-python
+  plot_total_power(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
+[[file:./.ob-jupyter/63a1770854b71f5dcda8e4144bd16326fc9f88ec.svg]]
+:END:
+
+#+begin_src jupyter-python
+  plot_excited_state(model, params, with_init=True)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
+[[file:./.ob-jupyter/6e0b8aa8971299bc06267feb6a9281a1ceb11220.svg]]
+:END:
+
+#+begin_src jupyter-python
+  plot_coherences(model, params)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
+[[file:./.ob-jupyter/ff290ee0e7503e3c6873e276c6d82524702c68e9.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 : \(\bar{P}=-0.00092\pm 0.00015\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((40000 -0.000918483945087149 0.00014770367528033136))) |
+[[file:./.ob-jupyter/1b13cea34f14a105c448c7131c897fcb5e3048f3.svg]]
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:title= | (center : \(\bar{P}=-0.00092\pm 0.00015\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((40000 -0.000918483945087149 0.00014770367528033136))) |
+[[file:./.ob-jupyter/b119ba866ff1a9d4cfc2944bbff78dbfebe743c8.svg]]
+:END:

python/energy_flow_proper/10_antizeno_engine/bcfshit.org

@@ -5,10 +5,23 @@
   import matplotlib.pyplot as plt
   import numpy as np
   import figsaver as fs
+  import plot_utils as pu
 #+end_src
 
 #+RESULTS:
 
+#+begin_src jupyter-python
+  tt = np.linspace(0,20,1000)
+  f, a = pu.plot_complex(tt, 1/(1 + 1j *tt)**2)
+  a.axhline(0, color="gray")
+  a.set_xlabel("$\tau$")
+  fs.export_fig("ohmic_bcf_example")
+#+end_src
+
+#+RESULTS:
+[[file:./.ob-jupyter/26bc0805aafe2726c138e8cf7d9377eddc9cc5cf.svg]]
+
+
 #+begin_src jupyter-python :results none
   def J(ω):
       return np.exp(-(ω)) * (ω)
@@ -37,11 +50,11 @@
 
   #plt.plot(ts, np.abs(refecene(ts))/np.abs(refecene(ts).max()), label=N)
 
-  plt.legend()
+  plt.legend(title=r"$N$")
   plt.yscale("log")
   plt.xscale("log")
-  plt.xlabel("$τ$")
-  plt.ylabel(r"$\frac{α_N(τ) - α(τ)}{α_N(τ)}$")
+  plt.xlabel(r"$\tau$")
+  plt.ylabel(r"$\frac{\alpha_N(\tau) - \alpha(\tau)}{\alpha_N(\tau)}$")
   # tikzplotlib.clean_figure()
   # tikzplotlib.save("test.tex", axis_width="\\figW", axis_height="\\figH", textsize=5)
   fs.export_fig("bcf_approx")
@@ -49,15 +62,54 @@
 
 #+RESULTS:
 :RESULTS:
-: /nix/store/akzgacnj2l97sldws5cnxjlgv27317xd-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:613: RuntimeWarning: divide by zero encountered in log10
+: /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:613: RuntimeWarning: divide by zero encountered in log10
 :   xData = np.log10(xData)
-: /nix/store/akzgacnj2l97sldws5cnxjlgv27317xd-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:616: RuntimeWarning: divide by zero encountered in log10
+: /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:616: RuntimeWarning: divide by zero encountered in log10
 :   yData = np.log10(yData)
-: /nix/store/akzgacnj2l97sldws5cnxjlgv27317xd-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:1285: RuntimeWarning: invalid value encountered in multiply
+: /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:1285: RuntimeWarning: invalid value encountered in multiply
 :   detA = -(X2[:, 0] - X1[:, 0]) * (X4[1] - X3[1]) + (X2[:, 1] - X1[:, 1]) * (
-: /nix/store/akzgacnj2l97sldws5cnxjlgv27317xd-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:1297: RuntimeWarning: invalid value encountered in matmul
+: /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:1297: RuntimeWarning: invalid value encountered in matmul
 :   Lambda[id_detA, 0] = (rhs[id_detA, :] @ Rotate @ (X4 - X3).T) / detA[id_detA]
-: /nix/store/akzgacnj2l97sldws5cnxjlgv27317xd-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:1300: RuntimeWarning: invalid value encountered in matmul
+: /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:1300: RuntimeWarning: invalid value encountered in matmul
 :   -(X2[id_detA, :] - X1[id_detA, :]) @ Rotate * rhs[id_detA, :], axis=1
-[[file:./.ob-jupyter/c0862d837dbdc839685fe05cf1b7de6c3728f24c.svg]]
+[[file:./.ob-jupyter/39e2cd547191a44e40edeb983d34a7ae20b8d2f8.svg]]
+:END:
+
+#+begin_src jupyter-python
+  from hiro_models.one_qubit_model import QubitModel
+  import hops
+
+  t = np.linspace(0, 3, 1000)
+  long_model = QubitModel(ω_c=0.5, bcf_norm_method="unit", t=t)
+  short_model = QubitModel(ω_c=3, bcf_norm_method="unit", t=t)
+
+  f, (ax1, ax2) = plt.subplots(ncols=2)
+  for model in [long_model, short_model]:
+      proc = model.driving_process
+      color = None
+      label = True
+      for _ in range(10):
+          z = hops.core.utility.uni_to_gauss(np.random.rand(proc.get_num_y() * 2))
+          proc.calc_deriv = True
+          proc.new_process(z)
+          lines = ax1.plot(long_model.t, proc(long_model.t).real, alpha=0.5, color=color, label=rf"$\omega_c={model.ω_c}$" if label else None)
+          lines = ax2.plot(long_model.t, proc.dot(long_model.t).real, alpha=0.5, color=color)
+          color = lines[0].get_color()
+          label = False
+
+  ax1.legend()
+  ax1.set_xlabel(r"$\tau$")
+  ax1.set_ylabel(r"$\Re \eta^\ast$")
+  ax2.set_xlabel(r"$\tau$")
+  ax2.set_ylabel(r"$\partial_t \Re {\eta}^\ast$")
+  fs.export_fig("stocproc_comparison", tikz=False)
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+: stocproc.stocproc - INFO - Loaded instance from cache.
+: INFO:stocproc.stocproc:Loaded instance from cache.
+: stocproc.stocproc - INFO - Loaded instance from cache.
+: INFO:stocproc.stocproc:Loaded instance from cache.
+[[file:./.ob-jupyter/01e08d1f2965621ed6e74a15578aab7fa5b234bb.svg]]
 :END:

python/energy_flow_proper/10_antizeno_engine/figures/bcf_approx.tex

@@ -1,375 +0,0 @@
-% This file was created with tikzplotlib v0.10.1.
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-
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-  draw opacity=1,
-  text opacity=1,
-  at={(0.97,0.03)},
-  anchor=south east,
-  draw=lightgray204
-},
-log basis x={10},
-log basis y={10},
-tick align=outside,
-tick pos=left,
-width=\figW,
-x grid style={darkgray176},
-xlabel={\(\displaystyle τ\)},
-xmin=0.00925843821720426, xmax=216235.390357936,
-xmode=log,
-xtick style={color=black},
-xtick={0.0001,0.001,0.01,0.1,1,10,100,1000,10000,100000,1000000,10000000},
-xticklabels={
-  \(\displaystyle {10^{-4}}\),
-  \(\displaystyle {10^{-3}}\),
-  \(\displaystyle {10^{-2}}\),
-  \(\displaystyle {10^{-1}}\),
-  \(\displaystyle {10^{0}}\),
-  \(\displaystyle {10^{1}}\),
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-ylabel={\(\displaystyle \frac{α_N(τ) - α(τ)}{α_N(τ)}\)},
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-\addlegendentry{1e+03}
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-\end{axis}
-
-\end{tikzpicture}

python/energy_flow_proper/10_antizeno_engine/flake.lock

@@ -32,11 +32,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1660485612,
-        "narHash": "sha256-sSLW1KaB1adKTJn9+Ja3h3AaS7QCZyhUKiSUStcLg80=",
+        "lastModified": 1661720780,
+        "narHash": "sha256-AJNGyaB2eKZAYaPNjBZOzap87yL+F9ZLaFzzMkvega0=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "6512b21eabb4d52e87ea2edcf31a288e67b2e4f8",
+        "rev": "a63021a330d8d33d862a8e29924b42d73037dd37",
         "type": "github"
       },
       "original": {
@@ -47,11 +47,11 @@
     },
     "nixpkgs_2": {
       "locked": {
-        "lastModified": 1660546381,
-        "narHash": "sha256-rEzCjeWVGhK5AyHxm1zet0lF6+AVSW3JuU5LAU2SMYU=",
+        "lastModified": 1661848632,
+        "narHash": "sha256-TZ0bBXckz5LVrYBup1rCv/o3qg0VKWnhQIQHJiX1nI0=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "eb642f80f9aecc19312909e08601a3c2020b5ce2",
+        "rev": "d4b68195fd0bd3559f58b23e130f8b4e0b2a6e19",
         "type": "github"
       },
       "original": {
@@ -62,36 +62,36 @@
     },
     "poetry2nix": {
       "inputs": {
-        "flake-utils": "flake-utils_2",
+        "flake-utils": "flake-utils",
         "nixpkgs": "nixpkgs_2"
       },
       "locked": {
-        "lastModified": 1660542485,
-        "narHash": "sha256-XfklMwJMLB7bLI5ZnQTrNaK7KyBnElLGoWOL++XO3zk=",
-        "owner": "nix-community",
-        "repo": "poetry2nix",
-        "rev": "8b6239cf2ded121f8f3d570d2563d69f05d4208f",
-        "type": "github"
+        "lastModified": 1661849972,
+        "narHash": "sha256-W35hlAYSrJBJW5N2rr6N9MT1VMKWbOq1Fl0Lmd0Q9CA=",
+        "path": "/home/hiro/src/poetry2nix",
+        "type": "path"
       },
       "original": {
-        "owner": "nix-community",
-        "repo": "poetry2nix",
-        "type": "github"
+        "path": "/home/hiro/src/poetry2nix",
+        "type": "path"
       }
     },
     "root": {
       "inputs": {
         "nixpkgs": "nixpkgs",
+        "poetry2nix": "poetry2nix",
         "utils": "utils"
       }
     },
     "utils": {
       "inputs": {
-        "flake-utils": "flake-utils",
+        "flake-utils": "flake-utils_2",
         "nixpkgs": [
           "nixpkgs"
         ],
-        "poetry2nix": "poetry2nix"
+        "poetry2nix": [
+          "poetry2nix"
+        ]
       },
       "locked": {
         "lastModified": 1660547406,

python/energy_flow_proper/10_antizeno_engine/flake.nix

@@ -3,8 +3,10 @@
 
   inputs = {
     nixpkgs.url = "nixpkgs/nixos-unstable";
+    poetry2nix.url = "path:/home/hiro/src/poetry2nix";
     utils.url = "github:vale981/hiro-flake-utils";
     utils.inputs.nixpkgs.follows = "nixpkgs";
+    utils.inputs.poetry2nix.follows = "poetry2nix";
   };
 
   outputs = { self, utils, nixpkgs, ... }:
@@ -20,10 +22,10 @@
       python = pkgs: pkgs.python39Full;
       shellOverride = (pkgs: oldAttrs: {
         shellHook = ''
-                    export PYTHONPATH=/home/hiro/src/two_qubit_model/:$PYTHONPATH
-                    export PYTHONPATH=/home/hiro/src/hops/:$PYTHONPATH
-                    export PYTHONPATH=/home/hiro/src/hopsflow/:$PYTHONPATH
-                    export PYTHONPATH=/home/hiro/src/stocproc/:$PYTHONPATH
+                    # export PYTHONPATH=/home/hiro/src/two_qubit_model/:$PYTHONPATH
+                    # export PYTHONPATH=/home/hiro/src/hops/:$PYTHONPATH
+                    # export PYTHONPATH=/home/hiro/src/hopsflow/:$PYTHONPATH
+                    # export PYTHONPATH=/home/hiro/src/stocproc/:$PYTHONPATH
 
                     export LD_LIBRARY_PATH="${(pkgs.lib.makeLibraryPath [pkgs.arb])}"
       #               '';

python/energy_flow_proper/10_antizeno_engine/integrate.py

@@ -0,0 +1,2 @@
+from model import *
+aux.integrate(model, 10, stream_file="results.fifo")

python/energy_flow_proper/10_antizeno_engine/integrate_gap.py

@@ -0,0 +1,5 @@
+from model_gap import *
+import ray
+ray.shutdown()
+ray.init()
+aux.integrate(model, 10_000, stream_file="results.fifo")

python/energy_flow_proper/10_antizeno_engine/model_cc.py

@@ -27,12 +27,12 @@ from anti_zeno_engine import *
     ε=1,#.1,
     ω_c=1,
     ε_couple=.68,
-    n=20,
+    n=5,
     detune=.5,
     ω_0=20,
-    T_c=5e3,
-    T_h=5e4,
-    δ=[3.2*.01/10, 1.08*.01/10],
+    T_c=10,
+    T_h=100,
+    δ=[3.2/2.5, 1.08/2],
     γ=.2,
     switch_cycles=1,
     therm_initial_state=False,
@@ -43,4 +43,4 @@ from anti_zeno_engine import *
     init_time_steps=10,
     interpolation_multiplier=10,
 )
-model.k_max = 3
+model.k_max = 4

python/energy_flow_proper/10_antizeno_engine/model_gap.py

@@ -0,0 +1,49 @@
+import figsaver as fs
+from hiro_models.one_qubit_model import QubitModelMutliBath, StocProcTolerances
+import hiro_models.model_auxiliary as aux
+from hops.util.utilities import relative_entropy, relative_entropy_single, entropy, trace_distance
+from hopsflow.util import EnsembleValue
+import numpy as np
+import qutip as qt
+import scipy
+import utilities as ut
+from hops.util.dynamic_matrix import SmoothStep, Periodic, Harmonic, ConstantMatrix, Piecewise, Shift
+import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib
+import plot_utils as pu
+
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO, show_stocproc=False)
+
+from anti_zeno_engine import *
+import anti_zeno_engine as az
+
+(
+    model,
+    params
+) = anti_zeno_engine(
+    Δ=11,
+    ε=.001,#.1,
+    ω_c=.8,
+    ε_couple=.85,
+    n=2,
+    detune=.6,
+    ω_0=20,
+    T_c=5,
+    T_h=20,
+    δ=[2.5, 2],
+    γ=.1,
+    switch_cycles=1,
+    therm_initial_state=False,
+    ε_init=.00001,
+    terms=7,
+    dt=0.01/2,
+    sp_tol=1e-4,
+    init_time_steps=10,
+    interpolation_multiplier=10,
+    cut_end=True,
+    initial_gap=False
+)
+model.k_max = 4

python/energy_flow_proper/10_antizeno_engine/moments_sketch.org

@@ -43,12 +43,10 @@
 #+end_src
 
 #+begin_src jupyter-python
-  %matplotlib inline
   c = coef(10)
   import figsaver as fs
   # from matplotlib.colors import LogNorm
   fig, ax = plt.subplots()
-  fig.set_size_inches(fs.get_figsize(fraction=1))
   ms = ax.matshow(c)
   fig.colorbar(ms)
   ax.set_xlabel("$k$")
@@ -58,14 +56,14 @@
 
 #+RESULTS:
 :RESULTS:
-[[file:./.ob-jupyter/5d755e0618ed8699c41488f4d4a6627183b5d741.svg]]
-[[file:./.ob-jupyter/15195a262630c4eff09cc1c25e629c2e4fd3307c.svg]]
+: /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/tikzplotlib/_cleanfigure.py:166: UserWarning: Cleaning Line Collections (scatter plot) is not supported yet.
+:   warnings.warn(
+[[file:./.ob-jupyter/531dce055bc42c918a0488082af2995f3d1d694c.svg]]
 :END:
 
 
 #+begin_src jupyter-python
     fig = plt.figure()
-    fig.set_size_inches(fs.get_figsize(fraction=1))
     import scipy
 
     for order in 10 ** np.arange(1, 4):
@@ -107,11 +105,13 @@
 
 #+RESULTS:
 :RESULTS:
-: /tmp/ipykernel_129770/256163567.py:11: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string "--o" (-> marker='o'). The keyword argument will take precedence.
+: /tmp/ipykernel_572734/2027820373.py:10: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string "--o" (-> marker='o'). The keyword argument will take precedence.
 :   plt.plot(
-: /nix/store/av32kn66py1m065lq75rmmlxrsqvgbaa-python3-3.9.13-env/lib/python3.9/site-packages/scipy/stats/_discrete_distns.py:69: RuntimeWarning: divide by zero encountered in _binom_pdf
+: /tmp/ipykernel_572734/2027820373.py:10: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string "--o" (-> marker='o'). The keyword argument will take precedence.
+:   plt.plot(
+: /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/scipy/stats/_discrete_distns.py:78: RuntimeWarning: divide by zero encountered in _binom_pdf
 :   return _boost._binom_pdf(x, n, p)
-[[file:./.ob-jupyter/625ccae9f05819d713eacd97b4a4064fdddc1c72.svg]]
+[[file:./.ob-jupyter/c0c0a867b86da64af2f24ee48e9744f58062458a.svg]]
 :END:
 
 #+begin_src jupyter-python

python/energy_flow_proper/10_antizeno_engine/mount_taurus.sh

@@ -1,6 +1,12 @@
 #!/usr/bin/env bash
-sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus  s8896854@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s8896854-10_ssd/.data/ .data
-sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus  s8896854@taurusexport.hrsk.tu-dresden.de:/beegfs/ws/0/s8896854-m_10t/project/python/energy_flow_proper/10_antizeno_engine/results results
+sshfs s4498638@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s4498638-m_10t/project/python/energy_flow_proper/10_antizeno_engine/.data/ .data
+sshfs s4498638@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s4498638-m_10t/project/python/energy_flow_proper/10_antizeno_engine/results results
+
+sshfs s4498638@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s4498638-m_10t/project/python/energy_flow_proper/10_antizeno_engine/ taurus
 
 
-sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus  s8896854@taurusexport.hrsk.tu-dresden.de:/beegfs/ws/0/s8896854-m_10t/project/python/energy_flow_proper/10_antizeno_engine/ taurus
+# sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus  s8896854@taurusexport.hrsk.tu-dresden.de:/lustre/ssd/ws/s8896854-10_ssd/.data/ .data
+# sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus  s8896854@taurusexport.hrsk.tu-dresden.de:/beegfs/ws/0/s8896854-m_10t/project/python/energy_flow_proper/10_antizeno_engine/results results
+
+
+# sshfs -oIdentityFile=~/.ssh/id_ed25519_taurus  s8896854@taurusexport.hrsk.tu-dresden.de:/beegfs/ws/0/s8896854-m_10t/project/python/energy_flow_proper/10_antizeno_engine/ taurus

python/energy_flow_proper/10_antizeno_engine/pyproject.toml

@@ -7,9 +7,9 @@ license = "GPLv3"
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.11"
-# 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" }
+hops = { git = "git@gitlab.hrz.tu-chemnitz.de:s8896854--tu-dresden.de/hops.git", branch="online_calculation" }
+stocproc = { git = "git@github.com:vale981/stocproc.git", branch="master" }
+hopsflow = { git = "git@github.com:vale981/hopsflow.git", branch="online_calculation" }
 hiro_models = { git = "git@github.com:vale981/two_qubit_model.git", branch="online_calculation" }
 matplotlib = "^3.5.0"
 jupyter = "^1.0.0"
@@ -22,6 +22,7 @@ statsmodels = "^0.13.2"
 protobuf = "==3.20.1"
 tabulate = "^0.8.9"
 tikzplotlib = "^0.10.1"
+brokenaxes = "^0.5.0"
 
 [tool.poetry.dev-dependencies]
 black = "^21.12b0"

python/energy_flow_proper/10_antizeno_engine/with_gap.org

@@ -25,9 +25,10 @@ Here we try to reproduce the anti zeno engine from the paper.
   logging_setup(logging.INFO, show_stocproc=False)
 #+end_src
 
-* Model Definition
+* TODO Model Definition
 #+begin_src jupyter-python :results none :tangle model_gap.py
   from anti_zeno_engine import *
+  import anti_zeno_engine as az
 #+end_src
 
 #+begin_src jupyter-python :tangle model_gap.py
@@ -36,25 +37,26 @@ Here we try to reproduce the anti zeno engine from the paper.
       params
   ) = anti_zeno_engine(
       Δ=11,
-      ε=.01,#.1,
-      ω_c=1,
-      ε_couple=.68,
-      n=4,
-      detune=.5,
+      ε=.001,#.1,
+      ω_c=.8,
+      ε_couple=.85,
+      n=2,
+      detune=.6,
       ω_0=20,
-      T_c=10,
-      T_h=100,
-      δ=[3.2/2.5, 1.08/2],
-      γ=.2,
+      T_c=5,
+      T_h=20,
+      δ=[2.5, 2],
+      γ=.1,
       switch_cycles=1,
       therm_initial_state=False,
-      ε_init=.001/2,
+      ε_init=.00001,
       terms=7,
       dt=0.01/2,
       sp_tol=1e-4,
       init_time_steps=10,
       interpolation_multiplier=10,
-      cut_end=True
+      cut_end=True,
+      initial_gap=False
   )
   model.k_max = 4
 #+end_src
@@ -67,7 +69,7 @@ Here we try to reproduce the anti zeno engine from the paper.
     import ray
     ray.shutdown()
     ray.init()
-    aux.integrate(model, 10, stream_file="results.fifo")
+    aux.integrate(model, 10_000, stream_file="results.fifo")
 #+end_src
 
 #+RESULTS:
@@ -168,34 +170,6 @@ Here we try to reproduce the anti zeno engine from the paper.
   print(model.all_energies_online("results.fifo"))
 #+end_src
 
-#+RESULTS:
-:RESULTS:
-# [goto error]
-#+begin_example
-  ---------------------------------------------------------------------------
-  KeyboardInterrupt                         Traceback (most recent call last)
-  Input In [7], in <cell line: 2>()
-        1 from model_gap import *
-  ----> 2 print(model.all_energies_online("results.fifo"))
-
-  File /nix/store/13dn9rbd9j677ap99g05vklq1lc3w3bd-python3-3.9.13-env/lib/python3.9/site-packages/hiro_models/model_base.py:411, in Model.all_energies_online(self, stream_pipe, **kwargs)
-      399 system_power_worker = hopsflow.util.make_operator_expectation_task(
-      400     self.system.derivative(), self.t, normalize=True, real=True
-      401 )
-      403 flow, interaction, interaction_power, system, system_power = (
-      404     None,
-      405     None,
-     (...)
-      408     None,
-      409 )
-  --> 411 with open(stream_pipe, "rb") as fifo:
-      412     while True:
-      413         try:
-
-  KeyboardInterrupt: 
-#+end_example
-:END:
-
 #+begin_src jupyter-python
   %matplotlib tk
   flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
@@ -215,8 +189,8 @@ Here we try to reproduce the anti zeno engine from the paper.
 
 #+RESULTS:
 :RESULTS:
-: 897
-| hline | <AxesSubplot:> | ((<matplotlib.lines.Line2D at 0x7f8110dac100>) <matplotlib.collections.PolyCollection at 0x7f8110d89fa0>) |
+: 1000
+| hline | <AxesSubplot:> | ((<matplotlib.lines.Line2D at 0x7fc17ce7d5e0>) <matplotlib.collections.PolyCollection at 0x7fc17ce7d850>) |
 :END:
 
 
@@ -227,7 +201,7 @@ Here we try to reproduce the anti zeno engine from the paper.
 #+RESULTS:
 :RESULTS:
 | <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\tau$ | > |
-[[file:./.ob-jupyter/ba66070842ce964bbf22617fe2187a53ce7b0eb7.svg]]
+[[file:./.ob-jupyter/ac1f2d29ca14cd56819bad81a9331fe58699b8a4.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -236,84 +210,107 @@ Here we try to reproduce the anti zeno engine from the paper.
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:xlabel= | $\omega$ | ylabel= | Spectral Density | > |
-[[file:./.ob-jupyter/ac8e211f0656dcabbdf35b0b21870882d451383a.svg]]
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot: | xlabel= | $\omega$ | ylabel= | Spectral Density | > |
+[[file:./.ob-jupyter/0750fb10f4322daf823b3a4dd2a778f611cd35ba.svg]]
 :END:
 
 
 #+begin_src jupyter-python
-  plot_total_power(model, params)
+  az.plot_total_power(model, params)
 #+end_src
 
 #+RESULTS:
 :RESULTS:
-# [goto error]
-#+begin_example
-  ---------------------------------------------------------------------------
-  RuntimeError                              Traceback (most recent call last)
-  Input In [37], in <cell line: 1>()
-  ----> 1 plot_total_power(model, params)
-
-  File ~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/10_antizeno_engine/plot_utils.py:40, in wrap_plot.<locals>.wrapped(ax, setup_function, *args, **kwargs)
-       37 if not ax:
-       38     fig, ax = setup_function()
-  ---> 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/masterarb/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.py:277, in plot_total_power(model, params, ax)
-      275 @wrap_plot
-      276 def plot_total_power(model, params, ax=None):
-  --> 277     with aux.get_data(model) as data:
-      278         mask = model.t > params.τ_init
-      279         plot_with_σ(
-      280             model.t[mask], model.total_power(data, gc_sleep=0).slice(mask), ax=ax
-      281         )
-
-  File /nix/store/jhaz6wa06sid3pc6c0g3wmid421vldwj-python3-3.9.13-env/lib/python3.9/site-packages/hiro_models/model_auxiliary.py:161, in get_data(model, data_path, read_only, **kwargs)
-      150         return HIData(
-      151             path,
-      152             hi_key=model.hops_config,
-     (...)
-      157             **kwargs,
-      158         )
-      160 else:
-  --> 161     raise RuntimeError(f"No data found for model with hash '{hexhash}'.")
-
-  RuntimeError: No data found for model with hash 'e51d6781660399bc02edc6fa6d2a8709342c8884dc390695bb4024e566f94788'.
-#+end_example
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot: | > |
+[[file:./.ob-jupyter/a0940099e4254277356021fe5efb2256a9b41d6f.svg]]
 :END:
 
 #+begin_src jupyter-python
-  plot_excited_state(model, params, with_init=True)
+  %matplotlib inline
+  az.plot_flow(model, params)
 #+end_src
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
-[[file:./.ob-jupyter/cb9dd30567ab3e585612450d945f68b293d62b49.svg]]
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot: | > |
+[[file:./.ob-jupyter/49b58ffd075a7a6568c33e09b51b2d56c5188a18.svg]]
 :END:
 
+#+begin_src jupyter-python
+  from brokenaxes import brokenaxes
+
+  flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
+  f, (ax, ax2) = plt.subplots(ncols=2, sharey=True)
+  mask = np.logical_and(model.t > params.τ_init, model.t < params.τ_init+params.τ_c)
+  plot_with_σ(model.t[mask], flow.for_bath(0).slice(mask), ax=ax, label="Cold", alpha=0.5)
+  plot_with_σ(model.t[mask], flow.for_bath(1).slice(mask), ax=ax, label="Hot", alpha=0.5)
+  mask = np.logical_and(model.t > (params.τ_init + params.τ_off + params.τ_c), model.t < (params.τ_init + params.τ_off + params.τ_c * 2))
+  plot_with_σ(model.t[mask], flow.for_bath(0).slice(mask), ax=ax2, label="Cold", alpha=0.5)
+  plot_with_σ(model.t[mask], flow.for_bath(1).slice(mask), ax=ax2, label="Hot", alpha=0.5)
+  ax.legend()
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+: 114592
+[[file:./.ob-jupyter/415a9e30963a519d4316460eb97c6ac7e59c4ab9.svg]]
+:END:
+
+#+begin_src jupyter-python
+  %matplotlib tk
+  flow, interaction, int_pow, system, sys_pow = model.all_energies_online_from_cache()
+  tot_power = (int_pow + sys_pow).sum_baths()
+  f, (ax) = plt.subplots(ncols=1, sharey=True)
+  mask_first = np.logical_and(model.t > params.τ_init, model.t < params.τ_init+params.τ_c)
+  plot_with_σ(model.t[mask_first], tot_power.slice(mask_first), ax=ax, label="Cold", linewidth=.1)
+  ax.axhline(max(tot_power.slice(mask).value))
+  mask = np.logical_and(model.t > (params.τ_init + params.τ_off + params.τ_c), model.t < (params.τ_init + params.τ_off + params.τ_c * 2))
+  plot_with_σ(model.t[mask] - params.τ_off - params.τ_c, tot_power.slice(mask), ax=ax, label="Cold", alpha=0.5, linewidth=.1)
+  ax2.axhline(max(tot_power.slice(mask).value))
+  ax.legend()
+
+#+end_src
+
+#+RESULTS:
+: <matplotlib.legend.Legend at 0x7f455af60460>
+
 #+begin_src jupyter-python
   plot_coherences(model, params)
 #+end_src
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:> |
-[[file:./.ob-jupyter/b53ff9a196a6e3f854b4fac57edc3a4f6341ab5c.svg]]
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot: | > |
+[[file:./.ob-jupyter/5ec9de8b7d5e3c6db100f422847f3213e4c82be7.svg]]
 :END:
 
 
 #+begin_src jupyter-python
-  plot_power_output(model, params, steady_index=2)
+  az.plot_power_output(model, params, steady_index=0)
 #+end_src
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot:title= | (center : \(\bar{P}=-0.1214\pm 0.0006\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((50000 -0.12137889710904826 0.0005822571770304311))) |
-[[file:./.ob-jupyter/308d2abac15b394ba335d0288fc22b63cc78d4aa.svg]]
+| <Figure | size | 520x320 | with | 1 | Axes> | <AxesSubplot: | title= | (center : \(\bar{P}=-0.01074\pm 0.00018\)) | xlabel= | $\tau$ | ylabel= | $\langle{P}\rangle$ | > | EnsembleValue | (((114592 -0.010740084942026365 0.0001783712160433793))) |
+[[file:./.ob-jupyter/ebb8a3d6c186256877b7b4f6758b2f4b6a9936f3.svg]]
 :END:
 
+#+begin_src jupyter-python
+  powers, *_ = az.get_powers(model, params)
+  δ = abs(powers[0] - powers[1]) * (1 / max([abs(power.value) for power in powers]))
+  δ.value, δ.σ, δ.N,   powers[1].value / powers[0].value, powers[1].σ, powers[0].σ, np.sqrt((powers[1].σ / powers[0].value) ** 2 + (powers[1].value / ((powers[0].value) ** 2) * (powers[0].σ)) ** 2)
+#+end_src
+
+#+RESULTS:
+| 0.848105965701148 | 0.01913064382265813 | 114592 | 6.583537033669784 | 0.00026142881805965064 | 0.0002427347027911536 | 0.5716895128604129 |
+
+#+begin_src jupyter-python
+  powers[0].value,powers[1].value 
+#+end_src
+
+#+RESULTS:
+| -0.0028324737900908177 | -0.018647696093961913 |
+
 #+begin_src jupyter-python
   model.hexhash
 #+end_src

python/energy_flow_proper/11_new_ho_comparison/figures/bcf_decay.tex

@@ -1,140 +0,0 @@
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-\end{tikzpicture}

python/energy_flow_proper/11_new_ho_comparison/figures/sqrt_convergence.tex

@@ -1,140 +0,0 @@
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-
-\end{tikzpicture}

python/energy_flow_proper/11_new_ho_comparison/ho_analytic_zero_temperature.org

@@ -86,18 +86,48 @@
 
 #+RESULTS:
 #+begin_example
-  stocproc.stocproc - INFO - Loaded instance from cache.
-  INFO:stocproc.stocproc:Loaded instance from cache.
+  stocproc.stocproc - INFO - non neg freq only
+  INFO:stocproc.stocproc:non neg freq only
   [ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.   7.5  8.5  9.5 10.5 11.5 12.5
    13.5]
-  stocproc.stocproc - INFO - Loaded instance from cache.
-  INFO:stocproc.stocproc:Loaded instance from cache.
+  stocproc.stocproc - INFO - Fourier Integral Boundaries: [0.000e+00, 1.527e+03]
+  INFO:stocproc.stocproc:Fourier Integral Boundaries: [0.000e+00, 1.527e+03]
+  stocproc.stocproc - INFO - Number of Nodes            : 131072
+  INFO:stocproc.stocproc:Number of Nodes            : 131072
+  stocproc.stocproc - INFO - yields dx                  : 1.165e-02
+  INFO:stocproc.stocproc:yields dx                  : 1.165e-02
+  stocproc.stocproc - INFO - yields dt                  : 4.114e-03
+  INFO:stocproc.stocproc:yields dt                  : 4.114e-03
+  stocproc.stocproc - INFO - yields t_max               : 5.392e+02
+  INFO:stocproc.stocproc:yields t_max               : 5.392e+02
+  stocproc.stocproc - INFO - non neg freq only
+  INFO:stocproc.stocproc:non neg freq only
   [ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.   7.5  8.5  9.5 10.5 11.5 12.5
    13.5]
-  stocproc.stocproc - INFO - Loaded instance from cache.
-  INFO:stocproc.stocproc:Loaded instance from cache.
+  stocproc.stocproc - INFO - Fourier Integral Boundaries: [0.000e+00, 5.527e+03]
+  INFO:stocproc.stocproc:Fourier Integral Boundaries: [0.000e+00, 5.527e+03]
+  stocproc.stocproc - INFO - Number of Nodes            : 262144
+  INFO:stocproc.stocproc:Number of Nodes            : 262144
+  stocproc.stocproc - INFO - yields dx                  : 2.108e-02
+  INFO:stocproc.stocproc:yields dx                  : 2.108e-02
+  stocproc.stocproc - INFO - yields dt                  : 1.137e-03
+  INFO:stocproc.stocproc:yields dt                  : 1.137e-03
+  stocproc.stocproc - INFO - yields t_max               : 2.980e+02
+  INFO:stocproc.stocproc:yields t_max               : 2.980e+02
+  stocproc.stocproc - INFO - non neg freq only
+  INFO:stocproc.stocproc:non neg freq only
   [ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.   7.5  8.5  9.5 10.5 11.5 12.5
    13.5]
+  stocproc.stocproc - INFO - Fourier Integral Boundaries: [0.000e+00, 3.235e+03]
+  INFO:stocproc.stocproc:Fourier Integral Boundaries: [0.000e+00, 3.235e+03]
+  stocproc.stocproc - INFO - Number of Nodes            : 262144
+  INFO:stocproc.stocproc:Number of Nodes            : 262144
+  stocproc.stocproc - INFO - yields dx                  : 1.234e-02
+  INFO:stocproc.stocproc:yields dx                  : 1.234e-02
+  stocproc.stocproc - INFO - yields dt                  : 1.942e-03
+  INFO:stocproc.stocproc:yields dt                  : 1.942e-03
+  stocproc.stocproc - INFO - yields t_max               : 5.091e+02
+  INFO:stocproc.stocproc:yields t_max               : 5.091e+02
   stocproc.stocproc - INFO - Loaded instance from cache.
   INFO:stocproc.stocproc:Loaded instance from cache.
   [ 0.5  1.5  2.5  3.5  4.5  5.5  6.5  7.   7.5  8.5  9.5 10.5 11.5 12.5
@@ -114,6 +144,26 @@
 #+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_2022-08-05_11-16-57_697267_44068/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-08-05_11-16-57_697267_44068/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-08-05_11-16-57_697267_44068', 'metrics_export_port': 59076, 'gcs_address': '141.30.17.225:59268', 'address': '141.30.17.225:59268', 'node_id': 'f205a3b0dd8a57759edce36de05aadac8b525793d4478cd44bede964'})
 
+#+begin_src jupyter-python
+  fig, ax = plt.subplots()
+  for i, (params, keys) in enumerate(zip(multi_params, model_keys)):
+        plt.axhline(
+            np.sqrt(params.SysP.g[0].sum().real) / keys["wc"],
+            label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
+            color=(ax.plot([],[])[0].get_color())
+        )
+
+  plt.ylim(0,.6)
+  plt.legend(fontsize="x-small")
+  plt.ylabel(r"$\sqrt{\alpha(0)} / \omega_c$")
+  plt.xticks([])
+  fs.export_fig("timescale_comparison", x_scaling=.3, tikz=False)
+#+end_src
+
+#+RESULTS:
+[[file:./.ob-jupyter/1a6e39e8cf92ba799e42a667460ebffb57909043.svg]]
+
+
 
 #+begin_src jupyter-python
   supervisors = []

python/energy_flow_proper/11_new_ho_comparison/zero_t.py

@@ -82,6 +82,20 @@ import ray
 ray.shutdown()
 ray.init()
 
+fig, ax = plt.subplots()
+for i, (params, keys) in enumerate(zip(multi_params, model_keys)):
+      plt.axhline(
+          np.sqrt(params.SysP.g[0].sum().real) / keys["wc"],
+          label=rf"$\alpha(0)={params.SysP.g[0].sum().real:.2f}$, $\omega_c={keys['wc']}$",
+          color=(ax.plot([],[])[0].get_color())
+      )
+
+plt.ylim(0,.6)
+plt.legend(fontsize="x-small")
+plt.ylabel(r"$\sqrt{\alpha(0)} / \omega_c$")
+plt.xticks([])
+fs.export_fig("timescale_comparison", x_scaling=.3, tikz=False)
+
 supervisors = []
 for params in multi_params:
       supervisor = HOPSSupervisor(

python/energy_flow_proper/plot_utils.py

@@ -117,14 +117,14 @@ def plot_complex(x, y, *args, ax=None, label="", absolute=False, **kwargs):
         x,
         abs(y.real) if absolute else y.real,
         *args,
-        label=f"{label}absolute real part",
+        label=f"{label}{'absolute' if absolute else ''} real part",
         **kwargs,
     )
     ax.plot(
         x,
         abs(y.imag) if absolute else y.imag,
         *args,
-        label=f"{label}absolute imag part",
+        label=f"{label}{'absolute' if absolute else ''} imag part",
         **kwargs,
     )
     ax.legend()
@@ -601,6 +601,7 @@ def plot_energy_overview(
     # total=True,
     # flow=True,
     # interaction=True,
+    bath_names=None,
     **kwargs,
 ):
     if not ensemble_args:
@@ -620,11 +621,12 @@ def plot_energy_overview(
 
         num_baths = interaction_energy.num_baths
         for bath in range(num_baths):
+            label = bath_names[bath] if bath_names else bath + 1
             # plot_with_σ(
             #     model.t, flow, bath=bath, ax=ax, label=f"Flow {bath+1}", **kwargs
             # )
             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 {label}", **kwargs
             )
 
             plot_with_σ(
@@ -632,7 +634,7 @@ def plot_energy_overview(
                 interaction_energy,
                 ax=ax,
                 bath=bath,
-                label=f"Interaction {bath+1}",
+                label=f"Interaction {label}",
                 **kwargs,
             )
 
@@ -695,12 +697,13 @@ def plot_σ_development(ensemble_value, ax=None, **kwargs):
 def plot_multi_energy_overview(
     models,
     label_fn=lambda m: fr"$\omega_c={m.ω_c:.1f},\,\alpha(0)={m.bcf(0).real:.2f}$",
+    ensemble_arg=dict(),
 ):
     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])
+    # 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"$\langle H_\mathrm{I}\rangle$")
@@ -734,11 +737,11 @@ def plot_multi_energy_overview(
             )
 
             mask = np.logical_and(7 <= data.time[:], data.time[:] <= 9)
-            plot_with_σ(
-                data.time[mask],
-                flow.slice(mask),
-                ax=flow_ins,
-            )
+            # plot_with_σ(
+            #     data.time[mask],
+            #     flow.slice(mask),
+            #     ax=flow_ins,
+            # )
 
             plot_with_σ(
                 data.time[:],