master-thesis/python/richard_hops/energy_flow_thermal.org

#+PROPERTY: header-args :session finite_temp :kernel python :pandoc t :async yes

* Setup
** Jupyter
#+begin_src jupyter-python
  %load_ext autoreload
  %autoreload 2
  %load_ext jupyter_spaces
#+end_src

#+RESULTS:
: The autoreload extension is already loaded. To reload it, use:
:   %reload_ext autoreload

** Matplotlib
#+begin_src jupyter-python
  import matplotlib
  import matplotlib.pyplot as plt

  #matplotlib.use("TkCairo", force=True)
  %gui tk
  %matplotlib inline
  plt.style.use('ggplot')
#+end_src

#+RESULTS:

** Richard (old) HOPS
#+begin_src jupyter-python
  import hierarchyLib
  import hierarchyData
  import numpy as np

  from stocproc.stocproc import StocProc_FFT
  import bcf
  from dataclasses import dataclass, field
  import scipy
  import scipy.misc
  import scipy.signal
  import pickle
  from scipy.special import gamma as gamma_func
  from scipy.optimize import curve_fit
#+end_src

#+RESULTS:

** Auxiliary Definitions
#+begin_src jupyter-python
  σ1 = np.matrix([[0,1],[1,0]])
  σ2 = np.matrix([[0,-1j],[1j,0]])
  σ3 = np.matrix([[1,0],[0,-1]])
#+end_src

#+RESULTS:

* Model Setup
Basic parameters.
#+begin_src jupyter-python
  class params:
      T = 2

      t_max = 10
      t_steps = int(t_max * 1/.01)
      k_max = 10
      N = 1000

      seed = 100
      dim = 2
      H_s = σ3 + np.eye(dim)
      L = σ2 #1 / 2 * (σ1 - 1j * σ2)
      ψ_0 = np.array([0, 1])

      s = 1
      num_exp_t = 4

      wc = 1

      with open("good_fit_data_abs_brute_force", "rb") as f:
          good_fit_data_abs = pickle.load(f)

      alpha = 0.8
      # _, g_tilde, w_tilde = good_fit_data_abs[(numExpFit, s)]
      # g_tilde = np.array(g_tilde)
      # w_tilde = np.array(w_tilde)
      # g = 1 / np.pi * gamma_func(s + 1) * wc ** (s + 1) * np.asarray(g_tilde)
      # w = wc * np.asarray(w_tilde)

      bcf_scale = np.pi / 2 * alpha * wc ** (1 - s)
#+end_src

#+RESULTS:

** BCF and Thermal BCF
#+begin_src jupyter-python
  @dataclass
  class CauchyBCF:
      δ: float
      wc: float

      def I(self, ω):
          return np.sqrt(self.δ) / (self.δ + (ω - self.wc) ** 2 / self.δ)

      def __call__(self, τ):
          return np.sqrt(self.δ) * np.exp(-1j * self.wc * τ - np.abs(τ) * self.δ)

      def __bfkey__(self):
          return self.δ, self.wc

  α = bcf.OBCF(s=params.s, eta=1, gamma=params.wc)
  I = bcf.OSD(s=params.s, eta=1, gamma=params.wc)
#+end_src

#+RESULTS:

*** Fit
We now fit a sum of exponentials against the BCF.
#+begin_src jupyter-python
  from lmfit import minimize, Parameters


  def α_apprx(τ, g, w):
      return np.sum(
          g[np.newaxis, :] * np.exp(-w[np.newaxis, :] * (τ[:, np.newaxis])), axis=1
      )


  def _fit():
      def residual(fit_params, x, data):
          resid = 0
          w = np.array([fit_params[f"w{i}"] for i in range(params.num_exp_t)]) + 1j * np.array(
              [fit_params[f"wi{i}"] for i in range(params.num_exp_t)]
          )
          g = np.array([fit_params[f"g{i}"] for i in range(params.num_exp_t)]) + 1j * np.array(
              [fit_params[f"gi{i}"] for i in range(params.num_exp_t)]
          )
          resid = data - α_apprx(x, g, w)

          return resid.view(float)

      fit_params = Parameters()
      for i in range(params.num_exp_t):
          fit_params.add(f"g{i}", value=.1)
          fit_params.add(f"gi{i}", value=.1)
          fit_params.add(f"w{i}", value=.1)
          fit_params.add(f"wi{i}", value=.1)

      ts = np.linspace(0, params.t_max, 1000)
      out = minimize(residual, fit_params, args=(ts, α(ts)))

      w = np.array([out.params[f"w{i}"] for i in range(params.num_exp_t)]) + 1j * np.array(
          [out.params[f"wi{i}"] for i in range(params.num_exp_t)]
      )
      g = np.array([out.params[f"g{i}"] for i in range(params.num_exp_t)]) + 1j * np.array(
          [out.params[f"gi{i}"] for i in range(params.num_exp_t)]
      )

      return w, g


  w, g = _fit()
#+end_src

#+RESULTS:

*** Plot
Let's look a the result.
#+begin_src jupyter-python
  class bcfplt:
      t = np.linspace(0, params.t_max, 1000)
      ω = np.linspace(params.wc - 10, params.wc + 10, 1000)
      fig, axs = plt.subplots(2)
      axs[0].plot(t, np.real(α(t)))
      axs[0].plot(t, np.imag(α(t)))
      axs[0].plot(t, np.real(α_apprx(t, g, w)))
      axs[0].plot(t, np.imag(α_apprx(t, g, w)))
      axs[1].plot(ω, I(ω).real)
      axs[1].plot(ω, I(ω).imag)
#+end_src

#+RESULTS:
[[file:./.ob-jupyter/b2ed70a60ed972b1a9df01e1cca7fdf0e33eff97.png]]

Seems ok for now.
** Hops setup
#+begin_src jupyter-python
  HierachyParam = hierarchyData.HiP(
      k_max=params.k_max,
      # g_scale=None,
      # sample_method='random',
      seed=params.seed,
      nonlinear=True,
      normalized=False,
      # terminator=False,
      result_type=hierarchyData.RESULT_TYPE_ALL,
      # accum_only=None,
      # rand_skip=None
  )
#+end_src

#+RESULTS:

Integration.
#+begin_src jupyter-python
  IntegrationParam = hierarchyData.IntP(
      t_max=params.t_max,
      t_steps=params.t_steps,
      # integrator_name='zvode',
      # atol=1e-8,
      # rtol=1e-8,
      # order=5,
      # nsteps=5000,
      # method='bdf',
      # t_steps_skip=1
  )
#+end_src

#+RESULTS:

And now the system.
#+begin_src jupyter-python
  SystemParam = hierarchyData.SysP(
      H_sys=params.H_s,
      L=params.L,
      psi0=params.ψ_0,  # excited qubit
      g=np.array(g),
      w=np.array(w),
      H_dynamic=[],
      bcf_scale=params.bcf_scale,  # some coupling strength (scaling of the fit parameters 'g_i')
      gw_hash=None,  # this is used to load g,w from some database
      len_gw=len(g),
  )
#+end_src

#+RESULTS:

The quantum noise.
#+begin_src jupyter-python
  Eta = StocProc_FFT(
      I,
      params.t_max,
      α,
      negative_frequencies=False,
      seed=params.seed,
      intgr_tol=1e-3,
      intpl_tol=1e-3,
      scale=params.bcf_scale,
  )
#+end_src

#+RESULTS:
#+begin_example
  stocproc.stocproc - INFO - non neg freq only
  stocproc.method_ft - INFO - get_dt_for_accurate_interpolation, please wait ...
  stocproc.method_ft - INFO - acc interp N 33 dt 6.25e-01 -> diff 1.51e-01
  stocproc.method_ft - INFO - acc interp N 65 dt 3.12e-01 -> diff 3.41e-02
  stocproc.method_ft - INFO - acc interp N 129 dt 1.56e-01 -> diff 7.19e-03
  stocproc.method_ft - INFO - acc interp N 257 dt 7.81e-02 -> diff 1.70e-03
  stocproc.method_ft - INFO - acc interp N 513 dt 3.91e-02 -> diff 4.21e-04
  stocproc.method_ft - INFO - requires dt < 3.906e-02
  stocproc.method_ft - INFO - get_N_a_b_for_accurate_fourier_integral, please wait ...
  stocproc.method_ft - INFO - J_w_min:1.00e-02 N 32 yields: interval [0.00e+00,6.47e+00] diff 9.83e-03
  stocproc.method_ft - INFO - J_w_min:1.00e-03 N 32 yields: interval [0.00e+00,9.12e+00] diff 6.55e-03
  stocproc.method_ft - INFO - J_w_min:1.00e-02 N 64 yields: interval [0.00e+00,6.47e+00] diff 1.11e-02
  stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
  stocproc.method_ft - INFO - J_w_min:1.00e-04 N 32 yields: interval [0.00e+00,1.17e+01] diff 1.32e-02
  stocproc.method_ft - INFO - J_w_min:1.00e-03 N 64 yields: interval [0.00e+00,9.12e+00] diff 9.90e-04
  stocproc.method_ft - INFO - return, cause tol of 0.001 was reached
  stocproc.method_ft - INFO - requires dx < 1.425e-01
  stocproc.stocproc - INFO - Fourier Integral Boundaries: [0.000e+00, 2.166e+02]
  stocproc.stocproc - INFO - Number of Nodes            : 2048
  stocproc.stocproc - INFO - yields dx                  : 1.058e-01
  stocproc.stocproc - INFO - yields dt                  : 2.900e-02
  stocproc.stocproc - INFO - yields t_max               : 5.937e+01
#+end_example

The sample trajectories are smooth.
#+begin_src jupyter-python
  %%space plot
  ts = np.linspace(0, params.t_max, 1000)
  Eta.new_process()
  plt.plot(ts, Eta(ts).real)
#+end_src

#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f70a4adb790> |
[[file:./.ob-jupyter/2203ae711b676c5126b800cfe209dd822f8a19ac.png]]
:END:

And now the thermal noise.
#+begin_src jupyter-python
  EtaTherm = StocProc_FFT(
      spectral_density=bcf.OFTDens(s=params.s, eta=1, gamma=params.wc, beta=1 / params.T),
      t_max=params.t_max,
      alpha=bcf.OFTCorr(s=params.s, eta=1, gamma=params.wc, beta=1 / params.T),
      intgr_tol=1e-3,
      intpl_tol=1e-3,
      seed=params.seed,
      negative_frequencies=False,
      scale=params.bcf_scale,
  )
#+end_src

#+RESULTS:
#+begin_example
  stocproc.stocproc - INFO - non neg freq only
  stocproc.method_ft - INFO - get_dt_for_accurate_interpolation, please wait ...
  stocproc.method_ft - INFO - acc interp N 33 dt 6.25e-01 -> diff 2.82e-02
  stocproc.method_ft - INFO - acc interp N 65 dt 3.12e-01 -> diff 5.93e-03
  stocproc.method_ft - INFO - acc interp N 129 dt 1.56e-01 -> diff 1.38e-03
  stocproc.method_ft - INFO - acc interp N 257 dt 7.81e-02 -> diff 3.38e-04
  stocproc.method_ft - INFO - requires dt < 7.812e-02
  stocproc.method_ft - INFO - get_N_a_b_for_accurate_fourier_integral, please wait ...
  stocproc.method_ft - INFO - J_w_min:1.00e-02 N 32 yields: interval [0.00e+00,4.10e+00] diff 1.21e-02
  stocproc.method_ft - INFO - J_w_min:1.00e-03 N 32 yields: interval [0.00e+00,5.82e+00] diff 2.95e-02
  stocproc.method_ft - INFO - J_w_min:1.00e-02 N 64 yields: interval [0.00e+00,4.10e+00] diff 7.88e-03
  stocproc.method_ft - INFO - J_w_min:1.00e-04 N 32 yields: interval [0.00e+00,7.50e+00] diff 4.90e-02
  stocproc.method_ft - INFO - J_w_min:1.00e-03 N 64 yields: interval [0.00e+00,5.82e+00] diff 6.80e-03
  stocproc.method_ft - INFO - J_w_min:1.00e-02 N 128 yields: interval [0.00e+00,4.10e+00] diff 7.56e-03
  stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
  stocproc.method_ft - INFO - J_w_min:1.00e-05 N 32 yields: interval [0.00e+00,9.16e+00] diff 8.38e-02
  stocproc.method_ft - INFO - J_w_min:1.00e-04 N 64 yields: interval [0.00e+00,7.50e+00] diff 1.10e-02
  stocproc.method_ft - INFO - J_w_min:1.00e-03 N 128 yields: interval [0.00e+00,5.82e+00] diff 1.60e-03
  stocproc.method_ft - INFO - J_w_min:1.00e-02 N 256 yields: interval [0.00e+00,4.10e+00] diff 7.48e-03
  stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
  stocproc.method_ft - INFO - J_w_min:1.00e-06 N 32 yields: interval [0.00e+00,1.08e+01] diff 1.22e-01
  stocproc.method_ft - INFO - J_w_min:1.00e-05 N 64 yields: interval [0.00e+00,9.16e+00] diff 1.75e-02
  stocproc.method_ft - INFO - J_w_min:1.00e-04 N 128 yields: interval [0.00e+00,7.50e+00] diff 2.67e-03
  stocproc.method_ft - INFO - J_w_min:1.00e-03 N 256 yields: interval [0.00e+00,5.82e+00] diff 7.86e-04
  stocproc.method_ft - INFO - return, cause tol of 0.001 was reached
  stocproc.method_ft - INFO - requires dx < 2.272e-02
  stocproc.stocproc - INFO - Fourier Integral Boundaries: [0.000e+00, 8.651e+01]
  stocproc.stocproc - INFO - Number of Nodes            : 4096
  stocproc.stocproc - INFO - yields dx                  : 2.112e-02
  stocproc.stocproc - INFO - yields dt                  : 7.263e-02
  stocproc.stocproc - INFO - yields t_max               : 2.974e+02
#+end_example

The sample trajectories are smooth too.
#+begin_src jupyter-python
  %%space plot
  ts = np.linspace(0, params.t_max, 1000)
  EtaTherm.new_process()
  plt.plot(ts, EtaTherm(ts).real)
#+end_src

#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f70a4a507c0> |
[[file:./.ob-jupyter/99b25e82eddc6ca1ac5bfa2026136468066d1be9.png]]
:END:

* Actual Hops
Generate the key for binary caching.
#+begin_src jupyter-python
  hi_key = hierarchyData.HIMetaKey_type(
      HiP=HierachyParam,
      IntP=IntegrationParam,
      SysP=SystemParam,
      Eta=Eta,
      EtaTherm=EtaTherm,
  )
#+end_src

#+RESULTS:

Initialize Hierarchy.
#+begin_src jupyter-python
  myHierarchy = hierarchyLib.HI(hi_key, number_of_samples=params.N, desc="calculate the heat flow")
#+end_src

#+RESULTS:
: init Hi class, use 2002 equation
: /home/hiro/Documents/Projects/UNI/master/masterarb/python/richard_hops/hierarchyLib.py:1058: UserWarning: sum_k_max is not implemented! DO SO BEFORE NEXT USAGE (use simplex).HierarchyParametersType does not yet know about sum_k_max
:   warnings.warn(

Run the integration.
#+begin_src jupyter-python
  myHierarchy.integrate_simple(data_path="data", data_name="energy_flow_therm_new_again_less.data")
#+end_src

#+RESULTS:
#+begin_example
  (10, 1000, 1001)
  samples     :0.0%
  integration :0.0%
  samples     :0.4%
  integration :45.6%
  samples     :0.9%
  integration :12.6%
  samples     :1.3%
  integration :95.3%
  samples     :1.8%
  integration :76.1%
  samples     :2.3%
  integration :48.0%
  samples     :2.3%
  integration :99.9%
  samples     :2.4%
  integration :74.3%
  samples     :2.9%
  integration :64.6%
  samples     :3.4%
  integration :46.3%
  samples     :3.9%
  integration :14.1%
  samples     :4.4%
  integration :1.5%
  samples     :4.8%
  integration :92.0%
  samples     :5.3%
  integration :66.0%
  samples     :5.8%
  integration :51.5%
  samples     :6.3%
  integration :35.0%
  samples     :6.4%
  integration :99.9%
  samples     :6.4%
  integration :99.9%
  samples     :6.4%
  integration :99.9%
  samples     :6.4%
  integration :99.9%
  samples     :6.9%
  integration :1.2%
  samples     :7.0%
  integration :99.9%
  samples     :7.5%
  integration :35.0%
  samples     :7.9%
  integration :95.6%
  samples     :8.0%
  integration :43.1%
  samples     :8.5%
  integration :23.9%
  samples     :8.7%
  integration :99.9%
  samples     :9.1%
  integration :0.2%
  samples     :9.5%
  integration :89.0%
  samples     :9.6%
  integration :99.9%
  samples     :10.1%
  integration :77.3%
  samples     :10.4%
  integration :99.9%
  samples     :10.6%
  integration :78.4%
  samples     :11.1%
  integration :54.3%
  samples     :11.2%
  integration :99.9%
  samples     :11.7%
  integration :67.8%
  samples     :12.1%
  integration :99.9%
  samples     :12.3%
  integration :66.9%
  samples     :12.8%
  integration :49.7%
  samples     :12.9%
  integration :99.9%
  samples     :13.4%
  integration :15.6%
  samples     :13.8%
  integration :99.9%
  samples     :13.9%
  integration :99.9%
  samples     :14.4%
  integration :79.3%
  samples     :14.6%
  integration :99.9%
  samples     :15.0%
  integration :56.3%
  samples     :15.5%
  integration :24.4%
  samples     :15.6%
  integration :53.3%
  samples     :16.1%
  integration :40.1%
  samples     :16.3%
  integration :99.9%
  samples     :16.7%
  integration :1.3%
  samples     :17.1%
  integration :85.3%
  samples     :17.2%
  integration :99.9%
  samples     :17.7%
  integration :1.7%
  samples     :18.0%
  integration :99.9%
  samples     :18.2%
  integration :80.1%
  samples     :18.7%
  integration :54.7%
  samples     :18.8%
  integration :99.9%
  samples     :19.3%
  integration :42.4%
  samples     :19.7%
  integration :99.9%
  samples     :19.9%
  integration :49.4%
  samples     :20.4%
  integration :28.7%
  samples     :20.5%
  integration :99.9%
  samples     :20.6%
  integration :48.9%
  samples     :21.0%
  integration :99.9%
  samples     :21.3%
  integration :39.7%
  samples     :21.8%
  integration :17.9%
  samples     :22.0%
  integration :99.9%
  samples     :22.3%
  integration :55.0%
  samples     :22.8%
  integration :23.0%
  samples     :22.9%
  integration :61.2%
  samples     :23.4%
  integration :34.9%
  samples     :23.6%
  integration :99.9%
  samples     :23.9%
  integration :92.7%
  samples     :24.4%
  integration :63.9%
  samples     :24.4%
  integration :99.9%
  samples     :24.9%
  integration :20.4%
  samples     :25.2%
  integration :99.9%
  samples     :25.5%
  integration :57.4%
  samples     :25.9%
  integration :95.2%
  samples     :26.0%
  integration :99.9%
  samples     :26.2%
  integration :93.2%
  samples     :26.6%
  integration :99.9%
  samples     :26.9%
  integration :53.1%
  samples     :27.4%
  integration :40.7%
  samples     :27.5%
  integration :99.9%
  samples     :27.9%
  integration :71.3%
  samples     :28.3%
  integration :99.9%
  samples     :28.5%
  integration :19.5%
  samples     :29.0%
  integration :8.5%
  samples     :29.2%
  integration :99.9%
  samples     :29.6%
  integration :0.1%
  samples     :30.0%
  integration :82.2%
  samples     :30.1%
  integration :69.8%
  samples     :30.6%
  integration :50.0%
  samples     :30.9%
  integration :99.9%
  samples     :31.0%
  integration :81.4%
  samples     :31.5%
  integration :57.7%
  samples     :31.6%
  integration :52.0%
  samples     :32.1%
  integration :34.3%
  samples     :32.4%
  integration :99.9%
  samples     :32.7%
  integration :22.2%
  samples     :33.2%
  integration :0.0%
  samples     :33.3%
  integration :99.9%
  samples     :33.8%
  integration :5.6%
  samples     :34.1%
  integration :99.9%
  samples     :34.3%
  integration :3.6%
  samples     :34.7%
  integration :84.6%
  samples     :34.9%
  integration :99.9%
  samples     :35.3%
  integration :89.0%
  samples     :35.8%
  integration :62.8%
  samples     :35.9%
  integration :39.1%
  samples     :36.4%
  integration :11.0%
  samples     :36.6%
  integration :99.9%
  samples     :36.6%
  integration :99.9%
  samples     :37.1%
  integration :10.6%
  samples     :37.3%
  integration :65.4%
  samples     :37.8%
  integration :44.3%
  samples     :38.1%
  integration :99.9%
  samples     :38.3%
  integration :99.9%
  samples     :38.8%
  integration :81.3%
  samples     :39.0%
  integration :7.2%
  samples     :39.4%
  integration :97.2%
  samples     :39.8%
  integration :99.9%
  samples     :40.0%
  integration :93.5%
  samples     :40.5%
  integration :70.7%
  samples     :40.6%
  integration :99.9%
  samples     :40.9%
  integration :45.0%
  samples     :41.3%
  integration :99.9%
  samples     :41.5%
  integration :88.2%
  samples     :42.0%
  integration :68.2%
  samples     :42.2%
  integration :99.9%
  samples     :42.5%
  integration :92.3%
  samples     :43.0%
  integration :68.6%
  samples     :43.1%
  integration :99.9%
  samples     :43.6%
  integration :71.8%
  samples     :44.1%
  integration :44.8%
  samples     :44.6%
  integration :25.6%
  samples     :45.1%
  integration :7.7%
  samples     :45.5%
  integration :97.1%
  samples     :46.0%
  integration :72.7%
  samples     :46.5%
  integration :40.4%
  samples     :47.0%
  integration :22.3%
  samples     :47.5%
  integration :3.1%
  samples     :47.9%
  integration :90.8%
  samples     :48.4%
  integration :67.0%
  samples     :48.9%
  integration :36.7%
  samples     :48.9%
  integration :99.9%
  samples     :48.9%
  integration :99.9%
  samples     :48.9%
  integration :99.9%
  samples     :48.9%
  integration :99.9%
  samples     :48.9%
  integration :99.9%
  samples     :49.3%
  integration :5.0%
  samples     :49.7%
  integration :85.1%
  samples     :50.0%
  integration :99.9%
  samples     :50.2%
  integration :41.6%
  samples     :50.7%
  integration :24.7%
  samples     :51.1%
  integration :99.9%
  samples     :51.6%
  integration :78.3%
  samples     :52.1%
  integration :46.2%
  samples     :52.6%
  integration :29.4%
  samples     :53.1%
  integration :5.9%
  samples     :53.5%
  integration :78.5%
  samples     :54.0%
  integration :53.3%
  samples     :54.5%
  integration :28.7%
  samples     :55.0%
  integration :10.4%
  samples     :55.5%
  integration :3.0%
  samples     :55.9%
  integration :97.2%
  samples     :56.4%
  integration :75.1%
  samples     :56.7%
  integration :11.9%
  samples     :57.2%
  integration :0.0%
  samples     :57.5%
  integration :99.9%
  samples     :57.5%
  integration :99.9%
  samples     :57.5%
  integration :99.9%
  samples     :57.5%
  integration :99.9%
  samples     :57.5%
  integration :99.9%
  samples     :57.5%
  integration :99.9%
  samples     :57.8%
  integration :77.7%
  samples     :58.3%
  integration :53.5%
  samples     :58.5%
  integration :99.9%
  samples     :58.7%
  integration :37.1%
  samples     :59.2%
  integration :15.8%
  samples     :59.4%
  integration :5.8%
  samples     :59.8%
  integration :99.5%
  samples     :60.1%
  integration :99.9%
  samples     :60.1%
  integration :99.9%
  samples     :60.5%
  integration :0.0%
  samples     :60.7%
  integration :77.9%
  samples     :61.2%
  integration :59.5%
  samples     :61.5%
  integration :99.9%
  samples     :61.7%
  integration :62.3%
  samples     :62.2%
  integration :28.4%
  samples     :62.3%
  integration :99.9%
  samples     :62.5%
  integration :59.4%
  samples     :62.8%
  integration :99.9%
  samples     :63.1%
  integration :62.1%
  samples     :63.6%
  integration :37.8%
  samples     :63.7%
  integration :99.9%
  samples     :64.1%
  integration :99.8%
  samples     :64.6%
  integration :79.1%
  samples     :64.7%
  integration :90.6%
  samples     :65.2%
  integration :68.2%
  samples     :65.4%
  integration :99.9%
  samples     :65.8%
  integration :47.8%
  samples     :66.3%
  integration :14.5%
  samples     :66.4%
  integration :37.2%
  samples     :66.9%
  integration :20.2%
  samples     :67.1%
  integration :99.9%
  samples     :67.3%
  integration :6.3%
  samples     :67.7%
  integration :95.5%
  samples     :67.9%
  integration :53.7%
  samples     :68.4%
  integration :36.6%
  samples     :68.7%
  integration :99.9%
  samples     :69.1%
  integration :23.1%
  samples     :69.6%
  integration :10.3%
  samples     :70.0%
  integration :91.6%
  samples     :70.5%
  integration :65.4%
  samples     :71.0%
  integration :42.1%
  samples     :71.5%
  integration :20.5%
  samples     :72.0%
  integration :5.8%
  samples     :72.4%
  integration :93.3%
  samples     :72.9%
  integration :76.1%
  samples     :73.4%
  integration :73.2%
  samples     :73.9%
  integration :56.9%
  samples     :74.4%
  integration :30.2%
  samples     :74.9%
  integration :11.4%
  samples     :75.2%
  integration :66.9%
  samples     :75.7%
  integration :28.5%
  samples     :75.9%
  integration :99.9%
  samples     :75.9%
  integration :99.9%
  samples     :75.9%
  integration :99.9%
  samples     :75.9%
  integration :99.9%
  samples     :75.9%
  integration :99.9%
  samples     :76.1%
  integration :10.6%
  samples     :76.5%
  integration :99.8%
  samples     :77.0%
  integration :81.5%
  samples     :77.1%
  integration :99.9%
  samples     :77.4%
  integration :73.1%
  samples     :77.8%
  integration :99.9%
  samples     :78.0%
  integration :57.8%
  samples     :78.5%
  integration :37.9%
  samples     :78.6%
  integration :99.9%
  samples     :78.7%
  integration :0.1%
  samples     :79.1%
  integration :82.8%
  samples     :79.4%
  integration :35.2%
  samples     :79.9%
  integration :16.9%
  samples     :80.1%
  integration :99.9%
  samples     :80.4%
  integration :81.1%
  samples     :80.9%
  integration :42.0%
  samples     :81.0%
  integration :81.5%
  samples     :81.5%
  integration :64.4%
  samples     :81.8%
  integration :99.9%
  samples     :82.1%
  integration :53.6%
  samples     :82.6%
  integration :38.6%
  samples     :82.7%
  integration :15.3%
  samples     :83.2%
  integration :0.1%
  samples     :83.4%
  integration :99.9%
  samples     :83.4%
  integration :99.9%
  samples     :83.9%
  integration :41.7%
  samples     :84.1%
  integration :37.1%
  samples     :84.6%
  integration :13.4%
  samples     :84.9%
  integration :99.9%
  samples     :85.0%
  integration :81.5%
  samples     :85.5%
  integration :59.9%
  samples     :85.6%
  integration :99.9%
  samples     :86.1%
  integration :30.6%
  samples     :86.5%
  integration :53.8%
  samples     :86.6%
  integration :99.9%
  samples     :87.1%
  integration :82.4%
  samples     :87.3%
  integration :99.9%
  samples     :87.5%
  integration :58.6%
  samples     :88.0%
  integration :36.3%
  samples     :88.2%
  integration :13.1%
  samples     :88.6%
  integration :98.9%
  samples     :88.9%
  integration :99.9%
  samples     :89.2%
  integration :55.8%
  samples     :89.7%
  integration :25.7%
  samples     :89.8%
  integration :22.9%
  samples     :90.3%
  integration :0.4%
  samples     :90.5%
  integration :99.9%
  samples     :90.7%
  integration :88.2%
  samples     :91.2%
  integration :66.2%
  samples     :91.3%
  integration :99.9%
  samples     :91.7%
  integration :94.0%
  samples     :92.1%
  integration :99.9%
  samples     :92.3%
  integration :18.3%
  samples     :92.7%
  integration :96.3%
  samples     :93.2%
  integration :78.6%
  samples     :93.7%
  integration :54.4%
  samples     :94.2%
  integration :33.1%
  samples     :94.7%
  integration :20.7%
  samples     :95.1%
  integration :98.3%
  samples     :95.6%
  integration :80.2%
  samples     :96.1%
  integration :57.7%
  samples     :96.6%
  integration :35.9%
  samples     :97.1%
  integration :18.9%
  samples     :97.6%
  integration :0.4%
  samples     :98.0%
  integration :75.9%
  samples     :98.5%
  integration :54.6%
  samples     :98.9%
  integration :34.8%
  samples     :99.3%
  integration :37.7%
  samples     :99.3%
  integration :99.9%
  samples     :99.3%
  integration :99.9%
  samples     :99.3%
  integration :99.9%
  samples     :99.3%
  integration :99.9%
  samples     :99.3%
  integration :99.9%
  samples     :99.3%
  integration :99.9%
  samples     :99.7%
  integration :63.5%
  samples     : 100%
  integration :0.0%
  
#+end_example
Get the samples.
#+BEGIN_SRC jupyter-python
  # to access the data the 'hi_key' is used to find the data in the hdf5 file
  class int_result:
      with hierarchyData.HIMetaData(
          hid_name="energy_flow_therm_new_again_less.data", hid_path="data"
      ) as metaData:
          with metaData.get_HIData(hi_key, read_only=True) as data:
              smp = data.get_samples()
              print("{} samples found in database".format(smp))
              τ = data.get_time()
              rho_τ = data.get_rho_t()
              #s_proc = np.array(data.stoc_proc)
              #states = np.array(data.aux_states).copy()
              ψ_1 = np.array(data.aux_states[:, :, 0 : params.num_exp_t * params.dim])
              ψ_0 = np.array(data.stoc_traj)
              y = np.array(data.y)
              #η = np.array(data.stoc_proc)
              temp_y = np.array(data.temp_y)
#+end_src

#+RESULTS:
: 1000 samples found in database

Calculate energy.
#+begin_src jupyter-python
  %matplotlib inline
  energy = np.einsum("ijk,kj", int_result.rho_τ,params.H_s).real
  plt.plot(int_result.τ, energy)
#+end_src

#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f70a4918400> |
[[file:./.ob-jupyter/7cb82ec4a27feaf82773ed589e3fef5b13a8b0af.png]]
:END:

* Energy Flow
:PROPERTIES:
:ID:       9ce93da8-d323-40ec-96a2-42ba184dc963
:END:
#+begin_src jupyter-python
  int_result.ψ_1.shape
#+end_src

#+RESULTS:
| 1280 | 1000 | 8 |

Let's look at the norm.
#+begin_src jupyter-python
  plt.plot(int_result.τ, (int_result.ψ_0[0].conj() * int_result.ψ_0[0]).sum(axis=1).real)
#+end_src

#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f70a48fabe0> |
[[file:./.ob-jupyter/fffb6bce0ad5b5e3fd7ae1b07c8cc3c93607fe3c.png]]
:END:

And try to calculate the energy flow.
#+begin_src jupyter-python
  def flow_for_traj(ψ_0, ψ_1, temp_y):
      a = np.array((params.L @ ψ_0.T).T)
      EtaTherm.new_process(temp_y)
      η_dot = scipy.misc.derivative(EtaTherm, int_result.τ, dx=1e-3, order=5)

      ψ_1 = (-w * g * params.bcf_scale)[None, :, None] * ψ_1.reshape(
          params.t_steps, params.num_exp_t, params.dim
      )

      # return np.array(np.sum(ψ_0.conj() * ψ_0, axis=1)).flatten().real
      j_0 = np.array(
          2
          ,* (
              1j
              ,* (np.sum(a.conj()[:, None, :] * ψ_1, axis=(1, 2)))
              / np.sum(ψ_0.conj() * ψ_0, axis=1)
          ).real
      ).flatten()

      j_therm = np.array(
          2
          ,* (
              1j
              ,* (np.sum(a.conj() * ψ_0, axis=1)) * η_dot
              / np.sum(ψ_0.conj() * ψ_0, axis=1)
          ).real
      ).flatten()
      return j_0, j_therm
#+end_src

#+RESULTS:

Now we calculate the average over all trajectories.
#+begin_src jupyter-python
  class Flow:
      j_0 = np.zeros_like(int_result.τ)
      j_therm = np.zeros_like(int_result.τ)
      for i in range(0, params.N):
          dj, dj_therm = flow_for_traj(
              int_result.ψ_0[i], int_result.ψ_1[i], int_result.temp_y[i]
          )

          j_0 += dj
          j_therm += dj_therm
      j_0 /= params.N
      j_therm /= params.N
      j = j_0 + j_therm

#+end_src

#+RESULTS:

And plot it :).
#+begin_src jupyter-python
  %matplotlib inline
  plt.plot(int_result.τ, Flow.j_0, label=r"$j_0$")
  plt.plot(int_result.τ, Flow.j_therm, label=r"$j_\mathrm{therm}$")
  plt.plot(int_result.τ, Flow.j, label=r"$j$")
  plt.legend()
#+end_src

#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7f70a49ccac0>
[[file:./.ob-jupyter/e6724f82c0e439f5b7d9c0501ee930facb205943.png]]
:END:

Let's calculate the integrated energy.
#+begin_src jupyter-python
  E_t = np.array(
      [0]
      + [
          scipy.integrate.simpson(Flow.j[0:n], int_result.τ[0:n])
          for n in range(1, len(int_result.τ))
      ]
  )
  E_t[-1]
#+end_src

#+RESULTS:
: 4.387773704387552

With this we can retrieve the energy of the interaction Hamiltonian.
#+begin_src jupyter-python
  E_I = - energy - E_t
#+end_src

#+RESULTS:

#+begin_src jupyter-python
  %%space plot
  #plt.plot(τ, j, label="$J$", linestyle='--')
  plt.plot(int_result.τ, E_t, label=r"$\langle H_{\mathrm{B}}\rangle$")
  plt.plot(int_result.τ, E_I, label=r"$\langle H_{\mathrm{I}}\rangle$")
  plt.plot(int_result.τ, energy, label=r"$\langle H_{\mathrm{S}}\rangle$")

  plt.xlabel("τ")
  plt.legend()
  plt.show()
#+end_src

#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f70a47fbaf0> |
| <matplotlib.lines.Line2D | at | 0x7f70a47fbee0> |
| <matplotlib.lines.Line2D | at | 0x7f70a47862e0> |
: Text(0.5, 0, 'τ')
: <matplotlib.legend.Legend at 0x7f70a47fbdf0>
[[file:./.ob-jupyter/34fdaa07b2b35134bac0341834aced72d448d1c1.png]]
:END:

* System + Interaction Energy
#+begin_src jupyter-python
  def h_si_for_traj(ψ_0, ψ_1, temp_y):
      a = np.array((params.L @ ψ_0.T).T)
      b = np.array((params.H_s @ ψ_0.T).T)
      ψ_1 = (g*params.bcf_scale)[None, :, None] * ψ_1.reshape(
          params.t_steps, params.num_exp_t, params.dim
      )
      EtaTherm.new_process(temp_y)

      E_i = np.array(
          2
          ,* (
              -1j
              ,* np.sum(
                  a.conj()[:, None, :]
                  ,* ψ_1,
                  axis=(1, 2),
              )
          ).real
      ).flatten()

      E_i += np.array(
          2
          ,* (
              -1j * EtaTherm(int_result.τ)
              ,* np.sum(
                  a.conj()
                  ,* ψ_0,
                  axis=1,
              )
          ).real
      ).flatten()

      E_s = np.array(np.sum(b.conj() * ψ_0, axis=1)).flatten().real

      return (E_i + E_s) / np.sum(ψ_0.conj() * ψ_0, axis=1).real
#+end_src

#+RESULTS:

#+begin_src jupyter-python
  e_si = np.zeros_like(int_result.τ)
  for i in range(0, params.N):
      e_si += h_si_for_traj(int_result.ψ_0[i], int_result.ψ_1[i], int_result.temp_y[i])
  e_si /= params.N
#+end_src

#+RESULTS:

Doesn't work out.
#+begin_src jupyter-python
  plt.plot(int_result.τ, e_si, label=r"direct")
  plt.plot(int_result.τ, E_I + energy)
  plt.legend()
#+end_src


#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7f70a45fddc0>
[[file:./.ob-jupyter/cbf733338ec8702f4c097598ed286cfc60811a0c.png]]
:END: