mirror of
https://github.com/vale981/master-thesis
synced 2025-03-09 20:56:40 -04:00
270 lines
10 KiB
Org Mode
270 lines
10 KiB
Org Mode
#+PROPERTY: header-args :session billohops :kernel python :pandoc t
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* Setup
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#+begin_src jupyter-python
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%load_ext autoreload
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%autoreload 2
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%load_ext jupyter_spaces
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#+end_src
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#+RESULTS:
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: The autoreload extension is already loaded. To reload it, use:
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: %reload_ext autoreload
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: The jupyter_spaces extension is already loaded. To reload it, use:
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: %reload_ext jupyter_spaces
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#+begin_src jupyter-python
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import hops
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import stocproc as sp
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import numpy as np
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import matplotlib.pyplot as plt
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import scipy
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plt.style.use('ggplot')
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from IPython.display import set_matplotlib_formats
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set_matplotlib_formats('pdf', 'svg')
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#+end_src
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#+RESULTS:
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: <ipython-input-145-e225c2f5410d>:8: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`
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: set_matplotlib_formats('pdf', 'svg')
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* Auxiliary Definitions
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#+begin_src jupyter-python
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σ1 = np.matrix([[0,1],[1,0]])
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σ2 = np.matrix([[0,-1j],[1j,0]])
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σ3 = np.matrix([[1,0],[0,-1]])
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#+end_src
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#+RESULTS:
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* Model
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Let's set up the basic parameters.
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#+begin_src jupyter-python
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γ = .01 # coupling ratio
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ω_c = 1 # center of spect. dens
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δ = 1 # breadth BCF
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W = -ω_c * 1j - δ # exponent BCF
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τ_max = 1.6 # the maximal simulation time
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seed = 100 # seed for all pseudo random generators
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H_s = σ3
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L = 1/2 * (σ1 - 1j * σ2) * γ
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L
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#+end_src
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#+RESULTS:
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: matrix([[0. +0.j, 0. +0.j],
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: [0.01+0.j, 0. +0.j]])
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And for fun: the BCF and the spectral density.
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#+begin_src jupyter-python
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def α(τ):
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return np.sqrt(δ) * np.exp(-1j * ω_c * τ - np.abs(τ) * δ)
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def I(ω):
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return np.sqrt(δ) / (δ + (ω - ω_c) ** 2 / δ)
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#+end_src
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#+RESULTS:
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** Visualize
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#+begin_src jupyter-python
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%%space plot
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t = np.linspace(0, τ_max, 1000)
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ω = np.linspace(ω_c - 10, ω_c + 10, 1000)
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fig, axs = plt.subplots(2)
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axs[0].plot(t, np.real(α(t)))
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axs[0].plot(t, np.imag(α(t)))
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axs[1].plot(ω, I(ω))
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4b46f79f10> |
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| <matplotlib.lines.Line2D | at | 0x7f4b46f052b0> |
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| <matplotlib.lines.Line2D | at | 0x7f4b46f05730> |
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[[file:./.ob-jupyter/531fd66ca2df41e72d6e1f473ebbfb0f48aec9c2.svg]]
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:END:
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* HOPS
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** Process
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Let's get ourselves a realiation of a stochastic process. Mostly stolen
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fromt the ~stocproc~ examples.
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#+begin_src jupyter-python
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η = sp.StocProc_FFT(
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I, τ_max, α, negative_frequencies=True, seed=seed, intgr_tol=1e-2, intpl_tol=1e-2
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)
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#+end_src
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#+RESULTS:
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#+begin_example
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stocproc.stocproc - INFO - use neg freq
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stocproc.method_ft - INFO - get_dt_for_accurate_interpolation, please wait ...
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stocproc.method_ft - INFO - acc interp N 33 dt 1.00e-01 -> diff 9.15e-04
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stocproc.method_ft - INFO - requires dt < 1.000e-01
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stocproc.method_ft - INFO - get_N_a_b_for_accurate_fourier_integral, please wait ...
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stocproc.method_ft - INFO - J_w_min:1.00e-02 N 32 yields: interval [-8.95e+00,1.09e+01] diff 2.01e-01
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stocproc.method_ft - INFO - J_w_min:1.00e-03 N 32 yields: interval [-3.06e+01,3.26e+01] diff 6.40e-01
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stocproc.method_ft - INFO - J_w_min:1.00e-02 N 64 yields: interval [-8.95e+00,1.09e+01] diff 2.00e-01
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stocproc.method_ft - INFO - J_w_min:1.00e-04 N 32 yields: interval [-9.90e+01,1.01e+02] diff 1.90e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-03 N 64 yields: interval [-3.06e+01,3.26e+01] diff 7.41e-02
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stocproc.method_ft - INFO - J_w_min:1.00e-02 N 128 yields: interval [-8.95e+00,1.09e+01] diff 2.00e-01
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stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
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stocproc.method_ft - INFO - J_w_min:1.00e-05 N 32 yields: interval [-3.15e+02,3.17e+02] diff 2.68e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-04 N 64 yields: interval [-9.90e+01,1.01e+02] diff 1.15e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-03 N 128 yields: interval [-3.06e+01,3.26e+01] diff 6.33e-02
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stocproc.method_ft - INFO - J_w_min:1.00e-02 N 256 yields: interval [-8.95e+00,1.09e+01] diff 2.00e-01
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stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
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stocproc.method_ft - INFO - J_w_min:1.00e-06 N 32 yields: interval [-9.99e+02,1.00e+03] diff 2.99e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-05 N 64 yields: interval [-3.15e+02,3.17e+02] diff 2.29e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-04 N 128 yields: interval [-9.90e+01,1.01e+02] diff 2.78e-01
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stocproc.method_ft - INFO - J_w_min:1.00e-03 N 256 yields: interval [-3.06e+01,3.26e+01] diff 6.33e-02
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stocproc.method_ft - INFO - J_w_min:1.00e-02 N 512 yields: interval [-8.95e+00,1.09e+01] diff 2.00e-01
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stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
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stocproc.method_ft - INFO - J_w_min:1.00e-07 N 32 yields: interval [-3.16e+03,3.16e+03] diff 3.09e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-06 N 64 yields: interval [-9.99e+02,1.00e+03] diff 2.84e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-05 N 128 yields: interval [-3.15e+02,3.17e+02] diff 1.66e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-04 N 256 yields: interval [-9.90e+01,1.01e+02] diff 2.20e-02
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stocproc.method_ft - INFO - J_w_min:1.00e-03 N 512 yields: interval [-3.06e+01,3.26e+01] diff 6.33e-02
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stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
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stocproc.method_ft - INFO - J_w_min:1.00e-08 N 32 yields: interval [-1.00e+04,1.00e+04] diff 3.13e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-07 N 64 yields: interval [-3.16e+03,3.16e+03] diff 3.04e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-06 N 128 yields: interval [-9.99e+02,1.00e+03] diff 2.57e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-05 N 256 yields: interval [-3.15e+02,3.17e+02] diff 8.81e-01
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stocproc.method_ft - INFO - J_w_min:1.00e-04 N 512 yields: interval [-9.90e+01,1.01e+02] diff 2.00e-02
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stocproc.method_ft - INFO - J_w_min:1.00e-03 N 1024 yields: interval [-3.06e+01,3.26e+01] diff 6.33e-02
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stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
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stocproc.method_ft - INFO - J_w_min:1.00e-09 N 32 yields: interval [-3.16e+04,3.16e+04] diff 3.14e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-08 N 64 yields: interval [-1.00e+04,1.00e+04] diff 3.11e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-07 N 128 yields: interval [-3.16e+03,3.16e+03] diff 2.95e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-06 N 256 yields: interval [-9.99e+02,1.00e+03] diff 2.10e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-05 N 512 yields: interval [-3.15e+02,3.17e+02] diff 9.94e-02
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stocproc.method_ft - INFO - J_w_min:1.00e-04 N 1024 yields: interval [-9.90e+01,1.01e+02] diff 2.00e-02
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stocproc.method_ft - INFO - J_w_min:1.00e-03 N 2048 yields: interval [-3.06e+01,3.26e+01] diff 6.33e-02
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stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
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stocproc.method_ft - INFO - J_w_min:1.00e-10 N 32 yields: interval [-1.00e+05,1.00e+05] diff 3.14e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-09 N 64 yields: interval [-3.16e+04,3.16e+04] diff 3.13e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-08 N 128 yields: interval [-1.00e+04,1.00e+04] diff 3.08e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-07 N 256 yields: interval [-3.16e+03,3.16e+03] diff 2.77e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-06 N 512 yields: interval [-9.99e+02,1.00e+03] diff 1.41e+00
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stocproc.method_ft - INFO - J_w_min:1.00e-05 N 1024 yields: interval [-3.15e+02,3.17e+02] diff 6.56e-03
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stocproc.method_ft - INFO - return, cause tol of 0.01 was reached
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stocproc.method_ft - INFO - requires dx < 6.176e-01
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stocproc.stocproc - INFO - Fourier Integral Boundaries: [-3.152e+02, 3.172e+02]
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stocproc.stocproc - INFO - Number of Nodes : 1024
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stocproc.stocproc - INFO - yields dx : 6.176e-01
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stocproc.stocproc - INFO - yields dt : 9.935e-03
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stocproc.stocproc - INFO - yields t_max : 1.016e+01
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#+end_example
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Let's plot it.
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#+begin_src jupyter-python
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%%space plot
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η.new_process(seed=seed)
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plt.plot(η.t, np.real(η(η.t)), label="Re")
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plt.plot(η.t, np.imag(η(η.t)), linestyle="--", label="Im")
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plt.ylabel("η")
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plt.xlabel("τ")
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plt.legend()
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#+end_src
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#+RESULTS:
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:RESULTS:
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: stocproc.stocproc - INFO - use fixed seed (100) for new process
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| <matplotlib.lines.Line2D | at | 0x7f4b46e96d90> |
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| <matplotlib.lines.Line2D | at | 0x7f4b46ea41f0> |
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: Text(0, 0.5, 'η')
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: Text(0.5, 0, 'τ')
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: <matplotlib.legend.Legend at 0x7f4b46ea44c0>
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[[file:./.ob-jupyter/7e3938f40b103b001892d8d9bd055b30abb4d166.svg]]
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:END:
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** TODO Actual Hops
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#+begin_src jupyter-python
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h = hops.Hops(η, H_s, L, W, 10, seed)
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#+end_src
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#+RESULTS:
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#+begin_src jupyter-python
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res = h.integrate_hops_trajectory([1, 0], τ_max, seed)
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#+end_src
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#+RESULTS:
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: stocproc.stocproc - INFO - use fixed seed (100) for new process
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#+begin_src jupyter-python
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%%space plot
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t = np.linspace(0, τ_max, 1000)
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plt.plot(t, np.abs(res.sol(t)[3]))
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4b468bb3d0> |
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[[file:./.ob-jupyter/f956b12a0e926cc85149714aaa9f81b87b035181.svg]]
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:END:
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#+begin_src jupyter-python :results none
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ts, ρs, js = h.integrate_hops_ensemble([1, 0], np.linspace(0, τ_max, 1000), 10000)
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#+end_src
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#+begin_src jupyter-python
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energy = [np.trace(ρ) for ρ in ρs]
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plt.plot(ts, energy)
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#+end_src
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#+RESULTS:
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:RESULTS:
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: /nix/store/z1lf15g2zxp79fwaajlnim22xxwh293l-python3-3.9.4-env/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part
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: return array(a, dtype, copy=False, order=order)
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| <matplotlib.lines.Line2D | at | 0x7f4b46307100> |
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[[file:./.ob-jupyter/4bea320be403bb6638004a735d38a82100cdde66.svg]]
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:END:
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#+begin_src jupyter-python
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energy = [np.real(np.trace(ρ @ σ3)/np.trace(ρ)) for ρ in ρs]
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plt.plot(ts, energy)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4b465bacd0> |
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[[file:./.ob-jupyter/9b8a70997f86a2ab2d4610dd63420af018aa40b4.svg]]
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:END:
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And let's plot the heat transfer rate.
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#+begin_src jupyter-python
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plt.plot(ts, js)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f4b462b5370> |
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[[file:./.ob-jupyter/284aa707d9174d730de5b77c3732f952d1b0ad45.svg]]
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:END:
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#+begin_src jupyter-python
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E_t = np.sum(((ts[1:] - ts[:-1]) * js[1:]))
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E_t
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#+end_src
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#+RESULTS:
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: 2.978852046217619e-07
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#+begin_src jupyter-python
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js.mean()
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#+end_src
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#+RESULTS:
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: 1.8599207463571254e-07
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#+begin_src jupyter-python
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(energy[0] - energy[-1])/E_t
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#+end_src
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#+RESULTS:
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: 340.77341790743804
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