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some reevaluations

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Valentin Boettcher 2021-12-02 13:26:09 +01:00
parent 85946280ff
commit 3fd05dd400
23 changed files with 78708 additions and 8674 deletions
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40
python/energy_flow_proper/03_gaussian/comparison_with_hops.org
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@ -274,12 +274,10 @@ We calculate the sytem energy just for fun.
result.N,
stg.__HI_nonlinear,
)
# for energy, var, n in zip(*e_sys):
# plt.plot(result.τ, energy.real, label=f"n={n}", alpha=n / e_sys[2][-1])
#+end_src
#+RESULTS:
: 100%|██████████| 999/999 [00:17<00:00, 56.69it/s]
: 100%|██████████| 999/999 [00:21<00:00, 47.50it/s]
#+begin_src jupyter-python
@ -289,7 +287,7 @@ We calculate the sytem energy just for fun.
#+RESULTS:
:RESULTS:
: <ErrorbarContainer object of 3 artists>
[[file:./.ob-jupyter/fcb24c52ebb5d9e374171da718bf814fa7ce9b80.svg]]
[[file:./.ob-jupyter/8ea9a10e8eb94231023fb1fd414cd976f05c541b.svg]]
:END:
** Heat Flow
@ -308,20 +306,15 @@ And then we calculate the flow.
#+end_src
#+RESULTS:
: 100%|██████████| 999/999 [01:13<00:00, 13.62it/s]
: 100%|██████████| 999/999 [01:17<00:00, 12.82it/s]
#+begin_src jupyter-python
for n, flow, flow_std in full_flow[:-1]:
plt.plot(result.τ, -flow, label=f"n={n}", alpha=n / full_flow[-1][0], linestyle="--")
plt.errorbar(result.τ, -full_flow[-1][1], yerr=full_flow[-1][2], ecolor="yellow", label="final")
plt.legend()
with ut.hiro_style():
ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
#+end_src
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7fa7d3ce11f0>
[[file:./.ob-jupyter/af7c0d19e2cd4cd0c6e57d6d98a30c968631eca2.svg]]
:END:
[[file:./.ob-jupyter/b6bfa7556dc73a35795cab91ab6b7cc0d88b2118.svg]]
** Analytic
#+begin_src jupyter-python :results none
@ -330,7 +323,7 @@ And then we calculate the flow.
Setting up the BCFs.
#+begin_src jupyter-python :results none
α = gf.BCF(
α_0 = gf.BCF(
stg.t_max,
stg.__bcf_zero_temp,
num_terms=4,
@ -347,27 +340,26 @@ Setting up the BCFs.
We can now initialize the parameter object for the alogrithm.
#+begin_src jupyter-python :results none
sys = gf.SystemParams(Ω=stg.OMEGA, η=stg.eta, α=α)
sys = gf.SystemParams(Ω=stg.OMEGA, η=stg.eta, α_0=α_0)
#+end_src
Now we can define the flow:
#+begin_src jupyter-python :results none
flow = gf.Flow(sys, α_0_dot, n=1)
flow = gf.Flow(sys, α_0, α_0_dot, n=1)
flow_τ = flow(result.τ)
#+end_src
And plot it against the numerically obtained flow.
#+begin_src jupyter-python
%matplotlib inline
plt.errorbar(result.τ, -full_flow[-1][1], yerr=full_flow[-1][2]*2, ecolor="yellow", label="HOPS")
plt.plot(result.τ, flow_τ, label="analytic")
plt.legend()
fig, ax = ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
ax.plot(result.τ, flow_τ, label="analytic", color="blue", linestyle="-.")
ax.legend()
#+end_src
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7fa7c719fdc0>
[[file:./.ob-jupyter/547af6e4599bfd19fc0d085fba9ce6e04ea19a58.svg]]
: <matplotlib.legend.Legend at 0x7fc6e49df370>
[[file:./.ob-jupyter/dccc2c78e24e15583a06d4875345490b56a15390.svg]]
:END:
#+begin_src jupyter-python
@ -381,6 +373,6 @@ And plot it against the numerically obtained flow.
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7fa7c6d07ca0>
[[file:./.ob-jupyter/67e5608a7e0453729d1d531fb8f6c92790037582.svg]]
: <matplotlib.legend.Legend at 0x7fc6e43b5070>
[[file:./.ob-jupyter/facdca9188abacca9ba42ce98ab3b4ce56b042ea.svg]]
:END:

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python/energy_flow_proper/04_gaussian_nonzero/comparison_with_hops.org Normal file
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#+PROPERTY: header-args :session comparison_hops_gauss_nonzero :kernel python :pandoc yes :async yes
* Setup Boilerplate
#+begin_src jupyter-python :results none
import numpy as np
import matplotlib.pyplot as plt
import stg_helper
import stg
from hopsflow import util, hopsflow
import utilities as ut
#+end_src
* Load Hops Data
#+begin_src jupyter-python :results none
system_params = stg_helper.get_system_param(stg)
class result:
hd = stg_helper.get_hierarchy_data(stg, read_only=True)
N = hd.samples[0]
τ = hd.get_time()
# ρ = hd.get_rho_t()
ψ_1 = hd.aux_states
ψ = hd.stoc_traj
therm_y = hd.therm_y
#+end_src
#+begin_src jupyter-python
_, e_sys, σ_e_sys = util.operator_expectation_ensemble(
iter(result.ψ),
system_params.H_sys.todense(),
result.N,
stg.__HI_nonlinear,
)
#+end_src
#+RESULTS:
: 100%|██████████| 4581/4581 [01:35<00:00, 47.89it/s]
#+begin_src jupyter-python
plt.errorbar(result.τ, e_sys.real, yerr=σ_e_sys.real, ecolor="yellow")
#+end_src
#+RESULTS:
:RESULTS:
: <ErrorbarContainer object of 3 artists>
[[file:./.ob-jupyter/0c841367a3fb206d42cd99485373f85699844767.svg]]
:END:
* Heat Flow
#+begin_src jupyter-python
hf_system = hopsflow.SystemParams(
system_params.L.todense(), stg.__g, stg.__w, stg.__bcf_scale, stg.__HI_nonlinear
)
ξ = stg_helper.get_eta_therm(stg)
ξ.calc_deriv = True
hf_therm = hopsflow.ThermalParams(ξ=ξ, τ=result.τ, num_deriv=False)
full_flow = hopsflow.heat_flow_ensemble(
iter(result.ψ), iter(result.ψ_1), hf_system, result.N, (iter(result.therm_y), hf_therm), every=int(result.N / 10)
)
#+end_src
#+RESULTS:
: 100%|██████████| 4581/4581 [06:43<00:00, 11.36it/s]
#+begin_src jupyter-python
with ut.hiro_style():
ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/12bd8c1c361e502dcda461df4f32460a0c99c07b.svg]]
#+RESULTS:
* Analytic Solution
#+begin_src jupyter-python :results none
from hopsflow import gaussflow as gf
#+end_src
Setting up the BCFs.
#+begin_src jupyter-python :results none
α = gf.BCF(
stg.t_max,
stg.__bcf,
num_terms=6,
resolution=0.001,
)
α_0 = gf.BCF(
stg.t_max,
stg.__bcf_zero_temp,
num_terms=4,
resolution=0.001,
)
α_0_dot = gf.BCF(
stg.t_max,
lambda t: 2 / (1j * np.pi) * (stg.wc / (1 + 1j * stg.wc * t)) ** 3,
num_terms=5,
resolution=0.001,
)
#+end_src
LGTM.
#+begin_src jupyter-python
fig, ax = ut.plot_complex(result.τ, α(result.τ)-α.approx(result.τ), label="finite")
#+end_src
#+RESULTS:
[[file:./.ob-jupyter/9a6fc74b82954ac7bda8251a2643fac6836313fb.svg]]
#+begin_src jupyter-python :results none
sys = gf.SystemParams(Ω=stg.OMEGA, η=stg.eta, α_0=α_0)
flow = gf.Flow(sys, α, α_0_dot, n=1)
flow_τ = flow(result.τ)
#+end_src
#+begin_src jupyter-python
plt.plot(result.τ, flow_τ)
#+end_src
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f7aeddf6e80> |
[[file:./.ob-jupyter/9560167a44dc90d2312c88fc8e2d1953c3324e64.svg]]
:END:
* Compare
#+begin_src jupyter-python
fig, ax = ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
ax.plot(result.τ, flow_τ, label="analytic")
ax.legend()
#+end_src
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7f7aee2a4be0>
[[file:./.ob-jupyter/d1252a21ce23059a7d0626a4843582a6197972c8.svg]]
:END:
#+begin_src jupyter-python
fig, ax = ut.plot_diff_vs_sigma(result.τ, full_flow, flow_τ, transform=lambda y: -y)
ax.legend()
#+end_src
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7f36da5b97f0>
[[file:./.ob-jupyter/7b082f4a36aee70924c9078088dbec1a515e36b5.svg]]
:END:
* Cleanup
#+begin_src jupyter-python :results none
result.hd.close()
#+end_src

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# native
import pathlib
import traceback
import pickle
# third party
import numpy as np
from scipy.special import gamma as gamma_func
import qutip
# hops
import hops.core.hierarchyData as hid
import hops.util.bcf as bcf
import hops.util.matrixLib as ml
import hops.util.truncation_schemes as truncation_schemes
import stg_helper
from hops.data.path import get_path as get_hops_data_path
################################################
#
# OVERALL PARAMETERS
#
# may be overwritten in later definitions
#
################################################
max_HO_level = 25
OMEGA = 1
wc = 2
s = 1
__BCF_FIT_n = 5
__T = 1
__HI_eta_type = "fft"
__HI_eta_temperature_type = "ts"
eta = 0.5
p_trunc = 1.5
q_trunc = 0.5
n_eq = None
kfac = 1.4
kmax_list_pre_sqrt = True
__HI_k_max = None
t_max = 16
sp_tol = 1e-4
__HI_seed = 0
__HI_accum_only = False
__HI_number_of_samples = 4582
__BCF_FIT_good_data_path = get_hops_data_path() + "/good_fit_data_abs_brute_force"
with open(__BCF_FIT_good_data_path, "rb") as f:
good_fit_data_abs = pickle.load(f)
try:
_, g_tilde, w_tilde = good_fit_data_abs[(__BCF_FIT_n, s)]
__g = 1 / np.pi * gamma_func(s + 1) * wc ** (s + 1) * np.asarray(g_tilde)
__w = wc * np.asarray(w_tilde)
__HI_truncation_scheme = stg_helper.get_truncation_scheme(
kmax_list_pre_sqrt=kmax_list_pre_sqrt,
g=__g,
w=__w,
q=q_trunc,
p=p_trunc,
kfac=kfac,
)
except KeyError:
raise KeyError(
"no fit data in {} for n={} and s={}".format(
__BCF_FIT_good_data_path, __BCF_FIT_n, s
)
)
__BCF_FIT_gw_hash = None
__temp_method = "stoc_pot"
__bcf_scale = eta
# print("eta", __bcf_scale)
__bcf_zero_temp = bcf.OhmicBCF_zeroTemp(s=s, eta=1, w_c=wc)
__spd_zero_temp = bcf.OhmicSD_zeroTemp(s=s, eta=1, w_c=wc)
if __T == 0:
__bcf = __bcf_zero_temp
__spec_dens = __spd_zero_temp
__stoc_temp_corr = None
__stoc_temp_dens = None
beta = np.inf
__HI_save_therm_y = False
else:
__bcf = bcf.OhmicBCF_nonZeroTemp(s=s, eta=1, w_c=wc, beta=1 / __T)
__spec_dens = bcf.PseudoSD(sd_at_t_zero=__spd_zero_temp, T=__T)
__stoc_temp_corr = bcf.Ohmic_StochasticPotentialCorrelations(
s=s, eta=1, w_c=wc, beta=1 / __T
)
__stoc_temp_dens = bcf.Ohmic_StochasticPotentialDensity(
s=s, eta=1, w_c=wc, beta=1 / __T
)
beta = 1 / __T
__HI_save_therm_y = True
q = qutip.operators.create(max_HO_level) + qutip.operators.destroy(max_HO_level)
p = (qutip.operators.destroy(max_HO_level) - qutip.operators.create(max_HO_level)) / 1j
__L = 0.5 * q
__H_sys = 0.25 * OMEGA * (p ** 2 + q ** 2)
__L = ml.Operator(data=__L, fmt="coo")
__H_sys = ml.Operator(data=__H_sys, fmt="coo")
#################################################
#
# settings for stoc proc
#
#################################################
__SP_t_max = t_max
__SP_db_name = "SPCache"
__SP_db_path = "."
__SP_KLE_tol = sp_tol
__SP_KLE_ng_fac = 4
__SP_KLE_meth = "fourpoint"
# __SP_KLE_meth = 'trapz'
# __SP_KLE_diff_method = 'full'
# __SP_KLE_dm_random_samples = None
__SP_KLE_diff_method = "random"
__SP_KLE_dm_random_samples = 10000
if __temp_method == "stoc_pot":
__SP_FFT_neg_frequ = False
else:
if __T == 0:
__SP_FFT_neg_frequ = False
else:
__SP_FFT_neg_frequ = True
# __SP_FFT_intgr_tol = 5e-3
# __SP_FFT_intpl_tol = 5e-3
__SP_FFT_intgr_tol = sp_tol
__SP_FFT_intpl_tol = sp_tol
################################################
#
# System and Hierarchy parameters
#
################################################
# __g = None
# __w = None
__H_dyn = []
__psi0 = np.array(qutip.states.fock(max_HO_level, n=1).data.todense()).flatten()
# for image: stoch_trj.png
# __INTGR_t_max = 6
# __INTGR_t_steps = 750
__INTGR_t_max = t_max
__INTGR_t_steps = int(t_max / 0.01)
__INTGR_name = "zvode"
__INTGR_atol = "1e-8"
__INTGR_rtol = "1e-8"
__INTGR_order = 5
__INTGR_nsteps = 10_000
__INTGR_method = "bdf"
__HI_g_scale = None
__HI_sample_method = "random"
__HI_nonlinear = True
__HI_normalized = False
__HI_normalized_by_hand = True
__HI_with_terminator = False
# __HI_result_data = hid.RESULT_TYPE_ZEROTH_ORDER_ONLY
__HI_result_data = hid.ResultType.ZEROTH_AND_FIRST_ORDER
#################################################
#
# DB and Server/Client parameters
#
#################################################
__db_name = "SBM"
__db_path = "."
__host = "localhost"
__verbose = 1
__authkey = __db_name
__desc = __db_name
__port = 35254
__nproc = 0
__nice = 19

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