mirror of
https://github.com/vale981/master-thesis
synced 2025-03-06 10:31:37 -05:00
upgrade 03
This commit is contained in:
parent
dca315c7d9
commit
102b79ee97
6 changed files with 286 additions and 468 deletions
|
|
@ -92,7 +92,7 @@ Now we read the trajectory data.
|
|||
#+begin_src jupyter-python
|
||||
class result:
|
||||
hd = HIMetaData("data", ".").get_HIData(params, read_only=True)
|
||||
N = 2000 #hd.samples
|
||||
N = hd.samples
|
||||
τ = hd.get_time()
|
||||
ψ_1 = hd.aux_states
|
||||
ψ = hd.stoc_traj
|
||||
|
|
|
|||
|
|
@ -1,283 +1,113 @@
|
|||
#+PROPERTY: header-args :session comparison_hops_gauss :kernel python :pandoc yes :async yes
|
||||
* Configuration and Setup
|
||||
The main process configuration is to be found [[file:stg.py][here]].
|
||||
This will be tangled into the [[file:config.py][config file]] that can be used with the HOPS cli.
|
||||
|
||||
** Stochastic Processes
|
||||
We then proceed to initialize the stochastic processes.
|
||||
#+begin_src jupyter-python :results none :tangle config.py
|
||||
from hops.core.hierarchy_parameters import HIParams, HiP, IntP, SysP, ResultType
|
||||
from hops.core.hierarchyLib import HI
|
||||
from hops.util.bcf_fits import get_ohm_g_w
|
||||
from hops.util.truncation_schemes import TruncationScheme_Power_multi
|
||||
import hops.util.bcf
|
||||
import numpy as np
|
||||
import hops.util.matrixLib as ml
|
||||
from stocproc import StocProc_FFT
|
||||
np.__config__.blas_opt_info = np.__config__.blas_ilp64_opt_info # fix for qutip
|
||||
import qutip
|
||||
|
||||
#+begin_src vterm
|
||||
python ../hops/sp.py -s stg.py
|
||||
max_HO_level = 25
|
||||
wc = 2
|
||||
s = 1
|
||||
|
||||
# The BCF fit
|
||||
bcf_terms = 5
|
||||
g, w = get_ohm_g_w(bcf_terms, s, wc)
|
||||
|
||||
integration = IntP(t_max=16, t_steps=int(16 // 0.01))
|
||||
|
||||
q = (qutip.operators.create(max_HO_level) + qutip.operators.destroy(max_HO_level)).data.todense()
|
||||
p = ((qutip.operators.destroy(max_HO_level) - qutip.operators.create(max_HO_level)) / 1j).data.todense()
|
||||
|
||||
system = SysP(
|
||||
H_sys=0.25 * (p ** 2 + q ** 2),
|
||||
L=0.5 * q,
|
||||
psi0=np.array(qutip.states.fock(max_HO_level, n=1).data.todense()).flatten(),
|
||||
g=g,
|
||||
w=w,
|
||||
bcf_scale=0.5,
|
||||
T=0,
|
||||
)
|
||||
|
||||
hops_bcf = hops.util.bcf.OhmicBCF_zeroTemp(
|
||||
s,
|
||||
1,
|
||||
wc,
|
||||
)
|
||||
params = HIParams(
|
||||
SysP=system,
|
||||
IntP=integration,
|
||||
HiP=HiP(
|
||||
nonlinear=True,
|
||||
normalized_by_hand=True,
|
||||
result_type=ResultType.ZEROTH_AND_FIRST_ORDER,
|
||||
truncation_scheme=TruncationScheme_Power_multi.from_g_w(
|
||||
g=g, w=w, p=1, q=0.5, kfac=1.4
|
||||
),
|
||||
save_therm_rng_seed=True,
|
||||
),
|
||||
Eta=StocProc_FFT(
|
||||
spectral_density=hops.util.bcf.OhmicSD_zeroTemp(
|
||||
s,
|
||||
1,
|
||||
wc,
|
||||
),
|
||||
alpha=hops_bcf,
|
||||
t_max=integration.t_max,
|
||||
intgr_tol=1e-4,
|
||||
intpl_tol=1e-4,
|
||||
negative_frequencies=False,
|
||||
),
|
||||
EtaTherm=None,
|
||||
)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
Linux ArLeenUX 5.15.2-zen1 x86_64
|
||||
14:56:17 up 3:23, 2 users, load average: 0.58, 0.95, 1.23
|
||||
impure ~/D/P/U/m/m/p/e/03_gaussian python ../hops/sp.py -s stg.py
|
||||
stocproc.stocproc - INFO - non neg freq only
|
||||
non neg freq only
|
||||
stocproc.method_ft - INFO - get_dt_for_accurate_interpolation, please wait ...
|
||||
get_dt_for_accurate_interpolation, please wait ...
|
||||
stocproc.method_ft - INFO - acc interp N 33 dt 1.56e+00 -> diff 4.52e-01
|
||||
acc interp N 33 dt 1.56e+00 -> diff 4.52e-01
|
||||
stocproc.method_ft - INFO - acc interp N 65 dt 7.81e-01 -> diff 2.19e-01
|
||||
acc interp N 65 dt 7.81e-01 -> diff 2.19e-01
|
||||
stocproc.method_ft - INFO - acc interp N 129 dt 3.91e-01 -> diff 5.71e-02
|
||||
acc interp N 129 dt 3.91e-01 -> diff 5.71e-02
|
||||
stocproc.method_ft - INFO - acc interp N 257 dt 1.95e-01 -> diff 1.17e-02
|
||||
acc interp N 257 dt 1.95e-01 -> diff 1.17e-02
|
||||
stocproc.method_ft - INFO - acc interp N 513 dt 9.77e-02 -> diff 2.69e-03
|
||||
acc interp N 513 dt 9.77e-02 -> diff 2.69e-03
|
||||
stocproc.method_ft - INFO - acc interp N 1025 dt 4.88e-02 -> diff 6.59e-04
|
||||
acc interp N 1025 dt 4.88e-02 -> diff 6.59e-04
|
||||
stocproc.method_ft - INFO - acc interp N 2049 dt 2.44e-02 -> diff 1.64e-04
|
||||
acc interp N 2049 dt 2.44e-02 -> diff 1.64e-04
|
||||
stocproc.method_ft - INFO - acc interp N 4097 dt 1.22e-02 -> diff 4.09e-05
|
||||
acc interp N 4097 dt 1.22e-02 -> diff 4.09e-05
|
||||
stocproc.method_ft - INFO - requires dt < 1.221e-02
|
||||
requires dt < 1.221e-02
|
||||
stocproc.method_ft - INFO - get_N_a_b_for_accurate_fourier_integral, please wait ...
|
||||
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
|
||||
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 8.12e-03
|
||||
J_w_min:1.00e-03 N 32 yields: interval [0.00e+00,9.12e+00] diff 8.12e-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
|
||||
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
|
||||
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
|
||||
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 2.05e-03
|
||||
J_w_min:1.00e-03 N 64 yields: interval [0.00e+00,9.12e+00] diff 2.05e-03
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-02 N 128 yields: interval [0.00e+00,6.47e+00] diff 1.14e-02
|
||||
J_w_min:1.00e-02 N 128 yields: interval [0.00e+00,6.47e+00] diff 1.14e-02
|
||||
stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
|
||||
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+00,1.42e+01] diff 1.94e-02
|
||||
J_w_min:1.00e-05 N 32 yields: interval [0.00e+00,1.42e+01] diff 1.94e-02
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-04 N 64 yields: interval [0.00e+00,1.17e+01] diff 3.35e-03
|
||||
J_w_min:1.00e-04 N 64 yields: interval [0.00e+00,1.17e+01] diff 3.35e-03
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-03 N 128 yields: interval [0.00e+00,9.12e+00] diff 8.98e-04
|
||||
J_w_min:1.00e-03 N 128 yields: interval [0.00e+00,9.12e+00] diff 8.98e-04
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-02 N 256 yields: interval [0.00e+00,6.47e+00] diff 1.15e-02
|
||||
J_w_min:1.00e-02 N 256 yields: interval [0.00e+00,6.47e+00] diff 1.15e-02
|
||||
stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
|
||||
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+00,1.66e+01] diff 2.65e-02
|
||||
J_w_min:1.00e-06 N 32 yields: interval [0.00e+00,1.66e+01] diff 2.65e-02
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-05 N 64 yields: interval [0.00e+00,1.42e+01] diff 4.93e-03
|
||||
J_w_min:1.00e-05 N 64 yields: interval [0.00e+00,1.42e+01] diff 4.93e-03
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-04 N 128 yields: interval [0.00e+00,1.17e+01] diff 5.42e-04
|
||||
J_w_min:1.00e-04 N 128 yields: interval [0.00e+00,1.17e+01] diff 5.42e-04
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-03 N 256 yields: interval [0.00e+00,9.12e+00] diff 1.06e-03
|
||||
J_w_min:1.00e-03 N 256 yields: interval [0.00e+00,9.12e+00] diff 1.06e-03
|
||||
stocproc.method_ft - INFO - increasing N while shrinking the interval does lower the error -> try next level
|
||||
increasing N while shrinking the interval does lower the error -> try next level
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-07 N 32 yields: interval [0.00e+00,1.91e+01] diff 3.44e-02
|
||||
J_w_min:1.00e-07 N 32 yields: interval [0.00e+00,1.91e+01] diff 3.44e-02
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-06 N 64 yields: interval [0.00e+00,1.66e+01] diff 6.78e-03
|
||||
J_w_min:1.00e-06 N 64 yields: interval [0.00e+00,1.66e+01] diff 6.78e-03
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-05 N 128 yields: interval [0.00e+00,1.42e+01] diff 9.98e-04
|
||||
J_w_min:1.00e-05 N 128 yields: interval [0.00e+00,1.42e+01] diff 9.98e-04
|
||||
stocproc.method_ft - INFO - J_w_min:1.00e-04 N 256 yields: interval [0.00e+00,1.17e+01] diff 9.67e-05
|
||||
J_w_min:1.00e-04 N 256 yields: interval [0.00e+00,1.17e+01] diff 9.67e-05
|
||||
stocproc.method_ft - INFO - return, cause tol of 0.0001 was reached
|
||||
return, cause tol of 0.0001 was reached
|
||||
stocproc.method_ft - INFO - requires dx < 4.557e-02
|
||||
requires dx < 4.557e-02
|
||||
stocproc.stocproc - INFO - Fourier Integral Boundaries: [0.000e+00, 6.199e+02]
|
||||
Fourier Integral Boundaries: [0.000e+00, 6.199e+02]
|
||||
stocproc.stocproc - INFO - Number of Nodes : 16384
|
||||
Number of Nodes : 16384
|
||||
stocproc.stocproc - INFO - yields dx : 3.784e-02
|
||||
yields dx : 3.784e-02
|
||||
stocproc.stocproc - INFO - yields dt : 1.013e-02
|
||||
yields dt : 1.013e-02
|
||||
stocproc.stocproc - INFO - yields t_max : 1.660e+02
|
||||
yields t_max : 1.660e+02
|
||||
added StocProc to database 'SPCache' at '.'
|
||||
:END:
|
||||
|
||||
The stochastic process is initialized and cached in ~./SPCache~.
|
||||
|
||||
* Hops Integration
|
||||
We can use multiple avenues.
|
||||
|
||||
** Local Integration
|
||||
#+begin_src vterm :term-name integration
|
||||
python ../hops/hi.py -s stg.py
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
Linux ArLeenUX 5.15.2-zen1 x86_64
|
||||
17:39:20 up 6:06, 2 users, load average: 0.19, 0.88, 1.71
|
||||
impure ~/D/P/U/m/m/p/e/03_gaussian python ../hops/hi.py -s stg.py
|
||||
run integrate
|
||||
init Hi class, use 3550 equation
|
||||
is up event is False
|
||||
wait ...
|
||||
[in server process] add args to server ...
|
||||
^C
|
||||
############## in JM SERVER EXIT
|
||||
|
||||
|
||||
HI_Server start at 2021-11-26 17:39:24.291498 | runtime 6.000e+00s
|
||||
HI_Server total number of jobs : 289457
|
||||
| processed : 0
|
||||
| succeeded : 0
|
||||
| failed : 0
|
||||
| not processed : 289457
|
||||
| queried : 0
|
||||
| not queried yet : 289457
|
||||
Traceback (most recent call last):
|
||||
File "/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/03_gaussian/../hops/hi.py", line 828, in
|
||||
<module>
|
||||
hi.integrate(
|
||||
File "/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/core/hierarchyLib.py", line 147
|
||||
2, in integrate
|
||||
mp_is_up_event.wait()
|
||||
File "/nix/store/cy49x6xnmzf4b7q8la495ic1lp91awq9-python3-3.9.4/lib/python3.9/multiprocessing/synchronize.py", line 349, in
|
||||
wait
|
||||
self._cond.wait(timeout)
|
||||
File "/nix/store/cy49x6xnmzf4b7q8la495ic1lp91awq9-python3-3.9.4/lib/python3.9/multiprocessing/synchronize.py", line 261, in
|
||||
wait
|
||||
return self._wait_semaphore.acquire(True, timeout)
|
||||
KeyboardInterrupt
|
||||
Process Process-1:
|
||||
Traceback (most recent call last):
|
||||
File "/nix/store/cy49x6xnmzf4b7q8la495ic1lp91awq9-python3-3.9.4/lib/python3.9/multiprocessing/process.py", line 315, in _boo
|
||||
tstrap
|
||||
self.run()
|
||||
File "/nix/store/cy49x6xnmzf4b7q8la495ic1lp91awq9-python3-3.9.4/lib/python3.9/multiprocessing/process.py", line 108, in run
|
||||
self._target(*self._args, **self._kwargs)
|
||||
File "/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/core/hierarchyLib.py", line 142
|
||||
4, in run_server
|
||||
hi_integrator_server.put_arg(arg)
|
||||
File "/nix/store/nv44kxf8ljllk4lad7pwlrjiwl0yfrwn-python3-3.9.4-env/lib/python3.9/site-packages/jobmanager/jobmanager.py", l
|
||||
ine 1763, in put_arg
|
||||
self.job_q.put(copy.copy(a))
|
||||
File "/nix/store/nv44kxf8ljllk4lad7pwlrjiwl0yfrwn-python3-3.9.4-env/lib/python3.9/site-packages/jobmanager/jobmanager.py", l
|
||||
ine 1289, in put
|
||||
self.data[str_id] = item
|
||||
KeyboardInterrupt
|
||||
! impure ~/D/P/U/m/m/p/e/03_gaussian exti 10.6s
|
||||
exti: command not found
|
||||
:END:
|
||||
|
||||
And there we go. It is better to run the above command in a
|
||||
vterm-session.
|
||||
|
||||
** Remote/Distributed Integration
|
||||
We start the server locally.
|
||||
#+begin_src vterm :term-name local-server
|
||||
python ../hops/hi.py -s stg.py server
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
Linux ArLeenUX 5.15.2-zen1 x86_64
|
||||
17:24:00 up 5:51, 2 users, load average: 0.54, 0.86, 1.60
|
||||
impure ~/D/P/U/m/m/p/e/03_gaussian python ../hops/hi.py -s stg.py server
|
||||
run server
|
||||
init Hi class, use 1420 equation
|
||||
JobManager started on ArLeenUX:35254 (bytearray(b'SBM'))
|
||||
hi server is running
|
||||
[T[TET 00:00:18 [0.0c/s] TTG -- 0.0% ETA -- ORT -
|
||||
[TET-00:06:37--[16.9c/min]-TTG-0.00ms-------------------------100%-------------------------ETA-20211126_17:30:41-ORT-00:06:37]
|
||||
res_q #0 20.52GB/s 5.31TB|rem.:0, done:100, failed:0, prog.:0
|
||||
|
||||
############## in JM SERVER EXIT
|
||||
|
||||
|
||||
HI_Server start at 2021-11-26 17:24:03.735185 | runtime 3.990e+02s
|
||||
HI_Server total number of jobs : 100
|
||||
| processed : 100
|
||||
| succeeded : 100
|
||||
| failed : 0
|
||||
| timing in sec: min 2.121e+01 | max 5.025e+01 | avr 4.283e+01
|
||||
| not processed : 0
|
||||
| queried : 0
|
||||
| not queried yet : 0
|
||||
:END:
|
||||
|
||||
And jack in with a remote client. In this case my box at home.
|
||||
|
||||
** Local Client
|
||||
#+begin_src vterm :term-name local-client
|
||||
python ../hops/hi.py -s stg.py client
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
Linux ArLeenUX 5.15.2-zen1 x86_64
|
||||
17:24:25 up 5:51, 2 users, load average: 0.86, 0.92, 1.60
|
||||
impure ~/D/P/U/m/m/p/e/03_gaussian python ../hops/hi.py -s stg.py client
|
||||
run client
|
||||
timeout from args None
|
||||
start client on host ArLeenUX
|
||||
/nix/store/nv44kxf8ljllk4lad7pwlrjiwl0yfrwn-python3-3.9.4-env/lib/python3.9/site-packages/jobmanager/jobmanager.py:130: UserWa
|
||||
rning: num_threads could not be set, MKL / openblas not found
|
||||
warnings.warn("num_threads could not be set, MKL / openblas not found")
|
||||
/nix/store/nv44kxf8ljllk4lad7pwlrjiwl0yfrwn-python3-3.9.4-env/lib/python3.9/site-packages/jobmanager/jobmanager.py:130: UserWa
|
||||
rning: num_threads could not be set, MKL / openblas not found
|
||||
warnings.warn("num_threads could not be set, MKL / openblas not found")
|
||||
/nix/store/nv44kxf8ljllk4lad7pwlrjiwl0yfrwn-python3-3.9.4-env/lib/python3.9/site-packages/jobmanager/jobmanager.py:130: UserWa
|
||||
rning: num_threads could not be set, MKL / openblas not found
|
||||
warnings.warn("num_threads could not be set, MKL / openblas not found")
|
||||
/nix/store/nv44kxf8ljllk4lad7pwlrjiwl0yfrwn-python3-3.9.4-env/lib/python3.9/site-packages/jobmanager/jobmanager.py:130: UserWa
|
||||
rning: num_threads could not be set, MKL / openblas not found
|
||||
warnings.warn("num_threads could not be set, MKL / openblas not found")
|
||||
w1:00:06:00 [1.5c/min] #9 - [TET 00:00:17 [0.0c/s] TTG -- 0.0% ETA -- ORT --]
|
||||
w2:00:06:00 [1.6c/min] #10 - [TET 5.72s [0.0c/s] TTG -- 0.0% ETA -- ORT --]
|
||||
w3:00:06:00 [1.5c/min] #9 - [TET 00:00:19 [0.0c/s] TTG -- 0.0% ETA -- ORT --]
|
||||
w4:00:06:00 [1.6c/min] #10 - [TET 5.75s [0.0c/s] TTG -- 0.0% ETA -- ORT --]
|
||||
local res_q 0 236.4GB/s
|
||||
:END:
|
||||
|
||||
Churn in with our local machine :).
|
||||
|
||||
* Heat Flow HOPS
|
||||
** Jupyter Setup
|
||||
#+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
|
||||
from hops.core.hierarchyData import HIMetaData
|
||||
#+end_src
|
||||
|
||||
** Loading the Data
|
||||
#+begin_src jupyter-python :results none
|
||||
system_params = stg_helper.get_system_param(stg)
|
||||
#+end_src
|
||||
|
||||
Now we can load the data.
|
||||
#+begin_src jupyter-python :results none
|
||||
#+begin_src jupyter-python
|
||||
class result:
|
||||
hd = stg_helper.get_hierarchy_data(stg, read_only=True)
|
||||
N = hd.samples[0]
|
||||
hd = HIMetaData("data", ".").get_HIData(params, read_only=True)
|
||||
N = hd.samples
|
||||
τ = hd.get_time()
|
||||
# ρ = hd.get_rho_t()
|
||||
ψ_1 = hd.aux_states
|
||||
ψ = hd.stoc_traj
|
||||
#therm_y = hd.therm_y
|
||||
|
||||
result.N
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: 10
|
||||
|
||||
We calculate the sytem energy just for fun.
|
||||
#+begin_src jupyter-python
|
||||
_, e_sys, σ_e_sys = util.operator_expectation_ensemble(
|
||||
iter(result.ψ),
|
||||
system_params.H_sys.todense(),
|
||||
system.H_sys,
|
||||
result.N,
|
||||
stg.__HI_nonlinear,
|
||||
params.HiP.nonlinear,
|
||||
)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: 100%|██████████| 999/999 [00:23<00:00, 41.67it/s]
|
||||
: 100% 9/9 [00:00<00:00, 124.55it/s]
|
||||
|
||||
|
||||
#+begin_src jupyter-python
|
||||
|
|
@ -287,26 +117,26 @@ We calculate the sytem energy just for fun.
|
|||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: <ErrorbarContainer object of 3 artists>
|
||||
[[file:./.ob-jupyter/e5a69bd7754bca0aa1af09eb146b618116a6c869.svg]]
|
||||
[[file:./.ob-jupyter/32a477f6ab175e22012ea4278d072c264fee212b.svg]]
|
||||
:END:
|
||||
|
||||
** Heat Flow
|
||||
First we set up some parameter objects for the alogrithm.
|
||||
#+begin_src jupyter-python :results none
|
||||
hf_system = hopsflow.SystemParams(
|
||||
system_params.L.todense(), stg.__g, stg.__w, stg.__bcf_scale, stg.__HI_nonlinear
|
||||
system.L, system.g, system.w, system.bcf_scale, params.HiP.nonlinear
|
||||
)
|
||||
#+end_src
|
||||
|
||||
And then we calculate the flow.
|
||||
#+begin_src jupyter-python
|
||||
full_flow = hopsflow.heat_flow_ensemble(
|
||||
iter(result.ψ), iter(result.ψ_1), hf_system, result.N, every=int(result.N / 6)
|
||||
iter(result.ψ), iter(result.ψ_1), hf_system, result.N, every=int(result.N // 6)
|
||||
)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: 100%|██████████| 999/999 [01:19<00:00, 12.63it/s]
|
||||
: 100% 9/9 [00:00<00:00, 36.82it/s]
|
||||
|
||||
#+begin_src jupyter-python
|
||||
with ut.hiro_style():
|
||||
|
|
@ -314,7 +144,7 @@ And then we calculate the flow.
|
|||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
[[file:./.ob-jupyter/4ba72b691569b11043e33f3d8e6bcf1e6b6a035d.svg]]
|
||||
[[file:./.ob-jupyter/32304a750ffed797cfa8c65c28cafe14a7053074.svg]]
|
||||
|
||||
** Analytic
|
||||
#+begin_src jupyter-python :results none
|
||||
|
|
@ -324,15 +154,15 @@ And then we calculate the flow.
|
|||
Setting up the BCFs.
|
||||
#+begin_src jupyter-python :results none
|
||||
α_0 = gf.BCF(
|
||||
stg.t_max,
|
||||
stg.__bcf_zero_temp,
|
||||
params.IntP.t_max,
|
||||
hops_bcf,
|
||||
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,
|
||||
params.IntP.t_max,
|
||||
lambda t: 2 / (1j * np.pi) * (wc / (1 + 1j * wc * t)) ** 3,
|
||||
num_terms=5,
|
||||
resolution=0.001,
|
||||
)
|
||||
|
|
@ -340,7 +170,7 @@ 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_0=α_0)
|
||||
sys = gf.SystemParams(Ω=1, η=system.bcf_scale, α_0=α_0)
|
||||
#+end_src
|
||||
|
||||
Now we can define the flow:
|
||||
|
|
@ -358,8 +188,8 @@ And plot it against the numerically obtained flow.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: <matplotlib.legend.Legend at 0x7ffaf9d490d0>
|
||||
[[file:./.ob-jupyter/c50861ae1d5935f9ccefaedcfb65294291d1a9f0.svg]]
|
||||
: <matplotlib.legend.Legend at 0x7fcae05550a0>
|
||||
[[file:./.ob-jupyter/495974be9b87f35c9d2005aa7bf4996b23653198.svg]]
|
||||
:END:
|
||||
|
||||
#+begin_src jupyter-python
|
||||
|
|
@ -373,6 +203,6 @@ And plot it against the numerically obtained flow.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
: <matplotlib.legend.Legend at 0x7ffae03e6100>
|
||||
[[file:./.ob-jupyter/9b75b8cedc82092c5fbe9bb22a72a6265296e455.svg]]
|
||||
: <matplotlib.legend.Legend at 0x7fcae18f7bb0>
|
||||
[[file:./.ob-jupyter/19aa40be77dc83e8bc59155e0bfc391e07a0a0f0.svg]]
|
||||
:END:
|
||||
|
|
|
|||
65
python/energy_flow_proper/03_gaussian/config.py
Normal file
65
python/energy_flow_proper/03_gaussian/config.py
Normal file
|
|
@ -0,0 +1,65 @@
|
|||
from hops.core.hierarchy_parameters import HIParams, HiP, IntP, SysP, ResultType
|
||||
from hops.core.hierarchyLib import HI
|
||||
from hops.util.bcf_fits import get_ohm_g_w
|
||||
from hops.util.truncation_schemes import TruncationScheme_Power_multi
|
||||
import hops.util.bcf
|
||||
import numpy as np
|
||||
import hops.util.matrixLib as ml
|
||||
from stocproc import StocProc_FFT
|
||||
np.__config__.blas_opt_info = np.__config__.blas_ilp64_opt_info # fix for qutip
|
||||
import qutip
|
||||
|
||||
max_HO_level = 25
|
||||
wc = 2
|
||||
s = 1
|
||||
|
||||
# The BCF fit
|
||||
bcf_terms = 5
|
||||
g, w = get_ohm_g_w(bcf_terms, s, wc)
|
||||
|
||||
integration = IntP(t_max=16, t_steps=int(16 // 0.01))
|
||||
|
||||
q = (qutip.operators.create(max_HO_level) + qutip.operators.destroy(max_HO_level)).data.todense()
|
||||
p = ((qutip.operators.destroy(max_HO_level) - qutip.operators.create(max_HO_level)) / 1j).data.todense()
|
||||
|
||||
system = SysP(
|
||||
H_sys=0.25 * (p ** 2 + q ** 2),
|
||||
L=0.5 * q,
|
||||
psi0=np.array(qutip.states.fock(max_HO_level, n=1).data.todense()).flatten(),
|
||||
g=g,
|
||||
w=w,
|
||||
bcf_scale=0.5,
|
||||
T=0,
|
||||
)
|
||||
|
||||
hops_bcf = hops.util.bcf.OhmicBCF_zeroTemp(
|
||||
s,
|
||||
1,
|
||||
wc,
|
||||
)
|
||||
params = HIParams(
|
||||
SysP=system,
|
||||
IntP=integration,
|
||||
HiP=HiP(
|
||||
nonlinear=True,
|
||||
normalized_by_hand=True,
|
||||
result_type=ResultType.ZEROTH_AND_FIRST_ORDER,
|
||||
truncation_scheme=TruncationScheme_Power_multi.from_g_w(
|
||||
g=g, w=w, p=1, q=0.5, kfac=1.4
|
||||
),
|
||||
save_therm_rng_seed=True,
|
||||
),
|
||||
Eta=StocProc_FFT(
|
||||
spectral_density=hops.util.bcf.OhmicSD_zeroTemp(
|
||||
s,
|
||||
1,
|
||||
wc,
|
||||
),
|
||||
alpha=hops_bcf,
|
||||
t_max=integration.t_max,
|
||||
intgr_tol=1e-4,
|
||||
intpl_tol=1e-4,
|
||||
negative_frequencies=False,
|
||||
),
|
||||
EtaTherm=None,
|
||||
)
|
||||
126
python/energy_flow_proper/03_gaussian/flake.lock
generated
Normal file
126
python/energy_flow_proper/03_gaussian/flake.lock
generated
Normal file
|
|
@ -0,0 +1,126 @@
|
|||
{
|
||||
"nodes": {
|
||||
"flake-utils": {
|
||||
"locked": {
|
||||
"lastModified": 1638122382,
|
||||
"narHash": "sha256-sQzZzAbvKEqN9s0bzWuYmRaA03v40gaJ4+iL1LXjaeI=",
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"rev": "74f7e4319258e287b0f9cb95426c9853b282730b",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"type": "github"
|
||||
}
|
||||
},
|
||||
"flake-utils_2": {
|
||||
"locked": {
|
||||
"lastModified": 1610051610,
|
||||
"narHash": "sha256-U9rPz/usA1/Aohhk7Cmc2gBrEEKRzcW4nwPWMPwja4Y=",
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"rev": "3982c9903e93927c2164caa727cd3f6a0e6d14cc",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"type": "github"
|
||||
}
|
||||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1642104392,
|
||||
"narHash": "sha256-m71b7MgMh9FDv4MnI5sg9MiBVW6DhE1zq+d/KlLWSC8=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "5aaed40d22f0d9376330b6fa413223435ad6fee5",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"id": "nixpkgs",
|
||||
"ref": "nixos-unstable",
|
||||
"type": "indirect"
|
||||
}
|
||||
},
|
||||
"nixpkgs_2": {
|
||||
"locked": {
|
||||
"lastModified": 1642104392,
|
||||
"narHash": "sha256-m71b7MgMh9FDv4MnI5sg9MiBVW6DhE1zq+d/KlLWSC8=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "5aaed40d22f0d9376330b6fa413223435ad6fee5",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"id": "nixpkgs",
|
||||
"ref": "nixos-unstable",
|
||||
"type": "indirect"
|
||||
}
|
||||
},
|
||||
"nixpkgs_3": {
|
||||
"locked": {
|
||||
"lastModified": 1610729867,
|
||||
"narHash": "sha256-bk/SBaBLqZX/PEqal27DMQwAHHl0dcZMp8NNksQr80s=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "04af07c659c6723a2259bb6bc00a47ec53330f20",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"type": "github"
|
||||
}
|
||||
},
|
||||
"poetry2nix": {
|
||||
"inputs": {
|
||||
"flake-utils": "flake-utils_2",
|
||||
"nixpkgs": "nixpkgs_3"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1641997288,
|
||||
"narHash": "sha256-l/oldhMwpIhKbsHrm0Uzl+UbtS0p/2uL4fjV0KMw980=",
|
||||
"owner": "nix-community",
|
||||
"repo": "poetry2nix",
|
||||
"rev": "cf1547ba2b009e20d92d5aab0691c5286408e2bb",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "nix-community",
|
||||
"repo": "poetry2nix",
|
||||
"type": "github"
|
||||
}
|
||||
},
|
||||
"root": {
|
||||
"inputs": {
|
||||
"nixpkgs": "nixpkgs",
|
||||
"utils": "utils"
|
||||
}
|
||||
},
|
||||
"utils": {
|
||||
"inputs": {
|
||||
"flake-utils": "flake-utils",
|
||||
"nixpkgs": "nixpkgs_2",
|
||||
"poetry2nix": "poetry2nix"
|
||||
},
|
||||
"locked": {
|
||||
"lastModified": 1642512830,
|
||||
"narHash": "sha256-73733rniMobC1gGsqMjvEgEYkKtX7VfQj5eafWGx9dQ=",
|
||||
"owner": "vale981",
|
||||
"repo": "hiro-flake-utils",
|
||||
"rev": "b8cca753d3cc543b7ecb1313b136cc95582fecb1",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "vale981",
|
||||
"repo": "hiro-flake-utils",
|
||||
"type": "github"
|
||||
}
|
||||
}
|
||||
},
|
||||
"root": "root",
|
||||
"version": 7
|
||||
}
|
||||
|
|
@ -324,7 +324,7 @@ develop = false
|
|||
|
||||
[package.dependencies]
|
||||
lmfit = "^1.0.3"
|
||||
numpy = "^1.21.4"
|
||||
numpy = "^1.20.0"
|
||||
scipy = "^1.7.3"
|
||||
sqlitedict = "^1.7.0"
|
||||
stocproc = {git = "https://github.com/vale981/stocproc", branch = "master"}
|
||||
|
|
@ -334,7 +334,7 @@ tqdm = "^4.62.3"
|
|||
type = "git"
|
||||
url = "https://github.com/vale981/hopsflow"
|
||||
reference = "main"
|
||||
resolved_reference = "37f384a0ff37c8a05e82ff4289c61268c24591e1"
|
||||
resolved_reference = "9e33c9ca5085cba3f082ca37f0847db4af741ffa"
|
||||
|
||||
[[package]]
|
||||
name = "ipykernel"
|
||||
|
|
|
|||
|
|
@ -1,203 +0,0 @@
|
|||
# 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 = 0
|
||||
|
||||
__HI_eta_type = "fft"
|
||||
__HI_eta_temperature_type = "fft"
|
||||
|
||||
eta = .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 = 1000
|
||||
|
||||
|
||||
__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
|
||||
Loading…
Add table
Reference in a new issue