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upgrade 05 to new hops :P and new truncation scheme

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This commit is contained in:
Valentin Boettcher 2022-02-25 21:18:20 +01:00
parent 9491e58363
commit 7c2f5624f4
3 changed files with 174 additions and 12 deletions
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109
python/energy_flow_proper/05_gaussian_two_baths/comparison_with_hops.org
View file

@ -6,10 +6,15 @@ This will be tangled into the [[file:config.py][config file]] that can be used w
from hops.core.hierarchy_parameters import HIParams, HiP, IntP, SysP, ResultType
from stocproc.stocproc import StocProc_TanhSinh
from hops.util.bcf_fits import get_ohm_g_w
from hops.util.truncation_schemes import TruncationScheme_Power_multi
from hops.util.truncation_schemes import (
TruncationScheme_Power_multi,
BathMemory,
)
from hops.util.abstract_truncation_scheme import (
TruncationScheme_Simplex
)
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
@ -35,8 +40,10 @@ This will be tangled into the [[file:config.py][config file]] that can be used w
bcf_terms: int = 4,
t_max: float = 10,
k_fac: float = 1.4,
inf_tol: float = 0.1,
sp_tol: float = 1e-3,
bcf_scale: list[float] = [0.5, 0.5],
truncation_scheme: str = "power",
T: float = 0.3,
):
# The BCF fit
@ -81,7 +88,13 @@ This will be tangled into the [[file:config.py][config file]] that can be used w
result_type=ResultType.ZEROTH_AND_FIRST_ORDER,
truncation_scheme=TruncationScheme_Power_multi.from_g_w(
g=system.g, w=system.w, p=[1, 1], q=[0.5, 0.5], kfac=[k_fac] * 2
),
)
if truncation_scheme == "power"
else BathMemory.from_system(
system, nonlinear=True, influence_tolerance=inf_tol
)
if truncation_scheme == "bath_memory"
else TruncationScheme_Simplex(5),
save_therm_rng_seed=True,
),
Eta=[
@ -191,14 +204,15 @@ This will be tangled into the [[file:config.py][config file]] that can be used w
- better
*** 8 HO Levels
#+begin_src jupyter-python :results none :async no :tangle config.py
params = make_config(
make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
k_fac=1.5,
inf_tol=.2,
sp_tol=1e-6,
bcf_scale=[.3, .3],
T=.2
T=.2,
truncation_scheme="bath_memory"
)
#+end_src
@ -215,6 +229,89 @@ This will be tangled into the [[file:config.py][config file]] that can be used w
logger.setLevel(logging.INFO)
#+end_src
** Convergence Testing
#+begin_src jupyter-python :results none
with_power = make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
k_fac=1.5,
sp_tol=1e-6,
bcf_scale=[.3, .3],
T=.2
)
#+end_src
#+begin_src jupyter-python :results none
with_inftol = make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
inf_tol=.2,
sp_tol=1e-6,
bcf_scale=[.3, .3],
T=.2,
truncation_scheme="bath_memory"
)
#+end_src
#+begin_src jupyter-python :results none
with_simplex = make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
inf_tol=0.2,
sp_tol=1e-6,
bcf_scale=[0.3, 0.3],
T=0.2,
truncation_scheme="simplex",
)
#+end_src
#+begin_src jupyter-python :results none
configs = [with_simplex, with_inftol, with_power]
from hops.core.integration import HOPSSupervisor
supervisors = [HOPSSupervisor(config, number_of_samples=1, data_name="data_conv") for config in configs]
#+end_src
#+begin_src jupyter-python
for sup in supervisors:
sup.integrate_single_process()
#+end_src
#+RESULTS:
:RESULTS:
: INFO:hops.core.integration:Some 1 trajectories have to be integrated.
: 0% 0/1 [00:00<?, ?it/s]INFO:hops.core.integration:Some 1 trajectories have to be integrated.
: 0% 0/1 [00:00<?, ?it/s]INFO:hops.core.integration:Some 1 trajectories have to be integrated.
: 0% 0/1 [00:00<?, ?it/s]
:END:
#+begin_src jupyter-python :results none
trajectories = []
for supervisor in supervisors:
with supervisor.get_data(True) as data:
τ = data.get_time()
trajectories.append(data.get_stoc_traj(0))
#+end_src
#+begin_src jupyter-python :results none
diffs = [np.linalg.norm(trajectories[0] - trajectories[1], axis=1), np.linalg.norm(trajectories[0] - trajectories[2], axis=1)]
#+end_src
#+begin_src jupyter-python
plt.plot(τ, diffs[0])
plt.plot(τ, diffs[1], label="power")
plt.legend()
#+end_src
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7fad74377670>
[[file:./.ob-jupyter/c3d123861a6902621b521c00b1b23db0ec8fe931.svg]]
:END:
- yay, new criterion is way better ;P
** Init Gaussflow
#+begin_src jupyter-python :results none
from hopsflow import gaussflow_two as gf

51
python/energy_flow_proper/05_gaussian_two_baths/comparison_with_hops.py
View file

@ -46,6 +46,57 @@ import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
with_power = make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
k_fac=1.5,
sp_tol=1e-6,
bcf_scale=[.3, .3],
T=.2
)
with_inftol = make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
inf_tol=.2,
sp_tol=1e-6,
bcf_scale=[.3, .3],
T=.2,
truncation_scheme="bath_memory"
)
with_simplex = make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
inf_tol=0.2,
sp_tol=1e-6,
bcf_scale=[0.3, 0.3],
T=0.2,
truncation_scheme="simplex",
)
configs = [with_simplex, with_inftol, with_power]
from hops.core.integration import HOPSSupervisor
supervisors = [HOPSSupervisor(config, number_of_samples=1, data_name="data_conv") for config in configs]
for sup in supervisors:
sup.integrate_single_process()
trajectories = []
for supervisor in supervisors:
with supervisor.get_data(True) as data:
τ = data.get_time()
trajectories.append(data.get_stoc_traj(0))
diffs = [np.linalg.norm(trajectories[0] - trajectories[1], axis=1), np.linalg.norm(trajectories[0] - trajectories[2], axis=1)]
plt.plot(τ, diffs[0])
plt.plot(τ, diffs[1], label="power")
plt.legend()
from hopsflow import gaussflow_two as gf
from hopsflow import util as util

26
python/energy_flow_proper/05_gaussian_two_baths/config.py
View file

@ -1,10 +1,15 @@
from hops.core.hierarchy_parameters import HIParams, HiP, IntP, SysP, ResultType
from stocproc.stocproc import StocProc_TanhSinh
from hops.util.bcf_fits import get_ohm_g_w
from hops.util.truncation_schemes import TruncationScheme_Power_multi
from hops.util.truncation_schemes import (
TruncationScheme_Power_multi,
BathMemory,
)
from hops.util.abstract_truncation_scheme import (
TruncationScheme_Simplex
)
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
@ -30,8 +35,10 @@ def make_config(
bcf_terms: int = 4,
t_max: float = 10,
k_fac: float = 1.4,
inf_tol: float = 0.1,
sp_tol: float = 1e-3,
bcf_scale: list[float] = [0.5, 0.5],
truncation_scheme: str = "power",
T: float = 0.3,
):
# The BCF fit
@ -76,7 +83,13 @@ def make_config(
result_type=ResultType.ZEROTH_AND_FIRST_ORDER,
truncation_scheme=TruncationScheme_Power_multi.from_g_w(
g=system.g, w=system.w, p=[1, 1], q=[0.5, 0.5], kfac=[k_fac] * 2
),
)
if truncation_scheme == "power"
else BathMemory.from_system(
system, nonlinear=True, influence_tolerance=inf_tol
)
if truncation_scheme == "bath_memory"
else TruncationScheme_Simplex(5),
save_therm_rng_seed=True,
),
Eta=[
@ -124,12 +137,13 @@ def make_config(
return params
params = make_config(
make_config(
max_HO_level=8,
bcf_terms=4,
t_max=50,
k_fac=1.5,
inf_tol=.2,
sp_tol=1e-6,
bcf_scale=[.3, .3],
T=.2
T=.2,
truncation_scheme="bath_memory"
)