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update finite temperature
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#+PROPERTY: header-args :session finite_temp_new :kernel python :pandoc yes :async yes
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* Configuration and Setup
The main process configuration is to be found [[file:stg.py][here]].
** Stochastic Processes
We then proceed to initialize the stochastic processes.
#+begin_src vterm
python ../hops/sp.py -s stg.py
#+end_src
#+RESULTS:
:RESULTS:
Linux ArLeenUX 5.14.15-zen1 x86_64
19:19:41 up 7:45, 2 users, load average: 0.63, 1.00, 1.18
impure  ~/D/P/U/m/m/p/e/02_finite_temperature  python ../hops/sp.py -s stg.py
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:185: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:228: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
StocProc found in database 'SPCache' at '.'
StocProc found in 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 5.14.15-zen1 x86_64
16:47:19 up 5:13, 2 users, load average: 0.82, 0.96, 0.92
impure  ~/D/P/U/m/m/p/e/02_finite_temperature  python ../hops/hi.py -s stg.py
run integrate
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:185: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:228: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
init Hi class, use 464 equation
is up event is False
wait ...
[in server process] add args to server ...
[in server process] befor bring him up
JobManager started on ArLeenUX:26524 (bytearray(b'HOPS26524'))
hi server is up
[in server process] set is_up
is up event is now True
[TET 9.61ms [0.0c/s] TTG -- 0.0% ETA -- ORT --]
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
anager/jobmanager.py:130: UserWarning: num_threads could not be set, MKL / openblas not found
warnings.warn("num_threads could not be set, MKL / openblas not found")
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
anager/jobmanager.py:130: UserWarning: num_threads could not be set, MKL / openblas not found
warnings.warn("num_threads could not be set, MKL / openblas not found")
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
anager/jobmanager.py:130: UserWarning: num_threads could not be set, MKL / openblas not found
warnings.warn("num_threads could not be set, MKL / openblas not found")
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
[TET 1.01s [0.0c/s] TTG -- 0.0% ETA -- ORT --]
res_q #0 0/s 0kB|rem.:1, done:0, failed:0, prog.:0
w1:3.02s [27.7c/min] #1 - [TET 848.21ms [0.0c/s] TTG -- 0.0% ETA -- ORT --]
w2:3.02s [0.0c/s] #0 - 3.03s [0.0c/s] #0
w3:3.02s [0.0c/s] #0 - 3.03s [0.0c/s] #0
w4:3.02s [0.0c/s] #0 - 3.03s [0.0c/s] #0
local res_q 0 365.1GB/s
,********************************ms------------100%------------ETA-20211111_16:47:25-ORT-3.01s]
,********************************ne:1, failed:0, prog.:0
,** the client has finished early! stop the server
,********************************
,********************************
[TET-4.02s--[19.9c/min]-TTG-0.00ms------------100%------------ETA-20211111_16:47:26-ORT-4.02s]
res_q #0 0kB/s 7.18GB|rem.:0, done:1, failed:0, prog.:0
[in server process] server has joined!
############## in JM SERVER EXIT
HI_Server start at 2021-11-11 16:47:22.000943 | runtime 6.000e+00s
HI_Server total number of jobs : 1
| processed : 1
| succeeded : 1
| failed : 0
| timing in sec: min 2.028e+00 | max 2.028e+00 | avr 2.028e+00
| not processed : 0
| queried : 0
| not queried yet : 0
,* has joined
server process is not running anymore (exit with 0)
: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 5.14.15-zen1 x86_64
19:28:43 up 7:54, 2 users, load average: 0.43, 0.70, 0.96
impure  ~/D/P/U/m/m/p/e/02_finite_temperature  python ../hops/hi.py -s stg.py server
run server
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:185: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:228: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
init Hi class, use 464 equation
JobManager started on ArLeenUX:35254 (bytearray(b'SBM'))
hi server is running
[TET-00:03:39-----[2.2c/s]-TTG-0.00ms---------100%---------ETA-20211111_19:32:25-ORT-00:03:39]
res_q #0 15.09GB/s 3.183TB|rem.:0, done:452, failed:0, prog.:0
############## in JM SERVER EXIT
HI_Server start at 2021-11-11 19:28:46.024888 | runtime 2.210e+02s
HI_Server total number of jobs : 452
| processed : 452
| succeeded : 452
| failed : 0
| timing in sec: min 4.039e+00 | max 9.583e+00 | avr 5.470e+00
| 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 5.14.15-zen1 x86_64
19:29:04 up 7:54, 2 users, load average: 0.46, 0.69, 0.95
impure  ~/D/P/U/m/m/p/e/02_finite_temperature  python ../hops/hi.py -s stg.py client
run client
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:185: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
/home/hiro/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/hops/hops/util/bc
f.py:228: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a be
tter performance consider the 'OhmEnv' package
warnings.warn(
timeout from args None
start client on host ArLeenUX
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
anager/jobmanager.py:130: UserWarning: num_threads could not be set, MKL / openblas not found
warnings.warn("num_threads could not be set, MKL / openblas not found")
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
anager/jobmanager.py:130: UserWarning: num_threads could not be set, MKL / openblas not found
warnings.warn("num_threads could not be set, MKL / openblas not found")
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
anager/jobmanager.py:130: UserWarning: num_threads could not be set, MKL / openblas not found
warnings.warn("num_threads could not be set, MKL / openblas not found")
/nix/store/00v56a021xv35zkdhr91dd4rbccpp2x0-python3-3.9.4-env/lib/python3.9/site-packages/jobm
anager/jobmanager.py:130: UserWarning: num_threads could not be set, MKL / openblas not found
warnings.warn("num_threads could not be set, MKL / openblas not found")
w1:00:03:30 [9.9c/min] #39 - [TET 00:00:12 [0.0c/s] TTG -- 0.0% ETA -- ORT --]
w2:00:03:30 [9.7c/min] #38 - [TET 00:00:15 [0.0c/s] TTG -- 0.0% ETA -- ORT --]
w3:00:03:30 [9.9c/min] #39 - [TET 00:00:13 [0.0c/s] TTG -- 0.0% ETA -- ORT --]
w4:00:03:30 [9.9c/min] #38 - [TET 00:00:16 [0.0c/s] TTG -- 0.0% ETA -- ORT --]
local res_q 0 319.4GB/s
:END:
Churn in with our local machine :).
* Using the Data
** Jupyter Setup
#+begin_src jupyter-python
import numpy as np
import matplotlib.pyplot as plt
#+end_src
#+RESULTS:
** Check the Freshness of the Data
This doesn't check for modifications in this file though!
#+begin_src jupyter-python
from deps import deps
deps.report()
#+end_src
#+RESULTS:
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: Is fresh: True
: Overall Hash: a94d2f6f1da1923ebad6d66b736e2cd3207b3b0b
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~True~ means that no important code has changed. In this case it even
checks if we have all the samples.
** Load the Data
Stghelper seems to be what we want.
#+begin_src jupyter-python
import stg_helper
import stg
import hopsflow
from hopsflow import util
#+end_src
#+RESULTS:
Now let's load the system parameters.
#+begin_src jupyter-python
system_params = stg_helper.get_system_param(stg)
system_params
#+end_src
#+RESULTS:
#+begin_example
H_dynamic : []
H_sys : Operator with format 'coo' and shape(2, 2)
(0, 0) -0.5
(1, 1) 0.5
L : Operator with format 'coo' and shape(2, 2)
(0, 1) 0.5
(1, 0) 0.5
bcf_scale : 0.8
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g : [-0.02721057-0.00954633j -0.2649888 -0.24446953j -0.42474792-0.17263986j
-0.34393704-1.88365973j -1.05844961+3.42100634j 3.63543978-3.87070472j
6.44166867+2.75995815j]
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psi0 : [0 1]
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w : [ 0.2139392 +2.65203434e-02j 0.9575499 +2.23185841e-01j
22.20034528+3.21863905e+01j 2.63533411+9.82383126e-01j
15.64050609+1.63103642e+01j 5.60430249+3.07507006e+00j
9.99803518+7.65897515e+00j]
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--- extra info ---
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T : 0.1
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T_method : stoc_pot
gw_info : None
len_gw : None
#+end_example
Now we read the trajectory data.
#+begin_src jupyter-python
class result:
hd = stg_helper.get_hierarchy_data(stg, read_only=True)
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N = 5000
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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
#+RESULTS:
** Calculate System Energy
Simple sanity check.
#+begin_src jupyter-python
e_sys = util.operator_expectation_ensemble(
iter(result.ψ), system_params.H_sys.todense(), result.N, stg.__HI_nonlinear
)
plt.plot(result.τ, e_sys)
#+end_src
#+RESULTS:
:RESULTS:
update finite temperature
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: /nix/store/k62y7zypgg51r34lyk8r0jzyrvnhkcc9-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 | 0x7fbe92a1f160> |
[[file:./.ob-jupyter/c0dc239491ac0a8c63db7dd6d912a22523ebfcf5.svg]]
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:END:
** Calculate the Heat Flow
Now let's calculate the heatflow. In this simple case it is engouh to
know the first hierarchy states.
First we set up some parameter objects for the alogrithm.
#+begin_src jupyter-python
hf_system = hopsflow.SystemParams(
system_params.L.todense(), stg.__g, stg.__w, stg.__bcf_scale, stg.__HI_nonlinear
)
η = stg_helper.get_eta(stg)
ξ = stg_helper.get_eta_therm(stg)
ξ.calc_deriv = True
hf_therm = hopsflow.ThermalParams(ξ=ξ, τ=result.τ, num_deriv=False)
#+end_src
#+RESULTS:
Now we can apply our tooling to one trajectory for testing.
#+begin_src jupyter-python
hf_sample_run = hopsflow.HOPSRun(result.ψ[0], result.ψ_1[0], hf_system)
hf_sample_run_therm = hopsflow.ThermalRunParams(hf_therm, result.therm_y[0])
first_flow = hopsflow.flow_trajectory_coupling(hf_sample_run, hf_system)
first_flow_therm = hopsflow.flow_trajectory_therm(hf_sample_run, hf_sample_run_therm)
plt.plot(result.τ, first_flow)
plt.plot(result.τ, first_flow_therm)
#+end_src
#+RESULTS:
:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7fbe92932a60> |
[[file:./.ob-jupyter/d3704acd8907cce8fff23234fd2e73729593caf3.svg]]
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:END:
And now for all trajectories.
#+begin_src jupyter-python
full_flow = hopsflow.heat_flow_ensemble(
iter(result.ψ), iter(result.ψ_1), hf_system, result.N, (iter(result.therm_y), hf_therm)
)
#+end_src
#+RESULTS:
#+begin_src jupyter-python
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plt.plot(result.τ, full_flow)
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#+end_src
#+RESULTS:
:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7fbe922a4400> |
[[file:./.ob-jupyter/aa3d7f22017d994caa199611df2e5e2f27ee8bec.svg]]
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:END:
We can integrate the energy change in the bath:
#+begin_src jupyter-python
e_bath = util.integrate_array(-full_flow, result.τ)
plt.plot(result.τ, e_bath)
#+end_src
#+RESULTS:
:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7fbe92484820> |
[[file:./.ob-jupyter/8f7fb585f526f7676f76c37a5aef0244b4d3ca88.svg]]
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:END:
** Calculate the Interaction Energy
First we calculate it from energy conservation.
#+begin_src jupyter-python
e_int = 1/2 - e_sys - e_bath
plt.plot(result.τ, e_int)
#+end_src
#+RESULTS:
:RESULTS:
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: /nix/store/k62y7zypgg51r34lyk8r0jzyrvnhkcc9-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 | 0x7fbe921de190> |
[[file:./.ob-jupyter/e53ffa1f94610fafd5f3ff2ab3f4d85d95619077.svg]]
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:END:
And then from first principles:
#+begin_src jupyter-python
e_int_ex = hopsflow.interaction_energy_ensemble(
result.ψ, result.ψ_1, hf_system, result.N, (result.therm_y, hf_therm)
)
#+end_src
#+RESULTS:
And both together:
#+begin_src jupyter-python
plt.plot(result.τ, e_int, label="integrated")
plt.plot(result.τ, e_int_ex, label="exact")
plt.legend()
#+end_src
#+RESULTS:
:RESULTS:
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: /nix/store/k62y7zypgg51r34lyk8r0jzyrvnhkcc9-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.legend.Legend at 0x7fbe9214d100>
[[file:./.ob-jupyter/63a6155c1d7b836db3f678b89cadd9d0bf90d92b.svg]]
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:END:
Seems to work :P.
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* Update Dependency Hash
When we're done we update the dependency hash. This helps us to check
if we have to recompute anything later on.
#+begin_src jupyter-python
deps.write_hash()
deps.get_hash()[0]
#+end_src
#+RESULTS:
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: a94d2f6f1da1923ebad6d66b736e2cd3207b3b0b
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