hiro/master-thesis
1
0
Fork 0
mirror of https://github.com/vale981/master-thesis synced 2025-03-05 10:01:43 -05:00
Code Issues Projects Releases Packages Wiki Activity Actions

two ho more time steps

Browse source
This commit is contained in:
Valentin Boettcher 2022-08-03 17:15:03 +02:00
parent cef8074ebe
commit 647b869eae
2 changed files with 20 additions and 18 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

27
python/energy_flow_proper/11_new_ho_comparison/ho_analytic_two_baths.org
View file

@ -31,7 +31,7 @@
hops_bcf = hops.util.bcf.OhmicBCF_zeroTemp(s, 1, wc)
g, w = hops_bcf.exponential_coefficients(bcf_terms)
integration = IntP(t_steps=(2, 100))
integration = IntP(t_steps=(20, 1000))
q_proto = qutip.operators.create(max_HO_level) + qutip.operators.destroy(
max_HO_level
@ -154,8 +154,8 @@
#+end_src
#+RESULTS:
: ho_data_local/two_baths/_6/two_baths_6be504a1a85fd41f80e35d1f604cbfd6_1.h5
: 0% 34/10000 [02:22<11:35:31, 4.19s/it]
: ho_data/two_baths/_6/two_baths_6be504a1a85fd41f80e35d1f604cbfd6_1.h5
: 0it [00:00, ?it/s]
* Flow
#+begin_src jupyter-python :results none
@ -202,8 +202,8 @@
#+RESULTS:
:RESULTS:
: WARNING:matplotlib.legend:No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
: <matplotlib.legend.Legend at 0x7eff1fe96400>
[[file:./.ob-jupyter/fa167f554a2a849afec72057205569a35a9ed376.svg]]
: <matplotlib.legend.Legend at 0x7ff07f657220>
[[file:./.ob-jupyter/7dc9909701b9a96b2e00e19325c23b2ec0a8aa2b.svg]]
:END:
* Analytic
@ -254,7 +254,8 @@
#+begin_src jupyter-python
fig, ax = plt.subplots()
for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
consistency = (-1 * flow).consistency(ex_flow)
consistency = (-1 * flow.for_bath(0)).consistency(ex_flow[0])
consistency1 = (-1 * flow.for_bath(1)).consistency(ex_flow[1])
pu.plot_with_σ(
params.IntP.t,
-1 * flow,
@ -267,7 +268,7 @@
-1 * flow,
bath=1,
ax=ax,
label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ ${consistency}\%$",
label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ ${consistency1}\%$",
)
ax.plot(params.IntP.t, ex_flow[0], linestyle="dotted", color="black")
ax.plot(params.IntP.t, ex_flow[1], linestyle="dotted", color="black")
@ -282,15 +283,15 @@
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7eff0ea6f8e0>
[[file:./.ob-jupyter/904cc3c3d597a77e7e3544b4a3463fdc110a9a36.svg]]
: <matplotlib.legend.Legend at 0x7ff06e7e79a0>
[[file:./.ob-jupyter/993434ccfc11251484909bde5f5f8eae60b822cf.svg]]
:END:
#+begin_src jupyter-python
pu.plot_convergence(
multi_params[-1].IntP.t,
(-1 * flow_hops[-1].for_bath(0)
-ex_flow[0]) * (1/flow_hops[-1].for_bath(1).max.value),
(-1 * flow_hops[-1].for_bath(1)
-ex_flow[1]) * (1/flow_hops[-1].for_bath(1).max.value),
reference=np.zeros_like(ex_flow[0])
)
plt.axhline(0)
@ -300,8 +301,8 @@
#+RESULTS:
:RESULTS:
: <matplotlib.legend.Legend at 0x7eff0ee60790>
[[file:./.ob-jupyter/23c886933441b9ce654cc2377af65a8009e23521.svg]]
: <matplotlib.legend.Legend at 0x7ff06e3b0820>
[[file:./.ob-jupyter/df60edc8869ab6d9fcce16d1d99cfc5624368eb4.svg]]
:END:
* Obesrvations

11
python/energy_flow_proper/11_new_ho_comparison/two_ho.py
View file

@ -26,7 +26,7 @@ def ho_duo(
hops_bcf = hops.util.bcf.OhmicBCF_zeroTemp(s, 1, wc)
g, w = hops_bcf.exponential_coefficients(bcf_terms)
integration = IntP(t_steps=(2, 100))
integration = IntP(t_steps=(20, 1000))
q_proto = qutip.operators.create(max_HO_level) + qutip.operators.destroy(
max_HO_level
@ -202,7 +202,8 @@ for params, keys in zip(multi_params, model_keys):
fig, ax = plt.subplots()
for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, model_keys):
consistency = (-1 * flow).consistency(ex_flow)
consistency = (-1 * flow.for_bath(0)).consistency(ex_flow[0])
consistency1 = (-1 * flow.for_bath(1)).consistency(ex_flow[1])
pu.plot_with_σ(
params.IntP.t,
-1 * flow,
@ -215,7 +216,7 @@ for params, flow, ex_flow, keys in zip(multi_params, flow_hops, exact_flows, mod
-1 * flow,
bath=1,
ax=ax,
label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ ${consistency}\%$",
label=rf"$α(0)={params.SysP.g[0].sum().real:.2f}$ $ω_c={keys['wc']}$ ${consistency1}\%$",
)
ax.plot(params.IntP.t, ex_flow[0], linestyle="dotted", color="black")
ax.plot(params.IntP.t, ex_flow[1], linestyle="dotted", color="black")
@ -229,8 +230,8 @@ ax.legend()
pu.plot_convergence(
multi_params[-1].IntP.t,
(-1 * flow_hops[-1].for_bath(0)
-ex_flow[0]) * (1/flow_hops[-1].for_bath(1).max.value),
(-1 * flow_hops[-1].for_bath(1)
-ex_flow[1]) * (1/flow_hops[-1].for_bath(1).max.value),
reference=np.zeros_like(ex_flow[0])
)
plt.axhline(0)