mirror of
https://github.com/vale981/master-thesis
synced 2025-03-05 10:01:43 -05:00
put plot routines into figsaver
This commit is contained in:
parent
eed3efb25f
commit
5e03157862
3 changed files with 117 additions and 119 deletions
|
|
@ -1,4 +1,9 @@
|
|||
import figsaver as fs
|
||||
from figsaver import (
|
||||
plot_interaction_consistency,
|
||||
plot_interaction_consistency_development,
|
||||
plot_flow_bcf,
|
||||
)
|
||||
from hiro_models.one_qubit_model import QubitModel, StocProcTolerances
|
||||
import hiro_models.model_auxiliary as aux
|
||||
import numpy as np
|
||||
|
|
|
|||
|
|
@ -3,6 +3,11 @@
|
|||
* Boilerplate
|
||||
#+begin_src jupyter-python :results none :tangle scripts/integrate_slip.py
|
||||
import figsaver as fs
|
||||
from figsaver import (
|
||||
plot_interaction_consistency,
|
||||
plot_interaction_consistency_development,
|
||||
plot_flow_bcf,
|
||||
)
|
||||
from hiro_models.one_qubit_model import QubitModel, StocProcTolerances
|
||||
import hiro_models.model_auxiliary as aux
|
||||
import numpy as np
|
||||
|
|
@ -25,123 +30,6 @@ Init ray and silence stocproc.
|
|||
logging_setup(logging.INFO)
|
||||
#+end_src
|
||||
|
||||
** Helper Functionality
|
||||
*** Plotting Consistency
|
||||
Interaction consistency for multiple models and a reference.
|
||||
#+begin_src jupyter-python :results none
|
||||
def plot_interaction_consistency(
|
||||
models, reference=None, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs
|
||||
):
|
||||
fig, ax = plt.subplots()
|
||||
if reference:
|
||||
with aux.get_data(reference) as data:
|
||||
reference_energy = reference.interaction_energy(data, **kwargs)
|
||||
|
||||
for model in models:
|
||||
with aux.get_data(model) as data:
|
||||
energy = model.interaction_energy(data, **kwargs)
|
||||
interaction_ref = model.interaction_energy_from_conservation(data, **kwargs)
|
||||
|
||||
diff = abs(interaction_ref - energy)
|
||||
self_consistency = (diff.value < diff.σ).sum() / len(diff.value[0]) * 100
|
||||
|
||||
if reference:
|
||||
diff = abs(interaction_ref - reference_energy)
|
||||
final_consistency = (
|
||||
(diff.value < diff.σ).sum() / len(diff.value[0]) * 100
|
||||
)
|
||||
|
||||
_, _, (line, _) = fs.plot_with_σ(
|
||||
data.time[:],
|
||||
energy,
|
||||
ax=ax,
|
||||
label=label_fn(model)
|
||||
+ fr", (${self_consistency:.0f}\%$"
|
||||
+ (fr", ${final_consistency:.0f}\%$)" if reference else ")"),
|
||||
bath=0,
|
||||
)
|
||||
|
||||
fs.plot_with_σ(
|
||||
data.time[:],
|
||||
interaction_ref,
|
||||
ax=ax,
|
||||
linestyle="--",
|
||||
bath=0,
|
||||
color=fs.lighten_color(line[0].get_color(), 0.8),
|
||||
)
|
||||
|
||||
ax.set_xlabel("$τ$")
|
||||
ax.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
|
||||
ax.legend()
|
||||
|
||||
return fig, ax
|
||||
#+end_src
|
||||
|
||||
Consistency for multiple sample counts.
|
||||
Interaction consistency for multiple models and a reference.
|
||||
#+begin_src jupyter-python :results none
|
||||
def plot_interaction_consistency_development(
|
||||
models, reference=None, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs
|
||||
):
|
||||
fig, ax = plt.subplots()
|
||||
if reference:
|
||||
with aux.get_data(reference) as data:
|
||||
reference_energy = reference.interaction_energy(data, **kwargs)
|
||||
|
||||
for model in models:
|
||||
with aux.get_data(model) as data:
|
||||
interaction_ref = model.interaction_energy_from_conservation(
|
||||
data, **kwargs
|
||||
)
|
||||
if reference:
|
||||
diff = abs(interaction_ref - reference_energy)
|
||||
else:
|
||||
energy = model.interaction_energy(data, **kwargs)
|
||||
diff = abs(interaction_ref - energy)
|
||||
|
||||
ns, values = [], []
|
||||
for N, val, σ in diff:
|
||||
ns.append(N)
|
||||
values.append((val < σ).sum() / len(val[0]) * 100)
|
||||
|
||||
ax.plot(ns, values, linestyle="--", marker=".", label=label_fn(model))
|
||||
|
||||
ax.axhline(68, linestyle="-.", color="grey", alpha=.5)
|
||||
ax.set_xlabel("$N$")
|
||||
ax.set_ylabel(("" if reference else "Self-") + r"Consistency [$\%$]")
|
||||
ax.legend()
|
||||
|
||||
return fig, ax
|
||||
#+end_src
|
||||
|
||||
*** Flow vs BCF
|
||||
#+begin_src jupyter-python :results none
|
||||
def plot_flow_bcf(models, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs):
|
||||
fig, ax = plt.subplots()
|
||||
for model in models:
|
||||
with aux.get_data(model) as data:
|
||||
flow = model.bath_energy_flow(data, **kwargs)
|
||||
_, _, (line, _) = fs.plot_with_σ(
|
||||
data.time[:],
|
||||
flow,
|
||||
ax=ax,
|
||||
label=label_fn(model),
|
||||
bath=0,
|
||||
transform=lambda y: -y,
|
||||
)
|
||||
|
||||
ax.plot(
|
||||
data.time[:],
|
||||
-model.L_expect * model.bcf_scale * model.bcf(data.time[:]).imag,
|
||||
linestyle="--",
|
||||
color=line[0].get_color(),
|
||||
)
|
||||
|
||||
return fig, ax
|
||||
#+end_src
|
||||
|
||||
*** TODO Maybe integrate into =hiro-models=
|
||||
|
||||
* Global Definitions
|
||||
We use a logspaced time to resolve the initial slip.
|
||||
#+begin_src jupyter-python :results none :tangle scripts/integrate_slip.py
|
||||
|
|
|
|||
|
|
@ -51,7 +51,7 @@ def scientific_round(val, *err, retprec=False):
|
|||
val = np.ones_like(err) * val
|
||||
|
||||
i = np.floor(np.log10(err))
|
||||
first_digit = (err // 10**i).astype(int)
|
||||
first_digit = (err // 10 ** i).astype(int)
|
||||
prec = (-i + np.ones_like(err) * (first_digit <= 3)).astype(int)
|
||||
prec = np.max(prec, axis=1)
|
||||
|
||||
|
|
@ -136,7 +136,7 @@ def wrap_plot(f):
|
|||
def get_figsize(
|
||||
columnwidth: float,
|
||||
wf: float = 0.5,
|
||||
hf: float = (5.0**0.5 - 1.0) / 2.0,
|
||||
hf: float = (5.0 ** 0.5 - 1.0) / 2.0,
|
||||
) -> tuple[float, float]:
|
||||
"""
|
||||
:param wf: Width fraction in columnwidth units.
|
||||
|
|
@ -253,6 +253,7 @@ def plot_with_σ(x, y, ax=None, transform=lambda y: y, bath=None, **kwargs):
|
|||
|
||||
return fancy_error(x, y_final, err, ax=ax, **kwargs)
|
||||
|
||||
|
||||
@wrap_plot
|
||||
def plot_diff_vs_sigma(
|
||||
x,
|
||||
|
|
@ -303,6 +304,110 @@ def plot_diff_vs_sigma(
|
|||
ax.set_ylabel(rf"$|{{{ylabel}}}_{{\mathrm{{ref}}}}-{{{ylabel}}}_{{N_i}}|$")
|
||||
|
||||
|
||||
def plot_interaction_consistency(
|
||||
models, reference=None, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs
|
||||
):
|
||||
fig, ax = plt.subplots()
|
||||
if reference:
|
||||
with aux.get_data(reference) as data:
|
||||
reference_energy = reference.interaction_energy(data, **kwargs)
|
||||
|
||||
for model in models:
|
||||
with aux.get_data(model) as data:
|
||||
energy = model.interaction_energy(data, **kwargs)
|
||||
interaction_ref = model.interaction_energy_from_conservation(data, **kwargs)
|
||||
|
||||
diff = abs(interaction_ref - energy)
|
||||
self_consistency = (diff.value < diff.σ).sum() / len(diff.value[0]) * 100
|
||||
|
||||
if reference:
|
||||
diff = abs(interaction_ref - reference_energy)
|
||||
final_consistency = (
|
||||
(diff.value < diff.σ).sum() / len(diff.value[0]) * 100
|
||||
)
|
||||
|
||||
_, _, (line, _) = fs.plot_with_σ(
|
||||
data.time[:],
|
||||
energy,
|
||||
ax=ax,
|
||||
label=label_fn(model)
|
||||
+ fr", (${self_consistency:.0f}\%$"
|
||||
+ (fr", ${final_consistency:.0f}\%$)" if reference else ")"),
|
||||
bath=0,
|
||||
)
|
||||
|
||||
fs.plot_with_σ(
|
||||
data.time[:],
|
||||
interaction_ref,
|
||||
ax=ax,
|
||||
linestyle="--",
|
||||
bath=0,
|
||||
color=fs.lighten_color(line[0].get_color(), 0.8),
|
||||
)
|
||||
|
||||
ax.set_xlabel("$τ$")
|
||||
ax.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
|
||||
ax.legend()
|
||||
|
||||
return fig, ax
|
||||
|
||||
|
||||
def plot_interaction_consistency_development(
|
||||
models, reference=None, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs
|
||||
):
|
||||
fig, ax = plt.subplots()
|
||||
if reference:
|
||||
with aux.get_data(reference) as data:
|
||||
reference_energy = reference.interaction_energy(data, **kwargs)
|
||||
|
||||
for model in models:
|
||||
with aux.get_data(model) as data:
|
||||
interaction_ref = model.interaction_energy_from_conservation(data, **kwargs)
|
||||
if reference:
|
||||
diff = abs(interaction_ref - reference_energy)
|
||||
else:
|
||||
energy = model.interaction_energy(data, **kwargs)
|
||||
diff = abs(interaction_ref - energy)
|
||||
|
||||
ns, values = [], []
|
||||
for N, val, σ in diff:
|
||||
ns.append(N)
|
||||
values.append((val < σ).sum() / len(val[0]) * 100)
|
||||
|
||||
ax.plot(ns, values, linestyle="--", marker=".", label=label_fn(model))
|
||||
|
||||
ax.axhline(68, linestyle="-.", color="grey", alpha=0.5)
|
||||
ax.set_xlabel("$N$")
|
||||
ax.set_ylabel(("" if reference else "Self-") + r"Consistency [$\%$]")
|
||||
ax.legend()
|
||||
|
||||
return fig, ax
|
||||
|
||||
|
||||
def plot_flow_bcf(models, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs):
|
||||
fig, ax = plt.subplots()
|
||||
for model in models:
|
||||
with aux.get_data(model) as data:
|
||||
flow = model.bath_energy_flow(data, **kwargs)
|
||||
_, _, (line, _) = fs.plot_with_σ(
|
||||
data.time[:],
|
||||
flow,
|
||||
ax=ax,
|
||||
label=label_fn(model),
|
||||
bath=0,
|
||||
transform=lambda y: -y,
|
||||
)
|
||||
|
||||
ax.plot(
|
||||
data.time[:],
|
||||
-model.L_expect * model.bcf_scale * model.bcf(data.time[:]).imag,
|
||||
linestyle="--",
|
||||
color=line[0].get_color(),
|
||||
)
|
||||
|
||||
return fig, ax
|
||||
|
||||
|
||||
###############################################################################
|
||||
# SIDE EFFECTS #
|
||||
###############################################################################
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue