mirror of
https://github.com/vale981/master-thesis
synced 2025-03-04 17:41:43 -05:00
update plot utils and figsaver
This commit is contained in:
parent
1f02b7c46a
commit
7689cbaba5
2 changed files with 226 additions and 52 deletions
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@ -2,34 +2,48 @@ import matplotlib
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import matplotlib.pyplot as plt
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import os
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from pathlib import Path
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import tikzplotlib
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from contextlib import contextmanager
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import numpy as np
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try:
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import tikzplotlib
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except:
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tikzplotlib = None # dirty
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fig_path = Path(os.getcwd()) / "figures"
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val_path = Path(os.getcwd()) / "values"
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def export_fig(name, fig=None, aspect="golden", **kwargs):
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def export_fig(name, fig=None, x_scaling=1, y_scaling=0.4, tikz=True, **kwargs):
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fig_path.mkdir(parents=True, exist_ok=True)
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if fig is None:
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fig = plt.gcf()
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w, _ = fig.get_size_inches()
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fig.set_size_inches((w * x_scaling, w * y_scaling))
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if not fig.get_constrained_layout():
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fig.tight_layout()
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fig.canvas.draw()
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# fig.savefig(fig_path / f"{name}.pdf")
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# fig.savefig(fig_path / f"{name}.svg")
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# fig.savefig(fig_path / f"{name}.pgf")
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fig.savefig(fig_path / f"{name}.pdf")
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tikzplotlib.clean_figure()
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fig.tight_layout()
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tikzplotlib.save(
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fig_path / f"{name}.tex",
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figure=fig,
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axis_width="\\figW",
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axis_height="\\figH",
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**kwargs,
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)
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if tikz and tikzplotlib:
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tikzplotlib.clean_figure()
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fig.tight_layout()
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tikzplotlib.save(
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fig_path / f"{name}.tex",
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figure=fig,
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axis_width="\\figW",
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axis_height="\\figH",
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**kwargs,
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)
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def scientific_round(val, *err, retprec=False):
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@ -120,11 +134,22 @@ def tex_value(val, err=None, unit=None, prefix="", suffix="", prec=0, save=None)
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###############################################################################
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MPL_RC = {
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"lines.linewidth": 1.5,
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"lines.linewidth": 0.5,
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"text.color": "black",
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"axes.labelcolor": "black",
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"xtick.color": "black",
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"ytick.color": "black",
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"figure.figsize": (5.2, 3.2),
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"text.usetex": True,
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"font.family": "serif",
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"font.serif": ["Palatino"],
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"font.size": 7,
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"axes.labelsize": 7,
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"axes.titlesize": 7,
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"legend.fontsize": 7,
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"xtick.labelsize": 5,
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"ytick.labelsize": 5,
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"figure.constrained_layout.use": True,
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}
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MPL_RC_POSTER = {
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@ -10,6 +10,7 @@ from hops.util.utilities import (
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entropy,
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trace_distance,
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)
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from matplotlib.gridspec import GridSpec
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try:
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import hiro_models.model_auxiliary as aux
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@ -306,35 +307,41 @@ def plot_diff_vs_sigma(
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def plot_interaction_consistency(
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models, reference=None, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs
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models,
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reference=None,
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label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$",
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inset=None,
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bath=0,
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**kwargs,
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):
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fig, ax = plt.subplots()
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fig, (ax, ax2) = plt.subplots(ncols=2)
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ax3 = None
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if inset:
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slc, bounds = inset
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ax3 = ax.inset_axes(bounds)
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if reference:
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with aux.get_data(reference) as data:
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reference_energy = reference.interaction_energy(data, **kwargs)
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for model in models:
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with aux.get_data(model) as data:
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energy = model.interaction_energy(data, **kwargs)
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interaction_ref = model.interaction_energy_from_conservation(data, **kwargs)
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diff = abs(interaction_ref - energy)
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energy = model.interaction_energy(data, **kwargs).for_bath(bath)
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interaction_ref = model.interaction_energy_from_conservation(
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data, **kwargs
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).for_bath(bath)
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self_consistency = (diff.value < diff.σ).sum() / len(diff.value[0]) * 100
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self_consistency = energy.consistency(interaction_ref)
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normalizer = 1 / abs(energy.value).max()
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if reference:
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diff = abs(interaction_ref - reference_energy)
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final_consistency = (
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(diff.value < diff.σ).sum() / len(diff.value[0]) * 100
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)
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final_consistency = reference_energy.consistency(interaction_ref)
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_, _, (line, _) = plot_with_σ(
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data.time[:],
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energy,
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ax=ax,
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label=label_fn(model)
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+ fr", (${self_consistency:.0f}\%$"
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+ (fr", ${final_consistency:.0f}\%$)" if reference else ")"),
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bath=0,
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)
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plot_with_σ(
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@ -342,21 +349,58 @@ def plot_interaction_consistency(
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interaction_ref,
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ax=ax,
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linestyle="--",
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bath=0,
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color=lighten_color(line[0].get_color(), 0.8),
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)
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ax.set_xlabel("$τ$")
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ax.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
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ax.legend()
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plot_with_σ(
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data.time[:],
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(energy - interaction_ref) * normalizer,
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label=label_fn(model)
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+ fr", (${self_consistency:.0f}\%$"
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+ (fr", ${final_consistency:.0f}\%$)" if reference else ")"),
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ax=ax2,
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color=line[0].get_color(),
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)
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return fig, ax
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if inset:
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plot_with_σ(
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data.time[slc],
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energy.slice(slc),
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ax=ax3,
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color=line[0].get_color(),
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)
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plot_with_σ(
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data.time[slc],
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interaction_ref.slice(slc),
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ax=ax3,
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linestyle="--",
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color=lighten_color(line[0].get_color(), 0.8),
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)
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ax.set_xlabel(r"$\tau$")
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ax2.set_xlabel(r"$\tau$")
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ax.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
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ax2.set_ylabel(
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r"$\langle (H_\mathrm{I}\rangle - \langle H_\mathrm{I}\rangle_\mathrm{ref}) / \langle H_\mathrm{I}\rangle_\mathrm{max}$"
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)
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ax2.legend()
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return fig, (ax, ax2, ax3)
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def plot_interaction_consistency_development(
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models, reference=None, label_fn=lambda model: f"$ω_c={model.ω_c:.2f}$", **kwargs
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models,
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reference=None,
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label_fn=lambda model: rf"$\omega_c={model.ω_c:.2f}$",
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**kwargs,
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):
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fig, (ax, ax2, ax3) = plt.subplots(nrows=1, ncols=3)
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fig = plt.figure(constrained_layout=True)
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gs = GridSpec(2, 2, figure=fig)
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ax = fig.add_subplot(gs[0, :])
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ax2 = fig.add_subplot(gs[1, 0])
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ax3 = fig.add_subplot(gs[1, 1])
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ax2.set_xscale("log")
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ax2.set_yscale("log")
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@ -376,23 +420,25 @@ def plot_interaction_consistency_development(
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else:
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energy = model.interaction_energy(data, **kwargs)
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diff = abs(interaction_ref - energy)
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normalizer = 1 / abs(energy.value).max()
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diff = abs(interaction_ref - energy) * normalizer
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ns, values, σs = [], [], []
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ns, values, σs, diffs = [], [], [], []
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for N, val, σ in diff.aggregate_iterator:
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ns.append(N)
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values.append((val < σ).sum() / len(val[0]) * 100)
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σs.append(σ.max())
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σs.append(σ.mean() * normalizer)
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diffs.append(val.mean() * normalizer)
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σ_ref, σ_int = [], []
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for _, _, σ in interaction_ref.aggregate_iterator:
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σ_ref.append(σ.max())
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σ_ref.append(σ.mean() * normalizer)
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for _, _, σ in energy.aggregate_iterator:
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σ_int.append(σ.max())
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σ_int.append(σ.mean() * normalizer)
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ax.plot(
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lines = ax.plot(
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ns,
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values,
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linestyle="--",
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@ -400,31 +446,64 @@ def plot_interaction_consistency_development(
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markersize=2,
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label=label_fn(model),
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)
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ax2.plot(ns, σs, label=label_fn(model))
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ax2.plot(
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ns,
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σs,
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label=label_fn(model),
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color=lines[0].get_color(),
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)
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ax2.plot(
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ns,
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diffs,
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label=label_fn(model),
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linestyle="--",
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color=lines[0].get_color(),
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)
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ax3.plot(
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ns,
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σ_ref,
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label=label_fn(model) + " (from conservation)",
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linestyle="--",
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color=lines[0].get_color(),
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)
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ax3.plot(ns, σ_int, label=label_fn(model) + " (direct)")
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ax.axhline(68, linestyle="-.", color="grey", alpha=0.5)
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ax.set_xlabel("$N$")
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ax.set_title("Consistency")
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ax2.set_xlabel("$N$")
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ax3.set_xlabel("$N$")
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ax2.set_ylabel("Maximum Allowed Deviation")
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ax3.set_ylabel("Maximum $σ$")
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ax2.set_ylabel(r"$[|\langle H_I\rangle|_\mathrm{max}]$")
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ax2.set_title(r"Mean Actual vs. Allowed deviation.")
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ax3.set_ylabel(r"$[|\langle H_I\rangle|_\mathrm{max}]$")
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ax3.set_title(r"Statistical Error of $\langle H_I\rangle$.")
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ax.set_ylabel(("" if reference else "Self-") + r"Consistency [$\%$]")
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ax.legend()
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ax2.legend()
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ax3.legend()
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straight_line = matplotlib.lines.Line2D(
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[], [], color="black", label=r"$\langle \sigma_\Delta\rangle$"
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)
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dashed_line = matplotlib.lines.Line2D(
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[], [], color="black", label=r"$\langle\Delta\rangle$", linestyle="--"
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)
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ax2.legend(handles=[straight_line, dashed_line])
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straight_line = matplotlib.lines.Line2D([], [], color="black", label=r"Direct")
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dashed_line = matplotlib.lines.Line2D(
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[], [], color="black", label=r"Energy Conservation", linestyle="--"
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)
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ax3.legend(handles=[dashed_line, straight_line])
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return fig, [ax, ax2, ax3]
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def plot_consistency_development(value, reference):
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fig, (ax, ax2) = plt.subplots(nrows=2, sharex=True)
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def plot_consistency_development(value, reference, fig=None):
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if fig:
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(ax, ax2) = fig.subplots(nrows=2, sharex=True)
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else:
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fig, (ax, ax2) = (fig or plt).subplots(nrows=2, sharex=True)
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ax.set_xscale("log")
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ax2.set_xscale("log")
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@ -466,14 +545,17 @@ def plot_consistency_development(value, reference):
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markersize=5,
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)
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ax2.plot(ns, np.array(σs) / abs(reference).max(), label=r"$\langle σ\rangle$")
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ax2.plot(ns, np.array(max_diff) / abs(reference).max(), label=r"$\langle Δ\rangle$")
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ax2.plot(ns, np.array(σs) / abs(reference).max(), label=r"$\langle \sigma\rangle$")
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ax2.plot(
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ns, np.array(max_diff) / abs(reference).max(), label=r"$\langle \Delta\rangle$"
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)
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ax.axhline(68, linestyle="-.", color="grey", alpha=0.5)
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ax.set_xlabel("$N$")
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ax2.set_xlabel("$N$")
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ax.set_ylabel(r"Consistency [$\%$]")
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ax2.set_ylabel(r"$\mathrm{Deviation}/{J_\mathrm{max}}$")
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ax.set_ylabel(r"[$\%$]")
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ax.set_title(r"Consistency")
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ax2.set_ylabel(r"$[{J_\mathrm{max}}]$")
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ax2.set_title(r"Deviation")
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ax2.legend()
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@ -589,13 +671,80 @@ def plot_distance_measures(model, strobe_indices, ax=None):
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@wrap_plot
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def plot_σ_development(ensemble_value, ax=None, **kwargs):
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# norm = abs(ensemble_value.value).max()
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ax.plot(
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ensemble_value.Ns,
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[σ.mean() for σ in ensemble_value.σs],
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marker=".",
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markersize=6,
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linestyle="dotted",
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**kwargs,
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)
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ax.set_ylabel("mean $σ$")
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ax.set_ylabel("mean normalized $σ$")
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ax.set_xlabel("$N$")
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def plot_multi_energy_overview(
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models,
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label_fn=lambda m: fr"$\omega_c={m.ω_c:.1f},\,\alpha(0)={m.bcf(0).real:.2f}$",
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):
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fig, ((ax_sys, ax_flow), (ax_inter, ax_bath)) = plt.subplots(
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nrows=2, ncols=2, sharex=True
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)
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flow_ins = ax_flow.inset_axes([0.4, 0.20, 0.5, 0.55])
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ax_sys.set_ylabel(r"$\langle H_\mathrm{S}\rangle$")
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ax_flow.set_ylabel("$J$")
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ax_inter.set_ylabel(r"$\langle H_\mathrm{I}\rangle$")
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ax_bath.set_ylabel(r"$\langle H_\mathrm{B}\rangle$")
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ax_sys.set_xlabel(r"$\tau$")
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ax_flow.set_xlabel(r"$\tau$")
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ax_inter.set_xlabel(r"$\tau$")
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ax_bath.set_xlabel(r"$\tau$")
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handles = []
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for model in models:
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with aux.get_data(model) as data:
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sys_energy = model.system_energy(data, **ensemble_arg)
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flow = model.bath_energy_flow(data, **ensemble_arg).for_bath(0)
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inter = model.interaction_energy(data, **ensemble_arg).for_bath(0)
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bath = model.bath_energy(data, **ensemble_arg).for_bath(0)
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_, _, (lines, _) = plot_with_σ(
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data.time[:],
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sys_energy,
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ax=ax_sys,
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label=label_fn(model),
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)
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handles.append(lines[0])
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plot_with_σ(
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data.time[:],
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flow,
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ax=ax_flow,
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)
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mask = np.logical_and(7 <= data.time[:], data.time[:] <= 9)
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plot_with_σ(
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data.time[mask],
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flow.slice(mask),
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ax=flow_ins,
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)
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plot_with_σ(
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data.time[:],
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inter,
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ax=ax_inter,
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)
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plot_with_σ(
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data.time[:],
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bath,
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ax=ax_bath,
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)
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ax_sys.legend(
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# ncol=2,
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)
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