tmin=tmin,
run_nb=run_nb,
key_var=("uy", "ux"),
cache=test_mode,
ax_inset=ax_inset,
n_colors=len(df),
)
if test_mode:
break
# fig12_atm(fig, ax_b)
if __name__ == "__main__":
matplotlib_rc(fontsize=fontsize)
path_fig = exit_if_figure_exists(__file__)
set_figsize(5, 4)
fig, ax = pl.subplots(2, 2, sharey=False, sharex=False)
df_w = load_df("df_w")
df_3840 = df_w[df_w["$n$"] == 3840]
df_7680 = df_w[df_w["$n$"] == 7680]
ax_inset3 = None # _ax_inset(fig, '$r/L_f$', 0.325, 0.362)
ax_inset7 = None # _ax_inset(fig, '$r:/L_f$', 0.775, 0.362)
sns.set_palette("cubehelix", 5)
plot_df(df_3840, fig, ax[:, 0], ax_inset3)
sns.set_palette("cubehelix", 3)
plot_df(df_7680, fig, ax[:, 1], ax_inset7)
for ax1 in ax[1, :]:
paths_sim[short_name],
fig,
ax,
Fr,
c,
t_start=tmin,
run_nb=run_nb,
n_colors=len(df),
)
if __name__ == "__main__":
sns.set_palette("cubehelix", 3)
matplotlib_rc(11)
path_fig = exit_if_figure_exists(__file__)
set_figsize(7, 3)
fig, ax = pl.subplots(1, 2, sharex=True, sharey=True)
df_w = load_df("df_w")
df_3840 = df_w[df_w["$n$"] == 3840]
df_7680 = df_w[df_w["$n$"] == 7680]
sns.set_palette("cubehelix", 5)
plot_df(df_3840, fig, ax[0])
sns.set_palette("cubehelix", 3)
plot_df(df_7680, fig, ax[1])
for ax1 in ax:
ax1.set_ylim(1e-2, 1e2)
ax1.set_xlim(2e-1, 5e2)
#!/usr/bin/env python
import pylab as pl
import fluidsim as fls
import os
import h5py
from base import _index_where, _k_f, _eps, set_figsize
from paths import paths_sim, path_pyfig, exit_if_figure_exists
def figX_(path, fig=None, ax=None, t_start=10):
pass
if __name__ == "__main__":
path_fig = exit_if_figure_exists(__file__)
set_figsize(10, 6)
fig, ax = pl.subplots()
figX_seb(paths_sim["noise_c100nh3840Buinf"], fig, ax, t_start=20)
pl.savefig(path_fig)
Y,
field,
norm=norm,
vmin=vmin,
vmax=vmax,
cmap=cmap,
zorder=0)
cbar = fig.colorbar(contours, ax=ax)
cbar.solids.set_rasterized(True)
if __name__ == "__main__":
matplotlib_rc(fontsize=10)
path_fig = exit_if_figure_exists(__file__)
set_figsize(6.65, 5.8)
fig, axes = plt.subplots(2, 2)
short_names = ["noise_c20nh1920Buinf", "noise_c400nh1920Buinf"]
sim0 = fls.load_state_phys_file(paths_sim[short_names[0]],
merge_missing_params=True)
sim1 = sim0
sim1 = fls.load_state_phys_file(paths_sim[short_names[1]],
merge_missing_params=True)
keys = ["div", "uy", "div", "uy"]
for ax, sim, key_field in zip(axes.ravel(), [sim0, sim0, sim1, sim1],
keys):
fig_phys_subplot(sim,
fig,
ax,
key_field,
x_slice=[0, 3],
ax.set_xlim([0.0, None])
ax.set_ylim([0.0, None])
if normalized:
ax.set_xlabel("$t (\epsilon/L_f^2)^{1/3}$")
# ax.set_ylabel("$E/E_f$")
ax.set_ylabel("$E/(\epsilon L_f)^{2/3}$")
# ax.grid(True, axis='y', linestyle=':')
else:
ax.set_xlabel("$t$")
ax.set_ylabel("$E$")
def get_legend_and_paths(c_list, nh_list):
keys = []
legend = []
for c in c_list:
for nh in nh_list:
keys.append("c{}nh{}".format(c, nh))
legend.append("c={}, n={}".format(c, nh))
return legend, [paths_sim["noise_" + k + "Buinf"] for k in keys]
if __name__ == "__main__":
matplotlib_rc(fontsize)
path_fig = exit_if_figure_exists(__file__, override_exit=False)
set_figsize(5.12, 3.0)
fig1_plot_all(paths_sim)
pl.savefig(path_fig)
pl.savefig(path_fig.replace(".png", ".pdf"))
PiEKd = cumsum_inv(transferEKd) * sim.oper.deltak
PiEAr = cumsum_inv(transferEAr) * sim.oper.deltak
PiEAd = cumsum_inv(transferEAd) * sim.oper.deltak
# PiEPd = cumsum_inv(transferEPd) * sim.oper.deltak
print(eps)
ax.axhline(1.0, color="k", ls=":")
PiEK = PiEKr + PiEKd
PiEA = PiEAr + PiEAd
PiE = PiEK + PiEA
ax.set_xlabel("$k/k_f$")
ax.set_ylabel(r"$\Pi(k)/\epsilon$")
ax.set_xscale("log")
ax.set_yscale("linear")
ax.plot(khE, PiE, "k", linewidth=2, label=r"$\Pi$")
ax.plot(khE, PiEK, "r:", linewidth=2, label=r"$\Pi_K$")
ax.plot(khE, PiEA, "b--", linewidth=2, label=r"$\Pi_A$")
ax.set_ylim([-0.1, 1.1])
ax.legend()
if __name__ == "__main__":
matplotlib_rc()
path_fig = exit_if_figure_exists(__file__)
set_figsize(5, 3)
fig, ax = pl.subplots()
fig2_seb(paths_sim["noise_c100nh3840Buinf"], fig, ax) # , t_start=20)
pl.savefig(path_fig)
def plot_energy(
df_main,
fig=None,
ax=None,
N=[960, 1920, 3840, 7680],
C=[10, 20, 40, 100, 400, 700],
):
if fig is None and ax is None:
set_figsize(6.5, 3)
fig, ax = plt.subplots(1, 2)
df_n = {}
for n in N:
df_n[n] = filter_df_by(df_main, ["$n$"], n)
E_eps = "$E/\sqrt{\epsilon}$"
mark = markers()
for n, df in df_n.items():
df = df.assign(**{E_eps: df["$E$"] / df["$\epsilon$"]**0.5})
c_eps = df["$c$"] * df["$\epsilon$"]
Cn = get_cn(c_eps, df["$E$"])
E_fit = model_func(c_eps.values, Cn) / df["$\epsilon$"].values**0.5
print("n =", n, "Cn =", Cn)
# ax[0].plot(
ax[0].loglog(df["$c$"].values, E_fit, ":", linewidth=1)
ax[0].scatter(
"$c$",
E_eps,
marker=next(mark),
data=df,
label="$n={}$".format(n),
# logx=True, logy=True,
)
ax[0].set_ylabel(E_eps)
ax[0].set_xlabel("$c$")
# ax[0].set_xticks(C)
# Subplot on right : Energy vs n
df_c = {}
for c in C:
df_c[c] = filter_df_by(df_main, ["$c$"], c)
E_eLc = "$E/\sqrt{\epsilon L_f c}$"
mark = markers()
for c, df in df_c.items():
df = df.assign(
**{E_eLc: df["$E$"] / (df["$\epsilon$"] * L_f * df["$c$"])**0.5})
n = df["$n$"]
# alpha = get_alpha(n, df['$E$'])
# E_fit = model_func2(n, alpha) / (df['$\epsilon$'] * L_f * df['$c$']) ** 0.5
alpha = get_alpha(n, df[E_eLc])
print("c =", c, "alpha =", alpha)
E_fit = model_func2(n, *alpha)
ax[1].scatter(
"$n$",
E_eLc,
data=df,
marker=next(mark),
label=r"$c={}; \alpha={:.2f}$".format(c, alpha[1]),
# logx=True, logy=True
)
ax[1].loglog(n, E_fit, ":", label="", linewidth=1)
# print('c =', c)
# print(df[E_eLc])
ax[1].set_ylabel(E_eLc)
ax[1].set_xlabel("$n$")
ax[1].set_xlim([None, 1e4])
# ax[1].set_xticks(N)
for a in ax.ravel():
a.legend(fontsize=7)
fig.tight_layout()
return fig, ax
