fig7_spectra(
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)
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"))
