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[1].set_ylabel(None)
fig.tight_layout()
from paths import shortname_from_path
df = pd.DataFrame({"path": paths})
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=optimize.OptimizeWarning)
params = df.path.apply(init_df)
df_params = pd.DataFrame(list(params))
df = df.join(df_params)
df = df.sort_values(["c", "$n$"])
short = df["path"].apply(str).apply(shortname_from_path)
df.set_index(short, inplace=True)
df.head()
# ## Load shock separation estimates from cache
df_shocks = load_df("shock_sep_laplacian_nupt1_by_counting_mean")
df_shocks
# _df = df[(df["$n$"] == 960) | (df["$n$"] == 2880)]
_df = df
try:
_df["d"] = df_shocks["mean"].values
except ValueError:
breakpoint()
raise
df = _df
fig, ax = plt.subplots(1, 2, figsize=(10, 3), dpi=150)
colors = [
mpl.colors.to_hex(c) for c in sns.color_palette("cubehelix", len(df))
import numpy as np
from peak_detection import run
from paths import paths_lap, load_df
df = load_df("df_lap")
df = df[np.logical_and(
df["$c$"] > 10, np.logical_not((df["$n$"] == 960) | (df["$n$"] == 2880)))]
print(df)
# FIXME: t=12,21
for t in range(22, 26):
print("Time:", t)
run(
nh_min=0,
df=df,
save_as=f"dataframes/shock_sep_laplacian_nupt1_by_counting_t{t}.csv",
dict_paths=paths_lap,
t_approx=t,
)
import matplotlib.pyplot as plt
from base import matplotlib_rc
from paths import load_df, exit_if_figure_exists
from base_fig_energy import plot_energy
matplotlib_rc(fontsize=10)
path_fig = exit_if_figure_exists(__file__)
df_w = load_df("df_lap")
fig, ax = plt.subplots(1, 2, figsize=(6.5, 3))
plot_energy(df_w,
fig,
ax,
N=[960, 1920, 2880, 3840, 7680],
C=[10, 20, 40, 100, 200])
fig.tight_layout()
fig.savefig(path_fig)
fig.savefig(path_fig.replace(".png", ".pdf"))
result = f"{short_name},{path},{mean},{std}\n"
f.write(result)
return mean, std
def run(
nh_min,
nh_max=10_000,
df=None,
save_as="dataframes/shock_sep.csv",
dict_paths=paths_sim,
t_approx=None,
overwrite=False,
):
if df is None:
df = load_df()
df = df[(df["$n$"] > nh_min) & (df["$n$"] < nh_max)]
for i in range(len(df)):
short = df.iloc[i]["short name"]
print(dict_paths[short])
if os.path.exists(save_as) and overwrite:
os.remove(save_as)
return (df["short name"].apply(
avg_shock_seperation_from_shortname,
dict_paths=dict_paths,
save_as=save_as,
t_approx=t_approx,
).apply(pd.Series))