Skip to content
Snippets Groups Projects
Commit bf4cfb07 authored by Pierre Augier's avatar Pierre Augier
Browse files

2022strat_turb_toro: simulation letters in figures and Fh/R2 limits

parent aec26d8e
No related branches found
No related tags found
No related merge requests found
......@@ -319,8 +319,9 @@
\includegraphics[width=0.7\textwidth]{%
../tmp/fig_isotropy_coef_vs_FhR}
}
\caption{Large and small scale isotropy coefficients.
\label{fig:isotropy-coefficients}}
\caption{Large and small scale isotropy coefficients. Red letters correspond to
simulations of table~\ref{table-simuls-regimes} analyzed in
subsection~\ref{spectra-seb-regimes}. \label{fig:isotropy-coefficients}}
\end{figure}
Figure~\ref{fig:isotropy-coefficients} ...
......@@ -348,10 +349,12 @@
../tmp/fig_mixing_coef_vs_Fh}
}
\caption{Mixing coefficient versus the horizontal Froude number. The colors represent
$\R_2$. \label{fig:mixing-coefficients-vs-Fh}}
$\R_2$. Red letters correspond to simulations of table~\ref{table-simuls-regimes}
analyzed in subsection~\ref{spectra-seb-regimes}.
\label{fig:mixing-coefficients-vs-Fh}}
\end{figure}
Figure~\ref{fig:mixing-coefficients-vs-Fh} ...
\subsection{Spatial spectra and spectral energy budget}
......@@ -352,9 +355,10 @@
\end{figure}
Figure~\ref{fig:mixing-coefficients-vs-Fh} ...
\subsection{Spatial spectra and spectral energy budget}
\label{spectra-seb-regimes}
\input{../tmp/table_simuls_regimes.tex}
......@@ -367,7 +371,9 @@
\includegraphics[width=0.48\textwidth]{%
../tmp/fig_seb_regime_D}
}
\caption{Spectra and spectral energy budget for the dissipative regime. \label{fig:spectra-seb-D}}
\caption{Spectra and spectral energy budget for simulation D (see
table~\ref{table-simuls-regimes}) corresponding to the dissipative regime.
\label{fig:spectra-seb-D}}
\end{figure}
Figure~\ref{fig:spectra-seb-D} ...
......@@ -380,7 +386,9 @@
\includegraphics[width=0.48\textwidth]{%
../tmp/fig_seb_regime_L}
}
\caption{Spectra and spectral energy budget for the LAST regime. \label{fig:spectra-seb-L}}
\caption{Spectra and spectral energy budget for simulation L (see
table~\ref{table-simuls-regimes}) corresponding to the LAST regime.
\label{fig:spectra-seb-L}}
\end{figure}
Figure~\ref{fig:spectra-seb-L} ...
......@@ -393,7 +401,9 @@
\includegraphics[width=0.48\textwidth]{%
../tmp/fig_seb_regime_O}
}
\caption{Spectra and spectral energy budget for the optimal stratified turbulence regime. \label{fig:spectra-seb-O}}
\caption{Spectra and spectral energy budget for simulation O (see
table~\ref{table-simuls-regimes}) corresponding to the optimal stratified turbulence
regime. \label{fig:spectra-seb-O}}
\end{figure}
Figure~\ref{fig:spectra-seb-O} ...
......@@ -406,7 +416,9 @@
\includegraphics[width=0.48\textwidth]{%
../tmp/fig_seb_regime_W}
}
\caption{Spectra and spectral energy budget for the weakly stratified turbulence regime. \label{fig:spectra-seb-W}}
\caption{Spectra and spectral energy budget for simulation W (see
table~\ref{table-simuls-regimes}) corresponding to the weakly stratified turbulence
regime. \label{fig:spectra-seb-W}}
\end{figure}
Figure~\ref{fig:spectra-seb-W} ...
......@@ -419,7 +431,9 @@
\includegraphics[width=0.48\textwidth]{%
../tmp/fig_seb_regime_P}
}
\caption{Spectra and spectral energy budget for the passive scalar turbulence regime. \label{fig:spectra-seb-P}}
\caption{Spectra and spectral energy budget for simulation P (see
table~\ref{table-simuls-regimes}) corresponding to the passive scalar turbulence
regime. \label{fig:spectra-seb-P}}
\end{figure}
Figure~\ref{fig:spectra-seb-P} ...
......
import numpy as np
import matplotlib.pyplot as plt
from util import save_fig, plot
from util import save_fig, plot, params_simuls_regimes, Fh_limit, R2_limit
from util_dataframe import df
......@@ -15,5 +15,27 @@
vmax=0.8,
s=50 * df["I_dissipation"],
)
df_tmp = df[["N", "Rb", "Fh", "R2"]]
letters = {v: k for k, v in params_simuls_regimes.items()}
for index, (N, Rb, Fh, R2) in df_tmp.iterrows():
if (N, Rb) not in letters:
continue
letter = letters[(N, Rb)]
if letter == "P":
continue
coef_x = 0.85
coef_y = 0.68
if letter == "O":
coef_x = 0.83
coef_y = 0.62
elif letter == "W":
coef_y = 0.6
elif letter == "L":
coef_y = 0.77
ax.text(coef_x * Fh, coef_y * R2, letter, color="r")
ax.set_xlim(right=1)
ax.set_ylim(top=1e3)
......@@ -18,3 +40,8 @@
ax.set_xlim(right=1)
ax.set_ylim(top=1e3)
ax.axvline(Fh_limit, linestyle=":")
Fh_min, Fh_max = ax.get_xlim()
ax.plot([Fh_min, Fh_limit], [R2_limit, R2_limit], linestyle=":")
ax.set_xlabel("$F_h$")
......@@ -20,5 +47,6 @@
ax.set_xlabel("$F_h$")
ax.set_ylabel("$\mathcal{R} = Re {F_h}^2$")
ax.set_ylabel(r"$\mathcal{R} = Re {F_h}^2$")
fig = ax.figure
fig.tight_layout()
......
import numpy as np
import matplotlib.pyplot as plt
from util import save_fig, plot
from util import save_fig, plot, R2_limit
from util_dataframe import df
......@@ -9,6 +9,8 @@
df, "R2", "I_dissipation", c=np.log10(df["Fh"]), vmin=-2, vmax=-1, logy=True
)
ax.axvline(R2_limit, linestyle=":")
ax.set_xlabel(r"$\mathcal{R}$")
ax.set_ylabel("$I_{diss}$")
......
import numpy as np
import matplotlib.pyplot as plt
from util import save_fig, plot
from util import save_fig, plot, Fh_limit
from util_dataframe import df
ax = plot(
df, "Fh", "I_velocity", c=np.log10(df["R2"]), vmin=0.5, vmax=2, logy=True
)
......@@ -5,9 +5,10 @@
from util_dataframe import df
ax = plot(
df, "Fh", "I_velocity", c=np.log10(df["R2"]), vmin=0.5, vmax=2, logy=True
)
ax.axvline(Fh_limit, linestyle=":")
ax.set_xlabel("$F_h$")
ax.set_ylabel(r"$I_{velocity}$")
......
import numpy as np
import matplotlib.pyplot as plt
from util import save_fig, plot
from util import save_fig, plot, params_simuls_regimes
from util_dataframe import df
......@@ -15,8 +15,30 @@
vmax=2,
s=35,
)
# ax.set_xlim(right=1)
# ax.set_ylim(top=1e3)
df_tmp = df[["N", "Rb", "Fh", "Gamma"]]
letters = {v: k for k, v in params_simuls_regimes.items()}
for index, (N, Rb, Fh, Gamma) in df_tmp.iterrows():
if (N, Rb) not in letters:
continue
letter = letters[(N, Rb)]
coef_x = 0.85
coef_y = 0.7
if letter == "O":
coef_x = 0.7
coef_y = 1.04
elif letter == "W":
coef_x = 1.08
coef_y = 0.95
elif letter == "L":
coef_y = 1.2
elif letter == "P":
coef_x = 0.95
coef_y = 1.13
ax.text(coef_x * Fh, coef_y * Gamma, letter, color="r")
ax.set_xlabel("$F_h$")
ax.set_ylabel(r"$\Gamma=\epsilon_A / \epsilon_K$")
......
......@@ -122,15 +122,7 @@
def plot(
df,
x,
y,
logx=True,
logy=False,
c=None,
vmin=None,
vmax=None,
s=None,
df, x, y, logx=True, logy=False, c=None, vmin=None, vmax=None, s=None, ax=None
):
ax = df.plot.scatter(
x=x,
......@@ -142,4 +134,5 @@
vmin=vmin,
vmax=vmax,
s=s,
ax=ax,
)
......@@ -145,5 +138,5 @@
)
pc = ax.collections[0]
pc = ax.collections[-1]
pc.set_cmap("inferno")
if c is not None:
......@@ -186,3 +179,6 @@
"R2": formatter_R,
"R4": lambda v: f"{v:.0f}",
}
Fh_limit = 0.06
R2_limit = 10.0
......@@ -2,6 +2,8 @@
paths = []
for N, Rb in sorted(couples320):
if N == 2.9:
continue
paths.append(get_path_finer_resol(N, Rb))
print(f"Using {len(paths)} simulations")
......
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment