fig9.ipynb

Kernel: Python 3 (system-wide)

import cmocean
import numpy as np
import h5py
import matplotlib.pyplot as plt
from matplotlib import cm
import seaborn as sns
from matplotlib.gridspec import GridSpec
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.ticker as mticker
rc = {'figure.figsize':(10,5),
      'axes.facecolor':'white',
      'axes.grid' : True,
      'grid.color': '.8',
      'font.size' : 11}
plt.rcParams.update(rc)

#1D Spectra
Ekx07 = np.load('Ekx07.npy')
Ekz07 = np.load('Ekz07.npy')
Ekz07_2 = np.load('Ekz07_2.npy')

Ekx1 = np.load('Ekx1.npy')
Ekz1 = np.load('Ekz1.npy')
Ekz1_2 = np.load('Ekz1_2.npy')

Ekx2 = np.load('Ekx2.npy')
Ekz2 = np.load('Ekz2.npy')
Ekz2_2 = np.load('Ekz2_2.npy')

#Wavenumbers
m = np.linspace(0,256,257)*(2*np.pi/14.28)
kx =  np.linspace(0,341,341)*(2*np.pi/28.56)

#Ozmidov wavenumbers
MO1 = (1/(0.004)**0.5)
MO2 = (0.5**1.5/(0.004)**0.5)
MO07 = (np.sqrt(2)**1.5/(0.004)**0.5)

#Spectra normalisation constants
N1 = np.trapz(Ekx07[:341], kx)
N2 = np.trapz(Ekx1[:341], kx)
N3 = np.trapz(Ekx2[:341], kx)

N4 = np.trapz(m[:170]**3*Ekz07[:170], m[:170])
N5 = np.trapz(m[:170]**3*Ekz1[:170], m[:170])
N6 = np.trapz(m[:170]**3*Ekz2[:170], m[:170])

N7 = np.trapz(m[:170]**3*Ekz07[:170], m[:170])
N8 = np.trapz(m[:170]**3*Ekz1[:170], m[:170])
N9 = np.trapz(m[:170]**(3)*Ekz2[:170], m[:170])

N10 = np.trapz(m[:170]**(5/3)*Ekz07[:170], m[:170])
N11 = np.trapz(m[:170]**(5/3)*Ekz1[:170], m[:170])
N12 = np.trapz(m[:170]**(5/3)*Ekz2[:170], m[:170])

fig, axs = plt.subplots(2,2, figsize=(9,6))
axs[0,0].plot(kx, kx**(5/3)*Ekx07[:341]/N1, color='midnightblue', label='$\\mathrm{F07}$')
axs[0,0].plot(kx, kx**(5/3)*Ekx1[:341]/N2, color='firebrick', label='$\\mathrm{F1}$')
axs[0,0].plot(kx, kx**(5/3)*Ekx2[:341]/N3, color='tab:orange', label='$\\mathrm{F2}$')
axs[0,0].set_ylim(0.001, 0.5)
axs[0,0].set_xscale('log')
axs[0,0].set_yscale('log')
axs[0,0].set_xlabel('$k_x$')
axs[0,0].set_title('$k_x^{5/3}E(k)$')
axs[0,0].annotate('$a)$', (0.09,0.8), annotation_clip=False)
axs[0,0].legend(frameon=False)
    
axs[0,1].plot(m[:170]/MO07, m[:170]**3*Ekz07[:170]/N4, color='midnightblue')
axs[0,1].plot(m[:170]/MO1, m[:170]**3*Ekz1[:170]/N5, color='firebrick')
axs[0,1].plot(m[:170]/MO2, m[:170]**3*Ekz2[:170]/N6, color='tab:orange')
axs[0,1].set_xlabel('$\hat{k_z}=k_z/k_O$')
axs[0,1].set_title('$k_z^{3}E(k_z)$')
axs[0,1].annotate('$b)$', (0.02,0.8), annotation_clip=False)

axs[1,0].plot(m[:170]/MO2, m[:170]**(3)*Ekz2_2[:170]/N9, color='tab:orange', linestyle='--')
axs[1,0].plot(m[:170]/MO07, m[:170]**(3)*Ekz07_2[:170]/N7,  color='midnightblue', linestyle='--')
axs[1,0].plot(m[:170]/MO1, m[:170]**(3)*Ekz1_2[:170]/N8,  color='firebrick', linestyle='--')
axs[1,0].plot(m[10:170]/MO1,0.003*(m[10:170]/7)**(4/3), linestyle='dotted', linewidth=1, color='k')
axs[1,0].annotate('$4/3$', (0.6, 0.003), size=10)
axs[1,0].set_xlabel('$\hat{k}_{z}$')
axs[1,0].set_title('$k_z^{3}E(k_z)[k_x\\neq 0]$')
axs[1,0].annotate('$c)$', (0.02,0.8), annotation_clip=False)

axs[1,1].plot(m[:170]/MO2, m[:170]**(5/3)*Ekz2_2[:170]/N12, color='tab:orange', )
axs[1,1].plot(m[:170]/MO07, m[:170]**(5/3)*Ekz07_2[:170]/N10,  color='midnightblue',)
axs[1,1].plot(m[:170]/MO1, m[:170]**(5/3)*Ekz1_2[:170]/N11,  color='firebrick', )

axs[1,1].plot(m[5:170]/MO1,0.015*(m[5:170]/7)**(-4/3), linestyle='dotted', linewidth=1, color='k')
axs[1,1].annotate('$-4/3$', (0.3, 0.008), size=10)
axs[1,1].set_xlabel('$\hat{k}_z$')
axs[1,1].set_title('$k_z^{5/3}E(k_z)[k_x\\neq 0]$')
axs[1,1].annotate('$d)$', (0.02,0.8), annotation_clip=False)

axs[0,1].set_ylim(0.001,0.5)
axs[0,1].set_xlim(0.03,15)
axs[0,1].set_xscale('log')
axs[0,1].set_yscale('log')

axs[1,0].set_ylim(0.001,0.5)
axs[1,0].set_xlim(0.03,15)
axs[1,0].set_xscale('log')
axs[1,0].set_yscale('log')

axs[1,1].set_ylim(0.001,0.5)
axs[1,1].set_xlim(0.03,15)
axs[1,1].set_xscale('log')
axs[1,1].set_yscale('log')

axs[1,1].set_yticklabels([])
axs[0,1].set_yticklabels([])
fig.tight_layout()
fig.show()

/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part
  return np.asarray(x, float)