def f(x):
c = correlator(1, x[0] * np.pi, x[1] * 2 * np.pi, 0, 0, 0, omega, w, tW,
tS, tP, N2)
print(c)
if np.sum(np.abs(c[2:, 2:])) > 1e-15:
print(1)
return 1.0
else:
print(0)
return 0
import numpy as np
from pyTurb import correlator
from multiprocessing import Pool
omega = 1.
tS = np.pi / 4
tP = np.pi / 4
tW = np.pi / 2
N2 = -1
tolr = 1e-30
tola = 1e-30
t = 1.
p = 1.
w = 0.00001
correl = correlator(1, t, p, 0, 0, 0, omega, w, tW, tS, tP, N2)
print(correl)
print('---')
w = 0.0001
correl = correlator(1, t, p, 0, 0, 0, omega, w, tW, tS, tP, N2)
print(correl)
print('---')
w = 0.001
correl = correlator(1, t, p, 0, 0, 0, omega, w, tW, tS, tP, N2)
print(correl)
print('---')
w = 0.01
correl = correlator(1, t, p, 0, 0, 0, omega, w, tW, tS, tP, N2)
print(correl)
print('---')
w = 1e-3 * omega
N2 = 1
tRan = np.linspace(0, np.pi, num=1000, endpoint=True)
pRan = np.linspace(0, 2 * np.pi, num=2000, endpoint=True)
correl = np.zeros((len(tRan), len(pRan), 4, 4))
for i, t in enumerate(tRan):
for j, p in enumerate(pRan):
correl[i, j] = correlator(1.,
t,
p,
0,
0,
0,
omega,
w,
tW,
tS,
tP,
N2,
eps=1e-20)
# correl[i,j] -= correlator(1., t, p, 0, 0, 0, omega, w, tW, tS, tP, N2, eps=1e-20)
#correl /= (w*omega)
correl = np.sum(np.sum(np.abs(correl[..., 2:, 2:])**2, axis=-1), axis=-1)**0.5
correl[np.abs(correl) < 1e-20] = 1e-20
correl = np.log10(np.abs(correl))
import matplotlib.pyplot as plt
import numpy as np
from pyTurb import correlator
B = 1.
tB = np.pi / 4
pB = 0
tS = np.pi / 2
tP = 0
tW = np.pi / 2
omega = 1.
w = -3. / 2
N2 = 0
eps = 1e-20
kRan = np.linspace(1e-3, 1 - 1e-3, num=40, endpoint=True)
tRan = np.linspace(0, np.pi, num=40, endpoint=True)
pRan = np.linspace(0, 2 * np.pi, num=40, endpoint=True)
correl = np.zeros((len(kRan), len(tRan), len(pRan), 4))
for l, k in enumerate(kRan):
for i, t in enumerate(tRan):
for j, p in enumerate(pRan):
correl[l, i, j, 0] = k / np.max(kRan)
correl[l, i, j, 1] = t / np.max(tRan)
correl[l, i, j, 2] = p / np.max(pRan)
correl[l, i, j, 3] = abs(
correlator(k**(-2. / 3), t, p, B, tB, pB, omega, w, tW, tS, tP,
N2))[3, 3]
np.savetxt('correl3D.txt', correl.reshape((-1, 4)))
N2 = 1
eps = 1e-20
tRan = np.linspace(0, np.pi, num=400, endpoint=True)
pRan = np.linspace(0, 2 * np.pi, num=400, endpoint=True)
correl = np.zeros((len(tRan), len(pRan), 4, 4))
for i, t in enumerate(tRan):
for j, p in enumerate(pRan):
correl[i, j] = correlator(1.,
t,
p,
B,
tB,
pB,
omega,
w,
tW,
tS,
tP,
N2,
eps=1e-2,
order=1)
correl = np.sum(np.sum(np.abs(correl[..., 2:, 2:])**2, axis=-1), axis=-1)**0.5
correl[np.abs(correl) < 1e-16] = 0
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import ticker
fig, ax = plt.subplots(figsize=(4, 4))
im = ax.imshow(np.abs(correl),