def update_frame(frame, format=1):
if format == 1:
t, vxm, vym, num = frame
phi = np.sqrt(vxm**2 + vym**2)
elif format == 2:
t, phi, num = frame
rho = num.astype(float)
rho_s = gaussian_filter(rho, sigma=[5, 1])
xh1, rho_h = half_peak.find_interface(rho, sigma=[5, 1])
xh2, rho_h = half_peak.find_interface(rho, sigma=[10, 1])
xh3, rho_h = half_peak.find_interface(rho, sigma=[15, 1])
xh4, rho_h = half_peak.find_interface(rho, sigma=[20, 1])
xh1 = half_rho.untangle(xh1, Lx)
xh2 = half_rho.untangle(xh2, Lx)
xh3 = half_rho.untangle(xh3, Lx)
xh4 = half_rho.untangle(xh4, Lx)
w1 = np.var(xh1)
w2 = np.var(xh2)
w3 = np.var(xh3)
w4 = np.var(xh4)
dx = np.round(100 - np.mean(xh1)).astype(int)
xh1 += dx
xh2 += dx
xh3 += dx
rho_s = np.roll(rho_s, dx, axis=1)
im.set_data(rho_s.T)
line1.set_data(yh, xh1)
line3.set_data(yh, xh3)
title.set_text(title_template % (eta, eps, Lx, Ly, N, t, phi, w1, w3))
writer.grab_frame()
print("t=", t)
f.write("%d\t%f\t%f\t%f\t%f\t%f\n" % (t, phi, w1, w2, w3, w4))
def handle_raw_snap():
import load_snap
from half_rho import untangle
import snake
import half_peak
os.chdir(r"D:\tmp")
Lx = 180
Ly = 1000
sigma = [5, 1]
r = np.round(np.logspace(2, 18, 17, base=np.sqrt(2))).astype(int)
G1 = np.zeros(r.size)
G2 = np.zeros(r.size)
count = 0
snap = load_snap.RawSnap(r"so_%g_%g_%d_%d_%d_%d_%d.bin" %
(0.35, 0, Lx, Ly, Lx * Ly, 2000, 1234))
n = snap.get_num_frames()
print("n=", n)
t_beg = 250
t_end = 300
for i, frame in enumerate(snap.gene_frames(t_beg, t_end)):
x, y, theta = frame
rho = load_snap.coarse_grain2(x,
y,
theta,
Lx=Lx,
Ly=Ly,
ncols=Lx,
nrows=Ly).astype(float)
xh1, rho_h = half_peak.find_interface(rho, sigma=sigma)
rho_s = gaussian_filter(rho, sigma=sigma)
xh2, yh2 = snake.find_interface(rho_s,
0.5,
0.1,
0.25,
400,
rho_h,
dx=5)
xh1 = untangle(xh1, Lx)
xh2 = untangle(xh2, Lx)
G1 += cal_G(xh1, r)
G2 += cal_G(xh2, r)
count += 1
print("i = ", i)
G1 /= count
G2 /= count
plt.plot(r, G1)
plt.plot(r, G2)
plt.xscale("log")
plt.yscale("log")
plt.show()
plt.close()
for i in range(G1.size):
print(r[i], G1[i], G2[i])
print(count)
def show_separated_snaps(Lx,
Ly,
seed,
t_beg,
t_end,
dt=None,
eta=0.35,
eps=0,
rho0=1,
transpos=False,
sigma=[5, 1]):
from half_peak import find_interface
from half_rho import untangle
if dt is None:
dt = t_beg
t = t_beg
while t <= t_end:
file = r's_%g_%g_%g_%d_%d_%d_%08d.bin' % (eta, eps, rho0, Lx, Ly, seed,
t)
snap = RawSnap(file)
for frame in snap.gene_frames():
try:
x, y, theta = frame
phi = np.sqrt(
np.mean(np.cos(theta))**2 + np.mean(np.sin(theta))**2)
rho = coarse_grain2(x, y, theta, Lx=Lx, Ly=Ly).astype(float)
yh = np.linspace(0.5, Ly - 0.5, Ly)
xh, rho_h = find_interface(rho, sigma=sigma)
w = np.var(untangle(xh, Lx))
print("t=%d, phi=%f, w=%f" % (t, phi, w))
plt.subplot(121)
if transpos:
x, y = y, x
xh, yh = yh, xh
plt.xlim(0, Ly)
plt.ylim(0, Lx)
else:
plt.xlim(0, Lx)
plt.ylim(0, Ly)
plt.scatter(x, y, s=1, c=theta, cmap="hsv")
plt.plot(xh, yh)
plt.title(r"$t=%d, \phi=%g, w^2=%g$" % (t, phi, w))
plt.colorbar()
plt.subplot(122)
plt.plot(rho.mean(axis=0))
plt.show()
plt.close()
except:
print("t=%d, Error" % t)
t += dt
def update_frame():
for i in range(nfile):
t, vxm, vym, num = next(frames[i])
xh, rho_h = half_peak.find_interface(num.astype(float),
sigma=[20, 1])
xh = half_rho.untangle(xh, Lx[i])
w = np.var(xh)
dx = 100 - np.mean(xh)
xh += dx
phi = np.sqrt(vxm**2 + vym**2)
line[i].set_data(yh[i], xh)
line[i].set_label(r"$L_y=%d, w^2=%.4f, \phi=%.4f$" %
(Lx[i], w, phi))
title.set_text(r"$\eta=%g, \epsilon=%g, L_x=%d, t=%d$" %
(eta[0], eps[0], Ly[0], t))
plt.legend()
writer.grab_frame()
print("t=", t)
def cal_spectrum(Lx, Ly, sigma_y=10, show=False, out=False, eps=0, dt=1):
file = r"so_0.35_%g_%d_%d_%d_2000_1234.bin" % (eps, Lx, Ly, Lx * Ly)
snap = load_snap.RawSnap(file)
if not isinstance(sigma_y, list):
nrows = 1
sigma_y = [sigma_y]
else:
nrows = len(sigma_y)
ncols = Ly // 2 + 1
q = np.arange(ncols) / Ly
spectrum = np.zeros((nrows, ncols))
count = np.zeros(nrows, int)
for frame in snap.gene_frames(beg_idx=300, interval=dt):
x, y, theta = frame
rho = load_snap.coarse_grain2(x, y, theta, Lx=Lx, Ly=Ly).astype(float)
for i, sy in enumerate(sigma_y):
try:
xh, rho_h = find_interface(rho, sigma=[sy, 1])
xh = untangle(xh, Lx)
h = xh - np.mean(xh)
hq = np.fft.rfft(h) / Ly
A2 = np.abs(hq)**2
spectrum[i] += A2
count[i] += 1
except:
pass
print("t=%d" % (dt * count[-1]))
for i in range(nrows):
spectrum[i] /= count[i]
if show:
plt.loglog(q, spectrum)
plt.show()
plt.close()
if out:
outfile = "hq_%g_%d_%d.dat" % (eps, Lx, Ly)
with open(outfile, "w") as f:
for i in range(q.size):
line = "%f" % (q[i]**2)
for j in range(nrows):
line += "\t%.8f" % (spectrum[j, i])
line += "\n"
f.write(line)
def time_ave(Lx, Ly, sigma_y=100, show=False, out=False, eps=0, dt=1):
file = r"so_0.35_%g_%d_%d_%d_2000_1234.bin" % (eps, Lx, Ly, Lx * Ly)
snap = load_snap.RawSnap(file)
if not isinstance(sigma_y, list):
nrows = 1
sigma_y = [sigma_y]
else:
nrows = len(sigma_y)
ncols = Lx
rho_mean = np.zeros((nrows, ncols))
count = np.zeros(nrows, int)
x0 = np.arange(Lx) + 0.5
for frame in snap.gene_frames(beg_idx=300, interval=dt):
x, y, theta = frame
rho = load_snap.coarse_grain2(x, y, theta, Lx=Lx, Ly=Ly).astype(float)
rho_real = load_snap.coarse_grain(x, y, Lx=Lx, Ly=Ly)
for i, sy in enumerate(sigma_y):
try:
xh, rho_h = find_interface(rho, sigma=[sy, 1])
rho_mean[i] += ave_one_frame(rho_real, xh)
count[i] += 1
except:
pass
print("t=%d" % (dt * count[-1]))
for i in range(nrows):
rho_mean[i] /= count[i]
if show:
for i in range(nrows):
plt.plot(x0, rho_mean[i])
plt.show()
plt.close()
if out:
outfile = "avePeak_%g_%d_%d.dat" % (eps, Lx, Ly)
with open(outfile, "w") as f:
for i in range(ncols):
line = "%f" % (x0[i])
for j in range(nrows):
line += "\t%.8f" % (rho_mean[j, i])
line += "\n"
f.write(line)
def handle_raw_snap(file):
import half_peak
import spatial_corr
from half_rho import untangle
path, file = file.split("/")
os.chdir(path)
str_list = file.split("_")
Lx = int(str_list[3])
Ly = int(str_list[4])
snap = RawSnap(file)
outfile = file.replace(".bin", ".dat")
f = open(outfile, "w")
corr2D = spatial_corr.Corr2D(file, 1)
for i, frame in enumerate(snap.gene_frames()):
x, y, theta = frame
vxm = np.mean(np.cos(theta))
vym = np.mean(np.sin(theta))
phi = np.sqrt(vxm**2 + vym**2)
rho = coarse_grain2(x, y, theta, Lx=Lx, Ly=Ly, ncols=Lx,
nrows=Ly).astype(float)
line = "%d\t%f" % (i, phi)
for sigma_y in [1, 5, 10, 15, 20]:
try:
xh, rho_h = half_peak.find_interface(rho, sigma=[sigma_y, 1])
xh = untangle(xh, Lx)
w = np.var(xh)
line += "\t%f" % (w)
except:
print("Error when sigma_y = %d and i = %d" % (sigma_y, i))
line += "\t"
line += "\n"
f.write(line)
if i >= 300:
rho, vx, vy = coarse_grain(x, y, theta, Lx, Ly)
corr2D.accu(vx, vy, rho)
f.close()
corr2D.outfile()
width1 = []
width2 = []
debug = True
t_beg = 0
t_end = None
for i, frame in enumerate(snap.gene_frames(t_beg, t_end, 100)):
x, y, theta = frame
rho = load_snap.coarse_grain2(x,
y,
theta,
Lx=Lx,
Ly=Ly,
ncols=Lx,
nrows=Ly).astype(float)
xh, rho_h = half_rho.find_interface(rho, sigma=[1, 1])
xh2, rho_h2 = half_peak.find_interface(rho, sigma=[1, 1])
rho_s = gaussian_filter(rho, sigma=[1, 1])
xh3, yh3 = find_interface(rho_s, 0.5, 0.1, 0.25, 500, rho_h2, dx=5)
yh = np.linspace(0.5, Ly - 0.5, Ly)
if debug:
# plt.scatter(y, x, s=0.5, c=theta, cmap="hsv")
plt.plot(y, x, "o", ms="1")
rho_s[rho_s > 5] = 5
# plt.imshow(rho_s.T, interpolation="none", origin="lower")
xh = untangle(xh, Lx)
xh2 = untangle(xh2, Lx)
xh3 = untangle(xh3, Lx)
plt.plot(yh, xh, "g")
plt.plot(yh, xh2, "r--")
plt.plot(yh3, xh3, "k:")
plt.show()
os.chdir(path)
outfile = file.replace(".bin", "_skew.dat")
f = open(outfile, "w")
snap = load_snap.CoarseGrainSnap(file)
tot_frames = snap.get_num_frames()
ts = np.zeros(tot_frames, int)
h10 = np.zeros((tot_frames, Ly))
h15 = np.zeros((tot_frames, Ly))
frames = snap.gene_frames()
for i, frame in enumerate(frames):
t, vxm, vym, num = frame
ts[i] = t
line = "%d" % t
rho = num.astype(float)
xh1, rho_h = half_peak.find_interface(rho, sigma=[10, 1])
xh1 = half_rho.untangle(xh1, Lx)
h10[i] = xh1
gamma1 = skew(xh1)
gamma2 = kurt(xh1)
line += "\t%f\t%f" % (gamma1, gamma2)
xh1, rho_h = half_peak.find_interface(rho, sigma=[15, 1])
xh1 = half_rho.untangle(xh1, Lx)
h15[i] = xh1
gamma1 = skew(xh1)
gamma2 = kurt(xh1)
line += "\t%f\t%f" % (gamma1, gamma2)
try:
xh2 = isoline(rho, Lx, Ly, Lx, Ly // 20)
gamma1 = skew(xh2)
def show(self,
i_beg=0,
i_end=None,
di=1,
lx=1,
ly=1,
transpos=True,
sigma=[5, 1],
output=True,
show=True):
import half_rho
import half_peak
if output:
f = open(self.file.replace(".bin", "_%d.dat" % (sigma[0])), "w")
for i, frame in enumerate(self.gene_frames(i_beg, i_end, di)):
t, vxm, vym, num = frame
rho = num.astype(np.float32)
yh = np.linspace(0.5, self.nrows - 0.5, self.nrows)
# xh1, rho_h1 = half_rho.find_interface(rho, sigma=sigma)
xh2, rho_h2 = half_peak.find_interface(rho, sigma=sigma)
# xh1 = half_rho.untangle(xh1, self.ncols)
xh2 = half_rho.untangle(xh2, self.ncols)
# w1 = np.var(xh1)
w2 = np.var(xh2)
if show:
if ly > 1:
rho = np.array([
np.mean(num[i * ly:(i + 1) * ly], axis=0)
for i in range(self.nrows // ly)
])
if lx > 1:
rho = np.array([
np.mean(rho[:, i * lx:(i + 1) * lx], axis=1)
for i in range(self.ncols // lx)
])
rho = gaussian_filter(rho, sigma=[5, 1])
if transpos:
rho = rho.T
box = [0, self.nrows, 0, self.ncols]
plt.figure(figsize=(14, 3))
plt.plot(yh, xh2, "r")
plt.xlabel(r"$y$")
plt.ylabel(r"$x$")
else:
box = [0, self.ncols, 0, self.nrows]
plt.figure(figsize=(4, 12))
plt.plot(xh2, yh, "r")
plt.xlabel(r"$x$")
plt.ylabel(r"$y$")
rho[rho > 4] = 4
plt.imshow(rho,
origin="lower",
interpolation="none",
extent=box,
aspect="auto")
plt.title(r"$t=%d, \phi=%g, w^2=%g$" %
(t, np.sqrt(vxm**2 + vym**2), w2))
plt.tight_layout()
plt.show()
plt.close()
print(t, np.sqrt(vxm**2 + vym**2), w2)
if output:
f.write("%d\t%f\t%f\n" % (t, np.sqrt(vxm**2 + vym**2), w2))
if output:
f.close()