def run_snap(dirf, s):
datfile = dirf + f'/correlation_grid{grid}/{s}.dat'
data = read_dat(datfile)
data = dict_to_np(data)
avg = np.copy(data)
n = 2
dirf = '-'.join([dirf.split('-')[0], str(n)])
datfile = dirf + f'/correlation_grid{grid}/{s}.dat'
# print(datfile)
while os.path.isfile(datfile):
# print(datfile)
data = read_dat(datfile)
avg += dict_to_np(data)
n += 1
dirf = '-'.join([dirf.split('-')[0], str(n)])
datfile = dirf + f'/correlation_grid{grid}/{s}.dat'
return avg / (n - 1)
def color(r, l):
cmap = matplotlib.cm.get_cmap('Spectral')
# return cmap(r/l)
return 'black'
from_freq = 5
r = 5
snap = 0
file = dir + f'/{snap}.dat'
plt.figure(1)
while os.path.isfile(file) and snap < 350:
print(file)
data = read_dat(file)
col = len(data)
max_freq = get_max(data['freq'][from_freq:], data[str(r)][from_freq:])
print(max_freq)
i = 1
plt.scatter(snap,
2 * np.pi / max_freq,
edgecolors=color(i, col),
facecolors=color(i, col))
snap += 1
file = dir + f'/{snap}.dat'
plt.xlabel(r'Time')
plt.ylabel(r'reduced wavenumber')
snaps = {50: [170], 60: [180], 70: [185, 200], 80: [200], 85: [215], 90: [240]}
plt.figure(1)
def func(x, a, b, c, d):
return a + b * x + c * x * 2 + d * x**3
def gaus(x, a, x0, sigma):
return a * exp(-(x - x0)**2 / (2 * sigma**2))
for snap in snaps[A]:
print(snap)
data = read_dat('/'.join([path_to_data, f'{snap}.dat']))
l = len(data['freq'][7:]) // 6
x = data['freq'][6:18]
y = data[str(6)][6:18]
n = len(x)
plt.plot(x, y, label=f'time={snap}', marker='.')
xx = np.linspace(x[0], x[-1], 1000)
# f = interp1d(x,y, kind='quadratic')
# # plt.plot(xx, f(xx), 'k--')
# tck = splrep(x, y, s=None, k=4)
# plt.plot(xx, splev(xx, tck), 'k--')
# ste = np.sqrt( sum((splev(x, tck) - ar(y))**2)/(n-2))
# chi = sum((splev(x, tck) - ar(y))**2/ar(y))
# chi2 = sum((splev(x, tck) - ar(y))**2/splev(x, tck))
