dr = 5.0
sphere = gal.ds.sphere(pos, (rmax, 'pc')) # do small for now
x = sphere['spherical_radius'].convert_to_units('pc').value
y = sphere['Fe_Fraction'].value
p = yt.ProfilePlot(sphere,
"radius", ["Fe_Fraction", 'Fe_over_H', 'O_over_Fe'],
weight_field='cell_volume',
accumulation=False)
p.set_unit('radius', 'pc')
p.save()
bins = np.arange(0.0, rmax + dr, dr)
#acf, bins = ACF_scargle(x, y, dy = 0.0000001, n_omega = 2**12, omega_max = np.pi/5.0) #, bins = bins)
acf, err, bins = ACF_EK(x, y, dy=1.0E-8, bins=bins)
print(acf)
print(bins)
simple_plot(0.5 * (bins[1:] + bins[:-1]), acf, 'Fe_Fraction_acf.png')
print('----------------------------------------------------------')
print('----------------------------------------------------------')
x = sphere['spherical_radius'].convert_to_units('pc').value
y = sphere['Fe_over_H'].value
bins = np.arange(0.0, rmax + dr, dr)
#acf, bins = ACF_scargle(x, y, dy = 0.0001, n_omega = 2**12, omega_max = np.pi/5.0) #, bins = bins)
acf, err, bins = ACF_EK(x, y, dy=0.00001, bins=bins)
# add errors
dy = 0.1
y_obs = np.random.normal(y, dy)
#------------------------------------------------------------
# compute ACF via scargle method
C_S, t_S = ACF_scargle(t, y_obs, dy, n_omega=2**12, omega_max=np.pi / 5.0)
ind = (t_S >= 0) & (t_S <= 500)
t_S = t_S[ind]
C_S = C_S[ind]
#------------------------------------------------------------
# compute ACF via E-K method
C_EK, C_EK_err, bins = ACF_EK(t, y_obs, dy, bins=np.linspace(0, 500, 51))
t_EK = 0.5 * (bins[1:] + bins[:-1])
#------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(5, 5))
# plot the input data
ax = fig.add_subplot(211)
ax.errorbar(t, y_obs, dy, fmt='.k', lw=1)
ax.set_xlabel('t (days)')
ax.set_ylabel('observed flux')
# plot the ACF
ax = fig.add_subplot(212)
ax.plot(t_S, C_S, '-', c='gray', lw=1, label='Scargle')
def compute_AF():
from astroML.time_series import lomb_scargle, generate_damped_RW
from astroML.time_series import ACF_scargle, ACF_EK
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
#------------------------------------------------------------
# Generate time-series data:
# we'll do 1000 days worth of magnitudes
t = np.arange(0, 1E3)
z = 2.0
tau = 300
tau_obs = tau / (1. + z)
np.random.seed(6)
y = generate_damped_RW(t, tau=tau, z=z, xmean=20)
# randomly sample 100 of these
ind = np.arange(len(t))
np.random.shuffle(ind)
ind = ind[:100]
ind.sort()
t = t[ind]
y = y[ind]
# add errors
dy = 0.1
y_obs = np.random.normal(y, dy)
#------------------------------------------------------------
# compute ACF via scargle method
C_S, t_S = ACF_scargle(t, y_obs, dy,
n_omega=2 ** 12, omega_max=np.pi / 5.0)
ind = (t_S >= 0) & (t_S <= 500)
t_S = t_S[ind]
C_S = C_S[ind]
#------------------------------------------------------------
# compute ACF via E-K method
C_EK, C_EK_err, bins = ACF_EK(t, y_obs, dy, bins=np.linspace(0, 500, 51))
t_EK = 0.5 * (bins[1:] + bins[:-1])
#------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(5, 5))
# plot the input data
ax = fig.add_subplot(211)
ax.errorbar(t, y_obs, dy, fmt='.k', lw=1)
ax.set_xlabel('t (days)')
ax.set_ylabel('observed flux')
# plot the ACF
ax = fig.add_subplot(212)
ax.plot(t_S, C_S, '-', c='gray', lw=1,
label='Scargle')
ax.errorbar(t_EK, C_EK, C_EK_err, fmt='.k', lw=1,
label='Edelson-Krolik')
ax.plot(t_S, np.exp(-abs(t_S) / tau_obs), '-k', label='True')
ax.legend(loc=3)
ax.plot(t_S, 0 * t_S, ':', lw=1, c='gray')
ax.set_xlim(0, 500)
ax.set_ylim(-1.0, 1.1)
ax.set_xlabel('t (days)')
ax.set_ylabel('ACF(t)')
plt.show()