def check_all():
filenames = [f for f in os.listdir("./") if f.endswith("smoothed.fits") and f.startswith("H")]
corrdir = "Cross_correlations/"
vsini_values = [1, 10, 20, 30, 40]
Temperatures = [3300, 3500, 3700, 3900, 4200, 4500, 5000, 5500]
Temperatures = range(3000, 6800, 100)
metals = [-0.5, 0.0, 0.5]
logg = 4.5
HelperFunctions.ensure_dir("Figures/")
for rootfile in sorted(filenames):
Tvals = []
Zvals = []
rotvals = []
significance = []
corrval = []
for T in Temperatures:
for metal in metals:
for vsini in vsini_values:
corrfile = "{0:s}{1:s}.{2:d}kps_{3:.1f}K{4:+.1f}{5:+.1f}".format(corrdir,
rootfile.split(".fits")[0],
vsini,
T,
logg,
metal)
print corrfile
try:
vel, corr = np.loadtxt(corrfile, unpack=True)
except IOError:
continue
# Check the significance of the highest peak within +/- 500 km/s
left = np.searchsorted(vel, -500)
right = np.searchsorted(vel, 500)
idx = np.argmax(corr[left:right]) + left
v = vel[idx]
goodindices = np.where(np.abs(vel - v) > vsini)[0]
std = np.std(corr[goodindices])
mean = np.mean(corr[goodindices])
mean = np.median(corr)
mad = HelperFunctions.mad(corr)
std = 1.4826 * mad
sigma = (corr[idx] - mean) / std
"""
# Plot if > 3 sigma peak
if sigma > 4:
fig = plt.figure(10)
ax = fig.add_subplot(111)
ax.plot(vel, corr, 'k-', lw=2)
ax.set_xlabel("Velocity (km/s)")
ax.set_ylabel("CCF")
ax.set_title(r'{0:s}: $T_s$={1:d}K & [Fe/H]={2:.1f}'.format(rootfile, T, metal))
ax.grid(True)
fig.savefig(u"Figures/{0:s}.pdf".format(corrfile.split("/")[-1]))
plt.close(fig)
"""
Tvals.append(T)
Zvals.append(metal)
rotvals.append(vsini)
significance.append(sigma)
corrval.append(corr[idx] - np.median(corr))
# Now, make a plot of the significance as a function of Temperature and metallicity for each vsini
Tvals = np.array(Tvals)
Zvals = np.array(Zvals)
rotvals = np.array(rotvals)
significance = np.array(significance)
corrval = np.array(corrval)
fig = plt.figure(1)
ax = fig.add_subplot(111, projection='3d')
ax.set_title("Significance Summary for %s" % (rootfile.split(".fits")[0].replace("_", " ")))
for i, rot in enumerate(vsini_values):
goodindices = np.where(abs(rotvals - rot) < 1e-5)[0]
ax.set_xlabel("Temperature (K)")
ax.set_ylabel("[Fe/H]")
ax.set_zlabel("Significance")
ax.plot(Tvals[goodindices], Zvals[goodindices], significance[goodindices], 'o', label="%i km/s" % rot)
#ax.plot(Tvals[goodindices], Zvals[goodindices], corrval[goodindices], 'o', label="{0:d} km/s".format(rot))
leg = ax.legend(loc='best', fancybox=True)
leg.get_frame().set_alpha(0.5)
fig.savefig("Figures/Summary_{0:s}.pdf".format(rootfile.split(".fits")[0]))
idx = np.argmax(significance)
#ax.plot(Tvals[idx], Zvals[idx], significance[idx], 'x', markersize=25, label="Most Significant")
print os.getcwd()
plt.show()
print corrfile
try:
vel, corr = np.loadtxt(corrfile, unpack=True)
except IOError:
continue
# Check the significance of the highest peak within +/- 500 km/s
left = np.searchsorted(vel, -500)
right = np.searchsorted(vel, 500)
idx = np.argmax(corr[left:right]) + left
v = vel[idx]
goodindices = np.where(np.abs(vel - v) > vsini)[0]
std = np.std(corr[goodindices])
mean = np.mean(corr[goodindices])
mean = np.median(corr)
mad = HelperFunctions.mad(corr)
std = 1.4826 * mad
sigma = (corr[idx] - mean) / std
"""
# Plot if > 3 sigma peak
if sigma > 4:
fig = plt.figure(10)
ax = fig.add_subplot(111)
ax.plot(vel, corr, 'k-', lw=2)
ax.set_xlabel("Velocity (km/s)")
ax.set_ylabel("CCF")
ax.set_title(r'{0:s}: $T_s$={1:d}K & [Fe/H]={2:.1f}'.format(rootfile, T, metal))
ax.grid(True)
fig.savefig(u"Figures/{0:s}.pdf".format(corrfile.split("/")[-1]))
plt.close(fig)
