def ConvolveSmooth(self, numiters=10, lowreject=2, highreject=3):
done = False
data = self.smoothing_data.copy()
# data.y /= data.cont
iterations = 0
if self.window_size % 2 == 0:
self.window_size += 1
while not done and iterations < numiters:
iterations += 1
done = True
y = FittingUtilities.savitzky_golay(data.y, self.window_size, 5)
#s = np.r_[data.y[self.window_size/2:0:-1], data.y, data.y[-1:-self.window_size/2:-1]]
#y = np.convolve(window/window.sum(), s, mode='valid')
reduced = data.y / y
sigma = np.std(reduced)
mean = np.mean(reduced)
badindices = \
np.where(np.logical_or((reduced - mean) / sigma < -lowreject, (reduced - mean) / sigma > highreject))[0]
if badindices.size > 0:
done = False
data.y[badindices] = y[badindices]
return DataStructures.xypoint(x=self.smoothing_data.x, y=y / self.smoothing_data.cont)
orders.pop(numorders - 1 - i)
else:
order.cont = FittingUtilities.Continuum(order.x, order.y, lowreject=3, highreject=3)
orders[numorders - 1 - i] = order.copy()
"""
for i, order in enumerate(orders):
plt.plot(order.x, order.y/order.cont+i)
plt.show()
sys.exit()
"""
#Smooth data
if smooth:
for order in orders:
order.y /= FittingUtilities.savitzky_golay(order.y, 91, 5)
output_dir = "Cross_correlations/"
outfilebase = fname.split(".fits")[0]
if "/" in fname:
dirs = fname.split("/")
output_dir = ""
outfilebase = dirs[-1].split(".fits")[0]
for directory in dirs[:-1]:
output_dir = output_dir + directory + "/"
output_dir = output_dir + "Cross_correlations/"
#Do the cross-correlation
for vsini in [10, 20, 30, 40]:
Correlate.PyCorr2(orders, resolution=60000, outdir=output_dir, models=model_data, stars=star_list,
temps=temp_list, gravities=gravity_list, metallicities=metal_list,
vsini=vsini * units.km.to(units.cm), debug=False, outfilebase=outfilebase)
def Filter(fname, vsini=100, numiters=100, lowreject=3, highreject=3):
orders = FitsUtils.MakeXYpoints(fname, extensions=True, x="wavelength", y="flux", errors="error")
column_list = []
for i, order in enumerate(orders):
smoothed = HelperFunctions.IterativeLowPass(
order, vsini * units.km.to(units.cm), numiter=numiters, lowreject=lowreject, highreject=highreject
)
plt.plot(order.x, order.y)
plt.plot(order.x, smoothed)
plt.show()
continue
done = False
x = order.x.copy()
y = order.y.copy()
indices = np.array([True] * x.size)
iteration = 0
while not done and iteration < numiters:
done = True
iteration += 1
smoothed = FittingUtilities.savitzky_golay(y, window_size, smoothorder)
residuals = y / smoothed
if iteration == 1:
# Save the first and last points, for interpolation
first, last = smoothed[0], smoothed[-1]
mean = residuals.mean()
std = residuals.std()
print residuals.size, x.size, y.size
# plt.plot((residuals - mean)/std)
# plt.show()
badindices = np.where(
np.logical_or((residuals - mean) / std > highreject, (residuals - mean) / std < -lowreject)
)[0]
if badindices.size > 1 and y.size - badindices.size > 2 * window_size and iteration < numiters:
done = False
x = np.delete(x, badindices)
y = np.delete(y, badindices)
print "iter = %i" % iteration
if x[0] > order.x[0]:
x = np.append(np.array([order.x[0]]), x)
smoothed = np.append(np.array([first]), smoothed)
if x[-1] < order.x[-1]:
x = np.append(x, np.array([order.x[-1]]))
smoothed = np.append(smoothed, np.array([last]))
print x.size, y.size, smoothed.size
smooth_fcn = interp(x, smoothed, k=1)
smoothed = smooth_fcn(order.x)
order.cont = FittingUtilities.Continuum(order.x, order.y)
plt.figure(1)
plt.plot(order.x, order.y / order.cont, "k-")
plt.plot(order.x, smoothed / order.cont, "r-")
# plt.figure(2)
# plt.plot(order.x, order.y/smoothed)
# plt.plot(order.x, smoothed)
# orders[i].y /= smoothed
column = {
"wavelength": order.x,
"flux": order.y / smoothed,
"continuum": np.ones(order.x.size),
"error": order.err,
}
column_list.append(column)
plt.show()
outfilename = "%s_smoothed.fits" % (fname.split(".fits")[0])
print "Outputting to %s" % outfilename
