def box_smooth(self, nbox, preserve=False, **kwargs):
""" Box car smooth the spectrum
Parameters
----------
nbox: int
Number of pixels to smooth over
preserve: bool (False)
If True, perform a convolution to ensure the new spectrum
has the same number of pixels as the original.
**kwargs: dict
If preserve=True, these keywords are passed on to
astropy.convoution.convolve
Returns
-------
A new XSpectrum1D instance of the smoothed spectrum
"""
if preserve:
from astropy.convolution import convolve, Box1DKernel
new_fx = convolve(self.flux, Box1DKernel(nbox), **kwargs)
new_sig = convolve(self.sig, Box1DKernel(nbox), **kwargs)
new_wv = self.wavelength
else:
# Truncate arrays as need be
npix = len(self.flux)
try:
new_npix = npix // nbox # New division
except ZeroDivisionError:
raise ZeroDivisionError('Dividing by zero..')
orig_pix = np.arange(new_npix * nbox)
# Rebin (mean)
new_wv = liu.scipy_rebin(self.wavelength[orig_pix], new_npix)
new_fx = liu.scipy_rebin(self.flux[orig_pix], new_npix)
if self.sig_is_set:
new_sig = liu.scipy_rebin(
self.sig[orig_pix], new_npix) / np.sqrt(nbox)
else:
new_sig = None
# Return
return XSpectrum1D.from_tuple(
(new_wv, new_fx, new_sig), meta=self.meta.copy())
def box_smooth(self, nbox, preserve=False):
""" Box car smooth spectrum and return a new one
Is a simple wrapper to the rebin routine
Parameters
----------
nbox: integer
Number of pixels to smooth over
preserve: bool (False)
Keep the new spectrum at the same number of pixels as original
Returns:
--------
XSpectrum1D of the smoothed spectrum
"""
if preserve:
from astropy.convolution import convolve, Box1DKernel
new_fx = convolve(self.flux, Box1DKernel(nbox))
new_sig = convolve(self.sig, Box1DKernel(nbox))
new_wv = self.dispersion
else:
# Truncate arrays as need be
npix = len(self.flux)
try:
new_npix = npix // nbox # New division
except ZeroDivisionError:
raise ZeroDivisionError("Dividing by zero..")
orig_pix = np.arange(new_npix * nbox)
# Rebin (mean)
new_wv = liu.scipy_rebin(self.dispersion[orig_pix], new_npix)
new_fx = liu.scipy_rebin(self.flux[orig_pix], new_npix)
new_sig = liu.scipy_rebin(self.sig[orig_pix], new_npix) / np.sqrt(nbox)
# Return
return XSpectrum1D.from_array(
new_wv, new_fx, meta=self.meta.copy(), uncertainty=apy.nddata.StdDevUncertainty(new_sig)
)
def box_smooth(self, nbox, preserve=False):
""" Box car smooth the spectrum
Parameters
----------
nbox: integer
Number of pixels to smooth over
preserve: bool (False)
If True, perform a convolution to ensure the new spectrum
has the same number of pixels as the original.
Returns
-------
A new XSpectrum1D instance of the smoothed spectrum
"""
if preserve:
from astropy.convolution import convolve, Box1DKernel
new_fx = convolve(self.flux, Box1DKernel(nbox))
new_sig = convolve(self.sig, Box1DKernel(nbox))
new_wv = self.wavelength
else:
# Truncate arrays as need be
npix = len(self.flux)
try:
new_npix = npix // nbox # New division
except ZeroDivisionError:
raise ZeroDivisionError('Dividing by zero..')
orig_pix = np.arange(new_npix * nbox)
# Rebin (mean)
new_wv = liu.scipy_rebin(self.wavelength[orig_pix], new_npix)
new_fx = liu.scipy_rebin(self.flux[orig_pix], new_npix)
new_sig = liu.scipy_rebin(
self.sig[orig_pix], new_npix) / np.sqrt(nbox)
# Return
return XSpectrum1D.from_array(
new_wv, new_fx, meta=self.meta.copy(),
uncertainty=apy.nddata.StdDevUncertainty(new_sig))
