p.plot(ssp_wave, ssp_flux[i_age], hold=False) p.xlim(1800., 11000.) # Pick out some wavelength range of interest: # Nah, not worth it... #lam_lo = 1750. #lam_hi = 11000. #ilam_lo = min(n.where(ssp_wave >= lam_lo)[0]) #ilam_hi = max(n.where(ssp_wave <= lam_hi)[0]) # Does a blur matrix at this stage give us something tractable? vdisp = 225. # (in km/s) c_in_km_per_s = 299792.458 sigconv = ssp_wave * vdisp / c_in_km_per_s ssp_bound = pxs.cen2bound(ssp_wave) gblur = pxs.gauss_blur_matrix(ssp_bound, sigconv) ssp_blur = gblur * ssp_flux[i_age] p.plot(ssp_wave, ssp_flux[i_age], hold=False) p.plot(ssp_wave, ssp_blur, hold=True) p.xlim(1800., 11000.) # Looks good. # Now let's bin it down to the BOSS coadd resoultion # and dial in the wavelength range we want. sspSpline = pxs.PixelSpline(ssp_bound, ssp_blur) ssp_coeff0 = 3.225 ssp_coeff1 = 0.0001 ssp_naxis1 = 2**13 ssp_loglam = ssp_coeff0 + ssp_coeff1 * n.arange(ssp_naxis1)
p.xlim(1800.,11000.) # Pick out some wavelength range of interest: # Nah, not worth it... #lam_lo = 1750. #lam_hi = 11000. #ilam_lo = min(n.where(ssp_wave >= lam_lo)[0]) #ilam_hi = max(n.where(ssp_wave <= lam_hi)[0]) # Does a blur matrix at this stage give us something tractable? vdisp = 225. # (in km/s) c_in_km_per_s = 299792.458 sigconv = ssp_wave * vdisp / c_in_km_per_s ssp_bound = pxs.cen2bound(ssp_wave) gblur = pxs.gauss_blur_matrix(ssp_bound, sigconv) ssp_blur = gblur * ssp_flux[i_age] p.plot(ssp_wave, ssp_flux[i_age], hold=False) p.plot(ssp_wave, ssp_blur, hold=True) p.xlim(1800.,11000.) # Looks good. # Now let's bin it down to the BOSS coadd resoultion # and dial in the wavelength range we want. sspSpline = pxs.PixelSpline(ssp_bound, ssp_blur) ssp_coeff0 = 3.225 ssp_coeff1 = 0.0001 ssp_naxis1 = 2**13 ssp_loglam = ssp_coeff0 + ssp_coeff1 * n.arange(ssp_naxis1)
# the end conditions that we could control to get the residuals # down even further, but it's in pretty good shape. # Last thing, out of the pixelsplines module, but not # a method of the PixelSpline class, is the convolution # by variable resolution. This works analytically, # but on pixelized data rather than on any spline # form of data. # Let's say we want to convolve our spectrum by something # with a sigma that would be 1 Angstrom at 3000 Ang, # and 1 Ang at 1.2 micron, and 2 Ang at 7500 Ang, varying # quadratically with wavelength. sig_conv = 1. - (wave - 3000.) * (wave - 12000.) / (4500.**2) blurmatrix = pxs.gauss_blur_matrix(wavebound, sig_conv) # Apply the blur matrix to our test spectrum to see what the outcome looks like: blurflux = blurmatrix * flux[istar] p.plot(wave, flux[istar], drawstyle='steps-mid', hold=False) p.plot(wave, blurflux, drawstyle='steps-mid', hold=True) # Let's make a second blur matrix, and a third that is purportedly # the quadrature-sum of the two, and see whether the composition # of the first two is equal to the third in its action: sig_conv_2 = 3. - sig_conv sig_conv_3 = n.sqrt(sig_conv**2 + sig_conv_2**2) blurmatrix_2 = pxs.gauss_blur_matrix(wavebound, sig_conv_2) blurmatrix_3 = pxs.gauss_blur_matrix(wavebound, sig_conv_3)
