a spectra is a flux assiociated to wavelengths, the other parameters
can be readfrom the SDSS data
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__init__(self,
fluxes_cs=np.array([]),
fluxes=np.array([]),
noise=np.array([]),
zsdss=0,
coeff0=3.5793,
coeff1=0.0001,
ra=0,
dec=0,
angst=np.array([])) |
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initmask(self)
initialize the mask to an array containing only 'False' values, this
fonction should be called only once the spectra is non-empty |
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pixtoangst(self,
index)
converts a pixel to a wavelenght in angstroems, using a formula from
SDSS site |
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angsttopix(self,
wavelength)
converts a wavelength in angstroems to a pixel (it is the inverse
function 'pixtoangst') |
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pixtoz(self,
index)
converts a pixel to a redshift |
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ztopix(self,
z)
converts a redshift to a pixel (it is the inverse function 'pixtoz') |
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setangst(self,
coeff0=3.5793,
coeff1=0.0001)
defines the wavelength of the spectra |
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setz(self,
z)
sets the wavelengths corrsponding to the redshift |
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calc_csc(self)
substracts the continuum of the spectra, using a median filter |
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read(self,
directory,
filename)
creates a spectra from a SDSS file (i.e. |
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mask_angst(self,
tupleslist,
z=0)
for a spectra, we give a list of tuples (each one containing a
wavelength and a width) and we hide the pixels at the given
wavelength |
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mask_height(self,
height,
fluxchoice)
hides the peaks higher than a given value [not used] |
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mask_borders(self,
lambda1=5007+20,
lambda2=6562.81-160)
eliminates the flux outside the two given wavelengths |
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specsim(self,
z1,
z2,
model,
noiseintensity,
secintensity=1)
simulates a spectra containing one main and one secondary spectra :
you give one spectra as a model, the noise intensity, the two
redshift and the ratio between the two spectra flux [not used] |
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