예제 #1
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def my_convolution(chann, head, newsize, outspax, psf, instpsf):

    #Convolve cube channel with PSF channel
    chann = psf_convolve(chann, psf)

    #Convolve cube channel with instrument PSF
    chann = psf_convolve(chann, instpsf)

    #Frebin datacube up to output spaxel scale
    newsize = (int(newsize[0]), int(newsize[1]))
    #chann *= (head['CDELT1']*head['CDELT2']*1.E-6)
    chann = frebin(chann, (newsize[0],newsize[1]), total=True)
    #chann /= (outspax[0]*outspax[1]*1.E-6)

    return chann
예제 #2
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def wavelength_loop(cube, head, wavels, out_cube, newsize, outspax):
    '''Function to take input datacube and process it iteratively through
    each wavelength channel as follows:
    - Generate PSF array for given channel
    - Add effect of ADR to channel
    - Convolve cube channel with PSF
    - Frebin up to chosen output spaxel scale

    Inputs:

        cube: Datacube
        head: Datacube header
        wavels: Wavelength array
        out_cube: Empty output cube
        newsize: tuple containing (x_newsize, y_newsize) array sizes
        outspax: output spaxels (x, y) (mas, mas)

    Output:

        cube: Processed cube
        head: Updated header

    '''

    nspec = wp.NIRSpec()
    nspec.pupilopd = None
    print 'OPD = ', nspec.pupilopd

    oversamp = 1000. / float(outspax[0])

    for l in xrange(len(wavels)):
        #Print percentage bar
        update_progress(n.round(l / float(len(wavels)), 2))

        #Create PSF channel
        psf = nspec.calcPSF(outfile=None,
                            source=None,
                            filter=None,
                            nlambda=None,
                            monochromatic=wavels[l] * 1.E-6,
                            oversample=oversamp,
                            fov_arcsec=5,
                            rebin=False)

        psf = psf[0].data

        #Convolve cube channel with PSF channel
        channel = psf_convolve(cube[l, :, :], psf)

        #Frebin datacube up to output spaxel scale
        newsize = (int(newsize[0]), int(newsize[1]))
        channel *= (head['CDELT1'] * head['CDELT2'] * 1.E-6)
        channel = frebin(channel, (newsize[0], newsize[1]), total=True)
        channel /= (outspax[0] * outspax[1] * 1.E-6)

        #Add channel to output cube
        out_cube[l, :, :] = channel

    return out_cube, head
예제 #3
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def pp_wavelength_channel(chann, head, wave, l, newsize, outspax):
    '''Function to take input datacube and process it iteratively through
    each wavelength channel as follows:
    - Generate PSF array for given channel
    - Add effect of ADR to channel
    - Convolve cube channel with PSF
    - Frebin up to chosen output spaxel scale

    Inputs:

        chann: cube channel
        head: Datacube header
        wave: wavelength [um]
        l: iteration no.
        out_cube: Empty output cube
        newsize: tuple containing (x_newsize, y_newsize) array sizes
        outspax: tuple containing (x, y) output spaxel scales

    Output:

        cube: Processed cube
        head: Updated header
        inspax: Input spaxel scale (mas, mas)
        outspax: Output spaxel scale (mas, mas)

    '''

    nspec = wp.NIRSpec()
    nspec.pupilopd = None
    #print 'OPD = ', nspec.pupilopd

    oversamp = 1000. / float(outspax[0])

    #Create PSF channel
    #print 'Wavelength = ', wave*1.E-6, 'm'
    psf = nspec.calcPSF(outfile=None,
                        source=None,
                        filter=None,
                        nlambda=None,
                        monochromatic=wave * 1.E-6,
                        oversample=oversamp,
                        fov_arcsec=5,
                        rebin=False)
    psf = psf[0].data

    #Convolve cube channel with PSF channel
    chann = psf_convolve(chann, psf)

    #Frebin datacube up to output spaxel scale
    newsize = (int(newsize[0]), int(newsize[1]))
    chann *= (head['CDELT1'] * head['CDELT2'] * 1.E-6)
    chann = frebin(chann, (newsize[0], newsize[1]), total=True)
    chann /= (outspax[0] * outspax[1] * 1.E-6)

    return chann, l
예제 #4
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def wavelength_loop(cube, head, wavels, out_cube, newsize, outspax):
    '''Function to take input datacube and process it iteratively through
    each wavelength channel as follows:
    - Generate PSF array for given channel
    - Add effect of ADR to channel
    - Convolve cube channel with PSF
    - Frebin up to chosen output spaxel scale

    Inputs:

        cube: Datacube
        head: Datacube header
        wavels: Wavelength array
        out_cube: Empty output cube
        newsize: tuple containing (x_newsize, y_newsize) array sizes
        outspax: output spaxels (x, y) (mas, mas)

    Output:

        cube: Processed cube
        head: Updated header

    '''

    nspec = wp.NIRSpec()
    nspec.pupilopd = None
    print 'OPD = ', nspec.pupilopd

    oversamp = 1000./float(outspax[0])

    for l in xrange(len(wavels)):
        #Print percentage bar
        update_progress(n.round(l/float(len(wavels)),2))

        #Create PSF channel
        psf = nspec.calcPSF(outfile=None, source=None, filter=None, nlambda=None,
                            monochromatic=wavels[l]*1.E-6, oversample=oversamp,
                            fov_arcsec=5, rebin=False)

        psf = psf[0].data

        #Convolve cube channel with PSF channel
        channel = psf_convolve(cube[l,:,:], psf)

        #Frebin datacube up to output spaxel scale
        newsize = (int(newsize[0]), int(newsize[1]))
        channel *= (head['CDELT1']*head['CDELT2']*1.E-6)
        channel = frebin(channel, (newsize[0],newsize[1]), total=True)
        channel /= (outspax[0]*outspax[1]*1.E-6)

        #Add channel to output cube
        out_cube[l,:,:] = channel

    return out_cube, head
예제 #5
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def pp_wavelength_channel(chann, head, wave, l, newsize, outspax):
    '''Function to take input datacube and process it iteratively through
    each wavelength channel as follows:
    - Generate PSF array for given channel
    - Add effect of ADR to channel
    - Convolve cube channel with PSF
    - Frebin up to chosen output spaxel scale

    Inputs:

        chann: cube channel
        head: Datacube header
        wave: wavelength [um]
        l: iteration no.
        out_cube: Empty output cube
        newsize: tuple containing (x_newsize, y_newsize) array sizes
        outspax: tuple containing (x, y) output spaxel scales

    Output:

        cube: Processed cube
        head: Updated header
        inspax: Input spaxel scale (mas, mas)
        outspax: Output spaxel scale (mas, mas)

    '''

    nspec = wp.NIRSpec()
    nspec.pupilopd = None
    #print 'OPD = ', nspec.pupilopd

    oversamp = 1000./float(outspax[0])

    #Create PSF channel
    #print 'Wavelength = ', wave*1.E-6, 'm'
    psf = nspec.calcPSF(outfile=None, source=None, filter=None, nlambda=None,
                        monochromatic=wave*1.E-6, oversample=oversamp,
                        fov_arcsec=5, rebin=False)
    psf = psf[0].data

    #Convolve cube channel with PSF channel
    chann = psf_convolve(chann, psf)

    #Frebin datacube up to output spaxel scale
    newsize = (int(newsize[0]), int(newsize[1]))
    chann *= (head['CDELT1']*head['CDELT2']*1.E-6)
    chann = frebin(chann, (newsize[0],newsize[1]), total=True)
    chann /= (outspax[0]*outspax[1]*1.E-6)

    return chann, l
예제 #6
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def wavelength_loop(cube,
                    head,
                    wavels,
                    out_cube,
                    AO,
                    psfvars,
                    adrvals,
                    newsize,
                    outspax,
                    adr_switch='ON'):
    '''Function to take input datacube and process it iteratively through
    each wavelength channel as follows:
    - Generate PSF array for given channel
    - Add effect of ADR to channel
    - Convolve cube channel with PSF
    - Frebin up to chosen output spaxel scale

    Inputs:

        cube: Datacube
        head: Datacube header
        wavels: Wavelength array
        out_cube: Empty output cube
        AO: AO mode [LTAO, SCAO, Gaussian]
        psdvars: list containing [psfparams, psfspax, psfsize, [D,eps],
                                  res_jitter, seeing, user_psf, user_psflams]
        adrvals: array of ADR values
        newsize: tuple containing (x_newsize, y_newsize) array sizes
        outspax: tuple containing (x, y) output spaxel scales
        adr_switch: ON or OFF. Turns ADR effect on or off.

    Output:

        cube: Processed cube
        head: Updated header
        inspax: Input spaxel scale (mas, mas)
        outspax: Output spaxel scale (mas, mas)

    '''
    for l in xrange(len(wavels)):
        #Print percentage bar
        update_progress(n.round(l / float(len(wavels)), 2))

        #Create PSF channel
        #If user PSF and 2D image
        upsf = psfvars[-2]
        upsflams = psfvars[-1]
        if type(upsf) != str and type(upsflams) == str:
            psf = upsf
        #If User PSF and 3D cube
        elif type(upsf) != str and type(upsflams) != str:
            #Interpolate PSF cube
            interp = interp1d(upsflams, upsf, axis=0)
            psf = interp(wavels[l])

        elif AO == 'LTAO' or AO == 'SCAO' or AO == 'GLAO':
            psf = create_psf_channel(psfvars[0], l, psfvars[1],
                                     (head['CDELT1'], head['CDELT2']),
                                     psfvars[2], psfvars[3], psfvars[4])

        elif AO == 'Gaussian':
            psf = create_Gausspsf_channel(wavels[l],
                                          psfvars[5],
                                          psfvars[3],
                                          psfvars[4],
                                          psfvars[2],
                                          Nyquist=False,
                                          psfspax=psfvars[1],
                                          output_spax=(head['CDELT1'],
                                                       head['CDELT2']))

        else:
            print 'AO = ', AO
            raise ValueError('AO choice error!')

        #Create instrument PSF array
        instpsf = create_instpsf(psfvars[2], psfvars[1], outspax,
                                 (head['CDELT1'], head['CDELT2']))

        #Add ADR effect to channel
        if adr_switch == 'ON':
            cube[l, :, :] = add_ADR(cube[l, :, :], head, adrvals[l], 'spline3')

        #Convolve cube channel with PSF channel
        channel = psf_convolve(cube[l, :, :], psf)

        #Convolve cube channel with instrument PSF
        channel = psf_convolve(channel, instpsf)

        #Frebin datacube up to output spaxel scale
        newsize = (int(newsize[0]), int(newsize[1]))
        channel *= (head['CDELT1'] * head['CDELT2'] * 1.E-6)
        channel = frebin(channel, (newsize[0], newsize[1]), total=True)
        channel /= (outspax[0] * outspax[1] * 1.E-6)

        #Correct ADR effect
        if adr_switch == 'ON':
            adrhead = head.copy()
            adrhead['CDELT1'] = outspax[0]
            adrhead['CDELT2'] = outspax[1]
            channel = add_ADR(channel, adrhead, -1. * adrvals[l], 'spline3')

        #Add channel to output cube
        out_cube[l, :, :] = channel

    return out_cube, head
예제 #7
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def pp_wavelength_channel(chann, head, wave, l, AO, psfvars, adrval, newsize,
                          outspax, adr_switch):
    '''Function to take input datacube and process it iteratively through
    each wavelength channel as follows:
    - Generate PSF array for given channel
    - Add effect of ADR to channel
    - Convolve cube channel with PSF
    - Frebin up to chosen output spaxel scale

    Inputs:

        chann: cube channel
        head: Datacube header
        wave: wavelength [um]
        l: iteration no.
        out_cube: Empty output cube
        AO: AO mode [LTAO, SCAO, Gaussian]
        psfvars: list containing [psfparams, psfspax, psfsize,
                                  [D,eps], res_jitter, seeing, user_psf,
                                  user_psflams]
        adrval: ADR value
        newsize: tuple containing (x_newsize, y_newsize) array sizes
        outspax: tuple containing (x, y) output spaxel scales
        adr_switch: On or OFF. Turns ADR effect on or off

    Output:

        cube: Processed cube
        head: Updated header
        inspax: Input spaxel scale (mas, mas)
        outspax: Output spaxel scale (mas, mas)

    '''

    #Create PSF channel
    #If user PSF and 2D image
    upsf = psfvars[-2]
    upsflams = psfvars[-1]
    if type(upsf) != str and type(upsflams) == str:
        psf = upsf
    #If User PSF and 3D cube
    elif type(upsf) != str and type(upsflams) != str:
        #Interpolate PSF cube
        interp = interp1d(upsflams, upsf, axis=0)
        psf = interp(wave)

    elif AO == 'LTAO' or AO == 'SCAO' or AO == 'GLAO':
        psf = create_psf_channel(psfvars[0], l, psfvars[1],
                                 (head['CDELT1'], head['CDELT2']), psfvars[2],
                                 psfvars[3], psfvars[4])

    elif AO == 'Gaussian':
        psf = create_Gausspsf_channel(wave, psfvars[5], psfvars[3], psfvars[4],
                                      psfvars[2], False, psfvars[1],
                                      (head['CDELT1'], head['CDELT2']))

    else:
        print 'AO = ', AO
        raise ValueError('AO choice or user_PSF error!')

    #Create instrument PSF array
    instpsf = create_instpsf(psfvars[2], psfvars[1], outspax,
                             (head['CDELT1'], head['CDELT2']))

    #Add ADR effect to channel
    if adr_switch == 'ON':
        chann = add_ADR(chann, head, adrval, 'spline3')

    #Convolve cube channel with PSF channel
    chann = psf_convolve(chann, psf)

    #Convolve cube channel with instrument PSF
    chann = psf_convolve(chann, instpsf)

    #Frebin datacube up to output spaxel scale
    newsize = (int(newsize[0]), int(newsize[1]))
    chann *= (head['CDELT1'] * head['CDELT2'] * 1.E-6)
    chann = frebin(chann, (newsize[0], newsize[1]), total=True)
    chann /= (outspax[0] * outspax[1] * 1.E-6)

    #"Correct" ADR effect
    if adr_switch == 'ON':
        adrhead = head.copy()
        adrhead['CDELT1'] = outspax[0]
        adrhead['CDELT2'] = outspax[1]
        chann = add_ADR(chann, adrhead, -1. * adrval, 'spline3')

    return chann, l