예제 #1
0
def test():
    import threedhst.prep_flt_astrodrizzle as prep
    
    asn_file = 'HOPS183B-10-140-G141_asn.fits'
    #asn_file = 'HOPS183B-10-140-F160W_asn.fits'
    
    threedhst.process_grism.fresh_flt_files(asn_file, from_path='../RAW/', preserve_dq=False)

    asn = threedhst.utils.ASNFile(asn_file)
    for exp in asn.exposures:
        prep.subtract_fixed_background(flt_file='%s_flt.fits' %(exp), path_to_raw='../RAW/')
예제 #2
0
파일: pab.py 프로젝트: gbrammer/pabeta
def m82():

    import glob
    import os

    import threedhst
    import threedhst.prep_flt_astrodrizzle as init
    from threedhst import catIO

    import unicorn
    import research.hawkiff

    import stsci.convolve
    import research.pab.align as align

    ### Make ACS associations
    unicorn.candels.make_asn_files(uniquename=True, translate={'-ROT': ''})
    threedhst.options['FLT_PERSISTENCE_PATH'] = '../Persistence/'

    #### IR
    files = glob.glob('*-F1*asn.fits')
    radec = None
    for asn_file in files:
        #init.prep_direct_grism_pair(direct_asn=asn_file, grism_asn=None, radec=radec, ACS=False, align_threshold=5, raw_path='../RAW/', order=0)
        threedhst.process_grism.fresh_flt_files(asn_file)
        asn = threedhst.utils.ASNFile(asn_file)
        for exp in asn.exposures:
            init.subtract_fixed_background(flt_file='%s_flt.fits' % (exp))

        #drizzlepac.astrodrizzle.AstroDrizzle(asn_file, static=False, skysub=False, driz_separate=True, driz_sep_wcs=False, median=True, blot=True, driz_cr=True, driz_combine=True, final_wht_type='IVM', clean=True, final_wcs=True, final_rot=0, final_scale=0.128, final_pixfrac=1, context=False, resetbits=0, final_bits=64, preserve=False)

    #
    ims = []
    hdrs = []

    for asn_file in files:
        asn = threedhst.utils.ASNFile(asn_file)

        ### cross correlation
        for exp in asn.exposures:
            flt = pyfits.open('%s_flt.fits' % (exp))
            slx, sly = slice(400, 600), slice(400, 600)
            #slx, sly = slice(450,550), slice(450,550)

            ims.append((flt[1].data * (flt[3].data == 0))[sly, slx])
            hdrs.append(flt[1].header)

    shifts = [[0, 0]]

    for i in range(1, len(ims)):

        ccorr = stsci.convolve.correlate2d(ims[0], ims[i], fft=1)
        from astropy.modeling import models, fitting
        poly = models.Polynomial2D(degree=4)
        lsq = fitting.LevMarLSQFitter()
        yp, xp = np.indices(ccorr.shape)
        pfit = lsq(poly, xp, yp, ccorr)

        #ix = np.unravel_index(np.argmax(ccorr-pfit(xp, yp)), ccorr.shape)
        sh = ccorr.shape
        ix = [sh[0] / 2, sh[1] / 2]
        ccorr[ix[0], ix[1]] = np.nan

        ds9.view(ccorr - pfit(xp, yp))
        status = raw_input('Mark center of ccorr peak')
        xcf = np.cast[float](ds9.get('pan image').split())
        xc = np.cast[int](np.round(xcf))

        N = 10
        sly2, slx2 = slice(xc[1] - N - 1,
                           xc[1] + N - 1), slice(xc[0] - N - 1, xc[0] + N - 1)
        sub = (ccorr - pfit(xp, yp))[sly2, slx2]

        flat = models.Polynomial2D(degree=1)
        gauss = models.Gaussian2D(100000,
                                  xcf[0],
                                  xcf[1],
                                  1,
                                  1,
                                  0,
                                  bounds={
                                      'x_mean': [xcf[0] - 5, xcf[0] + 5],
                                      'y_mean': [xcf[1] - 5, xcf[1] + 5]
                                  })
        ok = np.isfinite(sub)
        gfit = lsq(flat + gauss, xp[sly2, slx2][ok], yp[sly2, slx2][ok],
                   sub[ok])
        shifts.append([ix[1] - gfit.x_mean_1, ix[0] - gfit.y_mean_1])
        print shifts[-1]

    wcs = pywcs.WCS(hdrs[0])
    crpix = [[hdrs[0]['CRPIX1'], hdrs[0]['CRPIX2']]]

    for i in range(1, len(ims)):
        wcs = pywcs.WCS(hdrs[i])
        xy = wcs.all_world2pix([hdrs[0]['CRVAL1']], [hdrs[0]['CRVAL2']], 1)
        crpix.append([xy[0][0], xy[1][0]])
        print crpix[-1]

    dx = np.array(shifts) - (np.array(crpix) - crpix[0])

    ### M82
    dx = np.array([[0., 0.], [0.1692337, -0.03445068],
                   [0.11051269, 0.26955819], [0.04652894, 0.30199132]])

    i0 = 0
    for asn_file in files:
        asn = threedhst.utils.ASNFile(asn_file)
        fp = open(asn_file.replace('_asn.fits', '_shifts.txt'), 'w')
        fp.write("""# frame: output
# refimage: %s_flt.fits[1]
# form: delta
# units: pixels
""" % (asn.exposures[0]))

        for exp in asn.exposures:
            fp.write('%s_flt.fits  %.4f  %.4f  0, 1.0 0.1 0.1\n' %
                     (exp, dx[i0][0], dx[i0][1]))
            flt = pyfits.open('%s_flt.fits' % (exp), mode='update')
            flt[1].header['CRPIX1'] += dx[i0][0]
            flt[1].header['CRPIX2'] += dx[i0][1]
            flt.flush()

            i0 += 1

        fp.close()

    for asn_file in files:
        drizzlepac.astrodrizzle.AstroDrizzle(asn_file,
                                             static=False,
                                             skysub=True,
                                             driz_separate=True,
                                             driz_sep_wcs=False,
                                             median=True,
                                             blot=True,
                                             driz_cr=True,
                                             driz_combine=True,
                                             final_wht_type='IVM',
                                             clean=True,
                                             final_wcs=True,
                                             final_rot=0,
                                             final_scale=0.1,
                                             final_pixfrac=1,
                                             context=False,
                                             resetbits=4096,
                                             final_bits=64,
                                             preserve=False)

    for asn_file in files:
        align.coarse_align(image=asn_file.replace('_asn.fits',
                                                  '_drz_sci.fits'),
                           ref='h_m82_v_s20_drz_sci.fits')
예제 #3
0
파일: pab.py 프로젝트: gbrammer/pabeta
def m82():

    import glob
    import os

    import threedhst
    import threedhst.prep_flt_astrodrizzle as init
    from threedhst import catIO

    import unicorn
    import research.hawkiff

    import stsci.convolve
    import research.pab.align as align

    ### Make ACS associations
    unicorn.candels.make_asn_files(uniquename=True, translate={'-ROT': ''})
    threedhst.options['FLT_PERSISTENCE_PATH'] = '../Persistence/'

    #### IR
    files = glob.glob('*-F1*asn.fits')
    radec = None
    for asn_file in files:
        #init.prep_direct_grism_pair(direct_asn=asn_file, grism_asn=None, radec=radec, ACS=False, align_threshold=5, raw_path='../RAW/', order=0)
        threedhst.process_grism.fresh_flt_files(asn_file)
        asn = threedhst.utils.ASNFile(asn_file)
        for exp in asn.exposures:
            init.subtract_fixed_background(flt_file='%s_flt.fits' % (exp))

        #drizzlepac.astrodrizzle.AstroDrizzle(asn_file, static=False, skysub=False, driz_separate=True, driz_sep_wcs=False, median=True, blot=True, driz_cr=True, driz_combine=True, final_wht_type='IVM', clean=True, final_wcs=True, final_rot=0, final_scale=0.128, final_pixfrac=1, context=False, resetbits=0, final_bits=64, preserve=False)

    #
    ims = []
    hdrs = []

    for asn_file in files:
        asn = threedhst.utils.ASNFile(asn_file)

        ### cross correlation
        for exp in asn.exposures:
            flt = pyfits.open('%s_flt.fits' % (exp))
            slx, sly = slice(400, 600), slice(400, 600)
            #slx, sly = slice(450,550), slice(450,550)

            ims.append((flt[1].data * (flt[3].data == 0))[sly, slx])
            hdrs.append(flt[1].header)

    shifts = [[0, 0]]

    for i in range(1, len(ims)):

        ccorr = stsci.convolve.correlate2d(ims[0], ims[i], fft=1)
        from astropy.modeling import models, fitting
        poly = models.Polynomial2D(degree=4)
        lsq = fitting.LevMarLSQFitter()
        yp, xp = np.indices(ccorr.shape)
        pfit = lsq(poly, xp, yp, ccorr)

        #ix = np.unravel_index(np.argmax(ccorr-pfit(xp, yp)), ccorr.shape)
        sh = ccorr.shape
        ix = [sh[0] / 2, sh[1] / 2]
        ccorr[ix[0], ix[1]] = np.nan

        ds9.view(ccorr - pfit(xp, yp))
        status = raw_input('Mark center of ccorr peak')
        xcf = np.cast[float](ds9.get('pan image').split())
        xc = np.cast[int](np.round(xcf))

        N = 10
        sly2, slx2 = slice(xc[1] - N - 1, xc[1] + N - 1), slice(
            xc[0] - N - 1, xc[0] + N - 1)
        sub = (ccorr - pfit(xp, yp))[sly2, slx2]

        flat = models.Polynomial2D(degree=1)
        gauss = models.Gaussian2D(
            100000,
            xcf[0],
            xcf[1],
            1,
            1,
            0,
            bounds={
                'x_mean': [xcf[0] - 5, xcf[0] + 5],
                'y_mean': [xcf[1] - 5, xcf[1] + 5]
            })
        ok = np.isfinite(sub)
        gfit = lsq(flat + gauss, xp[sly2, slx2][ok], yp[sly2, slx2][ok],
                   sub[ok])
        shifts.append([ix[1] - gfit.x_mean_1, ix[0] - gfit.y_mean_1])
        print shifts[-1]

    wcs = pywcs.WCS(hdrs[0])
    crpix = [[hdrs[0]['CRPIX1'], hdrs[0]['CRPIX2']]]

    for i in range(1, len(ims)):
        wcs = pywcs.WCS(hdrs[i])
        xy = wcs.all_world2pix([hdrs[0]['CRVAL1']], [hdrs[0]['CRVAL2']], 1)
        crpix.append([xy[0][0], xy[1][0]])
        print crpix[-1]

    dx = np.array(shifts) - (np.array(crpix) - crpix[0])

    ### M82
    dx = np.array([[0., 0.], [0.1692337, -0.03445068],
                   [0.11051269, 0.26955819], [0.04652894, 0.30199132]])

    i0 = 0
    for asn_file in files:
        asn = threedhst.utils.ASNFile(asn_file)
        fp = open(asn_file.replace('_asn.fits', '_shifts.txt'), 'w')
        fp.write("""# frame: output
# refimage: %s_flt.fits[1]
# form: delta
# units: pixels
""" % (asn.exposures[0]))

        for exp in asn.exposures:
            fp.write('%s_flt.fits  %.4f  %.4f  0, 1.0 0.1 0.1\n' %
                     (exp, dx[i0][0], dx[i0][1]))
            flt = pyfits.open('%s_flt.fits' % (exp), mode='update')
            flt[1].header['CRPIX1'] += dx[i0][0]
            flt[1].header['CRPIX2'] += dx[i0][1]
            flt.flush()

            i0 += 1

        fp.close()

    for asn_file in files:
        drizzlepac.astrodrizzle.AstroDrizzle(
            asn_file,
            static=False,
            skysub=True,
            driz_separate=True,
            driz_sep_wcs=False,
            median=True,
            blot=True,
            driz_cr=True,
            driz_combine=True,
            final_wht_type='IVM',
            clean=True,
            final_wcs=True,
            final_rot=0,
            final_scale=0.1,
            final_pixfrac=1,
            context=False,
            resetbits=4096,
            final_bits=64,
            preserve=False)

    for asn_file in files:
        align.coarse_align(
            image=asn_file.replace('_asn.fits', '_drz_sci.fits'),
            ref='h_m82_v_s20_drz_sci.fits')