Пример #1
0
def clicker2(event):

    global mask, aid, bid, cid, did, eid, fid, done

    if event.inaxes:
        if event.button == 1:
            if (event.x > 601 and event.x < 801 and event.y > 422
                    and event.y < 482):
                disconnect(aid)
                disconnect(bid)
                disconnect(cid)
                disconnect(did)
                disconnect(eid)
                disconnect(fid)
                try:
                    lines, status = kepio.openascii(maskfile, 'r', None, False)
                    for line in lines:
                        mask = []
                        work = line.strip().split('|')
                        y0 = int(work[3])
                        x0 = int(work[4])
                        work = work[5].split(';')
                        for i in range(len(work)):
                            y = int(work[i].split(',')[0]) + y0
                            x = int(work[i].split(',')[1]) + x0
                            mask.append(str(x) + ',' + str(y))
                        pylab.clf()
                        plotimage(cmdLine)
                except:
                    txt = 'ERROR -- KEPMASK: Cannot open or read mask file ' + maskfile
                    kepmsg.err(logfile, txt, True)

    return
Пример #2
0
def clicker2(event):

    global mask, aid, bid, cid, did, eid, fid, done

    if event.inaxes:
        if event.button == 1:
            if (event.x > 601 and event.x < 801 and
                event.y > 422 and event.y < 482):
                disconnect(aid)
                disconnect(bid)
                disconnect(cid)
                disconnect(did)
                disconnect(eid)
                disconnect(fid)
                try:
                    lines, status = kepio.openascii(maskfile,'r',None,False)
                    for line in lines:
                        mask = []
                        work = line.strip().split('|')
                        y0 = int(work[3])
                        x0 = int(work[4])
                        work = work[5].split(';')
                        for i in range(len(work)):
                            y = int(work[i].split(',')[0]) + y0
                            x = int(work[i].split(',')[1]) + x0
                            mask.append(str(x) + ',' + str(y))
                        pylab.clf()
                        plotimage(cmdLine)
                except:
                    txt = 'ERROR -- KEPMASK: Cannot open or read mask file ' + maskfile
                    kepmsg.err(logfile,txt,True)
                     
    return
Пример #3
0
def clicker1(event):

    global mask, aid, bid, cid, did, eid, fid, cmdLine

    if event.inaxes:
        if event.button == 1:
            if (event.x > 83 and event.x < 383 and
                event.y > 12 and event.y < 68):
                if kepio.fileexists(rinf):
                    mask = []
                    lines, status = kepio.openascii(rinf,'r',logf,verb)
                    for line in lines:
                        line = line.strip().split(',')
                        try:
                            float(line[0])
                            float(line[1])
                            if barytime0 > 2.4e6:
                                mask.append(float(line[0]) - barytime0)
                                mask.append(float(line[1]) - barytime0)
                            else:
                                mask.append(float(line[0]) - barytime0 - 2.4e6)
                                mask.append(float(line[1]) - barytime0 - 2.4e6)
                        except:
                            message = 'ERROR -- KEPRANGE: ascii format of ranges '
                            message += 'file not recognized.'
                            status = kepmsg.err(logf,message,False)
                    disconnect(aid)
                    disconnect(bid)
                    disconnect(cid)
                    disconnect(did)
                    disconnect(eid)
                    disconnect(fid)
                    plotlc(cmdLine)
                else:
                    print 'WARNING -- KEPRANGE: input ranges file does not exist or was not provided'
    return
Пример #4
0
def kepffi(ffifile,kepid,ra,dec,aperfile,imin,imax,iscale,cmap,npix,
            verbose,logfile,status,cmdLine=False): 

    global pimg, zscale, zmin, zmax, xmin, xmax, ymin, ymax, quarter
    global kepmag, skygroup, season, channel
    global module, output, row, column, maskfile, plotfile
    global pkepid, pkepmag, pra, pdec, colmap, mask

# input arguments

    status = 0
    seterr(all="ignore") 
    maskfile = 'kepffi-' + str(kepid) + '.txt'
    plotfile = 'kepffi-' + str(kepid) + '.png'
    zmin = imin; zmax = imax; zscale = iscale; colmap = cmap

# logg the call 

    hashline = '----------------------------------------------------------------------------'
    kepmsg.log(logfile,hashline,verbose)
    call = 'KEPFFI -- '
    call += 'ffifile='+ffifile+' '
    call += 'kepid='+str(kepid)+' '
    call += 'ra='+ra+' '
    call += 'dec='+dec+' '
    call += 'aperfile='+aperfile+' '
    call += 'imin='+str(imin)+' '
    call += 'imax='+str(imax)+' '
    call += 'iscale='+str(iscale)+' '
    call += 'cmap'+str(cmap)+' '
    call += 'npix='+str(npix)+' '
    chatter = 'n'
    if (verbose): chatter = 'y'
    call += 'verbose='+chatter+' '
    call += 'logfile='+logfile
    kepmsg.log(logfile,call+'\n',verbose)

# start time

    kepmsg.clock('KEPFFI started at',logfile,verbose)

# reference color map

    if cmap == 'browse':
        status = cmap_plot(cmdLine)

# open existing mask file

    if kepio.fileexists(aperfile):
        lines, status = kepio.openascii(aperfile,'r',logfile,verbose)
        for line in lines:
            line = line.strip().split('|')
            y0 = int(line[3])
            x0 = int(line[4])
            pixels = line[5].split(';')
            for pixel in pixels:
                m = y0 + int(pixel.split(',')[0])
                n = x0 + int(pixel.split(',')[1])
                mask.append(str(m)+','+str(n))
        status = kepio.closeascii(lines,logfile,verbose)

# RA and Dec conversion

    if kepid == 'None' or kepid == 'none' or kepid.strip() == '':
        try:
            mra = float(ra)
            mdec = float(dec)
        except:
            try:
                mra,mdec = sex2dec(ra,dec)
            except:
                txt = 'ERROR -- no sensible RA and Dec coordinates provided'
                sys.exit(txt)

# open FFI FITS file

    if status == 0:
        ffi, status = openfits(ffifile,'readonly')
        try:
            quarter = ffi[0].header['QUARTER']
        except:
            try:
                dateobs = ffi[0].header['DATE-OBS']
                if dateobs == '2009-04-24': quarter = 0
                if dateobs == '2009-04-25': quarter = 0
                if dateobs == '2009-04-26': quarter = 0
                if dateobs == '2009-06-19': quarter = 2
                if dateobs == '2009-08-19': quarter = 2
                if dateobs == '2009-09-17': quarter = 2
                if dateobs == '2009-10-19': quarter = 3
                if dateobs == '2009-11-18': quarter = 3
                if dateobs == '2009-12-17': quarter = 3
            except:
                txt  = 'ERROR -- cannot determine quarter when FFI was taken. Either a\n'
                txt += 'QUARTER or DATE-OBS keyword is expected in the primary header'
                sys.exit(txt)
        if quarter == 0: quarter = 1
        if quarter < 0:
            txt  = 'ERROR -- cannot determine quarter from FFI. Try downloading a new\n'
            txt += 'version of KeplerFFI.py from http://keplergo.arc.nasa.gov'
            sys.exit()
    if int(quarter) == 0:
        season = 3
    else:
        season = (int(quarter) - 2) % 4

# locate target in MAST

        try:
            int(kepid)
            kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
                = MASTKepID(kepid,season)
            pkepmag = kepmag; pkepid = kepid
        except:
            kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
                = MASTRADec(mra,mdec,8.0,season)
            ra,dec = dec2sex(ra,dec)
        pra = ra; pdec = dec
        print kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column
       
# read and close FFI FITS file

        img, status = readimage(ffi,int(channel))
        status = closefits(ffi)

# print target data

        print ''
        print '      KepID:  %s' % kepid
        print ' RA (J2000):  %s' % ra
        print 'Dec (J2000): %s' % dec
        print '     KepMag:  %s' % kepmag
        print '   SkyGroup:    %2s' % skygroup
        print '     Season:    %2s' % str(season)
        print '    Channel:    %2s' % channel
        print '     Module:    %2s' % module
        print '     Output:     %1s' % output
        print '     Column:  %4s' % column
        print '        Row:  %4s' % row
        print ''

# subimage of channel for plot

        ymin = int(max([int(row)-npix/2,0]))
        ymax = int(min([int(row)+npix/2+1,img.shape[0]]))
        xmin = int(max([int(column)-npix/2,0]))
        xmax = int(min([int(column)+npix/2+1,img.shape[1]]))

# intensity scale

        nstat = 2; pixels = []
        for i in range(ymin,ymax+1):
            for j in range(xmin,xmax+1):
                pixels.append(img[i,j])
        pixels = array(sort(pixels),dtype=float32)
        if int(float(len(pixels)) / 10 + 0.5) > nstat:
            nstat = int(float(len(pixels)) / 10 + 0.5)
        if not zmin:
            zmin = median(pixels[:nstat])
        if not zmax:
            zmax = median(pixels[-nstat:])
        if 'log' in zscale:
            img = log10(img)
            zmin = log10(zmin)
            zmax = log10(zmax)
        if ('sq' in zscale):
            img = sqrt(img)
            zmin = sqrt(zmin)
            zmax = sqrt(zmax)
        pimg = img[ymin:ymax,xmin:xmax]

# plot limits

        ymin = float(ymin) - 0.5
        ymax = float(ymax) - 0.5
        xmin = float(xmin) - 0.5
        xmax = float(xmax) - 0.5

# plot style

        try:
            params = {'backend': 'png',
                      'axes.linewidth': 2.5,
                      'axes.labelsize': 24,
                      'axes.font': 'sans-serif',
                      'axes.fontweight' : 'bold',
                      'text.fontsize': 12,
                      'legend.fontsize': 12,
                      'xtick.labelsize': 16,
                      'ytick.labelsize': 16}
            pylab.rcParams.update(params)
        except:
            pass

    if status == 0:
        pylab.figure(figsize=[10,7])
        plotimage(cmdLine)

# render plot

        if cmdLine: 
            pylab.show()
        else: 
            pylab.ion()
            pylab.plot([])
            pylab.ioff()
	
    return
Пример #5
0
def keppca(infile,
           maskfile,
           outfile,
           components,
           plotpca,
           nreps,
           clobber,
           verbose,
           logfile,
           status,
           cmdLine=False):

    try:
        import mdp
    except:
        msg = 'ERROR -- KEPPCA: this task has an external python dependency to MDP, a Modular toolkit for Data Processing (http://mdp-toolkit.sourceforge.net). In order to take advantage of this PCA task, the user must first install MDP with their current python distribution. Note carefully that you may have more than python installation on your machine, and ensure that MDP is installed with the same version of python that the PyKE tools employ. Installation instructions for MDP can be found at the URL provided above.'
        status = kepmsg.err(None, msg, True)

# startup parameters

    status = 0
    labelsize = 32
    ticksize = 18
    xsize = 16
    ysize = 10
    lcolor = '#0000ff'
    lwidth = 1.0
    fcolor = '#ffff00'
    falpha = 0.2
    seterr(all="ignore")

    # log the call

    if status == 0:
        hashline = '----------------------------------------------------------------------------'
        kepmsg.log(logfile, hashline, verbose)
        call = 'KEPPCA -- '
        call += 'infile=' + infile + ' '
        call += 'maskfile=' + maskfile + ' '
        call += 'outfile=' + outfile + ' '
        call += 'components=' + components + ' '
        ppca = 'n'
        if (plotpca): ppca = 'y'
        call += 'plotpca=' + ppca + ' '
        call += 'nmaps=' + str(nreps) + ' '
        overwrite = 'n'
        if (clobber): overwrite = 'y'
        call += 'clobber=' + overwrite + ' '
        chatter = 'n'
        if (verbose): chatter = 'y'
        call += 'verbose=' + chatter + ' '
        call += 'logfile=' + logfile
        kepmsg.log(logfile, call + '\n', verbose)

# start time

    if status == 0:
        kepmsg.clock('KEPPCA started at', logfile, verbose)

# test log file

    if status == 0:
        logfile = kepmsg.test(logfile)

# clobber output file

    if status == 0:
        if clobber: status = kepio.clobber(outfile, logfile, verbose)
        if kepio.fileexists(outfile):
            message = 'ERROR -- KEPPCA: ' + outfile + ' exists. Use clobber=yes'
            status = kepmsg.err(logfile, message, verbose)

# Set output file names - text file with data and plot

    if status == 0:
        dataout = copy(outfile)
        repname = re.sub('.fits', '.png', outfile)

# open input file

    if status == 0:
        instr = pyfits.open(infile, mode='readonly', memmap=True)
        tstart, tstop, bjdref, cadence, status = kepio.timekeys(
            instr, infile, logfile, verbose, status)

# open TPF FITS file

    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
            kepio.readTPF(infile,'TIME',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
            kepio.readTPF(infile,'TIMECORR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
            kepio.readTPF(infile,'CADENCENO',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
            kepio.readTPF(infile,'FLUX',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
            kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
            kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
            kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
            kepio.readTPF(infile,'QUALITY',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, pcorr1, status = \
            kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, pcorr2, status = \
            kepio.readTPF(infile,'POS_CORR2',logfile,verbose)

# Save original data dimensions, in case of using maskfile

    if status == 0:
        xdimorig = xdim
        ydimorig = ydim

# read mask definition file if it has been supplied

    if status == 0 and 'aper' not in maskfile.lower(
    ) and maskfile.lower() != 'all':
        maskx = array([], 'int')
        masky = array([], 'int')
        lines, status = kepio.openascii(maskfile, 'r', logfile, verbose)
        for line in lines:
            line = line.strip().split('|')
            if len(line) == 6:
                y0 = int(line[3])
                x0 = int(line[4])
                line = line[5].split(';')
                for items in line:
                    try:
                        masky = numpy.append(masky,
                                             y0 + int(items.split(',')[0]))
                        maskx = numpy.append(maskx,
                                             x0 + int(items.split(',')[1]))
                    except:
                        continue
        status = kepio.closeascii(lines, logfile, verbose)
        if len(maskx) == 0 or len(masky) == 0:
            message = 'ERROR -- KEPPCA: ' + maskfile + ' contains no pixels.'
            status = kepmsg.err(logfile, message, verbose)
        xdim = max(maskx) - min(maskx) + 1  # Find largest x dimension of mask
        ydim = max(masky) - min(masky) + 1  # Find largest y dimension of mask

        # pad mask to ensure it is rectangular

        workx = array([], 'int')
        worky = array([], 'int')
        for ip in arange(min(maskx), max(maskx) + 1):
            for jp in arange(min(masky), max(masky) + 1):
                workx = append(workx, ip)
                worky = append(worky, jp)
        maskx = workx
        masky = worky

# define new subimage bitmap...

    if status == 0 and maskfile.lower() != 'all':
        aperx = numpy.array([], 'int')
        apery = numpy.array([], 'int')
        aperb = maskx - x0 + xdimorig * (
            masky - y0
        )  # aperb is an array that contains the pixel numbers in the mask
        npix = len(aperb)

# ...or use all pixels

    if status == 0 and maskfile.lower() == 'all':
        npix = xdimorig * ydimorig
        aperb = array([], 'int')
        aperb = numpy.r_[0:npix]

# legal mask defined?

    if status == 0:
        if len(aperb) == 0:
            message = 'ERROR -- KEPPCA: no legal pixels within the subimage are defined.'
            status = kepmsg.err(logfile, message, verbose)

# Identify principal components desired

    if status == 0:
        pcaout = []
        txt = components.strip().split(',')
        for work1 in txt:
            try:
                pcaout.append(int(work1.strip()))
            except:
                work2 = work1.strip().split('-')
                try:
                    for work3 in range(int(work2[0]), int(work2[1]) + 1):
                        pcaout.append(work3)
                except:
                    message = 'ERROR -- KEPPCA: cannot understand principal component list requested'
                    status = kepmsg.err(logfile, message, verbose)
    if status == 0:
        pcaout = set(sort(pcaout))
    pcarem = array(
        list(pcaout)) - 1  # The list of pca component numbers to be removed

    # Initialize arrays and variables, and apply pixel mask to the data

    if status == 0:
        ntim = 0
        time = numpy.array([], dtype='float64')
        timecorr = numpy.array([], dtype='float32')
        cadenceno = numpy.array([], dtype='int')
        pixseries = numpy.array([], dtype='float32')
        errseries = numpy.array([], dtype='float32')
        bkgseries = numpy.array([], dtype='float32')
        berseries = numpy.array([], dtype='float32')
        quality = numpy.array([], dtype='float32')
        pos_corr1 = numpy.array([], dtype='float32')
        pos_corr2 = numpy.array([], dtype='float32')
        nrows = numpy.size(fluxpixels, 0)

# Apply the pixel mask so we are left with only the desired pixels

    if status == 0:
        pixseriesb = fluxpixels[:, aperb]
        errseriesb = errpixels[:, aperb]
        bkgseriesb = flux_bkg[:, aperb]
        berseriesb = flux_bkg_err[:, aperb]

# Read in the data to various arrays

    if status == 0:
        for i in range(nrows):
            if qual[i] < 10000 and \
                    numpy.isfinite(barytime[i]) and \
                    numpy.isfinite(fluxpixels[i,int(ydim*xdim/2+0.5)]) and \
                    numpy.isfinite(fluxpixels[i,1+int(ydim*xdim/2+0.5)]):
                ntim += 1
                time = numpy.append(time, barytime[i])
                timecorr = numpy.append(timecorr, tcorr[i])
                cadenceno = numpy.append(cadenceno, cadno[i])
                pixseries = numpy.append(pixseries, pixseriesb[i])
                errseries = numpy.append(errseries, errseriesb[i])
                bkgseries = numpy.append(bkgseries, bkgseriesb[i])
                berseries = numpy.append(berseries, berseriesb[i])
                quality = numpy.append(quality, qual[i])
                pos_corr1 = numpy.append(pos_corr1, pcorr1[i])
                pos_corr2 = numpy.append(pos_corr2, pcorr2[i])
        pixseries = numpy.reshape(pixseries, (ntim, npix))
        errseries = numpy.reshape(errseries, (ntim, npix))
        bkgseries = numpy.reshape(bkgseries, (ntim, npix))
        berseries = numpy.reshape(berseries, (ntim, npix))
        tmp = numpy.median(pixseries, axis=1)
        for i in range(len(tmp)):
            pixseries[i] = pixseries[i] - tmp[i]

# Figure out which pixels are undefined/nan and remove them. Keep track for adding back in later

    if status == 0:
        nanpixels = numpy.array([], dtype='int')
        i = 0
        while (i < npix):
            if numpy.isnan(pixseries[0, i]):
                nanpixels = numpy.append(nanpixels, i)
                npix = npix - 1
            i = i + 1
        pixseries = numpy.delete(pixseries, nanpixels, 1)
        errseries = numpy.delete(errseries, nanpixels, 1)
        pixseries[numpy.isnan(pixseries)] = random.gauss(100, 10)
        errseries[numpy.isnan(errseries)] = 10

# Compute statistical weights, means, standard deviations

    if status == 0:
        weightseries = (pixseries / errseries)**2
        pixMean = numpy.average(pixseries, axis=0, weights=weightseries)
        pixStd = numpy.std(pixseries, axis=0)

# Normalize the input by subtracting the mean and divising by the standard deviation.
# This makes it a correlation-based PCA, which is what we want.

    if status == 0:
        pixseriesnorm = (pixseries - pixMean) / pixStd

# Number of principal components to compute. Setting it equal to the number of pixels

    if status == 0:
        nvecin = npix

# Run PCA using the MDP Whitening PCA, which produces normalized PCA components (zero mean and unit variance)

    if status == 0:
        pcan = mdp.nodes.WhiteningNode(svd=True)
        pcar = pcan.execute(pixseriesnorm)
        eigvec = pcan.get_recmatrix()
        model = pcar

# Re-insert nan columns as zeros

    if status == 0:
        for i in range(0, len(nanpixels)):
            nanpixels[i] = nanpixels[i] - i
        eigvec = numpy.insert(eigvec, nanpixels, 0, 1)
        pixMean = numpy.insert(pixMean, nanpixels, 0, 0)

#  Make output eigenvectors (correlation images) into xpix by ypix images

    if status == 0:
        eigvec = eigvec.reshape(nvecin, ydim, xdim)

# Calculate sum of all pixels to display as raw lightcurve and other quantities

    if status == 0:
        pixseriessum = sum(pixseries, axis=1)
        nrem = len(pcarem)  # Number of components to remove
        nplot = npix  # Number of pcas to plot - currently set to plot all components, but could set
        # nplot = nrem to just plot as many components as is being removed

# Subtract components by fitting them to the summed light curve

    if status == 0:
        x0 = numpy.tile(-1.0, 1)
        for k in range(0, nrem):

            def f(x):
                fluxcor = pixseriessum
                for k in range(0, len(x)):
                    fluxcor = fluxcor - x[k] * model[:, pcarem[k]]
                return mad(fluxcor)

            if k == 0:
                x0 = array([-1.0])
            else:
                x0 = numpy.append(x0, 1.0)
            myfit = scipy.optimize.fmin(f,
                                        x0,
                                        maxiter=50000,
                                        maxfun=50000,
                                        disp=False)
            x0 = myfit

# Now that coefficients for all components have been found, subtract them to produce a calibrated time-series,
# and then divide by the robust mean to produce a normalized time series as well

    if status == 0:
        c = myfit
        fluxcor = pixseriessum
        for k in range(0, nrem):
            fluxcor = fluxcor - c[k] * model[:, pcarem[k]]
            normfluxcor = fluxcor / mean(reject_outliers(fluxcor, 2))

# input file data

    if status == 0:
        cards0 = instr[0].header.cards
        cards1 = instr[1].header.cards
        cards2 = instr[2].header.cards
        table = instr[1].data[:]
        maskmap = copy(instr[2].data)

# subimage physical WCS data

    if status == 0:
        crpix1p = cards2['CRPIX1P'].value
        crpix2p = cards2['CRPIX2P'].value
        crval1p = cards2['CRVAL1P'].value
        crval2p = cards2['CRVAL2P'].value
        cdelt1p = cards2['CDELT1P'].value
        cdelt2p = cards2['CDELT2P'].value

# dummy columns for output file

    if status == 0:
        sap_flux_err = numpy.empty(len(time))
        sap_flux_err[:] = numpy.nan
        sap_bkg = numpy.empty(len(time))
        sap_bkg[:] = numpy.nan
        sap_bkg_err = numpy.empty(len(time))
        sap_bkg_err[:] = numpy.nan
        pdc_flux = numpy.empty(len(time))
        pdc_flux[:] = numpy.nan
        pdc_flux_err = numpy.empty(len(time))
        pdc_flux_err[:] = numpy.nan
        psf_centr1 = numpy.empty(len(time))
        psf_centr1[:] = numpy.nan
        psf_centr1_err = numpy.empty(len(time))
        psf_centr1_err[:] = numpy.nan
        psf_centr2 = numpy.empty(len(time))
        psf_centr2[:] = numpy.nan
        psf_centr2_err = numpy.empty(len(time))
        psf_centr2_err[:] = numpy.nan
        mom_centr1 = numpy.empty(len(time))
        mom_centr1[:] = numpy.nan
        mom_centr1_err = numpy.empty(len(time))
        mom_centr1_err[:] = numpy.nan
        mom_centr2 = numpy.empty(len(time))
        mom_centr2[:] = numpy.nan
        mom_centr2_err = numpy.empty(len(time))
        mom_centr2_err[:] = numpy.nan

# mask bitmap

    if status == 0 and 'aper' not in maskfile.lower(
    ) and maskfile.lower() != 'all':
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperx = append(aperx, crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = append(apery, crval2p + (i + 1 - crpix2p) * cdelt2p)
                if maskmap[i, j] == 0:
                    pass
                else:
                    maskmap[i, j] = 1
                    for k in range(len(maskx)):
                        if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
                            maskmap[i, j] = 3

# construct output primary extension

    if status == 0:
        hdu0 = pyfits.PrimaryHDU()
        for i in range(len(cards0)):
            if cards0[i].keyword not in list(hdu0.header.keys()):
                hdu0.header[cards0[i].keyword] = (cards0[i].value,
                                                  cards0[i].comment)
            else:
                hdu0.header.cards[
                    cards0[i].keyword].comment = cards0[i].comment
        status = kepkey.history(call, hdu0, outfile, logfile, verbose)
        outstr = HDUList(hdu0)

# construct output light curve extension

    if status == 0:
        col1 = Column(name='TIME',
                      format='D',
                      unit='BJD - 2454833',
                      array=time)
        col2 = Column(name='TIMECORR', format='E', unit='d', array=timecorr)
        col3 = Column(name='CADENCENO', format='J', array=cadenceno)
        col4 = Column(name='SAP_FLUX',
                      format='E',
                      unit='e-/s',
                      array=pixseriessum)
        col5 = Column(name='SAP_FLUX_ERR',
                      format='E',
                      unit='e-/s',
                      array=sap_flux_err)
        col6 = Column(name='SAP_BKG', format='E', unit='e-/s', array=sap_bkg)
        col7 = Column(name='SAP_BKG_ERR',
                      format='E',
                      unit='e-/s',
                      array=sap_bkg_err)
        col8 = Column(name='PDCSAP_FLUX',
                      format='E',
                      unit='e-/s',
                      array=pdc_flux)
        col9 = Column(name='PDCSAP_FLUX_ERR',
                      format='E',
                      unit='e-/s',
                      array=pdc_flux_err)
        col10 = Column(name='SAP_QUALITY', format='J', array=quality)
        col11 = Column(name='PSF_CENTR1',
                       format='E',
                       unit='pixel',
                       array=psf_centr1)
        col12 = Column(name='PSF_CENTR1_ERR',
                       format='E',
                       unit='pixel',
                       array=psf_centr1_err)
        col13 = Column(name='PSF_CENTR2',
                       format='E',
                       unit='pixel',
                       array=psf_centr2)
        col14 = Column(name='PSF_CENTR2_ERR',
                       format='E',
                       unit='pixel',
                       array=psf_centr2_err)
        col15 = Column(name='MOM_CENTR1',
                       format='E',
                       unit='pixel',
                       array=mom_centr1)
        col16 = Column(name='MOM_CENTR1_ERR',
                       format='E',
                       unit='pixel',
                       array=mom_centr1_err)
        col17 = Column(name='MOM_CENTR2',
                       format='E',
                       unit='pixel',
                       array=mom_centr2)
        col18 = Column(name='MOM_CENTR2_ERR',
                       format='E',
                       unit='pixel',
                       array=mom_centr2_err)
        col19 = Column(name='POS_CORR1',
                       format='E',
                       unit='pixel',
                       array=pos_corr1)
        col20 = Column(name='POS_CORR2',
                       format='E',
                       unit='pixel',
                       array=pos_corr2)
        col21 = Column(name='PCA_FLUX', format='E', unit='e-/s', array=fluxcor)
        col22 = Column(name='PCA_FLUX_NRM', format='E', array=normfluxcor)
        cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
                            col12,col13,col14,col15,col16,col17,col18,col19,col20,col21,col22])
        hdu1 = new_table(cols)
        hdu1.header['TTYPE1'] = ('TIME', 'column title: data time stamps')
        hdu1.header['TFORM1'] = ('D', 'data type: float64')
        hdu1.header['TUNIT1'] = ('BJD - 2454833',
                                 'column units: barycenter corrected JD')
        hdu1.header['TDISP1'] = ('D12.7', 'column display format')
        hdu1.header['TTYPE2'] = (
            'TIMECORR', 'column title: barycentric-timeslice correction')
        hdu1.header['TFORM2'] = ('E', 'data type: float32')
        hdu1.header['TUNIT2'] = ('d', 'column units: days')
        hdu1.header['TTYPE3'] = ('CADENCENO',
                                 'column title: unique cadence number')
        hdu1.header['TFORM3'] = ('J', 'column format: signed integer32')
        hdu1.header['TTYPE4'] = ('SAP_FLUX',
                                 'column title: aperture photometry flux')
        hdu1.header['TFORM4'] = ('E', 'column format: float32')
        hdu1.header['TUNIT4'] = ('e-/s', 'column units: electrons per second')
        hdu1.header['TTYPE5'] = ('SAP_FLUX_ERR',
                                 'column title: aperture phot. flux error')
        hdu1.header['TFORM5'] = ('E', 'column format: float32')
        hdu1.header['TUNIT5'] = (
            'e-/s', 'column units: electrons per second (1-sigma)')
        hdu1.header['TTYPE6'] = (
            'SAP_BKG', 'column title: aperture phot. background flux')
        hdu1.header['TFORM6'] = ('E', 'column format: float32')
        hdu1.header['TUNIT6'] = ('e-/s', 'column units: electrons per second')
        hdu1.header['TTYPE7'] = (
            'SAP_BKG_ERR', 'column title: ap. phot. background flux error')
        hdu1.header['TFORM7'] = ('E', 'column format: float32')
        hdu1.header['TUNIT7'] = (
            'e-/s', 'column units: electrons per second (1-sigma)')
        hdu1.header['TTYPE8'] = ('PDCSAP_FLUX',
                                 'column title: PDC photometry flux')
        hdu1.header['TFORM8'] = ('E', 'column format: float32')
        hdu1.header['TUNIT8'] = ('e-/s', 'column units: electrons per second')
        hdu1.header['TTYPE9'] = ('PDCSAP_FLUX_ERR',
                                 'column title: PDC flux error')
        hdu1.header['TFORM9'] = ('E', 'column format: float32')
        hdu1.header['TUNIT9'] = (
            'e-/s', 'column units: electrons per second (1-sigma)')
        hdu1.header['TTYPE10'] = (
            'SAP_QUALITY', 'column title: aperture photometry quality flag')
        hdu1.header['TFORM10'] = ('J', 'column format: signed integer32')
        hdu1.header['TTYPE11'] = ('PSF_CENTR1',
                                  'column title: PSF fitted column centroid')
        hdu1.header['TFORM11'] = ('E', 'column format: float32')
        hdu1.header['TUNIT11'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE12'] = ('PSF_CENTR1_ERR',
                                  'column title: PSF fitted column error')
        hdu1.header['TFORM12'] = ('E', 'column format: float32')
        hdu1.header['TUNIT12'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE13'] = ('PSF_CENTR2',
                                  'column title: PSF fitted row centroid')
        hdu1.header['TFORM13'] = ('E', 'column format: float32')
        hdu1.header['TUNIT13'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE14'] = ('PSF_CENTR2_ERR',
                                  'column title: PSF fitted row error')
        hdu1.header['TFORM14'] = ('E', 'column format: float32')
        hdu1.header['TUNIT14'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE15'] = (
            'MOM_CENTR1', 'column title: moment-derived column centroid')
        hdu1.header['TFORM15'] = ('E', 'column format: float32')
        hdu1.header['TUNIT15'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE16'] = ('MOM_CENTR1_ERR',
                                  'column title: moment-derived column error')
        hdu1.header['TFORM16'] = ('E', 'column format: float32')
        hdu1.header['TUNIT16'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE17'] = ('MOM_CENTR2',
                                  'column title: moment-derived row centroid')
        hdu1.header['TFORM17'] = ('E', 'column format: float32')
        hdu1.header['TUNIT17'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE18'] = ('MOM_CENTR2_ERR',
                                  'column title: moment-derived row error')
        hdu1.header['TFORM18'] = ('E', 'column format: float32')
        hdu1.header['TUNIT18'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE19'] = (
            'POS_CORR1', 'column title: col correction for vel. abbern')
        hdu1.header['TFORM19'] = ('E', 'column format: float32')
        hdu1.header['TUNIT19'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE20'] = (
            'POS_CORR2', 'column title: row correction for vel. abbern')
        hdu1.header['TFORM20'] = ('E', 'column format: float32')
        hdu1.header['TUNIT20'] = ('pixel', 'column units: pixel')
        hdu1.header['TTYPE21'] = ('PCA_FLUX',
                                  'column title: PCA-corrected flux')
        hdu1.header['TFORM21'] = ('E', 'column format: float32')
        hdu1.header['TUNIT21'] = ('pixel', 'column units: e-/s')
        hdu1.header['TTYPE22'] = (
            'PCA_FLUX_NRM', 'column title: normalized PCA-corrected flux')
        hdu1.header['TFORM22'] = ('E', 'column format: float32')
        hdu1.header['EXTNAME'] = ('LIGHTCURVE', 'name of extension')
        for i in range(len(cards1)):
            if (cards1[i].keyword not in list(hdu1.header.keys())
                    and cards1[i].keyword[:4] not in [
                        'TTYP', 'TFOR', 'TUNI', 'TDIS', 'TDIM', 'WCAX', '1CTY',
                        '2CTY', '1CRP', '2CRP', '1CRV', '2CRV', '1CUN', '2CUN',
                        '1CDE', '2CDE', '1CTY', '2CTY', '1CDL', '2CDL', '11PC',
                        '12PC', '21PC', '22PC'
                    ]):
                hdu1.header[cards1[i].keyword] = (cards1[i].value,
                                                  cards1[i].comment)
        outstr.append(hdu1)

# construct output mask bitmap extension

    if status == 0:
        hdu2 = ImageHDU(maskmap)
        for i in range(len(cards2)):
            if cards2[i].keyword not in list(hdu2.header.keys()):
                hdu2.header[cards2[i].keyword] = (cards2[i].value,
                                                  cards2[i].comment)
            else:
                hdu2.header.cards[
                    cards2[i].keyword].comment = cards2[i].comment
        outstr.append(hdu2)

# construct principal component table

    if status == 0:
        cols = [
            Column(name='TIME', format='E', unit='BJD - 2454833', array=time)
        ]
        for i in range(len(pcar[0, :])):
            colname = 'PC' + str(i + 1)
            col = Column(name=colname, format='E', array=pcar[:, i])
            cols.append(col)
        hdu3 = new_table(ColDefs(cols))
        hdu3.header['EXTNAME'] = ('PRINCIPAL_COMPONENTS', 'name of extension')
        hdu3.header['TTYPE1'] = ('TIME', 'column title: data time stamps')
        hdu3.header['TFORM1'] = ('D', 'data type: float64')
        hdu3.header['TUNIT1'] = ('BJD - 2454833',
                                 'column units: barycenter corrected JD')
        hdu3.header['TDISP1'] = ('D12.7', 'column display format')
        for i in range(len(pcar[0, :])):
            hdu3.header['TTYPE' + str(i + 2)] = \
                ('PC' + str(i + 1), 'column title: principal component number' + str(i + 1))
            hdu3.header['TFORM' + str(i + 2)] = ('E', 'column format: float32')
        outstr.append(hdu3)

# write output file

    if status == 0:
        outstr.writeto(outfile)

# close input structure

    if status == 0:
        status = kepio.closefits(instr, logfile, verbose)

# Create PCA report

    if status == 0 and plotpca:
        npp = 7  # Number of plots per page
        l = 1
        repcnt = 1
        for k in range(nreps):

            # First plot of every pagewith flux image, flux and calibrated time series

            status = kepplot.define(16, 12, logfile, verbose)
            if (k % (npp - 1) == 0):
                pylab.figure(figsize=[10, 16])
                subplot2grid((npp, 6), (0, 0), colspan=2)
                #                imshow(log10(pixMean.reshape(xdim,ydim).T-min(pixMean)+1),interpolation="nearest",cmap='RdYlBu')
                imshow(log10(
                    flipud(pixMean.reshape(ydim, xdim)) - min(pixMean) + 1),
                       interpolation="nearest",
                       cmap='RdYlBu')
                xticks([])
                yticks([])
                ax1 = subplot2grid((npp, 6), (0, 2), colspan=4)
                px = copy(time) + bjdref
                py = copy(pixseriessum)
                px, xlab, status = kepplot.cleanx(px, logfile, verbose)
                py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
                kepplot.RangeOfPlot(px, py, 0.01, False)
                kepplot.plot1d(px, py, cadence, lcolor, lwidth, fcolor, falpha,
                               True)
                py = copy(fluxcor)
                py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
                plot(px,
                     py,
                     marker='.',
                     color='r',
                     linestyle='',
                     markersize=1.0)
                kepplot.labels('', re.sub('\)', '',
                                          re.sub('Flux \(', '', ylab)), 'k',
                               18)
                grid()
                setp(ax1.get_xticklabels(), visible=False)

# plot principal components

            subplot2grid((npp, 6), (l, 0), colspan=2)
            imshow(eigvec[k], interpolation="nearest", cmap='RdYlBu')
            xlim(-0.5, xdim - 0.5)
            ylim(-0.5, ydim - 0.5)
            xticks([])
            yticks([])

            # The last plot on the page that should have the xlabel

            if (k % (npp - 1) == npp - 2 or k == nvecin - 1):
                subplot2grid((npp, 6), (l, 2), colspan=4)
                py = copy(model[:, k])
                kepplot.RangeOfPlot(px, py, 0.01, False)
                kepplot.plot1d(px, py, cadence, 'r', lwidth, 'g', falpha, True)
                kepplot.labels(xlab, 'PC ' + str(k + 1), 'k', 18)
                pylab.grid()
                pylab.tight_layout()
                l = 1
                pylab.savefig(re.sub('.png', '_%d.png' % repcnt, repname))
                if not cmdLine: kepplot.render(cmdLine)
                repcnt += 1

# The other plots on the page that should have no xlabel

            else:
                ax2 = subplot2grid((npp, 6), (l, 2), colspan=4)
                py = copy(model[:, k])
                kepplot.RangeOfPlot(px, py, 0.01, False)
                kepplot.plot1d(px, py, cadence, 'r', lwidth, 'g', falpha, True)
                kepplot.labels('', 'PC ' + str(k + 1), 'k', 18)
                grid()
                setp(ax2.get_xticklabels(), visible=False)
                pylab.tight_layout()
                l = l + 1
        pylab.savefig(re.sub('.png', '_%d.png' % repcnt, repname))
        if not cmdLine: kepplot.render(cmdLine)

# plot style and size

    if status == 0 and plotpca:
        status = kepplot.define(labelsize, ticksize, logfile, verbose)
        pylab.figure(figsize=[xsize, ysize])
        pylab.clf()

# plot aperture photometry and PCA corrected data

    if status == 0 and plotpca:
        ax = kepplot.location([0.06, 0.54, 0.93, 0.43])
        px = copy(time) + bjdref
        py = copy(pixseriessum)
        px, xlab, status = kepplot.cleanx(px, logfile, verbose)
        py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
        kepplot.RangeOfPlot(px, py, 0.01, False)
        kepplot.plot1d(px, py, cadence, lcolor, lwidth, fcolor, falpha, True)
        py = copy(fluxcor)
        py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
        kepplot.plot1d(px, py, cadence, 'r', 2, fcolor, 0.0, True)
        pylab.setp(pylab.gca(), xticklabels=[])
        kepplot.labels('', ylab, 'k', 24)
        pylab.grid()

# plot aperture photometry and PCA corrected data

    if status == 0 and plotpca:
        ax = kepplot.location([0.06, 0.09, 0.93, 0.43])
        yr = array([], 'float32')
        npc = min([6, nrem])
        for i in range(npc - 1, -1, -1):
            py = pcar[:, i] * c[i]
            py, ylab, status = kepplot.cleany(py, 1.0, logfile, verbose)
            cl = float(i) / (float(npc))
            kepplot.plot1d(px, py, cadence, [1.0 - cl, 0.0, cl], 2, fcolor,
                           0.0, True)
            yr = append(yr, py)
        y1 = max(yr)
        y2 = -min(yr)
        kepplot.RangeOfPlot(px, array([-y1, y1, -y2, y2]), 0.01, False)
        kepplot.labels(xlab, 'Principal Components', 'k', 24)
        pylab.grid()

# save plot to file

    if status == 0 and plotpca:
        pylab.savefig(repname)

# render plot

    if status == 0 and plotpca:
        kepplot.render(cmdLine)

# stop time

    if status == 0:
        kepmsg.clock('KEPPCA ended at', logfile, verbose)

    return
Пример #6
0
def keppca(infile,maskfile,outfile,components,clobber,verbose,logfile,status): 

# startup parameters

    cmdLine=False
    status = 0
    labelsize = 32
    ticksize = 18
    xsize = 16
    ysize = 10
    lcolor = '#0000ff'
    lwidth = 1.0
    fcolor = '#ffff00'
    falpha = 0.2
    seterr(all="ignore") 

# log the call 

    hashline = '----------------------------------------------------------------------------'
    kepmsg.log(logfile,hashline,verbose)
    call = 'KEPPCA -- '
    call += 'infile='+infile+' '
    call += 'maskfile='+maskfile+' '
    call += 'outfile='+outfile+' '
    call += 'components='+components+' '
    overwrite = 'n'
    if (clobber): overwrite = 'y'
    call += 'clobber='+overwrite+ ' '
    chatter = 'n'
    if (verbose): chatter = 'y'
    call += 'verbose='+chatter+' '
    call += 'logfile='+logfile
    kepmsg.log(logfile,call+'\n',verbose)

# start time

    kepmsg.clock('KEPPCA started at',logfile,verbose)

# test log file

    logfile = kepmsg.test(logfile)

# clobber output file

    if clobber: status = kepio.clobber(outfile,logfile,verbose)
    if kepio.fileexists(outfile): 
        message = 'ERROR -- KEPPCA: ' + outfile + ' exists. Use --clobber'
        status = kepmsg.err(logfile,message,verbose)

# open input file

    status = 0
    instr = pyfits.open(infile,mode='readonly',memmap=True)
    if status == 0:
        tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)

# fudge non-compliant FITS keywords with no values

    if status == 0:
        instr = kepkey.emptykeys(instr,file,logfile,verbose)

# input file data

    if status == 0:
        cards0 = instr[0].header.ascardlist()
        cards1 = instr[1].header.ascardlist()
        cards2 = instr[2].header.ascardlist()
        table = instr[1].data[:]
        maskmap = copy(instr[2].data)

# open TPF FITS file

    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
            kepio.readTPF(infile,'TIME',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
            kepio.readTPF(infile,'TIMECORR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
            kepio.readTPF(infile,'CADENCENO',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
            kepio.readTPF(infile,'FLUX',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
            kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
            kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
            kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
            kepio.readTPF(infile,'QUALITY',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, pcorr1, status = \
            kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, pcorr2, status = \
            kepio.readTPF(infile,'POS_CORR2',logfile,verbose)

# read mask definition file

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        maskx = array([],'int')
        masky = array([],'int')
        lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
        for line in lines:
            line = line.strip().split('|')
            if len(line) == 6:
                y0 = int(line[3])
                x0 = int(line[4])
                line = line[5].split(';')
                for items in line:
                    try:
                        masky = numpy.append(masky,y0 + int(items.split(',')[0]))
                        maskx = numpy.append(maskx,x0 + int(items.split(',')[1]))
                    except:
                        continue
        status = kepio.closeascii(lines,logfile,verbose)
        if len(maskx) == 0 or len(masky) == 0:
            message = 'ERROR -- KEPPCA: ' + maskfile + ' contains no pixels.'
            status = kepmsg.err(logfile,message,verbose)

# subimage physical WCS data

    if status == 0:
        crpix1p = cards2['CRPIX1P'].value
        crpix2p = cards2['CRPIX2P'].value
        crval1p = cards2['CRVAL1P'].value
        crval2p = cards2['CRVAL2P'].value
        cdelt1p = cards2['CDELT1P'].value
        cdelt2p = cards2['CDELT2P'].value

# define new subimage bitmap...

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        aperx = numpy.array([],'int')
        apery = numpy.array([],'int')
        aperb = numpy.array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperx = numpy.append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = numpy.append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
                if maskmap[i,j] == 0:
                    aperb = numpy.append(aperb,0)
                else:
                    aperb = numpy.append(aperb,1)
                    maskmap[i,j] = 1
                    for k in range(len(maskx)):
                        if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
                            aperb[-1] = 3
                            maskmap[i,j] = 3

# ...or use old subimage bitmap

    if status == 0 and 'aper' in maskfile.lower():
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperb = numpy.append(aperb,maskmap[i,j])

# ...or use all pixels

    if status == 0 and maskfile.lower() == 'all':
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                if maskmap[i,j] == 0:
                    aperb = numpy.append(aperb,0)
                else:
                    aperb = numpy.append(aperb,3)
                    maskmap[i,j] = 3

# legal mask defined?

    if status == 0:
        if len(aperb) == 0:
            message = 'ERROR -- KEPPCA: no legal pixels within the subimage are defined.'
            status = kepmsg.err(logfile,message,verbose)
        
# identify principal components to be combined

    if status == 0:
        pcaout = []
        txt = components.strip().split(',')
        for work1 in txt:
            try:
                pcaout.append(int(work1.strip()))
            except:
                work2 = work1.strip().split('-')
                try:
                    for work3 in range(int(work2[0]),int(work2[1]) + 1):
                        pcaout.append(work3)
                except:
                    message = 'ERROR -- KEPPCA: cannot understand principal component list requested'
                    status = kepmsg.err(logfile,message,verbose)
    if status == 0:
        pcaout = set(sort(pcaout))

# flux pixel array size

    if status == 0:
        ntim = 0
        time = numpy.array([],dtype='float64')
        timecorr = numpy.array([],dtype='float32')
        cadenceno = numpy.array([],dtype='int')
        pixseries = numpy.array([],dtype='float32')
        errseries = numpy.array([],dtype='float32')
        bkgseries = numpy.array([],dtype='float32')
        berseries = numpy.array([],dtype='float32')
        quality = numpy.array([],dtype='float32')
        pos_corr1 = numpy.array([],dtype='float32')
        pos_corr2 = numpy.array([],dtype='float32')
        nrows = numpy.size(fluxpixels,0)
        npix = numpy.size(fluxpixels,1)

# remove NaN timestamps

        for i in range(nrows):
            if qual[i] == 0 and \
                    numpy.isfinite(barytime[i]) and \
                    numpy.isfinite(fluxpixels[i,ydim*xdim/2]) and \
                    numpy.isfinite(fluxpixels[i,1+ydim*xdim/2]):
                ntim += 1
                time = numpy.append(time,barytime[i])
                timecorr = numpy.append(timecorr,tcorr[i])
                cadenceno = numpy.append(cadenceno,cadno[i])
                pixseries = numpy.append(pixseries,fluxpixels[i])
                errseries = numpy.append(errseries,errpixels[i])
                bkgseries = numpy.append(bkgseries,flux_bkg[i])
                berseries = numpy.append(berseries,flux_bkg_err[i])
                quality = numpy.append(quality,qual[i])
                pos_corr1 = numpy.append(pos_corr1,pcorr1[i])
                pos_corr2 = numpy.append(pos_corr2,pcorr2[i])
        pixseries = numpy.reshape(pixseries,(-1,npix))
        errseries = numpy.reshape(errseries,(-1,npix))
        bkgseries = numpy.reshape(bkgseries,(-1,npix))
        berseries = numpy.reshape(berseries,(-1,npix))

# dummy columns for output file

    if status == 0:
        pdc_flux = numpy.empty(len(time)); pdc_flux[:] = numpy.nan
        pdc_flux_err = numpy.empty(len(time)); pdc_flux_err[:] = numpy.nan
        psf_centr1 = numpy.empty(len(time)); psf_centr1[:] = numpy.nan
        psf_centr1_err = numpy.empty(len(time)); psf_centr1_err[:] = numpy.nan
        psf_centr2 = numpy.empty(len(time)); psf_centr2[:] = numpy.nan
        psf_centr2_err = numpy.empty(len(time)); psf_centr2_err[:] = numpy.nan
        mom_centr1 = numpy.empty(len(time)); mom_centr1[:] = numpy.nan
        mom_centr1_err = numpy.empty(len(time)); mom_centr1_err[:] = numpy.nan
        mom_centr2 = numpy.empty(len(time)); mom_centr2[:] = numpy.nan
        mom_centr2_err = numpy.empty(len(time)); mom_centr2_err[:] = numpy.nan

# subtract mean over time from each pixel in the mask

    if status == 0:
        nmask = 0
        for i in range(npix):
            if aperb[i] == 3:
                nmask += 1
        work1 = numpy.zeros((len(pixseries),nmask))
        nmask = -1
        for i in range(npix):
            if aperb[i] == 3:
                nmask += 1
                maskedFlux = numpy.ma.masked_invalid(pixseries[:,i])
                pixMean = numpy.mean(maskedFlux)
                if numpy.isfinite(pixMean):
                    work1[:,nmask] = maskedFlux - pixMean
                else:
                    work1[:,nmask] = numpy.zeros((ntim))

# calculate covariance matrix

    if status == 0:
        work2 = work1.T
        covariance = numpy.cov(work2)

# determine eigenfunctions and eigenvectors of the covariance matrix
        
    if status == 0:
        [latent,coeff] = numpy.linalg.eig(covariance)

# projection of the data in the new space

    if status == 0:
        score = numpy.dot(coeff.T,work2).T

# construct new table data

    if status == 0:
        sap_flux = numpy.array([],'float32')
        sap_flux_err = numpy.array([],'float32')
        sap_bkg = numpy.array([],'float32')
        sap_bkg_err = numpy.array([],'float32')
        for i in range(len(time)):
            work1 = numpy.array([],'float64')
            work2 = numpy.array([],'float64')
            work3 = numpy.array([],'float64')
            work4 = numpy.array([],'float64')
            work5 = numpy.array([],'float64')
            for j in range(len(aperb)):
                if (aperb[j] == 3):
                    work1 = numpy.append(work1,pixseries[i,j])
                    work2 = numpy.append(work2,errseries[i,j])
                    work3 = numpy.append(work3,bkgseries[i,j])
                    work4 = numpy.append(work4,berseries[i,j])
            sap_flux = numpy.append(sap_flux,kepstat.sum(work1))
            sap_flux_err = numpy.append(sap_flux_err,kepstat.sumerr(work2))
            sap_bkg = numpy.append(sap_bkg,kepstat.sum(work3))
            sap_bkg_err = numpy.append(sap_bkg_err,kepstat.sumerr(work4))
        sap_mean = scipy.stats.stats.nanmean(sap_flux)

# coadd principal components

    if status == 0:
        pca_flux = numpy.zeros((len(sap_flux)))
        for i in range(nmask):
            if (i + 1) in pcaout:
                pca_flux = pca_flux + score[:,i]
        pca_flux += sap_mean

# construct output primary extension

    if status == 0:
        hdu0 = pyfits.PrimaryHDU()
        for i in range(len(cards0)):
            if cards0[i].key not in hdu0.header.ascardlist().keys():
                hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
            else:
                hdu0.header.ascardlist()[cards0[i].key].comment = cards0[i].comment
        status = kepkey.history(call,hdu0,outfile,logfile,verbose)
        outstr = HDUList(hdu0)

# construct output light curve extension

    if status == 0:
        col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
        col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
        col3 = Column(name='CADENCENO',format='J',array=cadenceno)
        col4 = Column(name='SAP_FLUX',format='E',array=sap_flux)
        col5 = Column(name='SAP_FLUX_ERR',format='E',array=sap_flux_err)
        col6 = Column(name='SAP_BKG',format='E',array=sap_bkg)
        col7 = Column(name='SAP_BKG_ERR',format='E',array=sap_bkg_err)
        col8 = Column(name='PDCSAP_FLUX',format='E',array=pdc_flux)
        col9 = Column(name='PDCSAP_FLUX_ERR',format='E',array=pdc_flux_err)
        col10 = Column(name='SAP_QUALITY',format='J',array=quality)
        col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
        col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
        col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
        col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
        col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
        col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
        col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
        col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
        col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
        col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
        cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
                            col12,col13,col14,col15,col16,col17,col18,col19,col20])
        hdu1 = new_table(cols)
        hdu1.header.update('TTYPE1','TIME','column title: data time stamps')
        hdu1.header.update('TFORM1','D','data type: float64')
        hdu1.header.update('TUNIT1','BJD - 2454833','column units: barycenter corrected JD')
        hdu1.header.update('TDISP1','D12.7','column display format')
        hdu1.header.update('TTYPE2','TIMECORR','column title: barycentric-timeslice correction')
        hdu1.header.update('TFORM2','E','data type: float32')
        hdu1.header.update('TUNIT2','d','column units: days')
        hdu1.header.update('TTYPE3','CADENCENO','column title: unique cadence number')
        hdu1.header.update('TFORM3','J','column format: signed integer32')
        hdu1.header.update('TTYPE4','SAP_FLUX','column title: aperture photometry flux')
        hdu1.header.update('TFORM4','E','column format: float32')
        hdu1.header.update('TUNIT4','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE5','SAP_FLUX_ERR','column title: aperture phot. flux error')
        hdu1.header.update('TFORM5','E','column format: float32')
        hdu1.header.update('TUNIT5','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE6','SAP_BKG','column title: aperture phot. background flux')
        hdu1.header.update('TFORM6','E','column format: float32')
        hdu1.header.update('TUNIT6','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE7','SAP_BKG_ERR','column title: ap. phot. background flux error')
        hdu1.header.update('TFORM7','E','column format: float32')
        hdu1.header.update('TUNIT7','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE8','PDCSAP_FLUX','column title: PDC photometry flux')
        hdu1.header.update('TFORM8','E','column format: float32')
        hdu1.header.update('TUNIT8','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE9','PDCSAP_FLUX_ERR','column title: PDC flux error')
        hdu1.header.update('TFORM9','E','column format: float32')
        hdu1.header.update('TUNIT9','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE10','SAP_QUALITY','column title: aperture photometry quality flag')
        hdu1.header.update('TFORM10','J','column format: signed integer32')
        hdu1.header.update('TTYPE11','PSF_CENTR1','column title: PSF fitted column centroid')
        hdu1.header.update('TFORM11','E','column format: float32')
        hdu1.header.update('TUNIT11','pixel','column units: pixel')
        hdu1.header.update('TTYPE12','PSF_CENTR1_ERR','column title: PSF fitted column error')
        hdu1.header.update('TFORM12','E','column format: float32')
        hdu1.header.update('TUNIT12','pixel','column units: pixel')
        hdu1.header.update('TTYPE13','PSF_CENTR2','column title: PSF fitted row centroid')
        hdu1.header.update('TFORM13','E','column format: float32')
        hdu1.header.update('TUNIT13','pixel','column units: pixel')
        hdu1.header.update('TTYPE14','PSF_CENTR2_ERR','column title: PSF fitted row error')
        hdu1.header.update('TFORM14','E','column format: float32')
        hdu1.header.update('TUNIT14','pixel','column units: pixel')
        hdu1.header.update('TTYPE15','MOM_CENTR1','column title: moment-derived column centroid')
        hdu1.header.update('TFORM15','E','column format: float32')
        hdu1.header.update('TUNIT15','pixel','column units: pixel')
        hdu1.header.update('TTYPE16','MOM_CENTR1_ERR','column title: moment-derived column error')
        hdu1.header.update('TFORM16','E','column format: float32')
        hdu1.header.update('TUNIT16','pixel','column units: pixel')
        hdu1.header.update('TTYPE17','MOM_CENTR2','column title: moment-derived row centroid')
        hdu1.header.update('TFORM17','E','column format: float32')
        hdu1.header.update('TUNIT17','pixel','column units: pixel')
        hdu1.header.update('TTYPE18','MOM_CENTR2_ERR','column title: moment-derived row error')
        hdu1.header.update('TFORM18','E','column format: float32')
        hdu1.header.update('TUNIT18','pixel','column units: pixel')
        hdu1.header.update('TTYPE19','POS_CORR1','column title: col correction for vel. abbern')
        hdu1.header.update('TFORM19','E','column format: float32')
        hdu1.header.update('TUNIT19','pixel','column units: pixel')
        hdu1.header.update('TTYPE20','POS_CORR2','column title: row correction for vel. abbern')
        hdu1.header.update('TFORM20','E','column format: float32')
        hdu1.header.update('TUNIT20','pixel','column units: pixel')
        hdu1.header.update('EXTNAME','LIGHTCURVE','name of extension')
        for i in range(len(cards1)):
            if (cards1[i].key not in hdu1.header.ascardlist().keys() and
                cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
                                          '2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
                                          '1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
                                          '12PC','21PC','22PC']):
                hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
        outstr.append(hdu1)

# construct output mask bitmap extension

    if status == 0:
        hdu2 = ImageHDU(maskmap)
        for i in range(len(cards2)):
            if cards2[i].key not in hdu2.header.ascardlist().keys():
                hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
            else:
                hdu2.header.ascardlist()[cards2[i].key].comment = cards2[i].comment
        outstr.append(hdu2)

# construct principal component table

    if status == 0:
        cols = []
        for i in range(nmask):
            colname = 'PC' + str(i + 1)
            col = Column(name=colname,format='E',unit='e-/s',array=score[:,i])
            cols.append(col)
        hdu3 = new_table(ColDefs(cols))
        hdu3.header.update('EXTNAME','PRINCIPAL_COMPONENTS','name of extension')
        for i in range(nmask):
            hdu3.header.update('TTYPE' + str(i + 1),'PC' + str(i + 1),'column title: principal component number' + str(i + 1))
            hdu3.header.update('TFORM' + str(i + 1),'E','column format: float32')
            hdu3.header.update('TUNIT' + str(i + 1),'e-/s','column units: electrons per sec')
        outstr.append(hdu3)

# write output file

    if status == 0:
        outstr.writeto(outfile,checksum=True)

# close input structure

    if status == 0:
        status = kepio.closefits(instr,logfile,verbose)	    

# plotting defaults

    if status == 0:
        plotLatex = True
        try:
            params = {'backend': 'png',
                      'axes.linewidth': 2.5,
                      'axes.labelsize': labelsize,
                      'axes.font': 'sans-serif',
                      'axes.fontweight' : 'bold',
                      'text.fontsize': 12,
                      'legend.fontsize': 12,
                      'xtick.labelsize': ticksize,
                      'ytick.labelsize': ticksize}
            rcParams.update(params)
        except:
            plotLatex = False
    if status == 0:
        pylab.figure(figsize=[xsize,ysize])
        pylab.clf()

# clean up x-axis unit

    if status == 0:
	intime0 = float(int(tstart / 100) * 100.0)
	ptime = time + bjdref - intime0
	xlab = 'BJD $-$ %d' % intime0

# clean up y-axis units

    if status == 0:
        pout = copy(score)
	nrm = len(str(int(pout.max())))-1
	pout = pout / 10**nrm
	ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm

# data limits

	xmin = ptime.min()
	xmax = ptime.max()
	ymin = pout.min()
	ymax = pout.max()
	xr = xmax - xmin
	yr = ymax - ymin

# plot window

        ax = pylab.axes([0.06,0.54,0.93,0.43])

# force tick labels to be absolute rather than relative

        pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
        pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))

# rotate y labels by 90 deg

        labels = ax.get_yticklabels()
        pylab.setp(labels, 'rotation', 90)
        pylab.setp(pylab.gca(),xticklabels=[])

# plot principal components

        for i in range(nmask):
            pylab.plot(ptime,pout[:,i],linestyle='-',linewidth=lwidth)
        if not plotLatex:
            ylab = '10**%d electrons/sec' % nrm
        ylabel(ylab, {'color' : 'k'})
        grid()

# plot ranges

        pylab.xlim(xmin-xr*0.01,xmax+xr*0.01)
        pylab.ylim(ymin-yr*0.01,ymax+yr*0.01)

# plot output data

        ax = pylab.axes([0.06,0.09,0.93,0.43])

# force tick labels to be absolute rather than relative

        pylab.gca().xaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))
        pylab.gca().yaxis.set_major_formatter(pylab.ScalarFormatter(useOffset=False))

# rotate y labels by 90 deg

        labels = ax.get_yticklabels()
        setp(labels, 'rotation', 90)

# clean up y-axis units

    if status == 0:
        pout = copy(pca_flux)
	nrm = len(str(int(pout.max())))-1
	pout = pout / 10**nrm
	ylab = '10$^%d$ e$^-$ s$^{-1}$' % nrm

# data limits

	ymin = pout.min()
	ymax = pout.max()
	yr = ymax - ymin
        ptime = numpy.insert(ptime,[0],[ptime[0]]) 
        ptime = numpy.append(ptime,[ptime[-1]])
        pout = numpy.insert(pout,[0],[0.0]) 
        pout = numpy.append(pout,0.0)

# plot time coadded principal component series

        pylab.plot(ptime[1:-1],pout[1:-1],color=lcolor,linestyle='-',linewidth=lwidth)
        pylab.fill(ptime,pout,color=fcolor,linewidth=0.0,alpha=falpha)
	pylab.xlabel(xlab, {'color' : 'k'})
        pylab.ylabel(ylab, {'color' : 'k'})
        pylab.grid()

# plot ranges

        pylab.xlim(xmin-xr*0.01,xmax+xr*0.01)
        if ymin >= 0.0: 
            pylab.ylim(ymin-yr*0.01,ymax+yr*0.01)
        else:
            pylab.ylim(1.0e-10,ymax+yr*0.01)

# render plot

        if cmdLine: 
            pylab.show()
        else: 
            pylab.ion()
            pylab.plot([])
            pylab.ioff()
	
# stop time

    if status == 0:
        kepmsg.clock('KEPPCA ended at',logfile,verbose)

    return
Пример #7
0
def kepextract(infile,maskfile,outfile,subback,clobber,verbose,logfile,status): 

# startup parameters

    status = 0
    seterr(all="ignore") 

# log the call 

    hashline = '----------------------------------------------------------------------------'
    kepmsg.log(logfile,hashline,verbose)
    call = 'KEPEXTRACT -- '
    call += 'infile='+infile+' '
    call += 'maskfile='+maskfile+' '
    call += 'outfile='+outfile+' '
    backgr = 'n'
    if (subback): backgr = 'y'
    call += 'background='+backgr+ ' '
    overwrite = 'n'
    if (clobber): overwrite = 'y'
    call += 'clobber='+overwrite+ ' '
    chatter = 'n'
    if (verbose): chatter = 'y'
    call += 'verbose='+chatter+' '
    call += 'logfile='+logfile
    kepmsg.log(logfile,call+'\n',verbose)

# start time

    kepmsg.clock('KEPEXTRACT started at',logfile,verbose)

# test log file

    logfile = kepmsg.test(logfile)

# clobber output file

    if clobber: status = kepio.clobber(outfile,logfile,verbose)
    if kepio.fileexists(outfile): 
        message = 'ERROR -- KEPEXTRACT: ' + outfile + ' exists. Use --clobber'
        status = kepmsg.err(logfile,message,verbose)

# open input file

    status = 0
    instr = pyfits.open(infile,mode='readonly',memmap=True)
    if status == 0:
        tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)

# fudge non-compliant FITS keywords with no values

    if status == 0:
        instr = kepkey.emptykeys(instr,file,logfile,verbose)

# input file data

    if status == 0:
        cards0 = instr[0].header.cards
        cards1 = instr[1].header.cards
        cards2 = instr[2].header.cards
        table = instr[1].data[:]
        maskmap = copy(instr[2].data)

# input table data

    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, time, status = \
            kepio.readTPF(infile,'TIME',logfile,verbose)
        time = numpy.array(time,dtype='float64')
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, timecorr, status = \
            kepio.readTPF(infile,'TIMECORR',logfile,verbose)
        timecorr = numpy.array(timecorr,dtype='float32')
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, cadenceno, status = \
            kepio.readTPF(infile,'CADENCENO',logfile,verbose)
        cadenceno = numpy.array(cadenceno,dtype='int')
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, raw_cnts, status = \
            kepio.readTPF(infile,'RAW_CNTS',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux, status = \
            kepio.readTPF(infile,'FLUX',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_err, status = \
            kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
            kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
            kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, cosmic_rays, status = \
            kepio.readTPF(infile,'COSMIC_RAYS',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, quality, status = \
            kepio.readTPF(infile,'QUALITY',logfile,verbose)
        quality = numpy.array(quality,dtype='int')
    if status == 0:
        try:
            pos_corr1 = numpy.array(table.field('POS_CORR1'),dtype='float64')  #  ---for FITS wave #2
        except:
            pos_corr1 = empty(len(time)); pos_corr1[:] = numpy.nan   # ---temporary before FITS wave #2
        try:
            pos_corr2 = numpy.array(table.field('POS_CORR2'),dtype='float64')  #  ---for FITS wave #2
        except:
            pos_corr2 = empty(len(time)); pos_corr2[:] = numpy.nan   # ---temporary before FITS wave #2

# dummy columns for output file

        psf_centr1 = empty(len(time)); psf_centr1[:] = numpy.nan
        psf_centr1_err = empty(len(time)); psf_centr1_err[:] = numpy.nan
        psf_centr2 = empty(len(time)); psf_centr2[:] = numpy.nan
        psf_centr2_err = empty(len(time)); psf_centr2_err[:] = numpy.nan
#        mom_centr1 = empty(len(time)); mom_centr1[:] = numpy.nan
        mom_centr1_err = empty(len(time)); mom_centr1_err[:] = numpy.nan
#        mom_centr2 = empty(len(time)); mom_centr2[:] = numpy.nan
        mom_centr2_err = empty(len(time)); mom_centr2_err[:] = numpy.nan

# read mask definition file

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        maskx = array([],'int')
        masky = array([],'int')
        lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
        for line in lines:
            line = line.strip().split('|')
            if len(line) == 6:
                y0 = int(line[3])
                x0 = int(line[4])
                line = line[5].split(';')
                for items in line:
                    try:
                        masky = append(masky,y0 + int(items.split(',')[0]))
                        maskx = append(maskx,x0 + int(items.split(',')[1]))
                    except:
                        continue
        status = kepio.closeascii(lines,logfile,verbose)
        if len(maskx) == 0 or len(masky) == 0:
            message = 'ERROR -- KEPEXTRACT: ' + maskfile + ' contains no pixels.'
            status = kepmsg.err(logfile,message,verbose)

# subimage physical WCS data

    if status == 0:
        crpix1p = cards2['CRPIX1P'].value
        crpix2p = cards2['CRPIX2P'].value
        crval1p = cards2['CRVAL1P'].value
        crval2p = cards2['CRVAL2P'].value
        cdelt1p = cards2['CDELT1P'].value
        cdelt2p = cards2['CDELT2P'].value

# define new subimage bitmap...

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        aperx = array([],'int')
        apery = array([],'int')
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
                if maskmap[i,j] == 0:
                    aperb = append(aperb,0)
                else:
                    aperb = append(aperb,1)
                    maskmap[i,j] = 1
                    for k in range(len(maskx)):
                        if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
                            aperb[-1] = 3
                            maskmap[i,j] = 3

# trap case where no aperture needs to be defined but pixel positions are still required for centroiding

    if status == 0 and maskfile.lower() == 'all':
        aperx = array([],'int')
        apery = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)

# ...or use old subimage bitmap

    if status == 0 and 'aper' in maskfile.lower():
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperb = append(aperb,maskmap[i,j])

# ...or use all pixels

    if status == 0 and maskfile.lower() == 'all':
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                if maskmap[i,j] == 0:
                    aperb = append(aperb,0)
                else:
                    aperb = append(aperb,3)
                    maskmap[i,j] = 3

# subtract median pixel value for background?

    if status == 0:
        sky = array([],'float32')
        for i in range(len(time)):
            sky = append(sky,median(flux[i,:]))
        if not subback:
            sky[:] = 0.0

# legal mask defined?

    if status == 0:
        if len(aperb) == 0:
            message = 'ERROR -- KEPEXTRACT: no legal pixels within the subimage are defined.'
            status = kepmsg.err(logfile,message,verbose)
        
# construct new table flux data

    if status == 0:
        naper = (aperb == 3).sum()
        ntime = len(time)
        sap_flux = array([],'float32')
        sap_flux_err = array([],'float32')
        sap_bkg = array([],'float32')
        sap_bkg_err = array([],'float32')
        raw_flux = array([],'float32')
        for i in range(len(time)):
            work1 = array([],'float64')
            work2 = array([],'float64')
            work3 = array([],'float64')
            work4 = array([],'float64')
            work5 = array([],'float64')
            for j in range(len(aperb)):
                if (aperb[j] == 3):
                    work1 = append(work1,flux[i,j]-sky[i])
                    work2 = append(work2,flux_err[i,j])
                    work3 = append(work3,flux_bkg[i,j])
                    work4 = append(work4,flux_bkg_err[i,j])
                    work5 = append(work5,raw_cnts[i,j])
            sap_flux = append(sap_flux,kepstat.sum(work1))
            sap_flux_err = append(sap_flux_err,kepstat.sumerr(work2))
            sap_bkg = append(sap_bkg,kepstat.sum(work3))
            sap_bkg_err = append(sap_bkg_err,kepstat.sumerr(work4))
            raw_flux = append(raw_flux,kepstat.sum(work5))

# construct new table moment data

    if status == 0:
        mom_centr1 = zeros(shape=(ntime))
        mom_centr2 = zeros(shape=(ntime))
        mom_centr1_err = zeros(shape=(ntime))
        mom_centr2_err = zeros(shape=(ntime))
        for i in range(ntime):
            xf = zeros(shape=(naper))
            yf = zeros(shape=(naper))
            f = zeros(shape=(naper))
            xfe = zeros(shape=(naper))
            yfe = zeros(shape=(naper))
            fe = zeros(shape=(naper))
            k = -1
            for j in range(len(aperb)):
                if (aperb[j] == 3):
                    k += 1
                    xf[k] = aperx[j] * flux[i,j]
                    xfe[k] = aperx[j] * flux_err[i,j]
                    yf[k] = apery[j] * flux[i,j]
                    yfe[k] = apery[j] * flux_err[i,j]
                    f[k] = flux[i,j]
                    fe[k] = flux_err[i,j]
            xfsum = kepstat.sum(xf)
            yfsum = kepstat.sum(yf)
            fsum = kepstat.sum(f)
            xfsume = sqrt(kepstat.sum(square(xfe)) / naper)
            yfsume = sqrt(kepstat.sum(square(yfe)) / naper)
            fsume = sqrt(kepstat.sum(square(fe)) / naper)
            mom_centr1[i] = xfsum / fsum
            mom_centr2[i] = yfsum / fsum
            mom_centr1_err[i] = sqrt((xfsume / xfsum)**2 + ((fsume / fsum)**2))
            mom_centr2_err[i] = sqrt((yfsume / yfsum)**2 + ((fsume / fsum)**2))
        mom_centr1_err = mom_centr1_err * mom_centr1
        mom_centr2_err = mom_centr2_err * mom_centr2

# construct new table PSF data

    if status == 0:
        psf_centr1 = zeros(shape=(ntime))
        psf_centr2 = zeros(shape=(ntime))
        psf_centr1_err = zeros(shape=(ntime))
        psf_centr2_err = zeros(shape=(ntime))
        modx = zeros(shape=(naper))
        mody = zeros(shape=(naper))
        k = -1
        for j in range(len(aperb)):
            if (aperb[j] == 3):
                k += 1
                modx[k] = aperx[j]
                mody[k] = apery[j]
        for i in range(ntime):
            modf = zeros(shape=(naper))
            k = -1
            guess = [mom_centr1[i], mom_centr2[i], nanmax(flux[i:]), 1.0, 1.0, 0.0, 0.0]
            for j in range(len(aperb)):
                if (aperb[j] == 3):
                    k += 1
                    modf[k] = flux[i,j]
                    args = (modx, mody, modf)
            ans = leastsq(kepfunc.PRFgauss2d,guess,args=args,xtol=1.0e-8,ftol=1.0e-4,full_output=True)
            s_sq = (ans[2]['fvec']**2).sum() / (ntime-len(guess))
            psf_centr1[i] = ans[0][0]
            psf_centr2[i] = ans[0][1]
            try:
                psf_centr1_err[i] = sqrt(diag(ans[1] * s_sq))[0]
            except:
                psf_centr1_err[i] = numpy.nan
            try:
                psf_centr2_err[i] = sqrt(diag(ans[1] * s_sq))[1]
            except:
                psf_centr2_err[i] = numpy.nan

# construct output primary extension

    if status == 0:
        hdu0 = pyfits.PrimaryHDU()
        for i in range(len(cards0)):
            if cards0[i].key not in hdu0.header.keys():
                hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
            else:
                hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
        status = kepkey.history(call,hdu0,outfile,logfile,verbose)
        outstr = HDUList(hdu0)

# construct output light curve extension

    if status == 0:
        col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
        col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
        col3 = Column(name='CADENCENO',format='J',array=cadenceno)
        col4 = Column(name='SAP_FLUX',format='E',array=sap_flux)
        col5 = Column(name='SAP_FLUX_ERR',format='E',array=sap_flux_err)
        col6 = Column(name='SAP_BKG',format='E',array=sap_bkg)
        col7 = Column(name='SAP_BKG_ERR',format='E',array=sap_bkg_err)
        col8 = Column(name='PDCSAP_FLUX',format='E',array=sap_flux)
        col9 = Column(name='PDCSAP_FLUX_ERR',format='E',array=sap_flux_err)
        col10 = Column(name='SAP_QUALITY',format='J',array=quality)
        col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
        col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
        col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
        col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
        col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
        col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
        col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
        col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
        col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
        col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
        col21 = Column(name='RAW_FLUX',format='E',array=raw_flux)
        cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
                            col12,col13,col14,col15,col16,col17,col18,col19,col20,col21])
        hdu1 = new_table(cols)
        hdu1.header.update('TTYPE1','TIME','column title: data time stamps')
        hdu1.header.update('TFORM1','D','data type: float64')
        hdu1.header.update('TUNIT1','BJD - 2454833','column units: barycenter corrected JD')
        hdu1.header.update('TDISP1','D12.7','column display format')
        hdu1.header.update('TTYPE2','TIMECORR','column title: barycentric-timeslice correction')
        hdu1.header.update('TFORM2','E','data type: float32')
        hdu1.header.update('TUNIT2','d','column units: days')
        hdu1.header.update('TTYPE3','CADENCENO','column title: unique cadence number')
        hdu1.header.update('TFORM3','J','column format: signed integer32')
        hdu1.header.update('TTYPE4','SAP_FLUX','column title: aperture photometry flux')
        hdu1.header.update('TFORM4','E','column format: float32')
        hdu1.header.update('TUNIT4','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE5','SAP_FLUX_ERR','column title: aperture phot. flux error')
        hdu1.header.update('TFORM5','E','column format: float32')
        hdu1.header.update('TUNIT5','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE6','SAP_BKG','column title: aperture phot. background flux')
        hdu1.header.update('TFORM6','E','column format: float32')
        hdu1.header.update('TUNIT6','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE7','SAP_BKG_ERR','column title: ap. phot. background flux error')
        hdu1.header.update('TFORM7','E','column format: float32')
        hdu1.header.update('TUNIT7','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE8','PDCSAP_FLUX','column title: PDC photometry flux')
        hdu1.header.update('TFORM8','E','column format: float32')
        hdu1.header.update('TUNIT8','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE9','PDCSAP_FLUX_ERR','column title: PDC flux error')
        hdu1.header.update('TFORM9','E','column format: float32')
        hdu1.header.update('TUNIT9','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE10','SAP_QUALITY','column title: aperture photometry quality flag')
        hdu1.header.update('TFORM10','J','column format: signed integer32')
        hdu1.header.update('TTYPE11','PSF_CENTR1','column title: PSF fitted column centroid')
        hdu1.header.update('TFORM11','E','column format: float32')
        hdu1.header.update('TUNIT11','pixel','column units: pixel')
        hdu1.header.update('TTYPE12','PSF_CENTR1_ERR','column title: PSF fitted column error')
        hdu1.header.update('TFORM12','E','column format: float32')
        hdu1.header.update('TUNIT12','pixel','column units: pixel')
        hdu1.header.update('TTYPE13','PSF_CENTR2','column title: PSF fitted row centroid')
        hdu1.header.update('TFORM13','E','column format: float32')
        hdu1.header.update('TUNIT13','pixel','column units: pixel')
        hdu1.header.update('TTYPE14','PSF_CENTR2_ERR','column title: PSF fitted row error')
        hdu1.header.update('TFORM14','E','column format: float32')
        hdu1.header.update('TUNIT14','pixel','column units: pixel')
        hdu1.header.update('TTYPE15','MOM_CENTR1','column title: moment-derived column centroid')
        hdu1.header.update('TFORM15','E','column format: float32')
        hdu1.header.update('TUNIT15','pixel','column units: pixel')
        hdu1.header.update('TTYPE16','MOM_CENTR1_ERR','column title: moment-derived column error')
        hdu1.header.update('TFORM16','E','column format: float32')
        hdu1.header.update('TUNIT16','pixel','column units: pixel')
        hdu1.header.update('TTYPE17','MOM_CENTR2','column title: moment-derived row centroid')
        hdu1.header.update('TFORM17','E','column format: float32')
        hdu1.header.update('TUNIT17','pixel','column units: pixel')
        hdu1.header.update('TTYPE18','MOM_CENTR2_ERR','column title: moment-derived row error')
        hdu1.header.update('TFORM18','E','column format: float32')
        hdu1.header.update('TUNIT18','pixel','column units: pixel')
        hdu1.header.update('TTYPE19','POS_CORR1','column title: col correction for vel. abbern')
        hdu1.header.update('TFORM19','E','column format: float32')
        hdu1.header.update('TUNIT19','pixel','column units: pixel')
        hdu1.header.update('TTYPE20','POS_CORR2','column title: row correction for vel. abbern')
        hdu1.header.update('TFORM20','E','column format: float32')
        hdu1.header.update('TUNIT20','pixel','column units: pixel')
        hdu1.header.update('TTYPE21','RAW_FLUX','column title: raw aperture photometry flux')
        hdu1.header.update('TFORM21','E','column format: float32')
        hdu1.header.update('TUNIT21','e-/s','column units: electrons per second')
        hdu1.header.update('EXTNAME','LIGHTCURVE','name of extension')
        for i in range(len(cards1)):
            if (cards1[i].key not in hdu1.header.keys() and
                cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
                                          '2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
                                          '1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
                                          '12PC','21PC','22PC']):
                hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
        outstr.append(hdu1)

# construct output mask bitmap extension

    if status == 0:
        hdu2 = ImageHDU(maskmap)
        for i in range(len(cards2)):
            if cards2[i].key not in hdu2.header.keys():
                hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
            else:
                hdu2.header.cards[cards2[i].key].comment = cards2[i].comment
        outstr.append(hdu2)

# write output file

    if status == 0:
        outstr.writeto(outfile,checksum=True)

# close input structure

    if status == 0:
        status = kepio.closefits(instr,logfile,verbose)	    

# end time

    kepmsg.clock('KEPEXTRACT finished at',logfile,verbose)
Пример #8
0
def keppca(infile,maskfile,outfile,components,plotpca,nreps,clobber,verbose,logfile,status,cmdLine=False): 

    try:
        import mdp
    except:
        msg = 'ERROR -- KEPPCA: this task has an external python dependency to MDP, a Modular toolkit for Data Processing (http://mdp-toolkit.sourceforge.net). In order to take advantage of this PCA task, the user must first install MDP with their current python distribution. Note carefully that you may have more than python installation on your machine, and ensure that MDP is installed with the same version of python that the PyKE tools employ. Installation instructions for MDP can be found at the URL provided above.'
        status = kepmsg.err(None,msg,True)
    
# startup parameters

    status = 0
    labelsize = 32
    ticksize = 18
    xsize = 16
    ysize = 10
    lcolor = '#0000ff'
    lwidth = 1.0
    fcolor = '#ffff00'
    falpha = 0.2
    seterr(all="ignore") 

# log the call 

    if status == 0:
        hashline = '----------------------------------------------------------------------------'
        kepmsg.log(logfile,hashline,verbose)
        call = 'KEPPCA -- '
        call += 'infile='+infile+' '
        call += 'maskfile='+maskfile+' '
        call += 'outfile='+outfile+' '
        call += 'components='+components+' '
        ppca = 'n'
        if (plotpca): ppca = 'y'
        call += 'plotpca='+ppca+ ' '
        call += 'nmaps='+str(nreps)+' '
        overwrite = 'n'
        if (clobber): overwrite = 'y'
        call += 'clobber='+overwrite+ ' '
        chatter = 'n'
        if (verbose): chatter = 'y'
        call += 'verbose='+chatter+' '
        call += 'logfile='+logfile
        kepmsg.log(logfile,call+'\n',verbose)
        
# start time

    if status == 0:
        kepmsg.clock('KEPPCA started at',logfile,verbose)

# test log file

    if status == 0:
        logfile = kepmsg.test(logfile)
    
# clobber output file

    if status == 0:
        if clobber: status = kepio.clobber(outfile,logfile,verbose)
        if kepio.fileexists(outfile): 
            message = 'ERROR -- KEPPCA: ' + outfile + ' exists. Use clobber=yes'
            status = kepmsg.err(logfile,message,verbose)

# Set output file names - text file with data and plot

    if status == 0:
        dataout = copy(outfile)
        repname = re.sub('.fits','.png',outfile)

# open input file

    if status == 0:    
        instr = pyfits.open(infile,mode='readonly',memmap=True)
        tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)

# open TPF FITS file

    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, barytime, status = \
            kepio.readTPF(infile,'TIME',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, tcorr, status = \
            kepio.readTPF(infile,'TIMECORR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, cadno, status = \
            kepio.readTPF(infile,'CADENCENO',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, fluxpixels, status = \
            kepio.readTPF(infile,'FLUX',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, errpixels, status = \
            kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
            kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
            kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, qual, status = \
            kepio.readTPF(infile,'QUALITY',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, pcorr1, status = \
            kepio.readTPF(infile,'POS_CORR1',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, pcorr2, status = \
            kepio.readTPF(infile,'POS_CORR2',logfile,verbose)

# Save original data dimensions, in case of using maskfile

    if status == 0:
        xdimorig = xdim
        ydimorig = ydim
    
# read mask definition file if it has been supplied

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        maskx = array([],'int')
        masky = array([],'int')
        lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
        for line in lines:
            line = line.strip().split('|')
            if len(line) == 6:
                y0 = int(line[3])
                x0 = int(line[4])
                line = line[5].split(';')
                for items in line:
                    try:
                        masky = numpy.append(masky,y0 + int(items.split(',')[0]))
                        maskx = numpy.append(maskx,x0 + int(items.split(',')[1]))
                    except:
                        continue
        status = kepio.closeascii(lines,logfile,verbose)
        if len(maskx) == 0 or len(masky) == 0:
            message = 'ERROR -- KEPPCA: ' + maskfile + ' contains no pixels.'
            status = kepmsg.err(logfile,message,verbose)
        xdim = max(maskx) - min(maskx) + 1   # Find largest x dimension of mask
        ydim = max(masky) - min(masky) + 1   # Find largest y dimension of mask

# pad mask to ensure it is rectangular

        workx = array([],'int')
        worky = array([],'int')
        for ip in arange(min(maskx),max(maskx) + 1):
            for jp in arange(min(masky),max(masky) + 1):
                workx = append(workx,ip)
                worky = append(worky,jp)
        maskx = workx
        masky = worky

# define new subimage bitmap...

    if status == 0 and maskfile.lower() != 'all':
        aperx = numpy.array([],'int')
        apery = numpy.array([],'int')
        aperb = maskx - x0 + xdimorig * (masky - y0)   # aperb is an array that contains the pixel numbers in the mask
        npix = len(aperb)

# ...or use all pixels

    if status == 0 and maskfile.lower() == 'all':
        npix = xdimorig*ydimorig
        aperb = array([],'int')
        aperb = numpy.r_[0:npix]

# legal mask defined?

    if status == 0:
        if len(aperb) == 0:
            message = 'ERROR -- KEPPCA: no legal pixels within the subimage are defined.'
            status = kepmsg.err(logfile,message,verbose)

# Identify principal components desired

    if status == 0:
        pcaout = []
        txt = components.strip().split(',')
        for work1 in txt:
            try:
                pcaout.append(int(work1.strip()))
            except:
                work2 = work1.strip().split('-')
                try:
                    for work3 in range(int(work2[0]),int(work2[1]) + 1):
                        pcaout.append(work3)
                except:
                    message = 'ERROR -- KEPPCA: cannot understand principal component list requested'
                    status = kepmsg.err(logfile,message,verbose)
    if status == 0:
        pcaout = set(sort(pcaout))
    pcarem = array(list(pcaout))-1    # The list of pca component numbers to be removed

# Initialize arrays and variables, and apply pixel mask to the data

    if status == 0:
        ntim = 0
        time = numpy.array([],dtype='float64')
        timecorr = numpy.array([],dtype='float32')
        cadenceno = numpy.array([],dtype='int')
        pixseries = numpy.array([],dtype='float32')
        errseries = numpy.array([],dtype='float32')
        bkgseries = numpy.array([],dtype='float32')
        berseries = numpy.array([],dtype='float32')
        quality = numpy.array([],dtype='float32')
        pos_corr1 = numpy.array([],dtype='float32')
        pos_corr2 = numpy.array([],dtype='float32')
        nrows = numpy.size(fluxpixels,0)
        
# Apply the pixel mask so we are left with only the desired pixels       

    if status == 0:
        pixseriesb = fluxpixels[:,aperb]
        errseriesb = errpixels[:,aperb]
        bkgseriesb = flux_bkg[:,aperb]
        berseriesb = flux_bkg_err[:,aperb]

# Read in the data to various arrays 
   
    if status == 0:
        for i in range(nrows):
            if qual[i] < 10000 and \
                    numpy.isfinite(barytime[i]) and \
                    numpy.isfinite(fluxpixels[i,int(ydim*xdim/2+0.5)]) and \
                    numpy.isfinite(fluxpixels[i,1+int(ydim*xdim/2+0.5)]):
                ntim += 1
                time = numpy.append(time,barytime[i])
                timecorr = numpy.append(timecorr,tcorr[i])
                cadenceno = numpy.append(cadenceno,cadno[i])
                pixseries = numpy.append(pixseries,pixseriesb[i])
                errseries = numpy.append(errseries,errseriesb[i])
                bkgseries = numpy.append(bkgseries,bkgseriesb[i])
                berseries = numpy.append(berseries,berseriesb[i])
                quality = numpy.append(quality,qual[i])
                pos_corr1 = numpy.append(pos_corr1,pcorr1[i])
                pos_corr2 = numpy.append(pos_corr2,pcorr2[i])
        pixseries = numpy.reshape(pixseries,(ntim,npix))
        errseries = numpy.reshape(errseries,(ntim,npix))
        bkgseries = numpy.reshape(bkgseries,(ntim,npix))
        berseries = numpy.reshape(berseries,(ntim,npix))        
        tmp =  numpy.median(pixseries,axis=1)     
        for i in range(len(tmp)):
             pixseries[i] = pixseries[i] - tmp[i]

# Figure out which pixels are undefined/nan and remove them. Keep track for adding back in later

    if status == 0:
        nanpixels = numpy.array([],dtype='int')
        i = 0
        while (i < npix):
            if numpy.isnan(pixseries[0,i]):
                nanpixels = numpy.append(nanpixels,i)
                npix = npix - 1
            i = i + 1
        pixseries = numpy.delete(pixseries,nanpixels,1)
        errseries = numpy.delete(errseries,nanpixels,1)
        pixseries[numpy.isnan(pixseries)] = random.gauss(100,10)
        errseries[numpy.isnan(errseries)] = 10
 
# Compute statistical weights, means, standard deviations

    if status == 0:
        weightseries = (pixseries/errseries)**2
        pixMean = numpy.average(pixseries,axis=0,weights=weightseries)
        pixStd  = numpy.std(pixseries,axis=0)

# Normalize the input by subtracting the mean and divising by the standard deviation. 
# This makes it a correlation-based PCA, which is what we want.

    if status == 0:
        pixseriesnorm = (pixseries - pixMean)/pixStd

# Number of principal components to compute. Setting it equal to the number of pixels

    if status == 0:
        nvecin = npix  

# Run PCA using the MDP Whitening PCA, which produces normalized PCA components (zero mean and unit variance)
    
    if status == 0:
        pcan = mdp.nodes.WhiteningNode(svd=True)
        pcar = pcan.execute(pixseriesnorm)
        eigvec = pcan.get_recmatrix()
        model = pcar
 
# Re-insert nan columns as zeros

    if status == 0:
        for i in range(0,len(nanpixels)):
            nanpixels[i] = nanpixels[i]-i
        eigvec = numpy.insert(eigvec,nanpixels,0,1)
        pixMean = numpy.insert(pixMean,nanpixels,0,0)

#  Make output eigenvectors (correlation images) into xpix by ypix images

    if status == 0:
        eigvec = eigvec.reshape(nvecin,ydim,xdim)

# Calculate sum of all pixels to display as raw lightcurve and other quantities

    if status == 0:
        pixseriessum = sum(pixseries,axis=1)
        nrem=len(pcarem)  # Number of components to remove
        nplot = npix      # Number of pcas to plot - currently set to plot all components, but could set 
                          # nplot = nrem to just plot as many components as is being removed

# Subtract components by fitting them to the summed light curve

    if status == 0:
        x0 = numpy.tile(-1.0,1)
        for k in range(0,nrem):
            def f(x):
                fluxcor = pixseriessum
                for k in range(0,len(x)):
                    fluxcor = fluxcor - x[k]*model[:,pcarem[k]]
                return mad(fluxcor)
            if k==0:
                x0 = array([-1.0])
            else:
                x0 = numpy.append(x0,1.0)
            myfit = scipy.optimize.fmin(f,x0,maxiter=50000,maxfun=50000,disp=False)
            x0 = myfit
    
# Now that coefficients for all components have been found, subtract them to produce a calibrated time-series, 
# and then divide by the robust mean to produce a normalized time series as well

    if status == 0:
        c = myfit
        fluxcor = pixseriessum
        for k in range(0,nrem):
            fluxcor = fluxcor - c[k]*model[:,pcarem[k]]
            normfluxcor = fluxcor/mean(reject_outliers(fluxcor,2))

# input file data

    if status == 0:
        cards0 = instr[0].header.cards
        cards1 = instr[1].header.cards
        cards2 = instr[2].header.cards
        table = instr[1].data[:]
        maskmap = copy(instr[2].data)

# subimage physical WCS data

    if status == 0:
        crpix1p = cards2['CRPIX1P'].value
        crpix2p = cards2['CRPIX2P'].value
        crval1p = cards2['CRVAL1P'].value
        crval2p = cards2['CRVAL2P'].value
        cdelt1p = cards2['CDELT1P'].value
        cdelt2p = cards2['CDELT2P'].value

# dummy columns for output file

    if status == 0:
        sap_flux_err = numpy.empty(len(time)); sap_flux_err[:] = numpy.nan
        sap_bkg = numpy.empty(len(time)); sap_bkg[:] = numpy.nan
        sap_bkg_err = numpy.empty(len(time)); sap_bkg_err[:] = numpy.nan
        pdc_flux = numpy.empty(len(time)); pdc_flux[:] = numpy.nan
        pdc_flux_err = numpy.empty(len(time)); pdc_flux_err[:] = numpy.nan
        psf_centr1 = numpy.empty(len(time)); psf_centr1[:] = numpy.nan
        psf_centr1_err = numpy.empty(len(time)); psf_centr1_err[:] = numpy.nan
        psf_centr2 = numpy.empty(len(time)); psf_centr2[:] = numpy.nan
        psf_centr2_err = numpy.empty(len(time)); psf_centr2_err[:] = numpy.nan
        mom_centr1 = numpy.empty(len(time)); mom_centr1[:] = numpy.nan
        mom_centr1_err = numpy.empty(len(time)); mom_centr1_err[:] = numpy.nan
        mom_centr2 = numpy.empty(len(time)); mom_centr2[:] = numpy.nan
        mom_centr2_err = numpy.empty(len(time)); mom_centr2_err[:] = numpy.nan

# mask bitmap

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
                if maskmap[i,j] == 0:
                    pass
                else:
                    maskmap[i,j] = 1
                    for k in range(len(maskx)):
                        if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
                            maskmap[i,j] = 3

# construct output primary extension

    if status == 0:
        hdu0 = pyfits.PrimaryHDU()
        for i in range(len(cards0)):
            if cards0[i].keyword not in hdu0.header.keys():
                hdu0.header[cards0[i].keyword] = (cards0[i].value, cards0[i].comment)
            else:
                hdu0.header.cards[cards0[i].keyword].comment = cards0[i].comment
        status = kepkey.history(call,hdu0,outfile,logfile,verbose)
        outstr = HDUList(hdu0)

# construct output light curve extension

    if status == 0:
        col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
        col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
        col3 = Column(name='CADENCENO',format='J',array=cadenceno)
        col4 = Column(name='SAP_FLUX',format='E',unit='e-/s',array=pixseriessum)
        col5 = Column(name='SAP_FLUX_ERR',format='E',unit='e-/s',array=sap_flux_err)
        col6 = Column(name='SAP_BKG',format='E',unit='e-/s',array=sap_bkg)
        col7 = Column(name='SAP_BKG_ERR',format='E',unit='e-/s',array=sap_bkg_err)
        col8 = Column(name='PDCSAP_FLUX',format='E',unit='e-/s',array=pdc_flux)
        col9 = Column(name='PDCSAP_FLUX_ERR',format='E',unit='e-/s',array=pdc_flux_err)
        col10 = Column(name='SAP_QUALITY',format='J',array=quality)
        col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
        col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
        col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
        col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
        col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
        col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
        col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
        col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
        col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
        col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
        col21 = Column(name='PCA_FLUX',format='E',unit='e-/s',array=fluxcor)
        col22 = Column(name='PCA_FLUX_NRM',format='E',array=normfluxcor)
        cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
                            col12,col13,col14,col15,col16,col17,col18,col19,col20,col21,col22])
        hdu1 = new_table(cols)
        hdu1.header['TTYPE1'] = ('TIME','column title: data time stamps')
        hdu1.header['TFORM1'] = ('D','data type: float64')
        hdu1.header['TUNIT1'] = ('BJD - 2454833','column units: barycenter corrected JD')
        hdu1.header['TDISP1'] = ('D12.7','column display format')
        hdu1.header['TTYPE2'] = ('TIMECORR','column title: barycentric-timeslice correction')
        hdu1.header['TFORM2'] = ('E','data type: float32')
        hdu1.header['TUNIT2'] = ('d','column units: days')
        hdu1.header['TTYPE3'] = ('CADENCENO','column title: unique cadence number')
        hdu1.header['TFORM3'] = ('J','column format: signed integer32')
        hdu1.header['TTYPE4'] = ('SAP_FLUX','column title: aperture photometry flux')
        hdu1.header['TFORM4'] = ('E','column format: float32')
        hdu1.header['TUNIT4'] = ('e-/s','column units: electrons per second')
        hdu1.header['TTYPE5'] = ('SAP_FLUX_ERR','column title: aperture phot. flux error')
        hdu1.header['TFORM5'] = ('E','column format: float32')
        hdu1.header['TUNIT5'] = ('e-/s','column units: electrons per second (1-sigma)')
        hdu1.header['TTYPE6'] = ('SAP_BKG','column title: aperture phot. background flux')
        hdu1.header['TFORM6'] = ('E','column format: float32')
        hdu1.header['TUNIT6'] = ('e-/s','column units: electrons per second')
        hdu1.header['TTYPE7'] = ('SAP_BKG_ERR','column title: ap. phot. background flux error')
        hdu1.header['TFORM7'] = ('E','column format: float32')
        hdu1.header['TUNIT7'] = ('e-/s','column units: electrons per second (1-sigma)')
        hdu1.header['TTYPE8'] = ('PDCSAP_FLUX','column title: PDC photometry flux')
        hdu1.header['TFORM8'] = ('E','column format: float32')
        hdu1.header['TUNIT8'] = ('e-/s','column units: electrons per second')
        hdu1.header['TTYPE9'] = ('PDCSAP_FLUX_ERR','column title: PDC flux error')
        hdu1.header['TFORM9'] = ('E','column format: float32')
        hdu1.header['TUNIT9'] = ('e-/s','column units: electrons per second (1-sigma)')
        hdu1.header['TTYPE10'] = ('SAP_QUALITY','column title: aperture photometry quality flag')
        hdu1.header['TFORM10'] = ('J','column format: signed integer32')
        hdu1.header['TTYPE11'] = ('PSF_CENTR1','column title: PSF fitted column centroid')
        hdu1.header['TFORM11'] = ('E','column format: float32')
        hdu1.header['TUNIT11'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE12'] = ('PSF_CENTR1_ERR','column title: PSF fitted column error')
        hdu1.header['TFORM12'] = ('E','column format: float32')
        hdu1.header['TUNIT12'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE13'] = ('PSF_CENTR2','column title: PSF fitted row centroid')
        hdu1.header['TFORM13'] = ('E','column format: float32')
        hdu1.header['TUNIT13'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE14'] = ('PSF_CENTR2_ERR','column title: PSF fitted row error')
        hdu1.header['TFORM14'] = ('E','column format: float32')
        hdu1.header['TUNIT14'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE15'] = ('MOM_CENTR1','column title: moment-derived column centroid')
        hdu1.header['TFORM15'] = ('E','column format: float32')
        hdu1.header['TUNIT15'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE16'] = ('MOM_CENTR1_ERR','column title: moment-derived column error')
        hdu1.header['TFORM16'] = ('E','column format: float32')
        hdu1.header['TUNIT16'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE17'] = ('MOM_CENTR2','column title: moment-derived row centroid')
        hdu1.header['TFORM17'] = ('E','column format: float32')
        hdu1.header['TUNIT17'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE18'] = ('MOM_CENTR2_ERR','column title: moment-derived row error')
        hdu1.header['TFORM18'] = ('E','column format: float32')
        hdu1.header['TUNIT18'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE19'] = ('POS_CORR1','column title: col correction for vel. abbern')
        hdu1.header['TFORM19'] = ('E','column format: float32')
        hdu1.header['TUNIT19'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE20'] = ('POS_CORR2','column title: row correction for vel. abbern')
        hdu1.header['TFORM20'] = ('E','column format: float32')
        hdu1.header['TUNIT20'] = ('pixel','column units: pixel')
        hdu1.header['TTYPE21'] = ('PCA_FLUX','column title: PCA-corrected flux')
        hdu1.header['TFORM21'] = ('E','column format: float32')
        hdu1.header['TUNIT21'] = ('pixel','column units: e-/s')
        hdu1.header['TTYPE22'] = ('PCA_FLUX_NRM','column title: normalized PCA-corrected flux')
        hdu1.header['TFORM22'] = ('E','column format: float32')
        hdu1.header['EXTNAME'] = ('LIGHTCURVE','name of extension')
        for i in range(len(cards1)):
            if (cards1[i].keyword not in hdu1.header.keys() and
                cards1[i].keyword[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
                                          '2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
                                          '1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
                                          '12PC','21PC','22PC']):
                hdu1.header[cards1[i].keyword] = (cards1[i].value, cards1[i].comment)
        outstr.append(hdu1)

# construct output mask bitmap extension

    if status == 0:
        hdu2 = ImageHDU(maskmap)
        for i in range(len(cards2)):
            if cards2[i].keyword not in hdu2.header.keys():
                hdu2.header[cards2[i].keyword] = (cards2[i].value, cards2[i].comment)
            else:
                hdu2.header.cards[cards2[i].keyword].comment = cards2[i].comment
        outstr.append(hdu2)

# construct principal component table

    if status == 0:
        cols = [Column(name='TIME',format='E',unit='BJD - 2454833',array=time)]
        for i in range(len(pcar[0,:])):
            colname = 'PC' + str(i + 1)
            col = Column(name=colname,format='E',array=pcar[:,i])
            cols.append(col)
        hdu3 = new_table(ColDefs(cols))
        hdu3.header['EXTNAME'] = ('PRINCIPAL_COMPONENTS','name of extension')
        hdu3.header['TTYPE1'] = ('TIME','column title: data time stamps')
        hdu3.header['TFORM1'] = ('D','data type: float64')
        hdu3.header['TUNIT1'] = ('BJD - 2454833','column units: barycenter corrected JD')
        hdu3.header['TDISP1'] = ('D12.7','column display format')
        for i in range(len(pcar[0,:])):
            hdu3.header['TTYPE' + str(i + 2)] = \
                ('PC' + str(i + 1), 'column title: principal component number' + str(i + 1))
            hdu3.header['TFORM' + str(i + 2)] = ('E','column format: float32')
        outstr.append(hdu3)

# write output file

    if status == 0:
        outstr.writeto(outfile)
    
# close input structure

    if status == 0:
        status = kepio.closefits(instr,logfile,verbose)
        
# Create PCA report 

    if status == 0 and plotpca:
        npp = 7 # Number of plots per page
        l = 1
        repcnt = 1
        for k in range(nreps):

# First plot of every pagewith flux image, flux and calibrated time series 

            status = kepplot.define(16,12,logfile,verbose)
            if (k % (npp - 1) == 0):     
                pylab.figure(figsize=[10,16])
                subplot2grid((npp,6),(0,0), colspan=2)
#                imshow(log10(pixMean.reshape(xdim,ydim).T-min(pixMean)+1),interpolation="nearest",cmap='RdYlBu')
                imshow(log10(flipud(pixMean.reshape(ydim,xdim))-min(pixMean)+1),interpolation="nearest",cmap='RdYlBu')
                xticks([])
                yticks([])
                ax1 = subplot2grid((npp,6),(0,2), colspan=4)
                px = copy(time) + bjdref
                py = copy(pixseriessum)
                px, xlab, status = kepplot.cleanx(px,logfile,verbose) 
                py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
                kepplot.RangeOfPlot(px,py,0.01,False)
                kepplot.plot1d(px,py,cadence,lcolor,lwidth,fcolor,falpha,True)
                py = copy(fluxcor)
                py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
                plot(px,py,marker='.',color='r',linestyle='',markersize=1.0)
                kepplot.labels('',re.sub('\)','',re.sub('Flux \(','',ylab)),'k',18)
                grid()
                setp(ax1.get_xticklabels(), visible=False)

# plot principal components

            subplot2grid((npp,6),(l,0), colspan=2)
            imshow(eigvec[k],interpolation="nearest",cmap='RdYlBu')
            xlim(-0.5,xdim-0.5)
            ylim(-0.5,ydim-0.5)
            xticks([])
            yticks([])

# The last plot on the page that should have the xlabel

            if ( k% (npp - 1) == npp - 2 or k == nvecin - 1):  
                subplot2grid((npp,6),(l,2), colspan=4)
                py = copy(model[:,k])
                kepplot.RangeOfPlot(px,py,0.01,False)
                kepplot.plot1d(px,py,cadence,'r',lwidth,'g',falpha,True)
                kepplot.labels(xlab,'PC ' + str(k+1),'k',18)
                pylab.grid()
                pylab.tight_layout()
                l = 1
                pylab.savefig(re.sub('.png','_%d.png' % repcnt,repname))
                if not cmdLine: kepplot.render(cmdLine)
                repcnt += 1

# The other plots on the page that should have no xlabel

            else:
                ax2 = subplot2grid((npp,6),(l,2), colspan=4)
                py = copy(model[:,k])
                kepplot.RangeOfPlot(px,py,0.01,False)
                kepplot.plot1d(px,py,cadence,'r',lwidth,'g',falpha,True)
                kepplot.labels('','PC ' + str(k+1),'k',18)
                grid()
                setp(ax2.get_xticklabels(), visible=False)
                pylab.tight_layout()
                l=l+1
        pylab.savefig(re.sub('.png','_%d.png' % repcnt,repname))
        if not cmdLine: kepplot.render(cmdLine)

# plot style and size

    if status == 0 and plotpca:
        status = kepplot.define(labelsize,ticksize,logfile,verbose)
        pylab.figure(figsize=[xsize,ysize])
        pylab.clf()

# plot aperture photometry and PCA corrected data

    if status == 0 and plotpca:
        ax = kepplot.location([0.06,0.54,0.93,0.43])
        px = copy(time) + bjdref
        py = copy(pixseriessum)
        px, xlab, status = kepplot.cleanx(px,logfile,verbose) 
        py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
        kepplot.RangeOfPlot(px,py,0.01,False)
        kepplot.plot1d(px,py,cadence,lcolor,lwidth,fcolor,falpha,True)
        py = copy(fluxcor)
        py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
        kepplot.plot1d(px,py,cadence,'r',2,fcolor,0.0,True)
        pylab.setp(pylab.gca(),xticklabels=[])
        kepplot.labels('',ylab,'k',24)
        pylab.grid()

# plot aperture photometry and PCA corrected data

    if status == 0 and plotpca:
        ax = kepplot.location([0.06,0.09,0.93,0.43])
        yr = array([],'float32')
        npc = min([6,nrem])
        for i in range(npc-1,-1,-1):
            py = pcar[:,i] * c[i]
            py, ylab, status = kepplot.cleany(py,1.0,logfile,verbose)
            cl = float(i) / (float(npc))
            kepplot.plot1d(px,py,cadence,[1.0-cl,0.0,cl],2,fcolor,0.0,True)
            yr = append(yr,py)
        y1 = max(yr)
        y2 = -min(yr)
        kepplot.RangeOfPlot(px,array([-y1,y1,-y2,y2]),0.01,False)
        kepplot.labels(xlab,'Principal Components','k',24)
        pylab.grid()

# save plot to file

    if status == 0 and plotpca:
        pylab.savefig(repname)

# render plot

    if status == 0 and plotpca:
        kepplot.render(cmdLine)

# stop time

    if status == 0:
        kepmsg.clock('KEPPCA ended at',logfile,verbose)

    return
Пример #9
0
def kepextract(infile,maskfile,outfile,subback,clobber,verbose,logfile,status): 

# startup parameters

    status = 0
    seterr(all="ignore") 

# log the call 

    hashline = '----------------------------------------------------------------------------'
    kepmsg.log(logfile,hashline,verbose)
    call = 'KEPEXTRACT -- '
    call += 'infile='+infile+' '
    call += 'maskfile='+maskfile+' '
    call += 'outfile='+outfile+' '
    backgr = 'n'
    if (subback): backgr = 'y'
    call += 'background='+backgr+ ' '
    overwrite = 'n'
    if (clobber): overwrite = 'y'
    call += 'clobber='+overwrite+ ' '
    chatter = 'n'
    if (verbose): chatter = 'y'
    call += 'verbose='+chatter+' '
    call += 'logfile='+logfile
    kepmsg.log(logfile,call+'\n',verbose)

# start time

    kepmsg.clock('KEPEXTRACT started at',logfile,verbose)

# test log file

    logfile = kepmsg.test(logfile)

# clobber output file

    if clobber: status = kepio.clobber(outfile,logfile,verbose)
    if kepio.fileexists(outfile): 
        message = 'ERROR -- KEPEXTRACT: ' + outfile + ' exists. Use --clobber'
        status = kepmsg.err(logfile,message,verbose)

# open input file

    status = 0
    instr = pyfits.open(infile,mode='readonly',memmap=True)
    if status == 0:
        tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)

# fudge non-compliant FITS keywords with no values

    if status == 0:
        instr = kepkey.emptykeys(instr,file,logfile,verbose)

# input file data

    if status == 0:
        cards0 = instr[0].header.cards
        cards1 = instr[1].header.cards
        cards2 = instr[2].header.cards
        table = instr[1].data[:]
        maskmap = copy(instr[2].data)

# input table data

    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, time, status = \
            kepio.readTPF(infile,'TIME',logfile,verbose)
        time = numpy.array(time,dtype='float64')
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, timecorr, status = \
            kepio.readTPF(infile,'TIMECORR',logfile,verbose)
        timecorr = numpy.array(timecorr,dtype='float32')
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, cadenceno, status = \
            kepio.readTPF(infile,'CADENCENO',logfile,verbose)
        cadenceno = numpy.array(cadenceno,dtype='int')
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, raw_cnts, status = \
            kepio.readTPF(infile,'RAW_CNTS',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux, status = \
            kepio.readTPF(infile,'FLUX',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_err, status = \
            kepio.readTPF(infile,'FLUX_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg, status = \
            kepio.readTPF(infile,'FLUX_BKG',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, flux_bkg_err, status = \
            kepio.readTPF(infile,'FLUX_BKG_ERR',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, cosmic_rays, status = \
            kepio.readTPF(infile,'COSMIC_RAYS',logfile,verbose)
    if status == 0:
        kepid, channel, skygroup, module, output, quarter, season, \
            ra, dec, column, row, kepmag, xdim, ydim, quality, status = \
            kepio.readTPF(infile,'QUALITY',logfile,verbose)
        quality = numpy.array(quality,dtype='int')
    if status == 0:
        try:
            pos_corr1 = numpy.array(table.field('POS_CORR1'),dtype='float64')  #  ---for FITS wave #2
        except:
            pos_corr1 = empty(len(time)); pos_corr1[:] = numpy.nan   # ---temporary before FITS wave #2
        try:
            pos_corr2 = numpy.array(table.field('POS_CORR2'),dtype='float64')  #  ---for FITS wave #2
        except:
            pos_corr2 = empty(len(time)); pos_corr2[:] = numpy.nan   # ---temporary before FITS wave #2

# dummy columns for output file

        psf_centr1 = empty(len(time)); psf_centr1[:] = numpy.nan
        psf_centr1_err = empty(len(time)); psf_centr1_err[:] = numpy.nan
        psf_centr2 = empty(len(time)); psf_centr2[:] = numpy.nan
        psf_centr2_err = empty(len(time)); psf_centr2_err[:] = numpy.nan
#        mom_centr1 = empty(len(time)); mom_centr1[:] = numpy.nan
        mom_centr1_err = empty(len(time)); mom_centr1_err[:] = numpy.nan
#        mom_centr2 = empty(len(time)); mom_centr2[:] = numpy.nan
        mom_centr2_err = empty(len(time)); mom_centr2_err[:] = numpy.nan

# read mask definition file

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        maskx = array([],'int')
        masky = array([],'int')
        lines, status = kepio.openascii(maskfile,'r',logfile,verbose)
        for line in lines:
            line = line.strip().split('|')
            if len(line) == 6:
                y0 = int(line[3])
                x0 = int(line[4])
                line = line[5].split(';')
                for items in line:
                    try:
                        masky = append(masky,y0 + int(items.split(',')[0]))
                        maskx = append(maskx,x0 + int(items.split(',')[1]))
                    except:
                        continue
        status = kepio.closeascii(lines,logfile,verbose)
        if len(maskx) == 0 or len(masky) == 0:
            message = 'ERROR -- KEPEXTRACT: ' + maskfile + ' contains no pixels.'
            status = kepmsg.err(logfile,message,verbose)

# subimage physical WCS data

    if status == 0:
        crpix1p = cards2['CRPIX1P'].value
        crpix2p = cards2['CRPIX2P'].value
        crval1p = cards2['CRVAL1P'].value
        crval2p = cards2['CRVAL2P'].value
        cdelt1p = cards2['CDELT1P'].value
        cdelt2p = cards2['CDELT2P'].value

# define new subimage bitmap...

    if status == 0 and 'aper' not in maskfile.lower() and maskfile.lower() != 'all':
        aperx = array([],'int')
        apery = array([],'int')
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)
                if maskmap[i,j] == 0:
                    aperb = append(aperb,0)
                else:
                    aperb = append(aperb,1)
                    maskmap[i,j] = 1
                    for k in range(len(maskx)):
                        if aperx[-1] == maskx[k] and apery[-1] == masky[k]:
                            aperb[-1] = 3
                            maskmap[i,j] = 3

# trap case where no aperture needs to be defined but pixel positions are still required for centroiding

    if status == 0 and maskfile.lower() == 'all':
        aperx = array([],'int')
        apery = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)

# ...or use old subimage bitmap

    if status == 0 and 'aper' in maskfile.lower():
        aperx = array([],'int')
        apery = array([],'int')
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                aperb = append(aperb,maskmap[i,j])
                aperx = append(aperx,crval1p + (j + 1 - crpix1p) * cdelt1p)
                apery = append(apery,crval2p + (i + 1 - crpix2p) * cdelt2p)

# ...or use all pixels

    if status == 0 and maskfile.lower() == 'all':
        aperb = array([],'int')
        for i in range(maskmap.shape[0]):
            for j in range(maskmap.shape[1]):
                if maskmap[i,j] == 0:
                    aperb = append(aperb,0)
                else:
                    aperb = append(aperb,3)
                    maskmap[i,j] = 3

# subtract median pixel value for background?

    if status == 0:
        sky = array([],'float32')
        for i in range(len(time)):
            sky = append(sky,median(flux[i,:]))
        if not subback:
            sky[:] = 0.0

# legal mask defined?

    if status == 0:
        if len(aperb) == 0:
            message = 'ERROR -- KEPEXTRACT: no legal pixels within the subimage are defined.'
            status = kepmsg.err(logfile,message,verbose)
        
# construct new table flux data

    if status == 0:
        naper = (aperb == 3).sum()
        ntime = len(time)
        sap_flux = array([],'float32')
        sap_flux_err = array([],'float32')
        sap_bkg = array([],'float32')
        sap_bkg_err = array([],'float32')
        raw_flux = array([],'float32')
        for i in range(len(time)):
            work1 = array([],'float64')
            work2 = array([],'float64')
            work3 = array([],'float64')
            work4 = array([],'float64')
            work5 = array([],'float64')
            for j in range(len(aperb)):
                if (aperb[j] == 3):
                    work1 = append(work1,flux[i,j]-sky[i])
                    work2 = append(work2,flux_err[i,j])
                    work3 = append(work3,flux_bkg[i,j])
                    work4 = append(work4,flux_bkg_err[i,j])
                    work5 = append(work5,raw_cnts[i,j])
            sap_flux = append(sap_flux,kepstat.sum(work1))
            sap_flux_err = append(sap_flux_err,kepstat.sumerr(work2))
            sap_bkg = append(sap_bkg,kepstat.sum(work3))
            sap_bkg_err = append(sap_bkg_err,kepstat.sumerr(work4))
            raw_flux = append(raw_flux,kepstat.sum(work5))

# construct new table moment data

    if status == 0:
        mom_centr1 = zeros(shape=(ntime))
        mom_centr2 = zeros(shape=(ntime))
        mom_centr1_err = zeros(shape=(ntime))
        mom_centr2_err = zeros(shape=(ntime))
        for i in range(ntime):
            xf = zeros(shape=(naper))
            yf = zeros(shape=(naper))
            f = zeros(shape=(naper))
            xfe = zeros(shape=(naper))
            yfe = zeros(shape=(naper))
            fe = zeros(shape=(naper))
            k = -1
            for j in range(len(aperb)):
                if (aperb[j] == 3):
                    k += 1
                    xf[k] = aperx[j] * flux[i,j]
                    xfe[k] = aperx[j] * flux_err[i,j]
                    yf[k] = apery[j] * flux[i,j]
                    yfe[k] = apery[j] * flux_err[i,j]
                    f[k] = flux[i,j]
                    fe[k] = flux_err[i,j]
            xfsum = kepstat.sum(xf)
            yfsum = kepstat.sum(yf)
            fsum = kepstat.sum(f)
            xfsume = sqrt(kepstat.sum(square(xfe)) / naper)
            yfsume = sqrt(kepstat.sum(square(yfe)) / naper)
            fsume = sqrt(kepstat.sum(square(fe)) / naper)
            mom_centr1[i] = xfsum / fsum
            mom_centr2[i] = yfsum / fsum
            mom_centr1_err[i] = sqrt((xfsume / xfsum)**2 + ((fsume / fsum)**2))
            mom_centr2_err[i] = sqrt((yfsume / yfsum)**2 + ((fsume / fsum)**2))
        mom_centr1_err = mom_centr1_err * mom_centr1
        mom_centr2_err = mom_centr2_err * mom_centr2

# construct new table PSF data

    if status == 0:
        psf_centr1 = zeros(shape=(ntime))
        psf_centr2 = zeros(shape=(ntime))
        psf_centr1_err = zeros(shape=(ntime))
        psf_centr2_err = zeros(shape=(ntime))
        modx = zeros(shape=(naper))
        mody = zeros(shape=(naper))
        k = -1
        for j in range(len(aperb)):
            if (aperb[j] == 3):
                k += 1
                modx[k] = aperx[j]
                mody[k] = apery[j]
        for i in range(ntime):
            modf = zeros(shape=(naper))
            k = -1
            guess = [mom_centr1[i], mom_centr2[i], nanmax(flux[i:]), 1.0, 1.0, 0.0, 0.0]
            for j in range(len(aperb)):
                if (aperb[j] == 3):
                    k += 1
                    modf[k] = flux[i,j]
                    args = (modx, mody, modf)
            try:
                ans = leastsq(kepfunc.PRFgauss2d,guess,args=args,xtol=1.0e-8,ftol=1.0e-4,full_output=True)
                s_sq = (ans[2]['fvec']**2).sum() / (ntime-len(guess))
                psf_centr1[i] = ans[0][0]
                psf_centr2[i] = ans[0][1]
            except:
                pass
            try:
                psf_centr1_err[i] = sqrt(diag(ans[1] * s_sq))[0]
            except:
                psf_centr1_err[i] = numpy.nan
            try:
                psf_centr2_err[i] = sqrt(diag(ans[1] * s_sq))[1]
            except:
                psf_centr2_err[i] = numpy.nan

# construct output primary extension

    if status == 0:
        hdu0 = pyfits.PrimaryHDU()
        for i in range(len(cards0)):
            if cards0[i].key not in hdu0.header.keys():
                hdu0.header.update(cards0[i].key, cards0[i].value, cards0[i].comment)
            else:
                hdu0.header.cards[cards0[i].key].comment = cards0[i].comment
        status = kepkey.history(call,hdu0,outfile,logfile,verbose)
        outstr = HDUList(hdu0)

# construct output light curve extension

    if status == 0:
        col1 = Column(name='TIME',format='D',unit='BJD - 2454833',array=time)
        col2 = Column(name='TIMECORR',format='E',unit='d',array=timecorr)
        col3 = Column(name='CADENCENO',format='J',array=cadenceno)
        col4 = Column(name='SAP_FLUX',format='E',array=sap_flux)
        col5 = Column(name='SAP_FLUX_ERR',format='E',array=sap_flux_err)
        col6 = Column(name='SAP_BKG',format='E',array=sap_bkg)
        col7 = Column(name='SAP_BKG_ERR',format='E',array=sap_bkg_err)
        col8 = Column(name='PDCSAP_FLUX',format='E',array=sap_flux)
        col9 = Column(name='PDCSAP_FLUX_ERR',format='E',array=sap_flux_err)
        col10 = Column(name='SAP_QUALITY',format='J',array=quality)
        col11 = Column(name='PSF_CENTR1',format='E',unit='pixel',array=psf_centr1)
        col12 = Column(name='PSF_CENTR1_ERR',format='E',unit='pixel',array=psf_centr1_err)
        col13 = Column(name='PSF_CENTR2',format='E',unit='pixel',array=psf_centr2)
        col14 = Column(name='PSF_CENTR2_ERR',format='E',unit='pixel',array=psf_centr2_err)
        col15 = Column(name='MOM_CENTR1',format='E',unit='pixel',array=mom_centr1)
        col16 = Column(name='MOM_CENTR1_ERR',format='E',unit='pixel',array=mom_centr1_err)
        col17 = Column(name='MOM_CENTR2',format='E',unit='pixel',array=mom_centr2)
        col18 = Column(name='MOM_CENTR2_ERR',format='E',unit='pixel',array=mom_centr2_err)
        col19 = Column(name='POS_CORR1',format='E',unit='pixel',array=pos_corr1)
        col20 = Column(name='POS_CORR2',format='E',unit='pixel',array=pos_corr2)
        col21 = Column(name='RAW_FLUX',format='E',array=raw_flux)
        cols = ColDefs([col1,col2,col3,col4,col5,col6,col7,col8,col9,col10,col11, \
                            col12,col13,col14,col15,col16,col17,col18,col19,col20,col21])
        hdu1 = new_table(cols)
        hdu1.header.update('TTYPE1','TIME','column title: data time stamps')
        hdu1.header.update('TFORM1','D','data type: float64')
        hdu1.header.update('TUNIT1','BJD - 2454833','column units: barycenter corrected JD')
        hdu1.header.update('TDISP1','D12.7','column display format')
        hdu1.header.update('TTYPE2','TIMECORR','column title: barycentric-timeslice correction')
        hdu1.header.update('TFORM2','E','data type: float32')
        hdu1.header.update('TUNIT2','d','column units: days')
        hdu1.header.update('TTYPE3','CADENCENO','column title: unique cadence number')
        hdu1.header.update('TFORM3','J','column format: signed integer32')
        hdu1.header.update('TTYPE4','SAP_FLUX','column title: aperture photometry flux')
        hdu1.header.update('TFORM4','E','column format: float32')
        hdu1.header.update('TUNIT4','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE5','SAP_FLUX_ERR','column title: aperture phot. flux error')
        hdu1.header.update('TFORM5','E','column format: float32')
        hdu1.header.update('TUNIT5','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE6','SAP_BKG','column title: aperture phot. background flux')
        hdu1.header.update('TFORM6','E','column format: float32')
        hdu1.header.update('TUNIT6','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE7','SAP_BKG_ERR','column title: ap. phot. background flux error')
        hdu1.header.update('TFORM7','E','column format: float32')
        hdu1.header.update('TUNIT7','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE8','PDCSAP_FLUX','column title: PDC photometry flux')
        hdu1.header.update('TFORM8','E','column format: float32')
        hdu1.header.update('TUNIT8','e-/s','column units: electrons per second')
        hdu1.header.update('TTYPE9','PDCSAP_FLUX_ERR','column title: PDC flux error')
        hdu1.header.update('TFORM9','E','column format: float32')
        hdu1.header.update('TUNIT9','e-/s','column units: electrons per second (1-sigma)')
        hdu1.header.update('TTYPE10','SAP_QUALITY','column title: aperture photometry quality flag')
        hdu1.header.update('TFORM10','J','column format: signed integer32')
        hdu1.header.update('TTYPE11','PSF_CENTR1','column title: PSF fitted column centroid')
        hdu1.header.update('TFORM11','E','column format: float32')
        hdu1.header.update('TUNIT11','pixel','column units: pixel')
        hdu1.header.update('TTYPE12','PSF_CENTR1_ERR','column title: PSF fitted column error')
        hdu1.header.update('TFORM12','E','column format: float32')
        hdu1.header.update('TUNIT12','pixel','column units: pixel')
        hdu1.header.update('TTYPE13','PSF_CENTR2','column title: PSF fitted row centroid')
        hdu1.header.update('TFORM13','E','column format: float32')
        hdu1.header.update('TUNIT13','pixel','column units: pixel')
        hdu1.header.update('TTYPE14','PSF_CENTR2_ERR','column title: PSF fitted row error')
        hdu1.header.update('TFORM14','E','column format: float32')
        hdu1.header.update('TUNIT14','pixel','column units: pixel')
        hdu1.header.update('TTYPE15','MOM_CENTR1','column title: moment-derived column centroid')
        hdu1.header.update('TFORM15','E','column format: float32')
        hdu1.header.update('TUNIT15','pixel','column units: pixel')
        hdu1.header.update('TTYPE16','MOM_CENTR1_ERR','column title: moment-derived column error')
        hdu1.header.update('TFORM16','E','column format: float32')
        hdu1.header.update('TUNIT16','pixel','column units: pixel')
        hdu1.header.update('TTYPE17','MOM_CENTR2','column title: moment-derived row centroid')
        hdu1.header.update('TFORM17','E','column format: float32')
        hdu1.header.update('TUNIT17','pixel','column units: pixel')
        hdu1.header.update('TTYPE18','MOM_CENTR2_ERR','column title: moment-derived row error')
        hdu1.header.update('TFORM18','E','column format: float32')
        hdu1.header.update('TUNIT18','pixel','column units: pixel')
        hdu1.header.update('TTYPE19','POS_CORR1','column title: col correction for vel. abbern')
        hdu1.header.update('TFORM19','E','column format: float32')
        hdu1.header.update('TUNIT19','pixel','column units: pixel')
        hdu1.header.update('TTYPE20','POS_CORR2','column title: row correction for vel. abbern')
        hdu1.header.update('TFORM20','E','column format: float32')
        hdu1.header.update('TUNIT20','pixel','column units: pixel')
        hdu1.header.update('TTYPE21','RAW_FLUX','column title: raw aperture photometry flux')
        hdu1.header.update('TFORM21','E','column format: float32')
        hdu1.header.update('TUNIT21','e-/s','column units: electrons per second')
        hdu1.header.update('EXTNAME','LIGHTCURVE','name of extension')
        for i in range(len(cards1)):
            if (cards1[i].key not in hdu1.header.keys() and
                cards1[i].key[:4] not in ['TTYP','TFOR','TUNI','TDIS','TDIM','WCAX','1CTY',
                                          '2CTY','1CRP','2CRP','1CRV','2CRV','1CUN','2CUN',
                                          '1CDE','2CDE','1CTY','2CTY','1CDL','2CDL','11PC',
                                          '12PC','21PC','22PC']):
                hdu1.header.update(cards1[i].key, cards1[i].value, cards1[i].comment)
        outstr.append(hdu1)

# construct output mask bitmap extension

    if status == 0:
        hdu2 = ImageHDU(maskmap)
        for i in range(len(cards2)):
            if cards2[i].key not in hdu2.header.keys():
                hdu2.header.update(cards2[i].key, cards2[i].value, cards2[i].comment)
            else:
                hdu2.header.cards[cards2[i].key].comment = cards2[i].comment
        outstr.append(hdu2)

# write output file

    if status == 0:
        outstr.writeto(outfile,checksum=True)

# close input structure

    if status == 0:
        status = kepio.closefits(instr,logfile,verbose)	    

# end time

    kepmsg.clock('KEPEXTRACT finished at',logfile,verbose)
Пример #10
0
def kepconvert(infile,outfile,conversion,columns,baddata,clobber,verbose,logfile,status): 

# startup parameters

    status = 0

# log the call 

    hashline = '----------------------------------------------------------------------------'
    kepmsg.log(logfile,hashline,verbose)
    call = 'KEPCONVERT -- '
    call += 'infile='+infile+' '
    call += 'outfile='+outfile+' '
    call += 'conversion='+conversion+' '
    call += 'columns='+columns+ ' '
    writebad = 'n'
    if (baddata): writebad = 'y'
    call += 'baddata='+writebad+ ' '
    overwrite = 'n'
    if (clobber): overwrite = 'y'
    call += 'clobber='+overwrite+ ' '
    chatter = 'n'
    if (verbose): chatter = 'y'
    call += 'verbose='+chatter+' '
    call += 'logfile='+logfile
    kepmsg.log(logfile,call+'\n',verbose)
    
# start time

    kepmsg.clock('KEPCONVERT started at',logfile,verbose)

# test log file

    logfile = kepmsg.test(logfile)

# data columns

    if status == 0:
        colnames = columns.strip().split(',')
        ncol = len(colnames)
        if ncol < 1:
            message = 'ERROR -- KEPCONVERT: no data columns specified'
            status = kepmsg.err(logfile,message,verbose)

# input file exists

    if status == 0 and not kepio.fileexists(infile):
        message = 'ERROR -- KEPCONVERT: input file '+infile+' does not exist'
        status = kepmsg.err(logfile,message,verbose)

# clobber output file

    if status == 0:
        if clobber: status = kepio.clobber(outfile,logfile,verbose)
        if kepio.fileexists(outfile): 
            message = 'ERROR -- KEPCONVERT: ' + outfile + ' exists. Use clobber=yes'
            status = kepmsg.err(logfile,message,verbose)

# open FITS input file


    if status == 0 and conversion == 'fits2asc':
        instr, status = kepio.openfits(infile,'readonly',logfile,verbose)
        tstart, tstop, bjdref, cadence, status = kepio.timekeys(instr,infile,logfile,verbose,status)

# read FITS table data

    if status == 0 and conversion == 'fits2asc':
        table, status = kepio.readfitstab(infile,instr[1],logfile,verbose)

# check columns exist in FITS file
    if not baddata and status == 0 and conversion == 'fits2asc':
	    try:
	    	qualcol = table.field('SAP_QUALITY') == 0
	    except:
	    	message = 'No SAP_QUALITY column in data, are you using an old FITS file?'
	    	status = kepmsg.err(logfile,message,verbose)

    if status == 0 and conversion == 'fits2asc':
        work = []
        for colname in colnames:
            try:
                if colname.lower() == 'time':
                    work.append(table.field(colname) + bjdref)
                else:
                    work.append(table.field(colname))
            except:
                message = 'ERROR -- KEPCONVERT: no column ' + colname + ' in ' + infile
                status = kepmsg.err(logfile,message,verbose)
        if not baddata:
            for i in range(len(work)):
                work[i] = work[i][qualcol]
# close input file

    if status == 0 and conversion == 'fits2asc':
        status = kepio.closefits(instr,logfile,verbose)

## write output file

    if status == 0 and conversion == 'fits2asc':
        # table, status = kepio.openascii(outfile,'w',logfile,verbose)
        # for i in range(len(work[0])):
            # txt = ''
            # for j in range(len(work)):
                # if numpy.isfinite(work[j][i]):
                    # txt += str(work[j][i]) + ' '
            # txt = txt.strip()
            # if len(re.sub('\s+',',',txt).split(',')) == ncol:
                # table.write(txt + '\n')
        # status = kepio.closeascii(table,logfile,verbose)
        savetxt(outfile,array(work).T)
    	
## open and read ASCII input file

    if status == 0 and conversion == 'asc2fits':
        table, status = kepio.openascii(infile,'r',logfile,verbose)

## organize ASCII table into arrays

    if status == 0 and conversion == 'asc2fits':
        work = []
        for i in range(ncol):
            work.append([])
        nline = 0
        for line in table:
            line = line.strip()
            line = re.sub('\s+',',',line)
            line = re.sub('\|',',',line)
            line = re.sub(';',',',line)
            if '#' not in line:
                nline + 1
                line = line.split(',')
                if len(line) == ncol:
                    for i in range(len(line)):
                        try:
                            work[i].append(float(line[i]))
                        except:
                            message = 'ERROR --KEPCONVERT: ' + str(line[i]) + ' is not float'
                            status = kepmsg.err(logfile,message,verbose)
                            break
                else:
                    message  = 'ERROR --KEPCONVERT: ' + str(ncol) + ' columns required but '
                    message += str(len(line)) + ' columns supplied by ' + infile
                    message += ' at line' + str(nline)
                    status = kepmsg.err(logfile,message,verbose)
                    break
        for i in range(ncol):
            work[i] = numpy.array(work[i],dtype='float64')

## timing keywords for output file

    if status == 0 and conversion == 'asc2fits':
        for i in range(ncol):
            if 'time' in colnames[i].lower():
                if work[i][1] > 54000.0 and work[i][1] < 60000.0:
                    work[i] += 2.4e6
#                work[i] += 2.4553e6
                tstart = work[i].min()
                tstop = work[i].max()
                lc_start = tstart
                lc_end = tstop
                if lc_start > 2.4e6: lc_start -= 2.4e6
                if lc_end > 2.4e6: lc_end -= 2.4e6
                dts = []
                for j in range(1,len(work[i])):
                   dts.append(work[i][j] - work[i][j-1])
                dts = numpy.array(dts,dtype='float32')
                cadence = numpy.median(dts)
                if cadence * 86400.0 > 58.0 and cadence * 86400.0 < 61.0:
                    obsmode = 'short cadence'
                elif cadence * 86400.0 > 1600.0 and cadence * 86400.0 < 2000.0:
                    obsmode = 'long cadence'
                else:
                    obsmode = 'unknown'

## Create the outfile primary extension

    if status == 0 and conversion == 'asc2fits':
        hdu0 = PrimaryHDU()
        try:
            hdu0.header.update('EXTNAME','PRIMARY','name of extension')
            hdu0.header.update('EXTVER',1.0,'extension version number')
            hdu0.header.update('ORIGIN','NASA/Ames','organization that generated this file')
            hdu0.header.update('DATE',time.asctime(time.localtime()),'file creation date')
            hdu0.header.update('CREATOR','kepconvert','SW version used to create this file')
            hdu0.header.update('PROCVER','None','processing script version')
            hdu0.header.update('FILEVER','2.0','file format version')
            hdu0.header.update('TIMVERSN','OGIP/93-003','OGIP memo number for file format')
            hdu0.header.update('TELESCOP','Kepler','telescope')
            hdu0.header.update('INSTRUME','Kepler photometer','detector type')
            hdu0.header.update('OBJECT','Unknown','string version of kepID')
            hdu0.header.update('KEPLERID','Unknown','unique Kepler target identifier')
            hdu0.header.update('CHANNEL','Unknown','CCD channel')
            hdu0.header.update('SKYGROUP','Unknown','roll-independent location of channel')
            hdu0.header.update('MODULE','Unknown','CCD module')
            hdu0.header.update('OUTPUT','Unknown','CCD output')
            hdu0.header.update('QUARTER','Unknown','mission quarter during which data was collected')
            hdu0.header.update('SEASON','Unknown','mission season during which data was collected')
            hdu0.header.update('DATA_REL','Unknown','version of data release notes describing data')
            hdu0.header.update('OBSMODE',obsmode,'observing mode')
            hdu0.header.update('RADESYS','Unknown','reference frame of celestial coordinates')
            hdu0.header.update('RA_OBJ','Unknown','[deg] right ascension from KIC')
            hdu0.header.update('DEC_OBJ','Unknown','[deg] declination from KIC')
            hdu0.header.update('EQUINOX',2000.0,'equinox of celestial coordinate system')
            hdu0.header.update('PMRA','Unknown','[arcsec/yr] RA proper motion')
            hdu0.header.update('PMDEC','Unknown','[arcsec/yr] Dec proper motion')
            hdu0.header.update('PMTOTAL','Unknown','[arcsec/yr] total proper motion')
            hdu0.header.update('PARALLAX','Unknown','[arcsec] parallax')
            hdu0.header.update('GLON','Unknown','[deg] galactic longitude')
            hdu0.header.update('GLAT','Unknown','[deg] galactic latitude')
            hdu0.header.update('GMAG','Unknown','[mag] SDSS g band magnitude from KIC')
            hdu0.header.update('RMAG','Unknown','[mag] SDSS r band magnitude from KIC')
            hdu0.header.update('IMAG','Unknown','[mag] SDSS i band magnitude from KIC')
            hdu0.header.update('ZMAG','Unknown','[mag] SDSS z band magnitude from KIC')
            hdu0.header.update('D51MAG','Unknown','[mag] D51 magnitude, from KIC')
            hdu0.header.update('JMAG','Unknown','[mag] J band magnitude from 2MASS')
            hdu0.header.update('HMAG','Unknown','[mag] H band magnitude from 2MASS')
            hdu0.header.update('KMAG','Unknown','[mag] K band magnitude from 2MASS')
            hdu0.header.update('KEPMAG','Unknown','[mag] Kepler magnitude (Kp) from KIC')
            hdu0.header.update('GRCOLOR','Unknown','[mag] (g-r) color, SDSS bands')
            hdu0.header.update('JKCOLOR','Unknown','[mag] (J-K) color, 2MASS bands')
            hdu0.header.update('GKCOLOR','Unknown','[mag] (g-K) color, SDSS g - 2MASS K')
            hdu0.header.update('TEFF','Unknown','[K] effective temperature from KIC')
            hdu0.header.update('LOGG','Unknown','[cm/s2] log10 surface gravity from KIC')
            hdu0.header.update('FEH','Unknown','[log10([Fe/H])] metallicity from KIC')
            hdu0.header.update('EBMINUSV','Unknown','[mag] E(B-V) redenning from KIC')
            hdu0.header.update('AV','Unknown','[mag] A_v extinction from KIC')
            hdu0.header.update('RADIUS','Unknown','[solar radii] stellar radius from KIC')
            hdu0.header.update('TMINDEX','Unknown','unique 2MASS catalog ID from KIC')
            hdu0.header.update('SCPID','Unknown','unique SCP processing ID from KIC') 
            hdulist = HDUList(hdu0)
        except:
            message = 'ERROR -- KEPCONVERT: cannot create primary extension in ' + outfile
            status = kepmsg.err(logfile,message,verbose)
            
## create the outfile HDU 1 extension

    if status == 0 and conversion == 'asc2fits':
        try:
            fitscol = []
            for i in range(ncol):
                fitscol.append(Column(name=colnames[i],format='D',array=work[i]))
            fitscols = ColDefs(fitscol)
            hdu1 = new_table(fitscols)
            hdulist.append(hdu1)
            hdu1.header.update('INHERIT',True,'inherit primary keywords')
            hdu1.header.update('EXTNAME','LIGHTCURVE','name of extension')
            hdu1.header.update('EXTVER',1,'extension version number')
            hdu1.header.update('TELESCOP','Kepler','telescope')
            hdu1.header.update('INSTRUME','Kepler photometer','detector type')
            hdu1.header.update('OBJECT','Unknown','string version of kepID')
            hdu1.header.update('KEPLERID','Unknown','unique Kepler target identifier')
            hdu1.header.update('RADESYS','Unknown','reference frame of celestial coordinates')
            hdu1.header.update('RA_OBJ','Unknown','[deg] right ascension from KIC')
            hdu1.header.update('DEC_OBJ','Unknown','[deg] declination from KIC')
            hdu1.header.update('EQUINOX',2000.0,'equinox of celestial coordinate system')
            hdu1.header.update('TIMEREF','Unknown','barycentric correction applied to times')
            hdu1.header.update('TASSIGN','Unknown','where time is assigned')
            hdu1.header.update('TIMESYS','Unknown','time system is barycentric JD')
            hdu1.header.update('BJDREFI',0.0,'integer part of BJD reference date')
            hdu1.header.update('BJDREFF',0.0,'fraction of day in BJD reference date')
            hdu1.header.update('TIMEUNIT','Unknown','time unit for TIME, TSTART and TSTOP')
            hdu1.header.update('TSTART',tstart,'observation start time in JD - BJDREF')
            hdu1.header.update('TSTOP',tstop,'observation stop time in JD - BJDREF')
            hdu1.header.update('LC_START',lc_start,'observation start time in MJD')
            hdu1.header.update('LC_END',lc_end,'observation stop time in MJD')
            hdu1.header.update('TELAPSE',tstop-tstart,'[d] TSTOP - TSTART')
            hdu1.header.update('LIVETIME','Unknown','[d] TELAPSE multiplied by DEADC')
            hdu1.header.update('EXPOSURE','Unknown','[d] time on source')
            hdu1.header.update('DEADC','Unknown','deadtime correction')
            hdu1.header.update('TIMEPIXR','Unknown','bin time beginning=0 middle=0.5 end=1')
            hdu1.header.update('TIERRELA','Unknown','[d] relative time error')
            hdu1.header.update('TIERABSO','Unknown','[d] absolute time error')
            hdu1.header.update('INT_TIME','Unknown','[s] photon accumulation time per frame')
            hdu1.header.update('READTIME','Unknown','[s] readout time per frame')
            hdu1.header.update('FRAMETIM','Unknown','[s] frame time (INT_TIME + READTIME)')
            hdu1.header.update('NUM_FRM','Unknown','number of frames per time stamp')
            hdu1.header.update('TIMEDEL','Unknown','[d] time resolution of data')
            hdu1.header.update('DATE-OBS','Unknown','TSTART as UT calendar date')
            hdu1.header.update('DATE-END','Unknown','TSTOP as UT calendar date')
            hdu1.header.update('BACKAPP','Unknown','background is subtracted')
            hdu1.header.update('DEADAPP','Unknown','deadtime applied')
            hdu1.header.update('VIGNAPP','Unknown','vignetting or collimator correction applied')
            hdu1.header.update('GAIN','Unknown','channel gain [electrons/count]')
            hdu1.header.update('READNOIS','Unknown','read noise [electrons]')
            hdu1.header.update('NREADOUT','Unknown','number of reads per cadence')
            hdu1.header.update('TIMSLICE','Unknown','time-slice readout sequence section')
            hdu1.header.update('MEANBLCK','Unknown','FSW mean black level [count]')
            hdu1.header.update('PDCSAPFL','Unknown','SAP PDC processing flags (bit code)')
            hdu1.header.update('PDCDIAFL','Unknown','DIA PDC processing flags (bit code)')
            hdu1.header.update('MISPXSAP','Unknown','no of optimal aperture pixels missing from SAP')
            hdu1.header.update('MISPXDIA','Unknown','no of optimal aperture pixels missing from DIA')
            hdu1.header.update('CROWDSAP','Unknown','crowding metric evaluated over SAP opt. ap.')
            hdu1.header.update('CROWDDIA','Unknown','crowding metric evaluated over DIA aperture')
        except:
            message = 'ERROR -- KEPCONVERT: cannot create light curve extension in ' + outfile
            status = kepmsg.err(logfile,message,verbose)

## history keyword in output file

    if status == 0 and conversion == 'asc2fits':
        status = kepkey.history(call,hdu0,outfile,logfile,verbose)

## filter data table

    if status == 0 and conversion == 'asc2fits':
        instr, status = kepio.filterNaN(hdulist,colnames[min(array([1,len(colnames)-1],dtype='int'))],
                                        outfile,logfile,verbose)

## write output FITS file

    if status == 0 and conversion == 'asc2fits':
        hdulist.writeto(outfile,checksum=True)

## end time

    if (status == 0):
	    message = 'KEPCONVERT completed at'
    else:
	    message = '\nKEPCONVERT aborted at'
    kepmsg.clock(message,logfile,verbose)
Пример #11
0
def kepffi(ffifile,kepid,ra,dec,aperfile,imin,imax,iscale,cmap,npix,
            verbose,logfile,status,cmdLine=False): 

    global pimg, zscale, zmin, zmax, xmin, xmax, ymin, ymax, quarter
    global kepmag, skygroup, season, channel
    global module, output, row, column, maskfile, plotfile
    global pkepid, pkepmag, pra, pdec, colmap, mask

# input arguments

    status = 0
    seterr(all="ignore") 
    maskfile = 'kepffi-' + str(kepid) + '.txt'
    plotfile = 'kepffi-' + str(kepid) + '.png'
    zmin = imin; zmax = imax; zscale = iscale; colmap = cmap

# logg the call 

    hashline = '----------------------------------------------------------------------------'
    kepmsg.log(logfile,hashline,verbose)
    call = 'KEPFFI -- '
    call += 'ffifile='+ffifile+' '
    call += 'kepid='+str(kepid)+' '
    call += 'ra='+ra+' '
    call += 'dec='+dec+' '
    call += 'aperfile='+aperfile+' '
    call += 'imin='+str(imin)+' '
    call += 'imax='+str(imax)+' '
    call += 'iscale='+str(iscale)+' '
    call += 'cmap'+str(cmap)+' '
    call += 'npix='+str(npix)+' '
    chatter = 'n'
    if (verbose): chatter = 'y'
    call += 'verbose='+chatter+' '
    call += 'logfile='+logfile
    kepmsg.log(logfile,call+'\n',verbose)

# start time

    kepmsg.clock('KEPFFI started at',logfile,verbose)

# reference color map

    if cmap == 'browse':
        status = cmap_plot(cmdLine)

# open existing mask file

    if kepio.fileexists(aperfile):
        lines, status = kepio.openascii(aperfile,'r',logfile,verbose)
        for line in lines:
            line = line.strip().split('|')
            y0 = int(line[3])
            x0 = int(line[4])
            pixels = line[5].split(';')
            for pixel in pixels:
                m = y0 + int(pixel.split(',')[0])
                n = x0 + int(pixel.split(',')[1])
                mask.append(str(m)+','+str(n))
        status = kepio.closeascii(lines,logfile,verbose)

# RA and Dec conversion

    if kepid == 'None' or kepid == 'none' or kepid.strip() == '':
        try:
            mra = float(ra)
            mdec = float(dec)
        except:
            try:
                mra,mdec = sex2dec(ra,dec)
            except:
                txt = 'ERROR -- no sensible RA and Dec coordinates provided'
                sys.exit(txt)

# open FFI FITS file

    if status == 0:
        ffi, status = openfits(ffifile,'readonly')
        try:
            quarter = ffi[0].header['QUARTER']
        except:
            try:
                dateobs = ffi[0].header['DATE-OBS']
                if dateobs == '2009-04-24': quarter = 0
                if dateobs == '2009-04-25': quarter = 0
                if dateobs == '2009-04-26': quarter = 0
                if dateobs == '2009-06-19': quarter = 2
                if dateobs == '2009-08-19': quarter = 2
                if dateobs == '2009-09-17': quarter = 2
                if dateobs == '2009-10-19': quarter = 3
                if dateobs == '2009-11-18': quarter = 3
                if dateobs == '2009-12-17': quarter = 3
            except:
                txt  = 'ERROR -- cannot determine quarter when FFI was taken. Either a\n'
                txt += 'QUARTER or DATE-OBS keyword is expected in the primary header'
                sys.exit(txt)
        if quarter == 0: quarter = 1
        if quarter < 0:
            txt  = 'ERROR -- cannot determine quarter from FFI. Try downloading a new\n'
            txt += 'version of KeplerFFI.py from http://keplergo.arc.nasa.gov'
            sys.exit()
    if int(quarter) == 0:
        season = 3
    else:
        season = (int(quarter) - 2) % 4

# locate target in MAST

        try:
            int(kepid)
            kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
                = MASTKepID(kepid,season)
            pkepmag = kepmag; pkepid = kepid
        except:
            kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column \
                = MASTRADec(mra,mdec,8.0,season)
            ra,dec = dec2sex(ra,dec)
        pra = ra; pdec = dec
        print(kepid,ra,dec,kepmag,skygroup,channel,module,output,row,column)
       
# read and close FFI FITS file

        img, status = readimage(ffi,int(channel))
        status = closefits(ffi)

# print target data

        print('')
        print('      KepID:  %s' % kepid)
        print(' RA (J2000):  %s' % ra)
        print('Dec (J2000): %s' % dec)
        print('     KepMag:  %s' % kepmag)
        print('   SkyGroup:    %2s' % skygroup)
        print('     Season:    %2s' % str(season))
        print('    Channel:    %2s' % channel)
        print('     Module:    %2s' % module)
        print('     Output:     %1s' % output)
        print('     Column:  %4s' % column)
        print('        Row:  %4s' % row)
        print('')

# subimage of channel for plot

        ymin = int(max([int(row)-npix/2,0]))
        ymax = int(min([int(row)+npix/2+1,img.shape[0]]))
        xmin = int(max([int(column)-npix/2,0]))
        xmax = int(min([int(column)+npix/2+1,img.shape[1]]))

# intensity scale

        nstat = 2; pixels = []
        for i in range(ymin,ymax+1):
            for j in range(xmin,xmax+1):
                pixels.append(img[i,j])
        pixels = array(sort(pixels),dtype=float32)
        if int(float(len(pixels)) / 10 + 0.5) > nstat:
            nstat = int(float(len(pixels)) / 10 + 0.5)
        if not zmin:
            zmin = median(pixels[:nstat])
        if not zmax:
            zmax = median(pixels[-nstat:])
        if 'log' in zscale:
            img = log10(img)
            zmin = log10(zmin)
            zmax = log10(zmax)
        if ('sq' in zscale):
            img = sqrt(img)
            zmin = sqrt(zmin)
            zmax = sqrt(zmax)
        pimg = img[ymin:ymax,xmin:xmax]

# plot limits

        ymin = float(ymin) - 0.5
        ymax = float(ymax) - 0.5
        xmin = float(xmin) - 0.5
        xmax = float(xmax) - 0.5

# plot style

        try:
            params = {'backend': 'png',
                      'axes.linewidth': 2.5,
                      'axes.labelsize': 24,
                      'axes.font': 'sans-serif',
                      'axes.fontweight' : 'bold',
                      'text.fontsize': 12,
                      'legend.fontsize': 12,
                      'xtick.labelsize': 16,
                      'ytick.labelsize': 16}
            pylab.rcParams.update(params)
        except:
            pass

    if status == 0:
        pylab.figure(figsize=[10,7])
        plotimage(cmdLine)

# render plot

        if cmdLine: 
            pylab.show()
        else: 
            pylab.ion()
            pylab.plot([])
            pylab.ioff()
	
    return
def kepconvert(infile, outfile, conversion, columns, baddata, clobber, verbose,
               logfile, status):

    # startup parameters

    status = 0

    # log the call

    hashline = '----------------------------------------------------------------------------'
    kepmsg.log(logfile, hashline, verbose)
    call = 'KEPCONVERT -- '
    call += 'infile=' + infile + ' '
    call += 'outfile=' + outfile + ' '
    call += 'conversion=' + conversion + ' '
    call += 'columns=' + columns + ' '
    writebad = 'n'
    if (baddata): writebad = 'y'
    call += 'baddata=' + writebad + ' '
    overwrite = 'n'
    if (clobber): overwrite = 'y'
    call += 'clobber=' + overwrite + ' '
    chatter = 'n'
    if (verbose): chatter = 'y'
    call += 'verbose=' + chatter + ' '
    call += 'logfile=' + logfile
    kepmsg.log(logfile, call + '\n', verbose)

    # start time

    kepmsg.clock('KEPCONVERT started at', logfile, verbose)

    # test log file

    logfile = kepmsg.test(logfile)

    # data columns

    if status == 0:
        colnames = columns.strip().split(',')
        ncol = len(colnames)
        if ncol < 1:
            message = 'ERROR -- KEPCONVERT: no data columns specified'
            status = kepmsg.err(logfile, message, verbose)

# input file exists

    if status == 0 and not kepio.fileexists(infile):
        message = 'ERROR -- KEPCONVERT: input file ' + infile + ' does not exist'
        status = kepmsg.err(logfile, message, verbose)

# clobber output file

    if status == 0:
        if clobber: status = kepio.clobber(outfile, logfile, verbose)
        if kepio.fileexists(outfile):
            message = 'ERROR -- KEPCONVERT: ' + outfile + ' exists. Use clobber=yes'
            status = kepmsg.err(logfile, message, verbose)

# open FITS input file

    if status == 0 and conversion == 'fits2asc':
        instr, status = kepio.openfits(infile, 'readonly', logfile, verbose)
        tstart, tstop, bjdref, cadence, status = kepio.timekeys(
            instr, infile, logfile, verbose, status)

# read FITS table data

    if status == 0 and conversion == 'fits2asc':
        table, status = kepio.readfitstab(infile, instr[1], logfile, verbose)

# check columns exist in FITS file
    if not baddata and status == 0 and conversion == 'fits2asc':
        try:
            qualcol = table.field('SAP_QUALITY') == 0
        except:
            message = 'No SAP_QUALITY column in data, are you using an old FITS file?'
            status = kepmsg.err(logfile, message, verbose)

    if status == 0 and conversion == 'fits2asc':
        work = []
        for colname in colnames:
            try:
                if colname.lower() == 'time':
                    work.append(table.field(colname) + bjdref)
                else:
                    work.append(table.field(colname))
            except:
                message = 'ERROR -- KEPCONVERT: no column ' + colname + ' in ' + infile
                status = kepmsg.err(logfile, message, verbose)
        if not baddata:
            for i in range(len(work)):
                work[i] = work[i][qualcol]
# close input file

    if status == 0 and conversion == 'fits2asc':
        status = kepio.closefits(instr, logfile, verbose)

## write output file

    if status == 0 and conversion == 'fits2asc':
        # table, status = kepio.openascii(outfile,'w',logfile,verbose)
        # for i in range(len(work[0])):
        # txt = ''
        # for j in range(len(work)):
        # if numpy.isfinite(work[j][i]):
        # txt += str(work[j][i]) + ' '
        # txt = txt.strip()
        # if len(re.sub('\s+',',',txt).split(',')) == ncol:
        # table.write(txt + '\n')
        # status = kepio.closeascii(table,logfile,verbose)
        savetxt(outfile, array(work).T)

## open and read ASCII input file

    if status == 0 and conversion == 'asc2fits':
        table, status = kepio.openascii(infile, 'r', logfile, verbose)

## organize ASCII table into arrays

    if status == 0 and conversion == 'asc2fits':
        work = []
        for i in range(ncol):
            work.append([])
        nline = 0
        for line in table:
            line = line.strip()
            line = re.sub('\s+', ',', line)
            line = re.sub('\|', ',', line)
            line = re.sub(';', ',', line)
            if '#' not in line:
                nline + 1
                line = line.split(',')
                if len(line) == ncol:
                    for i in range(len(line)):
                        try:
                            work[i].append(float(line[i]))
                        except:
                            message = 'ERROR --KEPCONVERT: ' + str(
                                line[i]) + ' is not float'
                            status = kepmsg.err(logfile, message, verbose)
                            break
                else:
                    message = 'ERROR --KEPCONVERT: ' + str(
                        ncol) + ' columns required but '
                    message += str(
                        len(line)) + ' columns supplied by ' + infile
                    message += ' at line' + str(nline)
                    status = kepmsg.err(logfile, message, verbose)
                    break
        for i in range(ncol):
            work[i] = numpy.array(work[i], dtype='float64')

## timing keywords for output file

    if status == 0 and conversion == 'asc2fits':
        for i in range(ncol):
            if 'time' in colnames[i].lower():
                if work[i][1] > 54000.0 and work[i][1] < 60000.0:
                    work[i] += 2.4e6
#                work[i] += 2.4553e6
                tstart = work[i].min()
                tstop = work[i].max()
                lc_start = tstart
                lc_end = tstop
                if lc_start > 2.4e6: lc_start -= 2.4e6
                if lc_end > 2.4e6: lc_end -= 2.4e6
                dts = []
                for j in range(1, len(work[i])):
                    dts.append(work[i][j] - work[i][j - 1])
                dts = numpy.array(dts, dtype='float32')
                cadence = numpy.median(dts)
                if cadence * 86400.0 > 58.0 and cadence * 86400.0 < 61.0:
                    obsmode = 'short cadence'
                elif cadence * 86400.0 > 1600.0 and cadence * 86400.0 < 2000.0:
                    obsmode = 'long cadence'
                else:
                    obsmode = 'unknown'

## Create the outfile primary extension

    if status == 0 and conversion == 'asc2fits':
        hdu0 = PrimaryHDU()
        try:
            hdu0.header.update('EXTNAME', 'PRIMARY', 'name of extension')
            hdu0.header.update('EXTVER', 1.0, 'extension version number')
            hdu0.header.update('ORIGIN', 'NASA/Ames',
                               'organization that generated this file')
            hdu0.header.update('DATE', time.asctime(time.localtime()),
                               'file creation date')
            hdu0.header.update('CREATOR', 'kepconvert',
                               'SW version used to create this file')
            hdu0.header.update('PROCVER', 'None', 'processing script version')
            hdu0.header.update('FILEVER', '2.0', 'file format version')
            hdu0.header.update('TIMVERSN', 'OGIP/93-003',
                               'OGIP memo number for file format')
            hdu0.header.update('TELESCOP', 'Kepler', 'telescope')
            hdu0.header.update('INSTRUME', 'Kepler photometer',
                               'detector type')
            hdu0.header.update('OBJECT', 'Unknown', 'string version of kepID')
            hdu0.header.update('KEPLERID', 'Unknown',
                               'unique Kepler target identifier')
            hdu0.header.update('CHANNEL', 'Unknown', 'CCD channel')
            hdu0.header.update('SKYGROUP', 'Unknown',
                               'roll-independent location of channel')
            hdu0.header.update('MODULE', 'Unknown', 'CCD module')
            hdu0.header.update('OUTPUT', 'Unknown', 'CCD output')
            hdu0.header.update(
                'QUARTER', 'Unknown',
                'mission quarter during which data was collected')
            hdu0.header.update(
                'SEASON', 'Unknown',
                'mission season during which data was collected')
            hdu0.header.update(
                'DATA_REL', 'Unknown',
                'version of data release notes describing data')
            hdu0.header.update('OBSMODE', obsmode, 'observing mode')
            hdu0.header.update('RADESYS', 'Unknown',
                               'reference frame of celestial coordinates')
            hdu0.header.update('RA_OBJ', 'Unknown',
                               '[deg] right ascension from KIC')
            hdu0.header.update('DEC_OBJ', 'Unknown',
                               '[deg] declination from KIC')
            hdu0.header.update('EQUINOX', 2000.0,
                               'equinox of celestial coordinate system')
            hdu0.header.update('PMRA', 'Unknown',
                               '[arcsec/yr] RA proper motion')
            hdu0.header.update('PMDEC', 'Unknown',
                               '[arcsec/yr] Dec proper motion')
            hdu0.header.update('PMTOTAL', 'Unknown',
                               '[arcsec/yr] total proper motion')
            hdu0.header.update('PARALLAX', 'Unknown', '[arcsec] parallax')
            hdu0.header.update('GLON', 'Unknown', '[deg] galactic longitude')
            hdu0.header.update('GLAT', 'Unknown', '[deg] galactic latitude')
            hdu0.header.update('GMAG', 'Unknown',
                               '[mag] SDSS g band magnitude from KIC')
            hdu0.header.update('RMAG', 'Unknown',
                               '[mag] SDSS r band magnitude from KIC')
            hdu0.header.update('IMAG', 'Unknown',
                               '[mag] SDSS i band magnitude from KIC')
            hdu0.header.update('ZMAG', 'Unknown',
                               '[mag] SDSS z band magnitude from KIC')
            hdu0.header.update('D51MAG', 'Unknown',
                               '[mag] D51 magnitude, from KIC')
            hdu0.header.update('JMAG', 'Unknown',
                               '[mag] J band magnitude from 2MASS')
            hdu0.header.update('HMAG', 'Unknown',
                               '[mag] H band magnitude from 2MASS')
            hdu0.header.update('KMAG', 'Unknown',
                               '[mag] K band magnitude from 2MASS')
            hdu0.header.update('KEPMAG', 'Unknown',
                               '[mag] Kepler magnitude (Kp) from KIC')
            hdu0.header.update('GRCOLOR', 'Unknown',
                               '[mag] (g-r) color, SDSS bands')
            hdu0.header.update('JKCOLOR', 'Unknown',
                               '[mag] (J-K) color, 2MASS bands')
            hdu0.header.update('GKCOLOR', 'Unknown',
                               '[mag] (g-K) color, SDSS g - 2MASS K')
            hdu0.header.update('TEFF', 'Unknown',
                               '[K] effective temperature from KIC')
            hdu0.header.update('LOGG', 'Unknown',
                               '[cm/s2] log10 surface gravity from KIC')
            hdu0.header.update('FEH', 'Unknown',
                               '[log10([Fe/H])] metallicity from KIC')
            hdu0.header.update('EBMINUSV', 'Unknown',
                               '[mag] E(B-V) redenning from KIC')
            hdu0.header.update('AV', 'Unknown',
                               '[mag] A_v extinction from KIC')
            hdu0.header.update('RADIUS', 'Unknown',
                               '[solar radii] stellar radius from KIC')
            hdu0.header.update('TMINDEX', 'Unknown',
                               'unique 2MASS catalog ID from KIC')
            hdu0.header.update('SCPID', 'Unknown',
                               'unique SCP processing ID from KIC')
            hdulist = HDUList(hdu0)
        except:
            message = 'ERROR -- KEPCONVERT: cannot create primary extension in ' + outfile
            status = kepmsg.err(logfile, message, verbose)

## create the outfile HDU 1 extension

    if status == 0 and conversion == 'asc2fits':
        try:
            fitscol = []
            for i in range(ncol):
                fitscol.append(
                    Column(name=colnames[i], format='D', array=work[i]))
            fitscols = ColDefs(fitscol)
            hdu1 = new_table(fitscols)
            hdulist.append(hdu1)
            hdu1.header.update('INHERIT', True, 'inherit primary keywords')
            hdu1.header.update('EXTNAME', 'LIGHTCURVE', 'name of extension')
            hdu1.header.update('EXTVER', 1, 'extension version number')
            hdu1.header.update('TELESCOP', 'Kepler', 'telescope')
            hdu1.header.update('INSTRUME', 'Kepler photometer',
                               'detector type')
            hdu1.header.update('OBJECT', 'Unknown', 'string version of kepID')
            hdu1.header.update('KEPLERID', 'Unknown',
                               'unique Kepler target identifier')
            hdu1.header.update('RADESYS', 'Unknown',
                               'reference frame of celestial coordinates')
            hdu1.header.update('RA_OBJ', 'Unknown',
                               '[deg] right ascension from KIC')
            hdu1.header.update('DEC_OBJ', 'Unknown',
                               '[deg] declination from KIC')
            hdu1.header.update('EQUINOX', 2000.0,
                               'equinox of celestial coordinate system')
            hdu1.header.update('TIMEREF', 'Unknown',
                               'barycentric correction applied to times')
            hdu1.header.update('TASSIGN', 'Unknown', 'where time is assigned')
            hdu1.header.update('TIMESYS', 'Unknown',
                               'time system is barycentric JD')
            hdu1.header.update('BJDREFI', 0.0,
                               'integer part of BJD reference date')
            hdu1.header.update('BJDREFF', 0.0,
                               'fraction of day in BJD reference date')
            hdu1.header.update('TIMEUNIT', 'Unknown',
                               'time unit for TIME, TSTART and TSTOP')
            hdu1.header.update('TSTART', tstart,
                               'observation start time in JD - BJDREF')
            hdu1.header.update('TSTOP', tstop,
                               'observation stop time in JD - BJDREF')
            hdu1.header.update('LC_START', lc_start,
                               'observation start time in MJD')
            hdu1.header.update('LC_END', lc_end,
                               'observation stop time in MJD')
            hdu1.header.update('TELAPSE', tstop - tstart, '[d] TSTOP - TSTART')
            hdu1.header.update('LIVETIME', 'Unknown',
                               '[d] TELAPSE multiplied by DEADC')
            hdu1.header.update('EXPOSURE', 'Unknown', '[d] time on source')
            hdu1.header.update('DEADC', 'Unknown', 'deadtime correction')
            hdu1.header.update('TIMEPIXR', 'Unknown',
                               'bin time beginning=0 middle=0.5 end=1')
            hdu1.header.update('TIERRELA', 'Unknown',
                               '[d] relative time error')
            hdu1.header.update('TIERABSO', 'Unknown',
                               '[d] absolute time error')
            hdu1.header.update('INT_TIME', 'Unknown',
                               '[s] photon accumulation time per frame')
            hdu1.header.update('READTIME', 'Unknown',
                               '[s] readout time per frame')
            hdu1.header.update('FRAMETIM', 'Unknown',
                               '[s] frame time (INT_TIME + READTIME)')
            hdu1.header.update('NUM_FRM', 'Unknown',
                               'number of frames per time stamp')
            hdu1.header.update('TIMEDEL', 'Unknown',
                               '[d] time resolution of data')
            hdu1.header.update('DATE-OBS', 'Unknown',
                               'TSTART as UT calendar date')
            hdu1.header.update('DATE-END', 'Unknown',
                               'TSTOP as UT calendar date')
            hdu1.header.update('BACKAPP', 'Unknown',
                               'background is subtracted')
            hdu1.header.update('DEADAPP', 'Unknown', 'deadtime applied')
            hdu1.header.update('VIGNAPP', 'Unknown',
                               'vignetting or collimator correction applied')
            hdu1.header.update('GAIN', 'Unknown',
                               'channel gain [electrons/count]')
            hdu1.header.update('READNOIS', 'Unknown', 'read noise [electrons]')
            hdu1.header.update('NREADOUT', 'Unknown',
                               'number of reads per cadence')
            hdu1.header.update('TIMSLICE', 'Unknown',
                               'time-slice readout sequence section')
            hdu1.header.update('MEANBLCK', 'Unknown',
                               'FSW mean black level [count]')
            hdu1.header.update('PDCSAPFL', 'Unknown',
                               'SAP PDC processing flags (bit code)')
            hdu1.header.update('PDCDIAFL', 'Unknown',
                               'DIA PDC processing flags (bit code)')
            hdu1.header.update(
                'MISPXSAP', 'Unknown',
                'no of optimal aperture pixels missing from SAP')
            hdu1.header.update(
                'MISPXDIA', 'Unknown',
                'no of optimal aperture pixels missing from DIA')
            hdu1.header.update('CROWDSAP', 'Unknown',
                               'crowding metric evaluated over SAP opt. ap.')
            hdu1.header.update('CROWDDIA', 'Unknown',
                               'crowding metric evaluated over DIA aperture')
        except:
            message = 'ERROR -- KEPCONVERT: cannot create light curve extension in ' + outfile
            status = kepmsg.err(logfile, message, verbose)

## history keyword in output file

    if status == 0 and conversion == 'asc2fits':
        status = kepkey.history(call, hdu0, outfile, logfile, verbose)

## filter data table

    if status == 0 and conversion == 'asc2fits':
        instr, status = kepio.filterNaN(
            hdulist, colnames[min(array([1, len(colnames) - 1], dtype='int'))],
            outfile, logfile, verbose)

## write output FITS file

    if status == 0 and conversion == 'asc2fits':
        hdulist.writeto(outfile, checksum=True)

## end time

    if (status == 0):
        message = 'KEPCONVERT completed at'
    else:
        message = '\nKEPCONVERT aborted at'
    kepmsg.clock(message, logfile, verbose)