Ejemplo n.º 1
0
    def getPositionAtTime(self, t):
        from astrometry.util.starutil_numpy import radectoxyz, arcsecperrad, axistilt, xyztoradec

        dt = (t - self.epoch).toYears()
        # Assume "pos" is an RaDecPos
        p = self.pos + dt * self.pm
        suntheta = t.getSunTheta()

        # print 'dt', dt, 'pos', self.pos, 'pm', self.pm, 'dt*pm:', dt * self.pm
        # print 'p0: (%.8f, %.8f)' % (self.pos.ra, self.pos.dec)
        # print 'p1: (%.8f, %.8f)' % (p.ra, p.dec)

        xyz = radectoxyz(p.ra, p.dec)
        xyz = xyz[0]
        # d(celestial coords)/d(parallax)
        # - takes numerical derivatives when it could take analytic ones
        # output is in [degrees / arcsec].  Yep.    Crazy but true.
        # HACK: fmods dRA when it should do something continuous.
        # rd2xyz(0,0) is a unit vector; 1/arcsecperrad is (a good approximation to)
        # the distance on the unit sphere spanned by an angle of 1 arcsec.
        # We take a step of that length and return the change in RA,Dec.
        # It's about 1e-5 so we don't renormalize the xyz unit vector.
        dxyz1 = radectoxyz(0., 0.) / arcsecperrad
        dxyz1 = dxyz1[0]
        # - imprecise angle of obliquity
        # - implicitly assumes circular orbit
        # output is in [degrees / arcsec].  Yep.    Crazy but true.
        dxyz2 = radectoxyz(90., axistilt) / arcsecperrad
        dxyz2 = dxyz2[0]
        xyz += self.parallax.getValue() * (dxyz1 * np.cos(suntheta) +
                                           dxyz2 * np.sin(suntheta))
        r,d = xyztoradec(xyz)
        return RaDecPos(r,d)
Ejemplo n.º 2
0
def cat_sdss(req, ver):
    import json
    import numpy as np
    from astrometry.util.starutil_numpy import degrees_between, radectoxyz, xyztoradec
    from map.views import sdss_ccds_near
    from astrometry.util.fits import fits_table, merge_tables

    tag = 'sdss-cat'
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])

    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    rad = degrees_between(ralo, declo, rahi, dechi) / 2.
    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2)
    xyz /= np.sqrt(np.sum(xyz**2))
    rc,dc = xyztoradec(xyz)
    rad = rad + np.hypot(10.,14.)/2./60.
    ccds = sdss_ccds_near(rc[0], dc[0], rad)
    if ccds is None:
        print('No SDSS CCDs nearby')
        return HttpResponse(json.dumps(dict(rd=[])),
                            content_type='application/json')
    print(len(ccds), 'SDSS CCDs')

    T = []
    for ccd in ccds:
        # env/BOSS_PHOTOOBJ/301/2073/3/photoObj-002073-3-0088.fits
        fn = os.path.join(settings.SDSS_BASEDIR, 'env', 'BOSS_PHOTOOBJ',
                          str(ccd.rerun), str(ccd.run), str(ccd.camcol),
                          'photoObj-%06i-%i-%04i.fits' % (ccd.run, ccd.camcol, ccd.field))
        print('Reading', fn)
        T.append(fits_table(fn, columns='ra dec objid mode objc_type objc_flags objc_flags nchild tai expflux devflux psfflux cmodelflux fracdev mjd'.split()))
    T = merge_tables(T)
    T.cut((T.dec >= declo) * (T.dec <= dechi))
    # FIXME
    T.cut((T.ra  >= ralo) * (T.ra <= rahi))
    
    # primary
    T.cut(T.mode == 1)
    types = ['P' if t == 6 else 'C' for t in T.objc_type]
    fluxes = [p if t == 6 else c for t,p,c in zip(T.objc_type, T.psfflux, T.cmodelflux)]

    return HttpResponse(json.dumps(dict(
        rd=[(float(o.ra),float(o.dec)) for o in T],
        sourcetype=types,
        fluxes = [dict(u=float(f[0]), g=float(f[1]), r=float(f[2]),
                       i=float(f[3]), z=float(f[4])) for f in fluxes],
    )),
                        content_type='application/json')
Ejemplo n.º 3
0
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
                nearest=False, indexlist=False):
    '''
    (m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)

    Cross-matches numpy arrays of RA,Dec points.

    Behaves like spherematch.pro of IDL 

    ra1,dec1 (and 2): RA,Dec in degrees of points to match.
       Must be scalars or numpy arrays.

       radius_in_deg: search radius in degrees.

    notself: if True, avoids returning 'identity' matches;
        ASSUMES that ra1,dec1 == ra2,dec2.

    nearest: if True, returns only the nearest match in (ra2,dec2)
        for each point in (ra1,dec1).

    indexlist: returns a list of length len(ra1), containing None or a
    list of ints of matched points in ra2,dec2.  Returns this list.
        
    Returns:

    m1: indices into the "ra1,dec1" arrays of matching points.
       Numpy array of ints.
    m2: same, but for "ra2,dec2".
    d12: distance, in degrees, between the matching points.
    '''

    # Convert to coordinates on the unit sphere
    xyz1 = radectoxyz(ra1, dec1)
    #if all(ra1 == ra2) and all(dec1 == dec2):
    if ra1 is ra2 and dec1 is dec2:
        xyz2 = xyz1
    else:
        xyz2 = radectoxyz(ra2, dec2)
    r = deg2dist(radius_in_deg)

    if nearest:
        (inds,dists2) = _nearest_func(xyz2, xyz1, r, notself=notself)
        I = np.flatnonzero(inds >= 0)
        J = inds[I]
        d = distsq2deg(dists2[I])
    else:
        X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
        if indexlist:
            return X
        (inds,dists) = X
        dist_in_deg = dist2deg(dists)
        I,J = inds[:,0], inds[:,1]
        d = dist_in_deg[:,0]
        
    return (I, J, d)
Ejemplo n.º 4
0
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
                nearest=False):
    '''
    (m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)

    Cross-matches numpy arrays of RA,Dec points.

    Behaves like spherematch.pro of IDL 

    ra1,dec1 (and 2): RA,Dec in degrees of points to match.
       Must be scalars or numpy arrays.
    radius_in_deg: search radius in degrees.
    notself: if True, avoids returning 'identity' matches;
        ASSUMES that ra1,dec1 == ra2,dec2.
    nearest: if True, returns only the nearest match in (ra2,dec2)
        for each point in (ra1,dec1).
        
    Returns:

    m1: indices into the "ra1,dec1" arrays of matching points.
       Numpy array of ints.
    m2: same, but for "ra2,dec2".
    d12: distance, in degrees, between the matching points.
    '''

    # Convert to coordinates on the unit sphere
    xyz1 = radectoxyz(ra1, dec1)
    #if all(ra1 == ra2) and all(dec1 == dec2):
    if ra1 is ra2 and dec1 is dec2:
        xyz2 = xyz1
    else:
        xyz2 = radectoxyz(ra2, dec2)
    r = deg2dist(radius_in_deg)

    if nearest:
        (inds,dists2) = _nearest_func(xyz2, xyz1, r, notself=notself)
        I = np.flatnonzero(inds >= 0)
        J = inds[I]
        d = distsq2deg(dists2[I])
    else:
        (inds,dists) = match(xyz1, xyz2, r, notself)
        dist_in_deg = dist2deg(dists)
        I,J = inds[:,0], inds[:,1]
        d = dist_in_deg[:,0]
        
    return (I, J, d)
Ejemplo n.º 5
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def cat_kd(req, ver, tag, fn):
    tag = 'spec'
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])
    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    import numpy as np
    from astrometry.util.fits import fits_table, merge_tables
    from astrometry.libkd.spherematch import tree_open, tree_search_radec
    from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between

    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2) / 2.
    xyz /= np.sqrt(np.sum(xyz**2))
    rc, dc = xyztoradec(xyz)
    rc = rc[0]
    dc = dc[0]
    rad = degrees_between(rc, dc, ralo, declo)

    kd = tree_open(fn)
    I = tree_search_radec(kd, rc, dc, rad)
    print('Matched', len(I), 'from', fn)
    if len(I) == 0:
        return None
    T = fits_table(fn, rows=I)
    debug(len(T), 'spectra')
    if ralo > rahi:
        # RA wrap
        T.cut(
            np.logical_or(T.ra > ralo, T.ra < rahi) * (T.dec > declo) *
            (T.dec < dechi))
    else:
        T.cut(
            (T.ra > ralo) * (T.ra < rahi) * (T.dec > declo) * (T.dec < dechi))
    debug(len(T), 'in cut')

    return T
Ejemplo n.º 6
0
def cat_targets_dr2(req, ver):
    import json
    tag = 'targets-dr2'
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])

    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    from astrometry.util.fits import fits_table, merge_tables
    import numpy as np
    from cat.models import DR2_Target as Target

    from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2) / 2.
    xyz /= np.sqrt(np.sum(xyz**2))
    rc, dc = xyztoradec(xyz)
    rc = rc[0]
    dc = dc[0]
    rad = degrees_between(rc, dc, ralo, declo)

    objs = Target.objects.extra(where=[
        'q3c_radial_query(target.ra, target.dec, %.4f, %.4f, %g)' %
        (rc, dc, rad * 1.01)
    ])
    print('Got', objs.count(), 'targets')
    print('types:', np.unique([o.type for o in objs]))
    print('versions:', np.unique([o.version for o in objs]))

    return HttpResponse(json.dumps(
        dict(
            rd=[(float(o.ra), float(o.dec)) for o in objs],
            name=[o.type for o in objs],
        )),
                        content_type='application/json')
Ejemplo n.º 7
0
def cat_kd(req, ver, tag, fn):
    tag = 'spec'
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])
    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    import numpy as np
    from astrometry.util.fits import fits_table, merge_tables
    from astrometry.libkd.spherematch import tree_open, tree_search_radec
    from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between

    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2)/2.
    xyz /= np.sqrt(np.sum(xyz**2))
    rc,dc = xyztoradec(xyz)
    rc = rc[0]
    dc = dc[0]
    rad = degrees_between(rc, dc, ralo, declo)

    kd = tree_open(fn)
    I = tree_search_radec(kd, rc, dc, rad)
    print('Matched', len(I), 'from', fn)
    if len(I) == 0:
        return None
    T = fits_table(fn, rows=I)
    debug(len(T), 'spectra')
    if ralo > rahi:
        # RA wrap
        T.cut(np.logical_or(T.ra > ralo, T.ra < rahi) * (T.dec > declo) * (T.dec < dechi))
    else:
        T.cut((T.ra > ralo) * (T.ra < rahi) * (T.dec > declo) * (T.dec < dechi))
    debug(len(T), 'in cut')

    return T
Ejemplo n.º 8
0
def cat_targets_dr2(req, ver):
    import json
    tag = 'targets-dr2'
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])

    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    from astrometry.util.fits import fits_table, merge_tables
    import numpy as np
    from cat.models import DR2_Target as Target

    from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between
    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2)/2.
    xyz /= np.sqrt(np.sum(xyz**2))
    rc,dc = xyztoradec(xyz)
    rc = rc[0]
    dc = dc[0]
    rad = degrees_between(rc, dc, ralo, declo)

    objs = Target.objects.extra(where=[
            'q3c_radial_query(target.ra, target.dec, %.4f, %.4f, %g)'
            % (rc, dc, rad * 1.01)])
    print('Got', objs.count(), 'targets')
    print('types:', np.unique([o.type for o in objs]))
    print('versions:', np.unique([o.version for o in objs]))

    return HttpResponse(json.dumps(dict(
                rd=[(float(o.ra),float(o.dec)) for o in objs],
                name=[o.type for o in objs],
                )),
                        content_type='application/json')
Ejemplo n.º 9
0
def read_astrans(fn, hdu, hdr=None, W=None, H=None, fitsfile=None):
    from astrometry.sdss import AsTransWrapper, AsTrans
    from astrometry.util.util import Tan, fit_sip_wcs_py
    from astrometry.util.starutil_numpy import radectoxyz
    import numpy as np

    astrans = AsTrans.read(fn, F=fitsfile, primhdr=hdr)
    # Approximate as SIP.
    if hdr is None and fn.endswith('.bz2'):
        import fitsio
        hdr = fitsio.read_header(fn, 0)

    if hdr is not None:
        tan = Tan(*[
            float(hdr[k]) for k in [
                'CRVAL1', 'CRVAL2', 'CRPIX1', 'CRPIX2', 'CD1_1', 'CD1_2',
                'CD2_1', 'CD2_2', 'NAXIS1', 'NAXIS2'
            ]
        ])
    else:
        # Frame files include a TAN header... start there.
        tan = Tan(fn)

    # Evaluate AsTrans on a pixel grid...
    h, w = tan.shape
    xx = np.linspace(1, w, 20)
    yy = np.linspace(1, h, 20)
    xx, yy = np.meshgrid(xx, yy)
    xx = xx.ravel()
    yy = yy.ravel()
    rr, dd = astrans.pixel_to_radec(xx, yy)

    xyz = radectoxyz(rr, dd)
    fieldxy = np.vstack((xx, yy)).T

    sip_order = 5
    inv_order = 7
    sip = fit_sip_wcs_py(xyz, fieldxy, None, tan, sip_order, inv_order)
    return sip
Ejemplo n.º 10
0
def read_astrans(fn, hdu, hdr=None, W=None, H=None, fitsfile=None):
    from astrometry.sdss import AsTransWrapper, AsTrans
    from astrometry.util.util import Tan, fit_sip_wcs_py
    from astrometry.util.starutil_numpy import radectoxyz
    import numpy as np
    
    astrans = AsTrans.read(fn, F=fitsfile, primhdr=hdr)
    # Approximate as SIP.
    if hdr is None and fn.endswith('.bz2'):
        import fitsio
        hdr = fitsio.read_header(fn, 0)

    if hdr is not None:
        tan = Tan(*[float(hdr[k]) for k in [
                    'CRVAL1', 'CRVAL2', 'CRPIX1', 'CRPIX2',
                    'CD1_1', 'CD1_2', 'CD2_1', 'CD2_2', 'NAXIS1','NAXIS2']])
    else:
        # Frame files include a TAN header... start there.
        tan = Tan(fn)

    # Evaluate AsTrans on a pixel grid...
    h,w = tan.shape
    xx = np.linspace(1, w, 20)
    yy = np.linspace(1, h, 20)
    xx,yy = np.meshgrid(xx, yy)
    xx = xx.ravel()
    yy = yy.ravel()
    rr,dd = astrans.pixel_to_radec(xx, yy)

    xyz = radectoxyz(rr, dd)
    fieldxy = np.vstack((xx, yy)).T

    sip_order = 5
    inv_order = 7
    sip = fit_sip_wcs_py(xyz, fieldxy, None, tan, sip_order, inv_order)
    return sip
Ejemplo n.º 11
0
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
                nearest=False, indexlist=False, count=False):
    '''
    Cross-matches numpy arrays of RA,Dec points.

    Behaves like spherematch.pro of IDL.

    Parameters
    ----------
    ra1, dec1, ra2, dec2 : numpy arrays, or scalars.
        RA,Dec in degrees of points to match.

    radius_in_deg : float
        Search radius in degrees.

    notself : boolean
        If True, avoids returning 'identity' matches;
        ASSUMES that ra1,dec1 == ra2,dec2.

    nearest : boolean
        If True, returns only the nearest match in *(ra2,dec2)*
        for each point in *(ra1,dec1)*.

    indexlist : boolean
        If True, returns a list of length *len(ra1)*, containing *None*
        or a list of ints of matched points in *ra2,dec2*.
        

    Returns
    -------
    m1 : numpy array of integers
        Indices into the *ra1,dec1* arrays of matching points.
    m2 : numpy array of integers
        Same, but for *ra2,dec2*.
    d12 : numpy array, float
        Distance, in degrees, between the matching points.
    '''
    # Convert to coordinates on the unit sphere
    xyz1 = radectoxyz(ra1, dec1)
    #if all(ra1 == ra2) and all(dec1 == dec2):
    if ra1 is ra2 and dec1 is dec2:
        xyz2 = xyz1
    else:
        xyz2 = radectoxyz(ra2, dec2)
    r = deg2dist(radius_in_deg)

    extra = ()
    if nearest:
        X = _nearest_func(xyz2, xyz1, r, notself=notself, count=count)
        if not count:
            (inds,dists2) = X
            I = np.flatnonzero(inds >= 0)
            J = inds[I]
            d = distsq2deg(dists2[I])
        else:
            #print 'X', X
            #(inds,dists2,counts) = X
            J,I,d,counts = X
            extra = (counts,)
            print('I', I.shape, I.dtype)
            print('J', J.shape, J.dtype)
            print('counts', counts.shape, counts.dtype)
    else:
        X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
        if indexlist:
            return X
        (inds,dists) = X
        dist_in_deg = dist2deg(dists)
        I,J = inds[:,0], inds[:,1]
        d = dist_in_deg[:,0]
        
    return (I, J, d) + extra
Ejemplo n.º 12
0
def cat_targets_drAB(req,
                     ver,
                     cats=[],
                     tag='',
                     bgs=False,
                     sky=False,
                     bright=False,
                     dark=False,
                     color_name_func=desitarget_color_names):
    '''
    color_name_func: function that selects names and colors for targets
    (eg based on targeting bit values)
    '''

    import json
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])

    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    from astrometry.util.fits import fits_table, merge_tables
    from astrometry.libkd.spherematch import tree_open, tree_search_radec
    import numpy as np
    from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between

    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2) / 2.
    xyz /= np.sqrt(np.sum(xyz**2))
    rc, dc = xyztoradec(xyz)
    rc = rc[0]
    dc = dc[0]
    rad = degrees_between(rc, dc, ralo, declo)
    '''
    startree -i /project/projectdirs/desi/target/catalogs/targets-dr4-0.20.0.fits -o data/targets-dr4-0.20.0.kd.fits -P -k -T
    '''
    TT = []
    for fn in cats:
        kd = tree_open(fn)
        I = tree_search_radec(kd, rc, dc, rad)
        print('Matched', len(I), 'from', fn)
        if len(I) == 0:
            continue
        T = fits_table(fn, rows=I)
        TT.append(T)
    if len(TT) == 0:
        return HttpResponse(json.dumps(dict(rd=[], name=[])),
                            content_type='application/json')
    T = merge_tables(TT, columns='fillzero')

    if bgs:
        T.cut(T.bgs_target > 0)

    if bright:
        T.cut(np.logical_or(T.bgs_target > 0, T.mws_target > 0))

    if dark:
        T.cut(T.desi_target > 0)

    names = None
    colors = None
    if color_name_func is not None:
        names, colors = color_name_func(T)

    if sky:
        fluxes = [
            dict(g=float(g), r=float(r), z=float(z))
            for (g, r, z) in zip(T.apflux_g[:,
                                            0], T.apflux_r[:,
                                                           0], T.apflux_z[:,
                                                                          0])
        ]
        nobs = None
    else:
        fluxes = [
            dict(g=float(g),
                 r=float(r),
                 z=float(z),
                 W1=float(W1),
                 W2=float(W2))
            for (g, r, z, W1,
                 W2) in zip(T.flux_g, T.flux_r, T.flux_z, T.flux_w1, T.flux_w2)
        ]
        nobs = [
            dict(g=int(g), r=int(r), z=int(z))
            for g, r, z in zip(T.nobs_g, T.nobs_r, T.nobs_z)
        ],

    rtn = dict(
        rd=[(t.ra, t.dec) for t in T],
        targetid=[int(t) for t in T.targetid],
        fluxes=fluxes,
    )
    if names is not None:
        rtn.update(name=names)
    if colors is not None:
        rtn.update(color=colors)
    if nobs is not None:
        rtn.update(nobs=nobs)
    return HttpResponse(json.dumps(rtn), content_type='application/json')
Ejemplo n.º 13
0
def cat_targets_drAB(req, ver, cats=None, tag='', bgs=False, sky=False, bright=False, dark=False, color_name_func=desitarget_color_names):
    '''
    color_name_func: function that selects names and colors for targets
    (eg based on targeting bit values)
    '''
    if cats is None:
        cats = []

    import json
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])

    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    from astrometry.util.fits import fits_table, merge_tables
    from astrometry.libkd.spherematch import tree_open, tree_search_radec
    import numpy as np
    from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, degrees_between

    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2)/2.
    xyz /= np.sqrt(np.sum(xyz**2))
    rc,dc = xyztoradec(xyz)
    rc = rc[0]
    dc = dc[0]
    rad = degrees_between(rc, dc, ralo, declo)

    '''
    startree -i /project/projectdirs/desi/target/catalogs/targets-dr4-0.20.0.fits -o data/targets-dr4-0.20.0.kd.fits -P -k -T
    '''
    TT = []
    for fn in cats:
        kd = tree_open(fn)
        I = tree_search_radec(kd, rc, dc, rad)
        print('Matched', len(I), 'from', fn)
        if len(I) == 0:
            continue
        T = fits_table(fn, rows=I)
        TT.append(T)
    if len(TT) == 0:
        return HttpResponse(json.dumps(dict(rd=[], name=[])),
                            content_type='application/json')
    T = merge_tables(TT, columns='fillzero')

    if bgs:
        T.cut(T.bgs_target > 0)

    if bright:
        T.cut(np.logical_or(T.bgs_target > 0, T.mws_target > 0))

    if dark:
        T.cut(T.desi_target > 0)

    names = None
    colors = None
    if color_name_func is not None:
        names,colors = color_name_func(T)

    if sky:
        fluxes = [dict(g=float(g), r=float(r), z=float(z))
                  for (g,r,z) in zip(T.apflux_g[:,0], T.apflux_r[:,0], T.apflux_z[:,0])]
        nobs = None
    else:
        fluxes = [dict(g=float(g), r=float(r), z=float(z),
                       W1=float(W1), W2=float(W2))
                  for (g,r,z,W1,W2)
                  in zip(T.flux_g, T.flux_r, T.flux_z, T.flux_w1, T.flux_w2)]
        nobs=[dict(g=int(g), r=int(r), z=int(z)) for g,r,z
              in zip(T.nobs_g, T.nobs_r, T.nobs_z)],

    rtn = dict(rd=[(t.ra, t.dec) for t in T],
               targetid=[int(t) for t in T.targetid],
               fluxes=fluxes,
           )
    if names is not None:
        rtn.update(name=names)
    if colors is not None:
        rtn.update(color=colors)
    if nobs is not None:
        rtn.update(nobs=nobs)
    
    # Convert targetid to string to prevent rounding errors
    rtn['targetid'] = [str(s) for s in rtn['targetid']]
    
    return HttpResponse(json.dumps(rtn), content_type='application/json')
Ejemplo n.º 14
0
def cat_sdss(req, ver):
    import json
    import numpy as np
    from astrometry.util.starutil_numpy import degrees_between, radectoxyz, xyztoradec
    from map.views import sdss_ccds_near
    from astrometry.util.fits import fits_table, merge_tables

    tag = 'sdss-cat'
    ralo = float(req.GET['ralo'])
    rahi = float(req.GET['rahi'])
    declo = float(req.GET['declo'])
    dechi = float(req.GET['dechi'])

    ver = int(ver)
    if not ver in catversions[tag]:
        raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))

    rad = degrees_between(ralo, declo, rahi, dechi) / 2.
    xyz1 = radectoxyz(ralo, declo)
    xyz2 = radectoxyz(rahi, dechi)
    xyz = (xyz1 + xyz2)
    xyz /= np.sqrt(np.sum(xyz**2))
    rc, dc = xyztoradec(xyz)
    rad = rad + np.hypot(10., 14.) / 2. / 60.
    ccds = sdss_ccds_near(rc[0], dc[0], rad)
    if ccds is None:
        print('No SDSS CCDs nearby')
        return HttpResponse(json.dumps(dict(rd=[])),
                            content_type='application/json')
    print(len(ccds), 'SDSS CCDs')

    T = []
    for ccd in ccds:
        # env/BOSS_PHOTOOBJ/301/2073/3/photoObj-002073-3-0088.fits
        fn = os.path.join(
            settings.SDSS_BASEDIR, 'env', 'BOSS_PHOTOOBJ', str(ccd.rerun),
            str(ccd.run), str(ccd.camcol),
            'photoObj-%06i-%i-%04i.fits' % (ccd.run, ccd.camcol, ccd.field))
        print('Reading', fn)
        T.append(
            fits_table(
                fn,
                columns=
                'ra dec objid mode objc_type objc_flags objc_flags nchild tai expflux devflux psfflux cmodelflux fracdev mjd'
                .split()))
    T = merge_tables(T)
    T.cut((T.dec >= declo) * (T.dec <= dechi))
    # FIXME
    T.cut((T.ra >= ralo) * (T.ra <= rahi))

    # primary
    T.cut(T.mode == 1)
    types = ['P' if t == 6 else 'C' for t in T.objc_type]
    fluxes = [
        p if t == 6 else c
        for t, p, c in zip(T.objc_type, T.psfflux, T.cmodelflux)
    ]

    return HttpResponse(json.dumps(
        dict(
            rd=[(float(o.ra), float(o.dec)) for o in T],
            sourcetype=types,
            fluxes=[
                dict(u=float(f[0]),
                     g=float(f[1]),
                     r=float(f[2]),
                     i=float(f[3]),
                     z=float(f[4])) for f in fluxes
            ],
        )),
                        content_type='application/json')
Ejemplo n.º 15
0
    gridr = np.hypot(gridx, gridy)

    crpixx,crpixy = (petal.ccdw+1.)/2., (petal.ccdh+1.)/2.
    crx,cry = petal.gfa_pix_to_focal_mm(crpixx, crpixy)

    theta = Tpsfunc(gridr)
    gridu = theta * gridx / gridr
    gridv = theta * gridy / gridr
    crr = np.hypot(crx, cry)
    crd = Tpsfunc(crr)
    cru = crd * crx / crr
    crv = crd * cry / crr

    griddec = gridv
    gridra  = -gridu / np.cos(np.deg2rad(griddec))
    starxyz = radectoxyz(gridra, griddec)
    fieldxy = np.vstack((ccdgridpx, ccdgridpy)).T
    weights = np.ones(len(gridra))
    crdec = crv[0]
    crra  = -cru[0] / np.cos(np.deg2rad(crdec))
    ps = 0.2/3600.
    tan_in = Tan(crra, crdec, crpixx, crpixy, -ps, 0., 0., ps, float(petal.ccdw), float(petal.ccdh))
    sip_order = inv_order = 4
    sip = fit_sip_wcs_py(starxyz, fieldxy, weights, tan_in, sip_order, inv_order)

    hdr = fitsio.FITSHDR()
    sip.add_to_header(hdr)
    for i,(x,y) in enumerate(zip(petal.gfa.gif_1_pix_x, petal.gfa.gif_1_pix_y)):
        hdr.add_record(dict(name='GIF1X%i' % (i+1), value=x,
                            comment='Pinhole pixel pos'))
        hdr.add_record(dict(name='GIF1Y%i' % (i+1), value=y))
Ejemplo n.º 16
0
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
                nearest=False, indexlist=False, count=False):
    '''
    (m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)

    Cross-matches numpy arrays of RA,Dec points.

    Behaves like spherematch.pro of IDL 

    ra1,dec1 (and 2): RA,Dec in degrees of points to match.
       Must be scalars or numpy arrays.

       radius_in_deg: search radius in degrees.

    notself: if True, avoids returning 'identity' matches;
        ASSUMES that ra1,dec1 == ra2,dec2.

    nearest: if True, returns only the nearest match in (ra2,dec2)
        for each point in (ra1,dec1).

    indexlist: returns a list of length len(ra1), containing None or a
    list of ints of matched points in ra2,dec2.  Returns this list.
        
    Returns:

    m1: indices into the "ra1,dec1" arrays of matching points.
       Numpy array of ints.
    m2: same, but for "ra2,dec2".
    d12: distance, in degrees, between the matching points.
    '''

    # Convert to coordinates on the unit sphere
    xyz1 = radectoxyz(ra1, dec1)
    #if all(ra1 == ra2) and all(dec1 == dec2):
    if ra1 is ra2 and dec1 is dec2:
        xyz2 = xyz1
    else:
        xyz2 = radectoxyz(ra2, dec2)
    r = deg2dist(radius_in_deg)

    extra = ()
    if nearest:
        X = _nearest_func(xyz2, xyz1, r, notself=notself, count=count)
        if not count:
            (inds,dists2) = X
            I = np.flatnonzero(inds >= 0)
            J = inds[I]
            d = distsq2deg(dists2[I])
        else:
            #print 'X', X
            #(inds,dists2,counts) = X
            J,I,d,counts = X
            extra = (counts,)
            print 'I', I.shape, I.dtype
            print 'J', J.shape, J.dtype
            print 'counts', counts.shape, counts.dtype
    else:
        X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
        if indexlist:
            return X
        (inds,dists) = X
        dist_in_deg = dist2deg(dists)
        I,J = inds[:,0], inds[:,1]
        d = dist_in_deg[:,0]
        
    return (I, J, d) + extra
Ejemplo n.º 17
0
def build_groupcat_sky(parent, linking_length=2, verbose=True, groupcatfile='groupcat.fits',
                       parentfile='parent.fits'):
    """Build a group catalog based on just RA, Dec coordinates.

    """
    from astropy.table import Column, Table
    from astrometry.util.starutil_numpy import radectoxyz, xyztoradec, arcsec_between

    grp, mult, frst, nxt = fof_groups(parent, linking_length=linking_length, verbose=verbose)

    ngrp = max(grp) + 1    
    groupid = np.arange(ngrp)
    
    groupcat = Table()
    groupcat.add_column(Column(name='groupid', dtype='i4', length=ngrp, data=groupid)) # unique ID number
    #groupcat.add_column(Column(name='galaxy', dtype='S1000', length=ngrp))
    groupcat.add_column(Column(name='nmembers', dtype='i4', length=ngrp))
    groupcat.add_column(Column(name='ra', dtype='f8', length=ngrp))  # average RA
    groupcat.add_column(Column(name='dec', dtype='f8', length=ngrp)) # average Dec
    groupcat.add_column(Column(name='width', dtype='f4', length=ngrp)) # maximum separation
    groupcat.add_column(Column(name='d25max', dtype='f4', length=ngrp))
    groupcat.add_column(Column(name='d25min', dtype='f4', length=ngrp))
    groupcat.add_column(Column(name='fracmasked', dtype='f4', length=ngrp))
    
    # Add the groupid to the input catalog.
    outparent = parent.copy()
    
    #t0 = time.time()
    npergrp, _ = np.histogram(grp, bins=len(grp), range=(0, len(grp)))
    #print('Time to build the histogram = {:.3f} minutes.'.format( (time.time() - t0) / 60 ) )    
    
    big = np.where( npergrp > 1 )[0]
    small = np.where( npergrp == 1 )[0]

    if len(small) > 0:
        groupcat['nmembers'][small] = 1
        groupcat['groupid'][small] = groupid[small]
        groupcat['ra'][small] = parent['ra'][grp[small]]
        groupcat['dec'][small] = parent['dec'][grp[small]]
        groupcat['d25max'][small] = parent['d25'][grp[small]]
        groupcat['d25min'][small] = parent['d25'][grp[small]]
        groupcat['width'][small] = parent['d25'][grp[small]]
        
        outparent['groupid'][grp[small]] = groupid[small]

    for igrp in range(len(big)):
        jj = frst[big[igrp]]
        ig = list()
        ig.append(jj)
        while (nxt[jj] != -1):
            ig.append(nxt[jj])
            jj = nxt[jj]
        ig = np.array(ig)
        
        ra1, dec1 = parent['ra'][ig].data, parent['dec'][ig].data        
        ra2, dec2 = xyztoradec(np.mean(radectoxyz(ra1, dec1), axis=0))

        groupcat['ra'][big[igrp]] = ra2
        groupcat['dec'][big[igrp]] = dec2
        
        d25min, d25max = np.min(parent['d25'][ig]), np.max(parent['d25'][ig])

        groupcat['d25max'][big[igrp]] = d25max
        groupcat['d25min'][big[igrp]] = d25min
        
        groupcat['nmembers'][big[igrp]] = len(ig)
        outparent['groupid'][ig] = groupcat['groupid'][big[igrp]]
        
        # Get the distance of each object from every other object.
        #diff = arcsec_between(ra1, dec1, ra2, dec2) / 60 # [arcmin] # group center
        
        diff = list()
        for _ra, _dec in zip(ra1, dec1):
            diff.append(arcsec_between(ra1, dec1, _ra, _dec) / 60) # [arcmin]
        
        #if len(ig) > 2:
        #    import pdb ; pdb.set_trace()
        diameter = np.hstack(diff).max()
        groupcat['width'][big[igrp]] = diameter
            
    print('Writing {}'.format(groupcatfile))
    groupcat.write(groupcatfile, overwrite=True)    

    print('Writing {}'.format(parentfile))
    outparent.write(parentfile, overwrite=True)
    
    return groupcat, outparent
Ejemplo n.º 18
0
def arcsec_between(ra1, dec1, ra2, dec2):
    xyz1 = radectoxyz(ra1, dec1)
    xyz2 = radectoxyz(ra2, dec2)
    d2 = np.sum((xyz1 - xyz2)**2, axis=1)
    rad = np.arccos(1. - d2 / 2.)
    return 3600. * np.rad2deg(rad)