Python CircularAperture.do_photometry方法代码示例

# 需要导入模块: from photutils import CircularAperture [as 别名]
# 或者: from photutils.CircularAperture import do_photometry [as 别名]
def starbright(fnstar,fnflat,istar,axs,fg):
    #%% load data
    data = meanstack(fnstar,100)[0]
    #%% flat field
    flatnorm = readflat(fnflat,fnstar)
    data = (data/flatnorm).round().astype(data.dtype)
    #%% background
    mean, median, std = sigma_clipped_stats(data, sigma=3.0)

    rfact=data.shape[0]//40
    cfact=data.shape[1]//40
    bg = Background(data,(rfact,cfact),interp_order=1, sigclip_sigma=3)
# http://docs.astropy.org/en/stable/units/#module-astropy.units
    #dataphot = (data - bg.background)*u.ph/(1e-4*u.m**2 * u.s * u.sr)
 #   data = (data-0.97*data.min()/bg.background.min()*bg.background) * u.ph/(u.cm**2 * u.s * u.sr)
    data = data* u.ph/(u.cm**2 * u.s * u.sr)
    #%% source extraction
    sources = daofind(data, fwhm=3.0, threshold=5*std)
    #%% star identification and quantification
    XY = column_stack((sources['xcentroid'], sources['ycentroid']))
    apertures = CircularAperture(XY, r=4.)
    norm = ImageNormalize(stretch=SqrtStretch())

    flux = apertures.do_photometry(data,effective_gain=camgain)[0]
#%% plots
    fg.suptitle('{}'.format(fnflat.parent),fontsize='x-large')

    hi = axs[-3].imshow(flatnorm,interpolation='none',origin='lower')
    fg.colorbar(hi,ax=axs[-3])
    axs[-3].set_title('flatfield {}'.format(fnflat.name))

    hi = axs[-2].imshow(bg.background,interpolation='none',origin='lower')
    fg.colorbar(hi,ax=axs[-2])
    axs[-2].set_title('background {}'.format(fnstar.name))

    hi = axs[-1].imshow(data.value,
                    cmap='Greys', origin='lower', norm=norm,interpolation='none')
    fg.colorbar(hi,ax=axs[-1])
    for i,xy in enumerate(XY):
        axs[-1].text(xy[0],xy[1], str(i),ha='center',va='center',fontsize=16,color='w')
    apertures.plot(ax=axs[-1], color='blue', lw=1.5, alpha=0.5)
    axs[-1].set_title('star {}'.format(fnstar.name))

    return flux[istar]

# 需要导入模块: from photutils import CircularAperture [as 别名]
# 或者: from photutils.CircularAperture import do_photometry [as 别名]
def tso_aperture_photometry(datamodel, xcenter, ycenter, radius, radius_inner,
                            radius_outer):
    """
    Create a photometric catalog for NIRCam TSO imaging observations.

    Parameters
    ----------
    datamodel : `CubeModel`
        The input `CubeModel` of a NIRCam TSO imaging observation.

    xcenter, ycenter : float
        The ``x`` and ``y`` center of the aperture.

    radius : float
        The radius (in pixels) of the circular aperture.

    radius_inner, radius_outer : float
        The inner and outer radii (in pixels) of the circular-annulus
        aperture, used for local background estimation.

    Returns
    -------
    catalog : `~astropy.table.QTable`
        An astropy QTable (Quantity Table) containing the source
        photometry.
    """

    if not isinstance(datamodel, CubeModel):
        raise ValueError('The input data model must be a CubeModel.')

    # For the SUB64P subarray with the WLP8 pupil, the circular aperture
    # extends beyond the image and the circular annulus does not have any
    # overlap with the image.  In that case, we simply sum all values
    # in the array and skip the background subtraction.
    sub64p_wlp8 = False
    if (datamodel.meta.instrument.pupil == 'WLP8' and
            datamodel.meta.subarray.name == 'SUB64P'):
        sub64p_wlp8 = True

    if not sub64p_wlp8:
        phot_aper = CircularAperture((xcenter, ycenter), r=radius)
        bkg_aper = CircularAnnulus((xcenter, ycenter), r_in=radius_inner,
                                   r_out=radius_outer)

    aperture_sum = []
    aperture_sum_err = []
    annulus_sum = []
    annulus_sum_err = []

    nimg = datamodel.data.shape[0]

    if sub64p_wlp8:
        info = ('Photometry measured as the sum of all values in the '
               'subarray.  No background subtraction was performed.')

        for i in np.arange(nimg):
            aperture_sum.append(np.sum(datamodel.data[i, :, :]))
            aperture_sum_err.append(
                np.sqrt(np.sum(datamodel.err[i, :, :]**2)))
    else:
        info = ('Photometry measured in a circular aperture of r={0} '
                'pixels.  Background calculated as the mean in a '
                'circular annulus with r_inner={1} pixels and '
                'r_outer={2} pixels.'.format(radius, radius_inner,
                                                radius_outer))
        for i in np.arange(nimg):
            aper_sum, aper_sum_err = phot_aper.do_photometry(
                datamodel.data[i, :, :], error=datamodel.err[i, :, :])
            ann_sum, ann_sum_err = bkg_aper.do_photometry(
                datamodel.data[i, :, :], error=datamodel.err[i, :, :])

            aperture_sum.append(aper_sum[0])
            aperture_sum_err.append(aper_sum_err[0])
            annulus_sum.append(ann_sum[0])
            annulus_sum_err.append(ann_sum_err[0])

    aperture_sum = np.array(aperture_sum)
    aperture_sum_err = np.array(aperture_sum_err)
    annulus_sum = np.array(annulus_sum)
    annulus_sum_err = np.array(annulus_sum_err)

    # construct metadata for output table
    meta = OrderedDict()
    meta['instrument'] = datamodel.meta.instrument.name
    meta['detector'] = datamodel.meta.instrument.detector
    meta['channel'] = datamodel.meta.instrument.channel
    meta['subarray'] = datamodel.meta.subarray.name
    meta['filter'] = datamodel.meta.instrument.filter
    meta['pupil'] = datamodel.meta.instrument.pupil
    meta['target_name'] = datamodel.meta.target.catalog_name
    meta['xcenter'] = xcenter
    meta['ycenter'] = ycenter
    ra_icrs, dec_icrs = datamodel.meta.wcs(xcenter, ycenter)
    meta['ra_icrs'] = ra_icrs
    meta['dec_icrs'] = dec_icrs
    meta['apertures'] = info

    # initialize the output table
    tbl = QTable(meta=meta)

#.........这里部分代码省略.........