Python CircularAperture.to_mask方法代碼示例

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
def measure_one_annular_bg(image, center, innerRad, outerRad, metric, apMethod='exact'):
    """Class methods are similar to regular functions.

    Note:
        Do not include the `self` parameter in the ``Args`` section.

    Args:
        param1: The first parameter.
        param2: The second parameter.

    Returns:
        True if successful, False otherwise.

    """
    
    innerAperture   = CircularAperture(center, innerRad)
    outerAperture   = CircularAperture(center, outerRad)
    
    inner_aper_mask = innerAperture.to_mask(method=apMethod)[0]
    inner_aper_mask = inner_aper_mask.to_image(image.shape).astype(bool)
    
    outer_aper_mask = outerAperture.to_mask(method=apMethod)[0]
    outer_aper_mask = outer_aper_mask.to_image(image.shape).astype(bool)
    
    backgroundMask = (~inner_aper_mask)*outer_aper_mask
    
    return metric(image[backgroundMask])

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
def measure_one_background(image, center, aperRad, metric, apMethod='exact', bgMethod='circle'):
    """Class methods are similar to regular functions.

    Note:
        Do not include the `self` parameter in the ``Args`` section.

    Args:
        param1: The first parameter.
        param2: The second parameter.

    Returns:
        True if successful, False otherwise.

    """
    
    if np.ndim(aperRad) == 0:
        aperture  = CircularAperture(center, aperRad)
        aperture  = aperture.to_mask(method=apMethod)[0]    # list of ApertureMask objects (one for each position)
        aperture  = ~aperture.to_image(image).astype(bool) # inverse to keep 'outside' aperture
    else:
        innerRad, outerRad = aperRad
        
        innerAperture   = CircularAperture(center, innerRad)
        outerAperture   = CircularAperture(center, outerRad)
        
        inner_aper_mask = innerAperture.to_mask(method=method)[0]
        inner_aper_mask = inner_aper_mask.to_image(image.shape).astype(bool)
    
        outer_aper_mask = outerAperture.to_mask(method=method)[0]
        outer_aper_mask = outer_aper_mask.to_image(image.shape).astype(bool)     
        
        aperture        = (~inner_aper_mask)*outer_aper_mask
    
    if bgMethod == 'median':
        medFrame  = median(image[aperture])
        madFrame  = scale.mad(image[aperture])
        
        medianMask= abs(image - medFrame) < nSig*madFrame
        
        aperture  = medianMask*aperture
    
    if bgMethod == 'kde':
        kdeFrame = kde.KDEUnivariate(image[aperture].ravel())
        kdeFrame.fit()
        
        return kdeFrame.support[kdeFrame.density.argmax()]
    
    return metric(image[aperture])

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
def measure_one_median_bg(image, center, aperRad, metric, nSig, apMethod='exact'):
    """Class methods are similar to regular functions.

    Note:
        Do not include the `self` parameter in the ``Args`` section.

    Args:
        param1: The first parameter.
        param2: The second parameter.

    Returns:
        True if successful, False otherwise.

    """
    
    aperture       = CircularAperture(center, aperRad)
    aperture       = aperture.to_mask(method=apMethod)[0]
    aperture       = aperture.to_image(image.shape).astype(bool)
    backgroundMask = ~aperture
    
    medFrame  = median(image[backgroundMask])
    madFrame  = std(image[backgroundMask])
    
    medianMask= abs(image - medFrame) < nSig*madFrame
    
    maskComb  = medianMask*backgroundMask
    
    return median(image[maskComb])

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
def measure_one_circle_bg(image, center, aperRad, metric, apMethod='exact'):
    """Class methods are similar to regular functions.

    Note:
        Do not include the `self` parameter in the ``Args`` section.

    Args:
        param1: The first parameter.
        param2: The second parameter.

    Returns:
        True if successful, False otherwise.

    """
    
    aperture  = CircularAperture(center, aperRad)
    aper_mask = aperture.to_mask(method=apMethod)[0]    # list of ApertureMask objects (one for each position)
    
    # backgroundMask = abs(aperture.get_fractions(np.ones(self.imageCube[0].shape))-1)
    backgroundMask = aper_mask.to_image(image.shape).astype(bool)
    backgroundMask = ~backgroundMask#[backgroundMask == 0] = False
    
    return metric(image[backgroundMask])

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
def measure_one_KDE_bg(image, center, aperRad, metric, apMethod='exact'):
    """Class methods are similar to regular functions.

    Note:
        Do not include the `self` parameter in the ``Args`` section.

    Args:
        param1: The first parameter.
        param2: The second parameter.

    Returns:
        True if successful, False otherwise.

    """
    
    aperture       = CircularAperture(center, aperRad)
    aperture       = aperture.to_mask(method=apMethod)[0]
    aperture       = aperture.to_image(image.shape).astype(bool)
    backgroundMask = ~aperture
    
    kdeFrame = kde.KDEUnivariate(image[backgroundMask])
    kdeFrame.fit()
    
    return kdeFrame.support[kdeFrame.density.argmax()]

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
def photom_av(ima, pos, radius, r_in=False, r_out=False, mode='median'):
    '''
    Aperture photometry in an aperture located at pixel coordinates 
    pos = ( (x0, y0), (x1, y1), ... ) with a radius=radius.
    When r_in and r_out are given, background is estimated in CircularAnnulus and subtracted.
    
    mode refers to how the background is estimated within the circlar annulus.
    Can be 'median' or 'mean'
    
    Photometry is calculating by median averaging the pixels within the aperture and 
    multiplying by the number of pixels in the aperture (including fractions of pixels).

    '''
    # Setting up the mask 
    if hasattr(ima, 'mask'):
      if ima.mask.size == 1:
	mask = np.zeros(ima.shape, dtype=np.bool) | ima.mask
      else:
        mask = ima.mask.copy()
    else:
        mask = np.zeros(ima.shape, dtype=np.bool)
        
        
    ### Performing the actual photometry - identical for each method
    # Median averaging of flux in aperture
    # Setting up the aperture 
    apertures = CircularAperture(pos, r = radius) 
    ap_mask = apertures.to_mask(method='center')
    # Setting up arrays to store data
    nflx = len(ap_mask)
    flx = np.zeros(nflx, dtype=np.float)
    flux_max = np.zeros(nflx, dtype=np.float)
    flux_min = np.zeros(nflx, dtype=np.float)
    # Median averaging of flux
    for i, am in enumerate(ap_mask):
      fluxmask = ~mask & am.to_image(shape=mask.shape).astype(np.bool)
      flx[i] = np.median(ima[fluxmask])
      flux_max[i] = np.max(ima[fluxmask])
      flux_min[i] = np.min(ima[fluxmask])
      
      
      
      
    # Aperture photometry on mask to see how many masked pixels are in the 
    # aperture
    apm       = aperture_photometry(mask.astype(int), apertures)
    # Number of unmasked pixels in aperture
    ap_area   = Column(name = 'area_aper',
		       data=apertures.area() - apm['aperture_sum'].data)
    
    # Flux in aperture using median av flux and fractional no. pixels in aperture
    flux_init = flx*ap_area
    

    ### Two different modes for analysing the background
    if ( r_in and r_out and mode in ('mean', 'median') ):
      
      ### This stuff is the same regardless of method
      # Setting up the annulus
      anulus_apertures = CircularAnnulus(pos, r_in=r_in, r_out=r_out)
      # Performing annulus photometry on the mask
      bkgm = aperture_photometry(mask.astype(int), anulus_apertures)
      # Number of masked pixels in bkg
      mbkg_area = Column(name = 'bpix_bkg',
			 data=bkgm['aperture_sum'])  
      # Number of non-masked pixels in aperture and bkg        
      bkg_area  = Column(name = 'area_bkg',
			 data=anulus_apertures.area() - bkgm['aperture_sum'])
      
      
      ### This stuff is specific to the mean
      if mode == 'mean':
	# Perform the annulus photometry on the image
	bkg  = aperture_photometry(ima, anulus_apertures, mask=mask)
        # Average bkg where this divides by only number of NONMASKED pixels
        # as the aperture photometry ignores the masked pixels
        bkga = Column(name='background',
		      data=bkg['aperture_sum']/bkg_area)
        # Bkg subtracted flux
        flux = flux_init - bkga*ap_area
        # Adding that data
        ap.add_column(bkga)
        
        
      elif mode == 'median':
	# Number of pixels in the annulus, a different method
	aperture_mask = anulus_apertures.to_mask(method='center')
	nbkg = len(aperture_mask)
	
	# Background mask
	bkgm = np.zeros(nbkg, dtype=np.float)
	
	# Median averaging
	for i, am in enumerate(aperture_mask):
	  bmask = ~mask & am.to_image(shape=mask.shape).astype(np.bool)
	  bkgm[i] = np.median(ima[bmask])
		
	flux = flux_init - bkgm*ap_area
	bkgm = Column(name = 'background', data = bkgm)

#.........這裏部分代碼省略.........

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
 def circular_mask(self, radius=4):
     circ =  CircularAperture(self.centroid, radius)
     circ_mask = circ.to_mask()[0].to_image(self.image.shape).astype(bool)
     return circ_mask

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
    def do_detection(self):
        """Flag outlier pixels in DQ of input images."""
        self.build_suffix(**self.outlierpars)
        self._convert_inputs()

        pars = self.outlierpars
        save_intermediate_results = pars['save_intermediate_results']

        # Start by performing initial TSO Photometry on stack of DataModels
        # TODO:  need information about the actual source position in
        # TSO imaging mode (for all subarrays).
        # Meanwhile, this is a placeholder representing the geometric
        # center of the image.
        nints, ny, nx = self.inputs.data.shape
        xcenter = (ny - 1) / 2.
        ycenter = (ny - 1) / 2.

        # all radii are in pixel units
        if self.inputs.meta.instrument.pupil == 'WLP8':
            radius = 50
            radius_inner = 60
            radius_outer = 70
        else:
            radius = 3
            radius_inner = 4
            radius_outer = 5

        apertures = CircularAperture((xcenter, ycenter), r=radius)
        aperture_mask = apertures.to_mask(method='center')[0]
        # This mask has 1 for mask region, 0 for outside of mask
        median_mask = aperture_mask.to_image((ny, nx))
        inv_median_mask = np.abs(median_mask - 1)
        # Perform photometry
        catalog = tso_aperture_photometry(self.inputs, xcenter, ycenter,
                                          radius, radius_inner,
                                          radius_outer)

        # Extract net photometry for the source
        # This will be the value used for scaling the median image within
        # the aperture region
        phot_values = catalog['net_aperture_sum']

        # Convert CubeModel into ModelContainer of 2-D DataModels
        for image in self.input_models:
            image.wht = resample_utils.build_driz_weight(
                image,
                weight_type='exptime',
                good_bits=pars['good_bits']
            )

        # Initialize intermediate products used in the outlier detection
        input_shape = self.input_models[0].data.shape
        median_model = datamodels.ImageModel(init=input_shape)
        median_model.meta = deepcopy(self.input_models[0].meta)
        base_filename = self.inputs.meta.filename
        median_model.meta.filename = self.make_output_path(
            basepath=base_filename, suffix='median'
        )

        # Perform median combination on set of drizzled mosaics
        median_model.data = self.create_median(self.input_models)
        aper2 = CircularAnnulus((xcenter, ycenter), r_in=radius_inner,
                                r_out=radius_outer)

        tbl1 = aperture_photometry(median_model.data, apertures,
                                   error=median_model.data * 0.0 + 1.0)
        tbl2 = aperture_photometry(median_model.data, aper2,
                                   error=median_model.data * 0.0 + 1.0)

        aperture_sum = u.Quantity(tbl1['aperture_sum'][0])
        annulus_sum = u.Quantity(tbl2['aperture_sum'][0])
        annulus_mean = annulus_sum / aper2.area()
        aperture_bkg = annulus_mean * apertures.area()
        median_phot_value = aperture_sum - aperture_bkg

        if save_intermediate_results:
            log.info("Writing out MEDIAN image to: {}".format(
                     median_model.meta.filename))
            median_model.save(median_model.meta.filename)

        # Scale the median image by the initial photometry (only in aperture)
        # to create equivalent of 'blot' images
        # Area outside of aperture in median will remain unchanged
        blot_models = datamodels.ModelContainer()
        for i in range(nints):
            scale_factor = float(phot_values[i] / median_phot_value)
            scaled_image = datamodels.ImageModel(init=median_model.data.shape)
            scaled_image.meta = deepcopy(median_model.meta)
            scaled_data = (median_model.data * (scale_factor * median_mask) + (
                           median_model.data * inv_median_mask))
            scaled_image.data = scaled_data
            blot_models.append(scaled_image)

        if save_intermediate_results:
            log.info("Writing out Scaled Median images...")

            def make_output_path(ignored, idx=None):
                output_path = self.make_output_path(
                    basepath=base_filename, suffix='blot', idx=idx,
                    component_format='_{asn_id}_{idx}'
#.........這裏部分代碼省略.........

# 需要導入模塊: from photutils import CircularAperture [as 別名]
# 或者: from photutils.CircularAperture import to_mask [as 別名]
txdump_out.close()

g_iraf, ge_iraf, gf_iraf, gsky_iraf = np.loadtxt('phot_test_g.txdump', usecols=(1,2,3,4), unpack=True)
i_iraf, ie_iraf, if_iraf, isky_iraf = np.loadtxt('phot_test_i.txdump', usecols=(1,2,3,4), unpack=True)

# now try python
x, y = np.loadtxt(coords_file, usecols=(0,1), unpack=True)
positions = np.array(zip(x,y))

hdu_g = fits.open(fits_g)
hdu_i = fits.open(fits_i)

apertures = CircularAperture(positions, r=8.)
annulus_apertures = CircularAnnulus(positions, r_in=10., r_out=14.)
print apertures.area()
ap_mask = apertures.to_mask(method='subpixel', subpixels=7)
dummy = np.ones_like(hdu_g[0].data)
ann_mask = annulus_apertures.to_mask(method='center')
ap_g = [m.apply(hdu_g[0].data) for i,m in enumerate(ap_mask)]
ap_i = [m.apply(hdu_i[0].data) for i,m in enumerate(ap_mask)]
area_g = [np.sum(m.apply(dummy)) for i,m in enumerate(ap_mask)]
area_i = [np.sum(m.apply(dummy)) for i,m in enumerate(ap_mask)]

print area_g, area_i
# plt.imshow(ap_g[0], interpolation='nearest')
# plt.show()
ann_g = [m.apply(hdu_g[0].data, fill_value=-999.) for i,m in enumerate(ann_mask)]
ann_i = [m.apply(hdu_i[0].data, fill_value=-999.) for i,m in enumerate(ann_mask)]

flux_g = np.array([np.sum(a) for j,a in enumerate(ap_g)])
flux_i = np.array([np.sum(a) for j,a in enumerate(ap_i)])