本文整理汇总了Python中astropy.table.QTable.replace_column方法的典型用法代码示例。如果您正苦于以下问题：Python QTable.replace_column方法的具体用法？Python QTable.replace_column怎么用？Python QTable.replace_column使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类astropy.table.QTable的用法示例。
在下文中一共展示了QTable.replace_column方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: make_source_catalog
# 需要导入模块: from astropy.table import QTable [as 别名]
# 或者: from astropy.table.QTable import replace_column [as 别名]
#.........这里部分代码省略.........
The type of pixel connectivity used in determining how pixels
are grouped into a detected source. The options are 4 or 8
(default). 4-connected pixels touch along their edges.
8-connected pixels touch along their edges or corners. For
reference, SExtractor uses 8-connected pixels.
deblend : bool, optional
Whether to deblend overlapping sources. Source deblending
requires scikit-image.
Returns
-------
catalog : `~astropy.Table`
An astropy Table containing the source photometry and
morphologies.
"""
if not isinstance(model, DrizProductModel):
raise ValueError('The input model must be an DrizProductModel.')
# Remove until model.wht contains an IVM map
# Calculate "background-only" error assuming the weight image is an
# inverse-variance map (IVM). The weight image is clipped because it
# may contain zeros.
# bkg_error = np.sqrt(1.0 / np.clip(model.wht, 1.0e-20, 1.0e20))
# threshold = snr_threshold * bkg_error
# Estimate the 1-sigma noise in the image empirically because model.wht
# does not yet contain an IVM map
mask = (model.wht == 0)
data_mean, data_median, data_std = sigma_clipped_stats(
model.data, mask=mask, sigma=3.0, iters=10)
threshold = data_median + (data_std * snr_threshold)
sigma = kernel_fwhm * gaussian_fwhm_to_sigma
kernel = Gaussian2DKernel(sigma, x_size=kernel_xsize, y_size=kernel_ysize)
kernel.normalize()
segm = photutils.detect_sources(model.data, threshold, npixels=npixels,
filter_kernel=kernel,
connectivity=connectivity)
# source deblending requires scikit-image
if deblend:
segm = photutils.deblend_sources(model.data, segm, npixels=npixels,
filter_kernel=kernel,
nlevels=deblend_nlevels,
contrast=deblend_contrast,
mode=deblend_mode,
connectivity=connectivity,
relabel=True)
# Calculate total error, including source Poisson noise.
# This calculation assumes that the data and bkg_error images are in
# units of electron/s. Poisson noise is not included for pixels
# where data < 0.
exptime = model.meta.resample.product_exposure_time # total exptime
#total_error = np.sqrt(bkg_error**2 + np.maximum(model.data / exptime, 0))
total_error = np.sqrt(data_std**2 + np.maximum(model.data / exptime, 0))
wcs = model.get_fits_wcs()
source_props = photutils.source_properties(
model.data, segm, error=total_error, filter_kernel=kernel, wcs=wcs)
columns = ['id', 'xcentroid', 'ycentroid', 'ra_icrs_centroid',
'dec_icrs_centroid', 'area', 'source_sum',
'source_sum_err', 'semimajor_axis_sigma',
'semiminor_axis_sigma', 'orientation']
catalog = photutils.properties_table(source_props, columns=columns)
# convert orientation to degrees
catalog = QTable(catalog)
orient_deg = catalog['orientation'].to(u.deg)
catalog.replace_column('orientation', orient_deg)
# define orientation position angle
rot = _get_rotation(wcs)
catalog['orientation_sky'] = ((270. - rot +
catalog['orientation'].value) * u.deg)
# define AB mag and AB mag error
pixelarea = model.meta.photometry.pixelarea_arcsecsq
if pixelarea is None:
micro_Jy = 0.0
else:
micro_Jy = (catalog['source_sum'] *
model.meta.photometry.conversion_microjanskys *
model.meta.photometry.pixelarea_arcsecsq)
if micro_Jy > 0.0:
abmag = -2.5 * np.log10(micro_Jy) + 23.9
else:
abmag = 0.
catalog['abmag'] = abmag
# assuming SNR >> 1 (otherwise abmag_error is asymmetric):
catalog['abmag_error'] = (2.5 * np.log10(np.e) *
catalog['source_sum_err'] /
catalog['source_sum'])
return catalog