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Commit b6d51ba5 authored by David Rafferty's avatar David Rafferty
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Add weighting option

parent 8af260a7
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...@@ -58,7 +58,7 @@ def run(step, parset, LSM): ...@@ -58,7 +58,7 @@ def run(step, parset, LSM):
return result return result
def group(LSM, algorithm, targetFlux=None, numClusters=100, FWHM=None, def group(LSM, algorithm, targetFlux=None, weightBySize=False, numClusters=100, FWHM=None,
threshold=0.1, applyBeam=False, root='Patch', pad_index=False, method='mid', threshold=0.1, applyBeam=False, root='Patch', pad_index=False, method='mid',
facet="", byPatch=False, kernelSize=0.1, nIterations=100, lookDistance=0.2, facet="", byPatch=False, kernelSize=0.1, nIterations=100, lookDistance=0.2,
groupingDistance=0.01): groupingDistance=0.01):
...@@ -92,8 +92,13 @@ def group(LSM, algorithm, targetFlux=None, numClusters=100, FWHM=None, ...@@ -92,8 +92,13 @@ def group(LSM, algorithm, targetFlux=None, numClusters=100, FWHM=None,
own own
targetFlux : str or float, optional targetFlux : str or float, optional
Target flux for tessellation (the total flux of each tile will be close Target flux for tessellation (the total flux of each tile will be close
to this value). The target flux can be specified as either a float in Jy to this value) and voronoi algorithms. The target flux can be specified
or as a string with units (e.g., '25.0 mJy') as either a float in Jy or as a string with units (e.g., '25.0 mJy')
weightBySize : bool, optional
If True, fluxes are weighted by patch size (as mean size / size) when
the targetFlux criterion is applied. Patches with sizes below the mean
(flux-weighted) size are upweighted and those above the mean are
downweighted
numClusters : int, optional numClusters : int, optional
Number of clusters for clustering. Sources are grouped around the Number of clusters for clustering. Sources are grouped around the
numClusters brightest sources numClusters brightest sources
...@@ -252,6 +257,11 @@ def group(LSM, algorithm, targetFlux=None, numClusters=100, FWHM=None, ...@@ -252,6 +257,11 @@ def group(LSM, algorithm, targetFlux=None, numClusters=100, FWHM=None,
dirs_names = [] dirs_names = []
names = LSM.getPatchNames() names = LSM.getPatchNames()
fluxes = LSM.getColValues('I', aggregate='sum', units=units, applyBeam=applyBeam) fluxes = LSM.getColValues('I', aggregate='sum', units=units, applyBeam=applyBeam)
if weightBySize:
sizes = LSM.getPatchSizes(weight=True, applyBeam=applyBeam)
meanSize = np.mean(sizes)
weights = meanSize / sizes
fluxes *= weights
for name, flux in zip(names, fluxes): for name, flux in zip(names, fluxes):
if flux >= targetFlux: if flux >= targetFlux:
dirs_names.append(name) dirs_names.append(name)
......
...@@ -1986,7 +1986,7 @@ class SkyModel(object): ...@@ -1986,7 +1986,7 @@ class SkyModel(object):
operations.remove.remove(self, filterExpression, aggregate=aggregate, operations.remove.remove(self, filterExpression, aggregate=aggregate,
applyBeam=applyBeam, useRegEx=useRegEx, force=force) applyBeam=applyBeam, useRegEx=useRegEx, force=force)
def group(self, algorithm, targetFlux=None, numClusters=100, FWHM=None, def group(self, algorithm, targetFlux=None, weightBySize=False, numClusters=100, FWHM=None,
threshold=0.1, applyBeam=False, root='Patch', pad_index=False, threshold=0.1, applyBeam=False, root='Patch', pad_index=False,
method='mid', facet="", byPatch=False, kernelSize=0.1, method='mid', facet="", byPatch=False, kernelSize=0.1,
nIterations=100, lookDistance=0.2, groupingDistance=0.01): nIterations=100, lookDistance=0.2, groupingDistance=0.01):
...@@ -2000,17 +2000,17 @@ class SkyModel(object): ...@@ -2000,17 +2000,17 @@ class SkyModel(object):
algorithm : str algorithm : str
Algorithm to use for grouping: Algorithm to use for grouping:
- 'single' => all sources are grouped into a single patch - 'single' => all sources are grouped into a single patch
- 'every' => every source gets a separate patch - 'every' => every source gets a separate patch named 'source_patch'
- 'cluster' => SAGECAL clustering algorithm that groups sources into - 'cluster' => SAGECAL clustering algorithm that groups sources into
specified number of clusters (specified by the numClusters parameter). specified number of clusters (specified by the numClusters parameter)
- 'tessellate' => group into tiles whose total flux approximates - 'tessellate' => group into tiles whose total flux approximates
the target flux (specified by the targetFlux parameter). the target flux (specified by the targetFlux parameter)
- 'threshold' => group by convolving the sky model with a Gaussian beam - 'threshold' => group by convolving the sky model with a Gaussian beam
and then thresholding to find islands of emission (NOTE: all sources and then thresholding to find islands of emission (NOTE: all sources
are currently considered to be point sources of flux unity) are currently considered to be point sources of flux unity)
- 'facet' => group by facets using as an input a fits file. It requires - 'facet' => group by facets using as an input a fits file. It requires
the use of the additional parameter 'facet' to enter the name of the the use of the additional parameter 'facet' to enter the name of the
fits file (NOTE: This method is experimental). fits file.
- 'voronoi' => given a previously grouped sky model, voronoi tesselate - 'voronoi' => given a previously grouped sky model, voronoi tesselate
using the patch positions for patches above the target flux (specified using the patch positions for patches above the target flux (specified
by the targetFlux parameter) by the targetFlux parameter)
...@@ -2020,8 +2020,13 @@ class SkyModel(object): ...@@ -2020,8 +2020,13 @@ class SkyModel(object):
own own
targetFlux : str or float, optional targetFlux : str or float, optional
Target flux for tessellation (the total flux of each tile will be close Target flux for tessellation (the total flux of each tile will be close
to this value). The target flux can be specified as either a float in Jy to this value) and voronoi algorithms. The target flux can be specified
or as a string with units (e.g., '25.0 mJy') as either a float in Jy or as a string with units (e.g., '25.0 mJy')
weightBySize : bool, optional
If True, fluxes are weighted by patch size (as mean size / size) when
the targetFlux criterion is applied. Patches with sizes below the mean
(flux-weighted) size are upweighted and those above the mean are
downweighted
numClusters : int, optional numClusters : int, optional
Number of clusters for clustering. Sources are grouped around the Number of clusters for clustering. Sources are grouped around the
numClusters brightest sources. numClusters brightest sources.
...@@ -2070,7 +2075,7 @@ class SkyModel(object): ...@@ -2070,7 +2075,7 @@ class SkyModel(object):
>>> s.group('tessellate', targetFlux=30.0) >>> s.group('tessellate', targetFlux=30.0)
""" """
operations.group.group(self, algorithm, targetFlux=targetFlux, operations.group.group(self, algorithm, targetFlux=targetFlux, weightBySize=weightBySize
numClusters=numClusters, FWHM=FWHM, threshold=threshold, numClusters=numClusters, FWHM=FWHM, threshold=threshold,
applyBeam=applyBeam, root=root, pad_index=pad_index, applyBeam=applyBeam, root=root, pad_index=pad_index,
method=method, facet=facet, byPatch=byPatch, method=method, facet=facet, byPatch=byPatch,
......
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