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#!/usr/bin/env python 

# 

# LSST Data Management System 

# Copyright 2008-2015 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <http://www.lsstcorp.org/LegalNotices/>. 

# 

 

import numpy as np 

 

import lsst.geom 

import lsst.afw.table 

import lsst.afw.image 

import lsst.afw.detection 

import lsst.afw.geom 

import lsst.pex.exceptions 

 

from .sfm import SingleFrameMeasurementTask 

from .forcedMeasurement import ForcedMeasurementTask 

from . import CentroidResultKey 

 

__all__ = ("BlendContext", "TestDataset", "AlgorithmTestCase", "TransformTestCase", 

"SingleFramePluginTransformSetupHelper", "ForcedPluginTransformSetupHelper", 

"FluxTransformTestCase", "CentroidTransformTestCase") 

 

 

class BlendContext: 

"""! 

A Python context manager used to add multiple overlapping sources along with a parent source 

that represents all of them together. 

 

This is used as the return value for TestDataset.addBlend(), and this is the only way it should 

be used. The only public method is addChild(). 

""" 

 

def __init__(self, owner): 

self.owner = owner 

self.parentRecord = self.owner.catalog.addNew() 

self.parentImage = lsst.afw.image.ImageF(self.owner.exposure.getBBox()) 

self.children = [] 

 

def __enter__(self): 

# BlendContext is its own context manager, so we just return self. 

return self 

 

def addChild(self, instFlux, centroid, shape=None): 

"""! 

Add a child source to the blend, and return the truth catalog record that corresponds to it. 

 

@param[in] instFlux Total instFlux of the source to be added. 

@param[in] centroid Position of the source to be added (lsst.geom.Point2D). 

@param[in] shape 2nd moments of the source before PSF convolution 

(lsst.afw.geom.Quadrupole). Note that the truth catalog 

records post-convolution moments) 

""" 

record, image = self.owner.addSource(instFlux, centroid, shape) 

record.set(self.owner.keys["parent"], self.parentRecord.getId()) 

self.parentImage += image 

self.children.append((record, image)) 

return record 

 

def __exit__(self, type_, value, tb): 

# We're not using the context manager for any kind of exception safety or guarantees; 

# we just want the nice "with" statement syntax. 

if type_ is not None: # exception was raised; just skip all this and let it propagate 

return 

# On exit, we need to compute and set the truth values for the parent object. 

self.parentRecord.set(self.owner.keys["nChild"], len(self.children)) 

# Compute instFlux from sum of component fluxes 

instFlux = 0.0 

for record, image in self.children: 

instFlux += record.get(self.owner.keys["instFlux"]) 

self.parentRecord.set(self.owner.keys["instFlux"], instFlux) 

# Compute centroid from instFlux-weighted mean of component centroids 

x = 0.0 

y = 0.0 

for record, image in self.children: 

w = record.get(self.owner.keys["instFlux"])/instFlux 

x += record.get(self.owner.keys["centroid"].getX())*w 

y += record.get(self.owner.keys["centroid"].getY())*w 

self.parentRecord.set(self.owner.keys["centroid"], lsst.geom.Point2D(x, y)) 

# Compute shape from instFlux-weighted mean of offset component shapes 

xx = 0.0 

yy = 0.0 

xy = 0.0 

for record, image in self.children: 

w = record.get(self.owner.keys["instFlux"])/instFlux 

dx = record.get(self.owner.keys["centroid"].getX()) - x 

dy = record.get(self.owner.keys["centroid"].getY()) - y 

xx += (record.get(self.owner.keys["shape"].getIxx()) + dx**2)*w 

yy += (record.get(self.owner.keys["shape"].getIyy()) + dy**2)*w 

xy += (record.get(self.owner.keys["shape"].getIxy()) + dx*dy)*w 

self.parentRecord.set(self.owner.keys["shape"], lsst.afw.geom.Quadrupole(xx, yy, xy)) 

# Run detection on the parent image to get the parent Footprint. 

self.owner._installFootprint(self.parentRecord, self.parentImage) 

# Create perfect HeavyFootprints for all children; these will need to be modified later to account 

# for the noise we'll add to the image. 

deblend = lsst.afw.image.MaskedImageF(self.owner.exposure.getMaskedImage(), True) 

for record, image in self.children: 

deblend.getImage().getArray()[:, :] = image.getArray() 

heavyFootprint = lsst.afw.detection.HeavyFootprintF(self.parentRecord.getFootprint(), deblend) 

record.setFootprint(heavyFootprint) 

 

 

class TestDataset: 

"""! 

A simulated dataset consisting of a test image and an associated truth catalog. 

 

TestDataset creates an idealized image made of pure Gaussians (including a Gaussian PSF), 

with simple noise and idealized Footprints/HeavyFootprints that simulated the outputs 

of detection and deblending. Multiple noise realizations can be created from the same 

underlying sources, allowing uncertainty estimates to be verified via Monte Carlo. 

 

Typical usage: 

@code 

bbox = lsst.geom.Box2I(lsst.geom.Point2I(0,0), lsst.geom.Point2I(100, 100)) 

dataset = TestDataset(bbox) 

dataset.addSource(instFlux=1E5, centroid=lsst.geom.Point2D(25, 26)) 

dataset.addSource(instFlux=2E5, centroid=lsst.geom.Point2D(75, 24), 

shape=lsst.afw.geom.Quadrupole(8, 7, 2)) 

with dataset.addBlend() as family: 

family.addChild(instFlux=2E5, centroid=lsst.geom.Point2D(50, 72)) 

family.addChild(instFlux=1.5E5, centroid=lsst.geom.Point2D(51, 74)) 

exposure, catalog = dataset.realize(noise=100.0, schema=TestDataset.makeMinimalSchema()) 

@endcode 

""" 

 

@classmethod 

def makeMinimalSchema(cls): 

"""Return the minimal schema needed to hold truth catalog fields. 

 

When TestDataset.realize() is called, the schema must include at least these fields. 

Usually it will include additional fields for measurement algorithm outputs, allowing 

the same catalog to be used for both truth values (the fields from the minimal schema) 

and the measurements. 

""" 

if not hasattr(cls, "_schema"): 

schema = lsst.afw.table.SourceTable.makeMinimalSchema() 

cls.keys = {} 

cls.keys["parent"] = schema.find("parent").key 

cls.keys["nChild"] = schema.addField("deblend_nChild", type=np.int32) 

cls.keys["instFlux"] = schema.addField("truth_instFlux", type=np.float64, 

doc="true instFlux", units="count") 

cls.keys["centroid"] = lsst.afw.table.Point2DKey.addFields( 

schema, "truth", "true simulated centroid", "pixel" 

) 

cls.keys["centroid_sigma"] = lsst.afw.table.CovarianceMatrix2fKey.addFields( 

schema, "truth", ['x', 'y'], "pixel" 

) 

cls.keys["centroid_flag"] = schema.addField("truth_flag", type="Flag", 

doc="set if the object is a star") 

cls.keys["shape"] = lsst.afw.table.QuadrupoleKey.addFields( 

schema, "truth", "true shape after PSF convolution", lsst.afw.table.CoordinateType.PIXEL 

) 

cls.keys["isStar"] = schema.addField("truth_isStar", type="Flag", 

doc="set if the object is a star") 

schema.getAliasMap().set("slot_Shape", "truth") 

schema.getAliasMap().set("slot_Centroid", "truth") 

schema.getAliasMap().set("slot_ModelFlux", "truth") 

schema.getCitizen().markPersistent() 

cls._schema = schema 

schema = lsst.afw.table.Schema(cls._schema) 

schema.disconnectAliases() 

return schema 

 

@staticmethod 

def makePerturbedWcs(oldWcs, minScaleFactor=1.2, maxScaleFactor=1.5, 

minRotation=None, maxRotation=None, 

minRefShift=None, maxRefShift=None, 

minPixShift=2.0, maxPixShift=4.0, randomSeed=1): 

"""! 

Create a new undistorted TAN WCS that is similar but not identical to another, with random 

scaling, rotation, and offset (in both pixel position and reference position). 

 

The maximum and minimum arguments are interpreted as absolute values for a split 

range that covers both positive and negative values (as this method is used 

in testing, it is typically most important to avoid perturbations near zero). 

Scale factors are treated somewhat differently: the actual scale factor is chosen between 

minScaleFactor and maxScaleFactor OR (1/maxScaleFactor) and (1/minScaleFactor). 

 

The default range for rotation is 30-60 degrees, and the default range for reference shift 

is 0.5-1.0 arcseconds (these cannot be safely included directly as default values because Angle 

objects are mutable). 

 

The random number generator is primed with the seed given. If ``None``, a seed is 

automatically chosen. 

""" 

random_state = np.random.RandomState(randomSeed) 

if minRotation is None: 

minRotation = 30.0*lsst.geom.degrees 

if maxRotation is None: 

maxRotation = 60.0*lsst.geom.degrees 

if minRefShift is None: 

minRefShift = 0.5*lsst.geom.arcseconds 

if maxRefShift is None: 

maxRefShift = 1.0*lsst.geom.arcseconds 

 

def splitRandom(min1, max1, min2=None, max2=None): 

if min2 is None: 

min2 = -max1 

if max2 is None: 

max2 = -min1 

if random_state.uniform() > 0.5: 

return float(random_state.uniform(min1, max1)) 

else: 

return float(random_state.uniform(min2, max2)) 

# Generate random perturbations 

scaleFactor = splitRandom(minScaleFactor, maxScaleFactor, 1.0/maxScaleFactor, 1.0/minScaleFactor) 

rotation = splitRandom(minRotation.asRadians(), maxRotation.asRadians())*lsst.geom.radians 

refShiftRa = splitRandom(minRefShift.asRadians(), maxRefShift.asRadians())*lsst.geom.radians 

refShiftDec = splitRandom(minRefShift.asRadians(), maxRefShift.asRadians())*lsst.geom.radians 

pixShiftX = splitRandom(minPixShift, maxPixShift) 

pixShiftY = splitRandom(minPixShift, maxPixShift) 

# Compute new CD matrix 

oldTransform = lsst.geom.LinearTransform(oldWcs.getCdMatrix()) 

rTransform = lsst.geom.LinearTransform.makeRotation(rotation) 

sTransform = lsst.geom.LinearTransform.makeScaling(scaleFactor) 

newTransform = oldTransform*rTransform*sTransform 

matrix = newTransform.getMatrix() 

# Compute new coordinate reference pixel (CRVAL) 

oldSkyOrigin = oldWcs.getSkyOrigin() 

newSkyOrigin = lsst.geom.SpherePoint(oldSkyOrigin.getRa() + refShiftRa, 

oldSkyOrigin.getDec() + refShiftDec) 

# Compute new pixel reference pixel (CRPIX) 

oldPixOrigin = oldWcs.getPixelOrigin() 

newPixOrigin = lsst.geom.Point2D(oldPixOrigin.getX() + pixShiftX, 

oldPixOrigin.getY() + pixShiftY) 

return lsst.afw.geom.makeSkyWcs(crpix=newPixOrigin, crval=newSkyOrigin, cdMatrix=matrix) 

 

@staticmethod 

def makeEmptyExposure(bbox, wcs=None, crval=None, cdelt=None, psfSigma=2.0, psfDim=17, fluxMag0=1E12): 

"""! 

Create an Exposure, with a Calib, Wcs, and Psf, but no pixel values set. 

 

@param[in] bbox Bounding box of the image (image coordinates) as returned by makeCatalog. 

@param[in] wcs New Wcs for the exposure (created from crval and cdelt if None). 

@param[in] crval afw.geom.SpherePoint: ICRS center of the TAN WCS attached to the image. 

@param[in] cdelt afw.geom.Angle: pixel scale of the image 

@param[in] psfSigma Radius (sigma) of the Gaussian PSF attached to the image 

@param[in] psfDim Width and height of the image's Gaussian PSF attached to the image 

@param[in] fluxMag0 Flux at magnitude zero (in e-) used to set the Calib of the exposure. 

""" 

if wcs is None: 

if crval is None: 

crval = lsst.geom.SpherePoint(45.0, 45.0, lsst.geom.degrees) 

if cdelt is None: 

cdelt = 0.2*lsst.geom.arcseconds 

crpix = lsst.geom.Box2D(bbox).getCenter() 

wcs = lsst.afw.geom.makeSkyWcs(crpix=crpix, crval=crval, 

cdMatrix=lsst.afw.geom.makeCdMatrix(scale=cdelt)) 

exposure = lsst.afw.image.ExposureF(bbox) 

psf = lsst.afw.detection.GaussianPsf(psfDim, psfDim, psfSigma) 

calib = lsst.afw.image.Calib() 

calib.setFluxMag0(fluxMag0) 

exposure.setWcs(wcs) 

exposure.setPsf(psf) 

exposure.setCalib(calib) 

return exposure 

 

@staticmethod 

def drawGaussian(bbox, instFlux, ellipse): 

"""! 

Create an image of an elliptical Gaussian. 

 

@param[in,out] bbox Bounding box of image to create. 

@param[in] instFlux Total instFlux of the Gaussian (normalized analytically, 

not using pixel values) 

@param[in] ellipse lsst.afw.geom.Ellipse holding the centroid and shape. 

""" 

x, y = np.meshgrid(np.arange(bbox.getBeginX(), bbox.getEndX()), 

np.arange(bbox.getBeginY(), bbox.getEndY())) 

t = ellipse.getGridTransform() 

xt = t[t.XX] * x + t[t.XY] * y + t[t.X] 

yt = t[t.YX] * x + t[t.YY] * y + t[t.Y] 

image = lsst.afw.image.ImageF(bbox) 

image.getArray()[:, :] = np.exp(-0.5*(xt**2 + yt**2))*instFlux/(2.0*ellipse.getCore().getArea()) 

return image 

 

def __init__(self, bbox, threshold=10.0, exposure=None, **kwds): 

"""! 

Initialize the dataset. 

 

@param[in] bbox Bounding box of the test image. 

@param[in] threshold Threshold absolute value used to determine footprints for 

simulated sources. This thresholding will be applied before noise is 

actually added to images (or before the noise level is even known), so 

this will necessarily produce somewhat artificial footprints. 

@param[in] exposure lsst.afw.image.ExposureF test sources should be added to. Ownership should 

be considered transferred from the caller to the TestDataset. 

Must have a Gaussian Psf for truth catalog shapes to be exact. 

@param[in] **kwds Keyword arguments forwarded to makeEmptyExposure if exposure is None. 

""" 

if exposure is None: 

exposure = self.makeEmptyExposure(bbox, **kwds) 

self.threshold = lsst.afw.detection.Threshold(threshold, lsst.afw.detection.Threshold.VALUE) 

self.exposure = exposure 

self.psfShape = self.exposure.getPsf().computeShape() 

self.schema = self.makeMinimalSchema() 

self.catalog = lsst.afw.table.SourceCatalog(self.schema) 

 

def _installFootprint(self, record, image): 

"""Create a Footprint for a simulated source and add it to its truth catalog record. 

""" 

# Run detection on the single-source image 

fpSet = lsst.afw.detection.FootprintSet(image, self.threshold) 

# the call below to the FootprintSet ctor is actually a grow operation 

fpSet = lsst.afw.detection.FootprintSet(fpSet, int(self.psfShape.getDeterminantRadius() + 1.0), True) 

# Update the full exposure's mask plane to indicate the detection 

fpSet.setMask(self.exposure.getMaskedImage().getMask(), "DETECTED") 

# Attach the new footprint to the exposure 

if len(fpSet.getFootprints()) > 1: 

raise RuntimeError("Threshold value results in multiple Footprints for a single object") 

if len(fpSet.getFootprints()) == 0: 

raise RuntimeError("Threshold value results in zero Footprints for object") 

record.setFootprint(fpSet.getFootprints()[0]) 

 

def addSource(self, instFlux, centroid, shape=None): 

"""! 

Add a source to the simulation 

 

@param[in] instFlux Total instFlux of the source to be added. 

@param[in] centroid Position of the source to be added (lsst.geom.Point2D). 

@param[in] shape 2nd moments of the source before PSF convolution 

(lsst.afw.geom.Quadrupole). Note that the truth catalog 

records post-convolution moments). If None, a point source 

will be added. 

 

@return a truth catalog record and single-source image corresponding to the new source. 

""" 

# Create and set the truth catalog fields 

record = self.catalog.addNew() 

record.set(self.keys["instFlux"], instFlux) 

record.set(self.keys["centroid"], centroid) 

covariance = np.random.normal(0, 0.1, 4).reshape(2, 2) 

covariance[0, 1] = covariance[1, 0] # CovarianceMatrixKey assumes symmetric x_y_Cov 

record.set(self.keys["centroid_sigma"], covariance.astype(np.float32)) 

if shape is None: 

record.set(self.keys["isStar"], True) 

fullShape = self.psfShape 

else: 

record.set(self.keys["isStar"], False) 

fullShape = shape.convolve(self.psfShape) 

record.set(self.keys["shape"], fullShape) 

# Create an image containing just this source 

image = self.drawGaussian(self.exposure.getBBox(), instFlux, 

lsst.afw.geom.Ellipse(fullShape, centroid)) 

# Generate a footprint for this source 

self._installFootprint(record, image) 

# Actually add the source to the full exposure 

self.exposure.getMaskedImage().getImage().getArray()[:, :] += image.getArray() 

return record, image 

 

def addBlend(self): 

"""! 

Return a context manager that allows a blend of multiple sources to be added. 

 

Example: 

@code 

d = TestDataset(...) 

with d.addBlend() as b: 

b.addChild(flux1, centroid1) 

b.addChild(flux2, centroid2, shape2) 

@endcode 

 

Note that nothing stops you from creating overlapping sources just using the addSource() method, 

but addBlend() is necesssary to create a parent object and deblended HeavyFootprints of the type 

produced by the detection and deblending pipelines. 

""" 

return BlendContext(self) 

 

def transform(self, wcs, **kwds): 

"""! 

Create a copy of the dataset transformed to a new WCS, with new Psf and Calib. 

 

@param[in] wcs Wcs for the new dataset. 

@param[in] **kwds Additional keyword arguments passed on to makeEmptyExposure. If not 

specified, these revert to the defaults for makeEmptyExposure, not the 

values in the current dataset. 

""" 

bboxD = lsst.geom.Box2D() 

xyt = lsst.afw.geom.makeWcsPairTransform(self.exposure.getWcs(), wcs) 

for corner in lsst.geom.Box2D(self.exposure.getBBox()).getCorners(): 

bboxD.include(xyt.applyForward(lsst.geom.Point2D(corner))) 

bboxI = lsst.geom.Box2I(bboxD) 

result = TestDataset(bbox=bboxI, wcs=wcs, **kwds) 

oldCalib = self.exposure.getCalib() 

newCalib = result.exposure.getCalib() 

oldPsfShape = self.exposure.getPsf().computeShape() 

for record in self.catalog: 

if record.get(self.keys["nChild"]): 

raise NotImplementedError("Transforming blended sources in TestDatasets is not supported") 

magnitude = oldCalib.getMagnitude(record.get(self.keys["instFlux"])) 

newFlux = newCalib.getFlux(magnitude) 

oldCentroid = record.get(self.keys["centroid"]) 

newCentroid = xyt.applyForward(oldCentroid) 

if record.get(self.keys["isStar"]): 

newDeconvolvedShape = None 

else: 

affine = lsst.afw.geom.linearizeTransform(xyt, oldCentroid) 

oldFullShape = record.get(self.keys["shape"]) 

oldDeconvolvedShape = lsst.afw.geom.Quadrupole( 

oldFullShape.getIxx() - oldPsfShape.getIxx(), 

oldFullShape.getIyy() - oldPsfShape.getIyy(), 

oldFullShape.getIxy() - oldPsfShape.getIxy(), 

False 

) 

newDeconvolvedShape = oldDeconvolvedShape.transform(affine.getLinear()) 

result.addSource(newFlux, newCentroid, newDeconvolvedShape) 

return result 

 

def realize(self, noise, schema, randomSeed=1): 

"""! 

Create a simulated with noise and a simulated post-detection catalog with (Heavy)Footprints. 

 

@param[in] noise Standard deviation of noise to be added to the exposure. The noise will be 

Gaussian and constant, appropriate for the sky-limited regime. 

@param[in] schema Schema of the new catalog to be created. Must start with self.schema (i.e. 

schema.contains(self.schema) must be True), but typically contains fields for 

already-configured measurement algorithms as well. 

@param[in] randomSeed Seed for the random number generator. If None, a seed is chosen automatically. 

 

@return a tuple of (exposure, catalog) 

""" 

random_state = np.random.RandomState(randomSeed) 

assert schema.contains(self.schema) 

mapper = lsst.afw.table.SchemaMapper(self.schema) 

mapper.addMinimalSchema(self.schema, True) 

exposure = self.exposure.clone() 

exposure.getMaskedImage().getVariance().getArray()[:, :] = noise**2 

exposure.getMaskedImage().getImage().getArray()[:, :] \ 

+= random_state.randn(exposure.getHeight(), exposure.getWidth())*noise 

catalog = lsst.afw.table.SourceCatalog(schema) 

catalog.extend(self.catalog, mapper=mapper) 

# Loop over sources and generate new HeavyFootprints that divide up the noisy pixels, not the 

# ideal no-noise pixels. 

for record in catalog: 

# parent objects have non-Heavy Footprints, which don't need to be updated after adding noise. 

if record.getParent() == 0: 

continue 

# get flattened arrays that correspond to the no-noise and noisy parent images 

parent = catalog.find(record.getParent()) 

footprint = parent.getFootprint() 

parentFluxArrayNoNoise = np.zeros(footprint.getArea(), dtype=np.float32) 

footprint.spans.flatten(parentFluxArrayNoNoise, 

self.exposure.getMaskedImage().getImage().getArray(), 

self.exposure.getXY0()) 

parentFluxArrayNoisy = np.zeros(footprint.getArea(), dtype=np.float32) 

footprint.spans.flatten(parentFluxArrayNoisy, 

exposure.getMaskedImage().getImage().getArray(), 

exposure.getXY0()) 

oldHeavy = record.getFootprint() 

fraction = (oldHeavy.getImageArray() / parentFluxArrayNoNoise) 

# n.b. this isn't a copy ctor - it's a copy from a vanilla Footprint, so it doesn't copy 

# the arrays we don't want to change, and hence we have to do that ourselves below. 

newHeavy = lsst.afw.detection.HeavyFootprintF(oldHeavy) 

newHeavy.getImageArray()[:] = parentFluxArrayNoisy*fraction 

newHeavy.getMaskArray()[:] = oldHeavy.getMaskArray() 

newHeavy.getVarianceArray()[:] = oldHeavy.getVarianceArray() 

record.setFootprint(newHeavy) 

return exposure, catalog 

 

 

class AlgorithmTestCase: 

 

def makeSingleFrameMeasurementConfig(self, plugin=None, dependencies=()): 

"""Convenience function to create a Config instance for SingleFrameMeasurementTask 

 

The plugin and its dependencies will be the only plugins run, while the Centroid, Shape, 

and ModelFlux slots will be set to the truth fields generated by the TestDataset class. 

""" 

config = SingleFrameMeasurementTask.ConfigClass() 

config.slots.centroid = "truth" 

config.slots.shape = "truth" 

config.slots.modelFlux = None 

config.slots.apFlux = None 

config.slots.psfFlux = None 

config.slots.gaussianFlux = None 

config.slots.calibFlux = None 

config.plugins.names = (plugin,) + tuple(dependencies) 

return config 

 

def makeSingleFrameMeasurementTask(self, plugin=None, dependencies=(), config=None, schema=None, 

algMetadata=None): 

"""Convenience function to create a SingleFrameMeasurementTask with a simple configuration. 

""" 

if config is None: 

if plugin is None: 

raise ValueError("Either plugin or config argument must not be None") 

config = self.makeSingleFrameMeasurementConfig(plugin=plugin, dependencies=dependencies) 

if schema is None: 

schema = TestDataset.makeMinimalSchema() 

# Clear all aliases so only those defined by config are set. 

schema.setAliasMap(None) 

if algMetadata is None: 

algMetadata = lsst.daf.base.PropertyList() 

return SingleFrameMeasurementTask(schema=schema, algMetadata=algMetadata, config=config) 

 

def makeForcedMeasurementConfig(self, plugin=None, dependencies=()): 

"""Convenience function to create a Config instance for ForcedMeasurementTask 

 

In addition to the plugins specified in the plugin and dependencies arguments, 

the TransformedCentroid and TransformedShape plugins will be run and used as the 

Centroid and Shape slots; these simply transform the reference catalog centroid 

and shape to the measurement coordinate system. 

""" 

config = ForcedMeasurementTask.ConfigClass() 

config.slots.centroid = "base_TransformedCentroid" 

config.slots.shape = "base_TransformedShape" 

config.slots.modelFlux = None 

config.slots.apFlux = None 

config.slots.psfFlux = None 

config.slots.gaussianFlux = None 

config.plugins.names = (plugin,) + tuple(dependencies) + ("base_TransformedCentroid", 

"base_TransformedShape") 

return config 

 

def makeForcedMeasurementTask(self, plugin=None, dependencies=(), config=None, refSchema=None, 

algMetadata=None): 

"""Convenience function to create a ForcedMeasurementTask with a simple configuration. 

""" 

if config is None: 

if plugin is None: 

raise ValueError("Either plugin or config argument must not be None") 

config = self.makeForcedMeasurementConfig(plugin=plugin, dependencies=dependencies) 

if refSchema is None: 

refSchema = TestDataset.makeMinimalSchema() 

if algMetadata is None: 

algMetadata = lsst.daf.base.PropertyList() 

return ForcedMeasurementTask(refSchema=refSchema, algMetadata=algMetadata, config=config) 

 

 

class TransformTestCase: 

"""! 

Base class for testing measurement transformations. 

 

We test both that the transform itself operates successfully (fluxes are 

converted to magnitudes, flags are propagated properly) and that the 

transform is registered as the default for the appropriate measurement 

algorithms. 

 

In the simple case of one-measurement-per-transformation, the developer 

need not directly write any tests themselves: simply customizing the class 

variables is all that is required. More complex measurements (e.g. 

multiple aperture fluxes) require extra effort. 

""" 

# The name used for the measurement algorithm; determines the names of the 

# fields in the resulting catalog. This default should generally be fine, 

# but subclasses can override if required. 

name = "MeasurementTransformTest" 

 

# These should be customized by subclassing. 

controlClass = None 

algorithmClass = None 

transformClass = None 

 

# Flags which may be set by the algorithm being tested. Can be customized 

# in subclasses. 

flagNames = ("flag",) 

 

# The plugin being tested should be registered under these names for 

# single frame and forced measurement. Should be customized by 

# subclassing. 

singleFramePlugins = () 

forcedPlugins = () 

 

def setUp(self): 

bbox = lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Point2I(200, 200)) 

self.calexp = TestDataset.makeEmptyExposure(bbox) 

self._setupTransform() 

 

def tearDown(self): 

del self.calexp 

del self.inputCat 

del self.mapper 

del self.transform 

del self.outputCat 

 

def _populateCatalog(self, baseNames): 

records = [] 

for flagValue in (True, False): 

records.append(self.inputCat.addNew()) 

for baseName in baseNames: 

for flagName in self.flagNames: 

if records[-1].schema.join(baseName, flagName) in records[-1].schema: 

records[-1].set(records[-1].schema.join(baseName, flagName), flagValue) 

self._setFieldsInRecords(records, baseName) 

 

def _checkOutput(self, baseNames): 

for inSrc, outSrc in zip(self.inputCat, self.outputCat): 

for baseName in baseNames: 

self._compareFieldsInRecords(inSrc, outSrc, baseName) 

for flagName in self.flagNames: 

keyName = outSrc.schema.join(baseName, flagName) 

if keyName in inSrc.schema: 

self.assertEqual(outSrc.get(keyName), inSrc.get(keyName)) 

else: 

self.assertFalse(keyName in outSrc.schema) 

 

def _runTransform(self, doExtend=True): 

if doExtend: 

self.outputCat.extend(self.inputCat, mapper=self.mapper) 

self.transform(self.inputCat, self.outputCat, self.calexp.getWcs(), self.calexp.getCalib()) 

 

def testTransform(self, baseNames=None): 

""" 

Test the operation of the transformation on a catalog containing random data. 

 

We check that: 

 

* An appropriate exception is raised on an attempt to transform between catalogs with different 

numbers of rows; 

* Otherwise, all appropriate conversions are properly appled and that flags have been propagated. 

 

The `baseNames` argument requires some explanation. This should be an iterable of the leading parts of 

the field names for each measurement; that is, everything that appears before `_instFlux`, `_flag`, 

etc. In the simple case of a single measurement per plugin, this is simply equal to `self.name` (thus 

measurements are stored as `self.name + "_instFlux"`, etc). More generally, the developer may specify 

whatever iterable they require. For example, to handle multiple apertures, we could have 

`(self.name + "_0", self.name + "_1", ...)`. 

 

@param[in] baseNames Iterable of the initial parts of measurement field names. 

""" 

baseNames = baseNames or [self.name] 

self._populateCatalog(baseNames) 

self.assertRaises(lsst.pex.exceptions.LengthError, self._runTransform, False) 

self._runTransform() 

self._checkOutput(baseNames) 

 

def _checkRegisteredTransform(self, registry, name): 

# If this is a Python-based transform, we can compare directly; if 

# it's wrapped C++, we need to compare the wrapped class. 

self.assertEqual(registry[name].PluginClass.getTransformClass(), self.transformClass) 

 

def testRegistration(self): 

""" 

Test that the transformation is appropriately registered with the relevant measurement algorithms. 

""" 

for pluginName in self.singleFramePlugins: 

self._checkRegisteredTransform(lsst.meas.base.SingleFramePlugin.registry, pluginName) 

for pluginName in self.forcedPlugins: 

self._checkRegisteredTransform(lsst.meas.base.ForcedPlugin.registry, pluginName) 

 

 

class SingleFramePluginTransformSetupHelper: 

 

def _setupTransform(self): 

self.control = self.controlClass() 

inputSchema = lsst.afw.table.SourceTable.makeMinimalSchema() 

# Trick algorithms that depend on the slot centroid or alias into thinking they've been defined; 

# it doesn't matter for this test since we won't actually use the plugins for anything besides 

# defining the schema. 

inputSchema.getAliasMap().set("slot_Centroid", "dummy") 

inputSchema.getAliasMap().set("slot_Shape", "dummy") 

self.algorithmClass(self.control, self.name, inputSchema) 

inputSchema.getAliasMap().erase("slot_Centroid") 

inputSchema.getAliasMap().erase("slot_Shape") 

self.inputCat = lsst.afw.table.SourceCatalog(inputSchema) 

self.mapper = lsst.afw.table.SchemaMapper(inputSchema) 

self.transform = self.transformClass(self.control, self.name, self.mapper) 

self.outputCat = lsst.afw.table.BaseCatalog(self.mapper.getOutputSchema()) 

 

 

class ForcedPluginTransformSetupHelper: 

 

def _setupTransform(self): 

self.control = self.controlClass() 

inputMapper = lsst.afw.table.SchemaMapper(lsst.afw.table.SourceTable.makeMinimalSchema(), 

lsst.afw.table.SourceTable.makeMinimalSchema()) 

# Trick algorithms that depend on the slot centroid or alias into thinking they've been defined; 

# it doesn't matter for this test since we won't actually use the plugins for anything besides 

# defining the schema. 

inputMapper.editOutputSchema().getAliasMap().set("slot_Centroid", "dummy") 

inputMapper.editOutputSchema().getAliasMap().set("slot_Shape", "dummy") 

self.algorithmClass(self.control, self.name, inputMapper, lsst.daf.base.PropertyList()) 

inputMapper.editOutputSchema().getAliasMap().erase("slot_Centroid") 

inputMapper.editOutputSchema().getAliasMap().erase("slot_Shape") 

self.inputCat = lsst.afw.table.SourceCatalog(inputMapper.getOutputSchema()) 

self.mapper = lsst.afw.table.SchemaMapper(inputMapper.getOutputSchema()) 

self.transform = self.transformClass(self.control, self.name, self.mapper) 

self.outputCat = lsst.afw.table.BaseCatalog(self.mapper.getOutputSchema()) 

 

 

class FluxTransformTestCase(TransformTestCase): 

 

def _setFieldsInRecords(self, records, name): 

for record in records: 

record[record.schema.join(name, 'instFlux')] = np.random.random() 

record[record.schema.join(name, 'instFluxErr')] = np.random.random() 

 

# Negative instFluxes should be converted to NaNs. 

assert len(records) > 1 

records[0][record.schema.join(name, 'instFlux')] = -1 

 

def _compareFieldsInRecords(self, inSrc, outSrc, name): 

instFluxName = inSrc.schema.join(name, 'instFlux') 

instFluxErrName = inSrc.schema.join(name, 'instFluxErr') 

if inSrc[instFluxName] > 0: 

mag, magErr = self.calexp.getCalib().getMagnitude(inSrc[instFluxName], inSrc[instFluxErrName]) 

self.assertEqual(outSrc[outSrc.schema.join(name, 'mag')], mag) 

self.assertEqual(outSrc[outSrc.schema.join(name, 'magErr')], magErr) 

else: 

self.assertTrue(np.isnan(outSrc[outSrc.schema.join(name, 'mag')])) 

self.assertTrue(np.isnan(outSrc[outSrc.schema.join(name, 'magErr')])) 

 

 

class CentroidTransformTestCase(TransformTestCase): 

 

def _setFieldsInRecords(self, records, name): 

for record in records: 

record[record.schema.join(name, 'x')] = np.random.random() 

record[record.schema.join(name, 'y')] = np.random.random() 

# Some algorithms set no errors; some set only sigma on x & y; some provide 

# a full covariance matrix. Set only those which exist in the schema. 

for fieldSuffix in ('xErr', 'yErr', 'x_y_Cov'): 

fieldName = record.schema.join(name, fieldSuffix) 

if fieldName in record.schema: 

record[fieldName] = np.random.random() 

 

def _compareFieldsInRecords(self, inSrc, outSrc, name): 

centroidResultKey = CentroidResultKey(inSrc.schema[self.name]) 

centroidResult = centroidResultKey.get(inSrc) 

 

coord = lsst.afw.table.CoordKey(outSrc.schema[self.name]).get(outSrc) 

coordTruth = self.calexp.getWcs().pixelToSky(centroidResult.getCentroid()) 

self.assertEqual(coordTruth, coord) 

 

# If the centroid has an associated uncertainty matrix, the coordinate 

# must have one too, and vice versa. 

try: 

coordErr = lsst.afw.table.CovarianceMatrix2fKey(outSrc.schema[self.name], 

["ra", "dec"]).get(outSrc) 

except lsst.pex.exceptions.NotFoundError: 

self.assertFalse(centroidResultKey.getCentroidErr().isValid()) 

else: 

transform = self.calexp.getWcs().linearizePixelToSky(coordTruth, lsst.geom.radians) 

coordErrTruth = np.dot(np.dot(transform.getLinear().getMatrix(), 

centroidResult.getCentroidErr()), 

transform.getLinear().getMatrix().transpose()) 

np.testing.assert_array_almost_equal(np.array(coordErrTruth), coordErr)