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# 

# LSST Data Management System 

# Copyright 2008-2017 LSST/AURA 

# 

# 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/>. 

# 

 

""" 

Tests for InputCounts measurement algorithm 

""" 

import numpy as np 

import itertools 

from collections import namedtuple 

 

import unittest 

import lsst.utils.tests 

 

import lsst.geom 

import lsst.afw.detection as afwDetection 

import lsst.afw.geom as afwGeom 

import lsst.afw.image as afwImage 

import lsst.afw.table as afwTable 

import lsst.meas.base as measBase 

 

 

try: 

display 

import matplotlib.pyplot as plt 

import matplotlib.patches as patches 

except NameError: 

display = False 

 

 

def ccdVennDiagram(exp, showImage=True, legendLocation='best'): 

''' 

Create a figure with the bounding boxes for each of the images which go into a coadd, 

over-plotting the given exposure object. 

@param[in] exp (Exposure) The exposure object to plot, must be the product of a coadd 

Optional: 

@param[in] showImage (Bool) Plot image data in addition to it's bounding box, default True 

@param[in] legendLocation (String) Matplotlib legend location code, can be: 'best', 'upper right', 

'upper left', 'lower left', 'lower right', 'right', center left', 

'center right', 'lower center', 'upper center', 'center' 

''' 

# Create the figure object 

fig = plt.figure() 

# Use all the built in matplotib line style attributes to create a list of the possible styles 

linestyles = ['solid', 'dashed', 'dashdot', 'dotted'] 

colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] 

# Calculate the cartisian product of the styles, and randomize the order, to help each CCD get 

# it's own color 

pcomb = np.random.permutation(list(itertools.product(colors, linestyles))) 

# Filter out a black solid box, as that will be the style of the given exp object 

pcomb = pcomb[((pcomb[:, 0] == 'k') * (pcomb[:, 1] == 'solid')) is False] 

# Get the image properties 

origin = lsst.geom.PointD(exp.getXY0()) 

mainBox = exp.getBBox().getCorners() 

# Plot the exposure 

plt.gca().add_patch(patches.Rectangle((0, 0), *list(mainBox[2]-mainBox[0]), fill=False, label="exposure")) 

# Grab all of the CCDs that went into creating the exposure 

ccds = exp.getInfo().getCoaddInputs().ccds 

# Loop over and plot the extents of each ccd 

for i, ccd in enumerate(ccds): 

ccdBox = lsst.geom.Box2D(ccd.getBBox()) 

ccdCorners = ccdBox.getCorners() 

coaddCorners = [exp.getWcs().skyToPixel(ccd.getWcs().pixelToSky(point)) + 

(lsst.geom.PointD() - origin) for point in ccdCorners] 

plt.gca().add_patch(patches.Rectangle(coaddCorners[0], *list(coaddCorners[2]-coaddCorners[0]), 

fill=False, color=pcomb[i][0], ls=pcomb[i][1], 

label="CCD{}".format(i))) 

# If showImage is true, plot the data contained in exp as well as the boundrys 

if showImage: 

plt.imshow(exp.getMaskedImage().getArrays()[0], cmap='Greys', origin='lower') 

plt.colorbar() 

# Adjust plot parameters and plot 

plt.gca().relim() 

plt.gca().autoscale_view() 

ylim = plt.gca().get_ylim() 

xlim = plt.gca().get_xlim() 

plt.gca().set_ylim(1.5*ylim[0], 1.5*ylim[1]) 

plt.gca().set_xlim(1.5*xlim[0], 1.5*xlim[1]) 

plt.legend(loc=legendLocation) 

fig.canvas.draw() 

plt.show() 

 

 

class InputCountTest(lsst.utils.tests.TestCase): 

 

def testInputCounts(self, showPlot=False): 

# Generate a simulated coadd of four overlapping-but-offset CCDs. 

# Populate it with three sources. 

# Demonstrate that we can correctly recover the number of images which 

# contribute to each source. 

 

size = 20 # Size of images (pixels) 

value = 100.0 # Source flux 

 

ccdPositions = [ 

lsst.geom.Point2D(8, 0), 

lsst.geom.Point2D(10, 10), 

lsst.geom.Point2D(-8, -8), 

lsst.geom.Point2D(-8, 8) 

] 

 

# Represent sources by a tuple of position and expected number of 

# contributing CCDs (based on the size/positions given above). 

Source = namedtuple("Source", ["pos", "count"]) 

sources = [ 

Source(pos=lsst.geom.Point2D(6, 6), count=2), 

Source(pos=lsst.geom.Point2D(10, 10), count=3), 

Source(pos=lsst.geom.Point2D(14, 14), count=1) 

] 

 

# These lines are used in the creation of WCS information 

scale = 1.0e-5 * lsst.geom.degrees 

cdMatrix = afwGeom.makeCdMatrix(scale=scale) 

crval = lsst.geom.SpherePoint(0.0, 0.0, lsst.geom.degrees) 

 

# Construct the info needed to set the exposure object 

imageBox = lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Extent2I(size, size)) 

wcsRef = afwGeom.makeSkyWcs(crpix=lsst.geom.Point2D(0, 0), crval=crval, cdMatrix=cdMatrix) 

 

# Create the exposure object, and set it up to be the output of a coadd 

exp = afwImage.ExposureF(size, size) 

exp.setWcs(wcsRef) 

exp.getInfo().setCoaddInputs(afwImage.CoaddInputs(afwTable.ExposureTable.makeMinimalSchema(), 

afwTable.ExposureTable.makeMinimalSchema())) 

 

# Set the fake CCDs that "went into" making this coadd, using the 

# differing wcs objects created above. 

ccds = exp.getInfo().getCoaddInputs().ccds 

for pos in ccdPositions: 

record = ccds.addNew() 

record.setWcs(afwGeom.makeSkyWcs(crpix=pos, crval=crval, cdMatrix=cdMatrix)) 

record.setBBox(imageBox) 

record.setValidPolygon(afwGeom.Polygon(lsst.geom.Box2D(imageBox))) 

 

# Configure a SingleFrameMeasurementTask to run InputCounts. 

measureSourcesConfig = measBase.SingleFrameMeasurementConfig() 

measureSourcesConfig.plugins.names = ["base_PeakCentroid", "base_InputCount"] 

measureSourcesConfig.slots.centroid = "base_PeakCentroid" 

measureSourcesConfig.slots.psfFlux = None 

measureSourcesConfig.slots.apFlux = None 

measureSourcesConfig.slots.modelFlux = None 

measureSourcesConfig.slots.instFlux = None 

measureSourcesConfig.slots.calibFlux = None 

measureSourcesConfig.slots.shape = None 

measureSourcesConfig.validate() 

schema = afwTable.SourceTable.makeMinimalSchema() 

task = measBase.SingleFrameMeasurementTask(schema, config=measureSourcesConfig) 

catalog = afwTable.SourceCatalog(schema) 

 

# Add simulated sources to the measurement catalog. 

for src in sources: 

spans = afwGeom.SpanSet.fromShape(1) 

spans = spans.shiftedBy(int(src.pos.getX()), int(src.pos.getY())) 

foot = afwDetection.Footprint(spans) 

peak = foot.getPeaks().addNew() 

peak.setFx(src.pos[0]) 

peak.setFy(src.pos[1]) 

peak.setPeakValue(value) 

catalog.addNew().setFootprint(foot) 

 

task.run(catalog, exp) 

 

for src, rec in zip(sources, catalog): 

self.assertEqual(rec.get("base_InputCount_value"), src.count) 

 

184 ↛ 185line 184 didn't jump to line 185, because the condition on line 184 was never true if display: 

ccdVennDiagram(exp) 

 

def _preparePlugin(self, addCoaddInputs): 

""" 

Prepare a SingleFrameInputCountPlugin for running. 

 

Sets up an InputCount plugin to run on an empty catalog together with a synthetic, content-free 

Exposure. 

 

@param[in] addCoaddInputs Should we add the coadd inputs? 

@returns tuple of (initialized plugin, empty catalog, synthetic exposure) 

""" 

exp = afwImage.ExposureF(20, 20) 

scale = 1.0e-5*lsst.geom.degrees 

wcs = afwGeom.makeSkyWcs(crpix=lsst.geom.Point2D(0, 0), 

crval=lsst.geom.SpherePoint(0.0, 0.0, lsst.geom.degrees), 

cdMatrix=afwGeom.makeCdMatrix(scale=scale)) 

exp.setWcs(wcs) 

if addCoaddInputs: 

exp.getInfo().setCoaddInputs(afwImage.CoaddInputs(afwTable.ExposureTable.makeMinimalSchema(), 

afwTable.ExposureTable.makeMinimalSchema())) 

ccds = exp.getInfo().getCoaddInputs().ccds 

record = ccds.addNew() 

record.setWcs(wcs) 

record.setBBox(exp.getBBox()) 

record.setValidPolygon(afwGeom.Polygon(lsst.geom.Box2D(exp.getBBox()))) 

 

schema = afwTable.SourceTable.makeMinimalSchema() 

measBase.SingleFramePeakCentroidPlugin(measBase.SingleFramePeakCentroidConfig(), 

"centroid", schema, None) 

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

inputCount = measBase.SingleFrameInputCountPlugin(measBase.InputCountConfig(), 

"inputCount", schema, None) 

catalog = afwTable.SourceCatalog(schema) 

return inputCount, catalog, exp 

 

def testBadCentroid(self): 

""" 

The flag from the centroid slot should propagate to the badCentroid 

flag on InputCount and the algorithm should throw a MeasurementError 

when it encounters a NAN position. 

""" 

inputCount, catalog, exp = self._preparePlugin(True) 

record = catalog.addNew() 

 

# The inputCount's badCentroid flag is an alias to the centroid's global flag, 

# so it should be set immediately. 

record.set("centroid_flag", True) 

self.assertTrue(record.get("inputCount_flag_badCentroid")) 

 

# Even though the source is flagged as bad, if the position is good we should still get a measurement. 

record.set("slot_Centroid_x", 10) 

record.set("slot_Centroid_y", 10) 

inputCount.measure(record, exp) 

self.assertTrue(record.get("inputCount_flag_badCentroid")) 

self.assertEqual(record.get("inputCount_value"), 1) 

 

# The centroid is bad (with a NAN) even though the centroid isn't flagged, so we should get a 

# MeasurementError indicating an expected failure. 

record = catalog.addNew() 

record.set("slot_Centroid_x", float("nan")) 

record.set("slot_Centroid_y", 12.345) 

record.set("centroid_flag", False) 

with self.assertRaises(measBase.MeasurementError) as measErr: 

inputCount.measure(record, exp) 

 

# Calling the fail() method should set the global flag. 

record = catalog.addNew() 

record.set("inputCount_flag", False) 

inputCount.fail(record, measErr.exception) 

self.assertTrue(record.get("inputCount_flag")) 

 

def testBadCoaddInputs(self): 

""" 

When there are no coadd inputs on the input exposure we should throw a MeasurementError 

and set both the global flag and flag_noInputs. 

""" 

inputCount, catalog, exp = self._preparePlugin(False) 

record = catalog.addNew() 

 

# Initially, the record is not flagged. 

self.assertFalse(record.get("inputCount_flag")) 

self.assertFalse(record.get("inputCount_flag_noInputs")) 

 

# There are no coadd inputs, so we should get a MeasurementError indicating 

# an expected failure. 

with self.assertRaises(measBase.MeasurementError) as measErr: 

inputCount.measure(record, exp) 

 

# Calling the fail() method should set the noInputs and global flags. 

inputCount.fail(record, measErr.exception) 

self.assertTrue(record.get("inputCount_flag")) 

self.assertTrue(record.get("inputCount_flag_noInputs")) 

 

 

class TestMemory(lsst.utils.tests.MemoryTestCase): 

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

288 ↛ 289line 288 didn't jump to line 289, because the condition on line 288 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()