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# 

# LSST Data Management System 

# Copyright 2008-2017 LSST Corporation. 

# 

# 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/>. 

# 

from __future__ import absolute_import, division, print_function 

from future import standard_library 

standard_library.install_aliases() # noqa E402 

from builtins import zip 

import unittest 

import pickle 

 

import numpy as np 

 

try: 

import scipy.special 

except ImportError: 

scipy = None 

 

import lsst.utils.tests 

import lsst.shapelet.tests 

import lsst.afw.image 

import lsst.afw.geom as geom 

import lsst.afw.geom.ellipses as ellipses 

 

 

class ShapeletFunctionTestCase(lsst.shapelet.tests.ShapeletTestCase): 

 

def setUp(self): 

np.random.seed(500) 

order = 4 

self.ellipse = ellipses.Ellipse(ellipses.Axes(2.2, 0.8, 0.3), geom.Point2D(0.12, -0.08)) 

self.coefficients = np.random.randn(lsst.shapelet.computeSize(order)) 

self.x = np.random.randn(25) 

self.y = np.random.randn(25) 

self.bases = [ 

lsst.shapelet.BasisEvaluator(order, lsst.shapelet.HERMITE), 

lsst.shapelet.BasisEvaluator(order, lsst.shapelet.LAGUERRE), 

] 

self.functions = [ 

lsst.shapelet.ShapeletFunction(order, lsst.shapelet.HERMITE, self.coefficients), 

lsst.shapelet.ShapeletFunction(order, lsst.shapelet.LAGUERRE, self.coefficients), 

] 

for function in self.functions: 

function.setEllipse(self.ellipse) 

 

def testPickle(self): 

for function in self.functions: 

s = pickle.dumps(function, protocol=2) 

function2 = pickle.loads(s) 

self.assertEqual(function.getOrder(), function2.getOrder()) 

self.assertEqual(function.getBasisType(), function2.getBasisType()) 

self.assertFloatsAlmostEqual(function.getEllipse().getParameterVector(), 

function2.getEllipse().getParameterVector()) 

self.assertFloatsAlmostEqual(function.getCoefficients(), function2.getCoefficients()) 

 

def testConversion(self): 

for basis, function in zip(self.bases, self.functions): 

evaluator = function.evaluate() 

v = np.zeros(self.coefficients.shape, dtype=float) 

t = self.ellipse.getGridTransform() 

for x, y in zip(self.x, self.y): 

basis.fillEvaluation(v, t(geom.Point2D(x, y))) 

p1 = evaluator(x, y) 

p2 = np.dot(v, self.coefficients) * t.getLinear().computeDeterminant() 

self.assertFloatsAlmostEqual(p1, p2, rtol=1E-8) 

v = np.zeros(self.coefficients.shape, dtype=float) 

basis.fillIntegration(v) 

p1 = evaluator.integrate() 

p2 = np.dot(v, self.coefficients) 

self.assertFloatsAlmostEqual(p1, p2, rtol=1E-8) 

 

def testMoments(self): 

x = np.linspace(-15, 15, 151) 

y = x 

for function in self.functions: 

z = self.makeImage(function, x, y) 

self.checkMoments(function, x, y, z) 

 

def testDerivatives(self): 

eps = 1E-7 

v = np.zeros(self.coefficients.shape, dtype=float) 

v_lo = np.zeros(self.coefficients.shape, dtype=float) 

v_hi = np.zeros(self.coefficients.shape, dtype=float) 

dx_a = np.zeros(self.coefficients.shape, dtype=float) 

dy_a = np.zeros(self.coefficients.shape, dtype=float) 

for basis in self.bases: 

for x, y in zip(self.x, self.y): 

basis.fillEvaluation(v, x, y, dx_a, dy_a) 

basis.fillEvaluation(v_hi, x+eps, y) 

basis.fillEvaluation(v_lo, x-eps, y) 

dx_n = 0.5 * (v_hi - v_lo) / eps 

basis.fillEvaluation(v_hi, x, y+eps) 

basis.fillEvaluation(v_lo, x, y-eps) 

dy_n = 0.5 * (v_hi - v_lo) / eps 

self.assertFloatsAlmostEqual(dx_n, dx_a, rtol=1E-5) 

self.assertFloatsAlmostEqual(dy_n, dy_a, rtol=1E-5) 

 

def testAddToImage(self): 

bbox = geom.Box2I(geom.Point2I(5, 6), geom.Extent2I(20, 30)) 

image = lsst.afw.image.ImageD(bbox) 

x = np.arange(bbox.getBeginX(), bbox.getEndX(), dtype=float) 

y = np.arange(bbox.getBeginY(), bbox.getEndY(), dtype=float) 

array = np.zeros((bbox.getHeight(), bbox.getWidth()), dtype=float) 

for f in self.functions: 

image.getArray()[:] = 0.0 

array[:] = 0.0 

ev = f.evaluate() 

ev.addToImage(image) 

ev.addToImage(array, bbox.getMin()) 

check = self.makeImage(f, x, y) 

self.assertFloatsAlmostEqual(image.getArray(), check) 

self.assertFloatsAlmostEqual(array, check) 

 

def testConvolution(self): 

132 ↛ 133line 132 didn't jump to line 133, because the condition on line 132 was never true if scipy is None: 

print("Skipping convolution test; scipy could not be imported.") 

return 

e1 = ellipses.Ellipse(ellipses.Axes(10, 8, 0.3), geom.Point2D(1.5, 2.0)) 

e2 = ellipses.Ellipse(ellipses.Axes(12, 9, -0.5), geom.Point2D(-1.0, -0.25)) 

f1 = lsst.shapelet.ShapeletFunction(3, lsst.shapelet.HERMITE, e1) 

f2 = lsst.shapelet.ShapeletFunction(2, lsst.shapelet.LAGUERRE, e2) 

f1.getCoefficients()[:] = np.random.randn(*f1.getCoefficients().shape) 

f2.getCoefficients()[:] = np.random.randn(*f2.getCoefficients().shape) 

fc1, fc2 = self.checkConvolution(f1, f2) 

self.assertEqual(fc1.getBasisType(), lsst.shapelet.HERMITE) 

self.assertEqual(fc2.getBasisType(), lsst.shapelet.LAGUERRE) 

self.assertFloatsAlmostEqual(fc1.getEllipse().getParameterVector(), 

fc2.getEllipse().getParameterVector()) 

self.assertEqual(fc1.getOrder(), fc2.getOrder()) 

fc2.changeBasisType(lsst.shapelet.HERMITE) 

self.assertFloatsAlmostEqual(fc1.getCoefficients(), fc2.getCoefficients(), 1E-8) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

159 ↛ 160line 159 didn't jump to line 160, because the condition on line 159 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()