def testNativeLonLatVector(self):
"""
Test that _nativeLonLatFromRaDec works by considering stars and pointings
at intuitive locations (make sure it works in a vectorized way; we do this
by performing a bunch of tansformations passing in ra and dec as numpy arrays
and then comparing them to results computed in an element-wise way)
"""
from galsim.lsst.lsst_wcs import _nativeLonLatFromRaDec
start = time.clock()
raPoint = np.radians(145.0)
decPoint = np.radians(-35.0)
nSamples = 100
rng = np.random.RandomState(42)
raList = rng.random_sample(nSamples)*2.0*np.pi
decList = rng.random_sample(nSamples)*np.pi - 0.5*np.pi
lonList, latList = _nativeLonLatFromRaDec(raList, decList, raPoint, decPoint)
for rr, dd, lon, lat in zip(raList, decList, lonList, latList):
lonControl, latControl = _nativeLonLatFromRaDec(rr, dd, raPoint, decPoint)
self.assertAlmostEqual(lat, latControl, 10)
if np.abs(np.abs(lat) - 0.5*np.pi)>1.0e-9:
self.assertAlmostEqual(lon, lonControl, 10)
print 'time to run %s = %e sec' % (funcname(), time.clock()-start)
def testNativeLonLat(self):
"""
Test that nativeLonLatFromRaDec works by considering stars and pointings
at intuitive locations
"""
from galsim.lsst.lsst_wcs import _nativeLonLatFromRaDec
start = time.clock()
raList = [0.0, 0.0, 0.0, 1.5*np.pi]
decList = [0.5*np.pi, 0.5*np.pi, 0.0, 0.0]
raPointList = [0.0, 1.5*np.pi, 1.5*np.pi, 0.0]
decPointList = [0.0, 0.0,0.0, 0.0]
lonControlList = [np.pi, np.pi, 0.5*np.pi, 1.5*np.pi]
latControlList = [0.0, 0.0, 0.0, 0.0]
for rr, dd, rp, dp, lonc, latc in \
zip(raList, decList, raPointList, decPointList, lonControlList, latControlList):
lon, lat = _nativeLonLatFromRaDec(rr, dd, rp, dp)
self.assertAlmostEqual(lon, lonc, 10)
self.assertAlmostEqual(lat, latc, 10)
print 'time to run %s = %e seconds' % (funcname(), time.clock()-start)
def testNativeLongLatComplicated(self):
"""
Test that nativeLongLatFromRaDec works by considering stars and pointings
at non-intuitive locations.
"""
from galsim.lsst.lsst_wcs import _nativeLonLatFromRaDec
start = time.clock()
rng = np.random.RandomState(42)
nPointings = 10
raPointingList = rng.random_sample(nPointings)*2.0*np.pi
decPointingList = rng.random_sample(nPointings)*np.pi - 0.5*np.pi
nStars = 10
for raPointing, decPointing in zip(raPointingList, decPointingList):
raList = rng.random_sample(nStars)*2.0*np.pi
decList = rng.random_sample(nStars)*np.pi - 0.5*np.pi
for raRad, decRad in zip(raList, decList):
sinRa = np.sin(raRad)
cosRa = np.cos(raRad)
sinDec = np.sin(decRad)
cosDec = np.cos(decRad)
# the three dimensional position of the star
controlPosition = np.array([-cosDec*sinRa, cosDec*cosRa, sinDec])
# calculate the rotation matrices needed to transform the
# x, y, and z axes into the local x, y, and z axes
# (i.e. the axes with z lined up with raPointing, decPointing)
alpha = 0.5*np.pi - decPointing
ca = np.cos(alpha)
sa = np.sin(alpha)
rotX = np.array([[1.0, 0.0, 0.0],
[0.0, ca, sa],
[0.0, -sa, ca]])
cb = np.cos(raPointing)
sb = np.sin(raPointing)
rotZ = np.array([[cb, -sb, 0.0],
[sb, cb, 0.0],
[0.0, 0.0, 1.0]])
# rotate the coordinate axes into the local basis
xAxis = np.dot(rotZ, np.dot(rotX, np.array([1.0, 0.0, 0.0])))
yAxis = np.dot(rotZ, np.dot(rotX, np.array([0.0, 1.0, 0.0])))
zAxis = np.dot(rotZ, np.dot(rotX, np.array([0.0, 0.0, 1.0])))
# calculate the local longitude and latitude of the star
lon, lat = _nativeLonLatFromRaDec(raRad, decRad, raPointing, decPointing)
cosLon = np.cos(lon)
sinLon = np.sin(lon)
cosLat = np.cos(lat)
sinLat = np.sin(lat)
# the x, y, z position of the star in the local coordinate basis
transformedPosition = np.array([-cosLat*sinLon,
cosLat*cosLon,
sinLat])
# convert that position back into the un-rotated bases
testPosition = transformedPosition[0]*xAxis + \
transformedPosition[1]*yAxis + \
transformedPosition[2]*zAxis
# assert that testPosition and controlPosition should be equal
np.testing.assert_array_almost_equal(controlPosition, testPosition, decimal=10)
print 'time to run %s = %e sec' % (funcname(), time.clock()-start)
