def test_fits():
get_aardvark()
file_name = os.path.join('data','Aardvark.fit')
config = treecorr.read_config('Aardvark.params')
# Just test a few random particular values
cat1 = treecorr.Catalog(file_name, config)
numpy.testing.assert_equal(len(cat1.ra), 390935)
numpy.testing.assert_equal(cat1.nobj, 390935)
numpy.testing.assert_almost_equal(cat1.ra[0], 56.4195 * (pi/180.))
numpy.testing.assert_almost_equal(cat1.ra[390934], 78.4782 * (pi/180.))
numpy.testing.assert_almost_equal(cat1.dec[290333], 83.1579 * (pi/180.))
numpy.testing.assert_almost_equal(cat1.g1[46392], 0.0005066675)
numpy.testing.assert_almost_equal(cat1.g2[46392], -0.0001006742)
numpy.testing.assert_almost_equal(cat1.k[46392], -0.0008628797)
# The catalog doesn't have x, y, or w, but test that functionality as well.
del config['ra_col']
del config['dec_col']
config['x_col'] = 'RA'
config['y_col'] = 'DEC'
config['w_col'] = 'MU'
config['flag_col'] = 'INDEX'
config['ignore_flag'] = 64
cat2 = treecorr.Catalog(file_name, config)
numpy.testing.assert_almost_equal(cat2.x[390934], 78.4782, decimal=4)
numpy.testing.assert_almost_equal(cat2.y[290333], 83.1579, decimal=4)
numpy.testing.assert_almost_equal(cat2.w[46392], 0.) # index = 1200379
numpy.testing.assert_almost_equal(cat2.w[46393], 0.9995946) # index = 1200386
# Test using a limited set of rows
config['first_row'] = 101
config['last_row'] = 50000
cat3 = treecorr.Catalog(file_name, config)
numpy.testing.assert_equal(len(cat3.x), 49900)
numpy.testing.assert_equal(cat3.nobj, sum(cat3.w != 0))
numpy.testing.assert_almost_equal(cat3.g1[46292], 0.0005066675)
numpy.testing.assert_almost_equal(cat3.g2[46292], -0.0001006742)
numpy.testing.assert_almost_equal(cat3.k[46292], -0.0008628797)
def test_aardvark():
# Eric Suchyta did a brute force calculation of the Aardvark catalog, so it is useful to
# compare the output from my code with that.
get_aardvark()
file_name = os.path.join('data','Aardvark.fit')
config = treecorr.read_config('Aardvark.params')
cat1 = treecorr.Catalog(file_name, config)
gg = treecorr.GGCorrelation(config)
gg.process(cat1)
direct_file_name = os.path.join('data','Aardvark.direct')
direct_data = numpy.genfromtxt(direct_file_name)
direct_xip = direct_data[:,3]
direct_xim = direct_data[:,4]
#print 'gg.xip = ',gg.xip
#print 'direct.xip = ',direct_xip
xip_err = gg.xip - direct_xip
print 'xip_err = ',xip_err
print 'max = ',max(abs(xip_err))
assert max(abs(xip_err)) < 2.e-7
print 'xip_im = ',gg.xip_im
print 'max = ',max(abs(gg.xip_im))
assert max(abs(gg.xip_im)) < 3.e-7
xim_err = gg.xim - direct_xim
print 'xim_err = ',xim_err
print 'max = ',max(abs(xim_err))
assert max(abs(xim_err)) < 1.e-7
print 'xim_im = ',gg.xim_im
print 'max = ',max(abs(gg.xim_im))
assert max(abs(gg.xim_im)) < 1.e-7
# However, after some back and forth about the calculation, we concluded that Eric hadn't
# done the spherical trig correctly to get the shears relative to the great circle joining
# the two positions. So let's compare with my own brute force calculation (i.e. using
# bin_slop = 0):
# This also has the advantage that the radial bins are done the same way -- uniformly
# spaced in log of the chord distance, rather than the great circle distance.
bs0_file_name = os.path.join('data','Aardvark.bs0')
bs0_data = numpy.genfromtxt(bs0_file_name)
bs0_xip = bs0_data[:,2]
bs0_xim = bs0_data[:,3]
#print 'gg.xip = ',gg.xip
#print 'bs0.xip = ',bs0_xip
xip_err = gg.xip - bs0_xip
print 'xip_err = ',xip_err
print 'max = ',max(abs(xip_err))
assert max(abs(xip_err)) < 1.e-7
xim_err = gg.xim - bs0_xim
print 'xim_err = ',xim_err
print 'max = ',max(abs(xim_err))
assert max(abs(xim_err)) < 5.e-8
# Check that we get the same result using the corr2 executable:
# Note: This is the only test of the corr2 executable that we do with nosetests.
# The other similar tests are blocked out with: if __name__ == '__main__':
import subprocess
p = subprocess.Popen( ["corr2","Aardvark.params"] )
p.communicate()
corr2_output = numpy.genfromtxt(os.path.join('output','Aardvark.out'), names=True)
print 'gg.xip = ',gg.xip
print 'from corr2 output = ',corr2_output['xip']
print 'ratio = ',corr2_output['xip']/gg.xip
print 'diff = ',corr2_output['xip']-gg.xip
numpy.testing.assert_almost_equal(corr2_output['xip']/gg.xip, 1., decimal=3)
print 'gg.xim = ',gg.xim
print 'from corr2 output = ',corr2_output['xim']
print 'ratio = ',corr2_output['xim']/gg.xim
print 'diff = ',corr2_output['xim']-gg.xim
numpy.testing.assert_almost_equal(corr2_output['xim']/gg.xim, 1., decimal=3)
print 'xip_im from corr2 output = ',corr2_output['xip_im']
print 'max err = ',max(abs(corr2_output['xip_im']))
assert max(abs(corr2_output['xip_im'])) < 3.e-7
print 'xim_im from corr2 output = ',corr2_output['xim_im']
print 'max err = ',max(abs(corr2_output['xim_im']))
assert max(abs(corr2_output['xim_im'])) < 1.e-7
# As bin_slop decreases, the agreement should get even better.
# This test is slow, so only do it if running test_gg.py directly.
if __name__ == '__main__':
config['bin_slop'] = 0.2
gg = treecorr.GGCorrelation(config)
gg.process(cat1)
#print 'gg.xip = ',gg.xip
#print 'bs0.xip = ',bs0_xip
xip_err = gg.xip - bs0_xip
print 'xip_err = ',xip_err
print 'max = ',max(abs(xip_err))
assert max(abs(xip_err)) < 1.e-8
xim_err = gg.xim - bs0_xim
print 'xim_err = ',xim_err
print 'max = ',max(abs(xim_err))
assert max(abs(xim_err)) < 1.e-8
