def test_combine_srcdict(self):
catd1 = cd.catData()
srcdict1 = catd1._generate_srcdict(ras,
decs, [outfile],
flux_type='pflux')
catd2 = cd.catData()
srcdict2 = catd2._generate_srcdict(ras,
decs, [outfile],
flux_type='pflux')
catd = cd.catData()
srcdict = catd._combine_srcdict(srcdict1, srcdict2)
keys = list(srcdict.keys())
data = srcdict[keys[0]]['data']
nt.assert_equal(data.shape, (2, 1))
np.testing.assert_almost_equal(data, np.array([[0.75], [0.75]]))
def test_init(self):
catd = cd.catData()
bms = bs.BeamOnly(catd)
nt.assert_equal(bms.bm_pix, 61)
nt.assert_equal(bms.cat, catd)
bms = bs.BeamOnly(catd, 30)
nt.assert_equal(bms.bm_pix, 30)
nt.assert_equal(bms.cat, catd)
def test_read_hdf5(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
catd.write_hdf5(h5file, clobber=True)
catd.read_hdf5(h5file)
nt.assert_equal(catd.Nsrcs, len(ras))
np.testing.assert_almost_equal(
catd.data_array, np.array([[[0.75], [0.5], [1.], [0.5], [0.6]]]))
def create_catdata(azalt, data, error, nsrcs, npoints):
catd = cd.catData()
catd.azalt_array = azalt
catd.data_array = data
catd.error_array = error
catd.Nfits = npoints
catd.Nsrcs = nsrcs
return catd
def test_add_src(self):
catd = cd.catData()
catd.gen_catalog(ras[0:2], decs[0:2], [outfile])
catd.add_src(ras[2:3], decs[2:3], pols=['xx'], fitsfiles_xx=[outfile])
nt.assert_equal(catd.Nsrcs, 3)
nt.assert_equal(catd.data_array.shape, (1, 3, 1))
np.testing.assert_almost_equal(catd.pos_array[2],
np.array([ras[2], decs[2]]), 2)
def test_srcdict_catdata(self):
catd = cd.catData()
srcdict = catd._generate_srcdict(ras,
decs, [outfile],
flux_type='gauss_pflux')
catd._srcdict_catdata(srcdict)
nt.assert_equal(catd.data_array.shape, (1, len(ras), 1))
np.testing.assert_almost_equal(catd.pos_array, list(srcdict.keys()))
def test_get_unique(self):
catd = cd.catData()
ura, udec = catd.get_unique(ras, decs)
np.testing.assert_almost_equal(ura, ras)
np.testing.assert_almost_equal(udec, decs)
ura, udec = catd.get_unique([30.01713089, 30.01713089, 30.01713089],
[-30.88211818, -30.88211818, -30.85])
np.testing.assert_almost_equal(ura, [30.01713089], 2)
np.testing.assert_almost_equal(udec, [-30.88211818], 2)
def test_gen_catalog_1pol(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, fitsfiles_xx=[outfile])
nt.assert_equal(catd.pols, ['xx'])
nt.assert_equal(catd.Nfits, 1)
nt.assert_equal(catd.Nsrcs, len(ras))
nt.assert_equal(catd.azalt_array.shape, (2, len(ras), 1))
nt.assert_equal(catd.data_array.shape, (1, len(ras), 1))
nt.assert_equal(catd.ha_array.shape, (len(ras), 1))
def test_generate_srcdict(self):
catd = cd.catData()
srcdict = catd._generate_srcdict(ras,
decs, [outfile],
flux_type='pflux')
keys = list(srcdict.keys())
nt.assert_equal(len(keys), len(ras))
data = srcdict[keys[0]]['data']
nt.assert_equal(data.shape, (1, ))
np.testing.assert_almost_equal(data, np.array([0.75]))
def create_catdata(data, ha, nsrcs, npoints, error='0', pols=['xx']):
catd = cd.catData()
#catd.azalt_array = azalt
catd.data_array = data
catd.ha_array = ha
catd.error_array = error
catd.Nfits = npoints
catd.Nsrcs = nsrcs
catd.pols = pols
return catd
def test_delete_multiple_srcs(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
pos_array = catd.pos_array
catd.delete_src([(74.26, -52.02), (41.91, -43.23)])
nt.assert_equal(catd.Nsrcs, 3)
nt.assert_equal(catd.data_array.shape, (1, 3, 1))
nt.assert_equal(catd.ha_array.shape, (3, 1))
nt.assert_equal(catd.error_array.shape, (1, 3, 1))
np.testing.assert_almost_equal(catd.pos_array,
np.delete(pos_array, [0, 1], 0))
def test_delete_src(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
pos_array = catd.pos_array
catd.delete_src((74.26, -52.02))
nt.assert_equal(catd.Nsrcs, 4)
nt.assert_equal(catd.data_array.shape, (1, 4, 1))
nt.assert_equal(catd.ha_array.shape, (4, 1))
nt.assert_equal(catd.error_array.shape, (1, 4, 1))
np.testing.assert_almost_equal(catd.pos_array,
np.delete(pos_array, 0, 0))
def test_get_npoints(self):
catd = cd.catData()
npix = 91
x = np.linspace(0, np.pi, npix)
azs = np.array([[0, np.pi / 2, np.pi], [0, np.pi / 2, np.pi]])
alts = np.array([[0, np.pi / 2, np.pi], [0, np.pi / 2, np.pi]])
catd.azalt_array = np.zeros((2, azs.shape[0], azs.shape[1]))
catd.azalt_array[0, :, :] = azs
catd.azalt_array[1, :, :] = alts
npoints = catd._get_npoints(npix)
nt.assert_almost_equal(npoints, int(np.pi / (x[1] - x[0])) + 1)
def test_interpolate_data(self):
catd = cd.catData()
x = np.arange(0, 5)
y = np.array([0., 0.5, 1., 1.5, 2.])
f = catd._interpolate_data(x, y, kind='linear', bounds_error=False)
nt.assert_equal(f(0), 0.)
np.testing.assert_almost_equal(f(np.array([0, 1.5])),
np.array([0., 0.75]))
y = np.array([0., 0.5, 1., -0.55, -0.1])
f = catd._interpolate_data(x, y, kind='cubic', bounds_error=False)
nt.assert_true(-0.55 < f(np.array([2.5])) < 1.)
def test_calc_catalog_flux(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
beam = bt.get_fitsbeam(beamfits, 151e6)
catalog_flux = catd.calc_catalog_flux(beam, 'xx')
nt.assert_almost_equal(catalog_flux[2], 1.000, 3)
def test_get_azalt(self):
catd = cd.catData()
az, alt = catd._get_azalt(-30.721388888888885, 0)
nt.assert_almost_equal(az, np.pi / 2)
nt.assert_almost_equal(alt, np.pi / 2)
def test_get_lstha(self):
catd = cd.catData()
jd = 2458115.23736 + 5 / 24. / 60
lst, ha = catd._get_lstha(2458115.23736, 22.47460079)
np.testing.assert_allclose(ha, 0, atol=1e-1)
def test_get_jd(self):
ft.add_keyword(fitsfile, 'JD', 2458115.23736, outfile, overwrite=True)
catd = cd.catData()
jds = catd._get_jds([outfile])
nt.assert_equal(jds, [2458115.23736])
def test_check_jd(self):
catd = cd.catData()
nt.assert_raises(AssertionError, catd._get_jds, [fitsfile])
def test_length_radec(self):
catd = cd.catData()
nt.assert_raises(AssertionError, catd.get_unique, ras, decs[0:4])
def test_return_dict(self):
catd = cd.catData()
srcdict = catd.gen_catalog(ras, decs, [outfile], return_data=True)
nt.assert_equal(len(srcdict.keys()), len(ras))
np.testing.assert_almost_equal(list(srcdict.keys()), catd.pos_array)
def test_delete_valueerror(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
nt.assert_raises(ValueError, catd.delete_src, (45.6, -30.45))
def test_catalog_vals(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
np.testing.assert_almost_equal(
catd.data_array, np.array([[[0.75], [0.5], [1.0], [0.5], [0.6]]]))
def test_get_npoints(self):
catd = cd.catData()
ha = np.array([[np.pi / 2, np.pi / 4, 1e-5, -np.pi / 4, -np.pi / 2]])
catd.ha_array = ha
npoints = catd._get_npoints(0.01)
nt.assert_equal(npoints, int(np.pi / 0.01) + 1)
def test_write_hdf5(self):
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
catd.write_hdf5(h5file, clobber=True)
nt.assert_true(os.path.exists(h5file))
nt.assert_raises(IOError, catd.write_hdf5, h5file)
def test_no_hdf5(self):
catd = cd.catData()
nt.assert_raises(IOError, catd.read_hdf5, 'src.h5')
import numpy as np
import collections
import aipy
import os
import copy
import pylab
import linsolve
beamfits = os.path.join(DATA_PATH, 'HERA_NF_dipole_power.beamfits')
fitsfile = os.path.join(DATA_PATH, '2458115.23736.test.xx.fits')
outfile = fitsfile.replace('.fits', '.mod.fits')
ras = [74.26237654, 41.91116875, 22.47460079, 9.8393989, 356.25426296]
decs = [-52.0209015, -43.2292595, -30.27372862, -17.40763737, -0.3692835]
ft.add_keyword(fitsfile, 'JD', 2458115.23736, outfile, overwrite=True)
catd = cd.catData()
catd.gen_catalog(ras, decs, [outfile])
def create_catdata(azalt, data, error, nsrcs, npoints):
catd = cd.catData()
catd.azalt_array = azalt
catd.data_array = data
catd.error_array = error
catd.Nfits = npoints
catd.Nsrcs = nsrcs
return catd
def gen_catdata_zensrc(fluxval, sigma=1):
azs = np.array([[np.pi/2]]); alts=np.array([[np.pi/2]])
top = aipy.coord.azalt2top([azs, alts])
data = bt.get_src_tracks(top, fluxval, sigma_x=sigma)
