def get_dmag(lc_id_list, out_file_name, seed, lock, v_cache):
rng = np.random.RandomState(seed)
plc = ParametrizedLightCurveMixin()
v_cache = create_variability_cache()
plc.load_parametrized_light_curves(variability_cache=v_cache)
dt = (60.0 * 10.0) / (3600.0 * 24.0)
mjd_base = np.arange(0.0, 180.0 + 2.0 * dt, dt)
dmag_abs_list = np.zeros(len(lc_id_list), dtype=float)
dmag_true_list = np.zeros(len(lc_id_list), dtype=float)
for i_lc, lc_int in enumerate(lc_id_list):
dmag_abs_max = -1.0
dmag_true_max = -1.0
for mjd_start in np.arange(59580.0, 59580.0 + 3654.0 + 181.0, 180.0):
mjd_arr = mjd_base + mjd_start
d_mjd = rng.random_sample(len(mjd_arr)) * 0.9 * dt
mjd_arr += d_mjd
q_flux, d_flux = plc._calc_dflux(lc_int,
mjd_arr,
variability_cache=v_cache)
d_flux = d_flux / q_flux
d_flux_abs_max = np.abs(d_flux).max()
dmag_abs_max_local = 2.5 * np.log10(1.0 + d_flux_abs_max)
if dmag_abs_max_local > dmag_abs_max:
dmag_abs_max = dmag_abs_max_local
d_flux_true_max = d_flux.max()
dmag_true_max_local = 2.5 * np.log10(1.0 + d_flux_true_max)
if dmag_true_max_local > dmag_true_max:
dmag_true_max = dmag_true_max_local
dmag_abs_list[i_lc] = dmag_abs_max
dmag_true_list[i_lc] = dmag_true_max
lock.acquire()
with open(out_file_name, 'a') as out_file:
for lc_id, dmag_abs, dmag_true in zip(lc_id_list, dmag_abs_list,
dmag_true_list):
out_file.write('%d %e %e\n' % (lc_id, dmag_abs, dmag_true))
lock.release()
def test_ParametrizedLightCurve_in_catalog(self):
"""
Test the performance of applyParametrizedLightCurve()
in the context of an InstanceCatalog
"""
# Create dummy light curve parameters
lc_temp_file_name = tempfile.mktemp(
prefix='test_ParametrizedLightCurve_in_catalog', suffix='.gz')
rng = np.random.RandomState(1621145)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1) * 5.0
b1_list = (rng.random_sample(n_c_1) - 0.5) * 2.0
c1_list = (rng.random_sample(n_c_1) - 0.5) * 0.1
omega1_list = rng.random_sample(n_c_1) * 20.0
tau1_list = rng.random_sample(n_c_1) * 100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2) * 5.0
b2_list = (rng.random_sample(n_c_2) - 0.5) * 2.0
c2_list = (rng.random_sample(n_c_2) - 0.5) * 0.1
omega2_list = rng.random_sample(n_c_2) * 20.0
tau2_list = rng.random_sample(n_c_2) * 100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr999990000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr999990001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
# Create dummy database of astrophysical sources
db_temp_file_name = tempfile.mktemp(
prefix='test_ParametrizedLightCurve_in_catalog_db', suffix='.txt')
lc_list = [999990001, None, 999990001, 999990000]
t0_list = [1729.1, None, 2345.1, 10.9]
with open(db_temp_file_name, 'w') as out_file:
out_file.write('# a header\n')
for i_obj in range(len(lc_list)):
if lc_list[i_obj] is not None:
paramStr = '{"m":"kplr", "p":{"lc":%d, "t0":%.3f}}' % (
lc_list[i_obj], t0_list[i_obj])
else:
paramStr = None
out_file.write('%d;10.0;20.0;0.01;0.01;%s\n' %
(i_obj, paramStr))
dtype = np.dtype([('simobjid', int), ('ra', float), ('dec', float),
('ebv', float), ('parallax', float),
('varParamStr', str, 100)])
db = fileDBObject(db_temp_file_name,
runtable='test',
dtype=dtype,
delimiter=';',
idColKey='simobjid')
class ParametrizedVarParamStrCat(InstanceCatalog, VariabilityStars):
column_outputs = [
'simobjid', 'delta_lsst_u', 'delta_lsst_g', 'delta_lsst_r',
'delta_lsst_i', 'delta_lsst_z', 'delta_lsst_y'
]
default_formats = {'f': '%.15g'}
obs = ObservationMetaData(mjd=59580.0)
cat = ParametrizedVarParamStrCat(db, obs_metadata=obs)
cat.load_parametrized_light_curves(lc_temp_file_name)
cat_out_name = tempfile.mktemp(
prefix='test_ParametrizedLightCurve_in_cat_out', suffix='.txt')
cat.write_catalog(cat_out_name)
kp = ParametrizedLightCurveMixin()
cat_dtype = np.dtype([('simobjid', int), ('du', float), ('dg', float),
('dr', float), ('di', float), ('dz', float),
('dy', float)])
cat_data = np.genfromtxt(cat_out_name, dtype=cat_dtype, delimiter=', ')
for i_obj in range(len(cat_data)):
obj_id = cat_data['simobjid'][i_obj]
if lc_list[obj_id] is None:
self.assertEqual(cat_data['du'][i_obj], 0.0)
self.assertEqual(cat_data['dg'][i_obj], 0.0)
self.assertEqual(cat_data['dr'][i_obj], 0.0)
self.assertEqual(cat_data['di'][i_obj], 0.0)
self.assertEqual(cat_data['dz'][i_obj], 0.0)
self.assertEqual(cat_data['dy'][i_obj], 0.0)
else:
q_flux, d_flux = kp._calc_dflux(lc_list[obj_id],
obs.mjd.TAI - t0_list[obj_id])
d_mag_true = -2.5 * np.log10(1.0 + d_flux / q_flux)
self.assertGreater(np.abs(d_mag_true), 0.0001)
self.assertAlmostEqual(cat_data['du'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dg'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dr'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['di'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dz'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dy'][i_obj], d_mag_true, 15)
if os.path.exists(cat_out_name):
os.unlink(cat_out_name)
if os.path.exists(db_temp_file_name):
os.unlink(db_temp_file_name)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)
def test_applyParametrizedLightCurve_manyExpmjd(self):
"""
test applyParametrizedLightCurve on an array of expmjd values
by creating a dummy light curve file with known
parameters, generating magnitudes, and comparing to
the expected outputs.
We will use _calc_dflux() to calculate the known truth,
since that method was tested in test_calc_dflux()
"""
lc_temp_file_name = tempfile.mktemp(
prefix='test_applyParametrizedLightCurve_manyexpmjd', suffix='.gz')
rng = np.random.RandomState(13291)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1) * 5.0
b1_list = (rng.random_sample(n_c_1) - 0.5) * 2.0
c1_list = (rng.random_sample(n_c_1) - 0.5) * 0.1
omega1_list = rng.random_sample(n_c_1) * 20.0
tau1_list = rng.random_sample(n_c_1) * 100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2) * 5.0
b2_list = (rng.random_sample(n_c_2) - 0.5) * 2.0
c2_list = (rng.random_sample(n_c_2) - 0.5) * 0.1
omega2_list = rng.random_sample(n_c_2) * 20.0
tau2_list = rng.random_sample(n_c_2) * 100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr999900000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr999900001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
params = {}
params['lc'] = np.array([999900001, 999900000, None, 999900001])
params['t0'] = np.array([223.1, 1781.45, None, 32.0])
kp = ParametrizedLightCurveMixin()
kp.load_parametrized_light_curves(lc_temp_file_name)
# first test that passing in an empty set of params
# results in an empty numpy array (so that the 'dry
# run' of catalog generation does not fail)
d_mag_out = kp.applyParametrizedLightCurve([], {}, 1.0)
np.testing.assert_array_equal(d_mag_out,
np.array([[], [], [], [], [], []]))
expmjd = rng.random_sample(10) * 10000.0 + 59580.0
d_mag_out = kp.applyParametrizedLightCurve([], params, expmjd)
self.assertEqual(d_mag_out.shape, (6, 4, 10))
for i_obj in range(4):
if i_obj == 2:
for i_filter in range(6):
np.testing.assert_array_equal(d_mag_out[i_filter][i_obj],
np.zeros(10))
else:
q_flux, d_flux = kp._calc_dflux(params['lc'][i_obj],
expmjd - params['t0'][i_obj])
d_mag_truth = -2.5 * np.log10(1.0 + d_flux / q_flux)
nan_vals = np.where(np.isnan(d_mag_truth))
self.assertEqual(len(nan_vals[0]), 0)
for i_filter in range(6):
np.testing.assert_array_equal(d_mag_out[i_filter][i_obj],
d_mag_truth)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)
def test_calc_dflux(self):
"""
Test the method that calculates the flux of
parametrized light curves by generating a fake light
curve library with known parameters, calculating
the fluxes, and comparing to the expected results.
"""
lc_temp_file_name = tempfile.mktemp(prefix='test_calc_dflux_lc',
suffix='.gz')
rng = np.random.RandomState(7124)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1) * 5.0
b1_list = (rng.random_sample(n_c_1) - 0.5) * 2.0
c1_list = (rng.random_sample(n_c_1) - 0.5) * 0.1
omega1_list = rng.random_sample(n_c_1) * 20.0
tau1_list = rng.random_sample(n_c_1) * 100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2) * 5.0
b2_list = (rng.random_sample(n_c_2) - 0.5) * 2.0
c2_list = (rng.random_sample(n_c_2) - 0.5) * 0.1
omega2_list = rng.random_sample(n_c_2) * 20.0
tau2_list = rng.random_sample(n_c_2) * 100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr990000000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr990000001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0 / (i_c + 1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
expmjd = rng.random_sample(100) * 200.0
kp = ParametrizedLightCurveMixin()
kp.load_parametrized_light_curves(lc_temp_file_name)
q_flux, d_flux = kp._calc_dflux(990000000, expmjd)
self.assertAlmostEqual(q_flux, median1 + c1_list.sum(), 10)
true_flux = np.zeros(len(expmjd))
for i_c in range(n_c_1):
arg = omega1_list[i_c] * (expmjd - tau1_list[i_c])
true_flux += a1_list[i_c] * np.cos(arg)
true_flux += b1_list[i_c] * np.sin(arg)
self.assertEqual(len(d_flux), len(true_flux))
np.testing.assert_allclose(d_flux, true_flux, rtol=0.0, atol=1.0e-10)
q_flux, d_flux = kp._calc_dflux(990000001, expmjd)
self.assertAlmostEqual(q_flux, median2 + c2_list.sum(), 10)
true_flux = np.zeros(len(expmjd))
for i_c in range(n_c_2):
arg = omega2_list[i_c] * (expmjd - tau2_list[i_c])
true_flux += a2_list[i_c] * np.cos(arg)
true_flux += b2_list[i_c] * np.sin(arg)
self.assertEqual(len(d_flux), len(true_flux))
np.testing.assert_allclose(d_flux, true_flux, rtol=0.0, atol=1.0e-10)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)
_out_dir = os.path.join('/astro/store/pogo4/danielsf/dmag_max_data')
_out_dir = '.'
if not os.path.isdir(_out_dir):
raise RuntimeError('%s is not a dir' % _out_dir)
plc = ParametrizedLightCurveMixin()
variability_cache = create_variability_cache()
plc.load_parametrized_light_curves(variability_cache=variability_cache)
time_arr = np.arange(0.0, 3653.0, 0.1)
dmag_dict = {}
t_start = time.time()
for lc_id in variability_cache['_PARAMETRIZED_LC_MODELS']:
q_flux, d_flux = plc._calc_dflux(lc_id, time_arr,
variability_cache=variability_cache)
d_flux = np.where(d_flux>-q_flux, d_flux, -0.9*q_flux)
try:
dmag = 2.5*np.log10(1.0+d_flux/q_flux)
dmag = np.where(np.abs(dmag)>0.0, dmag, 1.0e-20)
dmag = np.where(np.abs(dmag)<1.0e10, dmag, 1.0e10)
bad = np.where(np.logical_or(np.isnan(dmag),np.isinf(dmag)))
assert len(bad[0]) == 0
dmag_dict[lc_id] = int(np.ceil(np.log10(np.abs(dmag).max())))
except:
print(lc_id)
print(d_flux.min(),d_flux.max(),q_flux)
print(dmag.min())
print(dmag.max())
is_inf = np.where(np.isinf(np.log10(np.abs(dmag).max())))
print(is_inf)
def test_ParametrizedLightCurve_in_catalog(self):
"""
Test the performance of applyParametrizedLightCurve()
in the context of an InstanceCatalog
"""
# Create dummy light curve parameters
lc_temp_file_name = tempfile.mktemp(prefix='test_ParametrizedLightCurve_in_catalog',
suffix='.gz')
rng = np.random.RandomState(1621145)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1)*5.0
b1_list = (rng.random_sample(n_c_1)-0.5)*2.0
c1_list = (rng.random_sample(n_c_1)-0.5)*0.1
omega1_list = rng.random_sample(n_c_1)*20.0
tau1_list = rng.random_sample(n_c_1)*100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2)*5.0
b2_list = (rng.random_sample(n_c_2)-0.5)*2.0
c2_list = (rng.random_sample(n_c_2)-0.5)*0.1
omega2_list = rng.random_sample(n_c_2)*20.0
tau2_list = rng.random_sample(n_c_2)*100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr999990000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr999990001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
# Create dummy database of astrophysical sources
db_temp_file_name = tempfile.mktemp(prefix='test_ParametrizedLightCurve_in_catalog_db',
suffix='.txt')
lc_list = [999990001, None, 999990001, 999990000]
t0_list = [1729.1, None, 2345.1, 10.9]
with open(db_temp_file_name, 'w') as out_file:
out_file.write('# a header\n')
for i_obj in range(len(lc_list)):
if lc_list[i_obj] is not None:
paramStr = '{"m":"kplr", "p":{"lc":%d, "t0":%.3f}}' % (lc_list[i_obj], t0_list[i_obj])
else:
paramStr = None
out_file.write('%d;10.0;20.0;0.01;0.01;%s\n' % (i_obj, paramStr))
dtype = np.dtype([('simobjid', int), ('ra', float), ('dec', float),
('ebv', float), ('parallax', float), ('varParamStr', str, 100)])
db = fileDBObject(db_temp_file_name, runtable='test', dtype=dtype, delimiter=';',
idColKey='simobjid')
class ParametrizedVarParamStrCat(InstanceCatalog, VariabilityStars):
column_outputs = ['simobjid', 'delta_lsst_u', 'delta_lsst_g', 'delta_lsst_r',
'delta_lsst_i', 'delta_lsst_z', 'delta_lsst_y']
default_formats = {'f':'%.15g'}
obs = ObservationMetaData(mjd=59580.0)
cat = ParametrizedVarParamStrCat(db, obs_metadata=obs)
cat.load_parametrized_light_curves(lc_temp_file_name)
cat_out_name = tempfile.mktemp(prefix='test_ParametrizedLightCurve_in_cat_out',
suffix='.txt')
cat.write_catalog(cat_out_name)
kp = ParametrizedLightCurveMixin()
cat_dtype = np.dtype([('simobjid', int), ('du', float), ('dg', float),
('dr', float), ('di', float), ('dz', float),
('dy', float)])
cat_data = np.genfromtxt(cat_out_name, dtype=cat_dtype, delimiter=', ')
for i_obj in range(len(cat_data)):
obj_id = cat_data['simobjid'][i_obj]
if lc_list[obj_id] is None:
self.assertEqual(cat_data['du'][i_obj], 0.0)
self.assertEqual(cat_data['dg'][i_obj], 0.0)
self.assertEqual(cat_data['dr'][i_obj], 0.0)
self.assertEqual(cat_data['di'][i_obj], 0.0)
self.assertEqual(cat_data['dz'][i_obj], 0.0)
self.assertEqual(cat_data['dy'][i_obj], 0.0)
else:
q_flux, d_flux = kp._calc_dflux(lc_list[obj_id], obs.mjd.TAI-t0_list[obj_id])
d_mag_true = -2.5*np.log10(1.0+d_flux/q_flux)
self.assertGreater(np.abs(d_mag_true), 0.0001)
self.assertAlmostEqual(cat_data['du'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dg'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dr'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['di'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dz'][i_obj], d_mag_true, 15)
self.assertAlmostEqual(cat_data['dy'][i_obj], d_mag_true, 15)
if os.path.exists(cat_out_name):
os.unlink(cat_out_name)
if os.path.exists(db_temp_file_name):
os.unlink(db_temp_file_name)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)
def test_applyParametrizedLightCurve_manyExpmjd(self):
"""
test applyParametrizedLightCurve on an array of expmjd values
by creating a dummy light curve file with known
parameters, generating magnitudes, and comparing to
the expected outputs.
We will use _calc_dflux() to calculate the known truth,
since that method was tested in test_calc_dflux()
"""
lc_temp_file_name = tempfile.mktemp(prefix='test_applyParametrizedLightCurve_manyexpmjd',
suffix='.gz')
rng = np.random.RandomState(13291)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1)*5.0
b1_list = (rng.random_sample(n_c_1)-0.5)*2.0
c1_list = (rng.random_sample(n_c_1)-0.5)*0.1
omega1_list = rng.random_sample(n_c_1)*20.0
tau1_list = rng.random_sample(n_c_1)*100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2)*5.0
b2_list = (rng.random_sample(n_c_2)-0.5)*2.0
c2_list = (rng.random_sample(n_c_2)-0.5)*0.1
omega2_list = rng.random_sample(n_c_2)*20.0
tau2_list = rng.random_sample(n_c_2)*100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr999900000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr999900001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
params = {}
params['lc'] = np.array([999900001, 999900000, None, 999900001])
params['t0'] = np.array([223.1, 1781.45, None, 32.0])
kp = ParametrizedLightCurveMixin()
kp.load_parametrized_light_curves(lc_temp_file_name)
# first test that passing in an empty set of params
# results in an empty numpy array (so that the 'dry
# run' of catalog generation does not fail)
d_mag_out = kp.applyParametrizedLightCurve([],{},1.0)
np.testing.assert_array_equal(d_mag_out,
np.array([[],[],[],[],[],[]]))
expmjd = rng.random_sample(10)*10000.0 + 59580.0
d_mag_out = kp.applyParametrizedLightCurve([], params, expmjd)
self.assertEqual(d_mag_out.shape, (6, 4, 10))
for i_obj in range(4):
if i_obj == 2:
for i_filter in range(6):
np.testing.assert_array_equal(d_mag_out[i_filter][i_obj],
np.zeros(10))
else:
q_flux, d_flux = kp._calc_dflux(params['lc'][i_obj],
expmjd-params['t0'][i_obj])
d_mag_truth = -2.5*np.log10(1.0+d_flux/q_flux)
nan_vals = np.where(np.isnan(d_mag_truth))
self.assertEqual(len(nan_vals[0]), 0)
for i_filter in range(6):
np.testing.assert_array_equal(d_mag_out[i_filter][i_obj], d_mag_truth)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)
def test_calc_dflux(self):
"""
Test the method that calculates the flux of
parametrized light curves by generating a fake light
curve library with known parameters, calculating
the fluxes, and comparing to the expected results.
"""
lc_temp_file_name = tempfile.mktemp(prefix='test_calc_dflux_lc',
suffix='.gz')
rng = np.random.RandomState(7124)
n_c_1 = 10
a1_list = rng.random_sample(n_c_1)*5.0
b1_list = (rng.random_sample(n_c_1)-0.5)*2.0
c1_list = (rng.random_sample(n_c_1)-0.5)*0.1
omega1_list = rng.random_sample(n_c_1)*20.0
tau1_list = rng.random_sample(n_c_1)*100.0
median1 = 100.0
n_c_2 = 15
a2_list = rng.random_sample(n_c_2)*5.0
b2_list = (rng.random_sample(n_c_2)-0.5)*2.0
c2_list = (rng.random_sample(n_c_2)-0.5)*0.1
omega2_list = rng.random_sample(n_c_2)*20.0
tau2_list = rng.random_sample(n_c_2)*100.0
median2 = 200.0
with gzip.open(lc_temp_file_name, 'w') as out_file:
out_file.write(b'# a header\n')
out_file.write(b'kplr990000000_lc.txt 100 1.0e+02 %d ' % n_c_1)
for i_c in range(n_c_1):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median1)
for i_c in range(n_c_1):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a1_list[i_c], b1_list[i_c], c1_list[i_c],
omega1_list[i_c], tau1_list[i_c]))
out_file.write(b'\n')
out_file.write(b'kplr990000001_lc.txt 100 1.0e+02 %d ' % n_c_2)
for i_c in range(n_c_2):
out_file.write(b'%e ' % (1.0/(i_c+1)))
out_file.write(b'%e ' % median2)
for i_c in range(n_c_2):
out_file.write(b'%.15e %.15e %.15e %.15e %.15e ' %
(a2_list[i_c], b2_list[i_c], c2_list[i_c],
omega2_list[i_c], tau2_list[i_c]))
out_file.write(b'\n')
expmjd = rng.random_sample(100)*200.0
kp = ParametrizedLightCurveMixin()
kp.load_parametrized_light_curves(lc_temp_file_name)
q_flux, d_flux = kp._calc_dflux(990000000, expmjd)
self.assertAlmostEqual(q_flux, median1+c1_list.sum(), 10)
true_flux = np.zeros(len(expmjd))
for i_c in range(n_c_1):
arg = omega1_list[i_c]*(expmjd-tau1_list[i_c])
true_flux += a1_list[i_c]*np.cos(arg)
true_flux += b1_list[i_c]*np.sin(arg)
self.assertEqual(len(d_flux), len(true_flux))
np.testing.assert_allclose(d_flux, true_flux, rtol=0.0, atol=1.0e-10)
q_flux, d_flux = kp._calc_dflux(990000001, expmjd)
self.assertAlmostEqual(q_flux, median2+c2_list.sum(), 10)
true_flux = np.zeros(len(expmjd))
for i_c in range(n_c_2):
arg = omega2_list[i_c]*(expmjd-tau2_list[i_c])
true_flux += a2_list[i_c]*np.cos(arg)
true_flux += b2_list[i_c]*np.sin(arg)
self.assertEqual(len(d_flux), len(true_flux))
np.testing.assert_allclose(d_flux, true_flux, rtol=0.0, atol=1.0e-10)
sims_clean_up()
if os.path.exists(lc_temp_file_name):
os.unlink(lc_temp_file_name)