def test_satellite_and_annual_parallax_calculation():
"""test parallax calculation with Spitzer data"""
model_with_par = mm.Model(
{
't_0': 2456836.22,
'u_0': 0.922,
't_E': 22.87,
'pi_E_N': -0.248,
'pi_E_E': 0.234
},
coords="17:47:12.25 -21:22:58.2")
model_with_par.parallax(satellite=True,
earth_orbital=True,
topocentric=False)
model_with_par.parameters.t_0_par = 2456836.2
data_OGLE = mm.MulensData(file_name=SAMPLE_FILE_02)
data_Spitzer = mm.MulensData(file_name=SAMPLE_FILE_03,
ephemerides_file=SAMPLE_FILE_03_EPH)
model_with_par.set_datasets([data_OGLE, data_Spitzer])
ref_OGLE = np.loadtxt(SAMPLE_FILE_02_REF, unpack=True, usecols=[5])
ref_Spitzer = np.loadtxt(SAMPLE_FILE_03_REF, unpack=True, usecols=[5])
np.testing.assert_almost_equal(model_with_par.data_magnification[0],
ref_OGLE,
decimal=2)
ratio = model_with_par.data_magnification[1] / ref_Spitzer
np.testing.assert_almost_equal(ratio, [1.] * len(ratio), decimal=4)
def test_event_get_chi2_5():
"""
De facto checks how information is passed between Event, Fit, and Model.
Also checks simple relations between different Event.get_chi2().
"""
kwargs = {'comments': ["\\", "|"]}
data_1 = mm.MulensData(file_name=SAMPLE_FILE_03, **kwargs)
data_2 = mm.MulensData(file_name=SAMPLE_FILE_04, phot_fmt='flux', **kwargs)
params = {'t_0': 2452848.06, 'u_0': 0.1317, 't_E': 61.5}
model = mm.Model(params)
event_1 = mm.Event(data_1, model)
event_2 = mm.Event(data_2, model)
event_1_2 = mm.Event([data_1, data_2], model)
event_2_1 = mm.Event([data_2, data_1], model)
chi2_1 = event_1.get_chi2()
chi2_2 = event_2.get_chi2()
# The line below was failing when the test was written:
chi2_1_2 = event_1_2.get_chi2()
chi2_2_1 = event_2_1.get_chi2()
np.testing.assert_almost_equal(chi2_1_2, chi2_1 + chi2_2)
np.testing.assert_almost_equal(chi2_1_2, chi2_2_1)
def test_event_get_chi2_3():
"""test on ob08092 OGLE-IV data - MulensData.good & MulensData.bad"""
t_0 = 5379.57091
u_0 = 0.52298
t_E = 17.94002
bad = np.zeros(383, dtype='bool')
bad[300:350] = True
data_1 = mm.MulensData(file_name=SAMPLE_FILE_01, bad=bad)
data_2 = mm.MulensData(file_name=SAMPLE_FILE_01, good=~bad)
ev = mm.Event()
mod = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
mod.set_datasets([data_1])
ev.model = mod
ev.datasets = [data_1]
chi2 = ev.get_chi2()
np.testing.assert_almost_equal(float(chi2),
343.46567,
decimal=4,
err_msg='problem in resulting chi2')
mod.set_datasets([data_2])
ev.model = mod
ev.datasets = [data_2]
chi2 = ev.get_chi2()
np.testing.assert_almost_equal(float(chi2),
343.46567,
decimal=4,
err_msg='problem in resulting chi2')
def test_data_coords():
coords = SkyCoord('18:00:00 -30:00:00', unit=(u.hourangle, u.deg))
data_1 = mm.MulensData(file_name=SAMPLE_FILE_01,
coords='18:00:00 -30:00:00')
assert isinstance(data_1.coords, SkyCoord)
assert data_1.coords.ra == coords.ra
assert data_1.coords.dec == coords.dec
assert data_1.coords.dec.deg == -30.00
data_3 = mm.MulensData(file_name=SAMPLE_FILE_01)
data_3.coords = '17:00:00 -27:32:14'
assert data_3.coords.to_string('hmsdms') == '17h00m00s -27d32m14s'
def generate_binary_source_datasets(model_1, model_2):
"""prepare fake data for binary source model"""
time = np.linspace(4900., 5200., 600)
mag_1 = model_1.get_magnification(time)
mag_2 = model_2.get_magnification(time)
flux = 100. * mag_1 + 300. * mag_2 + 50.
data_1 = mm.MulensData(data_list=[time, flux, 1. + 0. * time],
phot_fmt='flux')
flux = 20. * mag_1 + 30. * mag_2 + 50.
data_2 = mm.MulensData(data_list=[time, flux, 1. + 0. * time],
phot_fmt='flux')
return (data_1, data_2)
def test_binary_source_and_fluxes_for_bands():
"""
Test if setting different flux ratios for different bands in binary
source models works properly. The argument flux_ratio_constraint
is set as string.
"""
model = mm.Model({
't_0_1': 5000.,
'u_0_1': 0.05,
't_0_2': 5100.,
'u_0_2': 0.003,
't_E': 30.
})
times_I = np.linspace(4900., 5200., 3000)
times_V = np.linspace(4800., 5300., 250)
(f_s_1_I, f_s_2_I, f_b_I) = (10., 20., 3.)
(f_s_1_V, f_s_2_V, f_b_V) = (15., 5., 30.)
q_f_I = f_s_2_I / f_s_1_I
q_f_V = f_s_2_V / f_s_1_V
(mag_1_I, mag_2_I) = model.magnification(times_I, separate=True)
(mag_1_V, mag_2_V) = model.magnification(times_V, separate=True)
effective_mag_I = (mag_1_I + mag_2_I * q_f_I) / (1. + q_f_I)
effective_mag_V = (mag_1_V + mag_2_V * q_f_V) / (1. + q_f_V)
flux_I = mag_1_I * f_s_1_I + mag_2_I * f_s_2_I + f_b_I
flux_V = mag_1_V * f_s_1_V + mag_2_V * f_s_2_V + f_b_V
data_I = mm.MulensData(data_list=[times_I, flux_I, flux_I / 100.],
phot_fmt='flux',
bandpass='******')
data_V = mm.MulensData(data_list=[times_V, flux_V, flux_V / 1000.],
phot_fmt='flux',
bandpass='******')
model.set_datasets([data_V, data_I])
model.set_source_flux_ratio_for_band('I', q_f_I)
model.set_source_flux_ratio_for_band('V', q_f_V)
# Test Model.magnification()
result_I = model.magnification(times_I, flux_ratio_constraint='I')
result_V = model.magnification(times_V, flux_ratio_constraint='V')
almost(result_I, effective_mag_I)
almost(result_V, effective_mag_V)
# Test Model.get_data_magnification()
result_I = model.get_data_magnification(data_I)
result_V = model.get_data_magnification(data_V)
almost(result_I, effective_mag_I)
almost(result_V, effective_mag_V)
def test_satellite_and_annual_parallax_calculation():
"""
test that data magnifications are correctly retrieved for Spitzer data.
"""
# Create Model
model_parameters = {
't_0': 2456836.22,
'u_0': 0.922,
't_E': 22.87,
'pi_E_N': -0.248,
'pi_E_E': 0.234,
't_0_par': 2456836.2
}
coords = "17:47:12.25 -21:22:58.2"
model_with_par = mm.Model(model_parameters, coords=coords)
model_with_par.parallax(satellite=True,
earth_orbital=True,
topocentric=False)
# Load Spitzer data and answers
data_Spitzer = mm.MulensData(file_name=SAMPLE_FILE_03,
ephemerides_file=SAMPLE_FILE_03_EPH)
ref_Spitzer = np.loadtxt(SAMPLE_FILE_03_REF, unpack=True, usecols=[5])
# Test FitData.data_magnification()
my_fit = mm.FitData(dataset=data_Spitzer, model=model_with_par)
ratio = my_fit.get_data_magnification() / ref_Spitzer
np.testing.assert_almost_equal(ratio, [1.] * len(ratio), decimal=4)
def test_event_chi2_binary_source():
"""simple test if chi2 calculation for binary source works fine"""
model = mm.Model({
't_0_1': 5000.,
'u_0_1': 0.05,
't_0_2': 5100.,
'u_0_2': 0.15,
't_E': 25.
})
model_1 = mm.Model(model.parameters.source_1_parameters)
model_2 = mm.Model(model.parameters.source_2_parameters)
# prepare fake data:
time = np.linspace(4900., 5200., 600)
mag_1 = model_1.magnification(time)
mag_2 = model_2.magnification(time)
flux = 100. * mag_1 + 300. * mag_2 + 50.
data = mm.MulensData(data_list=[time, flux, 1. + 0. * time],
phot_fmt='flux')
# Calculate chi^2:
event = mm.Event([data], model)
np.testing.assert_almost_equal(event.get_chi2(), 0.)
# Make sure Model.set_source_flux_ratio() is taken into account.
model.set_source_flux_ratio(1.)
np.testing.assert_almost_equal(model.magnification(time),
(mag_1 + mag_2) / 2.)
assert event.get_chi2() > 1.
model.set_source_flux_ratio(3.)
np.testing.assert_almost_equal(event.get_chi2(), 0.)
def test_chi2_vs_get_chi2():
"""get_chi2 always updates after something changes in the model, chi2 does
not."""
t_0 = 5379.57091
u_0 = 0.52298
t_E = 17.94002
data = mm.MulensData(file_name=SAMPLE_FILE_01)
ev = mm.Event()
mod = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
ev.model = mod
ev.datasets = [data]
# New (v2.0) method of fixing the blending
ev.fix_blend_flux = {}
np.testing.assert_almost_equal(ev.get_chi2(), 427.20382, decimal=4)
np.testing.assert_almost_equal(ev.chi2, 427.20382, decimal=4)
fix_blend_flux = {}
for dataset in ev.datasets:
fix_blend_flux[dataset] = 0.
ev.fix_blend_flux = fix_blend_flux
np.testing.assert_almost_equal(ev.chi2, 427.20382, decimal=4)
np.testing.assert_almost_equal(ev.get_chi2(), 459.09826, decimal=4)
np.testing.assert_almost_equal(ev.chi2, 459.09826, decimal=4)
def test_chi2_gradient():
"""
test that fit_fluxes/update must be run in order to update the gradient
"""
data = mm.MulensData(file_name=SAMPLE_FILE_02)
event = mm.Event(datasets=[data],
model=mm.Model(chi2_gradient_test_1.parameters),
fix_blend_flux={data: 0.})
result = event.get_chi2_gradient(chi2_gradient_test_1.grad_params)
reference = np.array([
chi2_gradient_test_1.gradient[key]
for key in chi2_gradient_test_1.grad_params
])
np.testing.assert_almost_equal(reference / result, 1., decimal=4)
# changing something w/o updating: gives old values
event.model.parameters.t_0 += 0.1
result_1 = event.chi2_gradient
np.testing.assert_almost_equal(result_1 / result, 1.)
# run fit_fluxes: gives new values
event.fit_fluxes()
result_2 = event.calculate_chi2_gradient(chi2_gradient_test_1.grad_params)
assert all(result != result_2)
# Go back to previous results and test that calculate_chi2_gradient gives
# results that don't match anything.
event.model.parameters.t_0 -= 0.1
result_3 = event.calculate_chi2_gradient(chi2_gradient_test_1.grad_params)
assert all(result != result_3)
assert all(result_2 != result_3)
result_4 = event.get_chi2_gradient(chi2_gradient_test_1.grad_params)
np.testing.assert_almost_equal(result_4 / result, 1.)
def test_annual_parallax_calculation():
"""
This is a high-level unit test for parallax. The "true" values were
calculated from the sfit routine assuming fs=1.0, fb=0.0.
"""
t_0 = 2457479.5 # April 1 2016, a time when parallax is large
times = np.array([t_0 - 1., t_0, t_0 + 1., t_0 + 1.])
true_with_par = [
np.array([7.12376832, 10.0386009, 7.13323363, 7.13323363])
]
model_with_par = mm.Model(
{
't_0': t_0,
'u_0': 0.1,
't_E': 10.,
'pi_E': (0.3, 0.5)
},
coords='17:57:05 -30:22:59')
model_with_par.parallax(satellite=False,
earth_orbital=True,
topocentric=False)
ones = np.ones(len(times))
data = mm.MulensData(data_list=[times, ones, ones])
model_with_par.set_datasets([data])
model_with_par.parameters.t_0_par = 2457479.
np.testing.assert_almost_equal(model_with_par.data_magnification,
true_with_par,
decimal=4)
def _set_event_PSPL(self):
"""
Sets internal variable to MulensModel.Event instance
that uses PSPL model.
"""
datasets = []
for band in self.bands:
mask = (self._filter == band)
if not np.any(mask):
continue
times = self._JD[mask]
flux = self._simulated_flux[band]
sigma_flux = self._sigma_flux[band]
plot = {}
data = MM.MulensData([times, flux, sigma_flux],
phot_fmt='flux',
bandpass=band,
plot_properties={'label': 'LSST ' + band})
datasets.append(data)
if self._follow_up_Chilean is not None:
if self._follow_up_Chilean.n_epochs > 0:
datasets.append(self._follow_up_Chilean)
if self._follow_up_nonChilean is not None:
if self._follow_up_nonChilean.n_epochs > 0:
datasets.append(self._follow_up_nonChilean)
model = MM.Model(
{p: self._parameters[p]
for p in ['t_0', 'u_0', 't_E']})
self._event_PSPL = MM.Event(datasets, model)
def test_event_get_chi2_3():
"""
Test: If I change the model parameters, the chi2 should change.
"""
t_0 = 5380.
u_0 = 0.5
t_E = 18.
model = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
# Generate fake data
times = np.arange(5320, 5420.)
f_source = 0.1
f_blend = 0.5
mod_fluxes = f_source * model.magnification(times) + f_blend
I_mag = mm.Utils.get_mag_from_flux(mod_fluxes)
errors = 0.01 * np.ones(times.shape)
data = mm.MulensData(data_list=[times, I_mag, errors])
# Generate event and fit
event = mm.Event(datasets=data, model=model)
orig_chi2 = event.get_chi2()
# Change the model
event.model.parameters.t_0 = 5000.
assert event.get_chi2() != orig_chi2
def do_annual_parallax_test(filename):
"""testing functions called by a few unit tests"""
with open(filename) as data_file:
lines = data_file.readlines()
ulens_params = lines[3].split()
event_params = lines[4].split()
data = np.loadtxt(filename, dtype=None)
model = mm.Model(
{
't_0': float(ulens_params[1]) + 2450000.,
'u_0': float(ulens_params[3]),
't_E': float(ulens_params[4]),
'pi_E_N': float(ulens_params[5]),
'pi_E_E': float(ulens_params[6])
},
coords=SkyCoord(event_params[1] + ' ' + event_params[2],
unit=(u.deg, u.deg)))
model.parameters.t_0_par = float(ulens_params[2]) + 2450000.
time = data[:, 0]
dataset = mm.MulensData([time, 20. + time * 0., 0.1 + time * 0.],
add_2450000=True)
model.set_datasets([dataset])
model.parallax(satellite=False, earth_orbital=True, topocentric=False)
return np.testing.assert_almost_equal(model.data_magnification[0] /
data[:, 1],
1.0,
decimal=4)
def _add_follow_up_nonChilean(self):
"""
Add follow-up from observatories that are outside Chile.
"""
if self._nonChilean_follow_up_data is None:
temp = np.loadtxt(self._nonChilean_follow_up_file, unpack=True)
self._nonChilean_follow_up_data = {
'jd': temp[0],
'5sigma_depth': temp[1]
}
start = self.detection_time + self._dt_shift
stop = self._parameters[
't_0'] + self._t_E_factor * self._parameters['t_E']
mask = (self._nonChilean_follow_up_data['jd'] > start)
mask *= (self._nonChilean_follow_up_data['jd'] < stop)
times = self._nonChilean_follow_up_data['jd'][mask]
mag_5sig = self._nonChilean_follow_up_data['5sigma_depth'][mask]
mag_5sig -= self._d_5sigma
sim = self._simulate_flux(times, mag_5sig, self._band_follow_up)
self._follow_up_nonChilean = MM.MulensData(
[times, sim[0], sim[1]],
phot_fmt='flux',
bandpass=self._band_follow_up,
plot_properties={
"label": "follow-up i",
"zorder": -500
})
def gen_data(f_source_1,
f_blend,
q_flux,
times,
bandpass=None,
**kwargs):
"""generate perfect data for a given set of properties"""
if bandpass == 'I':
gamma = self.gamma['I']
elif bandpass == 'V':
gamma = self.gamma['V']
else:
gamma = None
mag_1 = model_1.get_magnification(times)
mag_2 = model_2.get_magnification(times, gamma=gamma)
f_source_2 = f_source_1 * q_flux
flux = f_source_1 * mag_1 + f_source_2 * mag_2 + f_blend
err = np.zeros(len(times)) + 0.01
data = mm.MulensData([times, flux, err],
phot_fmt='flux',
bandpass=bandpass,
**kwargs)
return data
def test_event_get_chi2_4():
"""test if best chi2 is remembered correctly"""
t_0 = 5379.57091
u_0 = 0.52298
t_E = 17.94002
data = mm.MulensData(file_name=SAMPLE_FILE_01)
ev = mm.Event()
params = {'t_0': t_0, 'u_0': u_0, 't_E': t_E * u.day}
mod = mm.Model(params)
mod.set_datasets([data])
ev.model = mod
ev.datasets = [data]
chi2_1 = ev.get_chi2() # This is the best model.
ev.model.parameters.parameters['t_0'] += 1.
ev.model.parameters.parameters['u_0'] += 0.1
ev.model.parameters.parameters['t_E'] += 1. * u.day
chi2_2 = ev.get_chi2()
assert chi2_2 > chi2_1
assert ev.best_chi2 == chi2_1
assert ev.best_chi2_parameters == params
def test_chi2_gradient_2():
"""
Double-up on the gradient test, i.e. duplicate the dataset and check that
the gradient values double.
"""
reference = np.array([
chi2_gradient_test_1.gradient[key]
for key in chi2_gradient_test_1.grad_params
])
data = mm.MulensData(file_name=SAMPLE_FILE_02)
event = mm.Event(datasets=[data, data],
model=mm.Model(chi2_gradient_test_1.parameters),
fix_blend_flux={data: 0.})
result_0 = event.get_chi2_gradient(chi2_gradient_test_1.grad_params)
result_1 = event.fits[1].chi2_gradient
np.testing.assert_almost_equal(2. * reference / result_0, 1., decimal=4)
np.testing.assert_almost_equal(2. * result_1 / result_0, 1.)
np.testing.assert_almost_equal(reference / result_1, 1., decimal=4)
# Change something and test fit.get_chi2_gradient
event.model.parameters.t_0 += 0.1
result_2 = event.fits[1].get_chi2_gradient(
chi2_gradient_test_1.grad_params)
assert result_2[0] != result_1[0]
event.model.parameters.t_0 -= 0.1
result_3 = event.fits[1].get_chi2_gradient(
chi2_gradient_test_1.grad_params)
np.testing.assert_almost_equal(result_3 / reference, 1., decimal=4)
def test_event_get_chi2_double_source_simple():
"""
basic test on ob08092 OGLE-IV data
"""
t_0 = 5379.57091
u_0 = 0.52298
t_E = 17.94002
data = mm.MulensData(file_name=SAMPLE_FILE_01)
ev = mm.Event()
mod = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
mod.set_datasets([data, data])
ev.model = mod
ev.datasets = [data, data]
chi2 = ev.get_chi2()
assert isinstance(chi2, float), 'wrong type of chi2'
message = 'problem in resulting chi2 for 2 exactly the same datasets'
np.testing.assert_almost_equal(chi2,
854.407644,
decimal=4,
err_msg=message)
def test_event_chi2_binary_source_2datasets():
"""
simple test if chi2 calculation for binary source
works fine for 2 datasets
"""
model = mm.Model({
't_0_1': 5000.,
'u_0_1': 0.05,
't_0_2': 5100.,
'u_0_2': 0.15,
't_E': 25.
})
model_1 = mm.Model(model.parameters.source_1_parameters)
model_2 = mm.Model(model.parameters.source_2_parameters)
# prepare fake data:
time = np.linspace(4900., 5200., 600)
mag_1 = model_1.magnification(time)
mag_2 = model_2.magnification(time)
flux = 100. * mag_1 + 300. * mag_2 + 50.
data_1 = mm.MulensData(data_list=[time, flux, 1. + 0. * time],
phot_fmt='flux')
flux = 20. * mag_1 + 30. * mag_2 + 50.
data_2 = mm.MulensData(data_list=[time, flux, 1. + 0. * time],
phot_fmt='flux')
# Calculate chi^2:
event = mm.Event([data_1, data_2], model)
np.testing.assert_almost_equal(event.get_chi2(), 0.)
np.testing.assert_almost_equal(event.get_chi2_for_dataset(0), 0.)
np.testing.assert_almost_equal(event.get_chi2_for_dataset(1), 0.)
# Make sure that changing parameters changes chi2:
model.parameters.t_E = 100.
assert event.get_chi2() > 1., 'wrong chi2'
model.parameters.t_E = 25.
np.testing.assert_almost_equal(event.get_chi2(), 0.)
model.parameters.t_0_1 = 5010.
assert event.get_chi2() > 1., 'wrong chi2'
model.parameters.t_0_1 = 5000.
# Test combination of Model.set_source_flux_ratio_for_band() and
# Event.get_chi2_for_dataset().
data_1.bandpass = '******'
event.model.set_source_flux_ratio_for_band('some', 3.)
np.testing.assert_almost_equal(event.get_chi2_for_dataset(0), 0.)
def test_file_read():
"""read sample file and check if values match"""
data = mm.MulensData(file_name=SAMPLE_FILE_01)
np.testing.assert_almost_equal(data.time[0],
5264.84100,
err_msg="time of first line doesn't match")
assert data.mag[-1] == 13.913, "magnitude of the last line doesn't match"
def test_data_list():
"""
check if initialization by list works fine
"""
t = np.array([7500., 7501.])
m = np.array([21.0, 21.1])
e = np.array([0.001, 1.000])
data = mm.MulensData(data_list=[t, m, e])
np.testing.assert_almost_equal(
data.time, t, err_msg='problem with time vector in MulensData')
def test_gradient_init_1(self):
"""
Test that fit_fluxes/update must be run before the gradient is accessed
"""
data = mm.MulensData(file_name=SAMPLE_FILE_02)
event = mm.Event(datasets=[data],
model=mm.Model(chi2_gradient_test_1.parameters),
fix_blend_flux={data: 0.})
with self.assertRaises(TypeError):
event.chi2_gradient()
def generate_dataset(time, flux_1, flux_2, blend_flux, flux_err,
model_1, model_2):
"""Generate simulated dataset assuming binary source model."""
A_1 = model_1.get_magnification(time)
A_2 = model_2.get_magnification(time)
flux = A_1 * flux_1 + A_2 * flux_2 + blend_flux
err_flux = flux_err + 0. * time
flux += flux_err * np.random.normal(size=len(time))
my_dataset = mm.MulensData([time, flux, err_flux], phot_fmt='flux')
return my_dataset
def test_event_get_chi2_2():
"""
Basic unit test on ob08092 OGLE-IV data. Same as above but with
the data input twice (to test behavior for multiple datasets);
also test if Event.get_chi2_for_dataset() gives correct answers.
"""
t_0 = 5379.57091
u_0 = 0.52298
t_E = 17.94002
answer = 427.20382201
data_1 = mm.MulensData(file_name=SAMPLE_FILE_01)
data_2 = mm.MulensData(file_name=SAMPLE_FILE_01)
ev = mm.Event()
mod = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
ev.model = mod
ev.datasets = [data_1, data_2]
chi2 = ev.get_chi2()
assert isinstance(chi2, float), 'wrong type of chi2'
np.testing.assert_almost_equal(float(chi2),
2. * answer,
decimal=4,
err_msg='problem in resulting chi2')
chi2_2 = ev.get_chi2_for_dataset(0)
np.testing.assert_almost_equal(chi2_2, answer)
chi2_3 = ev.get_chi2_for_dataset(1)
np.testing.assert_almost_equal(chi2_3, answer)
# New method of fixing the blending
# Both datasets have zero blending
ev.fix_blend_flux = {data_1: 0., data_2: 0.}
chi2_no_blend = ev.get_chi2()
assert isinstance(chi2_no_blend, float), 'wrong type of chi2'
np.testing.assert_almost_equal(
float(chi2_no_blend),
2. * 459.09826,
decimal=4,
err_msg='problem in resulting chi2 for fixed no blending')
assert (ev.blend_fluxes == [0., 0.])
def generate_dataset(f_mod, t):
"""
pass in f_mod and t, returns a MulensData
"""
# error in measurement
err = f_mod * 0.01
my_dataset = mm.MulensData(data_list=[t, f_mod, err], phot_fmt='flux')
return my_dataset
def test_event_coords_ra_dec_2():
"""checks setting coords of Event after initialization"""
ra_1_str = '01:00:00'
dec_1_str = '+44:15:00'
ra_1 = 15.
dec_1 = 44.25
data = mm.MulensData(file_name=SAMPLE_FILE_01)
model = mm.Model({'t_0': 2450000., 'u_0': 0.1, 't_E': 100.})
event = mm.Event(datasets=data, model=model)
event.coords = '{0} {1}'.format(ra_1_str, dec_1_str)
check_event_coords(event, ra_1, dec_1)
def _test_event_chi2_gradient_rho():
"""
test calculation of chi2 gradient including finite source effects
MB08310 is used as an example
"""
kwargs = {'comments': ["\\", "|"]}
datasets = [
mm.MulensData(file_name=SAMPLE_FILE_310_01, bandpass='******', **kwargs),
mm.MulensData(file_name=SAMPLE_FILE_310_02, bandpass='******', **kwargs),
mm.MulensData(file_name=SAMPLE_FILE_310_03, bandpass='******', **kwargs)
]
(gamma_I, gamma_V) = (0.44, 0.72)
t_0 = 2454656.39975
u_0 = 0.00300
t_E = 11.14
rho = 0.00492549
t_star = rho * t_E
parameters = {'t_0': t_0, 'u_0': u_0, 't_E': t_E, 'rho': rho}
model = mm.Model(parameters)
method = 'finite_source_LD_Yoo04'
model.set_magnification_methods(
[t_0 - 2. * t_star, method, t_0 + 2. * t_star])
model.set_limb_coeff_gamma('R', (gamma_V + gamma_I) / 2.)
model.set_limb_coeff_gamma('U', (gamma_V + gamma_I) / 2.)
model.set_limb_coeff_gamma('I', gamma_I)
# Set expected values
# JCY - see sandbox/rho_gradient on pink laptop
params = parameters.keys()
gradient = {
't_0': 1283513.3068849628,
'u_0': 20492801.742886964,
't_E': -9573.3589902395161,
'rho': -1503911.2409404013
}
reference = np.array([gradient[key] for key in params])
# Create event and run test
event = mm.Event(model=model, datasets=datasets)
def test_model_PSPL_1():
"""tests basic evaluation of Paczynski model"""
t_0 = 5379.57091
u_0 = 0.52298
t_E = 17.94002
times = np.array([t_0 - 2.5 * t_E, t_0, t_0 + t_E])
data = mm.MulensData(data_list=[times, times * 0., times * 0.])
model = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
model.parameters.u_0 = u_0
model.parameters.t_E = t_E
model.set_datasets([data])
almost(model.data_magnification,
[np.array([1.028720763, 2.10290259, 1.26317278])],
err_msg="PSPL model returns wrong values")
def test_BLPS_01():
"""simple binary lens with point source"""
params = mm.ModelParameters({
't_0': t_0,
'u_0': u_0,
't_E': t_E,
'alpha': alpha,
's': s,
'q': q
})
model = mm.Model(parameters=params)
t = np.array([2456112.5])
data = mm.MulensData(data_list=[t, t * 0. + 16., t * 0. + 0.01])
magnification = model.get_magnification(data.time[0])
almost(magnification, 4.691830781584699)
