def test_magnifications_for_orbital_motion():
"""
make sure that orbital motion parameters are properly passed to
magnification methods calculations
"""
dict_static = {
't_0': 100.,
'u_0': 0.1,
't_E': 100.,
'q': 0.99,
's': 1.1,
'alpha': 10.
}
dict_motion = dict_static.copy()
dict_motion.update({'ds_dt': -2, 'dalpha_dt': -300.})
static = mm.Model(dict_static)
motion = mm.Model(dict_motion)
t_1 = 100.
almost(static.magnification(t_1), motion.magnification(t_1))
t_2 = 130.
static.parameters.s = 0.93572895
static.parameters.alpha = 345.359342916
almost(static.magnification(t_2), motion.magnification(t_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.get_magnification(times_I, separate=True)
(mag_1_V, mag_2_V) = model.get_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
# model.set_source_flux_ratio_for_band('I', q_f_I)
# model.set_source_flux_ratio_for_band('V', q_f_V)
# Test Model.get_magnification()
result_I = model.get_magnification(times_I, source_flux_ratio=q_f_I)
result_V = model.get_magnification(times_V, source_flux_ratio=q_f_V)
almost(result_I, effective_mag_I)
almost(result_V, effective_mag_V)
def test_separate_method_for_each_source():
"""
Checks if setting separate magnification method for each source in
binary source models works properly.
"""
model = mm.Model({
't_0_1': 5000.,
'u_0_1': 0.01,
'rho_1': 0.005,
't_0_2': 5100.,
'u_0_2': 0.001,
'rho_2': 0.005,
't_E': 1000.
})
model.set_magnification_methods(
[4999., 'finite_source_uniform_Gould94', 5101.], source=1)
# In order not to get "no FS method" warning:
model.set_magnification_methods([0., 'finite_source_uniform_Gould94', 1.],
source=2)
out = model.magnification(5000., separate=True)
almost([out[0][0], out[1][0]], [103.46704167, 10.03696291])
model.set_magnification_methods(
[4999., 'finite_source_uniform_Gould94', 5001.], source=1)
model.set_magnification_methods(
[5099., 'finite_source_uniform_Gould94', 5101.], source=2)
out = model.magnification(5100., separate=True)
almost([out[0][0], out[1][0]], [9.98801936, 395.96963727])
def test_caustic_for_orbital_motion():
"""
check if caustics calculated for different epochs in orbital motion model
are as expected
"""
q = 0.1
s = 1.3
params = {
't_0': 100.,
'u_0': 0.1,
't_E': 10.,
'q': q,
's': s,
'ds_dt': 0.5,
'alpha': 0.,
'dalpha_dt': 0.
}
model = mm.Model(parameters=params)
model.update_caustics()
almost(model.caustics.get_caustics(), mm.Caustics(q=q, s=s).get_caustics())
model.update_caustics(100. + 365.25 / 2)
almost(model.caustics.get_caustics(),
mm.Caustics(q=q, s=1.55).get_caustics())
def test_limb_darkening():
"""check if limb_darkening coeffs are properly passed and converted"""
gamma = 0.4555
u = 3. * gamma / (2. + gamma)
model = mm.Model({'t_0': 2450000., 'u_0': 0.1, 't_E': 100., 'rho': 0.001})
model.set_limb_coeff_gamma('I', gamma)
almost(model.get_limb_coeff_gamma('I'), gamma)
almost(model.get_limb_coeff_u('I'), u)
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])
model = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
almost(model.get_magnification(times),
np.array([1.028720763, 2.10290259, 1.26317278]),
err_msg="PSPL model returns wrong values")
def test_t_E():
"""make sure t_E can be accessed properly"""
t_0 = 2460000.
u_0 = 0.1
t_E = 12.3456
t_star = 0.01234
params = dict(t_0=t_0, u_0=u_0, t_E=t_E)
model_1 = mm.Model(params)
params['t_star'] = t_star
model_2 = mm.Model(params)
almost(model_1.parameters.t_E, t_E)
almost(model_2.parameters.t_E, t_E)
def test_photfmt_scaled_2(self):
""" check phot_fmt='scaled'; true values of f_source, f_blend should
yield errorbars identical to the true values."""
f_source_0 = self.dataset_properties['f_source']
f_blend_0 = self.dataset_properties['f_blend']
# Bad = True
(residuals,
res_errors) = self.fit.get_residuals(phot_fmt='scaled',
source_flux=f_source_0,
blend_flux=f_blend_0,
bad=True)
almost(res_errors, self.dataset.err_mag)
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_bad_keyword(self):
"""
If bad = False, the magnification should be zero. Therefore, the flux
calculated for the bad data points should be f_blend. If bad=True,
the values should be the true values of the residuals.
"""
# Bad = False
(residuals, res_errors) = self.fit.get_residuals(phot_fmt='flux',
bad=False)
for index in self.outliers['index']:
exp_residual = (self.dataset.flux[index] -
self.dataset_properties['f_blend'])
almost(residuals[index], exp_residual)
# Check errorbars
almost(res_errors, self.dataset.err_flux)
# Bad = True
(residuals, res_errors) = self.fit.get_residuals(phot_fmt='flux',
bad=True)
for i, index in enumerate(self.outliers['index']):
exp_residual = self.outliers['values'][i]
almost(residuals[index], exp_residual)
# Check errorbars
almost(res_errors, self.dataset.err_flux)
def test_get_lc():
"""
Test if Model.get_lc() works properly; we test on binary source model
without finite source effect.
"""
model = mm.Model({
't_0_1': 5000.,
'u_0_1': 1.,
't_0_2': 5100.,
'u_0_2': 0.1,
't_E': 100.
})
out = model.get_lc(5050., source_flux=[1., 2.], blend_flux=3.)
almost(out, 19.668370500043526)
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)
def test_BLPS_02_AC():
"""
simple binary lens with extended source and different methods to evaluate
magnification - version with adaptivecontouring
"""
params = mm.ModelParameters({
't_0': t_0,
'u_0': u_0,
't_E': t_E,
'alpha': alpha,
's': s,
'q': q,
'rho': rho
})
model = mm.Model(parameters=params)
t = np.array([6112.5, 6113., 6114., 6115., 6116., 6117., 6118., 6119])
t += 2450000.
ac_name = 'Adaptive_Contouring'
methods = [
2456113.5, 'Quadrupole', 2456114.5, 'Hexadecapole', 2456116.5, ac_name,
2456117.5
]
accuracy_1 = {'accuracy': 0.04}
accuracy_2 = {'accuracy': 0.01, 'ld_accuracy': 0.00001}
model.set_magnification_methods(methods)
model.set_magnification_methods_parameters({ac_name: accuracy_1})
data = mm.MulensData(data_list=[t, t * 0. + 16., t * 0. + 0.01])
result = model.get_magnification(data.time)
expected = np.array([
4.69183078, 2.87659723, 1.83733975, 1.63865704, 1.61038135, 1.63603122,
1.69045492, 1.77012807
])
almost(result, expected, decimal=3)
# Below we test passing the limb coeff to VBBL function.
# data.bandpass = '******'
model.set_limb_coeff_u('I', 10.)
# This is an absurd value but I needed something quick.
model.set_magnification_methods_parameters({ac_name: accuracy_2})
# result = model.data_magnification[0]
result = model.get_magnification(data.time,
gamma=model.get_limb_coeff_gamma('I'))
almost(result[5], 1.6366862, decimal=3)
def test_intp_of_linear_arr():
h_in, w_in, d_in = 10, 20, 5
h_out, w_out, d_out = 5., 25., 5.
x_in, y_in, z_in = np.mgrid[:h_in, :w_in, :d_in]
x_out, y_out, z_out = np.mgrid[:h_out, :w_out, :d_out]
x_out = 1. * x_out / x_out.max() * (h_in - 1.)
y_out = 1. * y_out / y_out.max() * (w_in - 1.)
z_out = 1. * z_out / z_out.max() * (d_in - 1.)
# -- arbitray linear function
def f1(x, y):
a = 1.234
b = 0.987
return a * x + b * y
ref_out = f1(x_out, y_out)
out1 = resample(f1(x_in, y_in), (h_out, w_out, d_out), order=1)
almost(ref_out, out1)
def execute_test_blend_fixed(f_b):
# test for when source flux is to be determined, but blend flux is a
# fixed value
pspl, t, A = generate_model()
# secret source flux
f_s = 1.0
f_mod = f_s * A + f_b
my_dataset = generate_dataset(f_mod, t)
my_fit = mm.FitData(model=pspl,
dataset=my_dataset,
fix_blend_flux=f_b,
fix_source_flux=False)
my_fit.fit_fluxes()
almost(my_fit.source_flux, f_s)
def test_model_init_1():
"""tests if basic parameters of Model.__init__() are properly passed"""
t_0 = 5432.10987
u_0 = 0.001
t_E = 123.456
rho = 0.0123
my_model = mm.Model({'t_0': t_0, 'u_0': u_0, 't_E': t_E, 'rho': rho})
almost(my_model.parameters.t_0, t_0, err_msg='t_0 not set properly')
almost(my_model.parameters.u_0, u_0, err_msg='u_0 not set properly')
almost(my_model.parameters.t_E, t_E, err_msg='t_E not set properly')
almost(my_model.parameters.rho, rho, err_msg='rho not set properly')
def test_coords_transformation():
"""
this was tested using http://ned.ipac.caltech.edu/forms/calculator.html
"""
coords = "17:54:32.1 -30:12:34.0"
model = mm.Model({'t_0': 2450000., 'u_0': 0.1, 't_E': 100.}, coords=coords)
almost(model.coords.galactic_l.value, 359.90100049 - 360., decimal=4)
almost(model.coords.galactic_b.value, -2.31694073, decimal=3)
almost(model.coords.ecliptic_lon.value, 268.81102051, decimal=1)
almost(model.coords.ecliptic_lat.value, -6.77579203, decimal=2)
def test_both_fixed():
"""
test for when both fluxes are fixed --> evaluate chi2 but not fluxes.
"""
pspl, t, A = generate_model()
# secret blend flux, set source flux
f_s = 1.0
f_b = 0.5
f_mod = f_s * A + f_b
my_dataset = generate_dataset(f_mod, t)
my_fit = mm.FitData(model=pspl,
dataset=my_dataset,
fix_blend_flux=f_b,
fix_source_flux=f_s)
my_fit.update()
almost(my_fit.blend_flux, f_b)
almost(my_fit.source_flux, f_s)
def test_BLPS_shear():
"""
simple binary lens with point source and external convergence and shear
"""
params = mm.ModelParameters({
't_0': t_0,
'u_0': u_0,
't_E': t_E,
'alpha': alpha,
's': s,
'q': q,
'convergence_K': 0.0,
'shear_G': complex(0, 0)
})
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)
def test_BLPS_02():
"""
simple binary lens with extended source and different methods to
evaluate magnification
"""
params = mm.ModelParameters({
't_0': t_0,
'u_0': u_0,
't_E': t_E,
'alpha': alpha,
's': s,
'q': q,
'rho': rho
})
model = mm.Model(parameters=params)
t = np.array([6112.5, 6113., 6114., 6115., 6116., 6117., 6118., 6119])
t += 2450000.
methods = [
2456113.5, 'Quadrupole', 2456114.5, 'Hexadecapole', 2456116.5, 'VBBL',
2456117.5
]
model.set_magnification_methods(methods)
data = mm.MulensData(data_list=[t, t * 0. + 16., t * 0. + 0.01])
model.set_datasets([data])
result = model.data_magnification[0]
expected = np.array([
4.69183078, 2.87659723, 1.83733975, 1.63865704, 1.61038135, 1.63603122,
1.69045492, 1.77012807
])
almost(result, expected)
# Below we test passing the limb coeff to VBBL function.
data.bandpass = '******'
model.set_limb_coeff_u('I', 10.)
# This is an absurd value but I needed something quick.
result = model.data_magnification[0]
almost(result[5], 1.6366862)
def test_source_fixed():
"""
test for when blend flux is to be determined, but source flux is a fixed
value
"""
pspl, t, A = generate_model()
# secret blend flux, set source flux
f_s = 1.0
f_b = 0.5
f_mod = f_s * A + f_b
my_dataset = generate_dataset(f_mod, t)
my_fit = mm.FitData(model=pspl,
dataset=my_dataset,
fix_blend_flux=False,
fix_source_flux=f_s)
my_fit.fit_fluxes()
almost(my_fit.blend_flux, f_b)
def test_photfmt_scaled_1(self):
""" check phot_fmt='scaled' """
f_source_0 = 1.0
f_blend_0 = 0.1
# Bad = True
(residuals,
res_errors) = self.fit.get_residuals(phot_fmt='scaled',
source_flux=f_source_0,
blend_flux=f_blend_0,
bad=True)
model_flux = (
f_source_0 * self.model.get_magnification(self.dataset.time) +
f_blend_0)
model_mag = mm.Utils.get_mag_from_flux(model_flux)
for i in np.arange(len(self.dataset.time)):
exp_flux = (
f_source_0 *
(self.dataset.flux[i] - self.dataset_properties['f_blend']) /
self.dataset_properties['f_source'] + f_blend_0)
if i in self.outliers['index']:
exp_mag = mm.Utils.get_mag_from_flux(exp_flux)
exp_delta_mag = exp_mag - model_mag[i]
almost(exp_delta_mag, residuals[i])
else:
# Non-outliers should have zero residual
almost(residuals[i], 0)
# Check errorbars
exp_err_flux = (f_source_0 * self.dataset.err_flux[i] /
self.dataset_properties['f_source'])
exp_err_mag = 2.5 * exp_err_flux / exp_flux / np.log(10.)
almost(exp_err_mag, res_errors[i])
assert self.dataset.err_mag[i] != res_errors[i]
def test_photfmt_mag(self):
""" check phot_fmt = 'mag' ."""
# Bad = True
(residuals, res_errors) = self.fit.get_residuals(phot_fmt='mag',
bad=True)
# Simple sign check
for i, index in enumerate(self.outliers['index']):
if self.outliers['values'][i] > 0:
assert residuals[index] < 0
else:
assert residuals[index] > 0
# Value check
for i in np.arange(len(self.dataset.time)):
if i in self.outliers['index']:
index = np.where(self.outliers['index'] == i)
f_0 = self.dataset.flux[i] - self.outliers['values'][index]
f_obs = self.dataset.flux[i]
delta_mag = -2.5 * np.log10(f_obs / f_0)
almost(delta_mag, residuals[i])
else:
# Non-outliers should have zero residual
almost(residuals[i], 0)
# Check errorbars
almost(res_errors, self.dataset.err_mag)
def test_scale_fluxes():
"""Specify a source_flux, blend_flux and make sure it works"""
# Original Flux values
f_s = 1.0
f_b = 0.5
# Generate fake data from a fake model
pspl, t, A = generate_model()
f_mod = f_s * A + f_b
data = generate_dataset(f_mod, t)
fit = mm.FitData(dataset=data, model=pspl)
fit.fit_fluxes()
num = 100
# Test the same
(new_flux, new_err) = fit.scale_fluxes(source_flux=f_s, blend_flux=f_b)
almost(data.flux[num], new_flux[num])
# Test Different
(f_s_new, f_b_new) = (0.1, 0.)
exp_flux = (data.flux - f_b) * f_s_new / f_s + f_b_new
exp_err = data.err_flux * f_s_new / f_s
(new_flux, new_err) = fit.scale_fluxes(source_flux=f_s_new,
blend_flux=f_b_new)
assert np.abs(data.flux[num] - new_flux[num]) > 0.5
almost(exp_flux / new_flux, 1.)
almost(exp_err / new_err, 1.)
def test_default():
"""
test for when blend flux and source flux are to be determined
"""
pspl, t, A = generate_model()
# secrets
f_s = 1.0
f_b = 0.5
# generate f_mod
f_mod = f_s * A + f_b
my_dataset = generate_dataset(f_mod, t)
my_fit = mm.FitData(model=pspl,
dataset=my_dataset,
fix_blend_flux=False,
fix_source_flux=False)
my_fit.fit_fluxes()
almost(my_fit.blend_flux, f_b)
almost(my_fit.source_flux, f_s)
# Test get_model_fluxes() for 1 source
peak_index = 500
mod_fluxes = my_fit.get_model_fluxes()
almost(mod_fluxes[peak_index], my_dataset.flux[peak_index])
def test_init_parameters():
"""are parameters properly passed between Model and ModelParameters?"""
t_0 = 6141.593
u_0 = 0.5425
t_E = 62.63 * u.day
params = mm.ModelParameters({'t_0': t_0, 'u_0': u_0, 't_E': t_E})
model = mm.Model(parameters=params)
almost(model.parameters.t_0, t_0)
almost(model.parameters.u_0, u_0)
almost(model.parameters.t_E, t_E.value)
def run_test(self,
fix_blend_flux=False,
fix_source_flux=False,
fix_q_flux=False):
self.my_fit = mm.FitData(model=self.model,
dataset=self.dataset,
fix_blend_flux=fix_blend_flux,
fix_source_flux=fix_source_flux,
fix_source_flux_ratio=fix_q_flux)
self.my_fit.fit_fluxes()
almost(self.my_fit.blend_flux, self.f_b)
almost(self.my_fit.source_fluxes[0], self.f_s_1)
almost(self.my_fit.source_fluxes[1], self.f_s_2)
# Test get_model_fluxes() for 2 sources
peak_index = 500
mod_fluxes = self.my_fit.get_model_fluxes()
almost(mod_fluxes[peak_index], self.dataset.flux[peak_index])
def test_model_binary_and_finite_sources():
"""
test if model magnification calculation for binary source works with
finite sources (both rho and t_star given)
"""
model = mm.Model({
't_0_1': 5000.,
'u_0_1': 0.005,
'rho_1': 0.001,
't_0_2': 5100.,
'u_0_2': 0.0003,
't_star_2': 0.03,
't_E': 25.
})
model_1 = mm.Model(model.parameters.source_1_parameters)
model_2 = mm.Model(model.parameters.source_2_parameters)
(t1, t2) = (4999.95, 5000.05)
(t3, t4) = (5099.95, 5100.05)
finite = 'finite_source_uniform_Gould94'
model.set_magnification_methods(
[t1, finite, t2, 'point_source', t3, finite, t4])
model_1.set_magnification_methods([t1, finite, t2])
model_2.set_magnification_methods([t3, finite, t4])
# prepare fake data:
(f_s_1, f_s_2, f_b) = (100., 300., 50.)
time = np.linspace(4900., 5200., 4200)
mag_1 = model_1.magnification(time)
mag_2 = model_2.magnification(time)
flux = f_s_1 * mag_1 + f_s_2 * mag_2 + f_b
data = mm.MulensData(data_list=[time, flux, 1. + 0. * time],
phot_fmt='flux')
model.set_datasets([data])
model_1.set_datasets([data])
model_2.set_datasets([data])
# test:
fitted = model.get_data_magnification(data)
expected = (mag_1 * f_s_1 + mag_2 * f_s_2) / (f_s_1 + f_s_2)
almost(fitted, expected)
# test separate=True option:
(mag_1_, mag_2_) = model.magnification(time, separate=True)
almost(mag_1, mag_1_)
almost(mag_2, mag_2_)
def test_model_binary_and_finite_sources():
"""
test if model magnification calculation for binary source works with
finite sources (both rho and t_star given)
"""
model = mm.Model({
't_0_1': 5000.,
'u_0_1': 0.005,
'rho_1': 0.001,
't_0_2': 5100.,
'u_0_2': 0.0003,
't_star_2': 0.03,
't_E': 25.
})
model_1 = mm.Model(model.parameters.source_1_parameters)
model_2 = mm.Model(model.parameters.source_2_parameters)
(t1, t2) = (4999.95, 5000.05)
(t3, t4) = (5099.95, 5100.05)
finite = 'finite_source_uniform_Gould94'
model.set_magnification_methods(
[t1, finite, t2, 'point_source', t3, finite, t4])
model_1.set_magnification_methods([t1, finite, t2])
model_2.set_magnification_methods([t3, finite, t4])
(f_s_1, f_s_2, f_b) = (100., 300., 50.)
time = np.linspace(4900., 5200., 4200)
mag_1 = model_1.get_magnification(time)
mag_2 = model_2.get_magnification(time)
# test: model.set_source_flux_ratio(f_s_2/f_s_1)
fitted = model.get_magnification(time, source_flux_ratio=f_s_2 / f_s_1)
expected = (mag_1 * f_s_1 + mag_2 * f_s_2) / (f_s_1 + f_s_2)
almost(fitted, expected)
# test separate=True option:
(mag_1_, mag_2_) = model.get_magnification(time, separate=True)
almost(mag_1, mag_1_)
almost(mag_2, mag_2_)
def test_intp_of_constant_arr():
h_in, w_in, d_in = 10, 20, 5
h_out, w_out, d_out = 5., 25., 5.
x_in, y_in, z_in = np.mgrid[:h_in, :w_in, :d_in]
x_out, y_out, z_out = np.mgrid[:h_out, :w_out, :d_out]
x_out = 1. * x_out / x_out.max() * (h_in - 1.)
y_out = 1. * y_out / y_out.max() * (w_in - 1.)
z_out = 1. * z_out / z_out.max() * (d_in - 1.)
# -- arbitrary constant function
def f0(x, y):
a = 1.234
return a * np.ones(x.shape)
ref_out = f0(x_out, y_out)
out0 = resample(f0(x_in, y_in), (h_out, w_out, d_out), order=0)
almost(ref_out, out0)
out1 = resample(f0(x_in, y_in), (h_out, w_out, d_out), order=1)
almost(ref_out, out1)
out2 = resample(f0(x_in, y_in), (h_out, w_out, d_out), order=2)
almost(ref_out, out2)
out3 = resample(f0(x_in, y_in), (h_out, w_out, d_out), order=3)
almost(ref_out, out3)
out4 = resample(f0(x_in, y_in), (h_out, w_out, d_out), order=4)
almost(ref_out, out4)
out5 = resample(f0(x_in, y_in), (h_out, w_out, d_out), order=5)
almost(ref_out, out5)