def generate_data_rng_object_works(self):
pe = PSDParEst(self.ps)
sim_data1 = pe._generate_data(self.lpost, [2.0, 0.1, 100.0, 2.0],
seed=1)
sim_data2 = pe._generate_data(self.lpost, [2.0, 0.1, 100.0, 2.0],
seed=1)
assert np.allclose(sim_data1.power, sim_data2.power)
def generate_data_rng_object_works(self):
pe = PSDParEst(self.ps)
sim_data1 = pe._generate_data(self.lpost,
[2.0, 0.1, 100.0, 2.0],
seed=1)
sim_data2 = pe._generate_data(self.lpost,
[2.0, 0.1, 100.0, 2.0],
seed=1)
assert np.allclose(sim_data1.power, sim_data2.power)
def generate_data_rng_object_works(self):
pe = PSDParEst(self.ps)
sim_data1 = pe._generate_data(
self.lpost,
[self.x_0_0, self.fwhm_0, self.amplitude_0, self.amplitude_1],
seed=1)
sim_data2 = pe._generate_data(
self.lpost,
[self.x_0_0, self.fwhm_0, self.amplitude_0, self.amplitude_1],
seed=1)
assert np.allclose(sim_data1.power, sim_data2.power)
def test_generate_data_produces_correct_distribution(self):
model = models.Const1D()
model.amplitude = 2.0
p = model(self.ps.freq)
seed = 100
rng = np.random.RandomState(seed)
noise = rng.exponential(size=len(p))
power = noise*p
ps = Powerspectrum()
ps.freq = self.ps.freq
ps.power = power
ps.m = 1
ps.df = self.ps.freq[1]-self.ps.freq[0]
ps.norm = "leahy"
lpost = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
pe = PSDParEst(ps)
rng2 = np.random.RandomState(seed)
sim_data = pe._generate_data(lpost, [2.0], rng2)
assert np.allclose(ps.power, sim_data.power)
def test_generate_data_produces_correct_distribution(self):
model = models.Const1D()
model.amplitude = 2.0
p = model(self.ps.freq)
seed = 100
rng = np.random.RandomState(seed)
noise = rng.exponential(size=len(p))
power = noise*p
ps = Powerspectrum()
ps.freq = self.ps.freq
ps.power = power
ps.m = 1
ps.df = self.ps.freq[1]-self.ps.freq[0]
ps.norm = "leahy"
lpost = PSDLogLikelihood(ps.freq, ps.power, model, m=1)
pe = PSDParEst(ps)
rng2 = np.random.RandomState(seed)
sim_data = pe._generate_data(lpost, [2.0], rng2)
assert np.allclose(ps.power, sim_data.power)
