def test_gaussian_bounds():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
gs = fit_gaussian(x, y,
bounds={"mean": [1., 1.6], "amplitude": [1.7, 2.3]})
def fit(frequencies,
stats,
center_freq,
width=None,
obs_length=None,
baseline=0):
estimated_amp = stats[np.argmin(np.abs(frequencies - center_freq))]
if obs_length is not None:
s = fit_sinc(frequencies,
stats - baseline,
obs_length=obs_length,
amp=estimated_amp,
mean=center_freq)
else:
df = frequencies[1] - frequencies[0]
if width is None:
width = 2 * df
s = fit_gaussian(frequencies,
stats - baseline,
stddev=width,
amplitude=estimated_amp,
mean=center_freq)
return s
def test_gaussian_bounds():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
gs = fit_gaussian(x, y,
bounds={"mean": [1., 1.6], "amplitude": [1.7, 2.3]})
def test_gaussian_fixed():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
gs = fit_gaussian(x, y, mean=1.3, fixed={"mean": True, "amplitude": False})
assert gs.mean.fixed
assert not gs.amplitude.fixed
def test_gaussian_fixed():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
gs = fit_gaussian(x, y, mean=1.3, fixed={"mean": True, "amplitude": False})
assert gs.mean.fixed
assert not gs.amplitude.fixed
def test_gaussian_function():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
gs = fit_gaussian(x, y)
assert np.abs(gs.mean - 1.3) < 0.1
assert np.abs(gs.amplitude - 2) < 0.1
assert np.abs(gs.stddev - 0.7) < 0.1
def test_gaussian_function():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
gs = fit_gaussian(x, y)
assert np.abs(gs.mean - 1.3) < 0.1
assert np.abs(gs.amplitude - 2) < 0.1
assert np.abs(gs.stddev - 0.7) < 0.1
def test_gaussian_tied():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
def tiedgaussian(model):
mean = model.amplitude / 2
return mean
gs = fit_gaussian(x, y, tied={"mean": tiedgaussian})
assert np.abs(gs.mean/gs.amplitude - 0.5) < 0.1
def test_gaussian_tied():
x = np.linspace(-5., 5., 200)
y = 2 * np.exp(-0.5 * (x - 1.3)**2 / 0.7**2)
y += np.random.normal(0., 0.1, x.shape)
def tiedgaussian(model):
mean = model.amplitude / 2
return mean
gs = fit_gaussian(x, y, tied={"mean": tiedgaussian})
assert np.abs(gs.mean/gs.amplitude - 0.5) < 0.1
