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_sinc_fixed():
x = np.linspace(-5., 5., 200)
y = 2 * (np.sin(x)/x)**2
y += np.random.normal(0., 0.1, x.shape)
sf = fit_sinc(x, y, mean=1., fixed={"mean": True, "amplitude": False})
assert sf.mean.fixed
assert not sf.amplitude.fixed
def test_sinc_fixed():
x = np.linspace(-5., 5., 200)
y = 2 * (np.sin(x)/x)**2
y += np.random.normal(0., 0.1, x.shape)
sf = fit_sinc(x, y, mean=1., fixed={"mean": True, "amplitude": False})
assert sf.mean.fixed
assert not sf.amplitude.fixed
def test_sinc_function():
x = np.linspace(-5., 5., 200)
y = 2 * (np.sin(x)/x)**2
y += np.random.normal(0., 0.1, x.shape)
s = fit_sinc(x, y)
assert np.abs(s.mean) < 0.1
assert np.abs(s.amplitude - 2) < 0.1
assert np.abs(s.width - 1) < 0.1
def test_sinc_function():
x = np.linspace(-5., 5., 200)
y = 2 * (np.sin(x)/x)**2
y += np.random.normal(0., 0.1, x.shape)
s = fit_sinc(x, y)
assert np.abs(s.mean) < 0.1
assert np.abs(s.amplitude - 2) < 0.1
assert np.abs(s.width - 1) < 0.1
def test_sinc_obs():
obs_length = 0.32
x = np.linspace(-5., 5., 200)
w = 1 / (np.pi*obs_length)
y = 2 * (np.sin(x / w) / (x / w))**2
y += np.random.normal(0., 0.1, x.shape)
s = fit_sinc(x, y, obs_length=obs_length)
assert np.abs(1 / (np.pi*obs_length) - s.width) < 0.1
assert s.width.fixed
def test_sinc_obs():
obs_length = 0.32
x = np.linspace(-5., 5., 200)
w = 1 / (np.pi*obs_length)
y = 2 * (np.sin(x / w) / (x / w))**2
y += np.random.normal(0., 0.1, x.shape)
s = fit_sinc(x, y, obs_length=obs_length)
assert np.abs(1 / (np.pi*obs_length) - s.width) < 0.1
assert s.width.fixed