def test_get_data():
ex_f = np.array([1, 2, 3, 4, 5])
ex_PSD = np.array([[1.1, 1.2, 1.2, 1.3],
[2.4, 2.7, 2.4, 2.7],
[3.95, 3.9, 4, 3.8],
[5, 5.4, 5.7, 4.7],
[5, 6, 6, 4]])
# Create the test HDF5 file
with h5py.File('test.h5', 'w', driver='core', backing_store=False) as fh:
fh['x'] = ex_f
fh['y'] = ex_PSD.mean(axis=1)
fh['y'].attrs['n_avg'] = ex_PSD.shape[1]
# Need ddof=1 to get sample standard deviation, rather than population.
fh['y_std'] = ex_PSD.std(axis=1, ddof=1)
ex_mean = np.array([1.2, 2.55, 3.9125, 5.2, 5.25])
ex_ci = np.array([0.080016664930917122, 0.16974097914175013,
0.083683431255336824, 0.43090292797024871,
0.93827856560121137])
f, psd_mean, psd_ci = get_data(fh)
assert np.allclose(ex_f, f)
assert np.allclose(ex_mean, psd_mean)
assert np.allclose(ex_ci, psd_ci)
def test_convert_data(self):
convert_data(self.testv1, self.converted)
f, psd_mean, psd_ci = get_data(self.converted)
ex_mean = np.array([1.2, 2.55, 3.9125, 5.2, 5.25])
ex_ci = np.array([0.080016664930917122, 0.16974097914175013,
0.083683431255336824, 0.43090292797024871,
0.93827856560121137])
assert np.allclose(self.ex_f, f)
assert np.allclose(ex_mean, psd_mean)
assert np.allclose(ex_ci, psd_ci)
def calck(filename, f_min, f_max,
quality_factor, spring_constant, resonance_frequency, temperature):
"""Base function for the command line interface. TODO: Add numpy style
param strings."""
est_cant = Cantilever(f_c=resonance_frequency*u.Hz,
k_c=spring_constant*u.N/u.m,
Q=quality_factor*u.dimensionless)
data = get_data(filename)
bmf = BrownianMotionFitter(*data, T=temperature, est_cant=est_cant)
bmf.calc_fit(f_min, f_max)
return bmf
