Infer polynomial coefficients and their covariance structure for fitting radio-astronometric callibrator spectra.
- Author: Tim Molteno. tim@elec.ac.nz
- Publication: Molteno, Timothy CA. "Correlation structure in flux-density calibrator models." Monthly Notices of the Royal Astronomical Society 527.3 (2024): 5732-5740.
Machine readable data from this catalogue is available in the HDF file in the data directory. calibrator_catalogue.hdf5
sudo pip3 install specfit
develop
pip3 install -e .
Here is an example. This code is in the examples directory.
import numpy as np
import specfit as sf
import matplotlib.pyplot as plt
# Data from J.E. Reynolds for J1939-6342
original_data = np.array(
[[0.408, 6.24, 0.312 ],
[0.843, 13.65, 0.6825],
[1.38 , 14.96, 0.748 ],
[1.413, 14.87, 0.7435],
[1.612, 14.47, 0.7235],
[1.66 , 14.06, 0.703 ],
[1.665, 14.21, 0.7105],
[2.295, 11.95, 0.5975],
[2.378, 11.75, 0.5875],
[4.8 , 5.81, 0.2905],
[4.8 , 5.76, 0.288 ],
[4.835, 5.72, 0.286 ],
[4.85 , 5.74, 0.287 ],
[8.415, 2.99, 0.1495],
[8.42 , 2.97, 0.1485],
[8.64 , 2.81, 0.1405],
[8.64 , 2.81, 0.1405]])
freq_ghz, mu, sigma = original_data.T
freq = freq_ghz*1e9
names, stats, a_cov, a_corr, idata = \
sf.spectral_inference("J1939-6342",
freq=nu, mu=data, sigma=sigma, order=4, nu0=1.4e9)
Now we can plot the data and show the results.
fig, ax = sf.dataplot(plt, "J1939-6342", freq=freq, mu=data, sigma=sigma)
a = stats[0] # Means
nu = np.linspace(min_freq, max_freq, 100)
S = sf.flux(nu, a, nu0=1.4e9)
ax.plot(nu/1e9, S, label="polynomial fit")
ax.legend()
fig.tight_layout()
plt.show()
print(names, stats)
print(a_cov)
- Incorporate some ideas on using variances of parameters and constraints on flux uncertainties in place of requiring an explicit assumption of the sigma (in the case of data-free inference)
- Use smoothness as a prior (rather than model-order).
- 0.4.0b1 Update the marginal_likelihood method to correctly no longer use inferenceData objects to avoid a bug in pymc. Return the relative marginal likelihood (rather than the log marginal likelihood)
- 0.3.0b3 Clean up to use the natural log throughout! (IMPORTANT) Use consistent way to get names of variables from the posterior Add machine readable hdf5 file output.
- 0.3.0b2 Use pymc and upgrade to newer versions.
- 0.2.0b4 Include a separate function (marginal_likelihood) for estimating the marginal likelihood using SMC Change the likelihood to use a Student's t distribution for robustness.
- 0.2.0b3 Fix examples, move to github automation for release information.
- 0.1.0b3 First functioning release.
- 0.1.0b4 [In progress] Add the frequency range to the full_column output. Return the inference data to allow further processing Improved plotting and postprocessing. Added posterior PDF helper plotting function (slow) Use different tuning depending on polynomial order Output to a file, including lists of alternate names