specfit 0.5.0b2

Infer polynomial spectral models with covariancess

Specfit

Infer polynomial coefficients and their covariance structure for fitting radio-astronometric callibrator spectra.

• Author: Tim Molteno. [e-mail]
• Publication: Molteno, Timothy CA. "Correlation structure in flux-density calibrator models." Monthly Notices of the Royal Astronomical Society 527.3 (2024): 5732-5740.

Data

Machine readable data from this catalogue is available in the HDF file in the data directory. calibrator_catalogue.hdf5

Install

sudo pip3 install specfit

develop

pip3 install -e .

Examples

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)

TODO

• 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).

Changelog

• 0.5.0b2 Move to hatchling as the build system, add pyproject.toml.
• 0.5.0b1 Add spline fits, and piecewise linear fits. (WORK IN PROGRESS). Add a new function for processing marginal likelihood.
• 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