Spectral utils#

The madcubapy.utils package offers a set of utils to perform operations on spectra data.

Create spectral arrays#

The create_spectral_array() function creates an spectral array by providing the following parameters:

  • nchan : Number of channels in the spectrum.

  • cdelt : Width of a channel.

  • crpix : Reference channel of the spectrum.

  • crval : Value of the reference channel.

>>> from madcubapy.utils import create_spectral_array
>>> x_axis = create_spectral_array(nchan=5, cdelt=2, crpix=2, crval=10)
>>> x_axis
array([8, 10, 12, 14, 16])

Spectral data conversions#

The user can convert between rest frequency, observed frequency, and velocity using the predefined functions rest_to_obs(), obs_to_rest(), obs_to_vel(), vel_to_obs(), rest_to_vel(), and vel_to_rest(). The user can use different conventions:

  • Radio \(V = c \frac{f_0 - f}{f_0} ; f(V) = f_0 ( 1 - V/c )\)

  • Relativistic \(V = c \frac{f_0^2 - f^2}{f_0^2 + f^2} ; f(V) = f_0 \frac{\left(1 - (V/c)^2\right)^{1/2}}{(1+V/c)}\)

Here \(f_0\) is the rest frequency, \(f\) is the observed frequency, and \(V\) is the velocity.

For the input data, the functions accept single values and arrays of values, which must be Quantity objects. As an example, here is how to convert observed frequency to velocity using obs_to_vel()

>>> from madcubapy.utils import obs_to_vel
>>> obs_freq_array = (263.35134466, 264.3413873) * u.GHz
>>> line_rest_freq = 266 * u. GHz
>>> velocity_array = obs_to_vel(obs_freq=obs_freq_array,
                                doppler_rest=line_rest_freq,
                                doppler_convention="relativistic")
>>> velocity_array
[3000, 1875.1495799 ] km / s

To avoid the use of several functions, madcubapy.utils also offers the SpectralData class to work with spectral data with units and automatically perform conversions between frequencies, wavelengths, photon energies, and Doppler velocities. Check The SpectralData page to know more.

Spectral resolution units#

The user can convert between the spectral resolution between Frequency and velocity units using the convert_spectral_resolution() function.

SNR functions#

madcubapy offers functions to measure the signal-to-noise ratio of spectral lines in various ways:

  • measure_snr_peak() for a peak SNR measurement (peak intensity / rms).

  • measure_snr_profile_fit() for a SNR integrating the emission of the line profile assuming a gaussian fit using the equation: (eq.1)

  • measure_snr_profile_observed() for a SNR integrating the emission along the line profile on the observed channels inside a selected window width. The function allows selecting a binary or fractional mask to handle the side channels.

Similarly, madcubapy.utils also offers helpers to estimate the rms for a desired SNR with the functions estimate_rms_peak() and estimate_rms_profile_fit().