valska.beam_metrics

Functions

`chromaticity_test`(freq_array, test_param)	Test the variation of a parameter with frequency.
`fit_beam_width_vs_frequency`(freq, theta_deg, ...)	Fit beam shape vs frequency using lmfit.
`lst_formatter`(x, pos)	Format LST in hours, wrapped to [0, 24).
`plot_2d_lst_deg`(ax, data, extent, ...[, cmap])	Plot a 2D image with: - Left y-axis: LST (hours) - Right y-axis: angular separation (degrees)
`plot_baseline_heatmap`(ax, data, bl_counts, ...)	Baseline vs angle heatmap with axes: - Left: LST (hours) - Right: Angle (deg) - External colorbar
`plot_beam_shape`(ax, theta_deg, ydata, freq)	Plot the beam shape (normalized response) along with Gaussian and Airy fits.
`plot_spectrum`(ax, freq_array, parameter, ...)	Plot spectrum
`plot_waterfall_matplotlib`(ax, data, freqs, ...)	Create a waterfall plot (frequency vs LST).

Classes

`BeamMetrics`(filename)
`Loader`(stream)	Initialize the scanner.
`SimulationConfig`([latitude, sigma, ...])

class valska.beam_metrics.BeamMetrics(filename: str)

check_beam(save_path: str | Path | None = None, show: bool = True): Check beam parameters from pyuvsim data and produce validation report and plots.

compute_beam_metrics(): Compute beam metrics

make_plots(gauss_result, airy_result, fit_vs_freq, save_path: str | Path | None = None, show: bool = True): Create diagnostic plots

prepare_uv_data(uvd: pyuvdata.UVData): Resize and prepare UV data

read_simulation_config(beam_parameters: str): Read in the simulation config information

class valska.beam_metrics.Loader(stream): Initialize the scanner.

class valska.beam_metrics.SimulationConfig(latitude: float | None = None, sigma: float | None = None, beam_shape: str | None = None, diameter: float | None = None)

valska.beam_metrics._airy(theta: ndarray[tuple[Any, ...], dtype[_ScalarT]], freq_hz: float, A: float, theta0: float, diam: float) → ndarray[tuple[Any, ...], dtype[_ScalarT]]: Airy power beam for a circular aperture.

valska.beam_metrics.chromaticity_test(freq_array: ndarray[tuple[Any, ...], dtype[_ScalarT]], test_param: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → float

Test the variation of a parameter with frequency.

Parameters

freq_array :: Frequency array.
test_param :: The parameter to test against frequency.

valska.beam_metrics.fit_beam_width_vs_frequency(freq: ndarray[tuple[Any, ...], dtype[_ScalarT]], theta_deg: ndarray[tuple[Any, ...], dtype[_ScalarT]], v_auto: ndarray[tuple[Any, ...], dtype[_ScalarT]], shape: str) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ModelResult | None, ModelResult | None]

Fit beam shape vs frequency using lmfit.

Parameters

freq :: Frequency in Hz
theta_deg :: Angular coordinate in degrees.
v_auto :: Visibility data with shape (angle, frequency).
shape :: Either “GaussianBeam” or “AiryBeam”.

Returns

fit_vs_freq :: Gaussian FWHM values at each frequency.
gauss_result :: Gaussian result at middle frequency.
airy_result :: Airy result at middle frequency.

valska.beam_metrics.lst_formatter(x: float, pos: int) → str: Format LST in hours, wrapped to [0, 24).

valska.beam_metrics.plot_2d_lst_deg(ax: matplotlib.axes.Axes, data: ndarray[tuple[Any, ...], dtype[floating | complexfloating]], extent: tuple[float, float, float, float], lsts_hours: ndarray[tuple[Any, ...], dtype[floating]], theta_deg: ndarray[tuple[Any, ...], dtype[floating]], cmap: str = 'viridis') → matplotlib.axes.Axes: Plot a 2D image with: - Left y-axis: LST (hours) - Right y-axis: angular separation (degrees)

valska.beam_metrics.plot_baseline_heatmap(ax: matplotlib.axes.Axes, data: ndarray[tuple[Any, ...], dtype[floating | complexfloating]], bl_counts: ndarray[tuple[Any, ...], dtype[integer]], lsts_hours: ndarray[tuple[Any, ...], dtype[floating]], theta_deg: ndarray[tuple[Any, ...], dtype[floating]], freq: float, cmap: str = 'viridis') → matplotlib.axes.Axes: Baseline vs angle heatmap with axes: - Left: LST (hours) - Right: Angle (deg) - External colorbar

valska.beam_metrics.plot_beam_shape(ax: matplotlib.axes.Axes, theta_deg: ndarray[tuple[Any, ...], dtype[_ScalarT]], ydata: ndarray[tuple[Any, ...], dtype[_ScalarT]], freq: float, gauss_result: ModelResult | None = None, airy_result: ModelResult | None = None) → list[matplotlib.lines.Line2D]

Plot the beam shape (normalized response) along with Gaussian and Airy fits.

Parameters

ax :: The axes on which to plot the data.
theta_deg :: The angle array.
ydata :: The data to plot.
gauss_result :: Result from the Gaussian fit.
airy_result :: Result from the Airy fit.

valska.beam_metrics.plot_spectrum(ax: matplotlib.axes.Axes, freq_array: ndarray[tuple[Any, ...], dtype[_ScalarT]], parameter: ndarray[tuple[Any, ...], dtype[_ScalarT]], ylabel: str, title: str) → list[matplotlib.lines.Line2D]: Plot spectrum

valska.beam_metrics.plot_waterfall_matplotlib(ax: matplotlib.axes.Axes, data: ndarray[tuple[Any, ...], dtype[floating | complexfloating]], freqs: ndarray[tuple[Any, ...], dtype[floating]], lsts_hours: ndarray[tuple[Any, ...], dtype[floating]], theta_deg: ndarray[tuple[Any, ...], dtype[floating]], cmap: str = 'viridis') → matplotlib.axes.Axes: Create a waterfall plot (frequency vs LST).