nenupy.schedule.contamination.BeamLobes

class nenupy.schedule.contamination.BeamLobes(time, frequency, pointing, nenufar_config=<nenupy.instru.nenufar.NenuFAR_Configuration object>, miniarray_rotations=None, use_antenna_gain=True)[source]

Bases: object

__init__(time, frequency, pointing, nenufar_config=<nenupy.instru.nenufar.NenuFAR_Configuration object>, miniarray_rotations=None, use_antenna_gain=True)[source]

Methods

__init__(time, frequency, pointing[, ...])

compute_moc([relative_threshold, return_array])

Computes the MOC as sky patches where the array factor is above \({\rm max}(\mathcal{F}_{\rm MA}) \times r\).

compute_weight_moc(sources[, thresholds])

Compute moc with arrays of values between 0 and 1.

gsm_in_lobes([temperature_threshold])

plot(time, frequency, **kwargs)

Plots the figure.

sources_in_lobes(sources)

Attributes

miniarray_rotations

compute_moc(relative_threshold=0.5, return_array=False)[source]

Computes the MOC as sky patches where the array factor is above \({\rm max}(\mathcal{F}_{\rm MA}) \times r\). \(\mathcal{F}_{\rm MA}\) is the Mini-Array(s) normalized array factor and \(r\) is the relative_threshold.

Parameters:

relative_threshold – All the AF values above this threshold are included in the MOC.

compute_weight_moc(sources, thresholds=array([0.01, 0.01584893, 0.02511886, 0.03981072, 0.06309573, 0.1, 0.15848932, 0.25118864, 0.39810717, 0.63095734]))[source]

Compute moc with arrays of values between 0 and 1.

plot(time, frequency, **kwargs)[source]

Plots the figure.

plot()