API

SamRFI

class samrfi.RadioRFI(vis=False, dir_path=None)

SyntheticRFI

class samrfi.SyntheticRFI(points_freq=None, points_time=None, min_freq=None, max_freq=None, max_time=None, min_time=None, dir_path=False)

add_gaussian_rfi_spectrograph(amplitude, center, width, rfi_axis='FREQ', table=False)

Add Gaussian RFI to the spectrograph along the specified axis.

Parameters:: amplitude (float): The amplitude of the RFI signal. center (float): The center frequency or time of the RFI signal. width (float): The bandwidth or timewidth of the RFI signal. rfi_axis (str): The axis along which to add the RFI (‘FREQ’ or ‘TIME’). table (bool): Whether to add the RFI parameters to the RFI table.
Returns:: numpy.ndarray: The spectrograph with added Gaussian RFI.

add_square_rfi_spectrograph(amplitude, center, width, rfi_axis='FREQ', table=False)

Add Square RFI to the spectrograph along the specified axis.

Parameters:: amplitude (float): The amplitude of the RFI signal. center (float): The center frequency or time of the RFI signal. width (float): The bandwidth or timewidth of the RFI signal. rfi_axis (str): The axis along which to add the RFI (‘FREQ’ or ‘TIME’). table (bool): Whether to add the RFI parameters to the RFI table.
Returns:: numpy.ndarray: The spectrograph with added Square RFI.

baseline_profile()

Generates a baseline profile using a normal distribution.

Returns:: numpy.ndarray: The baseline profile.

create_model_spectrograph()

Create a model spectrograph based on the given table of RFI parameters.

Parameters:: table (pandas.DataFrame): A table containing RFI parameters such as amplitude, center frequency, and bandwidth. frequencies (numpy.ndarray): An array of frequencies. points_time (int): The number of time intervals. points_freq (int): The number of points_freq in the spectrograph.
Returns:: numpy.ndarray: The model spectrograph.

gaussian_function(x, amplitude, center, width)

Generates a radio frequency interference (RFI) signal.

Parameters:: amplitude (float): The amplitude of the RFI signal. center_freq (float): The center frequency of the RFI signal. bandwidth (float): The bandwidth of the RFI signal.
Returns:: numpy.ndarray: An array representing the RFI signal.

generate_waterfall(mean_rfi=1, std_rfi=5, pers_freq_gauss=1, pers_time_gauss=0, inter_freq_gauss=0, inter_freq_square=0, pers_freq_square=0, pers_time_square=0, noise=10, mean=5, edge_buffer=50)

Generate a waterfall plot with simulated radio frequency interference (RFI).

Parameters:: pers_freq_gauss (int): The number of persistent RFI signals to add to the waterfall plot in frequency. Default is 1. pers_time_gauss (int): The number of persistent RFI signals to add to the waterfall plot in time. Default is 0. inter_freq_gauss (int): The number of intermittent RFI signals to add to the waterfall plot. Default is 0. noise (float): The noise level of the waterfall plot. Default is 10. mean (float): The mean level of the waterfall plot. Default is 5. edge_buffer (int): The buffer size around the edges of the waterfall plot. Default is 50.
Returns:: None

intermittent_rfi(amplitude, center_freq, bandwidth, time_period, duty_cycle, time_offset=0, func_type='GAUSS', table=False)

Add intermittent RFI to the spectrograph in two channels with a specified frequency offset and time period.

Parameters: - spectrograph: The input spectrograph to which RFI will be added. - frequencies: Array of frequency values. - amplitude: Amplitude of the RFI signal. - center_freq: Center frequency of the first RFI channel. - bandwidth: Bandwidth of the RFI signal. - time_period: Time period for the intermittent RFI. - duty_cycle: Fraction of the time period during which the RFI is active. - time_offset: Offset in time for the intermittent RFI (default: 0).

Returns: - modified_spectrograph: The spectrograph with added intermittent RFI.

Plotter

class samrfi.Plotter(radiorfi_instance)

RFIModels

class samrfi.RFIModels(sam_checkpoint, sam_type, radiorfi_instance, device='cuda')

create_RGB_channels(zeroR=False, zeroG=False, zeroB=False)

Generate an RGB combined image from a spectrograph.

Parameters:: spectrograph (numpy.ndarray): The input spectrograph image.
Returns:: numpy.ndarray: The RGB combined image.

RFITraining

class samrfi.RFITraining(rfi_instance, device='cuda')

MetricsCalculator

utility functions

samrfi.utilities.calcquality(dat, flag): Need to minimize the score that it returns

samrfi.utilities.create_patchify_patches(rfi_combined, patch_size=128)

Create patches from a list of images.

Parameters:: rfi_combined (list of numpy.ndarray): List of images to be patched. patch_size (int): Size of each patch. Default is 128. step (int): Step size for patching. Default is 128.
Returns:: numpy.ndarray: Array of image patches.

samrfi.utilities.find_spectrograph_peaks(spectrograph, min_distance=10, threshold_abs=30)

Find peaks in the spectrograph image.

Parameters:: spectrograph (numpy.ndarray): The input spectrograph image. min_distance (int): The minimum distance between peaks. Default is 10. threshold_abs (int): The minimum intensity value for peaks. Default is 25.
Returns:: numpy.ndarray: An array of peak coordinates.