RFI

savgol_filter

your.utils.rfi.savgol_filter(bandpass, channel_bandwidth, frequency_window=15, sigma=6)

Apply savgol filter to the data. See Agarwal el al. 2020 for details.

Args:

bandpass (numpy.ndarray): bandpass of the data
channel_bandwidth (float): channel bandwidth (MHz)
frequency_window (float): frequency window (MHz)
sigma (float): sigma value to apply cutoff on

Returns:

numpy.ndarray: mask for channels

[source]

spectral_kurtosis

your.utils.rfi.spectral_kurtosis(data, N=1, d=None)

Compute spectral kurtosis. See Nita et al. (2016) for details.

Args:

data (numpy.ndarray): 2D frequency time data
N (int): Number of accumulations on the FPGA
d (float): shape factor

Returns:

 numpy.ndarray: Spectral Kurtosis along frequency axis

[source]

sk_filter

your.utils.rfi.sk_filter(data, channel_bandwidth, tsamp, N=None, d=None, sigma=5)

Apply Spectral Kurtosis filter to the data

Args:

data (numpy.ndarray): 2D frequency time data
channel_bandwidth (float): channel bandwidth (MHz)
tsamp (float): sampling time (seconds)
N (int): Number of accumulations on the FPGA
d (float): shape factor
sigma (float): sigma value to apply cutoff on

Returns:

 numpy.ndarray: mask for channels

[source]

calc_N

your.utils.rfi.calc_N(channel_bandwidth, tsamp)

Calculates number of accumulations on FPGA

Args:

channel_bandwidth (float): channel bandwidth (MHz)
tsamp (float): sampling time (seconds)

Returns:

int: FPGA accumulation length

[source]

sk_sg_filter

your.utils.rfi.sk_sg_filter(
    data, your_object, spectral_kurtosis_sigma=6, savgol_frequency_window=15, savgol_sigma=5
)

Apply Spectral Kurtosis and Savgol filter to the data

Args:

data (numpy.ndarray): 2D frequency time data
your_object: Your object
spectral_kurtosis_sigma (float): sigma value to apply cutoff on for SK filter
savgol_frequency_window (float): frequency window for savgol filter(MHz)
savgol_sigma (float): sigma value to apply cutoff on for savgol filter

Returns:

 numpy.ndarray: mask for channels