MeerKATgen package¶

Submodules¶

MeerKATgen.create_dataset module¶

MeerKATgen.observation module¶

class MeerKATgen.observation.Observation(num_beams=None, fchans=None, tchans=None, df=<Quantity 2.79396772 Hz>, dt=<Quantity 18.25361101 s>, fch1=900, ascending=False, SETI=None, coordinates=None, obs_data=None, telescope_beam_width=None, beamlet_width=None, **kwargs)¶

Bases: object

Core Object that facilitate the creation of entirely synthetic radio data of 64 anntena beams

…

num_beamsint: Number of beams
fchansint: Number of frequency bins
tchansint: Number of time bins
dffloat: Resolution in freq in [Hz]
dtfloat: Resolution in time in [seconds]
fch1float: start frequency in [MHz]
telescope_beam_widthfloat: The entire FOV full width half maximum in units of distance [sigma]
beamlet_widthfloat: the individual telescope full width half maximum in units of distance [sigma]
datanumpy array: Contains the data of observations [number of beams , time , frequency]
adj_matrix: numpy array: Contains the adjacency matrix describes the graph of the setup. [number of beams, number of beams]
coordinatesnumpy array: Contains the coordinates in X-Y plane [number of beams, 2]
labelsarray numpy: Contains the labels of 1 or 0 for SETI or No SETI
SETIdict: Contains dictionary for all the physical parameters to be injected. Key elements of dictionary: SETI[‘SETI_INDEX’] SETI[‘seti_start_index’] SETI[‘seti_snr’] SETI[‘seti_drift’] SETI[‘seti_width’] SETI[‘seti_mean’]

simulate_points(num=64): Simulate 64 points in the sky given the initialized FWHM values
generate_complete_observation_blank(): Take the initialized values and create blank observations from it with INJECTED SETI signals
generate_complete_observation_real(): Take the initialized values and create observations from REAL DATA and INJECT SETI signals
extract_all(): Extract all the important peices of data

extract_all()¶

Extracts the data, coordinates, adjacency matrix and the labels.

None

dataarray: the observations
coordinatesarray: the coordinates in the sky for these beams
adj_matrixarray: adjacency matrix for the graph
labelsarray: labels for where the SETI signal is or isn’t.

generate_complete_observation_blank()¶

Generate complete stack of signals. This simulates the observation including with SYNTHETIC background given the coordinates and inital values.

None

dataArray: Adds the simulated signal into the array
labelsarray: Records the labels with the injected signals

generate_complete_observation_real()¶

Generate complete stack of signals. This simulates the observation using REAL background given the coordinates and inital values.

None

dataArray: Adds the simulated signal into the array
labelsarray: Records the labels with the injected signals

simulate_points(num)¶

Generate random points given number of beams

numint: Number of beams

coordinatesnumpy Array: Returns the sky coordinates it simulated numpy array [num, 2]

MeerKATgen.observation.random() → x in the interval [0, 1).¶

MeerKATgen.sim_params module¶

MeerKATgen.utils module¶

MeerKATgen.utils.calc_rfi_snr(RFI_POINT, deviation, coordinates, snr_base=30)¶

calculate all SNR of RFI signal

RFI_POINTarray [vector]: single R^2 vector for where the signal originate from
deviationfloat: signal decay as standard deviation
coordinatesarray: coordinates of beams
snr_basefloat: the base SNR of the RFI signal

SNR_valslist: list of RFI signals as a function of Guassian and distance to the point

MeerKATgen.utils.construct_distance_adj(coordinates, sigma_beam, func=None)¶

Construct arbitrary Adjacency Matrix

coordinates : n-beams of R^2 vectors sigma_beam : spread function of beam func : python function two vectors and gives 1 scalar Returns ——- Distance :

Adjacency matrix weighted by arbitrary function

MeerKATgen.utils.construct_guassian_adj(coordinates, sigma_beam)¶

Construct Guassian Adjacency Matrix. We do so by looping through and checking the connections and computing the guassian between the points and weighting the adjacency matrix that way.

coordinatesarray: n-beams of R^2 vectors
sigma_beamfloat: Sigma spread of beam

adj_matrixarray: Adjacency matrix weighted by guassian function

MeerKATgen.utils.distance(x1, x2)¶

Compute Euclidean distance between two vectors

x1array: Sky point for obj 1 : [ra, dec] R^2 vector
x2array: Sky point for obj 2 : [ra, dec] R^2 vector

Distancefloat: computes the euclidean norm

MeerKATgen.utils.gaussian(x, mu, sig)¶

Compute 1-D Guassian given the value and the mean and deviation

xfloat: point value [1d]
mufloat: mean value
sigfloat: deviation

guassianfloat: guassian evaluated at x

MeerKATgen.utils.generate_multiple_signal_no_background(start_index, snr, drift, width, mean, num_freq_chans=256, num_time_chans=16, df=<Quantity 2.79396772 Hz>, dt=<Quantity 18.25361101 s>, fch1=<Quantity 6095.21484235 MHz>)¶

generate MULTIPLE signal on a single beam/observation. All with synthetic backgrounds

start_index, snr: float

SNR of injected signal

driftfloat: drift rate of signal
widthfloat: width of the signal in [Hz]
meanfloat: average background noise
num_freq_chansint: number of freq channels in index
num_time_chansint: number of time channels in index
dffloat: freq resolution in the data [Hz] astropy units
dtfloat: time resolution in data [seconds] astropy units
fch1float: start of frequency channel [mhz] astropy units

Distance: spectrogram of fake inject data

MeerKATgen.utils.generate_multiple_signal_real_background(start_index, snr, drift, width, background, num_freq_chans=256, num_time_chans=16, df=<Quantity 2.79396772 Hz>, dt=<Quantity 18.25361101 s>, fch1=<Quantity 6095.21484235 MHz>)¶

generate MULTIPLE signal REAL BACKGROUND on a single beam/observation.

start_index, snr: float

SNR of injected signal

driftfloat: drift rate of signal
widthfloat: width of the signal in [Hz]
backgroundarray: backgrounds to use
meanfloat: average background noise
num_freq_chansint: number of freq channels in index
num_time_chansint: number of time channels in index
dffloat: freq resolution in the data [Hz] astropy units
dtfloat: time resolution in data [seconds] astropy units
fch1float: start of frequency channel [mhz] astropy units

Distance: spectrogram of fake inject data

MeerKATgen.utils.generate_single_signal_no_background(start_index, snr, drift, width, mean, num_freq_chans=256, num_time_chans=16, df=<Quantity 2.79396772 Hz>, dt=<Quantity 18.25361101 s>, fch1=<Quantity 900. MHz>)¶

generate SINGLE signal on a single beam/observation.

start_index, snr: float

SNR of injected signal

driftfloat: drift rate of signal
widthfloat: width of the signal in [Hz]
meanfloat: average background noise
num_freq_chansint: number of freq channels in index
num_time_chansint: number of time channels in index
dffloat: freq resolution in the data [Hz] astropy units
dtfloat: time resolution in data [seconds] astropy units
fch1float: start of frequency channel [mhz] astropy units

Distance: spectrogram of fake inject data

MeerKATgen.utils.generate_single_signal_real_background(start_index, snr, drift, width, background, num_freq_chans=256, num_time_chans=16, df=<Quantity 2.79396772 Hz>, dt=<Quantity 18.25361101 s>, fch1=<Quantity 6095.21484235 MHz>)¶

generate SINGLE signal REAL BACKGROUND on a single beam/observation.

start_index, snr: float

SNR of injected signal

driftfloat: drift rate of signal
widthfloat: width of the signal in [Hz]
backgroundarray: SINGLE backgrounds to use
meanfloat: average background noise
num_freq_chansint: number of freq channels in index
num_time_chansint: number of time channels in index
dffloat: freq resolution in the data [Hz] astropy units
dtfloat: time resolution in data [seconds] astropy units
fch1float: start of frequency channel [mhz] astropy units

Distance: spectrogram of fake inject data

MeerKATgen.utils.move_point_guassian(coordinates, adj_matrix, point, new_location, sigma_beam)¶

Move point and recalculate Adjacency Matrix

coordinates : n-beams of R^2 vectors so its [N,2] shape adj_matrix: adjacency matrix point: index of point selected new_location: position in normalized sky coordinates where to place point sigma_beam : spread function of beam Returns ——- Distance :

new set of coordinates and the new adj matrix

MeerKATgen.utils.random() → x in the interval [0, 1).¶

MeerKATgen.visualizations module¶

MeerKATgen.visualizations.random() → x in the interval [0, 1).¶

MeerKATgen.visualizations.visualize_connection(coordinates, adj_matrix)¶

Visualize Connections

coordinatesarray: Sky point for objs : [ ra, dec] R^2 vector
adj_matrixarray: connections for graph

MeerKATgen.visualizations.visualize_connection_SETI(coordinates, adj_matrix, SETI_INDEX, line_true=False)¶

Visualize Connections Parameters ———- coordinates : array

Sky point for objs : [ ra, dec] R^2 vector

adj_matrixarray: connections for graph

MeerKATgen.visualizations.visualize_fullset_squares(data)¶

Visualize Connections

coordinatesarray: Sky point for objs : [ ra, dec] R^2 vector
adj_matrixarray: connections for graph

MeerKATgen.visualizations.visualize_polar_SETI(coordinates, adj_matrix, SETI_INDEX, line_true=False)¶

Visualize polarcoordinates

coordinatesarray: Sky point for objs : [ ra, dec] R^2 vector
adj_matrixarray: connections for graph

MeerKATgen Docs

MeerKATgen package

Contents

MeerKATgen package¶

Submodules¶

MeerKATgen.create_dataset module¶

MeerKATgen.observation module¶

MeerKATgen.sim_params module¶

MeerKATgen.utils module¶

MeerKATgen.visualizations module¶

Module contents¶