Simulation

rascil.processing_components.simulation.atmospheric_screen Module

Functions for tropospheric and ionospheric modeling : see SDP Memo 97

Functions

`find_pierce_points`(station_locations, ha, ...)	Find the pierce points for a flat screen at specified height
`create_gaintable_from_screen`(vis, sc, screen)	Create gaintables from a screen calculated using ARatmospy
`grid_gaintable_to_screen`(vis, gaintables, screen)	Grid a gaintable to a screen image
`calculate_sf_from_screen`(screen)	Calculate structure function image from screen
`plot_gaintable_on_screen`(vis, gaintables[, ...])	Plot a gaintable on an ionospheric screen

rascil.processing_components.simulation.noise Module

Functions that add noise.

Functions

`calculate_noise_visibility`(bandwidth, ...)	Calculate noise rms per visibility [nchan, npol]
`addnoise_visibility`(vis[, t_sys, eta, seed])	Add noise to a visibility

rascil.processing_components.simulation.pointing Module

Functions for simulating pointing errors

Functions

`simulate_gaintable_from_pointingtable`(vis, ...)	Create gaintables from a pointing table
`simulate_pointingtable_from_timeseries`(pt[, ...])	Create a pointing table with time series created from PSD.
`simulate_pointingtable`(pt, pointing_error[, ...])	Simulate a gain table

rascil.processing_components.simulation.rfi Module

Functions used to simulate RFI. Developed as part of SP-122/SIM.

The scenario is: * There is a TV station at a remote location (e.g. Perth), emitting a broadband signal (7MHz) of known power (50kW). * The emission from the TV station arrives at LOW stations with phase delay and attenuation. Neither of these are well known but they are probably static. * The RFI enters LOW stations in a side-lobe of the main beam. Calculations by Fred Dulwich indicate that this provides attenuation of about 55 - 60dB for a source close to the horizon. * The RFI enters each LOW station with fixed delay and zero fringe rate (assuming no e.g. ionospheric ducting) * In tracking a source on the sky, the signal from one station is delayed and fringe-rotated to stop the fringes for one direction on the sky. * The fringe rotation stops the fringe from a source at the phase tracking centre but phase rotates the RFI, which now becomes time-variable. * The correlation data are time- and frequency-averaged over a timescale appropriate for the station field of view. This averaging de-correlates the RFI signal. * We want to study the effects of this RFI on statistics of the images: on source and at the pole.

Functions

`calculate_averaged_correlation`(correlation, ...)	Average the correlation in time and frequency :param correlation: Correlation(ntimes, nant, nants, nchan] :param channel_width: Number of channels to average :param time_width: Number of integrations to average :return:
`simulate_rfi_block_prop`(bvis, ...[, ...])	Simulate RFI in a BlockVisility
`calculate_station_correlation_rfi`(...)	Form the correlation from the rfi at the station

rascil.processing_components.simulation.simulation_helpers Module

Functions that help with SKA simulations

Functions

`plot_visibility`(vis_list[, colors, title, ...])	Standard plot of visibility
`plot_visibility_pol`(vis_list[, title, y, x, ...])	Standard plot of visibility
`find_times_above_elevation_limit`(...)	Find all times for which a phasecentre is above the elevation limit
`plot_uvcoverage`(vis_list[, ax, plot_file, title])	Standard plot of uv coverage
`plot_uwcoverage`(vis_list[, ax, plot_file, title])	Standard plot of uw coverage
`plot_vwcoverage`(vis_list[, ax, plot_file, title])	Standard plot of vw coverage
`plot_configuration`(config[, ax, plot_file, ...])	Standard plot of uv coverage
`plot_azel`(bvis_list[, plot_file])	Standard plot of az el coverage
`plot_gaintable`(gt_list[, title, value, ...])	Standard plot of gain table
`plot_pointingtable`(pt_list, plot_file, ...)	Standard plot of pointing table
`find_pb_width_null`(pbtype, frequency, **kwargs)	Rough estimates of HWHM and null locations
`create_mid_simulation_components`(...[, ...])	Construct components for simulation
`plot_pa`(bvis_list[, plot_file])	Standard plot of parallactic angle coverage

rascil.processing_components.simulation.surface Module

Functions for dish surface modeling

Functions

`simulate_gaintable_from_zernikes`(vis, sc, ...)	Create gaintables for a set of zernikes
`simulate_gaintable_from_voltage_pattern`(vis, ...)	Create gaintables from a list of components and voltage patterns

rascil.processing_components.simulation.testing_support Module

Functions that aid testing in various ways. A typical use would be:

lowcore = create_named_configuration('LOWBD2-CORE')
times = numpy.linspace(-3, +3, 13) * (numpy.pi / 12.0)

frequency = numpy.array([1e8])
channel_bandwidth = numpy.array([1e7])

# Define the component and give it some polarisation and spectral behaviour
f = numpy.array([100.0])
flux = numpy.array([f])

phasecentre =
    SkyCoord(ra=+15.0 * u.deg, dec=-35.0 * u.deg, frame='icrs', equinox='J2000')
compabsdirection =
    SkyCoord(ra=17.0 * u.deg, dec=-36.5 * u.deg, frame='icrs', equinox='J2000')

comp = SkyComponent(flux=flux, frequency=frequency, direction=compabsdirection,
                                polarisation_frame=PolarisationFrame('stokesI'))
image = create_test_image(frequency=frequency,
                          phasecentre=phasecentre,
                          cellsize=0.001,
                          polarisation_frame=PolarisationFrame('stokesI'))
vis = create_visibility(lowcore, times=times, frequency=frequency,
                             channel_bandwidth=channel_bandwidth,
                             phasecentre=phasecentre, weight=1,
                             polarisation_frame=PolarisationFrame('stokesI'),
                             integration_time=1.0)

Functions

`create_low_test_image_from_gleam`([npixel, ...])	Create LOW test image from the GLEAM survey
`create_low_test_skycomponents_from_gleam`([...])	Create sky components from the GLEAM survey
`create_low_test_skymodel_from_gleam`([...])	Create LOW test skymodel from the GLEAM survey
`create_test_image`([cellsize, frequency, ...])	Create a useful test image
`create_test_image_from_s3`([npixel, ...])	Create MID test image from S3
`create_test_skycomponents_from_s3`([...])	Create test image from S3
`create_unittest_components`(model, flux[, ...])
`create_unittest_model`(vis, model_pol[, ...])
`ingest_unittest_visibility`(config, ...[, ...])	Make a standard visibility simulation
`insert_unittest_errors`(vt[, seed, ...])	Simulate gain errors and apply
`replicate_image`(im[, polarisation_frame, ...])	Make a new canonical shape Image, extended along third and fourth axes by replication.
`simulate_gaintable`(gt[, phase_error, ...])	Simulate a gain table