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simulate_spectral_calibration.py
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simulate_spectral_calibration.py
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import sys
import os
import numpy
import argparse
from scipy.constants import c
# Import local codes
# sys.path.append("../../beam_perturbations/code/tile_beam_perturbations/")
sys.path.append("../../CorrCal_UKZN_Development/corrcal")
from corrcal import grid_data
from src.covariance import thermal_variance
from src.radiotelescope import AntennaPositions
from src.radiotelescope import BaselineTable
from src.skymodel import SkyRealisation
from src.util import split_visibility
from src.util import find_sky_model_sources
from src.util import generate_sky_model_vectors
from src.util import generate_covariance_vectors
from src.calibrate import hybrid_calibration
from src.plottools import colorbar
def main(mode="run"):
# 12 500 sources within 2.5 primary beam width
# about 10 calibrator sources with primary beam weighted flux > 3x RMS
# beam width 13 wavelength dishes
# 8x8 array with 20 wavelength spacing
# position noise of 0.04 wavelengths
# per visibility noise 0.1*brightes source
output_path = "/data/rjoseph/Hybrid_Calibration/numerical_simulations/Testing/"
frequency_range = numpy.linspace(135, 165, 10)*1e6
tile_size = 4 # wavelengths
noise_fraction_brightest_source = 0.1
position_precision = 0
broken_tile_fraction = 0
sky_model_limit = 6
n_realisations = 10
if mode == "run":
print(f"Running Simulation")
calibration_simulation(frequency_range=frequency_range, antenna_diameter=tile_size,
noise_fraction_brightest_source=noise_fraction_brightest_source,
position_error=position_precision, broken_tile_fraction=broken_tile_fraction,
sky_model_limit=sky_model_limit, n_realisations=n_realisations, output_path=output_path)
return
def calibration_simulation(frequency_range, antenna_diameter, noise_fraction_brightest_source, position_error,
broken_tile_fraction, sky_model_limit, n_realisations, output_path, save_inputs=True):
if save_inputs:
setup_simulation_directory(output_path)
input_parameters = numpy.array([[position_error], [broken_tile_fraction], [sky_model_limit], [n_realisations]])
header_string = "Position_Precision[m] Broken_Tile[Fraction] Sky_Model_Depth[Jy]"
numpy.savetxt(output_path + "input_parameters.txt", input_parameters.T, header=header_string)
antenna_table = AntennaPositions(load=False, shape=['doublehex', 14, 0, 0, 100, 250])
antenna_table.antenna_ids = numpy.arange(0, len(antenna_table.antenna_ids), 1)
print(f"Progress: \r", )
for i in range(n_realisations):
if (i / n_realisations * 100 % 10) == 0.0:
print(f"{int(i / n_realisations * 100)}% ... \r", )
calibration_realisation(frequency_range=frequency_range, antenna_table=antenna_table,
noise_fraction_brightest_source=noise_fraction_brightest_source,
antenna_size=antenna_diameter, position_precision=position_error,
broken_tile_fraction=broken_tile_fraction,
sky_model_limit=sky_model_limit, seed=i, save_inputs=True,
output_path=output_path)
print("")
return
def calibration_realisation(frequency_range, antenna_table, noise_fraction_brightest_source, antenna_size,
position_precision=0, gain_precision = 0.2, broken_tile_fraction=0.0, sky_model_limit=1e-1, seed=None,
save_inputs=True, output_path=None):
if seed is None:
numpy.random.seed(seed)
position_errors = numpy.random.normal(loc=0, scale=position_precision, size=2 * antenna_table.antenna_ids.shape[0])
gain_errors = numpy.random.normal(loc=1, scale=0.2, size = )
antenna_table.antenna_ids = numpy.arange(0, len(antenna_table.antenna_ids), 1)
antenna_table.x_coordinates += position_errors[:len(antenna_table.antenna_ids)]
antenna_table.y_coordinates += position_errors[len(antenna_table.antenna_ids):]
antenna_table.antenna_gains = numpy.random.normal(loc=1, scale=0.2, size = )
baseline_table = BaselineTable(position_table=antenna_table, frequency_channels=frequency_range)
# We go down to 40 mili-Jansky to get about 10 calibration sources
sky_realisation = SkyRealisation(sky_type="random", flux_low=40e-3, flux_high=10, seed=seed)
sky_model_sources = find_sky_model_sources(sky_realisation, frequency_range, antenna_size=antenna_size,
sky_model_depth=sky_model_limit)
print(f"Including {len(sky_model_sources.l_coordinates)} sources in the sky model")
if save_inputs:
if not os.path.exists(output_path + f"realisation_{seed}"):
print(f"Creating folder for realisation {seed}")
os.makedirs(output_path + f"realisation_{seed}")
sky_realisation.save_table(output_path + f"realisation_{seed}/", "sky_realisation")
numpy.save(output_path + f"realisation_{seed}/" + "position_errors", position_errors)
numpy.save(output_path + f"realisation_{seed}/" + "antenna_gains", antenna_table.antenna_gains)
# Create thermal noise
noise_level = thermal_variance()
# now compute the visibilities
ideal_visibilities = sky_realisation.create_visibility_model(baseline_table, frequency_range, antenna_size=antenna_size)
noise_realisation = numpy.random.normal(scale=noise_level, size=(ideal_visibilities.shape[0], 2))
noise_visibilities = numpy.zeros_like(ideal_visibilities)
noise_visibilities[:, 0] = noise_realisation[:, 0] + 1j*noise_realisation[:, 1]
measured_visibilities = baseline_table.baseline_gains*ideal_visibilities + noise_visibilities
if save_inputs:
numpy.save(output_path + f"realisation_{seed}/" + "ideal_visibilities", ideal_visibilities)
numpy.save(output_path + f"realisation_{seed}/" + "noise_visibilities", noise_visibilities)
numpy.save(output_path + f"realisation_{seed}/" + "measured_visibilities", measured_visibilities)
data_sorted, u_sorted, v_sorted, noise_sorted, ant1_sorted, ant2_sorted, edges_sorted, sorting_indices, \
conjugation_flag = grid_data(measured_visibilities,
baseline_table.u_coordinates,
baseline_table.v_coordinates,
noise_visibilities,
baseline_table.antenna_id1.astype(int),
baseline_table.antenna_id2.astype(int))
data_vector = split_visibility(data_sorted)
model_vectors = generate_sky_model_vectors(sky_model_sources, baseline_table, frequency_range, antenna_size)
covariance_vectors = generate_covariance_vectors(baseline_table.number_of_baselines, frequency_range,
sky_model_limit)
noise_split = numpy.zeros(data_vector.shape[0]) + noise_level
print("Calibrating the Sky")
gain_solutions = hybrid_calibration(data_vector, noise_split, covariance_vectors, model_vectors, edges_sorted,
ant1_sorted, ant2_sorted, gain_guess=None, scale_factor=1000)
numpy.save(output_path + f"realisation_{seed}/" + "gain_solutions", gain_solutions)
return gain_solutions
def plot_sky_counts(fluxes):
bins = numpy.logspace(numpy.log10(fluxes.min()), numpy.log10(fluxes.max()), 100)
hist, edges = numpy.histogram(fluxes, bins=bins)
bin_centers = (edges[:len(edges) - 1] + edges[1:]) / 2.
pyplot.plot(bin_centers, hist)
pyplot.xscale('log')
pyplot.yscale('log')
return
def setup_simulation_directory(output_path):
if not os.path.exists(output_path + "/"):
print("Creating Project folder at output destination!")
os.makedirs(output_path)
return
if "__main__" == __name__:
parser = argparse.ArgumentParser(description='Redundant Calibration Simulation set up')
parser.add_argument("--ssh", action="store_true", dest="ssh_key", default=False)
parser.add_argument("-r", action="store_true", default=True)
parser.add_argument("-p", action="store_true", default=False)
args = parser.parse_args()
import matplotlib
if args.ssh_key:
matplotlib.use("Agg")
from matplotlib import pyplot
if args.p:
main(mode="process")
elif args.r:
main(mode="run")