/
simulate_beam_covariance_data.py
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/
simulate_beam_covariance_data.py
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import os
import sys
import argparse
import numpy
from numba import prange, njit
from src.util import hexagonal_array
from src.util import redundant_baseline_finder
from src.radiotelescope import RadioTelescope
from src.radiotelescope import AntennaPositions
from src.radiotelescope import BaselineTable
from src.radiotelescope import broken_mwa_beam_loader
from src.skymodel import SkyRealisation
from src.skymodel import create_visibilities_analytic
from src.generaltools import from_lm_to_theta_phi
from src.plottools import colorbar
sys.path.append("../../CorrCal_UKZN_Development/corrcal")
from corrcal import grid_data
def beam_covariance_simulation(array_size=3, create_signal=False, compute_covariance=True, plot_covariance=True,
show_plot=True):
output_path = "/data/rjoseph/Hybrid_Calibration/numerical_simulations/"
project_path = "redundant_based_beam_covariance/"
n_realisations = 10000
if not os.path.exists(output_path + project_path + "/"):
print("Creating Project folder at output destination!")
os.makedirs(output_path + project_path)
hex_telescope = create_hex_telescope(array_size)
if create_signal:
create_visibility_data(hex_telescope, n_realisations, output_path + project_path, output_data=True)
if compute_covariance:
compute_baseline_covariance(hex_telescope, output_path + project_path, n_realisations, data_type='model')
compute_baseline_covariance(hex_telescope, output_path + project_path, n_realisations, data_type='perturbed')
compute_baseline_covariance(hex_telescope, output_path + project_path, n_realisations, data_type='residual')
if plot_covariance:
plot_covariance_data(output_path + project_path, simulation_type = "Beam")
if show_plot:
pyplot.show()
return
def broken_tiles(telescope, fraction=25 / 128, seed=None, number_dipoles=16):
if seed is not None:
numpy.random.seed((seed))
number_antennas = len(telescope.antenna_positions.x_coordinates)
# Determine number of broken tiles
number_broken_tiles = numpy.random.binomial(n=number_antennas, p=fraction, size=1)
# Select which tiles are broken
broken_flags = numpy.zeros(number_antennas, dtype=int)
broken_tile_indices = numpy.random.randint(0, number_antennas, number_broken_tiles)
broken_dipole_indices = numpy.random.randint(0, number_dipoles, number_broken_tiles)
broken_flags[broken_tile_indices] = broken_dipole_indices
return broken_flags
def create_visibility_data(telescope_object, n_realisations, path, output_data=False):
print("Creating Signal Realisations")
if not os.path.exists(path + "/" + "Simulated_Visibilities") and output_data:
print("Creating realisation folder in Project path")
os.makedirs(path + "/" + "Simulated_Visibilities")
original_baselines = telescope_object.baseline_table
for i in range(n_realisations):
print(f"Realisation {i}")
broken_flags = broken_tiles(telescope_object, seed=i)
source_population = SkyRealisation(sky_type='random', flux_high=1, seed = i)
model_visibilities = source_population.create_visibility_model(source_population, redundant_baselines,
frequency_range = numpy.array([150e6]))
perturbed_visibilities = create_perturbed_visibilities(source_population, redundant_baselines, broken_flags)
residual_visibilities = model_visibilities.flatten() - perturbed_visibilities
numpy.save(path + "/" + "Simulated_Visibilities/" + f"model_realisation_{i}", model_visibilities)
numpy.save(path + "/" + "Simulated_Visibilities/" + f"perturbed_realisation_{i}", perturbed_visibilities)
numpy.save(path + "/" + "Simulated_Visibilities/" + f"residual_realisation_{i}", residual_visibilities)
return
def create_hex_telescope(size):
hex_telescope = RadioTelescope(load=False)
antenna_positions = hexagonal_array(size)
antenna_table = AntennaPositions(load=False)
antenna_table.antenna_ids = numpy.arange(0, antenna_positions.shape[0], 1)
antenna_table.x_coordinates = antenna_positions[:, 0]
antenna_table.y_coordinates = antenna_positions[:, 1]
antenna_table.z_coordinates = antenna_positions[:, 2]
antenna_table.antenna_gains = numpy.zeros(len(antenna_positions[:, 0]), dtype=complex) + 1 + 0j
hex_telescope.antenna_positions = antenna_table
hex_telescope.baseline_table = BaselineTable(position_table=antenna_table)
return hex_telescope
def apparent_flux_possibilities(source_population, number_of_dipoles=16, nu=150e6):
number_of_sources = len(source_population.fluxes)
theta, phi = from_lm_to_theta_phi(source_population.l_coordinates, source_population.m_coordinates)
beam_response = numpy.zeros((number_of_sources, number_of_dipoles + 1), dtype=complex)
flux_beam_product = numpy.zeros_like(beam_response)
for i in range(number_of_dipoles + 1):
if i == 0:
faulty_dipole_i = None
else:
faulty_dipole_i = i - 1
beam_response[:, i] = broken_mwa_beam_loader(theta, phi, frequency=nu, faulty_dipole=faulty_dipole_i,
load=False)
flux_beam_product[:, i] = beam_response[:, i] * source_population.fluxes
apparent_fluxes = numpy.einsum('ij,ik->ijk', flux_beam_product, numpy.conj(beam_response))
return apparent_fluxes
def create_perturbed_visibilities(source_population, baseline_table, broken_flags, frequency = 150e6):
observations = numpy.zeros(baseline_table.number_of_baselines, dtype = complex)
apparent_fluxes = apparent_flux_possibilities(source_population, nu = frequency)
flags_antenna1 = broken_flags[baseline_table.antenna_id1.astype(int)]
flags_antenna2 = broken_flags[baseline_table.antenna_id2.astype(int)]
numba_perturbed_loop(observations, apparent_fluxes, source_population.l_coordinates, source_population.m_coordinates
, baseline_table.u(frequency), baseline_table.v(frequency), flags_antenna1, flags_antenna2)
return observations
@njit(parallel=True)
def numba_perturbed_loop(observations, fluxes, l_source, m_source, u_baselines, v_baselines, broken_flags1,
broken_flags2):
for source_index in prange(len(fluxes)):
for baseline_index in range(u_baselines.shape[0]):
kernel = numpy.exp(-2j * numpy.pi * (u_baselines[baseline_index] * l_source[source_index] +
v_baselines[baseline_index] * m_source[source_index]))
observations[baseline_index] += fluxes[source_index, broken_flags1[baseline_index],
broken_flags2[baseline_index]] * kernel
def compute_baseline_covariance(telescope_object, path, n_realisations, data_type = "residual", figure = None,
axes = None):
original_table = telescope_object.baseline_table
dummy_data = numpy.zeros(telescope_object.baseline_table.number_of_baselines, dtype = complex)
data_sorted, u_sorted, v_sorted, noise_sorted, ant1_sorted, ant2_sorted, edges_sorted, sorting_indices, \
conjugation_flag = grid_data(dummy_data,
telescope_object.baseline_table.u_coordinates,
telescope_object.baseline_table.v_coordinates,
dummy_data,
telescope_object.baseline_table.antenna_id1.astype(int),
telescope_object.baseline_table.antenna_id2.astype(int))
if not os.path.exists(path + "/" + "Simulated_Covariance"):
print("Creating Covariance folder in Project path")
os.makedirs(path + "/" + "Simulated_Covariance")
residuals = numpy.zeros((2*telescope_object.baseline_table.number_of_baselines, n_realisations), dtype = complex)
for i in range(n_realisations):
residuals_realisation = numpy.load(path + "Simulated_Visibilities/" + f"{data_type}_realisation_{i}.npy").flatten()
# conjugate data if neccessary
residuals_realisation[conjugation_flag] = numpy.conjugate(residuals_realisation[conjugation_flag])
# Sort data according to redundant groupings of the idealised array
residuals[0::2, i] = numpy.real(residuals_realisation[sorting_indices])
residuals[1::2, i] = numpy.imag(residuals_realisation[sorting_indices])
baseline_covariance = numpy.cov(residuals)
numpy.save(path + "Simulated_Covariance/" + f"baseline_{data_type}_covariance", baseline_covariance)
return
def plot_covariance_data(path, simulation_type = "Unspecified", figure=None, axes=None):
if not os.path.exists(path + "/" + "Plots"):
print("Creating realisation folder in Project path")
os.makedirs(path + "/" + "Plots")
data_labels =['Ideal', 'Perturbed', 'Residual']
data = []
data.append(numpy.load(path + "Simulated_Covariance/" + f"baseline_model_covariance.npy"))
data.append(numpy.load(path + "Simulated_Covariance/" + f"baseline_perturbed_covariance.npy"))
data.append(numpy.load(path + "Simulated_Covariance/" + f"baseline_residual_covariance.npy"))
figure.suptitle(f"Baseline {simulation_type} Covariance")
for i in range(3):
realplot = axes[i].imshow(numpy.real(data[i]))
# imagplot = axes[i, 1].imshow(numpy.imag(data[i]))
colorbar(realplot)
# colorbar(imagplot)
axes[i].set_title(f"Re({data_labels[i]})")
# axes[i, 1].set_title(f"Im({data_labels[i]}) ")
if i == 0 :
axes[i].set_ylabel("Baseline Index")
axes[i].set_xlabel("Baseline Index")
# axes[i, 1].set_xlabel("Baseline Index")
figure.subplots_adjust(top=0.9)
figure.savefig(path + "Plots/" +f"{simulation_type}_Covariance_Plot.pdf")
return
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ssh", action= "store_true", dest="ssh_key", default = False)
params = parser.parse_args()
import matplotlib
if params.ssh_key:
matplotlib.use("Agg")
from matplotlib import pyplot
beam_covariance_simulation()