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Plot_Calibrated_Residuals_Relative.py
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Plot_Calibrated_Residuals_Relative.py
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import numpy
import argparse
from matplotlib import colors
from src.powerspectrum import from_frequency_to_eta
from src.powerspectrum import fiducial_eor_power_spectrum
from src.plottools import plot_2dpower_spectrum
# from src.plottools import plot_power_contours
from src.generaltools import from_jansky_to_milikelvin
from src.covariance import calibrated_residual_error
def main(labelfontsize = 20, ticksize= 15):
output_path = "./"#""/data/rjoseph/Hybrid_Calibration/theoretical_calculations/power_spectra/"
k_perp_range = numpy.array([1e-4, 1.1e-1])
u_range = numpy.logspace(-1, numpy.log10(500), 100)
frequency_range = numpy.linspace(135, 165, 251) * 1e6
contour_levels = numpy.array([1e1, 1e2, 1e3])
eta = from_frequency_to_eta(frequency_range)
eor_power_spectrum = fiducial_eor_power_spectrum(u_range, eta)
position_calibrated = calibrated_residual_error(u=u_range, nu=frequency_range, residuals='position',
calibration_type='relative')
beam_calibrated = calibrated_residual_error(u=u_range, nu=frequency_range, residuals='beam',
calibration_type='relative')
total_calibrated = calibrated_residual_error(u=u_range, nu=frequency_range, residuals='both',
calibration_type='relative')
figure, axes = pyplot.subplots(1, 3, figsize=(15, 5))
ps_norm = colors.LogNorm(vmin=1e3, vmax=1e15)
plot_2dpower_spectrum(u_range, eta, frequency_range, position_calibrated, title="Position Error", axes=axes[0],
axes_label_font=labelfontsize, tickfontsize=ticksize, colorbar_show=False,
xlabel_show=True, norm=ps_norm, ylabel_show=True)
plot_2dpower_spectrum(u_range, eta, frequency_range, beam_calibrated, title="Beam Variations", axes=axes[1],
axes_label_font=labelfontsize, tickfontsize=ticksize, colorbar_show=False,
xlabel_show=True, norm=ps_norm, ylabel_show=False)
plot_2dpower_spectrum(u_range, eta, frequency_range, total_calibrated, title="Total Error", axes=axes[2],
axes_label_font=labelfontsize, tickfontsize=ticksize, colorbar_show=True,
xlabel_show=True, norm=ps_norm, zlabel_show=True, ylabel_show=False)
# plot_power_contours(u_range, eta, frequency_range, from_jansky_to_milikelvin(position_calibrated, frequency_range)/eor_power_spectrum,
# axes=axes[0], ratio=True, axes_label_font=labelfontsize, tickfontsize=ticksize, xlabel_show=True, norm=ps_norm, ylabel_show=False, contour_levels=contour_levels)
#
# plot_power_contours(u_range, eta, frequency_range, from_jansky_to_milikelvin(beam_calibrated, frequency_range)/eor_power_spectrum,
# axes=axes[1], ratio=True, axes_label_font=labelfontsize, tickfontsize=ticksize, xlabel_show=True, norm=ps_norm, ylabel_show=False, contour_levels=contour_levels)
#
# plot_power_contours(u_range, eta, frequency_range, from_jansky_to_milikelvin(total_calibrated, frequency_range)/eor_power_spectrum,
# axes=axes[2], ratio=True, axes_label_font=labelfontsize, tickfontsize=ticksize, xlabel_show=True, norm=ps_norm, ylabel_show=False, contour_levels=contour_levels)
#
pyplot.tight_layout()
pyplot.savefig(output_path + "Calibrated_Residuals_Relative.pdf")
pyplot.show()
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
main()