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Plot_Calibrated_Residuals_Absolute.py
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Plot_Calibrated_Residuals_Absolute.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_1dpower_spectrum
# from src.plottools import plot_1dpower_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)
u_plot = 7
model_limit = 1e-1
broken_baseline_weight = 0.3
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)
sky_calibrated = calibrated_residual_error(u=u_range, nu=frequency_range, residuals='sky',
calibration_type='absolute', model_limit=model_limit,
broken_baselines_weight=broken_baseline_weight)
beam_calibrated = calibrated_residual_error(u=u_range, nu=frequency_range, residuals='beam',
calibration_type='absolute', model_limit=model_limit,
broken_baselines_weight=broken_baseline_weight)
total_calibrated = calibrated_residual_error(u=u_range, nu=frequency_range, residuals='both',
calibration_type='absolute', model_limit=model_limit,
broken_baselines_weight=broken_baseline_weight)
index = numpy.where(numpy.abs(u_range - u_plot) == numpy.min(numpy.abs(u_range - u_plot)))[0]
sky_calibrated = sky_calibrated[index, ...]
beam_calibrated= beam_calibrated[index,...]
total_calibrated= total_calibrated[index, ...]
eor_power_spectrum = eor_power_spectrum[index,...]
figure, axes = pyplot.subplots(1, 3, figsize=(15, 5))
ps_norm = colors.LogNorm(vmin=1e3, vmax=1e15)
plot_1dpower_spectrum(eta, frequency_range, sky_calibrated[0,:], title="Sky Error", axes=axes[0],
axes_label_font=labelfontsize, tickfontsize=ticksize, xlabel_show=True, ylabel_show=True)
plot_1dpower_spectrum(eta, frequency_range, beam_calibrated[0,:], title="Beam Variations", axes=axes[1],
axes_label_font=labelfontsize, tickfontsize=ticksize, xlabel_show=True, ylabel_show=False)
plot_1dpower_spectrum(eta, frequency_range, total_calibrated[0,:], title="Total Error", axes=axes[2],
axes_label_font=labelfontsize, tickfontsize=ticksize, xlabel_show=True, ylabel_show=False)
plot_1dpower_spectrum(eta, frequency_range, eor_power_spectrum[0, :], axes=axes[0], ratio=True, color = 'k')
plot_1dpower_spectrum(eta, frequency_range, eor_power_spectrum[0, :], axes=axes[1], ratio=True, color = 'k')
plot_1dpower_spectrum(eta, frequency_range, eor_power_spectrum[0, :], axes=axes[2], ratio=True, color = 'k')
# plot_power_contours(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_Absolute.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()