def main(labelfontsize = 16, ticksize= 11):
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
eta = from_frequency_to_eta(frequency_range)
start = time.time()
position_covariance = PositionCovariance(position_precision=1e-2)
position_covariance.compute_covariance(u=u_range, v=0, nu=frequency_range)
position_error_power =position_covariance.compute_power()
# position_error_power = calculate_sky_power_spectrum(u=u_range, nu=frequency_range)
lapse = time.time() - start
print(f"It took {lapse}")
figure, axes = pyplot.subplots(1, 1, figsize=(5, 5))
ps_norm = colors.LogNorm(vmin=1e3, vmax=1e15)
plot_2dpower_spectrum(u_range, eta, frequency_range, position_error_power, title="Position Error", axes=axes,
axes_label_font=labelfontsize, tickfontsize=ticksize, colorbar_show=True,
xlabel_show=True, norm=ps_norm, ylabel_show=True, zlabel_show=True)
figure.tight_layout()
# pyplot.show()
figure.savefig("../plots/Position_Class_Test.pdf")
return
def main(labelfontsize = 20, ticksize= 15):
tile_precision = 1e-2
broken_fraction = 0.25
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
eta = from_frequency_to_eta(frequency_range)
position_covariance = PositionCovariance(position_precision=tile_precision)
position_covariance.compute_covariance(u=u_range, v=0, nu=frequency_range)
beam_covariance = BeamCovariance(model_depth=10, calibration_type='redundant', broken_fraction=broken_fraction)
beam_covariance.compute_covariance(u=u_range, v=0, nu = frequency_range)
position_error_power = position_covariance.compute_power()
beam_error_power = beam_covariance.compute_power()
total_error_power = position_error_power + beam_error_power
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_error_power, 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_error_power, title="Beam Model Error", axes=axes[1],
axes_label_font=labelfontsize, tickfontsize=ticksize, colorbar_show=False,
xlabel_show=True, norm=ps_norm)
plot_2dpower_spectrum(u_range, eta, frequency_range, total_error_power, title="Total Error", axes=axes[2],
axes_label_font=labelfontsize, tickfontsize=ticksize, colorbar_show=True,
xlabel_show=True, norm=ps_norm, zlabel_show=True)
figure.tight_layout()
figure.savefig("../plots/Uncalibrated_redundant_residuals.pdf")
return
def main(labelfontsize=20, ticksize=20):
model_limit = 100e-3
position_error = 0.01
broken_fraction = 0.25
compute = False
save = False
load = True
telescope_position_path = "./data/MWA_Compact_Coordinates.txt"
u_range = numpy.logspace(-1, numpy.log10(500), 100)
frequency_range = numpy.linspace(135, 165, 251) * 1e6
eta = from_frequency_to_eta(frequency_range)
print(f"{eta.max()} blaah")
eor_power_spectrum = fiducial_eor_power_spectrum(u_range, eta)
telescope = RadioTelescope(load=True, path=telescope_position_path)
redundant_table = redundant_baseline_finder(telescope.baseline_table)
contour_levels = numpy.array([1e-1, 1e0, 1e2])
sky_clocations = None #[(6e-2, 0.21), (4e-2, 0.25), (1.1e-2, 0.35 )]
beam_clocations = None #[(6e-2, 0.21), (8e-2, 0.5), (1.1e-2, 0.35 )]
linestyles = ['dotted', 'dashed', 'solid']
if compute:
#### Initialise Sky Errors ###
sky_error = SkyCovariance(model_depth=model_limit)
sky_error.compute_covariance(u=u_range, v=0, nu=frequency_range)
sky_beam_covariance = BeamCovariance(model_depth=model_limit,
calibration_type='sky',
broken_fraction=broken_fraction)
sky_beam_covariance.compute_covariance(u=u_range,
v=0,
nu=frequency_range)
sky_total = sky_error + sky_beam_covariance
if load:
with open('unmodeled_sky.obj', 'rb') as f:
sky_error = pickle.load(f)
with open('sky_total.obj', 'rb') as f:
sky_total = pickle.load(f)
sky_gain = GainCovariance(sky_total,
calibration_type='sky',
baseline_table=redundant_table)
if compute:
###### Initialise Redundant Errors
position_covariance = PositionCovariance(
position_precision=position_error)
position_covariance.compute_covariance(u=u_range,
v=0,
nu=frequency_range)
beam_covariance = BeamCovariance(model_depth=model_limit,
calibration_type='relative',
broken_fraction=broken_fraction)
beam_covariance.compute_covariance(u=u_range, v=0, nu=frequency_range)
redundant_total = position_covariance + beam_covariance
if load:
with open('redundant_residuals.obj', 'rb') as f:
redundant_total = pickle.load(f)
relative_gain = GainCovariance(redundant_total,
calibration_type='relative',
baseline_table=redundant_table)
absolute_gain = GainCovariance(sky_total,
calibration_type='absolute',
baseline_table=redundant_table)
redundant_gain = relative_gain + absolute_gain
##### Rescale unmodeled sky covariance to model (avoiding heavy computation)
model_sky = residual_to_model_rescale(sky_error)
######### Compute residuals
redundant_residuals = CalibratedResiduals(redundant_gain,
model_matrix=model_sky,
residual_matrix=sky_error)
sky_residuals = CalibratedResiduals(sky_gain,
model_matrix=model_sky,
residual_matrix=sky_error)
###### Compute PS #########
redundant_power = redundant_residuals.compute_power()
sky_power = sky_residuals.compute_power()
if save:
###### Save covariance matrices
sky_error.save("unmodeled_sky")
sky_beam_covariance.save("sky_beam_error")
sky_total.save("sky_total")
position_covariance.save("position_error")
beam_covariance.save("redundant_beam")
redundant_total.save("redundant_residuals")
figure, axes = pyplot.subplots(1, 2, figsize=(11, 5))
ps_norm = colors.LogNorm(vmin=1e3, vmax=1e15)
plot_2dpower_spectrum(u_range,
eta,
frequency_range,
sky_power,
title="Sky Based",
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,
redundant_power,
title="Redundancy Based",
axes=axes[1],
axes_label_font=labelfontsize,
tickfontsize=ticksize,
colorbar_show=True,
zlabel_show=True,
xlabel_show=True,
norm=ps_norm,
ylabel_show=False)
plot_power_contours(u_range,
eta,
frequency_range,
from_jansky_to_milikelvin(sky_power, 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,
contour_label_locs=sky_clocations,
smooth=3,
contour_styles=linestyles)
plot_power_contours(
u_range,
eta,
frequency_range,
from_jansky_to_milikelvin(redundant_power, 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,
contour_label_locs=beam_clocations,
smooth=3,
contour_styles=linestyles)
pyplot.tight_layout()
pyplot.savefig("../plots/Calibrated_Residuals_Comparison_MWA.pdf")
pyplot.show()
return
def main(labelfontsize = 20, ticksize= 15):
model_limit = 100e-3
position_error = 0.01
broken_fraction = 0.25
telescope_position_path = "./data/MWA_Compact_Coordinates.txt"
u_range = numpy.logspace(-1, numpy.log10(500), 100)
frequency_range = numpy.linspace(135, 165, 251 )* 1e6
eta = from_frequency_to_eta(frequency_range)
eor_power_spectrum = fiducial_eor_power_spectrum(u_range, eta)
telescope = RadioTelescope(load=True, path=telescope_position_path)
redundant_table = redundant_baseline_finder(telescope.baseline_table)
contour_levels = numpy.array([1e0, 1e1, 1e2])
sky_clocations = [(6e-2, 0.21), (4e-2, 0.17), (3e-2, 0.07 )]
beam_clocations = [(6e-2, 0.21), (0.045, 0.15), (3e-2, 0.07 )]
total_clocations = [(6e-2, 0.24), (0.045, 0.18), (3e-2, 0.10)]
sky_covariance = SkyCovariance(model_depth=model_limit)
sky_covariance.compute_covariance(u=u_range, v = 0, nu=frequency_range)
model_sky = copy.deepcopy(sky_covariance)
unmodeled_mu = sky_moment_returner(2, s_low=sky_covariance.s_low, s_mid=sky_covariance.s_mid,
s_high=sky_covariance.model_depth, k1=sky_covariance.k1,
gamma1=sky_covariance.alpha1, k2=sky_covariance.k2, gamma2=sky_covariance.alpha2)
modeled_mu = sky_moment_returner(2, s_low=sky_covariance.model_depth, s_mid=sky_covariance.s_mid,
s_high=sky_covariance.s_high, k1=sky_covariance.k1,
gamma1=sky_covariance.alpha1, k2=sky_covariance.k2, gamma2=sky_covariance.alpha2)
model_sky.matrix *= modeled_mu / unmodeled_mu
position_covariance = PositionCovariance(position_precision=position_error)
position_covariance.compute_covariance(u=u_range, v = 0, nu=frequency_range)
beam_covariance = BeamCovariance(model_depth=model_limit, calibration_type='relative', broken_fraction=broken_fraction)
beam_covariance.compute_covariance(u=u_range, v = 0, nu=frequency_range)
total_covariance = position_covariance + beam_covariance
position_gain = GainCovariance(position_covariance, calibration_type='relative', baseline_table=redundant_table)
beam_gain = GainCovariance(beam_covariance, calibration_type='relative', baseline_table=redundant_table)
total_gain= GainCovariance(total_covariance, calibration_type='relative', baseline_table=redundant_table)
position_residuals = CalibratedResiduals(position_gain, model_matrix=model_sky, residual_matrix=sky_covariance)
beam_residuals = CalibratedResiduals(beam_gain, model_matrix=model_sky, residual_matrix=sky_covariance)
total_residuals = CalibratedResiduals(total_gain, model_matrix=model_sky, residual_matrix=sky_covariance)
position_power = position_residuals.compute_power()
beam_power = beam_residuals.compute_power()
total_power = total_residuals.compute_power()
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_power, 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_power, 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_power, 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, contour_label_locs=sky_clocations)
#
# 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, contour_label_locs=beam_clocations)
#
# 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, contour_label_locs=total_clocations)
#
pyplot.tight_layout()
pyplot.savefig("../plots/Calibrated_Residuals_Relative_MWA.pdf")
pyplot.show()
return
def main(labelfontsize = 20, ticksize= 15):
model_limit = 100e-3
broken_fraction = 0.25
u_plot = 7
telescope_position_path = "./data/MWA_Compact_Coordinates.txt"
u_range = numpy.logspace(-1, numpy.log10(500), 100)
frequency_range = numpy.linspace(135, 165,251 )* 1e6
eta = from_frequency_to_eta(frequency_range)
eor_power_spectrum = fiducial_eor_power_spectrum(u_range, eta)
telescope = RadioTelescope(load=True, path=telescope_position_path)
redundant_table = redundant_baseline_finder(telescope.baseline_table)
sky_covariance = SkyCovariance(model_depth=model_limit)
sky_covariance.compute_covariance(u=u_range, v = 0, nu=frequency_range)
model_sky = copy.deepcopy(sky_covariance)
unmodeled_mu = sky_moment_returner(2, s_low=sky_covariance.s_low, s_mid=sky_covariance.s_mid,
s_high=sky_covariance.model_depth, k1=sky_covariance.k1,
gamma1=sky_covariance.alpha1, k2=sky_covariance.k2, gamma2=sky_covariance.alpha2)
modeled_mu = sky_moment_returner(2, s_low=sky_covariance.model_depth, s_mid=sky_covariance.s_mid,
s_high=sky_covariance.s_high, k1=sky_covariance.k1,
gamma1=sky_covariance.alpha1, k2=sky_covariance.k2, gamma2=sky_covariance.alpha2)
model_sky.matrix *= modeled_mu / unmodeled_mu
beam_covariance = BeamCovariance(model_depth=model_limit, calibration_type='sky', broken_fraction=broken_fraction)
beam_covariance.compute_covariance(u=u_range, v = 0, nu=frequency_range)
total_covariance = sky_covariance + beam_covariance
sky_gain = GainCovariance(sky_covariance, calibration_type='absolute', baseline_table=redundant_table)
beam_gain = GainCovariance(beam_covariance, calibration_type='absolute', baseline_table=redundant_table)
total_gain= GainCovariance(total_covariance, calibration_type='absolute', baseline_table=redundant_table)
sky_residuals = CalibratedResiduals(sky_gain, model_matrix=model_sky, residual_matrix=sky_covariance)
beam_residuals = CalibratedResiduals(beam_gain, model_matrix=model_sky, residual_matrix=sky_covariance)
total_residuals = CalibratedResiduals(total_gain, model_matrix=model_sky, residual_matrix=sky_covariance)
sky_power = sky_residuals.compute_power()
beam_power = beam_residuals.compute_power()
total_power = total_residuals.compute_power()
index = numpy.where(numpy.abs(u_range - u_plot) == numpy.min(numpy.abs(u_range - u_plot)))[0]
sky_calibrated = sky_power[index, ...]
beam_calibrated= beam_power[index,...]
total_calibrated= total_power[index, ...]
eor_power_spectrum = eor_power_spectrum[index,...]
figure, axes = pyplot.subplots(1, 3, figsize=(15, 5))
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')
pyplot.tight_layout()
pyplot.savefig("../plots/Calibrated_Residuals_Absolute_MWA.pdf")
pyplot.show()
return
def main(labelfontsize=20, ticksize=15):
model_limit = 100e-3
broken_fraction = 0.25
u_range = numpy.logspace(-1, numpy.log10(500), 100)
frequency_range = numpy.linspace(135, 165, 251) * 1e6
eta = from_frequency_to_eta(frequency_range)
sky_covariance = SkyCovariance(model_depth=model_limit)
sky_covariance.compute_covariance(u=u_range, v=0, nu=frequency_range)
beam_covariance = BeamCovariance(model_depth=model_limit,
calibration_type='sky',
broken_fraction=broken_fraction)
beam_covariance.compute_covariance(u=u_range, v=0, nu=frequency_range)
sky_error_power = sky_covariance.compute_power()
beam_error_power = beam_covariance.compute_power()
total_error_power = sky_error_power + beam_error_power
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,
sky_error_power,
title="Sky Model 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_error_power,
title="Beam Model Error",
axes=axes[1],
axes_label_font=labelfontsize,
tickfontsize=ticksize,
colorbar_show=False,
xlabel_show=True,
norm=ps_norm)
plot_2dpower_spectrum(u_range,
eta,
frequency_range,
total_error_power,
title="Total Error",
axes=axes[2],
axes_label_font=labelfontsize,
tickfontsize=ticksize,
colorbar_show=True,
xlabel_show=True,
norm=ps_norm,
zlabel_show=True)
figure.tight_layout()
figure.savefig("../plots/Uncalibrated_sky_residuals.pdf")
return
