def main():
"""Compare a standard Survey Population with a Large Survey Population."""
from frbpoppy.cosmic_pop import CosmicPopulation
from frbpoppy.survey import Survey
from frbpoppy.survey_pop import SurveyPopulation
from frbpoppy.do_plot import plot
pop = CosmicPopulation.simple(int(3e3), generate=False)
pop.name = 'large'
survey = Survey('perfect')
large_pop = LargePopulation(pop, survey, max_size=1e3).pops[0]
# For comparison
surv_pop = SurveyPopulation(pop, survey)
surv_pop.name = 'normal'
plot(large_pop, surv_pop, mute=False, tns=False)
from tests.convenience import plot_aa_style, rel_path
OBSERVATORIES = [('parkes-htru', 'parkes-htru'),
('wsrt-apertif', 'wsrt-apertif')]
n = int(1e6)
plot_aa_style()
for obs in OBSERVATORIES:
pattern = obs[0]
survey = obs[1]
s = Survey(survey)
s.set_beam(model=pattern)
int_pro, offset = s.calc_beam(shape=n)
# Sort the values
sorted_int = np.argsort(offset)
int_pro = int_pro[sorted_int]
offset = offset[sorted_int]
bins = 1e2
bin_means, bin_edges, bin_numbers = bstat(offset,
int_pro,
statistic='mean',
bins=bins)
"""Plot intensity profile of sidelobes."""
import matplotlib.pyplot as plt
import numpy as np
from frbpoppy.survey import Survey
SIDELOBES = [0, 1, 2, 8]
SURVEY = 'apertif'
MIN_Y = 1e-7
n = 50000
for sidelobe in reversed(SIDELOBES):
args = {'sidelobes': sidelobe}
s = Survey(SURVEY, gain_pattern='airy', n_sidelobes=sidelobe)
int_pro, offset = s.intensity_profile(n_gen=n)
# Sort the values
sorted_int = np.argsort(offset)
int_pro = int_pro[sorted_int]
offset = offset[sorted_int]
# Clean up lower limit
offset = offset[int_pro > MIN_Y]
int_pro = int_pro[int_pro > MIN_Y]
label = f'{sidelobe} sidelobes'
if sidelobe == 1:
label = label[:-1]
from frbpoppy.survey import Survey
from tests.convenience import plot_aa_style, rel_path
SIDELOBES = [0, 1, 2, 8]
SURVEY = 'wsrt-apertif'
MIN_Y = 1e-7
n = 50000
plot_aa_style()
for sidelobe in reversed(SIDELOBES):
args = {'sidelobes': sidelobe}
s = Survey(SURVEY)
s.set_beam(model='airy', n_sidelobes=sidelobe)
int_pro, offset = s.calc_beam(shape=n)
# Sort the values
sorted_int = np.argsort(offset)
int_pro = int_pro[sorted_int]
offset = offset[sorted_int]
# Clean up lower limit
offset = offset[int_pro > MIN_Y]
int_pro = int_pro[int_pro > MIN_Y]
label = f'{sidelobe} sidelobes'
if sidelobe == 1:
label = label[:-1]
plot_aa_style()
for pattern in PATTERNS:
n_sidelobes = 1
p = pattern
z = 0
if pattern.startswith('perfect'):
n_sidelobes = 0.5
if pattern.startswith('airy'):
n_sidelobes = int(pattern[-1])
p = 'airy'
if n_sidelobes == 0:
z = 10
s = Survey(SURVEY)
s.set_beam(model=p, n_sidelobes=n_sidelobes)
int_pro, offset = s.calc_beam(shape=n)
# Sort the values
sorted_int = np.argsort(offset)
int_pro = int_pro[sorted_int]
offset = offset[sorted_int]
# Clean up lower limit
offset = offset[int_pro > MIN_Y]
int_pro = int_pro[int_pro > MIN_Y]
print(f'Beam size at FWHM: {s.beam_size_at_fwhm}')
print(f'Beam size with {n_sidelobes} sidelobes: {s.beam_size}')
import numpy as np
from scipy.stats import binned_statistic as bstat
from frbpoppy.survey import Survey
OBSERVATORIES = [('parkes', 'htru'),
('apertif', 'apertif')]
n = int(1e6)
for obs in OBSERVATORIES:
survey = obs[1]
pattern = obs[0]
s = Survey(survey, gain_pattern=pattern)
int_pro, offset = s.intensity_profile(n_gen=n)
# Sort the values
sorted_int = np.argsort(offset)
int_pro = int_pro[sorted_int]
offset = offset[sorted_int]
# Offset in degrees
offset = offset/60.
bins = 1e2
bin_means, bin_edges, bin_numbers = bstat(offset,
int_pro,
statistic='mean',
n = 500000
plot_aa_style()
for pattern in PATTERNS:
n_sidelobes = 1
p = pattern
z = 0
if pattern.startswith('airy'):
n_sidelobes = int(pattern[-1])
p = 'airy'
if n_sidelobes == 0:
z = 10
s = Survey(SURVEY, gain_pattern=p, n_sidelobes=n_sidelobes)
int_pro, offset = s.intensity_profile(shape=n)
# Sort the values
sorted_int = np.argsort(offset)
int_pro = int_pro[sorted_int]
offset = offset[sorted_int]
# Clean up lower limit
offset = offset[int_pro > MIN_Y]
int_pro = int_pro[int_pro > MIN_Y]
# Offset in degrees
offset = offset/60.
print(f'Beam size at FWHM: {s.beam_size_fwhm}')