elif src not in srcs_seen_twice:
# Timedelta at which repeater detected
time_rep_seen.append(dt)
srcs_seen_twice.append(src)
return time_rep_seen
if __name__ == '__main__':
import matplotlib.pyplot as plt
from tests.convenience import plot_aa_style, rel_path
import numpy as np
from tqdm import tqdm
# Set up plot style
plot_aa_style(cols=1)
f, ax1 = plt.subplots(1, 1)
# See how the real fraction changes over time
chime_fracs = []
dts = calc_rep_frac()
days = [d for d in range(301)]
for day in tqdm(days, desc='frbcat'):
n_rep = sum([dt <= day for dt in dts])
n_one_offs = 2 * day
try:
frac = n_rep / (n_rep + n_one_offs)
except ZeroDivisionError:
frac = np.nan
chime_fracs.append(frac)
"""Plot intensity profile of sidelobes."""
import matplotlib.pyplot as plt
import numpy as np
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]
def plot(self, run):
# Get data
# For each requested run
df = self.so.df
par_set = df[df.run == run].par_set.iloc[0]
# For each parameter
for main_par in self.run_pars[par_set]:
pprint(f'Plotting {main_par}')
other_pars = [e for e in self.run_pars[par_set] if e != main_par]
for compare_par in ['dm', 'snr']:
compare_col = f'{compare_par}_gof'
pprint(f' - {compare_col}')
for survey, group_surv in df[df.run == run].groupby('survey'):
pprint(f' - {survey}')
# Set up plot
plot_aa_style()
plt.rcParams["figure.figsize"] = (5.75373 * 3, 5.75373 * 3)
plt.rcParams['figure.max_open_warning'] = 125
n_x = group_surv[other_pars[0]].nunique()
if len(other_pars) > 1:
n_y = group_surv[other_pars[1]].nunique()
else:
n_y = 1
fig, ax = plt.subplots(n_x,
n_y,
sharex='col',
sharey='row')
groups = group_surv.groupby(other_pars)
x = -1
for i, (other_pars_vals, group) in enumerate(groups):
bins = group[main_par].values
values = group[compare_col].values
bins, values = self.add_edges_to_hist(bins, values)
if n_y > 1:
y = i % n_y
if y == 0:
x += 1
a = ax[y, x]
else:
y = i
a = ax[y]
a.step(bins, values, where='mid')
a.set_title = str(other_pars_vals)
diff = np.diff(bins)
if diff[1] != diff[0]:
a.set_xscale('log')
# Set axis label
if y == n_y - 1:
p = other_pars[0]
if isinstance(other_pars_vals, float):
val = other_pars_vals
else:
val = other_pars_vals[0]
p = p.replace('_', ' ')
a.set_xlabel(f'{p} = {val:.2}')
if x == 0:
p = other_pars[1]
val = other_pars_vals[1]
p = p.replace('_', ' ')
a.set_ylabel(f'{p} = {val:.2}')
# Set axis limits
subset = df[df.run == run][main_par]
y_subset = group_surv[compare_col].copy()
try:
low = np.nanmin(y_subset)
high = np.nanmax(y_subset)
except ValueError:
low = 0.0001
high = 1
log = False
if low > 0 and high > 0:
log = True
for a in ax.flatten():
a.set_xlim(subset.min(), subset.max())
if log:
a.set_yscale('log', nonposy='clip')
a.set_ylim(low, high)
p = main_par.replace('_', ' ')
fig.suptitle(f'{p} - {compare_par} - {survey}')
plt.tight_layout()
plt.subplots_adjust(top=0.95)
# Save to subdirectory
path_to_save = rel_path(f'./plots/mc/{main_par}_run{run}/')
if not os.path.isdir(path_to_save):
os.mkdir(path_to_save)
path_to_save += f'{compare_par}_{survey}.pdf'
plt.savefig(path_to_save)
plt.clf()
else:
pop.generate()
surv_pop = SurveyPopulation(pop, survey, scale_by_area=False)
surv_pop.source_rate.f_area = surv_f_area[name]
surv_pop.source_rate.scale_by_area()
# surv_pop.save()
surv_pops.append(surv_pop)
else:
surv_pops = []
for name in SURVEYS:
surv_pops.append(unpickle(f'optimal_{name}'))
# Start plot
plot_aa_style(cols=2)
plt.rcParams["figure.figsize"] = (3.556 * 3, 3.556)
fig, axes = plt.subplots(1, 3)
for ax in axes.flatten():
ax.set_aspect('auto')
# Get norm pop
y = 0
ys = []
names = []
rates = []
norm_sim_rate = surv_pops[0].source_rate.det
norm_real_rate = EXPECTED['parkes-htru'][0] / EXPECTED['parkes-htru'][1]
norm_rate = norm_sim_rate / norm_real_rate
def plot_dists(surv_pop, telescope):
"""Plot the fluence and DM distribution of a surveyed population.
Args:
surv_pop (Population): Population from which to plot
telescope (str): Name of the telescope with which to compare the
distribution. Necessary for Frbcat.
"""
plot_aa_style(cols=2)
# Plot dm distribution
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
dm_frbpoppy = surv_pop.frbs.dm
pprint(f'Number of detected FRBs: {len(dm_frbpoppy)}')
ax1.step(*hist(dm_frbpoppy), where='mid', linestyle='dashed')
df = Frbcat().df
dm_frbcat = df[df.telescope == telescope].dm
ax1.step(*hist(dm_frbcat), where='mid')
# Compare distributions
ks = ks_2samp(dm_frbpoppy, dm_frbcat)
text = fr'$p={round(ks[1], 2)}$'
anchored_text = AnchoredText(text, loc=1, borderpad=1., frameon=False)
ax1.add_artist(anchored_text)
ax1.set_xlabel(r'DM ($\textrm{pc}\ \textrm{cm}^{-3}$)')
ax1.set_ylabel('Fraction')
ax1.set_ylim([0, 1.1])
ax1.set_xlim([0, 3500])
# Plot fluence distributions
fluence_frbpoppy = surv_pop.frbs.fluence
ax2.step(*hist(fluence_frbpoppy, bin_type='log'),
where='mid',
label='frbpoppy',
linestyle='dashed')
fluence_frbcat = df[df.telescope == telescope].fluence
ax2.step(*hist(fluence_frbcat, bin_type='log'),
where='mid',
label='frbcat')
# Compare distributions
ks = ks_2samp(fluence_frbpoppy, fluence_frbcat)
text = fr'$p={round(ks[1], 2)}$'
anchored_text = AnchoredText(text, loc=1, borderpad=1., frameon=False)
ax2.add_artist(anchored_text)
ax2.set_xlabel(r'Fluence (Jy ms)')
ax2.set_ylim([0, 1.1])
ax2.set_xlim([5e-1, 1e4])
plt.xscale('log')
plt.figlegend(loc='upper center', ncol=2, framealpha=1)
plt.tight_layout()
plt.savefig(rel_path(f'plots/frbpoppy_{telescope}.pdf'))
plt.clf()
