def calc_cum_hist(self, pop):
# Bin up parameter in a cumulative bin
edges, values = hist(pop.frbs.snr, bin_type='log')
if isinstance(edges, float):
return np.nan, np.nan
n_gt_s = np.cumsum(values[::-1])[::-1]
# Normalise to S/N of 10^5
log_diff = (np.log10(edges[0]) - np.log10(1e0))
edges = 10**(np.log10(edges) - log_diff)
return edges, n_gt_s
if SCALE_TO == 'parkes-htru':
htru_pop = SurveyPopulation(pop, htru, mute=True)
n_frbs_htru = EXPECTED['parkes-htru'][0]
n_days_htru = EXPECTED['parkes-htru'][1]
scaled_n_days = n_days_htru * (htru_pop.source_rate.det / n_frbs_htru)
if SCALE_TO == 'askap':
askap_pop = SurveyPopulation(pop, askap, mute=True)
n_frbs_askap = EXPECTED['askap-fly'][0]
n_days_askap = EXPECTED['askap-fly'][1]
scaled_n_days = n_days_askap * (askap_pop.source_rate.det /
n_frbs_askap)
days_per_frb = scaled_n_days / apertif_pop.source_rate.det
# print(f'{days_per_frb} days per frb')
days_per_frbs.append(days_per_frb)
days_per_frbs = np.array(days_per_frbs)
mean = np.mean(days_per_frbs)
std = np.std(days_per_frbs)
poisson_std = poisson_interval(mean)
print(f'Mean rate for apertif is {mean}')
print(f'Standard deviation of {std}')
# Plot
rates, values = hist(days_per_frbs, bin_type='lin')
plot_aa_style()
plt.step(rates, values, where='mid')
plt.xlabel(f'Apertif days per burst scaled to {SCALE_TO}')
plt.savefig(rel_path('./plots/rate_apertif_dist.pdf'))
27.7, 18.9, 17.84, 10.2, 14.84, 10.25]
snrs['fast-crafts'] = [19]
# Start plot
plot_aa_style()
fig, ax1 = plt.subplots(1, 1)
# Fluence plot
ax1.set_xlabel('S/N')
ax1.set_xscale('log')
ax1.set_ylabel(r'\#(${>}\text{S/N}$)')
ax1.set_yscale('log')
# Update fluence plot
for survey in SURVEYS:
name = survey
snr = snrs[survey]
try:
bins, values = hist(snr, bin_type='log', norm=None)
# Cumulative sum
values = np.cumsum(values[::-1])[::-1]
plt.step(bins, values, where='mid', label=name)
except ValueError:
continue
plt.legend()
plt.tight_layout()
plt.savefig(rel_path('./plots/logn_logs_current.pdf'))
def plot(frbcat, frbpop):
"""Plot distributions."""
# Change working directory
plot_aa_style(cols=2)
f, axes = plt.subplots(2, 2, sharex='col', sharey='row')
axes[1, 0].set_xlabel(r'DM ($\textrm{pc}\ \textrm{cm}^{-3}$)')
axes[1, 1].set_xlabel(r'S/N')
axes[1, 1].set_xscale('log')
axes[1, 0].set_ylabel('Fraction')
axes[1, 0].set_yscale('log')
axes[1, 0].set_ylim(3e-2, 1.2e0)
axes[0, 0].set_ylabel('Fraction')
axes[0, 0].set_yscale('log')
axes[0, 0].set_ylim(3e-2, 1.2e0)
# Set colours
cmap = plt.get_cmap('tab10')([0, 1])
# Plot dm distribution
for i, p in enumerate((frbcat, frbpop)):
for t in ['r', 'o']:
# Line style
linestyle = 'solid'
label = 'one-offs'
alpha = 1
a = 0
if t == 'r':
linestyle = 'dashed'
label = 'repeaters'
a = 1
n_bins = 40
if len(p[t]['dm']) < 20:
n_bins = 10
bins = np.linspace(0, 2000, n_bins)
axes[a, 0].step(*hist(p[t]['dm'], norm='max', bins=bins),
where='mid', linestyle=linestyle, label=label,
color=cmap[i], alpha=alpha)
# Plot SNR distribution
bins = np.logspace(0.8, 3.5, n_bins)
axes[a, 1].step(*hist(p[t]['snr'], norm='max', bins=bins),
where='mid', linestyle=linestyle, label=label,
color=cmap[i], alpha=alpha)
for t in ['r', 'o']:
for p in ('dm', 'snr'):
row = 0
col = 0
if p == 'snr':
col = 1
if t == 'r':
row = 1
ks = ks_2samp(frbpop[t][p], frbcat[t][p])
print(t, p, ks)
text = fr'$p={round(ks[1], 2)}$'
if ks[1] < 0.01:
# text = r'$p < 0.01$'
text = fr'$p={round(ks[1], 3)}$'
anchored_text = AnchoredText(text, loc='upper right',
borderpad=0.5, frameon=False)
axes[row, col].add_artist(anchored_text)
# Set up layout options
f.subplots_adjust(hspace=0)
f.subplots_adjust(wspace=0.07)
# Add legend elements
elements = []
def patch(color):
return Patch(facecolor=color, edgecolor=color)
elements.append((patch(cmap[0]), 'Frbcat'))
elements.append((patch(cmap[1]), 'Frbpoppy'))
# Add line styles
elements.append((Line2D([0], [0], color='gray'), 'One-offs'))
elements.append((Line2D([0], [0], color='gray', linestyle='dashed'),
'Repeaters'))
lines, labels = zip(*elements)
lgd = plt.figlegend(lines, labels, loc='upper center', ncol=4,
framealpha=1, bbox_to_anchor=(0.485, 1.04),
columnspacing=1.1, handletextpad=0.3)
path = rel_path('./plots/frbpoppy_chime.pdf')
plt.savefig(path, bbox_extra_artists=(lgd,), bbox_inches='tight')
# Check p-value above S/N 15
for t in ['r', 'o']:
mask_frbpop = (frbpop[t]['snr'] > 15)
mask_frbcat = (frbcat[t]['snr'] > 15)
for par in ['dm', 'snr']:
ks = ks_2samp(frbpop[t][par][mask_frbpop],
frbcat[t][par][mask_frbcat])
print(t, par, ks, len(frbpop[t][par][mask_frbpop]),
len(frbcat[t][par][mask_frbcat]))
"""
mean, std = calc_lognormal_input(mean, std)
return np.random.lognormal(mean, std, shape)
fig, axes = plt.subplots(1, 1, sharey=True)
mean = 2.71**2
std = 5
size = int(1e4)
axes.set_xscale('log', basex=2.71)
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
data = lognormal(mean, std, size)
xy = hist(data, bin_type='log')
label = 'Distribution with input (frbpoppy)'
axes.step(*xy, where='mid', color=colors[0], label=label)
axes.axvline(mean, color=colors[0], linestyle='dotted')
axes.text(mean, 1, mean)
axes.axvline(mean + std, color=colors[0], linestyle='dotted')
axes.text(mean + std, 1, f'{mean}+{std}')
data = np.random.lognormal(mean, std, size)
xy = hist(data, bin_type='log')
label = 'Underlying normal input (numpy)'
axes.step(*xy, where='mid', color=colors[1], label=label)
axes.axvline(mean, color=colors[1], linestyle='dotted')
axes.text(mean, 1, mean)
axes.axvline(mean + std, color=colors[1], linestyle='dotted')
axes.text(mean + std, 1, f'{mean}+{std}')
if surv_pop.n_sources() == 0:
print(surv_pop.source_rate)
print(f'{name} | no FRBs in population')
continue
names.append(name)
ys.append(y)
# Dimensions measure plot
ax = axes[0]
ax.set_xlabel(r'DM ($\textrm{pc}\ \textrm{cm}^{-3}$)')
ax.set_ylabel(r'\#')
ax.set_yscale('log')
bins, values = hist(surv_pop.frbs.dm,
bin_type='lin',
norm='frac',
n_bins=20)
values = values.astype(np.float64)
values *= float(surv_pop.source_rate.f_area) * 1e6
ax.step(bins, values, where='mid', label=name)
# Fluence plot
ax = axes[1]
ax.set_xlabel('S/N')
ax.set_xscale('log')
ax.set_ylabel(r'\#(${>}\text{S/N}$)')
ax.set_yscale('log')
# Update fluence plot
bins, values = hist(surv_pop.frbs.snr,
bin_type='log',
# Distinguish populations
if pop.name.endswith('(1 burst)'):
label = '1 burst'
linestyle = 'solid'
elif pop.name.endswith('(> 1 burst)'):
label = '$>$1 burst'
linestyle = 'dashed'
else:
label = 'cosmic'
linestyle = 'dashdot'
pprint(f'Number of bursts in {label}: {pop.n_bursts()}')
# Do stuff with data
dm = pop.frbs.dm
x, y = hist(dm)
x *= 200 # Normalise x-axis z=0.01, z=2
# Plot DM distributions
ax1.step(x,
y,
where='mid',
linestyle=linestyle,
label=label,
color=colors[i])
# Plot fluence distributions
snr = pop.frbs.snr
if snr is None:
continue
time = surv_pop.frbs.time
# Set up plot
plot_aa_style()
if isinstance(rate_dist, np.ndarray):
min_rate = np.log10(np.min(rate_dist[rate_dist != 0]))
max_rate = np.log10(max(rate_dist))
else:
min_rate = np.log10(RATE) - 1
max_rate = np.log10(RATE) + 1
rate_dist = np.array(rate_dist)
bins = np.logspace(min_rate, max_rate, 20)
# Plot original distribution
rates, values = hist(rate_dist, bins=bins, norm='max')
plt.step(rates, values, where='mid', label='original')
# Plot the first frac of time
mask = (time < init_surv_time_frac * N_DAYS) & ~np.isnan(time)
half_n_bursts = np.count_nonzero(mask, axis=1)
half_rate = half_n_bursts / (init_surv_time_frac * N_DAYS)
rates, values = hist(half_rate, bins=bins, norm='max')
plt.step(rates,
values,
where='mid',
zorder=2,
label=fr'{int(init_surv_time_frac*100)}\% of survey time')
# Plot the full rate
n_bursts = np.count_nonzero(~np.isnan(time), axis=1)
def plot_dists(surv_pop, telescope):
"""Plot the fluence and DM distribution of a surveyed population.
Args:
surv_pop (Population): Population from which to plot
telescope (str): Name of the telescope with which to compare the
distribution. Necessary for Frbcat.
"""
plot_aa_style(cols=2)
# Plot dm distribution
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
dm_frbpoppy = surv_pop.frbs.dm
pprint(f'Number of detected FRBs: {len(dm_frbpoppy)}')
ax1.step(*hist(dm_frbpoppy), where='mid', linestyle='dashed')
df = Frbcat().df
dm_frbcat = df[df.telescope == telescope].dm
ax1.step(*hist(dm_frbcat), where='mid')
# Compare distributions
ks = ks_2samp(dm_frbpoppy, dm_frbcat)
text = fr'$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()
def update(*args):
"""Update plot if widgets change."""
# Which parameter is on the x axis of the rate plot?
x_axis = x_para.value_selected
ax2.set_xlabel(x_axis)
# What values do the parameters have?
vars = {s.label.get_text(): s.val for s in sliders}
# Update fluence plot
for i, line in enumerate(flnc_lines):
survey = line.get_label()
pop = get_pops(**vars, survey=survey)[0]
snr = pop.frbs.snr
try:
bins, values = hist(snr, bin_type='log', norm=None)
except ValueError:
bins, values = np.array([np.nan]), np.array([np.nan])
# Cumulative sum
values = np.cumsum(values[::-1])[::-1]
# Normalise to area on sky
values = values * pop.source_rate.f_area
line.set_xdata(bins)
line.set_ydata(values)
# Figure out new condition
masks = []
for p in parameters:
if p != x_axis:
masks.append((df[p] == vars[p]))
mask = np.bitwise_and.reduce(np.array(masks))
# Scale to which survey
scale_survey = scaling.value_selected
y_scaling = df[mask][scale_survey].values
real_scaling = rates[scale_survey]
# Update rate values
for i, line in enumerate(rate_lines):
survey = line.get_label()
x = df[x_axis].unique()
y = df[mask][survey] / y_scaling
line.set_xdata(x)
line.set_ydata(y)
# Set up expectation intervals
middle = rates[survey] / real_scaling
top, bot = poisson_interval(n_frbs[survey], sigma=2)
top = (top / n_days[survey]) / real_scaling
bot = (bot / n_days[survey]) / real_scaling
left = min(x)
right = max(x)
xy = list(zip([left, right, right, left], [top, top, bot, bot]))
intervals[i].set_xy(xy)
expects[i].set_xdata([left, right])
expects[i].set_ydata([middle] * 2)
# Fluence plot
# Rates plot
ax2.set_xlim(min(x), max(x))
ax2.set_ylim(1e-2, 1e2)
ax2.autoscale_view()
fig.canvas.draw_idle()
cosmic_pop.set_lum(model='powerlaw', low=1e40, high=1e45)
cosmic_pop.generate()
# Setup a survey
survey = Survey('perfect')
# Observe the FRB population
survey_pop = SurveyPopulation(cosmic_pop, survey)
plot_aa_style(cols=2)
f, axes = plt.subplots(1, 2)
s = survey_pop.frbs
bins, values = hist(s.snr, bin_type='log')
# Cumulative sum
values = np.cumsum(values[::-1])[::-1]
axes[0].step(bins, values, where='mid')
axes[0].set_title('SNR')
bins, values = hist(s.fluence, bin_type='log')
# Cumulative sum
values = np.cumsum(values[::-1])[::-1]
axes[1].step(bins, values, where='mid')
axes[1].set_title(r'Fluence')
# Set axis
axes[0].set_xscale('log')
axes[0].set_yscale('log')
axes[1].set_xscale('log')
from frbpoppy import CosmicPopulation, Survey, SurveyPopulation, hist
from tests.convenience import plot_aa_style, rel_path
pop = CosmicPopulation.simple(1e4, generate=False)
pop.generate()
survey = Survey('perfect')
survey_pop = SurveyPopulation(pop, survey)
plot_aa_style()
plt.rcParams["figure.figsize"] = (5.75373, 5.75373)
f, axes = plt.subplots(2, 2)
s = survey_pop.frbs
axes[0, 0].step(*hist(s.snr, bin_type='log'), where='mid')
axes[0, 0].set_title('SNR')
axes[1, 0].step(*hist(s.T_sys, bin_type='log'), where='mid')
axes[1, 0].set_title(r'T$_{\text{sys}}$')
axes[0, 1].step(*hist(s.s_peak, bin_type='log'), where='mid')
axes[0, 1].set_title(r'S$_{\text{peak}}$')
axes[1, 1].step(*hist(s.w_arr, bin_type='log'), where='mid')
axes[1, 1].set_title(r'w$_{\text{arr}}$')
for x in [0, 1]:
for y in [0, 1]:
axes[x, y].set_xscale('log')
axes[x, y].set_yscale('log')
ax1_elements = []
ax2_elements = [(Line2D([0], [0], color='gray'), 'perfect')]
for i, dist_type in enumerate(dist_types):
print(f'Distribution type: {dist_type}')
color = colors[i]
# Plot rates
rate = np.array([RATE])
if dist_type == 'mix':
rate = np.array([RATE, 0])
bins = np.linspace(0, 0.5, 40)
rates, values = hist(rate, bins=bins, norm='prob')
if dist_type == 'dist':
rate_dist = log10normal(RATE, 2, N_SRCS)
rates, values = hist(rate_dist, bins=bins, norm='prob')
if dist_type == 'weibull':
rate_dist = clustered(n_srcs=N_SRCS, n_days=MAX_DAYS, z=0)
rates, values = hist(rate_dist, bins=bins, norm='prob')
ax1.step(rates, values, where='mid', label=dist_type, color=color)
r = adapt_pop(r_pop, dist_type)
surv_pop = SurveyPopulation(r, survey)
linestyle = linestyles[0]
