def completeness_erf(bin_centres,
flux_limit,
stretch=1.0,
cosmo=False,
N_samples=100):
# calculates a grid for completeness.
# Should be done to the same size as N, take bin_centres from N.
# takes flux_limit and background (in nJy), and snr_target
# stretch is variable.
dz = bin_centres['z'][1] - bin_centres['z'][0]
dlog10L = bin_centres['log10L'][1] - bin_centres['log10L'][0]
if not cosmo: cosmo = flare.core.default_cosmo()
c = np.zeros((len(bin_centres['z']), len(bin_centres['log10L'])))
for i, z in enumerate(bin_centres['z']):
for j, log10L in enumerate(bin_centres['log10L']):
f = np.linspace(lum_to_flux(10**(log10L - dlog10L / 2), cosmo, z),
lum_to_flux(10**(log10L + dlog10L / 2), cosmo, z),
N_samples)
N_fract = 0.
for fluxx in f:
N_fract += 0.5 * (1.0 + cps.erf(stretch *
(fluxx - flux_limit)))
c[i, j] = N_fract / N_samples
return c.T
def flux_sample_bin_plot(samples,
cosmo=False,
bins=100,
range=[[0., 15.], [0., 3.]],
save_file=False):
# --- make nice plot
if not cosmo: cosmo = flare.core.default_cosmo()
fig = plt.figure(figsize=(6, 5))
z_array = samples['z']
f_array = lum_to_flux(10.**samples['log10L'], cosmo, z_array) * (10.**9)
cm = plt.get_cmap('plasma')
plt.hist2d(z_array, np.log10(f_array), bins=bins, range=range, cmap=cm)
bar = plt.colorbar(orientation='vertical')
bar.set_label(r'$\rm N$', rotation=90)
plt.ylabel(r"$\rm log_{10}(f_{\nu}/ [nJy])$")
plt.xlabel(r"$\rm z$")
plt.xlim(range[0][0], range[0][-1])
plt.ylim(range[1][0], range[1][-1])
if save_file == False:
return fig
else:
plt.savefig(save_file + '.png', dpi=300)
def completeness_sample(bin_centres,
flux_limit,
stretch=1.0,
cosmo=False,
N_samples=100):
# calculates a grid for completeness.
# Should be done to the same size as N, take bin_centres from N.
# takes flux_limit and background (in nJy), and snr_target
# stretch is variable.
dz = bin_centres['z'][1] - bin_centres['z'][0]
dlog10L = bin_centres['log10L'][1] - bin_centres['log10L'][0]
if not cosmo: cosmo = flare.core.default_cosmo()
c = np.zeros((len(bin_centres['z']), len(bin_centres['log10L'])))
for i, z in enumerate(bin_centres['z']):
for j, log10L in enumerate(bin_centres['log10L']):
z_sample = np.random.uniform(z - dz / 2, z + dz / 2, N_samples)
log10L_sample = np.random.uniform(log10L - dlog10L / 2,
log10L + dlog10L / 2, N_samples)
f = lum_to_flux(10**log10L_sample, cosmo, z_sample)
N_fract = 0.
for q in range(N_samples):
N_fract += 0.5 * (1.0 + cps.erf(stretch * (f[q] - flux_limit)))
c[i, j] = N_fract / N_samples
return c.T
def completeness_erf_legacy(bin_centres,
flux_limit,
stretch=1.0,
cosmo=False,
N_samples=100):
# calculates a grid for completeness. Due to a design fla.....feature, this only calculates completeness in the bin
# that gets dissected by the flux cut. Will most likely be removed in a future revision.
# Should be done to the same size as N, take bin_centres from N.
# takes flux_limit and background (in nJy), and snr_target
# stretch is variable.
dz = bin_centres['z'][1] - bin_centres['z'][0]
dlog10L = bin_centres['log10L'][1] - bin_centres['log10L'][0]
if not cosmo: cosmo = flare.core.default_cosmo()
c = np.zeros((len(bin_centres['z']), len(bin_centres['log10L'])))
for i, z in enumerate(bin_centres['z']):
for j, log10L in enumerate(bin_centres['log10L']):
if abs(log10L -
np.log10(flux_to_L(flux_limit, cosmo, z))) <= dlog10L:
# print(abs(log10L-np.log10(flux_to_L(flux_limit, cosmo, z))))
f = np.linspace(
lum_to_flux(10**(log10L - dlog10L / 2), cosmo, z),
lum_to_flux(10**(log10L + dlog10L / 2), cosmo, z),
N_samples)
N_fract = 0.
for fluxx in f:
N_fract += 0.5 * (1.0 + cps.erf(stretch *
(fluxx - flux_limit)))
c[i, j] = N_fract / N_samples
return c.T
def completeness_cut(bin_centres, flux_limit, stretch=1.0, cosmo=False):
# calculates a grid for completeness.
# Should be done to the same size as N, take bin_centres from N.
# takes flux_limit and background (in nJy), and snr_target
# stretch is variable.
dz = bin_centres['z'][1] - bin_centres['z'][0]
dlog10L = bin_centres['log10L'][1] - bin_centres['log10L'][0]
if not cosmo: cosmo = flare.core.default_cosmo()
c = np.zeros((len(bin_centres['z']), len(bin_centres['log10L'])))
for i, z in enumerate(bin_centres['z']):
for j, log10L in enumerate(bin_centres['log10L']):
f = lum_to_flux(10**log10L, cosmo, z)
c[i, j] = 0.5 * (1.0 + cps.erf(stretch * (f - flux_limit)))
return c.T
def M_to_m(M, cosmo, z):
lum = photconv.M_to_lum(M)
flux = photconv.lum_to_flux(lum, cosmo, z)
m = photconv.flux_to_m(flux)
return m
