def get_dropoutnbar(band='g', DES=False, year=5, printit=False):
if band in ['g', 'r', 'i', 'z']:
if DES and band == 'g':
'''
--- g-Dropouts ---
DES year 1 23.376 26.667
DES year 2 23.753 47.345
DES year 3 23.973 74.237
DES year 4 24.129 104.633
DES year 5 24.250 138.418
'''
print(
"\n\nLoading z0 and nbar for DES-like imaging, given %s.\n\n" %
band)
## DES-like Galaxies per sq. deg; SV equivalent to year 5.
gDES = {
'Y1': 26.667,
'Y2': 47.345,
'Y3': 74.237,
'Y4': 104.633,
'Y5': 138.418,
'SV': 138.418
}
z0 = 4.0
nbar = gDES['Y' + str(year)]
return z0, nbar
else:
from goldrush.specs import samplestats
stats = samplestats(printit=printit)
print(
"\n\nLoading z0 and nbar for HSC-like imaging, given %s.\n\n" %
band)
## BX: z ~ 2; LBG: z ~ 3
elif band in ['BX', 'LBG']:
from reddy import samplestats
## Retrieve galaxies per sq. deg. and z0 from Reddy.py
stats = samplestats(printit=printit)
print("\n\nLoading z0 and nbar for Reddy-like imaging, given %s.\n\n" %
band)
else:
raise ValueError("Requested type is not available for dropout p(z).")
return stats[band]['z'], stats[band]['nbar']
def get_nbar_nocontam(band, depth='W', printit=False):
'''
Get the number counts per magnitude bin, and the contamination corrected counts.
'''
from goldrush.specs import samplestats
magbins, pnbar, counts = get_nbarbymag(band, depth, printit=False)
stats = samplestats(printit=printit)
zee = np.ceil(stats[band]['z']) ## Round to the nearest integer.
stats[band]['nbar_noint'] = sum(counts * (1. - get_contamination(
magbins[:-1], zee=zee, depth=depth))) / stats['Total area'][depth]
if printit:
print(
sum(stats[band]['counts'].values()), counts.sum(),
sum(counts *
(1. - get_contamination(magbins[:-1], zee=zee, depth=depth))))
return stats
def get_nbarbymag(dropband, depth, printit=False):
'''
For selection bands, e.g. gri, and depth, [UD, D, W],
calculate the total number of objects with a magnitude less
than a given limit and the area of that field type.
From this, calculate the angular number density. NOTE: No zy for Ultra Deep.
'''
import os
import glob
from specs import samplestats
from selection_box import detection_bands
root = os.environ['LBGCMB']
## Loads from (hsc) specs.py
specs = samplestats()
## Catalogues labelled by bands used in selection criteria.
band2cat = {'g': 'gri', 'r': 'riz', 'i': 'izy', 'z': 'zY'}
if band2cat[dropband] == "zy":
columns = pd.read_csv(root + "/dropouts/goldrush/cats/column_zy.cat",
header=None,
names=["colname"],
delim_whitespace=True)
appradii = '10'
else:
columns = pd.read_csv(root + "/dropouts/goldrush/cats/column.cat",
header=None,
names=["colname"],
delim_whitespace=True)
appradii = '15'
## Define magnitude type to be retrieved.
mags = [
x + appradii for x in [
'gmag_aperture', 'rmag_aperture', 'imag_aperture', 'zmag_aperture',
'ymag_aperture'
]
]
## Columns to be retrieved from GoldRush catalogues.
names = ['object_id', 'ra', 'dec'] + mags
## Get all the fields for given depth.
files = glob.glob(root + "/dropouts/goldrush/cats/%s/%s/*" %
(depth, band2cat[dropband]))
counts = []
total_area = 0.0
## Loop over fields.
for file in files:
mid = file.split(".")[-2]
field = mid.split('_' + depth + '_')[-1]
## Area [deg^2] of e.g. COSMOS 'D'
area = specs['areas'][depth][field]
total_area += area
## Read and drop rows with undefined magnitudes.
input = pd.read_csv(file,
header=None,
names=columns["colname"],
delim_whitespace=True)
input = input.replace(99., np.nan)
input = input.dropna(
axis=0,
how='any',
thresh=None,
subset=[detection_bands[dropband] + 'mag_aperture' + appradii],
inplace=False)
## Copy the necessary part of the dataframe.
data = input[names].copy()
if printit:
print("\n\nHSC %s field with area %.6lf deg2 (from file %s). \n" %
(field, area, file))
pprint(data)
print("\n\nMagnitude limits of identified objects: %.6lf < mag < %.6lf" % \
(data[detection_bands[dropband] + 'mag_aperture' + appradii].min(), data[detection_bands[dropband] + 'mag_aperture' + appradii].max()))
if input.empty == False:
## Bands in mag. for the detection band, to calculate Ng( < mag).
magbins = np.arange(18., 26.5, 0.01)
## Catch all bin for objects brighter than 22nd mag and fainter than 26.5.
magbins = np.concatenate(
[np.array([0.]), magbins,
np.array([30.])])
## Defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths.
counts.append(
np.histogram(data[detection_bands[dropband] + 'mag_aperture' +
appradii],
bins=magbins)[0])
counts = np.array(counts)
## Sum the binned histogram counts over all fields.
counts = counts.sum(axis=0)
if not (depth is 'UD' and band2cat[dropband] is 'zy'):
cumulative = np.cumsum(
counts) ## Cumulative counts of all bins up to mag. limit;
pnbar = cumulative / total_area ## Projected nbar.
## Check.
print("\n\nTotal counts @ depth %s (%.2lf): %.3lf \t %.3lf" % (depth, effective_depth(dropband=dropband, depth=depth), cumulative[-1],\
specs[dropband]['total-%scounts' % {'W': '', 'D': 'deep', 'UD': 'udeep'}[depth]]))
print("Total area: %.3lf sq. deg. (%.3lf)" %
(total_area, specs['Total area'][depth]))
print("Effective nbar: %.3lf per sq. deg." %
(cumulative[-1] / total_area))
return magbins, pnbar, counts
else:
raise ValueError(
"\n\nThe combination of UD and zy band selection is not available.\n\n"
)
def plot_ilims(results, plot_des=False, plot_hsc=True):
import os
import pylab as pl
import matplotlib.pyplot as plt
from matplotlib.pyplot import text
from specs import samplestats
from selection_box import detection_bands
latexify(fig_width=None, fig_height=None, columns=2, equal=False)
colors = ['b', 'r', 'indigo']
## Load Goldrush basic stats.
stats = samplestats(printit=False)
## Create figure.
fig, axarray = plt.subplots(1, 1, sharey=False)
fig.set_size_inches(6.5, 3.5)
## Catch a one plot call.
if not isinstance(axarray, np.ndarray):
axarray = np.array([axarray])
for k, dropband in enumerate(results.keys()):
for ldepth, label in zip(['D'], ['GOLDRUSH Deep']):
magbins, pnbar, counts = results[dropband][ldepth]
## Plot ang_nbar against limiting mag. (rightmost edge).
axarray[k].semilogy(magbins[1:],
pnbar,
'-',
label=r'$g-$' + label,
markersize=3,
lw=1.)
for contamination, color in zip(['D', 'W'], ['dodgerblue', 'indigo']):
rate = get_contamination(magbins[1:],
round(stats[dropband]['z']),
contamination,
depth=ldepth)
axarray[k].semilogy(magbins[1:], pnbar * (1. - rate), '--', label='Less %s' % contamination + r' ($\simeq$' + '%.2lf) interlopers' % effective_depth(dropband, contamination),\
markersize=3, lw=1., c=color, dashes=[3, 1], alpha=0.4)
'''
if plot_des:
## DES depths/yr estimate
depths = des_depths()
## Plot the magnitude limit of DES SV.
axarray[k].axvline(depths['SV'][detection_bands[dropband]], c='k', linestyle='-', label='DES SV', lw = 0.5)
## and the magnitude limits per year (estimated for year greater than one).
for year in np.arange(1, 6, 1):
axarray[k].axvline(depths['Y' + str(year)][detection_bands[dropband]], c='k', linestyle='-', label='', lw = 0.5)
'''
root = os.environ['LBGCMB']
data = np.loadtxt(
root +
"/dropouts/nz/schechter/dat/schechter_estimate_%s_dropouts.txt" %
dropband)
## 0.59 completeness frac.
axarray[k].semilogy(data[:, 0],
data[:, 1],
'k',
label='Best-fit UV Schechter fn.',
alpha=0.5)
## 0.59 completeness frac. Galaxies only.
axarray[k].semilogy(data[:, 0],
galaxy_frac(data[:, 0]) * data[:, 1],
'k--',
label='Best-fit galaxy UV Schechter fn.',
dashes=[3, 1])
ymax = pnbar[magbins < 24.5].max()
axarray[k].set_xlim(23.0, 26.5) ## [magbins[1], magbins[-2]]
axarray[k].set_ylim([10., 8.e3])
axarray[k].set_xlabel(r'$%s_{\rm{AB}}$' % detection_bands[dropband])
axarray[k].set_ylabel(r'$N(<%s)$ / deg$^2$' %
detection_bands[dropband])
## axarray[k].get_yaxis().get_major_formatter().set_scientific(False)
## axarray[k].ticklabel_format(style='sci', scilimits=(-1, 2))
pl.legend(loc=4)
pl.savefig('plots/hsc_icut.pdf', bbox_inches='tight')
fmt='%.6le')
stats = get_nbar_nocontam(band, depth='W', printit=False)
print(
"\nGoldRush observed: %.3le g/deg2; Contamination corrected: %.3le g/deg2"
% (stats[band]['nbar'], stats[band]['nbar_nointerlopers']))
if __name__ == "__main__":
print(
"\n\nWelcome to a Schechter fn. calculator for the areal density of HSC Goldrush dropouts.\n\n"
)
band = 'g'
stats = samplestats()
z = stats[band]['z']
## Redshift distribution limits handles by completeness(z), rather than dz.
dz = 1.5
alpha = stats[band]['schechter']['alpha']
Mstar = stats[band]['schechter']['M_star']
phi_star = stats[band]['schechter']['phi_star']
mlims = np.arange(22., 26.5, 0.1)
for band in ['g']:
dropout_arealdensity(z, dz, band, stats, mlims)