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jackknife_limits.py
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jackknife_limits.py
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import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as fits
from collections import OrderedDict
from astropy.table import Table
jk_limits = {'JK0': {'ra_min': 129., 'ra_max': 133., 'dec_min': -2., 'dec_max': 3.},
'JK1': {'ra_min': 133., 'ra_max': 137., 'dec_min': -2., 'dec_max': 3.},
'JK2': {'ra_min': 137., 'ra_max': 141., 'dec_min': -2., 'dec_max': 3.},
'JK3': {'ra_min': 174., 'ra_max': 178., 'dec_min': -3., 'dec_max': 2.},
'JK4': {'ra_min': 178., 'ra_max': 182., 'dec_min': -3., 'dec_max': 2.},
'JK5': {'ra_min': 182., 'ra_max': 186., 'dec_min': -3., 'dec_max': 2.},
'JK6': {'ra_min': 211.5, 'ra_max': 215.5, 'dec_min': -2., 'dec_max': 3.},
'JK7': {'ra_min': 215.5, 'ra_max': 219.5, 'dec_min': -2., 'dec_max': 3.},
'JK8': {'ra_min': 219.5, 'ra_max': 223.5, 'dec_min': -2., 'dec_max': 3.}}
def set_jackknife(ras, decs, limits=None, debug=True):
result = np.array(['None'] * len(ras), dtype=str)
if limits == None:
limits = jk_limits
for strip in limits.keys():
ra_min = limits[strip]['ra_min']
ra_max = limits[strip]['ra_max']
dec_min = limits[strip]['dec_min']
dec_max = limits[strip]['dec_max']
in_strip = (ras >= ra_min) & (ras <= ra_max) & (decs >= dec_min) & (decs <= dec_max)
result[in_strip] = strip
if debug:
print(strip, limits[strip])
return result
def plot_jackknife(dat):
fig, ax = plt.subplots(1,1, figsize=(10,10))
for idx in np.unique(dat['JK']):
sub = dat[dat['JK'] == idx]
ax.scatter(sub['RA'], sub['DEC'], s=0.25, label=idx)
ax.set_xlabel('RA [deg.]')
ax.set_ylabel('DEC [deg.]')
ax.legend(frameon=True, ncol=6)
return fig, ax
def solve_jackknife(rand, ndiv=2):
'''
Splits up dat and rand into jackknife areas based on (ra, dec) in (ndiv x ndiv) chunks.
'''
njack = ndiv * ndiv
jk_volfrac = (njack - 1.) / njack
dpercentile = 100. / ndiv
percentiles = np.arange(dpercentile, 100. + dpercentile, dpercentile)
jk = 0
limits = OrderedDict()
for ra_per in percentiles:
# Given a vector V of length N, the q-th percentile of V is the q-th ranked value in a sorted copy of V.
# https://docs.scipy.org/doc/numpy-1.9.2/reference/generated/numpy.percentile.html
rahigh = np.percentile(rand[f'RANDOM_RA'], ra_per)
ralow = np.percentile(rand[f'RANDOM_RA'], ra_per - dpercentile)
print('{:.6f}\t{:.6f}'.format(ralow, rahigh))
for dec_per in percentiles:
isin = (rand['RANDOM_RA'] >= ralow) & (rand['RANDOM_RA'] <= rahigh)
dechigh = np.percentile(rand[f'RANDOM_DEC'][isin], dec_per)
declow = np.percentile(rand[f'RANDOM_DEC'][isin], dec_per - dpercentile)
print('\t{:.6f}\t{:.6f}'.format(declow, dechigh))
limits[f'JK{jk}'] = {'ra_min': float(ralow), 'ra_max': float(rahigh), 'dec_min': float(declow), 'dec_max': float(dechigh)}
jk += 1
jks = set_jackknife(rand['RANDOM_RA'], rand['RANDOM_DEC'], limits=limits)
return njack, jk_volfrac, limits, jks
def jackknife_mean(fpath):
print('Appending JK mean and error to lumfn. extension.')
with fits.open(fpath, mode='update') as hdulist:
nphi = 0
phis = []
for i, hdu in enumerate(hdulist):
# skip primary.
if i > 0:
phis.append(hdu.data['PHI_IVMAX'])
nphi += 1
phis = np.array(phis)
mean = np.mean(phis, axis=0)
err = np.std(phis, axis=0)
hdr = hdulist['LUMFN'].header
lumfn = hdulist['LUMFN'].data
lumfn = Table(lumfn, names=lumfn.names)
lumfn['PHI_IVMAX_JK'] = mean
lumfn['PHI_IVMAX_ERROR_JK'] = err
lumfn.pprint()
lumfn = fits.BinTableHDU(lumfn, name='LUMFN', header=hdr)
hdulist[1] = lumfn
hdulist.flush()
hdulist.close()
if __name__ == '__main__':
import pylab as pl
from astropy.table import Table
from findfile import findfile
field = 'G9'
version = 'v2'
dpath = findfile(ftype='gold', dryrun=False, survey='gama', version=version)
rpath = findfile(ftype='randoms', dryrun=False, field=field, survey='gama', version=version)
dat = Table.read(dpath)
dat = dat[dat['FIELD'] == field]
rand = Table.read(rpath)
dat, rand = set_jackknife(dat, rand, ndiv=4)
print(np.unique(dat['JK'].data))
fig, ax = plot_jackknife(dat)
fig.savefig('fig.pdf')