def __init__(self, dtxns=None, ulims=None, llims=None, all=None, ind=None):
"""
Find the Kaplan-Meier CDF estimate from right-censored (upper limits)
or left-censored (lower limits) data. The data may be entered as
detected values (dtxns) and EITHER uppler (ulims) or lower (llims)
limits, or as a single set of data values (all) accompanied by
an array of integer indicators (0=dtxn, -1=lower limit, 1=upper limit).
"""
if all != None:
if dtxns or ulims or llims:
raise ValueError, 'Illegal selection of input parameters!'
if (ind == None) or (len(ind) != len(all)):
raise ValueError, '"all" values must be accompanied by indicators!'
self.all = all
self.ind = ind
self.ntot = len(all)
else:
if (ulims != None) and (llims != None):
raise ValueError, 'Only one of upper or lower limits allowed!'
if ulims != None:
nlim = len(ulims)
self.limit = 'Upper'
self.all = concatenate([dtxns, ulims])
elif llims != None:
nlim = len(llims)
self.limit = 'Lower'
self.all = concatenate([dtxs, llims])
else:
raise ValueError, 'Must specify dtxns and 1 of ulims/llims!'
nd = len(dtxns)
self.ntot = nd + nlim
self.ind = zeros(self.ntot)
if self.limit == 'Upper':
self.ind[nd:] = 1
else:
self.ind[nd:] = -1
ierr, self.km_cdf, self.cdf_err, self.mean, self.std, self.nu, \
self.uncens, self.nc, self.cens = kmestm(self.ind, self.all)
if ierr != 0:
raise ValueError, 'Invalid indicator array!'
# Trim sizes of returned arrays.
self.km_cdf = self.km_cdf[:self.nu]
self.cdf_err = self.cdf_err[:self.nu]
self.uncens = self.uncens[:self.nu]
self.cens = self.cens[:self.nc]
# Note there is no binned dist'n (yet).
self.nbins = None
# Define a Population1D object
wts = zeros(len(self.uncens), Float)
for i in range(len(self.uncens)-1):
wts[i] = self.km_cdf[i]-self.km_cdf[i+1]
wts[-1] = self.km_cdf[-1]
Population1D.__init__(self, self.uncens, wts)
from numpy import * from _asurvkm import kmestm, kmdif, quart # Censoring index (-1 = censored): gal1_ind = array([0, 0, -1, -1, 0, -1]) # IR abs. magnitudes for galaxies: gal1_mag = array([28.5, 26.9, 29.7, 28.1, 30.1, 27.6]) # Diff'l KM estimate bin params: nb, w, m_l = 5, 2., 25. ierr,sx,vx,smean,err,nu,su,nc,sc = kmestm(gal1_ind,gal1_mag) sx = sx[:nu] su = su[:nu] sc = sc[:nc] ntot = gal1_mag.shape[0] print 'KM CDF:' print su print sc print # Diff'l: bin_l,bin_u,diff = kmdif(sx, su, ntot, m_l, w, nb) print 'Diff\'l:' print bin_l print bin_u print diff print # Quartiles: q = quart(su, sc)
from numpy import * from _asurvkm import kmestm, kmdif, quart # Censoring index (-1 = censored): gal1_ind = array([0, 0, -1, -1, 0, -1]) # IR abs. magnitudes for galaxies: gal1_mag = array([28.5, 26.9, 29.7, 28.1, 30.1, 27.6]) # Diff'l KM estimate bin params: nb, w, m_l = 5, 2., 25. ierr, sx, vx, smean, err, nu, su, nc, sc = kmestm(gal1_ind, gal1_mag) sx = sx[:nu] su = su[:nu] sc = sc[:nc] ntot = gal1_mag.shape[0] print 'KM CDF:' print su print sc print # Diff'l: bin_l, bin_u, diff = kmdif(sx, su, ntot, m_l, w, nb) print 'Diff\'l:' print bin_l print bin_u print diff print # Quartiles: q = quart(su, sc)
