First import all the modules such as healpy and astropy needed for analyzing the structure
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import healpix_util as hu
import astropy as ap
import numpy as np
from astropy.io import fits
from astropy.table import Table
import astropy.io.ascii as ascii
from astropy.io import fits
from astropy.constants import c
import matplotlib.pyplot as plt
import math as m
from math import pi
#from scipy.constants import c
import scipy.special as sp
from astroML.decorators import pickle_results
from scipy import integrate
import warnings
from sklearn.neighbors import BallTree
import pickle
import multiprocessing as mp
import time
from aptestmetricdt import *
from aptestmetricdz import *
from scipy.spatial import distance as d
from apcat import *
from progressbar import *
from tqdm import *
from functools import partial
import pymangle
from apdz import *
from apdt import *
from scipy.optimize import curve_fit
#from astroML.datasets import fetch_sdss_specgals
#from astroML.correlation import bootstrap_two_point_angular
%matplotlib inline
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# Getting back the objects:
with open('../output/datzAP.pkl') as f: # Python 3: open(..., 'rb')
dat = pickle.load(f)
dat
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Read the data file (taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html ) converted to ascii with comoving distance etc. in V01 reading from pkl files for faster read
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# Getting back the objects:
with open('../output/rdatzAP.pkl') as f: # Python 3: open(..., 'rb')
datR = pickle.load(f)
datR
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rr2d=np.zeros((20,20))
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rng = np.array([[0, 0.02], [0, 0.02]])
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%%time
rrbt=BallTree(datR,metric='pyfunc',func=APzdth)
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rng
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bins=np.arange(0.0,0.02001,0.001)
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bins
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%%time
for i in tqdm(xrange(len(datR))):
ind=rrbt.query_radius(datR[i].reshape(1,-1),0.021)
for j in ind:
dist0=d.cdist([datR[i],],datR[j],APdz)[0]
dist1=d.cdist([datR[i],],datR[j],APzdth)[0]
rr2d+=np.histogram2d(dist0, dist1,range=rng,bins=(bins,bins))[0]
print rr2d
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with open('rr2ddr72v06cdist200kopt2.pkl','w') as f:
pickle.dump(rr2d,f)
rr2d
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%%time
ind=ddbt.query_radius(dat[0].reshape(1,-1),bins[0])
ind1=ind.reshape(1,0)
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ind1
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for i in ind1:
print dat[i]
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%%time
for i in ind:
dist0=d.cdist([dat[0],],dat[i],APdz)[0]
dist1=d.cdist([dat[0],],dat[i],APzdth)[0]
print np.histogram2d(dist0, dist1,range=rng)
dd2d+=np.histogram2d(dist0, dist1,range=rng,bins=(bins,bins))[0]
print dd2d
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help(np.histogram2d)
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d.
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ind
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np.random.seed(0)
X = np.random.random((10, 3)) # 10 points in 3 dimensions
tree = BallTree(X, leaf_size=2) # doctest: +SKIP
#print(tree.query_radius(X[0], r=0.3, count_only=True))
ind = tree.query_radius(X[0].reshape(1,-1), r=0.3) # doctest: +SKIP
print(ind) # indices of neighbors within distance 0.3
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help(ddbt.query_radius)
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dr2d=np.zeros((20,20))
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rng = np.array([[0, 0.02], [0, 0.02]])
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%%time
dist0=d.cdist([dat[0],],datR,APdz)[0]
dist1=d.cdist([dat[0],],datR,APzdth)[0]
print np.histogram2d(dist0, dist1,range=rng)
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%%time
for i in tqdm(xrange(len(dat))):
dist0=d.cdist([dat[i],],datR,APdz)[0]
dist1=d.cdist([dat[i],],datR,APzdth)[0]
dr2d+=np.histogram2d(dist0, dist1,range=rng)[0]
print dr2d
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with open('dr2ddr72v06cdist200k.pkl','w') as f:
pickle.dump(dr2d,f)
dr2d
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dist0
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len(dist0)
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dist0.size
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dist0.flatten
print dist0
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len(dist0)
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%%time
dist0=d.cdist([dat[0],],dat,APdz)[0]
dist1=d.cdist([dat[0],],dat,APzdtheta)[0]
print np.histogram2d(dist0, dist1,range=rng)
#print dd2d
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dist0
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dist0=dist0[0]
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dist0[1]
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dist0[1:len(dist0)]
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len(dist0)
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dist0.size
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help(dist0.flatten)
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dist0[0]
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np.array.flatten(dist0)
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len(dist0)
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%%time
while len(dat)>0:
i=len(dat)-1
while i>0:
dist[i]=APcat(dat[0],dat[i])
i-=1
dd2d+=np.histogram2d(dist[:,0], dist[:,1],range=rng)[0]
dat=np.delete(dat,0,axis=0)
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dd2d
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%%time
while len(dat)>0:
i=len(dat)-1
while i>0:
dist=500*APcat(dat[0],dat[i])
dd2d+=np.histogram2d(dist[0], dist[1], bins = 10, range = rng)
i-=1
dat=np.delete(dat,0,axis=0)
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def binDists2d(dat, dz = lambda u, v: abs(u[0]-v[0]), zdth = lambda u, v: 0.5*(u[0]+v[0])*np.arccos(np.sin(u[2])*np.sin(v[2])+np.cos(u[2])*np.cos(v[2])*np.cos(u[1]-v[1]))):
i, j = np.triu_indices(dat.shape[0], 1)
dist0 = dz(dat[i], dat[j])
dist1 = zdth(dat[i], dat[j])
rng = np.array([[0, 0.02], [0, 0.02]])
return np.histogram2d(dist0, dist1, bins = 10, range = rng)
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td=np.random.rand(1000,3)
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binDists2d(td)
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%%time
binDists2d(dat)
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def binDists2d(dat, f1 = 'euclidean', f2 = 'cosine'):
dist0 = APcat(dat, f1)
dist1 = d.pdist(dat, f2)
return np.histogram2d(dist0, dist1, bins = 10)
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dd2d
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plt.contour(dd2d)
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with open('DR72DD2DMI.pkl', 'w') as f:
pickle.dump(dd2d,f)
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with open('DR72DD2DMI.pkl') as f:
DD2D = pickle.load(f)
DD2D
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dzbin=zdthbin=np.arange(0.002,0.022,0.002)
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plt.contour(dzbin,zdthbin,dd2d)
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dzbin
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plt.contour(dzbin,zdthbin,dd2d,levels=[ 5041., 13955., 23161., 31557., 38796., 46402., 53552.,
60708., 67437., 74549.])
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for i in range(len(dat)-1):
for j in range(len(dat)-1):
dist=APcat(dat[0],dat[i])
ind0=int(dist[0]/0.002)
ind1=int(dist[1]/0.002)
if ind0>9 or ind1>9:
pass
else:
dd2d[ind0,ind1]+=1
dat=np.delete(dat,0,0)
print len(dat)
print dd2d
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for i in range(len(dat)):
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bins=np.arange(0,0.022,0.002)
print bins
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%%time
from apmetric6 import *
BTdat6 = BallTree(dat,metric='pyfunc',func=APmetric6,leaf_size=5)
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BTdat6
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%%time
per6=BTdat6.two_point_correlation(dat,bins)
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print per6
One has to change if condition in the metric definition of dz<=0.002 to 0.002<dz<=0.004 and so on
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%%time
from apmetric4 import *
BTdat4 = BallTree(dat,metric='pyfunc',func=APmetric4,leaf_size=5)
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BTdat4
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%%time
per4=BTdat4.two_point_correlation(dat,bins)
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print per4
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%%time
from apmetric3 import *
BTdat3 = BallTree(dat,metric='pyfunc',func=APmetric3,leaf_size=5)
BTdat3
%%time
per3=BTdat3.two_point_correlation(dat,bins)
print per3print bins
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Nbins=len(bins)
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Nbins
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LCfmetric=LCDMmetric
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LCfmetric(dat[0],dat[1])
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%%time
start_time=time.time()
counts_DD=BTDLC.two_point_correlation(dat,bins)
print counts_DD
end_time=time.time()
tottime=end_time-start_time
print "Total run time:"
print tottime
with open('BTDcDDLCf.pkl', 'w') as f:
pickle.dump(counts_DD,f)
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with open('BTDcDDLCf.pkl') as f:
counts_DD = pickle.load(f)
counts_DD
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DD=np.diff(counts_DD)
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DD
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plt.plot(bins[1:len(bins)],DD,'ro-')
BallTree.two_point_correlation works almost 10 times faster! with leaf_size=5 Going with it to the random catalog
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dataR=ascii.read("./output/rand200kdr72.dat")
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dataR
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len(dataR)
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dataR=ascii.read("./output/rDR7200kLCsrarf.dat")
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dataR
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dataR.remove_column('z')
dataR.remove_column('ra')
dataR.remove_column('dec')
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dataR
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rs=np.array(dataR['s'])
rrar=np.array(dataR['rar'])
rdecr=np.array(dataR['decr'])
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datR=np.array([rs,rrar,rdecr])
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datR
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datR.reshape(3,len(dataR))
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datR=datR.transpose()
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datR
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# Saving the objects:
with open('rDR7200kLCsrarf.pkl', 'w') as f: # Python 3: open(..., 'wb')
pickle.dump(datR, f)
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# Getting back the objects:
with open('rDR7200kLCsrarf.pkl') as f: # Python 3: open(..., 'rb')
datR = pickle.load(f)
datR
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%%time
BT_RLC = BallTree(datR,metric='pyfunc',func=LCfmetric,leaf_size=5)
with open('BTR200kdatsLCf.pkl', 'w') as f:
pickle.dump(BT_RLC,f)
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with open('BTR200kdatsLCf.pkl') as f:
BTRLC = pickle.load(f)
BTRLC
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%%time
start_time=time.time()
counts_RR=BTRLC.two_point_correlation(datR,bins)
print counts_RR
end_time=time.time()
tottime=end_time-start_time
print "Total run time:"
print tottime
with open('BTR200kcRRLCf.pkl', 'w') as f:
pickle.dump(counts_RR,f)
with open('BTR200kcRRLCf.pkl') as f:
counts_RR = pickle.load(f)
counts_RR
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counts_RR
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RR=np.diff(counts_RR)
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RR
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plt.plot(bins[1:len(bins)],RR,'bo-')
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RR_zero = (RR == 0)
RR[RR_zero] = 1
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%%time
start_time=time.time()
counts_DR=BTRLC.two_point_correlation(dat,bins)
print counts_DR
end_time=time.time()
tottime=end_time-start_time
print "Total run time:"
print tottime
with open('BTR200kcDRLCf.pkl', 'w') as f:
pickle.dump(counts_DR,f)
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with open('BTR200kcDRLCf.pkl') as f:
counts_DR = pickle.load(f)
counts_DR
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DR=np.diff(counts_DR)
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DR
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corrells=(4.0 * DD - 4.0 * DR + RR) / RR
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corrells
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plt.plot(bins[1:len(bins)],corrells,'go-')
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plt.plot(bins[1:len(bins)],bins[1:len(bins)]*bins[1:len(bins)]*corrells*(c*1e-5)**2,'go-')
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plt.plot(bins[2:len(bins)],bins[2:len(bins)]*bins[2:len(bins)]*corrells[1:len(bins)]*(c*1e-5)**2,'go-')
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plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')
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plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')
plt.savefig("correl2xlsLCf.pdf")
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plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'bo-')
plt.savefig("correl2x1lsLCf.pdf")
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plt.yscale('log')
plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')
plt.savefig("correllsfiglogLCf.pdf")
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plt.yscale('log')
plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'ro-')
plt.savefig("correllslog2xLCf.pdf")
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plt.yscale('log')
plt.xscale('log')
plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')
plt.savefig("correllsloglogLCf.pdf")
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from functools import partial
def harvester(text, case):
X = case[0]
return text + str(X)
partial_harvester = partial(harvester, case=RAW_DATASET)
partial_qr=partial(BTD.query_radius,count_only=True)
if __name__ == '__main__':
pool = multiprocessing.Pool(processes=6)
case_data = RAW_DATASET
pool.map(partial_harvester, case_data, 1)
pool.close()
pool.join()
mapfunc = partial(BTD.query_radius, count_only=True)
map(mapfunc, volume_ids)
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#ascii.write("DR72DDbinned.dat",(bins[1:len(bins)],DDresult))
start_time=time.time()
@pickle_results("DR72DDmp1.pkl")
def ddcal(BTD,dat,bins,Nbins):
counts_DD=np.zeros(Nbins)
for i in tqdm(range(Nbins)):
counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))
DD = np.diff(counts_DD)
print counts_DD
print DD
return DD
def mf_wrap(args):
return ddcal(*args)
pool=mp.Pool(8)
arg=[(BTD,dat,bins,Nbins)]
%timeit DDresult=pool.map(mf_wrap,arg)
#DDresult = ddcal(BTD,dat,bins,Nbins)
end_time=time.time()
tottime=end_time-start_time
print "Total run time:"
print tottime
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%timeit dat
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DDresult[0]
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DDresult[1]
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plt.plot(bins[1:len(bins)],DDresult[0],'ro')
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def myfun(a,b):
print a + b
return a+b
def mf_wrap(args):
return myfun(*args)
p = mp.Pool(4)
fl = [(a,b) for a in range(3) for b in range(2)]
p.map(mf_wrap, fl)
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counts_DD=np.zeros(Nbins)
for i in range(Nbins):
counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))
DD = np.diff(counts_DD)
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print counts_DD
print DD
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plt.plot(bins[1:len(bins)],DD,'ro')
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dataR=fits.open("/Users/rohin/Downloads/random-DR7-Full.fits")
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dataR=dataR[1].data
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len(dataR)
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tdata=np.array(data)
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type(tdata[4])
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tdata.shape
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tdata.shape
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tdata=np.atleast_d(tdata)
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tdata.shape
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tdata.reshape(len(tdata),3)
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tdata=np.asarray(data)
tdata=tdata.transpose()
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tdata
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len(tdata)
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BTD.two_point_correlationpoint_correlationpoint_correlationpoint_correlationtime
stime=time.time()
tpcf=BTD.two_point_correlation(dat,bins)
print time.time()-stime
print tpcf
plt.plot(bins,tpcf)
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stime=time.time()
tpcfd=BTD.two_point_correlation(dat,bins,dualtree=True)
print time.time()-stime
print tpcfd
plt.plot(bins,tpcfd)
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X
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np.random.seed(0)
X = np.random.random((30,3))
r = np.linspace(0, 1, 10)
tree = BallTree(X,metric='pyfunc',func=LCDMmetric)
s = pickle.dumps(tree)
treedump = pickle.loads(s)
treedump.two_point_correlation(X,r)
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BT_D = BallTree(data)
BT_R = BallTree(data_R)
counts_DD = np.zeros(Nbins + 1)
counts_RR = np.zeros(Nbins + 1)
for i in range(Nbins + 1):
counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],
count_only=True))
counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],
count_only=True))
DD = np.diff(counts_DD)
RR = np.diff(counts_RR)
# check for zero in the denominator
RR_zero = (RR == 0)
RR[RR_zero] = 1
if method == 'standard':
corr = factor ** 2 * DD / RR - 1
elif method == 'landy-szalay':
if sklearn_has_two_point:
counts_DR = KDT_R.two_point_correlation(data, bins)
else:
counts_DR = np.zeros(Nbins + 1)
for i in range(Nbins + 1):
counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],
count_only=True))
DR = np.diff(counts_DR)
corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR
corr[RR_zero] = np.nan
return corr
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dr7fdat=np.array([data['s'][0:300] data['rar'][0:300] data['decr'][0:300]])
dr7fdat
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dr7fdat[2]
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def LCDMmetric(p1,p2):
costheta=m.sin(dec1)*m.sin(dec2)+m.cos(dec1)*m.cos(dec2)*m.cos(ra1-ra2)
s1=DC_LCDM(z1)
s2=DC_LCDM(z2)
return np.sqrt(s1**2+s2**2-2.0*s1*s2*costheta)