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KiDS_Mocks_HOD_zone_display_nonperiodic.py
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KiDS_Mocks_HOD_zone_display_nonperiodic.py
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import numpy as np
from numpy import *
import matplotlib
from matplotlib import pyplot as plt
import sys
sys.path.append('/Users/Vasiliy/Desktop/PhD/Scripts/ZOBOV')
sys.path.append('/Users/Vasiliy/Desktop/PhD/Scripts')
from read_vol_zone_void import *
from scipy.spatial import cKDTree
from periodic_kdtree import PeriodicCKDTree
import time
from itertools import cycle
from Cosmology_class import *
from astropy.io import fits
import matplotlib.colors as mclr
from scipy.integrate import quad
from mpl_toolkits.axes_grid1 import make_axes_locatable
font = {'family' : 'serif', 'serif' : ['Times'], 'size' : '35'}
matplotlib.rc('font', **font)
matplotlib.rc('text', usetex=True)
matplotlib.rcParams['mathtext.fontset'] = 'custom'
matplotlib.rcParams['mathtext.rm'] = 'Times'
matplotlib.rcParams['mathtext.it'] = 'Times'
plt.rc('legend', **{'fontsize':30})
t_begin = time.time()
LC = LC_cosmology()
Lbox = 252.5
pix_count = 1549#7745#
# WMAP9+SN+BAO cosmology
LC.H0 = 68.98 #km s^{-1} Mpc^{-1}
h = LC.H0/100.
c = 2.99792E5 #km/s
LC.omega_m = 0.2905
LC.omega_L = 0.7095
LC.z_box = 0.525
z_box_317 = 0.317
LC.Lbox_full = 505. #Mpc/h this is the size of the simulation box that the light cone was in, but only half of it is used so it's 252.5 Mpc/h
LC.theta = 10. #degrees on sky of each mock
LC.Lcube = 265.
nc = 3072.
D_H = c/LC.H0 #Hubble distance in units of Mpc
def comoving_dist(z_box,omega_m,omega_L,H0,h,c,theta,Lbox):
### FINDING COMOVING DISTANCE TO LOWER, MIDDLE, AND UPPER LIGHTCONE IN Z
### AND FINDING UPPER AND LOWER ANGULAR DIAMETER DISTANCE FOR X AND Y
# Find comoving distance to center of box and then to the z_max and z_min in order to find x_min(y_min)
# and x_max(y_max) at z_min and z_max
def integrand(z):
return (1./sqrt(omega_m*(1+z)**3 + omega_L))
ans, err = quad(integrand, 0, z_box)
D_box = h*D_H*ans #This is the comoving distance to the 'middle' of the light cone in units of Mpc/h
# Distance to 'lower' and 'upper' section of the light cone
# Lbox is divided by 4 because the lightcone already is taking up Lbox/2
D_low = D_box - Lbox/4.
D_upper = D_box + Lbox/4.
# Angular distance of 'lower' and 'upper' section of lightcone
DA_low = 2.*D_low*tan(radians(theta/2.))
DA_upper = 2.*D_upper*tan(radians(theta/2.))
return D_box, D_low, D_upper, DA_low, DA_upper
# Get comoving distances of middle of box, lower end, and upper end as well as angular distances of lower and upper edge of cone
D_box, D_low, D_upper, DA_low, DA_upper = LC.comoving_dist()
D_box_317, D_low_317, D_upper_317, DA_low_317, DA_upper_317 = comoving_dist(z_box_317,LC.omega_m,LC.omega_L,LC.H0,h,c,LC.theta,LC.Lbox_full)
### VOLUMES ###############################################################
# Volume of light cone
# vol_cone = (1./3.)*(DA_low**2 + (DA_low*DA_upper) + DA_upper**2)*(LC.Lbox_full/2.)
vol_cone = (1./3.)*DA_upper**2*D_upper # This is the volume if you have a whole light cone from z_i = 0 to some z
### LOAD DATA ###############################################################
numpart, numzones, zone = read_zone('KiDS_Mocks_upto_z_525.zone')
# numpart, numzones, zone = read_zone('Test_L1000/AllHalo_1000Mpc_wrapped_cai.zone')
numpart_vol, vol, non_zero_idx = read_vol('KiDS_Mocks_upto_z_525.vol.txt', vol_cone)
x,y,z = read_LC_gal_pos('KiDS_Mocks_upto_z_525_LC.txt')
ID_all, x_vol_all, y_vol_all, z_vol_all, x_denmin_all, y_denmin_all, z_denmin_all, zone_rad_all, dencon_all = read_vol_zone_txt('KiDS_Mocks_upto_z_525.vol.zone.txt')
adj_dict = read_adj('KiDS_Mocks_upto_z_525.adj.txt')
numpart_0_042, x_gal_0_042,y_gal_0_042,z_gal_0_042, x_halo_0_042,y_halo_0_042,z_halo_0_042, m200c_0_042, r200c_0_042, Rs_0_042, c_0_042 = read_HOD('L505Mpc_HOD+0.042.dat')
numpart_0_130, x_gal_0_130,y_gal_0_130,z_gal_0_130, x_halo_0_130,y_halo_0_130,z_halo_0_130, m200c_0_130, r200c_0_130, Rs_0_130, c_0_130 = read_HOD('L505Mpc_HOD+0.130.dat')
numpart_0_221, x_gal_0_221,y_gal_0_221,z_gal_0_221, x_halo_0_221,y_halo_0_221,z_halo_0_221, m200c_0_221, r200c_0_221, Rs_0_221, c_0_221 = read_HOD('L505Mpc_HOD+0.221.dat')
numpart_0_317, x_gal_0_317,y_gal_0_317,z_gal_0_317, x_halo_0_317,y_halo_0_317,z_halo_0_317, m200c_0_317, r200c_0_317, Rs_0_317, c_0_317 = read_HOD('L505Mpc_HOD+0.317.dat')
numpart_0_418, x_gal_0_418,y_gal_0_418,z_gal_0_418, x_halo_0_418,y_halo_0_418,z_halo_0_418, m200c_0_418, r200c_0_418, Rs_0_418, c_0_418 = read_HOD('L505Mpc_HOD+0.418.dat')
numpart_0_525, x_gal_0_525,y_gal_0_525,z_gal_0_525, x_halo_0_525,y_halo_0_525,z_halo_0_525, m200c_0_525, r200c_0_525, Rs_0_525, c_0_525 = read_HOD('L505Mpc_HOD+0.525.dat')
# kappa_fits = fits.open('../kappa_0.582_mass.dat_LOS500.fits')
kappa_LoRes = fits.open('../kappa_0.582_mass.dat_LOS500_LoRes.fits')
# xgal_kappa, ygal_kappa, gal_kappa = loadtxt('Source_galaxies_for_kappa_z_0_582.txt', unpack=True)
### DOWNGRADE KAPPA RESOLUTION ##############################################
# Open some Fits image called kappa_fits
# kappa_fits has dimensions of 7745*7745 --> I am lowering its resolution by factor of 5 on each axis
# Now lower the resolution of this thing to 1549*1549
# kappa_LoRes = fits.PrimaryHDU() # Make a new fits image object
# kappa_LoRes.data = np.empty([1549, 1549])
# Averaging the pixels in blocks of (4)^2 pxls^2
# jump = int(len(kappa_fits[0].data[0,:])/len(kappa_LoRes.data[0,:])) # =5
# For loop goes through pxls of lo res mask: 1549*1549
# for x in range(0, len(kappa_LoRes.data[0,:]) ):
# for y in range(0, len(kappa_LoRes.data[:,0]) ):
# avg_pxls = np.sum(kappa_fits[0].data[y*jump:(y+1)*jump, x*jump:(x+1)*jump])/(jump*jump)
# kappa_LoRes.data[y,x] = avg_pxls
# kappa_LoRes.writeto('../kappa_0.582_mass.dat_LOS500_LoRes.fits', output_verify='ignore', clobber=True)
############################################################################################
### GET KAPPA VALUES AND PIXELS ##############################################
# kappa = kappa_fits[0].data # Regular resolution kappa
kappa = kappa_LoRes[0].data # Lower resolution kappa
x_pix = np.arange(0,pix_count,1)
y_pix = np.arange(0,pix_count,1)
kappa_loc = [x_pix for i in range(pix_count)]
kappa_dict = {}
for i in xrange(len(kappa)):
kappa_dict[i] = {}
kappa_dict[i] = kappa[i]
# Create dictionary of random kappa values pulled from a guassian distribution
kappa_rand = np.random.normal(0, (0.3)**2, (pix_count,pix_count))
################################################################################################
# delta_map = read_delta_map('../0.221delta.dat_bicubic_LOS500.txt',pix_count)
### COMBINE ALL XYZ OF GAL AND HALOS UP TO Z=0.525 AND MASS #############################################
x_gal_upto_z_525 = hstack((x_gal_0_042,x_gal_0_130,x_gal_0_221,x_gal_0_317,x_gal_0_418,x_gal_0_525))
y_gal_upto_z_525 = hstack((y_gal_0_042,y_gal_0_130,y_gal_0_221,y_gal_0_317,y_gal_0_418,y_gal_0_525))
z_gal_upto_z_525 = hstack((z_gal_0_042,z_gal_0_130,z_gal_0_221,z_gal_0_317,z_gal_0_418,z_gal_0_525))
x_halo_upto_z_525 = hstack((x_halo_0_042,x_halo_0_130,x_halo_0_221,x_halo_0_317,x_halo_0_418,x_halo_0_525))
y_halo_upto_z_525 = hstack((y_halo_0_042,y_halo_0_130,y_halo_0_221,y_halo_0_317,y_halo_0_418,y_halo_0_525))
z_halo_upto_z_525 = hstack((z_halo_0_042,z_halo_0_130,z_halo_0_221,z_halo_0_317,z_halo_0_418,z_halo_0_525))
m200c_upto_z_525 = hstack((m200c_0_042,m200c_0_130,m200c_0_221,m200c_0_317,m200c_0_418,m200c_0_525))
################################################################################################
# Average density of survey
tot_numden = numpart_vol/(vol_cone)
# Zone per particle that has a non zero volume
zone_nonzero = [zone[q] for q in non_zero_idx]
void_idx_all = rm_duplicate(zone_nonzero)
# Get vol that isn't zero
vol = [vol[q] for q in non_zero_idx]
# Use only xyz of non zero volume particles
x = [x[q] for q in non_zero_idx]
y = [y[q] for q in non_zero_idx]
z = [z[q] for q in non_zero_idx]
# Find voids that are in overdensities
num_v_in_c = 0
void_in_cloud_idx = []
zone_vol = [(4./3.)*pi*zr**3 for zr in zone_rad_all]
for i,t in enumerate(zone_vol):
if 1./t >= tot_numden:
num_v_in_c += 1
void_in_cloud_idx.append(i)
x_cloud = [x_vol_all[q] for q in void_in_cloud_idx]
y_cloud = [y_vol_all[q] for q in void_in_cloud_idx]
z_cloud = [z_vol_all[q] for q in void_in_cloud_idx]
some_idx = np.arange(0,len(ID_all),1)
# Get rid of void in cloud
ID = [ID_all[i] for i in some_idx if i not in void_in_cloud_idx]
x_denmin = [x_denmin_all[i] for i in some_idx if i not in void_in_cloud_idx]
y_denmin = [y_denmin_all[i] for i in some_idx if i not in void_in_cloud_idx]
z_denmin = [z_denmin_all[i] for i in some_idx if i not in void_in_cloud_idx]
x_vol = [x_vol_all[i] for i in some_idx if i not in void_in_cloud_idx]
y_vol = [y_vol_all[i] for i in some_idx if i not in void_in_cloud_idx]
z_vol = [z_vol_all[i] for i in some_idx if i not in void_in_cloud_idx]
zone_rad = [zone_rad_all[i] for i in some_idx if i not in void_in_cloud_idx]
dencon = [dencon_all[i] for i in some_idx if i not in void_in_cloud_idx]
void_idx = [void_idx_all[i] for i in some_idx if i not in void_in_cloud_idx]
### FIND ZONES WITH RADIUS LARGER THAN 10 MPC
zone_rad_cut = []
x_vol_cut = []
y_vol_cut = []
z_vol_cut = []
x_denmin_cut = []
y_denmin_cut = []
z_denmin_cut = []
void_idx_cut = []
for i,t in enumerate(zone_rad):
if t > 10.:
zone_rad_cut.append(zone_rad[i])
x_vol_cut.append(x_vol[i])
y_vol_cut.append(y_vol[i])
z_vol_cut.append(z_vol[i])
x_denmin_cut.append(x_denmin[i])
y_denmin_cut.append(y_denmin[i])
z_denmin_cut.append(z_denmin[i])
void_idx_cut.append(void_idx[i])
# f = open('LOS500_vol_avg_void_centers.txt', 'w')
# for i in xrange(len(x_vol_cut)):
# f.write("{} {} {}\n".format(x_vol_cut[i],y_vol_cut[i],z_vol_cut[i]))
# f.close()
### FIND NUMBER OF HALOS WITH MASS OF MAX HALO AND THEIR INDEX
mass_idx = np.where(m200c_0_221==max(m200c_0_221))[0]
##############################################################
def interpolate2D(X, Y, grid):
# linear 2D interpolation
Xi = X.astype(np.int)
Yi = Y.astype(np.int) # these round down to integer
VAL_XYlo = grid[Yi, Xi] + (X - Xi)*( grid[Yi, Xi+1] - grid[Yi, Xi] )
VAL_XYhi = grid[Yi+1,Xi] + (X - Xi)*( grid[Yi+1,Xi+1] - grid[Yi+1, Xi] )
VAL_XY = VAL_XYlo + (Y - Yi)*( VAL_XYhi - VAL_XYlo )
return VAL_XY
def kappa_pix_to_mpc(coord,D_upper):
mpc_val = [D_upper*tan((value*(LC.theta/pix_count)*(pi/180.))-5*(pi/180.)) for value in coord]
return mpc_val
def mpc_to_pix(coord,z_coord):
# Converts Mpc to pixel values for KiDS Mocks configuration created by Joachim
pix_val = [(arctan(val/valz) + 5.*(pi/180.))*(pix_count/LC.theta)*(180./pi) for (val,valz) in zip(coord,z_coord)]
return pix_val
def spherical_stk(zone_rad_stk,x_zone_stk,y_zone_stk,z_zone_stk,numzone_stk):
delta_stk = [[] for i in xrange(numzone_stk)]
for r in range(0,(len(zone_rad_stk))):
# Create array from ~0 to 2 in order for it to be the same for all different sized zones
# R_stk = np.linspace(0,2,9)
# Array of volumes for each consecutive sphere for R values
V = ((4.*pi)/3.)*(R_stk*zone_rad_stk[r])**3
# Find index of halo representing min value of zone
ind_x_stk = find_nearest(x,x_zone_stk[r])
ind_y_stk = find_nearest(y,y_zone_stk[r])
ind_z_stk = find_nearest(z,z_zone_stk[r])
count_stk = []
nden_stk = []
den_stk = []
for n in range(0,len(R_stk)):
# This gives me a number density in each shell
# looks for number of particles within a volume given by input radius
if n==0:
# Density part of loop
loc_temp1 = tree.query_ball_point([x[ind_x_stk], y[ind_y_stk], z[ind_z_stk]], R_stk[n]*zone_rad_stk[r])
count_temp1 = len(loc_temp1)
# nden_temp1 = count_temp1/V[n]
# vol_temp1 = [vol[j] for j in loc_temp1]
sum_vol1 = sum([vol[j] for j in loc_temp1])
if sum_vol1 != 0.0:
den_temp1 = ((1./sum_vol1)*count_temp1)/(numpart_vol/vol_cone)
else:
den_temp1 = 0.0
else:
# Density part of loop
delta_r = (R_stk[n-1]*zone_rad_stk[r]) - (R_stk[n]*zone_rad_stk[r])
loc_temp11 = tree.query_ball_point([x[ind_x_stk], y[ind_y_stk], z[ind_z_stk]], R_stk[n]*zone_rad_stk[r])
loc_temp12 = tree.query_ball_point([x[ind_x_stk], y[ind_y_stk], z[ind_z_stk]], R_stk[n-1]*zone_rad_stk[r])
count_temp11 = len(loc_temp11)
count_temp12 = len(loc_temp12)
count_temp1 = count_temp11-count_temp12
# Index of particles in shell
# shell_loc = [i for i in loc_temp11 if i not in loc_temp12]
shell_loc = list(set(loc_temp11) - set(loc_temp12))
if len(shell_loc) != count_temp1:
print 'number of particles in shell doesnt add up!'
# nden_temp1 = count_temp1/(V[n]-V[n-1])
# vol_temp1 = [vol[j] for j in shell_loc]
sum_vol1 = sum([vol[j] for j in shell_loc])
if sum_vol1 != 0.0:
den_temp1 = ((1./sum_vol1)*count_temp1)/(numpart_vol/vol_cone)
else:
den_temp1 = 0.0
# Count of halos and number density in a each shell
count_stk.append(count_temp1)
# nden_stk.append(nden_temp1)
den_stk.append(den_temp1)
delta_stk[r] = den_stk
# Add elements of each zone's number count and number density per shell
if r==0:
avg_count_temp = count_stk
# avg_nden_temp = nden_stk
avg_den_temp = den_stk
else:
avg_count_temp = [a+b for a,b in zip(avg_count_temp,count_stk)]
# avg_nden_temp = [c+d for c,d in zip(avg_nden_temp,nden_stk)]
avg_den_temp = [c+d for c,d in zip(avg_den_temp,den_stk)]
# Divide total counts and number densities in shells by number of voids
avg_cnt = np.array(avg_count_temp)/numzone_stk
# avg_nden = np.array(avg_nden_temp)/numzone_stk
avg_den = np.array(avg_den_temp)/numzone_stk
return avg_cnt, avg_den, delta_stk#,avg_nden
def sph_kappa_stk(zn_rad_pix_stk,xpix_zone_stk,ypix_zone_stk,numzone_stk):
kappa_stk = [[] for i in xrange(numzone_stk)]
w = [[] for i in xrange(numzone_stk)]
for r in range(0,(len(zn_rad_pix_stk))):
print r
# Create array from ~0 to 2 in order for it to be the same for all different sized zones
# R_stk = np.linspace(0,2,9)
# Get XY location of void on map projected at z where the kappa map is
ind_x_pix = find_nearest(x_pix,xpix_zone_stk[r])
ind_y_pix = find_nearest(y_pix,ypix_zone_stk[r])
# Create array of X and Y that spans 2x zone radius
# x_zone_all = np.arange(x_pix[ind_x_pix]-R_mult*int(zn_rad_pix_stk[r]),x_pix[ind_x_pix]+R_mult*int(zn_rad_pix_stk[r]),1)
# y_zone_all = np.arange(y_pix[ind_y_pix]-R_mult*int(zn_rad_pix_stk[r]),y_pix[ind_y_pix]+R_mult*int(zn_rad_pix_stk[r]),1)
# # Non-grid values
x_zone_all = np.arange(x_pix[ind_x_pix]-R_mult*zn_rad_pix_stk[r],x_pix[ind_x_pix]+R_mult*zn_rad_pix_stk[r],1)
y_zone_all = np.arange(y_pix[ind_y_pix]-R_mult*zn_rad_pix_stk[r],y_pix[ind_y_pix]+R_mult*zn_rad_pix_stk[r],1)
# The '-2' on pix_count is to account for the interpolation that goes to X(Y) + 1 for indexing purposes
x_zone = x_zone_all[np.where(np.logical_and(x_zone_all>=0, x_zone_all<=pix_count-2))[0]]
y_zone = y_zone_all[np.where(np.logical_and(y_zone_all>=0, y_zone_all<=pix_count-2))[0]]
# print 'dist start'
t_dist1 = time.time()
# Find distance to each pixel from the void center
# Creates len(x_zone) x len(y_zone) array where the dist[3][1] is location (3,1) in xy coords x[:,None]
# dist = [[sqrt((i-x_pix[ind_x_pix])**2. + (j-y_pix[ind_y_pix])**2.) for j in y_zone] for i in x_zone]
dist = sqrt((x_zone[:,None]-x_pix[ind_x_pix])**2. + (y_zone[None,:]-y_pix[ind_y_pix])**2.)
t_dist2 = time.time()
# print 'dist done. It took \t%g minutes' % ((t_dist2-t_dist1)/60.)
# print ''
# print 'kappa start'
t_kappa1 = time.time()
# Get all kappa within 2x zone rad for a particular zone
kappa_seg = {}
for i,t in enumerate(x_zone):
# Need to put 'y' value first in 2D arrays
# kappa_seg[i] = [kappa_dict[j][t] for j in y_zone]
kappa_seg[i] = interpolate2D(t,y_zone,kappa)
# kappa_seg = [[kappa_dict[i][j] for j in y_zone] for i in x_zone]
t_kappa2 = time.time()
# print 'kappa done. It took \t%g minutes' % ((t_kappa2-t_kappa1)/60.)
t_sort1 = time.time()
# Get index of the bin where each distance would fall if it were sorted
dist_sort_idx = {}
for j in xrange(len(dist)):
dist_sort_idx[j] = np.searchsorted(R_stk*zn_rad_pix_stk[r], dist[j])
# dist_sort_idx = [np.searchsorted(R_stk*zn_rad_pix_stk[r], dist[j]) for j in xrange(len(dist))]
t_sort2 = time.time()
# print 'dist sort done. It took \t%g minutes' % ((t_sort2-t_sort1)/60.)
# Sort the values of the kappa segment based on the sorting of the distances
# kappa_seg_sorted = [[x for y,x in sorted(zip(dist[i],kappa_seg[i]))] for i in xrange(len(dist))]
k_rav = np.array(kappa_seg.values()).ravel()
dist_idx_rav = np.array(dist_sort_idx.values()).ravel()
t_bin1 = time.time()
# Put arrays of kappa into radii bins
kappa_sum_temp = [k_rav[np.where(dist_idx_rav==i)[0]] for i in xrange(len(R_stk))]# if k_rav[np.where(dist_idx_rav==i)[0]] not []]
t_bin2 = time.time()
# print 'kappa bin done. It took \t%g minutes' % ((t_bin2-t_bin1)/60.)
# Mean of kappa value for each radial bin
kappa_stk[r] = [nanmean(k) for k in kappa_sum_temp]
w[r] = [1 if isfinite(kappa_stk[r][i]) else 0 for i in xrange(len(kappa_stk[r]))]
# if r ==0:
# avg_kappa_temp = kappa_stk[r]
# else:
# avg_kappa_temp = [e+f for e,f in zip(avg_kappa_temp,kappa_stk[r])]
# avg_kappa = [np.nanmean(np.array(avg_kappa_temp[i])) for i in xrange(len(R_stk))]
return kappa_stk#, w
# def sph_kappa_stk(zn_rad_pix_stk,xpix_zone_stk,ypix_zone_stk,numzone_stk):
# for r in range(0,(len(zn_rad_pix_stk))):
# print r
def vol_avg_center(x_cell,y_cell,z_cell, x_adj,y_adj,z_adj, v_cell,v_adj):
# Gets volume weighted average location of a cell and it's adjacency
# The collection of these volume weighted locations is the location of the boundary for the zone
# Volume weighted average is obtained by this formula: (x_cell*vol_cell + x_adj[i]*vol_adj[i])/(vol_cell+vol_adj[i])
# Note that x_adj[i] and vol_adj[i] may contain more than one value so this is a summation
# Same procedure for y and z
x_vol_avg = []
y_vol_avg = []
z_vol_avg = []
# Loop over all cells that are on the boundary of a zone
for l in range(0,len(v_cell)):
# Numerator for xyz
num_x_adj = sum([x*volx for x,volx in zip(x_adj[l],v_adj[l])])
num_x = num_x_adj + (x_cell[l]*v_cell[l])
num_y_adj = sum([y*voly for y,voly in zip(y_adj[l],v_adj[l])])
num_y = num_y_adj + (y_cell[l]*v_cell[l])
num_z_adj = sum([z*volz for z,volz in zip(z_adj[l],v_adj[l])])
num_z = num_z_adj + (z_cell[l]*v_cell[l])
# Denominator
dem = v_cell[l]+sum(v_adj[l])
x_vol_avg.append(num_x/dem)
y_vol_avg.append(num_y/dem)
z_vol_avg.append(num_z/dem)
return x_vol_avg, y_vol_avg, z_vol_avg
def boundary_bin(vm, dist, sort_idx):
# Function to bin the boundary distance profile
# vm is an array of volume for each particle in a zone
# dist is an array of distance of each particle to the closest "boundary particle"
# min and max are the upper and lower bounds of the bin
bin_vol_temp = [[] for _ in range(len(bins)+1)]
bin_dist = [[] for _ in range(len(bins)+1)]
bin_den = [[] for _ in range(len(bins)+1)]
cnt = [0 for _ in range(len(bins)+1)]
# sort_idx = [len(bins)-1. if max(sort_idx) >= len(bins) else a for a in sort_idx]
for i in range(0,len(dist)):
bin_dist[sort_idx[i]].append(dist[i])
# bin_vol_temp[sort_idx[i].append([0. if math.isnan(t) else t for t in vm[i]])
bin_vol_temp[sort_idx[i]].append(vm[i])
cnt[sort_idx[i]] += 1
# if not isnan(vm[i]):
# bin_vol_temp[sort_idx[i]].append(vm[i])
# cnt[sort_idx[i]] += 1
# else:
# bin_vol_temp.append(0.0)
del cnt[0]
del bin_dist[0]
del bin_vol_temp[0]
bin_den = [((1./sum(v_val))*c)/(np.int(numpart_vol)/vol_cone) for (v_val,c) in zip(bin_vol_temp,cnt)]
# np.set_nan_to_num(bin_den)
bin_den = [0. if math.isnan(v) else v for v in bin_den]
return bin_den, bin_dist, cnt
def bin_zone(x_den_val,y_den_val,z_den_val, x_vol_val,y_vol_val,z_vol_val, x_pix_vol_val, y_pix_vol_val, zn_rad, dcon, zn_rad_pix, min_bin, max_bin):
# This function returns in xyz centers (volume averaged and density), zone radius, density contrast,
# and zone ID for particles in a specific range of zone radii
x_zone_stk = []
y_zone_stk = []
z_zone_stk = []
x_vol_zone_stk = []
y_vol_zone_stk = []
z_vol_zone_stk = []
x_pix_zone_stk = []
y_pix_zone_stk = []
zn_rad_stk = []
zn_rad_pix_stk = []
zone_dencon_stk = []
zn = []
# Loop over all zones and retain xyz and radius for those that have given size
for i in range(0,len(zn_rad)):
if zn_rad[i] > min_bin and zn_rad[i] <= max_bin:
x_zone_stk.append(x_den_val[i])
y_zone_stk.append(y_den_val[i])
z_zone_stk.append(z_den_val[i])
x_vol_zone_stk.append(x_vol_val[i])
y_vol_zone_stk.append(y_vol_val[i])
z_vol_zone_stk.append(z_vol_val[i])
x_pix_zone_stk.append(x_pix_vol_val[i])
y_pix_zone_stk.append(y_pix_vol_val[i])
zn_rad_stk.append(zn_rad[i])
zone_dencon_stk.append(dcon[i])
zn.append(zone_nonzero[i])
zn_rad_pix_stk.append(zn_rad_pix[i])
return x_zone_stk, y_zone_stk, z_zone_stk, x_vol_zone_stk, y_vol_zone_stk, z_vol_zone_stk, x_pix_zone_stk, y_pix_zone_stk, zn_rad_stk, zone_dencon_stk, zn, zn_rad_pix_stk
def adj_particles(same_zone_adj_bn,zn):
# This function takes in an array of adjacencies for a zone and
# finds the cells and their xyz and vol if any of their adjacencies
# are not in the same zone as the cell itself
x_non_zone_adj = []
y_non_zone_adj = []
z_non_zone_adj = []
x_non_zone_adj_slice = []
y_non_zone_adj_slice = []
z_non_zone_adj_slice = []
x_non_zone_adj_tot = []
y_non_zone_adj_tot = []
z_non_zone_adj_tot = []
x_non_zone_adj_arr_slice = []
y_non_zone_adj_arr_slice = []
z_non_zone_adj_arr_slice = []
x_non_zone_adj_arr_tot = []
y_non_zone_adj_arr_tot = []
z_non_zone_adj_arr_tot = []
adj_cell_vol = []
adj_cell_vol_tot = []
cell_on_boundary_temp = [] # ID of cells that have adjacent cells not in the same zone
cell_on_boundary_temp_tot = []
for a in range(0, len(same_zone_adj_bn)):
adj_cell_vol_temp = []
x_non_zone_adj_temp = []
y_non_zone_adj_temp = []
z_non_zone_adj_temp = []
adj_cell_vol_temp_tot = []
x_non_zone_adj_temp_tot = []
y_non_zone_adj_temp_tot = []
z_non_zone_adj_temp_tot = []
for b in same_zone_adj_bn[a]:
if zone_nonzero[b] != zn:
# xyz as array of arrays for each adjacency
x_non_zone_adj_temp_tot.append(x[b])
y_non_zone_adj_temp_tot.append(y[b])
z_non_zone_adj_temp_tot.append(z[b])
# Get volume for each adjacency
adj_cell_vol_temp_tot.append(vol[b])
# Get ID of cell thats on a boundary
cell_on_boundary_temp_tot.append(same_zone_id_bn[a])
if zone_nonzero[b] != zn and z[b] <= slice_max_bn[a] and z[b] >= slice_min_bn[a]:
# xyz in order for plots
x_non_zone_adj.append(x[b])
y_non_zone_adj.append(y[b])
z_non_zone_adj.append(z[b])
# xyz as array of arrays for each adjacency
x_non_zone_adj_temp.append(x[b])
y_non_zone_adj_temp.append(y[b])
z_non_zone_adj_temp.append(z[b])
# Get volume for each adjacency
adj_cell_vol_temp.append(vol[b])
# Get ID of cell thats on a boundary
cell_on_boundary_temp.append(same_zone_id_bn[a])
# Gets array of arrays which contain volumes for each adjacent cell not part of zone for each particle in same_zone_adj
if adj_cell_vol_temp != []:
adj_cell_vol.append(adj_cell_vol_temp)
x_non_zone_adj_arr_slice.append(x_non_zone_adj_temp)
y_non_zone_adj_arr_slice.append(y_non_zone_adj_temp)
z_non_zone_adj_arr_slice.append(z_non_zone_adj_temp)
# Gets array of arrays which contain volumes for each adjacent cell not part of zone for each particle in same_zone_adj
if adj_cell_vol_temp_tot != []:
adj_cell_vol_tot.append(adj_cell_vol_temp_tot)
x_non_zone_adj_arr_tot.append(x_non_zone_adj_temp_tot)
y_non_zone_adj_arr_tot.append(y_non_zone_adj_temp_tot)
z_non_zone_adj_arr_tot.append(z_non_zone_adj_temp_tot)
# Remove duplicate IDs of cells that are on a boundary
cell_on_boundary = rm_duplicate(cell_on_boundary_temp)
cell_on_boundary_tot = rm_duplicate(cell_on_boundary_temp_tot)
return cell_on_boundary_tot, cell_on_boundary, x_non_zone_adj_arr_tot, y_non_zone_adj_arr_tot, z_non_zone_adj_arr_tot, adj_cell_vol_tot
def boundary_stk(xvol, yvol, zvol, x_same_zone_bn, y_same_zone_bn, z_same_zone_bn, vol_same_zone_bn, zn, rad_val):
# This function takes in the location of the boundary points (likely volume averaged) and
# bins them to find the profile
# Create tree of volume weighted boundary points
boundary_pts = zip(xvol, yvol, zvol)
tree_boundary = cKDTree(boundary_pts)
x_part = []
y_part = []
z_part = []
zn_idx = np.where(np.array(void_idx_cut)==zn)[0]
# Find particles within rad_val of zone radius that are not in zone
idx = tree.query_ball_point([x_vol_cut[zn_idx],y_vol_cut[zn_idx],z_vol_cut[zn_idx]],rad_val)
new_idx = [] # index of particles not in zone, but within 2*R_eff of zone
for i in idx:
if zone[i] != zn:
new_idx.append(i)
x_part.append(x[i])
y_part.append(y[i])
z_part.append(z[i])
cls_dist = []
cls_idx = []
cls_dist_non_zn = []
cls_idx_non_zn = []
# Find closest distance for each particle in a zone to the boundary particle
for i in range(0,len(x_same_zone_bn)):
cls_dist.append(tree_boundary.query([x_same_zone_bn[i],y_same_zone_bn[i],z_same_zone_bn[i]])[0])
cls_idx.append(tree_boundary.query([x_same_zone_bn[i],y_same_zone_bn[i],z_same_zone_bn[i]])[1])
# Find closest distance for each particle not in a zone to the boundary particle
for i in range(0,len(x_part)):
cls_dist_non_zn.append(tree_boundary.query([x_part[i],y_part[i],z_part[i]])[0])
cls_idx_non_zn.append(tree_boundary.query([x_part[i],y_part[i],z_part[i]])[1])
# Calculate density for each cell in the zone
# den_same_zone_bn = [(1./volume) for volume in vol_same_zone_bn[0]]
# vol_same_zone_bn = [(volume) for volume in vol_same_zone_bn[0]]
# Calculate density for each particle
vol_non_zn_part = []
for i in new_idx:
# den_non_zn_part.append(1./vol[i])
vol_non_zn_part.append(vol[i])
# Bin the density, distance, and number counts from 0 to 2.5. This binning is normalized to effective radius of each zone
den_bins = []
dist_bins = []
ncnt_bins = []
den_bins_non_zn = []
dist_bins_non_zn = []
ncnt_bins_non_zn = []
sort_zn_idx = np.searchsorted(bins,cls_dist)
sort_non_zn_idx = np.searchsorted(bins,cls_dist_non_zn)
# Find den, dist, cnt for particles in zone
# Density, distance, and num counts of for each bin. Bins are normalized to the effective radius of each zone
den_bins, dist_bins, ncnt_bins = boundary_bin(vol_same_zone_bn, cls_dist, sort_zn_idx)
# Make arrays of den, dist, num counts of for bins
# if den_temp != np.nan:
# den_bins.append(den_temp)
# else:
# den_bins.append(0)
# if dist_temp != np.nan:
# dist_bins.append(dist_temp)
# else:
# dist_bins.append(0)
# if ncnt_temp != np.nan:
# ncnt_bins.append(ncnt_temp)
# else:
# ncnt_bins.append(0)
# Find den, dist, cnt for particle not in zone
# Density, distance, and num counts of for each bin. Bins are normalized to the effective radius of each zone
den_bins_non_zn, dist_bins_non_zn, ncnt_bins_non_zn = boundary_bin(vol_non_zn_part, cls_dist_non_zn, sort_non_zn_idx)
# Make arrays of den, dist, num counts of for bins
# if den_temp2 != np.nan:
# den_bins_non_zn.append(den_temp2)
# else:
# den_bins_non_zn.append(0)
# if dist_temp2 != np.nan:
# dist_bins_non_zn.append(dist_temp2)
# else:
# dist_bins.append(0)
# if ncnt_temp2 != np.nan:
# ncnt_bins_non_zn.append(ncnt_temp2)
# else:
# ncnt_bins_non_zn.append(0)
return den_bins, dist_bins, ncnt_bins, den_bins_non_zn, dist_bins_non_zn, ncnt_bins_non_zn
### GET XYZ OF HALOS IN PIXEL VALUES ###
def read_halo_pix(filename):
f = open(filename,'r')
x_halo_pix = []
y_halo_pix = []
z_halo_pix = []
mass_halo_pix = []
rows = f.readlines()
for row in rows:
data = row.split()
x_halo_pix.append(data[0])
y_halo_pix.append(data[1])
z_halo_pix.append(data[2])
mass_halo_pix.append(data[3])
x_halo_pix = np.array(x_halo_pix).astype(np.float)
y_halo_pix = np.array(y_halo_pix).astype(np.float)
z_halo_pix = np.array(z_halo_pix).astype(np.float)
mass_halo_pix = np.array(mass_halo_pix).astype(np.float)
return x_halo_pix,y_halo_pix,z_halo_pix,mass_halo_pix
x_halo_pix_042,y_halo_pix_042,z_halo_pix_042,mass_halo_pix_042 = read_halo_pix('../0.042LightCone_halo.dat_LOS500.txt')
x_halo_pix_130,y_halo_pix_130,z_halo_pix_130,mass_halo_pix_130 = read_halo_pix('../0.130LightCone_halo.dat_LOS500.txt')
x_halo_pix_221,y_halo_pix_221,z_halo_pix_221,mass_halo_pix_221 = read_halo_pix('../0.221LightCone_halo.dat_LOS500.txt')
x_halo_pix_317,y_halo_pix_317,z_halo_pix_317,mass_halo_pix_317 = read_halo_pix('../0.317LightCone_halo.dat_LOS500.txt')
x_halo_pix_418,y_halo_pix_418,z_halo_pix_418,mass_halo_pix_418 = read_halo_pix('../0.418LightCone_halo.dat_LOS500.txt')
x_halo_pix_525,y_halo_pix_525,z_halo_pix_525,mass_halo_pix_525 = read_halo_pix('../0.525LightCone_halo.dat_LOS500.txt')
x_halo_pix_upto_z_525 = hstack((x_halo_pix_042,x_halo_pix_130,x_halo_pix_221,x_halo_pix_317,x_halo_pix_418,x_halo_pix_525))
y_halo_pix_upto_z_525 = hstack((y_halo_pix_042,y_halo_pix_130,y_halo_pix_221,y_halo_pix_317,y_halo_pix_418,y_halo_pix_525))
z_halo_pix_upto_z_525 = hstack((z_halo_pix_042,z_halo_pix_130,z_halo_pix_221,z_halo_pix_317,z_halo_pix_418,z_halo_pix_525))
mass_halo_pix_upto_z_525 = hstack((mass_halo_pix_042,mass_halo_pix_130,mass_halo_pix_221,mass_halo_pix_317,mass_halo_pix_418,mass_halo_pix_525))
### GET XYZ OF TOP N MOST MASSIVE HALOS ###################################
# Get indicies of most massive halos
massive_halo_idx = np.argpartition(mass_halo_pix_upto_z_525, -500)[-500:]
# Coordinates of most massive halos
x_halo_massive = [x_halo_pix_upto_z_525[valx]/5. for valx in massive_halo_idx]
y_halo_massive = [y_halo_pix_upto_z_525[valy]/5. for valy in massive_halo_idx]
# Mass of most massive halos
mass_halo_massive = [mass_halo_pix_upto_z_525[mass] for mass in massive_halo_idx]
# Radii for halos is sent to be 1 Mpc at the center of the box ie whatever 1 Mpc at the middle
# of the box is, is coverted to pixels and used as the radius for all halos
rad_halo_massive = np.ones(len(x_halo_massive))*15#(pix_count/(2*D_box_317*tan(radians(5.))))
#############################################################################
#############################################################################
# x_halo_pix = mpc_to_pix(x_halo_0_221,z_halo_0_221)
# y_halo_pix = mpc_to_pix(y_halo_0_221,z_halo_0_221)
####################################################################################################################
### CONVERT KAPPA PIXEL ARRAYS TO MPC ARRAYS ##########
# x_pix_mpc = kappa_pix_to_mpc(x_pix,D_upper)
# y_pix_mpc = kappa_pix_to_mpc(y_pix,D_upper)
#################################################
# Array of effective radius of each cell
r_eff = []
for v in vol:
r = (v*(3./(4*pi)))**(1./3.)
r_eff.append(r)
# Find index of cell with largest radius
max_ind = np.int(find_nearest(r_eff, max(r_eff)))
# Find index of cell with second largest radius
sec_lrg_ind = np.int(find_nearest(r_eff, second_largest(r_eff)))
# Find index of cell with smallest radius
min_ind = np.int(find_nearest(r_eff, min(r_eff)))
# Find index of cell with arbitrary radius
arb_ind = np.int(find_nearest(np.asarray(r_eff), 7.))
# Find index of max zone radius from vol.zone.txt file
max_rad_idx = np.int(find_nearest(x_denmin, x[max_ind]))
### FIND ALL PARTICLES THAT BELONG TO ZONE #####################################################
same_zone_id = []
# Loop over all zones and get index for each particle in that zone
for (i,t) in enumerate(zone_nonzero):
if t == zone_nonzero[arb_ind]:
same_zone_id.append(i)
### FIND TOTAL VOLUME AND EFFECTIVE RADIUS OF A ZONE ###########################################################
tot_zone_vol = 0
vol_same_zone = []
for n in same_zone_id:
tot_zone_vol += vol[n]
vol_same_zone.append(vol[n])
r_eff_zone_tot = (tot_zone_vol*(3./(4*pi)))**(1./3.)
### FIND HISTOGRAM OF ZONE RADII ###########################################################
zone_rad_hist, zone_rad_hist_bin = np.histogram(log10(zone_rad))
######################################################################################################
### GET COORDINATES, RADIUS, AND ADJACENCIES OF PARTICLES THAT BELONG TO ZONE BELONG TO ZONE #####################################################
x_same_zone = []
y_same_zone = []
z_same_zone = []
r_eff_same_zone = []
same_zone_adj = []
for val in same_zone_id:
x_same_zone.append(x[val])
y_same_zone.append(y[val])
z_same_zone.append(z[val])
r_eff_same_zone.append(r_eff[val])
# Gets array of indices of adjacent particles to each particle in a specific zone
same_zone_adj.append(adj_dict[val])
### CREATE SLICE IN THE Y DIMENSION ########################################################
# Indices of slices in the y direction
yslice_gal_loc = np.where(np.logical_and(array(y)>=-6., array(y)<=0.))[0]
xslice = array(x)[yslice_gal_loc]
zslice = array(z)[yslice_gal_loc]
# Indices of void centers that fall into this slice
yslice_zone_loc = np.where(np.logical_and(array(y_vol_cut)>=-6., array(y_vol_cut)<=0.))[0]
yslice_zone_rad = array(zone_rad_cut)[yslice_zone_loc]
xslice_zone = array(x_vol_cut)[yslice_zone_loc]
zslice_zone = array(z_vol_cut)[yslice_zone_loc]
# Find locations most massive zone in this slice
yslice_zone_lg = np.argpartition(yslice_zone_rad, -5)[-5:]
### CREATE SLICE IN THE Z DIMENSION ########################################################
x_slice = []
y_slice = []
slice_idx = []
x_slice_zone = []
y_slice_zone = []
slice_zone_idx = []
r_eff_zone_slice = []
# mult = 1. #for the slice of zone
mult2 = 1. #for the slice of the boundary particles
slice_max = (mult2*r_eff[arb_ind]+z[arb_ind])
slice_min = (z[arb_ind]-mult2*r_eff[arb_ind])
# Loop over halo z values and create a slice in the cube with the size of the effective radius
# of chosen void
for (i,t) in enumerate(z):
if t <= slice_max and t >= slice_min:
slice_idx.append(i)
x_slice.append(x[i])
y_slice.append(y[i])
for (i,t) in enumerate(z_same_zone):
if t <= slice_max and t >= slice_min:
slice_zone_idx.append(i)
x_slice_zone.append(x_same_zone[i])
y_slice_zone.append(y_same_zone[i])
# Effective radii of each cell in slice that's within a zone
for i in slice_zone_idx:
r_eff_zone_slice.append(r_eff_same_zone[i])
arb_ind_void = np.where(void_idx==zone_nonzero[arb_ind])[0][0]
denminrad = np.int(find_nearest(x_slice_zone, x_denmin[arb_ind_void]))
# same_zone_id_bn = np.where(zone_nonzero==zone_nonzero[arb_ind])[0]
# cell_on_boundary_tot_2303, cell_on_boundary_2303, x_non_zone_adj_2303, y_non_zone_adj_2303, z_non_zone_adj_2303, adj_cell_vol_2303 = adj_particles(same_zone_adj,arb_ind_void)
# x_vol_avg_2303, y_vol_avg_2303, z_vol_avg_2303 = vol_avg_center(x[cell_on_boundary_tot_2303],y[cell_on_boundary_tot_2303],z[cell_on_boundary_tot_2303], x_non_zone_adj_2303,y_non_zone_adj_2303,z_non_zone_adj_2303, vol[cell_on_boundary_tot_2303],adj_cell_vol_2303)
#################################################################################
### CREATE TREE FOR X,Y,Z COORDINATES FOR ALL HALOS #########################
# Create tree for all particles
halos = zip(np.array(x).ravel(), np.array(y).ravel(), np.array(z).ravel()) #makes x,y,z single arrays
tree = cKDTree(halos)
# Create tree for xy coordinates given pixel values for kappa maps
pix_val = zip(x_pix.ravel(),y_pix.ravel())
pix_tree = cKDTree(pix_val)
# Create kappa tree
# kval = np.array(kappa_dict.values()).ravel()
# kappa_tree = cKDTree(kval)
# raise()
#############################################################################
### FIND RADIUS AT WHICH THE BACKGROUND NUMBER DENSITY IS REACHED PER VOID ######################
zone_rad_sph_den = -99.*np.ones(len(x_vol_cut))
numden_zone_rad = -99.*np.ones(len(x_vol_cut))
# Create a dictionary of distances from zone center to each particle
dist1 = {}
dist1 = [tree.query([x_vol_cut[i],y_vol_cut[i],z_vol_cut[i]], k=int(numpart_vol))[0] for i in xrange(len(x_vol_cut))]
for i in xrange(len(x_vol_cut)):
print i
for d in dist1[i]:
# Find how many particles are within radius d for each zone
part_loc = tree.query_ball_point([x_vol_cut[i], y_vol_cut[i], z_vol_cut[i]], d)
# Get number den at this particular radius
# numden = len(part_loc)/(((4.*pi)/3.)*d**3.)
numden = len(part_loc)/np.sum(array(vol)[part_loc])
# If the numden is greater than or equal to the total numden, add radius to radius array
if numden >= 0.7*tot_numden:
numden_zone_rad[i] = numden
zone_rad_sph_den[i] = d
break
# part_loc = [tree.query_ball_point([x_vol_cut[i], y_vol_cut[i], z_vol_cut[i]], d) for d in dist1[i]]
# numden = [len(part_loc[j])/(((4.*pi)/3.)*array(dist1[i])**3.) for j in xrange(len(part_loc))]
# den_idx = [np.where(array(numden[h])>=0.7*tot_numden)[0] for h in xrange(len(numden))]
# for k in xrange(len(numden)):
# if len(den_idx[k]) > 0:
# numden_zone_rad[i] = array(numden[k])[den_idx[k][0]]
# zone_rad_sph_den[i] = dist1[i][den_idx[k][0]]
# else:
# numden_zone_rad[i] = -99.
# zone_rad_sph_den[i] = -99.
zero_idx = np.where(zone_rad_sph_den==-99.)[0]
zone_rad_sph_den[zero_idx] = np.array(zone_rad)[zero_idx]
np.save('zone_rad_sph_0.7den_upto_z_525_cut', (zone_rad_sph_den,numden_zone_rad))
# zone_rad_sph_den, numden_zone_rad = np.load('zone_rad_sph_0.2den_upto_z_525_cut.npy')
###################################################################################################
#### CREATE PIXEL VALUES FOR XY COORDINATES OF VOID CENTERS ######################
# Conversion factors between pix/arcmin and deg/pix for KiDS mocks using 10 deg opening angle
pix_to_arcmin = 12.90833
deg_to_pix = 0.00129115558
### MPC TO PIXEL CONVERSION FACTOR ###