Initial import
This commit is contained in:
commit
56a50eead3
820 changed files with 192077 additions and 0 deletions
533
scripts/old_analysis/analysis.py
Normal file
533
scripts/old_analysis/analysis.py
Normal file
|
|
@ -0,0 +1,533 @@
|
|||
#+
|
||||
# ARES/HADES/BORG Package -- ./scripts/old_analysis/analysis.py
|
||||
# Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
|
||||
# Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
|
||||
#
|
||||
# Additional contributions from:
|
||||
# Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
|
||||
#
|
||||
#+
|
||||
from read_all_h5 import explore_chain
|
||||
from read_all_h5 import rebuild_spliced_h5
|
||||
import h5py as h5
|
||||
import numpy as np
|
||||
import healpy as hp
|
||||
import numexpr as ne
|
||||
import os
|
||||
import math
|
||||
from pylab import *
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.pyplot import figure, show
|
||||
|
||||
#import numba
|
||||
'''
|
||||
@numba.jit
|
||||
def _special_spectrum_builder(P, PP, tmp, alpha, N_i, Pk_i):
|
||||
|
||||
for i in range(Pk_i.size):
|
||||
for j in range(PP.size):
|
||||
v = PP[j]**(-alpha)*exp(- N_i[i]*Pk_i[i] / (2 * PP[j]))
|
||||
tmp[j] = v
|
||||
total += v
|
||||
|
||||
for j in range(PP.size):
|
||||
P[j] += tmp[j] / total
|
||||
'''
|
||||
|
||||
|
||||
#ARES/HADES/BORG image scanning class
|
||||
class IndexTracker:
|
||||
def __init__(self, ax, X):
|
||||
self.ax = ax
|
||||
self.ax.set_title('use up/down keys to navigate images')
|
||||
|
||||
self.X = X
|
||||
rows,cols,self.slices = X.shape
|
||||
self.ind = self.slices/2
|
||||
|
||||
cmax=X.max()
|
||||
cmin=X.min()
|
||||
|
||||
self.im = self.ax.imshow(self.X[:,self.ind,:],vmax=cmax,vmin=cmin)
|
||||
self.update()
|
||||
|
||||
def onscroll(self, event):
|
||||
#print ("%s " % (event.key))
|
||||
if event.key=='up':
|
||||
self.ind = np.clip(self.ind+1, 0, self.slices-1)
|
||||
else:
|
||||
self.ind = np.clip(self.ind-1, 0, self.slices-1)
|
||||
|
||||
|
||||
self.update()
|
||||
|
||||
def update(self):
|
||||
self.im.set_data(self.X[:,self.ind,:])
|
||||
self.ax.set_ylabel('slice %s'%self.ind)
|
||||
self.im.axes.figure.canvas.draw()
|
||||
|
||||
|
||||
#ARES/HADES/BORG analysis class
|
||||
class analysis:
|
||||
|
||||
def __init__(self, chain_path='.', LSS_framework='ARES'):
|
||||
self.chain_path = chain_path
|
||||
self.LSS_framework = LSS_framework
|
||||
|
||||
self.description = "This Class is part of the ARES/HADES/BORG analysis framework"
|
||||
self.author = "Copyright (C) 2009-2016 Jens Jasche \n Copyright (C) 2014-2016 Guilhem Lavaux"
|
||||
|
||||
#get chain setup
|
||||
|
||||
self.L0=0
|
||||
self.L1=0
|
||||
self.L2=0
|
||||
self.N0=0
|
||||
self.N1=0
|
||||
self.N2=0
|
||||
self.x0=0
|
||||
self.x1=0
|
||||
self.x2=0
|
||||
|
||||
with h5.File(os.path.join(self.chain_path, "restart.h5_0"), mode="r") as f:
|
||||
info=f.require_group('/info')
|
||||
markov=f.require_group('/markov')
|
||||
#print markov.keys()
|
||||
#print info.keys()
|
||||
print f['info']['scalars'].keys()
|
||||
#print f['markov']['scalars'].keys()
|
||||
|
||||
self.L0 = f['info']['scalars']['L0'][:]
|
||||
self.L1 = f['info']['scalars']['L1'][:]
|
||||
self.L2 = f['info']['scalars']['L2'][:]
|
||||
self.N0 = int(f['info']['scalars']['N0'][:])
|
||||
self.N1 = int(f['info']['scalars']['N1'][:])
|
||||
self.N2 = int(f['info']['scalars']['N2'][:])
|
||||
self.xmin0 = int(f['info']['scalars']['corner0'][:])
|
||||
self.xmin1 = int(f['info']['scalars']['corner1'][:])
|
||||
self.xmin2 = int(f['info']['scalars']['corner2'][:])
|
||||
self.ncat = int(f['info']['scalars']['NCAT'][:])
|
||||
|
||||
|
||||
if(LSS_framework!='BORG'):
|
||||
self.kmodes = f['/info/scalars/k_modes'][:]
|
||||
self.nmodes = len(self.kmodes)
|
||||
|
||||
#get brefs
|
||||
bref=[]
|
||||
for i in range(self.ncat):
|
||||
bref.append(f['info']['scalars']['galaxy_bias_ref_'+str(i)][:])
|
||||
self.bias_ref=np.array(bref)
|
||||
|
||||
|
||||
def check_biasref(self):
|
||||
return self.bias_ref
|
||||
|
||||
def get_ncat(self):
|
||||
return self.ncat
|
||||
|
||||
def get_mask_spliced(self,msknr,ncpu=0):
|
||||
if ncpu>0:
|
||||
mskkey = "info.scalars.galaxy_sel_window_" + str(msknr)
|
||||
a=rebuild_spliced_h5(os.path.join(self.chain_path, "restart.h5"),[mskkey],32)
|
||||
return np.array(a[mskkey][:,:,:,0])
|
||||
else:
|
||||
print 'Error: need number of processes to read files !'
|
||||
|
||||
def get_mask(self,msknr):
|
||||
|
||||
with h5.File(os.path.join(self.chain_path, "restart.h5_0"), mode="r") as f:
|
||||
mskkey = "galaxy_sel_window_" + str(msknr)
|
||||
mask = f['info']['scalars'][mskkey][:]
|
||||
return np.array(mask[:,:,:,0])
|
||||
|
||||
def get_data(self,datnr):
|
||||
|
||||
with h5.File(os.path.join(self.chain_path, "restart.h5_0"), mode="r") as f:
|
||||
datkey = "galaxy_data_" + str(datnr)
|
||||
data = f['info']['scalars'][datkey][:]
|
||||
return np.array(data)
|
||||
|
||||
def get_data_spliced(self,msknr,ncpu=0):
|
||||
if ncpu>0:
|
||||
mskkey = "info.scalars.galaxy_data_" + str(msknr)
|
||||
a=rebuild_spliced_h5(os.path.join(self.chain_path, "restart.h5"),[mskkey],32)
|
||||
return np.array(a[mskkey][:])
|
||||
else:
|
||||
print 'Error: need number of processes to read files !'
|
||||
|
||||
def scan_datacube(self,data):
|
||||
fig = figure()
|
||||
ax = fig.add_subplot(111)
|
||||
plt.jet()
|
||||
tracker = IndexTracker(ax, data)
|
||||
fig.canvas.mpl_connect('key_press_event', tracker.onscroll)
|
||||
show()
|
||||
|
||||
|
||||
def get_2d_marginal(self,attribute_a='s_field',attribute_b='s_field',id_a=None,id_b=None, first_sample=0,last_sample=1000):
|
||||
print '-'*60
|
||||
print 'Estimate 2d marginals for parameters ', attribute_a, ' and ', attribute_b , ' for ' , self.LSS_framework, ' run!'
|
||||
print '-'*60
|
||||
|
||||
if(id_a==None or id_b==None):
|
||||
print "Error: no index chosen"
|
||||
return -1
|
||||
|
||||
#2) collect chain
|
||||
samples_a = []
|
||||
samples_b = []
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, 1):
|
||||
d = a[attribute_a][:]
|
||||
e = a[attribute_b][:]
|
||||
|
||||
samples_a.append(d[id_a])
|
||||
samples_b.append(e[id_b])
|
||||
|
||||
H, xedges, yedges = np.histogram2d(samples_a, samples_b)
|
||||
|
||||
return xedges,yedges, H
|
||||
|
||||
def get_cross_corcoeff(self,attribute_a='s_field',attribute_b='s_field',id_a=None,id_b=None, first_sample=0,last_sample=1000):
|
||||
print '-'*60
|
||||
print 'Estimate 2d marginals for parameters ', attribute_a, ' and ', attribute_b , ' for ' , self.LSS_framework, ' run!'
|
||||
print '-'*60
|
||||
|
||||
if(id_a==None or id_b==None):
|
||||
print "Error: no index chosen"
|
||||
return -1
|
||||
|
||||
#2) collect chain
|
||||
samples_a = []
|
||||
samples_b = []
|
||||
|
||||
nelements_a = len(id_a[0])
|
||||
nelements_b = len(id_b[0])
|
||||
|
||||
mu_a = np.zeros(nelements_a)
|
||||
var_a = np.zeros(nelements_a)
|
||||
|
||||
mu_b = np.zeros(nelements_b)
|
||||
var_b = np.zeros(nelements_b)
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, 1):
|
||||
d = a[attribute_a][:]
|
||||
e = a[attribute_b][:]
|
||||
|
||||
aux_a = d[id_a]
|
||||
aux_b = e[id_b]
|
||||
|
||||
mu_a = (nn-1.)/float(nn)*mu_a +1./float(nn)*aux_a
|
||||
if(nn>1):
|
||||
aux = (mu_a-aux_a)**2
|
||||
var_a = (nn-1.)/nn*var_a+1./(nn-1)*aux
|
||||
|
||||
mu_b = (nn-1.)/float(nn)*mu_b +1./float(nn)*aux_b
|
||||
if(nn>1):
|
||||
aux = (mu_b-aux_b)**2
|
||||
var_b = (nn-1.)/nn*var_b+1./(nn-1)*aux
|
||||
|
||||
samples_a.append(aux_a)
|
||||
samples_b.append(aux_b)
|
||||
nn+=1
|
||||
|
||||
pc= np.zeros((nelements_a,nelements_b))
|
||||
cnt=0
|
||||
for n in range(nn-1):
|
||||
x=samples_a[n]
|
||||
y=samples_b[n]
|
||||
pc += np.multiply.outer(x-mu_a, y-mu_b)
|
||||
cnt+=1
|
||||
|
||||
return pc/float(cnt) #/np.sqrt(var_a*var_b)
|
||||
|
||||
|
||||
def get_trace(self,attribute='s_field',element_id=None, first_sample=0,last_sample=1000):
|
||||
print '-'*60
|
||||
print 'Record trace for parameters ', attribute , ' for ' , self.LSS_framework, ' run!'
|
||||
print '-'*60
|
||||
|
||||
'''
|
||||
if(element_id==None):
|
||||
print "Error: no list of indices provided"
|
||||
return -1
|
||||
'''
|
||||
|
||||
#1) collect chain
|
||||
samples = []
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, 1):
|
||||
d = a[attribute][:]
|
||||
if (element_id!=None):
|
||||
samples.append(d[element_id])
|
||||
else:
|
||||
samples.append(d)
|
||||
nn+=1
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
def get_corrlength(self,attribute='s_field',element_id=None,nlength=100, first_sample=0,last_sample=1000):
|
||||
print '-'*60
|
||||
print 'Estimate correlation length for parameters ', attribute , ' for ' , self.LSS_framework, ' run!'
|
||||
print '-'*60
|
||||
|
||||
if(element_id==None):
|
||||
print "Error: no list of indices provided"
|
||||
return -1
|
||||
|
||||
if(nlength>last_sample-first_sample):
|
||||
print "Warning: Chain setting not long enough set nlength to last_sample"
|
||||
nlength = last_sample-first_sample -1
|
||||
|
||||
nelements = len(element_id[0])
|
||||
|
||||
#1) calculate mean and variance
|
||||
mu = np.zeros(nelements)
|
||||
var = np.zeros(nelements)
|
||||
|
||||
#2) collect chain
|
||||
samples = []
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, 1):
|
||||
d = a[attribute][:]
|
||||
|
||||
print np.shape(d)
|
||||
|
||||
mu = (nn-1.)/float(nn)*mu +1./float(nn)*d[element_id]
|
||||
if(nn>1):
|
||||
aux = (mu-d[element_id])**2
|
||||
var = (nn-1.)/nn*var+1./(nn-1)*aux
|
||||
|
||||
samples.append(d[element_id])
|
||||
|
||||
nn+=1
|
||||
|
||||
|
||||
cl = np.zeros((nlength,nelements))
|
||||
cl_count= np.zeros(nlength)
|
||||
|
||||
for i in range(nlength):
|
||||
for j in range(len(samples)-i):
|
||||
cl[i]+= (samples[j]-mu)*(samples[j+i]-mu)/var
|
||||
cl_count[i] +=1.;
|
||||
|
||||
for i in range(nlength):
|
||||
cl[i]/=cl_count[i]
|
||||
|
||||
return np.array(range(nlength)), cl
|
||||
|
||||
def print_job(self,msg):
|
||||
print('-'*60)
|
||||
print(msg)
|
||||
print('-'*60)
|
||||
|
||||
|
||||
|
||||
def spectrum_pdf(self, first_sample=0, last_sample=-1, sample_steps=10, gridsize=1000, Pmin=None, Pmax=None):
|
||||
|
||||
P = np.zeros((gridsize, Npk), dtype=np.float64)
|
||||
|
||||
if Pmin is None or Pmax is None:
|
||||
P0m,P0M = np.inf,0
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
P0m = a['/scalars/powerspectrum'].min()
|
||||
P0M = a['/scalars/powerspectrum'].max()
|
||||
Pb_m,Pb_M = min(P0m, Pb_m),max(P0M,Pb_M)
|
||||
if Pmin is None:
|
||||
Pmin = Pb_m
|
||||
if Pmax is None:
|
||||
Pmax = Pb_M
|
||||
|
||||
PP = Pmin*np.exp(np.arange(gridsize)*np.log(Pmax/Pmin))
|
||||
N=0
|
||||
prior=0
|
||||
N_ib = 0.5*(self.Nk+prior)[None,:]
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
Pk_i = a['/scalars/powerspectrum'][:]
|
||||
N_i = self.Nk
|
||||
|
||||
_special_spectrum_builder(P, PP, tmp_PP, N_ib, N_i, Pk_i)
|
||||
|
||||
N += 1
|
||||
|
||||
P /= N
|
||||
|
||||
return P
|
||||
|
||||
def get_spherical_slice(self,vdata,nside=32, observer=np.array([0,0,0]),rslice = 150.):
|
||||
|
||||
def RenderSphere(VolumeData3D,image,rslice,observer,Larr,Narr):
|
||||
print "Rendering Sphere..."
|
||||
|
||||
NSIDE=hp.npix2nside(len(image))
|
||||
|
||||
idx=Larr[0]/Narr[0]
|
||||
idy=Larr[1]/Narr[1]
|
||||
idz=Larr[2]/Narr[2]
|
||||
|
||||
for ipix in range(len(image)):
|
||||
#get direction of pixel and calculate unit vectors
|
||||
dx,dy,dz=hp.pix2vec(NSIDE, ipix)
|
||||
d = math.sqrt(dx * dx + dy * dy + dz * dz)
|
||||
dx = dx / d; dy = dy / d; dz = dz / d # ray unit vector
|
||||
|
||||
rayX = observer[0]+rslice*dx; rayY = observer[1]+rslice*dy; rayZ = observer[2]+rslice*dz
|
||||
rayX /= idx; rayY /= idy; rayZ /= idz
|
||||
#find voxel inside box
|
||||
ix = int(round(rayX))
|
||||
iy = int(round(rayY))
|
||||
iz = int(round(rayZ))
|
||||
|
||||
image[ipix]=np.nan
|
||||
if ix > -1 and ix < Narr[0] \
|
||||
or iy > -1 and iy < Narr[1] \
|
||||
or iz > -1 and iz < Narr[2]:
|
||||
|
||||
jx = (ix+1) % Narr[0];
|
||||
jy = (iy+1) % Narr[1];
|
||||
jz = (iz+1) % Narr[2];
|
||||
rx = (rayX - ix);
|
||||
ry = (rayY - iy);
|
||||
rz = (rayZ - iz);
|
||||
|
||||
qx = 1.-rx;
|
||||
qy = 1.-ry;
|
||||
qz = 1.-rz;
|
||||
val = VolumeData3D[ix,iy,iz] * qx * qy * qz +VolumeData3D[ix,iy,jz] * qx * qy * rz +VolumeData3D[ix,jy,iz] * qx * ry * qz +VolumeData3D[ix,jy,jz] * qx * ry * rz +VolumeData3D[jx,iy,iz] * rx * qy * qz +VolumeData3D[jx,iy,jz] * rx * qy * rz +VolumeData3D[jx,jy,iz] * rx * ry * qz +VolumeData3D[jx,jy,jz] * rx * ry * rz;
|
||||
image[ipix]=val
|
||||
|
||||
print '\r'+str(100 * ipix / (len(image) - 1)).zfill(3) + "%"
|
||||
|
||||
|
||||
obs = np.array([observer[0]-self.xmin0,observer[1]-self.xmin1,observer[2]-self.xmin2])
|
||||
|
||||
Larr=np.array([self.L0,self.L1,self.L2])
|
||||
Narr=np.array([self.N0,self.N1,self.N2])
|
||||
image = np.zeros(hp.nside2npix(nside))
|
||||
RenderSphere(vdata,image,rslice,obs,Larr,Narr)
|
||||
return image
|
||||
|
||||
def mean_var_density(self, first_sample=0,last_sample=-1,sample_steps=10):
|
||||
self.print_job('Estimate mean and variance of density fields for %s run!' % self.LSS_framework)
|
||||
|
||||
|
||||
if(self.LSS_framework=='ARES'):
|
||||
mu_i = np.zeros((self.N0,self.N1,self.N2))
|
||||
|
||||
var_i = np.zeros((self.N0,self.N1,self.N2))
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
d = a['s_field'][:]
|
||||
mu_i = (nn-1.)/float(nn)*mu_i +1./float(nn)*d
|
||||
|
||||
if(nn>1):
|
||||
aux = (mu_i-d)**2
|
||||
var_i = (nn-1.)/nn*var_i+1./(nn-1)*aux
|
||||
|
||||
nn+=1
|
||||
|
||||
return mu_i, var_i
|
||||
|
||||
elif(self.LSS_framework=='HADES'):
|
||||
mu_i = np.zeros((self.N0,self.N1,self.N2))
|
||||
|
||||
var_i = np.zeros((self.N0,self.N1,self.N2))
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
d = a['s_field'][:]
|
||||
mu_i = (nn-1.)/float(nn)*mu_i +1./float(nn)*d
|
||||
|
||||
if(nn>1):
|
||||
aux = (mu_i-d)**2
|
||||
var_i = (nn-1.)/nn*var_i+1./(nn-1)*aux
|
||||
|
||||
nn+=1
|
||||
|
||||
return mu_i, var_i
|
||||
|
||||
else:
|
||||
mu_i = np.zeros((self.N0,self.N1,self.N2))
|
||||
mu_f = np.zeros((self.N0,self.N1,self.N2))
|
||||
|
||||
var_i = np.zeros((self.N0,self.N1,self.N2))
|
||||
var_f = np.zeros((self.N0,self.N1,self.N2))
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
d = a['s_field'][:]
|
||||
mu_i = (nn-1.)/float(nn)*mu_i +1./float(nn)*d
|
||||
|
||||
if(nn>1):
|
||||
aux = (mu_i-d)**2
|
||||
var_i = (nn-1.)/nn*var_i+1./(nn-1)*aux
|
||||
|
||||
d = a['BORG_final_density'][:]
|
||||
mu_f = (nn-1.)/float(nn)*mu_f +1./float(nn)*d
|
||||
|
||||
if(nn>1):
|
||||
aux = (mu_f-d)**2
|
||||
var_f = (nn-1.)/nn*var_f+1./(nn-1)*aux
|
||||
|
||||
nn+=1
|
||||
|
||||
|
||||
return mu_i, var_i, mu_f, var_f
|
||||
|
||||
def mean_var_spec(self, first_sample=0,last_sample=-1,sample_steps=10):
|
||||
self.print_job('Estimate mean and variance of density fields for %s run!' % self.LSS_framework)
|
||||
|
||||
if(self.LSS_framework=='ARES'):
|
||||
mu = np.zeros(self.nmodes)
|
||||
var = np.zeros(self.nmodes)
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
d = a['/scalars/powerspectrum'][:]
|
||||
mu = (nn-1.)/float(nn)*mu +1./float(nn)*d
|
||||
|
||||
if(nn>1):
|
||||
aux = (mu-d)**2
|
||||
var = (nn-1.)/nn*var+1./(nn-1)*aux
|
||||
|
||||
nn+=1
|
||||
|
||||
return self.kmodes,mu, var
|
||||
|
||||
elif(self.LSS_framework=='HADES'):
|
||||
mu = np.zeros(self.nmodes)
|
||||
var = np.zeros(self.nmodes)
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
d = a['/scalars/powerspectrum'][:]
|
||||
mu = (nn-1.)/float(nn)*mu +1./float(nn)*d
|
||||
|
||||
if(nn>1):
|
||||
aux = (mu-d)**2
|
||||
var = (nn-1.)/nn*var+1./(nn-1)*aux
|
||||
|
||||
nn+=1
|
||||
|
||||
return self.kmodes,mu, var
|
||||
|
||||
else:
|
||||
mu = np.zeros(self.nmodes)
|
||||
var = np.zeros(self.nmodes)
|
||||
|
||||
nn=1
|
||||
for i,a in explore_chain(self.chain_path, first_sample,last_sample, sample_steps):
|
||||
d = a['/scalars/powerspectrum'][:]
|
||||
mu = (nn-1.)/float(nn)*mu +1./float(nn)*d
|
||||
|
||||
if(nn>1):
|
||||
aux = (mu-d)**2
|
||||
var = (nn-1.)/nn*var+1./(nn-1)*aux
|
||||
|
||||
nn+=1
|
||||
|
||||
return self.kmodes,mu, var
|
||||
Loading…
Add table
Add a link
Reference in a new issue