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