def get_COV(fileCov, binFile):
boxZN = float(os.path.basename(fileCov).split('_')[1])
print boxZN
hf, boxLength, boxName, boxRedshift, logmp, boxLengthComoving, massCorrection = lib.get_basic_info(fileCov, boxZN, delta_wrt='mean')
bins = n.log10( 10**n.loadtxt(binFile) * massCorrection )
logmass = ( bins[1:] + bins[:-1] )/2.
mass = 10**logmass
dX = ( 10**bins[1:] - 10**bins[:-1] )
dlnbin = (bins[1:] - bins[:-1])*n.log(10)
m2sigma = interp1d(hf.M, hf.sigma )
sigma_i = m2sigma( mass )
data_i=cPickle.load(open(fileCov,'r'))
counts_i = n.sum(data_i, axis=0)
ok = (counts_i>10)
data = data_i.T[ok].T
counts = n.sum(data, axis=0)
sigma = sigma_i[ok]
count_matrix = n.outer(counts, counts)/1000.
cv = (n.cov(data.T, ddof=0)/count_matrix)**0.5
ctotal = cv + count_matrix**(-0.5)
xcv, ycv = n.meshgrid(sigma, sigma)
s1_i = n.hstack((n.log10(xcv)))
s2_i = n.hstack((n.log10(ycv)))
val_i = n.hstack((n.log10(ctotal)))
ok = (val_i != n.inf) & (n.isnan(val_i) == False )
return s1_i[ok], s2_i[ok], val_i[ok]
def get_COV(fileCov, binFile):
boxZN = float(os.path.basename(fileCov).split('_')[1])
print boxZN
hf, boxLength, boxName, boxRedshift, logmp, boxLengthComoving, massCorrection = lib.get_basic_info(
fileCov, boxZN, delta_wrt='mean')
bins = n.log10(10**n.loadtxt(binFile) * massCorrection)
logmass = (bins[1:] + bins[:-1]) / 2.
mass = 10**logmass
dX = (10**bins[1:] - 10**bins[:-1])
dlnbin = (bins[1:] - bins[:-1]) * n.log(10)
m2sigma = interp1d(hf.M, hf.sigma)
sigma_i = m2sigma(mass)
data_i = cPickle.load(open(fileCov, 'r'))
counts_i = n.sum(data_i, axis=0)
ok = (counts_i > 10)
data = data_i.T[ok].T
counts = n.sum(data, axis=0)
sigma = sigma_i[ok]
count_matrix = n.outer(counts, counts) / 1000.
cv = (n.cov(data.T, ddof=0) / count_matrix)**0.5
ctotal = cv + count_matrix**(-0.5)
xcv, ycv = n.meshgrid(sigma, sigma)
s1_i = n.hstack((n.log10(xcv)))
s2_i = n.hstack((n.log10(ycv)))
val_i = n.hstack((n.log10(ctotal)))
ok = (val_i != n.inf) & (n.isnan(val_i) == False)
return s1_i[ok], s2_i[ok], val_i[ok]
def plot_CRCoef_mvir(fileC, fileS, binFile):
boxZN = float(os.path.basename(fileC).split('_')[1])
print boxZN
hf, boxLength, boxName, boxRedshift, logmp, boxLengthComoving, massCorrection = lib.get_basic_info(fileC, boxZN, delta_wrt='mean')
bins = n.log10( 10**n.loadtxt(binFile) * massCorrection )
logmass = ( bins[1:] + bins[:-1] )/2.
mSel = (logmass > 9) & (logmass < 15)
mass = 10**logmass
dX = ( 10**bins[1:] - 10**bins[:-1] )
dlnbin = (bins[1:] - bins[:-1])*n.log(10)
m2sigma = interp1d(hf.M, hf.sigma )
sigma = m2sigma( mass )
data=cPickle.load(open(fileC,'r'))
dataS=cPickle.load(open(fileS,'r'))
counts = n.sum(data, axis=0)
count_matrix = n.outer(counts, counts)/1000.
cv = (n.cov(data.T, ddof=0)/count_matrix)**0.5
cr = n.corrcoef(data.T)
cvS = (n.cov(dataS.T, ddof=0)/count_matrix)**0.5
crS = n.corrcoef(dataS.T)
ctotal = cv + count_matrix**(-0.5)
mass2X = interp1d(logmass, n.arange(len(logmass)))
sigma2X = interp1d(sigma, n.arange(len(logmass)))
fig = p.figure(0,(6,6))
mat = p.matshow(cr)
p.xticks(n.arange(0,len(logmass),5), logmass[n.arange(0,len(logmass),5)],rotation=45)
p.yticks(n.arange(0,len(logmass),5), logmass[n.arange(0,len(logmass),5)])
p.axvline(mass2X(logmp+2), lw=2, color='k')
p.axhline(mass2X(logmp+2), lw=2, color='k')
#p.axvline(mass2X(logmp+1), lw=2, color='k')
#p.axhline(mass2X(logmp+1), lw=2, color='k')
cb = p.colorbar(shrink=0.8)
cb.set_label(r"R")
p.xlabel(r'log$_{10}[M_{vir}/(h^{-1}M_\odot)]$')
p.ylabel(r'log$_{10}[M_{vir}/(h^{-1}M_\odot)]$')
p.grid()
p.savefig(join(os.environ['MVIR_DIR'],plotDir,"mvir-cr-"+boxName+".png"))
p.clf()
fig = p.figure(0,(6,6))
mat = p.matshow(cr)
p.xticks(n.arange(0,len(sigma),5), n.round(sigma[n.arange(0,len(sigma),5)],3),rotation=45)
p.yticks(n.arange(0,len(sigma),5), n.round(sigma[n.arange(0,len(sigma),5)],3))
cb = p.colorbar(shrink=0.8)
cb.set_label(r"R($\sigma_1, \sigma_2$)")
p.xlabel(r'$\sigma$')
p.ylabel(r'$\sigma$')
p.grid()
p.savefig(join(os.environ['MVIR_DIR'],plotDir,"sigma-cr-"+boxName+".png"))
p.clf()
id = int(mass2X(logmp+2.5))
print id, len(logmass)
return cr, cv, logmass, sigma, id, ctotal
def plot_CRCoef_mvir(fileC, fileS, binFile):
boxZN = float(os.path.basename(fileC).split('_')[1])
print boxZN
hf, boxLength, boxName, boxRedshift, logmp, boxLengthComoving, massCorrection = lib.get_basic_info(fileC, boxZN, delta_wrt='mean')
bins = n.log10( 10**n.loadtxt(binFile) * massCorrection )
logmass = ( bins[1:] + bins[:-1] )/2.
mSel = (logmass > 9) & (logmass < 15)
mass = 10**logmass
dX = ( 10**bins[1:] - 10**bins[:-1] )
dlnbin = (bins[1:] - bins[:-1])*n.log(10)
m2sigma = interp1d(hf.M, hf.sigma )
sigma = m2sigma( mass )
data=cPickle.load(open(fileC,'r'))
dataS=cPickle.load(open(fileS,'r'))
counts = n.sum(data, axis=0)
count_matrix = n.outer(counts, counts)/1000.
cv = (n.cov(data.T, ddof=0)/count_matrix)**0.5
cr = n.corrcoef(data.T)
cvS = (n.cov(dataS.T, ddof=0)/count_matrix)**0.5
crS = n.corrcoef(dataS.T)
ctotal = cv + count_matrix**(-0.5)
mass2X = interp1d(logmass, n.arange(len(logmass)))
sigma2X = interp1d(sigma, n.arange(len(logmass)))
fig = p.figure(0,(6,6))
mat = p.matshow(cr)
p.xticks(n.arange(0,len(logmass),5), logmass[n.arange(0,len(logmass),5)],rotation=45)
p.yticks(n.arange(0,len(logmass),5), logmass[n.arange(0,len(logmass),5)])
p.axvline(mass2X(logmp+2), lw=2, color='k')
p.axhline(mass2X(logmp+2), lw=2, color='k')
#p.axvline(mass2X(logmp+1), lw=2, color='k')
#p.axhline(mass2X(logmp+1), lw=2, color='k')
cb = p.colorbar(shrink=0.8)
cb.set_label(r"R")
p.xlabel(r'log$_{10}[M_{vir}/(h^{-1}M_\odot)]$')
p.ylabel(r'log$_{10}[M_{vir}/(h^{-1}M_\odot)]$')
p.grid()
p.savefig(join(os.environ['MULTIDARK_LIGHTCONE_DIR'], 'mvir',"covariance","mvir-cr-"+boxName+".png"))
p.clf()
fig = p.figure(0,(6,6))
mat = p.matshow(cr)
p.xticks(n.arange(0,len(sigma),5), n.round(sigma[n.arange(0,len(sigma),5)],3),rotation=45)
p.yticks(n.arange(0,len(sigma),5), n.round(sigma[n.arange(0,len(sigma),5)],3))
cb = p.colorbar(shrink=0.8)
cb.set_label(r"R($\sigma_1, \sigma_2$)")
p.xlabel(r'$\sigma$')
p.ylabel(r'$\sigma$')
p.grid()
p.savefig(join(os.environ['MULTIDARK_LIGHTCONE_DIR'], 'mvir',"covariance","sigma-cr-"+boxName+".png"))
p.clf()
id = int(mass2X(logmp+2.5))
print id, len(logmass)
return cr, cv, logmass, sigma, id, ctotal
def plot_COV(fileCov, binFile):
boxZN = float(os.path.basename(fileCov).split('_')[1])
print boxZN
hf, boxLength, boxName, boxRedshift, logmp, boxLengthComoving, massCorrection = lib.get_basic_info(fileCov, boxZN, delta_wrt='mean')
bins = n.log10( 10**n.loadtxt(binFile) * massCorrection )
logmass = ( bins[1:] + bins[:-1] )/2.
mass = 10**logmass
dX = ( 10**bins[1:] - 10**bins[:-1] )
dlnbin = (bins[1:] - bins[:-1])*n.log(10)
m2sigma = interp1d(hf.M, hf.sigma )
sigma_i = m2sigma( mass )
data_i=cPickle.load(open(fileCov,'r'))
counts_i = n.sum(data_i, axis=0)
ok = (counts_i>10)
data = data_i.T[ok].T
counts = n.sum(data, axis=0)
sigma = sigma_i[ok]
count_matrix = n.outer(counts, counts)/1000.
cv = (n.cov(data.T, ddof=0)/count_matrix)**0.5
ctotal = cv + count_matrix**(-0.5)
xcv, ycv = n.meshgrid(sigma, sigma)
model = {"MD_0.4Gpc": dn_cov_L04, "MD_1Gpc": dn_cov_L10, "MD_2.5Gpc": dn_cov_L25, "MD_4Gpc": dn_cov_L40,"MD_2.5GpcNW": dn_cov_L25, "MD_4GpcNW": dn_cov_L40 }
fig, (ax1, ax2) = p.subplots(1,2, sharex=True, sharey=True)
ax1.set_title(boxName)
sca = ax1.scatter(-n.log10(xcv), -n.log10(ycv), c=n.log10(ctotal), s=10, edgecolors='none',vmin=-4,vmax=0)
#cb = sca.colorbar(shrink=0.8)
#cb.set_label("C($\sigma_1, \sigma_2$)")
ax1.set_xlabel(r'$log_{10}(\sigma_1^{-1})$')
ax1.set_ylabel(r'$log_{10}(\sigma_2^{-1})$')
ax1.set_xlim((-0.7, 0.6))
ax1.set_ylim((-0.7, 0.6))
ax1.grid()
#ax2=fig.add_subplot(122, sharex=ax1, sharey=ax1)
ax2.set_title("model")
ax2.scatter(-n.log10(xcv), -n.log10(ycv), c=n.log10(model[boxName](xcv, ycv)), s=10, edgecolors='none',vmin=-4,vmax=0)
ax2.set_xlim((-0.7, 0.6))
ax2.grid()
ax2.set_ylim((-0.7, 0.6))
ax2.set_xlabel(r'$log_{10}(\sigma_1^{-1})$')
fig.savefig(join(os.environ['MULTIDARK_LIGHTCONE_DIR'], 'mvir',"covariance","covariance_matrix_"+boxName+".png"))
fig.clf()
