def plot_wedge(self,ax,mubin,plot_label,colour,r_power=2,nr=40,rmax_plot=200.,abs_mu=False):
mumin = mubin[0]
mumax = mubin[1]
#Create the wedge, and wedgise the correlation.
b = wedgize.wedge(mumin=mumin,mumax=mumax,rtmax=self.rtmax,nrt=self.nt,
rpmin=self.rpmin,rpmax=self.rpmax,nrp=self.np,nr=nr,rmax=rmax_plot,
absoluteMu=abs_mu)
r, xi_wed, cov_wed = b.wedge(self.xi,self.cov)
print(mubin)
print(r[:4])
print(xi_wed[:4])
print(' ')
#Get the errors.
Nr = len(r)
err_wed = np.zeros(Nr)
for i in range(Nr):
err_wed[i] = np.sqrt(cov_wed[i][i])
#Define the variables to plot, and plot them.
cut = err_wed>0
r = r[cut]
xi_wed = xi_wed[cut]
err_wed = err_wed[cut]
ar = ax.errorbar(r,(r**r_power) * xi_wed,yerr=(r**r_power) * err_wed,fmt='o',label=plot_label,color=colour)
return ar, plot_label
def plot_wedge_fit(self,ax,mubin,plot_label,colour,r_power=2,nr=40,rmax_plot=200.,rmin_fit=None,rmax_fit=None,abs_mu=False):
#Using data in picca fit
mumin = mubin[0]
mumax = mubin[1]
b = wedgize.wedge(mumin=mumin,mumax=mumax,rtmax=self.rtmax,nrt=self.nt,
rpmin=self.rpmin,rpmax=self.rpmax,nrp=self.np,nr=nr,rmax=rmax_plot,
absoluteMu=abs_mu)
"""
#REMOVE NON_DIAGONALS FROM COV MATRIX
for i in range(self.cov_grid.shape[0]):
for j in range(self.cov_grid.shape[1]):
if i!=j:
self.cov_grid[i,j]=0
"""
r, fit_xi_wed, _ = b.wedge(self.fit['xi_grid'],self.cov)
if rmin_fit == None:
rmin_fit = 0.
if rmax_fit == None:
rmax_fit = rmax_plot
#Plot the fit up to rmax_plot in a dashed line.
ax.plot(r,(r**r_power) * fit_xi_wed,c=colour,linestyle='--')
#Plot the fit in the fitted region in a solid line.
fit_bins = (r>rmin_fit) * (r<rmax_fit)
ax.plot(r[fit_bins],(r[fit_bins]**r_power) * fit_xi_wed[fit_bins],c=colour,label=plot_label)
return
def plot_xir2(self,mubin,plot_label):
mumin = mubin[0]
mumax = mubin[1]
b = wedgize.wedge(mumin=mumin,mumax=mumax,rtmax=self.rtmax,nrt=self.nt,
rpmin=self.rpmin,rpmax=self.rpmax,nrp=self.np,nr=self.nr,
rmax=self.rmax)
#REMOVE NON_DIAGONALS FROM COV MATRIX
for i in range(self.cov_grid.shape[0]):
for j in range(self.cov_grid.shape[1]):
if i!=j:
self.cov_grid[i,j]=0
r, xi_wed, cov_wed = b.wedge(self.xi_grid,self.cov_grid)
Nr = len(r)
err_wed = np.zeros(Nr)
for i in range(Nr):
err_wed[i] = np.sqrt(cov_wed[i][i])
cut = err_wed>0
plt.errorbar(r[cut],xi_wed[cut],yerr=err_wed[cut],fmt='o',label=plot_label)
return
def plot_wedge_manual_model(self,ax,b1,b2,beta1,beta2,mubin,plot_label,colour,r_power=2,nr=40,rmax_plot=200.,abs_mu=False):
#Using data in picca fit
mumin = mubin[0]
mumax = mubin[1]
b = wedgize.wedge(mumin=mumin,mumax=mumax,rtmax=self.rtmax,nrt=self.nt,
rpmin=self.rpmin,rpmax=self.rpmax,nrp=self.np,nr=nr,rmax=rmax_plot,
absoluteMu=abs_mu)
#Using our model
model = 'Slosar11'
r,xi = correlation_model.get_model_xi(model,self.quantity_1,self.quantity_2,b1,b2,beta1,beta2,self.zeff,mubin)
indices = r<rmax_plot
r = r[indices]
xi = xi[indices]
ax.plot(r,(r**r_power) * xi,c=colour,label=plot_label,linestyle=':')
return
def get_plot_data(mumin,mumax,file_path):
#Read data from the filename to know how to wedgize etc
#filename format: ${CORRELTYPE}_${NPIXELS}_${BRANCH}_${OPTION}_${RSDOPTION}_rpmin${RPMIN}_rpmax${RPMAX}_rtmax${RTMAX}_np${NP}_nt${NT}_zmin${ZQSOMIN}_zmax${ZQSOMAX}.fits.gz
parameters = get_parameters_from_filename(file_path)
#file_parameters = {**file_parameters,**{filename:parameters}}
h = fits.open(file_path)
data = h[1].data
b = wedgize.wedge(mumin=mumin,mumax=mumax,rtmax=parameters['rtmax'],
nrt=int(parameters['nt']),rpmin=parameters['rpmin'],
rpmax=parameters['rpmax'],nrp=int(parameters['np']),
nr=int(parameters['nr']),rmax=parameters['rmax'])
rp = data['RP'][:]
rt = data['RT'][:]
z = data['Z'][:]
xi_grid = data['DA'][:]
cov_grid = data['CO'][:]
#REMOVE NON_DIAGONALS FROM COV MATRIX
for i in range(cov_grid.shape[0]):
for j in range(cov_grid.shape[1]):
if i!=j:
cov_grid[i,j]=0
r, xi_wed, cov_wed = b.wedge(xi_grid,cov_grid)
Nr = len(r)
err_wed = np.zeros(Nr)
for i in range(Nr):
err_wed[i] = np.sqrt(cov_wed[i][i])
cut = err_wed>0
return r, xi_wed, err_wed, cut, parameters
fout = open(args.out, 'w')
for i in range(len(data.RP)):
print(data.RP[i], data.RT[i], xid[i], file=fout)
fout.close()
if args.plot:
P.figure()
P.pcolormesh( sp.reshape(data.RP, (50, 50)), \
sp.reshape(data.RT, (50, 50)), \
sp.reshape(xid*(data.RP**2+data.RT**2), (50, 50)), \
vmin=-1, vmax=1. )
P.colorbar()
for mus in [[0., 0.5], [0.5, 0.8], [0.8, 0.95], [0.95, 1.]]:
w = wedge(rpmin=0.0, rpmax=200.0, nrp=50, \
rtmin=0.0, rtmax=200.0, nrt=50, \
rmin=0.0, rmax=200.0, nr=50, \
mumin=mus[0], mumax=mus[1], ss=10)
r, wedm, wedcov = w.wedge(xid, data.CO)
r, wed, wedcov = w.wedge(data.DA, data.CO)
dwed = N.sqrt(N.diag(wedcov))
P.figure()
#-- we multiply the wedges by r**2 so we can see the BAO peak
P.errorbar(r, wed * r**2, dwed * r**2, fmt='o')
P.plot(r, wedm * r**2)
P.ylim(-1.2, 0.5)
P.title(r'$%.1f < \mu < %.1f$' % (mus[0], mus[1]))
P.xlabel('r [Mpc/h]')
P.show()
rp = data['RP'][:]
rt = data['RT'][:]
z = data['Z'][:]
xi_grid = data['DA'][:]
cov_grid = data['CO'][:]
#b = wedgize.wedge(mumin=0.0, mumax=1.0,rtmax=20.0,nrt=5,rpmax=20.0,nrp=5,nr=20,rmax=40)
#b = wedgize.wedge(mumin=0.0, mumax=1.0,rtmax=40.0,nrt=10,rpmax=40.0,nrp=10,nr=40,rmax=80)
#b = wedgize.wedge(mumin=0.0, mumax=1.0,rtmax=80.0,nrt=20,rpmax=80.0,nrp=20,nr=80,rmax=160)
#b = wedgize.wedge(mumin=0.0, mumax=1.0,rtmax=100.0,nrt=25,rpmax=100.0,nrp=25,nr=100,rmax=200)
#b = wedgize.wedge(mumin=0.0, mumax=1.0,rtmax=140.0,nrt=35,rpmax=140.0,nrp=35,nr=150,rmax=300)
b = wedgize.wedge(mumin=wedgize_args[0],
mumax=wedgize_args[1],
rtmax=wedgize_args[2],
nrt=wedgize_args[3],
rpmax=wedgize_args[4],
nrp=wedgize_args[5],
nr=wedgize_args[6],
rmax=wedgize_args[7])
r, xi_wed, cov_wed = b.wedge(xi_grid, cov_grid)
Nr = len(r)
err_wed = np.zeros(Nr)
for i in range(Nr):
err_wed[i] = np.sqrt(cov_wed[i][i])
print(i, err_wed[i], cov_wed[i][i])
cut = err_wed > 0
plt.errorbar(r[cut],
xi_wed[cut] * (r[cut]**2),
def plot_wedge(self,
x_power=0,
mumin=-1.,
mumax=1.,
other=None,
fontsize=18):
list_corr = [self]
if not other is None:
list_corr += other
for c in list_corr:
rpmin = c._rp_min
rpmax = c._rp_max
nrp = c._np
rtmin = c._rt_min
rtmax = c._rt_max
nrt = c._nt
rmin = 0.
rmax = c._rt_max
nr = c._nt
ss = 10
wed = wedgize.wedge(rpmin=rpmin,
rpmax=rpmax,
nrp=nrp,
rtmin=rtmin,
rtmax=rtmax,
nrt=nrt,
rmin=rmin,
rmax=rmax,
nr=nr,
mumin=mumin,
mumax=mumax,
ss=ss)
r, d, ivar = wed.wedge(c._da, c._co)
e = sp.sqrt(sp.diag(ivar))
coef = sp.power(r, x_power)
if not c._isfit:
plt.errorbar(r,
coef * d,
yerr=coef * e,
linewidth=1,
label=r'$' + c._title + '$',
fmt='.',
color='c')
else:
plt.errorbar(r,
coef * d,
label=r'$' + c._title + '$',
color='red')
plt.title(r"$" + str(mumin) + " < \mu < " + str(mumax) + "$",
fontsize=fontsize)
plt.xlabel(r'$r \, [h^{-1} \, \mathrm{Mpc}]$', fontsize=fontsize)
if (x_power == 0):
plt.ylabel(r'$\xi^{qf}(r)$', fontsize=fontsize)
if (x_power == 1):
plt.ylabel(r'$r \cdot \xi^{qf}(r) \, [h^{-1} \, \mathrm{Mpc}]$',
fontsize=fontsize)
if (x_power == 2):
plt.ylabel(
r'$r^{2} \cdot \xi^{qf}(r) \, [(h^{-1} \, \mathrm{Mpc})^{2}]$',
fontsize=fontsize)
plt.legend(fontsize=8, numpoints=1, ncol=2)
#plt.grid()
#plt.tight_layout()
#plt.show()
return