def plot_A0(self):
sG=np.sqrt(np.var(self.gA0))
mG=np.mean(self.gA0)
amin, amax=plt.xlim()
alist=np.arange(3*amin, amax*3, amax/250.)
theoryGdist=norm.pdf(alist, loc=mG, scale=sG)
for i in range(len(self.fgnls)):
if (self.theoryplot==False):
lbl=self.TYPELABEL+"$=$"+NtoSTR(self.fgnls[i])
else:
lbl=None
neg=np.min(self.fgNLA0[i]-self.gA0)
const=9*6.*self.fgnls[i]*self.phisq
plot_hist(plt, (self.fgNLA0[i]-self.gA0+const)/(1-9.*3.*self.fgnls[i]*self.phisq), clr=self.clrs[i+1], alp=ALPHA, labl=lbl, ht='stepfilled')
amin, amax=plt.xlim()
alist=np.arange(3*amin, amax*3, 0.001)
if (self.theoryplot):
scl=6.0*self.A0const*self.Nconst*self.fgnls[i]
theorynGdist=np.sign(self.fgnls[i])*chi2.pdf(alist,1, scale=scl)
plt.plot(alist, theorynGdist, self.clrs[i+1], linestyle=self.ls[i+1], linewidth=LW, label=self.TYPELABEL+"="+NtoSTR(self.fgnls[i]))
plt.xlabel(r'$A_0$')
plt.ylabel(r'$p(A_0)$')
plt.yscale('log')
plt.ylim(0.01, 100)
plt.xlim(-0.02, 0.25)
plt.legend()
def plot_A(self, histtype="stepfilled"):
if histtype=="step":
ALPHAG=0.5
else:
ALPHAG=ALPHA
plot_hist(plt, self.gA, clr=self.clrs[0], alp=ALPHAG, ht=histtype)
sG=np.sqrt(np.var(self.gAi))
mG=np.mean(self.gAi)
amin, amax = plt.xlim()
alist=np.arange(0.0, np.max(self.fgNLA), 0.001)
theoryGdist=chi.pdf(alist, 3,scale=sG)
plt.plot(alist, theoryGdist,self.clrs[0], linestyle=self.ls[0], linewidth=LW, label=self.TYPELABEL+r"$=0$")
for i in range(len(self.fgnls)):
if (self.theoryplot==False):
lbl=self.TYPELABEL+"="+NtoSTR(self.fgnls[i])
else:
lbl=None
plot_hist(plt, self.fgNLA[i], clr=self.clrs[i+1], alp=ALPHA, labl=lbl)
if (self.theoryplot):
theorynGdist=chi.pdf(alist, 3, scale=np.sqrt((self.A1const*self.fgnls[i])**2.0+sG**2.0))
plt.plot(alist, theorynGdist, self.clrs[i+1], linestyle=self.ls[i+1], linewidth=LW, label=self.TYPELABEL+"="+NtoSTR(self.fgnls[i]))
# theory plots
#plt.xlim(0.0, 0.2)
#plt.xticks([0.02, 0.04, 0.06, 0.08, 0.10])
plt.xlabel(r'$A$')
plt.ylabel(r'$p(A)$')
plt.legend(fontsize=20)
def plot_Ai(self, select=0.2):
"""
the select option is used to choose only those data that have power modulation less than
this amount i.e. if
abs(A0)<select
this is necessary to only consider skies with the power spectra as what we see.
"""
sG=np.sqrt(np.var(self.gAi.flatten()))
print sG
mG=np.mean(self.gAi.flatten())
amin, amax = np.min(self.gAi), np.max(self.gAi)
alist=np.arange(2*amin, amax*2, amax/250.)
theoryGdist=norm.pdf(alist, loc=mG, scale=sG)
for i in range(len(self.fgnls)):
if (self.theoryplot==False):
lbl=self.TYPELABEL+"="+NtoSTR(self.fgnls[i])
else:
lbl=None
Aiselected = self.fgNLAi[i][np.abs(self.fgNLA0[i/3])<select]
gAiselected = self.gAi[np.abs(self.fgNLA0[i/3])<select]
print len(Aiselected)
plot_hist(plt, Aiselected - gAiselected, clr=self.clrs[i+1], alp=ALPHA, labl=lbl)
if (self.theoryplot):
sigmax=np.sqrt(6.*self.fgnls[i]*self.A0const*self.Nconst)
sigmay=np.sqrt(72.*self.fgnls[i]*2.2188E-10)
theorynGdist=kn(0, np.abs(alist)/sigmax/sigmay)/np.pi/sigmax/sigmay
plt.plot(alist, theorynGdist, self.clrs[i+1], linestyle=self.ls[i+1], linewidth=LW, label=self.TYPELABEL+"="+NtoSTR(self.fgnls[i]))
plt.xlim(-0.15, 0.15)
plt.xlabel(r'$A_i$')
plt.ylabel(r'$p(A_i)$')
plt.legend(fontsize=20)
