def __init__(self, NP, means, mins, maxs, sds, outfile, errlev=0.1, goodchi2=350.0):
"""
Instantiates the class by synthetically generating data.
"""
MCMC.__init__(self, TargetAcceptedPoints=1000, NumberOfParams=NP, Mins=mins, Maxs=maxs, SDs=sds, \
write2file=True, outputfilename=outfile, alpha=0.1, debug=False,\
EstimateCovariance=True, CovNum=100, goodchi2=goodchi2)
lcurv.readmap()
lcurv.mockdata(means,errlev)
def mockdata():
global pars
lcurv.readmap()
Rp = 39.4
Rn, a, b = 0.4, 0, 0.3
pars = [51, 290, 1.0, Rp, Rn, a, b]
pars = np.array(pars)
print(pars)
errlev = 0.04
lcurv.mockdata(pars, errlev)
def mockdata():
global pars
lcurv.readmap()
Rp = 39.4 # scaled by 30
Rn,a,b = 0.4, 0, 0.3
print('r_half = ',Rhalf(1,Rn,a,b)*Rp/30)
pars = [ 51, 290, 1.0, Rp, Rn, a, b ]
pars = np.array(pars)
print(pars)
errlev = 0.04
lcurv.mockdata(pars,errlev)
def mockdata():
global pars
lcurv.readmap()
Rp = 39.4 # scaled by 30
Rn, a, b = 0.4, 0, 0.3
print('r_half = ', Rhalf(1, Rn, a, b) * Rp / 30)
pars = [51, 290, 1.0, Rp, Rn, a, b]
pars = np.array(pars)
print(pars)
errlev = 0.04
lcurv.mockdata(pars, errlev)
def __init__(self,
NP,
means,
mins,
maxs,
sds,
outfile,
errlev=0.1,
goodchi2=350.0):
"""
Instantiates the class by synthetically generating data.
"""
MCMC.__init__(self, TargetAcceptedPoints=1000, NumberOfParams=NP, Mins=mins, Maxs=maxs, SDs=sds, \
write2file=True, outputfilename=outfile, alpha=0.1, debug=False,\
EstimateCovariance=True, CovNum=100, goodchi2=goodchi2)
lcurv.readmap()
lcurv.mockdata(means, errlev)
hi = []
for f in (t1, t2, norm, rp, rn, a, b):
p.append(f[1])
lo.append(f[0])
hi.append(f[2])
p = numpy.array(p)
lo = numpy.array(lo)
hi = numpy.array(hi)
errlev = 0.05
print(lo)
print(p)
print(hi)
lcurv.mockdata(p, errlev)
def resid(p):
nobs, x = lcurv.residuals(p)
x = x[:nobs]
return x
lo[0], hi[0] = 750, 850
lo[1], hi[1] = 450, 550
p[0] = 780
p[1] = 480
q = leastsq(resid, p)[0]
"""
Rn, a, b = 0.4, 0, 0.3
pars = [51, 290]
if fname[1] == 'c':
pars += [1.0, Rp, Rn, a, b]
elif fname[1] == 'g':
pars += [1.0, sig, -2, 0, 0]
else:
print('Filename error')
sys.exit()
pars = np.array(pars)
print(pars)
lcurv.readmap()
errlev = 0.04
lcurv.mockdata(pars, errlev)
chain = np.genfromtxt(fname + '.chain', skip_header=0, skip_footer=1)
print(chain.shape)
minchis = 1e30
for i in range(chain.shape[0]):
x = chain[i, :]
if x[1] < minchis:
q = x[3:]
minchis = x[1]
print(q)
print(minchis, lcurv.chis(q),
1 - st.chi2.cdf(minchis, pars[1] - pars[0] + 1 - 7))
lcurv.writecurves(q)
hi = []
for f in (t1,t2,norm,rp,rn,a,b):
p.append(f[1])
lo.append(f[0])
hi.append(f[2])
p = numpy.array(p)
lo = numpy.array(lo)
hi = numpy.array(hi)
errlev = 0.05
print(lo)
print(p)
print(hi)
lcurv.mockdata(p,errlev)
def resid(p):
nobs,x = lcurv.residuals(p)
x = x[:nobs]
return x
lo[0],hi[0] = 750,850
lo[1],hi[1] = 450,550
p[0] = 780
p[1] = 480
q = leastsq(resid,p)[0]
"""
rawlnp,ans = metrop.samp(lnprob, lo, hi, 200)