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)
import numpy
import lcurv
import metrop
from scipy.optimize import leastsq
lcurv.readmap()
t1 = (1, 100, 200)
t2 = (300, 400, 500)
norm = (0.5, 1, 2.5)
rp = (10, 20, 30)
rn = (0, 0.7, 1)
a = (-1, .5, 1)
b = (-1, .5, 1)
p = []
lo = []
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)
import numpy
import lcurv
import metrop
from scipy.optimize import leastsq
lcurv.readmap()
t1 = (1,100,200)
t2 = (300,400,500)
norm = (0.5,1,2.5)
rp = (10,20,30)
rn = (0,0.7,1)
a = (-1,.5,1)
b = (-1,.5,1)
p = []
lo = []
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)
