def my_custom_prior(u):
# prior distributions transform from 0:1 to the parameter range
# here: a gaussian prior distribution, cut below 1/above 3
x = scipy.stats.norm(1.9, 0.15).ppf(u)
if x < 1.:
x = 1
if x > 3:
x = 3
return x
# define prior
transformations = [
# jeffreys prior for nH (but see below)
bxa.create_jeffreys_prior_for(m, m.wabs.nH),
# jeffreys prior for scale variable
bxa.create_jeffreys_prior_for(m, m.powerlaw.norm),
# custom gaussian prior function for photon index
bxa.create_custom_prior_for(m, m.powerlaw.PhoIndex, my_custom_prior),
# and possibly many more
# jeffreys prior for scale variable
bxa.create_jeffreys_prior_for(m, m.gaussian.norm),
# uniform prior for location variable
bxa.create_uniform_prior_for(m, m.gaussian.LineE),
# jeffreys prior for scale variable
bxa.create_jeffreys_prior_for(m, m.gaussian.Sigma),
]
# we want nH to come out in logarithmic values, without offset of 22
# so we shift the existing jeffreys prior transformation:
def my_custom_prior(u):
# prior distributions transform from 0:1 to the parameter range
# here: a gaussian prior distribution, cut below 1/above 3
x = scipy.stats.norm(1.9, 0.15).ppf(u)
if x < 1.:
x = 1
if x > 3:
x = 3
return x
# define prior
transformations = [
# jeffreys prior for nH (but see below)
bxa.create_jeffreys_prior_for(m, m.wabs.nH),
# jeffreys prior for scale variable
bxa.create_jeffreys_prior_for(m, m.powerlaw.norm),
# custom gaussian prior function for photon index
bxa.create_custom_prior_for(m, m.powerlaw.PhoIndex, my_custom_prior),
# and possibly many more
]
# we want nH to come out in logarithmic values, without offset of 22
# so we shift the existing jeffreys prior transformation:
# first get the old transformation
prevtransform = transformations[0]['transform']
# shift it for storage
transformations[0]['transform'] = lambda x: prevtransform(x) + 22
# before putting it into xspec, we have to shift back and exponentiate
transformations[0]['aftertransform'] = lambda x: 10**(x - 22)
m.powerlaw.norm.values = ",,1e-10,1e-10,1e1,1e1" def my_custom_prior(u): # prior distributions transform from 0:1 to the parameter range # here: a gaussian prior distribution, cut below 1/above 3 x = scipy.stats.norm(1.9, 0.15).ppf(u) if x < 1.: x = 1 if x > 3: x = 3 return x # define prior transformations = [ # jeffreys prior for nH (but see below) bxa.create_jeffreys_prior_for(m, m.wabs.nH), # jeffreys prior for scale variable bxa.create_jeffreys_prior_for(m, m.powerlaw.norm), # custom gaussian prior function for photon index bxa.create_custom_prior_for( m, m.powerlaw.PhoIndex, my_custom_prior), # and possibly many more ] # we want nH to come out in logarithmic values, without offset of 22 # so we shift the existing jeffreys prior transformation: # first get the old transformation prevtransform = transformations[0]['transform'] # shift it for storage transformations[0]['transform'] = lambda x: prevtransform(x) + 22 # before putting it into xspec, we have to shift back and exponentiate transformations[0]['aftertransform'] = lambda x: 10**(x - 22)
from xspec import *
import bxa.xspec as bxa
Plot.xAxis='keV'
Plot.yLog=True
alldatastr='n4.pha'
AllData(alldatastr)
AllData.ignore('1:**-8.0,800.0-** 2:**-200.0,40000.0-**')
Fit.statMethod='cstat'
m = Model("grbm")
m.grbm.alpha.values = ',,-10,-10,5,10'
m.grbm.beta.values = ',,-10,-10,10,20'
m.grbm.tem.values = ',,1e-10,1e-5,1e5,1e6'
print(m.grbm.tem.values)
transformations = [bxa.create_uniform_prior_for( m, m.grbm.alpha),
bxa.create_uniform_prior_for( m, m.grbm.beta),
bxa.create_jeffreys_prior_for( m, m.grbm.tem)]
outputfiles_basename = 'simplest3-'
bxa.standard_analysis(transformations,outputfiles_basename = outputfiles_basename,verbose=True, resume=False,skipsteps = ['convolved'])
par3=AllModels(1)(3)
print(par3.values[0],par3.error[0])
m.gaussian.Sigma.values = ",,0.001,0.001,1,1" def my_custom_prior(u): # prior distributions transform from 0:1 to the parameter range # here: a gaussian prior distribution, cut below 1/above 3 x = scipy.stats.norm(1.9, 0.15).ppf(u) if x < 1.: x = 1 if x > 3: x = 3 return x # define prior transformations = [ # jeffreys prior for nH (but see below) bxa.create_jeffreys_prior_for(m, m.wabs.nH), # jeffreys prior for scale variable bxa.create_jeffreys_prior_for(m, m.powerlaw.norm), # custom gaussian prior function for photon index bxa.create_custom_prior_for( m, m.powerlaw.PhoIndex, my_custom_prior), # and possibly many more # jeffreys prior for scale variable bxa.create_jeffreys_prior_for(m, m.gaussian.norm), # uniform prior for location variable bxa.create_uniform_prior_for(m, m.gaussian.LineE), # jeffreys prior for scale variable bxa.create_jeffreys_prior_for(m, m.gaussian.Sigma), ] # we want nH to come out in logarithmic values, without offset of 22 # so we shift the existing jeffreys prior transformation:
def xspec_fit_kernel(filelist,datadir,savedir,num = 0,flux = False):
os.chdir(datadir)
Plot.xAxis='keV'
Plot.yLog=True
alldatastr = ' '.join(filelist)
AllData.clear()
AllModels.clear()
AllData(alldatastr)
#AllData.show()
Fit.statMethod='cstat'
AllData.ignore('1:**-200.0,40000.0-** 2-4:**-8.0,800.0-**')#我觉得这里可以留一个接口。
m = Model('grbm')
#--------------------------------------------
'''
设置grbm模型的三个参数的范围。
'''
#m.grbm.alpha.values = ',,-10,-10,5,5'
#m.grbm.beta.values = ',,-10,-10,10,10'
m.grbm.tem.values = ',,1e-10,1e-10,1e6,1e6'
#--------------------------------------------
transformations = [bxa.create_uniform_prior_for(m,m.grbm.alpha),
bxa.create_uniform_prior_for(m,m.grbm.beta),
#bxa.create_uniform_prior_for(m,m.grbm.tem)]
bxa.create_jeffreys_prior_for(m,m.grbm.tem)]
outputdir = savedir+'results'+str(num)+'/'
if os.path.exists(outputdir) == False:
os.makedirs(outputdir)
os.chdir(outputdir)
outputfiles_basename = 'simpest_'
bxa.standard_analysis(transformations,outputfiles_basename = outputfiles_basename,verbose =True,resume=False,skipsteps = ['convolved','qq'])
par3=AllModels(1)(3)#第一个模型的第三个参数
value = par3.values[0]
value_arr1,value_arr2,ffff = par3.error
Plot('eeufspec')
flux_list = []
for i in range(len(filelist)):
print(i)
if(flux):
flux = AllData(i+1).flux
flux_list.append(flux)
energies=Plot.x(i+1)
rates=Plot.y(i+1)
folded=Plot.model(i+1)
xErrs=Plot.xErr(i+1)
yErrs=Plot.yErr(i+1)
plt.errorbar(energies,rates,xerr=xErrs,yerr=yErrs,zorder=1,ls='None')
plt.plot(energies,folded,color='black',zorder=2)
plt.axvline(x = value,color = 'r')
plt.axvline(x = value_arr1,color = 'g')
plt.axvline(x = value_arr2,color = 'g')
plt.xlabel('Energy KeV')
plt.ylabel(r'${KeV^{2} (Photons cm^{-2}s^{-1}keV^{-1})}$')
plt.xscale('log')
plt.yscale('log')
plt.savefig(savedir + 'foldedspec.png')
#plt.close()
if flux :
return value,value_arr1,value_arr2,np.array(flux_list).T
else:
return value,value_arr1,value_arr2
