from astropy import units as u
from sherpa.models import ArithmeticModel
from sherpa.data import BaseData, Data
from sherpa.stats import Likelihood
from sherpa.optmethods import LevMar, NelderMead, MonCar, GridSearch
__all__ = [
'SherpaDataWrapper',
'SherpaModelWrapper',
'SherpaStatWrapper',
]
SHERPA_OPTMETHODS = OrderedDict()
SHERPA_OPTMETHODS['levmar'] = LevMar()
SHERPA_OPTMETHODS['simplex'] = NelderMead()
SHERPA_OPTMETHODS['moncar'] = MonCar()
SHERPA_OPTMETHODS['gridsearch'] = GridSearch()
class SherpaDataWrapper(Data):
def __init__(self, gp_data, name='GPData'):
# sherpa does some magic here: it sets class attributes from constructor
# arguments so `gp_data` will be available later on the instance.
self._data_dummy = np.empty_like(gp_data.e_ref)
BaseData.__init__(self)
def to_fit(self, staterr):
return self._data_dummy, None, None
def eval_model_to_fit(self, model):
return self._data_dummy
G1.fwhm = .05
G1.pos = 1083.03 + ref_value * 5
mdl = G1
mplot = ModelPlot()
mplot.prepare(d, mdl)
dplot = DataPlot()
dplot.prepare(d)
mplot.overplot()
#set error methods, ChiSq() or LeastSq()
#Chi square is a way to compare which profile best describes data, ie: is it more gaussian or lorentzian
#Least Square says how good the data fits the particular model instance
#opt - optimizers improve the fit. Monte Carlo is what I used, it is slow but it is most robust. Many options on sherpas site
ustat = LeastSq()
opt = MonCar() #LevMar() #NelderMead() #
#apply actual Fit
f = Fit(d, mdl, stat=ustat, method=opt)
res = f.fit()
fplot = FitPlot()
mplot.prepare(d, mdl)
fplot.prepare(dplot, mplot)
fplot.plot()
#param_errors = f.est_errors()
#plotting routine
plt.plot(d.x, d.y, "c.", label="Data")
plt.plot(d.x, mdl(d.x), linewidth=2, label="Gaussian")
plt.legend(loc=2)
def prepare_spectra(nH: float,
group: int = 1,
add_gal: bool = False,
redshift: Optional[float] = None,
**kwargs) -> float:
"""
Fit the spectra using an absorbed powerlaw model using the Wstat statistic. The function also returns a p-value for the gof.
:param nH: The galactic absorption column density in units of 10^22 /cm3
:param group: The number of counts per energy bin
:param add_gal: Setting this to True would add an intrinsic abrosption column density along side the galactic one
:param redshift: The redshift to use in the fit. Only takes effect if add_gal is set to True
...
:return: Returns the p-value of the gof. The null hypothesis states that the model and the observation differ while alternate says that the model explains the data
"""
pha = read_pha("core_spectrum.pi")
pha.set_analysis("energy")
pha.notice(0.5, 7.0)
tabs = ~pha.mask
pha.group_counts(group, tabStops=tabs)
x = pha.get_x()
x = pha.apply_filter(x, pha._middle)
y = pha.get_y(filter=True)
pha.set_analysis("energy")
model = xsphabs.abs1 * powlaw1d.srcp1
print("Fitting the spectrum")
zFlag = False
if (nH is not None) and (nH > 0.0):
if add_gal == 1:
model = xsphabs.gal * xszphabs.abs1 * powlaw1d.srcp
gal.nH = nH
freeze(gal.nH)
zFlag = True
else:
model = xsphabs.abs1 * powlaw1d.srcp1
abs1.nH = nH
freeze(abs1.nH)
else:
model = xszphabs.abs1 * powlaw1d.srcp1
zFlag = True
if zFlag is True and add_gal == 1:
# print('REDSHIFT',redshift)
abs1.redshift = redshift
freeze(abs1.redshift)
full_model = RSPModelPHA(pha.get_arf(), pha.get_rmf(), pha,
pha.exposure * model)
print(full_model)
fit = Fit(pha, full_model, method=MonCar(), stat=WStat())
res = fit.fit()
print(res.format())
print(fit.est_errors())
# calculate the p-value for wstat
mplot2 = ModelPlot()
mplot2.prepare(pha, full_model)
miu = mplot2.y * pha.exposure * 0.0146
obs = y * pha.exposure * 0.0146
c, ce, cv = SpecUtils.estimate_gof_cstat(miu, obs)
#print(f"C0={c},C_e={ce},C_v={cv}")
zval = (fit.calc_stat() - ce) / np.sqrt(cv)
if zval > 0:
pval = special.erfc(zval / np.sqrt(2))
else:
pval = special.erf(abs(zval) / np.sqrt(2))
print(f"p-value for wstat = {pval}")
set_data(pha)
set_model(model)
save_chart_spectrum("core_flux_chart.dat", elow=0.5, ehigh=7.0)
# save_chart_spectrum("core_flux_chart.rdb",format='text/tsv', elow=0.5, ehigh=7.0)
SAOTraceUtils.save_spectrum_rdb("core_flux_chart.dat")
return pval
def prepare_spectra(group, nH, add_gal, redshift):
pha = read_pha("core_spectrum.pi")
pha.set_analysis("energy")
pha.notice(0.5, 7.0)
tabs = ~pha.mask
pha.group_counts(group, tabStops=tabs)
x = pha.get_x()
x = pha.apply_filter(x, pha._middle)
y = pha.get_y(filter=True)
pha.set_analysis("energy")
model = xsphabs.abs1 * powlaw1d.srcp1
print("Fitting the spectrum")
zFlag = False
if (nH is not None) and (nH > 0.0):
if add_gal == 1:
model = xsphabs.gal * xszphabs.abs1 * powlaw1d.srcp
gal.nH = nH
freeze(gal.nH)
zFlag = True
else:
model = xsphabs.abs1 * powlaw1d.srcp1
abs1.nH = nH
freeze(abs1.nH)
else:
model = xszphabs.abs1 * powlaw1d.srcp1
zFlag = True
if zFlag is True and add_gal == 1:
# print('REDSHIFT',redshift)
abs1.redshift = redshift
freeze(abs1.redshift)
full_model = RSPModelPHA(pha.get_arf(), pha.get_rmf(), pha, pha.exposure * model)
print(full_model)
fit = Fit(pha, full_model, method=MonCar(), stat=WStat())
res = fit.fit()
print(res.format())
print(fit.est_errors())
# calculate the p-value for wstat
mplot2 = ModelPlot()
mplot2.prepare(pha, full_model)
miu = mplot2.y * pha.exposure * 0.0146
obs = y * pha.exposure * 0.0146
c, ce, cv = gof_cstat(miu, obs)
print(f"C0={c},C_e={ce},C_v={cv}")
zval = (fit.calc_stat() - ce) / np.sqrt(cv)
if zval > 0:
pval = special.erfc(zval / np.sqrt(2))
else:
pval = special.erf(abs(zval) / np.sqrt(2))
print(f"p-value for wstat = {pval}")
set_data(pha)
set_model(model)
save_chart_spectrum("core_flux_chart.dat", elow=0.5, ehigh=7.0)
# save_chart_spectrum("core_flux_chart.rdb",format='text/tsv', elow=0.5, ehigh=7.0)
save_spectrum_rdb("core_flux_chart.dat")
# Requires sherpa 4.9
import numpy as np
from astropy import units as u
from sherpa.models import ArithmeticModel, Parameter
from sherpa.data import DataND, BaseData, Data
from sherpa.stats import Likelihood
from sherpa.optmethods import LevMar, NelderMead, MonCar, GridSearch
__all__ = ['SherpaDataWrapper', 'SherpaModelWrapper', 'SherpaStatWrapper']
SHERPA_OPTMETHODS = {
'simplex': NelderMead(),
'moncar': MonCar(),
'gridsearch': GridSearch()
}
class SherpaDataWrapper(Data):
def __init__(self, gp_data, name='GPData'):
# sherpa does some magic here: it sets class attributes from constructor
# arguments so `gp-data` will be available later on the instance.
self._data_dummy = np.empty_like(gp_data.e_ref)
BaseData.__init__(self)
def to_fit(self, staterr):
return self._data_dummy, None, None
def eval_model_to_fit(self, model):
return self._data_dummy