def sherpa_spec_fit(in_files,
model,
noplot,
manual,
reproj,
do_conf):
"""Reads a set of pha files and performs a maximum likelihood fit.
"""
import logging
logging.basicConfig(level=logging.DEBUG, format='%(levelname)s - %(message)s')
# from sherpa.astro.ui import * # TEST if needed (only when outside sherpa env)
import sherpa.astro.ui as sau
from . import load_model
from . import make_plot
from .specsource import SpecSource
logger = logging.getLogger("sherpa")
logger.setLevel(logging.ERROR)
# Read and load the data and model:
list_data = []
emax = 9e10 # Maximum energy taken into account for the fit --> Compute properly (as a function of the max E event?)
# We don't use a specific maximum reduced statistic value since we don't expect the cstat to be anywhere near the large number limit
sau.set_conf_opt("max_rstat", 10000)
p1 = load_model.load_model(model[0]) # [-1] #load model returns an array of model components
# where the last component is the total model
spec = SpecSource('SRC', in_files)
if reproj == 0:
myspec = spec
elif reproj == 3:
myspec = spec.reproject(nbins={'offset': 5, 'eff': 10, 'zen': 10})
elif reproj == 2:
myspec = spec.reproject(nbins={'offset': 5, 'eff': 20, 'zen': 12})
elif reproj == 1:
myspec = spec.reproject(nbins={'offset': 25, 'eff': 40, 'zen': 30})
elif reproj == 4:
myspec = spec.reproject(nbins={'offset': 1, 'eff': 1, 'zen': 1})
myspec.set_source(p1)
if manual:
load_model.set_manual_model(sau.get_model(datid)) # Parameters for all runs are linked
else:
print('Using default initial values for model parameters')
myspec.fit(do_conf=do_conf)
if noplot:
quit()
make_plot.make_plot(list_data, p1)
raw_input('Press <ENTER> to continue')
def set_sherpa_env(stat=None):
shp.clean()
dsmod.clean()
shp.set_conf_opt("sigma", 1.6)
shp.set_conf_opt("numcores", 4)
shp.set_proj_opt("sigma", 1.6)
shp.set_proj_opt("numcores", 4)
if stat:
shp.set_stat(stat)
def fit(self, do_covar=False, do_conf=False):
"""Perform fit using profile likelihood technique for background estimation and subtraction."""
listnames = self.get_noticed_list() # [ids.name for ids in self.listids[self.noticed_ids]]
if len(listnames) > 0:
wfit(listnames)
print_fit()
if do_covar is True:
sau.covar(*listnames)
if do_conf is True:
sau.set_conf_opt('max_rstat', 10000)
sau.conf(*listnames)
print_conf()
else:
print("Empty noticed runs list. No fit")
def sherpa_spec_fit(in_files, model, noplot, manual, reproj, do_conf):
"""Reads a set of pha files and performs a maximum likelihood fit.
"""
# from sherpa.astro.ui import * # TEST if needed (only when outside sherpa env)
import sherpa.astro.ui as sau
from . import load_model
from . import make_plot
from .specsource import SpecSource
# Read and load the data and model:
list_data = []
emax = 9e10 # Maximum energy taken into account for the fit --> Compute properly (as a function of the max E event?)
# We don't use a specific maximum reduced statistic value since we don't expect the cstat to be anywhere near the large number limit
sau.set_conf_opt("max_rstat", 10000)
p1 = load_model.load_model(
model[0]) # [-1] #load model returns an array of model components
# where the last component is the total model
spec = SpecSource('SRC', in_files)
if reproj == 0:
myspec = spec
elif reproj == 3:
myspec = spec.reproject(nbins={'offset': 5, 'eff': 10, 'zen': 10})
elif reproj == 2:
myspec = spec.reproject(nbins={'offset': 5, 'eff': 20, 'zen': 12})
elif reproj == 1:
myspec = spec.reproject(nbins={'offset': 25, 'eff': 40, 'zen': 30})
elif reproj == 4:
myspec = spec.reproject(nbins={'offset': 1, 'eff': 1, 'zen': 1})
myspec.set_source(p1)
if manual:
load_model.set_manual_model(
sau.get_model(datid)) # Parameters for all runs are linked
else:
print('Using default initial values for model parameters')
myspec.fit(do_conf=do_conf)
if noplot:
quit()
make_plot.make_plot(list_data, p1)
raw_input('Press <ENTER> to continue')
def run_hspec_fit(self, model, thres_low, thres_high):
"""Run the gammapy.hspec fit
Parameters
----------
model : str
Sherpa model
thres_high : `~gammapy.spectrum.Energy`
Upper threshold of the spectral fit
thres_low : `~gammapy.spectrum.Energy`
Lower threshold of the spectral fit
"""
log.info("Starting HSPEC")
import sherpa.astro.ui as sau
from ..hspec import wstat
from sherpa.models import PowLaw1D
if model == 'PL':
p1 = PowLaw1D('p1')
p1.gamma = 2.2
p1.ref = 1e9
p1.ampl = 6e-19
else:
raise ValueError('Desired Model is not defined')
thres = thres_low.to('keV').value
emax = thres_high.to('keV').value
sau.freeze(p1.ref)
sau.set_conf_opt("max_rstat", 100)
list_data = []
for obs in self.observations:
datid = obs.phafile.parts[-1][7:12]
sau.load_data(datid, str(obs.phafile))
sau.notice_id(datid, thres, emax)
sau.set_source(datid, p1)
list_data.append(datid)
wstat.wfit(list_data)
sau.covar()
fit_val = sau.get_covar_results()
fit_attrs = ('parnames', 'parvals', 'parmins', 'parmaxes')
fit = dict((attr, getattr(fit_val, attr)) for attr in fit_attrs)
fit = self.apply_containment(fit)
sau.clean()
self.fit = fit
def wfit(dataids=None):
listids = ()
if dataids is None:
listids = sau.list_data_ids()
else:
listids = dataids
wstat = w_statistic(listids)
sau.load_user_stat("mystat", wstat, wstat.CATstat_err_LV)
sau.set_stat(mystat)
sau.set_method("neldermead") # set_method("moncar")
sau.set_conf_opt("max_rstat", 1000) # We don't use a specific maximum reduced statistic value
# since we don't expect the cstat to be anywhere near the
# large number limit
sau.fit(*listids)
sau.conf()
def wfit(dataids=None):
listids = ()
if dataids is None:
listids = sau.list_data_ids()
else:
listids = dataids
wstat = w_statistic(listids)
sau.load_user_stat("mystat", wstat, wstat.CATstat_err_LV)
sau.set_stat(mystat)
sau.set_method("neldermead") # set_method("moncar")
sau.set_conf_opt(
"max_rstat",
1000) # We don't use a specific maximum reduced statistic value
# since we don't expect the cstat to be anywhere near the
# large number limit
sau.fit(*listids)
sau.conf()
def _run_hspec_fit(self):
"""Run the gammapy.hspec fit
"""
log.info("Starting HSPEC")
import sherpa.astro.ui as sau
from ..hspec import wstat
sau.set_conf_opt("max_rstat", 100)
thres_lo = self.energy_threshold_low.to('keV').value
thres_hi = self.energy_threshold_high.to('keV').value
sau.freeze(self.model.ref)
list_data = []
for pha in self.pha:
datid = pha.parts[-1][7:12]
sau.load_data(datid, str(pha))
sau.notice_id(datid, thres_lo, thres_hi)
sau.set_source(datid, self.model)
list_data.append(datid)
wstat.wfit(list_data)
# theta = 57 deg (from positive north)
shp.set_par(chxe.xpos, 497.4, 497.4 - 4, 497.4 + 4)
shp.set_par(chxe.ypos, 499.1, 499.1 - 4, 499.1 + 4)
shp.set_par(chxe.fwhm, 30, 1, 200)
shp.set_par(chxe.ellip, .5)
shp.set_par(chxe.theta, -.9948)
shp.set_par(chxe.ampl, 1e-5)
shp.thaw(chxe.ellip, chxe.xpos, chxe.ypos)
print(shp.get_model())
shp.ignore2d('circle(500,500,1000)')
shp.notice2d('circle(500,500,60)')
shp.ignore2d('circle(500,500,' + str(r) + ')')
shp.fit()
print(shp.get_model())
shp.set_conf_opt('numcores', 3)
shp.set_conf_opt('maxiters', 50)
shp.set_conf_opt('fast', True)
shp.set_conf_opt('remin', 10000.0)
shp.set_conf_opt('soft_limits', True)
shp.freeze(chxe.xpos, chxe.ypos)
#shp.conf(chxe.ellip, chxe.fwhm)
utils.save_components('40-50 keV/chxe' + str(int(r * 2.5)) + '.model',
['chxe'])
shp.save('40-50 keV/fits/chxe150/chxe' + str(int(r * 2.5)) + 'fit.sav')
