def bkg_only_fit(output, steppar=False, error=False):
"""
Fit bkg region alone to XRB model
"""
out = g309.load_data_and_models(["bkg"], snr_model=None)
set_energy_range(out['bkg'])
xs.AllData.ignore("bad")
# Reset XRB to "typical" values
# As usual, fit is pretty sensitive to initial values
# (initial kT values that are too small drive fit to a bad local minimum)
xrb = xs.AllModels(1, 'xrb')
xrb.setPars({xrb.apec.kT.index : "0.2, , 0, 0, 0.5, 1"}, # Unabsorped apec (local bubble)
{xrb.tbnew_gas.nH.index : "1.5, , 0.01, 0.1, 5, 10"}, # Galactic absorption
{xrb.apec_6.kT.index : "0.7, , 0, 0, 2, 4"}, # Absorbed apec (galactic halo)
{xrb.apec.norm.index : 1e-3},
{xrb.apec_6.norm.index : 1e-3} )
xrb.apec.kT.frozen = False
xrb.tbnew_gas.nH.frozen = False
xrb.apec_6.kT.frozen = False
xrb.apec.norm.frozen = False
xrb.apec_6.norm.frozen = False
xs.Fit.perform()
if xs.Plot.device == "/xs":
xs.Plot("ldata delchi")
if steppar:
xs.Fit.steppar("xrb:{:d} 0.1 0.5 20".format(xs_utils.par_num(xrb, xrb.apec.kT)))
xs.Fit.steppar("xrb:{:d} 0.4 0.8 20".format(xs_utils.par_num(xrb, xrb.apec_6.kT)))
if xs.Plot.device == "/xs":
xs.Plot("ldata delchi")
if error:
xs.Xset.openLog(output + "_error.log")
print "Error run started:", datetime.now()
xs.Fit.error("xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec.kT))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec.norm))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.tbnew_gas.nH))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec_6.kT))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec_6.norm)))
print "Error run complete:", datetime.now()
xs.Xset.closeLog()
# Dump useful things here...
products(output)
print_model(xrb, output + "_xrb.txt")
def bkg_only_fit(output, steppar=False, error=False):
"""
Fit bkg region alone to XRB model
"""
out = g309.load_data_and_models(["bkg"], snr_model=None)
set_energy_range(out['bkg'])
xs.AllData.ignore("bad")
# Reset XRB to "typical" values
# As usual, fit is pretty sensitive to initial values
# (initial kT values that are too small drive fit to a bad local minimum)
xrb = xs.AllModels(1, 'xrb')
xrb.setPars({xrb.apec.kT.index : "0.2, , 0, 0, 0.5, 1"}, # Unabsorped apec (local bubble)
{xrb.tbnew_gas.nH.index : "1.5, , 0.01, 0.1, 5, 10"}, # Galactic absorption
{xrb.apec_6.kT.index : "0.7, , 0, 0, 2, 4"}, # Absorbed apec (galactic halo)
{xrb.apec.norm.index : 1e-3},
{xrb.apec_6.norm.index : 1e-3} )
xrb.apec.kT.frozen = False
xrb.tbnew_gas.nH.frozen = False
xrb.apec_6.kT.frozen = False
xrb.apec.norm.frozen = False
xrb.apec_6.norm.frozen = False
xs.Fit.perform()
if xs.Plot.device == "/xs":
xs.Plot("ldata delchi")
if steppar:
xs.Fit.steppar("xrb:{:d} 0.1 0.5 20".format(xs_utils.par_num(xrb, xrb.apec.kT)))
xs.Fit.steppar("xrb:{:d} 0.4 0.8 20".format(xs_utils.par_num(xrb, xrb.apec_6.kT)))
if xs.Plot.device == "/xs":
xs.Plot("ldata delchi")
if error:
xs.Xset.openLog(output + "_error.log")
print "Error run started:", datetime.now()
xs.Fit.error("xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec.kT))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec.norm))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.tbnew_gas.nH))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec_6.kT))
+ " xrb:{:d}".format(xs_utils.par_num(xrb, xrb.apec_6.norm)))
print "Error run complete:", datetime.now()
xs.Xset.closeLog()
# Dump useful things here...
products(output)
print_model(xrb, output + "_xrb.txt")
def annulus_fit(output, error=False, error_rerun=False,
free_center_elements=None, free_all_elements=None,
four_ann=False, **kwargs):
"""
Fit radial annuli simultaneously
Arguments:
output = output products file stem
error = run error commands?
error_rerun = run error commands 2nd time?
four_ann = fit only 4 instead of 5 annuli?
free_all_elements = (case-sensitive) elements to free in all annuli.
Element names much match XSPEC parameter names.
Default: Si, S free
free_center_elements = (case-sensitive) additional elements to free in
central circle (0-100 arcsec)
Element names much match XSPEC parameter names.
kwargs - passed to g309_models.load_data_and_models
(suffix, mosmerge, marfrmf)
Output: n/a
loads of stuff dumped to output*
XSPEC session left in fitted state
"""
if free_center_elements is None:
free_center_elements = []
if free_all_elements is None:
free_all_elements = ['Si', 'S']
regs = ["ann_000_100", "ann_100_200", "ann_200_300", "ann_300_400", "ann_400_500"]
if four_ann:
regs = ["ann_000_100", "ann_100_200", "ann_200_300", "ann_300_400"]
out = g309.load_data_and_models(regs, snr_model='vnei', **kwargs)
for reg in regs:
set_energy_range(out[reg])
xs.AllData.ignore("bad")
# Link nH across annuli
# Each region has n spectra (n exposures)
# [0] gets 1st of n ExtractedSpectra objects
# .models['...'] gets corresponding 1st of n XSPEC models
rings = [out[reg][0].models['snr'] for reg in regs]
for ring in rings[1:]: # Exclude center
ring.tbnew_gas.nH.link = xs_utils.link_name(rings[0], rings[0].tbnew_gas.nH)
# Start fit process
xs.Fit.renorm()
xs.Fit.perform()
for ring in rings:
ring.tbnew_gas.nH.frozen = False
ring.vnei.kT.frozen = False
ring.vnei.Tau.frozen = False
xs.Fit.perform()
for ring in rings:
for elem in free_all_elements:
comp = ring.vnei.__getattribute__(elem)
comp.frozen = False
for elem in free_center_elements:
comp = rings[0].vnei.__getattribute__(elem)
comp.frozen = False
xs.Fit.perform()
if xs.Plot.device == "/xs":
xs.Plot("ldata delchi")
# Error runs
if error:
xs.Xset.openLog(output + "_error.log")
print "First error run:", datetime.now()
for reg, ring in zip(regs, rings):
print "Running", reg, "errors:", datetime.now()
xs.Fit.error(error_str_all_free(ring))
print reg, "errors complete:", datetime.now() # Will not appear in error log
if error_rerun:
print "Second error run:", datetime.now()
for reg, ring in zip(regs, rings):
print "Running", reg, "errors:", datetime.now()
xs.Fit.error(error_str_all_free(ring))
print reg, "errors complete:", datetime.now() # Will not appear in error log
xs.Xset.closeLog()
print "Error runs complete:", datetime.now()
# Output products
products(output)
for ring in rings:
model_log = output + "_{}.txt".format(ring.name)
print_model(ring, model_log)
def single_fit(output, region='src', with_bkg=True, free_elements=None,
error=False, error_rerun=False,
tau_scan=False, tau_freeze=None, nH_freeze=None,
snr_model='vnei', **kwargs):
"""Fit any region to an arbitrary remnant model, possibly fitting XRB with
background region as well.
Arguments
output: file stem string
Keyword arguments
region: region spectrum to be fitted
with_bkg: fit to X-ray background region (bkg) simultaneously
snr_model: snr model expression (and parameter setup) to use
free_elements: (default) is [Si,S]
if [], use solar abundances
error: perform single error run
tau_scan: steppar over plausible Tau values to ensure convergence to "correct" best fit
tau_freeze: freeze ionization timescale to provided value
nH_freeze: freeze SNR absorption to provided value
kwargs - passed to g309_models.load_data_and_models
(suffix, mosmerge, marfrmf)
"""
if free_elements is None:
free_elements = ['Si', 'S']
# Set up spectra and models in XSPEC
if with_bkg:
out = g309.load_data_and_models([region, 'bkg'], snr_model=snr_model, **kwargs)
set_energy_range(out['bkg'])
else:
out = g309.load_data_and_models([region], **kwargs)
set_energy_range(out[region])
xs.AllData.ignore("bad")
xrb = xs.AllModels(1, 'xrb')
snr = xs.AllModels(1, 'snr_' + region)
# Reset XRB to "typical" values, do NOT vary yet
if with_bkg:
xrb.setPars({xrb.apec.kT.index : "0.1, , 0, 0, 0.5, 1"}, # Unabsorped apec (local bubble)
{xrb.tbnew_gas.nH.index : "1, , 0.1, 0.5, 5, 10"}, # Extragalactic absorption
{xrb.tbnew_gas_5.nH.index : "1, , 0.1, 0.5, 5, 10"}, # Ridge absorption
{xrb.apec_6.kT.index : "0.5, , 0, 0, 2, 4"}, # Galactic ridge (+ minimal halo maybe)
{xrb.apec.norm.index : 1e-3},
{xrb.apec_6.norm.index : 1e-3} )
xs_utils.freeze_model(xrb)
# Try floating norms to help initial fit
xrb.apec.norm.frozen = False
xrb.apec_6.norm.frozen = False
def thaw_bkg():
for c in [xrb.apec.kT, xrb.tbnew_gas.nH, xrb.tbnew_gas_5.nH,
xrb.apec_6.kT]:
c.frozen = False
xs.Fit.renorm()
# Let SNR model vary (NOTE: this assumes default to be vnei...)
if snr_model.startswith('vnei'):
xs_utils.freeze_model(snr)
# Configure initial SNR parameters
if nH_freeze:
snr.tbnew_gas.nH = nH_freeze
else:
snr.tbnew_gas.nH.frozen=False
snr.vnei.kT.frozen=False
snr.vnei.norm.frozen=False
if tau_freeze:
snr.vnei.Tau = tau_freeze
else:
snr.vnei.Tau.frozen = False
for elem in free_elements:
comp = snr.vnei.__getattribute__(elem)
comp.frozen = False
if snr_model == 'vnei+nei':
snr.nei.norm = 0
elif snr_model == 'vnei+powerlaw':
snr.powerlaw.PhoIndex = 2
snr.powerlaw.norm = 0 # zero
elif snr_model == 'vnei+srcutlog':
# srcutlog, w/ one free parameter, behaves better than powerlaw
snr.srcutlog.__getattribute__('break').frozen = False
# Run initial fit
if with_bkg:
# Fit has enormous trouble converging
if tau_freeze:
thaw_bkg()
xs.Fit.perform()
else:
snr.vnei.Tau = 2e10
snr.vnei.Tau.frozen = True
xs.Fit.perform()
thaw_bkg()
xs.Fit.perform()
snr.vnei.Tau.frozen = False
xs.Fit.perform()
else:
xs.Fit.perform()
# Post-processing on initial fit
if tau_scan:
xs.Fit.steppar("log {:s}:{:d} 1e9 5e13 15".format(snr.name,
xs_utils.par_num(snr, snr.vnei.Tau)))
if snr_model == 'vnei+nei':
snr.nei.kT.frozen = False
snr.nei.Tau.frozen = False
snr.nei.norm.frozen = False
xs.Fit.perform()
elif snr_model == 'vnei+powerlaw':
snr.powerlaw.PhoIndex = 2
snr.powerlaw.norm = 0 # zero
snr.powerlaw.norm.frozen = False
xs.Fit.perform()
snr.powerlaw.PhoIndex.frozen = False
xs.Fit.perform()
# Because powerlaw norm generally runs to zero, traverse moderately
# strong power law cases
xs.Fit.steppar("log {:s}:{:d} 1e-5 1e-2 30".format(snr.name,
xs_utils.par_num(snr, snr.powerlaw.norm)))
elif snr_model == 'vnei+srcutlog':
# Check reasonably high break values: 15 -- 17
xs.Fit.steppar("{:s}:{:d} 15 17 20".format(snr.name,
xs_utils.par_num(snr, snr.srcutlog.__getattribute__('break'))
))
elif snr_model == 'vpshock':
xs_utils.freeze_model(snr)
snr.tbnew_gas.nH.frozen=False
snr.vpshock.kT.frozen=False
snr.vpshock.norm.frozen=False
if tau_freeze:
raise Exception("ERROR: vpshock not configured for fixed Tau")
for elem in free_elements:
comp = snr.vpshock.__getattribute__(elem)
comp.frozen = False
# vpshock fits are very ill behaved, must coerce into best fit
snr.vpshock.Tau_l = 1e8
snr.vpshock.Tau_u = 5e10
xs.Fit.perform()
if with_bkg:
thaw_bkg()
xs.Fit.perform()
snr.vpshock.Tau_l.frozen = False
snr.vpshock.Tau_u.frozen = False
xs.Fit.perform()
if tau_scan:
# Since Tau_u is constrained to be greater than Tau_l (?) this
# should ensure that both Tau_u and Tau_l traverse a range of
# values. But I haven't checked it yet...
xs.Fit.steppar("log {:s}:{:d} 1e9 5e13 15".format(snr.name,
xs_utils.par_num(snr, snr.vpshock.Tau_u)))
else:
raise Exception("Invalid SNR model - please add branch")
# Compute standard 90% errors
if error:
xs.Xset.openLog(output + "_error.log")
if with_bkg:
xs.Fit.error(error_str_all_free(xrb))
xs.Fit.error(error_str_all_free(snr))
if error_rerun:
if with_bkg:
xs.Fit.error(error_str_all_free(xrb))
xs.Fit.error(error_str_all_free(snr))
xs.Xset.closeLog()
# Dump standard outputs
products(output)
print_model(snr, output + "_{:s}.txt".format(snr.name))
if with_bkg:
print_model(xrb, output + "_xrb.txt")
def annulus_fit(output, error=False, error_rerun=False,
free_center_elements=None, free_all_elements=None,
four_ann=False, **kwargs):
"""
Fit radial annuli simultaneously
Arguments:
output = output products file stem
error = run error commands?
error_rerun = run error commands 2nd time?
four_ann = fit only 4 instead of 5 annuli?
free_all_elements = (case-sensitive) elements to free in all annuli.
Element names much match XSPEC parameter names.
Default: Si, S free
free_center_elements = (case-sensitive) additional elements to free in
central circle (0-100 arcsec)
Element names much match XSPEC parameter names.
kwargs - passed to g309_models.load_data_and_models
(suffix, mosmerge, marfrmf)
Output: n/a
loads of stuff dumped to output*
XSPEC session left in fitted state
"""
if free_center_elements is None:
free_center_elements = []
if free_all_elements is None:
free_all_elements = ['Si', 'S']
regs = ["ann_000_100", "ann_100_200", "ann_200_300", "ann_300_400", "ann_400_500"]
if four_ann:
regs = ["ann_000_100", "ann_100_200", "ann_200_300", "ann_300_400"]
out = g309.load_data_and_models(regs, snr_model='vnei', **kwargs)
for reg in regs:
set_energy_range(out[reg])
xs.AllData.ignore("bad")
# Link nH across annuli
# Each region has n spectra (n exposures)
# [0] gets 1st of n ExtractedSpectra objects
# .models['...'] gets corresponding 1st of n XSPEC models
rings = [out[reg][0].models['snr'] for reg in regs]
for ring in rings[1:]: # Exclude center
ring.tbnew_gas.nH.link = xs_utils.link_name(rings[0], rings[0].tbnew_gas.nH)
# Start fit process
xs.Fit.renorm()
xs.Fit.perform()
for ring in rings:
ring.tbnew_gas.nH.frozen = False
ring.vnei.kT.frozen = False
ring.vnei.Tau.frozen = False
xs.Fit.perform()
for ring in rings:
for elem in free_all_elements:
comp = ring.vnei.__getattribute__(elem)
comp.frozen = False
for elem in free_center_elements:
comp = rings[0].vnei.__getattribute__(elem)
comp.frozen = False
xs.Fit.perform()
if xs.Plot.device == "/xs":
xs.Plot("ldata delchi")
# Error runs
if error:
xs.Xset.openLog(output + "_error.log")
print "First error run:", datetime.now()
for reg, ring in zip(regs, rings):
print "Running", reg, "errors:", datetime.now()
xs.Fit.error(error_str_all_free(ring))
print reg, "errors complete:", datetime.now() # Will not appear in error log
if error_rerun:
print "Second error run:", datetime.now()
for reg, ring in zip(regs, rings):
print "Running", reg, "errors:", datetime.now()
xs.Fit.error(error_str_all_free(ring))
print reg, "errors complete:", datetime.now() # Will not appear in error log
xs.Xset.closeLog()
print "Error runs complete:", datetime.now()
# Output products
products(output)
for ring in rings:
model_log = output + "_{}.txt".format(ring.name)
print_model(ring, model_log)
def single_fit(output, region='src', with_bkg=True, free_elements=None,
error=False, error_rerun=False,
tau_scan=False, tau_freeze=None, nH_freeze=None,
snr_model='vnei', **kwargs):
"""Fit any region to an arbitrary remnant model, possibly fitting XRB with
background region as well.
Arguments
output: file stem string
Keyword arguments
region: region spectrum to be fitted
with_bkg: fit to X-ray background region (bkg) simultaneously
snr_model: snr model expression (and parameter setup) to use
free_elements: (default) is [Si,S]
if [], use solar abundances
error: perform single error run
tau_scan: steppar over plausible Tau values to ensure convergence to "correct" best fit
tau_freeze: freeze ionization timescale to provided value
nH_freeze: freeze SNR absorption to provided value
kwargs - passed to g309_models.load_data_and_models
(suffix, mosmerge, marfrmf)
"""
if free_elements is None:
free_elements = ['Si', 'S']
# Set up spectra and models in XSPEC
if with_bkg:
out = g309.load_data_and_models([region, 'bkg'], snr_model=snr_model, **kwargs)
set_energy_range(out['bkg'])
else:
out = g309.load_data_and_models([region], snr_model=snr_model, **kwargs)
set_energy_range(out[region])
xs.AllData.ignore("bad")
if snr_model == 'gauss':
for extr in out[region]:
extr.spec.ignore("**-5.0, 8.0-**")
xrb = xs.AllModels(1, 'xrb')
snr = xs.AllModels(1, 'snr_' + region)
# Reset XRB to "typical" values, do NOT vary yet
if with_bkg:
xrb.setPars({xrb.apec.kT.index : "0.1, , 0, 0, 0.5, 1"}, # Unabsorped apec (local bubble)
{xrb.tbnew_gas.nH.index : "1, , 0.1, 0.5, 5, 10"}, # Extragalactic absorption
{xrb.tbnew_gas_5.nH.index : "1, , 0.1, 0.5, 5, 10"}, # Ridge absorption
{xrb.apec_6.kT.index : "0.5, , 0, 0, 2, 4"}, # Galactic ridge (+ minimal halo maybe)
{xrb.apec.norm.index : 1e-3},
{xrb.apec_6.norm.index : 1e-3} )
xs_utils.freeze_model(xrb)
# Try floating norms to help initial fit
xrb.apec.norm.frozen = False
xrb.apec_6.norm.frozen = False
def thaw_bkg():
for c in [xrb.apec.kT, xrb.tbnew_gas.nH, xrb.tbnew_gas_5.nH,
xrb.apec_6.kT]:
c.frozen = False
xs.Fit.renorm()
# Let SNR model vary (NOTE: this assumes default to be vnei...)
if snr_model.startswith('vnei'):
xs_utils.freeze_model(snr)
# Configure initial SNR parameters
if nH_freeze:
snr.tbnew_gas.nH = nH_freeze
else:
snr.tbnew_gas.nH.frozen=False
snr.vnei.kT.frozen=False
snr.vnei.norm.frozen=False
if tau_freeze:
snr.vnei.Tau = tau_freeze
else:
snr.vnei.Tau.frozen = False
for elem in free_elements:
comp = snr.vnei.__getattribute__(elem)
comp.frozen = False
if snr_model == 'vnei+nei':
snr.nei.norm = 0
elif snr_model == 'vnei+powerlaw':
snr.powerlaw.PhoIndex = 2
snr.powerlaw.norm = 0 # zero
elif snr_model == 'vnei+srcutlog':
# srcutlog, w/ one free parameter, behaves better than powerlaw
snr.srcutlog.__getattribute__('break').frozen = False
# Run initial fit
if with_bkg:
# Fit has enormous trouble converging
if tau_freeze:
thaw_bkg()
xs.Fit.perform()
else:
snr.vnei.Tau = 2e10
snr.vnei.Tau.frozen = True
xs.Fit.perform()
thaw_bkg()
xs.Fit.perform()
snr.vnei.Tau.frozen = False
xs.Fit.perform()
else:
xs.Fit.perform()
# Post-processing on initial fit
if tau_scan:
xs.Fit.steppar("log {:s}:{:d} 1e9 5e13 15".format(snr.name,
xs_utils.par_num(snr, snr.vnei.Tau)))
if snr_model == 'vnei+nei':
snr.nei.kT.frozen = False
snr.nei.Tau.frozen = False
snr.nei.norm.frozen = False
xs.Fit.perform()
elif snr_model == 'vnei+powerlaw':
snr.powerlaw.PhoIndex = 2
snr.powerlaw.norm = 0 # zero
snr.powerlaw.norm.frozen = False
xs.Fit.perform()
snr.powerlaw.PhoIndex.frozen = False
xs.Fit.perform()
# Because powerlaw norm generally runs to zero, traverse moderately
# strong power law cases
xs.Fit.steppar("log {:s}:{:d} 1e-5 1e-2 30".format(snr.name,
xs_utils.par_num(snr, snr.powerlaw.norm)))
elif snr_model == 'vnei+srcutlog':
# Check reasonably high break values: 15 -- 17
xs.Fit.steppar("{:s}:{:d} 15 17 20".format(snr.name,
xs_utils.par_num(snr, snr.srcutlog.__getattribute__('break'))
))
elif snr_model == 'vpshock':
xs_utils.freeze_model(snr)
snr.tbnew_gas.nH.frozen=False
snr.vpshock.kT.frozen=False
snr.vpshock.norm.frozen=False
if tau_freeze:
raise Exception("ERROR: vpshock not configured for fixed Tau")
for elem in free_elements:
comp = snr.vpshock.__getattribute__(elem)
comp.frozen = False
# vpshock fits are very ill behaved, must coerce into best fit
snr.vpshock.Tau_l = 1e8
snr.vpshock.Tau_u = 5e10
xs.Fit.perform()
if with_bkg:
thaw_bkg()
xs.Fit.perform()
snr.vpshock.Tau_l.frozen = False
snr.vpshock.Tau_u.frozen = False
xs.Fit.perform()
if tau_scan:
# Since Tau_u is constrained to be greater than Tau_l (?) this
# should ensure that both Tau_u and Tau_l traverse a range of
# values. But I haven't checked it yet...
xs.Fit.steppar("log {:s}:{:d} 1e9 5e13 15".format(snr.name,
xs_utils.par_num(snr, snr.vpshock.Tau_u)))
elif snr_model == 'gauss':
# Not supported! Not at all constrained . . .
assert not with_bkg
xs_utils.freeze_model(snr)
# Coupling between code is too tight, abstractions keep leaking.
# Because models must be addressed by NUMBER in XSPEC
# commands to tweak model parameters necessarily break
# abstraction interface between "load models" and "fit models"
# Possible solutions: (1) give up and let interfaces merge
# (monolithic "g309_models_fits"), or . . . (2) stop whining
if 'mosmerge' not in kwargs or kwargs['mosmerge']:
instr_1 = xs.AllModels(1, 'instr_1')
#instr_2 = xs.AllModels(2, 'instr_2') # Let PN instr lines fit
instr_3 = xs.AllModels(3, 'instr_3')
for instr in [instr_1, instr_3]:
# Must update parameter lower limits
instr.constant.factor.values = "0, , 0, 0, , "
instr.constant.factor.frozen = True
else: # No MOS merging
instr_1 = xs.AllModels(1, 'instr_1')
instr_2 = xs.AllModels(2, 'instr_2')
#instr_3 = xs.AllModels(3, 'instr_3') # Let PN instr lines fit
instr_4 = xs.AllModels(4, 'instr_4')
instr_5 = xs.AllModels(5, 'instr_5')
for instr in [instr_1, instr_2, instr_4, instr_5]:
# Must update parameter lower limits
instr.constant.factor.values = "0, , 0, 0, , "
instr.constant.factor.frozen = True
snr.tbnew_gas.nH = 0
snr.tbnew_gas.nH.frozen = True
# LineE: lower/upper bounds set from Yamaguchi+ 2014
# Sigma: prevent line from over-widening to fit as "constant" addition
# norm: no bounds
snr.setPars({snr.gaussian.LineE.index : "6.55, , 6.2, 6.3, 6.8, 6.9",
snr.gaussian.Sigma.index : "0.1, , 0, 0, 0.2, 0.5"} )
snr.gaussian.LineE.frozen=False
snr.gaussian.Sigma.frozen=False
snr.gaussian.norm.frozen=False
xs.Fit.perform()
else:
raise Exception("Invalid SNR model - please add branch")
# Compute standard 90% errors
if error:
xs.Xset.openLog(output + "_error.log")
if with_bkg:
xs.Fit.error(error_str_all_free(xrb))
xs.Fit.error(error_str_all_free(snr))
if error_rerun:
if with_bkg:
xs.Fit.error(error_str_all_free(xrb))
xs.Fit.error(error_str_all_free(snr))
xs.Xset.closeLog()
# Dump standard outputs
products(output)
print_model(snr, output + "_{:s}.txt".format(snr.name))
if with_bkg:
print_model(xrb, output + "_xrb.txt")
