def cmp_pha_intro(fit_result, p1, covarerr):
assert fit_result.statval == approx(37.9079, rel=1e-4)
assert fit_result.rstat == approx(0.902569, rel=1e-4)
assert fit_result.qval == approx(0.651155, rel=1e-4)
self.assertEqual(fit_result.nfev, 22)
self.assertEqual(fit_result.numpoints, 44)
self.assertEqual(fit_result.dof, 42)
p1.gamma.val == approx(2.15852, rel=1e-4)
p1.ampl.val == approx(0.00022484, rel=1e-4)
assert ui.calc_photon_flux() == approx(0.000469964, rel=1e-4)
assert ui.calc_energy_flux() == approx(9.614847e-13, rel=1e-4)
assert ui.calc_data_sum() == approx(706.85714092, rel=1e-4)
assert ui.calc_model_sum() == approx(638.45693377, rel=1e-4)
assert ui.calc_source_sum() == approx(0.046996409, rel=1e-4)
calc = ui.eqwidth(self.locals['p1'], ui.get_source())
assert calc == approx(-0.57731725, rel=1e-4)
calc = ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2)
# Prior to fixing #619 the expected values were
# expected = [0.93341286, 0.93752836, 0.94325233,
# 0.94990140, 0.95678054, 0.96393515]
expected = [0.93132747, 0.9352768, 0.94085917,
0.94738472, 0.95415463, 0.96121113]
assert calc == approx(expected, rel=1e-4)
def test_pha_intro(self):
self.run_thread('pha_intro')
# astro.ui imported as ui, instead of
# being in global namespace
self.assertEqualWithinTol(ui.get_fit_results().statval, 37.9079, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().rstat, 0.902569, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().qval, 0.651155, 1e-4)
self.assertEqualWithinTol(self.locals['p1'].gamma.val, 2.15852, 1e-4)
self.assertEqualWithinTol(self.locals['p1'].ampl.val, 0.00022484, 1e-4)
self.assertEqualWithinTol(ui.calc_photon_flux(), 0.000469964, 1e-4)
self.assertEqualWithinTol(ui.calc_energy_flux(), 9.614847e-13, 1e-4)
self.assertEqualWithinTol(ui.calc_data_sum(), 706.85714092, 1e-4)
self.assertEqualWithinTol(ui.calc_model_sum(), 638.45693377, 1e-4)
self.assertEqualWithinTol(ui.calc_source_sum(), 0.046996409, 1e-4)
self.assertEqualWithinTol(
ui.eqwidth(self.locals['p1'], ui.get_source()), -0.57731725, 1e-4)
self.assertEqualWithinTol(
ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2), [
0.93341286, 0.93752836, 0.94325233, 0.94990140, 0.95678054,
0.96393515
], 1e-4)
self.assertEqual(ui.get_fit_results().nfev, 22)
self.assertEqual(ui.get_fit_results().numpoints, 44)
self.assertEqual(ui.get_fit_results().dof, 42)
def test_pha_intro(self):
self.run_thread("pha_intro")
# astro.ui imported as ui, instead of
# being in global namespace
self.assertEqualWithinTol(ui.get_fit_results().statval, 37.9079, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().rstat, 0.902569, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().qval, 0.651155, 1e-4)
self.assertEqualWithinTol(self.locals["p1"].gamma.val, 2.15852, 1e-4)
self.assertEqualWithinTol(self.locals["p1"].ampl.val, 0.00022484, 1e-4)
self.assertEqualWithinTol(ui.calc_photon_flux(), 0.000469964, 1e-4)
self.assertEqualWithinTol(ui.calc_energy_flux(), 9.614847e-13, 1e-4)
self.assertEqualWithinTol(ui.calc_data_sum(), 706.85714092, 1e-4)
self.assertEqualWithinTol(ui.calc_model_sum(), 638.45693377, 1e-4)
self.assertEqualWithinTol(ui.calc_source_sum(), 0.046996409, 1e-4)
self.assertEqualWithinTol(ui.eqwidth(self.locals["p1"], ui.get_source()), -0.57731725, 1e-4)
self.assertEqualWithinTol(
ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2),
[0.93341286, 0.93752836, 0.94325233, 0.94990140, 0.95678054, 0.96393515],
1e-4,
)
self.assertEqual(ui.get_fit_results().nfev, 22)
self.assertEqual(ui.get_fit_results().numpoints, 44)
self.assertEqual(ui.get_fit_results().dof, 42)
def cmp_thread(fit_result, p1, covarerr):
assert fit_result.statval == approx(37.9079, rel=1e-4)
assert fit_result.rstat == approx(0.902569, rel=1e-4)
assert fit_result.qval == approx(0.651155, rel=1e-4)
assert fit_result.nfev == 22
assert fit_result.numpoints == 44
assert fit_result.dof == 42
p1.gamma.val == approx(2.15852, rel=1e-4)
p1.ampl.val == approx(0.00022484, rel=1e-4)
assert ui.calc_photon_flux() == approx(0.000469964, rel=1e-4)
assert ui.calc_energy_flux() == approx(9.614847e-13, rel=1e-4)
assert ui.calc_data_sum() == approx(706.85714092, rel=1e-4)
assert ui.calc_model_sum() == approx(638.45693377, rel=1e-4)
assert ui.calc_source_sum() == approx(0.046996409, rel=1e-4)
calc = ui.eqwidth(p1, ui.get_source())
assert calc == approx(-0.57731725, rel=1e-4)
calc = ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2)
expected = [
0.93132747, 0.9352768, 0.94085917, 0.94738472, 0.95415463,
0.96121113
]
assert calc == approx(expected, rel=1e-4)
def test_pha_intro(self):
self.run_thread('pha_intro')
# astro.ui imported as ui, instead of
# being in global namespace
fit_results = ui.get_fit_results()
covarerr = sqrt(fit_results.extra_output['covar'].diagonal())
assert covarerr[0] == approx(0.0790393, rel=1e-4)
assert covarerr[1] == approx(1.4564e-05, rel=1e-4)
assert fit_results.statval == approx(37.9079, rel=1e-4)
assert fit_results.rstat == approx(0.902569, rel=1e-4)
assert fit_results.qval == approx(0.651155, rel=1e-4)
assert self.locals['p1'].gamma.val == approx(2.15852, rel=1e-4)
assert self.locals['p1'].ampl.val == approx(0.00022484, rel=1e-4)
assert ui.calc_photon_flux() == approx(0.000469964, rel=1e-4)
assert ui.calc_energy_flux() == approx(9.614847e-13, rel=1e-4)
assert ui.calc_data_sum() == approx(706.85714092, rel=1e-4)
assert ui.calc_model_sum() == approx(638.45693377, rel=1e-4)
assert ui.calc_source_sum() == approx(0.046996409, rel=1e-4)
calc = ui.eqwidth(self.locals['p1'], ui.get_source())
assert calc == approx(-0.57731725, rel=1e-4)
calc = ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2)
expected = [0.93341286, 0.93752836, 0.94325233,
0.94990140, 0.95678054, 0.96393515]
assert calc == approx(expected, rel=1e-4)
self.assertEqual(ui.get_fit_results().nfev, 22)
self.assertEqual(ui.get_fit_results().numpoints, 44)
self.assertEqual(ui.get_fit_results().dof, 42)
def fit_sherpa(obsid_list,
redshift,
nH_Gal,
energy,
min_counts=25,
kT_guess=3,
Ab_guess=1,
fix_nH_Gal=True,
fix_abund=False,
find_errors=True):
spectra = []
for obs in obsid_list:
temp = glob.glob('xaf_*_' + obs + '.pi') # get spectra of regions
spectra.append(
temp
) # spectra will be made of lists which have xaf_*_obs.pi filenames - access with spectra[i][j]
spectra.sort()
num_obs = len(obsid_list)
num_reg = len(temp)
filename = 'spectra_wabs_mekal.dat'
results_file = open(filename, "w")
results_file.write(
'# Fit results for wabs*mekal (zeros indicate that no fitting was performed)\n'
)
results_file.write(
'# Reg_no. kT kT_loerr kT_hierr Z Z_loerr Z_hierr norm norm_loerr norm_hierr nH_Gal nH_loerr nH_hierr red_chisq total_counts num_bins\n'
)
for i in range(num_reg):
sherpa.clean() # clean everything
cnts = numpy.zeros(
num_obs
) # make array of zeros with index length same as num_obs to store counts
max_rate = numpy.zeros(num_obs) # max count rate [counts/s/keV]
data_set = 0 # data set number
good_src_ids = numpy.zeros(num_obs, dtype=int) - 1
for j in range(num_obs):
sherpa.load_pha(
data_set, spectra[j]
[i]) # load xaf_#_obs_####.pi and .arf and .rmf files.
sherpa.ignore_id(data_set, 0.0, energy[0])
sherpa.ignore_id(data_set, energy[1], None)
cnts[j] = sherpa.calc_data_sum(energy[0], energy[1], data_set)
cnt_rate = sherpa.get_rate(data_set, filter=True)
if len(cnt_rate) == 0:
max_rate[
j] = 0.0 # when few counts (<50), get_rate can return zero-length array
else:
max_rate[j] = numpy.max(cnt_rate)
sherpa.subtract(data_set) # subtract background
sherpa.set_source(
data_set, sherpa.xswabs.abs1 *
sherpa.xsmekal.plsm1) # 1 temperature mekal model fit
good_src_ids[j] = data_set
data_set += 1 # same run for region but different obs
# Filter out ignored obs
good_src_ids_indx = numpy.where(good_src_ids >= 0)
good_src_ids = good_src_ids[good_src_ids_indx]
max_rate = max_rate[good_src_ids_indx]
cnts = cnts[good_src_ids_indx]
totcnts = numpy.sum(cnts)
if totcnts >= min_counts:
print('Fitting spectra in region: ' + str(i))
abs1.nH = nH_Gal
abs1.cache = 0
if fix_nH_Gal:
sherpa.freeze(abs1.nH)
else:
sherpa.thaw(abs1.nH)
plsm1.kt = kT_guess
sherpa.thaw(plsm1.kt)
plsm1.Abundanc = Ab_guess
if fix_abund:
sherpa.freeze(plsm1.Abundanc)
else:
sherpa.thaw(plsm1.Abundanc)
plsm1.redshift = redshift
sherpa.freeze(plsm1.redshift)
plsm1.cache = 0
sherpa.fit()
fit_result = sherpa.get_fit_results()
red_chi2 = fit_result.rstat
num_bins = fit_result.numpoints
if fix_nH_Gal:
nH = nH_Gal
kT = fit_result.parvals[0]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[1]
else:
Z = fit_result.parvals[1]
norm = fit_result.parvals[2]
else:
nH = fit_result.parvals[0]
kT = fit_result.parvals[1]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[2]
else:
Z = fit_result.parvals[2]
norm = fit_result.parvals[3]
del fit_result
if find_errors:
sherpa.covar()
covar_result = sherpa.get_covar_results()
if fix_nH_Gal:
nH_loerr = 0.0
nH_hierr = 0.0
kT_loerr = covar_result.parmins[0]
kT_hierr = covar_result.parmaxes[0]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[1]
norm_hierr = covar_result.parmaxes[1]
else:
Z_loerr = covar_result.parmins[1]
Z_hierr = covar_result.parmaxes[1]
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
nH_loerr = covar_result.parmins[0]
nH_hierr = covar_result.parmaxes[0]
kT_loerr = covar_result.parmins[1]
kT_hierr = covar_result.parmaxes[1]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
Z_loerr = covar_result.parmins[2]
Z_hierr = covar_result.parmaxes[2]
norm_loerr = covar_result.parmins[3]
norm_hierr = covar_result.parmaxes[3]
del covar_result
# Check for failed errors (= None) and set them to +/- best-fit value
if not fix_nH_Gal:
if nH_loerr is None: nH_loerr = -nH # is was ==
if nH_hierr is None: nH_hierr = nH
if kT_loerr is None: kT_loerr = -kT
if kT_hierr is None: kT_hierr = kT
if not fix_abund:
if Z_loerr is None: Z_loerr = -Z
if Z_hierr is None: Z_hierr = Z
if norm_loerr is None: norm_loerr = -norm
if norm_hierr is None: norm_hierr = norm
else:
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
else: # if total counts < min_counts, just write zeros
print('\n Warning: no fit performed for for region: ' + str(i))
print(
'\n Spectra have insufficient counts after filtering or do not exist.'
)
print('\n --> All parameters for this region set to 0.0.')
kT = 0.0
Z = 0.0
nH = 0.0
norm = 0.0
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
red_chi2 = 0.0
num_bins = 0
reg_id = spectra[0][i].split(
'_'
) # Splits string after every underscore so that region number can be accessed. reg_id[1] is accessed because that is the region number after 'xaf'
results_file.write(
'%7r %7.4f %7.4f %7.4f %7.4f %7.4f %7.4f %6.4e %6.4e %6.4e %7.4f %7.4f %7.4f %7.4f %8.1f %8r\n'
% (int(reg_id[1]), kT, kT_loerr, kT_hierr, Z, Z_loerr, Z_hierr,
norm, norm_loerr, norm_hierr, nH, nH_loerr, nH_hierr, red_chi2,
totcnts, num_bins)) # Write all data to a file
results_file.close()
def fit_sherpa(obsid_list, redshift, nH_Gal, energy, min_counts=25, kT_guess=3, Ab_guess=1, fix_nH_Gal=True, fix_abund=False, find_errors=True):
spectra = []
for obs in obsid_list:
temp = glob.glob('xaf_*_' + obs + '.pi') # get spectra of regions
spectra.append(temp) # spectra will be made of lists which have xaf_*_obs.pi filenames - access with spectra[i][j]
spectra.sort()
num_obs = len(obsid_list)
num_reg = len(temp)
filename = 'spectra_wabs_mekal.dat'
results_file = open(filename, "w")
results_file.write('# Fit results for wabs*mekal (zeros indicate that no fitting was performed)\n')
results_file.write('# Reg_no. kT kT_loerr kT_hierr Z Z_loerr Z_hierr norm norm_loerr norm_hierr nH_Gal nH_loerr nH_hierr red_chisq total_counts num_bins\n')
for i in range(num_reg):
sherpa.clean() # clean everything
cnts = numpy.zeros(num_obs) # make array of zeros with index length same as num_obs to store counts
max_rate = numpy.zeros(num_obs) # max count rate [counts/s/keV]
data_set = 0 # data set number
good_src_ids = numpy.zeros(num_obs, dtype=int) - 1
for j in range(num_obs):
sherpa.load_pha(data_set, spectra[j][i]) # load xaf_#_obs_####.pi and .arf and .rmf files.
sherpa.ignore_id(data_set, 0.0, energy[0])
sherpa.ignore_id(data_set, energy[1], None)
cnts[j] = sherpa.calc_data_sum(energy[0], energy[1], data_set)
cnt_rate = sherpa.get_rate(data_set, filter=True)
if len(cnt_rate) == 0:
max_rate[j] = 0.0 # when few counts (<50), get_rate can return zero-length array
else:
max_rate[j] = numpy.max(cnt_rate)
sherpa.subtract(data_set) # subtract background
sherpa.set_source(data_set, sherpa.xswabs.abs1 * sherpa.xsmekal.plsm1) # 1 temperature mekal model fit
good_src_ids[j] = data_set
data_set += 1 # same run for region but different obs
# Filter out ignored obs
good_src_ids_indx = numpy.where(good_src_ids >= 0)
good_src_ids = good_src_ids[good_src_ids_indx]
max_rate = max_rate[good_src_ids_indx]
cnts = cnts[good_src_ids_indx]
totcnts = numpy.sum(cnts)
if totcnts >= min_counts:
print('Fitting spectra in region: ' + str(i))
abs1.nH = nH_Gal
abs1.cache = 0
if fix_nH_Gal:
sherpa.freeze(abs1.nH)
else:
sherpa.thaw(abs1.nH)
plsm1.kt = kT_guess
sherpa.thaw(plsm1.kt)
plsm1.Abundanc = Ab_guess
if fix_abund:
sherpa.freeze(plsm1.Abundanc)
else:
sherpa.thaw(plsm1.Abundanc)
plsm1.redshift = redshift
sherpa.freeze(plsm1.redshift)
plsm1.cache = 0
sherpa.fit()
fit_result = sherpa.get_fit_results()
red_chi2 = fit_result.rstat
num_bins = fit_result.numpoints
if fix_nH_Gal:
nH = nH_Gal
kT = fit_result.parvals[0]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[1]
else:
Z = fit_result.parvals[1]
norm = fit_result.parvals[2]
else:
nH = fit_result.parvals[0]
kT = fit_result.parvals[1]
if fix_abund:
Z = Ab_guess
norm = fit_result.parvals[2]
else:
Z = fit_result.parvals[2]
norm = fit_result.parvals[3]
del fit_result
if find_errors:
sherpa.covar()
covar_result = sherpa.get_covar_results()
if fix_nH_Gal:
nH_loerr = 0.0
nH_hierr = 0.0
kT_loerr = covar_result.parmins[0]
kT_hierr = covar_result.parmaxes[0]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[1]
norm_hierr = covar_result.parmaxes[1]
else:
Z_loerr = covar_result.parmins[1]
Z_hierr = covar_result.parmaxes[1]
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
nH_loerr = covar_result.parmins[0]
nH_hierr = covar_result.parmaxes[0]
kT_loerr = covar_result.parmins[1]
kT_hierr = covar_result.parmaxes[1]
if fix_abund:
Z_loerr = 0.0
Z_hierr = 0.0
norm_loerr = covar_result.parmins[2]
norm_hierr = covar_result.parmaxes[2]
else:
Z_loerr = covar_result.parmins[2]
Z_hierr = covar_result.parmaxes[2]
norm_loerr = covar_result.parmins[3]
norm_hierr = covar_result.parmaxes[3]
del covar_result
# Check for failed errors (= None) and set them to +/- best-fit value
if not fix_nH_Gal:
if nH_loerr is None: nH_loerr = -nH # is was ==
if nH_hierr is None: nH_hierr = nH
if kT_loerr is None: kT_loerr = -kT
if kT_hierr is None: kT_hierr = kT
if not fix_abund:
if Z_loerr is None: Z_loerr = -Z
if Z_hierr is None: Z_hierr = Z
if norm_loerr is None: norm_loerr = -norm
if norm_hierr is None: norm_hierr = norm
else:
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
else: # if total counts < min_counts, just write zeros
print('\n Warning: no fit performed for for region: ' + str(i))
print('\n Spectra have insufficient counts after filtering or do not exist.')
print('\n --> All parameters for this region set to 0.0.')
kT = 0.0
Z = 0.0
nH = 0.0
norm = 0.0
kT_loerr = 0.0
Z_loerr = 0.0
nH_loerr = 0.0
norm_loerr = 0.0
kT_hierr = 0.0
Z_hierr = 0.0
nH_hierr = 0.0
norm_hierr = 0.0
red_chi2 = 0.0
num_bins = 0
reg_id = spectra[0][i].split('_') # Splits string after every underscore so that region number can be accessed. reg_id[1] is accessed because that is the region number after 'xaf'
results_file.write('%7r %7.4f %7.4f %7.4f %7.4f %7.4f %7.4f %6.4e %6.4e %6.4e %7.4f %7.4f %7.4f %7.4f %8.1f %8r\n' % (int(reg_id[1]), kT, kT_loerr, kT_hierr, Z, Z_loerr, Z_hierr, norm, norm_loerr, norm_hierr, nH, nH_loerr, nH_hierr, red_chi2, totcnts, num_bins)) # Write all data to a file
results_file.close()