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fit_spectra.py
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"""
Fits a model to Chandra spectra.
This module can use the output of extract_spectra.py to fit
a model to the spectra. If run as a script, multiple spectra
may be specfied by giving a text file that lists the
spectra as an argument with '@' prepended.
Version 0.6: 3/5/2011 - Updated for Ciao 4.3
Version 0.7: 1/11/2013 - Updated for Ciao 4.5
Version 0.8: 29/5/2014 - Updated for Ciao 4.6
"""
import os
import sys
import subprocess
import numpy
import multiprocessing
import threading
# Check that CIAO was initialized
if os.environ.get("CALDB") == None:
sys.exit('Please initalize CIAO before running this script.')
from sherpa.astro.ui import *
from pychips import *
import gc
def call_sherpa_1T(spectra, redshift, nH_Gal, kT_guess, Ab_guess, root, lo_energy='0.5', hi_energy='7.0', plasma_model='mekal', min_counts=100, binning=None, reg_num_to_start=0, fix_nH_Gal=True, fix_abund=False, find_errors=False, make_plots=False, min_cnt_rate_ratio=0.3, clobber=False):
"""
Calls Sherpa to fit a single-temperature model to one or more spectra.
Inputs: spectra - list of input PI files. Can be a list of
lists if there is more than one observation:
e.g., [spectra_obs1, spectra_obs2], where
spectra_obs1 = ['reg1.pi, 'reg2.pi', ...]
redshift - redshift of source
nH_Gal - Galactic N_H (10^22 cm^-2)
kT_guess - initial guess for temperature (keV)
Ab_guess - initial guess for abundance
root - root of output file with fit results
lo_energy - lower bound of fitted energy range (keV)
hi_energy - upper bound of fitted energy range (keV)
plasma_model - specifies the plasma model (mekal or apec)
min_counts - minimum number of total counts required for
fitting
binning - number of counts per bin for fitting
reg_num_to_start - number to start from when numbering the
fit results by region
fix_nH_Gal - if True, freezes nH_Gal
fix_abund - if True, freezes abundance
find_errors - if True, calculates errors
make_plots - if True, make a plot of fit for each region
min_cnt_rate_ratio - min ratio (relative to max cnt rate
in region) below which to discard
observations
clobber - if True, overwrite existing file
Outputs: The fits results are saved to the file:
root+'_wabs_'+plasma_model+'.dat'
"""
if isinstance(spectra, str): spectra = [spectra]
if isinstance(spectra[0], str):
nreg = 1 # number of regions
else:
nreg = len(spectra[0]) # number of regions
nobs = len(spectra) # number of observations
fit_results_file = root + '_wabs_' + plasma_model + '.dat'
ccolor = ["blue", "gold", "cyan", "forest", "darkred", "red", "gray", "green", "magenta", "orange", "black", "yellow", "turquoise", "firebrick", "brown", "azure", "honeydew", "lime", "mistyrose", "navy"]
if type(redshift) == str: redshift = float(redshift)
if type(nH_Gal) == str: nH_Gal = float(nH_Gal)
if type(kT_guess) == str: kT_guess = float(kT_guess)
if type(Ab_guess) == str: Ab_guess = float(Ab_guess)
if type(lo_energy) == str: lo_energy = float(lo_energy)
if type(hi_energy) == str: hi_energy = float(hi_energy)
if os.path.isfile(fit_results_file) == False or clobber == True:
results_file = open(fit_results_file, "w")
results_file.write('# Fit results for wabs*'+plasma_model+' (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(nreg):
print('\n')
clean() # reset everything
gc.collect() # collect garbage every step to avoid memory problems when fitting a large number (>10) of observations simultaneously
nobs_current_reg = 0 # number of valid spectra for this region
if nobs > 1:
cnts = numpy.zeros(nobs) # array to store counts
max_rate = numpy.zeros(nobs) # max count rate [counts/s/keV]
src_id = 0 # index of source id
good_src_ids = numpy.zeros(nobs, dtype=int) - 1
for j in range(nobs):
pi_file = spectra[j][i]
pi_root = os.path.splitext(pi_file)[0]
if pi_root[-3:] == 'grp': # check if grouped or not
pi_root = pi_root[:-4]
bgd_file = pi_root[:-3] + 'bgd.pi'
rmf_file = pi_root + '.rmf'
arf_file = pi_root + '.arf'
pi_file_exists = os.path.isfile(pi_file)
bgd_file_exists = os.path.isfile(bgd_file)
if not bgd_file_exists:
bgd_file = pi_root[:-3] + 'bkg.pi'
bgd_file_exists = os.path.isfile(bgd_file)
rmf_file_exists = os.path.isfile(rmf_file)
arf_file_exists = os.path.isfile(arf_file)
if pi_file_exists and bgd_file_exists and rmf_file_exists and arf_file_exists: # make sure all required files exist before trying to load data
nobs_current_reg += 1
load_pha(src_id, pi_file)
if binning != None:
print('Grouping to ' + str(binning) + ' counts...')
group_counts(src_id, binning)
ignore_id(src_id, 0.0, lo_energy)
ignore_id(src_id, hi_energy, None)
cnts[j] = calc_data_sum(lo_energy, hi_energy, src_id) # get counts in filtered dataset
print('Counts for obs '+str(j+1)+': '+str(int(cnts[j])))
cnt_rate = get_rate(src_id, filter=True)
if len(cnt_rate) == 0: # when few counts (<50), get_rate can return zero-length array
max_rate[j] = 0.0
else:
max_rate[j] = numpy.max(cnt_rate)
subtract(src_id) # subtract the background
if src_id == 0:
if plasma_model == 'mekal':
set_source(src_id, xswabs.abs1 * xsmekal.plsm1)
if plasma_model == 'apec':
set_source(src_id, xswabs.abs1 * xsapec.plsm1)
else:
set_source(src_id, abs1 * plsm1)
good_src_ids[j] = src_id
src_id += 1
# Filter out ignored observations
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]
# If min_cnt_rate_ratio is specified, check whether the count rate
# of any observation falls below the limit.
if min_cnt_rate_ratio != None:
max_rate_overall = numpy.max(max_rate)
max_rate_ratios = max_rate / max_rate_overall
lowcr_src_ids_indx = numpy.where(max_rate_ratios < min_cnt_rate_ratio)
highcr_src_ids_indx = numpy.where(max_rate_ratios >= min_cnt_rate_ratio)
if len(lowcr_src_ids_indx) > 0:
lowcr_src_ids = good_src_ids[lowcr_src_ids_indx]
good_src_ids = good_src_ids[highcr_src_ids_indx]
cnts = cnts[highcr_src_ids_indx]
for b in range(len(lowcr_src_ids)):
print('Removing observation '+str(lowcr_src_ids[b]+1)+' (dataset '+str(lowcr_src_ids[b])+') for low count rate.')
delete_data(lowcr_src_ids[b])
nobs_current_reg -= 1
if nobs == 1:
cnts = 0
pi_file = spectra[0][i]
pi_root = os.path.splitext(pi_file)[0]
if pi_root[-3:] == 'grp': # check if grouped or not
pi_root = pi_root[:-4]
bgd_file = pi_root[:-3] + 'bgd.pi'
rmf_file = pi_root + '.rmf'
arf_file = pi_root + '.arf'
pi_file_exists = os.path.isfile(pi_file)
bgd_file_exists = os.path.isfile(bgd_file)
if not bgd_file_exists:
bgd_file = pi_root[:-3] + 'bkg.pi'
bgd_file_exists = os.path.isfile(bgd_file)
rmf_file_exists = os.path.isfile(rmf_file)
arf_file_exists = os.path.isfile(arf_file)
if pi_file_exists and bgd_file_exists and rmf_file_exists and arf_file_exists: # make sure all required files exist before trying to load data
nobs_current_reg += 1
load_pha(pi_file)
if binning != None:
group_counts(binning)
if plasma_model == 'mekal':
set_source(xswabs.abs1 * xsmekal.plsm1)
if plasma_model == 'apec':
set_source(xswabs.abs1 * xsapec.plsm1)
ignore(0.0, lo_energy)
ignore(hi_energy, None)
cnts = calc_data_sum(lo_energy, hi_energy) # get counts in filtered dataset
subtract()
# Check whether total counts >= min_counts.
# If so, fit; if not, skip the fit
totcnts = numpy.sum(cnts)
if totcnts >= min_counts:
if nobs_current_reg > 1:
print('\nFitting '+str(nobs_current_reg)+' spectra in region '+str(i + reg_num_to_start)+' ('+str(int(totcnts))+' counts total)...')
else:
print('\nFitting 1 spectrum in region '+str(i + reg_num_to_start)+' ('+str(int(totcnts))+' counts total)...')
abs1.nH = nH_Gal
abs1.cache = 0
if fix_nH_Gal:
freeze(abs1.nH)
else:
thaw(abs1.nH)
plsm1.kt = kT_guess
thaw(plsm1.kt)
plsm1.abundanc = Ab_guess
if fix_abund:
freeze(plsm1.abundanc)
else:
thaw(plsm1.abundanc)
plsm1.redshift = redshift
freeze(plsm1.redshift)
plsm1.cache = 0
fit()
fit_result = 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 make_plots:
if len(good_src_ids) > 10:
nplots = numpy.ceil(len(good_src_ids) / 10.0)
else:
nplots = 1
for plot_num in range(nplots):
start_indx = 0 + numpy.floor(len(good_src_ids) / nplots) * plot_num
if plot_num == nplots - 1:
end_indx = len(good_src_ids)
else:
end_indx = numpy.floor(len(good_src_ids) / nplots) * (plot_num + 1.0)
clear() # delete any plot windows
add_window()
#add_window(["display", False])
set_preference("frame.transparency", "true")
#set_preference("window.display", "false")
label_ypos = 0.2
ccolor_indx = 0
for p in good_src_ids[start_indx:end_indx]:
plot_fit_delchi(p, overplot=True, clearwindow=False)
log_scale(X_AXIS)
set_current_plot("plot1")
limits(X_AXIS, 0.1, 10) # keV
limits(Y_AXIS, 1E-7, 0.2)
log_scale(Y_AXIS)
set_curve("crv1", ["symbol.color", ccolor[ccolor_indx], "err.color", ccolor[ccolor_indx]])
set_curve("crv2", ["line.color", ccolor[ccolor_indx], "err.color", ccolor[ccolor_indx]])
label_ypos = label_ypos / 2.0
add_label(4.0, label_ypos, "obs"+str(p+1))
set_label(["color", ccolor[ccolor_indx]])
set_current_plot("plot2")
limits(X_AXIS, 0.1, 10) # keV
limits(Y_AXIS, -5.0, 5.0)
set_curve(["symbol.color", ccolor[ccolor_indx], "err.color", ccolor[ccolor_indx]])
set_plot_xlabel("Energy (keV)")
set_plot_ylabel("Sigma")
ccolor_indx += 1
set_current_plot("plot1")
set_plot_title("Fit for region "+str(i+reg_num_to_start))
set_plot_ylabel("Counts s^{-1} keV^{-1}")
if nplots > 1:
fit_plot_file = "reg" + str(i+reg_num_to_start) + "_plot" + str(plot_num) + "_1T_fit.pdf"
else:
fit_plot_file = "reg" + str(i+reg_num_to_start) + "_1T_fit.pdf"
print_window(fit_plot_file, ["orientation", "portrait", "clobber", True])
if find_errors:
covar()
covar_result = 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]
kT_loerr = covar_result.parmins[1]
nH_hierr = covar_result.parmaxes[0]
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 == None: nH_loerr = -nH
if nH_hierr == None: nH_hierr = nH
if kT_loerr == None: kT_loerr = -kT
if kT_hierr == None: kT_hierr = kT
if not fix_abund:
if Z_loerr == None: Z_loerr = -Z
if Z_hierr == None: Z_hierr = Z
if norm_loerr == None: norm_loerr = -norm
if norm_hierr == 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 + reg_num_to_start)+':')
if nobs > 1:
print(' Spectra have insufficient counts after filtering or do not exist.')
else:
print(' Spectrum has insufficient counts after filtering or does not exist.')
print(' --> 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
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' % (i+reg_num_to_start, 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) )
results_file.close()
# Finally, make sure all regions have an entry in the fit results file
# (this shouldn't be needed, but just in case...)
dtype = {'names': ('reg_id', 'kT', 'kT_lo', 'kT_hi', 'Z', 'Z_lo', 'Z_hi', 'norm', 'norm_lo', 'norm_hi', 'nH', 'nH_lo', 'nH_hi', 'chi2', 'totcnts', 'nbins'), 'formats': ('i4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'f4', 'i4')}
data = numpy.loadtxt(fit_results_file, dtype=dtype)
reg_num = data["reg_id"]
missing_regs = []
n_missing = 0
for i in range(len(reg_num)):
if int(reg_num[i]) != i + reg_num_to_start + n_missing:
missing_regs.append(i + reg_num_to_start + n_missing)
n_missing += 1
if n_missing > 0:
results_file = open(fit_results_file, "a") # append missing regions
for i in range(n_missing):
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' % (missing_regs[i]+reg_num_to_start, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0) )
else:
print('\n Output file ('+fit_results_file+') exists and clobber = False.')
def call_sherpa_2T(spectra, redshift, nH_Gal, kT_guess, Ab_guess, root, lo_energy='0.5', hi_energy='7.0', plasma_model='mekal', min_counts=100, binning=None, reg_num_to_start=0, fix_nH_Gal=True, fix_abund=False, find_errors=False, make_plots=False, min_cnt_rate_ratio=0.3, clobber=False):
"""
Calls Sherpa to fit a two-temperature model to one or more spectra.
Inputs: spectra - list of input PI files. Can be a list of
lists if there is more than one observation:
e.g., [spectra_obs1, spectra_obs2], where
spectra_obs1 = ['reg1.pi, 'reg2.pi', ...]
redshift - redshift of source
nH_Gal - Galactic N_H (10^22 cm^-2)
kT_guess - initial guess for temperature (keV)
Ab_guess - initial guess for abundance
root - root of output file with fit results
lo_energy - lower bound of fitted energy range (keV)
hi_energy - upper bound of fitted energy range (keV)
plasma_model - specifies the plasma model (mekal or apec)
min_counts - minimum number of total counts required for
fitting
binning - number of counts per bin for fitting
reg_num_to_start - number to start from when numbering the
fit results by region
fix_nH_Gal - if True, freezes nH_Gal
fix_abund - if True, freezes abundance
find_errors - if True, calculates errors
make_plots - if True, make a plot of fit for each region
min_cnt_rate_ratio - min ratio (relative to max cnt rate
in region) below which to discard
observations
clobber - if True, overwrite existing file
Outputs: The fits results are saved to the file:
root+'_wabs_2'+plasma_model+'.dat'
"""
if isinstance(spectra, str): spectra = [spectra]
if isinstance(spectra[0], str):
nreg = 1 # number of regions
else:
nreg = len(spectra[0]) # number of regions
nobs = len(spectra) # number of observations
fit_results_file = root + '_wabs_2' + plasma_model + '.dat'
ccolor = ["blue", "gold", "cyan", "forest", "darkred", "red", "gray", "green", "magenta", "orange", "black", "yellow", "turquoise", "firebrick", "brown", "azure", "honeydew", "lime", "mistyrose", "navy"]
if type(redshift) == str: redshift = float(redshift)
if type(nH_Gal) == str: nH_Gal = float(nH_Gal)
if type(kT_guess) == str: kT_guess = float(kT_guess)
if type(Ab_guess) == str: Ab_guess = float(Ab_guess)
if type(lo_energy) == str: lo_energy = float(lo_energy)
if type(hi_energy) == str: hi_energy = float(hi_energy)
if os.path.isfile(fit_results_file) == False or clobber == True:
results_file = open(fit_results_file, "w")
results_file.write('# Fit results for wabs*'+plasma_model+' (zeros indicate that no fitting was performed)\n')
results_file.write('# Reg_no. kT1 kT1_loerr kT1_hierr Z1 Z1_loerr Z1_hierr norm1 norm1_loerr norm1_hierr kT2 kT2_loerr kT2_hierr Z2 Z2_loerr Z2_hierr norm2 norm2_loerr norm2_hierr nH_Gal nH_loerr nH_hierr red_chisq total_counts num_bins\n')
if make_plots:
add_window(["display", False])
for i in range(nreg):
print('\n')
clean() # reset everything
gc.collect() # collect garbage every step to avoid memory problems when fitting a large number (>10) of observations simultaneously
nobs_current_reg = 0 # number of valid spectra for this region
if nobs > 1:
cnts = numpy.zeros(nobs) # array to store counts
max_rate = numpy.zeros(nobs) # max count rate [counts/s/keV]
src_id = 0 # index of source id
good_src_ids = numpy.zeros(nobs, dtype=int) - 1
for j in range(nobs):
pi_file = spectra[j][i]
pi_root = os.path.splitext(pi_file)[0]
if pi_root[-3:] == 'grp': # check if grouped or not
pi_root = pi_root[:-4]
bgd_file = pi_root[:-3] + 'bgd.pi'
rmf_file = pi_root + '.rmf'
arf_file = pi_root + '.arf'
pi_file_exists = os.path.isfile(pi_file)
bgd_file_exists = os.path.isfile(bgd_file)
rmf_file_exists = os.path.isfile(rmf_file)
arf_file_exists = os.path.isfile(arf_file)
if pi_file_exists and bgd_file_exists and rmf_file_exists and arf_file_exists: # make sure all required files exist before trying to load data
nobs_current_reg += 1
load_pha(src_id, pi_file)
if binning != None:
print('Grouping to ' + str(binning) + ' counts...')
group_counts(src_id, binning)
ignore_id(src_id, 0.0, lo_energy)
ignore_id(src_id, hi_energy, None)
cnts[j] = calc_data_sum(lo_energy, hi_energy, src_id) # get counts in filtered dataset
print('Counts for obs '+str(j+1)+': '+str(int(cnts[j])))
cnt_rate = get_rate(src_id, filter=True)
if len(cnt_rate) == 0: # when few counts (<50), get_rate can return zero-length array
max_rate[j] = 0.0
else:
max_rate[j] = numpy.max(cnt_rate)
subtract(src_id) # subtract the background
if src_id == 0:
if plasma_model == 'mekal':
set_source(src_id, xswabs.abs1 * (xsmekal.plsm1 + xsmekal.plsm2))
if plasma_model == 'apec':
set_source(src_id, xswabs.abs1 * (xsapec.plsm1 + xsapec.plsm2))
else:
set_source(src_id, abs1 * (plsm1 + plsm2))
good_src_ids[j] = src_id
src_id += 1
# Filter out ignored observations
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]
# If min_cnt_rate_ratio is specified, check whether the count rate
# of any observation falls below the limit.
if min_cnt_rate_ratio != None:
max_rate_overall = numpy.max(max_rate)
max_rate_ratios = max_rate / max_rate_overall
lowcr_src_ids_indx = numpy.where(max_rate_ratios < min_cnt_rate_ratio)
highcr_src_ids_indx = numpy.where(max_rate_ratios >= min_cnt_rate_ratio)
if len(lowcr_src_ids_indx) > 0:
lowcr_src_ids = good_src_ids[lowcr_src_ids_indx]
good_src_ids = good_src_ids[highcr_src_ids_indx]
cnts = cnts[highcr_src_ids_indx]
for b in range(len(lowcr_src_ids)):
print('Removing observation '+str(lowcr_src_ids[b]+1)+' (dataset '+str(lowcr_src_ids[b])+') for low count rate.')
delete_data(lowcr_src_ids[b])
nobs_current_reg -= 1
if nobs == 1:
pi_file = spectra[0][i]
pi_root = os.path.splitext(pi_file)[0]
if pi_root[-3:] == 'grp': # check if grouped or not
pi_root = pi_root[:-4]
bgd_file = pi_root[:-3] + 'bgd.pi'
rmf_file = pi_root + '.rmf'
arf_file = pi_root + '.arf'
pi_file_exists = os.path.isfile(pi_file)
bgd_file_exists = os.path.isfile(bgd_file)
if not bgd_file_exists:
bgd_file = pi_root[:-3] + 'bkg.pi'
bgd_file_exists = os.path.isfile(bgd_file)
rmf_file_exists = os.path.isfile(rmf_file)
arf_file_exists = os.path.isfile(arf_file)
if pi_file_exists and bgd_file_exists and rmf_file_exists and arf_file_exists: # make sure all required files exist before trying to load data
nobs_current_reg += 1
load_pha(pi_file)
if binning != None:
group_counts(binning)
if plasma_model == 'mekal':
set_source(xswabs.abs1 * (xsmekal.plsm1 + xsmekal.plsm2))
if plasma_model == 'apec':
set_source(xswabs.abs1 * (xsapec.plsm1 + xsapec.plsm2))
ignore(0.0, lo_energy)
ignore(hi_energy, None)
cnts = calc_data_sum(lo_energy, hi_energy) # get counts in filtered dataset
subtract()
# Check whether total counts >= min_counts.
# If so, fit; if not, skip the fit
totcnts = numpy.sum(cnts)
if totcnts >= min_counts:
if nobs_current_reg > 1:
print('\nFitting '+str(nobs_current_reg)+' spectra in region '+str(i + reg_num_to_start)+' ('+str(totcnts)+' counts total)...')
else:
print('\nFitting 1 spectrum in region '+str(i + reg_num_to_start)+' ('+str(numpy.sum(cnts))+' counts total)...')
abs1.nH = nH_Gal
abs1.cache = 0
if fix_nH_Gal:
freeze(abs1.nH)
else:
thaw(abs1.nH)
plsm1.kt = kT_guess
thaw(plsm1.kt)
plsm1.abundanc = Ab_guess
if fix_abund:
freeze(plsm1.abundanc)
else:
thaw(plsm1.abundanc)
plsm1.redshift = redshift
freeze(plsm1.redshift)
plsm1.cache = 0
plsm2.kt = kT_guess
thaw(plsm2.kt)
plsm2.abundanc = Ab_guess
if fix_abund:
freeze(plsm2.abundanc)
else:
thaw(plsm2.abundanc)
link(plsm1.abundanc, plsm2.abundanc)
plsm2.redshift = redshift
freeze(plsm2.redshift)
plsm2.cache = 0
set_method("moncar")
fit()
fit_result = get_fit_results()
red_chi2 = fit_result.rstat
num_bins = fit_result.numpoints
if fix_nH_Gal:
nH = nH_Gal
kT1 = fit_result.parvals[0]
if fix_abund:
Z1 = Ab_guess
norm1 = fit_result.parvals[1]
kT2 = fit_result.parvals[2]
norm2 = fit_result.parvals[3]
else:
Z1 = fit_result.parvals[1]
norm1 = fit_result.parvals[2]
kT2 = fit_result.parvals[3]
norm2 = fit_result.parvals[4]
else:
nH = fit_result.parvals[0]
kT1 = fit_result.parvals[1]
if fix_abund:
Z1 = Ab_guess
norm1 = fit_result.parvals[2]
kT2 = fit_result.parvals[3]
norm2 = fit_result.parvals[4]
else:
Z1 = fit_result.parvals[2]
norm1 = fit_result.parvals[3]
kT2 = fit_result.parvals[4]
norm2 = fit_result.parvals[5]
del fit_result
if find_errors:
covar()
covar_result = get_covar_results()
if fix_nH_Gal:
nH_loerr = 0.0
nH_hierr = 0.0
kT1_loerr = covar_result.parmins[0]
kT1_hierr = covar_result.parmaxes[0]
if fix_abund:
Z1_loerr = 0.0
Z1_hierr = 0.0
norm1_loerr = covar_result.parmins[1]
norm1_hierr = covar_result.parmaxes[1]
kT2_loerr = covar_result.parmins[2]
norm2_loerr = covar_result.parmins[3]
kT2_hierr = covar_result.parmaxes[2]
norm2_hierr = covar_result.parmaxes[3]
else:
Z1_loerr = covar_result.parmins[1]
norm1_loerr = covar_result.parmins[2]
kT1_hierr = covar_result.parmaxes[0]
Z1_hierr = covar_result.parmaxes[1]
norm1_hierr = covar_result.parmaxes[2]
kT2_loerr = covar_result.parmins[3]
norm2_loerr = covar_result.parmins[4]
kT2_hierr = covar_result.parmaxes[3]
norm2_hierr = covar_result.parmaxes[4]
else:
nH_loerr =covar_result.parmins[0]
nH_hierr = covar_result.parmaxes[0]
kT1_loerr = covar_result.parmins[1]
kT1_hierr = covar_result.parmaxes[1]
if fix_abund:
Z1_loerr = 0.0
Z1_hierr = 0.0
norm1_loerr = covar_result.parmins[2]
norm1_hierr = covar_result.parmaxes[2]
kT2_loerr = covar_result.parmins[3]
norm2_loerr = covar_result.parmins[4]
kT2_hierr = covar_result.parmaxes[3]
norm2_hierr = covar_result.parmaxes[4]
else:
Z1_loerr = covar_result.parmins[2]
norm1_loerr = covar_result.parmins[3]
Z1_hierr = covar_result.parmaxes[2]
norm1_hierr = covar_result.parmaxes[3]
kT2_loerr = covar_result.parmins[4]
norm2_loerr = covar_result.parmins[5]
kT2_hierr = covar_result.parmaxes[4]
norm2_hierr = covar_result.parmaxes[5]
del covar_result
# Check for failed errors (= None) and set them to +/- best-fit value
if not fix_nH_Gal:
if nH_loerr == None: nH_loerr = -nH
if nH_hierr == None: nH_hierr = nH
if kT1_loerr == None: kT1_loerr = -kT1
if kT1_hierr == None: kT1_hierr = kT1
if not fix_abund:
if Z1_loerr == None: Z1_loerr = -Z1
if Z1_hierr == None: Z1_hierr = Z1
if norm1_loerr == None: norm1_loerr = -norm1
if norm1_hierr == None: norm1_hierr = norm1
if kT2_loerr == None: kT2_loerr = -kT2
if kT2_hierr == None: kT2_hierr = kT2
if norm2_loerr == None: norm2_loerr = -norm2
if norm2_hierr == None: norm2_hierr = norm2
else:
kT1_loerr = 0.0
Z1_loerr = 0.0
nH_loerr = 0.0
norm1_loerr = 0.0
kT1_hierr = 0.0
Z1_hierr = 0.0
nH_hierr = 0.0
norm1_hierr = 0.0
kT2_loerr = 0.0
norm2_loerr = 0.0
kT2_hierr = 0.0
norm2_hierr = 0.0
else: # if total counts < min_counts, just write zeros
print('\n Warning: no fit performed for for region '+str(i + reg_num_to_start)+':')
if nobs > 1:
print(' Spectra have insufficient counts after filtering or do not exist.')
else:
print(' Spectrum has insufficient counts after filtering or does not exist.')
print(' --> All parameters for this region set to 0.0.')
kT1 = 0.0
Z1 = 0.0
nH = 0.0
norm1 = 0.0
kT2 = 0.0
norm2 = 0.0
kT1_loerr = 0.0
Z1_loerr = 0.0
nH_loerr = 0.0
norm1_loerr = 0.0
kT1_hierr = 0.0
Z1_hierr = 0.0
nH_hierr = 0.0
norm1_hierr = 0.0
kT2_loerr = 0.0
norm2_loerr = 0.0
kT2_hierr = 0.0
norm2_hierr = 0.0
red_chi2 = 0.0
num_bins = 0
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 %7.4f %7.4f %6.4e %6.4e %6.4e %7.4f %7.4f %7.4f %7.4f %8.1f %8r\n' % (i+reg_num_to_start, kT1, kT1_loerr, kT1_hierr, Z1, Z1_loerr, Z1_hierr, norm1, norm1_loerr, norm1_hierr, kT2, kT2_loerr, kT2_hierr, Z1, Z1_loerr, Z1_hierr, norm2, norm2_loerr, norm2_hierr, nH, nH_loerr, nH_hierr, red_chi2, totcnts, num_bins) )
results_file.close()
else:
print('\n Output file ('+fit_results_file+') exists and clobber = False.')
def call_sherpa_1T_plus_pow(spectra, redshift, nH_Gal, kT_guess, Ab_guess, plindx_guess, root, lo_energy='0.5', hi_energy='7.0', plasma_model='mekal', min_counts=100, binning=None, reg_num_to_start=0, fix_nH_Gal=True, fix_abund=False, find_errors=False, min_cnt_rate_ratio=0.3, make_plots=False, clobber=False):
"""
Calls Sherpa to fit a single-temperature-plus-power-law model to one or more spectra.
Inputs: spectra - list of input PI files. Can be a list of
lists if there is more than one observation:
e.g., [spectra_obs1, spectra_obs2], where
spectra_obs1 = ['reg1.pi, 'reg2.pi', ...]
redshift - redshift of source
nH_Gal - Galactic N_H (10^22 cm^-2)
kT_guess - initial guess for temperature (keV)
Ab_guess - initial guess for abundance
plindx_guess - intial guess for the power-law index
root - root of output file with fit results
lo_energy - lower bound of fitted energy range (keV)
hi_energy - upper bound of fitted energy range (keV)
plasma_model - specifies the plasma model (mekal or apec)
min_counts - minimum number of total counts required for
fitting
binning - number of counts per bin for fitting
reg_num_to_start - number to start from when numbering the
fit results by region
fix_nH_Gal - if True, freezes nH_Gal
fix_abund - if True, freezes abundance
find_errors - if True, calculates errors
make_plots - if True, make a plot of fit for each region
min_cnt_rate_ratio - min ratio (relative to max cnt rate
in region) below which to discard
observations
clobber - if True, overwrite existing file
Outputs: The fits results are saved to the file:
root+'_wabs_'+plasma_model+'_pow.dat'
"""
if isinstance(spectra, str): spectra = [spectra]
if isinstance(spectra[0], str):
nreg = 1 # number of regions
else:
nreg = len(spectra[0]) # number of regions
nobs = len(spectra) # number of observations
fit_results_file = root + '_wabs_' + plasma_model + '_pow.dat'
ccolor = ["blue", "gold", "cyan", "forest", "darkred", "red", "gray", "green", "magenta", "orange", "black", "yellow", "turquoise", "firebrick", "brown", "azure", "honeydew", "lime", "mistyrose", "navy"]
if type(redshift) == str: redshift = float(redshift)
if type(nH_Gal) == str: nH_Gal = float(nH_Gal)
if type(kT_guess) == str: kT_guess = float(kT_guess)
if type(Ab_guess) == str: Ab_guess = float(Ab_guess)
if type(plindx_guess) == str: plindx_guess = float(plindx_guess)
if type(lo_energy) == str: lo_energy = float(lo_energy)
if type(hi_energy) == str: hi_energy = float(hi_energy)
if os.path.isfile(fit_results_file) == False or clobber == True:
results_file = open(fit_results_file, "w")
results_file.write('# Fit results for wabs*'+plasma_model+' (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 plindx indx_loerr indx_hierr plnorm plnorm_loerr plnorm_hierr nH_Gal nH_loerr nH_hierr red_chisq total_counts num_bins\n')
for i in range(nreg):
print('\n')
clean() # reset everything
gc.collect() # collect garbage every step to avoid memory problems when fitting a large number (>10) of observations simultaneously
nobs_current_reg = 0 # number of valid spectra for this region
good_src_ids = numpy.zeros(nobs, dtype=int) - 1
if nobs > 1:
cnts = numpy.zeros(nobs) # array to store counts
max_rate = numpy.zeros(nobs) # max count rate [counts/s/keV]
src_id = 0 # index of source id
for j in range(nobs):
pi_file = spectra[j][i]
pi_root = os.path.splitext(pi_file)[0]
if pi_root[-3:] == 'grp': # check if grouped or not
pi_root = pi_root[:-4]
bgd_file = pi_root[:-3] + 'bgd.pi'
rmf_file = pi_root + '.rmf'
arf_file = pi_root + '.arf'
pi_file_exists = os.path.isfile(pi_file)
bgd_file_exists = os.path.isfile(bgd_file)
rmf_file_exists = os.path.isfile(rmf_file)
arf_file_exists = os.path.isfile(arf_file)
if pi_file_exists and bgd_file_exists and rmf_file_exists and arf_file_exists: # make sure all required files exist before trying to load data
nobs_current_reg += 1
load_pha(src_id, pi_file)
if binning != None:
print('Grouping to ' + str(binning) + ' counts...')
group_counts(src_id, binning)
ignore_id(src_id, 0.0, lo_energy)
ignore_id(src_id, hi_energy, None)
cnts[j] = calc_data_sum(lo_energy, hi_energy, src_id) # get counts in filtered dataset
print('Counts for obs '+str(j+1)+': '+str(int(cnts[j])))
cnt_rate = get_rate(src_id, filter=True)
if len(cnt_rate) == 0: # when few counts (<50), get_rate can return zero-length array
max_rate[j] = 0.0
else:
max_rate[j] = numpy.max(cnt_rate)
subtract(src_id) # subtract the background
if src_id == 0:
if plasma_model == 'mekal':
set_source(src_id, xswabs.abs1 * (xsmekal.plsm1 + xspowerlaw.pow1))
if plasma_model == 'apec':
set_source(src_id, xswabs.abs1 * (xsapec.plsm1 + xspowerlaw.pow1))
else:
set_source(src_id, abs1 * (plsm1 + pow1))
good_src_ids[j] = src_id
src_id += 1
# Filter out ignored observations
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]
# If min_cnt_rate_ratio is specified, check whether the count rate
# of any observation falls below the limit.
if min_cnt_rate_ratio != None:
max_rate_overall = numpy.max(max_rate)
max_rate_ratios = max_rate / max_rate_overall
lowcr_src_ids_indx = numpy.where(max_rate_ratios < min_cnt_rate_ratio)
highcr_src_ids_indx = numpy.where(max_rate_ratios >= min_cnt_rate_ratio)
if len(lowcr_src_ids_indx) > 0:
lowcr_src_ids = good_src_ids[lowcr_src_ids_indx]
good_src_ids = good_src_ids[highcr_src_ids_indx]
cnts = cnts[highcr_src_ids_indx]
for b in range(len(lowcr_src_ids)):
print('Removing observation '+str(lowcr_src_ids[b]+1)+' (dataset '+str(lowcr_src_ids[b])+') for low count rate.')
delete_data(lowcr_src_ids[b])
nobs_current_reg -= 1
if nobs == 1:
pi_file = spectra[0][i]
pi_root = os.path.splitext(pi_file)[0]
if pi_root[-3:] == 'grp': # check if grouped or not
pi_root = pi_root[:-4]
bgd_file = pi_root[:-3] + 'bgd.pi'
rmf_file = pi_root + '.rmf'
arf_file = pi_root + '.arf'
pi_file_exists = os.path.isfile(pi_file)
bgd_file_exists = os.path.isfile(bgd_file)
if not bgd_file_exists:
bgd_file = pi_root[:-3] + 'bkg.pi'
bgd_file_exists = os.path.isfile(bgd_file)
rmf_file_exists = os.path.isfile(rmf_file)
arf_file_exists = os.path.isfile(arf_file)
if pi_file_exists and bgd_file_exists and rmf_file_exists and arf_file_exists: # make sure all required files exist before trying to load data
nobs_current_reg += 1
load_pha(pi_file)
if binning != None:
group_counts(binning)
if plasma_model == 'mekal':
set_source(xswabs.abs1 * (xsmekal.plsm1 + xspowerlaw.pow1))
if plasma_model == 'apec':
set_source(xswabs.abs1 * (xsapec.plsm1 + xspowerlaw.pow1))
ignore(0.0, lo_energy)
ignore(hi_energy, None)
cnts = calc_data_sum(lo_energy, hi_energy) # get counts in filtered dataset
subtract()
# Check whether total counts >= min_counts.
# If so, fit; if not, skip the fit
totcnts = numpy.sum(cnts)
if totcnts >= min_counts:
if nobs_current_reg > 1:
print('\nFitting '+str(nobs_current_reg)+' spectra in region '+str(i + reg_num_to_start)+' ('+str(totcnts)+' counts total)...')
else:
print('\nFitting 1 spectrum in region '+str(i + reg_num_to_start)+' ('+str(numpy.sum(cnts))+' counts total)...')
abs1.nH = nH_Gal
if fix_nH_Gal:
freeze(abs1.nH)
else:
thaw(abs1.nH)
plsm1.kt = kT_guess
thaw(plsm1.kt)
plsm1.abundanc = Ab_guess
thaw(plsm1.abundanc)
plsm1.redshift = redshift
freeze(plsm1.redshift)
pow1.PhoIndex = plindx_guess
set_method("moncar")
fit()
fit_result = 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]
Z = fit_result.parvals[1]
norm = fit_result.parvals[2]
powindx = fit_result.parvals[3]
pownorm = fit_result.parvals[4]
else:
nH = fit_result.parvals[0]
kT = fit_result.parvals[1]
Z = fit_result.parvals[2]
norm = fit_result.parvals[3]
powindx = fit_result.parvals[4]
pownorm = fit_result.parvals[5]
del fit_result
if make_plots:
if len(good_src_ids) > 10:
nplots = numpy.ceil(len(good_src_ids) / 10.0)
else:
nplots = 1
for plot_num in range(nplots):
start_indx = 0 + numpy.floor(len(good_src_ids) / nplots) * plot_num
if plot_num == nplots - 1:
end_indx = len(good_src_ids)
else:
end_indx = numpy.floor(len(good_src_ids) / nplots) * (plot_num + 1.0)
clear() # delete any plot windows
add_window()
#add_window(["display", False])
set_preference("frame.transparency", "true")
#set_preference("window.display", "false")
label_ypos = 0.2
ccolor_indx = 0
for p in good_src_ids[start_indx:end_indx]:
plot_fit_delchi(p, overplot=True, clearwindow=False)
log_scale(X_AXIS)
set_current_plot("plot1")
limits(X_AXIS, 0.1, 10) # keV
limits(Y_AXIS, 1E-7, 0.2)
log_scale(Y_AXIS)
set_curve("crv1", ["symbol.color", ccolor[ccolor_indx], "err.color", ccolor[ccolor_indx]])
set_curve("crv2", ["line.color", ccolor[ccolor_indx], "err.color", ccolor[ccolor_indx]])
label_ypos = label_ypos / 2.0
add_label(4.0, label_ypos, "obs"+str(p+1))
set_label(["color", ccolor[ccolor_indx]])
set_current_plot("plot2")
limits(X_AXIS, 0.1, 10) # keV
limits(Y_AXIS, -5.0, 5.0)
set_curve(["symbol.color", ccolor[ccolor_indx], "err.color", ccolor[ccolor_indx]])
set_plot_xlabel("Energy (keV)")
set_plot_ylabel("Sigma")
ccolor_indx += 1
set_current_plot("plot1")
set_plot_title("Fit for region "+str(i+reg_num_to_start))
set_plot_ylabel("Counts s^{-1} keV^{-1}")
if nplots > 1:
fit_plot_file = "reg" + str(i+reg_num_to_start) + "_plot" + str(plot_num) + "_1T+pow_fit.pdf"
else:
fit_plot_file = "reg" + str(i+reg_num_to_start) + "_1T+pow_fit.pdf"
print_window(fit_plot_file, ["orientation", "portrait", "clobber", True])
if find_errors:
covar()
covar_result = get_covar_results()
if fix_nH_Gal:
nH_loerr = 0.0
nH_hierr = 0.0
kT_loerr = covar_result.parmins[0]
Z_loerr = covar_result.parmins[1]
norm_loerr = covar_result.parmins[2]
powindx_loerr = covar_result.parmins[3]
pownorm_loerr = covar_result.parmins[4]
kT_hierr = covar_result.parmaxes[0]
Z_hierr = covar_result.parmaxes[1]
norm_hierr = covar_result.parmaxes[2]
powindx_hierr = covar_result.parmaxes[3]
pownorm_hierr = covar_result.parmaxes[4]
else:
nH_loerr =covar_result.parmins[0]
kT_loerr = covar_result.parmins[1]
Z_loerr = covar_result.parmins[2]
norm_loerr = covar_result.parmins[3]
powindx_loerr = covar_result.parmins[4]
pownorm_loerr = covar_result.parmins[5]
nH_hierr = covar_result.parmaxes[0]
kT_hierr = covar_result.parmaxes[1]
Z_hierr = covar_result.parmaxes[2]
norm_hierr = covar_result.parmaxes[3]
powindx_hierr = covar_result.parmaxes[4]
pownorm_hierr = covar_result.parmaxes[5]
del covar_result