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This commit is contained in:
Roman Krivonos 2024-04-18 16:03:08 +03:00
parent 9841cf5ded
commit d1e1643f8a
7 changed files with 101 additions and 7 deletions
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12
data/xspec/GC06.xcm Normal file
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@ -0,0 +1,12 @@
method leven 10 0.01
abund angr
xsect vern
cosmo 70 0 0.73
xset delta 0.01
systematic 0
model atable{../../data/polarmodel.fits} + powerlaw
0.73486 1 0 0 3 3
2.27115e-09 0.01 0 0 1e+20 1e+24
1.23053 0.01 -3 -2 9 10
0.0103601 0.01 0 0 1e+20 1e+24
bayes off

12
data/xspec/GC10.xcm Normal file
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@ -0,0 +1,12 @@
method leven 10 0.01
abund angr
xsect vern
cosmo 70 0 0.73
xset delta 0.01
systematic 0
model atable{../../data/polarmodel.fits} + powerlaw
0.713934 1 0 0 3 3
2.27522e-09 0.01 0 0 1e+20 1e+24
1.80029 0.01 -3 -2 9 10
0.138919 0.01 0 0 1e+20 1e+24
bayes off

10
data/xspec/LON+20.xcm Normal file
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@ -0,0 +1,10 @@
method leven 10 0.01
abund angr
xsect vern
cosmo 70 0 0.73
xset delta 0.01
systematic 0
model atable{../../data/polarmodel.fits}
0.724678 1 0 0 3 3
1.03624e-09 0.01 0 0 1e+20 1e+24
bayes off

5
ridge/ridge/config.py
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@ -45,6 +45,11 @@ SEM означает standatd error on mean (~RMS/sqrt(n))
"""
sem_cut=98
"""
Измерение GRXE ((data-model)/crab) со значением ошибки выше этого персентиля будет отброшено
"""
grxe_err_cut=90
""" Координаты Краба """
crab_ra = 83.6287
crab_dec = 22.014

26
scripts/02_grxe_resid.py
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@ -43,8 +43,9 @@ with open(proddir+fn.replace(".fits",".ignored_rev.pkl"), 'rb') as fp:
with open(proddir+fn.replace(".fits",".crabmodel.pkl"), 'rb') as fp:
crabmodel, z = pickle.load(fp)
p = np.poly1d(z)
crabmodel_keys = list(crabmodel.keys())
#print(crabmodel)
#sys.exit()
crab_rev_max = np.max(list(crabmodel.keys()))
print("Crab is defined untill orbit {}".format(crab_rev_max))
@ -66,11 +67,13 @@ clean0=[]
model0=[]
resid0=[] # residuals in cts/s
grxe0=[] # mCrab
grxe_err0=[] # mCrab
crab0=[] # Crab count rate
mjd0=[]
a0=[]
b0=[]
err0=[]
model_err0=[]
crab_err0=[]
lon0=[]
lat0=[]
base0=[]
@ -114,6 +117,17 @@ for i, row in df.iterrows():
a = bgdmodel[orbit]['a']
b = bgdmodel[orbit]['b']
c = bgdmodel[orbit]['c']
# Crab error is defined only for Crab resolutions, so we interpolate between
if(orbit in crabmodel_keys):
crab_err = crabmodel[orbit]['err']
else:
left,right = find_nearest(crabmodel_keys, orbit)
crab_err = np.interp(orbit, [left,right], [crabmodel[left]['err'], crabmodel[right]['err']])
#print()
#print(orbit, left, right)
#print(orbit, 'err', crabmodel[left]['err'], crab_err, crabmodel[right]['err'])
#sys.exit()
err = bgdmodel[orbit]['err']
m = a*row['PHASE']+b
r1 = bgdmodel[orbit]['r1'] # nearest left orbit used for calibration
@ -126,12 +140,14 @@ for i, row in df.iterrows():
model0.append(m)
resid0.append(clean[i]-m)
grxe0.append(1000*(clean[i]-m)/p(orbit))
grxe_err0.append(1000*np.sqrt(err**2 + crab_err**2)/p(orbit))
crab0.append(p(orbit))
a0.append(a)
b0.append(b)
c0.append(c)
err0.append(err)
model_err0.append(err)
crab_err0.append(crab_err)
phase0.append(row['PHASE'])
rev0.append(orbit)
lon0.append(row['LON'])
@ -155,13 +171,15 @@ coldefs = fits.ColDefs([
fits.Column(name='PHASE', format='D', unit='', array=[phase0[index] for index in indices]),
fits.Column(name='CLEAN', format='D', unit='cts/s', array=[clean0[index] for index in indices]),
fits.Column(name='MODEL', format='D', unit='cts/s', array=[model0[index] for index in indices]),
fits.Column(name='MODEL_ERR', format='D', unit='', array=[model_err0[index] for index in indices]),
fits.Column(name='RESID', format='D', unit='cts/s', array=[resid0[index] for index in indices]),
fits.Column(name='GRXE', format='D', unit='mCrab', array=[grxe0[index] for index in indices]),
fits.Column(name='GRXE_ERR', format='D', unit='mCrab', array=[grxe_err0[index] for index in indices]),
fits.Column(name='CRAB', format='D', unit='cts/s', array=[crab0[index] for index in indices]),
fits.Column(name='CRAB_ERR', format='D', unit='', array=[crab_err0[index] for index in indices]),
fits.Column(name='A', format='D', unit='', array=[a0[index] for index in indices]),
fits.Column(name='B', format='D', unit='', array=[b0[index] for index in indices]),
fits.Column(name='C', format='D', unit='', array=[c0[index] for index in indices]),
fits.Column(name='ERR', format='D', unit='', array=[err0[index] for index in indices]),
])
fout = fn.replace(".fits",".{}.resid.fits".format(outkey))

11
scripts/03_grxe_spec.py
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@ -85,6 +85,13 @@ for skey in skeys:
#plt.show()
grxe = np.array(df['GRXE'])
grxe_err = np.array(df['GRXE_ERR'])
perc = np.percentile(grxe_err, grxe_err_cut, axis=0, keepdims=False)
print("{} {}: {}% cut of GRXE ERR: {:.2f} mCrab".format(skey,enkey,sem_cut,perc))
idx=np.where(grxe_err < perc)
grxe=grxe[idx]
grxe_err=grxe_err[idx]
filtered_data = sigma_clip(grxe, sigma=sigma, maxiters=10, return_bounds=True)
filtered_grxe = filtered_data[0]
@ -99,7 +106,7 @@ for skey in skeys:
#sg_sem*=1.5
#if(sg_mean<0.0):
# sg_mean=1e-9
# sg_mean=1e-6
# sg_sem*=2
ebands0[enkey]=[sg_mean,sg_sem]
@ -119,7 +126,7 @@ for skey in skeys:
fspec="{}{}.spec".format(specdir,skey)
with open(fspec, 'w') as fp:
for enkey in ebands0.keys():
fp.write("0 {} {:.2f} {:.2f} 0.0\n".format(bands[enkey],ebands0[enkey][0],ebands0[enkey][1]))
fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],ebands0[enkey][0],ebands0[enkey][1]))
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
try:

30
scripts/03_plot_skyreg.py
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@ -71,6 +71,7 @@ plt.show()
rev = np.array(df['REV'])
grxe = np.array(df['GRXE'])
grxe_err = np.array(df['GRXE_ERR'])
@ -86,6 +87,35 @@ sstr = '%-14s mean = %6.4f, variance = %6.4f, skew = %6.4f, kurtosis = %6.4f'
n, (smin, smax), sm, sv, ss, sk = describe(grxe)
print(sstr % ('sample:', sm, sv, ss, sk))
# Calculate the percentiles across the x dimension
errmax=np.max(grxe_err)
perc = np.percentile(grxe_err, grxe_err_cut, axis=0, keepdims=False)
print("{} {}: {}% cut of GRXE ERR: {:.2f} mCrab".format(skey,enkey,sem_cut,perc))
idx=np.where(grxe_err < perc)
plt.hist(grxe_err, bins=n_bins, range=[0,errmax], color="red")
rev=rev[idx]
grxe=grxe[idx]
grxe_err=grxe_err[idx]
plt.hist(grxe_err, bins=n_bins, range=[0,errmax], color="grey")
plt.xlabel("GRXE ERROR, mCrab")
plt.title("Distribution of errors {}".format(skey))
plt.show()
grxe_sign=np.divide(grxe,grxe_err)
plt.hist(grxe_sign, bins=n_bins)
plt.xlabel("GRXE S/N")
plt.title("Distribution of significance {}".format(skey))
plt.show()
A=np.sum(grxe/grxe_err**2)
B=np.sum(1.0/grxe_err**2)
wgt_mean=A/B
wgt_mean_err=np.sqrt(1.0/B)
print("Weighted mean: {:.2f}+/-{:.2f} normal mean: {:.2f}".format(wgt_mean,wgt_mean_err,np.mean(grxe)))
#filtered_grxe = sigma_clip(grxe, sigma=sigma, maxiters=10)
filtered_data = sigma_clip(grxe, sigma=sigma, maxiters=10, return_bounds=True)
filtered_grxe = filtered_data[0]