integral/ridge
1
0
Fork 0
generated from erosita/uds
Code Issues Pull Requests Packages Projects Releases Wiki Activity

minor

Browse Source
This commit is contained in:
Roman Krivonos
2024-07-06 17:35:25 +03:00
parent 265e153465
commit 2552bdb552
156 changed files with 158161 additions and 224 deletions
Download Patch File Download Diff File Expand all files Collapse all files

161
scripts/03_grxe_spec.py
View File

@@ -25,7 +25,6 @@ from astropy.stats import sigma_clipped_stats
from scipy.stats import norm
from scipy.stats import describe
from scipy.stats import sem
import subprocess
from numpy import absolute
@@ -77,6 +76,53 @@ ebands0={'B01':[0.0,0.0],
'B21':[0.0,0.0],
}
"""
ebands_sim={'B01':[[],[]],
'B02':[[],[]],
'B03':[[],[]],
'B04':[[],[]],
'B05':[[],[]],
'B06':[[],[]],
'B07':[[],[]],
'B08':[[],[]],
'B09':[[],[]],
'B10':[[],[]],
'B11':[[],[]],
'B12':[[],[]],
'B13':[[],[]],
'B14':[[],[]],
'B15':[[],[]],
'B16':[[],[]],
'B17':[[],[]],
'B18':[[],[]],
'B19':[[],[]],
'B20':[[],[]],
'B21':[[],[]],
}
"""
ebands_sim={'B01':[],
'B02':[],
'B03':[],
'B04':[],
'B05':[],
'B06':[],
'B07':[],
'B08':[],
'B09':[],
'B10':[],
'B11':[],
'B12':[],
'B13':[],
'B14':[],
'B15':[],
'B16':[],
'B17':[],
'B18':[],
'B19':[],
'B20':[],
'B21':[],
}
#skey='Geminga'
if len(sys.argv) > 1:
@@ -88,55 +134,61 @@ else:
if not os.path.exists(specdir):
os.makedirs(specdir)
with open(proddir+'detcnts.B21.ignored_rev.resid.pkl', 'rb') as fp:
ignored_rev = pickle.load(fp)
print(ignored_rev)
ign=ignored_rev.tolist()
sys.exit()
nsim=1000
for skey in skeys:
if not skey in skyreg.keys():
print("{} not found in {}".format(skey,list(skyreg.keys())))
sys.exit()
for enkey in ebands0.keys():
bkg_fn="detcnts.{}.BKG.resid.fits".format(enkey,inkey)
syserr, bkg_sem = get_syserror(proddir+bkg_fn)
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
d = fits.getdata(proddir+fn)
df=pd.DataFrame(np.array(d).byteswap().newbyteorder())
#print(df.columns)
df = df.query('LON > {} & LON < {} & LAT > {} & LAT < {}'.format(skyreg[skey]['lon'] - skyreg[skey]['wlon']/2,
skyreg[skey]['lon'] + skyreg[skey]['wlon']/2,
skyreg[skey]['lat'] - skyreg[skey]['wlat']/2,
skyreg[skey]['lat'] + skyreg[skey]['wlat']/2))
#df = df.query("REV == @ign")
#print("Number of ScWs: {}".format(df.shape[0]))
df = df.query("LON > {} & LON < {} & LAT > {} & LAT < {} & REV != @ign".format(
skyreg[skey]['lon'] - skyreg[skey]['wlon']/2,
skyreg[skey]['lon'] + skyreg[skey]['wlon']/2,
skyreg[skey]['lat'] - skyreg[skey]['wlat']/2,
skyreg[skey]['lat'] + skyreg[skey]['wlat']/2)
)
t = Table.from_pandas(df)
t.write("{}fits/{}.{}.fits".format(specdir,skey,enkey),overwrite=True)
texp = np.array(df['TEXP'])
print("{} Number of ScWs: {}, {:.1f} Ms".format(skey,df.shape[0],np.sum(texp)/1e6))
if not (df.shape[0]>0):
continue
#plt.scatter(df['LON'],df['LAT'])
#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,grxe_err_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]
filtered_min = filtered_data[1]
filtered_max = filtered_data[2]
#print("length orig: {} taken: {} filtered: {}".format(len(grxe),len(grxe[filtered_grxe.mask==False]),len(grxe[filtered_grxe.mask==True])))
sg_mean, sg_med, sg_std = sigma_clipped_stats(grxe, sigma=sigma, maxiters=10)
sg_sem = sem(grxe[filtered_grxe.mask==False])
print("{}: mean {:.2f} med {:.2f} std {:.2f} sem {:.2f} N={}".format(enkey, sg_mean, sg_med, sg_std, sg_sem, len(grxe[filtered_grxe.mask==False])))
#sg_sem*=1.5
if(sg_mean<0.0):
sg_mean=1e-6
#sg_sem*=2
print("*** Data frame size {} ***".format(df.size))
sg_mean,sg_sem = get_spec(df, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey)
ebands0[enkey]=[sg_mean,sg_sem]
nsel = int(df.shape[0]/10)
for n in range(nsim):
df0=df.sample(nsel)
sg_mean,sg_sem = get_spec(df0, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey)
ebands_sim[enkey].append(sg_mean)
#ebands_sim[enkey][1].append(sg_sem)
if(plotme):
k=1.2
plt.hist(grxe, bins=n_bins, range=[filtered_min*k, filtered_max*k])
@@ -149,12 +201,53 @@ for skey in skeys:
plt.xlabel("mCrab")
plt.show()
###
fspec="{}{}.spec".format(specdir,skey)
with open(fspec, 'w') as fp:
for enkey in ebands0.keys():
fp.write("0 {} {:.6f} {:.6f} 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])
###
fspec="{}{}.sim.spec".format(specdir,skey)
with open(fspec, 'w') as fp:
for enkey in ebands_sim.keys():
data=ebands_sim[enkey]
#print(type(data))
(mu, sg) = norm.fit(data)
#n, bins, patches = plt.hist(data, 60, density=True, facecolor='green', alpha=0.75)
if(plotme):
# add a 'best fit' line
y = norm.pdf( bins, mu, sg)
l = plt.plot(bins, y, 'r--', linewidth=2)
#plot
plt.xlabel('Flux')
plt.ylabel('Probability')
#plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=%.3f,\ \sigma=%.3f$' %(mu, sg))
plt.title("{} {:.2f} {:.2f}".format(enkey, mu, sg))
plt.grid(True)
plt.show()
print(mu,sg)
filtered_data = sigma_clip(data, sigma=sigma, maxiters=10, return_bounds=True)
filtered_arr=filtered_data[0]
filtered_min=filtered_data[1]
filtered_max=filtered_data[2]
sg_mean, sg_med, sg_std = sigma_clipped_stats(data, sigma=sigma, maxiters=10)
sg_sem = sem(data)
fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],sg_mean,sg_std))
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
try:
for remfile in ["cols","cols1","cols2","header",]:
os.remove(remfile)