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

spectra

Browse Source
This commit is contained in:
Roman Krivonos 2024-04-17 15:48:08 +03:00
parent ebf991bf58
commit 9841cf5ded
5 changed files with 99 additions and 60 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
ridge/ridge/config.py
View File

@ -70,8 +70,23 @@ basemin=20
skyreg={
'M81':{'lon':142.091816,'lat':40.900054,'wlon':20.0,'wlat':20.0},
'BKG':{'lon':-70.0,'lat':70.0,'wlon':40.0,'wlat':40.0},
'GC':{'lon':0.0,'lat':0.0,'wlon':10.0,'wlat':10.0},
'GC10':{'lon':0.0,'lat':0.0,'wlon':10.0,'wlat':10.0},
'GC06':{'lon':0.0,'lat':0.0,'wlon':6.0,'wlat':6.0},
'LAT+20':{'lon':-34.0,'lat':20.0,'wlon':10.0,'wlat':10.0},
'CenA':{'lon':-50,'lat':20.0,'wlon':15.0,'wlat':15.0},
'CygX1':{'lon':71.0,'lat':1.0,'wlon':15.0,'wlat':20.0},
'ScoX1':{'lon':0.0,'lat':23.0,'wlon':15.0,'wlat':15.0},
'LON-75':{'lon':-75.0,'lat':0.0,'wlon':20.0,'wlat':20.0},
'LON+115':{'lon':115.0,'lat':0.0,'wlon':20.0,'wlat':10.0},
'LON+40':{'lon':43.0,'lat':0.0,'wlon':20.0,'wlat':10.0},
'LON+20':{'lon':20.0,'lat':0.0,'wlon':20.0,'wlat':10.0},
'LON-20':{'lon':-20.0,'lat':0.0,'wlon':20.0,'wlat':10.0},
'Geminga':{'lon':195.133837,'lat':4.265811,'wlon':10.0,'wlat':10.0},
'GP':{'lon':0.0,'lat':0.0,'wlon':100.0,'wlat':10.0},
'HerX1':{'lon':58.1500665,'lat':37.5219674,'wlon':10.0,'wlat':10.0},
'3C273':{'lon':-71,'lat':69.0,'wlon':20.0,'wlat':20.0},
'SMC':{'lon':-59,'lat':-44,'wlon':20.0,'wlat':20.0},
'LMC':{'lon':-80,'lat':-32,'wlon':20.0,'wlat':20.0},
'LAT-30':{'lon':0.0,'lat':-30,'wlon':10.0,'wlat':10.0},
'3C120':{'lon':-159,'lat':-21,'wlon':25.0,'wlat':25.0},
'Geminga':{'lon':-164.866,'lat':4.265811,'wlon':10.0,'wlat':10.0},
}

7
scripts/03_grxe_flux.py
View File

@ -42,7 +42,12 @@ d = fits.getdata(proddir+fn)
df=pd.DataFrame(np.array(d).byteswap().newbyteorder())
#print(df.columns)
key="GC"
#key="GC"
#sz=5
#lon0=0.0
#lat0=0.0
key="BKG"
sz=5
lon0=0.0
lat0=0.0

124
scripts/03_grxe_spec.py
View File

@ -26,6 +26,7 @@ from scipy.stats import norm
from scipy.stats import describe
from scipy.stats import sem
import subprocess
from numpy import absolute
from numpy import arange
@ -52,64 +53,77 @@ ebands0={#'E02':[0.0,0.0],
'E12':[0.0,0.0],}
#skey='Geminga'
skey = sys.argv[1]
if len(sys.argv) > 1:
skeys = [sys.argv[1]]
else:
skeys = list(skyreg.keys())
if not skey in skyreg.keys():
print("{} not found in {}".format(skey,list(skyreg.keys())))
sys.exit()
for enkey in ebands0.keys():
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))
#print("Number of ScWs: {}".format(df.shape[0]))
if not (df.shape[0]>0):
continue
#plt.scatter(df['LON'],df['LAT'])
#plt.show()
grxe = np.array(df['GRXE'])
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-9
sg_sem*=2
ebands0[enkey]=[sg_mean,sg_sem]
if(plotme):
k=1.2
plt.hist(grxe, bins=n_bins, range=[filtered_min*k, filtered_max*k])
plt.hist(grxe[filtered_grxe.mask], bins=n_bins, range=[filtered_min*k, filtered_max*k])
plt.axvline(sg_mean, color="black")
plt.axvline(sg_mean+sg_sem, color="black", linestyle="dashed")
plt.axvline(sg_mean-sg_sem, color="black", linestyle="dashed")
plt.axvline(sg_mean+sg_std, color="blue", linestyle="dashed")
plt.axvline(sg_mean-sg_std, color="blue", linestyle="dashed")
plt.xlabel("mCrab")
plt.show()
if not os.path.exists(specdir):
os.makedirs(specdir)
with open("{}{}.spec".format(specdir,skey), 'w') as fp:
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():
fp.write("0 {} {:.2f} {:.2f} 0.0\n".format(bands[enkey],ebands0[enkey][0],ebands0[enkey][1]))
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))
#print("Number of ScWs: {}".format(df.shape[0]))
if not (df.shape[0]>0):
continue
#plt.scatter(df['LON'],df['LAT'])
#plt.show()
grxe = np.array(df['GRXE'])
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-9
# sg_sem*=2
ebands0[enkey]=[sg_mean,sg_sem]
if(plotme):
k=1.2
plt.hist(grxe, bins=n_bins, range=[filtered_min*k, filtered_max*k])
plt.hist(grxe[filtered_grxe.mask], bins=n_bins, range=[filtered_min*k, filtered_max*k])
plt.axvline(sg_mean, color="black")
plt.axvline(sg_mean+sg_sem, color="black", linestyle="dashed")
plt.axvline(sg_mean-sg_sem, color="black", linestyle="dashed")
plt.axvline(sg_mean+sg_std, color="blue", linestyle="dashed")
plt.axvline(sg_mean-sg_std, color="blue", linestyle="dashed")
plt.xlabel("mCrab")
plt.show()
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]))
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
try:
for remfile in ["cols","cols1","cols2","header"]:
os.remove(remfile)
except OSError:
pass

2
scripts/README.md
View File

@ -28,3 +28,5 @@ source <MY PATH>/venv/bin/activate.csh
```./03_grxe_spec.py GC``` или ```./03_grxe_spec.py M81```
Регионы неба задаются в ```config.py```. Спектры помещаются в директорию ```./products/spec```. Затем надо перейти в эту директорию и выполнить, например: ```perl ../../scripts/do_pha_igr_v3_mCrab.pl M81.spec```.
Если не задать регион неба, скрипт делает спектры для всех регионов.

7
scripts/do_pha_igr_v3_mCrab.pl
View File

@ -1,7 +1,7 @@
#! /usr/bin/perl
#require "utils.pl";
#require "getopts.pl";
use File::Basename;
#&Getopts('i:o:n:c:e');
#############################################
@ -138,6 +138,9 @@ print COLS "STAT_ERR 1D counts\n";
close(COLS);
$base = basename($rmff);
open(HEADER,">header");
print HEADER <<EOHEAD;
EXTNAME = 'SPECTRUM' / name of this binary table extension
@ -159,7 +162,7 @@ BACKSCAL= 1.00000000E+00 / Background scale factor
CORRSCAL= 0.00000000E+00 / Correlation scale factor
BACKFILE= 'NONE ' / Background FITS file for this object
CORRFILE= 'NONE ' / Correlation FITS file for this object
RESPFILE= '$rmff' / Redistribution matrix file (RMF)
RESPFILE= '$base' / Redistribution matrix file (RMF)
ANCRFILE= 'NONE ' / Ancillary response file (ARF)
XFLT0001= 'NONE ' / XSPEC selection filter description
CHANTYPE= 'PHA ' / Channels assigned by detector electronics