generated from erosita/uds
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This commit is contained in:
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908e25b200
@ -34,5 +34,5 @@ This directory contains preprocessed ISGRI detector count rate cleaned from the
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### cobe_ibis_resp_lon.dat
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This file comes from [Krivonos+ 2007](https://ui.adsabs.harvard.edu/abs/2007A%26A...463..957K/abstract) paper
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This file comes from [Krivonos+ 2007](https://ui.adsabs.harvard.edu/abs/2007A%26A...463..957K/abstract) paper, and is used in Figure 1 of the current paper.
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@ -1,12 +0,0 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model atable{../../data/polarmodel.fits} + powerlaw
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0.73486 1 0 0 3 3
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2.27115e-09 0.01 0 0 1e+20 1e+24
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1.23053 0.01 -3 -2 9 10
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0.0103601 0.01 0 0 1e+20 1e+24
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bayes off
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@ -1,12 +0,0 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model atable{../../data/polarmodel.fits} + powerlaw
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0.713934 1 0 0 3 3
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2.27522e-09 0.01 0 0 1e+20 1e+24
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1.80029 0.01 -3 -2 9 10
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0.138919 0.01 0 0 1e+20 1e+24
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bayes off
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@ -1,10 +0,0 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model atable{../../data/polarmodel.fits}
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0.724678 1 0 0 3 3
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1.03624e-09 0.01 0 0 1e+20 1e+24
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bayes off
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@ -1,18 +0,0 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model cflux*bremss + cflux*powerlaw
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20 -0.1 0 0 1e+06 1e+06
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60 -0.1 0 0 1e+06 1e+06
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-9.0642 0.01 -100 -100 100 100
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20.3536 1 0.0001 0.0001 100 200
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1e-05 -1 0 0 1e+20 1e+24
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80 -0.1 0 0 1e+06 1e+06
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200 -0.1 0 0 1e+06 1e+06
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-11.2811 0.01 -100 -100 100 100
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-1.5 -1 -3 -2 9 10
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1e-05 -1 0 0 1e+20 1e+24
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bayes off
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19
models/xspec/cutoffpl_gb.xcm
Normal file
19
models/xspec/cutoffpl_gb.xcm
Normal file
@ -0,0 +1,19 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model cflux*cutoffpl + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.01266 0.01 -100 -100 100 100
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0 -1 -3 -2 9 10
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11.1866 0.01 0.01 1 500 500
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.31022 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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19
models/xspec/cutoffpl_l+20.xcm
Normal file
19
models/xspec/cutoffpl_l+20.xcm
Normal file
@ -0,0 +1,19 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model cflux*cutoffpl + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.36476 0.01 -100 -100 100 100
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0 -1 -3 -2 9 10
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11.1662 0.01 0.01 1 500 500
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.75981 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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19
models/xspec/cutoffpl_l-20.xcm
Normal file
19
models/xspec/cutoffpl_l-20.xcm
Normal file
@ -0,0 +1,19 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0
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model cflux*cutoffpl + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.36741 0.01 -100 -100 100 100
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0 -1 -3 -2 9 10
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11.2463 0.01 0.01 1 500 500
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.65316 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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9
models/xspec/load_gb_cutoffpl.xcm
Normal file
9
models/xspec/load_gb_cutoffpl.xcm
Normal file
@ -0,0 +1,9 @@
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data GB.spec.pha
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setpl en
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cpd /xs
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setpl reb 2
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systematic 0
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setpl reb 3 3
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@cutoffpl_gb
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pl eeuf delchi
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9
models/xspec/load_gb_polar.xcm
Normal file
9
models/xspec/load_gb_polar.xcm
Normal file
@ -0,0 +1,9 @@
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data GB.spec.pha
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setpl en
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cpd /xs
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setpl reb 2
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systematic 0
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setpl reb 3 3
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@polar_gb_cflux
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pl eeuf delchi
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8
models/xspec/load_l+20_cutoffpl.xcm
Normal file
8
models/xspec/load_l+20_cutoffpl.xcm
Normal file
@ -0,0 +1,8 @@
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data L+20.spec.pha
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setpl en
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cpd /xs
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@cutoffpl_l+20
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setpl reb 3 3
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pl eeuf delchi
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#steppar 9 -10 -12 100
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#new 9 -10.34
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8
models/xspec/load_l+20_polar.xcm
Normal file
8
models/xspec/load_l+20_polar.xcm
Normal file
@ -0,0 +1,8 @@
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data L+20.spec.pha
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setpl en
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cpd /xs
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@polar_l+20_cflux
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setpl reb 3 3
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pl eeuf delchi
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#steppar 9 -10 -12 100
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#new 9 -10.34
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8
models/xspec/load_l-20_cutoffpl.xcm
Normal file
8
models/xspec/load_l-20_cutoffpl.xcm
Normal file
@ -0,0 +1,8 @@
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data L-20.spec.pha
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setpl en
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cpd /xs
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@cutoffpl_l-20
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setpl reb 3 3
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pl eeuf delchi
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#steppar 9 -10 -12 100
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#new 9 -10.34
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8
models/xspec/load_l-20_polar.xcm
Normal file
8
models/xspec/load_l-20_polar.xcm
Normal file
@ -0,0 +1,8 @@
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data L-20.spec.pha
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setpl en
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cpd /xs
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@polar_l-20_cflux
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setpl reb 2 2
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pl eeuf delchi
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#steppar 9 -10 -12 100
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#new 9 -10.34
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18
models/xspec/polar_gb_cflux.xcm
Normal file
18
models/xspec/polar_gb_cflux.xcm
Normal file
@ -0,0 +1,18 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0.05
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model cflux*atable{polarmodel.fits} + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.01521 0.01 -100 -100 100 100
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0.657219 0.01 0 0 3 3
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.1965 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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18
models/xspec/polar_l+20_cflux.xcm
Normal file
18
models/xspec/polar_l+20_cflux.xcm
Normal file
@ -0,0 +1,18 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0.05
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model cflux*atable{polarmodel.fits} + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.28956 0.01 -100 -100 100 100
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0.734431 0.01 0 0 3 3
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.95672 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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18
models/xspec/polar_l-20_cflux.xcm
Normal file
18
models/xspec/polar_l-20_cflux.xcm
Normal file
@ -0,0 +1,18 @@
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method leven 10 0.01
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abund angr
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xsect vern
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cosmo 70 0 0.73
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xset delta 0.01
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systematic 0.05
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model cflux*atable{polarmodel.fits} + cflux*powerlaw
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.25148 0.01 -100 -100 100 100
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0.79495 0.01 0 0 3 3
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1 -1 0 0 1e+20 1e+24
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30 -0.1 0 0 1e+06 1e+06
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80 -0.1 0 0 1e+06 1e+06
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-9.96087 0.01 -100 -100 100 100
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1.55 -1 -3 -2 9 10
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1 -1 0 0 1e+20 1e+24
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bayes off
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@ -3,6 +3,7 @@ All scripts must be ru in ridge/scripts directory
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"""
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# All directory paths below must be ended by /
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modelsdir="../models/"
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datadir="../data/"
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proddir="../products/"
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mapsdir="../products/Maps/"
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@ -124,18 +125,10 @@ skyreg={
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#'Geminga':{'lon':-164.866,'lat':4.265811,'wlon':10.0,'wlat':10.0},
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}
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""" Bootstrap settings """
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# Number of simulations
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nsim=100
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# Fraction of data, randomly selected during each simulation
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simfrac=10
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sigma=3
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# template for map
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modelrxte="grxe_model/modelrxte_ait_3to20keV_flux_2deg.fits"
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modelrxte="grxe/modelrxte_ait_3to20keV_flux_2deg.fits"
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# Galactic longitude profile
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lon_max=173.0
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@ -303,8 +303,7 @@ def get_spec(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60,
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resid = np.array(df['RESID'])
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if(plotme):
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print("get_spec: Initial size: {}".format(len(grxe)))
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#print(grxe)
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#print(grxe_err)
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perc = np.percentile(grxe_err, grxe_err_cut, axis=0, keepdims=False)
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#print("{} {}: {}% cut of GRXE ERR: {:.2f} mCrab".format(skey,enkey,grxe_err_cut,perc))
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@ -417,6 +416,7 @@ def get_spec(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60,
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ydata = n
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(mu, sg) = norm.fit(grxe)
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if(plotme==True):
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print("Initial Gaiss fit: mu={:.2f} sigma={:.2f}".format(mu,sg))
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y_peak = norm.pdf(mu, mu, sg)
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@ -443,8 +443,8 @@ def get_spec(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60,
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#pfit, perr = fit_curvefit(params, xdata, ydata, const_gaussian_ff)
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pfit, perr = fit_leastsq(params, xdata, ydata, const_gaussian_ff)
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print(pfit)
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print(perr)
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#print(pfit)
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#print(perr)
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#pfit, perr = fit_leastsq(params, xdata, ydata, double_gaussian2_ff)
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#pfit, perr = fit_curvefit(params, xdata, ydata, double_gaussian_ff)
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@ -27,7 +27,7 @@ from ridge.utils import *
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from ridge.config import *
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#enkey = sys.argv[1]
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enkey="E01"
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enkey="A01"
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inkey="ALL"
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fn="detcnts.{}.fits".format(enkey)
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@ -63,7 +63,7 @@ print("Total {} ScWs, {:.1f} Ms".format(df_tot.shape[0], np.sum(df_tot['TEXP'])/
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# read ignored orbits
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with open(proddir+'detcnts.B21.ignored_rev.resid.pkl', 'rb') as fp:
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with open(ignored_rev_file, 'rb') as fp:
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ignored_rev = pickle.load(fp)
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print("{} orbits ignored".format(len(ignored_rev)))
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ign=ignored_rev.tolist()
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@ -39,7 +39,6 @@ inkey="ALL"
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sigma=3
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plotme=False
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ebands0={
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@ -51,7 +50,6 @@ ebands0={
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if len(sys.argv) > 1:
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skeys = [sys.argv[1]]
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#simfrac = int(sys.argv[2])
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else:
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skeys = list(skyreg.keys())
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@ -67,24 +65,13 @@ with open(ignored_rev_file, 'rb') as fp:
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print("{} orbits ignored".format(len(ignored_rev)))
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ign=ignored_rev.tolist()
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for skey in skeys:
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if not skey in skyreg.keys():
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print("{} not found in {}".format(skey,list(skyreg.keys())))
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sys.exit()
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# generate array for bootstrap
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ebands_sim={}
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for enkey in ebands0.keys():
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ebands_sim[enkey] = []
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for enkey in ebands0.keys():
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#bkg_fn="detcnts.{}.BKG.resid.fits".format(enkey,inkey)
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#syserr, bkg_sem = get_syserror(proddir+bkg_fn)
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fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
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print("Reading {}".format(proddir+fn))
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dat = Table.read(proddir+fn, unit_parse_strict='silent')
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@ -113,13 +100,6 @@ for skey in skeys:
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ebands0[enkey]=[sg_mean,sg_sem]
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nsel = int(df.shape[0]*simfrac/100)
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for n in range(nsim):
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df0=df.sample(nsel)
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sg_mean,sg_sem,skew_val,skew_err = get_spec(df0, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey)
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ebands_sim[enkey].append(sg_mean)
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###
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fspec="{}{}.dat".format(fluxdir,skey)
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with open(fspec, 'w') as fp:
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for enkey in ebands0.keys():
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@ -129,33 +109,6 @@ for skey in skeys:
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fp.write("{} {} {} {:.6f} {:.6f}\n".format(skey,enkey,bands[enkey],flux,err))
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###
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fspec="{}{}.sim{:02d}.dat".format(fluxdir,skey,simfrac)
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with open(fspec, 'w') as fp:
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for enkey in ebands_sim.keys():
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data=ebands_sim[enkey]
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(mu, sg) = norm.fit(data)
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fp.write("{:02d} {} {} {} {:.6f} {:.6f}\n".format(simfrac,skey,enkey,bands[enkey],mu,sg))
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print("[BOOT] {}: {} {:.6f} {:.6f}".format(skey,enkey,mu,sg))
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if(plotme):
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n, bins, patches = plt.hist(data, 60, density=True, facecolor='green', alpha=0.75)
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# add a 'best fit' line
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y = norm.pdf(bins, mu, sg)
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l = plt.plot(bins, y, 'r--', linewidth=2)
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#plot
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plt.axvline(mu, color="black")
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plt.axvline(ebands0[enkey][0], color="black", linestyle="dashed")
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#plt.axvline(mu+sg_sem, color="black", linestyle="dashed")
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#plt.axvline(mu-sg_sem, color="black", linestyle="dashed")
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plt.axvline(mu+sg, color="blue", linestyle="dashed")
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plt.axvline(mu-sg, color="blue", linestyle="dashed")
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plt.xlabel('Flux, mCrab')
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plt.ylabel('Probability')
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plt.title("[BOOT] {}: {:.2f} {:.2f}".format(enkey, mu, sg))
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plt.grid(True)
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plt.show()
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|
||||
@ -113,14 +113,6 @@ for i in range(lon_nbin):
|
||||
nsel = int(df0.shape[0]*simfrac/100)
|
||||
print("lon {:.2f} ".format(glon[i]),"nsel=",nsel,df0.shape[0])
|
||||
|
||||
"""
|
||||
for n in range(nsim):
|
||||
df1=df0.sample(nsel)
|
||||
sg_mean1,sg_sem1,skew_val,skew_err = get_spec(df1, grxe_err_cut=grxe_err_cut, enkey=enkey, nscw_min=nscw_min)
|
||||
mean_sim[dkey].append(sg_mean1)
|
||||
"""
|
||||
|
||||
|
||||
#print('sg_sem',sg_sem)
|
||||
mean_map[i] = sg_mean
|
||||
sem_map[i] = sg_sem
|
||||
@ -130,7 +122,7 @@ for i in range(lon_nbin):
|
||||
lon_arr.append(row['LON'])
|
||||
lat_arr.append(row['LAT'])
|
||||
|
||||
#sys.exit()
|
||||
|
||||
|
||||
|
||||
if not os.path.exists(profdir):
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
./03_grxe_galprof.py E01 21
|
||||
./03_grxe_galprof.py E13 21
|
||||
./03_grxe_galprof.py E14 21
|
||||
|
||||
./03_grxe_galprof.py A01 21
|
||||
./03_grxe_galprof.py A02 21
|
||||
./03_grxe_galprof.py A03 21
|
||||
|
||||
@ -6,7 +6,7 @@ __copyright__ = "Space Research Institute (IKI)"
|
||||
from astropy.table import Table, Column
|
||||
from matplotlib import ticker
|
||||
import matplotlib.pyplot as plt
|
||||
import sys
|
||||
import sys, os
|
||||
|
||||
from ridge.utils import *
|
||||
from ridge.config import *
|
||||
@ -14,15 +14,15 @@ from ridge.config import *
|
||||
|
||||
scale = 100.0
|
||||
|
||||
fn="detcnts.E01.ALL.resid.galprof.fits"
|
||||
fn="detcnts.A01.ALL.resid.galprof.fits"
|
||||
dat = Table.read(profdir+fn, unit_parse_strict='silent')
|
||||
df1 = dat.to_pandas().sort_values(by=['LON1'])
|
||||
|
||||
fn="detcnts.E14.ALL.resid.galprof.fits"
|
||||
fn="detcnts.A02.ALL.resid.galprof.fits"
|
||||
dat = Table.read(profdir+fn, unit_parse_strict='silent')
|
||||
df2 = dat.to_pandas().sort_values(by=['LON1'])
|
||||
|
||||
fn="detcnts.E13.ALL.resid.galprof.fits"
|
||||
fn="detcnts.A03.ALL.resid.galprof.fits"
|
||||
dat = Table.read(profdir+fn, unit_parse_strict='silent')
|
||||
df3 = dat.to_pandas().sort_values(by=['LON1'])
|
||||
|
||||
@ -96,5 +96,10 @@ ax2.set_ylabel('GRXE flux, x100 mCrab',fontsize=14, fontweight='normal')
|
||||
#plt.xscale('linear')
|
||||
#plt.yscale('linear')
|
||||
|
||||
plt.savefig(profdir+'galprof.png', bbox_inches='tight')
|
||||
if not os.path.exists(figdir):
|
||||
os.makedirs(figdir)
|
||||
|
||||
filename=figdir+'galprof.png'
|
||||
plt.savefig(filename, bbox_inches='tight')
|
||||
plt.close(fig)
|
||||
print("Result is saved as {}".format(filename))
|
||||
|
||||
@ -64,7 +64,7 @@ with open(ignored_rev_file, 'rb') as fp:
|
||||
ign=ignored_rev.tolist()
|
||||
|
||||
|
||||
hdulist = fits.open(datadir+modelrxte)
|
||||
hdulist = fits.open(modelsdir+modelrxte)
|
||||
w = wcs.WCS(hdulist[0].header)
|
||||
smap =hdulist[0].data
|
||||
|
||||
@ -72,7 +72,7 @@ sx=int(hdulist[0].header['NAXIS1'])
|
||||
sy=int(hdulist[0].header['NAXIS2'])
|
||||
|
||||
# fill AITOF map indexes
|
||||
# Already done in 02_grxe_resid.py
|
||||
# -- Already done in 02_grxe_resid.py
|
||||
"""
|
||||
ds9x=[]
|
||||
ds9y=[]
|
||||
@ -136,23 +136,8 @@ for i in range(sx):
|
||||
if (df0.shape[0] < nscw_min):
|
||||
continue
|
||||
|
||||
#print("*** *** REV *** ***")
|
||||
#print(df0["REV"])
|
||||
|
||||
# check coordinates
|
||||
#print("***",i+1,j+1,lon,lat,smap[j][i])
|
||||
#for i0,row0 in df0.iterrows():
|
||||
# print(row0['LON'],row0['LAT'],row0['GRXE'])
|
||||
|
||||
|
||||
sg_mean,sg_sem = get_spec(df0, sigma=sigma, grxe_err_cut=grxe_err_cut, enkey=enkey, nscw_min=nscw_min)
|
||||
|
||||
nsel = int(df0.shape[0]*simfrac/100)
|
||||
#print("nsel=",nsel,df0.shape[0])
|
||||
for n in range(nsim):
|
||||
df1=df0.sample(nsel)
|
||||
sg_mean1,sg_sem1 = get_spec(df1, grxe_err_cut=grxe_err_cut, enkey=enkey, nscw_min=nscw_min)
|
||||
mean_sim[dkey].append(sg_mean1)
|
||||
sg_mean,sg_sem,skew_val,skew_err = get_spec(df0, sigma=4, grxe_err_cut=grxe_err_cut, enkey=enkey, plotme=False, bootstrap=False, gaussfit=True)
|
||||
|
||||
|
||||
#print('sg_sem',sg_sem)
|
||||
@ -161,19 +146,9 @@ for i in range(sx):
|
||||
sign_map[j][i] = sg_mean/sg_sem
|
||||
cnt_map[j][i] = df0.shape[0]
|
||||
|
||||
"""
|
||||
obsid_map[dkey] = obsid
|
||||
grxe_map[dkey] = grxe
|
||||
grxe_err_map[dkey] = grxe_err
|
||||
"""
|
||||
|
||||
|
||||
|
||||
|
||||
""" Filter by error map """
|
||||
# Calculate the percentiles across the x and y dimension
|
||||
|
||||
|
||||
perc = np.percentile(sem_map, sem_cut, axis=(0, 1), keepdims=False)
|
||||
print("{} {}: {}% cut of SEM map: {:.2f} mCrab".format(key,enkey,sem_cut,perc))
|
||||
|
||||
@ -184,10 +159,6 @@ sem_map[idx]=0.0
|
||||
cnt_map[idx]=0
|
||||
sign_map[idx]=0.0
|
||||
|
||||
#mean_sim_map[idx]=0.0
|
||||
#error_sim_map[idx]=0.0
|
||||
|
||||
|
||||
if not os.path.exists(mapsdir):
|
||||
os.makedirs(mapsdir)
|
||||
|
||||
@ -203,76 +174,8 @@ hdulist.writeto(mapsdir+fn.replace(".fits",".cnt.fits"),overwrite=True)
|
||||
hdulist[0].data=sign_map
|
||||
hdulist.writeto(mapsdir+fn.replace(".fits",".sign.fits"),overwrite=True)
|
||||
|
||||
print("saving simulations")
|
||||
for i in range(sx):
|
||||
for j in range(sy):
|
||||
dkey="{:04d}{:04d}".format(j,i)
|
||||
data=mean_sim[dkey]
|
||||
#print("{} size {}".format(dkey,len(data)))
|
||||
if(len(data)>10):
|
||||
(mu, sg) = norm.fit(data)
|
||||
mean_sim_map[j][i] = mu
|
||||
error_sim_map[j][i] = sg
|
||||
|
||||
perc = np.percentile(error_sim_map, sem_cut, axis=(0, 1), keepdims=False)
|
||||
print("{} {}: {}% cut of SEM map: {:.2f} mCrab".format(key,enkey,sem_cut,perc))
|
||||
|
||||
idx=np.where(error_sim_map > perc)
|
||||
print("index size {}".format(len(idx)))
|
||||
mean_sim_map[idx]=0.0
|
||||
error_sim_map[idx]=0.0
|
||||
|
||||
hdulist[0].data=mean_sim_map
|
||||
hdulist.writeto(mapsdir+fn.replace(".fits",".sim.mean.fits"),overwrite=True)
|
||||
hdulist[0].data=error_sim_map
|
||||
hdulist.writeto(mapsdir+fn.replace(".fits",".sim.error.fits"),overwrite=True)
|
||||
|
||||
sys.exit()
|
||||
|
||||
print("Prepare data for fine map")
|
||||
|
||||
obsid_list=[]
|
||||
grxe_list=[]
|
||||
grxe_err_list=[]
|
||||
for i in range(sx):
|
||||
for j in range(sy):
|
||||
""" Use mask """
|
||||
if not (cnt_map[j][i]>0):
|
||||
continue
|
||||
world = w.wcs_pix2world([(i+1,j+1)], 1)
|
||||
lon = world[0][0]
|
||||
lat = world[0][1]
|
||||
if(np.isnan(lon) or np.isnan(lat)):
|
||||
continue
|
||||
ds9i=i+1
|
||||
ds9j=j+1
|
||||
df0 = df.query('DS9X == {} & DS9Y == {}'.format(ds9i,ds9j))
|
||||
if (len(df0) <= nscw_min):
|
||||
continue
|
||||
|
||||
dkey="{:04d}{:04d}".format(j,i)
|
||||
for scw in obsid_map[dkey]:
|
||||
obsid_list.append(scw.decode("UTF-8"))
|
||||
for grxe in grxe_map[dkey]:
|
||||
grxe_list.append(grxe)
|
||||
for grxe in grxe_err_map[dkey]:
|
||||
grxe_err_list.append(grxe)
|
||||
|
||||
|
||||
coldefs = fits.ColDefs([
|
||||
fits.Column(name='OBSID', format='11A', array=obsid_list),
|
||||
])
|
||||
|
||||
fout = fn.replace(".fits",".grxe.fits")
|
||||
hdu = fits.BinTableHDU.from_columns(coldefs, name='GRXE')
|
||||
hdu.header['MISSION'] = ('INTEGRAL', '')
|
||||
#hdu.header['TELESCOP'] = (outkey, '')
|
||||
hdu.header['INSTITUT'] = ('IKI', 'Affiliation')
|
||||
hdu.header['AUTHOR'] = ('Roman Krivonos', 'Responsible person')
|
||||
hdu.header['EMAIL'] = ('krivonos@cosmos.ru', 'E-mail')
|
||||
#hdu.add_checksum()
|
||||
print(hdu.columns)
|
||||
hdu.writeto(proddir+fout, overwrite=True)
|
||||
|
||||
with fits.open(proddir+fout, mode='update') as hdus:
|
||||
hdus[1].add_checksum()
|
||||
|
||||
|
||||
@ -1,37 +1,10 @@
|
||||
./03_grxe_map.py E01
|
||||
#./03_grxe_map.py E02
|
||||
#./03_grxe_map.py E03
|
||||
#./03_grxe_map.py E04
|
||||
#./03_grxe_map.py E05
|
||||
#./03_grxe_map.py E06
|
||||
#./03_grxe_map.py E07
|
||||
#./03_grxe_map.py E08
|
||||
#./03_grxe_map.py E09
|
||||
#./03_grxe_map.py E10
|
||||
./03_grxe_map.py E13
|
||||
./03_grxe_map.py E14
|
||||
for band in A01 A02 A03
|
||||
do
|
||||
./03_grxe_map.py $band
|
||||
done
|
||||
|
||||
|
||||
|
||||
|
||||
./03_grxe_map.py B01
|
||||
./03_grxe_map.py B02
|
||||
./03_grxe_map.py B03
|
||||
./03_grxe_map.py B04
|
||||
./03_grxe_map.py B05
|
||||
./03_grxe_map.py B06
|
||||
./03_grxe_map.py B07
|
||||
./03_grxe_map.py B08
|
||||
./03_grxe_map.py B09
|
||||
./03_grxe_map.py B10
|
||||
./03_grxe_map.py B11
|
||||
./03_grxe_map.py B12
|
||||
./03_grxe_map.py B13
|
||||
./03_grxe_map.py B14
|
||||
./03_grxe_map.py B15
|
||||
./03_grxe_map.py B16
|
||||
./03_grxe_map.py B17
|
||||
./03_grxe_map.py B18
|
||||
./03_grxe_map.py B19
|
||||
./03_grxe_map.py B20
|
||||
./03_grxe_map.py B21
|
||||
# if somebody needs maps in narrow nands:
|
||||
#for band in B01 B02 B03 B04 B05 B06 B07 B08 B09 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 B20 B21
|
||||
#do
|
||||
#./03_grxe_map.py $band
|
||||
#done
|
||||
|
||||
@ -38,7 +38,7 @@ from ridge.config import *
|
||||
inkey="ALL"
|
||||
|
||||
|
||||
sigma=3
|
||||
|
||||
plotme=False
|
||||
|
||||
"""
|
||||
@ -117,11 +117,15 @@ else:
|
||||
if not os.path.exists(specdir):
|
||||
os.makedirs(specdir)
|
||||
|
||||
fitsdir = "{}fits/".format(specdir)
|
||||
if not os.path.exists(fitsdir):
|
||||
os.makedirs(fitsdir)
|
||||
|
||||
with open(ignored_rev_file, 'rb') as fp:
|
||||
ignored_rev = pickle.load(fp)
|
||||
print(ignored_rev)
|
||||
print("{} orbits ignored".format(len(ignored_rev)))
|
||||
#sys.exit()
|
||||
|
||||
ign=ignored_rev.tolist()
|
||||
|
||||
|
||||
@ -131,14 +135,7 @@ for skey in skeys:
|
||||
print("{} not found in {}".format(skey,list(skyreg.keys())))
|
||||
sys.exit()
|
||||
|
||||
# generate array for bootstrap
|
||||
ebands_sim={}
|
||||
for enkey in ebands0.keys():
|
||||
ebands_sim[enkey] = []
|
||||
|
||||
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)
|
||||
#d1 = fits.getdata(proddir+fn)
|
||||
@ -163,7 +160,7 @@ for skey in skeys:
|
||||
skyreg[skey]['lat'] - skyreg[skey]['wlat']/2,
|
||||
skyreg[skey]['lat'] + skyreg[skey]['wlat']/2)
|
||||
|
||||
#sys.exit()
|
||||
|
||||
df = df.query(query)
|
||||
print("{}, {}: {} N={}".format(skey, enkey, query, df.shape[0]))
|
||||
|
||||
@ -178,22 +175,12 @@ for skey in skeys:
|
||||
if not (df.shape[0]>0):
|
||||
continue
|
||||
|
||||
#plt.scatter(df['LON'],df['LAT'])
|
||||
#plt.show()
|
||||
#print("*** {} {} Data Frame size {} ***".format(skey, enkey, df.size))
|
||||
sg_mean,sg_sem,skew_val,skew_err = get_spec(df, sigma=3, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=True, bootstrap=False, gaussfit=True)
|
||||
sg_mean,sg_sem,skew_val,skew_err = get_spec(df, sigma=3, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey, plotme=False, bootstrap=False, gaussfit=True)
|
||||
ebands0[enkey]=[sg_mean,sg_sem]
|
||||
skew0[enkey]=[skew_val,skew_err]
|
||||
|
||||
nsel = int(df.shape[0]*simfrac/100)
|
||||
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)
|
||||
|
||||
|
||||
###
|
||||
fspec="{}{}.spec".format(specdir,skey)
|
||||
with open(fspec, 'w') as fp:
|
||||
for enkey in ebands0.keys():
|
||||
@ -203,8 +190,6 @@ for skey in skeys:
|
||||
fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],flux,err))
|
||||
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
|
||||
|
||||
###
|
||||
|
||||
fspec="{}{}.skew".format(specdir,skey)
|
||||
with open(fspec, 'w') as fp:
|
||||
fp.write("read serr 4\n")
|
||||
@ -213,37 +198,6 @@ for skey in skeys:
|
||||
fp.write("{} {} {:.6f} {:.6f}\n".format(count,bands[enkey],skew0[enkey][0],skew0[enkey][1]))
|
||||
count+=1
|
||||
|
||||
fspec="{}{}.sim.spec".format(specdir,skey)
|
||||
with open(fspec, 'w') as fp:
|
||||
for enkey in ebands_sim.keys():
|
||||
data=ebands_sim[enkey]
|
||||
|
||||
(mu, sg) = norm.fit(data)
|
||||
fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],mu,sg))
|
||||
print("[BOOT] {}: {} {:.6f} {:.6f}".format(skey,enkey,mu,sg))
|
||||
|
||||
if(plotme):
|
||||
n, bins, patches = plt.hist(data, 60, density=True, facecolor='green', alpha=0.75)
|
||||
# add a 'best fit' line
|
||||
y = norm.pdf(bins, mu, sg)
|
||||
l = plt.plot(bins, y, 'r--', linewidth=2)
|
||||
#plot
|
||||
plt.axvline(mu, color="black")
|
||||
plt.axvline(ebands0[enkey][0], color="black", linestyle="dashed")
|
||||
#plt.axvline(mu+sg_sem, color="black", linestyle="dashed")
|
||||
#plt.axvline(mu-sg_sem, color="black", linestyle="dashed")
|
||||
plt.axvline(mu+sg, color="blue", linestyle="dashed")
|
||||
plt.axvline(mu-sg, color="blue", linestyle="dashed")
|
||||
|
||||
plt.xlabel('Flux, mCrab')
|
||||
plt.ylabel('Probability')
|
||||
plt.title("[BOOT] {}: {:.2f} {:.2f}".format(enkey, mu, sg))
|
||||
plt.grid(True)
|
||||
plt.show()
|
||||
|
||||
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
|
||||
|
||||
|
||||
try:
|
||||
for remfile in ["cols","cols1","cols2","header",]:
|
||||
os.remove(remfile)
|
||||
|
||||
@ -20,7 +20,7 @@ Plot long-term Crab detector count rate approximated by cubic polynomial functio
|
||||
|
||||
### 01_crabmodel_plot_sys.py
|
||||
|
||||
Plots the normalized distribution of the residuals between the IBIS/ISGRI Crab count rate and the corresponding polynomial fit (Fig. B8 in paper). The distribution is approximated with a Gaussian function.
|
||||
Plots the normalized distribution of the residuals between the IBIS/ISGRI Crab count rate and the corresponding polynomial fit (Fig. B8 in the paper). The distribution is approximated with a Gaussian function.
|
||||
|
||||
### 02_grxe_resid.py
|
||||
|
||||
@ -28,21 +28,46 @@ Calculates difference between detector count rate and that predicted by backgrou
|
||||
|
||||
### 02_grxe_resid_plot.py
|
||||
|
||||
Plots normalized distribution of the relative residuals of the background model obtained in three energy bands for BKG region (Figs. A5 and A6 in paper).
|
||||
Plots normalized distribution of the relative residuals of the background model obtained in three energy bands for BKG region (Figs. A5 and A6 in the paper).
|
||||
|
||||
### 02_grxe_map.py
|
||||
|
||||
Makes the map of the residuals in mCrab units (not covered in the paper).
|
||||
Makes the map of the residuals in mCrab units (not shown in the paper).
|
||||
|
||||
### 03_grxe_flux.py
|
||||
|
||||
Estimates flux in regions defined in config.py, and places result in products/Flux, e.g.:
|
||||
|
||||
```
|
||||
cat ../products/Flux/GB.dat
|
||||
GB A01 25.00 60.00 154.886019 3.087652
|
||||
GB A02 60.00 80.00 96.472798 9.218161
|
||||
GB A03 80.00 200.00 102.207302 13.291950
|
||||
```
|
||||
wgere GB means Galactic Bulge region (see Table 1 in the paper).
|
||||
|
||||
### 03_grxe_galprof.py/03_grxe_galprof.sh and 03_grxe_galprof_plot.py
|
||||
|
||||
Makes Galactic longitude profile and plots it.
|
||||
|
||||
### 03_grxe_spec.py
|
||||
|
||||
Extract spectrum of selected sky region, e.g.:
|
||||
|
||||
```./03_grxe_spec.py GC``` or ```./03_grxe_spec.py M81```
|
||||
```./03_grxe_spec.py GB``` or ```./03_grxe_spec.py L+20```
|
||||
|
||||
If you do not specify the sky region, the script makes spectra for all regions.
|
||||
|
||||
Sky regions are defined in ```config.py```.
|
||||
|
||||
Spectra are stored in `./products/Spec`. After spectra files are ready, go to directory `./products/Spec` and run ```perl ../../scripts/do_pha_igr_v3_mCrab.pl M81.spec```.
|
||||
Spectra are stored in `../products/Spectra`.
|
||||
|
||||
To load the spectrum, e.g. Galactic Bulge, do the following steps:
|
||||
|
||||
```
|
||||
cd ../products/Spectra
|
||||
cp ../../models/xspec/* .
|
||||
xspec
|
||||
@load_gb_cutoffpl.xcm
|
||||
fit
|
||||
```
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user