generated from erosita/uds
update
This commit is contained in:
parent
2dc9aa6a8d
commit
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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### 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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@ -98,104 +98,104 @@
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-2.99981 -1.584635716374209E-004 4.05677620714671
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-2.99981 -1.584635716374209E-004 4.05677620714671
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||||||
-1.9998 -1.601728698576348E-004 4.14460425365896
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-1.9998 -1.601728698576348E-004 4.14460425365896
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||||||
-0.999804 -1.618485992135904E-004 4.20283820107557
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-0.999804 -1.618485992135904E-004 4.20283820107557
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7.391063585095475E-005 -1.634896152618052E-004 4.24714842919316
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7.391063585095475E-005 -1.634896152618052E-004 4.24714842919316
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||||||
1.00007538246518 -1.650948021933402E-004 4.23521323452837
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1.00007538246518 -1.650948021933402E-004 4.23521323452837
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||||||
2.00007685869151 -1.666630726222933E-004 4.19981639323352
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2.00007685869151 -1.666630726222933E-004 4.19981639323352
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3.00007833903195 -1.681933673218127E-004 4.14870274802363
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3.00007833903195 -1.681933673218127E-004 4.14870274802363
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||||||
4.00007982322146 -1.696846551414032E-004 4.09098444969177
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4.00007982322146 -1.696846551414032E-004 4.09098444969177
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||||||
5.00008131101137 -1.711359324710015E-004 3.99885667540513
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5.00008131101137 -1.711359324710015E-004 3.99885667540513
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||||||
6.00008280216830 -1.725462231105744E-004 3.89347299791858
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6.00008280216830 -1.725462231105744E-004 3.89347299791858
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7.00008429647310 -1.739145779075349E-004 3.76462574577249
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7.00008429647310 -1.739145779075349E-004 3.76462574577249
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8.00008579371966 -1.752400743718950E-004 3.63635072815927
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8.00008579371966 -1.752400743718950E-004 3.63635072815927
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9.00008729371401 -1.765218162898285E-004 3.50638304157508
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9.00008729371401 -1.765218162898285E-004 3.50638304157508
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10.0000887962735 -1.777589335948580E-004 3.37145501598623
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10.0000887962735 -1.777589335948580E-004 3.37145501598623
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11.0000903012260 -1.789505817842232E-004 3.24177379875826
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11.0000903012260 -1.789505817842232E-004 3.24177379875826
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12.0000918084088 -1.800959417821170E-004 3.11478207103756
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12.0000918084088 -1.800959417821170E-004 3.11478207103756
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13.0000933176683 -1.811942191922550E-004 2.99374761306871
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13.0000933176683 -1.811942191922550E-004 2.99374761306871
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14.0000948288589 -1.822446444250974E-004 2.87861908304001
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14.0000948288589 -1.822446444250974E-004 2.87861908304001
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15.0000963418429 -1.832464721404567E-004 2.77561053788302
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15.0000963418429 -1.832464721404567E-004 2.77561053788302
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16.0000978564895 -1.841989809262620E-004 2.68308013212301
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16.0000978564895 -1.841989809262620E-004 2.68308013212301
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17.0000993726742 -1.851014728922422E-004 2.60453777524445
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17.0000993726742 -1.851014728922422E-004 2.60453777524445
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18.0001008902786 -1.859532733439200E-004 2.53565104306417
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18.0001008902786 -1.859532733439200E-004 2.53565104306417
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19.0001024091895 -1.867537306728816E-004 2.47233716673087
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19.0001024091895 -1.867537306728816E-004 2.47233716673087
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20.0001039292990 -1.875022156165036E-004 2.41091856457831
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20.0001039292990 -1.875022156165036E-004 2.41091856457831
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21.0001054505034 -1.881981214837714E-004 2.35183597832713
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21.0001054505034 -1.881981214837714E-004 2.35183597832713
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22.0001069727033 -1.888408632217865E-004 2.29505497188558
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22.0001069727033 -1.888408632217865E-004 2.29505497188558
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23.0001084958029 -1.894298775692284E-004 2.23966311949082
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23.0001084958029 -1.894298775692284E-004 2.23966311949082
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24.0001100197099 -1.899646225331532E-004 2.18270405221428
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24.0001100197099 -1.899646225331532E-004 2.18270405221428
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25.0001115443348 -1.904445771997502E-004 2.12658153420184
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25.0001115443348 -1.904445771997502E-004 2.12658153420184
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26.0001130695907 -1.908692410715939E-004 2.06970103422394
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26.0001130695907 -1.908692410715939E-004 2.06970103422394
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27.0001145953933 -1.912381344397690E-004 2.01055711062610
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27.0001145953933 -1.912381344397690E-004 2.01055711062610
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28.0001161216601 -1.915507972881687E-004 1.95086601011597
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28.0001161216601 -1.915507972881687E-004 1.95086601011597
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29.0001176483103 -1.918067894801539E-004 1.89349532821088
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29.0001176483103 -1.918067894801539E-004 1.89349532821088
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30.0001191752645 -1.920056903595918E-004 1.83465529800941
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30.0001191752645 -1.920056903595918E-004 1.83465529800941
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31.0001207024444 -1.921470982763575E-004 1.77267803851111
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31.0001207024444 -1.921470982763575E-004 1.77267803851111
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32.0001222297725 -1.922306305796736E-004 1.70824972704387
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32.0001222297725 -1.922306305796736E-004 1.70824972704387
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33.0001237571719 -1.922559227833552E-004 1.64434312285321
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33.0001237571719 -1.922559227833552E-004 1.64434312285321
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34.0001252845660 -1.922226288049937E-004 1.58380085501745
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34.0001252845660 -1.922226288049937E-004 1.58380085501745
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35.0001268118780 -1.921304202843803E-004 1.52350376647231
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35.0001268118780 -1.921304202843803E-004 1.52350376647231
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36.0001283390309 -1.919789863003307E-004 1.46309418488510
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36.0001283390309 -1.919789863003307E-004 1.46309418488510
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37.0001298659472 -1.917680331569949E-004 1.40282681792836
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37.0001298659472 -1.917680331569949E-004 1.40282681792836
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38.0001313925484 -1.914972839266613E-004 1.34490940128067
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38.0001313925484 -1.914972839266613E-004 1.34490940128067
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39.0001329187551 -1.911664780462734E-004 1.28528094690644
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39.0001329187551 -1.911664780462734E-004 1.28528094690644
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40.0001344444861 -1.907753711016494E-004 1.22954122097706
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40.0001344444861 -1.907753711016494E-004 1.22954122097706
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41.0001359696588 -1.903237342239712E-004 1.17455169070665
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41.0001359696588 -1.903237342239712E-004 1.17455169070665
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42.0001374941884 -1.898113540913749E-004 1.12244635417390
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42.0001374941884 -1.898113540913749E-004 1.12244635417390
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43.0001390179878 -1.892380319898919E-004 1.07219353857889
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43.0001390179878 -1.892380319898919E-004 1.07219353857889
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44.0001405409671 -1.886035840694833E-004 1.02430360841000
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44.0001405409671 -1.886035840694833E-004 1.02430360841000
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45.0001420630336 -1.879078402523141E-004 0.979428719444290
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45.0001420630336 -1.879078402523141E-004 0.979428719444290
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46.0001435840910 -1.871506442657522E-004 0.935356858468708
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46.0001435840910 -1.871506442657522E-004 0.935356858468708
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47.0001451040393 -1.863318530396520E-004 0.894827492395949
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47.0001451040393 -1.863318530396520E-004 0.894827492395949
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48.0001466227743 -1.854513363215080E-004 0.855432838859234
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48.0001466227743 -1.854513363215080E-004 0.855432838859234
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49.0001481401873 -1.845089759862736E-004 0.817676828920911
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49.0001481401873 -1.845089759862736E-004 0.817676828920911
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50.0001496561647 -1.835046660514693E-004 0.781697224403438
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50.0001496561647 -1.835046660514693E-004 0.781697224403438
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51.0001511705870 -1.824383114971961E-004 0.746880450942950
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51.0001511705870 -1.824383114971961E-004 0.746880450942950
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52.0001526833291 -1.813098283567813E-004 0.714481382326025
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52.0001526833291 -1.813098283567813E-004 0.714481382326025
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53.0001541942592 -1.801191429264108E-004 0.683907557802099
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53.0001541942592 -1.801191429264108E-004 0.683907557802099
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54.0001557032385 -1.788661911306080E-004 0.655179352879342
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54.0001557032385 -1.788661911306080E-004 0.655179352879342
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55.0001572101204 -1.775509181874801E-004 0.629516307759393
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55.0001572101204 -1.775509181874801E-004 0.629516307759393
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56.0001587147500 -1.761732780107737E-004 0.605500599333575
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56.0001587147500 -1.761732780107737E-004 0.605500599333575
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57.0001602169637 -1.747332325530905E-004 0.583744119521001
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57.0001602169637 -1.747332325530905E-004 0.583744119521001
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58.0001617165877 -1.732307511904491E-004 0.564742241822184
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58.0001617165877 -1.732307511904491E-004 0.564742241822184
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59.0001632134378 -1.716658101147992E-004 0.547030353536971
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59.0001632134378 -1.716658101147992E-004 0.547030353536971
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60.0001647073187 -1.700383920016535E-004 0.531224684628053
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60.0001647073187 -1.700383920016535E-004 0.531224684628053
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61.0001661980224 -1.683484850463790E-004 0.517929610622562
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61.0001661980224 -1.683484850463790E-004 0.517929610622562
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62.0001676853279 -1.665960823503505E-004 0.507160103381866
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62.0001676853279 -1.665960823503505E-004 0.507160103381866
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63.0001691689998 -1.647811816903592E-004 0.498444379609143
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63.0001691689998 -1.647811816903592E-004 0.498444379609143
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64.0001706487874 -1.629037844165511E-004 0.494034330260365
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64.0001706487874 -1.629037844165511E-004 0.494034330260365
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65.0001721244231 -1.609638950946140E-004 0.492719032342890
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65.0001721244231 -1.609638950946140E-004 0.492719032342890
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66.0001735956217 -1.589615210223331E-004 0.493270940429287
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66.0001735956217 -1.589615210223331E-004 0.493270940429287
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67.0001750620784 -1.568966712897368E-004 0.495773721904444
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67.0001750620784 -1.568966712897368E-004 0.495773721904444
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68.0001765234679 -1.547693564880757E-004 0.500681739849555
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68.0001765234679 -1.547693564880757E-004 0.500681739849555
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69.0001779794421 -1.525795881118784E-004 0.506208278777099
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69.0001779794421 -1.525795881118784E-004 0.506208278777099
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70.0001794296292 -1.503273780500624E-004 0.511988313341823
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70.0001794296292 -1.503273780500624E-004 0.511988313341823
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71.0001808736314 -1.480127383410312E-004 0.518444152681345
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71.0001808736314 -1.480127383410312E-004 0.518444152681345
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72.0001823110226 -1.456356805333828E-004 0.524610710513296
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72.0001823110226 -1.456356805333828E-004 0.524610710513296
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73.0001837413472 -1.431962154441874E-004 0.531530819999874
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73.0001837413472 -1.431962154441874E-004 0.531530819999874
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74.0001851641172 -1.406943534929457E-004 0.541253395874888
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74.0001851641172 -1.406943534929457E-004 0.541253395874888
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75.0001865788098 -1.381301039000888E-004 0.547252179962178
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75.0001865788098 -1.381301039000888E-004 0.547252179962178
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76.0001879848655 -1.355034754089646E-004 0.550328944960229
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76.0001879848655 -1.355034754089646E-004 0.550328944960229
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77.0001893816847 -1.328144763017401E-004 0.548374638305539
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77.0001893816847 -1.328144763017401E-004 0.548374638305539
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78.0001907686254 -1.300631150450550E-004 0.543457647253208
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78.0001907686254 -1.300631150450550E-004 0.543457647253208
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79.0001921449999 -1.272494010247291E-004 0.534844103019597
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79.0001921449999 -1.272494010247291E-004 0.534844103019597
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80.0001935100720 -1.243733454872078E-004 0.525534664790709
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80.0001935100720 -1.243733454872078E-004 0.525534664790709
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81.0001948630535 -1.214349630980337E-004 0.513037455404691
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81.0001948630535 -1.214349630980337E-004 0.513037455404691
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82.0001962031013 -1.184342737515831E-004 0.496574141874924
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82.0001962031013 -1.184342737515831E-004 0.496574141874924
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83.0001975293133 -1.153713045970800E-004 0.477024601855444
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83.0001975293133 -1.153713045970800E-004 0.477024601855444
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84.0001988407258 -1.122460930887494E-004 0.457166893096085
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84.0001988407258 -1.122460930887494E-004 0.457166893096085
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85.0002001363093 -1.090586902617891E-004 0.431005130023210
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85.0002001363093 -1.090586902617891E-004 0.431005130023210
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86.0002014149656 -1.058091646539957E-004 0.403735383331875
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86.0002014149656 -1.058091646539957E-004 0.403735383331875
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87.0002026755239 -1.024976071592920E-004 0.374798473396735
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87.0002026755239 -1.024976071592920E-004 0.374798473396735
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88.0002039167387 -9.912413684496408E-005 0.347173148457788
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88.0002039167387 -9.912413684496408E-005 0.347173148457788
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89.0002051372859 -9.568890728414767E-005 0.319852120748145
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89.0002051372859 -9.568890728414767E-005 0.319852120748145
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90.0002063357613 -9.219211437681474E-005 0.292631801156368
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90.0002063357613 -9.219211437681474E-005 0.292631801156368
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91.0002075106785 -8.863400506449633E-005 0.266228044396145
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91.0002075106785 -8.863400506449633E-005 0.266228044396145
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92.0002086604679 -8.501488724089448E-005 0.240945560706418
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92.0002086604679 -8.501488724089448E-005 0.240945560706418
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93.0002097834773 -8.133514139907735E-005 0.218025197616317
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93.0002097834773 -8.133514139907735E-005 0.218025197616317
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94.0002108779727 -7.759523291160514E-005 0.196663382713245
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94.0002108779727 -7.759523291160514E-005 0.196663382713245
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95.0002119421417 -7.379572672469782E-005 0.178672240628803
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95.0002119421417 -7.379572672469782E-005 0.178672240628803
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96.0002129740982 -6.993730257401385E-005 0.163732183028421
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96.0002129740982 -6.993730257401385E-005 0.163732183028421
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97.0002139718896 -6.602077225506821E-005 0.151594421381940
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97.0002139718896 -6.602077225506821E-005 0.151594421381940
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98.0002149335069 -6.204709821040018E-005 0.140721254721218
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98.0002149335069 -6.204709821040018E-005 0.140721254721218
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99.0002158568978 -5.801741279101933E-005 0.133490005530158
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99.0002158568978 -5.801741279101933E-005 0.133490005530158
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100.000216739984 -5.393303921308732E-005 0.128219990691853
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100.000216739984 -5.393303921308732E-005 0.128219990691853
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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 @@
|
|||||||
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
|
|
||||||
@ -1,18 +0,0 @@
|
|||||||
method leven 10 0.01
|
|
||||||
abund angr
|
|
||||||
xsect vern
|
|
||||||
cosmo 70 0 0.73
|
|
||||||
xset delta 0.01
|
|
||||||
systematic 0
|
|
||||||
model cflux*bremss + cflux*powerlaw
|
|
||||||
20 -0.1 0 0 1e+06 1e+06
|
|
||||||
60 -0.1 0 0 1e+06 1e+06
|
|
||||||
-9.0642 0.01 -100 -100 100 100
|
|
||||||
20.3536 1 0.0001 0.0001 100 200
|
|
||||||
1e-05 -1 0 0 1e+20 1e+24
|
|
||||||
80 -0.1 0 0 1e+06 1e+06
|
|
||||||
200 -0.1 0 0 1e+06 1e+06
|
|
||||||
-11.2811 0.01 -100 -100 100 100
|
|
||||||
-1.5 -1 -3 -2 9 10
|
|
||||||
1e-05 -1 0 0 1e+20 1e+24
|
|
||||||
bayes off
|
|
||||||
19
models/xspec/cutoffpl_gb.xcm
Normal file
19
models/xspec/cutoffpl_gb.xcm
Normal file
@ -0,0 +1,19 @@
|
|||||||
|
method leven 10 0.01
|
||||||
|
abund angr
|
||||||
|
xsect vern
|
||||||
|
cosmo 70 0 0.73
|
||||||
|
xset delta 0.01
|
||||||
|
systematic 0
|
||||||
|
model cflux*cutoffpl + cflux*powerlaw
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.01266 0.01 -100 -100 100 100
|
||||||
|
0 -1 -3 -2 9 10
|
||||||
|
11.1866 0.01 0.01 1 500 500
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.31022 0.01 -100 -100 100 100
|
||||||
|
1.55 -1 -3 -2 9 10
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
bayes off
|
||||||
19
models/xspec/cutoffpl_l+20.xcm
Normal file
19
models/xspec/cutoffpl_l+20.xcm
Normal file
@ -0,0 +1,19 @@
|
|||||||
|
method leven 10 0.01
|
||||||
|
abund angr
|
||||||
|
xsect vern
|
||||||
|
cosmo 70 0 0.73
|
||||||
|
xset delta 0.01
|
||||||
|
systematic 0
|
||||||
|
model cflux*cutoffpl + cflux*powerlaw
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.36476 0.01 -100 -100 100 100
|
||||||
|
0 -1 -3 -2 9 10
|
||||||
|
11.1662 0.01 0.01 1 500 500
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.75981 0.01 -100 -100 100 100
|
||||||
|
1.55 -1 -3 -2 9 10
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
bayes off
|
||||||
19
models/xspec/cutoffpl_l-20.xcm
Normal file
19
models/xspec/cutoffpl_l-20.xcm
Normal file
@ -0,0 +1,19 @@
|
|||||||
|
method leven 10 0.01
|
||||||
|
abund angr
|
||||||
|
xsect vern
|
||||||
|
cosmo 70 0 0.73
|
||||||
|
xset delta 0.01
|
||||||
|
systematic 0
|
||||||
|
model cflux*cutoffpl + cflux*powerlaw
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.36741 0.01 -100 -100 100 100
|
||||||
|
0 -1 -3 -2 9 10
|
||||||
|
11.2463 0.01 0.01 1 500 500
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.65316 0.01 -100 -100 100 100
|
||||||
|
1.55 -1 -3 -2 9 10
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
bayes off
|
||||||
9
models/xspec/load_gb_cutoffpl.xcm
Normal file
9
models/xspec/load_gb_cutoffpl.xcm
Normal file
@ -0,0 +1,9 @@
|
|||||||
|
data GB.spec.pha
|
||||||
|
setpl en
|
||||||
|
cpd /xs
|
||||||
|
setpl reb 2
|
||||||
|
systematic 0
|
||||||
|
setpl reb 3 3
|
||||||
|
@cutoffpl_gb
|
||||||
|
pl eeuf delchi
|
||||||
|
|
||||||
9
models/xspec/load_gb_polar.xcm
Normal file
9
models/xspec/load_gb_polar.xcm
Normal file
@ -0,0 +1,9 @@
|
|||||||
|
data GB.spec.pha
|
||||||
|
setpl en
|
||||||
|
cpd /xs
|
||||||
|
setpl reb 2
|
||||||
|
systematic 0
|
||||||
|
setpl reb 3 3
|
||||||
|
@polar_gb_cflux
|
||||||
|
pl eeuf delchi
|
||||||
|
|
||||||
8
models/xspec/load_l+20_cutoffpl.xcm
Normal file
8
models/xspec/load_l+20_cutoffpl.xcm
Normal file
@ -0,0 +1,8 @@
|
|||||||
|
data L+20.spec.pha
|
||||||
|
setpl en
|
||||||
|
cpd /xs
|
||||||
|
@cutoffpl_l+20
|
||||||
|
setpl reb 3 3
|
||||||
|
pl eeuf delchi
|
||||||
|
#steppar 9 -10 -12 100
|
||||||
|
#new 9 -10.34
|
||||||
8
models/xspec/load_l+20_polar.xcm
Normal file
8
models/xspec/load_l+20_polar.xcm
Normal file
@ -0,0 +1,8 @@
|
|||||||
|
data L+20.spec.pha
|
||||||
|
setpl en
|
||||||
|
cpd /xs
|
||||||
|
@polar_l+20_cflux
|
||||||
|
setpl reb 3 3
|
||||||
|
pl eeuf delchi
|
||||||
|
#steppar 9 -10 -12 100
|
||||||
|
#new 9 -10.34
|
||||||
8
models/xspec/load_l-20_cutoffpl.xcm
Normal file
8
models/xspec/load_l-20_cutoffpl.xcm
Normal file
@ -0,0 +1,8 @@
|
|||||||
|
data L-20.spec.pha
|
||||||
|
setpl en
|
||||||
|
cpd /xs
|
||||||
|
@cutoffpl_l-20
|
||||||
|
setpl reb 3 3
|
||||||
|
pl eeuf delchi
|
||||||
|
#steppar 9 -10 -12 100
|
||||||
|
#new 9 -10.34
|
||||||
8
models/xspec/load_l-20_polar.xcm
Normal file
8
models/xspec/load_l-20_polar.xcm
Normal file
@ -0,0 +1,8 @@
|
|||||||
|
data L-20.spec.pha
|
||||||
|
setpl en
|
||||||
|
cpd /xs
|
||||||
|
@polar_l-20_cflux
|
||||||
|
setpl reb 2 2
|
||||||
|
pl eeuf delchi
|
||||||
|
#steppar 9 -10 -12 100
|
||||||
|
#new 9 -10.34
|
||||||
18
models/xspec/polar_gb_cflux.xcm
Normal file
18
models/xspec/polar_gb_cflux.xcm
Normal file
@ -0,0 +1,18 @@
|
|||||||
|
method leven 10 0.01
|
||||||
|
abund angr
|
||||||
|
xsect vern
|
||||||
|
cosmo 70 0 0.73
|
||||||
|
xset delta 0.01
|
||||||
|
systematic 0.05
|
||||||
|
model cflux*atable{polarmodel.fits} + cflux*powerlaw
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.01521 0.01 -100 -100 100 100
|
||||||
|
0.657219 0.01 0 0 3 3
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.1965 0.01 -100 -100 100 100
|
||||||
|
1.55 -1 -3 -2 9 10
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
bayes off
|
||||||
18
models/xspec/polar_l+20_cflux.xcm
Normal file
18
models/xspec/polar_l+20_cflux.xcm
Normal file
@ -0,0 +1,18 @@
|
|||||||
|
method leven 10 0.01
|
||||||
|
abund angr
|
||||||
|
xsect vern
|
||||||
|
cosmo 70 0 0.73
|
||||||
|
xset delta 0.01
|
||||||
|
systematic 0.05
|
||||||
|
model cflux*atable{polarmodel.fits} + cflux*powerlaw
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.28956 0.01 -100 -100 100 100
|
||||||
|
0.734431 0.01 0 0 3 3
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.95672 0.01 -100 -100 100 100
|
||||||
|
1.55 -1 -3 -2 9 10
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
bayes off
|
||||||
18
models/xspec/polar_l-20_cflux.xcm
Normal file
18
models/xspec/polar_l-20_cflux.xcm
Normal file
@ -0,0 +1,18 @@
|
|||||||
|
method leven 10 0.01
|
||||||
|
abund angr
|
||||||
|
xsect vern
|
||||||
|
cosmo 70 0 0.73
|
||||||
|
xset delta 0.01
|
||||||
|
systematic 0.05
|
||||||
|
model cflux*atable{polarmodel.fits} + cflux*powerlaw
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.25148 0.01 -100 -100 100 100
|
||||||
|
0.79495 0.01 0 0 3 3
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
30 -0.1 0 0 1e+06 1e+06
|
||||||
|
80 -0.1 0 0 1e+06 1e+06
|
||||||
|
-9.96087 0.01 -100 -100 100 100
|
||||||
|
1.55 -1 -3 -2 9 10
|
||||||
|
1 -1 0 0 1e+20 1e+24
|
||||||
|
bayes off
|
||||||
@ -3,6 +3,7 @@ All scripts must be ru in ridge/scripts directory
|
|||||||
"""
|
"""
|
||||||
|
|
||||||
# All directory paths below must be ended by /
|
# All directory paths below must be ended by /
|
||||||
|
modelsdir="../models/"
|
||||||
datadir="../data/"
|
datadir="../data/"
|
||||||
proddir="../products/"
|
proddir="../products/"
|
||||||
mapsdir="../products/Maps/"
|
mapsdir="../products/Maps/"
|
||||||
@ -124,18 +125,10 @@ skyreg={
|
|||||||
#'Geminga':{'lon':-164.866,'lat':4.265811,'wlon':10.0,'wlat':10.0},
|
#'Geminga':{'lon':-164.866,'lat':4.265811,'wlon':10.0,'wlat':10.0},
|
||||||
}
|
}
|
||||||
|
|
||||||
""" Bootstrap settings """
|
|
||||||
|
|
||||||
# Number of simulations
|
|
||||||
nsim=100
|
|
||||||
|
|
||||||
# Fraction of data, randomly selected during each simulation
|
|
||||||
simfrac=10
|
|
||||||
|
|
||||||
sigma=3
|
sigma=3
|
||||||
|
|
||||||
# template for map
|
# template for map
|
||||||
modelrxte="grxe_model/modelrxte_ait_3to20keV_flux_2deg.fits"
|
modelrxte="grxe/modelrxte_ait_3to20keV_flux_2deg.fits"
|
||||||
|
|
||||||
# Galactic longitude profile
|
# Galactic longitude profile
|
||||||
lon_max=173.0
|
lon_max=173.0
|
||||||
|
|||||||
@ -303,8 +303,7 @@ def get_spec(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60,
|
|||||||
resid = np.array(df['RESID'])
|
resid = np.array(df['RESID'])
|
||||||
if(plotme):
|
if(plotme):
|
||||||
print("get_spec: Initial size: {}".format(len(grxe)))
|
print("get_spec: Initial size: {}".format(len(grxe)))
|
||||||
#print(grxe)
|
|
||||||
#print(grxe_err)
|
|
||||||
|
|
||||||
perc = np.percentile(grxe_err, grxe_err_cut, axis=0, keepdims=False)
|
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))
|
#print("{} {}: {}% cut of GRXE ERR: {:.2f} mCrab".format(skey,enkey,grxe_err_cut,perc))
|
||||||
@ -417,7 +416,8 @@ def get_spec(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60,
|
|||||||
ydata = n
|
ydata = n
|
||||||
(mu, sg) = norm.fit(grxe)
|
(mu, sg) = norm.fit(grxe)
|
||||||
|
|
||||||
print("Initial Gaiss fit: mu={:.2f} sigma={:.2f}".format(mu,sg))
|
if(plotme==True):
|
||||||
|
print("Initial Gaiss fit: mu={:.2f} sigma={:.2f}".format(mu,sg))
|
||||||
|
|
||||||
y_peak = norm.pdf(mu, mu, sg)
|
y_peak = norm.pdf(mu, mu, sg)
|
||||||
plt.xlim(filtered_min*k, filtered_max*k)
|
plt.xlim(filtered_min*k, filtered_max*k)
|
||||||
@ -443,8 +443,8 @@ def get_spec(df, grxe_err_cut=None, skey=None, enkey=None, sigma=3, n_bins = 60,
|
|||||||
|
|
||||||
#pfit, perr = fit_curvefit(params, xdata, ydata, const_gaussian_ff)
|
#pfit, perr = fit_curvefit(params, xdata, ydata, const_gaussian_ff)
|
||||||
pfit, perr = fit_leastsq(params, xdata, ydata, const_gaussian_ff)
|
pfit, perr = fit_leastsq(params, xdata, ydata, const_gaussian_ff)
|
||||||
print(pfit)
|
#print(pfit)
|
||||||
print(perr)
|
#print(perr)
|
||||||
|
|
||||||
#pfit, perr = fit_leastsq(params, xdata, ydata, double_gaussian2_ff)
|
#pfit, perr = fit_leastsq(params, xdata, ydata, double_gaussian2_ff)
|
||||||
#pfit, perr = fit_curvefit(params, xdata, ydata, double_gaussian_ff)
|
#pfit, perr = fit_curvefit(params, xdata, ydata, double_gaussian_ff)
|
||||||
|
|||||||
@ -27,7 +27,7 @@ from ridge.utils import *
|
|||||||
from ridge.config import *
|
from ridge.config import *
|
||||||
|
|
||||||
#enkey = sys.argv[1]
|
#enkey = sys.argv[1]
|
||||||
enkey="E01"
|
enkey="A01"
|
||||||
inkey="ALL"
|
inkey="ALL"
|
||||||
|
|
||||||
fn="detcnts.{}.fits".format(enkey)
|
fn="detcnts.{}.fits".format(enkey)
|
||||||
@ -63,7 +63,7 @@ print("Total {} ScWs, {:.1f} Ms".format(df_tot.shape[0], np.sum(df_tot['TEXP'])/
|
|||||||
|
|
||||||
|
|
||||||
# read ignored orbits
|
# read ignored orbits
|
||||||
with open(proddir+'detcnts.B21.ignored_rev.resid.pkl', 'rb') as fp:
|
with open(ignored_rev_file, 'rb') as fp:
|
||||||
ignored_rev = pickle.load(fp)
|
ignored_rev = pickle.load(fp)
|
||||||
print("{} orbits ignored".format(len(ignored_rev)))
|
print("{} orbits ignored".format(len(ignored_rev)))
|
||||||
ign=ignored_rev.tolist()
|
ign=ignored_rev.tolist()
|
||||||
|
|||||||
@ -39,7 +39,6 @@ inkey="ALL"
|
|||||||
|
|
||||||
|
|
||||||
|
|
||||||
sigma=3
|
|
||||||
plotme=False
|
plotme=False
|
||||||
|
|
||||||
ebands0={
|
ebands0={
|
||||||
@ -51,7 +50,6 @@ ebands0={
|
|||||||
|
|
||||||
if len(sys.argv) > 1:
|
if len(sys.argv) > 1:
|
||||||
skeys = [sys.argv[1]]
|
skeys = [sys.argv[1]]
|
||||||
#simfrac = int(sys.argv[2])
|
|
||||||
else:
|
else:
|
||||||
skeys = list(skyreg.keys())
|
skeys = list(skyreg.keys())
|
||||||
|
|
||||||
@ -67,24 +65,13 @@ with open(ignored_rev_file, 'rb') as fp:
|
|||||||
print("{} orbits ignored".format(len(ignored_rev)))
|
print("{} orbits ignored".format(len(ignored_rev)))
|
||||||
ign=ignored_rev.tolist()
|
ign=ignored_rev.tolist()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
for skey in skeys:
|
for skey in skeys:
|
||||||
if not skey in skyreg.keys():
|
if not skey in skyreg.keys():
|
||||||
print("{} not found in {}".format(skey,list(skyreg.keys())))
|
print("{} not found in {}".format(skey,list(skyreg.keys())))
|
||||||
sys.exit()
|
sys.exit()
|
||||||
|
|
||||||
# generate array for bootstrap
|
|
||||||
ebands_sim={}
|
|
||||||
for enkey in ebands0.keys():
|
|
||||||
ebands_sim[enkey] = []
|
|
||||||
|
|
||||||
for enkey in ebands0.keys():
|
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)
|
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
|
||||||
print("Reading {}".format(proddir+fn))
|
print("Reading {}".format(proddir+fn))
|
||||||
dat = Table.read(proddir+fn, unit_parse_strict='silent')
|
dat = Table.read(proddir+fn, unit_parse_strict='silent')
|
||||||
@ -113,13 +100,6 @@ for skey in skeys:
|
|||||||
ebands0[enkey]=[sg_mean,sg_sem]
|
ebands0[enkey]=[sg_mean,sg_sem]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
nsel = int(df.shape[0]*simfrac/100)
|
|
||||||
for n in range(nsim):
|
|
||||||
df0=df.sample(nsel)
|
|
||||||
sg_mean,sg_sem,skew_val,skew_err = get_spec(df0, grxe_err_cut=grxe_err_cut, skey=skey, enkey=enkey)
|
|
||||||
ebands_sim[enkey].append(sg_mean)
|
|
||||||
###
|
|
||||||
fspec="{}{}.dat".format(fluxdir,skey)
|
fspec="{}{}.dat".format(fluxdir,skey)
|
||||||
with open(fspec, 'w') as fp:
|
with open(fspec, 'w') as fp:
|
||||||
for enkey in ebands0.keys():
|
for enkey in ebands0.keys():
|
||||||
@ -129,33 +109,6 @@ for skey in skeys:
|
|||||||
fp.write("{} {} {} {:.6f} {:.6f}\n".format(skey,enkey,bands[enkey],flux,err))
|
fp.write("{} {} {} {:.6f} {:.6f}\n".format(skey,enkey,bands[enkey],flux,err))
|
||||||
|
|
||||||
|
|
||||||
###
|
|
||||||
fspec="{}{}.sim{:02d}.dat".format(fluxdir,skey,simfrac)
|
|
||||||
with open(fspec, 'w') as fp:
|
|
||||||
for enkey in ebands_sim.keys():
|
|
||||||
data=ebands_sim[enkey]
|
|
||||||
|
|
||||||
(mu, sg) = norm.fit(data)
|
|
||||||
fp.write("{:02d} {} {} {} {:.6f} {:.6f}\n".format(simfrac,skey,enkey,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()
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -113,14 +113,6 @@ for i in range(lon_nbin):
|
|||||||
nsel = int(df0.shape[0]*simfrac/100)
|
nsel = int(df0.shape[0]*simfrac/100)
|
||||||
print("lon {:.2f} ".format(glon[i]),"nsel=",nsel,df0.shape[0])
|
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)
|
#print('sg_sem',sg_sem)
|
||||||
mean_map[i] = sg_mean
|
mean_map[i] = sg_mean
|
||||||
sem_map[i] = sg_sem
|
sem_map[i] = sg_sem
|
||||||
@ -130,7 +122,7 @@ for i in range(lon_nbin):
|
|||||||
lon_arr.append(row['LON'])
|
lon_arr.append(row['LON'])
|
||||||
lat_arr.append(row['LAT'])
|
lat_arr.append(row['LAT'])
|
||||||
|
|
||||||
#sys.exit()
|
|
||||||
|
|
||||||
|
|
||||||
if not os.path.exists(profdir):
|
if not os.path.exists(profdir):
|
||||||
|
|||||||
@ -1,4 +1,3 @@
|
|||||||
./03_grxe_galprof.py E01 21
|
./03_grxe_galprof.py A01 21
|
||||||
./03_grxe_galprof.py E13 21
|
./03_grxe_galprof.py A02 21
|
||||||
./03_grxe_galprof.py E14 21
|
./03_grxe_galprof.py A03 21
|
||||||
|
|
||||||
|
|||||||
@ -6,7 +6,7 @@ __copyright__ = "Space Research Institute (IKI)"
|
|||||||
from astropy.table import Table, Column
|
from astropy.table import Table, Column
|
||||||
from matplotlib import ticker
|
from matplotlib import ticker
|
||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
import sys
|
import sys, os
|
||||||
|
|
||||||
from ridge.utils import *
|
from ridge.utils import *
|
||||||
from ridge.config import *
|
from ridge.config import *
|
||||||
@ -14,15 +14,15 @@ from ridge.config import *
|
|||||||
|
|
||||||
scale = 100.0
|
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')
|
dat = Table.read(profdir+fn, unit_parse_strict='silent')
|
||||||
df1 = dat.to_pandas().sort_values(by=['LON1'])
|
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')
|
dat = Table.read(profdir+fn, unit_parse_strict='silent')
|
||||||
df2 = dat.to_pandas().sort_values(by=['LON1'])
|
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')
|
dat = Table.read(profdir+fn, unit_parse_strict='silent')
|
||||||
df3 = dat.to_pandas().sort_values(by=['LON1'])
|
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.xscale('linear')
|
||||||
#plt.yscale('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)
|
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()
|
ign=ignored_rev.tolist()
|
||||||
|
|
||||||
|
|
||||||
hdulist = fits.open(datadir+modelrxte)
|
hdulist = fits.open(modelsdir+modelrxte)
|
||||||
w = wcs.WCS(hdulist[0].header)
|
w = wcs.WCS(hdulist[0].header)
|
||||||
smap =hdulist[0].data
|
smap =hdulist[0].data
|
||||||
|
|
||||||
@ -72,7 +72,7 @@ sx=int(hdulist[0].header['NAXIS1'])
|
|||||||
sy=int(hdulist[0].header['NAXIS2'])
|
sy=int(hdulist[0].header['NAXIS2'])
|
||||||
|
|
||||||
# fill AITOF map indexes
|
# fill AITOF map indexes
|
||||||
# Already done in 02_grxe_resid.py
|
# -- Already done in 02_grxe_resid.py
|
||||||
"""
|
"""
|
||||||
ds9x=[]
|
ds9x=[]
|
||||||
ds9y=[]
|
ds9y=[]
|
||||||
@ -136,23 +136,8 @@ for i in range(sx):
|
|||||||
if (df0.shape[0] < nscw_min):
|
if (df0.shape[0] < nscw_min):
|
||||||
continue
|
continue
|
||||||
|
|
||||||
#print("*** *** REV *** ***")
|
|
||||||
#print(df0["REV"])
|
|
||||||
|
|
||||||
# check coordinates
|
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("***",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)
|
|
||||||
|
|
||||||
|
|
||||||
#print('sg_sem',sg_sem)
|
#print('sg_sem',sg_sem)
|
||||||
@ -161,19 +146,9 @@ for i in range(sx):
|
|||||||
sign_map[j][i] = sg_mean/sg_sem
|
sign_map[j][i] = sg_mean/sg_sem
|
||||||
cnt_map[j][i] = df0.shape[0]
|
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 """
|
""" Filter by error map """
|
||||||
# Calculate the percentiles across the x and y dimension
|
# Calculate the percentiles across the x and y dimension
|
||||||
|
|
||||||
|
|
||||||
perc = np.percentile(sem_map, sem_cut, axis=(0, 1), keepdims=False)
|
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))
|
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
|
cnt_map[idx]=0
|
||||||
sign_map[idx]=0.0
|
sign_map[idx]=0.0
|
||||||
|
|
||||||
#mean_sim_map[idx]=0.0
|
|
||||||
#error_sim_map[idx]=0.0
|
|
||||||
|
|
||||||
|
|
||||||
if not os.path.exists(mapsdir):
|
if not os.path.exists(mapsdir):
|
||||||
os.makedirs(mapsdir)
|
os.makedirs(mapsdir)
|
||||||
|
|
||||||
@ -203,76 +174,8 @@ hdulist.writeto(mapsdir+fn.replace(".fits",".cnt.fits"),overwrite=True)
|
|||||||
hdulist[0].data=sign_map
|
hdulist[0].data=sign_map
|
||||||
hdulist.writeto(mapsdir+fn.replace(".fits",".sign.fits"),overwrite=True)
|
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
|
for band in A01 A02 A03
|
||||||
#./03_grxe_map.py E02
|
do
|
||||||
#./03_grxe_map.py E03
|
./03_grxe_map.py $band
|
||||||
#./03_grxe_map.py E04
|
done
|
||||||
#./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
|
|
||||||
|
|
||||||
|
# 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 B01
|
#./03_grxe_map.py $band
|
||||||
./03_grxe_map.py B02
|
#done
|
||||||
./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
|
|
||||||
|
|||||||
@ -38,7 +38,7 @@ from ridge.config import *
|
|||||||
inkey="ALL"
|
inkey="ALL"
|
||||||
|
|
||||||
|
|
||||||
sigma=3
|
|
||||||
plotme=False
|
plotme=False
|
||||||
|
|
||||||
"""
|
"""
|
||||||
@ -117,11 +117,15 @@ else:
|
|||||||
if not os.path.exists(specdir):
|
if not os.path.exists(specdir):
|
||||||
os.makedirs(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:
|
with open(ignored_rev_file, 'rb') as fp:
|
||||||
ignored_rev = pickle.load(fp)
|
ignored_rev = pickle.load(fp)
|
||||||
print(ignored_rev)
|
print(ignored_rev)
|
||||||
print("{} orbits ignored".format(len(ignored_rev)))
|
print("{} orbits ignored".format(len(ignored_rev)))
|
||||||
#sys.exit()
|
|
||||||
ign=ignored_rev.tolist()
|
ign=ignored_rev.tolist()
|
||||||
|
|
||||||
|
|
||||||
@ -131,14 +135,7 @@ for skey in skeys:
|
|||||||
print("{} not found in {}".format(skey,list(skyreg.keys())))
|
print("{} not found in {}".format(skey,list(skyreg.keys())))
|
||||||
sys.exit()
|
sys.exit()
|
||||||
|
|
||||||
# generate array for bootstrap
|
|
||||||
ebands_sim={}
|
|
||||||
for enkey in ebands0.keys():
|
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)
|
fn="detcnts.{}.{}.resid.fits".format(enkey,inkey)
|
||||||
#d1 = fits.getdata(proddir+fn)
|
#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,
|
||||||
skyreg[skey]['lat'] + skyreg[skey]['wlat']/2)
|
skyreg[skey]['lat'] + skyreg[skey]['wlat']/2)
|
||||||
|
|
||||||
#sys.exit()
|
|
||||||
df = df.query(query)
|
df = df.query(query)
|
||||||
print("{}, {}: {} N={}".format(skey, enkey, query, df.shape[0]))
|
print("{}, {}: {} N={}".format(skey, enkey, query, df.shape[0]))
|
||||||
|
|
||||||
@ -178,22 +175,12 @@ for skey in skeys:
|
|||||||
if not (df.shape[0]>0):
|
if not (df.shape[0]>0):
|
||||||
continue
|
continue
|
||||||
|
|
||||||
#plt.scatter(df['LON'],df['LAT'])
|
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)
|
||||||
#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)
|
|
||||||
ebands0[enkey]=[sg_mean,sg_sem]
|
ebands0[enkey]=[sg_mean,sg_sem]
|
||||||
skew0[enkey]=[skew_val,skew_err]
|
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)
|
fspec="{}{}.spec".format(specdir,skey)
|
||||||
with open(fspec, 'w') as fp:
|
with open(fspec, 'w') as fp:
|
||||||
for enkey in ebands0.keys():
|
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))
|
fp.write("0 {} {:.6f} {:.6f} 0.0\n".format(bands[enkey],flux,err))
|
||||||
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
|
subprocess.run(["perl", "do_pha_igr_v3_mCrab.pl", fspec])
|
||||||
|
|
||||||
###
|
|
||||||
|
|
||||||
fspec="{}{}.skew".format(specdir,skey)
|
fspec="{}{}.skew".format(specdir,skey)
|
||||||
with open(fspec, 'w') as fp:
|
with open(fspec, 'w') as fp:
|
||||||
fp.write("read serr 4\n")
|
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]))
|
fp.write("{} {} {:.6f} {:.6f}\n".format(count,bands[enkey],skew0[enkey][0],skew0[enkey][1]))
|
||||||
count+=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:
|
try:
|
||||||
for remfile in ["cols","cols1","cols2","header",]:
|
for remfile in ["cols","cols1","cols2","header",]:
|
||||||
os.remove(remfile)
|
os.remove(remfile)
|
||||||
|
|||||||
@ -20,7 +20,7 @@ Plot long-term Crab detector count rate approximated by cubic polynomial functio
|
|||||||
|
|
||||||
### 01_crabmodel_plot_sys.py
|
### 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
|
### 02_grxe_resid.py
|
||||||
|
|
||||||
@ -28,21 +28,46 @@ Calculates difference between detector count rate and that predicted by backgrou
|
|||||||
|
|
||||||
### 02_grxe_resid_plot.py
|
### 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
|
### 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
|
### 03_grxe_spec.py
|
||||||
|
|
||||||
Extract spectrum of selected sky region, e.g.:
|
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.
|
If you do not specify the sky region, the script makes spectra for all regions.
|
||||||
|
|
||||||
Sky regions are defined in ```config.py```.
|
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