maps

scripts/02_grxe_resid.py

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+#!/usr/bin/env python
+
+__author__      = "Roman Krivonos"
+__copyright__   = "Space Research Institute (IKI)"
+
+import numpy as np
+import pandas as pd
+from astropy.io import fits
+from astropy.table import Table, Column
+import matplotlib.pyplot as plt
+import math, sys
+import pickle
+
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import HuberRegressor
+from sklearn.linear_model import RANSACRegressor
+from sklearn.linear_model import TheilSenRegressor
+from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import RepeatedKFold
+
+#from statsmodels.robust.scale import huber
+from astropy.stats import sigma_clip
+from numpy import absolute
+from numpy import arange
+
+from ridge.utils import *
+from ridge.config import *
+
+enkey = sys.argv[1]
+outkey = sys.argv[2]
+
+fn="detcnts.{}.fits".format(enkey)
+
+with open(proddir+fn.replace(".fits",".pkl"), 'rb') as fp:
+    bgdmodel = pickle.load(fp)
+
+with open(proddir+fn.replace(".fits",".ignored_scw.pkl"), 'rb') as fp:
+    ignored_scw = pickle.load(fp)
+
+with open(proddir+fn.replace(".fits",".crabmodel.pkl"), 'rb') as fp:
+    crabmodel, z = pickle.load(fp)
+    p = np.poly1d(z)
+    #print(crabmodel)
+
+crab_rev_max = np.max(list(crabmodel.keys()))
+print("Crab is defined untill orbit {}".format(crab_rev_max))
+
+with fits.open(datadir+fn) as hdul:
+    data=hdul[1].data
+
+#print(data.columns)
+
+rev   = data.field('rev')
+mjd   = data.field('mjd')
+clean = data.field('clean')
+phase = data.field('phase')
+
+
+rev0=[]
+phase0=[]
+clean0=[]
+model0=[]
+resid0=[] # residuals in cts/s
+grxe0=[] # mCrab
+crab0=[] # Crab count rate
+mjd0=[]
+a0=[]
+b0=[]
+err0=[]
+lon0=[]
+lat0=[]
+
+d  = fits.getdata(datadir+fn)
+df = pd.DataFrame(np.array(d).byteswap().newbyteorder())
+
+# BKG
+if(outkey == 'BKG'):
+    df = df.query('CLEAN > 0.0 & ( abs(LAT) > {} | abs(LON) > {}) & PHASE > {} & PHASE < {}'.format(bmax,lmax,phmin,phmax))
+
+# GAL
+if(outkey=='GAL'): 
+    df = df.query('CLEAN > 0.0 & abs(LAT) < {} & abs(LON) < {} & PHASE > {} & PHASE < {}'.format(bmax,lmax,phmin,phmax))
+
+# ALL
+if(outkey=='ALL'): 
+    df = df.query('CLEAN > 0.0 & PHASE > {} & PHASE < {}'.format(phmin,phmax))
+    
+for i, row in df.iterrows():
+    orbit=row['REV']
+    obsid=row['OBSID'].decode("UTF-8")
+    
+    if not (orbit > revmin and orbit < revmax):
+        print("Skip orbit",orbit,row['OBSID'])
+        continue
+
+    if not (orbit < crab_rev_max):
+        print("Skip orbit",orbit,row['OBSID'])
+        continue
+
+    if (obsid in ignored_scw):
+        print("Skip ScW",obsid)
+        continue
+
+    a = bgdmodel[orbit]['a']
+    b = bgdmodel[orbit]['b']
+    err = bgdmodel[orbit]['err']
+    m = a*row['PHASE']+b
+
+    clean0.append(clean[i])
+    mjd0.append(mjd[i])
+    model0.append(m)
+    resid0.append(clean[i]-m)
+    grxe0.append(1000*(clean[i]-m)/p(orbit))
+    crab0.append(p(orbit))
+        
+    a0.append(a)
+    b0.append(b)
+    err0.append(err)
+    phase0.append(row['PHASE'])
+    rev0.append(orbit)
+    lon0.append(row['LON'])
+    lat0.append(row['LAT'])
+
+
+indices = sorted(
+    range(len(mjd0)),
+    key=lambda index: mjd0[index]
+)
+
+coldefs = fits.ColDefs([
+    #fits.Column(name='OBSID', format='11A', array=[obs_id[index] for index in indices]), 
+    #fits.Column(name='RA', format='D', unit='deg',  array=[ra[index] for index in indices]),
+    #fits.Column(name='DEC', format='D', unit='deg', array=[dec[index] for index in indices]),
+    fits.Column(name='LON', format='D', unit='deg', array=[lon0[index] for index in indices]),
+    fits.Column(name='LAT', format='D', unit='deg', array=[lat0[index] for index in indices]),
+    fits.Column(name='REV', format='J', unit='', array=[rev0[index] for index in indices]),
+    fits.Column(name='MJD', format='D', unit='', array=[mjd0[index] for index in indices]),
+    fits.Column(name='PHASE', format='D', unit='', array=[phase0[index] for index in indices]),
+    fits.Column(name='CLEAN', format='D', unit='cts/s', array=[clean0[index] for index in indices]),
+    fits.Column(name='MODEL', format='D', unit='cts/s', array=[model0[index] for index in indices]),
+    fits.Column(name='RESID', format='D', unit='cts/s', array=[resid0[index] for index in indices]),
+    fits.Column(name='GRXE', format='D', unit='mCrab', array=[grxe0[index] for index in indices]),
+    fits.Column(name='CRAB', format='D', unit='cts/s', array=[crab0[index] for index in indices]),
+    fits.Column(name='A', format='D', unit='', array=[a0[index] for index in indices]),
+    fits.Column(name='B', format='D', unit='', array=[b0[index] for index in indices]),
+    fits.Column(name='ERR', format='D', unit='', array=[err0[index] for index in indices]),
+])
+
+fout = fn.replace(".fits",".{}.resid.fits".format(outkey))
+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() 
+
+    
+