IKI

scripts/03_grxe_galprof.py

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+#!/usr/bin/env python
+
+__author__      = "Roman Krivonos"
+__copyright__   = "Space Research Institute (IKI)"
+
+import numpy as np
+import numpy.ma as ma
+import pandas as pd
+from astropy.wcs import WCS
+from astropy import wcs
+from astropy.io import fits
+from astropy.table import Table, Column
+import matplotlib.pyplot as plt
+import math, sys, os
+import pickle
+
+from astropy.coordinates import SkyCoord  # High-level coordinates
+from astropy.coordinates import ICRS, Galactic, FK4, FK5  # Low-level frames
+from astropy.coordinates import Angle, Latitude, Longitude  # Angles
+import astropy.units as u
+
+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 astropy.stats import sigma_clip
+from astropy.stats import sigma_clipped_stats
+
+from scipy.stats import norm
+from scipy.stats import describe
+from scipy.stats import sem
+
+
+from numpy import absolute
+from numpy import arange
+
+from ridge.utils import *
+from ridge.config import *
+
+enkey = sys.argv[1]
+lon_nbin = int(sys.argv[2])
+key="ALL"
+fn='detcnts.{}.{}.resid.fits'.format(enkey,key)
+
+print("Reading {}".format(proddir+fn))
+dat = Table.read(proddir+fn, unit_parse_strict='silent')
+df = dat.to_pandas()
+
+#df = df.query('abs(LAT) < {} & abs(LON) < {}'.format(5,5))
+
+n_bins = 80
+minmax=[-300,800]
+sigma=3
+maxiters=10
+
+with open(ignored_rev_file, 'rb') as fp:
+    ignored_rev = pickle.load(fp)
+    print(ignored_rev)
+    print("{} orbits ignored".format(len(ignored_rev)))
+
+ign=ignored_rev.tolist()
+
+
+glon, step = np.linspace(-lon_max, lon_max, num=lon_nbin, endpoint=False,retstep=True)
+print(glon,step)
+
+
+#
+# initiate 2d arrays
+#
+mean_map = np.array([0.0 for j in range(lon_nbin)])
+sign_map = np.array([0.0 for j in range(lon_nbin)])
+sem_map = np.array([0.0 for j in range(lon_nbin)])
+cnt_map = np.array([0 for j in range(lon_nbin)])
+
+mean_sim_map = np.array([0.0 for j in range(lon_nbin)])
+error_sim_map = np.array([0.0 for j in range(lon_nbin)])
+
+mean_sim={}
+for i in range(lon_nbin):
+    dkey="{:04d}".format(i)
+    mean_sim[dkey] = []
+
+
+obsid_map = {}
+grxe_map = {}
+grxe_err_map = {}
+
+# redefine simfrac for low number of ScWs in pixel
+#simfrac=2
+#nsim=10
+
+lon_arr=[]
+lat_arr=[]
+
+for i in range(lon_nbin):
+    dkey="{:04d}".format(i)
+
+    
+    df0 = df.query('LON > {} & LON <= {} & LAT > {} & LAT < {} & REV != @ign'.format(glon[i],glon[i]+step,-lat_max,lat_max))
+
+    if  (df0.shape[0] < nscw_min):
+        continue
+
+    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)
+    print("lon {:.2f} ".format(glon[i]),"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)
+    mean_map[i] = sg_mean
+    sem_map[i]  = sg_sem
+    cnt_map[i]  = df0.shape[0]
+
+    for index, row in df0.iterrows():
+        lon_arr.append(row['LON'])
+        lat_arr.append(row['LAT'])
+
+
+
+        
+if not os.path.exists(profdir):
+    os.makedirs(profdir)
+
+print("saving simulations")
+for i in range(lon_nbin):
+    dkey="{:04d}".format(i)
+    data=mean_sim[dkey]
+    #print("{} size {}".format(dkey,len(data)))
+    #if(len(data)>10):
+    (mu, sg) = norm.fit(data)
+    mean_sim_map[i] = mu
+    error_sim_map[i] = sg
+    
+
+coldefs = fits.ColDefs([
+    fits.Column(name='LON1', format='D', array=glon), 
+    fits.Column(name='LON2', format='D', array=glon+step), 
+    fits.Column(name='GRXE_FLUX', format='D', array=mean_map), 
+    fits.Column(name='GRXE_ERROR', format='D', array=sem_map), 
+    fits.Column(name='GRXE_SIM_FLUX', format='D', array=mean_sim_map), 
+    fits.Column(name='GRXE_SIM_ERROR', format='D', array=error_sim_map), 
+    fits.Column(name='nScW', format='J', array=cnt_map), 
+])
+
+fout = fn.replace(".fits",".galprof.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(profdir+fout, overwrite=True)
+
+with fits.open(profdir+fout, mode='update') as hdus:
+    hdus[1].add_checksum() 
+
+    
+# to check, which ScWs were used
+coldefs = fits.ColDefs([
+    fits.Column(name='LON', format='D', array=lon_arr), 
+    fits.Column(name='LAT', format='D', array=lat_arr), 
+])
+
+
+fout = fn.replace(".fits",".gal.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(profdir+fout, overwrite=True)
+
+with fits.open(profdir+fout, mode='update') as hdus:
+    hdus[1].add_checksum() 
+
+