generated from erosita/uds
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uds/setup.py
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uds/setup.py
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from setuptools import setup, find_packages
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setup(name="uds",
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version="0.1",
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description='eROSITA UDS field analysis',
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author='Roman Krivonos',
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author_email='krivonos@cosmos.ru',
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url='https://www.srg.cosmos.ru',
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packages=find_packages())
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uds/uds/__init__.py
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uds/uds/__init__.py
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468
uds/uds/astrometry.py
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uds/uds/astrometry.py
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import glob
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import os
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import sys
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import numpy as np
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from astropy.io import fits
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import pickle
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from scipy.stats import sem
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from astropy.wcs import WCS
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from astropy.coordinates import SkyCoord # High-level coordinates
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from astropy.coordinates import ICRS, Galactic, FK4, FK5 # Low-level frames
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from astropy.coordinates import Angle, Latitude, Longitude # Angles
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import matplotlib.pyplot as plt
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from astropy.table import QTable, Table, Column
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from mpdaf.obj import WCS as mWCS
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from sherpa.astro.ui import *
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from uds.config import *
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from scipy.optimize import minimize
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from random import uniform
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def sky2pix(wcs,dsrc):
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# converts ra,dec --> x,y
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# checks
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#sk = wcs.pix2sky([0.0,0.0])
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#print(sk)
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#px = wcs.sky2pix([-6.86525105,36.54071803],nearest=False)
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#print(px)
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#sk = wcs.pix2sky([px[0][1],px[0][0]])
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#print(sk)
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for idx, s in enumerate(dsrc):
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ra0=s['ra']
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dec0=s['dec']
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px = wcs.sky2pix([dec0,ra0],nearest=False)
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s['x']=px[0][1]
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s['y']=px[0][0]
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#sk = wcs.pix2sky([y0,x0])
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#ra=sk[0][1]
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#dec=sk[0][0]
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#px = wcs.sky2pix([dec,ra],nearest=False)
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#x=px[0][1]
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#y=px[0][0]
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#print("{:.4f} {:.4f} --> {} {} --> {:.4f} {:.4f} --> {} {}".format(ra0,dec0,x0,y0,ra,dec,x,y))
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def pix2sky(wcs,dsrc):
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# converts x,y --> ra1,dec1
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for idx, s in enumerate(dsrc):
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sk = wcs.pix2sky([s['y'],s['x']])
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s['ra1']=sk[0][1]
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s['dec1']=sk[0][0]
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def plot_resid(dsrc,dref,crval1,crval2):
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# calculates RMS of original ra,dec
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center_crd = SkyCoord(crval1, crval2, frame=FK5(), unit="deg")
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offset=[]
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resid=[]
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error=[]
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for idx, s in enumerate(dsrc):
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src_crd = SkyCoord(dsrc[idx]['ra'], dsrc[idx]['dec'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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offset.append(src_crd.separation(center_crd).arcmin)
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resid.append(src_crd.separation(ref_crd).arcsec)
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error.append(s['radec_err'])
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indices = sorted(
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range(len(offset)),
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key=lambda index: offset[index]
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)
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return ([offset[index] for index in indices],
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[resid[index] for index in indices],
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[error[index] for index in indices])
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def plot_resid1(dsrc,dref,crval1,crval2):
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# calculates RMS of original ra,dec
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center_crd = SkyCoord(crval1, crval2, frame=FK5(), unit="deg")
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offset=[]
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resid=[]
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error=[]
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for idx, s in enumerate(dsrc):
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src_crd = SkyCoord(dsrc[idx]['ra1'], dsrc[idx]['dec1'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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offset.append(src_crd.separation(center_crd).arcmin)
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resid.append(src_crd.separation(ref_crd).arcsec)
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error.append(s['radec_err'])
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indices = sorted(
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range(len(offset)),
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key=lambda index: offset[index]
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)
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return ([offset[index] for index in indices],
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[resid[index] for index in indices],
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[error[index] for index in indices])
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def get_rms(dsrc,dref):
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# calculates RMS of original ra,dec
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resid=0.0
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n=0
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for idx, s in enumerate(dsrc):
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radec_err=s['radec_err']
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src_crd = SkyCoord(dsrc[idx]['ra'], dsrc[idx]['dec'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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sep=src_crd.separation(ref_crd).arcsec
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resid = resid+sep**2
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n=n+1
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return np.sqrt(resid/n)
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def get_rms_w(dsrc,dref):
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# calculates RMS of original ra,dec
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resid=0.0
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n=0
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A=0.0
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B=0.0
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for idx, s in enumerate(dsrc):
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radec_err=s['radec_err']
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src_crd = SkyCoord(dsrc[idx]['ra'], dsrc[idx]['dec'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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sep=src_crd.separation(ref_crd).arcsec
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A=A+sep/radec_err**2
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B=B+1.0/radec_err**2
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n=n+1
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return A/B
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# A=Total(table_lc_sav.rate/(table_lc_sav.err)^2)
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# B=Total(1.0/(table_lc_sav.err)^2)
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def get_rms1_w(dsrc,dref):
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# calculates RMS of original ra,dec
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resid=0.0
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n=0
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A=0.0
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B=0.0
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for idx, s in enumerate(dsrc):
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radec_err=s['radec_err']
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src_crd = SkyCoord(dsrc[idx]['ra1'], dsrc[idx]['dec1'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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sep=src_crd.separation(ref_crd).arcsec
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A=A+sep/radec_err**2
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B=B+1.0/radec_err**2
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n=n+1
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return A/B
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def get_chi2_radec(dsrc,dref):
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# calculates RMS of original ra,dec
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resid=0.0
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n=0
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for idx, s in enumerate(dsrc):
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radec_err=s['radec_err']
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src_crd = SkyCoord(dsrc[idx]['ra'], dsrc[idx]['dec'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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sep=src_crd.separation(ref_crd).arcsec
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resid = resid+sep**2/radec_err**2
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n=n+1
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return resid
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def get_rms1(dsrc,dref):
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# calculates RMS of modified ra1,dec1
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resid=0.0
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n=0
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for idx, s in enumerate(dsrc):
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radec_err=s['radec_err']
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src_crd = SkyCoord(dsrc[idx]['ra1'], dsrc[idx]['dec1'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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sep=src_crd.separation(ref_crd).arcsec
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resid = resid+sep*sep
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n=n+1
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return np.sqrt(resid/n)
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def get_chi2_radec1(dsrc,dref):
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# calculates RMS of modified ra1,dec1
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resid=0.0
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n=0
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for idx, s in enumerate(dsrc):
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radec_err=s['radec_err']
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src_crd = SkyCoord(dsrc[idx]['ra1'], dsrc[idx]['dec1'], frame=FK5(), unit="deg")
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ref_crd = SkyCoord(dref[idx]['ra'], dref[idx]['dec'], frame=FK5(), unit="deg")
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sep=src_crd.separation(ref_crd).arcsec
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resid = resid+sep**2/radec_err**2
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n=n+1
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return resid
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def do_transform_resid(params, wcs, dsrc, dref, crval1, crval2):
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angle_in, crval1_in, crval2_in = params
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# fills x,y
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sky2pix(wcs,dsrc)
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wcs1=wcs.copy()
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# do transform here
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#wcs1.rotate(angle_in/60)
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wcs1.set_crval1(crval1_in)
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wcs1.set_crval2(crval2_in)
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# fills ra1,dec1
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pix2sky(wcs1,dsrc)
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# caclulate new statistics
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resid1 = get_chi2_radec1(dsrc,dref)
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#print("--> {:+.4f} {:+.4f} {:.4f}".format((crval1-wcs.get_crval1())*3600,(crval2-wcs.get_crval2())*3600,resid1))
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return resid1
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def do_astro_corr(src=None, ref=None, catalog=None):
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""" calculates astronomy correction """
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log=catalog.replace(".fits", ".shift.log")
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log0=catalog.replace(".fits", ".orig.qdp")
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log1=catalog.replace(".fits", ".shift.qdp")
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fout=catalog.replace(".fits", ".shift-map.fits")
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title=os.path.split(catalog)[1]
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key=title.replace("_SourceCatalog_en0.fits","")
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#png=catalog.replace(".fits", ".opti.png")
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#png=png.replace(key,"{}_{}".format(obslist[key],key))
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t = Table(names=('rota', 'shift_ra','shift_dec','chi2',), dtype=('f8','f8','f8','f8'))
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print(src)
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hdul = fits.open(src)
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src_map_data = hdul[0].data
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src_map_hdr = hdul[0].header
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src_tab_data = hdul[1].data
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src_tab_hdr = hdul[1].header
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hdul.close()
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dsrc=[]
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for rec in src_tab_data:
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d = dict(ra = rec['RA'], dec = rec['DEC'], ra_err = rec['RA_ERR'], dec_err = rec['DEC_ERR'], radec_err = rec['RADEC_ERR'])
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dsrc.append(d)
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print(ref)
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hdul = fits.open(ref)
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ref_map_data = hdul[0].data
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ref_map_hdr = hdul[0].header
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ref_tab_data = hdul[1].data
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ref_tab_hdr = hdul[1].header
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hdul.close()
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dref=[]
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for rec in ref_tab_data:
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d = dict(ra = rec['RA'], dec = rec['DEC'], ra_err = rec['RA_ERR'], dec_err = rec['DEC_ERR'])
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dref.append(d)
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rms = get_rms(dsrc,dref)
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chi2_radec = get_chi2_radec(dsrc,dref)
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wcs_src=mWCS(src_map_hdr)
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wcs_src.info()
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crval1=wcs_src.get_crval1()
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crval2=wcs_src.get_crval2()
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#print(crval1,crval2)
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offset0, sep0, err0 = plot_resid(dsrc,dref,crval1,crval2)
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with open(log0, 'w') as writer:
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writer.write("label Y Separation, arcsec\nlabel X Offset, arcmin\nr y -1 15\n")
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writer.write("ma 9 on\n")
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writer.write("read serr 2\n")
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for idx, s in enumerate(offset0):
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writer.write("{:.2f} {:.2f} {:.2f}\n".format(offset0[idx],sep0[idx],err0[idx]))
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wcs_ref=mWCS(ref_map_hdr)
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wcs_ref.info()
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chi2_radec1_opt=1000
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rota_opt=0.0
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crval1_opt=crval1
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crval2_opt=crval2
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rota_min=-30
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rota_max=30
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Rsim=10.0
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Nsim=20000
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crd = SkyCoord(crval1, crval2, frame=FK5(), unit="deg")
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i=0
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while i < Nsim:
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#rota_rand = uniform(rota_min, rota_max)
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rota_rand=0.0
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crval1_rand = uniform(crval1-Rsim/3600, crval1+Rsim/3600)
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crval2_rand = uniform(crval2-Rsim/3600, crval2+Rsim/3600)
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#crval1_rand = crval1
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#crval2_rand = crval2
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sim_crd = SkyCoord(crval1_rand, crval2_rand, frame=FK5(), unit="deg")
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if(sim_crd.separation(crd).arcsec > Rsim):
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continue
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chi2_radec1 = do_transform_resid([rota_rand, crval1_rand, crval2_rand], wcs_src, dsrc, dref, crval1, crval2)
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t.add_row((rota_rand,(crval1-crval1_rand)*3600,(crval2-crval2_rand)*3600,chi2_radec1))
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if(chi2_radec1 < chi2_radec1_opt):
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chi2_radec1_opt = chi2_radec1
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rota_opt = rota_rand
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crval1_opt = crval1_rand
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crval2_opt = crval2_rand
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i += 1
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# set optimal parameters
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r = do_transform_resid([rota_opt, crval1_opt, crval2_opt], wcs_src, dsrc, dref, crval1, crval2)
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print(chi2_radec1_opt,'==',r)
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shift_ra=(crval1-crval1_opt)*3600
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shift_dec=(crval2-crval2_opt)*3600
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t.write(fout, format='fits', overwrite=True)
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rms1 = get_rms1(dsrc,dref)
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offset1, sep1, err1 = plot_resid1(dsrc,dref,crval1,crval2)
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with open(log1, 'w') as writer:
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writer.write("label Y Separation, arcsec\nlabel X Offset, arcmin\nr y -1 15\n")
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writer.write("ma 9 on\n")
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writer.write("read serr 2\n")
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for idx, s in enumerate(offset1):
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writer.write("{:.2f} {:.2f} {:.2f}\n".format(offset1[idx],sep1[idx],err1[idx]))
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with open(log, 'w') as writer:
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writer.write("{} {:+.2f} {:+.2f} {:+.2f} ({:.2f} - {:.2f}) = {:.2f} {:.2f} ({:.2f}) {:.2f} ({:.2f}) N={} -- {}\n".format(obslist[key],
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rota_opt,
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shift_ra,
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shift_dec,
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get_chi2_radec(dsrc,dref),
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get_chi2_radec1(dsrc,dref),
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chi2_radec-chi2_radec1_opt,
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rms,get_rms_w(dsrc,dref),
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rms1,get_rms1_w(dsrc,dref),
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len(dsrc),key))
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"""
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Nsim=20000 Shift only
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N01 +0.00 +4.76 +0.07 (171.22 - 28.06) = 143.15 6.35 (5.11) 3.92 (1.40) N=22 -- tm1_obs_1
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N02 +0.00 +2.77 -2.74 (354.15 - 225.27) = 128.88 10.10 (5.34) 9.38 (2.61) N=31 -- tm5_obs_1
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N03 +0.00 +3.88 -3.65 (542.25 - 303.25) = 239.00 8.74 (6.87) 6.73 (3.07) N=36 -- tm5_obs_2
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N04 +0.00 +0.28 +0.03 (43.16 - 43.04) = 0.12 6.04 (3.39) 6.07 (3.36) N=17 -- tm6_park_1
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N05 +0.00 +0.65 -0.78 (175.10 - 169.55) = 5.56 6.77 (4.47) 6.54 (4.51) N=38 -- tm6_scan_1
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N06 +0.00 -0.38 +0.27 (26.60 - 26.37) = 0.23 6.54 (4.27) 6.52 (4.23) N=11 -- tm6_park_2
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N07 +0.00 +0.04 -0.35 (128.82 - 127.98) = 0.84 5.76 (4.21) 5.75 (4.18) N=35 -- tm6_scan_2
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N08 +0.00 +0.58 -0.22 (10.07 - 9.79) = 0.29 3.94 (2.83) 3.89 (2.82) N= 9 -- tm6_park_3
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N09 +0.00 +1.35 -0.56 (95.06 - 87.04) = 8.02 5.73 (4.45) 5.60 (4.28) N=38 -- tm6_scan_3
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N10 +0.00 +0.56 +0.36 (15.71 - 15.41) = 0.30 4.22 (3.49) 4.22 (3.43) N=10 -- tm6_park_4
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N11 +0.00 +1.08 -0.62 (116.34 - 108.60) = 7.73 5.38 (4.41) 5.11 (4.26) N=37 -- tm6_scan_4
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N12 +0.00 -0.56 -1.56 (84.93 - 59.86) = 25.07 4.20 (2.45) 4.09 (1.88) N=30 -- tm6_obs_1
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N13 +0.00 +0.19 -1.03 (17.58 - 13.28) = 4.30 3.38 (1.90) 3.23 (1.31) N=13 -- tm6_obs_2_badpix
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N14 +0.00 +1.03 -1.99 (63.23 - 35.27) = 27.97 4.36 (2.73) 3.27 (2.05) N=23 -- tm6_obs_3_badpix
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N15 +0.00 +0.88 -1.66 (187.51 - 164.26) = 23.25 6.93 (3.29) 6.62 (2.68) N=33 -- tm7_obs_1
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N16 +0.00 +2.74 -0.52 (113.70 - 75.09) = 38.61 5.34 (4.23) 4.73 (3.42) N=26 -- tm7_obs_2
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Nsim=20000 Shift+Rotation
|
||||
N01 -1.50 +4.93 +0.00 (171.22 - 27.67) = 143.55 6.35 (5.11) 3.83 (1.38) N=22 -- tm1_obs_1
|
||||
N02 -2.15 +2.47 -3.05 (354.15 - 223.27) = 130.88 10.10 (5.34) 9.29 (2.71) N=31 -- tm5_obs_1
|
||||
N03 -5.08 +3.87 -4.04 (542.25 - 296.00) = 246.25 8.74 (6.87) 6.93 (2.82) N=36 -- tm5_obs_2
|
||||
N04 +5.90 -0.19 +0.50 (43.16 - 39.56) = 3.60 6.04 (3.39) 5.57 (3.57) N=17 -- tm6_park_1
|
||||
N05 -5.84 +1.37 -0.85 (175.10 - 111.02) = 64.08 6.77 (4.47) 5.53 (3.58) N=38 -- tm6_scan_1
|
||||
N06 -0.30 -0.27 +0.31 (26.60 - 26.36) = 0.24 6.54 (4.27) 6.50 (4.27) N=11 -- tm6_park_2
|
||||
N07 -5.49 +0.73 -0.98 (128.82 - 86.16) = 42.66 5.76 (4.21) 4.94 (3.62) N=35 -- tm6_scan_2
|
||||
N08 -0.28 +0.51 -0.29 (10.07 - 9.78) = 0.29 3.94 (2.83) 3.90 (2.82) N= 9 -- tm6_park_3
|
||||
N09 -4.56 +0.91 -0.40 (95.06 - 64.56) = 30.49 5.73 (4.45) 4.93 (3.56) N=38 -- tm6_scan_3
|
||||
N10 +2.01 +0.54 +0.63 (15.71 - 15.17) = 0.54 4.22 (3.49) 4.03 (3.39) N=10 -- tm6_park_4
|
||||
N11 -3.83 +1.01 -1.29 (116.34 - 82.25) = 34.08 5.38 (4.41) 4.75 (3.72) N=37 -- tm6_scan_4
|
||||
N12 -1.35 -0.23 -1.27 (84.93 - 60.30) = 24.63 4.20 (2.45) 4.20 (1.82) N=30 -- tm6_obs_1
|
||||
N13 -1.52 +0.17 -1.38 (17.58 - 13.30) = 4.29 3.38 (1.90) 3.21 (1.28) N=13 -- tm6_obs_2_badpix
|
||||
N14 -1.24 +0.63 -2.18 (63.23 - 34.42) = 28.81 4.36 (2.73) 3.28 (1.92) N=23 -- tm6_obs_3_badpix
|
||||
N15 -5.23 +0.82 -1.47 (187.51 - 161.20) = 26.31 6.93 (3.29) 6.31 (2.66) N=33 -- tm7_obs_1
|
||||
N16 -5.41 +2.47 -0.97 (113.70 - 60.40) = 53.30 5.34 (4.23) 4.59 (2.89) N=26 -- tm7_obs_2
|
||||
"""
|
||||
def do_astro_corr_rota(src=None, ref=None, catalog=None):
|
||||
""" calculates astronomy correction """
|
||||
|
||||
title=os.path.split(catalog)[1]
|
||||
png=catalog.replace(".fits", ".rota.png")
|
||||
log=catalog.replace(".fits", ".rota.log")
|
||||
key=title.replace("_SourceCatalog_en0.fits","")
|
||||
png=png.replace(key,"{}_{}".format(obslist[key],key))
|
||||
|
||||
print(src)
|
||||
hdul = fits.open(src)
|
||||
src_map_data = hdul[0].data
|
||||
src_map_hdr = hdul[0].header
|
||||
src_tab_data = hdul[1].data
|
||||
src_tab_hdr = hdul[1].header
|
||||
hdul.close()
|
||||
|
||||
dsrc=[]
|
||||
for rec in src_tab_data:
|
||||
d = dict(ra = rec['RA'], dec = rec['DEC'], ra_err = rec['RA_ERR'], dec_err = rec['DEC_ERR'], radec_err = rec['RADEC_ERR'])
|
||||
dsrc.append(d)
|
||||
|
||||
|
||||
|
||||
print(ref)
|
||||
hdul = fits.open(ref)
|
||||
ref_map_data = hdul[0].data
|
||||
ref_map_hdr = hdul[0].header
|
||||
ref_tab_data = hdul[1].data
|
||||
ref_tab_hdr = hdul[1].header
|
||||
hdul.close()
|
||||
|
||||
dref=[]
|
||||
for rec in ref_tab_data:
|
||||
d = dict(ra = rec['RA'], dec = rec['DEC'], ra_err = rec['RA_ERR'], dec_err = rec['DEC_ERR'])
|
||||
dref.append(d)
|
||||
|
||||
|
||||
resid = get_rms(dsrc,dref)
|
||||
print(resid)
|
||||
|
||||
wcs_src=mWCS(src_map_hdr)
|
||||
wcs_src.info()
|
||||
|
||||
wcs_ref=mWCS(ref_map_hdr)
|
||||
wcs_ref.info()
|
||||
|
||||
x=[]
|
||||
y=[]
|
||||
rms_min=1000.0
|
||||
rota_min=1000.0
|
||||
for angle in np.arange(-0.5, 0.5, 0.01):
|
||||
resid1=do_transform(angle,wcs_src,dsrc,dref)
|
||||
x.append(angle*60)
|
||||
y.append(resid1/resid)
|
||||
if(resid1<rms_min):
|
||||
rms_min=resid1
|
||||
rota_min=angle*60
|
||||
|
||||
with open(log, 'w') as writer:
|
||||
writer.write("{} {} {:.2f} {:.2f} {:.2f} {:.2f}%\n".format(obslist[key],title,rota_min, rms_min, resid, (1.0-rms_min/resid)*100.0))
|
||||
|
||||
|
||||
fig, ax = plt.subplots( nrows=1, ncols=1 ) # create figure & 1 axis
|
||||
ax.plot(x,y)
|
||||
plt.ylabel('RMS/RMS0, RMS0={:.2f} arcsec'.format(resid))
|
||||
plt.xlabel('Rotation, arcmin')
|
||||
plt.title("{} {} {} pairs, {:.1f}%".format(obslist[key], title,len(dsrc),(1.0-rms_min/resid)*100.0))
|
||||
plt.grid(True)
|
||||
plt.axvline(x = 0.0, color = 'b', label = 'zero')
|
||||
fig.savefig(png)
|
||||
plt.close(fig) # close the figure window
|
||||
|
||||
def do_transform(angle,wcs,dsrc,dref):
|
||||
# fills x,y
|
||||
sky2pix(wcs,dsrc)
|
||||
|
||||
# do transform here
|
||||
wcs.rotate(angle)
|
||||
|
||||
# fills ra1,dec1
|
||||
pix2sky(wcs,dsrc)
|
||||
|
||||
resid1 = get_rms1(dsrc,dref)
|
||||
return resid1
|
||||
109
uds/uds/config.py
Normal file
109
uds/uds/config.py
Normal file
@@ -0,0 +1,109 @@
|
||||
from pathlib import Path
|
||||
|
||||
work_dir = Path('/path/to/some/logical/parent/dir')
|
||||
|
||||
"""
|
||||
Координаты сентрального кадра, к которому будут
|
||||
приводиться изображения всех списков событий
|
||||
"""
|
||||
ra_cen=34.5342131
|
||||
de_cen=-4.7956710
|
||||
|
||||
|
||||
"""
|
||||
Словарь камер со списком наблюдений каждой камеры.
|
||||
Номера камер должны быть отсортированы
|
||||
"""
|
||||
keylist_tm={'1':['tm1_obs_1',],
|
||||
'5':['tm5_obs_1','tm5_obs_2',],
|
||||
'6':['tm6_obs_1','tm6_obs_2_badpix','tm6_obs_3_badpix',
|
||||
'tm6_park_1','tm6_park_2','tm6_park_3','tm6_park_4',
|
||||
'tm6_scan_1','tm6_scan_2','tm6_scan_3','tm6_scan_4'],
|
||||
'7':['tm7_obs_1','tm7_obs_2',]}
|
||||
|
||||
"""
|
||||
Примерные центры изображений каждого наблюдения.
|
||||
Требуется для астрокоррекции. Будет расчитываться матрица сдвигов и поворотов,
|
||||
так вот, повороты будут проводиться вокруг данных координат.
|
||||
"""
|
||||
wcslist={'tm1_obs_1':[34.7279760,-5.0680267],
|
||||
'tm5_obs_1':[34.7351248,-4.4407314],
|
||||
'tm5_obs_2':[34.8748997,-4.4871658],
|
||||
'tm7_obs_1':[35.0015120,-4.7602124],
|
||||
'tm7_obs_2':[34.9810029,-4.5915912],
|
||||
'tm6_obs_1':[34.4227062,-4.7207170],
|
||||
'tm6_obs_2_badpix':[34.7272339,-4.4294153],
|
||||
'tm6_obs_3_badpix':[34.8750291,-4.4708468],
|
||||
'tm6_park_1':[35.0544951,-4.0613619],
|
||||
'tm6_park_2':[35.0558675,-4.0683084],
|
||||
'tm6_park_3':[35.0565263,-4.0583538],
|
||||
'tm6_park_4':[35.0602986,-4.0622220],
|
||||
'tm6_scan_1':[34.5405596,-4.8088748],
|
||||
'tm6_scan_2':[34.5405596,-4.8088748],
|
||||
'tm6_scan_3':[34.5405596,-4.8088748],
|
||||
'tm6_scan_4':[34.5405596,-4.8088748]}
|
||||
|
||||
""" like in the paper (Table 1) """
|
||||
obslist={'tm1_obs_1':'N01',
|
||||
'tm5_obs_1':'N02',
|
||||
'tm5_obs_2':'N03',
|
||||
'tm7_obs_1':'N15',
|
||||
'tm7_obs_2':'N16',
|
||||
'tm6_obs_1':'N12',
|
||||
'tm6_obs_2_badpix':'N13',
|
||||
'tm6_obs_3_badpix':'N14',
|
||||
'tm6_park_1':'N04',
|
||||
'tm6_park_2':'N06',
|
||||
'tm6_park_3':'N08',
|
||||
'tm6_park_4':'N10',
|
||||
'tm6_scan_1':'N05',
|
||||
'tm6_scan_2':'N07',
|
||||
'tm6_scan_3':'N09',
|
||||
'tm6_scan_4':'N11'}
|
||||
|
||||
""" Это просто индекс диапазона для выходных файлов. """
|
||||
eband=[ "0", "1", "2", "3", "4", "5", "6", "7", "8"]
|
||||
""" Диапазоны энергий. """
|
||||
emin_ev=[ 300, 300, 600, 2300, 200, 300, 5000, 500, 1000]
|
||||
emax_ev=[2300, 600, 2300, 5000, 10000,8000, 8000, 1000, 2000]
|
||||
|
||||
emin_kev=[0.3, 0.3, 0.6, 2.3, 0.2, 0.3, 5.0, 0.5, 1.0]
|
||||
emax_kev=[2.3, 0.6, 2.3, 5.0, 10.0, 8.0, 8.0, 1.0, 2.0]
|
||||
|
||||
#ecf = [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
# something is wrong here
|
||||
#ecf = [9.7817E+11, 3.2982E+12, 1.3903E+12, 2.3322E+12, 5.2022E+11, 5.8453E+11, 3.8468E+12]
|
||||
"""
|
||||
*** en0 ecf 9.7817E+11 +/- 2.4606E+10 2.52% N=17
|
||||
*** en1 ecf 3.2982E+12 +/- 8.2963E+10 2.52% N=17
|
||||
*** en2 ecf 1.3903E+12 +/- 3.5036E+10 2.52% N=17
|
||||
*** en3 ecf 2.3322E+12 +/- 5.8717E+10 2.52% N=17
|
||||
*** en4 ecf 5.2022E+11 +/- 1.3110E+10 2.52% N=17
|
||||
*** en5 ecf 5.8453E+11 +/- 1.4743E+10 2.52% N=17
|
||||
"""
|
||||
|
||||
# finally use Pavel's file ../data/ECF/ecf_tbabspow_g2nh0.02.pkl
|
||||
"""
|
||||
for e in [(0.3, 2.3), (0.3, 0.6), (0.6, 2.3), (2.3, 5.0), (5.0, 8.0)]:
|
||||
print(f'{ecf[e]:g}')
|
||||
"""
|
||||
ecf = [1.0911e+12, # (0.3, 2.3)
|
||||
1.07252e+12, # (0.3, 0.6)
|
||||
1.08963e+12, # (0.6, 2.3)
|
||||
1.14674e+11, # (2.3, 5.0)
|
||||
1.0,
|
||||
1.0,
|
||||
2.77581e+10, # (5.0, 8.0)
|
||||
1354632916123.6191, # (0.5, 1.0) 4XMM-DR12 EP2 band
|
||||
1014764099304.4968] # (1.0, 2.0) 4XMM-DR12 EP3 band
|
||||
|
||||
|
||||
outfile_post='.fits'
|
||||
|
||||
"""
|
||||
Pavel Medvedev:
|
||||
0.3-2.3: 9.135819435325375e-13
|
||||
0.3-0.6: 9.160477830652834e-13
|
||||
0.6-2.3: 9.201664167869427e-13
|
||||
2.3-5.0: 8.721504826794627e-12
|
||||
"""
|
||||
359
uds/uds/sherpa.py
Normal file
359
uds/uds/sherpa.py
Normal file
@@ -0,0 +1,359 @@
|
||||
import glob
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
from astropy.io import fits
|
||||
import pickle
|
||||
from scipy.stats import sem
|
||||
|
||||
#import matplotlib.pyplot as plt
|
||||
#plt.style.use('ggplot')
|
||||
|
||||
from sherpa.astro.ui import *
|
||||
|
||||
def calc_ecf(path,catprep=None, emin=None, emax=None, eband=None, outfile=None, fitmin=0.2, fitmax=5.0, simnum=100):
|
||||
"""
|
||||
Do uncorrection of SPECRESP for CORRCOMP=CORRPSF*CORRVIGN, like this:
|
||||
SPECRESP=SPECRESP/CORRCOMB, because because ermldet does not correct for PSF and vignetting.
|
||||
"""
|
||||
|
||||
data={}
|
||||
|
||||
if (catprep == None):
|
||||
print("catprep file is not provided, exit")
|
||||
sys.exit()
|
||||
else:
|
||||
print("Reading {}".format(catprep))
|
||||
|
||||
catprep_hdul = fits.open(catprep,mode='readonly')
|
||||
catprep_data = catprep_hdul[1].data
|
||||
#print(catprep_data['detUID'])
|
||||
mlrate = {}
|
||||
mlrate_err = {}
|
||||
dl = {}
|
||||
for rec in catprep_data:
|
||||
srcid=rec['detUID']
|
||||
|
||||
""" consider only non-extended sources """
|
||||
#if(rec['EXT'] > 0.0):
|
||||
# continue
|
||||
|
||||
if(srcid in mlrate.keys()):
|
||||
print("Duplicate SrcID! {}".format(srcid))
|
||||
sys.exit()
|
||||
else:
|
||||
mlrate[srcid]=rec['ML_RATE_0']
|
||||
mlrate_err[srcid]=rec['ML_RATE_ERR_0']
|
||||
dl[srcid]=rec['DET_LIKE_0']
|
||||
|
||||
|
||||
flist=glob.glob(path)
|
||||
for filename in flist:
|
||||
|
||||
print("Reading {}".format(filename))
|
||||
|
||||
#fout=filename.replace(".fits", ".uncorr.fits")
|
||||
|
||||
pha=filename.replace("ARF", "SourceSpec")
|
||||
pha=pha.replace(".fits", ".fits.grp")
|
||||
pha_header = fits.getheader(pha,ext=1)
|
||||
object = pha_header['OBJECT']
|
||||
|
||||
""" check a given source (testing...) """
|
||||
#if (int(object) > 10):
|
||||
# continue
|
||||
|
||||
srcid="ML{}".format(object)
|
||||
if not (srcid in mlrate.keys()):
|
||||
print("Skip {}".format(srcid))
|
||||
continue
|
||||
|
||||
|
||||
#hdul = fits.open(filename,mode='readonly')
|
||||
#specresp = np.divide(hdul[1].data['SPECRESP'], hdul[1].data['CORRCOMB'])
|
||||
#hdul[1].data['SPECRESP'] = specresp
|
||||
#hdul.writeto(fout, overwrite=True)
|
||||
clean() # sherpa
|
||||
set_xsxsect("vern")
|
||||
set_xsabund('wilm')
|
||||
load_pha(pha) # sherpa
|
||||
|
||||
""" load uncorrected ARF """
|
||||
# load_arf(fout) # sherpa
|
||||
|
||||
# Define the source model
|
||||
#sh.set_source("powlaw1d.p1")
|
||||
set_source("xstbabs.abs1 * powlaw1d.p1") # sherpa
|
||||
abs1.nH = 0.02
|
||||
# Change reference energy of the model
|
||||
p1.ref = 1 # 1 keV
|
||||
p1.gamma = 2.0
|
||||
p1.ampl = 1e-4 # in cm**-2 s**-1 keV**-1
|
||||
freeze(abs1.nH, p1.gamma) # sherpa
|
||||
|
||||
### Define the statistic
|
||||
set_stat("wstat") # sherpa
|
||||
|
||||
ignore_bad() # sherpa
|
||||
### Define the fit range
|
||||
print("Notice 0.3-5.0 keV:")
|
||||
notice(fitmin, fitmax) # sherpa
|
||||
|
||||
""" Check wether spectrum still has counts after filtering """
|
||||
dep = get_dep(filter=True)
|
||||
if not (len(dep)):
|
||||
print("*** Skip {} due to low counts".format(srcid))
|
||||
continue
|
||||
|
||||
|
||||
|
||||
### Do the fit
|
||||
fit()
|
||||
res_fit=get_fit_results()
|
||||
rstat=res_fit.rstat
|
||||
|
||||
set_analysis('energy')
|
||||
|
||||
photon_flx=[]
|
||||
energy_flx=[]
|
||||
skipme=False
|
||||
for ie in range(len(eband)):
|
||||
ph_flx=calc_photon_flux(emin[ie], emax[ie])
|
||||
en_flx=calc_energy_flux(emin[ie], emax[ie])
|
||||
if not (ph_flx>0):
|
||||
skipme=True
|
||||
photon_flx.append(ph_flx)
|
||||
energy_flx.append(en_flx)
|
||||
print("en{} Photon flux: {:.4E} Energy flux: {:.4E}".format(eband[ie], ph_flx, en_flx))
|
||||
|
||||
if (skipme == True):
|
||||
print("*** Skip zero flux for {}".format(srcid))
|
||||
continue
|
||||
|
||||
print("----> Success: {} pow norm: {:.4E} reduced stat: {:.2f}".format(res_fit.succeeded,res_fit.parvals[0], rstat))
|
||||
|
||||
sample_flx=[]
|
||||
sample_flx_lo=[]
|
||||
sample_flx_hi=[]
|
||||
if (res_fit.rstat < 3.0):
|
||||
conf()
|
||||
res_conf=get_conf_results()
|
||||
#print(res_conf)
|
||||
component=abs1+p1
|
||||
#print(component)
|
||||
#covar()
|
||||
#errs = get_covar_results().parmaxes
|
||||
#ans = sample_flux(correlated=False, scales=errs, num=500)
|
||||
for ie in range(len(eband)):
|
||||
pars_flux = sample_flux(modelcomponent=component, lo=emin[ie], hi=emax[ie], num=simnum, correlated=True, numcores=10, confidence=68)
|
||||
sample_flx.append(pars_flux[0][0])
|
||||
sample_flx_lo.append(pars_flux[0][2])
|
||||
sample_flx_hi.append(pars_flux[0][1])
|
||||
|
||||
else:
|
||||
continue
|
||||
|
||||
print("### SAMPLE FLUX: {}".format(sample_flx))
|
||||
data[srcid]={'sample_flx':sample_flx,
|
||||
'sample_flx_lo':sample_flx_lo,
|
||||
'sample_flx_hi':sample_flx_hi,
|
||||
'photon_flx':photon_flx,
|
||||
'energy_flx':energy_flx,
|
||||
'ml_rate':mlrate[srcid],
|
||||
'ml_rate_err':mlrate_err[srcid],
|
||||
'det_like':dl[srcid]}
|
||||
|
||||
with open(outfile, 'wb') as f:
|
||||
pickle.dump(data, f)
|
||||
|
||||
def calc_flux(path,catprep=None, emin=None, emax=None, eband=None, outfile=None, fitmin=0.2, fitmax=5.0, simnum=100):
|
||||
""" """
|
||||
|
||||
data={}
|
||||
|
||||
if (catprep == None):
|
||||
print("catprep file is not provided, exit")
|
||||
sys.exit()
|
||||
else:
|
||||
print("Reading {}".format(catprep))
|
||||
|
||||
catprep_hdul = fits.open(catprep,mode='readonly')
|
||||
catprep_data = catprep_hdul[1].data
|
||||
#print(catprep_data['detUID'])
|
||||
mlrate = {}
|
||||
mlrate_err = {}
|
||||
dl = {}
|
||||
for rec in catprep_data:
|
||||
srcid=rec['detUID']
|
||||
|
||||
""" consider only non-extended sources """
|
||||
#if(rec['EXT'] > 0.0):
|
||||
# continue
|
||||
|
||||
if(srcid in mlrate.keys()):
|
||||
print("Duplicate SrcID! {}".format(srcid))
|
||||
sys.exit()
|
||||
else:
|
||||
mlrate[srcid]=rec['ML_RATE_0']
|
||||
mlrate_err[srcid]=rec['ML_RATE_ERR_0']
|
||||
dl[srcid]=rec['DET_LIKE_0']
|
||||
|
||||
|
||||
flist=glob.glob(path)
|
||||
for filename in flist:
|
||||
|
||||
print("Reading {}".format(filename))
|
||||
|
||||
pha=filename.replace("ARF", "SourceSpec")
|
||||
pha=pha.replace(".fits", ".fits.grp")
|
||||
pha_header = fits.getheader(pha,ext=1)
|
||||
object = pha_header['OBJECT']
|
||||
|
||||
|
||||
srcid="ML{}".format(object)
|
||||
if not (srcid in mlrate.keys()):
|
||||
print("Skip {}".format(srcid))
|
||||
continue
|
||||
|
||||
|
||||
clean() # sherpa
|
||||
set_xsxsect("vern")
|
||||
set_xsabund('wilm')
|
||||
load_pha(pha) # sherpa
|
||||
|
||||
# Define the source model
|
||||
#sh.set_source("powlaw1d.p1")
|
||||
set_source("xstbabs.abs1 * powlaw1d.p1") # sherpa
|
||||
abs1.nH = 0.02
|
||||
# Change reference energy of the model
|
||||
p1.ref = 1 # 1 keV
|
||||
p1.gamma = 2.0
|
||||
p1.ampl = 1e-4 # in cm**-2 s**-1 keV**-1
|
||||
freeze(abs1.nH, p1.gamma) # sherpa
|
||||
|
||||
### Define the statistic
|
||||
set_stat("wstat") # sherpa
|
||||
|
||||
photon_flx=[]
|
||||
energy_flx=[]
|
||||
skipme=False
|
||||
for ie in range(len(eband)):
|
||||
ignore_bad() # sherpa
|
||||
### Define the fit range
|
||||
print("Notice {}-{} keV:".format(emin[ie], emax[ie]))
|
||||
notice(emin[ie], emax[ie]) # sherpa
|
||||
|
||||
""" Check wether spectrum still has counts after filtering """
|
||||
dep = get_dep(filter=True)
|
||||
if not (len(dep)):
|
||||
print("*** Skip {} due to low counts".format(srcid))
|
||||
continue
|
||||
|
||||
### Do the fit
|
||||
fit()
|
||||
res_fit=get_fit_results()
|
||||
rstat=res_fit.rstat
|
||||
|
||||
set_analysis('energy')
|
||||
|
||||
ph_flx=calc_photon_flux(emin[ie], emax[ie])
|
||||
en_flx=calc_energy_flux(emin[ie], emax[ie])
|
||||
if not (ph_flx>0):
|
||||
skipme=True
|
||||
photon_flx.append(ph_flx)
|
||||
energy_flx.append(en_flx)
|
||||
print("en{} Photon flux: {:.4E} Energy flux: {:.4E}".format(eband[ie], ph_flx, en_flx))
|
||||
print("----> Success: {} pow norm: {:.4E} reduced stat: {:.2f}".format(res_fit.succeeded,res_fit.parvals[0], rstat))
|
||||
|
||||
if (skipme == True):
|
||||
print("*** Skip zero flux for {}".format(srcid))
|
||||
continue
|
||||
|
||||
|
||||
sample_flx=[]
|
||||
sample_flx_lo=[]
|
||||
sample_flx_hi=[]
|
||||
if (res_fit.rstat < 3.0):
|
||||
conf()
|
||||
res_conf=get_conf_results()
|
||||
#print(res_conf)
|
||||
component=abs1+p1
|
||||
#print(component)
|
||||
#covar()
|
||||
#errs = get_covar_results().parmaxes
|
||||
#ans = sample_flux(correlated=False, scales=errs, num=500)
|
||||
for ie in range(len(eband)):
|
||||
pars_flux = sample_flux(modelcomponent=component, lo=emin[ie], hi=emax[ie], num=simnum, correlated=True, numcores=10, confidence=68)
|
||||
sample_flx.append(pars_flux[0][0])
|
||||
sample_flx_lo.append(pars_flux[0][2])
|
||||
sample_flx_hi.append(pars_flux[0][1])
|
||||
|
||||
else:
|
||||
continue
|
||||
|
||||
print("### SAMPLE FLUX: {}".format(sample_flx))
|
||||
data[srcid]={'sample_flx':sample_flx,
|
||||
'sample_flx_lo':sample_flx_lo,
|
||||
'sample_flx_hi':sample_flx_hi,
|
||||
'photon_flx':photon_flx,
|
||||
'energy_flx':energy_flx,
|
||||
'ml_rate':mlrate[srcid],
|
||||
'ml_rate_err':mlrate_err[srcid],
|
||||
'det_like':dl[srcid]}
|
||||
|
||||
with open(outfile, 'wb') as f:
|
||||
pickle.dump(data, f)
|
||||
|
||||
|
||||
def print_ecf(infile=None, emin=None, emax=None, eband=None, skipfrac=5.0):
|
||||
|
||||
|
||||
|
||||
with open(infile, 'rb') as f:
|
||||
data = pickle.load(f)
|
||||
|
||||
for ie in range(len(eband)):
|
||||
print()
|
||||
ecf=[]
|
||||
|
||||
for key in data.keys():
|
||||
ar=data[key]
|
||||
ml = ar['ml_rate']
|
||||
dml = ar['ml_rate_err']
|
||||
|
||||
flx = ar['sample_flx'][ie]
|
||||
dflx1 = ar['sample_flx'][ie] - ar['sample_flx_lo'][ie]
|
||||
dflx2 = ar['sample_flx_hi'][ie] - ar['sample_flx'][ie]
|
||||
|
||||
# take maximum error as conservative approach
|
||||
dflx = np.maximum(dflx1,dflx2)
|
||||
|
||||
ecf0 = ml/flx
|
||||
decf0 = abs(flx*dml - ml*dflx) / np.power(flx,2)
|
||||
frac0 = 100 * abs(flx*dml - ml*dflx) / (flx*ml)
|
||||
|
||||
skipme=False
|
||||
if((100*dflx/flx) < skipfrac):
|
||||
skipme=False
|
||||
else:
|
||||
skipme=True
|
||||
|
||||
#if not (len(ar['sample_flx'])>0):
|
||||
# continue
|
||||
print("en{} {} DL={:.2f} FLUX: {:4E} (-{:.4E} +{:.4E} {:.2f}%) ML_RATE: {:.4f} +/- {:.4f} ({:.2f}%) --> {:.4E} +/- {:.4E} {:.2f}% skip={}".format(eband[ie],key,ar['det_like'],
|
||||
flx,dflx1,dflx2,100*dflx/flx,
|
||||
ml,dml,100*dml/ml,
|
||||
ecf0,decf0,frac0,skipme))
|
||||
if(skipme==True):
|
||||
continue
|
||||
ecf.append(ecf0)
|
||||
|
||||
|
||||
|
||||
ecf_mean = np.mean(ecf)
|
||||
ecf_err = np.std(ecf, ddof=1)/np.sqrt(np.size(ecf))
|
||||
print("\n*** en{} ecf {:.4E} +/- {:.4E} {:.2f}% N={}".format(eband[ie],
|
||||
ecf_mean,
|
||||
ecf_err,100*ecf_err/ecf_mean,
|
||||
np.size(ecf)))
|
||||
sys.exit()
|
||||
3506
uds/uds/utils.py
Normal file
3506
uds/uds/utils.py
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user