revanshe

scripts/09_sas_convol.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+from pysas.wrapper import Wrapper as w
+import os, sys
+from os.path import dirname
+import inspect
+import glob
+
+import os.path
+from os import path
+import subprocess
+import numpy as np
+import matplotlib.pyplot as plt
+from astropy.io import fits
+from astropy.table import Table
+from matplotlib.colors import LogNorm
+
+from astropy.convolution import interpolate_replace_nans, convolve, convolve_fft
+from astropy.convolution import Gaussian2DKernel
+
+import re
+import pyds9
+
+import arches
+from arches.utils import *
+from arches.config import *
+
+root_path=dirname(dirname(dirname(inspect.getfile(arches))))
+print("Arches root path: {}".format(root_path))
+
+archive_dir=root_path+'/data/archive'
+events_dir=root_path+'/data/processed'
+products_dir=root_path+'/products/sas'
+create_folder(products_dir)
+mdir=root_path+'/products/mosaic'
+create_folder(mdir)
+
+ds9reg_dir=root_path+'/data/ds9reg'
+
+
+inargs = ['--version']
+t = w('sasver', inargs)
+t.run()
+
+# run over energies
+for idx,e in enumerate(emin):
+    label=f"_{idx}"
+    e1=emin[idx]
+    e2=emax[idx]
+
+    for ikey,key in enumerate(['','m1_','m2_','mos_','pn_']):
+        in_cts=f'{mdir}/{key}cts{label}.fits'
+        if not (test_file(in_cts,alive=True)):
+            continue
+
+        hdul0 = fits.open(in_cts)
+        c_data = hdul0[0].data
+        c_header = hdul0[0].header
+        c_wcs = WCS(c_header)
+        hdul0.close()
+        
+        in_exp=f'{mdir}/{key}exp{label}.fits'
+        if not (test_file(in_exp,alive=True)):
+            continue
+
+        hdul0 = fits.open(in_exp)
+        e_data = hdul0[0].data
+        e_header = hdul0[0].header
+        e_wcs = WCS(e_header)
+        hdul0.close()
+        
+        in_qpb=f'{mdir}/{key}qpb{label}.fits'
+        if not (test_file(in_qpb,alive=True)):
+            continue
+
+        hdul0 = fits.open(in_qpb)
+        q_data = hdul0[0].data
+        q_header = hdul0[0].header
+        q_wcs = WCS(q_header)
+        hdul0.close()
+        
+        gauss2d = Gaussian2DKernel(x_stddev=0.530785)
+
+        convol_cts = convolve(c_data, gauss2d)
+        out_cts = f'{mdir}/{key}cts{label}_gauss.fits'
+        hdul0[0].data=convol_cts
+        hdul0.writeto(out_cts,overwrite=True)
+
+        convol_qpb = convolve(q_data, gauss2d)
+        out_qpb = f'{mdir}/{key}qpb{label}_gauss.fits'
+        hdul0[0].data=convol_qpb
+        hdul0.writeto(out_qpb,overwrite=True)
+
+        (nx,ny) = c_data.shape
+        map_flx = np.zeros(c_data.shape,dtype=np.float64)
+        index = e_data > 0
+        map_flx[index]=(convol_cts[index]-convol_qpb[index])/e_data[index]
+        out_flx = f'{mdir}/{key}flx{label}_gauss.fits'
+        hdul0[0].data=map_flx
+        hdul0.writeto(out_flx,overwrite=True)
+