found error in the estimation of the energy index for energy interpolation; added script to estimate likelihood for specified location

lkl_solver.py

@@ -0,0 +1,101 @@
+import numpy as np
+from astropy.io import fits
+import matplotlib.pyplot as plt
+import pickle
+from astropy.wcs import WCS
+import tqdm
+from multiprocessing.pool import ThreadPool
+from chan_psf import solve_for_locations, solve_for_locations_eintp, solve_for_rates
+
+psfe = np.array([1.8, 1.9, 3.0, 4.0, 6.0, 7.0, 8.0, 9.0])
+
+def prepare_psf(evt):
+    """
+    find all unique psf for observation and load in single 3d data cuve
+    return data cube with events slices indexes
+    """
+    u, ui = np.unique(evt["psf_cube"], return_inverse=True)
+    data = np.array([np.load(p[3:])[:, ::-1,::-1].copy() for p in u])
+    return data, ui
+
+def select_xychunksize(wcs, halfpsfsize=36./3600.):
+    """
+    get wcs and find wcs pixel size of psf reach
+    """
+    sizex = int(abs(halfpsfsize/wcs.wcs.cdelt[1])) + 2
+    sizey = int(abs(halfpsfsize/wcs.wcs.cdelt[0])) + 2
+    return sizex, sizey
+
+def read_wcs(h):
+    """
+    read events wcs header
+    """
+    w = WCS(naxis=2)
+    w.wcs.ctype = [h["TCTYP11"], h["TCTYP12"]]
+    w.wcs.crval = [h["TCRVL11"], h["TCRVL12"]]
+    w.wcs.cdelt = [h["TCDLT11"], h["TCDLT12"]]
+    w.wcs.crpix = [h["TCRPX11"], h["TCRPX12"]]
+    w = WCS(w.to_header())
+    return w
+
+
+def create_neighboring_blocks(x, y, sizex, sizey):
+    """
+    schematically all sky is splitted on squares, which are approximatelly ~ 10 times greater then the psf
+    events for corresponding square are joined :: squer + diluttaion of psf reach
+
+    coordinate system with events and all required coefficiets are fed to psf solver
+    current psf size is 25*0.5 arcsec (with up to \sqrt(2) factor in case of worst rolls -> 36''
+
+    """
+    """
+    event list already contains x and y for each event
+    """
+    iix = (x//sizex + 0.5).astype(int)
+    iiy = (y//sizey + 0.5).astype(int)
+    isx, isy = np.mgrid[-1:2:1, -1:2:1]
+    oidx = np.repeat(np.arange(x.size), 9)
+    xyu, iixy, xyc = np.unique(np.array([np.repeat(iix, 9) + np.tile(isx.ravel(), x.size), 
+                                         np.repeat(iiy, 9)+  np.tile(isy.ravel(), x.size)]), axis=1, return_counts=True, return_inverse=True)
+
+    sord = np.argsort(iixy)
+    return oidx[sord], xyu, xyc
+
+
+def lkls_for_rates(evt, expv, wcs, srcx, srcy, rates):
+    sizex, sizey = select_xychunksize(wcs)
+    x, y = evt["x"].astype(float), evt["y"].astype(float)
+    mask = np.logical_and.reduce([x > srcx - sizex//2, y > srcy - sizey//2, x < srcx + sizex//2, y < srcy + sizey//2], axis=0)
+    print("mask sum", srcx, srcy, mask.sum())
+    eloc = evt[mask]
+    pickle.dump(eloc, open("eloc.pkl", "wb"))
+    psfdata, ui = prepare_psf(eloc)
+    xe, ye = np.copy(x[mask]), np.copy(y[mask])
+    eidx = np.maximum(np.searchsorted(psfe*1e3, eloc["ENERGY"]) - 1, 0)
+    ee = np.maximum((eloc["ENERGY"]/1000. - psfe[eidx])/(psfe[eidx + 1] - psfe[eidx]), 0.).astype(float) + eidx
+    pk = np.copy(eloc["src_spec"]/eloc["bkg_spec"]).astype(float)
+    roll = np.copy(np.deg2rad(eloc["roll_pnt"])).astype(float)
+    #"OOOOOOOdddO", &psfi, &eidx, &x, &y, &roll, &pk, &rates, &xc, &yc, &eloc, &smat
+    #                       O   O   O   O   O    O    O      d     d     d      O"
+    print(ui, ee, xe, ye, roll, pk)
+    lkls = solve_for_rates(ui, ee, xe, ye, roll, pk, rates, srcx, srcy, expv, psfdata)
+    return lkls
+
+
+if __name__ == "__main__":
+    p1 = fits.open("test.fits")
+    ewcs = read_wcs(p1[1].header)
+    wcs = WCS(fits.getheader("eR_spec_asp_0.fits.gz", 0))
+    xc, yc = 4290, 4147
+    xc, yc = 4643, 4223.7
+    #xc, yc = 4147,4290 
+    xc, yc = ewcs.all_world2pix(wcs.all_pix2world([[xc, yc],], 0), 0).T
+    print(xc, yc)
+    eloc = 0.025 #0.0283
+    #rates = np.array([4.2/eloc,]) #np.logspace(-0.5, 0.5, 129)*4.2/eloc
+    rates = np.logspace(-0.5, 0.5, 129)*1352/eloc #*4.2/eloc
+    lkls = lkls_for_rates(p1[1].data, eloc, ewcs, xc, yc, rates)
+    plt.plot(rates, lkls)
+    plt.axvline(rates[64])
+    plt.show()
+