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

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Андрей Семена
2025-07-22 13:42:28 +03:00
parent 54febb8bbf
commit 23abdd884f
3 changed files with 252 additions and 24 deletions
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161
chan_psf.c
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@@ -180,21 +180,6 @@ static PyObject * solve_for_locations(PyObject *self, PyObject *args)
double pval, eloc, p2, p3;
int idx1d, idx2d;
/*
pval = *psfvalfromptr(smatd, smat->dimensions, 1744, 50, 50);
printf("1744 50 50 %f\n", pval);
pval = *psfvalfromptr(smatd, smat->dimensions, 1744, 40, 48);
printf("1744 40 48 %f\n", pval);
pval = *psfvalfromptr(smatd, smat->dimensions, 1744, 20, 52);
printf("1744 20 52 %f\n", pval);
printf("bwd %f %f %f\n", xptr[0], xptr[10], xptr[20]);
printf("xptr %f %f %f\n",xcptr[0], xcptr[10], xcptr[20]);
printf("dimension %d\n", xc->dimensions[0]);
*/
for (loc=0; loc < xc->dimensions[0]; loc++) // loop over sky locations
{
msum = 0;
@@ -251,8 +236,6 @@ static PyObject * solve_for_locations(PyObject *self, PyObject *args)
if (pval > 1e-10)
{
bw[msum] = pval;
//printf("%d %d %d %f %f %f %f %f\n", k[ctr], idx1d, idx2d, inpixdx, inpixdy, dx, dy, pval);
//printf("%d %d %d %f %f %f %f %f\n", k[ctr], idx1d, idx2d, xptr[ctr], yptr[ctr], xcptr[loc], ycptr[loc], pval);
msum += 1;
};
@@ -347,7 +330,7 @@ static PyObject * solve_for_locations_eintp(PyObject *self, PyObject *args)
long ctr, msum=0;
double lkl, erf;
double pval, eloc, p2, p3, ptmp;
double pval, eloc, p2, p3;
int idx1d, idx2d;
//return psfdata + ((k*dims[1] + ei)*dims[2] + xi)*dims[3] + yi;
@@ -462,11 +445,153 @@ static PyObject * solve_for_locations_eintp(PyObject *self, PyObject *args)
static PyObject * solve_for_rates(PyObject *self, PyObject *args)
{
//xc, yc --- wcs locations, events has coordinates in the same locations, and psf have the same grid as well
// the only additional parameter to events are pk scale (rate scale in respect to psf) and rotation angle
PyArrayObject *psfi, *eidx, *x, *y, *rates, *roll, *pk, *smat;
double xc, yc;
int rid, ctr;
double x1, y1, dx, dy, eloc;
if (!PyArg_ParseTuple(args, "OOOOOOOdddO", &psfi, &eidx, &x, &y, &roll, &pk, &rates, &xc, &yc, &eloc, &smat)) return NULL;
// -------------------------- ===============
// those are events properties those for sky smat it array for psf matrices
npy_intp snew = {rates->dimensions[0]};
PyArrayObject * lkls = PyArray_SimpleNew(1, &snew, NPY_DOUBLE);
double * lklsd = (double*) lkls->data;
double * smatd = (double*) smat->data;
double *ca = (double*)malloc(sizeof(double)*x->dimensions[0]);
double *sa = (double*)malloc(sizeof(double)*x->dimensions[0]);
double* nparrptr = (double*) roll->data;
for (ctr=0; ctr < x->dimensions[0]; ctr++)
{
ca[ctr] = cos(nparrptr[ctr]);
sa[ctr] = sin(nparrptr[ctr]);
};
double * bw = (double*)malloc(sizeof(double)*psfi->dimensions[0]); //not more then thet will be used for each location
Py_BEGIN_ALLOW_THREADS;
double inpixdx, inpixdy, rate;
double * pkd = (double*) pk->data;
long * k = (long*)psfi->data;
double * ek = (double*)eidx->data;
int ei;
double* xptr = (double*) x->data;
double* yptr = (double*) y->data;
long msum=0;
double lkl, erf;
double pval, p2, p3;
int idx1d, idx2d;
for (rid=0; rid < rates->dimensions[0]; rid++) // loop over sky locations
{
msum = 0;
for (ctr=0; ctr < psfi->dimensions[0]; ctr++) // for each sky location loop over all provided events
{
x1 = (xc - xptr[ctr]);
y1 = (yc - yptr[ctr]);
//rotate by the event roll angle, dx dy centered at the psf center (central pixel of 101x101 map)
dx = x1*ca[ctr] - y1*sa[ctr]; //+ 50;
dy = y1*ca[ctr] + x1*sa[ctr]; // + 50.;
// temporary hardcode psf shape is 101x101
ei = (int)(ek[ctr]);
erf = ek[ctr] - (double)(ei);
//printf("evt %d ei %d erf %f ek %f dx %f dy %f\n", ctr, ei, erf, ek[ctr], dx, dy);
//current psf shape is 101:
if ((dx > -50) && (dx < 50))
{
if ((dy > -50) && (dy < 50))
{
idx1d = (int)((dx + 50.5)); // float dx from -0.5 to 0.5 should fell in the 50-th pixel
idx2d = (int)((dy + 50.5));
pval = (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei, idx1d, idx2d))*(1. - erf) + (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei + 1, idx1d, idx2d))*erf;
//naive interpolation block
//-------------------------------------------------------------------------------------------------------
inpixdx = dx - (idx1d - 50);
inpixdy = dy - (idx2d - 50);
if (inpixdx > 0.)
{
p2 = (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei, idx1d + 1, idx2d))*(1. - erf) + (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei + 1, idx1d + 1, idx2d))*erf;
if (inpixdy > 0.)
{
p3 = (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei, idx1d, idx2d + 1))*(1. - erf) + (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei + 1, idx1d, idx2d + 1))*erf;
}else{
inpixdy = -inpixdy;
p3 = (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei, idx1d, idx2d - 1))*(1. - erf) + (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei + 1, idx1d, idx2d - 1))*erf;
}
}else{
p2 = (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei, idx1d - 1, idx2d))*(1. - erf) + (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei + 1, idx1d - 1, idx2d))*erf;
inpixdx = -inpixdx;
if (inpixdy > 0.)
{
p3 = (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei, idx1d, idx2d + 1))*(1. - erf) + (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei + 1, idx1d, idx2d + 1))*erf;
}else{
inpixdy = -inpixdy;
p3 = (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei, idx1d, idx2d - 1))*(1. - erf) + (* eepsfvalfromptr(smatd, smat->dimensions, *(k + ctr), ei + 1, idx1d, idx2d - 1))*erf;
}
}
//printf("pval %f %f %f %f %f %d %d %d\n", pval, p2, p3, inpixdx, inpixdy, idx1d, idx2d, k[ctr]);
pval = (pval + inpixdx*(p2 - pval) + inpixdy*(p3 - pval))* (*(pkd + ctr));
// interpolation up to here
//-------------------------------------------------------------------------------------------------------
if (pval > 1e-10)
{
bw[msum] = pval;
msum += 1;
//printf("%f %d\n", pval, msum);
};
};
};
};
if (msum > 0)
{
rate = (double) *((double*) rates->data + rid);
lkl = 0.;
for (ctr=0; ctr < msum; ctr ++)
{
lkl = lkl + log(rate*bw[ctr] + 1.);
}
*(lklsd + rid) = lkl - eloc*rate;
}else{
*(lklsd + rid) = 0.;
};
};
//printf("loop done\n");
Py_END_ALLOW_THREADS;
free(bw);
PyObject *res = Py_BuildValue("O", lkls);
Py_DECREF(lkls);
return res;
}
static PyMethodDef PSFMethods[] = {
{"solve_for_locations", solve_for_locations, METH_VARARGS, "get coordinates within pixel based on its coordinates"},
{"solve_for_locations_eintp", solve_for_locations_eintp, METH_VARARGS, "compute likelihood using psf energy interpolation"},
{"put_psf_on_img", put_psf_on, METH_VARARGS, "put psf as is on img for all cooreindates "},
{"solve_for_rates", solve_for_rates, METH_VARARGS, "computed likelihood at specified position for a series of rates"},
{NULL, NULL, 0, NULL}
};

101
lkl_solver.py Normal file
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@@ -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()

14
source_detection2.py
View File

@@ -71,10 +71,10 @@ def make_srccount_and_detmap(emap, evt, h, wcs=None):
x, y = evt["x"], evt["y"]
else:
ewcs = read_wcs(h)
x, y = wcs.all_world2pix(ewcs.all_pix2world(np.array([x, y]).T, 0), 0).T
x, y = wcs.all_world2pix(ewcs.all_pix2world(np.array([evt["x"], evt["y"]]).T, 0), 0).T
eidx = np.searchsorted(psfe*1e3, evt["ENERGY"])
eidx = np.maximum((evt["ENERGY"]/1000. - psfe[eidx])/(psfe[eidx + 1] - psfe[eidx]), 0.)
eidx = np.maximum(np.searchsorted(psfe*1e3, evt["ENERGY"]) - 1, 0)
eidx = np.maximum((evt["ENERGY"]/1000. - psfe[eidx])/(psfe[eidx + 1] - psfe[eidx]), 0.) + eidx
sizex, sizey = select_xychunksize(wcs)
iidx, xyu, cts = create_neighboring_blocks(x, y, sizex, sizey)
cc = np.zeros(cts.size + 1, int)
@@ -115,8 +115,10 @@ def make_srccount_and_detmap(emap, evt, h, wcs=None):
if __name__ == "__main__":
p1 = fits.open("test.fits")
#emap = fits.getdata("exp.map.gz") #np.full((8192, 8192), 10000.)
emap = fits.getdata("eR_spec_asp_0.fits.gz") #np.full((8192, 8192), 10000.)
emapf = fits.open("eR_spec_asp_0.fits.gz") #np.full((8192, 8192), 10000.)
emap = emapf[0].data
w = WCS(emapf[0].header)
wcs, cmap, pmap = make_srccount_and_detmap(emap, p1[1].data, p1[1].header)
fits.HDUList([fits.PrimaryHDU(), fits.ImageHDU(pmap - cmap, header=p1[1].header), fits.ImageHDU(cmap, header=p1[1].header)]).writeto("tmap4.fits.gz", overwrite=True)
wcs, cmap, pmap = make_srccount_and_detmap(emap, p1[1].data, p1[1].header, wcs=w)
fits.HDUList([fits.PrimaryHDU(), fits.ImageHDU(pmap - cmap, header=p1[1].header), fits.ImageHDU(cmap, header=p1[1].header)]).writeto("tmap5.fits.gz", overwrite=True)
#fits.ImageHDU(data=pmap, header=wcs.to_header()).writeto("tmap4.fits.gz", overwrite=True)