generated from erosita/uds
na Kubani
This commit is contained in:
Roman Krivonos
@@ -45,9 +45,9 @@ enkey = sys.argv[1]
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key="ALL"
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fn='detcnts.{}.{}.resid.fits'.format(enkey,key)
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d = fits.getdata(proddir+fn)
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df=pd.DataFrame(np.array(d).byteswap().newbyteorder())
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#print(df.columns)
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print("Reading {}".format(proddir+fn))
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dat = Table.read(proddir+fn, unit_parse_strict='silent')
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df = dat.to_pandas()
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#df = df.query('abs(LAT) < {} & abs(LON) < {}'.format(5,5))
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@@ -56,7 +56,14 @@ minmax=[-300,800]
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sigma=3
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maxiters=10
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modelrxte="allsky/modelrxte_ait_3to20keV_flux_2deg.fits"
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with open(ignored_rev_file, 'rb') as fp:
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ignored_rev = pickle.load(fp)
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print(ignored_rev)
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print("{} orbits ignored".format(len(ignored_rev)))
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ign=ignored_rev.tolist()
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hdulist = fits.open(datadir+modelrxte)
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w = wcs.WCS(hdulist[0].header)
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smap =hdulist[0].data
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@@ -82,75 +89,81 @@ for i,row in df.iterrows():
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df['DS9Y']=ds9x
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df['DS9X']=ds9y
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#
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# initiate 2d arrays
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#
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mean_map = np.array([[0.0 for i in range(sx)] for j in range(sy)])
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sign_map = np.array([[0.0 for i in range(sx)] for j in range(sy)])
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sem_map = np.array([[0.0 for i in range(sx)] for j in range(sy)])
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cnt_map = np.array([[0 for i in range(sx)] for j in range(sy)])
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# simulations
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mean_sim_map = np.array([[0.0 for i in range(sx)] for j in range(sy)])
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error_sim_map = np.array([[0.0 for i in range(sx)] for j in range(sy)])
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sign_sim_map = np.array([[0.0 for i in range(sx)] for j in range(sy)])
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mean_sim={}
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for i in range(sx):
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for j in range(sy):
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dkey="{:04d}{:04d}".format(j,i)
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mean_sim[dkey] = []
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obsid_map = {}
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grxe_map = {}
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grxe_err_map = {}
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# redefine simfrac for low number of ScWs in pixel
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simfrac=2
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nsim=100
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for i in range(sx):
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for j in range(sy):
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dkey="{:04d}{:04d}".format(j,i)
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world = w.wcs_pix2world([(i+1,j+1)], 1)
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lon = world[0][0]
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lat = world[0][1]
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if(np.isnan(lon) or np.isnan(lat)):
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continue
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ds9i=i+1
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ds9j=j+1
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df0 = df.query('DS9X == {} & DS9Y == {}'.format(ds9i,ds9j))
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if (len(df0) <= nscw_min):
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df0 = df.query('DS9X == {} & DS9Y == {} & REV != @ign'.format(ds9i,ds9j))
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if (df0.shape[0] <= nscw_min):
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continue
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print("*** *** SUM *** ***")
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print(np.sum(df0["GRXE"]))
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# check coordinates
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#print("***",i+1,j+1,lon,lat,smap[j][i])
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#for i0,row0 in df0.iterrows():
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# print(row0['LON'],row0['LAT'],row0['GRXE'])
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grxe_err = np.array(df0['GRXE_ERR'])
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perc = np.percentile(grxe_err, grxe_err_cut, axis=0, keepdims=False)
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print("{} {}: {}% cut of GRXE ERR: {:.2f} mCrab".format(key,enkey,grxe_err_cut,perc))
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df0 = df.query('DS9X == {} & DS9Y == {} & GRXE_ERR < {}'.format(ds9i,ds9j,perc))
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if (len(df0) <= nscw_min):
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continue
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sg_mean,sg_sem = get_spec(df0, sigma=sigma, grxe_err_cut=grxe_err_cut, enkey=enkey)
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nsel = int(df0.shape[0]/simfrac)
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print("nsel=",nsel,df0.shape[0],len(df0['GRXE']))
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for n in range(nsim):
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df1=df0.sample(nsel)
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sg_mean1,sg_sem1 = get_spec(df1, grxe_err_cut=grxe_err_cut, enkey=enkey)
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mean_sim[dkey].append(sg_mean1)
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obsid = np.array(df0['OBSID'])
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grxe = np.array(df0['GRXE'])
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grxe_err = np.array(df0['GRXE_ERR'])
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sg_mean, sg_med, sg_std = sigma_clipped_stats(grxe, sigma=sigma, maxiters=maxiters)
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filtered_data = sigma_clip(grxe, sigma=sigma, maxiters=maxiters, return_bounds=True)
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filtered_grxe = filtered_data[0]
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filtered_min = filtered_data[1]
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filtered_max = filtered_data[2]
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# final error on flux measurement ~RMS/sqrt(n)
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sg_sem = sem(grxe[filtered_grxe.mask==False])
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obsid=obsid[filtered_grxe.mask==False]
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#print("Sigma clipping: mean {:.2f} med {:.2f} std {:.2f} sem {:.2f}".format(sg_mean, sg_med, sg_std, sg_sem))
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#plt.hist(grxe, bins=n_bins, range=minmax)
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#plt.hist(grxe[filtered_grxe.mask], bins=n_bins, range=minmax)
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#plt.show()
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print('sg_sem',sg_sem)
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mean_map[j][i] = sg_mean
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sem_map[j][i] = sg_sem
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sign_map[j][i] = sg_mean/sg_sem
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cnt_map[j][i] = df0.shape[0]
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dkey="{:04d}{:04d}".format(j,i)
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"""
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obsid_map[dkey] = obsid
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grxe_map[dkey] = grxe
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grxe_err_map[dkey] = grxe_err
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"""
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""" Filter by error map """
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# Calculate the percentiles across the x and y dimension
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perc = np.percentile(sem_map, sem_cut, axis=(0, 1), keepdims=False)
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@@ -178,7 +191,23 @@ hdulist.writeto(mapsdir+fn.replace(".fits",".cnt.fits"),overwrite=True)
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hdulist[0].data=sign_map
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hdulist.writeto(mapsdir+fn.replace(".fits",".sign.fits"),overwrite=True)
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for i in range(sx):
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for j in range(sy):
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dkey="{:04d}{:04d}".format(j,i)
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data=mean_sim[dkey]
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(mu, sg) = norm.fit(data)
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mean_sim_map[j][i] = mu
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error_sim_map[j][i] = sg
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hdulist[0].data=mean_sim_map
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hdulist.writeto(mapsdir+fn.replace(".fits",".sim.mean.fits"),overwrite=True)
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hdulist[0].data=error_sim_map
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hdulist.writeto(mapsdir+fn.replace(".fits",".sim.error.fits"),overwrite=True)
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sys.exit()
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print("Prepare data for fine map")
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obsid_list=[]
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