if phibins > 0:
phibin_fct = np.ceil(nphi / float(phibins))
else:
phibin_fct = 1
nphi = int(np.ceil(2 * np.pi * interp_rmax / phibin_fct) *
phibin_fct) # number of azimuthal samples per bin
rbin_new = (interp_rmax - interp_rmin) / rbin_fct
phibin_new = nphi / phibin_fct
binned_pol_img_sh = (int(rbin_new), int(phibin_new))
print("polar image dimensions: %d x %d" % (rbin_new, phibin_new))
Interp = InterpSimple(cent[0], cent[1], interp_rmax, interp_rmin, nphi, img_sh)
pmask = Interp.nearest(mask).astype(int).astype(float)
pmask_bn = bin_ndarray(pmask, binned_pol_img_sh)
# print pmask.shape,pmask_bn.shape
# if (rbin_fct==1 and phibin_fct==1):
# print('not binning polar img')
# sys.exit()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~ data get parameters
#load the data events for the given run
ds_str = 'exp=cxilr6716:run=%d:smd' % run
ds = psana.MPIDataSource(ds_str)
events = ds.events()
# open the detector obj
nphi = int( 2 * np.pi * interp_rmax )
if phibins>0:
phibin_fct = np.ceil( nphi / float( phibins ) )
else:
phibin_fct=1
nphi = int( np.ceil( 2 * np.pi * interp_rmax/phibin_fct)*phibin_fct) # number of azimuthal samples per bin
rbin_new = (interp_rmax- interp_rmin ) / rbin_fct
phibin_new = nphi / phibin_fct
binned_pol_img_sh = ( int(rbin_new), int(phibin_new) )
print("polar image dimensions: %d x %d"%(rbin_new, phibin_new))
Interp = InterpSimple( cent[0], cent[1] , interp_rmax, interp_rmin, nphi, img_sh)
pmask = Interp.nearest(mask).astype(int).astype(float)
pmask_bn = bin_ndarray( pmask, binned_pol_img_sh)
# print pmask.shape,pmask_bn.shape
# if (rbin_fct==1 and phibin_fct==1):
# print('not binning polar img')
# sys.exit()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~ data get parameters
#load the data events for the given run
ds_str = 'exp=cxilp6715:run=%d:smd' % run
ds = psana.MPIDataSource(ds_str)
events = ds.events()
# min ring radii
# desired dimension of image, these will be approximate
rbins =35
phibins = 360
interp_rmin = 100
interp_rmax = 450
rbin_fct = np.floor( (interp_rmax - interp_rmin) / rbins)
# adjust so our edge is a multiple of rbin factor
interp_rmax = int( interp_rmin + np.ceil( (interp_rmax - interp_rmin) / rbin_fct)*rbin_fct )
nphi = int( 2 * np.pi * interp_rmax )
phibin_fct = np.ceil( nphi / float( phibins ) )
nphi = int( np.ceil( 2 * np.pi * interp_rmax/phibin_fct)*phibin_fct) # number of azimuthal samples per bin
rbin_new = (interp_rmax- interp_rmin ) / rbin_fct
phibin_new = nphi / phibin_fct
binned_pol_img_sh = ( int(rbin_new), int(phibin_new) )
print("polar image dimensions: %d x %d"%(rbin_new, phibin_new))
Interp = InterpSimple( cent[0], cent[1] , interp_rmax, interp_rmin, nphi, img_sh)
pmask = Interp.nearest(mask).astype(int).astype(float)
pmask_bn = bin_ndarray( pmask, binned_pol_img_sh)
pmask_bn = pmask_bn.astype(int)
pmask_bn = np.array(pmask_bn==pmask_bn.max(), dtype = int)
#np.save('/reg/d/psdm/cxi/cxilp6715/scratch/water_data/binned_pmask_basic.npy', pmask_bn)
np.save('/reg/d/psdm/cxi/cxilp6715/results/shared_files/binned_pmask_basic3.npy', pmask_bn)