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Bugs fixed in reading with noise map and color_image speeded up with boost histogram

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This commit is contained in:
bergamaschi 2024-04-25 18:34:58 +02:00
parent 8eabf6a008
commit e71a0a166b
3 changed files with 70 additions and 63 deletions
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72
examples/clustersFunctions.py
View File

@ -5,56 +5,15 @@ import numpy as np
#from creader import ClusterFileReader
import creader as cr
#import clusterFunctions as cf
"""
fname = "/mnt/myData/230914_30s_star_100um_nofi/star_"
fnameff = "/mnt/myData/230914_30s_flat_100um_nofi/flat_"
from PIL import Image
import boost_histogram as bh
xmin=161+20
xmax=xmin+40
ymin=161+20
ymax=ymin+40
emin=0
emax=30
ecutmin=8
ecutmax=12
subpix=5
i = 0
nbins=100
hist1=np.zeros(nbins)
#hist=np.zeros(10000)
bin_edges=np.zeros(nbins+1)
gain=150
im=np.zeros((xmax-xmin,ymax-ymin))
intim=np.zeros((subpix*(xmax-xmin),subpix*(ymax-ymin)))
etabins=251
etas=np.zeros((etabins,etabins))
csize=3
if csize==3:
etamin=-0.6
etamax=0.6
else:
etamin=-0.1
etamax=1.1
for i in range(1,21):
ff=fname+str(i)+".clust"
print(ff)
r = cr.ClusterFileReader(ff)
"""
def color_images(r, emin, emax, ebins, xmin, xmax, ymin, ymax, images=None, csize=3,gain=150):
n=0
while (cl:=r.read(100000,None)).size:
while (((cl:=r.read(100000,None)).size) ):
v=cr.clusterize(csize,cl['data'])
vv=[cl['x'],cl['y'],v['tot']/gain]
image,bins=np.histogramdd(vv, bins=[xmax-xmin,yxmax-ymin,ebins], range=[[xmin,xmax-1],[ymin,ymax-1],[emin,emax]])
image,bins=np.histogramdd(vv, bins=[xmax-xmin,ymax-ymin,ebins], range=[[xmin,xmax-1],[ymin,ymax-1],[emin,emax]])
if images is None:
images = image.copy()
@ -63,8 +22,25 @@ def color_images(r, emin, emax, ebins, xmin, xmax, ymin, ymax, images=None, csi
n+=v.shape[0]
#print(v.shape)
print("Read ",n," clusters",np.sum(images))
return images, bins
def color_images_bh(r, emin, emax, ebins, xmin, xmax, ymin, ymax, hist3d=None, csize=3,gain=150,noisemap=None):
if hist3d is None:
hist3d = bh.Histogram(
bh.axis.Regular(xmax-xmin+1, xmin-0.5, xmax+0.5),
bh.axis.Regular(ymax-ymin+1, xmin-0.5, xmax+0.5),
bh.axis.Regular(ebins, emin, emax),
)
n=0
while (((cl:=r.read(100000,noisemap)).size)and (n<1E7)):
v=cr.clusterize(csize,cl['data'])
vv=[cl['x'],cl['y'],v['tot']/gain]
hist3d.fill(*vv)
n+=v.shape[0]
if n%1000000 ==0:
print("Read ",n," clusters",hist3d.sum())
return hist3d
def analyze_clusters(r, emin, emax, ecutmin, ecutmax, xmin, xmax, ymin, ymax, ietax=None, ietay=None, im=None, sp=None, etas=None, intim=None, csize=3,gain=150, nbins=100, etabins=250, subpix=5):
if csize==3:
@ -170,3 +146,9 @@ def plot_colz(hist2d, vmax=-1, vmin=0):
fig1.colorbar(vv, ax=axs1)
fig1.show()
def read_tiff_map(fname):
im_tot = Image.open(fname)
return np.transpose(np.flip(np.array(im_tot),axis=0))
def plothist2d(h):
return plt.pcolormesh(*h.axes.edges.T, h.values().T)

37
examples/color_imaging.py
View File

@ -6,42 +6,55 @@ import numpy as np
import creader as cr
import clustersFunctions as cf
fname = "/mnt/myData/230914_30s_star_100um_nofi/star_"
imfname= "/mnt/moench_data/moenchLGAD_maxIV_202403/clust/En1600eV_300V_-20deg_W17_ExitSlit0/En1600eV_300V_-20deg_W17_xray_d0_f0_1_f00000.tiff"
fname = "/mnt/moench_data/moenchLGAD_maxIV_202403/clust/En1600eV_300V_-20deg_W17_ExitSlit0/En1600eV_300V_-20deg_W17_xray_d0_f{}_1.clust"
noisefile="/mnt/moench_data/moenchLGAD_maxIV_202403/clust/En1600eV_300V_-20deg_W17_ExitSlit0/En1600eV_300V_-20deg_W17_dark_d0_f0_0_ped.tiff"
xmin=0
xmax=400
ymin=0
ymax=400
emin=0
emax=30
emax=16000
csize=3
gain=150
gain=1
nbins=100
indmin=1
indmax=20
indmin=0
indmax=0
im=None
h3d=None
noisemap=np.sqrt(cf.read_tiff_map(noisefile))
cf.plot_colz(noisemap)
for i in range(indmin,indmax+1):
#ff=fname
ff=fname+str(i)+".clust"
ff=fname.format(str(i))
print(ff)
r = cr.ClusterFileReader(ff)
im, bins=cf.color_images(r, emin, emax, nbins, xmin, xmax, ymin, ymax, im, csize, gain)
#im, bins=cf.color_images(r, emin, emax, nbins, xmin, xmax, ymin, ymax, im, csize, gain)
h3d=cf.color_images_bh(r, emin, emax, nbins, xmin, xmax, ymin, ymax, h3d, csize, gain, 3*noisemap)
im,xx,yy,ee=h3d.to_numpy()
sp=np.sum(im,axis=(0,1))
fig, ax = plt.subplots()
ax.plot(bins[2][:-1],sp)
ax.plot(ee[:-1],sp)
#ax.plot(bins[2][:-1],sp)
##ax.set_yscale('log')
fig.show()
cu=np.sum(im[:,:,10:35],axis=2)
cu=np.sum(im,axis=2)
cf.plot_colz(cu)
mo=np.sum(im[:,:,50:70],axis=2)
cf.plot_colz(mo)

22
src/cluster_reader.c
View File

@ -49,6 +49,8 @@ size_t read_clusters_with_cut(FILE *fp, size_t n_clusters, Cluster *buf,
int iframe = 0;
uint32_t nph = *n_left;
uint32_t nn = *n_left;
size_t nph_read = 0;
int32_t t2max, tot1;
@ -57,8 +59,18 @@ size_t read_clusters_with_cut(FILE *fp, size_t n_clusters, Cluster *buf,
int good = 1;
double noise;
// read photons left from previous frame
// if (noise_map)
// printf("Using moise map\n");
if (nph) {
for (size_t iph = 0; iph < nph; iph++) {
if (nph > n_clusters) {
// if we have more photons left in the frame then photons to read we
// read directly the requested number
nn = n_clusters;
} else {
nn = nph;
}
for (size_t iph = 0; iph < nn; iph++) {
// read photons 1 by 1
size_t n_read = fread((void *)(ptr), sizeof(Cluster), 1, fp);
if (n_read != 1) {
@ -83,9 +95,9 @@ size_t read_clusters_with_cut(FILE *fp, size_t n_clusters, Cluster *buf,
}
if (good) {
ptr++;
nph_read++;
(*n_left)--;
nph_read++;
}
(*n_left)--;
if (nph_read >= n_clusters)
break;
}
@ -126,9 +138,9 @@ size_t read_clusters_with_cut(FILE *fp, size_t n_clusters, Cluster *buf,
}
if (good) {
ptr++;
nph_read++;
(*n_left)--;
nph_read++;
}
(*n_left)--;
if (nph_read >= n_clusters)
break;
}