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Added fitting, fixed roi etc (#129)

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Co-authored-by: Patrick <patrick.sieberer@psi.ch>
Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
This commit is contained in:
Erik Fröjdh
2025-02-12 16:50:31 +01:00
committed by GitHub
parent 7d6223d52d
commit dadf5f4869
55 changed files with 2931 additions and 693 deletions
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86
python/examples/play.py
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@ -8,51 +8,61 @@ import numpy as np
import boost_histogram as bh
import time
from aare import File, ClusterFinder, VarClusterFinder
<<<<<<< HEAD
from aare import File, ClusterFinder, VarClusterFinder, ClusterFile, CtbRawFile
from aare import gaus, fit_gaus
base = Path('/mnt/sls_det_storage/matterhorn_data/aare_test_data/')
f = File(base/'Moench03new/cu_half_speed_master_4.json')
cf = ClusterFinder((400,400), (3,3))
for i in range(1000):
cf.push_pedestal_frame(f.read_frame())
fig, ax = plt.subplots()
im = ax.imshow(cf.pedestal())
cf.pedestal()
cf.noise()
N = 500
t0 = time.perf_counter()
hist1 = bh.Histogram(bh.axis.Regular(40, -2, 4000))
f.seek(0)
base = Path('/mnt/sls_det_storage/moench_data/Julian/MOENCH05/20250113_first_xrays_redo/raw_files/')
cluster_file = Path('/home/l_msdetect/erik/tmp/Cu.clust')
t0 = time.perf_counter()
data = f.read_n(N)
offset= -0.5
hist3d = bh.Histogram(
bh.axis.Regular(160, 0+offset, 160+offset), #x
bh.axis.Regular(150, 0+offset, 150+offset), #y
bh.axis.Regular(200, 0, 6000), #ADU
)
total_clusters = 0
with ClusterFile(cluster_file, chunk_size = 1000) as f:
for i, clusters in enumerate(f):
arr = np.array(clusters)
total_clusters += clusters.size
hist3d.fill(arr['y'],arr['x'], clusters.sum_2x2()) #python talks [row, col] cluster finder [x,y]
=======
from aare import RawFile
f = RawFile('/mnt/sls_det_storage/jungfrau_data1/vadym_tests/jf12_M431/laser_scan/laserScan_pedestal_G0_master_0.json')
print(f'{f.frame_number(1)}')
for i in range(10):
header, img = f.read_frame()
print(header['frameNumber'], img.shape)
>>>>>>> developer
t_elapsed = time.perf_counter()-t0
print(f'Histogram filling took: {t_elapsed:.3f}s {total_clusters/t_elapsed/1e6:.3f}M clusters/s')
histogram_data = hist3d.counts()
x = hist3d.axes[2].edges[:-1]
n_bytes = data.itemsize*data.size
y = histogram_data[100,100,:]
xx = np.linspace(x[0], x[-1])
# fig, ax = plt.subplots()
# ax.step(x, y, where = 'post')
print(f'Reading {N} frames took {t_elapsed:.3f}s {N/t_elapsed:.0f} FPS, {n_bytes/1024**2:.4f} GB/s')
y_err = np.sqrt(y)
y_err = np.zeros(y.size)
y_err += 1
# par = fit_gaus2(y,x, y_err)
# ax.plot(xx, gaus(xx,par))
# print(par)
for frame in data:
a = cf.find_clusters(frame)
res = fit_gaus(y,x)
res2 = fit_gaus(y,x, y_err)
print(res)
print(res2)
clusters = cf.steal_clusters()
# t_elapsed = time.perf_counter()-t0
# print(f'Clustering {N} frames took {t_elapsed:.2f}s {N/t_elapsed:.0f} FPS')
# t0 = time.perf_counter()
# total_clusters = clusters.size
# hist1.fill(clusters.sum())
# t_elapsed = time.perf_counter()-t0
# print(f'Filling histogram with the sum of {total_clusters} clusters took: {t_elapsed:.3f}s, {total_clusters/t_elapsed:.3g} clust/s')
# print(f'Average number of clusters per frame {total_clusters/N:.3f}')