mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2025-04-20 05:40:03 +02:00
7309cff47c
- added stand alone fitting using: https://jugit.fz-juelich.de/mlz/lmfit.git - fit_gaus, fit_pol1 with and without errors - multi threaded fitting --------- Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
import sys
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sys.path.append('/home/l_msdetect/erik/aare/build')
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#Our normal python imports
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import boost_histogram as bh
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import time
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<<<<<<< HEAD
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from aare import File, ClusterFinder, VarClusterFinder, ClusterFile, CtbRawFile
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from aare import gaus, fit_gaus
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base = Path('/mnt/sls_det_storage/moench_data/Julian/MOENCH05/20250113_first_xrays_redo/raw_files/')
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cluster_file = Path('/home/l_msdetect/erik/tmp/Cu.clust')
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t0 = time.perf_counter()
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offset= -0.5
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hist3d = bh.Histogram(
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bh.axis.Regular(160, 0+offset, 160+offset), #x
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bh.axis.Regular(150, 0+offset, 150+offset), #y
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bh.axis.Regular(200, 0, 6000), #ADU
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)
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total_clusters = 0
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with ClusterFile(cluster_file, chunk_size = 1000) as f:
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for i, clusters in enumerate(f):
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arr = np.array(clusters)
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total_clusters += clusters.size
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hist3d.fill(arr['y'],arr['x'], clusters.sum_2x2()) #python talks [row, col] cluster finder [x,y]
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=======
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from aare import RawFile
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f = RawFile('/mnt/sls_det_storage/jungfrau_data1/vadym_tests/jf12_M431/laser_scan/laserScan_pedestal_G0_master_0.json')
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print(f'{f.frame_number(1)}')
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for i in range(10):
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header, img = f.read_frame()
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print(header['frameNumber'], img.shape)
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>>>>>>> developer
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t_elapsed = time.perf_counter()-t0
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print(f'Histogram filling took: {t_elapsed:.3f}s {total_clusters/t_elapsed/1e6:.3f}M clusters/s')
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histogram_data = hist3d.counts()
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x = hist3d.axes[2].edges[:-1]
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y = histogram_data[100,100,:]
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xx = np.linspace(x[0], x[-1])
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# fig, ax = plt.subplots()
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# ax.step(x, y, where = 'post')
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y_err = np.sqrt(y)
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y_err = np.zeros(y.size)
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y_err += 1
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# par = fit_gaus2(y,x, y_err)
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# ax.plot(xx, gaus(xx,par))
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# print(par)
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res = fit_gaus(y,x)
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res2 = fit_gaus(y,x, y_err)
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print(res)
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print(res2)
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