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https://github.com/slsdetectorgroup/aare.git
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Added fitting with lmfit (#128)
- 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>
This commit is contained in:
Erik Fröjdh
79
python/examples/fits.py
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79
python/examples/fits.py
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@ -0,0 +1,79 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from aare import fit_gaus, fit_pol1
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from aare import gaus, pol1
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textpm = f"±" #
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textmu = f"μ" #
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textsigma = f"σ" #
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# ================================= Gauss fit =================================
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# Parameters
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mu = np.random.uniform(1, 100) # Mean of Gaussian
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sigma = np.random.uniform(4, 20) # Standard deviation
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num_points = 10000 # Number of points for smooth distribution
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noise_sigma = 100
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# Generate Gaussian distribution
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data = np.random.normal(mu, sigma, num_points)
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# Generate errors for each point
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errors = np.abs(np.random.normal(0, sigma, num_points)) # Errors with mean 0, std 0.5
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# Create subplot
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fig0, ax0 = plt.subplots(1, 1, num=0, figsize=(12, 8))
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x = np.histogram(data, bins=30)[1][:-1] + 0.05
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y = np.histogram(data, bins=30)[0]
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yerr = errors[:30]
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# Add the errors as error bars in the step plot
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ax0.errorbar(x, y, yerr=yerr, fmt=". ", capsize=5)
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ax0.grid()
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par, err = fit_gaus(x, y, yerr)
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print(par, err)
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x = np.linspace(x[0], x[-1], 1000)
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ax0.plot(x, gaus(x, par), marker="")
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ax0.set(xlabel="x", ylabel="Counts", title=f"A0 = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
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f"{textmu} = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
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f"{textsigma} = {par[2]:0.2f}{textpm}{err[2]:0.2f}\n"
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f"(init: {textmu}: {mu:0.2f}, {textsigma}: {sigma:0.2f})")
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fig0.tight_layout()
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# ================================= pol1 fit =================================
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# Parameters
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n_points = 40
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# Generate random slope and intercept (origin)
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slope = np.random.uniform(-10, 10) # Random slope between 0.5 and 2.0
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intercept = np.random.uniform(-10, 10) # Random intercept between -10 and 10
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# Generate random x values
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x_values = np.random.uniform(-10, 10, n_points)
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# Calculate y values based on the linear function y = mx + b + error
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errors = np.abs(np.random.normal(0, np.random.uniform(1, 5), n_points))
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var_points = np.random.normal(0, np.random.uniform(0.1, 2), n_points)
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y_values = slope * x_values + intercept + var_points
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fig1, ax1 = plt.subplots(1, 1, num=1, figsize=(12, 8))
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ax1.errorbar(x_values, y_values, yerr=errors, fmt=". ", capsize=5)
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par, err = fit_pol1(x_values, y_values, errors)
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x = np.linspace(np.min(x_values), np.max(x_values), 1000)
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ax1.plot(x, pol1(x, par), marker="")
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ax1.set(xlabel="x", ylabel="y", title=f"a = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
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f"b = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
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f"(init: {slope:0.2f}, {intercept:0.2f})")
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fig1.tight_layout()
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plt.show()
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@ -8,6 +8,28 @@ 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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@ -17,104 +39,30 @@ 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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# for i, frame in enumerate(f):
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# print(f'{i}', end='\r')
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# print()
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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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# from aare._aare import ClusterFinderMT, ClusterCollector, ClusterFileSink
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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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# cf = ClusterFinderMT((400,400), (3,3), n_threads = 3)
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# # collector = ClusterCollector(cf)
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# out_file = ClusterFileSink(cf, "test.clust")
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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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# for i in range(1000):
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# img = f.read_frame()
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# cf.push_pedestal_frame(img)
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# print('Pedestal done')
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# cf.sync()
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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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# for i in range(100):
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# img = f.read_frame()
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# cf.find_clusters(img)
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# # time.sleep(1)
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# cf.stop()
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# time.sleep(1)
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# print('Second run')
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# cf.start()
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# for i in range(100):
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# img = f.read_frame()
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# cf.find_clusters(img)
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# cf.stop()
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# print('Third run')
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# cf.start()
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# for i in range(129):
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# img = f.read_frame()
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# cf.find_clusters(img)
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# cf.stop()
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# out_file.stop()
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# print('Done')
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# cfile = ClusterFile("test.clust")
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# i = 0
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# while True:
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# try:
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# cv = cfile.read_frame()
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# i+=1
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# except RuntimeError:
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# break
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# print(f'Read {i} frames')
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# # cf = ClusterFinder((400,400), (3,3))
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# # for i in range(1000):
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# # cf.push_pedestal_frame(f.read_frame())
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# # fig, ax = plt.subplots()
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# # im = ax.imshow(cf.pedestal())
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# # cf.pedestal()
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# # cf.noise()
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# # N = 500
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# # t0 = time.perf_counter()
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# # hist1 = bh.Histogram(bh.axis.Regular(40, -2, 4000))
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# # f.seek(0)
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# # t0 = time.perf_counter()
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# # data = f.read_n(N)
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# # t_elapsed = time.perf_counter()-t0
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# # n_bytes = data.itemsize*data.size
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# # print(f'Reading {N} frames took {t_elapsed:.3f}s {N/t_elapsed:.0f} FPS, {n_bytes/1024**2:.4f} GB/s')
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# # for frame in data:
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# # a = cf.find_clusters(frame)
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# # clusters = cf.steal_clusters()
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# # t_elapsed = time.perf_counter()-t0
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# # print(f'Clustering {N} frames took {t_elapsed:.2f}s {N/t_elapsed:.0f} FPS')
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# # t0 = time.perf_counter()
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# # total_clusters = clusters.size
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# # hist1.fill(clusters.sum())
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# # t_elapsed = time.perf_counter()-t0
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# # print(f'Filling histogram with the sum of {total_clusters} clusters took: {t_elapsed:.3f}s, {total_clusters/t_elapsed:.3g} clust/s')
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# # print(f'Average number of clusters per frame {total_clusters/N:.3f}')
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