Added fitting, fixed roi etc (#129)

Co-authored-by: Patrick <patrick.sieberer@psi.ch>
Co-authored-by: JulianHeymes <julian.heymes@psi.ch>

python/examples/fits.py

@@ -0,0 +1,79 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from aare import fit_gaus, fit_pol1
+from aare import gaus, pol1
+
+textpm = f"±"  #
+textmu = f"μ"  #
+textsigma = f"σ"  #
+
+
+
+# ================================= Gauss fit =================================
+# Parameters
+mu = np.random.uniform(1, 100)  # Mean of Gaussian
+sigma = np.random.uniform(4, 20)  # Standard deviation
+num_points = 10000  # Number of points for smooth distribution
+noise_sigma = 100
+
+# Generate Gaussian distribution
+data = np.random.normal(mu, sigma, num_points)
+
+# Generate errors for each point
+errors = np.abs(np.random.normal(0, sigma, num_points))  # Errors with mean 0, std 0.5
+
+# Create subplot
+fig0, ax0 = plt.subplots(1, 1, num=0, figsize=(12, 8))
+
+x = np.histogram(data, bins=30)[1][:-1] + 0.05
+y = np.histogram(data, bins=30)[0]
+yerr = errors[:30]
+
+
+# Add the errors as error bars in the step plot
+ax0.errorbar(x, y, yerr=yerr, fmt=". ", capsize=5)
+ax0.grid()
+
+par, err = fit_gaus(x, y, yerr)
+print(par, err)
+
+x = np.linspace(x[0], x[-1], 1000)
+ax0.plot(x, gaus(x, par), marker="")
+ax0.set(xlabel="x", ylabel="Counts", title=f"A0 = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
+                                           f"{textmu} = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
+                                           f"{textsigma} = {par[2]:0.2f}{textpm}{err[2]:0.2f}\n"
+                                           f"(init: {textmu}: {mu:0.2f}, {textsigma}: {sigma:0.2f})")
+fig0.tight_layout()
+
+
+
+# ================================= pol1 fit =================================
+# Parameters
+n_points = 40
+
+# Generate random slope and intercept (origin)
+slope = np.random.uniform(-10, 10)  # Random slope between 0.5 and 2.0
+intercept = np.random.uniform(-10, 10)  # Random intercept between -10 and 10
+
+# Generate random x values
+x_values = np.random.uniform(-10, 10, n_points)
+
+# Calculate y values based on the linear function y = mx + b + error
+errors = np.abs(np.random.normal(0, np.random.uniform(1, 5), n_points))
+var_points = np.random.normal(0, np.random.uniform(0.1, 2), n_points)
+y_values = slope * x_values + intercept + var_points
+
+fig1, ax1 = plt.subplots(1, 1, num=1, figsize=(12, 8))
+ax1.errorbar(x_values, y_values, yerr=errors, fmt=". ", capsize=5)
+par, err = fit_pol1(x_values, y_values, errors)
+
+
+x = np.linspace(np.min(x_values), np.max(x_values), 1000)
+ax1.plot(x, pol1(x, par), marker="")
+ax1.set(xlabel="x", ylabel="y", title=f"a = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
+                                      f"b = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
+                                      f"(init: {slope:0.2f}, {intercept:0.2f})")
+fig1.tight_layout()
+
+plt.show()
+

python/examples/play.py

@@ -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}')