Added chi2 to fit results (#131)

- fit_gaus and fit_pol1 now return a dict
- calculate chi2 after fit
- cleaned up code

python/examples/play.py

@@ -8,61 +8,15 @@ import numpy as np
 import boost_histogram as bh
 import time
 
-<<<<<<< HEAD
-from aare import File, ClusterFinder, VarClusterFinder, ClusterFile, CtbRawFile
-from aare import gaus, fit_gaus
+import aare
 
-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')
+data = np.random.normal(10, 1, 1000)
 
-t0 = time.perf_counter()
-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
-)
+hist = bh.Histogram(bh.axis.Regular(10, 0, 20))
+hist.fill(data)
 
-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]
-
-y = histogram_data[100,100,:]
-xx = np.linspace(x[0], x[-1])
-# fig, ax = plt.subplots()
-# ax.step(x, y, where = 'post')
-
-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)
-
-res = fit_gaus(y,x)
-res2 = fit_gaus(y,x, y_err)
-print(res)
-print(res2)
 
+x = hist.axes[0].centers
+y = hist.values()
+y_err = np.sqrt(y)+1
+res = aare.fit_gaus(x, y, y_err, chi2 = True)