Interpolate (#137)

- added eta based interpolation

python/examples/play.py

@@ -1,50 +1,77 @@
 import sys
 sys.path.append('/home/l_msdetect/erik/aare/build')
 
-#Our normal python imports
-from pathlib import Path
-import matplotlib.pyplot as plt
+from aare._aare import ClusterVector_i, Interpolator
+
+import pickle 
 import numpy as np
+import matplotlib.pyplot as plt
 import boost_histogram as bh
+import torch
+import math
 import time
 
 
-import aare
+def gaussian_2d(mx, my, sigma = 1, res=100, grid_size = 2):
+    """
+    Generate a 2D gaussian as position mx, my, with sigma=sigma. 
+    The gaussian is placed on a 2x2 pixel matrix with resolution 
+    res in one dimesion.
+    """
+    x = torch.linspace(0, pixel_size*grid_size, res)
+    x,y = torch.meshgrid(x,x, indexing="ij")
+    return 1 / (2*math.pi*sigma**2) * \
+      torch.exp(-((x - my)**2 / (2*sigma**2) + (y - mx)**2 / (2*sigma**2)))
 
-data = np.random.normal(10, 1, 1000)
+scale = 1000 #Scale factor when converting to integer
+pixel_size = 25 #um
+grid = 2
+resolution = 100
+sigma_um = 10
+xa = np.linspace(0,grid*pixel_size,resolution)
+ticks = [0, 25, 50]
 
-hist = bh.Histogram(bh.axis.Regular(10, 0, 20))
-hist.fill(data)
+hit = np.array((20,20))
+etahist_fname = "/home/l_msdetect/erik/tmp/test_hist.pkl"
+
+local_resolution = 99
+grid_size = 3
+xaxis = np.linspace(0,grid_size*pixel_size, local_resolution)
+t = gaussian_2d(hit[0],hit[1], grid_size = grid_size, sigma = 10, res = local_resolution)
+pixels = t.reshape(grid_size, t.shape[0] // grid_size, grid_size, t.shape[1] // grid_size).sum(axis = 3).sum(axis = 1)
+pixels = pixels.numpy()
+pixels = (pixels*scale).astype(np.int32)
+v = ClusterVector_i(3,3)
+v.push_back(1,1, pixels)
+
+with open(etahist_fname, "rb") as f:
+        hist = pickle.load(f)
+eta = hist.view().copy()
+etabinsx = np.array(hist.axes.edges.T[0].flat)
+etabinsy = np.array(hist.axes.edges.T[1].flat)
+ebins = np.array(hist.axes.edges.T[2].flat)
+p = Interpolator(eta, etabinsx[0:-1], etabinsy[0:-1], ebins[0:-1])
 
 
-x = hist.axes[0].centers
-y = hist.values()
-y_err = np.sqrt(y)+1
-res = aare.fit_gaus(x, y, y_err, chi2 = True)
+
+#Generate the hit
 
 
-        
-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')
+tmp = p.interpolate(v)
+print(f'tmp:{tmp}')
+pos = np.array((tmp['x'], tmp['y']))*25
 
-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)
 
+print(pixels)
+fig, ax = plt.subplots(figsize = (7,7))
+ax.pcolormesh(xaxis, xaxis, t)
+ax.plot(*pos, 'o')
+ax.set_xticks([0,25,50,75])
+ax.set_yticks([0,25,50,75])
+ax.set_xlim(0,75)
+ax.set_ylim(0,75)
+ax.grid()
+print(f'{hit=}')
+print(f'{pos=}')