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