mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2025-06-03 19:40:40 +02:00
94ac58b09e
Co-authored-by: Patrick <patrick.sieberer@psi.ch> Co-authored-by: JulianHeymes <julian.heymes@psi.ch> Co-authored-by: Dhanya Thattil <dhanya.thattil@psi.ch> Co-authored-by: Xiangyu Xie <45243914+xiangyuxie@users.noreply.github.com> Co-authored-by: xiangyu.xie <xiangyu.xie@psi.ch> Co-authored-by: AliceMazzoleni99 <alice.mazzoleni@psi.ch> Co-authored-by: Mazzoleni Alice Francesca <mazzol_a@pc17378.psi.ch> Co-authored-by: siebsi <sieb.patr@gmail.com>
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
import sys
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sys.path.append('/home/l_msdetect/erik/aare/build')
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from aare import RawSubFile, DetectorType, RawFile
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from pathlib import Path
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path = Path("/home/l_msdetect/erik/data/aare-test-data/raw/jungfrau/")
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f = RawSubFile(path/"jungfrau_single_d0_f0_0.raw", DetectorType.Jungfrau, 512, 1024, 16)
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# f = RawFile(path/"jungfrau_single_master_0.json")
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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=}') |