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rebin_example.py(vytvořil soubor)
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1 | + | from mantid.simpleapi import * | |
2 | + | import matplotlib.pyplot as plt | |
3 | + | import numpy as np | |
4 | + | ||
5 | + | ||
6 | + | def rebin_data(datax, datay, xmin, xmax, xbin): | |
7 | + | """Rebin input data with specified binning params | |
8 | + | ||
9 | + | Parameters: | |
10 | + | datax (numpy.ndarray): Input data X array | |
11 | + | datay (numpy.ndarray): Input data Y array | |
12 | + | xmin (float): X Min for rebinning | |
13 | + | xmax (float): X Max for rebinning | |
14 | + | xbin (float): Bin size for rebinning | |
15 | + | ||
16 | + | Returns: | |
17 | + | tuple: Rebinned data X and Y arrays | |
18 | + | """ | |
19 | + | wksp = CreateWorkspace( | |
20 | + | DataX=datax, | |
21 | + | DataY=datay, | |
22 | + | NSpec=1, | |
23 | + | Distribution=True | |
24 | + | ) | |
25 | + | ||
26 | + | Rebin( | |
27 | + | InputWorkspace=wksp, | |
28 | + | OutputWorkspace="wksp_rebin", | |
29 | + | Params=f"{xmin}, {xbin}, {xmax}" | |
30 | + | ) | |
31 | + | ||
32 | + | datax_rebinned = mtd["wksp_rebin"].extractX()[0] | |
33 | + | datay_rebinned = mtd["wksp_rebin"].extractY()[0] | |
34 | + | ||
35 | + | return (datax_rebinned, datay_rebinned) | |
36 | + | ||
37 | + | ||
38 | + | if __name__ == "__main__": | |
39 | + | # Dummy data of a simple Gaussian distribution | |
40 | + | mu = 0 | |
41 | + | sigma = 1 | |
42 | + | amplitude = 1 | |
43 | + | ||
44 | + | datax = np.linspace(-5, 5, 100) | |
45 | + | datay = amplitude * np.exp(-((datax - mu) ** 2) / (2 * sigma ** 2)) | |
46 | + | ||
47 | + | rebinned_data = rebin_data(datax, datay, -4., 4., .2) | |
48 | + | ||
49 | + | # Plot the original and rebinned data together | |
50 | + | plt.plot(datax, datay, label="Original Data") | |
51 | + | plt.plot(rebinned_data[0], rebinned_data[1], label="Rebinned Data") | |
52 | + | plt.xlabel('x') | |
53 | + | plt.ylabel('y') | |
54 | + | plt.legend() | |
55 | + | plt.show() |
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