Revision of rebin_example.py

1 file changed, 55 insertions

rebin_example.py(file created)

		@@ -0,0 +1,55 @@
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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