yuanpeng/rebin_example.py

rebin_example.py · 1.4 KiB · Python Raw

from mantid.simpleapi import *
import matplotlib.pyplot as plt
import numpy as np

def rebin_data(datax, datay, xmin, xmax, xbin):
    """Rebin input data with specified binning params

Parameters:
    datax (numpy.ndarray): Input data X array
    datay (numpy.ndarray): Input data Y array
    xmin (float): X Min for rebinning
    xmax (float): X Max for rebinning
    xbin (float): Bin size for rebinning

Returns:
    tuple: Rebinned data X and Y arrays
    """
    wksp = CreateWorkspace(
        DataX=datax,
        DataY=datay,
        NSpec=1,
        Distribution=True
    )

Rebin(
        InputWorkspace=wksp,
        OutputWorkspace="wksp_rebin",
        Params=f"{xmin}, {xbin}, {xmax}"
    )

datax_rebinned = mtd["wksp_rebin"].extractX()[0]
    datay_rebinned = mtd["wksp_rebin"].extractY()[0]

return (datax_rebinned, datay_rebinned)

if __name__ == "__main__":
    # Dummy data of a simple Gaussian distribution
    mu = 0
    sigma = 1
    amplitude = 1

datax = np.linspace(-5, 5, 100)
    datay = amplitude * np.exp(-((datax - mu) ** 2) / (2 * sigma ** 2))

rebinned_data = rebin_data(datax, datay, -4., 4., .2)

# Plot the original and rebinned data together
    plt.plot(datax, datay, label="Original Data")
    plt.plot(rebinned_data[0], rebinned_data[1], label="Rebinned Data")
    plt.xlabel('x')
    plt.ylabel('y')
    plt.legend()
    plt.show()

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()