rebin_example.py
· 1.4 KiB · Python
原始文件
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() |