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R13109/Titanium - Ti6Al4V-Fe/ML classifier/Deep Learning/Triplet Loss/Preprocessing.py
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Preprocessing.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 26 07:14:15 2023
@author: srpv
contact: vigneashwara.solairajapandiyan@empa.ch
contact: vigneashwara.pandiyan@tii.ae
The codes in this following script will be used for the publication of the following work
"Dynamics of in-situ alloying of Ti6Al4V-Fe by means of acoustic emission monitoring
supported by operando synchrotron X-ray diffraction"
@any reuse of this code should be authorized by the first owner, code author
"""
# %%
# Librariies to import
import numpy as np
import os
'''
This is a custom script to compute the PSD and visualize it'''
# %%
# point to the folder based on Dataprep_Whole.py
dataset = 'KM'
path = r'C:\Users\srpv\Desktop\Sinergia Offline\Reza\Code\Pre_processed Dataset'
window_size = 5000 # rate at which the acquistion happend.
Rawspace = np.zeros((0, window_size)) # size of the PSD features computed
classspace = []
r = ['Ti64', 'Ti64_3Fe', 'Ti64_6Fe'] # --> Total number of classes/ Categories
for sensor in r:
dataset_name = path+'/'+str(dataset)+'-'+str(sensor)+'_raw'+'.npy'
print(dataset_name)
data = np.load(dataset_name)
Rawspace = np.concatenate((Rawspace, data), axis=0)
print(Rawspace.shape)
# Featurespace.append(data)
dataset_label = path+'/'+'Classlabel_'+str(dataset)+'-'+str(sensor)+'.npy'
print(dataset_label)
label = np.load(dataset_label)
classspace = np.concatenate((classspace, label), axis=0)
print(classspace.shape)
data_filename = dataset+'_' + 'Rawspace'+'.npy'
np.save(os.path.join(path, data_filename), Rawspace, allow_pickle=True)
data_filename = dataset+'_' + 'Class'+'_' + 'label'+'.npy'
np.save(os.path.join(path, data_filename), classspace, allow_pickle=True)
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