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Diffusion LPBF Acoustic Dynamics of in-situ alloying of Titanium-Fe (master) Browse

R13225/Crack dynamics/Main_Visualize.py
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Main_Visualize.py
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# -*- coding: utf-8 -*-
"""
@author: srpv
contact: vigneashwara.solairajapandiyan@empa.ch, vigneashpandiyan@gmail.com
The codes in this following script will be used for the publication of the following work
"Exploring Acoustic Emission Monitoring during Laser Powder Bed Fusion of premixed Ti6Al4V-Fe powder: Evidence of martensitic phase transformation supported by operando X-ray diffraction "
@any reuse of this code should be authorized by the first owner, code author
"""
# libraries to import
import random
from matplotlib import cm
import pywt
from operator import itemgetter
from itertools import groupby
from scipy import signal
import os
from pathlib import Path
import ntpath
import re
import matplotlib.pyplot as plt
import pandas as pd
import glob
import numpy as np
import glob
import seaborn as sns
import matplotlib.pyplot as mpl
from Utils import *
mpl.rcParams['agg.path.chunksize'] = 1000000
def path_leaf(path):
"""
Extracts the file name from a given path.
Args:
path (str): The path from which to extract the file name.
Returns:
str: The file name extracted from the path.
"""
head, tail = ntpath.split(path)
return tail
# return tail or ntpath.basename(head)
# %%
# path to the data
path = r'C:\Users\vigneashwara.p\Desktop\C4Science\lpbf-acoustic-dynamics-of-in-situ-alloying-of-titanium-fe\Crack dynamics\Data\Ti64_6Fe_KM_23'
sample_rate = 1500000 # sample rate of the data
isDirectory = os.path.isdir(path)
file = os.path.join(os.getcwd(), os.listdir(os.getcwd())[0])
total_path = os.path.dirname(file)
print(total_path)
if isDirectory:
print(path)
print(isDirectory)
Channel0 = (os.path.join(path, 'channel_0_decoded'))
print(Channel0)
Channel1 = (os.path.join(path, 'channel_1_decoded'))
print(Channel1)
all_files = glob.glob(Channel0 + "/*.csv")
for filename in all_files:
tail = path_leaf(filename)
A = tail.split('.')
print(A)
data_file_name = A[0]
print(data_file_name)
# Create a folder to save the data
folder_created = os.path.join(total_path, data_file_name)
print(folder_created)
try:
os.makedirs(folder_created, exist_ok=True)
print("Directory created....")
except OSError as error:
print("Directory already exists....")
Channel_0 = (os.path.join(Channel0, tail))
print(Channel0)
Channel_1 = (os.path.join(Channel1, tail))
print(Channel1)
data1 = pd.read_csv(Channel_0)
data1 = data1.to_numpy()
data1 = np.ravel(data1)
data2 = pd.read_csv(Channel_1)
data2 = data2.to_numpy()
data2 = np.ravel(data2)
length = len(data1)
N = length
t0 = 0
dt = 1/sample_rate
time = np.arange(0, N) * dt + t0
# Plot the raw data
uniq = str(random.randint(0, 9999))
plt.rcParams.update(plt.rcParamsDefault)
fig = plt.figure(figsize=(10, 6), dpi=800)
fig, ((ax1, ax2)) = plt.subplots(2, 1)
title = ' AE '+'Signal'
fig.suptitle(title)
plt.rc("font", size=8)
ax1.plot(time[0:800000], data1[0:800000], 'red', linewidth=2.5, label='Laser')
ax1.legend(fontsize=15)
ax1.set_ylabel('Voltage (V)')
ax2.plot(time[0:800000], data2[0:800000], 'blue', linewidth=1, label='AE')
ax2.legend(fontsize=15)
# ax1.set_xlim(0,0.3)
ax2.set_xlabel('Time (sec)', labelpad=5)
ax2.set_ylabel('Voltage (V)')
# ax2.set_xlim(0,0.3)
# Plot the raw data
for ax in fig.get_axes():
ax.label_outer()
# plt.xlim((0, 40e5))
tail = path_leaf(path)
A = tail.split('.')
data_file_name = A[0]
graph_2 = str(data_file_name)+'_'+uniq+'_'+'RawAE'+'signal'+'.png'
plt.savefig(os.path.join(folder_created, graph_2), bbox_inches='tight', dpi=200)
plt.show()
data1_ = data1[570000:750000] # truncating from whole data
data2_ = data2[570000:750000] # truncating from whole data
length = len(data1_)
N = length
t0 = 0
dt = 1/sample_rate
time_ = np.arange(0, N) * dt + t0
plt.rcParams.update(plt.rcParamsDefault)
fig = plt.figure(figsize=(10, 6), dpi=800)
fig, ((ax1, ax2)) = plt.subplots(2, 1)
title = ' AE '+'Signal'
fig.suptitle(title)
plt.rc("font", size=8)
ax1.plot(time_, data1_, 'red', linewidth=2.5, label='Laser')
ax1.legend(fontsize=15)
ax1.set_ylabel('Voltage (V)')
ax2.plot(time_, data2_, 'blue', linewidth=1, label='AE')
ax2.legend(fontsize=15)
# ax1.set_xlim(0,0.3)
ax2.set_xlabel('Time (sec)', labelpad=5)
ax2.set_ylabel('Voltage (V)')
# ax2.set_xlim(0,0.3)
for ax in fig.get_axes():
ax.label_outer()
# plt.xlim((0, 40e5))
tail = path_leaf(path)
A = tail.split('.')
data_file_name = A[0]
graph_2 = str(data_file_name)+'_'+uniq+'_'+'AE'+'signal'+'.png'
plt.savefig(os.path.join(folder_created, graph_2), bbox_inches='tight', dpi=200)
plt.show()
# Plot the spectrogram
specgram3d(data_file_name, uniq, data2_, sample_rate, time_, folder_created)
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