Page MenuHomec4science
Files F97194000

EnergyBands.py
No OneTemporary
Actions

File Metadata

Created
Fri, Jan 3, 08:12

EnergyBands.py
View Options

# -*- 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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import seaborn as sns
from matplotlib import cm
file = os.path.join(os.getcwd(), os.listdir(os.getcwd())[0])
total_path = os.path.dirname(file)
print(total_path)
# %%
sns.set(font_scale=1.5)
sns.set_style("whitegrid", {'axes.grid': False})
sns.set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8})
sample_rate = 1500000
windowsize = 5000
t0 = 0
dt = 1/sample_rate
time = np.arange(0, windowsize) * dt + t0
path = r'C:\Users\srpv\Desktop\Git\Additive-Manufacturing-Acoustic-Dynamics-of-in-situ-alloying-of-Titanium-Fe\ML classifier\Feature extraction'
# %%
def boxcomparisonplots(Featurespace, classspace, Material):
df2 = pd.DataFrame(classspace)
df2.columns = ['Categorical']
df2 = df2['Categorical'].replace(0, 'Ti64')
df2 = pd.DataFrame(df2)
df2 = df2['Categorical'].replace(1, 'Ti64_3Fe')
df2 = pd.DataFrame(df2)
df2 = df2['Categorical'].replace(2, 'Ti64_6Fe')
classspace = pd.DataFrame(df2)
classspace.columns = ['Categorical']
data = pd.concat([Featurespace, classspace], axis=1)
print("Respective windows per category", data.Categorical.value_counts())
minval = min(data.Categorical.value_counts())
print("windows of the class: ", minval)
data = pd.concat([data[data.Categorical == cat].head(minval)
for cat in data.Categorical.unique()])
print("Balanced dataset: ", data.Categorical.value_counts())
Featurespace = data.iloc[:, :-1]
classspace = data.iloc[:, -1]
Featurespace = Featurespace.to_numpy()
Featurespace = (Featurespace[:, 0:5]).astype(np.float64)
classspace = classspace.to_numpy()
classes = np.unique(classspace)
color = iter(cm.rainbow(np.linspace(0, 1, len(classes))))
values, counts = np.unique(classspace, return_counts=True)
print(values, counts)
c = len(Featurespace)
df1 = pd.DataFrame(Featurespace)
df1 = np.ravel(df1, order='F')
df1 = pd.DataFrame(df1)
df2 = pd.DataFrame(classspace)
df2.columns = ['Categorical']
filename = '0-150 kHZ'
numbers = np.random.randn(c)
df3 = pd.DataFrame({'labels': filename, 'numbers': numbers})
df3 = df3.drop(['numbers'], axis=1)
filename = '150-300 kHZ'
numbers = np.random.randn(c)
df4 = pd.DataFrame({'labels': filename, 'numbers': numbers})
df4 = df4.drop(['numbers'], axis=1)
filename = '300-450 kHZ'
numbers = np.random.randn(c)
df5 = pd.DataFrame({'labels': filename, 'numbers': numbers})
df5 = df5.drop(['numbers'], axis=1)
filename = '450-600 kHZ'
numbers = np.random.randn(c)
df6 = pd.DataFrame({'labels': filename, 'numbers': numbers})
df6 = df6.drop(['numbers'], axis=1)
filename = '600-750 kHZ'
numbers = np.random.randn(c)
df7 = pd.DataFrame({'labels': filename, 'numbers': numbers})
df7 = df7.drop(['numbers'], axis=1)
Energyband = np.concatenate((df3, df4, df5, df6, df7), axis=0)
Modes = np.concatenate((df2, df2, df2, df2, df2), axis=0)
Energybands = np.concatenate((df1, Energyband, Modes), axis=1)
Energybands = pd.DataFrame(Energybands)
Energybands.columns = ['Frequency distribution', 'Frequency levels', 'Categorical']
plt.rcParams.update(plt.rcParamsDefault)
plt.figure(figsize=(7, 5))
sns.set(font_scale=3.5)
sns.set_style("whitegrid", {'axes.grid': False})
ax = sns.catplot(y="Frequency levels", x="Frequency distribution", hue="Categorical", kind="bar", data=Energybands, height=12,
aspect=1.8, palette=color)
ax.set_xticklabels(rotation=0)
ax.tick_params(axis='both', which='major', labelsize=25)
ax.tick_params(axis='both', which='minor', labelsize=25)
ax._legend.remove()
plt.legend(loc='lower right', frameon=False, fontsize=40)
# plt.ticklabel_format(axis='y', style='sci',scilimits=(0,0))
plt.title('Frequency distribution', fontsize=50)
plotname = str(Material)+"_Frequency distribution"+'.png'
plt.savefig(plotname, dpi=800, bbox_inches='tight')
plt.show()
# %%
M = ['KM', 'CM']
for Material in M:
classfile = str(Material)+'_Class_label'+'.npy'
featurefile = str(Material)+'_PSD'+'.npy'
classfile = os.path.join(path, classfile)
featurefile = os.path.join(path, featurefile)
classspace = np.load(classfile).astype(np.float64)
classspace = pd.DataFrame(classspace)
featurespace = np.load(featurefile).astype(np.float64)
featurespace = pd.DataFrame(featurespace)
boxcomparisonplots(featurespace, classspace, Material)

Event Timeline

c4science · Help