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line_class.py
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line_class.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Apr 25 20:15:43 2021
@author: aserafeim
"""
import pandas as pd
from scipy.fftpack import fft, fftfreq, ifft
import numpy as np
import matplotlib.pyplot as plt
import statistics as stat
N_sheets=9
plt.close('all')
elements=['Mn Wt%','Si Wt%']
class lineFFT:
def __init__(self,filename,N_sheets,stepsize=5,elements=['Mn Wt%','Si Wt%'],threshold=0.1):
self.filename=filename
self.N_sheets=N_sheets
self.L=stepsize
self.elements=elements
self.threshold=threshold
def open_clean(self):
df = pd.read_excel (self.filename,[i for i in range(N_sheets)])
for i in range(len(df)):
ind=np.where(df[i][self.elements[0]]>7.5)
df[i][self.elements[0]+'_C']=df[i][self.elements[0]]
df[i][self.elements[0]+'_C'][ind[0]]=df[i][self.elements[0]].mean()
return df
# df_norm = pd.read_exciel (r'Lines_10pct.xlsx',[i for i in range(N_sheets)])
def calc_fft(self):
d=self.open_clean() # df contains the composition after inverse FFT
Lf_real={} #Dictionary with the segregation lengths in real space
ft_mn_v={} #Dictionary with the values after FFT
Lf_v={} # Dictionary with segregation length in frequency space
thres_2=0.5
x_new, x_temp=[], []
Lf_conc_temp=[]
Lf_conc={} # Dictionary with segreagation length in real space after all of the simmilar frequencies have been averaged
power=[]
for i in range(self.N_sheets):
N=len(df[i])
#FFT
ft_mn=fft(np.array(df[i][self.elements[0]+'_C']))
ft_mn_v[i]=ft_mn[1:N//2]
#Calculate frequencies
Lf=fftfreq(N, self.L)
Lf_v[i]=Lf[1:N//2]
#Filter out frequencies based on the 5% highest
sort_ft=np.sort((2/N)*np.abs(ft_mn))
#Make an index vector to find which values of ft are higher than the cutoff value of 10%
index=(2/N)*np.abs(ft_mn)>sort_ft[-round(self.threshold*len(sort_ft))]
#Zero out the low frequencies
ft_clean=ft_mn*index
power.append(sum(abs(ft_clean))/sum(abs(ft_mn)))
#Calculate inverse frequncies
Lf_clean=(1/Lf)*index
Lf_clean=Lf_clean[1:N//2] #take half the elements due to symmetry
#Assigng values to a dictoíonary
Lf_real[i]=Lf_clean
#Inverse FFT
y=ifft(ft_clean)
#Addting the results to the pandas datastructure
df[i]['FFT '+elements[0]]=y
#Average similar segregation length based on thres_2
x_new, x_temp=[], []
Lf_conc_temp=[]
Lf_nozeros=Lf_real[i][np.nonzero(Lf_real[i])]
x_temp.append(Lf_nozeros[0])
print ('line'+str(i))
for j in range(1,len(Lf_nozeros),1):
# print((Lf_nozeros[j]-x_temp[0])/Lf_nozeros[j])
if (np.abs(Lf_nozeros[j]-x_temp[0]))/Lf_nozeros[j]<thres_2:
x_temp.append(Lf_nozeros[j])
else:
Lf_conc_temp.append(stat.mean(x_temp))
x_temp=[]
x_temp.append(Lf_nozeros[j])
if j==len(Lf_nozeros)-1:
Lf_conc_temp.append(Lf_nozeros[j])
print(Lf_conc_temp)
Lf_conc[i]=Lf_conc_temp
return df, Lf_real, ft_mn_v, Lf_v, Lf_conc, power
# def average_per_line():
# for i in range
def subplotting(df,Lf_real,elements,name):
i=0
for i in range(len(df)):
fig, ax = plt.subplots(3,1)
ax[0].plot(df[i]['Distance '], df[i]['FFT '+elements[0]])
ax[0].plot(df[i]['Distance '],df[i]['Mn Wt%'])
ax[0].set_ylabel('Mn Concentration')
ax[0].set_xlabel('Distance (μm)')
ax[1].plot(df[i]['Distance '], df[i]['FFT '+elements[0]])
ax[1].set_ylabel('Mn Concentration')
ax[1].set_xlabel('Distance (μm)')
ax[2].plot(Lf_real[i])
ax[2].set_yscale('log')
ax[2].set_ylabel('Band separation (μm)')
ax[2].set_xlabel('Harmonic number')
fig.savefig('Subplot_'+str(i)+'_'+elements[0]+'.png',dpi=600,bbox_inches='tight')
def plotfreq(ft,Lf,thres):
#Plotting frequency-magnitude diagrams for all lines
for i in range(len(Lf)):
fig, ax =plt.subplots()
# plt.figure()
ax.plot(Lf[i][1:],np.abs(ft[i][1:]))
ax.set_xlabel('Frequency (1/μm)',fontsize=16)
ax.set_ylabel('Signal magnitude',fontsize=16)
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(16)
fig.savefig('MagnitudevsFrequency'+'_'+elements[0]+'Thresh'+str(thres)+'line'+str(i)+'.png',dpi=600,bbox_inches='tight')
def adjust_lengths(Lf_conc,N_freq):
#dictionary with each component and a list of the freq of each line for this component.
comp_L_conc={}
final_L={}
for j in range(N_freq):
comp_L_conc[j]=[]
final_L[j]=[]
for j in range(N_freq):
for i in range(len(Lf_conc)):
comp_L_conc[j].append(Lf_conc[i][j])
for j in range(N_freq):
final_L[j].append(np.mean(comp_L_conc[j][0:2]))
final_L[j].append(np.mean(comp_L_conc[j][3:5]))
final_L[j].append(np.mean(comp_L_conc[j][6:8]))
return comp_L_conc, final_L
# def twoline(Lf1,Lf2):
file20='Lines_20pct.xlsx'
file10='Lines_10pct.xlsx'
thres=0.5
line20=lineFFT(file20,9,threshold=thres)
df=line20.open_clean()
line10=lineFFT(file10,8,threshold=thres)
y , Lf_real,ft_mn,Lf, Lf_conc, power =line20.calc_fft()
comp_L_conc,final_L=adjust_lengths(Lf_conc,5)
# y50 , Lf_real50,ft_mn50,Lf50, Lf_conc50, power50 =line10.calc_fft()
# subplotting(y,Lf_real,elements,file20)
#
plotfreq(ft_mn,Lf,thres)
# fig, ax =plt.subplots()
# ax.plot(Lf_conc[4], label='10 pct threshold')
# ax.plot(Lf_conc50[4],label='50 pct threshold')
# ax.legend()
# fig2, ax =plt.subplots()
# ax.plot(power, label='10 pct threshold')
# ax.plot(power50,label='50 pct threshold')
# ax.legend()
# fig3, ax =plt.subplots()
# ax.plot(Lf_real[4], label='10 pct threshold')
# ax.plot(Lf_real50[4],label='50 pct threshold')
# ax.legend()
# fig,ax=plt.subplots()
# for i in range(3):
# ax.plot(Lf_real[i])
fig2, ax =plt.subplots()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(12)
sections=['Top', 'Middle', 'Center']
ax.plot(sections,final_L[2],label='Component 2')
ax.plot(sections,final_L[3],label='Component 3')
ax.plot(sections,final_L[4],label='Component 4')
ax.set_xlabel('Section')
ax.set_ylabel('Segregation lenght (μm)')
ax.legend()
fig2.savefig('segregation_lengths'+'_'+elements[0]+'Thresh'+str(thres)+'.png',dpi=600,bbox_inches='tight')