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duplicate_matrix.py
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duplicate_matrix.py
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import numpy as np
import scipy.spatial.distance as eucd
from scipy.stats.stats import pearsonr
import sys
line_array=[]
vec_array=[]
print("Parameters:")
mzdiff=0.01
rtdiff=0.1
corrthresh=0.99
print("Median MZ Diff: "+ str(mzdiff))
print("Median RT Diff: "+ str(rtdiff))
print("Correlation Threshold: "+ str(corrthresh))
table_labels=0
if(len(sys.argv)<2):
print("python metric_euc.py csv_file.csv")
sys.exit(1)
with open(sys.argv[1]) as f:
first=0
for line in f:
line=line.rstrip('\n')
if(first==0):
first=1
table_labels=line.split(',')
print("Removing Blanks")
continue
temp_array=line.split(',')
t=5
while(t!=0):
temp_array.remove('')
t-=1
line_array.append(map(float,temp_array))
temp_array=temp_array[9:]
vec_array.append(map(float,temp_array))
line_array=np.array(line_array)
vec_array=np.array(vec_array)
print("Row Count: ", len(line_array))
print("Correlation Matrix Based")
nvec=len(line_array)
correlation_matrix=np.zeros((nvec,nvec))
for i in range(0,nvec):
correlation_matrix[i][i]=1
# for i in range(nvec):
# for j in range(nvec):
# print(correlation_matrix[i][j]),
# print('\n')
for i in range(nvec):
for j in range(i+1,nvec):
if(abs(line_array[i][3]-line_array[j][3])<=mzdiff and abs(line_array[i][4]-line_array[j][4])<=rtdiff ):
first_corr=pearsonr(vec_array[i],vec_array[j])[0]
if(first_corr>=corrthresh):
# print(line_array[i][1],line_array[j][1])
correlation_matrix[i][j]=1
correlation_matrix[j][i]=1
else:
correlation_matrix[i][j]=0
correlation_matrix[j][i]=0
for i in range(nvec):
for j in range(nvec):
if i!=j and correlation_matrix[i][j]==1:
correlation_matrix[i][j]=0
correlation_matrix[j][i]=0
correlation_matrix[j][j]=0
count=0
for i in range(nvec):
if(correlation_matrix[i][i]==1):
# print(line_array[i][1])
count+=1
print(count)