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similarity copy.py
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similarity copy.py
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from scipy.optimize import linear_sum_assignment
import numpy as np
import csv
import math
import configparser
import sys
import os
import matplotlib.pyplot as plt
import pandas as pd
import nmrplot.core as npl
from adjustText import adjust_text
def Two_Column_List(file):
with open(file) as input:
type=""
mycsv = csv.reader(input, delimiter='\t', skipinitialspace=True)
peaks = []
for cols in mycsv:
if len(cols)==2:
peaks.append([float(cols[0].strip()),float(cols[1].strip()),type])
elif len(cols)==1:
type=cols[0]
return peaks
def similarity(cp, project, echo):
datapath=cp.get('datadir')
fp = open(datapath+os.sep+project+os.sep+'result'+os.sep+cp.get('predictionoutput'),'r')
spectra_simulated = []
spectrum_simulated= []
spectra_simulated_dicts = []
spectrum_simulated_dict = {}
line=fp.readline().strip()
while line:
if line is '/':
spectra_simulated.append(spectrum_simulated)
spectra_simulated_dicts.append(spectrum_simulated_dict)
spectrum_simulated=[]
spectrum_simulated_dict = {}
else:
peaks_string=line.split(",")
peaks_float=[float(peaks_string[0]),float(peaks_string[1]),peaks_string[2]]
#Add the relevent experiment peak to the right dictionary for the labelling afterwards
peak_x = float(peaks_string[0])
peak_y = float(peaks_string[1])
peak_type = peaks_string[2]
peak_c = int(peaks_string[3])
peak_h = int(peaks_string[4])
if peak_type in spectrum_simulated_dict:
spectrum_simulated_dict[peak_type].append([peak_x, peak_y, peak_c, peak_h])
else:
spectrum_simulated_dict[peak_type] = [[peak_x, peak_y, peak_c, peak_h]]
spectrum_simulated.append(peaks_float)
line=fp.readline().strip()
#spectra_simulated.append(spectrum_simulated)
#print(spectra_simulated)
fp.close()
fp = open(datapath+os.sep+project+os.sep+'result'+os.sep+cp.get('louvainoutput'),'r')
clusters_real = []
cluster_real= []
line=fp.readline().strip()
while line:
if line is '/':
if len(cluster_real)>0:
clusters_real.append(cluster_real)
cluster_real=[]
else:
peaks_string=line.split(",")
peaks_float=[float(peaks_string[1]),float(peaks_string[2])]
cluster_real.append(peaks_float)
line=fp.readline().strip()
clusters_real.append(cluster_real)
#print(clusters_real)
fp.close()
spectrum_real = Two_Column_List(datapath+os.sep+project+os.sep+cp.get('spectruminput'))
costs = {}
stddevs = {}
costspercompound = {}
stddevspercompound = {}
s=0
noshifts= []
xreal=[]
yreal=[]
xsim=[]
ysim=[]
xrealunassigned=[]
yrealunassigned=[]
linesnames=[]
#this is for calculating the optimal set
spectraforcover=[]
for spectrum_simulated in spectra_simulated:
xreallocal=[]
xreallocal.append([])
xreallocal.append([])
xreallocal.append([])
yreallocal=[]
yreallocal.append([])
yreallocal.append([])
yreallocal.append([])
xsimlocal=[]
xsimlocal.append([])
xsimlocal.append([])
xsimlocal.append([])
ysimlocal=[]
ysimlocal.append([])
ysimlocal.append([])
ysimlocal.append([])
xreallocalunassigned=[]
xreallocalunassigned.append([])
xreallocalunassigned.append([])
xreallocalunassigned.append([])
yreallocalunassigned=[]
yreallocalunassigned.append([])
yreallocalunassigned.append([])
yreallocalunassigned.append([])
spectrumforcover=[]
if len(spectrum_simulated)>0:
#print(str(len(spectrum_simulated))+' b '+str(len(spectrum_real)))
cost=np.zeros((len(spectrum_simulated),len(spectrum_real)))
i=0
#print(cost)
for peak_simulated in spectrum_simulated:
k=0
#print(str(i)+' a '+str(k))
type=2
if peak_simulated[2]=='b':
type=0
elif peak_simulated[2]=='q':
type=1
xsimlocal[type].append(peak_simulated[0])
ysimlocal[type].append(peak_simulated[1])
for peak_real in spectrum_real:
x=abs(peak_real[0]-peak_simulated[0])+abs((peak_real[1]-peak_simulated[1])*10)
if (type==0 and "HMBC" in peak_real[2]) or (type==1 and "HSQC" in peak_real[2] and not "TOCSY" in peak_real[2]) or (type==2 and "HSQCTOCSY" in peak_real[2]):
cost[i][k]=x*x
else:
cost[i][k]=sys.float_info.max/10000
#if(peak_real[0]>69 and peak_real[0]<70 and peak_real[1]>5.3 and peak_real[1]<5.4):
# print(peak_real)
# print(peak_simulated)
# print(cost[i][k])
k+=1
i+=1
#print(cost)
row_ind, col_ind=linear_sum_assignment(cost)
#print('cost: '+str(cost[row_ind,col_ind].sum())+' '+str(len(spectrum_simulated)));
#print('cost: '+str(cost[row_ind,col_ind].sum()/len(spectrum_simulated)));
costs.setdefault(cost[row_ind,col_ind].sum()/len(spectrum_simulated), [])
costs[cost[row_ind,col_ind].sum()/len(spectrum_simulated)].append(s)
costspercompound[s]=cost[row_ind,col_ind].sum()/len(spectrum_simulated)
#print("___________")
#print(costspercompound[s])
#print(row_ind)
#print(col_ind)
#i=0
#for row in row_ind:
# print(str(row)+' '+str(col_ind[i]))
# print('1_ '+str(spectrum_real[col_ind[i]])+' '+str(spectrum_simulated[row])+' '+str(cost[row][col_ind[i]]))
# i+=1
hits_clusters=[]
for cluster_real in clusters_real:
#print('cluster')
number_of_peaks=0
number_of_hits=0
for peak in cluster_real:
mincost=sys.float_info.max
indexmin=-1
rowmin=-1
number_of_peaks+=1
i=0
#print(str(peak[0])+'______ '+str(peak[1]))
for row in row_ind:
#print(str(peak[0])+' '+str(spectrum_real[col_ind[row]][0])+' '+str(peak[1])+' '+str(spectrum_real[col_ind[row]][1]))
if peak[0]==spectrum_real[col_ind[i]][0] and peak[1]==spectrum_real[col_ind[i]][1] and cost[row][col_ind[i]]<mincost:
#print('hit'+str(peak)+' '+str(spectrum_real[col_ind[i]]));
mincost=cost[row][col_ind[i]]
indexmin=i
rowmin=row
i+=1
#print(number_of_hits)
#if not found:
#print('no hit')
if indexmin>-1:
#print(mincost)
#print(str(spectrum_real[col_ind[indexmin]][0])+' '+str(spectrum_real[col_ind[indexmin]][1]))
#print(str(spectrum_simulated[col_ind[rowmin]][0])+' '+str(spectrum_simulated[col_ind[rowmin]][1]))
type=2
if 'HMBC' in spectrum_real[col_ind[indexmin]][2]:
type=0
elif 'HSQC' in spectrum_real[col_ind[indexmin]][2] and not 'TOCSY' in spectrum_real[col_ind[indexmin]][2]:
type=1
if mincost<9:
number_of_hits+=1
xreallocal[type].append(spectrum_real[col_ind[indexmin]][0])
yreallocal[type].append(spectrum_real[col_ind[indexmin]][1])
spectrumforcover.append(peak)
else:
xreallocalunassigned[type].append(spectrum_real[col_ind[indexmin]][0])
yreallocalunassigned[type].append(spectrum_real[col_ind[indexmin]][1])
hits_clusters.append(number_of_hits/number_of_peaks)
#print(hits_clusters)
stddevs.setdefault(np.std(hits_clusters), [])
stddevs[np.std(hits_clusters)].append(s)
stddevspercompound[s]=np.std(hits_clusters)
#print('standard deviation '+str(np.std(hits_clusters)))
else:
noshifts.append(s)
s+=1
xreal.append(xreallocal)
yreal.append(yreallocal)
xsim.append(xsimlocal)
ysim.append(ysimlocal)
xrealunassigned.append(xreallocalunassigned)
yrealunassigned.append(yreallocalunassigned)
spectraforcover.append(spectrumforcover)
#we have now got the costs in costs and the standard deviations of the cluster distributions in stddists, so we can do the normalisation
#print(costs)
#print(stddevs)
costsorder=[]
stddevsorder=[]
maxcost=0
mincost=sys.float_info.max
maxstddev=0
minstddev=sys.float_info.max
costsum=0
stdsum=0
for cost in costs:
if cost<mincost:
mincost=cost
if cost>maxcost:
maxcost=cost
costsum=costsum+len(costs[cost])
for stddev in stddevs:
if stddev<minstddev:
minstddev=stddev
if stddev>maxstddev:
maxstddev=stddev
stdsum=stdsum+len(stddevs[stddev])
#print(maxcost,costsum, stdsum)
costspercompound_norm = {}
stddevspercompound_norm = {}
overallcosts={}
for i in costspercompound:
if maxcost-mincost!=0:
costspercompound_norm[i]=(costspercompound[i]-mincost)/(maxcost-mincost)
else:
costspercompound_norm[i]=1
if maxstddev-minstddev!=0:
stddevspercompound_norm[i]=(stddevspercompound[i]-minstddev)/(maxstddev-minstddev)
overallcosts.setdefault((costspercompound_norm[i]+(1-stddevspercompound_norm[i]))/2, [])
overallcosts[(costspercompound_norm[i]+(1-stddevspercompound_norm[i]))/2].append(i)
else:
overallcosts.setdefault((costspercompound_norm[i]+1)/2, [])
overallcosts[(costspercompound_norm[i]+1)/2].append(i)
i+=1
fp = open(datapath+os.sep+project+os.sep+cp.get('msmsinput'),'r')
line=fp.readline().strip()
smiles=[]
while line:
smiles.append(line)
line=fp.readline().strip()
usehsqctocsy = cp.get('usehsqctocsy')
debug = cp.get('debug')
usehmbc = cp.get('usehmbc')
if os.path.exists(datapath+os.sep+project+os.sep+cp.get('msmsinput')[0:len(cp.get('msmsinput'))-4]+'names.txt'):
with open(datapath+os.sep+project+os.sep+cp.get('msmsinput')[0:len(cp.get('msmsinput'))-4]+'names.txt') as f:
linesnames = f.read().splitlines()
#we make plot
print("DICTIONARY!")
#print(spectra_simulated_dicts)
i=0
for name in linesnames:
#print(name)
#print(ysim)
#print(xsim)
#datapath+os.sep+project+os.sep+'result'+os.sep+cp.get('predictionoutput'),'r'
#hmbc_bruker_ar = cp.get('hmbcbruker').split(",")
#hmbc_bruker_fp = datapath+os.sep+project+os.sep+hmbc_bruker_ar[0]
#hsqc_bruker_ar = cp.get('hsqcbruker').split(",")
#hsqc_bruker_fp = datapath+os.sep+project+os.sep+hsqc_bruker_ar[0]
#spectra_simulated_dicts = []
fig = plt.figure(figsize=(30,10))
mosaic = "AAA;BCD"
try:
skeletal_structure = plt.imread(datapath+os.sep+project+os.sep+"reports"+os.sep+name+"jpg")
except:
print("Structure image not found.")
if usehmbc!= 'false':
ax = fig.add_subplot(1,3,1)
ax.set_xlim([0, 10])
ax.set_ylim([0, 200])
ax.invert_xaxis()
ax.invert_yaxis()
if 'hmbcbruker' in cp.keys():
try:
hmbc_bg_img = plt.imread(datapath+os.sep+project+os.sep+'plots'+ os.sep + "hmbc_spectrum.png")
ax.imshow(hmbc_bg_img, extent=[10, 0, 200, 0], aspect='auto', alpha=0.5)
except:
pass
# YKS VARIANT ON PANNA UUESTI PEAK SIMULATED I-ga läbi
ax.scatter(ysim[i][0], xsim[i][0], c='grey', label='simulated hmbc ('+str(len(ysim[i][0]))+')', alpha=0.6, edgecolors='none', s=12)
if len(xreal[i][0])>0:
ax.scatter(yreal[i][0], xreal[i][0], c='green', label='measured assigned ('+str(len(yreal[i][0]))+')', alpha=1, edgecolors='none', s=12)
ax.scatter(yrealunassigned[i][0], xrealunassigned[i][0], c='blue', label='measured unassigned closest shifts ('+str(len(yrealunassigned[i][0]))+')', alpha=0.6, edgecolors='none', s=12)
texts = []
for point_b in spectra_simulated_dicts[i]['b']:
x, y, c_nr, h_nr = point_b
#print("Scattering HMBC point label of x: " + str(x) + " y: " + str(y) + " the C idx: " + str(c_nr) + " H idx: " + str(h_nr))
texts.append(ax.annotate(f'C: {c_nr}, H: {h_nr}', (y, x), xytext=(y+1.8, x+1.8), arrowprops=dict(arrowstyle="->")))
adjust_text(texts)
if debug=='true':
xrealrest=[]
yrealrest=[]
for peak_real in spectrum_real:
if peak_real[2] == "HMBC":
valuecontained=False
if peak_real[0] in xreal[i][0] and peak_real[1] in yreal[i][0]:
if xreal[i][0].index(peak_real[0])!=yreal[i][0].index(peak_real[1]):
valuecontained=True
if peak_real[0] in xrealunassigned[i][0] and peak_real[1] in yrealunassigned[i][0]:
if xrealunassigned[i][0].index(peak_real[0])!=yrealunassigned[i][0].index(peak_real[1]):
valuecontained=True
if not valuecontained:
xrealrest.append(peak_real[0])
yrealrest.append(peak_real[1])
if len(xrealrest)>0:
ax.scatter(yrealrest, xrealrest, c='red', label='measured unused ('+str(len(yrealrest))+')', alpha=0.2, edgecolors='none', s=12)
ax.legend()
ax.grid(True)
ax = fig.add_subplot(1,3,2)
ax.set_xlim([0, 10])
ax.set_ylim([0, 200])
ax.invert_xaxis()
ax.invert_yaxis()
if 'hsqcbruker' in cp.keys():
try:
hsqc_bg_img = plt.imread(datapath+os.sep+project+os.sep+'plots'+ os.sep + "hsqc_spectrum.png")
ax.imshow(hsqc_bg_img, extent=[10, 0, 200, 0], aspect='auto', alpha=0.5)
except:
pass
ax.scatter(ysim[i][1], xsim[i][1], c='grey', label='simulated hsqc ('+str(len(ysim[i][1]))+')', alpha=0.6, edgecolors='none', s=12)
if len(xreal[i][1])>0:
ax.scatter(yreal[i][1], xreal[i][1], c='green', label='measured assigned ('+str(len(yreal[i][1]))+')', alpha=1, edgecolors='none', s=12)
ax.scatter(yrealunassigned[i][1], xrealunassigned[i][1], c='blue', label='measured unassigned closest shifts ('+str(len(yrealunassigned[i][1]))+')', alpha=0.6, edgecolors='none', s=12)
texts = []
for point_q in spectra_simulated_dicts[i]['q']:
x, y, c_nr, h_nr = point_q
print("Scattering HSQC point label of x: " + str(x) + " y: " + str(y) + " the C idx: " + str(c_nr) + " H idx: " + str(h_nr))
texts.append(ax.annotate(f'C: {c_nr}, H: {h_nr}', (y, x), xytext=(y+1.8, x+1.8), arrowprops=dict(arrowstyle="->")))
adjust_text(texts)
if debug=='true':
xrealrest=[]
yrealrest=[]
#print(xreal[i][1])
#print(yreal[i][1])
#print(xrealunassigned[i][1])
#print(yrealunassigned[i][1])
#print(xsim[i][1])
#print(ysim[i][1])
for peak_real in spectrum_real:
if "HSQC" in peak_real[2] and not "TOCSY" in peak_real[2]:
valuecontained=False
#print(peak_real)
#print(peak_real[0] in xrealunassigned[i][1])
#print(peak_real[1] in yrealunassigned[i][1])
if peak_real[0] in xreal[i][1] and peak_real[1] in yreal[i][1]:
#if xreal[i][1].index(peak_real[0])!=yreal[i][1].index(peak_real[1]):
valuecontained=True
if peak_real[0] in xrealunassigned[i][1] and peak_real[1] in yrealunassigned[i][1]:
#if xrealunassigned[i][1].index(peak_real[0])!=yrealunassigned[i][1].index(peak_real[1]):
valuecontained=True
if not valuecontained:
xrealrest.append(peak_real[0])
yrealrest.append(peak_real[1])
if len(xrealrest)>0:
ax.scatter(yrealrest, xrealrest, c='red', label='measured unused ('+str(len(yrealrest))+')', alpha=0.2, edgecolors='none', s=12)
#print(xrealrest)
#print(yrealrest)
ax.legend()
ax.grid(True)
if usehsqctocsy== 'true':
ax = fig.add_subplot(1,3,3)
ax.set_xlim([0, 10])
ax.set_ylim([0, 200])
ax.invert_xaxis()
ax.invert_yaxis()
if 'hsqctocsybruker' in cp.keys():
try:
hsqctocsy_bg_img = plt.imread(datapath+os.sep+project+os.sep+'plots'+ os.sep + "hsqctocsy_spectrum.png")
ax.imshow(hsqctocsy_bg_img, extent=[10, 0, 200, 0], aspect='auto', alpha=0.5)
except:
pass
ax.scatter(ysim[i][2], xsim[i][2], c='grey', label='simulated hsqc ('+str(len(ysim[i][2]))+')', alpha=0.6, edgecolors='none', s=12)
if len(xreal[i][2])>0:
ax.scatter(yreal[i][2], xreal[i][2], c='green', label='measured assigned ('+str(len(yreal[i][2]))+')', alpha=1, edgecolors='none', s=12)
ax.scatter(yrealunassigned[i][2], xrealunassigned[i][2], c='blue', label='measured unassigned closest shifts ('+str(len(yrealunassigned[i][2]))+')', alpha=0.6, edgecolors='none', s=12)
texts = []
for point_t in spectra_simulated_dicts[i]['t']:
x, y, c_nr, h_nr = point_t
print("Scattering HSQCTOCSY point label of x: " + str(x) + " y: " + str(y) + " the C idx: " + str(c_nr) + " H idx: " + str(h_nr))
ax.annotate(f'C: {c_nr}, H: {h_nr}', (y, x), xytext=(y+0.8, x+0.8), arrowprops=dict(arrowstyle="->"))
adjust_text[texts]
if debug=='true':
xrealrest=[]
yrealrest=[]
for peak_real in spectrum_real:
if "HSQCTOCSY" in peak_real[2]:
valuecontained=False
if peak_real[0] in xreal[i][2] and peak_real[1] in yreal[i][2]:
if xreal[i][2].index(peak_real[0])!=yreal[i][2].index(peak_real[1]):
valuecontained=True
if peak_real[0] in xrealunassigned[i][2] and peak_real[1] in yrealunassigned[i][2]:
if xrealunassigned[i][2].index(peak_real[0])!=yrealunassigned[i][2].index(peak_real[1]):
valuecontained=True
if not valuecontained:
xrealrest.append(peak_real[0])
yrealrest.append(peak_real[1])
if len(xrealrest)>0:
ax.scatter(yrealrest, xrealrest, c='red', label='measured unused ('+str(len(yrealrest))+')', alpha=0.2, edgecolors='none', s=12)
ax.legend()
ax.grid(True)
fig.savefig(datapath+os.sep+project+os.sep+'plots'+os.sep+name+'.png', transparent=False, dpi=80, bbox_inches="tight")
i+=1
plt.close()
i=0
fp = open(datapath+os.sep+project+os.sep+'result'+os.sep+cp.get('result'),'w')
fpcsv = open(datapath+os.sep+project+os.sep+'result'+os.sep+cp.get('result')+'.csv','w')
if len(linesnames)>0:
fpcsv.write('ID\tsmiles\tname\tdistance\tstandard deviation')
else:
fpcsv.write('ID\tsmiles\tdistance\tstandard deviation')
if usehmbc!= 'false':
fpcsv.write('\tmatching rate (HMBC)\t')
fpcsv.write('\tmatching rate (HSQC)\t')
if usehsqctocsy== 'true':
fpcsv.write('\tmatching rate (HSQC-TOCSY)\t')
fpcsv.write('\n')
result=[]
for cost in sorted(overallcosts):
for position in overallcosts[cost]:
localresult=[]
matchingresult=[]
matchingrate=''
matchingratecsv=''
if usehmbc!= 'false':
rate='n/a'
ratecsv='n/a'
if len(ysim[position][0])>0:
rate='{:.2%}'.format(len(yreal[position][0])/len(ysim[position][0]))
ratecsv='{:.2%}'.format(len(yreal[position][0])/len(ysim[position][0]))
matchingrate=matchingrate+', matching rate: '+str(len(yreal[position][0]))+'/'+str(len(ysim[position][0]))+', '+rate+' (HMBC)'
matchingresult.append(str(len(yreal[position][0]))+'/'+str(len(ysim[position][0])))
matchingresult.append(rate)
matchingratecsv=matchingratecsv+'\t'+str(len(yreal[position][0]))+'/'+str(len(ysim[position][0]))+'\t'+ratecsv
matchingrate=matchingrate+', matching rate: '+str(len(yreal[position][1]))+'/'+str(len(ysim[position][1]))+', '+'{:.2%}'.format(len(yreal[position][1])/len(ysim[position][1]))+' (HSQC)'
matchingratecsv=matchingratecsv+'\t'+str(len(yreal[position][1]))+'/'+str(len(ysim[position][1]))+'\t'+'{:.2%}'.format(len(yreal[position][1])/len(ysim[position][1]))
matchingresult.append(str(len(yreal[position][1]))+'/'+str(len(ysim[position][1])))
matchingresult.append('{:.2%}'.format(len(yreal[position][1])/len(ysim[position][1])))
if usehsqctocsy== 'true':
matchingrate=matchingrate+', matching rate: '+str(len(yreal[position][2]))+'/'+str(len(ysim[position][2]))+', '+'{:.2%}'.format(len(yreal[position][2])/len(ysim[i][2]))+' (HSQC-TOCSY)'
matchingratecsv=matchingratecsv+'\t'+str(len(yreal[position][2]))+'/'+str(len(ysim[position][2]))+'\t'+'{:.2%}'.format(len(yreal[position][2])/len(ysim[i][2]))
matchingresult.append(str(len(yreal[position][2]))+'/'+str(len(ysim[position][2])))
matchingresult.append('{:.2%}'.format(len(yreal[position][2])/len(ysim[i][2])))
localresult.append(smiles[position])
if len(linesnames)>0:
localresult.append(linesnames[position]);
if maxstddev-minstddev!=0:
if echo:
print(str(i+1)+': '+str(smiles[position])+'/'+str(linesnames[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: '+"{0:.2f}".format(stddevspercompound_norm[position])+matchingrate)
fp.write(str(i+1)+': '+str(smiles[position])+'/'+str(linesnames[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: '+"{0:.2f}".format(stddevspercompound_norm[position])+matchingrate+'\n')
fpcsv.write(str(i+1)+'\t'+str(smiles[position])+'\t'+str(linesnames[position])+'\t'+"{0:.2f}".format(costspercompound_norm[position])+'\t'+"{0:.2f}".format(stddevspercompound_norm[position])+matchingratecsv+'\n')
else:
if echo:
print(str(i+1)+': '+str(smiles[position])+'/'+str(linesnames[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: n/a'+matchingrate)
fp.write(str(i+1)+': '+str(smiles[position])+'/'+str(linesnames[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: n/a'+matchingrate+'\n')
fpcsv.write(str(i+1)+'\t'+str(smiles[position])+'\t'+str(linesnames[position])+'\t'+"{0:.2f}".format(costspercompound_norm[position])+'\t'+matchingratecsv+'\n')
else:
if maxstddev-minstddev!=0:
if echo:
print(str(i+1)+': '+str(smiles[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: '+"{0:.2f}".format(stddevspercompound_norm[position])+matchingrate)
fp.write(str(i+1)+': '+str(smiles[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: '+"{0:.2f}".format(stddevspercompound_norm[position])+matchingrate+'\n')
fpcsv.write(str(i+1)+'\t'+str(smiles[position])+'\t'+"{0:.2f}".format(costspercompound_norm[position])+'\t'+"{0:.2f}".format(stddevspercompound_norm[position])+matchingratecsv+'\n')
else:
if echo:
print(str(i+1)+': '+str(smiles[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: n/a'+matchingrate)
fp(str(i+1)+': '+str(smiles[position])+', distance: '+"{0:.2f}".format(costspercompound_norm[position])+', standard deviation: n/a'+'\n')
fpcsv(str(i+1)+'\t'+str(smiles[position])+'\t'+"{0:.2f}".format(costspercompound_norm[position])+'\tn/a'+'\n')
i+=1
localresult.append("{0:.2f}".format(costspercompound_norm[position]))
if maxstddev-minstddev!=0:
localresult.append("{0:.2f}".format(stddevspercompound_norm[position]))
else:
localresult.append("n/a")
localresult.extend(matchingresult)
result.append(localresult)
for noshift in noshifts:
print('no shifts were predicted for '+str(smiles[noshift])+' and we cannot say anything about it!')
fp.close()
fpcsv.close()
columns=['SMILES']
if len(linesnames)>0:
columns.append('Name')
columns.extend(['Distance', 'Standard deviation'])
if usehmbc!= 'false':
columns.append('Matching peaks HMBC')
columns.append('Mathcing rate HMBC')
columns.append('Matching peaks HSQC')
columns.append('Mathcing rate HSQC')
if usehsqctocsy== 'true':
columns.append('Matching peaks HSQC-TOCSY')
columns.append('Mathcing rate HSQC-TOCSY')
df = pd.DataFrame(result, columns = columns)
df.index = np.arange(1, len(df) + 1)
return df
#now we find the optimal set. We have spectraforcover containing the peaks covered by each spectrum, costspercompound_norm containing the distances, and stddevspercompound_norm containing the std devs
#usedspectra = [False for i in range(len(spectraforcover))]
#addspectrum(usedspectra, spectraforcover,0, costspercompound_norm, stddevspercompound_norm)
best=0
bestspectrum=[]
count=0
def addspectrum(usedspectra, spectraforcover, start, costspercompound_norm, stddevspercompound_norm):
global best
global bestspectrum
global count
for x in range(start,len(usedspectra)):
if count% 10000 == 0:
print(count)
count+=1
newusedspectra=usedspectra.copy()
if newusedspectra[x]==False:
newusedspectra[x]=True
usedpeaks=[]
numberofpeaks=0
numberofmultiplepeaks=0
distance=0
stddev=0
for y in range(0,len(newusedspectra)):
if newusedspectra[y]==True:
distance+=costspercompound_norm[y]
stddev+=stddevspercompound_norm[y]
for peak in spectraforcover[y]:
if peak in usedpeaks:
numberofmultiplepeaks+=1
else:
numberofpeaks+=1
usedpeaks.append(peak)
if (numberofpeaks-numberofmultiplepeaks)>best:
best=numberofpeaks-numberofmultiplepeaks
bestspectrum=newusedspectra
print("combination "+str(newusedspectra).strip('[]')+", best "+str(best)+" "+str((numberofpeaks-numberofmultiplepeaks))+", peaks: "+str(numberofpeaks)+", multiple peaks: "+str(numberofmultiplepeaks)+", distance: "+"{0:.2f}".format(distance)+", std dev: "+"{0:.2f}".format(stddev))
addspectrum(newusedspectra, spectraforcover, x+1, costspercompound_norm, stddevspercompound_norm)