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mol2net.py
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mol2net.py
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##python3
'''
=========================================
mol2net
Network analysis from a list of molecules
=========================================
AUTHOR: Alexander Ruf ([email protected])
If you use this code for your work, please cite the corresponding paper:
10.1021/acs.analchem.2c01271
, this github repository and/or the corresponding Zenodo DOI:
@software{rufalexan_2022_7025094,
author = {rufalexan},
title = {rufalexan/mol2net: v0.1.0},
month = aug,
year = 2022,
publisher = {Zenodo},
version = {v0.1.0},
doi = {10.5281/zenodo.7025094},
url = {https://doi.org/10.5281/zenodo.7025094}
}
'''
import datetime
begin_time = datetime.datetime.now()
import os
import glob
import string
import chemparse
import re
import numpy as np
import pandas as pd
from collections import namedtuple
import networkx as nx
from networkx.drawing.nx_agraph import graphviz_layout
import pydot
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.ticker as ticker
from mycolorpy import colorlist as mcp
import math
m_h = 1.007825
m_c = 12.
m_n = 14.003074
m_o = 15.994915
m_s = 31.972071
masses = (m_h, m_c, m_n, m_o, m_s)
valence_h = 1
valence_c = 4
valence_n = 3
valence_o = 2
valence_s = 2
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
trafos = ['H2','O','CO','NH3']
##trafoWeights = [ 1, 5, 1, 2]
trafoWeights = [1]*len(trafos)
columnNames = ['molecular_formula','abundance_int'] ## for molFormula & abundance/intensity
sample = 'Ice composition H2O_CH3OH_NH3'
nComponents = 1
only_uniques = 'yes'
color_type = sample
node_size = 'Degree'
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
######################################
## TRAFOS
######################################
cf = re.compile('([A-Z][a-z]?)(\d?\d?\d?)')
all_elements = []
for xxxx in trafos:
for el, count in re.findall(cf, xxxx):
all_elements += [el]
atoms = set(all_elements)
counts = [[""] + trafos]
for e in atoms:
count = [e]
for xxxx in trafos:
d = dict(re.findall(cf, xxxx))
n = d.get(e, 0)
if n == '': n = 1
count += [int(n)]
counts += [count]
A = np.array(counts)
A_t = (A.transpose())
x2 = pd.DataFrame(A_t)
new_header = x2.iloc[0]
x2 = x2[1:]
x2.columns = new_header
x2 = x2.rename(columns={'': 'Molecular Formula'})
for i in ['H', 'C', 'N', 'O', 'S']:
if i in x2.columns:
x2[i] = x2[i].astype(int)
else:
x2[i] = 0
h = x2['H'].to_numpy()
c = x2['C'].to_numpy()
n = x2['N'].to_numpy()
o = x2['O'].to_numpy()
s = x2['S'].to_numpy()
elements = np.transpose(np.array([h, c, n, o, s]))
massExact = np.dot(elements, masses)
x2['Mass (exact)'] = massExact.round(6)
weights = pd.DataFrame(trafoWeights,columns = ['trafoWeights'])
weights['trafos'] = trafos
x3 = x2.join(weights.set_index(['trafos'], verify_integrity=True ),on=['Molecular Formula'], how='left')
xTRAFOS = x3
mdTRAFOS = xTRAFOS['Mass (exact)'].round(6).to_numpy()
######################################
## DETECTIONS
######################################
path = os.getcwd()
files = glob.glob(os.path.join("*.tsv"))
d = {}
for f in files:
key = f
name = f.replace(".tsv", "")
x = globals()[f"orig_{name}"] = pd.read_csv(f, sep='\t', skiprows=0, encoding = 'unicode_escape')
if 'id' in x.columns:
pass
else:
x.insert(0, 'id', range(1, 1 + len(x)))
#
try:
x = x[['id',sample,columnNames[0],columnNames[1]]].rename(columns={columnNames[0]: 'Molecular Formula',columnNames[1]:'Intensity'})
except:
x = x[['id',columnNames[0],columnNames[1]]].rename(columns={columnNames[0]: 'Molecular Formula',columnNames[1]:'Intensity'})
#
molFormulas = x['Molecular Formula']
a = []
for i in molFormulas:
tmp_a = chemparse.parse_formula(i)
a.append(tmp_a)
#
x_a = pd.DataFrame(a).fillna(0)
x2 = pd.concat([x,x_a], axis=1)
#
if 'H' in x2.columns:
x2['H'] = x2['H'].astype(int)
else:
x2['H'] = 0
#
if 'C' in x2.columns:
x2['C'] = x2['C'].astype(int)
else:
x2['C'] = 0
#
if 'N' in x2.columns:
x2['N'] = x2['N'].astype(int)
else:
x2['N'] = 0
#
if 'O' in x2.columns:
x2['O'] = x2['O'].astype(int)
else:
x2['O'] = 0
#
if 'S' in x2.columns:
x2['S'] = x2['S'].astype(int)
else:
x2['S'] = 0
#
h = x2['H'].to_numpy()
c = x2['C'].to_numpy()
o = x2['O'].to_numpy()
n = x2['N'].to_numpy()
s = x2['S'].to_numpy()
elements = np.transpose(np.array([h, c, n, o, s]))
massExact = np.dot(elements, masses)
x2['Mass (exact)'] = massExact.round(6)
x2['mu'] = mu = ( (h*valence_h + c*valence_c + n*valence_n + o*valence_o + s*valence_s) / 2 ) - (h + c + o + n + s) + 1
x2['DBE'] = dbe = c - (h/2) + (n/2) + 1
x2['HC'] = h/c
x2['NC'] = n/c
x2['OC'] = o/c
x2['SC'] = s/c
x2['FILTER'] = 1 * ( (mu >= 0) )
atom_list = ['H','C','N','O','S']
for i in range(len(atom_list)-1):
x2['Molecular Formula'] = x2['Molecular Formula'].replace({atom_list[i]+'0'}, {atom_list[i]}, regex=True)
x2['Molecular Formula'] = x2['Molecular Formula'].replace({atom_list[i]+'1'+atom_list[i+1]}, {atom_list[i]}, regex=True)
x2['Molecular Formula'] = x2['Molecular Formula'].replace({'S'+'0'}, {''}, regex=True)
x2['Molecular Formula'] = x2['Molecular Formula'].replace({'S'+'1'}, {'S'}, regex=True)
#
x2['Mass (frequency)'] = x2['Mass (exact)'].map(dict(x2['Mass (exact)'].value_counts()))
try:
if only_uniques == 'yes':
x3 = x2.drop_duplicates(subset=['Mass (exact)'], keep='first')
else:
pass
except:
x3 = x2
#
xNODES0 = xDET = x3[(x3['FILTER']==1)]
globals()[f"{name}"] = xDET
mass2 = mass = xDET['Mass (exact)'].round(6).to_numpy()
data = xDET[['id','Mass (exact)']]
#
########################################## x x
## MD MATCHES
##########################################
md_matches = namedtuple('md_matches', 'md_matches hits')
new_list = []
for md in mdTRAFOS:
for element in mass:
if element+md in mass2:
tmp_new_list = md_matches(element, element+md)
new_list.append(tmp_new_list)
#
matches = np.array(new_list).reshape(len(new_list),2)
matches = matches[np.argsort(matches[:,0])]
sources = data.rename(columns={'id': 'Source', 'Mass (exact)': 'Mass (source)'})
targets = data.rename(columns={'id': 'Target', 'Mass (exact)': 'Mass (target)'})
matches2 = pd.DataFrame({'Mass (source)': matches[:, 0], 'Mass (target)': matches[:, 1]})
source_match = matches2.merge(sources, how='left', on=['Mass (source)'])
target_match = source_match.merge(targets, how='left', on=['Mass (target)'])
target_match['Mass difference'] = mass_diff = np.around(target_match['Mass (target)'] - target_match['Mass (source)'],6)
xxxx = xTRAFOS[['Mass (exact)','Molecular Formula']].rename(columns={'Mass (exact)': 'Mass difference'})
x = target_match.merge(xxxx, how='left', on=['Mass difference'])
x['type'] = type = np.full((len(mass_diff),1), 'Undirected')
x['Label'] = Label = np.full((len(mass_diff),1), 'x')
xxxx = xTRAFOS[['Mass (exact)','trafoWeights']]
x2 = x.join(xxxx.set_index(['Mass (exact)'], verify_integrity=True ),on=['Mass difference'], how='left').rename(columns={'trafoWeights': 'Weight'}).sort_values(by=['Source'])
x2['Mass difference (frequency)'] = x2['Mass difference'].map(dict(x2['Mass difference'].value_counts()))
#
xEDGES0 = x2
#
md_freq = x2[['Molecular Formula','Mass difference (frequency)']]
md_freq = md_freq.drop_duplicates(subset=['Molecular Formula'], keep='first')
md_freq = md_freq.sort_values(by=['Mass difference (frequency)'], ascending=False)
print('\n\n\n\n')
print(name+', '+str(len(xNODES0))+' nodes,', str(len(xEDGES0))+' edges')
print(md_freq)
print('\n\n\n\n')
#
########################################## x x
## NETWORK ANA
##########################################
G0 = nx.from_pandas_edgelist(xEDGES0, 'Source', 'Target', create_using=nx.Graph())
G0cc = list(nx.connected_components(G0))
d = {name:k for k,comp in enumerate(G0cc) for name in comp} #dict(enumerate(G0cc))
df_comp = pd.DataFrame.from_dict(d, orient='index', columns=['component']).rename_axis('id').reset_index()
nodes2 = xNODES0.merge(df_comp, how='left', on='id')
nans = nodes2[nodes2.isna().any(axis=1)]
xNODES0 = nodes2[~nodes2['component'].isnull()]
#
df_source = xEDGES0.rename(columns={'Source':'id'})
edges2 = df_source.merge(df_comp, how='left', on='id')
edges2 = edges2.rename(columns={'component':'component source','id':'Source'})
df_target = edges2.rename(columns={'Target':'id'})
edges3 = df_target.merge(df_comp, how='left', on='id')
edges3 = edges3.rename(columns={'component':'component target','id':'Target','mass_difference':'mass difference','molecular_formula':'molecular formula'})
#
df_comp_freq = pd.DataFrame(xNODES0['component'].value_counts()).rename(columns={'component': 'freq_component'})
xNODES0['df_comp_freq'] = xNODES0['component'].map(dict(xNODES0['component'].value_counts()))
df_comp_freq_norm = pd.DataFrame(xNODES0['component'].value_counts(normalize=True)).rename(columns={'component': 'freq_component_norm [0-1]'})
df_comp_freq_comb = pd.concat([df_comp_freq,df_comp_freq_norm], axis=1)
print('\n\n\n\n')
print(name+' (all components)')
print(df_comp_freq_comb)
print('\n\n\n\n')
#
FILTER_COMPONENTS = list(df_comp_freq_comb.index)[:nComponents]
xEDGES = edges3[(edges3['component source'].isin(FILTER_COMPONENTS))]
xNODES = xNODES0[(xNODES0['component'].isin(FILTER_COMPONENTS))]
G = nx.from_pandas_edgelist(xEDGES, 'Source', 'Target', create_using=nx.Graph())
a = pd.DataFrame(G.nodes(data = True)).rename(columns={0:'id'}).drop([1], axis=1)
a2 = a.join(xNODES.set_index('id'), on='id')
a2['id_G0_nodes'] = range(1,1+len(a2))
xNODES = a2
#
degrees = pd.DataFrame(G.degree(), columns=["id", "Degree"])
degrees['id'] = degrees['id'].astype(int)
xNODES = xNODES.join(degrees.set_index('id'), on='id')
xNODES['Degree counts'] = xNODES['Degree'].map(dict(xNODES['Degree'].value_counts()))
#
## bc = nx.betweenness_centrality(G, normalized=True) ## takes a while....
## bc2 = pd.DataFrame.from_dict(bc, orient='index', columns=['betCen']).rename_axis('id').reset_index()
## xNODES = xNODES.merge(bc2, how='left', on='id')
#
colors = mcp.gen_color(cmap='gist_rainbow',n=len(xNODES[color_type].unique())) ## cmap AUTUMN tab20b coolwarm gist_rainbow tab20c brg https://matplotlib.org/stable/tutorials/colors/colormaps.html
##colors = ['#15B01A', 'blue', 'orange', 'lightgray', 'red', 'green']
samples0 = xNODES[color_type].unique().tolist()
try:
samples = sorted(samples0, key=lambda x: int("".join([i for i in x if i.isdigit()])))
except:
samples = sorted(samples0)
#
sample_colors = pd.DataFrame({color_type: samples, 'color': colors})
xNODES = xNODES.merge(sample_colors, how='left', on=color_type)
xNODES = xNODES.sort_values(by=['id_G0_nodes'], ascending=True) ## re-ordering/re-indexing according to graph G0 (id_G0_nodes)
node_colors = xNODES['color'].tolist()
#
xNODES = xNODES.sort_values(by=['id_G0_nodes'], ascending=True)
xNODES['node_size'] = (xNODES[node_size] - np.min(xNODES[node_size])) / (np.max(xNODES[node_size]) - np.min(xNODES[node_size]))
node_sizes = xNODES['node_size'].tolist()
node_sizes = [i * 1e2 for i in node_sizes]
#
########### l = ['Degree','Time point']
########### for i0 in l:
########### x = xNODES[['id',i0]]
########### for i in ['Source','Target']:
########### x2 = xEDGES.join(x.set_index(['id'], verify_integrity=True ),on=[i], how='left').rename(columns={i0:i0+' ('+str(i)+')'})
########### x3 = x2[i0+' ('+str(i)+')']
########### xEDGES[i0+' ('+str(i)+')'] = x3
###########################################
#### DEGREE DISTRIBUTION
###########################################
##x_name = 'Degree'
##y_name = 'Degree counts'
##text = 'id'
##x = xNODES[x_name].tolist()
##y = xNODES[y_name].tolist()
##text = xNODES[text].tolist()
###
##plt.figure()
##plt.title(name+'\n, Comp'+str(FILTER_COMPONENTS)+', Trafos'+str(trafos), wrap=True, fontsize=12)
##plt.scatter(x,y,c='blue')
#### for i in range(len(x)):
#### plt.annotate(text[i], (x[i], y[i] + 0.2), fontsize=10)
###
#### plt.xscale('log',base=10)
#### plt.yscale('log',base=10)
##plt.xlabel(x_name)
##plt.ylabel(y_name)
#### plt.savefig(name+'_'+str(FILTER_COMPONENTS)+'_'+str(trafos)+' degreeDistri'+'.png')
##plt.show()
###########################################
#### LAYOUT
###########################################
##plt.figure()
##plt.title(name, wrap=True, fontsize=12)
#### nx.draw(G, pos = nx.nx_pydot.graphviz_layout(G), with_labels=False, node_color=node_colors, node_size=20, width=.03, font_size=5)
#### nx.draw(G, pos = nx.nx_pydot.graphviz_layout(G), with_labels=False, node_color=node_colors, node_size=node_sizes, width=.05, alpha=1)
#### nx.draw(G, pos = nx.spring_layout(G, scale=1), with_labels=False, node_color=node_colors, node_size=node_sizes, width=.05, alpha=1)
#### nx.draw(G, pos = nx.spring_layout(G, k=.5/math.sqrt(G.order()), scale=10), with_labels=False, node_color=node_colors, node_size=node_sizes, width=.05, alpha=1)
##nx.draw(G, pos = nx.nx_pydot.graphviz_layout(G), with_labels=False, node_color=node_colors, node_size=2, width=.01, alpha=1)
##for i in range(len(sample_colors)):
## plt.plot([], [], sample_colors.values[i][1], marker='o', markersize=10, label=sample_colors.values[i][0])
###
##plt.legend() ## loc='upper left'
#### plt.text(.6, -1.1,str(G.number_of_nodes())+' nodes, '+str(G.number_of_edges())+' edges, node size ~ '+str(node_size), fontsize=8, wrap=True)
##plt.text(.6, -1.1,str(G.number_of_nodes())+' nodes, '+str(G.number_of_edges())+' edges', fontsize=8, wrap=True)
#### plt.savefig(name+'_Comp'+str(FILTER_COMPONENTS)+' '+str(color_type)+' network'+'.png')
##plt.show()
####################################################
#### CONTINOUS COLOR GRADIENT (e.g. element maps)
####################################################
##fontsize=20
##l = ['H', 'C', 'N', 'O', 'S', 'HC', 'NC', 'OC', 'SC', 'Mass (exact)', 'Intensity', 'DBE']
###
##for i in l:
## color_type = i
## carac = xNODES[['id', color_type]]
## carac = carac.set_index('id')
## carac = carac.reindex(G.nodes())
## carac[color_type]=pd.Categorical(carac[color_type])
## carac[color_type].cat.codes
## nodes = G.nodes()
## plt.figure()
## plt.title(color_type, wrap=True, fontsize=25)
#### nx.draw(G, pos = nx.nx_pydot.graphviz_layout(G), with_labels=False, node_color=carac[color_type].cat.codes, cmap=plt.cm.jet, node_size=3, width=.1, font_size=10)
#### pos = nx.spring_layout(G)
## pos = nx.nx_pydot.graphviz_layout(G)
#### pos = nx.fruchterman_reingold_layout(G)
#### pos = nx.nx_pydot.graphviz_layout(G)
## nx.draw(G, pos, with_labels=False, node_color=node_colors, node_size=2, width=.01, alpha=1)
## ec = nx.draw_networkx_edges(G, pos, width=.01, alpha=0.2)
### nc = nx.draw_networkx_nodes(G, pos, nodelist=nodes, node_color=carac[color_type], with_labels=False, node_size=5, cmap=plt.cm.jet)
## nc = nx.draw_networkx_nodes(G, pos, nodelist=nodes, node_color=carac[color_type], node_size=3, cmap=plt.cm.jet)
#### plt.colorbar(nc)
## cb = plt.colorbar(nc, orientation='vertical')
## nc.figure.axes[0].tick_params(axis="both", labelsize=21) ## change the label size
## nc.figure.axes[1].tick_params(axis="y", labelsize=21) ## change the tick label size of colorbar
## plt.axis('off')
#### plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+' '+color_type+' network'+'.png')
## plt.show()
###########################################
#### edges vs edge counts
###########################################
##edge_edgeCounts = xEDGES.drop_duplicates(subset=['mass difference'], keep='first')
##edge_edgeCounts = edge_edgeCounts.sort_values(by=['mass difference'], ascending = True)
##x_name = 'mass difference'
##y_name = 'mass difference counts'
##text = 'trafo'
##x = edge_edgeCounts[x_name].tolist()
##y = edge_edgeCounts[y_name].tolist()
##text = edge_edgeCounts[text].tolist()
###
##plt.figure()
##plt.title(name, wrap=True, fontsize=12)
##plt.scatter(x,y,c='black')
##for i in range(len(x)):
## plt.annotate(text[i], (x[i], y[i] + 0.2), fontsize=10)
###
##plt.xlabel('mass (transformation)')
##plt.ylabel('transformation counts')
##plt.savefig(name+'_Comp'+str(FILTER_COMPONENTS)+' '+str(color_type)+' edge_edgeCounts'+'.png')
####plt.show()
###########################################
#### degree vs abundance
###########################################
##xNODES2 = xNODES[ (xNODES['sample'] == 'detections') ]
##x_name = 'Degree'
##y_name = 'abundance'
##text = 'molecular_formula'
##x = xNODES2[x_name].tolist()
##y = xNODES2[y_name].tolist()
##ann = xNODES2[text].tolist()
##fig, ax = plt.subplots()
##plt.title(name, wrap=True, fontsize=12)
##ax.scatter(x,y,c='black')
##for i, txt in enumerate(ann):
## ax.annotate(txt, (x[i], y[i]))
###
##plt.yscale('log',base=2)
##plt.xlabel(x_name)
####plt.xlabel(y_name)
##plt.ylabel('log('+str(abundance)+')')
##plt.savefig(name+'_Comp'+str(FILTER_COMPONENTS)+' '+str(color_type)+' degree_abundance'+'.png')
####plt.show()
#################################
#### COMPONENT HISTOGRAM
#################################
##plt.figure()
##pd.DataFrame(xNODES0['component'].value_counts(normalize=True)).sort_index().plot(kind='bar', rot=0, ylabel='', legend=False, color='black', width=1)
###pd.DataFrame(xNODES0['component'].value_counts(normalize=False)).sort_index().plot(kind='bar', rot=0, ylabel='', legend=False, color='black', width=1)
##plt.title(name+', Component '+str(FILTER_COMPONENTS), wrap=True)
##plt.xticks(np.arange(0, len(df_comp_freq), 20), fontsize=15) ## ice
####plt.xticks(np.arange(0, len(df_comp_freq), 100), fontsize=15) ## paris
##plt.yticks(fontsize=15)
##plt.yscale('log')
##plt.xlabel('Component number', fontsize=15)
###plt.ylabel('Frequency (log scale)')
##plt.ylabel('Frequency (log scale, normalized)', fontsize=15)
###plt.ylabel('Frequency (normalized)')
####plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+ ' hist_components'+'.svg')
##plt.show()
#######################################################
####### component map
#######################################################
##fontsize=20
##color_type = 'component'
##carac = xNODES0[['id', color_type]]
##carac = carac.set_index('id')
##carac = carac.reindex(G.nodes())
##carac[color_type]=pd.Categorical(carac[color_type])
##carac[color_type].cat.codes
##nodes = G.nodes()
##plt.figure()
##plt.title(color_type, wrap=True, fontsize=20)
##nx.draw(G, pos = nx.nx_pydot.graphviz_layout(G), with_labels=False, node_color=carac[color_type].cat.codes, cmap=plt.cm.jet, node_size=3, width=.1, font_size=10)
###pos = nx.spring_layout(G)
###pos = nx.fruchterman_reingold_layout(G)
##pos = nx.nx_pydot.graphviz_layout(G)
##ec = nx.draw_networkx_edges(G, pos, alpha=0.2)
### nc = nx.draw_networkx_nodes(G, pos, nodelist=nodes, node_color=carac[color_type], with_labels=False, node_size=5, cmap=plt.cm.jet)
##nc = nx.draw_networkx_nodes(G, pos, nodelist=nodes, node_color=carac[color_type], node_size=3, cmap=plt.cm.jet)
####plt.colorbar(nc)
##cb = plt.colorbar(nc, orientation='vertical').set_label(label=color_type, size=fontsize+2)
##nc.figure.axes[0].tick_params(axis="both", labelsize=21) ## change the label size
##nc.figure.axes[1].tick_params(axis="y", labelsize=21) ## change the tick label size of colorbar
##plt.axis('off')
####plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+' '+color_type+' network'+'.png')
##plt.show()
###########################
### CLUSTER TRANSITIONS fig 2: min_set directions between nh3_poor to medium, etc..
### WITH DIRECTIONS
###########################
###~~~~~~~~~~~~~~~~~~~~~~~
##FROM_TO = 'Ice composition H2O_CH3OH_NH3'
###~~~~~~~~~~~~~~~~~~~~~~~
##df_samples = xNODES[['id', FROM_TO]]
##df_edges4 = xEDGES.rename(columns={'Source':'id'})
##df_edges4 = df_edges4.merge(df_samples, how='left', on='id')
##df_edges4 = df_edges4.rename(columns={FROM_TO: FROM_TO+str('_Source'), 'id':'Source'})
##df_edges5 = df_edges4.rename(columns={'Target':'id'})
##df_edges5 = df_edges5.merge(df_samples, how='left', on='id')
##df_edges5 = df_edges5.rename(columns={FROM_TO: FROM_TO+str('_Target'), 'id':'Target'})
###~~~~~~~~~~~~~~~~~~~~~~~
##SAMPLE_START = '3_1_0.2'
##SAMPLE_END = '3_1_1'
###SAMPLE_START = '3_1_1'
###SAMPLE_END = '10_1_1'
###SAMPLE_START = '10_1_1'
###SAMPLE_END = '3_1_5'
###~~~~~~~~~~~~~~~~~~~~~~~
##df_edges6 = df_edges5[(df_edges5[FROM_TO+str('_Source')] == SAMPLE_START) & (df_edges5[FROM_TO+str('_Target')] == SAMPLE_END)]
##df_edges6_backward= df_edges5[(df_edges5[FROM_TO+str('_Source')] == SAMPLE_END) & (df_edges5[FROM_TO+str('_Target')] == SAMPLE_START)]
##products6 = df_edges6['Mass (target)'] - df_edges6['Mass (source)']
##products6_backward = df_edges6_backward['Mass (target)'] - df_edges6_backward['Mass (source)']
##for x in trafos:
## TRAFO = x
## globals()['df_edges6_%s' % x] = df_edges6[(df_edges6['Molecular Formula'].str.strip() == TRAFO)]
## globals()['products6_%s' % x] = globals()['df_edges6_%s' % x]['Mass (target)'] - globals()['df_edges6_%s' % x]['Mass (source)']
## globals()['df_edges6_backward_%s' % x] = df_edges6_backward[(df_edges6_backward['Molecular Formula'].str.strip() == TRAFO)]
## globals()['products6_backward_%s' % x] = globals()['df_edges6_backward_%s' % x]['Mass (target)'] - globals()['df_edges6_backward_%s' % x]['Mass (source)']
##forward = []
##for x in trafos:
## a = globals()['products6_%s' % x].shape[0]
## b = int(a)
## forward.append(b)
##backward = []
##for x in trafos:
## a = globals()['products6_backward_%s' % x].shape[0]
## b = int(a)
## backward.append(b)
##df = pd.DataFrame({'Forward': forward, 'Backward': backward}, index=trafos)
##fontsize = 12
##df.plot(kind='bar', rot=0, width=.8, color={"Forward": "black", 'Backward': 'w'}, edgecolor='black', stacked=True, fontsize=fontsize)
##plt.title(SAMPLE_START+r'$\rightarrow$'+SAMPLE_END+' ('+str(sample)+', Component ' +str(FILTER_COMPONENTS)+')', fontsize=fontsize+2)
##plt.xlabel('Transformations', fontsize=fontsize+2)
##plt.ylabel('Frequency', fontsize=fontsize+2)
##plt.legend(fontsize=fontsize)
####plt.savefig(name+'_Component'+str(FILTER_COMPONENTS)+'_'+SAMPLE_START+SAMPLE_END+' trafo_bar_chart'+'.png')
##plt.show()
##xEDGES['xxxx'] = xEDGES['Source']*1.123456 + xEDGES['Target']*1.123456
#xNODES.plot.scatter('id','node_size')
#plt.show()
##x = x
##x.to_csv('xxxxxxxxxxxxxxx.txt', header="'\t'.join(list(x.columns))", index=None, sep='\t')
##xEXPORT.to_csv(name+'_Comp'+str(FILTER_COMPONENTS)+' C'+str(C_min)+'-C'+str(C_max)+' '+str(trafos)+' '+str(no_generations)+' generations max_path_length '+str(max_path_length)+'.txt', sep='\t', index=False)
print('Time code = '+str(datetime.datetime.now() - begin_time)+' [h:min:sec]')