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3_netAna.py
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3_netAna.py
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#python3
'''
==============================
3_netAna -- NETWORK ANALYSIS
==============================
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
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib import cm
from networkx.drawing.nx_agraph import graphviz_layout
import pydot
import matplotlib as mpl
import matplotlib.ticker as ticker
begin_time = datetime.datetime.now()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#name = 'PubChem 1-10000[H2 ], [O ], [CO ], [H3N ]'
#name2 = 'pubchem_database_CHNO_1_10000'
name = 'EDGES_TEST_PubChem 10001-20000[[H2 ], [O ], [CO ], [H3N ]]'
#name2 = 'pubchem_database_CHNO_10001_20000'
#name = 'Ice residue[H2 ], [O ], [CO ], [H3N ]'
#name = 'EDGES_[H2 ], [O ], [CO ], [H3N ]_min_set'
#name = 'EDGES_[H2 ], [O ], [CO ], [H3N ], [CHN ], [CH3N ], [H2O ], [CO2 ], [CH2O ], [CHNO ]'
#name = 'Ice residue[O ], [CO ], [H3N ]' # min_set - H2
#name = 'Ice residue[H2 ], [CO ], [H3N ]' # min_set - O
#name = 'Ice residue[H2 ], [O ], [H3N ]' # min_set - CO
#name = 'Ice residue[H2 ], [O ], [CO ]' # min_set - NH3
name2 = 'TEST_pubchem_database_CHNO_10001-20000'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
df_edges = pd.read_csv(name + '.tsv', sep='\t', header=0)
df_nodes = pd.read_csv(name2 + '.tsv', sep='\t', header=0)
G = nx.from_pandas_edgelist(df_edges, 'Source', 'Target', create_using=nx.Graph())
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#name = 'PubChem subset 1-10000'
#name = 'PubChem subset 10001-20000'
#name = 'Ice residue'
#name2 = 'H$_2$O:CH$_3$OH:NH$_3$'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
####################
# COMPONENTS
####################
Gcc = list(nx.connected_components(G))
d = {name:k for k,comp in enumerate(Gcc) for name in comp} #dict(enumerate(Gcc))
df_comp = pd.DataFrame.from_dict(d, orient='index', columns=['component']).rename_axis('id').reset_index()
df_nodes2 = df_nodes.merge(df_comp, how='left', on='id')
#nans = df_nodes2[df_nodes2.isna().any(axis=1)]
df_nodes_cc = df_nodes2[~df_nodes2['component'].isnull()]
df_source = df_edges.rename(columns={'Source':'id'})
df_edges2 = df_source.merge(df_comp, how='left', on='id')
df_edges2 = df_edges2.rename(columns={'component':'component source'})
df_edges2 = df_edges2.rename(columns={'id':'Source'})
df_target = df_edges2.rename(columns={'Target':'id'})
df_edges3 = df_target.merge(df_comp, how='left', on='id')
df_edges3 = df_edges3.rename(columns={'component':'component target'})
df_edges3 = df_edges3.rename(columns={'id':'Target'})
df_edges3 = df_edges3.rename(columns={'mass_difference':'mass difference'})
df_edges3 = df_edges3.rename(columns={'molecular_formula':'molecular formula'})
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
FILTER_COMPONENTS = [0,1] # [3,12,35,1,17] # np.inf list(range(0,10+1)) PubChem 1-10000 [24,2] PubChem 10001 [24,3] min_set - H2 [7,17,34,38,6] min_set - O [13,5,8,25,15] min_set - CO [18,6,37,50,34] min_set - NH3 [3,12,35,1,17]
#df_edges_cc = df_edges3[(df_edges3['component source'] <= FILTER_COMPONENTS)]
#df_nodes_cc = df_nodes_cc[(df_nodes_cc['component'] <= FILTER_COMPONENTS)]
#FILTER_COMPONENTS = '0-'+ str(FILTER_COMPONENTS)
#df_edges_cc = df_edges3[(df_edges3['component source'] == FILTER_COMPONENTS)] # das brauche ich wegen list/isin nicht mehr?
#df_nodes_cc = df_nodes_cc[(df_nodes_cc['component'] == FILTER_COMPONENTS)]
df_edges_cc = df_edges3[(df_edges3['component source'].isin(FILTER_COMPONENTS))]
df_nodes_cc = df_nodes_cc[(df_nodes_cc['component'].isin(FILTER_COMPONENTS))]
#df_nodes_cc['lg_abundance_int'] = np.log10(df_nodes_cc['abundance_int']) # because range of intensities is too large for colorbar legend
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
G_cc = nx.from_pandas_edgelist(df_edges_cc, 'Source', 'Target', create_using=nx.Graph())
####################
# COLORS
####################
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PUBCHEM
color_type = 'Compound class' ## H C N O H/C N/C O/C exact_mass DBE Compound class Compound class_aromatics X_C complexity logP component ### "/" filenames cannot be saved !
#######sample_colors = pd.DataFrame({color_type: ['Other', 'CH_aromatics', 'Acids', 'Alcohols', 'Ketones', 'Esters', 'Amides', 'Nitriles', 'Amines', 'N_cyclic_aliphatics', 'N_aromatics'],
####### 'sample_color': ['whitesmoke', 'black', 'lightseagreen','blue', 'skyblue', 'cyan', 'gold', 'lime', 'green', 'magenta', 'red'] })
sample_colors = pd.DataFrame({color_type: ['CH_aromatics', 'Acids', 'Alcohols', 'Ketones', 'Esters', 'Amides', 'Nitriles', 'Amines', 'N_cyclic_aliphatics', 'N_aromatics'],
'sample_color': ['black', 'lightseagreen','blue', 'skyblue', 'cyan', 'gold', 'lime', 'green', 'magenta', 'red'] })
######## CH |---- CHO ---| |- CHNO-||--- CHN ---||--- N heterocycles ---|
sample_colors = pd.DataFrame({color_type: ['Other', 'CH_aromatics', 'N_aromatics', 'CH_aromatics_N_aromatics'],
'sample_color': ['whitesmoke', 'black', 'red', 'blue'] })
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#######~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ICE_RESIDUE
##color_type = 'Ice composition H2O_CH3OH_NH3' # Ice composition H2O_CH3OH_NH3 H C N O H/C N/C O/C mz_theo_neutral abundance_int - lg_abundance_int mu DBE N/O O/H component ### "/" filenames cannot be saved !
##sample_colors = pd.DataFrame({color_type: ['3_1_0.2', '3_1_1', '3_1_1 16h', '3_1_5', '10_1_1', '3_1_1 overirradiation'],
## 'sample_color': ['blue', '#15B01A', 'green', 'red', 'lightgray', 'orange']})
#######~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
carac = df_nodes_cc[['id', color_type]]
carac = carac.set_index('id')
carac = carac.reindex(G_cc.nodes())
carac[color_type]=pd.Categorical(carac[color_type])
carac[color_type].cat.codes
## DISCRETE, MANUAL COLORS (e.g. colored by samples)
sample_names = df_nodes_cc[color_type].unique() ###### no CH_aromatics dabei, und dann nan dabei
carac2 = carac.merge(sample_colors, how='left', on=color_type)
node_colors = carac2['sample_color'].tolist()
############
### LABELS
############
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##node_label = 'molecular_formula'
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##node_labels = df_nodes_cc[['id', node_label]]
##df_source = df_edges_cc.rename(columns={'Source':'id'})
##df_edges2 = df_source.merge(node_labels, how='left', on='id')
##df_edges2 = df_edges2.rename(columns={'id':'Source'})
##df_edges2 = df_edges2.rename(columns={node_label:node_label+'_source'})
##df_target = df_edges2.rename(columns={'Target':'id'})
##df_edges2 = df_target.merge(node_labels, how='left', on='id')
##df_edges2 = df_edges2.rename(columns={'id':'Target'})
##df_edges2 = df_edges2.rename(columns={node_label:node_label+'_target'})
###df_edges2 = df_edges2.rename(columns={'molecular_formula_x':node_label})
###df_edges2[node_label+'_source'].astype(str)
###df_edges2[node_label+'_target'].astype(str)
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##edge_label = 'molecular formula'
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##df_edges2 = df_edges2.rename(columns={edge_label:'Edge_label'})
##G_cc = nx.from_pandas_edgelist(df_edges2, node_label+'_source', node_label+'_target', edge_attr='Edge_label', create_using=nx.Graph())
##edge_labels = nx.get_edge_attributes(G_cc, "Edge_label")
##label_size=30 #12
##node_sizes=2000 #400
##plt.figure()
##nx.draw_networkx_edge_labels(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), edge_labels=edge_labels, font_size=label_size)
##nx.draw(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), with_labels=True, node_color=node_colors, node_size=node_sizes, width=1, font_size=label_size, alpha=.5)
###nx.draw(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), with_labels=True, node_color=carac[color_type].cat.codes, cmap=plt.cm.jet, node_size=node_size, width=.3, font_size=label_size, alpha=.5)
##plt.show()
################################
### DEGREE DISTRIBUTION
################################
##m=0
##degree_freq = nx.degree_histogram(G_cc)
##degrees = range(len(degree_freq))
####plt.figure(figsize=(6, 5))
#####plt.loglog(degrees[m:], degree_freq[m:],'ko-')
####plt.plot(degrees[m:], degree_freq[m:],'ko-', label='')
####plt.axvline(x=np.mean(degrees[m:]), color='k', linestyle='--', alpha=.3)
####plt.axhline(y=np.mean(degree_freq[m:]), color='k', linestyle='--', alpha=.3)
####plt.xlim(-.5,26.5)
####plt.xlabel('Degree')
####plt.ylabel('Frequency')
#####plt.legend()
####plt.title(name+', Component '+str(FILTER_COMPONENTS), wrap=True)
######plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+' degree distribution'+'.png')
##G = G_cc
##degrees = [val for (node, val) in G.degree()]
##df_nodes_cc['degrees'] = degrees
###df_nodes_cc.to_csv('__________________.txt', header="'\t'.join(list(df_export.columns))", index=None, sep='\t')
###df_edges_cc.to_csv('__________________edges.txt', header="'\t'.join(list(df_export.columns))", index=None, sep='\t')
#df_nodes_cc[(df_nodes_cc['id'] == 122)]
#df_nodes_cc.plot.scatter('mz_theo_neutral', 'lg_abundance_int')
####df_nodes_cc['abundance_int'].value_counts().plot()
#####df_nodes_cc['abundance_int'].cumsum().plot()
#plt.show()
#######df = pd.DataFrame(df_edges_cc['Source'].unique())
#######coverage_nodes = int(round(df.shape[0] / df_nodes_cc.shape[0] * 100, 0)) # maybe not correct as some nodes can be also uniquely as targets
#coverage_nodes = int(round(df_nodes_cc.shape[0] / df_nodes.shape[0] * 100, 0))
#print(name2+', '+name+' -- Component '+str(FILTER_COMPONENTS))
#print('Coverage_nodes = '+str(coverage_nodes)+' %')
############################
##### AVERAGE VALUES
############################
#C_min = df_nodes_cc['C'].min().astype(int)
#C_max = df_nodes_cc['C'].max().astype(int)
#H_min = df_nodes_cc['H'].min().astype(int)
#H_max = df_nodes_cc['H'].max().astype(int)
#N_min = df_nodes_cc['N'].min().astype(int)
#N_max = df_nodes_cc['N'].max().astype(int)
#O_min = df_nodes_cc['O'].min().astype(int)
#O_max = df_nodes_cc['O'].max().astype(int)
#mz_min = df_nodes_cc['mz_theo_neutral'].min().astype(int)
#mz_max = df_nodes_cc['mz_theo_neutral'].max().astype(int)
#DBE_min = df_nodes_cc['DBE'].min().astype(int)
#DBE_max = df_nodes_cc['DBE'].max().astype(int)
#C_median = df_nodes_cc['C'].median().astype(int)
#H_median = df_nodes_cc['H'].median().astype(int)
#N_median = df_nodes_cc['N'].median().astype(int)
#O_median = df_nodes_cc['O'].median().astype(int)
#mz_median = df_nodes_cc['mz_theo_neutral'].median().astype(int)
#DBE_median = df_nodes_cc['DBE'].median().astype(int)
##print('C_min = '+str(df_nodes_cc['C'].min()))
##print('C_max = '+str(df_nodes_cc['C'].max()))
##print('H_min = '+str(df_nodes_cc['H'].min()))
##print('H_max = '+str(df_nodes_cc['H'].max()))
##print('N_min = '+str(df_nodes_cc['N'].min()))
##print('N_max = '+str(df_nodes_cc['N'].max()))
##print('O_min = '+str(df_nodes_cc['O'].min()))
##print('O_max = '+str(df_nodes_cc['O'].max()))
##print('mz_min = '+str(df_nodes_cc['mz_theo_neutral'].min()))
##print('mz_max = '+str(df_nodes_cc['mz_theo_neutral'].max()))
##print('DBE_min = '+str(df_nodes_cc['DBE'].min()))
##print('DBE_max = '+str(df_nodes_cc['DBE'].max()))
##PERCENT = int(df_nodes_cc.shape[0] / df_nodes.shape[0] * 100)
#PERCENT = np.round(df_nodes_cc.shape[0] / df_nodes.shape[0] * 100, 1)
#print('%'+name+' -- Component '+str(FILTER_COMPONENTS))
##print('C$_{'+str(C_min)+'-'+str(C_max)+'}$H$_{'+str(H_min)+'-'+str(H_max)+'}$N$_{'+str(N_min)+'-'+str(N_max)+'}$O$_{'+str(O_min)+'-'+str(O_max)+'}$, '+'m/z = '+str(mz_min)+'-'+str(mz_max)+' amu, '+'DBE = '+str(DBE_min)+'-'+str(DBE_max))
##print('C$_{'+str(C_median)+'}$H$_{'+str(H_median)+'}$N$_{'+str(N_median)+'}$O$_{'+str(O_median)+'}$, \\\ '+'m/z = '+str(mz_median)+' amu, '+'DBE = '+str(DBE_median)+' \\\ (median values) \\\ '+str(df_nodes_cc.shape[0])+' molecular formulas ('+str(PERCENT)+' % of all formulas) \\\ ')
##print('C$_{'+str(C_median)+'}$H$_{'+str(H_median)+'}$N$_{'+str(N_median)+'}$O$_{'+str(O_median)+'}$, \\\ '+'m/z = '+str(mz_median)+' amu, '+'DBE = '+str(DBE_median)+' \\\ (median values) \\\ '+str(df_nodes_cc.shape[0])+' molecular formulas \\\ ')
#print('C$_{'+str(C_median)+'}$H$_{'+str(H_median)+'}$N$_{'+str(N_median)+'}$O$_{'+str(O_median)+'}$, \\\ '+'DBE = '+str(DBE_median)+' \\vspace{.5cm} ')
################################
### ELEMENT HISTOGRAMS
################################
#####~~~~~~~~~~~~~~
#ELEMENT = 'H'
#####~~~~~~~~~~~~~~
#df_element_freq = pd.DataFrame(df_nodes_cc[ELEMENT].value_counts())
#plt.figure()
#pd.DataFrame(df_nodes_cc[ELEMENT].value_counts(normalize=False)).sort_index().plot(kind='bar', rot=0, ylabel='', legend=False, color='black', width=.5)
#plt.title('Component '+str(FILTER_COMPONENTS))
#plt.xticks(())
#plt.yticks(())
##plt.yscale('log')
#plt.xlabel('#'+str(ELEMENT), fontsize=25)
##plt.ylabel('Frequency (log scale, normalized)')
#plt.ylabel('Frequency', fontsize=25)
#plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+ ' element_components_'+str(ELEMENT)+'.png')
#################################
#### COMPONENT HISTOGRAM
#################################
#df_comp_freq = pd.DataFrame(df_nodes_cc['component'].value_counts()).rename(columns={'component': 'freq_component'})
##plt.figure()
##pd.DataFrame(df_nodes_cc['component'].value_counts(normalize=True)).sort_index().plot(kind='bar', rot=0, ylabel='', legend=False, color='black', width=1)
###pd.DataFrame(df_nodes_cc['component'].value_counts(normalize=False)).sort_index().plot(kind='bar', rot=0, ylabel='', legend=False, color='black', width=1)
##plt.title('Component '+str(FILTER_COMPONENTS))
##plt.xticks(np.arange(0, len(df_comp_freq), 10))
##plt.yticks()
##plt.yscale('log')
##plt.xlabel('Component number')
##plt.ylabel('Frequency (log scale, normalized)')
###plt.ylabel('Frequency (log scale)')
##plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+ ' hist_components'+'.png')
#print(name)
#print(df_comp_freq)
####################
## NORMAL LAYOUT
####################
#plt.figure()
#plt.title(name, wrap=True, fontsize=20)
##nx.draw(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), with_labels=False, node_color=node_colors, node_size=20, width=.03, font_size=5)
#nx.draw(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), with_labels=False, node_color=node_colors, node_size=20, width=.1)
#plt.plot([], [], sample_colors.values[0][1], marker='o', markersize=10, label=sample_colors.values[0][0]) # Legend
#plt.plot([], [], sample_colors.values[1][1], marker='o', markersize=10, label=sample_colors.values[1][0])
#plt.plot([], [], sample_colors.values[2][1], marker='o', markersize=10, label=sample_colors.values[2][0])
#plt.plot([], [], sample_colors.values[3][1], marker='o', markersize=10, label=sample_colors.values[3][0])
#plt.plot([], [], sample_colors.values[4][1], marker='o', markersize=10, label=sample_colors.values[4][0])
#plt.plot([], [], sample_colors.values[5][1], marker='o', markersize=10, label=sample_colors.values[5][0])
##plt.plot([], [], sample_colors.values[6][1], marker='o', markersize=10, label=sample_colors.values[6][0])
##plt.plot([], [], sample_colors.values[7][1], marker='o', markersize=10, label=sample_colors.values[7][0])
##plt.plot([], [], sample_colors.values[8][1], marker='o', markersize=10, label=sample_colors.values[8][0])
##plt.plot([], [], sample_colors.values[9][1], marker='o', markersize=10, label=sample_colors.values[9][0])
##plt.legend() #loc='upper left'
##plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+' '+color_type+' network'+'.png')
#plt.show()
####################################################
#### CONTINOUS COLOR GRADIENT (e.g. element maps)
####################################################
#nodes = G_cc.nodes()
#plt.figure()
#plt.title(color_type, wrap=True, fontsize=20)
#nx.draw(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), with_labels=False, node_color=carac[color_type].cat.codes, cmap=plt.cm.jet, node_size=20, width=.1, font_size=10)
##pos = nx.spring_layout(G_cc)
##pos = nx.fruchterman_reingold_layout(G_cc)
#pos = nx.nx_pydot.graphviz_layout(G_cc)
#ec = nx.draw_networkx_edges(G_cc, pos, alpha=0.2)
#nc = nx.draw_networkx_nodes(G_cc, pos, nodelist=nodes, node_color=carac[color_type], with_labels=False, node_size=5, cmap=plt.cm.jet)
#nc = nx.draw_networkx_nodes(G_cc, pos, nodelist=nodes, node_color=carac[color_type], node_size=5, cmap=plt.cm.jet)
#plt.colorbar(nc)
#plt.axis('off')
##plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+' '+color_type+' network'+'.png')
#plt.show()
###############################
## COMPOUND CLASS HISTOGRAM -- PubChem
###############################
#df_comp_class_freq = pd.DataFrame(df_nodes_cc['Compound class'].value_counts()).rename(columns={'Compound class': 'freq_component'})
#plt.figure()
#pd.DataFrame(df_nodes_cc['Compound class'].value_counts(normalize=True)).sort_index().plot(kind='bar', rot=90, ylabel='', legend=False, color='black', width=1)
##pd.DataFrame(df_nodes_cc['Compound class'].value_counts(normalize=False)).sort_index().plot(kind='bar', rot=90, ylabel='', legend=False, color='black', width=1)
#plt.title(str(name)+', Component '+str(FILTER_COMPONENTS))
#plt.yticks()
#plt.xlabel('Compound class')
#plt.ylabel('Frequency (normalized)')
##plt.ylabel('Frequency')
#plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+ ' hist_comp_class'+'.svg')
###############################
## COMPOUND CLASS PIE CHART -- PubChem
###############################
#df_nodes_cc['comp_class_counts'] = df_nodes_cc['Compound class'].map(df_nodes_cc['Compound class'].value_counts())
#df = df_nodes_cc[['Compound class', 'comp_class_counts']]
#df = df.drop_duplicates(subset = ['comp_class_counts'])
#sample_colors = pd.DataFrame({color_type: ['CH_aromatics', 'Acids', 'Alcohols', 'Ketones', 'Esters', 'Amides', 'Nitriles', 'Amines', 'N_cyclic_aliphatics', 'N_aromatics'],
# 'sample_color': ['black', 'lightseagreen','blue', 'skyblue', 'cyan', 'gold', 'lime', 'green', 'magenta', 'red'] })
#sample_colors = sample_colors.rename(columns={'DBE':'Compound class'})
#df = df.merge(sample_colors, how='left', on='Compound class')
#plt.figure()
#df.plot(kind='pie', y = 'comp_class_counts', autopct='%1.1f%%',
# startangle=90, shadow=False, labels=df['Compound class'], legend = False, fontsize=14, colors=df['sample_color'])
#plt.savefig(name+', Comp'+str(FILTER_COMPONENTS)+ ' pie_comp_class'+'.svg')
##plt.show()
#######################
## FULL-SCREEN NETWORK
#######################
#plt.figure()
#nx.draw(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), with_labels=False, node_color=node_colors, node_size=15, width=.1)
##nx.draw(G_cc, pos = nx.nx_pydot.graphviz_layout(G_cc), with_labels=True, node_color=carac[color_type].cat.codes, cmap=plt.cm.jet, node_size=7, width=.1, font_size=10)
#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 = df_nodes_cc[['id', FROM_TO]]
#df_edges4 = df_edges_cc.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['mz_targets'] - df_edges6['mz_sources']
#products6_backward = df_edges6_backward['mz_targets'] - df_edges6_backward['mz_sources']
##trafo_names = ['H2', 'CO', 'O', 'H3N'] ### automatize later
##trafo_names = df_edges['molecular_formula'].unique().tolist() ## doesn't work.. maybe because of order below
#trafo_names = ['H2', 'O', 'CO', 'H3N', 'CHN', 'CH3N', 'H2O', 'CO2', 'CH2O', 'CHNO']
#for x in trafo_names:
# 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]['mz_targets'] - globals()['df_edges6_%s' % x]['mz_sources']
# 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]['mz_targets'] - globals()['df_edges6_backward_%s' % x]['mz_sources']
##forward = [products6_H2.shape[0], products6_O.shape[0], products6_CO.shape[0], products6_H3N.shape[0]]
##backward = [products6_backward_H2.shape[0], products6_backward_O.shape[0], products6_backward_CO.shape[0], products6_backward_H3N.shape[0]]
##df = pd.DataFrame({'Forward+Backward': np.add(forward, backward), 'Forward': forward, 'Backward': backward}, index=l)
#forward = [products6_H2.shape[0], products6_O.shape[0], products6_CO.shape[0], products6_H3N.shape[0], products6_CHN.shape[0], products6_CH3N.shape[0], products6_H2O.shape[0], products6_CO2.shape[0], products6_CH2O.shape[0], products6_CHNO.shape[0]]
#backward = [products6_backward_H2.shape[0], products6_backward_O.shape[0], products6_backward_CO.shape[0], products6_backward_H3N.shape[0], products6_backward_CHN.shape[0], products6_backward_CH3N.shape[0], products6_backward_H2O.shape[0], products6_backward_CO2.shape[0], products6_backward_CH2O.shape[0], products6_backward_CHNO.shape[0]]
#df = pd.DataFrame({'Forward': forward, 'Backward': backward}, index=trafo_names)
#fontsize = 12
##plt.figure()
##df.plot.bar(rot=0, stacked=True)
##df.plot(kind='bar', rot=0, width=.8, color={"Forward+Backward": "lightgray", "Forward": "black", 'Backward': 'w'}, edgecolor='black')
#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(name2)+', Component ' +str(FILTER_COMPONENTS)+')', fontsize=fontsize+2)
##plt.xticks()
##plt.yticks()
#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()
### ----------------------- old long version -----------------------------------------
##########################
## CLUSTER TRANSITIONS fig 2: min_set directions between nh3_poor to medium, etc..
## WITH DIRECTIONS
##########################
#df_samples = df_nodes[['id', 'Ice composition H2O_CH3OH_NH3']]
#df_edges4 = df_edges_cc.rename(columns={'Source':'id'})
#df_edges4 = df_edges4.merge(df_samples, how='left', on='id')
#df_edges4 = df_edges4.rename(columns={'Ice composition H2O_CH3OH_NH3': 'H2O_CH3OH_NH3_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={'Ice composition H2O_CH3OH_NH3': 'H2O_CH3OH_NH3_Target', 'id':'Target'})
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '3_1_0.2'
#SAMPLE_END = '3_1_1'
##~~~~~~~~~~~~~~~~~~~~~~~
#df_edges6 = df_edges5[(df_edges5['H2O_CH3OH_NH3_Source'] == SAMPLE_START) & (df_edges5['H2O_CH3OH_NH3_Target'] == SAMPLE_END)]
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '3_1_1'
#SAMPLE_END = '3_1_0.2'
##~~~~~~~~~~~~~~~~~~~~~~~
#df_edges6_backward = df_edges5[(df_edges5['H2O_CH3OH_NH3_Source'] == SAMPLE_START) & (df_edges5['H2O_CH3OH_NH3_Target'] == SAMPLE_END)]
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '3_1_1'
#SAMPLE_END = '10_1_1'
##~~~~~~~~~~~~~~~~~~~~~~~
#df_edges7 = df_edges5[(df_edges5['H2O_CH3OH_NH3_Source'] == SAMPLE_START) & (df_edges5['H2O_CH3OH_NH3_Target'] == SAMPLE_END)]
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '10_1_1'
#SAMPLE_END = '3_1_1'
##~~~~~~~~~~~~~~~~~~~~~~~
#df_edges7_backward = df_edges5[(df_edges5['H2O_CH3OH_NH3_Source'] == SAMPLE_START) & (df_edges5['H2O_CH3OH_NH3_Target'] == SAMPLE_END)]
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '10_1_1'
#SAMPLE_END = '3_1_5'
##~~~~~~~~~~~~~~~~~~~~~~~
#df_edges8 = df_edges5[(df_edges5['H2O_CH3OH_NH3_Source'] == SAMPLE_START) & (df_edges5['H2O_CH3OH_NH3_Target'] == SAMPLE_END)]
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '3_1_5'
#SAMPLE_END = '10_1_1'
##~~~~~~~~~~~~~~~~~~~~~~~
#df_edges8_backward = df_edges5[(df_edges5['H2O_CH3OH_NH3_Source'] == SAMPLE_START) & (df_edges5['H2O_CH3OH_NH3_Target'] == SAMPLE_END)]
#products6 = df_edges6['mz_targets'] - df_edges6['mz_sources']
#products7 = df_edges7['mz_targets'] - df_edges7['mz_sources']
#products8 = df_edges8['mz_targets'] - df_edges8['mz_sources']
#df_edges6_H2 = df_edges6[(df_edges6['molecular formula'].str.strip() == 'H2')]
#products6_H2 = df_edges6_H2['mz_targets'] - df_edges6_H2['mz_sources']
#df_edges6_backward_H2 = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'H2')]
#products6_backward_H2 = df_edges6_backward_H2['mz_targets'] - df_edges6_backward_H2['mz_sources']
#df_edges7_H2 = df_edges7[(df_edges7['molecular formula'].str.strip() == 'H2')]
#products7_H2 = df_edges7_H2['mz_targets'] - df_edges7_H2['mz_sources']
#df_edges7_backward_H2 = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'H2')]
#products7_backward_H2 = df_edges7_backward_H2['mz_targets'] - df_edges7_backward_H2['mz_sources']
#df_edges8_H2 = df_edges8[(df_edges8['molecular formula'].str.strip() == 'H2')]
#products8_H2 = df_edges8_H2['mz_targets'] - df_edges8_H2['mz_sources']
#df_edges8_backward_H2 = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'H2')]
#products8_backward_H2 = df_edges8_backward_H2['mz_targets'] - df_edges8_backward_H2['mz_sources']
#df_edges6_O = df_edges6[(df_edges6['molecular formula'].str.strip() == 'O')]
#products6_O = df_edges6_O['mz_targets'] - df_edges6_O['mz_sources']
#df_edges6_backward_O = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'O')]
#products6_backward_O = df_edges6_backward_O['mz_targets'] - df_edges6_backward_O['mz_sources']
#df_edges7_O = df_edges7[(df_edges7['molecular formula'].str.strip() == 'O')]
#products7_O = df_edges7_O['mz_targets'] - df_edges7_O['mz_sources']
#df_edges7_backward_O = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'O')]
#products7_backward_O = df_edges7_backward_O['mz_targets'] - df_edges7_backward_O['mz_sources']
#df_edges8_O = df_edges8[(df_edges8['molecular formula'].str.strip() == 'O')]
#products8_O = df_edges8_O['mz_targets'] - df_edges8_O['mz_sources']
#df_edges8_backward_O = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'O')]
#products8_backward_O = df_edges8_backward_O['mz_targets'] - df_edges8_backward_O['mz_sources']
#df_edges6_CO = df_edges6[(df_edges6['molecular formula'].str.strip() == 'CO')]
#products6_CO = df_edges6_CO['mz_targets'] - df_edges6_CO['mz_sources']
#df_edges6_backward_CO = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'CO')]
#products6_backward_CO = df_edges6_backward_CO['mz_targets'] - df_edges6_backward_CO['mz_sources']
#df_edges7_CO = df_edges7[(df_edges7['molecular formula'].str.strip() == 'CO')]
#products7_CO = df_edges7_CO['mz_targets'] - df_edges7_CO['mz_sources']
#df_edges7_backward_CO = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'CO')]
#products7_backward_CO = df_edges7_backward_CO['mz_targets'] - df_edges7_backward_CO['mz_sources']
#df_edges8_CO = df_edges8[(df_edges8['molecular formula'].str.strip() == 'CO')]
#products8_CO = df_edges8_CO['mz_targets'] - df_edges8_CO['mz_sources']
#df_edges8_backward_CO = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'CO')]
#products8_backward_CO = df_edges8_backward_CO['mz_targets'] - df_edges8_backward_CO['mz_sources']
#df_edges6_H3N = df_edges6[(df_edges6['molecular formula'].str.strip() == 'H3N')]
#products6_H3N = df_edges6_H3N['mz_targets'] - df_edges6_H3N['mz_sources']
#df_edges6_backward_H3N = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'H3N')]
#products6_backward_H3N = df_edges6_backward_H3N['mz_targets'] - df_edges6_backward_H3N['mz_sources']
#df_edges7_H3N = df_edges7[(df_edges7['molecular formula'].str.strip() == 'H3N')]
#products7_H3N = df_edges7_H3N['mz_targets'] - df_edges7_H3N['mz_sources']
#df_edges7_backward_H3N = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'H3N')]
#products7_backward_H3N = df_edges7_backward_H3N['mz_targets'] - df_edges7_backward_H3N['mz_sources']
#df_edges8_H3N = df_edges8[(df_edges8['molecular formula'].str.strip() == 'H3N')]
#products8_H3N = df_edges8_H3N['mz_targets'] - df_edges8_H3N['mz_sources']
#df_edges8_backward_H3N = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'H3N')]
#products8_backward_H3N = df_edges8_backward_H3N['mz_targets'] - df_edges8_backward_H3N['mz_sources']
#df_edges6_CHN = df_edges6[(df_edges6['molecular formula'].str.strip() == 'CHN')]
#products6_CHN = df_edges6_CHN['mz_targets'] - df_edges6_CHN['mz_sources']
#df_edges6_backward_CHN = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'CHN')]
#products6_backward_CHN = df_edges6_backward_CHN['mz_targets'] - df_edges6_backward_CHN['mz_sources']
#df_edges7_CHN = df_edges7[(df_edges7['molecular formula'].str.strip() == 'CHN')]
#products7_CHN = df_edges7_CHN['mz_targets'] - df_edges7_CHN['mz_sources']
#df_edges7_backward_CHN = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'CHN')]
#products7_backward_CHN = df_edges7_backward_CHN['mz_targets'] - df_edges7_backward_CHN['mz_sources']
#df_edges8_CHN = df_edges8[(df_edges8['molecular formula'].str.strip() == 'CHN')]
#products8_CHN = df_edges8_CHN['mz_targets'] - df_edges8_CHN['mz_sources']
#df_edges8_backward_CHN = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'CHN')]
#products8_backward_CHN = df_edges8_backward_CHN['mz_targets'] - df_edges8_backward_CHN['mz_sources']
#df_edges6_CH3N = df_edges6[(df_edges6['molecular formula'].str.strip() == 'CH3N')]
#products6_CH3N = df_edges6_CH3N['mz_targets'] - df_edges6_CH3N['mz_sources']
#df_edges6_backward_CH3N = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'CH3N')]
#products6_backward_CH3N = df_edges6_backward_CH3N['mz_targets'] - df_edges6_backward_CH3N['mz_sources']
#df_edges7_CH3N = df_edges7[(df_edges7['molecular formula'].str.strip() == 'CH3N')]
#products7_CH3N = df_edges7_CH3N['mz_targets'] - df_edges7_CH3N['mz_sources']
#df_edges7_backward_CH3N = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'CH3N')]
#products7_backward_CH3N = df_edges7_backward_CH3N['mz_targets'] - df_edges7_backward_CH3N['mz_sources']
#df_edges8_CH3N = df_edges8[(df_edges8['molecular formula'].str.strip() == 'CH3N')]
#products8_CH3N = df_edges8_CH3N['mz_targets'] - df_edges8_CH3N['mz_sources']
#df_edges8_backward_CH3N = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'CH3N')]
#products8_backward_CH3N = df_edges8_backward_CH3N['mz_targets'] - df_edges8_backward_CH3N['mz_sources']
#df_edges6_H2O = df_edges6[(df_edges6['molecular formula'].str.strip() == 'H2O')]
#products6_H2O = df_edges6_H2O['mz_targets'] - df_edges6_H2O['mz_sources']
#df_edges6_backward_H2O = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'H2O')]
#products6_backward_H2O = df_edges6_backward_H2O['mz_targets'] - df_edges6_backward_H2O['mz_sources']
#df_edges7_H2O = df_edges7[(df_edges7['molecular formula'].str.strip() == 'H2O')]
#products7_H2O = df_edges7_H2O['mz_targets'] - df_edges7_H2O['mz_sources']
#df_edges7_backward_H2O = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'H2O')]
#products7_backward_H2O = df_edges7_backward_H2O['mz_targets'] - df_edges7_backward_H2O['mz_sources']
#df_edges8_H2O = df_edges8[(df_edges8['molecular formula'].str.strip() == 'H2O')]
#products8_H2O = df_edges8_H2O['mz_targets'] - df_edges8_H2O['mz_sources']
#df_edges8_backward_H2O = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'H2O')]
#products8_backward_H2O = df_edges8_backward_H2O['mz_targets'] - df_edges8_backward_H2O['mz_sources']
#df_edges6_CO2 = df_edges6[(df_edges6['molecular formula'].str.strip() == 'CO2')]
#products6_CO2 = df_edges6_CO2['mz_targets'] - df_edges6_CO2['mz_sources']
#df_edges6_backward_CO2 = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'CO2')]
#products6_backward_CO2 = df_edges6_backward_CO2['mz_targets'] - df_edges6_backward_CO2['mz_sources']
#df_edges7_CO2 = df_edges7[(df_edges7['molecular formula'].str.strip() == 'CO2')]
#products7_CO2 = df_edges7_CO2['mz_targets'] - df_edges7_CO2['mz_sources']
#df_edges7_backward_CO2 = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'CO2')]
#products7_backward_CO2 = df_edges7_backward_CO2['mz_targets'] - df_edges7_backward_CO2['mz_sources']
#df_edges8_CO2 = df_edges8[(df_edges8['molecular formula'].str.strip() == 'CO2')]
#products8_CO2 = df_edges8_CO2['mz_targets'] - df_edges8_CO2['mz_sources']
#df_edges8_backward_CO2 = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'CO2')]
#products8_backward_CO2 = df_edges8_backward_CO2['mz_targets'] - df_edges8_backward_CO2['mz_sources']
#df_edges6_CH2O = df_edges6[(df_edges6['molecular formula'].str.strip() == 'CH2O')]
#products6_CH2O = df_edges6_CH2O['mz_targets'] - df_edges6_CH2O['mz_sources']
#df_edges6_backward_CH2O = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'CH2O')]
#products6_backward_CH2O = df_edges6_backward_CH2O['mz_targets'] - df_edges6_backward_CH2O['mz_sources']
#df_edges7_CH2O = df_edges7[(df_edges7['molecular formula'].str.strip() == 'CH2O')]
#products7_CH2O = df_edges7_CH2O['mz_targets'] - df_edges7_CH2O['mz_sources']
#df_edges7_backward_CH2O = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'CH2O')]
#products7_backward_CH2O = df_edges7_backward_CH2O['mz_targets'] - df_edges7_backward_CH2O['mz_sources']
#df_edges8_CH2O = df_edges8[(df_edges8['molecular formula'].str.strip() == 'CH2O')]
#products8_CH2O = df_edges8_CH2O['mz_targets'] - df_edges8_CH2O['mz_sources']
#df_edges8_backward_CH2O = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'CH2O')]
#products8_backward_CH2O = df_edges8_backward_CH2O['mz_targets'] - df_edges8_backward_CH2O['mz_sources']
#df_edges6_CHNO = df_edges6[(df_edges6['molecular formula'].str.strip() == 'CHNO')]
#products6_CHNO = df_edges6_CHNO['mz_targets'] - df_edges6_CHNO['mz_sources']
#df_edges6_backward_CHNO = df_edges6_backward[(df_edges6_backward['molecular formula'].str.strip() == 'CHNO')]
#products6_backward_CHNO = df_edges6_backward_CHNO['mz_targets'] - df_edges6_backward_CHNO['mz_sources']
#df_edges7_CHNO = df_edges7[(df_edges7['molecular formula'].str.strip() == 'CHNO')]
#products7_CHNO = df_edges7_CHNO['mz_targets'] - df_edges7_CHNO['mz_sources']
#df_edges7_backward_CHNO = df_edges7_backward[(df_edges7_backward['molecular formula'].str.strip() == 'CHNO')]
#products7_backward_CHNO = df_edges7_backward_CHNO['mz_targets'] - df_edges7_backward_CHNO['mz_sources']
#df_edges8_CHNO = df_edges8[(df_edges8['molecular formula'].str.strip() == 'CHNO')]
#products8_CHNO = df_edges8_CHNO['mz_targets'] - df_edges8_CHNO['mz_sources']
#df_edges8_backward_CHNO = df_edges8_backward[(df_edges8_backward['molecular formula'].str.strip() == 'CHNO')]
#products8_backward_CHNO = df_edges8_backward_CHNO['mz_targets'] - df_edges8_backward_CHNO['mz_sources']
#direction_H2_6 = products6_H2.shape[0] / products6_backward_H2.shape[0]
#direction_O_6 = products6_O.shape[0] / products6_backward_O.shape[0]
#direction_CO_6 = products6_CO.shape[0] / products6_backward_CO.shape[0]
#direction_H3N_6 = products6_H3N.shape[0] / products6_backward_H3N.shape[0]
#direction_CHN_6 = products6_CHN.shape[0] / products6_backward_CHN.shape[0]
#direction_CH3N_6 = products6_CH3N.shape[0] / products6_backward_CH3N.shape[0]
#direction_H2O_6 = products6_H2O.shape[0] / products6_backward_H2O.shape[0]
#direction_CO2_6 = products6_CO2.shape[0] / products6_backward_CO2.shape[0]
#direction_CH2O_6 = products6_CH2O.shape[0] / products6_backward_CH2O.shape[0]
#direction_CHNO_6 = products6_CHNO.shape[0] / products6_backward_CHNO.shape[0]
#direction_H2_7 = products7_H2.shape[0] / products7_backward_H2.shape[0]
#direction_O_7 = products7_O.shape[0] / products7_backward_O.shape[0]
#direction_CO_7 = products7_CO.shape[0] / products7_backward_CO.shape[0]
#direction_H3N_7 = products7_H3N.shape[0] / products7_backward_H3N.shape[0]
#direction_CHN_7 = products7_CHN.shape[0] / products7_backward_CHN.shape[0]
#direction_CH3N_7 = products7_CH3N.shape[0] / products7_backward_CH3N.shape[0]
#direction_H2O_7 = products7_H2O.shape[0] / products7_backward_H2O.shape[0]
#direction_CO2_7 = products7_CO2.shape[0] / products7_backward_CO2.shape[0]
#direction_CH2O_7 = products7_CH2O.shape[0] / products7_backward_CH2O.shape[0]
#direction_CHNO_7 = products7_CHNO.shape[0] / products7_backward_CHNO.shape[0]
#direction_H2_8 = products8_H2.shape[0] / products8_backward_H2.shape[0]
#direction_O_8 = products8_O.shape[0] / products8_backward_O.shape[0]
#direction_CO_8 = products8_CO.shape[0] / products8_backward_CO.shape[0]
#direction_H3N_8 = products8_H3N.shape[0] / products8_backward_H3N.shape[0]
#direction_CHN_8 = products8_CHN.shape[0] / products8_backward_CHN.shape[0]
#direction_CH3N_8 = products8_CH3N.shape[0] / products8_backward_CH3N.shape[0]
#direction_H2O_8 = products8_H2O.shape[0] / products8_backward_H2O.shape[0]
#direction_CO2_8 = products8_CO2.shape[0] / products8_backward_CO2.shape[0]
#direction_CH2O_8 = products8_CH2O.shape[0] / products8_backward_CH2O.shape[0]
#direction_CHNO_8 = products8_CHNO.shape[0] / products8_backward_CHNO.shape[0]
#print('Direction_H2_6: '+str(direction_H2_6))
#print('Direction_O_6: '+str(direction_O_6))
#print('Direction_CO_6: '+str(direction_CO_6))
#print('Direction_H3N_6: '+str(direction_H3N_6))
#print('Direction_CHN_6: '+str(direction_CHN_6))
#print('Direction_CH3N_6: '+str(direction_CH3N_6))
#print('Direction_H2O_6: '+str(direction_H2O_6))
#print('Direction_CO2_6: '+str(direction_CO2_6))
#print('Direction_CH2O_6: '+str(direction_CH2O_6))
#print('Direction_CHNO_6: '+str(direction_CHNO_6))
#print('Direction_H2_7: '+str(direction_H2_7))
#print('Direction_O_7: '+str(direction_O_7))
#print('Direction_CO_7: '+str(direction_CO_7))
#print('Direction_H3N_7: '+str(direction_H3N_7))
#print('Direction_CHN_7: '+str(direction_CHN_7))
#print('Direction_CH3N_7: '+str(direction_CH3N_7))
#print('Direction_H2O_7: '+str(direction_H2O_7))
#print('Direction_CO2_7: '+str(direction_CO2_7))
#print('Direction_CH2O_7: '+str(direction_CH2O_7))
#print('Direction_CHNO_7: '+str(direction_CHNO_7))
#print('Direction_H2_8: '+str(direction_H2_8))
#print('Direction_O_8: '+str(direction_O_8))
#print('Direction_CO_8: '+str(direction_CO_8))
#print('Direction_H3N_8: '+str(direction_H3N_8))
#print('Direction_CHN_8: '+str(direction_CHN_8))
#print('Direction_CH3N_8: '+str(direction_CH3N_8))
#print('Direction_H2O_8: '+str(direction_H2O_8))
#print('Direction_CO2_8: '+str(direction_CO2_8))
#print('Direction_CH2O_8: '+str(direction_CH2O_8))
#print('Direction_CHNO_8: '+str(direction_CHNO_8))
#index = ['H2', 'O', 'CO', 'H3N', 'CHN', 'CH3N', 'H2O', 'CO2', 'CH2O', 'CHNO']
#forward = [products6_H2.shape[0], products6_O.shape[0], products6_CO.shape[0], products6_H3N.shape[0], products6_CHN.shape[0], products6_CH3N.shape[0], products6_H2O.shape[0], products6_CO2.shape[0], products6_CH2O.shape[0], products6_CHNO.shape[0]]
#backward = [products6_backward_H2.shape[0], products6_backward_O.shape[0], products6_backward_CO.shape[0], products6_backward_H3N.shape[0], products6_backward_CHN.shape[0], products6_backward_CH3N.shape[0], products6_backward_H2O.shape[0], products6_backward_CO2.shape[0], products6_backward_CH2O.shape[0], products6_backward_CHNO.shape[0]]
##spacer = a = [0] * len(forward)
#df = pd.DataFrame({'Forward+Backward': np.add(forward, backward), 'Forward': forward, 'Backward': backward}, index=index)
#plt.figure()
#df.plot(kind='bar', rot=0, width=.8, color={"Forward+Backward": "lightgray", "Forward": "black", 'Backward': 'w'}, edgecolor='black')
##ax = df.plot.bar(rot=0, stacked=True)
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '3_1_02'
#SAMPLE_END = '3_1_1'
##~~~~~~~~~~~~~~~~~~~~~~~
#plt.title(SAMPLE_START+r'$\rightarrow$'+SAMPLE_END+' (H$_2$O:CH$_3$OH:NH$_3$), Component '+str(FILTER_COMPONENTS))
#plt.xticks()
#plt.yticks()
#plt.xlabel('Transformations')
#plt.ylabel('Frequency')
#plt.savefig(name+'_Component'+str(FILTER_COMPONENTS)+'_'+SAMPLE_START+SAMPLE_END+' trafo_bar_chart'+'.png')
#forward = [products7_H2.shape[0], products7_O.shape[0], products7_CO.shape[0], products7_H3N.shape[0], products7_CHN.shape[0], products7_CH3N.shape[0], products7_H2O.shape[0], products7_CO2.shape[0], products7_CH2O.shape[0], products7_CHNO.shape[0]]
#backward = [products7_backward_H2.shape[0], products7_backward_O.shape[0], products7_backward_CO.shape[0], products7_backward_H3N.shape[0], products7_backward_CHN.shape[0], products7_backward_CH3N.shape[0], products7_backward_H2O.shape[0], products7_backward_CO2.shape[0], products7_backward_CH2O.shape[0], products7_backward_CHNO.shape[0]]
#df = pd.DataFrame({'Forward+Backward': np.add(forward, backward), 'Forward': forward, 'Backward': backward}, index=index)
#plt.figure()
#df.plot(kind='bar', rot=0, width=.8, color={"Forward+Backward": "lightgray", "Forward": "black", 'Backward': 'w'}, edgecolor='black')
##ax = df.plot.bar(rot=0, stacked=True)
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '3_1_1'
#SAMPLE_END = '10_1_1'
##~~~~~~~~~~~~~~~~~~~~~~~
#plt.title(SAMPLE_START+r'$\rightarrow$'+SAMPLE_END+' (H$_2$O:CH$_3$OH:NH$_3$), Component '+str(FILTER_COMPONENTS))
#plt.xticks()
#plt.yticks()
#plt.xlabel('Transformations')
#plt.ylabel('Frequency')
#plt.savefig(name+'_Component'+str(FILTER_COMPONENTS)+'_'+SAMPLE_START+SAMPLE_END+' trafo_bar_chart'+'.png')
#forward = [products8_H2.shape[0], products8_O.shape[0], products8_CO.shape[0], products8_H3N.shape[0], products8_CHN.shape[0], products8_CH3N.shape[0], products8_H2O.shape[0], products8_CO2.shape[0], products8_CH2O.shape[0], products8_CHNO.shape[0]]
#backward = [products8_backward_H2.shape[0], products8_backward_O.shape[0], products8_backward_CO.shape[0], products8_backward_H3N.shape[0], products8_backward_CHN.shape[0], products8_backward_CH3N.shape[0], products8_backward_H2O.shape[0], products8_backward_CO2.shape[0], products8_backward_CH2O.shape[0], products8_backward_CHNO.shape[0]]
#df = pd.DataFrame({'Forward+Backward': np.add(forward, backward), 'Forward': forward, 'Backward': backward}, index=index)
#plt.figure()
#df.plot(kind='bar', rot=0, width=.8, color={"Forward+Backward": "lightgray", "Forward": "black", 'Backward': 'w'}, edgecolor='black')
##ax = df.plot.bar(rot=0, stacked=True)
##~~~~~~~~~~~~~~~~~~~~~~~
#SAMPLE_START = '10_1_1'
#SAMPLE_END = '3_1_5'
##~~~~~~~~~~~~~~~~~~~~~~~
#plt.title(SAMPLE_START+r'$\rightarrow$'+SAMPLE_END+' (H$_2$O:CH$_3$OH:NH$_3$), Component '+str(FILTER_COMPONENTS))
#plt.xticks()