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1 from swiglpk import *
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2 import random
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3 import pandas as pd
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4 import numpy as np
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5 import cobra as cb
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6
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7 # Initialize LP problem
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8 def initialize_lp_problem(S):
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9
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10 len_vector=len(S.keys())
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11 values=list(S.values())
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12 indexes=list(S.keys())
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13 ia = intArray(len_vector+1);
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14 ja = intArray(len_vector+1);
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15 ar = doubleArray(len_vector+1);
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16
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17 i=0
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18 ind_row=[indexes[i][0]+1 for i in range(0, len(values) )]
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19 ind_col=[indexes[i][1]+1 for i in range(0, len(values) )]
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20 for i in range(1, len(values) + 1):
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21 ia[i]=ind_row[i-1]
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22 ja[i]=ind_col[i-1]
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23 ar[i] = values[i-1]
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24
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25 nrows=S.shape[0]
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26 ncol=S.shape[1]
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27
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28 return len_vector, values, indexes, ia, ja, ar, nrows, ncol
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29
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30
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31
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32 # Solve LP problem from the structure of the metabolic model
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33 def create_and_solve_lp_problem(lb,ub,nrows, ncol, len_vector, ia, ja, ar,
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34 obj_coefs,reactions,return_lp=False):
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35
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36
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37 lp = glp_create_prob();
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38 glp_set_prob_name(lp, "sample");
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39 glp_set_obj_dir(lp, GLP_MAX);
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40 glp_add_rows(lp, nrows);
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41 eps = 1e-16
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42 for i in range(nrows):
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43 glp_set_row_name(lp, i+1, "constrain_"+str(i+1));
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44 glp_set_row_bnds(lp, i+1, GLP_FX, 0.0, 0.0);
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45 glp_add_cols(lp, ncol);
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46 for i in range(ncol):
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47 glp_set_col_name(lp, i+1, "flux_"+str(i+1));
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48 glp_set_col_bnds(lp, i+1, GLP_DB,lb[i]-eps,ub[i]+eps);
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49 glp_load_matrix(lp, len_vector, ia, ja, ar);
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50
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51 try:
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52 fluxes,Z=solve_lp_problem(lp,obj_coefs,reactions)
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53 if return_lp:
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54 return fluxes,Z,lp
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55 else:
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56 glp_delete_prob(lp);
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57 return fluxes,Z
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58 except Exception as e:
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59 glp_delete_prob(lp)
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60 raise Exception(e)
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61
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62
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63 # Solve LP problem from the structure of the metabolic model
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64 def solve_lp_problem(lp,obj_coefs,reactions):
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65
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66 # Set the coefficients of the objective function
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67 i=1
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68 for ind_coef in obj_coefs:
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69 glp_set_obj_coef(lp, i, ind_coef);
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70 i+=1
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71
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72 # Initialize the parameters
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73 params=glp_smcp()
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74 params.presolve=GLP_ON
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75 params.msg_lev = GLP_MSG_ALL
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76 params.tm_lim=4000
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77 glp_init_smcp(params)
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78
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79 # Solve the problem
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80 glp_scale_prob(lp,GLP_SF_AUTO)
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81
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82 value=glp_simplex(lp, params)
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83
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84 Z = glp_get_obj_val(lp);
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85
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86 if value == 0:
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87 fluxes = []
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88 for i in range(len(reactions)): fluxes.append(glp_get_col_prim(lp, i+1))
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89 return fluxes,Z
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90 else:
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91 raise Exception("error in LP problem. Problem:",str(value))
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92
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93
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94 # Create LP structure
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95 def create_lp_structure(model):
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96
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97 reactions=[el.id for el in model.reactions]
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98 coefs_obj=[reaction.objective_coefficient for reaction in model.reactions]
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99
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100 # Lower and upper bounds
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101 lb=[reaction.lower_bound for reaction in model.reactions]
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102 ub=[reaction.upper_bound for reaction in model.reactions]
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103
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104 # Create S matrix
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105 S=cb.util.create_stoichiometric_matrix(model,array_type="dok")
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106
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107 return S,lb,ub,coefs_obj,reactions
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108
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109 # CBS sampling interface
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110 def randomObjectiveFunctionSampling(model, nsample, coefficients_df, df_sample):
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111
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112 S,lb,ub,coefs_obj,reactions = create_lp_structure(model)
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113 len_vector, values, indexes, ia, ja, ar, nrow, ncol = initialize_lp_problem(S)
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114
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115 for i in range(nsample):
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116
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117 coefs_obj=coefficients_df.iloc[:,i].values
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118
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119 if coefs_obj[-1]==1: #minimize
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120 coefs_obj= coefs_obj[0:-1] * -1
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121 else:
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122 coefs_obj=coefs_obj[0:-1]
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123
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124 fluxes,Z = create_and_solve_lp_problem(lb,ub, nrow, ncol, len_vector,
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125 ia, ja, ar, coefs_obj,reactions,return_lp=False)
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126 df_sample.loc[i] = fluxes
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127 pass
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128
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129 def randomObjectiveFunctionSampling_cobrapy(model, nsample, coefficients_df, df_sample):
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130
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131 for i in range(nsample):
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132
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133 dict_coeff={}
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134 if(coefficients_df.iloc[-1][i]==1):
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135 type_problem = -1 #minimize
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136 else:
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137 type_problem = 1
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138
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139 for rxn in [reaction.id for reaction in model.reactions]:
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140 dict_coeff[model.reactions.get_by_id(rxn)] = coefficients_df.loc[rxn][i] * type_problem
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141
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142 model.objective = dict_coeff
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143 solution = model.optimize().fluxes
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144 for rxn, flux in solution.items():
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145 df_sample.loc[i][rxn] = flux
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146
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147 pass
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148
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149 # Create random coefficients for CBS
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150 def randomObjectiveFunction(model, n_samples, df_fva, seed=0):
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151
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152
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153 #reactions = model.reactions
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154 reactions = [reaction.id for reaction in model.reactions]
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155 cont=seed
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156 list_ex=reactions.copy()
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157 list_ex.append("type_of_problem")
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158 coefficients_df = pd.DataFrame(index=list_ex,columns=[str(i) for i in range(n_samples)])
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159
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160 for i in range(0, n_samples):
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161
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162 cont=cont+1
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163 random.seed(cont)
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164
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165 # Genera un numero casuale tra 0 e 1
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166 threshold = random.random() #coefficiente tra 0 e 1
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167
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168 for reaction in reactions:
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169
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170 cont=cont+1
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171 random.seed(cont)
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172
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173 val=random.random()
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174
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175 if val>threshold:
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176
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177 cont=cont+1
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178 random.seed(cont)
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179
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180 c=2*random.random()-1 #coefficiente tra -1 e 1
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181
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182 val_max=np.max([df_fva.loc[reaction,"minimum"],df_fva.loc[reaction,"maximum"]])
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183
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184 if val_max!=0: #solo se la fva รจ diversa da zero
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185 coefficients_df.loc[reaction,str(i)] = c/val_max #divido per la fva
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186 else:
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187 coefficients_df.loc[reaction,str(i)] = 0
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188
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189 else:
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190 coefficients_df.loc[reaction,str(i)] = 0
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191
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192 cont=cont+1
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193 random.seed(cont)
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194
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195 if random.random()<0.5:
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196 coefficients_df.loc["type_of_problem",str(i)] = 0 #maximize
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197 else:
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198 coefficients_df.loc["type_of_problem",str(i)] = 1 #minimize
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199
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200 return coefficients_df |
