--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/COBRAxy/src/utils/CBS_backend.py	Sat Oct 25 14:55:13 2025 +0000
@@ -0,0 +1,297 @@
+"""
+CBS backend utilities using GLPK for constraint-based sampling.
+
+This module builds and solves LP problems from COBRA models, supports random
+objective function generation (CBS), and provides both swiglpk-based and
+COBRApy-based sampling fallbacks.
+"""
+from swiglpk import *
+import random
+import pandas as pd
+import numpy as np
+import cobra as cb
+
+# Initialize LP problem
+def initialize_lp_problem(S):
+    """
+    Prepare sparse matrix structures for GLPK given a stoichiometric matrix.
+
+    Args:
+        S: Stoichiometric matrix (DOK or similar) as returned by cobra.util.create_stoichiometric_matrix.
+
+    Returns:
+        tuple: (len_vector, values, indexes, ia, ja, ar, nrows, ncol)
+            - len_vector: number of non-zero entries
+            - values: list of non-zero values
+            - indexes: list of (row, col) indices for non-zero entries
+            - ia, ja, ar: GLPK-ready arrays for the sparse matrix
+            - nrows, ncol: matrix shape
+    """
+
+    len_vector=len(S.keys())
+    values=list(S.values())
+    indexes=list(S.keys())
+    ia = intArray(len_vector+1); 
+    ja = intArray(len_vector+1);
+    ar = doubleArray(len_vector+1);
+    
+    i=0
+    ind_row=[indexes[i][0]+1 for i in range(0, len(values) )]
+    ind_col=[indexes[i][1]+1 for i in range(0, len(values) )]
+    for i in range(1, len(values) + 1): 
+        ia[i]=ind_row[i-1]
+        ja[i]=ind_col[i-1]
+        ar[i] = values[i-1]
+    
+    nrows=S.shape[0]
+    ncol=S.shape[1]
+    
+    return len_vector, values, indexes, ia, ja, ar, nrows, ncol
+    
+    
+
+def create_and_solve_lp_problem(lb,ub,nrows, ncol, len_vector, ia, ja, ar, 
+                                obj_coefs,reactions,return_lp=False):
+    """
+    Create and solve a GLPK LP problem for a metabolic model.
+
+    Args:
+        lb, ub: Lower/upper bounds per reaction (lists of floats).
+        nrows, ncol: Dimensions of the S matrix.
+        len_vector, ia, ja, ar: Sparse matrix data prepared for GLPK.
+        obj_coefs: Objective function coefficients (list of floats).
+        reactions: Reaction identifiers (list of str), used for output mapping.
+        return_lp: If True, also return the GLPK problem object (caller must delete).
+
+    Returns:
+        tuple: (fluxes, Z) or (fluxes, Z, lp) if return_lp=True.
+    """
+    
+    
+    lp = glp_create_prob();
+    glp_set_prob_name(lp, "sample");
+    glp_set_obj_dir(lp, GLP_MAX);
+    glp_add_rows(lp, nrows);
+    eps = 1e-16
+    for i in range(nrows):
+        glp_set_row_name(lp, i+1, "constrain_"+str(i+1));
+        glp_set_row_bnds(lp, i+1, GLP_FX, 0.0, 0.0);
+    glp_add_cols(lp, ncol);
+    for i in range(ncol):
+        glp_set_col_name(lp, i+1, "flux_"+str(i+1));
+        glp_set_col_bnds(lp, i+1, GLP_DB,lb[i]-eps,ub[i]+eps);
+    glp_load_matrix(lp, len_vector, ia, ja, ar);
+    
+    try:
+        fluxes,Z=solve_lp_problem(lp,obj_coefs,reactions)
+        if return_lp:
+            return fluxes,Z,lp
+        else:
+            glp_delete_prob(lp);
+            return fluxes,Z
+    except Exception as e:
+        glp_delete_prob(lp)
+        raise Exception(e)
+    
+    
+def solve_lp_problem(lp,obj_coefs,reactions):
+    """
+    Configure and solve an LP with GLPK using provided objective coefficients.
+
+    Args:
+        lp: GLPK problem handle.
+        obj_coefs: Objective coefficients.
+        reactions: Reaction identifiers (unused in computation; length used for output).
+
+    Returns:
+        tuple: (fluxes, Z) where fluxes is a list of primal column values and Z is the objective value.
+    """
+   
+    # Set the coefficients of the objective function
+    i=1
+    for ind_coef in obj_coefs:
+        glp_set_obj_coef(lp, i, ind_coef);
+        i+=1
+
+    # Initialize the parameters    
+    params=glp_smcp()
+    params.presolve=GLP_ON
+    params.msg_lev = GLP_MSG_ERR
+    params.tm_lim=4000
+    glp_init_smcp(params)
+
+    glp_term_out(GLP_OFF)
+
+    try:
+    
+        # Solve the problem
+        glp_scale_prob(lp,GLP_SF_AUTO)
+        
+        value=glp_simplex(lp, params) 
+
+        Z = glp_get_obj_val(lp);
+
+        if value == 0:
+            fluxes = []
+            for i in range(len(reactions)): fluxes.append(glp_get_col_prim(lp, i+1))
+            return fluxes,Z
+        else:
+            raise Exception("error in LP problem. Problem:",str(value)) 
+    except Exception as e:
+        # Re-enable terminal output for error reporting
+        glp_term_out(GLP_ON)
+        raise Exception(e)
+    finally:
+        # Re-enable terminal output after solving
+        glp_term_out(GLP_ON)
+    
+def create_lp_structure(model):
+    """
+    Extract the LP structure (S matrix, bounds, objective) from a COBRA model.
+
+    Args:
+        model (cobra.Model): The COBRA model.
+
+    Returns:
+        tuple: (S, lb, ub, coefs_obj, reactions)
+    """
+    
+    reactions=[el.id for el in model.reactions]
+    coefs_obj=[reaction.objective_coefficient for reaction in model.reactions]
+    
+    # Lower and upper bounds
+    lb=[reaction.lower_bound for reaction in model.reactions]
+    ub=[reaction.upper_bound for reaction in model.reactions]
+    
+    # Create S matrix
+    S=cb.util.create_stoichiometric_matrix(model,array_type="dok")
+    
+    return S,lb,ub,coefs_obj,reactions
+
+def randomObjectiveFunctionSampling(model, nsample, coefficients_df, df_sample):
+    """
+    Sample fluxes using GLPK by iterating over random objective functions.
+
+    Args:
+        model: COBRA model.
+        nsample: Number of samples to generate.
+        coefficients_df: DataFrame of objective coefficients (columns are samples; last row is type_of_problem).
+        df_sample: Output DataFrame to fill with flux samples (index: sample, columns: reactions).
+
+    Returns:
+        None
+    """
+
+    S,lb,ub,coefs_obj,reactions = create_lp_structure(model)
+    len_vector, values, indexes, ia, ja, ar, nrow, ncol = initialize_lp_problem(S)
+    
+    for i in range(nsample):
+      
+        coefs_obj=coefficients_df.iloc[:,i].values
+            
+        if coefs_obj[-1]==1: # minimize
+            coefs_obj= coefs_obj[0:-1] * -1
+        else:
+            coefs_obj=coefs_obj[0:-1]
+
+        fluxes,Z = create_and_solve_lp_problem(lb,ub, nrow, ncol, len_vector, 
+                                                        ia, ja, ar, coefs_obj,reactions,return_lp=False)
+        df_sample.loc[i] = fluxes 
+    return
+
+def randomObjectiveFunctionSampling_cobrapy(model, nsample, coefficients_df, df_sample):
+    """
+    Fallback sampling using COBRApy's optimize with per-sample randomized objectives.
+
+    Args:
+        model: COBRA model.
+        nsample: Number of samples to generate.
+        coefficients_df: DataFrame of objective coefficients (columns are samples; last row is type_of_problem).
+        df_sample: Output DataFrame to fill with flux samples (index: sample, columns: reactions).
+
+    Returns:
+        None
+    """
+    
+    for i in range(nsample):
+
+        dict_coeff={}
+        if(coefficients_df.iloc[-1][i]==1):
+            type_problem = -1 # minimize
+        else:
+            type_problem = 1 # maximize
+            
+        for rxn in [reaction.id for reaction in model.reactions]:
+            dict_coeff[model.reactions.get_by_id(rxn)] = coefficients_df.loc[rxn][i] * type_problem
+            
+        model.objective = dict_coeff
+        solution =  model.optimize().fluxes
+        for rxn, flux in solution.items():
+            df_sample.loc[i][rxn] = flux
+
+    return
+
+def randomObjectiveFunction(model, n_samples, df_fva, seed=0):
+    """
+    Create random objective function coefficients for CBS sampling.
+
+    The last row 'type_of_problem' encodes 0 for maximize and 1 for minimize.
+
+    Args:
+        model: COBRA model.
+        n_samples: Number of random objectives to generate.
+        df_fva: Flux Variability Analysis results with 'minimum' and 'maximum' per reaction.
+        seed: Seed for reproducibility.
+
+    Returns:
+        pandas.DataFrame: Coefficients DataFrame indexed by reaction IDs plus 'type_of_problem'.
+    """
+    # reactions = model.reactions
+    reactions = [reaction.id for reaction in model.reactions]
+    cont = seed
+    list_ex = reactions.copy()
+    list_ex.append("type_of_problem")
+    coefficients_df = pd.DataFrame(index=list_ex, columns=[str(i) for i in range(n_samples)])
+
+    for i in range(0, n_samples):
+
+        cont = cont + 1
+        random.seed(cont)
+
+        # Generate a random threshold in [0, 1]
+        threshold = random.random()
+
+        for reaction in reactions:
+
+            cont = cont + 1
+            random.seed(cont)
+
+            val = random.random()
+
+            if val > threshold:
+
+                cont = cont + 1
+                random.seed(cont)
+
+                # Coefficient in [-1, 1]
+                c = 2 * random.random() - 1
+
+                val_max = np.max([abs(df_fva.loc[reaction, "minimum"]), abs(df_fva.loc[reaction, "maximum"])])
+
+                if val_max != 0: # only if FVA is non-zero
+                    coefficients_df.loc[reaction, str(i)] = c / val_max # scale by FVA
+                else:
+                    coefficients_df.loc[reaction, str(i)] = 0
+
+            else:
+                coefficients_df.loc[reaction, str(i)] = 0
+
+        cont = cont + 1
+        random.seed(cont)
+
+        if random.random() < 0.5:
+            coefficients_df.loc["type_of_problem", str(i)] = 0 # maximize
+        else:
+            coefficients_df.loc["type_of_problem", str(i)] = 1 # minimize
+
+    return coefficients_df