comparison COBRAxy/flux_simulation_beta.py @ 480:ecaf3c9852b3 draft

Uploaded

479:a036b8d3642f

    # Merge all batches into a single DataFrame
    all_samples = []
    
    for i in range(n_batches):
        batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
        batch_data = np.load(batch_filename)
        all_samples.append(batch_data)
    
    # Concatenate all batches
    samplesTotal_array = np.vstack(all_samples)
    

    # Merge all batches into a single DataFrame
    all_samples = []
    
    for i in range(n_batches):
        batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
        batch_data = np.load(batch_filename)
        all_samples.append(batch_data)
    
    # Concatenate all batches
    samplesTotal_array = np.vstack(all_samples)
    
    # Convert back to DataFrame with proper column names
    samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
    
    # Save the final merged result as CSV
    write_to_file(samplesTotal.T, model_name, True)
    

480:ecaf3c9852b3

    # Merge all batches into a single DataFrame
    all_samples = []
    
    for i in range(n_batches):
        batch_filename = f"{ARGS.output_path}/{model_name}_{i}_OPTGP.npy"
        batch_data = np.load(batch_filename, allow_pickle=True)
        all_samples.append(batch_data)
    
    # Concatenate all batches
    samplesTotal_array = np.vstack(all_samples)
    

    # Merge all batches into a single DataFrame
    all_samples = []
    
    for i in range(n_batches):
        batch_filename = f"{ARGS.output_path}/{model_name}_{i}_CBS.npy"
        batch_data = np.load(batch_filename, allow_pickle=True)
        all_samples.append(batch_data)
    
    # Concatenate all batches
    samplesTotal_array = np.vstack(all_samples)
    
    # Convert back to DataFrame with proper column namesq
    samplesTotal = pd.DataFrame(samplesTotal_array, columns=reaction_ids)
    
    # Save the final merged result as CSV
    write_to_file(samplesTotal.T, model_name, True)